{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/dilohn/ch-en-scaffolding/blob/5baac0446c23cc8ca2fc2e64643c6a7029909340/data_preprocessing.py", "id": "python-0-a6c50455441e584629b895f6764a7265", "text": "import pandas as pd\nimport numpy as np\nfrom sklearn.preprocessing import LabelEncoder, StandardScaler\nfrom sklearn.model_selection import train_test_split\n\ndef load_data(file_path):\n    try:\n        return pd.read_csv(file_path)\n    except Exception:\n        return create_synthetic_vocab_dataset()\n\ndef create_synthetic_vocab_dataset(n_samples=200):\n    np.random.seed(42)\n    \n    directions = ['M→E', 'E→M']\n    fields = ['STEM', 'General', 'Academic', 'Business']\n    learner_levels = ['Beginner', 'Intermediate', 'Advanced']\n    frequencies = ['Common', 'Uncommon', 'Rare']\n    \n    data = {\n        'Word': [f\"word_{i}\" for i in range(n_samples)],\n        'Direction': np.random.choice(directions, n_samples),\n        'Field': np.random.choice(fields, n_samples),\n        'Learner Level': np.random.choice(learner_levels, n_samples),\n        'Frequency': np.random.choice(frequencies, n_samples),\n        'Difficulty': np.random.randint(1, 11, n_samples)\n    }\n    \n    return pd.DataFrame(data)\n\ndef preprocess_data(df):\n    df_processed = df.copy()\n    \n    for col in df_processed.columns:\n        if df_processed[col].dtype == 'object':\n            df_processed[col].fillna('Unknown', inplace=True)\n        else:\n            df_processed[col].fillna(df_processed[col].median(), inplace=True)\n    \n    categorical_cols = ['Direction', 'Field', 'Learner Level', 'Frequency']\n    label_encoders = {}\n    \n    for col in categorical_cols:\n        if col in df_processed.columns:\n            le = LabelEncoder()\n            df_processed[f'{col}_encoded'] = le.fit_transform(df_processed[col])\n            label_encoders[col] = le\n    \n    if 'Field' in df_processed.columns:\n        df_processed['is_stem'] = (df_processed['Field'] == 'STEM').astype(int)\n    \n    if 'Direction' in df_processed.columns:\n        df_processed['is_m_to_e'] = (df_processed['Direction'] == 'M→E').astype(int)\n    \n    return df_processed, label_encoders\n\ndef generate_synthetic_scaffolding_data(vocab_df, n_samples=500):\n    np.random.seed(42)\n    \n    translation_methods = ['all_terms', 'difficulty_based', 'field_specific', 'random', 'frequency_based']\n    method_factors = {'all_terms': 1.1, 'difficulty_based': 1.0, 'field_specific': 1.1, \n                     'random': 0.8, 'frequency_based': 0.9}\n    \n    sampled_words = vocab_df.sample(n=n_samples, replace=True).reset_index(drop=True)\n    scaffolding_data = []\n    \n    for _, word_row in sampled_words.iterrows():\n        method = np.random.choice(translation_methods)\n        base_effectiveness = np.random.uniform(0.5, 0.9)\n        difficulty_factor = 1.0 - (word_row['Difficulty'] / 20.0)\n        method_factor = method_factors.get(method, 1.0)\n        field_factor = 1.2 if word_row['Field'] == 'STEM' else 1.0\n        \n        effectiveness = base_effectiveness * difficulty_factor * method_factor * field_factor\n        effectiveness = min(1.0, max(0.0, effectiveness))\n        effectiveness_score = 1 + 9 * effectiveness\n        \n        record = {\n            'word': word_row['Word'],\n            'direction': word_row['Direction'],\n            'field': word_row['Field'],\n            'learner_level': word_row['Learner Level'],\n            'difficulty': word_row['Difficulty'],\n            'frequency': word_row['Frequency'],\n            'scaffolding_method': method,\n            'effectiveness_score': effectiveness_score\n        }\n        \n        for col in ['Direction_encoded', 'Field_encoded', 'Learner Level_encoded', 'Frequency_encoded']:\n            if col in word_row:\n                record[col.lower()] = word_row[col]\n        \n        scaffolding_data.append(record)\n    \n    return pd.DataFrame(scaffolding_data)\n\ndef prepare_features_for_scaffolding_model(scaffolding_data):\n    df = scaffolding_data.copy()\n    categorical_cols = ['direction', 'field', 'learner_level', 'frequency', 'scaffolding_method']\n    label_encoders = {}\n    \n    for col in categorical_cols:\n        if col in df.columns and f'{col}_encoded' not in df.columns:\n            le = LabelEncoder()\n            df[f'{col}_encoded'] = le.fit_transform(df[col])\n            label_encoders[col] = le\n    \n    features = ['difficulty', 'direction_encoded', 'field_encoded', 'learner_level_encoded', \n               'frequency_encoded', 'scaffolding_method_encoded']\n    \n    df['is_stem'] = (df['field'] == 'STEM').astype(int)\n    df['is_m_to_e'] = (df['direction'] == 'M→E').astype(int)\n    features.extend(['is_stem', 'is_m_to_e'])\n    \n    return df, features, label_encoders\n\ndef split_scaffolding_dataset(df, features, target='effectiveness_score', test_size=0.2, random_state=42):\n    X = df[features].values\n    y = df[target].values\n    \n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)\n    \n    scaler = StandardScaler()\n    X_train = scaler.fit_transform(X_train)\n    X_test = scaler.transform(X_test)\n    \n    return X_train, X_test, y_train, y_test, scaler, features\n", "text_preview": "import pandas as pd import numpy as np from sklearn.preprocessing import LabelEncoder, StandardScaler from sklearn.model_selection import train_test_split def load_data(file_path): try: return pd.read_csv(file_path) except Exception: return", "input_sha256": "c0c1a0195d03e758d0d4f4372f7dbc81d1efe9e9bee059ce18474127472f1b39", "text_sha256": "a6c50455441e584629b895f6764a7265c54ca4dd6fbfecfa1dbd2912d9ac758a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12001, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 2, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.8850925277829443, "noise_severity": 0.005013275966708176, "missing_content_severity": 0.01938195967680545, "factual_density": 3.1479041778381562, "pretraining_utility": 0.630074571796476, "math_density": 2.191950145861972, "code_density": 3.8574470224925914, "reasoning_density": 0.48962401582660475, "education_level": 2.1800921207667283, "creative_writing_quality": -0.25378967659104934}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.4641 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.018715687929903302, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9932622045089804, "secondary": 0.9627819896406656, "tertiary": 0.9823114031327386, "structured_records": 0.10474217869669275, "procedural_steps": 0.054777209284521663, "worked_solution": 0.0031871931291870617, "contextual_dialogue": 4.410672070170875e-06, "content_quality": 0.5731822451963895, "noise_severity": 0.9958222700277432, "missing_content_severity": 0.9838483669359954, "factual_density": 0.28992014819846346, "pretraining_utility": 0.5035217564608021, "math_density": 0.11441437203962435, "code_density": 0.8812058520771594, "reasoning_density": 0.5919799868111626, "education_level": 0.7672151048153419, "creative_writing_quality": 0.999711342138309, "primary_topic": 0.8593204603682068, "secondary_topic": 0.4426679112792096}, "probability_margin": {"primary": 0.9912214485543511, "secondary": 0.9509455451835672, "tertiary": 0.9765307982759533, "education_level": 0.6408187843169865, "creative_writing_quality": 0.9996511182171436, "primary_topic": 0.7854785830485848, "secondary_topic": 0.16828107239017992}, "raw_probabilities": {"primary": {"Academic Writing": 0.00013931481574736696, "Content Listing": 2.4125839772143548e-05, "Creative Writing": 2.102351449389091e-05, "Customer Support Page": 1.1597997962305188e-05, "Q&A Forum": 1.77570147807424e-05, "Discussion Thread": 1.1041849094403927e-05, "Comment Section": 1.6938061810041666e-05, "Missing Content": 2.955374790324307e-05, "Knowledge Article": 0.00010908546375305092, "Legal Notices": 2.398216894482425e-05, "News Article": 7.193017779275015e-06, "Organizational Announcement": 1.4209676291022899e-05, "Nonfiction Writing": 2.4654889923436434e-05, "Organizational About Page": 1.7071999475995087e-05, "Personal About Page": 1.6247347452012188e-05, "Personal Blog": 1.1361237921507465e-05, "Product Page": 1.0344889868459808e-05, "Spam / Ads": 1.6577501881525357e-05, "Structured Data": 0.0016400893506716796, "Technical Writing": 0.9935429459877729, "Transcript / Interview": 1.7866995804409175e-05, "Tutorial / How-To Guide": 0.002321497433421829, "User Reviews": 8.195497295959947e-06, "Other / Unknown": 0.001947323700177879}, "secondary": {"Academic Writing": 0.0026669445968966867, "Content Listing": 0.00013946098125645746, "Creative Writing": 0.0001683332682853267, "Customer Support Page": 9.479353919403133e-05, "Q&A Forum": 6.517854878409436e-05, "Discussion Thread": 5.5155492939529225e-05, "Comment Section": 0.00010144289758293682, "Missing Content": 0.00022088239284309122, "Knowledge Article": 0.0005516496358401481, "Legal Notices": 0.00012666886073499988, "News Article": 9.087188175815426e-05, "Organizational Announcement": 7.203806688731499e-05, "Nonfiction Writing": 0.00010169682791942751, "Organizational About Page": 9.594515049459608e-05, "Personal About Page": 8.800459639660907e-05, "Personal Blog": 8.566745665383868e-05, "Product Page": 5.532011430083264e-05, "Spam / Ads": 8.484201154893497e-05, "Structured Data": 0.013387194888737275, "Technical Writing": 0.012071437772959425, "Transcript / Interview": 0.00010897057539666667, "Tutorial / How-To Guide": 0.005189247039329068, "User Reviews": 4.5513330956084955e-05, "NONE": 0.9643327400723045}, "tertiary": {"Academic Writing": 0.0012448394231884224, "Content Listing": 5.1993110873517393e-05, "Creative Writing": 6.4784704379028e-05, "Customer Support Page": 3.435992428396632e-05, "Q&A Forum": 2.235620105132464e-05, "Discussion Thread": 1.9792514065406257e-05, "Comment Section": 3.774399473444894e-05, "Missing Content": 0.00011840231713355977, "Knowledge Article": 0.0003007368998221106, "Legal Notices": 5.875585870059453e-05, "News Article": 3.2850849401671875e-05, "Organizational Announcement": 2.770101948055116e-05, "Nonfiction Writing": 4.6950185104484234e-05, "Organizational About Page": 4.5818431156890566e-05, "Personal About Page": 3.4261809255678294e-05, "Personal Blog": 2.842872558857873e-05, "Product Page": 2.3602137632149816e-05, "Spam / Ads": 3.465085248708508e-05, "Structured Data": 0.006517629726254511, "Technical Writing": 0.005332842497582611, "Transcript / Interview": 4.0276273319753005e-05, "Tutorial / How-To Guide": 0.002816233619918808, "User Reviews": 1.6560922377108456e-05, "NONE": 0.9830484280022078}, "structured_records": {"false": 0.8952578213033072, "true": 0.10474217869669275}, "procedural_steps": {"false": 0.9452227907154783, "true": 0.054777209284521663}, "worked_solution": {"false": 0.996812806870813, "true": 0.0031871931291870617}, "contextual_dialogue": {"false": 0.9999955893279299, "true": 4.410672070170875e-06}, "content_quality": {"-2": 0.00766611818504995, "-1": 0.027086786476931194, "0": 0.2363724614816818, "1": 0.5302377170826987, "2": 0.19863691677363843}, "noise_severity": {"0": 0.9958919993775479, "1": 0.0035346396867476306, "2": 0.00034319692279226517, "3": 0.00012841361727316253, "4": 0.00010175039563913188}, "missing_content_severity": {"0": 0.9898022906853647, "1": 0.0033680532209591652, "2": 0.00482160652289662, "3": 0.0016615048730646777, "4": 0.00034654469771475225}, "factual_density": {"0": 0.012301318484425387, "1": 0.033487133172841306, "2": 0.15822969872743897, "3": 0.3859697512507404, "4": 0.41001209836455393}, "pretraining_utility": {"-2": 0.007013079320113154, "-1": 0.058215484297648404, "0": 0.3453794536696445, "1": 0.47646775069083735, "2": 0.11292423202175664}, "math_density": {"0": 0.10110629630769818, "1": 0.23316371122984292, "2": 0.24091478364553168, "3": 0.22230396792664295, "4": 0.20251124089028422}, "code_density": {"0": 0.00021717701293719866, "1": 0.00042937758551484434, "2": 0.017795373961138908, "3": 0.10480538877683755, "4": 0.8767526826635715}, "reasoning_density": {"0": 0.7658731282560755, "1": 0.06061008127374479, "2": 0.1147703115542501, "3": 0.035512604219359326, "4": 0.023233874696570466}, "education_level": {"none": 0.0009312741877046983, "0": 0.0013627026424611896, "1": 0.011847752570042709, "2": 0.8060125873461182, "3": 0.16519380302913178, "4": 0.01465188022454122}, "creative_writing_quality": {"not_applicable": 0.9997594517819242, "-2": 3.513034811998528e-05, "-1": 5.125342237484594e-05, "0": 0.00010833356478060764, "1": 3.119630145605977e-05, "2": 1.463458134436504e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00018838533245682434, "Art & Design": 0.00026229299420376013, "Law & Crime": 0.00027695776056794037, "Education & Careers": 0.017307946745440946, "Entertainment & Media": 0.00023830813063766695, "Fashion & Beauty": 0.0002306426830365911, "Business, Economics & Finance": 0.00032406658172289644, "Food & Dining": 0.00018993095045555404, "Games & Gaming": 0.0002421124220548999, "Electronics & Hardware": 0.0002685876211977174, "Health, Healthcare & Wellness": 0.00023209234390096466, "History & Geography": 0.0002955732116346912, "Home, Pets & Hobbies": 0.0001760501003949615, "Industry & Production": 0.00020439363817342955, "Language, Literature & Philosophy": 0.023428618657033093, "Politics & Government": 0.0001574227035425913, "Religion & Spirituality": 0.00019955911497619307, "Science, Mathematics & Engineering": 0.0792526288439217, "Relationships & Community": 0.00016883338262157628, "Software Usage & Applications": 0.0007886207864924341, "Programming & Software Engineering": 0.8647312118925065, "Sports & Fitness": 0.0002100735618314196, "Vehicles & Transportation": 0.00010149625858376745, "Travel & Tourism": 0.00024596524624836025, "Other / Uncovered Subject": 0.010001168379023637, "Unknown / Insufficient Content": 0.00027706065733998836}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007684340674989628, "Art & Design": 0.0008062206669913358, "Law & Crime": 0.0007808377908954686, "Education & Careers": 0.07024877087386322, "Entertainment & Media": 0.0006747654449486606, "Fashion & Beauty": 0.0004454860181231851, "Business, Economics & Finance": 0.0008184385845949614, "Food & Dining": 0.0003509839889549097, "Games & Gaming": 0.0004391835744092308, "Electronics & Hardware": 0.0006315489013370874, "Health, Healthcare & Wellness": 0.0005909748237692753, "History & Geography": 0.0005998515498650349, "Home, Pets & Hobbies": 0.00043523544977456955, "Industry & Production": 0.00042069965759644585, "Language, Literature & Philosophy": 0.051184281108032116, "Politics & Government": 0.0003738110088760183, "Religion & Spirituality": 0.0005573378231815586, "Science, Mathematics & Engineering": 0.46410376084539384, "Relationships & Community": 0.0002637208179128254, "Software Usage & Applications": 0.0007811867443726601, "Programming & Software Engineering": 0.09696974626348388, "Sports & Fitness": 0.00047184789976251966, "Vehicles & Transportation": 0.0002233934437210299, "Travel & Tourism": 0.000617567778162887, "Other / Uncovered Subject": 0.010619226419264228, "NONE": 0.2958226884552139}}, "probabilities": {"primary": {"Academic Writing": 0.00013931481574736698, "Content Listing": 2.412583977214355e-05, "Creative Writing": 2.1023514493890913e-05, "Customer Support Page": 1.159799796230519e-05, "Q&A Forum": 1.7757014780742402e-05, "Discussion Thread": 1.1041849094403928e-05, "Comment Section": 1.693806181004167e-05, "Missing Content": 2.9553747903243073e-05, "Knowledge Article": 0.00010908546375305094, "Legal Notices": 2.3982168944824254e-05, "News Article": 7.193017779275016e-06, "Organizational Announcement": 1.42096762910229e-05, "Nonfiction Writing": 2.4654889923436437e-05, "Organizational About Page": 1.707199947599509e-05, "Personal About Page": 1.624734745201219e-05, "Personal Blog": 1.1361237921507467e-05, "Product Page": 1.034488986845981e-05, "Spam / Ads": 1.657750188152536e-05, "Structured Data": 0.0016400893506716798, "Technical Writing": 0.993542945987773, "Transcript / Interview": 1.786699580440918e-05, "Tutorial / How-To Guide": 0.0023214974334218293, "User Reviews": 8.195497295959949e-06, "Other / Unknown": 0.0019473237001778792}, "secondary": {"Academic Writing": 0.0026669445968966867, "Content Listing": 0.00013946098125645746, "Creative Writing": 0.0001683332682853267, "Customer Support Page": 9.479353919403133e-05, "Q&A Forum": 6.517854878409436e-05, "Discussion Thread": 5.5155492939529225e-05, "Comment Section": 0.00010144289758293682, "Missing Content": 0.00022088239284309122, "Knowledge Article": 0.0005516496358401481, "Legal Notices": 0.00012666886073499988, "News Article": 9.087188175815426e-05, "Organizational Announcement": 7.203806688731499e-05, "Nonfiction Writing": 0.00010169682791942751, "Organizational About Page": 9.594515049459608e-05, "Personal About Page": 8.800459639660907e-05, "Personal Blog": 8.566745665383868e-05, "Product Page": 5.532011430083264e-05, "Spam / Ads": 8.484201154893497e-05, "Structured Data": 0.013387194888737275, "Technical Writing": 0.012071437772959425, "Transcript / Interview": 0.00010897057539666667, "Tutorial / How-To Guide": 0.005189247039329068, "User Reviews": 4.5513330956084955e-05, "NONE": 0.9643327400723045}, "tertiary": {"Academic Writing": 0.0012448394231884224, "Content Listing": 5.1993110873517393e-05, "Creative Writing": 6.4784704379028e-05, "Customer Support Page": 3.435992428396632e-05, "Q&A Forum": 2.235620105132464e-05, "Discussion Thread": 1.9792514065406257e-05, "Comment Section": 3.774399473444894e-05, "Missing Content": 0.00011840231713355977, "Knowledge Article": 0.0003007368998221106, "Legal Notices": 5.875585870059453e-05, "News Article": 3.2850849401671875e-05, "Organizational Announcement": 2.770101948055116e-05, "Nonfiction Writing": 4.6950185104484234e-05, "Organizational About Page": 4.5818431156890566e-05, "Personal About Page": 3.4261809255678294e-05, "Personal Blog": 2.842872558857873e-05, "Product Page": 2.3602137632149816e-05, "Spam / Ads": 3.465085248708508e-05, "Structured Data": 0.006517629726254511, "Technical Writing": 0.005332842497582611, "Transcript / Interview": 4.0276273319753005e-05, "Tutorial / How-To Guide": 0.002816233619918808, "User Reviews": 1.6560922377108456e-05, "NONE": 0.9830484280022078}, "structured_records": {"false": 0.8952578213033072, "true": 0.10474217869669275}, "procedural_steps": {"false": 0.9452227907154783, "true": 0.054777209284521663}, "worked_solution": {"false": 0.996812806870813, "true": 0.0031871931291870617}, "contextual_dialogue": {"false": 0.9999955893279299, "true": 4.410672070170875e-06}, "content_quality": {"-2": 0.007666118185049948, "-1": 0.027086786476931187, "0": 0.23637246148168176, "1": 0.5302377170826986, "2": 0.19863691677363837}, "noise_severity": {"0": 0.9958919993775479, "1": 0.0035346396867476306, "2": 0.00034319692279226517, "3": 0.00012841361727316253, "4": 0.00010175039563913188}, "missing_content_severity": {"0": 0.9898022906853647, "1": 0.0033680532209591652, "2": 0.00482160652289662, "3": 0.0016615048730646777, "4": 0.00034654469771475225}, "factual_density": {"0": 0.012301318484425387, "1": 0.033487133172841306, "2": 0.15822969872743897, "3": 0.3859697512507404, "4": 0.41001209836455393}, "pretraining_utility": {"-2": 0.007013079320113154, "-1": 0.058215484297648404, "0": 0.3453794536696445, "1": 0.47646775069083735, "2": 0.11292423202175664}, "math_density": {"0": 0.10110629630769818, "1": 0.23316371122984292, "2": 0.24091478364553168, "3": 0.22230396792664295, "4": 0.20251124089028422}, "code_density": {"0": 0.00021717701293719866, "1": 0.00042937758551484434, "2": 0.017795373961138908, "3": 0.10480538877683755, "4": 0.8767526826635715}, "reasoning_density": {"0": 0.7658731282560752, "1": 0.06061008127374477, "2": 0.11477031155425008, "3": 0.03551260421935932, "4": 0.023233874696570463}, "education_level": {"none": 0.0009312741877046984, "0": 0.0013627026424611899, "1": 0.01184775257004271, "2": 0.8060125873461184, "3": 0.1651938030291318, "4": 0.014651880224541221}, "creative_writing_quality": {"not_applicable": 0.9997594517819242, "-2": 3.513034811998528e-05, "-1": 5.125342237484594e-05, "0": 0.00010833356478060764, "1": 3.119630145605977e-05, "2": 1.463458134436504e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00018838533245682434, "Art & Design": 0.00026229299420376013, "Law & Crime": 0.00027695776056794037, "Education & Careers": 0.017307946745440946, "Entertainment & Media": 0.00023830813063766695, "Fashion & Beauty": 0.0002306426830365911, "Business, Economics & Finance": 0.00032406658172289644, "Food & Dining": 0.00018993095045555404, "Games & Gaming": 0.0002421124220548999, "Electronics & Hardware": 0.0002685876211977174, "Health, Healthcare & Wellness": 0.00023209234390096466, "History & Geography": 0.0002955732116346912, "Home, Pets & Hobbies": 0.0001760501003949615, "Industry & Production": 0.00020439363817342955, "Language, Literature & Philosophy": 0.023428618657033093, "Politics & Government": 0.0001574227035425913, "Religion & Spirituality": 0.00019955911497619307, "Science, Mathematics & Engineering": 0.0792526288439217, "Relationships & Community": 0.00016883338262157628, "Software Usage & Applications": 0.0007886207864924341, "Programming & Software Engineering": 0.8647312118925065, "Sports & Fitness": 0.0002100735618314196, "Vehicles & Transportation": 0.00010149625858376745, "Travel & Tourism": 0.00024596524624836025, "Other / Uncovered Subject": 0.010001168379023637, "Unknown / Insufficient Content": 0.00027706065733998836}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007684340674989629, "Art & Design": 0.0008062206669913359, "Law & Crime": 0.0007808377908954687, "Education & Careers": 0.07024877087386323, "Entertainment & Media": 0.0006747654449486607, "Fashion & Beauty": 0.0004454860181231852, "Business, Economics & Finance": 0.0008184385845949615, "Food & Dining": 0.00035098398895490974, "Games & Gaming": 0.00043918357440923085, "Electronics & Hardware": 0.0006315489013370875, "Health, Healthcare & Wellness": 0.0005909748237692754, "History & Geography": 0.000599851549865035, "Home, Pets & Hobbies": 0.0004352354497745696, "Industry & Production": 0.0004206996575964459, "Language, Literature & Philosophy": 0.05118428110803212, "Politics & Government": 0.00037381100887601834, "Religion & Spirituality": 0.0005573378231815587, "Science, Mathematics & Engineering": 0.4641037608453939, "Relationships & Community": 0.00026372081791282547, "Software Usage & Applications": 0.0007811867443726602, "Programming & Software Engineering": 0.0969697462634839, "Sports & Fitness": 0.0004718478997625197, "Vehicles & Transportation": 0.00022339344372102992, "Travel & Tourism": 0.0006175677781628871, "Other / Uncovered Subject": 0.01061922641926423, "NONE": 0.295822688455214}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9773, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9773, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:05.084192+00:00", "scoring_key_slot": "primary", "dataset_uid": "267f0b69-9901-4510-ba2b-acf6e2bb8ca5", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "5127d3b32318614720b866a0bd9a49f455a7166025bdc5237192b4fada37281d", "source_metadata": {"uuid": "267f0b69-9901-4510-ba2b-acf6e2bb8ca5", "repo_name": "dilohn/ch-en-scaffolding", "relative_path": "data_preprocessing.py", "category": "DATA", "algo_rel_score": 0.9249479174613953, "quality_score": 7.211342811584473, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2025-05-24T08:18:51.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "09aea7d3dc1952d07c38fde10f3f8d785426fcfe", "size_bytes": 5113, "file_timestamp": 1748498681, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "5baac0446c23cc8ca2fc2e64643c6a7029909340"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2025-05-24T08:18:51.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "09aea7d3dc1952d07c38fde10f3f8d785426fcfe", "size_bytes": 5113, "file_timestamp": 1748498681, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "5baac0446c23cc8ca2fc2e64643c6a7029909340", "url": "https://github.com/dilohn/ch-en-scaffolding/blob/5baac0446c23cc8ca2fc2e64643c6a7029909340/data_preprocessing.py"}, "gpt4o_tokens": 1195, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.8850925277829444, "noise_severity": 0.005013275966708176, "missing_content_severity": 0.01938195967680545, "factual_density": 3.1479041778381562, "pretraining_utility": 0.630074571796476, "math_density": 2.191950145861972, "code_density": 3.8574470224925914, "reasoning_density": 0.4896240158266048, "education_level": 2.1800921207667283, "creative_writing_quality": -0.25378967659104934}, "score_applicability": {"education_level": 0.9990687258122953, "creative_writing_quality": 0.00024054821807586367}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/llazzaro/chem-fingerprints/blob/44b0a2e0cee9307d302b307255889e09db22184a/chemfp/slow.py", "id": "python-0-84b93ceeb3bd7170c5cad424537e1621", "text": "import heapq\nimport operator\n\nfrom . import bitops\nfrom . import Fingerprints\nfrom . import fps_search\n\ndef count_tanimoto_hits_fp(query_fp, targets, threshold):\n    return sum(1 for target in targets\n                   if bitops.byte_tanimoto(query_fp, target[1]) >= threshold)\n\n## def iter_count_tanimoto_hits(queries, targets, threshold):\n##     for query_id, query_fp in queries:\n##         yield query_id, sum(1 for target in targets\n##                                if bitops.byte_tanimoto(query_fp, target[1]) >= threshold)\n\ndef count_tanimoto_hits_arena(queries, targets, threshold):\n    counts = []\n    for query_id, query_fp in queries:\n        counts.append(sum(1 for target in targets\n                          if bitops.byte_tanimoto(query_fp, target[1]) >= threshold))\n    return counts\n\ndef tanimoto_count_once(queries, targets, threshold):\n    # Only go through the list of queries once\n    results = [0] * len(queries)\n    query_ids = []\n    query_fps = []\n    for (id, fp) in queries:\n        query_ids.append(id)\n        query_fps.append(fp)\n    for target_id, target_fp in targets:\n        for i, query_fp in enumerate(query_fps):\n            if bitops.byte_tanimoto(query_fp, target_fp) >= threshold:\n                results[i] += 1\n    return zip(query_ids, results)\n\n##########\n\ndef threshold_tanimoto_search_fp(query_fp, targets, threshold):\n    ids = []\n    scores = []\n    for target_id, target_fp in targets:\n        score = bitops.byte_tanimoto(query_fp, target_fp)\n        if score >= threshold:\n            ids.append(target_id)\n            scores.append(score)\n    return fps_search.FPSSearchResult(ids, scores)\n\ndef _iter_threshold_tanimoto_search(queries, targets, threshold):\n    for query_id, query_fp in queries:\n        yield threshold_tanimoto_search_fp(query_fp, targets, threshold)\n\ndef threshold_tanimoto_search_arena(queries, targets, threshold):\n    results = []\n    for query_id, query_fp in queries:\n        results.append(threshold_tanimoto_search_fp(query_fp, targets, threshold))\n    return fps_search.FPSSearchResults(results)\n\n#def tanimoto_search_all(queries, targets, threshold):\n#    results = [[] for i in xrange(len(queries))]\n#    query_fps = [query[1] for query in queries]\n#    for target_id, target_fp in targets:\n#        for i, query_fp in enumerate(query_fps):\n#            score = bitops.byte_tanimoto(query_fp, target_fp)\n#            if score >= threshold:\n#                results[i].append( (target_id, score) )\n#    return results\n\n##########\n\n\ndef knearest_tanimoto_search_fp(query_fp, targets, k, threshold):\n    hits = heapq.nlargest(k, threshold_tanimoto_search_fp(query_fp, targets, threshold),\n                          key = operator.itemgetter(1))\n    if hits:\n        ids, scores = zip(*hits)\n    else:\n        ids, scores = [], []\n    return fps_search.FPSSearchResult(ids, scores)\n\ndef _iter_knearest_tanimoto_search(queries, targets, k, threshold):\n    for hits in _iter_threshold_tanimoto_search(queries, targets, threshold):\n        hits = heapq.nlargest(k, hits, key = operator.itemgetter(1))\n        ids, scores = zip(*hits)\n        yield fps_search.FPSSearchResult(ids, scores)\n\ndef knearest_tanimoto_search_arena(queries, targets, k, threshold):\n    results = []\n    for row in _iter_knearest_tanimoto_search(queries, targets, k, threshold):\n        results.append(row)\n    return fps_search.FPSSearchResults(results)\n\n# I am not going to optimize this.\n#def knearest_tanimoto_search_all(queries, targets, k, threshold):\n#    results = tanimoto_search(queries, targets, threshold)\n#    for i, (id, hits) in enumerate(results):\n#        results[i] = (id, heapq.nlargest(k, hits, key = operator.itemgetter(1)))\n#    return results\n\ndef _check_threshold(threshold):\n    if not (0.0 <= threshold <= 1.0):\n        raise ValueError(\"threshold must between 0.0 and 1.0, inclusive\")\n\nclass SlowFingerprints(Fingerprints):\n    def count_tanimoto_hits_fp(self, query_fp, threshold=0.7):\n        if not (0.0 <= threshold <= 1.0):\n            raise ValueError(\"threshold must between 0.0 and 1.0, inclusive\")\n        return count_tanimoto_hits_fp(query_fp, self._id_fp_pairs, threshold)\n\n    def count_tanimoto_hits_arena(self, queries, threshold=0.7):\n        _check_threshold(threshold)\n        return count_tanimoto_hits_arena(queries, self._id_fp_pairs, threshold)\n\n    def threshold_tanimoto_search_fp(self, fp, threshold=0.7):\n        _check_threshold(threshold)\n        return threshold_tanimoto_search_fp(fp, self._id_fp_pairs, threshold)\n\n    def threshold_tanimoto_search_arena(self, queries, threshold=0.7):\n        _check_threshold(threshold)\n        return threshold_tanimoto_search_arena(queries, self._id_fp_pairs, threshold)\n\n    def knearest_tanimoto_search_fp(self, fp, k=3, threshold=0.7):\n        if k < 0:\n            raise ValueError(\"k must be non-negative\")\n        _check_threshold(threshold)\n        return knearest_tanimoto_search_fp(fp, self._id_fp_pairs, k, threshold)\n\n    def knearest_tanimoto_search_arena(self, queries, k=3, threshold=0.7, batch_size=100):\n        if k < 0:\n            raise ValueError(\"k must be non-negative\")\n        _check_threshold(threshold)\n        return knearest_tanimoto_search_arena(queries, self._id_fp_pairs, k, threshold)\n", "text_preview": "import heapq import operator from . import bitops from . import Fingerprints from . import fps_search def count_tanimoto_hits_fp(query_fp, targets, threshold): return sum(1 for target in targets if bitops.byte_tanimoto(query_fp, target[1]) ", "input_sha256": "91c0a881938fb5e4f4aac7d5ddb519445bbb0c5521e83f43dcd9e6c2196d4682", "text_sha256": "84b93ceeb3bd7170c5cad424537e1621314de51b2fe5740ea07dfa93845f835f", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12004, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.8216802643298189, "noise_severity": 0.03275796822269472, "missing_content_severity": 0.031291169324666385, "factual_density": 3.227739192355503, "pretraining_utility": 0.3836314412023249, "math_density": 2.074161822989135, "code_density": 3.801354305511669, "reasoning_density": 0.32340426406502687, "education_level": 2.4436010030478066, "creative_writing_quality": -0.15881893646856604}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.001455426198034404, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9980972484077381, "secondary": 0.994154196135856, "tertiary": 0.995672901733805, "structured_records": 0.001960301773471877, "procedural_steps": 0.002797756302826415, "worked_solution": 0.0011621785066190866, "contextual_dialogue": 5.626700594730198e-06, "content_quality": 0.5157071244712249, "noise_severity": 0.9727016931477543, "missing_content_severity": 0.973924025562778, "factual_density": 0.3564493269629193, "pretraining_utility": 0.4456805227971701, "math_density": 0.0, "code_density": 0.8344619212597242, "reasoning_density": 0.7304964466124776, "education_level": 0.5365784964315635, "creative_writing_quality": 0.9993062844806347, "primary_topic": 0.9252377563653822, "secondary_topic": 0.5777430270869514}, "probability_margin": {"primary": 0.9970376810018876, "secondary": 0.9911831276120165, "tertiary": 0.9934200654244026, "education_level": 0.32407132762895535, "creative_writing_quality": 0.9991037473135433, "primary_topic": 0.861590596419637, "secondary_topic": 0.3083632905231501}, "raw_probabilities": {"primary": {"Academic Writing": 3.0870366217998405e-05, "Content Listing": 1.0266562313136556e-05, "Creative Writing": 8.413957031536663e-06, "Customer Support Page": 4.3567627128981605e-06, "Q&A Forum": 6.502118297695356e-06, "Discussion Thread": 5.121723713793332e-06, "Comment Section": 5.951111000937762e-06, "Missing Content": 1.7734360391983583e-05, "Knowledge Article": 6.811078851516464e-05, "Legal Notices": 1.544438590802968e-05, "News Article": 2.351780357107403e-06, "Organizational Announcement": 6.08215099721756e-06, "Nonfiction Writing": 7.524128948686156e-06, "Organizational About Page": 7.535209001187681e-06, "Personal About Page": 6.732484752022761e-06, "Personal Blog": 5.846122070683927e-06, "Product Page": 4.142695530935389e-06, "Spam / Ads": 5.012647565679324e-06, "Structured Data": 0.00030518777546535534, "Technical Writing": 0.9981765297240823, "Transcript / Interview": 9.89866017774394e-06, "Tutorial / How-To Guide": 0.00014770123928598946, "User Reviews": 3.834523467002764e-06, "Other / Unknown": 0.0011388487221947077}, "secondary": {"Academic Writing": 0.00019945019878011904, "Content Listing": 3.70799227249964e-05, "Creative Writing": 4.270148771083069e-05, "Customer Support Page": 1.8596219316903505e-05, "Q&A Forum": 1.634876991843998e-05, "Discussion Thread": 1.8362642431795886e-05, "Comment Section": 3.529312556246115e-05, "Missing Content": 0.00016120222696194912, "Knowledge Article": 0.00022206978852613743, "Legal Notices": 6.15473727462977e-05, "News Article": 2.4989320720360925e-05, "Organizational Announcement": 2.159247131004225e-05, "Nonfiction Writing": 3.399523083294841e-05, "Organizational About Page": 2.8481439351915123e-05, "Personal About Page": 2.4641427546578802e-05, "Personal Blog": 4.850489285499754e-05, "Product Page": 1.3071166117853084e-05, "Spam / Ads": 2.4308378722484686e-05, "Structured Data": 0.0008851828037794852, "Technical Writing": 0.003214643684845513, "Transcript / Interview": 2.774975058471789e-05, "Tutorial / How-To Guide": 0.00042893706854247324, "User Reviews": 1.3479313248699934e-05, "NONE": 0.994397771296862}, "tertiary": {"Academic Writing": 9.44793102515993e-05, "Content Listing": 2.760567360791944e-05, "Creative Writing": 4.784424963868082e-05, "Customer Support Page": 1.6725525670250455e-05, "Q&A Forum": 1.4124947534821554e-05, "Discussion Thread": 1.6665499487484133e-05, "Comment Section": 4.804696889031733e-05, "Missing Content": 0.00014779769068724275, "Knowledge Article": 0.00015264526845752436, "Legal Notices": 7.244902324633868e-05, "News Article": 2.189381269622943e-05, "Organizational Announcement": 2.2050928760044713e-05, "Nonfiction Writing": 6.44571629398301e-05, "Organizational About Page": 3.0279545322371856e-05, "Personal About Page": 2.1152706109075405e-05, "Personal Blog": 0.00011220054152265906, "Product Page": 1.2774750291658292e-05, "Spam / Ads": 2.3202199372101974e-05, "Structured Data": 0.0004438861895497159, "Technical Writing": 0.0024331320704938076, "Transcript / Interview": 2.353336383369409e-05, "Tutorial / How-To Guide": 0.00028684876247368546, "User Reviews": 1.3006314266587138e-05, "NONE": 0.9958531974948964}, "structured_records": {"false": 0.9980396982265282, "true": 0.001960301773471877}, "procedural_steps": {"false": 0.9972022436971736, "true": 0.002797756302826415}, "worked_solution": {"false": 0.998837821493381, "true": 0.0011621785066190866}, "contextual_dialogue": {"false": 0.9999943732994052, "true": 5.626700594730198e-06}, "content_quality": {"-2": 0.00739244248572001, "-1": 0.042713626204765036, "0": 0.2721310132856655, "1": 0.47634706054167497, "2": 0.2014158574821745}, "noise_severity": {"0": 0.9720759407017676, "1": 0.024254005147810533, "2": 0.0027652819300913586, "3": 0.000645689666621035, "4": 0.000259082553709593}, "missing_content_severity": {"0": 0.9760286358884029, "1": 0.018605239795745188, "2": 0.003675403251657832, "3": 0.0014277612311696932, "4": 0.00026295983302411253}, "factual_density": {"0": 0.011712408689297498, "1": 0.02443629569278586, "2": 0.14373065866247772, "3": 0.3646409684839938, "4": 0.45547966847144505}, "pretraining_utility": {"-2": 0.009857017256472086, "-1": 0.12106193120759136, "0": 0.42515078975306053, "1": 0.36345311664289176, "2": 0.08047714513998433}, "math_density": {"0": 0.2242740343883724, "1": 0.1967387558479217, "2": 0.14378225511592932, "3": 0.1509612616817513, "4": 0.28424369296602525}, "code_density": {"0": 0.000263928477319141, "1": 0.0005768032990105802, "2": 0.02413507372568401, "3": 0.14758942323065463, "4": 0.8274347712673316}, "reasoning_density": {"0": 0.8401523072600374, "1": 0.05698511857711383, "2": 0.05946449095776858, "3": 0.026102169247945083, "4": 0.01729591395713516}, "education_level": {"none": 0.0011652818529016562, "0": 0.0014874206836434695, "1": 0.010420252420214451, "2": 0.6138154136929695, "3": 0.2897440860640142, "4": 0.08336754528625663}, "creative_writing_quality": {"not_applicable": 0.9994219037338623, "-2": 5.680409548507334e-05, "-1": 0.00010323521708886558, "0": 0.0003181564203189913, "1": 7.477029259453572e-05, "2": 2.5130240650034783e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00010502521569367106, "Art & Design": 0.00018248723306135112, "Law & Crime": 0.00013783696321223958, "Education & Careers": 0.00010477976077437422, "Entertainment & Media": 0.00010792946807775455, "Fashion & Beauty": 0.00014531533009699276, "Business, Economics & Finance": 0.0001435091265557083, "Food & Dining": 0.00014363872394399684, "Games & Gaming": 0.00017397815436826576, "Electronics & Hardware": 0.0002082583479334723, "Health, Healthcare & Wellness": 0.0004106920403950382, "History & Geography": 0.00016606614013483915, "Home, Pets & Hobbies": 9.585494767471258e-05, "Industry & Production": 0.00019982002386645267, "Language, Literature & Philosophy": 0.00013082583722375345, "Politics & Government": 6.6115916012948e-05, "Religion & Spirituality": 0.00010945966012370495, "Science, Mathematics & Engineering": 0.06652263085476894, "Relationships & Community": 5.891934104921168e-05, "Software Usage & Applications": 0.0005190643714946222, "Programming & Software Engineering": 0.9281132272744059, "Sports & Fitness": 0.00015648696417035798, "Vehicles & Transportation": 6.853067719848004e-05, "Travel & Tourism": 0.00013497245203337392, "Other / Uncovered Subject": 0.0015021430512377014, "Unknown / Insufficient Content": 0.00029243212449241813}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007838172924172357, "Art & Design": 0.0006061111861611413, "Law & Crime": 0.000670254817912357, "Education & Careers": 0.0004005552440595744, "Entertainment & Media": 0.0004597421273960422, "Fashion & Beauty": 0.00045180027041673575, "Business, Economics & Finance": 0.0005929108547633593, "Food & Dining": 0.00037527353269517364, "Games & Gaming": 0.0004767008528286605, "Electronics & Hardware": 0.0006709832677478281, "Health, Healthcare & Wellness": 0.0009612711560477335, "History & Geography": 0.00046278001635010523, "Home, Pets & Hobbies": 0.0003256660337661049, "Industry & Production": 0.0004913613257796569, "Language, Literature & Philosophy": 0.0002805912418639282, "Politics & Government": 0.0002685926382299794, "Religion & Spirituality": 0.0005073854116756307, "Science, Mathematics & Engineering": 0.2856203893681493, "Relationships & Community": 0.0001883557067145921, "Software Usage & Applications": 0.0008339491320608917, "Programming & Software Engineering": 0.10715096610745989, "Sports & Fitness": 0.00039495285443089676, "Vehicles & Transportation": 0.0002057534227794452, "Travel & Tourism": 0.0005715033681944279, "Other / Uncovered Subject": 0.0022646528787999966, "NONE": 0.5939836798912994}}, "probabilities": {"primary": {"Academic Writing": 3.087036621799841e-05, "Content Listing": 1.0266562313136558e-05, "Creative Writing": 8.413957031536665e-06, "Customer Support Page": 4.356762712898161e-06, "Q&A Forum": 6.502118297695358e-06, "Discussion Thread": 5.121723713793333e-06, "Comment Section": 5.9511110009377635e-06, "Missing Content": 1.7734360391983586e-05, "Knowledge Article": 6.811078851516466e-05, "Legal Notices": 1.5444385908029683e-05, "News Article": 2.3517803571074032e-06, "Organizational Announcement": 6.082150997217562e-06, "Nonfiction Writing": 7.524128948686158e-06, "Organizational About Page": 7.535209001187682e-06, "Personal About Page": 6.732484752022762e-06, "Personal Blog": 5.846122070683929e-06, "Product Page": 4.14269553093539e-06, "Spam / Ads": 5.012647565679325e-06, "Structured Data": 0.0003051877754653554, "Technical Writing": 0.9981765297240826, "Transcript / Interview": 9.898660177743941e-06, "Tutorial / How-To Guide": 0.0001477012392859895, "User Reviews": 3.834523467002765e-06, "Other / Unknown": 0.001138848722194708}, "secondary": {"Academic Writing": 0.00019945019878011904, "Content Listing": 3.70799227249964e-05, "Creative Writing": 4.270148771083069e-05, "Customer Support Page": 1.8596219316903505e-05, "Q&A Forum": 1.634876991843998e-05, "Discussion Thread": 1.8362642431795886e-05, "Comment Section": 3.529312556246115e-05, "Missing Content": 0.00016120222696194912, "Knowledge Article": 0.00022206978852613743, "Legal Notices": 6.15473727462977e-05, "News Article": 2.4989320720360925e-05, "Organizational Announcement": 2.159247131004225e-05, "Nonfiction Writing": 3.399523083294841e-05, "Organizational About Page": 2.8481439351915123e-05, "Personal About Page": 2.4641427546578802e-05, "Personal Blog": 4.850489285499754e-05, "Product Page": 1.3071166117853084e-05, "Spam / Ads": 2.4308378722484686e-05, "Structured Data": 0.0008851828037794852, "Technical Writing": 0.003214643684845513, "Transcript / Interview": 2.774975058471789e-05, "Tutorial / How-To Guide": 0.00042893706854247324, "User Reviews": 1.3479313248699934e-05, "NONE": 0.994397771296862}, "tertiary": {"Academic Writing": 9.44793102515993e-05, "Content Listing": 2.760567360791944e-05, "Creative Writing": 4.784424963868082e-05, "Customer Support Page": 1.6725525670250455e-05, "Q&A Forum": 1.4124947534821554e-05, "Discussion Thread": 1.6665499487484133e-05, "Comment Section": 4.804696889031733e-05, "Missing Content": 0.00014779769068724275, "Knowledge Article": 0.00015264526845752436, "Legal Notices": 7.244902324633868e-05, "News Article": 2.189381269622943e-05, "Organizational Announcement": 2.2050928760044713e-05, "Nonfiction Writing": 6.44571629398301e-05, "Organizational About Page": 3.0279545322371856e-05, "Personal About Page": 2.1152706109075405e-05, "Personal Blog": 0.00011220054152265906, "Product Page": 1.2774750291658292e-05, "Spam / Ads": 2.3202199372101974e-05, "Structured Data": 0.0004438861895497159, "Technical Writing": 0.0024331320704938076, "Transcript / Interview": 2.353336383369409e-05, "Tutorial / How-To Guide": 0.00028684876247368546, "User Reviews": 1.3006314266587138e-05, "NONE": 0.9958531974948964}, "structured_records": {"false": 0.9980396982265282, "true": 0.001960301773471877}, "procedural_steps": {"false": 0.9972022436971736, "true": 0.002797756302826415}, "worked_solution": {"false": 0.998837821493381, "true": 0.0011621785066190866}, "contextual_dialogue": {"false": 0.9999943732994052, "true": 5.626700594730198e-06}, "content_quality": {"-2": 0.00739244248572001, "-1": 0.042713626204765036, "0": 0.2721310132856655, "1": 0.47634706054167497, "2": 0.2014158574821745}, "noise_severity": {"0": 0.9720759407017674, "1": 0.024254005147810526, "2": 0.002765281930091358, "3": 0.0006456896666210349, "4": 0.00025908255370959293}, "missing_content_severity": {"0": 0.9760286358884032, "1": 0.01860523979574519, "2": 0.003675403251657833, "3": 0.0014277612311696935, "4": 0.0002629598330241126}, "factual_density": {"0": 0.0117124086892975, "1": 0.024436295692785864, "2": 0.14373065866247775, "3": 0.36464096848399385, "4": 0.4554796684714451}, "pretraining_utility": {"-2": 0.009857017256472086, "-1": 0.12106193120759136, "0": 0.42515078975306053, "1": 0.36345311664289176, "2": 0.08047714513998433}, "math_density": {"0": 0.2242740343883724, "1": 0.1967387558479217, "2": 0.14378225511592932, "3": 0.1509612616817513, "4": 0.28424369296602525}, "code_density": {"0": 0.00026392847731914105, "1": 0.0005768032990105803, "2": 0.024135073725684013, "3": 0.14758942323065466, "4": 0.8274347712673317}, "reasoning_density": {"0": 0.8401523072600374, "1": 0.05698511857711383, "2": 0.05946449095776858, "3": 0.026102169247945083, "4": 0.01729591395713516}, "education_level": {"none": 0.0011652818529016562, "0": 0.0014874206836434695, "1": 0.010420252420214451, "2": 0.6138154136929695, "3": 0.2897440860640142, "4": 0.08336754528625663}, "creative_writing_quality": {"not_applicable": 0.9994219037338625, "-2": 5.680409548507335e-05, "-1": 0.00010323521708886561, "0": 0.00031815642031899136, "1": 7.477029259453574e-05, "2": 2.513024065003479e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00010502521569367103, "Art & Design": 0.0001824872330613511, "Law & Crime": 0.00013783696321223956, "Education & Careers": 0.0001047797607743742, "Entertainment & Media": 0.00010792946807775452, "Fashion & Beauty": 0.00014531533009699274, "Business, Economics & Finance": 0.00014350912655570827, "Food & Dining": 0.00014363872394399681, "Games & Gaming": 0.00017397815436826574, "Electronics & Hardware": 0.00020825834793347225, "Health, Healthcare & Wellness": 0.0004106920403950381, "History & Geography": 0.00016606614013483912, "Home, Pets & Hobbies": 9.585494767471256e-05, "Industry & Production": 0.00019982002386645262, "Language, Literature & Philosophy": 0.00013082583722375343, "Politics & Government": 6.611591601294799e-05, "Religion & Spirituality": 0.00010945966012370492, "Science, Mathematics & Engineering": 0.06652263085476892, "Relationships & Community": 5.891934104921167e-05, "Software Usage & Applications": 0.0005190643714946221, "Programming & Software Engineering": 0.9281132272744057, "Sports & Fitness": 0.00015648696417035795, "Vehicles & Transportation": 6.853067719848003e-05, "Travel & Tourism": 0.0001349724520333739, "Other / Uncovered Subject": 0.001502143051237701, "Unknown / Insufficient Content": 0.0002924321244924181}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007838172924172355, "Art & Design": 0.0006061111861611412, "Law & Crime": 0.0006702548179123569, "Education & Careers": 0.0004005552440595743, "Entertainment & Media": 0.0004597421273960421, "Fashion & Beauty": 0.00045180027041673564, "Business, Economics & Finance": 0.0005929108547633591, "Food & Dining": 0.00037527353269517353, "Games & Gaming": 0.0004767008528286604, "Electronics & Hardware": 0.000670983267747828, "Health, Healthcare & Wellness": 0.0009612711560477333, "History & Geography": 0.0004627800163501051, "Home, Pets & Hobbies": 0.00032566603376610483, "Industry & Production": 0.0004913613257796568, "Language, Literature & Philosophy": 0.00028059124186392815, "Politics & Government": 0.00026859263822997934, "Religion & Spirituality": 0.0005073854116756306, "Science, Mathematics & Engineering": 0.28562038936814926, "Relationships & Community": 0.00018835570671459203, "Software Usage & Applications": 0.0008339491320608915, "Programming & Software Engineering": 0.10715096610745986, "Sports & Fitness": 0.00039495285443089665, "Vehicles & Transportation": 0.00020575342277944514, "Travel & Tourism": 0.0005715033681944278, "Other / Uncovered Subject": 0.002264652878799996, "NONE": 0.5939836798912993}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9899, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9899, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:06.504238+00:00", "scoring_key_slot": "primary", "dataset_uid": "896cf7bc-cca8-4372-8c28-207fe6ef4234", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "fc13ca38a6be655d658038be9e8d143e18d1519d929f8a471e48a664b47c245b", "source_metadata": {"uuid": "896cf7bc-cca8-4372-8c28-207fe6ef4234", "repo_name": "llazzaro/chem-fingerprints", "relative_path": "chemfp/slow.py", "category": "ALGO", "algo_rel_score": 0.9359116554260254, "quality_score": 6.43178129196167, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 1092, "repo_created_at": "2015-11-05T04:32:52.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 3, "forks": 1, "issues": 11, "pull_requests": 0}, "file_meta": {"content_id": "f80c294dc162341f5980a1500d850e4cc610d518", "size_bytes": 5262, "file_timestamp": 1370475196, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "44b0a2e0cee9307d302b307255889e09db22184a"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 1092, "repo_created_at": "2015-11-05T04:32:52.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 3, "forks": 1, "issues": 11, "pull_requests": 0}, "file_meta": {"content_id": "f80c294dc162341f5980a1500d850e4cc610d518", "size_bytes": 5262, "file_timestamp": 1370475196, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "44b0a2e0cee9307d302b307255889e09db22184a", "url": "https://github.com/llazzaro/chem-fingerprints/blob/44b0a2e0cee9307d302b307255889e09db22184a/chemfp/slow.py"}, "gpt4o_tokens": 1307, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.8216802643298189, "noise_severity": 0.03275796822269473, "missing_content_severity": 0.031291169324666385, "factual_density": 3.227739192355503, "pretraining_utility": 0.3836314412023249, "math_density": 2.074161822989135, "code_density": 3.801354305511669, "reasoning_density": 0.32340426406502687, "education_level": 2.4436010030478066, "creative_writing_quality": -0.15881893646856604}, "score_applicability": {"education_level": 0.9988347181470983, "creative_writing_quality": 0.0005780962661375009}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/zeek/broker/blob/dffb17dd53a40b221ddc83bb8e2c05714d0a56fa/tests/btest/web-socket/encrypted.py", "id": "python-0-ca4ddd606c921799f9ffb5b82e8ab4bd", "text": "# @TEST-GROUP: web-socket\n#\n# @TEST-PORT: BROKER_WEB_SOCKET_PORT\n#\n# @TEST-COPY-FILE: ${CERTS_ROOT}/ca.pem\n# @TEST-COPY-FILE: ${CERTS_ROOT}/cert.1.pem\n# @TEST-COPY-FILE: ${CERTS_ROOT}/key.1.enc.pem\n# @TEST-COPY-FILE: ${CERTS_ROOT}/cert.2.pem\n# @TEST-COPY-FILE: ${CERTS_ROOT}/key.2.pem\n# @TEST-COPY-FILE: ${API_ROOT}/v1-broker-out.json\n#\n# @TEST-EXEC: btest-bg-run node \"broker-node --config-file=../node.cfg\"\n# @TEST-EXEC: btest-bg-run recv \"python3 ../recv.py >recv.out\"\n# @TEST-EXEC: $SCRIPTS/wait-for-file recv/ready 15 || (btest-bg-wait -k 1 && false)\n#\n# @TEST-EXEC: btest-bg-run send \"python3 ../send.py\"\n#\n# @TEST-EXEC: $SCRIPTS/wait-for-file recv/done 30 || (btest-bg-wait -k 1 && false)\n# @TEST-EXEC: btest-diff recv/recv.out\n#\n# @TEST-EXEC: btest-bg-wait -k 1\n\n@TEST-START-FILE node.cfg\n\nbroker {\n  ssl {\n    cafile = \"../ca.pem\"\n    certificate = \"../cert.1.pem\"\n    key = \"../key.1.enc.pem\"\n    passphrase = \"<PASSWORD>\"\n  }\n}\ntopics = [\"/test\"]\nverbose = true\n\n@TEST-END-FILE\n\n@TEST-START-FILE recv.py\n\nimport asyncio, websockets, os, time, json, sys, ssl\n\nfrom jsonschema import validate\n\nws_port = os.environ['BROKER_WEB_SOCKET_PORT'].split('/')[0]\n\nws_url = f'wss://localhost:{ws_port}/v1/messages/json'\n\nssl_ctx = ssl.SSLContext(ssl.PROTOCOL_TLS)\nssl_ctx.load_verify_locations(\"../ca.pem\")\nssl_ctx.load_cert_chain(certfile=\"../cert.2.pem\", keyfile=\"../key.2.pem\")\n\nschema = json.load(open('../v1-broker-out.json'))\n\nasync def do_run():\n    # Try up to 30 times.\n    connected  = False\n    for i in range(30):\n        try:\n            ws = await websockets.connect(ws_url, ssl=ssl_ctx)\n            connected  = True\n            # send filter and wait for ack\n            await ws.send('[\"/test\"]')\n            ack_json = await ws.recv()\n            try:\n                validate(json.loads(ack_json), schema)\n            except Exception as err:\n                print(f'received invalid ack: {err}')\n                sys.exit(1)\n            # tell btest to start the sender now\n            with open('ready', 'w') as f:\n                f.write('ready')\n            # dump messages to stdout (redirected to recv.out)\n            for i in range(10):\n                msg = await ws.recv()\n                try:\n                    validate(json.loads(msg), schema)\n                except Exception as err:\n                    print(f'received invalid data message: {err}')\n                    sys.exit(1)\n                print(f'{msg}')\n            # tell btest we're done\n            with open('done', 'w') as f:\n                f.write('done')\n            await ws.close()\n            sys.exit()\n        except:\n            if not connected:\n                print(f'failed to connect to {ws_url}, try again', file=sys.stderr)\n                time.sleep(1)\n            else:\n                sys.exit()\n\nloop = asyncio.get_event_loop()\nloop.run_until_complete(do_run())\n\n@TEST-END-FILE\n\n@TEST-START-FILE send.py\n\nimport asyncio, websockets, os, json, sys, ssl\n\nws_port = os.environ['BROKER_WEB_SOCKET_PORT'].split('/')[0]\n\nws_url = f'wss://localhost:{ws_port}/v1/messages/json'\n\nssl_ctx = ssl.SSLContext(ssl.PROTOCOL_TLS)\nssl_ctx.load_verify_locations(\"../ca.pem\")\nssl_ctx.load_cert_chain(certfile=\"../cert.2.pem\", keyfile=\"../key.2.pem\")\n\nmsg = {\n    'type': 'data-message',\n    'topic': '/test',\n    '@data-type': \"count\",\n    \"data\": 0\n}\n\nasync def do_run():\n    async with websockets.connect(ws_url, ssl=ssl_ctx) as ws:\n      await ws.send('[]')\n      await ws.recv() # wait for ACK\n      for i in range(10):\n          msg['data'] += 1\n          await ws.send(json.dumps(msg))\n      await ws.close()\n\nloop = asyncio.get_event_loop()\nloop.run_until_complete(do_run())\n\n@TEST-END-FILE\n", "text_preview": "# @TEST-GROUP: web-socket # # @TEST-PORT: BROKER_WEB_SOCKET_PORT # # @TEST-COPY-FILE: ${CERTS_ROOT}/ca.pem # @TEST-COPY-FILE: ${CERTS_ROOT}/cert.1.pem # @TEST-COPY-FILE: ${CERTS_ROOT}/key.1.enc.pem # @TEST-COPY-FILE: ${CERTS_ROOT}/cert.2.pe", "input_sha256": "66076a757835340212231bfc2dc3592fc629e860f5957fef48e748ab0e411c23", "text_sha256": "ca4ddd606c921799f9ffb5b82e8ab4bdbbb4ffe99b8e7d30ce2c40eca507588a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12003, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.0753066858416955, "noise_severity": 0.07241816368292872, "missing_content_severity": 0.305287869516792, "factual_density": 3.3134797246782766, "pretraining_utility": 0.5686492298856919, "math_density": 0.12966873678104007, "code_density": 3.4155060069683914, "reasoning_density": 0.20090323872869728, "education_level": 2.63008634135256, "creative_writing_quality": -0.2520741316722677}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.05919031833893018, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9843920288423605, "secondary": 0.6726862432662394, "tertiary": 0.7344500537068619, "structured_records": 0.3058040114409279, "procedural_steps": 0.1598531883294698, "worked_solution": 0.007194884652298536, "contextual_dialogue": 0.00022659138306309627, "content_quality": 0.5094398130127539, "noise_severity": 0.9396515302642261, "missing_content_severity": 0.74559344206934, "factual_density": 0.5238800364715301, "pretraining_utility": 0.4958394775097855, "math_density": 0.8919427193491333, "code_density": 0.5129216724736596, "reasoning_density": 0.8325806343927522, "education_level": 0.40002879682375586, "creative_writing_quality": 0.9996639237466318, "primary_topic": 0.9957724718231243, "secondary_topic": 0.8919176864309527}, "probability_margin": {"primary": 0.9764878290114969, "secondary": 0.45463186008835715, "tertiary": 0.5711678928763227, "education_level": 0.07338509955330541, "creative_writing_quality": 0.999599235566189, "primary_topic": 0.9942309895362568, "secondary_topic": 0.8479111310861255}, "raw_probabilities": {"primary": {"Academic Writing": 4.909283454168374e-05, "Content Listing": 5.607213996608168e-05, "Creative Writing": 1.9660509634828006e-05, "Customer Support Page": 2.1980688562738216e-05, "Q&A Forum": 1.7985914495884595e-05, "Discussion Thread": 1.311243848332378e-05, "Comment Section": 1.931223339174905e-05, "Missing Content": 3.446557334962379e-05, "Knowledge Article": 6.481629516332551e-05, "Legal Notices": 2.745527356578696e-05, "News Article": 8.816356180282667e-06, "Organizational Announcement": 1.7189044399964222e-05, "Nonfiction Writing": 1.6212575333459747e-05, "Organizational About Page": 1.8214516924636605e-05, "Personal About Page": 1.4099458248787667e-05, "Personal Blog": 1.1375271976858829e-05, "Product Page": 1.3580339454955722e-05, "Spam / Ads": 1.3750192619482034e-05, "Structured Data": 0.0031540040333507898, "Technical Writing": 0.9850423609739288, "Transcript / Interview": 3.2566637631395566e-05, "Tutorial / How-To Guide": 0.0027690857290178523, "User Reviews": 1.0259007345542809e-05, "Other / Unknown": 0.008554531962431902}, "secondary": {"Academic Writing": 0.0008123847913277202, "Content Listing": 0.0007908551476627774, "Creative Writing": 0.000544345393931741, "Customer Support Page": 0.00037829713617792277, "Q&A Forum": 0.0003977130227523887, "Discussion Thread": 0.0003117026669447946, "Comment Section": 0.0004060128861366056, "Missing Content": 0.0014001321050792618, "Knowledge Article": 0.0008630156013715974, "Legal Notices": 0.0005035144960262922, "News Article": 0.0003508764278707387, "Organizational Announcement": 0.00042520091940388466, "Nonfiction Writing": 0.0004380567181616538, "Organizational About Page": 0.0004864874406092812, "Personal About Page": 0.00036977802043668725, "Personal Blog": 0.00030901856231965854, "Product Page": 0.00027667705060637556, "Spam / Ads": 0.00038991047412458826, "Structured Data": 0.2316924563751222, "Technical Writing": 0.05617271177921757, "Transcript / Interview": 0.0005850498152076553, "Tutorial / How-To Guide": 0.015552699577254882, "User Reviews": 0.00021878712877447979, "NONE": 0.6863243164634794}, "tertiary": {"Academic Writing": 0.0007832546231942787, "Content Listing": 0.0006509234754619892, "Creative Writing": 0.0007074412092710861, "Customer Support Page": 0.00044909392624511367, "Q&A Forum": 0.00033781109183741954, "Discussion Thread": 0.0002971253858978591, "Comment Section": 0.0004029181672592461, "Missing Content": 0.0018997290920640228, "Knowledge Article": 0.0008418170945507553, "Legal Notices": 0.0005379627429261361, "News Article": 0.0003437218465409463, "Organizational Announcement": 0.00039049393339573777, "Nonfiction Writing": 0.00040410070778470374, "Organizational About Page": 0.0004771142798238242, "Personal About Page": 0.00032407928035369996, "Personal Blog": 0.00031264743234135585, "Product Page": 0.0002711855281043165, "Spam / Ads": 0.0003793659550976429, "Structured Data": 0.1743467419260866, "Technical Writing": 0.057163177404989926, "Transcript / Interview": 0.0005786488407011705, "Tutorial / How-To Guide": 0.012384942960155316, "User Reviews": 0.00020106829350745474, "NONE": 0.7455146348024093}, "structured_records": {"false": 0.694195988559072, "true": 0.3058040114409279}, "procedural_steps": {"false": 0.8401468116705302, "true": 0.1598531883294698}, "worked_solution": {"false": 0.9928051153477014, "true": 0.007194884652298536}, "contextual_dialogue": {"false": 0.9997734086169369, "true": 0.00022659138306309627}, "content_quality": {"-2": 0.006078734910593227, "-1": 0.03859134065823206, "0": 0.1612638832232562, "1": 0.462076586094723, "2": 0.3319894551131954}, "noise_severity": {"0": 0.9383728081680918, "1": 0.053379009266356324, "2": 0.006357800256142252, "3": 0.0012379753333505957, "4": 0.0006524069760590264}, "missing_content_severity": {"0": 0.7092256437054637, "1": 0.28135842709118086, "2": 0.00583514920975061, "3": 0.0020639759683090714, "4": 0.001516804025295665}, "factual_density": {"0": 0.0028605607956079964, "1": 0.008100077038365976, "2": 0.10415027931438754, "3": 0.4424772423954181, "4": 0.4424118404562204}, "pretraining_utility": {"-2": 0.008749331758901102, "-1": 0.07213492282538835, "0": 0.34765385762380274, "1": 0.4846409593549331, "2": 0.08682092843697464}, "math_density": {"0": 0.88587880022087, "1": 0.1058263445787216, "2": 0.004152144939164784, "3": 0.0010327387209852605, "4": 0.0031099715402582787}, "code_density": {"0": 0.0006859527658454516, "1": 0.0016212572391285467, "2": 0.0642976228918194, "3": 0.4482911644672022, "4": 0.4851040026360044}, "reasoning_density": {"0": 0.8772291942197625, "1": 0.05953853549281567, "2": 0.05019484041917223, "3": 0.011174697075461247, "4": 0.0018627327927883527}, "education_level": {"none": 0.00092601310908251, "0": 0.001126185368543614, "1": 0.0036128554047560432, "2": 0.42663889779982445, "3": 0.5000239973531299, "4": 0.06767205096466347}, "creative_writing_quality": {"not_applicable": 0.9997199364555265, "-2": 4.170457909050714e-05, "-1": 6.0785912781149915e-05, "0": 0.0001207008893374532, "1": 4.014603035278605e-05, "2": 1.6726132911584027e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.2412714586391495e-05, "Art & Design": 2.080325065282671e-05, "Law & Crime": 3.591976125349809e-05, "Education & Careers": 1.6925306888947307e-05, "Entertainment & Media": 1.443249075037532e-05, "Fashion & Beauty": 9.17480295636613e-06, "Business, Economics & Finance": 3.0243404532343016e-05, "Food & Dining": 1.3727684253376318e-05, "Games & Gaming": 2.9540577054895375e-05, "Electronics & Hardware": 7.481828139627241e-05, "Health, Healthcare & Wellness": 2.2412949706357625e-05, "History & Geography": 2.2142622072074115e-05, "Home, Pets & Hobbies": 1.2135123015095641e-05, "Industry & Production": 1.8213537728740014e-05, "Language, Literature & Philosophy": 2.041373396214119e-05, "Politics & Government": 1.2409021768509602e-05, "Religion & Spirituality": 2.3130875580540723e-05, "Science, Mathematics & Engineering": 0.0017040795244397697, "Relationships & Community": 1.0204667308611462e-05, "Software Usage & Applications": 0.0016263107866664411, "Programming & Software Engineering": 0.9959350690606965, "Sports & Fitness": 2.313072116580244e-05, "Vehicles & Transportation": 1.1808429396813377e-05, "Travel & Tourism": 3.119621778707965e-05, "Other / Uncovered Subject": 0.00022731072283621053, "Unknown / Insufficient Content": 3.20337315440491e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006714813085025981, "Art & Design": 0.0003531821297736082, "Law & Crime": 0.004417421714054144, "Education & Careers": 0.0002655366197884666, "Entertainment & Media": 0.00032342904678271157, "Fashion & Beauty": 0.00017222713932113176, "Business, Economics & Finance": 0.0004798061959674869, "Food & Dining": 0.00017492881182389823, "Games & Gaming": 0.00028033608048857984, "Electronics & Hardware": 0.0003926194286251449, "Health, Healthcare & Wellness": 0.00030723398275748695, "History & Geography": 0.0002558483107788416, "Home, Pets & Hobbies": 0.0001709409718311194, "Industry & Production": 0.00019644071489367023, "Language, Literature & Philosophy": 0.00016785039601385234, "Politics & Government": 0.00023317235863807266, "Religion & Spirituality": 0.00034927005972512626, "Science, Mathematics & Engineering": 0.022889903027361068, "Relationships & Community": 0.00013614141000749827, "Software Usage & Applications": 0.020965889870010183, "Programming & Software Engineering": 0.048163567405175196, "Sports & Fitness": 0.00021209861979955524, "Vehicles & Transportation": 0.0001229043725569156, "Travel & Tourism": 0.00040819673396967827, "Other / Uncovered Subject": 0.0018148748000533308, "NONE": 0.8960746984913007}}, "probabilities": {"primary": {"Academic Writing": 4.9092834541683755e-05, "Content Listing": 5.6072139966081695e-05, "Creative Writing": 1.966050963482801e-05, "Customer Support Page": 2.198068856273822e-05, "Q&A Forum": 1.79859144958846e-05, "Discussion Thread": 1.3112438483323783e-05, "Comment Section": 1.9312233391749053e-05, "Missing Content": 3.4465573349623796e-05, "Knowledge Article": 6.481629516332553e-05, "Legal Notices": 2.7455273565786967e-05, "News Article": 8.816356180282669e-06, "Organizational Announcement": 1.7189044399964226e-05, "Nonfiction Writing": 1.621257533345975e-05, "Organizational About Page": 1.821451692463661e-05, "Personal About Page": 1.409945824878767e-05, "Personal Blog": 1.137527197685883e-05, "Product Page": 1.3580339454955725e-05, "Spam / Ads": 1.3750192619482038e-05, "Structured Data": 0.0031540040333507906, "Technical Writing": 0.985042360973929, "Transcript / Interview": 3.256663763139557e-05, "Tutorial / How-To Guide": 0.0027690857290178527, "User Reviews": 1.025900734554281e-05, "Other / Unknown": 0.008554531962431904}, "secondary": {"Academic Writing": 0.00081238479132772, "Content Listing": 0.0007908551476627772, "Creative Writing": 0.0005443453939317409, "Customer Support Page": 0.00037829713617792266, "Q&A Forum": 0.0003977130227523886, "Discussion Thread": 0.00031170266694479455, "Comment Section": 0.0004060128861366055, "Missing Content": 0.0014001321050792616, "Knowledge Article": 0.0008630156013715972, "Legal Notices": 0.0005035144960262921, "News Article": 0.00035087642787073864, "Organizational Announcement": 0.00042520091940388455, "Nonfiction Writing": 0.0004380567181616537, "Organizational About Page": 0.00048648744060928107, "Personal About Page": 0.00036977802043668714, "Personal Blog": 0.0003090185623196585, "Product Page": 0.0002766770506063755, "Spam / Ads": 0.00038991047412458815, "Structured Data": 0.23169245637512215, "Technical Writing": 0.05617271177921756, "Transcript / Interview": 0.0005850498152076552, "Tutorial / How-To Guide": 0.015552699577254879, "User Reviews": 0.00021878712877447973, "NONE": 0.6863243164634792}, "tertiary": {"Academic Writing": 0.0007832546231942788, "Content Listing": 0.0006509234754619893, "Creative Writing": 0.0007074412092710862, "Customer Support Page": 0.0004490939262451137, "Q&A Forum": 0.0003378110918374196, "Discussion Thread": 0.00029712538589785916, "Comment Section": 0.00040291816725924616, "Missing Content": 0.001899729092064023, "Knowledge Article": 0.0008418170945507554, "Legal Notices": 0.0005379627429261362, "News Article": 0.00034372184654094635, "Organizational Announcement": 0.0003904939333957378, "Nonfiction Writing": 0.0004041007077847038, "Organizational About Page": 0.00047711427982382427, "Personal About Page": 0.0003240792803537, "Personal Blog": 0.0003126474323413559, "Product Page": 0.00027118552810431653, "Spam / Ads": 0.00037936595509764295, "Structured Data": 0.17434674192608662, "Technical Writing": 0.05716317740498993, "Transcript / Interview": 0.0005786488407011706, "Tutorial / How-To Guide": 0.012384942960155318, "User Reviews": 0.00020106829350745477, "NONE": 0.7455146348024094}, "structured_records": {"false": 0.694195988559072, "true": 0.3058040114409279}, "procedural_steps": {"false": 0.8401468116705302, "true": 0.1598531883294698}, "worked_solution": {"false": 0.9928051153477014, "true": 0.007194884652298536}, "contextual_dialogue": {"false": 0.9997734086169369, "true": 0.00022659138306309627}, "content_quality": {"-2": 0.006078734910593228, "-1": 0.03859134065823207, "0": 0.16126388322325622, "1": 0.4620765860947231, "2": 0.33198945511319544}, "noise_severity": {"0": 0.9383728081680918, "1": 0.053379009266356324, "2": 0.006357800256142252, "3": 0.0012379753333505957, "4": 0.0006524069760590264}, "missing_content_severity": {"0": 0.7092256437054638, "1": 0.2813584270911809, "2": 0.0058351492097506105, "3": 0.002063975968309072, "4": 0.0015168040252956652}, "factual_density": {"0": 0.0028605607956079964, "1": 0.008100077038365976, "2": 0.10415027931438754, "3": 0.4424772423954181, "4": 0.4424118404562204}, "pretraining_utility": {"-2": 0.008749331758901102, "-1": 0.07213492282538835, "0": 0.34765385762380274, "1": 0.4846409593549331, "2": 0.08682092843697464}, "math_density": {"0": 0.8858788002208701, "1": 0.10582634457872162, "2": 0.004152144939164785, "3": 0.0010327387209852607, "4": 0.003109971540258279}, "code_density": {"0": 0.0006859527658454516, "1": 0.0016212572391285467, "2": 0.0642976228918194, "3": 0.4482911644672022, "4": 0.4851040026360044}, "reasoning_density": {"0": 0.8772291942197625, "1": 0.05953853549281567, "2": 0.05019484041917223, "3": 0.011174697075461247, "4": 0.0018627327927883527}, "education_level": {"none": 0.0009260131090825101, "0": 0.0011261853685436143, "1": 0.0036128554047560437, "2": 0.4266388977998245, "3": 0.50002399735313, "4": 0.06767205096466349}, "creative_writing_quality": {"not_applicable": 0.9997199364555265, "-2": 4.170457909050714e-05, "-1": 6.0785912781149915e-05, "0": 0.0001207008893374532, "1": 4.014603035278605e-05, "2": 1.6726132911584027e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.2412714586391495e-05, "Art & Design": 2.080325065282671e-05, "Law & Crime": 3.591976125349809e-05, "Education & Careers": 1.6925306888947307e-05, "Entertainment & Media": 1.443249075037532e-05, "Fashion & Beauty": 9.17480295636613e-06, "Business, Economics & Finance": 3.0243404532343016e-05, "Food & Dining": 1.3727684253376318e-05, "Games & Gaming": 2.9540577054895375e-05, "Electronics & Hardware": 7.481828139627241e-05, "Health, Healthcare & Wellness": 2.2412949706357625e-05, "History & Geography": 2.2142622072074115e-05, "Home, Pets & Hobbies": 1.2135123015095641e-05, "Industry & Production": 1.8213537728740014e-05, "Language, Literature & Philosophy": 2.041373396214119e-05, "Politics & Government": 1.2409021768509602e-05, "Religion & Spirituality": 2.3130875580540723e-05, "Science, Mathematics & Engineering": 0.0017040795244397697, "Relationships & Community": 1.0204667308611462e-05, "Software Usage & Applications": 0.0016263107866664411, "Programming & Software Engineering": 0.9959350690606965, "Sports & Fitness": 2.313072116580244e-05, "Vehicles & Transportation": 1.1808429396813377e-05, "Travel & Tourism": 3.119621778707965e-05, "Other / Uncovered Subject": 0.00022731072283621053, "Unknown / Insufficient Content": 3.20337315440491e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006714813085025981, "Art & Design": 0.0003531821297736082, "Law & Crime": 0.004417421714054144, "Education & Careers": 0.0002655366197884666, "Entertainment & Media": 0.00032342904678271157, "Fashion & Beauty": 0.00017222713932113176, "Business, Economics & Finance": 0.0004798061959674869, "Food & Dining": 0.00017492881182389823, "Games & Gaming": 0.00028033608048857984, "Electronics & Hardware": 0.0003926194286251449, "Health, Healthcare & Wellness": 0.00030723398275748695, "History & Geography": 0.0002558483107788416, "Home, Pets & Hobbies": 0.0001709409718311194, "Industry & Production": 0.00019644071489367023, "Language, Literature & Philosophy": 0.00016785039601385234, "Politics & Government": 0.00023317235863807266, "Religion & Spirituality": 0.00034927005972512626, "Science, Mathematics & Engineering": 0.022889903027361068, "Relationships & Community": 0.00013614141000749827, "Software Usage & Applications": 0.020965889870010183, "Programming & Software Engineering": 0.048163567405175196, "Sports & Fitness": 0.00021209861979955524, "Vehicles & Transportation": 0.0001229043725569156, "Travel & Tourism": 0.00040819673396967827, "Other / Uncovered Subject": 0.0018148748000533308, "NONE": 0.8960746984913007}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9594, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9594, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:06.764142+00:00", "scoring_key_slot": "primary", "dataset_uid": "856b0f09-a412-4255-aefd-ed268e0efdf2", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "0c12931e3958a90c67bfbe9f0f3a4b004da71745e6c9dff8635cb9129c7bb70e", "source_metadata": {"uuid": "856b0f09-a412-4255-aefd-ed268e0efdf2", "repo_name": "zeek/broker", "relative_path": "tests/btest/web-socket/encrypted.py", "category": "TEST", "algo_rel_score": 0.9780579209327698, "quality_score": 6.076479434967041, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 2176, "repo_created_at": "2014-07-09T15:12:39.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 68, "forks": 25, "issues": 41, "pull_requests": 4}, "file_meta": {"content_id": "22d53235d6871572b2ee66a5dae38678ed6cb446", "size_bytes": 3698, "file_timestamp": 1752322531, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "dffb17dd53a40b221ddc83bb8e2c05714d0a56fa"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 2176, "repo_created_at": "2014-07-09T15:12:39.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 68, "forks": 25, "issues": 41, "pull_requests": 4}, "file_meta": {"content_id": "22d53235d6871572b2ee66a5dae38678ed6cb446", "size_bytes": 3698, "file_timestamp": 1752322531, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "dffb17dd53a40b221ddc83bb8e2c05714d0a56fa", "url": "https://github.com/zeek/broker/blob/dffb17dd53a40b221ddc83bb8e2c05714d0a56fa/tests/btest/web-socket/encrypted.py"}, "gpt4o_tokens": 1013, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.0753066858416955, "noise_severity": 0.07241816368292872, "missing_content_severity": 0.305287869516792, "factual_density": 3.3134797246782766, "pretraining_utility": 0.5686492298856919, "math_density": 0.12966873678104007, "code_density": 3.4155060069683914, "reasoning_density": 0.20090323872869728, "education_level": 2.63008634135256, "creative_writing_quality": -0.2520741316722677}, "score_applicability": {"education_level": 0.9990739868909176, "creative_writing_quality": 0.00028006354447348034}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/MissMais/CCCSolution/blob/a960de3cd291587614c75ccf8252281217dc9af6/FinanceDatabase/FinanceToolkit-main/tests/ratios/test_profitability.py", "id": "python-0-ecfd8f2b3c79e9b08443799140933915", "text": "\"\"\"Profitability Ratios Tests\"\"\" \"\"\nimport pandas as pd\n\nfrom financetoolkit.ratios import profitability\n\n\n# pylint: disable=missing-function-docstring\ndef test_get_gross_margin(recorder):\n    recorder.capture(\n        profitability.get_gross_margin(\n            revenue=pd.Series([100, 110, 120, 130, 80]),\n            cost_of_goods_sold=pd.Series([30, 40, 60, 60, 20]),\n        )\n    )\n\n\ndef test_get_operating_margin(recorder):\n    recorder.capture(\n        profitability.get_operating_margin(\n            operating_income=pd.Series([80, 60, 70, 80, 90]),\n            revenue=pd.Series([100, 110, 120, 130, 80]),\n        )\n    )\n\n\ndef test_get_net_profit_margin(recorder):\n    recorder.capture(\n        profitability.get_net_profit_margin(\n            net_income=pd.Series([60, 50, 40, 30, 50]),\n            revenue=pd.Series([100, 110, 120, 130, 80]),\n        )\n    )\n\n\ndef test_get_interest_coverage_ratio(recorder):\n    recorder.capture(\n        profitability.get_interest_coverage_ratio(\n            operating_income=pd.Series([60, 50, 40, 30, 50]),\n            interest_expense=pd.Series([80, 60, 70, 80, 90]),\n        )\n    )\n\n\ndef test_get_income_before_tax_profit_margin(recorder):\n    recorder.capture(\n        profitability.get_income_before_tax_profit_margin(\n            income_before_tax=pd.Series([60, 50, 40, 30, 50]),\n            revenue=pd.Series([200, 150, 130, 100, 90]),\n        )\n    )\n\n\ndef test_get_effective_tax_rate(recorder):\n    recorder.capture(\n        profitability.get_effective_tax_rate(\n            income_tax_expense=pd.Series([80, 40, 40, 30, 20]),\n            income_before_tax=pd.Series([200, 150, 130, 100, 90]),\n        )\n    )\n\n\ndef test_get_return_on_assets(recorder):\n    recorder.capture(\n        profitability.get_return_on_assets(\n            net_income=pd.Series([80, 40, 40, 30, 20]),\n            total_assets_begin=pd.Series([200, 300, 400, 500, 450]),\n            total_assets_end=pd.Series([300, 400, 500, 450, 600]),\n        )\n    )\n\n\ndef test_get_return_on_equity(recorder):\n    recorder.capture(\n        profitability.get_return_on_equity(\n            net_income=pd.Series([80, 40, 40, 30, 20]),\n            total_equity_begin=pd.Series([130, 200, 210, 200, 150]),\n            total_equity_end=pd.Series([250, 400, 410, 400, 350]),\n        )\n    )\n\n\ndef test_get_return_on_invested_capital(recorder):\n    recorder.capture(\n        profitability.get_return_on_invested_capital(\n            net_income=pd.Series([80, 40, 40, 30, 20]),\n            dividends=pd.Series([4, 3, 2, 1, 10]),\n            effective_tax_rate=pd.Series([0.2, 0.2, 0.2, 0.2, 0.2]),\n            total_equity_begin=pd.Series([130, 200, 210, 200, 150]),\n            total_equity_end=pd.Series([130, 200, 210, 200, 150]),\n            total_debt_begin=pd.Series([130, 200, 210, 200, 150]),\n            total_debt_end=pd.Series([130, 200, 210, 200, 150]),\n        )\n    )\n\n\ndef test_get_income_quality_ratio(recorder):\n    recorder.capture(\n        profitability.get_income_quality_ratio(\n            cash_flow_from_operating_activities=pd.Series([130, 200, 210, 200, 150]),\n            net_income=pd.Series([80, 40, 40, 30, 20]),\n        )\n    )\n\n\ndef test_get_return_on_tangible_assets(recorder):\n    recorder.capture(\n        profitability.get_return_on_tangible_assets(\n            net_income=pd.Series([80, 40, 40, 30, 20]),\n            total_assets_begin=pd.Series([200, 300, 400, 500, 450]),\n            total_assets_end=pd.Series([200, 300, 400, 500, 450]),\n            intangible_assets_begin=pd.Series([20, 20, 21, 20, 15]),\n            intangible_assets_end=pd.Series([20, 20, 21, 20, 15]),\n            total_liabilities_begin=pd.Series([130, 200, 210, 200, 150]),\n            total_liabilities_end=pd.Series([130, 200, 210, 200, 150]),\n        )\n    )\n\n\ndef test_get_return_on_capital_employed(recorder):\n    recorder.capture(\n        profitability.get_return_on_capital_employed(\n            net_income=pd.Series([80, 40, 40, 30, 20]),\n            interest_expense=pd.Series([20, 20, 21, 20, 15]),\n            tax_expense=pd.Series([5, 10, 20, 10, 5]),\n            total_assets=pd.Series([200, 300, 400, 500, 450]),\n            total_current_liabilities=pd.Series([130, 200, 210, 200, 150]),\n        )\n    )\n\n\ndef test_get_net_income_per_ebt(recorder):\n    recorder.capture(\n        profitability.get_net_income_per_ebt(\n            net_income=pd.Series([80, 40, 40, 30, 20]),\n            income_tax_expense=pd.Series([5, 10, 20, 10, 5]),\n        )\n    )\n\n\ndef test_get_free_cash_flow_operating_cash_flow_ratio(recorder):\n    recorder.capture(\n        profitability.get_free_cash_flow_operating_cash_flow_ratio(\n            free_cash_flow=pd.Series([80, 40, 40, 30, 20]),\n            operating_cash_flow=pd.Series([200, 100, 200, 150, 50]),\n        )\n    )\n\n\ndef test_get_tax_burden_ratio(recorder):\n    recorder.capture(\n        profitability.get_tax_burden_ratio(\n            net_income=pd.Series([80, 40, 40, 30, 20]),\n            income_before_tax=pd.Series([200, 100, 50, 150, 50]),\n        )\n    )\n\n\ndef test_get_EBIT_to_revenue(recorder):\n    recorder.capture(\n        profitability.get_EBIT_to_revenue(\n            earnings_before_interest_and_taxes=pd.Series([80, 50, 40, 30, 20]),\n            revenue=pd.Series([230, 150, 50, 150, 50]),\n        )\n    )\n", "text_preview": "\"\"\"Profitability Ratios Tests\"\"\" \"\" import pandas as pd from financetoolkit.ratios import profitability # pylint: disable=missing-function-docstring def test_get_gross_margin(recorder): recorder.capture( profitability.get_gross_margin( reve", "input_sha256": "5f69dd37289f7ad3e607ffea6a46aede5f5609275f9235c4337343b1c7ecae9f", "text_sha256": "ecfd8f2b3c79e9b08443799140933915e105d1da90e0c696e858588dedffeb5d", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12002, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.0110513498304603, "noise_severity": 0.0752278282655685, "missing_content_severity": 0.01897841957851594, "factual_density": 2.8054015479194367, "pretraining_utility": 0.19811264339153328, "math_density": 2.8366223321782247, "code_density": 3.900919819659628, "reasoning_density": 0.03134763062060501, "education_level": 2.3011009831111844, "creative_writing_quality": -0.3756137113949587}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Programming & Software Engineering"}, "review_reasons": ["Secondary Topic must be a different main subject in both modes", "secondary_topic probability 0.4389 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.025434545204345738, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9896614957268074, "secondary": 0.9197643946595547, "tertiary": 0.9463047896553938, "structured_records": 0.41733733903087195, "procedural_steps": 0.002200432397552023, "worked_solution": 0.007923145272329391, "contextual_dialogue": 7.224522207488088e-06, "content_quality": 0.53736541135842, "noise_severity": 0.9373101431120263, "missing_content_severity": 0.9841846503512367, "factual_density": 0.004501289932863584, "pretraining_utility": 0.4707413672092906, "math_density": 0.030518610148520753, "code_density": 0.9174331830496901, "reasoning_density": 0.9738769744828292, "education_level": 0.6343150474940249, "creative_writing_quality": 0.9996449789264612, "primary_topic": 0.8252931997731481, "secondary_topic": 0.41643920238752147}, "probability_margin": {"primary": 0.9842532400879871, "secondary": 0.8863003211161788, "tertiary": 0.9257663431998852, "education_level": 0.4407234544763651, "creative_writing_quality": 0.999592452480869, "primary_topic": 0.6770541076713806, "secondary_topic": 0.02406056078613472}, "raw_probabilities": {"primary": {"Academic Writing": 0.00011234556600146452, "Content Listing": 4.732722375566583e-05, "Creative Writing": 2.420624551296568e-05, "Customer Support Page": 1.7114870075950377e-05, "Q&A Forum": 1.8037143269001707e-05, "Discussion Thread": 1.257382339771443e-05, "Comment Section": 2.0074266935536363e-05, "Missing Content": 4.798848899212669e-05, "Knowledge Article": 0.000138678387975972, "Legal Notices": 3.303331548299805e-05, "News Article": 8.24672616417223e-06, "Organizational Announcement": 1.777588713083081e-05, "Nonfiction Writing": 2.3852744853499542e-05, "Organizational About Page": 1.9892031655673744e-05, "Personal About Page": 1.6694171425737923e-05, "Personal Blog": 1.1115822251046992e-05, "Product Page": 1.416449804459914e-05, "Spam / Ads": 1.629218644858106e-05, "Structured Data": 0.002470495022718739, "Technical Writing": 0.9900922667381904, "Transcript / Interview": 2.528467203073445e-05, "Tutorial / How-To Guide": 0.0009611382568834154, "User Reviews": 1.2375260599900816e-05, "Other / Unknown": 0.0058390266502033016}, "secondary": {"Academic Writing": 0.001387167833711179, "Content Listing": 0.0006164037860302948, "Creative Writing": 0.00028932352609587197, "Customer Support Page": 0.00017034595897377536, "Q&A Forum": 0.00015462435660391278, "Discussion Thread": 0.00010864780630982841, "Comment Section": 0.00019448850837661602, "Missing Content": 0.0007752906803909853, "Knowledge Article": 0.0020913234394792316, "Legal Notices": 0.0003501769865933769, "News Article": 0.00014981876462770566, "Organizational Announcement": 0.0001641156506817285, "Nonfiction Writing": 0.0002289098739464399, "Organizational About Page": 0.00019962161429054392, "Personal About Page": 0.00014033205310809562, "Personal Blog": 0.00016652682116716536, "Product Page": 0.00010897187713016127, "Spam / Ads": 0.00016699828652742122, "Structured Data": 0.03680722376589435, "Technical Writing": 0.0293141735526514, "Transcript / Interview": 0.0002424952849046389, "Tutorial / How-To Guide": 0.0029716840164194457, "User Reviews": 9.379067401261186e-05, "NONE": 0.9231075448820732}, "tertiary": {"Academic Writing": 0.0006986107232396449, "Content Listing": 0.00031810424007217106, "Creative Writing": 0.0001830683041193567, "Customer Support Page": 9.087047449829211e-05, "Q&A Forum": 7.797015005027136e-05, "Discussion Thread": 6.375382966723952e-05, "Comment Section": 0.00011965425973224696, "Missing Content": 0.000642971968331198, "Knowledge Article": 0.0025110059216061926, "Legal Notices": 0.0003792491131515347, "News Article": 9.161362369535317e-05, "Organizational Announcement": 9.150932017667628e-05, "Nonfiction Writing": 0.00018195781738754025, "Organizational About Page": 0.00014482302259695605, "Personal About Page": 7.72747862632032e-05, "Personal Blog": 8.738661441990739e-05, "Product Page": 7.436580002950077e-05, "Spam / Ads": 0.00010181272012729472, "Structured Data": 0.020677163954163663, "Technical Writing": 0.0227757468865338, "Transcript / Interview": 0.00011374083010359837, "Tutorial / How-To Guide": 0.001901931604767208, "User Reviews": 5.332394884810777e-05, "NONE": 0.948542090086419}, "structured_records": {"false": 0.5826626609691281, "true": 0.41733733903087195}, "procedural_steps": {"false": 0.9977995676024479, "true": 0.002200432397552023}, "worked_solution": {"false": 0.9920768547276706, "true": 0.007923145272329391}, "contextual_dialogue": {"false": 0.9999927754777925, "true": 7.224522207488088e-06}, "content_quality": {"-2": 0.00681605854478094, "-1": 0.028894180396871062, "0": 0.19381854184163294, "1": 0.487364791116537, "2": 0.28310642810017816}, "noise_severity": {"0": 0.931028909849601, "1": 0.06469422776168494, "2": 0.0028731594049298103, "3": 0.0008275302411126247, "4": 0.000576172742671509}, "missing_content_severity": {"0": 0.9879118872060011, "1": 0.007376810974572739, "2": 0.0030356710152373806, "3": 0.0011722566432865685, "4": 0.0005033741609021838}, "factual_density": {"0": 0.03041356123636124, "1": 0.10142790691834452, "2": 0.23131218416235638, "3": 0.30603611805537234, "4": 0.33081022962756557}, "pretraining_utility": {"-2": 0.018269030111063317, "-1": 0.18196079775653234, "0": 0.4459662234630687, "1": 0.2909963959684787, "2": 0.06280755270085675}, "math_density": {"0": 0.055719488588439565, "1": 0.09959850886307384, "2": 0.1852340390044849, "3": 0.2712361088698257, "4": 0.388211854674176}, "code_density": {"0": 0.00019801641636221017, "1": 0.0004747200593201179, "2": 0.011585431978144997, "3": 0.07369309054067266, "4": 0.9140487410055}, "reasoning_density": {"0": 0.9808000773816412, "1": 0.009199118510419237, "2": 0.008241963975674317, "3": 0.0013707763702234452, "4": 0.0003880637620416992}, "education_level": {"none": 0.0012295776066799493, "0": 0.0015035095045521773, "1": 0.015243962141577614, "2": 0.695262539578354, "3": 0.25453908510198897, "4": 0.0322213260668473}, "creative_writing_quality": {"not_applicable": 0.9997041491053843, "-2": 5.798077859177681e-05, "-1": 6.899072684099195e-05, "0": 0.00011169662451521706, "1": 4.053889785705454e-05, "2": 1.6643866810680633e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.000182090053200791, "Art & Design": 0.00027862022236491135, "Law & Crime": 0.00033826653603791766, "Education & Careers": 0.00018774027126306934, "Entertainment & Media": 0.0001826752524700924, "Fashion & Beauty": 0.00024355898573826987, "Business, Economics & Finance": 0.15495858441818477, "Food & Dining": 0.00015997433101038299, "Games & Gaming": 0.00024322172250894333, "Electronics & Hardware": 0.00020212730178131708, "Health, Healthcare & Wellness": 0.00019610383618510167, "History & Geography": 0.0002431630451476361, "Home, Pets & Hobbies": 0.0001640559518193213, "Industry & Production": 0.0002724000837609709, "Language, Literature & Philosophy": 0.00017664003494676634, "Politics & Government": 0.00015375516512798718, "Religion & Spirituality": 0.0001921505265306442, "Science, Mathematics & Engineering": 0.007706528556992386, "Relationships & Community": 0.0001174219088176802, "Software Usage & Applications": 0.0004678895171853901, "Programming & Software Engineering": 0.8320126920895654, "Sports & Fitness": 0.00021517744911375513, "Vehicles & Transportation": 0.00011119615625629006, "Travel & Tourism": 0.00016887144187738407, "Other / Uncovered Subject": 0.0005957895754440016, "Unknown / Insufficient Content": 0.00022930556666872783}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006152472598503398, "Art & Design": 0.0006095724800316938, "Law & Crime": 0.0007507643554541296, "Education & Careers": 0.00040660901108901794, "Entertainment & Media": 0.0004593873551949613, "Fashion & Beauty": 0.0004531574030551003, "Business, Economics & Finance": 0.4148232876634052, "Food & Dining": 0.0003330732612348892, "Games & Gaming": 0.00047793242014673937, "Electronics & Hardware": 0.0005244059743561338, "Health, Healthcare & Wellness": 0.0004958937827661548, "History & Geography": 0.00048631021298924744, "Home, Pets & Hobbies": 0.00039555654039769895, "Industry & Production": 0.0004814801891553981, "Language, Literature & Philosophy": 0.00036460798763219757, "Politics & Government": 0.0003150215932677607, "Religion & Spirituality": 0.0005124086115885986, "Science, Mathematics & Engineering": 0.028759944357502624, "Relationships & Community": 0.00022431575732638443, "Software Usage & Applications": 0.0005775341038706199, "Programming & Software Engineering": 0.4388838484495399, "Sports & Fitness": 0.00039928768937742744, "Vehicles & Transportation": 0.00025615727504626085, "Travel & Tourism": 0.00044988954579895966, "Other / Uncovered Subject": 0.0006305324744145463, "NONE": 0.10731377424550806}}, "probabilities": {"primary": {"Academic Writing": 0.00011234556600146452, "Content Listing": 4.732722375566583e-05, "Creative Writing": 2.420624551296568e-05, "Customer Support Page": 1.7114870075950377e-05, "Q&A Forum": 1.8037143269001707e-05, "Discussion Thread": 1.257382339771443e-05, "Comment Section": 2.0074266935536363e-05, "Missing Content": 4.798848899212669e-05, "Knowledge Article": 0.000138678387975972, "Legal Notices": 3.303331548299805e-05, "News Article": 8.24672616417223e-06, "Organizational Announcement": 1.777588713083081e-05, "Nonfiction Writing": 2.3852744853499542e-05, "Organizational About Page": 1.9892031655673744e-05, "Personal About Page": 1.6694171425737923e-05, "Personal Blog": 1.1115822251046992e-05, "Product Page": 1.416449804459914e-05, "Spam / Ads": 1.629218644858106e-05, "Structured Data": 0.002470495022718739, "Technical Writing": 0.9900922667381904, "Transcript / Interview": 2.528467203073445e-05, "Tutorial / How-To Guide": 0.0009611382568834154, "User Reviews": 1.2375260599900816e-05, "Other / Unknown": 0.0058390266502033016}, "secondary": {"Academic Writing": 0.001387167833711179, "Content Listing": 0.0006164037860302948, "Creative Writing": 0.00028932352609587197, "Customer Support Page": 0.00017034595897377536, "Q&A Forum": 0.00015462435660391278, "Discussion Thread": 0.00010864780630982841, "Comment Section": 0.00019448850837661602, "Missing Content": 0.0007752906803909853, "Knowledge Article": 0.0020913234394792316, "Legal Notices": 0.0003501769865933769, "News Article": 0.00014981876462770566, "Organizational Announcement": 0.0001641156506817285, "Nonfiction Writing": 0.0002289098739464399, "Organizational About Page": 0.00019962161429054392, "Personal About Page": 0.00014033205310809562, "Personal Blog": 0.00016652682116716536, "Product Page": 0.00010897187713016127, "Spam / Ads": 0.00016699828652742122, "Structured Data": 0.03680722376589435, "Technical Writing": 0.0293141735526514, "Transcript / Interview": 0.0002424952849046389, "Tutorial / How-To Guide": 0.0029716840164194457, "User Reviews": 9.379067401261186e-05, "NONE": 0.9231075448820732}, "tertiary": {"Academic Writing": 0.0006986107232396449, "Content Listing": 0.00031810424007217106, "Creative Writing": 0.0001830683041193567, "Customer Support Page": 9.087047449829211e-05, "Q&A Forum": 7.797015005027136e-05, "Discussion Thread": 6.375382966723952e-05, "Comment Section": 0.00011965425973224696, "Missing Content": 0.000642971968331198, "Knowledge Article": 0.0025110059216061926, "Legal Notices": 0.0003792491131515347, "News Article": 9.161362369535317e-05, "Organizational Announcement": 9.150932017667628e-05, "Nonfiction Writing": 0.00018195781738754025, "Organizational About Page": 0.00014482302259695605, "Personal About Page": 7.72747862632032e-05, "Personal Blog": 8.738661441990739e-05, "Product Page": 7.436580002950077e-05, "Spam / Ads": 0.00010181272012729472, "Structured Data": 0.020677163954163663, "Technical Writing": 0.0227757468865338, "Transcript / Interview": 0.00011374083010359837, "Tutorial / How-To Guide": 0.001901931604767208, "User Reviews": 5.332394884810777e-05, "NONE": 0.948542090086419}, "structured_records": {"false": 0.5826626609691281, "true": 0.41733733903087195}, "procedural_steps": {"false": 0.9977995676024479, "true": 0.002200432397552023}, "worked_solution": {"false": 0.9920768547276706, "true": 0.007923145272329391}, "contextual_dialogue": {"false": 0.9999927754777925, "true": 7.224522207488088e-06}, "content_quality": {"-2": 0.006816058544780938, "-1": 0.028894180396871055, "0": 0.19381854184163289, "1": 0.4873647911165369, "2": 0.2831064281001781}, "noise_severity": {"0": 0.9310289098496011, "1": 0.06469422776168496, "2": 0.0028731594049298107, "3": 0.0008275302411126248, "4": 0.0005761727426715092}, "missing_content_severity": {"0": 0.9879118872060011, "1": 0.007376810974572739, "2": 0.0030356710152373806, "3": 0.0011722566432865685, "4": 0.0005033741609021838}, "factual_density": {"0": 0.03041356123636124, "1": 0.10142790691834452, "2": 0.23131218416235638, "3": 0.30603611805537234, "4": 0.33081022962756557}, "pretraining_utility": {"-2": 0.01826903011106332, "-1": 0.18196079775653237, "0": 0.4459662234630688, "1": 0.29099639596847876, "2": 0.06280755270085676}, "math_density": {"0": 0.055719488588439565, "1": 0.09959850886307384, "2": 0.1852340390044849, "3": 0.2712361088698257, "4": 0.388211854674176}, "code_density": {"0": 0.00019801641636221017, "1": 0.0004747200593201179, "2": 0.011585431978144997, "3": 0.07369309054067266, "4": 0.9140487410055}, "reasoning_density": {"0": 0.9808000773816413, "1": 0.009199118510419239, "2": 0.008241963975674319, "3": 0.0013707763702234454, "4": 0.00038806376204169923}, "education_level": {"none": 0.0012295776066799493, "0": 0.0015035095045521773, "1": 0.015243962141577614, "2": 0.695262539578354, "3": 0.25453908510198897, "4": 0.0322213260668473}, "creative_writing_quality": {"not_applicable": 0.9997041491053843, "-2": 5.798077859177681e-05, "-1": 6.899072684099195e-05, "0": 0.00011169662451521706, "1": 4.053889785705454e-05, "2": 1.6643866810680633e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00018209005320079104, "Art & Design": 0.0002786202223649114, "Law & Crime": 0.0003382665360379177, "Education & Careers": 0.00018774027126306937, "Entertainment & Media": 0.00018267525247009242, "Fashion & Beauty": 0.0002435589857382699, "Business, Economics & Finance": 0.1549585844181848, "Food & Dining": 0.000159974331010383, "Games & Gaming": 0.00024322172250894336, "Electronics & Hardware": 0.0002021273017813171, "Health, Healthcare & Wellness": 0.0001961038361851017, "History & Geography": 0.00024316304514763613, "Home, Pets & Hobbies": 0.00016405595181932133, "Industry & Production": 0.00027240008376097093, "Language, Literature & Philosophy": 0.00017664003494676636, "Politics & Government": 0.0001537551651279872, "Religion & Spirituality": 0.00019215052653064423, "Science, Mathematics & Engineering": 0.007706528556992387, "Relationships & Community": 0.00011742190881768021, "Software Usage & Applications": 0.00046788951718539014, "Programming & Software Engineering": 0.8320126920895655, "Sports & Fitness": 0.00021517744911375515, "Vehicles & Transportation": 0.00011119615625629008, "Travel & Tourism": 0.0001688714418773841, "Other / Uncovered Subject": 0.0005957895754440017, "Unknown / Insufficient Content": 0.00022930556666872785}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006152472598503398, "Art & Design": 0.0006095724800316938, "Law & Crime": 0.0007507643554541296, "Education & Careers": 0.00040660901108901794, "Entertainment & Media": 0.0004593873551949613, "Fashion & Beauty": 0.0004531574030551003, "Business, Economics & Finance": 0.4148232876634052, "Food & Dining": 0.0003330732612348892, "Games & Gaming": 0.00047793242014673937, "Electronics & Hardware": 0.0005244059743561338, "Health, Healthcare & Wellness": 0.0004958937827661548, "History & Geography": 0.00048631021298924744, "Home, Pets & Hobbies": 0.00039555654039769895, "Industry & Production": 0.0004814801891553981, "Language, Literature & Philosophy": 0.00036460798763219757, "Politics & Government": 0.0003150215932677607, "Religion & Spirituality": 0.0005124086115885986, "Science, Mathematics & Engineering": 0.028759944357502624, "Relationships & Community": 0.00022431575732638443, "Software Usage & Applications": 0.0005775341038706199, "Programming & Software Engineering": 0.4388838484495399, "Sports & Fitness": 0.00039928768937742744, "Vehicles & Transportation": 0.00025615727504626085, "Travel & Tourism": 0.00044988954579895966, "Other / Uncovered Subject": 0.0006305324744145463, "NONE": 0.10731377424550806}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10421, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10421, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:06.904165+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "5d6c7f04-cf5c-4bf9-b11b-41929a14ecc1", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "396bfa3e4ecc6cae069ee5ff4d773ea54c34d1d9398666e4f5b8cb5fda8a2436", "source_metadata": {"uuid": "5d6c7f04-cf5c-4bf9-b11b-41929a14ecc1", "repo_name": "MissMais/CCCSolution", "relative_path": "FinanceDatabase/FinanceToolkit-main/tests/ratios/test_profitability.py", "category": "TEST", "algo_rel_score": 0.977168083190918, "quality_score": 7.941538333892822, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2024-10-29T14:38:09.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "7223a4a26050de75fd08241bd79e02c57ca73b27", "size_bytes": 5297, "file_timestamp": 1730214133, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a960de3cd291587614c75ccf8252281217dc9af6"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2024-10-29T14:38:09.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "7223a4a26050de75fd08241bd79e02c57ca73b27", "size_bytes": 5297, "file_timestamp": 1730214133, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a960de3cd291587614c75ccf8252281217dc9af6", "url": "https://github.com/MissMais/CCCSolution/blob/a960de3cd291587614c75ccf8252281217dc9af6/FinanceDatabase/FinanceToolkit-main/tests/ratios/test_profitability.py"}, "gpt4o_tokens": 1463, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.0110513498304605, "noise_severity": 0.0752278282655685, "missing_content_severity": 0.01897841957851594, "factual_density": 2.8054015479194367, "pretraining_utility": 0.19811264339153328, "math_density": 2.8366223321782247, "code_density": 3.900919819659628, "reasoning_density": 0.03134763062060501, "education_level": 2.3011009831111844, "creative_writing_quality": -0.3756137113949587}, "score_applicability": {"education_level": 0.9987704223933201, "creative_writing_quality": 0.000295850894615721}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/clancycottrill/PIA-4E/blob/ece7b43c56cac038f80350f78b7bab9794b73ac2/file_encrypt.py", "id": "python-0-b13b6c7f10e111ca833aa4aa4952a72b", "text": "from cryptography.fernet import Fernet, InvalidToken\nimport os\nimport logging\n\n\ndef write_to_txt(message):\n    with open(\"file_encrypt_results.txt\", \"a\") as txt_file:\n        txt_file.write(message + \"\\n\")\n\ndef main():\n    logging.basicConfig (filename = 'Encrypt-Decrypt_Files.log',\n                     filemode = 'a',\n                     format =\"%(asctime)s - %(levelname)s - %(message)s\",\n                     datefmt = \"%m/%d/%Y %I:%M:%S %p\", level = logging.INFO)\n\n    def generate_key():\n        key = Fernet.generate_key()\n        with open(\"secret.key\", \"wb\") as key_file:\n            key_file.write(key)\n        logging.info(f'Clave generada: {key}')\n        write_to_txt(f'Clave generada: {key}')  \n    def load_key():\n        try:\n            return open(\"secret.key\", \"rb\").read()\n        except FileNotFoundError:\n            print(\"Error: El archivo de clave 'secret.key' no se encontró.\")\n            logging.warning('El archivo ''secret.key'' no se encontro')\n            write_to_txt('Error: El archivo de clave \"secret.key\" no se encontró.')  \n            return None\n\n    def encrypt_file(file_name):\n        key = load_key()\n        if key is None:\n            return\n        fernet = Fernet(key)\n        try:\n            with open(file_name, \"rb\") as file:\n                original = file.read()\n            encrypted = fernet.encrypt(original)\n            with open(file_name + \".encrypted\", \"wb\") as encrypted_file:\n                encrypted_file.write(encrypted)\n            os.remove(file_name)\n            message = f\"{file_name} ha sido cifrado, eliminado y guardado como {file_name}.encrypted\"\n            print(message)\n            logging.info(message)\n            write_to_txt(message) \n        except Exception as e:\n            print(f\"Error al cifrar el archivo: {e}\")\n            logging.warning(f'No se pudo cifrar el archivo, ocurrio un error: {e}')\n            write_to_txt(f'Error al cifrar el archivo: {e}')  \n\n    def decrypt_file(file_name):\n        key = load_key()\n        if key is None:\n            return\n        fernet = Fernet(key)\n        try:\n            with open(file_name, \"rb\") as encrypted_file:\n                encrypted = encrypted_file.read()\n            decrypted = fernet.decrypt(encrypted)\n            with open(file_name.replace(\".encrypted\", \"\"), \"wb\") as decrypted_file:\n                decrypted_file.write(decrypted)\n            os.remove(file_name)\n            message = f\"{file_name} ha sido descifrado y guardado como {file_name.replace('.encrypted', '')}\"\n            print(message)\n            logging.info(message)\n            write_to_txt(message)  \n        except InvalidToken:\n            print(\"Error: El archivo no es un archivo cifrado válido.\")\n            write_to_txt(\"Error: El archivo no es un archivo cifrado válido.\")  \n        except Exception as e:\n            print(f\"Error al descifrar el archivo: {e}\")\n            logging.warning(f'No se pudo descifrar el archivo, ocurrio un error: {e}')\n            write_to_txt(f'Error al descifrar el archivo: {e}')  \n\n    def ValidateFile(file_name):\n        if os.path.exists(file_name):\n            print(f\"El archivo {file_name} existe\")\n            return True\n        else:\n            print(f\"El archivo {file_name} no existe o no se puede acceder.\")\n            write_to_txt(f\"El archivo {file_name} no existe o no se puede acceder.\")  \n            return False\n\n    while True:\n        x = input('¿Quiere cifrar un archivo? (s/n): ')\n        \n        if x == 's':\n            print('Generando clave')\n\n            generate_key()\n            print('Clave guardada en el archivo: secret.key')\n            logging.info('Clave guardada en el archivo  ''secret.key')\n            write_to_txt('Clave guardada en el archivo  ''secret.key')  \n            file_name = input('Ingrese la ruta completa del archivo que desea encriptar: ')\n            try:\n                if ValidateFile(file_name):\n                    encrypt_file(file_name)\n                else:\n                    print('Por favor, ingrese un archivo que exista.')\n            except Exception as e:\n                print(f'Error: {e}')\n                logging.warning(f'Ocurrió un error: {e}')\n                write_to_txt(f'Ocurrió un error: {e}')  \n                \n        elif x == 'n':\n            decrypt_choice = input('¿Quiere desencriptar un archivo? (s/n): ')\n            if decrypt_choice == 's':\n                file_name = input('Ingrese la ruta completa del archivo que desea desencriptar (debe tener la extensión .encrypted): ')\n                try:\n                    if ValidateFile(file_name):\n                        decrypt_file(file_name)\n                    else:\n                        print('Por favor, ingrese un archivo que exista.')\n                except Exception as e:\n                    print(f'Error: {e}')\n                    logging.warning(f'Ocurrió un error: {e}')\n                    write_to_txt(f'Ocurrió un error: {e}') \n            else:\n                break\n        else:\n            print('Por favor, solo ingrese (s/n)')\n            continue\n\nmain()\n", "text_preview": "from cryptography.fernet import Fernet, InvalidToken import os import logging def write_to_txt(message): with open(\"file_encrypt_results.txt\", \"a\") as txt_file: txt_file.write(message + \"\\n\") def main(): logging.basicConfig (filename = 'Enc", "input_sha256": "a98119b2fca0b3af27e339b02d73b899bb7cff6933033af77eb2f08946917413", "text_sha256": "b13b6c7f10e111ca833aa4aa4952a72b689ae247d33efeb124562b94bdda1a12", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12005, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.492122261654697, "noise_severity": 0.023208319361085975, "missing_content_severity": 0.007772584554882241, "factual_density": 3.1207947656666515, "pretraining_utility": 0.5845885411272459, "math_density": 0.1456855877612215, "code_density": 3.732676117139017, "reasoning_density": 0.5208362115884618, "education_level": 2.044249810120747, "creative_writing_quality": -0.33487052595383987}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.01613052471321086, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9932668425592787, "secondary": 0.9709756647266934, "tertiary": 0.9878075166013482, "structured_records": 0.0004170703795818626, "procedural_steps": 0.05336781048618531, "worked_solution": 0.01025550154041084, "contextual_dialogue": 0.00010213502929709774, "content_quality": 0.3901395954687762, "noise_severity": 0.9806597338657617, "missing_content_severity": 0.9935228462042648, "factual_density": 0.26732897138887624, "pretraining_utility": 0.5096380129745428, "math_density": 0.8785953435323154, "code_density": 0.7772300976158475, "reasoning_density": 0.5659698236762819, "education_level": 0.8075334979403707, "creative_writing_quality": 0.9983429092512393, "primary_topic": 0.988141286420189, "secondary_topic": 0.8602349386752118}, "probability_margin": {"primary": 0.9898277231331845, "secondary": 0.9579656455179881, "tertiary": 0.9835874510581964, "education_level": 0.7489741309608685, "creative_writing_quality": 0.9978369278402354, "primary_topic": 0.9811793496100615, "secondary_topic": 0.7926000130957491}, "raw_probabilities": {"primary": {"Academic Writing": 0.00011263144345405036, "Content Listing": 2.5266939144173444e-05, "Creative Writing": 3.29688868128365e-05, "Customer Support Page": 1.3798938370268825e-05, "Q&A Forum": 1.8661702882098258e-05, "Discussion Thread": 1.272565988571916e-05, "Comment Section": 2.4303960679000164e-05, "Missing Content": 3.983384829251283e-05, "Knowledge Article": 0.00015743170436549223, "Legal Notices": 4.000318011484107e-05, "News Article": 7.471092461248155e-06, "Organizational Announcement": 1.5218765571128799e-05, "Nonfiction Writing": 2.312246370424808e-05, "Organizational About Page": 1.7306607343974876e-05, "Personal About Page": 1.8751992161574063e-05, "Personal Blog": 1.1873044382006359e-05, "Product Page": 9.918114711302408e-06, "Spam / Ads": 1.6473890243789456e-05, "Structured Data": 0.00018055788695598983, "Technical Writing": 0.9935473907859754, "Transcript / Interview": 2.8021641631553937e-05, "Tutorial / How-To Guide": 0.003719667652790962, "User Reviews": 9.587783852403846e-06, "Other / Unknown": 0.0019170120142133055}, "secondary": {"Academic Writing": 0.0004957374479761071, "Content Listing": 0.00010121398688345307, "Creative Writing": 0.00015421063910732113, "Customer Support Page": 7.844004655120206e-05, "Q&A Forum": 6.869117954494857e-05, "Discussion Thread": 4.9952359266647985e-05, "Comment Section": 0.00011554944174985339, "Missing Content": 0.0001740665196422269, "Knowledge Article": 0.0004339203800482034, "Legal Notices": 0.0001305346412098898, "News Article": 7.118896429729375e-05, "Organizational Announcement": 6.427848941399388e-05, "Nonfiction Writing": 7.964703640517994e-05, "Organizational About Page": 7.972839025049803e-05, "Personal About Page": 7.851698509242664e-05, "Personal Blog": 7.259671009579673e-05, "Product Page": 4.355522174034042e-05, "Spam / Ads": 7.077734714326813e-05, "Structured Data": 0.000425448473646779, "Technical Writing": 0.010562130096613795, "Transcript / Interview": 0.00020643064662717473, "Tutorial / How-To Guide": 0.014219366511759654, "User Reviews": 3.900645518617421e-05, "NONE": 0.9721850120297478}, "tertiary": {"Academic Writing": 0.0003107576348664867, "Content Listing": 5.59325056394535e-05, "Creative Writing": 0.00011429496396878986, "Customer Support Page": 5.743802214724161e-05, "Q&A Forum": 3.894112259005471e-05, "Discussion Thread": 3.0475676461138724e-05, "Comment Section": 7.806208503285803e-05, "Missing Content": 0.0002131026707669782, "Knowledge Article": 0.0003602310233268617, "Legal Notices": 0.0001608268899335813, "News Article": 4.634153734038111e-05, "Organizational Announcement": 4.354300101384787e-05, "Nonfiction Writing": 7.57455945217944e-05, "Organizational About Page": 6.144390158769316e-05, "Personal About Page": 5.268826558114816e-05, "Personal Blog": 4.994494011742233e-05, "Product Page": 3.302512980702021e-05, "Spam / Ads": 5.252105673027725e-05, "Structured Data": 0.00034178549076968427, "Technical Writing": 0.004536071455225799, "Transcript / Interview": 0.00021446560690852276, "Tutorial / How-To Guide": 0.004728085684762292, "User Reviews": 2.873899794206556e-05, "NONE": 0.9883155367429587}, "structured_records": {"false": 0.9995829296204182, "true": 0.0004170703795818626}, "procedural_steps": {"false": 0.9466321895138147, "true": 0.05336781048618531}, "worked_solution": {"false": 0.9897444984595891, "true": 0.01025550154041084}, "contextual_dialogue": {"false": 0.9998978649707029, "true": 0.00010213502929709774}, "content_quality": {"-2": 0.02229731488717914, "-1": 0.11537243165482243, "0": 0.32221830392020345, "1": 0.4281345759917121, "2": 0.11197737354608281}, "noise_severity": {"0": 0.9815716476223688, "1": 0.0149007825176758, "2": 0.0026343214737098645, "3": 0.0005340996489919902, "4": 0.0003591487372536187}, "missing_content_severity": {"0": 0.9953312321614505, "1": 0.002297686768894757, "2": 0.0017715856247080292, "3": 0.00046625524321512274, "4": 0.00013324020173151397}, "factual_density": {"0": 0.015372176741350479, "1": 0.03813070211124719, "2": 0.1637111328016339, "3": 0.3759021554309372, "4": 0.4068838329148312}, "pretraining_utility": {"-2": 0.008431152960588014, "-1": 0.059031204346732, "0": 0.3585670540764233, "1": 0.4874591258373594, "2": 0.08651146277889722}, "math_density": {"0": 0.9332359308660054, "1": 0.03414562919882137, "2": 0.006319056707202044, "3": 0.006295687763888435, "4": 0.020003695464082684}, "code_density": {"0": 0.0005390002423559366, "1": 0.0010711558866184172, "2": 0.04444102813312099, "3": 0.17307235796546203, "4": 0.7808764577724426}, "reasoning_density": {"0": 0.750547542689109, "1": 0.06858397677607186, "2": 0.11558639252240017, "3": 0.04004890228208589, "4": 0.025233185730332966}, "education_level": {"none": 0.0014829815638393153, "0": 0.0023096113571645887, "1": 0.05791726302849004, "2": 0.8396112482836422, "3": 0.09063711732277367, "4": 0.00804177844409016}, "creative_writing_quality": {"not_applicable": 0.9986190910426994, "-2": 0.00012288729957449384, "-1": 0.00036077481574084825, "0": 0.0007821632024640855, "1": 8.604357297786992e-05, "2": 2.9040066543191364e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.472882226386985e-05, "Art & Design": 5.923598676219772e-05, "Law & Crime": 0.00019763132111028347, "Education & Careers": 7.247003640640326e-05, "Entertainment & Media": 3.460724982070906e-05, "Fashion & Beauty": 2.6784536693993687e-05, "Business, Economics & Finance": 6.830138081456828e-05, "Food & Dining": 3.4163361892042786e-05, "Games & Gaming": 6.420783295211623e-05, "Electronics & Hardware": 0.0002039085074212211, "Health, Healthcare & Wellness": 3.924934871453385e-05, "History & Geography": 4.131272346116971e-05, "Home, Pets & Hobbies": 3.81184362934992e-05, "Industry & Production": 3.208575055648494e-05, "Language, Literature & Philosophy": 6.539833463777071e-05, "Politics & Government": 3.409393784927241e-05, "Religion & Spirituality": 4.13970244679534e-05, "Science, Mathematics & Engineering": 0.007418041178581721, "Relationships & Community": 2.240440965951166e-05, "Software Usage & Applications": 0.0011604044662445183, "Programming & Software Engineering": 0.9885973907886432, "Sports & Fitness": 5.328181540845619e-05, "Vehicles & Transportation": 2.3943044821382982e-05, "Travel & Tourism": 5.207571976795633e-05, "Other / Uncovered Subject": 0.0014955908266990267, "Unknown / Insufficient Content": 6.917315805598037e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.000717647395161571, "Art & Design": 0.0004569560132830527, "Law & Crime": 0.0029548506417757902, "Education & Careers": 0.0005784293151681457, "Entertainment & Media": 0.0004892301450332374, "Fashion & Beauty": 0.00030268648963277734, "Business, Economics & Finance": 0.0006205720583363975, "Food & Dining": 0.0002359957645298853, "Games & Gaming": 0.0003633667014848881, "Electronics & Hardware": 0.0006072209376300064, "Health, Healthcare & Wellness": 0.00035595780636411145, "History & Geography": 0.00032156313875328634, "Home, Pets & Hobbies": 0.00027064583408298044, "Industry & Production": 0.000235070435933355, "Language, Literature & Philosophy": 0.0004220620172584273, "Politics & Government": 0.00030306270853782996, "Religion & Spirituality": 0.00039489000349397264, "Science, Mathematics & Engineering": 0.07301050486118534, "Relationships & Community": 0.0001633543214145011, "Software Usage & Applications": 0.0070609561839333045, "Programming & Software Engineering": 0.036140028919954006, "Sports & Fitness": 0.00028743712558017764, "Vehicles & Transportation": 0.0001295846919797405, "Travel & Tourism": 0.00044467026528776304, "Other / Uncovered Subject": 0.007522738267271011, "NONE": 0.8656105179569344}}, "probabilities": {"primary": {"Academic Writing": 0.00011263144345405038, "Content Listing": 2.5266939144173448e-05, "Creative Writing": 3.296888681283651e-05, "Customer Support Page": 1.3798938370268826e-05, "Q&A Forum": 1.866170288209826e-05, "Discussion Thread": 1.2725659885719162e-05, "Comment Section": 2.4303960679000168e-05, "Missing Content": 3.9833848292512836e-05, "Knowledge Article": 0.00015743170436549226, "Legal Notices": 4.0003180114841075e-05, "News Article": 7.471092461248156e-06, "Organizational Announcement": 1.52187655711288e-05, "Nonfiction Writing": 2.3122463704248085e-05, "Organizational About Page": 1.730660734397488e-05, "Personal About Page": 1.8751992161574066e-05, "Personal Blog": 1.187304438200636e-05, "Product Page": 9.91811471130241e-06, "Spam / Ads": 1.647389024378946e-05, "Structured Data": 0.00018055788695598985, "Technical Writing": 0.9935473907859755, "Transcript / Interview": 2.802164163155394e-05, "Tutorial / How-To Guide": 0.0037196676527909626, "User Reviews": 9.587783852403848e-06, "Other / Unknown": 0.0019170120142133057}, "secondary": {"Academic Writing": 0.0004957374479761071, "Content Listing": 0.00010121398688345307, "Creative Writing": 0.00015421063910732113, "Customer Support Page": 7.844004655120206e-05, "Q&A Forum": 6.869117954494857e-05, "Discussion Thread": 4.9952359266647985e-05, "Comment Section": 0.00011554944174985339, "Missing Content": 0.0001740665196422269, "Knowledge Article": 0.0004339203800482034, "Legal Notices": 0.0001305346412098898, "News Article": 7.118896429729375e-05, "Organizational Announcement": 6.427848941399388e-05, "Nonfiction Writing": 7.964703640517994e-05, "Organizational About Page": 7.972839025049803e-05, "Personal About Page": 7.851698509242664e-05, "Personal Blog": 7.259671009579673e-05, "Product Page": 4.355522174034042e-05, "Spam / Ads": 7.077734714326813e-05, "Structured Data": 0.000425448473646779, "Technical Writing": 0.010562130096613795, "Transcript / Interview": 0.00020643064662717473, "Tutorial / How-To Guide": 0.014219366511759654, "User Reviews": 3.900645518617421e-05, "NONE": 0.9721850120297478}, "tertiary": {"Academic Writing": 0.0003107576348664867, "Content Listing": 5.59325056394535e-05, "Creative Writing": 0.00011429496396878986, "Customer Support Page": 5.743802214724161e-05, "Q&A Forum": 3.894112259005471e-05, "Discussion Thread": 3.0475676461138724e-05, "Comment Section": 7.806208503285803e-05, "Missing Content": 0.0002131026707669782, "Knowledge Article": 0.0003602310233268617, "Legal Notices": 0.0001608268899335813, "News Article": 4.634153734038111e-05, "Organizational Announcement": 4.354300101384787e-05, "Nonfiction Writing": 7.57455945217944e-05, "Organizational About Page": 6.144390158769316e-05, "Personal About Page": 5.268826558114816e-05, "Personal Blog": 4.994494011742233e-05, "Product Page": 3.302512980702021e-05, "Spam / Ads": 5.252105673027725e-05, "Structured Data": 0.00034178549076968427, "Technical Writing": 0.004536071455225799, "Transcript / Interview": 0.00021446560690852276, "Tutorial / How-To Guide": 0.004728085684762292, "User Reviews": 2.873899794206556e-05, "NONE": 0.9883155367429587}, "structured_records": {"false": 0.9995829296204182, "true": 0.0004170703795818626}, "procedural_steps": {"false": 0.9466321895138147, "true": 0.05336781048618531}, "worked_solution": {"false": 0.9897444984595891, "true": 0.01025550154041084}, "contextual_dialogue": {"false": 0.9998978649707029, "true": 0.00010213502929709774}, "content_quality": {"-2": 0.02229731488717914, "-1": 0.11537243165482243, "0": 0.32221830392020345, "1": 0.4281345759917121, "2": 0.11197737354608281}, "noise_severity": {"0": 0.9815716476223688, "1": 0.0149007825176758, "2": 0.0026343214737098645, "3": 0.0005340996489919902, "4": 0.0003591487372536187}, "missing_content_severity": {"0": 0.9953312321614506, "1": 0.0022976867688947575, "2": 0.0017715856247080294, "3": 0.0004662552432151228, "4": 0.000133240201731514}, "factual_density": {"0": 0.015372176741350479, "1": 0.03813070211124719, "2": 0.1637111328016339, "3": 0.3759021554309372, "4": 0.4068838329148312}, "pretraining_utility": {"-2": 0.008431152960588016, "-1": 0.05903120434673201, "0": 0.35856705407642336, "1": 0.48745912583735945, "2": 0.08651146277889724}, "math_density": {"0": 0.9332359308660054, "1": 0.03414562919882137, "2": 0.006319056707202044, "3": 0.006295687763888435, "4": 0.020003695464082684}, "code_density": {"0": 0.0005390002423559366, "1": 0.0010711558866184172, "2": 0.04444102813312099, "3": 0.17307235796546203, "4": 0.7808764577724426}, "reasoning_density": {"0": 0.7505475426891091, "1": 0.06858397677607188, "2": 0.11558639252240019, "3": 0.040048902282085894, "4": 0.02523318573033297}, "education_level": {"none": 0.0014829815638393153, "0": 0.0023096113571645887, "1": 0.05791726302849004, "2": 0.8396112482836422, "3": 0.09063711732277367, "4": 0.00804177844409016}, "creative_writing_quality": {"not_applicable": 0.9986190910426995, "-2": 0.00012288729957449386, "-1": 0.0003607748157408483, "0": 0.0007821632024640856, "1": 8.604357297786993e-05, "2": 2.9040066543191367e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.472882226386986e-05, "Art & Design": 5.9235986762197734e-05, "Law & Crime": 0.00019763132111028352, "Education & Careers": 7.247003640640327e-05, "Entertainment & Media": 3.4607249820709064e-05, "Fashion & Beauty": 2.6784536693993693e-05, "Business, Economics & Finance": 6.83013808145683e-05, "Food & Dining": 3.416336189204279e-05, "Games & Gaming": 6.420783295211625e-05, "Electronics & Hardware": 0.00020390850742122116, "Health, Healthcare & Wellness": 3.9249348714533855e-05, "History & Geography": 4.1312723461169715e-05, "Home, Pets & Hobbies": 3.811843629349921e-05, "Industry & Production": 3.2085750556484946e-05, "Language, Literature & Philosophy": 6.539833463777073e-05, "Politics & Government": 3.409393784927242e-05, "Religion & Spirituality": 4.1397024467953403e-05, "Science, Mathematics & Engineering": 0.0074180411785817225, "Relationships & Community": 2.2404409659511664e-05, "Software Usage & Applications": 0.0011604044662445186, "Programming & Software Engineering": 0.9885973907886434, "Sports & Fitness": 5.3281815408456204e-05, "Vehicles & Transportation": 2.394304482138299e-05, "Travel & Tourism": 5.207571976795634e-05, "Other / Uncovered Subject": 0.0014955908266990271, "Unknown / Insufficient Content": 6.917315805598038e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.000717647395161571, "Art & Design": 0.0004569560132830527, "Law & Crime": 0.0029548506417757902, "Education & Careers": 0.0005784293151681457, "Entertainment & Media": 0.0004892301450332374, "Fashion & Beauty": 0.00030268648963277734, "Business, Economics & Finance": 0.0006205720583363975, "Food & Dining": 0.0002359957645298853, "Games & Gaming": 0.0003633667014848881, "Electronics & Hardware": 0.0006072209376300064, "Health, Healthcare & Wellness": 0.00035595780636411145, "History & Geography": 0.00032156313875328634, "Home, Pets & Hobbies": 0.00027064583408298044, "Industry & Production": 0.000235070435933355, "Language, Literature & Philosophy": 0.0004220620172584273, "Politics & Government": 0.00030306270853782996, "Religion & Spirituality": 0.00039489000349397264, "Science, Mathematics & Engineering": 0.07301050486118534, "Relationships & Community": 0.0001633543214145011, "Software Usage & Applications": 0.0070609561839333045, "Programming & Software Engineering": 0.036140028919954006, "Sports & Fitness": 0.00028743712558017764, "Vehicles & Transportation": 0.0001295846919797405, "Travel & Tourism": 0.00044467026528776304, "Other / Uncovered Subject": 0.007522738267271011, "NONE": 0.8656105179569344}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9689, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9689, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:08.574109+00:00", "scoring_key_slot": "primary", "dataset_uid": "c84d5dd5-074a-4fb5-aaf5-e18f330a250c", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "47f74aa279ef085e64dc110b59bb2853812df46874cd35c453b02b46df1edf7c", "source_metadata": {"uuid": "c84d5dd5-074a-4fb5-aaf5-e18f330a250c", "repo_name": "clancycottrill/PIA-4E", "relative_path": "file_encrypt.py", "category": "TOOL", "algo_rel_score": 0.9037100672721863, "quality_score": 5.842343807220459, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 7, "repo_created_at": "2024-11-12T00:45:57.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "6b5450a058a5d6c6263f3e7610f8cd7506ac04d0", "size_bytes": 5236, "file_timestamp": 1731381841, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "ece7b43c56cac038f80350f78b7bab9794b73ac2"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 7, "repo_created_at": "2024-11-12T00:45:57.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "6b5450a058a5d6c6263f3e7610f8cd7506ac04d0", "size_bytes": 5236, "file_timestamp": 1731381841, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "ece7b43c56cac038f80350f78b7bab9794b73ac2", "url": "https://github.com/clancycottrill/PIA-4E/blob/ece7b43c56cac038f80350f78b7bab9794b73ac2/file_encrypt.py"}, "gpt4o_tokens": 1098, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.492122261654697, "noise_severity": 0.023208319361085975, "missing_content_severity": 0.007772584554882241, "factual_density": 3.1207947656666515, "pretraining_utility": 0.5845885411272459, "math_density": 0.1456855877612215, "code_density": 3.732676117139017, "reasoning_density": 0.5208362115884618, "education_level": 2.044249810120747, "creative_writing_quality": -0.33487052595383987}, "score_applicability": {"education_level": 0.9985170184361607, "creative_writing_quality": 0.001380908957300489}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/christokita/representative-democracy-districts/blob/d56cd85d90965cc353274741ecb60fccd0321779/scripts/democracy/model.py", "id": "python-0-2c7ce020ab1e2ec84f2ecd5bd0bbd6ad", "text": "import math\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\nfrom typing import Tuple\n\nclass LegislativeDistricts:\n    \"\"\"\n    Class that takes results of Schelling model and constructs legislative districts for election.\n    \"\"\"\n\n    def __init__(\n            self, voters: pd.DataFrame, x_column: str = \"grid_x\", y_column: str = \"grid_y\", identity_column: str = \"identity\"\n        ) -> None:\n        \"\"\"\n        Parameters:\n            voters:             long-form of Schelling model population grid with three colums: x coordinate, y coordinate, and voter identity.\n            x_column:           name of column containing voter's X coordinate in grid.\n            x_column:           name of column containing voter's Y coordinate in grid.\n            identity_column:    name of column containing voter's identty, which dictates voting behavior.\n\n        Returns:\n            None\n        \"\"\"\n        self.voters = voters\n        self.x_col = x_column\n        self.y_col = y_column\n        self.identity_col = identity_column\n\n    def create_districts(self, district_width: int, district_height: int, verbose: bool = True) -> None:\n        \"\"\"\n        Assign each voter to a legislative district.\n        In order to make sure districts are evenly sized,\n        choose appropriate district width and height.\n\n        Parameters:\n            district_width:     desired width of each district.\n            district_height:    desired height of each district.\n            verbose:            whether to display message upon district creation.\n\n        Returns:\n            None. Adds `district` column to self.voters dataframe.\n        \"\"\"\n        # Get dimensions of population grid\n        grid_width = len( self.voters[self.x_col].unique() )\n        grid_height = len( self.voters[self.y_col].unique() )\n        \n        # Assign user to district\n        district_x = self.voters[self.x_col] // district_width\n        district_y = self.voters[self.y_col] // district_height\n        \n        num_districts_per_row = grid_width // district_width\n        num_districts_per_col = grid_height // district_height\n        self.voters[\"district\"] = district_x + 1 + district_y * num_districts_per_row\n        \n        # Print message\n        n_districts = num_districts_per_row*num_districts_per_col\n        if verbose:\n            print(f\"Generated {n_districts:,} voting districts, each with {int(self.voters.shape[0]/n_districts):,} voters.\")\n        return None \n\n    def conduct_election(self, members_per_district: int = 1) -> Tuple[pd.DataFrame]:\n        \"\"\"\n        Simulate election of N-member districts in our electorate.\n        With multi-member districts, in the event there is only one type of voter there,\n        two representatives of the same identity will be elected.\n\n        Parameters:\n            members_per_district:   number of representatives elected per district (1 = single-member, 2+ = multi-member)\n            \n        Returns:\n            district_votes:     breakdown of votes for each candidate.\n            elected:            list of who won in each district.\n        \"\"\"\n        district_votes = (\n            self.voters\n                .groupby([\"district\", self.identity_col])\n                .size()\n                .reset_index(name=\"votes\")\n                .sort_values(by=[\"district\", \"votes\"], ascending=[True,False])\n                .reset_index(drop=True)\n        )\n        elected = (\n            district_votes\n                .groupby(\"district\")\n                .head(members_per_district)\n                .set_index(\"district\")\n                .groupby(level=0)\n                .apply(lambda x: x.reindex(x.index.repeat(members_per_district))[:members_per_district] if len(x) < members_per_district else x)\n                .reset_index(level=1)\n                .reset_index(drop=True)\n                .rename(columns={self.identity_col: \"representive_identity\"})\n                .assign(order_elected=lambda x: x.groupby(\"district\").cumcount())\n                .drop(columns=[\"votes\"])\n        )\n        return district_votes, elected\n    \n    def calculate_segregation(self) -> pd.DataFrame:\n        \"\"\"\n        Calculate the social segregation within each district and across the entire population.\n        \"\"\"\n        # Calculate identity breakdown by district\n        district_demographics = (\n            self.voters\n            .groupby([\"district\", \"identity\"])\n            .size()\n            .reset_index(name=\"voters\")\n            .pivot(index=\"district\", columns=\"identity\", values=\"voters\")\n            .fillna(0)\n        )\n\n        # Drop vancancies\n        if -1 in district_demographics.columns:\n            district_demographics = district_demographics.drop(columns=[-1]) \n\n        # Calculate overall entropy\n        E = stats.entropy( district_demographics.sum(axis=0) )\n        T = district_demographics.values.sum()\n\n        # Calcualte segregation by district\n        E_i = district_demographics.apply(stats.entropy, axis=1)\n        T_i = district_demographics.sum(axis=1)\n\n        district_segregation = pd.DataFrame({\n            \"district\": district_demographics.index,\n            \"n_voters\": T_i,\n            \"diversity\": E_i,\n            \"segregation\": (E - E_i) / E\n        })\n        district_segregation = district_segregation.reset_index(drop=True)\n\n        # Calculate Thiel's Index\n        H = sum(district_segregation.n_voters/T * district_segregation.segregation)\n        return district_segregation, H\n    \n    def calculate_representation(self, elected: pd.DataFrame) -> pd.DataFrame:\n        \"\"\"\n        Determine the number and percent of voters who now have an elected official that is of the same identity.\n\n        Parameters:\n            elected:    dataframe of representatives elected in each district.\n\n        Return:\n            representation:     \n        \"\"\"\n        representation = pd.DataFrame({\n            \"district\": self.voters.district.unique(), \n            \"n_voters\": np.nan,\n            \"n_represented\": np.nan, \n            \"prop_represented\": np.nan\n        })\n        for _, row in representation.iterrows():\n            district_voters = self.voters[self.voters.district == row.district]\n            district_voters = district_voters[district_voters.identity != -1]\n            district_representatives = elected[elected.district == row.district]\n            representation.loc[representation.district == row.district, \"n_voters\"] = district_voters.shape[0]\n            representation.loc[representation.district == row.district, \"n_represented\"] = sum(district_voters.identity.isin(district_representatives.representive_identity))\n            representation.loc[representation.district == row.district, \"prop_represented\"] = sum(district_voters.identity.isin(district_representatives.representive_identity)) / district_voters.shape[0]\n        return representation\n", "text_preview": "import math import numpy as np import pandas as pd from scipy import stats from typing import Tuple class LegislativeDistricts: \"\"\" Class that takes results of Schelling model and constructs legislative districts for election. \"\"\" def __ini", "input_sha256": "c68c49a531f526453a4b15cea2e72a3c46f1c870d1f7f19da05be18569048358", "text_sha256": "2c7ce020ab1e2ec84f2ecd5bd0bbd6ad76f2f480efd1edcc535fe2ec2fa992fb", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12006, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Politics & Government", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 3, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.517804531475842, "noise_severity": 0.015349314885830845, "missing_content_severity": 0.016627573775030092, "factual_density": 3.223867547865835, "pretraining_utility": 0.7334224555979101, "math_density": 2.8586255025394984, "code_density": 3.816724250768317, "reasoning_density": 1.063015309240867, "education_level": 2.377099581252647, "creative_writing_quality": -0.26646366462181803}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Politics & Government"}, "review_reasons": ["secondary_topic probability 0.5376 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.024774579078333803, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.997630519079188, "secondary": 0.9637525277512223, "tertiary": 0.9896042624416577, "structured_records": 0.015655666422570193, "procedural_steps": 0.04602891888840224, "worked_solution": 0.007045954631413946, "contextual_dialogue": 5.236547218703297e-06, "content_quality": 0.4192178440192804, "noise_severity": 0.9872089042618076, "missing_content_severity": 0.9861436885208082, "factual_density": 0.3532229565548629, "pretraining_utility": 0.5539036187381783, "math_density": 0.36695251174349885, "code_density": 0.8472702089735972, "reasoning_density": 0.11415390896594413, "education_level": 0.5877141301350556, "creative_writing_quality": 0.9992407702320746, "primary_topic": 0.5875048669176074, "secondary_topic": 0.5191186065683441}, "probability_margin": {"primary": 0.9966780857941012, "secondary": 0.9497958107292769, "tertiary": 0.9866105001902239, "education_level": 0.3890022823689526, "creative_writing_quality": 0.9990685521316001, "primary_topic": 0.3498257248198104, "secondary_topic": 0.33382500629392753}, "raw_probabilities": {"primary": {"Academic Writing": 0.00022262824244561916, "Content Listing": 1.0072046781976555e-05, "Creative Writing": 1.4036133573956586e-05, "Customer Support Page": 6.164738889331297e-06, "Q&A Forum": 8.327991108345408e-06, "Discussion Thread": 4.984253773392762e-06, "Comment Section": 6.858913610665457e-06, "Missing Content": 1.0158533676650408e-05, "Knowledge Article": 0.00010489179962965757, "Legal Notices": 1.5095598112089902e-05, "News Article": 3.0298515556125854e-06, "Organizational Announcement": 7.346798468114008e-06, "Nonfiction Writing": 3.073841885213329e-05, "Organizational About Page": 8.457879486086223e-06, "Personal About Page": 8.904640851549477e-06, "Personal Blog": 4.7611816656679026e-06, "Product Page": 4.249110496007418e-06, "Spam / Ads": 6.288558045276337e-06, "Structured Data": 0.00036779056215221334, "Technical Writing": 0.9977292474508885, "Transcript / Interview": 1.052432481404384e-05, "Tutorial / How-To Guide": 0.00036045849501563457, "User Reviews": 3.8228193199839935e-06, "Other / Unknown": 0.0010511616567872762}, "secondary": {"Academic Writing": 0.009188378962852978, "Content Listing": 0.00013389841718504475, "Creative Writing": 0.00019258189051232164, "Customer Support Page": 0.00011533621001404552, "Q&A Forum": 8.012083387752488e-05, "Discussion Thread": 6.455183887237843e-05, "Comment Section": 0.00010443983096675349, "Missing Content": 0.00024883261290089415, "Knowledge Article": 0.001498406797390545, "Legal Notices": 0.0001422189759073414, "News Article": 9.441387695469334e-05, "Organizational Announcement": 9.54994713330281e-05, "Nonfiction Writing": 0.00038411870517010587, "Organizational About Page": 0.00011520127238085384, "Personal About Page": 0.00010761333571970879, "Personal Blog": 8.02351853342056e-05, "Product Page": 5.629060505664451e-05, "Spam / Ads": 8.417822254528299e-05, "Structured Data": 0.0028381650382868413, "Technical Writing": 0.015467028365644469, "Transcript / Interview": 0.00014384196728377217, "Tutorial / How-To Guide": 0.0034531424375684417, "User Reviews": 4.8666051320666864e-05, "NONE": 0.9652628390949214}, "tertiary": {"Academic Writing": 0.0032266621764776137, "Content Listing": 3.626399776731135e-05, "Creative Writing": 6.719985298667898e-05, "Customer Support Page": 3.099660986490752e-05, "Q&A Forum": 2.4625041101523605e-05, "Discussion Thread": 1.6560795770092464e-05, "Comment Section": 3.346122530949266e-05, "Missing Content": 0.00014463502416251913, "Knowledge Article": 0.00047283415643905154, "Legal Notices": 7.386638913801527e-05, "News Article": 3.316196885680914e-05, "Organizational Announcement": 2.9139496604734057e-05, "Nonfiction Writing": 0.0001591659131489886, "Organizational About Page": 4.785229763369715e-05, "Personal About Page": 3.124079183820552e-05, "Personal Blog": 2.6081967923203756e-05, "Product Page": 1.9796695147117652e-05, "Spam / Ads": 2.634223043972204e-05, "Structured Data": 0.0007095205035615801, "Technical Writing": 0.0034269179830314312, "Transcript / Interview": 3.5968871854009315e-05, "Tutorial / How-To Guide": 0.001274864986877237, "User Reviews": 1.5422850810775608e-05, "NONE": 0.9900374181732553}, "structured_records": {"false": 0.9843443335774298, "true": 0.015655666422570193}, "procedural_steps": {"false": 0.9539710811115978, "true": 0.04602891888840224}, "worked_solution": {"false": 0.992954045368586, "true": 0.007045954631413946}, "contextual_dialogue": {"false": 0.9999947634527813, "true": 5.236547218703297e-06}, "content_quality": {"-2": 0.01101602659680732, "-1": 0.0986457026926758, "0": 0.35922754267473717, "1": 0.4237391687094269, "2": 0.10737155932635277}, "noise_severity": {"0": 0.9869813461076317, "1": 0.011418716686355877, "2": 0.001080406102961678, "3": 0.0003083384186507796, "4": 0.00021119268439981795}, "missing_content_severity": {"0": 0.9898117539868112, "1": 0.005312971010574703, "2": 0.003550398179618802, "3": 0.00108570088676347, "4": 0.0002391759362318443}, "factual_density": {"0": 0.006737658063344732, "1": 0.028030331051122744, "2": 0.13527547886980348, "3": 0.39453986898781035, "4": 0.4354166630279188}, "pretraining_utility": {"-2": 0.0066026585425322, "-1": 0.04044053664079858, "0": 0.3002575520489442, "1": 0.518330196211677, "2": 0.13436905655604803}, "math_density": {"0": 0.020699048178021186, "1": 0.0653766122484998, "2": 0.25766537265308825, "3": 0.3471177226967409, "4": 0.3091412442236499}, "code_density": {"0": 0.00033537014830225116, "1": 0.0006697041974495381, "2": 0.021240442926988293, "3": 0.13744427019214916, "4": 0.8403102125351108}, "reasoning_density": {"0": 0.5426982797743113, "1": 0.12570016114049512, "2": 0.13818027909315772, "3": 0.112730530054087, "4": 0.08069074993794885}, "education_level": {"none": 0.0010569951077581078, "0": 0.0014779142728616342, "1": 0.011663440294741834, "2": 0.656428441779213, "3": 0.2674261594102604, "4": 0.061947049135165154}, "creative_writing_quality": {"not_applicable": 0.9993673085267288, "-2": 7.995165975949691e-05, "-1": 0.00014723491114065364, "0": 0.00029875639512877227, "1": 7.494807236767406e-05, "2": 3.180043487457937e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00026394336750300583, "Art & Design": 0.00044466024262578474, "Law & Crime": 0.0010792880582142755, "Education & Careers": 0.0003295094342211929, "Entertainment & Media": 0.00036266577466779614, "Fashion & Beauty": 0.0003002643413293209, "Business, Economics & Finance": 0.0015604912760029419, "Food & Dining": 0.00031597778983075695, "Games & Gaming": 0.000990862927480413, "Electronics & Hardware": 0.0003520760500289623, "Health, Healthcare & Wellness": 0.00040070195350983467, "History & Geography": 0.0005166003603516859, "Home, Pets & Hobbies": 0.0002893685664804823, "Industry & Production": 0.0003518679367119191, "Language, Literature & Philosophy": 0.0003933963786348992, "Politics & Government": 0.2535443395240428, "Religion & Spirituality": 0.0003825337440095892, "Science, Mathematics & Engineering": 0.08413837256294089, "Relationships & Community": 0.0027083752099951214, "Software Usage & Applications": 0.0005586298474120737, "Programming & Software Engineering": 0.6033700643438532, "Sports & Fitness": 0.00034779939663572967, "Vehicles & Transportation": 0.00021031623022757105, "Travel & Tourism": 0.0005215396699771124, "Other / Uncovered Subject": 0.0456958816437667, "Unknown / Insufficient Content": 0.0005704733695460134}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006322989153324412, "Art & Design": 0.0007425262913639447, "Law & Crime": 0.0010326067599225521, "Education & Careers": 0.0005298287771622947, "Entertainment & Media": 0.0005735147893326109, "Fashion & Beauty": 0.0004592710464286715, "Business, Economics & Finance": 0.0028034998914058256, "Food & Dining": 0.00043415098393471285, "Games & Gaming": 0.0012451369220008494, "Electronics & Hardware": 0.0005398453045465992, "Health, Healthcare & Wellness": 0.0006214182858584788, "History & Geography": 0.0007402404384358549, "Home, Pets & Hobbies": 0.00037345184547339736, "Industry & Production": 0.00047041462782761444, "Language, Literature & Philosophy": 0.0003491010539955979, "Politics & Government": 0.5376140447772539, "Religion & Spirituality": 0.0006502258012177764, "Science, Mathematics & Engineering": 0.2037890384833264, "Relationships & Community": 0.00869998727163571, "Software Usage & Applications": 0.000513077053263073, "Programming & Software Engineering": 0.1473181872281197, "Sports & Fitness": 0.0004208553671505195, "Vehicles & Transportation": 0.0002842952806016106, "Travel & Tourism": 0.0006122224107862159, "Other / Uncovered Subject": 0.02941301811031556, "NONE": 0.059137742283307905}}, "probabilities": {"primary": {"Academic Writing": 0.00022262824244561921, "Content Listing": 1.0072046781976557e-05, "Creative Writing": 1.4036133573956589e-05, "Customer Support Page": 6.164738889331299e-06, "Q&A Forum": 8.32799110834541e-06, "Discussion Thread": 4.984253773392763e-06, "Comment Section": 6.8589136106654584e-06, "Missing Content": 1.015853367665041e-05, "Knowledge Article": 0.0001048917996296576, "Legal Notices": 1.5095598112089906e-05, "News Article": 3.029851555612586e-06, "Organizational Announcement": 7.3467984681140094e-06, "Nonfiction Writing": 3.07384188521333e-05, "Organizational About Page": 8.457879486086225e-06, "Personal About Page": 8.904640851549478e-06, "Personal Blog": 4.761181665667903e-06, "Product Page": 4.249110496007419e-06, "Spam / Ads": 6.288558045276339e-06, "Structured Data": 0.00036779056215221345, "Technical Writing": 0.9977292474508888, "Transcript / Interview": 1.0524324814043842e-05, "Tutorial / How-To Guide": 0.0003604584950156346, "User Reviews": 3.822819319983994e-06, "Other / Unknown": 0.0010511616567872764}, "secondary": {"Academic Writing": 0.00918837896285298, "Content Listing": 0.00013389841718504478, "Creative Writing": 0.00019258189051232167, "Customer Support Page": 0.00011533621001404554, "Q&A Forum": 8.012083387752489e-05, "Discussion Thread": 6.455183887237845e-05, "Comment Section": 0.0001044398309667535, "Missing Content": 0.0002488326129008942, "Knowledge Article": 0.0014984067973905452, "Legal Notices": 0.00014221897590734143, "News Article": 9.441387695469336e-05, "Organizational Announcement": 9.549947133302811e-05, "Nonfiction Writing": 0.0003841187051701059, "Organizational About Page": 0.00011520127238085386, "Personal About Page": 0.0001076133357197088, "Personal Blog": 8.023518533420562e-05, "Product Page": 5.6290605056644515e-05, "Spam / Ads": 8.4178222545283e-05, "Structured Data": 0.0028381650382868417, "Technical Writing": 0.01546702836564447, "Transcript / Interview": 0.0001438419672837722, "Tutorial / How-To Guide": 0.003453142437568442, "User Reviews": 4.866605132066687e-05, "NONE": 0.9652628390949215}, "tertiary": {"Academic Writing": 0.0032266621764776137, "Content Listing": 3.626399776731135e-05, "Creative Writing": 6.719985298667898e-05, "Customer Support Page": 3.099660986490752e-05, "Q&A Forum": 2.4625041101523605e-05, "Discussion Thread": 1.6560795770092464e-05, "Comment Section": 3.346122530949266e-05, "Missing Content": 0.00014463502416251913, "Knowledge Article": 0.00047283415643905154, "Legal Notices": 7.386638913801527e-05, "News Article": 3.316196885680914e-05, "Organizational Announcement": 2.9139496604734057e-05, "Nonfiction Writing": 0.0001591659131489886, "Organizational About Page": 4.785229763369715e-05, "Personal About Page": 3.124079183820552e-05, "Personal Blog": 2.6081967923203756e-05, "Product Page": 1.9796695147117652e-05, "Spam / Ads": 2.634223043972204e-05, "Structured Data": 0.0007095205035615801, "Technical Writing": 0.0034269179830314312, "Transcript / Interview": 3.5968871854009315e-05, "Tutorial / How-To Guide": 0.001274864986877237, "User Reviews": 1.5422850810775608e-05, "NONE": 0.9900374181732553}, "structured_records": {"false": 0.9843443335774298, "true": 0.015655666422570193}, "procedural_steps": {"false": 0.9539710811115978, "true": 0.04602891888840224}, "worked_solution": {"false": 0.992954045368586, "true": 0.007045954631413946}, "contextual_dialogue": {"false": 0.9999947634527813, "true": 5.236547218703297e-06}, "content_quality": {"-2": 0.01101602659680732, "-1": 0.0986457026926758, "0": 0.35922754267473717, "1": 0.4237391687094269, "2": 0.10737155932635277}, "noise_severity": {"0": 0.9869813461076319, "1": 0.011418716686355879, "2": 0.0010804061029616781, "3": 0.00030833841865077964, "4": 0.000211192684399818}, "missing_content_severity": {"0": 0.9898117539868112, "1": 0.005312971010574703, "2": 0.003550398179618802, "3": 0.00108570088676347, "4": 0.0002391759362318443}, "factual_density": {"0": 0.00673765806334473, "1": 0.028030331051122737, "2": 0.13527547886980346, "3": 0.39453986898781024, "4": 0.4354166630279187}, "pretraining_utility": {"-2": 0.0066026585425322, "-1": 0.04044053664079858, "0": 0.3002575520489442, "1": 0.518330196211677, "2": 0.13436905655604803}, "math_density": {"0": 0.020699048178021186, "1": 0.0653766122484998, "2": 0.25766537265308825, "3": 0.3471177226967409, "4": 0.3091412442236499}, "code_density": {"0": 0.00033537014830225116, "1": 0.0006697041974495381, "2": 0.021240442926988293, "3": 0.13744427019214916, "4": 0.8403102125351108}, "reasoning_density": {"0": 0.5426982797743113, "1": 0.12570016114049512, "2": 0.13818027909315772, "3": 0.112730530054087, "4": 0.08069074993794885}, "education_level": {"none": 0.0010569951077581078, "0": 0.0014779142728616342, "1": 0.011663440294741834, "2": 0.656428441779213, "3": 0.2674261594102604, "4": 0.061947049135165154}, "creative_writing_quality": {"not_applicable": 0.9993673085267288, "-2": 7.995165975949691e-05, "-1": 0.00014723491114065364, "0": 0.00029875639512877227, "1": 7.494807236767406e-05, "2": 3.180043487457937e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00026394336750300583, "Art & Design": 0.00044466024262578474, "Law & Crime": 0.0010792880582142755, "Education & Careers": 0.0003295094342211929, "Entertainment & Media": 0.00036266577466779614, "Fashion & Beauty": 0.0003002643413293209, "Business, Economics & Finance": 0.0015604912760029419, "Food & Dining": 0.00031597778983075695, "Games & Gaming": 0.000990862927480413, "Electronics & Hardware": 0.0003520760500289623, "Health, Healthcare & Wellness": 0.00040070195350983467, "History & Geography": 0.0005166003603516859, "Home, Pets & Hobbies": 0.0002893685664804823, "Industry & Production": 0.0003518679367119191, "Language, Literature & Philosophy": 0.0003933963786348992, "Politics & Government": 0.2535443395240428, "Religion & Spirituality": 0.0003825337440095892, "Science, Mathematics & Engineering": 0.08413837256294089, "Relationships & Community": 0.0027083752099951214, "Software Usage & Applications": 0.0005586298474120737, "Programming & Software Engineering": 0.6033700643438532, "Sports & Fitness": 0.00034779939663572967, "Vehicles & Transportation": 0.00021031623022757105, "Travel & Tourism": 0.0005215396699771124, "Other / Uncovered Subject": 0.0456958816437667, "Unknown / Insufficient Content": 0.0005704733695460134}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006322989153324413, "Art & Design": 0.0007425262913639448, "Law & Crime": 0.0010326067599225523, "Education & Careers": 0.0005298287771622948, "Entertainment & Media": 0.000573514789332611, "Fashion & Beauty": 0.00045927104642867154, "Business, Economics & Finance": 0.002803499891405826, "Food & Dining": 0.0004341509839347129, "Games & Gaming": 0.0012451369220008496, "Electronics & Hardware": 0.0005398453045465993, "Health, Healthcare & Wellness": 0.0006214182858584789, "History & Geography": 0.000740240438435855, "Home, Pets & Hobbies": 0.0003734518454733974, "Industry & Production": 0.0004704146278276145, "Language, Literature & Philosophy": 0.00034910105399559797, "Politics & Government": 0.537614044777254, "Religion & Spirituality": 0.0006502258012177765, "Science, Mathematics & Engineering": 0.20378903848332644, "Relationships & Community": 0.008699987271635711, "Software Usage & Applications": 0.0005130770532630731, "Programming & Software Engineering": 0.14731818722811973, "Sports & Fitness": 0.00042085536715051955, "Vehicles & Transportation": 0.00028429528060161063, "Travel & Tourism": 0.000612222410786216, "Other / Uncovered Subject": 0.029413018110315565, "NONE": 0.05913774228330791}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10010, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10010, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:08.734126+00:00", "scoring_key_slot": "primary", "dataset_uid": "bc5ecd34-eeab-4711-b6e9-6e719e24133a", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "098dc723812b53b73de603e92df0e40b4d9561ef74ae9da482f7be21f3870477", "source_metadata": {"uuid": "bc5ecd34-eeab-4711-b6e9-6e719e24133a", "repo_name": "christokita/representative-democracy-districts", "relative_path": "scripts/democracy/model.py", "category": "DATA", "algo_rel_score": 0.9767605066299438, "quality_score": 5.345131874084473, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 17, "repo_created_at": "2024-04-20T12:29:33.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "d3cc758db9a19df3dccc2faa7867309b303c024f", "size_bytes": 6902, "file_timestamp": 1716235863, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "d56cd85d90965cc353274741ecb60fccd0321779"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 17, "repo_created_at": "2024-04-20T12:29:33.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "d3cc758db9a19df3dccc2faa7867309b303c024f", "size_bytes": 6902, "file_timestamp": 1716235863, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "d56cd85d90965cc353274741ecb60fccd0321779", "url": "https://github.com/christokita/representative-democracy-districts/blob/d56cd85d90965cc353274741ecb60fccd0321779/scripts/democracy/model.py"}, "gpt4o_tokens": 1442, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.517804531475842, "noise_severity": 0.015349314885830845, "missing_content_severity": 0.016627573775030092, "factual_density": 3.2238675478658356, "pretraining_utility": 0.7334224555979101, "math_density": 2.8586255025394984, "code_density": 3.816724250768317, "reasoning_density": 1.063015309240867, "education_level": 2.377099581252647, "creative_writing_quality": -0.26646366462181803}, "score_applicability": {"education_level": 0.998943004892242, "creative_writing_quality": 0.0006326914732711763}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/reddyroxx/DBMS-Class_project/blob/ad24e121ef2d3a02a19ece6207a18e87536ec1f4/Programs/proj.py", "id": "python-0-b3b3905b6c39a040ad17abc3bb4642bc", "text": "\nfrom flask import Flask,render_template ,redirect, url_for , request\n#import requests\nimport sqlite3 as sql\n#import os.path\nimport datetime\n\nconn=sql.connect(\"restaurant.db\")\n\n \napp = Flask(__name__)\n@app.route('/home')\ndef home():\n    return render_template(\"homepage.html\")\n\n@app.route(\"/branch\",methods=[\"POST\",\"GET\"])\ndef branch():\n    #tableName = \"Girinagar\"\n    if request.method == 'POST':\n        tableName=request.form['branch']\n    conn=sql.connect(\"restaurant.db\")\n\t#conn.row_factory=sql.Row\n\t#cur=conn.cursor()\n    command=\"select branch_id from branch where location = '\"+tableName+\"';\"\n    #command=\"select branch_id from branch where location = 'Girinagar';\"\n    b_id = list(conn.execute(command))[0][0]\n    #print(b_id)\n    #conn.commit()\n    #rows=cur.fetchall()\n    #menu = [\"Spicy Delight Veg Pizza\",\"Country Special Veg Pizza\",\"Farm House Veg Pizza\",\"Mexican Green Wave Veg Pizza\",\"5 Pepper Veg Pizza\",\"Cheese and Barbeque Chicken Non-Veg Pizza\",\"Chicken Fiesta Non-Veg Pizza\",\"Spicy Chicken Non-Veg Pizza\",\"Zesty Chicken Non-Veg Pizza\",\"Chicken Mexicana Non-Veg Pizza\"]    \n    \n    return render_template(\"branch_\"+b_id+\".html\")\n\n'''\n@app.route(\"/queries\",methods=[\"POST\",\"GET\"])\ndef queries():\n    return render_template(\"queries.html\")\n'''\n\n\n@app.route(\"/check_user\",methods=[\"POST\",\"GET\"])\ndef check_user():\n    if request.method == 'POST':\n        contact=request.form['contact_no']\n    conn=sql.connect(\"restaurant.db\")\n    command = \"select * from customer where contact_no ='\"+contact+\"';\"    \n    user = list(conn.execute(command))\n    rows = [contact]\n    if len(user)==0:\n        return render_template(\"registration.html\",rows=rows)\n    else:\n        return render_template(\"prajwal.html\",rows=rows)\n    \n@app.route(\"/user_created\",methods=[\"POST\",\"GET\"])\ndef user_created():\n    if request.method=='POST':\n        name = request.form['name']\n        contact_no = request.form['contact_no']\n        email = request.form['email']\n    \n        \n    conn = sql.connect(\"restaurant.db\")\n    command = \"select count(*) from customer;\"\n    n = int(list(conn.execute(command))[0][0])\n    \n    insert_command = \"INSERT INTO customer (customer_id,name,contact_no,email_id) values\\\n                        ('\"+\"c\"+str(n+1)+\"',\"+\"'\"+name+\"','\"+contact_no+\"','\"+email+\"');\"\n    conn.execute(insert_command)\n    conn.commit()    \n    return render_template(\"homepage.html\")\n    #return redirect(url_for('home'))\n    \n\n\n@app.route(\"/transaction_success\",methods=[\"POST\",\"GET\"])\ndef transaction_success():\n    \n    \n    \n    if request.method == 'POST':\n        item=request.form.getlist('pizza')\n        quantity = request.form.getlist('quantity')\n        contact_no = request.form['contact_no']\n        branch = request.form['branch']\n    #conn=sql.connect(\"restaurant.db\")\n    \n    #print(item)\n    #print(quantity)\n    \n    item = list(item)\n    rows = []\n    rows.append(item)\n    rows.append(quantity)\n    rows.append(contact_no)\n    rows.append(branch)\n    \n    \n    conn=sql.connect(\"restaurant.db\")\n    order_command = \"select count(*) from orders;\"\n    order_number = int(list(conn.execute(order_command))[0][0])+1\n    #bill_number = order_number\n    date = datetime.datetime.today()\n    today_date = str(date.year)+\"/\"+str(date.month)+\"/\"+str(date.day)\n    rows.append(order_number)\n    #rows.append(bill_number)\n    rows.append(today_date)\n    base_prices = []\n    final_prices = []\n    for i in range(len(rows[0])):\n        command = \"select price from menu where dish_name = '\"+rows[0][i]+\"'\"\n        l = list(conn.execute(command))[0][0]\n        base_prices.append(str(l))\n        final_prices.append(str(int(l)*int(rows[1][i])))\n    \n    rows.append(base_prices)\n    rows.append(final_prices)\n    bill_total = sum([int(i) for i in final_prices])\n    rows.append(bill_total)\n    \n    c_name = list(conn.execute(\"select name from customer where contact_no='\"+rows[2]+\"'\"))[0][0]\n    rows.append(c_name)\n    print(rows)\n    \n    branch_id = list(conn.execute(\"select branch_id from branch where location='\"+rows[3]+\"'\"))[0][0]\n    c_id = list(conn.execute(\"select customer_id from customer where contact_no='\"+rows[2]+\"'\"))[0][0]\n    x = list(conn.execute(\"select visit_count from visits where branch_id='\"+branch_id+\"' and customer_id='\"+c_id+\"' and visit_date='\"+rows[5]+\"'\"))\n    \n    #visits table insertion\n    if(len(x)>0):\n        visit_count = int(x[0][0])\n        command = \"update visits set visit_count='\"+str(visit_count+1)+\"' where \"+\"branch_id='\"+branch_id+\"' and customer_id='\"+c_id+\"' and visit_date='\"+rows[5]+\"'\"\n        conn.execute(command)\n        conn.commit()\n    else:\n        visit_count = 0\n        visits_command = \"insert into visits (branch_id,customer_id,visit_date,visit_count) values('\"+branch_id+\"','\"+c_id+\"','\"+rows[5]+\"','\"+str(visit_count+1)+\"')\"    \n        conn.execute(visits_command)\n        conn.commit()\n    \n    \n    \n    \n    \n    #orders table insertion\n    orders_command = \"insert into orders (order_id,customer_name,customer_id,branch_id,order_date) values('or\"+str(rows[4])+\"','\"+c_name+\"','\"+c_id+\"','\"+branch_id+\"','\"+rows[5]+\"')\"\n    conn.execute(orders_command)\n    conn.commit()\n    \n    #bill table insertion\n    bill_command = \"insert into bill (bill_id,bill_date,price,order_id) values ('bi\"+str(rows[4])+\"','\"+rows[5]+\"','\"+str(rows[8])+\"','or\"+str(rows[4])+\"')\"\n    conn.execute(bill_command)\n    conn.commit()\n    \n    #order_items_command\n    for j in range(len(rows[0])):\n        dish_id = list(conn.execute(\"select dish_id from menu where dish_name='\"+rows[0][j]+\"'\"))[0][0]\n        order_items_command = \"insert into order_items (order_id,quantity,dish_id) values ('or\"+str(rows[4])+\"','\"+str(rows[1][j])+\"','\"+dish_id+\"')\"\n        conn.execute(order_items_command)\n        conn.commit()\n    \n    return render_template(\"transaction_success.html\",rows=rows)\n    \n\n\n\nif __name__ == '__main__':\n    app.run(debug=True)\n    \n", "text_preview": "from flask import Flask,render_template ,redirect, url_for , request #import requests import sqlite3 as sql #import os.path import datetime conn=sql.connect(\"restaurant.db\") app = Flask(__name__) @app.route('/home') def home(): return rende", "input_sha256": "e2fe4dd80b640d617e8a87de6379fff2fb027b32a30d128ca0d23976356a1b5e", "text_sha256": "b3b3905b6c39a040ad17abc3bb4642bc203adb04417186d81bbb7504b9d63aee", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12008, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Food & Dining", "sub_topic": null}}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": -0.03816098891863029, "noise_severity": 0.34925753482497324, "missing_content_severity": 0.06839259984531568, "factual_density": 3.1702726195818265, "pretraining_utility": 0.2949317318383773, "math_density": 0.6065934810266457, "code_density": 3.904722782727519, "reasoning_density": 0.7675239543903948, "education_level": 2.004456781655321, "creative_writing_quality": -0.6048119945732884}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Food & Dining"}, "review_reasons": ["secondary_topic probability 0.4534 below 0.70", "secondary_topic vs NONE margin 0.0063 below 0.10"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.002946907814700861, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9965066977180885, "secondary": 0.9748424838353114, "tertiary": 0.9779175180767383, "structured_records": 0.04338103321759602, "procedural_steps": 0.003638642829661006, "worked_solution": 0.010902558842090857, "contextual_dialogue": 6.598347560996086e-06, "content_quality": 0.4990038173995919, "noise_severity": 0.7089520543125223, "missing_content_severity": 0.9430061667955703, "factual_density": 0.3085605163181889, "pretraining_utility": 0.5197439770939127, "math_density": 0.4945054324777953, "code_density": 0.920602318939599, "reasoning_density": 0.360396704674671, "education_level": 0.8866476569977657, "creative_writing_quality": 0.9987550601721206, "primary_topic": 0.9634654579265646, "secondary_topic": 0.43154523343775447}, "probability_margin": {"primary": 0.9950939927515651, "secondary": 0.962954727526799, "tertiary": 0.9687997519978403, "education_level": 0.8583699349057922, "creative_writing_quality": 0.9985596410332684, "primary_topic": 0.9353750320676198, "secondary_topic": 0.006264239700426388}, "raw_probabilities": {"primary": {"Academic Writing": 0.00011333544538411805, "Content Listing": 2.8168247364174336e-05, "Creative Writing": 2.560488386393409e-05, "Customer Support Page": 9.876590600750736e-06, "Q&A Forum": 1.3456491043492199e-05, "Discussion Thread": 8.757028087103848e-06, "Comment Section": 1.3525678349851619e-05, "Missing Content": 3.207265472839201e-05, "Knowledge Article": 5.901674955296881e-05, "Legal Notices": 2.8206459488769337e-05, "News Article": 5.311352836868615e-06, "Organizational Announcement": 1.1381874504581678e-05, "Nonfiction Writing": 1.4129306228226907e-05, "Organizational About Page": 1.3632398587600169e-05, "Personal About Page": 1.702506714184433e-05, "Personal Blog": 9.501585548110473e-06, "Product Page": 9.712342363513205e-06, "Spam / Ads": 1.2465603111761758e-05, "Structured Data": 0.0002887077266179296, "Technical Writing": 0.9966522519798349, "Transcript / Interview": 1.8081656609335204e-05, "Tutorial / How-To Guide": 0.00104956845026442, "User Reviews": 7.951199617726268e-06, "Other / Unknown": 0.0015582592282698093}, "secondary": {"Academic Writing": 0.0009576807624645884, "Content Listing": 0.00047819555744837625, "Creative Writing": 0.0002084888104813111, "Customer Support Page": 7.751216705774947e-05, "Q&A Forum": 7.091482549326988e-05, "Discussion Thread": 5.3838752982491526e-05, "Comment Section": 0.00016516668723485066, "Missing Content": 0.0006091352764262812, "Knowledge Article": 0.0004496457667192108, "Legal Notices": 0.00023010403597594642, "News Article": 8.901496294443419e-05, "Organizational Announcement": 8.097619023067304e-05, "Nonfiction Writing": 0.00010517287810475785, "Organizational About Page": 0.0001036377560180119, "Personal About Page": 9.693651568242635e-05, "Personal Blog": 8.189426996574718e-05, "Product Page": 9.506046053106473e-05, "Spam / Ads": 9.380600840202418e-05, "Structured Data": 0.004737392662752743, "Technical Writing": 0.012935986148707746, "Transcript / Interview": 0.00010242565080655522, "Tutorial / How-To Guide": 0.002238957779118837, "User Reviews": 4.7342398944158275e-05, "NONE": 0.9758907136755067}, "tertiary": {"Academic Writing": 0.0005058445701102591, "Content Listing": 0.0014840329475674708, "Creative Writing": 0.00022382470538530997, "Customer Support Page": 5.889217149494996e-05, "Q&A Forum": 4.517497657237977e-05, "Discussion Thread": 3.689657733762468e-05, "Comment Section": 0.0002495866540666617, "Missing Content": 0.0008340006792246032, "Knowledge Article": 0.00045092615368854175, "Legal Notices": 0.00023708966574884457, "News Article": 5.743411452187236e-05, "Organizational Announcement": 5.982238368684614e-05, "Nonfiction Writing": 0.00013261276096256122, "Organizational About Page": 9.952385533744855e-05, "Personal About Page": 7.11299174445294e-05, "Personal Blog": 5.543778665457672e-05, "Product Page": 8.055461057767353e-05, "Spam / Ads": 9.188822749750919e-05, "Structured Data": 0.003812231643763668, "Technical Writing": 0.010037869492367329, "Transcript / Interview": 7.258192399496095e-05, "Tutorial / How-To Guide": 0.0024278096449204356, "User Reviews": 3.721304686646037e-05, "NONE": 0.9788376214902076}, "structured_records": {"false": 0.956618966782404, "true": 0.04338103321759602}, "procedural_steps": {"false": 0.996361357170339, "true": 0.003638642829661006}, "worked_solution": {"false": 0.9890974411579091, "true": 0.010902558842090857}, "contextual_dialogue": {"false": 0.999993401652439, "true": 6.598347560996086e-06}, "content_quality": {"-2": 0.028702930300963596, "-1": 0.2622723434176328, "0": 0.4631601028863176, "1": 0.21021203168924235, "2": 0.035652591705843666}, "noise_severity": {"0": 0.7150793108496771, "1": 0.22808123253843132, "2": 0.051208205247535055, "3": 0.0037651136659540255, "4": 0.0018661376984024252}, "missing_content_severity": {"0": 0.949953570637393, "1": 0.036546636827375106, "2": 0.009331033042410687, "3": 0.0034911410381654267, "4": 0.0006776184546557307}, "factual_density": {"0": 0.011607412646669368, "1": 0.03258247716287622, "2": 0.14852543681181402, "3": 0.38849942471923915, "4": 0.41878524865940137}, "pretraining_utility": {"-2": 0.010448825636974883, "-1": 0.11979009655051387, "0": 0.48115414119958466, "1": 0.3415943935630125, "2": 0.04701254304991423}, "math_density": {"0": 0.5108672559648422, "1": 0.40310460836249273, "2": 0.06664277190261063, "3": 0.00733812622128636, "4": 0.012047237548768141}, "code_density": {"0": 0.00019305298367582185, "1": 0.0004067980807593956, "2": 0.011718662339479212, "3": 0.0698472864165413, "4": 0.9178342001795442}, "reasoning_density": {"0": 0.6552950387617841, "1": 0.07256502407122783, "2": 0.1575925500566009, "3": 0.07841571823558381, "4": 0.03613166887480342}, "education_level": {"none": 0.0008203705507016859, "0": 0.0012743317897199412, "1": 0.043519866236064825, "2": 0.9055397141648047, "3": 0.047169779259012515, "4": 0.0016759379996962162}, "creative_writing_quality": {"not_applicable": 0.9989625501434338, "-2": 0.00019852913942319697, "-1": 0.0003453973392672052, "0": 0.0004029091101654278, "1": 6.62350343267431e-05, "2": 2.4379233383637213e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.938852960583536e-05, "Art & Design": 0.00011023381881063151, "Law & Crime": 8.002716174258814e-05, "Education & Careers": 0.00027899571332663917, "Entertainment & Media": 7.430607030816722e-05, "Fashion & Beauty": 0.00011327076117889255, "Business, Economics & Finance": 0.001973791772704771, "Food & Dining": 0.029495600554076802, "Games & Gaming": 0.00019740561981126373, "Electronics & Hardware": 9.02860543549018e-05, "Health, Healthcare & Wellness": 7.502256959280244e-05, "History & Geography": 4.829441470958215e-05, "Home, Pets & Hobbies": 5.691265094800153e-05, "Industry & Production": 7.693436430210626e-05, "Language, Literature & Philosophy": 5.231641154660862e-05, "Politics & Government": 3.4455856012314764e-05, "Religion & Spirituality": 4.790967417010858e-05, "Science, Mathematics & Engineering": 0.0002282913116479694, "Relationships & Community": 3.93942232973688e-05, "Software Usage & Applications": 0.0008838394691656435, "Programming & Software Engineering": 0.9648706326216967, "Sports & Fitness": 6.810451463397717e-05, "Vehicles & Transportation": 4.581727362017488e-05, "Travel & Tourism": 7.901915857360115e-05, "Other / Uncovered Subject": 0.0007993319346004018, "Unknown / Insufficient Content": 0.00013041749556222734}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009223461837843633, "Art & Design": 0.001115323946628396, "Law & Crime": 0.0009118774616644817, "Education & Careers": 0.0026260292340713385, "Entertainment & Media": 0.0007505858351386733, "Fashion & Beauty": 0.0006789286445173999, "Business, Economics & Finance": 0.029440454397378, "Food & Dining": 0.45340887830553317, "Games & Gaming": 0.0007737715714215282, "Electronics & Hardware": 0.0007640508305548949, "Health, Healthcare & Wellness": 0.000691346461919161, "History & Geography": 0.0004612676596290966, "Home, Pets & Hobbies": 0.0005261147034603849, "Industry & Production": 0.0005724861681898992, "Language, Literature & Philosophy": 0.0003504504850057118, "Politics & Government": 0.0002563047798563816, "Religion & Spirituality": 0.0005255999172303349, "Science, Mathematics & Engineering": 0.0016678229702923313, "Relationships & Community": 0.0002800185529337134, "Software Usage & Applications": 0.001677419280993697, "Programming & Software Engineering": 0.05047354787401138, "Sports & Fitness": 0.00034229141989673435, "Vehicles & Transportation": 0.00036190590206349013, "Travel & Tourism": 0.0006913530551442446, "Other / Uncovered Subject": 0.002585185753574355, "NONE": 0.4471446386051068}}, "probabilities": {"primary": {"Academic Writing": 0.00011333544538411803, "Content Listing": 2.816824736417433e-05, "Creative Writing": 2.5604883863934083e-05, "Customer Support Page": 9.876590600750734e-06, "Q&A Forum": 1.3456491043492195e-05, "Discussion Thread": 8.757028087103846e-06, "Comment Section": 1.3525678349851615e-05, "Missing Content": 3.2072654728392007e-05, "Knowledge Article": 5.90167495529688e-05, "Legal Notices": 2.820645948876933e-05, "News Article": 5.311352836868614e-06, "Organizational Announcement": 1.1381874504581676e-05, "Nonfiction Writing": 1.4129306228226904e-05, "Organizational About Page": 1.3632398587600165e-05, "Personal About Page": 1.7025067141844327e-05, "Personal Blog": 9.501585548110471e-06, "Product Page": 9.712342363513203e-06, "Spam / Ads": 1.2465603111761755e-05, "Structured Data": 0.00028870772661792953, "Technical Writing": 0.9966522519798346, "Transcript / Interview": 1.80816566093352e-05, "Tutorial / How-To Guide": 0.0010495684502644198, "User Reviews": 7.951199617726266e-06, "Other / Unknown": 0.0015582592282698089}, "secondary": {"Academic Writing": 0.0009576807624645884, "Content Listing": 0.00047819555744837625, "Creative Writing": 0.0002084888104813111, "Customer Support Page": 7.751216705774947e-05, "Q&A Forum": 7.091482549326988e-05, "Discussion Thread": 5.3838752982491526e-05, "Comment Section": 0.00016516668723485066, "Missing Content": 0.0006091352764262812, "Knowledge Article": 0.0004496457667192108, "Legal Notices": 0.00023010403597594642, "News Article": 8.901496294443419e-05, "Organizational Announcement": 8.097619023067304e-05, "Nonfiction Writing": 0.00010517287810475785, "Organizational About Page": 0.0001036377560180119, "Personal About Page": 9.693651568242635e-05, "Personal Blog": 8.189426996574718e-05, "Product Page": 9.506046053106473e-05, "Spam / Ads": 9.380600840202418e-05, "Structured Data": 0.004737392662752743, "Technical Writing": 0.012935986148707746, "Transcript / Interview": 0.00010242565080655522, "Tutorial / How-To Guide": 0.002238957779118837, "User Reviews": 4.7342398944158275e-05, "NONE": 0.9758907136755067}, "tertiary": {"Academic Writing": 0.0005058445701102591, "Content Listing": 0.0014840329475674708, "Creative Writing": 0.00022382470538530997, "Customer Support Page": 5.889217149494996e-05, "Q&A Forum": 4.517497657237977e-05, "Discussion Thread": 3.689657733762468e-05, "Comment Section": 0.0002495866540666617, "Missing Content": 0.0008340006792246032, "Knowledge Article": 0.00045092615368854175, "Legal Notices": 0.00023708966574884457, "News Article": 5.743411452187236e-05, "Organizational Announcement": 5.982238368684614e-05, "Nonfiction Writing": 0.00013261276096256122, "Organizational About Page": 9.952385533744855e-05, "Personal About Page": 7.11299174445294e-05, "Personal Blog": 5.543778665457672e-05, "Product Page": 8.055461057767353e-05, "Spam / Ads": 9.188822749750919e-05, "Structured Data": 0.003812231643763668, "Technical Writing": 0.010037869492367329, "Transcript / Interview": 7.258192399496095e-05, "Tutorial / How-To Guide": 0.0024278096449204356, "User Reviews": 3.721304686646037e-05, "NONE": 0.9788376214902076}, "structured_records": {"false": 0.956618966782404, "true": 0.04338103321759602}, "procedural_steps": {"false": 0.996361357170339, "true": 0.003638642829661006}, "worked_solution": {"false": 0.9890974411579091, "true": 0.010902558842090857}, "contextual_dialogue": {"false": 0.999993401652439, "true": 6.598347560996086e-06}, "content_quality": {"-2": 0.028702930300963596, "-1": 0.2622723434176328, "0": 0.4631601028863176, "1": 0.21021203168924235, "2": 0.035652591705843666}, "noise_severity": {"0": 0.7150793108496771, "1": 0.22808123253843132, "2": 0.051208205247535055, "3": 0.0037651136659540255, "4": 0.0018661376984024252}, "missing_content_severity": {"0": 0.949953570637393, "1": 0.036546636827375106, "2": 0.009331033042410687, "3": 0.0034911410381654267, "4": 0.0006776184546557307}, "factual_density": {"0": 0.011607412646669367, "1": 0.03258247716287621, "2": 0.148525436811814, "3": 0.38849942471923904, "4": 0.41878524865940125}, "pretraining_utility": {"-2": 0.010448825636974881, "-1": 0.11979009655051384, "0": 0.48115414119958455, "1": 0.34159439356301247, "2": 0.047012543049914216}, "math_density": {"0": 0.5108672559648422, "1": 0.40310460836249273, "2": 0.06664277190261063, "3": 0.00733812622128636, "4": 0.012047237548768141}, "code_density": {"0": 0.00019305298367582188, "1": 0.00040679808075939563, "2": 0.011718662339479213, "3": 0.06984728641654131, "4": 0.9178342001795443}, "reasoning_density": {"0": 0.6552950387617841, "1": 0.07256502407122783, "2": 0.1575925500566009, "3": 0.07841571823558381, "4": 0.03613166887480342}, "education_level": {"none": 0.000820370550701686, "0": 0.0012743317897199414, "1": 0.04351986623606483, "2": 0.9055397141648048, "3": 0.04716977925901252, "4": 0.0016759379996962164}, "creative_writing_quality": {"not_applicable": 0.9989625501434338, "-2": 0.00019852913942319697, "-1": 0.0003453973392672052, "0": 0.0004029091101654278, "1": 6.62350343267431e-05, "2": 2.4379233383637213e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.938852960583536e-05, "Art & Design": 0.00011023381881063151, "Law & Crime": 8.002716174258814e-05, "Education & Careers": 0.00027899571332663917, "Entertainment & Media": 7.430607030816722e-05, "Fashion & Beauty": 0.00011327076117889255, "Business, Economics & Finance": 0.001973791772704771, "Food & Dining": 0.029495600554076802, "Games & Gaming": 0.00019740561981126373, "Electronics & Hardware": 9.02860543549018e-05, "Health, Healthcare & Wellness": 7.502256959280244e-05, "History & Geography": 4.829441470958215e-05, "Home, Pets & Hobbies": 5.691265094800153e-05, "Industry & Production": 7.693436430210626e-05, "Language, Literature & Philosophy": 5.231641154660862e-05, "Politics & Government": 3.4455856012314764e-05, "Religion & Spirituality": 4.790967417010858e-05, "Science, Mathematics & Engineering": 0.0002282913116479694, "Relationships & Community": 3.93942232973688e-05, "Software Usage & Applications": 0.0008838394691656435, "Programming & Software Engineering": 0.9648706326216967, "Sports & Fitness": 6.810451463397717e-05, "Vehicles & Transportation": 4.581727362017488e-05, "Travel & Tourism": 7.901915857360115e-05, "Other / Uncovered Subject": 0.0007993319346004018, "Unknown / Insufficient Content": 0.00013041749556222734}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009223461837843634, "Art & Design": 0.0011153239466283962, "Law & Crime": 0.0009118774616644818, "Education & Careers": 0.002626029234071339, "Entertainment & Media": 0.0007505858351386734, "Fashion & Beauty": 0.0006789286445174, "Business, Economics & Finance": 0.029440454397378003, "Food & Dining": 0.4534088783055332, "Games & Gaming": 0.0007737715714215283, "Electronics & Hardware": 0.000764050830554895, "Health, Healthcare & Wellness": 0.0006913464619191611, "History & Geography": 0.00046126765962909664, "Home, Pets & Hobbies": 0.000526114703460385, "Industry & Production": 0.0005724861681898993, "Language, Literature & Philosophy": 0.00035045048500571184, "Politics & Government": 0.0002563047798563817, "Religion & Spirituality": 0.000525599917230335, "Science, Mathematics & Engineering": 0.0016678229702923315, "Relationships & Community": 0.00028001855293371345, "Software Usage & Applications": 0.0016774192809936971, "Programming & Software Engineering": 0.050473547874011385, "Sports & Fitness": 0.0003422914198967344, "Vehicles & Transportation": 0.0003619059020634902, "Travel & Tourism": 0.0006913530551442447, "Other / Uncovered Subject": 0.0025851857535743555, "NONE": 0.44714463860510684}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10048, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10048, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:11.134198+00:00", "scoring_key_slot": "primary", "dataset_uid": "2bfc668c-9935-469d-882e-2ae25add7074", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "8d4af0ec03fe6d48c0f48bede71858247182401adbee122812b2144395ff9c96", "source_metadata": {"uuid": "2bfc668c-9935-469d-882e-2ae25add7074", "repo_name": "reddyroxx/DBMS-Class_project", "relative_path": "Programs/proj.py", "category": "WEB", "algo_rel_score": 0.9546769857406616, "quality_score": 3.1962783336639404, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 1, "repo_created_at": "2019-09-29T15:27:17.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "6c69a053e7d1b6e7f9ad02bdda463e5b0f0410c8", "size_bytes": 5920, "file_timestamp": 1569770884, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "ad24e121ef2d3a02a19ece6207a18e87536ec1f4"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 1, "repo_created_at": "2019-09-29T15:27:17.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "6c69a053e7d1b6e7f9ad02bdda463e5b0f0410c8", "size_bytes": 5920, "file_timestamp": 1569770884, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "ad24e121ef2d3a02a19ece6207a18e87536ec1f4", "url": "https://github.com/reddyroxx/DBMS-Class_project/blob/ad24e121ef2d3a02a19ece6207a18e87536ec1f4/Programs/proj.py"}, "gpt4o_tokens": 1444, "dataset_split": "python", "probability_weighted_scores": {"content_quality": -0.03816098891863029, "noise_severity": 0.34925753482497324, "missing_content_severity": 0.06839259984531568, "factual_density": 3.170272619581827, "pretraining_utility": 0.2949317318383773, "math_density": 0.6065934810266457, "code_density": 3.904722782727519, "reasoning_density": 0.7675239543903948, "education_level": 2.004456781655321, "creative_writing_quality": -0.6048119945732884}, "score_applicability": {"education_level": 0.9991796294492984, "creative_writing_quality": 0.0010374498565662103}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/KatieJ16/book_predictor/blob/2184cd08f1e55ca5e785e0107f707e17418cdea8/app.py", "id": "python-0-48df6bdadfeceb61ea228ecef77764b1", "text": "import streamlit as st\nimport pickle\n\n#make model \n\n#doing masked autoencoder\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom sklearn.model_selection import KFold\nfrom goodreads_helpers import *\nfrom sklearn.neighbors import NearestNeighbors\nfrom PIL import Image,  ImageOps, ImageDraw\nfrom bs4 import BeautifulSoup\nfrom io import BytesIO\nimport base64\n\n\n\nmethod_options = ['Average', 'Neural Network', 'K nearest neighbors', 'All']\n\nst.set_page_config(\n    page_title=\"The Bibliobrain\",\n    page_icon=\"logo.png\"  # Replace with the actual path to your logo image\n)\n\nst.sidebar.success(\"Select a demo above.\")\n\n\n#initialize the model\nnum_users = np.load(\"num_users.npy\").item()\nnum_items = np.load(\"num_items.npy\").item()\nlatent_dim = 377#100  # Number of latent features\n\n\nclass SparseAutoencoder(nn.Module):\n    def __init__(self, num_items, latent_dim):\n        super(SparseAutoencoder, self).__init__()\n        hidden1 = latent_dim*2\n        self.encoder1 = nn.Linear(num_items, hidden1)\n        self.encoder2 = nn.Linear(hidden1, latent_dim)\n        self.decoder1 = nn.Linear(latent_dim, hidden1)\n        self.decoder2 = nn.Linear(hidden1, num_items)\n        \n    def forward(self, x):\n        x = torch.relu(self.encoder1(x))\n        x = torch.relu(self.encoder2(x))\n        x = torch.relu(self.decoder1(x))\n        x = self.decoder2(x)\n        # Scale sigmoid output to [1, 5]\n        return 1 + 4 * torch.sigmoid(x)\n    \nmodel = SparseAutoencoder(num_items, latent_dim)\n\n# Load your machine learning model (replace \"model.pkl\" with your actual model file)\n# Example: A model trained to predict numerical output based on text input\ntry:\n    model.load_state_dict(torch.load(\"2model{}.pkl\".format(latent_dim)))\nexcept FileNotFoundError:\n    st.error(\"Model file not found! Make sure 'model.pkl' is in the same directory.\")\n\n#load info that we will need\n# Load the list from the file\nwith open(\"titles.pkl\", \"rb\") as file:\n    titles = pickle.load(file)\n    \nwith open(\"top_100.pkl\", \"rb\") as file:\n    top_100 = pickle.load(file)\n    \nwith open(\"suggest.pkl\", \"rb\") as file:\n    suggest = pickle.load(file)\n    \nwith open(\"average_ratings_list.pkl\", \"rb\") as file:\n    average_ratings_list = np.array(pickle.load(file))\n    \nwith open(\"images_dict.pkl\", \"rb\") as file:\n    images_dict = pickle.load(file)\n    \nnum_titles = len(titles)\n\n# Load the list from the file\nwith open(\"ratings_df.pkl\", \"rb\") as file:\n    ratings_df = pickle.load(file)\n\n# App title\n# Load the logo image\nlogo = Image.open(\"logo.png\") \n\n# Create a container to hold the logo and title\nwith st.container():\n    col1, col2 = st.columns([1, 3])  # Adjust column widths as needed (e.g., [1, 3] for a smaller logo)\n    with col1:\n        st.image(logo)#, width=50)  # Adjust width as needed\n\n    with col2:\n        st.title(\"The Bibliobrain\")#Book Recommendations\")\n        st.subheader(\"Use AI to recommend your next great Read!\") \n\n# Input fields\nst.write(\"Your goodreads user id number is the number in your url. Got to your profile and look at the number after the last /. My goodreads url is https://www.goodreads.com/user/show/169695558-katie, so my user id is 169695558.\")\nuser_id = int(st.number_input(\"What is your User ID for goodreads:\", step=1))\n\n\nnum_entries = 250#st.slider(\"Number of Books to import (the more you have, the better the recommendations, but the longer it will take):\", min_value=1, max_value=1000, value=100, step=1)\n\ninclude_rereads = st.checkbox('Include Rereads?')\nmethod = \"Average\"#st.selectbox('Choose an method (Choose Average for best results):', method_options)\n# Predict button\nif st.button(\"Predict\"):\n    if user_id:\n        try:\n            col1, col2 = st.columns([0.1, 0.9])\n            with col1:\n                try:\n                    #get user id picture:\n                    url = f\"https://www.goodreads.com/user/show/{user_id}\"\n                    headers = {\"User-Agent\": \"Mozilla/5.0\"}\n                    response = requests.get(url, headers=headers)\n\n\n                    # Check if the request was successful\n                    if response.status_code == 200:\n                        soup = BeautifulSoup(response.content, 'html.parser')\n\n                        # Extract the profile picture (typically within an <img> tag)\n                        profile_picture_tag = soup.find('img', class_='og:image')\n                        og_image_tag = soup.find('meta', property='og:image')\n                        profile_picture_url = og_image_tag['content']\n                        if og_image_tag:\n                            img_response = requests.get(profile_picture_url)\n                            if img_response.status_code == 200:\n                                img = Image.open(BytesIO(img_response.content))\n\n                            # Display the profile picture\n                            st.image(crop_circle(img))\n                    else:\n                        print(\"Profile picture not found.\")\n                except:\n                    pass\n                \n            with col2:\n                #get user data\n                ratings_data = get_user_data(user_id, num_entries=num_entries)\n            st.write(\"Finding best matches! Comparing to \", num_users, \" readers and \", num_items, \"books.\")\n\n            #make matrix of ratings\n            ratings = np.zeros((1, num_titles)) #average_ratings_list.reshape((1, num_titles))#\n\n            for index, row in ratings_data.iterrows():\n                if row['Title'] in titles:\n                    try:\n                        ratings[0, titles.index(row[\"Title\"])] = int(row[\"Rating\"])\n                    except:\n                        pass\n\n            if method == 'Average':\n                sum_ratings = np.zeros(ratings.shape[1])\n            if method in ('Neural Network', 'All', 'Average'):\n                ratings_torch = torch.tensor(ratings).float()\n\n                #Evaulating the model\n                model.eval()\n                with torch.no_grad():\n                    reconstructed = model(ratings_torch)\n\n                pred_ratings_list = reconstructed[0].detach().numpy()\n                #give a list sorted out with books you've already read:\n                sorted_indices = np.argsort(pred_ratings_list)[::-1]\n                list_num = 1\n                if method == 'Average':\n                    sum_ratings += pred_ratings_list\n                else:\n                    st.write(\"Top books are:\")\n                    for idx in sorted_indices[:10]: \n                        if include_rereads:\n                            if  (np.isnan(pred_ratings_list[idx])) :\n                                continue\n                            st.write( str(list_num) , titles[idx], \" - Predicted Rating:\", str(round(pred_ratings_list[idx], 1)))\n                        else:#don't include rereads\n                            if  (ratings[0, idx] > 0) or(np.isnan(pred_ratings_list[idx])) :\n                                continue\n                            st.write( str(list_num) , titles[idx], \" - Predicted Rating:\", str(round(pred_ratings_list[idx], 1)))\n                        list_num += 1\n                \n            \n            if method in ('K nearest neighbors', 'All', 'Average'):\n                #load knn \n                with open(\"knn_model.pkl\", \"rb\") as file:\n                    knn = pickle.load(file)\n\n                #get neighbors of new \n                distances, indices = knn.kneighbors(ratings)\n                ratings_matrix = ratings_df.values\n\n                pred_ratings_list = np.array([])\n                rankings_list = np.array([])\n                for item_id in range(num_titles):\n                    # Get the ratings for the neighbors on item 2\n                    neighbor_ratings = np.array([ratings_matrix[i, item_id] for i in indices[0] if not np.isnan(ratings_matrix[i, item_id])])\n\n                    predicted_rating = np.mean(neighbor_ratings[np.nonzero(neighbor_ratings)])\n                    rankings = np.sum(neighbor_ratings[np.nonzero(neighbor_ratings)])\n                    \n                    pred_ratings_list = np.append(pred_ratings_list, predicted_rating)\n                    rankings_list = np.append(rankings_list, rankings)\n\n                best_book_rating = np.max(pred_ratings_list)\n                best_book_idx = np.argmax(pred_ratings_list)\n\n                sorted_indices = np.argsort(pred_ratings_list)[::-1]\n                list_num = 1 \n                if method == 'Average':\n                    sum_ratings += np.where(np.isnan(pred_ratings_list), 0, pred_ratings_list)\n                else:\n                    st.write(\"Top books are:\")\n                    for i, idx in enumerate(sorted_indices): \n                        if (ratings[0, idx] > 0) or (np.isnan(pred_ratings_list[idx])):\n                            pass\n                        else:\n                            st.write( str(list_num) , titles[idx], \" - Predicted Rating:\", str(round(pred_ratings_list[idx], 1)))\n                            list_num += 1\n                        if list_num > 10:\n                            break\n                            \n            #print our results for average\n            if method == 'Average':\n                list_num = 1\n                sum_ratings = sum_ratings/2\n                if include_rereads: #get average of their rating and predicted rating\n#                     sum_ratings = (sum_ratings + ratings[0]) / 2 #Their own rating get's as much weight as the predicted\n                    # Initialize an array to store the results\n                    result = sum_ratings.copy()\n\n                    # Loop through the arrays and calculate the average ignoring zero elements\n                    for i in range(len(sum_ratings)):\n                        if ratings[0][i] != 0 and sum_ratings[i] != 0:\n                            result[i] = (ratings[0][i] + sum_ratings[i]) / 2\n                        elif ratings[0][i] != 0:\n                            result[i] = ratings[0][i]\n                        elif sum_ratings[i] != 0:\n                            result[i] = sum_ratings[i]\n                        else:\n                            result[i] = 0\n                    sum_ratings = result\n                    \n                sorted_indices = np.argsort(sum_ratings)[::-1]\n                score = np.zeros(sum_ratings.shape)\n                for idx, rating in enumerate(sum_ratings):\n                    score[idx] += sum_ratings[idx]\n                    #add a point for every person that read the book in your group\n                    neighbor_ratings = np.array([ratings_matrix[i, idx] for i in indices[0] if not np.isnan(ratings_matrix[i, idx])])\n                    score[idx] += min(len(neighbor_ratings[np.nonzero(neighbor_ratings)]>5) *0.05, 1)\n                    if include_rereads and (ratings[0, idx] == 5): #if they rated 5, give big boost.\n                        score[idx] += 0.1\n    \n                score += ((average_ratings_list - 5))\n                #sort based on score\n                sorted_indices = np.argsort(score)[::-1]\n                for idx in sorted_indices[:99]: \n                    if include_rereads:\n                        if  (np.isnan(pred_ratings_list[idx])) :\n                            continue\n                        col1, col2 = st.columns([0.2, 0.8])\n                        cover_url = images_dict[titles[idx]]\n                        with col1:\n                            try:\n                                st.image(cover_url)\n                            except:\n                                pass\n                        with col2:\n                            st.write( str(list_num) , titles[idx], \" - Predicted Rating:\", str(round(sum_ratings[idx], 1)))\n                            list_num += 1\n                    else:#don't include rereads\n                        if  (ratings[0, idx] > 0) or(np.isnan(pred_ratings_list[idx])) :\n                            continue\n                        if suggest[idx]: #exclude later books in series\n                            col1, col2 = st.columns([0.2, 0.8])\n                            cover_url = images_dict[titles[idx]]\n                            with col1:\n                                try:\n                                    st.image(cover_url)\n                                except:\n                                    pass\n                            with col2:\n                                st.write( str(list_num)+'.' , titles[idx], \" - Predicted Rating:\", str(round(sum_ratings[idx], 1)))#,  ' - Score: ', str(round(score[idx],1)))\n                            list_num += 1\n                \n        except Exception as e:\n            st.error(f\"An error occurred: {e}\")\n    else:\n        st.warning(\"Need your User ID.\")\n\n\n# Footer\nst.title(\"Support Me ☕\")\n\nst.write(\"If you enjoy this app, feel free to buy me a coffee! Your support is much appreciated!\")\n\n# Add a PayPal link\ncoffee_link = \"https://www.paypal.com/paypalme/16katiej\"\nst.markdown(f\"[Buy Me a Coffee! ☕]({coffee_link})\", unsafe_allow_html=True)\nst.write(\"Made with ❤️ using Streamlit.\")\n\n", "text_preview": "import streamlit as st import pickle #make model #doing masked autoencoder import torch import torch.nn as nn import torch.optim as optim from sklearn.model_selection import KFold from goodreads_helpers import * from sklearn.neighbors impor", "input_sha256": "a58aff683011840d62c5b4339e8ae131ac77176fd0cd0c3cda0bcdfb75e647c8", "text_sha256": "48df6bdadfeceb61ea228ecef77764b144e1b4729cc05bf5a9fbfb6cf47696f5", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12007, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 2, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.0016482400034399936, "noise_severity": 0.5081793761325587, "missing_content_severity": 0.10152746185252404, "factual_density": 2.968970883435035, "pretraining_utility": 0.6143433019884412, "math_density": 2.411707595495367, "code_density": 3.8073561503882254, "reasoning_density": 1.2617263341591451, "education_level": 2.4013907644261754, "creative_writing_quality": -0.4664054116287682}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.020672761088990632, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9920091079923757, "secondary": 0.4588207593716478, "tertiary": 0.4372491825831359, "structured_records": 0.010069826849181176, "procedural_steps": 0.18137915023753595, "worked_solution": 0.017828504143573575, "contextual_dialogue": 9.992229189401481e-06, "content_quality": 0.45051362769818193, "noise_severity": 0.5765171865562011, "missing_content_severity": 0.9153937817895633, "factual_density": 0.14080906952919559, "pretraining_utility": 0.474508714780712, "math_density": 0.20061374877896965, "code_density": 0.8394634586568546, "reasoning_density": 0.0, "education_level": 0.5692516972523411, "creative_writing_quality": 0.9920402066248746, "primary_topic": 0.8999853207697238, "secondary_topic": 0.36962049202244984}, "probability_margin": {"primary": 0.9898037044076281, "secondary": 0.060710431032692136, "tertiary": 0.053559871945189186, "education_level": 0.34586292280904507, "creative_writing_quality": 0.9897060110426247, "primary_topic": 0.8587284024068236, "secondary_topic": 0.0885485192663873}, "raw_probabilities": {"primary": {"Academic Writing": 0.0001136188657223001, "Content Listing": 5.207131771548899e-05, "Creative Writing": 4.0746764842592335e-05, "Customer Support Page": 4.176879656267319e-05, "Q&A Forum": 2.0151557070746588e-05, "Discussion Thread": 1.511890826693445e-05, "Comment Section": 2.7597867330042453e-05, "Missing Content": 5.2553215801411513e-05, "Knowledge Article": 9.148253286697059e-05, "Legal Notices": 4.0004177385689117e-05, "News Article": 1.1234336444919663e-05, "Organizational Announcement": 4.100740715274623e-05, "Nonfiction Writing": 3.219434308815897e-05, "Organizational About Page": 6.787679669841633e-05, "Personal About Page": 0.0011451447069450815, "Personal Blog": 6.213625577380501e-05, "Product Page": 4.376704071676967e-05, "Spam / Ads": 0.0004964206505293753, "Structured Data": 0.0002331595690378188, "Technical Writing": 0.9923420618260268, "Transcript / Interview": 3.430990925908348e-05, "Tutorial / How-To Guide": 0.0024367277578698724, "User Reviews": 2.0487978493749028e-05, "Other / Unknown": 0.0025383574183986545}, "secondary": {"Academic Writing": 0.0013750200838865468, "Content Listing": 0.0015080039249372003, "Creative Writing": 0.0011157894304554292, "Customer Support Page": 0.006793742991827672, "Q&A Forum": 0.0005171061889768322, "Discussion Thread": 0.00039079204289753735, "Comment Section": 0.000753944077933848, "Missing Content": 0.0009694710532964806, "Knowledge Article": 0.00241442553981507, "Legal Notices": 0.0009276961131746764, "News Article": 0.0006092613541557116, "Organizational Announcement": 0.0009876319150908842, "Nonfiction Writing": 0.0014171398999313011, "Organizational About Page": 0.002386040622139115, "Personal About Page": 0.04045675812413143, "Personal Blog": 0.001319394983774052, "Product Page": 0.0009969469569507594, "Spam / Ads": 0.42065946336513704, "Structured Data": 0.0008674566531582288, "Technical Writing": 0.014178990100792661, "Transcript / Interview": 0.0010008302847537207, "Tutorial / How-To Guide": 0.016481785698386278, "User Reviews": 0.0005024141965682653, "NONE": 0.4813698943978292}, "tertiary": {"Academic Writing": 0.0016982091748404327, "Content Listing": 0.00166689934716048, "Creative Writing": 0.0015875371091586244, "Customer Support Page": 0.012480321297506227, "Q&A Forum": 0.0006084110854263978, "Discussion Thread": 0.00043859250013642573, "Comment Section": 0.0009655603211953859, "Missing Content": 0.0010718052706089048, "Knowledge Article": 0.00269212169643333, "Legal Notices": 0.0010975227559747289, "News Article": 0.0006795418542894732, "Organizational Announcement": 0.0010556217738144048, "Nonfiction Writing": 0.0020739042350804635, "Organizational About Page": 0.003665325588278137, "Personal About Page": 0.0521299685229759, "Personal Blog": 0.0018223382321669076, "Product Page": 0.0013720670343842676, "Spam / Ads": 0.4071372613636494, "Structured Data": 0.0008850176549370985, "Technical Writing": 0.008907344596898483, "Transcript / Interview": 0.0010557602068044403, "Tutorial / How-To Guide": 0.033660205331494955, "User Reviews": 0.0005515297379464893, "NONE": 0.4606971333088386}, "structured_records": {"false": 0.9899301731508188, "true": 0.010069826849181176}, "procedural_steps": {"false": 0.8186208497624641, "true": 0.18137915023753595}, "worked_solution": {"false": 0.9821714958564264, "true": 0.017828504143573575}, "contextual_dialogue": {"false": 0.9999900077708106, "true": 9.992229189401481e-06}, "content_quality": {"-2": 0.04163252763570597, "-1": 0.24560264810795887, "0": 0.43253426877411694, "1": 0.22994516758162536, "2": 0.05028538790059272}, "noise_severity": {"0": 0.5834819371731781, "1": 0.3374983321796043, "2": 0.06995265497864304, "3": 0.0054925686786303, "4": 0.0035745069899443646}, "missing_content_severity": {"0": 0.9408113576264571, "1": 0.02853189943328749, "2": 0.02025647244404594, "3": 0.009118464453692928, "4": 0.0012818060425164657}, "factual_density": {"0": 0.019237645736524887, "1": 0.09104330954301948, "2": 0.1678565535788656, "3": 0.34523549783207597, "4": 0.37662699330951405}, "pretraining_utility": {"-2": 0.010335090408605462, "-1": 0.07720181962264747, "0": 0.3227142096662408, "1": 0.4672824581767128, "2": 0.12246642212579335}, "math_density": {"0": 0.050436343832737957, "1": 0.17290526531945868, "2": 0.299189743840587, "3": 0.2694517455341308, "4": 0.20801690147308552}, "code_density": {"0": 0.0002687158370832007, "1": 0.0005485327481352488, "2": 0.01689255078008141, "3": 0.15613828645887312, "4": 0.826151914175827}, "reasoning_density": {"0": 0.4873228599346538, "1": 0.11248570730768193, "2": 0.16696141352858215, "3": 0.11760227712202981, "4": 0.1156277421070524}, "education_level": {"none": 0.0009287644006573005, "0": 0.0013008274128227062, "1": 0.004884958084054328, "2": 0.6410430810436176, "3": 0.29518015823457255, "4": 0.05666221082427558}, "creative_writing_quality": {"not_applicable": 0.9933668388540622, "-2": 0.0007646526709963034, "-1": 0.0019277634334779177, "0": 0.003660827811437522, "1": 0.00019650793925261184, "2": 8.340929077342537e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00020692440964063862, "Art & Design": 0.0005410985780437711, "Law & Crime": 0.00024706504072955653, "Education & Careers": 0.0002378442938368843, "Entertainment & Media": 0.0008919735601821921, "Fashion & Beauty": 0.00030837366286438125, "Business, Economics & Finance": 0.0003397740675725379, "Food & Dining": 0.00021184673353003545, "Games & Gaming": 0.0004418034496073099, "Electronics & Hardware": 0.00017901975254030496, "Health, Healthcare & Wellness": 0.00023299030414775622, "History & Geography": 0.00023043350768824728, "Home, Pets & Hobbies": 0.002517626363752019, "Industry & Production": 0.00015990306032293356, "Language, Literature & Philosophy": 0.032692773732929545, "Politics & Government": 0.00012702660529415497, "Religion & Spirituality": 0.00020128490037489048, "Science, Mathematics & Engineering": 0.04510363679483403, "Relationships & Community": 0.0002583261734038454, "Software Usage & Applications": 0.004084377793607382, "Programming & Software Engineering": 0.9038320392016576, "Sports & Fitness": 0.00025290399992639723, "Vehicles & Transportation": 0.00011406323079716299, "Travel & Tourism": 0.00024222838057185155, "Other / Uncovered Subject": 0.006086413204468717, "Unknown / Insufficient Content": 0.00025824919767568284}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0016411334701320064, "Art & Design": 0.003688566263906886, "Law & Crime": 0.0011069493137392612, "Education & Careers": 0.0008047504083988664, "Entertainment & Media": 0.00907387657723809, "Fashion & Beauty": 0.0010258498022702464, "Business, Economics & Finance": 0.0026912622665858175, "Food & Dining": 0.0009147516430096938, "Games & Gaming": 0.0010762600516973412, "Electronics & Hardware": 0.0005689870845558906, "Health, Healthcare & Wellness": 0.000976468580566652, "History & Geography": 0.0008647159522902654, "Home, Pets & Hobbies": 0.005025607615196645, "Industry & Production": 0.0005153587406191576, "Language, Literature & Philosophy": 0.1358158292927749, "Politics & Government": 0.0004721542285633374, "Religion & Spirituality": 0.0009794409348963297, "Science, Mathematics & Engineering": 0.3053173384475068, "Relationships & Community": 0.0006227480873912209, "Software Usage & Applications": 0.009545084730664658, "Programming & Software Engineering": 0.11037400609857619, "Sports & Fitness": 0.0005819913151150251, "Vehicles & Transportation": 0.000315231883088066, "Travel & Tourism": 0.0009213253798772711, "Other / Uncovered Subject": 0.011214454117445346, "NONE": 0.3938658577138941}}, "probabilities": {"primary": {"Academic Writing": 0.0001136188657223001, "Content Listing": 5.207131771548899e-05, "Creative Writing": 4.0746764842592335e-05, "Customer Support Page": 4.176879656267319e-05, "Q&A Forum": 2.0151557070746588e-05, "Discussion Thread": 1.511890826693445e-05, "Comment Section": 2.7597867330042453e-05, "Missing Content": 5.2553215801411513e-05, "Knowledge Article": 9.148253286697059e-05, "Legal Notices": 4.0004177385689117e-05, "News Article": 1.1234336444919663e-05, "Organizational Announcement": 4.100740715274623e-05, "Nonfiction Writing": 3.219434308815897e-05, "Organizational About Page": 6.787679669841633e-05, "Personal About Page": 0.0011451447069450815, "Personal Blog": 6.213625577380501e-05, "Product Page": 4.376704071676967e-05, "Spam / Ads": 0.0004964206505293753, "Structured Data": 0.0002331595690378188, "Technical Writing": 0.9923420618260268, "Transcript / Interview": 3.430990925908348e-05, "Tutorial / How-To Guide": 0.0024367277578698724, "User Reviews": 2.0487978493749028e-05, "Other / Unknown": 0.0025383574183986545}, "secondary": {"Academic Writing": 0.001375020083886547, "Content Listing": 0.0015080039249372006, "Creative Writing": 0.0011157894304554294, "Customer Support Page": 0.006793742991827673, "Q&A Forum": 0.0005171061889768323, "Discussion Thread": 0.0003907920428975374, "Comment Section": 0.0007539440779338481, "Missing Content": 0.0009694710532964807, "Knowledge Article": 0.0024144255398150702, "Legal Notices": 0.0009276961131746765, "News Article": 0.0006092613541557117, "Organizational Announcement": 0.0009876319150908845, "Nonfiction Writing": 0.0014171398999313013, "Organizational About Page": 0.0023860406221391153, "Personal About Page": 0.04045675812413144, "Personal Blog": 0.0013193949837740522, "Product Page": 0.0009969469569507596, "Spam / Ads": 0.4206594633651371, "Structured Data": 0.000867456653158229, "Technical Writing": 0.014178990100792663, "Transcript / Interview": 0.001000830284753721, "Tutorial / How-To Guide": 0.01648178569838628, "User Reviews": 0.0005024141965682654, "NONE": 0.48136989439782923}, "tertiary": {"Academic Writing": 0.0016982091748404327, "Content Listing": 0.00166689934716048, "Creative Writing": 0.0015875371091586244, "Customer Support Page": 0.012480321297506227, "Q&A Forum": 0.0006084110854263978, "Discussion Thread": 0.00043859250013642573, "Comment Section": 0.0009655603211953859, "Missing Content": 0.0010718052706089048, "Knowledge Article": 0.00269212169643333, "Legal Notices": 0.0010975227559747289, "News Article": 0.0006795418542894732, "Organizational Announcement": 0.0010556217738144048, "Nonfiction Writing": 0.0020739042350804635, "Organizational About Page": 0.003665325588278137, "Personal About Page": 0.0521299685229759, "Personal Blog": 0.0018223382321669076, "Product Page": 0.0013720670343842676, "Spam / Ads": 0.4071372613636494, "Structured Data": 0.0008850176549370985, "Technical Writing": 0.008907344596898483, "Transcript / Interview": 0.0010557602068044403, "Tutorial / How-To Guide": 0.033660205331494955, "User Reviews": 0.0005515297379464893, "NONE": 0.4606971333088386}, "structured_records": {"false": 0.9899301731508188, "true": 0.010069826849181176}, "procedural_steps": {"false": 0.8186208497624641, "true": 0.18137915023753595}, "worked_solution": {"false": 0.9821714958564264, "true": 0.017828504143573575}, "contextual_dialogue": {"false": 0.9999900077708106, "true": 9.992229189401481e-06}, "content_quality": {"-2": 0.041632527635705976, "-1": 0.2456026481079589, "0": 0.432534268774117, "1": 0.2299451675816254, "2": 0.050285387900592726}, "noise_severity": {"0": 0.5834819371731781, "1": 0.3374983321796043, "2": 0.06995265497864304, "3": 0.0054925686786303, "4": 0.0035745069899443646}, "missing_content_severity": {"0": 0.9408113576264572, "1": 0.028531899433287492, "2": 0.020256472444045943, "3": 0.00911846445369293, "4": 0.001281806042516466}, "factual_density": {"0": 0.019237645736524887, "1": 0.09104330954301948, "2": 0.1678565535788656, "3": 0.34523549783207597, "4": 0.37662699330951405}, "pretraining_utility": {"-2": 0.010335090408605464, "-1": 0.07720181962264748, "0": 0.32271420966624087, "1": 0.4672824581767129, "2": 0.12246642212579337}, "math_density": {"0": 0.050436343832737957, "1": 0.17290526531945868, "2": 0.299189743840587, "3": 0.2694517455341308, "4": 0.20801690147308552}, "code_density": {"0": 0.0002687158370832007, "1": 0.0005485327481352488, "2": 0.01689255078008141, "3": 0.15613828645887312, "4": 0.826151914175827}, "reasoning_density": {"0": 0.4873228599346538, "1": 0.11248570730768193, "2": 0.16696141352858215, "3": 0.11760227712202981, "4": 0.1156277421070524}, "education_level": {"none": 0.0009287644006573005, "0": 0.0013008274128227062, "1": 0.004884958084054328, "2": 0.6410430810436176, "3": 0.29518015823457255, "4": 0.05666221082427558}, "creative_writing_quality": {"not_applicable": 0.9933668388540622, "-2": 0.0007646526709963034, "-1": 0.0019277634334779177, "0": 0.003660827811437522, "1": 0.00019650793925261184, "2": 8.340929077342537e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00020692440964063867, "Art & Design": 0.0005410985780437712, "Law & Crime": 0.0002470650407295566, "Education & Careers": 0.00023784429383688434, "Entertainment & Media": 0.0008919735601821923, "Fashion & Beauty": 0.0003083736628643813, "Business, Economics & Finance": 0.00033977406757253794, "Food & Dining": 0.0002118467335300355, "Games & Gaming": 0.00044180344960731, "Electronics & Hardware": 0.000179019752540305, "Health, Healthcare & Wellness": 0.00023299030414775627, "History & Geography": 0.00023043350768824734, "Home, Pets & Hobbies": 0.0025176263637520196, "Industry & Production": 0.00015990306032293358, "Language, Literature & Philosophy": 0.03269277373292955, "Politics & Government": 0.000127026605294155, "Religion & Spirituality": 0.00020128490037489054, "Science, Mathematics & Engineering": 0.045103636794834034, "Relationships & Community": 0.00025832617340384545, "Software Usage & Applications": 0.004084377793607383, "Programming & Software Engineering": 0.9038320392016578, "Sports & Fitness": 0.0002529039999263973, "Vehicles & Transportation": 0.00011406323079716302, "Travel & Tourism": 0.0002422283805718516, "Other / Uncovered Subject": 0.006086413204468719, "Unknown / Insufficient Content": 0.0002582491976756829}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0016411334701320064, "Art & Design": 0.003688566263906886, "Law & Crime": 0.0011069493137392612, "Education & Careers": 0.0008047504083988664, "Entertainment & Media": 0.00907387657723809, "Fashion & Beauty": 0.0010258498022702464, "Business, Economics & Finance": 0.0026912622665858175, "Food & Dining": 0.0009147516430096938, "Games & Gaming": 0.0010762600516973412, "Electronics & Hardware": 0.0005689870845558906, "Health, Healthcare & Wellness": 0.000976468580566652, "History & Geography": 0.0008647159522902654, "Home, Pets & Hobbies": 0.005025607615196645, "Industry & Production": 0.0005153587406191576, "Language, Literature & Philosophy": 0.1358158292927749, "Politics & Government": 0.0004721542285633374, "Religion & Spirituality": 0.0009794409348963297, "Science, Mathematics & Engineering": 0.3053173384475068, "Relationships & Community": 0.0006227480873912209, "Software Usage & Applications": 0.009545084730664658, "Programming & Software Engineering": 0.11037400609857619, "Sports & Fitness": 0.0005819913151150251, "Vehicles & Transportation": 0.000315231883088066, "Travel & Tourism": 0.0009213253798772711, "Other / Uncovered Subject": 0.011214454117445346, "NONE": 0.3938658577138941}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11428, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11428, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:11.364128+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "5e9fe2e8-9ab1-4acb-849a-9283af47a977", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "33b2006754b0ac48068f2c58b2920803cee34101a20c2d223c01d4d7c9615863", "source_metadata": {"uuid": "5e9fe2e8-9ab1-4acb-849a-9283af47a977", "repo_name": "KatieJ16/book_predictor", "relative_path": "app.py", "category": "DATA", "algo_rel_score": 0.908858060836792, "quality_score": 5.088521957397461, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 76, "repo_created_at": "2024-12-19T22:45:53.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "f0d9195a5204a4ee0ed34648e993221615788f83", "size_bytes": 13153, "file_timestamp": 1736548144, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2184cd08f1e55ca5e785e0107f707e17418cdea8"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 76, "repo_created_at": "2024-12-19T22:45:53.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "f0d9195a5204a4ee0ed34648e993221615788f83", "size_bytes": 13153, "file_timestamp": 1736548144, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2184cd08f1e55ca5e785e0107f707e17418cdea8", "url": "https://github.com/KatieJ16/book_predictor/blob/2184cd08f1e55ca5e785e0107f707e17418cdea8/app.py"}, "gpt4o_tokens": 2780, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.0016482400034399936, "noise_severity": 0.5081793761325587, "missing_content_severity": 0.10152746185252404, "factual_density": 2.968970883435035, "pretraining_utility": 0.6143433019884412, "math_density": 2.411707595495367, "code_density": 3.8073561503882254, "reasoning_density": 1.2617263341591451, "education_level": 2.4013907644261754, "creative_writing_quality": -0.4664054116287682}, "score_applicability": {"education_level": 0.9990712355993427, "creative_writing_quality": 0.00663316114593778}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/camila314/tap/blob/1e811e0caf99bca006a7d508b56bd81e104eff68/clk.py", "id": "python-0-6ff755a89f6796410ec0c80d64e15c3e", "text": "from pydub import AudioSegment\nimport struct\nfrom pydub.playback import play\nfrom tqdm import tqdm\nimport random\nimport math\nimport sys\nimport os\nimport glob\nfrom pathlib import Path\n\ndef parse_xbot(dat):\n\tout = b''\n\tclicks = dat.split('\\n')\n\tfps = int(clicks[0][5:])\n\tfor i in clicks[2:]:\n\t\tif len(i)<1: break\n\n\t\thold, pos = i.split(' ')\n\n\t\treal_hold = True if int(hold)%2==1 else False\n\t\treal_pos = float(pos)/fps\n\n\t\tout += struct.pack('di?xxx', real_pos, 32, real_hold)\n\treturn out\n\ndef parse_zbot(dat):\n\tout = b''\n\tdelta, = struct.unpack(\"f\", dat[:4])\n\tdat = dat[8:]\n\tprint(delta)\n\tfor i in range(0, len(dat), 6):\n\t\tframe, hold, p1 = struct.unpack(\"IBB\", dat[i:i+6])\n\t\tif frame>0:\n\t\t\tout += struct.pack(\"di?BBB\", frame*delta, 32, hold-0x30, 0,0,0)\n\treturn out\n\ndef getRandomPath(hold=True, soft=False):\n\ts_char = 's' if soft else ''\n\tif hold:\n\t\tpaths = glob.glob(str(Path.home() / \"Desktop\" / \"holds\" / \"{}*.wav\".format(s_char)))\n\t\tif len(paths) == 0:\n\t\t\tpaths = glob.glob(str(Path.home() / \"Desktop\" / \"holds\" / \"*.wav\"))\n\telse:\n\t\tpaths = glob.glob(str(Path.home() / \"Desktop\" / \"releases\" / \"*.wav\"))\n\treturn random.choice(paths)\n\naudLength = 1\ninput = input if sys.version[0]=='3' else raw_input\ndef db2mag(db):\n\treturn 10**(db/20.)\ndef mag2db(mag):\n\treturn 20.*math.log10(mag)\n\ndef nerve(x):\n\treturn math.tanh(math.exp(0.1*(x-70.) -0.9))/2.\n\nclass Click(object):\n\tdef __init__(self, data):\n\t\tself.position, _, self.hold = struct.unpack(\"di?xxx\", data)\n\t\tself.useful = self.position!=0\n\t\tself.position += 0.1\n\n\t\tif self.hold:\n\t\t\tself.audio = AudioSegment.from_wav(getRandomPath())\n\t\telse:\n\t\t\tself.audio = AudioSegment.from_wav(getRandomPath(hold=False))\n\tdef multVolume(self, amount):\n\t\t#print(math.log2(amount)*10)\n\t\ttry:\n\t\t\tself.audio = self.audio.apply_gain(mag2db(db2mag(self.audio.dBFS)*amount)-self.audio.dBFS)\n\t\t\t#self.audio = self.audio.pan(amount-1).split_to_mono()[1]\n\t\texcept Exception as e:\n\t\t\tprint(e)\n\t\t\texit()\n\tdef process(self, lastClick):\n\t\tbias = random.uniform(0.8, 1.2)\n\t\tif self.position/audLength>0.5:\n\t\t\tbias += nerve(100*self.position/audLength)\n\t\tif lastClick==None:\n\t\t\tself.multVolume(bias)\n\t\t\treturn\n\t\tif (self.position-lastClick.position)/2 < 0.1 and self.hold:\n\t\t\tself.audio = AudioSegment.from_wav(getRandomPath(soft=True))\n\n\t\ttimeSince = (self.position-lastClick.position)/2\n\t\t#print(timeSince)\n\t\tif timeSince>0.1  or ~-self.hold:\n\t\t\tself.multVolume(bias)\n\t\telse:\n\t\t\tret = (90.*timeSince*timeSince)+0.0613\n\t\t\tself.multVolume(ret*bias)\n\n\t\toctaves = random.uniform(-0.1,0.1)\n\t\tself.audio = self.audio._spawn(self.audio.raw_data, overrides={'frame_rate': int(self.audio.frame_rate * (2.0 ** octaves))})\n\ndef main():\n\tglobal audLength\n\tok = input(\"Drag the macro file here:\").strip()\t\n\tmacro = open(ok,'rb').read()\n\ttry:\n\t\tif macro.startswith(b'fps:'):\n\t\t\tmacro = parse_xbot(macro.decode().replace('\\r\\n','\\n'))\n\t\tif ok.endswith(\"zbf\"):\n\t\t\tmacro = parse_zbot(macro)\n\t\t\tprint(\"hi\")\n\t\topen(\"test.xgd\", \"wb\").write(macro)\n\t\tclicks_ = list([Click(macro[i:i+16]) for i in range(0, len(macro), 16)])\n\texcept struct.error:\n\t\tprint('\\u001b[31;1m[Error]\\u001b[0m Invalid macro file')\n\t\texit()\n\tclicks = []\n\tfor i in clicks_:\n\t\tif clicks==[] or (clicks[-1].position<i.position and i.position < 3600):\n\t\t\tclicks.append(i)\n\t\telse:\n\t\t\tbreak\n\n\taudLength = max([c.position for c in clicks])+1\n\tprint(audLength)\n\tmainClip = AudioSegment.silent(duration=round(audLength*1100))\n\n\tlastH = None\n\tlastC = None\n\tfor c in tqdm(clicks, colour='yellow', desc='Mapping clicks'):\n\t\tif lastC==None or c.hold != lastC.hold:\n\t\t\tc.process(lastH)\n\t\t\t#print(c.hold)\n\t\t\tif c.hold:\n\t\t\t\tlastH = c\n\t\t\tlastC = c\n\t\t\tmainClip = mainClip.overlay(c.audio, position=c.position*1000)\n\tmainClip.export(str(Path.home() / \"Desktop\" / \"tapOutput.wav\"))\n\tprint(\"Success. Check your desktop for the output file\")\nif __name__ == '__main__':\n\tmain()", "text_preview": "from pydub import AudioSegment import struct from pydub.playback import play from tqdm import tqdm import random import math import sys import os import glob from pathlib import Path def parse_xbot(dat): out = b'' clicks = dat.split('\\n') f", "input_sha256": "bb2b1a4bc1e65679df381b7c9aca9f59b2109a84e3c2df0ae2de6c29a367d1eb", "text_sha256": "6ff755a89f6796410ec0c80d64e15c3e81bf6debd430680197c504d89d8910ae", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12009, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 1, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5315323901673392, "noise_severity": 0.027180970243796385, "missing_content_severity": 0.011221109477814796, "factual_density": 3.369762799121806, "pretraining_utility": 0.3800792950797128, "math_density": 1.678640031218902, "code_density": 3.929579378413472, "reasoning_density": 0.2723549566234864, "education_level": 2.2214406441495744, "creative_writing_quality": -0.4864311761108372}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.007146390207918185, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.994832230090759, "secondary": 0.987066416310367, "tertiary": 0.9945235191360208, "structured_records": 0.003029381347339136, "procedural_steps": 0.01094335931535458, "worked_solution": 0.0022295572973148464, "contextual_dialogue": 9.077504954693637e-06, "content_quality": 0.40285356720079124, "noise_severity": 0.977349191463503, "missing_content_severity": 0.9906490754351543, "factual_density": 0.4748023326015053, "pretraining_utility": 0.45377435953982914, "math_density": 0.263187897307117, "code_density": 0.9413161486778934, "reasoning_density": 0.7730375361470947, "education_level": 0.6984944920109062, "creative_writing_quality": 0.9993367458138137, "primary_topic": 0.9560379785232108, "secondary_topic": 0.7604146310780047}, "probability_margin": {"primary": 0.9921914020909626, "secondary": 0.980127438243368, "tertiary": 0.9923168965720418, "education_level": 0.5682573260679902, "creative_writing_quality": 0.9992288009833972, "primary_topic": 0.9309932455045083, "secondary_topic": 0.695289825433359}, "raw_probabilities": {"primary": {"Academic Writing": 4.539763864873005e-05, "Content Listing": 2.8320984188183072e-05, "Creative Writing": 4.1087222084959867e-05, "Customer Support Page": 1.3848013136710375e-05, "Q&A Forum": 1.6987397302904204e-05, "Discussion Thread": 1.4154045137531623e-05, "Comment Section": 2.4096286560249858e-05, "Missing Content": 5.235551550552293e-05, "Knowledge Article": 9.50521871995046e-05, "Legal Notices": 4.142175673119785e-05, "News Article": 7.84934567477843e-06, "Organizational Announcement": 1.544020161782271e-05, "Nonfiction Writing": 2.281494994337998e-05, "Organizational About Page": 1.8071146670917e-05, "Personal About Page": 2.3532810848650174e-05, "Personal Blog": 1.3503733396051477e-05, "Product Page": 1.2368923406069071e-05, "Spam / Ads": 2.1551695349096944e-05, "Structured Data": 0.0004673348272695002, "Technical Writing": 0.9950475538369774, "Transcript / Interview": 2.722143067833691e-05, "Tutorial / How-To Guide": 0.001082511899595371, "User Reviews": 1.1372406062371887e-05, "Other / Unknown": 0.002856151746014781}, "secondary": {"Academic Writing": 8.553240210255314e-05, "Content Listing": 4.5680686748756756e-05, "Creative Writing": 7.098456348300205e-05, "Customer Support Page": 3.3610451888868066e-05, "Q&A Forum": 2.4850125184827224e-05, "Discussion Thread": 2.1149403928246723e-05, "Comment Section": 5.423642397089674e-05, "Missing Content": 0.00016075906640237506, "Knowledge Article": 0.00021300508225459257, "Legal Notices": 9.209856802254997e-05, "News Article": 3.11962650896719e-05, "Organizational Announcement": 3.121181390091585e-05, "Nonfiction Writing": 3.198344309791254e-05, "Organizational About Page": 3.645964417602905e-05, "Personal About Page": 3.661602917880848e-05, "Personal Blog": 3.391737806427077e-05, "Product Page": 2.0761611321404475e-05, "Spam / Ads": 3.910592118348461e-05, "Structured Data": 0.0009768726743041415, "Technical Writing": 0.007477877387400367, "Transcript / Interview": 4.9755143770636943e-05, "Tutorial / How-To Guide": 0.002806820742810001, "User Reviews": 2.0199540947381847e-05, "NONE": 0.9876053156307684}, "tertiary": {"Academic Writing": 4.7640479765344915e-05, "Content Listing": 3.264049872830639e-05, "Creative Writing": 5.2792252758498185e-05, "Customer Support Page": 2.4877067885345006e-05, "Q&A Forum": 1.5110412484543825e-05, "Discussion Thread": 1.3641118847286178e-05, "Comment Section": 4.23510294399959e-05, "Missing Content": 0.00017466744063076247, "Knowledge Article": 0.0001419729532661125, "Legal Notices": 0.00011243743333195034, "News Article": 2.404780599443003e-05, "Organizational Announcement": 2.1500407971752793e-05, "Nonfiction Writing": 2.813778819671393e-05, "Organizational About Page": 3.782850457385053e-05, "Personal About Page": 2.3797908387152915e-05, "Personal Blog": 2.560106335097727e-05, "Product Page": 1.919194634450248e-05, "Spam / Ads": 3.4587039603199354e-05, "Structured Data": 0.0004437023154097802, "Technical Writing": 0.0024348092666447558, "Transcript / Interview": 3.5003569108058094e-05, "Tutorial / How-To Guide": 0.0014462298514307202, "User Reviews": 1.572600715943091e-05, "NONE": 0.9947517058386866}, "structured_records": {"false": 0.9969706186526609, "true": 0.003029381347339136}, "procedural_steps": {"false": 0.9890566406846454, "true": 0.01094335931535458}, "worked_solution": {"false": 0.9977704427026851, "true": 0.0022295572973148464}, "contextual_dialogue": {"false": 0.9999909224950453, "true": 9.077504954693637e-06}, "content_quality": {"-2": 0.023342623259395453, "-1": 0.09649451839979019, "0": 0.32950475801808893, "1": 0.42660404555953046, "2": 0.1240540547631949}, "noise_severity": {"0": 0.9754417297629984, "1": 0.02284925175925092, "2": 0.0010667533050009244, "3": 0.00037084881645599245, "4": 0.00027141635629391035}, "missing_content_severity": {"0": 0.9929713778780176, "1": 0.004007645604427375, "2": 0.002027765184183045, "3": 0.0008149118284660058, "4": 0.00017829950490582771}, "factual_density": {"0": 0.013484430727986356, "1": 0.023395506288874317, "2": 0.09987486165858538, "3": 0.30636323578245445, "4": 0.5568819655420995}, "pretraining_utility": {"-2": 0.01171450065086759, "-1": 0.11426673543451092, "0": 0.4299700493490895, "1": 0.370322397315105, "2": 0.07372631725042691}, "math_density": {"0": 0.10276724600627878, "1": 0.37774369612800285, "2": 0.3428457511272094, "3": 0.09136839411755569, "4": 0.08527491262095335}, "code_density": {"0": 0.0001888149991601474, "1": 0.0003508726385170291, "2": 0.008141319550037654, "3": 0.052330104574261016, "4": 0.9389888882380242}, "reasoning_density": {"0": 0.8658469077183644, "1": 0.041985436354960104, "2": 0.05896586125817504, "3": 0.020369380921825244, "4": 0.012832413746675118}, "education_level": {"none": 0.0016674081236991334, "0": 0.0020252045535883658, "1": 0.029838016452790224, "2": 0.7487454100090885, "3": 0.18048808394109828, "4": 0.03723587691973547}, "creative_writing_quality": {"not_applicable": 0.9994472881781781, "-2": 0.00010975743337232991, "-1": 0.00014829665953328082, "0": 0.0002184871947808732, "1": 5.338580353268204e-05, "2": 2.2784730602975427e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0007270887489393365, "Art & Design": 0.0004426676992018727, "Law & Crime": 0.0003654213986525884, "Education & Careers": 0.00014207556580862835, "Entertainment & Media": 0.003470946974671985, "Fashion & Beauty": 0.00014825941093783037, "Business, Economics & Finance": 0.00013835793548380707, "Food & Dining": 9.415263732512042e-05, "Games & Gaming": 0.026735579998578955, "Electronics & Hardware": 0.0004670769745310294, "Health, Healthcare & Wellness": 0.00010180492469493838, "History & Geography": 0.00013458514461098465, "Home, Pets & Hobbies": 0.0002723301979082364, "Industry & Production": 0.00011181718073441209, "Language, Literature & Philosophy": 0.000147051294377025, "Politics & Government": 9.013491897466582e-05, "Religion & Spirituality": 0.00011280078416205384, "Science, Mathematics & Engineering": 0.003600079927980526, "Relationships & Community": 7.470412963206548e-05, "Software Usage & Applications": 0.0018095296476150124, "Programming & Software Engineering": 0.9577288255030874, "Sports & Fitness": 0.0003380354578282962, "Vehicles & Transportation": 7.617139800949195e-05, "Travel & Tourism": 0.00016556923821880835, "Other / Uncovered Subject": 0.0021525112785706476, "Unknown / Insufficient Content": 0.0003524216294646283}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.004513925612778627, "Art & Design": 0.002555511268550949, "Law & Crime": 0.0015284574825390844, "Education & Careers": 0.000543669235004046, "Entertainment & Media": 0.06313564241472894, "Fashion & Beauty": 0.00047376279346197095, "Business, Economics & Finance": 0.0007708439654601602, "Food & Dining": 0.0003560398558169555, "Games & Gaming": 0.07433962752626079, "Electronics & Hardware": 0.001232836416043815, "Health, Healthcare & Wellness": 0.0004981615336456535, "History & Geography": 0.00040113125186645905, "Home, Pets & Hobbies": 0.0007905065502769195, "Industry & Production": 0.00030372172400439557, "Language, Literature & Philosophy": 0.00040347660111861414, "Politics & Government": 0.00032157898533428166, "Religion & Spirituality": 0.0005254596520615219, "Science, Mathematics & Engineering": 0.027416177103598997, "Relationships & Community": 0.00024303658482321147, "Software Usage & Applications": 0.014945081952028158, "Programming & Software Engineering": 0.028099998220997, "Sports & Fitness": 0.0007840331972651955, "Vehicles & Transportation": 0.00019527900356804386, "Travel & Tourism": 0.0006247076351620243, "Other / Uncovered Subject": 0.005367880473984227, "NONE": 0.7696294529596198}}, "probabilities": {"primary": {"Academic Writing": 4.539763864873005e-05, "Content Listing": 2.8320984188183072e-05, "Creative Writing": 4.1087222084959867e-05, "Customer Support Page": 1.3848013136710375e-05, "Q&A Forum": 1.6987397302904204e-05, "Discussion Thread": 1.4154045137531623e-05, "Comment Section": 2.4096286560249858e-05, "Missing Content": 5.235551550552293e-05, "Knowledge Article": 9.50521871995046e-05, "Legal Notices": 4.142175673119785e-05, "News Article": 7.84934567477843e-06, "Organizational Announcement": 1.544020161782271e-05, "Nonfiction Writing": 2.281494994337998e-05, "Organizational About Page": 1.8071146670917e-05, "Personal About Page": 2.3532810848650174e-05, "Personal Blog": 1.3503733396051477e-05, "Product Page": 1.2368923406069071e-05, "Spam / Ads": 2.1551695349096944e-05, "Structured Data": 0.0004673348272695002, "Technical Writing": 0.9950475538369774, "Transcript / Interview": 2.722143067833691e-05, "Tutorial / How-To Guide": 0.001082511899595371, "User Reviews": 1.1372406062371887e-05, "Other / Unknown": 0.002856151746014781}, "secondary": {"Academic Writing": 8.553240210255314e-05, "Content Listing": 4.5680686748756756e-05, "Creative Writing": 7.098456348300205e-05, "Customer Support Page": 3.3610451888868066e-05, "Q&A Forum": 2.4850125184827224e-05, "Discussion Thread": 2.1149403928246723e-05, "Comment Section": 5.423642397089674e-05, "Missing Content": 0.00016075906640237506, "Knowledge Article": 0.00021300508225459257, "Legal Notices": 9.209856802254997e-05, "News Article": 3.11962650896719e-05, "Organizational Announcement": 3.121181390091585e-05, "Nonfiction Writing": 3.198344309791254e-05, "Organizational About Page": 3.645964417602905e-05, "Personal About Page": 3.661602917880848e-05, "Personal Blog": 3.391737806427077e-05, "Product Page": 2.0761611321404475e-05, "Spam / Ads": 3.910592118348461e-05, "Structured Data": 0.0009768726743041415, "Technical Writing": 0.007477877387400367, "Transcript / Interview": 4.9755143770636943e-05, "Tutorial / How-To Guide": 0.002806820742810001, "User Reviews": 2.0199540947381847e-05, "NONE": 0.9876053156307684}, "tertiary": {"Academic Writing": 4.7640479765344915e-05, "Content Listing": 3.264049872830639e-05, "Creative Writing": 5.2792252758498185e-05, "Customer Support Page": 2.4877067885345006e-05, "Q&A Forum": 1.5110412484543825e-05, "Discussion Thread": 1.3641118847286178e-05, "Comment Section": 4.23510294399959e-05, "Missing Content": 0.00017466744063076247, "Knowledge Article": 0.0001419729532661125, "Legal Notices": 0.00011243743333195034, "News Article": 2.404780599443003e-05, "Organizational Announcement": 2.1500407971752793e-05, "Nonfiction Writing": 2.813778819671393e-05, "Organizational About Page": 3.782850457385053e-05, "Personal About Page": 2.3797908387152915e-05, "Personal Blog": 2.560106335097727e-05, "Product Page": 1.919194634450248e-05, "Spam / Ads": 3.4587039603199354e-05, "Structured Data": 0.0004437023154097802, "Technical Writing": 0.0024348092666447558, "Transcript / Interview": 3.5003569108058094e-05, "Tutorial / How-To Guide": 0.0014462298514307202, "User Reviews": 1.572600715943091e-05, "NONE": 0.9947517058386866}, "structured_records": {"false": 0.9969706186526609, "true": 0.003029381347339136}, "procedural_steps": {"false": 0.9890566406846454, "true": 0.01094335931535458}, "worked_solution": {"false": 0.9977704427026851, "true": 0.0022295572973148464}, "contextual_dialogue": {"false": 0.9999909224950453, "true": 9.077504954693637e-06}, "content_quality": {"-2": 0.023342623259395456, "-1": 0.0964945183997902, "0": 0.329504758018089, "1": 0.4266040455595305, "2": 0.12405405476319491}, "noise_severity": {"0": 0.9754417297629981, "1": 0.022849251759250917, "2": 0.0010667533050009242, "3": 0.00037084881645599234, "4": 0.0002714163562939103}, "missing_content_severity": {"0": 0.9929713778780177, "1": 0.004007645604427376, "2": 0.0020277651841830455, "3": 0.000814911828466006, "4": 0.00017829950490582774}, "factual_density": {"0": 0.013484430727986356, "1": 0.023395506288874317, "2": 0.09987486165858538, "3": 0.30636323578245445, "4": 0.5568819655420995}, "pretraining_utility": {"-2": 0.01171450065086759, "-1": 0.11426673543451092, "0": 0.4299700493490895, "1": 0.370322397315105, "2": 0.07372631725042691}, "math_density": {"0": 0.10276724600627878, "1": 0.37774369612800285, "2": 0.3428457511272094, "3": 0.09136839411755569, "4": 0.08527491262095335}, "code_density": {"0": 0.0001888149991601474, "1": 0.0003508726385170291, "2": 0.008141319550037654, "3": 0.052330104574261016, "4": 0.9389888882380242}, "reasoning_density": {"0": 0.8658469077183645, "1": 0.04198543635496011, "2": 0.058965861258175045, "3": 0.020369380921825248, "4": 0.01283241374667512}, "education_level": {"none": 0.0016674081236991336, "0": 0.002025204553588366, "1": 0.029838016452790227, "2": 0.7487454100090886, "3": 0.1804880839410983, "4": 0.037235876919735476}, "creative_writing_quality": {"not_applicable": 0.9994472881781779, "-2": 0.00010975743337232988, "-1": 0.0001482966595332808, "0": 0.00021848719478087314, "1": 5.338580353268203e-05, "2": 2.2784730602975424e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0007270887489393364, "Art & Design": 0.0004426676992018726, "Law & Crime": 0.00036542139865258837, "Education & Careers": 0.00014207556580862833, "Entertainment & Media": 0.003470946974671984, "Fashion & Beauty": 0.00014825941093783035, "Business, Economics & Finance": 0.00013835793548380704, "Food & Dining": 9.415263732512039e-05, "Games & Gaming": 0.026735579998578948, "Electronics & Hardware": 0.0004670769745310293, "Health, Healthcare & Wellness": 0.00010180492469493835, "History & Geography": 0.00013458514461098462, "Home, Pets & Hobbies": 0.00027233019790823634, "Industry & Production": 0.00011181718073441206, "Language, Literature & Philosophy": 0.00014705129437702497, "Politics & Government": 9.01349189746658e-05, "Religion & Spirituality": 0.00011280078416205382, "Science, Mathematics & Engineering": 0.0036000799279805253, "Relationships & Community": 7.470412963206547e-05, "Software Usage & Applications": 0.001809529647615012, "Programming & Software Engineering": 0.9577288255030871, "Sports & Fitness": 0.00033803545782829615, "Vehicles & Transportation": 7.617139800949194e-05, "Travel & Tourism": 0.00016556923821880833, "Other / Uncovered Subject": 0.002152511278570647, "Unknown / Insufficient Content": 0.00035242162946462827}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0045139256127786275, "Art & Design": 0.0025555112685509496, "Law & Crime": 0.0015284574825390846, "Education & Careers": 0.0005436692350040462, "Entertainment & Media": 0.06313564241472895, "Fashion & Beauty": 0.000473762793461971, "Business, Economics & Finance": 0.0007708439654601603, "Food & Dining": 0.00035603985581695555, "Games & Gaming": 0.0743396275262608, "Electronics & Hardware": 0.0012328364160438152, "Health, Healthcare & Wellness": 0.0004981615336456536, "History & Geography": 0.0004011312518664591, "Home, Pets & Hobbies": 0.0007905065502769196, "Industry & Production": 0.0003037217240043956, "Language, Literature & Philosophy": 0.0004034766011186142, "Politics & Government": 0.0003215789853342817, "Religion & Spirituality": 0.000525459652061522, "Science, Mathematics & Engineering": 0.027416177103599, "Relationships & Community": 0.0002430365848232115, "Software Usage & Applications": 0.01494508195202816, "Programming & Software Engineering": 0.028099998220997003, "Sports & Fitness": 0.0007840331972651956, "Vehicles & Transportation": 0.0001952790035680439, "Travel & Tourism": 0.0006247076351620244, "Other / Uncovered Subject": 0.005367880473984228, "NONE": 0.7696294529596199}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9812, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9812, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:13.934034+00:00", "scoring_key_slot": "primary", "dataset_uid": "568df502-8132-44cc-96cf-74d357f0bb45", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "ce601c246a2bd91ff4dea97a9b73da0c902724a674bda305582133a211b7edbc", "source_metadata": {"uuid": "568df502-8132-44cc-96cf-74d357f0bb45", "repo_name": "camila314/tap", "relative_path": "clk.py", "category": "TOOL", "algo_rel_score": 0.9568852186203003, "quality_score": 3.264463424682617, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 17, "repo_created_at": "2020-12-27T19:54:42.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 4, "forks": 4, "issues": 2, "pull_requests": 0}, "file_meta": {"content_id": "e581e3b56a3651b5e6914ab8f0d9060478b7ac01", "size_bytes": 3831, "file_timestamp": 1619933460, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "1e811e0caf99bca006a7d508b56bd81e104eff68"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 17, "repo_created_at": "2020-12-27T19:54:42.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 4, "forks": 4, "issues": 2, "pull_requests": 0}, "file_meta": {"content_id": "e581e3b56a3651b5e6914ab8f0d9060478b7ac01", "size_bytes": 3831, "file_timestamp": 1619933460, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "1e811e0caf99bca006a7d508b56bd81e104eff68", "url": "https://github.com/camila314/tap/blob/1e811e0caf99bca006a7d508b56bd81e104eff68/clk.py"}, "gpt4o_tokens": 1204, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.5315323901673392, "noise_severity": 0.027180970243796388, "missing_content_severity": 0.011221109477814796, "factual_density": 3.369762799121806, "pretraining_utility": 0.3800792950797128, "math_density": 1.678640031218902, "code_density": 3.929579378413472, "reasoning_density": 0.2723549566234864, "education_level": 2.2214406441495744, "creative_writing_quality": -0.4864311761108372}, "score_applicability": {"education_level": 0.998332591876301, "creative_writing_quality": 0.0005527118218221413}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/captainmoha/AndromedaBot/blob/ba36b140fda00b9de06e3dd9c613686ef2f6168f/bot.py", "id": "python-0-47edd871a18f49f0126bfb34d99f870b", "text": "from flask import Flask, request\nimport json\nimport requests\nimport string\nimport sqlite3\nimport random\nimport os\nimport csv\nimport re\n\napp = Flask(__name__)\n\n# database\nDB = 'movies.db'\ndb_con = sqlite3.connect(DB, check_same_thread=False)\ncursor = db_con.cursor()\n\n# read emoji\nwith open('emoji.csv', 'r') as fCSV:\n\treader = csv.reader(fCSV)\n\temoji = next(reader)\n\tprint (str(len(emoji)) + ' ' + str(type(emoji))) \n\n# read smilys\nwith open('smiley.csv', 'r') as fCSV:\n\treader = csv.reader(fCSV)\n\tsmileys = {}\n\n\tfor row in reader:\n\t\tsmileys[row[0]] = row[1]\n\n\tprint (str(len(smileys)) + ' ' + str(type(smileys))) \n\n# This needs to be filled with the Page Access Token that will be provided\n# by the Facebook App that will be created. get your own.\nPAT = os.environ['PAT']\n\n\n# # Verification token for webhooks. get your own\n# VERIFIY_TOKEN = ''\n\n# api key from openweathermap. get your own.\nWEATHER_API_KEY = os.environ['WEATHER']\n\nCURRENT_WEATHER_API = 'http://api.openweathermap.org/data/2.5/weather'\nDAILY_WEATHER_API = 'http://api.openweathermap.org/data/2.5/forecast/daily'\nMSG_API = 'https://graph.facebook.com/v2.6/me/messages'\nFB_SETTINGS_API = 'https://graph.facebook.com/v2.6/me/thread_settings'\n\n# for article summary\nWIKI_API = 'https://en.wikipedia.org/w/api.php'\n\n# Movie data\nIMDB_API = 'http://www.omdbapi.com'\n\n@app.route('/', methods=['GET'])\ndef verify():\n\t'''\n\t\tVerify webhooks with facebook\n\t'''\n\n\n\tif (request.args.get('hub.verify_token', '') == VERIFIY_TOKEN):\n\n\t\tprint ('Verification successful!')\n\t\tadd_persist_menu()\n\t\tadd_url_whitelist()\n\t\tadd_get_started_button()\n\t\treturn request.args.get('hub.challenge', '')\n\n\telse:\n\t\tprint ('Verification faild!')\n\t\treturn 'Error, wrong token'\n\n\n\n@app.route('/', methods=['POST'])\ndef handle_webhook_requests():\n\t'''\n\t\tHandle events sent by facebook via webhooks\n\t'''\n\n\n\tprint ('handling messages')\n\n\tpayload = request.get_json()\n\n\tprint (payload)\n\n\t# handle messages\n\tmessages = messaging_events(payload)\n\n\tfor sender, message in messages:\n\t\tprint ('Incoming from %s: %s' % (sender, message))\n\n\t\t# handle different types of messages. text or attachments\n\t\tsend_text_message(sender, message)\n\n\treturn 'ok'\n\n\n\ndef messaging_events(payload):\n\t'''\n\tGenerate tuples of (sender_id, message_text) from the provided payload.\n \t'''\n\n\n\tevents = payload['entry'][0]['messaging']\n\n\tfor event in events:\n\t\tprint(event)\n\t\tif 'message' in event:\n\n\t\t\t# handle different types of content\n\t\t\tif 'text' in event['message']:\n\t\t\t\tyield event['sender']['id'], event['message']['text']\n\n\t\t\t# cases other than text\n\n\t\t\t# else if 'attachments' in event['message']:\n\t\t\t#\tyield event['sender']['id'], event['message']['attachments']\n\t\t\t\t\n\t\t\telse:\n\t\t\t\tyield event['sender']['id'], 'u wot m8?'\n\n\t\telif 'postback' in event:\n\t\t\t# handle postbacks\n\t\t\tsend_post(event['sender']['id'], text_only=False, args={'postback': event['postback']['payload']})\n\n\n\ndef send_text_message(recipient, text):\n\t\"\"\"\n\t\tSend the message text to recipient with id recipient.\n\t\"\"\"\n\n\n\t# see if the user is commanding and handle user commands\n\n\tcommandList = text.split(' ')\n\tif (commandList[0].lower() in ['andromeda', 'rommie', 'rom', 'bot', 'lassie']):\n\t\thandle_commands(recipient, commandList)\n\n\t# if it's a normal message\n\telse:\n\t\t# send post request to the api with the message\n\t\tsend_post(recipient, get_reply(text))\n\n\ndef get_reply(text):\n\t'''\n\t\tget suitable reply for the user's text message\n\t'''\n\n\n\t# the user sent an image (not supported yet)\n\tif (text == 'u wot m8?'):\n\t\treturn 'u wot m8?'\n\n\traw_txt = text\n\temoji_msg = extract_emoji(raw_txt)\n\ttext = escape_query(text).lower()\n\treply = None\n\trows = None\n\n\tcursor.execute(\"SELECT line_id, text FROM LineSearch WHERE text Match ? AND text LIKE ?\", ('^'+text+'*', '%'+text+'%'))\n\trows = cursor.fetchall()\n\n\t# nothing in the database then see if it's an emoji message\n\tif (rows == None or len(rows) == 0):\n\t\t\n\n\t\t# if it's not an emoji message try the database again less strictly\n\t\tif (len(emoji_msg) == 0):\n\t\t\tcursor.execute(\"SELECT line_id, text FROM LineSearch WHERE text Match ? AND text LIKE ?\", ('^'+text.split(' ')[0]+'*', '%'+text.split(' ')[0]+'%'))\n\t\t\trows = cursor.fetchall()\n\n\t\t\tif (rows == None or len(rows) == 0):\n\t\t\t\treturn apologize() + emoji_msg\n\t\t\telse:\n\t\t\t\treturn smart_reply(rows, bad=True) + emoji_msg\n\n\t\telse:\n\t\t\treturn emoji_msg\n\n\telse:\n\t\treturn smart_reply(rows) + emoji_msg\n\n\t\t\n\ndef smart_reply(rows, bad=False):\n\t'''\n\t\tget supposedly smart reply from database and return it\n\t'''\n\n\n\tprint(\"Rows: \" + str(len(rows)))\n\tif (len(rows) > 10):\n\t\trandomIndex = random.randrange(0, 10)\n\telse:\n\t\trandomIndex = random.randrange(0, len(rows))\n\trow = rows[randomIndex]\n\n\tprint(\"id: %s - text: %s\" % (row[0], row[1]))\n\treply_id = int(row[0]) + 1\n\tprint(\"reply id: \" + str(reply_id))\n\n\tcursor.execute('SELECT text FROM LineSearch WHERE line_id = ?', (reply_id,))\n\treply = cursor.fetchone()\n\tif (reply == None):\n\t\t# if we don't find an answer just reply with what we have!\n\t\treturn rows[randomIndex][1]\n\n\tif (not bad):\n\t\tprint(\"reply: %s\" % reply[0])\n\telse:\n\t\tprint(\"bad reply: %s\" % reply[0])\n\t\n\n\treturn reply[0]\n\n\ndef extract_emoji(txt):\n\t'''\n\t\textract emojis and smileys from a message contact them\n\t'''\n\tmsg = \"\"\n\t\n\tfor emo in txt:\n\t\tif emo in emoji:\n\t\t\tmsg += emo\n\n\ttokens = txt.split(' ')\n\tprint(tokens)\n\t# print(smileys.keys())\n\tfor smiley in tokens:\n\t\tif smiley in smileys.keys():\n\t\t\tmsg += smileys[smiley]\n\n\treturn msg\n\n\ndef escape_query(s):\n\t'''\n\t\tremove all but alphanumeric charcters and white space\n\t'''\n\treturn re.sub(r'[^\\s\\w]', '', s)\n\n\ndef apologize():\n\t'''\n\t\tWhen we've got nothin to say\n\t'''\n\treturn \"Let's talk about something else...\"\n\n\ndef handle_commands(recipient, commandList):\n\t'''\n\t\tHandle user commands\n\t'''\n\n\n\tsupported_commands = ['movie', 'weather', 'wiki']\n\n\tprint(\"Gonna handle commands baby!\")\n\n\tif (len(commandList) < 3) or (commandList[1] not in supported_commands):\n\t\tsend_post(recipient, \"Invalid command my friend!\")\n\n\t# movie\n\telif (commandList[1] == 'movie'):\n\t\tmovie_title = \" \".join(commandList[2:])\n\n\t\t# len of response is 2 when the movie is not found\n\t\tresponse = get_movie_json(movie_title)\n\n\t\t\n\t\tif (response and len(response) > 2):\n\t\t\t# we found a movie!\n\t\t\tsend_post(recipient, \"\", text_only=False, args=response)\n\t\telse:\n\t\t\tsend_post(recipient, \"Invalid movie or series name. Please, try again honey.\")\n\t\t\tsend_post(recipient, \"\", text_only=False, args={'img_url': 'http://i.imgur.com/DhgMkzW.jpg'})\n\n\t# weather\n\telif (commandList[1] == 'weather'):\n\t\tcity = \" \".join(commandList[2:4])\n\t\tweather_json = get_weather_json(city)\n\n\t\tif (weather_json and len(weather_json) > 2):\n\t\t\t# we found the city!\n\t\t\tsend_post(recipient, text_only=False, args=weather_json)\n\t\telse:\n\t\t\tsend_post(recipient, \"Invalid city name. Please, try again sweetie.\")\n\t\t\tsend_post(recipient, text_only=False, args={'img_url': 'http://i.imgur.com/DhgMkzW.jpg'})\n\n\t# handle wikipedia\n\telif (commandList[1] == 'wiki'):\n\t\twiki_query = \" \".join(commandList[2:]).title()\n\t\twiki_json = get_wiki_json(wiki_query)\n\n\t\tif 'error' in wiki_json:\n\t\t\tsend_post(recipient, 'No article found on wikipedia for ' + wiki_query + ' Please, try again my friend.')\n\n\t\telse:\n\t\t\tsend_post(recipient, wiki_json['title'])\n\t\t\tsend_post(recipient, text_only=False, args={'img_url': wiki_json['thumbnail']})\n\t\t\tsend_post(recipient, wiki_json['summary'])\n\t\t\tsend_post(recipient, 'Read more on wikipedia: ' + wiki_json['url'])\n\n\n\n\n\n\t\ndef send_post(recipient, text=\"\", text_only=True, args={}):\n\t'''\n\t\t send post request to the api with the message\n\t'''\n\n\n\tprint (\"id: \" + str(recipient))\n\n\t# handle text\n\tif text_only:\n\t\tdata = json.dumps({\n\t\t\t'recipient': {'id': recipient},\n\t\t\t'message': {'text': text}\n\t\t\t})\n\n\t# handle movies\n\telif 'Title' in args:\n\t\tdata = json.dumps({\n\t\t'recipient': {'id': recipient},\n\t\t'message':{\n\t\t\t'attachment':{\n\t\t\t\t'type':\"template\",\n\t\t\t\t'payload':{\n\t\t\t\t\t'template_type':\"generic\",\n\t\t\t\t\t'elements':[\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\t'title': args['Title'] + ' (' + args['Year'] + ')\\nRating: ' + args['imdbRating'],\n\t\t\t\t\t\t\t'subtitle': args['Plot'],\n\t\t\t\t\t\t\t'image_url': args['Poster'] if not (args['Poster'] == 'N/A') else 'https://cdn2.iconfinder.com/data/icons/stickerweather/256/na.png',\n\t\t\t\t\t\t\t'buttons':[\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t'type':\"web_url\",\n\t\t\t\t\t\t\t\t\t'url':\"http://www.imdb.com/title/\" + args['imdbID'] if not (args['imdbID'] == 'N/A') else 'http://www.imdb.com',\n\t\t\t\t\t\t\t\t\t'title':\"View on imdb\"\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t],\n\t\t\t\n\t\t\t\t\t\t}\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t})\n\n\t# handle weather\n\telif 'city' in args:\n\n\t\tweather = \"Today, \" + args['list'][0]['weather'][0]['main'] + ' in ' + args['city']['name'] + ', ' + args['city']['country']\n\t\tweather += \"\\nMax : \" + str(int(args['list'][0]['temp']['max'])) + \" Min: \"\n\t\tweather += str(int(args['list'][0]['temp']['min']))\n\n\t\tdata = json.dumps({\n\t\t\t'recipient': {'id':recipient},\n\t\t\t'message': {'text': weather}\n\t\t\t})\n\n\t# handle image\n\telif 'img_url' in args:\n\t\tdata = json.dumps({\n\t\t\t'recipient': {'id': recipient},\n\t\t\t'message': {\n\t\t\t\t'attachment': {\n\t\t\t\t\t'type': 'image',\n\t\t\t\t\t'payload': {\n\t\t\t\t\t\t'url': args['img_url']\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\t})\n\t\n\t# handle postbacks\n\telif 'postback' in args:\n\n\t\tif args['postback'] == 'share':\n\t\t\tmessage = {\n\t\t\t        \"attachment\":{\n\t\t\t            \"type\":\"template\",\n\t\t\t            \"payload\":{\n\t\t\t                \"template_type\":\"generic\",\n\t\t\t                \"elements\":[\n\t\t\t                    {\n\t\t\t                        \"title\":\"I am Andromeda your bot friend!\",\n\t\t\t                        'subtitle': \"Click the share button to tell your friends!\",\n\t\t\t                        'image_url': \"http://i.imgur.com/aBO45Yp.png\",\n\t\t\t                        \"default_action\":{\n\t\t\t\t\t\t\t\t\t    \"type\":\"web_url\",\n\t\t\t\t\t\t\t\t\t    \"url\":\"https://www.facebook.com/pg/AndromedaBot\",\n\t\t\t\t\t\t\t\t\t    \"messenger_extensions\":True,\n\t\t\t\t\t\t\t\t\t    \"webview_height_ratio\":\"tall\",\n\t\t\t\t\t\t\t\t\t    \"fallback_url\":\"https://www.facebook.com/pg/AndromedaBot\"\n\t\t\t\t\t\t\t\t\t},\n\t\t\t                        \"buttons\":[\n\t\t\t                            {\n\t\t\t                                \"type\":\"element_share\"\n\t\t\t                            }\n\t\t\t                        ]\n\t\t\t                    }\n\t\t\t                ]\n\t\t\t            }\n\t\t\t        }\n\t\t\t    }\n\t\t\n\n\t\telif args['postback'] == 'help' or args['postback'] == 'start':\n\t\t\thelp_msg = 'Hi, Welcome :D My name is Andromeda. I am an android. I am always happy to help :D\\n\\n'\n\t\t\thelp_msg += '-I can chat with you anytime. Just message me and I will reply!\\n\\n'\n\t\t\thelp_msg += '-You can also ask me about a movie or series or the weather or something on wikipedia. Like this.\\n\\n'\n\t\t\thelp_msg += 'andromeda weather london'\n\t\t\thelp_msg += '\\n\\nor\\n\\n'\n\t\t\thelp_msg += 'andromeda movie interstellar'\n\t\t\thelp_msg += '\\n\\nor\\n\\n'\n\t\t\thelp_msg += 'andromeda wiki The Matrix'\n\t\t\thelp_msg += '\\n\\n Go ahead! try it :D'\n\n\t\t\tmessage = {'text': help_msg}\n\n\n\t\tdata = json.dumps({\n\t\t\t'recipient': {'id': recipient},\n\t\t\t'message': message \n\t\t\t})\n\n\t# finally. send the message\n\tsend_json_post_requst(MSG_API, data)\n\n\n\ndef get_movie_json(movie_title):\n\t'''\n\t\tMake get request to the oIMDB API to get movie info and return it as json\n\t'''\n\tapikey = os.environ['IMDB']\n\tmovie = requests.get(IMDB_API, params={'t': movie_title, 'apikey': apikey})\n\n\treturn movie.json()\n\n\ndef get_weather_json(city):\n\t'''\n\t\tGet weather data from openweathermap.org and return it as json\n\t'''\n\n\n\t# first api call to get city id\n\tcurrent_weather = requests.get(CURRENT_WEATHER_API, params={'q': city, 'appid': WEATHER_API_KEY, 'units': 'metric'})\n\n\tif (current_weather and len(current_weather.json()) > 2):\n\t\tcity_id = current_weather.json()['id']\n\n\t\t# second api call to get weather json\n\t\tcurrent_weather = requests.get(DAILY_WEATHER_API, params={'id':city_id, 'appid': WEATHER_API_KEY, 'units': 'metric', 'cnt': '1'})\n\t\tprint(\"Weather \" + str(current_weather))\n\n\treturn current_weather.json()\n\t\n\ndef get_wiki_json(query):\n\n\t# search the api for query\n\tparams = {'action': 'query', 'format': 'json', 'prop':'extracts', 'exintro': 'null', 'explaintext': 'null', 'titles': query}\n\tsearch_json = send_json_get_request(WIKI_API, params)\n\n\tpage = search_json['query']['pages']\n\tpageid = list(page.keys())[0]\n\n\t# if nothing found\n\tif (pageid == '-1'):\n\t\treturn json.dumps({'error': '404'})\n\n\t# we got a hit!\n\telse:\n\t\t# get title and summary\n\t\ttitle = page[str(pageid)]['title']\n\t\tsummary = page[str(pageid)]['extract']\n\t\t# get thumbnail\n\t\tparams = {'action': 'query', 'format': 'json', 'prop':'pageimages', 'piprop':'original', 'pageids': str(pageid)}\n\t\tthumb_json = send_json_get_request(WIKI_API, params)\n\t\tthumb_page = thumb_json['query']['pages'][str(pageid)]\n\n\t\tif 'thumbnail' in thumb_page:\n\t\t\tthumbnail = thumb_page['thumbnail']['original']\n\t\t\tprint('got thumb: ' + str(thumbnail))\n\t\telse:\n\t\t\tthumbnail = 'null'\n\n\t\tarticle_url = 'https://en.wikipedia.org/?curid=' + str(pageid)\n\n\t\twiki_json = {\n\t\t\t'id': str(pageid),\n\t\t\t'title': title,\n\t\t\t'thumbnail': thumbnail,\n\t\t\t'url': article_url,\n\t\t\t'summary': summary[:640]\n\t\t}\n\n\t\treturn wiki_json\n\n\n\ndef add_persist_menu():\n\t'''\n\t\tadds a persistant menu to chat in order to help users or share the bot\n\t\thttps://developers.facebook.com/docs/messenger-platform/thread-settings/persistent-menu\n\t'''\n\n\n\tmenu = json.dumps({\n\t\t\t\"setting_type\" : \"call_to_actions\",\n\t\t\t\"thread_state\" : \"existing_thread\",\n\t\t\t\"call_to_actions\":[\n\t\t\t  {\n\t\t\t    \"type\":\"postback\",\n\t\t\t    \"title\":\"Help\",\n\t\t\t    \"payload\": 'help'\n\t\t\t  },\n\t\t\t  {\n\t\t\t    \"type\":\"postback\",\n\t\t\t    \"title\":\"Share me\",\n\t\t\t    \"payload\": 'share'\n\t\t\t  }\n\t\t\t]\n\t\t})\n\n\n\tsend_json_post_requst(FB_SETTINGS_API, menu)\n\n\ndef remove_persist_menu():\n\t'''\n\t\tremove persistent menu by sending a delete request\n\t'''\n\n\n\tdelete_menu = json.dumps({\n\t\t\t\t\"setting_type\":\"call_to_actions\",\n\t\t\t\t\"thread_state\":\"existing_thread\"\n\t\t\t\t})\n\n\tparams = {'access_token': PAT}\n\theaders = {'Content-type': 'application/json'}\n\t\n\treq = requests.delete(FB_SETTINGS_API, params=params, data=delete_menu, headers=headers)\n\t\n\tif req.status_code != requests.codes.ok:\n\t\tprint (\"failed to delete persistant menu \" + req.text)\n\n\n\ndef add_url_whitelist():\n\t'''\n\t\tadd urls used by the bot to a whitelist as required by facebook\n\t'''\n\n\tdata = json.dumps({\n\t    \"setting_type\": \"domain_whitelisting\",\n\t    \"whitelisted_domains\": [\"https://www.facebook.com\"],\n\t    \"domain_action_type\": \"add\"\n\t})\n\n\tsend_json_post_requst(FB_SETTINGS_API, data)\n\n\ndef add_get_started_button():\n\t'''\n\t\tsend post request to add a postback action for the get started button\n\t'''\n\n\n\tdata = json.dumps({\n\t\t    \"setting_type\":\"call_to_actions\",\n\t\t    \"thread_state\":\"new_thread\",\n\t\t    \"call_to_actions\":[\n\t\t        {\n\t\t            \"payload\":\"start\"\n\t\t        }\n\t\t    ]\n\t\t})\n\n\tsend_json_post_requst(FB_SETTINGS_API, data)\n\n\ndef send_json_post_requst(url, json_data):\n\t'''\n\t\tSend a generic post request to the api in url with the json payload from json_data\n\t'''\n\n\n\tparams = {'access_token': PAT}\n\theaders = {'Content-type': 'application/json'}\n\t\n\treq = requests.post(url, params=params, data=json_data, headers=headers)\n\t\n\tif req.status_code != requests.codes.ok:\n\t\tprint(\"json post request faild\" + req.text)\n\telse:\n\t\tprint(\"json post request succes! \" + req.text)\n\n\ndef send_json_get_request(url, params):\n\n\treq = requests.get(url, params=params)\n\n\tif req.status_code != requests.codes.ok:\n\t\tprint(\"json get request faild\" + req.text)\n\telse:\n\t\tprint(\"json get request success! \" + req.text)\n\n\treturn req.json()\n\n\n\n\nif __name__ == '__main__':\n\t# for c9\n\t# app.run(debug=True, host=os.getenv('IP', '0.0.0.0'),port=int(os.getenv('PORT', 8080)))\n\t\n\t# heroku\n\tapp.run()\n\n\t\t\t\n", "text_preview": "from flask import Flask, request import json import requests import string import sqlite3 import random import os import csv import re app = Flask(__name__) # database DB = 'movies.db' db_con = sqlite3.connect(DB, check_same_thread=False) c", "input_sha256": "19b8191d33d8ab7c4187cb031212386568b638cbfee827bb22cec1c9de19fac3", "text_sha256": "47edd871a18f49f0126bfb34d99f870bd2e11e42d4996804dfb8dbc70c1204d2", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12010, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.22039306695016986, "noise_severity": 0.11402999303956873, "missing_content_severity": 0.07935404958184042, "factual_density": 2.9019064702296524, "pretraining_utility": 0.5240701406422074, "math_density": 0.13133514864653495, "code_density": 3.7431915362863535, "reasoning_density": 0.9275032120930279, "education_level": 2.063398959087327, "creative_writing_quality": -0.289988983594425}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.03172015511207926, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.994658276899542, "secondary": 0.8804421960835376, "tertiary": 0.8473429037926723, "structured_records": 0.06940881941922189, "procedural_steps": 0.027396201029588306, "worked_solution": 0.005023952250060357, "contextual_dialogue": 0.00019162050195291758, "content_quality": 0.42646713010807513, "noise_severity": 0.9049750058003594, "missing_content_severity": 0.9338716253484662, "factual_density": 0.3508655301669149, "pretraining_utility": 0.48810991776991963, "math_density": 0.8905540427945542, "code_density": 0.7859929469052946, "reasoning_density": 0.22708065658914345, "education_level": 0.8361305861325, "creative_writing_quality": 0.9863552648056013, "primary_topic": 0.9873043215383258, "secondary_topic": 0.7463123915742013}, "probability_margin": {"primary": 0.9928165562722674, "secondary": 0.8274742922693874, "tertiary": 0.7741682713389116, "education_level": 0.7714984031787855, "creative_writing_quality": 0.9811774111626592, "primary_topic": 0.9827978436471879, "secondary_topic": 0.5906970753632639}, "raw_probabilities": {"primary": {"Academic Writing": 4.3987277535841366e-05, "Content Listing": 3.2758192798898354e-05, "Creative Writing": 4.2122204354574564e-05, "Customer Support Page": 2.294641531929342e-05, "Q&A Forum": 1.865327549070118e-05, "Discussion Thread": 1.4663928823227608e-05, "Comment Section": 2.2837095396746927e-05, "Missing Content": 4.127824756278526e-05, "Knowledge Article": 9.234864681180971e-05, "Legal Notices": 4.0158957999235974e-05, "News Article": 8.649643396647703e-06, "Organizational Announcement": 1.7975447499436073e-05, "Nonfiction Writing": 2.2875917025103206e-05, "Organizational About Page": 2.8273928070239967e-05, "Personal About Page": 3.08346224968242e-05, "Personal Blog": 1.551221654135548e-05, "Product Page": 1.6045148743192767e-05, "Spam / Ads": 2.815341335411214e-05, "Structured Data": 0.002064292423127001, "Technical Writing": 0.9948808486953944, "Transcript / Interview": 2.8908631489739354e-05, "Tutorial / How-To Guide": 0.0009678004636891258, "User Reviews": 1.2941186475160376e-05, "Other / Unknown": 0.0015051340206045395}, "secondary": {"Academic Writing": 0.00062198525156188, "Content Listing": 0.0006674213284675541, "Creative Writing": 0.0011197389705995709, "Customer Support Page": 0.006951540217635722, "Q&A Forum": 0.00033716891528852845, "Discussion Thread": 0.00030031697273433515, "Comment Section": 0.0008293702063896648, "Missing Content": 0.0009908055530622524, "Knowledge Article": 0.0011037002641417968, "Legal Notices": 0.0008459224686574326, "News Article": 0.0003691516059300527, "Organizational Announcement": 0.0004449445739986436, "Nonfiction Writing": 0.0005606936821802133, "Organizational About Page": 0.0007185285195175415, "Personal About Page": 0.0005061468702237855, "Personal Blog": 0.0004661758567776883, "Product Page": 0.0003116077582713648, "Spam / Ads": 0.0021976175801178385, "Structured Data": 0.05794947897733614, "Technical Writing": 0.02813798291653047, "Transcript / Interview": 0.0009000753933373551, "Tutorial / How-To Guide": 0.008040287672312758, "User Reviews": 0.00020556719820392265, "NONE": 0.8854237712467236}, "tertiary": {"Academic Writing": 0.0005565439455014159, "Content Listing": 0.0008277428835448387, "Creative Writing": 0.0016726767031664332, "Customer Support Page": 0.015073355576131459, "Q&A Forum": 0.00036012391092863353, "Discussion Thread": 0.0002808879761182319, "Comment Section": 0.0011061803220485237, "Missing Content": 0.0012770691844859493, "Knowledge Article": 0.001851607937056082, "Legal Notices": 0.0012607923298537267, "News Article": 0.0003670045746261125, "Organizational Announcement": 0.00044392376312640044, "Nonfiction Writing": 0.0008261963223811663, "Organizational About Page": 0.0014073326063734366, "Personal About Page": 0.0007719010944213481, "Personal Blog": 0.0004485684297295184, "Product Page": 0.00035903393677861966, "Spam / Ads": 0.0051538925794787985, "Structured Data": 0.0795353447957327, "Technical Writing": 0.01985382187610532, "Transcript / Interview": 0.0013599867701554879, "Tutorial / How-To Guide": 0.011273068571570871, "User Reviews": 0.0002293277760406637, "NONE": 0.8537036161346443}, "structured_records": {"false": 0.930591180580778, "true": 0.06940881941922189}, "procedural_steps": {"false": 0.9726037989704117, "true": 0.027396201029588306}, "worked_solution": {"false": 0.9949760477499396, "true": 0.005023952250060357}, "contextual_dialogue": {"false": 0.9998083794980471, "true": 0.00019162050195291758}, "content_quality": {"-2": 0.024508987413724218, "-1": 0.18490521363262155, "0": 0.39810649932696257, "1": 0.3306423438431436, "2": 0.06183695578354811}, "noise_severity": {"0": 0.9035733458771966, "1": 0.08215089955498144, "2": 0.01182770406713716, "3": 0.0015685166524253941, "4": 0.0008795338482591942}, "missing_content_severity": {"0": 0.9343084538871884, "1": 0.055287909290885326, "2": 0.007801199449615415, "3": 0.001946008097519589, "4": 0.0006564292747913796}, "factual_density": {"0": 0.02235014559601146, "1": 0.08633994374500009, "2": 0.19879712250699033, "3": 0.352078871137321, "4": 0.3404339170146772}, "pretraining_utility": {"-2": 0.008183022991272378, "-1": 0.07414234109707737, "0": 0.37226522784897265, "1": 0.4762402884035257, "2": 0.0691691196591519}, "math_density": {"0": 0.8911836034767766, "1": 0.09511416781880372, "2": 0.008526681988650343, "3": 0.0015345700126469135, "4": 0.003640976703122452}, "code_density": {"0": 0.0003291080997784049, "1": 0.0006797290133669957, "2": 0.02521477137230471, "3": 0.2030233015298224, "4": 0.7707530899847275}, "reasoning_density": {"0": 0.5340881983491228, "1": 0.14971539932518085, "2": 0.2103808400036243, "3": 0.06623611652768967, "4": 0.03957944579438235}, "education_level": {"none": 0.0011150495269013647, "0": 0.0017133228780157527, "1": 0.03625342705789283, "2": 0.8634421551104167, "3": 0.0919437519316312, "4": 0.005532293495142077}, "creative_writing_quality": {"not_applicable": 0.9886293873380011, "-2": 0.0005923036443992547, "-1": 0.0027870336471687253, "0": 0.007451976175341896, "1": 0.0004043098629098015, "2": 0.00013498933217923496}, "primary_topic": {"Adult & Explicit Sexual Content": 8.270614010681316e-05, "Art & Design": 8.641373654073716e-05, "Law & Crime": 7.294866484763408e-05, "Education & Careers": 7.880789079785878e-05, "Entertainment & Media": 0.00499477321658696, "Fashion & Beauty": 5.559767706419844e-05, "Business, Economics & Finance": 8.664772791734965e-05, "Food & Dining": 5.18796493262525e-05, "Games & Gaming": 0.0006556961008581986, "Electronics & Hardware": 0.00010688358017388846, "Health, Healthcare & Wellness": 4.7898903644256605e-05, "History & Geography": 5.42483913833302e-05, "Home, Pets & Hobbies": 8.138434622990819e-05, "Industry & Production": 4.9196955239607034e-05, "Language, Literature & Philosophy": 0.00025955217134144, "Politics & Government": 4.0359508342673834e-05, "Religion & Spirituality": 6.846171210560709e-05, "Science, Mathematics & Engineering": 0.001532211755447488, "Relationships & Community": 0.0007982324941224303, "Software Usage & Applications": 0.001727764743255081, "Programming & Software Engineering": 0.9877926168637748, "Sports & Fitness": 7.83418723536786e-05, "Vehicles & Transportation": 4.177709055844676e-05, "Travel & Tourism": 0.0001241828509256038, "Other / Uncovered Subject": 0.0009072432906920458, "Unknown / Insufficient Content": 0.00012417266636364788}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009964673173699454, "Art & Design": 0.0011604785570995765, "Law & Crime": 0.0008615530757365773, "Education & Careers": 0.0005591938407376485, "Entertainment & Media": 0.16537253191962195, "Fashion & Beauty": 0.0005320889625665103, "Business, Economics & Finance": 0.0010993819610033856, "Food & Dining": 0.0005385027817612573, "Games & Gaming": 0.0011636186850103355, "Electronics & Hardware": 0.0005421104771607137, "Health, Healthcare & Wellness": 0.0008106782641037315, "History & Geography": 0.0008968971428903587, "Home, Pets & Hobbies": 0.0006714880797828693, "Industry & Production": 0.0002935086847573878, "Language, Literature & Philosophy": 0.001837270936009753, "Politics & Government": 0.0004963989162636341, "Religion & Spirituality": 0.00046640479492578596, "Science, Mathematics & Engineering": 0.008169675635405119, "Relationships & Community": 0.0037348964145209763, "Software Usage & Applications": 0.027609073523329874, "Programming & Software Engineering": 0.020606190112260517, "Sports & Fitness": 0.0004506324417025979, "Vehicles & Transportation": 0.0002454782166073388, "Travel & Tourism": 0.0011541317908913215, "Other / Uncovered Subject": 0.0036617401855950234, "NONE": 0.7560696072828859}}, "probabilities": {"primary": {"Academic Writing": 4.3987277535841366e-05, "Content Listing": 3.2758192798898354e-05, "Creative Writing": 4.2122204354574564e-05, "Customer Support Page": 2.294641531929342e-05, "Q&A Forum": 1.865327549070118e-05, "Discussion Thread": 1.4663928823227608e-05, "Comment Section": 2.2837095396746927e-05, "Missing Content": 4.127824756278526e-05, "Knowledge Article": 9.234864681180971e-05, "Legal Notices": 4.0158957999235974e-05, "News Article": 8.649643396647703e-06, "Organizational Announcement": 1.7975447499436073e-05, "Nonfiction Writing": 2.2875917025103206e-05, "Organizational About Page": 2.8273928070239967e-05, "Personal About Page": 3.08346224968242e-05, "Personal Blog": 1.551221654135548e-05, "Product Page": 1.6045148743192767e-05, "Spam / Ads": 2.815341335411214e-05, "Structured Data": 0.002064292423127001, "Technical Writing": 0.9948808486953944, "Transcript / Interview": 2.8908631489739354e-05, "Tutorial / How-To Guide": 0.0009678004636891258, "User Reviews": 1.2941186475160376e-05, "Other / Unknown": 0.0015051340206045395}, "secondary": {"Academic Writing": 0.00062198525156188, "Content Listing": 0.0006674213284675541, "Creative Writing": 0.0011197389705995709, "Customer Support Page": 0.006951540217635722, "Q&A Forum": 0.00033716891528852845, "Discussion Thread": 0.00030031697273433515, "Comment Section": 0.0008293702063896648, "Missing Content": 0.0009908055530622524, "Knowledge Article": 0.0011037002641417968, "Legal Notices": 0.0008459224686574326, "News Article": 0.0003691516059300527, "Organizational Announcement": 0.0004449445739986436, "Nonfiction Writing": 0.0005606936821802133, "Organizational About Page": 0.0007185285195175415, "Personal About Page": 0.0005061468702237855, "Personal Blog": 0.0004661758567776883, "Product Page": 0.0003116077582713648, "Spam / Ads": 0.0021976175801178385, "Structured Data": 0.05794947897733614, "Technical Writing": 0.02813798291653047, "Transcript / Interview": 0.0009000753933373551, "Tutorial / How-To Guide": 0.008040287672312758, "User Reviews": 0.00020556719820392265, "NONE": 0.8854237712467236}, "tertiary": {"Academic Writing": 0.0005565439455014159, "Content Listing": 0.0008277428835448387, "Creative Writing": 0.0016726767031664332, "Customer Support Page": 0.015073355576131459, "Q&A Forum": 0.00036012391092863353, "Discussion Thread": 0.0002808879761182319, "Comment Section": 0.0011061803220485237, "Missing Content": 0.0012770691844859493, "Knowledge Article": 0.001851607937056082, "Legal Notices": 0.0012607923298537267, "News Article": 0.0003670045746261125, "Organizational Announcement": 0.00044392376312640044, "Nonfiction Writing": 0.0008261963223811663, "Organizational About Page": 0.0014073326063734366, "Personal About Page": 0.0007719010944213481, "Personal Blog": 0.0004485684297295184, "Product Page": 0.00035903393677861966, "Spam / Ads": 0.0051538925794787985, "Structured Data": 0.0795353447957327, "Technical Writing": 0.01985382187610532, "Transcript / Interview": 0.0013599867701554879, "Tutorial / How-To Guide": 0.011273068571570871, "User Reviews": 0.0002293277760406637, "NONE": 0.8537036161346443}, "structured_records": {"false": 0.930591180580778, "true": 0.06940881941922189}, "procedural_steps": {"false": 0.9726037989704117, "true": 0.027396201029588306}, "worked_solution": {"false": 0.9949760477499396, "true": 0.005023952250060357}, "contextual_dialogue": {"false": 0.9998083794980471, "true": 0.00019162050195291758}, "content_quality": {"-2": 0.024508987413724218, "-1": 0.18490521363262155, "0": 0.39810649932696257, "1": 0.3306423438431436, "2": 0.06183695578354811}, "noise_severity": {"0": 0.9035733458771968, "1": 0.08215089955498145, "2": 0.011827704067137164, "3": 0.0015685166524253946, "4": 0.0008795338482591944}, "missing_content_severity": {"0": 0.9343084538871882, "1": 0.05528790929088531, "2": 0.0078011994496154135, "3": 0.0019460080975195885, "4": 0.0006564292747913795}, "factual_density": {"0": 0.02235014559601146, "1": 0.08633994374500009, "2": 0.19879712250699033, "3": 0.352078871137321, "4": 0.3404339170146772}, "pretraining_utility": {"-2": 0.008183022991272378, "-1": 0.07414234109707737, "0": 0.37226522784897265, "1": 0.4762402884035257, "2": 0.0691691196591519}, "math_density": {"0": 0.8911836034767766, "1": 0.09511416781880372, "2": 0.008526681988650343, "3": 0.0015345700126469135, "4": 0.003640976703122452}, "code_density": {"0": 0.0003291080997784049, "1": 0.0006797290133669957, "2": 0.02521477137230471, "3": 0.2030233015298224, "4": 0.7707530899847275}, "reasoning_density": {"0": 0.5340881983491228, "1": 0.14971539932518085, "2": 0.2103808400036243, "3": 0.06623611652768967, "4": 0.03957944579438235}, "education_level": {"none": 0.0011150495269013649, "0": 0.001713322878015753, "1": 0.036253427057892834, "2": 0.8634421551104168, "3": 0.09194375193163122, "4": 0.005532293495142078}, "creative_writing_quality": {"not_applicable": 0.9886293873380011, "-2": 0.0005923036443992547, "-1": 0.0027870336471687253, "0": 0.007451976175341896, "1": 0.0004043098629098015, "2": 0.00013498933217923496}, "primary_topic": {"Adult & Explicit Sexual Content": 8.270614010681316e-05, "Art & Design": 8.641373654073716e-05, "Law & Crime": 7.294866484763408e-05, "Education & Careers": 7.880789079785878e-05, "Entertainment & Media": 0.00499477321658696, "Fashion & Beauty": 5.559767706419844e-05, "Business, Economics & Finance": 8.664772791734965e-05, "Food & Dining": 5.18796493262525e-05, "Games & Gaming": 0.0006556961008581986, "Electronics & Hardware": 0.00010688358017388846, "Health, Healthcare & Wellness": 4.7898903644256605e-05, "History & Geography": 5.42483913833302e-05, "Home, Pets & Hobbies": 8.138434622990819e-05, "Industry & Production": 4.9196955239607034e-05, "Language, Literature & Philosophy": 0.00025955217134144, "Politics & Government": 4.0359508342673834e-05, "Religion & Spirituality": 6.846171210560709e-05, "Science, Mathematics & Engineering": 0.001532211755447488, "Relationships & Community": 0.0007982324941224303, "Software Usage & Applications": 0.001727764743255081, "Programming & Software Engineering": 0.9877926168637748, "Sports & Fitness": 7.83418723536786e-05, "Vehicles & Transportation": 4.177709055844676e-05, "Travel & Tourism": 0.0001241828509256038, "Other / Uncovered Subject": 0.0009072432906920458, "Unknown / Insufficient Content": 0.00012417266636364788}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009964673173699454, "Art & Design": 0.0011604785570995765, "Law & Crime": 0.0008615530757365773, "Education & Careers": 0.0005591938407376485, "Entertainment & Media": 0.16537253191962195, "Fashion & Beauty": 0.0005320889625665103, "Business, Economics & Finance": 0.0010993819610033856, "Food & Dining": 0.0005385027817612573, "Games & Gaming": 0.0011636186850103355, "Electronics & Hardware": 0.0005421104771607137, "Health, Healthcare & Wellness": 0.0008106782641037315, "History & Geography": 0.0008968971428903587, "Home, Pets & Hobbies": 0.0006714880797828693, "Industry & Production": 0.0002935086847573878, "Language, Literature & Philosophy": 0.001837270936009753, "Politics & Government": 0.0004963989162636341, "Religion & Spirituality": 0.00046640479492578596, "Science, Mathematics & Engineering": 0.008169675635405119, "Relationships & Community": 0.0037348964145209763, "Software Usage & Applications": 0.027609073523329874, "Programming & Software Engineering": 0.020606190112260517, "Sports & Fitness": 0.0004506324417025979, "Vehicles & Transportation": 0.0002454782166073388, "Travel & Tourism": 0.0011541317908913215, "Other / Uncovered Subject": 0.0036617401855950234, "NONE": 0.7560696072828859}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 13078, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 13078, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:14.674066+00:00", "scoring_key_slot": "primary", "dataset_uid": "82a0c8d1-5a68-47a4-a2bb-f0719831d078", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "faf7b7edae632007aba5b20a7b3155bc6f7ef43c1b1f6f26df5ae3ca129fc6d3", "source_metadata": {"uuid": "82a0c8d1-5a68-47a4-a2bb-f0719831d078", "repo_name": "captainmoha/AndromedaBot", "relative_path": "bot.py", "category": "WEB", "algo_rel_score": 0.9819040894508362, "quality_score": 3.574826955795288, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 122, "repo_created_at": "2017-02-07T17:37:36.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "b0aa2021f9eb6eb5ca3267106d95723b444becd9", "size_bytes": 15523, "file_timestamp": 1620563592, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "ba36b140fda00b9de06e3dd9c613686ef2f6168f"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 122, "repo_created_at": "2017-02-07T17:37:36.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "b0aa2021f9eb6eb5ca3267106d95723b444becd9", "size_bytes": 15523, "file_timestamp": 1620563592, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "ba36b140fda00b9de06e3dd9c613686ef2f6168f", "url": "https://github.com/captainmoha/AndromedaBot/blob/ba36b140fda00b9de06e3dd9c613686ef2f6168f/bot.py"}, "gpt4o_tokens": 4213, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.22039306695016986, "noise_severity": 0.11402999303956873, "missing_content_severity": 0.07935404958184043, "factual_density": 2.9019064702296524, "pretraining_utility": 0.5240701406422074, "math_density": 0.13133514864653495, "code_density": 3.7431915362863535, "reasoning_density": 0.9275032120930279, "education_level": 2.063398959087327, "creative_writing_quality": -0.289988983594425}, "score_applicability": {"education_level": 0.9988849504730987, "creative_writing_quality": 0.011370612661998913}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/florianfei/light-matching-engine/blob/828319982345165371ca11013e96489eef49ee91/python_matching_engine/trade_server/alphalab_submission.py", "id": "python-0-fcb0e925dcb20ba6444598dab92b5d56", "text": "# Enter your code here. Read input from STDIN. Print output to STDOUT\nclass Order:\n    def __init__(self, amount, price, id, side, type, display_size=None):\n        self.amount = amount\n        self.price = price\n        self.id = id\n        self.side = side\n        self.type = type\n        self.display_size = display_size\n\n    def __str__(self):\n        if self.type == 'ICE' and self.display_size:\n            return '{}({})@{}#{}'.format(self.display_size, self.amount, self.price, self.id)\n        return '{}@{}#{}'.format(self.amount, self.price, self.id)\n\nclass Trade:\n    def __init__(self, buy_id, sell_id, amount, price):\n        self.buy_id = buy_id\n        self.sell_id = sell_id\n        self.amount = amount\n        self.price = price\n\nclass OrderBook:\n    def __init__(self):\n        self.buy_orders = []\n        self.sell_orders = []\n        self.id_mapping = {}\n\n    def process(self, order):\n        if order.side == 'B':\n            if order.type == 'MO':\n                order.price = float('inf')\n            self.process_buy(order)\n        elif order.side == 'S':\n            if order.type == 'MO':\n                order.price = 0\n            self.process_sell(order)\n    \n    def process_buy(self, order):\n        trades, to_remove = [], []\n        index = n = len(self.sell_orders)\n        # Market order price == 0\n        # Limit order price > 0\n        if order.price == 0:\n            index = 0\n        elif n != 0 and self.sell_orders[0].price <= order.price:\n            index = self.find_sell_order_match(order.price)\n        \n        while index < len(self.sell_orders):\n            sell_order = self.sell_orders[index]\n            if sell_order.price > order.price:\n                break\n            if sell_order.type != 'ICE':\n                min_amount = min(sell_order.amount, order.amount)\n                trades.append(Trade(order.id, sell_order.id, min_amount, sell_order.price))\n                order.amount -= min_amount\n                if sell_order.amount == min_amount:\n                    to_remove.append(index)\n                else:\n                    sell_order.amount -= min_amount\n                index += 1\n            else:\n                if index+1 == len(self.sell_orders) or self.sell_orders[index+1].price > order.price:\n                    min_amount = min(sell_order.amount, order.amount)\n                    trades.append(Trade(order.id, sell_order.id, min_amount, sell_order.price))\n                    order.amount -= min_amount\n                    if sell_order.amount == min_amount:\n                        to_remove.append(index)\n                    else:\n                        sell_order.amount -= min_amount\n                        sell_order.display_size -= (min_amount % sell_order.display_size)\n                    break\n                else:\n                    min_amount = min(sell_order.display_size, order.amount)\n                    trades.append(Trade(order.id, sell_order.id, min_amount, sell_order.price))\n                    order.amount -= min_amount\n                    if sell_order.display_size == min_amount:\n                        updated_order = sell_order\n                        self.remove_sell_order(index)\n                        updated_order.amount -= min_amount\n                        updated_order.display_size = min(updated_order.amount, updated_order.display_size)\n                        self.add_sell_order(updated_order)\n                    else:\n                        sell_order.amount -= min_amount\n                        sell_order.display_size -= min_amount\n            if order.amount == 0:\n                break\n        if order.amount > 0:\n            if order.type == 'LO' or order.type == 'ICE':\n                self.add_buy_order(order)\n            if order.type == 'FOK':\n                print(0)\n                return\n        if to_remove:\n            self.remove_sell_orders(to_remove[0], to_remove[-1]+1)\n        # Output total cost\n        total = 0\n        for t in trades:\n            total += t.amount * t.price\n        print(total)\n\n    def process_sell(self, order):\n        trades, to_remove = [], []\n        index = n = len(self.buy_orders)\n        if order.price == 0:\n            index = 0\n        elif n != 0 and self.buy_orders[0].price >= order.price:\n            index = self.find_buy_order_match(order.price)\n        while index < len(self.buy_orders):\n            buy_order = self.buy_orders[index]\n            if buy_order.price < order.price:\n                break\n            if buy_order.type != 'ICE':\n                min_amount = min(buy_order.amount, order.amount)\n                trades.append(Trade(order.id, buy_order.id, min_amount, buy_order.price))\n                order.amount -= min_amount\n                if buy_order.amount == min_amount:\n                    to_remove.append(index)\n                else:\n                    buy_order.amount -= min_amount\n                index += 1\n            else:\n                if index+1 == len(self.buy_orders) or self.buy_orders[index+1].price < order.price:\n                    min_amount = min(buy_order.amount, order.amount)\n                    trades.append(Trade(order.id, buy_order.id, min_amount, buy_order.price))\n                    order.amount -= min_amount\n                    if buy_order.amount == min_amount:\n                        to_remove.append(index)\n                    else:\n                        buy_order.amount -= min_amount\n                        buy_order.display_size -= (min_amount % buy_order.display_size)\n                    break\n                else:\n                    min_amount = min(buy_order.display_size, order.amount)\n                    trades.append(Trade(order.id, buy_order.id, min_amount, buy_order.price))\n                    order.amount -= min_amount\n                    if buy_order.display_size == min_amount:\n                        updated_order = buy_order\n                        self.remove_buy_order(index)\n                        updated_order.amount -= min_amount\n                        updated_order.display_size = min(updated_order.amount, updated_order.display_size)\n                        self.add_buy_order(updated_order)\n                    else:\n                        buy_order.amount -= min_amount\n                        buy_order.display_size -= min_amount\n            if order.amount == 0:\n                break\n        if order.amount > 0:\n            if order.type == 'LO' or order.type == 'ICE':\n                self.add_sell_order(order)\n            if order.type == 'FOK':\n                print(0)\n                return\n        if to_remove:\n            self.remove_buy_orders(to_remove[0], to_remove[-1]+1)\n        # Output total cost\n        total = 0\n        for t in trades:\n            total += t.amount * t.price\n        print(total)\n    \n    def add_buy_order(self, order):\n        index = self.find_buy_order(order.price)\n        if index >= len(self.buy_orders):\n            self.buy_orders.append(order)\n            self.id_mapping[order.id] = (len(self.buy_orders)-1, 'B')\n        else:\n            self.buy_orders.insert(index, order)\n            for i in range(index+1, len(self.buy_orders)):\n                oid = self.buy_orders[i].id\n                self.id_mapping[oid] = (self.id_mapping[oid][0]+1, self.id_mapping[oid][1])\n            self.id_mapping[order.id] = (index, 'B')\n    \n    def add_sell_order(self, order):\n        index = self.find_sell_order(order.price)\n        if index >= len(self.sell_orders):\n            self.sell_orders.append(order)\n            self.id_mapping[order.id] = (len(self.sell_orders)-1, 'S')\n        else:\n            self.sell_orders.insert(index, order)\n            for i in range(index+1, len(self.sell_orders)):\n                oid = self.sell_orders[i].id\n                self.id_mapping[oid] = (self.id_mapping[oid][0]+1, self.id_mapping[oid][1])\n            self.id_mapping[order.id] = (index, 'S')\n    \n    def cancel_order(self, order_id):\n        if order_id in self.id_mapping:\n            idx, side = self.id_mapping[order_id]\n            if side == 'S':\n                self.remove_sell_order(idx)\n            elif side == 'B':\n                self.remove_buy_order(idx)\n        # else:\n        #     print('Could not find order for id: {}'.format(order_id))\n    \n    def cancel_and_replace_order(self, order_id, amount, price):\n        if order_id not in self.id_mapping:\n            return\n        idx, side = self.id_mapping[order_id]\n        if side == 'S':\n            original_order = self.sell_orders[idx]\n            if original_order.price == price and original_order.amount > amount:\n                original_order.amount = amount\n            else:\n                updated_order = original_order\n                self.remove_sell_order(idx)\n                updated_order.amount = amount\n                updated_order.price = price\n                self.add_sell_order(updated_order)\n        elif side == 'B':\n            original_order = self.buy_orders[idx]\n            if original_order.price == price and original_order.amount > amount:\n                original_order.amount = amount\n            else:\n                updated_order = original_order\n                self.remove_buy_order(idx)\n                updated_order.amount = amount\n                updated_order.price = price\n                self.add_buy_order(updated_order)\n            \n    def remove_buy_order(self, index):\n        order_id = self.buy_orders[index].id\n        for i in range(index+1, len(self.buy_orders)):\n            oid = self.buy_orders[i].id\n            self.id_mapping[oid] = (self.id_mapping[oid][0]-1, self.id_mapping[oid][1])\n        del self.buy_orders[index]\n        del self.id_mapping[order_id]\n    \n    def remove_buy_orders(self, start, end):\n        order_ids = [self.buy_orders[idx].id for idx in range(start, end)]\n        for i in range(end, len(self.buy_orders)):\n            oid = self.buy_orders[i].id\n            self.id_mapping[oid] = (self.id_mapping[oid][0]-(end-start), self.id_mapping[oid][1])\n        del self.buy_orders[start:end]\n        for oid in order_ids:\n            del self.id_mapping[oid]\n\n    def remove_sell_order(self, index):\n        order_id = self.sell_orders[index].id\n        for i in range(index+1, len(self.sell_orders)):\n            oid = self.sell_orders[i].id\n            self.id_mapping[oid] = (self.id_mapping[oid][0]-1, self.id_mapping[oid][1])\n        del self.sell_orders[index]\n        del self.id_mapping[order_id]\n    \n    def remove_sell_orders(self, start, end):\n        order_ids = [self.sell_orders[idx].id for idx in range(start, end)]\n        for i in range(end, len(self.sell_orders)):\n            oid = self.sell_orders[i].id\n            self.id_mapping[oid] = (self.id_mapping[oid][0]-(end-start), self.id_mapping[oid][1])\n        del self.sell_orders[start:end]\n        for oid in order_ids:\n            del self.id_mapping[oid]\n    \n    def find_sell_order(self, price):\n        n = len(self.sell_orders)\n        l, r, idx = 0, n-1, n\n        while l <= r:\n            mid = (l+r)//2\n            # [1,2,3,4,5,5,6,7,7] insert 5\n            if self.sell_orders[mid].price > price:\n                idx = mid\n                r = mid-1\n            else:\n                l = mid+1\n        return idx\n\n    def find_buy_order(self, price):\n        n = len(self.buy_orders)\n        l, r, idx = 0, n-1, n\n        while l <= r:\n            mid = (l+r)//2\n            # [7,7,6,6,5,3,2,2,2,2,1] insert 5\n            if self.buy_orders[mid].price < price:\n                idx = mid\n                r = mid-1\n            else:\n                l = mid+1\n        return idx\n\n    def find_sell_order_match(self, price):\n        n = len(self.sell_orders)\n        l, r, idx = 0, n-1, 0\n        while l <= r:\n            mid = (l+r)//2\n            if self.sell_orders[mid].price <= price:\n                idx = mid\n                r = mid-1\n            else:\n                l = mid+1\n        return idx\n\n    def find_buy_order_match(self, price):\n        n = len(self.buy_orders)\n        l, r, idx = 0, n-1, 0\n        while l <= r:\n            mid = (l+r)//2\n            if self.buy_orders[mid].price >= price:\n                idx = mid\n                r = mid-1\n            else:\n                l = mid+1\n        return idx\n        \n# MAIN CODE EXECUTION\n# Essentially the matching engine\norder_book = OrderBook()\ncmd = input()\ncount = 0\nwhile cmd != 'END':\n    # print(cmd)\n    args = cmd.split(' ')\n    if args[0] == 'SUB':\n        price = -1 if args[1] == 'MO' else int(args[5])\n        if args[1] == 'ICE':\n            order = Order(int(args[4]), price, args[3], args[2], args[1], int(args[6]))\n        else:\n            order = Order(int(args[4]), price, args[3], args[2], args[1])\n        order_book.process(order)\n    elif args[0] == 'CXL':\n        order_book.cancel_order(args[1])\n    elif args[0] == 'CRP':\n        order_book.cancel_and_replace_order(args[1], int(args[2]), int(args[3]))\n    # if count >= 9:\n    #     print('===============')\n    #     print('CURRENT STATE')\n    #     print('===============')\n    #     print('B:', ' '.join([str(order) for order in order_book.buy_orders]))\n    #     print('S:', ' '.join([str(order) for order in order_book.sell_orders]))\n    cmd = input()\n    count += 1\n\nprint('B:', ' '.join([str(order) for order in order_book.buy_orders]))\nprint('S:', ' '.join([str(order) for order in order_book.sell_orders]))\n\n\nTEST CASE 8\n\nSUB ICE B 8w3k 500 12 80\nSUB FOK S k3ow 300 10\nSUB FOK S 420c 250 10\n\nTEST CASE 9\n\nSUB ICE B Uo7Z 400 12 120\nCRP Uo7Z 600 10\nSUB LO S nmqX 90 12\nSUB LO S rpDs 200 10\n\nTEST CASE 10\n\nSUB FOK B iUMF 434 977\nCRP iUMF 425 977\nSUB LO S SJZB 395 569\nCRP iUMF 391 977\nSUB MO S kdWj 440\nCRP SJZB 686 765\nCXL iUMF\nCRP kdWj 517 459\nSUB FOK B RrQj 270 71\nSUB ICE S kCYM 982 313 762\nCRP iUMF 13 977\nCRP iUMF 229 977\nSUB MO S RBgY 643\nSUB LO B iAM8 34 641\nCXL SJZB\nSUB MO B DoiC 516\nSUB MO B KMOK 304\nSUB ICE S cdl1 784 437 559\nCRP cdl1 482 437\nSUB FOK S QDfw 374 345\nSUB MO S VWKD 851\nSUB LO B GSY2 277 294\nSUB FOK B TRI1 531 803\nSUB MO S G6H0 448\nCRP iAM8 193 586\nSUB MO B 2KIE 344\nSUB IOC B qdzL 874 106\nSUB FOK B PnSN 240 347\nCXL 2KIE\nCRP PnSN 183 347\nCXL TRI1\nSUB FOK S RI61 864 54\nSUB FOK B TVMC 76 104\nSUB MO S Xsc2 490\nSUB ICE S SnN3 768 475 511\nSUB ICE S HAbL 569 192 89\nCXL SnN3\nSUB ICE S OjSf 456 495 199\nSUB IOC S q8QU 7 633\nSUB MO S VnQ4 336\nCRP RrQj 22 71\nCRP q8QU 791 926\nSUB LO B aA4S 26 613\nSUB IOC S rAGT 538 533\nSUB MO S oYiO 571\nSUB IOC S oEnm 887 93\nCXL TRI1\nSUB ICE B sb8D 907 802 816\nSUB ICE B kGOQ 101 889 99\nSUB ICE B K32L 302 380 41\nCRP VnQ4 458 875\nSUB MO B GAn8 801\nCRP RBgY 459 762\n\nSUB IOC B E6oj 376 393\nSUB MO S FYGB 632\nCXL FYGB\nSUB MO S Z5iW 782\nSUB MO B iI4G 178\nSUB LO S uCmN 723 914\nCRP E6oj 181 393\nSUB LO B NTQL 407 241\nSUB FOK B Yy0e 47 402\nSUB MO S iyye 325\nSUB MO B gJXV 158\nSUB IOC B tZAM 694 191\nSUB ICE B uWk4 131 933 68\nSUB ICE B 9Zlv 236 732 47\nSUB LO S XZxE 615 919\nSUB MO B O4FC 975\nSUB LO B klAI 949 978\nSUB ICE S WsPq 819 737 70\nSUB ICE B W19V 881 609 737\nCRP klAI 597 978\nCRP FYGB 589 360\nCRP 9Zlv 16 732\nCRP WsPq 305 737\nSUB ICE B OXzh 268 783 16\nCXL WsPq\nCXL W19V\nSUB MO B FMQQ 910\nCXL O4FC\nCRP E6oj 218 393\nCXL OXzh\nSUB ICE B 7gsG 107 413 106\nSUB IOC S Tkdm 726 61\nSUB IOC B Z9bK 278 205\nCRP iyye 352 378\nCRP O4FC 856 104\nCXL OXzh\nSUB LO B m14U 430 314\nSUB FOK S Q5Du 37 69\nCRP uWk4 172 541\nCRP E6oj 235 384\nSUB FOK B VBnr 565 300\nSUB IOC S mf3A 397 374\nSUB LO S tXnh 277 752\nSUB LO B EGyU 448 196\nSUB MO B fZRG 240\nCXL tXnh\nSUB ICE B AWEQ 965 853 743\nSUB IOC S jiDJ 896 833\nSUB FOK S NjlW 99 529\nSUB MO S q98j 127\nCRP EGyU 87 144\nSUB LO B S7JG 859 80\nCXL Z9bK\n\n1)\n\nTime complexity\n1. Searching for a match / insertion location takes O(log n) time\n2. Searching for an order by ID takes O(1) time\n3. Inserting an order into the orderbook buy_orders / sell_orders takes O(n) time (inserting into array + updating all index_mapping locations)\n4. Cancelling / removing an order takes O(n) time (updating all index_mapping locations of subsequent elements)\n5. Processing a new order (find match + iterate price <= / >= order.price + possible deletion + insertion) takes O(k*n) time, k depends on how much larger the order amount is than the display_size of the Iceberg orders.\n\nSpace complexity\nThe OrderBook object takes O(n) space, O(n) space is each required by the buy_orders, sell_orders and hashmap for the mapping of IDs to index positions.\n\nMore suitable data structure\nUsing a TreeMap (like in Java) for both buy_orders and sell_orders (+ hashmap to keep track of index mapping, index being the price) could have improved the time complexity. The TreeMap would use the price as the key. As multiple orders can be associated with a given price, the stored value is a LinkedList. This also improves the runtime for processing orders as deletion and appending to the end of a list (for Iceberg orders - main bottleneck) is O(1) for an LinkedList. Finding all values that are smaller  / larger than a given price is also achieved in O(log n). Cancelling orders will need to iterate through the list of orders with the same price.\n\n2) \nAbstracting classes, strengths\nMy code defined classes for the orderbook, trades and orders. The orders could have been split into multiple order classes that inherit from an abstract order class, but I chose to stick with using an attirbute called type to discern between the different orders.\n\nMy code relies on maintaining a sorted python array (arraylist equivalent). The advantage is then that searching is relatively fast O(log n), and the functions to do the search for the right starting / insertion point are used quite frequently. Space complexity is also kept to a minimum. The main downside is that modifications (which are frequent) make the runtime O(n) at minimum. In the worst case (many iceberg orders), my code is too slow as the iceberg orders need to be frequently removed and reinserted.\n\nImprove the design to make it more robust\nApart from opting for a different data structure (such as treemap), I would introduce more helper functions to refactor the process_sell / process_buy functions especially. The classes need to handle different cases based on the type of order that was received. Furthermore, opting for a different key - value data structure would help make the code more robust when a different algorithm is used for matching (i.e. pro rata instead of price time).\n\n3)\nDifficulties\nThe addition of the Iceberg order in part 3 was the main difficulty of the challenge. The iceberg orders could be fairly easily handled given the functions that I already had (remove / add orders), however the runtime is sub-optimal. The test cases 10 and 11 would not be able to pass due to TLE, test case 9 I went through manually and am not sure if there might be a bug with the test case (?).\n\nI introduced an optimization in the case that an iceberg order is the last order that matches the order:\nIn such a case, the frequent deletion and reinsertion is not necessary and the updating of the order can be handled quickly. Realistically this can be expanded to optimize for the case that all remaining orders that still match are iceberg orders.", "text_preview": "# Enter your code here. Read input from STDIN. Print output to STDOUT class Order: def __init__(self, amount, price, id, side, type, display_size=None): self.amount = amount self.price = price self.id = id self.side = side self.type = type ", "input_sha256": "6d693d7024fbbbc8d915b22a77710eeec506c143eaa577d0736a351aa3a32721", "text_sha256": "fcb0e925dcb20ba6444598dab92b5d5605b743db6e6d7d32ab74c9f3d466d6b0", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12013, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 1, "code_density": 3, "reasoning_density": 3, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7200227299375306, "noise_severity": 0.3262833032150015, "missing_content_severity": 0.08587710908511388, "factual_density": 3.2849723819662127, "pretraining_utility": 0.8342753771375934, "math_density": 2.142011651080067, "code_density": 3.2313663739562557, "reasoning_density": 2.494988344010662, "education_level": 2.3291818668670934, "creative_writing_quality": -0.2605057885590824}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.048010567253176706, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9885407809460482, "secondary": 0.4135712308664461, "tertiary": 0.4636692140871523, "structured_records": 0.6178563054278802, "procedural_steps": 0.06910992236949576, "worked_solution": 0.24627727745901798, "contextual_dialogue": 8.803714841028097e-05, "content_quality": 0.4876436517904351, "noise_severity": 0.728097247320832, "missing_content_severity": 0.9284357424290718, "factual_density": 0.5404636841322902, "pretraining_utility": 0.6138866469033204, "math_density": 0.0, "code_density": 0.6776511859436695, "reasoning_density": 0.15656900400068674, "education_level": 0.6249664359142305, "creative_writing_quality": 0.9959523080040562, "primary_topic": 0.9567168214536386, "secondary_topic": 0.46483184479029094}, "probability_margin": {"primary": 0.9847803621524455, "secondary": 0.3065997748745086, "tertiary": 0.35050091557589536, "education_level": 0.4212002141907948, "creative_writing_quality": 0.9947432711209896, "primary_topic": 0.932572933874157, "secondary_topic": 0.20029345164419}, "raw_probabilities": {"primary": {"Academic Writing": 0.00011175292200382909, "Content Listing": 3.328306332460029e-05, "Creative Writing": 3.3842566112752405e-05, "Customer Support Page": 2.3280759818150564e-05, "Q&A Forum": 0.00020518481240406887, "Discussion Thread": 4.513237252227158e-05, "Comment Section": 5.349460384531279e-05, "Missing Content": 4.2243159792738776e-05, "Knowledge Article": 0.00032659721956077004, "Legal Notices": 3.823768788923882e-05, "News Article": 1.3313716952576349e-05, "Organizational Announcement": 3.258381122730747e-05, "Nonfiction Writing": 0.0009305237742324211, "Organizational About Page": 3.378988629332927e-05, "Personal About Page": 3.866380130848513e-05, "Personal Blog": 0.00023921153269828625, "Product Page": 2.1335243688274554e-05, "Spam / Ads": 2.8837334309313443e-05, "Structured Data": 0.0011073726479449474, "Technical Writing": 0.9890182484066296, "Transcript / Interview": 0.00029387821802845534, "Tutorial / How-To Guide": 0.004237886254184116, "User Reviews": 3.3241952137112205e-05, "Other / Unknown": 0.0030580642530923037}, "secondary": {"Academic Writing": 0.004910509991432488, "Content Listing": 0.0012745702885704506, "Creative Writing": 0.0011480287416126046, "Customer Support Page": 0.0007131114707650102, "Q&A Forum": 0.016198804624874514, "Discussion Thread": 0.0014189829309422064, "Comment Section": 0.0016253514047887532, "Missing Content": 0.0018812924812778875, "Knowledge Article": 0.044951259377948694, "Legal Notices": 0.0009834562965488963, "News Article": 0.000812332048633342, "Organizational Announcement": 0.0011612368153715589, "Nonfiction Writing": 0.12609645995356328, "Organizational About Page": 0.0012185527123869048, "Personal About Page": 0.0011172687822260461, "Personal Blog": 0.01865494559881239, "Product Page": 0.000905785981628361, "Spam / Ads": 0.0008266232473018539, "Structured Data": 0.13140598803916892, "Technical Writing": 0.1305039228545566, "Transcript / Interview": 0.010736674475475525, "Tutorial / How-To Guide": 0.06111645344047813, "User Reviews": 0.002332625527957922, "NONE": 0.43800576291367754}, "tertiary": {"Academic Writing": 0.005557567379219039, "Content Listing": 0.0011316507128890727, "Creative Writing": 0.0013612911898368211, "Customer Support Page": 0.0007412584487861133, "Q&A Forum": 0.009589574366909838, "Discussion Thread": 0.0013780754050899495, "Comment Section": 0.0016555398459222844, "Missing Content": 0.0017928937853559205, "Knowledge Article": 0.05257432813191352, "Legal Notices": 0.0009150317684655092, "News Article": 0.000718282421949375, "Organizational Announcement": 0.0010698106163225549, "Nonfiction Writing": 0.13551541459095895, "Organizational About Page": 0.0012921533716728406, "Personal About Page": 0.001100274526201379, "Personal Blog": 0.04265922590858543, "Product Page": 0.0007311734946641514, "Spam / Ads": 0.0008350812904286616, "Structured Data": 0.11801122305822659, "Technical Writing": 0.09801946666701675, "Transcript / Interview": 0.009930970749044664, "Tutorial / How-To Guide": 0.024855915833077807, "User Reviews": 0.0025474662706084614, "NONE": 0.4860163301668543}, "structured_records": {"false": 0.38214369457211983, "true": 0.6178563054278802}, "procedural_steps": {"false": 0.9308900776305042, "true": 0.06910992236949576}, "worked_solution": {"false": 0.753722722540982, "true": 0.24627727745901798}, "contextual_dialogue": {"false": 0.9999119628515897, "true": 8.803714841028097e-05}, "content_quality": {"-2": 0.008963824474632414, "-1": 0.0677006782228896, "0": 0.2851096140872971, "1": 0.47080070932067675, "2": 0.16742517389450412}, "noise_severity": {"0": 0.7421425230989818, "1": 0.20258479614187616, "2": 0.04485874982465127, "3": 0.007674716314140752, "4": 0.002739214620350127}, "missing_content_severity": {"0": 0.9332110375056006, "1": 0.05086063697115163, "2": 0.013232660704077673, "3": 0.002231508570873546, "4": 0.00046415624829656464}, "factual_density": {"0": 0.0022867865058985967, "1": 0.00835941032453563, "2": 0.10965641837075236, "3": 0.4614894042950811, "4": 0.41820798050373226}, "pretraining_utility": {"-2": 0.006930159288735834, "-1": 0.03979784830822157, "0": 0.21414414880656038, "1": 0.5903221431696778, "2": 0.14880570042680438}, "math_density": {"0": 0.12094855579115617, "1": 0.24000744367271148, "2": 0.2313060582997445, "3": 0.191559678137685, "4": 0.21617826409870286}, "code_density": {"0": 0.001028086497516069, "1": 0.002356028346396452, "2": 0.06992978527032932, "3": 0.6175936244738319, "4": 0.3090924754119262}, "reasoning_density": {"0": 0.07281707171092706, "1": 0.1585181733093849, "2": 0.22307686384270603, "3": 0.292035121532063, "4": 0.253552769604919}, "education_level": {"none": 0.0008614364309962895, "0": 0.0012820146783811509, "1": 0.00767829840505008, "2": 0.6874720299285254, "3": 0.2662718157377306, "4": 0.0364344048193163}, "creative_writing_quality": {"not_applicable": 0.9966269233367135, "-2": 0.0002656437377410009, "-1": 0.0008412323899629735, "0": 0.0018836522157239552, "1": 0.00027128277031201017, "2": 0.00011126554954662946}, "primary_topic": {"Adult & Explicit Sexual Content": 7.266443235366584e-05, "Art & Design": 9.826154271029795e-05, "Law & Crime": 0.00011469093360254879, "Education & Careers": 9.780222477461579e-05, "Entertainment & Media": 6.066684240766313e-05, "Fashion & Beauty": 4.401992139148609e-05, "Business, Economics & Finance": 0.025808625215880145, "Food & Dining": 5.8433635159608266e-05, "Games & Gaming": 0.0006001234166789036, "Electronics & Hardware": 8.991756730290183e-05, "Health, Healthcare & Wellness": 5.8257276191859816e-05, "History & Geography": 8.795273420500176e-05, "Home, Pets & Hobbies": 5.247011517408648e-05, "Industry & Production": 7.757080364168196e-05, "Language, Literature & Philosophy": 7.61013595426577e-05, "Politics & Government": 4.820400451615301e-05, "Religion & Spirituality": 6.160128847837528e-05, "Science, Mathematics & Engineering": 0.011453239811321243, "Relationships & Community": 3.574353295423755e-05, "Software Usage & Applications": 0.00026921775954979283, "Programming & Software Engineering": 0.9583815590900371, "Sports & Fitness": 9.083929214713133e-05, "Vehicles & Transportation": 4.583342866273434e-05, "Travel & Tourism": 8.81781097914641e-05, "Other / Uncovered Subject": 0.002037630046400028, "Unknown / Insufficient Content": 9.039561512459986e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009727112238034558, "Art & Design": 0.0008101799942266526, "Law & Crime": 0.0012515250142393335, "Education & Careers": 0.00153565859104853, "Entertainment & Media": 0.0006279316499408495, "Fashion & Beauty": 0.00039396251559500726, "Business, Economics & Finance": 0.28512178373108976, "Food & Dining": 0.0004033002596768892, "Games & Gaming": 0.0014428867319815838, "Electronics & Hardware": 0.0006070931381019856, "Health, Healthcare & Wellness": 0.0005064808342531354, "History & Geography": 0.0004923763772841886, "Home, Pets & Hobbies": 0.0004184526520579429, "Industry & Production": 0.0004536299541587142, "Language, Literature & Philosophy": 0.00042245496375852275, "Politics & Government": 0.00035809906174777574, "Religion & Spirituality": 0.0005734135566118436, "Science, Mathematics & Engineering": 0.13618163046121018, "Relationships & Community": 0.00021540147888450878, "Software Usage & Applications": 0.0009308494815616137, "Programming & Software Engineering": 0.07228193893195443, "Sports & Fitness": 0.0004758906902423258, "Vehicles & Transportation": 0.00029830740646356614, "Travel & Tourism": 0.0006294923727927691, "Other / Uncovered Subject": 0.007179313552034685, "NONE": 0.48541523537527975}}, "probabilities": {"primary": {"Academic Writing": 0.00011175292200382906, "Content Listing": 3.328306332460028e-05, "Creative Writing": 3.38425661127524e-05, "Customer Support Page": 2.3280759818150557e-05, "Q&A Forum": 0.00020518481240406881, "Discussion Thread": 4.5132372522271576e-05, "Comment Section": 5.3494603845312775e-05, "Missing Content": 4.224315979273877e-05, "Knowledge Article": 0.00032659721956077, "Legal Notices": 3.8237687889238816e-05, "News Article": 1.3313716952576346e-05, "Organizational Announcement": 3.2583811227307466e-05, "Nonfiction Writing": 0.0009305237742324208, "Organizational About Page": 3.3789886293329266e-05, "Personal About Page": 3.866380130848512e-05, "Personal Blog": 0.0002392115326982862, "Product Page": 2.133524368827455e-05, "Spam / Ads": 2.8837334309313436e-05, "Structured Data": 0.0011073726479449472, "Technical Writing": 0.9890182484066293, "Transcript / Interview": 0.0002938782180284553, "Tutorial / How-To Guide": 0.004237886254184115, "User Reviews": 3.32419521371122e-05, "Other / Unknown": 0.003058064253092303}, "secondary": {"Academic Writing": 0.004910509991432489, "Content Listing": 0.0012745702885704508, "Creative Writing": 0.0011480287416126048, "Customer Support Page": 0.0007131114707650103, "Q&A Forum": 0.016198804624874517, "Discussion Thread": 0.0014189829309422066, "Comment Section": 0.0016253514047887534, "Missing Content": 0.0018812924812778877, "Knowledge Article": 0.0449512593779487, "Legal Notices": 0.0009834562965488965, "News Article": 0.0008123320486333421, "Organizational Announcement": 0.001161236815371559, "Nonfiction Writing": 0.1260964599535633, "Organizational About Page": 0.001218552712386905, "Personal About Page": 0.0011172687822260463, "Personal Blog": 0.018654945598812395, "Product Page": 0.0009057859816283612, "Spam / Ads": 0.000826623247301854, "Structured Data": 0.13140598803916895, "Technical Writing": 0.13050392285455661, "Transcript / Interview": 0.010736674475475527, "Tutorial / How-To Guide": 0.061116453440478134, "User Reviews": 0.0023326255279579222, "NONE": 0.4380057629136776}, "tertiary": {"Academic Writing": 0.005557567379219039, "Content Listing": 0.0011316507128890727, "Creative Writing": 0.0013612911898368211, "Customer Support Page": 0.0007412584487861133, "Q&A Forum": 0.009589574366909838, "Discussion Thread": 0.0013780754050899495, "Comment Section": 0.0016555398459222844, "Missing Content": 0.0017928937853559205, "Knowledge Article": 0.05257432813191352, "Legal Notices": 0.0009150317684655092, "News Article": 0.000718282421949375, "Organizational Announcement": 0.0010698106163225549, "Nonfiction Writing": 0.13551541459095895, "Organizational About Page": 0.0012921533716728406, "Personal About Page": 0.001100274526201379, "Personal Blog": 0.04265922590858543, "Product Page": 0.0007311734946641514, "Spam / Ads": 0.0008350812904286616, "Structured Data": 0.11801122305822659, "Technical Writing": 0.09801946666701675, "Transcript / Interview": 0.009930970749044664, "Tutorial / How-To Guide": 0.024855915833077807, "User Reviews": 0.0025474662706084614, "NONE": 0.4860163301668543}, "structured_records": {"false": 0.38214369457211983, "true": 0.6178563054278802}, "procedural_steps": {"false": 0.9308900776305042, "true": 0.06910992236949576}, "worked_solution": {"false": 0.753722722540982, "true": 0.24627727745901798}, "contextual_dialogue": {"false": 0.9999119628515897, "true": 8.803714841028097e-05}, "content_quality": {"-2": 0.008963824474632414, "-1": 0.0677006782228896, "0": 0.2851096140872971, "1": 0.47080070932067675, "2": 0.16742517389450412}, "noise_severity": {"0": 0.7421425230989818, "1": 0.20258479614187616, "2": 0.04485874982465127, "3": 0.007674716314140752, "4": 0.002739214620350127}, "missing_content_severity": {"0": 0.9332110375056006, "1": 0.05086063697115163, "2": 0.013232660704077673, "3": 0.002231508570873546, "4": 0.00046415624829656464}, "factual_density": {"0": 0.0022867865058985967, "1": 0.00835941032453563, "2": 0.10965641837075236, "3": 0.4614894042950811, "4": 0.41820798050373226}, "pretraining_utility": {"-2": 0.006930159288735834, "-1": 0.03979784830822157, "0": 0.21414414880656038, "1": 0.5903221431696778, "2": 0.14880570042680438}, "math_density": {"0": 0.12094855579115617, "1": 0.24000744367271148, "2": 0.2313060582997445, "3": 0.191559678137685, "4": 0.21617826409870286}, "code_density": {"0": 0.001028086497516069, "1": 0.002356028346396452, "2": 0.06992978527032932, "3": 0.6175936244738319, "4": 0.3090924754119262}, "reasoning_density": {"0": 0.07281707171092706, "1": 0.1585181733093849, "2": 0.22307686384270603, "3": 0.292035121532063, "4": 0.253552769604919}, "education_level": {"none": 0.0008614364309962896, "0": 0.001282014678381151, "1": 0.007678298405050081, "2": 0.6874720299285255, "3": 0.2662718157377307, "4": 0.036434404819316306}, "creative_writing_quality": {"not_applicable": 0.9966269233367133, "-2": 0.00026564373774100086, "-1": 0.0008412323899629733, "0": 0.0018836522157239547, "1": 0.0002712827703120101, "2": 0.00011126554954662943}, "primary_topic": {"Adult & Explicit Sexual Content": 7.266443235366584e-05, "Art & Design": 9.826154271029795e-05, "Law & Crime": 0.00011469093360254879, "Education & Careers": 9.780222477461579e-05, "Entertainment & Media": 6.066684240766313e-05, "Fashion & Beauty": 4.401992139148609e-05, "Business, Economics & Finance": 0.025808625215880145, "Food & Dining": 5.8433635159608266e-05, "Games & Gaming": 0.0006001234166789036, "Electronics & Hardware": 8.991756730290183e-05, "Health, Healthcare & Wellness": 5.8257276191859816e-05, "History & Geography": 8.795273420500176e-05, "Home, Pets & Hobbies": 5.247011517408648e-05, "Industry & Production": 7.757080364168196e-05, "Language, Literature & Philosophy": 7.61013595426577e-05, "Politics & Government": 4.820400451615301e-05, "Religion & Spirituality": 6.160128847837528e-05, "Science, Mathematics & Engineering": 0.011453239811321243, "Relationships & Community": 3.574353295423755e-05, "Software Usage & Applications": 0.00026921775954979283, "Programming & Software Engineering": 0.9583815590900371, "Sports & Fitness": 9.083929214713133e-05, "Vehicles & Transportation": 4.583342866273434e-05, "Travel & Tourism": 8.81781097914641e-05, "Other / Uncovered Subject": 0.002037630046400028, "Unknown / Insufficient Content": 9.039561512459986e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009727112238034558, "Art & Design": 0.0008101799942266526, "Law & Crime": 0.0012515250142393335, "Education & Careers": 0.00153565859104853, "Entertainment & Media": 0.0006279316499408495, "Fashion & Beauty": 0.00039396251559500726, "Business, Economics & Finance": 0.28512178373108976, "Food & Dining": 0.0004033002596768892, "Games & Gaming": 0.0014428867319815838, "Electronics & Hardware": 0.0006070931381019856, "Health, Healthcare & Wellness": 0.0005064808342531354, "History & Geography": 0.0004923763772841886, "Home, Pets & Hobbies": 0.0004184526520579429, "Industry & Production": 0.0004536299541587142, "Language, Literature & Philosophy": 0.00042245496375852275, "Politics & Government": 0.00035809906174777574, "Religion & Spirituality": 0.0005734135566118436, "Science, Mathematics & Engineering": 0.13618163046121018, "Relationships & Community": 0.00021540147888450878, "Software Usage & Applications": 0.0009308494815616137, "Programming & Software Engineering": 0.07228193893195443, "Sports & Fitness": 0.0004758906902423258, "Vehicles & Transportation": 0.00029830740646356614, "Travel & Tourism": 0.0006294923727927691, "Other / Uncovered Subject": 0.007179313552034685, "NONE": 0.48541523537527975}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 14022, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 14022, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:17.054000+00:00", "scoring_key_slot": "primary", "dataset_uid": "64bb6ca0-8b41-405c-be90-27c17c8032c4", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "a8e78496192faafce66ff46aae6608a01821b51157a6e6290edc6cef9531e11c", "source_metadata": {"uuid": "64bb6ca0-8b41-405c-be90-27c17c8032c4", "repo_name": "florianfei/light-matching-engine", "relative_path": "python_matching_engine/trade_server/alphalab_submission.py", "category": "TOOL", "algo_rel_score": 0.9910694360733032, "quality_score": 3.360379695892334, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 1, "repo_created_at": "2022-02-23T02:05:06.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "e2bda3a0d11ab7c255d555af0aad5e066c51ee28", "size_bytes": 19218, "file_timestamp": 1645582997, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "828319982345165371ca11013e96489eef49ee91"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 1, "repo_created_at": "2022-02-23T02:05:06.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "e2bda3a0d11ab7c255d555af0aad5e066c51ee28", "size_bytes": 19218, "file_timestamp": 1645582997, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "828319982345165371ca11013e96489eef49ee91", "url": "https://github.com/florianfei/light-matching-engine/blob/828319982345165371ca11013e96489eef49ee91/python_matching_engine/trade_server/alphalab_submission.py"}, "gpt4o_tokens": 4918, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.7200227299375306, "noise_severity": 0.3262833032150015, "missing_content_severity": 0.08587710908511388, "factual_density": 3.2849723819662127, "pretraining_utility": 0.8342753771375934, "math_density": 2.142011651080067, "code_density": 3.2313663739562557, "reasoning_density": 2.494988344010662, "education_level": 2.3291818668670934, "creative_writing_quality": -0.2605057885590824}, "score_applicability": {"education_level": 0.9991385635690038, "creative_writing_quality": 0.0033730766632865685}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/gudzek/python-/blob/20329562fadf28de70671e92737f77bfea362724/program2.py", "id": "python-0-9d3b8f0c332a1c1f85b8ed02fda65971", "text": "\"\"\"\nProgram do rysowania wykresow interpolowanych wielomianów\nprzy podanych przez użytkownika węzłach\nCopyright (C) 2018 by <NAME> and <NAME>.\n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\nSee the GNU General Public License for more details,\navailable at <https://www.gnu.org/licenses/>.\n\nStarted on October 26, 2018. Last revision: October 29, 2018.\n\"\"\"\n\n\n\n\n\ntitle = \"\\\nProgram2 v. 1.00, 10/29/2018. Copyright (C) by <NAME> and <NAME>.\\n\\\nThis is free software. No warranty. See GNU GPL for details.\\n\";\n#\n\nhelpinfo = \"\\\nTen program rysuje wielomiany, wyliczone na podstawie interpolacji metodą Newtona\\n\\\nDo znalezienia odpowiadającego wielomianu program wykorzystuje węzły podane przez użytkownika\\n\\\nProgram uruchomiamy podając dane w następujący sposób:\\n\\\nx_0 f(x_0) x_1 f(x_1) x_2 f(x_2)...\\n\\\n\";\n\n\n\n\n\n#=============================================================\n##############Oznaczenia - part_0\n# X - [x_0, x_1, x_2, ...] - argumenty\n# Y - [f(x_0), f(x_1), f(x_2), ...] - wartości\n# Z - [[x_0, x_1, x_2], [f(x_0), f(x_1), f(x_2)], [f[x_0 x_1],f[x_1 x_2]]]\n    #Z jest listą w której przetrzymujemy wszystkie wyliczone lub wprowadzone wartości\n\n# P - [f(x_0), f[x_0 x_1], f[x_0 x_1 x_2]] - zawiera wszystkie wartości niezbędne do obliczenia wielomianu\n# Lista - np. [[-2, 1], [0, 1], [-3, 2]] - lista dwumianów wykorzystywanych przy wyliczaniu wielomianu\n    #Precyzując [-2, 1] oznacza dwumian (x-2)\n    #           [ 0, 1] oznacza dwumian (x-0)\n\n# Lista2 - np. [a_0 , [b_0 , b_1], [c_0, c_1, c_2], [d_0, d_1, d_2, d_3]]\n    #Lista sum częściowych, które po zsumowaniu dają nam wielomian, który chcemy narysować\n\n# Wynik - współczynniki wielomianu wynikowego podane według szablonu [a_0, a_1, a_2, ...]\n    #       F(x) = a_0 + a_1*x + a_2*x^2 + a_3*x^3 + ...\n\n\n\n\n#Import niezbędnych modułów\nimport sys\nimport matplotlib.pyplot as plt\n\n\n\n\n\n#=============================================================\n##############Funkcje - part_1\n#Fukcja mnożąca dwumiany\ndef multi(a,b):\n    prod = []\n\n    for i in range(0,len(a)+len(b)-1):\n        prod.append(0)\n\n    for i in range(0,len(a)):\n        for j in range(0,len(b)):\n            prod[i+j] += a[i] * b[j]\n\n    print(prod)\n    return(prod)\n\n\n\n\n\n#=============================================================\n##############Sprawdzamy, czy można uruchomić pozostałą część kodu - part_2\n#sprawdzamy poprawność wprowadzonych danych\nif len(sys.argv)%2 == 0:\n    print(\"Podano złą liczbę argumentów. Spróbuj jeszcze raz.\")\n    quit()\n\n\n\n\n\n#=============================================================\n##############Segregujemy wprowadzone dane - part_3\n#Dzielimy wprowadzone węzły na dwie listy\n#X - argumenty\n#Y - wartości\nX = []\nY = []\nfor i in range(1,int((len(sys.argv)+1)/2)):\n    X.append(int(sys.argv[2*i-1]))\n    Y.append(int(sys.argv[2*i]))\n\n\n\n\n\n#=============================================================\n###############Informacja zwrotna dla użytkownika - part_4\n#Informujemy użytkownika o podanych argumentach i wartościach\nprint(\"\\nJako argumenty węzłów podano: \", X)\nprint(\"\\nJako wartości węzłów podano: \", Y, \"\\n\")\n\n\n\n\n\n#=============================================================\n##############Wyliczanie f[x_0,x_1,x_2,...] - part_5\n#Korzystamy z algorytmu Newtona\n#Lista Z jest nam potrzebna do zachowywania obliczanych wyrażeń\nZ = []\nZ.append(X)\nZ.append(Y)\n\n#Obliczamy Divided differences i umieszczamy je w listach\nfor j in range(0,len(X)-1):\n    Y_new = []\n\n    for i in range(1, len(X)-j):\n        Y_new.append((Z[j+1][i]-Z[j+1][i-1])/(Z[0][i+j] - Z[0][i-1]))\n    Z.append(Y_new)\n\n#Wyciągamy z listy tylko elemetny niezbędne do utworzenia wielomianu\nP = []\nfor i in range(1,len(X)+1):\n    P.append(Z[i][0])\n\n\n\n\n\n#=============================================================\n##############Tworzenie dwumianów - part_6\n#towrzymy dwumiany - (x - x_i) - wzór newtona\n#będą potrzebne do późniejszego wymnożenia\nX_new = []\nfor i in range(0,len(X)):\n    X_new.append(i)\n\nfor i in range(0,len(X)):\n    X_new[i] = (-1)*X[i]\n\nNew_stuff = []\nfor i in range(0,len(X)):\n    New_stuff.append(1)\n\nLista = []\nfor i in range(0,len(X)):\n    Lista.append(i)\n\nfor i in range(0,len(X)):\n    Lista[i] = [X_new[i], New_stuff[i]]\n\n\n\n\n\n#=============================================================\n##############Wymnażamy wcześniej utworzone dwumiany - part_7\nLista2 = []\nLista2.append(P[0])\nLista2.append(Lista[0])\n\nPolyn = multi(Lista[0],Lista[1])\nLista2.append(Polyn)\n\nfor i in range(2, len(X)-1):\n    Polyn = multi(Polyn,Lista[i])\n    Lista2.append(Polyn)\n\n##############Wymnażamy wcześniej utworzone dwumiany przez wartość z P - part_7.5\nfor j in range(1, len(Lista2)):\n    for i in range(1, len(Lista2[j])+1):\n        Lista2[j][i-1] = Lista2[j][i-1] * P[j]\n\n\n\n\n\n#=============================================================\n##############Dodawanie odpowiednich wyrazów - part_8\nWynik = []\nfor i in range(0,len(Lista2)):\n    Wynik.append(0)\n\nWynik[0] = Lista2[0]\n\nfor j in range(1,len(Lista2)):\n    for i in range(0,len(Lista2[j])):\n        Wynik[i] += Lista2[j][i]\n\nprint(\"\\n\\n\\nNasz wielomian to [a_0,a_1,a_2, ...]:  \",Wynik)\n\n\n\n\n\n#=============================================================\n##############Wyliczanie wartości funkcji - part_9\nX_wyk = []\nY_wyk = []\n\np = -100\nwhile p<=100:\n    X_wyk.append(p)\n\n    U = 0\n    for i in range(0,len(Wynik)):\n        U += Wynik[i]*p**i\n    Y_wyk.append(U)\n    p = p + 1\n\n#Rysuję wykres i podane węzły\nplt.plot(X_wyk,Y_wyk)\nplt.plot(X, Y, 'o', color='black')\nplt.show()\n", "text_preview": "\"\"\" Program do rysowania wykresow interpolowanych wielomianów przy podanych przez użytkownika węzłach Copyright (C) 2018 by <NAME> and <NAME>. This program is free software: you can redistribute it and/or modify it under the terms of the GN", "input_sha256": "61f49fbb3adc750602498ba1c074aecd1d371d6c4f7abeebcda5922857b5d9f5", "text_sha256": "9d3b8f0c332a1c1f85b8ed02fda6597140c93dbf765d6e3a2ee1debae7f26219", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12012, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Legal Notices", "sub_format": null}, "tertiary": {"main_format": "Legal Notices", "sub_format": null}, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 3, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5020712328165621, "noise_severity": 0.5932308277175549, "missing_content_severity": 0.040776968804047034, "factual_density": 2.9532068562759948, "pretraining_utility": 0.5390212308060933, "math_density": 3.237779631746417, "code_density": 3.4016057166108045, "reasoning_density": 1.5899973222847719, "education_level": 1.9240503869389387, "creative_writing_quality": -0.5136784285257966}, "api_choices": {"primary": "Technical Writing", "secondary": "Legal Notices", "tertiary": "Legal Notices", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["Duplicate Document Type slots", "tertiary probability 0.5250 below 0.70", "secondary_topic probability 0.5299 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.2922195703031828, "secondary_choice": "Legal Notices", "tertiary_choice": "Legal Notices"}, "confidence": {"primary": 0.983281155383817, "secondary": 0.8092301393394624, "tertiary": 0.5043053703274455, "structured_records": 0.009486322502178268, "procedural_steps": 0.11841784776012237, "worked_solution": 0.052986605683653834, "contextual_dialogue": 8.728909262778989e-06, "content_quality": 0.40141539454536423, "noise_severity": 0.5056409769020376, "missing_content_severity": 0.9660191926632942, "factual_density": 0.5028383383887186, "pretraining_utility": 0.4727403091487864, "math_density": 0.5147243472609551, "code_density": 0.5013380971756702, "reasoning_density": 0.0, "education_level": 0.7508136444230946, "creative_writing_quality": 0.997809717238881, "primary_topic": 0.8069963733585057, "secondary_topic": 0.5111237166780771}, "probability_margin": {"primary": 0.9777683522322044, "secondary": 0.736197740179657, "tertiary": 0.1855080346302198, "education_level": 0.6544049402083907, "creative_writing_quality": 0.9974296569453809, "primary_topic": 0.6379843743384245, "secondary_topic": 0.22841430839028187}, "raw_probabilities": {"primary": {"Academic Writing": 0.0002513837410543815, "Content Listing": 4.7664141026878106e-05, "Creative Writing": 6.990240792403397e-05, "Customer Support Page": 0.00013866797360398954, "Q&A Forum": 3.813238459836405e-05, "Discussion Thread": 2.4431598025762042e-05, "Comment Section": 4.223368669304105e-05, "Missing Content": 9.829890774840922e-05, "Knowledge Article": 0.0004587725550796262, "Legal Notices": 0.006209421677286906, "News Article": 1.7104798308429216e-05, "Organizational Announcement": 5.9734872138009375e-05, "Nonfiction Writing": 4.904800918914525e-05, "Organizational About Page": 5.3145131216136007e-05, "Personal About Page": 5.0847882187148354e-05, "Personal Blog": 3.059574624620838e-05, "Product Page": 2.4243007248341624e-05, "Spam / Ads": 3.256949931287404e-05, "Structured Data": 0.0003289024916152129, "Technical Writing": 0.9839777739094913, "Transcript / Interview": 4.44584873140463e-05, "Tutorial / How-To Guide": 0.005324989844321428, "User Reviews": 2.207371980101894e-05, "Other / Unknown": 0.0026056035285694132}, "secondary": {"Academic Writing": 0.0017507513423923049, "Content Listing": 0.00042310483810096547, "Creative Writing": 0.0005227121822548123, "Customer Support Page": 0.005277572538724012, "Q&A Forum": 0.0002475638127745085, "Discussion Thread": 0.00021696025607828817, "Comment Section": 0.0005007541042765008, "Missing Content": 0.0005603293649015617, "Knowledge Article": 0.004579830676546585, "Legal Notices": 0.8171788835336514, "News Article": 0.00031200382963765755, "Organizational Announcement": 0.0005588742929497619, "Nonfiction Writing": 0.0004777755151692941, "Organizational About Page": 0.0004941862663630024, "Personal About Page": 0.000354481847972521, "Personal Blog": 0.00028537769114996545, "Product Page": 0.00030631261865853493, "Spam / Ads": 0.0003057722311210919, "Structured Data": 0.0008272199987965194, "Technical Writing": 0.045995012403686544, "Transcript / Interview": 0.0004744773940274649, "Tutorial / How-To Guide": 0.03718393984586121, "User Reviews": 0.00018496006091117648, "NONE": 0.0809811433539944}, "tertiary": {"Academic Writing": 0.0033466679602396072, "Content Listing": 0.0006381044820050653, "Creative Writing": 0.0008775702848899134, "Customer Support Page": 0.016260084376753827, "Q&A Forum": 0.00038992355538724853, "Discussion Thread": 0.00036564696477716713, "Comment Section": 0.00069163141905513, "Missing Content": 0.0008107719526789312, "Knowledge Article": 0.006937139311360744, "Legal Notices": 0.5249593132304686, "News Article": 0.0005374749437921743, "Organizational Announcement": 0.0008501611503658968, "Nonfiction Writing": 0.0008299600704823265, "Organizational About Page": 0.0008592184089264666, "Personal About Page": 0.0006203032213731466, "Personal Blog": 0.0004407846828510601, "Product Page": 0.00048752509407760193, "Spam / Ads": 0.0005877995026250272, "Structured Data": 0.0012856254568602951, "Technical Writing": 0.026374354328946144, "Transcript / Interview": 0.0008001616089787053, "Tutorial / How-To Guide": 0.07126374961483378, "User Reviews": 0.00033474977802226577, "NONE": 0.3394512786002488}, "structured_records": {"false": 0.9905136774978217, "true": 0.009486322502178268}, "procedural_steps": {"false": 0.8815821522398777, "true": 0.11841784776012237}, "worked_solution": {"false": 0.9470133943163461, "true": 0.052986605683653834}, "contextual_dialogue": {"false": 0.9999912710907373, "true": 8.728909262778989e-06}, "content_quality": {"-2": 0.025074811248206614, "-1": 0.1051583773988756, "0": 0.3225739583221294, "1": 0.437006473349726, "2": 0.11018637968106249}, "noise_severity": {"0": 0.49123698045301634, "1": 0.43458515441457407, "2": 0.06688985702746134, "3": 0.004286073171734661, "4": 0.0030019349332135463}, "missing_content_severity": {"0": 0.9701357612399701, "1": 0.021984488514588733, "2": 0.005575756457704492, "3": 0.001575007776896649, "4": 0.0007289860108398402}, "factual_density": {"0": 0.006914469728876534, "1": 0.049218587429628154, "2": 0.2025129847828855, "3": 0.4664535329538437, "4": 0.2749004251047662}, "pretraining_utility": {"-2": 0.009576639636528727, "-1": 0.0781278639615081, "0": 0.3618595522750791, "1": 0.4645695142131092, "2": 0.08586642991377477}, "math_density": {"0": 0.003642505850330198, "1": 0.014597554701012979, "2": 0.13215294881720194, "3": 0.43955178311481924, "4": 0.4100552075166355}, "code_density": {"0": 0.0007891642078938416, "1": 0.001807492751907069, "2": 0.06548518884169718, "3": 0.4588447706185047, "4": 0.47307338357999723}, "reasoning_density": {"0": 0.3951480074457175, "1": 0.09891860710494364, "2": 0.17144381276994272, "3": 0.18976720107764222, "4": 0.14472237160175402}, "education_level": {"none": 0.0018945936513150013, "0": 0.003021452992942178, "1": 0.13793976347752146, "2": 0.7923447036859121, "3": 0.061422022327548766, "4": 0.00337746386476045}, "creative_writing_quality": {"not_applicable": 0.9981747643657342, "-2": 0.00030846578196092, "-1": 0.0005703954166587026, "0": 0.0007451074203532551, "1": 0.00015279122230425597, "2": 4.847579298867124e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0002561378727390195, "Art & Design": 0.0005823589110915683, "Law & Crime": 0.0005408159163451198, "Education & Careers": 0.00031423667531600577, "Entertainment & Media": 0.00027144002837186477, "Fashion & Beauty": 0.0002015218412335405, "Business, Economics & Finance": 0.0003554025239200261, "Food & Dining": 0.000235279684797236, "Games & Gaming": 0.00038655088904794045, "Electronics & Hardware": 0.0003262020996279643, "Health, Healthcare & Wellness": 0.0003200508470140253, "History & Geography": 0.00033608425507786725, "Home, Pets & Hobbies": 0.00023787403182711254, "Industry & Production": 0.0002775515437730467, "Language, Literature & Philosophy": 0.00032456753910811996, "Politics & Government": 0.00019860245661132252, "Religion & Spirituality": 0.00021593154414839728, "Science, Mathematics & Engineering": 0.1764352154293695, "Relationships & Community": 0.0001404556118287586, "Software Usage & Applications": 0.0021237695018914296, "Programming & Software Engineering": 0.8144195897677939, "Sports & Fitness": 0.0003004591579911453, "Vehicles & Transportation": 0.00015978135379418018, "Travel & Tourism": 0.0002519969776501028, "Other / Uncovered Subject": 0.00048083092821697606, "Unknown / Insufficient Content": 0.000307292611413695}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007425638807873778, "Art & Design": 0.002619804312037873, "Law & Crime": 0.017718024165702343, "Education & Careers": 0.0008326280409521338, "Entertainment & Media": 0.0005617150289337795, "Fashion & Beauty": 0.0004808556876609004, "Business, Economics & Finance": 0.0007729683369003905, "Food & Dining": 0.00039009495436669565, "Games & Gaming": 0.0005408788393743447, "Electronics & Hardware": 0.0006223883379216798, "Health, Healthcare & Wellness": 0.0006198909307994293, "History & Geography": 0.000566972939348615, "Home, Pets & Hobbies": 0.0004497006184950175, "Industry & Production": 0.0005075228973780861, "Language, Literature & Philosophy": 0.0004999438080439725, "Politics & Government": 0.0005977598010172749, "Religion & Spirituality": 0.0005552809876022464, "Science, Mathematics & Engineering": 0.5299266506519972, "Relationships & Community": 0.00023505372874647597, "Software Usage & Applications": 0.004107361727594683, "Programming & Software Engineering": 0.3015123422617153, "Sports & Fitness": 0.0005104041376234412, "Vehicles & Transportation": 0.00031851954702022276, "Travel & Tourism": 0.0005507483092007303, "Other / Uncovered Subject": 0.0007237182315342769, "NONE": 0.13303620783724557}}, "probabilities": {"primary": {"Academic Writing": 0.0002513837410543815, "Content Listing": 4.7664141026878106e-05, "Creative Writing": 6.990240792403397e-05, "Customer Support Page": 0.00013866797360398954, "Q&A Forum": 3.813238459836405e-05, "Discussion Thread": 2.4431598025762042e-05, "Comment Section": 4.223368669304105e-05, "Missing Content": 9.829890774840922e-05, "Knowledge Article": 0.0004587725550796262, "Legal Notices": 0.006209421677286906, "News Article": 1.7104798308429216e-05, "Organizational Announcement": 5.9734872138009375e-05, "Nonfiction Writing": 4.904800918914525e-05, "Organizational About Page": 5.3145131216136007e-05, "Personal About Page": 5.0847882187148354e-05, "Personal Blog": 3.059574624620838e-05, "Product Page": 2.4243007248341624e-05, "Spam / Ads": 3.256949931287404e-05, "Structured Data": 0.0003289024916152129, "Technical Writing": 0.9839777739094913, "Transcript / Interview": 4.44584873140463e-05, "Tutorial / How-To Guide": 0.005324989844321428, "User Reviews": 2.207371980101894e-05, "Other / Unknown": 0.0026056035285694132}, "secondary": {"Academic Writing": 0.0017507513423923049, "Content Listing": 0.00042310483810096547, "Creative Writing": 0.0005227121822548123, "Customer Support Page": 0.005277572538724012, "Q&A Forum": 0.0002475638127745085, "Discussion Thread": 0.00021696025607828817, "Comment Section": 0.0005007541042765008, "Missing Content": 0.0005603293649015617, "Knowledge Article": 0.004579830676546585, "Legal Notices": 0.8171788835336514, "News Article": 0.00031200382963765755, "Organizational Announcement": 0.0005588742929497619, "Nonfiction Writing": 0.0004777755151692941, "Organizational About Page": 0.0004941862663630024, "Personal About Page": 0.000354481847972521, "Personal Blog": 0.00028537769114996545, "Product Page": 0.00030631261865853493, "Spam / Ads": 0.0003057722311210919, "Structured Data": 0.0008272199987965194, "Technical Writing": 0.045995012403686544, "Transcript / Interview": 0.0004744773940274649, "Tutorial / How-To Guide": 0.03718393984586121, "User Reviews": 0.00018496006091117648, "NONE": 0.0809811433539944}, "tertiary": {"Academic Writing": 0.0033466679602396072, "Content Listing": 0.0006381044820050653, "Creative Writing": 0.0008775702848899134, "Customer Support Page": 0.016260084376753827, "Q&A Forum": 0.00038992355538724853, "Discussion Thread": 0.00036564696477716713, "Comment Section": 0.00069163141905513, "Missing Content": 0.0008107719526789312, "Knowledge Article": 0.006937139311360744, "Legal Notices": 0.5249593132304686, "News Article": 0.0005374749437921743, "Organizational Announcement": 0.0008501611503658968, "Nonfiction Writing": 0.0008299600704823265, "Organizational About Page": 0.0008592184089264666, "Personal About Page": 0.0006203032213731466, "Personal Blog": 0.0004407846828510601, "Product Page": 0.00048752509407760193, "Spam / Ads": 0.0005877995026250272, "Structured Data": 0.0012856254568602951, "Technical Writing": 0.026374354328946144, "Transcript / Interview": 0.0008001616089787053, "Tutorial / How-To Guide": 0.07126374961483378, "User Reviews": 0.00033474977802226577, "NONE": 0.3394512786002488}, "structured_records": {"false": 0.9905136774978217, "true": 0.009486322502178268}, "procedural_steps": {"false": 0.8815821522398777, "true": 0.11841784776012237}, "worked_solution": {"false": 0.9470133943163461, "true": 0.052986605683653834}, "contextual_dialogue": {"false": 0.9999912710907373, "true": 8.728909262778989e-06}, "content_quality": {"-2": 0.025074811248206614, "-1": 0.1051583773988756, "0": 0.3225739583221294, "1": 0.437006473349726, "2": 0.11018637968106249}, "noise_severity": {"0": 0.49123698045301634, "1": 0.43458515441457407, "2": 0.06688985702746134, "3": 0.004286073171734661, "4": 0.0030019349332135463}, "missing_content_severity": {"0": 0.9701357612399703, "1": 0.021984488514588736, "2": 0.005575756457704493, "3": 0.0015750077768966492, "4": 0.0007289860108398404}, "factual_density": {"0": 0.006914469728876534, "1": 0.049218587429628154, "2": 0.2025129847828855, "3": 0.4664535329538437, "4": 0.2749004251047662}, "pretraining_utility": {"-2": 0.009576639636528729, "-1": 0.07812786396150811, "0": 0.36185955227507915, "1": 0.46456951421310927, "2": 0.08586642991377479}, "math_density": {"0": 0.0036425058503301985, "1": 0.01459755470101298, "2": 0.13215294881720197, "3": 0.4395517831148193, "4": 0.41005520751663554}, "code_density": {"0": 0.0007891642078938416, "1": 0.001807492751907069, "2": 0.06548518884169718, "3": 0.4588447706185047, "4": 0.47307338357999723}, "reasoning_density": {"0": 0.3951480074457175, "1": 0.09891860710494364, "2": 0.17144381276994272, "3": 0.18976720107764222, "4": 0.14472237160175402}, "education_level": {"none": 0.0018945936513150013, "0": 0.003021452992942178, "1": 0.13793976347752146, "2": 0.7923447036859121, "3": 0.061422022327548766, "4": 0.00337746386476045}, "creative_writing_quality": {"not_applicable": 0.9981747643657342, "-2": 0.00030846578196092, "-1": 0.0005703954166587026, "0": 0.0007451074203532551, "1": 0.00015279122230425597, "2": 4.847579298867124e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00025613787273901954, "Art & Design": 0.0005823589110915685, "Law & Crime": 0.00054081591634512, "Education & Careers": 0.0003142366753160058, "Entertainment & Media": 0.0002714400283718648, "Fashion & Beauty": 0.00020152184123354053, "Business, Economics & Finance": 0.0003554025239200262, "Food & Dining": 0.00023527968479723603, "Games & Gaming": 0.0003865508890479405, "Electronics & Hardware": 0.00032620209962796434, "Health, Healthcare & Wellness": 0.0003200508470140254, "History & Geography": 0.0003360842550778673, "Home, Pets & Hobbies": 0.00023787403182711257, "Industry & Production": 0.00027755154377304673, "Language, Literature & Philosophy": 0.00032456753910812, "Politics & Government": 0.00019860245661132254, "Religion & Spirituality": 0.0002159315441483973, "Science, Mathematics & Engineering": 0.17643521542936952, "Relationships & Community": 0.00014045561182875863, "Software Usage & Applications": 0.00212376950189143, "Programming & Software Engineering": 0.814419589767794, "Sports & Fitness": 0.00030045915799114533, "Vehicles & Transportation": 0.0001597813537941802, "Travel & Tourism": 0.00025199697765010285, "Other / Uncovered Subject": 0.0004808309282169761, "Unknown / Insufficient Content": 0.00030729261141369503}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007425638807873778, "Art & Design": 0.002619804312037873, "Law & Crime": 0.017718024165702343, "Education & Careers": 0.0008326280409521338, "Entertainment & Media": 0.0005617150289337795, "Fashion & Beauty": 0.0004808556876609004, "Business, Economics & Finance": 0.0007729683369003905, "Food & Dining": 0.00039009495436669565, "Games & Gaming": 0.0005408788393743447, "Electronics & Hardware": 0.0006223883379216798, "Health, Healthcare & Wellness": 0.0006198909307994293, "History & Geography": 0.000566972939348615, "Home, Pets & Hobbies": 0.0004497006184950175, "Industry & Production": 0.0005075228973780861, "Language, Literature & Philosophy": 0.0004999438080439725, "Politics & Government": 0.0005977598010172749, "Religion & Spirituality": 0.0005552809876022464, "Science, Mathematics & Engineering": 0.5299266506519972, "Relationships & Community": 0.00023505372874647597, "Software Usage & Applications": 0.004107361727594683, "Programming & Software Engineering": 0.3015123422617153, "Sports & Fitness": 0.0005104041376234412, "Vehicles & Transportation": 0.00031851954702022276, "Travel & Tourism": 0.0005507483092007303, "Other / Uncovered Subject": 0.0007237182315342769, "NONE": 0.13303620783724557}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10427, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10427, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:17.195170+00:00", "scoring_key_slot": "primary", "dataset_uid": "cac208db-348e-4ed5-a88f-40a07c853550", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "7783b4422bfb90206e3689da39c8f14f9170502775e1d1973742b71a8f767b73", "source_metadata": {"uuid": "cac208db-348e-4ed5-a88f-40a07c853550", "repo_name": "gudzek/python-", "relative_path": "program2.py", "category": "DATA", "algo_rel_score": 0.9823250770568848, "quality_score": 4.415121078491211, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 11, "repo_created_at": "2020-04-28T19:39:04.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "067a8d561836d2961f45b1ac0dc5adfa699eca95", "size_bytes": 5943, "file_timestamp": 1589755641, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "20329562fadf28de70671e92737f77bfea362724"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 11, "repo_created_at": "2020-04-28T19:39:04.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "067a8d561836d2961f45b1ac0dc5adfa699eca95", "size_bytes": 5943, "file_timestamp": 1589755641, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "20329562fadf28de70671e92737f77bfea362724", "url": "https://github.com/gudzek/python-/blob/20329562fadf28de70671e92737f77bfea362724/program2.py"}, "gpt4o_tokens": 1871, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.5020712328165621, "noise_severity": 0.5932308277175549, "missing_content_severity": 0.040776968804047034, "factual_density": 2.9532068562759948, "pretraining_utility": 0.5390212308060933, "math_density": 3.237779631746417, "code_density": 3.4016057166108045, "reasoning_density": 1.5899973222847719, "education_level": 1.9240503869389387, "creative_writing_quality": -0.5136784285257966}, "score_applicability": {"education_level": 0.9981054063486849, "creative_writing_quality": 0.001825235634265805}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/elen04ka/Snake/blob/31091e9ff2f96265edbe0cb149bb608276a221a7/menu.py", "id": "python-0-3e0ce91200e602899e589087da78edb8", "text": "import pygame\nimport sys\nfrom random import randrange\n\n# Ініціалізація Pygame\npygame.init()\n\n# Кольори\nWHITE = (255, 255, 255)\nBLACK = (0, 0, 0)\n\n# Розміри екрану\nSCREEN_WIDTH = 700\nSCREEN_HEIGHT = 700\n\n# Ініціалізація екрану\nscreen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))\npygame.display.set_caption(\"Snake Game\")\n\n# Завантаження фонового зображення\nbackground = pygame.transform.scale(pygame.image.load('background.jpg'), (SCREEN_HEIGHT, SCREEN_WIDTH))\n\n# Шрифти\nfont = pygame.font.SysFont('Comic Sans MS', 36, bold=True)\n\n# Функція для створення тексту\ndef create_text(text, color, x, y):\n    text_surface = font.render(text, True, color)\n    text_rect = text_surface.get_rect()\n    text_rect.center = (x, y)\n    return text_surface, text_rect\n\n# Головний цикл гри\ndef main_menu():\n    menu_items = [\"Easy\", \"Medium\", \"Hard\"]\n    selected_item = 0  # Початковий вибраний пункт меню\n\n    while True:\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                pygame.quit()\n                sys.exit()\n\n        # Відображення фону меню\n        screen.blit(background, (0, 0))\n\n        title_text, title_rect = create_text(\"Snake Game\", BLACK, SCREEN_WIDTH // 2, SCREEN_HEIGHT // 4)\n\n        screen.blit(title_text, title_rect)\n\n        # Відображення пунктів меню та позначення вибраного пункту\n        for i, item in enumerate(menu_items):\n            color = WHITE if i == selected_item else BLACK\n            item_text, item_rect = create_text(item, color, SCREEN_WIDTH // 2, SCREEN_HEIGHT // 2 + i * 50)\n            screen.blit(item_text, item_rect)\n\n        pygame.display.flip()\n\n        # Обробка клавіш w та s для перемикання по пунктах меню\n        for event in pygame.event.get():\n            if event.type == pygame.KEYDOWN:\n                if event.key == pygame.K_w:\n                    selected_item = (selected_item - 1) % len(menu_items)\n                elif event.key == pygame.K_s:\n                    selected_item = (selected_item + 1) % len(menu_items)\n                elif event.key == pygame.K_RETURN:\n                    start_game(menu_items[selected_item])\n\n            if event.type == pygame.QUIT:\n                pygame.quit()\n                sys.exit()\n\n# Код гри\n\nRES = 700\nSIZE = 50\nx, y = randrange(0, RES, SIZE), randrange(0, RES, SIZE)\napple = randrange(0, RES, SIZE), randrange(0, RES, SIZE)\ndris = {'W': True, 'S': True, 'A': True, 'D': True}\nlength = 1\nsnake = [(x, y)]\ndx, dy = 0, 0\nfps = 8\nscore = 0\npaused = False\ngame_over = False\ngame_over_on_wall_collision = False\n\npygame.mixer.init()\nsc = pygame.display.set_mode([RES, RES])\nclock = pygame.time.Clock()\nfont_score = pygame.font.SysFont('Comic Sans MS', 26, bold=True)\nfont_end = pygame.font.SysFont('Comic Sans MS', 70, bold=True)\nfont_pause = pygame.font.SysFont('Comic Sans MS', 25, bold=True)\n\napple_texture = pygame.image.load('apple_texture.png')\nsnake_head_texture = pygame.image.load('snake_head_texture.png')\nsnake_body_texture = pygame.image.load('snake_body_texture.png')\n\npygame.mixer.music.load('background_music.mp3')\ngame_over_sound = pygame.mixer.Sound('game_over_sound.mp3')\n\ndef reset_game():\n    global x, y, apple, dris, length, snake, dx, dy, score, paused, game_over\n    x, y = randrange(0, RES, SIZE), randrange(0, RES, SIZE)\n    apple = randrange(0, RES, SIZE), randrange(0, RES, SIZE)\n    dris = {'W': True, 'S': True, 'A': True, 'D': True}\n    length = 1\n    snake = [(x, y)]\n    dx, dy = 0, 0\n    score = 0\n    paused = False\n    game_over = False\n\ndef start_game(difficulty):\n    global game_over_on_wall_collision, fps\n    if difficulty == \"Easy\":\n        fps = 8\n        game_over_on_wall_collision = False\n    elif difficulty == \"Medium\":\n        fps = 12\n        game_over_on_wall_collision = False\n    elif difficulty == \"Hard\":\n        fps = 14\n        game_over_on_wall_collision = True\n\n    reset_game()\n    main_loop()\n\ndef main_loop():\n    global x, y, apple, dris, length, snake, dx, dy, fps, score, paused, game_over, game_over_on_wall_collision\n\n    while True:\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                pygame.quit()\n                sys.exit()\n            if event.type == pygame.KEYDOWN:\n                if event.key == pygame.K_p:\n                    paused = not paused\n                if event.key == pygame.K_b:\n                    game_over == True\n                    main_menu()\n                elif game_over and event.key == pygame.K_RETURN:\n                    reset_game()\n\n        key = pygame.key.get_pressed()\n        if not paused and not game_over:\n            if key[pygame.K_w] and dris['W']:\n                dris = {'W': True, 'S': False, 'A': True, 'D': True}\n                dx, dy = 0, -1\n            if key[pygame.K_s] and dris['S']:\n                dx, dy = 0, 1\n                dris = {'W': False, 'S': True, 'A': True, 'D': True}\n            if key[pygame.K_a] and dris['A']:\n                dx, dy = -1, 0\n                dris = {'W': True, 'S': True, 'A': True, 'D': False}\n            if key[pygame.K_d] and dris['D']:\n                dx, dy = 1, 0\n                dris = {'W': True, 'S': True, 'A': False, 'D': True}\n\n            sc.fill(pygame.Color('black'))\n\n            if snake[0] == apple:\n                apple = randrange(0, RES, SIZE), randrange(0, RES, SIZE)\n                score += 1\n                snake.append((0, 0))\n                length += 1\n\n            for i in range(length - 1, 0, -1):\n                snake[i] = (snake[i-1][0], snake[i-1][1])\n\n            x += dx * SIZE\n            y += dy * SIZE\n\n            if x < 0:\n                x = RES - SIZE\n            elif x > RES - SIZE:\n                x = 0\n            elif y < 0:\n                y = RES - SIZE\n            elif y > RES - SIZE:\n                y = 0\n\n            snake[0] = (x, y)\n\n            if game_over_on_wall_collision and (x < 0 or x >= RES or y < 0 or y >= RES):\n                game_over = True\n                pygame.mixer.music.stop()\n                game_over_sound.play()\n\n            for segment in snake[1:]:\n                if snake[0] == segment:\n                    game_over = True\n                    pygame.mixer.music.stop()\n                    game_over_sound.play()\n\n            for i, segment in enumerate(snake):\n                if i == 0:\n                    sc.blit(snake_head_texture, (*segment, SIZE, SIZE))\n                else:\n                    sc.blit(snake_body_texture, (*segment, SIZE, SIZE))\n\n            sc.blit(apple_texture, (*apple, SIZE, SIZE))\n\n        if game_over:\n            render_end = font_end.render('GAME OVER', 1, pygame.Color('white'))\n            sc.blit(render_end, (RES // 2 - 200, RES // 2.5))\n            render_restart = font_score.render('Press ENTER to restart', 1, pygame.Color('white'))\n            sc.blit(render_restart, (RES // 1.63 - 250, RES // 2.5 + 80))\n\n        if paused:\n            render_pause = font_end.render('PAUSE', 1, pygame.Color('white'))\n            sc.blit(render_pause, (RES // 1.9 - 150, RES // 2.3))\n\n        if not game_over:\n            render_score = font_score.render(f'SCORE: {score}', 1, pygame.Color('white'))\n            sc.blit(render_score, (5, 5))\n\n            render_paused = font_pause.render('Press P to pause the game', 1, pygame.Color('white'))\n            sc.blit(render_paused, (RES - 330, 5))\n\n            back_to_menu = font_pause.render('Press B to back to menu', 1, pygame.Color('white'))\n            sc.blit(back_to_menu, (RES - 330, 30))\n\n        if not paused and not game_over and not pygame.mixer.music.get_busy():\n            pygame.mixer.music.play(-1)\n\n        pygame.display.flip()\n        clock.tick(fps)\n\n# Основний цикл меню\nif __name__ == \"__main__\":\n    main_menu()\n", "text_preview": "import pygame import sys from random import randrange # Ініціалізація Pygame pygame.init() # Кольори WHITE = (255, 255, 255) BLACK = (0, 0, 0) # Розміри екрану SCREEN_WIDTH = 700 SCREEN_HEIGHT = 700 # Ініціалізація екрану screen = pygame.di", "input_sha256": "10083b44782679be9be620916a0495735db4ebb257c44c245d1ae34bc59301f4", "text_sha256": "3e0ce91200e602899e589087da78edb8f0606c58b5212be49431445059c2e2c6", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12011, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.434913423805999, "noise_severity": 0.05646730935128565, "missing_content_severity": 0.01994343591323849, "factual_density": 2.9009868234922362, "pretraining_utility": 0.4669801805353321, "math_density": 0.6599035447116406, "code_density": 3.80639878924537, "reasoning_density": 0.2287708759554944, "education_level": 1.6555576148794868, "creative_writing_quality": -0.3381271599589904}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.007212328719802152, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.989237990964177, "secondary": 0.9779775483711771, "tertiary": 0.985503456600536, "structured_records": 0.006069318772724883, "procedural_steps": 0.0033471513346080115, "worked_solution": 0.00763888366555437, "contextual_dialogue": 5.320859675254319e-06, "content_quality": 0.3867655249075307, "noise_severity": 0.9529439088739287, "missing_content_severity": 0.9833804700723012, "factual_density": 0.4027324185222717, "pretraining_utility": 0.43842423692353716, "math_density": 0.4500803794069661, "code_density": 0.8386656577044748, "reasoning_density": 0.8093576033704213, "education_level": 0.5724425644769222, "creative_writing_quality": 0.9939768521885005, "primary_topic": 0.934723715620327, "secondary_topic": 0.7298471034475792}, "probability_margin": {"primary": 0.9850277144104469, "secondary": 0.9689570421104892, "tertiary": 0.981219599993001, "education_level": 0.31058024902735887, "creative_writing_quality": 0.9918744364495032, "primary_topic": 0.8786740085237033, "secondary_topic": 0.5280264925434018}, "raw_probabilities": {"primary": {"Academic Writing": 6.313324642075212e-05, "Content Listing": 5.201570911644648e-05, "Creative Writing": 0.00022803055999061182, "Customer Support Page": 2.250902754840136e-05, "Q&A Forum": 2.8987501005353552e-05, "Discussion Thread": 2.107288283566626e-05, "Comment Section": 4.0544582489676024e-05, "Missing Content": 8.758945356093424e-05, "Knowledge Article": 0.00014574260655312145, "Legal Notices": 4.816418645479229e-05, "News Article": 1.2971265530657932e-05, "Organizational Announcement": 2.3815072047220466e-05, "Nonfiction Writing": 3.3278291578893275e-05, "Organizational About Page": 2.9290664491582858e-05, "Personal About Page": 4.8314529485953236e-05, "Personal Blog": 2.212074180406133e-05, "Product Page": 2.0890874121116668e-05, "Spam / Ads": 3.0700041616626236e-05, "Structured Data": 0.0003916852421525124, "Technical Writing": 0.9896864080073363, "Transcript / Interview": 3.4096472534202305e-05, "Tutorial / How-To Guide": 0.004250414004912725, "User Reviews": 1.9531439522854838e-05, "Other / Unknown": 0.004658693596889473}, "secondary": {"Academic Writing": 0.0001655793801103293, "Content Listing": 0.000291666051770335, "Creative Writing": 0.0014811727658276554, "Customer Support Page": 7.609535933231418e-05, "Q&A Forum": 5.2272473516123423e-05, "Discussion Thread": 4.469858820990878e-05, "Comment Section": 0.00016680524127728186, "Missing Content": 0.0014401709562376487, "Knowledge Article": 0.00041354244078333657, "Legal Notices": 0.00012546966956773078, "News Article": 6.482975962881489e-05, "Organizational Announcement": 6.8514012358869e-05, "Nonfiction Writing": 7.844367742912666e-05, "Organizational About Page": 7.776455660212437e-05, "Personal About Page": 0.00012631603484689936, "Personal Blog": 8.440389358405922e-05, "Product Page": 4.651256700304021e-05, "Spam / Ads": 9.527440156906156e-05, "Structured Data": 0.0015060460718064838, "Technical Writing": 0.00993810841188895, "Transcript / Interview": 9.375755585744738e-05, "Tutorial / How-To Guide": 0.004618701910343478, "User Reviews": 4.870369807082735e-05, "NONE": 0.9788951505223781}, "tertiary": {"Academic Writing": 8.334739128272917e-05, "Content Listing": 0.00028074965876287965, "Creative Writing": 0.0011666112556806348, "Customer Support Page": 4.691312112772333e-05, "Q&A Forum": 3.244633077151798e-05, "Discussion Thread": 2.8979782005882966e-05, "Comment Section": 0.00011329165318971004, "Missing Content": 0.0017199190754736344, "Knowledge Article": 0.0003848062654223822, "Legal Notices": 0.00016511340123297308, "News Article": 4.471725899994248e-05, "Organizational Announcement": 4.27169197243668e-05, "Nonfiction Writing": 8.215751225204885e-05, "Organizational About Page": 5.755693976970249e-05, "Personal About Page": 7.437114358033496e-05, "Personal Blog": 4.954129515837815e-05, "Product Page": 3.4849674453591186e-05, "Spam / Ads": 6.164384962994603e-05, "Structured Data": 0.0012063118118361296, "Technical Writing": 0.004887879249179299, "Transcript / Interview": 5.4610455249432174e-05, "Tutorial / How-To Guide": 0.0032398421713244517, "User Reviews": 3.414454171201427e-05, "NONE": 0.9861074792421803}, "structured_records": {"false": 0.9939306812272751, "true": 0.006069318772724883}, "procedural_steps": {"false": 0.9966528486653919, "true": 0.0033471513346080115}, "worked_solution": {"false": 0.9923611163344457, "true": 0.00763888366555437}, "contextual_dialogue": {"false": 0.9999946791403247, "true": 5.320859675254319e-06}, "content_quality": {"-2": 0.02782729313736783, "-1": 0.11304349312333176, "0": 0.34091510749371506, "1": 0.43281670928710453, "2": 0.08539739695848099}, "noise_severity": {"0": 0.954272680672001, "1": 0.03777595549823666, "2": 0.005930222093922348, "3": 0.0012536572781566143, "4": 0.0007674844576836152}, "missing_content_severity": {"0": 0.9893221518015072, "1": 0.0033465636828983653, "2": 0.005685540268115389, "3": 0.0013571852958069093, "4": 0.00028855895167215537}, "factual_density": {"0": 0.025175731563614853, "1": 0.0689196287603103, "2": 0.19450068492905387, "3": 0.40254999411426606, "4": 0.30885396063275483}, "pretraining_utility": {"-2": 0.009664801701920562, "-1": 0.08805791498339896, "0": 0.39834513250565207, "1": 0.4334966026954847, "2": 0.07043554811354374}, "math_density": {"0": 0.5693135459319427, "1": 0.3007660177991883, "2": 0.07131916701683001, "3": 0.0179058841293639, "4": 0.04069538512267516}, "code_density": {"0": 0.00028807867914730024, "1": 0.0006180703221538121, "2": 0.01716484733568627, "3": 0.1562649904002071, "4": 0.8256640132628056}, "reasoning_density": {"0": 0.8791576712621814, "1": 0.04515115645094193, "2": 0.05293773574104931, "3": 0.01326949816085606, "4": 0.009483938384971411}, "education_level": {"none": 0.003035878592777158, "0": 0.010793025172253744, "1": 0.333121888036743, "2": 0.6437021370641018, "3": 0.0073829037440785944, "4": 0.0019641673900455945}, "creative_writing_quality": {"not_applicable": 0.9949807101570838, "-2": 0.0004042327140606654, "-1": 0.0012291234218150156, "0": 0.003106273707580507, "1": 0.00021888936858044145, "2": 6.0770630879785346e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00011295869786052131, "Art & Design": 0.00025377561251109537, "Law & Crime": 0.00012832133686154393, "Education & Careers": 0.00012746730937773998, "Entertainment & Media": 0.0007901736490641251, "Fashion & Beauty": 0.00010440428119408043, "Business, Economics & Finance": 0.00013213094378517523, "Food & Dining": 9.14147353558165e-05, "Games & Gaming": 0.05856033341891886, "Electronics & Hardware": 0.00010941490378257403, "Health, Healthcare & Wellness": 9.026807467325727e-05, "History & Geography": 0.00012282810352982126, "Home, Pets & Hobbies": 0.00013531446387611622, "Industry & Production": 9.980884210530264e-05, "Language, Literature & Philosophy": 0.00013480202740449255, "Politics & Government": 5.0372389909441605e-05, "Religion & Spirituality": 9.169300030958853e-05, "Science, Mathematics & Engineering": 0.0002070436594876895, "Relationships & Community": 8.045789559681803e-05, "Software Usage & Applications": 0.0007071606829643648, "Programming & Software Engineering": 0.9372343419426221, "Sports & Fitness": 0.00018416547844746212, "Vehicles & Transportation": 5.631705114373314e-05, "Travel & Tourism": 0.00010503479802449283, "Other / Uncovered Subject": 0.00017724335728777906, "Unknown / Insufficient Content": 0.00011275334390579369}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010244648204584075, "Art & Design": 0.004252537721377045, "Law & Crime": 0.0006151360014711136, "Education & Careers": 0.00041407775106681806, "Entertainment & Media": 0.0026598228280704348, "Fashion & Beauty": 0.00032210878315027693, "Business, Economics & Finance": 0.0006033961327233594, "Food & Dining": 0.0002983768954772805, "Games & Gaming": 0.21221110692542433, "Electronics & Hardware": 0.00045871004527231983, "Health, Healthcare & Wellness": 0.00034851505181173186, "History & Geography": 0.00037699008361263825, "Home, Pets & Hobbies": 0.00049024797531478, "Industry & Production": 0.0002874935365992788, "Language, Literature & Philosophy": 0.0010039009153786354, "Politics & Government": 0.00026516484382008024, "Religion & Spirituality": 0.0003716917665708708, "Science, Mathematics & Engineering": 0.0008358942658032422, "Relationships & Community": 0.00025425908712569344, "Software Usage & Applications": 0.0013474275021105814, "Programming & Software Engineering": 0.029816471246991473, "Sports & Fitness": 0.000325247297372566, "Vehicles & Transportation": 0.00015783884635079797, "Travel & Tourism": 0.000494060761431658, "Other / Uncovered Subject": 0.0005274594463883876, "NONE": 0.7402375994688262}}, "probabilities": {"primary": {"Academic Writing": 6.313324642075212e-05, "Content Listing": 5.201570911644648e-05, "Creative Writing": 0.00022803055999061182, "Customer Support Page": 2.250902754840136e-05, "Q&A Forum": 2.8987501005353552e-05, "Discussion Thread": 2.107288283566626e-05, "Comment Section": 4.0544582489676024e-05, "Missing Content": 8.758945356093424e-05, "Knowledge Article": 0.00014574260655312145, "Legal Notices": 4.816418645479229e-05, "News Article": 1.2971265530657932e-05, "Organizational Announcement": 2.3815072047220466e-05, "Nonfiction Writing": 3.3278291578893275e-05, "Organizational About Page": 2.9290664491582858e-05, "Personal About Page": 4.8314529485953236e-05, "Personal Blog": 2.212074180406133e-05, "Product Page": 2.0890874121116668e-05, "Spam / Ads": 3.0700041616626236e-05, "Structured Data": 0.0003916852421525124, "Technical Writing": 0.9896864080073363, "Transcript / Interview": 3.4096472534202305e-05, "Tutorial / How-To Guide": 0.004250414004912725, "User Reviews": 1.9531439522854838e-05, "Other / Unknown": 0.004658693596889473}, "secondary": {"Academic Writing": 0.00016557938011032932, "Content Listing": 0.00029166605177033503, "Creative Writing": 0.0014811727658276556, "Customer Support Page": 7.60953593323142e-05, "Q&A Forum": 5.227247351612343e-05, "Discussion Thread": 4.4698588209908784e-05, "Comment Section": 0.00016680524127728189, "Missing Content": 0.001440170956237649, "Knowledge Article": 0.0004135424407833366, "Legal Notices": 0.0001254696695677308, "News Article": 6.48297596288149e-05, "Organizational Announcement": 6.851401235886902e-05, "Nonfiction Writing": 7.844367742912668e-05, "Organizational About Page": 7.776455660212439e-05, "Personal About Page": 0.0001263160348468994, "Personal Blog": 8.440389358405923e-05, "Product Page": 4.6512567003040214e-05, "Spam / Ads": 9.527440156906157e-05, "Structured Data": 0.001506046071806484, "Technical Writing": 0.009938108411888951, "Transcript / Interview": 9.37575558574474e-05, "Tutorial / How-To Guide": 0.004618701910343479, "User Reviews": 4.870369807082736e-05, "NONE": 0.9788951505223782}, "tertiary": {"Academic Writing": 8.334739128272917e-05, "Content Listing": 0.00028074965876287965, "Creative Writing": 0.0011666112556806348, "Customer Support Page": 4.691312112772333e-05, "Q&A Forum": 3.244633077151798e-05, "Discussion Thread": 2.8979782005882966e-05, "Comment Section": 0.00011329165318971004, "Missing Content": 0.0017199190754736344, "Knowledge Article": 0.0003848062654223822, "Legal Notices": 0.00016511340123297308, "News Article": 4.471725899994248e-05, "Organizational Announcement": 4.27169197243668e-05, "Nonfiction Writing": 8.215751225204885e-05, "Organizational About Page": 5.755693976970249e-05, "Personal About Page": 7.437114358033496e-05, "Personal Blog": 4.954129515837815e-05, "Product Page": 3.4849674453591186e-05, "Spam / Ads": 6.164384962994603e-05, "Structured Data": 0.0012063118118361296, "Technical Writing": 0.004887879249179299, "Transcript / Interview": 5.4610455249432174e-05, "Tutorial / How-To Guide": 0.0032398421713244517, "User Reviews": 3.414454171201427e-05, "NONE": 0.9861074792421803}, "structured_records": {"false": 0.9939306812272751, "true": 0.006069318772724883}, "procedural_steps": {"false": 0.9966528486653919, "true": 0.0033471513346080115}, "worked_solution": {"false": 0.9923611163344457, "true": 0.00763888366555437}, "contextual_dialogue": {"false": 0.9999946791403247, "true": 5.320859675254319e-06}, "content_quality": {"-2": 0.02782729313736782, "-1": 0.11304349312333173, "0": 0.340915107493715, "1": 0.4328167092871044, "2": 0.08539739695848098}, "noise_severity": {"0": 0.9542726806720008, "1": 0.03777595549823665, "2": 0.005930222093922346, "3": 0.001253657278156614, "4": 0.000767484457683615}, "missing_content_severity": {"0": 0.9893221518015072, "1": 0.0033465636828983653, "2": 0.005685540268115389, "3": 0.0013571852958069093, "4": 0.00028855895167215537}, "factual_density": {"0": 0.025175731563614856, "1": 0.06891962876031031, "2": 0.1945006849290539, "3": 0.4025499941142661, "4": 0.3088539606327549}, "pretraining_utility": {"-2": 0.009664801701920562, "-1": 0.08805791498339896, "0": 0.39834513250565207, "1": 0.4334966026954847, "2": 0.07043554811354374}, "math_density": {"0": 0.5693135459319426, "1": 0.30076601779918827, "2": 0.07131916701683, "3": 0.017905884129363895, "4": 0.040695385122675155}, "code_density": {"0": 0.00028807867914730024, "1": 0.0006180703221538121, "2": 0.01716484733568627, "3": 0.1562649904002071, "4": 0.8256640132628056}, "reasoning_density": {"0": 0.8791576712621814, "1": 0.04515115645094193, "2": 0.05293773574104931, "3": 0.01326949816085606, "4": 0.009483938384971411}, "education_level": {"none": 0.0030358785927771586, "0": 0.010793025172253745, "1": 0.33312188803674303, "2": 0.643702137064102, "3": 0.007382903744078595, "4": 0.001964167390045595}, "creative_writing_quality": {"not_applicable": 0.9949807101570836, "-2": 0.0004042327140606653, "-1": 0.0012291234218150154, "0": 0.003106273707580506, "1": 0.0002188893685804414, "2": 6.077063087978533e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00011295869786052134, "Art & Design": 0.0002537756125110954, "Law & Crime": 0.00012832133686154396, "Education & Careers": 0.00012746730937774, "Entertainment & Media": 0.0007901736490641253, "Fashion & Beauty": 0.00010440428119408046, "Business, Economics & Finance": 0.00013213094378517525, "Food & Dining": 9.141473535581651e-05, "Games & Gaming": 0.05856033341891887, "Electronics & Hardware": 0.00010941490378257405, "Health, Healthcare & Wellness": 9.026807467325728e-05, "History & Geography": 0.00012282810352982128, "Home, Pets & Hobbies": 0.00013531446387611624, "Industry & Production": 9.980884210530266e-05, "Language, Literature & Philosophy": 0.00013480202740449258, "Politics & Government": 5.037238990944162e-05, "Religion & Spirituality": 9.169300030958855e-05, "Science, Mathematics & Engineering": 0.00020704365948768955, "Relationships & Community": 8.045789559681804e-05, "Software Usage & Applications": 0.0007071606829643649, "Programming & Software Engineering": 0.9372343419426223, "Sports & Fitness": 0.00018416547844746217, "Vehicles & Transportation": 5.631705114373315e-05, "Travel & Tourism": 0.00010503479802449285, "Other / Uncovered Subject": 0.00017724335728777908, "Unknown / Insufficient Content": 0.00011275334390579372}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010244648204584075, "Art & Design": 0.004252537721377045, "Law & Crime": 0.0006151360014711136, "Education & Careers": 0.00041407775106681806, "Entertainment & Media": 0.0026598228280704348, "Fashion & Beauty": 0.00032210878315027693, "Business, Economics & Finance": 0.0006033961327233594, "Food & Dining": 0.0002983768954772805, "Games & Gaming": 0.21221110692542433, "Electronics & Hardware": 0.00045871004527231983, "Health, Healthcare & Wellness": 0.00034851505181173186, "History & Geography": 0.00037699008361263825, "Home, Pets & Hobbies": 0.00049024797531478, "Industry & Production": 0.0002874935365992788, "Language, Literature & Philosophy": 0.0010039009153786354, "Politics & Government": 0.00026516484382008024, "Religion & Spirituality": 0.0003716917665708708, "Science, Mathematics & Engineering": 0.0008358942658032422, "Relationships & Community": 0.00025425908712569344, "Software Usage & Applications": 0.0013474275021105814, "Programming & Software Engineering": 0.029816471246991473, "Sports & Fitness": 0.000325247297372566, "Vehicles & Transportation": 0.00015783884635079797, "Travel & Tourism": 0.000494060761431658, "Other / Uncovered Subject": 0.0005274594463883876, "NONE": 0.7402375994688262}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10681, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10681, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:17.195170+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "789be52c-0e4e-4700-bcfb-b2cd653f336b", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "7c58247435fcf527a816b4adbe707797ffe17b03cdc4f89d6492a2ad07a3d84a", "source_metadata": {"uuid": "789be52c-0e4e-4700-bcfb-b2cd653f336b", "repo_name": "elen04ka/Snake", "relative_path": "menu.py", "category": "TOOL", "algo_rel_score": 0.9018571376800537, "quality_score": 4.038947105407715, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2023-10-07T15:56:25.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "73f261a2ebd9e4344dd10b23c81784806d52cc5d", "size_bytes": 8289, "file_timestamp": 1696694258, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["CC0-1.0"]}, "commit_id": "31091e9ff2f96265edbe0cb149bb608276a221a7"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2023-10-07T15:56:25.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "73f261a2ebd9e4344dd10b23c81784806d52cc5d", "size_bytes": 8289, "file_timestamp": 1696694258, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["CC0-1.0"]}, "commit_id": "31091e9ff2f96265edbe0cb149bb608276a221a7", "url": "https://github.com/elen04ka/Snake/blob/31091e9ff2f96265edbe0cb149bb608276a221a7/menu.py"}, "gpt4o_tokens": 2029, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.434913423805999, "noise_severity": 0.05646730935128565, "missing_content_severity": 0.01994343591323849, "factual_density": 2.9009868234922362, "pretraining_utility": 0.4669801805353321, "math_density": 0.6599035447116407, "code_density": 3.80639878924537, "reasoning_density": 0.2287708759554944, "education_level": 1.6555576148794868, "creative_writing_quality": -0.3381271599589904}, "score_applicability": {"education_level": 0.9969641214072229, "creative_writing_quality": 0.0050192898429164134}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/Ocram95/pcap_injector/blob/3fd2108921db9977b79ac581a7fcb0a493b71240/MQTTv3.1.1/bulk_mode/extractor_bulk.py", "id": "python-0-8f9e42cea0293630535b356e80c54581", "text": "import optparse\nfrom scapy.contrib import mqtt\nfrom scapy.contrib.mqtt import MQTTConnect, MQTTPublish\nfrom scapy.all import *\nfrom scapy.layers.inet import IP, TCP\nimport csv\n\n\ndef process_command_line(argv):\n    parser = optparse.OptionParser()\n    parser.add_option('-r', '--pcap', help='Specify the pcap to read.', action='store', type='string', dest='pcap')\n    parser.add_option('-i', '--injected_flows', help='Specify csv file of the injected flows.', action='store', type='string', dest='injected_flows')\n\n    settings, args = parser.parse_args(argv)\n\n    if not settings.pcap:\n        raise ValueError(\"A pcap file must be specified.\")\n    if not settings.injected_flows:\n        raise ValueError(\"A csv file with the injected flows must be specified.\")\n\n    return settings, args\n\n\ndef create_dict_attack(injected_flows):\n    list_of_dict = []\n    with open(injected_flows) as csv_file:\n        csv_reader = csv.reader(csv_file, delimiter=',')\n        # Skip header\n        next(csv_reader, None)\n        for row in csv_reader:\n            tmp_dict = {'target_field': row[0], 'src': row[1], 'dst': row[2], 'psrc': int(row[3]), 'pdst': int(row[4]),\n                        'proto': int(row[5]), 'plenght': int(row[6]), 'blength': int(row[7]), 'counter': 0,\n                        'counter_pub': 0, 'extracted': '', 'prev_time_packet': 0}\n            list_of_dict.append(tmp_dict)\n\n    return list_of_dict\n\n\ndef extract(pcap, attack_dict):\n    print(\"Reading input pcap. This might takes few minutes...\")\n    pkts = rdpcap(pcap, 900)\n    delta = 10\n    print(\"Extracting...\")\n\n    for pkt in pkts:\n        if TCP in pkt:\n            # Packet info\n            p_source, p_destination, p_psrc, p_pdst, p_proto = pkt[IP].src, pkt[IP].dst, pkt.sport, pkt.dport, pkt[IP].proto\n            if mqtt.MQTT in pkt:\n                mqtt_pkt = pkt[mqtt.MQTT]\n                for attack in attack_dict:\n                    a_source, a_destination, a_psrc, a_pdst, a_proto = attack['src'], attack['dst'], attack['psrc'], attack['pdst'], attack['proto']\n\n                    if p_source == a_source and p_destination == a_destination and p_proto == a_proto:\n                        if MQTTConnect in mqtt_pkt and p_pdst == a_pdst:\n                            # CONNECT Packet Handling\n                            if attack['counter'] < attack['blength']:\n                                targeted_field = attack['target_field']\n                                if targeted_field == \"KA\":\n                                    attack['extracted'] += mqtt_pkt.klive.to_bytes(2, 'big').decode('utf-8')\n                                    attack['counter'] += 16\n                                elif targeted_field == \"CID\":\n                                    clientId = mqtt_pkt.clientId.decode('utf-8')\n                                    attack['extracted'] += clientId[0]\n                                    attack['counter'] += 8\n                                elif targeted_field == \"PW\":\n                                    password = mqtt_pkt.password.decode('utf-8')\n                                    attack['extracted'] += password[0]\n                                    attack['counter'] += 8\n                                elif targeted_field == \"USR\":\n                                    username = mqtt_pkt.username.decode('utf-8')\n                                    attack['extracted'] += username[0]\n                                    attack['counter'] += 8\n\n                        if MQTTPublish in mqtt_pkt and p_psrc == a_psrc and p_pdst == a_pdst:\n                            # PUBLISH Packet Handling\n                            if attack['counter_pub'] < attack['blength']:\n                                targeted_field = attack['target_field']\n                                if targeted_field == \"AM\":\n                                    am = mqtt_pkt.value.decode('utf-8')\n                                    attack['extracted'] += am[0]\n                                    attack['counter_pub'] += 8\n                                elif targeted_field == \"TN\":\n                                    topic_name = mqtt_pkt.topic.decode('utf-8')\n                                    first_char = topic_name[0]\n                                    if first_char.isupper():\n                                        attack['extracted'] += '1'\n                                    else:\n                                        attack['extracted'] += '0'\n                                attack['counter_pub'] += 1\n\n    extracted_attacks_in_chunks = []\n    for attack in attack_dict:\n        if attack['target_field'] == \"TN\":\n            extracted_attacks_in_chunks.append((attack['target_field'],list((attack['extracted'][0+i:8+i] for i in range(0, len(attack['extracted']), 8)))))\n        else:\n            extracted_attacks_in_chunks.append((attack['target_field'], attack['extracted']))\n    extracted_attacks = []\n    for attack in extracted_attacks_in_chunks:\n        secret_string = ''\n        if attack[0] == \"TN\":\n            for chunk in attack[1]:\n                secret_string += chr(int(chunk, 2))\n            extracted_attacks.append((attack[0], secret_string))\n        else:\n            secret_string += attack[1]\n            extracted_attacks.append((attack[0], secret_string))\n    return extracted_attacks\n\n\ndef write_extracted(attacks):\n    print(\"Writing extracted payloads...\")\n    file = open(\"extracted_attacks.txt\", \"w\")\n    for x in attacks:\n        file.write(x[0] + ', ' + x[1] + '\\n')\n\n\nsettings, args = process_command_line(sys.argv)\nattack_dict = create_dict_attack(settings.injected_flows)\nattacks = extract(settings.pcap, attack_dict)\nwrite_extracted(attacks)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "text_preview": "import optparse from scapy.contrib import mqtt from scapy.contrib.mqtt import MQTTConnect, MQTTPublish from scapy.all import * from scapy.layers.inet import IP, TCP import csv def process_command_line(argv): parser = optparse.OptionParser()", "input_sha256": "cf0c3dc083d3d5f301aa110df6f4f939be2964b09df8281829bcf6cbf7c26bd0", "text_sha256": "8f9e42cea0293630535b356e80c54581c3974c7d6cb3bad44bfb19a11236dfc1", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12014, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.4382405767982553, "noise_severity": 0.1421251944799665, "missing_content_severity": 0.05532324290232251, "factual_density": 3.3236368680804587, "pretraining_utility": 0.42324555251625234, "math_density": 0.27862468844925264, "code_density": 3.6864700214216377, "reasoning_density": 0.5018270948571791, "education_level": 2.694076129801453, "creative_writing_quality": -0.4085949008383027}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.009864913854021218, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9958283225089686, "secondary": 0.9827429129481643, "tertiary": 0.9930367361001864, "structured_records": 0.015866910059264543, "procedural_steps": 0.010686788275142235, "worked_solution": 0.003754685934915643, "contextual_dialogue": 7.930093822502002e-06, "content_quality": 0.35473870660403084, "noise_severity": 0.8815623379333613, "missing_content_severity": 0.953897297581398, "factual_density": 0.43636405673371537, "pretraining_utility": 0.4375530376747827, "math_density": 0.7678127596256228, "code_density": 0.7387250178513649, "reasoning_density": 0.5818107542856841, "education_level": 0.32006108193448257, "creative_writing_quality": 0.9995601808815731, "primary_topic": 0.9811149494170757, "secondary_topic": 0.8245081381685642}, "probability_margin": {"primary": 0.9940692168787268, "secondary": 0.9763221913295353, "tertiary": 0.9908698495773025, "education_level": 0.004801461431341136, "creative_writing_quality": 0.9994892989038139, "primary_topic": 0.9754433412660881, "secondary_topic": 0.7638837736125174}, "raw_probabilities": {"primary": {"Academic Writing": 4.703046544009279e-05, "Content Listing": 1.9734489462318943e-05, "Creative Writing": 1.992268049130196e-05, "Customer Support Page": 7.500249554076803e-06, "Q&A Forum": 1.126813999501518e-05, "Discussion Thread": 7.855697692981295e-06, "Comment Section": 1.252991435816012e-05, "Missing Content": 4.4023543004038627e-05, "Knowledge Article": 0.0001043648857544809, "Legal Notices": 2.3884268115182624e-05, "News Article": 3.8534306237230706e-06, "Organizational Announcement": 9.34276102218293e-06, "Nonfiction Writing": 1.5449266854741955e-05, "Organizational About Page": 1.1791533818016081e-05, "Personal About Page": 1.0326587590366402e-05, "Personal Blog": 7.3467690730763054e-06, "Product Page": 6.707576218265591e-06, "Spam / Ads": 9.725896215447592e-06, "Structured Data": 0.000523470775897372, "Technical Writing": 0.9960021424044282, "Transcript / Interview": 1.6293963407454214e-05, "Tutorial / How-To Guide": 0.0011467767762183476, "User Reviews": 5.732399063777181e-06, "Other / Unknown": 0.0019329255257013094}, "secondary": {"Academic Writing": 0.0001927793988420566, "Content Listing": 6.181569773982492e-05, "Creative Writing": 8.138409393072757e-05, "Customer Support Page": 4.3229382407923214e-05, "Q&A Forum": 3.2248839050524453e-05, "Discussion Thread": 2.732558848107165e-05, "Comment Section": 7.102099978658068e-05, "Missing Content": 0.00027348153347139337, "Knowledge Article": 0.0003673160806206859, "Legal Notices": 0.00014831360762040867, "News Article": 4.430257124083058e-05, "Organizational Announcement": 3.955921487883911e-05, "Nonfiction Writing": 5.2475511384839946e-05, "Organizational About Page": 5.3792858282539044e-05, "Personal About Page": 4.276918225101553e-05, "Personal Blog": 4.2175292032240094e-05, "Product Page": 2.971272034789849e-05, "Spam / Ads": 4.313737988546343e-05, "Structured Data": 0.005436913690734681, "Technical Writing": 0.007139766912455467, "Transcript / Interview": 5.735059858284919e-05, "Tutorial / How-To Guide": 0.0022342561552173242, "User Reviews": 2.2914448764026266e-05, "NONE": 0.9834619582419908}, "tertiary": {"Academic Writing": 7.036456713212202e-05, "Content Listing": 2.7050222280750274e-05, "Creative Writing": 4.302345859386228e-05, "Customer Support Page": 2.0623233330016267e-05, "Q&A Forum": 1.3168975943667211e-05, "Discussion Thread": 1.0953767886331391e-05, "Comment Section": 3.4254839172192375e-05, "Missing Content": 0.00023767505199979086, "Knowledge Article": 0.00025732742423721167, "Legal Notices": 0.00010726164145905485, "News Article": 2.0880098649899224e-05, "Organizational Announcement": 1.732480100269125e-05, "Nonfiction Writing": 3.220741294471174e-05, "Organizational About Page": 3.241544144383662e-05, "Personal About Page": 2.030384826859581e-05, "Personal Blog": 2.013668015484972e-05, "Product Page": 1.5342593427684786e-05, "Spam / Ads": 1.960523774204679e-05, "Structured Data": 0.0022694484868043953, "Technical Writing": 0.0024570225187094765, "Transcript / Interview": 2.4287388032994696e-05, "Tutorial / How-To Guide": 0.000911789151135594, "User Reviews": 1.0661063636271515e-05, "NONE": 0.993326872096012}, "structured_records": {"false": 0.9841330899407355, "true": 0.015866910059264543}, "procedural_steps": {"false": 0.9893132117248578, "true": 0.010686788275142235}, "worked_solution": {"false": 0.9962453140650843, "true": 0.003754685934915643}, "contextual_dialogue": {"false": 0.9999920699061775, "true": 7.930093822502002e-06}, "content_quality": {"-2": 0.02912506711985287, "-1": 0.11780520995624884, "0": 0.34505086636639754, "1": 0.40174179212079175, "2": 0.10627706443670905}, "noise_severity": {"0": 0.8807462456540396, "1": 0.10250197485020655, "2": 0.01204128378723545, "3": 0.0033013307787851977, "4": 0.0014091649297333683}, "missing_content_severity": {"0": 0.97001380230499, "1": 0.010105832781683127, "2": 0.015013582816697306, "3": 0.004276883899273201, "4": 0.0005898981973562903}, "factual_density": {"0": 0.01076627933162509, "1": 0.02182617098775072, "2": 0.11706401943919499, "3": 0.333691462751399, "4": 0.5166520674900302}, "pretraining_utility": {"-2": 0.01129014486251776, "-1": 0.10326511141196866, "0": 0.41276786451396985, "1": 0.396262804769831, "2": 0.07641407444171276}, "math_density": {"0": 0.8153793228256572, "1": 0.14376510502691034, "2": 0.011227911405662845, "3": 0.006106882356062102, "4": 0.023520778385707573}, "code_density": {"0": 0.0003221291575284891, "1": 0.0007302575676503059, "2": 0.04161878581889517, "3": 0.22681311760750686, "4": 0.7305157098484192}, "reasoning_density": {"0": 0.7922236930797135, "1": 0.03661109505925146, "2": 0.08517523614844949, "3": 0.04909437534931346, "4": 0.036895600363272066}, "education_level": {"none": 0.0009105954228320901, "0": 0.0011171429340466167, "1": 0.0032387827300221026, "2": 0.428582773514061, "3": 0.4333842349454021, "4": 0.132766470453636}, "creative_writing_quality": {"not_applicable": 0.9996334840679776, "-2": 6.42099334393587e-05, "-1": 9.867607976942087e-05, "0": 0.0001441851641636584, "1": 4.155010355158689e-05, "2": 1.7894651098146994e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001452460003839631, "Art & Design": 6.473663080567327e-05, "Law & Crime": 0.005450960523025106, "Education & Careers": 5.337304844358292e-05, "Entertainment & Media": 4.602391652250857e-05, "Fashion & Beauty": 3.517330781430863e-05, "Business, Economics & Finance": 6.355241662942019e-05, "Food & Dining": 3.732702580100538e-05, "Games & Gaming": 7.922066798145305e-05, "Electronics & Hardware": 0.0003394597568537467, "Health, Healthcare & Wellness": 5.12355838431899e-05, "History & Geography": 6.430192034356547e-05, "Home, Pets & Hobbies": 4.78000338508217e-05, "Industry & Production": 9.041539730305543e-05, "Language, Literature & Philosophy": 4.929635066339388e-05, "Politics & Government": 3.21445371585461e-05, "Religion & Spirituality": 4.9152045361422246e-05, "Science, Mathematics & Engineering": 0.004141982925884182, "Relationships & Community": 2.4261123591071744e-05, "Software Usage & Applications": 0.000602390906811903, "Programming & Software Engineering": 0.9818412975164189, "Sports & Fitness": 6.0232224053812384e-05, "Vehicles & Transportation": 4.039333046565935e-05, "Travel & Tourism": 7.249488073447347e-05, "Other / Uncovered Subject": 0.006397956250330771, "Unknown / Insufficient Content": 0.00011957167892438956}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009284522231818708, "Art & Design": 0.00048014124137172833, "Law & Crime": 0.0673740515495636, "Education & Careers": 0.0003891590954570402, "Entertainment & Media": 0.0004977923813793196, "Fashion & Beauty": 0.0002566200012247503, "Business, Economics & Finance": 0.0006444001180538425, "Food & Dining": 0.00024087626752377104, "Games & Gaming": 0.00042405889122923597, "Electronics & Hardware": 0.0008078669596878906, "Health, Healthcare & Wellness": 0.00040358769871468714, "History & Geography": 0.00033270990207050113, "Home, Pets & Hobbies": 0.00032254069785734386, "Industry & Production": 0.00041073670295247534, "Language, Literature & Philosophy": 0.0002486932064999322, "Politics & Government": 0.0003002381391977439, "Religion & Spirituality": 0.0004585277003438687, "Science, Mathematics & Engineering": 0.013190777074218155, "Relationships & Community": 0.00016328938817833784, "Software Usage & Applications": 0.00422862720541617, "Programming & Software Engineering": 0.05794567751096588, "Sports & Fitness": 0.00030522399366902307, "Vehicles & Transportation": 0.00018069567095883987, "Travel & Tourism": 0.0005027105281885982, "Other / Uncovered Subject": 0.01770472069001444, "NONE": 0.8312578251620809}}, "probabilities": {"primary": {"Academic Writing": 4.7030465440092794e-05, "Content Listing": 1.9734489462318947e-05, "Creative Writing": 1.9922680491301963e-05, "Customer Support Page": 7.500249554076804e-06, "Q&A Forum": 1.1268139995015181e-05, "Discussion Thread": 7.855697692981297e-06, "Comment Section": 1.2529914358160121e-05, "Missing Content": 4.402354300403863e-05, "Knowledge Article": 0.00010436488575448092, "Legal Notices": 2.3884268115182627e-05, "News Article": 3.853430623723071e-06, "Organizational Announcement": 9.342761022182932e-06, "Nonfiction Writing": 1.544926685474196e-05, "Organizational About Page": 1.1791533818016083e-05, "Personal About Page": 1.0326587590366404e-05, "Personal Blog": 7.346769073076306e-06, "Product Page": 6.707576218265592e-06, "Spam / Ads": 9.725896215447594e-06, "Structured Data": 0.0005234707758973721, "Technical Writing": 0.9960021424044283, "Transcript / Interview": 1.6293963407454217e-05, "Tutorial / How-To Guide": 0.0011467767762183478, "User Reviews": 5.7323990637771816e-06, "Other / Unknown": 0.0019329255257013096}, "secondary": {"Academic Writing": 0.0001927793988420566, "Content Listing": 6.181569773982492e-05, "Creative Writing": 8.138409393072757e-05, "Customer Support Page": 4.3229382407923214e-05, "Q&A Forum": 3.2248839050524453e-05, "Discussion Thread": 2.732558848107165e-05, "Comment Section": 7.102099978658068e-05, "Missing Content": 0.00027348153347139337, "Knowledge Article": 0.0003673160806206859, "Legal Notices": 0.00014831360762040867, "News Article": 4.430257124083058e-05, "Organizational Announcement": 3.955921487883911e-05, "Nonfiction Writing": 5.2475511384839946e-05, "Organizational About Page": 5.3792858282539044e-05, "Personal About Page": 4.276918225101553e-05, "Personal Blog": 4.2175292032240094e-05, "Product Page": 2.971272034789849e-05, "Spam / Ads": 4.313737988546343e-05, "Structured Data": 0.005436913690734681, "Technical Writing": 0.007139766912455467, "Transcript / Interview": 5.735059858284919e-05, "Tutorial / How-To Guide": 0.0022342561552173242, "User Reviews": 2.2914448764026266e-05, "NONE": 0.9834619582419908}, "tertiary": {"Academic Writing": 7.036456713212202e-05, "Content Listing": 2.7050222280750274e-05, "Creative Writing": 4.302345859386228e-05, "Customer Support Page": 2.0623233330016267e-05, "Q&A Forum": 1.3168975943667211e-05, "Discussion Thread": 1.0953767886331391e-05, "Comment Section": 3.4254839172192375e-05, "Missing Content": 0.00023767505199979086, "Knowledge Article": 0.00025732742423721167, "Legal Notices": 0.00010726164145905485, "News Article": 2.0880098649899224e-05, "Organizational Announcement": 1.732480100269125e-05, "Nonfiction Writing": 3.220741294471174e-05, "Organizational About Page": 3.241544144383662e-05, "Personal About Page": 2.030384826859581e-05, "Personal Blog": 2.013668015484972e-05, "Product Page": 1.5342593427684786e-05, "Spam / Ads": 1.960523774204679e-05, "Structured Data": 0.0022694484868043953, "Technical Writing": 0.0024570225187094765, "Transcript / Interview": 2.4287388032994696e-05, "Tutorial / How-To Guide": 0.000911789151135594, "User Reviews": 1.0661063636271515e-05, "NONE": 0.993326872096012}, "structured_records": {"false": 0.9841330899407355, "true": 0.015866910059264543}, "procedural_steps": {"false": 0.9893132117248578, "true": 0.010686788275142235}, "worked_solution": {"false": 0.9962453140650843, "true": 0.003754685934915643}, "contextual_dialogue": {"false": 0.9999920699061775, "true": 7.930093822502002e-06}, "content_quality": {"-2": 0.02912506711985287, "-1": 0.11780520995624884, "0": 0.34505086636639754, "1": 0.40174179212079175, "2": 0.10627706443670905}, "noise_severity": {"0": 0.8807462456540394, "1": 0.10250197485020653, "2": 0.012041283787235447, "3": 0.003301330778785197, "4": 0.001409164929733368}, "missing_content_severity": {"0": 0.9700138023049901, "1": 0.010105832781683128, "2": 0.015013582816697308, "3": 0.0042768838992732015, "4": 0.0005898981973562904}, "factual_density": {"0": 0.01076627933162509, "1": 0.02182617098775072, "2": 0.11706401943919499, "3": 0.333691462751399, "4": 0.5166520674900302}, "pretraining_utility": {"-2": 0.01129014486251776, "-1": 0.10326511141196866, "0": 0.41276786451396985, "1": 0.396262804769831, "2": 0.07641407444171276}, "math_density": {"0": 0.8153793228256572, "1": 0.14376510502691034, "2": 0.011227911405662845, "3": 0.006106882356062102, "4": 0.023520778385707573}, "code_density": {"0": 0.0003221291575284891, "1": 0.0007302575676503059, "2": 0.04161878581889517, "3": 0.22681311760750686, "4": 0.7305157098484192}, "reasoning_density": {"0": 0.7922236930797135, "1": 0.03661109505925146, "2": 0.08517523614844949, "3": 0.04909437534931346, "4": 0.036895600363272066}, "education_level": {"none": 0.0009105954228320902, "0": 0.001117142934046617, "1": 0.003238782730022103, "2": 0.42858277351406104, "3": 0.4333842349454022, "4": 0.13276647045363602}, "creative_writing_quality": {"not_applicable": 0.9996334840679778, "-2": 6.420993343935871e-05, "-1": 9.86760797694209e-05, "0": 0.00014418516416365843, "1": 4.1550103551586895e-05, "2": 1.7894651098146998e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00014524600038396312, "Art & Design": 6.473663080567328e-05, "Law & Crime": 0.005450960523025107, "Education & Careers": 5.3373048443582925e-05, "Entertainment & Media": 4.602391652250858e-05, "Fashion & Beauty": 3.517330781430864e-05, "Business, Economics & Finance": 6.35524166294202e-05, "Food & Dining": 3.7327025801005386e-05, "Games & Gaming": 7.922066798145306e-05, "Electronics & Hardware": 0.00033945975685374674, "Health, Healthcare & Wellness": 5.1235583843189906e-05, "History & Geography": 6.430192034356548e-05, "Home, Pets & Hobbies": 4.7800033850821705e-05, "Industry & Production": 9.041539730305544e-05, "Language, Literature & Philosophy": 4.929635066339389e-05, "Politics & Government": 3.214453715854611e-05, "Religion & Spirituality": 4.915204536142225e-05, "Science, Mathematics & Engineering": 0.0041419829258841825, "Relationships & Community": 2.4261123591071747e-05, "Software Usage & Applications": 0.0006023909068119031, "Programming & Software Engineering": 0.981841297516419, "Sports & Fitness": 6.023222405381239e-05, "Vehicles & Transportation": 4.039333046565936e-05, "Travel & Tourism": 7.249488073447348e-05, "Other / Uncovered Subject": 0.0063979562503307715, "Unknown / Insufficient Content": 0.00011957167892438958}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009284522231818708, "Art & Design": 0.00048014124137172833, "Law & Crime": 0.0673740515495636, "Education & Careers": 0.0003891590954570402, "Entertainment & Media": 0.0004977923813793196, "Fashion & Beauty": 0.0002566200012247503, "Business, Economics & Finance": 0.0006444001180538425, "Food & Dining": 0.00024087626752377104, "Games & Gaming": 0.00042405889122923597, "Electronics & Hardware": 0.0008078669596878906, "Health, Healthcare & Wellness": 0.00040358769871468714, "History & Geography": 0.00033270990207050113, "Home, Pets & Hobbies": 0.00032254069785734386, "Industry & Production": 0.00041073670295247534, "Language, Literature & Philosophy": 0.0002486932064999322, "Politics & Government": 0.0003002381391977439, "Religion & Spirituality": 0.0004585277003438687, "Science, Mathematics & Engineering": 0.013190777074218155, "Relationships & Community": 0.00016328938817833784, "Software Usage & Applications": 0.00422862720541617, "Programming & Software Engineering": 0.05794567751096588, "Sports & Fitness": 0.00030522399366902307, "Vehicles & Transportation": 0.00018069567095883987, "Travel & Tourism": 0.0005027105281885982, "Other / Uncovered Subject": 0.01770472069001444, "NONE": 0.8312578251620809}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9806, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9806, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:17.734166+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "4fc57945-4cda-4e69-b31f-20efcdd69b59", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "08a56d6ae787fa9e4f3095019ca6436aa76d4633b05919c1a6d4d49a1233edd8", "source_metadata": {"uuid": "4fc57945-4cda-4e69-b31f-20efcdd69b59", "repo_name": "Ocram95/pcap_injector", "relative_path": "MQTTv3.1.1/bulk_mode/extractor_bulk.py", "category": "TOOL", "algo_rel_score": 0.9990336894989014, "quality_score": 3.7918508052825928, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 64, "repo_created_at": "2021-03-22T15:07:26.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 12, "forks": 2, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c02d601c145d84123b58d015c754dfd275dd3a84", "size_bytes": 5719, "file_timestamp": 1718186487, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "3fd2108921db9977b79ac581a7fcb0a493b71240"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 64, "repo_created_at": "2021-03-22T15:07:26.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 12, "forks": 2, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c02d601c145d84123b58d015c754dfd275dd3a84", "size_bytes": 5719, "file_timestamp": 1718186487, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "3fd2108921db9977b79ac581a7fcb0a493b71240", "url": "https://github.com/Ocram95/pcap_injector/blob/3fd2108921db9977b79ac581a7fcb0a493b71240/MQTTv3.1.1/bulk_mode/extractor_bulk.py"}, "gpt4o_tokens": 1213, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.4382405767982553, "noise_severity": 0.14212519447996652, "missing_content_severity": 0.05532324290232251, "factual_density": 3.3236368680804587, "pretraining_utility": 0.42324555251625234, "math_density": 0.27862468844925264, "code_density": 3.6864700214216377, "reasoning_density": 0.5018270948571791, "education_level": 2.694076129801453, "creative_writing_quality": -0.4085949008383027}, "score_applicability": {"education_level": 0.999089404577168, "creative_writing_quality": 0.00036651593202217193}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/hanakysela/pyladies/blob/759aa300c5a5f9b6803c8fa13364dd587d5de535/Piskvorky/strategie.py", "id": "python-0-f04ba46a4eb50b4e20300f081b05debd", "text": "from random import randint\n\n\ndef vyhodnot(pole):\n    if 'xxx' in pole:\n        return 'x'\n    elif 'ooo' in pole:\n        return 'o'\n    elif '-' not in pole:  # Nikdy nepouzivejte not '-' in pole, je to mene citelne\n        return '!'\n    else:\n        return '-'\n\n\ndef tah(pole, index, symbol):  # Ukazka DRY principu, vytahli jsme spolecny kod do jedne funkce\n    \"\"\"Vrati herni pole s danym symbolem umistnym na danou pozici.\"\"\"\n    return pole[:index] + symbol + pole[index + 1:]\n\n\ndef tah_hrace(pole):\n    \"\"\"Ziska od uzivatele pozici, kam chce tahnout, a vrati pole se zaznamenanym tahem hrace.\"\"\"\n    while True:\n        pozice = input('Kam chces hrat? (0..{})'.format(len(pole) - 1))\n        if pozice.isdigit():  # Staci opravdu isdigit?\n            pozice = int(pozice)\n            if pozice < 0 or pozice >= len(pole):\n                print('Nemuzes hrat venku z pole!')\n            elif pole[pozice] != '-':\n                print('Tam neni volno!')\n            else:\n                return tah(pole, pozice, 'o')\n        else:\n            print('To neni cislo!')\n\n\ndef tah_pocitace(pole):\n    \"\"\"Vrati herni pole se zaznamenaným tahem pocitace.\"\"\"\n    from random import randint\n    pozice = -1\n\n    ## vitezne tahy\n    if (\"x-x\") in pole:\n        pozice = (pole.index(\"x-x\")) + 1\n    elif(\"-xx\") in pole:\n        pozice = (pole.index(\"-xx\"))\n    elif(\"xx-\") in pole:\n        pozice = (pole.index(\"xx-\")) + 2\n\n    ## zabraneni vyhre cloveka na posledni okamzik\n    elif (\"o-o\") in pole:\n        pozice = (pole.index(\"o-o\")) + 1\n    elif(\"-oo\") in pole:\n        pozice = (pole.index(\"-oo\"))\n    elif(\"oo-\") in pole:\n        pozice = (pole.index(\"oo-\")) + 2\n\n    ##nenechej soupere dychat I\n    elif (\"--o-\") in pole:\n        pozice = (pole.index(\"--o\")) + 1\n    elif (\"-o--\") in pole:\n        pozice = (pole.index(\"o--\")) + 2\n\n    ##pokud to jde, utoc, ale nehraj hned u soupere\n    elif(\"-x--\") in pole:\n        pozice = (pole.index(\"x--\")) + 2\n    elif(\"--x-\") in pole:\n        pozice = (pole.index(\"--x\")) + 1\n\n    ## pokud nenajdes peknou volnou sekvenci, hraj takhle:\n    elif(\"x--\") in pole:\n        pozice = (pole.index(\"x--\")) + 1\n    elif(\"--x\") in pole:\n        pozice = (pole.index(\"--x\")) + 1\n\n    #nenechej soupere dychat II\n    elif(\"--ox--\") in pole:\n        pozice = (pole.index(\"--ox\")) + 4\n    elif(\"--xo--\") in pole:\n        pozice = (pole.index(\"xo--\")) + 1\n\n    # hraj doprostred (pokud nahodou nenastane zadna situace vyse)\n    elif(\"-----\") in pole:\n        pozice = (pole.index(\"-----\")) + 3\n    elif(\"----\") in pole:\n        pozice = (pole.index(\"----\")) + 2\n    elif(\"---3\") in pole:\n        pozice = (pole.index(\"---\")) + 1\n\n    else:\n        pozice = randint(0, len(pole) - 1)\n\n    while pole[pozice] != '-':\n        pozice = randint(0, len(pole) - 1)\n    return tah(pole, pozice, 'x')\n\n\ndef piskvorky1d():\n    pole = '-' * 20\n    i = 0\n    while True:\n        if i % 2 == 0:\n            pole = tah_hrace(pole)\n        else:\n            pole = tah_pocitace(pole)\n        print(pole)\n\n        if vyhodnot(pole) == 'o':\n            print('Vyhral hrac.')\n        elif vyhodnot(pole) == 'x':\n            print('Vyhral pocitac.')\n        elif vyhodnot(pole) == '!':\n            print('Remiza!')\n\n        if vyhodnot(pole) != '-':\n            break\n\n        i += 1\n\npiskvorky1d()\n", "text_preview": "from random import randint def vyhodnot(pole): if 'xxx' in pole: return 'x' elif 'ooo' in pole: return 'o' elif '-' not in pole: # Nikdy nepouzivejte not '-' in pole, je to mene citelne return '!' else: return '-' def tah(pole, index, symbo", "input_sha256": "1567e76386bc8f2c8fca179df10fa4cb9489c1eb7e90c09321998901f80e270a", "text_sha256": "f04ba46a4eb50b4e20300f081b05debd5d245389319f7dbcc6a7d093b6ffadb7", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12016, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.4700194147240249, "noise_severity": 0.022087045462579256, "missing_content_severity": 0.004999657386712768, "factual_density": 3.2465150363760857, "pretraining_utility": 0.502853440590686, "math_density": 0.9123324182672922, "code_density": 3.7138791530783855, "reasoning_density": 1.5745349711039305, "education_level": 1.529943946826457, "creative_writing_quality": -0.19028418825689974}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.007728951137127571, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.986713606348451, "secondary": 0.9648482280835485, "tertiary": 0.972913220574464, "structured_records": 0.0011767021432362722, "procedural_steps": 0.010389804552923619, "worked_solution": 0.027785161400718062, "contextual_dialogue": 0.00010555219236438238, "content_quality": 0.3791679115674921, "noise_severity": 0.9815941287811839, "missing_content_severity": 0.995833618844406, "factual_density": 0.3720958636467381, "pretraining_utility": 0.44507256650532656, "math_density": 0.2397229847772565, "code_density": 0.7615659608986542, "reasoning_density": 0.0, "education_level": 0.4330898686312827, "creative_writing_quality": 0.9946252332264321, "primary_topic": 0.9093503696889029, "secondary_topic": 0.6270330907619169}, "probability_margin": {"primary": 0.9808572872265441, "secondary": 0.9517904228729286, "tertiary": 0.96398231938461, "education_level": 0.0826436421355718, "creative_writing_quality": 0.9925590654645442, "primary_topic": 0.8321805803003017, "secondary_topic": 0.36873099063635467}, "raw_probabilities": {"primary": {"Academic Writing": 5.300409200713891e-05, "Content Listing": 3.784088780176454e-05, "Creative Writing": 0.0006959204884769855, "Customer Support Page": 1.852852252126336e-05, "Q&A Forum": 3.1557569377170855e-05, "Discussion Thread": 1.89062576259234e-05, "Comment Section": 3.9416709432335045e-05, "Missing Content": 4.447309772992631e-05, "Knowledge Article": 0.000178313114858802, "Legal Notices": 3.559442805570695e-05, "News Article": 1.0361859238095867e-05, "Organizational Announcement": 1.771306748787399e-05, "Nonfiction Writing": 4.1111558041720106e-05, "Organizational About Page": 2.6978104829086897e-05, "Personal About Page": 3.574655646566767e-05, "Personal Blog": 2.818297841987895e-05, "Product Page": 1.3446949507273362e-05, "Spam / Ads": 2.7225218238788692e-05, "Structured Data": 0.00027502724969408253, "Technical Writing": 0.9872672060839321, "Transcript / Interview": 3.4100950728222943e-05, "Tutorial / How-To Guide": 0.0046429644807180065, "User Reviews": 1.646091742393641e-05, "Other / Unknown": 0.006409918857388038}, "secondary": {"Academic Writing": 0.00020290716176738605, "Content Listing": 9.119439590692649e-05, "Creative Writing": 0.002982811019160393, "Customer Support Page": 9.475010379735725e-05, "Q&A Forum": 7.479958183020637e-05, "Discussion Thread": 5.566614193954921e-05, "Comment Section": 0.00017382652180300654, "Missing Content": 0.0001940451445823126, "Knowledge Article": 0.0012678143125543053, "Legal Notices": 0.00013468580235566607, "News Article": 7.702251066039501e-05, "Organizational Announcement": 7.140898261468766e-05, "Nonfiction Writing": 0.00014364409433459592, "Organizational About Page": 8.927689519715854e-05, "Personal About Page": 0.0001230995819959174, "Personal Blog": 0.0001156401422341108, "Product Page": 4.727778809165737e-05, "Spam / Ads": 9.117165615207503e-05, "Structured Data": 0.0007583764669923616, "Technical Writing": 0.012222761621376914, "Transcript / Interview": 0.00010860589144431433, "Tutorial / How-To Guide": 0.014522462373805365, "User Reviews": 4.386656266940199e-05, "NONE": 0.966312885246734}, "tertiary": {"Academic Writing": 0.00015822905381494183, "Content Listing": 6.589822288232033e-05, "Creative Writing": 0.0020445063021115326, "Customer Support Page": 8.706993881870344e-05, "Q&A Forum": 6.0371806148673974e-05, "Discussion Thread": 4.603184660199257e-05, "Comment Section": 0.00021053571799000328, "Missing Content": 0.00020098216033209249, "Knowledge Article": 0.0014070043602572996, "Legal Notices": 0.00016680449062266527, "News Article": 6.000318918449885e-05, "Organizational Announcement": 5.079618881211406e-05, "Nonfiction Writing": 0.00024047098251580947, "Organizational About Page": 8.090347952196458e-05, "Personal About Page": 8.111419527306186e-05, "Personal Blog": 9.04715620121091e-05, "Product Page": 3.746683607310468e-05, "Spam / Ads": 8.756261209479455e-05, "Structured Data": 0.0005773816640705126, "Technical Writing": 0.01002324237072577, "Transcript / Interview": 8.607179449434126e-05, "Tutorial / How-To Guide": 0.010059516999251422, "User Reviews": 3.5727842528710277e-05, "NONE": 0.9740418363838614}, "structured_records": {"false": 0.9988232978567637, "true": 0.0011767021432362722}, "procedural_steps": {"false": 0.9896101954470764, "true": 0.010389804552923619}, "worked_solution": {"false": 0.9722148385992819, "true": 0.027785161400718062}, "contextual_dialogue": {"false": 0.9998944478076356, "true": 0.00010555219236438238}, "content_quality": {"-2": 0.024171494544121427, "-1": 0.1117971745361969, "0": 0.3413807129927342, "1": 0.41514165750543014, "2": 0.10750896042151724}, "noise_severity": {"0": 0.9820289156436622, "1": 0.01505707878254115, "2": 0.0020489687694785516, "3": 0.0005281180761908757, "4": 0.00033691872812709303}, "missing_content_severity": {"0": 0.9968721682607681, "1": 0.0017053673897326914, "2": 0.0010645528233928145, "3": 0.00026646175423109713, "4": 9.144977187528905e-05}, "factual_density": {"0": 0.012913143555844645, "1": 0.023950316425525177, "2": 0.13788871251386026, "3": 0.35420401509623967, "4": 0.47104381240853027}, "pretraining_utility": {"-2": 0.009361037135116016, "-1": 0.07917482336028328, "0": 0.39509698167554647, "1": 0.4319839774369073, "2": 0.084383180392147}, "math_density": {"0": 0.6241323209842247, "1": 0.14427563415903444, "2": 0.050815834384910795, "3": 0.0566797265488843, "4": 0.12409648392294581}, "code_density": {"0": 0.00046316638312154126, "1": 0.0010637284063796872, "2": 0.045565074511325285, "3": 0.18994684714733903, "4": 0.7629611835518345}, "reasoning_density": {"0": 0.36377873908613967, "1": 0.1562006186808637, "2": 0.1732330416762271, "3": 0.15528213315646594, "4": 0.15150546740030366}, "education_level": {"none": 0.002218779620220901, "0": 0.017107744452519395, "1": 0.4449312483904971, "2": 0.5275748905260689, "3": 0.006201039108251592, "4": 0.0019662979024421224}, "creative_writing_quality": {"not_applicable": 0.9955210276886934, "-2": 0.00021432250334126951, "-1": 0.000903227699479148, "0": 0.0029619622241492173, "1": 0.00031932467299462325, "2": 8.013521134246677e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00012257524298821734, "Art & Design": 0.000205292424513385, "Law & Crime": 0.0001518329775702836, "Education & Careers": 0.00015597989998897524, "Entertainment & Media": 0.0004112859561259108, "Fashion & Beauty": 9.717198848964414e-05, "Business, Economics & Finance": 0.00014936103706177702, "Food & Dining": 9.388839767077731e-05, "Games & Gaming": 0.0806563136313357, "Electronics & Hardware": 0.00010830450241605547, "Health, Healthcare & Wellness": 0.00010627394065289033, "History & Geography": 0.00016020020455996702, "Home, Pets & Hobbies": 0.00016666798221561167, "Industry & Production": 0.00012074465137670178, "Language, Literature & Philosophy": 0.0002093779927377776, "Politics & Government": 8.013761811062025e-05, "Religion & Spirituality": 0.00010758369460696272, "Science, Mathematics & Engineering": 0.0025803336200647424, "Relationships & Community": 8.569074429848683e-05, "Software Usage & Applications": 0.0005121090042135607, "Programming & Software Engineering": 0.9128368939316375, "Sports & Fitness": 0.00024364168494891958, "Vehicles & Transportation": 6.682712424489717e-05, "Travel & Tourism": 0.0001266557549756861, "Other / Uncovered Subject": 0.0003013247911785238, "Unknown / Insufficient Content": 0.00014353120201639064}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009375990153783813, "Art & Design": 0.0007969948469772239, "Law & Crime": 0.0006358600917630456, "Education & Careers": 0.0011652589323344572, "Entertainment & Media": 0.0010828621496257569, "Fashion & Beauty": 0.000389586560243491, "Business, Economics & Finance": 0.0005933761925402917, "Food & Dining": 0.00034357506865067487, "Games & Gaming": 0.27264698125010395, "Electronics & Hardware": 0.00045962075460539097, "Health, Healthcare & Wellness": 0.000421863834264794, "History & Geography": 0.000510616917365886, "Home, Pets & Hobbies": 0.0007264825685842127, "Industry & Production": 0.0003504513961703292, "Language, Literature & Philosophy": 0.0012134480038890625, "Politics & Government": 0.0003304735780002378, "Religion & Spirituality": 0.0004587136533998079, "Science, Mathematics & Engineering": 0.011453169052406901, "Relationships & Community": 0.00027440210061797617, "Software Usage & Applications": 0.0006939311826575713, "Programming & Software Engineering": 0.06099872155760526, "Sports & Fitness": 0.0005014742582938964, "Vehicles & Transportation": 0.0001951182288633429, "Travel & Tourism": 0.0005820602136033742, "Other / Uncovered Subject": 0.0008593867055960106, "NONE": 0.6413779718864586}}, "probabilities": {"primary": {"Academic Writing": 5.300409200713892e-05, "Content Listing": 3.784088780176455e-05, "Creative Writing": 0.0006959204884769856, "Customer Support Page": 1.8528522521263365e-05, "Q&A Forum": 3.155756937717086e-05, "Discussion Thread": 1.8906257625923404e-05, "Comment Section": 3.941670943233505e-05, "Missing Content": 4.447309772992632e-05, "Knowledge Article": 0.00017831311485880203, "Legal Notices": 3.5594428055706954e-05, "News Article": 1.0361859238095868e-05, "Organizational Announcement": 1.7713067487873994e-05, "Nonfiction Writing": 4.111155804172011e-05, "Organizational About Page": 2.69781048290869e-05, "Personal About Page": 3.574655646566768e-05, "Personal Blog": 2.8182978419878952e-05, "Product Page": 1.3446949507273364e-05, "Spam / Ads": 2.7225218238788695e-05, "Structured Data": 0.0002750272496940826, "Technical Writing": 0.9872672060839323, "Transcript / Interview": 3.410095072822295e-05, "Tutorial / How-To Guide": 0.004642964480718007, "User Reviews": 1.6460917423936412e-05, "Other / Unknown": 0.006409918857388039}, "secondary": {"Academic Writing": 0.00020290716176738605, "Content Listing": 9.119439590692649e-05, "Creative Writing": 0.002982811019160393, "Customer Support Page": 9.475010379735725e-05, "Q&A Forum": 7.479958183020637e-05, "Discussion Thread": 5.566614193954921e-05, "Comment Section": 0.00017382652180300654, "Missing Content": 0.0001940451445823126, "Knowledge Article": 0.0012678143125543053, "Legal Notices": 0.00013468580235566607, "News Article": 7.702251066039501e-05, "Organizational Announcement": 7.140898261468766e-05, "Nonfiction Writing": 0.00014364409433459592, "Organizational About Page": 8.927689519715854e-05, "Personal About Page": 0.0001230995819959174, "Personal Blog": 0.0001156401422341108, "Product Page": 4.727778809165737e-05, "Spam / Ads": 9.117165615207503e-05, "Structured Data": 0.0007583764669923616, "Technical Writing": 0.012222761621376914, "Transcript / Interview": 0.00010860589144431433, "Tutorial / How-To Guide": 0.014522462373805365, "User Reviews": 4.386656266940199e-05, "NONE": 0.966312885246734}, "tertiary": {"Academic Writing": 0.00015822905381494186, "Content Listing": 6.589822288232035e-05, "Creative Writing": 0.002044506302111533, "Customer Support Page": 8.706993881870346e-05, "Q&A Forum": 6.037180614867398e-05, "Discussion Thread": 4.603184660199258e-05, "Comment Section": 0.0002105357179900033, "Missing Content": 0.0002009821603320925, "Knowledge Article": 0.0014070043602572998, "Legal Notices": 0.0001668044906226653, "News Article": 6.000318918449886e-05, "Organizational Announcement": 5.079618881211407e-05, "Nonfiction Writing": 0.0002404709825158095, "Organizational About Page": 8.09034795219646e-05, "Personal About Page": 8.111419527306188e-05, "Personal Blog": 9.047156201210911e-05, "Product Page": 3.7466836073104687e-05, "Spam / Ads": 8.756261209479457e-05, "Structured Data": 0.0005773816640705127, "Technical Writing": 0.010023242370725771, "Transcript / Interview": 8.607179449434127e-05, "Tutorial / How-To Guide": 0.010059516999251424, "User Reviews": 3.572784252871028e-05, "NONE": 0.9740418363838615}, "structured_records": {"false": 0.9988232978567637, "true": 0.0011767021432362722}, "procedural_steps": {"false": 0.9896101954470764, "true": 0.010389804552923619}, "worked_solution": {"false": 0.9722148385992819, "true": 0.027785161400718062}, "contextual_dialogue": {"false": 0.9998944478076356, "true": 0.00010555219236438238}, "content_quality": {"-2": 0.02417149454412143, "-1": 0.11179717453619692, "0": 0.34138071299273426, "1": 0.4151416575054302, "2": 0.10750896042151725}, "noise_severity": {"0": 0.9820289156436623, "1": 0.015057078782541151, "2": 0.002048968769478552, "3": 0.0005281180761908758, "4": 0.0003369187281270931}, "missing_content_severity": {"0": 0.9968721682607681, "1": 0.0017053673897326914, "2": 0.0010645528233928145, "3": 0.00026646175423109713, "4": 9.144977187528905e-05}, "factual_density": {"0": 0.012913143555844645, "1": 0.023950316425525177, "2": 0.13788871251386026, "3": 0.35420401509623967, "4": 0.47104381240853027}, "pretraining_utility": {"-2": 0.009361037135116016, "-1": 0.07917482336028328, "0": 0.39509698167554647, "1": 0.4319839774369073, "2": 0.084383180392147}, "math_density": {"0": 0.6241323209842247, "1": 0.14427563415903444, "2": 0.050815834384910795, "3": 0.0566797265488843, "4": 0.12409648392294581}, "code_density": {"0": 0.00046316638312154126, "1": 0.0010637284063796872, "2": 0.045565074511325285, "3": 0.18994684714733903, "4": 0.7629611835518345}, "reasoning_density": {"0": 0.36377873908613967, "1": 0.1562006186808637, "2": 0.1732330416762271, "3": 0.15528213315646594, "4": 0.15150546740030366}, "education_level": {"none": 0.002218779620220901, "0": 0.017107744452519395, "1": 0.4449312483904971, "2": 0.5275748905260689, "3": 0.006201039108251592, "4": 0.0019662979024421224}, "creative_writing_quality": {"not_applicable": 0.9955210276886932, "-2": 0.00021432250334126946, "-1": 0.0009032276994791478, "0": 0.0029619622241492164, "1": 0.0003193246729946232, "2": 8.013521134246675e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00012257524298821737, "Art & Design": 0.00020529242451338501, "Law & Crime": 0.00015183297757028364, "Education & Careers": 0.00015597989998897526, "Entertainment & Media": 0.00041128595612591087, "Fashion & Beauty": 9.717198848964415e-05, "Business, Economics & Finance": 0.00014936103706177705, "Food & Dining": 9.388839767077732e-05, "Games & Gaming": 0.08065631363133571, "Electronics & Hardware": 0.00010830450241605548, "Health, Healthcare & Wellness": 0.00010627394065289034, "History & Geography": 0.00016020020455996705, "Home, Pets & Hobbies": 0.0001666679822156117, "Industry & Production": 0.0001207446513767018, "Language, Literature & Philosophy": 0.00020937799273777763, "Politics & Government": 8.013761811062026e-05, "Religion & Spirituality": 0.00010758369460696273, "Science, Mathematics & Engineering": 0.002580333620064743, "Relationships & Community": 8.569074429848684e-05, "Software Usage & Applications": 0.0005121090042135608, "Programming & Software Engineering": 0.9128368939316376, "Sports & Fitness": 0.0002436416849489196, "Vehicles & Transportation": 6.682712424489718e-05, "Travel & Tourism": 0.00012665575497568612, "Other / Uncovered Subject": 0.00030132479117852385, "Unknown / Insufficient Content": 0.00014353120201639067}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009375990153783814, "Art & Design": 0.000796994846977224, "Law & Crime": 0.0006358600917630458, "Education & Careers": 0.0011652589323344574, "Entertainment & Media": 0.001082862149625757, "Fashion & Beauty": 0.00038958656024349106, "Business, Economics & Finance": 0.0005933761925402918, "Food & Dining": 0.0003435750686506749, "Games & Gaming": 0.272646981250104, "Electronics & Hardware": 0.000459620754605391, "Health, Healthcare & Wellness": 0.00042186383426479404, "History & Geography": 0.0005106169173658861, "Home, Pets & Hobbies": 0.0007264825685842128, "Industry & Production": 0.00035045139617032923, "Language, Literature & Philosophy": 0.0012134480038890627, "Politics & Government": 0.00033047357800023786, "Religion & Spirituality": 0.00045871365339980797, "Science, Mathematics & Engineering": 0.011453169052406903, "Relationships & Community": 0.0002744021006179762, "Software Usage & Applications": 0.0006939311826575714, "Programming & Software Engineering": 0.060998721557605266, "Sports & Fitness": 0.0005014742582938965, "Vehicles & Transportation": 0.00019511822886334292, "Travel & Tourism": 0.0005820602136033743, "Other / Uncovered Subject": 0.0008593867055960107, "NONE": 0.6413779718864587}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9631, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9631, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:04.273601+00:00", "scoring_key_slot": "primary", "dataset_uid": "fb3ca792-39e2-49fa-9e65-caff1298fe44", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "b9d22b80fcadd4b1ee8c6b8c7f490a616ef383b1c0e627f0f41825cbd1b8f08f", "source_metadata": {"uuid": "fb3ca792-39e2-49fa-9e65-caff1298fe44", "repo_name": "hanakysela/pyladies", "relative_path": "Piskvorky/strategie.py", "category": "TOOL", "algo_rel_score": 0.9438432455062866, "quality_score": 3.4753072261810303, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 13, "repo_created_at": "2016-10-24T17:54:12.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "3144d87b2a1ba1ab30a1d2a0984ac3444dc1e43e", "size_bytes": 3315, "file_timestamp": 1480487578, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "759aa300c5a5f9b6803c8fa13364dd587d5de535"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 13, "repo_created_at": "2016-10-24T17:54:12.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "3144d87b2a1ba1ab30a1d2a0984ac3444dc1e43e", "size_bytes": 3315, "file_timestamp": 1480487578, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "759aa300c5a5f9b6803c8fa13364dd587d5de535", "url": "https://github.com/hanakysela/pyladies/blob/759aa300c5a5f9b6803c8fa13364dd587d5de535/Piskvorky/strategie.py"}, "gpt4o_tokens": 1057, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.4700194147240249, "noise_severity": 0.022087045462579256, "missing_content_severity": 0.004999657386712768, "factual_density": 3.2465150363760857, "pretraining_utility": 0.502853440590686, "math_density": 0.9123324182672922, "code_density": 3.7138791530783855, "reasoning_density": 1.5745349711039305, "education_level": 1.529943946826457, "creative_writing_quality": -0.19028418825689974}, "score_applicability": {"education_level": 0.997781220379779, "creative_writing_quality": 0.004478972311306724}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/shadimohagheghi/Strategic-Network-Formation/blob/79b3ae51a479ee75b2bb202ad288ff76df70a87b/NetworkFormation_1.py", "id": "python-0-4edc4b87cd4ec8d3edd631627131515b", "text": "import networkx as nx\nfrom numpy import linalg as LA\nimport math\nimport collections\nimport numpy as np \nimport matplotlib\nfrom Payoff_1 import payoff_1\nfrom numpy import inf\nmatplotlib.rc('xtick', labelsize=25)\nmatplotlib.rc('ytick', labelsize=25)\nfrom random import random\nfrom MinimumSpanningTree import MinimumSpanningTree as mst\nimport matplotlib.pyplot as plt\nimport scipy.stats as st\nfrom networkx.algorithms import community as comm\nfrom community_louvain import best_partition\nplt.close(\"all\")\n\n##########################################################\n\nclique_sizes=3\n\nW_interconnection=[]\n\n'''\nW_interconnection.append([0   , 0.3 , 0.1 ])\nW_interconnection.append([0.3 , 0   , 0.3])\nW_interconnection.append([0.1 , 0.3, 0   ])\n'''\n\nW_interconnection.append([0   , 0.9 , 0.1 , 0.1 ])\nW_interconnection.append([0.9   , 0   , 0.1, 0.1])\nW_interconnection.append([0.1 , 0.1, 0   , 0   ])\nW_interconnection.append([0.1 , 0.1, 0   , 0   ])\n\n'''\nW_interconnection.append([0   , 0.9 , 0.1 , 0.05, 0.3])\nW_interconnection.append([0.9 , 0   , 0.15 , 0.2, 0.1])\nW_interconnection.append([0.1 , 0.15 , 0   , 0  , 0.5])\nW_interconnection.append([0.05 , 0.2 , 0   , 0  , 0.35])\nW_interconnection.append([0.3 , 0.1 , 0.5 , 0.35, 0  ])\n\n\nrowsums=A1.sum(axis=1)\nD1_out=np.diag(rowsums, k=0)\nA1_EN1= np.dot(LA.inv(D1_out+np.identity(n1)),A1+np.identity(n1))\n'''\nn_cliques=int(len(W_interconnection))\nn=clique_sizes*n_cliques\nW=np.zeros((n, n))\n\n#Comembership\n\nfor i in range(n_cliques):\n    for j in range(n_cliques):\n        for k in range(clique_sizes):\n            for l in range(clique_sizes):\n                W[i*clique_sizes+l][j*clique_sizes+k]=W_interconnection[i][j]\n\n\n#redundant ties\nfor i in range(n_cliques):\n    '''\n    for j in range(n_cliques):\n        for k in range(clique_sizes):\n            W[i*clique_sizes+k][j*clique_sizes+k]=W_interconnection[i][j]\n    '''\n    W[i*clique_sizes][i*clique_sizes+1]=1\n    W[i*clique_sizes][i*clique_sizes+2]=1\n\n    W[i*clique_sizes+1][i*clique_sizes]=1\n    W[i*clique_sizes+2][i*clique_sizes]=1\n\n    W[i*clique_sizes+1][i*clique_sizes+2]=1\n    W[i*clique_sizes+2][i*clique_sizes+1]=1\n\nfor i in range(n):\n    W[i][i]=0\n\nprint W-np.transpose(W)\n\nW1=np.kron(W_interconnection, np.ones((clique_sizes,clique_sizes)))\nW1=W1+np.kron(np.identity(n_cliques), np.ones((clique_sizes,clique_sizes)))\n\nfor i in range(n):\n    W1[i][i]=0\n\ndef Formation1(N):\n    \n    G=nx.Graph()\n    for i in range(N):\n        G.add_node(i)\n    \n    ### *** ### *** ### *** ### *** ### *** ### *** ### *** ### *** ### *** ### \n\n    U=payoff_1(G,W)\n    print \"U= \", np.around(U, 5)\n    np.set_printoptions(precision=5)\n    U_updated=[]\n\n    for u in range(500): \n        [i, j]= np.random.choice(N, 2, replace=False)\n        i=int(i)\n        j=int(j)\n        print [i,j]\n\n        if G.has_edge(i,j):\n            G.remove_edge(i,j)\n            U_updated=payoff_1(G,W)\n\n            #print \"U_updated[i], U_updated[j] \", U_updated[i], U_updated[j]\n            #print \"U[i], U[j] \", U[i], U[j]\n            #print \"U_updated= \", np.around(U_updated, 5)\n\n            if (U_updated[i]<=U[i] and U_updated[j]<=U[j]):\n            #if (sum(U_updated)<=sum(U)):\n                G.add_edge(i,j)\n                print \"didn't remove\"\n            else:\n                print \"removed\"\n                for q in range(len(U_updated)):\n                    U[q]=U_updated[q]\n            \n        else:\n            G.add_edge(i,j)\n            U_updated=payoff_1(G,W)\n\n            #print \"U_updated[i], U_updated[j] \", U_updated[i], U_updated[j]\n            #print \"U[i], U[j] \", U[i], U[j]\n            #print \"U_updated= \", np.around(U_updated, 5)\n\n            np.around(U_updated, 5)\n            np.set_printoptions(precision=5)\n\n            if (U_updated[i]>=U[i] and U_updated[j]>=U[j]):  \n            #if (sum(U_updated)>=sum(U)):              \n                print \"added\"\n                for q in range(len(U_updated)):\n                    U[q]=U_updated[q]\n            else:\n                G.remove_edge(i,j)\n                print \"didn't add\"\n\n    U_updated=payoff_1(G,W)\n\n    print \"sum(U), sum(U_updated): \", sum(U), sum(U_updated)\n    \n    A= nx.to_numpy_matrix(G)\n    A = np.array(A)\n\n    print W\n    print W1\n    print np.kron(np.identity(n_cliques), np.ones((clique_sizes,clique_sizes)))\n    weights=[]\n    allWeights=[]\n    '''\n    e=G.number_of_edges()\n    for i in range(n):\n        for j in range(i, n):\n            if i != j and A[i][j]:\n                weights.append(W[i][j])\n            if i != j and W[i][j]:\n                allWeights.append(W[i][j])\n    '''\n    GW = nx.from_numpy_matrix(W, create_using=nx.MultiGraph())\n    for (i, j) in GW.edges():\n        allWeights.append(W[i][j])\n\n    for (i, j) in G.edges():\n        weights.append(W[i][j])\n\n    print allWeights, np.around(U, 3)\n    np.set_printoptions(precision=3)\n\n    #partition = best_partition(G) \n\n    weights=np.multiply(weights, 1)\n    allWeights=np.multiply(allWeights, 10)\n\n    plt.figure(1)\n    nx.draw(G, pos=nx.shell_layout(G), weight=weights, width=weights, color= weights, node_size=500,cmap=plt.cm.Reds, with_labels = True)\n    plt.show() \n    '''\n    plt.figure(2)\n    print GW.edges()\n    nx.draw(GW, pos=nx.shell_layout(GW), weight=allWeights, width=allWeights, color= allWeights, node_size=500,cmap=plt.cm.Reds, with_labels = True)\n    #nx.draw(G, pos=nx.circular_layout(G), node_size=500,cmap=plt.cm.Reds, with_labels = True)\n    plt.show() \n    '''\n    return G\n\n#G = Formation(6)\n\n", "text_preview": "import networkx as nx from numpy import linalg as LA import math import collections import numpy as np import matplotlib from Payoff_1 import payoff_1 from numpy import inf matplotlib.rc('xtick', labelsize=25) matplotlib.rc('ytick', labelsi", "input_sha256": "b23d2f923c43f77fa0819aa75bf4a4ef622920a3c840d3df1ad84378b7b0e233", "text_sha256": "4edc4b87cd4ec8d3edd631627131515b76d0c1f715f8b77c1e6d89c5b060679a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12015, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.1286223455738068, "noise_severity": 0.20853412168790902, "missing_content_severity": 0.10853353160368501, "factual_density": 2.6739492491137, "pretraining_utility": 0.26588853813167956, "math_density": 3.3120393131506978, "code_density": 3.900146238497933, "reasoning_density": 0.49830734255667486, "education_level": 2.63942732924324, "creative_writing_quality": -0.560500135329162}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.5468 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.019754405593062185, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9938517527092946, "secondary": 0.9639808340055386, "tertiary": 0.9845941267982992, "structured_records": 0.03433591497431041, "procedural_steps": 0.010019689248814755, "worked_solution": 0.012333799973181818, "contextual_dialogue": 4.64166093125131e-06, "content_quality": 0.4674446893989679, "noise_severity": 0.8262215652600757, "missing_content_severity": 0.9095553903302626, "factual_density": 0.23691954258467762, "pretraining_utility": 0.4692853978201861, "math_density": 0.42669942762558144, "code_density": 0.9167885320816105, "reasoning_density": 0.584743881202771, "education_level": 0.35304305427097415, "creative_writing_quality": 0.9989419419188261, "primary_topic": 0.770400655482071, "secondary_topic": 0.5286405578625357}, "probability_margin": {"primary": 0.9917516900805302, "secondary": 0.9530285085001858, "tertiary": 0.9804485698303872, "education_level": 0.05004514114653108, "creative_writing_quality": 0.9987791083808595, "primary_topic": 0.6034262295903405, "secondary_topic": 0.31151460380189866}, "raw_probabilities": {"primary": {"Academic Writing": 0.0007536821044196406, "Content Listing": 3.226861540388124e-05, "Creative Writing": 2.6388919453457207e-05, "Customer Support Page": 1.1036086392700339e-05, "Q&A Forum": 1.7824688145301078e-05, "Discussion Thread": 1.2608078400780222e-05, "Comment Section": 1.7650885357665266e-05, "Missing Content": 4.004094423396603e-05, "Knowledge Article": 0.00012488600884809708, "Legal Notices": 3.140171318937328e-05, "News Article": 7.284952996377306e-06, "Organizational Announcement": 1.4400161473848245e-05, "Nonfiction Writing": 4.679278877464917e-05, "Organizational About Page": 1.8814821369057282e-05, "Personal About Page": 1.8808004183868204e-05, "Personal Blog": 1.0942175219976825e-05, "Product Page": 8.836336249195073e-06, "Spam / Ads": 1.5109449475203133e-05, "Structured Data": 0.0011301706765156874, "Technical Writing": 0.9941079296797407, "Transcript / Interview": 2.557344455659617e-05, "Tutorial / How-To Guide": 0.0011615599786996764, "User Reviews": 9.749887689897613e-06, "Other / Unknown": 0.002356239599210491}, "secondary": {"Academic Writing": 0.007929037587190886, "Content Listing": 0.00010352663085063948, "Creative Writing": 0.0001464594935873416, "Customer Support Page": 5.856645076108914e-05, "Q&A Forum": 5.98001344971819e-05, "Discussion Thread": 5.969008272730024e-05, "Comment Section": 0.0001058090018477991, "Missing Content": 0.0005482908044360309, "Knowledge Article": 0.0005726295514761121, "Legal Notices": 0.00012280065313117802, "News Article": 6.830832507218617e-05, "Organizational Announcement": 6.459283030550429e-05, "Nonfiction Writing": 0.00014720360864690102, "Organizational About Page": 9.177384684345115e-05, "Personal About Page": 7.484582753113119e-05, "Personal Blog": 6.151059830441449e-05, "Product Page": 4.3126779762467174e-05, "Spam / Ads": 6.700398993934921e-05, "Structured Data": 0.01245312408845531, "Technical Writing": 0.009135949730903967, "Transcript / Interview": 0.00010599614843580935, "Tutorial / How-To Guide": 0.0024545028677378402, "User Reviews": 4.3818378914924095e-05, "NONE": 0.9654816325886412}, "tertiary": {"Academic Writing": 0.003399900448864028, "Content Listing": 4.945328016816099e-05, "Creative Writing": 7.69075396128908e-05, "Customer Support Page": 2.7699786812601648e-05, "Q&A Forum": 2.412868400416874e-05, "Discussion Thread": 2.6914617187084385e-05, "Comment Section": 5.305332271329214e-05, "Missing Content": 0.0003968990282303287, "Knowledge Article": 0.0004136802412334637, "Legal Notices": 9.265812009351973e-05, "News Article": 3.556126096368121e-05, "Organizational Announcement": 2.8875995346113297e-05, "Nonfiction Writing": 9.535178708672178e-05, "Organizational About Page": 5.476613440367465e-05, "Personal About Page": 3.5786344405967144e-05, "Personal Blog": 3.231309877564035e-05, "Product Page": 2.1503702713457384e-05, "Spam / Ads": 3.559454602912641e-05, "Structured Data": 0.004787468351316163, "Technical Writing": 0.004083395480075804, "Transcript / Interview": 4.678257083030509e-05, "Tutorial / How-To Guide": 0.0009239265594342293, "User Reviews": 2.1340917996323718e-05, "NONE": 0.9852360381817034}, "structured_records": {"false": 0.9656640850256896, "true": 0.03433591497431041}, "procedural_steps": {"false": 0.9899803107511852, "true": 0.010019689248814755}, "worked_solution": {"false": 0.9876662000268182, "true": 0.012333799973181818}, "contextual_dialogue": {"false": 0.9999953583390687, "true": 4.64166093125131e-06}, "content_quality": {"-2": 0.03290214853971756, "-1": 0.18941771649428074, "0": 0.4554180909642327, "1": 0.26067972885601526, "2": 0.061582315145753685}, "noise_severity": {"0": 0.8417340247803127, "1": 0.11492260063638049, "2": 0.03791451415846259, "3": 0.00393294896477407, "4": 0.0014959114600702965}, "missing_content_severity": {"0": 0.9223102985709067, "1": 0.05268908026653501, "2": 0.019918120756960412, "3": 0.00432179179916127, "4": 0.0007607086064363357}, "factual_density": {"0": 0.07031754983093291, "1": 0.10846735154793291, "2": 0.19298629730367908, "3": 0.33340590231141204, "4": 0.29482289900604314}, "pretraining_utility": {"-2": 0.014178501653109103, "-1": 0.1571274889358304, "0": 0.4441799085570677, "1": 0.31765517133425736, "2": 0.06685892951973539}, "math_density": {"0": 0.008717423451078338, "1": 0.02429208851800408, "2": 0.12311142403235009, "3": 0.3339918794262765, "4": 0.509887184572291}, "code_density": {"0": 0.00017974913270765592, "1": 0.0003546251517053561, "2": 0.01045835555845982, "3": 0.07715417839920109, "4": 0.9118530917579262}, "reasoning_density": {"0": 0.7888295231913701, "1": 0.05222521043205288, "2": 0.07135467659534504, "3": 0.046989580190996104, "4": 0.040601009590235895}, "education_level": {"none": 0.0016858609133359086, "0": 0.0016928344957519484, "1": 0.00631503398582199, "2": 0.4608692118924785, "3": 0.4108240707459474, "4": 0.11861298796666422}, "creative_writing_quality": {"not_applicable": 0.9991182849323551, "-2": 0.000182655763471331, "-1": 0.0002593495711416227, "0": 0.0003391765514956425, "1": 7.06066797256179e-05, "2": 2.9926501810907492e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00023539523778688798, "Art & Design": 0.0005059428225069179, "Law & Crime": 0.0004587074518510049, "Education & Careers": 0.00027126609061980933, "Entertainment & Media": 0.0002665526245037599, "Fashion & Beauty": 0.00026509886822548577, "Business, Economics & Finance": 0.006580065282631358, "Food & Dining": 0.00022217231480030057, "Games & Gaming": 0.0010256648673489153, "Electronics & Hardware": 0.0003464071054284278, "Health, Healthcare & Wellness": 0.0003874144270992511, "History & Geography": 0.0004212874307637186, "Home, Pets & Hobbies": 0.0002409116422572957, "Industry & Production": 0.0003007972824363258, "Language, Literature & Philosophy": 0.00032605027954955195, "Politics & Government": 0.00027390241996196084, "Religion & Spirituality": 0.0002948004747465245, "Science, Mathematics & Engineering": 0.17580516991165082, "Relationships & Community": 0.0025743638020793887, "Software Usage & Applications": 0.0005279997557925314, "Programming & Software Engineering": 0.7792313995019914, "Sports & Fitness": 0.00032813590520391764, "Vehicles & Transportation": 0.0001797515196322386, "Travel & Tourism": 0.00041117662434160524, "Other / Uncovered Subject": 0.027942177125544337, "Unknown / Insufficient Content": 0.0005773892312462044}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006721525554731695, "Art & Design": 0.0017712948663511387, "Law & Crime": 0.0009767272169006246, "Education & Careers": 0.0004917165153732541, "Entertainment & Media": 0.0004782532605561527, "Fashion & Beauty": 0.0004368340116283823, "Business, Economics & Finance": 0.008944398048455553, "Food & Dining": 0.0004296696141606216, "Games & Gaming": 0.0015479565097422248, "Electronics & Hardware": 0.0006156429606002616, "Health, Healthcare & Wellness": 0.0006661253579066862, "History & Geography": 0.0006064492611230309, "Home, Pets & Hobbies": 0.00039029333634330607, "Industry & Production": 0.0004854620859141495, "Language, Literature & Philosophy": 0.0003575265689067072, "Politics & Government": 0.0005601853627503062, "Religion & Spirituality": 0.0006293403877082228, "Science, Mathematics & Engineering": 0.5467697671755151, "Relationships & Community": 0.003864877242046999, "Software Usage & Applications": 0.0005972920240720571, "Programming & Software Engineering": 0.1559146952463141, "Sports & Fitness": 0.000538372668795582, "Vehicles & Transportation": 0.00030488309991743656, "Travel & Tourism": 0.0006982062356793311, "Other / Uncovered Subject": 0.03599671501414912, "NONE": 0.23525516337361638}}, "probabilities": {"primary": {"Academic Writing": 0.0007536821044196406, "Content Listing": 3.226861540388124e-05, "Creative Writing": 2.6388919453457207e-05, "Customer Support Page": 1.1036086392700339e-05, "Q&A Forum": 1.7824688145301078e-05, "Discussion Thread": 1.2608078400780222e-05, "Comment Section": 1.7650885357665266e-05, "Missing Content": 4.004094423396603e-05, "Knowledge Article": 0.00012488600884809708, "Legal Notices": 3.140171318937328e-05, "News Article": 7.284952996377306e-06, "Organizational Announcement": 1.4400161473848245e-05, "Nonfiction Writing": 4.679278877464917e-05, "Organizational About Page": 1.8814821369057282e-05, "Personal About Page": 1.8808004183868204e-05, "Personal Blog": 1.0942175219976825e-05, "Product Page": 8.836336249195073e-06, "Spam / Ads": 1.5109449475203133e-05, "Structured Data": 0.0011301706765156874, "Technical Writing": 0.9941079296797407, "Transcript / Interview": 2.557344455659617e-05, "Tutorial / How-To Guide": 0.0011615599786996764, "User Reviews": 9.749887689897613e-06, "Other / Unknown": 0.002356239599210491}, "secondary": {"Academic Writing": 0.007929037587190886, "Content Listing": 0.00010352663085063948, "Creative Writing": 0.0001464594935873416, "Customer Support Page": 5.856645076108914e-05, "Q&A Forum": 5.98001344971819e-05, "Discussion Thread": 5.969008272730024e-05, "Comment Section": 0.0001058090018477991, "Missing Content": 0.0005482908044360309, "Knowledge Article": 0.0005726295514761121, "Legal Notices": 0.00012280065313117802, "News Article": 6.830832507218617e-05, "Organizational Announcement": 6.459283030550429e-05, "Nonfiction Writing": 0.00014720360864690102, "Organizational About Page": 9.177384684345115e-05, "Personal About Page": 7.484582753113119e-05, "Personal Blog": 6.151059830441449e-05, "Product Page": 4.3126779762467174e-05, "Spam / Ads": 6.700398993934921e-05, "Structured Data": 0.01245312408845531, "Technical Writing": 0.009135949730903967, "Transcript / Interview": 0.00010599614843580935, "Tutorial / How-To Guide": 0.0024545028677378402, "User Reviews": 4.3818378914924095e-05, "NONE": 0.9654816325886412}, "tertiary": {"Academic Writing": 0.003399900448864028, "Content Listing": 4.945328016816099e-05, "Creative Writing": 7.69075396128908e-05, "Customer Support Page": 2.7699786812601648e-05, "Q&A Forum": 2.412868400416874e-05, "Discussion Thread": 2.6914617187084385e-05, "Comment Section": 5.305332271329214e-05, "Missing Content": 0.0003968990282303287, "Knowledge Article": 0.0004136802412334637, "Legal Notices": 9.265812009351973e-05, "News Article": 3.556126096368121e-05, "Organizational Announcement": 2.8875995346113297e-05, "Nonfiction Writing": 9.535178708672178e-05, "Organizational About Page": 5.476613440367465e-05, "Personal About Page": 3.5786344405967144e-05, "Personal Blog": 3.231309877564035e-05, "Product Page": 2.1503702713457384e-05, "Spam / Ads": 3.559454602912641e-05, "Structured Data": 0.004787468351316163, "Technical Writing": 0.004083395480075804, "Transcript / Interview": 4.678257083030509e-05, "Tutorial / How-To Guide": 0.0009239265594342293, "User Reviews": 2.1340917996323718e-05, "NONE": 0.9852360381817034}, "structured_records": {"false": 0.9656640850256896, "true": 0.03433591497431041}, "procedural_steps": {"false": 0.9899803107511852, "true": 0.010019689248814755}, "worked_solution": {"false": 0.9876662000268182, "true": 0.012333799973181818}, "contextual_dialogue": {"false": 0.9999953583390687, "true": 4.64166093125131e-06}, "content_quality": {"-2": 0.03290214853971757, "-1": 0.18941771649428077, "0": 0.45541809096423275, "1": 0.2606797288560153, "2": 0.06158231514575369}, "noise_severity": {"0": 0.8417340247803125, "1": 0.11492260063638046, "2": 0.03791451415846258, "3": 0.003932948964774069, "4": 0.001495911460070296}, "missing_content_severity": {"0": 0.922310298570907, "1": 0.05268908026653502, "2": 0.019918120756960415, "3": 0.004321791799161271, "4": 0.000760708606436336}, "factual_density": {"0": 0.07031754983093291, "1": 0.10846735154793291, "2": 0.19298629730367908, "3": 0.33340590231141204, "4": 0.29482289900604314}, "pretraining_utility": {"-2": 0.014178501653109105, "-1": 0.15712748893583042, "0": 0.44417990855706774, "1": 0.3176551713342574, "2": 0.0668589295197354}, "math_density": {"0": 0.008717423451078338, "1": 0.02429208851800408, "2": 0.12311142403235009, "3": 0.3339918794262765, "4": 0.509887184572291}, "code_density": {"0": 0.00017974913270765592, "1": 0.0003546251517053561, "2": 0.01045835555845982, "3": 0.07715417839920109, "4": 0.9118530917579262}, "reasoning_density": {"0": 0.7888295231913701, "1": 0.05222521043205288, "2": 0.07135467659534504, "3": 0.046989580190996104, "4": 0.040601009590235895}, "education_level": {"none": 0.0016858609133359086, "0": 0.0016928344957519484, "1": 0.00631503398582199, "2": 0.4608692118924785, "3": 0.4108240707459474, "4": 0.11861298796666422}, "creative_writing_quality": {"not_applicable": 0.9991182849323549, "-2": 0.00018265576347133096, "-1": 0.00025934957114162263, "0": 0.00033917655149564245, "1": 7.060667972561788e-05, "2": 2.9926501810907485e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.000235395237786888, "Art & Design": 0.000505942822506918, "Law & Crime": 0.00045870745185100495, "Education & Careers": 0.0002712660906198094, "Entertainment & Media": 0.00026655262450375994, "Fashion & Beauty": 0.0002650988682254858, "Business, Economics & Finance": 0.006580065282631359, "Food & Dining": 0.0002221723148003006, "Games & Gaming": 0.0010256648673489155, "Electronics & Hardware": 0.00034640710542842783, "Health, Healthcare & Wellness": 0.00038741442709925114, "History & Geography": 0.00042128743076371865, "Home, Pets & Hobbies": 0.00024091164225729572, "Industry & Production": 0.00030079728243632585, "Language, Literature & Philosophy": 0.000326050279549552, "Politics & Government": 0.0002739024199619609, "Religion & Spirituality": 0.00029480047474652455, "Science, Mathematics & Engineering": 0.17580516991165085, "Relationships & Community": 0.002574363802079389, "Software Usage & Applications": 0.0005279997557925316, "Programming & Software Engineering": 0.7792313995019915, "Sports & Fitness": 0.0003281359052039177, "Vehicles & Transportation": 0.00017975151963223863, "Travel & Tourism": 0.0004111766243416053, "Other / Uncovered Subject": 0.02794217712554434, "Unknown / Insufficient Content": 0.0005773892312462045}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006721525554731696, "Art & Design": 0.001771294866351139, "Law & Crime": 0.0009767272169006248, "Education & Careers": 0.0004917165153732542, "Entertainment & Media": 0.00047825326055615276, "Fashion & Beauty": 0.0004368340116283824, "Business, Economics & Finance": 0.008944398048455555, "Food & Dining": 0.00042966961416062165, "Games & Gaming": 0.001547956509742225, "Electronics & Hardware": 0.0006156429606002617, "Health, Healthcare & Wellness": 0.0006661253579066863, "History & Geography": 0.000606449261123031, "Home, Pets & Hobbies": 0.0003902933363433061, "Industry & Production": 0.00048546208591414953, "Language, Literature & Philosophy": 0.00035752656890670727, "Politics & Government": 0.0005601853627503063, "Religion & Spirituality": 0.0006293403877082229, "Science, Mathematics & Engineering": 0.5467697671755152, "Relationships & Community": 0.0038648772420469993, "Software Usage & Applications": 0.0005972920240720572, "Programming & Software Engineering": 0.15591469524631413, "Sports & Fitness": 0.0005383726687955821, "Vehicles & Transportation": 0.0003048830999174366, "Travel & Tourism": 0.0006982062356793312, "Other / Uncovered Subject": 0.035996715014149125, "NONE": 0.2352551633736164}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10303, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10303, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:06.542684+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "a2fe855e-a5eb-4373-84a7-f487f2b9af4d", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "eea084a5678c92198b418bcce30c18549534802a6b4260c2e48007788ba29d75", "source_metadata": {"uuid": "a2fe855e-a5eb-4373-84a7-f487f2b9af4d", "repo_name": "shadimohagheghi/Strategic-Network-Formation", "relative_path": "NetworkFormation_1.py", "category": "DATA", "algo_rel_score": 0.9921702742576599, "quality_score": 3.9865798950195312, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 4, "repo_created_at": "2019-01-11T10:03:12.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "f38ffc73fd2a384ccb6f5a0d7b07cf5200212727", "size_bytes": 5485, "file_timestamp": 1576747930, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "79b3ae51a479ee75b2bb202ad288ff76df70a87b"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 4, "repo_created_at": "2019-01-11T10:03:12.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "f38ffc73fd2a384ccb6f5a0d7b07cf5200212727", "size_bytes": 5485, "file_timestamp": 1576747930, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "79b3ae51a479ee75b2bb202ad288ff76df70a87b", "url": "https://github.com/shadimohagheghi/Strategic-Network-Formation/blob/79b3ae51a479ee75b2bb202ad288ff76df70a87b/NetworkFormation_1.py"}, "gpt4o_tokens": 1673, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.1286223455738068, "noise_severity": 0.20853412168790905, "missing_content_severity": 0.10853353160368501, "factual_density": 2.6739492491137, "pretraining_utility": 0.26588853813167956, "math_density": 3.3120393131506978, "code_density": 3.900146238497933, "reasoning_density": 0.49830734255667486, "education_level": 2.63942732924324, "creative_writing_quality": -0.560500135329162}, "score_applicability": {"education_level": 0.998314139086664, "creative_writing_quality": 0.0008817150676451214}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/mtfelix/coding-the-matrix/blob/c959f4e49749e3526147d0ca732ec85277226d1d/vec/test_vec.py", "id": "python-0-e57ed6dadcd117fee13d63b2a93fd7fc", "text": "\"\"\"\n    \n>>> from vec import Vec\n    \nFor getitem(v,d):\n\nReturn the value of entry d in v.\nBe sure getitem(v,d) returns 0 if d is not represented in v.f.\n    \n>>> v = Vec({'a','b','c', 'd'},{'a':2,'c':1,'d':3})\n>>> v['d']\n3\n>>> v['b']\n0\n\nFor setitem(v,d,val):\n\nSet the element of v with label d to be val.\nsetitem(v,d,val) should set the value for key d even if d\n  is not previously represented in v.f. \n\n>>> v = Vec({'a', 'b', 'c'}, {'b':0})\n>>> v['b'] = 5\n>>> v['b']\n5\n>>> v['a'] = 1\n>>> v['a']\n1\n\nFor equal(u,v):\n    \nReturn true iff u is equal to v.\nBecause of sparse representation, it is not enough to compare dictionaries\n\n>>> Vec({'a', 'b', 'c'}, {'a':0}) == Vec({'a', 'b', 'c'}, {'b':0})\nTrue\n\nBe sure that equal(u, v) check equalities for all keys from u.f and v.f even if some keys in u.f do not exist in v.f (or vice versa)\n\n>>> Vec({'x','y','z'},{'y':1,'x':2}) == Vec({'x','y','z'},{'y':1,'z':0})\nFalse\n>>> Vec({'a','b','c'}, {'a':0,'c':1}) == Vec({'a','b','c'}, {'a':0,'c':1,'b':4})\nFalse\n    \nFor add(u, v):\n\nReturns the addition of the two vectors. \nBe sure to add together values for all keys from u.f and v.f even if some keys in u.f do not exist in v.f (or vice versa)\n    \n>>> a = Vec({'a','e','i','o','u'}, {'a':0,'e':1,'i':2})\n>>> b = Vec({'a','e','i','o','u'}, {'o':4,'u':7})\n>>> c = Vec({'a','e','i','o','u'}, {'a':0,'e':1,'i':2,'o':4,'u':7}) \n>>> a + b == c\nTrue\n>>> d = Vec({'x','y','z'}, {'x':2,'y':1})\n>>> e = Vec({'x','y','z'}, {'z':4,'y':-1})\n>>> f = Vec({'x','y','z'}, {'x':2,'y':0,'z':4})\n>>> d + e == f\nTrue\n>>> b + Vec({'a','e','i','o','u'}, {}) == b\nTrue\n    \nFor dot(u, v):\n    \nReturns the dot product of the two vectors.\n\n>>> u1 = Vec({'a','b'}, {'a':1, 'b':2})\n>>> u2 = Vec({'a','b'}, {'b':2, 'a':1})\n>>> u1*u2\n5\n>>> v1 = Vec({'p','q','r','s'}, {'p':2,'s':3,'q':-1,'r':0})\n>>> v2 = Vec({'p','q','r','s'}, {'p':-2,'r':5})\n>>> v1*v2\n-4\n>>> w1 = Vec({'a','b','c'}, {'a':2,'b':3,'c':4})\n>>> w2 = Vec({'a','b','c'}, {'a':12,'b':8,'c':6})\n>>> w1*w2\n72\n\nFor scalar_mul(v, alpha):\n    \nReturns the scalar-vector product alpha times v.\n\n>>> zero = Vec({'x','y','z','w'}, {})\n>>> u = Vec({'x','y','z','w'},{'x':1,'y':2,'z':3,'w':4})\n>>> 0*u == zero\nTrue\n>>> 1*u == u\nTrue\n>>> 0.5*u == Vec({'x','y','z','w'},{'x':0.5,'y':1,'z':1.5,'w':2})\nTrue\n    \nFor neg(v):\n\nReturns the negation of vector v. Be sure that all values in v.f are negated\n>>> zero = Vec({'x','y','z'}, {})\n>>> -zero == zero\nTrue\n>>> v = Vec({0,1,2,3,4,'a'}, {0:1,1:-1,2:-1,3:1})\n>>> -v == Vec({0,1,2,3,4,'a'}, {0:-1,1:1,2:1,3:-1}) \nTrue\n>>> -(-v) == v\nTrue\n>>> v == -v\nFalse\n\n\"\"\"\n\nif __name__ == \"__main__\":\n    import doctest\n    doctest.testmod()\n", "text_preview": "\"\"\" >>> from vec import Vec For getitem(v,d): Return the value of entry d in v. Be sure getitem(v,d) returns 0 if d is not represented in v.f. >>> v = Vec({'a','b','c', 'd'},{'a':2,'c':1,'d':3}) >>> v['d'] 3 >>> v['b'] 0 For setitem(v,d,val", "input_sha256": "e5b84014ed78ab80c208f811ac7c542fb76550e935f13e96b7b28f8b72d7908b", "text_sha256": "e57ed6dadcd117fee13d63b2a93fd7fc2a0bd5d38458c6bd234081be29f56ac0", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12018, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 3, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.29083976504786, "noise_severity": 0.13075079293406597, "missing_content_severity": 0.07175216173130117, "factual_density": 3.423745925084686, "pretraining_utility": 0.7486565200983225, "math_density": 3.336179295053399, "code_density": 3.2699052927950176, "reasoning_density": 0.6418482310070774, "education_level": 1.9133351766920559, "creative_writing_quality": -0.1465843181534374}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.5177 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0607910399731163, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.971123938078896, "secondary": 0.7750804127546188, "tertiary": 0.8385145414222184, "structured_records": 0.22952543125565553, "procedural_steps": 0.029688773986207742, "worked_solution": 0.3454019110382502, "contextual_dialogue": 2.3974165981202156e-05, "content_quality": 0.5858549856276452, "noise_severity": 0.8910410058882784, "missing_content_severity": 0.9402065318905823, "factual_density": 0.519788270903905, "pretraining_utility": 0.5679636010686534, "math_density": 0.4468160792111656, "code_density": 0.5167360200997404, "reasoning_density": 0.4651264741607688, "education_level": 0.7747144894050008, "creative_writing_quality": 0.9991288668548859, "primary_topic": 0.9175021258864304, "secondary_topic": 0.49845251992019135}, "probability_margin": {"primary": 0.9518247188270617, "secondary": 0.684577023647576, "tertiary": 0.7738905765068969, "education_level": 0.6770132061833382, "creative_writing_quality": 0.9989055695545804, "primary_topic": 0.8439575738146372, "secondary_topic": 0.2171038713523996}, "raw_probabilities": {"primary": {"Academic Writing": 0.00011748484725969127, "Content Listing": 6.68276284018729e-05, "Creative Writing": 4.188354167355233e-05, "Customer Support Page": 5.897275666759429e-05, "Q&A Forum": 4.175685390205755e-05, "Discussion Thread": 2.4220773264283644e-05, "Comment Section": 4.142468907616019e-05, "Missing Content": 5.338954733748797e-05, "Knowledge Article": 0.0004691664872055625, "Legal Notices": 4.1433480018616626e-05, "News Article": 1.6105876533374206e-05, "Organizational Announcement": 3.480371752390171e-05, "Nonfiction Writing": 3.7001543734611176e-05, "Organizational About Page": 4.255680682837109e-05, "Personal About Page": 3.358183384775288e-05, "Personal Blog": 2.3431627076642436e-05, "Product Page": 2.5151631405683238e-05, "Spam / Ads": 3.2237206623192416e-05, "Structured Data": 0.0010886975992128237, "Technical Writing": 0.9723271073256087, "Transcript / Interview": 5.700457090442836e-05, "Tutorial / How-To Guide": 0.020502388498547023, "User Reviews": 2.0499180156266378e-05, "Other / Unknown": 0.004802871977190506}, "secondary": {"Academic Writing": 0.0020552113372006913, "Content Listing": 0.0004179510579087176, "Creative Writing": 0.00046290636264403526, "Customer Support Page": 0.00046118082190650347, "Q&A Forum": 0.00031714030842405977, "Discussion Thread": 0.000178645184290325, "Comment Section": 0.00030136847352488374, "Missing Content": 0.0005163305727562011, "Knowledge Article": 0.0033500191512209196, "Legal Notices": 0.00027911754272708127, "News Article": 0.0002282677523443985, "Organizational Announcement": 0.000268412174378716, "Nonfiction Writing": 0.00028325639572263957, "Organizational About Page": 0.0003339975217471459, "Personal About Page": 0.00026409412606591176, "Personal Blog": 0.0002515855536741593, "Product Page": 0.00020854937358869325, "Spam / Ads": 0.00022270058195539551, "Structured Data": 0.0061207661518365535, "Technical Writing": 0.09987503857560036, "Transcript / Interview": 0.0004449252546576605, "Tutorial / How-To Guide": 0.09855963633332031, "User Reviews": 0.00014683716932831976, "NONE": 0.7844520622231763}, "tertiary": {"Academic Writing": 0.004033705876939278, "Content Listing": 0.00023995771315754892, "Creative Writing": 0.0003131401510717299, "Customer Support Page": 0.00027400659862034717, "Q&A Forum": 0.00016680162600742667, "Discussion Thread": 0.0001024532517139991, "Comment Section": 0.00019192040263988722, "Missing Content": 0.0004638115688640132, "Knowledge Article": 0.0053561417575549684, "Legal Notices": 0.00033544691067178856, "News Article": 0.00014547066212356588, "Organizational Announcement": 0.00016196435484976658, "Nonfiction Writing": 0.0002055391544509554, "Organizational About Page": 0.00023885791170834167, "Personal About Page": 0.0001601911099676355, "Personal Blog": 0.00013341871064534073, "Product Page": 0.00014040178950015523, "Spam / Ads": 0.00016422422016826543, "Structured Data": 0.0077370374653281705, "Technical Writing": 0.06245411308419596, "Transcript / Interview": 0.00029805049709866313, "Tutorial / How-To Guide": 0.07135252568939579, "User Reviews": 8.771729703379952e-05, "NONE": 0.8452431021962926}, "structured_records": {"false": 0.7704745687443445, "true": 0.22952543125565553}, "procedural_steps": {"false": 0.9703112260137923, "true": 0.029688773986207742}, "worked_solution": {"false": 0.6545980889617498, "true": 0.3454019110382502}, "contextual_dialogue": {"false": 0.9999760258340188, "true": 2.3974165981202156e-05}, "content_quality": {"-2": 0.002677518079091493, "-1": 0.006305115810263344, "0": 0.08242434024168172, "1": 0.5146861347216206, "2": 0.3939068911473429}, "noise_severity": {"0": 0.878040419103028, "1": 0.1161146916301876, "2": 0.003786514746309567, "3": 0.0011704262706400317, "4": 0.0008879482498347836}, "missing_content_severity": {"0": 0.9581135813793527, "1": 0.019096039352413614, "2": 0.01655607490030165, "3": 0.005393244893444693, "4": 0.0008410594744875482}, "factual_density": {"0": 0.001851137253084822, "1": 0.004208596833519535, "2": 0.07112907094952543, "3": 0.41396559350336515, "4": 0.5088456014605051}, "pretraining_utility": {"-2": 0.006837663257581717, "-1": 0.04764671418585059, "0": 0.2690871611652004, "1": 0.5428783619833981, "2": 0.1335500994079691}, "math_density": {"0": 0.004666925177599504, "1": 0.011627508379059879, "2": 0.10727833875649509, "3": 0.39571380158603325, "4": 0.4807134261008123}, "code_density": {"0": 0.0014369961964673557, "1": 0.004481410965055542, "2": 0.14173193102313392, "3": 0.42743862747767875, "4": 0.4249110343376645}, "reasoning_density": {"0": 0.5301239028770875, "1": 0.3339838856436827, "2": 0.10960286809992877, "3": 0.01649876435366647, "4": 0.009790579025634436}, "education_level": {"none": 0.001271253277107632, "0": 0.0026631343837538214, "1": 0.1352488683208292, "2": 0.8122620745041673, "3": 0.043088852307251554, "4": 0.0054658172068906144}, "creative_writing_quality": {"not_applicable": 0.9992740557124049, "-2": 7.535890578815679e-05, "-1": 0.00013993167995920958, "0": 0.0003684861578244239, "1": 0.0001000976449256766, "2": 4.2069899097665615e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 8.440322067788218e-05, "Art & Design": 0.00014038456689624578, "Law & Crime": 0.00012524164768595034, "Education & Careers": 0.00010247258652668065, "Entertainment & Media": 8.883508130569521e-05, "Fashion & Beauty": 7.561372784685598e-05, "Business, Economics & Finance": 0.00010536531944308003, "Food & Dining": 7.206265507430096e-05, "Games & Gaming": 0.00012059991699131619, "Electronics & Hardware": 0.00010140167786557961, "Health, Healthcare & Wellness": 9.975150548538539e-05, "History & Geography": 0.00012699645350048894, "Home, Pets & Hobbies": 7.348245689072259e-05, "Industry & Production": 7.246370803147717e-05, "Language, Literature & Philosophy": 9.76669779301244e-05, "Politics & Government": 4.781412266002064e-05, "Religion & Spirituality": 8.059662567456198e-05, "Science, Mathematics & Engineering": 0.0767175472300074, "Relationships & Community": 3.997365193860865e-05, "Software Usage & Applications": 0.00035051739016257906, "Programming & Software Engineering": 0.9206751210446447, "Sports & Fitness": 0.000117837740968364, "Vehicles & Transportation": 4.726897066973784e-05, "Travel & Tourism": 9.39411363601074e-05, "Other / Uncovered Subject": 0.00023053111744245004, "Unknown / Insufficient Content": 0.00011210946731993029}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005309756019955425, "Art & Design": 0.0006086957020710017, "Law & Crime": 0.0005329556773496219, "Education & Careers": 0.0010382671296489236, "Entertainment & Media": 0.000344800590784228, "Fashion & Beauty": 0.00028776765944400605, "Business, Economics & Finance": 0.00037903369412031464, "Food & Dining": 0.00024814410555512036, "Games & Gaming": 0.0003558113903891797, "Electronics & Hardware": 0.0004084174024072105, "Health, Healthcare & Wellness": 0.00041635298152534856, "History & Geography": 0.0004219187887425311, "Home, Pets & Hobbies": 0.00026256261293959875, "Industry & Production": 0.00030990900013161936, "Language, Literature & Philosophy": 0.00024169746671733253, "Politics & Government": 0.00021535045050792733, "Religion & Spirituality": 0.00037307103273545513, "Science, Mathematics & Engineering": 0.5177428076155686, "Relationships & Community": 0.0001737397715003992, "Software Usage & Applications": 0.0004879481178808268, "Programming & Software Engineering": 0.17245498784149293, "Sports & Fitness": 0.00035315575770995117, "Vehicles & Transportation": 0.00018951720817423644, "Travel & Tourism": 0.00048362641242982745, "Other / Uncovered Subject": 0.0004995497250094433, "NONE": 0.30063893626316895}}, "probabilities": {"primary": {"Academic Writing": 0.00011748484725969124, "Content Listing": 6.682762840187288e-05, "Creative Writing": 4.1883541673552324e-05, "Customer Support Page": 5.8972756667594274e-05, "Q&A Forum": 4.175685390205754e-05, "Discussion Thread": 2.4220773264283637e-05, "Comment Section": 4.142468907616018e-05, "Missing Content": 5.3389547337487955e-05, "Knowledge Article": 0.0004691664872055624, "Legal Notices": 4.143348001861662e-05, "News Article": 1.6105876533374203e-05, "Organizational Announcement": 3.48037175239017e-05, "Nonfiction Writing": 3.700154373461117e-05, "Organizational About Page": 4.2556806828371085e-05, "Personal About Page": 3.358183384775287e-05, "Personal Blog": 2.343162707664243e-05, "Product Page": 2.515163140568323e-05, "Spam / Ads": 3.223720662319241e-05, "Structured Data": 0.0010886975992128235, "Technical Writing": 0.9723271073256085, "Transcript / Interview": 5.7004570904428345e-05, "Tutorial / How-To Guide": 0.02050238849854702, "User Reviews": 2.0499180156266375e-05, "Other / Unknown": 0.004802871977190505}, "secondary": {"Academic Writing": 0.0020552113372006913, "Content Listing": 0.0004179510579087176, "Creative Writing": 0.00046290636264403526, "Customer Support Page": 0.00046118082190650347, "Q&A Forum": 0.00031714030842405977, "Discussion Thread": 0.000178645184290325, "Comment Section": 0.00030136847352488374, "Missing Content": 0.0005163305727562011, "Knowledge Article": 0.0033500191512209196, "Legal Notices": 0.00027911754272708127, "News Article": 0.0002282677523443985, "Organizational Announcement": 0.000268412174378716, "Nonfiction Writing": 0.00028325639572263957, "Organizational About Page": 0.0003339975217471459, "Personal About Page": 0.00026409412606591176, "Personal Blog": 0.0002515855536741593, "Product Page": 0.00020854937358869325, "Spam / Ads": 0.00022270058195539551, "Structured Data": 0.0061207661518365535, "Technical Writing": 0.09987503857560036, "Transcript / Interview": 0.0004449252546576605, "Tutorial / How-To Guide": 0.09855963633332031, "User Reviews": 0.00014683716932831976, "NONE": 0.7844520622231763}, "tertiary": {"Academic Writing": 0.004033705876939278, "Content Listing": 0.00023995771315754892, "Creative Writing": 0.0003131401510717299, "Customer Support Page": 0.00027400659862034717, "Q&A Forum": 0.00016680162600742667, "Discussion Thread": 0.0001024532517139991, "Comment Section": 0.00019192040263988722, "Missing Content": 0.0004638115688640132, "Knowledge Article": 0.0053561417575549684, "Legal Notices": 0.00033544691067178856, "News Article": 0.00014547066212356588, "Organizational Announcement": 0.00016196435484976658, "Nonfiction Writing": 0.0002055391544509554, "Organizational About Page": 0.00023885791170834167, "Personal About Page": 0.0001601911099676355, "Personal Blog": 0.00013341871064534073, "Product Page": 0.00014040178950015523, "Spam / Ads": 0.00016422422016826543, "Structured Data": 0.0077370374653281705, "Technical Writing": 0.06245411308419596, "Transcript / Interview": 0.00029805049709866313, "Tutorial / How-To Guide": 0.07135252568939579, "User Reviews": 8.771729703379952e-05, "NONE": 0.8452431021962926}, "structured_records": {"false": 0.7704745687443445, "true": 0.22952543125565553}, "procedural_steps": {"false": 0.9703112260137923, "true": 0.029688773986207742}, "worked_solution": {"false": 0.6545980889617498, "true": 0.3454019110382502}, "contextual_dialogue": {"false": 0.9999760258340188, "true": 2.3974165981202156e-05}, "content_quality": {"-2": 0.002677518079091493, "-1": 0.006305115810263344, "0": 0.08242434024168172, "1": 0.5146861347216206, "2": 0.3939068911473429}, "noise_severity": {"0": 0.878040419103028, "1": 0.1161146916301876, "2": 0.003786514746309567, "3": 0.0011704262706400317, "4": 0.0008879482498347836}, "missing_content_severity": {"0": 0.9581135813793524, "1": 0.01909603935241361, "2": 0.016556074900301646, "3": 0.0053932448934446925, "4": 0.000841059474487548}, "factual_density": {"0": 0.001851137253084822, "1": 0.004208596833519535, "2": 0.07112907094952543, "3": 0.41396559350336515, "4": 0.5088456014605051}, "pretraining_utility": {"-2": 0.006837663257581718, "-1": 0.0476467141858506, "0": 0.26908716116520043, "1": 0.5428783619833982, "2": 0.13355009940796914}, "math_density": {"0": 0.004666925177599504, "1": 0.011627508379059879, "2": 0.10727833875649509, "3": 0.39571380158603325, "4": 0.4807134261008123}, "code_density": {"0": 0.0014369961964673557, "1": 0.004481410965055542, "2": 0.14173193102313392, "3": 0.42743862747767875, "4": 0.4249110343376645}, "reasoning_density": {"0": 0.5301239028770877, "1": 0.33398388564368275, "2": 0.10960286809992878, "3": 0.016498764353666474, "4": 0.009790579025634438}, "education_level": {"none": 0.0012712532771076317, "0": 0.002663134383753821, "1": 0.13524886832082916, "2": 0.8122620745041671, "3": 0.04308885230725155, "4": 0.005465817206890614}, "creative_writing_quality": {"not_applicable": 0.9992740557124049, "-2": 7.535890578815679e-05, "-1": 0.00013993167995920958, "0": 0.0003684861578244239, "1": 0.0001000976449256766, "2": 4.2069899097665615e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 8.440322067788216e-05, "Art & Design": 0.00014038456689624576, "Law & Crime": 0.0001252416476859503, "Education & Careers": 0.00010247258652668062, "Entertainment & Media": 8.88350813056952e-05, "Fashion & Beauty": 7.561372784685597e-05, "Business, Economics & Finance": 0.00010536531944308, "Food & Dining": 7.206265507430094e-05, "Games & Gaming": 0.00012059991699131616, "Electronics & Hardware": 0.00010140167786557959, "Health, Healthcare & Wellness": 9.975150548538536e-05, "History & Geography": 0.0001269964535004889, "Home, Pets & Hobbies": 7.348245689072257e-05, "Industry & Production": 7.246370803147716e-05, "Language, Literature & Philosophy": 9.766697793012437e-05, "Politics & Government": 4.781412266002063e-05, "Religion & Spirituality": 8.059662567456196e-05, "Science, Mathematics & Engineering": 0.07671754723000739, "Relationships & Community": 3.9973651938608646e-05, "Software Usage & Applications": 0.000350517390162579, "Programming & Software Engineering": 0.9206751210446444, "Sports & Fitness": 0.00011783774096836397, "Vehicles & Transportation": 4.726897066973783e-05, "Travel & Tourism": 9.394113636010737e-05, "Other / Uncovered Subject": 0.00023053111744245, "Unknown / Insufficient Content": 0.00011210946731993026}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005309756019955425, "Art & Design": 0.0006086957020710017, "Law & Crime": 0.0005329556773496219, "Education & Careers": 0.0010382671296489236, "Entertainment & Media": 0.000344800590784228, "Fashion & Beauty": 0.00028776765944400605, "Business, Economics & Finance": 0.00037903369412031464, "Food & Dining": 0.00024814410555512036, "Games & Gaming": 0.0003558113903891797, "Electronics & Hardware": 0.0004084174024072105, "Health, Healthcare & Wellness": 0.00041635298152534856, "History & Geography": 0.0004219187887425311, "Home, Pets & Hobbies": 0.00026256261293959875, "Industry & Production": 0.00030990900013161936, "Language, Literature & Philosophy": 0.00024169746671733253, "Politics & Government": 0.00021535045050792733, "Religion & Spirituality": 0.00037307103273545513, "Science, Mathematics & Engineering": 0.5177428076155686, "Relationships & Community": 0.0001737397715003992, "Software Usage & Applications": 0.0004879481178808268, "Programming & Software Engineering": 0.17245498784149293, "Sports & Fitness": 0.00035315575770995117, "Vehicles & Transportation": 0.00018951720817423644, "Travel & Tourism": 0.00048362641242982745, "Other / Uncovered Subject": 0.0004995497250094433, "NONE": 0.30063893626316895}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9611, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9611, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:07.383292+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "ab8fe461-c7d9-4887-9ebd-c859a89eed49", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "bcffaeda15c8ab8690b6e89d4c68dbd5753d22c35c3f359e7a5bcc8777331508", "source_metadata": {"uuid": "ab8fe461-c7d9-4887-9ebd-c859a89eed49", "repo_name": "mtfelix/coding-the-matrix", "relative_path": "vec/test_vec.py", "category": "TOOL", "algo_rel_score": 0.9197890758514404, "quality_score": 4.721138000488281, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 77, "repo_created_at": "2013-07-07T09:27:45.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 46, "forks": 35, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "a37e6c67173dce1ab145bc36998f683ac6260d93", "size_bytes": 2645, "file_timestamp": 1645670699, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "c959f4e49749e3526147d0ca732ec85277226d1d"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 77, "repo_created_at": "2013-07-07T09:27:45.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 46, "forks": 35, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "a37e6c67173dce1ab145bc36998f683ac6260d93", "size_bytes": 2645, "file_timestamp": 1645670699, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "c959f4e49749e3526147d0ca732ec85277226d1d", "url": "https://github.com/mtfelix/coding-the-matrix/blob/c959f4e49749e3526147d0ca732ec85277226d1d/vec/test_vec.py"}, "gpt4o_tokens": 1067, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.29083976504786, "noise_severity": 0.13075079293406597, "missing_content_severity": 0.07175216173130118, "factual_density": 3.423745925084686, "pretraining_utility": 0.7486565200983225, "math_density": 3.336179295053399, "code_density": 3.2699052927950176, "reasoning_density": 0.6418482310070774, "education_level": 1.9133351766920559, "creative_writing_quality": -0.1465843181534374}, "score_applicability": {"education_level": 0.9987287467228922, "creative_writing_quality": 0.0007259442875951325}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/speechbrain/speechbrain/blob/ec1425368dd3fc9dd41edc7b50a9148cd463abec/tests/utils/refactoring_checks.py", "id": "python-0-d7235ede2c1bf5af29c5063ebfbb078b", "text": "#!/usr/bin/env/python3\n\"\"\"This is a test script for creating a list of expected outcomes (before refactoring);\nthen, manual editing might change YAMLs and/or code; another test runs to compare results\n(after refactoring to before). The target is a list of known HF repos.\n\nThe goal is to identify to which extent changes break existing functionality.\nThen, larger changes to code base can be rolled out more assured.\n\nAuthors\n * <NAME>, 2022, 2023\n\"\"\"\n\nimport importlib  # noqa\nimport os\nimport subprocess\nimport sys\nfrom copy import deepcopy\nfrom glob import glob\n\nimport torch  # noqa\nimport yaml\nfrom hyperpyyaml import load_hyperpyyaml\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\n\nimport speechbrain  # noqa\nfrom speechbrain.dataio.dataloader import LoopedLoader, make_dataloader\nfrom speechbrain.inference.interfaces import foreign_class  # noqa\nfrom speechbrain.utils.distributed import run_on_main  # noqa\nfrom speechbrain.utils.train_logger import FileTrainLogger\n\n\ndef init(\n    new_interfaces_git=\"https://github.com/speechbrain/speechbrain\",\n    new_interfaces_branch=\"hf-interface-testing\",\n    new_interfaces_local_dir=\"tests/tmp/hf_interfaces\",\n):\n    \"\"\"Initialises a PR branch to: https://github.com/speechbrain/speechbrain/tree/hf-interface-testing\n\n    Skip if the path as of `new_interfaces_local_dir` exists (e.g. by DIY init instead of via this script).\n\n    Arguments\n    ---------\n    new_interfaces_git: str\n        Your git repo (or default: `https://github.com/speechbrain/speechbrain`);\n        can be specified in tests/utils/overrides.yaml\n    new_interfaces_branch: str\n        Default is `hf-interface-testing` (a git branch); can be specified in tests/utils/overrides.yaml\n    new_interfaces_local_dir: str\n        Default is `tests/tmp/hf_interfaces` (a local path); can be specified in tests/utils/overrides.yaml\n\n    Returns\n    -------\n    str\n        Local path of `updates_pretrained_models` where the update HF yaml/interface files can be found.\n    \"\"\"\n    # set up git etc\n    if not os.path.exists(new_interfaces_local_dir):\n        # note: not checking for anything, whether it exists or not - or if there is a previous one already\n        # clone repo with PR on updates_pretrained_models into local folder\n        cmd_out_clone = subprocess.run(\n            [\"git\", \"clone\", new_interfaces_git, new_interfaces_local_dir],\n            capture_output=True,\n        )\n        print(f\"\\tgit clone log: {cmd_out_clone}\")\n\n        # cd into that local folder, switch branch to the one containing updates_pretrained_models & cd back\n        cwd = os.getcwd()\n        os.chdir(new_interfaces_local_dir)\n        cmd_out_co = subprocess.run(\n            [\"git\", \"checkout\", new_interfaces_branch], capture_output=True\n        )\n        print(f\"\\tgit checkout log: {cmd_out_co}\")\n        os.chdir(cwd)\n\n    # return the valid local path with updates_pretrained_models\n    updates_dir = f\"{new_interfaces_local_dir}/updates_pretrained_models\"\n    return updates_dir\n\n\ndef get_model(repo, values, updates_dir=None, run_opts=None):\n    \"\"\"Fetches a pretrained model with the option the re-specify its hyperparameters & interface.\n\n    Arguments\n    ---------\n    repo: str\n        Source of pretrained model (assuming its within the HF speechbrain collection).\n    values: dict\n        Interface specification.\n        Example: speechbrain:hf-interface-testing/updates_pretrained_models/ssl-wav2vec2-base-librispeech/test.yaml\n    updates_dir: str\n        Local folder with yaml:interface updates; None (default) = take original yaml/interface specification.\n    run_opts: dict\n        Run options, such as device\n\n    Returns\n    -------\n    A pretrained model with a speechbrain.inference.interface or a custom interface.\n    \"\"\"\n    # get the pretrained class; model & predictions\n    kwargs = {\n        \"source\": f\"speechbrain/{repo}\",\n        \"savedir\": f\"pretrained_models/{repo}\",\n    }\n\n    # adjust symlinks\n    hparams = f\"pretrained_models/{repo}/hyperparams.yaml\"\n    if (\n        \"foreign\" in values.keys()\n    ):  # it's a custom model which has its own Python filename\n        custom = f'pretrained_models/{repo}/{values[\"foreign\"]}'\n    # prepare model loading: is it the old -or- the new yaml/interface?\n    if updates_dir is not None:\n        # testing the refactoring; assuming all model data has been loaded already\n        kwargs[\"source\"] = f\"{updates_dir}/{repo}\"\n        os.unlink(hparams)\n        os.symlink(f\"{updates_dir}/{repo}/hyperparams.yaml\", hparams)\n        if \"foreign\" in values.keys():\n            os.unlink(custom)\n            os.symlink(\n                f'{updates_dir}/{repo}/{values[\"foreign\"]}',\n                custom,\n            )\n    else:\n        # re:testing on develop? => simply unlink anything before and re:link from cached HF hub\n        if os.path.exists(hparams):\n            os.unlink(hparams)\n        if \"foreign\" in values.keys():\n            if os.path.exists(custom):\n                os.unlink(custom)\n\n    if run_opts is not None:\n        kwargs[\"run_opts\"] = run_opts\n\n    print(f\"\\trepo: {repo}\")\n    # load pretrained model either via specified pretrained class or custom interface\n    if \"foreign\" not in values.keys():\n        print(f'\\tspeechbrain.inference.{values[\"cls\"]}')\n        print(f\"\\tobj.from_hparams({kwargs})\")\n        obj = eval(f'speechbrain.inference.{values[\"cls\"]}')\n        model = obj.from_hparams(**kwargs)\n    else:\n        kwargs[\"pymodule_file\"] = values[\"foreign\"]\n        kwargs[\"classname\"] = values[\"cls\"]\n        model = foreign_class(**kwargs)\n\n    return model\n\n\ndef get_prediction(repo, values, updates_dir=None):\n    \"\"\"Gets the prediction for one predefined audio example, pattern: {repo}/{values[\"sample\"]} (see HF model card).\n\n    Arguments\n    ---------\n    repo: str\n        Source of pretrained model (assuming its within the HF speechbrain collection).\n    values: dict\n        Interface specification.\n        Examples: speechbrain:hf-interface-testing/updates_pretrained_models/ssl-wav2vec2-base-librispeech/test.yaml\n                  speechbrain:hf-interface-testing/updates_pretrained_models/asr-wav2vec2-librispeech/test.yaml\n    updates_dir: str\n        Controls whether/not we are in the refactored results (None: expected results; before refactoring).\n\n    Returns\n    -------\n    Cleaned-up prediction results for yaml output (result logging & comparison through yaml de/serialization).\n    \"\"\"\n\n    def sanitize(data):\n        # cleanup data for yaml output (w/o this, yaml will make attempts to save torch/numpy arrays in their format)\n        if isinstance(data, torch.Tensor):\n            data = data.detach().cpu().numpy()\n            if data.ndim:\n                data = list(data)\n        return data\n\n    # get the pretrained model (before/after yaml/interface update)\n    model = get_model(repo=repo, values=values, updates_dir=updates_dir)  # noqa\n\n    try:\n        # simulate batch from single file\n        prediction = eval(\n            f'model.{values[\"fnx\"]}(model.load_audio(\"{repo}/{values[\"sample\"]}\", savedir=\"pretrained_models/{repo}\").unsqueeze(0), torch.tensor([1.0]))'\n        )\n\n    except Exception:\n        # use an example audio if no audio can be loaded\n        print(f'\\tWARNING - no audio found on HF: {repo}/{values[\"sample\"]}')\n        prediction = eval(\n            f'model.{values[\"fnx\"]}(model.load_audio(\"tests/samples/single-mic/example1.wav\", savedir=\"pretrained_models/{repo}\").unsqueeze(0), torch.tensor([1.0]))'\n        )\n\n    finally:\n        del model\n\n    return [sanitize(x[0]) for x in prediction]\n\n\ndef gather_expected_results(\n    glob_filter=\"*\",\n    new_interfaces_git=\"https://github.com/speechbrain/speechbrain\",\n    new_interfaces_branch=\"hf-interface-testing\",\n    new_interfaces_local_dir=\"tests/tmp/hf_interfaces\",\n    yaml_path=\"tests/tmp/refactoring_results.yaml\",\n):\n    \"\"\"Before refactoring HF YAMLs and/or code, gather prediction results.\n\n    Arguments\n    ---------\n    glob_filter: str\n        Filter for a repo subset or a specific repo.\n    new_interfaces_git: str\n        Your git repo (or default: `https://github.com/speechbrain/speechbrain`);\n        can be specified in tests/utils/overrides.yaml\n    new_interfaces_branch: str\n        Default is `hf-interface-testing` (a git branch); can be specified in tests/utils/overrides.yaml\n    new_interfaces_local_dir: str\n        Default is `tests/tmp/hf_interfaces` (a local path); can be specified in tests/utils/overrides.yaml\n    yaml_path : str\n        Path where to store/load refactoring testing results for later comparison.\n\n    \"\"\"\n    # load results, if existing -or- new from scratch\n    if os.path.exists(yaml_path):\n        with open(yaml_path, encoding=\"utf-8\") as yaml_in:\n            results = yaml.safe_load(yaml_in)\n    else:\n        results = {}\n\n    # go through each repo\n    updates_dir = init(\n        new_interfaces_git, new_interfaces_branch, new_interfaces_local_dir\n    )\n    repos = map(os.path.basename, glob(f\"{updates_dir}/{glob_filter}\"))\n    for repo in repos:\n        # skip if results are there\n        if repo not in results.keys():\n            # get values\n            with open(\n                f\"{updates_dir}/{repo}/test.yaml\", encoding=\"utf-8\"\n            ) as yaml_test:\n                values = load_hyperpyyaml(yaml_test)\n\n            print(f\"Collecting results for: {repo} w/ values={values}\")\n            prediction = get_prediction(repo, values)\n\n            # extend the results\n            results[repo] = {\"before\": prediction}\n            with open(yaml_path, \"w\", encoding=\"utf-8\") as yaml_out:\n                yaml.dump(results, yaml_out, default_flow_style=None)\n\n\ndef gather_refactoring_results(\n    glob_filter=\"*\",\n    new_interfaces_git=\"https://github.com/speechbrain/speechbrain\",\n    new_interfaces_branch=\"hf-interface-testing\",\n    new_interfaces_local_dir=\"tests/tmp/hf_interfaces\",\n    yaml_path=\"tests/tmp/refactoring_results.yaml\",\n):\n    \"\"\"After refactoring HF YAMLs and/or code, gather prediction results.\n\n    Arguments\n    ---------\n    glob_filter: str\n        Filter for a repo subset or a specific repo.\n    new_interfaces_git: str\n        Your git repo (or default: `https://github.com/speechbrain/speechbrain`);\n        can be specified in tests/utils/overrides.yaml\n    new_interfaces_branch: str\n        Default is `hf-interface-testing` (a git branch); can be specified in tests/utils/overrides.yaml\n    new_interfaces_local_dir: str\n        Default is `tests/tmp/hf_interfaces` (a local path); can be specified in tests/utils/overrides.yaml\n    yaml_path: str\n        Path where to store/load refactoring testing results for later comparison.\n    \"\"\"\n    # expected results need to exist\n    if os.path.exists(yaml_path):\n        with open(yaml_path, encoding=\"utf-8\") as yaml_in:\n            results = yaml.safe_load(yaml_in)\n\n    # go through each repo\n    updates_dir = init(\n        new_interfaces_git, new_interfaces_branch, new_interfaces_local_dir\n    )\n    repos = map(os.path.basename, glob(f\"{updates_dir}/{glob_filter}\"))\n    for repo in repos:\n        # skip if results are there\n        if \"after\" not in results[repo].keys():\n            # get values\n            with open(\n                f\"{updates_dir}/{repo}/test.yaml\", encoding=\"utf-8\"\n            ) as yaml_test:\n                values = load_hyperpyyaml(yaml_test)\n\n            print(\n                f\"Collecting refactoring results for: {repo} w/ values={values}\"\n            )\n\n            # extend the results\n            results[repo][\"after\"] = get_prediction(repo, values, updates_dir)\n            results[repo][\"same\"] = (\n                results[repo][\"before\"] == results[repo][\"after\"]\n            )\n\n            # update\n            with open(yaml_path, \"w\", encoding=\"utf-8\") as yaml_out:\n                yaml.dump(results, yaml_out, default_flow_style=None)\n\n            print(f\"\\tsame: {results[repo]['same']}\")\n\n\ndef test_performance(\n    repo, values, run_opts, updates_dir=None, recipe_overrides={}\n):\n    \"\"\"Runs the evaluation partition of a recipe dataset for a pretrained model.\n\n    Arguments\n    ---------\n    repo: str\n        Source of pretrained model (assuming its within the HF speechbrain collection).\n    values: dict\n        Interface specification.\n        Examples: speechbrain:hf-interface-testing/updates_pretrained_models/ssl-wav2vec2-base-librispeech/test.yaml\n                  speechbrain:hf-interface-testing/updates_pretrained_models/asr-wav2vec2-librispeech/test.yaml\n    run_opts: dict\n        Run options, such as device\n    updates_dir: str\n        Controls whether/not we are in the refactored results (None: expected results; before refactoring).\n    recipe_overrides: dict\n        Recipe YAMLs contain placeholders and flags which need to be overwritten (e.g. data_folder & skip_prep).\n        See: overrides.yaml\n\n    Returns\n    -------\n    Dict for export to yaml with performance statistics, as specified in the test.yaml files.\n    \"\"\"\n    # Dataset depending file structure\n    tmp_dir = f'tests/tmp/{values[\"dataset\"]}'\n    speechbrain.create_experiment_directory(experiment_directory=tmp_dir)\n    stats_meta = {\n        f'[{values[\"dataset\"]}] - {\"BEFORE\" if updates_dir is None else \"AFTER\"}': repo\n    }\n\n    # Load pretrained\n    model = get_model(\n        repo=repo, values=values, updates_dir=updates_dir, run_opts=run_opts\n    )  # noqa\n\n    # Dataio preparation; we need the test sets only\n    with open(values[\"recipe_yaml\"], encoding=\"utf-8\") as fin:\n        recipe_hparams = load_hyperpyyaml(\n            fin, values[\"overrides\"] | recipe_overrides\n        )\n\n    # Dataset preparation is assumed to be done through recipes; before running this.\n    exec(values[\"dataio\"])\n    test_datasets = deepcopy(eval(values[\"test_datasets\"]))\n\n    # harmonise\n    if type(test_datasets) is not dict:\n        tmp = {}\n        if type(test_datasets) is list:\n            for i, x in enumerate(test_datasets):\n                tmp[i] = x\n        else:\n            tmp[0] = test_datasets\n        test_datasets = tmp\n\n    # prepare testing\n    logger = FileTrainLogger(save_file=f\"{tmp_dir}/{repo}.log\")\n    reporting = deepcopy(values[\"performance\"])\n    for metric, specs in reporting.items():\n        reporting[metric][\"tracker\"] = deepcopy(\n            recipe_hparams[specs[\"handler\"]]()\n        )\n\n    test_loader_kwargs = deepcopy(recipe_hparams[values[\"test_loader\"]])\n    del recipe_hparams\n\n    stats = {}\n    for k in test_datasets.keys():  # keys are test_clean, test_other etc\n        test_set = test_datasets[k]\n        if not (\n            isinstance(test_set, DataLoader)\n            or isinstance(test_set, LoopedLoader)\n        ):\n            test_set = make_dataloader(test_set, **test_loader_kwargs)\n\n        with torch.no_grad():\n            for batch in tqdm(test_set, dynamic_ncols=True, disable=False):\n                batch = batch.to(model.device)\n                wavs, wav_lens = batch.sig\n                wavs, wav_lens = (  # noqa\n                    wavs.to(model.device),\n                    wav_lens.to(model.device),\n                )\n                predictions = eval(  # noqa\n                    f'model.{values[\"fnx\"]}(wavs, wav_lens)'\n                )\n                predicted = eval(values[\"predicted\"])  # noqa\n                targeted = eval(values[\"targeted\"])  # noqa\n                ids = batch.id  # noqa\n                for metric in reporting.keys():\n                    reporting[metric][\"tracker\"].append(\n                        *eval(values[\"to_stats\"])\n                    )\n\n        stats[k] = {}\n        for metric, specs in reporting.items():\n            stats[k][metric] = specs[\"tracker\"].summarize(specs[\"field\"])\n        logger.log_stats(\n            stats_meta=stats_meta | {\"set\": k}, test_stats=stats[k]\n        )\n\n    return stats\n\n\n# run first w/ \"--after=False\" on latest develop, then checkout the refactoring branch and run w/ \"--after=True\"\n# PYTHONPATH=`realpath .` python tests/utils/refactoring_checks.py tests/utils/overrides.yaml --LibriSpeech_data=\"\" --CommonVoice_EN_data=\"\" --CommonVoice_FR_data=\"\" --IEMOCAP_data=\"\" --after=False\nif __name__ == \"__main__\":\n    hparams_file, run_opts, overrides = speechbrain.parse_arguments(\n        sys.argv[1:]\n    )\n\n    with open(hparams_file, encoding=\"utf-8\") as fin:\n        dataset_overrides = load_hyperpyyaml(fin, overrides)\n\n    # go through each repo\n    updates_dir = init(\n        dataset_overrides[\"new_interfaces_git\"],\n        dataset_overrides[\"new_interfaces_branch\"],\n        dataset_overrides[\"new_interfaces_local_dir\"],\n    )\n\n    # load results, if existing -or- new from scratch\n    yaml_path = f'{dataset_overrides[\"new_interfaces_local_dir\"]}.yaml'\n    if os.path.exists(yaml_path):\n        with open(yaml_path, encoding=\"utf-8\") as yaml_in:\n            results = yaml.safe_load(yaml_in)\n    else:\n        results = {}\n\n    repos = map(\n        os.path.basename,\n        glob(f'{updates_dir}/{dataset_overrides[\"glob_filter\"]}'),\n    )\n    for repo in repos:\n        # get values\n        with open(\n            f\"{updates_dir}/{repo}/test.yaml\", encoding=\"utf-8\"\n        ) as yaml_test:\n            values = load_hyperpyyaml(yaml_test)\n\n        # for this testing, some fields need to exist; skip otherwise\n        if any(\n            [\n                entry not in values\n                for entry in [\n                    \"dataset\",\n                    \"overrides\",\n                    \"dataio\",\n                    \"test_datasets\",\n                    \"test_loader\",\n                    \"performance\",\n                    \"predicted\",\n                ]\n            ]\n        ):\n            continue\n\n        # skip if datasets is not given\n        if not dataset_overrides[f'{values[\"dataset\"]}_data']:\n            continue\n\n        print(f\"Run tests on: {repo}\")\n        if repo not in results.keys():\n            results[repo] = {}\n\n        # Before refactoring\n        if \"before\" not in results[repo].keys():\n            results[repo][\"before\"] = test_performance(\n                repo,\n                values,\n                updates_dir=None,\n                run_opts=run_opts,\n                recipe_overrides=dataset_overrides[values[\"dataset\"]],\n            )\n\n            # update\n            with open(yaml_path, \"w\", encoding=\"utf-8\") as yaml_out:\n                yaml.dump(results, yaml_out, default_flow_style=None)\n\n        # After refactoring\n        if (\n            \"after\" not in results[repo].keys()\n            and dataset_overrides[\"after\"] is True\n        ):\n            results[repo][\"after\"] = test_performance(\n                repo,\n                values,\n                run_opts=run_opts,\n                updates_dir=updates_dir,\n                recipe_overrides=dataset_overrides[values[\"dataset\"]],\n            )\n\n            results[repo][\"same\"] = (\n                results[repo][\"before\"] == results[repo][\"after\"]\n            )\n            print(f'\\tbefore: {results[repo][\"before\"]}')\n            print(f'\\t after: {results[repo][\"after\"]}')\n            print(f'\\t  same: {results[repo][\"same\"]}')\n\n            # update\n            with open(yaml_path, \"w\", encoding=\"utf-8\") as yaml_out:\n                yaml.dump(results, yaml_out, default_flow_style=None)\n\n    # update\n    with open(yaml_path, \"w\", encoding=\"utf-8\") as yaml_out:\n        yaml.dump(results, yaml_out, default_flow_style=None)\n", "text_preview": "#!/usr/bin/env/python3 \"\"\"This is a test script for creating a list of expected outcomes (before refactoring); then, manual editing might change YAMLs and/or code; another test runs to compare results (after refactoring to before). The targ", "input_sha256": "ba2e6815a4c41ab75b3f6fc1af898d0e5120c31b327be73362e3097c4af2c322", "text_sha256": "d7235ede2c1bf5af29c5063ebfbb078bc3d173557a4f4606a6d1daa25c660e5a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12019, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9439073660650397, "noise_severity": 0.06860795618754355, "missing_content_severity": 0.01788023433806859, "factual_density": 3.2178055727732042, "pretraining_utility": 0.6508503025632109, "math_density": 0.06579618734613013, "code_density": 3.810234402002646, "reasoning_density": 0.7777613734317274, "education_level": 3.031102911802093, "creative_writing_quality": -0.22891901755337107}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.017802383591614585, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.994715926186689, "secondary": 0.9209244329194433, "tertiary": 0.9327768245283673, "structured_records": 0.011545789752375963, "procedural_steps": 0.18422885511309497, "worked_solution": 0.0035044890929189125, "contextual_dialogue": 5.037137652036176e-06, "content_quality": 0.5579435865967654, "noise_severity": 0.9428267031770471, "missing_content_severity": 0.9850998047182762, "factual_density": 0.5011779460073351, "pretraining_utility": 0.5487544695283225, "math_density": 0.9451698438782249, "code_density": 0.8418620016688717, "reasoning_density": 0.35186552214022704, "education_level": 0.49828735654300904, "creative_writing_quality": 0.9995271669050594, "primary_topic": 0.9802986470358678, "secondary_topic": 0.6502176197972064}, "probability_margin": {"primary": 0.9919889667173136, "secondary": 0.8765530326886732, "tertiary": 0.90571395823884, "education_level": 0.3549891173443178, "creative_writing_quality": 0.9994141958474866, "primary_topic": 0.9649954228906377, "secondary_topic": 0.42775702034328866}, "raw_probabilities": {"primary": {"Academic Writing": 3.9944837804314255e-05, "Content Listing": 1.6265560608848187e-05, "Creative Writing": 1.227095946075431e-05, "Customer Support Page": 1.3002927233319538e-05, "Q&A Forum": 9.245925859416983e-06, "Discussion Thread": 6.56705327994125e-06, "Comment Section": 1.1433811196020824e-05, "Missing Content": 1.594801293418691e-05, "Knowledge Article": 6.223935822258461e-05, "Legal Notices": 1.705696701940174e-05, "News Article": 4.917551743306114e-06, "Organizational Announcement": 1.5038737730156963e-05, "Nonfiction Writing": 1.0127612250900086e-05, "Organizational About Page": 1.3244733060497443e-05, "Personal About Page": 9.072369702562606e-06, "Personal Blog": 6.50833093731693e-06, "Product Page": 6.757490599827564e-06, "Spam / Ads": 8.254647282311046e-06, "Structured Data": 0.00020674973198923682, "Technical Writing": 0.9949360959289103, "Transcript / Interview": 1.4021462080349739e-05, "Tutorial / How-To Guide": 0.002947129211596653, "User Reviews": 6.703886775081607e-06, "Other / Unknown": 0.0016114028917227545}, "secondary": {"Academic Writing": 0.0007545224700263268, "Content Listing": 0.00033572825815027897, "Creative Writing": 0.00025722037591037244, "Customer Support Page": 0.00030148577852297053, "Q&A Forum": 0.00015254059981946406, "Discussion Thread": 0.00011622957480156212, "Comment Section": 0.0003520452689307982, "Missing Content": 0.00048306339732185634, "Knowledge Article": 0.001086183401729843, "Legal Notices": 0.0004500911500909544, "News Article": 0.00017807205800622703, "Organizational Announcement": 0.0002820212247364743, "Nonfiction Writing": 0.0002688857844676269, "Organizational About Page": 0.0002420476399917831, "Personal About Page": 0.0002079446513361575, "Personal Blog": 0.00015594168500926108, "Product Page": 0.00012888921715994387, "Spam / Ads": 0.00017934140054664808, "Structured Data": 0.0018535380975267964, "Technical Writing": 0.04766621552579317, "Transcript / Interview": 0.0002325448491481151, "Tutorial / How-To Guide": 0.019989710652716784, "User Reviews": 0.00010648872379011804, "NONE": 0.9242192482144664}, "tertiary": {"Academic Writing": 0.0005939137439468375, "Content Listing": 0.0002871063178261013, "Creative Writing": 0.00018651468776829477, "Customer Support Page": 0.0002435138985043541, "Q&A Forum": 8.293218264609962e-05, "Discussion Thread": 7.015290357102576e-05, "Comment Section": 0.0002319121807009235, "Missing Content": 0.0005501003099028097, "Knowledge Article": 0.0013734505906956675, "Legal Notices": 0.0022857619832573695, "News Article": 0.00011847863838598471, "Organizational Announcement": 0.00025469950788522617, "Nonfiction Writing": 0.00033863687616108435, "Organizational About Page": 0.00022688219885056538, "Personal About Page": 0.00023491502078656473, "Personal Blog": 0.00010742876700099053, "Product Page": 8.980603174020103e-05, "Spam / Ads": 0.00013897649276377224, "Structured Data": 0.0023344962918910037, "Technical Writing": 0.029863831934178582, "Transcript / Interview": 0.00015272201558120334, "Tutorial / How-To Guide": 0.024585215919223437, "User Reviews": 7.076133371308888e-05, "NONE": 0.9355777901730187}, "structured_records": {"false": 0.988454210247624, "true": 0.011545789752375963}, "procedural_steps": {"false": 0.815771144886905, "true": 0.18422885511309497}, "worked_solution": {"false": 0.9964955109070811, "true": 0.0035044890929189125}, "contextual_dialogue": {"false": 0.999994962862348, "true": 5.037137652036176e-06}, "content_quality": {"-2": 0.005365434851055301, "-1": 0.028552205127262014, "0": 0.22007945020173103, "1": 0.508815378745491, "2": 0.23718753107446064}, "noise_severity": {"0": 0.9375128705356819, "1": 0.05866791456666116, "2": 0.002237499897916396, "3": 0.0008618181739125486, "4": 0.0007198968258279885}, "missing_content_severity": {"0": 0.9858957748112747, "1": 0.011277454517771447, "2": 0.0020684583073947217, "3": 0.0005673862487278701, "4": 0.0001909261148310205}, "factual_density": {"0": 0.004374624459841083, "1": 0.017596453497495862, "2": 0.14207366563448204, "3": 0.42775923762598034, "4": 0.40819601878220074}, "pretraining_utility": {"-2": 0.00662834492869491, "-1": 0.05497617641723837, "0": 0.3154847793808396, "1": 0.5267382297086154, "2": 0.09617246956461185}, "math_density": {"0": 0.9576915433497882, "1": 0.028043058692128777, "2": 0.008509034584995027, "3": 0.0022903940083406037, "4": 0.0034659693647473727}, "code_density": {"0": 0.0002952074819770074, "1": 0.0005623558442931346, "2": 0.0114752650049304, "3": 0.16394717052670568, "4": 0.8237200011420938}, "reasoning_density": {"0": 0.6012004524040646, "1": 0.178856747911173, "2": 0.11400953859044043, "3": 0.0528474960376141, "4": 0.0530857650567078}, "education_level": {"none": 0.0009255348083725493, "0": 0.0012323269941743578, "1": 0.003126912514332122, "2": 0.18589208212242375, "3": 0.5819061304525075, "4": 0.2269170131081897}, "creative_writing_quality": {"not_applicable": 0.9996059724208828, "-2": 4.885440445647977e-05, "-1": 8.264581435230023e-05, "0": 0.0001917765733962384, "1": 5.134735685957321e-05, "2": 1.9403430052620573e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.954257695210778e-05, "Art & Design": 5.897049692194058e-05, "Law & Crime": 4.736420128914818e-05, "Education & Careers": 5.197536381363601e-05, "Entertainment & Media": 5.5315009405665525e-05, "Fashion & Beauty": 4.045605104670823e-05, "Business, Economics & Finance": 6.081098063336786e-05, "Food & Dining": 3.639029468852058e-05, "Games & Gaming": 6.669804848528109e-05, "Electronics & Hardware": 8.704368266913785e-05, "Health, Healthcare & Wellness": 5.436506441126551e-05, "History & Geography": 4.704152671754899e-05, "Home, Pets & Hobbies": 2.8537080948826043e-05, "Industry & Production": 4.611195586544396e-05, "Language, Literature & Philosophy": 0.000482338897361494, "Politics & Government": 2.493767157213111e-05, "Religion & Spirituality": 4.15411698213788e-05, "Science, Mathematics & Engineering": 0.016060968490004428, "Relationships & Community": 2.0837595515924663e-05, "Software Usage & Applications": 0.0009274774132550169, "Programming & Software Engineering": 0.981056391380642, "Sports & Fitness": 5.5893916838898355e-05, "Vehicles & Transportation": 2.3106175847802898e-05, "Travel & Tourism": 5.530271943778792e-05, "Other / Uncovered Subject": 0.00045557550209193454, "Unknown / Insufficient Content": 6.500673376244426e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009441577348669595, "Art & Design": 0.0006221723431426924, "Law & Crime": 0.0006708432276811783, "Education & Careers": 0.0004823440308804975, "Entertainment & Media": 0.0008802607840764919, "Fashion & Beauty": 0.0003760115548290148, "Business, Economics & Finance": 0.0006888941771472857, "Food & Dining": 0.00030041375342613444, "Games & Gaming": 0.00044684106719962386, "Electronics & Hardware": 0.0006992543692702448, "Health, Healthcare & Wellness": 0.0006317896509111925, "History & Geography": 0.0003936876737925676, "Home, Pets & Hobbies": 0.000279040862421686, "Industry & Production": 0.00037359027818472073, "Language, Literature & Philosophy": 0.0014248758289977111, "Politics & Government": 0.0003100854678508483, "Religion & Spirituality": 0.00046147862587604975, "Science, Mathematics & Engineering": 0.2359137679232559, "Relationships & Community": 0.00017800695509537567, "Software Usage & Applications": 0.006879209134691757, "Programming & Software Engineering": 0.08127669143047471, "Sports & Fitness": 0.0003458844316219023, "Vehicles & Transportation": 0.00017172700853302906, "Travel & Tourism": 0.0005005382511930794, "Other / Uncovered Subject": 0.0010776451680347126, "NONE": 0.6636707882665446}}, "probabilities": {"primary": {"Academic Writing": 3.9944837804314255e-05, "Content Listing": 1.6265560608848187e-05, "Creative Writing": 1.227095946075431e-05, "Customer Support Page": 1.3002927233319538e-05, "Q&A Forum": 9.245925859416983e-06, "Discussion Thread": 6.56705327994125e-06, "Comment Section": 1.1433811196020824e-05, "Missing Content": 1.594801293418691e-05, "Knowledge Article": 6.223935822258461e-05, "Legal Notices": 1.705696701940174e-05, "News Article": 4.917551743306114e-06, "Organizational Announcement": 1.5038737730156963e-05, "Nonfiction Writing": 1.0127612250900086e-05, "Organizational About Page": 1.3244733060497443e-05, "Personal About Page": 9.072369702562606e-06, "Personal Blog": 6.50833093731693e-06, "Product Page": 6.757490599827564e-06, "Spam / Ads": 8.254647282311046e-06, "Structured Data": 0.00020674973198923682, "Technical Writing": 0.9949360959289103, "Transcript / Interview": 1.4021462080349739e-05, "Tutorial / How-To Guide": 0.002947129211596653, "User Reviews": 6.703886775081607e-06, "Other / Unknown": 0.0016114028917227545}, "secondary": {"Academic Writing": 0.0007545224700263268, "Content Listing": 0.00033572825815027897, "Creative Writing": 0.00025722037591037244, "Customer Support Page": 0.00030148577852297053, "Q&A Forum": 0.00015254059981946406, "Discussion Thread": 0.00011622957480156212, "Comment Section": 0.0003520452689307982, "Missing Content": 0.00048306339732185634, "Knowledge Article": 0.001086183401729843, "Legal Notices": 0.0004500911500909544, "News Article": 0.00017807205800622703, "Organizational Announcement": 0.0002820212247364743, "Nonfiction Writing": 0.0002688857844676269, "Organizational About Page": 0.0002420476399917831, "Personal About Page": 0.0002079446513361575, "Personal Blog": 0.00015594168500926108, "Product Page": 0.00012888921715994387, "Spam / Ads": 0.00017934140054664808, "Structured Data": 0.0018535380975267964, "Technical Writing": 0.04766621552579317, "Transcript / Interview": 0.0002325448491481151, "Tutorial / How-To Guide": 0.019989710652716784, "User Reviews": 0.00010648872379011804, "NONE": 0.9242192482144664}, "tertiary": {"Academic Writing": 0.0005939137439468376, "Content Listing": 0.00028710631782610135, "Creative Writing": 0.0001865146877682948, "Customer Support Page": 0.00024351389850435411, "Q&A Forum": 8.293218264609963e-05, "Discussion Thread": 7.015290357102577e-05, "Comment Section": 0.00023191218070092352, "Missing Content": 0.0005501003099028098, "Knowledge Article": 0.0013734505906956677, "Legal Notices": 0.00228576198325737, "News Article": 0.00011847863838598473, "Organizational Announcement": 0.0002546995078852262, "Nonfiction Writing": 0.0003386368761610844, "Organizational About Page": 0.0002268821988505654, "Personal About Page": 0.00023491502078656475, "Personal Blog": 0.00010742876700099054, "Product Page": 8.980603174020104e-05, "Spam / Ads": 0.00013897649276377227, "Structured Data": 0.002334496291891004, "Technical Writing": 0.029863831934178586, "Transcript / Interview": 0.00015272201558120337, "Tutorial / How-To Guide": 0.02458521591922344, "User Reviews": 7.07613337130889e-05, "NONE": 0.9355777901730188}, "structured_records": {"false": 0.988454210247624, "true": 0.011545789752375963}, "procedural_steps": {"false": 0.815771144886905, "true": 0.18422885511309497}, "worked_solution": {"false": 0.9964955109070811, "true": 0.0035044890929189125}, "contextual_dialogue": {"false": 0.999994962862348, "true": 5.037137652036176e-06}, "content_quality": {"-2": 0.005365434851055301, "-1": 0.028552205127262014, "0": 0.22007945020173103, "1": 0.508815378745491, "2": 0.23718753107446064}, "noise_severity": {"0": 0.9375128705356819, "1": 0.05866791456666116, "2": 0.002237499897916396, "3": 0.0008618181739125486, "4": 0.0007198968258279885}, "missing_content_severity": {"0": 0.985895774811275, "1": 0.01127745451777145, "2": 0.0020684583073947225, "3": 0.0005673862487278704, "4": 0.00019092611483102054}, "factual_density": {"0": 0.004374624459841083, "1": 0.017596453497495862, "2": 0.14207366563448204, "3": 0.42775923762598034, "4": 0.40819601878220074}, "pretraining_utility": {"-2": 0.00662834492869491, "-1": 0.05497617641723837, "0": 0.3154847793808396, "1": 0.5267382297086154, "2": 0.09617246956461185}, "math_density": {"0": 0.9576915433497882, "1": 0.028043058692128777, "2": 0.008509034584995027, "3": 0.0022903940083406037, "4": 0.0034659693647473727}, "code_density": {"0": 0.0002952074819770074, "1": 0.0005623558442931346, "2": 0.0114752650049304, "3": 0.16394717052670568, "4": 0.8237200011420938}, "reasoning_density": {"0": 0.6012004524040646, "1": 0.178856747911173, "2": 0.11400953859044043, "3": 0.0528474960376141, "4": 0.0530857650567078}, "education_level": {"none": 0.0009255348083725493, "0": 0.0012323269941743578, "1": 0.003126912514332122, "2": 0.18589208212242375, "3": 0.5819061304525075, "4": 0.2269170131081897}, "creative_writing_quality": {"not_applicable": 0.9996059724208828, "-2": 4.885440445647977e-05, "-1": 8.264581435230023e-05, "0": 0.0001917765733962384, "1": 5.134735685957321e-05, "2": 1.9403430052620573e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.954257695210779e-05, "Art & Design": 5.897049692194059e-05, "Law & Crime": 4.736420128914819e-05, "Education & Careers": 5.197536381363602e-05, "Entertainment & Media": 5.531500940566553e-05, "Fashion & Beauty": 4.0456051046708236e-05, "Business, Economics & Finance": 6.0810980633367866e-05, "Food & Dining": 3.6390294688520584e-05, "Games & Gaming": 6.66980484852811e-05, "Electronics & Hardware": 8.704368266913787e-05, "Health, Healthcare & Wellness": 5.436506441126552e-05, "History & Geography": 4.7041526717549e-05, "Home, Pets & Hobbies": 2.8537080948826047e-05, "Industry & Production": 4.6111955865443966e-05, "Language, Literature & Philosophy": 0.00048233889736149405, "Politics & Government": 2.4937671572131112e-05, "Religion & Spirituality": 4.154116982137881e-05, "Science, Mathematics & Engineering": 0.01606096849000443, "Relationships & Community": 2.0837595515924666e-05, "Software Usage & Applications": 0.000927477413255017, "Programming & Software Engineering": 0.9810563913806422, "Sports & Fitness": 5.589391683889836e-05, "Vehicles & Transportation": 2.31061758478029e-05, "Travel & Tourism": 5.530271943778793e-05, "Other / Uncovered Subject": 0.0004555755020919346, "Unknown / Insufficient Content": 6.500673376244427e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009441577348669595, "Art & Design": 0.0006221723431426924, "Law & Crime": 0.0006708432276811783, "Education & Careers": 0.0004823440308804975, "Entertainment & Media": 0.0008802607840764919, "Fashion & Beauty": 0.0003760115548290148, "Business, Economics & Finance": 0.0006888941771472857, "Food & Dining": 0.00030041375342613444, "Games & Gaming": 0.00044684106719962386, "Electronics & Hardware": 0.0006992543692702448, "Health, Healthcare & Wellness": 0.0006317896509111925, "History & Geography": 0.0003936876737925676, "Home, Pets & Hobbies": 0.000279040862421686, "Industry & Production": 0.00037359027818472073, "Language, Literature & Philosophy": 0.0014248758289977111, "Politics & Government": 0.0003100854678508483, "Religion & Spirituality": 0.00046147862587604975, "Science, Mathematics & Engineering": 0.2359137679232559, "Relationships & Community": 0.00017800695509537567, "Software Usage & Applications": 0.006879209134691757, "Programming & Software Engineering": 0.08127669143047471, "Sports & Fitness": 0.0003458844316219023, "Vehicles & Transportation": 0.00017172700853302906, "Travel & Tourism": 0.0005005382511930794, "Other / Uncovered Subject": 0.0010776451680347126, "NONE": 0.6636707882665446}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 13119, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 13119, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:08.106660+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "d77e4884-b9e8-426c-baeb-6e0f6b535bcf", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "3b2bfb241a87eb6a6aef15186b58fe70992dcbd502150204537ef0ec57e5f605", "source_metadata": {"uuid": "d77e4884-b9e8-426c-baeb-6e0f6b535bcf", "repo_name": "speechbrain/speechbrain", "relative_path": "tests/utils/refactoring_checks.py", "category": "TEST", "algo_rel_score": 0.9898965358734131, "quality_score": 7.662249565124512, "source_meta": {"github_metadata": {"branch": "develop", "commit_count": 10544, "repo_created_at": "2020-04-28T17:48:45.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 10141, "forks": 1527, "issues": 120, "pull_requests": 57}, "file_meta": {"content_id": "ce4fc7e69223580d6071c7febf6cc959e242ae9e", "size_bytes": 19461, "file_timestamp": 1752856185, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "ec1425368dd3fc9dd41edc7b50a9148cd463abec"}}, "parsed_source_meta": {"github_metadata": {"branch": "develop", "commit_count": 10544, "repo_created_at": "2020-04-28T17:48:45.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 10141, "forks": 1527, "issues": 120, "pull_requests": 57}, "file_meta": {"content_id": "ce4fc7e69223580d6071c7febf6cc959e242ae9e", "size_bytes": 19461, "file_timestamp": 1752856185, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "ec1425368dd3fc9dd41edc7b50a9148cd463abec", "url": "https://github.com/speechbrain/speechbrain/blob/ec1425368dd3fc9dd41edc7b50a9148cd463abec/tests/utils/refactoring_checks.py"}, "gpt4o_tokens": 4372, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.9439073660650397, "noise_severity": 0.06860795618754355, "missing_content_severity": 0.01788023433806859, "factual_density": 3.2178055727732042, "pretraining_utility": 0.6508503025632109, "math_density": 0.06579618734613013, "code_density": 3.810234402002646, "reasoning_density": 0.7777613734317274, "education_level": 3.031102911802093, "creative_writing_quality": -0.22891901755337107}, "score_applicability": {"education_level": 0.9990744651916275, "creative_writing_quality": 0.00039402757911721217}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/SophonAlpha/codewars/blob/53a8525bd1dd927701ca1ae499cd81c4ea25cd74/solutions/four_pass_transport.py", "id": "python-0-8623a053d96e3f51728b7064bd981fea", "text": "\"\"\"\n\nMy solution for Four Pass Transport kata:\nhttps://www.codewars.com/kata/four-pass-transport\n\nLevel: 1 kyu\n\nUseful learning sources:\nhttps://www.redblobgames.com/pathfinding/a-star/introduction.html\nhttps://en.wikipedia.org/wiki/Travelling_salesman_problem\nhttps://en.wikipedia.org/wiki/A*_search_algorithm\nhttps://www.geeksforgeeks.org/a-search-algorithm/\n\n\"\"\"\n\nimport itertools\nimport copy\n\nCONV_BELT_MOD = 'c' # representation for one conveyer belt module\nATTENUATION = 0.001\n\ndef show(stations, path):\n    \"\"\"\n    Helper function to display factory floor, stations and conveyer belt modules.\n    \"\"\"\n    if not path:\n        return\n    symbols = {0: 'a', 1: 'b', 2: 'c', 3: 'd'}\n    symbol = None\n    floor = [[None] * 10 for _ in range(0, 10)]\n    for pos in path:\n        if pos in stations:\n            symbol = symbols[stations.index(pos)]\n        row, col = convert(pos)\n        floor[row][col] = symbol\n    for i, station in enumerate(stations):\n        row, col = convert(station)\n        floor[row][col] = i + 1\n    print('  0 1 2 3 4 5 6 7 8 9')\n    for i, row in enumerate(floor):\n        line = str(i) + ' ' + ' '.join([str(col) if col else '.'\n                                        for col in row])\n        print(line)\n\ndef convert(pos):\n    \"\"\"\n    Convert single integer representation of position coordinates (e.g. 34) to\n    two integer representation (e.g. row: 3, column: 4).\n    \"\"\"\n    row = pos // 10\n    col = pos % 10\n    return row, col\n\nclass NoNeighbourError(Exception):\n    \"\"\" Error to be raised when tile has no neighbours.\"\"\"\n    pass\n\nclass NoPathError(Exception):\n    \"\"\" Error to be raised when issues with finding a test_path between stations.\"\"\"\n    pass\n\nclass TileOccupiedError(Exception):\n    \"\"\" Error to be raised when a occupied tile is selected as test_path element.\"\"\"\n    pass\n\nclass Factory:\n    \"\"\"\n    Represent the factory floor as an object.\n    \"\"\"\n\n    def __init__(self, stations):\n        self.floor = [[None] * 10 for _ in range(0, 10)]\n        self.stations = {}\n        self.occupied = set()\n        self.place_stations(stations)\n        self.empty_floor = copy.deepcopy(self.floor)\n        self.empty_occupied = copy.deepcopy(self.occupied)\n\n    def place_stations(self, stations):\n        \"\"\"\n        Place the stations on the factory floor.\n        \"\"\"\n        for i, station in enumerate(stations):\n            station_num = i + 1\n            row = station // 10\n            col = station % 10\n            self.floor[row][col] = station_num\n            self.stations[station_num] = (row, col)\n            self.mark_occupied([(row, col)])\n\n    def place_conveyer_belt(self, row, col):\n        \"\"\"\n        Place one conveyer belt module on the factory floor.\n        \"\"\"\n        self.floor[row][col] = CONV_BELT_MOD\n        self.mark_occupied([(row, col)])\n\n    def remove_conveyer_belts(self):\n        \"\"\"\n        Remove all conveyer belts from factory floor.\n        \"\"\"\n        self.floor = copy.deepcopy(self.empty_floor)\n        self.occupied = copy.deepcopy(self.empty_occupied)\n\n    def mark_occupied(self, tiles):\n        \"\"\"\n        Mark tile occupied.\n        \"\"\"\n        for row, col in tiles:\n            self.occupied.add((row, col))\n\n    def unmark_occupied(self, tiles):\n        \"\"\"\n        Unmark tile occupied status.\n        \"\"\"\n        for row, col in tiles:\n            self.occupied.discard((row, col))\n\n    def is_occupied(self, tile):\n        \"\"\"\n        Return true if a tile of the factory floor is occupied either by a\n        station or by a conveyer belt module.\n        \"\"\"\n        row, col = tile\n        return (row, col) in self.occupied\n\n    def show_state(self):\n        \"\"\"\n        Print a top view of the factory floor. Useful for troubleshooting.\n        \"\"\"\n        print('  0 1 2 3 4 5 6 7 8 9')\n        for i, row in enumerate(self.floor):\n            line = str(i) + ' ' + ' '.join([str(col) if col else '.'\n                                            for col in row])\n            print(line)\n\nclass PathPlanner:\n    \"\"\"\n    Plans the shortest path through the factory floor.\n    \"\"\"\n\n    def __init__(self, factory):\n        self.factory = factory\n        self.occupied_tiles = list(self.factory.stations.values())\n        self.min_path_segments = None\n        self.min_path_order = None\n        self.min_path_len = None\n\n    def plan(self):\n        \"\"\"\n        Main function.\n\n        By default we use the \"Manhattan path\" from start to end tile. If the\n        direct Manhattan path is not possible the A* search algorithm is used\n        to find the shortest path.\n\n        In addition, the algorithm uses combinations of heuristics (for A*) and\n        path segment orders to find shortest overall path that crosses\n        a series of stations.\n\n        A path segment is the path between two stations. With four stations\n        there are three segments: station 1 - station 2, station 2 - station 3,\n        station 3 - station 4. The algorithm finds the shortest path for\n        individual segments. However, this does not guarantee overall shortest\n        path. The overall path length is highly dependent on the order in which\n        the segments are processed. The algorithm runs for all permutations of\n        the path segments.\n\n        The algorithm varies the heuristics used by the A* search function.\n        Tests showed that forcing A* to prefer straight horizontal or vertical\n        paths seems to help finding good solutions.\n        \"\"\"\n        segment_variants = self.get_segment_variants()\n        for segment_set in segment_variants:\n            self.factory.remove_conveyer_belts()\n            order, segments = self.get_order_segments(segment_set)\n            try:\n                path_segments = self.plan_stations_set(segments)\n            except NoNeighbourError:\n                continue\n            except NoPathError:\n                continue\n            except TileOccupiedError:\n                continue\n            self.save_min_path(path_segments, order)\n        min_path = self.join_paths()\n        return min_path\n\n    def get_segment_variants(self):\n        \"\"\"\n        Generate a list of all segment orders + A* heuristics to be tested.\n\n        First, all permutations of segment orders are generated. Then, for each\n        order, each segment is combined with each heuristic.\n\n        For four stations and two heuristics this generates the following number\n        of segment variants:\n            - permutations of 3 segments = 6 variants in segment order\n            - each segment tested with 2 heuristics = 2^3 = 8 heuristic variants\n              for one segment order\n            - 6 segment orders * 8 heuristic variants = 48 segment variants to\n              be tested\n        \"\"\"\n        stations = list(self.factory.stations.values())\n        ordered_segments = [(i, (stations[i], stations[i + 1]))\n                            for i in range(len(stations) - 1)]\n        segment_variants = list(itertools.permutations(ordered_segments, 3))\n        heur_funcs = [\n            #'heur_no_heuristic',\n            #'heur_manhattan',\n            #'heur_manhattan_cross',\n            'heur_manhattan_vertical',\n            'heur_manhattan_horizontal'\n            ]\n        heur_combs = list(itertools.product(heur_funcs, repeat=3))\n        segment_variants = [((o1, (s1, h1)), (o2, (s2, h2)), (o3, (s3, h3)))\n                            for ((o1, s1), (o2, s2), (o3, s3)) in segment_variants\n                            for (h1, h2, h3) in heur_combs]\n        return segment_variants\n\n    def get_order_segments(self, segment_set):\n        \"\"\"\n        Return the order of segments. This is required to recombine the\n        individual path segments in the correct order to the total path.\n        \"\"\"\n        order = [o for o, _ in segment_set]\n        segments = [s for _, s in segment_set]\n        return order, segments\n\n    def plan_stations_set(self, segments):\n        \"\"\"\n        Plan the shortest path between stations and place conveyer belt modules.\n        \"\"\"\n        path_segments = []\n        for (start_tile, end_tile), heuristic in segments:\n            self.factory.unmark_occupied([start_tile, end_tile])\n            path = self.get_manhattan_path(start_tile, end_tile, heuristic)\n            if not path:\n                path = self.get_astar_path(start_tile, end_tile, heuristic)\n            self.factory.mark_occupied([start_tile, end_tile])\n            self.place_conveyer_modules(path)\n            path_segments.append(path)\n        return path_segments\n\n    def save_min_path(self, path_segments, order):\n        \"\"\"\n        Keep the shortest of two paths.\n        \"\"\"\n        if not self.min_path_segments:\n            self.min_path_segments = path_segments\n            self.min_path_order = order\n            self.min_path_len = sum(map(len, path_segments))\n        else:\n            lenght = sum(map(len, path_segments))\n            if lenght < self.min_path_len:\n                self.min_path_segments = path_segments\n                self.min_path_order = order\n                self.min_path_len = lenght\n\n    def join_paths(self):\n        \"\"\"\n        Join the path segments together to one list. Avoid duplicate\n        elements when joining the segments.\n        \"\"\"\n        if not self.min_path_order:\n            return None\n        stations = self.factory.stations\n        test_path = []\n        for i, _ in enumerate(self.min_path_order):\n            seg = self.min_path_segments[self.min_path_order.index(i)]\n            test_path = test_path + seg[:-1]\n        test_path = test_path + [stations[len(stations)]] # add last station\n        test_path = self.convert(test_path)\n        return test_path\n\n    def get_manhattan_path(self, start_tile, end_tile, heuristic):\n        \"\"\"\n        Generate the Manhattan path between start and end tile.\n        \"\"\"\n        s_row, s_col = start_tile\n        e_row, e_col = end_tile\n        if e_row >= s_row:\n            row_start = s_row\n            row_end = e_row + 1\n            row_step = 1\n        else:\n            row_start = s_row\n            row_end = e_row -1\n            row_step = -1\n        if e_col >= s_col:\n            col_start = s_col\n            col_end = e_col + 1\n            col_step = 1\n        else:\n            col_start = s_col\n            col_end = e_col - 1\n            col_step = -1\n        if heuristic == 'heur_manhattan_vertical':\n            path = [(r, s_col) for r in list(range(row_start, row_end, row_step))]\n            path = path[:-1] + [(e_row, c) for c in list(range(col_start, col_end, col_step))]\n        if heuristic == 'heur_manhattan_horizontal':\n            path = [(s_row, c) for c in list(range(col_start, col_end, col_step))]\n            path = path[:-1] + [(r, e_col) for r in list(range(row_start, row_end, row_step))]\n        if set(path) & self.factory.occupied:\n            # check if the path collides with already occupied tiles\n            path = None\n        return path\n\n    def get_astar_path(self, start_tile, end_tile, heuristic):\n        \"\"\"\n        Generate graph representation of factory floor and run A* search to find\n        shortest path.\n        \"\"\"\n        graph = self.build_graph(start_tile, end_tile)\n        path = self.astar(start_tile, end_tile, heuristic, graph)\n        if not path:\n            raise NoPathError('ERROR: no path from {} to {} found.'.format(start_tile, end_tile))\n        return path\n\n    def astar(self, start_tile, end_tile, heuristic, graph):\n        \"\"\"\n        Perform A* search to find shortest path between start and end tile.\n        Function inputs are heuristic function to be used with A* and graph to\n        search on.\n        \"\"\"\n        heuristic_func = getattr(self, heuristic, self.heur_generic)\n        closed_set = {}\n        open_set = {start_tile: heuristic_func(start_tile, start_tile, end_tile)}\n        came_from = {}\n        g_score = {start_tile: 0}\n        while open_set:\n            current = min(open_set, key=open_set.get)\n            if current == end_tile:\n                return self.reconstruct_path(came_from, current)\n            closed_set[current] = open_set[current]\n            open_set.pop(current)\n            neighbour_dists = graph[current]\n            for neighbour in neighbour_dists:\n                neighbour_g = g_score[current] + neighbour_dists[neighbour]\n                neighbour_f = neighbour_g + \\\n                              heuristic_func(start_tile, neighbour, end_tile)\n                if neighbour in open_set and \\\n                   neighbour_f > open_set[neighbour]:\n                    continue\n                if neighbour in closed_set and \\\n                   neighbour_f > closed_set[neighbour]:\n                    continue\n                elif neighbour in g_score and \\\n                   neighbour_g < g_score[neighbour]:\n                    continue\n                came_from[neighbour] = current\n                g_score[neighbour] = neighbour_g\n                open_set[neighbour] = neighbour_f\n        return None\n\n    def heur_generic(self, start, current, end):\n        \"\"\"\n        Only to be called if an unknown name for a heuristic function is passed\n        on (this would only happen if there is a typo in the list of heuristic\n        function in this source code).\n        \"\"\"\n        raise Exception('Unknown heuristic function')\n\n    def heur_no_heuristic(self, start, current, end):\n        \"\"\"\n        No heuristic. Using this turns A* into the Djikstra algorithm.\n        \"\"\"\n        heur = 0\n        return heur\n\n    def heur_manhattan(self, start, current, end):\n        \"\"\"\n        Manhattan distance heuristics\n        \"\"\"\n        heur = self.manhattan_dist(current, end)\n        return heur\n\n    def heur_manhattan_cross(self, start, current, end):\n        \"\"\"\n        Manhattan distance + cross product heuristics. This gives preference to\n        paths that are more in a straight line between start and end tile.\n        \"\"\"\n        heur = self.manhattan_dist(current, end) + \\\n               self.cross_product(start, current, end)\n        return heur\n\n    def heur_manhattan_vertical(self, start, current, end):\n        \"\"\"\n        Manhattan distance + a factor that gives preference to vertical paths\n        between start and end tile.\n        \"\"\"\n        heur = self.manhattan_dist(current, end) + \\\n               self.vertical_dist(current, end)\n        return heur\n\n    def heur_manhattan_horizontal(self, start, current, end):\n        \"\"\"\n        Manhattan distance + a factor that gives preference to horizontal paths\n        between start and end tile.\n        \"\"\"\n        heur = self.manhattan_dist(current, end) + \\\n               self.horizontal_dist(current, end)\n        return heur\n\n    def manhattan_dist(self, start, end):\n        \"\"\"\n        Calculate the Manhattan distance between start and end tile.\n        \"\"\"\n        s_row, s_col = start\n        e_row, e_col = end\n        dist = abs(e_row - s_row) + abs(e_col - s_col)\n        return dist\n\n    def cross_product(self, start, current, end):\n        \"\"\"\n        The cross product between start tile - end tile vector and\n        current tile - end tile vector represents a factor for how aligned the\n        two vectors are. This factor is used to prefer paths that are in a\n        straight line from start to end tile.\n        \"\"\"\n        s_row, s_col = start\n        c_row, c_col = current\n        e_row, e_col = end\n        dx1 = c_col - e_col\n        dy1 = c_row - e_row\n        dx2 = s_col - e_col\n        dy2 = s_row - e_row\n        cross = abs(dx1 * dy2 - dx2*dy1)\n        return cross * ATTENUATION\n\n    def vertical_dist(self, current, end):\n        \"\"\"\n        vertical distance\n        \"\"\"\n        c_row, _ = current\n        e_row, _ = end\n        return abs(e_row - c_row) * ATTENUATION\n\n    def horizontal_dist(self, current, end):\n        \"\"\"\n        horizontal distance\n        \"\"\"\n        _, c_col = current\n        _, e_col = end\n        return abs(e_col - c_col) * ATTENUATION\n\n    def reconstruct_path(self, came_from, current):\n        \"\"\"\n        Generate path after A* search completed.\n        \"\"\"\n        path = []\n        while current in came_from:\n            path.append(current)\n            current = came_from[current]\n        path.append(current)\n        path.reverse()\n        return path\n\n    def place_conveyer_modules(self, path):\n        \"\"\"\n        Place conveyer belt modules along the list of tiles provided.\n        \"\"\"\n        for tile in path[1:-1]: # exclude start and end station tiles\n            row, col = tile\n            self.factory.place_conveyer_belt(row, col)\n\n    def convert(self, path):\n        \"\"\"\n        Convert tile coordinates from '(row, col)' to single integer notation.\n        \"\"\"\n        path_converted = list(map(lambda tile: tile[0] * 10 + tile[1], path))\n        return path_converted\n\n    def build_graph(self, start_tile, end_tile):\n        \"\"\"\n        Build a graph representation of the factory floor. The floor tiles are\n        the nodes and distance between nodes is always 1. Tiles with conveyer\n        belt modules are considered as occupied and will be excluded when\n        building the graph. Stations except the start and end station are\n        excluded as well. This way the algorithm calculates paths around\n        occupied tiles.\n        \"\"\"\n        floor_dim = len(self.factory.floor)\n        tiles = set(itertools.product(range(0, floor_dim), repeat=2))\n        tiles = tiles - self.factory.occupied\n        distance = 1\n        graph = {}\n        for row, col in tiles:\n            n_tiles = self.get_neighbour_tiles(row, col)\n            if ((row, col) == start_tile or (row, col) == end_tile) and \\\n               not n_tiles:\n                # start and/or end tile are isolated, they have no neighbours,\n                # stop algorithm, not worth continuing\n                raise NoNeighbourError('ERROR: tile {} has no neighbour tiles.'.format((row, col)))\n            distances = {(n_row, n_col): distance for n_row, n_col in n_tiles \\\n                        if not self.factory.is_occupied((n_row, n_col))}\n            graph[(row, col)] = distances\n        return graph\n\n    def get_neighbour_tiles(self, row, col):\n        \"\"\"\n        Get all tiles around a given tile.\n        \"\"\"\n        positions = [(-1, 0), (0, 1), (1, 0), (0, -1)]\n        max_row = len(self.factory.floor) - 1\n        max_col = len(self.factory.floor[0]) - 1\n        neighbours = []\n        for d_row, d_col in positions:\n            n_row = row + d_row\n            n_col = col + d_col\n            if n_row >= 0 and n_row <= max_row and \\\n               n_col >= 0 and n_col <= max_col:\n                neighbours.append((n_row, n_col))\n        return neighbours\n\ndef four_pass(stations):\n    \"\"\"\n    main function\n    \"\"\"\n    factory = Factory(stations)\n    path_planner = PathPlanner(factory)\n    test_path = path_planner.plan()\n    return test_path\n\nprint('with rotation index:\\n')\nshow([62, 67, 36, 86], [62, 63, 64, 65, 66, 67, 57, 56, 46, 36, 37, 38, 48, 58, 68, 78, 88, 87, 86])\nprint('\\nno rotation index:\\n')\nshow([62, 67, 36, 86], [62, 63, 64, 65, 66, 67, 57, 47, 37, 36, 26, 27, 28, 38, 48, 58, 68, 78, 88, 87, 86])\n\nimport timeit\nresults = timeit.timeit(stmt='four_pass([3, 7, 22, 6])',\n                        setup='from __main__ import four_pass',\n                        number=100)\nprint('total runtime: {}s'.format(results))\n\n", "text_preview": "\"\"\" My solution for Four Pass Transport kata: https://www.codewars.com/kata/four-pass-transport Level: 1 kyu Useful learning sources: https://www.redblobgames.com/pathfinding/a-star/introduction.html https://en.wikipedia.org/wiki/Travelling", "input_sha256": "89b7e6a9aceff1c6624f21b1de3fd0b41f7ce3cbc9f5e3097494a77c8b2b8af7", "text_sha256": "8623a053d96e3f51728b7064bd981fea37db86b5a5023b15a6d3b43ba1e2cc20", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12017, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 4, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7701108516074503, "noise_severity": 0.1659157235360221, "missing_content_severity": 0.01733540889907189, "factual_density": 3.30389150075523, "pretraining_utility": 0.8761775769186189, "math_density": 2.87998321708475, "code_density": 3.5259661755843035, "reasoning_density": 2.886883618407342, "education_level": 2.0502655036054613, "creative_writing_quality": -0.27353890284570426}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.05818188796825526, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9798858047152503, "secondary": 0.7731043595730405, "tertiary": 0.7380551716344238, "structured_records": 0.013262962326947196, "procedural_steps": 0.1632171142390402, "worked_solution": 0.26087720352097243, "contextual_dialogue": 6.4537677518775045e-06, "content_quality": 0.4823196645145038, "noise_severity": 0.8617368970533149, "missing_content_severity": 0.9855538259174401, "factual_density": 0.5222933397316831, "pretraining_utility": 0.5758436267348774, "math_density": 0.06665268090395826, "code_density": 0.6049718129869192, "reasoning_density": 0.07240301533945115, "education_level": 0.787943848549388, "creative_writing_quality": 0.9962494734539159, "primary_topic": 0.9288710193896592, "secondary_topic": 0.4292107953437023}, "probability_margin": {"primary": 0.968288244246842, "secondary": 0.7280577152182814, "tertiary": 0.6362870221421854, "education_level": 0.7317129390647631, "creative_writing_quality": 0.9951512087903308, "primary_topic": 0.9044172655033257, "secondary_topic": 0.12076481765874392}, "raw_probabilities": {"primary": {"Academic Writing": 0.0001432208556877135, "Content Listing": 0.0004017343096827496, "Creative Writing": 7.055189938311567e-05, "Customer Support Page": 3.915690502227921e-05, "Q&A Forum": 0.0001689289087732704, "Discussion Thread": 4.413160192350682e-05, "Comment Section": 7.238237728330938e-05, "Missing Content": 0.0001118455841938495, "Knowledge Article": 0.0005893974772434676, "Legal Notices": 5.4409673919315827e-05, "News Article": 1.999505153063641e-05, "Organizational Announcement": 4.433317443816573e-05, "Nonfiction Writing": 8.632320090272777e-05, "Organizational About Page": 4.498768769264673e-05, "Personal About Page": 8.323405667463826e-05, "Personal Blog": 0.00030245172634845677, "Product Page": 2.4732701047381465e-05, "Spam / Ads": 5.3299495540215537e-05, "Structured Data": 0.0001792061169106924, "Technical Writing": 0.9807238961854482, "Transcript / Interview": 4.5040769288142434e-05, "Tutorial / How-To Guide": 0.012435651938606216, "User Reviews": 3.888217047080726e-05, "Other / Unknown": 0.004222206131988273}, "secondary": {"Academic Writing": 0.0023957132169130546, "Content Listing": 0.054500629372548924, "Creative Writing": 0.0009640178188904016, "Customer Support Page": 0.0005699297799180729, "Q&A Forum": 0.0011802497835769852, "Discussion Thread": 0.0005468360146922176, "Comment Section": 0.0008303785513322675, "Missing Content": 0.0013325552238302608, "Knowledge Article": 0.01943837695722075, "Legal Notices": 0.0007359388206357625, "News Article": 0.00043434245157196195, "Organizational Announcement": 0.0009061586565191172, "Nonfiction Writing": 0.0035652151795955525, "Organizational About Page": 0.0007063641440536414, "Personal About Page": 0.0016991229034442256, "Personal Blog": 0.037655477910674286, "Product Page": 0.00038761524300819886, "Spam / Ads": 0.0009492137675194817, "Structured Data": 0.001094091922338834, "Technical Writing": 0.035173018949930614, "Transcript / Interview": 0.0006585500967878846, "Tutorial / How-To Guide": 0.051070641834270056, "User Reviews": 0.0006472168098970295, "NONE": 0.7825583445908304}, "tertiary": {"Academic Writing": 0.002222971931788977, "Content Listing": 0.11268251734080417, "Creative Writing": 0.0012790368014135806, "Customer Support Page": 0.0007136517449850921, "Q&A Forum": 0.0014528577654762146, "Discussion Thread": 0.0005904383312871546, "Comment Section": 0.0009743268938959896, "Missing Content": 0.002111041884803933, "Knowledge Article": 0.026321219687556872, "Legal Notices": 0.0010753793844600693, "News Article": 0.0005599962414454015, "Organizational Announcement": 0.0010347430954451406, "Nonfiction Writing": 0.004523530607830039, "Organizational About Page": 0.0007989171125238464, "Personal About Page": 0.00174982563773245, "Personal Blog": 0.04028462070646097, "Product Page": 0.0004847006009705388, "Spam / Ads": 0.0014919837710047617, "Structured Data": 0.0013648530875387337, "Technical Writing": 0.01843073734928998, "Transcript / Interview": 0.0007674941656443149, "Tutorial / How-To Guide": 0.029314365950638, "User Reviews": 0.0008012504240141889, "NONE": 0.7489695394829895}, "structured_records": {"false": 0.9867370376730528, "true": 0.013262962326947196}, "procedural_steps": {"false": 0.8367828857609598, "true": 0.1632171142390402}, "worked_solution": {"false": 0.7391227964790276, "true": 0.26087720352097243}, "contextual_dialogue": {"false": 0.9999935462322481, "true": 6.4537677518775045e-06}, "content_quality": {"-2": 0.014223290099410542, "-1": 0.06386989981491892, "0": 0.2551431055595031, "1": 0.4711000774311447, "2": 0.1956636270950228}, "noise_severity": {"0": 0.8511456395892977, "1": 0.13651878346216267, "2": 0.008927162779226023, "3": 0.0020910421618465114, "4": 0.0013173720074669529}, "missing_content_severity": {"0": 0.9879974828243845, "1": 0.008358223590783654, "2": 0.0023493149890886937, "3": 0.0009013590528618706, "4": 0.00039361954288130957}, "factual_density": {"0": 0.0031326698612513687, "1": 0.010257708860880795, "2": 0.1047648184778597, "3": 0.44327505626140334, "4": 0.43856974653860487}, "pretraining_utility": {"-2": 0.007260391742645867, "-1": 0.03273317225298281, "0": 0.2291575157658609, "1": 0.5382663078201274, "2": 0.19258261241838298}, "math_density": {"0": 0.018454998453717, "1": 0.06765597356225278, "2": 0.2608245849266207, "3": 0.32157969856038243, "4": 0.3314847444970271}, "code_density": {"0": 0.0006683782757817472, "1": 0.0014103860156003839, "2": 0.056169808088271406, "3": 0.35478953708922606, "4": 0.5869618905311205}, "reasoning_density": {"0": 0.065904209900476, "1": 0.09978722407585579, "2": 0.1335668685207918, "3": 0.2830041327216035, "4": 0.41773756478127294}, "education_level": {"none": 0.0016029251106282306, "0": 0.0023835718852284965, "1": 0.06630941605386295, "2": 0.8232865404578233, "3": 0.0915736013930603, "4": 0.01484394509939671}, "creative_writing_quality": {"not_applicable": 0.9968745612115966, "-2": 0.0002705241188092116, "-1": 0.0007700637615778841, "0": 0.0017233524212657422, "1": 0.00026681407139598855, "2": 9.468441535452888e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00018100772253112365, "Art & Design": 0.00020434804805068927, "Law & Crime": 0.00015219174430620245, "Education & Careers": 0.00018397271261430377, "Entertainment & Media": 0.0002719103801017881, "Fashion & Beauty": 9.60399244992256e-05, "Business, Economics & Finance": 0.00022956764146049151, "Food & Dining": 9.906078037841596e-05, "Games & Gaming": 0.012659088756527494, "Electronics & Hardware": 0.0001346408978027032, "Health, Healthcare & Wellness": 0.00012202935698422294, "History & Geography": 0.00017357726801608793, "Home, Pets & Hobbies": 0.00011497784928038757, "Industry & Production": 0.010642255660248229, "Language, Literature & Philosophy": 0.00011678013387997758, "Politics & Government": 6.504577345243716e-05, "Religion & Spirituality": 0.00011327730176544617, "Science, Mathematics & Engineering": 0.027189483909808135, "Relationships & Community": 7.690861900907191e-05, "Software Usage & Applications": 0.00028473103850794497, "Programming & Software Engineering": 0.9316067494131338, "Sports & Fitness": 0.0001839117538230874, "Vehicles & Transportation": 0.01416222318194377, "Travel & Tourism": 0.00016898521895832721, "Other / Uncovered Subject": 0.0006197436920584978, "Unknown / Insufficient Content": 0.00014749122085823917}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0015132875239614416, "Art & Design": 0.0009875127543510207, "Law & Crime": 0.0006942035850786822, "Education & Careers": 0.0013439269245966573, "Entertainment & Media": 0.0015965997004743762, "Fashion & Beauty": 0.00039795378545956646, "Business, Economics & Finance": 0.0009199891977145299, "Food & Dining": 0.0003800665497125674, "Games & Gaming": 0.041766417338292394, "Electronics & Hardware": 0.000650731793668561, "Health, Healthcare & Wellness": 0.0005564257881379044, "History & Geography": 0.0006128773640550649, "Home, Pets & Hobbies": 0.00045128596407185127, "Industry & Production": 0.025325365980104278, "Language, Literature & Philosophy": 0.00040739817517341544, "Politics & Government": 0.0002655465347920389, "Religion & Spirituality": 0.000532869509075468, "Science, Mathematics & Engineering": 0.3303994086332776, "Relationships & Community": 0.00023315943984166464, "Software Usage & Applications": 0.0006122639615528156, "Programming & Software Engineering": 0.10675482326375371, "Sports & Fitness": 0.0006716783502601443, "Vehicles & Transportation": 0.029858393855229606, "Travel & Tourism": 0.0006262641558347112, "Other / Uncovered Subject": 0.0012773235795083616, "NONE": 0.4511642262920215}}, "probabilities": {"primary": {"Academic Writing": 0.00014322085568771353, "Content Listing": 0.0004017343096827497, "Creative Writing": 7.055189938311568e-05, "Customer Support Page": 3.9156905022279214e-05, "Q&A Forum": 0.00016892890877327041, "Discussion Thread": 4.413160192350683e-05, "Comment Section": 7.23823772833094e-05, "Missing Content": 0.00011184558419384953, "Knowledge Article": 0.0005893974772434677, "Legal Notices": 5.440967391931584e-05, "News Article": 1.9995051530636414e-05, "Organizational Announcement": 4.433317443816574e-05, "Nonfiction Writing": 8.632320090272778e-05, "Organizational About Page": 4.498768769264674e-05, "Personal About Page": 8.323405667463827e-05, "Personal Blog": 0.0003024517263484568, "Product Page": 2.4732701047381472e-05, "Spam / Ads": 5.329949554021555e-05, "Structured Data": 0.00017920611691069242, "Technical Writing": 0.9807238961854484, "Transcript / Interview": 4.504076928814244e-05, "Tutorial / How-To Guide": 0.01243565193860622, "User Reviews": 3.888217047080727e-05, "Other / Unknown": 0.004222206131988274}, "secondary": {"Academic Writing": 0.0023957132169130546, "Content Listing": 0.054500629372548924, "Creative Writing": 0.0009640178188904016, "Customer Support Page": 0.0005699297799180729, "Q&A Forum": 0.0011802497835769852, "Discussion Thread": 0.0005468360146922176, "Comment Section": 0.0008303785513322675, "Missing Content": 0.0013325552238302608, "Knowledge Article": 0.01943837695722075, "Legal Notices": 0.0007359388206357625, "News Article": 0.00043434245157196195, "Organizational Announcement": 0.0009061586565191172, "Nonfiction Writing": 0.0035652151795955525, "Organizational About Page": 0.0007063641440536414, "Personal About Page": 0.0016991229034442256, "Personal Blog": 0.037655477910674286, "Product Page": 0.00038761524300819886, "Spam / Ads": 0.0009492137675194817, "Structured Data": 0.001094091922338834, "Technical Writing": 0.035173018949930614, "Transcript / Interview": 0.0006585500967878846, "Tutorial / How-To Guide": 0.051070641834270056, "User Reviews": 0.0006472168098970295, "NONE": 0.7825583445908304}, "tertiary": {"Academic Writing": 0.0022229719317889774, "Content Listing": 0.11268251734080419, "Creative Writing": 0.0012790368014135808, "Customer Support Page": 0.0007136517449850922, "Q&A Forum": 0.0014528577654762149, "Discussion Thread": 0.0005904383312871547, "Comment Section": 0.0009743268938959897, "Missing Content": 0.0021110418848039334, "Knowledge Article": 0.026321219687556876, "Legal Notices": 0.0010753793844600695, "News Article": 0.0005599962414454016, "Organizational Announcement": 0.0010347430954451408, "Nonfiction Writing": 0.00452353060783004, "Organizational About Page": 0.0007989171125238465, "Personal About Page": 0.0017498256377324502, "Personal Blog": 0.04028462070646098, "Product Page": 0.00048470060097053887, "Spam / Ads": 0.0014919837710047619, "Structured Data": 0.001364853087538734, "Technical Writing": 0.018430737349289985, "Transcript / Interview": 0.000767494165644315, "Tutorial / How-To Guide": 0.029314365950638004, "User Reviews": 0.000801250424014189, "NONE": 0.7489695394829896}, "structured_records": {"false": 0.9867370376730528, "true": 0.013262962326947196}, "procedural_steps": {"false": 0.8367828857609598, "true": 0.1632171142390402}, "worked_solution": {"false": 0.7391227964790276, "true": 0.26087720352097243}, "contextual_dialogue": {"false": 0.9999935462322481, "true": 6.4537677518775045e-06}, "content_quality": {"-2": 0.014223290099410542, "-1": 0.06386989981491892, "0": 0.2551431055595031, "1": 0.4711000774311447, "2": 0.1956636270950228}, "noise_severity": {"0": 0.8511456395892978, "1": 0.1365187834621627, "2": 0.008927162779226025, "3": 0.002091042161846512, "4": 0.001317372007466953}, "missing_content_severity": {"0": 0.9879974828243845, "1": 0.008358223590783654, "2": 0.0023493149890886937, "3": 0.0009013590528618706, "4": 0.00039361954288130957}, "factual_density": {"0": 0.0031326698612513687, "1": 0.010257708860880795, "2": 0.1047648184778597, "3": 0.44327505626140334, "4": 0.43856974653860487}, "pretraining_utility": {"-2": 0.007260391742645867, "-1": 0.03273317225298281, "0": 0.2291575157658609, "1": 0.5382663078201274, "2": 0.19258261241838298}, "math_density": {"0": 0.018454998453717, "1": 0.06765597356225278, "2": 0.2608245849266207, "3": 0.32157969856038243, "4": 0.3314847444970271}, "code_density": {"0": 0.0006683782757817472, "1": 0.0014103860156003839, "2": 0.056169808088271406, "3": 0.35478953708922606, "4": 0.5869618905311205}, "reasoning_density": {"0": 0.065904209900476, "1": 0.09978722407585579, "2": 0.1335668685207918, "3": 0.2830041327216035, "4": 0.41773756478127294}, "education_level": {"none": 0.0016029251106282306, "0": 0.0023835718852284965, "1": 0.06630941605386295, "2": 0.8232865404578233, "3": 0.0915736013930603, "4": 0.01484394509939671}, "creative_writing_quality": {"not_applicable": 0.9968745612115967, "-2": 0.00027052411880921167, "-1": 0.0007700637615778842, "0": 0.0017233524212657424, "1": 0.0002668140713959886, "2": 9.468441535452889e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00018100772253112362, "Art & Design": 0.00020434804805068922, "Law & Crime": 0.00015219174430620243, "Education & Careers": 0.00018397271261430371, "Entertainment & Media": 0.00027191038010178803, "Fashion & Beauty": 9.603992449922557e-05, "Business, Economics & Finance": 0.00022956764146049146, "Food & Dining": 9.906078037841593e-05, "Games & Gaming": 0.01265908875652749, "Electronics & Hardware": 0.00013464089780270317, "Health, Healthcare & Wellness": 0.00012202935698422291, "History & Geography": 0.0001735772680160879, "Home, Pets & Hobbies": 0.00011497784928038755, "Industry & Production": 0.010642255660248227, "Language, Literature & Philosophy": 0.00011678013387997756, "Politics & Government": 6.504577345243715e-05, "Religion & Spirituality": 0.00011327730176544615, "Science, Mathematics & Engineering": 0.02718948390980813, "Relationships & Community": 7.69086190090719e-05, "Software Usage & Applications": 0.0002847310385079449, "Programming & Software Engineering": 0.9316067494131336, "Sports & Fitness": 0.00018391175382308735, "Vehicles & Transportation": 0.014162223181943767, "Travel & Tourism": 0.0001689852189583272, "Other / Uncovered Subject": 0.0006197436920584977, "Unknown / Insufficient Content": 0.00014749122085823914}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0015132875239614416, "Art & Design": 0.0009875127543510207, "Law & Crime": 0.0006942035850786822, "Education & Careers": 0.0013439269245966573, "Entertainment & Media": 0.0015965997004743762, "Fashion & Beauty": 0.00039795378545956646, "Business, Economics & Finance": 0.0009199891977145299, "Food & Dining": 0.0003800665497125674, "Games & Gaming": 0.041766417338292394, "Electronics & Hardware": 0.000650731793668561, "Health, Healthcare & Wellness": 0.0005564257881379044, "History & Geography": 0.0006128773640550649, "Home, Pets & Hobbies": 0.00045128596407185127, "Industry & Production": 0.025325365980104278, "Language, Literature & Philosophy": 0.00040739817517341544, "Politics & Government": 0.0002655465347920389, "Religion & Spirituality": 0.000532869509075468, "Science, Mathematics & Engineering": 0.3303994086332776, "Relationships & Community": 0.00023315943984166464, "Software Usage & Applications": 0.0006122639615528156, "Programming & Software Engineering": 0.10675482326375371, "Sports & Fitness": 0.0006716783502601443, "Vehicles & Transportation": 0.029858393855229606, "Travel & Tourism": 0.0006262641558347112, "Other / Uncovered Subject": 0.0012773235795083616, "NONE": 0.4511642262920215}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 13376, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 13376, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:08.323401+00:00", "scoring_key_slot": "primary", "dataset_uid": "30b18cf7-cc18-4e07-894b-d5bb14a45541", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "45c1e5dc9c6f3243d145bd6ce5fb5b725067e56abd3a57398498ea58531f91b8", "source_metadata": {"uuid": "30b18cf7-cc18-4e07-894b-d5bb14a45541", "repo_name": "SophonAlpha/codewars", "relative_path": "solutions/four_pass_transport.py", "category": "ALGO", "algo_rel_score": 0.9906879663467407, "quality_score": 7.299619197845459, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 480, "repo_created_at": "2018-05-15T18:25:08.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 3, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "b31fec85dcd783b6aa25a0fff5a71b00a56a367f", "size_bytes": 19968, "file_timestamp": 1597242635, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "53a8525bd1dd927701ca1ae499cd81c4ea25cd74"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 480, "repo_created_at": "2018-05-15T18:25:08.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 3, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "b31fec85dcd783b6aa25a0fff5a71b00a56a367f", "size_bytes": 19968, "file_timestamp": 1597242635, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "53a8525bd1dd927701ca1ae499cd81c4ea25cd74", "url": "https://github.com/SophonAlpha/codewars/blob/53a8525bd1dd927701ca1ae499cd81c4ea25cd74/solutions/four_pass_transport.py"}, "gpt4o_tokens": 4474, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.7701108516074503, "noise_severity": 0.1659157235360221, "missing_content_severity": 0.01733540889907189, "factual_density": 3.30389150075523, "pretraining_utility": 0.8761775769186189, "math_density": 2.87998321708475, "code_density": 3.5259661755843035, "reasoning_density": 2.886883618407342, "education_level": 2.0502655036054613, "creative_writing_quality": -0.27353890284570426}, "score_applicability": {"education_level": 0.9983970748893718, "creative_writing_quality": 0.0031254387884033555}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/govindak-umd/ENPM661/blob/0341598fd14f3d7ee22b8987ba16f7cd66530f63/Project%203/Project%203%20Phase%203%20Gazebo%20version/661_proj3_p3_FINAL_CODE_for_gazebo.py", "id": "python-0-101e3d36865afd41f311aaca6de14db2", "text": "# Importing all the libraries\n\nimport numpy as np\nimport copy\nimport math\nimport heapq\nimport time\nimport matplotlib.pyplot as plt\n\n# %%\n# wheel dia = 76mm\n# full robot dia = 354 mm\n# distance between the wheels = 317.5mm\n# clearance =  can be given by the user = 5 mm\n# for solving the path using eucledian heuristic\n\n# Getting the start time to measure the time taken for solving\n\n####################User Inputs######################\n\nprint('                             WELCOME TO THE A * PROGRAMME                             ')\nprint('THIS PROGRAMME TAKES THE USER INPUTS BELOW AND SOLVES THE OBSTACLE SPACE VERY QUICKLY')\nprint('time taken (for A* solving) ~ <1 second')\nprint(\n    'To solve the necessary test cases, please know that the start and the goal points here are adjusted based on the clearance and the width of the robot')\nprint('This avoids the goal or the start getting into the borders or any obstacles')\nprint('For the turtle bot, this value is fixed at 35')\nprint('If you wish to change this value, please rerun the code after doing so.')\nprint('ALSO !! NOTE !!, the coordinates of the obstacle points have been multiplied by a factor of 100')\nprint(\n    'This ensures a clearer graph. Hence, please multiply by a factor of 100, before input. For example: 5.5 will be = 5.5*100 = 550')\nprint('Range of x coordinates you can enter  =  0 - 1020')\nprint('Range of y coordinates you can enter  =  0 - 1020')\nprint('Please enter angles in multiples of 15 only, from 0 to 360. example : 15,30,225,etc . ')\nprint('NOTE :  ENTER THE COORDINATE VALUES MULTIPLIED BY 100')\nx_start, y_start, start_orientation = input(\"Enter the x and y coordinate of the start and the initial orientation here, with a blank in between them (suggested : -420 -300 30) : > \").split()\nx_goal, y_goal =  input(\"Enter the x and y coordinate of the goal and the , in the form of blanks (suggested : 450 450) : > \").split()\nRPM_L, RPM_R = input(\"Enter the LEFT WHEEL RPM AND RIGHT WHEEL RPM  : > \").split()\nclearance = int(input(\"Enter the clearance of the robot with the nearby obstacles : (suggested value  < 5) : > \"))\nstep_size = int(input(\"Enter the step (1-10): (suggested values  = 1) : > \"))\nradius = 1\nstart = (int(x_start) + radius + clearance, int(y_start) + radius + clearance)\ngoal = (int(x_goal) - radius - clearance, int(y_goal) - radius - clearance)\nprint('Wheel radius is calculated to be 76mm/2 -> approximating to 0.5')\ntime_run = int(input(('Please enter the time you wish to choose to run the robot ((suggested  value = 1 second) : ')))\nstart_orientation = int(start_orientation)\nRPM_L = int(RPM_L)\nRPM_R = int(RPM_R)\nprint('Please wait while the map is being generated. Approximate wait time < 8 seconds . ')\n\n# %%\n# function to round the point 5\ndef Round2Point5(num):\n    return (round(num * 2) / 2)\n\n\ndef RoundToFifteen(x, base=15):\n    return base * round(x / base)\n\n\n# for solving the path using eucledian heuristic\n\ndef EucledianDistance(a, b):\n    x1 = a[0]\n    x2 = b[0]\n    y1 = a[1]\n    y2 = b[1]\n    dist = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)\n    dist = Round2Point5(dist)\n    return dist\n\n\ndef plot_curve(X0, Y0, Theta0, UL, UR):\n    global time_run\n    t = 0\n    r = 0.5\n    L = 3.5\n    dt = 0.1\n    X1 = 0\n    Y1 = 0\n    dtheta = 0\n    Theta0 = 3.14 * Theta0 / 180\n    Theta1 = Theta0\n    while t < time_run:\n        t = t + dt\n        X0 = X0 + X1\n        Y0 = Y0 + Y1\n\n        dx = (r/2) * (UL + UR) * math.cos(Theta1) * dt\n        dy = (r/2) * (UL + UR) * math.sin(Theta1) * dt\n        dtheta = (r / L) * (UR - UL) * dt\n\n        X1 = X1 + dx\n        Y1 = Y1 + dy\n        Theta1 = Theta1 + 0.5 * dtheta\n\n        plt.quiver(X0, Y0, X1, Y1, units='xy', scale=1, color='r', width=1, headwidth=1, headlength=0)\n\n        Xn = X0 + X1\n        Yn = Y0 + Y1\n        Thetan = 180 * (Theta1) / 3.14\n    return Xn, Yn, Thetan\n\n\n# can use manhattan or eucledian heuristic as for caclulation of the value of cost to go to the goal.\n\n#####\n\n# function to move\n\n# Possible motions :\n\n# [0, RPM1]\n# [RPM1, 0]\n# [RPM1, RPM1]\n# [0, RPM2]\n# [RPM2, 0]\n# [RPM2, RPM2]\n# [RPM1, RPM2]\n# [RPM2, RPM1]\n\n# inding neighbours using the non-holonomic drive conditions\n# fucntion finds the immediete neighbour to which the robot drives to\n\ndef ActionMove(curr_node, orientation_facing, RPM_L, RPM_R, d=1.0, L=3.5, step_size=1.0):\n    global time_run\n    curr_start_x = curr_node[0]\n    curr_start_y = curr_node[1]\n\n    #r = d / 2\n    r = 1\n    # radius = dia / 2\n    t = 0\n\n    dt = 0.1\n    X1 = 0\n    Y1 = 0\n    dtheta = 0\n    # x_new, y_new, new_orientation = GoTo(x,y,degree,RPM_L,RPM_R)\n\n    orientation_facing_RADIANS = 3.14 * orientation_facing / 180\n\n    while t < time_run:\n        t = t + dt\n        curr_start_x = curr_start_x + X1\n        curr_start_y = curr_start_y + Y1\n        dx = (r/2) * (RPM_L + RPM_R) * math.cos(orientation_facing_RADIANS) * dt\n        dy = (r/2) * (RPM_L + RPM_R) * math.sin(orientation_facing_RADIANS) * dt\n        dtheta = (r / L) * (RPM_R - RPM_L) * dt\n        X1 = X1 + dx\n        Y1 = Y1 + dy\n        orientation_facing_RADIANS = orientation_facing_RADIANS + 0.5 * dtheta\n        new_x = curr_start_x + X1\n        new_y = curr_start_y + Y1\n        new_final_orientation = 180 * (orientation_facing_RADIANS) / 3.14\n    # calculating the degrees rotated   from the start to the new orientation and assigning costs accordingly\n    degrees_rotated = abs(orientation_facing - new_final_orientation)\n    degrees_rotated = abs(degrees_rotated % 360)\n    cost = 10 + (10 * degrees_rotated / 360)\n    #     new_final_orientation = round(new_final_orientation,2)\n    new_final_orientation = RoundToFifteen(new_final_orientation)\n    new_node = ((round(new_x, 2), round(new_y, 2)), new_final_orientation, cost)\n    same_out = (curr_node, orientation_facing, 100000)\n    if -510.00 <= new_node[0][0] <= 510.00 and -510.00 <= new_node[0][1] <= 510.00:\n        return new_node, True\n    else:\n        return same_out, False\n\n\n# %%\n######################################     POINTS FOR REMOVING VALUES FROM THE GRAPH\n\n# calculating all the points within the entire canvas\n\n# all possible points in integer format\n\nall_possible_int_points = []\n\n# points to draw the map, without considering the radius and clearance of the robot. This works with\n# different sets of points as compared to the points created\n\nuntraversable_points = []\n# SCALING all by 100 times\nfor i in range(-510, 511):  # 1020 width\n    for j in range(-510, 511):  # 1020 width\n        all_possible_int_points.append((i, j))  # appending\n\nfor pt in all_possible_int_points:\n    x = pt[0]\n    y = pt[1]\n    # circle shaped obstacles\n    # circle shaped obstacle at the center of the image\n    # for path traversal\n    if (x - 0) ** 2 + (y - 0) ** 2 <= (100 + radius + clearance) ** 2:\n        untraversable_points.append((x, y))\n\n    # circle shaped obstacle on top right\n    # for path traversal\n    if (x + 200) ** 2 + (y +300) ** 2 <= (100 + radius + clearance) ** 2:\n        untraversable_points.append((x, y))\n\n    # circle shaped obstacle on bottom right\n    # for path traversal\n    if (x + 200) ** 2 + (y - 300) ** 2 <= (100 + radius + clearance) ** 2:\n        untraversable_points.append((x, y))\n\n    # circle shaped obstacle on bottom left\n    # for path traversal\n    if (x - 200) ** 2 + (y -300) ** 2 <= (100 + radius + clearance) ** 2:\n        untraversable_points.append((x, y))\n\n    # borders\n\n    # left vertical border\n    if -510 < x < -500 + radius + clearance:\n        if 0 < y < 510:\n            untraversable_points.append((x, y))\n\n    # right vertical border\n    if 500 - radius - clearance < x < 510:\n        if -510 < y < 510:\n            untraversable_points.append((x, y))\n    # bottom horizontal border\n    if -510 < x < 510:\n        if -510 < y < -500 + radius + clearance:\n            untraversable_points.append((x, y))\n    # top horizontal border\n    if -510 < x < 510:\n        if 500 - radius - clearance < y < 510:\n            untraversable_points.append((x, y))\n\n    # squares\n\n    # left square\n    if -475 - radius - clearance <= x <= -325 + radius + clearance:\n        if -75 - radius - clearance <= y <= 75 + radius + clearance:\n            untraversable_points.append((x, y))\n\n    # right square\n    if 325 - radius - clearance <= x <= 475 + radius + clearance:\n        if -75 - radius - clearance <= y <= 75 + radius + clearance:\n            untraversable_points.append((x, y))\n\n    # top left square (inverted to bottom right square)\n    if 125- radius - clearance <= x <= 275 + radius + clearance:\n        if -375 - radius - clearance <= y <= -225 + radius + clearance:\n            untraversable_points.append((x, y))\n\n#        POINTS FOR DRAWING THE GRAPH\n\n##################function to check if any point is within the obstacle space#################\n# including the region covered by the robot radius and its clearance\n\n# %%\ndef checkObstaclespace(point):\n    global radius\n    global clearance\n    x = point[0]\n    y = point[1]\n    if (x - 0) ** 2 + (y - 0) ** 2 <= (100 + radius + clearance) ** 2:\n        return False\n\n    # circle shaped obstacle on top right\n    # for path traversal\n    elif (x + 200) ** 2 + (y +300) ** 2 <= (100 + radius + clearance) ** 2:\n        return False        \n\n    # circle shaped obstacle on bottom right\n    # for path traversal\n    elif (x + 200) ** 2 + (y - 300) ** 2 <= (100 + radius + clearance) ** 2:\n        return False\n\n    # circle shaped obstacle on bottom left\n    # for path traversal\n    elif (x - 200) ** 2 + (y -300) ** 2 <= (100 + radius + clearance) ** 2:\n        return False\n\n    # borders\n\n    # left vertical border\n    elif -510 < x < -500 + radius + clearance and  0 < y < 510:\n        return False\n\n    # right vertical border\n    elif 500 - radius - clearance < x < 510 and  -510 < y < 510:\n        \n        return False\n    # bottom horizontal border\n    elif -510 < x < 510 and  -510 < y < -500 + radius + clearance:\n        return False\n    # top horizontal border\n    elif -510 < x < 510 and  500 - radius - clearance < y < 510:\n        \n        return False\n\n    # squares\n\n    # left square\n    elif -475 - radius - clearance <= x <= -325 + radius + clearance and  -75 - radius - clearance <= y <= 75 + radius + clearance:\n        \n        return False\n\n    # right square\n    elif 325 - radius - clearance <= x <= 475 + radius + clearance and  -75 - radius - clearance <= y <= 75 + radius + clearance:\n        \n        return False\n\n    # top left square (inverted to bottom right square)\n    elif 125- radius - clearance <= x <= 275 + radius + clearance and  -375 - radius - clearance <= y <= -225 + radius + clearance:\n        \n        return False\n\n\n    else:\n\n        return True\n\n\n# %%\n# showing what every child node of each parent nodes are connected to\n\n\ndef generateGraph(point, degree,\n                  step_size=1):  # remember that this size_x and size_y are the sizes of the matrix, so not the end coordinates\n    global RPM_R\n    global RPM_L\n\n    i = point[0]  # x coordinate\n    j = point[1]  # y coordinate\n\n    if i <= 510 and j <= 510 and i >= -510 and j >= -510:\n\n        all_neighbours = {}\n\n        pos1 = ActionMove(point, degree, 0, RPM_L)[0]\n        pos2 = ActionMove(point, degree, RPM_L, 0)[0]\n        pos3 = ActionMove(point, degree, RPM_L, RPM_L)[0]\n        pos4 = ActionMove(point, degree, 0, RPM_R)[0]\n        pos5 = ActionMove(point, degree, RPM_R, 0)[0]\n        pos6 = ActionMove(point, degree, RPM_R, RPM_R)[0]\n        pos7 = ActionMove(point, degree, RPM_L, RPM_R)[0]\n        pos8 = ActionMove(point, degree, RPM_R, RPM_L)[0]\n\n        if pos1[0][0] >= -510 and pos1[0][1] >= -510 and pos1[0][0] <= 510 and pos1[0][1] <= 510:\n            all_neighbours[pos1[0]] = (round(pos1[2], 2), pos1[1])\n\n        if pos2[0][0] >= -510 and pos2[0][1] >= -510 and pos2[0][0] <= 510 and pos2[0][1] <= 510:\n            all_neighbours[pos2[0]] = (round(pos2[2], 2), pos2[1])\n\n        if pos3[0][0] >= -510 and pos3[0][1] >= -510 and pos3[0][0] <= 510 and pos3[0][1] <= 510:\n            all_neighbours[pos3[0]] = (round(pos3[2], 2), pos3[1])\n\n        if pos4[0][0] >= -510 and pos4[0][1] >= -510 and pos4[0][0] <= 510 and pos4[0][1] <= 510:\n            all_neighbours[pos1[0]] = (round(pos4[2], 2), pos4[1])\n\n        if pos5[0][0] >= -510 and pos5[0][1] >= -510 and pos5[0][0] <= 510 and pos5[0][1] <= 510:\n            all_neighbours[pos5[0]] = (round(pos5[2], 2), pos5[1])\n\n        if pos6[0][0] >= -510 and pos6[0][1] >= -510 and pos6[0][0] <= 510 and pos6[0][1] <= 510:\n            all_neighbours[pos6[0]] = (round(pos6[2], 2), pos6[1])\n\n        if pos7[0][0] >= -510 and pos7[0][1] >= -510 and pos7[0][0] <= 510 and pos7[0][1] <= 510:\n            all_neighbours[pos7[0]] = (round(pos7[2], 2), pos7[1])\n\n        if pos8[0][0] >= -510 and pos8[0][1] >= -510 and pos8[0][0] <= 510 and pos8[0][1] <= 510:\n            all_neighbours[pos8[0]] = (round(pos8[2], 2), pos8[1])\n\n        return all_neighbours\n\n    else:\n\n        pass\n\n\n# %%\n# function to backtrack so that the path from the goal to the end is obtained\n\n\n\n\ndef BackTrack(backtrack_dict, goal, start):  # goal is the starting point now and start is the goal point now\n    point_and_angle_list = []\n    # initializing the backtracked list\n    back_track_list = []\n    # appending the start variable to the back_track_list list\n    back_track_list.append(start)\n    # while the goal is not found\n\n    while goal != []:\n        # for key and values in the backtracking dictionary\n        for k, v in backtracking_dict.items():\n\n            # for the key and values in the values, v\n            for k2, v2 in v.items():\n                # checking if the first key is the start\n                if k == start:\n\n                    # checking if not in the backtrackedlist\n\n                    if v2[0] not in back_track_list:\n                        back_track_list.append(start)\n                        point_and_angle_list.append((start, v2[1]))\n                    # updating the start variable\n                    start = v2[0]\n\n                    # checking if it is the goal\n                    if v2[0] == goal:\n                        goal = []\n                        break\n                        # returns the backtracked list\n    return (back_track_list)\n\n\n######### < < < < A* algorithm code > > > > #########\n\n# %%\n# empty dictionary for backtracking from child to parent upto the start\nbacktracking = {}\n# list of all the visited nodes\nvisited = set()\nglobal orientation_to_layer\n# Mapping of Angles to layers\norientation_to_layer = {0: 0, 15: 1, 30: 2, 45: 3, 60: 4, 75: 5, 90: 6, 105: 7, 120: 8, 135: 9, 150: 10, 165: 11,\n                        180: 12,\n                        195: 13, 210: 14, 225: 15, 240: 16, 255: 17, 270: 18, 285: 19, 300: 20, 315: 21, 330: 22,\n                        345: 23, 360: 24}\n# array to store cost from\n# creating a 200rows, by 300 column by 12 layers for the various costs and to check if a node is visited\ncost_from = np.array(np.ones((2041, 2041, 24)) * np.inf)\n# Initializing visited nodes as empty array\nV = np.zeros((2041, 2041, 24))\n# array to store Heuristic distance\nheur = np.array(np.ones((2041, 2041)) * np.inf)\n# array to store total cost f\nf = np.array(np.ones((2041, 2041, 24)) * np.inf)\n# list for Explored nodes\npriority_queue = []\n# append start point,start orientation of the bot and initialize it's cost to zero\nheapq.heappush(priority_queue, (0, start, start_orientation))\n# initialize cost  for start node to zero\ncost_from[int(2 * start[0])][int(2 * start[1])][orientation_to_layer[start_orientation]] = 0\nf[int(2 * start[0])][int(2 * start[1])][orientation_to_layer[start_orientation]] = 0\n\n\n######### < < < < A* algorithm code > > > > #########\n\ndef a_star_Algorithm(start, goal):\n    global step_size\n    global untraversable_points\n    global V\n    break_while = 0\n\n    # create a dictionary for backtracked parents\n    backtracking = {}\n\n    # check if goal/start in obstacle space\n\n    if goal in untraversable_points or start in untraversable_points:\n\n        print('!!!!!!!!!!GOAL/START IS INSIDE OBSTACLE SPACE!!!!!!!!!!')\n        print(\n            'Please check if the radius and clearance of the robot was \\n taken into consideration and reassign again.')\n        backtracking = 0\n        rounded_neighbour = 0\n\n        return False\n\n    else:\n        while True:\n            if break_while == 1:\n                break\n            _, curr_vert, curr_orient = heapq.heappop(priority_queue)\n            # append visited nodes\n            visited.add(curr_vert)\n            # checking if the neighbour is the goal. If goal found reached\n            if ((curr_vert[0] - goal[0]) ** 2 + (curr_vert[1] - goal[1]) ** 2 <= (1.5) ** 2):\n                print(curr_vert)\n                break\n            # check whether node is in the obstacle space\n            if checkObstaclespace(curr_vert) == True:\n                # generate neighbours\n                graph = generateGraph(curr_vert, curr_orient)\n                graph_list = []\n                # put neighbours in a list for easy access\n                for key, cost_value in graph.items():\n                    graph_list.append((key, cost_value))\n                for neighbour, cost in graph_list:\n                    this_cost = graph[neighbour][0]\n                    breakflag = 0\n                    orientation = graph[neighbour][1]\n                    orientation = orientation % 360\n                    # Round the node\n                    rounded_neighbour = (Round2Point5(neighbour[0]), Round2Point5(neighbour[1]))\n                    #                     print(\"cost for this\"+str(rounded_neighbour)+str(this_cost))\n                    if rounded_neighbour in visited:\n                        breakflag = 1\n                        # exit if found\n                    if breakflag == 1:\n                        continue\n                    # check if this neighbour is goal\n                    if ((rounded_neighbour[0] - goal[0]) ** 2 + (rounded_neighbour[1] - goal[1]) ** 2 <= (1.5) ** 2):\n                        break_while = 1\n                        break\n                    ##check whether current neighbour node is in the obstacle space\n                    if checkObstaclespace(rounded_neighbour) == True:\n                        # check if visited\n                        if V[int(2 * rounded_neighbour[0])][int(2 * rounded_neighbour[1])][\n                            orientation_to_layer[orientation]] == 0:\n                            # if not, make it visited\n                            V[int(2 * rounded_neighbour[0])][int(2 * rounded_neighbour[1])][\n                                orientation_to_layer[orientation]] = 1\n                            # calculate cost from\n                            cost_from[int(2 * rounded_neighbour[0])][int(2 * rounded_neighbour[1])][\n                                orientation_to_layer[int(orientation)]] = (\n                                    this_cost + cost_from[int(2 * curr_vert[0])][int(2 * curr_vert[1])][\n                                orientation_to_layer[curr_orient]])\n                            # calculate cost to go values\n                            heur[int(2 * rounded_neighbour[0])][int(2 * rounded_neighbour[1])] = EucledianDistance(\n                                rounded_neighbour, goal)\n                            # calculate f = g+h\n                            f[int(2 * rounded_neighbour[0])][int(2 * rounded_neighbour[1])][\n                                orientation_to_layer[orientation]] = \\\n                                cost_from[int(2 * rounded_neighbour[0])][int(2 * rounded_neighbour[1])][\n                                    orientation_to_layer[orientation]] + heur[int(2 * rounded_neighbour[0])][\n                                    int(2 * rounded_neighbour[1])]\n                            # push to the explored node queue\n                            heapq.heappush(priority_queue, (\n                                f[int(2 * rounded_neighbour[0])][int(2 * rounded_neighbour[1])][\n                                    orientation_to_layer[orientation]], rounded_neighbour, orientation))\n                            backtracking[rounded_neighbour] = {}\n                            # adding to the backtracking dictionary\n                            backtracking[rounded_neighbour][\n                                f[int(2 * rounded_neighbour[0])][int(2 * rounded_neighbour[1])][\n                                    orientation_to_layer[orientation]]] = (curr_vert, curr_orient)\n                        else:\n                            # if visited, check cost. if newly genrated neighbour has a lower cost, update it\n                            if (f[int(2 * rounded_neighbour[0])][int(2 * rounded_neighbour[1])][\n                                orientation_to_layer[orientation]]) > (\n                                    f[int(2 * curr_vert[0])][int(2 * curr_vert[1])][orientation_to_layer[curr_orient]]):\n                                f[int(2 * rounded_neighbour[0])][int(2 * rounded_neighbour[1])][\n                                    orientation_to_layer[orientation]] = (\n                                    f[int(2 * curr_vert[0])][int(2 * curr_vert[1])][orientation_to_layer[curr_orient]])\n                                backtracking[rounded_neighbour][\n                                    f[int(2 * rounded_neighbour[0])][int(2 * rounded_neighbour[1])][\n                                        orientation_to_layer[orientation]]] = (curr_vert, curr_orient)\n\n    return (curr_vert, backtracking)\n    # return the last parent node and backtracked dictionary\n# calling the function to solve the obstacle space using A* Algorithm\nnew_goal_rounded, backtracking_dict = a_star_Algorithm(start, goal)\n# Backtracking back to the goal\nbacktracked_final = BackTrack(backtracking_dict, start, new_goal_rounded)\n# printing the nodes from initial to the end\nprint(backtracked_final)\n    \nros_visited_points = []\nfor visit in list(visited):\n     ros_visited_points.append((visit[0]/100,visit[1]/100))\n\nros_backtracked_points = []\nfor backtr in backtracked_final:\n    ros_backtracked_points.append((backtr[0]/100,backtr[1]/100))\nros_backtracked_points = ros_backtracked_points[::-1]", "text_preview": "# Importing all the libraries import numpy as np import copy import math import heapq import time import matplotlib.pyplot as plt # %% # wheel dia = 76mm # full robot dia = 354 mm # distance between the wheels = 317.5mm # clearance = can be", "input_sha256": "3723297576b0621b24edb9ff5167f938bd17ea3757f913b648758ba54ca88ceb", "text_sha256": "101e3d36865afd41f311aaca6de14db2eff67a5b467a5bd6caf7d0539b18fa66", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12020, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 4, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": -0.04524737054120029, "noise_severity": 0.2585505705445948, "missing_content_severity": 0.11341804553317651, "factual_density": 3.173307282636798, "pretraining_utility": 0.5597732613643559, "math_density": 3.339101229264688, "code_density": 3.7864094771344248, "reasoning_density": 2.197449996135736, "education_level": 2.365766056114848, "creative_writing_quality": -0.6100845280073974}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.5690 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.021108474023383672, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9904436150064604, "secondary": 0.8823622912450252, "tertiary": 0.8603360574814944, "structured_records": 0.03792021581698151, "procedural_steps": 0.09748550653172783, "worked_solution": 0.07287185393484583, "contextual_dialogue": 1.5569948920104677e-05, "content_quality": 0.4500523798133762, "noise_severity": 0.7845411912128377, "missing_content_severity": 0.9054849620556862, "factual_density": 0.49944360492191164, "pretraining_utility": 0.46109410151387875, "math_density": 0.4492510243872403, "code_density": 0.8220078976120203, "reasoning_density": 0.0, "education_level": 0.576451141452061, "creative_writing_quality": 0.9989601441913362, "primary_topic": 0.8311805241906401, "secondary_topic": 0.5517727878006718}, "probability_margin": {"primary": 0.9852074523768264, "secondary": 0.8331873183815505, "tertiary": 0.7929222334043619, "education_level": 0.3445620272296354, "creative_writing_quality": 0.9988376135320124, "primary_topic": 0.6900608439640321, "secondary_topic": 0.3693271421815243}, "raw_probabilities": {"primary": {"Academic Writing": 0.00028169549619327017, "Content Listing": 3.666271769521574e-05, "Creative Writing": 3.845363184620857e-05, "Customer Support Page": 2.2052862779056703e-05, "Q&A Forum": 2.8989572359092612e-05, "Discussion Thread": 1.6004021072929884e-05, "Comment Section": 3.552909940171675e-05, "Missing Content": 4.792935102690462e-05, "Knowledge Article": 0.00019446018095517777, "Legal Notices": 3.770370414023175e-05, "News Article": 1.0941549037001885e-05, "Organizational Announcement": 2.1184945863229248e-05, "Nonfiction Writing": 2.984161956594112e-05, "Organizational About Page": 2.5248696541224804e-05, "Personal About Page": 2.410751727777612e-05, "Personal Blog": 1.839230651692616e-05, "Product Page": 1.8153801531765755e-05, "Spam / Ads": 2.646709480467077e-05, "Structured Data": 0.00044421691232513985, "Technical Writing": 0.9908417977145245, "Transcript / Interview": 3.9449077612294766e-05, "Tutorial / How-To Guide": 0.005634345337698107, "User Reviews": 1.5881695521749507e-05, "Other / Unknown": 0.0021104910937098558}, "secondary": {"Academic Writing": 0.003670358588120141, "Content Listing": 0.0003446536165037278, "Creative Writing": 0.0004915842710210141, "Customer Support Page": 0.0008846487711913478, "Q&A Forum": 0.0002831650843338639, "Discussion Thread": 0.00015891811255116617, "Comment Section": 0.0005989776047217737, "Missing Content": 0.0005488733058313965, "Knowledge Article": 0.0031088585105758094, "Legal Notices": 0.0003539045531842044, "News Article": 0.00020143433135859994, "Organizational Announcement": 0.00028267267670045786, "Nonfiction Writing": 0.0003648283804105734, "Organizational About Page": 0.0002810445516251565, "Personal About Page": 0.000286233802581225, "Personal Blog": 0.0002881430424646324, "Product Page": 0.00018354487768251334, "Spam / Ads": 0.0005442102139876442, "Structured Data": 0.004606860552890749, "Technical Writing": 0.0405666732690033, "Transcript / Interview": 0.00044825127079440593, "Tutorial / How-To Guide": 0.054076544061598626, "User Reviews": 0.00016175410771852953, "NONE": 0.8872638624431491}, "tertiary": {"Academic Writing": 0.0019285755747785976, "Content Listing": 0.0003240393987006868, "Creative Writing": 0.000480066368964722, "Customer Support Page": 0.001907570883864774, "Q&A Forum": 0.00024237406290097488, "Discussion Thread": 0.00014480499380978924, "Comment Section": 0.0010576201329367883, "Missing Content": 0.000562779312533298, "Knowledge Article": 0.007653561115300095, "Legal Notices": 0.00046399594164752924, "News Article": 0.00018376535151267857, "Organizational Announcement": 0.0002847565515272573, "Nonfiction Writing": 0.0005317031227595482, "Organizational About Page": 0.00026964103966203943, "Personal About Page": 0.0002701796627303243, "Personal Blog": 0.0002592485710620347, "Product Page": 0.0002168933791034502, "Spam / Ads": 0.0010194223064651678, "Structured Data": 0.0079622249298675, "Technical Writing": 0.03423529126804566, "Transcript / Interview": 0.000465385258154417, "Tutorial / How-To Guide": 0.07323315501540363, "User Reviews": 0.0001475573385036261, "NONE": 0.8661553884197655}, "structured_records": {"false": 0.9620797841830185, "true": 0.03792021581698151}, "procedural_steps": {"false": 0.9025144934682722, "true": 0.09748550653172783}, "worked_solution": {"false": 0.9271281460651541, "true": 0.07287185393484583}, "contextual_dialogue": {"false": 0.9999844300510798, "true": 1.5569948920104677e-05}, "content_quality": {"-2": 0.040100796574552545, "-1": 0.2723906642334693, "0": 0.4190986794030525, "1": 0.22947483273647692, "2": 0.038935027052448605}, "noise_severity": {"0": 0.7990795169755481, "1": 0.1524728195907291, "2": 0.04161787153067339, "3": 0.0044771597196787295, "4": 0.0023526321833706896}, "missing_content_severity": {"0": 0.9378275863726171, "1": 0.020757164307471383, "2": 0.032582002373235094, "3": 0.007836111307471647, "4": 0.0009971356392050059}, "factual_density": {"0": 0.007146706586226662, "1": 0.02165319606070956, "2": 0.14893368384835506, "3": 0.435278935139457, "4": 0.3869874783652518}, "pretraining_utility": {"-2": 0.013222593463855863, "-1": 0.07749350661011127, "0": 0.3488021147977047, "1": 0.4572516153544777, "2": 0.10323016977385065}, "math_density": {"0": 0.003227819689884064, "1": 0.007151710186467338, "2": 0.10664246947415189, "3": 0.41324742246806956, "4": 0.4697305781814273}, "code_density": {"0": 0.000315409894261941, "1": 0.0006217376247149972, "2": 0.020537957578375666, "3": 0.16938775525763156, "4": 0.8091371396450158}, "reasoning_density": {"0": 0.2643414461030934, "1": 0.09345237490909945, "2": 0.1296852192401371, "3": 0.20545665624431836, "4": 0.3070643035033518}, "education_level": {"none": 0.001697399900618032, "0": 0.001709245901470463, "1": 0.009352395114938565, "2": 0.6470426178767175, "3": 0.30248059064708205, "4": 0.0377177505591734}, "creative_writing_quality": {"not_applicable": 0.9991334534927802, "-2": 0.0001982272662380282, "-1": 0.00026786993919523006, "0": 0.00029583996076772444, "1": 7.356082722009189e-05, "2": 3.104851379876112e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0004967379211884949, "Art & Design": 0.00039589163714899816, "Law & Crime": 0.00031248376911415757, "Education & Careers": 0.0002560192704075413, "Entertainment & Media": 0.00023166014575783046, "Fashion & Beauty": 0.00017417056018497398, "Business, Economics & Finance": 0.0002850044623879573, "Food & Dining": 0.00017461361846216937, "Games & Gaming": 0.0007438200645058576, "Electronics & Hardware": 0.003128426373382581, "Health, Healthcare & Wellness": 0.00032493434132412053, "History & Geography": 0.0003104398913571213, "Home, Pets & Hobbies": 0.0003723332292874539, "Industry & Production": 0.0004978400818009753, "Language, Literature & Philosophy": 0.00020906666893634655, "Politics & Government": 0.00010400779414108664, "Religion & Spirituality": 0.00020820989253260064, "Science, Mathematics & Engineering": 0.1476127369885065, "Relationships & Community": 9.456608652419154e-05, "Software Usage & Applications": 0.0006424481875492962, "Programming & Software Engineering": 0.8376735809525386, "Sports & Fitness": 0.00036600497826317724, "Vehicles & Transportation": 0.0020164668319134476, "Travel & Tourism": 0.00023017136425407145, "Other / Uncovered Subject": 0.002843972353853211, "Unknown / Insufficient Content": 0.0002943925346772405}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010555891167298393, "Art & Design": 0.0011723623706514175, "Law & Crime": 0.0006048846124588118, "Education & Careers": 0.0006595472535143712, "Entertainment & Media": 0.00042058909295735315, "Fashion & Beauty": 0.0003805624261498148, "Business, Economics & Finance": 0.00047330220032533004, "Food & Dining": 0.00038150631220845764, "Games & Gaming": 0.0010350769129548367, "Electronics & Hardware": 0.02062039867094004, "Health, Healthcare & Wellness": 0.0007469584408959235, "History & Geography": 0.0005745115663934893, "Home, Pets & Hobbies": 0.0006652138417864112, "Industry & Production": 0.0008484344103882429, "Language, Literature & Philosophy": 0.0002578081979621393, "Politics & Government": 0.0002731342011604315, "Religion & Spirituality": 0.0004806050369489836, "Science, Mathematics & Engineering": 0.5690122959621845, "Relationships & Community": 0.00021133053664000383, "Software Usage & Applications": 0.0008517550887813487, "Programming & Software Engineering": 0.18862264096912, "Sports & Fitness": 0.0007583860227570441, "Vehicles & Transportation": 0.007608440200673052, "Travel & Tourism": 0.0005390858675431005, "Other / Uncovered Subject": 0.0020604269072148147, "NONE": 0.1996851537806602}}, "probabilities": {"primary": {"Academic Writing": 0.00028169549619327017, "Content Listing": 3.666271769521574e-05, "Creative Writing": 3.845363184620857e-05, "Customer Support Page": 2.2052862779056703e-05, "Q&A Forum": 2.8989572359092612e-05, "Discussion Thread": 1.6004021072929884e-05, "Comment Section": 3.552909940171675e-05, "Missing Content": 4.792935102690462e-05, "Knowledge Article": 0.00019446018095517777, "Legal Notices": 3.770370414023175e-05, "News Article": 1.0941549037001885e-05, "Organizational Announcement": 2.1184945863229248e-05, "Nonfiction Writing": 2.984161956594112e-05, "Organizational About Page": 2.5248696541224804e-05, "Personal About Page": 2.410751727777612e-05, "Personal Blog": 1.839230651692616e-05, "Product Page": 1.8153801531765755e-05, "Spam / Ads": 2.646709480467077e-05, "Structured Data": 0.00044421691232513985, "Technical Writing": 0.9908417977145245, "Transcript / Interview": 3.9449077612294766e-05, "Tutorial / How-To Guide": 0.005634345337698107, "User Reviews": 1.5881695521749507e-05, "Other / Unknown": 0.0021104910937098558}, "secondary": {"Academic Writing": 0.003670358588120141, "Content Listing": 0.0003446536165037278, "Creative Writing": 0.0004915842710210141, "Customer Support Page": 0.0008846487711913478, "Q&A Forum": 0.0002831650843338639, "Discussion Thread": 0.00015891811255116617, "Comment Section": 0.0005989776047217737, "Missing Content": 0.0005488733058313965, "Knowledge Article": 0.0031088585105758094, "Legal Notices": 0.0003539045531842044, "News Article": 0.00020143433135859994, "Organizational Announcement": 0.00028267267670045786, "Nonfiction Writing": 0.0003648283804105734, "Organizational About Page": 0.0002810445516251565, "Personal About Page": 0.000286233802581225, "Personal Blog": 0.0002881430424646324, "Product Page": 0.00018354487768251334, "Spam / Ads": 0.0005442102139876442, "Structured Data": 0.004606860552890749, "Technical Writing": 0.0405666732690033, "Transcript / Interview": 0.00044825127079440593, "Tutorial / How-To Guide": 0.054076544061598626, "User Reviews": 0.00016175410771852953, "NONE": 0.8872638624431491}, "tertiary": {"Academic Writing": 0.0019285755747785976, "Content Listing": 0.0003240393987006868, "Creative Writing": 0.000480066368964722, "Customer Support Page": 0.001907570883864774, "Q&A Forum": 0.00024237406290097488, "Discussion Thread": 0.00014480499380978924, "Comment Section": 0.0010576201329367883, "Missing Content": 0.000562779312533298, "Knowledge Article": 0.007653561115300095, "Legal Notices": 0.00046399594164752924, "News Article": 0.00018376535151267857, "Organizational Announcement": 0.0002847565515272573, "Nonfiction Writing": 0.0005317031227595482, "Organizational About Page": 0.00026964103966203943, "Personal About Page": 0.0002701796627303243, "Personal Blog": 0.0002592485710620347, "Product Page": 0.0002168933791034502, "Spam / Ads": 0.0010194223064651678, "Structured Data": 0.0079622249298675, "Technical Writing": 0.03423529126804566, "Transcript / Interview": 0.000465385258154417, "Tutorial / How-To Guide": 0.07323315501540363, "User Reviews": 0.0001475573385036261, "NONE": 0.8661553884197655}, "structured_records": {"false": 0.9620797841830185, "true": 0.03792021581698151}, "procedural_steps": {"false": 0.9025144934682722, "true": 0.09748550653172783}, "worked_solution": {"false": 0.9271281460651541, "true": 0.07287185393484583}, "contextual_dialogue": {"false": 0.9999844300510798, "true": 1.5569948920104677e-05}, "content_quality": {"-2": 0.04010079657455255, "-1": 0.27239066423346936, "0": 0.41909867940305257, "1": 0.22947483273647695, "2": 0.03893502705244861}, "noise_severity": {"0": 0.7990795169755481, "1": 0.1524728195907291, "2": 0.04161787153067339, "3": 0.0044771597196787295, "4": 0.0023526321833706896}, "missing_content_severity": {"0": 0.9378275863726169, "1": 0.02075716430747138, "2": 0.03258200237323509, "3": 0.007836111307471645, "4": 0.0009971356392050057}, "factual_density": {"0": 0.007146706586226662, "1": 0.02165319606070956, "2": 0.14893368384835506, "3": 0.435278935139457, "4": 0.3869874783652518}, "pretraining_utility": {"-2": 0.01322259346385586, "-1": 0.07749350661011126, "0": 0.3488021147977046, "1": 0.4572516153544776, "2": 0.10323016977385062}, "math_density": {"0": 0.0032278196898840633, "1": 0.007151710186467336, "2": 0.10664246947415186, "3": 0.41324742246806945, "4": 0.4697305781814272}, "code_density": {"0": 0.00031540989426194103, "1": 0.0006217376247149973, "2": 0.02053795757837567, "3": 0.16938775525763158, "4": 0.8091371396450159}, "reasoning_density": {"0": 0.2643414461030934, "1": 0.09345237490909945, "2": 0.1296852192401371, "3": 0.20545665624431836, "4": 0.3070643035033518}, "education_level": {"none": 0.001697399900618032, "0": 0.001709245901470463, "1": 0.009352395114938565, "2": 0.6470426178767175, "3": 0.30248059064708205, "4": 0.0377177505591734}, "creative_writing_quality": {"not_applicable": 0.9991334534927802, "-2": 0.0001982272662380282, "-1": 0.00026786993919523006, "0": 0.00029583996076772444, "1": 7.356082722009189e-05, "2": 3.104851379876112e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0004967379211884949, "Art & Design": 0.00039589163714899816, "Law & Crime": 0.00031248376911415757, "Education & Careers": 0.0002560192704075413, "Entertainment & Media": 0.00023166014575783046, "Fashion & Beauty": 0.00017417056018497398, "Business, Economics & Finance": 0.0002850044623879573, "Food & Dining": 0.00017461361846216937, "Games & Gaming": 0.0007438200645058576, "Electronics & Hardware": 0.003128426373382581, "Health, Healthcare & Wellness": 0.00032493434132412053, "History & Geography": 0.0003104398913571213, "Home, Pets & Hobbies": 0.0003723332292874539, "Industry & Production": 0.0004978400818009753, "Language, Literature & Philosophy": 0.00020906666893634655, "Politics & Government": 0.00010400779414108664, "Religion & Spirituality": 0.00020820989253260064, "Science, Mathematics & Engineering": 0.1476127369885065, "Relationships & Community": 9.456608652419154e-05, "Software Usage & Applications": 0.0006424481875492962, "Programming & Software Engineering": 0.8376735809525386, "Sports & Fitness": 0.00036600497826317724, "Vehicles & Transportation": 0.0020164668319134476, "Travel & Tourism": 0.00023017136425407145, "Other / Uncovered Subject": 0.002843972353853211, "Unknown / Insufficient Content": 0.0002943925346772405}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010555891167298393, "Art & Design": 0.0011723623706514175, "Law & Crime": 0.0006048846124588118, "Education & Careers": 0.0006595472535143712, "Entertainment & Media": 0.00042058909295735315, "Fashion & Beauty": 0.0003805624261498148, "Business, Economics & Finance": 0.00047330220032533004, "Food & Dining": 0.00038150631220845764, "Games & Gaming": 0.0010350769129548367, "Electronics & Hardware": 0.02062039867094004, "Health, Healthcare & Wellness": 0.0007469584408959235, "History & Geography": 0.0005745115663934893, "Home, Pets & Hobbies": 0.0006652138417864112, "Industry & Production": 0.0008484344103882429, "Language, Literature & Philosophy": 0.0002578081979621393, "Politics & Government": 0.0002731342011604315, "Religion & Spirituality": 0.0004806050369489836, "Science, Mathematics & Engineering": 0.5690122959621845, "Relationships & Community": 0.00021133053664000383, "Software Usage & Applications": 0.0008517550887813487, "Programming & Software Engineering": 0.18862264096912, "Sports & Fitness": 0.0007583860227570441, "Vehicles & Transportation": 0.007608440200673052, "Travel & Tourism": 0.0005390858675431005, "Other / Uncovered Subject": 0.0020604269072148147, "NONE": 0.1996851537806602}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 15262, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 15262, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:10.163105+00:00", "scoring_key_slot": "primary", "dataset_uid": "4dac652e-411e-40c5-a0e9-1b13353443c3", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "c3946f5e9f4c855f3c0d7a4e60ccb358d36344f42b3b302154b927123a528dd5", "source_metadata": {"uuid": "4dac652e-411e-40c5-a0e9-1b13353443c3", "repo_name": "govindak-umd/ENPM661", "relative_path": "Project 3/Project 3 Phase 3 Gazebo version/661_proj3_p3_FINAL_CODE_for_gazebo.py", "category": "DATA", "algo_rel_score": 0.98297518491745, "quality_score": 3.229591131210327, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 265, "repo_created_at": "2020-02-06T22:28:49.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 4, "forks": 1, "issues": 0, "pull_requests": 4}, "file_meta": {"content_id": "76af9ab182d6fc860bba22b760bb27224c8866f8", "size_bytes": 22773, "file_timestamp": 1588952451, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0341598fd14f3d7ee22b8987ba16f7cd66530f63"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 265, "repo_created_at": "2020-02-06T22:28:49.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 4, "forks": 1, "issues": 0, "pull_requests": 4}, "file_meta": {"content_id": "76af9ab182d6fc860bba22b760bb27224c8866f8", "size_bytes": 22773, "file_timestamp": 1588952451, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0341598fd14f3d7ee22b8987ba16f7cd66530f63", "url": "https://github.com/govindak-umd/ENPM661/blob/0341598fd14f3d7ee22b8987ba16f7cd66530f63/Project%203/Project%203%20Phase%203%20Gazebo%20version/661_proj3_p3_FINAL_CODE_for_gazebo.py"}, "gpt4o_tokens": 6111, "dataset_split": "python", "probability_weighted_scores": {"content_quality": -0.04524737054120029, "noise_severity": 0.2585505705445948, "missing_content_severity": 0.11341804553317651, "factual_density": 3.173307282636798, "pretraining_utility": 0.5597732613643559, "math_density": 3.3391012292646884, "code_density": 3.7864094771344248, "reasoning_density": 2.197449996135736, "education_level": 2.365766056114848, "creative_writing_quality": -0.6100845280073974}, "score_applicability": {"education_level": 0.998302600099382, "creative_writing_quality": 0.0008665465072198357}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/aisxyz/arithmetic/blob/db8779ca63a810f06643eba9e07596dffb10cc7b/Btree/btree.py", "id": "python-0-fbdc860a1701d0d43679cbb87dbe2077", "text": "# coding: utf8\n'''\nB树的性质：\n1、每个节点x有以下域：\n a) n[x]：当前存储在节点x中的关键字数。\n b) n[x]个关键字本身，以非降序存放，即 key1[x]<=key2[x]<=...<=keyn[x]。\n c) leaf[x]，表明节点x是否为叶子的布尔值。\n2、每个内节点x还包含n[x]+1个指向其子女的指针c1[x], c2[x], ..., cn[x], cn1[x]，叶\n 节点的ci域无定义。\n3、各关键字keyi[x]对存储在各子树中的关键字范围加以分隔：如果ki为存储在以ci[x]为根的子树\n 中的关键字，则： k1<=key1[x]<=k2<=key2[x]<=...<=keyn[x]<=kn1 。\n4、每个叶节点具有相同的深度。\n5、每个节点能包含的关键字数有上界和下界，它们共用一个称作B数的最小度数的固定整数t>=2来表示。\n a) 每个**非根**的节点必须至少有t-1个关键字，每个**非根**的内节点至少有t个子女。\n b) 每个节点可包含至多2t-1个关键字，所以一个内节点至多可有2t个子女。如果一个节点恰好有2t-1\n 个关键字，就说它是满的。\n 注：t=2时，树的每个内节点有2个、3个或4个子女，亦即一棵2-3-4树。\n\n定理：一棵关键字个数为n>=1，最小度数为t>=2的B数的高度h满足： h <= log_t((n+1)/2)。\n'''\n\nfrom bisect import bisect_left\n\ng_min_degree = 3\n\nclass Btree_node:\n    def __init__(self, keys=None):\n        # keys needs to be sorted.\n        if keys is None:\n            keys = []\n        self.num_key = len(keys)\n        self.keys = keys    #sorted(keys)\n        self.is_leaf = True\n        #self.parent = None\n        self.children = [None] * (self.num_key+1)\n\n    def __repr__(self):\n        return '<BNode(%s)>' % str(self.keys)\n\n\ndef Btree_search(node, key):\n    i = 0\n    while i < node.num_key and key > node.keys[i]:\n        i += 1\n    if i < node.num_key and key == node.keys[i]:\n        return node, i\n    if node.is_leaf:\n        return None, -1\n    else:\n        # DISK_READ(node.children[i])   # simulate reading disk.\n        return Btree_search(node.children[i], key)\n\n\ndef Btree_create():\n    root = Btree_node()\n    # DISK_WRITE()\n    return root\n\n\ndef is_full_node(node):\n    return node.num_key == 2 * g_mid_degree - 1\n\n\ndef Btree_split_child(node, i, child_i):\n    # child_i needs to be a full node.\n    mid_key_i = child_i.num_key // 2   # get the tree's min-degree t.\n    new_node = Btree_node(child_i.keys[mid_key_i+1:])\n    new_node.is_leaf = child_i.is_leaf\n    new_node.children = child_i.children[mid_key_i+1: ]\n    node.keys.insert(i, child_i.keys[mid_key_i])\n    node.children.insert(i+1, new_node)\n    node.num_key += 1\n    child_i.num_key = mid_key_i\n    child_i.keys = child_i.keys[0: mid_key_i]\n    child_i.children = child_i.children[: mid_key_i+1]\n    # DISK_WRITE(child_i)\n    # DISK_WRITE(new_node)\n    # DIST_WRITE(node)\n\ndef Btree_insert_nonfull(node, key):\n    tail_index = node.num_key - 1\n    while tail_index >= 0 and key < node.keys[tail_index]:\n        tail_index -= 1\n    if node.is_leaf:\n        node.keys.insert(tail_index+1, key)\n        node.num_key += 1\n        # DISK-WRITE(node)\n    else:\n        tail_index += 1\n        # DISK-READ(node.children[tail_index])\n        if is_full_node(node.children[tail_index]):\n            Btree_split_child(node, tail_index, node.children[tail_index])\n            if key > node.keys[tail_index]:\n                tail_index += 1\n        Btree_insert_nonfull(node.children[tail_index], key)\n    \ndef Btree_insert(root, key):\n    # This is the only way to increse the height of the B tree.\n    if is_full_node(root):\n        new_root = Btree_node()\n        new_root.is_leaf = False\n        new_root.chilren.append(root)\n        Btree_split_child(new_root, 0, root)\n        Btree_insert_nonfull(new_root, key)\n        root = new_root\n    else:\n        Btree_insert_nonfull(root, key)\n    return root\n    \n\ndef pop_precursor_key(node):\n    while not node.is_leaf:\n        node = node.children[-1]\n    node.num_key -= 1\n    node.children.pop()\n    return node.keys.pop()\n\ndef pop_succeed_key(node):\n    while not node.is_leaf:\n        node = node.children[0]\n    node.num_key -= 1\n    node.children.pop(0)\n    return node.keys.pop(0)\n\n\ndef Btree_delete(node, key):\n    #print('[+] Tracing node:', node)\n    if key in node.keys and node.is_leaf:\n        node.num_key -= 1\n        node.children.pop()     # Any child of leaf-node is none.\n        node.keys.remove(key)\n        return node\n    elif key in node.keys:\n        i = node.keys.index(key)\n        left_child = node.children[i]\n        right_child = node.children[i+1]\n        if left_child.num_key >= g_min_degree:\n            pre_key = pop_precursor_key(left_child)\n            node.keys[i] = pre_key\n        elif right_child.num_key >= g_min_degree:\n            suc_key = pop_succeed_key(right_child)\n            node.keys[i] = suc_key\n        else:\n            left_child.num_key = 2*g_min_degree - 1\n            left_child.keys.extend( [key] + right_child.keys )\n            left_child.children.extend( right_child.children )\n            node.num_key -= 1\n            node.keys.remove(key)\n            node.children.remove(right_child)\n            node.children[i] = Btree_delete(left_child, key)\n            if node.num_key == 0:\n                node = node.children[0]\n    else:\n        if node.is_leaf:\n            return node\n        i = bisect_left(node.keys, key)\n        child = node.children[i]\n        if child.num_key == g_min_degree-1:\n            left_brother = node.children[i-1] if i>0 else None\n            right_brother = node.children[i+1] if i<node.num_key else None\n            #print('left_brother:', left_brother)\n            #print('right_brother:', right_brother)\n            if right_brother and right_brother.num_key >= g_min_degree:\n                child.keys.append(node.keys[i])\n                child.num_key += 1\n                child.children.append(right_brother.children[0])\n                node.keys[i] = right_brother.keys[0]\n                right_brother.num_key -= 1\n                right_brother.keys = right_brother.keys[1: ]\n                right_brother.children = right_brother.children[1: ]\n            elif left_brother and left_brother.num_key >= g_min_degree:\n                child.keys.insert(0, node.keys[i])\n                child.num_key += 1\n                child.children.insert(0, left_brother.children[-1])\n                node.keys[i] = left_brother.keys[-1]\n                left_brother.num_key -= 1\n                left_brother.keys.pop()\n                left_brother.children.pop()\n            else:\n                if right_brother:\n                    child.num_key = 2*g_min_degree - 1\n                    child.keys += [node.keys[i]] + right_brother.keys\n                    child.children.extend(right_brother.children)\n                    node.num_key -= 1\n                    node.keys.pop(i)\n                    node.children.remove(right_brother)\n                else:       # left_brother is not none\n                    child.num_key = 2*g_min_degree - 1\n                    child.keys = left_brother.keys + [node.keys[i-1]] + child.keys\n                    child.children = left_brother.children + child.children\n                    node.num_key -= 1\n                    node.keys.pop(i-1)\n                    node.children.remove(left_brother)\n                    i -= 1             \n                    \n        node.children[i] = Btree_delete(child, key)\n        if node.num_key == 0:\n            node = node.children[0]\n        \n    return node\n    \n\ndef Btree_walk(node, level=0):\n    if node is None:\n        return\n    print('  '*level + '---', node)\n    for child in node.children:\n        Btree_walk(child, level+1)\n        \n        \nif __name__ == '__main__':\n    root = Btree_node(['p'])\n    root.is_leaf = False\n    node1 = Btree_node(['c', 'g', 'm'])\n    node1.is_leaf = False\n    node2 = Btree_node(['t', 'x'])\n    node2.is_leaf = False\n    node3 = Btree_node(['a', 'b'])\n    node4 = Btree_node(['d', 'e', 'f'])\n    node5 = Btree_node(['j', 'k', 'l'])\n    node6 = Btree_node(['n', 'o'])\n    node7 = Btree_node(['q', 'r', 's'])\n    node8 = Btree_node(['u', 'v'])\n    node9 = Btree_node(['y', 'z'])\n    root.children = [node1, node2]\n    node1.children = [node3, node4, node5, node6]\n    node2.children = [node7, node8, node9]\n    #print('search key 18:', Btree_search(root, 18))\n    root = Btree_delete(root, 'f')\n    root = Btree_delete(root, 'm')\n    root = Btree_delete(root, 'g')\n    root = Btree_delete(root, 'd')\n    root = Btree_delete(root, 'b')\n    root = Btree_delete(root, 'c')\n    root = Btree_delete(root, 'p')\n    root = Btree_delete(root, 'v')\n    Btree_walk(root)\n", "text_preview": "# coding: utf8 ''' B树的性质： 1、每个节点x有以下域： a) n[x]：当前存储在节点x中的关键字数。 b) n[x]个关键字本身，以非降序存放，即 key1[x]<=key2[x]<=...<=keyn[x]。 c) leaf[x]，表明节点x是否为叶子的布尔值。 2、每个内节点x还包含n[x]+1个指向其子女的指针c1[x], c2[x], ..., cn[x], cn1[x]，叶 节点的ci域无定义。 3、各关键字keyi[x]对存储在各子树中的关", "input_sha256": "93754607b7afd4fdc7f26fdfb33830cdefd234792780904b0e614855904a4ce3", "text_sha256": "fbdc860a1701d0d43679cbb87dbe2077a72032ed4eabbf4b4f5b86baba6ec4c2", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12021, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 2, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.20276948782913054, "noise_severity": 0.1223457331374833, "missing_content_severity": 0.06691295244194295, "factual_density": 3.3295647384184046, "pretraining_utility": 0.8693801905406656, "math_density": 2.253810186282784, "code_density": 3.618662249145466, "reasoning_density": 1.3091316083922302, "education_level": 2.1579834599144645, "creative_writing_quality": -0.25886295421668554}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.2669761527952079, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9912928667996438, "secondary": 0.5132534792963332, "tertiary": 0.7918372909087241, "structured_records": 0.030549721476323673, "procedural_steps": 0.05012603976985557, "worked_solution": 0.13945988798496828, "contextual_dialogue": 7.552064030959631e-06, "content_quality": 0.37546395672546473, "noise_severity": 0.8980452223854306, "missing_content_severity": 0.9442392062983809, "factual_density": 0.4413039486820035, "pretraining_utility": 0.6021463737716404, "math_density": 0.30387739148575754, "code_density": 0.6822185409545547, "reasoning_density": 0.0, "education_level": 0.776855991194416, "creative_writing_quality": 0.998118742941994, "primary_topic": 0.8799714166331281, "secondary_topic": 0.39618876694914107}, "probability_margin": {"primary": 0.9886210924592527, "secondary": 0.19240218601818176, "tertiary": 0.6933643603964558, "education_level": 0.6644583692082979, "creative_writing_quality": 0.9977081038794905, "primary_topic": 0.7746124382837601, "secondary_topic": 0.04171202503049887}, "raw_probabilities": {"primary": {"Academic Writing": 0.0003789195713538572, "Content Listing": 3.9538697341861284e-05, "Creative Writing": 2.8853001080361986e-05, "Customer Support Page": 1.9769782268993648e-05, "Q&A Forum": 3.043416695373445e-05, "Discussion Thread": 1.755188482536055e-05, "Comment Section": 2.801988047239768e-05, "Missing Content": 4.413014694624289e-05, "Knowledge Article": 0.0030345715570726742, "Legal Notices": 3.6164843808473216e-05, "News Article": 8.475783969972688e-06, "Organizational Announcement": 1.947944481228444e-05, "Nonfiction Writing": 4.323134117623786e-05, "Organizational About Page": 2.321278747053588e-05, "Personal About Page": 1.888508646558438e-05, "Personal Blog": 1.6320184701732546e-05, "Product Page": 1.5166954189328962e-05, "Spam / Ads": 1.7898517066555463e-05, "Structured Data": 0.00043341211909973365, "Technical Writing": 0.9916556640163253, "Transcript / Interview": 3.780101909191825e-05, "Tutorial / How-To Guide": 0.0028610603339063708, "User Reviews": 1.2538324574603174e-05, "Other / Unknown": 0.0011789005550258194}, "secondary": {"Academic Writing": 0.05174457649292274, "Content Listing": 0.0008126036063911191, "Creative Writing": 0.0007811107608682727, "Customer Support Page": 0.00048811788856213754, "Q&A Forum": 0.0005461356352737223, "Discussion Thread": 0.0004276097740255052, "Comment Section": 0.0006458400782201972, "Missing Content": 0.0007883903609819441, "Knowledge Article": 0.34113239830747094, "Legal Notices": 0.0006509172591250122, "News Article": 0.00040697890536306653, "Organizational Announcement": 0.0005466032852682446, "Nonfiction Writing": 0.0015164330502202247, "Organizational About Page": 0.0007366266973699972, "Personal About Page": 0.0005403611472475262, "Personal Blog": 0.0004147308687720253, "Product Page": 0.0004377698691736536, "Spam / Ads": 0.00045840374968506356, "Structured Data": 0.0013636565633452391, "Technical Writing": 0.048132594397818904, "Transcript / Interview": 0.001089847393318355, "Tutorial / How-To Guide": 0.012515060522586201, "User Reviews": 0.00028864906033728284, "NONE": 0.5335345843256527}, "tertiary": {"Academic Writing": 0.04088864049905948, "Content Listing": 0.0005525235651470698, "Creative Writing": 0.0006305788148297901, "Customer Support Page": 0.0003510337336964312, "Q&A Forum": 0.0003418110583779976, "Discussion Thread": 0.00023985255532957766, "Comment Section": 0.0004990370711137632, "Missing Content": 0.000591330619186098, "Knowledge Article": 0.10714637672440482, "Legal Notices": 0.0006107246437326622, "News Article": 0.00026407242889694597, "Organizational Announcement": 0.0003058442271353188, "Nonfiction Writing": 0.000945557533936144, "Organizational About Page": 0.00043147199721400755, "Personal About Page": 0.0003282559771878152, "Personal Blog": 0.0002604141034288126, "Product Page": 0.0003136347432556535, "Spam / Ads": 0.00033895349955225684, "Structured Data": 0.0014944009618375223, "Technical Writing": 0.02968734866503703, "Transcript / Interview": 0.0006719412966773459, "Tutorial / How-To Guide": 0.012429795359436956, "User Reviews": 0.00016566280066585308, "NONE": 0.8005107371208606}, "structured_records": {"false": 0.9694502785236764, "true": 0.030549721476323673}, "procedural_steps": {"false": 0.9498739602301445, "true": 0.05012603976985557}, "worked_solution": {"false": 0.8605401120150318, "true": 0.13945988798496828}, "contextual_dialogue": {"false": 0.9999924479359691, "true": 7.552064030959631e-06}, "content_quality": {"-2": 0.036737523568818285, "-1": 0.19986183491251933, "0": 0.3710733540107407, "1": 0.30854820513655684, "2": 0.08377908237136478}, "noise_severity": {"0": 0.8955466263517311, "1": 0.09060230893844606, "2": 0.010715948504385451, "3": 0.0022289376314826906, "4": 0.0009061785739545623}, "missing_content_severity": {"0": 0.9545882629885278, "1": 0.0270875657626601, "2": 0.015638614271209144, "3": 0.0021940697735469226, "4": 0.0004914872040559509}, "factual_density": {"0": 0.0024806106340033783, "1": 0.011080131413904461, "2": 0.09802378111488716, "3": 0.4312248625740945, "4": 0.4571906142631106}, "pretraining_utility": {"-2": 0.007454912773887056, "-1": 0.030363396901637696, "0": 0.22093054834174633, "1": 0.5678488709753805, "2": 0.17340227100734856}, "math_density": {"0": 0.01828765266128127, "1": 0.2541931666445909, "2": 0.36170675362965465, "3": 0.18704619587900878, "4": 0.1787662311854644}, "code_density": {"0": 0.0006526240121518019, "1": 0.0013504415664919044, "2": 0.02053574656962686, "3": 0.33360443696719766, "4": 0.6438567508845319}, "reasoning_density": {"0": 0.5046846139737389, "1": 0.1083140891617396, "2": 0.11308733450912332, "3": 0.12101299920934878, "4": 0.15290096314604942}, "education_level": {"none": 0.000939162153241076, "0": 0.0017514779807454453, "1": 0.018533022388239957, "2": 0.8140466593286799, "3": 0.149588290120382, "4": 0.015141388028711646}, "creative_writing_quality": {"not_applicable": 0.998432285784995, "-2": 0.00017235647788775674, "-1": 0.0004029864475887614, "0": 0.0007241819055044451, "1": 0.00019450249774747146, "2": 7.368688627655925e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00018988005831107064, "Art & Design": 0.0005815324301259362, "Law & Crime": 0.0002469858630138043, "Education & Careers": 0.00019276201852968928, "Entertainment & Media": 0.0002013429586117376, "Fashion & Beauty": 0.00012702120316248678, "Business, Economics & Finance": 0.0002274230741976138, "Food & Dining": 0.00013052904034776291, "Games & Gaming": 0.00025159243264771244, "Electronics & Hardware": 0.00024980587497446275, "Health, Healthcare & Wellness": 0.00020523294842780143, "History & Geography": 0.0002327469936508212, "Home, Pets & Hobbies": 0.00015874940606321018, "Industry & Production": 0.00016354890400561574, "Language, Literature & Philosophy": 0.00017748782643161245, "Politics & Government": 9.115255142741004e-05, "Religion & Spirituality": 0.00014938777134125653, "Science, Mathematics & Engineering": 0.10997546232501683, "Relationships & Community": 8.252777683696114e-05, "Software Usage & Applications": 0.0005238133675261976, "Programming & Software Engineering": 0.884587900608777, "Sports & Fitness": 0.00020783027707362597, "Vehicles & Transportation": 0.00010267271154160416, "Travel & Tourism": 0.00017968672396498171, "Other / Uncovered Subject": 0.0005566782933688765, "Unknown / Insufficient Content": 0.00020624656062384088}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007190638595057843, "Art & Design": 0.0008808188719531553, "Law & Crime": 0.0006051793744681856, "Education & Careers": 0.0008722373616278292, "Entertainment & Media": 0.00043856673938180523, "Fashion & Beauty": 0.00029785898136799394, "Business, Economics & Finance": 0.0004583345653560919, "Food & Dining": 0.00023520657506243098, "Games & Gaming": 0.00043544458705282383, "Electronics & Hardware": 0.0005470093748008297, "Health, Healthcare & Wellness": 0.00044973348242039276, "History & Geography": 0.00044184399092172014, "Home, Pets & Hobbies": 0.0002933437577894171, "Industry & Production": 0.000340152171300654, "Language, Literature & Philosophy": 0.00031675914425063504, "Politics & Government": 0.00021152306516474605, "Religion & Spirituality": 0.0004220868119695458, "Science, Mathematics & Engineering": 0.37770025088213677, "Relationships & Community": 0.00018259064088426943, "Software Usage & Applications": 0.0005515665104280184, "Programming & Software Engineering": 0.19225345909677632, "Sports & Fitness": 0.00034536120281911516, "Vehicles & Transportation": 0.00020853420512162852, "Travel & Tourism": 0.0005193237098149639, "Other / Uncovered Subject": 0.0008614751249892616, "NONE": 0.41941227591263563}}, "probabilities": {"primary": {"Academic Writing": 0.0003789195713538572, "Content Listing": 3.9538697341861284e-05, "Creative Writing": 2.8853001080361986e-05, "Customer Support Page": 1.9769782268993648e-05, "Q&A Forum": 3.043416695373445e-05, "Discussion Thread": 1.755188482536055e-05, "Comment Section": 2.801988047239768e-05, "Missing Content": 4.413014694624289e-05, "Knowledge Article": 0.0030345715570726742, "Legal Notices": 3.6164843808473216e-05, "News Article": 8.475783969972688e-06, "Organizational Announcement": 1.947944481228444e-05, "Nonfiction Writing": 4.323134117623786e-05, "Organizational About Page": 2.321278747053588e-05, "Personal About Page": 1.888508646558438e-05, "Personal Blog": 1.6320184701732546e-05, "Product Page": 1.5166954189328962e-05, "Spam / Ads": 1.7898517066555463e-05, "Structured Data": 0.00043341211909973365, "Technical Writing": 0.9916556640163253, "Transcript / Interview": 3.780101909191825e-05, "Tutorial / How-To Guide": 0.0028610603339063708, "User Reviews": 1.2538324574603174e-05, "Other / Unknown": 0.0011789005550258194}, "secondary": {"Academic Writing": 0.05174457649292274, "Content Listing": 0.0008126036063911191, "Creative Writing": 0.0007811107608682727, "Customer Support Page": 0.00048811788856213754, "Q&A Forum": 0.0005461356352737223, "Discussion Thread": 0.0004276097740255052, "Comment Section": 0.0006458400782201972, "Missing Content": 0.0007883903609819441, "Knowledge Article": 0.34113239830747094, "Legal Notices": 0.0006509172591250122, "News Article": 0.00040697890536306653, "Organizational Announcement": 0.0005466032852682446, "Nonfiction Writing": 0.0015164330502202247, "Organizational About Page": 0.0007366266973699972, "Personal About Page": 0.0005403611472475262, "Personal Blog": 0.0004147308687720253, "Product Page": 0.0004377698691736536, "Spam / Ads": 0.00045840374968506356, "Structured Data": 0.0013636565633452391, "Technical Writing": 0.048132594397818904, "Transcript / Interview": 0.001089847393318355, "Tutorial / How-To Guide": 0.012515060522586201, "User Reviews": 0.00028864906033728284, "NONE": 0.5335345843256527}, "tertiary": {"Academic Writing": 0.04088864049905948, "Content Listing": 0.0005525235651470698, "Creative Writing": 0.0006305788148297901, "Customer Support Page": 0.0003510337336964312, "Q&A Forum": 0.0003418110583779976, "Discussion Thread": 0.00023985255532957766, "Comment Section": 0.0004990370711137632, "Missing Content": 0.000591330619186098, "Knowledge Article": 0.10714637672440482, "Legal Notices": 0.0006107246437326622, "News Article": 0.00026407242889694597, "Organizational Announcement": 0.0003058442271353188, "Nonfiction Writing": 0.000945557533936144, "Organizational About Page": 0.00043147199721400755, "Personal About Page": 0.0003282559771878152, "Personal Blog": 0.0002604141034288126, "Product Page": 0.0003136347432556535, "Spam / Ads": 0.00033895349955225684, "Structured Data": 0.0014944009618375223, "Technical Writing": 0.02968734866503703, "Transcript / Interview": 0.0006719412966773459, "Tutorial / How-To Guide": 0.012429795359436956, "User Reviews": 0.00016566280066585308, "NONE": 0.8005107371208606}, "structured_records": {"false": 0.9694502785236764, "true": 0.030549721476323673}, "procedural_steps": {"false": 0.9498739602301445, "true": 0.05012603976985557}, "worked_solution": {"false": 0.8605401120150318, "true": 0.13945988798496828}, "contextual_dialogue": {"false": 0.9999924479359691, "true": 7.552064030959631e-06}, "content_quality": {"-2": 0.03673752356881829, "-1": 0.19986183491251935, "0": 0.37107335401074076, "1": 0.3085482051365569, "2": 0.08377908237136479}, "noise_severity": {"0": 0.8955466263517312, "1": 0.09060230893844608, "2": 0.010715948504385453, "3": 0.002228937631482691, "4": 0.0009061785739545624}, "missing_content_severity": {"0": 0.9545882629885278, "1": 0.0270875657626601, "2": 0.015638614271209144, "3": 0.0021940697735469226, "4": 0.0004914872040559509}, "factual_density": {"0": 0.0024806106340033783, "1": 0.011080131413904461, "2": 0.09802378111488716, "3": 0.4312248625740945, "4": 0.4571906142631106}, "pretraining_utility": {"-2": 0.0074549127738870544, "-1": 0.03036339690163769, "0": 0.22093054834174627, "1": 0.5678488709753804, "2": 0.17340227100734854}, "math_density": {"0": 0.01828765266128127, "1": 0.2541931666445909, "2": 0.36170675362965465, "3": 0.18704619587900878, "4": 0.1787662311854644}, "code_density": {"0": 0.0006526240121518019, "1": 0.0013504415664919044, "2": 0.02053574656962686, "3": 0.33360443696719766, "4": 0.6438567508845319}, "reasoning_density": {"0": 0.5046846139737389, "1": 0.1083140891617396, "2": 0.11308733450912332, "3": 0.12101299920934878, "4": 0.15290096314604942}, "education_level": {"none": 0.000939162153241076, "0": 0.0017514779807454453, "1": 0.018533022388239957, "2": 0.8140466593286799, "3": 0.149588290120382, "4": 0.015141388028711646}, "creative_writing_quality": {"not_applicable": 0.998432285784995, "-2": 0.00017235647788775674, "-1": 0.0004029864475887614, "0": 0.0007241819055044451, "1": 0.00019450249774747146, "2": 7.368688627655925e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00018988005831107067, "Art & Design": 0.0005815324301259363, "Law & Crime": 0.0002469858630138044, "Education & Careers": 0.0001927620185296893, "Entertainment & Media": 0.00020134295861173762, "Fashion & Beauty": 0.0001270212031624868, "Business, Economics & Finance": 0.00022742307419761383, "Food & Dining": 0.00013052904034776294, "Games & Gaming": 0.0002515924326477125, "Electronics & Hardware": 0.0002498058749744628, "Health, Healthcare & Wellness": 0.00020523294842780145, "History & Geography": 0.00023274699365082122, "Home, Pets & Hobbies": 0.0001587494060632102, "Industry & Production": 0.00016354890400561577, "Language, Literature & Philosophy": 0.00017748782643161248, "Politics & Government": 9.115255142741005e-05, "Religion & Spirituality": 0.00014938777134125656, "Science, Mathematics & Engineering": 0.10997546232501684, "Relationships & Community": 8.252777683696115e-05, "Software Usage & Applications": 0.0005238133675261977, "Programming & Software Engineering": 0.8845879006087771, "Sports & Fitness": 0.000207830277073626, "Vehicles & Transportation": 0.00010267271154160417, "Travel & Tourism": 0.00017968672396498174, "Other / Uncovered Subject": 0.0005566782933688766, "Unknown / Insufficient Content": 0.0002062465606238409}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007190638595057843, "Art & Design": 0.0008808188719531553, "Law & Crime": 0.0006051793744681856, "Education & Careers": 0.0008722373616278292, "Entertainment & Media": 0.00043856673938180523, "Fashion & Beauty": 0.00029785898136799394, "Business, Economics & Finance": 0.0004583345653560919, "Food & Dining": 0.00023520657506243098, "Games & Gaming": 0.00043544458705282383, "Electronics & Hardware": 0.0005470093748008297, "Health, Healthcare & Wellness": 0.00044973348242039276, "History & Geography": 0.00044184399092172014, "Home, Pets & Hobbies": 0.0002933437577894171, "Industry & Production": 0.000340152171300654, "Language, Literature & Philosophy": 0.00031675914425063504, "Politics & Government": 0.00021152306516474605, "Religion & Spirituality": 0.0004220868119695458, "Science, Mathematics & Engineering": 0.37770025088213677, "Relationships & Community": 0.00018259064088426943, "Software Usage & Applications": 0.0005515665104280184, "Programming & Software Engineering": 0.19225345909677632, "Sports & Fitness": 0.00034536120281911516, "Vehicles & Transportation": 0.00020853420512162852, "Travel & Tourism": 0.0005193237098149639, "Other / Uncovered Subject": 0.0008614751249892616, "NONE": 0.41941227591263563}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10938, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10938, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:10.733128+00:00", "scoring_key_slot": "primary", "dataset_uid": "3944e792-d701-4577-aa33-7552d0d5ddba", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "238d18d883d90ec9b02321126e3703d1897c471b335c761976b6632505c71615", "source_metadata": {"uuid": "3944e792-d701-4577-aa33-7552d0d5ddba", "repo_name": "aisxyz/arithmetic", "relative_path": "Btree/btree.py", "category": "ALGO", "algo_rel_score": 0.9962007403373718, "quality_score": 6.016284942626953, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 3, "repo_created_at": "2017-02-19T08:30:26.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "86e109d6c0435c33be3114e3eb576b9da033c44e", "size_bytes": 8672, "file_timestamp": 1487493213, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "db8779ca63a810f06643eba9e07596dffb10cc7b"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 3, "repo_created_at": "2017-02-19T08:30:26.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "86e109d6c0435c33be3114e3eb576b9da033c44e", "size_bytes": 8672, "file_timestamp": 1487493213, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "db8779ca63a810f06643eba9e07596dffb10cc7b", "url": "https://github.com/aisxyz/arithmetic/blob/db8779ca63a810f06643eba9e07596dffb10cc7b/Btree/btree.py"}, "gpt4o_tokens": 2350, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.20276948782913054, "noise_severity": 0.1223457331374833, "missing_content_severity": 0.06691295244194295, "factual_density": 3.3295647384184046, "pretraining_utility": 0.8693801905406656, "math_density": 2.253810186282784, "code_density": 3.618662249145466, "reasoning_density": 1.3091316083922302, "education_level": 2.1579834599144645, "creative_writing_quality": -0.25886295421668554}, "score_applicability": {"education_level": 0.9990608378467589, "creative_writing_quality": 0.001567714215004994}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/abdulaziz12k/coffee_shop_full_stack_project/blob/44a3b9aa6de6806e75e6a98fb2320c7ed2172978/starter_code/backend/src/api.py", "id": "python-0-de266cfc13b2757a09ca6ff488a5f018", "text": "import os\nfrom flask import Flask, request, jsonify, abort\nfrom sqlalchemy import exc\nimport json\nfrom flask_cors import CORS\nfrom .database.models import db_drop_and_create_all, setup_db, Drink\nfrom .auth.auth import AuthError, requires_auth\n\napp = Flask(__name__)\nsetup_db(app)\nCORS(app)\n\n\n'''\n@TODO uncomment the following line to initialize the datbase\n!! NOTE THIS WILL DROP ALL RECORDS AND START YOUR DB FROM SCRATCH\n!! NOTE THIS MUST BE UNCOMMENTED ON FIRST RUN\n!! Running this funciton will add one\n'''\nwith app.app_context():\n    db_drop_and_create_all()\n\n# ROUTES\n'''\n@TODO implement endpoint\n    GET /drinks\n        it should be a public endpoint\n        it should contain only the drink.short() data representation\n    returns status code 200 and json {\"success\": True, \"drinks\": drinks} where drinks is the list of drinks\n        or appropriate status code indicating reason for failure\n'''\n\n# GET DRINKS\n\n\n@app.route('/drinks', methods=['GET'])\ndef get_drinks():\n    response = Drink.query.order_by(Drink.id).all()\n    if response is None:\n        print('no drinks available at the moment')\n        abort(404)\n    else:\n        return ({\n            \"success\": True,\n            \"drinks\": [i.short() for i in response]\n        })\n\n\n'''\n@TODO implement endpoint\n    GET /drinks-detail\n        it should require the 'get:drinks-detail' permission\n        it should contain the drink.long() data representation\n    returns status code 200 and json {\"success\": True, \"drinks\": drinks} where drinks is the list of drinks\n        or appropriate status code indicating reason for failure\n'''\n\n# GET DRINKS INFORMATIONS\n\n\n@app.route('/drinks-details', methods=['GET'])\ndef get_drinks_details():\n    response = Drink.query.order_by(Drink.id).all()\n    if response is None:\n        print('no drinks avilable at the moment')\n        abort(404)\n    else:\n        return jsonify({\n            \"success\": True,\n            \"drinks\": [i.long() for i in response]\n        }), 200\n\n\n'''\n@TODO implement endpoint\n    POST /drinks\n        it should create a new row in the drinks table\n        it should require the 'post:drinks' permission\n        it should contain the drink.long() data representation\n    returns status code 200 and json {\"success\": True, \"drinks\": drink} where drink an array containing only the newly created drink\n        or appropriate status code indicating reason for failure\n'''\n\n# CREATE DRINK\n\n\n@app.route('/drinks', methods=['POST'])\ndef create_drinks():\n    response = request.get_json()\n    if response is None:\n        abort(404)\n    else:\n        new_drink = Drink(\n            id=response.get('id'),\n            title=response.get('title'),\n            recipe=response.get('recipe')\n        )\n        new_drink.insert()\n        return jsonify({\n            \"success\": True,\n            \"drinks\": [new_drink.long()]\n        }), 200\n\n\n'''\n@TODO implement endpoint\n    PATCH /drinks/<id>\n        where <id> is the existing model id\n        it should respond with a 404 error if <id> is not found\n        it should update the corresponding row for <id>\n        it should require the 'patch:drinks' permission\n        it should contain the drink.long() data representation\n    returns status code 200 and json {\"success\": True, \"drinks\": drink} where drink an array containing only the updated drink\n        or appropriate status code indicating reason for failure\n'''\n\n\n# EDITING DRINKS\n\n\n@app.route('/drinks/<int:drink_id>', methods=['PATCH'])\ndef update_drink(drink_id):\n    response = request.get_json()\n    if response is None:\n        abort(404)\n    else:\n        drink = Drink.query.filter(Drink.id == drink_id).first()\n        if drink is None:\n            abort(404)\n        else:\n            drink.title = response.get('title')\n            drink.recipe = response.get('recipe')\n            drink.update()\n            return jsonify({\n                \"success\": True,\n                \"drinks\": [drink.long()]\n            }), 200\n\n\n'''\n@TODO implement endpoint\n    DELETE /drinks/<id>\n        where <id> is the existing model id\n        it should respond with a 404 error if <id> is not found\n        it should delete the corresponding row for <id>\n        it should require the 'delete:drinks' permission\n    returns status code 200 and json {\"success\": True, \"delete\": id} where id is the id of the deleted record\n        or appropriate status code indicating reason for failure\n'''\n\n# DELETING DRINKS\n\n\n@app.route('/drinks/<int:drink_id>/delete', methods=['DELETE'])\ndef delete_drink(drink_id):\n    response = Drink.query.filter(Drink.id == drink_id).first()\n    if response is None:\n        abort(404)\n    else:\n        response.delete()\n        return jsonify({\n            \"success\": True,\n            \"delete\": [response.long()]\n        }), 200\n\n\n# Error Handling\n'''\nExample error handling for unprocessable entity\n'''\n\n\n@app.errorhandler(422)\ndef unprocessable(error):\n    return jsonify({\n        \"success\": False,\n        \"error\": 422,\n        \"message\": \"unprocessable\"\n    }), 422\n\n\n'''\n@TODO implement error handlers using the @app.errorhandler(error) decorator\n    each error handler should return (with approprate messages):\n             jsonify({\n                    \"success\": False,\n                    \"error\": 404,\n                    \"message\": \"resource not found\"\n                    }), 404\n\n'''\n\n\n'''\n@TODO implement error handler for 404\n    error handler should conform to general task above\n'''\n# ERROR HANDLING\n\n\n@app.errorhandler(404)\ndef resource_not_found(error):\n    return jsonify({\n        \"success\": False,\n        \"error\": 404,\n        \"message\": \"resource not found\"\n    }), 404\n\n\n@app.errorhandler(500)\ndef internal_server_error(error):\n    return jsonify({\n        \"success\": False,\n        \"error\": 500,\n        \"message\": 'Internal Server Error'\n    }), 500\n\n\n'''\n@TODO implement error handler for AuthError\n    error handler should conform to general task above\n'''\n\n\n@app.errorhandler(AuthError)\ndef handle_auth_error(exception):\n    return jsonify({\n        \"success\": False,\n        \"error\": exception.status_code,\n        \"message\": exception.error\n    }), exception.status_code\n", "text_preview": "import os from flask import Flask, request, jsonify, abort from sqlalchemy import exc import json from flask_cors import CORS from .database.models import db_drop_and_create_all, setup_db, Drink from .auth.auth import AuthError, requires_au", "input_sha256": "0c74ee286b32b85e6ba48d6e6406eb1db834d9b214633cff79a913872b632a4f", "text_sha256": "de266cfc13b2757a09ca6ff488a5f018796d89340433e3e23c8818a092e9fc0f", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12022, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.393965085702356, "noise_severity": 0.19196045641443849, "missing_content_severity": 0.24669439500123344, "factual_density": 3.175553841564649, "pretraining_utility": 0.5605471232241538, "math_density": 0.02667395797390009, "code_density": 3.540544230440755, "reasoning_density": 0.3058662328441196, "education_level": 2.079433039088673, "creative_writing_quality": -0.32421589345971674}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.07659423528624076, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9915087851956751, "secondary": 0.7620323490586753, "tertiary": 0.8419567684877961, "structured_records": 0.023610833933657406, "procedural_steps": 0.015205462727828665, "worked_solution": 0.013236267496321457, "contextual_dialogue": 5.648219090691634e-06, "content_quality": 0.3556545782083801, "noise_severity": 0.8400329529879679, "missing_content_severity": 0.7944213374989721, "factual_density": 0.4747153361993406, "pretraining_utility": 0.5045947294568378, "math_density": 0.9777717016884167, "code_density": 0.6171201920339623, "reasoning_density": 0.7451114726299003, "education_level": 0.8320613016205805, "creative_writing_quality": 0.9976404480103033, "primary_topic": 0.9767004474191504, "secondary_topic": 0.6505503172835828}, "probability_margin": {"primary": 0.9852931122854687, "secondary": 0.624964952220285, "tertiary": 0.7481530285907874, "education_level": 0.7563787027249501, "creative_writing_quality": 0.9969270893276998, "primary_topic": 0.9576471675993319, "secondary_topic": 0.38280475162583033}, "raw_probabilities": {"primary": {"Academic Writing": 4.216750133110486e-05, "Content Listing": 3.918448146943098e-05, "Creative Writing": 2.1010647843573577e-05, "Customer Support Page": 1.3728757451060066e-05, "Q&A Forum": 2.0015192660853312e-05, "Discussion Thread": 1.048976643888629e-05, "Comment Section": 2.1393143709211682e-05, "Missing Content": 5.0389836616186344e-05, "Knowledge Article": 6.328214507302348e-05, "Legal Notices": 2.466876780154512e-05, "News Article": 6.806151520123487e-06, "Organizational Announcement": 1.4459665199827277e-05, "Nonfiction Writing": 1.5260562524304684e-05, "Organizational About Page": 1.7908911500256748e-05, "Personal About Page": 1.6670803572767536e-05, "Personal Blog": 1.2014218773313405e-05, "Product Page": 1.2396028413536184e-05, "Spam / Ads": 1.6185950840506298e-05, "Structured Data": 0.0004274126475414438, "Technical Writing": 0.991862585812522, "Transcript / Interview": 2.1311345288064335e-05, "Tutorial / How-To Guide": 0.006569473527053266, "User Reviews": 9.644415716084838e-06, "Other / Unknown": 0.0006915397191398423}, "secondary": {"Academic Writing": 0.0007900115437896395, "Content Listing": 0.0007368445008367041, "Creative Writing": 0.0006858638705595527, "Customer Support Page": 0.0006206101492684472, "Q&A Forum": 0.0004573086401129864, "Discussion Thread": 0.00027763530053458244, "Comment Section": 0.0012241639836266735, "Missing Content": 0.006431860161660395, "Knowledge Article": 0.0016712395204614048, "Legal Notices": 0.0011683678080297527, "News Article": 0.00036009941219406524, "Organizational Announcement": 0.0004143094581979851, "Nonfiction Writing": 0.0004454894157681219, "Organizational About Page": 0.0004401225635495498, "Personal About Page": 0.0003900884024158763, "Personal Blog": 0.00034958995879747715, "Product Page": 0.0003013803271385721, "Spam / Ads": 0.0003906803585984261, "Structured Data": 0.009625852965593575, "Technical Writing": 0.053522090298250516, "Transcript / Interview": 0.000517555291774516, "Tutorial / How-To Guide": 0.14698271562761214, "User Reviews": 0.0002484525933318872, "NONE": 0.7719476678478971}, "tertiary": {"Academic Writing": 0.0004685722088335777, "Content Listing": 0.0005244255214735881, "Creative Writing": 0.0004205041638423308, "Customer Support Page": 0.0003891519069891519, "Q&A Forum": 0.0002671680381210397, "Discussion Thread": 0.0001443816782132049, "Comment Section": 0.0008615740400091504, "Missing Content": 0.007424626565736564, "Knowledge Article": 0.0012499936791154126, "Legal Notices": 0.0034071998604548437, "News Article": 0.00018421026889186572, "Organizational Announcement": 0.00023955126913708231, "Nonfiction Writing": 0.00026504961114734215, "Organizational About Page": 0.0002603463184468573, "Personal About Page": 0.00020706577347070053, "Personal Blog": 0.00019558878006086024, "Product Page": 0.0001938419584724746, "Spam / Ads": 0.0002564644309040202, "Structured Data": 0.013977540772710798, "Technical Writing": 0.019730336844671324, "Transcript / Interview": 0.0002829071578486578, "Tutorial / How-To Guide": 0.10038887454335058, "User Reviews": 0.00011872147396074065, "NONE": 0.8485419031341379}, "structured_records": {"false": 0.9763891660663426, "true": 0.023610833933657406}, "procedural_steps": {"false": 0.9847945372721714, "true": 0.015205462727828665}, "worked_solution": {"false": 0.9867637325036785, "true": 0.013236267496321457}, "contextual_dialogue": {"false": 0.9999943517809093, "true": 5.648219090691634e-06}, "content_quality": {"-2": 0.0179316639105164, "-1": 0.12996556492928849, "0": 0.3758985886336678, "1": 0.3926143866003774, "2": 0.08358979592614993}, "noise_severity": {"0": 0.8460120282544152, "1": 0.12226726101364069, "2": 0.02699134447207729, "3": 0.003206958582824187, "4": 0.0015224076770426658}, "missing_content_severity": {"0": 0.8095008447231841, "1": 0.14341275346581767, "2": 0.03974570563817285, "3": 0.005572554432230712, "4": 0.0017681417405944755}, "factual_density": {"0": 0.009186010743860433, "1": 0.025376357283267762, "2": 0.14908313069995438, "3": 0.41340678221019733, "4": 0.4029477190627201}, "pretraining_utility": {"-2": 0.00873314866494818, "-1": 0.06494562645038945, "0": 0.36087890181819693, "1": 0.4879255991284914, "2": 0.07751672393797412}, "math_density": {"0": 0.9806074101351084, "1": 0.015500975182745317, "2": 0.0018051280322279105, "3": 0.0007832198729740922, "4": 0.0013032667769441678}, "code_density": {"0": 0.0005770541234938552, "1": 0.0013093995815176664, "2": 0.05477279519149706, "3": 0.3436737639377227, "4": 0.5996669871657688}, "reasoning_density": {"0": 0.8154280897030887, "1": 0.08497392861388053, "2": 0.08417109841580334, "3": 0.009157425670277433, "4": 0.006269457596950026}, "education_level": {"none": 0.0009801186972071565, "0": 0.0015122252628237862, "1": 0.02962037498196768, "2": 0.8600510846838171, "3": 0.10367238195886692, "4": 0.00416381441531729}, "creative_writing_quality": {"not_applicable": 0.9980337066752527, "-2": 0.0001852666240637569, "-1": 0.0004955694537497457, "0": 0.001106617347552891, "1": 0.0001290806439718967, "2": 4.9759255409233716e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.677805268140785e-05, "Art & Design": 7.213890608907223e-05, "Law & Crime": 5.583614008219222e-05, "Education & Careers": 8.791251503701517e-05, "Entertainment & Media": 6.231214455772183e-05, "Fashion & Beauty": 7.025231676778867e-05, "Business, Economics & Finance": 0.00042234201137067304, "Food & Dining": 0.01994941645754357, "Games & Gaming": 0.00010041351102366715, "Electronics & Hardware": 7.566465048583585e-05, "Health, Healthcare & Wellness": 4.554901399073453e-05, "History & Geography": 4.432797353014498e-05, "Home, Pets & Hobbies": 4.666492230384396e-05, "Industry & Production": 5.185671280832938e-05, "Language, Literature & Philosophy": 4.3656237848470544e-05, "Politics & Government": 2.4975455732366247e-05, "Religion & Spirituality": 3.960951988438014e-05, "Science, Mathematics & Engineering": 0.0001306559621052315, "Relationships & Community": 2.705552163451613e-05, "Software Usage & Applications": 0.0005045050994510308, "Programming & Software Engineering": 0.9775965840568754, "Sports & Fitness": 5.7672582380650425e-05, "Vehicles & Transportation": 2.8302869354713327e-05, "Travel & Tourism": 4.903716957407809e-05, "Other / Uncovered Subject": 0.00027908687234579006, "Unknown / Insufficient Content": 8.739332454144342e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008304875194129525, "Art & Design": 0.000804141215198161, "Law & Crime": 0.0007152937177511502, "Education & Careers": 0.0008281942451477088, "Entertainment & Media": 0.0006657339787513908, "Fashion & Beauty": 0.0004641858076425628, "Business, Economics & Finance": 0.0023134554127925582, "Food & Dining": 0.28118593806992237, "Games & Gaming": 0.0005960809454211694, "Electronics & Hardware": 0.0005985492095697925, "Health, Healthcare & Wellness": 0.0005186566301461261, "History & Geography": 0.0003539746875502989, "Home, Pets & Hobbies": 0.000460485645985322, "Industry & Production": 0.0003733818431903618, "Language, Literature & Philosophy": 0.00026595210351880834, "Politics & Government": 0.00022586447040376262, "Religion & Spirituality": 0.00042735200104962435, "Science, Mathematics & Engineering": 0.0008002258412559655, "Relationships & Community": 0.00023312153450993374, "Software Usage & Applications": 0.002984611597073619, "Programming & Software Engineering": 0.03837611223031092, "Sports & Fitness": 0.00032485656639724594, "Vehicles & Transportation": 0.000199515923549108, "Travel & Tourism": 0.0004790359861922501, "Other / Uncovered Subject": 0.0009841031215040259, "NONE": 0.6639906896957527}}, "probabilities": {"primary": {"Academic Writing": 4.2167501331104856e-05, "Content Listing": 3.918448146943097e-05, "Creative Writing": 2.1010647843573574e-05, "Customer Support Page": 1.3728757451060063e-05, "Q&A Forum": 2.001519266085331e-05, "Discussion Thread": 1.0489766438886289e-05, "Comment Section": 2.139314370921168e-05, "Missing Content": 5.038983661618633e-05, "Knowledge Article": 6.328214507302347e-05, "Legal Notices": 2.4668767801545113e-05, "News Article": 6.806151520123485e-06, "Organizational Announcement": 1.4459665199827274e-05, "Nonfiction Writing": 1.526056252430468e-05, "Organizational About Page": 1.7908911500256745e-05, "Personal About Page": 1.6670803572767533e-05, "Personal Blog": 1.2014218773313402e-05, "Product Page": 1.239602841353618e-05, "Spam / Ads": 1.6185950840506295e-05, "Structured Data": 0.0004274126475414437, "Technical Writing": 0.9918625858125217, "Transcript / Interview": 2.131134528806433e-05, "Tutorial / How-To Guide": 0.006569473527053264, "User Reviews": 9.644415716084836e-06, "Other / Unknown": 0.0006915397191398422}, "secondary": {"Academic Writing": 0.0007900115437896395, "Content Listing": 0.0007368445008367041, "Creative Writing": 0.0006858638705595527, "Customer Support Page": 0.0006206101492684472, "Q&A Forum": 0.0004573086401129864, "Discussion Thread": 0.00027763530053458244, "Comment Section": 0.0012241639836266735, "Missing Content": 0.006431860161660395, "Knowledge Article": 0.0016712395204614048, "Legal Notices": 0.0011683678080297527, "News Article": 0.00036009941219406524, "Organizational Announcement": 0.0004143094581979851, "Nonfiction Writing": 0.0004454894157681219, "Organizational About Page": 0.0004401225635495498, "Personal About Page": 0.0003900884024158763, "Personal Blog": 0.00034958995879747715, "Product Page": 0.0003013803271385721, "Spam / Ads": 0.0003906803585984261, "Structured Data": 0.009625852965593575, "Technical Writing": 0.053522090298250516, "Transcript / Interview": 0.000517555291774516, "Tutorial / How-To Guide": 0.14698271562761214, "User Reviews": 0.0002484525933318872, "NONE": 0.7719476678478971}, "tertiary": {"Academic Writing": 0.0004685722088335777, "Content Listing": 0.0005244255214735881, "Creative Writing": 0.0004205041638423308, "Customer Support Page": 0.0003891519069891519, "Q&A Forum": 0.0002671680381210397, "Discussion Thread": 0.0001443816782132049, "Comment Section": 0.0008615740400091504, "Missing Content": 0.007424626565736564, "Knowledge Article": 0.0012499936791154126, "Legal Notices": 0.0034071998604548437, "News Article": 0.00018421026889186572, "Organizational Announcement": 0.00023955126913708231, "Nonfiction Writing": 0.00026504961114734215, "Organizational About Page": 0.0002603463184468573, "Personal About Page": 0.00020706577347070053, "Personal Blog": 0.00019558878006086024, "Product Page": 0.0001938419584724746, "Spam / Ads": 0.0002564644309040202, "Structured Data": 0.013977540772710798, "Technical Writing": 0.019730336844671324, "Transcript / Interview": 0.0002829071578486578, "Tutorial / How-To Guide": 0.10038887454335058, "User Reviews": 0.00011872147396074065, "NONE": 0.8485419031341379}, "structured_records": {"false": 0.9763891660663426, "true": 0.023610833933657406}, "procedural_steps": {"false": 0.9847945372721714, "true": 0.015205462727828665}, "worked_solution": {"false": 0.9867637325036785, "true": 0.013236267496321457}, "contextual_dialogue": {"false": 0.9999943517809093, "true": 5.648219090691634e-06}, "content_quality": {"-2": 0.0179316639105164, "-1": 0.12996556492928849, "0": 0.3758985886336678, "1": 0.3926143866003774, "2": 0.08358979592614993}, "noise_severity": {"0": 0.8460120282544152, "1": 0.12226726101364069, "2": 0.02699134447207729, "3": 0.003206958582824187, "4": 0.0015224076770426658}, "missing_content_severity": {"0": 0.8095008447231843, "1": 0.1434127534658177, "2": 0.039745705638172854, "3": 0.0055725544322307126, "4": 0.001768141740594476}, "factual_density": {"0": 0.009186010743860433, "1": 0.025376357283267762, "2": 0.14908313069995438, "3": 0.41340678221019733, "4": 0.4029477190627201}, "pretraining_utility": {"-2": 0.008733148664948178, "-1": 0.06494562645038944, "0": 0.3608789018181969, "1": 0.4879255991284913, "2": 0.07751672393797411}, "math_density": {"0": 0.9806074101351085, "1": 0.015500975182745319, "2": 0.0018051280322279107, "3": 0.0007832198729740924, "4": 0.001303266776944168}, "code_density": {"0": 0.0005770541234938552, "1": 0.0013093995815176664, "2": 0.05477279519149706, "3": 0.3436737639377227, "4": 0.5996669871657688}, "reasoning_density": {"0": 0.8154280897030887, "1": 0.08497392861388053, "2": 0.08417109841580334, "3": 0.009157425670277433, "4": 0.006269457596950026}, "education_level": {"none": 0.0009801186972071567, "0": 0.0015122252628237865, "1": 0.029620374981967682, "2": 0.8600510846838172, "3": 0.10367238195886694, "4": 0.004163814415317291}, "creative_writing_quality": {"not_applicable": 0.9980337066752525, "-2": 0.00018526662406375685, "-1": 0.0004955694537497456, "0": 0.0011066173475528908, "1": 0.00012908064397189667, "2": 4.97592554092337e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.677805268140785e-05, "Art & Design": 7.213890608907223e-05, "Law & Crime": 5.583614008219222e-05, "Education & Careers": 8.791251503701517e-05, "Entertainment & Media": 6.231214455772183e-05, "Fashion & Beauty": 7.025231676778867e-05, "Business, Economics & Finance": 0.00042234201137067304, "Food & Dining": 0.01994941645754357, "Games & Gaming": 0.00010041351102366715, "Electronics & Hardware": 7.566465048583585e-05, "Health, Healthcare & Wellness": 4.554901399073453e-05, "History & Geography": 4.432797353014498e-05, "Home, Pets & Hobbies": 4.666492230384396e-05, "Industry & Production": 5.185671280832938e-05, "Language, Literature & Philosophy": 4.3656237848470544e-05, "Politics & Government": 2.4975455732366247e-05, "Religion & Spirituality": 3.960951988438014e-05, "Science, Mathematics & Engineering": 0.0001306559621052315, "Relationships & Community": 2.705552163451613e-05, "Software Usage & Applications": 0.0005045050994510308, "Programming & Software Engineering": 0.9775965840568754, "Sports & Fitness": 5.7672582380650425e-05, "Vehicles & Transportation": 2.8302869354713327e-05, "Travel & Tourism": 4.903716957407809e-05, "Other / Uncovered Subject": 0.00027908687234579006, "Unknown / Insufficient Content": 8.739332454144342e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008304875194129526, "Art & Design": 0.0008041412151981611, "Law & Crime": 0.0007152937177511503, "Education & Careers": 0.0008281942451477089, "Entertainment & Media": 0.0006657339787513909, "Fashion & Beauty": 0.00046418580764256284, "Business, Economics & Finance": 0.0023134554127925587, "Food & Dining": 0.2811859380699224, "Games & Gaming": 0.0005960809454211695, "Electronics & Hardware": 0.0005985492095697926, "Health, Healthcare & Wellness": 0.0005186566301461262, "History & Geography": 0.00035397468755029893, "Home, Pets & Hobbies": 0.00046048564598532204, "Industry & Production": 0.0003733818431903619, "Language, Literature & Philosophy": 0.0002659521035188084, "Politics & Government": 0.00022586447040376265, "Religion & Spirituality": 0.0004273520010496244, "Science, Mathematics & Engineering": 0.0008002258412559656, "Relationships & Community": 0.00023312153450993377, "Software Usage & Applications": 0.0029846115970736196, "Programming & Software Engineering": 0.03837611223031093, "Sports & Fitness": 0.000324856566397246, "Vehicles & Transportation": 0.00019951592354910803, "Travel & Tourism": 0.00047903598619225015, "Other / Uncovered Subject": 0.000984103121504026, "NONE": 0.6639906896957528}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10063, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10063, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:11.520643+00:00", "scoring_key_slot": "primary", "dataset_uid": "eea04989-c8bc-47c8-a315-c49354fee7ea", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "b498bee04e31bbaaa259ca634eb1aa770046929c9851c41fb82b9ad76c6e61f6", "source_metadata": {"uuid": "eea04989-c8bc-47c8-a315-c49354fee7ea", "repo_name": "abdulaziz12k/coffee_shop_full_stack_project", "relative_path": "starter_code/backend/src/api.py", "category": "WEB", "algo_rel_score": 0.9948516488075256, "quality_score": 5.333845615386963, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 3, "repo_created_at": "2023-07-05T07:21:14.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "7cf4acbb0ab12a177580437f3ece4508589c5aca", "size_bytes": 6160, "file_timestamp": 1689875241, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "44a3b9aa6de6806e75e6a98fb2320c7ed2172978"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 3, "repo_created_at": "2023-07-05T07:21:14.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "7cf4acbb0ab12a177580437f3ece4508589c5aca", "size_bytes": 6160, "file_timestamp": 1689875241, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "44a3b9aa6de6806e75e6a98fb2320c7ed2172978", "url": "https://github.com/abdulaziz12k/coffee_shop_full_stack_project/blob/44a3b9aa6de6806e75e6a98fb2320c7ed2172978/starter_code/backend/src/api.py"}, "gpt4o_tokens": 1382, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.393965085702356, "noise_severity": 0.19196045641443849, "missing_content_severity": 0.24669439500123344, "factual_density": 3.175553841564649, "pretraining_utility": 0.5605471232241539, "math_density": 0.02667395797390009, "code_density": 3.540544230440755, "reasoning_density": 0.3058662328441196, "education_level": 2.079433039088673, "creative_writing_quality": -0.32421589345971674}, "score_applicability": {"education_level": 0.9990198813027928, "creative_writing_quality": 0.0019662933247475236}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/JYANG2002/Pygame/blob/9c17aa78b043cace2edf5316be665820d389ea2b/Hangman/Hangman.py", "id": "python-0-318485038bc05208731734eeb4f9637b", "text": "import pygame\nimport random\n\npygame.init()\nscreen = pygame.display.set_mode((800, 400))\npygame.display.set_caption(\"Hangman\")\nfont = pygame.font.Font('arial.ttf', 30)\ntext = font.render('Enter a letter:', True, 'Black')\ntextRect = text.get_rect()\ntextRect.midleft = (330, 190)\nchances = 6  # There are 6 chances to get it wrong\nusedw = []\nusedrec = []\ncorrect_text = []\ncorrect_rec = []\nused_letter = []\nnum = 0\na = random.randint(0, 5)\nf = open(\"words.txt\", 'r')\nr = f.readlines()\nstripped = list(map(str.strip, r))\noriginal_word = stripped[a]\nf.close()\n\n\nloser = pygame.mixer.Sound('Lose.wav')\nwinner = pygame.mixer.Sound('congrats.wav')\n\n\ndef end():\n    pygame.quit()\n    exit()\n\n\ndef draw(chance, word, letter, num):\n    a = \"abcdefghijklmnopqrstuvwxyz\"\n    screen.fill((255, 255, 255))  # Fill screen white\n    pygame.draw.line(screen, 'Black', (200, 50), (200, 250), 5)\n    pygame.draw.line(screen, 'Black', (150, 250), (250, 250), 5)\n    pygame.draw.line(screen, 'Black', (198, 50), (260, 50), 5)\n    pygame.draw.line(screen, 'Black', (260, 50), (260, 70), 5)\n\n    # Body parts\n    if chance < 6:\n        pygame.draw.circle(screen, 'Black', (260, 100), 30, 5)\n    if chance < 5:\n        pygame.draw.line(screen, 'Black', (260, 130), (260, 200), 5)\n    if chance < 4:\n        pygame.draw.line(screen, 'Black', (260, 140), (300, 130), 5)\n    if chance < 3:\n        pygame.draw.line(screen, 'Black', (260, 140), (220, 130), 5)\n    if chance < 2:\n        pygame.draw.line(screen, 'Black', (260, 200), (220, 230), 5)\n    if chance < 1:\n        pygame.draw.line(screen, 'Black', (260, 200), (300, 230), 5)\n\n    # Display lines of hidden word\n    for i in range(len(word)):\n        p1 = (330 + i * 60, 140)\n        p2 = (370 + i * 60, 140)\n        pygame.draw.line(screen, 'Black', p1, p2, 5)  # Starting point for first row\n\n    screen.blit(text, textRect)\n    t = font.render(letter, True, 'Black')\n\n    if letter in word and letter not in used_letter:\n        l = check_word(letter, word)\n        for i in l:\n            trec = t.get_rect()\n            trec.midleft = (340 + i * 60, 110)\n            correct_text.append(font.render(original_word[i], True, 'Black'))\n            correct_rec.append(trec)\n\n    if letter in a and letter not in used_letter:\n        trec = t.get_rect()\n        trec.midleft = (50 + num * 50, 300)\n        usedw.append(font.render(letter, True, 'Black'))\n        usedrec.append(trec)\n\n    for i in range(len(correct_text)):\n        screen.blit(correct_text[i], correct_rec[i])\n\n    #  Draw out the used words\n    for i in range(len(usedw)):\n        screen.blit(usedw[i], usedrec[i])\n\n    used_letter.append(letter)\n    pygame.display.update()\n\n\n# Return list of num of index that matches the letter to word\ndef check_word(letter, word):\n    lw = list(word)\n    l = []\n    index = 0\n    for i in lw:\n        if i.lower() == letter:\n            l.append(index)\n        index += 1\n    return l\n\n\nwhile True:\n    word = original_word.lower()\n    letter = chr(32)\n    for event in pygame.event.get():  # Get a list of all different events\n        if event.type == pygame.QUIT:  # Quit the game\n            end()\n        if event.type == pygame.KEYDOWN:  # If a key is pressed\n            letter = chr(event.key)\n            if letter not in used_letter:\n                num += 1\n            if letter not in word:\n                chances -= 1\n    screen.fill((135, 206, 235))\n\n    if len(correct_rec) == len(original_word):\n        winner.play()\n        pygame.time.wait(5000)\n        end()\n\n    else:\n        draw(chances, word, letter, num)\n        if chances == 0:\n            loser.play()\n            pygame.time.wait(5000)\n            end()\n", "text_preview": "import pygame import random pygame.init() screen = pygame.display.set_mode((800, 400)) pygame.display.set_caption(\"Hangman\") font = pygame.font.Font('arial.ttf', 30) text = font.render('Enter a letter:', True, 'Black') textRect = text.get_r", "input_sha256": "25826b89872b9947affedbfa91d894aee2a8d0942241c89939bac65f26e0eaa9", "text_sha256": "318485038bc05208731734eeb4f9637b5dd998b2c521473bf615367113367662", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12023, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.6337675293281801, "noise_severity": 0.008862846510872702, "missing_content_severity": 0.036389726580969166, "factual_density": 3.0099995886533244, "pretraining_utility": 0.42823580494309, "math_density": 0.5208257127650439, "code_density": 3.853411949574784, "reasoning_density": 0.4517861041716758, "education_level": 1.6352168836306917, "creative_writing_quality": -0.3445271683361926}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.005719019136283476, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9921871021797933, "secondary": 0.9879439026642096, "tertiary": 0.9939115748064185, "structured_records": 0.001088068226233491, "procedural_steps": 0.0027289624275405704, "worked_solution": 0.013923633681496015, "contextual_dialogue": 9.513320817727946e-06, "content_quality": 0.48400722608673374, "noise_severity": 0.9926142945742727, "missing_content_severity": 0.9696752278491924, "factual_density": 0.17499965721110355, "pretraining_utility": 0.4603132933498091, "math_density": 0.5659785726957968, "code_density": 0.8778432913123199, "reasoning_density": 0.6235115798569368, "education_level": 0.5494594508697765, "creative_writing_quality": 0.9971745154835361, "primary_topic": 0.9432266591710587, "secondary_topic": 0.7349791275119125}, "probability_margin": {"primary": 0.9882655354687488, "secondary": 0.9823179329725681, "tertiary": 0.9919455568288829, "education_level": 0.2668450512126624, "creative_writing_quality": 0.9962065723468102, "primary_topic": 0.8940791643767773, "secondary_topic": 0.5333428982888069}, "raw_probabilities": {"primary": {"Academic Writing": 4.381336386200885e-05, "Content Listing": 2.8954184446884572e-05, "Creative Writing": 0.00024308351059474826, "Customer Support Page": 1.4638680386811991e-05, "Q&A Forum": 1.7430070763947576e-05, "Discussion Thread": 1.5650841803637177e-05, "Comment Section": 2.4973603271420845e-05, "Missing Content": 5.11113160258357e-05, "Knowledge Article": 0.00010215227078751521, "Legal Notices": 3.651630795246678e-05, "News Article": 9.26396681334888e-06, "Organizational Announcement": 1.55624257422738e-05, "Nonfiction Writing": 2.5352381493712567e-05, "Organizational About Page": 2.0161557315303345e-05, "Personal About Page": 2.8262821028787452e-05, "Personal Blog": 1.3196510307886102e-05, "Product Page": 1.1958783155954109e-05, "Spam / Ads": 1.9770975269607025e-05, "Structured Data": 0.0002484569183025927, "Technical Writing": 0.9925126395889686, "Transcript / Interview": 2.4891214662052425e-05, "Tutorial / How-To Guide": 0.0022312788903402565, "User Reviews": 1.3775696484327937e-05, "Other / Unknown": 0.004247104120219768}, "secondary": {"Academic Writing": 7.785166246820865e-05, "Content Listing": 4.494901740227851e-05, "Creative Writing": 0.001025816348006112, "Customer Support Page": 3.3651455893303984e-05, "Q&A Forum": 2.7045042898520127e-05, "Discussion Thread": 2.289535431719134e-05, "Comment Section": 5.04550457558753e-05, "Missing Content": 0.0001499239673686884, "Knowledge Article": 0.0001965283708062714, "Legal Notices": 5.562565930129932e-05, "News Article": 3.255490535214215e-05, "Organizational Announcement": 3.0002627309464927e-05, "Nonfiction Writing": 2.8832257179136797e-05, "Organizational About Page": 3.5881023473357106e-05, "Personal About Page": 3.8962637012002037e-05, "Personal Blog": 3.353464972270956e-05, "Product Page": 2.139532842994146e-05, "Spam / Ads": 4.095811448415178e-05, "Structured Data": 0.0004970894841179286, "Technical Writing": 0.0061283070806328575, "Transcript / Interview": 3.7568131082640866e-05, "Tutorial / How-To Guide": 0.002922067031756354, "User Reviews": 2.1864752028732306e-05, "NONE": 0.9884462400532009}, "tertiary": {"Academic Writing": 3.702791641165373e-05, "Content Listing": 2.916424878653681e-05, "Creative Writing": 0.0010256993879720613, "Customer Support Page": 2.03997312656685e-05, "Q&A Forum": 1.3282640146679147e-05, "Discussion Thread": 1.2772145724619787e-05, "Comment Section": 3.0153966745688152e-05, "Missing Content": 0.00013982874790215426, "Knowledge Article": 0.00012107039314563595, "Legal Notices": 5.6346148701411555e-05, "News Article": 1.866289332016974e-05, "Organizational Announcement": 1.7496805292378675e-05, "Nonfiction Writing": 2.584918328214708e-05, "Organizational About Page": 3.1506115989988034e-05, "Personal About Page": 2.3038059928276705e-05, "Personal Blog": 2.1916953011152183e-05, "Product Page": 1.682269445198864e-05, "Spam / Ads": 2.57826355407354e-05, "Structured Data": 0.0002368053811928658, "Technical Writing": 0.002219702360601563, "Transcript / Interview": 2.274536161110066e-05, "Tutorial / How-To Guide": 0.001674460173781763, "User Reviews": 1.4206865709429813e-05, "NONE": 0.9941652591894844}, "structured_records": {"false": 0.9989119317737665, "true": 0.001088068226233491}, "procedural_steps": {"false": 0.9972710375724594, "true": 0.0027289624275405704}, "worked_solution": {"false": 0.986076366318504, "true": 0.013923633681496015}, "contextual_dialogue": {"false": 0.9999904866791822, "true": 9.513320817727946e-06}, "content_quality": {"-2": 0.017925754345423938, "-1": 0.058814526570463245, "0": 0.3213055835066713, "1": 0.4754747065653917, "2": 0.12647942901204973}, "noise_severity": {"0": 0.9927457148476735, "1": 0.0061779103130232445, "2": 0.0006950198827422021, "3": 0.0002305233938790194, "4": 0.00015083156268199868}, "missing_content_severity": {"0": 0.9809184683845006, "1": 0.004848420383413721, "2": 0.011589036396256878, "3": 0.0022130659382728944, "4": 0.000431008897555749}, "factual_density": {"0": 0.03932912391463789, "1": 0.05989779151965513, "2": 0.1666297596931988, "3": 0.31973102174276125, "4": 0.414412303129747}, "pretraining_utility": {"-2": 0.008890189324873234, "-1": 0.09191374286882309, "0": 0.4292988260697029, "1": 0.4018645570115422, "2": 0.06803268472505866}, "math_density": {"0": 0.6721878214671628, "1": 0.22893715517380028, "2": 0.04227273133368278, "3": 0.01906607317753854, "4": 0.0375362188478156}, "code_density": {"0": 0.00023884464791510386, "1": 0.0004935809126478006, "2": 0.02087371707179999, "3": 0.10240449495201236, "4": 0.8759893624156248}, "reasoning_density": {"0": 0.7878728009876527, "1": 0.055031582711850664, "2": 0.0980200487977621, "3": 0.03558784614663711, "4": 0.023487721356097387}, "education_level": {"none": 0.002156889390013461, "0": 0.007880376980216767, "1": 0.35770449117881803, "2": 0.6245495423914804, "3": 0.005948474515646019, "4": 0.001760225543825224}, "creative_writing_quality": {"not_applicable": 0.9976454295696134, "-2": 0.00024297092856569418, "-1": 0.0005198696620753249, "0": 0.0014388572228032778, "1": 0.00011114719770661314, "2": 4.1725419235474384e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 8.575180370955255e-05, "Art & Design": 0.00016301327445613056, "Law & Crime": 7.652361409756976e-05, "Education & Careers": 9.235867931219719e-05, "Entertainment & Media": 0.0006735407799911628, "Fashion & Beauty": 7.939410152781759e-05, "Business, Economics & Finance": 8.728130862427344e-05, "Food & Dining": 7.25498361683498e-05, "Games & Gaming": 0.05133108482616387, "Electronics & Hardware": 7.621978859327623e-05, "Health, Healthcare & Wellness": 6.0023302276530776e-05, "History & Geography": 8.424038450357227e-05, "Home, Pets & Hobbies": 8.845753305467924e-05, "Industry & Production": 6.918166822519439e-05, "Language, Literature & Philosophy": 0.00031597708354469205, "Politics & Government": 3.755238194403697e-05, "Religion & Spirituality": 7.33450263654841e-05, "Science, Mathematics & Engineering": 0.00021952188520475143, "Relationships & Community": 5.617096806902931e-05, "Software Usage & Applications": 0.0004102947650714066, "Programming & Software Engineering": 0.9454102492029411, "Sports & Fitness": 0.00011575031062686236, "Vehicles & Transportation": 3.822112480201256e-05, "Travel & Tourism": 7.605869883995755e-05, "Other / Uncovered Subject": 0.00012827117495428387, "Unknown / Insufficient Content": 7.896647693213731e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.001008616031054531, "Art & Design": 0.004850273780199281, "Law & Crime": 0.000520615275040786, "Education & Careers": 0.0004780503841008242, "Entertainment & Media": 0.0016658226803018571, "Fashion & Beauty": 0.00030834493233097555, "Business, Economics & Finance": 0.0005015137951735758, "Food & Dining": 0.0002818248196859304, "Games & Gaming": 0.21182933970341664, "Electronics & Hardware": 0.0004260765764785242, "Health, Healthcare & Wellness": 0.000364574531488726, "History & Geography": 0.00040165090228419504, "Home, Pets & Hobbies": 0.0004358902300005469, "Industry & Production": 0.00024060049459988925, "Language, Literature & Philosophy": 0.0027446496616841736, "Politics & Government": 0.00024019722245463063, "Religion & Spirituality": 0.0004097851020931797, "Science, Mathematics & Engineering": 0.001875391483887909, "Relationships & Community": 0.00024593273495372584, "Software Usage & Applications": 0.0012956247607532735, "Programming & Software Engineering": 0.02323934423414688, "Sports & Fitness": 0.0003043735626495965, "Vehicles & Transportation": 0.0001565971180366009, "Travel & Tourism": 0.00048089660029146305, "Other / Uncovered Subject": 0.0005217753906686765, "NONE": 0.7451722379922235}}, "probabilities": {"primary": {"Academic Writing": 4.381336386200886e-05, "Content Listing": 2.895418444688458e-05, "Creative Writing": 0.00024308351059474832, "Customer Support Page": 1.4638680386811995e-05, "Q&A Forum": 1.743007076394758e-05, "Discussion Thread": 1.565084180363718e-05, "Comment Section": 2.497360327142085e-05, "Missing Content": 5.1111316025835715e-05, "Knowledge Article": 0.00010215227078751523, "Legal Notices": 3.6516307952466784e-05, "News Article": 9.263966813348881e-06, "Organizational Announcement": 1.5562425742273803e-05, "Nonfiction Writing": 2.5352381493712573e-05, "Organizational About Page": 2.016155731530335e-05, "Personal About Page": 2.826282102878746e-05, "Personal Blog": 1.3196510307886106e-05, "Product Page": 1.1958783155954112e-05, "Spam / Ads": 1.977097526960703e-05, "Structured Data": 0.00024845691830259275, "Technical Writing": 0.9925126395889688, "Transcript / Interview": 2.4891214662052432e-05, "Tutorial / How-To Guide": 0.002231278890340257, "User Reviews": 1.377569648432794e-05, "Other / Unknown": 0.004247104120219769}, "secondary": {"Academic Writing": 7.785166246820865e-05, "Content Listing": 4.494901740227851e-05, "Creative Writing": 0.001025816348006112, "Customer Support Page": 3.3651455893303984e-05, "Q&A Forum": 2.7045042898520127e-05, "Discussion Thread": 2.289535431719134e-05, "Comment Section": 5.04550457558753e-05, "Missing Content": 0.0001499239673686884, "Knowledge Article": 0.0001965283708062714, "Legal Notices": 5.562565930129932e-05, "News Article": 3.255490535214215e-05, "Organizational Announcement": 3.0002627309464927e-05, "Nonfiction Writing": 2.8832257179136797e-05, "Organizational About Page": 3.5881023473357106e-05, "Personal About Page": 3.8962637012002037e-05, "Personal Blog": 3.353464972270956e-05, "Product Page": 2.139532842994146e-05, "Spam / Ads": 4.095811448415178e-05, "Structured Data": 0.0004970894841179286, "Technical Writing": 0.0061283070806328575, "Transcript / Interview": 3.7568131082640866e-05, "Tutorial / How-To Guide": 0.002922067031756354, "User Reviews": 2.1864752028732306e-05, "NONE": 0.9884462400532009}, "tertiary": {"Academic Writing": 3.702791641165373e-05, "Content Listing": 2.916424878653681e-05, "Creative Writing": 0.0010256993879720613, "Customer Support Page": 2.03997312656685e-05, "Q&A Forum": 1.3282640146679147e-05, "Discussion Thread": 1.2772145724619787e-05, "Comment Section": 3.0153966745688152e-05, "Missing Content": 0.00013982874790215426, "Knowledge Article": 0.00012107039314563595, "Legal Notices": 5.6346148701411555e-05, "News Article": 1.866289332016974e-05, "Organizational Announcement": 1.7496805292378675e-05, "Nonfiction Writing": 2.584918328214708e-05, "Organizational About Page": 3.1506115989988034e-05, "Personal About Page": 2.3038059928276705e-05, "Personal Blog": 2.1916953011152183e-05, "Product Page": 1.682269445198864e-05, "Spam / Ads": 2.57826355407354e-05, "Structured Data": 0.0002368053811928658, "Technical Writing": 0.002219702360601563, "Transcript / Interview": 2.274536161110066e-05, "Tutorial / How-To Guide": 0.001674460173781763, "User Reviews": 1.4206865709429813e-05, "NONE": 0.9941652591894844}, "structured_records": {"false": 0.9989119317737665, "true": 0.001088068226233491}, "procedural_steps": {"false": 0.9972710375724594, "true": 0.0027289624275405704}, "worked_solution": {"false": 0.986076366318504, "true": 0.013923633681496015}, "contextual_dialogue": {"false": 0.9999904866791822, "true": 9.513320817727946e-06}, "content_quality": {"-2": 0.01792575434542394, "-1": 0.05881452657046325, "0": 0.3213055835066714, "1": 0.47547470656539176, "2": 0.12647942901204975}, "noise_severity": {"0": 0.9927457148476735, "1": 0.0061779103130232445, "2": 0.0006950198827422021, "3": 0.0002305233938790194, "4": 0.00015083156268199868}, "missing_content_severity": {"0": 0.9809184683845008, "1": 0.004848420383413722, "2": 0.01158903639625688, "3": 0.002213065938272895, "4": 0.0004310088975557491}, "factual_density": {"0": 0.03932912391463789, "1": 0.05989779151965513, "2": 0.1666297596931988, "3": 0.31973102174276125, "4": 0.414412303129747}, "pretraining_utility": {"-2": 0.008890189324873234, "-1": 0.09191374286882309, "0": 0.4292988260697029, "1": 0.4018645570115422, "2": 0.06803268472505866}, "math_density": {"0": 0.6721878214671628, "1": 0.22893715517380028, "2": 0.04227273133368278, "3": 0.01906607317753854, "4": 0.0375362188478156}, "code_density": {"0": 0.00023884464791510386, "1": 0.0004935809126478006, "2": 0.02087371707179999, "3": 0.10240449495201236, "4": 0.8759893624156248}, "reasoning_density": {"0": 0.7878728009876528, "1": 0.05503158271185067, "2": 0.09802004879776212, "3": 0.03558784614663712, "4": 0.02348772135609739}, "education_level": {"none": 0.0021568893900134616, "0": 0.007880376980216769, "1": 0.3577044911788181, "2": 0.6245495423914805, "3": 0.00594847451564602, "4": 0.0017602255438252242}, "creative_writing_quality": {"not_applicable": 0.9976454295696137, "-2": 0.00024297092856569424, "-1": 0.000519869662075325, "0": 0.001438857222803278, "1": 0.00011114719770661316, "2": 4.172541923547439e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 8.575180370955255e-05, "Art & Design": 0.00016301327445613056, "Law & Crime": 7.652361409756976e-05, "Education & Careers": 9.235867931219719e-05, "Entertainment & Media": 0.0006735407799911628, "Fashion & Beauty": 7.939410152781759e-05, "Business, Economics & Finance": 8.728130862427344e-05, "Food & Dining": 7.25498361683498e-05, "Games & Gaming": 0.05133108482616387, "Electronics & Hardware": 7.621978859327623e-05, "Health, Healthcare & Wellness": 6.0023302276530776e-05, "History & Geography": 8.424038450357227e-05, "Home, Pets & Hobbies": 8.845753305467924e-05, "Industry & Production": 6.918166822519439e-05, "Language, Literature & Philosophy": 0.00031597708354469205, "Politics & Government": 3.755238194403697e-05, "Religion & Spirituality": 7.33450263654841e-05, "Science, Mathematics & Engineering": 0.00021952188520475143, "Relationships & Community": 5.617096806902931e-05, "Software Usage & Applications": 0.0004102947650714066, "Programming & Software Engineering": 0.9454102492029411, "Sports & Fitness": 0.00011575031062686236, "Vehicles & Transportation": 3.822112480201256e-05, "Travel & Tourism": 7.605869883995755e-05, "Other / Uncovered Subject": 0.00012827117495428387, "Unknown / Insufficient Content": 7.896647693213731e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010086160310545313, "Art & Design": 0.0048502737801992815, "Law & Crime": 0.0005206152750407861, "Education & Careers": 0.00047805038410082427, "Entertainment & Media": 0.0016658226803018573, "Fashion & Beauty": 0.0003083449323309756, "Business, Economics & Finance": 0.0005015137951735759, "Food & Dining": 0.0002818248196859305, "Games & Gaming": 0.21182933970341666, "Electronics & Hardware": 0.00042607657647852426, "Health, Healthcare & Wellness": 0.0003645745314887261, "History & Geography": 0.0004016509022841951, "Home, Pets & Hobbies": 0.00043589023000054696, "Industry & Production": 0.00024060049459988928, "Language, Literature & Philosophy": 0.002744649661684174, "Politics & Government": 0.00024019722245463066, "Religion & Spirituality": 0.00040978510209317974, "Science, Mathematics & Engineering": 0.0018753914838879092, "Relationships & Community": 0.0002459327349537259, "Software Usage & Applications": 0.0012956247607532737, "Programming & Software Engineering": 0.023239344234146884, "Sports & Fitness": 0.00030437356264959655, "Vehicles & Transportation": 0.00015659711803660094, "Travel & Tourism": 0.0004808966002914631, "Other / Uncovered Subject": 0.0005217753906686766, "NONE": 0.7451722379922237}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9741, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9741, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:11.873253+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "66b48ba3-a7a3-4e14-9b83-2303571962c9", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "5c048927e80ae00ebb481068ae0f48e7edd01ac9371824fccf7428e14f67aeb0", "source_metadata": {"uuid": "66b48ba3-a7a3-4e14-9b83-2303571962c9", "repo_name": "JYANG2002/Pygame", "relative_path": "Hangman/Hangman.py", "category": "TOOL", "algo_rel_score": 0.9455888271331787, "quality_score": 4.867114543914795, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 4, "repo_created_at": "2021-07-27T14:37:13.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "252cbc31f9208d3e24f30b8fe07b1c815123248c", "size_bytes": 3672, "file_timestamp": 1636409285, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "9c17aa78b043cace2edf5316be665820d389ea2b"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 4, "repo_created_at": "2021-07-27T14:37:13.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "252cbc31f9208d3e24f30b8fe07b1c815123248c", "size_bytes": 3672, "file_timestamp": 1636409285, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "9c17aa78b043cace2edf5316be665820d389ea2b", "url": "https://github.com/JYANG2002/Pygame/blob/9c17aa78b043cace2edf5316be665820d389ea2b/Hangman/Hangman.py"}, "gpt4o_tokens": 1040, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.6337675293281801, "noise_severity": 0.008862846510872702, "missing_content_severity": 0.036389726580969166, "factual_density": 3.0099995886533244, "pretraining_utility": 0.42823580494309, "math_density": 0.5208257127650439, "code_density": 3.853411949574784, "reasoning_density": 0.4517861041716758, "education_level": 1.6352168836306917, "creative_writing_quality": -0.3445271683361926}, "score_applicability": {"education_level": 0.9978431106099866, "creative_writing_quality": 0.002354570430386385}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/USC-EE-250L-Spring-2023/lab-10-teamkg/blob/8cc00cf98cf183e7c038553e7597d34871257f25/main.py", "id": "python-0-2c7a60e6642638935c30fd90f86845aa", "text": "import time\nimport numpy as np\nfrom typing import List, Optional\n\nimport threading\nimport pandas as pd\nimport requests\nimport plotly.express as px\n\ndef generate_data() -> List[int]:\n    \"\"\"Generate some random data.\"\"\"\n    return np.random.randint(100, 10000, 1000).tolist()\n\ndef process1(data: List[int]) -> List[int]:\n    \"\"\"TODO: Document this function. What does it do? What are the inputs and outputs?\n    process1 passes in a list data structure that consists of ints and returns this data structure with ints\"\"\"\n    \n    def foo(x):\n        \"\"\"Find the next largest prime number.\"\"\"\n        while True:\n            x += 1\n            if all(x % i for i in range(2, x)):\n                return x\n    return [foo(x) for x in data]\n\ndef process2(data: List[int]) -> List[int]:\n    \"\"\"TODO: Document this function. What does it do? What are the inputs and outputs?\n    process2 takes in a list data structure as data input and returns a list of the integers \"\"\"\n    def foo(x):\n        \"\"\"Find the next largest prime number.\"\"\"\n        while True:\n            x += 1\n            if int(np.sqrt(x)) ** 2 == x:\n                return x\n    return [foo(x) for x in data]\n\ndef final_process(data1: List[int], data2: List[int]) -> List[int]:\n    \"\"\"TODO: Document this function. What does it do? What are the inputs and outputs?\n    final_process takes in two list data structures consisting of integers and returns the average\"\"\"\n    return np.mean([x - y for x, y in zip(data1, data2)])\n\noffload_url = 'http://192.168.2.74:8000' # TODO: Change this to the IP address of your server\n\ndef run(offload: Optional[str] = None) -> float:\n    \"\"\"Run the program, offloading the specified function(s) to the server.\n    \n    Args:\n        offload: Which function(s) to offload to the server. Can be None, 'process1', 'process2', or 'both'.\n\n    Returns:\n        float: the final result of the program.\n    \"\"\"\n    data = generate_data()\n    if offload == 'None': # in this case, we run the program locally\n        data1 = process1(data)\n        data2 = process2(data)\n    elif offload == 'process1':\n        data1 = None\n        def offload_process1(data):\n            nonlocal data1\n            # TODO: Send a POST request to the server with the input data\n            theResponse = requests.post(f'{offload_url}/process1', json=data)\n            data1 = theResponse.json() \n        thread = threading.Thread(target=offload_process1, args=(data,))\n        thread.start()\n        data2 = process2(data)\n        thread.join()\n        # Question 2: Why do we need to join the thread here?\n        # Question 3: Are the processing functions executing in parallel or just concurrently? What is the difference?\n        #   See this article: https://oxylabs.io/blog/concurrency-vs-parallelism\n        #   ChatGPT is also good at explaining the difference between parallel and concurrent execution!\n        #   Make sure to cite any sources you use to answer this question.\n    elif offload == 'process2':\n        # TODO: Implement this case\n        #pass\n        data2 = None\n        def offload_process2(data):\n            nonlocal data2\n            theResponse = requests.post(f'{offload_url}/process2', json=data)\n            data2 = theResponse.json() \n        thread = threading.Thread(target=offload_process2, args=(data,))\n        thread.start()\n        data1 = process1(data)\n        thread.join()\n\n    elif offload == 'both':\n        # TODO: Implement this case\n        #pass\n        data1 = None\n        data2 = None\n        def offload_process1(data):\n            nonlocal data1\n            theResponse = requests.post(f'{offload_url}/process1', json=data)\n            data1 = theResponse.json()\n        def offload_process2(data):\n            nonlocal data2\n            theResponse = requests.post(f'{offload_url}/process2', json=data)\n            data2 = theResponse.json() \n        thread1 = threading.Thread(target=offload_process1, args=(data,))\n        thread1.start()\n        thread1.join()\n        thread2 = threading.Thread(target=offload_process2, args=(data,))\n        thread2.start()\n        thread2.join()\n    ans = final_process(data1, data2)\n    return ans \n\ndef main():\n    # TODO: Run the program 5 times for each offloading mode, and record the total execution time\n    #   Compute the mean and standard deviation of the execution times\n    #   Hint: store the results in a pandas DataFrame, use previous labs as a reference\n    rows =[] \n    totalTime = 0\n    samples = 5\n    listofModes = {\"None\", \"process1\", \"process2\", \"both\"}\n    for i in listofModes:\n        times = []\n        for j in range(samples):\n            start = time.time()\n            run(i)\n            end = time.time() #end - start \n            totalTime = end - start \n            times.append(totalTime)\n            print(f\"Offloading {i} - sample{j+1}: {times[-1]:.2f}\")\n        rows.append([str(i), np.mean(times), np.std(times)])\n    \n    df = pd.DataFrame(rows, columns=['Mode', 'Mean Time', 'Standard Deviation Time'])\n\n    # TODO: Plot makespans (total execution time) as a bar chart with error bars\n    # Make sure to include a title and x and y labels\n    graph = px.bar(df,\n            x='Mode',\n            y='Mean Time',\n            error_y='Standard Deviation Time'\n    )\n\n    # TODO: save plot to \"makespan.png\"\n    graph.write_image('makespan.png')\n\n    # Question 4: What is the best offloading mode? Why do you think that is?\n    # Question 5: What is the worst offloading mode? Why do you think that is?\n    # Question 6: The processing functions in the example aren't very likely to be used in a real-world application. \n    #   What kind of processing functions would be more likely to be used in a real-world application?\n    #   When would you want to offload these functions to a server?\n    \n    \nif __name__ == '__main__':\n    main()\n", "text_preview": "import time import numpy as np from typing import List, Optional import threading import pandas as pd import requests import plotly.express as px def generate_data() -> List[int]: \"\"\"Generate some random data.\"\"\" return np.random.randint(10", "input_sha256": "2f3beacfc1f3a9b1fdd3daa1179bda0e18cc064bc7bf1c44d6cf68e7239c6a3e", "text_sha256": "2c7a60e6642638935c30fd90f86845aab84ca9dc55ed6e339e50cefd806d1099", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12024, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 1, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": -0.22151927542614308, "noise_severity": 0.1770887708364541, "missing_content_severity": 0.516496556381061, "factual_density": 2.875648777453809, "pretraining_utility": 0.47949995796999384, "math_density": 1.6590324175708075, "code_density": 3.7441777819412176, "reasoning_density": 0.41324063081552753, "education_level": 2.1072921583355106, "creative_writing_quality": -0.3591610481885977}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.029511787584664065, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9666227993633385, "secondary": 0.7809200407476204, "tertiary": 0.8114754991366595, "structured_records": 0.011236314526033676, "procedural_steps": 0.1353809266477709, "worked_solution": 0.02372732827502753, "contextual_dialogue": 9.816740016582769e-06, "content_quality": 0.489175000859306, "noise_severity": 0.8524260243029549, "missing_content_severity": 0.5695862030157826, "factual_density": 0.408231034285124, "pretraining_utility": 0.46973228037430836, "math_density": 0.21333367816572923, "code_density": 0.7868148182843478, "reasoning_density": 0.6556328076537271, "education_level": 0.8194103753796113, "creative_writing_quality": 0.9984756685991321, "primary_topic": 0.9777618874440415, "secondary_topic": 0.6384863505106344}, "probability_margin": {"primary": 0.9392550961741063, "secondary": 0.6589811529915331, "tertiary": 0.7177752548656928, "education_level": 0.7256214025686125, "creative_writing_quality": 0.9980522999691422, "primary_topic": 0.9607327454529513, "secondary_topic": 0.4302868388830814}, "raw_probabilities": {"primary": {"Academic Writing": 0.0001336458623102084, "Content Listing": 5.607255741587714e-05, "Creative Writing": 5.159718136804403e-05, "Customer Support Page": 3.469186971539809e-05, "Q&A Forum": 0.00010685063497535837, "Discussion Thread": 3.082505839367234e-05, "Comment Section": 6.430571525409508e-05, "Missing Content": 9.702084047199088e-05, "Knowledge Article": 0.00013256706678484945, "Legal Notices": 4.1939031306895416e-05, "News Article": 1.763665290056273e-05, "Organizational Announcement": 3.268902753441082e-05, "Nonfiction Writing": 5.0148180120499125e-05, "Organizational About Page": 4.2180359989430564e-05, "Personal About Page": 3.538638002957719e-05, "Personal Blog": 2.690232917395322e-05, "Product Page": 2.5012137608379637e-05, "Spam / Ads": 4.187009462056063e-05, "Structured Data": 0.00026350724166641096, "Technical Writing": 0.9680135160565327, "Transcript / Interview": 6.897637659226369e-05, "Tutorial / How-To Guide": 0.028758419882426393, "User Reviews": 2.477907302866942e-05, "Other / Unknown": 0.0018494603897796812}, "secondary": {"Academic Writing": 0.0036191371413573704, "Content Listing": 0.0004133615856751637, "Creative Writing": 0.000574442750129257, "Customer Support Page": 0.0005547475397011752, "Q&A Forum": 0.004936740354141739, "Discussion Thread": 0.0004207372839329869, "Comment Section": 0.0014321317483362817, "Missing Content": 0.02261982412612159, "Knowledge Article": 0.0031368879861817323, "Legal Notices": 0.0004761321304539026, "News Article": 0.0003111701803704336, "Organizational Announcement": 0.0003226030792925326, "Nonfiction Writing": 0.0006913527918794099, "Organizational About Page": 0.00037890486460951723, "Personal About Page": 0.0003572590305201578, "Personal Blog": 0.0003580851808944525, "Product Page": 0.0002760398172471838, "Spam / Ads": 0.0003628393627109481, "Structured Data": 0.001012313526684268, "Technical Writing": 0.03575566159870149, "Transcript / Interview": 0.0006074816659435379, "Tutorial / How-To Guide": 0.13106721939160318, "User Reviews": 0.000266554480375433, "NONE": 0.7900483723831363}, "tertiary": {"Academic Writing": 0.00378131092897731, "Content Listing": 0.0002933395598766665, "Creative Writing": 0.0005231532846750595, "Customer Support Page": 0.0005315179520233074, "Q&A Forum": 0.007196068567178036, "Discussion Thread": 0.0004082910268981267, "Comment Section": 0.0013042623843768843, "Missing Content": 0.035006229810444704, "Knowledge Article": 0.004872508455890146, "Legal Notices": 0.0004657889862397104, "News Article": 0.0002555270946967753, "Organizational Announcement": 0.00023794473328721846, "Nonfiction Writing": 0.0007396485274152795, "Organizational About Page": 0.00031436694714481907, "Personal About Page": 0.0002869462080625039, "Personal Blog": 0.0002684791145458182, "Product Page": 0.0002604229514176024, "Spam / Ads": 0.0003168553711990751, "Structured Data": 0.0008378374921826833, "Technical Writing": 0.020439760834365706, "Transcript / Interview": 0.0005738759982591902, "Tutorial / How-To Guide": 0.10155543180693911, "User Reviews": 0.00019974529127229896, "NONE": 0.819330686672632}, "structured_records": {"false": 0.9887636854739663, "true": 0.011236314526033676}, "procedural_steps": {"false": 0.8646190733522291, "true": 0.1353809266477709}, "worked_solution": {"false": 0.9762726717249725, "true": 0.02372732827502753}, "contextual_dialogue": {"false": 0.9999901832599835, "true": 9.816740016582769e-06}, "content_quality": {"-2": 0.04651647947930292, "-1": 0.32422167823888204, "0": 0.4557787345525514, "1": 0.15123085368718264, "2": 0.022252254042081077}, "noise_severity": {"0": 0.8551923491552813, "1": 0.11787014524915912, "2": 0.022881047718425956, "3": 0.0027693013580909797, "4": 0.0012871565190425292}, "missing_content_severity": {"0": 0.6100678402676981, "1": 0.2760135007608198, "2": 0.1032140817543862, "3": 0.008763416756914387, "4": 0.0019411604601813823}, "factual_density": {"0": 0.012832349970178644, "1": 0.07310342053367734, "2": 0.2325330997241305, "3": 0.3886453616161836, "4": 0.29288576815582995}, "pretraining_utility": {"-2": 0.007966028379756147, "-1": 0.08086711714385407, "0": 0.3927783333634415, "1": 0.4604779103525362, "2": 0.05791061076041196}, "math_density": {"0": 0.14248358431515876, "1": 0.36341110074111205, "2": 0.2896028854463959, "3": 0.10159417205242938, "4": 0.10290825744490384}, "code_density": {"0": 0.0003617517941938672, "1": 0.0007679011686966118, "2": 0.02182738296285875, "3": 0.2084167414501997, "4": 0.768626222624051}, "reasoning_density": {"0": 0.7601441850860927, "1": 0.11424786535755664, "2": 0.08797847595794177, "3": 0.027482080851548043, "4": 0.0101473927468608}, "education_level": {"none": 0.0006378083733548958, "0": 0.0011797605122851004, "1": 0.021292359608401982, "2": 0.849508646149676, "3": 0.12388724358106357, "4": 0.0034941817752186187}, "creative_writing_quality": {"not_applicable": 0.99872972383261, "-2": 0.00012618029330349322, "-1": 0.0003516540861471491, "0": 0.0006774238634678529, "1": 8.22548959568785e-05, "2": 3.276302851429459e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.682175384474303e-05, "Art & Design": 6.072189130224566e-05, "Law & Crime": 5.286515690772511e-05, "Education & Careers": 0.0007145237841364546, "Entertainment & Media": 3.919557879538105e-05, "Fashion & Beauty": 3.287862527440603e-05, "Business, Economics & Finance": 7.770037516478322e-05, "Food & Dining": 4.4510684974607314e-05, "Games & Gaming": 7.262698930615717e-05, "Electronics & Hardware": 0.000626105595860518, "Health, Healthcare & Wellness": 4.835191453921346e-05, "History & Geography": 5.82964604735922e-05, "Home, Pets & Hobbies": 4.012006259424241e-05, "Industry & Production": 5.3367123837765427e-05, "Language, Literature & Philosophy": 4.276955788141003e-05, "Politics & Government": 3.336566786901426e-05, "Religion & Spirituality": 3.723748372670289e-05, "Science, Mathematics & Engineering": 0.017884454012473138, "Relationships & Community": 2.388173485528964e-05, "Software Usage & Applications": 0.0009405889584493819, "Programming & Software Engineering": 0.9786171994654245, "Sports & Fitness": 7.243048088868251e-05, "Vehicles & Transportation": 3.556666634006416e-05, "Travel & Tourism": 5.790223509227497e-05, "Other / Uncovered Subject": 0.0002143659231749447, "Unknown / Insufficient Content": 7.215181681298817e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007829512882050191, "Art & Design": 0.0012651476826671627, "Law & Crime": 0.0008153413712463626, "Education & Careers": 0.02931937590311783, "Entertainment & Media": 0.0006161229013889494, "Fashion & Beauty": 0.00037482530710687185, "Business, Economics & Finance": 0.0008453330050678889, "Food & Dining": 0.0003173813294596776, "Games & Gaming": 0.0004893718683162849, "Electronics & Hardware": 0.0022356029540471847, "Health, Healthcare & Wellness": 0.0005468798157917636, "History & Geography": 0.0005317159929926053, "Home, Pets & Hobbies": 0.00038525001434125747, "Industry & Production": 0.0004231531051048514, "Language, Literature & Philosophy": 0.0003408793039390771, "Politics & Government": 0.00039536224163866775, "Religion & Spirituality": 0.0004537969428954198, "Science, Mathematics & Engineering": 0.22210388276175944, "Relationships & Community": 0.00021531766772330482, "Software Usage & Applications": 0.003738245148779414, "Programming & Software Engineering": 0.07927889175834243, "Sports & Fitness": 0.0004295488024478202, "Vehicles & Transportation": 0.00025227157254009507, "Travel & Tourism": 0.0005682080379410531, "Other / Uncovered Subject": 0.0008844215782987166, "NONE": 0.6523907216448408}}, "probabilities": {"primary": {"Academic Writing": 0.00013364586231020843, "Content Listing": 5.607255741587715e-05, "Creative Writing": 5.159718136804404e-05, "Customer Support Page": 3.46918697153981e-05, "Q&A Forum": 0.00010685063497535838, "Discussion Thread": 3.082505839367235e-05, "Comment Section": 6.430571525409509e-05, "Missing Content": 9.702084047199089e-05, "Knowledge Article": 0.00013256706678484948, "Legal Notices": 4.193903130689542e-05, "News Article": 1.7636652900562735e-05, "Organizational Announcement": 3.2689027534410825e-05, "Nonfiction Writing": 5.014818012049913e-05, "Organizational About Page": 4.218035998943057e-05, "Personal About Page": 3.5386380029577194e-05, "Personal Blog": 2.6902329173953223e-05, "Product Page": 2.501213760837964e-05, "Spam / Ads": 4.187009462056064e-05, "Structured Data": 0.000263507241666411, "Technical Writing": 0.9680135160565329, "Transcript / Interview": 6.89763765922637e-05, "Tutorial / How-To Guide": 0.028758419882426396, "User Reviews": 2.4779073028669423e-05, "Other / Unknown": 0.0018494603897796814}, "secondary": {"Academic Writing": 0.0036191371413573704, "Content Listing": 0.0004133615856751637, "Creative Writing": 0.000574442750129257, "Customer Support Page": 0.0005547475397011752, "Q&A Forum": 0.004936740354141739, "Discussion Thread": 0.0004207372839329869, "Comment Section": 0.0014321317483362817, "Missing Content": 0.02261982412612159, "Knowledge Article": 0.0031368879861817323, "Legal Notices": 0.0004761321304539026, "News Article": 0.0003111701803704336, "Organizational Announcement": 0.0003226030792925326, "Nonfiction Writing": 0.0006913527918794099, "Organizational About Page": 0.00037890486460951723, "Personal About Page": 0.0003572590305201578, "Personal Blog": 0.0003580851808944525, "Product Page": 0.0002760398172471838, "Spam / Ads": 0.0003628393627109481, "Structured Data": 0.001012313526684268, "Technical Writing": 0.03575566159870149, "Transcript / Interview": 0.0006074816659435379, "Tutorial / How-To Guide": 0.13106721939160318, "User Reviews": 0.000266554480375433, "NONE": 0.7900483723831363}, "tertiary": {"Academic Writing": 0.00378131092897731, "Content Listing": 0.0002933395598766665, "Creative Writing": 0.0005231532846750595, "Customer Support Page": 0.0005315179520233074, "Q&A Forum": 0.007196068567178036, "Discussion Thread": 0.0004082910268981267, "Comment Section": 0.0013042623843768843, "Missing Content": 0.035006229810444704, "Knowledge Article": 0.004872508455890146, "Legal Notices": 0.0004657889862397104, "News Article": 0.0002555270946967753, "Organizational Announcement": 0.00023794473328721846, "Nonfiction Writing": 0.0007396485274152795, "Organizational About Page": 0.00031436694714481907, "Personal About Page": 0.0002869462080625039, "Personal Blog": 0.0002684791145458182, "Product Page": 0.0002604229514176024, "Spam / Ads": 0.0003168553711990751, "Structured Data": 0.0008378374921826833, "Technical Writing": 0.020439760834365706, "Transcript / Interview": 0.0005738759982591902, "Tutorial / How-To Guide": 0.10155543180693911, "User Reviews": 0.00019974529127229896, "NONE": 0.819330686672632}, "structured_records": {"false": 0.9887636854739663, "true": 0.011236314526033676}, "procedural_steps": {"false": 0.8646190733522291, "true": 0.1353809266477709}, "worked_solution": {"false": 0.9762726717249725, "true": 0.02372732827502753}, "contextual_dialogue": {"false": 0.9999901832599835, "true": 9.816740016582769e-06}, "content_quality": {"-2": 0.04651647947930292, "-1": 0.32422167823888204, "0": 0.4557787345525514, "1": 0.15123085368718264, "2": 0.022252254042081077}, "noise_severity": {"0": 0.8551923491552814, "1": 0.11787014524915913, "2": 0.02288104771842596, "3": 0.00276930135809098, "4": 0.0012871565190425295}, "missing_content_severity": {"0": 0.6100678402676982, "1": 0.2760135007608199, "2": 0.10321408175438622, "3": 0.008763416756914388, "4": 0.0019411604601813825}, "factual_density": {"0": 0.012832349970178644, "1": 0.07310342053367734, "2": 0.2325330997241305, "3": 0.3886453616161836, "4": 0.29288576815582995}, "pretraining_utility": {"-2": 0.007966028379756148, "-1": 0.08086711714385408, "0": 0.39277833336344153, "1": 0.46047791035253627, "2": 0.057910610760411964}, "math_density": {"0": 0.14248358431515878, "1": 0.3634111007411121, "2": 0.28960288544639595, "3": 0.10159417205242939, "4": 0.10290825744490385}, "code_density": {"0": 0.00036175179419386725, "1": 0.0007679011686966119, "2": 0.021827382962858754, "3": 0.20841674145019973, "4": 0.7686262226240511}, "reasoning_density": {"0": 0.7601441850860927, "1": 0.11424786535755664, "2": 0.08797847595794177, "3": 0.027482080851548043, "4": 0.0101473927468608}, "education_level": {"none": 0.0006378083733548957, "0": 0.0011797605122851002, "1": 0.02129235960840198, "2": 0.8495086461496758, "3": 0.12388724358106354, "4": 0.003494181775218618}, "creative_writing_quality": {"not_applicable": 0.9987297238326104, "-2": 0.00012618029330349327, "-1": 0.0003516540861471492, "0": 0.0006774238634678531, "1": 8.225489595687853e-05, "2": 3.27630285142946e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.6821753844743015e-05, "Art & Design": 6.072189130224565e-05, "Law & Crime": 5.28651569077251e-05, "Education & Careers": 0.0007145237841364544, "Entertainment & Media": 3.919557879538104e-05, "Fashion & Beauty": 3.287862527440602e-05, "Business, Economics & Finance": 7.770037516478321e-05, "Food & Dining": 4.451068497460731e-05, "Games & Gaming": 7.262698930615716e-05, "Electronics & Hardware": 0.0006261055958605179, "Health, Healthcare & Wellness": 4.835191453921345e-05, "History & Geography": 5.8296460473592184e-05, "Home, Pets & Hobbies": 4.01200625942424e-05, "Industry & Production": 5.336712383776541e-05, "Language, Literature & Philosophy": 4.2769557881410024e-05, "Politics & Government": 3.336566786901425e-05, "Religion & Spirituality": 3.723748372670288e-05, "Science, Mathematics & Engineering": 0.017884454012473135, "Relationships & Community": 2.3881734855289633e-05, "Software Usage & Applications": 0.0009405889584493817, "Programming & Software Engineering": 0.9786171994654242, "Sports & Fitness": 7.24304808886825e-05, "Vehicles & Transportation": 3.556666634006415e-05, "Travel & Tourism": 5.7902235092274955e-05, "Other / Uncovered Subject": 0.00021436592317494464, "Unknown / Insufficient Content": 7.215181681298816e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007829512882050191, "Art & Design": 0.0012651476826671627, "Law & Crime": 0.0008153413712463626, "Education & Careers": 0.02931937590311783, "Entertainment & Media": 0.0006161229013889494, "Fashion & Beauty": 0.00037482530710687185, "Business, Economics & Finance": 0.0008453330050678889, "Food & Dining": 0.0003173813294596776, "Games & Gaming": 0.0004893718683162849, "Electronics & Hardware": 0.0022356029540471847, "Health, Healthcare & Wellness": 0.0005468798157917636, "History & Geography": 0.0005317159929926053, "Home, Pets & Hobbies": 0.00038525001434125747, "Industry & Production": 0.0004231531051048514, "Language, Literature & Philosophy": 0.0003408793039390771, "Politics & Government": 0.00039536224163866775, "Religion & Spirituality": 0.0004537969428954198, "Science, Mathematics & Engineering": 0.22210388276175944, "Relationships & Community": 0.00021531766772330482, "Software Usage & Applications": 0.003738245148779414, "Programming & Software Engineering": 0.07927889175834243, "Sports & Fitness": 0.0004295488024478202, "Vehicles & Transportation": 0.00025227157254009507, "Travel & Tourism": 0.0005682080379410531, "Other / Uncovered Subject": 0.0008844215782987166, "NONE": 0.6523907216448408}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10020, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10020, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:14.993216+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "12101ddb-de02-4a35-ad3e-1e057dc9c4c0", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "43e11343c12b2ef31ea88fd1f9657e221e14b9b9cf64d117c1e6563d152ed20a", "source_metadata": {"uuid": "12101ddb-de02-4a35-ad3e-1e057dc9c4c0", "repo_name": "USC-EE-250L-Spring-2023/lab-10-teamkg", "relative_path": "main.py", "category": "DATA", "algo_rel_score": 0.979407548904419, "quality_score": 4.053912162780762, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 12, "repo_created_at": "2023-04-11T23:26:51.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "6d3769b44d67f995f78e8e25394d77bff5b09472", "size_bytes": 5847, "file_timestamp": 1681864492, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "8cc00cf98cf183e7c038553e7597d34871257f25"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 12, "repo_created_at": "2023-04-11T23:26:51.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "6d3769b44d67f995f78e8e25394d77bff5b09472", "size_bytes": 5847, "file_timestamp": 1681864492, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "8cc00cf98cf183e7c038553e7597d34871257f25", "url": "https://github.com/USC-EE-250L-Spring-2023/lab-10-teamkg/blob/8cc00cf98cf183e7c038553e7597d34871257f25/main.py"}, "gpt4o_tokens": 1404, "dataset_split": "python", "probability_weighted_scores": {"content_quality": -0.22151927542614308, "noise_severity": 0.1770887708364541, "missing_content_severity": 0.516496556381061, "factual_density": 2.875648777453809, "pretraining_utility": 0.47949995796999384, "math_density": 1.6590324175708075, "code_density": 3.7441777819412176, "reasoning_density": 0.41324063081552753, "education_level": 2.1072921583355106, "creative_writing_quality": -0.3591610481885977}, "score_applicability": {"education_level": 0.9993621916266451, "creative_writing_quality": 0.0012702761673896688}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/nstarman/astropy/blob/355180733157e6606676df6b0d792d9b0184f891/astropy/io/fits/hdu/compressed/tests/test_compression_failures.py", "id": "python-0-132022392eb6f7a18af71ddda669e3d6", "text": "# Licensed under a 3-clause BSD style license - see PYFITS.rst\n\nimport numpy as np\nimport pytest\n\nfrom astropy.io import fits\nfrom astropy.io.fits.hdu.compressed._tiled_compression import compress_image_data\nfrom astropy.io.fits.tests.conftest import FitsTestCase\n\nMAX_INT = np.iinfo(np.intc).max\nMAX_LONG = np.iinfo(int).max\nMAX_LONGLONG = np.iinfo(np.longlong).max\n\n\nclass TestCompressionFunction(FitsTestCase):\n    def test_wrong_argument_number(self):\n        with pytest.raises(TypeError):\n            compress_image_data(1, 2)\n\n    def test_unknown_compression_type(self):\n        hdu = fits.CompImageHDU(np.ones((10, 10)))\n        hdu._header[\"ZCMPTYPE\"] = \"fun\"\n        with pytest.raises(ValueError) as exc:\n            compress_image_data(\n                hdu.data, hdu.compression_type, hdu._header, hdu.columns\n            )\n        assert \"Unrecognized compression type: fun\" in str(exc.value)\n\n    def test_zbitpix_unknown(self):\n        hdu = fits.CompImageHDU(np.ones((10, 10)))\n        hdu._header[\"ZBITPIX\"] = 13\n        with pytest.raises(ValueError) as exc:\n            compress_image_data(\n                hdu.data, hdu.compression_type, hdu._header, hdu.columns\n            )\n        assert \"Invalid value for BITPIX: 13\" in str(exc.value)\n\n    def test_data_none(self):\n        hdu = fits.CompImageHDU(np.ones((10, 10)))\n        hdu.data = None\n        with pytest.raises(TypeError) as exc:\n            compress_image_data(\n                hdu.data, hdu.compression_type, hdu._header, hdu.columns\n            )\n        assert \"Image data must be a numpy.ndarray\" in str(exc.value)\n\n    def test_missing_internal_header(self):\n        hdu = fits.CompImageHDU(np.ones((10, 10)))\n        del hdu._header\n        with pytest.raises(AttributeError) as exc:\n            compress_image_data(\n                hdu.data, hdu.compression_type, hdu._header, hdu.columns\n            )\n        assert \"_header\" in str(exc.value)\n\n    def test_invalid_tform(self):\n        hdu = fits.CompImageHDU(np.ones((10, 10)))\n        hdu._header[\"TFORM1\"] = \"TX\"\n        with pytest.raises(RuntimeError) as exc:\n            compress_image_data(\n                hdu.data, hdu.compression_type, hdu._header, hdu.columns\n            )\n        assert \"TX\" in str(exc.value) and \"TFORM\" in str(exc.value)\n\n    def test_invalid_zdither(self):\n        hdu = fits.CompImageHDU(np.ones((10, 10)), quantize_method=1)\n        hdu._header[\"ZDITHER0\"] = \"a\"\n        with pytest.raises(TypeError):\n            compress_image_data(\n                hdu.data, hdu.compression_type, hdu._header, hdu.columns\n            )\n\n    @pytest.mark.parametrize(\"kw\", [\"ZNAXIS\", \"ZBITPIX\"])\n    def test_header_missing_keyword(self, kw):\n        hdu = fits.CompImageHDU(np.ones((10, 10)))\n        del hdu._header[kw]\n        with pytest.raises(KeyError) as exc:\n            compress_image_data(\n                hdu.data, hdu.compression_type, hdu._header, hdu.columns\n            )\n        assert kw in str(exc.value)\n\n    @pytest.mark.parametrize(\"kw\", [\"ZNAXIS\", \"ZVAL1\", \"ZVAL2\", \"ZBLANK\", \"BLANK\"])\n    def test_header_value_int_overflow(self, kw):\n        hdu = fits.CompImageHDU(np.ones((10, 10)))\n        hdu._header[kw] = MAX_INT + 1\n        with pytest.raises(OverflowError):\n            compress_image_data(\n                hdu.data, hdu.compression_type, hdu._header, hdu.columns\n            )\n\n    @pytest.mark.parametrize(\"kw\", [\"ZTILE1\", \"ZNAXIS1\"])\n    def test_header_value_long_overflow(self, kw):\n        hdu = fits.CompImageHDU(np.ones((10, 10)))\n        hdu._header[kw] = MAX_LONG + 1\n        with pytest.raises(OverflowError):\n            compress_image_data(\n                hdu.data, hdu.compression_type, hdu._header, hdu.columns\n            )\n\n    @pytest.mark.parametrize(\"kw\", [\"NAXIS1\", \"NAXIS2\", \"TNULL1\", \"PCOUNT\", \"THEAP\"])\n    def test_header_value_longlong_overflow(self, kw):\n        hdu = fits.CompImageHDU(np.ones((10, 10)))\n        hdu._header[kw] = MAX_LONGLONG + 1\n        with pytest.raises(OverflowError):\n            compress_image_data(\n                hdu.data, hdu.compression_type, hdu._header, hdu.columns\n            )\n\n    @pytest.mark.parametrize(\"kw\", [\"ZVAL3\"])\n    def test_header_value_float_overflow(self, kw):\n        hdu = fits.CompImageHDU(np.ones((10, 10)))\n        hdu._header[kw] = 1e300\n        with pytest.raises(OverflowError):\n            compress_image_data(\n                hdu.data, hdu.compression_type, hdu._header, hdu.columns\n            )\n\n    @pytest.mark.parametrize(\"kw\", [\"NAXIS1\", \"NAXIS2\", \"TFIELDS\", \"PCOUNT\"])\n    def test_header_value_negative(self, kw):\n        hdu = fits.CompImageHDU(np.ones((10, 10)))\n        hdu._header[kw] = -1\n        with pytest.raises(ValueError) as exc:\n            compress_image_data(\n                hdu.data, hdu.compression_type, hdu._header, hdu.columns\n            )\n        assert f\"{kw} should not be negative.\" in str(exc.value)\n\n    @pytest.mark.parametrize((\"kw\", \"limit\"), [(\"ZNAXIS\", 999), (\"TFIELDS\", 999)])\n    def test_header_value_exceeds_custom_limit(self, kw, limit):\n        hdu = fits.CompImageHDU(np.ones((10, 10)))\n        hdu._header[kw] = limit + 1\n        with pytest.raises(ValueError) as exc:\n            compress_image_data(\n                hdu.data, hdu.compression_type, hdu._header, hdu.columns\n            )\n        assert kw in str(exc.value)\n\n    @pytest.mark.parametrize(\n        \"kw\", [\"TTYPE1\", \"TFORM1\", \"ZCMPTYPE\", \"ZNAME1\", \"ZQUANTIZ\"]\n    )\n    def test_header_value_no_string(self, kw):\n        hdu = fits.CompImageHDU(np.ones((10, 10)))\n        hdu._header[kw] = 1\n        with pytest.raises(TypeError):\n            compress_image_data(\n                hdu.data, hdu.compression_type, hdu._header, hdu.columns\n            )\n\n    @pytest.mark.parametrize(\"kw\", [\"TZERO1\", \"TSCAL1\"])\n    def test_header_value_no_double(self, kw):\n        hdu = fits.CompImageHDU(np.ones((10, 10)))\n        hdu._header[kw] = \"1\"\n        with pytest.raises(TypeError):\n            compress_image_data(\n                hdu.data, hdu.compression_type, hdu._header, hdu.columns\n            )\n\n    @pytest.mark.parametrize(\"kw\", [\"ZSCALE\", \"ZZERO\"])\n    def test_header_value_no_double_int_image(self, kw):\n        hdu = fits.CompImageHDU(np.ones((10, 10), dtype=np.int32))\n        hdu._header[kw] = \"1\"\n        with pytest.raises(TypeError):\n            compress_image_data(\n                hdu.data, hdu.compression_type, hdu._header, hdu.columns\n            )\n", "text_preview": "# Licensed under a 3-clause BSD style license - see PYFITS.rst import numpy as np import pytest from astropy.io import fits from astropy.io.fits.hdu.compressed._tiled_compression import compress_image_data from astropy.io.fits.tests.conftes", "input_sha256": "5edd001a38d9f0a19e1b9469d4cc072e074cdd59f2c28ebcf3280bb626f2c80f", "text_sha256": "132022392eb6f7a18af71ddda669e3d6d9e8ab62ba0bca9dee42fdac03aad58b", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12025, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 2, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.4508668061138608, "noise_severity": 0.10764687371098185, "missing_content_severity": 0.0033178340403115097, "factual_density": 3.306677896326501, "pretraining_utility": 0.40564102435056804, "math_density": 0.6947534882927037, "code_density": 3.9140655267259303, "reasoning_density": 0.9780050360514365, "education_level": 2.838926316040916, "creative_writing_quality": -0.23911324183049695}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.03477497305243532, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9922287856309775, "secondary": 0.7080006082090403, "tertiary": 0.6865197332984797, "structured_records": 0.18954042838136245, "procedural_steps": 0.004795110204976592, "worked_solution": 0.1395431148402486, "contextual_dialogue": 5.6126190541600335e-06, "content_quality": 0.5423890050948841, "noise_severity": 0.9102942719075151, "missing_content_severity": 0.9972351382997404, "factual_density": 0.4222315802720841, "pretraining_utility": 0.3727052265850872, "math_density": 0.4210387597560803, "code_density": 0.9283879389382753, "reasoning_density": 0.18499580329046972, "education_level": 0.36630520261663485, "creative_writing_quality": 0.9993931152125088, "primary_topic": 0.9675157361434362, "secondary_topic": 0.7616573915077686}, "probability_margin": {"primary": 0.9874477130258756, "secondary": 0.47532347991129614, "tertiary": 0.41996266840290686, "education_level": 0.1372699658848206, "creative_writing_quality": 0.9992378029443134, "primary_topic": 0.9396152818232837, "secondary_topic": 0.5924150431817684}, "raw_probabilities": {"primary": {"Academic Writing": 9.135166646287073e-05, "Content Listing": 2.835007468652958e-05, "Creative Writing": 2.0501551372770187e-05, "Customer Support Page": 1.640957967528603e-05, "Q&A Forum": 1.6578707595923135e-05, "Discussion Thread": 1.1725373581486286e-05, "Comment Section": 1.8269531259185112e-05, "Missing Content": 2.5998203867532885e-05, "Knowledge Article": 7.357879773248613e-05, "Legal Notices": 0.0003140436920256272, "News Article": 7.491978733512625e-06, "Organizational Announcement": 1.6514978935410407e-05, "Nonfiction Writing": 1.6011446707282313e-05, "Organizational About Page": 1.5578376668310934e-05, "Personal About Page": 1.090937063847561e-05, "Personal Blog": 9.202113700368949e-06, "Product Page": 8.300998619898547e-06, "Spam / Ads": 1.0865201735204964e-05, "Structured Data": 0.0013389495276846836, "Technical Writing": 0.9925525862296868, "Transcript / Interview": 2.168673075281848e-05, "Tutorial / How-To Guide": 0.00026107638755144954, "User Reviews": 9.146276515046376e-06, "Other / Unknown": 0.005104873203811125}, "secondary": {"Academic Writing": 0.0008918114429065485, "Content Listing": 0.000469380970375831, "Creative Writing": 0.0005654719321025183, "Customer Support Page": 0.0002895010370130733, "Q&A Forum": 0.0003298378010226682, "Discussion Thread": 0.0002663230357861142, "Comment Section": 0.00039033584441741024, "Missing Content": 0.000624273538473234, "Knowledge Article": 0.000828980864168076, "Legal Notices": 0.24484376962236748, "News Article": 0.00048479433588541045, "Organizational Announcement": 0.0005157012450559953, "Nonfiction Writing": 0.00039561497041905986, "Organizational About Page": 0.00039957848299870433, "Personal About Page": 0.00028085250874984363, "Personal Blog": 0.0002631187578335837, "Product Page": 0.00025752254706291086, "Spam / Ads": 0.0003264178519185902, "Structured Data": 0.003372498496613994, "Technical Writing": 0.021931134223945645, "Transcript / Interview": 0.0005880364666461283, "Tutorial / How-To Guide": 0.0013152808312806119, "User Reviews": 0.00020251365929302757, "NONE": 0.7201672495336636}, "tertiary": {"Academic Writing": 0.0007408091733012643, "Content Listing": 0.00034452008770646185, "Creative Writing": 0.0004626744748614802, "Customer Support Page": 0.00024114170399497993, "Q&A Forum": 0.00021567340975030052, "Discussion Thread": 0.00018275716346320526, "Comment Section": 0.00028047642389378877, "Missing Content": 0.00043352698928944073, "Knowledge Article": 0.0006259200301836984, "Legal Notices": 0.2796187426748028, "News Article": 0.00036690046980984877, "Organizational Announcement": 0.0003621826977878782, "Nonfiction Writing": 0.0002755003027172884, "Organizational About Page": 0.00036113728889377005, "Personal About Page": 0.00019942906249209377, "Personal Blog": 0.00020042519163724262, "Product Page": 0.0002280346341962772, "Spam / Ads": 0.0002738103246014962, "Structured Data": 0.002790902943267762, "Technical Writing": 0.010760484882355517, "Transcript / Interview": 0.00042717124543747867, "Tutorial / How-To Guide": 0.000875527714154614, "User Reviews": 0.00015084003369162966, "NONE": 0.6995814110777097}, "structured_records": {"false": 0.8104595716186376, "true": 0.18954042838136245}, "procedural_steps": {"false": 0.9952048897950234, "true": 0.004795110204976592}, "worked_solution": {"false": 0.8604568851597514, "true": 0.1395431148402486}, "contextual_dialogue": {"false": 0.9999943873809458, "true": 5.6126190541600335e-06}, "content_quality": {"-2": 0.002122059661232875, "-1": 0.0038476727488492143, "0": 0.03581045988550958, "1": 0.45748101722364076, "2": 0.5007387904807675}, "noise_severity": {"0": 0.8969854392567532, "1": 0.10029108801915712, "2": 0.001455623984480611, "3": 0.0006268572355724954, "4": 0.0006409915040365033}, "missing_content_severity": {"0": 0.9980947912034528, "1": 0.0009679250203923224, "2": 0.0005690430694054643, "3": 0.0002611399458892156, "4": 0.00010710076086015296}, "factual_density": {"0": 0.004250880148769079, "1": 0.01235067407332508, "2": 0.12697376063746274, "3": 0.38531903958352204, "4": 0.47110564555692114}, "pretraining_utility": {"-2": 0.012362281728064077, "-1": 0.12486212422013881, "0": 0.38674525824919376, "1": 0.39683295957837145, "2": 0.07919737622423172}, "math_density": {"0": 0.5690366850420517, "1": 0.30319548381253997, "2": 0.04948286585935979, "3": 0.02054758838274981, "4": 0.057737376903298665}, "code_density": {"0": 0.00015716050555006909, "1": 0.00033456505582613657, "2": 0.0068321635247869245, "3": 0.07063780903481708, "4": 0.9220383018790198}, "reasoning_density": {"0": 0.5552520532936227, "1": 0.1508261695889945, "2": 0.14740255539414662, "3": 0.05370313121879581, "4": 0.0928160905044403}, "education_level": {"none": 0.0011093803502592245, "0": 0.0014202898374764095, "1": 0.004293792502348339, "2": 0.33465103629570847, "3": 0.47192100218052907, "4": 0.1866044988336784}, "creative_writing_quality": {"not_applicable": 0.9994942626770907, "-2": 7.154297714737553e-05, "-1": 9.009003296865328e-05, "0": 0.0002564597327773377, "1": 6.304166356392678e-05, "2": 2.4602916451981953e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.3625536833250896e-05, "Art & Design": 6.096793007151761e-05, "Law & Crime": 3.989404586733088e-05, "Education & Careers": 4.501997407650062e-05, "Entertainment & Media": 3.540911098337487e-05, "Fashion & Beauty": 4.3230880836181003e-05, "Business, Economics & Finance": 4.4990745247257475e-05, "Food & Dining": 3.672545422359669e-05, "Games & Gaming": 5.6312602914538674e-05, "Electronics & Hardware": 8.846087814022101e-05, "Health, Healthcare & Wellness": 4.407386511297798e-05, "History & Geography": 4.8797419593474746e-05, "Home, Pets & Hobbies": 2.939416535240384e-05, "Industry & Production": 4.744950591524743e-05, "Language, Literature & Philosophy": 3.760781266027957e-05, "Politics & Government": 2.560814914282054e-05, "Religion & Spirituality": 4.300437088760892e-05, "Science, Mathematics & Engineering": 0.02914984908386657, "Relationships & Community": 2.1849430882094002e-05, "Software Usage & Applications": 0.0005914302792809282, "Programming & Software Engineering": 0.9687651309071502, "Sports & Fitness": 6.586568176221746e-05, "Vehicles & Transportation": 2.738075635724822e-05, "Travel & Tourism": 5.191887879514305e-05, "Other / Uncovered Subject": 0.0004858071630013011, "Unknown / Insufficient Content": 6.019537104594861e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005543286050005546, "Art & Design": 0.0004526907208344374, "Law & Crime": 0.002576192613233223, "Education & Careers": 0.0003456587048361444, "Entertainment & Media": 0.0003791261981010836, "Fashion & Beauty": 0.000291606716570019, "Business, Economics & Finance": 0.0004656039577134823, "Food & Dining": 0.00022817979067448686, "Games & Gaming": 0.00028777174198810657, "Electronics & Hardware": 0.0004131356992473076, "Health, Healthcare & Wellness": 0.00031587448077001316, "History & Geography": 0.0003011558267104192, "Home, Pets & Hobbies": 0.00019131514455740444, "Industry & Production": 0.00022534684312012696, "Language, Literature & Philosophy": 0.00018254365365844236, "Politics & Government": 0.00022147540041636513, "Religion & Spirituality": 0.00032596202550986403, "Science, Mathematics & Engineering": 0.17840937172954754, "Relationships & Community": 0.00014792129088029782, "Software Usage & Applications": 0.0010667959806334242, "Programming & Software Engineering": 0.04028033550202765, "Sports & Fitness": 0.00023860334963415555, "Vehicles & Transportation": 0.00013159271789000196, "Travel & Tourism": 0.00038956666886656754, "Other / Uncovered Subject": 0.0007534297262627873, "NONE": 0.7708244149113159}}, "probabilities": {"primary": {"Academic Writing": 9.135166646287073e-05, "Content Listing": 2.835007468652958e-05, "Creative Writing": 2.0501551372770187e-05, "Customer Support Page": 1.640957967528603e-05, "Q&A Forum": 1.6578707595923135e-05, "Discussion Thread": 1.1725373581486286e-05, "Comment Section": 1.8269531259185112e-05, "Missing Content": 2.5998203867532885e-05, "Knowledge Article": 7.357879773248613e-05, "Legal Notices": 0.0003140436920256272, "News Article": 7.491978733512625e-06, "Organizational Announcement": 1.6514978935410407e-05, "Nonfiction Writing": 1.6011446707282313e-05, "Organizational About Page": 1.5578376668310934e-05, "Personal About Page": 1.090937063847561e-05, "Personal Blog": 9.202113700368949e-06, "Product Page": 8.300998619898547e-06, "Spam / Ads": 1.0865201735204964e-05, "Structured Data": 0.0013389495276846836, "Technical Writing": 0.9925525862296868, "Transcript / Interview": 2.168673075281848e-05, "Tutorial / How-To Guide": 0.00026107638755144954, "User Reviews": 9.146276515046376e-06, "Other / Unknown": 0.005104873203811125}, "secondary": {"Academic Writing": 0.0008918114429065485, "Content Listing": 0.000469380970375831, "Creative Writing": 0.0005654719321025183, "Customer Support Page": 0.0002895010370130733, "Q&A Forum": 0.0003298378010226682, "Discussion Thread": 0.0002663230357861142, "Comment Section": 0.00039033584441741024, "Missing Content": 0.000624273538473234, "Knowledge Article": 0.000828980864168076, "Legal Notices": 0.24484376962236748, "News Article": 0.00048479433588541045, "Organizational Announcement": 0.0005157012450559953, "Nonfiction Writing": 0.00039561497041905986, "Organizational About Page": 0.00039957848299870433, "Personal About Page": 0.00028085250874984363, "Personal Blog": 0.0002631187578335837, "Product Page": 0.00025752254706291086, "Spam / Ads": 0.0003264178519185902, "Structured Data": 0.003372498496613994, "Technical Writing": 0.021931134223945645, "Transcript / Interview": 0.0005880364666461283, "Tutorial / How-To Guide": 0.0013152808312806119, "User Reviews": 0.00020251365929302757, "NONE": 0.7201672495336636}, "tertiary": {"Academic Writing": 0.0007408091733012643, "Content Listing": 0.00034452008770646185, "Creative Writing": 0.0004626744748614802, "Customer Support Page": 0.00024114170399497993, "Q&A Forum": 0.00021567340975030052, "Discussion Thread": 0.00018275716346320526, "Comment Section": 0.00028047642389378877, "Missing Content": 0.00043352698928944073, "Knowledge Article": 0.0006259200301836984, "Legal Notices": 0.2796187426748028, "News Article": 0.00036690046980984877, "Organizational Announcement": 0.0003621826977878782, "Nonfiction Writing": 0.0002755003027172884, "Organizational About Page": 0.00036113728889377005, "Personal About Page": 0.00019942906249209377, "Personal Blog": 0.00020042519163724262, "Product Page": 0.0002280346341962772, "Spam / Ads": 0.0002738103246014962, "Structured Data": 0.002790902943267762, "Technical Writing": 0.010760484882355517, "Transcript / Interview": 0.00042717124543747867, "Tutorial / How-To Guide": 0.000875527714154614, "User Reviews": 0.00015084003369162966, "NONE": 0.6995814110777097}, "structured_records": {"false": 0.8104595716186376, "true": 0.18954042838136245}, "procedural_steps": {"false": 0.9952048897950234, "true": 0.004795110204976592}, "worked_solution": {"false": 0.8604568851597514, "true": 0.1395431148402486}, "contextual_dialogue": {"false": 0.9999943873809458, "true": 5.6126190541600335e-06}, "content_quality": {"-2": 0.002122059661232875, "-1": 0.0038476727488492143, "0": 0.03581045988550958, "1": 0.45748101722364076, "2": 0.5007387904807675}, "noise_severity": {"0": 0.8969854392567532, "1": 0.10029108801915712, "2": 0.001455623984480611, "3": 0.0006268572355724954, "4": 0.0006409915040365033}, "missing_content_severity": {"0": 0.9980947912034528, "1": 0.0009679250203923224, "2": 0.0005690430694054643, "3": 0.0002611399458892156, "4": 0.00010710076086015296}, "factual_density": {"0": 0.004250880148769079, "1": 0.01235067407332508, "2": 0.12697376063746274, "3": 0.38531903958352204, "4": 0.47110564555692114}, "pretraining_utility": {"-2": 0.01236228172806408, "-1": 0.12486212422013884, "0": 0.38674525824919387, "1": 0.39683295957837156, "2": 0.07919737622423173}, "math_density": {"0": 0.5690366850420517, "1": 0.30319548381253997, "2": 0.04948286585935979, "3": 0.02054758838274981, "4": 0.057737376903298665}, "code_density": {"0": 0.00015716050555006909, "1": 0.00033456505582613657, "2": 0.0068321635247869245, "3": 0.07063780903481708, "4": 0.9220383018790198}, "reasoning_density": {"0": 0.5552520532936228, "1": 0.15082616958899453, "2": 0.14740255539414665, "3": 0.053703131218795816, "4": 0.09281609050444031}, "education_level": {"none": 0.0011093803502592247, "0": 0.0014202898374764098, "1": 0.00429379250234834, "2": 0.3346510362957085, "3": 0.4719210021805291, "4": 0.18660449883367844}, "creative_writing_quality": {"not_applicable": 0.9994942626770907, "-2": 7.154297714737553e-05, "-1": 9.009003296865328e-05, "0": 0.0002564597327773377, "1": 6.304166356392678e-05, "2": 2.4602916451981953e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.362553683325088e-05, "Art & Design": 6.09679300715176e-05, "Law & Crime": 3.9894045867330876e-05, "Education & Careers": 4.5019974076500616e-05, "Entertainment & Media": 3.540911098337486e-05, "Fashion & Beauty": 4.3230880836181e-05, "Business, Economics & Finance": 4.499074524725747e-05, "Food & Dining": 3.672545422359668e-05, "Games & Gaming": 5.631260291453866e-05, "Electronics & Hardware": 8.8460878140221e-05, "Health, Healthcare & Wellness": 4.4073865112977975e-05, "History & Geography": 4.879741959347473e-05, "Home, Pets & Hobbies": 2.9394165352403834e-05, "Industry & Production": 4.744950591524742e-05, "Language, Literature & Philosophy": 3.7607812660279566e-05, "Politics & Government": 2.5608149142820533e-05, "Religion & Spirituality": 4.300437088760891e-05, "Science, Mathematics & Engineering": 0.02914984908386656, "Relationships & Community": 2.1849430882094e-05, "Software Usage & Applications": 0.0005914302792809281, "Programming & Software Engineering": 0.96876513090715, "Sports & Fitness": 6.586568176221745e-05, "Vehicles & Transportation": 2.7380756357248214e-05, "Travel & Tourism": 5.191887879514304e-05, "Other / Uncovered Subject": 0.000485807163001301, "Unknown / Insufficient Content": 6.0195371045948593e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005543286050005547, "Art & Design": 0.00045269072083443745, "Law & Crime": 0.0025761926132332235, "Education & Careers": 0.00034565870483614446, "Entertainment & Media": 0.00037912619810108363, "Fashion & Beauty": 0.00029160671657001906, "Business, Economics & Finance": 0.00046560395771348234, "Food & Dining": 0.0002281797906744869, "Games & Gaming": 0.0002877717419881066, "Electronics & Hardware": 0.00041313569924730765, "Health, Healthcare & Wellness": 0.0003158744807700132, "History & Geography": 0.00030115582671041925, "Home, Pets & Hobbies": 0.00019131514455740447, "Industry & Production": 0.00022534684312012698, "Language, Literature & Philosophy": 0.00018254365365844239, "Politics & Government": 0.00022147540041636516, "Religion & Spirituality": 0.0003259620255098641, "Science, Mathematics & Engineering": 0.17840937172954757, "Relationships & Community": 0.00014792129088029784, "Software Usage & Applications": 0.0010667959806334244, "Programming & Software Engineering": 0.040280335502027656, "Sports & Fitness": 0.00023860334963415558, "Vehicles & Transportation": 0.00013159271789000198, "Travel & Tourism": 0.0003895666688665676, "Other / Uncovered Subject": 0.0007534297262627874, "NONE": 0.770824414911316}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10366, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10366, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:16.313068+00:00", "scoring_key_slot": "primary", "dataset_uid": "34f08730-32a3-473e-9647-4dda60c75719", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "4aac7019339da504b12c7f64ea24c77a719e057406efd8bd7c7cf4905eb10f29", "source_metadata": {"uuid": "34f08730-32a3-473e-9647-4dda60c75719", "repo_name": "nstarman/astropy", "relative_path": "astropy/io/fits/hdu/compressed/tests/test_compression_failures.py", "category": "TEST", "algo_rel_score": 0.9889963865280151, "quality_score": 6.996245384216309, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 37168, "repo_created_at": "2020-05-26T18:22:56.000Z", "is_fork": true, "is_org_owned": false, "forked_from": "astropy/astropy", "stars": 2, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "ae8af3f667d0932b5faf8ee979e318109a1d6b57", "size_bytes": 6464, "file_timestamp": 1713394356, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "355180733157e6606676df6b0d792d9b0184f891"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 37168, "repo_created_at": "2020-05-26T18:22:56.000Z", "is_fork": true, "is_org_owned": false, "forked_from": "astropy/astropy", "stars": 2, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "ae8af3f667d0932b5faf8ee979e318109a1d6b57", "size_bytes": 6464, "file_timestamp": 1713394356, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "355180733157e6606676df6b0d792d9b0184f891", "url": "https://github.com/nstarman/astropy/blob/355180733157e6606676df6b0d792d9b0184f891/astropy/io/fits/hdu/compressed/tests/test_compression_failures.py"}, "gpt4o_tokens": 1722, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.4508668061138608, "noise_severity": 0.10764687371098185, "missing_content_severity": 0.0033178340403115097, "factual_density": 3.306677896326501, "pretraining_utility": 0.40564102435056804, "math_density": 0.6947534882927037, "code_density": 3.9140655267259303, "reasoning_density": 0.9780050360514363, "education_level": 2.838926316040916, "creative_writing_quality": -0.23911324183049695}, "score_applicability": {"education_level": 0.9988906196497408, "creative_writing_quality": 0.0005057373229092752}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/Rupesh528/ContestBuddy/blob/61e15da6a9c2ccae9b991c232e9c766d269f7912/src/database/mongodb_client.py", "id": "python-0-1b83014d7fd31d4a05107eed44a82e84", "text": "from pymongo import MongoClient\nfrom pymongo.server_api import ServerApi\nfrom datetime import datetime, UTC\nimport bcrypt\nimport certifi\nimport os\nimport json\n\nclass MongoDB:\n    def __init__(self):\n        # Your MongoDB credentials\n        username = \"sahanirupesh528\"\n        password = \"<PASSWORD>\"\n        cluster = \"cluster0.6muby.mongodb.net\"\n        \n        # Use Atlas connection string format\n        self.connection_string = f\"mongodb+srv://{username}:{password}@{cluster}/?retryWrites=true&w=majority\"\n        \n        self.is_connected = False\n        self.client = None\n        self.db = None\n        self.users = None\n        \n        try:\n            # Use certifi for SSL certificate validation but remove directConnection\n            self.client = MongoClient(\n                self.connection_string,\n                tlsCAFile=certifi.where(),\n                serverSelectionTimeoutMS=5000  # 5 second timeout\n            )\n            \n            # Test the connection\n            self.client.admin.command('ping')\n            self.is_connected = True\n            \n            # Set up database and collections\n            self.db = self.client['competitive_programming_app']\n            self.users = self.db['users']\n            \n            # Create index if connected\n            if self.is_connected:\n                try:\n                    self.users.create_index(\"email\", unique=True)\n                except Exception as e:\n                    print(f\"Index creation error: {str(e)}\")\n            \n            print(\"MongoDB connection successful\")\n            \n        except Exception as e:\n            print(f\"MongoDB Connection Error: {str(e)}\")\n            self._setup_local_storage()\n\n    def _setup_local_storage(self):\n        \"\"\"Set up local storage as a fallback\"\"\"\n        try:\n            # Create local storage directory\n            app_dir = self._get_app_directory()\n            data_dir = os.path.join(app_dir, \"data\")\n            \n            if not os.path.exists(data_dir):\n                os.makedirs(data_dir)\n                \n            # Check if local user data exists\n            self.users_file = os.path.join(data_dir, \"users.json\")\n            if not os.path.exists(self.users_file):\n                with open(self.users_file, \"w\") as f:\n                    json.dump([], f)\n                    \n            print(\"Local storage setup complete\")\n        except Exception as e:\n            print(f\"Local storage setup error: {str(e)}\")\n\n    def _get_app_directory(self):\n        \"\"\"Get the app directory\"\"\"\n        return os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n        \n    def _save_users_to_file(self, users):\n        \"\"\"Save users to local file\"\"\"\n        try:\n            with open(self.users_file, \"w\") as f:\n                json.dump(users, f)\n        except Exception as e:\n            print(f\"Error saving users to file: {str(e)}\")\n            \n    def _load_users_from_file(self):\n        \"\"\"Load users from local file\"\"\"\n        try:\n            with open(self.users_file, \"r\") as f:\n                return json.load(f)\n        except Exception as e:\n            print(f\"Error loading users from file: {str(e)}\")\n            return []\n\n    def authenticate_user(self, email, password):\n        \"\"\"Authenticate a user with email and password\"\"\"\n        # Try MongoDB first if connected\n        if self.is_connected:\n            try:\n                user = self.users.find_one({\"email\": email})\n                if user and bcrypt.checkpw(password.encode('utf-8'), user['password']):\n                    return {\n                        \"user_id\": str(user['_id']),\n                        \"email\": user['email'],\n                        \"name\": user.get('name', '')\n                    }\n            except Exception as e:\n                print(f\"MongoDB authentication error: {str(e)}\")\n        \n        # Fall back to local file if MongoDB failed or not connected\n        try:\n            users = self._load_users_from_file()\n            for user in users:\n                if user[\"email\"] == email:\n                    # Check password - stored as string in local file\n                    if bcrypt.checkpw(password.encode('utf-8'), user['password'].encode('utf-8')):\n                        return {\n                            \"user_id\": user.get('user_id', ''),\n                            \"email\": user['email'],\n                            \"name\": user.get('name', '')\n                        }\n        except Exception as e:\n            print(f\"Local file authentication error: {str(e)}\")\n            \n        return None\n\n    def create_user(self, email, password, name):\n        \"\"\"Create a new user\"\"\"\n        # Check if user exists in MongoDB or local file\n        existing_user = self.get_user_by_email(email)\n        if existing_user:\n            raise ValueError(\"Email already exists\")\n            \n        # Hash the password\n        hashed_password = bcrypt.hashpw(password.encode('utf-8'), <PASSWORD>alt())\n        \n        # Try to create in MongoDB if connected\n        user_id = None\n        if self.is_connected:\n            try:\n                user = {\n                    \"email\": email,\n                    \"password\": <PASSWORD>,\n                    \"name\": name,\n                    \"created_at\": datetime.now(UTC)\n                }\n                \n                result = self.users.insert_one(user)\n                user_id = str(result.inserted_id)\n            except Exception as e:\n                print(f\"MongoDB user creation error: {str(e)}\")\n        \n        # Always create locally as well or as fallback\n        try:\n            users = self._load_users_from_file()\n            \n            # Generate a simple ID if none from MongoDB\n            if not user_id:\n                import uuid\n                user_id = str(uuid.uuid4())\n                \n            local_user = {\n                \"user_id\": user_id,\n                \"email\": email,\n                \"password\": <PASSWORD>.decode('utf-8'),  # Store as string\n                \"name\": name,\n                \"created_at\": datetime.now(UTC).isoformat()\n            }\n            \n            users.append(local_user)\n            self._save_users_to_file(users)\n            \n            return user_id\n        except Exception as e:\n            print(f\"Local user creation error: {str(e)}\")\n            if user_id:  # If MongoDB succeeded but local failed\n                return user_id\n            raise\n\n    def get_user_by_email(self, email):\n        \"\"\"Get user info by email\"\"\"\n        # Try MongoDB first if connected\n        if self.is_connected:\n            try:\n                user = self.users.find_one({\"email\": email})\n                if user:\n                    return {\n                        \"user_id\": str(user['_id']),\n                        \"email\": user['email'],\n                        \"name\": user.get('name', '')\n                    }\n            except Exception as e:\n                print(f\"MongoDB get user error: {str(e)}\")\n        \n        # Fall back to local file\n        try:\n            users = self._load_users_from_file()\n            for user in users:\n                if user[\"email\"] == email:\n                    return {\n                        \"user_id\": user.get('user_id', ''),\n                        \"email\": user['email'],\n                        \"name\": user.get('name', '')\n                    }\n        except Exception as e:\n            print(f\"Local file get user error: {str(e)}\")\n            \n        return None", "text_preview": "from pymongo import MongoClient from pymongo.server_api import ServerApi from datetime import datetime, UTC import bcrypt import certifi import os import json class MongoDB: def __init__(self): # Your MongoDB credentials username = \"sahanir", "input_sha256": "9eb036351a0d06d83d6c8c738c2c8acebf2b80baed19c64bf724963767e8bdb1", "text_sha256": "1b83014d7fd31d4a05107eed44a82e842fa5a563cf7af412269923eaaa5c6eff", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12027, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": -1, "noise_severity": 0, "missing_content_severity": 1, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": -0.18221634378442625, "noise_severity": 0.7666236693657178, "missing_content_severity": 0.7323795216844535, "factual_density": 3.196367698398229, "pretraining_utility": 0.4099103593794485, "math_density": 0.018280779927724256, "code_density": 3.8424705575743445, "reasoning_density": 0.5134045377493405, "education_level": 2.134119545861477, "creative_writing_quality": -0.38486179157456685}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.004409245666288815, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9963496607927772, "secondary": 0.930837813025831, "tertiary": 0.9335588294567583, "structured_records": 0.033436110019726184, "procedural_steps": 0.02739767492185174, "worked_solution": 0.0011898306502230065, "contextual_dialogue": 5.510868751199652e-06, "content_quality": 0.13852031883899163, "noise_severity": 0.36114694219523513, "missing_content_severity": 0.5250148798909801, "factual_density": 0.3303064153318577, "pretraining_utility": 0.38136092238414565, "math_density": 0.9847660167268965, "code_density": 0.8687254646452868, "reasoning_density": 0.5721628852088828, "education_level": 0.7952227396178988, "creative_writing_quality": 0.9990779802917262, "primary_topic": 0.9965183706415001, "secondary_topic": 0.933814809388254}, "probability_margin": {"primary": 0.9953330513881496, "secondary": 0.8926622316970754, "tertiary": 0.8913634447534035, "education_level": 0.6865367759407368, "creative_writing_quality": 0.9988660801302778, "primary_topic": 0.9949069520815371, "secondary_topic": 0.9099801876459932}, "raw_probabilities": {"primary": {"Academic Writing": 3.939333220102284e-05, "Content Listing": 2.857829435070721e-05, "Creative Writing": 2.4190430989781323e-05, "Customer Support Page": 1.2429262519278994e-05, "Q&A Forum": 1.536293241560752e-05, "Discussion Thread": 1.0013090387091736e-05, "Comment Section": 1.7298544473571954e-05, "Missing Content": 0.00023033930182371663, "Knowledge Article": 0.00012858233989598166, "Legal Notices": 3.899300377839977e-05, "News Article": 6.998187876438298e-06, "Organizational Announcement": 1.3272712488645007e-05, "Nonfiction Writing": 1.809060249504275e-05, "Organizational About Page": 1.661425329320634e-05, "Personal About Page": 1.9349040971329127e-05, "Personal Blog": 1.0385017741501166e-05, "Product Page": 1.0170356542071782e-05, "Spam / Ads": 1.585015831466224e-05, "Structured Data": 0.0006314141545629023, "Technical Writing": 0.9965017582597449, "Transcript / Interview": 2.1611409504424484e-05, "Tutorial / How-To Guide": 0.00101104573853922, "User Reviews": 9.552703495222682e-06, "Other / Unknown": 0.0011687068715952292}, "secondary": {"Academic Writing": 0.0002462546410460117, "Content Listing": 0.0001792063523611011, "Creative Writing": 0.00023590120922049475, "Customer Support Page": 0.00015847266997238613, "Q&A Forum": 9.6276189857098e-05, "Discussion Thread": 9.54544732391138e-05, "Comment Section": 0.00023373995677108316, "Missing Content": 0.041057339119346004, "Knowledge Article": 0.0008903476657727978, "Legal Notices": 0.00026696779582734126, "News Article": 0.00016236826383306876, "Organizational Announcement": 0.0001418972595211534, "Nonfiction Writing": 0.00017562659241343576, "Organizational About Page": 0.00017196854141445498, "Personal About Page": 0.00013982156803573343, "Personal Blog": 0.00011917361127088503, "Product Page": 8.710408927848102e-05, "Spam / Ads": 0.0001614820275551151, "Structured Data": 0.004544860562000517, "Technical Writing": 0.012288491171916098, "Transcript / Interview": 0.00021677860081347768, "Tutorial / How-To Guide": 0.004534846336260069, "User Reviews": 7.605048585262226e-05, "NONE": 0.9337195708164214}, "tertiary": {"Academic Writing": 0.0001682042469655887, "Content Listing": 0.00013341826814955546, "Creative Writing": 0.00017861281606143016, "Customer Support Page": 0.00011888917411930462, "Q&A Forum": 7.348726830332855e-05, "Discussion Thread": 6.493354389225778e-05, "Comment Section": 0.00019822763045520924, "Missing Content": 0.04496376680932322, "Knowledge Article": 0.001018336725921838, "Legal Notices": 0.00041812268346664156, "News Article": 0.00014345593565636853, "Organizational Announcement": 0.0001075142562147375, "Nonfiction Writing": 0.00018326189044197386, "Organizational About Page": 0.0001875466413786118, "Personal About Page": 0.00010168979636559675, "Personal Blog": 9.233975197923993e-05, "Product Page": 7.936015694163986e-05, "Spam / Ads": 0.0001478029300732606, "Structured Data": 0.003190067536190533, "Technical Writing": 0.007879245505627283, "Transcript / Interview": 0.00015888076908780648, "Tutorial / How-To Guide": 0.004005498069277396, "User Reviews": 6.012603138049113e-05, "NONE": 0.9363272115627267}, "structured_records": {"false": 0.9665638899802738, "true": 0.033436110019726184}, "procedural_steps": {"false": 0.9726023250781483, "true": 0.02739767492185174}, "worked_solution": {"false": 0.998810169349777, "true": 0.0011898306502230065}, "contextual_dialogue": {"false": 0.9999944891312488, "true": 5.510868751199652e-06}, "content_quality": {"-2": 0.10799598058881811, "-1": 0.3176276884917796, "0": 0.2910460135714041, "1": 0.21525732881100682, "2": 0.06807298853699137}, "noise_severity": {"0": 0.5621339759173067, "1": 0.23144658727745576, "2": 0.09935344735971183, "3": 0.09179377041326432, "4": 0.01527221903226135}, "missing_content_severity": {"0": 0.41880131122318515, "1": 0.4469419029810302, "2": 0.11974600960208442, "3": 0.012097505275546407, "4": 0.002413270918153817}, "factual_density": {"0": 0.006128065734961425, "1": 0.023079564232067005, "2": 0.1522416254950408, "3": 0.40539809497564255, "4": 0.4131526495622882}, "pretraining_utility": {"-2": 0.02623784388737335, "-1": 0.11566422152435518, "0": 0.3561862921689579, "1": 0.42577301616007673, "2": 0.0761386262592368}, "math_density": {"0": 0.986552615031162, "1": 0.011173114977742675, "2": 0.000726700794218425, "3": 0.0005360134259627729, "4": 0.0010115557709141029}, "code_density": {"0": 0.0003210476652874202, "1": 0.000590645997509625, "2": 0.018376648939238766, "3": 0.11772001589349979, "4": 0.8629916415044645}, "reasoning_density": {"0": 0.7371746785370521, "1": 0.09097064388244923, "2": 0.1176235352996493, "3": 0.029737745855805398, "4": 0.02449339642504414}, "education_level": {"none": 0.0012091984258015204, "0": 0.0014053686738496399, "1": 0.018827562210411975, "2": 0.8293522830149157, "3": 0.14281550707417895, "4": 0.006390080600842146}, "creative_writing_quality": {"not_applicable": 0.9992316502431052, "-2": 9.974953437921355e-05, "-1": 0.0002129575539963222, "0": 0.000365570112827494, "1": 6.339695262309384e-05, "2": 2.6675603068629036e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.2296118182495396e-05, "Art & Design": 3.161090782550985e-05, "Law & Crime": 3.641763965418041e-05, "Education & Careers": 4.542586202945199e-05, "Entertainment & Media": 3.098796993015735e-05, "Fashion & Beauty": 9.798408298547785e-06, "Business, Economics & Finance": 4.834893824973349e-05, "Food & Dining": 1.5005710583954965e-05, "Games & Gaming": 0.0006380377530464971, "Electronics & Hardware": 5.6262482836247476e-05, "Health, Healthcare & Wellness": 1.6092192362985723e-05, "History & Geography": 1.7605617236517284e-05, "Home, Pets & Hobbies": 2.0265100771357093e-05, "Industry & Production": 1.727504166600532e-05, "Language, Literature & Philosophy": 2.3561188469816058e-05, "Politics & Government": 1.1066469424384953e-05, "Religion & Spirituality": 1.9807623432206137e-05, "Science, Mathematics & Engineering": 0.00022246026624358605, "Relationships & Community": 1.4326949498023967e-05, "Software Usage & Applications": 0.0017453273814437792, "Programming & Software Engineering": 0.9966522794629809, "Sports & Fitness": 0.0001005925151370885, "Vehicles & Transportation": 1.1534203732966981e-05, "Travel & Tourism": 2.293352692694207e-05, "Other / Uncovered Subject": 0.00011490808937153844, "Unknown / Insufficient Content": 4.577258066518774e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005289328486294324, "Art & Design": 0.0004099954413320511, "Law & Crime": 0.002996955907574668, "Education & Careers": 0.0013167511056428093, "Entertainment & Media": 0.0010230369482575046, "Fashion & Beauty": 0.00020237537301801315, "Business, Economics & Finance": 0.0006031628371716856, "Food & Dining": 0.0001728576700238818, "Games & Gaming": 0.00441875260665996, "Electronics & Hardware": 0.0003982843759760044, "Health, Healthcare & Wellness": 0.00023496011364033915, "History & Geography": 0.00019167039745628225, "Home, Pets & Hobbies": 0.0002156042206431699, "Industry & Production": 0.00014021715666675484, "Language, Literature & Philosophy": 0.00019531548038302605, "Politics & Government": 0.00019734066862650305, "Religion & Spirituality": 0.0002471592803633883, "Science, Mathematics & Engineering": 0.0010309933729033482, "Relationships & Community": 0.00016892147579100953, "Software Usage & Applications": 0.020519567597514363, "Programming & Software Engineering": 0.026380205996558784, "Sports & Fitness": 0.0008074702119099396, "Vehicles & Transportation": 8.671765948282314e-05, "Travel & Tourism": 0.00024459833948138783, "Other / Uncovered Subject": 0.0009077592717408146, "NONE": 0.936360393642552}}, "probabilities": {"primary": {"Academic Writing": 3.939333220102285e-05, "Content Listing": 2.8578294350707213e-05, "Creative Writing": 2.4190430989781326e-05, "Customer Support Page": 1.2429262519278996e-05, "Q&A Forum": 1.5362932415607523e-05, "Discussion Thread": 1.0013090387091738e-05, "Comment Section": 1.7298544473571958e-05, "Missing Content": 0.00023033930182371666, "Knowledge Article": 0.0001285823398959817, "Legal Notices": 3.899300377839978e-05, "News Article": 6.998187876438299e-06, "Organizational Announcement": 1.3272712488645008e-05, "Nonfiction Writing": 1.8090602495042753e-05, "Organizational About Page": 1.6614253293206343e-05, "Personal About Page": 1.934904097132913e-05, "Personal Blog": 1.0385017741501168e-05, "Product Page": 1.0170356542071784e-05, "Spam / Ads": 1.5850158314662245e-05, "Structured Data": 0.0006314141545629024, "Technical Writing": 0.996501758259745, "Transcript / Interview": 2.1611409504424487e-05, "Tutorial / How-To Guide": 0.0010110457385392201, "User Reviews": 9.552703495222684e-06, "Other / Unknown": 0.0011687068715952294}, "secondary": {"Academic Writing": 0.0002462546410460117, "Content Listing": 0.0001792063523611011, "Creative Writing": 0.00023590120922049475, "Customer Support Page": 0.00015847266997238613, "Q&A Forum": 9.6276189857098e-05, "Discussion Thread": 9.54544732391138e-05, "Comment Section": 0.00023373995677108316, "Missing Content": 0.041057339119346004, "Knowledge Article": 0.0008903476657727978, "Legal Notices": 0.00026696779582734126, "News Article": 0.00016236826383306876, "Organizational Announcement": 0.0001418972595211534, "Nonfiction Writing": 0.00017562659241343576, "Organizational About Page": 0.00017196854141445498, "Personal About Page": 0.00013982156803573343, "Personal Blog": 0.00011917361127088503, "Product Page": 8.710408927848102e-05, "Spam / Ads": 0.0001614820275551151, "Structured Data": 0.004544860562000517, "Technical Writing": 0.012288491171916098, "Transcript / Interview": 0.00021677860081347768, "Tutorial / How-To Guide": 0.004534846336260069, "User Reviews": 7.605048585262226e-05, "NONE": 0.9337195708164214}, "tertiary": {"Academic Writing": 0.0001682042469655887, "Content Listing": 0.00013341826814955546, "Creative Writing": 0.00017861281606143016, "Customer Support Page": 0.00011888917411930462, "Q&A Forum": 7.348726830332855e-05, "Discussion Thread": 6.493354389225778e-05, "Comment Section": 0.00019822763045520924, "Missing Content": 0.04496376680932322, "Knowledge Article": 0.001018336725921838, "Legal Notices": 0.00041812268346664156, "News Article": 0.00014345593565636853, "Organizational Announcement": 0.0001075142562147375, "Nonfiction Writing": 0.00018326189044197386, "Organizational About Page": 0.0001875466413786118, "Personal About Page": 0.00010168979636559675, "Personal Blog": 9.233975197923993e-05, "Product Page": 7.936015694163986e-05, "Spam / Ads": 0.0001478029300732606, "Structured Data": 0.003190067536190533, "Technical Writing": 0.007879245505627283, "Transcript / Interview": 0.00015888076908780648, "Tutorial / How-To Guide": 0.004005498069277396, "User Reviews": 6.012603138049113e-05, "NONE": 0.9363272115627267}, "structured_records": {"false": 0.9665638899802738, "true": 0.033436110019726184}, "procedural_steps": {"false": 0.9726023250781483, "true": 0.02739767492185174}, "worked_solution": {"false": 0.998810169349777, "true": 0.0011898306502230065}, "contextual_dialogue": {"false": 0.9999944891312488, "true": 5.510868751199652e-06}, "content_quality": {"-2": 0.10799598058881811, "-1": 0.3176276884917796, "0": 0.2910460135714041, "1": 0.21525732881100682, "2": 0.06807298853699137}, "noise_severity": {"0": 0.5621339759173067, "1": 0.23144658727745576, "2": 0.09935344735971183, "3": 0.09179377041326432, "4": 0.01527221903226135}, "missing_content_severity": {"0": 0.41880131122318515, "1": 0.4469419029810302, "2": 0.11974600960208442, "3": 0.012097505275546407, "4": 0.002413270918153817}, "factual_density": {"0": 0.006128065734961425, "1": 0.023079564232067005, "2": 0.1522416254950408, "3": 0.40539809497564255, "4": 0.4131526495622882}, "pretraining_utility": {"-2": 0.02623784388737335, "-1": 0.11566422152435518, "0": 0.3561862921689579, "1": 0.42577301616007673, "2": 0.0761386262592368}, "math_density": {"0": 0.986552615031162, "1": 0.011173114977742675, "2": 0.000726700794218425, "3": 0.0005360134259627729, "4": 0.0010115557709141029}, "code_density": {"0": 0.0003210476652874202, "1": 0.000590645997509625, "2": 0.018376648939238766, "3": 0.11772001589349979, "4": 0.8629916415044645}, "reasoning_density": {"0": 0.737174678537052, "1": 0.09097064388244921, "2": 0.11762353529964928, "3": 0.02973774585580539, "4": 0.024493396425044132}, "education_level": {"none": 0.0012091984258015206, "0": 0.00140536867384964, "1": 0.01882756221041198, "2": 0.8293522830149158, "3": 0.14281550707417898, "4": 0.006390080600842147}, "creative_writing_quality": {"not_applicable": 0.9992316502431052, "-2": 9.974953437921355e-05, "-1": 0.0002129575539963222, "0": 0.000365570112827494, "1": 6.339695262309384e-05, "2": 2.6675603068629036e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.2296118182495396e-05, "Art & Design": 3.161090782550985e-05, "Law & Crime": 3.641763965418041e-05, "Education & Careers": 4.542586202945199e-05, "Entertainment & Media": 3.098796993015735e-05, "Fashion & Beauty": 9.798408298547785e-06, "Business, Economics & Finance": 4.834893824973349e-05, "Food & Dining": 1.5005710583954965e-05, "Games & Gaming": 0.0006380377530464971, "Electronics & Hardware": 5.6262482836247476e-05, "Health, Healthcare & Wellness": 1.6092192362985723e-05, "History & Geography": 1.7605617236517284e-05, "Home, Pets & Hobbies": 2.0265100771357093e-05, "Industry & Production": 1.727504166600532e-05, "Language, Literature & Philosophy": 2.3561188469816058e-05, "Politics & Government": 1.1066469424384953e-05, "Religion & Spirituality": 1.9807623432206137e-05, "Science, Mathematics & Engineering": 0.00022246026624358605, "Relationships & Community": 1.4326949498023967e-05, "Software Usage & Applications": 0.0017453273814437792, "Programming & Software Engineering": 0.9966522794629809, "Sports & Fitness": 0.0001005925151370885, "Vehicles & Transportation": 1.1534203732966981e-05, "Travel & Tourism": 2.293352692694207e-05, "Other / Uncovered Subject": 0.00011490808937153844, "Unknown / Insufficient Content": 4.577258066518774e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005289328486294325, "Art & Design": 0.00040999544133205117, "Law & Crime": 0.0029969559075746686, "Education & Careers": 0.0013167511056428095, "Entertainment & Media": 0.0010230369482575048, "Fashion & Beauty": 0.00020237537301801318, "Business, Economics & Finance": 0.0006031628371716857, "Food & Dining": 0.00017285767002388184, "Games & Gaming": 0.004418752606659961, "Electronics & Hardware": 0.00039828437597600445, "Health, Healthcare & Wellness": 0.00023496011364033918, "History & Geography": 0.00019167039745628228, "Home, Pets & Hobbies": 0.00021560422064316993, "Industry & Production": 0.00014021715666675486, "Language, Literature & Philosophy": 0.00019531548038302608, "Politics & Government": 0.00019734066862650307, "Religion & Spirituality": 0.00024715928036338837, "Science, Mathematics & Engineering": 0.0010309933729033484, "Relationships & Community": 0.00016892147579100955, "Software Usage & Applications": 0.020519567597514367, "Programming & Software Engineering": 0.026380205996558787, "Sports & Fitness": 0.0008074702119099397, "Vehicles & Transportation": 8.671765948282316e-05, "Travel & Tourism": 0.0002445983394813879, "Other / Uncovered Subject": 0.0009077592717408147, "NONE": 0.9363603936425521}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10107, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10107, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:17.712974+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "d128f889-e6a5-45e5-afca-24ee82f63e4e", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "983bc5c40b39cfb30235659fbd23bc32f40e362a68c0d0035b3edce14673092c", "source_metadata": {"uuid": "d128f889-e6a5-45e5-afca-24ee82f63e4e", "repo_name": "Rupesh528/ContestBuddy", "relative_path": "src/database/mongodb_client.py", "category": "TOOL", "algo_rel_score": 0.9395707845687866, "quality_score": 6.9438276290893555, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2025-06-26T04:39:28.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "19eb2e081f946ca048ee7cd6547c57eee42d7242", "size_bytes": 7553, "file_timestamp": 1750912895, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "61e15da6a9c2ccae9b991c232e9c766d269f7912"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2025-06-26T04:39:28.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "19eb2e081f946ca048ee7cd6547c57eee42d7242", "size_bytes": 7553, "file_timestamp": 1750912895, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "61e15da6a9c2ccae9b991c232e9c766d269f7912", "url": "https://github.com/Rupesh528/ContestBuddy/blob/61e15da6a9c2ccae9b991c232e9c766d269f7912/src/database/mongodb_client.py"}, "gpt4o_tokens": 1475, "dataset_split": "python", "probability_weighted_scores": {"content_quality": -0.18221634378442625, "noise_severity": 0.7666236693657178, "missing_content_severity": 0.7323795216844535, "factual_density": 3.196367698398229, "pretraining_utility": 0.4099103593794485, "math_density": 0.018280779927724256, "code_density": 3.8424705575743445, "reasoning_density": 0.5134045377493405, "education_level": 2.134119545861477, "creative_writing_quality": -0.38486179157456685}, "score_applicability": {"education_level": 0.9987908015741985, "creative_writing_quality": 0.0007683497568947526}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/python/peps/blob/3432147495ce2851557173450e57f6e1e78e8301/check-peps.py", "id": "python-0-358aba68e28b3270336c0edc075ff001", "text": "#!/usr/bin/env python3\n\n# This file is placed in the public domain or under the\n# CC0-1.0-Universal license, whichever is more permissive.\n\n\"\"\"check-peps: Check PEPs for common mistakes.\n\nUsage: check-peps [-d | --detailed] <PEP files...>\n\nOnly the PEPs specified are checked.\nIf none are specified, all PEPs are checked.\n\nUse \"--detailed\" to show the contents of lines where errors were found.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport datetime as dt\nimport re\nimport sys\nfrom pathlib import Path\n\nTYPE_CHECKING = False\nif TYPE_CHECKING:\n    from collections.abc import Iterable, Iterator, KeysView, Sequence\n    from typing import TypeAlias\n\n    # (line number, warning message)\n    Message: TypeAlias = tuple[int, str]\n    MessageIterator: TypeAlias = Iterator[Message]\n\n\n# get the directory with the PEP sources\nROOT_DIR = Path(__file__).resolve().parent\nPEP_ROOT = ROOT_DIR / \"peps\"\n\n# See PEP 12 for the order\n# Note we retain \"BDFL-Delegate\"\nALL_HEADERS = (\n    \"PEP\",\n    \"Title\",\n    \"Author\",\n    \"Sponsor\",\n    \"BDFL-Delegate\", \"PEP-Delegate\",\n    \"Discussions-To\",\n    \"Status\",\n    \"Type\",\n    \"Topic\",\n    \"Requires\",\n    \"Created\",\n    \"Python-Version\",\n    \"Post-History\",\n    \"Replaces\",\n    \"Superseded-By\",\n    \"Resolution\",\n)\nREQUIRED_HEADERS = frozenset({\"PEP\", \"Title\", \"Author\", \"Status\", \"Type\", \"Created\"})\n\n# See PEP 1 for the full list\nALL_STATUSES = frozenset({\n    \"Accepted\",\n    \"Active\",\n    \"April Fool!\",\n    \"Deferred\",\n    \"Draft\",\n    \"Final\",\n    \"Provisional\",\n    \"Rejected\",\n    \"Superseded\",\n    \"Withdrawn\",\n})\n\n# PEPs that are allowed to link directly to PEPs\nSKIP_DIRECT_PEP_LINK_CHECK = frozenset({\"0009\", \"0287\", \"0676\", \"0684\", \"8001\"})\n\nDEFAULT_FLAGS = re.ASCII | re.IGNORECASE  # Insensitive latin\n\n# any sequence of letters or '-', followed by a single ':' and a space or end of line\nHEADER_PATTERN = re.compile(r\"^([a-z\\-]+):(?: |$)\", DEFAULT_FLAGS)\n# any sequence of unicode letters or legal special characters\nNAME_PATTERN = re.compile(r\"(?:[^\\W\\d_]|[ ',\\-.])+(?: |$)\")\n# any sequence of ASCII letters, digits, or legal special characters\nEMAIL_LOCAL_PART_PATTERN = re.compile(r\"[\\w!#$%&'*+\\-/=?^{|}~.]+\", DEFAULT_FLAGS)\n\nDISCOURSE_THREAD_PATTERN = re.compile(r\"([\\w\\-]+/)?\\d+\", DEFAULT_FLAGS)\nDISCOURSE_POST_PATTERN = re.compile(r\"([\\w\\-]+/)?\\d+(/\\d+)?\", DEFAULT_FLAGS)\n\nMAILMAN_2_PATTERN = re.compile(r\"[\\w\\-]+/\\d{4}-[a-z]+/\\d+\\.html\", DEFAULT_FLAGS)\nMAILMAN_3_THREAD_PATTERN = re.compile(r\"[\\w\\-]+@python\\.org/thread/[a-z0-9]+/?\", DEFAULT_FLAGS)\nMAILMAN_3_MESSAGE_PATTERN = re.compile(r\"[\\w\\-]+@python\\.org/message/[a-z0-9]+/?(#[a-z0-9]+)?\", DEFAULT_FLAGS)\n\n# Controlled by the \"--detailed\" flag\nDETAILED_ERRORS = False\n\n\ndef check(filenames: Sequence[str] = (), /) -> int:\n    \"\"\"The main entry-point.\"\"\"\n    if filenames:\n        filenames = map(Path, filenames)\n    else:\n        filenames = PEP_ROOT.glob(\"pep-????.rst\")\n    if (count := sum(map(check_file, filenames))) > 0:\n        s = \"s\" * (count != 1)\n        print(f\"check-peps failed: {count} error{s}\", file=sys.stderr)\n        return 1\n    return 0\n\n\ndef check_file(filename: Path, /) -> int:\n    filename = filename.resolve()\n    try:\n        content = filename.read_text(encoding=\"utf-8\")\n    except FileNotFoundError:\n        return _output_error(filename, [\"\"], [(0, \"Could not read PEP!\")])\n    else:\n        lines = content.splitlines()\n        return _output_error(filename, lines, check_peps(filename, lines))\n\n\ndef check_peps(filename: Path, lines: Sequence[str], /) -> MessageIterator:\n    yield from check_headers(lines)\n    for line_num, line in enumerate(lines, start=1):\n        if filename.stem.removeprefix(\"pep-\") in SKIP_DIRECT_PEP_LINK_CHECK:\n            continue\n        yield from check_direct_links(line_num, line.lstrip())\n\n\ndef check_headers(lines: Sequence[str], /) -> MessageIterator:\n    yield from _validate_pep_number(next(iter(lines), \"\"))\n\n    found_headers = {}\n    found_header_lines: list[tuple[str, int]] = []\n    line_num = 0\n    for line_num, line in enumerate(lines, start=1):\n        if line.strip() == \"\":\n            headers_end_line_num = line_num\n            break\n        if match := HEADER_PATTERN.match(line):\n            header = match[1]\n            found_header_lines.append((header, line_num))\n            if header in ALL_HEADERS:\n                if header not in found_headers:\n                    found_headers[header] = None\n                else:\n                    yield line_num, f\"Must not have duplicate header: {header} \"\n            else:\n                yield line_num, f\"Must not have invalid header: {header}\"\n    else:\n        headers_end_line_num = line_num\n\n    yield from _validate_required_headers(found_headers.keys())\n\n    shifted_line_nums = [line for _, line in found_header_lines[1:]]\n    for i, (header, line_num) in enumerate(found_header_lines):\n        start = line_num - 1\n        end = headers_end_line_num - 1\n        if i < len(found_header_lines) - 1:\n            end = shifted_line_nums[i] - 1\n        remainder = \"\\n\".join(lines[start:end]).removeprefix(f\"{header}:\")\n        if remainder != \"\":\n            if remainder[0] not in {\" \", \"\\n\"}:\n                yield line_num, f\"Headers must have a space after the colon: {header}\"\n            remainder = remainder.lstrip()\n        yield from _validate_header(header, line_num, remainder)\n\n\ndef _validate_header(header: str, line_num: int, content: str) -> MessageIterator:\n    if header == \"Title\":\n        yield from _validate_title(line_num, content)\n    elif header == \"Author\":\n        yield from _validate_author(line_num, content)\n    elif header == \"Sponsor\":\n        yield from _validate_sponsor(line_num, content)\n    elif header in {\"BDFL-Delegate\", \"PEP-Delegate\"}:\n        yield from _validate_delegate(line_num, content)\n    elif header == \"Discussions-To\":\n        yield from _validate_discussions_to(line_num, content)\n    elif header == \"Status\":\n        yield from _validate_status(line_num, content)\n    elif header == \"Type\":\n        yield from _validate_type(line_num, content)\n    elif header == \"Topic\":\n        yield from _validate_topic(line_num, content)\n    elif header in {\"Requires\", \"Replaces\", \"Superseded-By\"}:\n        yield from _validate_pep_references(line_num, content)\n    elif header == \"Created\":\n        yield from _validate_created(line_num, content)\n    elif header == \"Python-Version\":\n        yield from _validate_python_version(line_num, content)\n    elif header == \"Post-History\":\n        yield from _validate_post_history(line_num, content)\n    elif header == \"Resolution\":\n        yield from _validate_resolution(line_num, content)\n\n\ndef check_direct_links(line_num: int, line: str) -> MessageIterator:\n    \"\"\"Check that PEPs and RFCs aren't linked directly\"\"\"\n\n    line = line.lower()\n    if \"dev/peps/pep-\" in line or \"peps.python.org/pep-\" in line:\n        yield line_num, \"Use the :pep:`NNN` role to refer to PEPs\"\n    if \"rfc-editor.org/rfc/\" in line or \"ietf.org/doc/html/rfc\" in line:\n        yield line_num, \"Use the :rfc:`NNN` role to refer to RFCs\"\n\n\ndef _output_error(filename: Path, lines: Sequence[str], errors: Iterable[Message]) -> int:\n    relative_filename = filename.relative_to(ROOT_DIR)\n    err_count = 0\n    for line_num, msg in errors:\n        err_count += 1\n\n        print(f\"{relative_filename}:{line_num}:  {msg}\")\n        if not DETAILED_ERRORS:\n            continue\n\n        line = lines[line_num - 1]\n        print(\"     |\")\n        print(f\"{line_num: >4} | '{line}'\")\n        print(\"     |\")\n\n    return err_count\n\n\n###########################\n#  PEP Header Validators  #\n###########################\n\n\ndef _validate_required_headers(found_headers: KeysView[str]) -> MessageIterator:\n    \"\"\"PEPs must have all required headers, in the PEP 12 order\"\"\"\n\n    if missing := REQUIRED_HEADERS.difference(found_headers):\n        for missing_header in sorted(missing, key=ALL_HEADERS.index):\n            yield 1, f\"Must have required header: {missing_header}\"\n\n    ordered_headers = sorted(found_headers, key=ALL_HEADERS.index)\n    if list(found_headers) != ordered_headers:\n        order_str = \", \".join(ordered_headers)\n        yield 1, \"Headers must be in PEP 12 order. Correct order: \" + order_str\n\n\ndef _validate_pep_number(line: str) -> MessageIterator:\n    \"\"\"'PEP' header must be a number 1-9999\"\"\"\n\n    if not line.startswith(\"PEP: \"):\n        yield 1, \"PEP must begin with the 'PEP:' header\"\n        return\n\n    pep_number = line.removeprefix(\"PEP: \").lstrip()\n    yield from _pep_num(1, pep_number, \"'PEP:' header\")\n\n\ndef _validate_title(line_num: int, line: str) -> MessageIterator:\n    \"\"\"'Title' must be 1-79 characters\"\"\"\n\n    if len(line) == 0:\n        yield line_num, \"PEP must have a title\"\n    elif len(line) > 79:\n        yield line_num, \"PEP title must be less than 80 characters\"\n\n\ndef _validate_author(line_num: int, body: str) -> MessageIterator:\n    \"\"\"'Author' must be list of 'Name <<EMAIL>>, …'\"\"\"\n\n    lines = body.split(\"\\n\")\n    for offset, line in enumerate(lines):\n        if offset >= 1 and line[:9].isspace():\n            # Checks for:\n            # Author: Alice\n            #             Bob\n            #         ^^^^\n            # Note that len(\"Author: \") == 8\n            yield line_num + offset, \"Author line must not be over-indented\"\n        if offset < len(lines) - 1:\n            if not line.endswith(\",\"):\n                yield line_num + offset, \"Author continuation lines must end with a comma\"\n        for part in line.removesuffix(\",\").split(\", \"):\n            yield from _email(line_num + offset, part, \"Author\")\n\n\ndef _validate_sponsor(line_num: int, line: str) -> MessageIterator:\n    \"\"\"'Sponsor' must have format 'Name <<EMAIL>>'\"\"\"\n\n    yield from _email(line_num, line, \"Sponsor\")\n\n\ndef _validate_delegate(line_num: int, line: str) -> MessageIterator:\n    \"\"\"'Delegate' must have format 'Name <<EMAIL>>'\"\"\"\n\n    if line == \"\":\n        return\n\n    # PEP 451\n    if \", \" in line:\n        for part in line.removesuffix(\",\").split(\", \"):\n            yield from _email(line_num, part, \"Delegate\")\n        return\n\n    yield from _email(line_num, line, \"Delegate\")\n\n\ndef _validate_discussions_to(line_num: int, line: str) -> MessageIterator:\n    \"\"\"'Discussions-To' must be a thread URL\"\"\"\n\n    yield from _thread(line_num, line, \"Discussions-To\", discussions_to=True)\n    if line == \"Pending\":\n        return\n    if line.startswith(\"https://\"):\n        return\n    for suffix in \"@python.org\", \"@googlegroups.com\":\n        if line.endswith(suffix):\n            remainder = line.removesuffix(suffix)\n            if re.fullmatch(r\"[\\w\\-]+\", remainder) is None:\n                yield line_num, \"Discussions-To must be a valid mailing list\"\n            return\n    yield line_num, \"Discussions-To must be a valid thread URL, mailing list, or 'Pending'\"\n\n\ndef _validate_status(line_num: int, line: str) -> MessageIterator:\n    \"\"\"'Status' must be a valid PEP status\"\"\"\n\n    if line not in ALL_STATUSES:\n        yield line_num, \"Status must be a valid PEP status\"\n\n\ndef _validate_type(line_num: int, line: str) -> MessageIterator:\n    \"\"\"'Type' must be a valid PEP type\"\"\"\n\n    if line not in {\"Standards Track\", \"Informational\", \"Process\"}:\n        yield line_num, \"Type must be a valid PEP type\"\n\n\ndef _validate_topic(line_num: int, line: str) -> MessageIterator:\n    \"\"\"'Topic' must be for a valid sub-index\"\"\"\n\n    topics = line.split(\", \")\n    unique_topics = set(topics)\n    if len(topics) > len(unique_topics):\n        yield line_num, \"Topic must not contain duplicates\"\n\n    if unique_topics - {\"Governance\", \"Packaging\", \"Typing\", \"Release\"}:\n        if not all(map(str.istitle, unique_topics)):\n            yield line_num, \"Topic must be properly capitalised (Title Case)\"\n        if unique_topics - {\"governance\", \"packaging\", \"typing\", \"release\"}:\n            yield line_num, \"Topic must be for a valid sub-index\"\n    if sorted(topics) != topics:\n        yield line_num, \"Topic must be sorted lexicographically\"\n\n\ndef _validate_pep_references(line_num: int, line: str) -> MessageIterator:\n    \"\"\"`Requires`/`Replaces`/`Superseded-By` must be 'NNN' PEP IDs\"\"\"\n\n    line = line.removesuffix(\",\").rstrip()\n    if line.count(\", \") != line.count(\",\"):\n        yield line_num, \"PEP references must be separated by comma-spaces (', ')\"\n        return\n\n    references = line.split(\", \")\n    for reference in references:\n        yield from _pep_num(line_num, reference, \"PEP reference\")\n\n\ndef _validate_created(line_num: int, line: str) -> MessageIterator:\n    \"\"\"'Created' must be a 'DD-mmm-YYYY' date\"\"\"\n\n    yield from _date(line_num, line, \"Created\")\n\n\ndef _validate_python_version(line_num: int, line: str) -> MessageIterator:\n    \"\"\"'Python-Version' must be an ``X.Y[.Z]`` version\"\"\"\n\n    versions = line.split(\", \")\n    for version in versions:\n        if version.count(\".\") not in {1, 2}:\n            yield line_num, f\"Python-Version must have two or three segments: {version}\"\n            continue\n\n        try:\n            major, minor, micro = version.split(\".\", 2)\n        except ValueError:\n            major, minor = version.split(\".\", 1)\n            micro = \"\"\n\n        if major not in \"123\":\n            yield line_num, f\"Python-Version major part must be 1, 2, or 3: {version}\"\n        if not _is_digits(minor) and minor != \"x\":\n            yield line_num, f\"Python-Version minor part must be numeric: {version}\"\n        elif minor != \"0\" and minor[0] == \"0\":\n            yield line_num, f\"Python-Version minor part must not have leading zeros: {version}\"\n\n        if micro == \"\":\n            return\n        if minor == \"x\":\n            yield line_num, f\"Python-Version micro part must be empty if minor part is 'x': {version}\"\n        elif micro[0] == \"0\":\n            yield line_num, f\"Python-Version micro part must not have leading zeros: {version}\"\n        elif not _is_digits(micro):\n            yield line_num, f\"Python-Version micro part must be numeric: {version}\"\n\n\ndef _validate_post_history(line_num: int, body: str) -> MessageIterator:\n    \"\"\"'Post-History' must be '`DD-mmm-YYYY <Thread URL>`__, …' or `DD-mmm-YYYY`\"\"\"\n\n    if body == \"\":\n        return\n\n    for offset, line in enumerate(body.removesuffix(\",\").split(\"\\n\"), start=line_num):\n        for post in line.removesuffix(\",\").strip().split(\", \"):\n            prefix, postfix = (post.startswith(\"`\"), post.endswith(\">`__\"))\n            if not prefix and not postfix:\n                yield from _date(offset, post, \"Post-History\")\n            elif prefix and postfix:\n                post_date, post_url = post[1:-4].split(\" <\")\n                yield from _date(offset, post_date, \"Post-History\")\n                yield from _thread(offset, post_url, \"Post-History\")\n            else:\n                yield offset, \"post line must be a date or both start with “`” and end with “>`__”\"\n\n\ndef _validate_resolution(line_num: int, line: str) -> MessageIterator:\n    \"\"\"'Resolution' must be a direct thread/message URL or a link with a date.\"\"\"\n\n    prefix, postfix = (line.startswith(\"`\"), line.endswith(\">`__\"))\n    if not prefix and not postfix:\n        yield from _thread(line_num, line, \"Resolution\", allow_message=True)\n    elif prefix and postfix:\n        post_date, post_url = line[1:-4].split(\" <\")\n        yield from _date(line_num, post_date, \"Resolution\")\n        yield from _thread(line_num, post_url, \"Resolution\", allow_message=True)\n    else:\n        yield line_num, \"Resolution line must be a link or both start with “`” and end with “>`__”\"\n\n\n########################\n#  Validation Helpers  #\n########################\n\n\ndef _pep_num(line_num: int, pep_number: str, prefix: str) -> MessageIterator:\n    if pep_number == \"\":\n        yield line_num, f\"{prefix} must not be blank: {pep_number!r}\"\n        return\n    if pep_number.startswith(\"0\") and pep_number != \"0\":\n        yield line_num, f\"{prefix} must not contain leading zeros: {pep_number!r}\"\n    if not _is_digits(pep_number):\n        yield line_num, f\"{prefix} must be numeric: {pep_number!r}\"\n    elif not 0 <= int(pep_number) <= 9999:\n        yield line_num, f\"{prefix} must be between 0 and 9999: {pep_number!r}\"\n\n\ndef _is_digits(string: str) -> bool:\n    \"\"\"Match a string of ASCII digits ([0-9]+).\"\"\"\n    return string.isascii() and string.isdigit()\n\n\ndef _email(line_num: int, author_email: str, prefix: str) -> MessageIterator:\n    author_email = author_email.strip()\n\n    if author_email.count(\"<\") > 1:\n        msg = f\"{prefix} entries must not contain multiple '<': {author_email!r}\"\n        yield line_num, msg\n    if author_email.count(\">\") > 1:\n        msg = f\"{prefix} entries must not contain multiple '>': {author_email!r}\"\n        yield line_num, msg\n    if author_email.count(\"@\") > 1:\n        msg = f\"{prefix} entries must not contain multiple '@': {author_email!r}\"\n        yield line_num, msg\n\n    author = author_email.split(\"<\", 1)[0].rstrip()\n    if NAME_PATTERN.fullmatch(author) is None:\n        msg = f\"{prefix} entries must begin with a valid 'Name': {author_email!r}\"\n        yield line_num, msg\n        return\n\n    email_text = author_email.removeprefix(author)\n    if not email_text:\n        # Does not have the optional email part\n        return\n\n    if not email_text.startswith(\" <\") or not email_text.endswith(\">\"):\n        msg = f\"{prefix} entries must be formatted as 'Name <<EMAIL>>': {author_email!r}\"\n        yield line_num, msg\n    email_text = email_text.removeprefix(\" <\").removesuffix(\">\")\n\n    if \"@\" in email_text:\n        local, domain = email_text.rsplit(\"@\", 1)\n    elif \" at \" in email_text:\n        local, domain = email_text.rsplit(\" at \", 1)\n    else:\n        yield line_num, f\"{prefix} entries must contain a valid email address: {author_email!r}\"\n        return\n    if EMAIL_LOCAL_PART_PATTERN.fullmatch(local) is None or _invalid_domain(domain):\n        yield line_num, f\"{prefix} entries must contain a valid email address: {author_email!r}\"\n\n\ndef _invalid_domain(domain_part: str) -> bool:\n    *labels, root = domain_part.split(\".\")\n    for label in labels:\n        if not label.replace(\"-\", \"\").isalnum():\n            return True\n    return not root.isalnum() or not root.isascii()\n\n\ndef _thread(line_num: int, url: str, prefix: str, *, allow_message: bool = False, discussions_to: bool = False) -> MessageIterator:\n    if allow_message and discussions_to:\n        msg = \"allow_message and discussions_to cannot both be True\"\n        raise ValueError(msg)\n\n    msg = f\"{prefix} must be a valid thread URL\"\n\n    if not url.startswith(\"https://\"):\n        if not discussions_to:\n            yield line_num, msg\n        return\n\n    if url.startswith(\"https://discuss.python.org/t/\"):\n        remainder = url.removeprefix(\"https://discuss.python.org/t/\").removesuffix(\"/\")\n\n        # Discussions-To links must be the thread itself, not a post\n        if discussions_to:\n            # The equivalent pattern is similar to '([\\w\\-]+/)?\\d+',\n            # but the topic name must contain a non-numeric character\n\n            # We use ``str.rpartition`` as the topic name is optional\n            topic_name, _, topic_id = remainder.rpartition(\"/\")\n            if topic_name == '' and _is_digits(topic_id):\n                return\n            topic_name = topic_name.replace(\"-\", \"0\").replace(\"_\", \"0\")\n            # the topic name must not be entirely numeric\n            valid_topic_name = not _is_digits(topic_name) and topic_name.isalnum()\n            if valid_topic_name and _is_digits(topic_id):\n                return\n        else:\n            # The equivalent pattern is similar to '([\\w\\-]+/)?\\d+(/\\d+)?',\n            # but the topic name must contain a non-numeric character\n            if remainder.count(\"/\") == 2:\n                # When there are three parts, the URL must be \"topic-name/topic-id/post-id\".\n                topic_name, topic_id, post_id = remainder.rsplit(\"/\", 2)\n                topic_name = topic_name.replace(\"-\", \"0\").replace(\"_\", \"0\")\n                valid_topic_name = not _is_digits(topic_name) and topic_name.isalnum()\n                if valid_topic_name and _is_digits(topic_id) and _is_digits(post_id):\n                    # the topic name must not be entirely numeric\n                    return\n            elif remainder.count(\"/\") == 1:\n                # When there are only two parts, there's an ambiguity between\n                # \"topic-name/topic-id\" and \"topic-id/post-id\".\n                # We disambiguate by checking if the LHS is a valid name and\n                # the RHS is a valid topic ID (for the former),\n                # and then if both the LHS and RHS are valid IDs (for the latter).\n                left, right = remainder.rsplit(\"/\")\n                left = left.replace(\"-\", \"0\").replace(\"_\", \"0\")\n                # the topic name must not be entirely numeric\n                left_is_name = not _is_digits(left) and left.isalnum()\n                if left_is_name and _is_digits(right):\n                    return\n                elif _is_digits(left) and _is_digits(right):\n                    return\n            else:\n                # When there's only one part, it must be a valid topic ID.\n                if _is_digits(remainder):\n                    return\n\n    if url.startswith(\"https://mail.python.org/pipermail/\"):\n        remainder = url.removeprefix(\"https://mail.python.org/pipermail/\")\n        if MAILMAN_2_PATTERN.fullmatch(remainder) is not None:\n            return\n\n    if url.startswith(\"https://mail.python.org/archives/list/\"):\n        remainder = url.removeprefix(\"https://mail.python.org/archives/list/\")\n        if allow_message and MAILMAN_3_MESSAGE_PATTERN.fullmatch(remainder) is not None:\n            return\n        if MAILMAN_3_THREAD_PATTERN.fullmatch(remainder) is not None:\n            return\n\n    yield line_num, msg\n\n\ndef _date(line_num: int, date_str: str, prefix: str) -> MessageIterator:\n    try:\n        parsed_date = dt.datetime.strptime(date_str, \"%d-%b-%Y\")\n    except ValueError:\n        yield line_num, f\"{prefix} must be a 'DD-mmm-YYYY' date: {date_str!r}\"\n        return\n    else:\n        if date_str[1] == \"-\":  # Date must be zero-padded\n            yield line_num, f\"{prefix} must be a 'DD-mmm-YYYY' date: {date_str!r}\"\n            return\n\n    if parsed_date.year < 1990:\n        yield line_num, f\"{prefix} must not be before Python was invented: {date_str!r}\"\n    if parsed_date > (dt.datetime.now() + dt.timedelta(days=14)):\n        yield line_num, f\"{prefix} must not be in the future: {date_str!r}\"\n\n\nif __name__ == \"__main__\":\n    if {\"-h\", \"--help\", \"-?\"}.intersection(sys.argv[1:]):\n        print(__doc__, file=sys.stderr)\n        raise SystemExit(0)\n\n    files = {}\n    for arg in sys.argv[1:]:\n        if not arg.startswith(\"-\"):\n            files[arg] = None\n        elif arg in {\"-d\", \"--detailed\"}:\n            DETAILED_ERRORS = True\n        else:\n            print(f\"Unknown option: {arg!r}\", file=sys.stderr)\n            raise SystemExit(1)\n    raise SystemExit(check(files))\n", "text_preview": "#!/usr/bin/env python3 # This file is placed in the public domain or under the # CC0-1.0-Universal license, whichever is more permissive. \"\"\"check-peps: Check PEPs for common mistakes. Usage: check-peps [-d | --detailed] <PEP files...> Only", "input_sha256": "0a2aaea8aa5a436a352b7f310a2bf3bc1e63e7687bd69322d4ac48363f9d7523", "text_sha256": "358aba68e28b3270336c0edc075ff001a761bd3994eae1446410ca471470f291", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12026, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.326389416731886, "noise_severity": 0.047136666684864624, "missing_content_severity": 0.004423197804243306, "factual_density": 3.283062174730315, "pretraining_utility": 0.7021318662183913, "math_density": 0.04448543161150337, "code_density": 3.809231852927643, "reasoning_density": 2.0095855046235283, "education_level": 2.304510900022574, "creative_writing_quality": -0.03206656198734202}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.10198100911586405, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9954492150219421, "secondary": 0.5078911572139565, "tertiary": 0.6143061232479018, "structured_records": 0.1614440142588242, "procedural_steps": 0.045152456730853455, "worked_solution": 0.005662044796578246, "contextual_dialogue": 7.95069856107648e-06, "content_quality": 0.5539448734133524, "noise_severity": 0.9607194444292795, "missing_content_severity": 0.9963140018297972, "factual_density": 0.4025518122752625, "pretraining_utility": 0.5501273115896765, "math_density": 0.9629288069904138, "code_density": 0.8410265441063692, "reasoning_density": 0.0, "education_level": 0.6389485452261444, "creative_writing_quality": 0.9985484805928465, "primary_topic": 0.9937793877294088, "secondary_topic": 0.9313814447397487}, "probability_margin": {"primary": 0.9933996039374218, "secondary": 0.12495249721128143, "tertiary": 0.3109487159062432, "education_level": 0.46899064929305073, "creative_writing_quality": 0.9981542440578356, "primary_topic": 0.9906449655706614, "secondary_topic": 0.9015838741025497}, "raw_probabilities": {"primary": {"Academic Writing": 3.2342959164028946e-05, "Content Listing": 2.0839450713120805e-05, "Creative Writing": 1.4065589942193253e-05, "Customer Support Page": 1.4387183245921436e-05, "Q&A Forum": 9.064247821934565e-06, "Discussion Thread": 8.11001230250251e-06, "Comment Section": 1.1768283998933004e-05, "Missing Content": 1.889392906651311e-05, "Knowledge Article": 9.47050845162488e-05, "Legal Notices": 0.0005836639731471042, "News Article": 5.746413696168804e-06, "Organizational Announcement": 2.073488824532207e-05, "Nonfiction Writing": 1.3252040436810661e-05, "Organizational About Page": 1.4213561302416097e-05, "Personal About Page": 8.111133855060026e-06, "Personal Blog": 6.86770564161302e-06, "Product Page": 6.770013178042177e-06, "Spam / Ads": 9.003509634983891e-06, "Structured Data": 0.0005047752371855892, "Technical Writing": 0.9956388310626945, "Transcript / Interview": 1.2406561189102598e-05, "Tutorial / How-To Guide": 0.0007058590758036609, "User Reviews": 6.360957945481816e-06, "Other / Unknown": 0.002239227125272692}, "secondary": {"Academic Writing": 0.0007853256192568174, "Content Listing": 0.000897453527664522, "Creative Writing": 0.0005670360862365594, "Customer Support Page": 0.0007288498152746959, "Q&A Forum": 0.0003365190010918072, "Discussion Thread": 0.0003571609574725173, "Comment Section": 0.0005479691225581167, "Missing Content": 0.0008766356137812826, "Knowledge Article": 0.0018453296958616183, "Legal Notices": 0.40344319511876026, "News Article": 0.0005888543750290767, "Organizational Announcement": 0.000707538618421276, "Nonfiction Writing": 0.0005826212610118285, "Organizational About Page": 0.0006588287072300905, "Personal About Page": 0.00038258824565633816, "Personal Blog": 0.00038303263495776676, "Product Page": 0.0004085956947038873, "Spam / Ads": 0.0004896333080824438, "Structured Data": 0.004082650141830167, "Technical Writing": 0.04128961559883844, "Transcript / Interview": 0.0006987059193258702, "Tutorial / How-To Guide": 0.010668654773582968, "User Reviews": 0.0002775138333297541, "NONE": 0.5283956923300417}, "tertiary": {"Academic Writing": 0.0007022139503957602, "Content Listing": 0.000794154361799103, "Creative Writing": 0.0005868098510883852, "Customer Support Page": 0.0005938410846532511, "Q&A Forum": 0.0002881298336106147, "Discussion Thread": 0.00027246642195850803, "Comment Section": 0.00048365990789920465, "Missing Content": 0.0005725280353664253, "Knowledge Article": 0.0014650875462752168, "Legal Notices": 0.3194279855396627, "News Article": 0.0004599279964904481, "Organizational Announcement": 0.00053651041018906, "Nonfiction Writing": 0.0004827955991508428, "Organizational About Page": 0.0005536324126397964, "Personal About Page": 0.000306394020737826, "Personal Blog": 0.00028147886775517364, "Product Page": 0.00038140244223286055, "Spam / Ads": 0.00035689911043551917, "Structured Data": 0.009079995234254836, "Technical Writing": 0.019561401877518883, "Transcript / Interview": 0.0005136431680180532, "Tutorial / How-To Guide": 0.011717772237496479, "User Reviews": 0.0002045686444651895, "NONE": 0.6303767014459059}, "structured_records": {"false": 0.8385559857411757, "true": 0.1614440142588242}, "procedural_steps": {"false": 0.9548475432691466, "true": 0.045152456730853455}, "worked_solution": {"false": 0.9943379552034217, "true": 0.005662044796578246}, "contextual_dialogue": {"false": 0.9999920493014389, "true": 7.95069856107648e-06}, "content_quality": {"-2": 0.002502110466822661, "-1": 0.006219760554893027, "0": 0.0844925150757916, "1": 0.4759578295845613, "2": 0.43082778431793145}, "noise_severity": {"0": 0.9570583829347308, "1": 0.04026066507674421, "2": 0.0016381279390428626, "3": 0.0005715504678933673, "4": 0.0004712735815886457}, "missing_content_severity": {"0": 0.9974610985301524, "1": 0.0013214633515538068, "2": 0.0007068344179814082, "3": 0.0003543491845234786, "4": 0.00015625451578906178}, "factual_density": {"0": 0.004033753478277069, "1": 0.014790289012148794, "2": 0.12970704160964597, "3": 0.39701786110083837, "4": 0.4544510547990899}, "pretraining_utility": {"-2": 0.006720529086134815, "-1": 0.047233742451102394, "0": 0.3042286077763892, "1": 0.5208275745309837, "2": 0.12098954615538973}, "math_density": {"0": 0.9657034431843792, "1": 0.0290241014072692, "2": 0.002448742788193393, "3": 0.0007310058527863744, "4": 0.0020927067673720662}, "code_density": {"0": 0.0003453840603320768, "1": 0.0006140084751974257, "2": 0.012706430790937587, "3": 0.16213172382356117, "4": 0.8242024528499716}, "reasoning_density": {"0": 0.24098046437438667, "1": 0.1440778148193393, "2": 0.18976511577989078, "3": 0.214728961861126, "4": 0.2104476431652573}, "education_level": {"none": 0.0011122028064377838, "0": 0.0018764834933354718, "1": 0.019238908165498326, "2": 0.6991237876884536, "3": 0.2301331383954029, "4": 0.04851547945087205}, "creative_writing_quality": {"not_applicable": 0.9987904004940388, "-2": 0.00010307508278510425, "-1": 0.00018885478888851364, "0": 0.0006361564362031678, "1": 0.0002068091392475203, "2": 7.470405883672214e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.492096236903932e-05, "Art & Design": 4.934058771986504e-05, "Law & Crime": 0.0003660357466657927, "Education & Careers": 4.414511683650079e-05, "Entertainment & Media": 2.840312066106694e-05, "Fashion & Beauty": 1.935558101753735e-05, "Business, Economics & Finance": 4.1039628666199e-05, "Food & Dining": 1.9949292553890142e-05, "Games & Gaming": 4.030555857943117e-05, "Electronics & Hardware": 5.708824096652789e-05, "Health, Healthcare & Wellness": 2.0472706629937523e-05, "History & Geography": 3.258920790978267e-05, "Home, Pets & Hobbies": 1.9622867452290116e-05, "Industry & Production": 2.7103571432923262e-05, "Language, Literature & Philosophy": 0.0003021121538793122, "Politics & Government": 0.00010092936806943376, "Religion & Spirituality": 3.124219719547613e-05, "Science, Mathematics & Engineering": 0.00016900876520929594, "Relationships & Community": 3.416555959390633e-05, "Software Usage & Applications": 0.0010059146191014753, "Programming & Software Engineering": 0.9940186420475084, "Sports & Fitness": 3.6515101822162727e-05, "Vehicles & Transportation": 1.8124256595806643e-05, "Travel & Tourism": 5.003446923008275e-05, "Other / Uncovered Subject": 0.0033736764768470353, "Unknown / Insufficient Content": 5.926279548668667e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00047732200791694555, "Art & Design": 0.0003534572218288508, "Law & Crime": 0.010672537667212741, "Education & Careers": 0.0003443984108798537, "Entertainment & Media": 0.0003615220592827046, "Fashion & Beauty": 0.00018011581793569287, "Business, Economics & Finance": 0.0004733794215269519, "Food & Dining": 0.00015260136586215436, "Games & Gaming": 0.00023597029199568564, "Electronics & Hardware": 0.0002645522016766341, "Health, Healthcare & Wellness": 0.00019761111529092198, "History & Geography": 0.00021665098428804032, "Home, Pets & Hobbies": 0.0001533105854334739, "Industry & Production": 0.00015784190148976127, "Language, Literature & Philosophy": 0.0016104257723888662, "Politics & Government": 0.0005138823150215943, "Religion & Spirituality": 0.00025842237781645104, "Science, Mathematics & Engineering": 0.0008479770236709271, "Relationships & Community": 0.00018999218704271635, "Software Usage & Applications": 0.010439695152502992, "Programming & Software Engineering": 0.03243674583951637, "Sports & Fitness": 0.00016058064635978447, "Vehicles & Transportation": 9.755776966868426e-05, "Travel & Tourism": 0.00030388270924683504, "Other / Uncovered Subject": 0.004878947212078329, "NONE": 0.9340206199420661}}, "probabilities": {"primary": {"Academic Writing": 3.234295916402895e-05, "Content Listing": 2.083945071312081e-05, "Creative Writing": 1.4065589942193255e-05, "Customer Support Page": 1.4387183245921437e-05, "Q&A Forum": 9.064247821934567e-06, "Discussion Thread": 8.110012302502511e-06, "Comment Section": 1.1768283998933006e-05, "Missing Content": 1.8893929066513115e-05, "Knowledge Article": 9.470508451624881e-05, "Legal Notices": 0.0005836639731471043, "News Article": 5.746413696168805e-06, "Organizational Announcement": 2.0734888245322074e-05, "Nonfiction Writing": 1.3252040436810663e-05, "Organizational About Page": 1.42135613024161e-05, "Personal About Page": 8.111133855060027e-06, "Personal Blog": 6.8677056416130205e-06, "Product Page": 6.770013178042178e-06, "Spam / Ads": 9.003509634983893e-06, "Structured Data": 0.0005047752371855893, "Technical Writing": 0.9956388310626946, "Transcript / Interview": 1.24065611891026e-05, "Tutorial / How-To Guide": 0.000705859075803661, "User Reviews": 6.360957945481817e-06, "Other / Unknown": 0.0022392271252726923}, "secondary": {"Academic Writing": 0.0007853256192568176, "Content Listing": 0.0008974535276645222, "Creative Writing": 0.0005670360862365595, "Customer Support Page": 0.000728849815274696, "Q&A Forum": 0.0003365190010918073, "Discussion Thread": 0.00035716095747251737, "Comment Section": 0.0005479691225581168, "Missing Content": 0.0008766356137812828, "Knowledge Article": 0.0018453296958616187, "Legal Notices": 0.40344319511876037, "News Article": 0.0005888543750290768, "Organizational Announcement": 0.0007075386184212761, "Nonfiction Writing": 0.0005826212610118287, "Organizational About Page": 0.0006588287072300907, "Personal About Page": 0.00038258824565633827, "Personal Blog": 0.00038303263495776687, "Product Page": 0.0004085956947038874, "Spam / Ads": 0.000489633308082444, "Structured Data": 0.004082650141830168, "Technical Writing": 0.041289615598838446, "Transcript / Interview": 0.0006987059193258703, "Tutorial / How-To Guide": 0.01066865477358297, "User Reviews": 0.00027751383332975417, "NONE": 0.5283956923300418}, "tertiary": {"Academic Writing": 0.0007022139503957602, "Content Listing": 0.000794154361799103, "Creative Writing": 0.0005868098510883852, "Customer Support Page": 0.0005938410846532511, "Q&A Forum": 0.0002881298336106147, "Discussion Thread": 0.00027246642195850803, "Comment Section": 0.00048365990789920465, "Missing Content": 0.0005725280353664253, "Knowledge Article": 0.0014650875462752168, "Legal Notices": 0.3194279855396627, "News Article": 0.0004599279964904481, "Organizational Announcement": 0.00053651041018906, "Nonfiction Writing": 0.0004827955991508428, "Organizational About Page": 0.0005536324126397964, "Personal About Page": 0.000306394020737826, "Personal Blog": 0.00028147886775517364, "Product Page": 0.00038140244223286055, "Spam / Ads": 0.00035689911043551917, "Structured Data": 0.009079995234254836, "Technical Writing": 0.019561401877518883, "Transcript / Interview": 0.0005136431680180532, "Tutorial / How-To Guide": 0.011717772237496479, "User Reviews": 0.0002045686444651895, "NONE": 0.6303767014459059}, "structured_records": {"false": 0.8385559857411757, "true": 0.1614440142588242}, "procedural_steps": {"false": 0.9548475432691466, "true": 0.045152456730853455}, "worked_solution": {"false": 0.9943379552034217, "true": 0.005662044796578246}, "contextual_dialogue": {"false": 0.9999920493014389, "true": 7.95069856107648e-06}, "content_quality": {"-2": 0.002502110466822661, "-1": 0.006219760554893027, "0": 0.0844925150757916, "1": 0.4759578295845613, "2": 0.43082778431793145}, "noise_severity": {"0": 0.9570583829347309, "1": 0.040260665076744216, "2": 0.0016381279390428629, "3": 0.0005715504678933674, "4": 0.00047127358158864576}, "missing_content_severity": {"0": 0.9974610985301522, "1": 0.0013214633515538066, "2": 0.0007068344179814081, "3": 0.00035434918452347853, "4": 0.00015625451578906176}, "factual_density": {"0": 0.004033753478277068, "1": 0.01479028901214879, "2": 0.12970704160964594, "3": 0.39701786110083825, "4": 0.4544510547990898}, "pretraining_utility": {"-2": 0.006720529086134816, "-1": 0.0472337424511024, "0": 0.30422860777638927, "1": 0.5208275745309838, "2": 0.12098954615538975}, "math_density": {"0": 0.965703443184379, "1": 0.029024101407269194, "2": 0.0024487427881933928, "3": 0.0007310058527863742, "4": 0.002092706767372066}, "code_density": {"0": 0.00034538406033207686, "1": 0.0006140084751974258, "2": 0.01270643079093759, "3": 0.1621317238235612, "4": 0.8242024528499717}, "reasoning_density": {"0": 0.24098046437438667, "1": 0.1440778148193393, "2": 0.18976511577989078, "3": 0.214728961861126, "4": 0.2104476431652573}, "education_level": {"none": 0.0011122028064377836, "0": 0.0018764834933354714, "1": 0.019238908165498322, "2": 0.6991237876884535, "3": 0.23013313839540284, "4": 0.048515479450872034}, "creative_writing_quality": {"not_applicable": 0.998790400494039, "-2": 0.00010307508278510428, "-1": 0.0001888547888885137, "0": 0.0006361564362031679, "1": 0.00020680913924752035, "2": 7.470405883672215e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.492096236903933e-05, "Art & Design": 4.934058771986505e-05, "Law & Crime": 0.00036603574666579274, "Education & Careers": 4.41451168365008e-05, "Entertainment & Media": 2.8403120661066942e-05, "Fashion & Beauty": 1.9355581017537352e-05, "Business, Economics & Finance": 4.1039628666199005e-05, "Food & Dining": 1.9949292553890145e-05, "Games & Gaming": 4.030555857943118e-05, "Electronics & Hardware": 5.70882409665279e-05, "Health, Healthcare & Wellness": 2.0472706629937526e-05, "History & Geography": 3.2589207909782675e-05, "Home, Pets & Hobbies": 1.962286745229012e-05, "Industry & Production": 2.7103571432923266e-05, "Language, Literature & Philosophy": 0.00030211215387931223, "Politics & Government": 0.00010092936806943377, "Religion & Spirituality": 3.124219719547614e-05, "Science, Mathematics & Engineering": 0.00016900876520929597, "Relationships & Community": 3.416555959390634e-05, "Software Usage & Applications": 0.0010059146191014755, "Programming & Software Engineering": 0.9940186420475086, "Sports & Fitness": 3.651510182216273e-05, "Vehicles & Transportation": 1.8124256595806646e-05, "Travel & Tourism": 5.0034469230082756e-05, "Other / Uncovered Subject": 0.0033736764768470357, "Unknown / Insufficient Content": 5.926279548668668e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00047732200791694555, "Art & Design": 0.0003534572218288508, "Law & Crime": 0.010672537667212741, "Education & Careers": 0.0003443984108798537, "Entertainment & Media": 0.0003615220592827046, "Fashion & Beauty": 0.00018011581793569287, "Business, Economics & Finance": 0.0004733794215269519, "Food & Dining": 0.00015260136586215436, "Games & Gaming": 0.00023597029199568564, "Electronics & Hardware": 0.0002645522016766341, "Health, Healthcare & Wellness": 0.00019761111529092198, "History & Geography": 0.00021665098428804032, "Home, Pets & Hobbies": 0.0001533105854334739, "Industry & Production": 0.00015784190148976127, "Language, Literature & Philosophy": 0.0016104257723888662, "Politics & Government": 0.0005138823150215943, "Religion & Spirituality": 0.00025842237781645104, "Science, Mathematics & Engineering": 0.0008479770236709271, "Relationships & Community": 0.00018999218704271635, "Software Usage & Applications": 0.010439695152502992, "Programming & Software Engineering": 0.03243674583951637, "Sports & Fitness": 0.00016058064635978447, "Vehicles & Transportation": 9.755776966868426e-05, "Travel & Tourism": 0.00030388270924683504, "Other / Uncovered Subject": 0.004878947212078329, "NONE": 0.9340206199420661}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 14609, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 14609, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:19.981278+00:00", "scoring_key_slot": "primary", "dataset_uid": "07aa484a-e6a5-40f3-bb62-5d5fd85ddeea", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "2477e849f5e0cd4a577a08dbd449c9ac42fdaa29261049388c2b3d7fe2516a6e", "source_metadata": {"uuid": "07aa484a-e6a5-40f3-bb62-5d5fd85ddeea", "repo_name": "python/peps", "relative_path": "check-peps.py", "category": "TOOL", "algo_rel_score": 0.9358665347099304, "quality_score": 7.558267116546631, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 11086, "repo_created_at": "2013-10-08T13:27:12.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 4671, "forks": 1624, "issues": 21, "pull_requests": 31}, "file_meta": {"content_id": "fdf8c0585cec0be044ab7d61ae5255a3aa0e8cf2", "size_bytes": 23001, "file_timestamp": 1752697938, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "3432147495ce2851557173450e57f6e1e78e8301"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 11086, "repo_created_at": "2013-10-08T13:27:12.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 4671, "forks": 1624, "issues": 21, "pull_requests": 31}, "file_meta": {"content_id": "fdf8c0585cec0be044ab7d61ae5255a3aa0e8cf2", "size_bytes": 23001, "file_timestamp": 1752697938, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "3432147495ce2851557173450e57f6e1e78e8301", "url": "https://github.com/python/peps/blob/3432147495ce2851557173450e57f6e1e78e8301/check-peps.py"}, "gpt4o_tokens": 5727, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.326389416731886, "noise_severity": 0.047136666684864624, "missing_content_severity": 0.004423197804243306, "factual_density": 3.2830621747303153, "pretraining_utility": 0.7021318662183913, "math_density": 0.04448543161150337, "code_density": 3.809231852927643, "reasoning_density": 2.0095855046235283, "education_level": 2.304510900022574, "creative_writing_quality": -0.03206656198734202}, "score_applicability": {"education_level": 0.9988877971935621, "creative_writing_quality": 0.0012095995059610283}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/maxchanhi/music-theory-app/blob/9b5325d0263bb798006ab558c677fa3b1a676561/inversion/inversion_gen.py", "id": "python-0-029f93a097ae46f54dfa709d6c4eb1e4", "text": "from inversion.notation import easymode,tonal_triad,roman_numerial,inversion_type\nfrom inversion.grand_staff_gen import lilypond_generation_grand_staff\nimport random, subprocess \nfrom PIL import Image\ndef simple_triad(clef=\"grand\"):\n    if clef == \"grand staff\":\n        clef = \"grand\"\n    \n    key = random.choice(easymode)\n    triad_dict = tonal_triad(key)\n    triad_list = []\n    value_list = []\n    for triad_key, value in triad_dict.items():\n        triad_list.append(triad_key)\n        value_list.append(value)\n\n    picked_triad = random.choice(triad_list)\n    triad_notes = triad_dict[picked_triad]\n    exception_pitch=[\"a\",\"b\"]\n    inversion_keys = [\"<KEY>\"]\n    inversion = random.choice(inversion_keys)\n    if inversion == \"a\": #['fs', 'a', 'c']\n        if triad_notes[0][0] in exception_pitch:\n            triad_notes[1]=triad_notes[1]+\"'\"\n            triad_notes[2]=triad_notes[2]+\"'\"\n        elif triad_notes[1][0] in exception_pitch: #c e g\n            triad_notes[2]=triad_notes[2]+\"'\"\n    elif inversion == \"b\": \n        triad_notes[0], triad_notes[1],triad_notes[2] = triad_notes[1], triad_notes[2], triad_notes[0]\n        if triad_notes[0][0] not in exception_pitch:\n            if triad_notes[2][0] not in exception_pitch:\n                triad_notes[2]=triad_notes[2]+\"'\"\n            elif triad_notes[2][0] in exception_pitch:\n                pass\n        elif triad_notes[0][0] in exception_pitch:\n            triad_notes[1]=triad_notes[1]+\"'\"\n            triad_notes[2]=triad_notes[2]+\"'\"\n    elif inversion == \"c\":\n        triad_notes[0], triad_notes[1], triad_notes[2] = triad_notes[2], triad_notes[0],triad_notes[1]\n        if triad_notes[0][0] not in exception_pitch:\n            if triad_notes[2][0] in exception_pitch:\n                pass\n            elif triad_notes[2][0] not in exception_pitch:# e a c\n                triad_notes[2]=triad_notes[2]+\"'\"\n                if triad_notes[1][0] not in exception_pitch:\n                    triad_notes[1]=triad_notes[1]+\"'\"\n        elif triad_notes[0][0] in exception_pitch: #b e g\n            if triad_notes[2][0] not in exception_pitch:\n                triad_notes[2]=triad_notes[2]+\"'\"\n            if triad_notes[1][0] not in exception_pitch:\n                triad_notes[1]=triad_notes[1]+\"'\"\n    print(triad_notes)\n    # first note is always the bass and below middle c, c'\n    adjust_notes=[]\n    if clef ==\"treble\":\n        for note in triad_notes:\n            adjust_notes.append(note+\"'\")\n    elif clef == \"bass\":\n        if triad_notes[0][0] in exception_pitch:\n            triad_notes[0]=triad_notes[0]+\",\"\n            triad_notes[1]=triad_notes[1][:-1]\n            triad_notes[2]=triad_notes[2][:-1]\n            print(\"bass\",triad_notes)\n        adjust_notes=triad_notes\n    elif clef == \"grand\":\n        high_note = random.choice(triad_notes)\n        triad_notes.append(high_note+\"'\")\n        adjust_notes=adjust_notes+triad_notes\n        if adjust_notes[2] not in exception_pitch and \"'\" not in adjust_notes[2]:\n            adjust_notes[2]=adjust_notes[2]+\"'\"\n    print(adjust_notes)\n    \n    question_data = {\"clef\":clef,\"key_sign\": key, \"triad\": picked_triad,\n                     \"inversion_type\": inversion, \"notes\": adjust_notes}\n    return question_data\n\ndef chord(chord_notes=[\"b'\", \"ds'\", 'fs']):\n    chord_str = \"<\" +\" \".join(chord_notes) + \">1\"\n    return chord_str\n\ndef lilypond_generation(accompany,clef):\n    str_accompany = chord(accompany)\n    lilypond_score = f\"\"\"\n\\\\version \"2.22.0\"  \n\\\\header {{\n  tagline = \"\" \\\\language \"english\"\n}}\n\n#(set-global-staff-size 26)\n\\\\score {{  \n {{\\\\clef {clef} \\\\omit Staff.TimeSignature\n      {str_accompany}\n      \\\\bar \"|\"\n    }}\n}}\n\"\"\"\n\n    with open('score.ly', 'w') as f:\n        f.write(lilypond_score)\n\n    # Generate PNG image and MIDI file\n    subprocess.run(['lilypond', '-dpreview', '-dbackend=eps', '--png', '-dresolution=300', \n                    '--output=inversion/cropped_score_inversion', 'score.ly'],\n                   check=True)\n\ndef main_generation(clef=\"grand\"):\n    data = simple_triad(clef)\n    triad_notes = data[\"notes\"]\n    clef= data[\"clef\"]\n    if clef == \"bass\"  or clef ==\"treble\":\n        lilypond_generation(triad_notes,clef= data[\"clef\"])\n    else:\n        lilypond_generation_grand_staff(triad_notes)\n    print(data[\"inversion_type\"], triad_notes)\n    return data\n\n\n", "text_preview": "from inversion.notation import easymode,tonal_triad,roman_numerial,inversion_type from inversion.grand_staff_gen import lilypond_generation_grand_staff import random, subprocess from PIL import Image def simple_triad(clef=\"grand\"): if clef ", "input_sha256": "4db4092c832dbadc40e7c03e488edadc021f9775946cb87cd6fd7f127ff0ab12", "text_sha256": "029f93a097ae46f54dfa709d6c4eb1e4389404db11e8fb5e0a9b85a76769ea2a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12031, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Entertainment & Media", "sub_topic": null}}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.16712705805199407, "noise_severity": 0.09839556390659412, "missing_content_severity": 0.18194255914967333, "factual_density": 3.0110443061684733, "pretraining_utility": 0.26558638418685426, "math_density": 0.6906285153633479, "code_density": 3.8901878411740825, "reasoning_density": 0.3515071936973165, "education_level": 2.1729460300309933, "creative_writing_quality": -0.5628734309488733}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Entertainment & Media"}, "review_reasons": ["secondary_topic probability 0.4191 below 0.70", "secondary_topic vs NONE margin 0.0193 below 0.10"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.01224755441747627, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9955893368323394, "secondary": 0.9639377110499768, "tertiary": 0.9767177678334303, "structured_records": 0.024486635346352172, "procedural_steps": 0.01625008791061665, "worked_solution": 0.0062976678425334795, "contextual_dialogue": 5.173501846455799e-06, "content_quality": 0.37660653620026274, "noise_severity": 0.9180036967445049, "missing_content_severity": 0.8483812007086056, "factual_density": 0.17587025514039423, "pretraining_utility": 0.4806418345060316, "math_density": 0.42447623719721006, "code_density": 0.9084898676450687, "reasoning_density": 0.7070773385855696, "education_level": 0.7076456231759847, "creative_writing_quality": 0.9982882154750349, "primary_topic": 0.8792207128283769, "secondary_topic": 0.39586689171540185}, "probability_margin": {"primary": 0.9936185421655871, "secondary": 0.9506031738728853, "tertiary": 0.9694980918218263, "education_level": 0.5926484956557797, "creative_writing_quality": 0.9979994684286826, "primary_topic": 0.8154766081221975, "secondary_topic": 0.01934501957847834}, "raw_probabilities": {"primary": {"Academic Writing": 5.1023020394141666e-05, "Content Listing": 2.2208212329606844e-05, "Creative Writing": 3.471926784271354e-05, "Customer Support Page": 1.0569659539215276e-05, "Q&A Forum": 1.451658405254102e-05, "Discussion Thread": 1.0139492084713928e-05, "Comment Section": 1.553754907505294e-05, "Missing Content": 4.398381688454023e-05, "Knowledge Article": 0.00010825994268420475, "Legal Notices": 3.0169211365688503e-05, "News Article": 6.1390721134938475e-06, "Organizational Announcement": 1.3379726969808533e-05, "Nonfiction Writing": 1.828840681150865e-05, "Organizational About Page": 1.558239293933818e-05, "Personal About Page": 1.6888592721542876e-05, "Personal Blog": 1.0253144633588482e-05, "Product Page": 9.77406930480393e-06, "Spam / Ads": 1.4922335393738056e-05, "Structured Data": 0.0005298897806556051, "Technical Writing": 0.9957731144643253, "Transcript / Interview": 2.0028176680756212e-05, "Tutorial / How-To Guide": 0.0010674413320288102, "User Reviews": 8.599450431287819e-06, "Other / Unknown": 0.002154572298738139}, "secondary": {"Academic Writing": 0.0004301438339466696, "Content Listing": 0.00014693146109519828, "Creative Writing": 0.0003746996920983362, "Customer Support Page": 9.099239007021357e-05, "Q&A Forum": 7.892824651872335e-05, "Discussion Thread": 7.465119022520088e-05, "Comment Section": 0.0001798104832925673, "Missing Content": 0.0010440525507957547, "Knowledge Article": 0.0018479299272918498, "Legal Notices": 0.00021444816469072607, "News Article": 0.00012119187539077518, "Organizational Announcement": 0.00011843940732538364, "Nonfiction Writing": 0.000153000270313835, "Organizational About Page": 0.00011219056993188235, "Personal About Page": 0.00013298935852889177, "Personal Blog": 0.0001033901877651491, "Product Page": 6.355092004013628e-05, "Spam / Ads": 0.00012211263699709, "Structured Data": 0.009619350141920155, "Technical Writing": 0.014837132550009168, "Transcript / Interview": 0.000183035461891017, "Tutorial / How-To Guide": 0.004454218354921126, "User Reviews": 5.650390204572925e-05, "NONE": 0.9654403064228945}, "tertiary": {"Academic Writing": 0.00035487850898802966, "Content Listing": 0.00011083620317825418, "Creative Writing": 0.0002996001404613871, "Customer Support Page": 6.383486194771563e-05, "Q&A Forum": 4.179116546473611e-05, "Discussion Thread": 4.253717860656389e-05, "Comment Section": 0.0001261248790376333, "Missing Content": 0.0012960784373783744, "Knowledge Article": 0.0016516696416633574, "Legal Notices": 0.00025279570735926453, "News Article": 8.348223505259676e-05, "Organizational Announcement": 7.745048387843728e-05, "Nonfiction Writing": 0.00014331586632242805, "Organizational About Page": 8.821674186004664e-05, "Personal About Page": 8.526568151040551e-05, "Personal Blog": 6.903431897521308e-05, "Product Page": 5.228351533822409e-05, "Spam / Ads": 8.850772173243378e-05, "Structured Data": 0.006098041379549639, "Technical Writing": 0.008189769018544419, "Transcript / Interview": 0.00011355499789206114, "Tutorial / How-To Guide": 0.0029397254222299067, "User Reviews": 4.334505265814253e-05, "NONE": 0.9776878608403707}, "structured_records": {"false": 0.9755133646536478, "true": 0.024486635346352172}, "procedural_steps": {"false": 0.9837499120893833, "true": 0.01625008791061665}, "worked_solution": {"false": 0.9937023321574665, "true": 0.0062976678425334795}, "contextual_dialogue": {"false": 0.9999948264981535, "true": 5.173501846455799e-06}, "content_quality": {"-2": 0.04911846745688252, "-1": 0.1922356143400803, "0": 0.376631842198864, "1": 0.3064285447025069, "2": 0.07558553130166623}, "noise_severity": {"0": 0.918935268612532, "1": 0.069354386803637, "2": 0.007423426757408235, "3": 0.002953347717550686, "4": 0.0013335701088721572}, "missing_content_severity": {"0": 0.8621949793092109, "1": 0.10252082278771596, "2": 0.02835649973184462, "3": 0.00500205578664667, "4": 0.0019256423845820452}, "factual_density": {"0": 0.027147828873778077, "1": 0.06623140190013194, "2": 0.16680126216142163, "3": 0.3480676483131755, "4": 0.39175185875149277}, "pretraining_utility": {"-2": 0.01481569148334619, "-1": 0.14919032423626155, "0": 0.453688651864886, "1": 0.32020257344120434, "2": 0.06210275897430192}, "math_density": {"0": 0.7316118798915355, "1": 0.08330648393314656, "2": 0.0455864544392852, "3": 0.04183160439249994, "4": 0.09766357734353279}, "code_density": {"0": 0.000223290000679553, "1": 0.00043623269946312907, "2": 0.01243879860349163, "3": 0.08273270351782674, "4": 0.904168975178539}, "reasoning_density": {"0": 0.8261743664266317, "1": 0.05506444928697912, "2": 0.0778166368000615, "3": 0.02296871913509623, "4": 0.0179758283512314}, "education_level": {"none": 0.0018443297144798102, "0": 0.002212940262861727, "1": 0.046122318902201885, "2": 0.7563713526466539, "3": 0.1637228569908742, "4": 0.029726201482928583}, "creative_writing_quality": {"not_applicable": 0.9985735128958624, "-2": 0.0003121473619534974, "-1": 0.00038260142476956234, "0": 0.0005740444671797409, "1": 0.00011142324230306427, "2": 4.627060793164499e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00020415479985346727, "Art & Design": 0.003460361406254976, "Law & Crime": 0.0002739929330577047, "Education & Careers": 0.0007444981402615349, "Entertainment & Media": 0.06838946190508796, "Fashion & Beauty": 0.00018013574079489764, "Business, Economics & Finance": 0.00017276068657332856, "Food & Dining": 0.0001603176846334501, "Games & Gaming": 0.0007457342254884188, "Electronics & Hardware": 0.0001911059525077347, "Health, Healthcare & Wellness": 0.00017322435844207093, "History & Geography": 0.0002587884876814387, "Home, Pets & Hobbies": 0.000610685229584311, "Industry & Production": 0.00010592593344458178, "Language, Literature & Philosophy": 0.0002538278440595721, "Politics & Government": 8.401832417878263e-05, "Religion & Spirituality": 0.00021182479597270644, "Science, Mathematics & Engineering": 0.006135020718746883, "Relationships & Community": 8.918535214083757e-05, "Software Usage & Applications": 0.0008308336801328362, "Programming & Software Engineering": 0.8838660700272855, "Sports & Fitness": 0.00021047181762951964, "Vehicles & Transportation": 8.586438712132167e-05, "Travel & Tourism": 0.00023122300125580827, "Other / Uncovered Subject": 0.0320511419860436, "Unknown / Insufficient Content": 0.000279370581766833}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0013107107346275856, "Art & Design": 0.013057925617344868, "Law & Crime": 0.0008634779170575878, "Education & Careers": 0.0029874756015778343, "Entertainment & Media": 0.4191027804955787, "Fashion & Beauty": 0.0005723647043336365, "Business, Economics & Finance": 0.000656623770487661, "Food & Dining": 0.00047144743456064176, "Games & Gaming": 0.0011104370819880892, "Electronics & Hardware": 0.0006303578961269796, "Health, Healthcare & Wellness": 0.0006466302796867314, "History & Geography": 0.0007294786450506738, "Home, Pets & Hobbies": 0.0009493032216565391, "Industry & Production": 0.0003295192311157899, "Language, Literature & Philosophy": 0.0005080469016303953, "Politics & Government": 0.00028166081262649256, "Religion & Spirituality": 0.0007087520622604335, "Science, Mathematics & Engineering": 0.018074591046407365, "Relationships & Community": 0.00027085189320078055, "Software Usage & Applications": 0.001524183614973575, "Programming & Software Engineering": 0.07532049550961137, "Sports & Fitness": 0.000580165998527687, "Vehicles & Transportation": 0.00024918301788794876, "Travel & Tourism": 0.0007830131357473208, "Other / Uncovered Subject": 0.05852276245883298, "NONE": 0.39975776091710036}}, "probabilities": {"primary": {"Academic Writing": 5.1023020394141666e-05, "Content Listing": 2.2208212329606844e-05, "Creative Writing": 3.471926784271354e-05, "Customer Support Page": 1.0569659539215276e-05, "Q&A Forum": 1.451658405254102e-05, "Discussion Thread": 1.0139492084713928e-05, "Comment Section": 1.553754907505294e-05, "Missing Content": 4.398381688454023e-05, "Knowledge Article": 0.00010825994268420475, "Legal Notices": 3.0169211365688503e-05, "News Article": 6.1390721134938475e-06, "Organizational Announcement": 1.3379726969808533e-05, "Nonfiction Writing": 1.828840681150865e-05, "Organizational About Page": 1.558239293933818e-05, "Personal About Page": 1.6888592721542876e-05, "Personal Blog": 1.0253144633588482e-05, "Product Page": 9.77406930480393e-06, "Spam / Ads": 1.4922335393738056e-05, "Structured Data": 0.0005298897806556051, "Technical Writing": 0.9957731144643253, "Transcript / Interview": 2.0028176680756212e-05, "Tutorial / How-To Guide": 0.0010674413320288102, "User Reviews": 8.599450431287819e-06, "Other / Unknown": 0.002154572298738139}, "secondary": {"Academic Writing": 0.0004301438339466696, "Content Listing": 0.00014693146109519828, "Creative Writing": 0.0003746996920983362, "Customer Support Page": 9.099239007021357e-05, "Q&A Forum": 7.892824651872335e-05, "Discussion Thread": 7.465119022520088e-05, "Comment Section": 0.0001798104832925673, "Missing Content": 0.0010440525507957547, "Knowledge Article": 0.0018479299272918498, "Legal Notices": 0.00021444816469072607, "News Article": 0.00012119187539077518, "Organizational Announcement": 0.00011843940732538364, "Nonfiction Writing": 0.000153000270313835, "Organizational About Page": 0.00011219056993188235, "Personal About Page": 0.00013298935852889177, "Personal Blog": 0.0001033901877651491, "Product Page": 6.355092004013628e-05, "Spam / Ads": 0.00012211263699709, "Structured Data": 0.009619350141920155, "Technical Writing": 0.014837132550009168, "Transcript / Interview": 0.000183035461891017, "Tutorial / How-To Guide": 0.004454218354921126, "User Reviews": 5.650390204572925e-05, "NONE": 0.9654403064228945}, "tertiary": {"Academic Writing": 0.00035487850898802966, "Content Listing": 0.00011083620317825418, "Creative Writing": 0.0002996001404613871, "Customer Support Page": 6.383486194771563e-05, "Q&A Forum": 4.179116546473611e-05, "Discussion Thread": 4.253717860656389e-05, "Comment Section": 0.0001261248790376333, "Missing Content": 0.0012960784373783744, "Knowledge Article": 0.0016516696416633574, "Legal Notices": 0.00025279570735926453, "News Article": 8.348223505259676e-05, "Organizational Announcement": 7.745048387843728e-05, "Nonfiction Writing": 0.00014331586632242805, "Organizational About Page": 8.821674186004664e-05, "Personal About Page": 8.526568151040551e-05, "Personal Blog": 6.903431897521308e-05, "Product Page": 5.228351533822409e-05, "Spam / Ads": 8.850772173243378e-05, "Structured Data": 0.006098041379549639, "Technical Writing": 0.008189769018544419, "Transcript / Interview": 0.00011355499789206114, "Tutorial / How-To Guide": 0.0029397254222299067, "User Reviews": 4.334505265814253e-05, "NONE": 0.9776878608403707}, "structured_records": {"false": 0.9755133646536478, "true": 0.024486635346352172}, "procedural_steps": {"false": 0.9837499120893833, "true": 0.01625008791061665}, "worked_solution": {"false": 0.9937023321574665, "true": 0.0062976678425334795}, "contextual_dialogue": {"false": 0.9999948264981535, "true": 5.173501846455799e-06}, "content_quality": {"-2": 0.049118467456882524, "-1": 0.19223561434008032, "0": 0.37663184219886403, "1": 0.30642854470250697, "2": 0.07558553130166625}, "noise_severity": {"0": 0.9189352686125318, "1": 0.06935438680363698, "2": 0.007423426757408234, "3": 0.002953347717550685, "4": 0.001333570108872157}, "missing_content_severity": {"0": 0.8621949793092106, "1": 0.10252082278771593, "2": 0.028356499731844613, "3": 0.005002055786646669, "4": 0.0019256423845820448}, "factual_density": {"0": 0.02714782887377808, "1": 0.06623140190013195, "2": 0.16680126216142166, "3": 0.34806764831317555, "4": 0.3917518587514928}, "pretraining_utility": {"-2": 0.01481569148334619, "-1": 0.14919032423626155, "0": 0.453688651864886, "1": 0.32020257344120434, "2": 0.06210275897430192}, "math_density": {"0": 0.7316118798915355, "1": 0.08330648393314656, "2": 0.0455864544392852, "3": 0.04183160439249994, "4": 0.09766357734353279}, "code_density": {"0": 0.000223290000679553, "1": 0.00043623269946312907, "2": 0.01243879860349163, "3": 0.08273270351782674, "4": 0.904168975178539}, "reasoning_density": {"0": 0.8261743664266318, "1": 0.05506444928697913, "2": 0.07781663680006151, "3": 0.022968719135096235, "4": 0.017975828351231404}, "education_level": {"none": 0.0018443297144798102, "0": 0.002212940262861727, "1": 0.046122318902201885, "2": 0.7563713526466539, "3": 0.1637228569908742, "4": 0.029726201482928583}, "creative_writing_quality": {"not_applicable": 0.9985735128958625, "-2": 0.0003121473619534975, "-1": 0.0003826014247695624, "0": 0.000574044467179741, "1": 0.00011142324230306428, "2": 4.6270607931644996e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00020415479985346727, "Art & Design": 0.003460361406254976, "Law & Crime": 0.0002739929330577047, "Education & Careers": 0.0007444981402615349, "Entertainment & Media": 0.06838946190508796, "Fashion & Beauty": 0.00018013574079489764, "Business, Economics & Finance": 0.00017276068657332856, "Food & Dining": 0.0001603176846334501, "Games & Gaming": 0.0007457342254884188, "Electronics & Hardware": 0.0001911059525077347, "Health, Healthcare & Wellness": 0.00017322435844207093, "History & Geography": 0.0002587884876814387, "Home, Pets & Hobbies": 0.000610685229584311, "Industry & Production": 0.00010592593344458178, "Language, Literature & Philosophy": 0.0002538278440595721, "Politics & Government": 8.401832417878263e-05, "Religion & Spirituality": 0.00021182479597270644, "Science, Mathematics & Engineering": 0.006135020718746883, "Relationships & Community": 8.918535214083757e-05, "Software Usage & Applications": 0.0008308336801328362, "Programming & Software Engineering": 0.8838660700272855, "Sports & Fitness": 0.00021047181762951964, "Vehicles & Transportation": 8.586438712132167e-05, "Travel & Tourism": 0.00023122300125580827, "Other / Uncovered Subject": 0.0320511419860436, "Unknown / Insufficient Content": 0.000279370581766833}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0013107107346275856, "Art & Design": 0.013057925617344868, "Law & Crime": 0.0008634779170575878, "Education & Careers": 0.0029874756015778343, "Entertainment & Media": 0.4191027804955787, "Fashion & Beauty": 0.0005723647043336365, "Business, Economics & Finance": 0.000656623770487661, "Food & Dining": 0.00047144743456064176, "Games & Gaming": 0.0011104370819880892, "Electronics & Hardware": 0.0006303578961269796, "Health, Healthcare & Wellness": 0.0006466302796867314, "History & Geography": 0.0007294786450506738, "Home, Pets & Hobbies": 0.0009493032216565391, "Industry & Production": 0.0003295192311157899, "Language, Literature & Philosophy": 0.0005080469016303953, "Politics & Government": 0.00028166081262649256, "Religion & Spirituality": 0.0007087520622604335, "Science, Mathematics & Engineering": 0.018074591046407365, "Relationships & Community": 0.00027085189320078055, "Software Usage & Applications": 0.001524183614973575, "Programming & Software Engineering": 0.07532049550961137, "Sports & Fitness": 0.000580165998527687, "Vehicles & Transportation": 0.00024918301788794876, "Travel & Tourism": 0.0007830131357473208, "Other / Uncovered Subject": 0.05852276245883298, "NONE": 0.39975776091710036}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9792, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9792, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:06.622301+00:00", "scoring_key_slot": "primary", "dataset_uid": "c53b4054-ea77-4cf2-b34b-597d5a60d1bb", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "52adfc3dda982d2520d7df040509d1a1a8abb084925a9a9d1e920f0bfb7ef6a6", "source_metadata": {"uuid": "c53b4054-ea77-4cf2-b34b-597d5a60d1bb", "repo_name": "maxchanhi/music-theory-app", "relative_path": "inversion/inversion_gen.py", "category": "TOOL", "algo_rel_score": 0.9230144023895264, "quality_score": 3.3641703128814697, "source_meta": {"github_metadata": {"branch": "login_feature", "commit_count": 464, "repo_created_at": "2024-05-11T07:41:16.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "6f89b2a6e3f89c9b7d195ad8f1c0c814584a4391", "size_bytes": 4343, "file_timestamp": 1724395412, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "9b5325d0263bb798006ab558c677fa3b1a676561"}}, "parsed_source_meta": {"github_metadata": {"branch": "login_feature", "commit_count": 464, "repo_created_at": "2024-05-11T07:41:16.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "6f89b2a6e3f89c9b7d195ad8f1c0c814584a4391", "size_bytes": 4343, "file_timestamp": 1724395412, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "9b5325d0263bb798006ab558c677fa3b1a676561", "url": "https://github.com/maxchanhi/music-theory-app/blob/9b5325d0263bb798006ab558c677fa3b1a676561/inversion/inversion_gen.py"}, "gpt4o_tokens": 1205, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.16712705805199407, "noise_severity": 0.09839556390659414, "missing_content_severity": 0.18194255914967336, "factual_density": 3.0110443061684733, "pretraining_utility": 0.26558638418685426, "math_density": 0.6906285153633479, "code_density": 3.8901878411740825, "reasoning_density": 0.3515071936973165, "education_level": 2.1729460300309933, "creative_writing_quality": -0.5628734309488733}, "score_applicability": {"education_level": 0.9981556702855203, "creative_writing_quality": 0.0014264871041375102}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/tisobe/Python_scripts/blob/46bd9ba398e23752173b8aee3be57a905592db83/gaussian_fit.py", "id": "python-0-f78aa5e1af3b3bade07cdc7b24b4f854", "text": "#!/usr/bin/env /proj/sot/ska/bin/python\n\n#########################################################################################################\n#                                                                                                       #\n#       gaussian_fit.py: fitting a Gaussian profile for a given data                                    #\n#                                                                                                       #\n#           author: <NAME> (<EMAIL>)                                                   #\n#                                                                                                       #\n#           last update: Jul 24, 2013                                                                   #\n#                                                                                                       #\n#           Ref: http://www.astro.rug.nl/software/kapteyn/kmpfittutorial.html#gaussian-profiles         #\n#                                                                                                       #\n#########################################################################################################\n\nimport os\nimport sys\nimport re\nimport string\nimport random\nimport operator\nimport math\nimport numpy\n#\n#--- pylab plotting routine related modules\n#\nimport matplotlib as mpl\n\nfrom matplotlib.pyplot import figure, show, rc\n#\n#--- reading directory list\n#\npath = '/data/mta/Script/Python_script2.7/house_keeping/dir_list'\n\nf    = open(path, 'r')\ndata = [line.strip() for line in f.readlines()]\nf.close()\n\nfor ent in data:\n    atemp = re.split(':', ent)\n    var  = atemp[1].strip()\n    line = atemp[0].strip()\n    exec \"%s = %s\" %(var, line)\n#\n#--- append a path to a private folder to python directory\n#\nsys.path.append(bin_dir)\nsys.path.append(mta_dir)\n\n#\n#---  importing kapteyn routines\n#\nfrom kapteyn import kmpfit\n\n#--------------------------------------------------------------------------------------------------------------\n#-- fit_gaussian: fitting gaussian profile: control function                                                ---\n#--------------------------------------------------------------------------------------------------------------\n\ndef fit_gaussian(data, plot_op='no', xname='na', yname='na', tname='na', detail='no'):\n\n    \"\"\"\n    fitting gaussian profile: control function\n    Input:      data        --- input data list of lists [xdata, ydata, error]\n                plot_op     --- whether you want to create a plot, if so, \"yes\". Default: \"no\"\n                xname       --- x axis name \n                yname       --- y axis name\n                tname       --- title\n                detail      --- if you want to print out a detail fitting profile, set \"yes'. Default: 'no'\n    Output:     fitting parameter [noarmalization, center, sigma, zero level]\n                if plotting is asked, \"out.png\" is created\n                if detail is requested, kmpfit_info is created\n    \"\"\"\n\n    x, y, err = data\n#\n#--- fitting parameters\n#\n    pinitial = [1600, 10.5, 1.5, 0]                     #---initial guess of:  [noarmalization, center, sigma, zero level]\n    param = fit_model(x, y, err, pinitial,  detail)\n\n    if plot_op == 'yes':\n#\n#--- plotting the result\n#\n        plot_fig(x, y, param, xname, yname, tname)\n\n    return param\n\n#--------------------------------------------------------------------------------------------------------------\n#--------------------------------------------------------------------------------------------------------------\n#--------------------------------------------------------------------------------------------------------------\n\ndef fit_model(x, y, err, p0,  detail='no'):\n\n    \"\"\"\n    fitting a model to a given data\n    Input:      data        --- input data list of lists [xdata, ydata, error]\n                p0          --- initial guess of the parameters in a list\n                detail      --- if you want to print out a detail fitting profile, set \"yes'. Default: 'no'\n    Output:     fitting parameter [noarmalization, center, sigma, zero level]\n    \"\"\"\n#\n#--- convert the data to numpy array\n#\n    d   = numpy.array(x)\n    v   = numpy.array(y)\n    err = numpy.array(err)\n#\n#--- fitting routine starts\n#\n    fitobj = kmpfit.Fitter(residuals=my_residuals, deriv=my_derivs, data=(d,v,err))\n\n    try:\n        fitobj.fit(params0=p0)\n    except Exception, mes:\n        print \"Something wrong with fit:\", mes\n        raise SystemExit\n#\n#--- if detail fitting profile is asked, print it out\n#\n    if detail == 'yes':\n        line = \"\\n\\n======== Results kmpfit with explicit partial derivatives =========\" + \"\\n\"\n        line = line +  \"Params:        \" +  str(fitobj.params) + \"\\n\"\n        line = line +  \"Errors from covariance matrix         : \" +  str(fitobj.xerror) + \"\\n\"\n        line = line +  \"Uncertainties assuming reduced Chi^2=1: \" +  str(fitobj.stderr)  + \"\\n\"\n        line = line +  \"Chi^2 min:     \" +  str(fitobj.chi2_min) + \"\\n\"\n        line = line +  \"Reduced Chi^2: \" +  str(fitobj.rchi2_min) + \"\\n\"\n        line = line +  \"Iterations:    \" +  str(fitobj.niter) + \"\\n\"\n        line = line +  \"Function ev:   \" +  str(fitobj.nfev)  + \"\\n\"\n        line = line +  \"Status:        \" +  str(fitobj.status) + \"\\n\"\n        line = line +  \"Status Message:\" +  str(fitobj.message) + \"\\n\"\n        line = line +  \"Covariance:\\n\"   +  str(fitobj.covar)  + \"\\n\"\n\n        f = open('./kmpfit_info', 'w')\n        f.write(line)\n        f.close()\n\n    return fitobj.params\n\n#--------------------------------------------------------------------------------------------------------------\n#-- my_derivs: fitting derivatives                                                                          ---\n#--------------------------------------------------------------------------------------------------------------\n\ndef my_derivs(p, data, dflags):\n\n    \"\"\"\n    create fitting derivatives\n    Input:  p       --- parameter list\n            data    --- data list of lists [x, y, error]\n            dflags  --- ??? but used by kmfit\n    Output  pderiv  --- a list of derivatives\n    \"\"\"\n\n    x, y, err = data\n    A, mu, sigma, zerolev  = p\n    pderiv = numpy.zeros([len(p), len(x)])      # You need to create the required array\n\n    sig2 = sigma*sigma\n    sig3 = sig2 * sigma\n    xmu  = x-mu\n    xmu2 = xmu**2\n    expo = numpy.exp(-xmu2/(2.0*sig2))\n    fx   = A * expo\n\n    for i, flag in enumerate(dflags):\n        if flag:\n            if i == 0:\n                pderiv[0] = expo\n            elif i == 1:\n                pderiv[1] = fx * xmu/(sig2)\n            elif i == 2:\n                pderiv[2] = fx * xmu2/(sig3)\n            elif i == 3:\n                pderiv[3] = 1.0\n\n    pderiv /= -err\n    return pderiv\n\n#--------------------------------------------------------------------------------------------------------------\n#-- my_residuals: compute residuals                                                                         ---\n#--------------------------------------------------------------------------------------------------------------\n\ndef my_residuals(p, data):\n\n    \"\"\"\n    compute residuals\n    Input:      p    --- parameter list\n                data --- data list [x, y, error], the data must be numpy array\n    Output:     residuals\n    \"\"\"\n\n    x, y, err = data\n    \n    return (y-my_model(p,x)) / err\n\n#--------------------------------------------------------------------------------------------------------------\n#-- my_model: model you want to fit on the data                                                             ---\n#--------------------------------------------------------------------------------------------------------------\n\ndef my_model(p, x):\n\n    \"\"\"\n    model you want to fit on the data\n    Input:  p    ---- parameter list\n            x    ---- an numpy array of independent variable\n    Output  an numpy array of estimated values\n    \"\"\"\n\n    A, mu, sigma, zerolev = p\n\n    return( A * numpy.exp(-(x-mu)*(x-mu)/(2.0*sigma*sigma)) + zerolev )\n\n#--------------------------------------------------------------------------------------------------------------\n#-- plot_fig: plotting routine                                                                               --\n#--------------------------------------------------------------------------------------------------------------\n\ndef plot_fig(x, y, p, xname, yname, tname):\n\n    \"\"\"\n    plotting routine\n    Input:  x        --- a list of x values\n            y        --- a list of y values\n            p        --- a list of parameters\n            xname    --- a name of x axis\n            yname    --- a name of y axis\n            tname    --- title of the plot\n    Output: out.png  --- a png file\n    \"\"\"\n    rc('font', size=9)\n    fig   = figure()\n    frame = fig.add_subplot(1,1,1)\n#\n#--- plot actual data points\n#\n    frame.plot(x, y,  color='red', lw =0 , marker='+', markersize=10.)\n#\n#--- to make a model plot smooth, take  equally spanced 100 points between min and max value of x data\n#\n    xmin = min(x)\n    xmax = max(x)\n    diff = xmax - xmin\n    step = diff / 100.0\n    xs   = []\n    for i in range(0, 100):\n        xs.append(step * i)\n\n    frame.plot(xs, my_model(p, xs), 'b', lw=2)\n#\n#--- axis names and title\n#\n    frame.set_xlabel(xname)\n    frame.set_ylabel(yname)\n    frame.set_title(tname,fontsize=10)\n#\n#--- set the size of the plotting area in inch (width: 10.0in, height 5.0in)\n#\n    fig = mpl.pyplot.gcf()\n    fig.set_size_inches(10.0, 5.0)\n#\n#--- save the plot in png format\n#\n    fig.savefig('out.png', format='png', dpi=100)\n\n", "text_preview": "#!/usr/bin/env /proj/sot/ska/bin/python ######################################################################################################### # # # gaussian_fit.py: fitting a Gaussian profile for a given data # # # # author: <NAME> (<EM", "input_sha256": "f72b5eda7380a994b4ea46bcce0ed05eff471271d7fb3307280a6e18383c040d", "text_sha256": "f78aa5e1af3b3bade07cdc7b24b4f85419148eebded0f9bf0d7f98178a362451", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12030, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 3, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.6148349086516273, "noise_severity": 0.2299081902411961, "missing_content_severity": 0.1297088859160146, "factual_density": 3.1027376560946007, "pretraining_utility": 0.5898472220512417, "math_density": 2.768350982032542, "code_density": 3.7326150289008124, "reasoning_density": 0.8157783549889421, "education_level": 2.4408458585572075, "creative_writing_quality": -0.3231236518453132}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.5259 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0264053022678582, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9862586182601181, "secondary": 0.925281984214754, "tertiary": 0.952835343102954, "structured_records": 0.0049178881377408435, "procedural_steps": 0.050730296727987616, "worked_solution": 0.014460133519566175, "contextual_dialogue": 8.487743154758777e-06, "content_quality": 0.4598525215455824, "noise_severity": 0.80840984146567, "missing_content_severity": 0.8919092617366545, "factual_density": 0.4754088180120055, "pretraining_utility": 0.48994493115928384, "math_density": 0.40169036113873957, "code_density": 0.777179190750677, "reasoning_density": 0.32018470417588174, "education_level": 0.5052627136109643, "creative_writing_quality": 0.9987542949228152, "primary_topic": 0.778086283426621, "secondary_topic": 0.5069047839205463}, "probability_margin": {"primary": 0.9779318950873114, "secondary": 0.8959692652851289, "tertiary": 0.937231413263095, "education_level": 0.2551566809298917, "creative_writing_quality": 0.9984747119987757, "primary_topic": 0.5824089471938165, "secondary_topic": 0.27663868622392046}, "raw_probabilities": {"primary": {"Academic Writing": 0.00018969755770866932, "Content Listing": 5.434317037690938e-05, "Creative Writing": 4.3463052104930404e-05, "Customer Support Page": 3.302397865993491e-05, "Q&A Forum": 3.763848327323708e-05, "Discussion Thread": 2.383894277195802e-05, "Comment Section": 5.313678575920657e-05, "Missing Content": 9.490542031197712e-05, "Knowledge Article": 0.00036117078934265966, "Legal Notices": 6.441977094469072e-05, "News Article": 1.5141964370653017e-05, "Organizational Announcement": 2.6076276682477025e-05, "Nonfiction Writing": 4.697722156966882e-05, "Organizational About Page": 3.651203750589553e-05, "Personal About Page": 4.252588997844574e-05, "Personal Blog": 2.5208542398879415e-05, "Product Page": 2.060750491349845e-05, "Spam / Ads": 3.123369193578318e-05, "Structured Data": 0.0004722736087432511, "Technical Writing": 0.9868311758326131, "Transcript / Interview": 4.2136381652699195e-05, "Tutorial / How-To Guide": 0.008899280745301774, "User Reviews": 1.880146410225417e-05, "Other / Unknown": 0.0025364108869775654}, "secondary": {"Academic Writing": 0.0036097569979708145, "Content Listing": 0.0002572476571229974, "Creative Writing": 0.00032881428293200614, "Customer Support Page": 0.0002587791347282309, "Q&A Forum": 0.00016645977673131958, "Discussion Thread": 0.0001167433890690821, "Comment Section": 0.0002810727905141235, "Missing Content": 0.001710527644334008, "Knowledge Article": 0.003092679690586964, "Legal Notices": 0.001432899190680943, "News Article": 0.00018730833583996344, "Organizational Announcement": 0.0002094140928810048, "Nonfiction Writing": 0.0002739467136126441, "Organizational About Page": 0.00023643366929746503, "Personal About Page": 0.0006784473222871101, "Personal Blog": 0.0002164909338334716, "Product Page": 0.00011992220918282737, "Spam / Ads": 0.00021170515842770798, "Structured Data": 0.0011468480139172953, "Technical Writing": 0.024286322812862086, "Transcript / Interview": 0.00024541148666752275, "Tutorial / How-To Guide": 0.03242596958734377, "User Reviews": 0.00011156423670389968, "NONE": 0.9283952348724727}, "tertiary": {"Academic Writing": 0.003880501781016082, "Content Listing": 0.00017495145462311762, "Creative Writing": 0.0002489976280779856, "Customer Support Page": 0.00017702056408502233, "Q&A Forum": 0.00011386659043637518, "Discussion Thread": 7.510215679296872e-05, "Comment Section": 0.0001852936125137987, "Missing Content": 0.0023009725270233474, "Knowledge Article": 0.003823963394395725, "Legal Notices": 0.002015002549942824, "News Article": 0.0001165045109066386, "Organizational Announcement": 0.0001590206094949717, "Nonfiction Writing": 0.00029879700872644226, "Organizational About Page": 0.00021460690014706067, "Personal About Page": 0.001202896317626733, "Personal Blog": 0.0001831671024902061, "Product Page": 8.680090418898417e-05, "Spam / Ads": 0.0001740211801872918, "Structured Data": 0.000876131108416351, "Technical Writing": 0.011078858397022566, "Transcript / Interview": 0.0001722041820140427, "Tutorial / How-To Guide": 0.017569123877235984, "User Reviews": 7.165850230451211e-05, "NONE": 0.954800537140331}, "structured_records": {"false": 0.9950821118622591, "true": 0.0049178881377408435}, "procedural_steps": {"false": 0.9492697032720124, "true": 0.050730296727987616}, "worked_solution": {"false": 0.9855398664804338, "true": 0.014460133519566175}, "contextual_dialogue": {"false": 0.9999915122568452, "true": 8.487743154758777e-06}, "content_quality": {"-2": 0.014617993931400054, "-1": 0.07313888559106005, "0": 0.32653927977051667, "1": 0.45419789930855897, "2": 0.1315059413984642}, "noise_severity": {"0": 0.8069636474996666, "1": 0.16224705741412696, "2": 0.026261422746364144, "3": 0.0029732020250279895, "4": 0.0015546703148142228}, "missing_content_severity": {"0": 0.8938238232638188, "1": 0.08908737708051959, "2": 0.01229607997203194, "3": 0.003141529843087412, "4": 0.0016511898405422237}, "factual_density": {"0": 0.007142399158651422, "1": 0.03693335760346544, "2": 0.16809196846261132, "3": 0.42170873753517496, "4": 0.3661235372400969}, "pretraining_utility": {"-2": 0.009513222454735812, "-1": 0.06556009273375338, "0": 0.3514495774470946, "1": 0.4725204550343658, "2": 0.10095665233005045}, "math_density": {"0": 0.010057788462633421, "1": 0.06119912926686435, "2": 0.32223866837885623, "3": 0.36334313955861874, "4": 0.24316127433302726}, "code_density": {"0": 0.0003810028445049722, "1": 0.0007606838713223374, "2": 0.029022566414982215, "3": 0.20553377527723624, "4": 0.7643019715919542}, "reasoning_density": {"0": 0.663836082828934, "1": 0.07724703475728885, "2": 0.10935494594363541, "3": 0.0784263175361837, "4": 0.0711356189339578}, "education_level": {"none": 0.0017208158377475485, "0": 0.0020675091404572725, "1": 0.013400328214453432, "2": 0.5877189280091369, "3": 0.33256224707924525, "4": 0.06253017171895975}, "creative_writing_quality": {"not_applicable": 0.9989619124356793, "-2": 0.0001390044780846869, "-1": 0.00025348647738685236, "0": 0.0004872004369036122, "1": 0.00012072755505375808, "2": 3.766861689195234e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00028570768210686633, "Art & Design": 0.0005628196038305631, "Law & Crime": 0.00039241972065242995, "Education & Careers": 0.0002943197071547101, "Entertainment & Media": 0.0002695355179529452, "Fashion & Beauty": 0.0002538112892571474, "Business, Economics & Finance": 0.0004838952875932728, "Food & Dining": 0.00025103031598858536, "Games & Gaming": 0.000385835065177324, "Electronics & Hardware": 0.00042367488903829374, "Health, Healthcare & Wellness": 0.00038044599797144917, "History & Geography": 0.00038406409816421763, "Home, Pets & Hobbies": 0.0002689639335714029, "Industry & Production": 0.0003778874956428455, "Language, Literature & Philosophy": 0.00027806232230626586, "Politics & Government": 0.0001820545895487678, "Religion & Spirituality": 0.0002560700772931783, "Science, Mathematics & Engineering": 0.20421247917793434, "Relationships & Community": 0.00013612427299003783, "Software Usage & Applications": 0.00155304952587884, "Programming & Software Engineering": 0.7866214263717509, "Sports & Fitness": 0.0003048741778190315, "Vehicles & Transportation": 0.0001722795426793222, "Travel & Tourism": 0.00026448865251865657, "Other / Uncovered Subject": 0.0006309309744399588, "Unknown / Insufficient Content": 0.0003737497107387395}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006612681171542709, "Art & Design": 0.00325351333671006, "Law & Crime": 0.0006310580583574705, "Education & Careers": 0.0005326695933961443, "Entertainment & Media": 0.00047807473154446936, "Fashion & Beauty": 0.00040986245032595615, "Business, Economics & Finance": 0.0006437104905019487, "Food & Dining": 0.0003555095118135061, "Games & Gaming": 0.0004828770402000464, "Electronics & Hardware": 0.0006871613257475183, "Health, Healthcare & Wellness": 0.0005784638414393248, "History & Geography": 0.000498040124100861, "Home, Pets & Hobbies": 0.00038360769638550905, "Industry & Production": 0.0005460474759539921, "Language, Literature & Philosophy": 0.00031736873016873515, "Politics & Government": 0.0003301304073806827, "Religion & Spirituality": 0.0005101547906315342, "Science, Mathematics & Engineering": 0.5258699845389868, "Relationships & Community": 0.0002082435528363503, "Software Usage & Applications": 0.001717927937735982, "Programming & Software Engineering": 0.24923129831506635, "Sports & Fitness": 0.0004654998922618351, "Vehicles & Transportation": 0.00030543145542394947, "Travel & Tourism": 0.000498999056694962, "Other / Uncovered Subject": 0.0009643799855234357, "NONE": 0.20943871754365817}}, "probabilities": {"primary": {"Academic Writing": 0.00018969755770866927, "Content Listing": 5.434317037690937e-05, "Creative Writing": 4.34630521049304e-05, "Customer Support Page": 3.30239786599349e-05, "Q&A Forum": 3.7638483273237076e-05, "Discussion Thread": 2.3838942771958013e-05, "Comment Section": 5.3136785759206555e-05, "Missing Content": 9.490542031197709e-05, "Knowledge Article": 0.0003611707893426596, "Legal Notices": 6.44197709446907e-05, "News Article": 1.5141964370653013e-05, "Organizational Announcement": 2.6076276682477018e-05, "Nonfiction Writing": 4.697722156966881e-05, "Organizational About Page": 3.6512037505895523e-05, "Personal About Page": 4.2525889978445734e-05, "Personal Blog": 2.5208542398879408e-05, "Product Page": 2.0607504913498447e-05, "Spam / Ads": 3.1233691935783177e-05, "Structured Data": 0.000472273608743251, "Technical Writing": 0.9868311758326129, "Transcript / Interview": 4.213638165269919e-05, "Tutorial / How-To Guide": 0.008899280745301772, "User Reviews": 1.8801464102254167e-05, "Other / Unknown": 0.002536410886977565}, "secondary": {"Academic Writing": 0.003609756997970815, "Content Listing": 0.00025724765712299745, "Creative Writing": 0.0003288142829320062, "Customer Support Page": 0.00025877913472823096, "Q&A Forum": 0.0001664597767313196, "Discussion Thread": 0.00011674338906908212, "Comment Section": 0.00028107279051412356, "Missing Content": 0.0017105276443340081, "Knowledge Article": 0.0030926796905869644, "Legal Notices": 0.0014328991906809433, "News Article": 0.00018730833583996347, "Organizational Announcement": 0.00020941409288100483, "Nonfiction Writing": 0.00027394671361264414, "Organizational About Page": 0.00023643366929746506, "Personal About Page": 0.0006784473222871102, "Personal Blog": 0.00021649093383347163, "Product Page": 0.00011992220918282739, "Spam / Ads": 0.000211705158427708, "Structured Data": 0.0011468480139172955, "Technical Writing": 0.02428632281286209, "Transcript / Interview": 0.0002454114866675228, "Tutorial / How-To Guide": 0.03242596958734378, "User Reviews": 0.00011156423670389969, "NONE": 0.9283952348724728}, "tertiary": {"Academic Writing": 0.003880501781016082, "Content Listing": 0.00017495145462311762, "Creative Writing": 0.0002489976280779856, "Customer Support Page": 0.00017702056408502233, "Q&A Forum": 0.00011386659043637518, "Discussion Thread": 7.510215679296872e-05, "Comment Section": 0.0001852936125137987, "Missing Content": 0.0023009725270233474, "Knowledge Article": 0.003823963394395725, "Legal Notices": 0.002015002549942824, "News Article": 0.0001165045109066386, "Organizational Announcement": 0.0001590206094949717, "Nonfiction Writing": 0.00029879700872644226, "Organizational About Page": 0.00021460690014706067, "Personal About Page": 0.001202896317626733, "Personal Blog": 0.0001831671024902061, "Product Page": 8.680090418898417e-05, "Spam / Ads": 0.0001740211801872918, "Structured Data": 0.000876131108416351, "Technical Writing": 0.011078858397022566, "Transcript / Interview": 0.0001722041820140427, "Tutorial / How-To Guide": 0.017569123877235984, "User Reviews": 7.165850230451211e-05, "NONE": 0.954800537140331}, "structured_records": {"false": 0.9950821118622591, "true": 0.0049178881377408435}, "procedural_steps": {"false": 0.9492697032720124, "true": 0.050730296727987616}, "worked_solution": {"false": 0.9855398664804338, "true": 0.014460133519566175}, "contextual_dialogue": {"false": 0.9999915122568452, "true": 8.487743154758777e-06}, "content_quality": {"-2": 0.014617993931400055, "-1": 0.07313888559106006, "0": 0.3265392797705167, "1": 0.454197899308559, "2": 0.13150594139846422}, "noise_severity": {"0": 0.8069636474996666, "1": 0.16224705741412696, "2": 0.026261422746364144, "3": 0.0029732020250279895, "4": 0.0015546703148142228}, "missing_content_severity": {"0": 0.8938238232638188, "1": 0.08908737708051959, "2": 0.01229607997203194, "3": 0.003141529843087412, "4": 0.0016511898405422237}, "factual_density": {"0": 0.007142399158651422, "1": 0.03693335760346544, "2": 0.16809196846261132, "3": 0.42170873753517496, "4": 0.3661235372400969}, "pretraining_utility": {"-2": 0.009513222454735812, "-1": 0.06556009273375338, "0": 0.3514495774470946, "1": 0.4725204550343658, "2": 0.10095665233005045}, "math_density": {"0": 0.010057788462633421, "1": 0.06119912926686435, "2": 0.32223866837885623, "3": 0.36334313955861874, "4": 0.24316127433302726}, "code_density": {"0": 0.0003810028445049722, "1": 0.0007606838713223374, "2": 0.029022566414982215, "3": 0.20553377527723624, "4": 0.7643019715919542}, "reasoning_density": {"0": 0.6638360828289341, "1": 0.07724703475728886, "2": 0.10935494594363544, "3": 0.07842631753618372, "4": 0.07113561893395781}, "education_level": {"none": 0.001720815837747548, "0": 0.002067509140457272, "1": 0.013400328214453429, "2": 0.5877189280091368, "3": 0.3325622470792452, "4": 0.06253017171895973}, "creative_writing_quality": {"not_applicable": 0.9989619124356791, "-2": 0.00013900447808468688, "-1": 0.0002534864773868523, "0": 0.0004872004369036121, "1": 0.00012072755505375805, "2": 3.7668616891952335e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00028570768210686633, "Art & Design": 0.0005628196038305631, "Law & Crime": 0.00039241972065242995, "Education & Careers": 0.0002943197071547101, "Entertainment & Media": 0.0002695355179529452, "Fashion & Beauty": 0.0002538112892571474, "Business, Economics & Finance": 0.0004838952875932728, "Food & Dining": 0.00025103031598858536, "Games & Gaming": 0.000385835065177324, "Electronics & Hardware": 0.00042367488903829374, "Health, Healthcare & Wellness": 0.00038044599797144917, "History & Geography": 0.00038406409816421763, "Home, Pets & Hobbies": 0.0002689639335714029, "Industry & Production": 0.0003778874956428455, "Language, Literature & Philosophy": 0.00027806232230626586, "Politics & Government": 0.0001820545895487678, "Religion & Spirituality": 0.0002560700772931783, "Science, Mathematics & Engineering": 0.20421247917793434, "Relationships & Community": 0.00013612427299003783, "Software Usage & Applications": 0.00155304952587884, "Programming & Software Engineering": 0.7866214263717509, "Sports & Fitness": 0.0003048741778190315, "Vehicles & Transportation": 0.0001722795426793222, "Travel & Tourism": 0.00026448865251865657, "Other / Uncovered Subject": 0.0006309309744399588, "Unknown / Insufficient Content": 0.0003737497107387395}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.000661268117154271, "Art & Design": 0.0032535133367100604, "Law & Crime": 0.0006310580583574706, "Education & Careers": 0.0005326695933961444, "Entertainment & Media": 0.0004780747315444694, "Fashion & Beauty": 0.0004098624503259562, "Business, Economics & Finance": 0.0006437104905019488, "Food & Dining": 0.00035550951181350613, "Games & Gaming": 0.00048287704020004646, "Electronics & Hardware": 0.0006871613257475184, "Health, Healthcare & Wellness": 0.0005784638414393249, "History & Geography": 0.0004980401241008611, "Home, Pets & Hobbies": 0.0003836076963855091, "Industry & Production": 0.0005460474759539922, "Language, Literature & Philosophy": 0.0003173687301687352, "Politics & Government": 0.0003301304073806828, "Religion & Spirituality": 0.0005101547906315343, "Science, Mathematics & Engineering": 0.5258699845389869, "Relationships & Community": 0.00020824355283635034, "Software Usage & Applications": 0.0017179279377359821, "Programming & Software Engineering": 0.24923129831506638, "Sports & Fitness": 0.00046549989226183513, "Vehicles & Transportation": 0.0003054314554239495, "Travel & Tourism": 0.0004989990566949621, "Other / Uncovered Subject": 0.0009643799855234358, "NONE": 0.2094387175436582}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10723, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10723, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:06.662431+00:00", "scoring_key_slot": "primary", "dataset_uid": "541740a5-33b7-40a8-a25f-34648e67a839", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "71b5143019eff13d345c073582e8e6bf53f517de575205960ae3bb1b94fe35fc", "source_metadata": {"uuid": "541740a5-33b7-40a8-a25f-34648e67a839", "repo_name": "tisobe/Python_scripts", "relative_path": "gaussian_fit.py", "category": "DATA", "algo_rel_score": 0.9775769114494324, "quality_score": 5.242796897888184, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 6, "repo_created_at": "2013-06-17T15:45:38.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "2d80d21cb489fe200bd066a4ccead861c489a21f", "size_bytes": 9612, "file_timestamp": 1442424969, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "46bd9ba398e23752173b8aee3be57a905592db83"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 6, "repo_created_at": "2013-06-17T15:45:38.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "2d80d21cb489fe200bd066a4ccead861c489a21f", "size_bytes": 9612, "file_timestamp": 1442424969, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "46bd9ba398e23752173b8aee3be57a905592db83", "url": "https://github.com/tisobe/Python_scripts/blob/46bd9ba398e23752173b8aee3be57a905592db83/gaussian_fit.py"}, "gpt4o_tokens": 2084, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.6148349086516273, "noise_severity": 0.2299081902411961, "missing_content_severity": 0.1297088859160146, "factual_density": 3.1027376560946007, "pretraining_utility": 0.5898472220512417, "math_density": 2.768350982032542, "code_density": 3.7326150289008124, "reasoning_density": 0.8157783549889421, "education_level": 2.4408458585572075, "creative_writing_quality": -0.3231236518453132}, "score_applicability": {"education_level": 0.9982791841622525, "creative_writing_quality": 0.0010380875643208618}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/bcgov/ppr/blob/d972b65167102ffedf0d2089f3732582dfb9cd4c/ppr-api/src/ppr_api/resources/v1/search_results.py", "id": "python-0-24c43f93c5152b13c6f78c32a2b76639", "text": "# Copyright © 2019 Province of British Columbia\n#\n# Licensed under the Apache License, Version 2.0 (the 'License');\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an 'AS IS' BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"API endpoints for executing PPR search detail requests (search step 2).\"\"\"\n\n# pylint: disable=too-many-return-statements\nimport json\nfrom http import HTTPStatus\n\nimport requests\nfrom flask import Blueprint, Response, current_app, jsonify, request, stream_with_context\nfrom flask_cors import cross_origin\nfrom registry_schemas import utils as schema_utils\n\nfrom ppr_api.callback.document_storage.storage_service import GoogleStorageService\nfrom ppr_api.callback.reports.report_service import get_search_report\nfrom ppr_api.callback.utils.exceptions import ReportDataException, ReportException, StorageException\nfrom ppr_api.exceptions import BusinessException, DatabaseException\nfrom ppr_api.models import EventTracking, SearchRequest, SearchResult\nfrom ppr_api.models import utils as model_utils\nfrom ppr_api.models.search_request import REPORT_STATUS_PENDING\nfrom ppr_api.reports import ReportTypes, get_pdf\nfrom ppr_api.resources import utils as resource_utils\nfrom ppr_api.services.authz import authorized\nfrom ppr_api.services.queue_service import GoogleQueueService\nfrom ppr_api.utils.auth import jwt\nfrom ppr_api.utils.logging import logger\n\nbp = Blueprint(\"SEARCH_RESULTS1\", __name__, url_prefix=\"/api/v1/search-results\")  # pylint: disable=invalid-name\nVAL_ERROR = \"Search details request data validation errors.\"  # Validation error prefix\nGET_DETAILS_ERROR = \"Submit a search step 2 select results request before getting search result details.\"\nSEARCH_RESULTS_DOC_NAME = \"search-results-report-{search_id}.pdf\"\nCALLBACK_MESSAGES = {\n    resource_utils.CallbackExceptionCodes.UNKNOWN_ID: \"01: no search result data found for id={search_id}.\",\n    resource_utils.CallbackExceptionCodes.MAX_RETRIES: \"02: maximum retries reached for id={search_id}.\",\n    resource_utils.CallbackExceptionCodes.INVALID_ID: \"03: search result report setup not async for id={search_id}.\",\n    resource_utils.CallbackExceptionCodes.DEFAULT: \"04: default error for id={search_id}.\",\n    resource_utils.CallbackExceptionCodes.REPORT_DATA_ERR: \"05: report data error for id={search_id}.\",\n    resource_utils.CallbackExceptionCodes.REPORT_ERR: \"06: generate report failed for id={search_id}.\",\n    resource_utils.CallbackExceptionCodes.STORAGE_ERR: \"07: document storage save failed for id={search_id}.\",\n    resource_utils.CallbackExceptionCodes.NOTIFICATION_ERR: \"08: notification failed for id={search_id}.\",\n}\nCALLBACK_PARAM = \"callbackURL\"\nREPORT_URL = \"/ppr/api/v1/search-results/{search_id}\"\nUSE_CURRENT_PARAM = \"useCurrent\"\nSTREAMING_THRESHOLD = 31 * 1024 * 1024\nHTTP_OK = \"200\"\n\n\n@bp.route(\"/<string:search_id>\", methods=[\"POST\", \"OPTIONS\"])\n@cross_origin(origin=\"*\")\n@jwt.requires_auth\ndef post_search_results(search_id: str):  # pylint: disable=too-many-branches,too-many-locals\n    \"\"\"Execute a search detail request using selection choices in the request body.\"\"\"\n    try:\n        if search_id is None:\n            return resource_utils.path_param_error_response(\"search ID\")\n\n        # Quick check: must provide an account ID.\n        account_id = resource_utils.get_account_id(request)\n        if account_id is None:\n            return resource_utils.account_required_response()\n\n        # Verify request JWT and account ID\n        if not authorized(account_id, jwt):\n            return resource_utils.unauthorized_error_response(account_id)\n\n        request_json = None\n        use_current_selection = request.args.get(USE_CURRENT_PARAM)\n        if use_current_selection:\n            search_request = SearchRequest.find_by_id(search_id)\n            request_json = search_request.updated_selection or []\n        else:\n            request_json = request.get_json(silent=True)\n            # Validate schema.\n            valid_format, errors = schema_utils.validate(request_json, \"searchSummary\", \"ppr\")\n            if not valid_format:\n                return resource_utils.validation_error_response(errors, VAL_ERROR)\n\n        # Perform any extra data validation such as start and end dates here\n        search_detail = SearchResult.validate_search_select(request_json, search_id)\n\n        # Large report threshold check, require/save callbackURL parameter.\n        # UI may not request a report in step 2.\n        callback_url = request.args.get(CALLBACK_PARAM)\n        is_ui_pdf = callback_url is not None and callback_url == current_app.config.get(\"UI_SEARCH_CALLBACK_URL\")\n        is_callback: bool = is_async(search_detail, request_json)\n        if is_callback and (resource_utils.is_pdf(request) or is_ui_pdf):\n            if callback_url is None:\n                error = f\"Large search report required callbackURL parameter missing for {search_id}.\"\n                logger.warning(error)\n                return resource_utils.error_response(HTTPStatus.BAD_REQUEST, error)\n        elif callback_url and not is_ui_pdf and resource_utils.is_pdf(request):\n            logger.debug(f\"Search {search_id} results size < threshold callback={callback_url}.\")\n        else:\n            callback_url = None\n            is_ui_pdf = False\n        # Save the search query selection and details that match the selection.\n        account_name = resource_utils.get_account_name(jwt.get_token_auth_header(), account_id)\n        search_detail.update_selection(request_json, account_name, callback_url)\n        if not search_detail.search_response:\n            return resource_utils.unprocessable_error_response(\"search result details\")\n\n        response_data = search_detail.json\n        # Results data that is too large for real time report generation (small number of results) is also\n        # asynchronous.\n        if callback_url is None and search_detail.callback_url is not None:\n            callback_url = search_detail.callback_url\n            is_ui_pdf = True\n        elif not is_callback and callback_url is not None:\n            results_length = len(json.dumps(response_data))\n            logger.debug(f\"Search id={search_id} data size={results_length}.\")\n            if results_length <= current_app.config.get(\"MAX_SIZE_SEARCH_RT\"):\n                callback_url = None\n                logger.debug(f\"Search {search_id} results data size < threshold ignoring callback\")\n        if resource_utils.is_pdf(request) or is_ui_pdf:\n            return results_pdf_response(response_data, search_id, account_id, search_detail, callback_url)\n\n        return jsonify(response_data), HTTPStatus.OK\n\n    except DatabaseException as db_exception:\n        return resource_utils.db_exception_response(db_exception, account_id, \"POST search select id=\" + search_id)\n    except BusinessException as exception:\n        return resource_utils.business_exception_response(exception)\n    except Exception as default_exception:  # noqa: B902; return nicer default error\n        return resource_utils.default_exception_response(default_exception)\n\n\n@bp.route(\"/<string:search_id>\", methods=[\"GET\", \"OPTIONS\"])\n@cross_origin(origin=\"*\")\n@jwt.requires_auth\ndef get_search_results(search_id: str):  # pylint: disable=too-many-branches,too-many-locals\n    \"\"\"Get search detail information for a previous search.\"\"\"\n    try:\n        if search_id is None:\n            return resource_utils.path_param_error_response(\"search ID\")\n        # Quick check: must have an account ID.\n        account_id = resource_utils.get_account_id(request)\n        if account_id is None:\n            return resource_utils.account_required_response()\n        # Verify request JWT and account ID\n        if not authorized(account_id, jwt):\n            return resource_utils.unauthorized_error_response(account_id)\n        # Try to fetch search detail by search id. Remove _PENDING if it is in the id.\n        if search_id.find((\"_\" + REPORT_STATUS_PENDING)) != -1:\n            search_id = search_id.replace((\"_\" + REPORT_STATUS_PENDING), \"\")\n        logger.info(f\"Fetching search detail for {search_id}.\")\n        search_detail: SearchResult = SearchResult.find_by_search_id(search_id, True)\n        if not search_detail:\n            return resource_utils.not_found_error_response(\"searchId\", search_id)\n        # If no search selection (step 2) return an error. Could be results\n        # with no exact matches and no results selected - nil, which is valid.\n        if search_detail.search_select is None and search_detail.search.total_results_size > 0:\n            return resource_utils.bad_request_response(GET_DETAILS_ERROR)\n        response_data = search_detail.json\n        if resource_utils.is_pdf(request):\n            # If the request is for an async large report and report not available, return an error.\n            if search_detail.callback_url is not None and search_detail.doc_storage_url is None:\n                error_msg = f\"Search report not yet available for {search_id}.\"\n                logger.info(error_msg)\n                return resource_utils.bad_request_response(error_msg)\n\n            # If report in doc storage, fetch and return it.\n            if search_detail.doc_storage_url is not None:\n                # Fetch binary data from doc storage.\n                doc_name = search_detail.doc_storage_url\n                logger.info(f\"Fetching search report {doc_name} from doc storage.\")\n                raw_data = GoogleStorageService.get_document(doc_name)\n                size = len(raw_data)\n                if size < STREAMING_THRESHOLD:\n                    return raw_data, HTTPStatus.OK, {\"Content-Type\": \"application/pdf\"}\n                logger.info(f\"streaming response report size={size}\")\n\n                def stream(begin, end, raw_data):\n                    offset = max(0, begin)\n                    while offset < end:\n                        end_chunk = offset + (10 * 1024)\n                        buf = raw_data[offset:end_chunk]\n                        offset += len(buf)\n                        yield buf\n\n                resp = Response(stream_with_context(stream(0, size, raw_data)))\n                resp.headers[\"Content-Type\"] = \"application/pdf\"\n                resp.status = HTTP_OK\n                return resp\n\n            # If get to here report not yet generated: create, store, return it.\n            logger.info(f\"Generating search report for {search_id}.\")\n            raw_data, status_code, headers = get_pdf(\n                response_data, account_id, ReportTypes.SEARCH_DETAIL_REPORT.value, jwt.get_token_auth_header()\n            )\n            logger.debug(f\"report api call status={status_code}, headers=\" + json.dumps(headers))\n            if raw_data and status_code in (HTTPStatus.OK, HTTPStatus.CREATED):\n                doc_name = model_utils.get_search_doc_storage_name(search_detail.search)\n                logger.info(f\"Saving report output to doc storage: name={doc_name}.\")\n                response = GoogleStorageService.save_document(doc_name, raw_data)\n                logger.info(f\"Save document storage response: {response}.\")\n                search_detail.doc_storage_url = doc_name\n                search_detail.save()\n                return raw_data, HTTPStatus.OK, {\"Content-Type\": \"application/pdf\"}\n            # Report generation error: return error response.\n            if status_code not in (HTTPStatus.OK, HTTPStatus.CREATED):\n                return resource_utils.report_exception_response(raw_data, status_code)\n        response_data[\"reportAvailable\"] = search_detail.doc_storage_url is not None\n        return jsonify(response_data), HTTPStatus.OK\n    except DatabaseException as db_exception:\n        return resource_utils.db_exception_response(db_exception, account_id, \"GET search details id=\" + search_id)\n    except BusinessException as exception:\n        return resource_utils.business_exception_response(exception)\n    except Exception as default_exception:  # noqa: B902; return nicer default error\n        return resource_utils.default_exception_response(default_exception)\n\n\n@bp.route(\"/callback/<string:search_id>\", methods=[\"POST\", \"OPTIONS\"])\n@cross_origin(origin=\"*\")\ndef post_callback(search_id: str):  # pylint: disable=too-many-branches, too-many-locals\n    \"\"\"Generate and store a report, update search_result.doc_storage_url.\"\"\"\n    try:\n        logger.info(f\"Search report callback starting id={search_id}.\")\n        if search_id is None:\n            return resource_utils.path_param_error_response(\"search ID\")\n        # Authenticate with request api key\n        if not resource_utils.valid_api_key(request):\n            return resource_utils.unauthorized_error_response(\"Search report callback\")\n        # If exceeded max retries we're done.\n        event_count: int = 0\n        events = EventTracking.find_by_key_id_type(search_id, EventTracking.EventTrackingTypes.SEARCH_REPORT)\n        if events:\n            event_count = len(events)\n        if event_count > current_app.config.get(\"EVENT_MAX_RETRIES\"):\n            return callback_error(\n                resource_utils.CallbackExceptionCodes.MAX_RETRIES,\n                search_id,\n                HTTPStatus.INTERNAL_SERVER_ERROR,\n                \"Max retries reached.\",\n            )\n\n        search_detail = SearchResult.find_by_search_id(search_id, False)\n        if not search_detail:\n            return callback_error(resource_utils.CallbackExceptionCodes.UNKNOWN_ID, search_id, HTTPStatus.NOT_FOUND)\n\n        # Check if report already generated.\n        if search_detail.doc_storage_url is not None:\n            doc_ref = search_detail.doc_storage_url\n            logger.warning(f\"Search detail report for {search_id} already exists: {doc_ref}.\")\n            return {}, HTTPStatus.OK\n\n        # Check if search result is async: always have callback_url.\n        # if search_detail.callback_url is None:\n        #    return callback_error(resource_utils.CallbackExceptionCodes.INVALID_ID,\n        #                          search_id,\n        #                          HTTPStatus.BAD_REQUEST)\n\n        # Generate the report with an API call\n        logger.info(f\"Generating search detail report for {search_id}.\")\n        raw_data, status_code, headers = get_search_report(search_id)\n        logger.info(f\"Search detail report {search_id} response status={status_code}.\")\n        if not raw_data or not status_code:\n            return callback_error(\n                resource_utils.CallbackExceptionCodes.REPORT_DATA_ERR,\n                search_id,\n                HTTPStatus.INTERNAL_SERVER_ERROR,\n                \"No data or status code.\",\n            )\n        logger.debug(\"report api call status=\" + str(status_code) + \" headers=\" + json.dumps(headers))\n        if status_code not in (HTTPStatus.OK, HTTPStatus.CREATED):\n            message = f\"Status code={status_code}. Response: {raw_data}\"\n            return callback_error(\n                resource_utils.CallbackExceptionCodes.REPORT_ERR, search_id, HTTPStatus.INTERNAL_SERVER_ERROR, message\n            )\n\n        doc_name = model_utils.get_search_doc_storage_name(search_detail.search)\n        logger.info(f\"Saving report output to doc storage: name={doc_name}.\")\n        response = GoogleStorageService.save_document(doc_name, raw_data)\n        logger.info(f\"Save document storage response: {response}\")\n        search_detail.doc_storage_url = doc_name\n        search_detail.save()\n\n        # Track success event.\n        EventTracking.create(search_id, EventTracking.EventTrackingTypes.SEARCH_REPORT, int(HTTPStatus.OK))\n\n        # Enqueue notication event if not from UI.\n        if search_detail.is_ui_callback() or search_detail.callback_url is None:\n            logger.info(f\"Skipping report available notification for id={search_id}.\")\n        else:  # Enqueue notification event.\n            logger.info(f\"Queueing report notification for id={search_id}, callback=\" + search_detail.callback_url)\n            enqueue_notification(search_id)\n        return search_detail.json, HTTPStatus.OK\n\n    except ReportException as report_err:\n        return callback_error(\n            resource_utils.CallbackExceptionCodes.REPORT_ERR,\n            search_id,\n            HTTPStatus.INTERNAL_SERVER_ERROR,\n            str(report_err),\n        )\n    except ReportDataException as report_data_err:\n        return callback_error(\n            resource_utils.CallbackExceptionCodes.REPORT_DATA_ERR,\n            search_id,\n            HTTPStatus.INTERNAL_SERVER_ERROR,\n            str(report_data_err),\n        )\n    except StorageException as storage_err:\n        return callback_error(\n            resource_utils.CallbackExceptionCodes.STORAGE_ERR,\n            search_id,\n            HTTPStatus.INTERNAL_SERVER_ERROR,\n            str(storage_err),\n        )\n    except DatabaseException as db_exception:\n        return resource_utils.db_exception_response(db_exception, None, \"POST large report event\")\n    except Exception as default_err:  # noqa: B902; return nicer default error\n        return callback_error(\n            resource_utils.CallbackExceptionCodes.DEFAULT, search_id, HTTPStatus.INTERNAL_SERVER_ERROR, str(default_err)\n        )\n\n\n@bp.route(\"/notifications/<string:search_id>\", methods=[\"POST\", \"OPTIONS\"])\n@cross_origin(origin=\"*\")\ndef post_notifications(search_id: str):  # pylint: disable=too-many-branches, too-many-locals\n    \"\"\"Notify originator of async report request that the document is available.\"\"\"\n    try:\n        if search_id is None:\n            return resource_utils.path_param_error_response(\"search ID\")\n\n        # If exceeded max retries we're done.\n        event_count: int = 0\n        events = EventTracking.find_by_key_id_type(search_id, EventTracking.EventTrackingTypes.API_NOTIFICATION)\n        if events:\n            event_count = len(events)\n        if event_count > current_app.config.get(\"EVENT_MAX_RETRIES\"):\n            return notification_error(\n                resource_utils.CallbackExceptionCodes.MAX_RETRIES,\n                search_id,\n                HTTPStatus.INTERNAL_SERVER_ERROR,\n                \"Max retries reached.\",\n            )\n\n        search_detail = SearchResult.find_by_search_id(search_id, False)\n        if not search_detail:\n            return notification_error(resource_utils.CallbackExceptionCodes.UNKNOWN_ID, search_id, HTTPStatus.NOT_FOUND)\n\n        # Check if search result is async: always have callback_url.\n        callback_url = search_detail.callback_url\n        if callback_url is None:\n            return notification_error(\n                resource_utils.CallbackExceptionCodes.INVALID_ID,\n                search_id,\n                HTTPStatus.BAD_REQUEST,\n                \"Required callbackURL is missing.\",\n            )\n\n        # Send the notification.\n        response = send_notification(callback_url, search_id)\n        status_code = response.status_code\n        if status_code not in (HTTPStatus.OK, HTTPStatus.ACCEPTED, HTTPStatus.NO_CONTENT):\n            message = f\"Status code={status_code}. Response: \" + str(response.content)\n            return notification_error(\n                resource_utils.CallbackExceptionCodes.NOTIFICATION_ERR,\n                search_id,\n                HTTPStatus.INTERNAL_SERVER_ERROR,\n                message,\n            )\n\n        # Track success event.\n        EventTracking.create(search_id, EventTracking.EventTrackingTypes.API_NOTIFICATION, int(status_code))\n\n        return response.content, response.status_code\n\n    except DatabaseException as db_exception:\n        return resource_utils.db_exception_response(db_exception, None, \"POST notificaton event\")\n    except Exception as default_err:  # noqa: B902; return nicer default error\n        return notification_error(\n            resource_utils.CallbackExceptionCodes.DEFAULT, search_id, HTTPStatus.INTERNAL_SERVER_ERROR, str(default_err)\n        )\n\n\ndef results_pdf_response(\n    response_data: dict, search_id: str, account_id: str, search_detail: SearchResult, callback_url: str = None\n):\n    \"\"\"For PDF search result requests build the response with conditional async/callback set up.\"\"\"\n    # If results over threshold return JSON with callbackURL, getReportURL\n    if callback_url is not None:\n        # Add enqueue report generation event here.\n        enqueue_search_report(search_id)\n        response_data[\"callbackURL\"] = requests.utils.unquote(callback_url)\n        response_data[\"getReportURL\"] = REPORT_URL.format(search_id=search_id)\n        return jsonify(response_data), HTTPStatus.OK\n    raw_data, status_code, headers = get_pdf(\n        response_data, account_id, ReportTypes.SEARCH_DETAIL_REPORT.value, jwt.get_token_auth_header()\n    )\n    logger.debug(f\"report api call status={status_code}, headers=\" + json.dumps(headers))\n    if raw_data and status_code in (HTTPStatus.OK, HTTPStatus.CREATED):\n        doc_name = model_utils.get_search_doc_storage_name(search_detail.search)\n        response = GoogleStorageService.save_document(doc_name, raw_data)\n        logger.info(f\"Save {doc_name} document storage response: {response}\")\n        search_detail.doc_storage_url = doc_name\n        search_detail.save()\n        return raw_data, HTTPStatus.OK, {\"Content-Type\": \"application/pdf\"}\n    # Report generation error: return Accepted status code to identify error.\n    response_data[\"reportAvailable\"] = False\n    return jsonify(response_data), HTTPStatus.ACCEPTED, {\"Content-Type\": \"application/json\"}\n\n\ndef callback_error(code: str, search_id: str, status_code, message: str = None):\n    \"\"\"Return to the event listener callback error response based on the code.\"\"\"\n    error = CALLBACK_MESSAGES[code].format(search_id=search_id)\n    if message:\n        error += \" \" + message\n    logger.error(error)\n    # Track event here.\n    EventTracking.create(search_id, EventTracking.EventTrackingTypes.SEARCH_REPORT, status_code, message)\n    if status_code != HTTPStatus.BAD_REQUEST and code not in (\n        resource_utils.CallbackExceptionCodes.MAX_RETRIES.value,\n        resource_utils.CallbackExceptionCodes.UNKNOWN_ID.value,\n    ):\n        # set up retry\n        enqueue_search_report(search_id)\n    return resource_utils.error_response(status_code, error)\n\n\ndef notification_error(code: str, search_id: str, status_code, message: str = None):\n    \"\"\"Return to the event listener a notification error response based on the status code.\"\"\"\n    error = CALLBACK_MESSAGES[code].format(search_id=search_id)\n    if message:\n        error += \" \" + message\n    logger.error(error)\n    # Track event here.\n    EventTracking.create(search_id, EventTracking.EventTrackingTypes.API_NOTIFICATION, status_code, message)\n    if status_code != HTTPStatus.BAD_REQUEST and code not in (\n        resource_utils.CallbackExceptionCodes.MAX_RETRIES.value,\n        resource_utils.CallbackExceptionCodes.UNKNOWN_ID.value,\n    ):\n        # set up retry\n        enqueue_notification(search_id)\n    return resource_utils.error_response(status_code, error)\n\n\ndef send_notification(callback_url: str, search_id):\n    \"\"\"Send a notification message that the search results report is available.\"\"\"\n    gateway_url = current_app.config.get(\"GATEWAY_URL\")\n    search_report_url = gateway_url + REPORT_URL.format(search_id=search_id)\n    data = {\"id\": search_id, \"getReportURL\": search_report_url}\n    data_json = json.dumps(data)\n    headers = {\"Content-Type\": \"application/json\"}\n    logger.info(\"Sending \" + data_json + \" to \" + callback_url)\n    response = requests.post(url=callback_url, headers=headers, data=data_json, timeout=30.0)\n    return response\n\n\ndef is_async(search_detail: SearchResult, search_select) -> bool:\n    \"\"\"Check if the request number of financing statements exceeds the real time threshold.\"\"\"\n    threshold = current_app.config.get(\"SEARCH_PDF_ASYNC_THRESHOLD\")\n    if search_detail.search.total_results_size < threshold:\n        return False\n    if search_detail.exact_match_count > threshold:\n        return True\n    exact_count = 0\n    similar_count = 0\n    for result in search_detail.search_response:\n        # Always include exact matches.\n        if result[\"matchType\"] == model_utils.SEARCH_MATCH_EXACT:\n            exact_count += 1\n        else:\n            reg_num = result[\"financingStatement\"][\"baseRegistrationNumber\"]\n            for select in search_select:\n                # Verified: have to explicitly select a similar result to include.\n                if select[\"baseRegistrationNumber\"] == reg_num and (\"selected\" not in select or select[\"selected\"]):\n                    similar_count += 1\n    if (similar_count + exact_count) > threshold:\n        return True\n\n    return False\n\n\ndef enqueue_search_report(search_id: str):\n    \"\"\"Add the search report request to the queue.\"\"\"\n    try:\n        payload = {\"searchId\": search_id}\n        apikey = current_app.config.get(\"SUBSCRIPTION_API_KEY\")\n        if apikey:\n            payload[\"apikey\"] = apikey\n        GoogleQueueService().publish_search_report(payload)\n        logger.info(f\"Enqueue search report successful for id={search_id}.\")\n    except Exception as err:  # noqa: B902; do not alter app processing\n        logger.error(f\"Enqueue search report failed for id={search_id}: \" + str(err))\n        EventTracking.create(\n            search_id,\n            EventTracking.EventTrackingTypes.SEARCH_REPORT,\n            int(HTTPStatus.INTERNAL_SERVER_ERROR),\n            \"Enqueue search report event failed: \" + str(err),\n        )\n\n\ndef enqueue_notification(search_id: str):\n    \"\"\"Add the notification request to the queue.\"\"\"\n    try:\n        payload = {\"searchId\": search_id}\n        apikey = current_app.config.get(\"SUBSCRIPTION_API_KEY\")\n        if apikey:\n            payload[\"apikey\"] = apikey\n        GoogleQueueService().publish_notification(payload)\n        logger.info(f\"Enqueue notification successful for id={search_id}.\")\n    except Exception as err:  # noqa: B902; do not alter app processing\n        logger.error(f\"Enqueue notification failed for id={search_id}: \" + str(err))\n        EventTracking.create(\n            search_id,\n            EventTracking.EventTrackingTypes.API_NOTIFICATION,\n            int(HTTPStatus.INTERNAL_SERVER_ERROR),\n            \"Enqueue api notification event failed: \" + str(err),\n        )\n", "text_preview": "# Copyright © 2019 Province of British Columbia # # Licensed under the Apache License, Version 2.0 (the 'License'); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.", "input_sha256": "43ce651adc57e713741b38a8d5872d9f8da165124bc1f12335b55229bb0a0693", "text_sha256": "24c43f93c5152b13c6f78c32a2b766395715528b7522ee39d6ea99ec5d88861b", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12028, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Legal Notices", "sub_format": null}, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 1, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.0423775960160833, "noise_severity": 0.5890002195643416, "missing_content_severity": 0.01784020607704008, "factual_density": 3.309406610042262, "pretraining_utility": 0.6060582371538128, "math_density": 0.4420191978406283, "code_density": 3.680734131634654, "reasoning_density": 1.3739280376318777, "education_level": 2.7069727020339127, "creative_writing_quality": -0.17461884511780784}, "api_choices": {"primary": "Technical Writing", "secondary": "Legal Notices", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.5719677432488071, "secondary_choice": "Legal Notices", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9937408118863269, "secondary": 0.7470860795085265, "tertiary": 0.754203042358198, "structured_records": 0.0957993974827728, "procedural_steps": 0.016128543036631367, "worked_solution": 0.003893294857209006, "contextual_dialogue": 7.916772042976926e-06, "content_quality": 0.5499018101173612, "noise_severity": 0.60350294008706, "missing_content_severity": 0.9851331616024666, "factual_density": 0.4245055083685515, "pretraining_utility": 0.5293827752809102, "math_density": 0.631650668466143, "code_density": 0.7339451096955449, "reasoning_density": 0.0, "education_level": 0.4968751591940795, "creative_writing_quality": 0.9995058319080985, "primary_topic": 0.9845844463149286, "secondary_topic": 0.8344529141896679}, "probability_margin": {"primary": 0.9895088743843263, "secondary": 0.5651473205178721, "tertiary": 0.5481999437786568, "education_level": 0.2318393412214202, "creative_writing_quality": 0.9993826412176979, "primary_topic": 0.9764235985739365, "secondary_topic": 0.7700346954710675}, "raw_probabilities": {"primary": {"Academic Writing": 2.7234315566230137e-05, "Content Listing": 1.9427603586683566e-05, "Creative Writing": 1.1207857530118007e-05, "Customer Support Page": 9.499189566810754e-06, "Q&A Forum": 8.621518251351723e-06, "Discussion Thread": 4.919594183941607e-06, "Comment Section": 1.1166526113205574e-05, "Missing Content": 1.924397897341366e-05, "Knowledge Article": 4.539989918013924e-05, "Legal Notices": 0.004492737006736881, "News Article": 4.716025317172307e-06, "Organizational Announcement": 1.6075711106235658e-05, "Nonfiction Writing": 7.319137053100418e-06, "Organizational About Page": 1.189959557287385e-05, "Personal About Page": 5.565162138453916e-06, "Personal Blog": 6.543719894571566e-06, "Product Page": 6.735009073713603e-06, "Spam / Ads": 7.379282327218052e-06, "Structured Data": 0.0003012539332841256, "Technical Writing": 0.9940016113910632, "Transcript / Interview": 1.1499134798692556e-05, "Tutorial / How-To Guide": 6.640122569666633e-05, "User Reviews": 5.2734083509745785e-06, "Other / Unknown": 0.0008982697746342305}, "secondary": {"Academic Writing": 0.0005626292700600062, "Content Listing": 0.0005880193347005144, "Creative Writing": 0.00045981265934209636, "Customer Support Page": 0.00032113998305910956, "Q&A Forum": 0.00027906474083509556, "Discussion Thread": 0.00022127895337756712, "Comment Section": 0.0004365583726825727, "Missing Content": 0.0004498538633561546, "Knowledge Article": 0.0008832280765619302, "Legal Notices": 0.7576241595290045, "News Article": 0.00038466941205946616, "Organizational Announcement": 0.000500462466154274, "Nonfiction Writing": 0.0003213302286826172, "Organizational About Page": 0.0005139477055082717, "Personal About Page": 0.00023809753299988817, "Personal Blog": 0.00024161983137724403, "Product Page": 0.0003386944380104543, "Spam / Ads": 0.0003427265707860255, "Structured Data": 0.004014271077650904, "Technical Writing": 0.037812340321945345, "Transcript / Interview": 0.00046326347414865025, "Tutorial / How-To Guide": 0.00034378556173526436, "User Reviews": 0.00018220758482971201, "NONE": 0.1924768390111325}, "tertiary": {"Academic Writing": 0.0003719364024488064, "Content Listing": 0.00036996416299775784, "Creative Writing": 0.0003258127541703467, "Customer Support Page": 0.00022603564154719235, "Q&A Forum": 0.00011515982814625098, "Discussion Thread": 0.00011714916185721494, "Comment Section": 0.00024753597392618336, "Missing Content": 0.00026308385489867616, "Knowledge Article": 0.000545540028071222, "Legal Notices": 0.21624463848128295, "News Article": 0.00024699596648112366, "Organizational Announcement": 0.00027319892073603266, "Nonfiction Writing": 0.00018218715901601037, "Organizational About Page": 0.0002810242200391801, "Personal About Page": 0.0001557375542584422, "Personal Blog": 0.00015789969544112198, "Product Page": 0.0001812470144803049, "Spam / Ads": 0.00019557631880656216, "Structured Data": 0.006863705558531587, "Technical Writing": 0.007589065573220154, "Transcript / Interview": 0.00025474060240458313, "Tutorial / How-To Guide": 0.00026019210219371244, "User Reviews": 8.6990765105012e-05, "NONE": 0.7644445822599397}, "structured_records": {"false": 0.9042006025172272, "true": 0.0957993974827728}, "procedural_steps": {"false": 0.9838714569633686, "true": 0.016128543036631367}, "worked_solution": {"false": 0.996106705142791, "true": 0.003893294857209006}, "contextual_dialogue": {"false": 0.9999920832279571, "true": 7.916772042976926e-06}, "content_quality": {"-2": 0.004117824561508555, "-1": 0.015174620970664646, "0": 0.20616740029568661, "1": 0.4832924422345153, "2": 0.2912477119376249}, "noise_severity": {"0": 0.4433981261655932, "1": 0.5307757577496527, "2": 0.02137097317525726, "3": 0.0023380561738122743, "4": 0.0021170867356843587}, "missing_content_severity": {"0": 0.9851642907588473, "1": 0.012731609896928084, "2": 0.0013927360593966177, "3": 0.0005223290779938373, "4": 0.00018903420683431335}, "factual_density": {"0": 0.004210704436069073, "1": 0.01219617351591291, "2": 0.1110502335721245, "3": 0.4150615845214742, "4": 0.45748130395441944}, "pretraining_utility": {"-2": 0.006015444743310048, "-1": 0.05527829238975264, "0": 0.350738297245112, "1": 0.5025685122134651, "2": 0.08539945340836022}, "math_density": {"0": 0.6031042967958185, "1": 0.3691082529600553, "2": 0.017661019051569107, "3": 0.00291681799279361, "4": 0.007209613199763497}, "code_density": {"0": 0.0003065710263871437, "1": 0.0006304733434362949, "2": 0.023928769686476056, "3": 0.26829062485653643, "4": 0.7068435610871641}, "reasoning_density": {"0": 0.375493502390224, "1": 0.18607218528167274, "2": 0.21411528759448056, "3": 0.1376508217732468, "4": 0.08666820296037579}, "education_level": {"none": 0.0008330593797122722, "0": 0.0010581071161429622, "1": 0.003069495146787382, "2": 0.34888995810697937, "3": 0.5807292993283996, "4": 0.06542008092197839}, "creative_writing_quality": {"not_applicable": 0.9995881932567487, "-2": 4.5718084141955954e-05, "-1": 8.141518356628116e-05, "0": 0.00020555203905079318, "1": 5.7300739053217784e-05, "2": 2.1820697439329597e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.722378896844776e-05, "Art & Design": 5.134611961703094e-05, "Law & Crime": 0.0023378821079109006, "Education & Careers": 5.3519040216279234e-05, "Entertainment & Media": 3.137263437724336e-05, "Fashion & Beauty": 3.0256315977274893e-05, "Business, Economics & Finance": 0.0014017911348015045, "Food & Dining": 5.577234708621077e-05, "Games & Gaming": 4.6075046563505684e-05, "Electronics & Hardware": 0.00011852663397196544, "Health, Healthcare & Wellness": 4.635429696535658e-05, "History & Geography": 4.289341355120859e-05, "Home, Pets & Hobbies": 2.81870913542779e-05, "Industry & Production": 6.791132846334678e-05, "Language, Literature & Philosophy": 4.186008851657031e-05, "Politics & Government": 0.00875375365195639, "Religion & Spirituality": 4.5428167344037817e-05, "Science, Mathematics & Engineering": 0.000143767118070802, "Relationships & Community": 2.2263221595165913e-05, "Software Usage & Applications": 0.0003591185225368982, "Programming & Software Engineering": 0.9851773522258929, "Sports & Fitness": 4.673595049508667e-05, "Vehicles & Transportation": 8.936183543505829e-05, "Travel & Tourism": 4.487613926497014e-05, "Other / Uncovered Subject": 0.0007961126524690687, "Unknown / Insufficient Content": 0.00012025912659865226}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007183795234431866, "Art & Design": 0.0006344073999936573, "Law & Crime": 0.050968438025079985, "Education & Careers": 0.00047275204017212003, "Entertainment & Media": 0.0004946160778778129, "Fashion & Beauty": 0.0003600754868044208, "Business, Economics & Finance": 0.008882159493662263, "Food & Dining": 0.0003832963500208188, "Games & Gaming": 0.00039040970804960146, "Electronics & Hardware": 0.0006888732725028971, "Health, Healthcare & Wellness": 0.0004476600841157725, "History & Geography": 0.0003974190757024025, "Home, Pets & Hobbies": 0.00029095369984772783, "Industry & Production": 0.0003677283910631307, "Language, Literature & Philosophy": 0.0002751121125853981, "Politics & Government": 0.07078541432669021, "Religion & Spirituality": 0.000678049713361485, "Science, Mathematics & Engineering": 0.0006072407365913856, "Relationships & Community": 0.00015221356185364662, "Software Usage & Applications": 0.002102090225865908, "Programming & Software Engineering": 0.01466680541078942, "Sports & Fitness": 0.0002613638393300524, "Vehicles & Transportation": 0.00029991228154411555, "Travel & Tourism": 0.00039958263829216614, "Other / Uncovered Subject": 0.0034549367270027163, "NONE": 0.8408201097977577}}, "probabilities": {"primary": {"Academic Writing": 2.7234315566230137e-05, "Content Listing": 1.9427603586683566e-05, "Creative Writing": 1.1207857530118007e-05, "Customer Support Page": 9.499189566810754e-06, "Q&A Forum": 8.621518251351723e-06, "Discussion Thread": 4.919594183941607e-06, "Comment Section": 1.1166526113205574e-05, "Missing Content": 1.924397897341366e-05, "Knowledge Article": 4.539989918013924e-05, "Legal Notices": 0.004492737006736881, "News Article": 4.716025317172307e-06, "Organizational Announcement": 1.6075711106235658e-05, "Nonfiction Writing": 7.319137053100418e-06, "Organizational About Page": 1.189959557287385e-05, "Personal About Page": 5.565162138453916e-06, "Personal Blog": 6.543719894571566e-06, "Product Page": 6.735009073713603e-06, "Spam / Ads": 7.379282327218052e-06, "Structured Data": 0.0003012539332841256, "Technical Writing": 0.9940016113910632, "Transcript / Interview": 1.1499134798692556e-05, "Tutorial / How-To Guide": 6.640122569666633e-05, "User Reviews": 5.2734083509745785e-06, "Other / Unknown": 0.0008982697746342305}, "secondary": {"Academic Writing": 0.0005626292700600061, "Content Listing": 0.0005880193347005143, "Creative Writing": 0.00045981265934209626, "Customer Support Page": 0.0003211399830591095, "Q&A Forum": 0.0002790647408350955, "Discussion Thread": 0.00022127895337756707, "Comment Section": 0.0004365583726825726, "Missing Content": 0.0004498538633561545, "Knowledge Article": 0.00088322807656193, "Legal Notices": 0.7576241595290043, "News Article": 0.00038466941205946606, "Organizational Announcement": 0.0005004624661542738, "Nonfiction Writing": 0.00032133022868261716, "Organizational About Page": 0.0005139477055082715, "Personal About Page": 0.00023809753299988812, "Personal Blog": 0.00024161983137724398, "Product Page": 0.00033869443801045425, "Spam / Ads": 0.00034272657078602544, "Structured Data": 0.004014271077650903, "Technical Writing": 0.03781234032194534, "Transcript / Interview": 0.00046326347414865014, "Tutorial / How-To Guide": 0.0003437855617352643, "User Reviews": 0.000182207584829712, "NONE": 0.19247683901113244}, "tertiary": {"Academic Writing": 0.0003719364024488063, "Content Listing": 0.00036996416299775773, "Creative Writing": 0.00032581275417034665, "Customer Support Page": 0.0002260356415471923, "Q&A Forum": 0.00011515982814625096, "Discussion Thread": 0.00011714916185721491, "Comment Section": 0.0002475359739261833, "Missing Content": 0.0002630838548986761, "Knowledge Article": 0.0005455400280712218, "Legal Notices": 0.2162446384812829, "News Article": 0.0002469959664811236, "Organizational Announcement": 0.0002731989207360326, "Nonfiction Writing": 0.00018218715901601034, "Organizational About Page": 0.00028102422003918007, "Personal About Page": 0.00015573755425844218, "Personal Blog": 0.00015789969544112195, "Product Page": 0.00018124701448030486, "Spam / Ads": 0.0001955763188065621, "Structured Data": 0.006863705558531585, "Technical Writing": 0.007589065573220153, "Transcript / Interview": 0.0002547406024045831, "Tutorial / How-To Guide": 0.0002601921021937124, "User Reviews": 8.699076510501199e-05, "NONE": 0.7644445822599395}, "structured_records": {"false": 0.9042006025172272, "true": 0.0957993974827728}, "procedural_steps": {"false": 0.9838714569633686, "true": 0.016128543036631367}, "worked_solution": {"false": 0.996106705142791, "true": 0.003893294857209006}, "contextual_dialogue": {"false": 0.9999920832279571, "true": 7.916772042976926e-06}, "content_quality": {"-2": 0.004117824561508555, "-1": 0.015174620970664646, "0": 0.20616740029568661, "1": 0.4832924422345153, "2": 0.2912477119376249}, "noise_severity": {"0": 0.4433981261655933, "1": 0.5307757577496528, "2": 0.021370973175257265, "3": 0.0023380561738122747, "4": 0.002117086735684359}, "missing_content_severity": {"0": 0.985164290758847, "1": 0.01273160989692808, "2": 0.0013927360593966175, "3": 0.0005223290779938372, "4": 0.0001890342068343133}, "factual_density": {"0": 0.004210704436069073, "1": 0.01219617351591291, "2": 0.1110502335721245, "3": 0.4150615845214742, "4": 0.45748130395441944}, "pretraining_utility": {"-2": 0.006015444743310048, "-1": 0.05527829238975264, "0": 0.350738297245112, "1": 0.5025685122134651, "2": 0.08539945340836022}, "math_density": {"0": 0.6031042967958185, "1": 0.3691082529600553, "2": 0.017661019051569107, "3": 0.00291681799279361, "4": 0.007209613199763497}, "code_density": {"0": 0.0003065710263871437, "1": 0.0006304733434362949, "2": 0.023928769686476056, "3": 0.26829062485653643, "4": 0.7068435610871641}, "reasoning_density": {"0": 0.37549350239022405, "1": 0.18607218528167277, "2": 0.2141152875944806, "3": 0.13765082177324683, "4": 0.0866682029603758}, "education_level": {"none": 0.0008330593797122723, "0": 0.0010581071161429624, "1": 0.0030694951467873824, "2": 0.3488899581069794, "3": 0.5807292993283997, "4": 0.0654200809219784}, "creative_writing_quality": {"not_applicable": 0.9995881932567485, "-2": 4.571808414195595e-05, "-1": 8.141518356628115e-05, "0": 0.00020555203905079313, "1": 5.730073905321777e-05, "2": 2.1820697439329594e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.722378896844775e-05, "Art & Design": 5.134611961703093e-05, "Law & Crime": 0.0023378821079109, "Education & Careers": 5.351904021627922e-05, "Entertainment & Media": 3.137263437724335e-05, "Fashion & Beauty": 3.0256315977274886e-05, "Business, Economics & Finance": 0.0014017911348015043, "Food & Dining": 5.577234708621076e-05, "Games & Gaming": 4.607504656350567e-05, "Electronics & Hardware": 0.00011852663397196542, "Health, Healthcare & Wellness": 4.6354296965356565e-05, "History & Geography": 4.289341355120858e-05, "Home, Pets & Hobbies": 2.818709135427789e-05, "Industry & Production": 6.791132846334677e-05, "Language, Literature & Philosophy": 4.1860088516570303e-05, "Politics & Government": 0.008753753651956388, "Religion & Spirituality": 4.542816734403781e-05, "Science, Mathematics & Engineering": 0.00014376711807080196, "Relationships & Community": 2.226322159516591e-05, "Software Usage & Applications": 0.00035911852253689815, "Programming & Software Engineering": 0.9851773522258926, "Sports & Fitness": 4.6735950495086656e-05, "Vehicles & Transportation": 8.936183543505828e-05, "Travel & Tourism": 4.4876139264970136e-05, "Other / Uncovered Subject": 0.0007961126524690684, "Unknown / Insufficient Content": 0.00012025912659865223}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007183795234431866, "Art & Design": 0.0006344073999936573, "Law & Crime": 0.050968438025079985, "Education & Careers": 0.00047275204017212003, "Entertainment & Media": 0.0004946160778778129, "Fashion & Beauty": 0.0003600754868044208, "Business, Economics & Finance": 0.008882159493662263, "Food & Dining": 0.0003832963500208188, "Games & Gaming": 0.00039040970804960146, "Electronics & Hardware": 0.0006888732725028971, "Health, Healthcare & Wellness": 0.0004476600841157725, "History & Geography": 0.0003974190757024025, "Home, Pets & Hobbies": 0.00029095369984772783, "Industry & Production": 0.0003677283910631307, "Language, Literature & Philosophy": 0.0002751121125853981, "Politics & Government": 0.07078541432669021, "Religion & Spirituality": 0.000678049713361485, "Science, Mathematics & Engineering": 0.0006072407365913856, "Relationships & Community": 0.00015221356185364662, "Software Usage & Applications": 0.002102090225865908, "Programming & Software Engineering": 0.01466680541078942, "Sports & Fitness": 0.0002613638393300524, "Vehicles & Transportation": 0.00029991228154411555, "Travel & Tourism": 0.00039958263829216614, "Other / Uncovered Subject": 0.0034549367270027163, "NONE": 0.8408201097977577}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 14154, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 14154, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:07.045224+00:00", "scoring_key_slot": "primary", "dataset_uid": "e3450467-c22f-4bc4-9bc9-6bee0f156019", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "ec52f7e3cea7137263bbbc7ee8a3bf3e7a0b77671b8c45fabe77fc0217cf86fb", "source_metadata": {"uuid": "e3450467-c22f-4bc4-9bc9-6bee0f156019", "repo_name": "bcgov/ppr", "relative_path": "ppr-api/src/ppr_api/resources/v1/search_results.py", "category": "WEB", "algo_rel_score": 0.9614982604980469, "quality_score": 7.483300685882568, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 3144, "repo_created_at": "2021-01-25T22:06:53.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 8, "forks": 32, "issues": 1, "pull_requests": 0}, "file_meta": {"content_id": "5675adcd19aa34d9fe68caca063b3231b3ebea49", "size_bytes": 26591, "file_timestamp": 1752855528, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "d972b65167102ffedf0d2089f3732582dfb9cd4c"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 3144, "repo_created_at": "2021-01-25T22:06:53.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 8, "forks": 32, "issues": 1, "pull_requests": 0}, "file_meta": {"content_id": "5675adcd19aa34d9fe68caca063b3231b3ebea49", "size_bytes": 26591, "file_timestamp": 1752855528, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "d972b65167102ffedf0d2089f3732582dfb9cd4c", "url": "https://github.com/bcgov/ppr/blob/d972b65167102ffedf0d2089f3732582dfb9cd4c/ppr-api/src/ppr_api/resources/v1/search_results.py"}, "gpt4o_tokens": 5272, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.0423775960160833, "noise_severity": 0.5890002195643416, "missing_content_severity": 0.017840206077040083, "factual_density": 3.309406610042262, "pretraining_utility": 0.6060582371538128, "math_density": 0.4420191978406283, "code_density": 3.680734131634654, "reasoning_density": 1.3739280376318777, "education_level": 2.7069727020339127, "creative_writing_quality": -0.17461884511780784}, "score_applicability": {"education_level": 0.9991669406202879, "creative_writing_quality": 0.0004118067432515776}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/sagemath/sage-archive-2023-02-01/blob/9116c5584f94500731fdc9462df519229fa6ea85/src/sage/groups/additive_abelian/qmodnz_element.py", "id": "python-0-e148a18410f7367690de581f7553980d", "text": "r\"\"\"\nElements of `\\Q/n\\Z`.\n\nEXAMPLES::\n\n    sage: A = QQ / (3*ZZ)\n    sage: x = A(11/3); x\n    2/3\n    sage: x*14\n    1/3\n    sage: x.additive_order()\n    9\n\"\"\"\n\n# ****************************************************************************\n#       Copyright (C) 2017 <NAME> <<EMAIL>>\n#\n#  Distributed under the terms of the GNU General Public License (GPL)\n#  as published by the Free Software Foundation; either version 2 of\n#  the License, or (at your option) any later version.\n#                  https://www.gnu.org/licenses/\n# ****************************************************************************\n\nfrom sage.structure.element import AdditiveGroupElement\nfrom sage.rings.integer_ring import ZZ\nfrom sage.rings.infinity import infinity\nfrom sage.structure.richcmp import richcmp, op_EQ, op_NE\n\n\nclass QmodnZ_Element(AdditiveGroupElement):\n    r\"\"\"\n    The ``QmodnZ_Element`` class represents an element of the abelian\n    group `\\Q/n\\Z`.\n\n    INPUT:\n\n    - ``q`` -- a rational number.\n\n    - ``parent`` -- the parent abelian group `\\Q/n\\Z`.\n\n    OUTPUT:\n\n    The element `q` of abelian group `\\Q/n\\Z`, in standard form.\n\n    EXAMPLES::\n\n        sage: G = QQ/(19*ZZ)\n        sage: G(400/19)\n        39/19\n    \"\"\"\n    def __init__(self, parent, x, construct=False):\n        r\"\"\"\n        Create an element of `\\Q/n\\Z`.\n\n        EXAMPLES::\n\n            sage: G = QQ/(3*ZZ)\n            sage: G.random_element().parent() is G\n            True\n        \"\"\"\n\n        AdditiveGroupElement.__init__(self, parent)\n        # x = (a/b) = q(n/m) + r/mb\n        # am = q(nb) + r\n        # r < nb so r/mb < n/m\n        if construct:\n            self._x = x\n            return\n        n = parent.n.numerator()\n        if n == 0:\n            self._x = x\n        else:\n            m = parent.n.denominator()\n            a = x.numerator()\n            b = x.denominator()\n            q, r = (a * m).quo_rem(n * b)\n            self._x = r / (m * b)\n\n    def lift(self):\n        r\"\"\"\n        Return the smallest non-negative rational number reducing to\n        this element.\n\n        EXAMPLES::\n\n            sage: G = QQ/(5*ZZ)\n            sage: g = G(2/4); g\n            1/2\n            sage: q = lift(g); q\n            1/2\n\n        TESTS::\n\n            sage: q.parent() is QQ\n            True\n        \"\"\"\n        return self._x\n\n    def _rational_(self):\n        r\"\"\"\n        Lift to `\\Q`.\n\n        TESTS::\n\n            sage: QQ((QQ/ZZ)(4/3)) # indirect doctest\n            1/3\n        \"\"\"\n        return self._x\n\n    def _integer_(self, Z):\n        r\"\"\"\n        Lift to `\\Z`.\n\n        This is the smallest non-negative integer reducing to this element,\n        or a ``ValueError`` if none exists.\n\n        TESTS::\n\n            sage: G = QQ/(2*ZZ)\n            sage: ZZ(G(3))\n            1\n            sage: from sage.groups.additive_abelian.qmodnz import QmodnZ\n            sage: G = QmodnZ(8/3)\n            sage: ZZ(G(1/3))\n            3\n\n            sage: all(ZZ(G(i)) == i for i in range(8))\n            True\n\n            sage: G = QmodnZ(101/34)\n            sage: all(ZZ(G(i)) == i for i in range(101))\n            True\n        \"\"\"\n        QZ = self.parent()\n        b = self._x.denominator()\n        n = QZ.n.numerator()\n        m = QZ.n.denominator()\n        if not b.divides(m):\n            raise ValueError(\"No integral lift\")\n        a = self._x.numerator() * (m // b)\n        # a/m + q*(n/m) = (a + qn)/m = km/m so a + qn = km,\n        # k = a*m^(-1) mod n.\n        return (a * m.inverse_mod(n)) % n\n\n    def __neg__(self):\n        r\"\"\"\n        Return the additive inverse of this element in `\\Q/n\\Z`.\n\n        EXAMPLES::\n\n            sage: from sage.groups.additive_abelian.qmodnz import QmodnZ\n            sage: G = QmodnZ(5/7)\n            sage: g = G(13/21)\n            sage: -g\n            2/21\n\n        TESTS::\n\n            sage: G = QmodnZ(19/23)\n            sage: g = G(15/23)\n            sage: -g\n            4/23\n            sage: g + -g == G(0)\n            True\n        \"\"\"\n        if self._x == 0:\n            return self\n        else:\n            QZ = self.parent()\n            return QZ.element_class(QZ, QZ.n - self._x, True)\n\n    def _add_(self, other):\n        r\"\"\"\n        Return the sum of two elements in `\\Q/n\\Z`.\n\n        EXAMPLES::\n\n            sage: from sage.groups.additive_abelian.qmodnz import QmodnZ\n            sage: G = QmodnZ(9/10)\n            sage: g = G(5)\n            sage: h = G(1/2)\n            sage: g + h\n            1/10\n            sage: g + h == G(1/10)\n            True\n\n        TESTS::\n\n            sage: h + g == G(1/10)\n            True\n        \"\"\"\n        QZ = self.parent()\n        ans = self._x + other._x\n        if ans >= QZ.n:\n            ans -= QZ.n\n        return QZ.element_class(QZ, ans, True)\n\n    def _sub_(self, other):\n        r\"\"\"\n        Return the difference of two elements in `\\Q/n\\Z`.\n\n        EXAMPLES::\n\n            sage: from sage.groups.additive_abelian.qmodnz import QmodnZ\n            sage: G = QmodnZ(9/10)\n            sage: g = G(4)\n            sage: h = G(1/2)\n            sage: g - h\n            4/5\n            sage: h - g\n            1/10\n            sage: g - h == G(4/5)\n            True\n            sage: h - g == G(1/10)\n            True\n        \"\"\"\n        QZ = self.parent()\n        ans = self._x - other._x\n        if ans < 0:\n            ans += QZ.n\n        return QZ.element_class(QZ, ans, True)\n\n    def _rmul_(self, c):\n        r\"\"\"\n        Return the (right) scalar product of this element by ``c`` in `\\Q/n\\Z`.\n\n        EXAMPLES::\n\n            sage: from sage.groups.additive_abelian.qmodnz import QmodnZ\n            sage: G = QmodnZ(5/7)\n            sage: g = G(13/21)\n            sage: g*6\n            1/7\n        \"\"\"\n        QZ = self.parent()\n        return QZ.element_class(QZ, self._x * c)\n\n    def _lmul_(self, c):\n        r\"\"\"\n        Return the (left) scalar product of this element by ``c`` in `\\Q/n\\Z`.\n\n        EXAMPLES::\n\n            sage: from sage.groups.additive_abelian.qmodnz import QmodnZ\n            sage: G = QmodnZ(5/7)\n            sage: g = G(13/21)\n            sage: 6*g\n            1/7\n\n        TESTS::\n\n            sage: 6*g == g*6\n            True\n            sage: 6*g == 5*g\n            False\n        \"\"\"\n        return self._rmul_(c)\n\n    def division_by(self, other):\n        r\"\"\"\n        Division by an integer.\n\n        .. WARNING::\n\n            Division of `x` by `m` does not yield a well defined\n            result, since there are `m` elements `y` of `\\Q/n\\Z`\n            with the property that `x = my`.  We return the one\n            with the smallest non-negative lift.\n\n        EXAMPLES::\n\n            sage: G = QQ/(4*ZZ)\n            sage: x = G(3/8)\n            sage: x.division_by(4)\n            3/32\n        \"\"\"\n        QZ = self.parent()\n        other = ZZ(other)\n        return QZ.element_class(QZ, self._x / other, True)\n\n    def _repr_(self):\n        r\"\"\"\n        Display the element.\n\n        EXAMPLES::\n\n            sage: G = QQ/(8*ZZ)\n            sage: g = G(25/7); g\n            25/7\n        \"\"\"\n        return repr(self._x)\n\n    def __hash__(self):\n        r\"\"\"\n        Hashing.\n\n        TESTS::\n\n            sage: G = QQ/(4*ZZ)\n            sage: g = G(4/5)\n            sage: hash(g)\n            2135587864 # 32-bit\n            -7046029254386353128 # 64-bit\n            sage: hash(G(3/4))\n            527949074 # 32-bit\n            3938850096065010962 # 64-bit\n            sage: hash(G(1))\n            1\n        \"\"\"\n        return hash(self._x)\n\n    def _richcmp_(self, right, op):\n        r\"\"\"\n        Compare two elements.\n\n        EXAMPLES::\n\n            sage: G = QQ/(4*ZZ)\n            sage: g = G(4/5)\n            sage: h = G(6/7)\n            sage: g == h\n            False\n            sage: g == g\n            True\n        \"\"\"\n        if op == op_EQ or op == op_NE:\n            return richcmp(self._x, right._x, op)\n        else:\n            return NotImplemented\n\n    def additive_order(self):\n        r\"\"\"\n        Returns the order of this element in the abelian group `\\Q/n\\Z`.\n\n        EXAMPLES::\n\n            sage: G = QQ/(12*ZZ)\n            sage: g = G(5/3)\n            sage: g.additive_order()\n            36\n            sage: (-g).additive_order()\n            36\n        \"\"\"\n        # a/b * k = n/m * r\n        QZ = self.parent()\n        if QZ.n == 0:\n            if self._x == 0:\n                return ZZ.one()\n            return infinity\n        return (self._x / QZ.n).denominator()\n", "text_preview": "r\"\"\" Elements of `\\Q/n\\Z`. EXAMPLES:: sage: A = QQ / (3*ZZ) sage: x = A(11/3); x 2/3 sage: x*14 1/3 sage: x.additive_order() 9 \"\"\" # **************************************************************************** # Copyright (C) 2017 <NAME> <<", "input_sha256": "bb9a00f98b11a5b8f3ff8e68d561061e14f9ccb8824d051f005958791a1d1b1e", "text_sha256": "e148a18410f7367690de581f7553980d3aad6f1d0d4babe7d9cdcd7f0159fe2b", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12029, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Legal Notices", "sub_format": null}, "tertiary": {"main_format": "Legal Notices", "sub_format": null}, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 1, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.3274500825349502, "noise_severity": 0.5872454773605448, "missing_content_severity": 0.009096908314603276, "factual_density": 3.3485639920869734, "pretraining_utility": 0.6799629803064976, "math_density": 3.3212946330043445, "code_density": 3.6007207468875904, "reasoning_density": 1.6855640672294934, "education_level": 2.5769661873011214, "creative_writing_quality": -0.06178761150366841}, "api_choices": {"primary": "Technical Writing", "secondary": "Legal Notices", "tertiary": "Legal Notices", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["Duplicate Document Type slots", "secondary probability 0.5912 below 0.70", "tertiary probability 0.5197 below 0.70", "tertiary vs NONE margin 0.0813 below 0.10", "secondary_topic probability 0.4247 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.10769299098050727, "secondary_choice": "Legal Notices", "tertiary_choice": "Legal Notices"}, "confidence": {"primary": 0.9975699719047695, "secondary": 0.5734235683069612, "tertiary": 0.4988469919129045, "structured_records": 0.02370696868094075, "procedural_steps": 0.017182248157140565, "worked_solution": 0.30889909964360923, "contextual_dialogue": 5.92931242628535e-06, "content_quality": 0.5421711489840311, "noise_severity": 0.5927859949862534, "missing_content_severity": 0.9924192430711639, "factual_density": 0.45713666007247755, "pretraining_utility": 0.5444418587296315, "math_density": 0.434412194170287, "code_density": 0.6672672890729918, "reasoning_density": 0.0, "education_level": 0.36422474822194495, "creative_writing_quality": 0.9987953613593188, "primary_topic": 0.7849678624395189, "secondary_topic": 0.4016710132946734}, "probability_margin": {"primary": 0.9968481200225264, "secondary": 0.26049937020565717, "tertiary": 0.08133716018084552, "education_level": 0.006167811857844541, "creative_writing_quality": 0.9983915971338018, "primary_topic": 0.5939594016512382, "secondary_topic": 0.032899000329787764}, "raw_probabilities": {"primary": {"Academic Writing": 0.00015611560072771575, "Content Listing": 1.3306023543720488e-05, "Creative Writing": 6.679544576270525e-06, "Customer Support Page": 1.521971229633768e-05, "Q&A Forum": 8.298716275388044e-06, "Discussion Thread": 4.096244049746002e-06, "Comment Section": 6.033585207603957e-06, "Missing Content": 1.249867222875993e-05, "Knowledge Article": 0.00031425780125401645, "Legal Notices": 0.0003299992674287723, "News Article": 2.7909752269482358e-06, "Organizational Announcement": 9.793774350278815e-06, "Nonfiction Writing": 1.0591847302644761e-05, "Organizational About Page": 8.407420232659083e-06, "Personal About Page": 5.270904240735638e-06, "Personal Blog": 4.83822842321857e-06, "Product Page": 6.756408863094594e-06, "Spam / Ads": 5.607443529407809e-06, "Structured Data": 7.904611417650137e-05, "Technical Writing": 0.9976712230754041, "Transcript / Interview": 9.53751395787236e-06, "Tutorial / How-To Guide": 0.000492603786789948, "User Reviews": 3.924287036901375e-06, "Other / Unknown": 0.0008231030528777357}, "secondary": {"Academic Writing": 0.006024842865719078, "Content Listing": 0.000709734445252015, "Creative Writing": 0.0006271967928727426, "Customer Support Page": 0.0005893013832052162, "Q&A Forum": 0.0003986731990714337, "Discussion Thread": 0.0003318872434233589, "Comment Section": 0.0005950577986099247, "Missing Content": 0.000772431846277327, "Knowledge Article": 0.003876162576497373, "Legal Notices": 0.5911975862941711, "News Article": 0.0005135920877097658, "Organizational Announcement": 0.0007309726941717082, "Nonfiction Writing": 0.0005404093700163733, "Organizational About Page": 0.000596732263363522, "Personal About Page": 0.00035929194097215596, "Personal Blog": 0.00028805763461466045, "Product Page": 0.0004659924109071362, "Spam / Ads": 0.00047093039915640036, "Structured Data": 0.0009064976611479987, "Technical Writing": 0.049268132325663475, "Transcript / Interview": 0.0006975322775580095, "Tutorial / How-To Guide": 0.009055260769502527, "User Reviews": 0.00028550763160284907, "NONE": 0.3306982160885139}, "tertiary": {"Academic Writing": 0.002336937019506045, "Content Listing": 0.0006051635796753818, "Creative Writing": 0.0007116072614387725, "Customer Support Page": 0.000512358195966863, "Q&A Forum": 0.00034469476733313084, "Discussion Thread": 0.00031479089682039547, "Comment Section": 0.0005091417136329401, "Missing Content": 0.0007022337125132767, "Knowledge Article": 0.0022670630326540936, "Legal Notices": 0.5197283672498668, "News Article": 0.0005469374583477779, "Organizational Announcement": 0.0006274464423297655, "Nonfiction Writing": 0.0004892414505090679, "Organizational About Page": 0.0005983094619993258, "Personal About Page": 0.0003365439478685801, "Personal Blog": 0.00027542313895657233, "Product Page": 0.00044802633725032594, "Spam / Ads": 0.00047825515043124257, "Structured Data": 0.0007372246741382477, "Technical Writing": 0.01995948983926206, "Transcript / Interview": 0.0006161672752014895, "Tutorial / How-To Guide": 0.008208491866006674, "User Reviews": 0.0002548784592700082, "NONE": 0.4383912070690213}, "structured_records": {"false": 0.9762930313190592, "true": 0.02370696868094075}, "procedural_steps": {"false": 0.9828177518428595, "true": 0.017182248157140565}, "worked_solution": {"false": 0.6911009003563908, "true": 0.30889909964360923}, "contextual_dialogue": {"false": 0.9999940706875737, "true": 5.92931242628535e-06}, "content_quality": {"-2": 0.0026221257685357427, "-1": 0.01231335441154864, "0": 0.07847918321340175, "1": 0.46816298472945755, "2": 0.43842235187705636}, "noise_severity": {"0": 0.4507056643279756, "1": 0.5192148836621446, "2": 0.024529985365748335, "3": 0.0032272436096222944, "4": 0.0023222230345091364}, "missing_content_severity": {"0": 0.9941543127192102, "1": 0.0034465807944679097, "2": 0.0017341730772299172, "3": 0.0004777522706931526, "4": 0.0001871811383990193}, "factual_density": {"0": 0.001905375538078857, "1": 0.006235923038539066, "2": 0.08822984066096078, "3": 0.4486470553231727, "4": 0.4549818054392486}, "pretraining_utility": {"-2": 0.006118987747346697, "-1": 0.05625113662310546, "0": 0.30249415812072133, "1": 0.5218193425933565, "2": 0.11331637491546989}, "math_density": {"0": 0.0031735236198717357, "1": 0.012452427473108056, "2": 0.12739373365433276, "3": 0.3738665227881789, "4": 0.4831137924645084}, "code_density": {"0": 0.000502993524401758, "1": 0.0011705851557320375, "2": 0.0334243171892161, "3": 0.3269068891691745, "4": 0.6379952149614756}, "reasoning_density": {"0": 0.25125281265634675, "1": 0.21895023139916453, "2": 0.24224674540694785, "3": 0.16808049713373033, "4": 0.11946971340381055}, "education_level": {"none": 0.0009023536397807998, "0": 0.0012106038575715307, "1": 0.004171086277051386, "2": 0.4701872901849541, "3": 0.46401947832710955, "4": 0.05950918771353253}, "creative_writing_quality": {"not_applicable": 0.998996134466099, "-2": 7.1292082694465e-05, "-1": 0.00014669761302234095, "0": 0.000604537332297286, "1": 0.0001354216869731934, "2": 4.591681891373918e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0002376370744659996, "Art & Design": 0.0004359612115450682, "Law & Crime": 0.00036366238811469045, "Education & Careers": 0.00023388079812132386, "Entertainment & Media": 0.00028597074457914257, "Fashion & Beauty": 0.00020822261897375605, "Business, Economics & Finance": 0.00027784099012099123, "Food & Dining": 0.00018405493676675448, "Games & Gaming": 0.00043742591206943877, "Electronics & Hardware": 0.0002608279243063405, "Health, Healthcare & Wellness": 0.0002860939055066877, "History & Geography": 0.0003537850551387337, "Home, Pets & Hobbies": 0.00023121509178241557, "Industry & Production": 0.00023468156309577838, "Language, Literature & Philosophy": 0.00028232133415802393, "Politics & Government": 0.00013993791061573708, "Religion & Spirituality": 0.0003614724526444378, "Science, Mathematics & Engineering": 0.19927892761752997, "Relationships & Community": 0.00012053144233613947, "Software Usage & Applications": 0.0005417207749153142, "Programming & Software Engineering": 0.7932383292687681, "Sports & Fitness": 0.00043944503243226646, "Vehicles & Transportation": 0.00015261593556911858, "Travel & Tourism": 0.00027579333451094784, "Other / Uncovered Subject": 0.0008127485807480241, "Unknown / Insufficient Content": 0.00032489610118480476}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005341623490977328, "Art & Design": 0.0006560485217422593, "Law & Crime": 0.005635087484705945, "Education & Careers": 0.0004946025508189151, "Entertainment & Media": 0.0005429815595090098, "Fashion & Beauty": 0.00041191219784884203, "Business, Economics & Finance": 0.0005705330253113911, "Food & Dining": 0.0003326353941359015, "Games & Gaming": 0.0005613808771773453, "Electronics & Hardware": 0.000551192595042005, "Health, Healthcare & Wellness": 0.000498595635434459, "History & Geography": 0.0006014483824022885, "Home, Pets & Hobbies": 0.0003957937421603972, "Industry & Production": 0.0004311314418426316, "Language, Literature & Philosophy": 0.00035840731302247457, "Politics & Government": 0.0003414359173588386, "Religion & Spirituality": 0.0005982691114840577, "Science, Mathematics & Engineering": 0.4246836666294937, "Relationships & Community": 0.00020855183864963256, "Software Usage & Applications": 0.000723546766795987, "Programming & Software Engineering": 0.39178466629970593, "Sports & Fitness": 0.00047674614985407875, "Vehicles & Transportation": 0.0002755424222950185, "Travel & Tourism": 0.0005863672367947018, "Other / Uncovered Subject": 0.0006914209504458427, "NONE": 0.16705387360687066}}, "probabilities": {"primary": {"Academic Writing": 0.00015611560072771566, "Content Listing": 1.3306023543720483e-05, "Creative Writing": 6.679544576270521e-06, "Customer Support Page": 1.5219712296337674e-05, "Q&A Forum": 8.29871627538804e-06, "Discussion Thread": 4.096244049746e-06, "Comment Section": 6.033585207603954e-06, "Missing Content": 1.2498672228759926e-05, "Knowledge Article": 0.0003142578012540163, "Legal Notices": 0.00032999926742877214, "News Article": 2.7909752269482345e-06, "Organizational Announcement": 9.79377435027881e-06, "Nonfiction Writing": 1.0591847302644756e-05, "Organizational About Page": 8.40742023265908e-06, "Personal About Page": 5.270904240735635e-06, "Personal Blog": 4.8382284232185675e-06, "Product Page": 6.756408863094591e-06, "Spam / Ads": 5.607443529407806e-06, "Structured Data": 7.904611417650133e-05, "Technical Writing": 0.9976712230754037, "Transcript / Interview": 9.537513957872355e-06, "Tutorial / How-To Guide": 0.0004926037867899477, "User Reviews": 3.924287036901373e-06, "Other / Unknown": 0.0008231030528777353}, "secondary": {"Academic Writing": 0.006024842865719078, "Content Listing": 0.000709734445252015, "Creative Writing": 0.0006271967928727426, "Customer Support Page": 0.0005893013832052162, "Q&A Forum": 0.0003986731990714337, "Discussion Thread": 0.0003318872434233589, "Comment Section": 0.0005950577986099247, "Missing Content": 0.000772431846277327, "Knowledge Article": 0.003876162576497373, "Legal Notices": 0.5911975862941711, "News Article": 0.0005135920877097658, "Organizational Announcement": 0.0007309726941717082, "Nonfiction Writing": 0.0005404093700163733, "Organizational About Page": 0.000596732263363522, "Personal About Page": 0.00035929194097215596, "Personal Blog": 0.00028805763461466045, "Product Page": 0.0004659924109071362, "Spam / Ads": 0.00047093039915640036, "Structured Data": 0.0009064976611479987, "Technical Writing": 0.049268132325663475, "Transcript / Interview": 0.0006975322775580095, "Tutorial / How-To Guide": 0.009055260769502527, "User Reviews": 0.00028550763160284907, "NONE": 0.3306982160885139}, "tertiary": {"Academic Writing": 0.0023369370195060447, "Content Listing": 0.0006051635796753817, "Creative Writing": 0.0007116072614387724, "Customer Support Page": 0.0005123581959668629, "Q&A Forum": 0.0003446947673331308, "Discussion Thread": 0.0003147908968203954, "Comment Section": 0.00050914171363294, "Missing Content": 0.0007022337125132766, "Knowledge Article": 0.002267063032654093, "Legal Notices": 0.5197283672498667, "News Article": 0.0005469374583477778, "Organizational Announcement": 0.0006274464423297654, "Nonfiction Writing": 0.0004892414505090678, "Organizational About Page": 0.0005983094619993257, "Personal About Page": 0.00033654394786858003, "Personal Blog": 0.0002754231389565723, "Product Page": 0.00044802633725032583, "Spam / Ads": 0.00047825515043124246, "Structured Data": 0.0007372246741382475, "Technical Writing": 0.019959489839262056, "Transcript / Interview": 0.0006161672752014893, "Tutorial / How-To Guide": 0.008208491866006673, "User Reviews": 0.00025487845927000814, "NONE": 0.4383912070690212}, "structured_records": {"false": 0.9762930313190592, "true": 0.02370696868094075}, "procedural_steps": {"false": 0.9828177518428595, "true": 0.017182248157140565}, "worked_solution": {"false": 0.6911009003563908, "true": 0.30889909964360923}, "contextual_dialogue": {"false": 0.9999940706875737, "true": 5.92931242628535e-06}, "content_quality": {"-2": 0.0026221257685357427, "-1": 0.01231335441154864, "0": 0.07847918321340175, "1": 0.46816298472945755, "2": 0.43842235187705636}, "noise_severity": {"0": 0.4507056643279756, "1": 0.5192148836621446, "2": 0.024529985365748335, "3": 0.0032272436096222944, "4": 0.0023222230345091364}, "missing_content_severity": {"0": 0.99415431271921, "1": 0.003446580794467909, "2": 0.0017341730772299168, "3": 0.0004777522706931525, "4": 0.00018718113839901924}, "factual_density": {"0": 0.001905375538078857, "1": 0.006235923038539066, "2": 0.08822984066096078, "3": 0.4486470553231727, "4": 0.4549818054392486}, "pretraining_utility": {"-2": 0.006118987747346698, "-1": 0.05625113662310547, "0": 0.3024941581207214, "1": 0.5218193425933566, "2": 0.1133163749154699}, "math_density": {"0": 0.003173523619871736, "1": 0.012452427473108058, "2": 0.1273937336543328, "3": 0.37386652278817895, "4": 0.4831137924645085}, "code_density": {"0": 0.000502993524401758, "1": 0.0011705851557320375, "2": 0.0334243171892161, "3": 0.3269068891691745, "4": 0.6379952149614756}, "reasoning_density": {"0": 0.25125281265634675, "1": 0.21895023139916453, "2": 0.24224674540694785, "3": 0.16808049713373033, "4": 0.11946971340381055}, "education_level": {"none": 0.0009023536397807999, "0": 0.001210603857571531, "1": 0.004171086277051387, "2": 0.47018729018495414, "3": 0.4640194783271096, "4": 0.05950918771353254}, "creative_writing_quality": {"not_applicable": 0.998996134466099, "-2": 7.1292082694465e-05, "-1": 0.00014669761302234095, "0": 0.000604537332297286, "1": 0.0001354216869731934, "2": 4.591681891373918e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0002376370744659996, "Art & Design": 0.0004359612115450682, "Law & Crime": 0.00036366238811469045, "Education & Careers": 0.00023388079812132386, "Entertainment & Media": 0.00028597074457914257, "Fashion & Beauty": 0.00020822261897375605, "Business, Economics & Finance": 0.00027784099012099123, "Food & Dining": 0.00018405493676675448, "Games & Gaming": 0.00043742591206943877, "Electronics & Hardware": 0.0002608279243063405, "Health, Healthcare & Wellness": 0.0002860939055066877, "History & Geography": 0.0003537850551387337, "Home, Pets & Hobbies": 0.00023121509178241557, "Industry & Production": 0.00023468156309577838, "Language, Literature & Philosophy": 0.00028232133415802393, "Politics & Government": 0.00013993791061573708, "Religion & Spirituality": 0.0003614724526444378, "Science, Mathematics & Engineering": 0.19927892761752997, "Relationships & Community": 0.00012053144233613947, "Software Usage & Applications": 0.0005417207749153142, "Programming & Software Engineering": 0.7932383292687681, "Sports & Fitness": 0.00043944503243226646, "Vehicles & Transportation": 0.00015261593556911858, "Travel & Tourism": 0.00027579333451094784, "Other / Uncovered Subject": 0.0008127485807480241, "Unknown / Insufficient Content": 0.00032489610118480476}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005341623490977328, "Art & Design": 0.0006560485217422593, "Law & Crime": 0.005635087484705945, "Education & Careers": 0.0004946025508189151, "Entertainment & Media": 0.0005429815595090098, "Fashion & Beauty": 0.00041191219784884203, "Business, Economics & Finance": 0.0005705330253113911, "Food & Dining": 0.0003326353941359015, "Games & Gaming": 0.0005613808771773453, "Electronics & Hardware": 0.000551192595042005, "Health, Healthcare & Wellness": 0.000498595635434459, "History & Geography": 0.0006014483824022885, "Home, Pets & Hobbies": 0.0003957937421603972, "Industry & Production": 0.0004311314418426316, "Language, Literature & Philosophy": 0.00035840731302247457, "Politics & Government": 0.0003414359173588386, "Religion & Spirituality": 0.0005982691114840577, "Science, Mathematics & Engineering": 0.4246836666294937, "Relationships & Community": 0.00020855183864963256, "Software Usage & Applications": 0.000723546766795987, "Programming & Software Engineering": 0.39178466629970593, "Sports & Fitness": 0.00047674614985407875, "Vehicles & Transportation": 0.0002755424222950185, "Travel & Tourism": 0.0005863672367947018, "Other / Uncovered Subject": 0.0006914209504458427, "NONE": 0.16705387360687066}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000004, "mass_delta": 4.440892098500626e-16, "missing_mass": 0.0, "excess_mass": 4.440892098500626e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11161, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11161, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:07.292502+00:00", "scoring_key_slot": "primary", "dataset_uid": "17353945-bc4c-47ae-b08e-2c77847a2300", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "a28483fd2aa1981345e6c679cd4bb24e7057ee4c336c3085782abc74a0959a38", "source_metadata": {"uuid": "17353945-bc4c-47ae-b08e-2c77847a2300", "repo_name": "sagemath/sage-archive-2023-02-01", "relative_path": "src/sage/groups/additive_abelian/qmodnz_element.py", "category": "ALGO", "algo_rel_score": 0.955346941947937, "quality_score": 7.874449253082275, "source_meta": {"github_metadata": {"branch": "develop", "commit_count": 110169, "repo_created_at": "2012-06-08T10:11:02.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 2007, "forks": 408, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c81a70b7fffd98c6adecffaa43189ff1de14945e", "size_bytes": 8443, "file_timestamp": 1675030173, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "9116c5584f94500731fdc9462df519229fa6ea85"}}, "parsed_source_meta": {"github_metadata": {"branch": "develop", "commit_count": 110169, "repo_created_at": "2012-06-08T10:11:02.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 2007, "forks": 408, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c81a70b7fffd98c6adecffaa43189ff1de14945e", "size_bytes": 8443, "file_timestamp": 1675030173, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "9116c5584f94500731fdc9462df519229fa6ea85", "url": "https://github.com/sagemath/sage-archive-2023-02-01/blob/9116c5584f94500731fdc9462df519229fa6ea85/src/sage/groups/additive_abelian/qmodnz_element.py"}, "gpt4o_tokens": 2387, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.3274500825349502, "noise_severity": 0.5872454773605448, "missing_content_severity": 0.009096908314603276, "factual_density": 3.3485639920869734, "pretraining_utility": 0.6799629803064976, "math_density": 3.3212946330043445, "code_density": 3.6007207468875904, "reasoning_density": 1.6855640672294934, "education_level": 2.5769661873011214, "creative_writing_quality": -0.06178761150366841}, "score_applicability": {"education_level": 0.9990976463602192, "creative_writing_quality": 0.0010038655339010246}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/Vasuo/GAnvil/blob/da4f9c84e8773c0279440012d52ad2a0b4ebe0b1/app.py", "id": "python-0-3d17ab178743475921919b7608e038b7", "text": "import eventlet\neventlet.monkey_patch()\nfrom flask import Flask, render_template, redirect, url_for, flash, request, make_response\nfrom flask_socketio import SocketIO\nfrom threading import Thread\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_login import LoginManager, UserMixin, login_user, logout_user, login_required, current_user\nfrom flask_wtf import FlaskForm\nfrom wtforms import StringField, PasswordField, SubmitField\nfrom wtforms.validators import DataRequired, Length, Email, EqualTo, ValidationError\nfrom wtforms.fields import TextAreaField\nfrom werkzeug.security import generate_password_hash, check_password_hash\nfrom collections import defaultdict\nimport json\nimport time\nimport random\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'your_secret_key'\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///site.db'  # Используем SQLite для простоты\nsocketio = SocketIO(app, cors_allowed_origins=\"*\")\nuser_connections = defaultdict(set)\ndb = SQLAlchemy(app)\nrunning = False\n\n# Модель пользователя\nclass User(db.Model, UserMixin):\n    id = db.Column(db.Integer, primary_key=True)\n    username = db.Column(db.String(20), unique=True, nullable=False)\n    email = db.Column(db.String(120), unique=True, nullable=False)\n    password_hash = db.Column(db.String(128), nullable=False)\n    def set_password(self, password):\n        self.password_hash = generate_password_hash(password)\n    def check_password(self, password):\n        return check_password_hash(self.password_hash, password)\n\n# Модель игры\nclass Game(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(20), unique=True, nullable=False)\n    description = db.Column(db.String(200), nullable=False)\n    picture = db.Column(db.String(200), nullable=False)\n    code = db.Column(db.String(10000), nullable=False)\n    author = db.Column(db.String(20), nullable=False)\n    release = db.Column(db.String(1), nullable=False)\n\n# Модель сессии\nclass Session(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    game = db.Column(db.String(20), nullable=False)\n    host = db.Column(db.String(20), nullable=False)\n    players = db.Column(db.String(300), nullable=False)\n    active_players = db.Column(db.String(300), nullable=False)\n    active = db.Column(db.String(1), nullable=False)\n    description = db.Column(db.String(100), nullable=False)\n\n# Настройка Flask-Login\nlogin_manager = LoginManager(app)\nlogin_manager.login_view = 'login'  # Страница входа\n\n# Загрузчик пользователя для Flask-Login\n@login_manager.user_loader\ndef load_user(user_id):\n    return User.query.get(int(user_id))\n\n# Форма регистрации\nclass RegistrationForm(FlaskForm):\n    username = StringField('Логин', validators=[DataRequired(), Length(min=2, max=20)])\n    email = StringField('Email', validators=[DataRequired(), Email()])\n    password = PasswordField('<PASSWORD>', validators=[DataRequired(), Length(min=6)])\n    confirm_password = PasswordField('', validators=[DataRequired(), EqualTo('password')])\n    submit = SubmitField('Зарегистрироваться')\n    # Кастомная валидация для username\n    def validate_username(self, username):\n        user = User.query.filter_by(username=username.data).first()\n        if user:\n            raise ValidationError('Это имя пользователя уже занято. Пожалуйста, выберите другое.')\n    # Кастомная валидация для email\n    def validate_email(self, email):\n        user = User.query.filter_by(email=email.data).first()\n        if user:\n            raise ValidationError('Этот email уже используется. Пожалуйста, используйте другой.')\n\n# Форма входа\nclass LoginForm(FlaskForm):\n    username = StringField('Логин', validators=[DataRequired(), Length(min=2, max=20)])\n    password = PasswordField('<PASSWORD>', validators=[DataRequired()])\n    submit = SubmitField('Войти')\n\n# Маршрут регистрации\n@app.route('/register', methods=['GET', 'POST'])\ndef register():\n    if current_user.is_authenticated:\n        return redirect(url_for('gallery'))  # Если пользователь уже авторизован, перенаправляем на главную\n    theme = request.cookies.get('theme', 'light')  # Получаем тему из куки\n    form = RegistrationForm()\n    if form.validate_on_submit():\n        # Создаем нового пользователя\n        user = User(\n            username=form.username.data,\n            email=form.email.data,\n        )\n        user.set_password(form.password.data)\n        db.session.add(user)\n        db.session.commit()\n        flash('Ваш аккаунт успешно создан! Теперь вы можете войти.', 'success')\n        return redirect(url_for('login'))\n    return render_template('register.html', form=form, theme=theme)  # Передаём тему в шаблон\n\n# Маршрут входа\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n    if current_user.is_authenticated:\n        return redirect(url_for('gallery'))  # Если пользователь уже авторизован, перенаправляем на главную\n    theme = request.cookies.get('theme', 'light')  # Получаем тему из куки\n    form = LoginForm()\n    if form.validate_on_submit():\n        user = User.query.filter_by(username=form.username.data).first()\n        if user and user.check_password(form.password.data):\n            login_user(user)  # Сохраняем сессию пользователя\n            flash('Вы успешно вошли в систему!', 'success')\n            return redirect(url_for('gallery'))\n        else:\n            flash('Неверный username или пароль. Пожалуйста, попробуйте снова.', 'danger')\n    return render_template('login.html', form=form, theme=theme)  # Передаём тему в шаблон\n\n# Маршрут выхода\n@app.route('/logout')\n@login_required  # Только для авторизованных пользователей\ndef logout():\n    logout_user()  # Завершаем сессию\n    flash('Вы вышли из системы.', 'success')\n    return redirect(url_for('index'))\n\n# Начальная страница\n@app.route('/')\ndef index():\n    theme = request.cookies.get('theme', 'light')  # Получаем тему из куки\n    return render_template('index.html', theme=theme)\n\n# Главная страница\n@app.route('/gallery')\ndef gallery():\n    username = current_user.username\n    games = Game.query.filter(Game.release == 'T').all()\n    games = [{key: value for key, value in item.__dict__.items() if not key.startswith('_')} for item in games]\n    return render_template('gallery.html', games=games)\n\n@app.route('/workshop')\ndef workshop():\n    username = current_user.username\n    games = Game.query.filter(Game.author == username).all()\n    games = [{key: value for key, value in item.__dict__.items() if not key.startswith('_')} for item in games]\n    return render_template('workshop.html', games=games)\n\n@app.route('/search')\ndef search():\n    username = current_user.username\n    query = request.args.get('query', '')\n    query = Game.query.filter(Game.name == query).all()\n    if (len(query) > 0):\n        query = ([{key: value for key, value in item.__dict__.items() if not key.startswith('_')} for item in query])[0]\n        check = Game.query.filter(Game.name == query['name']+'_copy').all()\n        if (len(check) == 0):\n            game = Game(\n                name=query['name']+'_copy',\n                description=query['description'],\n                picture=query['picture'],\n                code=query['code'],\n                author=username,\n                release = 'F',\n            )\n            db.session.add(game)\n            db.session.commit()\n    return redirect(url_for('workshop'))\n\n@app.route('/create')\n@login_required\ndef create_game():\n    username = current_user.username\n    query = 'base game'\n    query = Game.query.filter(Game.name == query).all()\n    query = ([{key: value for key, value in item.__dict__.items() if not key.startswith('_')} for item in query])[0]\n    r = random.randint(1,1000)\n    check = Game.query.filter(Game.name == 'base game'+str(r)).all()\n    if (len(check) == 0):\n        game = Game(\n            name='base game'+str(r),\n            description=query['description'],\n            picture=query['picture'],\n            code=query['code'],\n            author=username,\n            release = 'F',\n        )\n        db.session.add(game)\n        db.session.commit()\n    return redirect(url_for('workshop'))\n\nclass GameForm(FlaskForm):\n    name = StringField('Название игры', validators=[\n        DataRequired(message=\"Поле обязательно для заполнения\"),\n        Length(min=3, max=100, message=\"Название должно быть от 3 до 100 символов\")\n    ])\n    description = TextAreaField('Описание', validators=[\n        DataRequired(message=\"Поле обязательно для заполнения\"),\n        Length(min=10, message=\"Описание должно быть не менее 10 символов\")\n    ])\n    picture = TextAreaField('Постер', validators=[\n        DataRequired(message=\"Поле обязательно для заполнения\"),\n        Length(min=10, message=\"Описание должно быть не менее 10 символов\")\n    ])\n    submit = SubmitField('Сохранить изменения')\n\n@app.route('/rename/<int:game_id>', methods=['GET', 'POST'])\ndef rename_game(game_id):\n    game = Game.query.get_or_404(game_id)  # Получаем игру или возвращаем 404\n    form = GameForm()\n    \n    if form.validate_on_submit():\n        # Обновляем данные игры\n        game.name = form.name.data\n        game.description = form.description.data\n        game.picture = form.picture.data\n        db.session.commit()\n        flash('Игра успешно обновлена!', 'success')\n        return redirect(url_for('workshop'))\n    \n    # Для GET запроса заполняем форму текущими данными\n    if request.method == 'GET':\n        form.name.data = game.name\n        form.description.data = game.description\n    \n    return render_template('rename.html', form=form, game_id=game_id)\n\n@app.route('/publish/<int:game_id>')\ndef publish_game(game_id):\n    username = current_user.username\n    game = Game.query.get(game_id)\n    if game:\n        game.release = 'T'\n        db.session.commit()\n    games = Game.query.filter(Game.author == username).all()\n    games = [{key: value for key, value in item.__dict__.items() if not key.startswith('_')}for item in games]\n    return render_template('workshop.html', games=games)\n\n@app.route('/edit_game/<int:game_id>')\ndef edit_game(game_id):\n    username = current_user.username\n    game = Game.query.get_or_404(game_id)\n    return redirect(url_for('game_host',game=game.name))\n\n@app.route('/sessions')\ndef sessions():\n    username = current_user.username\n    sessions = Session.query.all()\n    sessions = [{key: value for key, value in item.__dict__.items() if not key.startswith('_')} for item in sessions]\n    return render_template('sessions.html', sessions=sessions)\n\n@app.route('/create_session/<int:game_id>')\n@login_required\ndef create_session(game_id):\n    username = current_user.username\n    # Проверяем, есть ли уже активная сессия у пользователя\n    existing_session = Session.query.filter_by(host=username, active='T').first()\n    if not existing_session:\n        game_name = Game.query.get_or_404(game_id)\n        game_name = game_name.name\n        new_session = Session(\n            game = game_name,\n            host=username,\n            players='',\n            active_players='',\n            active='F',\n            description = 'Играем 31 вефраля в 25:79 в viber.',\n        )\n        db.session.add(new_session)\n        db.session.commit()\n        flash('Новая сессия успешно создана!', 'success')\n    else:\n        flash('У вас уже есть активная сессия!', 'warning')\n    # Перенаправляем на страницу сессий\n    return redirect(url_for('sessions'))\n\n@app.route('/delete_session/<int:session_id>', methods=['DELETE'])\n@login_required\ndef delete_session(session_id):\n    session = Session.query.get_or_404(session_id)\n    if session.host == current_user.username:\n        db.session.delete(session)\n        db.session.commit()\n        return '', 200\n    else:\n        return '', 403  # Forbidden\n\n@app.route('/join_session/<int:session_id>')\n@login_required\ndef join_session(session_id):\n    username = current_user.username\n    session = Session.query.get(session_id)\n    if session:\n        if (session.active == 'F'):\n            if (session.host != username):\n                if username not in session.players.split('%'):\n                    if (session.players == ''):\n                        session.players = username\n                    else:\n                        session.players += username\n                    db.session.commit()\n        else:\n            return redirect(url_for('game_player', session_id=session_id))\n    return redirect(url_for('sessions'))\n\n@app.route('/start_session/<int:session_id>')\n@login_required\ndef start_session(session_id):\n    session = Session.query.get_or_404(session_id)\n    if session.host == current_user.username:\n        # Логика начала игры\n        session.active = 'T'\n        db.session.commit()\n        return redirect(url_for('game_host',game=session.game))\n    else:\n        flash('Только хост может начать сессию!', 'warning')\n        return redirect(url_for('sessions'))    \n\n@app.route('/game_host/<game>')\ndef game_host(game):\n    username = current_user.username\n    gm = Game.query.filter(Game.name == game).first()\n    \n    # Преобразуем данные в правильный JSON с двойными кавычками\n    if isinstance(gm.code, dict):\n        game_data = json.dumps(gm.code)\n    else:\n        try:\n            # Пробуем заменить одинарные кавычки на двойные\n            fixed_code = gm.code.replace(\"'\", '\"')\n            game_data = fixed_code\n        except Exception as e:\n            game_data = json.dumps({\"speed\": 5, \"walls\": [], \"players\": {}})\n    \n    return render_template('game_host.html', \n                        name=username, \n                        game_name=game, \n                        code=game_data)\n\n@app.route('/game_player/<int:session_id>')\ndef game_player(session_id):\n    username = current_user.username\n    session = Session.query.get_or_404(session_id)\n    if session:\n        if (session.active_players == ''):\n            session.active_players = username\n        else:\n            session.active_players += '%'+username\n        db.session.commit()\n    socketio.emit('new_player', {'name':username, 'host':session.host})\n    return render_template('game_player.html', name=username, host=session.host)\n\n# Новый маршрут для обработки отключения игрока\n@app.route('/player_disconnected', methods=['POST'])\ndef handle_player_disconnected_http():\n    username = request.args.get('name')\n    if username:\n        session = Session.query.filter(Session.players.contains(username)).first()\n        if session:\n            active_players = session.active_players.split('%')\n            if username in active_players:\n                active_players.remove(username)\n                session.active_players = '%'.join(active_players)\n                db.session.commit()\n                socketio.emit('player_list_updated', {'players': active_players})\n        session = Session.query.filter(Session.host == username).first()\n        if session:\n            session.active = 'F'\n            db.session.commit()\n    return '', 200\n\n@socketio.on('player_input')\ndef players_inputs(inputs):\n    socketio.emit('players_update', [{'name': inputs['name'],'keys': inputs['keys']}])\n\n@socketio.on('game_state_update')\ndef game_state_update(data):\n    socketio.emit('players_draw', data)\n\n@socketio.on('save_game_speed')\ndef handle_save_game_speed(data):\n    game_name = data['game']\n    game_code = data['game_code']\n    # Находим игру в базе данных\n    game = Game.query.filter_by(name=game_name).first()\n    if game:\n        # Обновляем поле code с новым значением скорости\n        game.code = str(game_code)\n        db.session.commit()\n\ndef background_thread():\n    \"\"\"Поток для периодической отправки данных клиенту\"\"\"\n    counter = 0\n    while running:\n        time.sleep(5)  # Отправляем сообщения каждые 3 секунды\n        counter += 1\n        #print(f\"Отправлено обновление клиенту\")\n\n# Запуск приложения\nif __name__ == '__main__':\n    with app.app_context():\n        db.create_all()\n    socketio.run(app, host='0.0.0.0', port=1303)\n", "text_preview": "import eventlet eventlet.monkey_patch() from flask import Flask, render_template, redirect, url_for, flash, request, make_response from flask_socketio import SocketIO from threading import Thread from flask_sqlalchemy import SQLAlchemy from", "input_sha256": "2dda8a59cb1c57d278c34a5a58aa2ba81d132eca94d79eb96cff3f21e49b07d3", "text_sha256": "3d17ab178743475921919b7608e038b7ea3150b75e0541ada49e102911941fdf", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12033, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.15187242267532747, "noise_severity": 0.1612696806476729, "missing_content_severity": 0.04185944884803955, "factual_density": 3.3578299916528658, "pretraining_utility": 0.5538047557984604, "math_density": 0.12937154396402378, "code_density": 3.8719570980860616, "reasoning_density": 0.4299975615851048, "education_level": 2.1630904642347395, "creative_writing_quality": -0.4660296149808794}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0065597485510562326, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9964390367166053, "secondary": 0.9687110335074735, "tertiary": 0.9755559885172714, "structured_records": 0.05902045924278306, "procedural_steps": 0.007499080677487859, "worked_solution": 0.0028424434079270455, "contextual_dialogue": 8.457435023998175e-06, "content_quality": 0.4246221755422709, "noise_severity": 0.8656085994602726, "missing_content_severity": 0.965117125959967, "factual_density": 0.4648583263773881, "pretraining_utility": 0.49858310714734455, "math_density": 0.8921903800299802, "code_density": 0.8932975817383847, "reasoning_density": 0.6416686986790793, "education_level": 0.7674799520776991, "creative_writing_quality": 0.9982150073541555, "primary_topic": 0.9703590520919481, "secondary_topic": 0.8062283037364186}, "probability_margin": {"primary": 0.9949503489928571, "secondary": 0.9579668538841198, "tertiary": 0.9690542217679191, "education_level": 0.6395431897635335, "creative_writing_quality": 0.9977582055261901, "primary_topic": 0.9451925324331506, "secondary_topic": 0.6717807576216632}, "raw_probabilities": {"primary": {"Academic Writing": 3.1647217493380365e-05, "Content Listing": 4.206356065052696e-05, "Creative Writing": 2.4737621285141335e-05, "Customer Support Page": 8.927129048325536e-06, "Q&A Forum": 1.2381054835604337e-05, "Discussion Thread": 8.379322456197093e-06, "Comment Section": 1.7565178783794283e-05, "Missing Content": 2.551889831454193e-05, "Knowledge Article": 7.218066759896228e-05, "Legal Notices": 2.9802736529468634e-05, "News Article": 5.529767537723432e-06, "Organizational Announcement": 1.3731947537210758e-05, "Nonfiction Writing": 1.3278189705639341e-05, "Organizational About Page": 1.533330873198473e-05, "Personal About Page": 2.3293438919000712e-05, "Personal Blog": 9.232395448792826e-06, "Product Page": 1.1776676698025474e-05, "Spam / Ads": 1.3040908447253446e-05, "Structured Data": 0.0006471254731156864, "Technical Writing": 0.9965874101867467, "Transcript / Interview": 1.634890088052317e-05, "Tutorial / How-To Guide": 0.0007250724570381143, "User Reviews": 8.561768307969424e-06, "Other / Unknown": 0.0016370611938896433}, "secondary": {"Academic Writing": 0.0002298742377557216, "Content Listing": 0.000996294742205584, "Creative Writing": 0.00031492140278373184, "Customer Support Page": 0.00013556890526350863, "Q&A Forum": 8.790215883717146e-05, "Discussion Thread": 7.513211833271254e-05, "Comment Section": 0.0014700471504602935, "Missing Content": 0.0007650485151030655, "Knowledge Article": 0.001537182551727344, "Legal Notices": 0.0004192277885905838, "News Article": 0.00011526021656089513, "Organizational Announcement": 0.00013996733208174314, "Nonfiction Writing": 0.00023165286112075963, "Organizational About Page": 0.00016180923731494166, "Personal About Page": 0.00019848778415268087, "Personal Blog": 0.00016831516798244785, "Product Page": 9.04972566893397e-05, "Spam / Ads": 0.00034555550234141363, "Structured Data": 0.007270549843201828, "Technical Writing": 0.012047886560542247, "Transcript / Interview": 0.00016019397747385329, "Tutorial / How-To Guide": 0.0029487190878466677, "User Reviews": 7.516515696930075e-05, "NONE": 0.9700147404446621}, "tertiary": {"Academic Writing": 0.0001492678226754187, "Content Listing": 0.0016485132499456077, "Creative Writing": 0.00031131128099940076, "Customer Support Page": 0.00012188706885118996, "Q&A Forum": 6.670550138572624e-05, "Discussion Thread": 6.459159097094459e-05, "Comment Section": 0.0012996031099494001, "Missing Content": 0.0009314815131822537, "Knowledge Article": 0.0014544069298773193, "Legal Notices": 0.0004406131491006206, "News Article": 8.878655481105287e-05, "Organizational Announcement": 0.00011472643404753213, "Nonfiction Writing": 0.0003260234371466486, "Organizational About Page": 0.00014615558130470074, "Personal About Page": 0.0001636572975883479, "Personal Blog": 0.0001539342151004471, "Product Page": 8.469353896484065e-05, "Spam / Ads": 0.0005040720829141454, "Structured Data": 0.0050471017844291545, "Technical Writing": 0.007520267227799256, "Transcript / Interview": 0.00011649416631753722, "Tutorial / How-To Guide": 0.0026104333221139407, "User Reviews": 6.0784144806126365e-05, "NONE": 0.9765744889957184}, "structured_records": {"false": 0.9409795407572169, "true": 0.05902045924278306}, "procedural_steps": {"false": 0.9925009193225122, "true": 0.007499080677487859}, "worked_solution": {"false": 0.997157556592073, "true": 0.0028424434079270455}, "contextual_dialogue": {"false": 0.999991542564976, "true": 8.457435023998175e-06}, "content_quality": {"-2": 0.02802748709260081, "-1": 0.2132355091517721, "0": 0.40087494486886277, "1": 0.29456121176122735, "2": 0.0633008471255369}, "noise_severity": {"0": 0.8624600622318473, "1": 0.11853562775745732, "2": 0.015588875730885085, "3": 0.0021054356907960845, "4": 0.0013099985890142956}, "missing_content_severity": {"0": 0.969799149580625, "1": 0.0214532973344292, "2": 0.006246779327248876, "3": 0.002090502171676396, "4": 0.000410271586020855}, "factual_density": {"0": 0.005055126444445837, "1": 0.010233434804950913, "2": 0.10160583407534764, "3": 0.3880375300038028, "4": 0.49506807467145275}, "pretraining_utility": {"-2": 0.009261953164295752, "-1": 0.07255346138059897, "0": 0.35105497555827786, "1": 0.48937709628600384, "2": 0.07775251361082351}, "math_density": {"0": 0.8874494085753442, "1": 0.10325816236766401, "2": 0.004861365237979987, "3": 0.0013336041556473563, "4": 0.0030974596633644353}, "code_density": {"0": 0.0002343021892759135, "1": 0.00041942858745060564, "2": 0.01055409200200826, "3": 0.10473922339046643, "4": 0.8840529538307988}, "reasoning_density": {"0": 0.7425730237640626, "1": 0.11514667202101724, "2": 0.11893513323133206, "3": 0.016400060832929005, "4": 0.006945110150659119}, "education_level": {"none": 0.0011862286809542428, "0": 0.001560029732277687, "1": 0.016444577209930773, "2": 0.8062332933980826, "3": 0.16669010363454906, "4": 0.007885767344205607}, "creative_writing_quality": {"not_applicable": 0.9985125061284629, "-2": 0.0001914539189233889, "-1": 0.00044012380064531914, "0": 0.000754300602272808, "1": 7.341565713824072e-05, "2": 2.8199892557471914e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 7.607086226751136e-05, "Art & Design": 0.00010056054663486014, "Law & Crime": 5.4085314826991945e-05, "Education & Careers": 5.89225644735741e-05, "Entertainment & Media": 0.0002600724070884158, "Fashion & Beauty": 5.323238014438684e-05, "Business, Economics & Finance": 0.00011263825948772242, "Food & Dining": 4.613636224456999e-05, "Games & Gaming": 0.02630655611679944, "Electronics & Hardware": 5.193519734260202e-05, "Health, Healthcare & Wellness": 3.6518626825303656e-05, "History & Geography": 4.969868469619741e-05, "Home, Pets & Hobbies": 5.193710425426652e-05, "Industry & Production": 4.809525461031262e-05, "Language, Literature & Philosophy": 5.1957391193575245e-05, "Politics & Government": 2.6229708962847233e-05, "Religion & Spirituality": 4.5794246307204595e-05, "Science, Mathematics & Engineering": 9.966732600222133e-05, "Relationships & Community": 8.674286004875959e-05, "Software Usage & Applications": 0.0005488393650977715, "Programming & Software Engineering": 0.97149908854995, "Sports & Fitness": 7.509299520493874e-05, "Vehicles & Transportation": 2.7457530217310112e-05, "Travel & Tourism": 5.942815378459807e-05, "Other / Uncovered Subject": 0.00010372112198652053, "Unknown / Insufficient Content": 6.952106954805808e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.001166602313936362, "Art & Design": 0.0009604274875509334, "Law & Crime": 0.0007054152540338812, "Education & Careers": 0.0004693970396216077, "Entertainment & Media": 0.0013768920976589265, "Fashion & Beauty": 0.0003307990399261517, "Business, Economics & Finance": 0.0010333789036355945, "Food & Dining": 0.0003208936728425001, "Games & Gaming": 0.14190030366335474, "Electronics & Hardware": 0.0004674820964871209, "Health, Healthcare & Wellness": 0.00034873728329092496, "History & Geography": 0.00035210520139643004, "Home, Pets & Hobbies": 0.0004279253510177717, "Industry & Production": 0.000276094458433148, "Language, Literature & Philosophy": 0.0004903891515641439, "Politics & Government": 0.0002483397920293015, "Religion & Spirituality": 0.0003686239201150274, "Science, Mathematics & Engineering": 0.000746747210046741, "Relationships & Community": 0.0007799743229378248, "Software Usage & Applications": 0.003922967324122176, "Programming & Software Engineering": 0.028041398310560163, "Sports & Fitness": 0.0003535594967969106, "Vehicles & Transportation": 0.00016363247108593175, "Travel & Tourism": 0.00045203507106956575, "Other / Uncovered Subject": 0.0006148177814681343, "NONE": 0.8136810612850179}}, "probabilities": {"primary": {"Academic Writing": 3.164721749338036e-05, "Content Listing": 4.2063560650526954e-05, "Creative Writing": 2.473762128514133e-05, "Customer Support Page": 8.927129048325534e-06, "Q&A Forum": 1.2381054835604333e-05, "Discussion Thread": 8.379322456197092e-06, "Comment Section": 1.756517878379428e-05, "Missing Content": 2.5518898314541923e-05, "Knowledge Article": 7.218066759896227e-05, "Legal Notices": 2.9802736529468627e-05, "News Article": 5.529767537723431e-06, "Organizational Announcement": 1.3731947537210755e-05, "Nonfiction Writing": 1.3278189705639338e-05, "Organizational About Page": 1.5333308731984727e-05, "Personal About Page": 2.3293438919000705e-05, "Personal Blog": 9.232395448792824e-06, "Product Page": 1.177667669802547e-05, "Spam / Ads": 1.3040908447253443e-05, "Structured Data": 0.0006471254731156863, "Technical Writing": 0.9965874101867465, "Transcript / Interview": 1.6348900880523168e-05, "Tutorial / How-To Guide": 0.0007250724570381142, "User Reviews": 8.561768307969422e-06, "Other / Unknown": 0.0016370611938896428}, "secondary": {"Academic Writing": 0.00022987423775572163, "Content Listing": 0.0009962947422055843, "Creative Writing": 0.0003149214027837319, "Customer Support Page": 0.00013556890526350866, "Q&A Forum": 8.790215883717147e-05, "Discussion Thread": 7.513211833271255e-05, "Comment Section": 0.0014700471504602937, "Missing Content": 0.0007650485151030656, "Knowledge Article": 0.0015371825517273442, "Legal Notices": 0.00041922778859058383, "News Article": 0.00011526021656089514, "Organizational Announcement": 0.00013996733208174316, "Nonfiction Writing": 0.00023165286112075966, "Organizational About Page": 0.0001618092373149417, "Personal About Page": 0.0001984877841526809, "Personal Blog": 0.00016831516798244787, "Product Page": 9.049725668933972e-05, "Spam / Ads": 0.0003455555023414137, "Structured Data": 0.007270549843201829, "Technical Writing": 0.012047886560542249, "Transcript / Interview": 0.0001601939774738533, "Tutorial / How-To Guide": 0.002948719087846668, "User Reviews": 7.516515696930076e-05, "NONE": 0.9700147404446622}, "tertiary": {"Academic Writing": 0.0001492678226754187, "Content Listing": 0.0016485132499456077, "Creative Writing": 0.00031131128099940076, "Customer Support Page": 0.00012188706885118996, "Q&A Forum": 6.670550138572624e-05, "Discussion Thread": 6.459159097094459e-05, "Comment Section": 0.0012996031099494001, "Missing Content": 0.0009314815131822537, "Knowledge Article": 0.0014544069298773193, "Legal Notices": 0.0004406131491006206, "News Article": 8.878655481105287e-05, "Organizational Announcement": 0.00011472643404753213, "Nonfiction Writing": 0.0003260234371466486, "Organizational About Page": 0.00014615558130470074, "Personal About Page": 0.0001636572975883479, "Personal Blog": 0.0001539342151004471, "Product Page": 8.469353896484065e-05, "Spam / Ads": 0.0005040720829141454, "Structured Data": 0.0050471017844291545, "Technical Writing": 0.007520267227799256, "Transcript / Interview": 0.00011649416631753722, "Tutorial / How-To Guide": 0.0026104333221139407, "User Reviews": 6.0784144806126365e-05, "NONE": 0.9765744889957184}, "structured_records": {"false": 0.9409795407572169, "true": 0.05902045924278306}, "procedural_steps": {"false": 0.9925009193225122, "true": 0.007499080677487859}, "worked_solution": {"false": 0.997157556592073, "true": 0.0028424434079270455}, "contextual_dialogue": {"false": 0.999991542564976, "true": 8.457435023998175e-06}, "content_quality": {"-2": 0.028027487092600813, "-1": 0.21323550915177214, "0": 0.4008749448688628, "1": 0.2945612117612274, "2": 0.06330084712553691}, "noise_severity": {"0": 0.8624600622318471, "1": 0.1185356277574573, "2": 0.015588875730885082, "3": 0.002105435690796084, "4": 0.0013099985890142954}, "missing_content_severity": {"0": 0.9697991495806247, "1": 0.021453297334429196, "2": 0.006246779327248874, "3": 0.0020905021716763955, "4": 0.00041027158602085486}, "factual_density": {"0": 0.005055126444445837, "1": 0.010233434804950913, "2": 0.10160583407534764, "3": 0.3880375300038028, "4": 0.49506807467145275}, "pretraining_utility": {"-2": 0.009261953164295754, "-1": 0.07255346138059898, "0": 0.3510549755582779, "1": 0.4893770962860039, "2": 0.07775251361082353}, "math_density": {"0": 0.8874494085753442, "1": 0.10325816236766401, "2": 0.004861365237979987, "3": 0.0013336041556473563, "4": 0.0030974596633644353}, "code_density": {"0": 0.0002343021892759135, "1": 0.00041942858745060564, "2": 0.01055409200200826, "3": 0.10473922339046643, "4": 0.8840529538307988}, "reasoning_density": {"0": 0.7425730237640626, "1": 0.11514667202101724, "2": 0.11893513323133206, "3": 0.016400060832929005, "4": 0.006945110150659119}, "education_level": {"none": 0.0011862286809542428, "0": 0.001560029732277687, "1": 0.016444577209930773, "2": 0.8062332933980826, "3": 0.16669010363454906, "4": 0.007885767344205607}, "creative_writing_quality": {"not_applicable": 0.9985125061284629, "-2": 0.0001914539189233889, "-1": 0.00044012380064531914, "0": 0.000754300602272808, "1": 7.341565713824072e-05, "2": 2.8199892557471914e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 7.607086226751136e-05, "Art & Design": 0.00010056054663486014, "Law & Crime": 5.4085314826991945e-05, "Education & Careers": 5.89225644735741e-05, "Entertainment & Media": 0.0002600724070884158, "Fashion & Beauty": 5.323238014438684e-05, "Business, Economics & Finance": 0.00011263825948772242, "Food & Dining": 4.613636224456999e-05, "Games & Gaming": 0.02630655611679944, "Electronics & Hardware": 5.193519734260202e-05, "Health, Healthcare & Wellness": 3.6518626825303656e-05, "History & Geography": 4.969868469619741e-05, "Home, Pets & Hobbies": 5.193710425426652e-05, "Industry & Production": 4.809525461031262e-05, "Language, Literature & Philosophy": 5.1957391193575245e-05, "Politics & Government": 2.6229708962847233e-05, "Religion & Spirituality": 4.5794246307204595e-05, "Science, Mathematics & Engineering": 9.966732600222133e-05, "Relationships & Community": 8.674286004875959e-05, "Software Usage & Applications": 0.0005488393650977715, "Programming & Software Engineering": 0.97149908854995, "Sports & Fitness": 7.509299520493874e-05, "Vehicles & Transportation": 2.7457530217310112e-05, "Travel & Tourism": 5.942815378459807e-05, "Other / Uncovered Subject": 0.00010372112198652053, "Unknown / Insufficient Content": 6.952106954805808e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.001166602313936362, "Art & Design": 0.0009604274875509334, "Law & Crime": 0.0007054152540338812, "Education & Careers": 0.0004693970396216077, "Entertainment & Media": 0.0013768920976589265, "Fashion & Beauty": 0.0003307990399261517, "Business, Economics & Finance": 0.0010333789036355945, "Food & Dining": 0.0003208936728425001, "Games & Gaming": 0.14190030366335474, "Electronics & Hardware": 0.0004674820964871209, "Health, Healthcare & Wellness": 0.00034873728329092496, "History & Geography": 0.00035210520139643004, "Home, Pets & Hobbies": 0.0004279253510177717, "Industry & Production": 0.000276094458433148, "Language, Literature & Philosophy": 0.0004903891515641439, "Politics & Government": 0.0002483397920293015, "Religion & Spirituality": 0.0003686239201150274, "Science, Mathematics & Engineering": 0.000746747210046741, "Relationships & Community": 0.0007799743229378248, "Software Usage & Applications": 0.003922967324122176, "Programming & Software Engineering": 0.028041398310560163, "Sports & Fitness": 0.0003535594967969106, "Vehicles & Transportation": 0.00016363247108593175, "Travel & Tourism": 0.00045203507106956575, "Other / Uncovered Subject": 0.0006148177814681343, "NONE": 0.8136810612850179}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 12381, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 12381, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:08.171860+00:00", "scoring_key_slot": "primary", "dataset_uid": "a28a0e25-8a7c-4156-9fb4-a84d20137f9f", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "26992afd4a016a5aaa995948f7af7e49fc38b826303b3e0d2d0f182d0b90d1a9", "source_metadata": {"uuid": "a28a0e25-8a7c-4156-9fb4-a84d20137f9f", "repo_name": "Vasuo/GAnvil", "relative_path": "app.py", "category": "WEB", "algo_rel_score": 0.9827971458435059, "quality_score": 6.091944217681885, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 9, "repo_created_at": "2025-05-13T08:55:01.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "fe8d4c682320dd079435b802664446bc3ca5bac6", "size_bytes": 17529, "file_timestamp": 1748944330, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "da4f9c84e8773c0279440012d52ad2a0b4ebe0b1"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 9, "repo_created_at": "2025-05-13T08:55:01.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "fe8d4c682320dd079435b802664446bc3ca5bac6", "size_bytes": 17529, "file_timestamp": 1748944330, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "da4f9c84e8773c0279440012d52ad2a0b4ebe0b1", "url": "https://github.com/Vasuo/GAnvil/blob/da4f9c84e8773c0279440012d52ad2a0b4ebe0b1/app.py"}, "gpt4o_tokens": 3580, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.15187242267532747, "noise_severity": 0.16126968064767291, "missing_content_severity": 0.04185944884803955, "factual_density": 3.3578299916528658, "pretraining_utility": 0.5538047557984604, "math_density": 0.12937154396402378, "code_density": 3.8719570980860616, "reasoning_density": 0.4299975615851048, "education_level": 2.1630904642347395, "creative_writing_quality": -0.4660296149808794}, "score_applicability": {"education_level": 0.9988137713190457, "creative_writing_quality": 0.0014874938715372286}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/oliviax727/hvc-magnetic-honours-programs/blob/9bb87b9293110cec13d1e56f73312c9a7cf4d6f8/collation/group_proj_functions.py", "id": "python-0-65f4114fb980e9387f73c4151a9bd9ae", "text": "#! /Users/canders/miniconda3/envs/possum-mamba-env/bin/python python\n\n\"\"\"\nFunctions to be read in by groups_basic_pipeline_output_fdf_analyser_ver10_collab_restructure.py\nor later versions.\n\"\"\"\n\nimport numpy as np\nfrom astropy.io import fits\nfrom astropy.coordinates import SkyCoord\nfrom astropy.wcs import WCS\nfrom scipy.constants import c\nimport astropy.units as u\n\n# Only include this if measure_FDF_parms is indeed utilized in the script.\nfrom RMutils.util_RM import measure_FDF_parms\n\n\n##########################################################################################\n###################################### FUNCITONS #########################################\n##########################################################################################\n\ndef get_wcs_object_from_3D_fits_header(header_object):\n\t\"\"\"\n\tGet WCS object from a 3D FITS header.\n\n\tParameters\n\t----------\n\theader_object : astropy.io.fits.Header\n\t\tThe FITS header\n\n\tReturns\n\t-------\n\tastropy.wcs.WCS\n\t\tThe World Coordinate System (WCS) object\n\t\"\"\"\n\tfrom astropy.wcs import WCS\n\twcs = WCS(header_object)\n\twcs = wcs.dropaxis(2)\n\treturn wcs\n\ndef read_casda_fits(file):\n\t\"\"\"\n\tOpens a FITS file and returns the header and data.\n\n\tParameters:\n\t----------\n\tfile (str): The filename of the FITS file.\n\n\tReturns:\n\t-------\n\ttuple: A tuple containing the header, the HDU (Header/Data Unit) list, and the parent sbid.\n\t\"\"\"\n\tfrom astropy.io import fits\n\n\t#print('Opening %s'%print(file))\n\thdu = fits.open(file)\n\theader = hdu[0].header\n\tsbid = header['SBID'].strip()\n\treturn header, hdu, sbid\n\ndef load_and_parse_fits(file):\n\n\t\"\"\"\n\tLoads and parses FITS data from a file.\n\n\tParameters\n\t----------\n\tfile : str\n\t\tThe file path of the FITS file.\n\n\tReturns\n\t-------\n\ttuple\n\t\tA tuple containing the SkyCoord objects for the source and the pointing centre, the pixel offsets in the RA and Dec directions, and the pixel scale in the mosaic. Returns None if the SBID is in the ignore list.\n\t\"\"\"\n\n\t# Read in the source FDF fits\n\theader, hdu, sbid = read_casda_fits(file)\n\n\t# Get ra, decl coordinates of source associated with this FDF\n\twcs = get_wcs_object_from_3D_fits_header(header)\n\twx, wy = wcs.wcs_pix2world(1, 1, 1) # The fits cubes are 1 x 1 pix in RA and dec, so calculate the position of that pixel\n\tcoord_obj_source = SkyCoord(wx,wy,unit=(u.deg, u.deg),frame='icrs')\n\n\t# Get ra, decl mosaic reference value\n\tra_pointing_centre = header['CRVAL1']\n\tdec_pointing_centre = header['CRVAL2']\n\tra_direction_pix_offset_in_mosaic = header['CRPIX1'] \n\tdec_direction_pix_offset_in_mosaic = header['CRPIX2'] \n\tra_pixel_scale_in_mosaic = np.abs(header['CDELT1']) \n\tdec_pixel_scale_in_mosaic = np.abs(header['CDELT2'])\n\tcoord_obj_pointing_centre = SkyCoord(ra_pointing_centre,dec_pointing_centre,unit=(u.deg, u.deg),frame='icrs')\n\n\t# Check if pixel scales are equal\n\tassert ra_pixel_scale_in_mosaic==dec_pixel_scale_in_mosaic,'The pixel deltas in RA and Decl are different!'\n\n\treturn coord_obj_source, coord_obj_pointing_centre, ra_direction_pix_offset_in_mosaic, dec_direction_pix_offset_in_mosaic, ra_pixel_scale_in_mosaic, sbid, header, hdu\n\ndef load_and_parse_noise_spectra_fits(file):\n\n\t\"\"\"\n\tLoads and parses FITS data from a file that contains QU noise.\n\n\tParameters\n\t----------\n\tfile : str\n\t\tThe file path of the FITS file.\n\n\tReturns\n\t-------\n\ttuple\n\t\tA tuple containing the SkyCoord objects for the source and the pointing centre, the pixel offsets in the RA and Dec directions, and the pixel scale in the mosaic. Returns None if the SBID is in the ignore list.\n\t\"\"\"\n\n\t# Read in the source FDF fits\n\theader, hdu, sbid = read_casda_fits(file)\n\n\t# Get noise spectrum\n\tnoise_spec = hdu[0].data.flatten()\n\n\t# Return it\n\treturn noise_spec\n\ndef parse_polarisation(header, FDF, band_average_stokes_noise, c=None, freq_lo_hz=799990740.7407, freq_hi_hz=1086990740.7407):\n\t\"\"\"\n\tGiven the FDF FITS header and frequency range (the latter is not in the former, preposterously), \n\tcalculate the Faraday depth sampling and full width half maximum (FWHM) of the RMSF. \n\tAdditionally, it extracts RM, PSNR, uncertainties, and other parameters from the FITS \n\tHDU.\n\n\tParameters:\n\theader : dict\n\t\tA header from an FDF FITS file containing the Faraday depth information.\n\tFDF : Complex Array\n\t\tA complex-valued FDF\n\tc : float, optional\n\t\tThe speed of light in m/s (default is 3e8 m/s).\n\tfreq_lo_hz : float, optional\n\t\tThe lower bound of the frequency range in Hz (default is 799990740.7407 Hz).\n\tfreq_hi_hz : float, optional\n\t\tThe upper bound of the frequency range in Hz (default is 1086990740.7407 Hz).\n\n\tReturns:\n\ttuple\n\t\tThe Faraday depth sampling, the FWHM of the RMSF, and a dictionary containing the \n\t\tfitted peak Faraday depth, its uncertainty, and the fitted signal-to-noise ratio.\n\t\"\"\"\n\tif c is None:\n\t\tfrom scipy.constants import c\n\tcrval3 = header['CRVAL3'] #cube Faraday depth reference\n\tcdelt3 = header['CDELT3'] #cube Faraday depth reference\n\tnaxis3 = header['NAXIS3'] #number of faraday depth samples along FD axis\n\tphiArr = np.array([crval3+(i*cdelt3) for i in range(naxis3)]) #FD sampling\n\n\t#band_average_stokes_noise = 15e-6\n\n\tlsq_freq_lo = (c/freq_lo_hz)**2\n\tlsq_freq_hi = (c/freq_hi_hz)**2\n\tfwhmRMSF = (2*np.sqrt(3))/(lsq_freq_lo-lsq_freq_hi) \n\n\tlamSqArr_m2 = (c/np.array([7.999907407407E+08+(1e6*i) for i in range(288)]))**2\n\n\t#FDF = np.squeeze(hdu[0].data)\n\tFDF_params_dict = measure_FDF_parms(FDF, phiArr, fwhmRMSF, dFDF=band_average_stokes_noise, lamSqArr_m2=lamSqArr_m2,lam0Sq=np.nanmean(lamSqArr_m2), snrDoBiasCorrect=5.0)\n\n\treturn phiArr, fwhmRMSF, FDF_params_dict\n\n\n\n\n\n", "text_preview": "#! /Users/canders/miniconda3/envs/possum-mamba-env/bin/python python \"\"\" Functions to be read in by groups_basic_pipeline_output_fdf_analyser_ver10_collab_restructure.py or later versions. \"\"\" import numpy as np from astropy.io import fits ", "input_sha256": "16fdbbadd31441127995113ef8999e8a411279224cfdd5482c228d1709041b16", "text_sha256": "65f4114fb980e9387f73c4151a9bd9aed737875e7980deaf98907a547d9c2429", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12032, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 2, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.37954638279375286, "noise_severity": 0.27064156318621146, "missing_content_severity": 0.05658434078991201, "factual_density": 3.110638668388384, "pretraining_utility": 0.4895046533342491, "math_density": 2.289918305598721, "code_density": 3.6780156899476686, "reasoning_density": 0.6847532683444582, "education_level": 3.2714674396692844, "creative_writing_quality": -0.3602494598389791}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.5627 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.01020607721379474, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.997889089461156, "secondary": 0.9626654072760209, "tertiary": 0.9733152269773718, "structured_records": 0.010748042862803524, "procedural_steps": 0.04272929655206068, "worked_solution": 0.005751486974828451, "contextual_dialogue": 5.257245427146604e-06, "content_quality": 0.41683917966418704, "noise_severity": 0.7744653640114905, "missing_content_severity": 0.9528463826750734, "factual_density": 0.5152137626907238, "pretraining_utility": 0.44279084750085385, "math_density": 0.22976512042504582, "code_density": 0.7316797416230569, "reasoning_density": 0.42937227637961817, "education_level": 0.4180544524847562, "creative_writing_quality": 0.9991558226740639, "primary_topic": 0.7577914351146846, "secondary_topic": 0.5451564919195383}, "probability_margin": {"primary": 0.9970322104712755, "secondary": 0.9472348990410299, "tertiary": 0.9645848602617738, "education_level": 0.13482717823837131, "creative_writing_quality": 0.9989988093750788, "primary_topic": 0.5450614399647039, "secondary_topic": 0.34036913814844705}, "raw_probabilities": {"primary": {"Academic Writing": 0.0002257056738295701, "Content Listing": 1.2990272508986799e-05, "Creative Writing": 7.187078238492846e-06, "Customer Support Page": 4.830129979173344e-06, "Q&A Forum": 6.663704208411455e-06, "Discussion Thread": 4.4102504186853685e-06, "Comment Section": 5.443691752419506e-06, "Missing Content": 1.088072485653714e-05, "Knowledge Article": 7.31265451889803e-05, "Legal Notices": 8.980121872661826e-06, "News Article": 2.2238693431071055e-06, "Organizational Announcement": 4.996110326314793e-06, "Nonfiction Writing": 8.260459097005297e-06, "Organizational About Page": 6.8366241914910175e-06, "Personal About Page": 6.76619947341126e-06, "Personal Blog": 4.509464046446946e-06, "Product Page": 3.3291332662466253e-06, "Spam / Ads": 5.409267142066551e-06, "Structured Data": 0.00030287933476097083, "Technical Writing": 0.9979770440669411, "Transcript / Interview": 9.582560640024872e-06, "Tutorial / How-To Guide": 0.00035977348048778473, "User Reviews": 3.3376417642864313e-06, "Other / Unknown": 0.0009448335956655806}, "secondary": {"Academic Writing": 0.009635958604753166, "Content Listing": 0.0001678231410956302, "Creative Writing": 0.00014802693917844717, "Customer Support Page": 0.00013358288927468753, "Q&A Forum": 8.201127440943932e-05, "Discussion Thread": 7.115138584967446e-05, "Comment Section": 9.195493336052503e-05, "Missing Content": 0.0003250901841816184, "Knowledge Article": 0.0012463941603453575, "Legal Notices": 0.00016868963287274764, "News Article": 7.961930488908286e-05, "Organizational Announcement": 8.033132252974578e-05, "Nonfiction Writing": 0.00011239783342233543, "Organizational About Page": 0.00010005964819690785, "Personal About Page": 8.764908586239873e-05, "Personal Blog": 7.42939740436044e-05, "Product Page": 5.214097331681133e-05, "Spam / Ads": 8.22138587733976e-05, "Structured Data": 0.001812963658403399, "Technical Writing": 0.016986116265156784, "Transcript / Interview": 0.00011361060270374849, "Tutorial / How-To Guide": 0.004085366276867785, "User Reviews": 4.153874432613424e-05, "NONE": 0.9642210153061866}, "tertiary": {"Academic Writing": 0.008679479514109108, "Content Listing": 9.48828657066579e-05, "Creative Writing": 0.00010043475910626978, "Customer Support Page": 6.321055770985592e-05, "Q&A Forum": 4.131491903090671e-05, "Discussion Thread": 3.906851492563147e-05, "Comment Section": 8.849526927563135e-05, "Missing Content": 0.000505792501963616, "Knowledge Article": 0.0014377638428894927, "Legal Notices": 0.00021238084688013424, "News Article": 5.045466363502951e-05, "Organizational Announcement": 5.161164677036873e-05, "Nonfiction Writing": 0.00010646212609763284, "Organizational About Page": 7.826634985809953e-05, "Personal About Page": 5.7251001539252224e-05, "Personal Blog": 4.119012797640248e-05, "Product Page": 3.136992823964597e-05, "Spam / Ads": 5.989345795359115e-05, "Structured Data": 0.0008848506081863747, "Technical Writing": 0.009842232258207614, "Transcript / Interview": 6.821800556781967e-05, "Tutorial / How-To Guide": 0.0030111958598286696, "User Reviews": 2.7087854560860057e-05, "NONE": 0.9744270925199814}, "structured_records": {"false": 0.9892519571371965, "true": 0.010748042862803524}, "procedural_steps": {"false": 0.9572707034479393, "true": 0.04272929655206068}, "worked_solution": {"false": 0.9942485130251716, "true": 0.005751486974828451}, "contextual_dialogue": {"false": 0.9999947427545729, "true": 5.257245427146604e-06}, "content_quality": {"-2": 0.01561904465066333, "-1": 0.12888521150328464, "0": 0.3923962179794038, "1": 0.3865293681349323, "2": 0.07657015773171592}, "noise_severity": {"0": 0.7711446040722572, "1": 0.19516862266009374, "2": 0.027757378895328498, "3": 0.003759394753821819, "4": 0.002169999618498815}, "missing_content_severity": {"0": 0.9624164840975388, "1": 0.023185555487326284, "2": 0.010437779476318146, "3": 0.0033174974053172337, "4": 0.0006426835334994297}, "factual_density": {"0": 0.006388034721233418, "1": 0.02821582899640944, "2": 0.15995664603485463, "3": 0.45924841366774477, "4": 0.34619107657975773}, "pretraining_utility": {"-2": 0.009096242847337621, "-1": 0.09604185450134987, "0": 0.3702007272876505, "1": 0.4455833571970497, "2": 0.07907781816661223}, "math_density": {"0": 0.04451573412387653, "1": 0.228150306697859, "2": 0.33708623269496885, "3": 0.17339537242225841, "4": 0.21685235406103728}, "code_density": {"0": 0.00038836528727997086, "1": 0.0008377996932294521, "2": 0.031860746248930864, "3": 0.2541959573256617, "4": 0.7127171314448979}, "reasoning_density": {"0": 0.6718977248542363, "1": 0.1023692952079349, "2": 0.12625679874432086, "3": 0.06803434912615079, "4": 0.03144183206735729}, "education_level": {"none": 0.0015044568687531431, "0": 0.0014873236844899188, "1": 0.0029525556198899024, "2": 0.098792087923978, "3": 0.5150453770706301, "4": 0.3802181988322588}, "creative_writing_quality": {"not_applicable": 0.9992965188950532, "-2": 0.00011052239899507203, "-1": 0.00018144159737715253, "0": 0.0002977095199744225, "1": 7.8557469897242e-05, "2": 3.5250118703004074e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.000369460795879573, "Art & Design": 0.0004044980971224564, "Law & Crime": 0.0004216739890255505, "Education & Careers": 0.0002980594352808144, "Entertainment & Media": 0.0003101688798368516, "Fashion & Beauty": 0.00035462316271029894, "Business, Economics & Finance": 0.0002774453043046119, "Food & Dining": 0.00030938939239923667, "Games & Gaming": 0.0004149111330609518, "Electronics & Hardware": 0.0004780129332841322, "Health, Healthcare & Wellness": 0.00041640576816545524, "History & Geography": 0.0004605391004715592, "Home, Pets & Hobbies": 0.0002695025233808936, "Industry & Production": 0.0004018726888204518, "Language, Literature & Philosophy": 0.00027588227793426867, "Politics & Government": 0.00020511412794699984, "Religion & Spirituality": 0.0003111361723155562, "Science, Mathematics & Engineering": 0.22204570918403135, "Relationships & Community": 0.00014858885688261592, "Software Usage & Applications": 0.0011045994067291772, "Programming & Software Engineering": 0.7671071491487352, "Sports & Fitness": 0.00039917408896915504, "Vehicles & Transportation": 0.00022772632160055837, "Travel & Tourism": 0.0003263998749430859, "Other / Uncovered Subject": 0.0021189910405276643, "Unknown / Insufficient Content": 0.0005429662956414598}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006628990933067683, "Art & Design": 0.0005674872111937194, "Law & Crime": 0.0005601594863166152, "Education & Careers": 0.0003832444324803715, "Entertainment & Media": 0.00041018429140609567, "Fashion & Beauty": 0.0004026059944176683, "Business, Economics & Finance": 0.00038554678290298876, "Food & Dining": 0.000298291934381669, "Games & Gaming": 0.0003698740160580461, "Electronics & Hardware": 0.0006264670132773821, "Health, Healthcare & Wellness": 0.0005009098349662967, "History & Geography": 0.000491211758292174, "Home, Pets & Hobbies": 0.00028170778586944354, "Industry & Production": 0.00040805846284628944, "Language, Literature & Philosophy": 0.00023429686477621664, "Politics & Government": 0.00025473439990912036, "Religion & Spirituality": 0.00047949708616691427, "Science, Mathematics & Engineering": 0.5626504729995561, "Relationships & Community": 0.00016139051698814676, "Software Usage & Applications": 0.000753742474422399, "Programming & Software Engineering": 0.22228133485110904, "Sports & Fitness": 0.0004435919545845686, "Vehicles & Transportation": 0.0002537412846006234, "Travel & Tourism": 0.00043401530788347273, "Other / Uncovered Subject": 0.002094652832440906, "NONE": 0.20360988132984684}}, "probabilities": {"primary": {"Academic Writing": 0.00022570567382957017, "Content Listing": 1.2990272508986802e-05, "Creative Writing": 7.187078238492848e-06, "Customer Support Page": 4.830129979173345e-06, "Q&A Forum": 6.663704208411457e-06, "Discussion Thread": 4.410250418685369e-06, "Comment Section": 5.443691752419507e-06, "Missing Content": 1.0880724856537141e-05, "Knowledge Article": 7.312654518898031e-05, "Legal Notices": 8.980121872661828e-06, "News Article": 2.223869343107106e-06, "Organizational Announcement": 4.996110326314794e-06, "Nonfiction Writing": 8.260459097005299e-06, "Organizational About Page": 6.836624191491019e-06, "Personal About Page": 6.766199473411261e-06, "Personal Blog": 4.509464046446947e-06, "Product Page": 3.329133266246626e-06, "Spam / Ads": 5.409267142066552e-06, "Structured Data": 0.0003028793347609709, "Technical Writing": 0.9979770440669413, "Transcript / Interview": 9.582560640024874e-06, "Tutorial / How-To Guide": 0.0003597734804877848, "User Reviews": 3.337641764286432e-06, "Other / Unknown": 0.0009448335956655808}, "secondary": {"Academic Writing": 0.009635958604753166, "Content Listing": 0.0001678231410956302, "Creative Writing": 0.00014802693917844717, "Customer Support Page": 0.00013358288927468753, "Q&A Forum": 8.201127440943932e-05, "Discussion Thread": 7.115138584967446e-05, "Comment Section": 9.195493336052503e-05, "Missing Content": 0.0003250901841816184, "Knowledge Article": 0.0012463941603453575, "Legal Notices": 0.00016868963287274764, "News Article": 7.961930488908286e-05, "Organizational Announcement": 8.033132252974578e-05, "Nonfiction Writing": 0.00011239783342233543, "Organizational About Page": 0.00010005964819690785, "Personal About Page": 8.764908586239873e-05, "Personal Blog": 7.42939740436044e-05, "Product Page": 5.214097331681133e-05, "Spam / Ads": 8.22138587733976e-05, "Structured Data": 0.001812963658403399, "Technical Writing": 0.016986116265156784, "Transcript / Interview": 0.00011361060270374849, "Tutorial / How-To Guide": 0.004085366276867785, "User Reviews": 4.153874432613424e-05, "NONE": 0.9642210153061866}, "tertiary": {"Academic Writing": 0.008679479514109108, "Content Listing": 9.48828657066579e-05, "Creative Writing": 0.00010043475910626978, "Customer Support Page": 6.321055770985592e-05, "Q&A Forum": 4.131491903090671e-05, "Discussion Thread": 3.906851492563147e-05, "Comment Section": 8.849526927563135e-05, "Missing Content": 0.000505792501963616, "Knowledge Article": 0.0014377638428894927, "Legal Notices": 0.00021238084688013424, "News Article": 5.045466363502951e-05, "Organizational Announcement": 5.161164677036873e-05, "Nonfiction Writing": 0.00010646212609763284, "Organizational About Page": 7.826634985809953e-05, "Personal About Page": 5.7251001539252224e-05, "Personal Blog": 4.119012797640248e-05, "Product Page": 3.136992823964597e-05, "Spam / Ads": 5.989345795359115e-05, "Structured Data": 0.0008848506081863747, "Technical Writing": 0.009842232258207614, "Transcript / Interview": 6.821800556781967e-05, "Tutorial / How-To Guide": 0.0030111958598286696, "User Reviews": 2.7087854560860057e-05, "NONE": 0.9744270925199814}, "structured_records": {"false": 0.9892519571371965, "true": 0.010748042862803524}, "procedural_steps": {"false": 0.9572707034479393, "true": 0.04272929655206068}, "worked_solution": {"false": 0.9942485130251716, "true": 0.005751486974828451}, "contextual_dialogue": {"false": 0.9999947427545729, "true": 5.257245427146604e-06}, "content_quality": {"-2": 0.01561904465066333, "-1": 0.12888521150328464, "0": 0.3923962179794038, "1": 0.3865293681349323, "2": 0.07657015773171592}, "noise_severity": {"0": 0.7711446040722572, "1": 0.19516862266009374, "2": 0.027757378895328498, "3": 0.003759394753821819, "4": 0.002169999618498815}, "missing_content_severity": {"0": 0.9624164840975389, "1": 0.023185555487326287, "2": 0.010437779476318148, "3": 0.003317497405317234, "4": 0.0006426835334994298}, "factual_density": {"0": 0.006388034721233418, "1": 0.02821582899640944, "2": 0.15995664603485463, "3": 0.45924841366774477, "4": 0.34619107657975773}, "pretraining_utility": {"-2": 0.009096242847337621, "-1": 0.09604185450134987, "0": 0.3702007272876505, "1": 0.4455833571970497, "2": 0.07907781816661223}, "math_density": {"0": 0.04451573412387653, "1": 0.228150306697859, "2": 0.33708623269496885, "3": 0.17339537242225841, "4": 0.21685235406103728}, "code_density": {"0": 0.0003883652872799709, "1": 0.0008377996932294522, "2": 0.03186074624893087, "3": 0.25419595732566175, "4": 0.712717131444898}, "reasoning_density": {"0": 0.6718977248542363, "1": 0.1023692952079349, "2": 0.12625679874432086, "3": 0.06803434912615079, "4": 0.03144183206735729}, "education_level": {"none": 0.0015044568687531434, "0": 0.001487323684489919, "1": 0.002952555619889903, "2": 0.09879208792397802, "3": 0.5150453770706303, "4": 0.3802181988322589}, "creative_writing_quality": {"not_applicable": 0.999296518895053, "-2": 0.000110522398995072, "-1": 0.0001814415973771525, "0": 0.00029770951997442247, "1": 7.855746989724198e-05, "2": 3.525011870300407e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.000369460795879573, "Art & Design": 0.0004044980971224564, "Law & Crime": 0.0004216739890255505, "Education & Careers": 0.0002980594352808144, "Entertainment & Media": 0.0003101688798368516, "Fashion & Beauty": 0.00035462316271029894, "Business, Economics & Finance": 0.0002774453043046119, "Food & Dining": 0.00030938939239923667, "Games & Gaming": 0.0004149111330609518, "Electronics & Hardware": 0.0004780129332841322, "Health, Healthcare & Wellness": 0.00041640576816545524, "History & Geography": 0.0004605391004715592, "Home, Pets & Hobbies": 0.0002695025233808936, "Industry & Production": 0.0004018726888204518, "Language, Literature & Philosophy": 0.00027588227793426867, "Politics & Government": 0.00020511412794699984, "Religion & Spirituality": 0.0003111361723155562, "Science, Mathematics & Engineering": 0.22204570918403135, "Relationships & Community": 0.00014858885688261592, "Software Usage & Applications": 0.0011045994067291772, "Programming & Software Engineering": 0.7671071491487352, "Sports & Fitness": 0.00039917408896915504, "Vehicles & Transportation": 0.00022772632160055837, "Travel & Tourism": 0.0003263998749430859, "Other / Uncovered Subject": 0.0021189910405276643, "Unknown / Insufficient Content": 0.0005429662956414598}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006628990933067685, "Art & Design": 0.0005674872111937195, "Law & Crime": 0.0005601594863166153, "Education & Careers": 0.00038324443248037153, "Entertainment & Media": 0.0004101842914060957, "Fashion & Beauty": 0.0004026059944176684, "Business, Economics & Finance": 0.0003855467829029888, "Food & Dining": 0.00029829193438166905, "Games & Gaming": 0.00036987401605804613, "Electronics & Hardware": 0.0006264670132773822, "Health, Healthcare & Wellness": 0.0005009098349662969, "History & Geography": 0.0004912117582921741, "Home, Pets & Hobbies": 0.0002817077858694436, "Industry & Production": 0.0004080584628462895, "Language, Literature & Philosophy": 0.00023429686477621667, "Politics & Government": 0.0002547343999091204, "Religion & Spirituality": 0.0004794970861669143, "Science, Mathematics & Engineering": 0.5626504729995562, "Relationships & Community": 0.0001613905169881468, "Software Usage & Applications": 0.0007537424744223991, "Programming & Software Engineering": 0.22228133485110907, "Sports & Fitness": 0.00044359195458456863, "Vehicles & Transportation": 0.00025374128460062343, "Travel & Tourism": 0.0004340153078834728, "Other / Uncovered Subject": 0.0020946528324409064, "NONE": 0.20360988132984686}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10159, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10159, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:08.242320+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "b32cab1e-e57f-4d2c-8c32-8a598ddeddde", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "ba72ac8f43d9523aad6874553fa7f74a33c3b3a48852f24f8887c6d972ce8b94", "source_metadata": {"uuid": "b32cab1e-e57f-4d2c-8c32-8a598ddeddde", "repo_name": "oliviax727/hvc-magnetic-honours-programs", "relative_path": "collation/group_proj_functions.py", "category": "DATA", "algo_rel_score": 0.9917861819267273, "quality_score": 6.080735683441162, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 133, "repo_created_at": "2024-02-03T06:39:39.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c2806505b951138bca6b02301a34a6c024e0242b", "size_bytes": 5543, "file_timestamp": 1729724895, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "9bb87b9293110cec13d1e56f73312c9a7cf4d6f8"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 133, "repo_created_at": "2024-02-03T06:39:39.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c2806505b951138bca6b02301a34a6c024e0242b", "size_bytes": 5543, "file_timestamp": 1729724895, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "9bb87b9293110cec13d1e56f73312c9a7cf4d6f8", "url": "https://github.com/oliviax727/hvc-magnetic-honours-programs/blob/9bb87b9293110cec13d1e56f73312c9a7cf4d6f8/collation/group_proj_functions.py"}, "gpt4o_tokens": 1568, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.37954638279375286, "noise_severity": 0.27064156318621146, "missing_content_severity": 0.05658434078991201, "factual_density": 3.110638668388384, "pretraining_utility": 0.4895046533342491, "math_density": 2.289918305598721, "code_density": 3.6780156899476686, "reasoning_density": 0.6847532683444582, "education_level": 3.2714674396692844, "creative_writing_quality": -0.3602494598389791}, "score_applicability": {"education_level": 0.998495543131247, "creative_writing_quality": 0.000703481104946893}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/RahulGahalaut/Movie-Recommender/blob/ee35776701aba1eedd4eb71526bf57aa2bf5852a/server/app.py", "id": "python-0-ae9408a987d598cfba383ce4ffc7e86b", "text": "from flask import Flask, request, jsonify\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_jwt_extended import JWTManager, jwt_required, get_jwt_identity, create_access_token\nfrom dotenv import load_dotenv\nfrom flask_cors import CORS\nimport bcrypt\nimport os\nimport requests\nimport datetime\nimport binascii\n\n\nload_dotenv()\napp = Flask(__name__)\nCORS(app)\n# Configure SQLAlchemy and JWTManager\napp.config['SQLALCHEMY_DATABASE_URI'] = os.environ['SQLALCHEMY_DATABASE_URI']\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.config['JWT_SECRET_KEY'] = os.environ['JWT_SECRET_KEY']\ndb = SQLAlchemy(app)\njwt = JWTManager(app)\n\n\n# User model\nclass User(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(200), nullable=False)\n    email = db.Column(db.String(200), unique=True, nullable=False)\n    password = db.Column(db.LargeBinary, nullable=False)\n    interests = db.Column(db.JSON)\n\n\nTMDB_HEADERS = {\n    \"accept\": \"application/json\",\n    \"Authorization\": \"Bearer {}\".format(os.environ['TMDB_AUTHORIZATION_TOKEN'])\n}\n\n\n# Register route\n@app.route('/users/register', methods=['POST'])\ndef register():\n    try:\n        data = request.json\n        existing_user = User.query.filter_by(email=data['email']).first()\n        if existing_user:\n            return jsonify({\"message\": \"User already exists\"}), 409\n\n        hashed_password = bcrypt.hashpw(\n            data['password'].encode('utf-8'), bcrypt.gensalt())\n        new_user = User(name=data['name'], email=data['email'],\n                        password=<PASSWORD>, interests=data['interests'])\n        db.session.add(new_user)\n        db.session.commit()\n        expires = datetime.timedelta(days=30)\n        access_token = create_access_token(\n            identity=new_user.id, expires_delta=expires)\n        return jsonify({\"token\": access_token}), 201\n    except Exception as e:\n        print(e)\n        return jsonify({\"message\": \"Internal server error\"}), 500\n\n\n# Login route\n@app.route('/users/login', methods=['POST'])\ndef login():\n    try:\n        data = request.json\n        user = User.query.filter_by(email=data['email']).first()\n        if user and bcrypt.checkpw(data['password'].encode('utf-8'), user.password):\n            expires = datetime.timedelta(days=30)\n            access_token = create_access_token(\n                identity=user.id, expires_delta=expires)\n            return jsonify({\"token\": access_token}), 200\n        else:\n            return jsonify({\"message\": \"Invalid credentials\"}), 401\n    except Exception as e:\n        print(e)\n        return jsonify({\"message\": \"Internal server error\"}), 500\n\n\n@app.route('/movies/genres', methods=['GET'])\ndef get_genres():\n    try:\n        url = \"https://api.themoviedb.org/3/genre/movie/list?language=en\"\n        response = requests.get(url, headers=TMDB_HEADERS)\n        return jsonify(response.json()), 200\n    except Exception as e:\n        print(e)\n        return jsonify({\"message\": \"Internal server error\"}), 500\n\n\n# Protected route\n@app.route('/movies/search', methods=['GET'])\n@jwt_required()\ndef search_movie():\n    try:\n        movie_name = request.args.get('movie_name')\n        page = request.args.get('page')\n        url = \"https://api.themoviedb.org/3/search/movie?query={}&include_adult=false&language=en-US&page={}\".format(\n            movie_name, page)\n        response = requests.get(url, headers=TMDB_HEADERS)\n        return jsonify(response.json()), 200\n    except Exception as e:\n        print(e)\n        return jsonify({\"message\": \"Internal server error\"}), 500\n\n\n@app.route('/movies/recommended', methods=['GET'])\n@jwt_required()\ndef recommend_movies():\n    try:\n        page = request.args.get('page')\n        movie_id = request.args.get('movie_id')\n        url = \"https://api.themoviedb.org/3/movie/{}/similar?language=en-US&page={}\".format(\n            movie_id, page)\n        response = requests.get(url, headers=TMDB_HEADERS)\n        return jsonify(response.json()), 200\n    except Exception as e:\n        print(e)\n        return jsonify({\"message\": \"Internal server error\"}), 500\n\n\n@app.route('/movies/info', methods=['GET'])\n@jwt_required()\ndef movie_info():\n    try:\n        movie_id = request.args.get('movie_id')\n        url = \"https://api.themoviedb.org/3/movie/{}?language=en-US\".format(\n            movie_id)\n        response = requests.get(url, headers=TMDB_HEADERS)\n        return jsonify(response.json()), 200\n    except Exception as e:\n        print(e)\n        return jsonify({\"message\": \"Internal server error\"}), 500\n\n\n@app.route('/movies/interested', methods=['GET'])\n@jwt_required()\ndef interested_movies():\n    try:\n        page = request.args.get('page')\n        current_user_id = get_jwt_identity()\n        user = User.query.get(current_user_id)\n        genres = '|'.join(user.interests)\n        url = url = \"https://api.themoviedb.org/3/discover/movie?include_adult=false&include_video=false&language=en-US&page={}&sort_by=popularity.desc&with_genres={}\".format(page,\n                                                                                                                                                                               genres)\n        response = requests.get(url, headers=TMDB_HEADERS)\n        return jsonify(response.json()), 200\n    except Exception as e:\n        print(e)\n        return jsonify({\"message\": \"Internal server error\"}), 500\n\n\nwith app.app_context():\n    db.create_all()\n\nif __name__ == '__main__':\n    app.run(port=os.environ['PORT'], debug=True)\n", "text_preview": "from flask import Flask, request, jsonify from flask_sqlalchemy import SQLAlchemy from flask_jwt_extended import JWTManager, jwt_required, get_jwt_identity, create_access_token from dotenv import load_dotenv from flask_cors import CORS impo", "input_sha256": "0a112a42abeecbed64200ce0bbeb9dba616c583754e3e9ff39c09a8b9b7d18f9", "text_sha256": "ae9408a987d598cfba383ce4ffc7e86bde1a6451d9ae0db2cea698f044df2414", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12034, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Entertainment & Media", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.2882862849977544, "noise_severity": 0.2522041370394123, "missing_content_severity": 0.5876117520395802, "factual_density": 3.17955890883603, "pretraining_utility": 0.6185607017106564, "math_density": 0.06341413340469262, "code_density": 3.748058993229135, "reasoning_density": 0.2670167175288052, "education_level": 2.1972778225485587, "creative_writing_quality": -0.1985619428349816}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Entertainment & Media"}, "review_reasons": ["secondary_topic probability 0.5248 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.008541626468070244, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9953951851251008, "secondary": 0.9264607446465298, "tertiary": 0.9344225014736532, "structured_records": 0.03529501263419572, "procedural_steps": 0.0032780934391583587, "worked_solution": 0.0033303732818293665, "contextual_dialogue": 1.0149282251928763e-05, "content_quality": 0.251668603082555, "noise_severity": 0.7898298858004897, "missing_content_severity": 0.5103235399670164, "factual_density": 0.4945507579829519, "pretraining_utility": 0.5165925535453261, "math_density": 0.9471548888294228, "code_density": 0.7900491610242789, "reasoning_density": 0.7774860687259957, "education_level": 0.747911758780864, "creative_writing_quality": 0.9975451693545339, "primary_topic": 0.9654784484950724, "secondary_topic": 0.5058117437473263}, "probability_margin": {"primary": 0.9938884998933001, "secondary": 0.8906294812561395, "tertiary": 0.8944207624849261, "education_level": 0.5971631340869518, "creative_writing_quality": 0.9966965721104243, "primary_topic": 0.9363917354101828, "secondary_topic": 0.12180722767654883}, "raw_probabilities": {"primary": {"Academic Writing": 4.909274804979298e-05, "Content Listing": 3.8203637893943315e-05, "Creative Writing": 3.2606529683167356e-05, "Customer Support Page": 1.186488808737775e-05, "Q&A Forum": 1.8388899876673808e-05, "Discussion Thread": 1.3240632699543124e-05, "Comment Section": 1.8671483641442366e-05, "Missing Content": 0.00025292577668112033, "Knowledge Article": 0.00010216060587052484, "Legal Notices": 2.9723258764424852e-05, "News Article": 8.889933716567057e-06, "Organizational Announcement": 1.4900798786661732e-05, "Nonfiction Writing": 2.2574362271651945e-05, "Organizational About Page": 2.0064558684695904e-05, "Personal About Page": 2.5686687706005303e-05, "Personal Blog": 1.362889148486386e-05, "Product Page": 1.3956489576338205e-05, "Spam / Ads": 2.034770846892595e-05, "Structured Data": 0.0005233149965362336, "Technical Writing": 0.995587052411555, "Transcript / Interview": 2.4743294032685847e-05, "Tutorial / How-To Guide": 0.001448365341861785, "User Reviews": 1.104354581581568e-05, "Other / Unknown": 0.0016985525182549482}, "secondary": {"Academic Writing": 0.0003170979320625391, "Content Listing": 0.000475522179612489, "Creative Writing": 0.00033198460375871664, "Customer Support Page": 0.00015709541631011385, "Q&A Forum": 0.00013434417855571188, "Discussion Thread": 0.00011935066706849636, "Comment Section": 0.00020862931802425873, "Missing Content": 0.038895399030118266, "Knowledge Article": 0.0008621119912928768, "Legal Notices": 0.0004416371014543563, "News Article": 0.00019994624787825907, "Organizational Announcement": 0.00015941385986433746, "Nonfiction Writing": 0.00022033787840455867, "Organizational About Page": 0.00020287783991203016, "Personal About Page": 0.00016590668194855308, "Personal Blog": 0.0001389822563196804, "Product Page": 0.00010456535630490527, "Spam / Ads": 0.00021642417486779668, "Structured Data": 0.009449545111288381, "Technical Writing": 0.0141261553217482, "Transcript / Interview": 0.00023359471010841922, "Tutorial / How-To Guide": 0.003221094781587195, "User Reviews": 9.31030752521194e-05, "NONE": 0.9295248802862578}, "tertiary": {"Academic Writing": 0.0001930260580931579, "Content Listing": 0.00047475531356763756, "Creative Writing": 0.00023041213286736402, "Customer Support Page": 0.00012477719245622347, "Q&A Forum": 9.036877552211814e-05, "Discussion Thread": 9.052317373003524e-05, "Comment Section": 0.00018496425224031165, "Missing Content": 0.042734134760658286, "Knowledge Article": 0.0008041157492960888, "Legal Notices": 0.0003863915328425589, "News Article": 0.00013998825758015766, "Organizational Announcement": 0.00010764676001220712, "Nonfiction Writing": 0.0001854219469360682, "Organizational About Page": 0.00019357208572518157, "Personal About Page": 0.0001224666347566614, "Personal Blog": 0.00010218768905175241, "Product Page": 9.196072048449559e-05, "Spam / Ads": 0.00015354320353655677, "Structured Data": 0.008482094189608701, "Technical Writing": 0.0055845288536779565, "Transcript / Interview": 0.0001629323909449456, "Tutorial / How-To Guide": 0.0021394185557577824, "User Reviews": 6.587252506942466e-05, "NONE": 0.9371548972455843}, "structured_records": {"false": 0.9647049873658042, "true": 0.03529501263419572}, "procedural_steps": {"false": 0.9967219065608416, "true": 0.0032780934391583587}, "worked_solution": {"false": 0.9966696267181706, "true": 0.0033303732818293665}, "contextual_dialogue": {"false": 0.999989850717748, "true": 1.0149282251928763e-05}, "content_quality": {"-2": 0.03562966020176235, "-1": 0.1816697631230209, "0": 0.33462718880026093, "1": 0.3549314072256115, "2": 0.09314198064934422}, "noise_severity": {"0": 0.8048308740082405, "1": 0.15355603374081478, "2": 0.02965344062691687, "3": 0.008497384451348237, "4": 0.003462267172679779}, "missing_content_severity": {"0": 0.5018080511321152, "1": 0.42166743653070277, "2": 0.06623948337444897, "3": 0.007674767090952811, "4": 0.0026102618717802517}, "factual_density": {"0": 0.006883215025465329, "1": 0.02198704155057732, "2": 0.14886636261466316, "3": 0.4292143811810502, "4": 0.39304899962824397}, "pretraining_utility": {"-2": 0.011669615099678626, "-1": 0.07066031886872576, "0": 0.3044346339809888, "1": 0.5139106133224742, "2": 0.09932481872813258}, "math_density": {"0": 0.9494748476507314, "1": 0.04363920074825872, "2": 0.0031939743503010694, "3": 0.0013809250470035595, "4": 0.002311052203705272}, "code_density": {"0": 0.0003396865153365768, "1": 0.0006360205884676446, "2": 0.028748899941297604, "3": 0.19117639906152084, "4": 0.7790989938933773}, "reasoning_density": {"0": 0.847151687370747, "1": 0.05892147096991858, "2": 0.07867191078366961, "3": 0.010268298511112035, "4": 0.004986632364552813}, "education_level": {"none": 0.0008851009139797468, "0": 0.0011866486235447985, "1": 0.007921243015046653, "2": 0.78992646565072, "3": 0.19276333156376826, "4": 0.007317210232940667}, "creative_writing_quality": {"not_applicable": 0.9979543077954449, "-2": 0.00012221454463222852, "-1": 0.0004394263981369966, "0": 0.0012577356850206106, "1": 0.00017497228470789912, "2": 5.1343292057359206e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.4414126261261115e-05, "Art & Design": 0.0001077254877985773, "Law & Crime": 6.902723680403187e-05, "Education & Careers": 5.61457797755274e-05, "Entertainment & Media": 0.030414465065848325, "Fashion & Beauty": 5.547205787133797e-05, "Business, Economics & Finance": 0.00013314068315407892, "Food & Dining": 5.480921668184682e-05, "Games & Gaming": 0.00020726704162362842, "Electronics & Hardware": 7.14020485771851e-05, "Health, Healthcare & Wellness": 3.8748050756744845e-05, "History & Geography": 4.5027489861277804e-05, "Home, Pets & Hobbies": 5.725839797887749e-05, "Industry & Production": 4.608177032723365e-05, "Language, Literature & Philosophy": 5.068501833664137e-05, "Politics & Government": 3.0437631854031848e-05, "Religion & Spirituality": 4.1733352613968984e-05, "Science, Mathematics & Engineering": 0.00024323983204093555, "Relationships & Community": 3.938458334277663e-05, "Software Usage & Applications": 0.0008073142432575422, "Programming & Software Engineering": 0.9668062004760312, "Sports & Fitness": 6.601103982494221e-05, "Vehicles & Transportation": 3.102017578404007e-05, "Travel & Tourism": 6.222584851756475e-05, "Other / Uncovered Subject": 0.00033280696233857975, "Unknown / Insufficient Content": 7.795638273799814e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007875096248119482, "Art & Design": 0.0013746126361392239, "Law & Crime": 0.0008572950748151725, "Education & Careers": 0.0005075884085420617, "Entertainment & Media": 0.5248189843724291, "Fashion & Beauty": 0.0006028922808099243, "Business, Economics & Finance": 0.0012973503543500502, "Food & Dining": 0.00042506260133686857, "Games & Gaming": 0.0006841747717988897, "Electronics & Hardware": 0.0005131851641945056, "Health, Healthcare & Wellness": 0.0004953808854959752, "History & Geography": 0.00040975486670416565, "Home, Pets & Hobbies": 0.0005788480533529119, "Industry & Production": 0.00034095460238093683, "Language, Literature & Philosophy": 0.0003762584567505833, "Politics & Government": 0.00026211220911738676, "Religion & Spirituality": 0.0004630554665383213, "Science, Mathematics & Engineering": 0.0011865179036661092, "Relationships & Community": 0.00028841811188799076, "Software Usage & Applications": 0.0055537677963546995, "Programming & Software Engineering": 0.05293209525653156, "Sports & Fitness": 0.00039079492697367254, "Vehicles & Transportation": 0.00022745409679371395, "Travel & Tourism": 0.0005982179360101542, "Other / Uncovered Subject": 0.0010159574463338105, "NONE": 0.4030117566958803}}, "probabilities": {"primary": {"Academic Writing": 4.9092748049792964e-05, "Content Listing": 3.820363789394331e-05, "Creative Writing": 3.260652968316735e-05, "Customer Support Page": 1.1864888087377746e-05, "Q&A Forum": 1.8388899876673805e-05, "Discussion Thread": 1.3240632699543121e-05, "Comment Section": 1.8671483641442363e-05, "Missing Content": 0.0002529257766811203, "Knowledge Article": 0.00010216060587052481, "Legal Notices": 2.9723258764424845e-05, "News Article": 8.889933716567056e-06, "Organizational Announcement": 1.4900798786661729e-05, "Nonfiction Writing": 2.257436227165194e-05, "Organizational About Page": 2.00645586846959e-05, "Personal About Page": 2.5686687706005296e-05, "Personal Blog": 1.3628891484863856e-05, "Product Page": 1.3956489576338201e-05, "Spam / Ads": 2.0347708468925947e-05, "Structured Data": 0.0005233149965362335, "Technical Writing": 0.9955870524115548, "Transcript / Interview": 2.474329403268584e-05, "Tutorial / How-To Guide": 0.0014483653418617848, "User Reviews": 1.1043545815815678e-05, "Other / Unknown": 0.0016985525182549477}, "secondary": {"Academic Writing": 0.0003170979320625391, "Content Listing": 0.000475522179612489, "Creative Writing": 0.00033198460375871664, "Customer Support Page": 0.00015709541631011385, "Q&A Forum": 0.00013434417855571188, "Discussion Thread": 0.00011935066706849636, "Comment Section": 0.00020862931802425873, "Missing Content": 0.038895399030118266, "Knowledge Article": 0.0008621119912928768, "Legal Notices": 0.0004416371014543563, "News Article": 0.00019994624787825907, "Organizational Announcement": 0.00015941385986433746, "Nonfiction Writing": 0.00022033787840455867, "Organizational About Page": 0.00020287783991203016, "Personal About Page": 0.00016590668194855308, "Personal Blog": 0.0001389822563196804, "Product Page": 0.00010456535630490527, "Spam / Ads": 0.00021642417486779668, "Structured Data": 0.009449545111288381, "Technical Writing": 0.0141261553217482, "Transcript / Interview": 0.00023359471010841922, "Tutorial / How-To Guide": 0.003221094781587195, "User Reviews": 9.31030752521194e-05, "NONE": 0.9295248802862578}, "tertiary": {"Academic Writing": 0.0001930260580931579, "Content Listing": 0.00047475531356763756, "Creative Writing": 0.00023041213286736402, "Customer Support Page": 0.00012477719245622347, "Q&A Forum": 9.036877552211814e-05, "Discussion Thread": 9.052317373003524e-05, "Comment Section": 0.00018496425224031165, "Missing Content": 0.042734134760658286, "Knowledge Article": 0.0008041157492960888, "Legal Notices": 0.0003863915328425589, "News Article": 0.00013998825758015766, "Organizational Announcement": 0.00010764676001220712, "Nonfiction Writing": 0.0001854219469360682, "Organizational About Page": 0.00019357208572518157, "Personal About Page": 0.0001224666347566614, "Personal Blog": 0.00010218768905175241, "Product Page": 9.196072048449559e-05, "Spam / Ads": 0.00015354320353655677, "Structured Data": 0.008482094189608701, "Technical Writing": 0.0055845288536779565, "Transcript / Interview": 0.0001629323909449456, "Tutorial / How-To Guide": 0.0021394185557577824, "User Reviews": 6.587252506942466e-05, "NONE": 0.9371548972455843}, "structured_records": {"false": 0.9647049873658042, "true": 0.03529501263419572}, "procedural_steps": {"false": 0.9967219065608416, "true": 0.0032780934391583587}, "worked_solution": {"false": 0.9966696267181706, "true": 0.0033303732818293665}, "contextual_dialogue": {"false": 0.999989850717748, "true": 1.0149282251928763e-05}, "content_quality": {"-2": 0.03562966020176236, "-1": 0.18166976312302094, "0": 0.334627188800261, "1": 0.35493140722561156, "2": 0.09314198064934423}, "noise_severity": {"0": 0.8048308740082403, "1": 0.15355603374081475, "2": 0.029653440626916862, "3": 0.008497384451348235, "4": 0.0034622671726797782}, "missing_content_severity": {"0": 0.5018080511321152, "1": 0.42166743653070277, "2": 0.06623948337444897, "3": 0.007674767090952811, "4": 0.0026102618717802517}, "factual_density": {"0": 0.006883215025465329, "1": 0.02198704155057732, "2": 0.14886636261466316, "3": 0.4292143811810502, "4": 0.39304899962824397}, "pretraining_utility": {"-2": 0.011669615099678626, "-1": 0.07066031886872576, "0": 0.3044346339809888, "1": 0.5139106133224742, "2": 0.09932481872813258}, "math_density": {"0": 0.9494748476507314, "1": 0.04363920074825872, "2": 0.0031939743503010694, "3": 0.0013809250470035595, "4": 0.002311052203705272}, "code_density": {"0": 0.0003396865153365768, "1": 0.0006360205884676446, "2": 0.028748899941297604, "3": 0.19117639906152084, "4": 0.7790989938933773}, "reasoning_density": {"0": 0.847151687370747, "1": 0.05892147096991858, "2": 0.07867191078366961, "3": 0.010268298511112035, "4": 0.004986632364552813}, "education_level": {"none": 0.0008851009139797466, "0": 0.0011866486235447983, "1": 0.007921243015046651, "2": 0.7899264656507198, "3": 0.1927633315637682, "4": 0.007317210232940665}, "creative_writing_quality": {"not_applicable": 0.9979543077954449, "-2": 0.00012221454463222852, "-1": 0.0004394263981369966, "0": 0.0012577356850206106, "1": 0.00017497228470789912, "2": 5.1343292057359206e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.44141262612611e-05, "Art & Design": 0.00010772548779857728, "Law & Crime": 6.902723680403186e-05, "Education & Careers": 5.6145779775527384e-05, "Entertainment & Media": 0.030414465065848318, "Fashion & Beauty": 5.547205787133796e-05, "Business, Economics & Finance": 0.0001331406831540789, "Food & Dining": 5.4809216681846805e-05, "Games & Gaming": 0.00020726704162362836, "Electronics & Hardware": 7.140204857718509e-05, "Health, Healthcare & Wellness": 3.874805075674484e-05, "History & Geography": 4.50274898612778e-05, "Home, Pets & Hobbies": 5.725839797887747e-05, "Industry & Production": 4.6081770327233637e-05, "Language, Literature & Philosophy": 5.0685018336641355e-05, "Politics & Government": 3.043763185403184e-05, "Religion & Spirituality": 4.173335261396898e-05, "Science, Mathematics & Engineering": 0.0002432398320409355, "Relationships & Community": 3.9384583342776625e-05, "Software Usage & Applications": 0.000807314243257542, "Programming & Software Engineering": 0.9668062004760309, "Sports & Fitness": 6.60110398249422e-05, "Vehicles & Transportation": 3.1020175784040066e-05, "Travel & Tourism": 6.222584851756473e-05, "Other / Uncovered Subject": 0.0003328069623385797, "Unknown / Insufficient Content": 7.795638273799812e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007875096248119482, "Art & Design": 0.0013746126361392239, "Law & Crime": 0.0008572950748151725, "Education & Careers": 0.0005075884085420617, "Entertainment & Media": 0.5248189843724291, "Fashion & Beauty": 0.0006028922808099243, "Business, Economics & Finance": 0.0012973503543500502, "Food & Dining": 0.00042506260133686857, "Games & Gaming": 0.0006841747717988897, "Electronics & Hardware": 0.0005131851641945056, "Health, Healthcare & Wellness": 0.0004953808854959752, "History & Geography": 0.00040975486670416565, "Home, Pets & Hobbies": 0.0005788480533529119, "Industry & Production": 0.00034095460238093683, "Language, Literature & Philosophy": 0.0003762584567505833, "Politics & Government": 0.00026211220911738676, "Religion & Spirituality": 0.0004630554665383213, "Science, Mathematics & Engineering": 0.0011865179036661092, "Relationships & Community": 0.00028841811188799076, "Software Usage & Applications": 0.0055537677963546995, "Programming & Software Engineering": 0.05293209525653156, "Sports & Fitness": 0.00039079492697367254, "Vehicles & Transportation": 0.00022745409679371395, "Travel & Tourism": 0.0005982179360101542, "Other / Uncovered Subject": 0.0010159574463338105, "NONE": 0.4030117566958803}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9849, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9849, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:08.412418+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "b100dec7-e098-43c6-a007-c6dc218de68c", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "2f07fc27d66a224c926f5d19605f5d3d77e467a6a36f7a44313e3924a9264fbc", "source_metadata": {"uuid": "b100dec7-e098-43c6-a007-c6dc218de68c", "repo_name": "RahulGahalaut/Movie-Recommender", "relative_path": "server/app.py", "category": "WEB", "algo_rel_score": 0.981196403503418, "quality_score": 6.681423187255859, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 14, "repo_created_at": "2023-08-13T12:51:52.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "9fbe3f9e908fe27b53db12c4040fd37976a855b0", "size_bytes": 5509, "file_timestamp": 1692354300, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "ee35776701aba1eedd4eb71526bf57aa2bf5852a"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 14, "repo_created_at": "2023-08-13T12:51:52.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "9fbe3f9e908fe27b53db12c4040fd37976a855b0", "size_bytes": 5509, "file_timestamp": 1692354300, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "ee35776701aba1eedd4eb71526bf57aa2bf5852a", "url": "https://github.com/RahulGahalaut/Movie-Recommender/blob/ee35776701aba1eedd4eb71526bf57aa2bf5852a/server/app.py"}, "gpt4o_tokens": 1224, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.2882862849977544, "noise_severity": 0.25220413703941236, "missing_content_severity": 0.5876117520395802, "factual_density": 3.17955890883603, "pretraining_utility": 0.6185607017106564, "math_density": 0.06341413340469262, "code_density": 3.748058993229135, "reasoning_density": 0.2670167175288052, "education_level": 2.1972778225485587, "creative_writing_quality": -0.1985619428349816}, "score_applicability": {"education_level": 0.9991148990860201, "creative_writing_quality": 0.002045692204555094}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/anthonykozar/pythoncode/blob/0befa8ef81558f1147258d6fb31b5dd8fba4fce5/Collatz/CollatzModularMaps.py", "id": "python-0-d790fb529288b84f24d1bc502f026f1f", "text": "# Maps of generalized Collatz functions modulo N\nfrom collections import defaultdict\n\ncoll = lambda x: 3*x + 1\n\ndef makemap(func, mod):\n    m = {}\n    for i in range(mod):\n        m[i] = func(i) % mod\n    return m\n\n# This is a useful piece of code but only works if fmap is bijective.\n# Could be generalized by allowing fmap to be a collection or a callable and replacing mod with an iterable \"domain\".\ndef findcycles(fmap, mod):\n    cycles = []\n    used = set()\n    start = 0\n    while start < mod:\n        # start a new cycle\n        curcycle = []\n        curcycle.append(start)\n        used.add(start)\n        # iterate fmap to find cycle\n        next = fmap[start]\n        while next != start:\n            curcycle.append(next)\n            used.add(next)\n            next = fmap[next]\n        cycles.append(curcycle)\n        # find the smallest unused value\n        while start in used: start += 1\n    return cycles\n\nfilepath = \"/sdcard/Documents/python/\"\nwritefiles = True\n\n# prime moduli (except for 3) seem to make bijective maps for 3x+1\nprimes = [2]\nfor n in xrange(3, 10000, 2):\n    isprime = True\n    for p in primes:\n        isprime = isprime and (n%p != 0)\n    if isprime: primes.append(n)\n\n# all cycles for prime moduli\ncycles = {}\nfor p in primes[2:]:  # skip 2 and 3\n    cycles[p] = findcycles(makemap(coll, p), p)\n\nif writefiles:\n    fout = open(filepath + \"3x+1modp-cycles.txt\", 'w')\n    for p in primes[2:]:\n        fout.write(\"%d %s\\n\" % (p, cycles[p]))\n    fout.close()\n\n# lengths of all cycles for prime moduli\nallcyclens = {}\nfor p in primes[2:]:  # skip 2 and 3\n    allcyclens[p] = map(len, cycles[p])\n\nif writefiles:\n    fout = open(filepath + \"3x+1modp-cycle-lengths.txt\", 'w')\n    for p in primes[2:]:\n        fout.write(\"%d %s\\n\" % (p, allcyclens[p]))\n    fout.close()\n\n# every prime moduli appears to produce cycles of two lengths: one cycle of length 1 (the fixed point floor(p/2)) and one or more cycles of a second length k that divides p-1. There are (p-1)/k of these length k cycles.\n# the length of the \"long\" cycles and their multiplicity for each prime moduli\ncyclenmultiplicity = {}\nfor p in primes[2:]:  # skip 2 and 3\n    longlen = allcyclens[p][0]\n    multiplicity = allcyclens[p].count(longlen)\n    cyclenmultiplicity[p] = (longlen, multiplicity)\n    if longlen * multiplicity != p - 1:\n        print \"Cycle length * multiplicity != p-1 for p=%d\" % p\n\nif writefiles:\n    fout = open(filepath + \"3x+1modp-cycle-multiplicities.txt\", 'w')\n    fout.write(\"p length multiplicity\\n\")\n    for p in primes[2:]:\n        fout.write(\"%d\\t%d\\t%d\\n\" % (p, cyclenmultiplicity[p][0], cyclenmultiplicity[p][1]))\n    fout.close()\n\n# build an inverse map from each cycle length to its prime moduli\ncyclentop = defaultdict(list)\nfor p in primes[2:]:  # skip 2 and 3\n    cyclentop[cyclenmultiplicity[p][0]].append(p)\n\nif writefiles:\n    fout = open(filepath + \"3x+1modp-length-to-p.txt\", 'w')\n    lengths = cyclentop.keys()\n    lengths.sort()\n    for k in lengths:\n        fout.write(\"%d %s\\n\" % (k, cyclentop[k]))\n    fout.close()\n\n# build an inverse map from each multiplicity value to its prime moduli\nmultiplicitytop = defaultdict(list)\nfor p in primes[2:]:  # skip 2 and 3\n    multiplicitytop[cyclenmultiplicity[p][1]].append(p)\n\nif writefiles:\n    fout = open(filepath + \"3x+1modp-multiplicity-to-p.txt\", 'w')\n    mults = multiplicitytop.keys()\n    mults.sort()\n    for k in mults:\n        fout.write(\"%d %s\\n\" % (k, multiplicitytop[k]))\n    fout.close()\n\n########\nprimes = findprimes4(10000000, True, True)\n\ny = 1\nc = []\nfor i in xrange(50):\n    y = coll(y)\n    c.append(y)\n\nfor n in c:\n    print \"%d: \" % n,\n    tryfactors(n, primes)\n", "text_preview": "# Maps of generalized Collatz functions modulo N from collections import defaultdict coll = lambda x: 3*x + 1 def makemap(func, mod): m = {} for i in range(mod): m[i] = func(i) % mod return m # This is a useful piece of code but only works ", "input_sha256": "e9673e023e9baa390084ec38d2daf1a9be6a4fc9c94604c12894e10762fb5898", "text_sha256": "d790fb529288b84f24d1bc502f026f1fa37b8b730b485b2694f6b909c95202ad", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12036, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.39344591618993663, "noise_severity": 0.047684284314531956, "missing_content_severity": 0.05891974962097108, "factual_density": 3.08877733722776, "pretraining_utility": 0.4967530270250424, "math_density": 3.4364144827320073, "code_density": 3.7928075192886133, "reasoning_density": 0.7829587558369483, "education_level": 2.25036716894738, "creative_writing_quality": -0.32817748490750204}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.4461 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.004964742113168996, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9934060866124113, "secondary": 0.9555170653935228, "tertiary": 0.960697665859438, "structured_records": 0.013319205756072965, "procedural_steps": 0.017678315982753687, "worked_solution": 0.021766850546589524, "contextual_dialogue": 4.753350009926687e-06, "content_quality": 0.3368857014080666, "noise_severity": 0.9602630964045566, "missing_content_severity": 0.9509002086491908, "factual_density": 0.4843187108358088, "pretraining_utility": 0.4157642939709486, "math_density": 0.5303454022766727, "code_density": 0.827339599407178, "reasoning_density": 0.3475343701358763, "education_level": 0.6282914157875777, "creative_writing_quality": 0.9984676943357491, "primary_topic": 0.6726670453208768, "secondary_topic": 0.42396538381714494}, "probability_margin": {"primary": 0.9905532576027667, "secondary": 0.9442599855443129, "tertiary": 0.9526735645443494, "education_level": 0.517339332133625, "creative_writing_quality": 0.9981236659315527, "primary_topic": 0.3797973665649112, "secondary_topic": 0.15030480961693737}, "raw_probabilities": {"primary": {"Academic Writing": 0.0005359230159888292, "Content Listing": 2.6215188746637357e-05, "Creative Writing": 3.1932875600711916e-05, "Customer Support Page": 9.287388330677174e-06, "Q&A Forum": 1.9591754015668184e-05, "Discussion Thread": 1.47849864881451e-05, "Comment Section": 2.8940600851920997e-05, "Missing Content": 4.35389332611091e-05, "Knowledge Article": 0.0003212790052474345, "Legal Notices": 3.201572522306952e-05, "News Article": 7.236399189034839e-06, "Organizational Announcement": 1.4541947148180212e-05, "Nonfiction Writing": 9.803101390044142e-05, "Organizational About Page": 1.811364848110036e-05, "Personal About Page": 2.5609502007270194e-05, "Personal Blog": 1.3712577169366888e-05, "Product Page": 8.498671736433358e-06, "Spam / Ads": 1.4608270439583327e-05, "Structured Data": 0.0005152604181416907, "Technical Writing": 0.9936808330035608, "Transcript / Interview": 2.4559062723420557e-05, "Tutorial / How-To Guide": 0.0013778173415276717, "User Reviews": 1.0093269426810062e-05, "Other / Unknown": 0.0031275754007941306}, "secondary": {"Academic Writing": 0.010067459956593024, "Content Listing": 0.00018525219680943166, "Creative Writing": 0.00023447626256094088, "Customer Support Page": 8.22791648341869e-05, "Q&A Forum": 0.00010676450254054357, "Discussion Thread": 0.00010773191003510188, "Comment Section": 0.0003232660191082292, "Missing Content": 0.0029039860471802366, "Knowledge Article": 0.004513697678355139, "Legal Notices": 0.00021091023400729062, "News Article": 0.00010697409788264456, "Organizational Announcement": 0.00010881522939404263, "Nonfiction Writing": 0.0021343027114230444, "Organizational About Page": 0.0001507871785194534, "Personal About Page": 0.0001352779131193069, "Personal Blog": 0.00012988592843318644, "Product Page": 6.127970816727803e-05, "Spam / Ads": 0.00011770927955325329, "Structured Data": 0.004905103278942426, "Technical Writing": 0.013110535457813042, "Transcript / Interview": 0.0001473079640624169, "Tutorial / How-To Guide": 0.0027248520522112156, "User Reviews": 6.08242263286109e-05, "NONE": 0.9573705210021259}, "tertiary": {"Academic Writing": 0.009661698570945452, "Content Listing": 0.00014038074216441346, "Creative Writing": 0.00016856922308450625, "Customer Support Page": 5.668477158684686e-05, "Q&A Forum": 6.442034899602542e-05, "Discussion Thread": 8.463454702485581e-05, "Comment Section": 0.00020101863691951354, "Missing Content": 0.0011247877006202774, "Knowledge Article": 0.006362764665330029, "Legal Notices": 0.00020635065665099286, "News Article": 6.629210335229408e-05, "Organizational Announcement": 7.198592619745568e-05, "Nonfiction Writing": 0.0036668069401908404, "Organizational About Page": 0.00012005935527671362, "Personal About Page": 9.920594865342239e-05, "Personal Blog": 0.0001142545166401398, "Product Page": 4.597616278807327e-05, "Spam / Ads": 9.213130486417223e-05, "Structured Data": 0.0032066507152181444, "Technical Writing": 0.009227956737910378, "Transcript / Interview": 0.00010688824969947628, "Tutorial / How-To Guide": 0.0027299804845078157, "User Reviews": 4.523857608322865e-05, "NONE": 0.9623352631152948}, "structured_records": {"false": 0.9866807942439271, "true": 0.013319205756072965}, "procedural_steps": {"false": 0.9823216840172463, "true": 0.017678315982753687}, "worked_solution": {"false": 0.9782331494534104, "true": 0.021766850546589524}, "contextual_dialogue": {"false": 0.9999952466499901, "true": 4.753350009926687e-06}, "content_quality": {"-2": 0.03136189658420406, "-1": 0.11957946874696657, "0": 0.3679009938136464, "1": 0.38656610360505456, "2": 0.09459153725012834}, "noise_severity": {"0": 0.9604506168765569, "1": 0.03351640117678456, "2": 0.004466239407397853, "3": 0.0010315658340912667, "4": 0.0005351767051694725}, "missing_content_severity": {"0": 0.9644570419000466, "1": 0.01568100984639227, "2": 0.016814109216861453, "3": 0.0025808348059426778, "4": 0.00046700423075696473}, "factual_density": {"0": 0.01159405640396022, "1": 0.029058209666397685, "2": 0.17212151633995876, "3": 0.4334287754772886, "4": 0.35379744211239467}, "pretraining_utility": {"-2": 0.010616601801615867, "-1": 0.0940373876342195, "0": 0.382240329431623, "1": 0.41418774400258956, "2": 0.098917937129952}, "math_density": {"0": 0.004844877910138631, "1": 0.013174360063148318, "2": 0.0862931740136699, "3": 0.33209657741065335, "4": 0.5635910106023898}, "code_density": {"0": 0.0003134685841292149, "1": 0.0006250697441435687, "2": 0.022634274568225345, "3": 0.15879484800598806, "4": 0.8176323390975138}, "reasoning_density": {"0": 0.6128524977711419, "1": 0.16384032024082418, "2": 0.10794219872205578, "3": 0.05822589491189991, "4": 0.05713908835407821}, "education_level": {"none": 0.0014867542581864447, "0": 0.0020013258816715877, "1": 0.06187901537626145, "2": 0.6902428464896481, "3": 0.172903514356023, "4": 0.07148654363820951}, "creative_writing_quality": {"not_applicable": 0.9987230786131243, "-2": 0.00016288256702164927, "-1": 0.0003281695706745838, "0": 0.000599412681571583, "1": 0.0001380352796672451, "2": 4.842128794059286e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00033377465356930816, "Art & Design": 0.0005358768291481809, "Law & Crime": 0.0005314916462707803, "Education & Careers": 0.0002984678037480799, "Entertainment & Media": 0.00035114279564092227, "Fashion & Beauty": 0.0002597003882094731, "Business, Economics & Finance": 0.00038578035108640475, "Food & Dining": 0.0002494388859582615, "Games & Gaming": 0.0005091145060502866, "Electronics & Hardware": 0.00040982790161034317, "Health, Healthcare & Wellness": 0.00039599882453348446, "History & Geography": 0.0004351619186891552, "Home, Pets & Hobbies": 0.00041276027999575675, "Industry & Production": 0.00035805429554648573, "Language, Literature & Philosophy": 0.00033911297404584537, "Politics & Government": 0.00019316142253204412, "Religion & Spirituality": 0.00030322552878511374, "Science, Mathematics & Engineering": 0.30545940778208575, "Relationships & Community": 0.00018381295840247242, "Software Usage & Applications": 0.0005564401912223139, "Programming & Software Engineering": 0.6852567743469969, "Sports & Fitness": 0.00047290761238647134, "Vehicles & Transportation": 0.00023214589307368906, "Travel & Tourism": 0.00033868150531695773, "Other / Uncovered Subject": 0.0007914249567529292, "Unknown / Insufficient Content": 0.0004063137483426364}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005495532823368686, "Art & Design": 0.000586544957757766, "Law & Crime": 0.0006053607264504804, "Education & Careers": 0.0003908677803439072, "Entertainment & Media": 0.00043858668032412216, "Fashion & Beauty": 0.00034212538159623305, "Business, Economics & Finance": 0.0004410965462295997, "Food & Dining": 0.00027797340097573335, "Games & Gaming": 0.0004966431574069339, "Electronics & Hardware": 0.0005512124457315037, "Health, Healthcare & Wellness": 0.0004575858432952446, "History & Geography": 0.00041465588860088183, "Home, Pets & Hobbies": 0.0003658112867105093, "Industry & Production": 0.0004200880206962317, "Language, Literature & Philosophy": 0.0003200561261164376, "Politics & Government": 0.00023596133790000542, "Religion & Spirituality": 0.00043894884146026473, "Science, Mathematics & Engineering": 0.4461205613626394, "Relationships & Community": 0.0002129820358025059, "Software Usage & Applications": 0.0005327364938027294, "Programming & Software Engineering": 0.295815751745702, "Sports & Fitness": 0.0003800852969626489, "Vehicles & Transportation": 0.00024002607442992526, "Travel & Tourism": 0.0005289260405868722, "Other / Uncovered Subject": 0.0005278738529302134, "NONE": 0.248307985393211}}, "probabilities": {"primary": {"Academic Writing": 0.0005359230159888291, "Content Listing": 2.621518874663735e-05, "Creative Writing": 3.193287560071191e-05, "Customer Support Page": 9.287388330677172e-06, "Q&A Forum": 1.959175401566818e-05, "Discussion Thread": 1.4784986488145096e-05, "Comment Section": 2.894060085192099e-05, "Missing Content": 4.3538933261109095e-05, "Knowledge Article": 0.00032127900524743443, "Legal Notices": 3.2015725223069516e-05, "News Article": 7.236399189034837e-06, "Organizational Announcement": 1.4541947148180209e-05, "Nonfiction Writing": 9.80310139004414e-05, "Organizational About Page": 1.8113648481100355e-05, "Personal About Page": 2.5609502007270187e-05, "Personal Blog": 1.3712577169366884e-05, "Product Page": 8.498671736433356e-06, "Spam / Ads": 1.4608270439583323e-05, "Structured Data": 0.0005152604181416906, "Technical Writing": 0.9936808330035606, "Transcript / Interview": 2.455906272342055e-05, "Tutorial / How-To Guide": 0.0013778173415276715, "User Reviews": 1.009326942681006e-05, "Other / Unknown": 0.0031275754007941297}, "secondary": {"Academic Writing": 0.010067459956593024, "Content Listing": 0.00018525219680943166, "Creative Writing": 0.00023447626256094088, "Customer Support Page": 8.22791648341869e-05, "Q&A Forum": 0.00010676450254054357, "Discussion Thread": 0.00010773191003510188, "Comment Section": 0.0003232660191082292, "Missing Content": 0.0029039860471802366, "Knowledge Article": 0.004513697678355139, "Legal Notices": 0.00021091023400729062, "News Article": 0.00010697409788264456, "Organizational Announcement": 0.00010881522939404263, "Nonfiction Writing": 0.0021343027114230444, "Organizational About Page": 0.0001507871785194534, "Personal About Page": 0.0001352779131193069, "Personal Blog": 0.00012988592843318644, "Product Page": 6.127970816727803e-05, "Spam / Ads": 0.00011770927955325329, "Structured Data": 0.004905103278942426, "Technical Writing": 0.013110535457813042, "Transcript / Interview": 0.0001473079640624169, "Tutorial / How-To Guide": 0.0027248520522112156, "User Reviews": 6.08242263286109e-05, "NONE": 0.9573705210021259}, "tertiary": {"Academic Writing": 0.009661698570945454, "Content Listing": 0.00014038074216441348, "Creative Writing": 0.00016856922308450628, "Customer Support Page": 5.6684771586846866e-05, "Q&A Forum": 6.442034899602543e-05, "Discussion Thread": 8.463454702485582e-05, "Comment Section": 0.00020101863691951357, "Missing Content": 0.0011247877006202776, "Knowledge Article": 0.00636276466533003, "Legal Notices": 0.0002063506566509929, "News Article": 6.62921033522941e-05, "Organizational Announcement": 7.19859261974557e-05, "Nonfiction Writing": 0.003666806940190841, "Organizational About Page": 0.00012005935527671363, "Personal About Page": 9.92059486534224e-05, "Personal Blog": 0.00011425451664013982, "Product Page": 4.597616278807328e-05, "Spam / Ads": 9.213130486417224e-05, "Structured Data": 0.003206650715218145, "Technical Writing": 0.00922795673791038, "Transcript / Interview": 0.00010688824969947629, "Tutorial / How-To Guide": 0.002729980484507816, "User Reviews": 4.5238576083228656e-05, "NONE": 0.9623352631152949}, "structured_records": {"false": 0.9866807942439271, "true": 0.013319205756072965}, "procedural_steps": {"false": 0.9823216840172463, "true": 0.017678315982753687}, "worked_solution": {"false": 0.9782331494534104, "true": 0.021766850546589524}, "contextual_dialogue": {"false": 0.9999952466499901, "true": 4.753350009926687e-06}, "content_quality": {"-2": 0.03136189658420407, "-1": 0.11957946874696658, "0": 0.36790099381364644, "1": 0.3865661036050546, "2": 0.09459153725012835}, "noise_severity": {"0": 0.9604506168765569, "1": 0.03351640117678456, "2": 0.004466239407397853, "3": 0.0010315658340912667, "4": 0.0005351767051694725}, "missing_content_severity": {"0": 0.9644570419000467, "1": 0.015681009846392275, "2": 0.016814109216861457, "3": 0.002580834805942678, "4": 0.0004670042307569648}, "factual_density": {"0": 0.011594056403960221, "1": 0.02905820966639769, "2": 0.17212151633995879, "3": 0.43342877547728864, "4": 0.3537974421123947}, "pretraining_utility": {"-2": 0.010616601801615869, "-1": 0.09403738763421951, "0": 0.38224032943162306, "1": 0.4141877440025896, "2": 0.09891793712995202}, "math_density": {"0": 0.004844877910138631, "1": 0.013174360063148318, "2": 0.0862931740136699, "3": 0.33209657741065335, "4": 0.5635910106023898}, "code_density": {"0": 0.0003134685841292149, "1": 0.0006250697441435687, "2": 0.022634274568225345, "3": 0.15879484800598806, "4": 0.8176323390975138}, "reasoning_density": {"0": 0.6128524977711419, "1": 0.16384032024082418, "2": 0.10794219872205578, "3": 0.05822589491189991, "4": 0.05713908835407821}, "education_level": {"none": 0.0014867542581864447, "0": 0.0020013258816715877, "1": 0.06187901537626145, "2": 0.6902428464896481, "3": 0.172903514356023, "4": 0.07148654363820951}, "creative_writing_quality": {"not_applicable": 0.9987230786131244, "-2": 0.0001628825670216493, "-1": 0.0003281695706745839, "0": 0.0005994126815715831, "1": 0.00013803527966724512, "2": 4.842128794059287e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00033377465356930816, "Art & Design": 0.0005358768291481809, "Law & Crime": 0.0005314916462707803, "Education & Careers": 0.0002984678037480799, "Entertainment & Media": 0.00035114279564092227, "Fashion & Beauty": 0.0002597003882094731, "Business, Economics & Finance": 0.00038578035108640475, "Food & Dining": 0.0002494388859582615, "Games & Gaming": 0.0005091145060502866, "Electronics & Hardware": 0.00040982790161034317, "Health, Healthcare & Wellness": 0.00039599882453348446, "History & Geography": 0.0004351619186891552, "Home, Pets & Hobbies": 0.00041276027999575675, "Industry & Production": 0.00035805429554648573, "Language, Literature & Philosophy": 0.00033911297404584537, "Politics & Government": 0.00019316142253204412, "Religion & Spirituality": 0.00030322552878511374, "Science, Mathematics & Engineering": 0.30545940778208575, "Relationships & Community": 0.00018381295840247242, "Software Usage & Applications": 0.0005564401912223139, "Programming & Software Engineering": 0.6852567743469969, "Sports & Fitness": 0.00047290761238647134, "Vehicles & Transportation": 0.00023214589307368906, "Travel & Tourism": 0.00033868150531695773, "Other / Uncovered Subject": 0.0007914249567529292, "Unknown / Insufficient Content": 0.0004063137483426364}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005495532823368686, "Art & Design": 0.000586544957757766, "Law & Crime": 0.0006053607264504804, "Education & Careers": 0.0003908677803439072, "Entertainment & Media": 0.00043858668032412216, "Fashion & Beauty": 0.00034212538159623305, "Business, Economics & Finance": 0.0004410965462295997, "Food & Dining": 0.00027797340097573335, "Games & Gaming": 0.0004966431574069339, "Electronics & Hardware": 0.0005512124457315037, "Health, Healthcare & Wellness": 0.0004575858432952446, "History & Geography": 0.00041465588860088183, "Home, Pets & Hobbies": 0.0003658112867105093, "Industry & Production": 0.0004200880206962317, "Language, Literature & Philosophy": 0.0003200561261164376, "Politics & Government": 0.00023596133790000542, "Religion & Spirituality": 0.00043894884146026473, "Science, Mathematics & Engineering": 0.4461205613626394, "Relationships & Community": 0.0002129820358025059, "Software Usage & Applications": 0.0005327364938027294, "Programming & Software Engineering": 0.295815751745702, "Sports & Fitness": 0.0003800852969626489, "Vehicles & Transportation": 0.00024002607442992526, "Travel & Tourism": 0.0005289260405868722, "Other / Uncovered Subject": 0.0005278738529302134, "NONE": 0.248307985393211}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9657, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9657, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:09.492307+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "9fb3e0d1-67f3-4e59-9a31-1cd4fece6e44", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "492bd10ada50002482f4176ee50448398c5362e84b990ebf9feafb68499973de", "source_metadata": {"uuid": "9fb3e0d1-67f3-4e59-9a31-1cd4fece6e44", "repo_name": "anthonykozar/pythoncode", "relative_path": "Collatz/CollatzModularMaps.py", "category": "ALGO", "algo_rel_score": 0.999935507774353, "quality_score": 4.056485176086426, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 93, "repo_created_at": "2023-04-26T17:00:30.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "3d50b0ff340ab9ece38b5598b215df4c9f2a2ce2", "size_bytes": 3664, "file_timestamp": 1751284982, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0befa8ef81558f1147258d6fb31b5dd8fba4fce5"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 93, "repo_created_at": "2023-04-26T17:00:30.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "3d50b0ff340ab9ece38b5598b215df4c9f2a2ce2", "size_bytes": 3664, "file_timestamp": 1751284982, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0befa8ef81558f1147258d6fb31b5dd8fba4fce5", "url": "https://github.com/anthonykozar/pythoncode/blob/0befa8ef81558f1147258d6fb31b5dd8fba4fce5/Collatz/CollatzModularMaps.py"}, "gpt4o_tokens": 1073, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.39344591618993663, "noise_severity": 0.047684284314531956, "missing_content_severity": 0.05891974962097108, "factual_density": 3.08877733722776, "pretraining_utility": 0.4967530270250424, "math_density": 3.4364144827320073, "code_density": 3.7928075192886133, "reasoning_density": 0.7829587558369483, "education_level": 2.25036716894738, "creative_writing_quality": -0.32817748490750204}, "score_applicability": {"education_level": 0.9985132457418137, "creative_writing_quality": 0.0012769213868756542}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/home-assistant/core/blob/b33a556ca5699bdb5b9221558c689f7d9e934ab3/tests/components/axis/conftest.py", "id": "python-0-880030051167b83560575bd881395d0f", "text": "\"\"\"Axis conftest.\"\"\"\n\nfrom __future__ import annotations\n\nfrom collections.abc import Callable, Coroutine, Generator\nfrom copy import deepcopy\nfrom types import MappingProxyType\nfrom typing import Any, Protocol\nfrom unittest.mock import AsyncMock, patch\n\nfrom axis.rtsp import Signal, State\nimport pytest\nimport respx\n\nfrom homeassistant.components.axis.const import DOMAIN\nfrom homeassistant.const import (\n    CONF_HOST,\n    CONF_MODEL,\n    CONF_NAME,\n    CONF_PASSWORD,\n    CONF_PORT,\n    CONF_USERNAME,\n)\nfrom homeassistant.core import HomeAssistant\n\nfrom .const import (\n    API_DISCOVERY_RESPONSE,\n    APP_AOA_RESPONSE,\n    APP_VMD4_RESPONSE,\n    APPLICATIONS_LIST_RESPONSE,\n    BASIC_DEVICE_INFO_RESPONSE,\n    BRAND_RESPONSE,\n    DEFAULT_HOST,\n    FORMATTED_MAC,\n    IMAGE_RESPONSE,\n    MODEL,\n    MQTT_CLIENT_RESPONSE,\n    NAME,\n    PORT_MANAGEMENT_RESPONSE,\n    PORTS_RESPONSE,\n    PROPERTIES_RESPONSE,\n    PTZ_RESPONSE,\n    STREAM_PROFILES_RESPONSE,\n    VIEW_AREAS_RESPONSE,\n)\n\nfrom tests.common import MockConfigEntry\n\ntype ConfigEntryFactoryType = Callable[[], Coroutine[Any, Any, MockConfigEntry]]\ntype RtspStateType = Callable[[bool], None]\n\n\nclass RtspEventMock(Protocol):\n    \"\"\"Fixture to allow mocking received RTSP events.\"\"\"\n\n    def __call__(\n        self,\n        topic: str,\n        data_type: str,\n        data_value: str,\n        operation: str = \"Initialized\",\n        source_name: str = \"\",\n        source_idx: str = \"\",\n    ) -> None:\n        \"\"\"Send RTSP event.\"\"\"\n\n\nclass _RtspClientMock(Protocol):\n    async def __call__(\n        self, data: dict[str, Any] | None = None, state: str = \"\"\n    ) -> None: ...\n\n\n@pytest.fixture(name=\"mock_setup_entry\")\ndef fixture_setup_entry() -> Generator[AsyncMock]:\n    \"\"\"Override async_setup_entry.\"\"\"\n    with patch(\n        \"homeassistant.components.axis.async_setup_entry\", return_value=True\n    ) as mock_setup_entry:\n        yield mock_setup_entry\n\n\n# Config entry fixtures\n\n\n@pytest.fixture(name=\"config_entry\")\ndef fixture_config_entry(\n    config_entry_data: MappingProxyType[str, Any],\n    config_entry_options: MappingProxyType[str, Any],\n    config_entry_version: int,\n) -> MockConfigEntry:\n    \"\"\"Define a config entry fixture.\"\"\"\n    return MockConfigEntry(\n        domain=DOMAIN,\n        entry_id=\"676abe5b73621446e6550a2e86ffe3dd\",\n        unique_id=FORMATTED_MAC,\n        data=config_entry_data,\n        options=config_entry_options,\n        version=config_entry_version,\n    )\n\n\n@pytest.fixture(name=\"config_entry_version\")\ndef fixture_config_entry_version() -> int:\n    \"\"\"Define a config entry version fixture.\"\"\"\n    return 3\n\n\n@pytest.fixture(name=\"config_entry_data\")\ndef fixture_config_entry_data() -> MappingProxyType[str, Any]:\n    \"\"\"Define a config entry data fixture.\"\"\"\n    return {\n        CONF_HOST: DEFAULT_HOST,\n        CONF_USERNAME: \"root\",\n        CONF_PASSWORD: \"<PASSWORD>\",\n        CONF_PORT: 80,\n        CONF_MODEL: MODEL,\n        CONF_NAME: NAME,\n    }\n\n\n@pytest.fixture(name=\"config_entry_options\")\ndef fixture_config_entry_options() -> MappingProxyType[str, Any]:\n    \"\"\"Define a config entry options fixture.\"\"\"\n    return {}\n\n\n# Axis API fixtures\n\n\n@pytest.fixture(autouse=True)\ndef reset_mock_requests() -> Generator[None]:\n    \"\"\"Reset respx mock routes after the test.\"\"\"\n    yield\n    respx.mock.clear()\n\n\n@pytest.fixture(name=\"mock_requests\")\ndef fixture_request(\n    respx_mock: respx.MockRouter,\n    port_management_payload: dict[str, Any],\n    param_properties_payload: str,\n    param_ports_payload: str,\n    mqtt_status_code: int,\n) -> Callable[[str], None]:\n    \"\"\"Mock default Vapix requests responses.\"\"\"\n\n    def __mock_default_requests(host: str) -> None:\n        respx_mock(base_url=f\"http://{host}:80\")\n\n        if host != DEFAULT_HOST:\n            respx.post(\"/axis-cgi/apidiscovery.cgi\").respond(\n                json=API_DISCOVERY_RESPONSE,\n            )\n        respx.post(\"/axis-cgi/basicdeviceinfo.cgi\").respond(\n            json=BASIC_DEVICE_INFO_RESPONSE,\n        )\n        respx.post(\"/axis-cgi/io/portmanagement.cgi\").respond(\n            json=port_management_payload,\n        )\n        respx.post(\"/axis-cgi/mqtt/client.cgi\").respond(\n            json=MQTT_CLIENT_RESPONSE, status_code=mqtt_status_code\n        )\n        respx.post(\"/axis-cgi/streamprofile.cgi\").respond(\n            json=STREAM_PROFILES_RESPONSE,\n        )\n        respx.post(\"/axis-cgi/viewarea/info.cgi\").respond(json=VIEW_AREAS_RESPONSE)\n        respx.post(\n            \"/axis-cgi/param.cgi\",\n            data={\"action\": \"list\", \"group\": \"root.Brand\"},\n        ).respond(\n            text=BRAND_RESPONSE,\n            headers={\"Content-Type\": \"text/plain\"},\n        )\n        respx.post(\n            \"/axis-cgi/param.cgi\",\n            data={\"action\": \"list\", \"group\": \"root.Image\"},\n        ).respond(\n            text=IMAGE_RESPONSE,\n            headers={\"Content-Type\": \"text/plain\"},\n        )\n        respx.post(\n            \"/axis-cgi/param.cgi\",\n            data={\"action\": \"list\", \"group\": \"root.Input\"},\n        ).respond(\n            text=PORTS_RESPONSE,\n            headers={\"Content-Type\": \"text/plain\"},\n        )\n        respx.post(\n            \"/axis-cgi/param.cgi\",\n            data={\"action\": \"list\", \"group\": \"root.IOPort\"},\n        ).respond(\n            text=param_ports_payload,\n            headers={\"Content-Type\": \"text/plain\"},\n        )\n        respx.post(\n            \"/axis-cgi/param.cgi\",\n            data={\"action\": \"list\", \"group\": \"root.Output\"},\n        ).respond(\n            text=PORTS_RESPONSE,\n            headers={\"Content-Type\": \"text/plain\"},\n        )\n        respx.post(\n            \"/axis-cgi/param.cgi\",\n            data={\"action\": \"list\", \"group\": \"root.Properties\"},\n        ).respond(\n            text=param_properties_payload,\n            headers={\"Content-Type\": \"text/plain\"},\n        )\n        respx.post(\n            \"/axis-cgi/param.cgi\",\n            data={\"action\": \"list\", \"group\": \"root.PTZ\"},\n        ).respond(\n            text=PTZ_RESPONSE,\n            headers={\"Content-Type\": \"text/plain\"},\n        )\n        respx.post(\n            \"/axis-cgi/param.cgi\",\n            data={\"action\": \"list\", \"group\": \"root.StreamProfile\"},\n        ).respond(\n            text=STREAM_PROFILES_RESPONSE,\n            headers={\"Content-Type\": \"text/plain\"},\n        )\n        respx.post(\"/axis-cgi/applications/list.cgi\").respond(\n            text=APPLICATIONS_LIST_RESPONSE,\n            headers={\"Content-Type\": \"text/xml\"},\n        )\n        respx.post(\"/local/fenceguard/control.cgi\").respond(json=APP_VMD4_RESPONSE)\n        respx.post(\"/local/loiteringguard/control.cgi\").respond(json=APP_VMD4_RESPONSE)\n        respx.post(\"/local/motionguard/control.cgi\").respond(json=APP_VMD4_RESPONSE)\n        respx.post(\"/local/vmd/control.cgi\").respond(json=APP_VMD4_RESPONSE)\n        respx.post(\"/local/objectanalytics/control.cgi\").respond(json=APP_AOA_RESPONSE)\n\n    return __mock_default_requests\n\n\n@pytest.fixture\ndef api_discovery_items() -> dict[str, Any]:\n    \"\"\"Additional Apidiscovery items.\"\"\"\n    return {}\n\n\n@pytest.fixture(autouse=True)\ndef fixture_api_discovery(api_discovery_items: dict[str, Any]) -> None:\n    \"\"\"Apidiscovery mock response.\"\"\"\n    data = deepcopy(API_DISCOVERY_RESPONSE)\n    if api_discovery_items:\n        data[\"data\"][\"apiList\"].append(api_discovery_items)\n    respx.post(f\"http://{DEFAULT_HOST}:80/axis-cgi/apidiscovery.cgi\").respond(json=data)\n\n\n@pytest.fixture(name=\"port_management_payload\")\ndef fixture_io_port_management_data() -> dict[str, Any]:\n    \"\"\"Property parameter data.\"\"\"\n    return PORT_MANAGEMENT_RESPONSE\n\n\n@pytest.fixture(name=\"param_properties_payload\")\ndef fixture_param_properties_data() -> str:\n    \"\"\"Property parameter data.\"\"\"\n    return PROPERTIES_RESPONSE\n\n\n@pytest.fixture(name=\"param_ports_payload\")\ndef fixture_param_ports_data() -> str:\n    \"\"\"Property parameter data.\"\"\"\n    return PORTS_RESPONSE\n\n\n@pytest.fixture(name=\"mqtt_status_code\")\ndef fixture_mqtt_status_code() -> int:\n    \"\"\"Property parameter data.\"\"\"\n    return 200\n\n\n@pytest.fixture(name=\"mock_default_requests\")\ndef fixture_default_requests(mock_requests: Callable[[str], None]) -> None:\n    \"\"\"Mock default Vapix requests responses.\"\"\"\n    mock_requests(DEFAULT_HOST)\n\n\n@pytest.fixture(name=\"config_entry_factory\")\nasync def fixture_config_entry_factory(\n    hass: HomeAssistant,\n    config_entry: MockConfigEntry,\n    mock_requests: Callable[[str], None],\n) -> ConfigEntryFactoryType:\n    \"\"\"Fixture factory to set up Axis network device.\"\"\"\n\n    async def __mock_setup_config_entry() -> MockConfigEntry:\n        config_entry.add_to_hass(hass)\n        mock_requests(config_entry.data[CONF_HOST])\n        await hass.config_entries.async_setup(config_entry.entry_id)\n        await hass.async_block_till_done()\n        return config_entry\n\n    return __mock_setup_config_entry\n\n\n@pytest.fixture(name=\"config_entry_setup\")\nasync def fixture_config_entry_setup(\n    config_entry_factory: ConfigEntryFactoryType,\n) -> MockConfigEntry:\n    \"\"\"Define a fixture to set up Axis network device.\"\"\"\n    return await config_entry_factory()\n\n\n# RTSP fixtures\n\n\n@pytest.fixture(autouse=True, name=\"_mock_rtsp_client\")\ndef fixture_axis_rtsp_client() -> Generator[_RtspClientMock]:\n    \"\"\"No real RTSP communication allowed.\"\"\"\n    with patch(\"axis.stream_manager.RTSPClient\") as rtsp_client_mock:\n        rtsp_client_mock.return_value.session.state = State.STOPPED\n\n        async def start_stream() -> None:\n            \"\"\"Set state to playing when calling RTSPClient.start.\"\"\"\n            rtsp_client_mock.return_value.session.state = State.PLAYING\n\n        rtsp_client_mock.return_value.start = start_stream\n\n        def stop_stream() -> None:\n            \"\"\"Set state to stopped when calling RTSPClient.stop.\"\"\"\n            rtsp_client_mock.return_value.session.state = State.STOPPED\n\n        rtsp_client_mock.return_value.stop = stop_stream\n\n        def make_rtsp_call(data: dict[str, Any] | None = None, state: str = \"\") -> None:\n            \"\"\"Generate a RTSP call.\"\"\"\n            axis_streammanager_session_callback = rtsp_client_mock.call_args[0][4]\n\n            if data:\n                rtsp_client_mock.return_value.rtp.data = data\n                axis_streammanager_session_callback(signal=Signal.DATA)\n            elif state:\n                axis_streammanager_session_callback(signal=state)\n            else:\n                raise NotImplementedError\n\n        yield make_rtsp_call\n\n\n@pytest.fixture(autouse=True, name=\"mock_rtsp_event\")\ndef fixture_rtsp_event(_mock_rtsp_client: _RtspClientMock) -> RtspEventMock:\n    \"\"\"Fixture to allow mocking received RTSP events.\"\"\"\n\n    def send_event(\n        topic: str,\n        data_type: str,\n        data_value: str,\n        operation: str = \"Initialized\",\n        source_name: str = \"\",\n        source_idx: str = \"\",\n    ) -> None:\n        source = \"\"\n        if source_name != \"\" and source_idx != \"\":\n            source = f'<tt:SimpleItem Name=\"{source_name}\" Value=\"{source_idx}\"/>'\n\n        event = f\"\"\"<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<tt:MetadataStream xmlns:tt=\"http://www.onvif.org/ver10/schema\">\n    <tt:Event>\n        <wsnt:NotificationMessage xmlns:tns1=\"http://www.onvif.org/ver10/topics\"\n                                  xmlns:tnsaxis=\"http://www.axis.com/2009/event/topics\"\n                                  xmlns:wsnt=\"http://docs.oasis-open.org/wsn/b-2\"\n                                  xmlns:wsa5=\"http://www.w3.org/2005/08/addressing\">\n            <wsnt:Topic Dialect=\"http://docs.oasis-open.org/wsn/t-1/TopicExpression/Simple\">\n                {topic}\n            </wsnt:Topic>\n            <wsnt:ProducerReference>\n                <wsa5:Address>\n                    uri://bf32a3b9-e5e7-4d57-a48d-1b5be9ae7b16/ProducerReference\n                </wsa5:Address>\n            </wsnt:ProducerReference>\n            <wsnt:Message>\n                <tt:Message UtcTime=\"2020-11-03T20:21:48.346022Z\"\n                            PropertyOperation=\"{operation}\">\n                    <tt:Source>{source}</tt:Source>\n                    <tt:Key></tt:Key>\n                    <tt:Data>\n                        <tt:SimpleItem Name=\"{data_type}\" Value=\"{data_value}\"/>\n                    </tt:Data>\n                </tt:Message>\n            </wsnt:Message>\n        </wsnt:NotificationMessage>\n    </tt:Event>\n</tt:MetadataStream>\n\"\"\"\n\n        _mock_rtsp_client(data=event.encode(\"utf-8\"))\n\n    return send_event\n\n\n@pytest.fixture(autouse=True, name=\"mock_rtsp_signal_state\")\ndef fixture_rtsp_signal_state(_mock_rtsp_client: _RtspClientMock) -> RtspStateType:\n    \"\"\"Fixture to allow mocking RTSP state signalling.\"\"\"\n\n    def send_signal(connected: bool) -> None:\n        \"\"\"Signal state change of RTSP connection.\"\"\"\n        signal = Signal.PLAYING if connected else Signal.FAILED\n        _mock_rtsp_client(state=signal)\n\n    return send_signal\n", "text_preview": "\"\"\"Axis conftest.\"\"\" from __future__ import annotations from collections.abc import Callable, Coroutine, Generator from copy import deepcopy from types import MappingProxyType from typing import Any, Protocol from unittest.mock import Async", "input_sha256": "d33d5692ae4b2d5bce953ab6c85021ed63a37af3317e9360dbf632f62ccca7e3", "text_sha256": "880030051167b83560575bd881395d0f885d238dc79a179c417b773831535f21", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12035, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.3009730816961493, "noise_severity": 0.02414995361273776, "missing_content_severity": 0.017488756062177858, "factual_density": 3.4535369435651115, "pretraining_utility": 0.4407466018510292, "math_density": 0.013767566519091959, "code_density": 3.919048294310765, "reasoning_density": 0.11502397667486558, "education_level": 3.038089992764189, "creative_writing_quality": -0.15935701084214376}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0273477917647168, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9940662716406143, "secondary": 0.8668748884503281, "tertiary": 0.8420127758725408, "structured_records": 0.13877368433256132, "procedural_steps": 0.021044251439385177, "worked_solution": 0.0017484731003910281, "contextual_dialogue": 4.703506270095723e-06, "content_quality": 0.5932271105817511, "noise_severity": 0.9798750386560519, "missing_content_severity": 0.9854260366148517, "factual_density": 0.5446141196375929, "pretraining_utility": 0.42018932030210776, "math_density": 0.9885270279007567, "code_density": 0.9325402452589712, "reasoning_density": 0.9041466861042786, "education_level": 0.472706088631989, "creative_writing_quality": 0.9997262960292317, "primary_topic": 0.9861015985669574, "secondary_topic": 0.7788369472952777}, "probability_margin": {"primary": 0.9914699823272084, "secondary": 0.7859708412364319, "tertiary": 0.7347968582513356, "education_level": 0.31995829602170545, "creative_writing_quality": 0.9996605143770743, "primary_topic": 0.9806527809038492, "secondary_topic": 0.713329812954003}, "raw_probabilities": {"primary": {"Academic Writing": 3.6755931225658145e-05, "Content Listing": 1.8243313720402993e-05, "Creative Writing": 1.4701397629860613e-05, "Customer Support Page": 8.953933713180443e-06, "Q&A Forum": 8.507615540158546e-06, "Discussion Thread": 5.165432395456561e-06, "Comment Section": 9.98537750111232e-06, "Missing Content": 2.66983877620735e-05, "Knowledge Article": 5.094585009809845e-05, "Legal Notices": 2.7742834874199324e-05, "News Article": 4.539446309201219e-06, "Organizational Announcement": 9.971460116828898e-06, "Nonfiction Writing": 7.577665696270624e-06, "Organizational About Page": 1.0243340555834253e-05, "Personal About Page": 7.311542988697547e-06, "Personal Blog": 5.523802336519906e-06, "Product Page": 5.629924712724904e-06, "Spam / Ads": 7.5969382792579805e-06, "Structured Data": 0.0023618568564613075, "Technical Writing": 0.9943135103222555, "Transcript / Interview": 1.5519724202434344e-05, "Tutorial / How-To Guide": 0.00019382485850253576, "User Reviews": 5.666048075868679e-06, "Other / Unknown": 0.00284352799504702}, "secondary": {"Academic Writing": 0.0005194602049311964, "Content Listing": 0.00044975795761145705, "Creative Writing": 0.000488928678495214, "Customer Support Page": 0.000236273129072201, "Q&A Forum": 0.00021628483179510013, "Discussion Thread": 0.00016922765681727904, "Comment Section": 0.00029832877944298167, "Missing Content": 0.0006792505959186094, "Knowledge Article": 0.0009954103664873246, "Legal Notices": 0.00047391394515032835, "News Article": 0.00028160719489914545, "Organizational Announcement": 0.00026781373784753035, "Nonfiction Writing": 0.00026162855175531247, "Organizational About Page": 0.00029503625793259074, "Personal About Page": 0.00022233453047467762, "Personal Blog": 0.00020649766767424343, "Product Page": 0.00017057045992998913, "Spam / Ads": 0.00027169958293164287, "Structured Data": 0.08645092686179921, "Technical Writing": 0.033202710498894825, "Transcript / Interview": 0.0004338965372547322, "Tutorial / How-To Guide": 0.0008670150748745312, "User Reviews": 0.00011965879977886862, "NONE": 0.8724217680982311}, "tertiary": {"Academic Writing": 0.00042556474766227353, "Content Listing": 0.0003615398155440964, "Creative Writing": 0.0003799151662230195, "Customer Support Page": 0.00022223100788307625, "Q&A Forum": 0.0001628262515961682, "Discussion Thread": 0.0001580949187862011, "Comment Section": 0.0002558495096115081, "Missing Content": 0.000540812736884637, "Knowledge Article": 0.0013035830775304832, "Legal Notices": 0.0004228058313071102, "News Article": 0.0002444656565298924, "Organizational Announcement": 0.00023195106587125457, "Nonfiction Writing": 0.0002556592626365839, "Organizational About Page": 0.0002666676702881369, "Personal About Page": 0.0002000785911812512, "Personal Blog": 0.00015736776885558055, "Product Page": 0.00016579617248726173, "Spam / Ads": 0.00022324403259755076, "Structured Data": 0.113798718626516, "Technical Writing": 0.03030963472846314, "Transcript / Interview": 0.0003618984037472241, "Tutorial / How-To Guide": 0.0008503781942171829, "User Reviews": 0.00010533988572878719, "NONE": 0.8485955768778516}, "structured_records": {"false": 0.8612263156674387, "true": 0.13877368433256132}, "procedural_steps": {"false": 0.9789557485606148, "true": 0.021044251439385177}, "worked_solution": {"false": 0.9982515268996089, "true": 0.0017484731003910281}, "contextual_dialogue": {"false": 0.9999952964937299, "true": 4.703506270095723e-06}, "content_quality": {"-2": 0.002527762167770276, "-1": 0.004917413010955399, "0": 0.07615908027765303, "1": 0.5218454700445974, "2": 0.39455027449902397}, "noise_severity": {"0": 0.979931865080097, "1": 0.01715944534616759, "2": 0.00209100255547022, "3": 0.0004622449174311337, "4": 0.0003554421008340826}, "missing_content_severity": {"0": 0.9856964310887731, "1": 0.01210084610904299, "2": 0.0014706314313940043, "3": 0.0004817183928124748, "4": 0.00025037297797735834}, "factual_density": {"0": 0.002642427734733885, "1": 0.005451629486088381, "2": 0.05610322812591164, "3": 0.40733200078586457, "4": 0.5284707138674015}, "pretraining_utility": {"-2": 0.008834498608636937, "-1": 0.10818810394906865, "0": 0.38463340316917266, "1": 0.4300842855288719, "2": 0.06825970874424993}, "math_density": {"0": 0.9905676888936538, "1": 0.0074183578148643665, "2": 0.0006934563608530578, "3": 0.00031969173999344877, "4": 0.0010008051906352823}, "code_density": {"0": 0.00014381309068760485, "1": 0.00027476397876845574, "2": 0.006499738383983145, "3": 0.06655268462221255, "4": 0.9265289999243481}, "reasoning_density": {"0": 0.9155561759450404, "1": 0.05874702788737008, "2": 0.022111593048329308, "3": 0.0022870497862042915, "4": 0.0012981533330560036}, "education_level": {"none": 0.0008624056824460966, "0": 0.0010533344328552796, "1": 0.002547222615312689, "2": 0.19431851890444293, "3": 0.5605884071933241, "4": 0.24063011117161873}, "creative_writing_quality": {"not_applicable": 0.9997719133576931, "-2": 2.537574600121045e-05, "-1": 4.4755136148192906e-05, "0": 0.00011139898061884708, "1": 3.395413645748219e-05, "2": 1.2602643081050692e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.441329284787456e-05, "Art & Design": 2.9276470706539663e-05, "Law & Crime": 2.5851170324428915e-05, "Education & Careers": 2.2910808456628736e-05, "Entertainment & Media": 2.5634441970602313e-05, "Fashion & Beauty": 2.0995998010597234e-05, "Business, Economics & Finance": 3.855824682212966e-05, "Food & Dining": 3.0021848052362763e-05, "Games & Gaming": 3.956747426805957e-05, "Electronics & Hardware": 0.005983371564379169, "Health, Healthcare & Wellness": 4.104292638774119e-05, "History & Geography": 2.705665288812521e-05, "Home, Pets & Hobbies": 0.004833740353948711, "Industry & Production": 0.00013028490352397584, "Language, Literature & Philosophy": 2.2540675055870135e-05, "Politics & Government": 1.697338858896637e-05, "Religion & Spirituality": 2.771544486961866e-05, "Science, Mathematics & Engineering": 0.00027487648787111256, "Relationships & Community": 1.2426952695634217e-05, "Software Usage & Applications": 0.0012947848824283374, "Programming & Software Engineering": 0.9866361524682283, "Sports & Fitness": 3.9281167045351834e-05, "Vehicles & Transportation": 3.965019914527992e-05, "Travel & Tourism": 2.8199056861329686e-05, "Other / Uncovered Subject": 0.0002665332783999419, "Unknown / Insufficient Content": 5.813984622328124e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.000739139433521564, "Art & Design": 0.0005556749305317406, "Law & Crime": 0.0006157665803925927, "Education & Careers": 0.00036752116490830613, "Entertainment & Media": 0.0004970735678642545, "Fashion & Beauty": 0.00028901010606024693, "Business, Economics & Finance": 0.0007344694944090768, "Food & Dining": 0.00036210927385509686, "Games & Gaming": 0.0004809933483594429, "Electronics & Hardware": 0.07049878615182402, "Health, Healthcare & Wellness": 0.00048752763652483834, "History & Geography": 0.0003439317797865802, "Home, Pets & Hobbies": 0.07401340559914873, "Industry & Production": 0.0006178112164013258, "Language, Literature & Philosophy": 0.00026112738470887185, "Politics & Government": 0.000330514899218296, "Religion & Spirituality": 0.00040951726946325236, "Science, Mathematics & Engineering": 0.0021496444706878295, "Relationships & Community": 0.00016707478593279868, "Software Usage & Applications": 0.00962948668230483, "Programming & Software Engineering": 0.045377961969314484, "Sports & Fitness": 0.0003399533820093787, "Vehicles & Transportation": 0.00029506466514700154, "Travel & Tourism": 0.0004495881174731926, "Other / Uncovered Subject": 0.002643627537000524, "NONE": 0.7873432185531517}}, "probabilities": {"primary": {"Academic Writing": 3.675593122565814e-05, "Content Listing": 1.824331372040299e-05, "Creative Writing": 1.470139762986061e-05, "Customer Support Page": 8.953933713180441e-06, "Q&A Forum": 8.507615540158544e-06, "Discussion Thread": 5.1654323954565605e-06, "Comment Section": 9.985377501112318e-06, "Missing Content": 2.6698387762073492e-05, "Knowledge Article": 5.0945850098098436e-05, "Legal Notices": 2.7742834874199318e-05, "News Article": 4.539446309201218e-06, "Organizational Announcement": 9.971460116828896e-06, "Nonfiction Writing": 7.577665696270622e-06, "Organizational About Page": 1.0243340555834252e-05, "Personal About Page": 7.311542988697545e-06, "Personal Blog": 5.523802336519905e-06, "Product Page": 5.6299247127249034e-06, "Spam / Ads": 7.596938279257979e-06, "Structured Data": 0.002361856856461307, "Technical Writing": 0.9943135103222552, "Transcript / Interview": 1.551972420243434e-05, "Tutorial / How-To Guide": 0.0001938248585025357, "User Reviews": 5.6660480758686785e-06, "Other / Unknown": 0.0028435279950470194}, "secondary": {"Academic Writing": 0.0005194602049311963, "Content Listing": 0.00044975795761145694, "Creative Writing": 0.0004889286784952139, "Customer Support Page": 0.00023627312907220096, "Q&A Forum": 0.00021628483179510007, "Discussion Thread": 0.00016922765681727902, "Comment Section": 0.0002983287794429816, "Missing Content": 0.0006792505959186093, "Knowledge Article": 0.0009954103664873244, "Legal Notices": 0.00047391394515032824, "News Article": 0.0002816071948991454, "Organizational Announcement": 0.0002678137378475303, "Nonfiction Writing": 0.0002616285517553124, "Organizational About Page": 0.0002950362579325907, "Personal About Page": 0.00022233453047467756, "Personal Blog": 0.00020649766767424338, "Product Page": 0.0001705704599299891, "Spam / Ads": 0.0002716995829316428, "Structured Data": 0.0864509268617992, "Technical Writing": 0.03320271049889482, "Transcript / Interview": 0.0004338965372547321, "Tutorial / How-To Guide": 0.000867015074874531, "User Reviews": 0.00011965879977886859, "NONE": 0.8724217680982309}, "tertiary": {"Academic Writing": 0.00042556474766227353, "Content Listing": 0.0003615398155440964, "Creative Writing": 0.0003799151662230195, "Customer Support Page": 0.00022223100788307625, "Q&A Forum": 0.0001628262515961682, "Discussion Thread": 0.0001580949187862011, "Comment Section": 0.0002558495096115081, "Missing Content": 0.000540812736884637, "Knowledge Article": 0.0013035830775304832, "Legal Notices": 0.0004228058313071102, "News Article": 0.0002444656565298924, "Organizational Announcement": 0.00023195106587125457, "Nonfiction Writing": 0.0002556592626365839, "Organizational About Page": 0.0002666676702881369, "Personal About Page": 0.0002000785911812512, "Personal Blog": 0.00015736776885558055, "Product Page": 0.00016579617248726173, "Spam / Ads": 0.00022324403259755076, "Structured Data": 0.113798718626516, "Technical Writing": 0.03030963472846314, "Transcript / Interview": 0.0003618984037472241, "Tutorial / How-To Guide": 0.0008503781942171829, "User Reviews": 0.00010533988572878719, "NONE": 0.8485955768778516}, "structured_records": {"false": 0.8612263156674387, "true": 0.13877368433256132}, "procedural_steps": {"false": 0.9789557485606148, "true": 0.021044251439385177}, "worked_solution": {"false": 0.9982515268996089, "true": 0.0017484731003910281}, "contextual_dialogue": {"false": 0.9999952964937299, "true": 4.703506270095723e-06}, "content_quality": {"-2": 0.002527762167770276, "-1": 0.004917413010955399, "0": 0.07615908027765303, "1": 0.5218454700445974, "2": 0.39455027449902397}, "noise_severity": {"0": 0.979931865080097, "1": 0.01715944534616759, "2": 0.00209100255547022, "3": 0.0004622449174311337, "4": 0.0003554421008340826}, "missing_content_severity": {"0": 0.9856964310887731, "1": 0.01210084610904299, "2": 0.0014706314313940043, "3": 0.0004817183928124748, "4": 0.00025037297797735834}, "factual_density": {"0": 0.002642427734733885, "1": 0.005451629486088381, "2": 0.05610322812591164, "3": 0.40733200078586457, "4": 0.5284707138674015}, "pretraining_utility": {"-2": 0.008834498608636937, "-1": 0.10818810394906865, "0": 0.38463340316917266, "1": 0.4300842855288719, "2": 0.06825970874424993}, "math_density": {"0": 0.9905676888936539, "1": 0.007418357814864367, "2": 0.0006934563608530579, "3": 0.0003196917399934488, "4": 0.0010008051906352825}, "code_density": {"0": 0.00014381309068760487, "1": 0.0002747639787684558, "2": 0.006499738383983146, "3": 0.06655268462221256, "4": 0.9265289999243482}, "reasoning_density": {"0": 0.9155561759450404, "1": 0.05874702788737008, "2": 0.022111593048329308, "3": 0.0022870497862042915, "4": 0.0012981533330560036}, "education_level": {"none": 0.0008624056824460967, "0": 0.0010533344328552798, "1": 0.0025472226153126893, "2": 0.19431851890444296, "3": 0.5605884071933243, "4": 0.24063011117161875}, "creative_writing_quality": {"not_applicable": 0.9997719133576932, "-2": 2.5375746001210455e-05, "-1": 4.475513614819291e-05, "0": 0.00011139898061884709, "1": 3.3954136457482196e-05, "2": 1.2602643081050693e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.441329284787456e-05, "Art & Design": 2.9276470706539663e-05, "Law & Crime": 2.5851170324428915e-05, "Education & Careers": 2.2910808456628736e-05, "Entertainment & Media": 2.5634441970602313e-05, "Fashion & Beauty": 2.0995998010597234e-05, "Business, Economics & Finance": 3.855824682212966e-05, "Food & Dining": 3.0021848052362763e-05, "Games & Gaming": 3.956747426805957e-05, "Electronics & Hardware": 0.005983371564379169, "Health, Healthcare & Wellness": 4.104292638774119e-05, "History & Geography": 2.705665288812521e-05, "Home, Pets & Hobbies": 0.004833740353948711, "Industry & Production": 0.00013028490352397584, "Language, Literature & Philosophy": 2.2540675055870135e-05, "Politics & Government": 1.697338858896637e-05, "Religion & Spirituality": 2.771544486961866e-05, "Science, Mathematics & Engineering": 0.00027487648787111256, "Relationships & Community": 1.2426952695634217e-05, "Software Usage & Applications": 0.0012947848824283374, "Programming & Software Engineering": 0.9866361524682283, "Sports & Fitness": 3.9281167045351834e-05, "Vehicles & Transportation": 3.965019914527992e-05, "Travel & Tourism": 2.8199056861329686e-05, "Other / Uncovered Subject": 0.0002665332783999419, "Unknown / Insufficient Content": 5.813984622328124e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.000739139433521564, "Art & Design": 0.0005556749305317406, "Law & Crime": 0.0006157665803925927, "Education & Careers": 0.00036752116490830613, "Entertainment & Media": 0.0004970735678642545, "Fashion & Beauty": 0.00028901010606024693, "Business, Economics & Finance": 0.0007344694944090768, "Food & Dining": 0.00036210927385509686, "Games & Gaming": 0.0004809933483594429, "Electronics & Hardware": 0.07049878615182402, "Health, Healthcare & Wellness": 0.00048752763652483834, "History & Geography": 0.0003439317797865802, "Home, Pets & Hobbies": 0.07401340559914873, "Industry & Production": 0.0006178112164013258, "Language, Literature & Philosophy": 0.00026112738470887185, "Politics & Government": 0.000330514899218296, "Religion & Spirituality": 0.00040951726946325236, "Science, Mathematics & Engineering": 0.0021496444706878295, "Relationships & Community": 0.00016707478593279868, "Software Usage & Applications": 0.00962948668230483, "Programming & Software Engineering": 0.045377961969314484, "Sports & Fitness": 0.0003399533820093787, "Vehicles & Transportation": 0.00029506466514700154, "Travel & Tourism": 0.0004495881174731926, "Other / Uncovered Subject": 0.002643627537000524, "NONE": 0.7873432185531517}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11730, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11730, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:09.553279+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "c822e1ea-580d-4805-9597-6ccf230b023e", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "4ccde987e4ec5a7d6d2c53ea67e9f3a5eb52e5430237c78ed9cf4802dc39d657", "source_metadata": {"uuid": "c822e1ea-580d-4805-9597-6ccf230b023e", "repo_name": "home-assistant/core", "relative_path": "tests/components/axis/conftest.py", "category": "TEST", "algo_rel_score": 0.9872884750366211, "quality_score": 7.937950134277344, "source_meta": {"github_metadata": {"branch": "dev", "commit_count": 96732, "repo_created_at": "2013-09-17T07:29:48.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 80205, "forks": 34352, "issues": 2573, "pull_requests": 494}, "file_meta": {"content_id": "fada626f4f634760c1a3e6adf473cad58dc0e8b2", "size_bytes": 12878, "file_timestamp": 1752758403, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "b33a556ca5699bdb5b9221558c689f7d9e934ab3"}}, "parsed_source_meta": {"github_metadata": {"branch": "dev", "commit_count": 96732, "repo_created_at": "2013-09-17T07:29:48.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 80205, "forks": 34352, "issues": 2573, "pull_requests": 494}, "file_meta": {"content_id": "fada626f4f634760c1a3e6adf473cad58dc0e8b2", "size_bytes": 12878, "file_timestamp": 1752758403, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "b33a556ca5699bdb5b9221558c689f7d9e934ab3", "url": "https://github.com/home-assistant/core/blob/b33a556ca5699bdb5b9221558c689f7d9e934ab3/tests/components/axis/conftest.py"}, "gpt4o_tokens": 2904, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.3009730816961493, "noise_severity": 0.02414995361273776, "missing_content_severity": 0.017488756062177858, "factual_density": 3.4535369435651115, "pretraining_utility": 0.4407466018510292, "math_density": 0.013767566519091959, "code_density": 3.919048294310765, "reasoning_density": 0.11502397667486558, "education_level": 3.038089992764189, "creative_writing_quality": -0.15935701084214376}, "score_applicability": {"education_level": 0.9991375943175539, "creative_writing_quality": 0.00022808664230678335}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/if000else/ATM/blob/1afde3ec351a776ce5473eb4cd8fed8a90435bec/modules/functions.py", "id": "python-0-e7798d880a14251d974842db3aa56850", "text": "import json,os,sys\nfrom modules import api_func\nfrom modules import creditcard\nfrom modules import log\nprint(sys.path)\npath = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nPathOfDatabase = os.path.join(path,\"database\")\ndef colordisplay(string,arg):\n    '''\n    :param string: print strings with colors\n    :param arg: red,green...\n    :return:\n    '''\n    if arg == 'red':\n     print(\"\\033[1;31;1m%s\\033[0m\"%string)#红色\n    elif arg == 'green':\n     print(\"\\033[1;32;1m%s\\033[0m\"%string)#绿色\n    elif arg == 'yellow':\n     print(\"\\033[1;33;1m%s\\033[0m\"%string)#黄色\n    elif arg == 'blue':\n     print(\"\\033[1;34;1m%s\\033[0m\"%string)#蓝色\n    elif arg == 'purple':\n     print(\"\\033[1;34;1m%s\\033[0m\"%string)#紫色\n    else:\n     print(\"\\033[1;30;1m%s\\033[0m\" %string)#白色\n\n\ndef login():\n    '''\n    call while login\n    :return: info of user, dic;else return None\n    '''\n    count = [0,\"\"]\n    while True:\n        all_userdata = api_func.db_hander(\"select\", \"user_info\")  # get user_info\n        print(\"All users:\")##########\n        for item in all_userdata.keys():############\n            print(item)##########\n        username = input(\"\\033[1;33;1mPlease input username:\\033[0m\")\n        username = username.strip()\n        log.set_log(\"access\",20,\"User %s attempts to login!\"%username)\n        if username in all_userdata.keys():\n            userdata = all_userdata[username] # get specific user\n            psd = input(\"\\033[1;33;1mPlease input password:\\033[0m\")\n            psd = psd.strip()\n            if psd == userdata[\"password\"]:\n                if userdata[\"locked\"]:#locked\n                    print(\"\\033[1;31;1m User has been locked!!\\033[0m\")\n                else:\n                    log.set_log(\"access\",20,\"User %s  login successfully!\" % username)\n                    print(\"\\033[1;33;1m Login accept!\\033[0m\")\n                    return userdata\n            else: #password is incorrect\n                colordisplay(\"password is incorrect!\",\"red\")\n                log.set_log(\"access\",20,\"User %s failed to login!\" % username)\n                if count[1] == username:# tha same user\n                    count[0] += 1\n                    if count[0] >= 3:#more than 3 times\n                        colordisplay(\"User %s is locked because of attempting too many times!\",\"red\")\n                        api_func.db_hander(\"modify\",\"user_info\",all_userdata)# write in file\n                        log.set_log(\"access\",20,\"User %s is locked because of attempting too many times!\" % username)\n                else:# another user\n                    count[1] = username\n                    count[0] += 1\n\n        elif username == \"b\": # back\n            break\n        else:\n            print(\"\\033[1;31;1m Can not find such user!!!\\033[0m\")\n    return None\n\ndef db_handler(data,filename):\n    '''\n    update data timely\n    :param data: type of dic\n    :param filename: filename\n    :return: data\n    '''\n    path = \"%s/%s.txt\"%(PathOfDatabase,filename)\n    if filename == \"user_info\":\n        with open(path, \"w\") as f:\n            json.dump(data,f)\n    elif filename == \"card_info\":\n        with open(path, \"w\") as f:\n            json.dump(data, f)\n    return data\n\ndef inpmsg(message, limit_value=tuple()):\n    '''\n    inspect input message is valid or not\n    :param message:sting type  to display\n    :param limit_value:\n    :return:\n    '''\n    is_null_flag = True\n    while is_null_flag:\n        input_value = input(message).strip().lower()\n        if not input_value:\n            colordisplay(\"Input is empty!\", \"red\")\n            continue\n        elif len(limit_value) > 0:\n            if input_value not in limit_value:\n                colordisplay(\"Input is invalid,please retry!\",\"red\")\n                continue\n            else:\n                is_null_flag = False\n        else:\n            is_null_flag = False\n            continue\n    return input_value", "text_preview": "import json,os,sys from modules import api_func from modules import creditcard from modules import log print(sys.path) path = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) PathOfDatabase = os.path.join(path,\"database\") def col", "input_sha256": "f10d8db1847dc0f2fe0f0fd662f4d749f69444781c2f637c7dffa7993900b17c", "text_sha256": "e7798d880a14251d974842db3aa5685082db6a1c690d07894b4a3b9fce4b557d", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12037, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.21910168750304043, "noise_severity": 0.14955886083796893, "missing_content_severity": 0.026197272147307334, "factual_density": 3.2428183138420374, "pretraining_utility": 0.26294458833931333, "math_density": 0.12909720382946624, "code_density": 3.8225175337138837, "reasoning_density": 0.6712698601383493, "education_level": 1.8797456221338582, "creative_writing_quality": -0.6239890314704974}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.002710600774320615, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9960314888056622, "secondary": 0.9883285509085175, "tertiary": 0.9911570038904172, "structured_records": 0.0015734821843177605, "procedural_steps": 0.011222517129363615, "worked_solution": 0.001990557278851031, "contextual_dialogue": 6.778666839480503e-05, "content_quality": 0.4566539826445798, "noise_severity": 0.8753676159683592, "missing_content_severity": 0.9781689398772438, "factual_density": 0.3690152615350307, "pretraining_utility": 0.5176834738831061, "math_density": 0.8924189968087781, "code_density": 0.8520979447615697, "reasoning_density": 0.4406084498847088, "education_level": 0.7718760784600561, "creative_writing_quality": 0.998841025965219, "primary_topic": 0.9787851349154348, "secondary_topic": 0.822401436068877}, "probability_margin": {"primary": 0.994038287178651, "secondary": 0.9833253369410061, "tertiary": 0.9873796468905475, "education_level": 0.6594558058323698, "creative_writing_quality": 0.99866075226731, "primary_topic": 0.9658335741023235, "secondary_topic": 0.7187038186815726}, "raw_probabilities": {"primary": {"Academic Writing": 4.145228064100969e-05, "Content Listing": 2.214660452861159e-05, "Creative Writing": 2.3176927721606205e-05, "Customer Support Page": 8.870388198916736e-06, "Q&A Forum": 1.4488419342073877e-05, "Discussion Thread": 8.681432086848816e-06, "Comment Section": 1.5482481891483797e-05, "Missing Content": 3.356826353181255e-05, "Knowledge Article": 0.00010286028765543619, "Legal Notices": 3.149633994940108e-05, "News Article": 5.207900548854154e-06, "Organizational Announcement": 1.0384815724523437e-05, "Nonfiction Writing": 1.5453819583474288e-05, "Organizational About Page": 1.3636881452043425e-05, "Personal About Page": 1.6743707815584934e-05, "Personal Blog": 9.74844244029783e-06, "Product Page": 7.702744310037928e-06, "Spam / Ads": 1.6417013292754713e-05, "Structured Data": 0.0006082492408255665, "Technical Writing": 0.9961968434387596, "Transcript / Interview": 1.8873660251717537e-05, "Tutorial / How-To Guide": 0.0006125026272294483, "User Reviews": 7.456022110172617e-06, "Other / Unknown": 0.0021585562601086137}, "secondary": {"Academic Writing": 0.00010818491503366262, "Content Listing": 6.0945315959363064e-05, "Creative Writing": 9.307259580389066e-05, "Customer Support Page": 4.221337231476444e-05, "Q&A Forum": 2.874031726745356e-05, "Discussion Thread": 2.2321525384227163e-05, "Comment Section": 9.813826756311092e-05, "Missing Content": 0.0002542984682872792, "Knowledge Article": 0.0006955174033240776, "Legal Notices": 0.000113758543691605, "News Article": 3.720837945920627e-05, "Organizational Announcement": 3.4622208477889427e-05, "Nonfiction Writing": 4.759108163946489e-05, "Organizational About Page": 4.653505517668469e-05, "Personal About Page": 4.953907208615486e-05, "Personal Blog": 3.993961253968445e-05, "Product Page": 2.6065254572012906e-05, "Spam / Ads": 5.187972782900202e-05, "Structured Data": 0.0021419623990144186, "Technical Writing": 0.005489524346323154, "Transcript / Interview": 7.028714921356033e-05, "Tutorial / How-To Guide": 0.001613043172626828, "User Reviews": 1.975052908334312e-05, "NONE": 0.9888148612873292}, "tertiary": {"Academic Writing": 7.526290983289888e-05, "Content Listing": 5.317712207679805e-05, "Creative Writing": 9.467139777862647e-05, "Customer Support Page": 3.684228684520227e-05, "Q&A Forum": 2.3251223629925973e-05, "Discussion Thread": 1.9153570030245585e-05, "Comment Section": 9.929485263984037e-05, "Missing Content": 0.00030800312074049513, "Knowledge Article": 0.0005278959434606071, "Legal Notices": 0.00011810485285795007, "News Article": 3.4526504988647166e-05, "Organizational Announcement": 3.092290961219954e-05, "Nonfiction Writing": 7.734352101301558e-05, "Organizational About Page": 5.3708679291136194e-05, "Personal About Page": 4.212331072359321e-05, "Personal Blog": 3.5366823360908424e-05, "Product Page": 2.345940022146172e-05, "Spam / Ads": 5.467376646100391e-05, "Structured Data": 0.0012206460166747553, "Technical Writing": 0.004145815171102348, "Transcript / Interview": 7.197767251639359e-05, "Tutorial / How-To Guide": 0.0013075865032208167, "User Reviews": 2.073037927132254e-05, "NONE": 0.9915254620616498}, "structured_records": {"false": 0.9984265178156823, "true": 0.0015734821843177605}, "procedural_steps": {"false": 0.9887774828706364, "true": 0.011222517129363615}, "worked_solution": {"false": 0.998009442721149, "true": 0.001990557278851031}, "contextual_dialogue": {"false": 0.9999322133316052, "true": 6.778666839480503e-05}, "content_quality": {"-2": 0.021169919546092444, "-1": 0.17411692756954697, "0": 0.42975497909865845, "1": 0.31435789340663195, "2": 0.06060028037907017}, "noise_severity": {"0": 0.8764985385651236, "1": 0.1021979870075857, "2": 0.01766888273791127, "3": 0.0025152584029570946, "4": 0.0011193332864223523}, "missing_content_severity": {"0": 0.9861362384709824, "1": 0.005024078521043244, "2": 0.005778103464715965, "3": 0.0026293314762016644, "4": 0.0004322480670567917}, "factual_density": {"0": 0.014708657675087491, "1": 0.021597822620835753, "2": 0.12848936968889615, "3": 0.3765748482173136, "4": 0.4586293017978671}, "pretraining_utility": {"-2": 0.012220559412620268, "-1": 0.13347650267523914, "0": 0.47346176451352107, "1": 0.340820136957446, "2": 0.0400210364411735}, "math_density": {"0": 0.9102232448737343, "1": 0.07190175602085881, "2": 0.00562631851850096, "3": 0.003051911576017768, "4": 0.009196769010888044}, "code_density": {"0": 0.0003224374764999817, "1": 0.0007401019749651617, "2": 0.02291090900212005, "3": 0.1281505924509808, "4": 0.8478759590954339}, "reasoning_density": {"0": 0.6271293456409455, "1": 0.15323433582433588, "2": 0.1552898070277416, "3": 0.04993013576937828, "4": 0.014416375737598849}, "education_level": {"none": 0.001830613498762012, "0": 0.003505336322115815, "1": 0.15044092621767685, "2": 0.8098967320500468, "3": 0.031235423539623185, "4": 0.0030909683717753283}, "creative_writing_quality": {"not_applicable": 0.9990341883043491, "-2": 0.00017063583931989238, "-1": 0.0003734360370390671, "0": 0.000335669932236223, "1": 6.008796298444187e-05, "2": 2.5981924071189794e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001552151201015256, "Art & Design": 8.185610104624914e-05, "Law & Crime": 0.0021171078622766173, "Education & Careers": 8.249641488004482e-05, "Entertainment & Media": 5.965406611501862e-05, "Fashion & Beauty": 4.8864038870510263e-05, "Business, Economics & Finance": 0.013767517162517713, "Food & Dining": 6.742446240632511e-05, "Games & Gaming": 0.0009249272407845155, "Electronics & Hardware": 0.0002101214375835059, "Health, Healthcare & Wellness": 4.930155974387364e-05, "History & Geography": 5.8580728890177014e-05, "Home, Pets & Hobbies": 4.98834007258588e-05, "Industry & Production": 6.136172711046207e-05, "Language, Literature & Philosophy": 6.43871113144668e-05, "Politics & Government": 4.010968352536716e-05, "Religion & Spirituality": 5.3377899267978985e-05, "Science, Mathematics & Engineering": 0.00024353810377906877, "Relationships & Community": 3.251309800890751e-05, "Software Usage & Applications": 0.00105550404148752, "Programming & Software Engineering": 0.9796010912648412, "Sports & Fitness": 6.504436872775503e-05, "Vehicles & Transportation": 3.2897246245495444e-05, "Travel & Tourism": 5.762089623583996e-05, "Other / Uncovered Subject": 0.0008193088414207632, "Unknown / Insufficient Content": 0.00020029612209302081}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0011174830716689573, "Art & Design": 0.0007628967453843177, "Law & Crime": 0.010406833935234203, "Education & Careers": 0.0006139431833347857, "Entertainment & Media": 0.0005340337242851768, "Fashion & Beauty": 0.0003286030256729271, "Business, Economics & Finance": 0.11052833138465518, "Food & Dining": 0.00045158902461871474, "Games & Gaming": 0.0015610332562827729, "Electronics & Hardware": 0.0008330096844304657, "Health, Healthcare & Wellness": 0.00037675079410513546, "History & Geography": 0.00033777456290730025, "Home, Pets & Hobbies": 0.00032786412355645904, "Industry & Production": 0.0003111073774799165, "Language, Literature & Philosophy": 0.0003393738317073283, "Politics & Government": 0.00031806544486086383, "Religion & Spirituality": 0.00042576454298921715, "Science, Mathematics & Engineering": 0.0012018750430113619, "Relationships & Community": 0.00021015675917070834, "Software Usage & Applications": 0.008161249990009105, "Programming & Software Engineering": 0.029165846883230435, "Sports & Fitness": 0.000247085329569697, "Vehicles & Transportation": 0.0001668865373043724, "Travel & Tourism": 0.00039159765986722535, "Other / Uncovered Subject": 0.0016486940184355613, "NONE": 0.8292321500662279}}, "probabilities": {"primary": {"Academic Writing": 4.1452280641009694e-05, "Content Listing": 2.2146604528611592e-05, "Creative Writing": 2.3176927721606208e-05, "Customer Support Page": 8.870388198916737e-06, "Q&A Forum": 1.4488419342073878e-05, "Discussion Thread": 8.681432086848818e-06, "Comment Section": 1.54824818914838e-05, "Missing Content": 3.356826353181256e-05, "Knowledge Article": 0.0001028602876554362, "Legal Notices": 3.1496339949401086e-05, "News Article": 5.207900548854155e-06, "Organizational Announcement": 1.0384815724523438e-05, "Nonfiction Writing": 1.545381958347429e-05, "Organizational About Page": 1.3636881452043426e-05, "Personal About Page": 1.6743707815584938e-05, "Personal Blog": 9.748442440297832e-06, "Product Page": 7.70274431003793e-06, "Spam / Ads": 1.6417013292754717e-05, "Structured Data": 0.0006082492408255666, "Technical Writing": 0.9961968434387597, "Transcript / Interview": 1.887366025171754e-05, "Tutorial / How-To Guide": 0.0006125026272294484, "User Reviews": 7.456022110172618e-06, "Other / Unknown": 0.002158556260108614}, "secondary": {"Academic Writing": 0.00010818491503366262, "Content Listing": 6.0945315959363064e-05, "Creative Writing": 9.307259580389066e-05, "Customer Support Page": 4.221337231476444e-05, "Q&A Forum": 2.874031726745356e-05, "Discussion Thread": 2.2321525384227163e-05, "Comment Section": 9.813826756311092e-05, "Missing Content": 0.0002542984682872792, "Knowledge Article": 0.0006955174033240776, "Legal Notices": 0.000113758543691605, "News Article": 3.720837945920627e-05, "Organizational Announcement": 3.4622208477889427e-05, "Nonfiction Writing": 4.759108163946489e-05, "Organizational About Page": 4.653505517668469e-05, "Personal About Page": 4.953907208615486e-05, "Personal Blog": 3.993961253968445e-05, "Product Page": 2.6065254572012906e-05, "Spam / Ads": 5.187972782900202e-05, "Structured Data": 0.0021419623990144186, "Technical Writing": 0.005489524346323154, "Transcript / Interview": 7.028714921356033e-05, "Tutorial / How-To Guide": 0.001613043172626828, "User Reviews": 1.975052908334312e-05, "NONE": 0.9888148612873292}, "tertiary": {"Academic Writing": 7.526290983289888e-05, "Content Listing": 5.317712207679805e-05, "Creative Writing": 9.467139777862647e-05, "Customer Support Page": 3.684228684520227e-05, "Q&A Forum": 2.3251223629925973e-05, "Discussion Thread": 1.9153570030245585e-05, "Comment Section": 9.929485263984037e-05, "Missing Content": 0.00030800312074049513, "Knowledge Article": 0.0005278959434606071, "Legal Notices": 0.00011810485285795007, "News Article": 3.4526504988647166e-05, "Organizational Announcement": 3.092290961219954e-05, "Nonfiction Writing": 7.734352101301558e-05, "Organizational About Page": 5.3708679291136194e-05, "Personal About Page": 4.212331072359321e-05, "Personal Blog": 3.5366823360908424e-05, "Product Page": 2.345940022146172e-05, "Spam / Ads": 5.467376646100391e-05, "Structured Data": 0.0012206460166747553, "Technical Writing": 0.004145815171102348, "Transcript / Interview": 7.197767251639359e-05, "Tutorial / How-To Guide": 0.0013075865032208167, "User Reviews": 2.073037927132254e-05, "NONE": 0.9915254620616498}, "structured_records": {"false": 0.9984265178156823, "true": 0.0015734821843177605}, "procedural_steps": {"false": 0.9887774828706364, "true": 0.011222517129363615}, "worked_solution": {"false": 0.998009442721149, "true": 0.001990557278851031}, "contextual_dialogue": {"false": 0.9999322133316052, "true": 6.778666839480503e-05}, "content_quality": {"-2": 0.021169919546092444, "-1": 0.17411692756954697, "0": 0.42975497909865845, "1": 0.31435789340663195, "2": 0.06060028037907017}, "noise_severity": {"0": 0.8764985385651236, "1": 0.1021979870075857, "2": 0.01766888273791127, "3": 0.0025152584029570946, "4": 0.0011193332864223523}, "missing_content_severity": {"0": 0.9861362384709824, "1": 0.005024078521043244, "2": 0.005778103464715965, "3": 0.0026293314762016644, "4": 0.0004322480670567917}, "factual_density": {"0": 0.014708657675087491, "1": 0.021597822620835753, "2": 0.12848936968889615, "3": 0.3765748482173136, "4": 0.4586293017978671}, "pretraining_utility": {"-2": 0.012220559412620268, "-1": 0.13347650267523914, "0": 0.47346176451352107, "1": 0.340820136957446, "2": 0.0400210364411735}, "math_density": {"0": 0.9102232448737344, "1": 0.07190175602085883, "2": 0.005626318518500961, "3": 0.0030519115760177682, "4": 0.009196769010888046}, "code_density": {"0": 0.00032243747649998174, "1": 0.0007401019749651618, "2": 0.022910909002120054, "3": 0.12815059245098082, "4": 0.847875959095434}, "reasoning_density": {"0": 0.6271293456409455, "1": 0.15323433582433588, "2": 0.1552898070277416, "3": 0.04993013576937828, "4": 0.014416375737598849}, "education_level": {"none": 0.0018306134987620122, "0": 0.0035053363221158153, "1": 0.15044092621767688, "2": 0.8098967320500469, "3": 0.031235423539623188, "4": 0.0030909683717753288}, "creative_writing_quality": {"not_applicable": 0.9990341883043492, "-2": 0.0001706358393198924, "-1": 0.0003734360370390672, "0": 0.00033566993223622303, "1": 6.008796298444188e-05, "2": 2.5981924071189797e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00015521512010152562, "Art & Design": 8.185610104624915e-05, "Law & Crime": 0.0021171078622766177, "Education & Careers": 8.249641488004483e-05, "Entertainment & Media": 5.965406611501863e-05, "Fashion & Beauty": 4.886403887051028e-05, "Business, Economics & Finance": 0.013767517162517716, "Food & Dining": 6.742446240632513e-05, "Games & Gaming": 0.0009249272407845157, "Electronics & Hardware": 0.00021012143758350596, "Health, Healthcare & Wellness": 4.9301559743873655e-05, "History & Geography": 5.858072889017703e-05, "Home, Pets & Hobbies": 4.988340072585882e-05, "Industry & Production": 6.136172711046209e-05, "Language, Literature & Philosophy": 6.438711131446682e-05, "Politics & Government": 4.0109683525367164e-05, "Religion & Spirituality": 5.3377899267979e-05, "Science, Mathematics & Engineering": 0.00024353810377906882, "Relationships & Community": 3.2513098008907514e-05, "Software Usage & Applications": 0.0010555040414875203, "Programming & Software Engineering": 0.9796010912648414, "Sports & Fitness": 6.504436872775504e-05, "Vehicles & Transportation": 3.289724624549545e-05, "Travel & Tourism": 5.762089623583997e-05, "Other / Uncovered Subject": 0.0008193088414207634, "Unknown / Insufficient Content": 0.00020029612209302087}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0011174830716689573, "Art & Design": 0.0007628967453843177, "Law & Crime": 0.010406833935234203, "Education & Careers": 0.0006139431833347857, "Entertainment & Media": 0.0005340337242851768, "Fashion & Beauty": 0.0003286030256729271, "Business, Economics & Finance": 0.11052833138465518, "Food & Dining": 0.00045158902461871474, "Games & Gaming": 0.0015610332562827729, "Electronics & Hardware": 0.0008330096844304657, "Health, Healthcare & Wellness": 0.00037675079410513546, "History & Geography": 0.00033777456290730025, "Home, Pets & Hobbies": 0.00032786412355645904, "Industry & Production": 0.0003111073774799165, "Language, Literature & Philosophy": 0.0003393738317073283, "Politics & Government": 0.00031806544486086383, "Religion & Spirituality": 0.00042576454298921715, "Science, Mathematics & Engineering": 0.0012018750430113619, "Relationships & Community": 0.00021015675917070834, "Software Usage & Applications": 0.008161249990009105, "Programming & Software Engineering": 0.029165846883230435, "Sports & Fitness": 0.000247085329569697, "Vehicles & Transportation": 0.0001668865373043724, "Travel & Tourism": 0.00039159765986722535, "Other / Uncovered Subject": 0.0016486940184355613, "NONE": 0.8292321500662279}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9606, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9606, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:10.912181+00:00", "scoring_key_slot": "primary", "dataset_uid": "82d25acd-c1d0-4e66-aa24-db59e2e88f8a", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "2331746406c7a709e530e0ae4d8303fdac65299e1dc79c774248bd9d2403c41d", "source_metadata": {"uuid": "82d25acd-c1d0-4e66-aa24-db59e2e88f8a", "repo_name": "if000else/ATM", "relative_path": "modules/functions.py", "category": "TOOL", "algo_rel_score": 0.9038703441619873, "quality_score": 3.9994869232177734, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 5, "repo_created_at": "2017-08-27T08:32:16.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c475eda659df0a6ae4562f8d55c07cbe825c5491", "size_bytes": 3912, "file_timestamp": 1504172447, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "1afde3ec351a776ce5473eb4cd8fed8a90435bec"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 5, "repo_created_at": "2017-08-27T08:32:16.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c475eda659df0a6ae4562f8d55c07cbe825c5491", "size_bytes": 3912, "file_timestamp": 1504172447, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "1afde3ec351a776ce5473eb4cd8fed8a90435bec", "url": "https://github.com/if000else/ATM/blob/1afde3ec351a776ce5473eb4cd8fed8a90435bec/modules/functions.py"}, "gpt4o_tokens": 1003, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.21910168750304043, "noise_severity": 0.14955886083796893, "missing_content_severity": 0.026197272147307334, "factual_density": 3.2428183138420374, "pretraining_utility": 0.26294458833931333, "math_density": 0.12909720382946624, "code_density": 3.8225175337138837, "reasoning_density": 0.6712698601383493, "education_level": 1.8797456221338582, "creative_writing_quality": -0.6239890314704974}, "score_applicability": {"education_level": 0.9981693865012381, "creative_writing_quality": 0.0009658116956508143}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/yanggelinux/algorithm-data-structure/blob/3b13b36f37eb364410b3b5b4f10a1808d8b1111e/algorithm/sort_some/quick_sort.py", "id": "python-0-7d4fe5bcc572b512d536d9353ed0d24b", "text": "# -*- coding: utf8 -*-\n\n#快速排序\n#\n# 算法思想\n# 快速排序的核心思想在于：首先在这个序列中随便找一个数作为基准数，然后将这个序列中所有比基准数大的数放在该数字的右边，比基准数小的数放在该数字的左边。\n# 第一轮排序结束之后，再分别对已经好的基准书左边（比基准数小）和基准书右边（比基准书大）的数字序列重复上述操作，用递归形式即可实现快速排序，完成对整个序列的排序。\n#\n# 算法步骤\n# 为了清晰地展示快速排序的原理，这里使用一个例子来具体说明快速排序算法排序的过程。\n# 假定现在要对数字序列 [4, 2, 7，8, 0，1, 5，23] 进行快速排序。\n# 我们假设最左边的编号为i，最左边的编号为j，不失一般性，假定以4作为基准进行排序（每一次总总是让j先出发，向左移动，再让i出发，向右移动）。\n#\n# （一）第一轮排序\n# [4, 2, 7，8, 0，1, 5，23]\n# j向左出发寻找第一个小于4的数，遇到1的时候停下来；i向右出发寻找第一个大于4的数，遇到7的时候停下来，交换两者的位置，数字序列变为\n# [4,2,1,8,0,7,5,23]\n# 接下继续让j向左移动，寻找第二个小于4的数，遇到0的时候停下来；让i向右移动，寻找第二个大于4的数，遇到8的时候停下来，交换两者的位置，数字序列变为\n# [4,2,1,0,8,7,5,23]\n# 此时发现i与j相遇了，第一轮排序结束，调换4和0的位置，数字序列变为\n# [0,2,1,4,8,7,5,23],\n# 所有小于4的数字都在4的左边，所有大于4的数字都在4的右边。\n#\n# （二）第二轮排序\n# 分别对4左边和4右边的数字序列进行排序处理，首先对4左边的数字序列[0,2,1]排序。\n# 以第一个数字0为基准，j向左出发寻找第一个小于0的数，i向右出发寻找第一个大于0的数，遇到0的时候停下来，如果找到就交换两者的位置，否则不变。数字序列变为[0,2,1]，所有小于0的数字都在0的左边，所有大于0的数字都在0的右边。\n# 再对4右边的数字序列[8, 7，5,23]排序。\n# 以第一个数字8为基准，j向左出发寻找第一个小于8的数，遇到5的时候停下来；i向右出发寻找第一个大于8的数，如果找到就交换两者的位置，同时保证i小于j，那么数字序列变为[5,7,8,23]。所有小于8的数字都在8的左边，所有大于8的数字都在8的右边。\n# 总体数字序列变为[0,2,1,4,5,7,8,23]。\n#\n# （三）第三轮排序\n# 好了动手算一算，类似第一和第二轮的排序方法，对0右边的数字序列进行排序，得到[0,1,2]；对8左边的数字序列进行排序，得到[5,7，8，23]。第三轮排序结束。\n# 得到最终的排序结果为\n# [0,1,2,4,5,7,8,23]。\n# 快排过程结束。\n\n\ndef fake_quick_sort(arr):\n    \"\"\"\n    快速排序伪代码，时间复杂度O(nlogn)，空间复杂度O(n),不稳定排序算法\n    效率较低\n    :param arr:\n    :return:\n    \"\"\"\n    if len(arr) < 2:\n        return arr\n    pivot = arr[0]\n    less_arr = fake_quick_sort([i for i in arr[1:] if i <= pivot])\n    large_arr = fake_quick_sort([i for i in arr[1:] if i > pivot])\n    return less_arr + [pivot] + large_arr\n\ndef partition(arr,low,high):\n    \"\"\"\n    找到最佳分隔点\n    :param arr:\n    :param low:\n    :param high:\n    :return:\n    \"\"\"\n    i = low - 1\n    pivot = arr[high]\n    for j in range(low,high):\n        if arr[j] <= pivot:\n            i += 1\n            arr[i],arr[j] = arr[j],arr[i]\n    arr[i+1],arr[high] = arr[high],arr[i+1]\n    return i + 1\n\ndef quick_sort(arr,low,high):\n    \"\"\"\n    快速排序，时间复杂度O(nlogn)，空间复杂度O(1),不稳定排序算法\n    :param arr:\n    :param low:\n    :param high:\n    :return:\n    \"\"\"\n    if low < high:\n        p = partition(arr,low,high)\n        quick_sort(arr, low, p - 1)\n        quick_sort(arr, p + 1, high)\n    return arr\n\ndef partition2(arr,low,high):\n    \"\"\"\n    数据分隔算法\n    :param arr:\n    :param low:\n    :param high:\n    :return:\n    \"\"\"\n    #选取最右边的元素当作分隔点\n    pivot = arr[low]\n    while low < high:\n        #从右往左走，high -1\n        while low < high and arr[high] >= pivot:\n            high -= 1\n        #如果遇到比pivot小的数据，交换位置,小于pivot的数据，移动到左边\n        arr[low] = arr[high]\n        #从右往左走,low + 1\n        while low < high and arr[low] <= pivot:\n            low += 1\n        #遇到比pivot大的数据，互换位置，大于pivot的数据移动到右边\n        arr[high] = arr[low]\n    #当两点相遇的时候low=high\n    arr[low] = pivot\n    return low\n\ndef quick_sort2(arr,low,high):\n    \"\"\"\n\n    :param arr:\n    :param low:\n    :param high:\n    :return:\n    \"\"\"\n    if low < high:\n        p = partition2(arr,low,high)\n        quick_sort2(arr,low,p-1)\n        quick_sort2(arr,p+1,high)\n    return arr\n\n\n\nif __name__ == '__main__':\n    arr = [2, 5, 4, 6, 1, 3,2]\n    # arr = [1, 4, 2, 6, 5, 3, 2]\n    # print(fake_quick_sort(arr))\n    print(quick_sort2(arr,0,len(arr)-1))", "text_preview": "# -*- coding: utf8 -*- #快速排序 # # 算法思想 # 快速排序的核心思想在于：首先在这个序列中随便找一个数作为基准数，然后将这个序列中所有比基准数大的数放在该数字的右边，比基准数小的数放在该数字的左边。 # 第一轮排序结束之后，再分别对已经好的基准书左边（比基准数小）和基准书右边（比基准书大）的数字序列重复上述操作，用递归形式即可实现快速排序，完成对整个序列的排序。 # # 算法步骤 # 为了清晰地展示快速排序的原理，这里使用一个例子来具体说明快速排", "input_sha256": "76c1b4bd2a6ae250ad2a5fe5e2847f984b4431bdc10f180b85f628355ce45934", "text_sha256": "7d4fe5bcc572b512d536d9353ed0d24b9024940a67770193867e23a66cc42b72", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12039, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Technical Writing", "sub_format": null}, "tertiary": null, "content_structure": ["procedural_steps", "worked_solution"]}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 3, "code_density": 3, "reasoning_density": 3, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.16161076312488118, "noise_severity": 0.2830932957188814, "missing_content_severity": 0.026230403612641208, "factual_density": 2.909409488597684, "pretraining_utility": 0.9588569696140659, "math_density": 3.078279802904553, "code_density": 2.633789921996514, "reasoning_density": 2.892493108641037, "education_level": 1.7157810118363517, "creative_writing_quality": -0.4130615681139114}, "api_choices": {"primary": "Technical Writing", "secondary": "Technical Writing", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": ["Duplicate Document Type slots", "secondary probability 0.4739 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.5819120195988721, "secondary_choice": "Technical Writing", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.6929347694716272, "secondary": 0.4510777499701959, "tertiary": 0.6670187340514467, "structured_records": 0.012796204169124498, "procedural_steps": 0.7873373111857008, "worked_solution": 0.9422344490412329, "contextual_dialogue": 1.119365031076874e-05, "content_quality": 0.40081181127841614, "noise_severity": 0.7640889202342654, "missing_content_severity": 0.978141330322799, "factual_density": 0.595936845631059, "pretraining_utility": 0.673957641559946, "math_density": 0.4846255354635315, "code_density": 0.6061978373133494, "reasoning_density": 0.5093055243484088, "education_level": 0.6454289777496437, "creative_writing_quality": 0.9728929187363091, "primary_topic": 0.849538355638631, "secondary_topic": 0.5086617151914395}, "probability_margin": {"primary": 0.45271266360445467, "secondary": 0.1498764205225353, "tertiary": 0.5717714473941902, "education_level": 0.43020820075947497, "creative_writing_quality": 0.9660318410054014, "primary_topic": 0.7169407858188487, "secondary_topic": 0.2949515779108278}, "raw_probabilities": {"primary": {"Academic Writing": 0.0004446090251007768, "Content Listing": 0.00027966161656252803, "Creative Writing": 0.0002553816502407244, "Customer Support Page": 0.0002133034657011901, "Q&A Forum": 0.00027507548605765073, "Discussion Thread": 0.00016292857895758213, "Comment Section": 0.00031168441805313194, "Missing Content": 0.00021808761973890348, "Knowledge Article": 0.03438217631617016, "Legal Notices": 0.00021150752919197439, "News Article": 0.00013443720483606364, "Organizational Announcement": 0.00020504187085087906, "Nonfiction Writing": 0.000422705145579058, "Organizational About Page": 0.00027222814718245157, "Personal About Page": 0.00020970031401806527, "Personal Blog": 0.0001584133305912488, "Product Page": 0.00015983737840529266, "Spam / Ads": 0.00017089291624118111, "Structured Data": 0.00034531080791120445, "Technical Writing": 0.7057291540769761, "Transcript / Interview": 0.00035148047399161976, "Tutorial / How-To Guide": 0.25301649047252145, "User Reviews": 0.00012849284991960457, "Other / Unknown": 0.0019413993052011013}, "secondary": {"Academic Writing": 0.0012373607460266064, "Content Listing": 0.0004932084299133112, "Creative Writing": 0.0004477070738378182, "Customer Support Page": 0.00035638451826963127, "Q&A Forum": 0.00042187982020624554, "Discussion Thread": 0.000288399322322711, "Comment Section": 0.0004851023331320967, "Missing Content": 0.00041010903481625044, "Knowledge Article": 0.09355458522293819, "Legal Notices": 0.0004334794608599577, "News Article": 0.0002968070220250583, "Organizational Announcement": 0.0004530566528443769, "Nonfiction Writing": 0.0007643199047650531, "Organizational About Page": 0.000626786787897374, "Personal About Page": 0.0003643496712860537, "Personal Blog": 0.000310959143165011, "Product Page": 0.00036274070866767355, "Spam / Ads": 0.00035174157947746024, "Structured Data": 0.0004521972206617573, "Technical Writing": 0.47394951038810446, "Transcript / Interview": 0.0006423795194858916, "Tutorial / How-To Guide": 0.32407308986556915, "User Reviews": 0.00024291170663011386, "NONE": 0.09898093386709773}, "tertiary": {"Academic Writing": 0.0019420034753293539, "Content Listing": 0.0003209076485755189, "Creative Writing": 0.0004744460587473305, "Customer Support Page": 0.0003035018644914324, "Q&A Forum": 0.00026970083782984066, "Discussion Thread": 0.00021388283825469272, "Comment Section": 0.0004474130329185733, "Missing Content": 0.00043007824640563903, "Knowledge Article": 0.09628130067669456, "Legal Notices": 0.0003930652352118892, "News Article": 0.00023517096867587902, "Organizational Announcement": 0.0002918622258377126, "Nonfiction Writing": 0.0007278548536654964, "Organizational About Page": 0.0004793737266149565, "Personal About Page": 0.0002856484114023796, "Personal Blog": 0.0002576112371169601, "Product Page": 0.00023370453020609593, "Spam / Ads": 0.0003046904636408421, "Structured Data": 0.0009687684341728332, "Technical Writing": 0.10912150607177958, "Transcript / Interview": 0.0006270893822356216, "Tutorial / How-To Guide": 0.10433927886501948, "User Reviews": 0.00015818744920353045, "NONE": 0.6808929534659698}, "structured_records": {"false": 0.9872037958308755, "true": 0.012796204169124498}, "procedural_steps": {"false": 0.21266268881429917, "true": 0.7873373111857008}, "worked_solution": {"false": 0.05776555095876712, "true": 0.9422344490412329}, "contextual_dialogue": {"false": 0.9999888063496892, "true": 1.119365031076874e-05}, "content_quality": {"-2": 0.02863733274679529, "-1": 0.22143286617691915, "0": 0.373128667111682, "1": 0.31328397313381623, "2": 0.06351716083078734}, "noise_severity": {"0": 0.7630717528744754, "1": 0.19751948763505006, "2": 0.03426926562174257, "3": 0.0035226986345815703, "4": 0.001616795234150384}, "missing_content_severity": {"0": 0.97877089993306, "1": 0.017218606214396984, "2": 0.0032461776985011387, "3": 0.0005378226149251561, "4": 0.00022649353911661837}, "factual_density": {"0": 0.002806066420153945, "1": 0.018613707868207326, "2": 0.24208753332564623, "3": 0.5393500554657861, "4": 0.19714263692020637}, "pretraining_utility": {"-2": 0.00385344693681418, "-1": 0.01740721031862645, "0": 0.16982216880930406, "1": 0.6338632740641902, "2": 0.17505389987106532}, "math_density": {"0": 0.00958342976081512, "1": 0.029722898179440186, "2": 0.18188869162827903, "3": 0.4304404002573084, "4": 0.34836458017415733}, "code_density": {"0": 0.004241113478465265, "1": 0.01194570349513008, "2": 0.3827715891880773, "3": 0.54786533522808, "4": 0.05317625861024734}, "reasoning_density": {"0": 0.02744383607269437, "1": 0.04148124334308308, "2": 0.18287613616618686, "3": 0.5075355447065625, "4": 0.2406632397114732}, "education_level": {"none": 0.0011771724514930148, "0": 0.010524026637676176, "1": 0.27431594736522813, "2": 0.7045241481247031, "3": 0.007437823601809637, "4": 0.0020208818190899955}, "creative_writing_quality": {"not_applicable": 0.977410765613591, "-2": 0.0008230752571948601, "-1": 0.009152348098627706, "0": 0.011378924608189596, "1": 0.001002018809919128, "2": 0.0002328676124777462}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00020185733897767593, "Art & Design": 0.00029732659436533315, "Law & Crime": 0.0002765547119263454, "Education & Careers": 0.00029106013911158916, "Entertainment & Media": 0.00021074867939297577, "Fashion & Beauty": 0.00012852957299064858, "Business, Economics & Finance": 0.0002029307094636449, "Food & Dining": 0.0001362592296740386, "Games & Gaming": 0.00027021493540003956, "Electronics & Hardware": 0.00023901722125030183, "Health, Healthcare & Wellness": 0.00022879530911621367, "History & Geography": 0.00023227544834813593, "Home, Pets & Hobbies": 0.00016164973426345327, "Industry & Production": 0.0001870613311390057, "Language, Literature & Philosophy": 0.00019389783717345616, "Politics & Government": 0.00010241174723205701, "Religion & Spirituality": 0.00019724376016529692, "Science, Mathematics & Engineering": 0.13838455614137346, "Relationships & Community": 9.275343207946314e-05, "Software Usage & Applications": 0.0011569918247485626, "Programming & Software Engineering": 0.8553253419602221, "Sports & Fitness": 0.00023887753205493936, "Vehicles & Transportation": 0.00011811967229605238, "Travel & Tourism": 0.0002098300726775558, "Other / Uncovered Subject": 0.0006947013988337981, "Unknown / Insufficient Content": 0.00022099366572396413}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006209318362268468, "Art & Design": 0.0005526949863350976, "Law & Crime": 0.0006203597707527139, "Education & Careers": 0.007762433065491883, "Entertainment & Media": 0.0004792056964372825, "Fashion & Beauty": 0.00030574515266032986, "Business, Economics & Finance": 0.0004968830945489899, "Food & Dining": 0.0002531701354776006, "Games & Gaming": 0.00039100011909300625, "Electronics & Hardware": 0.00042532330434532414, "Health, Healthcare & Wellness": 0.00044043658498876776, "History & Geography": 0.0004407277641077335, "Home, Pets & Hobbies": 0.0002628100956189498, "Industry & Production": 0.0003388563026849534, "Language, Literature & Philosophy": 0.00032964030306711537, "Politics & Government": 0.0002413813430306618, "Religion & Spirituality": 0.00044198797277199763, "Science, Mathematics & Engineering": 0.2219067757540568, "Relationships & Community": 0.00019305452422933132, "Software Usage & Applications": 0.0014008730643205443, "Programming & Software Engineering": 0.23260776361940252, "Sports & Fitness": 0.0003491279714974348, "Vehicles & Transportation": 0.00020360317988917236, "Travel & Tourism": 0.0005122580414555889, "Other / Uncovered Subject": 0.0008636147872790509, "NONE": 0.5275593415302303}}, "probabilities": {"primary": {"Academic Writing": 0.0004446090251007768, "Content Listing": 0.00027966161656252803, "Creative Writing": 0.0002553816502407244, "Customer Support Page": 0.0002133034657011901, "Q&A Forum": 0.00027507548605765073, "Discussion Thread": 0.00016292857895758213, "Comment Section": 0.00031168441805313194, "Missing Content": 0.00021808761973890348, "Knowledge Article": 0.03438217631617016, "Legal Notices": 0.00021150752919197439, "News Article": 0.00013443720483606364, "Organizational Announcement": 0.00020504187085087906, "Nonfiction Writing": 0.000422705145579058, "Organizational About Page": 0.00027222814718245157, "Personal About Page": 0.00020970031401806527, "Personal Blog": 0.0001584133305912488, "Product Page": 0.00015983737840529266, "Spam / Ads": 0.00017089291624118111, "Structured Data": 0.00034531080791120445, "Technical Writing": 0.7057291540769761, "Transcript / Interview": 0.00035148047399161976, "Tutorial / How-To Guide": 0.25301649047252145, "User Reviews": 0.00012849284991960457, "Other / Unknown": 0.0019413993052011013}, "secondary": {"Academic Writing": 0.0012373607460266064, "Content Listing": 0.0004932084299133112, "Creative Writing": 0.0004477070738378182, "Customer Support Page": 0.00035638451826963127, "Q&A Forum": 0.00042187982020624554, "Discussion Thread": 0.000288399322322711, "Comment Section": 0.0004851023331320967, "Missing Content": 0.00041010903481625044, "Knowledge Article": 0.09355458522293819, "Legal Notices": 0.0004334794608599577, "News Article": 0.0002968070220250583, "Organizational Announcement": 0.0004530566528443769, "Nonfiction Writing": 0.0007643199047650531, "Organizational About Page": 0.000626786787897374, "Personal About Page": 0.0003643496712860537, "Personal Blog": 0.000310959143165011, "Product Page": 0.00036274070866767355, "Spam / Ads": 0.00035174157947746024, "Structured Data": 0.0004521972206617573, "Technical Writing": 0.47394951038810446, "Transcript / Interview": 0.0006423795194858916, "Tutorial / How-To Guide": 0.32407308986556915, "User Reviews": 0.00024291170663011386, "NONE": 0.09898093386709773}, "tertiary": {"Academic Writing": 0.0019420034753293539, "Content Listing": 0.0003209076485755189, "Creative Writing": 0.0004744460587473305, "Customer Support Page": 0.0003035018644914324, "Q&A Forum": 0.00026970083782984066, "Discussion Thread": 0.00021388283825469272, "Comment Section": 0.0004474130329185733, "Missing Content": 0.00043007824640563903, "Knowledge Article": 0.09628130067669456, "Legal Notices": 0.0003930652352118892, "News Article": 0.00023517096867587902, "Organizational Announcement": 0.0002918622258377126, "Nonfiction Writing": 0.0007278548536654964, "Organizational About Page": 0.0004793737266149565, "Personal About Page": 0.0002856484114023796, "Personal Blog": 0.0002576112371169601, "Product Page": 0.00023370453020609593, "Spam / Ads": 0.0003046904636408421, "Structured Data": 0.0009687684341728332, "Technical Writing": 0.10912150607177958, "Transcript / Interview": 0.0006270893822356216, "Tutorial / How-To Guide": 0.10433927886501948, "User Reviews": 0.00015818744920353045, "NONE": 0.6808929534659698}, "structured_records": {"false": 0.9872037958308755, "true": 0.012796204169124498}, "procedural_steps": {"false": 0.21266268881429917, "true": 0.7873373111857008}, "worked_solution": {"false": 0.05776555095876712, "true": 0.9422344490412329}, "contextual_dialogue": {"false": 0.9999888063496892, "true": 1.119365031076874e-05}, "content_quality": {"-2": 0.02863733274679529, "-1": 0.22143286617691915, "0": 0.373128667111682, "1": 0.31328397313381623, "2": 0.06351716083078734}, "noise_severity": {"0": 0.7630717528744754, "1": 0.19751948763505006, "2": 0.03426926562174257, "3": 0.0035226986345815703, "4": 0.001616795234150384}, "missing_content_severity": {"0": 0.9787708999330601, "1": 0.017218606214396987, "2": 0.003246177698501139, "3": 0.0005378226149251562, "4": 0.0002264935391166184}, "factual_density": {"0": 0.0028060664201539453, "1": 0.01861370786820733, "2": 0.24208753332564625, "3": 0.5393500554657862, "4": 0.1971426369202064}, "pretraining_utility": {"-2": 0.003853446936814179, "-1": 0.017407210318626447, "0": 0.16982216880930404, "1": 0.6338632740641901, "2": 0.1750538998710653}, "math_density": {"0": 0.00958342976081512, "1": 0.029722898179440186, "2": 0.18188869162827903, "3": 0.4304404002573084, "4": 0.34836458017415733}, "code_density": {"0": 0.004241113478465265, "1": 0.01194570349513008, "2": 0.3827715891880773, "3": 0.54786533522808, "4": 0.05317625861024734}, "reasoning_density": {"0": 0.02744383607269437, "1": 0.04148124334308308, "2": 0.18287613616618686, "3": 0.5075355447065625, "4": 0.2406632397114732}, "education_level": {"none": 0.0011771724514930148, "0": 0.010524026637676176, "1": 0.27431594736522813, "2": 0.7045241481247031, "3": 0.007437823601809637, "4": 0.0020208818190899955}, "creative_writing_quality": {"not_applicable": 0.977410765613591, "-2": 0.0008230752571948601, "-1": 0.009152348098627706, "0": 0.011378924608189596, "1": 0.001002018809919128, "2": 0.0002328676124777462}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00020185733897767587, "Art & Design": 0.0002973265943653331, "Law & Crime": 0.00027655471192634533, "Education & Careers": 0.0002910601391115891, "Entertainment & Media": 0.0002107486793929757, "Fashion & Beauty": 0.00012852957299064856, "Business, Economics & Finance": 0.00020293070946364485, "Food & Dining": 0.00013625922967403856, "Games & Gaming": 0.0002702149354000395, "Electronics & Hardware": 0.00023901722125030178, "Health, Healthcare & Wellness": 0.00022879530911621362, "History & Geography": 0.00023227544834813587, "Home, Pets & Hobbies": 0.00016164973426345325, "Industry & Production": 0.00018706133113900565, "Language, Literature & Philosophy": 0.0001938978371734561, "Politics & Government": 0.00010241174723205698, "Religion & Spirituality": 0.00019724376016529686, "Science, Mathematics & Engineering": 0.13838455614137343, "Relationships & Community": 9.275343207946311e-05, "Software Usage & Applications": 0.0011569918247485624, "Programming & Software Engineering": 0.8553253419602219, "Sports & Fitness": 0.0002388775320549393, "Vehicles & Transportation": 0.00011811967229605235, "Travel & Tourism": 0.00020983007267755575, "Other / Uncovered Subject": 0.000694701398833798, "Unknown / Insufficient Content": 0.00022099366572396408}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006209318362268468, "Art & Design": 0.0005526949863350976, "Law & Crime": 0.0006203597707527139, "Education & Careers": 0.007762433065491883, "Entertainment & Media": 0.0004792056964372825, "Fashion & Beauty": 0.00030574515266032986, "Business, Economics & Finance": 0.0004968830945489899, "Food & Dining": 0.0002531701354776006, "Games & Gaming": 0.00039100011909300625, "Electronics & Hardware": 0.00042532330434532414, "Health, Healthcare & Wellness": 0.00044043658498876776, "History & Geography": 0.0004407277641077335, "Home, Pets & Hobbies": 0.0002628100956189498, "Industry & Production": 0.0003388563026849534, "Language, Literature & Philosophy": 0.00032964030306711537, "Politics & Government": 0.0002413813430306618, "Religion & Spirituality": 0.00044198797277199763, "Science, Mathematics & Engineering": 0.2219067757540568, "Relationships & Community": 0.00019305452422933132, "Software Usage & Applications": 0.0014008730643205443, "Programming & Software Engineering": 0.23260776361940252, "Sports & Fitness": 0.0003491279714974348, "Vehicles & Transportation": 0.00020360317988917236, "Travel & Tourism": 0.0005122580414555889, "Other / Uncovered Subject": 0.0008636147872790509, "NONE": 0.5275593415302303}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10066, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10066, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:11.862357+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "26f314f9-8a0d-49da-8d18-4b1c7f12c5e6", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "d1f2a92f444afc6a8bc881022d4d726f57b5f57364bf5ed4f03c5ef6343e81db", "source_metadata": {"uuid": "26f314f9-8a0d-49da-8d18-4b1c7f12c5e6", "repo_name": "yanggelinux/algorithm-data-structure", "relative_path": "algorithm/sort_some/quick_sort.py", "category": "ALGO", "algo_rel_score": 0.9993606209754944, "quality_score": 4.073615074157715, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 46, "repo_created_at": "2019-10-18T07:51:28.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "f51fa9220a05892351f879e68807515ce9fab616", "size_bytes": 5142, "file_timestamp": 1599207624, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "3b13b36f37eb364410b3b5b4f10a1808d8b1111e"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 46, "repo_created_at": "2019-10-18T07:51:28.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "f51fa9220a05892351f879e68807515ce9fab616", "size_bytes": 5142, "file_timestamp": 1599207624, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "3b13b36f37eb364410b3b5b4f10a1808d8b1111e", "url": "https://github.com/yanggelinux/algorithm-data-structure/blob/3b13b36f37eb364410b3b5b4f10a1808d8b1111e/algorithm/sort_some/quick_sort.py"}, "gpt4o_tokens": 1640, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.16161076312488118, "noise_severity": 0.2830932957188814, "missing_content_severity": 0.026230403612641208, "factual_density": 2.909409488597684, "pretraining_utility": 0.9588569696140659, "math_density": 3.078279802904553, "code_density": 2.633789921996514, "reasoning_density": 2.892493108641037, "education_level": 1.7157810118363517, "creative_writing_quality": -0.4130615681139114}, "score_applicability": {"education_level": 0.998822827548507, "creative_writing_quality": 0.022589234386409036}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/jswoo/lecture/blob/fb14fb1c6a842e2ee2f79b0225ac9f4d11c3ca47/w5/finterstellar/simulation.py", "id": "python-0-22c91a99e3bd5c4bd23e62d48b814400", "text": "import pandas as pd\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\n\n\n\nclass PairAnalyze:\n\n    def sampling(self, prices_df, start_date, pair):\n        sample = pd.DataFrame()\n        sample = prices_df.loc[start_date:][pair].copy()\n        sample[pair[1]+' expected'] = 0.0\n        sample[pair[1]+' spread'] = 0.0\n        return (sample)\n    \n    \n    def regression(self, sample, s_codes):\n        sample.dropna(inplace=True)\n        x = sample[s_codes[0]]\n        y = sample[s_codes[1]]\n        # 1개 컬럼 np.array로 변환\n        x = np.array(x).reshape(-1, 1)\n        y = np.array(y).reshape(-1, 1)\n        # Linear Regression\n        regr = LinearRegression()\n        regr.fit(x, y)\n        result = {'Slope':regr.coef_[0,0], 'Intercept':regr.intercept_[0], 'R2':regr.score(x, y) }\n        #result = {'Slope':regr.coef_, 'Intercept':regr.intercept_, 'R2':regr.score(x, y) }\n        return(result)\n    \n    \n    def compare_r2(self, prices_df, base_date, s_codes):\n        comp_df = pd.DataFrame()\n        s_df = self.sampling(prices_df, base_date, s_codes)\n        s_df = s_df.dropna()\n        n = len(s_codes)\n        for i in range(0, n, 1):\n            for j in range(i, n, 1):\n                if i != j:\n                    code_pairs = [ s_codes[i], s_codes[j] ]\n                    regr = self.regression(s_df, code_pairs)\n                    c_pair = s_codes[i]+' vs. '+s_codes[j]\n                    #print(s_codes[i], '-', s_codes[j], ' : ', '{:,.2f}'.format(regr['R2']*100))\n                    comp_df.loc[c_pair, 'R2'] = round(regr['R2'], 4)\n                    comp_df.loc[c_pair, 'Slope'] = round(regr['Slope'], 4)\n        comp_df.index.name = 'pair'\n        comp_df = comp_df.sort_values(by='R2', ascending=False)\n        return (comp_df)\n\n    \n    def compare_r2_single(self, prices_df, base_date, cd_dict):\n        comp_df = pd.DataFrame()\n        s_codes = list(cd_dict.keys())\n        s_names = list(cd_dict.values())\n        s_df = self.sampling(prices_df, base_date, s_codes)\n        s_df = s_df.dropna()\n        n = len(s_codes)\n        for i in range(0, n, 1):\n            for j in range(i, n, 1):\n                if i != j:\n                    code_pairs = [ s_codes[i], s_codes[j] ]\n                    regr = self.regression(s_df, code_pairs)\n                    c_pair = s_codes[i]+' vs. '+s_codes[j]\n                    #print(s_codes[i], '-', s_codes[j], ' : ', '{:,.2f}'.format(regr['R2']*100))\n                    comp_df.loc[c_pair, 'R2'] = round(regr['R2'], 4)\n                    comp_df.loc[c_pair, 'Slope'] = round(regr['Slope'], 4)\n                    comp_df.loc[c_pair, 'X code'] = s_codes[i]\n                    comp_df.loc[c_pair, 'Y code'] = s_codes[j]\n                    comp_df.loc[c_pair, 'X name'] = s_names[i]\n                    comp_df.loc[c_pair, 'Y name'] = s_names[j]\n        comp_df.index.name = 'pair'\n        comp_df = comp_df.sort_values(by='R2', ascending=False)\n        return (comp_df)\n    \n    \n    def regression_rolling(self, sample, code_pair, period=60, step=10):\n        result_dict = {}\n        R2_lst = []\n\n        sample.dropna(inplace=True)\n        sample_size = len(sample)\n\n        if sample_size > period:\n            for i in range(0, sample_size, step):\n                sub_sample = sample.iloc[i:i+period].copy()\n                x = sub_sample[code_pair[0]]\n                y = sub_sample[code_pair[1]]\n                # 1개 컬럼 np.array로 변환\n                x = np.array(x).reshape(-1, 1)\n                y = np.array(y).reshape(-1, 1)\n                # Linear Regression\n                regr = LinearRegression()\n                regr.fit(x, y)\n                result_dict[i] = [regr.coef_[0,0], regr.intercept_[0], regr.score(x, y)]\n                # slope, intercept, R2\n                R2_lst.append(regr.score(x, y))\n        else:\n            x = sample[code_pair[0]]\n            y = sample[code_pair[1]]\n            # 1개 컬럼 np.array로 변환\n            x = np.array(x).reshape(-1, 1)\n            y = np.array(y).reshape(-1, 1)\n            # Linear Regression\n            regr = LinearRegression()\n            regr.fit(x, y)\n            result_dict[0] = [regr.coef_[0,0], regr.intercept_[0], regr.score(x, y)]\n            R2_lst.append(regr.score(x, y))\n\n        R2_mean = round(np.mean(R2_lst), 4)    # mean\n        R2_std = round(np.std(R2_lst), 4)    # standard deviation\n        return (R2_mean, R2_std, result_dict)\n\n    \n    \n    def compare_r2_rolling(self, prices_df, base_date, cd_dict, period=60, step=5):\n        \n        comp_df = pd.DataFrame()\n        s_codes = list(cd_dict.keys())\n        s_names = list(cd_dict.values())\n        s_df = self.sampling(prices_df, base_date, s_codes)\n        s_df = s_df.dropna()\n        n = len(s_codes)\n        \n        for i in range(0, n, 1):\n            for j in range(i, n, 1):\n                if i != j:\n                    cp = ( s_codes[i], s_codes[j] )\n                    rr = self.regression_rolling(s_df, cp, period, step)\n                    c_pair = s_codes[i]+' vs. '+s_codes[j]\n                    #print(s_codes[i], '-', s_codes[j], ' : ', '{:,.2f}'.format(regr['R2']*100))\n                    comp_df.loc[c_pair, 'R2 mean'] = round(rr[0], 4)\n                    comp_df.loc[c_pair, 'R2 std'] = round(rr[1], 4)\n                    comp_df.loc[c_pair, 'X code'] = s_codes[i]\n                    comp_df.loc[c_pair, 'Y code'] = s_codes[j]\n                    comp_df.loc[c_pair, 'X name'] = s_names[i]\n                    comp_df.loc[c_pair, 'Y name'] = s_names[j]\n        comp_df.index.name = 'pair'\n        comp_df = comp_df.sort_values(by='R2 mean', ascending=False)\n        return (comp_df)\n    \n    \n\n\n    \n    \nclass Simulate(PairAnalyze):\n    \n    \n    def rolling_sub_sampling(self, sample, cds, start_pt, period):\n        sub_sample = pd.DataFrame()\n        sub_sample = sample.loc[:, cds].copy()\n        sub_sample = sub_sample.iloc[start_pt:start_pt+period].copy()\n        return (sub_sample)\n    \n    \n    def rolling_expected_y(self, sample, cds, regr, start_pt, anz_period=20, apply_period=5):\n        sp = start_pt + anz_period\n        ep = start_pt + anz_period + apply_period\n        for i in sample.index[sp:ep]:\n            sample.loc[i, cds[1]+' expected'] = sample.loc[i, cds[0]] * regr['Slope'] + regr['Intercept']\n            sample.loc[i, cds[1]+' spread'] = sample.loc[i, cds[1]] - sample.loc[i, cds[1]+' expected'] \n        return ()\n    \n    \n    def pair_simulation_prep(self, sample, pair, anz_period=20, apply_period=5):\n        sample_size = len(sample)\n        if sample_size > anz_period:\n            for i in range(0, sample_size, apply_period):    # sample 데이터 내에서 apply_period 간격으로 rolling\n                sub_sample = self.rolling_sub_sampling(sample, pair, i, anz_period)    # anz_period 동안의 sub_sample을 만들고\n                regr = self.regression(sub_sample, pair)    # sub_sample로 regression을 돌려 추세선 산출\n                self.rolling_expected_y(sample, pair, regr, i, anz_period, apply_period)    # 다음 apply_period 동안 regr 값 적용\n        else:\n            print('Your sample is too small!')\n            print('The analyze period is', anz_period, ', but you have only', sample_size, 'samples.')\n            print('Cannot proceed SIMULATION.')\n        trd_sample = sample[anz_period:].copy()    # 트레이딩을 위한 데이터프레임 준비\n        return(trd_sample)", "text_preview": "import pandas as pd import numpy as np from sklearn.linear_model import LinearRegression class PairAnalyze: def sampling(self, prices_df, start_date, pair): sample = pd.DataFrame() sample = prices_df.loc[start_date:][pair].copy() sample[pai", "input_sha256": "3e0c903a658ea5a85cb24383b3ccd26ff2a5dc06c3cf0cf6dbb084c7656d939a", "text_sha256": "22c91a99e3bd5c4bd23e62d48b814400b031c6907ad1c37be5ce8702a6c02ca7", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12040, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5962685464313427, "noise_severity": 0.05218743715469017, "missing_content_severity": 0.0900046077369927, "factual_density": 3.1509446171317106, "pretraining_utility": 0.6425667240347779, "math_density": 3.1890798181592936, "code_density": 3.8809923032265425, "reasoning_density": 0.6631653507052067, "education_level": 2.266794807931497, "creative_writing_quality": -0.3645094249854119}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.5562 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.005757700084497275, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9941494780167467, "secondary": 0.9877934121048748, "tertiary": 0.9938014469756544, "structured_records": 0.008519475557639638, "procedural_steps": 0.06175994584314799, "worked_solution": 0.0026234951022991774, "contextual_dialogue": 9.718536877179092e-06, "content_quality": 0.44591728623041993, "noise_severity": 0.9565104690377582, "missing_content_severity": 0.9249961602191727, "factual_density": 0.29245384760975857, "pretraining_utility": 0.5253252249229823, "math_density": 0.3242331817994113, "code_density": 0.9008269193554522, "reasoning_density": 0.44736220774566116, "education_level": 0.6787995820584352, "creative_writing_quality": 0.999341674657964, "primary_topic": 0.7782162710499769, "secondary_topic": 0.5384808203811356}, "probability_margin": {"primary": 0.991979041816625, "secondary": 0.98315739390952, "tertiary": 0.9917263649019518, "education_level": 0.5009009189921919, "creative_writing_quality": 0.9992012051023546, "primary_topic": 0.6431653129607826, "secondary_topic": 0.41049777895869144}, "raw_probabilities": {"primary": {"Academic Writing": 0.00013961154662657516, "Content Listing": 2.979527190158765e-05, "Creative Writing": 2.3487772823712086e-05, "Customer Support Page": 1.1567643125365735e-05, "Q&A Forum": 1.8670591630757463e-05, "Discussion Thread": 1.2512913610168978e-05, "Comment Section": 1.6241422111571793e-05, "Missing Content": 3.530804708757527e-05, "Knowledge Article": 0.00016244660160310103, "Legal Notices": 3.207401924851026e-05, "News Article": 8.042613841608176e-06, "Organizational Announcement": 1.4189296314204326e-05, "Nonfiction Writing": 2.6158598319247163e-05, "Organizational About Page": 1.7406008853173178e-05, "Personal About Page": 1.854486373971108e-05, "Personal Blog": 1.0991780875203985e-05, "Product Page": 1.1211124022375304e-05, "Spam / Ads": 1.581285781229082e-05, "Structured Data": 0.0007868994699245249, "Technical Writing": 0.9943932497660489, "Transcript / Interview": 3.729966581671382e-05, "Tutorial / How-To Guide": 0.0024142079494238943, "User Reviews": 1.0059299939729595e-05, "Other / Unknown": 0.0017542108752994655}, "secondary": {"Academic Writing": 0.0012945378906565777, "Content Listing": 5.284961342336456e-05, "Creative Writing": 6.465360055696256e-05, "Customer Support Page": 3.113203788554122e-05, "Q&A Forum": 2.772148395583143e-05, "Discussion Thread": 2.0144059666263465e-05, "Comment Section": 3.361537101235995e-05, "Missing Content": 0.00011280638901287979, "Knowledge Article": 0.0004130656570097619, "Legal Notices": 6.15295814880239e-05, "News Article": 3.203021989376072e-05, "Organizational Announcement": 2.8284663849269883e-05, "Nonfiction Writing": 4.6443525288751836e-05, "Organizational About Page": 3.540249342745082e-05, "Personal About Page": 3.478323312467902e-05, "Personal Blog": 3.0170522625720936e-05, "Product Page": 1.8789538911598777e-05, "Spam / Ads": 3.543196342296672e-05, "Structured Data": 0.0012238824591926166, "Technical Writing": 0.005144626024318376, "Transcript / Interview": 5.569203610812287e-05, "Tutorial / How-To Guide": 0.0028844456212027514, "User Reviews": 1.5942080128035603e-05, "NONE": 0.9883020199338384}, "tertiary": {"Academic Writing": 0.0007189032133415286, "Content Listing": 3.358595843187913e-05, "Creative Writing": 4.439229505537485e-05, "Customer Support Page": 2.1294334617122317e-05, "Q&A Forum": 1.6997015129986558e-05, "Discussion Thread": 1.2351277609029739e-05, "Comment Section": 2.682878385992712e-05, "Missing Content": 0.000149551915655076, "Knowledge Article": 0.0002996480083120626, "Legal Notices": 7.046894719496092e-05, "News Article": 2.4206751318181092e-05, "Organizational Announcement": 1.927121124236269e-05, "Nonfiction Writing": 4.2285460831689634e-05, "Organizational About Page": 3.554343168784898e-05, "Personal About Page": 2.2928154244215895e-05, "Personal Blog": 2.0644787249450373e-05, "Product Page": 1.443745578174938e-05, "Spam / Ads": 2.5749675900795264e-05, "Structured Data": 0.000657844293401822, "Technical Writing": 0.002333355116383726, "Transcript / Interview": 3.5985203609233405e-05, "Tutorial / How-To Guide": 0.0013011350387316302, "User Reviews": 1.2871652074720687e-05, "NONE": 0.9940597200183355}, "structured_records": {"false": 0.9914805244423603, "true": 0.008519475557639638}, "procedural_steps": {"false": 0.938240054156852, "true": 0.06175994584314799}, "worked_solution": {"false": 0.9973765048977008, "true": 0.0026234951022991774}, "contextual_dialogue": {"false": 0.9999902814631229, "true": 9.718536877179092e-06}, "content_quality": {"-2": 0.012858335289768062, "-1": 0.07424894597243364, "0": 0.3472424572319052, "1": 0.43506636002847376, "2": 0.13058390147741936}, "noise_severity": {"0": 0.9528687082480647, "1": 0.04345706427663949, "2": 0.0027103739361282776, "3": 0.000545789150875288, "4": 0.00041806438829206213}, "missing_content_severity": {"0": 0.94379690963537, "1": 0.02783837127686147, "2": 0.023575454562510357, "3": 0.004141730765922134, "4": 0.000647533759336032}, "factual_density": {"0": 0.015009645162167799, "1": 0.03132818172549947, "2": 0.15751477583618262, "3": 0.38000270537075476, "4": 0.41614469190539544}, "pretraining_utility": {"-2": 0.007532878929230023, "-1": 0.052549920413215094, "0": 0.33582302541470144, "1": 0.4980059481792538, "2": 0.1060882270635996}, "math_density": {"0": 0.016168623949189415, "1": 0.04039254403109137, "2": 0.1565477442252932, "3": 0.3119725655000882, "4": 0.4749185222943379}, "code_density": {"0": 0.0002042907010290738, "1": 0.00040816429689059417, "2": 0.01299685031085938, "3": 0.09097234045695042, "4": 0.8954183542342705}, "reasoning_density": {"0": 0.7310896446928589, "1": 0.05188729949036769, "2": 0.09474559974577908, "3": 0.06732297256069676, "4": 0.054954483510297615}, "education_level": {"none": 0.0009788036086085825, "0": 0.001204619723704805, "1": 0.010197403174444184, "2": 0.7323329850486959, "3": 0.23143206605650404, "4": 0.023854122388042475}, "creative_writing_quality": {"not_applicable": 0.9994513955483033, "-2": 7.737204600765394e-05, "-1": 0.00014377515335363625, "0": 0.0002501904459487364, "1": 5.5985860636613727e-05, "2": 2.1280945749979215e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00018914370234362847, "Art & Design": 0.0003381890760569297, "Law & Crime": 0.0003397783178494994, "Education & Careers": 0.0003861522297099904, "Entertainment & Media": 0.00028940806878819254, "Fashion & Beauty": 0.00025063253067748214, "Business, Economics & Finance": 0.06066320364413664, "Food & Dining": 0.00019603340956527958, "Games & Gaming": 0.0005190515833032726, "Electronics & Hardware": 0.0002971180522439199, "Health, Healthcare & Wellness": 0.00026372475048907715, "History & Geography": 0.00031789653801168676, "Home, Pets & Hobbies": 0.00022000287034902558, "Industry & Production": 0.0002901215815908921, "Language, Literature & Philosophy": 0.0002062692229594842, "Politics & Government": 0.00018841636318183913, "Religion & Spirituality": 0.0002070654790871195, "Science, Mathematics & Engineering": 0.14358110151034906, "Relationships & Community": 9.747339105034599e-05, "Software Usage & Applications": 0.0008846255351589557, "Programming & Software Engineering": 0.7867464144711317, "Sports & Fitness": 0.0003352425233405744, "Vehicles & Transportation": 0.0001706765263176439, "Travel & Tourism": 0.0002517146410052028, "Other / Uncovered Subject": 0.002363862413835459, "Unknown / Insufficient Content": 0.00040668156746736037}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005601665462323769, "Art & Design": 0.0006015555685655976, "Law & Crime": 0.0008961049196497511, "Education & Careers": 0.0023290644363659465, "Entertainment & Media": 0.0005177828844446966, "Fashion & Beauty": 0.00048352346219390665, "Business, Economics & Finance": 0.14448015908880693, "Food & Dining": 0.00035207655368389273, "Games & Gaming": 0.000638627871167128, "Electronics & Hardware": 0.0005535870836055095, "Health, Healthcare & Wellness": 0.0004759456442283573, "History & Geography": 0.00047080552899497215, "Home, Pets & Hobbies": 0.00040101791603977234, "Industry & Production": 0.0004762900497566134, "Language, Literature & Philosophy": 0.0003115434918710897, "Politics & Government": 0.00033407771929997083, "Religion & Spirituality": 0.000465282538150083, "Science, Mathematics & Engineering": 0.5562315580587842, "Relationships & Community": 0.00016687430319783087, "Software Usage & Applications": 0.0008356631955720809, "Programming & Software Engineering": 0.1395253484215612, "Sports & Fitness": 0.0005392365834185253, "Vehicles & Transportation": 0.0002783502492725338, "Travel & Tourism": 0.00044033542217612184, "Other / Uncovered Subject": 0.0019012433628680765, "NONE": 0.1457337791000928}}, "probabilities": {"primary": {"Academic Writing": 0.00013961154662657516, "Content Listing": 2.979527190158765e-05, "Creative Writing": 2.3487772823712086e-05, "Customer Support Page": 1.1567643125365735e-05, "Q&A Forum": 1.8670591630757463e-05, "Discussion Thread": 1.2512913610168978e-05, "Comment Section": 1.6241422111571793e-05, "Missing Content": 3.530804708757527e-05, "Knowledge Article": 0.00016244660160310103, "Legal Notices": 3.207401924851026e-05, "News Article": 8.042613841608176e-06, "Organizational Announcement": 1.4189296314204326e-05, "Nonfiction Writing": 2.6158598319247163e-05, "Organizational About Page": 1.7406008853173178e-05, "Personal About Page": 1.854486373971108e-05, "Personal Blog": 1.0991780875203985e-05, "Product Page": 1.1211124022375304e-05, "Spam / Ads": 1.581285781229082e-05, "Structured Data": 0.0007868994699245249, "Technical Writing": 0.9943932497660489, "Transcript / Interview": 3.729966581671382e-05, "Tutorial / How-To Guide": 0.0024142079494238943, "User Reviews": 1.0059299939729595e-05, "Other / Unknown": 0.0017542108752994655}, "secondary": {"Academic Writing": 0.0012945378906565777, "Content Listing": 5.284961342336456e-05, "Creative Writing": 6.465360055696256e-05, "Customer Support Page": 3.113203788554122e-05, "Q&A Forum": 2.772148395583143e-05, "Discussion Thread": 2.0144059666263465e-05, "Comment Section": 3.361537101235995e-05, "Missing Content": 0.00011280638901287979, "Knowledge Article": 0.0004130656570097619, "Legal Notices": 6.15295814880239e-05, "News Article": 3.203021989376072e-05, "Organizational Announcement": 2.8284663849269883e-05, "Nonfiction Writing": 4.6443525288751836e-05, "Organizational About Page": 3.540249342745082e-05, "Personal About Page": 3.478323312467902e-05, "Personal Blog": 3.0170522625720936e-05, "Product Page": 1.8789538911598777e-05, "Spam / Ads": 3.543196342296672e-05, "Structured Data": 0.0012238824591926166, "Technical Writing": 0.005144626024318376, "Transcript / Interview": 5.569203610812287e-05, "Tutorial / How-To Guide": 0.0028844456212027514, "User Reviews": 1.5942080128035603e-05, "NONE": 0.9883020199338384}, "tertiary": {"Academic Writing": 0.0007189032133415287, "Content Listing": 3.3585958431879133e-05, "Creative Writing": 4.439229505537486e-05, "Customer Support Page": 2.129433461712232e-05, "Q&A Forum": 1.699701512998656e-05, "Discussion Thread": 1.235127760902974e-05, "Comment Section": 2.6828783859927123e-05, "Missing Content": 0.00014955191565507603, "Knowledge Article": 0.00029964800831206264, "Legal Notices": 7.046894719496094e-05, "News Article": 2.4206751318181095e-05, "Organizational Announcement": 1.9271211242362692e-05, "Nonfiction Writing": 4.228546083168964e-05, "Organizational About Page": 3.554343168784899e-05, "Personal About Page": 2.2928154244215898e-05, "Personal Blog": 2.0644787249450377e-05, "Product Page": 1.4437455781749381e-05, "Spam / Ads": 2.5749675900795267e-05, "Structured Data": 0.0006578442934018221, "Technical Writing": 0.0023333551163837265, "Transcript / Interview": 3.598520360923341e-05, "Tutorial / How-To Guide": 0.0013011350387316305, "User Reviews": 1.2871652074720689e-05, "NONE": 0.9940597200183356}, "structured_records": {"false": 0.9914805244423603, "true": 0.008519475557639638}, "procedural_steps": {"false": 0.938240054156852, "true": 0.06175994584314799}, "worked_solution": {"false": 0.9973765048977008, "true": 0.0026234951022991774}, "contextual_dialogue": {"false": 0.9999902814631229, "true": 9.718536877179092e-06}, "content_quality": {"-2": 0.012858335289768062, "-1": 0.07424894597243364, "0": 0.3472424572319052, "1": 0.43506636002847376, "2": 0.13058390147741936}, "noise_severity": {"0": 0.9528687082480649, "1": 0.0434570642766395, "2": 0.002710373936128278, "3": 0.0005457891508752881, "4": 0.00041806438829206224}, "missing_content_severity": {"0": 0.94379690963537, "1": 0.02783837127686147, "2": 0.023575454562510357, "3": 0.004141730765922134, "4": 0.000647533759336032}, "factual_density": {"0": 0.015009645162167799, "1": 0.03132818172549947, "2": 0.15751477583618262, "3": 0.38000270537075476, "4": 0.41614469190539544}, "pretraining_utility": {"-2": 0.007532878929230023, "-1": 0.052549920413215094, "0": 0.33582302541470144, "1": 0.4980059481792538, "2": 0.1060882270635996}, "math_density": {"0": 0.016168623949189415, "1": 0.04039254403109137, "2": 0.1565477442252932, "3": 0.3119725655000882, "4": 0.4749185222943379}, "code_density": {"0": 0.0002042907010290738, "1": 0.00040816429689059417, "2": 0.01299685031085938, "3": 0.09097234045695042, "4": 0.8954183542342705}, "reasoning_density": {"0": 0.7310896446928589, "1": 0.05188729949036769, "2": 0.09474559974577908, "3": 0.06732297256069676, "4": 0.054954483510297615}, "education_level": {"none": 0.0009788036086085825, "0": 0.001204619723704805, "1": 0.010197403174444184, "2": 0.7323329850486959, "3": 0.23143206605650404, "4": 0.023854122388042475}, "creative_writing_quality": {"not_applicable": 0.9994513955483034, "-2": 7.737204600765395e-05, "-1": 0.00014377515335363628, "0": 0.00025019044594873645, "1": 5.598586063661373e-05, "2": 2.128094574997922e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00018914370234362842, "Art & Design": 0.00033818907605692966, "Law & Crime": 0.0003397783178494993, "Education & Careers": 0.0003861522297099903, "Entertainment & Media": 0.0002894080687881925, "Fashion & Beauty": 0.0002506325306774821, "Business, Economics & Finance": 0.060663203644136625, "Food & Dining": 0.00019603340956527953, "Games & Gaming": 0.0005190515833032725, "Electronics & Hardware": 0.0002971180522439198, "Health, Healthcare & Wellness": 0.0002637247504890771, "History & Geography": 0.0003178965380116867, "Home, Pets & Hobbies": 0.00022000287034902553, "Industry & Production": 0.00029012158159089205, "Language, Literature & Philosophy": 0.00020626922295948415, "Politics & Government": 0.00018841636318183907, "Religion & Spirituality": 0.00020706547908711943, "Science, Mathematics & Engineering": 0.14358110151034903, "Relationships & Community": 9.747339105034596e-05, "Software Usage & Applications": 0.0008846255351589554, "Programming & Software Engineering": 0.7867464144711315, "Sports & Fitness": 0.00033524252334057435, "Vehicles & Transportation": 0.00017067652631764388, "Travel & Tourism": 0.00025171464100520274, "Other / Uncovered Subject": 0.0023638624138354585, "Unknown / Insufficient Content": 0.00040668156746736026}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005601665462323769, "Art & Design": 0.0006015555685655976, "Law & Crime": 0.0008961049196497511, "Education & Careers": 0.0023290644363659465, "Entertainment & Media": 0.0005177828844446966, "Fashion & Beauty": 0.00048352346219390665, "Business, Economics & Finance": 0.14448015908880693, "Food & Dining": 0.00035207655368389273, "Games & Gaming": 0.000638627871167128, "Electronics & Hardware": 0.0005535870836055095, "Health, Healthcare & Wellness": 0.0004759456442283573, "History & Geography": 0.00047080552899497215, "Home, Pets & Hobbies": 0.00040101791603977234, "Industry & Production": 0.0004762900497566134, "Language, Literature & Philosophy": 0.0003115434918710897, "Politics & Government": 0.00033407771929997083, "Religion & Spirituality": 0.000465282538150083, "Science, Mathematics & Engineering": 0.5562315580587842, "Relationships & Community": 0.00016687430319783087, "Software Usage & Applications": 0.0008356631955720809, "Programming & Software Engineering": 0.1395253484215612, "Sports & Fitness": 0.0005392365834185253, "Vehicles & Transportation": 0.0002783502492725338, "Travel & Tourism": 0.00044033542217612184, "Other / Uncovered Subject": 0.0019012433628680765, "NONE": 0.1457337791000928}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10568, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10568, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:14.626725+00:00", "scoring_key_slot": "primary", "dataset_uid": "532d3491-98f9-4f65-9592-76dec874f198", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "6d99052aa6c5c853babefcbb92b3d15f3db66b443c6506dc7c637bce0c40b5ec", "source_metadata": {"uuid": "532d3491-98f9-4f65-9592-76dec874f198", "repo_name": "jswoo/lecture", "relative_path": "w5/finterstellar/simulation.py", "category": "DATA", "algo_rel_score": 0.9436454772949219, "quality_score": 3.027341604232788, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 51, "repo_created_at": "2020-03-01T13:50:22.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 6, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "96b74493f848e9948dce1bba306df844c9962a0c", "size_bytes": 7464, "file_timestamp": 1561806391, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "fb14fb1c6a842e2ee2f79b0225ac9f4d11c3ca47"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 51, "repo_created_at": "2020-03-01T13:50:22.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 6, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "96b74493f848e9948dce1bba306df844c9962a0c", "size_bytes": 7464, "file_timestamp": 1561806391, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "fb14fb1c6a842e2ee2f79b0225ac9f4d11c3ca47", "url": "https://github.com/jswoo/lecture/blob/fb14fb1c6a842e2ee2f79b0225ac9f4d11c3ca47/w5/finterstellar/simulation.py"}, "gpt4o_tokens": 1972, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.5962685464313427, "noise_severity": 0.05218743715469017, "missing_content_severity": 0.0900046077369927, "factual_density": 3.1509446171317106, "pretraining_utility": 0.6425667240347779, "math_density": 3.1890798181592936, "code_density": 3.8809923032265425, "reasoning_density": 0.6631653507052067, "education_level": 2.266794807931497, "creative_writing_quality": -0.3645094249854119}, "score_applicability": {"education_level": 0.9990211963913914, "creative_writing_quality": 0.0005486044516966196}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/eoinlarkin/trax/blob/6cbe7c007d49cf2f4139c607e3bf2a061871ca7d/modules/gpx_helper.py", "id": "python-0-807f47e66d594580f4e1bd25e0a0b57f", "text": "from typing import Dict, Union\nfrom datetime import datetime\nimport gpxpy\nimport pandas as pd\n\n\ndef get_gpx_point_data(\n    point: gpxpy.gpx.GPXTrackPoint,\n) -> Dict[str, Union[float, datetime, int]]:\n    \"\"\"\n    Return a tuple containing some key data about `point`.\n    \"\"\"\n\n    # The XML namespaces used by the GPX file for extensions,\n    # used when parsing the extensions\n    NAMESPACES = {\n        \"garmin_tpe\": \"http://www.garmin.com/xmlschemas/TrackPointExtension/v1\"\n    }\n\n    data = {\n        \"latitude\": point.latitude,\n        \"longitude\": point.longitude,\n        \"elevation\": point.elevation,\n        \"time\": point.time,\n    }\n\n    # Parse extensions for heart rate and cadence data, if available\n    elem = point.extensions[0]  # Assuming we know there is only one extension\n    try:\n        data[\"heart_rate\"] = int(elem.find(\"garmin_tpe:hr\", NAMESPACES).text)\n    except AttributeError:\n        # \"text\" attribute not found, so data not available\n        pass\n\n    try:\n        data[\"cadence\"] = int(elem.find(\"garmin_tpe:cad\", NAMESPACES).text)\n    except AttributeError:\n        pass\n\n    return data\n\n\ndef get_dataframe_from_gpx(fname: str) -> pd.DataFrame:\n    \"\"\"\n    Takes the path to a GPX file (as a string) and returns a Pandas\n    DataFrame.\n    \"\"\"\n    # The names of the columns we will use in our DataFrame\n    COLUMN_NAMES = [\n        \"latitude\",\n        \"longitude\",\n        \"elevation\",\n        \"time\",\n        \"heart_rate\",\n        \"cadence\",\n    ]\n\n    with open(fname) as f:\n        gpx = gpxpy.parse(f)\n    segment = gpx.tracks[0].segments[\n        0\n    ]  # Assuming we know that there is only one track and one segment\n    data = [get_gpx_point_data(point) for point in segment.points]\n    return pd.DataFrame(data, columns=COLUMN_NAMES)\n\n\ndef gpx_metrics(file_name):\n    \"\"\"\n    Function to return key metrics from the gpx file\n    \"\"\"\n    import os\n\n    # gpx = gpxpy.parse(open(file_name))\n    gpx = gpxpy.parse(open(file_name))\n    track = gpx.tracks[0]\n    tot_distance = round(track.segments[0].length_3d() / 1000, 2)\n    gpx_df = get_dataframe_from_gpx(os.path.join(os.getcwd(), file_name))\n    avg_heartrate = int(gpx_df[\"heart_rate\"].mean())\n    start_time = gpx_df[\"time\"].min()\n    end_time = gpx_df[\"time\"].max()\n    max_elev = int(gpx_df[\"elevation\"].max())\n    min_elev = int(gpx_df[\"elevation\"].min())\n    return (\n        tot_distance,\n        avg_heartrate,\n        start_time,\n        end_time,\n        max_elev,\n        min_elev,\n    )\n\n\ndef gpx_download(webpath):\n    ''' Used to download the gpx files to a temporary file for plotting / reading'''\n    import urllib.request\n\n    urllib.request.urlretrieve(webpath, \"temp.gpx\")\n\n\ndef generate_plots(gpxPath, export_png=False, download_file=True):\n    import os\n    import folium\n    import gpxpy\n    import urllib.request\n    import plotly.offline as opy\n    import plotly.express as px\n\n    def heart_rate():\n        fig = px.area(\n            gpx_df,\n            x=\"time\",\n            y=\"heart_rate\",\n            color_discrete_sequence=[\"crimson\"],\n        )\n        return opy.plot(fig, auto_open=False, output_type=\"div\")\n\n    def elevation_plot():\n        fig = px.area(\n            gpx_df,\n            x=\"time\",\n            y=\"elevation\",\n            color_discrete_sequence=[\"darkorchid\"],\n        )\n        return opy.plot(fig, auto_open=False, output_type=\"div\")\n\n    if download_file:\n        urllib.request.urlretrieve(gpxPath, \"temp.gpx\")\n\n    gpx_df = get_dataframe_from_gpx(os.path.join(os.getcwd(), \"temp.gpx\"))\n    gpx_file = open(os.path.join(os.getcwd(), \"temp.gpx\"), \"r\")\n    gpx = gpxpy.parse(gpx_file)\n    points = []\n    for track in gpx.tracks:\n        for segment in track.segments:\n            for point in segment.points:\n                points.append(tuple([point.latitude, point.longitude]))\n    latitude = sum(p[0] for p in points) / len(points)\n    longitude = sum(p[1] for p in points) / len(points)\n    myMap = folium.Map(location=[latitude, longitude], tiles=\"Stamen Terrain\")\n    folium.PolyLine(points, color=\"red\", weight=2.5, opacity=1).add_to(myMap)\n\n    # getting max and min lat and longitude to optimise zoom level\n    max_lat, max_lon = max(p[0] for p in points), max(p[1] for p in points)\n    min_lat, min_lon = min(p[0] for p in points), min(p[1] for p in points)\n    myMap.fit_bounds(\n        [\n            [min_lat, min_lon],\n            [max_lat, max_lon],\n        ]\n    )\n\n    if export_png:\n        export_map_to_img(myMap)\n    else:\n        myMap = myMap._repr_html_()  # exporting for use in django\n        return myMap, elevation_plot(), heart_rate()\n\n\ndef generate_thumbnail():\n    import os\n    import plotly.offline as opy\n    import plotly.express as px\n\n    gpx_df = get_dataframe_from_gpx(os.path.join(os.getcwd(), \"temp.gpx\"))\n    fig = px.scatter(\n        gpx_df,\n        y=\"latitude\",\n        x=\"longitude\",\n        color_discrete_sequence=[\"darkcyan\"],\n    )\n    fig.update_layout(\n        xaxis=dict(showgrid=False),\n        yaxis=dict(showgrid=False),\n        paper_bgcolor=\"rgba(0,0,0,0)\",\n        plot_bgcolor=\"rgba(0,0,0,0)\",\n        margin=dict(l=0, r=0, t=0, b=0),\n    )\n    fig.update_xaxes(visible=False)\n    fig.update_yaxes(visible=False)\n    fig.write_image(\"activity_thumbnail.png\", width=300, height=300)\n", "text_preview": "from typing import Dict, Union from datetime import datetime import gpxpy import pandas as pd def get_gpx_point_data( point: gpxpy.gpx.GPXTrackPoint, ) -> Dict[str, Union[float, datetime, int]]: \"\"\" Return a tuple containing some key data a", "input_sha256": "27b97f8ca637e72a99ea1fec20a7ddd0623f9a51c2bbf1ab72f20ebfbf8fe22f", "text_sha256": "807f47e66d594580f4e1bd25e0a0b57fed0b865ea557ecd99682d39af693bedc", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12038, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 1, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.34938742855512217, "noise_severity": 0.01390097159832777, "missing_content_severity": 0.041185007531159924, "factual_density": 3.24053776313656, "pretraining_utility": 0.5856417759907475, "math_density": 1.1385029403215383, "code_density": 3.8622344981539065, "reasoning_density": 0.5532211568722021, "education_level": 2.1437033999190365, "creative_writing_quality": -0.46025294883343265}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.016237846130653488, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9955995731789038, "secondary": 0.9640250914733781, "tertiary": 0.98096893091406, "structured_records": 0.024216803032705257, "procedural_steps": 0.021159039833574048, "worked_solution": 0.0044907545444025174, "contextual_dialogue": 5.567717515274151e-06, "content_quality": 0.4244716834185689, "noise_severity": 0.9884158570013936, "missing_content_severity": 0.9656791603907, "factual_density": 0.3671148026137998, "pretraining_utility": 0.5160467928286302, "math_density": 0.3573046029989885, "code_density": 0.8851954151282554, "reasoning_density": 0.5389823692731649, "education_level": 0.7621755992138833, "creative_writing_quality": 0.9997063622609763, "primary_topic": 0.9698157883929428, "secondary_topic": 0.6960873440895893}, "probability_margin": {"primary": 0.994441721496895, "secondary": 0.9519430942122082, "tertiary": 0.9737866924709637, "education_level": 0.6466515647969073, "creative_writing_quality": 0.9996539793136007, "primary_topic": 0.9601542022060132, "secondary_topic": 0.6181492602318439}, "raw_probabilities": {"primary": {"Academic Writing": 3.115014670381204e-05, "Content Listing": 1.869173664743455e-05, "Creative Writing": 1.2033584382335796e-05, "Customer Support Page": 8.99208616653734e-06, "Q&A Forum": 1.3319398424200118e-05, "Discussion Thread": 6.919053537458098e-06, "Comment Section": 1.5419139379891562e-05, "Missing Content": 2.3775619478071757e-05, "Knowledge Article": 0.00012713756678622897, "Legal Notices": 2.452044500889118e-05, "News Article": 4.088957029806991e-06, "Organizational Announcement": 9.74750096524061e-06, "Nonfiction Writing": 1.2742238932302775e-05, "Organizational About Page": 1.2926931440133835e-05, "Personal About Page": 1.8674302209507183e-05, "Personal Blog": 7.702588040726554e-06, "Product Page": 7.496056935756841e-06, "Spam / Ads": 9.433178992650938e-06, "Structured Data": 0.001154875496786025, "Technical Writing": 0.9957829242964494, "Transcript / Interview": 1.5800472262240275e-05, "Tutorial / How-To Guide": 0.0013412027995543605, "User Reviews": 6.601382236622779e-06, "Other / Unknown": 0.0013338250216504586}, "secondary": {"Academic Writing": 0.00021954746361206712, "Content Listing": 0.00011017226917946283, "Creative Writing": 0.00013675763751964742, "Customer Support Page": 0.00012324420085356748, "Q&A Forum": 7.56922783737351e-05, "Discussion Thread": 5.4076959295063296e-05, "Comment Section": 0.00024007939698211176, "Missing Content": 0.0003517492758543093, "Knowledge Article": 0.0009191991492158874, "Legal Notices": 0.00018479218436302116, "News Article": 8.330078354098495e-05, "Organizational Announcement": 7.215289408354332e-05, "Nonfiction Writing": 9.717477551208906e-05, "Organizational About Page": 0.000103029393111609, "Personal About Page": 9.825598577883023e-05, "Personal Blog": 8.446105364502113e-05, "Product Page": 4.913178796292582e-05, "Spam / Ads": 8.335323164507633e-05, "Structured Data": 0.012818260743439518, "Technical Writing": 0.01358095178311252, "Transcript / Interview": 0.00010286185478487521, "Tutorial / How-To Guide": 0.00484364758167288, "User Reviews": 4.406132114056172e-05, "NONE": 0.9655240459953207}, "tertiary": {"Academic Writing": 9.081890488513055e-05, "Content Listing": 6.672844644157177e-05, "Creative Writing": 7.063425021150015e-05, "Customer Support Page": 5.945227028775256e-05, "Q&A Forum": 3.32397904876688e-05, "Discussion Thread": 2.4857660229248693e-05, "Comment Section": 0.00021750781416677502, "Missing Content": 0.0003646348424120655, "Knowledge Article": 0.0006273955239365323, "Legal Notices": 0.00015356773564430818, "News Article": 3.421936863967586e-05, "Organizational Announcement": 3.4952886623450656e-05, "Nonfiction Writing": 5.841623093837584e-05, "Organizational About Page": 6.778514900628385e-05, "Personal About Page": 5.0835396101779195e-05, "Personal Blog": 3.629208480839874e-05, "Product Page": 2.98353230570986e-05, "Spam / Ads": 4.240593932314766e-05, "Structured Data": 0.007975199655010486, "Technical Writing": 0.00537011271538024, "Transcript / Interview": 4.7003489217927396e-05, "Tutorial / How-To Guide": 0.002761148613093568, "User Reviews": 2.106378412283208e-05, "NONE": 0.9817618921259742}, "structured_records": {"false": 0.9757831969672948, "true": 0.024216803032705257}, "procedural_steps": {"false": 0.978840960166426, "true": 0.021159039833574048}, "worked_solution": {"false": 0.9955092454555975, "true": 0.0044907545444025174}, "contextual_dialogue": {"false": 0.9999944322824847, "true": 5.567717515274151e-06}, "content_quality": {"-2": 0.014991101460232121, "-1": 0.14064107275083335, "0": 0.39619900699976907, "1": 0.37632693335191103, "2": 0.07184188543725434}, "noise_severity": {"0": 0.9882943894304301, "1": 0.010172748429480863, "2": 0.0010530938163633684, "3": 0.0002970377587818164, "4": 0.00018273056494367968}, "missing_content_severity": {"0": 0.9697044272602173, "1": 0.021388081442440397, "2": 0.007251807889610297, "3": 0.0013294233214294435, "4": 0.00032626008630265256}, "factual_density": {"0": 0.00816339855978894, "1": 0.027252881750009707, "2": 0.14215404064295573, "3": 0.36074191608834383, "4": 0.46168776295890185}, "pretraining_utility": {"-2": 0.0071373450811609875, "-1": 0.06124304547800456, "0": 0.3536529101079561, "1": 0.49477388703468284, "2": 0.08319281229819561}, "math_density": {"0": 0.3163657680398378, "1": 0.40796252252926557, "2": 0.16300823202270445, "3": 0.04612995588590482, "4": 0.06653352152228732}, "code_density": {"0": 0.00023298184385135137, "1": 0.0004600273918740019, "2": 0.018522713791834827, "3": 0.0984080647113965, "4": 0.8823762122610433}, "reasoning_density": {"0": 0.7171513921541112, "1": 0.09889456887896098, "2": 0.11990163978647571, "3": 0.04168628830151882, "4": 0.0223661108789333}, "education_level": {"none": 0.0009571085835606046, "0": 0.0015606446083743875, "1": 0.02982997033336435, "2": 0.8018129993449028, "3": 0.1551614345479954, "4": 0.010677842581802448}, "creative_writing_quality": {"not_applicable": 0.999755301884147, "-2": 4.85827732185971e-05, "-1": 6.006359068012604e-05, "0": 0.0001013225705463369, "1": 2.4852255094217592e-05, "2": 9.876926313856388e-06}, "primary_topic": {"Adult & Explicit Sexual Content": 7.440661598121458e-05, "Art & Design": 0.00023868329145593125, "Law & Crime": 9.611472404800721e-05, "Education & Careers": 7.573210223390296e-05, "Entertainment & Media": 7.20034237772723e-05, "Fashion & Beauty": 9.035560101234637e-05, "Business, Economics & Finance": 0.00016918966575234625, "Food & Dining": 8.55518180243516e-05, "Games & Gaming": 0.0001405127868099213, "Electronics & Hardware": 0.0009702435149518632, "Health, Healthcare & Wellness": 0.0007316113827452478, "History & Geography": 0.0014260452400639233, "Home, Pets & Hobbies": 0.0002569676027334313, "Industry & Production": 7.641719137845185e-05, "Language, Literature & Philosophy": 7.406403318498886e-05, "Politics & Government": 4.193048947382263e-05, "Religion & Spirituality": 7.294561464481725e-05, "Science, Mathematics & Engineering": 0.008186172695159116, "Relationships & Community": 4.867782987563417e-05, "Software Usage & Applications": 0.0012148417868425689, "Programming & Software Engineering": 0.9709767196085989, "Sports & Fitness": 0.010822517402585614, "Vehicles & Transportation": 0.0006640472479700328, "Travel & Tourism": 0.0014883977829152032, "Other / Uncovered Subject": 0.0017962238529039736, "Unknown / Insufficient Content": 0.00010962669487710178}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0014623600721827999, "Art & Design": 0.007721924138099043, "Law & Crime": 0.0009249352588381163, "Education & Careers": 0.000773236601720741, "Entertainment & Media": 0.0009724302221825168, "Fashion & Beauty": 0.0006059952708237749, "Business, Economics & Finance": 0.001615668384522803, "Food & Dining": 0.0005588512279702286, "Games & Gaming": 0.0007258210871954764, "Electronics & Hardware": 0.0038449267670155984, "Health, Healthcare & Wellness": 0.02403855249075244, "History & Geography": 0.01417207183988059, "Home, Pets & Hobbies": 0.0011222659096505453, "Industry & Production": 0.00044639461652837503, "Language, Literature & Philosophy": 0.0004117257689635005, "Politics & Government": 0.0005224015732121168, "Religion & Spirituality": 0.0007262458742664394, "Science, Mathematics & Engineering": 0.08374003800321889, "Relationships & Community": 0.0003312573924906429, "Software Usage & Applications": 0.005430156580537924, "Programming & Software Engineering": 0.037954888925367844, "Sports & Fitness": 0.08962703216199197, "Vehicles & Transportation": 0.0016775461196637605, "Travel & Tourism": 0.009180236273052863, "Other / Uncovered Subject": 0.003636745046035195, "NONE": 0.7077762923938359}}, "probabilities": {"primary": {"Academic Writing": 3.115014670381203e-05, "Content Listing": 1.8691736647434547e-05, "Creative Writing": 1.2033584382335792e-05, "Customer Support Page": 8.992086166537338e-06, "Q&A Forum": 1.3319398424200115e-05, "Discussion Thread": 6.9190535374580965e-06, "Comment Section": 1.541913937989156e-05, "Missing Content": 2.377561947807175e-05, "Knowledge Article": 0.00012713756678622894, "Legal Notices": 2.4520445008891172e-05, "News Article": 4.08895702980699e-06, "Organizational Announcement": 9.747500965240608e-06, "Nonfiction Writing": 1.2742238932302772e-05, "Organizational About Page": 1.2926931440133832e-05, "Personal About Page": 1.867430220950718e-05, "Personal Blog": 7.702588040726552e-06, "Product Page": 7.4960569357568395e-06, "Spam / Ads": 9.433178992650936e-06, "Structured Data": 0.0011548754967860247, "Technical Writing": 0.9957829242964492, "Transcript / Interview": 1.580047226224027e-05, "Tutorial / How-To Guide": 0.0013412027995543603, "User Reviews": 6.601382236622777e-06, "Other / Unknown": 0.0013338250216504584}, "secondary": {"Academic Writing": 0.00021954746361206712, "Content Listing": 0.00011017226917946283, "Creative Writing": 0.00013675763751964742, "Customer Support Page": 0.00012324420085356748, "Q&A Forum": 7.56922783737351e-05, "Discussion Thread": 5.4076959295063296e-05, "Comment Section": 0.00024007939698211176, "Missing Content": 0.0003517492758543093, "Knowledge Article": 0.0009191991492158874, "Legal Notices": 0.00018479218436302116, "News Article": 8.330078354098495e-05, "Organizational Announcement": 7.215289408354332e-05, "Nonfiction Writing": 9.717477551208906e-05, "Organizational About Page": 0.000103029393111609, "Personal About Page": 9.825598577883023e-05, "Personal Blog": 8.446105364502113e-05, "Product Page": 4.913178796292582e-05, "Spam / Ads": 8.335323164507633e-05, "Structured Data": 0.012818260743439518, "Technical Writing": 0.01358095178311252, "Transcript / Interview": 0.00010286185478487521, "Tutorial / How-To Guide": 0.00484364758167288, "User Reviews": 4.406132114056172e-05, "NONE": 0.9655240459953207}, "tertiary": {"Academic Writing": 9.081890488513055e-05, "Content Listing": 6.672844644157177e-05, "Creative Writing": 7.063425021150015e-05, "Customer Support Page": 5.945227028775256e-05, "Q&A Forum": 3.32397904876688e-05, "Discussion Thread": 2.4857660229248693e-05, "Comment Section": 0.00021750781416677502, "Missing Content": 0.0003646348424120655, "Knowledge Article": 0.0006273955239365323, "Legal Notices": 0.00015356773564430818, "News Article": 3.421936863967586e-05, "Organizational Announcement": 3.4952886623450656e-05, "Nonfiction Writing": 5.841623093837584e-05, "Organizational About Page": 6.778514900628385e-05, "Personal About Page": 5.0835396101779195e-05, "Personal Blog": 3.629208480839874e-05, "Product Page": 2.98353230570986e-05, "Spam / Ads": 4.240593932314766e-05, "Structured Data": 0.007975199655010486, "Technical Writing": 0.00537011271538024, "Transcript / Interview": 4.7003489217927396e-05, "Tutorial / How-To Guide": 0.002761148613093568, "User Reviews": 2.106378412283208e-05, "NONE": 0.9817618921259742}, "structured_records": {"false": 0.9757831969672948, "true": 0.024216803032705257}, "procedural_steps": {"false": 0.978840960166426, "true": 0.021159039833574048}, "worked_solution": {"false": 0.9955092454555975, "true": 0.0044907545444025174}, "contextual_dialogue": {"false": 0.9999944322824847, "true": 5.567717515274151e-06}, "content_quality": {"-2": 0.014991101460232123, "-1": 0.14064107275083337, "0": 0.3961990069997691, "1": 0.3763269333519111, "2": 0.07184188543725435}, "noise_severity": {"0": 0.9882943894304302, "1": 0.010172748429480865, "2": 0.0010530938163633686, "3": 0.00029703775878181645, "4": 0.0001827305649436797}, "missing_content_severity": {"0": 0.9697044272602171, "1": 0.021388081442440393, "2": 0.007251807889610295, "3": 0.0013294233214294433, "4": 0.0003262600863026525}, "factual_density": {"0": 0.00816339855978894, "1": 0.027252881750009707, "2": 0.14215404064295573, "3": 0.36074191608834383, "4": 0.46168776295890185}, "pretraining_utility": {"-2": 0.0071373450811609875, "-1": 0.06124304547800456, "0": 0.3536529101079561, "1": 0.49477388703468284, "2": 0.08319281229819561}, "math_density": {"0": 0.3163657680398378, "1": 0.40796252252926557, "2": 0.16300823202270445, "3": 0.04612995588590482, "4": 0.06653352152228732}, "code_density": {"0": 0.00023298184385135137, "1": 0.0004600273918740019, "2": 0.018522713791834827, "3": 0.0984080647113965, "4": 0.8823762122610433}, "reasoning_density": {"0": 0.7171513921541112, "1": 0.09889456887896098, "2": 0.11990163978647571, "3": 0.04168628830151882, "4": 0.0223661108789333}, "education_level": {"none": 0.0009571085835606046, "0": 0.0015606446083743875, "1": 0.02982997033336435, "2": 0.8018129993449028, "3": 0.1551614345479954, "4": 0.010677842581802448}, "creative_writing_quality": {"not_applicable": 0.999755301884147, "-2": 4.85827732185971e-05, "-1": 6.006359068012604e-05, "0": 0.0001013225705463369, "1": 2.4852255094217592e-05, "2": 9.876926313856388e-06}, "primary_topic": {"Adult & Explicit Sexual Content": 7.440661598121458e-05, "Art & Design": 0.00023868329145593125, "Law & Crime": 9.611472404800721e-05, "Education & Careers": 7.573210223390296e-05, "Entertainment & Media": 7.20034237772723e-05, "Fashion & Beauty": 9.035560101234637e-05, "Business, Economics & Finance": 0.00016918966575234625, "Food & Dining": 8.55518180243516e-05, "Games & Gaming": 0.0001405127868099213, "Electronics & Hardware": 0.0009702435149518632, "Health, Healthcare & Wellness": 0.0007316113827452478, "History & Geography": 0.0014260452400639233, "Home, Pets & Hobbies": 0.0002569676027334313, "Industry & Production": 7.641719137845185e-05, "Language, Literature & Philosophy": 7.406403318498886e-05, "Politics & Government": 4.193048947382263e-05, "Religion & Spirituality": 7.294561464481725e-05, "Science, Mathematics & Engineering": 0.008186172695159116, "Relationships & Community": 4.867782987563417e-05, "Software Usage & Applications": 0.0012148417868425689, "Programming & Software Engineering": 0.9709767196085989, "Sports & Fitness": 0.010822517402585614, "Vehicles & Transportation": 0.0006640472479700328, "Travel & Tourism": 0.0014883977829152032, "Other / Uncovered Subject": 0.0017962238529039736, "Unknown / Insufficient Content": 0.00010962669487710178}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0014623600721827999, "Art & Design": 0.007721924138099043, "Law & Crime": 0.0009249352588381163, "Education & Careers": 0.000773236601720741, "Entertainment & Media": 0.0009724302221825168, "Fashion & Beauty": 0.0006059952708237749, "Business, Economics & Finance": 0.001615668384522803, "Food & Dining": 0.0005588512279702286, "Games & Gaming": 0.0007258210871954764, "Electronics & Hardware": 0.0038449267670155984, "Health, Healthcare & Wellness": 0.02403855249075244, "History & Geography": 0.01417207183988059, "Home, Pets & Hobbies": 0.0011222659096505453, "Industry & Production": 0.00044639461652837503, "Language, Literature & Philosophy": 0.0004117257689635005, "Politics & Government": 0.0005224015732121168, "Religion & Spirituality": 0.0007262458742664394, "Science, Mathematics & Engineering": 0.08374003800321889, "Relationships & Community": 0.0003312573924906429, "Software Usage & Applications": 0.005430156580537924, "Programming & Software Engineering": 0.037954888925367844, "Sports & Fitness": 0.08962703216199197, "Vehicles & Transportation": 0.0016775461196637605, "Travel & Tourism": 0.009180236273052863, "Other / Uncovered Subject": 0.003636745046035195, "NONE": 0.7077762923938359}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10005, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10005, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:15.167038+00:00", "scoring_key_slot": "primary", "dataset_uid": "2a7c41d3-870e-4ceb-acb9-70ac710e546b", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "9e17f0292d572cf5a794524684e9ddca57128be244f840fba693c8f55e9424b5", "source_metadata": {"uuid": "2a7c41d3-870e-4ceb-acb9-70ac710e546b", "repo_name": "eoinlarkin/trax", "relative_path": "modules/gpx_helper.py", "category": "DATA", "algo_rel_score": 0.9309900999069214, "quality_score": 5.604114532470703, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 151, "repo_created_at": "2022-05-25T21:20:34.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 1, "pull_requests": 1}, "file_meta": {"content_id": "c704d601ffc3ce7e532f33ff30fe71eda74d0853", "size_bytes": 5294, "file_timestamp": 1666813230, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "6cbe7c007d49cf2f4139c607e3bf2a061871ca7d"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 151, "repo_created_at": "2022-05-25T21:20:34.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 1, "pull_requests": 1}, "file_meta": {"content_id": "c704d601ffc3ce7e532f33ff30fe71eda74d0853", "size_bytes": 5294, "file_timestamp": 1666813230, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "6cbe7c007d49cf2f4139c607e3bf2a061871ca7d", "url": "https://github.com/eoinlarkin/trax/blob/6cbe7c007d49cf2f4139c607e3bf2a061871ca7d/modules/gpx_helper.py"}, "gpt4o_tokens": 1392, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.34938742855512217, "noise_severity": 0.01390097159832777, "missing_content_severity": 0.04118500753115993, "factual_density": 3.24053776313656, "pretraining_utility": 0.5856417759907475, "math_density": 1.1385029403215383, "code_density": 3.8622344981539065, "reasoning_density": 0.5532211568722021, "education_level": 2.1437033999190365, "creative_writing_quality": -0.46025294883343265}, "score_applicability": {"education_level": 0.9990428914164394, "creative_writing_quality": 0.000244698115853134}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/home-assistant/core/blob/b33a556ca5699bdb5b9221558c689f7d9e934ab3/tests/components/hdmi_cec/test_switch.py", "id": "python-0-cba6887b3d8571f4b3340ce34c399475", "text": "\"\"\"Tests for the HDMI-CEC switch platform.\"\"\"\n\nfrom pycec.const import POWER_OFF, POWER_ON, STATUS_PLAY, STATUS_STILL, STATUS_STOP\nfrom pycec.network import PhysicalAddress\nimport pytest\n\nfrom homeassistant.components.hdmi_cec import EVENT_HDMI_CEC_UNAVAILABLE\nfrom homeassistant.components.switch import DOMAIN as SWITCH_DOMAIN\nfrom homeassistant.const import (\n    ATTR_ENTITY_ID,\n    SERVICE_TURN_OFF,\n    SERVICE_TURN_ON,\n    STATE_OFF,\n    STATE_ON,\n    STATE_UNAVAILABLE,\n)\nfrom homeassistant.core import HomeAssistant\n\nfrom . import MockHDMIDevice\nfrom .conftest import CecEntityCreator, HDMINetworkCreator\n\n\n@pytest.mark.parametrize(\"config\", [{}, {\"platform\": \"switch\"}])\nasync def test_load_platform(\n    hass: HomeAssistant,\n    create_hdmi_network: HDMINetworkCreator,\n    create_cec_entity: CecEntityCreator,\n    config,\n) -> None:\n    \"\"\"Test that switch entity is loaded.\"\"\"\n    hdmi_network = await create_hdmi_network(config=config)\n    mock_hdmi_device = MockHDMIDevice(logical_address=3)\n    await create_cec_entity(hdmi_network, mock_hdmi_device)\n    mock_hdmi_device.set_update_callback.assert_called_once()\n    state = hass.states.get(\"media_player.hdmi_3\")\n    assert state is None\n\n    state = hass.states.get(\"switch.hdmi_3\")\n    assert state is not None\n\n\nasync def test_load_types(\n    hass: HomeAssistant,\n    create_hdmi_network: HDMINetworkCreator,\n    create_cec_entity: CecEntityCreator,\n) -> None:\n    \"\"\"Test that switch entity is loaded when types is set.\"\"\"\n    config = {\"platform\": \"media_player\", \"types\": {\"hdmi_cec.hdmi_3\": \"switch\"}}\n    hdmi_network = await create_hdmi_network(config=config)\n    mock_hdmi_device = MockHDMIDevice(logical_address=3)\n    await create_cec_entity(hdmi_network, mock_hdmi_device)\n    mock_hdmi_device.set_update_callback.assert_called_once()\n    state = hass.states.get(\"media_player.hdmi_3\")\n    assert state is None\n\n    state = hass.states.get(\"switch.hdmi_3\")\n    assert state is not None\n\n    mock_hdmi_device = MockHDMIDevice(logical_address=4)\n    await create_cec_entity(hdmi_network, mock_hdmi_device)\n    mock_hdmi_device.set_update_callback.assert_called_once()\n    state = hass.states.get(\"media_player.hdmi_4\")\n    assert state is not None\n\n    state = hass.states.get(\"switch.hdmi_4\")\n    assert state is None\n\n\nasync def test_service_on(\n    hass: HomeAssistant,\n    create_hdmi_network: HDMINetworkCreator,\n    create_cec_entity: CecEntityCreator,\n) -> None:\n    \"\"\"Test that switch triggers on `on` service.\"\"\"\n    hdmi_network = await create_hdmi_network()\n    mock_hdmi_device = MockHDMIDevice(logical_address=3, power_status=3)\n    await create_cec_entity(hdmi_network, mock_hdmi_device)\n    state = hass.states.get(\"switch.hdmi_3\")\n    assert state.state != STATE_ON\n\n    await hass.services.async_call(\n        SWITCH_DOMAIN, SERVICE_TURN_ON, {ATTR_ENTITY_ID: \"switch.hdmi_3\"}, blocking=True\n    )\n\n    mock_hdmi_device.turn_on.assert_called_once_with()\n\n    state = hass.states.get(\"switch.hdmi_3\")\n    assert state.state == STATE_ON\n\n\nasync def test_service_off(\n    hass: HomeAssistant,\n    create_hdmi_network: HDMINetworkCreator,\n    create_cec_entity: CecEntityCreator,\n) -> None:\n    \"\"\"Test that switch triggers on `off` service.\"\"\"\n    hdmi_network = await create_hdmi_network()\n    mock_hdmi_device = MockHDMIDevice(logical_address=3, power_status=4)\n    await create_cec_entity(hdmi_network, mock_hdmi_device)\n    state = hass.states.get(\"switch.hdmi_3\")\n    assert state.state != STATE_OFF\n\n    await hass.services.async_call(\n        SWITCH_DOMAIN,\n        SERVICE_TURN_OFF,\n        {ATTR_ENTITY_ID: \"switch.hdmi_3\"},\n        blocking=True,\n    )\n\n    mock_hdmi_device.turn_off.assert_called_once_with()\n\n    state = hass.states.get(\"switch.hdmi_3\")\n    assert state.state == STATE_OFF\n\n\n@pytest.mark.parametrize(\n    (\"power_status\", \"expected_state\"),\n    [(3, STATE_OFF), (POWER_OFF, STATE_OFF), (4, STATE_ON), (POWER_ON, STATE_ON)],\n)\n@pytest.mark.parametrize(\n    \"status\",\n    [\n        None,\n        STATUS_PLAY,\n        STATUS_STOP,\n        STATUS_STILL,\n    ],\n)\nasync def test_device_status_change(\n    hass: HomeAssistant,\n    create_hdmi_network: HDMINetworkCreator,\n    create_cec_entity: CecEntityCreator,\n    power_status,\n    expected_state,\n    status,\n) -> None:\n    \"\"\"Test state change on device status change.\"\"\"\n    hdmi_network = await create_hdmi_network()\n    mock_hdmi_device = MockHDMIDevice(logical_address=3, status=status)\n    await create_cec_entity(hdmi_network, mock_hdmi_device)\n\n    mock_hdmi_device.power_status = power_status\n    await hass.async_block_till_done()\n\n    state = hass.states.get(\"switch.hdmi_3\")\n    if power_status in (POWER_ON, 4) and status is not None:\n        pytest.xfail(\n            reason=\"`CecSwitchEntity.is_on` returns `False` here instead of `true` as expected.\"\n        )\n    assert state.state == expected_state\n\n\n@pytest.mark.parametrize(\n    (\"device_values\", \"expected\"),\n    [\n        ({\"osd_name\": \"Switch\", \"vendor\": \"Nintendo\"}, \"Nintendo Switch\"),\n        ({\"type_name\": \"TV\"}, \"TV 3\"),\n        ({\"type_name\": \"Playback\", \"osd_name\": \"Switch\"}, \"Playback 3 (Switch)\"),\n        ({\"type_name\": \"TV\", \"vendor\": \"Samsung\"}, \"TV 3\"),\n        (\n            {\"type_name\": \"Playback\", \"osd_name\": \"Super PC\", \"vendor\": \"Unknown\"},\n            \"Playback 3 (Super PC)\",\n        ),\n    ],\n)\nasync def test_friendly_name(\n    hass: HomeAssistant,\n    create_hdmi_network: HDMINetworkCreator,\n    create_cec_entity: CecEntityCreator,\n    device_values,\n    expected,\n) -> None:\n    \"\"\"Test friendly name setup.\"\"\"\n    hdmi_network = await create_hdmi_network()\n    mock_hdmi_device = MockHDMIDevice(logical_address=3, **device_values)\n    await create_cec_entity(hdmi_network, mock_hdmi_device)\n\n    state = hass.states.get(\"switch.hdmi_3\")\n    assert state.attributes[\"friendly_name\"] == expected\n\n\n@pytest.mark.parametrize(\n    (\"device_values\", \"expected_attributes\"),\n    [\n        (\n            {\"physical_address\": PhysicalAddress(\"3.0.0.0\")},\n            {\"physical_address\": \"3.0.0.0\"},\n        ),\n        pytest.param(\n            {},\n            {},\n            marks=pytest.mark.xfail(\n                reason=\"physical address logic returns a string 'None' instead of not being set.\"\n            ),\n        ),\n        (\n            {\"physical_address\": PhysicalAddress(\"3.0.0.0\"), \"vendor_id\": 5},\n            {\"physical_address\": \"3.0.0.0\", \"vendor_id\": 5, \"vendor_name\": None},\n        ),\n        (\n            {\n                \"physical_address\": PhysicalAddress(\"3.0.0.0\"),\n                \"vendor_id\": 5,\n                \"vendor\": \"Samsung\",\n            },\n            {\"physical_address\": \"3.0.0.0\", \"vendor_id\": 5, \"vendor_name\": \"Samsung\"},\n        ),\n        (\n            {\"physical_address\": PhysicalAddress(\"3.0.0.0\"), \"type\": 1},\n            {\"physical_address\": \"3.0.0.0\", \"type_id\": 1, \"type\": None},\n        ),\n        (\n            {\n                \"physical_address\": PhysicalAddress(\"3.0.0.0\"),\n                \"type\": 1,\n                \"type_name\": \"TV\",\n            },\n            {\"physical_address\": \"3.0.0.0\", \"type_id\": 1, \"type\": \"TV\"},\n        ),\n    ],\n)\nasync def test_extra_state_attributes(\n    hass: HomeAssistant,\n    create_hdmi_network: HDMINetworkCreator,\n    create_cec_entity: CecEntityCreator,\n    device_values,\n    expected_attributes,\n) -> None:\n    \"\"\"Test extra state attributes.\"\"\"\n    hdmi_network = await create_hdmi_network()\n    mock_hdmi_device = MockHDMIDevice(logical_address=3, **device_values)\n    await create_cec_entity(hdmi_network, mock_hdmi_device)\n\n    state = hass.states.get(\"switch.hdmi_3\")\n    attributes = state.attributes\n    # We don't care about these attributes, so just copy them to the expected attributes\n    for att in (\"friendly_name\", \"icon\"):\n        expected_attributes[att] = attributes[att]\n    assert attributes == expected_attributes\n\n\n@pytest.mark.parametrize(\n    (\"device_type\", \"expected_icon\"),\n    [\n        (None, \"mdi:help\"),\n        (0, \"mdi:television\"),\n        (1, \"mdi:microphone\"),\n        (2, \"mdi:help\"),\n        (3, \"mdi:radio\"),\n        (4, \"mdi:play\"),\n        (5, \"mdi:speaker\"),\n    ],\n)\nasync def test_icon(\n    hass: HomeAssistant,\n    create_hdmi_network: HDMINetworkCreator,\n    create_cec_entity: CecEntityCreator,\n    device_type,\n    expected_icon,\n) -> None:\n    \"\"\"Test icon selection.\"\"\"\n    hdmi_network = await create_hdmi_network()\n    mock_hdmi_device = MockHDMIDevice(logical_address=3, type=device_type)\n    await create_cec_entity(hdmi_network, mock_hdmi_device)\n\n    state = hass.states.get(\"switch.hdmi_3\")\n    assert state.attributes[\"icon\"] == expected_icon\n\n\nasync def test_unavailable_status(\n    hass: HomeAssistant,\n    create_hdmi_network: HDMINetworkCreator,\n    create_cec_entity: CecEntityCreator,\n) -> None:\n    \"\"\"Test entity goes into unavailable status when expected.\"\"\"\n    hdmi_network = await create_hdmi_network()\n    mock_hdmi_device = MockHDMIDevice(logical_address=3)\n    await create_cec_entity(hdmi_network, mock_hdmi_device)\n\n    hass.bus.async_fire(EVENT_HDMI_CEC_UNAVAILABLE)\n    await hass.async_block_till_done()\n\n    state = hass.states.get(\"switch.hdmi_3\")\n    assert state.state == STATE_UNAVAILABLE\n", "text_preview": "\"\"\"Tests for the HDMI-CEC switch platform.\"\"\" from pycec.const import POWER_OFF, POWER_ON, STATUS_PLAY, STATUS_STILL, STATUS_STOP from pycec.network import PhysicalAddress import pytest from homeassistant.components.hdmi_cec import EVENT_HD", "input_sha256": "8e860e8719453f615e26051dcbb074ae422b844f6ca306ced23efc99f2fbadaa", "text_sha256": "cba6887b3d8571f4b3340ce34c3994754aae88ad113ed6e04192d92e0c359784", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12042, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.399376587435131, "noise_severity": 0.015462249983471447, "missing_content_severity": 0.004276140092391229, "factual_density": 3.3657686769317694, "pretraining_utility": 0.5518572810669633, "math_density": 0.0461782363117765, "code_density": 3.9428126154370053, "reasoning_density": 1.5336412798374484, "education_level": 2.679212400606865, "creative_writing_quality": -0.11139136150670426}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.029107318062408516, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9897891238754287, "secondary": 0.9389986401698907, "tertiary": 0.9693714938002302, "structured_records": 0.47756748339991556, "procedural_steps": 0.019802292919459587, "worked_solution": 0.23754173240828572, "contextual_dialogue": 5.168410194489732e-06, "content_quality": 0.5588320496893345, "noise_severity": 0.9871147916804405, "missing_content_severity": 0.9964365499230073, "factual_density": 0.47147389744314105, "pretraining_utility": 0.4658902065074243, "math_density": 0.9615181364068529, "code_density": 0.9523438461975045, "reasoning_density": 0.0, "education_level": 0.3232340191763335, "creative_writing_quality": 0.9991052900285426, "primary_topic": 0.9730158305821948, "secondary_topic": 0.5848266921640592}, "probability_margin": {"primary": 0.982467580630991, "secondary": 0.9129366971482401, "tertiary": 0.9566269937039176, "education_level": 0.002203712001095537, "creative_writing_quality": 0.9988219346136789, "primary_topic": 0.9560773728029826, "secondary_topic": 0.37524420383398177}, "raw_probabilities": {"primary": {"Academic Writing": 8.581965868738999e-05, "Content Listing": 2.2325043649418017e-05, "Creative Writing": 2.0196164158656484e-05, "Customer Support Page": 1.446299321919647e-05, "Q&A Forum": 1.295919702570688e-05, "Discussion Thread": 7.418938263149697e-06, "Comment Section": 1.4150139596307424e-05, "Missing Content": 2.638994536449688e-05, "Knowledge Article": 7.211706883412665e-05, "Legal Notices": 3.0151545364684625e-05, "News Article": 5.865511583446492e-06, "Organizational Announcement": 1.6140721645278664e-05, "Nonfiction Writing": 1.679037080362432e-05, "Organizational About Page": 1.298137540621415e-05, "Personal About Page": 9.223218358181035e-06, "Personal Blog": 6.231686922495484e-06, "Product Page": 8.219870989387778e-06, "Spam / Ads": 8.599916362640779e-06, "Structured Data": 0.0014118019321704756, "Technical Writing": 0.9902145770472859, "Transcript / Interview": 2.647859513734827e-05, "Tutorial / How-To Guide": 0.00020260771549525651, "User Reviews": 7.494927381854757e-06, "Other / Unknown": 0.007746996416294865}, "secondary": {"Academic Writing": 0.00047739767896532446, "Content Listing": 0.00020164133108255185, "Creative Writing": 0.0002448195215111055, "Customer Support Page": 0.00014655204389302183, "Q&A Forum": 0.00010911043236737422, "Discussion Thread": 7.937184509938958e-05, "Comment Section": 0.00014316863803860304, "Missing Content": 0.0004007027824645028, "Knowledge Article": 0.000649084043229583, "Legal Notices": 0.0001900484043625961, "News Article": 0.000125033527229869, "Organizational Announcement": 0.00012647125431388046, "Nonfiction Writing": 0.0001267535002448671, "Organizational About Page": 0.00011995705489747066, "Personal About Page": 9.913621205886554e-05, "Personal Blog": 9.472322511026263e-05, "Product Page": 7.005402524800551e-05, "Spam / Ads": 0.00010488980697637119, "Structured Data": 0.02551244736102366, "Technical Writing": 0.028603666347905173, "Transcript / Interview": 0.00015664631185329523, "Tutorial / How-To Guide": 0.0006220112847427508, "User Reviews": 5.594987123635424e-05, "NONE": 0.9415403634961452}, "tertiary": {"Academic Writing": 0.00018884970004717937, "Content Listing": 0.00010707336876840242, "Creative Writing": 0.00011662804098227119, "Customer Support Page": 7.343306005921429e-05, "Q&A Forum": 4.4870924117895057e-05, "Discussion Thread": 3.692838802459654e-05, "Comment Section": 9.790248184111608e-05, "Missing Content": 0.0003047846216454606, "Knowledge Article": 0.0006753486985414674, "Legal Notices": 0.00020823769467235255, "News Article": 5.498497541952199e-05, "Organizational Announcement": 6.004587831748177e-05, "Nonfiction Writing": 7.188990968612202e-05, "Organizational About Page": 7.094342332036618e-05, "Personal About Page": 4.350441195245618e-05, "Personal Blog": 3.929051877689737e-05, "Product Page": 3.647103702018091e-05, "Spam / Ads": 4.8513844542770523e-05, "Structured Data": 0.014020687854636428, "Technical Writing": 0.012496282184686212, "Transcript / Interview": 6.456814606922407e-05, "Tutorial / How-To Guide": 0.0004631734011603866, "User Reviews": 2.790587715814435e-05, "NONE": 0.970647681558554}, "structured_records": {"false": 0.5224325166000845, "true": 0.47756748339991556}, "procedural_steps": {"false": 0.9801977070805404, "true": 0.019802292919459587}, "worked_solution": {"false": 0.7624582675917143, "true": 0.23754173240828572}, "contextual_dialogue": {"false": 0.9999948315898055, "true": 5.168410194489732e-06}, "content_quality": {"-2": 0.0019117631636820442, "-1": 0.0039928879165896545, "0": 0.05129141114460836, "1": 0.4784148738711553, "2": 0.4643890639039647}, "noise_severity": {"0": 0.9863356860889609, "1": 0.012501479429932006, "2": 0.0007105918048549613, "3": 0.00026938376117879804, "4": 0.00018285891507328135}, "missing_content_severity": {"0": 0.9968849709599533, "1": 0.0023612494327831805, "2": 0.00043575892779135303, "3": 0.00022870991386399227, "4": 8.931076560834145e-05}, "factual_density": {"0": 0.004045656291030795, "1": 0.009116479440896443, "2": 0.09659470804286054, "3": 0.3975098434956971, "4": 0.49273331272951515}, "pretraining_utility": {"-2": 0.0077246590003341485, "-1": 0.08629269056757798, "0": 0.3487778774259053, "1": 0.4608102563771554, "2": 0.09639451662902708}, "math_density": {"0": 0.9691915558160297, "1": 0.022885400620312926, "2": 0.0036127626589764596, "3": 0.0011738132452138767, "4": 0.0031364676594672577}, "code_density": {"0": 9.498445450170163e-05, "1": 0.00016752814499938014, "2": 0.004033564494250825, "3": 0.04823773332148805, "4": 0.9474661895847599}, "reasoning_density": {"0": 0.29747711444423613, "1": 0.22781427036238439, "2": 0.22211057740015208, "3": 0.14878629649814984, "4": 0.10381174129507759}, "education_level": {"none": 0.0008487690850490947, "0": 0.0011146309245929978, "1": 0.0038434693579465144, "2": 0.4338246373125157, "3": 0.43602834931361123, "4": 0.12434014400628456}, "creative_writing_quality": {"not_applicable": 0.9992544083571189, "-2": 7.346582648327604e-05, "-1": 0.00010553740450462769, "0": 0.00043247374344004765, "1": 9.881274766356537e-05, "2": 3.530192078953671e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.41765213161495e-05, "Art & Design": 5.4523140737676985e-05, "Law & Crime": 3.82145398251193e-05, "Education & Careers": 2.994580358550392e-05, "Entertainment & Media": 0.00012382077934572203, "Fashion & Beauty": 4.234484466818538e-05, "Business, Economics & Finance": 3.650614883728939e-05, "Food & Dining": 6.898535818862646e-05, "Games & Gaming": 7.1619336437358e-05, "Electronics & Hardware": 0.017976310449127803, "Health, Healthcare & Wellness": 5.247921594388917e-05, "History & Geography": 4.07043320224356e-05, "Home, Pets & Hobbies": 0.005614442543624767, "Industry & Production": 3.964886125628887e-05, "Language, Literature & Philosophy": 2.9465697944862322e-05, "Politics & Government": 2.1211549331652748e-05, "Religion & Spirituality": 3.2270652336728303e-05, "Science, Mathematics & Engineering": 0.00035791130232395876, "Relationships & Community": 1.7537687908286614e-05, "Software Usage & Applications": 0.0008784056743213565, "Programming & Software Engineering": 0.9740536832521104, "Sports & Fitness": 5.0842194330998185e-05, "Vehicles & Transportation": 7.166490804487483e-05, "Travel & Tourism": 3.31627450028318e-05, "Other / Uncovered Subject": 0.000191892174453391, "Unknown / Insufficient Content": 3.82302869739717e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008906323339116651, "Art & Design": 0.0009937034100325802, "Law & Crime": 0.0007191121338387257, "Education & Careers": 0.0004503003697708625, "Entertainment & Media": 0.002505166383360159, "Fashion & Beauty": 0.00036737271594660363, "Business, Economics & Finance": 0.0007689256860349782, "Food & Dining": 0.000751207385182434, "Games & Gaming": 0.0006861845364664797, "Electronics & Hardware": 0.22555069247761367, "Health, Healthcare & Wellness": 0.0006843135544886296, "History & Geography": 0.00046868818590018596, "Home, Pets & Hobbies": 0.09680870373403955, "Industry & Production": 0.0004640789884972847, "Language, Literature & Philosophy": 0.00027678090445856367, "Politics & Government": 0.000354488234273822, "Religion & Spirituality": 0.00048161524573464066, "Science, Mathematics & Engineering": 0.001322707660569501, "Relationships & Community": 0.00021997727349735475, "Software Usage & Applications": 0.0051918947415428894, "Programming & Software Engineering": 0.05627394506597934, "Sports & Fitness": 0.0003373985297836313, "Vehicles & Transportation": 0.0005108753045495419, "Travel & Tourism": 0.000485213026443407, "Other / Uncovered Subject": 0.0016411258064881159, "NONE": 0.6007948963115954}}, "probabilities": {"primary": {"Academic Writing": 8.581965868738999e-05, "Content Listing": 2.2325043649418017e-05, "Creative Writing": 2.0196164158656484e-05, "Customer Support Page": 1.446299321919647e-05, "Q&A Forum": 1.295919702570688e-05, "Discussion Thread": 7.418938263149697e-06, "Comment Section": 1.4150139596307424e-05, "Missing Content": 2.638994536449688e-05, "Knowledge Article": 7.211706883412665e-05, "Legal Notices": 3.0151545364684625e-05, "News Article": 5.865511583446492e-06, "Organizational Announcement": 1.6140721645278664e-05, "Nonfiction Writing": 1.679037080362432e-05, "Organizational About Page": 1.298137540621415e-05, "Personal About Page": 9.223218358181035e-06, "Personal Blog": 6.231686922495484e-06, "Product Page": 8.219870989387778e-06, "Spam / Ads": 8.599916362640779e-06, "Structured Data": 0.0014118019321704756, "Technical Writing": 0.9902145770472859, "Transcript / Interview": 2.647859513734827e-05, "Tutorial / How-To Guide": 0.00020260771549525651, "User Reviews": 7.494927381854757e-06, "Other / Unknown": 0.007746996416294865}, "secondary": {"Academic Writing": 0.00047739767896532446, "Content Listing": 0.00020164133108255185, "Creative Writing": 0.0002448195215111055, "Customer Support Page": 0.00014655204389302183, "Q&A Forum": 0.00010911043236737422, "Discussion Thread": 7.937184509938958e-05, "Comment Section": 0.00014316863803860304, "Missing Content": 0.0004007027824645028, "Knowledge Article": 0.000649084043229583, "Legal Notices": 0.0001900484043625961, "News Article": 0.000125033527229869, "Organizational Announcement": 0.00012647125431388046, "Nonfiction Writing": 0.0001267535002448671, "Organizational About Page": 0.00011995705489747066, "Personal About Page": 9.913621205886554e-05, "Personal Blog": 9.472322511026263e-05, "Product Page": 7.005402524800551e-05, "Spam / Ads": 0.00010488980697637119, "Structured Data": 0.02551244736102366, "Technical Writing": 0.028603666347905173, "Transcript / Interview": 0.00015664631185329523, "Tutorial / How-To Guide": 0.0006220112847427508, "User Reviews": 5.594987123635424e-05, "NONE": 0.9415403634961452}, "tertiary": {"Academic Writing": 0.00018884970004717932, "Content Listing": 0.0001070733687684024, "Creative Writing": 0.00011662804098227116, "Customer Support Page": 7.343306005921427e-05, "Q&A Forum": 4.487092411789505e-05, "Discussion Thread": 3.692838802459653e-05, "Comment Section": 9.790248184111605e-05, "Missing Content": 0.00030478462164546053, "Knowledge Article": 0.0006753486985414673, "Legal Notices": 0.0002082376946723525, "News Article": 5.4984975419521976e-05, "Organizational Announcement": 6.004587831748176e-05, "Nonfiction Writing": 7.1889909686122e-05, "Organizational About Page": 7.094342332036616e-05, "Personal About Page": 4.350441195245617e-05, "Personal Blog": 3.929051877689736e-05, "Product Page": 3.6471037020180904e-05, "Spam / Ads": 4.851384454277051e-05, "Structured Data": 0.014020687854636425, "Technical Writing": 0.012496282184686208, "Transcript / Interview": 6.456814606922405e-05, "Tutorial / How-To Guide": 0.0004631734011603865, "User Reviews": 2.7905877158144345e-05, "NONE": 0.9706476815585537}, "structured_records": {"false": 0.5224325166000845, "true": 0.47756748339991556}, "procedural_steps": {"false": 0.9801977070805404, "true": 0.019802292919459587}, "worked_solution": {"false": 0.7624582675917143, "true": 0.23754173240828572}, "contextual_dialogue": {"false": 0.9999948315898055, "true": 5.168410194489732e-06}, "content_quality": {"-2": 0.0019117631636820442, "-1": 0.0039928879165896545, "0": 0.05129141114460836, "1": 0.4784148738711553, "2": 0.4643890639039647}, "noise_severity": {"0": 0.9863356860889609, "1": 0.012501479429932006, "2": 0.0007105918048549613, "3": 0.00026938376117879804, "4": 0.00018285891507328135}, "missing_content_severity": {"0": 0.9968849709599531, "1": 0.00236124943278318, "2": 0.0004357589277913529, "3": 0.00022870991386399222, "4": 8.931076560834144e-05}, "factual_density": {"0": 0.004045656291030795, "1": 0.009116479440896443, "2": 0.09659470804286054, "3": 0.3975098434956971, "4": 0.49273331272951515}, "pretraining_utility": {"-2": 0.007724659000334149, "-1": 0.08629269056757799, "0": 0.3487778774259054, "1": 0.46081025637715545, "2": 0.0963945166290271}, "math_density": {"0": 0.9691915558160294, "1": 0.022885400620312923, "2": 0.0036127626589764587, "3": 0.0011738132452138764, "4": 0.003136467659467257}, "code_density": {"0": 9.498445450170164e-05, "1": 0.00016752814499938016, "2": 0.004033564494250826, "3": 0.048237733321488056, "4": 0.94746618958476}, "reasoning_density": {"0": 0.29747711444423613, "1": 0.22781427036238439, "2": 0.22211057740015208, "3": 0.14878629649814984, "4": 0.10381174129507759}, "education_level": {"none": 0.0008487690850490947, "0": 0.0011146309245929978, "1": 0.0038434693579465144, "2": 0.4338246373125157, "3": 0.43602834931361123, "4": 0.12434014400628456}, "creative_writing_quality": {"not_applicable": 0.999254408357119, "-2": 7.346582648327605e-05, "-1": 0.0001055374045046277, "0": 0.0004324737434400477, "1": 9.881274766356538e-05, "2": 3.530192078953672e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.4176521316149496e-05, "Art & Design": 5.452314073767697e-05, "Law & Crime": 3.8214539825119295e-05, "Education & Careers": 2.9945803585503914e-05, "Entertainment & Media": 0.000123820779345722, "Fashion & Beauty": 4.234484466818537e-05, "Business, Economics & Finance": 3.650614883728938e-05, "Food & Dining": 6.898535818862644e-05, "Games & Gaming": 7.161933643735799e-05, "Electronics & Hardware": 0.0179763104491278, "Health, Healthcare & Wellness": 5.247921594388916e-05, "History & Geography": 4.0704332022435596e-05, "Home, Pets & Hobbies": 0.005614442543624766, "Industry & Production": 3.9648861256288865e-05, "Language, Literature & Philosophy": 2.9465697944862315e-05, "Politics & Government": 2.1211549331652744e-05, "Religion & Spirituality": 3.22706523367283e-05, "Science, Mathematics & Engineering": 0.0003579113023239587, "Relationships & Community": 1.753768790828661e-05, "Software Usage & Applications": 0.0008784056743213563, "Programming & Software Engineering": 0.9740536832521102, "Sports & Fitness": 5.084219433099817e-05, "Vehicles & Transportation": 7.166490804487482e-05, "Travel & Tourism": 3.316274500283179e-05, "Other / Uncovered Subject": 0.00019189217445339095, "Unknown / Insufficient Content": 3.823028697397169e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008906323339116651, "Art & Design": 0.0009937034100325802, "Law & Crime": 0.0007191121338387257, "Education & Careers": 0.0004503003697708625, "Entertainment & Media": 0.002505166383360159, "Fashion & Beauty": 0.00036737271594660363, "Business, Economics & Finance": 0.0007689256860349782, "Food & Dining": 0.000751207385182434, "Games & Gaming": 0.0006861845364664797, "Electronics & Hardware": 0.22555069247761367, "Health, Healthcare & Wellness": 0.0006843135544886296, "History & Geography": 0.00046868818590018596, "Home, Pets & Hobbies": 0.09680870373403955, "Industry & Production": 0.0004640789884972847, "Language, Literature & Philosophy": 0.00027678090445856367, "Politics & Government": 0.000354488234273822, "Religion & Spirituality": 0.00048161524573464066, "Science, Mathematics & Engineering": 0.001322707660569501, "Relationships & Community": 0.00021997727349735475, "Software Usage & Applications": 0.0051918947415428894, "Programming & Software Engineering": 0.05627394506597934, "Sports & Fitness": 0.0003373985297836313, "Vehicles & Transportation": 0.0005108753045495419, "Travel & Tourism": 0.000485213026443407, "Other / Uncovered Subject": 0.0016411258064881159, "NONE": 0.6007948963115954}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11011, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11011, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:17.082340+00:00", "scoring_key_slot": "primary", "dataset_uid": "14960688-cec0-41d7-a602-9b1518017018", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "e55ba6a65c0918a98cd0452468d5bf02c47650180dda79d22086e9eaa0063a4e", "source_metadata": {"uuid": "14960688-cec0-41d7-a602-9b1518017018", "repo_name": "home-assistant/core", "relative_path": "tests/components/hdmi_cec/test_switch.py", "category": "TEST", "algo_rel_score": 0.9956591129302979, "quality_score": 7.543509006500244, "source_meta": {"github_metadata": {"branch": "dev", "commit_count": 96732, "repo_created_at": "2013-09-17T07:29:48.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 80205, "forks": 34352, "issues": 2573, "pull_requests": 494}, "file_meta": {"content_id": "57e5165e01703a2db78b9272fbfddd8e6c4bd79a", "size_bytes": 9236, "file_timestamp": 1752758403, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "b33a556ca5699bdb5b9221558c689f7d9e934ab3"}}, "parsed_source_meta": {"github_metadata": {"branch": "dev", "commit_count": 96732, "repo_created_at": "2013-09-17T07:29:48.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 80205, "forks": 34352, "issues": 2573, "pull_requests": 494}, "file_meta": {"content_id": "57e5165e01703a2db78b9272fbfddd8e6c4bd79a", "size_bytes": 9236, "file_timestamp": 1752758403, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "b33a556ca5699bdb5b9221558c689f7d9e934ab3", "url": "https://github.com/home-assistant/core/blob/b33a556ca5699bdb5b9221558c689f7d9e934ab3/tests/components/hdmi_cec/test_switch.py"}, "gpt4o_tokens": 2302, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.399376587435131, "noise_severity": 0.015462249983471447, "missing_content_severity": 0.004276140092391229, "factual_density": 3.3657686769317694, "pretraining_utility": 0.5518572810669633, "math_density": 0.0461782363117765, "code_density": 3.9428126154370053, "reasoning_density": 1.5336412798374484, "education_level": 2.679212400606865, "creative_writing_quality": -0.11139136150670426}, "score_applicability": {"education_level": 0.999151230914951, "creative_writing_quality": 0.0007455916428810536}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/antgroup/glake/blob/fb24ee8cc7beeb55b9792f2da58e0ee673d8c864/GLakeServe/tests/core/test_chunked_prefill_scheduler.py", "id": "python-0-87b462069fb87503b4c9c364534a7c85", "text": "from typing import List\nfrom unittest.mock import MagicMock\n\nimport pytest  # noqa\n\nfrom vllm.config import CacheConfig, SchedulerConfig\nfrom vllm.core.interfaces import AllocStatus\nfrom vllm.core.scheduler import Scheduler\nfrom vllm.sequence import Logprob, SequenceGroup\n\nfrom .utils import create_dummy_prompt\n\n\ndef get_sequence_groups(scheduler_output):\n    return [s.seq_group for s in scheduler_output.scheduled_seq_groups]\n\n\ndef append_new_token(seq_group, token_id: int):\n    for seq in seq_group.get_seqs():\n        seq.append_token_id(token_id, {token_id: Logprob(token_id)})\n\n\ndef schedule_and_update_computed_tokens(scheduler):\n    metas, out = scheduler.schedule()\n    for s, meta in zip(out.scheduled_seq_groups, metas):\n        s.seq_group.update_num_computed_tokens(meta.token_chunk_size)\n    return metas, out\n\n\ndef test_simple():\n    \"\"\"Verify basic scheduling works.\"\"\"\n    block_size = 4\n    num_seq_group = 4\n    max_model_len = 16\n    max_num_batched_tokens = 64\n    scheduler_config = SchedulerConfig(max_num_batched_tokens,\n                                       num_seq_group,\n                                       max_model_len,\n                                       enable_chunked_prefill=True)\n    cache_config = CacheConfig(block_size, 1.0, 1, \"auto\")\n    cache_config.num_cpu_blocks = 8\n    cache_config.num_gpu_blocks = 8\n    scheduler = Scheduler(scheduler_config, cache_config, None)\n    running: List[SequenceGroup] = []\n\n    # Add seq groups to scheduler.\n    for i in range(num_seq_group):\n        _, seq_group = create_dummy_prompt(str(i), prompt_length=block_size)\n        scheduler.add_seq_group(seq_group)\n        running.append(seq_group)\n\n    # Schedule seq groups prompts.\n    num_tokens = block_size * num_seq_group\n    seq_group_meta, out = schedule_and_update_computed_tokens(scheduler)\n    assert set(get_sequence_groups(out)) == set(running)\n    assert out.num_batched_tokens == num_tokens\n    assert (not out.blocks_to_copy and not out.blocks_to_swap_in\n            and not out.blocks_to_swap_out)\n    assert len(seq_group_meta) == num_seq_group\n    for s in running:\n        append_new_token(s, 1)\n\n    # Schedule seq groups generation.\n    seq_group_meta, out = schedule_and_update_computed_tokens(scheduler)\n    assert set(get_sequence_groups(out)) == set(running)\n    assert out.num_batched_tokens == num_seq_group\n    assert (not out.blocks_to_copy and not out.blocks_to_swap_in\n            and not out.blocks_to_swap_out)\n    assert len(seq_group_meta) == num_seq_group\n\n\ndef test_chunk():\n    \"\"\"Verify prefills are chunked properly.\"\"\"\n    block_size = 4\n    max_seqs = 60\n    max_model_len = 80\n    max_num_batched_tokens = 64\n    scheduler_config = SchedulerConfig(max_num_batched_tokens,\n                                       max_seqs,\n                                       max_model_len,\n                                       enable_chunked_prefill=True)\n    cache_config = CacheConfig(block_size, 1.0, 1, \"auto\")\n    cache_config.num_cpu_blocks = 8\n    cache_config.num_gpu_blocks = 8\n    scheduler = Scheduler(scheduler_config, cache_config, None)\n    running: List[SequenceGroup] = []\n\n    # Add seq groups to scheduler.\n    for i in range(2):\n        _, seq_group = create_dummy_prompt(str(i), prompt_length=60)\n        scheduler.add_seq_group(seq_group)\n        running.append(seq_group)\n\n    # Verify the second request is chunked.\n    seq_group_meta, out = schedule_and_update_computed_tokens(scheduler)\n    assert set(get_sequence_groups(out)) == set(running)\n    assert seq_group_meta[0].token_chunk_size == 60\n    # Verify it is chunked.\n    assert seq_group_meta[1].token_chunk_size == 4\n    assert out.num_prefill_groups == 2\n    assert out.num_batched_tokens == 64\n    # Only the first seq group has a new token appended.\n    append_new_token(running[0], 1)\n\n    # One chunked prefill, and one decoding.\n    seq_group_meta, out = schedule_and_update_computed_tokens(scheduler)\n    assert set(get_sequence_groups(out)) == set(running)\n    # The first one is prefill. Scheduler guarantees ordering.\n    assert seq_group_meta[0].token_chunk_size == 56\n    # The second one is a chunked prefill.\n    assert seq_group_meta[1].token_chunk_size == 1\n    assert out.num_prefill_groups == 1\n    assert out.num_batched_tokens == 57\n\n\ndef test_complex():\n    block_size = 4\n    max_seqs = 60\n    max_model_len = 80\n    max_num_batched_tokens = 64\n    scheduler_config = SchedulerConfig(max_num_batched_tokens,\n                                       max_seqs,\n                                       max_model_len,\n                                       enable_chunked_prefill=True)\n    cache_config = CacheConfig(block_size, 1.0, 1, \"auto\")\n    cache_config.num_cpu_blocks = 8\n    cache_config.num_gpu_blocks = 8\n    scheduler = Scheduler(scheduler_config, cache_config, None)\n    running: List[SequenceGroup] = []\n\n    # Add seq groups to scheduler.\n    for i in range(2):\n        _, seq_group = create_dummy_prompt(str(i), prompt_length=60)\n        scheduler.add_seq_group(seq_group)\n        running.append(seq_group)\n        assert seq_group.is_prefill()\n\n    # Verify the second request is chunked.\n    seq_group_meta, out = schedule_and_update_computed_tokens(scheduler)\n\n    assert set(get_sequence_groups(out)) == set(running)\n    assert seq_group_meta[0].token_chunk_size == 60\n    # Verify it is chunked.\n    assert seq_group_meta[1].token_chunk_size == 4\n    assert not running[0].is_prefill()\n    assert running[1].is_prefill()\n    assert out.num_prefill_groups == 2\n    assert out.num_batched_tokens == 64\n    # Only the first seq group has a new token appended.\n    append_new_token(running[0], 1)\n\n    # Add 2 more requsets.\n    for i in range(2, 4):\n        _, seq_group = create_dummy_prompt(str(i), prompt_length=60)\n        scheduler.add_seq_group(seq_group)\n        running.append(seq_group)\n\n    # Decoding & chunked prefill & first chunk of 3rd request is scheduled.\n    seq_group_meta, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(get_sequence_groups(out)) == 3\n    # The first one is the first chunked prefill.\n    assert seq_group_meta[0].token_chunk_size == 7\n    # The second one is the second new chunked prefill.\n    assert seq_group_meta[1].token_chunk_size == 56\n    # The last one is decode.\n    assert seq_group_meta[2].token_chunk_size == 1\n    # Two of them are in chunked prefill.\n    assert out.num_prefill_groups == 2\n    assert out.num_batched_tokens == 64\n    # The first 2 requests are now in decodine phase.\n    append_new_token(running[0], 1)\n    assert not running[0].is_prefill()\n    append_new_token(running[1], 1)\n    assert not running[1].is_prefill()\n    # The third request is still in prefill stage.\n    assert running[2].is_prefill()\n\n\ndef test_maximal_decoding():\n    \"\"\"Verify decoding requests are prioritized.\"\"\"\n    block_size = 4\n    max_seqs = 2\n    max_model_len = 2\n    max_num_batched_tokens = 2\n    scheduler_config = SchedulerConfig(max_num_batched_tokens,\n                                       max_seqs,\n                                       max_model_len,\n                                       enable_chunked_prefill=True)\n    cache_config = CacheConfig(block_size, 1.0, 1, \"auto\")\n    cache_config.num_cpu_blocks = 8\n    cache_config.num_gpu_blocks = 8\n    scheduler = Scheduler(scheduler_config, cache_config, None)\n    running: List[SequenceGroup] = []\n\n    # Add seq groups to scheduler.\n    for i in range(2):\n        _, seq_group = create_dummy_prompt(str(i), prompt_length=2)\n        scheduler.add_seq_group(seq_group)\n        running.append(seq_group)\n        assert seq_group.is_prefill()\n\n    # The first prefill is scheduled.\n    seq_group_meta, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(get_sequence_groups(out)) == 1\n    assert seq_group_meta[0].token_chunk_size == 2\n    assert not running[0].is_prefill()\n    assert running[1].is_prefill()\n    assert out.num_prefill_groups == 1\n    assert out.num_batched_tokens == 2\n    # Only the first seq group has a new token appended.\n    append_new_token(running[0], 1)\n\n    # Create one more seq_group.\n    _, seq_group = create_dummy_prompt(\"3\", prompt_length=2)\n    scheduler.add_seq_group(seq_group)\n    running.append(seq_group)\n    assert seq_group.is_prefill()\n    # The first decoding + second chunk is scheduled.\n    seq_group_meta, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(get_sequence_groups(out)) == 2\n    assert seq_group_meta[0].token_chunk_size == 1\n    assert seq_group_meta[1].token_chunk_size == 1\n    assert not running[0].is_prefill()\n    assert running[1].is_prefill()\n    assert running[2].is_prefill()\n    assert out.num_prefill_groups == 1\n    assert out.num_batched_tokens == 2\n    append_new_token(running[0], 1)\n\n    # Decoding + running prefill is prioritized.\n    seq_group_meta, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(get_sequence_groups(out)) == 2\n    assert seq_group_meta[0].token_chunk_size == 1\n    assert seq_group_meta[1].token_chunk_size == 1\n    assert not running[0].is_prefill()\n    assert not running[1].is_prefill()\n    assert out.num_prefill_groups == 1\n    assert out.num_batched_tokens == 2\n    append_new_token(running[0], 1)\n    append_new_token(running[1], 1)\n\n    # Only decoding is prioritized.\n    seq_group_meta, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(get_sequence_groups(out)) == 2\n    assert seq_group_meta[0].token_chunk_size == 1\n    assert seq_group_meta[1].token_chunk_size == 1\n    assert not running[0].is_prefill()\n    assert not running[1].is_prefill()\n    assert out.num_prefill_groups == 0\n    assert out.num_batched_tokens == 2\n    append_new_token(running[0], 1)\n    append_new_token(running[1], 1)\n\n    # After aborting the decoding request, the fcfs new prefill is prioritized.\n    scheduler.abort_seq_group(running[0].request_id)\n    seq_group_meta, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(get_sequence_groups(out)) == 2\n    assert seq_group_meta[0].token_chunk_size == 1\n    assert seq_group_meta[1].token_chunk_size == 1\n    assert not running[1].is_prefill()\n    assert running[2].is_prefill()\n    assert out.num_prefill_groups == 1\n    assert out.num_batched_tokens == 2\n\n\ndef test_prompt_limit():\n    \"\"\"Verify max_num_batched_tokens < max_model_len is possible.\"\"\"\n    block_size = 4\n    max_seqs = 32\n    max_model_len = 64\n    max_num_batched_tokens = 32\n    scheduler_config = SchedulerConfig(max_num_batched_tokens,\n                                       max_seqs,\n                                       max_model_len,\n                                       enable_chunked_prefill=True)\n    cache_config = CacheConfig(block_size, 1.0, 1, \"auto\")\n    cache_config.num_cpu_blocks = 8\n    cache_config.num_gpu_blocks = 8\n    scheduler = Scheduler(scheduler_config, cache_config, None)\n    running: List[SequenceGroup] = []\n\n    _, seq_group = create_dummy_prompt(\"1\", prompt_length=48)\n    scheduler.add_seq_group(seq_group)\n    running.append(seq_group)\n    assert seq_group.is_prefill()\n\n    # The prompt length > max_num_batched_tokens should be still scheduled.\n    seq_group_meta, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(get_sequence_groups(out)) == 1\n    assert seq_group_meta[0].token_chunk_size == 32\n    assert running[0].is_prefill()\n    assert out.num_prefill_groups == 1\n    assert out.num_batched_tokens == 32\n\n\ndef test_prompt_limit_exceed():\n    block_size = 4\n    max_seqs = 64\n    max_model_len = 32\n    max_num_batched_tokens = 64\n    scheduler_config = SchedulerConfig(max_num_batched_tokens,\n                                       max_seqs,\n                                       max_model_len,\n                                       enable_chunked_prefill=True)\n    cache_config = CacheConfig(block_size, 1.0, 1, \"auto\")\n    cache_config.num_cpu_blocks = 8\n    cache_config.num_gpu_blocks = 8\n    scheduler = Scheduler(scheduler_config, cache_config, None)\n    running: List[SequenceGroup] = []\n\n    _, seq_group = create_dummy_prompt(\"2\", prompt_length=48)\n    scheduler.add_seq_group(seq_group)\n    running.append(seq_group)\n    assert seq_group.is_prefill()\n    seq_group_meta, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(out.ignored_seq_groups) == 1\n    assert out.ignored_seq_groups[0] == seq_group\n\n\ndef test_swap():\n    \"\"\"Verify swapping works with chunked prefill requests\"\"\"\n    block_size = 4\n    max_seqs = 30\n    max_model_len = 200\n    max_num_batched_tokens = 30\n    scheduler_config = SchedulerConfig(max_num_batched_tokens,\n                                       max_seqs,\n                                       max_model_len,\n                                       enable_chunked_prefill=True)\n    cache_config = CacheConfig(block_size, 1.0, 1, \"auto\")\n    cache_config.num_cpu_blocks = 8\n    cache_config.num_gpu_blocks = 8\n    scheduler = Scheduler(scheduler_config, cache_config, None)\n\n    _, seq_group = create_dummy_prompt(\"1\", prompt_length=60, best_of=2)\n    scheduler.add_seq_group(seq_group)\n    _, out = schedule_and_update_computed_tokens(scheduler)\n    # The request is chunked.\n    # prefill scheduled now.\n    assert len(out.scheduled_seq_groups) == 1\n    assert out.num_prefill_groups == 1\n    assert seq_group.is_prefill()\n    assert out.num_batched_tokens == max_num_batched_tokens\n\n    # The last request should be swapped out.\n    scheduler.block_manager.can_append_slots = MagicMock()\n\n    def cannot_append_second_group(seq_group, num_lookahead_slots):\n        return seq_group.request_id != \"1\"\n\n    scheduler.block_manager.can_append_slots.side_effect = (\n        cannot_append_second_group)\n\n    # The running prefill is now swapped.\n    _, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(out.scheduled_seq_groups) == 0\n    assert out.num_batched_tokens == 0\n    assert out.blocks_to_swap_out != []\n    assert out.blocks_to_swap_in == []\n\n    # Add 1 more task. Swap should be prioritized over new prefill.\n    _, seq_group = create_dummy_prompt(\"2\", prompt_length=60)\n    scheduler.add_seq_group(seq_group)\n    _, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(out.scheduled_seq_groups) == 1\n    # 3 decodes. It is swapped in.\n    assert out.num_batched_tokens == 30\n    assert out.blocks_to_swap_in != []\n    assert out.blocks_to_swap_out == []\n\n\ndef test_running_prefill_prioritized_over_swap():\n    block_size = 4\n    max_seqs = 30\n    max_model_len = 200\n    max_num_batched_tokens = 30\n    scheduler_config = SchedulerConfig(max_num_batched_tokens,\n                                       max_seqs,\n                                       max_model_len,\n                                       enable_chunked_prefill=True)\n    cache_config = CacheConfig(block_size, 1.0, 1, \"auto\")\n    cache_config.num_cpu_blocks = 8\n    cache_config.num_gpu_blocks = 8\n    scheduler = Scheduler(scheduler_config, cache_config, None)\n\n    _, seq_group = create_dummy_prompt(\"1\", prompt_length=60, best_of=2)\n    scheduler.add_seq_group(seq_group)\n    _, out = schedule_and_update_computed_tokens(scheduler)\n    # The request is chunked.\n    # prefill scheduled now.\n    assert len(out.scheduled_seq_groups) == 1\n    assert out.num_prefill_groups == 1\n    assert seq_group.is_prefill()\n    assert out.num_batched_tokens == max_num_batched_tokens\n\n    # The request should be swapped out.\n    scheduler.block_manager.can_append_slots = MagicMock()\n\n    def cannot_append_second_group(seq_group, num_lookahead_slots):\n        return seq_group.request_id != \"1\"\n\n    scheduler.block_manager.can_append_slots.side_effect = (\n        cannot_append_second_group)\n\n    # The running prefill is now swapped.\n    _, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(out.scheduled_seq_groups) == 0\n    assert out.num_batched_tokens == 0\n    assert out.blocks_to_swap_out != []\n    assert out.blocks_to_swap_in == []\n\n    # Add 1 more task. Swap is not possible, so prefill is running.\n    scheduler.block_manager.can_swap_in = MagicMock()\n    scheduler.block_manager.can_swap_in.return_value = AllocStatus.LATER\n\n    _, seq_group2 = create_dummy_prompt(\"2\", prompt_length=60)\n    scheduler.add_seq_group(seq_group2)\n    _, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(out.scheduled_seq_groups) == 1\n    # 3 decodes. It is swapped in.\n    assert out.num_batched_tokens == 30\n    assert out.blocks_to_swap_in == []\n    assert out.blocks_to_swap_out == []\n    assert out.scheduled_seq_groups[0].seq_group == seq_group2\n\n    # Now although swap is possible, running prefill is prioritized.\n    scheduler.block_manager.can_swap_in.return_value = AllocStatus.OK\n    _, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(out.scheduled_seq_groups) == 1\n    # 3 decodes. It is swapped in.\n    assert out.num_batched_tokens == 30\n    assert out.blocks_to_swap_in == []\n    assert out.blocks_to_swap_out == []\n    assert not seq_group2.is_prefill()\n    assert out.scheduled_seq_groups[0].seq_group == seq_group2\n    append_new_token(seq_group2, 1)\n\n    # Decoding is prioritized.\n    _, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(out.scheduled_seq_groups) == 1\n    # 3 decodes. It is swapped in.\n    assert out.num_batched_tokens == 1\n    assert out.blocks_to_swap_in == []\n    assert out.blocks_to_swap_out == []\n    assert not seq_group2.is_prefill()\n    assert out.scheduled_seq_groups[0].seq_group == seq_group2\n    append_new_token(seq_group2, 1)\n\n    # Since we abort the sequence group, we can finally swap.\n    scheduler.abort_seq_group(seq_group2.request_id)\n    _, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(out.scheduled_seq_groups) == 1\n    assert out.num_batched_tokens == 30\n    assert out.blocks_to_swap_in != []\n    assert out.blocks_to_swap_out == []\n\n\ndef test_chunked_prefill_preempt():\n    \"\"\"Verify preempt works with chunked prefill requests\"\"\"\n    block_size = 4\n    max_seqs = 30\n    max_model_len = 200\n    max_num_batched_tokens = 30\n    scheduler_config = SchedulerConfig(max_num_batched_tokens,\n                                       max_seqs,\n                                       max_model_len,\n                                       enable_chunked_prefill=True)\n    cache_config = CacheConfig(block_size, 1.0, 1, \"auto\")\n    cache_config.num_cpu_blocks = 8\n    cache_config.num_gpu_blocks = 8\n    scheduler = Scheduler(scheduler_config, cache_config, None)\n\n    _, seq_group = create_dummy_prompt(\"1\", prompt_length=60)\n    scheduler.add_seq_group(seq_group)\n    _, out = schedule_and_update_computed_tokens(scheduler)\n    # The request is chunked.\n    # prefill scheduled now.\n    assert len(out.scheduled_seq_groups) == 1\n    assert out.num_prefill_groups == 1\n    assert seq_group.is_prefill()\n    assert out.num_batched_tokens == max_num_batched_tokens\n\n    # The request should be preempted.\n    scheduler.block_manager.can_append_slots = MagicMock()\n\n    def cannot_append_second_group(seq_group, num_lookahead_slots):\n        return seq_group.request_id != \"1\"\n\n    scheduler.block_manager.can_append_slots.side_effect = (\n        cannot_append_second_group)\n\n    # The running prefill is now preempted.\n    _, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(out.scheduled_seq_groups) == 0\n    assert out.num_batched_tokens == 0\n    assert out.blocks_to_swap_out == []\n    assert out.blocks_to_swap_in == []\n\n    # Make sure we can reschedule preempted request.\n    _, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(out.scheduled_seq_groups) == 1\n    assert out.num_prefill_groups == 1\n    assert seq_group.is_prefill()\n    assert out.num_batched_tokens == max_num_batched_tokens\n    assert seq_group.get_num_uncomputed_tokens() == 30\n\n    # We should be able to run prefill twice as it is chunked.\n    def cannot_append_second_group(seq_group, num_lookahead_slots):\n        return True\n\n    scheduler.block_manager.can_append_slots.side_effect = (\n        cannot_append_second_group)\n    _, out = schedule_and_update_computed_tokens(scheduler)\n    assert len(out.scheduled_seq_groups) == 1\n    assert out.num_prefill_groups == 1\n    assert not seq_group.is_prefill()\n    assert out.num_batched_tokens == max_num_batched_tokens\n\n\ndef test_chunked_prefill_max_seqs():\n    block_size = 4\n    max_seqs = 2\n    max_model_len = 80\n    max_num_batched_tokens = 64\n    scheduler_config = SchedulerConfig(max_num_batched_tokens,\n                                       max_seqs,\n                                       max_model_len,\n                                       enable_chunked_prefill=True)\n    cache_config = CacheConfig(block_size, 1.0, 1, \"auto\")\n    cache_config.num_cpu_blocks = 8\n    cache_config.num_gpu_blocks = 8\n    scheduler = Scheduler(scheduler_config, cache_config, None)\n    running = []\n\n    _, seq_group = create_dummy_prompt(\"1\", prompt_length=65)\n    scheduler.add_seq_group(seq_group)\n    running.append(seq_group)\n    # The first prefill is chunked.\n    seq_group_meta, out = schedule_and_update_computed_tokens(scheduler)\n    assert seq_group_meta[0].token_chunk_size == max_num_batched_tokens\n    assert len(get_sequence_groups(out)) == 1\n\n    # Add new requests.\n    for i in range(4):\n        _, seq_group = create_dummy_prompt(str(i), prompt_length=65)\n        scheduler.add_seq_group(seq_group)\n        running.append(seq_group)\n\n    # Make sure only 2 requests are scheduled.\n    seq_group_meta, out = schedule_and_update_computed_tokens(scheduler)\n    assert out.num_batched_tokens == max_num_batched_tokens\n    assert len(get_sequence_groups(out)) == 2\n    assert not running[0].is_prefill()\n    assert running[1].is_prefill()\n    append_new_token(running[0], 1)\n\n    # Although we have enough token budget, we can only schedule max_seqs.\n    seq_group_meta, out = schedule_and_update_computed_tokens(scheduler)\n    assert seq_group_meta[0].token_chunk_size == 2\n    assert seq_group_meta[1].token_chunk_size == 1\n    assert out.num_batched_tokens == 3\n    assert len(get_sequence_groups(out)) == max_seqs\n    assert not running[0].is_prefill()\n    assert not running[1].is_prefill()\n", "text_preview": "from typing import List from unittest.mock import MagicMock import pytest # noqa from vllm.config import CacheConfig, SchedulerConfig from vllm.core.interfaces import AllocStatus from vllm.core.scheduler import Scheduler from vllm.sequence ", "input_sha256": "4c547bc58c1b6219d8e44a27e769cfa9df8bfbf12b6c4e190ca559c1dac47fba", "text_sha256": "87b462069fb87503b4c9c364534a7c8559e9992386ce3e321bb0ad4bd23342f0", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12044, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.1910919426198987, "noise_severity": 0.01720998396950317, "missing_content_severity": 0.0025911213174778258, "factual_density": 3.1917841372549334, "pretraining_utility": 0.5447783770035621, "math_density": 0.8869132668325908, "code_density": 3.953655271430705, "reasoning_density": 1.7622550422859256, "education_level": 3.2520751034166397, "creative_writing_quality": -0.31626561909899525}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.01206701480170036, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9924839683833182, "secondary": 0.978096895769117, "tertiary": 0.9906885633882824, "structured_records": 0.0377414420328581, "procedural_steps": 0.03264529435262646, "worked_solution": 0.3455550209662422, "contextual_dialogue": 7.757173258254974e-06, "content_quality": 0.5589352318295984, "noise_severity": 0.9856583466920806, "missing_content_severity": 0.9978407322354351, "factual_density": 0.5333883565715307, "pretraining_utility": 0.4636357404264396, "math_density": 0.26090561097284104, "code_density": 0.9613793928589207, "reasoning_density": 0.0, "education_level": 0.413295531333866, "creative_writing_quality": 0.9997697976381255, "primary_topic": 0.9807418511112052, "secondary_topic": 0.8100781791609047}, "probability_margin": {"primary": 0.987092077295234, "secondary": 0.9628060013528863, "tertiary": 0.9847867376756626, "education_level": 0.13884795130848648, "creative_writing_quality": 0.999733617107451, "primary_topic": 0.9643574839373841, "secondary_topic": 0.687769138694005}, "raw_probabilities": {"primary": {"Academic Writing": 9.334126025297286e-05, "Content Listing": 2.6157003394171192e-05, "Creative Writing": 1.951269445021564e-05, "Customer Support Page": 1.2097511381354466e-05, "Q&A Forum": 1.4892089851953654e-05, "Discussion Thread": 9.152365338340719e-06, "Comment Section": 1.631135907583014e-05, "Missing Content": 2.955114330185478e-05, "Knowledge Article": 7.885115993672414e-05, "Legal Notices": 3.9677884580560405e-05, "News Article": 7.057627323519508e-06, "Organizational Announcement": 1.2850486233784241e-05, "Nonfiction Writing": 1.4589322888017173e-05, "Organizational About Page": 1.3831835866992187e-05, "Personal About Page": 8.95601453805384e-06, "Personal Blog": 7.130571876863586e-06, "Product Page": 8.750367705843194e-06, "Spam / Ads": 1.0097592955204102e-05, "Structured Data": 0.0006732964886374221, "Technical Writing": 0.9927971363673466, "Transcript / Interview": 2.5531079252708346e-05, "Tutorial / How-To Guide": 0.00036735367284765813, "User Reviews": 8.815028850522478e-06, "Other / Unknown": 0.00570505907211263}, "secondary": {"Academic Writing": 0.00017481189386053965, "Content Listing": 6.361350189597461e-05, "Creative Writing": 7.916141196415021e-05, "Customer Support Page": 4.4767400300872724e-05, "Q&A Forum": 3.8959836622381206e-05, "Discussion Thread": 2.8207383873576016e-05, "Comment Section": 6.489743364633531e-05, "Missing Content": 0.00012929988107851745, "Knowledge Article": 0.0003497091561776523, "Legal Notices": 8.143684945363204e-05, "News Article": 3.937159396621439e-05, "Organizational Announcement": 3.666909647369859e-05, "Nonfiction Writing": 3.962551819252399e-05, "Organizational About Page": 4.4667608852961854e-05, "Personal About Page": 3.2458667267479884e-05, "Personal Blog": 3.2355997486642456e-05, "Product Page": 2.478037285770289e-05, "Spam / Ads": 3.816102161562064e-05, "Structured Data": 0.00253199018577933, "Technical Writing": 0.016203523759184085, "Transcript / Interview": 5.436604826455017e-05, "Tutorial / How-To Guide": 0.0008377094443184692, "User Reviews": 1.9930824796752534e-05, "NONE": 0.9790095251120704}, "tertiary": {"Academic Writing": 0.00011234491194788576, "Content Listing": 3.54748055306045e-05, "Creative Writing": 4.766314752944576e-05, "Customer Support Page": 2.2588556805242746e-05, "Q&A Forum": 1.707416893821679e-05, "Discussion Thread": 1.4775461117845547e-05, "Comment Section": 3.575576572313523e-05, "Missing Content": 0.00013005562464486024, "Knowledge Article": 0.00025314310915440936, "Legal Notices": 7.61133573146342e-05, "News Article": 2.366260524483541e-05, "Organizational Announcement": 1.957082451060394e-05, "Nonfiction Writing": 2.669385116155527e-05, "Organizational About Page": 3.21033888475977e-05, "Personal About Page": 1.6954671464969625e-05, "Personal Blog": 1.6370291706863753e-05, "Product Page": 1.5229161697790478e-05, "Spam / Ads": 2.2759794697740367e-05, "Structured Data": 0.0007902880458199001, "Technical Writing": 0.006289802238108005, "Transcript / Interview": 2.6083136650352378e-05, "Tutorial / How-To Guide": 0.0008858701917692469, "User Reviews": 1.3082975843525941e-05, "NONE": 0.9910765399137706}, "structured_records": {"false": 0.9622585579671419, "true": 0.0377414420328581}, "procedural_steps": {"false": 0.9673547056473736, "true": 0.03264529435262646}, "worked_solution": {"false": 0.6544449790337579, "true": 0.3455550209662422}, "contextual_dialogue": {"false": 0.9999922428267417, "true": 7.757173258254974e-06}, "content_quality": {"-2": 0.003398966121688282, "-1": 0.010377636496277192, "0": 0.1381407182346723, "1": 0.4878978469351719, "2": 0.3601848322121903}, "noise_severity": {"0": 0.9853703637603533, "1": 0.012925769837379088, "2": 0.0010959558736310761, "3": 0.00033933972968507053, "4": 0.0002685707989516808}, "missing_content_severity": {"0": 0.9986722512997454, "1": 0.0004515365124736059, "2": 0.0005696435872979929, "3": 0.00022597677152496725, "4": 8.059182895833321e-05}, "factual_density": {"0": 0.004980230227588659, "1": 0.013329293483713088, "2": 0.1424756397794227, "3": 0.4633557818247273, "4": 0.3758590546845483}, "pretraining_utility": {"-2": 0.008692474655191329, "-1": 0.08981721955103612, "0": 0.34371750417470887, "1": 0.46356505737314646, "2": 0.09420774424591723}, "math_density": {"0": 0.5998680627145546, "1": 0.16275955769149422, "2": 0.08929577222115873, "3": 0.04674426479239055, "4": 0.10133234258040184}, "code_density": {"0": 8.528880287185622e-05, "1": 0.00013945738717403223, "2": 0.0029060261068261143, "3": 0.03977314898263342, "4": 0.9570960787204946}, "reasoning_density": {"0": 0.36336429747375554, "1": 0.10025449749493388, "2": 0.14347378378884673, "3": 0.19657670775655756, "4": 0.1963307134859064}, "education_level": {"none": 0.0011343349040172587, "0": 0.0011192281110522831, "1": 0.002649638571204128, "2": 0.11178553083243618, "3": 0.5110796094448883, "4": 0.3722316581364018}, "creative_writing_quality": {"not_applicable": 0.9998081646984379, "-2": 3.639099561160642e-05, "-1": 4.024038400482246e-05, "0": 7.454759098688029e-05, "1": 2.8961197103572246e-05, "2": 1.1695133855411082e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.6387341056507e-05, "Art & Design": 4.222442743371762e-05, "Law & Crime": 3.116235256259453e-05, "Education & Careers": 3.1861182325514684e-05, "Entertainment & Media": 2.8070561426475585e-05, "Fashion & Beauty": 2.6013342971590045e-05, "Business, Economics & Finance": 4.339271765840315e-05, "Food & Dining": 2.44217695045546e-05, "Games & Gaming": 6.308950553459424e-05, "Electronics & Hardware": 0.00019006620391538164, "Health, Healthcare & Wellness": 3.2587164788788055e-05, "History & Geography": 3.390264774192261e-05, "Home, Pets & Hobbies": 2.0896498342979134e-05, "Industry & Production": 4.0135148108162515e-05, "Language, Literature & Philosophy": 3.33608220426666e-05, "Politics & Government": 1.672393540976819e-05, "Religion & Spirituality": 2.8665303213256323e-05, "Science, Mathematics & Engineering": 0.01712506520800553, "Relationships & Community": 1.4562287682730166e-05, "Software Usage & Applications": 0.0003376276245975017, "Programming & Software Engineering": 0.9814825491453896, "Sports & Fitness": 4.6156057764963457e-05, "Vehicles & Transportation": 1.9266321864017882e-05, "Travel & Tourism": 3.894072181315406e-05, "Other / Uncovered Subject": 0.0001635971767309257, "Unknown / Insufficient Content": 4.927453211490266e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005331469260942331, "Art & Design": 0.00032678991514941785, "Law & Crime": 0.00043637546328884725, "Education & Careers": 0.00030113955221276566, "Entertainment & Media": 0.0003389508631318916, "Fashion & Beauty": 0.0001969384353007862, "Business, Economics & Finance": 0.00040020374190255454, "Food & Dining": 0.00018318967107751718, "Games & Gaming": 0.00032331938486450146, "Electronics & Hardware": 0.001158086458109794, "Health, Healthcare & Wellness": 0.0002756179282967419, "History & Geography": 0.00022990700348187704, "Home, Pets & Hobbies": 0.00014957445198004709, "Industry & Production": 0.00019459169325275908, "Language, Literature & Philosophy": 0.000156970040799138, "Politics & Government": 0.0001777860725805199, "Religion & Spirituality": 0.0002751100454159507, "Science, Mathematics & Engineering": 0.12961372588378797, "Relationships & Community": 0.0001255544147684731, "Software Usage & Applications": 0.0007766480055694801, "Programming & Software Engineering": 0.04538662627110336, "Sports & Fitness": 0.00020200775998441023, "Vehicles & Transportation": 0.00010117956028506264, "Travel & Tourism": 0.0003034636492270026, "Other / Uncovered Subject": 0.00045023223054192275, "NONE": 0.817382864577793}}, "probabilities": {"primary": {"Academic Writing": 9.334126025297289e-05, "Content Listing": 2.61570033941712e-05, "Creative Writing": 1.9512694450215644e-05, "Customer Support Page": 1.209751138135447e-05, "Q&A Forum": 1.4892089851953657e-05, "Discussion Thread": 9.15236533834072e-06, "Comment Section": 1.6311359075830142e-05, "Missing Content": 2.9551143301854787e-05, "Knowledge Article": 7.885115993672415e-05, "Legal Notices": 3.967788458056041e-05, "News Article": 7.05762732351951e-06, "Organizational Announcement": 1.2850486233784244e-05, "Nonfiction Writing": 1.4589322888017176e-05, "Organizational About Page": 1.383183586699219e-05, "Personal About Page": 8.956014538053841e-06, "Personal Blog": 7.130571876863587e-06, "Product Page": 8.750367705843195e-06, "Spam / Ads": 1.0097592955204104e-05, "Structured Data": 0.0006732964886374222, "Technical Writing": 0.9927971363673468, "Transcript / Interview": 2.5531079252708352e-05, "Tutorial / How-To Guide": 0.00036735367284765824, "User Reviews": 8.81502885052248e-06, "Other / Unknown": 0.005705059072112631}, "secondary": {"Academic Writing": 0.00017481189386053965, "Content Listing": 6.361350189597461e-05, "Creative Writing": 7.916141196415021e-05, "Customer Support Page": 4.4767400300872724e-05, "Q&A Forum": 3.8959836622381206e-05, "Discussion Thread": 2.8207383873576016e-05, "Comment Section": 6.489743364633531e-05, "Missing Content": 0.00012929988107851745, "Knowledge Article": 0.0003497091561776523, "Legal Notices": 8.143684945363204e-05, "News Article": 3.937159396621439e-05, "Organizational Announcement": 3.666909647369859e-05, "Nonfiction Writing": 3.962551819252399e-05, "Organizational About Page": 4.4667608852961854e-05, "Personal About Page": 3.2458667267479884e-05, "Personal Blog": 3.2355997486642456e-05, "Product Page": 2.478037285770289e-05, "Spam / Ads": 3.816102161562064e-05, "Structured Data": 0.00253199018577933, "Technical Writing": 0.016203523759184085, "Transcript / Interview": 5.436604826455017e-05, "Tutorial / How-To Guide": 0.0008377094443184692, "User Reviews": 1.9930824796752534e-05, "NONE": 0.9790095251120704}, "tertiary": {"Academic Writing": 0.00011234491194788577, "Content Listing": 3.5474805530604506e-05, "Creative Writing": 4.7663147529445767e-05, "Customer Support Page": 2.258855680524275e-05, "Q&A Forum": 1.7074168938216792e-05, "Discussion Thread": 1.4775461117845549e-05, "Comment Section": 3.5755765723135234e-05, "Missing Content": 0.00013005562464486026, "Knowledge Article": 0.0002531431091544094, "Legal Notices": 7.611335731463421e-05, "News Article": 2.3662605244835412e-05, "Organizational Announcement": 1.9570824510603944e-05, "Nonfiction Writing": 2.6693851161555273e-05, "Organizational About Page": 3.2103388847597704e-05, "Personal About Page": 1.695467146496963e-05, "Personal Blog": 1.6370291706863757e-05, "Product Page": 1.522916169779048e-05, "Spam / Ads": 2.275979469774037e-05, "Structured Data": 0.0007902880458199002, "Technical Writing": 0.006289802238108006, "Transcript / Interview": 2.608313665035238e-05, "Tutorial / How-To Guide": 0.000885870191769247, "User Reviews": 1.3082975843525943e-05, "NONE": 0.9910765399137708}, "structured_records": {"false": 0.9622585579671419, "true": 0.0377414420328581}, "procedural_steps": {"false": 0.9673547056473736, "true": 0.03264529435262646}, "worked_solution": {"false": 0.6544449790337579, "true": 0.3455550209662422}, "contextual_dialogue": {"false": 0.9999922428267417, "true": 7.757173258254974e-06}, "content_quality": {"-2": 0.003398966121688282, "-1": 0.010377636496277192, "0": 0.1381407182346723, "1": 0.4878978469351719, "2": 0.3601848322121903}, "noise_severity": {"0": 0.985370363760353, "1": 0.012925769837379085, "2": 0.001095955873631076, "3": 0.0003393397296850705, "4": 0.00026857079895168073}, "missing_content_severity": {"0": 0.9986722512997451, "1": 0.0004515365124736058, "2": 0.0005696435872979928, "3": 0.0002259767715249672, "4": 8.05918289583332e-05}, "factual_density": {"0": 0.004980230227588659, "1": 0.013329293483713088, "2": 0.1424756397794227, "3": 0.4633557818247273, "4": 0.3758590546845483}, "pretraining_utility": {"-2": 0.008692474655191329, "-1": 0.08981721955103612, "0": 0.34371750417470887, "1": 0.46356505737314646, "2": 0.09420774424591723}, "math_density": {"0": 0.5998680627145546, "1": 0.16275955769149422, "2": 0.08929577222115873, "3": 0.04674426479239055, "4": 0.10133234258040184}, "code_density": {"0": 8.528880287185622e-05, "1": 0.00013945738717403223, "2": 0.0029060261068261143, "3": 0.03977314898263342, "4": 0.9570960787204946}, "reasoning_density": {"0": 0.36336429747375554, "1": 0.10025449749493388, "2": 0.14347378378884673, "3": 0.19657670775655756, "4": 0.1963307134859064}, "education_level": {"none": 0.001134334904017259, "0": 0.0011192281110522834, "1": 0.0026496385712041284, "2": 0.1117855308324362, "3": 0.5110796094448884, "4": 0.37223165813640186}, "creative_writing_quality": {"not_applicable": 0.9998081646984377, "-2": 3.639099561160641e-05, "-1": 4.0240384004822455e-05, "0": 7.454759098688027e-05, "1": 2.896119710357224e-05, "2": 1.1695133855411078e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.638734105650699e-05, "Art & Design": 4.222442743371761e-05, "Law & Crime": 3.116235256259452e-05, "Education & Careers": 3.186118232551468e-05, "Entertainment & Media": 2.807056142647558e-05, "Fashion & Beauty": 2.601334297159004e-05, "Business, Economics & Finance": 4.3392717658403144e-05, "Food & Dining": 2.4421769504554595e-05, "Games & Gaming": 6.308950553459423e-05, "Electronics & Hardware": 0.0001900662039153816, "Health, Healthcare & Wellness": 3.258716478878805e-05, "History & Geography": 3.39026477419226e-05, "Home, Pets & Hobbies": 2.089649834297913e-05, "Industry & Production": 4.013514810816251e-05, "Language, Literature & Philosophy": 3.3360822042666596e-05, "Politics & Government": 1.6723935409768187e-05, "Religion & Spirituality": 2.8665303213256316e-05, "Science, Mathematics & Engineering": 0.017125065208005528, "Relationships & Community": 1.4562287682730162e-05, "Software Usage & Applications": 0.00033762762459750166, "Programming & Software Engineering": 0.9814825491453893, "Sports & Fitness": 4.615605776496344e-05, "Vehicles & Transportation": 1.926632186401788e-05, "Travel & Tourism": 3.894072181315405e-05, "Other / Uncovered Subject": 0.00016359717673092568, "Unknown / Insufficient Content": 4.9274532114902647e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005331469260942331, "Art & Design": 0.00032678991514941785, "Law & Crime": 0.00043637546328884725, "Education & Careers": 0.00030113955221276566, "Entertainment & Media": 0.0003389508631318916, "Fashion & Beauty": 0.0001969384353007862, "Business, Economics & Finance": 0.00040020374190255454, "Food & Dining": 0.00018318967107751718, "Games & Gaming": 0.00032331938486450146, "Electronics & Hardware": 0.001158086458109794, "Health, Healthcare & Wellness": 0.0002756179282967419, "History & Geography": 0.00022990700348187704, "Home, Pets & Hobbies": 0.00014957445198004709, "Industry & Production": 0.00019459169325275908, "Language, Literature & Philosophy": 0.000156970040799138, "Politics & Government": 0.0001777860725805199, "Religion & Spirituality": 0.0002751100454159507, "Science, Mathematics & Engineering": 0.12961372588378797, "Relationships & Community": 0.0001255544147684731, "Software Usage & Applications": 0.0007766480055694801, "Programming & Software Engineering": 0.04538662627110336, "Sports & Fitness": 0.00020200775998441023, "Vehicles & Transportation": 0.00010117956028506264, "Travel & Tourism": 0.0003034636492270026, "Other / Uncovered Subject": 0.00045023223054192275, "NONE": 0.817382864577793}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 14176, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 14176, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:02.217792+00:00", "scoring_key_slot": "primary", "dataset_uid": "b88fefa1-7d3c-47c4-b4b3-ff2e96ad7bce", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "7eb56a24c5b6f4ab633958c3eb32b33976febb20fc74cdc8c02c62e0fd652b48", "source_metadata": {"uuid": "b88fefa1-7d3c-47c4-b4b3-ff2e96ad7bce", "repo_name": "antgroup/glake", "relative_path": "GLakeServe/tests/core/test_chunked_prefill_scheduler.py", "category": "TEST", "algo_rel_score": 0.9323449730873108, "quality_score": 7.911637306213379, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 54, "repo_created_at": "2023-06-06T04:19:31.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 469, "forks": 41, "issues": 13, "pull_requests": 0}, "file_meta": {"content_id": "1dbff723403d66b8a03de6f0d77e1b71eeb2b8bd", "size_bytes": 22255, "file_timestamp": 1732696856, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "fb24ee8cc7beeb55b9792f2da58e0ee673d8c864"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 54, "repo_created_at": "2023-06-06T04:19:31.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 469, "forks": 41, "issues": 13, "pull_requests": 0}, "file_meta": {"content_id": "1dbff723403d66b8a03de6f0d77e1b71eeb2b8bd", "size_bytes": 22255, "file_timestamp": 1732696856, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "fb24ee8cc7beeb55b9792f2da58e0ee673d8c864", "url": "https://github.com/antgroup/glake/blob/fb24ee8cc7beeb55b9792f2da58e0ee673d8c864/GLakeServe/tests/core/test_chunked_prefill_scheduler.py"}, "gpt4o_tokens": 5333, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.1910919426198987, "noise_severity": 0.017209983969503174, "missing_content_severity": 0.0025911213174778258, "factual_density": 3.1917841372549334, "pretraining_utility": 0.5447783770035621, "math_density": 0.8869132668325908, "code_density": 3.953655271430705, "reasoning_density": 1.7622550422859256, "education_level": 3.2520751034166397, "creative_writing_quality": -0.31626561909899525}, "score_applicability": {"education_level": 0.9988656650959828, "creative_writing_quality": 0.00019183530156229245}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/RemasteredGod/dock-shift/blob/0a5eac2cfcd2aba4b9a3f61f80ef0863fdad7192/container-yard-management-system/backend/routes/container2_routes.py", "id": "python-0-754ace6b1dae6e7aaf71cd08f9109027", "text": "from flask import Blueprint, request, jsonify\nfrom models import db, Container2\nfrom flask_login import current_user, login_required\nimport datetime\n\n# Create blueprint for Container2 routes\ncontainer2_bp = Blueprint('container2', __name__)\n\n@container2_bp.route('/api/containers2', methods=['GET'])\n@login_required\ndef get_containers2():\n    \"\"\"Get all containers2 for the current user\"\"\"\n    try:\n        containers = Container2.query.filter_by(user_id=current_user.id).all()\n        return jsonify({\n            'success': True,\n            'containers': [container.to_dict() for container in containers]\n        })\n    except Exception as e:\n        return jsonify({\n            'success': False,\n            'error': str(e)\n        }), 500\n\n@container2_bp.route('/api/containers2/<container_id>', methods=['GET'])\n@login_required\ndef get_container2(container_id):\n    \"\"\"Get a specific container2 by ID\"\"\"\n    try:\n        container = Container2.query.filter_by(\n            container_id=container_id, \n            user_id=current_user.id\n        ).first()\n        \n        if not container:\n            return jsonify({\n                'success': False,\n                'error': 'Container not found'\n            }), 404\n            \n        return jsonify({\n            'success': True,\n            'container': container.to_dict()\n        })\n        \n    except Exception as e:\n        return jsonify({\n            'success': False,\n            'error': str(e)\n        }), 500\n\n@container2_bp.route('/api/containers2', methods=['POST'])\n@login_required\ndef create_container2():\n    \"\"\"Create a new dashboard container (Container2)\"\"\"\n    try:\n        data = request.get_json()\n        \n        # Validate required fields\n        required_fields = ['container_id', 'status', 'destination', 'arrival']\n        for field in required_fields:\n            if field not in data or not data[field]:\n                return jsonify({\n                    'success': False,\n                    'error': f'Missing required field: {field}'\n                }), 400\n        \n        # Check if container_id already exists\n        existing_container = Container2.query.filter_by(\n            container_id=data['container_id']\n        ).first()\n        \n        if existing_container:\n            return jsonify({\n                'success': False,\n                'error': 'Container ID already exists'\n            }), 400\n        \n        # Create new container\n        new_container = Container2(\n            container_id=data['container_id'],\n            status=data['status'],\n            destination=data['destination'],\n            arrival=data['arrival'],\n            user_id=current_user.id\n        )\n        \n        db.session.add(new_container)\n        db.session.commit()\n        \n        return jsonify({\n            'success': True,\n            'message': 'Container created successfully',\n            'container': new_container.to_dict()\n        }), 201\n        \n    except Exception as e:\n        db.session.rollback()\n        return jsonify({\n            'success': False,\n            'error': str(e)\n        }), 500\n\n@container2_bp.route('/api/containers2/<container_id>', methods=['PUT'])\n@login_required\ndef update_container2(container_id):\n    \"\"\"Update an existing dashboard container (Container2)\"\"\"\n    try:\n        # Get the container\n        container = Container2.query.filter_by(\n            container_id=container_id,\n            user_id=current_user.id\n        ).first()\n        \n        if not container:\n            return jsonify({\n                'success': False,\n                'error': 'Container not found'\n            }), 404\n            \n        # Get the JSON data\n        data = request.get_json()\n        \n        # Update container fields if provided\n        if 'status' in data:\n            container.status = data['status']\n            \n        if 'destination' in data:\n            container.destination = data['destination']\n            \n        if 'arrival' in data:\n            container.arrival = data['arrival']\n        \n        # Update the timestamp\n        container.updated_at = datetime.datetime.utcnow()\n        \n        # Save changes\n        db.session.commit()\n        \n        return jsonify({\n            'success': True,\n            'message': 'Container updated successfully',\n            'container': container.to_dict()\n        })\n        \n    except Exception as e:\n        db.session.rollback()\n        return jsonify({\n            'success': False,\n            'error': str(e)\n        }), 500\n\n@container2_bp.route('/api/containers2/<container_id>', methods=['DELETE'])\n@login_required\ndef delete_container2(container_id):\n    \"\"\"Delete a dashboard container (Container2)\"\"\"\n    try:\n        # Get the container\n        container = Container2.query.filter_by(\n            container_id=container_id,\n            user_id=current_user.id\n        ).first()\n        \n        if not container:\n            return jsonify({\n                'success': False,\n                'error': 'Container not found'\n            }), 404\n            \n        # Delete the container\n        db.session.delete(container)\n        db.session.commit()\n        \n        return jsonify({\n            'success': True,\n            'message': 'Container deleted successfully'\n        })\n        \n    except Exception as e:\n        db.session.rollback()\n        return jsonify({\n            'success': False,\n            'error': str(e)\n        }), 500\n\n@container2_bp.route('/api/dashboard2', methods=['GET'])\n@login_required\ndef get_dashboard2_data():\n    \"\"\"Get dashboard container data for current user\"\"\"\n    try:\n        # Get containers specific to this dashboard\n        containers = Container2.query.filter_by(user_id=current_user.id).all()\n        \n        # Count containers by status\n        status_counts = {\n            'In Transit': 0,\n            'At Yard': 0,\n            'Delivered': 0,\n            'Delayed': 0\n        }\n        \n        for container in containers:\n            status = container.status\n            if status in status_counts:\n                status_counts[status] += 1\n        \n        # Calculate total containers\n        total_containers = len(containers)\n        \n        # Calculate percentage delivered\n        percentage_delivered = 0\n        if total_containers > 0:\n            percentage_delivered = (status_counts['Delivered'] / total_containers) * 100\n        \n        # Get containers due in the next 7 days\n        today = datetime.datetime.now().date()\n        upcoming_arrivals = []\n        \n        for container in containers:\n            try:\n                arrival_date = datetime.datetime.strptime(container.arrival, \"%Y-%m-%d\").date()\n                days_left = (arrival_date - today).days\n                \n                if 0 <= days_left <= 7:\n                    upcoming_arrivals.append({\n                        'container_id': container.container_id,\n                        'destination': container.destination,\n                        'arrival': container.arrival,\n                        'days_left': days_left\n                    })\n            except ValueError:\n                # Skip if date format is invalid\n                pass\n        \n        # Sort upcoming arrivals by days left\n        upcoming_arrivals.sort(key=lambda x: x['days_left'])\n        \n        # Prepare dashboard data\n        dashboard_data = {\n            'total_containers': total_containers,\n            'status_counts': status_counts,\n            'percentage_delivered': round(percentage_delivered, 1),\n            'upcoming_arrivals': upcoming_arrivals[:5],  # Top 5 upcoming arrivals\n            'containers': [container.to_dict() for container in containers]\n        }\n        \n        return jsonify(dashboard_data)\n        \n    except Exception as e:\n        return jsonify({\n            'success': False,\n            'error': str(e)\n        }), 500", "text_preview": "from flask import Blueprint, request, jsonify from models import db, Container2 from flask_login import current_user, login_required import datetime # Create blueprint for Container2 routes container2_bp = Blueprint('container2', __name__) ", "input_sha256": "e062c1eb0b034c5c5ee5d76108060ba355fc61049d2a3db20c3514d12663141f", "text_sha256": "754ace6b1dae6e7aaf71cd08f9109027455c989fbb8b558efc23ecef522111bc", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12041, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 1, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9563493473433189, "noise_severity": 0.037981775757561884, "missing_content_severity": 0.011862105322490636, "factual_density": 3.162987760834923, "pretraining_utility": 0.5398875172079566, "math_density": 0.7421542284526665, "code_density": 3.854219828561847, "reasoning_density": 0.7701625052311317, "education_level": 2.1028129008576864, "creative_writing_quality": -0.2611901995251601}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0374433699877903, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9962863439080217, "secondary": 0.9364631110384987, "tertiary": 0.9755344536344541, "structured_records": 0.07224543120993558, "procedural_steps": 0.011325964080321696, "worked_solution": 0.006312103188196585, "contextual_dialogue": 4.708294832704287e-06, "content_quality": 0.5792672428294617, "noise_severity": 0.9683485202020318, "missing_content_severity": 0.9901149122312578, "factual_density": 0.4792403143979249, "pretraining_utility": 0.4912031240483319, "math_density": 0.6465721392791484, "code_density": 0.878516523801539, "reasoning_density": 0.35819791230739007, "education_level": 0.8265567687765428, "creative_writing_quality": 0.9994769382410761, "primary_topic": 0.9647110885609886, "secondary_topic": 0.45542140747252263}, "probability_margin": {"primary": 0.994888398266743, "secondary": 0.9039514743670385, "tertiary": 0.9626048894327949, "education_level": 0.737205151573374, "creative_writing_quality": 0.9993536227543953, "primary_topic": 0.9414199601161982, "secondary_topic": 0.12307132270119503}, "raw_probabilities": {"primary": {"Academic Writing": 3.0208183180315608e-05, "Content Listing": 2.1696493829630427e-05, "Creative Writing": 1.485514791282846e-05, "Customer Support Page": 7.830830755552362e-06, "Q&A Forum": 1.0225584344234304e-05, "Discussion Thread": 5.787809209558187e-06, "Comment Section": 9.94667851533084e-06, "Missing Content": 1.6552750167163787e-05, "Knowledge Article": 6.303345654446962e-05, "Legal Notices": 2.1306074865364673e-05, "News Article": 3.953793911498022e-06, "Organizational Announcement": 9.042076860474591e-06, "Nonfiction Writing": 9.331527918310234e-06, "Organizational About Page": 9.524123538662966e-06, "Personal About Page": 8.699732335250763e-06, "Personal Blog": 6.310114846913124e-06, "Product Page": 6.959314448191734e-06, "Spam / Ads": 8.16759711632729e-06, "Structured Data": 0.0015526813117777924, "Technical Writing": 0.9964410795785208, "Transcript / Interview": 1.1232064037182975e-05, "Tutorial / How-To Guide": 0.0005910852435515555, "User Reviews": 5.154927160930567e-06, "Other / Unknown": 0.0011353355846519708}, "secondary": {"Academic Writing": 0.0003050556662598825, "Content Listing": 0.0003421954231772774, "Creative Writing": 0.00023347019631568522, "Customer Support Page": 0.00016668273364945485, "Q&A Forum": 0.00010324728380939001, "Discussion Thread": 8.839179074603941e-05, "Comment Section": 0.00018914855886792138, "Missing Content": 0.00039906727914987066, "Knowledge Article": 0.0005174188657839166, "Legal Notices": 0.0002455547689645583, "News Article": 0.00013734157189442006, "Organizational Announcement": 0.00013066811547224734, "Nonfiction Writing": 0.0001344347166278468, "Organizational About Page": 0.00015132389401607618, "Personal About Page": 0.000110724652427755, "Personal Blog": 0.00012228635810092181, "Product Page": 9.516407931388205e-05, "Spam / Ads": 0.00013476371291968554, "Structured Data": 0.03515900704485603, "Technical Writing": 0.020207802746379823, "Transcript / Interview": 0.00016752632146571617, "Tutorial / How-To Guide": 0.001687551751938096, "User Reviews": 6.069105596886554e-05, "NONE": 0.9391104814118946}, "tertiary": {"Academic Writing": 8.295807608210469e-05, "Content Listing": 0.00013162401731980827, "Creative Writing": 8.210610096651238e-05, "Customer Support Page": 5.96566613180755e-05, "Q&A Forum": 2.849980712224661e-05, "Discussion Thread": 2.6604685143617775e-05, "Comment Section": 0.00010260454173205849, "Missing Content": 0.00021933884738849045, "Knowledge Article": 0.00042335357086078704, "Legal Notices": 0.00012917398457413813, "News Article": 4.1625440883474614e-05, "Organizational Announcement": 3.897462811350143e-05, "Nonfiction Writing": 4.947442226848448e-05, "Organizational About Page": 7.026511195569685e-05, "Personal About Page": 3.6822166609989627e-05, "Personal Blog": 3.6186096015915734e-05, "Product Page": 3.9375481349036085e-05, "Spam / Ads": 4.498867198697998e-05, "Structured Data": 0.013948961966890376, "Technical Writing": 0.006835405761449685, "Transcript / Interview": 5.2442693622593014e-05, "Tutorial / How-To Guide": 0.0009458807648710878, "User Reviews": 1.9825101790271606e-05, "NONE": 0.9765538513996852}, "structured_records": {"false": 0.9277545687900645, "true": 0.07224543120993558}, "procedural_steps": {"false": 0.9886740359196783, "true": 0.011325964080321696}, "worked_solution": {"false": 0.9936878968118034, "true": 0.006312103188196585}, "contextual_dialogue": {"false": 0.9999952917051673, "true": 4.708294832704287e-06}, "content_quality": {"-2": 0.00567402875110722, "-1": 0.016442257710935354, "0": 0.22435837895547112, "1": 0.5229110066085039, "2": 0.23061432797398243}, "noise_severity": {"0": 0.9693580688181501, "1": 0.025210991919059584, "2": 0.0041255607197369, "3": 0.0007018517731853414, "4": 0.0006035267698681198}, "missing_content_severity": {"0": 0.9931281306102778, "1": 0.0031554937556035564, "2": 0.00264422650951933, "3": 0.0008704379505477859, "4": 0.0002017111740512649}, "factual_density": {"0": 0.006959877881133785, "1": 0.01911902317436293, "2": 0.1718442509516562, "3": 0.4081271562141406, "4": 0.39394969177870665}, "pretraining_utility": {"-2": 0.007397862751274746, "-1": 0.0676712297782282, "0": 0.3777983191567419, "1": 0.47191070413877595, "2": 0.07522188417497917}, "math_density": {"0": 0.34097960220617773, "1": 0.5955779935917013, "2": 0.05095759516796149, "3": 0.005278191611595301, "4": 0.007206617422564066}, "code_density": {"0": 0.0002399944779908926, "1": 0.0004733601987735333, "2": 0.020406029782834043, "3": 0.1025880533642009, "4": 0.8762925621762006}, "reasoning_density": {"0": 0.6166364464261063, "1": 0.1084708946896563, "2": 0.18792544089526267, "3": 0.06202814320494951, "4": 0.02493907478402544}, "education_level": {"none": 0.0006937658507579623, "0": 0.0010858721397189017, "1": 0.020780212197857088, "2": 0.8554639739804524, "3": 0.11825882240707843, "4": 0.003717353424135306}, "creative_writing_quality": {"not_applicable": 0.9995641152008967, "-2": 5.4138641726045616e-05, "-1": 9.899676627519954e-05, "0": 0.00021049244650136567, "1": 5.108867712151492e-05, "2": 2.1168267479020393e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.4410560152182305e-05, "Art & Design": 7.980365278554104e-05, "Law & Crime": 6.326392423220269e-05, "Education & Careers": 5.496611762530323e-05, "Entertainment & Media": 4.071826271521435e-05, "Fashion & Beauty": 4.6023377330235264e-05, "Business, Economics & Finance": 0.004563505348013212, "Food & Dining": 6.565201921558064e-05, "Games & Gaming": 0.00010290281457356684, "Electronics & Hardware": 7.730889822990536e-05, "Health, Healthcare & Wellness": 4.802164628324009e-05, "History & Geography": 5.1607729125010445e-05, "Home, Pets & Hobbies": 4.0091992997543045e-05, "Industry & Production": 0.002495834403614016, "Language, Literature & Philosophy": 5.030751470535259e-05, "Politics & Government": 2.861980672204604e-05, "Religion & Spirituality": 4.2283055227142814e-05, "Science, Mathematics & Engineering": 0.00010302580306602077, "Relationships & Community": 2.8548943908500236e-05, "Software Usage & Applications": 0.00027586205152216016, "Programming & Software Engineering": 0.966068354385566, "Sports & Fitness": 7.503103555497271e-05, "Vehicles & Transportation": 0.02464839426936787, "Travel & Tourism": 8.014488158399154e-05, "Other / Uncovered Subject": 0.0007625411760079331, "Unknown / Insufficient Content": 6.277632987547372e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008716203497925266, "Art & Design": 0.0011448997756700703, "Law & Crime": 0.0009146301250322525, "Education & Careers": 0.000609098400370584, "Entertainment & Media": 0.0006604043951618883, "Fashion & Beauty": 0.0004486920298010039, "Business, Economics & Finance": 0.060582008503955516, "Food & Dining": 0.0005426176861535456, "Games & Gaming": 0.000773630672946355, "Electronics & Hardware": 0.0008843244770968848, "Health, Healthcare & Wellness": 0.0005877414497089886, "History & Geography": 0.0005051705577937262, "Home, Pets & Hobbies": 0.00048447533623481814, "Industry & Production": 0.009042468539753492, "Language, Literature & Philosophy": 0.00036005192892189563, "Politics & Government": 0.0003324169995102986, "Religion & Spirituality": 0.0006021862207326971, "Science, Mathematics & Engineering": 0.0007411599374604189, "Relationships & Community": 0.00025827215296976406, "Software Usage & Applications": 0.0018406971589441817, "Programming & Software Engineering": 0.08414163991825054, "Sports & Fitness": 0.0004810834778840303, "Vehicles & Transportation": 0.35329541525315367, "Travel & Tourism": 0.0007540134728005225, "Other / Uncovered Subject": 0.0027745432255517665, "NONE": 0.4763667379543487}}, "probabilities": {"primary": {"Academic Writing": 3.02081831803156e-05, "Content Listing": 2.1696493829630424e-05, "Creative Writing": 1.4855147912828457e-05, "Customer Support Page": 7.83083075555236e-06, "Q&A Forum": 1.0225584344234302e-05, "Discussion Thread": 5.787809209558185e-06, "Comment Section": 9.946678515330838e-06, "Missing Content": 1.6552750167163783e-05, "Knowledge Article": 6.30334565444696e-05, "Legal Notices": 2.130607486536467e-05, "News Article": 3.953793911498021e-06, "Organizational Announcement": 9.04207686047459e-06, "Nonfiction Writing": 9.331527918310232e-06, "Organizational About Page": 9.524123538662965e-06, "Personal About Page": 8.699732335250762e-06, "Personal Blog": 6.3101148469131225e-06, "Product Page": 6.959314448191732e-06, "Spam / Ads": 8.167597116327288e-06, "Structured Data": 0.001552681311777792, "Technical Writing": 0.9964410795785206, "Transcript / Interview": 1.1232064037182973e-05, "Tutorial / How-To Guide": 0.0005910852435515554, "User Reviews": 5.154927160930566e-06, "Other / Unknown": 0.0011353355846519706}, "secondary": {"Academic Writing": 0.00030505566625988254, "Content Listing": 0.00034219542317727745, "Creative Writing": 0.00023347019631568525, "Customer Support Page": 0.00016668273364945488, "Q&A Forum": 0.00010324728380939002, "Discussion Thread": 8.839179074603942e-05, "Comment Section": 0.0001891485588679214, "Missing Content": 0.0003990672791498707, "Knowledge Article": 0.0005174188657839167, "Legal Notices": 0.00024555476896455836, "News Article": 0.00013734157189442008, "Organizational Announcement": 0.00013066811547224737, "Nonfiction Writing": 0.00013443471662784683, "Organizational About Page": 0.0001513238940160762, "Personal About Page": 0.00011072465242775502, "Personal Blog": 0.00012228635810092184, "Product Page": 9.516407931388207e-05, "Spam / Ads": 0.00013476371291968556, "Structured Data": 0.035159007044856035, "Technical Writing": 0.020207802746379826, "Transcript / Interview": 0.0001675263214657162, "Tutorial / How-To Guide": 0.0016875517519380962, "User Reviews": 6.069105596886555e-05, "NONE": 0.9391104814118947}, "tertiary": {"Academic Writing": 8.295807608210467e-05, "Content Listing": 0.00013162401731980824, "Creative Writing": 8.210610096651236e-05, "Customer Support Page": 5.965666131807549e-05, "Q&A Forum": 2.8499807122246604e-05, "Discussion Thread": 2.660468514361777e-05, "Comment Section": 0.00010260454173205846, "Missing Content": 0.0002193388473884904, "Knowledge Article": 0.00042335357086078693, "Legal Notices": 0.0001291739845741381, "News Article": 4.162544088347461e-05, "Organizational Announcement": 3.897462811350142e-05, "Nonfiction Writing": 4.947442226848447e-05, "Organizational About Page": 7.026511195569683e-05, "Personal About Page": 3.682216660998962e-05, "Personal Blog": 3.618609601591573e-05, "Product Page": 3.937548134903608e-05, "Spam / Ads": 4.4988671986979974e-05, "Structured Data": 0.013948961966890373, "Technical Writing": 0.006835405761449683, "Transcript / Interview": 5.2442693622593e-05, "Tutorial / How-To Guide": 0.0009458807648710875, "User Reviews": 1.9825101790271603e-05, "NONE": 0.976553851399685}, "structured_records": {"false": 0.9277545687900645, "true": 0.07224543120993558}, "procedural_steps": {"false": 0.9886740359196783, "true": 0.011325964080321696}, "worked_solution": {"false": 0.9936878968118034, "true": 0.006312103188196585}, "contextual_dialogue": {"false": 0.9999952917051673, "true": 4.708294832704287e-06}, "content_quality": {"-2": 0.00567402875110722, "-1": 0.016442257710935354, "0": 0.22435837895547112, "1": 0.5229110066085039, "2": 0.23061432797398243}, "noise_severity": {"0": 0.9693580688181501, "1": 0.025210991919059584, "2": 0.0041255607197369, "3": 0.0007018517731853414, "4": 0.0006035267698681198}, "missing_content_severity": {"0": 0.993128130610278, "1": 0.0031554937556035573, "2": 0.0026442265095193303, "3": 0.0008704379505477862, "4": 0.00020171117405126494}, "factual_density": {"0": 0.006959877881133783, "1": 0.019119023174362926, "2": 0.17184425095165617, "3": 0.4081271562141405, "4": 0.39394969177870653}, "pretraining_utility": {"-2": 0.007397862751274746, "-1": 0.0676712297782282, "0": 0.3777983191567419, "1": 0.47191070413877595, "2": 0.07522188417497917}, "math_density": {"0": 0.3409796022061778, "1": 0.5955779935917014, "2": 0.0509575951679615, "3": 0.005278191611595302, "4": 0.0072066174225640664}, "code_density": {"0": 0.00023999447799089263, "1": 0.00047336019877353337, "2": 0.020406029782834047, "3": 0.10258805336420092, "4": 0.8762925621762007}, "reasoning_density": {"0": 0.6166364464261062, "1": 0.10847089468965627, "2": 0.1879254408952626, "3": 0.062028143204949496, "4": 0.024939074784025433}, "education_level": {"none": 0.0006937658507579623, "0": 0.0010858721397189017, "1": 0.020780212197857088, "2": 0.8554639739804524, "3": 0.11825882240707843, "4": 0.003717353424135306}, "creative_writing_quality": {"not_applicable": 0.9995641152008968, "-2": 5.413864172604562e-05, "-1": 9.899676627519956e-05, "0": 0.0002104924465013657, "1": 5.108867712151493e-05, "2": 2.1168267479020396e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.44105601521823e-05, "Art & Design": 7.980365278554103e-05, "Law & Crime": 6.326392423220267e-05, "Education & Careers": 5.496611762530322e-05, "Entertainment & Media": 4.071826271521434e-05, "Fashion & Beauty": 4.602337733023525e-05, "Business, Economics & Finance": 0.004563505348013211, "Food & Dining": 6.565201921558063e-05, "Games & Gaming": 0.00010290281457356681, "Electronics & Hardware": 7.730889822990534e-05, "Health, Healthcare & Wellness": 4.802164628324008e-05, "History & Geography": 5.160772912501043e-05, "Home, Pets & Hobbies": 4.009199299754304e-05, "Industry & Production": 0.0024958344036140156, "Language, Literature & Philosophy": 5.0307514705352576e-05, "Politics & Government": 2.8619806722046032e-05, "Religion & Spirituality": 4.228305522714281e-05, "Science, Mathematics & Engineering": 0.00010302580306602074, "Relationships & Community": 2.854894390850023e-05, "Software Usage & Applications": 0.0002758620515221601, "Programming & Software Engineering": 0.9660683543855658, "Sports & Fitness": 7.50310355549727e-05, "Vehicles & Transportation": 0.024648394269367862, "Travel & Tourism": 8.014488158399153e-05, "Other / Uncovered Subject": 0.0007625411760079329, "Unknown / Insufficient Content": 6.277632987547371e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008716203497925264, "Art & Design": 0.00114489977567007, "Law & Crime": 0.0009146301250322523, "Education & Careers": 0.0006090984003705839, "Entertainment & Media": 0.0006604043951618882, "Fashion & Beauty": 0.0004486920298010038, "Business, Economics & Finance": 0.0605820085039555, "Food & Dining": 0.0005426176861535455, "Games & Gaming": 0.0007736306729463548, "Electronics & Hardware": 0.0008843244770968846, "Health, Healthcare & Wellness": 0.0005877414497089885, "History & Geography": 0.0005051705577937261, "Home, Pets & Hobbies": 0.00048447533623481804, "Industry & Production": 0.00904246853975349, "Language, Literature & Philosophy": 0.0003600519289218956, "Politics & Government": 0.00033241699951029857, "Religion & Spirituality": 0.000602186220732697, "Science, Mathematics & Engineering": 0.0007411599374604187, "Relationships & Community": 0.000258272152969764, "Software Usage & Applications": 0.0018406971589441812, "Programming & Software Engineering": 0.08414163991825052, "Sports & Fitness": 0.0004810834778840302, "Vehicles & Transportation": 0.3532954152531536, "Travel & Tourism": 0.0007540134728005222, "Other / Uncovered Subject": 0.002774543225551766, "NONE": 0.4763667379543486}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10218, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10218, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:06.883274+00:00", "scoring_key_slot": "primary", "dataset_uid": "8fdf28c0-af87-4efe-a4d6-0647b55bc1e3", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "30c1974a35a2498a1beb2bf73f5893ba9309df85b9ab50dd10a7dc74b808a2a7", "source_metadata": {"uuid": "8fdf28c0-af87-4efe-a4d6-0647b55bc1e3", "repo_name": "RemasteredGod/dock-shift", "relative_path": "container-yard-management-system/backend/routes/container2_routes.py", "category": "WEB", "algo_rel_score": 0.9193006157875061, "quality_score": 5.656985282897949, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 20, "repo_created_at": "2025-04-25T16:37:31.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c0164f153179449fcc549fee5d488e8be83c1e59", "size_bytes": 7934, "file_timestamp": 1745730257, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0a5eac2cfcd2aba4b9a3f61f80ef0863fdad7192"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 20, "repo_created_at": "2025-04-25T16:37:31.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c0164f153179449fcc549fee5d488e8be83c1e59", "size_bytes": 7934, "file_timestamp": 1745730257, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0a5eac2cfcd2aba4b9a3f61f80ef0863fdad7192", "url": "https://github.com/RemasteredGod/dock-shift/blob/0a5eac2cfcd2aba4b9a3f61f80ef0863fdad7192/container-yard-management-system/backend/routes/container2_routes.py"}, "gpt4o_tokens": 1533, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.9563493473433189, "noise_severity": 0.037981775757561884, "missing_content_severity": 0.011862105322490636, "factual_density": 3.1629877608349233, "pretraining_utility": 0.5398875172079566, "math_density": 0.7421542284526665, "code_density": 3.854219828561847, "reasoning_density": 0.7701625052311317, "education_level": 2.1028129008576864, "creative_writing_quality": -0.2611901995251601}, "score_applicability": {"education_level": 0.9993062341492421, "creative_writing_quality": 0.0004358847991031462}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/biokit/biokit/blob/53cb3f7d24c79fd9c0ee613204fb3e14fd370e67/biokit/rtools/r4python.py", "id": "python-0-59ffeae79fc9e2abc246e03bdac232e0", "text": "# -*- python -*-\n#\n#  This file is part of biokit software\n#\n#  Copyright (c) 2014 - EBI-EMBL\n#\n#  File author(s): <NAME> <<EMAIL>>\n#\n#  Distributed under the GPLv3 License.\n#  See accompanying file LICENSE.txt or copy at\n#      http://www.gnu.org/licenses/gpl-3.0.html\n#\n#  website: http://github.com/cellnopt\n#\n##############################################################################\n\"\"\"Code use by pyper module original version available from pyper pacakge on pip\"\"\"\n\n__all__ = [\"r4python\"]\n\nr4python = r\"\"\"\n\n.getRvalue4Python__ <- function(x, use_dict=NULL) {\n\n    headstr <- 'numpy.array('\n    tailstr <- ')'\n\n    SpecialLocs <- function(x) { # find locations of special values: NULL, NA, NaN, Inf\n        rlt <- list()\n\n            idx <- which(is.null(x) | is.na(x))\n            if (length(idx) > 0) rlt$None <- idx\n            idx <- which(is.nan(x))\n            if (length(idx) > 0) rlt$numpy.NaN <- idx\n            idx <- which(is.infinite(x))\n            if (length(idx) > 0) {\n                v <- x[idx]\n                iidx <- which(v > 0)\n                if (length(iidx) > 0) rlt$numpy.Inf <- idx[iidx]\n                iidx <- which(v < 0)\n                if (length(iidx) > 0) rlt['-numpy.Inf'] <- idx[iidx]\n                }\n\n        return(rlt)\n        }\n\n\n    SpecialVals <- function(x, valoc) {\n        for (val in names(valoc)) x[valoc[[val]]] <- val\n        return(x)\n        }\n\n    NullStr <- function(x) 'None'\n\n    VectorStr <- function(x) {\n        #nms <- names(x)\n        #if (!is.null(nms) &&  length(nms)>0) return(ListStr(as.list(x)))\n        complx <- is.complex(x)\n        special_locs <- SpecialLocs(x)\n        if (is.character(x)) {\n            x <- gsub('\\\\\\\\', '\\\\\\\\\\\\\\\\', x)\n            x <- gsub('\"', '\\\\\\\\\"', x)\n            x <- paste('\"', x, '\"', sep='') }\n        else if (is.logical(x)) x <- ifelse(x, 'True', 'False')\n        if (length(special_locs) > 0) x <- SpecialVals(x, special_locs)\n        if (length(x)==1) x <- paste(x) # convert to character using paste, \"gettext\", or \"as.character\"\n        else x <- paste(headstr, '[', paste(x, collapse=','), ']', tailstr, sep='')\n        if (complx) x <- gsub('i', 'j', x)\n        return(x)\n    }\n\n    MatrixStr <- function(x) {\n        complx <- is.complex(x)\n        special_locs <- SpecialLocs(x)\n        if (is.character(x)) x <- matrix(paste('\"', x, '\"', sep=''), nrow=nrow(x))\n        else if (is.logical(x)) x <- ifelse(x, 'True', 'False')\n        if (length(special_locs) > 0) x <- SpecialVals(x, special_locs)\n        x <- apply(x, 1, function(r) paste('[', paste(r, collapse=','), ']', sep=''))\n        x <- paste(headstr, '[', paste(x, collapse=','), ']', tailstr, sep='')\n        if (complx) x <- gsub('i', 'j', x)\n        return(x)\n    }\n\n    ArrayStr <- function(x) {\n        complx <- is.complex(x)\n        ndim <- length(dim(x))\n        if (ndim == 1) return(VectorStr(x))\n        if (ndim == 2) return(MatrixStr(x))\n        # ndim >= 3\n        if (is.character(x)) x <- array(paste('\"', x, '\"', sep=''), dim=dim(x))\n        else if (is.logical(x)) x <- ifelse(x, 'True', 'False')\n        # do col first\n        x <- apply(x, seq(dim(x))[-2], function(r) paste('[', paste(r, collapse=','), ']', sep=''))\n        for (i in seq(ndim-2))\n            x <- apply(x, seq(dim(x))[-1], function(r) paste('[', paste(r, collapse=','), ']', sep=''))\n        x <- paste(headstr, '[', paste(x, collapse=','), ']', tailstr, sep='')\n        if (complx) x <- gsub('i', 'j', x)\n        return(x)\n    }\n\n    DataFrameStr <- function(x) {\n        if (ncol(x) == 0) {\n            return('pandas.DataFrame()')\n            }\n\n            cnms <- colnames(x) # get column names\n            ctp <- list()\n            for (i in seq(x)) {\n                xi <- as.vector(x[[i]])\n                special_locs <- SpecialLocs(xi)\n                if (is.character(xi)) {\n                    ctp[i] <- sprintf('(\"%s\", \"|S%d\")', cnms[i], if (length(xi) > 0) max(nchar(xi)) else 0 )\n                    # Hack TC July 2017 S%d above was sometime replaced by SNA\n                    # in Python3. Let us use object for now\n                    ctp[i] <- sprintf('(\"%s\", \"object\")', cnms[i], 50 )\n\n                    xi <- paste('\"', xi, '\"', sep='') }\n                else if (is.logical(xi)) {\n                    xi <- ifelse(xi, 'True', 'False')\n                    ctp[i] <- paste('(\"', cnms[i], '\", \"<?\")' ) }\n                else if (is.integer(xi)) {\n                    xi <- paste(xi)\n                    ctp[i] <- paste('(\"', cnms[i], '\", \"<q\")' ) }\n                else if (is.double(xi)) {\n                    xi <- paste(xi)\n                    ctp[i] <- paste('(\"', cnms[i], '\", \"<g\")' ) }\n                else if (is.complex(xi)) {\n                    xi <- gsub('i', 'j', paste(xi))\n                    ctp[i] <- paste('(\"', cnms[i], '\", \"<G\")') }\n                if (length(special_locs) > 0) xi <- SpecialVals(xi, special_locs)\n                if (nrow(x) > 0) x[[i]] <- xi }\n            tailstr <- paste(', dtype=[', paste(ctp, collapse=','), ']', tailstr, sep='')\n\n        x <- as.matrix(x)\n        x <- apply(x, 1, function(r) paste('(', paste(r, collapse=','), if(length(r)<2) ',)' else ')', sep=''))\n        x <- paste(headstr, '[', paste(x, collapse=','), ']', tailstr, sep='')\n        x <- paste('pandas.DataFrame(', x, ')', sep='')\n        return(x)\n    }\n\n    ListStr <- function(x) {\n        nms <- names(x) # get column names\n        x <- sapply(x, Str4Py)\n        return(zipVecWithName(x, nms))\n    }\n\n    zipVecWithName <- function(x, nms) {\n        if (!is.null(nms) &&  length(nms)>0) {\n            nms <- paste('\"', nms, '\"', sep='')\n            x <- sapply(seq(nms), function(i) paste('(', nms[i], ',', x[i], ')') )\n            if (identical(use_dict, TRUE)) x <- paste('dict([', paste(x, collapse=','), '])', sep='')\n            else if (identical(use_dict, FALSE))  x <- paste('[', paste(x, collapse=','), ']', sep='')\n            else { # should be NULL or something else\n                if (any(duplicated(nms))) x <- paste('[', paste(x, collapse=','), ']', sep='')\n                else x <- paste('dict([', paste(x, collapse=','), '])', sep='') } }\n        else x <- paste('[', paste(x, collapse=','), ']', sep='')\n        return(x)\n    }\n\n    Str4Py <- function(x) {\n        # no considering on NA, Inf, ...\n        # use is.XXX, typeof, class, mode, storage.mode, sprintf\n        if (is.factor(x)) x <- as.vector(x)\n        rlt <- {\n            if (is.null(x)) NullStr(x)\n            else if (is.vector(x) && !is.list(x)) VectorStr(x)\n            else if (is.matrix(x) || is.array(x)) ArrayStr(x)\n            else if (is.data.frame(x)) DataFrameStr(x)\n            else if (is.list(x)) ListStr(x)\n            else if (is.object(x)) 'None'\n            else Str4Py(as.character(x)) }\n            # other objects will be convert to character (instead of NullStr), or use \"gettext\"\n        return(rlt)\n    }\n    Str4Py(x)\n    }\n\n    # initalize library path for TCL/TK based environment on Windows, e.g. Python IDLE\n    .addLibs <- function() {\n        ruser <- Sys.getenv('R_USER')\n        userpath <- Sys.getenv('R_LIBS_USER')\n        libpaths <- .libPaths()\n        for (apath in userpath) {\n            if (length(grep(apath, libpaths)) > 0) next\n            if (file.exists(apath)) .libPaths(apath)\n            else {\n                d <- '/Documents'\n                if (substr(ruser, nchar(ruser)-nchar(d)+1, nchar(ruser)) != d) {\n                    apath <- paste(ruser,d, substr(apath, nchar(ruser)+1, nchar(apath)), sep='')\n                    if (file.exists(apath)) .libPaths(apath)\n                }\n            }\n        }\n    }\n\n    if(identical(.Platform$OS.type, 'windows')) .addLibs()\n    rm(.addLibs)\n\n\"\"\"\n", "text_preview": "# -*- python -*- # # This file is part of biokit software # # Copyright (c) 2014 - EBI-EMBL # # File author(s): <NAME> <<EMAIL>> # # Distributed under the GPLv3 License. # See accompanying file LICENSE.txt or copy at # http://www.gnu.org/li", "input_sha256": "25d5ca9006047724562b7edfe59a1dff2ef8be57cd4c457a845db6167bcd8da2", "text_sha256": "59ffeae79fc9e2abc246e03bdac232e036f0f8ffd32b31b1140c778aa6a148f5", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12043, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Legal Notices", "sub_format": null}, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.6297279828401086, "noise_severity": 0.38667898469446316, "missing_content_severity": 0.06357987716413055, "factual_density": 3.318425840952132, "pretraining_utility": 0.3867429733916552, "math_density": 0.31644940628548535, "code_density": 3.8147433166091846, "reasoning_density": 0.8178801495648109, "education_level": 2.6298467319645846, "creative_writing_quality": -0.3510433121075493}, "api_choices": {"primary": "Technical Writing", "secondary": "Legal Notices", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": ["secondary probability 0.5454 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.382145192203568, "secondary_choice": "Legal Notices", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9955082433777478, "secondary": 0.525612468096697, "tertiary": 0.7712669151693553, "structured_records": 0.002912491217412922, "procedural_steps": 0.00551976547932814, "worked_solution": 0.0016947621475537624, "contextual_dialogue": 7.714208133853234e-06, "content_quality": 0.4455378514638624, "noise_severity": 0.677767512754614, "missing_content_severity": 0.9470167690298912, "factual_density": 0.43202153412677635, "pretraining_utility": 0.45193905929457856, "math_density": 0.7362921614287622, "code_density": 0.8456194305076539, "reasoning_density": 0.3184332086959909, "education_level": 0.3483986944179326, "creative_writing_quality": 0.9984572337943114, "primary_topic": 0.9716627281868442, "secondary_topic": 0.8487914191313924}, "probability_margin": {"primary": 0.9942895407552985, "secondary": 0.14672634709227061, "tertiary": 0.5873707720301249, "education_level": 0.015435326198281696, "creative_writing_quality": 0.9981239973522679, "primary_topic": 0.9502282649504944, "secondary_topic": 0.7624891319975791}, "raw_probabilities": {"primary": {"Academic Writing": 3.8187359374886904e-05, "Content Listing": 1.969185151327545e-05, "Creative Writing": 1.3256819457780503e-05, "Customer Support Page": 1.1683514830466324e-05, "Q&A Forum": 1.2577425827145177e-05, "Discussion Thread": 8.426081053653644e-06, "Comment Section": 1.341963760849291e-05, "Missing Content": 4.8152259004363654e-05, "Knowledge Article": 9.372648597265204e-05, "Legal Notices": 0.0012655164083423375, "News Article": 5.740569699019494e-06, "Organizational Announcement": 2.0936723498966342e-05, "Nonfiction Writing": 1.2845115161952175e-05, "Organizational About Page": 2.1128599954915763e-05, "Personal About Page": 1.0118999955845047e-05, "Personal Blog": 7.956599033434333e-06, "Product Page": 7.247810342620179e-06, "Spam / Ads": 1.0343446847555554e-05, "Structured Data": 0.0009652897154666729, "Technical Writing": 0.995695399903675, "Transcript / Interview": 1.4437193711598217e-05, "Tutorial / How-To Guide": 0.00029080739257055513, "User Reviews": 7.250938720462716e-06, "Other / Unknown": 0.0014058591483763936}, "secondary": {"Academic Writing": 0.0007820282418143355, "Content Listing": 0.0005325424566602253, "Creative Writing": 0.0005946413185312372, "Customer Support Page": 0.0003932226822076058, "Q&A Forum": 0.00035111452972933394, "Discussion Thread": 0.00034234912047329934, "Comment Section": 0.0005811440662086006, "Missing Content": 0.0007492193346454011, "Knowledge Article": 0.0011367039324305813, "Legal Notices": 0.5453786152593346, "News Article": 0.0005638569538359331, "Organizational Announcement": 0.0006919828297647946, "Nonfiction Writing": 0.0005117724543284734, "Organizational About Page": 0.0007903193167188943, "Personal About Page": 0.0004978915751369695, "Personal Blog": 0.0002745626340289642, "Product Page": 0.00039997518080736214, "Spam / Ads": 0.00043056411725466044, "Structured Data": 0.0019045605769028582, "Technical Writing": 0.04276900105347837, "Transcript / Interview": 0.0006555899339536572, "Tutorial / How-To Guide": 0.0007509708595808986, "User Reviews": 0.0002651034051090715, "NONE": 0.398652268167064}, "tertiary": {"Academic Writing": 0.0005278765346247466, "Content Listing": 0.00031584966683775525, "Creative Writing": 0.0004259451127954557, "Customer Support Page": 0.0002594331431857988, "Q&A Forum": 0.00018995844999284098, "Discussion Thread": 0.00016541031225820787, "Comment Section": 0.00029302920368008684, "Missing Content": 0.0005279445010094502, "Knowledge Article": 0.0012739926919035365, "Legal Notices": 0.19342668834050727, "News Article": 0.0002886187085694465, "Organizational Announcement": 0.00043338639495804073, "Nonfiction Writing": 0.00029501246620035576, "Organizational About Page": 0.0007315052448931678, "Personal About Page": 0.00033394289406877745, "Personal Blog": 0.0001857694801657138, "Product Page": 0.00023408267019699123, "Spam / Ads": 0.0003194879360412669, "Structured Data": 0.001101682180098482, "Technical Writing": 0.016777394060226485, "Transcript / Interview": 0.00034486159383727446, "Tutorial / How-To Guide": 0.0006149482076681649, "User Reviews": 0.00013571983564875507, "NONE": 0.7807974603706321}, "structured_records": {"false": 0.9970875087825871, "true": 0.002912491217412922}, "procedural_steps": {"false": 0.9944802345206719, "true": 0.00551976547932814}, "worked_solution": {"false": 0.9983052378524463, "true": 0.0016947621475537624}, "contextual_dialogue": {"false": 0.9999922857918662, "true": 7.714208133853234e-06}, "content_quality": {"-2": 0.016302254958781074, "-1": 0.07736890327666941, "0": 0.3141687262719462, "1": 0.4446188349508667, "2": 0.14754128054173674}, "noise_severity": {"0": 0.6341861408719461, "1": 0.3515499804699254, "2": 0.00974935544288081, "3": 0.002427799522214321, "4": 0.002086723693033291}, "missing_content_severity": {"0": 0.9628413921882338, "1": 0.017534936474223727, "2": 0.013955985964433126, "3": 0.004537772731396842, "4": 0.0011299126417125115}, "factual_density": {"0": 0.00786804649702747, "1": 0.027802515826086335, "2": 0.1052068568907459, "3": 0.35628071180000764, "4": 0.5028418689861326}, "pretraining_utility": {"-2": 0.011895459903039748, "-1": 0.11167415792134582, "0": 0.42096219409736857, "1": 0.38872832503741106, "2": 0.0667398630408347}, "math_density": {"0": 0.8536294657208301, "1": 0.07026041228618184, "2": 0.021638665183046427, "3": 0.01497416360655562, "4": 0.03949729320338594}, "code_density": {"0": 0.0003309530184408549, "1": 0.0007509850895806998, "2": 0.01651844332413159, "3": 0.14864302940004662, "4": 0.8337565891678003}, "reasoning_density": {"0": 0.6531987957867007, "1": 0.09069039321879517, "2": 0.1133664541174037, "3": 0.07052057939719351, "4": 0.07222377747990695}, "education_level": {"none": 0.0017624027227569603, "0": 0.0017515859188118401, "1": 0.006868692953906737, "2": 0.4415635858166621, "3": 0.4569989120149438, "4": 0.09105482057291854}, "creative_writing_quality": {"not_applicable": 0.9987143614952595, "-2": 0.00018747513850434795, "-1": 0.0003176744719239758, "0": 0.0005903641429915787, "1": 0.00013893955258534075, "2": 5.118519873513559e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.834854263047272e-05, "Art & Design": 8.049019145542951e-05, "Law & Crime": 0.00013109754335943985, "Education & Careers": 6.847997848777443e-05, "Entertainment & Media": 4.403421054408982e-05, "Fashion & Beauty": 4.1136525802565564e-05, "Business, Economics & Finance": 8.906665540476181e-05, "Food & Dining": 6.262535407732636e-05, "Games & Gaming": 7.142320194299652e-05, "Electronics & Hardware": 0.00011497915776273552, "Health, Healthcare & Wellness": 0.0009982715247828086, "History & Geography": 8.15312264214255e-05, "Home, Pets & Hobbies": 4.077593631482282e-05, "Industry & Production": 6.485152605148669e-05, "Language, Literature & Philosophy": 0.0002022454373352985, "Politics & Government": 4.0401897685898246e-05, "Religion & Spirituality": 4.9251679458305645e-05, "Science, Mathematics & Engineering": 0.022524358306086577, "Relationships & Community": 2.3043392478839768e-05, "Software Usage & Applications": 0.001185690462516381, "Programming & Software Engineering": 0.972752623256581, "Sports & Fitness": 6.868977331229519e-05, "Vehicles & Transportation": 2.755724511394087e-05, "Travel & Tourism": 6.689989367213159e-05, "Other / Uncovered Subject": 0.0008820265604966594, "Unknown / Insufficient Content": 0.00022010052022455913}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005797643547971148, "Art & Design": 0.0004879380054835526, "Law & Crime": 0.006210900628100783, "Education & Careers": 0.00041986136502901255, "Entertainment & Media": 0.0004231239114895982, "Fashion & Beauty": 0.0002763404117403347, "Business, Economics & Finance": 0.0005742922417643658, "Food & Dining": 0.0002555171711236963, "Games & Gaming": 0.0003333613182992456, "Electronics & Hardware": 0.0004606618020182392, "Health, Healthcare & Wellness": 0.002087422421796122, "History & Geography": 0.00036778088673868753, "Home, Pets & Hobbies": 0.00022633281731807874, "Industry & Production": 0.000267734243374255, "Language, Literature & Philosophy": 0.000765750361625905, "Politics & Government": 0.000416937581094554, "Religion & Spirituality": 0.0003471934362494994, "Science, Mathematics & Engineering": 0.09211800178260601, "Relationships & Community": 0.00014410818717653696, "Software Usage & Applications": 0.005962785678447226, "Programming & Software Engineering": 0.030347207871866896, "Sports & Fitness": 0.00024330216107256523, "Vehicles & Transportation": 0.00011543969292259217, "Travel & Tourism": 0.0003696262009161821, "Other / Uncovered Subject": 0.0015914816867638626, "NONE": 0.854607133780185}}, "probabilities": {"primary": {"Academic Writing": 3.8187359374886904e-05, "Content Listing": 1.969185151327545e-05, "Creative Writing": 1.3256819457780503e-05, "Customer Support Page": 1.1683514830466324e-05, "Q&A Forum": 1.2577425827145177e-05, "Discussion Thread": 8.426081053653644e-06, "Comment Section": 1.341963760849291e-05, "Missing Content": 4.8152259004363654e-05, "Knowledge Article": 9.372648597265204e-05, "Legal Notices": 0.0012655164083423375, "News Article": 5.740569699019494e-06, "Organizational Announcement": 2.0936723498966342e-05, "Nonfiction Writing": 1.2845115161952175e-05, "Organizational About Page": 2.1128599954915763e-05, "Personal About Page": 1.0118999955845047e-05, "Personal Blog": 7.956599033434333e-06, "Product Page": 7.247810342620179e-06, "Spam / Ads": 1.0343446847555554e-05, "Structured Data": 0.0009652897154666729, "Technical Writing": 0.995695399903675, "Transcript / Interview": 1.4437193711598217e-05, "Tutorial / How-To Guide": 0.00029080739257055513, "User Reviews": 7.250938720462716e-06, "Other / Unknown": 0.0014058591483763936}, "secondary": {"Academic Writing": 0.0007820282418143353, "Content Listing": 0.0005325424566602251, "Creative Writing": 0.0005946413185312371, "Customer Support Page": 0.0003932226822076057, "Q&A Forum": 0.0003511145297293339, "Discussion Thread": 0.0003423491204732993, "Comment Section": 0.0005811440662086005, "Missing Content": 0.0007492193346454009, "Knowledge Article": 0.001136703932430581, "Legal Notices": 0.5453786152593345, "News Article": 0.000563856953835933, "Organizational Announcement": 0.0006919828297647945, "Nonfiction Writing": 0.0005117724543284732, "Organizational About Page": 0.0007903193167188941, "Personal About Page": 0.0004978915751369694, "Personal Blog": 0.00027456263402896416, "Product Page": 0.00039997518080736203, "Spam / Ads": 0.00043056411725466033, "Structured Data": 0.0019045605769028578, "Technical Writing": 0.04276900105347836, "Transcript / Interview": 0.0006555899339536571, "Tutorial / How-To Guide": 0.0007509708595808984, "User Reviews": 0.00026510340510907144, "NONE": 0.3986522681670639}, "tertiary": {"Academic Writing": 0.0005278765346247465, "Content Listing": 0.0003158496668377552, "Creative Writing": 0.0004259451127954556, "Customer Support Page": 0.00025943314318579877, "Q&A Forum": 0.00018995844999284092, "Discussion Thread": 0.00016541031225820785, "Comment Section": 0.0002930292036800868, "Missing Content": 0.0005279445010094501, "Knowledge Article": 0.0012739926919035363, "Legal Notices": 0.19342668834050722, "News Article": 0.0002886187085694464, "Organizational Announcement": 0.0004333863949580406, "Nonfiction Writing": 0.0002950124662003557, "Organizational About Page": 0.0007315052448931677, "Personal About Page": 0.0003339428940687774, "Personal Blog": 0.00018576948016571374, "Product Page": 0.00023408267019699118, "Spam / Ads": 0.0003194879360412668, "Structured Data": 0.0011016821800984818, "Technical Writing": 0.01677739406022648, "Transcript / Interview": 0.0003448615938372744, "Tutorial / How-To Guide": 0.0006149482076681648, "User Reviews": 0.00013571983564875505, "NONE": 0.7807974603706319}, "structured_records": {"false": 0.9970875087825871, "true": 0.002912491217412922}, "procedural_steps": {"false": 0.9944802345206719, "true": 0.00551976547932814}, "worked_solution": {"false": 0.9983052378524463, "true": 0.0016947621475537624}, "contextual_dialogue": {"false": 0.9999922857918662, "true": 7.714208133853234e-06}, "content_quality": {"-2": 0.016302254958781074, "-1": 0.07736890327666941, "0": 0.3141687262719462, "1": 0.4446188349508667, "2": 0.14754128054173674}, "noise_severity": {"0": 0.6341861408719461, "1": 0.3515499804699254, "2": 0.00974935544288081, "3": 0.002427799522214321, "4": 0.002086723693033291}, "missing_content_severity": {"0": 0.9628413921882338, "1": 0.017534936474223727, "2": 0.013955985964433126, "3": 0.004537772731396842, "4": 0.0011299126417125115}, "factual_density": {"0": 0.007868046497027471, "1": 0.027802515826086338, "2": 0.10520685689074591, "3": 0.3562807118000077, "4": 0.5028418689861327}, "pretraining_utility": {"-2": 0.01189545990303975, "-1": 0.11167415792134583, "0": 0.4209621940973686, "1": 0.3887283250374111, "2": 0.06673986304083471}, "math_density": {"0": 0.8536294657208302, "1": 0.07026041228618185, "2": 0.02163866518304643, "3": 0.014974163606555622, "4": 0.03949729320338595}, "code_density": {"0": 0.0003309530184408549, "1": 0.0007509850895806998, "2": 0.01651844332413159, "3": 0.14864302940004662, "4": 0.8337565891678003}, "reasoning_density": {"0": 0.6531987957867007, "1": 0.09069039321879517, "2": 0.1133664541174037, "3": 0.07052057939719351, "4": 0.07222377747990695}, "education_level": {"none": 0.0017624027227569603, "0": 0.0017515859188118401, "1": 0.006868692953906737, "2": 0.4415635858166621, "3": 0.4569989120149438, "4": 0.09105482057291854}, "creative_writing_quality": {"not_applicable": 0.9987143614952596, "-2": 0.00018747513850434798, "-1": 0.00031767447192397586, "0": 0.0005903641429915788, "1": 0.00013893955258534077, "2": 5.11851987351356e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.834854263047272e-05, "Art & Design": 8.049019145542951e-05, "Law & Crime": 0.00013109754335943985, "Education & Careers": 6.847997848777443e-05, "Entertainment & Media": 4.403421054408982e-05, "Fashion & Beauty": 4.1136525802565564e-05, "Business, Economics & Finance": 8.906665540476181e-05, "Food & Dining": 6.262535407732636e-05, "Games & Gaming": 7.142320194299652e-05, "Electronics & Hardware": 0.00011497915776273552, "Health, Healthcare & Wellness": 0.0009982715247828086, "History & Geography": 8.15312264214255e-05, "Home, Pets & Hobbies": 4.077593631482282e-05, "Industry & Production": 6.485152605148669e-05, "Language, Literature & Philosophy": 0.0002022454373352985, "Politics & Government": 4.0401897685898246e-05, "Religion & Spirituality": 4.9251679458305645e-05, "Science, Mathematics & Engineering": 0.022524358306086577, "Relationships & Community": 2.3043392478839768e-05, "Software Usage & Applications": 0.001185690462516381, "Programming & Software Engineering": 0.972752623256581, "Sports & Fitness": 6.868977331229519e-05, "Vehicles & Transportation": 2.755724511394087e-05, "Travel & Tourism": 6.689989367213159e-05, "Other / Uncovered Subject": 0.0008820265604966594, "Unknown / Insufficient Content": 0.00022010052022455913}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005797643547971148, "Art & Design": 0.0004879380054835526, "Law & Crime": 0.006210900628100783, "Education & Careers": 0.00041986136502901255, "Entertainment & Media": 0.0004231239114895982, "Fashion & Beauty": 0.0002763404117403347, "Business, Economics & Finance": 0.0005742922417643658, "Food & Dining": 0.0002555171711236963, "Games & Gaming": 0.0003333613182992456, "Electronics & Hardware": 0.0004606618020182392, "Health, Healthcare & Wellness": 0.002087422421796122, "History & Geography": 0.00036778088673868753, "Home, Pets & Hobbies": 0.00022633281731807874, "Industry & Production": 0.000267734243374255, "Language, Literature & Philosophy": 0.000765750361625905, "Politics & Government": 0.000416937581094554, "Religion & Spirituality": 0.0003471934362494994, "Science, Mathematics & Engineering": 0.09211800178260601, "Relationships & Community": 0.00014410818717653696, "Software Usage & Applications": 0.005962785678447226, "Programming & Software Engineering": 0.030347207871866896, "Sports & Fitness": 0.00024330216107256523, "Vehicles & Transportation": 0.00011543969292259217, "Travel & Tourism": 0.0003696262009161821, "Other / Uncovered Subject": 0.0015914816867638626, "NONE": 0.854607133780185}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10824, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10824, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:06.916016+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "e9c88bbf-4002-4b31-9a76-2a1353cea172", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "7c435b8872a9f7dc3f8320a7456aa3a4b691a91a4544c284030c8f7862c0d911", "source_metadata": {"uuid": "e9c88bbf-4002-4b31-9a76-2a1353cea172", "repo_name": "biokit/biokit", "relative_path": "biokit/rtools/r4python.py", "category": "TOOL", "algo_rel_score": 0.9760645031929016, "quality_score": 5.901961326599121, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 478, "repo_created_at": "2014-09-09T10:24:31.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 59, "forks": 23, "issues": 13, "pull_requests": 2}, "file_meta": {"content_id": "029d2c8b973867aa188a1c947f18090affb8ffc8", "size_bytes": 7745, "file_timestamp": 1631201614, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "53cb3f7d24c79fd9c0ee613204fb3e14fd370e67"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 478, "repo_created_at": "2014-09-09T10:24:31.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 59, "forks": 23, "issues": 13, "pull_requests": 2}, "file_meta": {"content_id": "029d2c8b973867aa188a1c947f18090affb8ffc8", "size_bytes": 7745, "file_timestamp": 1631201614, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "53cb3f7d24c79fd9c0ee613204fb3e14fd370e67", "url": "https://github.com/biokit/biokit/blob/53cb3f7d24c79fd9c0ee613204fb3e14fd370e67/biokit/rtools/r4python.py"}, "gpt4o_tokens": 2201, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.6297279828401086, "noise_severity": 0.38667898469446316, "missing_content_severity": 0.06357987716413055, "factual_density": 3.318425840952131, "pretraining_utility": 0.3867429733916552, "math_density": 0.31644940628548535, "code_density": 3.8147433166091846, "reasoning_density": 0.8178801495648109, "education_level": 2.6298467319645846, "creative_writing_quality": -0.3510433121075493}, "score_applicability": {"education_level": 0.998237597277243, "creative_writing_quality": 0.001285638504740379}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/huji-hcnl/pEYES/blob/def6a7d4f2e14f7e008cc3f81bdde20045eee5ea/peyes/_utils/metric_utils.py", "id": "python-0-7ccf14cfa723c195f4e36ce6a1ce0d21", "text": "import warnings\nfrom itertools import islice\nfrom typing import Sequence, Optional\n\nimport numpy as np\nimport pandas as pd\nimport Levenshtein\nfrom scipy.stats import norm\n\n\ndef transition_matrix(seq: Sequence, normalize_rows: bool = False) -> pd.DataFrame:\n    \"\"\"\n    Calculates the transition matrix of a sequence of events. The matrix is a DataFrame where the rows represent the\n    \"from\" event and the columns represent the \"to\" event. The values in the matrix are the counts of transitions\n    between the events. If `normalize_rows` is True, the values in each row are normalized to sum to 1.\n    See implementation details at https://stackoverflow.com/a/47298184/8543025.\n    \"\"\"\n    def window(s, n=2):\n        \"\"\" Sliding window width n from sequence s \"\"\"\n        it = iter(s)\n        result = tuple(islice(it, n))\n        if len(result) == n:\n            yield result\n        for elem in it:\n            result = result[1:] + (elem,)\n            yield result\n    pairs = pd.DataFrame(window(seq), columns=['From', 'To'])\n    counts = pairs.groupby('From')['To'].value_counts().unstack(fill_value=0)\n    if normalize_rows:\n        counts = counts.div(counts.sum(axis=1), axis=0)\n    return counts\n\n\ndef normalized_levenshtein_distance(gt: Sequence, pred: Sequence) -> float:\n    \"\"\"\n    Calculates the normalized Levenshtein distance between two sequences. Normalization is with respect to the size of\n    the GT, which may result in a value exceeding 1 if the predicted sequence is longer than the GT sequence.\n    This is the same as the Word Error Rate (WER) used in speech recognition.\n    For more information on using 1-NLD in eye-tracking, see https://doi.org/10.3758/s13428-021-01763-7\n    \"\"\"\n    d = Levenshtein.distance(gt, pred)\n    normalized_d = d / len(gt)\n    return normalized_d\n\n\ndef dprime_and_criterion(p: int, n: float, pp: int, tp: int, correction: Optional[str]) -> (float, float):\n    \"\"\"\n    Calculates d-prime and criterion while optionally applying a correction for floor/ceiling effects on the hit-rate\n    and/or false-alarm rate. See information on correction methods at https://stats.stackexchange.com/a/134802/288290.\n    See implementation details at https://lindeloev.net/calculating-d-in-python-and-php/.\n\n    :param p: int; number of positive GT events\n    :param n: int; number of negative GT events\n    :param pp: int; number of positive predicted events\n    :param tp: int; number of true positive predictions\n    :param correction: str; optional correction method for floor/ceiling effects\n    :return:\n        - d_prime: float; the d-prime value\n        - criterion: float; the criterion value\n    \"\"\"\n    hr, far = _dprime_rates(p, n, pp, tp, correction)\n    with warnings.catch_warnings():\n        warnings.simplefilter(\"ignore\", RuntimeWarning)\n        d_prime = norm.ppf(hr) - norm.ppf(far)\n        criterion = -0.5 * (norm.ppf(hr) + norm.ppf(far))\n        return d_prime, criterion\n\n\ndef _dprime_rates(p: int, n: float, pp: int, tp: int, correction: Optional[str]) -> (float, float):\n    \"\"\"\n    Calculates hit-rate and false-alarm rate for computing d-prime. Optionally applies a correction for floor/ceiling\n    effects on the rates. See information on correction methods at https://stats.stackexchange.com/a/134802/288290.\n    Returns a tuple of (hit-rate, false-alarm rate).\n    \"\"\"\n    fp = pp - tp\n    assert 0 <= tp <= min(p, pp), f\"True Positive count must be between 0 and min(p, pp) = {min(p, pp)}; got: {tp}\"\n    # assert 0 <= fp <= n, f\"False Positive count must be between 0 and n = {n}; got: {fp}\"\n    # NOTE: when comparing events (not labels), it doesn't hold that 0 <= fp <= n\n    hit_rate = tp / p if p > 0 else np.nan\n    false_alarm_rate = fp / n if n > 0 else np.nan\n    if 0 < hit_rate < 1 and 0 < false_alarm_rate < 1:\n        # no correction needed\n        return hit_rate, false_alarm_rate\n    corr = (correction or \"\").lower().strip().replace(\" \", \"_\").replace(\"-\", \"_\")\n    if corr is None or not corr:\n        return hit_rate, false_alarm_rate\n    if corr in {\"mk\", \"m&k\", \"macmillan_kaplan\", \"macmillan\"}:\n        # apply Macmillan & Kaplan (1985) correction\n        if hit_rate == 0:\n            hit_rate = 0.5 / p\n        if hit_rate == 1:\n            hit_rate = 1 - 0.5 / p\n        if false_alarm_rate == 0:\n            false_alarm_rate = 0.5 / n\n        if false_alarm_rate == 1:\n            false_alarm_rate = 1 - 0.5 / n\n        return hit_rate, false_alarm_rate\n    if correction in {\"ll\", \"loglinear\", \"log_linear\", \"hautus\"}:\n        # apply Hautus (1995) correction\n        prevalence = p / (p + n) if p + n > 0 else np.nan\n        new_tp, new_fp = tp + prevalence, fp + 1 - prevalence\n        new_p, new_n = p + 2 * prevalence, n + 2 * (1 - prevalence)\n        hit_rate = new_tp / new_p if new_p > 0 else np.nan\n        false_alarm_rate = new_fp / new_n if new_n > 0 else np.nan\n        return hit_rate, false_alarm_rate\n    raise ValueError(f\"Invalid correction: {correction}\")\n\n", "text_preview": "import warnings from itertools import islice from typing import Sequence, Optional import numpy as np import pandas as pd import Levenshtein from scipy.stats import norm def transition_matrix(seq: Sequence, normalize_rows: bool = False) -> ", "input_sha256": "15bf7b8be07476d655f45586143b3c13cb068b1da83778143b41736bb7122056", "text_sha256": "7ccf14cfa723c195f4e36ce6a1ce0d2135447874dbbd08f4026d093cee90942d", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12047, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.012204977167773, "noise_severity": 0.011561422599410376, "missing_content_severity": 0.02361289732491071, "factual_density": 3.3526514910479164, "pretraining_utility": 0.797036891828678, "math_density": 3.1861652063681696, "code_density": 3.7302975350320535, "reasoning_density": 1.3365817539908287, "education_level": 2.6069590954339024, "creative_writing_quality": -0.15922949670568276}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.6336 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.00896063633163946, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.997010898284716, "secondary": 0.9686224461593186, "tertiary": 0.9779726753749424, "structured_records": 0.0032938981783862775, "procedural_steps": 0.0313604052681981, "worked_solution": 0.009309621378191576, "contextual_dialogue": 6.523499688524626e-06, "content_quality": 0.544068195480456, "noise_severity": 0.9903654811671581, "missing_content_severity": 0.9803225855625745, "factual_density": 0.4605429092065969, "pretraining_utility": 0.5872127797515443, "math_density": 0.321804338640141, "code_density": 0.7752479458600444, "reasoning_density": 0.0, "education_level": 0.37699701883381037, "creative_writing_quality": 0.9996623668077907, "primary_topic": 0.8364840427464738, "secondary_topic": 0.6189479319383853}, "probability_margin": {"primary": 0.9957196306570828, "secondary": 0.9587597588876177, "tertiary": 0.9718785889395205, "education_level": 0.07185269039100806, "creative_writing_quality": 0.9995847968695329, "primary_topic": 0.6971560276445506, "secondary_topic": 0.45783077359528535}, "raw_probabilities": {"primary": {"Academic Writing": 0.0001785288746592412, "Content Listing": 1.4497036761342241e-05, "Creative Writing": 7.710961316775932e-06, "Customer Support Page": 7.498117928512515e-06, "Q&A Forum": 8.350432161896787e-06, "Discussion Thread": 5.129008085496184e-06, "Comment Section": 7.214129891623356e-06, "Missing Content": 1.2847546149963129e-05, "Knowledge Article": 0.00020133699013092534, "Legal Notices": 1.9199721374701415e-05, "News Article": 2.8326457265885823e-06, "Organizational Announcement": 6.647831545189416e-06, "Nonfiction Writing": 1.0310522366515563e-05, "Organizational About Page": 8.39234986426383e-06, "Personal About Page": 6.491747986515397e-06, "Personal Blog": 4.08401836860751e-06, "Product Page": 5.496208226633109e-06, "Spam / Ads": 6.3185239344369525e-06, "Structured Data": 0.0006016954722066496, "Technical Writing": 0.9971354441895195, "Transcript / Interview": 1.1577329008389295e-05, "Tutorial / How-To Guide": 0.0003181440830842974, "User Reviews": 4.438727265303199e-06, "Other / Unknown": 0.0014158135324366813}, "secondary": {"Academic Writing": 0.008721092845091863, "Content Listing": 0.00012507628835701194, "Creative Writing": 0.00011724057530286204, "Customer Support Page": 0.00010865805865732622, "Q&A Forum": 0.00010554887557304584, "Discussion Thread": 5.4603547930328184e-05, "Comment Section": 0.00010090397041097584, "Missing Content": 0.00022278140140918385, "Knowledge Article": 0.004394103308133738, "Legal Notices": 0.00017140657331118412, "News Article": 6.887506687252215e-05, "Organizational Announcement": 6.499918531100331e-05, "Nonfiction Writing": 0.00011987826479745255, "Organizational About Page": 9.469229479906464e-05, "Personal About Page": 7.236541004564069e-05, "Personal Blog": 6.156417643288011e-05, "Product Page": 4.838275235082004e-05, "Spam / Ads": 7.154842218607612e-05, "Structured Data": 0.0025159946988920234, "Technical Writing": 0.011170085348396062, "Transcript / Interview": 0.00010175246131729465, "Tutorial / How-To Guide": 0.001524891732163103, "User Reviews": 3.3710506244827784e-05, "NONE": 0.9699298442360137}, "tertiary": {"Academic Writing": 0.006849637703979415, "Content Listing": 8.440131762866907e-05, "Creative Writing": 8.92663193572826e-05, "Customer Support Page": 7.624187410148346e-05, "Q&A Forum": 9.31411576604461e-05, "Discussion Thread": 3.4005995254909916e-05, "Comment Section": 8.127593214842116e-05, "Missing Content": 0.00022532983099540806, "Knowledge Article": 0.0036951023771028936, "Legal Notices": 0.0002434541758761889, "News Article": 4.9009301690559556e-05, "Organizational Announcement": 4.765683055051738e-05, "Nonfiction Writing": 0.00010940773697567834, "Organizational About Page": 7.482921126126877e-05, "Personal About Page": 4.629165881804242e-05, "Personal Blog": 4.022358901098047e-05, "Product Page": 3.5810435290303535e-05, "Spam / Ads": 5.366225045187049e-05, "Structured Data": 0.0008674014252972662, "Technical Writing": 0.007011891628132703, "Transcript / Interview": 6.190451294094487e-05, "Tutorial / How-To Guide": 0.0012148227634320601, "User Reviews": 2.4751404389581256e-05, "NONE": 0.9788904805676532}, "structured_records": {"false": 0.9967061018216137, "true": 0.0032938981783862775}, "procedural_steps": {"false": 0.968639594731802, "true": 0.0313604052681981}, "worked_solution": {"false": 0.9906903786218084, "true": 0.009309621378191576}, "contextual_dialogue": {"false": 0.9999934765003115, "true": 6.523499688524626e-06}, "content_quality": {"-2": 0.005220631640847671, "-1": 0.029124246909301522, "0": 0.19354620538669384, "1": 0.49244734476754415, "2": 0.2796615712956129}, "noise_severity": {"0": 0.9897139170251852, "1": 0.009476050984292831, "2": 0.0004789488759141104, "3": 0.0001968585951423061, "4": 0.00013422451946560154}, "missing_content_severity": {"0": 0.9801650082616572, "1": 0.017174825806400014, "2": 0.0017911971285927805, "3": 0.0006201979520748257, "4": 0.00024877085127516466}, "factual_density": {"0": 0.0027984542133440483, "1": 0.014215383028845315, "2": 0.10651273416454561, "3": 0.38048307468308035, "4": 0.49599035391018464}, "pretraining_utility": {"-2": 0.005107992747730187, "-1": 0.03318128788376298, "0": 0.2674673322240179, "1": 0.5480526090810764, "2": 0.14619077806341246}, "math_density": {"0": 0.00788984692667277, "1": 0.022952433956509116, "2": 0.16244707213995457, "3": 0.3885239597757032, "4": 0.41818668720116037}, "code_density": {"0": 0.00038808495429049024, "1": 0.0008666558484110367, "2": 0.030962616351102902, "3": 0.2036249249033458, "4": 0.7641577179428498}, "reasoning_density": {"0": 0.4205020544360736, "1": 0.14511153319076556, "2": 0.19365510493285767, "3": 0.15876521882686506, "4": 0.08196608861343814}, "education_level": {"none": 0.0009338311679386085, "0": 0.0013941365763385871, "1": 0.0051745455174427475, "2": 0.48083084902817536, "3": 0.4089781586371673, "4": 0.10268847907293728}, "creative_writing_quality": {"not_applicable": 0.9997186390064923, "-2": 3.633956782044232e-05, "-1": 5.0475305256375373e-05, "0": 0.00013384213695935394, "1": 4.305449543456035e-05, "2": 1.7649488036931746e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001781696902981469, "Art & Design": 0.00027996845859680686, "Law & Crime": 0.0002675072395589415, "Education & Careers": 0.00021783036091380106, "Entertainment & Media": 0.00019664165324128995, "Fashion & Beauty": 0.00020358807366969845, "Business, Economics & Finance": 0.0002575918507494315, "Food & Dining": 0.00017395386618794146, "Games & Gaming": 0.0002543979858544768, "Electronics & Hardware": 0.00023821034447380466, "Health, Healthcare & Wellness": 0.0018598607695500362, "History & Geography": 0.0003600982365116581, "Home, Pets & Hobbies": 0.00016679786347163607, "Industry & Production": 0.00019092208304251056, "Language, Literature & Philosophy": 0.0008686407574323326, "Politics & Government": 0.00010173066420761002, "Religion & Spirituality": 0.00016542322204495108, "Science, Mathematics & Engineering": 0.14561709038090498, "Relationships & Community": 0.00012402086837206926, "Software Usage & Applications": 0.0006435354630243685, "Programming & Software Engineering": 0.8427731180254556, "Sports & Fitness": 0.0004144730988829599, "Vehicles & Transportation": 0.0001145492612253343, "Travel & Tourism": 0.00022196267301447008, "Other / Uncovered Subject": 0.0038253414702531423, "Unknown / Insufficient Content": 0.00028457563906210637}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007488955201957034, "Art & Design": 0.0006729994859143648, "Law & Crime": 0.0005937305290985262, "Education & Careers": 0.0004196068289452332, "Entertainment & Media": 0.0005780369557084841, "Fashion & Beauty": 0.00042600112854381515, "Business, Economics & Finance": 0.0005542120570053793, "Food & Dining": 0.0003753444411427947, "Games & Gaming": 0.000561676202206417, "Electronics & Hardware": 0.0006237529432738082, "Health, Healthcare & Wellness": 0.014467651364810982, "History & Geography": 0.0006238516973793362, "Home, Pets & Hobbies": 0.0003997630405104839, "Industry & Production": 0.00041889373547555886, "Language, Literature & Philosophy": 0.001337659404652609, "Politics & Government": 0.000312133852911064, "Religion & Spirituality": 0.00046661323619331474, "Science, Mathematics & Engineering": 0.6336037807099858, "Relationships & Community": 0.00037829064905280427, "Software Usage & Applications": 0.0006165624366014944, "Programming & Software Engineering": 0.17577300711470048, "Sports & Fitness": 0.0022543132758758165, "Vehicles & Transportation": 0.0003183734755761908, "Travel & Tourism": 0.0005851128770078188, "Other / Uncovered Subject": 0.010443680087895991, "NONE": 0.15244605694933572}}, "probabilities": {"primary": {"Academic Writing": 0.0001785288746592412, "Content Listing": 1.4497036761342241e-05, "Creative Writing": 7.710961316775932e-06, "Customer Support Page": 7.498117928512515e-06, "Q&A Forum": 8.350432161896787e-06, "Discussion Thread": 5.129008085496184e-06, "Comment Section": 7.214129891623356e-06, "Missing Content": 1.2847546149963129e-05, "Knowledge Article": 0.00020133699013092534, "Legal Notices": 1.9199721374701415e-05, "News Article": 2.8326457265885823e-06, "Organizational Announcement": 6.647831545189416e-06, "Nonfiction Writing": 1.0310522366515563e-05, "Organizational About Page": 8.39234986426383e-06, "Personal About Page": 6.491747986515397e-06, "Personal Blog": 4.08401836860751e-06, "Product Page": 5.496208226633109e-06, "Spam / Ads": 6.3185239344369525e-06, "Structured Data": 0.0006016954722066496, "Technical Writing": 0.9971354441895195, "Transcript / Interview": 1.1577329008389295e-05, "Tutorial / How-To Guide": 0.0003181440830842974, "User Reviews": 4.438727265303199e-06, "Other / Unknown": 0.0014158135324366813}, "secondary": {"Academic Writing": 0.008721092845091863, "Content Listing": 0.00012507628835701194, "Creative Writing": 0.00011724057530286204, "Customer Support Page": 0.00010865805865732622, "Q&A Forum": 0.00010554887557304584, "Discussion Thread": 5.4603547930328184e-05, "Comment Section": 0.00010090397041097584, "Missing Content": 0.00022278140140918385, "Knowledge Article": 0.004394103308133738, "Legal Notices": 0.00017140657331118412, "News Article": 6.887506687252215e-05, "Organizational Announcement": 6.499918531100331e-05, "Nonfiction Writing": 0.00011987826479745255, "Organizational About Page": 9.469229479906464e-05, "Personal About Page": 7.236541004564069e-05, "Personal Blog": 6.156417643288011e-05, "Product Page": 4.838275235082004e-05, "Spam / Ads": 7.154842218607612e-05, "Structured Data": 0.0025159946988920234, "Technical Writing": 0.011170085348396062, "Transcript / Interview": 0.00010175246131729465, "Tutorial / How-To Guide": 0.001524891732163103, "User Reviews": 3.3710506244827784e-05, "NONE": 0.9699298442360137}, "tertiary": {"Academic Writing": 0.006849637703979415, "Content Listing": 8.440131762866907e-05, "Creative Writing": 8.92663193572826e-05, "Customer Support Page": 7.624187410148346e-05, "Q&A Forum": 9.31411576604461e-05, "Discussion Thread": 3.4005995254909916e-05, "Comment Section": 8.127593214842116e-05, "Missing Content": 0.00022532983099540806, "Knowledge Article": 0.0036951023771028936, "Legal Notices": 0.0002434541758761889, "News Article": 4.9009301690559556e-05, "Organizational Announcement": 4.765683055051738e-05, "Nonfiction Writing": 0.00010940773697567834, "Organizational About Page": 7.482921126126877e-05, "Personal About Page": 4.629165881804242e-05, "Personal Blog": 4.022358901098047e-05, "Product Page": 3.5810435290303535e-05, "Spam / Ads": 5.366225045187049e-05, "Structured Data": 0.0008674014252972662, "Technical Writing": 0.007011891628132703, "Transcript / Interview": 6.190451294094487e-05, "Tutorial / How-To Guide": 0.0012148227634320601, "User Reviews": 2.4751404389581256e-05, "NONE": 0.9788904805676532}, "structured_records": {"false": 0.9967061018216137, "true": 0.0032938981783862775}, "procedural_steps": {"false": 0.968639594731802, "true": 0.0313604052681981}, "worked_solution": {"false": 0.9906903786218084, "true": 0.009309621378191576}, "contextual_dialogue": {"false": 0.9999934765003115, "true": 6.523499688524626e-06}, "content_quality": {"-2": 0.005220631640847671, "-1": 0.029124246909301522, "0": 0.19354620538669384, "1": 0.49244734476754415, "2": 0.2796615712956129}, "noise_severity": {"0": 0.9897139170251852, "1": 0.009476050984292831, "2": 0.0004789488759141104, "3": 0.0001968585951423061, "4": 0.00013422451946560154}, "missing_content_severity": {"0": 0.9801650082616572, "1": 0.017174825806400014, "2": 0.0017911971285927805, "3": 0.0006201979520748257, "4": 0.00024877085127516466}, "factual_density": {"0": 0.0027984542133440483, "1": 0.014215383028845315, "2": 0.10651273416454561, "3": 0.38048307468308035, "4": 0.49599035391018464}, "pretraining_utility": {"-2": 0.005107992747730187, "-1": 0.03318128788376298, "0": 0.2674673322240179, "1": 0.5480526090810764, "2": 0.14619077806341246}, "math_density": {"0": 0.00788984692667277, "1": 0.022952433956509116, "2": 0.16244707213995457, "3": 0.3885239597757032, "4": 0.41818668720116037}, "code_density": {"0": 0.00038808495429049024, "1": 0.0008666558484110367, "2": 0.030962616351102902, "3": 0.2036249249033458, "4": 0.7641577179428498}, "reasoning_density": {"0": 0.4205020544360736, "1": 0.14511153319076556, "2": 0.19365510493285767, "3": 0.15876521882686506, "4": 0.08196608861343814}, "education_level": {"none": 0.0009338311679386086, "0": 0.0013941365763385873, "1": 0.005174545517442748, "2": 0.4808308490281754, "3": 0.40897815863716735, "4": 0.1026884790729373}, "creative_writing_quality": {"not_applicable": 0.9997186390064924, "-2": 3.633956782044233e-05, "-1": 5.047530525637538e-05, "0": 0.00013384213695935397, "1": 4.305449543456036e-05, "2": 1.764948803693175e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00017816969029814687, "Art & Design": 0.0002799684585968068, "Law & Crime": 0.0002675072395589414, "Education & Careers": 0.000217830360913801, "Entertainment & Media": 0.0001966416532412899, "Fashion & Beauty": 0.0002035880736696984, "Business, Economics & Finance": 0.00025759185074943145, "Food & Dining": 0.00017395386618794143, "Games & Gaming": 0.00025439798585447673, "Electronics & Hardware": 0.0002382103444738046, "Health, Healthcare & Wellness": 0.0018598607695500357, "History & Geography": 0.00036009823651165807, "Home, Pets & Hobbies": 0.00016679786347163604, "Industry & Production": 0.0001909220830425105, "Language, Literature & Philosophy": 0.0008686407574323324, "Politics & Government": 0.00010173066420760999, "Religion & Spirituality": 0.00016542322204495105, "Science, Mathematics & Engineering": 0.14561709038090495, "Relationships & Community": 0.00012402086837206923, "Software Usage & Applications": 0.0006435354630243684, "Programming & Software Engineering": 0.8427731180254554, "Sports & Fitness": 0.0004144730988829598, "Vehicles & Transportation": 0.00011454926122533427, "Travel & Tourism": 0.00022196267301447003, "Other / Uncovered Subject": 0.0038253414702531414, "Unknown / Insufficient Content": 0.0002845756390621063}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007488955201957034, "Art & Design": 0.0006729994859143648, "Law & Crime": 0.0005937305290985262, "Education & Careers": 0.0004196068289452332, "Entertainment & Media": 0.0005780369557084841, "Fashion & Beauty": 0.00042600112854381515, "Business, Economics & Finance": 0.0005542120570053793, "Food & Dining": 0.0003753444411427947, "Games & Gaming": 0.000561676202206417, "Electronics & Hardware": 0.0006237529432738082, "Health, Healthcare & Wellness": 0.014467651364810982, "History & Geography": 0.0006238516973793362, "Home, Pets & Hobbies": 0.0003997630405104839, "Industry & Production": 0.00041889373547555886, "Language, Literature & Philosophy": 0.001337659404652609, "Politics & Government": 0.000312133852911064, "Religion & Spirituality": 0.00046661323619331474, "Science, Mathematics & Engineering": 0.6336037807099858, "Relationships & Community": 0.00037829064905280427, "Software Usage & Applications": 0.0006165624366014944, "Programming & Software Engineering": 0.17577300711470048, "Sports & Fitness": 0.0022543132758758165, "Vehicles & Transportation": 0.0003183734755761908, "Travel & Tourism": 0.0005851128770078188, "Other / Uncovered Subject": 0.010443680087895991, "NONE": 0.15244605694933572}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9907, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9907, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:07.671383+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "ae6848d4-d208-4a27-8fcc-a15cbd74c0eb", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "af55b43dc88ef91bd9cc2c2a38f9f84113bcff0663d5a48f4c18b25f93c8a190", "source_metadata": {"uuid": "ae6848d4-d208-4a27-8fcc-a15cbd74c0eb", "repo_name": "huji-hcnl/pEYES", "relative_path": "peyes/_utils/metric_utils.py", "category": "DATA", "algo_rel_score": 0.9875220656394958, "quality_score": 7.804806709289551, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 822, "repo_created_at": "2024-06-06T15:05:35.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 2, "issues": 2, "pull_requests": 0}, "file_meta": {"content_id": "ed3bb5e7e2548f223f5d236a4ade7f3bd7615a42", "size_bytes": 4977, "file_timestamp": 1752651111, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "def6a7d4f2e14f7e008cc3f81bdde20045eee5ea"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 822, "repo_created_at": "2024-06-06T15:05:35.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 2, "issues": 2, "pull_requests": 0}, "file_meta": {"content_id": "ed3bb5e7e2548f223f5d236a4ade7f3bd7615a42", "size_bytes": 4977, "file_timestamp": 1752651111, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "def6a7d4f2e14f7e008cc3f81bdde20045eee5ea", "url": "https://github.com/huji-hcnl/pEYES/blob/def6a7d4f2e14f7e008cc3f81bdde20045eee5ea/peyes/_utils/metric_utils.py"}, "gpt4o_tokens": 1321, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.012204977167773, "noise_severity": 0.011561422599410376, "missing_content_severity": 0.02361289732491071, "factual_density": 3.3526514910479164, "pretraining_utility": 0.797036891828678, "math_density": 3.1861652063681696, "code_density": 3.7302975350320535, "reasoning_density": 1.3365817539908287, "education_level": 2.6069590954339024, "creative_writing_quality": -0.15922949670568276}, "score_applicability": {"education_level": 0.9990661688320613, "creative_writing_quality": 0.0002813609935076638}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/python-postgres/fe/blob/6ce98853b28b0389e4325aacbce81dd4b62132ed/postgresql/types/io/stdlib_datetime.py", "id": "python-0-8f09b51fea4e5cc4e5f9e9a8fffdbfc1", "text": "##\n# stdlib_datetime - support for the stdlib's datetime.\n#\n# I/O routines for date, time, timetz, timestamp, timestamptz, and interval.\n# Supported by the datetime module.\n##\nimport datetime\nimport warnings\nfrom functools import partial\nfrom operator import methodcaller, add\n\nfrom ...python.datetime import UTC, FixedOffset, \\\n\tinfinity_date, infinity_datetime, \\\n\tnegative_infinity_date, negative_infinity_datetime\nfrom ...python.functools import Composition as compose\nfrom ...exceptions import TypeConversionWarning\n\nfrom .. import \\\n\tDATEOID, INTERVALOID, \\\n\tTIMEOID, TIMETZOID, \\\n\tTIMESTAMPOID, TIMESTAMPTZOID\n\nfrom . import lib\n\noid_to_type = {\n\tDATEOID: datetime.date,\n\tTIMESTAMPOID: datetime.datetime,\n\tTIMESTAMPTZOID: datetime.datetime,\n\tTIMEOID: datetime.time,\n\tTIMETZOID: datetime.time,\n\n\t# XXX: datetime.timedelta doesn't support months.\n\tINTERVALOID: datetime.timedelta,\n}\n\nseconds_in_day = 24 * 60 * 60\nseconds_in_hour = 60 * 60\n\npg_epoch_datetime = datetime.datetime(2000, 1, 1)\npg_epoch_datetime_utc = pg_epoch_datetime.replace(tzinfo = UTC)\npg_epoch_date = pg_epoch_datetime.date()\npg_date_offset = pg_epoch_date.toordinal()\n\n## Difference between PostgreSQL epoch and Unix epoch.\n## Used to convert a PostgreSQL ordinal to an ordinal usable by datetime\npg_time_days = (pg_date_offset - datetime.date(1970, 1, 1).toordinal())\n\n##\n# Constants used to special case infinity and -infinity.\ntime64_pack_constants = {\n\tinfinity_datetime: lib.time64_infinity,\n\tnegative_infinity_datetime: lib.time64_negative_infinity,\n\t'infinity': lib.time64_infinity,\n\t'-infinity': lib.time64_negative_infinity,\n}\ntime_pack_constants = {\n\tinfinity_datetime: lib.time_infinity,\n\tnegative_infinity_datetime: lib.time_negative_infinity,\n\t'infinity': lib.time_infinity,\n\t'-infinity': lib.time_negative_infinity,\n}\ndate_pack_constants = {\n\tinfinity_date: lib.date_infinity,\n\tnegative_infinity_date: lib.date_negative_infinity,\n\t'infinity': lib.date_infinity,\n\t'-infinity': lib.date_negative_infinity,\n}\ntime64_unpack_constants = {\n\tlib.time64_infinity: infinity_datetime,\n\tlib.time64_negative_infinity: negative_infinity_datetime,\n}\ntime_unpack_constants = {\n\tlib.time_infinity: infinity_datetime,\n\tlib.time_negative_infinity: negative_infinity_datetime,\n}\ndate_unpack_constants = {\n\tlib.date_infinity: infinity_date,\n\tlib.date_negative_infinity: negative_infinity_date,\n}\n\ndef date_pack(x,\n\tpack = lib.date_pack,\n\toffset = pg_date_offset,\n\tget = date_pack_constants.get,\n):\n\treturn get(x) or pack(x.toordinal() - offset)\n\ndef date_unpack(x,\n\tunpack = lib.date_unpack,\n\toffset = pg_date_offset,\n\tfrom_ord = datetime.date.fromordinal,\n\tget = date_unpack_constants.get,\n):\n\treturn get(x) or from_ord(unpack(x) + pg_date_offset)\n\ndef timestamp_pack(x,\n\tseconds_in_day = seconds_in_day,\n\tpg_epoch_datetime = pg_epoch_datetime,\n):\n\t\"\"\"\n\tCreate a (seconds, microseconds) pair from a `datetime.datetime` instance.\n\t\"\"\"\n\tx = (x - pg_epoch_datetime)\n\treturn ((x.days * seconds_in_day) + x.seconds, x.microseconds)\n\ndef timestamp_unpack(seconds,\n\ttimedelta = datetime.timedelta,\n\trelative_to = pg_epoch_datetime.__add__,\n):\n\t\"\"\"\n\tCreate a `datetime.datetime` instance from a (seconds, microseconds) pair.\n\t\"\"\"\n\treturn relative_to(timedelta(0, *seconds))\n\ndef timestamptz_pack(x,\n\tseconds_in_day = seconds_in_day,\n\tpg_epoch_datetime_utc = pg_epoch_datetime_utc,\n\tUTC = UTC,\n):\n\t\"\"\"\n\tCreate a (seconds, microseconds) pair from a `datetime.datetime` instance.\n\t\"\"\"\n\tx = (x.astimezone(UTC) - pg_epoch_datetime_utc)\n\treturn ((x.days * seconds_in_day) + x.seconds, x.microseconds)\n\ndef timestamptz_unpack(seconds,\n\ttimedelta = datetime.timedelta,\n\trelative_to = pg_epoch_datetime_utc.__add__,\n):\n\t\"\"\"\n\tCreate a `datetime.datetime` instance from a (seconds, microseconds) pair.\n\t\"\"\"\n\treturn relative_to(timedelta(0, *seconds))\n\ndef time_pack(x, seconds_in_hour = seconds_in_hour):\n\t\"\"\"\n\tCreate a (seconds, microseconds) pair from a `datetime.time` instance.\n\t\"\"\"\n\treturn (\n\t\t(x.hour * seconds_in_hour) + (x.minute * 60) + x.second,\n\t\tx.microsecond\n\t)\n\ndef time_unpack(seconds_ms, time = datetime.time, divmod = divmod):\n\t\"\"\"\n\tCreate a `datetime.time` instance from a (seconds, microseconds) pair.\n\tSeconds being offset from epoch.\n\t\"\"\"\n\tseconds, ms = seconds_ms\n\tminutes, sec = divmod(seconds, 60)\n\thours, min = divmod(minutes, 60)\n\treturn time(hours, min, sec, ms)\n\ndef interval_pack(x):\n\t\"\"\"\n\tCreate a (months, days, (seconds, microseconds)) tuple from a\n\t`datetime.timedelta` instance.\n\t\"\"\"\n\treturn (0, x.days, (x.seconds, x.microseconds))\n\ndef interval_unpack(mds, timedelta = datetime.timedelta):\n\t\"\"\"\n\tGiven a (months, days, (seconds, microseconds)) tuple, create a\n\t`datetime.timedelta` instance.\n\t\"\"\"\n\tmonths, days, seconds_ms = mds\n\tif months != 0:\n\t\t# XXX: Should this raise an exception?\n\t\tw = TypeConversionWarning(\n\t\t\t\"datetime.timedelta cannot represent relative intervals\",\n\t\t\tdetails = {\n\t\t\t\t'hint': 'An interval was unpacked with a non-zero \"month\" field.'\n\t\t\t},\n\t\t\tsource = 'DRIVER'\n\t\t)\n\t\twarnings.warn(w)\n\treturn timedelta(\n\t\tdays = days + (months * 30),\n\t\tseconds = seconds_ms[0], microseconds = seconds_ms[1]\n\t)\n\ndef timetz_pack(x,\n\ttime_pack = time_pack,\n):\n\t\"\"\"\n\tCreate a ((seconds, microseconds), timezone) tuple from a `datetime.time`\n\tinstance.\n\t\"\"\"\n\ttd = x.tzinfo.utcoffset(x)\n\tseconds = (td.days * seconds_in_day + td.seconds)\n\treturn (time_pack(x), seconds)\n\ndef timetz_unpack(tstz,\n\ttime_unpack = time_unpack,\n\tFixedOffset = FixedOffset,\n):\n\t\"\"\"\n\tCreate a `datetime.time` instance from a ((seconds, microseconds), timezone)\n\ttuple.\n\t\"\"\"\n\tt = time_unpack(tstz[0])\n\treturn t.replace(tzinfo = FixedOffset(tstz[1]))\n\nFloatTimes = False\nIntTimes = True\nNoDay = True\nWithDay = False\n\n# Used to handle the special cases: infinity and -infinity.\ndef proc_when_not_in(proc, dict):\n\tdef _proc(x, get=dict.get):\n\t\treturn get(x) or proc(x)\n\treturn _proc\n\nid_to_io = {\n\t(FloatTimes, TIMEOID) : (\n\t\tcompose((time_pack, lib.time_pack)),\n\t\tcompose((lib.time_unpack, time_unpack)),\n\t\tdatetime.time\n\t),\n\t(FloatTimes, TIMETZOID) : (\n\t\tcompose((timetz_pack, lib.timetz_pack)),\n\t\tcompose((lib.timetz_unpack, timetz_unpack)),\n\t\tdatetime.time\n\t),\n\t(FloatTimes, TIMESTAMPOID) : (\n\t\tproc_when_not_in(compose((timestamp_pack, lib.time_pack)), time_pack_constants),\n\t\tproc_when_not_in(compose((lib.time_unpack, timestamp_unpack)), time_unpack_constants),\n\t\tdatetime.datetime\n\t),\n\t(FloatTimes, TIMESTAMPTZOID) : (\n\t\tproc_when_not_in(compose((timestamptz_pack, lib.time_pack)), time_pack_constants),\n\t\tproc_when_not_in(compose((lib.time_unpack, timestamptz_unpack)), time_unpack_constants),\n\t\tdatetime.datetime\n\t),\n\t(FloatTimes, WithDay, INTERVALOID): (\n\t\tcompose((interval_pack, lib.interval_pack)),\n\t\tcompose((lib.interval_unpack, interval_unpack)),\n\t\tdatetime.timedelta\n\t),\n\t(FloatTimes, NoDay, INTERVALOID): (\n\t\tcompose((interval_pack, lib.interval_noday_pack)),\n\t\tcompose((lib.interval_noday_unpack, interval_unpack)),\n\t\tdatetime.timedelta\n\t),\n\n\t(IntTimes, TIMEOID) : (\n\t\tcompose((time_pack, lib.time64_pack)),\n\t\tcompose((lib.time64_unpack, time_unpack)),\n\t\tdatetime.time\n\t),\n\t(IntTimes, TIMETZOID) : (\n\t\tcompose((timetz_pack, lib.timetz64_pack)),\n\t\tcompose((lib.timetz64_unpack, timetz_unpack)),\n\t\tdatetime.time\n\t),\n\t(IntTimes, TIMESTAMPOID) : (\n\t\tproc_when_not_in(compose((timestamp_pack, lib.time64_pack)), time64_pack_constants),\n\t\tproc_when_not_in(compose((lib.time64_unpack, timestamp_unpack)), time64_unpack_constants),\n\t\tdatetime.datetime\n\t),\n\t(IntTimes, TIMESTAMPTZOID) : (\n\t\tproc_when_not_in(compose((timestamptz_pack, lib.time64_pack)), time64_pack_constants),\n\t\tproc_when_not_in(compose((lib.time64_unpack, timestamptz_unpack)), time64_unpack_constants),\n\t\tdatetime.datetime\n\t),\n\t(IntTimes, WithDay, INTERVALOID) : (\n\t\tcompose((interval_pack, lib.interval64_pack)),\n\t\tcompose((lib.interval64_unpack, interval_unpack)),\n\t\tdatetime.timedelta\n\t),\n\t(IntTimes, NoDay, INTERVALOID) : (\n\t\tcompose((interval_pack, lib.interval64_noday_pack)),\n\t\tcompose((lib.interval64_noday_unpack, interval_unpack)),\n\t\tdatetime.timedelta\n\t),\n}\n\n##\n# Identify whether it's IntTimes or FloatTimes\ndef time_type(typio):\n\tidt = typio.database.settings.get('integer_datetimes', None)\n\tif idt is None:\n\t\t# assume its absence means its on after 9.0\n\t\treturn bool(typio.database.version_info >= (9,0))\n\telif idt.__class__ is bool:\n\t\treturn idt\n\telse:\n\t\treturn (idt.lower() in ('on', 'true', 't', True))\n\ndef select_format(oid, typio, get = id_to_io.__getitem__):\n\treturn get((time_type(typio), oid))\n\ndef select_day_format(oid, typio, get = id_to_io.__getitem__):\n\treturn get((time_type(typio), typio.database.version_info[:2] <= (8,0), oid))\n\noid_to_io = {\n\tDATEOID : (date_pack, date_unpack, datetime.date,),\n\tTIMEOID : select_format,\n\tTIMETZOID : select_format,\n\tTIMESTAMPOID : select_format,\n\tTIMESTAMPTZOID : select_format,\n\tINTERVALOID : select_day_format,\n}\n", "text_preview": "## # stdlib_datetime - support for the stdlib's datetime. # # I/O routines for date, time, timetz, timestamp, timestamptz, and interval. # Supported by the datetime module. ## import datetime import warnings from functools import partial fr", "input_sha256": "6b587b7d1754048e2ba0167bd7953b3162402dd4d3059380d4ced2618f46bc3c", "text_sha256": "8f09b51fea4e5cc4e5f9e9a8fffdbfc1882bf3c7601838d9fca2a07069811a8a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12046, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 1, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.0601358572893085, "noise_severity": 0.016878116678829765, "missing_content_severity": 0.018927571390723624, "factual_density": 3.478883117039747, "pretraining_utility": 0.48226739681624664, "math_density": 1.4136389312042492, "code_density": 3.902118795748061, "reasoning_density": 0.5290432007196758, "education_level": 2.434307607956226, "creative_writing_quality": -0.19406662263946534}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.03766266370889282, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9926647399580474, "secondary": 0.8943061621837308, "tertiary": 0.9336063330104015, "structured_records": 0.3727551263616992, "procedural_steps": 0.0016302212437662974, "worked_solution": 0.008640680820896116, "contextual_dialogue": 5.0220045524367225e-06, "content_quality": 0.5641549670149575, "noise_severity": 0.9859349027676418, "missing_content_severity": 0.9842270238410636, "factual_density": 0.5657359308664553, "pretraining_utility": 0.4310542655165719, "math_density": 0.23186073121894557, "code_density": 0.9184323297900507, "reasoning_density": 0.5591306660669368, "education_level": 0.5174150466496018, "creative_writing_quality": 0.999249875104763, "primary_topic": 0.9935459560352953, "secondary_topic": 0.8671794392864014}, "probability_margin": {"primary": 0.9880540708779385, "secondary": 0.8321580028995464, "tertiary": 0.8971372092050525, "education_level": 0.2687663332568009, "creative_writing_quality": 0.9990709934372112, "primary_topic": 0.9904735565037667, "secondary_topic": 0.8082123485602141}, "raw_probabilities": {"primary": {"Academic Writing": 4.2308383973253145e-05, "Content Listing": 3.5075450911837584e-05, "Creative Writing": 1.859858268696721e-05, "Customer Support Page": 1.4856569347823044e-05, "Q&A Forum": 1.4312911973137227e-05, "Discussion Thread": 1.0835517056316644e-05, "Comment Section": 1.67161676649326e-05, "Missing Content": 3.271007010738676e-05, "Knowledge Article": 0.00014743984904735843, "Legal Notices": 3.456509856525759e-05, "News Article": 5.853408877913566e-06, "Organizational Announcement": 1.422146467277092e-05, "Nonfiction Writing": 1.4195394068027994e-05, "Organizational About Page": 1.5902173783303748e-05, "Personal About Page": 1.1210894383069475e-05, "Personal Blog": 9.069140911351898e-06, "Product Page": 8.818530558226641e-06, "Spam / Ads": 1.0437233094363056e-05, "Structured Data": 0.0049163049151902906, "Technical Writing": 0.9929703757931287, "Transcript / Interview": 1.9314555590788306e-05, "Tutorial / How-To Guide": 0.0002556721314004642, "User Reviews": 7.754410730101598e-06, "Other / Unknown": 0.0013734513522762626}, "secondary": {"Academic Writing": 0.0004510321993146368, "Content Listing": 0.00039218901543994466, "Creative Writing": 0.00033726317795497706, "Customer Support Page": 0.0002743290311885595, "Q&A Forum": 0.00016541222991705112, "Discussion Thread": 0.0001426414510479195, "Comment Section": 0.00025628026897379496, "Missing Content": 0.0005013027139718871, "Knowledge Article": 0.0020579868879289013, "Legal Notices": 0.00035191320516933045, "News Article": 0.00020294281307762837, "Organizational Announcement": 0.00022110434864341353, "Nonfiction Writing": 0.00021097421327314215, "Organizational About Page": 0.0002301594946700763, "Personal About Page": 0.0001921882093568047, "Personal Blog": 0.00016961932952344716, "Product Page": 0.00013702566514630984, "Spam / Ads": 0.0001882120358271406, "Structured Data": 0.06655206919319553, "Technical Writing": 0.02715759268678729, "Transcript / Interview": 0.0002670276259845566, "Tutorial / How-To Guide": 0.0007387484169730738, "User Reviews": 9.191369389263656e-05, "NONE": 0.898710072092742}, "tertiary": {"Academic Writing": 0.00022619314703012331, "Content Listing": 0.00022184652401303095, "Creative Writing": 0.00017927529216411246, "Customer Support Page": 0.00014152672756283943, "Q&A Forum": 8.162694193454618e-05, "Discussion Thread": 8.152230683441617e-05, "Comment Section": 0.000340815553069378, "Missing Content": 0.00043639888626134524, "Knowledge Article": 0.0018962324497853492, "Legal Notices": 0.0004009732597824353, "News Article": 9.946971271572947e-05, "Organizational Announcement": 0.0001144392690756996, "Nonfiction Writing": 0.00013504014461924766, "Organizational About Page": 0.0001436796551921708, "Personal About Page": 9.936764648572787e-05, "Personal Blog": 8.32118878832541e-05, "Product Page": 8.116468572365555e-05, "Spam / Ads": 0.00011047811635369395, "Structured Data": 0.039235526596582415, "Technical Writing": 0.01868559217483864, "Transcript / Interview": 0.00012941020017959474, "Tutorial / How-To Guide": 0.0006539007751565429, "User Reviews": 4.957224512128138e-05, "NONE": 0.9363727358016348}, "structured_records": {"false": 0.6272448736383007, "true": 0.3727551263616992}, "procedural_steps": {"false": 0.9983697787562337, "true": 0.0016302212437662974}, "worked_solution": {"false": 0.9913593191791039, "true": 0.008640680820896116}, "contextual_dialogue": {"false": 0.9999949779954476, "true": 5.0220045524367225e-06}, "content_quality": {"-2": 0.005254771902594802, "-1": 0.014609101749543902, "0": 0.18645657193949908, "1": 0.5021046059726824, "2": 0.2915749484356798}, "noise_severity": {"0": 0.9853367794413471, "1": 0.013234128852773433, "2": 0.0008697145634505297, "3": 0.00033294987056107193, "4": 0.00022642727186801518}, "missing_content_severity": {"0": 0.9865948582634282, "1": 0.009529858232090203, "2": 0.0025459419990489763, "3": 0.0010115368611949432, "4": 0.00031780464423765995}, "factual_density": {"0": 0.0024747348044691607, "1": 0.00620024521024704, "2": 0.06127238985421933, "3": 0.3700724284031971, "4": 0.5599802017278674}, "pretraining_utility": {"-2": 0.008702977096907543, "-1": 0.09673871869786792, "0": 0.38064737359547507, "1": 0.4314097915115692, "2": 0.0825011390981802}, "math_density": {"0": 0.25406409566650806, "1": 0.3600635083536479, "2": 0.20371836919046155, "3": 0.08247742268785133, "4": 0.09967660410153101}, "code_density": {"0": 0.0002171104484256319, "1": 0.00039602867514201427, "2": 0.006911287417746696, "3": 0.08200210159731716, "4": 0.9104734718613684}, "reasoning_density": {"0": 0.7130547345542593, "1": 0.1240586070710052, "2": 0.10454855114966521, "3": 0.03746493755094097, "4": 0.020873169674129326}, "education_level": {"none": 0.0013295386793840392, "0": 0.0017094414704012092, "1": 0.010667231399908871, "2": 0.5978458722080014, "3": 0.32907953895120057, "4": 0.05936837729110388}, "creative_writing_quality": {"not_applicable": 0.9993748959206359, "-2": 7.77292433621516e-05, "-1": 0.0001211744011242324, "0": 0.00030390248342470006, "1": 8.927485253816349e-05, "2": 3.302309891500013e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.116689638319884e-05, "Art & Design": 4.463304742618529e-05, "Law & Crime": 3.2883537204645714e-05, "Education & Careers": 4.1923909754439085e-05, "Entertainment & Media": 2.4416737920600778e-05, "Fashion & Beauty": 2.0858199926446687e-05, "Business, Economics & Finance": 4.814991259758186e-05, "Food & Dining": 3.0200561761333413e-05, "Games & Gaming": 5.817994485151647e-05, "Electronics & Hardware": 9.50311025816684e-05, "Health, Healthcare & Wellness": 2.809356484786714e-05, "History & Geography": 5.880532123087302e-05, "Home, Pets & Hobbies": 2.717243249290483e-05, "Industry & Production": 3.3131823309274636e-05, "Language, Literature & Philosophy": 9.405078786254656e-05, "Politics & Government": 1.8762804557056602e-05, "Religion & Spirituality": 3.113895609414076e-05, "Science, Mathematics & Engineering": 0.003320631991709539, "Relationships & Community": 1.6079738540924803e-05, "Software Usage & Applications": 0.0013708859499616923, "Programming & Software Engineering": 0.9937941884954762, "Sports & Fitness": 4.1359463258640204e-05, "Vehicles & Transportation": 2.145217814996805e-05, "Travel & Tourism": 4.167957634063478e-05, "Other / Uncovered Subject": 0.0005769691559968199, "Unknown / Insufficient Content": 8.815390976327389e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007301689085855748, "Art & Design": 0.00044720674747749555, "Law & Crime": 0.0007067273645862467, "Education & Careers": 0.00041306648047377434, "Entertainment & Media": 0.00043208587862341157, "Fashion & Beauty": 0.0002544484160254846, "Business, Economics & Finance": 0.0006967865273333203, "Food & Dining": 0.00023628104194873086, "Games & Gaming": 0.0004535334876612656, "Electronics & Hardware": 0.0007040412491937675, "Health, Healthcare & Wellness": 0.00034559518849317476, "History & Geography": 0.0009805941498929839, "Home, Pets & Hobbies": 0.0002807403037807841, "Industry & Production": 0.0002631261588015398, "Language, Literature & Philosophy": 0.0003791693356383525, "Politics & Government": 0.0002755248869014477, "Religion & Spirituality": 0.00039907673916710664, "Science, Mathematics & Engineering": 0.042620648709210014, "Relationships & Community": 0.00014973408138075622, "Software Usage & Applications": 0.008260270283189822, "Programming & Software Engineering": 0.06407557383055655, "Sports & Fitness": 0.00024942118404888274, "Vehicles & Transportation": 0.00016096187940122485, "Travel & Tourism": 0.0004754021466748877, "Other / Uncovered Subject": 0.003721892630182886, "NONE": 0.8722879223907706}}, "probabilities": {"primary": {"Academic Writing": 4.2308383973253145e-05, "Content Listing": 3.5075450911837584e-05, "Creative Writing": 1.859858268696721e-05, "Customer Support Page": 1.4856569347823044e-05, "Q&A Forum": 1.4312911973137227e-05, "Discussion Thread": 1.0835517056316644e-05, "Comment Section": 1.67161676649326e-05, "Missing Content": 3.271007010738676e-05, "Knowledge Article": 0.00014743984904735843, "Legal Notices": 3.456509856525759e-05, "News Article": 5.853408877913566e-06, "Organizational Announcement": 1.422146467277092e-05, "Nonfiction Writing": 1.4195394068027994e-05, "Organizational About Page": 1.5902173783303748e-05, "Personal About Page": 1.1210894383069475e-05, "Personal Blog": 9.069140911351898e-06, "Product Page": 8.818530558226641e-06, "Spam / Ads": 1.0437233094363056e-05, "Structured Data": 0.0049163049151902906, "Technical Writing": 0.9929703757931287, "Transcript / Interview": 1.9314555590788306e-05, "Tutorial / How-To Guide": 0.0002556721314004642, "User Reviews": 7.754410730101598e-06, "Other / Unknown": 0.0013734513522762626}, "secondary": {"Academic Writing": 0.0004510321993146368, "Content Listing": 0.00039218901543994466, "Creative Writing": 0.00033726317795497706, "Customer Support Page": 0.0002743290311885595, "Q&A Forum": 0.00016541222991705112, "Discussion Thread": 0.0001426414510479195, "Comment Section": 0.00025628026897379496, "Missing Content": 0.0005013027139718871, "Knowledge Article": 0.0020579868879289013, "Legal Notices": 0.00035191320516933045, "News Article": 0.00020294281307762837, "Organizational Announcement": 0.00022110434864341353, "Nonfiction Writing": 0.00021097421327314215, "Organizational About Page": 0.0002301594946700763, "Personal About Page": 0.0001921882093568047, "Personal Blog": 0.00016961932952344716, "Product Page": 0.00013702566514630984, "Spam / Ads": 0.0001882120358271406, "Structured Data": 0.06655206919319553, "Technical Writing": 0.02715759268678729, "Transcript / Interview": 0.0002670276259845566, "Tutorial / How-To Guide": 0.0007387484169730738, "User Reviews": 9.191369389263656e-05, "NONE": 0.898710072092742}, "tertiary": {"Academic Writing": 0.00022619314703012331, "Content Listing": 0.00022184652401303095, "Creative Writing": 0.00017927529216411246, "Customer Support Page": 0.00014152672756283943, "Q&A Forum": 8.162694193454618e-05, "Discussion Thread": 8.152230683441617e-05, "Comment Section": 0.000340815553069378, "Missing Content": 0.00043639888626134524, "Knowledge Article": 0.0018962324497853492, "Legal Notices": 0.0004009732597824353, "News Article": 9.946971271572947e-05, "Organizational Announcement": 0.0001144392690756996, "Nonfiction Writing": 0.00013504014461924766, "Organizational About Page": 0.0001436796551921708, "Personal About Page": 9.936764648572787e-05, "Personal Blog": 8.32118878832541e-05, "Product Page": 8.116468572365555e-05, "Spam / Ads": 0.00011047811635369395, "Structured Data": 0.039235526596582415, "Technical Writing": 0.01868559217483864, "Transcript / Interview": 0.00012941020017959474, "Tutorial / How-To Guide": 0.0006539007751565429, "User Reviews": 4.957224512128138e-05, "NONE": 0.9363727358016348}, "structured_records": {"false": 0.6272448736383007, "true": 0.3727551263616992}, "procedural_steps": {"false": 0.9983697787562337, "true": 0.0016302212437662974}, "worked_solution": {"false": 0.9913593191791039, "true": 0.008640680820896116}, "contextual_dialogue": {"false": 0.9999949779954476, "true": 5.0220045524367225e-06}, "content_quality": {"-2": 0.005254771902594802, "-1": 0.014609101749543902, "0": 0.18645657193949908, "1": 0.5021046059726824, "2": 0.2915749484356798}, "noise_severity": {"0": 0.9853367794413469, "1": 0.01323412885277343, "2": 0.0008697145634505295, "3": 0.0003329498705610719, "4": 0.00022642727186801513}, "missing_content_severity": {"0": 0.9865948582634282, "1": 0.009529858232090203, "2": 0.0025459419990489763, "3": 0.0010115368611949432, "4": 0.00031780464423765995}, "factual_density": {"0": 0.0024747348044691607, "1": 0.00620024521024704, "2": 0.06127238985421933, "3": 0.3700724284031971, "4": 0.5599802017278674}, "pretraining_utility": {"-2": 0.008702977096907543, "-1": 0.09673871869786792, "0": 0.38064737359547507, "1": 0.4314097915115692, "2": 0.0825011390981802}, "math_density": {"0": 0.2540640956665081, "1": 0.360063508353648, "2": 0.20371836919046157, "3": 0.08247742268785134, "4": 0.09967660410153102}, "code_density": {"0": 0.00021711044842563193, "1": 0.0003960286751420143, "2": 0.006911287417746697, "3": 0.08200210159731718, "4": 0.9104734718613685}, "reasoning_density": {"0": 0.7130547345542593, "1": 0.1240586070710052, "2": 0.10454855114966521, "3": 0.03746493755094097, "4": 0.020873169674129326}, "education_level": {"none": 0.0013295386793840392, "0": 0.0017094414704012092, "1": 0.010667231399908871, "2": 0.5978458722080014, "3": 0.32907953895120057, "4": 0.05936837729110388}, "creative_writing_quality": {"not_applicable": 0.9993748959206357, "-2": 7.772924336215158e-05, "-1": 0.00012117440112423237, "0": 0.0003039024834247, "1": 8.927485253816348e-05, "2": 3.3023098915000125e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.116689638319884e-05, "Art & Design": 4.463304742618529e-05, "Law & Crime": 3.2883537204645714e-05, "Education & Careers": 4.1923909754439085e-05, "Entertainment & Media": 2.4416737920600778e-05, "Fashion & Beauty": 2.0858199926446687e-05, "Business, Economics & Finance": 4.814991259758186e-05, "Food & Dining": 3.0200561761333413e-05, "Games & Gaming": 5.817994485151647e-05, "Electronics & Hardware": 9.50311025816684e-05, "Health, Healthcare & Wellness": 2.809356484786714e-05, "History & Geography": 5.880532123087302e-05, "Home, Pets & Hobbies": 2.717243249290483e-05, "Industry & Production": 3.3131823309274636e-05, "Language, Literature & Philosophy": 9.405078786254656e-05, "Politics & Government": 1.8762804557056602e-05, "Religion & Spirituality": 3.113895609414076e-05, "Science, Mathematics & Engineering": 0.003320631991709539, "Relationships & Community": 1.6079738540924803e-05, "Software Usage & Applications": 0.0013708859499616923, "Programming & Software Engineering": 0.9937941884954762, "Sports & Fitness": 4.1359463258640204e-05, "Vehicles & Transportation": 2.145217814996805e-05, "Travel & Tourism": 4.167957634063478e-05, "Other / Uncovered Subject": 0.0005769691559968199, "Unknown / Insufficient Content": 8.815390976327389e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007301689085855748, "Art & Design": 0.00044720674747749555, "Law & Crime": 0.0007067273645862467, "Education & Careers": 0.00041306648047377434, "Entertainment & Media": 0.00043208587862341157, "Fashion & Beauty": 0.0002544484160254846, "Business, Economics & Finance": 0.0006967865273333203, "Food & Dining": 0.00023628104194873086, "Games & Gaming": 0.0004535334876612656, "Electronics & Hardware": 0.0007040412491937675, "Health, Healthcare & Wellness": 0.00034559518849317476, "History & Geography": 0.0009805941498929839, "Home, Pets & Hobbies": 0.0002807403037807841, "Industry & Production": 0.0002631261588015398, "Language, Literature & Philosophy": 0.0003791693356383525, "Politics & Government": 0.0002755248869014477, "Religion & Spirituality": 0.00039907673916710664, "Science, Mathematics & Engineering": 0.042620648709210014, "Relationships & Community": 0.00014973408138075622, "Software Usage & Applications": 0.008260270283189822, "Programming & Software Engineering": 0.06407557383055655, "Sports & Fitness": 0.00024942118404888274, "Vehicles & Transportation": 0.00016096187940122485, "Travel & Tourism": 0.0004754021466748877, "Other / Uncovered Subject": 0.003721892630182886, "NONE": 0.8722879223907706}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11121, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11121, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:08.021471+00:00", "scoring_key_slot": "primary", "dataset_uid": "aabbdcd5-bbfd-4733-8fd8-558f2346549d", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "a5d7822fb99811e83d3a62ebd94f050cd9964131186ff3bcd47159f5729db5d6", "source_metadata": {"uuid": "aabbdcd5-bbfd-4733-8fd8-558f2346549d", "repo_name": "python-postgres/fe", "relative_path": "postgresql/types/io/stdlib_datetime.py", "category": "TOOL", "algo_rel_score": 0.9487122893333435, "quality_score": 7.799356460571289, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 888, "repo_created_at": "2009-01-03T15:06:05.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 87, "forks": 35, "issues": 17, "pull_requests": 2}, "file_meta": {"content_id": "a2f34524cdc7ae19c41a68f9e1b4b52ac98d6a13", "size_bytes": 8828, "file_timestamp": 1741529919, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "6ce98853b28b0389e4325aacbce81dd4b62132ed"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 888, "repo_created_at": "2009-01-03T15:06:05.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 87, "forks": 35, "issues": 17, "pull_requests": 2}, "file_meta": {"content_id": "a2f34524cdc7ae19c41a68f9e1b4b52ac98d6a13", "size_bytes": 8828, "file_timestamp": 1741529919, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "6ce98853b28b0389e4325aacbce81dd4b62132ed", "url": "https://github.com/python-postgres/fe/blob/6ce98853b28b0389e4325aacbce81dd4b62132ed/postgresql/types/io/stdlib_datetime.py"}, "gpt4o_tokens": 2369, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.0601358572893085, "noise_severity": 0.016878116678829765, "missing_content_severity": 0.018927571390723624, "factual_density": 3.478883117039747, "pretraining_utility": 0.48226739681624664, "math_density": 1.4136389312042492, "code_density": 3.902118795748061, "reasoning_density": 0.5290432007196758, "education_level": 2.434307607956226, "creative_writing_quality": -0.19406662263946534}, "score_applicability": {"education_level": 0.998670461320616, "creative_writing_quality": 0.0006251040793642475}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/srikardevulapalli/persona-context-mcp/blob/cc706de4edf82af8da53e75d3619041471d92c14/mcp_server.py", "id": "python-0-728c458e1b60135e6b23a2b7923f7cc5", "text": "from mcp.server.fastmcp import FastMCP\nimport json\nimport os\nfrom typing import List, Dict, Optional\n\n\n\ntry:\n    from mcp.server.fastmcp import FastMCP\n    MCP_AVAILABLE = True\nexcept ImportError:\n    MCP_AVAILABLE = False\n    from fastapi import FastAPI, HTTPException\n\n\n# Create data directory if it doesn't exist\nDATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"data\")\nos.makedirs(DATA_DIR, exist_ok=True)\n\n\n #Create server instance based on available framework\nif MCP_AVAILABLE:\n    server = FastMCP(\n        name=\"Persona Context MCP\",\n        host=\"0.0.0.0\",\n        port=5002\n    )\nelse:\n    server = FastAPI(title=\"Persona Context Server\")\n\n# Models\nclass PersonaFact:\n    def __init__(self, category: str, value: str, description: Optional[str] = None):\n        self.category = category\n        self.value = value\n        self.description = description\n\n    def to_dict(self):\n        return {\n            \"category\": self.category,\n            \"value\": self.value,\n            \"description\": self.description\n        }\n\nasync def get_persona(user_id: str) -> List[Dict]:\n    \"\"\"Get persona facts for a user\n        Args:\n            user_id: The ID of the user to get facts for\n        Returns:\n            List of persona facts\n    \"\"\"\n    try:\n        file_path = os.path.join(DATA_DIR, f\"{user_id}_persona.json\")\n        print(f\"[DEBUG] Attempting to read file: {file_path}\")\n        with open(file_path, \"r\") as f:\n            data = json.load(f)\n            print(f\"[DEBUG] Successfully read data: {data}\")\n            \n            # Return the data directly\n            if isinstance(data, dict) and \"data\" in data:\n                return data[\"data\"]\n            elif isinstance(data, list):\n                return data\n            else:\n                print(f\"[DEBUG] Unexpected data format: {type(data)}\")\n                return []\n            \n    except FileNotFoundError:\n        print(f\"[DEBUG] File not found: {file_path}\")\n        return []\n    except json.JSONDecodeError as e:\n        print(f\"[DEBUG] JSON decode error: {str(e)}\")\n        return []\n    except Exception as e:\n        print(f\"[DEBUG] Unexpected error: {str(e)}\")\n        return []\n    \n\ndef extract_user_id(message: str) -> str:\n    \"\"\"Extract user ID from any natural language message.\n    \n    Args:\n        message: The input message (e.g., \"what are the facts for srikar\", \"show me srikar's info\", \"srikar\")\n    Returns:\n        The extracted user ID\n    \"\"\"\n    message = message.lower().strip()\n    \n    # Common patterns that might indicate a user ID\n    patterns = [\n        r\"for\\s+(\\w+)\",  # \"for srikar\"\n        r\"about\\s+(\\w+)\",  # \"about srikar\"\n        r\"show\\s+(\\w+)\",  # \"show srikar\"\n        r\"get\\s+(\\w+)\",  # \"get srikar\"\n        r\"tell\\s+me\\s+about\\s+(\\w+)\",  # \"tell me about srikar\"\n        r\"what\\s+about\\s+(\\w+)\",  # \"what about srikar\"\n        r\"who\\s+is\\s+(\\w+)\",  # \"who is srikar\"\n        r\"(\\w+)'s\",  # \"srikar's\"\n    ]\n    \n    import re\n    for pattern in patterns:\n        match = re.search(pattern, message)\n        if match:\n            return match.group(1)\n    \n    # If no pattern matches, assume the whole message is the user ID\n    # but only if it's a single word\n    words = message.split()\n    if len(words) == 1:\n        return words[0]\n    \n    # If we can't extract a user ID, return the original message\n    return message\n\n# Register routes based on framework\nif MCP_AVAILABLE:\n    @server.tool()\n    async def get_persona_tool(query: str) -> Dict:\n        \"\"\"Get persona facts for a user.\n        \n        Args:\n            query: The query string (e.g., \"what are the facts for srikar\" or just \"srikar\")\n        \"\"\"\n        user_id = extract_user_id(query)\n        try:\n            facts = await get_persona(user_id)\n            \n            if not facts:\n                return {\n                    \"type\": \"text\",\n                    \"text\": f\"No persona facts found for user: {user_id}\",\n                    \"uuid\": \"56b2a590-10e8-4706-be22-b8bdb198884e\"\n                }\n            \n            # Format the facts into a readable string\n            formatted_facts = []\n            for fact in facts:\n                formatted_fact = (\n                    f\"Category: {fact.get('category', '')}\\n\"\n                    f\"Value: {fact.get('value', '')}\\n\"\n                    f\"Description: {fact.get('description', '')}\"\n                )\n                formatted_facts.append(formatted_fact)\n            \n            return {\n                \"type\": \"text\",\n                \"text\": f\"Persona facts for {user_id}:\\n\\n\" + \"\\n---\\n\".join(formatted_facts),\n                \"uuid\": \"56b2a590-10e8-4706-be22-b8bdb198884e\"\n            }\n                \n        except Exception as e:\n            print(f\"[DEBUG] Unexpected error: {str(e)}\")\n            return {\n                \"type\": \"text\",\n                \"text\": f\"Error retrieving persona data for user: {user_id}\",\n                \"uuid\": \"56b2a590-10e8-4706-be22-b8bdb198884e\"\n            }\nelse:\n    @server.get(\"/mcp/persona/{user_id}\")\n    async def get_persona_endpoint(user_id: str) -> List[Dict]:\n        return await get_persona(user_id)\n\nif __name__ == \"__main__\":\n    if not MCP_AVAILABLE:\n        import uvicorn\n        import asyncio\n        # Create data directory if it doesn't exist\n        os.makedirs(DATA_DIR, exist_ok=True)\n        uvicorn.run(server, host=\"0.0.0.0\", port=5002)\n    else:\n        # MCP framework will handle server startup\n        print(\"Running server with stdio transport\")\n        server.run(transport=\"stdio\")", "text_preview": "from mcp.server.fastmcp import FastMCP import json import os from typing import List, Dict, Optional try: from mcp.server.fastmcp import FastMCP MCP_AVAILABLE = True except ImportError: MCP_AVAILABLE = False from fastapi import FastAPI, HTT", "input_sha256": "5d9a482a14d7578bf3f81fe5b589154b9b554adbc62b411dc437ef8345163897", "text_sha256": "728c458e1b60135e6b23a2b7923f7cc59d10de982985c0d1bb995e8b63f84c7f", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12049, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.46545437725104694, "noise_severity": 0.13866206455115027, "missing_content_severity": 0.011560102670473305, "factual_density": 3.0292769680754237, "pretraining_utility": 0.4400292071067013, "math_density": 0.01566135531392095, "code_density": 3.7804123494813386, "reasoning_density": 0.5302918336560472, "education_level": 2.1774906178224787, "creative_writing_quality": -0.3116861233156211}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.009346197161709171, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.996734037842835, "secondary": 0.9735472942693834, "tertiary": 0.9832774555403632, "structured_records": 0.04277663904699327, "procedural_steps": 0.019138711508155697, "worked_solution": 0.00609407228866244, "contextual_dialogue": 6.8184852658991e-06, "content_quality": 0.3849808432673104, "noise_severity": 0.8844482795407081, "missing_content_severity": 0.990366581107939, "factual_density": 0.4243221669745877, "pretraining_utility": 0.43269672783498037, "math_density": 0.9869488705717325, "code_density": 0.8170102912344486, "reasoning_density": 0.5580901386199607, "education_level": 0.758962992294336, "creative_writing_quality": 0.9993346018951019, "primary_topic": 0.9947247762439928, "secondary_topic": 0.9674716089297136}, "probability_margin": {"primary": 0.9956190672983509, "secondary": 0.9594514087280985, "tertiary": 0.9781223437744967, "education_level": 0.6227438550922008, "creative_writing_quality": 0.9991577061940706, "primary_topic": 0.9936701424686992, "secondary_topic": 0.9547196330794682}, "raw_probabilities": {"primary": {"Academic Writing": 4.146945835441278e-05, "Content Listing": 1.956913286085408e-05, "Creative Writing": 1.2965955722491848e-05, "Customer Support Page": 9.134411400585359e-06, "Q&A Forum": 1.2398541262614712e-05, "Discussion Thread": 7.155937714085741e-06, "Comment Section": 1.08990167155035e-05, "Missing Content": 2.5341142261881386e-05, "Knowledge Article": 7.922004205172101e-05, "Legal Notices": 2.2305543993876937e-05, "News Article": 4.096080696070427e-06, "Organizational Announcement": 1.0349963061758185e-05, "Nonfiction Writing": 1.251783206027071e-05, "Organizational About Page": 1.2689235874553572e-05, "Personal About Page": 2.5384668469809484e-05, "Personal Blog": 7.5069933450528525e-06, "Product Page": 9.087877217771487e-06, "Spam / Ads": 1.0938267831040353e-05, "Structured Data": 0.0004732595047100071, "Technical Writing": 0.9968701195993835, "Transcript / Interview": 1.3404849766868196e-05, "Tutorial / How-To Guide": 0.0010530627458284838, "User Reviews": 6.070898384165934e-06, "Other / Unknown": 0.0012510523010325924}, "secondary": {"Academic Writing": 0.00012085881153876317, "Content Listing": 7.55508076638824e-05, "Creative Writing": 9.273899947026792e-05, "Customer Support Page": 8.9879591525865e-05, "Q&A Forum": 4.43510891009158e-05, "Discussion Thread": 3.5196044683606404e-05, "Comment Section": 9.548777485684725e-05, "Missing Content": 0.0002514717149912791, "Knowledge Article": 0.000497100554732457, "Legal Notices": 0.0001602560436912279, "News Article": 5.6684807127244714e-05, "Organizational Announcement": 5.761261617247456e-05, "Nonfiction Writing": 7.423968011630411e-05, "Organizational About Page": 7.719322457649151e-05, "Personal About Page": 7.211865888210887e-05, "Personal Blog": 5.25907761466563e-05, "Product Page": 4.6241677304197245e-05, "Spam / Ads": 6.556150849937387e-05, "Structured Data": 0.005810680284854979, "Technical Writing": 0.015198081613393906, "Transcript / Interview": 8.961157272555819e-05, "Tutorial / How-To Guide": 0.00225566715127676, "User Reviews": 3.133465517649794e-05, "NONE": 0.9746494903414924}, "tertiary": {"Academic Writing": 9.762208577831228e-05, "Content Listing": 7.515358199935935e-05, "Creative Writing": 8.505489436815753e-05, "Customer Support Page": 9.07547257282847e-05, "Q&A Forum": 3.462434506985282e-05, "Discussion Thread": 2.620646006377399e-05, "Comment Section": 0.00013177599460037503, "Missing Content": 0.000330864409697842, "Knowledge Article": 0.0005997780499772703, "Legal Notices": 0.000214640110202034, "News Article": 4.408120870476079e-05, "Organizational Announcement": 4.4787152442229945e-05, "Nonfiction Writing": 6.754991039771487e-05, "Organizational About Page": 7.328572466023258e-05, "Personal About Page": 6.033274119497695e-05, "Personal Blog": 4.1012197665294424e-05, "Product Page": 4.407551277249477e-05, "Spam / Ads": 5.271439285682323e-05, "Structured Data": 0.004265411613861434, "Technical Writing": 0.005851884451684733, "Transcript / Interview": 6.63082573464757e-05, "Tutorial / How-To Guide": 0.0037029851899120655, "User Reviews": 2.486876283399318e-05, "NONE": 0.9839742282261814}, "structured_records": {"false": 0.9572233609530068, "true": 0.04277663904699327}, "procedural_steps": {"false": 0.9808612884918443, "true": 0.019138711508155697}, "worked_solution": {"false": 0.9939059277113376, "true": 0.00609407228866244}, "contextual_dialogue": {"false": 0.9999931815147342, "true": 6.8184852658991e-06}, "content_quality": {"-2": 0.01496752275594493, "-1": 0.10811301867945283, "0": 0.3751556997873498, "1": 0.4000250761121153, "2": 0.10173868266513718}, "noise_severity": {"0": 0.8867008115344899, "1": 0.09305203629540378, "2": 0.016540161696065123, "3": 0.002298257032548845, "4": 0.0014087334414924298}, "missing_content_severity": {"0": 0.9934584444060636, "1": 0.0028705344760724927, "2": 0.002568038709417072, "3": 0.0008584388582199695, "4": 0.00024454355022668925}, "factual_density": {"0": 0.015457950030817114, "1": 0.04918951282196418, "2": 0.18601534004115594, "3": 0.38929201325310375, "4": 0.360045183852959}, "pretraining_utility": {"-2": 0.008902463855488162, "-1": 0.0898133595998735, "0": 0.41403324897944965, "1": 0.4268543607128265, "2": 0.06039656685236236}, "math_density": {"0": 0.9888436367421448, "1": 0.009046670017640363, "2": 0.0006950595568133689, "3": 0.00043396855095219865, "4": 0.0009806651324493132}, "code_density": {"0": 0.00043099613326145944, "1": 0.0007936816401649112, "2": 0.027771052668136356, "3": 0.15994051572884835, "4": 0.8110637538295888}, "reasoning_density": {"0": 0.7131130640804633, "1": 0.09433073953550991, "2": 0.1538183171204315, "3": 0.02662705717470659, "4": 0.0121108220888886}, "education_level": {"none": 0.000941619662520399, "0": 0.0013808209437926925, "1": 0.013535454029775053, "2": 0.7991358269119466, "3": 0.17639197181974584, "4": 0.0086143066322193}, "creative_writing_quality": {"not_applicable": 0.9994455015792516, "-2": 6.238660361700032e-05, "-1": 0.00013568716077950698, "0": 0.00028779538518095666, "1": 4.962763747634076e-05, "2": 1.9001633694713524e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.773955809405719e-05, "Art & Design": 4.715377628037227e-05, "Law & Crime": 3.508812649001292e-05, "Education & Careers": 5.084828509880873e-05, "Entertainment & Media": 4.910896360117984e-05, "Fashion & Beauty": 3.948526944762991e-05, "Business, Economics & Finance": 7.651842829663537e-05, "Food & Dining": 3.1080102434420837e-05, "Games & Gaming": 8.007958204757538e-05, "Electronics & Hardware": 8.451647028865854e-05, "Health, Healthcare & Wellness": 7.93365734678846e-05, "History & Geography": 2.9087441859744524e-05, "Home, Pets & Hobbies": 3.078465329653101e-05, "Industry & Production": 3.135551793967838e-05, "Language, Literature & Philosophy": 0.00023300898670577724, "Politics & Government": 2.8569815254089884e-05, "Religion & Spirituality": 5.525599033489788e-05, "Science, Mathematics & Engineering": 0.0012575269966784222, "Relationships & Community": 0.0009775667218879128, "Software Usage & Applications": 0.0006802770054084861, "Programming & Software Engineering": 0.9949276694653777, "Sports & Fitness": 4.322965581948749e-05, "Vehicles & Transportation": 2.0343881992191892e-05, "Travel & Tourism": 4.8796688341661e-05, "Other / Uncovered Subject": 0.0009303362264828421, "Unknown / Insufficient Content": 8.523581707305546e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.000349898447441494, "Art & Design": 0.0002271822308595822, "Law & Crime": 0.00027242076468250736, "Education & Careers": 0.00019489598723905355, "Entertainment & Media": 0.00023629331585936261, "Fashion & Beauty": 0.00013216185105644596, "Business, Economics & Finance": 0.00031029882608785353, "Food & Dining": 0.00010037161026288938, "Games & Gaming": 0.00021357503742712596, "Electronics & Hardware": 0.00028082571565324387, "Health, Healthcare & Wellness": 0.0002485614656083028, "History & Geography": 0.00011591426744909964, "Home, Pets & Hobbies": 9.651377755526376e-05, "Industry & Production": 9.06408858996158e-05, "Language, Literature & Philosophy": 0.0013082564351549638, "Politics & Government": 0.00012839903876759264, "Religion & Spirituality": 0.00016332133639135001, "Science, Mathematics & Engineering": 0.004915304729130343, "Relationships & Community": 0.0008609980487672668, "Software Usage & Applications": 0.004842664026841256, "Programming & Software Engineering": 0.014003067814487189, "Sports & Fitness": 0.00010858778916832935, "Vehicles & Transportation": 4.9798865256117426e-05, "Travel & Tourism": 0.00014632820471530933, "Other / Uncovered Subject": 0.0018810186342830754, "NONE": 0.9687227008939554}}, "probabilities": {"primary": {"Academic Writing": 4.146945835441278e-05, "Content Listing": 1.956913286085408e-05, "Creative Writing": 1.2965955722491848e-05, "Customer Support Page": 9.134411400585359e-06, "Q&A Forum": 1.2398541262614712e-05, "Discussion Thread": 7.155937714085741e-06, "Comment Section": 1.08990167155035e-05, "Missing Content": 2.5341142261881386e-05, "Knowledge Article": 7.922004205172101e-05, "Legal Notices": 2.2305543993876937e-05, "News Article": 4.096080696070427e-06, "Organizational Announcement": 1.0349963061758185e-05, "Nonfiction Writing": 1.251783206027071e-05, "Organizational About Page": 1.2689235874553572e-05, "Personal About Page": 2.5384668469809484e-05, "Personal Blog": 7.5069933450528525e-06, "Product Page": 9.087877217771487e-06, "Spam / Ads": 1.0938267831040353e-05, "Structured Data": 0.0004732595047100071, "Technical Writing": 0.9968701195993835, "Transcript / Interview": 1.3404849766868196e-05, "Tutorial / How-To Guide": 0.0010530627458284838, "User Reviews": 6.070898384165934e-06, "Other / Unknown": 0.0012510523010325924}, "secondary": {"Academic Writing": 0.00012085881153876317, "Content Listing": 7.55508076638824e-05, "Creative Writing": 9.273899947026792e-05, "Customer Support Page": 8.9879591525865e-05, "Q&A Forum": 4.43510891009158e-05, "Discussion Thread": 3.5196044683606404e-05, "Comment Section": 9.548777485684725e-05, "Missing Content": 0.0002514717149912791, "Knowledge Article": 0.000497100554732457, "Legal Notices": 0.0001602560436912279, "News Article": 5.6684807127244714e-05, "Organizational Announcement": 5.761261617247456e-05, "Nonfiction Writing": 7.423968011630411e-05, "Organizational About Page": 7.719322457649151e-05, "Personal About Page": 7.211865888210887e-05, "Personal Blog": 5.25907761466563e-05, "Product Page": 4.6241677304197245e-05, "Spam / Ads": 6.556150849937387e-05, "Structured Data": 0.005810680284854979, "Technical Writing": 0.015198081613393906, "Transcript / Interview": 8.961157272555819e-05, "Tutorial / How-To Guide": 0.00225566715127676, "User Reviews": 3.133465517649794e-05, "NONE": 0.9746494903414924}, "tertiary": {"Academic Writing": 9.762208577831229e-05, "Content Listing": 7.515358199935937e-05, "Creative Writing": 8.505489436815754e-05, "Customer Support Page": 9.075472572828471e-05, "Q&A Forum": 3.4624345069852825e-05, "Discussion Thread": 2.6206460063773993e-05, "Comment Section": 0.00013177599460037506, "Missing Content": 0.00033086440969784205, "Knowledge Article": 0.0005997780499772704, "Legal Notices": 0.00021464011020203404, "News Article": 4.40812087047608e-05, "Organizational Announcement": 4.478715244222995e-05, "Nonfiction Writing": 6.754991039771488e-05, "Organizational About Page": 7.328572466023259e-05, "Personal About Page": 6.0332741194976954e-05, "Personal Blog": 4.101219766529443e-05, "Product Page": 4.4075512772494774e-05, "Spam / Ads": 5.2714392856823235e-05, "Structured Data": 0.004265411613861435, "Technical Writing": 0.005851884451684734, "Transcript / Interview": 6.630825734647572e-05, "Tutorial / How-To Guide": 0.003702985189912066, "User Reviews": 2.4868762833993184e-05, "NONE": 0.9839742282261815}, "structured_records": {"false": 0.9572233609530068, "true": 0.04277663904699327}, "procedural_steps": {"false": 0.9808612884918443, "true": 0.019138711508155697}, "worked_solution": {"false": 0.9939059277113376, "true": 0.00609407228866244}, "contextual_dialogue": {"false": 0.9999931815147342, "true": 6.8184852658991e-06}, "content_quality": {"-2": 0.01496752275594493, "-1": 0.10811301867945283, "0": 0.3751556997873498, "1": 0.4000250761121153, "2": 0.10173868266513718}, "noise_severity": {"0": 0.8867008115344899, "1": 0.09305203629540378, "2": 0.016540161696065123, "3": 0.002298257032548845, "4": 0.0014087334414924298}, "missing_content_severity": {"0": 0.9934584444060638, "1": 0.002870534476072493, "2": 0.0025680387094170723, "3": 0.0008584388582199697, "4": 0.0002445435502266893}, "factual_density": {"0": 0.015457950030817114, "1": 0.04918951282196418, "2": 0.18601534004115594, "3": 0.38929201325310375, "4": 0.360045183852959}, "pretraining_utility": {"-2": 0.00890246385548816, "-1": 0.08981335959987348, "0": 0.41403324897944954, "1": 0.4268543607128264, "2": 0.060396566852362346}, "math_density": {"0": 0.9888436367421448, "1": 0.009046670017640363, "2": 0.0006950595568133689, "3": 0.00043396855095219865, "4": 0.0009806651324493132}, "code_density": {"0": 0.0004309961332614595, "1": 0.0007936816401649113, "2": 0.02777105266813636, "3": 0.15994051572884838, "4": 0.811063753829589}, "reasoning_density": {"0": 0.7131130640804634, "1": 0.09433073953550992, "2": 0.15381831712043154, "3": 0.026627057174706592, "4": 0.012110822088888602}, "education_level": {"none": 0.0009416196625203991, "0": 0.0013808209437926927, "1": 0.013535454029775055, "2": 0.7991358269119467, "3": 0.17639197181974586, "4": 0.008614306632219302}, "creative_writing_quality": {"not_applicable": 0.9994455015792514, "-2": 6.23866036170003e-05, "-1": 0.00013568716077950695, "0": 0.0002877953851809566, "1": 4.962763747634075e-05, "2": 1.900163369471352e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.7739558094057206e-05, "Art & Design": 4.7153776280372286e-05, "Law & Crime": 3.5088126490012936e-05, "Education & Careers": 5.084828509880874e-05, "Entertainment & Media": 4.910896360117985e-05, "Fashion & Beauty": 3.9485269447629924e-05, "Business, Economics & Finance": 7.65184282966354e-05, "Food & Dining": 3.108010243442085e-05, "Games & Gaming": 8.007958204757541e-05, "Electronics & Hardware": 8.451647028865856e-05, "Health, Healthcare & Wellness": 7.933657346788463e-05, "History & Geography": 2.9087441859744534e-05, "Home, Pets & Hobbies": 3.0784653296531024e-05, "Industry & Production": 3.135551793967839e-05, "Language, Literature & Philosophy": 0.00023300898670577732, "Politics & Government": 2.8569815254089894e-05, "Religion & Spirituality": 5.52559903348979e-05, "Science, Mathematics & Engineering": 0.0012575269966784226, "Relationships & Community": 0.0009775667218879132, "Software Usage & Applications": 0.0006802770054084863, "Programming & Software Engineering": 0.994927669465378, "Sports & Fitness": 4.3229655819487504e-05, "Vehicles & Transportation": 2.03438819921919e-05, "Travel & Tourism": 4.879668834166101e-05, "Other / Uncovered Subject": 0.0009303362264828424, "Unknown / Insufficient Content": 8.523581707305549e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.000349898447441494, "Art & Design": 0.0002271822308595822, "Law & Crime": 0.00027242076468250736, "Education & Careers": 0.00019489598723905355, "Entertainment & Media": 0.00023629331585936261, "Fashion & Beauty": 0.00013216185105644596, "Business, Economics & Finance": 0.00031029882608785353, "Food & Dining": 0.00010037161026288938, "Games & Gaming": 0.00021357503742712596, "Electronics & Hardware": 0.00028082571565324387, "Health, Healthcare & Wellness": 0.0002485614656083028, "History & Geography": 0.00011591426744909964, "Home, Pets & Hobbies": 9.651377755526376e-05, "Industry & Production": 9.06408858996158e-05, "Language, Literature & Philosophy": 0.0013082564351549638, "Politics & Government": 0.00012839903876759264, "Religion & Spirituality": 0.00016332133639135001, "Science, Mathematics & Engineering": 0.004915304729130343, "Relationships & Community": 0.0008609980487672668, "Software Usage & Applications": 0.004842664026841256, "Programming & Software Engineering": 0.014003067814487189, "Sports & Fitness": 0.00010858778916832935, "Vehicles & Transportation": 4.9798865256117426e-05, "Travel & Tourism": 0.00014632820471530933, "Other / Uncovered Subject": 0.0018810186342830754, "NONE": 0.9687227008939554}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10014, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10014, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:09.000779+00:00", "scoring_key_slot": "primary", "dataset_uid": "43f3ff86-bd78-4e6d-af01-c0d24b215981", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "4c27801a2164dc951e27648d6e3c1087381bc2f2a755f541516529a503aca666", "source_metadata": {"uuid": "43f3ff86-bd78-4e6d-af01-c0d24b215981", "repo_name": "srikardevulapalli/persona-context-mcp", "relative_path": "mcp_server.py", "category": "WEB", "algo_rel_score": 0.947944700717926, "quality_score": 6.179713726043701, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 12, "repo_created_at": "2025-05-22T12:39:50.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "4e9984fbb9b598ef288c940a34fb5050c1cc742f", "size_bytes": 5571, "file_timestamp": 1747967557, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "cc706de4edf82af8da53e75d3619041471d92c14"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 12, "repo_created_at": "2025-05-22T12:39:50.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "4e9984fbb9b598ef288c940a34fb5050c1cc742f", "size_bytes": 5571, "file_timestamp": 1747967557, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "cc706de4edf82af8da53e75d3619041471d92c14", "url": "https://github.com/srikardevulapalli/persona-context-mcp/blob/cc706de4edf82af8da53e75d3619041471d92c14/mcp_server.py"}, "gpt4o_tokens": 1373, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.46545437725104694, "noise_severity": 0.13866206455115027, "missing_content_severity": 0.011560102670473305, "factual_density": 3.0292769680754237, "pretraining_utility": 0.44002920710670135, "math_density": 0.01566135531392095, "code_density": 3.7804123494813386, "reasoning_density": 0.5302918336560472, "education_level": 2.1774906178224787, "creative_writing_quality": -0.3116861233156211}, "score_applicability": {"education_level": 0.9990583803374796, "creative_writing_quality": 0.0005544984207485181}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/carryty/test2/blob/9f1c29aba85e4f6cd40c82e0f504ad7a7543e810/highway_env/envs/square_env.py", "id": "python-0-a723bf6eca3678ebec3c2c77d3ec9e3e", "text": "import time\nfrom typing import Dict, Text, Tuple\nfrom gym import register\nimport numpy as np\nimport math\nimport random\nfrom highway_env import utils\nfrom highway_env.envs.common.abstract import AbstractEnv\nfrom highway_env.envs.common.action import Action\nfrom highway_env.road.road import Road, RoadNetwork\nfrom highway_env.road.lane import LineType, StraightLane, CircularLane, AbstractLane, SineLane\nfrom highway_env.utils import near_split\nfrom highway_env.vehicle.controller import ControlledVehicle\nfrom highway_env.vehicle.kinematics import Vehicle\nfrom highway_env.vehicle.objects import Obstacle\nfrom highway_env.road.regulation import RegulatedRoad\n\nObservation = np.ndarray\n\n\nclass SquareEnv(AbstractEnv):\n    \"\"\"\n    A highway driving environment.\n\n    The vehicle is driving on a straight highway with several lanes, and is rewarded for reaching a high speed,\n    staying on the rightmost lanes and avoiding collisions.\n    \"\"\"\n\n    @classmethod\n    def default_config(cls) -> dict:\n        config = super().default_config()\n        config.update({\n            \"observation\": {\n                \"type\": \"Kinematics\",\n                # \"absolute\": True,\n                \"features\": [\"presence\", \"x\", \"y\", \"vx\", \"vy\"],  #\n                \"features_range\": {\"x\": [-250, 250], \"y\": [-250, 250], \"vx\": [-30, 30], \"vy\": [-30, 30]},\n            },\n            \"action\": {\n                \"type\": \"DiscreteMetaAction\",\n            },\n            \"Create_vehicles\": True,\n            \"screen_width\": 960,\n            \"screen_height\":540,\n            \"incoming_vehicle_destination\": None,\n            \"spawn_probability\": 0.6,\n            \"lanes_count\": 2,\n            \"vehicles_count\": 60,\n            \"controlled_vehicles\": 1,\n            \"initial_lane_id\": None,\n            \"duration\": 80,  # [s]40\n            \"ego_spacing\": 0.15,\n            \"vehicles_density\": 0.8,\n            # \"b_reward\": 10,\n            # \"d_reward\": 10,\n            # \"f_reward\": 10,\n            # \"h_reward\": 10,\n            \"collision_reward\": -80,    # The reward received when colliding with a vehicle.    发生碰撞的奖励\n            # \"right_lane_reward\": 0.1,  # The reward received when driving on the right-most lanes, linearly mapped to\n                                       # zero for other lanes.\n            \"high_speed_reward\": 3,  # The reward received when driving at full speed, linearly mapped to zero for\n                                       # lower speeds according to config[\"reward_speed_range\"].\n            \"low_speed_reward\": -3,\n            \"lane_change_reward\": 0,   # The reward received at each lane change action.\n            \"reward_speed_range\": [20, 30],\n            \"low_reward_speed_range\": [10, 20],\n            \"normalize_reward\": False,\n            \"offroad_terminal\": True,\n            # \"real_time_rendering\": True,\n            \"is_truncated\": True,\n            \"check_safe\" : True\n        })\n        return config\n\n    def _reset(self) -> None:\n        self._create_road()\n        self._create_vehicles()\n        # self.t = 0\n        # self.cls = 0\n        # self.Arrival = [1,1,1,1]\n\n    def _create_road(self) -> None:\n        net = RoadNetwork()\n\n        r1 = 5\n\n        # 直道ab\n        net.add_lane(\"a\", \"b\",\n                     StraightLane([-200, 205], [200, 205], line_types=(LineType.CONTINUOUS, LineType.STRIPED),\n                                  width=5, speed_limit=30))\n        net.add_lane(\"a\", \"b\",\n                     StraightLane([-200, 210], [200, 210], line_types=(LineType.NONE, LineType.STRIPED),\n                                  width=5, speed_limit=30))\n        net.add_lane(\"a\", \"b\",\n                     StraightLane([-200, 215], [200, 215], line_types=(LineType.STRIPED, LineType.CONTINUOUS),\n                                  width=5, speed_limit=30))\n\n        # 弯道bc\n        center1 = [200, 200]\n        net.add_lane(\"b\", \"c\",\n                     CircularLane(center1, r1, np.deg2rad(90), np.deg2rad(0), width=5,\n                                  clockwise=False, line_types=[LineType.CONTINUOUS, LineType.NONE],\n                                  speed_limit=30))\n        net.add_lane(\"b\", \"c\",\n                     CircularLane(center1, r1+5, np.deg2rad(90), np.deg2rad(0), width=5,\n                                  clockwise=False, line_types=[LineType.STRIPED, LineType.NONE],\n                                  speed_limit=30))\n        net.add_lane(\"b\", \"c\",\n                     CircularLane(center1, r1 + 10, np.deg2rad(90), np.deg2rad(0), width=5,\n                                  clockwise=False, line_types=[LineType.STRIPED, LineType.CONTINUOUS],\n                                  speed_limit=30))\n        # 直道cd\n        net.add_lane(\"c\", \"d\",\n                     StraightLane([205, 200], [205, -200], line_types=(LineType.CONTINUOUS, LineType.STRIPED),\n                                  width=5, speed_limit=30))\n        net.add_lane(\"c\", \"d\",\n                     StraightLane([210, 200], [210, -200], line_types=(LineType.NONE, LineType.STRIPED),\n                                  width=5, speed_limit=30))\n        net.add_lane(\"c\", \"d\",\n                     StraightLane([215, 200], [215, -200], line_types=(LineType.NONE, LineType.CONTINUOUS),\n                                  width=5, speed_limit=30))\n        # 弯道de\n        center2 = [200, -200]\n        net.add_lane(\"d\", \"e\",\n                     CircularLane(center2, r1, np.deg2rad(0), np.deg2rad(-90), width=5,\n                                  clockwise=False, line_types=[LineType.CONTINUOUS, LineType.NONE],\n                                  speed_limit=30))\n        net.add_lane(\"d\", \"e\",\n                     CircularLane(center2, r1 + 5, np.deg2rad(0), np.deg2rad(-90), width=5,\n                                  clockwise=False, line_types=[LineType.STRIPED, LineType.NONE],\n                                  speed_limit=30))\n        net.add_lane(\"d\", \"e\",\n                     CircularLane(center2, r1 + 10, np.deg2rad(0), np.deg2rad(-90), width=5,\n                                  clockwise=False, line_types=[LineType.STRIPED, LineType.CONTINUOUS],\n                                  speed_limit=30))\n        # 直道ef\n        net.add_lane(\"e\", \"f\",\n                     StraightLane([200, -205], [-200, -205], line_types=(LineType.CONTINUOUS, LineType.STRIPED),\n                                  width=5, speed_limit=30))\n        net.add_lane(\"e\", \"f\",\n                     StraightLane([200, -210], [-200, -210], line_types=(LineType.NONE, LineType.STRIPED),\n                                  width=5, speed_limit=30))\n        net.add_lane(\"e\", \"f\",\n                     StraightLane([200, -215], [-200, -215], line_types=(LineType.NONE, LineType.CONTINUOUS),\n                                  width=5, speed_limit=30))\n        # 弯道fg\n        center3 = [-200, -200]\n        net.add_lane(\"f\", \"g\",\n                     CircularLane(center3, r1, np.deg2rad(-90), np.deg2rad(-180), width=5,\n                                  clockwise=False, line_types=[LineType.CONTINUOUS, LineType.NONE],\n                                  speed_limit=30))\n        net.add_lane(\"f\", \"g\",\n                     CircularLane(center3, r1 + 5, np.deg2rad(-90), np.deg2rad(-180), width=5,\n                                  clockwise=False, line_types=[LineType.STRIPED, LineType.NONE],\n                                  speed_limit=30))\n        net.add_lane(\"f\", \"g\",\n                     CircularLane(center3, r1 + 10, np.deg2rad(-90), np.deg2rad(-180), width=5,\n                                  clockwise=False, line_types=[LineType.STRIPED, LineType.CONTINUOUS],\n                                  speed_limit=30))\n        # 直道gh\n        net.add_lane(\"g\", \"h\",\n                     StraightLane([-205, -200], [-205, 200], line_types=(LineType.CONTINUOUS, LineType.NONE),\n                                  width=5, speed_limit=30))\n        net.add_lane(\"g\", \"h\",\n                     StraightLane([-210, -200], [-210, 200], line_types=(LineType.STRIPED, LineType.NONE),\n                                  width=5, speed_limit=30))\n        net.add_lane(\"g\", \"h\",\n                     StraightLane([-215, -200], [-215, 200], line_types=(LineType.STRIPED, LineType.CONTINUOUS),\n                                  width=5, speed_limit=30))\n\n        # 弯道ha\n        center3 = [-200, 200]\n        net.add_lane(\"h\", \"a\",\n                     CircularLane(center3, r1, np.deg2rad(180), np.deg2rad(90), width=5,\n                                  clockwise=False, line_types=[LineType.CONTINUOUS, LineType.NONE],\n                                  speed_limit=30))\n        net.add_lane(\"h\", \"a\",\n                     CircularLane(center3, r1 + 5, np.deg2rad(180), np.deg2rad(90), width=5,\n                                  clockwise=False, line_types=[LineType.STRIPED, LineType.NONE],\n                                  speed_limit=30))\n        net.add_lane(\"h\", \"a\",\n                     CircularLane(center3, r1 + 10, np.deg2rad(180), np.deg2rad(90), width=5,\n                                  clockwise=False, line_types=[LineType.STRIPED, LineType.CONTINUOUS],\n                                  speed_limit=30))\n\n        road = Road(network=net, np_random=self.np_random, record_history=self.config[\"show_trajectories\"])\n        self.road = road\n\n\n    def _create_vehicles(self) -> None:\n        other_vehicles_type = utils.class_from_path(self.config[\"other_vehicles_type\"])\n        other_per_controlled = near_split(self.config[\"vehicles_count\"], num_bins=self.config[\"controlled_vehicles\"])\n\n        ego_lane = self.road.network.get_lane((\"a\", \"b\", 0))\n        self.controlled_vehicles = []\n        for others in other_per_controlled:\n            vehicle = Vehicle.create_random(\n                self.road,\n                speed=25,\n                lane_id=self.config[\"initial_lane_id\"],\n                spacing=self.config[\"ego_spacing\"]\n            )\n            vehicle = self.action_type.vehicle_class(self.road, ego_lane.position(0, 0), heading=0, speed = 25)\n            self.controlled_vehicles.append(vehicle)\n            self.road.vehicles.append(vehicle)\n\n            for _ in range(others):\n                vehicle = other_vehicles_type.lane_create_random(self.road, lane_from = [\"a\", \"c\", \"e\", \"g\"], speed = 20, spacing = 1/self.config['vehicles_density'])\n                vehicle.randomize_behavior()\n                self.road.vehicles.append(vehicle)\n\n\n    def _reward(self, action: Action) -> float:\n        \"\"\"\n        The reward is defined to foster driving at high speed, on the rightmost lanes, and to avoid collisions.\n        :param action: the last action performed\n        :return: the corresponding reward\n        \"\"\"\n        rewards = self._rewards(action)\n        reward = sum(self.config.get(name, 0) * reward for name, reward in rewards.items())\n        # reward = reward - (self.time - self.t) * 0.8\n        # self.t = self.time\n        if self.config[\"normalize_reward\"]:\n            reward = utils.lmap(reward,\n                                [self.config[\"collision_reward\"],\n                                 self.config[\"high_speed_reward\"]],\n                                [0, 1])\n        reward *= rewards['on_road_reward']\n        return reward\n\n    def _rewards(self, action: Action) -> Dict[Text, float]:\n        neighbours = self.road.network.all_side_lanes(self.vehicle.lane_index)\n        lane = self.vehicle.target_lane_index[2] if isinstance(self.vehicle, ControlledVehicle) \\\n            else self.vehicle.lane_index[2]\n        # Use forward speed rather than speed, see https://github.com/eleurent/highway-env/issues/268\n        # print(self.vehicle.speed)\n        forward_speed = self.vehicle.speed     # * np.cos(self.vehicle.heading)\n        # self.cls+=1\n        # if self.cls%30 == 0:\n        #     print(forward_speed)\n        scaled_speed = utils.lmap(forward_speed, self.config[\"reward_speed_range\"], [0, 1])\n        low_speed = utils.lmap(forward_speed, self.config[\"low_reward_speed_range\"], [1, 0])\n        return {\n            \"collision_reward\": float(self.vehicle.crashed),\n            \"high_speed_reward\": np.clip(scaled_speed, 0, 1),\n            \"on_road_reward\": float(self.vehicle.on_road),\n            \"low_speed_reward\": np.clip(low_speed, 0, 1),\n        }\n\n    def _is_terminated(self) -> bool:\n        \"\"\"The episode is over if the ego vehicle crashed.\"\"\"\n        return (self.vehicle.crashed or\n                self.config[\"offroad_terminal\"] and not self.vehicle.on_road)\n\n    def _is_truncated(self) -> bool:\n        \"\"\"The episode is truncated if the time limit is reached.\"\"\"\n        return self.time >= self.config[\"duration\"]\n\n    def safety_check(self, action: int):\n        dt = 2 / self.config[\"policy_frequency\"]\n        self.error = [4, 4]\n        self.available_actions_list = self.get_available_actions()\n        # print(self.available_actions_list)\n        _obs = self.observation_type.observe()\n        _obs = np.delete(_obs, 0, axis=1)\n        obs = self.obs_restore(_obs)\n        print(obs)\n        heading = self.controlled_vehicles[0].to_dict()['heading']\n        if action in self.available_actions_list:\n            if not self._is_safe(action,obs,heading,dt):\n                print(\"有危险\")\n                return self.get_new_action(action,obs,heading,dt)\n            else:\n                return action\n        else:\n            return self.get_new_action(action,obs,heading,dt)\n\n    def get_new_action(self,action,obs,heading,dt):\n        action_list = []\n        for i in self.available_actions_list:\n            if i == action:\n                continue\n            if self._is_safe(i, obs, heading, dt):\n                action_list.append(i)\n        if len(action_list) == 0:\n            print(\"没有安全动作\")\n            action = 4\n            return action\n        else:\n            print(\"可用安全动作有：\",action_list)\n            action = random.choice(action_list)\n            print(\"新选择动作为：\", action)\n            return action\n\n    def _is_safe(self, action, obs, heading,dt):\n        ego_vehicle = obs[0]\n        npc_action = 1\n        # IDLE\n        if action == 1:\n            vehicle = self.road.vehicles[0]\n            for other in self.road.vehicles[1:]:\n                if vehicle.new_handle_collisions(other, dt):\n                        return False\n            return True\n\n        # FASTER\n        elif action == 3:\n            new_state = []\n            vehicle = self.road.vehicles[0]\n            # for other in self.road.vehicles[1:]:\n            #     if vehicle.new_handle_collisions(other, dt):\n            #         return False\n            new_ego_vehicle = self.move(action, ego_vehicle, dt)\n            for i in range(1,5):\n                new_state.append(self.move(npc_action, obs[i], dt))\n            for i in range(1,5):\n                if self.error[0] > abs(new_ego_vehicle[0] - new_state[i-1][0]) and \\\n                    self.error[1] > abs(new_ego_vehicle[1] - new_state[i-1][1]):\n                    return False\n            return True\n\n        # SLOWER\n        elif action == 4:\n            new_state = []\n            new_ego_vehicle = self.move(action, ego_vehicle, dt)\n            for i in range(1,5):\n                new_state.append(self.move(npc_action, obs[i], dt))\n            for i in range(1,5):\n                if self.error[0] >abs(new_ego_vehicle[0] - new_state[i-1][0]) and \\\n                    self.error[1] >abs(new_ego_vehicle[1] - new_state[i-1][1]):\n                    return False\n            return True\n\n        # LANE_RIGHT\n        elif action == 2:\n            offset = self.vehicle.speed * dt * 0.5\n            A = (ego_vehicle[0] +5 * math.cos(heading), ego_vehicle[1] + 5 * math.sin(heading))\n            B = (A[0] - 7.5 * math.cos(math.pi/2 - heading), A[1] + 7.5 * math.sin(math.pi/2 - heading))\n            C = (ego_vehicle[0] - 4 * math.cos(heading), ego_vehicle[1] - 4 * math.sin(heading))\n            D = (C[0] - 7.5 * math.cos(math.pi / 2 - heading), C[1] + 7.5 * math.sin(math.pi / 2 - heading))\n            E = (B[0] + offset * math.cos(heading), B[1] + offset * math.sin(heading))\n            F = (E[0] + 5 * math.cos(math.pi / 2 - heading), E[1] - 5 * math.sin(math.pi / 2 - heading))\n            G = (B[0] + 5 * math.cos(math.pi / 2 - heading), B[1] - 5 * math.sin(math.pi / 2 - heading))\n            polygon_1 = [A, B, C, D]\n            polygon_2 = [G, B, E, F]\n            for n in range(1, 5):\n                p = (obs[n][0], obs[n][1])\n                count_1 = 0\n                count_2 = 0\n                for i in range(4):\n                    p1, p2 = polygon_1[i], polygon_1[(i + 1) % n]\n                    if p1[1] == p2[1]:\n                        continue\n                    if p[1] < min(p1[1], p2[1]) or p[1] >= max(p1[1], p2[1]):\n                        continue\n                    x = (p[1] - p1[1]) * (p2[0] - p1[0]) / (p2[1] - p1[1]) + p1[0]\n                    if x > p[0]:\n                        count_1 += 1\n                if count_1 % 2 == 1:\n                    return False\n                for i in range(4):\n                    p1, p2 = polygon_2[i], polygon_2[(i + 1) % n]\n                    if p1[1] == p2[1]:\n                        continue\n                    if p[1] < min(p1[1], p2[1]) or p[1] >= max(p1[1], p2[1]):\n                        continue\n                    x = (p[1] - p1[1]) * (p2[0] - p1[0]) / (p2[1] - p1[1]) + p1[0]\n                    if x > p[0]:\n                        count_2 += 1\n                if count_2 % 2 == 1:\n                    return False\n\n            return True\n\n        # LANE_LEFT\n        elif action == 0:\n            offset = self.vehicle.speed * dt * 0.5\n            # print(f\"offset为{offset},dt为{dt}\")\n            A = (ego_vehicle[0] + 5 * math.cos(heading), ego_vehicle[1] + 5 * math.sin(heading))\n            B = (A[0] + 7.5 * math.cos(math.pi/2 - heading), A[1] - 7.5 * math.sin(math.pi/2 - heading))\n            C = (ego_vehicle[0] - 4 * math.cos(heading), ego_vehicle[1] - 4 * math.sin(heading))\n            D = (C[0] + 7.5 * math.cos(math.pi / 2 - heading), C[1] - 7.5 * math.sin(math.pi / 2 - heading))\n            E = (B[0] + offset * math.cos(heading), B[1] + offset * math.sin(heading))\n            F = (E[0] - 5 * math.cos(math.pi / 2 - heading), E[1] + 5 * math.sin(math.pi / 2 - heading))\n            G = (B[0] - 5 * math.cos(math.pi / 2 - heading), B[1] + 5 * math.sin(math.pi / 2 - heading))\n            polygon_1 = [A, B, C, D]\n            polygon_2 = [G, B, E, F]\n            for n in range(1, 5):\n                p = (obs[n][0], obs[n][1])\n                count_1 = 0\n                count_2 = 0\n                for i in range(4):\n                    p1, p2 = polygon_1[i], polygon_1[(i + 1) % n]\n                    if p1[1] == p2[1]:\n                        continue\n                    if p[1] < min(p1[1], p2[1]) or p[1] >= max(p1[1], p2[1]):\n                        continue\n                    x = (p[1] - p1[1]) * (p2[0] - p1[0]) / (p2[1] - p1[1]) + p1[0]\n                    if x > p[0]:\n                        count_1 += 1\n                if count_1 % 2 == 1:\n                    return False\n                for i in range(4):\n                    p1, p2 = polygon_2[i], polygon_2[(i + 1) % n]\n                    if p1[1] == p2[1]:\n                        continue\n                    if p[1] < min(p1[1], p2[1]) or p[1] >= max(p1[1], p2[1]):\n                        continue\n                    x = (p[1] - p1[1]) * (p2[0] - p1[0]) / (p2[1] - p1[1]) + p1[0]\n                    if x > p[0]:\n                        count_2 += 1\n                if count_2 % 2 == 1:\n                    return False\n            return True\n\n    def move(self,action, state, dt):\n        dt *= 4\n        a = 1\n        if action == 1:   # IDLE\n            state[0] = state[0] + state[2] * dt\n            state[1] = state[1] + state[3] * dt\n\n        elif action == 3:   # FASTER\n            state[0] = state[0] + 0.5 * a * dt**2 + state[2] * dt\n            state[1] = state[1] + 0.5 * a * dt ** 2 + state[3] * dt\n            state[2] = state[2] + a * dt # / math.sqrt(2)\n            state[3] = state[3] + a * dt # / math.sqrt(2)\n        elif action == 4:   # SLOWER\n            state[0] = state[0] - 0.5 * a * dt**2 + state[2] * dt\n            state[1] = state[1] - 0.5 * a * dt ** 2 + state[3] * dt\n            state[2] = state[2] - a * dt # / math.sqrt(2)\n            state[3] = state[3] - a * dt # / math.sqrt(2)\n\n        return state\n\n    def obs_restore(self,obs):\n        for i in range(5):\n            obs[i][0] = obs[i][0] * 250\n            obs[i][1] = obs[i][1] * 250\n            obs[i][2] = obs[i][2] * 30\n            obs[i][3] = obs[i][3] * 30\n        ego = obs[0]\n        for i in range(1,5):\n            for j in range(4):\n                obs[i][j] += ego[j]\n        return obs\n\n    def step(self, action: int) -> tuple[np.ndarray, float, bool, bool, dict]:\n        if self.config['check_safe']:\n            action = self.safety_check(action)\n        obs, reward, terminated, truncated, info = super().step(action)\n        if terminated:\n            print(action)\n            print(\"发生碰撞\")\n        return obs, reward, terminated, truncated, info\n\n", "text_preview": "import time from typing import Dict, Text, Tuple from gym import register import numpy as np import math import random from highway_env import utils from highway_env.envs.common.abstract import AbstractEnv from highway_env.envs.common.actio", "input_sha256": "6cd5c222e58751efdbd442b8c750e4da29caaa83409f4be2e8c510e35917baa7", "text_sha256": "a723bf6eca3678ebec3c2c77d3ec9e3e9d87352361fe6755f725a183b7b4bf46", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12048, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Vehicles & Transportation", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 2, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.44936005020177, "noise_severity": 0.17027037589117627, "missing_content_severity": 0.027599447701515765, "factual_density": 3.3436689211483444, "pretraining_utility": 0.577936107232744, "math_density": 2.605893519583272, "code_density": 3.911237769585735, "reasoning_density": 1.078211971645009, "education_level": 2.539321261018971, "creative_writing_quality": -0.45049795580791535}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Vehicles & Transportation"}, "review_reasons": ["secondary_topic probability 0.3558 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.010977076133096286, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9969029382924123, "secondary": 0.9377831790839685, "tertiary": 0.9492375193967646, "structured_records": 0.24738287400419587, "procedural_steps": 0.006483930869152406, "worked_solution": 0.005019182593631417, "contextual_dialogue": 6.803961010688165e-06, "content_quality": 0.36076334143717137, "noise_severity": 0.8581080200906864, "missing_content_severity": 0.9770004602487369, "factual_density": 0.4530574342902871, "pretraining_utility": 0.49422911212832077, "math_density": 0.2617230371031962, "code_density": 0.926031474654779, "reasoning_density": 0.10149002362915915, "education_level": 0.4464128887779738, "creative_writing_quality": 0.9993351670751447, "primary_topic": 0.9030549694276871, "secondary_topic": 0.3299894407381798}, "probability_margin": {"primary": 0.9954471883564081, "secondary": 0.9134970214947818, "tertiary": 0.9312189990255637, "education_level": 0.1814422899747435, "creative_writing_quality": 0.9992417542181927, "primary_topic": 0.8753567304790709, "secondary_topic": 0.15288637903761898}, "raw_probabilities": {"primary": {"Academic Writing": 7.78862465868159e-05, "Content Listing": 2.108202720543691e-05, "Creative Writing": 2.0753567546151087e-05, "Customer Support Page": 7.143526277401675e-06, "Q&A Forum": 9.917860799867572e-06, "Discussion Thread": 7.04510768498706e-06, "Comment Section": 1.1111794648795775e-05, "Missing Content": 2.2874570702251578e-05, "Knowledge Article": 7.052872328635132e-05, "Legal Notices": 2.368088217690718e-05, "News Article": 4.575016534010627e-06, "Organizational Announcement": 9.297387505656458e-06, "Nonfiction Writing": 1.0177568362307933e-05, "Organizational About Page": 1.0471837766284434e-05, "Personal About Page": 1.0626717538685598e-05, "Personal Blog": 7.615950572356784e-06, "Product Page": 8.272773780811631e-06, "Spam / Ads": 8.856066752374117e-06, "Structured Data": 0.0006457535675375973, "Technical Writing": 0.9970319825302284, "Transcript / Interview": 1.4098360861105945e-05, "Tutorial / How-To Guide": 0.000375060656680015, "User Reviews": 6.39308514522103e-06, "Other / Unknown": 0.0015847941738202649}, "secondary": {"Academic Writing": 0.0009872052621954723, "Content Listing": 0.0001973074967731278, "Creative Writing": 0.0003446882801778947, "Customer Support Page": 0.0001795798612531829, "Q&A Forum": 0.00012297232617491347, "Discussion Thread": 0.00010251657635952184, "Comment Section": 0.0002413767986401317, "Missing Content": 0.000517896781807165, "Knowledge Article": 0.002236678361479248, "Legal Notices": 0.00033360863167483884, "News Article": 0.00015168822967592875, "Organizational Announcement": 0.00014441534906101437, "Nonfiction Writing": 0.00019093565923415832, "Organizational About Page": 0.00017965360412192663, "Personal About Page": 0.00015049486487517518, "Personal Blog": 0.00013652982376010744, "Product Page": 0.00010649875892203926, "Spam / Ads": 0.00014748990091433375, "Structured Data": 0.024438657623037492, "Technical Writing": 0.026878525127354523, "Transcript / Interview": 0.00020173239846000288, "Tutorial / How-To Guide": 0.0015614501669146596, "User Reviews": 7.255149499676396e-05, "NONE": 0.9403755466221364}, "tertiary": {"Academic Writing": 0.0011279563807819724, "Content Listing": 0.0001286746611908801, "Creative Writing": 0.0002179633088001454, "Customer Support Page": 0.00011350394057420046, "Q&A Forum": 6.502021089212421e-05, "Discussion Thread": 6.39233435575336e-05, "Comment Section": 0.00018894245275392546, "Missing Content": 0.0005425460882248742, "Knowledge Article": 0.002977573480285001, "Legal Notices": 0.00036044500660625546, "News Article": 9.457556848725115e-05, "Organizational Announcement": 8.868658679121795e-05, "Nonfiction Writing": 0.00022453664887813908, "Organizational About Page": 0.00014372558241420664, "Personal About Page": 8.60310589602946e-05, "Personal Blog": 9.051001835992466e-05, "Product Page": 8.012348812620278e-05, "Spam / Ads": 0.00010340468007330843, "Structured Data": 0.020063011655562957, "Technical Writing": 0.020133623729669023, "Transcript / Interview": 0.00010050450927088986, "Tutorial / How-To Guide": 0.0016022665745427955, "User Reviews": 4.982826996420538e-05, "NONE": 0.9513526227552327}, "structured_records": {"false": 0.7526171259958041, "true": 0.24738287400419587}, "procedural_steps": {"false": 0.9935160691308476, "true": 0.006483930869152406}, "worked_solution": {"false": 0.9949808174063686, "true": 0.005019182593631417}, "contextual_dialogue": {"false": 0.9999931960389893, "true": 6.803961010688165e-06}, "content_quality": {"-2": 0.021792537531695404, "-1": 0.11928651695657631, "0": 0.35491132352857335, "1": 0.3957876017445726, "2": 0.10822202023858223}, "noise_severity": {"0": 0.8579415199535623, "1": 0.11931626364867896, "2": 0.018831399840576673, "3": 0.002351953667384517, "4": 0.0015588628897976024}, "missing_content_severity": {"0": 0.9826261032230943, "1": 0.009764633634920675, "2": 0.005419258433737418, "3": 0.0017637216338700227, "4": 0.0004262830743775463}, "factual_density": {"0": 0.004560616473820935, "1": 0.013355175763355685, "2": 0.10008289943378462, "3": 0.39785728679873544, "4": 0.48414402153030334}, "pretraining_utility": {"-2": 0.008750033487747605, "-1": 0.07098127034100392, "0": 0.3462818379613849, "1": 0.4815562718704841, "2": 0.0924305863393796}, "math_density": {"0": 0.022731660485310178, "1": 0.09455609697582577, "2": 0.3416364916351367, "3": 0.3362385642777366, "4": 0.2048371866259909}, "code_density": {"0": 0.0002076416116187642, "1": 0.00036895110620962113, "2": 0.007212529585665914, "3": 0.07239975147782952, "4": 0.9198111262186761}, "reasoning_density": {"0": 0.5585255809819732, "1": 0.11344549397851837, "2": 0.12278297091475597, "3": 0.10178328066203123, "4": 0.10346267346272123}, "education_level": {"none": 0.0011044127724471806, "0": 0.0014468607939634442, "1": 0.006229390526403766, "2": 0.5386774073149782, "3": 0.35723511734023466, "4": 0.09530681125197293}, "creative_writing_quality": {"not_applicable": 0.9994459725626206, "-2": 0.0001181344750918172, "-1": 0.0001350271413475613, "0": 0.0002042183444278705, "1": 7.158708949490342e-05, "2": 2.5060387017606504e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00012975244745893496, "Art & Design": 0.0002257662148672966, "Law & Crime": 0.00010851251066481418, "Education & Careers": 0.00014170790311987072, "Entertainment & Media": 0.00041383014717274037, "Fashion & Beauty": 0.00012352052615373534, "Business, Economics & Finance": 0.00017209253574695057, "Food & Dining": 0.00012396471591722734, "Games & Gaming": 0.028133647387490104, "Electronics & Hardware": 0.0001750091012084488, "Health, Healthcare & Wellness": 0.0001341522722117255, "History & Geography": 0.00014438306611764076, "Home, Pets & Hobbies": 0.000107371056622235, "Industry & Production": 0.0001677669647934782, "Language, Literature & Philosophy": 0.00010321254624824855, "Politics & Government": 7.174120239634358e-05, "Religion & Spirituality": 0.00011981145894228594, "Science, Mathematics & Engineering": 0.02865113044553561, "Relationships & Community": 6.300847267789084e-05, "Software Usage & Applications": 0.0003658869265931656, "Programming & Software Engineering": 0.9067836244496992, "Sports & Fitness": 0.00023371750649270516, "Vehicles & Transportation": 0.03142689397062825, "Travel & Tourism": 0.000141085318683631, "Other / Uncovered Subject": 0.0015559042596379222, "Unknown / Insufficient Content": 0.0001825065929198166}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0011683677672111744, "Art & Design": 0.0013219489137069084, "Law & Crime": 0.0007491389838577815, "Education & Careers": 0.0006893846658478737, "Entertainment & Media": 0.0020871356854170126, "Fashion & Beauty": 0.0004852547119417615, "Business, Economics & Finance": 0.0009612463536404516, "Food & Dining": 0.00046251923264281093, "Games & Gaming": 0.12822456417105713, "Electronics & Hardware": 0.000868205034417716, "Health, Healthcare & Wellness": 0.0007164915668513578, "History & Geography": 0.000711463127411798, "Home, Pets & Hobbies": 0.000488777469652044, "Industry & Production": 0.0006949217908545563, "Language, Literature & Philosophy": 0.0004232453704684463, "Politics & Government": 0.0003482080126159171, "Religion & Spirituality": 0.0006700116092287079, "Science, Mathematics & Engineering": 0.16318809176973692, "Relationships & Community": 0.00024697786301257666, "Software Usage & Applications": 0.000714261338684758, "Programming & Software Engineering": 0.13184909922043464, "Sports & Fitness": 0.0009821065874656327, "Vehicles & Transportation": 0.35575907763286524, "Travel & Tourism": 0.0006707631430022194, "Other / Uncovered Subject": 0.00264603938272835, "NONE": 0.20287269859524626}}, "probabilities": {"primary": {"Academic Writing": 7.78862465868159e-05, "Content Listing": 2.108202720543691e-05, "Creative Writing": 2.0753567546151087e-05, "Customer Support Page": 7.143526277401675e-06, "Q&A Forum": 9.917860799867572e-06, "Discussion Thread": 7.04510768498706e-06, "Comment Section": 1.1111794648795775e-05, "Missing Content": 2.2874570702251578e-05, "Knowledge Article": 7.052872328635132e-05, "Legal Notices": 2.368088217690718e-05, "News Article": 4.575016534010627e-06, "Organizational Announcement": 9.297387505656458e-06, "Nonfiction Writing": 1.0177568362307933e-05, "Organizational About Page": 1.0471837766284434e-05, "Personal About Page": 1.0626717538685598e-05, "Personal Blog": 7.615950572356784e-06, "Product Page": 8.272773780811631e-06, "Spam / Ads": 8.856066752374117e-06, "Structured Data": 0.0006457535675375973, "Technical Writing": 0.9970319825302284, "Transcript / Interview": 1.4098360861105945e-05, "Tutorial / How-To Guide": 0.000375060656680015, "User Reviews": 6.39308514522103e-06, "Other / Unknown": 0.0015847941738202649}, "secondary": {"Academic Writing": 0.0009872052621954723, "Content Listing": 0.0001973074967731278, "Creative Writing": 0.0003446882801778947, "Customer Support Page": 0.0001795798612531829, "Q&A Forum": 0.00012297232617491347, "Discussion Thread": 0.00010251657635952184, "Comment Section": 0.0002413767986401317, "Missing Content": 0.000517896781807165, "Knowledge Article": 0.002236678361479248, "Legal Notices": 0.00033360863167483884, "News Article": 0.00015168822967592875, "Organizational Announcement": 0.00014441534906101437, "Nonfiction Writing": 0.00019093565923415832, "Organizational About Page": 0.00017965360412192663, "Personal About Page": 0.00015049486487517518, "Personal Blog": 0.00013652982376010744, "Product Page": 0.00010649875892203926, "Spam / Ads": 0.00014748990091433375, "Structured Data": 0.024438657623037492, "Technical Writing": 0.026878525127354523, "Transcript / Interview": 0.00020173239846000288, "Tutorial / How-To Guide": 0.0015614501669146596, "User Reviews": 7.255149499676396e-05, "NONE": 0.9403755466221364}, "tertiary": {"Academic Writing": 0.0011279563807819724, "Content Listing": 0.0001286746611908801, "Creative Writing": 0.0002179633088001454, "Customer Support Page": 0.00011350394057420046, "Q&A Forum": 6.502021089212421e-05, "Discussion Thread": 6.39233435575336e-05, "Comment Section": 0.00018894245275392546, "Missing Content": 0.0005425460882248742, "Knowledge Article": 0.002977573480285001, "Legal Notices": 0.00036044500660625546, "News Article": 9.457556848725115e-05, "Organizational Announcement": 8.868658679121795e-05, "Nonfiction Writing": 0.00022453664887813908, "Organizational About Page": 0.00014372558241420664, "Personal About Page": 8.60310589602946e-05, "Personal Blog": 9.051001835992466e-05, "Product Page": 8.012348812620278e-05, "Spam / Ads": 0.00010340468007330843, "Structured Data": 0.020063011655562957, "Technical Writing": 0.020133623729669023, "Transcript / Interview": 0.00010050450927088986, "Tutorial / How-To Guide": 0.0016022665745427955, "User Reviews": 4.982826996420538e-05, "NONE": 0.9513526227552327}, "structured_records": {"false": 0.7526171259958041, "true": 0.24738287400419587}, "procedural_steps": {"false": 0.9935160691308476, "true": 0.006483930869152406}, "worked_solution": {"false": 0.9949808174063686, "true": 0.005019182593631417}, "contextual_dialogue": {"false": 0.9999931960389893, "true": 6.803961010688165e-06}, "content_quality": {"-2": 0.021792537531695407, "-1": 0.11928651695657633, "0": 0.3549113235285734, "1": 0.39578760174457267, "2": 0.10822202023858224}, "noise_severity": {"0": 0.8579415199535623, "1": 0.11931626364867896, "2": 0.018831399840576673, "3": 0.002351953667384517, "4": 0.0015588628897976024}, "missing_content_severity": {"0": 0.9826261032230943, "1": 0.009764633634920675, "2": 0.005419258433737418, "3": 0.0017637216338700227, "4": 0.0004262830743775463}, "factual_density": {"0": 0.004560616473820935, "1": 0.013355175763355685, "2": 0.10008289943378462, "3": 0.39785728679873544, "4": 0.48414402153030334}, "pretraining_utility": {"-2": 0.008750033487747604, "-1": 0.0709812703410039, "0": 0.34628183796138484, "1": 0.481556271870484, "2": 0.09243058633937959}, "math_density": {"0": 0.022731660485310175, "1": 0.09455609697582575, "2": 0.34163649163513665, "3": 0.3362385642777365, "4": 0.20483718662599085}, "code_density": {"0": 0.00020764161161876423, "1": 0.0003689511062096212, "2": 0.007212529585665915, "3": 0.07239975147782954, "4": 0.9198111262186762}, "reasoning_density": {"0": 0.5585255809819732, "1": 0.11344549397851837, "2": 0.12278297091475597, "3": 0.10178328066203123, "4": 0.10346267346272123}, "education_level": {"none": 0.0011044127724471804, "0": 0.001446860793963444, "1": 0.006229390526403764, "2": 0.538677407314978, "3": 0.3572351173402346, "4": 0.0953068112519729}, "creative_writing_quality": {"not_applicable": 0.9994459725626201, "-2": 0.00011813447509181714, "-1": 0.00013502714134756125, "0": 0.0002042183444278704, "1": 7.15870894949034e-05, "2": 2.5060387017606494e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00012975244745893494, "Art & Design": 0.00022576621486729656, "Law & Crime": 0.00010851251066481415, "Education & Careers": 0.0001417079031198707, "Entertainment & Media": 0.00041383014717274026, "Fashion & Beauty": 0.0001235205261537353, "Business, Economics & Finance": 0.00017209253574695054, "Food & Dining": 0.0001239647159172273, "Games & Gaming": 0.028133647387490097, "Electronics & Hardware": 0.00017500910120844877, "Health, Healthcare & Wellness": 0.00013415227221172548, "History & Geography": 0.00014438306611764073, "Home, Pets & Hobbies": 0.00010737105662223498, "Industry & Production": 0.00016776696479347817, "Language, Literature & Philosophy": 0.00010321254624824853, "Politics & Government": 7.174120239634357e-05, "Religion & Spirituality": 0.00011981145894228592, "Science, Mathematics & Engineering": 0.028651130445535602, "Relationships & Community": 6.300847267789082e-05, "Software Usage & Applications": 0.00036588692659316553, "Programming & Software Engineering": 0.906783624449699, "Sports & Fitness": 0.0002337175064927051, "Vehicles & Transportation": 0.03142689397062824, "Travel & Tourism": 0.00014108531868363097, "Other / Uncovered Subject": 0.0015559042596379217, "Unknown / Insufficient Content": 0.00018250659291981657}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0011683677672111744, "Art & Design": 0.0013219489137069084, "Law & Crime": 0.0007491389838577815, "Education & Careers": 0.0006893846658478737, "Entertainment & Media": 0.0020871356854170126, "Fashion & Beauty": 0.0004852547119417615, "Business, Economics & Finance": 0.0009612463536404516, "Food & Dining": 0.00046251923264281093, "Games & Gaming": 0.12822456417105713, "Electronics & Hardware": 0.000868205034417716, "Health, Healthcare & Wellness": 0.0007164915668513578, "History & Geography": 0.000711463127411798, "Home, Pets & Hobbies": 0.000488777469652044, "Industry & Production": 0.0006949217908545563, "Language, Literature & Philosophy": 0.0004232453704684463, "Politics & Government": 0.0003482080126159171, "Religion & Spirituality": 0.0006700116092287079, "Science, Mathematics & Engineering": 0.16318809176973692, "Relationships & Community": 0.00024697786301257666, "Software Usage & Applications": 0.000714261338684758, "Programming & Software Engineering": 0.13184909922043464, "Sports & Fitness": 0.0009821065874656327, "Vehicles & Transportation": 0.35575907763286524, "Travel & Tourism": 0.0006707631430022194, "Other / Uncovered Subject": 0.00264603938272835, "NONE": 0.20287269859524626}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000004, "mass_delta": 4.440892098500626e-16, "missing_mass": 0.0, "excess_mass": 4.440892098500626e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 14560, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 14560, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:09.061410+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "99ade19f-60ac-4d58-9bb1-60bb8305f3c1", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "44276be3b9d15d8d12b01f14fd8e4dccf4b833b10e4161c30531c3e00166cd7d", "source_metadata": {"uuid": "99ade19f-60ac-4d58-9bb1-60bb8305f3c1", "repo_name": "carryty/test2", "relative_path": "highway_env/envs/square_env.py", "category": "DATA", "algo_rel_score": 0.9715769290924072, "quality_score": 6.4745564460754395, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 2, "repo_created_at": "2024-04-03T12:39:44.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "6a72494c6b3128d1737491a27b1b3c4e27250cc4", "size_bytes": 21217, "file_timestamp": 1712149688, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "9f1c29aba85e4f6cd40c82e0f504ad7a7543e810"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 2, "repo_created_at": "2024-04-03T12:39:44.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "6a72494c6b3128d1737491a27b1b3c4e27250cc4", "size_bytes": 21217, "file_timestamp": 1712149688, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "9f1c29aba85e4f6cd40c82e0f504ad7a7543e810", "url": "https://github.com/carryty/test2/blob/9f1c29aba85e4f6cd40c82e0f504ad7a7543e810/highway_env/envs/square_env.py"}, "gpt4o_tokens": 5571, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.44936005020177, "noise_severity": 0.17027037589117627, "missing_content_severity": 0.027599447701515765, "factual_density": 3.3436689211483444, "pretraining_utility": 0.5779361072327441, "math_density": 2.6058935195832724, "code_density": 3.911237769585735, "reasoning_density": 1.078211971645009, "education_level": 2.539321261018971, "creative_writing_quality": -0.45049795580791535}, "score_applicability": {"education_level": 0.9988955872275528, "creative_writing_quality": 0.0005540274373797587}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/ayazs/aif-rag-api/blob/d8a7f17059f33e92e03f3ee001b50a61d739ae29/tests/services/vector_storage/test_pinecone_integration.py", "id": "python-0-a31e0ef494a218458cd1bb3266c76972", "text": "\"\"\"\nIntegration tests for the Pinecone vector storage service.\nThese tests use the real Pinecone client and should be run with caution.\n\"\"\"\n\nimport os\nimport pytest\nfrom pinecone import ServerlessSpec, PodSpec\nfrom app.config import Settings\nfrom app.services.vector_storage.pinecone import PineconeService\nfrom app.services.vector_storage.exceptions import (\n    IndexNotFoundError,\n    IndexCreationError,\n    IndexDeletionError,\n    RateLimitError,\n    ServiceTimeoutError\n)\n\n# Test configuration\nTEST_INDEX_NAME = 'test-index-integration'\nTEST_DIMENSION = 1536\nTEST_METRIC = 'cosine'\n\n@pytest.fixture\ndef settings():\n    \"\"\"Create settings instance with Pinecone configuration.\"\"\"\n    # Get Pinecone API key from environment\n    api_key = os.getenv(\"SERVICE_PINECONE_API_KEY\")\n    if not api_key:\n        pytest.skip(\"SERVICE_PINECONE_API_KEY not set in environment\")\n    \n    # Get Pinecone cloud and region from environment\n    cloud = os.getenv(\"SERVICE_PINECONE_CLOUD\", \"aws\")  # Default to AWS if not specified\n    region = os.getenv(\"SERVICE_PINECONE_REGION\", \"us-east-1\")  # Default to us-east-1 if not specified\n    \n    # Create settings with Pinecone configuration\n    return Settings(\n        _env_file=None,  # Don't load from .env file\n        SERVICE_PINECONE_API_KEY=api_key,\n        SERVICE_PINECONE_CLOUD=cloud,\n        SERVICE_PINECONE_REGION=region,\n        SERVICE_PINECONE_INDEX_NAME=TEST_INDEX_NAME\n    )\n\n@pytest.fixture\ndef pinecone_service(settings):\n    \"\"\"Create PineconeService instance with real client.\"\"\"\n    config = settings.get_service_config(\"pinecone\")\n    return PineconeService(config)\n\ndef test_create_and_delete_index(pinecone_service):\n    \"\"\"Test creating and deleting an index with the real Pinecone client.\"\"\"\n    try:\n        # Create index\n        pinecone_service.create_index(\n            name=TEST_INDEX_NAME,\n            dimension=TEST_DIMENSION,\n            metric=TEST_METRIC\n        )\n        \n        # Verify index exists\n        indices = pinecone_service.list_indices()\n        assert TEST_INDEX_NAME in indices\n        \n        # Verify index is ready\n        assert pinecone_service.check_index_health(TEST_INDEX_NAME) is True\n        \n        # Get index details\n        index_details = pinecone_service.describe_index(TEST_INDEX_NAME)\n        assert index_details['name'] == TEST_INDEX_NAME\n        assert index_details['dimension'] == TEST_DIMENSION\n        \n    finally:\n        # Clean up: delete the index\n        try:\n            pinecone_service.delete_index(TEST_INDEX_NAME)\n        except IndexNotFoundError:\n            pass  # Index might have been deleted already\n        \n        # Verify index is deleted\n        indices = pinecone_service.list_indices()\n        assert TEST_INDEX_NAME not in indices\n\ndef test_create_index_already_exists(pinecone_service):\n    \"\"\"Test creating an index that already exists.\"\"\"\n    try:\n        # Create index first\n        pinecone_service.create_index(\n            name=TEST_INDEX_NAME,\n            dimension=TEST_DIMENSION,\n            metric=TEST_METRIC\n        )\n        \n        # Try to create it again (should not raise an error)\n        pinecone_service.create_index(\n            name=TEST_INDEX_NAME,\n            dimension=TEST_DIMENSION,\n            metric=TEST_METRIC\n        )\n        \n    finally:\n        # Clean up\n        try:\n            pinecone_service.delete_index(TEST_INDEX_NAME)\n        except IndexNotFoundError:\n            pass\n\ndef test_delete_nonexistent_index(pinecone_service):\n    \"\"\"Test deleting an index that doesn't exist.\"\"\"\n    with pytest.raises(IndexNotFoundError):\n        pinecone_service.delete_index('nonexistent-index')\n\ndef test_create_index_with_serverless_spec(pinecone_service):\n    \"\"\"Test creating an index with explicit ServerlessSpec.\"\"\"\n    try:\n        spec = ServerlessSpec(\n            cloud=pinecone_service.cloud,\n            region=pinecone_service.region\n        )\n        \n        pinecone_service.create_index(\n            name=TEST_INDEX_NAME,\n            dimension=TEST_DIMENSION,\n            metric=TEST_METRIC,\n            spec=spec\n        )\n        \n        # Verify index exists and has correct configuration\n        index_details = pinecone_service.describe_index(TEST_INDEX_NAME)\n        assert index_details['name'] == TEST_INDEX_NAME\n        assert index_details['dimension'] == TEST_DIMENSION\n        assert index_details['metric'] == TEST_METRIC\n        assert index_details['spec']['serverless']['cloud'] == pinecone_service.cloud\n        assert index_details['spec']['serverless']['region'] == pinecone_service.region\n        \n    finally:\n        try:\n            pinecone_service.delete_index(TEST_INDEX_NAME)\n        except IndexNotFoundError:\n            pass\n\ndef test_index_health_checks(pinecone_service):\n    \"\"\"Test various index health check scenarios.\"\"\"\n    try:\n        # Create index\n        pinecone_service.create_index(\n            name=TEST_INDEX_NAME,\n            dimension=TEST_DIMENSION,\n            metric=TEST_METRIC\n        )\n        \n        # Test health check on existing index\n        assert pinecone_service.check_index_health(TEST_INDEX_NAME) is True\n        \n        # Test health check on non-existent index\n        with pytest.raises(IndexNotFoundError):\n            pinecone_service.check_index_health('nonexistent-index')\n            \n    finally:\n        try:\n            pinecone_service.delete_index(TEST_INDEX_NAME)\n        except IndexNotFoundError:\n            pass\n\ndef test_list_indices(pinecone_service):\n    \"\"\"Test listing indices functionality.\"\"\"\n    try:\n        # Create test index\n        pinecone_service.create_index(\n            name=TEST_INDEX_NAME,\n            dimension=TEST_DIMENSION,\n            metric=TEST_METRIC\n        )\n        \n        # List indices\n        indices = pinecone_service.list_indices()\n        assert isinstance(indices, list)\n        assert TEST_INDEX_NAME in indices\n        \n    finally:\n        try:\n            pinecone_service.delete_index(TEST_INDEX_NAME)\n        except IndexNotFoundError:\n            pass ", "text_preview": "\"\"\" Integration tests for the Pinecone vector storage service. These tests use the real Pinecone client and should be run with caution. \"\"\" import os import pytest from pinecone import ServerlessSpec, PodSpec from app.config import Settings", "input_sha256": "bb55c9f314b367dcf9c388e5329d65fe3961a1618afc88c54e3094df155dbd3b", "text_sha256": "a31e0ef494a218458cd1bb3266c769726b3562404497af742e0e7a08707d5492", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12051, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.1076446455787075, "noise_severity": 0.03906718077138561, "missing_content_severity": 0.014521006966984279, "factual_density": 2.895449264850986, "pretraining_utility": 0.6326960684270955, "math_density": 0.03810234286283912, "code_density": 3.7235551654189583, "reasoning_density": 0.7212733905067696, "education_level": 2.5124386186851018, "creative_writing_quality": -0.1086160003590815}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.009848974837756375, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9939264779167539, "secondary": 0.9640111491037046, "tertiary": 0.9742883402387547, "structured_records": 0.017389673259454768, "procedural_steps": 0.10263168292693897, "worked_solution": 0.026961548289327782, "contextual_dialogue": 5.925284892658588e-06, "content_quality": 0.5918925424560384, "noise_severity": 0.9674440160238453, "missing_content_severity": 0.9878991608608464, "factual_density": 0.5304646070736442, "pretraining_utility": 0.550639986906543, "math_density": 0.9682480476143007, "code_density": 0.7696293045157987, "reasoning_density": 0.39893884124435863, "education_level": 0.41016662457147907, "creative_writing_quality": 0.9992060955545674, "primary_topic": 0.9953805197956918, "secondary_topic": 0.9300377452923188}, "probability_margin": {"primary": 0.9904549220107425, "secondary": 0.944655515855772, "tertiary": 0.96420722513388, "education_level": 0.05908754880835221, "creative_writing_quality": 0.9989553508238649, "primary_topic": 0.9924772776639048, "secondary_topic": 0.900725670839966}, "raw_probabilities": {"primary": {"Academic Writing": 5.908462239587742e-05, "Content Listing": 2.379470180697219e-05, "Creative Writing": 1.9609486328893313e-05, "Customer Support Page": 1.731600034736334e-05, "Q&A Forum": 1.5176262757103663e-05, "Discussion Thread": 9.383567781518817e-06, "Comment Section": 1.6628045060217138e-05, "Missing Content": 2.6220280071475353e-05, "Knowledge Article": 9.743051863341134e-05, "Legal Notices": 2.9690962295062023e-05, "News Article": 7.083149364336286e-06, "Organizational Announcement": 1.4678488174059163e-05, "Nonfiction Writing": 1.4757187974025523e-05, "Organizational About Page": 1.4427612983366288e-05, "Personal About Page": 1.283348800638873e-05, "Personal Blog": 1.0264332844915162e-05, "Product Page": 1.1593570559103248e-05, "Spam / Ads": 1.26383976348953e-05, "Structured Data": 0.0003522308148306723, "Technical Writing": 0.9941795413368891, "Transcript / Interview": 2.312879220567913e-05, "Tutorial / How-To Guide": 0.0012953696469394768, "User Reviews": 1.249940796961362e-05, "Other / Unknown": 0.003724619326146604}, "secondary": {"Academic Writing": 0.0002607898092337431, "Content Listing": 0.00014522034128519452, "Creative Writing": 0.00016152390507010483, "Customer Support Page": 0.00015234113924530646, "Q&A Forum": 0.00010086640928602467, "Discussion Thread": 6.790274624074625e-05, "Comment Section": 0.00014087559278915839, "Missing Content": 0.00031789401667985383, "Knowledge Article": 0.0013484712212933141, "Legal Notices": 0.0003704764673110559, "News Article": 0.00010221766727034873, "Organizational Announcement": 0.0001118644410493394, "Nonfiction Writing": 0.00010995289365134964, "Organizational About Page": 0.00010574575709532717, "Personal About Page": 9.301208201806298e-05, "Personal Blog": 0.0001021209617982564, "Product Page": 7.310307029772837e-05, "Spam / Ads": 8.81939785899879e-05, "Structured Data": 0.0018887372286474707, "Technical Writing": 0.020855168701944825, "Transcript / Interview": 0.0001286016017549199, "Tutorial / How-To Guide": 0.00769932235556425, "User Reviews": 6.491305416680531e-05, "NONE": 0.9655106845577168}, "tertiary": {"Academic Writing": 0.0001214102888466641, "Content Listing": 9.119250567539716e-05, "Creative Writing": 0.00010046733512520435, "Customer Support Page": 0.00010102333849005899, "Q&A Forum": 4.9112023924763145e-05, "Discussion Thread": 3.53959817121471e-05, "Comment Section": 0.00011541167048870947, "Missing Content": 0.00030601193877533825, "Knowledge Article": 0.0017810452232539134, "Legal Notices": 0.0008074382786782664, "News Article": 5.726001381107181e-05, "Organizational Announcement": 8.097512738894452e-05, "Nonfiction Writing": 8.376582709021345e-05, "Organizational About Page": 6.703768466459892e-05, "Personal About Page": 4.41637215686966e-05, "Personal Blog": 4.8421398204025156e-05, "Product Page": 4.806048167669449e-05, "Spam / Ads": 6.610781224017229e-05, "Structured Data": 0.0022634738013417397, "Technical Writing": 0.01115243426159323, "Transcript / Interview": 6.54370630846302e-05, "Tutorial / How-To Guide": 0.007114437985478321, "User Reviews": 4.025684141398523e-05, "NONE": 0.9753596593954732}, "structured_records": {"false": 0.9826103267405453, "true": 0.017389673259454768}, "procedural_steps": {"false": 0.8973683170730611, "true": 0.10263168292693897}, "worked_solution": {"false": 0.9730384517106723, "true": 0.026961548289327782}, "contextual_dialogue": {"false": 0.9999940747151074, "true": 5.925284892658588e-06}, "content_quality": {"-2": 0.0040971360329196405, "-1": 0.011477099906225802, "0": 0.15579654382581273, "1": 0.5299424229193114, "2": 0.29868679731573056}, "noise_severity": {"0": 0.9661256385110383, "1": 0.0304960977877552, "2": 0.0021760496145116982, "3": 0.0005898725921722718, "4": 0.0006123414945225486}, "missing_content_severity": {"0": 0.9913514203945156, "1": 0.0043183143388478605, "2": 0.003106173650622525, "3": 0.0009060211371644491, "4": 0.00031807047884950513}, "factual_density": {"0": 0.006682698833335717, "1": 0.03655757782870622, "2": 0.2408333511729008, "3": 0.48648050398375065, "4": 0.2294458681813066}, "pretraining_utility": {"-2": 0.006304423432693811, "-1": 0.053494055452687046, "0": 0.3273665924390709, "1": 0.5268708866059264, "2": 0.08596404206962184}, "math_density": {"0": 0.9716373351988387, "1": 0.02308828211010339, "2": 0.002465158463734475, "3": 0.001153153084027355, "4": 0.0016560711432961775}, "code_density": {"0": 0.00038301961028702725, "1": 0.0007433821707112955, "2": 0.023726863342028014, "3": 0.22522888294370352, "4": 0.7499178519332701}, "reasoning_density": {"0": 0.5525816539638099, "1": 0.23486846896741484, "2": 0.16707324564126827, "3": 0.02964809545320986, "4": 0.015828535974297172}, "education_level": {"none": 0.0008847700937378133, "0": 0.0013593146305724158, "1": 0.00482632011649228, "2": 0.5084721871428992, "3": 0.449384638334547, "4": 0.03507276968175142}, "creative_writing_quality": {"not_applicable": 0.9993384129621395, "-2": 5.055723423350794e-05, "-1": 0.00011435331964249174, "0": 0.0003830621382746375, "1": 8.361984125202975e-05, "2": 2.9994504457829273e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.292665724497488e-05, "Art & Design": 3.080109291756298e-05, "Law & Crime": 2.0455372355360222e-05, "Education & Careers": 2.6182051294616857e-05, "Entertainment & Media": 1.7331969577677115e-05, "Fashion & Beauty": 1.1862377537732799e-05, "Business, Economics & Finance": 4.172165541105758e-05, "Food & Dining": 1.4862452882387964e-05, "Games & Gaming": 3.440932322937564e-05, "Electronics & Hardware": 5.156161454549748e-05, "Health, Healthcare & Wellness": 1.9160861786501274e-05, "History & Geography": 2.0194918897973005e-05, "Home, Pets & Hobbies": 1.1220303437189616e-05, "Industry & Production": 1.867136973714327e-05, "Language, Literature & Philosophy": 2.5765211812306235e-05, "Politics & Government": 1.5556403608737626e-05, "Religion & Spirituality": 2.1210242996376725e-05, "Science, Mathematics & Engineering": 0.0030809144473372147, "Relationships & Community": 1.365443737581542e-05, "Software Usage & Applications": 0.0006862298737543534, "Programming & Software Engineering": 0.9955581921112421, "Sports & Fitness": 3.156220692630213e-05, "Vehicles & Transportation": 1.1224370369845585e-05, "Travel & Tourism": 2.8797991466469484e-05, "Other / Uncovered Subject": 0.00014340302164256126, "Unknown / Insufficient Content": 3.212766061298133e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00039546768341746714, "Art & Design": 0.00024379455487433932, "Law & Crime": 0.0002810065717347278, "Education & Careers": 0.00021426225687933082, "Entertainment & Media": 0.00023571894827351778, "Fashion & Beauty": 0.00012443990241858423, "Business, Economics & Finance": 0.00044205988473250006, "Food & Dining": 0.00011921021772656196, "Games & Gaming": 0.0001880973874283405, "Electronics & Hardware": 0.00028186934334690686, "Health, Healthcare & Wellness": 0.00015571997718153666, "History & Geography": 0.00012663929375078566, "Home, Pets & Hobbies": 9.203474463614098e-05, "Industry & Production": 0.00011052203247212114, "Language, Literature & Philosophy": 0.00011833345797232374, "Politics & Government": 0.0001597922263897993, "Religion & Spirituality": 0.0001896184894722516, "Science, Mathematics & Engineering": 0.032002930402648115, "Relationships & Community": 7.944574209622415e-05, "Software Usage & Applications": 0.010108888225071722, "Programming & Software Engineering": 0.020791207301491102, "Sports & Fitness": 0.00013030361631877007, "Vehicles & Transportation": 5.888214081465649e-05, "Travel & Tourism": 0.00018236788443780705, "Other / Uncovered Subject": 0.0004387864718002677, "NONE": 0.9327286012426141}}, "probabilities": {"primary": {"Academic Writing": 5.908462239587741e-05, "Content Listing": 2.3794701806972182e-05, "Creative Writing": 1.960948632889331e-05, "Customer Support Page": 1.7316000347363338e-05, "Q&A Forum": 1.517626275710366e-05, "Discussion Thread": 9.383567781518815e-06, "Comment Section": 1.6628045060217135e-05, "Missing Content": 2.6220280071475346e-05, "Knowledge Article": 9.743051863341132e-05, "Legal Notices": 2.9690962295062016e-05, "News Article": 7.083149364336285e-06, "Organizational Announcement": 1.4678488174059159e-05, "Nonfiction Writing": 1.475718797402552e-05, "Organizational About Page": 1.4427612983366285e-05, "Personal About Page": 1.2833488006388726e-05, "Personal Blog": 1.026433284491516e-05, "Product Page": 1.1593570559103245e-05, "Spam / Ads": 1.2638397634895297e-05, "Structured Data": 0.00035223081483067226, "Technical Writing": 0.9941795413368889, "Transcript / Interview": 2.3128792205679125e-05, "Tutorial / How-To Guide": 0.0012953696469394766, "User Reviews": 1.2499407969613616e-05, "Other / Unknown": 0.003724619326146603}, "secondary": {"Academic Writing": 0.0002607898092337431, "Content Listing": 0.00014522034128519452, "Creative Writing": 0.00016152390507010483, "Customer Support Page": 0.00015234113924530646, "Q&A Forum": 0.00010086640928602467, "Discussion Thread": 6.790274624074625e-05, "Comment Section": 0.00014087559278915839, "Missing Content": 0.00031789401667985383, "Knowledge Article": 0.0013484712212933141, "Legal Notices": 0.0003704764673110559, "News Article": 0.00010221766727034873, "Organizational Announcement": 0.0001118644410493394, "Nonfiction Writing": 0.00010995289365134964, "Organizational About Page": 0.00010574575709532717, "Personal About Page": 9.301208201806298e-05, "Personal Blog": 0.0001021209617982564, "Product Page": 7.310307029772837e-05, "Spam / Ads": 8.81939785899879e-05, "Structured Data": 0.0018887372286474707, "Technical Writing": 0.020855168701944825, "Transcript / Interview": 0.0001286016017549199, "Tutorial / How-To Guide": 0.00769932235556425, "User Reviews": 6.491305416680531e-05, "NONE": 0.9655106845577168}, "tertiary": {"Academic Writing": 0.0001214102888466641, "Content Listing": 9.119250567539716e-05, "Creative Writing": 0.00010046733512520435, "Customer Support Page": 0.00010102333849005899, "Q&A Forum": 4.9112023924763145e-05, "Discussion Thread": 3.53959817121471e-05, "Comment Section": 0.00011541167048870947, "Missing Content": 0.00030601193877533825, "Knowledge Article": 0.0017810452232539134, "Legal Notices": 0.0008074382786782664, "News Article": 5.726001381107181e-05, "Organizational Announcement": 8.097512738894452e-05, "Nonfiction Writing": 8.376582709021345e-05, "Organizational About Page": 6.703768466459892e-05, "Personal About Page": 4.41637215686966e-05, "Personal Blog": 4.8421398204025156e-05, "Product Page": 4.806048167669449e-05, "Spam / Ads": 6.610781224017229e-05, "Structured Data": 0.0022634738013417397, "Technical Writing": 0.01115243426159323, "Transcript / Interview": 6.54370630846302e-05, "Tutorial / How-To Guide": 0.007114437985478321, "User Reviews": 4.025684141398523e-05, "NONE": 0.9753596593954732}, "structured_records": {"false": 0.9826103267405453, "true": 0.017389673259454768}, "procedural_steps": {"false": 0.8973683170730611, "true": 0.10263168292693897}, "worked_solution": {"false": 0.9730384517106723, "true": 0.026961548289327782}, "contextual_dialogue": {"false": 0.9999940747151074, "true": 5.925284892658588e-06}, "content_quality": {"-2": 0.0040971360329196405, "-1": 0.011477099906225802, "0": 0.15579654382581273, "1": 0.5299424229193114, "2": 0.29868679731573056}, "noise_severity": {"0": 0.9661256385110383, "1": 0.0304960977877552, "2": 0.0021760496145116982, "3": 0.0005898725921722718, "4": 0.0006123414945225486}, "missing_content_severity": {"0": 0.9913514203945156, "1": 0.0043183143388478605, "2": 0.003106173650622525, "3": 0.0009060211371644491, "4": 0.00031807047884950513}, "factual_density": {"0": 0.006682698833335717, "1": 0.03655757782870622, "2": 0.2408333511729008, "3": 0.48648050398375065, "4": 0.2294458681813066}, "pretraining_utility": {"-2": 0.006304423432693811, "-1": 0.053494055452687046, "0": 0.3273665924390709, "1": 0.5268708866059264, "2": 0.08596404206962184}, "math_density": {"0": 0.9716373351988387, "1": 0.02308828211010339, "2": 0.002465158463734475, "3": 0.001153153084027355, "4": 0.0016560711432961775}, "code_density": {"0": 0.00038301961028702725, "1": 0.0007433821707112955, "2": 0.023726863342028014, "3": 0.22522888294370352, "4": 0.7499178519332701}, "reasoning_density": {"0": 0.5525816539638099, "1": 0.23486846896741484, "2": 0.16707324564126827, "3": 0.02964809545320986, "4": 0.015828535974297172}, "education_level": {"none": 0.0008847700937378131, "0": 0.0013593146305724156, "1": 0.004826320116492279, "2": 0.5084721871428991, "3": 0.4493846383345469, "4": 0.035072769681751416}, "creative_writing_quality": {"not_applicable": 0.9993384129621395, "-2": 5.055723423350794e-05, "-1": 0.00011435331964249174, "0": 0.0003830621382746375, "1": 8.361984125202975e-05, "2": 2.9994504457829273e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.292665724497488e-05, "Art & Design": 3.080109291756298e-05, "Law & Crime": 2.0455372355360222e-05, "Education & Careers": 2.6182051294616857e-05, "Entertainment & Media": 1.7331969577677115e-05, "Fashion & Beauty": 1.1862377537732799e-05, "Business, Economics & Finance": 4.172165541105758e-05, "Food & Dining": 1.4862452882387964e-05, "Games & Gaming": 3.440932322937564e-05, "Electronics & Hardware": 5.156161454549748e-05, "Health, Healthcare & Wellness": 1.9160861786501274e-05, "History & Geography": 2.0194918897973005e-05, "Home, Pets & Hobbies": 1.1220303437189616e-05, "Industry & Production": 1.867136973714327e-05, "Language, Literature & Philosophy": 2.5765211812306235e-05, "Politics & Government": 1.5556403608737626e-05, "Religion & Spirituality": 2.1210242996376725e-05, "Science, Mathematics & Engineering": 0.0030809144473372147, "Relationships & Community": 1.365443737581542e-05, "Software Usage & Applications": 0.0006862298737543534, "Programming & Software Engineering": 0.9955581921112421, "Sports & Fitness": 3.156220692630213e-05, "Vehicles & Transportation": 1.1224370369845585e-05, "Travel & Tourism": 2.8797991466469484e-05, "Other / Uncovered Subject": 0.00014340302164256126, "Unknown / Insufficient Content": 3.212766061298133e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00039546768341746714, "Art & Design": 0.00024379455487433932, "Law & Crime": 0.0002810065717347278, "Education & Careers": 0.00021426225687933082, "Entertainment & Media": 0.00023571894827351778, "Fashion & Beauty": 0.00012443990241858423, "Business, Economics & Finance": 0.00044205988473250006, "Food & Dining": 0.00011921021772656196, "Games & Gaming": 0.0001880973874283405, "Electronics & Hardware": 0.00028186934334690686, "Health, Healthcare & Wellness": 0.00015571997718153666, "History & Geography": 0.00012663929375078566, "Home, Pets & Hobbies": 9.203474463614098e-05, "Industry & Production": 0.00011052203247212114, "Language, Literature & Philosophy": 0.00011833345797232374, "Politics & Government": 0.0001597922263897993, "Religion & Spirituality": 0.0001896184894722516, "Science, Mathematics & Engineering": 0.032002930402648115, "Relationships & Community": 7.944574209622415e-05, "Software Usage & Applications": 0.010108888225071722, "Programming & Software Engineering": 0.020791207301491102, "Sports & Fitness": 0.00013030361631877007, "Vehicles & Transportation": 5.888214081465649e-05, "Travel & Tourism": 0.00018236788443780705, "Other / Uncovered Subject": 0.0004387864718002677, "NONE": 0.9327286012426141}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9832, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9832, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:10.207925+00:00", "scoring_key_slot": "primary", "dataset_uid": "34b619b6-49a6-4655-b5f7-72ac4d2fcfc2", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "aa9d4b7235ddff176a839f6981426d7977103504864e4069804cbc78198e1c05", "source_metadata": {"uuid": "34b619b6-49a6-4655-b5f7-72ac4d2fcfc2", "repo_name": "ayazs/aif-rag-api", "relative_path": "tests/services/vector_storage/test_pinecone_integration.py", "category": "TEST", "algo_rel_score": 0.9864183068275452, "quality_score": 7.837586879730225, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 22, "repo_created_at": "2025-04-18T13:45:46.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "eb44b8acc01c4f541311881af13b824e933677ed", "size_bytes": 6085, "file_timestamp": 1745068552, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "d8a7f17059f33e92e03f3ee001b50a61d739ae29"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 22, "repo_created_at": "2025-04-18T13:45:46.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "eb44b8acc01c4f541311881af13b824e933677ed", "size_bytes": 6085, "file_timestamp": 1745068552, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "d8a7f17059f33e92e03f3ee001b50a61d739ae29", "url": "https://github.com/ayazs/aif-rag-api/blob/d8a7f17059f33e92e03f3ee001b50a61d739ae29/tests/services/vector_storage/test_pinecone_integration.py"}, "gpt4o_tokens": 1236, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.1076446455787075, "noise_severity": 0.03906718077138561, "missing_content_severity": 0.014521006966984279, "factual_density": 2.895449264850986, "pretraining_utility": 0.6326960684270955, "math_density": 0.03810234286283912, "code_density": 3.7235551654189583, "reasoning_density": 0.7212733905067696, "education_level": 2.5124386186851018, "creative_writing_quality": -0.1086160003590815}, "score_applicability": {"education_level": 0.9991152299062621, "creative_writing_quality": 0.0006615870378604961}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/semihbceylan/db2024/blob/e81c14dbe1de7f55978c996d6032a654efff76ec/server/routes/blocks.py", "id": "python-0-479f57944c29998e3e0deb2f1aaf2b47", "text": "from flask import Blueprint, jsonify, request\nfrom mysql.connector import Error\n\nfrom utils.get_db_connection import get_db_connection\nfrom moralis import evm_api\n\nfrom dotenv import load_dotenv\nimport os\n\nblocks = Blueprint('blocks', __name__)\n\n@blocks.route(\"/<int:chain_id>/<int:block_number>\", methods=['GET'])\ndef get_block(chain_id, block_number):\n    connection = get_db_connection()\n\n    if not connection:\n        return jsonify({\"error\": \"Database connection failed\"}), 500\n\n    try:\n        cursor = connection.cursor(dictionary=True)\n        cursor.execute(f\"SELECT * FROM blocks WHERE chain_id = {chain_id} AND block_number = {block_number}\")\n        data = cursor.fetchone()\n        if not data:\n            return jsonify({\"error\": \"Block not found\"}), 404\n        return jsonify(data), 200\n    except Error as e:\n        return jsonify({\"error\": str(e)}), 500\n    finally:\n        if connection.is_connected():\n            connection.commit()\n            cursor.close()\n            connection.close()\n\n@blocks.route(\"/<int:chain_id>/<int:block_number>\", methods=['DELETE'])\ndef delete_block(chain_id, block_number):\n    connection = get_db_connection()\n\n    if not connection:\n        return jsonify({\"error\": \"Database connection failed\"}), 500\n\n    try:\n        cursor = connection.cursor(dictionary=True)\n        cursor.execute(f\"SELECT * FROM blocks WHERE chain_id = {chain_id} AND block_number = {block_number}\")\n        data = cursor.fetchone()\n        if not data:\n            return jsonify({\"error\": \"Block not found\"}), 404\n        cursor.execute(f\"DELETE FROM transactions WHERE chain_id = {chain_id} AND block_number = {block_number}\")\n        transactions_deleted = cursor.rowcount\n        cursor.execute(f\"DELETE FROM blocks WHERE chain_id = {chain_id} AND block_number = {block_number}\")\n        return jsonify({\n            \"message\": \"BLock and related data deleted successfully\",\n            \"transactions_deleted\": transactions_deleted,\n        }), 200\n    except Error as e:\n        return jsonify({\"error\": str(e)}), 500\n    finally:\n        if connection.is_connected():\n            connection.commit()\n            cursor.close()\n            connection.close()\n\n@blocks.route(\"/<int:chain_id>/<int:block_number>\", methods=[\"POST\"])\ndef add_block(chain_id, block_number):\n    connection = get_db_connection()\n    if not connection:\n        return jsonify({\"error\": \"Database connection failed\"}), 500\n\n    try:\n        cursor = connection.cursor(dictionary=True)\n        cursor.execute(f\"SELECT * FROM chains WHERE chain_id = {chain_id}\")\n        chain = cursor.fetchone()\n        if not chain:\n            return jsonify({\"error\": \"Chain not found\"}), 404\n        cursor.execute(f\"SELECT * FROM blocks WHERE chain_id = {chain_id} AND block_number = {block_number}\")\n        data = cursor.fetchone()\n        if data:\n            return jsonify({\"error\": \"Block already exists\"}), 400\n\n\n        block = evm_api.block.get_block(os.getenv(\"MORALIS_API_KEY_1\"), {\n            \"chain\": f\"0x{chain_id:x}\",\n            \"block_number_or_hash\": f\"{block_number}\"\n        })\n\n        block_hash = block[\"hash\"]\n        parent_hash = block[\"parent_hash\"]\n        miner = block[\"miner\"]\n        transactions = block[\"transactions\"]\n        timestamp = block[\"timestamp\"]\n        timestamp = f\"{timestamp[:10]} {timestamp[11:19]}\"\n\n        # First, we need to insert the miner address to addresses table !\n\n        cursor.execute(\"SELECT chain_id FROM chains\")\n        chains = cursor.fetchall()\n\n        native_dollar_balance = 0\n        erc20_count = 0\n        erc20_dollar_balance = 0\n        nft_count = 0\n        \n        for chain in chains:\n            _chain_id = chain[\"chain_id\"]\n\n            tokens = (evm_api.wallets.get_wallet_token_balances_price(os.getenv(\"MORALIS_API_KEY_2\"), {\n                \"chain\": f\"0x{_chain_id:x}\",\n                \"address\": miner\n            }))[\"result\"]\n\n            for token in tokens:\n                if token[\"native_token\"]:\n                    native_dollar_balance += float(token[\"usd_value\"] if token[\"usd_value\"] else 0)\n                else:\n                    erc20_count += 1\n                    erc20_dollar_balance += float(token[\"usd_value\"] if token[\"usd_value\"] else 0)\n\n            nfts = (evm_api.nft.get_wallet_nfts(os.getenv(\"MORALIS_API_KEY_3\"), {\n                \"chain\": f\"0x{_chain_id:x}\",\n                \"format\": \"decimal\",\n                \"media_items\": False,\n                \"address\": miner\n            }))[\"result\"]\n\n            nft_count += len(nfts)\n\n        sql = f\"\"\"\n            INSERT INTO addresses (address, native_dollar_balance, erc20_count, erc20_dollar_balance, nft_count)\n            VALUES (%s, %s, %s, %s, %s)\n            ON DUPLICATE KEY UPDATE \n                native_dollar_balance = VALUES(native_dollar_balance),\n                erc20_count = VALUES(erc20_count),\n                erc20_dollar_balance = VALUES(erc20_dollar_balance),\n                nft_count = VALUES(nft_count)\n        \"\"\" \n\n        cursor.execute(sql, (\n            miner,\n            round(float(native_dollar_balance), 18),\n            int(erc20_count),\n            round(float(erc20_dollar_balance), 18),\n            int(nft_count)\n        ))\n\n        sql = f\"\"\"\n            INSERT INTO blocks (chain_id, block_number, block_hash, parent_hash, miner, tx_count, timestamp)\n            VALUES (%s, %s, %s, %s, %s, %s, %s)\n        \"\"\"\n\n        cursor.execute(sql, (\n            int(chain_id),\n            int(block_number),\n            block_hash,\n            parent_hash,\n            miner,\n            int(len(transactions)),\n            timestamp\n        ))\n\n        return jsonify({\"message\": \"Block added successfully\"}), 201\n    except Error as e:\n        return jsonify({\"error\": str(e)}), 500\n    finally:\n        if connection.is_connected():\n            connection.commit()\n            cursor.close()\n            connection.close()\n\n@blocks.route(\"/full/<int:chain_id>/<int:block_number>\", methods=['POST'])\ndef full_add_block(chain_id, block_number):\n    connection = get_db_connection()\n    if not connection:\n        return jsonify({\"error\": \"Database connection failed\"}), 500\n\n    try:\n        cursor = connection.cursor(dictionary=True)\n        cursor.execute(f\"SELECT * FROM chains WHERE chain_id = {chain_id}\")\n        chain = cursor.fetchone()\n        if not chain:\n            return jsonify({\"error\": \"Chain not found\"}), 404\n\n        block = evm_api.block.get_block(os.getenv(\"MORALIS_API_KEY_1\"), {\n            \"chain\": f\"0x{chain_id:x}\",\n            \"block_number_or_hash\": f\"{block_number}\"\n        })\n\n        block_hash = block[\"hash\"]\n        parent_hash = block[\"parent_hash\"]\n        miner = block[\"miner\"]\n        transactions = block[\"transactions\"]\n        timestamp = block[\"timestamp\"]\n        timestamp = f\"{timestamp[:10]} {timestamp[11:19]}\"\n\n        # First, we need to insert the miner address to addresses table !\n\n        cursor.execute(\"SELECT chain_id FROM chains\")\n        chains = cursor.fetchall()\n\n        native_dollar_balance = 0\n        erc20_count = 0\n        erc20_dollar_balance = 0\n        nft_count = 0\n        \n        for chain in chains:\n            _chain_id = chain[\"chain_id\"]\n\n            tokens = (evm_api.wallets.get_wallet_token_balances_price(os.getenv(\"MORALIS_API_KEY_4\"), {\n                \"chain\": f\"0x{_chain_id:x}\",\n                \"address\": miner\n            }))[\"result\"]\n\n            for token in tokens:\n                if token[\"native_token\"]:\n                    native_dollar_balance += float(token[\"usd_value\"] if token[\"usd_value\"] else 0)\n                else:\n                    erc20_count += 1\n                    erc20_dollar_balance += float(token[\"usd_value\"] if token[\"usd_value\"] else 0)\n\n            nfts = (evm_api.nft.get_wallet_nfts(os.getenv(\"MORALIS_API_KEY_5\"), {\n                \"chain\": f\"0x{_chain_id:x}\",\n                \"format\": \"decimal\",\n                \"media_items\": False,\n                \"address\": miner\n            }))[\"result\"]\n\n            nft_count += len(nfts)\n\n        sql = f\"\"\"\n            INSERT INTO addresses (address, native_dollar_balance, erc20_count, erc20_dollar_balance, nft_count)\n            VALUES (%s, %s, %s, %s, %s)\n            ON DUPLICATE KEY UPDATE \n                native_dollar_balance = VALUES(native_dollar_balance),\n                erc20_count = VALUES(erc20_count),\n                erc20_dollar_balance = VALUES(erc20_dollar_balance),\n                nft_count = VALUES(nft_count)\n        \"\"\" \n\n        cursor.execute(sql, (\n            miner,\n            round(float(native_dollar_balance), 18),\n            int(erc20_count),\n            round(float(erc20_dollar_balance), 18),\n            int(nft_count)\n        ))\n\n        sql = f\"\"\"\n            INSERT IGNORE INTO blocks (chain_id, block_number, block_hash, parent_hash, miner, tx_count, timestamp)\n            VALUES (%s, %s, %s, %s, %s, %s, %s)\n        \"\"\"\n\n        cursor.execute(sql, (\n            int(chain_id),\n            int(block_number),\n            block_hash,\n            parent_hash,\n            miner,\n            int(len(transactions)),\n            timestamp\n        ))\n\n        for tx in transactions:\n            _address = tx[\"from_address\"]\n            \n            native_dollar_balance = 0\n            erc20_count = 0\n            erc20_dollar_balance = 0\n            nft_count = 0\n            \n            for chain in chains:\n                _chain_id = chain[\"chain_id\"]\n\n                tokens = (evm_api.wallets.get_wallet_token_balances_price(os.getenv(\"MORALIS_API_KEY_2\"), {\n                    \"chain\": f\"0x{_chain_id:x}\",\n                    \"address\": _address\n                }))[\"result\"]\n\n                for token in tokens:\n                    if token[\"native_token\"]:\n                        native_dollar_balance += float(token[\"usd_value\"] if token[\"usd_value\"] else 0)\n                    else:\n                        erc20_count += 1\n                        erc20_dollar_balance += float(token[\"usd_value\"] if token[\"usd_value\"] else 0)\n\n                nfts = (evm_api.nft.get_wallet_nfts(os.getenv(\"MORALIS_API_KEY_3\"), {\n                    \"chain\": f\"0x{_chain_id:x}\",\n                    \"format\": \"decimal\",\n                    \"media_items\": False,\n                    \"address\": _address\n                }))[\"result\"]\n\n                nft_count += len(nfts)\n\n            sql = f\"\"\"\n                INSERT INTO addresses (address, native_dollar_balance, erc20_count, erc20_dollar_balance, nft_count)\n                VALUES (%s, %s, %s, %s, %s)\n                ON DUPLICATE KEY UPDATE \n                    native_dollar_balance = VALUES(native_dollar_balance),\n                    erc20_count = VALUES(erc20_count),\n                    erc20_dollar_balance = VALUES(erc20_dollar_balance),\n                    nft_count = VALUES(nft_count)\n            \"\"\" \n\n            cursor.execute(sql, (\n                _address,\n                round(float(native_dollar_balance), 18),\n                int(erc20_count),\n                round(float(erc20_dollar_balance), 18),\n                int(nft_count)\n            ))\n\n            native_dollar_balance = 0\n            erc20_count = 0\n            erc20_dollar_balance = 0\n            nft_count = 0\n            \n            for chain in chains:\n                _chain_id = chain[\"chain_id\"]\n\n                tokens = (evm_api.wallets.get_wallet_token_balances_price(os.getenv(\"MORALIS_API_KEY_4\"), {\n                    \"chain\": f\"0x{_chain_id:x}\",\n                    \"address\": _address\n                }))[\"result\"]\n\n                for token in tokens:\n                    if token[\"native_token\"]:\n                        native_dollar_balance += float(token[\"usd_value\"] if token[\"usd_value\"] else 0)\n                    else:\n                        erc20_count += 1\n                        erc20_dollar_balance += float(token[\"usd_value\"] if token[\"usd_value\"] else 0)\n\n                nfts = (evm_api.nft.get_wallet_nfts(os.getenv(\"MORALIS_API_KEY_5\"), {\n                    \"chain\": f\"0x{_chain_id:x}\",\n                    \"format\": \"decimal\",\n                    \"media_items\": False,\n                    \"address\": _address\n                }))[\"result\"]\n\n                nft_count += len(nfts)\n\n            sql = f\"\"\"\n                INSERT INTO addresses (address, native_dollar_balance, erc20_count, erc20_dollar_balance, nft_count)\n                VALUES (%s, %s, %s, %s, %s)\n                ON DUPLICATE KEY UPDATE \n                    native_dollar_balance = VALUES(native_dollar_balance),\n                    erc20_count = VALUES(erc20_count),\n                    erc20_dollar_balance = VALUES(erc20_dollar_balance),\n                    nft_count = VALUES(nft_count)\n            \"\"\" \n\n            cursor.execute(sql, (\n                _address,\n                round(float(native_dollar_balance), 18),\n                int(erc20_count),\n                round(float(erc20_dollar_balance), 18),\n                int(nft_count)\n            ))\n\n            tx_hash = tx[\"hash\"]\n            from_address = tx[\"from_address\"]\n            to_address = tx[\"to_address\"]\n            value = tx[\"value\"]\n\n            # It is impossible the duplicate case for transactions, so we can directly insert the transaction\n            sql = f\"\"\"\n                INSERT IGNORE INTO transactions (chain_id, tx_hash, from_address, to_address, block_number, value, timestamp)\n                VALUES (%s, %s, %s, %s, %s, %s, %s)\n            \"\"\"\n\n            cursor.execute(sql, (\n                int(chain_id),\n                tx_hash,\n                from_address,\n                to_address,\n                int(block_number),\n                float(value),\n                timestamp\n            ))\n\n        return jsonify({\"message\": \"Block added successfully\"}), 201\n    except Error as e:\n        return jsonify({\"error\": str(e)}), 500\n    finally:\n        if connection.is_connected():\n            connection.commit()\n            cursor.close()\n            connection.close()\n\n@blocks.route(\"/latest-blocks-for-each-chain\", methods=['GET'])\ndef latest_blocks_for_each_chain():\n    connection = get_db_connection()\n\n    if not connection:\n        return jsonify({\"error\": \"Database connection failed\"}), 500\n\n    try:\n        cursor = connection.cursor(dictionary=True)\n        cursor.execute(\"\"\"\n            SELECT b.*\n            FROM blocks b\n            JOIN (\n                SELECT chain_id, MAX(block_number) as latest_block_number\n                FROM blocks\n                GROUP BY chain_id\n            ) latest_blocks\n            ON b.chain_id = latest_blocks.chain_id AND b.block_number = latest_blocks.latest_block_number\n        \"\"\")\n\n        data = cursor.fetchall()\n        return jsonify(data), 200\n    except Error as e:\n        return jsonify({\"error\": str(e)}), 500\n    finally:\n        if connection.is_connected():\n            connection.commit()\n            cursor.close()\n            connection.close()", "text_preview": "from flask import Blueprint, jsonify, request from mysql.connector import Error from utils.get_db_connection import get_db_connection from moralis import evm_api from dotenv import load_dotenv import os blocks = Blueprint('blocks', __name__", "input_sha256": "e0c93f46c0ee19d4257e6bfd899d6c409b59a8ca94203342da2362ed487dc6a2", "text_sha256": "479f57944c29998e3e0deb2f1aaf2b47dd683f70d2149ce96c68d92ab42f33b4", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12045, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.21615704313835823, "noise_severity": 0.0749889921144086, "missing_content_severity": 0.014964219721075853, "factual_density": 3.029566929025413, "pretraining_utility": 0.4984035620767726, "math_density": 0.3428668405324915, "code_density": 3.9175366487687158, "reasoning_density": 0.5629132984242338, "education_level": 2.452321694162611, "creative_writing_quality": -0.4518804459497002}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.01785994044374184, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9967912377140189, "secondary": 0.9672053902641808, "tertiary": 0.9858418498576507, "structured_records": 0.04163860954836028, "procedural_steps": 0.02447636346141745, "worked_solution": 0.007478375694198474, "contextual_dialogue": 6.878460598453188e-06, "content_quality": 0.429502029436464, "noise_severity": 0.9375091732379929, "missing_content_severity": 0.9875298168991035, "factual_density": 0.48602228464601915, "pretraining_utility": 0.46260878733682653, "math_density": 0.7142776328895903, "code_density": 0.9312805406405964, "reasoning_density": 0.5309055846464719, "education_level": 0.491707360894332, "creative_writing_quality": 0.9992545826694443, "primary_topic": 0.9911397244812805, "secondary_topic": 0.8204950157208247}, "probability_margin": {"primary": 0.9955637658558357, "secondary": 0.9547583740100223, "tertiary": 0.9811009506180748, "education_level": 0.19836629591741795, "creative_writing_quality": 0.9991176638519788, "primary_topic": 0.9876314454576012, "secondary_topic": 0.7687595338312142}, "raw_probabilities": {"primary": {"Academic Writing": 3.206179368068481e-05, "Content Listing": 2.326919510010856e-05, "Creative Writing": 1.5578532628774797e-05, "Customer Support Page": 7.410406079920969e-06, "Q&A Forum": 1.1873962361376452e-05, "Discussion Thread": 7.328949233250757e-06, "Comment Section": 1.3446906216858344e-05, "Missing Content": 2.1828814652248334e-05, "Knowledge Article": 8.518932152538341e-05, "Legal Notices": 2.5173919502600645e-05, "News Article": 4.470543960234715e-06, "Organizational Announcement": 1.0178984710176213e-05, "Nonfiction Writing": 1.0004730640668262e-05, "Organizational About Page": 1.0683544853779821e-05, "Personal About Page": 1.1107820555993738e-05, "Personal Blog": 7.429083224609016e-06, "Product Page": 6.883046165578225e-06, "Spam / Ads": 9.174878814671714e-06, "Structured Data": 0.0008210790151458581, "Technical Writing": 0.9969249361426015, "Transcript / Interview": 1.6134256136945023e-05, "Tutorial / How-To Guide": 0.0005578363183408829, "User Reviews": 5.749547102291938e-06, "Other / Unknown": 0.0013611702867657835}, "secondary": {"Academic Writing": 0.00017441558273457227, "Content Listing": 0.0001525504548704636, "Creative Writing": 0.00011680787376845727, "Customer Support Page": 6.399530175498526e-05, "Q&A Forum": 5.053699584466647e-05, "Discussion Thread": 4.5098473086661345e-05, "Comment Section": 0.0003129861953993497, "Missing Content": 0.0003388542639951588, "Knowledge Article": 0.0005622678455120207, "Legal Notices": 0.00022105315134967875, "News Article": 6.408116843995321e-05, "Organizational Announcement": 6.267919560649067e-05, "Nonfiction Writing": 7.040947081720309e-05, "Organizational About Page": 8.187629822740036e-05, "Personal About Page": 6.015994747254459e-05, "Personal Blog": 5.868474445560104e-05, "Product Page": 4.1674399415549064e-05, "Spam / Ads": 6.722453861251837e-05, "Structured Data": 0.013251753919845176, "Technical Writing": 0.01381345832648439, "Transcript / Interview": 8.414418726462609e-05, "Tutorial / How-To Guide": 0.0017023500967064844, "User Reviews": 3.110523182933146e-05, "NONE": 0.9685718323365067}, "tertiary": {"Academic Writing": 8.495563771888768e-05, "Content Listing": 8.796066150838396e-05, "Creative Writing": 6.289683098104431e-05, "Customer Support Page": 3.23025717048452e-05, "Q&A Forum": 2.185517909697519e-05, "Discussion Thread": 2.124425734264194e-05, "Comment Section": 0.0003117462274473535, "Missing Content": 0.00032988332534345953, "Knowledge Article": 0.0004572380544813404, "Legal Notices": 0.0001821414377138259, "News Article": 3.537587538097218e-05, "Organizational Announcement": 3.162957430112677e-05, "Nonfiction Writing": 5.392227505352807e-05, "Organizational About Page": 5.190646849540005e-05, "Personal About Page": 3.0198540572766168e-05, "Personal Blog": 2.866484564967576e-05, "Product Page": 2.517439214247136e-05, "Spam / Ads": 3.772267970003907e-05, "Structured Data": 0.005248816110487227, "Technical Writing": 0.005330822162173727, "Transcript / Interview": 4.060924623989545e-05, "Tutorial / How-To Guide": 0.0010452438247259587, "User Reviews": 1.5917041489924923e-05, "NONE": 0.9864317727802485}, "structured_records": {"false": 0.9583613904516397, "true": 0.04163860954836028}, "procedural_steps": {"false": 0.9755236365385825, "true": 0.02447636346141745}, "worked_solution": {"false": 0.9925216243058015, "true": 0.007478375694198474}, "contextual_dialogue": {"false": 0.9999931215394016, "true": 6.878460598453188e-06}, "content_quality": {"-2": 0.017695679869754958, "-1": 0.19882890102943254, "0": 0.4036614175583173, "1": 0.3092506991776896, "2": 0.07056330236480554}, "noise_severity": {"0": 0.9370467757176655, "1": 0.0530404004433159, "2": 0.008367471908137285, "3": 0.0009677598687067375, "4": 0.000577592062174476}, "missing_content_severity": {"0": 0.9922123217229979, "1": 0.0026676445503737672, "2": 0.0033289476552017944, "3": 0.0015256644254090965, "4": 0.00026542164601780333}, "factual_density": {"0": 0.011501822008715377, "1": 0.032449264454934006, "2": 0.19419916976366805, "3": 0.43867965004758774, "4": 0.32317009372509475}, "pretraining_utility": {"-2": 0.008886764067086689, "-1": 0.08254627057369528, "0": 0.3814801132108671, "1": 0.4554503435120607, "2": 0.07163650863629034}, "math_density": {"0": 0.7747069923168891, "1": 0.1503543371244114, "2": 0.049021857159600175, "3": 0.009198464507517521, "4": 0.016718348891581802}, "code_density": {"0": 0.0001853279159123787, "1": 0.00033693718277295686, "2": 0.007501662836187752, "3": 0.0657079023469404, "4": 0.9262681697181865}, "reasoning_density": {"0": 0.6896077152269106, "1": 0.13297576157711472, "2": 0.12053915680293067, "3": 0.03865024233091838, "4": 0.01822712406212563}, "education_level": {"none": 0.0008336925152552241, "0": 0.0010740732492182852, "1": 0.0037298729008346473, "2": 0.5764228007452766, "3": 0.3780565048278587, "4": 0.03988305576155647}, "creative_writing_quality": {"not_applicable": 0.9993788188912036, "-2": 9.38906399268814e-05, "-1": 0.00018948441795874557, "0": 0.00026115503922485205, "1": 5.673592201828207e-05, "2": 1.991508966784362e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.084267404853785e-05, "Art & Design": 4.4702927632907845e-05, "Law & Crime": 2.5028620684317902e-05, "Education & Careers": 3.835109403837503e-05, "Entertainment & Media": 3.6336274248976e-05, "Fashion & Beauty": 2.0541285728763477e-05, "Business, Economics & Finance": 0.002661234418799995, "Food & Dining": 2.2965560244558934e-05, "Games & Gaming": 0.00027046640759241226, "Electronics & Hardware": 6.66551114928203e-05, "Health, Healthcare & Wellness": 2.1500482142767938e-05, "History & Geography": 2.96961278943127e-05, "Home, Pets & Hobbies": 2.499080473969393e-05, "Industry & Production": 4.149044225430312e-05, "Language, Literature & Philosophy": 3.3959433897155196e-05, "Politics & Government": 1.8433444131157244e-05, "Religion & Spirituality": 4.3048827885347325e-05, "Science, Mathematics & Engineering": 0.00043515773273650557, "Relationships & Community": 1.616888738973979e-05, "Software Usage & Applications": 0.0006080279206842641, "Programming & Software Engineering": 0.9914805043089235, "Sports & Fitness": 3.993041286217388e-05, "Vehicles & Transportation": 2.4014898320597553e-05, "Travel & Tourism": 4.943116338733252e-05, "Other / Uncovered Subject": 0.0038490588513223453, "Unknown / Insufficient Content": 5.746188691701586e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007479387110618024, "Art & Design": 0.0005519300253214608, "Law & Crime": 0.0007090220779329773, "Education & Careers": 0.00044715329688400267, "Entertainment & Media": 0.0007810139848105735, "Fashion & Beauty": 0.00031128528436539653, "Business, Economics & Finance": 0.05095292276075344, "Food & Dining": 0.00029881590881109893, "Games & Gaming": 0.0024579798047021754, "Electronics & Hardware": 0.0006068859936542391, "Health, Healthcare & Wellness": 0.00030719766548568267, "History & Geography": 0.0002997162098595624, "Home, Pets & Hobbies": 0.00034808827658871583, "Industry & Production": 0.0003399992037492448, "Language, Literature & Philosophy": 0.00024330600693510464, "Politics & Government": 0.00027744898475722023, "Religion & Spirituality": 0.0005102143234260263, "Science, Mathematics & Engineering": 0.0018719691799010698, "Relationships & Community": 0.00015234252120377973, "Software Usage & Applications": 0.007337056085521268, "Programming & Software Engineering": 0.058639519746501854, "Sports & Fitness": 0.00032664086587084595, "Vehicles & Transportation": 0.0001835432854031339, "Travel & Tourism": 0.0004733142501899586, "Other / Uncovered Subject": 0.04342564196859325, "NONE": 0.827399053577716}}, "probabilities": {"primary": {"Academic Writing": 3.2061793680684803e-05, "Content Listing": 2.3269195100108554e-05, "Creative Writing": 1.5578532628774794e-05, "Customer Support Page": 7.410406079920967e-06, "Q&A Forum": 1.1873962361376449e-05, "Discussion Thread": 7.328949233250755e-06, "Comment Section": 1.344690621685834e-05, "Missing Content": 2.182881465224833e-05, "Knowledge Article": 8.51893215253834e-05, "Legal Notices": 2.5173919502600638e-05, "News Article": 4.4705439602347145e-06, "Organizational Announcement": 1.0178984710176211e-05, "Nonfiction Writing": 1.000473064066826e-05, "Organizational About Page": 1.068354485377982e-05, "Personal About Page": 1.1107820555993736e-05, "Personal Blog": 7.429083224609014e-06, "Product Page": 6.883046165578223e-06, "Spam / Ads": 9.174878814671712e-06, "Structured Data": 0.0008210790151458579, "Technical Writing": 0.9969249361426012, "Transcript / Interview": 1.613425613694502e-05, "Tutorial / How-To Guide": 0.0005578363183408828, "User Reviews": 5.749547102291936e-06, "Other / Unknown": 0.0013611702867657833}, "secondary": {"Academic Writing": 0.00017441558273457227, "Content Listing": 0.0001525504548704636, "Creative Writing": 0.00011680787376845727, "Customer Support Page": 6.399530175498526e-05, "Q&A Forum": 5.053699584466647e-05, "Discussion Thread": 4.5098473086661345e-05, "Comment Section": 0.0003129861953993497, "Missing Content": 0.0003388542639951588, "Knowledge Article": 0.0005622678455120207, "Legal Notices": 0.00022105315134967875, "News Article": 6.408116843995321e-05, "Organizational Announcement": 6.267919560649067e-05, "Nonfiction Writing": 7.040947081720309e-05, "Organizational About Page": 8.187629822740036e-05, "Personal About Page": 6.015994747254459e-05, "Personal Blog": 5.868474445560104e-05, "Product Page": 4.1674399415549064e-05, "Spam / Ads": 6.722453861251837e-05, "Structured Data": 0.013251753919845176, "Technical Writing": 0.01381345832648439, "Transcript / Interview": 8.414418726462609e-05, "Tutorial / How-To Guide": 0.0017023500967064844, "User Reviews": 3.110523182933146e-05, "NONE": 0.9685718323365067}, "tertiary": {"Academic Writing": 8.495563771888768e-05, "Content Listing": 8.796066150838396e-05, "Creative Writing": 6.289683098104431e-05, "Customer Support Page": 3.23025717048452e-05, "Q&A Forum": 2.185517909697519e-05, "Discussion Thread": 2.124425734264194e-05, "Comment Section": 0.0003117462274473535, "Missing Content": 0.00032988332534345953, "Knowledge Article": 0.0004572380544813404, "Legal Notices": 0.0001821414377138259, "News Article": 3.537587538097218e-05, "Organizational Announcement": 3.162957430112677e-05, "Nonfiction Writing": 5.392227505352807e-05, "Organizational About Page": 5.190646849540005e-05, "Personal About Page": 3.0198540572766168e-05, "Personal Blog": 2.866484564967576e-05, "Product Page": 2.517439214247136e-05, "Spam / Ads": 3.772267970003907e-05, "Structured Data": 0.005248816110487227, "Technical Writing": 0.005330822162173727, "Transcript / Interview": 4.060924623989545e-05, "Tutorial / How-To Guide": 0.0010452438247259587, "User Reviews": 1.5917041489924923e-05, "NONE": 0.9864317727802485}, "structured_records": {"false": 0.9583613904516397, "true": 0.04163860954836028}, "procedural_steps": {"false": 0.9755236365385825, "true": 0.02447636346141745}, "worked_solution": {"false": 0.9925216243058015, "true": 0.007478375694198474}, "contextual_dialogue": {"false": 0.9999931215394016, "true": 6.878460598453188e-06}, "content_quality": {"-2": 0.017695679869754958, "-1": 0.19882890102943254, "0": 0.4036614175583173, "1": 0.3092506991776896, "2": 0.07056330236480554}, "noise_severity": {"0": 0.9370467757176656, "1": 0.053040400443315906, "2": 0.008367471908137287, "3": 0.0009677598687067376, "4": 0.0005775920621744761}, "missing_content_severity": {"0": 0.9922123217229974, "1": 0.002667644550373766, "2": 0.003328947655201793, "3": 0.001525664425409096, "4": 0.0002654216460178032}, "factual_density": {"0": 0.01150182200871538, "1": 0.03244926445493401, "2": 0.19419916976366808, "3": 0.4386796500475878, "4": 0.3231700937250948}, "pretraining_utility": {"-2": 0.008886764067086687, "-1": 0.08254627057369526, "0": 0.381480113210867, "1": 0.4554503435120606, "2": 0.07163650863629033}, "math_density": {"0": 0.7747069923168891, "1": 0.1503543371244114, "2": 0.049021857159600175, "3": 0.009198464507517521, "4": 0.016718348891581802}, "code_density": {"0": 0.0001853279159123787, "1": 0.00033693718277295686, "2": 0.007501662836187752, "3": 0.0657079023469404, "4": 0.9262681697181865}, "reasoning_density": {"0": 0.6896077152269106, "1": 0.13297576157711472, "2": 0.12053915680293067, "3": 0.03865024233091838, "4": 0.01822712406212563}, "education_level": {"none": 0.0008336925152552241, "0": 0.0010740732492182852, "1": 0.0037298729008346473, "2": 0.5764228007452766, "3": 0.3780565048278587, "4": 0.03988305576155647}, "creative_writing_quality": {"not_applicable": 0.9993788188912034, "-2": 9.389063992688137e-05, "-1": 0.0001894844179587455, "0": 0.000261155039224852, "1": 5.6735922018282054e-05, "2": 1.9915089667843617e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.084267404853786e-05, "Art & Design": 4.470292763290785e-05, "Law & Crime": 2.5028620684317905e-05, "Education & Careers": 3.8351094038375034e-05, "Entertainment & Media": 3.6336274248976006e-05, "Fashion & Beauty": 2.054128572876348e-05, "Business, Economics & Finance": 0.0026612344187999953, "Food & Dining": 2.2965560244558937e-05, "Games & Gaming": 0.0002704664075924123, "Electronics & Hardware": 6.665511149282031e-05, "Health, Healthcare & Wellness": 2.150048214276794e-05, "History & Geography": 2.9696127894312702e-05, "Home, Pets & Hobbies": 2.4990804739693933e-05, "Industry & Production": 4.149044225430313e-05, "Language, Literature & Philosophy": 3.39594338971552e-05, "Politics & Government": 1.8433444131157247e-05, "Religion & Spirituality": 4.304882788534733e-05, "Science, Mathematics & Engineering": 0.0004351577327365056, "Relationships & Community": 1.6168887389739793e-05, "Software Usage & Applications": 0.0006080279206842642, "Programming & Software Engineering": 0.9914805043089236, "Sports & Fitness": 3.9930412862173884e-05, "Vehicles & Transportation": 2.4014898320597556e-05, "Travel & Tourism": 4.943116338733253e-05, "Other / Uncovered Subject": 0.0038490588513223458, "Unknown / Insufficient Content": 5.746188691701587e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007479387110618025, "Art & Design": 0.0005519300253214609, "Law & Crime": 0.0007090220779329774, "Education & Careers": 0.0004471532968840027, "Entertainment & Media": 0.0007810139848105736, "Fashion & Beauty": 0.0003112852843653966, "Business, Economics & Finance": 0.050952922760753444, "Food & Dining": 0.000298815908811099, "Games & Gaming": 0.002457979804702176, "Electronics & Hardware": 0.0006068859936542392, "Health, Healthcare & Wellness": 0.0003071976654856827, "History & Geography": 0.00029971620985956245, "Home, Pets & Hobbies": 0.0003480882765887159, "Industry & Production": 0.00033999920374924486, "Language, Literature & Philosophy": 0.00024330600693510466, "Politics & Government": 0.0002774489847572203, "Religion & Spirituality": 0.0005102143234260265, "Science, Mathematics & Engineering": 0.00187196917990107, "Relationships & Community": 0.00015234252120377976, "Software Usage & Applications": 0.007337056085521269, "Programming & Software Engineering": 0.05863951974650186, "Sports & Fitness": 0.000326640865870846, "Vehicles & Transportation": 0.00018354328540313392, "Travel & Tourism": 0.00047331425018995866, "Other / Uncovered Subject": 0.043425641968593255, "NONE": 0.8273990535777161}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000004, "mass_delta": 4.440892098500626e-16, "missing_mass": 0.0, "excess_mass": 4.440892098500626e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 12171, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 12171, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:10.523385+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "8d7739ba-7610-448b-b67e-e92c10033557", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "0063bc26fc734f5d049f17f8fb337dd332842ade5ad330431226ed735dbabd0a", "source_metadata": {"uuid": "8d7739ba-7610-448b-b67e-e92c10033557", "repo_name": "semihbceylan/db2024", "relative_path": "server/routes/blocks.py", "category": "WEB", "algo_rel_score": 0.9895930290222168, "quality_score": 5.836332321166992, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 90, "repo_created_at": "2024-10-30T06:06:34.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "56dfc4a275ce5492f5c581d360b38fcd6e72ab02", "size_bytes": 15005, "file_timestamp": 1735541694, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "e81c14dbe1de7f55978c996d6032a654efff76ec"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 90, "repo_created_at": "2024-10-30T06:06:34.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "56dfc4a275ce5492f5c581d360b38fcd6e72ab02", "size_bytes": 15005, "file_timestamp": 1735541694, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "e81c14dbe1de7f55978c996d6032a654efff76ec", "url": "https://github.com/semihbceylan/db2024/blob/e81c14dbe1de7f55978c996d6032a654efff76ec/server/routes/blocks.py"}, "gpt4o_tokens": 3271, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.21615704313835823, "noise_severity": 0.0749889921144086, "missing_content_severity": 0.014964219721075854, "factual_density": 3.029566929025413, "pretraining_utility": 0.49840356207677267, "math_density": 0.3428668405324915, "code_density": 3.9175366487687158, "reasoning_density": 0.5629132984242338, "education_level": 2.452321694162611, "creative_writing_quality": -0.4518804459497002}, "score_applicability": {"education_level": 0.9991663074847448, "creative_writing_quality": 0.0006211811087966046}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/SGalindo831/CST205-Project/blob/3b26e255a3061aa377cee5c30f8e7144a4c32ef3/main.py", "id": "python-0-3a3734e73e65963757b58211ece9c0bb", "text": "#Course: CST-205\n#TItle: Main Functionality\n#Abstract: This Flask application provides a web interface where users can enter a city name to receive current weather \n# information and a relevant image. It integrates with the OpenWeatherAPI to fetch weather data and Unsplash to\n# obtain images. The application features a random city selection option and processes the weather data to determine \n# an appropriate color filter for the image based on the temperature. The image is then encoded in base64 format for \n# easy embedding in HTML. Additionally, the app converts Unix timestamps to local times and displays a 24-hour weather \n# forecast. The main functionalities are defined in routes that handle the home page and process both GET and POST requests.\n#Authors: <NAME> and <NAME>\n#Date: 05/16/2024\n#Sabino Worked on Filters, importing the weather API and some parts of def Home\n#Cesar worked on Unsplash API, Converting time, encoding images and the rst of def Home\nfrom flask import Flask, render_template, request\nimport requests\nfrom PIL import Image\nimport io\nimport base64\nimport cv2\nimport numpy as np\nimport random\nfrom datetime import datetime, timedelta\n\napp = Flask(__name__)\n\n# Weather API endpoint\nAPI_KEY = \"<KEY>\"\nWEATHER_API_URL = \"http://api.openweathermap.org/data/2.5/weather\"\n\n# Unsplash API endpoint\nFORECAST_API_URL = \"http://api.openweathermap.org/data/2.5/forecast\"\nUNSPLASH_API_KEY = \"<KEY>\"\nUNSPLASH_API_URL = \"https://api.unsplash.com/search/photos\"\n\n# List of cities for random selection\nCITIES = [\"New York\", \"London\", \"Tokyo\", \"Sydney\", \"Paris\", \"Berlin\", \"Moscow\", \"Los Angeles\", \"Rio de Janeiro\", \"Cape Town\"]\n\n# \"\"\"Applies a color map based on temperature.\"\"\"\ndef custom_temperature_filter(image, temp):\n\n    image_cv = np.array(image)\n\n    image_cv = cv2.cvtColor(image_cv, cv2.COLOR_RGB2BGR)\n\n    if temp > 78:\n        color_map = cv2.COLORMAP_HOT\n    elif temp >= 65 and temp <= 77:\n        color_map = None\n    elif temp <= 64:\n        color_map = cv2.COLORMAP_WINTER\n\n    if color_map is not None:\n        filtered_image = cv2.applyColorMap(image_cv, color_map)\n    else:\n        filtered_image = image_cv\n\n    filtered_image = cv2.cvtColor(filtered_image, cv2.COLOR_BGR2RGB)\n    return Image.fromarray(filtered_image)\n\ndef apply_temperature_based_filter(image, temp):\n    return custom_temperature_filter(image, temp)\n\n\"\"\"Encodes an image to base64 format.\"\"\"\ndef encode_image_to_base64(image):\n    buffered = io.BytesIO()\n    image.save(buffered, format=\"JPEG\")\n    return base64.b64encode(buffered.getvalue()).decode(\"utf-8\")\n\n\"\"\"Converts a Unix timestamp to a local time, taking into account the timezone offset.\"\"\"\ndef convert_to_local_time(unix_time, timezone_offset):\n    utc_time = datetime.utcfromtimestamp(unix_time)\n    local_time = utc_time + timedelta(seconds=timezone_offset)\n    return local_time.strftime(\"%Y-%m-%d %H:%M:%S\")\n\n\"\"\"Handles requests to the home page.\"\"\"\n@app.route('/', methods=['GET', 'POST'])\ndef home():\n    if request.method == 'POST':\n        if 'lucky' in request.form:\n            city_name = random.choice(CITIES)\n        else:\n            city_name = request.form['city']\n\n        # Fetch weather data from OpenWeatherAPI\n        params = {\n            \"q\": city_name,\n            \"appid\": API_KEY,\n            \"units\": \"imperial\",\n        }\n        response = requests.get(WEATHER_API_URL, params=params)\n        weather_data = response.json()\n\n        if response.status_code != 200 or 'main' not in weather_data:\n            return render_template('index.html', error=\"City not found or API error occurred.\")\n\n        temperature = weather_data['main']['temp']\n        weather_description = weather_data['weather'][0]['description']\n        weather_icon = weather_data['weather'][0]['icon']\n        unix_time = weather_data['dt']\n        timezone_offset = weather_data['timezone']\n\n        # Convert Unix timestamp to local time\n        local_time = convert_to_local_time(unix_time, timezone_offset)\n\n        # Fetch an image from Unsplash\n        headers = {\n            \"Authorization\": f\"Client-ID {UNSPLASH_API_KEY}\"\n        }\n        unsplash_params = {\n            \"query\": city_name,\n            \"per_page\": 1,\n        }\n        unsplash_response = requests.get(UNSPLASH_API_URL, headers=headers, params=unsplash_params)\n        unsplash_data = unsplash_response.json()\n\n        if unsplash_response.status_code != 200 or not unsplash_data['results']:\n            return render_template('index.html', error=\"No image found for the city.\")\n\n        image_url = unsplash_data['results'][0]['urls']['regular']\n\n        # Load the image as a PIL object directly from the URL\n        image_response = requests.get(image_url, stream=True)\n        original_image = Image.open(image_response.raw)\n\n        # Apply a color map filter based on temperature\n        filtered_image = apply_temperature_based_filter(original_image, temperature)\n\n        # Encode the original image to a base64 string for rendering\n        encoded_original_image = encode_image_to_base64(original_image)\n\n        # Encode the filtered image to a base64 string for rendering\n        encoded_filtered_image = encode_image_to_base64(filtered_image)\n\n        # Fetch forecast data\n        forecast_response = requests.get(FORECAST_API_URL, params=params)\n        forecast_data = forecast_response.json()\n\n        forecast_for_next_day = []\n\n        if forecast_data.get(\"list\"):\n            forecast_for_next_day = [\n                forecast for forecast in forecast_data[\"list\"]\n                if \"dt_txt\" in forecast and \"12:00:00\" in forecast[\"dt_txt\"]\n            ]\n\n        return render_template(\n            'index.html', city_name=city_name, temperature=temperature,\n            weather_description=weather_description, weather_icon=weather_icon,\n            local_time=local_time, forecast_for_next_day=forecast_for_next_day,\n            image=encoded_original_image, filtered_image=encoded_filtered_image\n        )\n\n    return render_template('home.html')\n\n@app.route('/home.html')\ndef home_page():\n    return render_template('home.html')\n\nif __name__ == '__main__':\n    app.run(debug=True)\n    ", "text_preview": "#Course: CST-205 #TItle: Main Functionality #Abstract: This Flask application provides a web interface where users can enter a city name to receive current weather # information and a relevant image. It integrates with the OpenWeatherAPI to", "input_sha256": "17032acbee28129fb27ed3e204370df7ae6ef348e8e2592bfe2316effd543256", "text_sha256": "3a3734e73e65963757b58211ece9c0bb7f76aaad47c16bfb3d93756d288d1fe5", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12050, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.6206612412954668, "noise_severity": 0.1690663478551164, "missing_content_severity": 0.412225528440881, "factual_density": 3.0786958574836154, "pretraining_utility": 0.580329287165616, "math_density": 0.4369984590897156, "code_density": 3.4377622992848926, "reasoning_density": 0.6482788802395009, "education_level": 1.9874825320877545, "creative_writing_quality": -0.25045777511163475}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.031759306743569776, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9911755107513333, "secondary": 0.7506963852777648, "tertiary": 0.7243761364771523, "structured_records": 0.027608724717452498, "procedural_steps": 0.024225097539532704, "worked_solution": 0.026656230275220884, "contextual_dialogue": 9.021151017507638e-06, "content_quality": 0.46743831147754455, "noise_severity": 0.859111376787403, "missing_content_severity": 0.6564787262992657, "factual_density": 0.46590106856965174, "pretraining_utility": 0.5016064086033714, "math_density": 0.635834617425237, "code_density": 0.5314685827374107, "reasoning_density": 0.4597675998004158, "education_level": 0.8560113804168219, "creative_writing_quality": 0.9944447166176352, "primary_topic": 0.9755623806994167, "secondary_topic": 0.7276292292381795}, "probability_margin": {"primary": 0.9882810726981027, "secondary": 0.5887885370231559, "tertiary": 0.5318056585123324, "education_level": 0.8172592832479701, "creative_writing_quality": 0.9924652580920439, "primary_topic": 0.9684733622069466, "secondary_topic": 0.6627246378074227}, "raw_probabilities": {"primary": {"Academic Writing": 0.0032621251052583496, "Content Listing": 3.0033966533831623e-05, "Creative Writing": 3.8251660247923964e-05, "Customer Support Page": 1.2383696112525363e-05, "Q&A Forum": 2.350252403156088e-05, "Discussion Thread": 1.3393512158739786e-05, "Comment Section": 2.625584253209526e-05, "Missing Content": 0.00010972916615867217, "Knowledge Article": 0.00011136338425683731, "Legal Notices": 3.343645873316262e-05, "News Article": 9.791490399507547e-06, "Organizational Announcement": 2.6287586781840196e-05, "Nonfiction Writing": 2.7648843031722315e-05, "Organizational About Page": 2.6451134858807215e-05, "Personal About Page": 3.1717108692946457e-05, "Personal Blog": 1.6155520847316845e-05, "Product Page": 1.7015262370384827e-05, "Spam / Ads": 1.9401430571221485e-05, "Structured Data": 0.00039220122841258996, "Technical Writing": 0.9915431978033611, "Transcript / Interview": 3.0257230893774113e-05, "Tutorial / How-To Guide": 0.0020979830283052864, "User Reviews": 1.4061200357624124e-05, "Other / Unknown": 0.002087355815092221}, "secondary": {"Academic Writing": 0.17229549886803538, "Content Listing": 0.0009261971398698578, "Creative Writing": 0.0010687194710785393, "Customer Support Page": 0.0004325730683452837, "Q&A Forum": 0.0005137777428701801, "Discussion Thread": 0.0003298224284601422, "Comment Section": 0.001245144667803907, "Missing Content": 0.0096023952369745, "Knowledge Article": 0.002408192097018187, "Legal Notices": 0.0007426842370531928, "News Article": 0.0005032170791112301, "Organizational Announcement": 0.0007464739576493996, "Nonfiction Writing": 0.0012616760532239714, "Organizational About Page": 0.0007336167048468755, "Personal About Page": 0.000750737154591608, "Personal Blog": 0.00046439572777049024, "Product Page": 0.00040258920194528863, "Spam / Ads": 0.0005440990932740834, "Structured Data": 0.006844757182531669, "Technical Writing": 0.02992556708257724, "Transcript / Interview": 0.0007872555179540121, "Tutorial / How-To Guide": 0.006065527142442987, "User Reviews": 0.0003210472533805393, "NONE": 0.7610840358911913}, "tertiary": {"Academic Writing": 0.20405480561160516, "Content Listing": 0.0018137692885423441, "Creative Writing": 0.0012488069098591098, "Customer Support Page": 0.0004449044043299144, "Q&A Forum": 0.000510188064995932, "Discussion Thread": 0.00040268387864951543, "Comment Section": 0.0018719388900663716, "Missing Content": 0.008272215052860434, "Knowledge Article": 0.002778174321187336, "Legal Notices": 0.0010273816235264396, "News Article": 0.0005248935968624409, "Organizational Announcement": 0.0009276827562443573, "Nonfiction Writing": 0.0015178395531775348, "Organizational About Page": 0.0009788714020889427, "Personal About Page": 0.0012512507558857034, "Personal Blog": 0.0005233678058506025, "Product Page": 0.0005023736642160797, "Spam / Ads": 0.000577633843535393, "Structured Data": 0.01371443441259207, "Technical Writing": 0.016706955607615995, "Transcript / Interview": 0.0007976171806104622, "Tutorial / How-To Guide": 0.0033655141975748567, "User Reviews": 0.00032623305418534555, "NONE": 0.7358604641239376}, "structured_records": {"false": 0.9723912752825475, "true": 0.027608724717452498}, "procedural_steps": {"false": 0.9757749024604673, "true": 0.024225097539532704}, "worked_solution": {"false": 0.9733437697247791, "true": 0.026656230275220884}, "contextual_dialogue": {"false": 0.9999909788489825, "true": 9.021151017507638e-06}, "content_quality": {"-2": 0.014362888216055922, "-1": 0.07223481283849437, "0": 0.32164810214058337, "1": 0.4618865630436595, "2": 0.12986763376120675}, "noise_severity": {"0": 0.8550808110838519, "1": 0.12659403237007477, "2": 0.014017152384265978, "3": 0.0027940059307194473, "4": 0.001513998231087832}, "missing_content_severity": {"0": 0.7074077397907296, "1": 0.1937968168166276, "2": 0.08124066902606647, "3": 0.014271723894184714, "4": 0.003283050472391579}, "factual_density": {"0": 0.005425194896823228, "1": 0.04251074086229509, "2": 0.17981436370134135, "3": 0.4124424129395238, "4": 0.3598072876000166}, "pretraining_utility": {"-2": 0.009185757554032333, "-1": 0.06426660445550411, "0": 0.352781029682064, "1": 0.4845658098876143, "2": 0.08920079842078527}, "math_density": {"0": 0.675048419542743, "1": 0.24953341818887523, "2": 0.051666742192240635, "3": 0.010874123788206017, "4": 0.012877296287935252}, "code_density": {"0": 0.0008192404543711587, "1": 0.0017031064506679888, "2": 0.06594444375979748, "3": 0.42196253202602363, "4": 0.5095706773091397}, "reasoning_density": {"0": 0.647453457811129, "1": 0.12750987046407444, "2": 0.16834835007583762, "3": 0.04268097697208517, "4": 0.014007344676873952}, "education_level": {"none": 0.001466971914320513, "0": 0.0028016546905692964, "1": 0.06275020043271476, "2": 0.8800094836806849, "3": 0.050088973887947696, "4": 0.002882715393762903}, "creative_writing_quality": {"not_applicable": 0.9953705971813627, "-2": 0.0002664277954913767, "-1": 0.0010940240401003064, "0": 0.0029053390893188365, "1": 0.0002598140864222713, "2": 0.00010379780730466089}, "primary_topic": {"Adult & Explicit Sexual Content": 4.854411898381428e-05, "Art & Design": 0.0005164063445550002, "Law & Crime": 0.00010196212557192407, "Education & Careers": 0.0012853316456121558, "Entertainment & Media": 0.0001079673684996744, "Fashion & Beauty": 8.848997225010901e-05, "Business, Economics & Finance": 9.992551073136537e-05, "Food & Dining": 0.00011756139210674499, "Games & Gaming": 0.00011473365260722568, "Electronics & Hardware": 0.0001317403857371815, "Health, Healthcare & Wellness": 0.00016600844733737598, "History & Geography": 0.0007481877199669926, "Home, Pets & Hobbies": 0.00016855533358244343, "Industry & Production": 9.570424643586879e-05, "Language, Literature & Philosophy": 6.946724032751427e-05, "Politics & Government": 5.258327558282578e-05, "Religion & Spirituality": 8.449543805013586e-05, "Science, Mathematics & Engineering": 0.008028926927108026, "Relationships & Community": 3.311726999868684e-05, "Software Usage & Applications": 0.0018607532421173129, "Programming & Software Engineering": 0.9765022891340546, "Sports & Fitness": 0.00011895057175016703, "Vehicles & Transportation": 7.641462262708311e-05, "Travel & Tourism": 0.002165563144988886, "Other / Uncovered Subject": 0.0070805247916284056, "Unknown / Insufficient Content": 0.00013579607778844966}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.001279519943213515, "Art & Design": 0.07538039030621135, "Law & Crime": 0.0012731721620848986, "Education & Careers": 0.00797151098203598, "Entertainment & Media": 0.0015445132343366408, "Fashion & Beauty": 0.000688882698062422, "Business, Economics & Finance": 0.0011967294041438452, "Food & Dining": 0.000886034308398639, "Games & Gaming": 0.0007105112893942221, "Electronics & Hardware": 0.000960002911857279, "Health, Healthcare & Wellness": 0.0016079170008923204, "History & Geography": 0.008786195007526513, "Home, Pets & Hobbies": 0.0008597351121078608, "Industry & Production": 0.0007124664685272255, "Language, Literature & Philosophy": 0.00041769660380836275, "Politics & Government": 0.0005916888670671803, "Religion & Spirituality": 0.0007979793850695723, "Science, Mathematics & Engineering": 0.046681669468539876, "Relationships & Community": 0.0002752264227917613, "Software Usage & Applications": 0.016001743026796147, "Programming & Software Engineering": 0.04263128949465126, "Sports & Fitness": 0.0008052238058996875, "Vehicles & Transportation": 0.0005157687512302463, "Travel & Tourism": 0.008224267619082511, "Other / Uncovered Subject": 0.041094837612636596, "NONE": 0.7381050281136341}}, "probabilities": {"primary": {"Academic Writing": 0.0032621251052583496, "Content Listing": 3.0033966533831623e-05, "Creative Writing": 3.8251660247923964e-05, "Customer Support Page": 1.2383696112525363e-05, "Q&A Forum": 2.350252403156088e-05, "Discussion Thread": 1.3393512158739786e-05, "Comment Section": 2.625584253209526e-05, "Missing Content": 0.00010972916615867217, "Knowledge Article": 0.00011136338425683731, "Legal Notices": 3.343645873316262e-05, "News Article": 9.791490399507547e-06, "Organizational Announcement": 2.6287586781840196e-05, "Nonfiction Writing": 2.7648843031722315e-05, "Organizational About Page": 2.6451134858807215e-05, "Personal About Page": 3.1717108692946457e-05, "Personal Blog": 1.6155520847316845e-05, "Product Page": 1.7015262370384827e-05, "Spam / Ads": 1.9401430571221485e-05, "Structured Data": 0.00039220122841258996, "Technical Writing": 0.9915431978033611, "Transcript / Interview": 3.0257230893774113e-05, "Tutorial / How-To Guide": 0.0020979830283052864, "User Reviews": 1.4061200357624124e-05, "Other / Unknown": 0.002087355815092221}, "secondary": {"Academic Writing": 0.1722954988680354, "Content Listing": 0.0009261971398698579, "Creative Writing": 0.0010687194710785396, "Customer Support Page": 0.00043257306834528376, "Q&A Forum": 0.0005137777428701802, "Discussion Thread": 0.00032982242846014227, "Comment Section": 0.0012451446678039071, "Missing Content": 0.009602395236974502, "Knowledge Article": 0.0024081920970181873, "Legal Notices": 0.0007426842370531929, "News Article": 0.0005032170791112302, "Organizational Announcement": 0.0007464739576493997, "Nonfiction Writing": 0.0012616760532239717, "Organizational About Page": 0.0007336167048468756, "Personal About Page": 0.0007507371545916081, "Personal Blog": 0.0004643957277704903, "Product Page": 0.0004025892019452887, "Spam / Ads": 0.0005440990932740835, "Structured Data": 0.00684475718253167, "Technical Writing": 0.029925567082577244, "Transcript / Interview": 0.0007872555179540122, "Tutorial / How-To Guide": 0.006065527142442988, "User Reviews": 0.00032104725338053935, "NONE": 0.7610840358911914}, "tertiary": {"Academic Writing": 0.2040548056116052, "Content Listing": 0.0018137692885423443, "Creative Writing": 0.00124880690985911, "Customer Support Page": 0.0004449044043299145, "Q&A Forum": 0.0005101880649959321, "Discussion Thread": 0.0004026838786495155, "Comment Section": 0.0018719388900663718, "Missing Content": 0.008272215052860436, "Knowledge Article": 0.0027781743211873363, "Legal Notices": 0.0010273816235264399, "News Article": 0.000524893596862441, "Organizational Announcement": 0.0009276827562443574, "Nonfiction Writing": 0.001517839553177535, "Organizational About Page": 0.000978871402088943, "Personal About Page": 0.0012512507558857036, "Personal Blog": 0.0005233678058506026, "Product Page": 0.0005023736642160798, "Spam / Ads": 0.0005776338435353932, "Structured Data": 0.013714434412592071, "Technical Writing": 0.016706955607615998, "Transcript / Interview": 0.0007976171806104623, "Tutorial / How-To Guide": 0.003365514197574857, "User Reviews": 0.0003262330541853456, "NONE": 0.7358604641239377}, "structured_records": {"false": 0.9723912752825475, "true": 0.027608724717452498}, "procedural_steps": {"false": 0.9757749024604673, "true": 0.024225097539532704}, "worked_solution": {"false": 0.9733437697247791, "true": 0.026656230275220884}, "contextual_dialogue": {"false": 0.9999909788489825, "true": 9.021151017507638e-06}, "content_quality": {"-2": 0.014362888216055924, "-1": 0.07223481283849438, "0": 0.3216481021405834, "1": 0.46188656304365955, "2": 0.12986763376120677}, "noise_severity": {"0": 0.8550808110838519, "1": 0.12659403237007477, "2": 0.014017152384265978, "3": 0.0027940059307194473, "4": 0.001513998231087832}, "missing_content_severity": {"0": 0.7074077397907296, "1": 0.1937968168166276, "2": 0.08124066902606647, "3": 0.014271723894184714, "4": 0.003283050472391579}, "factual_density": {"0": 0.005425194896823228, "1": 0.04251074086229509, "2": 0.17981436370134135, "3": 0.4124424129395238, "4": 0.3598072876000166}, "pretraining_utility": {"-2": 0.009185757554032333, "-1": 0.06426660445550411, "0": 0.352781029682064, "1": 0.4845658098876143, "2": 0.08920079842078527}, "math_density": {"0": 0.675048419542743, "1": 0.24953341818887523, "2": 0.051666742192240635, "3": 0.010874123788206017, "4": 0.012877296287935252}, "code_density": {"0": 0.0008192404543711587, "1": 0.0017031064506679888, "2": 0.06594444375979748, "3": 0.42196253202602363, "4": 0.5095706773091397}, "reasoning_density": {"0": 0.6474534578111288, "1": 0.12750987046407442, "2": 0.1683483500758376, "3": 0.042680976972085165, "4": 0.014007344676873949}, "education_level": {"none": 0.001466971914320513, "0": 0.0028016546905692964, "1": 0.06275020043271476, "2": 0.8800094836806849, "3": 0.050088973887947696, "4": 0.002882715393762903}, "creative_writing_quality": {"not_applicable": 0.9953705971813624, "-2": 0.00026642779549137667, "-1": 0.0010940240401003062, "0": 0.002905339089318836, "1": 0.0002598140864222712, "2": 0.00010379780730466086}, "primary_topic": {"Adult & Explicit Sexual Content": 4.8544118983814285e-05, "Art & Design": 0.0005164063445550003, "Law & Crime": 0.00010196212557192409, "Education & Careers": 0.001285331645612156, "Entertainment & Media": 0.00010796736849967441, "Fashion & Beauty": 8.848997225010902e-05, "Business, Economics & Finance": 9.992551073136538e-05, "Food & Dining": 0.000117561392106745, "Games & Gaming": 0.00011473365260722569, "Electronics & Hardware": 0.00013174038573718152, "Health, Healthcare & Wellness": 0.000166008447337376, "History & Geography": 0.0007481877199669927, "Home, Pets & Hobbies": 0.00016855533358244346, "Industry & Production": 9.57042464358688e-05, "Language, Literature & Philosophy": 6.946724032751428e-05, "Politics & Government": 5.258327558282579e-05, "Religion & Spirituality": 8.449543805013587e-05, "Science, Mathematics & Engineering": 0.008028926927108028, "Relationships & Community": 3.311726999868685e-05, "Software Usage & Applications": 0.001860753242117313, "Programming & Software Engineering": 0.9765022891340547, "Sports & Fitness": 0.00011895057175016705, "Vehicles & Transportation": 7.641462262708313e-05, "Travel & Tourism": 0.0021655631449888865, "Other / Uncovered Subject": 0.007080524791628406, "Unknown / Insufficient Content": 0.00013579607778844968}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.001279519943213515, "Art & Design": 0.07538039030621135, "Law & Crime": 0.0012731721620848986, "Education & Careers": 0.00797151098203598, "Entertainment & Media": 0.0015445132343366408, "Fashion & Beauty": 0.000688882698062422, "Business, Economics & Finance": 0.0011967294041438452, "Food & Dining": 0.000886034308398639, "Games & Gaming": 0.0007105112893942221, "Electronics & Hardware": 0.000960002911857279, "Health, Healthcare & Wellness": 0.0016079170008923204, "History & Geography": 0.008786195007526513, "Home, Pets & Hobbies": 0.0008597351121078608, "Industry & Production": 0.0007124664685272255, "Language, Literature & Philosophy": 0.00041769660380836275, "Politics & Government": 0.0005916888670671803, "Religion & Spirituality": 0.0007979793850695723, "Science, Mathematics & Engineering": 0.046681669468539876, "Relationships & Community": 0.0002752264227917613, "Software Usage & Applications": 0.016001743026796147, "Programming & Software Engineering": 0.04263128949465126, "Sports & Fitness": 0.0008052238058996875, "Vehicles & Transportation": 0.0005157687512302463, "Travel & Tourism": 0.008224267619082511, "Other / Uncovered Subject": 0.041094837612636596, "NONE": 0.7381050281136341}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9994, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9994, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:12.126931+00:00", "scoring_key_slot": "primary", "dataset_uid": "d3cb8225-56df-4ca0-98c6-d1f0eb6bc237", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "709e2baf6e224071ea46ad329b396c062d2a92474e5f81cb972669a5251c1905", "source_metadata": {"uuid": "d3cb8225-56df-4ca0-98c6-d1f0eb6bc237", "repo_name": "SGalindo831/CST205-Project", "relative_path": "main.py", "category": "WEB", "algo_rel_score": 0.998155415058136, "quality_score": 7.87474250793457, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 18, "repo_created_at": "2024-04-22T22:22:02.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "91507f1856030802a7df2e50a456e9222b358a24", "size_bytes": 6163, "file_timestamp": 1715915405, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "3b26e255a3061aa377cee5c30f8e7144a4c32ef3"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 18, "repo_created_at": "2024-04-22T22:22:02.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "91507f1856030802a7df2e50a456e9222b358a24", "size_bytes": 6163, "file_timestamp": 1715915405, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "3b26e255a3061aa377cee5c30f8e7144a4c32ef3", "url": "https://github.com/SGalindo831/CST205-Project/blob/3b26e255a3061aa377cee5c30f8e7144a4c32ef3/main.py"}, "gpt4o_tokens": 1374, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.6206612412954668, "noise_severity": 0.1690663478551164, "missing_content_severity": 0.412225528440881, "factual_density": 3.0786958574836154, "pretraining_utility": 0.580329287165616, "math_density": 0.4369984590897156, "code_density": 3.4377622992848926, "reasoning_density": 0.6482788802395009, "education_level": 1.9874825320877545, "creative_writing_quality": -0.25045777511163475}, "score_applicability": {"education_level": 0.9985330280856796, "creative_writing_quality": 0.004629402818637451}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/Spreng624/ctf-repository/blob/2b8b7be8b11498d023c042150b8cbb80a4ef8ae8/2025%20%E4%B8%8A%E5%8D%8A%E5%B9%B4/XYCTF%202025/chanllenge/Crypto/%E6%96%B0%E5%8B%92%E7%B4%A2%E7%97%85%E6%AF%92NTRU/task.py", "id": "python-0-23e3d0cf3b7268a1e415303714a2b00f", "text": "# Visit https://www.lddgo.net/string/pyc-compile-decompile for more information\n# Version : Python 3.8\n\n\"\"\"\nCreated on Sun Mar 30 18:25:08 2025\n\n@author: Crypto0\n\nimport re\nimport base64\nimport os\nimport sys\nfrom gmssl import sm4\nfrom Crypto.Util.Padding import pad\nimport binascii\nfrom random import shuffle, randrange\n\nN = 49\np = 3\nq = 128\nd = 3\nassert q > (6 * d + 1) * p\nR.<x> = ZZ[]\ndef generate_T(d1, d2):\n    assert N >= d1 + d2\n    s = [1] * d1 + [-1] * d2 + [0] * (N - d1 - d2)\n    shuffle(s)\n    return R(s)\n\ndef invert_mod_prime(f, p):\n    Rp = R.change_ring(Integers(p)).quotient(x^N - 1)\n    return R(lift(1 / Rp(f)))\n\ndef convolution(f, g):\n    return (f * g) % (x^N - 1)\n\ndef lift_mod(f, q):\n    return R([((f[i] + q // 2) % q) - q // 2 for i in range(N)])\n\ndef poly_mod(f, q):\n    return R([f[i] % q for i in range(N)])\n\ndef invert_mod_pow2(f, q):\n    assert q.is_power_of(2)\n    g = invert_mod_prime(f, 2)\n    while True:\n        r = lift_mod(convolution(g, f), q)\n        if r == 1:\n            return g\n        g = lift_mod(convolution(g, 2 - r), q)\n\ndef generate_message():\n    return R([randrange(p) - 1 for _ in range(N)])\n\ndef generate_key():\n    while True:\n        try:\n            f = generate_T(d + 1, d)\n            g = generate_T(d, d)\n            Fp = poly_mod(invert_mod_prime(f, p), p)\n            Fq = poly_mod(invert_mod_pow2(f, q), q)\n            break\n        except:\n            continue\n    h = poly_mod(convolution(Fq, g), q)\n    return h, (f, g)\n\ndef encrypt_message(m, h):\n    e = lift_mod(p * convolution(h, generate_T(d, d)) + m, q)\n    return e\n\ndef save_ntru_keys():\n    h, secret = generate_key()\n    with open(\"pub_key.txt\", \"w\") as f:\n        f.write(str(h))\n    m = generate_message()\n    with open(\"priv_key.txt\", \"w\") as f:\n        f.write(str(m))\n    e = encrypt_message(m, h)\n    with open(\"enc.txt\", \"w\") as f:\n        f.write(str(e))\n\ndef terms(poly_str):\n    terms = []\n    pattern = r\\'([+-]?\\\\s*x\\\\^?\\\\d*|[-+]?\\\\s*\\\\d+)\\'\n    matches = re.finditer(pattern, poly_str.replace(\\' \\', \\'\\'))\n\n    for match in matches:\n        term = match.group()\n        if term == \\'+x\\' or term == \\'x\\':\n            terms.append(1)\n        elif term == \\'-x\\':\n            terms.append(-1)\n        elif \\'x^\\' in term:\n            coeff_part = term.split(\\'x^\\')[0]\n            exponent = int(term.split(\\'x^\\')[1])\n            if not coeff_part or coeff_part == \\'+\\':\n                coeff = 1\n            elif coeff_part == \\'-\\':\n                coeff = -1\n            else:\n                coeff = int(coeff_part)\n            terms.append(coeff * exponent)\n        elif \\'x\\' in term:\n            coeff_part = term.split(\\'x\\')[0]\n            if not coeff_part or coeff_part == \\'+\\':\n                terms.append(1)\n            elif coeff_part == \\'-\\':\n                terms.append(-1)\n            else:\n                terms.append(int(coeff_part))\n        else:\n            if term == \\'+1\\' or term == \\'1\\':\n                terms.append(0)\n                terms.append(-0)\n    return terms\n\ndef gen_key(poly_terms):\n    binary = [0] * 128\n    for term in poly_terms:\n        exponent = abs(term)\n        if term > 0 and exponent <= 127:\n            binary[127 - exponent] = 1\n    binary_str = \\'\\'.join(map(str, binary))\n    hex_key = hex(int(binary_str, 2))[2:].upper().zfill(32)\n    return hex_key\n\ndef read_polynomial_from_file(filename):\n    with open(filename, \\'r\\') as file:\n        return file.read().strip()\n\n\ndef sm4_encrypt(key, plaintext):\n    assert len(key) == 16, \"SM4 key must be 16 bytes\"\n    cipher = sm4.CryptSM4()\n    cipher.set_key(key, sm4.SM4_ENCRYPT)\n    padded_plaintext = pad(plaintext, 16)\n    return cipher.crypt_ecb(padded_plaintext)\n\ndef sm4_encrypt_file(input_path, output_path, key):\n    with open(input_path, \\'rb\\') as f:\n        plaintext = f.read()\n\n    ciphertext = sm4_encrypt(key, plaintext)\n\n    with open(output_path, \\'wb\\') as f:\n        f.write(ciphertext)\n\ndef resource_path(relative_path):\n    if getattr(sys, \\'frozen\\', False):\n        base_path = sys._MEIPASS\n    else:\n        base_path = os.path.abspath(\".\")\n    return os.path.join(base_path, relative_path)\n\ndef encrypt_directory(directory, sm4_key, extensions=[\".txt\"]):\n    if not os.path.exists(directory):\n        print(f\"Directory does not exist: {directory}\")\n        return\n\n    for root, _, files in os.walk(directory):\n        for file in files:\n            if any(file.endswith(ext) for ext in extensions):\n                input_path = os.path.join(root, file)\n                output_path = input_path + \".enc\"\n\n                try:\n                    sm4_encrypt_file(input_path, output_path, sm4_key)\n                    os.remove(input_path)\n                    print(f\"Encrypted: {input_path} -> {output_path}\")\n                except Exception as e:\n                    print(f\"Error encrypting {input_path}: {str(e)}\")\n\ndef main():\n    try:\n        save_ntru_keys()\n        poly_str = read_polynomial_from_file(\"priv_key.txt\")\n        poly_terms = terms(poly_str)\n        sm4_key = binascii.unhexlify(poly_terms)\n        user_name = os.getlogin()\n        target_dir = os.path.join(\"C:\\\\Users\", user_name, \"Desktop\", \"test_files\")\n\n        if not os.path.exists(target_dir):\n            os.makedirs(target_dir, exist_ok=True)\n            print(f\"Created directory: {target_dir}\")\n            return\n\n        txt_files = [f for f in os.listdir(target_dir)\n                    if f.endswith(\\'.txt\\') and os.path.isfile(os.path.join(target_dir, f))]\n\n        if not txt_files:\n            print(\"No .txt files found in directory\")\n            return\n\n        for txt_file in txt_files:\n            file_path = os.path.join(target_dir, txt_file)\n            try:\n                with open(file_path, \\'rb\\') as f:\n                    test_data = f.read()\n\n                ciphertext = sm4_encrypt(sm4_key, test_data)\n                encrypted_path = file_path + \\'.enc\\'\n\n                with open(encrypted_path, \\'wb\\') as f:\n                f.write(ciphertext)\n            except Exception as e:\n                print(f\"Error processing {txt_file}: {str(e)}\")\n\n    except Exception as e:\n        print(f\"Fatal error: {str(e)}\")\n\nif __name__ == \"__main__\":\n    main()\n\"\"\"\n", "text_preview": "# Visit https://www.lddgo.net/string/pyc-compile-decompile for more information # Version : Python 3.8 \"\"\" Created on Sun Mar 30 18:25:08 2025 @author: Crypto0 import re import base64 import os import sys from gmssl import sm4 from Crypto.U", "input_sha256": "d3526833b3242bba7027bc64c6e97e38d8c05570a6ff23566e7b26163c7951c8", "text_sha256": "23e3d0cf3b7268a1e415303714a2b00fed1c94830e795ec1154ded2ad9715b00", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12052, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": -1, "noise_severity": 1, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 3, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": -0.5804568218349242, "noise_severity": 0.7356275491432921, "missing_content_severity": 0.2559085875962789, "factual_density": 3.398381861473836, "pretraining_utility": 0.32873819933785947, "math_density": 3.00038060097003, "code_density": 3.6001670512938504, "reasoning_density": 0.6323965209540002, "education_level": 3.219205252299867, "creative_writing_quality": -0.6089791824545334}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.3948 below 0.70", "secondary_topic vs NONE margin 0.0654 below 0.10"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.04927755449495197, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9769926200361745, "secondary": 0.7283038496019861, "tertiary": 0.6854151294362357, "structured_records": 0.001050045635975737, "procedural_steps": 0.04431120938148352, "worked_solution": 0.00626732725864024, "contextual_dialogue": 8.715321836974414e-06, "content_quality": 0.3652788593675258, "noise_severity": 0.5921178867614492, "missing_content_severity": 0.7867428436697675, "factual_density": 0.49865155122819615, "pretraining_utility": 0.2785319405401767, "math_density": 0.4574310606283597, "code_density": 0.6668058760782085, "reasoning_density": 0.4730028992049998, "education_level": 0.42267914879239044, "creative_writing_quality": 0.9979666087391157, "primary_topic": 0.8527797505117566, "secondary_topic": 0.37060505224318585}, "probability_margin": {"primary": 0.969336701166799, "secondary": 0.5091352599824706, "tertiary": 0.41875601532867446, "education_level": 0.1670993231683941, "creative_writing_quality": 0.9977248077417255, "primary_topic": 0.7506026816115753, "secondary_topic": 0.0653521508594681}, "raw_probabilities": {"primary": {"Academic Writing": 0.00013648559418066503, "Content Listing": 0.00010586392171469368, "Creative Writing": 0.00011476218859927367, "Customer Support Page": 3.196786860648785e-05, "Q&A Forum": 6.78660432489088e-05, "Discussion Thread": 4.726549038047122e-05, "Comment Section": 0.00011686663027856198, "Missing Content": 0.0009936155245959548, "Knowledge Article": 0.0002348899836907506, "Legal Notices": 0.00013418428350281893, "News Article": 3.065551367505631e-05, "Organizational Announcement": 5.104887162923349e-05, "Nonfiction Writing": 7.10576124649839e-05, "Organizational About Page": 8.029246208157851e-05, "Personal About Page": 6.035218501876134e-05, "Personal Blog": 3.913489155865706e-05, "Product Page": 2.7713178910763195e-05, "Spam / Ads": 0.008614559701201487, "Structured Data": 0.0002840394155571483, "Technical Writing": 0.9779512608680005, "Transcript / Interview": 7.830285749220983e-05, "Tutorial / How-To Guide": 0.005513086576020342, "User Reviews": 3.432905166469241e-05, "Other / Unknown": 0.005180399285925909}, "secondary": {"Academic Writing": 0.001292568738917849, "Content Listing": 0.0005214991825387652, "Creative Writing": 0.0007786434466195435, "Customer Support Page": 0.00031308636313489197, "Q&A Forum": 0.00038655438444952175, "Discussion Thread": 0.00027500391294962384, "Comment Section": 0.0005257946496216787, "Missing Content": 0.001514052470385896, "Knowledge Article": 0.0013041254047060848, "Legal Notices": 0.001001475125126622, "News Article": 0.00045455590441281904, "Organizational Announcement": 0.00047612796206762735, "Nonfiction Writing": 0.0005680518590046355, "Organizational About Page": 0.0006131555879401535, "Personal About Page": 0.0005486401179584336, "Personal Blog": 0.0003895592324651492, "Product Page": 0.0003128824977417956, "Spam / Ads": 0.23048926255276614, "Structured Data": 0.0006118439350996511, "Technical Writing": 0.013596266433155195, "Transcript / Interview": 0.0006442828125991382, "Tutorial / How-To Guide": 0.0035373666614664594, "User Reviews": 0.0002206782296356061, "NONE": 0.7396245225352367}, "tertiary": {"Academic Writing": 0.0015218463273812624, "Content Listing": 0.00039404370085955893, "Creative Writing": 0.0006614369980927053, "Customer Support Page": 0.00026700460354616855, "Q&A Forum": 0.0002867859620262558, "Discussion Thread": 0.00022414173199073337, "Comment Section": 0.0005121904388898325, "Missing Content": 0.0011734771503876301, "Knowledge Article": 0.0015195288432017842, "Legal Notices": 0.0013832208896400063, "News Article": 0.00036168656578718176, "Organizational Announcement": 0.0004122793970051544, "Nonfiction Writing": 0.0005512219468701132, "Organizational About Page": 0.0006054456406871935, "Personal About Page": 0.0005155788298976988, "Personal Blog": 0.0003594375647210996, "Product Page": 0.0002733429521750878, "Spam / Ads": 0.2797668170477181, "Structured Data": 0.0004994895809568362, "Technical Writing": 0.00722548551011147, "Transcript / Interview": 0.000491124960422733, "Tutorial / How-To Guide": 0.0022798563644071284, "User Reviews": 0.0001917246168317371, "NONE": 0.6985228323763926}, "structured_records": {"false": 0.9989499543640242, "true": 0.001050045635975737}, "procedural_steps": {"false": 0.9556887906185165, "true": 0.04431120938148352}, "worked_solution": {"false": 0.9937326727413598, "true": 0.00626732725864024}, "contextual_dialogue": {"false": 0.999991284678163, "true": 8.715321836974414e-06}, "content_quality": {"-2": 0.1710610952969466, "-1": 0.4473313926590539, "0": 0.2163246104828476, "1": 0.12156904170428029, "2": 0.04371385985687141}, "noise_severity": {"0": 0.37691549337148444, "1": 0.5438173376189545, "2": 0.05369782801502842, "3": 0.017862808483849923, "4": 0.007706532510682762}, "missing_content_severity": {"0": 0.874750693849857, "1": 0.04164109837766785, "2": 0.04815549318421193, "3": 0.023854355502866288, "4": 0.011598359085397088}, "factual_density": {"0": 0.006776116878786741, "1": 0.013356492685024266, "2": 0.08350706738353392, "3": 0.367430058188877, "4": 0.528930264863778}, "pretraining_utility": {"-2": 0.05408119905858574, "-1": 0.14002214321213313, "0": 0.3262238524069408, "1": 0.3824228699775165, "2": 0.09724993534482376}, "math_density": {"0": 0.01312928925565477, "1": 0.03663546509662139, "2": 0.21269226517776232, "3": 0.4118113163619626, "4": 0.325731664107999}, "code_density": {"0": 0.0007647511030581097, "1": 0.0016918868906057511, "2": 0.05731901721439477, "3": 0.2770602491933105, "4": 0.6631640955986309}, "reasoning_density": {"0": 0.757390000400058, "1": 0.04645431224331458, "2": 0.07056369461468052, "3": 0.05755315148646264, "4": 0.06803884125548415}, "education_level": {"none": 0.0018818932607608321, "0": 0.0013526488443339096, "1": 0.002883089803440637, "2": 0.12318310993920797, "3": 0.5188992906603254, "4": 0.35179996749193126}, "creative_writing_quality": {"not_applicable": 0.9983055072825964, "-2": 0.00036138214007982024, "-1": 0.000580699540870877, "0": 0.0005445109878784122, "1": 0.0001442470658381943, "2": 6.365298273634446e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0003721299470884212, "Art & Design": 0.00038076914994272895, "Law & Crime": 0.00400245311192208, "Education & Careers": 0.00027454977833989115, "Entertainment & Media": 0.0002875122959223712, "Fashion & Beauty": 0.00017217430636218829, "Business, Economics & Finance": 0.00030509894855294736, "Food & Dining": 0.00017678893726845451, "Games & Gaming": 0.0007098408875634403, "Electronics & Hardware": 0.00038269860975861117, "Health, Healthcare & Wellness": 0.0002712450972120245, "History & Geography": 0.0003175685830550983, "Home, Pets & Hobbies": 0.00023967848258264076, "Industry & Production": 0.0003955409919773746, "Language, Literature & Philosophy": 0.000277629826914996, "Politics & Government": 0.0001452256740129533, "Religion & Spirituality": 0.00022706752652001323, "Science, Mathematics & Engineering": 0.10783938618819067, "Relationships & Community": 0.00012556344055416845, "Software Usage & Applications": 0.0005162803834299395, "Programming & Software Engineering": 0.858442067799766, "Sports & Fitness": 0.0004145829004991298, "Vehicles & Transportation": 0.00016484144312414362, "Travel & Tourism": 0.000337674460654367, "Other / Uncovered Subject": 0.02273745976649019, "Unknown / Insufficient Content": 0.00048417146229511087}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010135979329803228, "Art & Design": 0.0007185350741826698, "Law & Crime": 0.026475145616115282, "Education & Careers": 0.0005613518076799518, "Entertainment & Media": 0.0009576649145418857, "Fashion & Beauty": 0.00037398870988845033, "Business, Economics & Finance": 0.0006854139074940313, "Food & Dining": 0.00034475886899481515, "Games & Gaming": 0.001599679395753973, "Electronics & Hardware": 0.0006937751492367337, "Health, Healthcare & Wellness": 0.0005265007714642866, "History & Geography": 0.00047317716937478, "Home, Pets & Hobbies": 0.00042608065159135803, "Industry & Production": 0.0006381842445868399, "Language, Literature & Philosophy": 0.00032032123235988625, "Politics & Government": 0.0003930550630178826, "Religion & Spirituality": 0.0004805518558477776, "Science, Mathematics & Engineering": 0.3948125502338326, "Relationships & Community": 0.00021504211968929006, "Software Usage & Applications": 0.0007446269614685235, "Programming & Software Engineering": 0.22254956367899603, "Sports & Fitness": 0.0005376051861904052, "Vehicles & Transportation": 0.0002500409849602485, "Travel & Tourism": 0.0005974818097895103, "Other / Uncovered Subject": 0.014150907285598029, "NONE": 0.3294603993743645}}, "probabilities": {"primary": {"Academic Writing": 0.00013648559418066503, "Content Listing": 0.00010586392171469368, "Creative Writing": 0.00011476218859927367, "Customer Support Page": 3.196786860648785e-05, "Q&A Forum": 6.78660432489088e-05, "Discussion Thread": 4.726549038047122e-05, "Comment Section": 0.00011686663027856198, "Missing Content": 0.0009936155245959548, "Knowledge Article": 0.0002348899836907506, "Legal Notices": 0.00013418428350281893, "News Article": 3.065551367505631e-05, "Organizational Announcement": 5.104887162923349e-05, "Nonfiction Writing": 7.10576124649839e-05, "Organizational About Page": 8.029246208157851e-05, "Personal About Page": 6.035218501876134e-05, "Personal Blog": 3.913489155865706e-05, "Product Page": 2.7713178910763195e-05, "Spam / Ads": 0.008614559701201487, "Structured Data": 0.0002840394155571483, "Technical Writing": 0.9779512608680005, "Transcript / Interview": 7.830285749220983e-05, "Tutorial / How-To Guide": 0.005513086576020342, "User Reviews": 3.432905166469241e-05, "Other / Unknown": 0.005180399285925909}, "secondary": {"Academic Writing": 0.001292568738917849, "Content Listing": 0.0005214991825387652, "Creative Writing": 0.0007786434466195435, "Customer Support Page": 0.00031308636313489197, "Q&A Forum": 0.00038655438444952175, "Discussion Thread": 0.00027500391294962384, "Comment Section": 0.0005257946496216787, "Missing Content": 0.001514052470385896, "Knowledge Article": 0.0013041254047060848, "Legal Notices": 0.001001475125126622, "News Article": 0.00045455590441281904, "Organizational Announcement": 0.00047612796206762735, "Nonfiction Writing": 0.0005680518590046355, "Organizational About Page": 0.0006131555879401535, "Personal About Page": 0.0005486401179584336, "Personal Blog": 0.0003895592324651492, "Product Page": 0.0003128824977417956, "Spam / Ads": 0.23048926255276614, "Structured Data": 0.0006118439350996511, "Technical Writing": 0.013596266433155195, "Transcript / Interview": 0.0006442828125991382, "Tutorial / How-To Guide": 0.0035373666614664594, "User Reviews": 0.0002206782296356061, "NONE": 0.7396245225352367}, "tertiary": {"Academic Writing": 0.0015218463273812624, "Content Listing": 0.00039404370085955893, "Creative Writing": 0.0006614369980927053, "Customer Support Page": 0.00026700460354616855, "Q&A Forum": 0.0002867859620262558, "Discussion Thread": 0.00022414173199073337, "Comment Section": 0.0005121904388898325, "Missing Content": 0.0011734771503876301, "Knowledge Article": 0.0015195288432017842, "Legal Notices": 0.0013832208896400063, "News Article": 0.00036168656578718176, "Organizational Announcement": 0.0004122793970051544, "Nonfiction Writing": 0.0005512219468701132, "Organizational About Page": 0.0006054456406871935, "Personal About Page": 0.0005155788298976988, "Personal Blog": 0.0003594375647210996, "Product Page": 0.0002733429521750878, "Spam / Ads": 0.2797668170477181, "Structured Data": 0.0004994895809568362, "Technical Writing": 0.00722548551011147, "Transcript / Interview": 0.000491124960422733, "Tutorial / How-To Guide": 0.0022798563644071284, "User Reviews": 0.0001917246168317371, "NONE": 0.6985228323763926}, "structured_records": {"false": 0.9989499543640242, "true": 0.001050045635975737}, "procedural_steps": {"false": 0.9556887906185165, "true": 0.04431120938148352}, "worked_solution": {"false": 0.9937326727413598, "true": 0.00626732725864024}, "contextual_dialogue": {"false": 0.999991284678163, "true": 8.715321836974414e-06}, "content_quality": {"-2": 0.17106109529694663, "-1": 0.44733139265905403, "0": 0.21632461048284765, "1": 0.12156904170428032, "2": 0.043713859856871415}, "noise_severity": {"0": 0.37691549337148444, "1": 0.5438173376189545, "2": 0.05369782801502842, "3": 0.017862808483849923, "4": 0.007706532510682762}, "missing_content_severity": {"0": 0.8747506938498568, "1": 0.04164109837766784, "2": 0.04815549318421192, "3": 0.02385435550286628, "4": 0.011598359085397086}, "factual_density": {"0": 0.006776116878786742, "1": 0.013356492685024268, "2": 0.08350706738353393, "3": 0.36743005818887703, "4": 0.5289302648637781}, "pretraining_utility": {"-2": 0.054081199058585744, "-1": 0.14002214321213316, "0": 0.32622385240694085, "1": 0.38242286997751657, "2": 0.09724993534482378}, "math_density": {"0": 0.01312928925565477, "1": 0.03663546509662139, "2": 0.21269226517776232, "3": 0.4118113163619626, "4": 0.325731664107999}, "code_density": {"0": 0.0007647511030581097, "1": 0.0016918868906057511, "2": 0.05731901721439477, "3": 0.2770602491933105, "4": 0.6631640955986309}, "reasoning_density": {"0": 0.7573900004000581, "1": 0.04645431224331459, "2": 0.07056369461468054, "3": 0.057553151486462645, "4": 0.06803884125548416}, "education_level": {"none": 0.0018818932607608321, "0": 0.0013526488443339096, "1": 0.002883089803440637, "2": 0.12318310993920797, "3": 0.5188992906603254, "4": 0.35179996749193126}, "creative_writing_quality": {"not_applicable": 0.9983055072825964, "-2": 0.00036138214007982024, "-1": 0.000580699540870877, "0": 0.0005445109878784122, "1": 0.0001442470658381943, "2": 6.365298273634446e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00037212994708842127, "Art & Design": 0.000380769149942729, "Law & Crime": 0.0040024531119220805, "Education & Careers": 0.0002745497783398912, "Entertainment & Media": 0.00028751229592237126, "Fashion & Beauty": 0.0001721743063621883, "Business, Economics & Finance": 0.0003050989485529474, "Food & Dining": 0.00017678893726845454, "Games & Gaming": 0.0007098408875634404, "Electronics & Hardware": 0.0003826986097586112, "Health, Healthcare & Wellness": 0.00027124509721202455, "History & Geography": 0.00031756858305509835, "Home, Pets & Hobbies": 0.0002396784825826408, "Industry & Production": 0.00039554099197737464, "Language, Literature & Philosophy": 0.00027762982691499605, "Politics & Government": 0.00014522567401295333, "Religion & Spirituality": 0.00022706752652001326, "Science, Mathematics & Engineering": 0.10783938618819068, "Relationships & Community": 0.00012556344055416848, "Software Usage & Applications": 0.0005162803834299396, "Programming & Software Engineering": 0.8584420677997661, "Sports & Fitness": 0.00041458290049912984, "Vehicles & Transportation": 0.00016484144312414365, "Travel & Tourism": 0.0003376744606543671, "Other / Uncovered Subject": 0.022737459766490192, "Unknown / Insufficient Content": 0.0004841714622951109}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010135979329803228, "Art & Design": 0.0007185350741826698, "Law & Crime": 0.026475145616115282, "Education & Careers": 0.0005613518076799518, "Entertainment & Media": 0.0009576649145418857, "Fashion & Beauty": 0.00037398870988845033, "Business, Economics & Finance": 0.0006854139074940313, "Food & Dining": 0.00034475886899481515, "Games & Gaming": 0.001599679395753973, "Electronics & Hardware": 0.0006937751492367337, "Health, Healthcare & Wellness": 0.0005265007714642866, "History & Geography": 0.00047317716937478, "Home, Pets & Hobbies": 0.00042608065159135803, "Industry & Production": 0.0006381842445868399, "Language, Literature & Philosophy": 0.00032032123235988625, "Politics & Government": 0.0003930550630178826, "Religion & Spirituality": 0.0004805518558477776, "Science, Mathematics & Engineering": 0.3948125502338326, "Relationships & Community": 0.00021504211968929006, "Software Usage & Applications": 0.0007446269614685235, "Programming & Software Engineering": 0.22254956367899603, "Sports & Fitness": 0.0005376051861904052, "Vehicles & Transportation": 0.0002500409849602485, "Travel & Tourism": 0.0005974818097895103, "Other / Uncovered Subject": 0.014150907285598029, "NONE": 0.3294603993743645}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10413, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10413, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:12.151554+00:00", "scoring_key_slot": "primary", "dataset_uid": "ac97190a-9bc7-4ad0-94fc-f15096c73b26", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "0471ed02418fa0859b073b15af7443f29d2221c1aabfd8f8129f968fe69e196e", "source_metadata": {"uuid": "ac97190a-9bc7-4ad0-94fc-f15096c73b26", "repo_name": "Spreng624/ctf-repository", "relative_path": "2025 上半年/XYCTF 2025/chanllenge/Crypto/新勒索病毒NTRU/task.py", "category": "TOOL", "algo_rel_score": 0.9017722010612488, "quality_score": 5.241092681884766, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 11, "repo_created_at": "2025-03-01T03:06:09.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "67174feea2bd83b54b131b66a0354bf8bfcf51cb", "size_bytes": 6243, "file_timestamp": 1746454941, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2b8b7be8b11498d023c042150b8cbb80a4ef8ae8"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 11, "repo_created_at": "2025-03-01T03:06:09.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "67174feea2bd83b54b131b66a0354bf8bfcf51cb", "size_bytes": 6243, "file_timestamp": 1746454941, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2b8b7be8b11498d023c042150b8cbb80a4ef8ae8", "url": "https://github.com/Spreng624/ctf-repository/blob/2b8b7be8b11498d023c042150b8cbb80a4ef8ae8/2025%20%E4%B8%8A%E5%8D%8A%E5%B9%B4/XYCTF%202025/chanllenge/Crypto/%E6%96%B0%E5%8B%92%E7%B4%A2%E7%97%85%E6%AF%92NTRU/task.py"}, "gpt4o_tokens": 1661, "dataset_split": "python", "probability_weighted_scores": {"content_quality": -0.5804568218349242, "noise_severity": 0.7356275491432921, "missing_content_severity": 0.2559085875962789, "factual_density": 3.398381861473835, "pretraining_utility": 0.32873819933785947, "math_density": 3.00038060097003, "code_density": 3.6001670512938504, "reasoning_density": 0.6323965209540002, "education_level": 3.219205252299867, "creative_writing_quality": -0.6089791824545334}, "score_applicability": {"education_level": 0.9981181067392392, "creative_writing_quality": 0.0016944927174036481}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/TalEliyahu/XComment.io/blob/f7bb49ae13e9959d0a2c56f76f97f1f1dea76757/tests/sources/multi/Organise.py", "id": "python-0-50510e3bcd77ef2b35f96e1000f2b321", "text": "import os\nimport sys\nimport shutil\n\nMusic = ['MP3', 'WAV', 'WMA', 'MKA', 'AAC', 'MID', 'RA', 'RAM', 'RM', 'OGG']\nCodes = ['CPP', 'RB', 'PY', 'HTML', 'CSS', 'JS']\nCompressed = ['RAR', 'JAR', 'ZIP', 'TAR', 'MAR', 'ISO', 'LZ', '7ZIP', 'TGZ', 'GZ', 'BZ2']\nDocuments = ['DOC', 'DOCX', 'PPT', 'PPTX', 'PAGES', 'PDF', 'ODT', 'ODP', 'XLSX', 'XLS', 'ODS', 'TXT', 'IN', 'OUT', 'MD']\nImages = ['JPG', 'JPEG', 'GIF', 'PNG', 'SVG']\nExecutables = ['LNK','DEB', 'EXE', 'SH', 'BUNDLE']\nVideo = ['FLV', 'WMV', 'MOV', 'MP4', 'MPEG', '3GP', 'MKV','AVI']\n\n\ndef getVideo():\n\treturn Video\n\ndef getMusic():\n\treturn Music\n\ndef getCodes():\n\treturn Codes\n\ndef getCompressed():\n\treturn Compressed\n\ndef getImages():\n\treturn Images\n\ndef getExe():\n\treturn Executables\n\ndef getDoc():\n\treturn Documents\n\n# taking the location of the Folder to Arrange\ntry:\n\tarrange_dir = str(sys.argv[1])\nexcept IndexError:\n\tarrange_dir = str(raw_input(\"Enter the Path of directory: \"))\n\n# when we make a folder that already exist then WindowsError happen\n# changing directory may give WindowsError\n\ndef change(direc):\n\ttry:\n\t\tos.chdir(direc)\n\t\t#print \"path changed\"\n\texcept WindowsError:\n\t\tprint \"Error! Cannot change the Directory\"\n\t\tprint \"Enter a valid directory!\"\n\t\tdirec = str(raw_input(\"Enter the Path of directory: \"))\n\t\tchange(direc)\n\nchange(arrange_dir)\n\n# now we will get the list of all the directories in the folder\n\nlist_dir = os.listdir(os.getcwd())\n\n#print list_dir\n\n#check_Folder = False # for organising Folders\ncheck_Music = False\ncheck_Video = False\ncheck_Exe = False\ncheck_Code = False\ncheck_Compressed = False\ncheck_Img = False\ncheck_Docs = False\n\n\nmain_names = ['Video','Folders','Images','Documents','Music','Codes','Executables','Compressed']\n\nfor name in list_dir:\n\t#print name.split('.')\n\tif len(name.split('.')) == 2:\n\n\t\tif name.split('.')[1].upper() in getVideo():\n\t\t\ttry:\n\t\t\t\tos.mkdir(\"Video\")\n\t\t\t\tprint \"Video Folder Created\"\n\t\t\texcept WindowsError:\n\t\t\t\tprint \"Images Folder Exists\"\n\t\n\t\t\told_dir = arrange_dir + \"\\\\\" + name\n\t\t\tnew_dir = arrange_dir + \"\\Video\"\n\t\t\tos.chdir(new_dir)\n\t\t\tshutil.move(old_dir, new_dir + \"\\\\\" + name)\n\t\t\tprint os.getcwd()\n\t\t\tos.chdir(arrange_dir)\n\t\t\t#print \"It is a folder\"\n\t\telif name.split('.')[1].upper() in getImages():\n\t\t\ttry:\n\t\t\t\tos.mkdir(\"Images\")\n\t\t\t\tprint \"Images Folder Created\"\n\t\t\texcept WindowsError:\n\t\t\t\tprint \"Images Folder Exists\"\n\t\n\t\t\told_dir = arrange_dir + \"\\\\\" + name\n\t\t\tnew_dir = arrange_dir + \"\\Images\"\n\t\t\tos.chdir(new_dir)\n\t\t\tshutil.move(old_dir, new_dir + \"\\\\\" + name)\n\t\t\tprint os.getcwd()\n\t\t\tos.chdir(arrange_dir)\n\t\t\t#print \"It is a folder\"\n\t\telif name.split('.')[1].upper() in getMusic():\n\t\t\ttry:\n\t\t\t\tos.mkdir(\"Music\")\n\t\t\t\tprint \"Music Folder Created\"\n\t\t\texcept WindowsError:\n\t\t\t\tprint \"Music Folder Exists\"\n\t\n\t\t\told_dir = arrange_dir + \"\\\\\" + name\n\t\t\tnew_dir = arrange_dir + \"\\Music\"\n\t\t\tos.chdir(new_dir)\n\t\t\tshutil.move(old_dir, new_dir + \"\\\\\" + name)\n\t\t\tprint os.getcwd()\n\t\t\tos.chdir(arrange_dir)\n\t\t\t#print \"It is a folder\"\n\t\telif name.split('.')[1].upper() in getDoc():\n\t\t\ttry:\n\t\t\t\tos.mkdir(\"Documents\")\n\t\t\t\tprint \"Documents Folder Created\"\n\t\t\texcept WindowsError:\n\t\t\t\tprint \"Documents Folder Exists\"\n\t\n\t\t\told_dir = arrange_dir + \"\\\\\" + name\n\t\t\tnew_dir = arrange_dir + \"\\Documents\"\n\t\t\tos.chdir(new_dir)\n\t\t\tshutil.move(old_dir, new_dir + \"\\\\\" + name)\n\t\t\tprint os.getcwd()\n\t\t\tos.chdir(arrange_dir)\n\t\t\t#print \"It is a folder\"\n\t\telif name.split('.')[1].upper() in getCodes():\n\t\t\ttry:\n\t\t\t\tos.mkdir(\"Codes\")\n\t\t\t\tprint \"Codes Folder Created\"\n\t\t\texcept WindowsError:\n\t\t\t\tprint \"Codes Folder Exists\"\n\t\n\t\t\told_dir = arrange_dir + \"\\\\\" + name\n\t\t\tnew_dir = arrange_dir + \"\\Codes\"\n\t\t\tos.chdir(new_dir)\n\t\t\tshutil.move(old_dir, new_dir + \"\\\\\" + name)\n\t\t\tprint os.getcwd()\n\t\t\tos.chdir(arrange_dir)\n\t\t\t#print \"It is a folder\"\n\t\telif name.split('.')[1].upper() in getCompressed():\n\t\t\ttry:\n\t\t\t\tos.mkdir(\"Compressed\")\n\t\t\t\tprint \"Compressed Folder Created\"\n\t\t\texcept WindowsError:\n\t\t\t\tprint \"Compressed Folder Exists\"\n\t\n\t\t\told_dir = arrange_dir + \"\\\\\" + name\n\t\t\tnew_dir = arrange_dir + \"\\Compressed\"\n\t\t\tos.chdir(new_dir)\n\t\t\tshutil.move(old_dir, new_dir + \"\\\\\" + name)\n\t\t\tprint os.getcwd()\n\t\t\tos.chdir(arrange_dir)\n\t\t\t#print \"It is a folder\"\n\t\telif name.split('.')[1].upper() in getExe():\n\t\t\ttry:\n\t\t\t\tos.mkdir(\"Executables\")\n\t\t\t\tprint \"Executables Folder Created\"\n\t\t\texcept WindowsError:\n\t\t\t\tprint \"Executables Folder Exists\"\n\t\n\t\t\told_dir = arrange_dir + \"\\\\\" + name\n\t\t\tnew_dir = arrange_dir + \"\\Executables\"\n\t\t\tos.chdir(new_dir)\n\t\t\tshutil.move(old_dir, new_dir + \"\\\\\" + name)\n\t\t\tprint os.getcwd()\n\t\t\tos.chdir(arrange_dir)\n\t\t\t#print \"It is a folder\"\n\telse:\n\t\tif name not in main_names:\n\t\t\ttry:\n\t\t\t\tos.mkdir(\"Folders\")\n\t\t\t\tprint \"Folders Folder Created\"\n\t\t\texcept WindowsError:\n\t\t\t\tprint \"Folders Folder Exists\"\n\t\n\t\t\told_dir = arrange_dir + \"\\\\\" + name\n\t\t\tnew_dir = arrange_dir + \"\\Folders\"\n\t\t\tos.chdir(new_dir)\n\t\t\tshutil.move(old_dir, new_dir + \"\\\\\" + name)\n\t\t\tprint os.getcwd()\n\t\t\tos.chdir(arrange_dir)\n\n\n\nprint \"Done Arranging Files and Folder in your specified directory\"", "text_preview": "import os import sys import shutil Music = ['MP3', 'WAV', 'WMA', 'MKA', 'AAC', 'MID', 'RA', 'RAM', 'RM', 'OGG'] Codes = ['CPP', 'RB', 'PY', 'HTML', 'CSS', 'JS'] Compressed = ['RAR', 'JAR', 'ZIP', 'TAR', 'MAR', 'ISO', 'LZ', '7ZIP', 'TGZ', 'G", "input_sha256": "c6229eb6b2db3a58191867196cb8a0258d6fa37e574f4416ecc35fdba11355e5", "text_sha256": "50510e3bcd77ef2b35f96e1000f2b3212e9945aa0be5bf07638c238aa2222ab9", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12053, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.07278493712792236, "noise_severity": 0.10163030999942024, "missing_content_severity": 0.006859401104956289, "factual_density": 2.6687520188079477, "pretraining_utility": 0.23725299225227997, "math_density": 0.012875924348219811, "code_density": 3.810374387274631, "reasoning_density": 0.6895494070920487, "education_level": 1.7534812036294378, "creative_writing_quality": -0.5897537551816644}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.03344824336788599, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9792348533623779, "secondary": 0.9137343791277552, "tertiary": 0.9486368939464189, "structured_records": 0.04536909942703634, "procedural_steps": 0.02643053706990681, "worked_solution": 0.006849727416575615, "contextual_dialogue": 1.6410318281000704e-05, "content_quality": 0.44560577684991054, "noise_severity": 0.9153080750004832, "missing_content_severity": 0.9942838324125364, "factual_density": 0.387127056645695, "pretraining_utility": 0.514886038133331, "math_density": 0.9892700630431501, "code_density": 0.8419786560621924, "reasoning_density": 0.42537549408995945, "education_level": 0.6724150714338695, "creative_writing_quality": 0.9982560681134338, "primary_topic": 0.9853508131037573, "secondary_topic": 0.9272312942575452}, "probability_margin": {"primary": 0.9704809427568424, "secondary": 0.8744334154429794, "tertiary": 0.9223217599050733, "education_level": 0.4795341740169877, "creative_writing_quality": 0.9979737597778899, "primary_topic": 0.9763023258841588, "secondary_topic": 0.8842793365549271}, "raw_probabilities": {"primary": {"Academic Writing": 8.602310035668713e-05, "Content Listing": 6.549883684118313e-05, "Creative Writing": 6.135271431520412e-05, "Customer Support Page": 3.265380005073247e-05, "Q&A Forum": 4.269254457208564e-05, "Discussion Thread": 3.212393626694491e-05, "Comment Section": 6.859167335921002e-05, "Missing Content": 7.68901988745912e-05, "Knowledge Article": 0.0002625215778599504, "Legal Notices": 6.175240868026842e-05, "News Article": 1.8571723746342248e-05, "Organizational Announcement": 3.2614072635078165e-05, "Nonfiction Writing": 4.888244828043901e-05, "Organizational About Page": 4.451064983443192e-05, "Personal About Page": 4.456748239819241e-05, "Personal Blog": 2.7389597749015685e-05, "Product Page": 2.517503340456797e-05, "Spam / Ads": 3.9071177446869826e-05, "Structured Data": 0.00501373945792052, "Technical Writing": 0.9801000678056122, "Transcript / Interview": 5.525768979414645e-05, "Tutorial / How-To Guide": 0.009619125048769854, "User Reviews": 2.1682354747330205e-05, "Other / Unknown": 0.004119244666484311}, "secondary": {"Academic Writing": 0.0003706820165399477, "Content Listing": 0.0003510827242213513, "Creative Writing": 0.00027830225393454296, "Customer Support Page": 0.00018347098195514597, "Q&A Forum": 0.00013944850382897604, "Discussion Thread": 0.00010469450514775346, "Comment Section": 0.00021042766424620075, "Missing Content": 0.0003154636498104885, "Knowledge Article": 0.000885920884229016, "Legal Notices": 0.00021423752695636354, "News Article": 0.00016736748982088708, "Organizational Announcement": 0.00015116852976155477, "Nonfiction Writing": 0.00017204719969499563, "Organizational About Page": 0.0001803472541159734, "Personal About Page": 0.00016155958707533483, "Personal Blog": 0.0001621603621244615, "Product Page": 0.0001078258125524842, "Spam / Ads": 0.0001817437952012223, "Structured Data": 0.042895364554452746, "Technical Writing": 0.019689600358174165, "Transcript / Interview": 0.00022144262624494707, "Tutorial / How-To Guide": 0.0154472505096809, "User Reviews": 7.96112127984247e-05, "NONE": 0.9173287799974321}, "tertiary": {"Academic Writing": 0.00018528203513489686, "Content Listing": 0.00022870911234419822, "Creative Writing": 0.0001310881403394615, "Customer Support Page": 9.389305224532833e-05, "Q&A Forum": 5.7953892245716424e-05, "Discussion Thread": 5.041786010129099e-05, "Comment Section": 0.00013193238844784333, "Missing Content": 0.00024072269122201636, "Knowledge Article": 0.0008690568507124765, "Legal Notices": 0.00012773784151054018, "News Article": 8.137860475202938e-05, "Organizational Announcement": 6.692518189354204e-05, "Nonfiction Writing": 0.00010488676277749796, "Organizational About Page": 0.00011142475447643402, "Personal About Page": 8.637192517617488e-05, "Personal Blog": 8.182229583508237e-05, "Product Page": 6.776982780079215e-05, "Spam / Ads": 0.0001040945486378642, "Structured Data": 0.0284552634602448, "Technical Writing": 0.009513647062080318, "Transcript / Interview": 0.0001028273705853667, "Tutorial / How-To Guide": 0.008286657603482541, "User Reviews": 4.3113372635720016e-05, "NONE": 0.9507770233653181}, "structured_records": {"false": 0.9546309005729636, "true": 0.04536909942703634}, "procedural_steps": {"false": 0.9735694629300932, "true": 0.02643053706990681}, "worked_solution": {"false": 0.9931502725834244, "true": 0.006849727416575615}, "contextual_dialogue": {"false": 0.999983589681719, "true": 1.6410318281000704e-05}, "content_quality": {"-2": 0.034800490985718094, "-1": 0.22664308335465633, "0": 0.4200077188819989, "1": 0.26806841110123836, "2": 0.050480295676388245}, "noise_severity": {"0": 0.916839227661228, "1": 0.0680615197687805, "2": 0.012622106061287138, "3": 0.0015840079267518696, "4": 0.0008931385819524636}, "missing_content_severity": {"0": 0.9953235943895813, "1": 0.0031077679671180996, "2": 0.0010840965492961688, "3": 0.00035472433677129564, "4": 0.00012981675723299093}, "factual_density": {"0": 0.024392920301908788, "1": 0.0905057989613837, "2": 0.2791573977801152, "3": 0.4038441075400353, "4": 0.20209977541655696}, "pretraining_utility": {"-2": 0.013979485051136133, "-1": 0.14448291089158913, "0": 0.4748901763398434, "1": 0.32359998218872144, "2": 0.04304744552870997}, "math_density": {"0": 0.9909100131367307, "1": 0.007324707034294191, "2": 0.0005412439686689882, "3": 0.00042741406463786606, "4": 0.0007966217956685118}, "code_density": {"0": 0.0003251084130152883, "1": 0.0006764729297879778, "2": 0.01554703392360942, "3": 0.1552016924367252, "4": 0.828249692296862}, "reasoning_density": {"0": 0.6656389400073354, "1": 0.09815600028978864, "2": 0.15143833397353842, "3": 0.05055016406216679, "4": 0.034216561667170686}, "education_level": {"none": 0.002125363318965227, "0": 0.007990795025752718, "1": 0.2474783855112368, "2": 0.7270125595282245, "3": 0.013320672032220034, "4": 0.0020722245836006918}, "creative_writing_quality": {"not_applicable": 0.9985467234278615, "-2": 0.0002897514080294115, "-1": 0.00045601469871821743, "0": 0.0005729636499716475, "1": 9.065143179795909e-05, "2": 4.389538362139383e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 7.907210720712617e-05, "Art & Design": 0.00011689293300488528, "Law & Crime": 7.071368699947688e-05, "Education & Careers": 9.384387183462853e-05, "Entertainment & Media": 0.0001570266237555613, "Fashion & Beauty": 5.87060598294264e-05, "Business, Economics & Finance": 0.00012486338892724177, "Food & Dining": 7.137090294517902e-05, "Games & Gaming": 0.00013320247111230316, "Electronics & Hardware": 0.0009086231825695946, "Health, Healthcare & Wellness": 5.4772222460726716e-05, "History & Geography": 6.729363968836044e-05, "Home, Pets & Hobbies": 0.00024919565616337165, "Industry & Production": 6.0618131125929194e-05, "Language, Literature & Philosophy": 0.00011613905345936344, "Politics & Government": 4.016365401426815e-05, "Religion & Spirituality": 5.906039948425219e-05, "Science, Mathematics & Engineering": 0.0003278953097370506, "Relationships & Community": 5.0917665422812704e-05, "Software Usage & Applications": 0.009611917484838632, "Programming & Software Engineering": 0.9859142433689974, "Sports & Fitness": 9.977503075835437e-05, "Vehicles & Transportation": 3.903618671543228e-05, "Travel & Tourism": 8.42337730013901e-05, "Other / Uncovered Subject": 0.001158868566351678, "Unknown / Insufficient Content": 0.0002515546295956836}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005178917253316963, "Art & Design": 0.0004356166103294214, "Law & Crime": 0.0003717436971121857, "Education & Careers": 0.0003368420939389406, "Entertainment & Media": 0.001989436377183784, "Fashion & Beauty": 0.00018627368995871883, "Business, Economics & Finance": 0.00046969530476847327, "Food & Dining": 0.0001651289971599292, "Games & Gaming": 0.00034523579142687884, "Electronics & Hardware": 0.001521278826891106, "Health, Healthcare & Wellness": 0.00019399504203277022, "History & Geography": 0.00018972375161325843, "Home, Pets & Hobbies": 0.00042063001164384205, "Industry & Production": 0.00014475686482863084, "Language, Literature & Philosophy": 0.00022015435571201008, "Politics & Government": 0.00015814527597918105, "Religion & Spirituality": 0.00022606174308228958, "Science, Mathematics & Engineering": 0.0006061654543229463, "Relationships & Community": 0.0001524540777428615, "Software Usage & Applications": 0.04575075407732794, "Programming & Software Engineering": 0.01390342004794187, "Sports & Fitness": 0.00018980736184827366, "Vehicles & Transportation": 8.754342125133954e-05, "Travel & Tourism": 0.00025182947529707326, "Other / Uncovered Subject": 0.0011353252930195257, "NONE": 0.9300300906322551}}, "probabilities": {"primary": {"Academic Writing": 8.602310035668711e-05, "Content Listing": 6.549883684118312e-05, "Creative Writing": 6.13527143152041e-05, "Customer Support Page": 3.2653800050732465e-05, "Q&A Forum": 4.269254457208563e-05, "Discussion Thread": 3.2123936266944904e-05, "Comment Section": 6.859167335921e-05, "Missing Content": 7.689019887459118e-05, "Knowledge Article": 0.00026252157785995036, "Legal Notices": 6.17524086802684e-05, "News Article": 1.8571723746342244e-05, "Organizational Announcement": 3.261407263507816e-05, "Nonfiction Writing": 4.8882448280439e-05, "Organizational About Page": 4.451064983443191e-05, "Personal About Page": 4.4567482398192405e-05, "Personal Blog": 2.7389597749015678e-05, "Product Page": 2.5175033404567963e-05, "Spam / Ads": 3.907117744686982e-05, "Structured Data": 0.005013739457920519, "Technical Writing": 0.980100067805612, "Transcript / Interview": 5.5257689794146434e-05, "Tutorial / How-To Guide": 0.009619125048769852, "User Reviews": 2.1682354747330202e-05, "Other / Unknown": 0.00411924466648431}, "secondary": {"Academic Writing": 0.0003706820165399477, "Content Listing": 0.0003510827242213513, "Creative Writing": 0.00027830225393454296, "Customer Support Page": 0.00018347098195514597, "Q&A Forum": 0.00013944850382897604, "Discussion Thread": 0.00010469450514775346, "Comment Section": 0.00021042766424620075, "Missing Content": 0.0003154636498104885, "Knowledge Article": 0.000885920884229016, "Legal Notices": 0.00021423752695636354, "News Article": 0.00016736748982088708, "Organizational Announcement": 0.00015116852976155477, "Nonfiction Writing": 0.00017204719969499563, "Organizational About Page": 0.0001803472541159734, "Personal About Page": 0.00016155958707533483, "Personal Blog": 0.0001621603621244615, "Product Page": 0.0001078258125524842, "Spam / Ads": 0.0001817437952012223, "Structured Data": 0.042895364554452746, "Technical Writing": 0.019689600358174165, "Transcript / Interview": 0.00022144262624494707, "Tutorial / How-To Guide": 0.0154472505096809, "User Reviews": 7.96112127984247e-05, "NONE": 0.9173287799974321}, "tertiary": {"Academic Writing": 0.00018528203513489686, "Content Listing": 0.00022870911234419822, "Creative Writing": 0.0001310881403394615, "Customer Support Page": 9.389305224532833e-05, "Q&A Forum": 5.7953892245716424e-05, "Discussion Thread": 5.041786010129099e-05, "Comment Section": 0.00013193238844784333, "Missing Content": 0.00024072269122201636, "Knowledge Article": 0.0008690568507124765, "Legal Notices": 0.00012773784151054018, "News Article": 8.137860475202938e-05, "Organizational Announcement": 6.692518189354204e-05, "Nonfiction Writing": 0.00010488676277749796, "Organizational About Page": 0.00011142475447643402, "Personal About Page": 8.637192517617488e-05, "Personal Blog": 8.182229583508237e-05, "Product Page": 6.776982780079215e-05, "Spam / Ads": 0.0001040945486378642, "Structured Data": 0.0284552634602448, "Technical Writing": 0.009513647062080318, "Transcript / Interview": 0.0001028273705853667, "Tutorial / How-To Guide": 0.008286657603482541, "User Reviews": 4.3113372635720016e-05, "NONE": 0.9507770233653181}, "structured_records": {"false": 0.9546309005729636, "true": 0.04536909942703634}, "procedural_steps": {"false": 0.9735694629300932, "true": 0.02643053706990681}, "worked_solution": {"false": 0.9931502725834244, "true": 0.006849727416575615}, "contextual_dialogue": {"false": 0.999983589681719, "true": 1.6410318281000704e-05}, "content_quality": {"-2": 0.0348004909857181, "-1": 0.22664308335465636, "0": 0.42000771888199895, "1": 0.2680684111012384, "2": 0.05048029567638825}, "noise_severity": {"0": 0.916839227661228, "1": 0.0680615197687805, "2": 0.012622106061287138, "3": 0.0015840079267518696, "4": 0.0008931385819524636}, "missing_content_severity": {"0": 0.9953235943895814, "1": 0.0031077679671181, "2": 0.001084096549296169, "3": 0.0003547243367712957, "4": 0.00012981675723299096}, "factual_density": {"0": 0.024392920301908788, "1": 0.0905057989613837, "2": 0.2791573977801152, "3": 0.4038441075400353, "4": 0.20209977541655696}, "pretraining_utility": {"-2": 0.013979485051136133, "-1": 0.14448291089158913, "0": 0.4748901763398434, "1": 0.32359998218872144, "2": 0.04304744552870997}, "math_density": {"0": 0.9909100131367304, "1": 0.00732470703429419, "2": 0.0005412439686689881, "3": 0.00042741406463786595, "4": 0.0007966217956685116}, "code_density": {"0": 0.00032510841301528837, "1": 0.000676472929787978, "2": 0.015547033923609422, "3": 0.15520169243672524, "4": 0.8282496922968621}, "reasoning_density": {"0": 0.6656389400073355, "1": 0.09815600028978866, "2": 0.15143833397353845, "3": 0.050550164062166794, "4": 0.03421656166717069}, "education_level": {"none": 0.002125363318965227, "0": 0.007990795025752718, "1": 0.2474783855112368, "2": 0.7270125595282245, "3": 0.013320672032220034, "4": 0.0020722245836006918}, "creative_writing_quality": {"not_applicable": 0.9985467234278613, "-2": 0.00028975140802941146, "-1": 0.0004560146987182173, "0": 0.0005729636499716474, "1": 9.065143179795908e-05, "2": 4.3895383621393826e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 7.907210720712617e-05, "Art & Design": 0.00011689293300488528, "Law & Crime": 7.071368699947688e-05, "Education & Careers": 9.384387183462853e-05, "Entertainment & Media": 0.0001570266237555613, "Fashion & Beauty": 5.87060598294264e-05, "Business, Economics & Finance": 0.00012486338892724177, "Food & Dining": 7.137090294517902e-05, "Games & Gaming": 0.00013320247111230316, "Electronics & Hardware": 0.0009086231825695946, "Health, Healthcare & Wellness": 5.4772222460726716e-05, "History & Geography": 6.729363968836044e-05, "Home, Pets & Hobbies": 0.00024919565616337165, "Industry & Production": 6.0618131125929194e-05, "Language, Literature & Philosophy": 0.00011613905345936344, "Politics & Government": 4.016365401426815e-05, "Religion & Spirituality": 5.906039948425219e-05, "Science, Mathematics & Engineering": 0.0003278953097370506, "Relationships & Community": 5.0917665422812704e-05, "Software Usage & Applications": 0.009611917484838632, "Programming & Software Engineering": 0.9859142433689974, "Sports & Fitness": 9.977503075835437e-05, "Vehicles & Transportation": 3.903618671543228e-05, "Travel & Tourism": 8.42337730013901e-05, "Other / Uncovered Subject": 0.001158868566351678, "Unknown / Insufficient Content": 0.0002515546295956836}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005178917253316963, "Art & Design": 0.0004356166103294214, "Law & Crime": 0.0003717436971121857, "Education & Careers": 0.0003368420939389406, "Entertainment & Media": 0.001989436377183784, "Fashion & Beauty": 0.00018627368995871883, "Business, Economics & Finance": 0.00046969530476847327, "Food & Dining": 0.0001651289971599292, "Games & Gaming": 0.00034523579142687884, "Electronics & Hardware": 0.001521278826891106, "Health, Healthcare & Wellness": 0.00019399504203277022, "History & Geography": 0.00018972375161325843, "Home, Pets & Hobbies": 0.00042063001164384205, "Industry & Production": 0.00014475686482863084, "Language, Literature & Philosophy": 0.00022015435571201008, "Politics & Government": 0.00015814527597918105, "Religion & Spirituality": 0.00022606174308228958, "Science, Mathematics & Engineering": 0.0006061654543229463, "Relationships & Community": 0.0001524540777428615, "Software Usage & Applications": 0.04575075407732794, "Programming & Software Engineering": 0.01390342004794187, "Sports & Fitness": 0.00018980736184827366, "Vehicles & Transportation": 8.754342125133954e-05, "Travel & Tourism": 0.00025182947529707326, "Other / Uncovered Subject": 0.0011353252930195257, "NONE": 0.9300300906322551}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10031, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10031, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:13.957878+00:00", "scoring_key_slot": "primary", "dataset_uid": "56a1438b-9394-462c-b4a9-2869fccbd6ed", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "647af1fbcea3ef0b49249084c63f3c4e6c08e31eb36de456ff19948d637fc953", "source_metadata": {"uuid": "56a1438b-9394-462c-b4a9-2869fccbd6ed", "repo_name": "TalEliyahu/XComment.io", "relative_path": "tests/sources/multi/Organise.py", "category": "TOOL", "algo_rel_score": 0.9378873705863953, "quality_score": 3.0723209381103516, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 94, "repo_created_at": "2017-09-04T23:55:08.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 44, "forks": 21, "issues": 8, "pull_requests": 2}, "file_meta": {"content_id": "b706b207e74887ee9280b039c41905e894d8a40e", "size_bytes": 5181, "file_timestamp": 1511285196, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "f7bb49ae13e9959d0a2c56f76f97f1f1dea76757"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 94, "repo_created_at": "2017-09-04T23:55:08.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 44, "forks": 21, "issues": 8, "pull_requests": 2}, "file_meta": {"content_id": "b706b207e74887ee9280b039c41905e894d8a40e", "size_bytes": 5181, "file_timestamp": 1511285196, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "f7bb49ae13e9959d0a2c56f76f97f1f1dea76757", "url": "https://github.com/TalEliyahu/XComment.io/blob/f7bb49ae13e9959d0a2c56f76f97f1f1dea76757/tests/sources/multi/Organise.py"}, "gpt4o_tokens": 1381, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.07278493712792236, "noise_severity": 0.10163030999942024, "missing_content_severity": 0.006859401104956289, "factual_density": 2.6687520188079477, "pretraining_utility": 0.23725299225227997, "math_density": 0.012875924348219811, "code_density": 3.810374387274631, "reasoning_density": 0.6895494070920487, "education_level": 1.7534812036294378, "creative_writing_quality": -0.5897537551816644}, "score_applicability": {"education_level": 0.9978746366810347, "creative_writing_quality": 0.001453276572138629}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/aristidedovi/removegb-kgtech/blob/b1ee1e219651634511cc888fa6391fb5ed279ed2/backend_flask_app/flaskr/api1/routes-old.py", "id": "python-0-a451ecbef3b0e52b475cdbb5aebbc304", "text": "# routes.py\n# for rendering api routes\nfrom . import api1\nfrom flask import abort, request, jsonify, current_app\nimport os\nfrom rembg import remove\nimport base64\nfrom datetime import datetime\nfrom flask_jwt_extended import create_access_token, jwt_required, get_jwt_identity\nfrom werkzeug.security import generate_password_hash, check_password_hash\nimport csv\n\nimport random\n\n# Define folder to save uploaded files to process further\nUPLOAD_FOLDER = os.path.join('staticFiles', 'uploads')\n\n# Ensure the directories exist\n#os.makedirs(UPLOAD_FOLDER, exist_ok=True)\n#os.makedirs(OUTPUT_FOLDER, exist_ok=True)\n \n# Define allowed files (for this example I want only csv file)\nALLOWED_EXTENSIONS = {'csv'}\n\n\n@api1.after_request\ndef after_request(response):\n    '''defining extra headers'''\n    response.headers.add('Access-Control-Allow-Headers',\n                         'Content-Type,Authorization,true')\n    response.headers.add('Access-Control-Allow-Methods',\n                         'GET,PATCH,POST,DELETE,OPTIONS')\n    response.headers.add('Content-Type', 'application/json')\n    return response\n\n\n# In-memory user data (replace with database in real use)\nusers = {}\n\n\n\n# Route for use registration\n@api1.route('/register', methods=['POST'])\ndef register():\n    username = request.json.get('username')\n    password = request.json.get('<PASSWORD>')\n\n    if not username or not password:\n        abort(400)\n    \n    if username in users:\n        abort(403)\n\n    # Save the user with hashed password\n    users[username] = generate_password_hash(password)\n    \n    return jsonify({\n        'message': 'User registred successfully'\n    })\n\n# Route for user login\n@api1.route('/login', methods=['POST'])\ndef login():\n    username = request.json.get('username')\n    password = request.json.get('password')\n\n    if not username or not password:\n        abort(400)\n\n    # Check if the user exists and the password matches\n    user_password_hash = users.get(username)\n    if not user_password_hash or not check_password_hash(user_password_hash, password):\n        abort(401)\n    \n    # Create JWT token\n    access_token = create_access_token(identity=username)\n    return jsonify(access_token=access_token), 200\n\n# Protected route (require validat JWT)\n@api1.route('/protected', methods=['GET'])\n@jwt_required()\ndef protected():\n    # Get the current user identity for the JWT\n    current_user = get_jwt_identity()\n    return jsonify(logged_in_as=current_user), 200\n\n\n# Uploade image route\n@api1.route('/upload_data', methods=['POST'])\ndef post_csv_file():\n        uploaded_file = request.files.get('image')\n\n        if uploaded_file and uploaded_file.filename != '':\n              # Generate unique filenames based on current timestamp\n             timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')\n             original_filename = f'{timestamp}_original.png'\n             modified_filename = f'{timestamp}_modified.png'\n\n             # Save the original image to the 'uploads/' directory\n             original_filepath = os.path.join(current_app.config['UPLOAD_FOLDER'], original_filename)\n             #original_filepath = os.path.join(UPLOAD_FOLDER, original_filename)\n             uploaded_file.save(original_filepath)\n\n             # Read the upload image and process (e.g., remove bacground)\n             input_image = open(original_filepath, 'rb').read()\n             output_image = remove(input_image)\n\n              # Save the modified image to the 'outputs/' directory\n             #modified_filepath = os.path.join(OUTPUT_FOLDER, modified_filename)\n             modified_filepath = os.path.join(current_app.config['OUTPUT_FOLDER'], modified_filename)\n             with open(modified_filepath, 'wb') as f:\n                f.write(output_image)\n\n             # Convert images to base64\n             encoded_original = base64.b64encode(input_image).decode(('utf-8'))\n             encoded_modified = base64.b64encode(output_image).decode(('utf-8'))\n\n            # Return the base64 images in JSON repsonse\n             return jsonify({\n                'original_image_path': original_filepath,\n                'modified_image_path': modified_filepath,\n                'original_image': f'data:image/png;base64,{encoded_original}',\n                'modified_oimage': f'data:image/png;base64,{encoded_modified}'\n            })\n        else:\n            abort(404)\n\n\n# error handlers\n\n\n@api1.errorhandler(400)\ndef bad_request(error):\n    return jsonify({\n        'success': False,\n        'error': 400,\n        'message': 'bad request'\n    }), 400\n\n@api1.errorhandler(401)\ndef unauthorized(error):\n    return jsonify({\n        'success': False,\n        'error': 401,\n        'message': 'Unauthorized'\n    }), 401\n\n@api1.errorhandler(403)\ndef unauthorized(error):\n    return jsonify({\n        'success': False,\n        'error': 403,\n        'message': 'Already exists'\n    }), 403\n\n\n@api1.errorhandler(404)\ndef not_found(error):\n    return jsonify({\n        'success': False,\n        'error': 404,\n        'message': 'resource not found'\n    }), 404\n\n\n@api1.errorhandler(405)\ndef not_allowed(error):\n    return jsonify({\n        'success': False,\n        'error': 405,\n        'message': 'method not allowed'\n    }), 405\n\n\n@api1.errorhandler(422)\ndef unprocessable(error):\n    return jsonify({\n        'success': False,\n        'error': 422,\n        'message': 'unprocessable'\n    }), 422\n\n\n@api1.errorhandler(500)\ndef server_error(error):\n    return jsonify({\n        'success': False,\n        'error': 500,\n        'message': 'internal error'\n    }), 500", "text_preview": "# routes.py # for rendering api routes from . import api1 from flask import abort, request, jsonify, current_app import os from rembg import remove import base64 from datetime import datetime from flask_jwt_extended import create_access_tok", "input_sha256": "7371fce84daf7dec464871b209c27d9226ebb2b4d95424022fb6ac8a99ceb253", "text_sha256": "a451ecbef3b0e52b475cdbb5aebbc30425f40ae61110c35d82501682fc6baade", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12055, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": -0.21769458424957205, "noise_severity": 0.21511666073335395, "missing_content_severity": 0.07861417788671644, "factual_density": 3.2438925651196397, "pretraining_utility": 0.4419976132946785, "math_density": 0.06237352910688257, "code_density": 3.790539855991944, "reasoning_density": 0.23496519054938006, "education_level": 2.175128736284132, "creative_writing_quality": -0.48483741080404835}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.005919183652500104, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9947549484631986, "secondary": 0.9445529999385395, "tertiary": 0.950729539402018, "structured_records": 0.06920121069606208, "procedural_steps": 0.029713537012171002, "worked_solution": 0.003991625997168121, "contextual_dialogue": 6.530941438483247e-06, "content_quality": 0.4618193398437397, "noise_severity": 0.8207361160555384, "missing_content_severity": 0.934488185094403, "factual_density": 0.36991047093303286, "pretraining_utility": 0.4513713738769809, "math_density": 0.9480220590775978, "code_density": 0.8254498799932866, "reasoning_density": 0.8041956745421832, "education_level": 0.7510138109829276, "creative_writing_quality": 0.9990905850918326, "primary_topic": 0.9972651961381453, "secondary_topic": 0.9392616942439088}, "probability_margin": {"primary": 0.9933119153019534, "secondary": 0.9180473134620835, "tertiary": 0.9278012733892741, "education_level": 0.6161246244062317, "creative_writing_quality": 0.9989409400273496, "primary_topic": 0.9963153901091348, "secondary_topic": 0.9220613768876962}, "raw_probabilities": {"primary": {"Academic Writing": 4.728070606658236e-05, "Content Listing": 3.116303654535783e-05, "Creative Writing": 2.049336934770079e-05, "Customer Support Page": 1.0641669772771612e-05, "Q&A Forum": 1.5729295865634995e-05, "Discussion Thread": 1.0716979467680451e-05, "Comment Section": 2.311859421013334e-05, "Missing Content": 4.436266746501024e-05, "Knowledge Article": 0.00011117167441732973, "Legal Notices": 2.377066132948162e-05, "News Article": 6.249312447175681e-06, "Organizational Announcement": 1.2485951854276796e-05, "Nonfiction Writing": 1.4208750751999747e-05, "Organizational About Page": 1.565997367747972e-05, "Personal About Page": 1.536661964981876e-05, "Personal Blog": 1.2872897380571544e-05, "Product Page": 9.119432479697813e-06, "Spam / Ads": 1.253271076061414e-05, "Structured Data": 0.0016615769752785358, "Technical Writing": 0.994973492277232, "Transcript / Interview": 2.2057749898042486e-05, "Tutorial / How-To Guide": 0.0015663917887449538, "User Reviews": 8.51084344198851e-06, "Other / Unknown": 0.0013310260619155444}, "secondary": {"Academic Writing": 0.00027475033552924196, "Content Listing": 0.00023664210346450526, "Creative Writing": 0.00022575724047367639, "Customer Support Page": 0.00015181231144985435, "Q&A Forum": 0.00010779528547369703, "Discussion Thread": 9.831533659854611e-05, "Comment Section": 0.00031434649939917687, "Missing Content": 0.000681642674842533, "Knowledge Article": 0.0010044215075857947, "Legal Notices": 0.0002970767953325494, "News Article": 0.00014258536578200606, "Organizational Announcement": 0.00013041576079580926, "Nonfiction Writing": 0.00013864565729342535, "Organizational About Page": 0.0001550077505452751, "Personal About Page": 0.00013021144987958538, "Personal Blog": 0.00017155094243315983, "Product Page": 8.93581865850268e-05, "Spam / Ads": 0.00013877867108584964, "Structured Data": 0.028815978145683447, "Technical Writing": 0.014264685111399758, "Transcript / Interview": 0.00018330554934235436, "Tutorial / How-To Guide": 0.005316362377443208, "User Reviews": 6.726333381459388e-05, "NONE": 0.9468632916077669}, "tertiary": {"Academic Writing": 0.00019793027811115205, "Content Listing": 0.00018074218746839003, "Creative Writing": 0.0001872481672389112, "Customer Support Page": 0.0001493109615308826, "Q&A Forum": 8.899590215309775e-05, "Discussion Thread": 7.854385118556921e-05, "Comment Section": 0.00041264882189475267, "Missing Content": 0.0011075877768551498, "Knowledge Article": 0.0015578001703897128, "Legal Notices": 0.0004043850756160387, "News Article": 0.00011022752972067448, "Organizational Announcement": 0.00010779139167874538, "Nonfiction Writing": 0.000163384314560167, "Organizational About Page": 0.00014070652513098006, "Personal About Page": 0.00010513230870994004, "Personal Blog": 0.00012437805014695508, "Product Page": 7.730630706118272e-05, "Spam / Ads": 0.00013502332861064253, "Structured Data": 0.024981201870993154, "Technical Writing": 0.009361145618602917, "Transcript / Interview": 0.00014528978811037, "Tutorial / How-To Guide": 0.007342169747237509, "User Reviews": 5.857476672595312e-05, "NONE": 0.9527824752602673}, "structured_records": {"false": 0.9307987893039379, "true": 0.06920121069606208}, "procedural_steps": {"false": 0.970286462987829, "true": 0.029713537012171002}, "worked_solution": {"false": 0.9960083740028319, "true": 0.003991625997168121}, "contextual_dialogue": {"false": 0.9999934690585616, "true": 6.530941438483247e-06}, "content_quality": {"-2": 0.03443084800740469, "-1": 0.3628939922037328, "0": 0.4175968168382801, "1": 0.15609558193219464, "2": 0.028982761018387742}, "noise_severity": {"0": 0.825595990021887, "1": 0.14085796967716727, "2": 0.02831538575816368, "3": 0.0032946986312688145, "4": 0.0019359559115132187}, "missing_content_severity": {"0": 0.9375475164412672, "1": 0.04974794073083327, "2": 0.009922137118269406, "3": 0.0021076599191763305, "4": 0.0006747457904538401}, "factual_density": {"0": 0.006834168759743993, "1": 0.017110060253915444, "2": 0.13115486218701133, "3": 0.41513085470561545, "4": 0.4297700540937137}, "pretraining_utility": {"-2": 0.008881021973529269, "-1": 0.0866584638128059, "0": 0.4082183754802725, "1": 0.4460661564122416, "2": 0.050175982321150676}, "math_density": {"0": 0.9476544510626475, "1": 0.04697665210550832, "2": 0.0025359747668577707, "3": 0.0010067607922868442, "4": 0.0018261612726995424}, "code_density": {"0": 0.0003854317420467399, "1": 0.000789407811922878, "2": 0.021475751942379318, "3": 0.16259868971934183, "4": 0.8147507187843093}, "reasoning_density": {"0": 0.8556365396465803, "1": 0.06945268515443623, "2": 0.06272295637822947, "3": 0.008684682644531863, "4": 0.0035031361762223405}, "education_level": {"none": 0.0012304590824731344, "0": 0.0015263526744974587, "1": 0.018370173515335986, "2": 0.7925115091524397, "3": 0.17638688474620803, "4": 0.009974620829045742}, "creative_writing_quality": {"not_applicable": 0.9992421542431938, "-2": 0.00011732269169946914, "-1": 0.00025071040649854366, "0": 0.0003012142158442827, "1": 5.927307014835055e-05, "2": 2.9325372615260564e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.490703619111451e-05, "Art & Design": 0.0005451689697442868, "Law & Crime": 2.2524075133741473e-05, "Education & Careers": 2.8106550049012636e-05, "Entertainment & Media": 1.6964568904934736e-05, "Fashion & Beauty": 2.7128054150788815e-05, "Business, Economics & Finance": 4.501370859257555e-05, "Food & Dining": 1.4093868610802582e-05, "Games & Gaming": 6.585174209836692e-05, "Electronics & Hardware": 9.309457261548075e-05, "Health, Healthcare & Wellness": 2.314240640143628e-05, "History & Geography": 2.101862654431351e-05, "Home, Pets & Hobbies": 1.675500924325806e-05, "Industry & Production": 2.4026526910564293e-05, "Language, Literature & Philosophy": 2.7684510825285532e-05, "Politics & Government": 1.4881755580528683e-05, "Religion & Spirituality": 2.0194234444152185e-05, "Science, Mathematics & Engineering": 0.0002655778575127907, "Relationships & Community": 1.693287217404355e-05, "Software Usage & Applications": 0.0010549907929280834, "Programming & Software Engineering": 0.9973703809020629, "Sports & Fitness": 3.2649254457786654e-05, "Vehicles & Transportation": 1.4759384131246933e-05, "Travel & Tourism": 2.9994903975776905e-05, "Other / Uncovered Subject": 0.000127499798940728, "Unknown / Insufficient Content": 4.665801777580849e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005454694315270731, "Art & Design": 0.019536406039139205, "Law & Crime": 0.000645103311084992, "Education & Careers": 0.0002807115775456062, "Entertainment & Media": 0.00031652221125670567, "Fashion & Beauty": 0.00024751774972547224, "Business, Economics & Finance": 0.0004963894505578054, "Food & Dining": 0.00014721460805206625, "Games & Gaming": 0.00028756831700025936, "Electronics & Hardware": 0.0006214607132714193, "Health, Healthcare & Wellness": 0.00022855187962627748, "History & Geography": 0.00015280731099912422, "Home, Pets & Hobbies": 0.00014543749380044538, "Industry & Production": 0.00012615852520955875, "Language, Literature & Philosophy": 0.00015627193330937183, "Politics & Government": 0.00015888430983171614, "Religion & Spirituality": 0.00019606724719740427, "Science, Mathematics & Engineering": 0.001142664077195593, "Relationships & Community": 0.00012221326506019672, "Software Usage & Applications": 0.014374886251510304, "Programming & Software Engineering": 0.01754465082980314, "Sports & Fitness": 0.00014634058784420244, "Vehicles & Transportation": 7.842917791152743e-05, "Travel & Tourism": 0.0001831402662957036, "Other / Uncovered Subject": 0.0005213505084095536, "NONE": 0.9415977829268354}}, "probabilities": {"primary": {"Academic Writing": 4.728070606658234e-05, "Content Listing": 3.116303654535782e-05, "Creative Writing": 2.049336934770078e-05, "Customer Support Page": 1.0641669772771607e-05, "Q&A Forum": 1.572929586563499e-05, "Discussion Thread": 1.0716979467680446e-05, "Comment Section": 2.311859421013333e-05, "Missing Content": 4.4362667465010217e-05, "Knowledge Article": 0.00011117167441732968, "Legal Notices": 2.377066132948161e-05, "News Article": 6.249312447175678e-06, "Organizational Announcement": 1.2485951854276791e-05, "Nonfiction Writing": 1.420875075199974e-05, "Organizational About Page": 1.5659973677479715e-05, "Personal About Page": 1.5366619649818752e-05, "Personal Blog": 1.2872897380571539e-05, "Product Page": 9.11943247969781e-06, "Spam / Ads": 1.2532710760614135e-05, "Structured Data": 0.0016615769752785351, "Technical Writing": 0.9949734922772315, "Transcript / Interview": 2.2057749898042475e-05, "Tutorial / How-To Guide": 0.0015663917887449531, "User Reviews": 8.510843441988506e-06, "Other / Unknown": 0.0013310260619155438}, "secondary": {"Academic Writing": 0.00027475033552924196, "Content Listing": 0.00023664210346450526, "Creative Writing": 0.00022575724047367639, "Customer Support Page": 0.00015181231144985435, "Q&A Forum": 0.00010779528547369703, "Discussion Thread": 9.831533659854611e-05, "Comment Section": 0.00031434649939917687, "Missing Content": 0.000681642674842533, "Knowledge Article": 0.0010044215075857947, "Legal Notices": 0.0002970767953325494, "News Article": 0.00014258536578200606, "Organizational Announcement": 0.00013041576079580926, "Nonfiction Writing": 0.00013864565729342535, "Organizational About Page": 0.0001550077505452751, "Personal About Page": 0.00013021144987958538, "Personal Blog": 0.00017155094243315983, "Product Page": 8.93581865850268e-05, "Spam / Ads": 0.00013877867108584964, "Structured Data": 0.028815978145683447, "Technical Writing": 0.014264685111399758, "Transcript / Interview": 0.00018330554934235436, "Tutorial / How-To Guide": 0.005316362377443208, "User Reviews": 6.726333381459388e-05, "NONE": 0.9468632916077669}, "tertiary": {"Academic Writing": 0.000197930278111152, "Content Listing": 0.00018074218746839, "Creative Writing": 0.00018724816723891114, "Customer Support Page": 0.00014931096153088258, "Q&A Forum": 8.899590215309773e-05, "Discussion Thread": 7.85438511855692e-05, "Comment Section": 0.00041264882189475256, "Missing Content": 0.0011075877768551496, "Knowledge Article": 0.0015578001703897123, "Legal Notices": 0.0004043850756160386, "News Article": 0.00011022752972067446, "Organizational Announcement": 0.00010779139167874536, "Nonfiction Writing": 0.00016338431456016698, "Organizational About Page": 0.00014070652513098003, "Personal About Page": 0.00010513230870994001, "Personal Blog": 0.00012437805014695505, "Product Page": 7.730630706118271e-05, "Spam / Ads": 0.0001350233286106425, "Structured Data": 0.024981201870993147, "Technical Writing": 0.009361145618602915, "Transcript / Interview": 0.00014528978811036999, "Tutorial / How-To Guide": 0.007342169747237507, "User Reviews": 5.8574766725953105e-05, "NONE": 0.952782475260267}, "structured_records": {"false": 0.9307987893039379, "true": 0.06920121069606208}, "procedural_steps": {"false": 0.970286462987829, "true": 0.029713537012171002}, "worked_solution": {"false": 0.9960083740028319, "true": 0.003991625997168121}, "contextual_dialogue": {"false": 0.9999934690585616, "true": 6.530941438483247e-06}, "content_quality": {"-2": 0.03443084800740469, "-1": 0.3628939922037328, "0": 0.4175968168382801, "1": 0.15609558193219464, "2": 0.028982761018387742}, "noise_severity": {"0": 0.825595990021887, "1": 0.14085796967716727, "2": 0.02831538575816368, "3": 0.0032946986312688145, "4": 0.0019359559115132187}, "missing_content_severity": {"0": 0.9375475164412672, "1": 0.04974794073083327, "2": 0.009922137118269406, "3": 0.0021076599191763305, "4": 0.0006747457904538401}, "factual_density": {"0": 0.006834168759743994, "1": 0.017110060253915448, "2": 0.13115486218701136, "3": 0.4151308547056155, "4": 0.42977005409371377}, "pretraining_utility": {"-2": 0.008881021973529269, "-1": 0.0866584638128059, "0": 0.4082183754802725, "1": 0.4460661564122416, "2": 0.050175982321150676}, "math_density": {"0": 0.9476544510626476, "1": 0.046976652105508325, "2": 0.002535974766857771, "3": 0.0010067607922868444, "4": 0.0018261612726995426}, "code_density": {"0": 0.0003854317420467399, "1": 0.000789407811922878, "2": 0.021475751942379318, "3": 0.16259868971934183, "4": 0.8147507187843093}, "reasoning_density": {"0": 0.85563653964658, "1": 0.06945268515443621, "2": 0.06272295637822946, "3": 0.008684682644531861, "4": 0.0035031361762223397}, "education_level": {"none": 0.0012304590824731344, "0": 0.0015263526744974587, "1": 0.018370173515335986, "2": 0.7925115091524397, "3": 0.17638688474620803, "4": 0.009974620829045742}, "creative_writing_quality": {"not_applicable": 0.9992421542431941, "-2": 0.00011732269169946917, "-1": 0.0002507104064985437, "0": 0.0003012142158442828, "1": 5.927307014835056e-05, "2": 2.932537261526057e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.4907036191114514e-05, "Art & Design": 0.0005451689697442869, "Law & Crime": 2.2524075133741476e-05, "Education & Careers": 2.8106550049012643e-05, "Entertainment & Media": 1.696456890493474e-05, "Fashion & Beauty": 2.7128054150788822e-05, "Business, Economics & Finance": 4.501370859257556e-05, "Food & Dining": 1.4093868610802585e-05, "Games & Gaming": 6.585174209836693e-05, "Electronics & Hardware": 9.309457261548078e-05, "Health, Healthcare & Wellness": 2.3142406401436287e-05, "History & Geography": 2.1018626544313513e-05, "Home, Pets & Hobbies": 1.6755009243258063e-05, "Industry & Production": 2.40265269105643e-05, "Language, Literature & Philosophy": 2.768451082528554e-05, "Politics & Government": 1.4881755580528686e-05, "Religion & Spirituality": 2.019423444415219e-05, "Science, Mathematics & Engineering": 0.0002655778575127908, "Relationships & Community": 1.6932872174043555e-05, "Software Usage & Applications": 0.0010549907929280837, "Programming & Software Engineering": 0.9973703809020631, "Sports & Fitness": 3.264925445778666e-05, "Vehicles & Transportation": 1.4759384131246936e-05, "Travel & Tourism": 2.999490397577691e-05, "Other / Uncovered Subject": 0.00012749979894072802, "Unknown / Insufficient Content": 4.66580177758085e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005454694315270731, "Art & Design": 0.019536406039139205, "Law & Crime": 0.000645103311084992, "Education & Careers": 0.0002807115775456062, "Entertainment & Media": 0.00031652221125670567, "Fashion & Beauty": 0.00024751774972547224, "Business, Economics & Finance": 0.0004963894505578054, "Food & Dining": 0.00014721460805206625, "Games & Gaming": 0.00028756831700025936, "Electronics & Hardware": 0.0006214607132714193, "Health, Healthcare & Wellness": 0.00022855187962627748, "History & Geography": 0.00015280731099912422, "Home, Pets & Hobbies": 0.00014543749380044538, "Industry & Production": 0.00012615852520955875, "Language, Literature & Philosophy": 0.00015627193330937183, "Politics & Government": 0.00015888430983171614, "Religion & Spirituality": 0.00019606724719740427, "Science, Mathematics & Engineering": 0.001142664077195593, "Relationships & Community": 0.00012221326506019672, "Software Usage & Applications": 0.014374886251510304, "Programming & Software Engineering": 0.01754465082980314, "Sports & Fitness": 0.00014634058784420244, "Vehicles & Transportation": 7.842917791152743e-05, "Travel & Tourism": 0.0001831402662957036, "Other / Uncovered Subject": 0.0005213505084095536, "NONE": 0.9415977829268354}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000004, "mass_delta": 4.440892098500626e-16, "missing_mass": 0.0, "excess_mass": 4.440892098500626e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9866, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9866, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:14.315243+00:00", "scoring_key_slot": "primary", "dataset_uid": "5c75f960-d63a-4665-bf48-e4de8c6d9f2c", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "2d48595bb7a85dce59a64ed5b2cd3afa81489940ce27396e841492d48eb6df3f", "source_metadata": {"uuid": "5c75f960-d63a-4665-bf48-e4de8c6d9f2c", "repo_name": "aristidedovi/removegb-kgtech", "relative_path": "backend_flask_app/flaskr/api1/routes-old.py", "category": "WEB", "algo_rel_score": 0.9710829854011536, "quality_score": 4.0221123695373535, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 3, "repo_created_at": "2024-09-26T21:25:15.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "23fa44a289c224f8554b602f62275172f440633c", "size_bytes": 5555, "file_timestamp": 1727438683, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "b1ee1e219651634511cc888fa6391fb5ed279ed2"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 3, "repo_created_at": "2024-09-26T21:25:15.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "23fa44a289c224f8554b602f62275172f440633c", "size_bytes": 5555, "file_timestamp": 1727438683, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "b1ee1e219651634511cc888fa6391fb5ed279ed2", "url": "https://github.com/aristidedovi/removegb-kgtech/blob/b1ee1e219651634511cc888fa6391fb5ed279ed2/backend_flask_app/flaskr/api1/routes-old.py"}, "gpt4o_tokens": 1206, "dataset_split": "python", "probability_weighted_scores": {"content_quality": -0.21769458424957205, "noise_severity": 0.21511666073335395, "missing_content_severity": 0.07861417788671644, "factual_density": 3.2438925651196397, "pretraining_utility": 0.4419976132946785, "math_density": 0.06237352910688257, "code_density": 3.790539855991944, "reasoning_density": 0.2349651905493801, "education_level": 2.175128736284132, "creative_writing_quality": -0.48483741080404835}, "score_applicability": {"education_level": 0.9987695409175269, "creative_writing_quality": 0.0007578457568059068}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/jparyani/wave/blob/2359e3bb20df587f182fba48f755cadff9ecd6aa/src/python/api/util.py", "id": "python-0-379bc4f3687cf3e89f19c7b0eb30597c", "text": "#!/usr/bin/python2.4\n#\n# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements.  See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership.  The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License.  You may obtain a copy of the License at\n#\n#   http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied.  See the License for the\n# specific language governing permissions and limitations\n# under the License.\n\n\"\"\"Utility library containing various helpers used by the API.\"\"\"\n\nimport re\n\nCUSTOM_SERIALIZE_METHOD_NAME = 'serialize'\n\nMARKUP_RE = re.compile(r'<([^>]*?)>')\nRESERVED_PROXY_FOR_CHARS_RE = re.compile(ur'[\\s\\u0000-\\u001F@,:<>\\u007F]')\n\ndef force_unicode(object):\n  \"\"\" Return the Unicode string version of object, with UTF-8 encoding. \"\"\"\n  if isinstance(object, unicode):\n    return object\n  return unicode(str(object), 'utf-8')\n\ndef parse_markup(markup):\n  \"\"\"Parses a bit of markup into robot compatible text.\n  \n  For now this is a rough approximation.\n  \"\"\"\n  def replace_tag(group):\n    if not group.groups:\n      return ''\n    tag = group.groups()[0].split(' ', 1)[0]\n    if (tag == 'p' or tag == 'br'):\n      return '\\n'\n    return ''\n\n  return MARKUP_RE.sub(replace_tag, markup)\n\ndef is_iterable(inst):\n  \"\"\"Returns whether or not this is a list, tuple, set or dict .\n\n  Note that this does not return true for strings.\n  \"\"\"\n  return hasattr(inst, '__iter__')\n\ndef is_dict(inst):\n  \"\"\"Returns whether or not the specified instance is a dict.\"\"\"\n  return hasattr(inst, 'iteritems')\n\n\ndef is_user_defined_new_style_class(obj):\n  \"\"\"Returns whether or not the specified instance is a user-defined type.\"\"\"\n  return type(obj).__module__ != '__builtin__'\n\ndef lower_camel_case(s):\n  \"\"\"Converts a string to lower camel case.\n\n  Examples:\n    foo => foo\n    foo_bar => fooBar\n    foo__bar => fooBar\n    foo_bar_baz => fooBarBaz\n\n  Args:\n    s: The string to convert to lower camel case.\n\n  Returns:\n    The lower camel cased string.\n  \"\"\"\n  return reduce(lambda a, b: a + (a and b.capitalize() or b), s.split('_'))\n\ndef non_none_dict(d):\n  \"\"\"return a copy of the dictionary without none values.\"\"\"\n  return dict([a for a in d.items() if a[1] is not None])\n\ndef _serialize_attributes(obj):\n  \"\"\"Serializes attributes of an instance.\n\n  Iterates all attributes of an object and invokes serialize if they are\n  public and not callable.\n\n  Args:\n    obj: The instance to serialize.\n\n  Returns:\n    The serialized object.\n  \"\"\"\n  data = {}\n  for attr_name in dir(obj):\n    if attr_name.startswith('_'):\n      continue\n    attr = getattr(obj, attr_name)\n    if attr is None or callable(attr):\n      continue\n    # Looks okay, serialize it.\n    data[lower_camel_case(attr_name)] = serialize(attr)\n  return data\n\n\ndef _serialize_dict(d):\n  \"\"\"Invokes serialize on all of its key/value pairs.\n\n  Args:\n    d: The dict instance to serialize.\n\n  Returns:\n    The serialized dict.\n  \"\"\"\n  data = {}\n  for k, v in d.items():\n    data[lower_camel_case(k)] = serialize(v)\n  return data\n\n\ndef serialize(obj):\n  \"\"\"Serializes any instance.\n\n  If this is a user-defined instance\n  type, it will first check for a custom Serialize() function and use that\n  if it exists. Otherwise, it will invoke serialize all of its public\n  attributes. Lists and dicts are serialized trivially.\n\n  Args:\n    obj: The instance to serialize.\n\n  Returns:\n    The serialized object.\n  \"\"\"\n  if is_user_defined_new_style_class(obj):\n    if obj and hasattr(obj, CUSTOM_SERIALIZE_METHOD_NAME):\n      method = getattr(obj, CUSTOM_SERIALIZE_METHOD_NAME)\n      if callable(method):\n        return method()\n    return _serialize_attributes(obj)\n  elif is_dict(obj):\n    return _serialize_dict(obj)\n  elif is_iterable(obj):\n    return [serialize(v) for v in obj]\n  return obj\n\ndef is_valid_proxy_for_id(s):\n  \"\"\" Checks if the given string is a valid proxy id.\n\n  This method asserts whether the string contains reserved characters or not.\n  This check is to ensure that when we concatenate the robot id and the proxy\n  id, it doesn't result in an invalid participant id.\n\n  The reserved characters are:\n  - whitespaces\n  - non-printing characters: decimal 0 - 31 (hex 00 - 1F), and decimal 127\n    (hex 7F)\n  - @, comma, :, <, and >\n  If you need to pass in an arbitrary string as the proxy id, please consider\n  encoding the string with a URL encoder (for example, urllib.quote_plus) or\n  base64 encoder (for example, base64.b64encode) first.\n\n  Args:\n    s: the string to be checked.\n\n  Returns:\n    True if the string is a valid proxy for id.\n  \"\"\"\n  return RESERVED_PROXY_FOR_CHARS_RE.search(s) == None\n\ndef check_is_valid_proxy_for_id(s):\n  \"\"\" Checks if the given string is a valid proxy id.\n\n  Please see isValidProxyForId(String) for more details on the assertion. This\n  method raises a ValueError exception if the input string is not a valid proxy\n  id.\n\n  Args:\n    s: the string to be checked.\n  \"\"\"\n  if s != None and not is_valid_proxy_for_id(s):\n    raise ValueError(s + ' is not a valid proxy for id.')\n\nclass StringEnum(object):\n  \"\"\"Enum like class that is configured with a list of values.\n\n  This class effectively implements an enum for Elements, except for that\n  the actual values of the enums will be the string values.\n  \"\"\"\n\n  def __init__(self, *values):\n    for name in values:\n      setattr(self, name, name)\n", "text_preview": "#!/usr/bin/python2.4 # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The A", "input_sha256": "4efc1d95676f85d93aad8ba3d61bd48c9190686424145de4be3f1bcf656cd1d1", "text_sha256": "379bc4f3687cf3e89f19c7b0eb30597c9e6096b9e38dbc42436f871c3d3197ec", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12054, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Legal Notices", "sub_format": null}, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 1, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9656278915162158, "noise_severity": 0.6336860422928662, "missing_content_severity": 0.01524466903857038, "factual_density": 3.3823860979558042, "pretraining_utility": 0.39628995470155876, "math_density": 0.18241704454544608, "code_density": 3.5998228713184144, "reasoning_density": 0.5878835744212572, "education_level": 2.0989269007339546, "creative_writing_quality": -0.2160787040715915}, "api_choices": {"primary": "Technical Writing", "secondary": "Legal Notices", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.7080582519747649, "secondary_choice": "Legal Notices", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9863211834800029, "secondary": 0.7483091145944999, "tertiary": 0.89455043414891, "structured_records": 0.004207808598488498, "procedural_steps": 0.013781747523471635, "worked_solution": 0.05204714031624662, "contextual_dialogue": 5.297187412421999e-06, "content_quality": 0.5830878503128086, "noise_severity": 0.6039781932341566, "missing_content_severity": 0.9872961091345247, "factual_density": 0.48532174829650354, "pretraining_utility": 0.47473283921199605, "math_density": 0.8479857962121282, "code_density": 0.6665190594320118, "reasoning_density": 0.5100970213156188, "education_level": 0.7549748300248608, "creative_writing_quality": 0.9991947943051415, "primary_topic": 0.9911820973083459, "secondary_topic": 0.8552603816508251}, "probability_margin": {"primary": 0.9765740995605843, "secondary": 0.5679103207351219, "tertiary": 0.8116780110768053, "education_level": 0.6566593990861761, "creative_writing_quality": 0.9989972592868276, "primary_topic": 0.9872436400756789, "secondary_topic": 0.7559996606092729}, "raw_probabilities": {"primary": {"Academic Writing": 4.955497329990354e-05, "Content Listing": 2.690632835476501e-05, "Creative Writing": 2.269823233778369e-05, "Customer Support Page": 1.4652879179367542e-05, "Q&A Forum": 1.6282055885562075e-05, "Discussion Thread": 1.0401357908545658e-05, "Comment Section": 2.2354513831532593e-05, "Missing Content": 4.319519252023675e-05, "Knowledge Article": 0.00011902236711725577, "Legal Notices": 0.010317034607751834, "News Article": 9.178938537477303e-06, "Organizational Announcement": 2.358760957931274e-05, "Nonfiction Writing": 1.991377022251745e-05, "Organizational About Page": 2.4099432400212625e-05, "Personal About Page": 1.4771803440404518e-05, "Personal Blog": 1.1077442337712038e-05, "Product Page": 1.1949310374245473e-05, "Spam / Ads": 1.3144210485823808e-05, "Structured Data": 0.0003324083934619829, "Technical Writing": 0.9868911341683361, "Transcript / Interview": 2.4505010060198046e-05, "Tutorial / How-To Guide": 0.00014268729202107392, "User Reviews": 9.423272586744574e-06, "Other / Unknown": 0.0018300168379691284}, "secondary": {"Academic Writing": 0.0005997993848063704, "Content Listing": 0.0004430750958075503, "Creative Writing": 0.0004562289023220774, "Customer Support Page": 0.0002777029084309554, "Q&A Forum": 0.0002765233158136529, "Discussion Thread": 0.0002531877452314622, "Comment Section": 0.0004300279323038122, "Missing Content": 0.0004975099070105293, "Knowledge Article": 0.0012403414765763691, "Legal Notices": 0.758796234819729, "News Article": 0.0003708778686070632, "Organizational Announcement": 0.0005120399278429955, "Nonfiction Writing": 0.0003687555941379827, "Organizational About Page": 0.00046125100151419093, "Personal About Page": 0.00029375278420768104, "Personal Blog": 0.0002262974321519844, "Product Page": 0.00032658891865035836, "Spam / Ads": 0.00032196855304142933, "Structured Data": 0.0008690672684733528, "Technical Writing": 0.040926939464861006, "Transcript / Interview": 0.0004613983859394343, "Tutorial / How-To Guide": 0.0005262229058734845, "User Reviews": 0.00017829432205977098, "NONE": 0.1908859140846071}, "tertiary": {"Academic Writing": 0.000306126958556348, "Content Listing": 0.00021127463753958046, "Creative Writing": 0.0002643278887150197, "Customer Support Page": 0.00016238298500766084, "Q&A Forum": 0.00010427326808082896, "Discussion Thread": 8.736844657501169e-05, "Comment Section": 0.00018274664348961399, "Missing Content": 0.0002681060039804186, "Knowledge Article": 0.002443109170132452, "Legal Notices": 0.08726615498256687, "News Article": 0.0001694618622547951, "Organizational Announcement": 0.00018224375238705517, "Nonfiction Writing": 0.0001476693982833304, "Organizational About Page": 0.00018631012737241838, "Personal About Page": 0.00013964640553760358, "Personal Blog": 0.00011748533166454087, "Product Page": 0.00012514468782315482, "Spam / Ads": 0.00016944626746982227, "Structured Data": 0.0008335503560745831, "Technical Writing": 0.006475777820131061, "Transcript / Interview": 0.00018849101206031848, "Tutorial / How-To Guide": 0.0009576070497836395, "User Reviews": 6.712888514177715e-05, "NONE": 0.8989441660593721}, "structured_records": {"false": 0.9957921914015115, "true": 0.004207808598488498}, "procedural_steps": {"false": 0.9862182524765284, "true": 0.013781747523471635}, "worked_solution": {"false": 0.9479528596837534, "true": 0.05204714031624662}, "contextual_dialogue": {"false": 0.9999947028125876, "true": 5.297187412421999e-06}, "content_quality": {"-2": 0.00480977231974737, "-1": 0.017535107390594558, "0": 0.21783381231377577, "1": 0.5268600724054596, "2": 0.2329612355704227}, "noise_severity": {"0": 0.420770062913073, "1": 0.5334424267529417, "2": 0.039999559318350246, "3": 0.0029073071593162087, "4": 0.002880643856318836}, "missing_content_severity": {"0": 0.9887020959593384, "1": 0.008554463130309164, "2": 0.0018137513002375017, "3": 0.0006560551326724322, "4": 0.0002736344774422281}, "factual_density": {"0": 0.0032511508249565505, "1": 0.009506525621265785, "2": 0.09112310511008166, "3": 0.3938435116604089, "4": 0.5022757067832871}, "pretraining_utility": {"-2": 0.007976106706607988, "-1": 0.10106310570880701, "0": 0.4362496662689404, "1": 0.39611696880770747, "2": 0.05859415250793714}, "math_density": {"0": 0.8646966723802677, "1": 0.11298541813010035, "2": 0.008415173447225937, "3": 0.003009664648731515, "4": 0.01089307139367485}, "code_density": {"0": 0.0005496694488951377, "1": 0.0012171067660063425, "2": 0.04210501032172499, "3": 0.3101171099445362, "4": 0.6460111035188373}, "reasoning_density": {"0": 0.6419474233147155, "1": 0.1828805114489858, "2": 0.13049380426705057, "3": 0.034697589438822694, "4": 0.009980671530425561}, "education_level": {"none": 0.001150748393006763, "0": 0.002026590439846203, "1": 0.05333380157223849, "2": 0.7958123583540506, "3": 0.13915295926787452, "4": 0.008523541972983311}, "creative_writing_quality": {"not_applicable": 0.9993289952542845, "-2": 6.830062242853259e-05, "-1": 0.00015729628348350267, "0": 0.0003317359674569586, "1": 7.843605223241199e-05, "2": 3.523582011403753e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.352156643880887e-05, "Art & Design": 6.182382595330417e-05, "Law & Crime": 0.004277607336192211, "Education & Careers": 6.611767297272935e-05, "Entertainment & Media": 4.417559597997608e-05, "Fashion & Beauty": 2.469320178540654e-05, "Business, Economics & Finance": 0.00014658917840449973, "Food & Dining": 3.420905988410382e-05, "Games & Gaming": 0.00011090095458942581, "Electronics & Hardware": 0.00011202645016948951, "Health, Healthcare & Wellness": 3.377770358004609e-05, "History & Geography": 3.673184752116043e-05, "Home, Pets & Hobbies": 3.125850578277117e-05, "Industry & Production": 4.1206824070633995e-05, "Language, Literature & Philosophy": 0.00027224362537798684, "Politics & Government": 7.62142408807562e-05, "Religion & Spirituality": 3.5998603488459804e-05, "Science, Mathematics & Engineering": 0.0013287386592784071, "Relationships & Community": 2.7538878015256403e-05, "Software Usage & Applications": 0.0007174730289351232, "Programming & Software Engineering": 0.9915212474118711, "Sports & Fitness": 5.3735322797465223e-05, "Vehicles & Transportation": 3.168957358817698e-05, "Travel & Tourism": 5.592096737933547e-05, "Other / Uncovered Subject": 0.0005863572750452195, "Unknown / Insufficient Content": 0.000218202690018113}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007420168085368015, "Art & Design": 0.0005773968440990907, "Law & Crime": 0.10482762943959738, "Education & Careers": 0.0005253844862404713, "Entertainment & Media": 0.0006056303304100473, "Fashion & Beauty": 0.0002947777944512367, "Business, Economics & Finance": 0.0013542390031371027, "Food & Dining": 0.00023962728223516744, "Games & Gaming": 0.0005932031405316131, "Electronics & Hardware": 0.0005551833953030438, "Health, Healthcare & Wellness": 0.0003233251986927558, "History & Geography": 0.0003404048277726328, "Home, Pets & Hobbies": 0.0002580077741069437, "Industry & Production": 0.00024695792374112717, "Language, Literature & Philosophy": 0.0006996970758143871, "Politics & Government": 0.0006866029365853695, "Religion & Spirituality": 0.00045360870387397303, "Science, Mathematics & Engineering": 0.002998307800487741, "Relationships & Community": 0.00018624365385306945, "Software Usage & Applications": 0.0034314272739819542, "Programming & Software Engineering": 0.017036595686289593, "Sports & Fitness": 0.00026150569849879183, "Vehicles & Transportation": 0.00018283133083748975, "Travel & Tourism": 0.00037029631502631866, "Other / Uncovered Subject": 0.001381809227025694, "NONE": 0.8608272900488703}}, "probabilities": {"primary": {"Academic Writing": 4.955497329990356e-05, "Content Listing": 2.690632835476502e-05, "Creative Writing": 2.2698232337783698e-05, "Customer Support Page": 1.4652879179367547e-05, "Q&A Forum": 1.6282055885562082e-05, "Discussion Thread": 1.0401357908545661e-05, "Comment Section": 2.23545138315326e-05, "Missing Content": 4.319519252023676e-05, "Knowledge Article": 0.00011902236711725581, "Legal Notices": 0.010317034607751838, "News Article": 9.178938537477306e-06, "Organizational Announcement": 2.3587609579312747e-05, "Nonfiction Writing": 1.9913770222517456e-05, "Organizational About Page": 2.409943240021263e-05, "Personal About Page": 1.4771803440404523e-05, "Personal Blog": 1.1077442337712041e-05, "Product Page": 1.1949310374245477e-05, "Spam / Ads": 1.3144210485823813e-05, "Structured Data": 0.000332408393461983, "Technical Writing": 0.9868911341683364, "Transcript / Interview": 2.4505010060198053e-05, "Tutorial / How-To Guide": 0.00014268729202107397, "User Reviews": 9.423272586744578e-06, "Other / Unknown": 0.001830016837969129}, "secondary": {"Academic Writing": 0.0005997993848063706, "Content Listing": 0.00044307509580755045, "Creative Writing": 0.00045622890232207755, "Customer Support Page": 0.0002777029084309555, "Q&A Forum": 0.000276523315813653, "Discussion Thread": 0.0002531877452314623, "Comment Section": 0.00043002793230381237, "Missing Content": 0.0004975099070105295, "Knowledge Article": 0.0012403414765763696, "Legal Notices": 0.7587962348197292, "News Article": 0.0003708778686070633, "Organizational Announcement": 0.0005120399278429957, "Nonfiction Writing": 0.0003687555941379828, "Organizational About Page": 0.0004612510015141911, "Personal About Page": 0.00029375278420768115, "Personal Blog": 0.00022629743215198448, "Product Page": 0.00032658891865035847, "Spam / Ads": 0.00032196855304142944, "Structured Data": 0.0008690672684733531, "Technical Writing": 0.04092693946486102, "Transcript / Interview": 0.00046139838593943445, "Tutorial / How-To Guide": 0.0005262229058734847, "User Reviews": 0.00017829432205977103, "NONE": 0.19088591408460714}, "tertiary": {"Academic Writing": 0.000306126958556348, "Content Listing": 0.00021127463753958046, "Creative Writing": 0.0002643278887150197, "Customer Support Page": 0.00016238298500766084, "Q&A Forum": 0.00010427326808082896, "Discussion Thread": 8.736844657501169e-05, "Comment Section": 0.00018274664348961399, "Missing Content": 0.0002681060039804186, "Knowledge Article": 0.002443109170132452, "Legal Notices": 0.08726615498256687, "News Article": 0.0001694618622547951, "Organizational Announcement": 0.00018224375238705517, "Nonfiction Writing": 0.0001476693982833304, "Organizational About Page": 0.00018631012737241838, "Personal About Page": 0.00013964640553760358, "Personal Blog": 0.00011748533166454087, "Product Page": 0.00012514468782315482, "Spam / Ads": 0.00016944626746982227, "Structured Data": 0.0008335503560745831, "Technical Writing": 0.006475777820131061, "Transcript / Interview": 0.00018849101206031848, "Tutorial / How-To Guide": 0.0009576070497836395, "User Reviews": 6.712888514177715e-05, "NONE": 0.8989441660593721}, "structured_records": {"false": 0.9957921914015115, "true": 0.004207808598488498}, "procedural_steps": {"false": 0.9862182524765284, "true": 0.013781747523471635}, "worked_solution": {"false": 0.9479528596837534, "true": 0.05204714031624662}, "contextual_dialogue": {"false": 0.9999947028125876, "true": 5.297187412421999e-06}, "content_quality": {"-2": 0.00480977231974737, "-1": 0.017535107390594558, "0": 0.21783381231377577, "1": 0.5268600724054596, "2": 0.2329612355704227}, "noise_severity": {"0": 0.420770062913073, "1": 0.5334424267529417, "2": 0.039999559318350246, "3": 0.0029073071593162087, "4": 0.002880643856318836}, "missing_content_severity": {"0": 0.9887020959593387, "1": 0.008554463130309166, "2": 0.0018137513002375021, "3": 0.0006560551326724323, "4": 0.00027363447744222814}, "factual_density": {"0": 0.0032511508249565505, "1": 0.009506525621265785, "2": 0.09112310511008166, "3": 0.3938435116604089, "4": 0.5022757067832871}, "pretraining_utility": {"-2": 0.007976106706607988, "-1": 0.10106310570880701, "0": 0.4362496662689404, "1": 0.39611696880770747, "2": 0.05859415250793714}, "math_density": {"0": 0.8646966723802674, "1": 0.1129854181301003, "2": 0.008415173447225933, "3": 0.003009664648731514, "4": 0.010893071393674845}, "code_density": {"0": 0.0005496694488951378, "1": 0.0012171067660063427, "2": 0.042105010321725, "3": 0.31011710994453623, "4": 0.6460111035188374}, "reasoning_density": {"0": 0.6419474233147154, "1": 0.18288051144898576, "2": 0.13049380426705054, "3": 0.03469758943882269, "4": 0.00998067153042556}, "education_level": {"none": 0.0011507483930067632, "0": 0.0020265904398462033, "1": 0.0533338015722385, "2": 0.7958123583540507, "3": 0.13915295926787455, "4": 0.008523541972983313}, "creative_writing_quality": {"not_applicable": 0.9993289952542845, "-2": 6.830062242853259e-05, "-1": 0.00015729628348350267, "0": 0.0003317359674569586, "1": 7.843605223241199e-05, "2": 3.523582011403753e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.352156643880887e-05, "Art & Design": 6.182382595330417e-05, "Law & Crime": 0.004277607336192211, "Education & Careers": 6.611767297272935e-05, "Entertainment & Media": 4.417559597997608e-05, "Fashion & Beauty": 2.469320178540654e-05, "Business, Economics & Finance": 0.00014658917840449973, "Food & Dining": 3.420905988410382e-05, "Games & Gaming": 0.00011090095458942581, "Electronics & Hardware": 0.00011202645016948951, "Health, Healthcare & Wellness": 3.377770358004609e-05, "History & Geography": 3.673184752116043e-05, "Home, Pets & Hobbies": 3.125850578277117e-05, "Industry & Production": 4.1206824070633995e-05, "Language, Literature & Philosophy": 0.00027224362537798684, "Politics & Government": 7.62142408807562e-05, "Religion & Spirituality": 3.5998603488459804e-05, "Science, Mathematics & Engineering": 0.0013287386592784071, "Relationships & Community": 2.7538878015256403e-05, "Software Usage & Applications": 0.0007174730289351232, "Programming & Software Engineering": 0.9915212474118711, "Sports & Fitness": 5.3735322797465223e-05, "Vehicles & Transportation": 3.168957358817698e-05, "Travel & Tourism": 5.592096737933547e-05, "Other / Uncovered Subject": 0.0005863572750452195, "Unknown / Insufficient Content": 0.000218202690018113}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007420168085368015, "Art & Design": 0.0005773968440990907, "Law & Crime": 0.10482762943959738, "Education & Careers": 0.0005253844862404713, "Entertainment & Media": 0.0006056303304100473, "Fashion & Beauty": 0.0002947777944512367, "Business, Economics & Finance": 0.0013542390031371027, "Food & Dining": 0.00023962728223516744, "Games & Gaming": 0.0005932031405316131, "Electronics & Hardware": 0.0005551833953030438, "Health, Healthcare & Wellness": 0.0003233251986927558, "History & Geography": 0.0003404048277726328, "Home, Pets & Hobbies": 0.0002580077741069437, "Industry & Production": 0.00024695792374112717, "Language, Literature & Philosophy": 0.0006996970758143871, "Politics & Government": 0.0006866029365853695, "Religion & Spirituality": 0.00045360870387397303, "Science, Mathematics & Engineering": 0.002998307800487741, "Relationships & Community": 0.00018624365385306945, "Software Usage & Applications": 0.0034314272739819542, "Programming & Software Engineering": 0.017036595686289593, "Sports & Fitness": 0.00026150569849879183, "Vehicles & Transportation": 0.00018283133083748975, "Travel & Tourism": 0.00037029631502631866, "Other / Uncovered Subject": 0.001381809227025694, "NONE": 0.8608272900488703}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0000000000000004, "mass_delta": 4.440892098500626e-16, "missing_mass": 0.0, "excess_mass": 4.440892098500626e-16, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10018, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10018, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:14.737305+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "8f5a625c-1b72-4a9c-8d6c-651224a015a3", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "c3ae53c7d749b2449c94f8051749c2d8cf78a276a39ebad625de1705c0d43f64", "source_metadata": {"uuid": "8f5a625c-1b72-4a9c-8d6c-651224a015a3", "repo_name": "jparyani/wave", "relative_path": "src/python/api/util.py", "category": "TOOL", "algo_rel_score": 0.950986921787262, "quality_score": 5.920175075531006, "source_meta": {"github_metadata": {"branch": "sandstorm", "commit_count": 305, "repo_created_at": "2014-08-13T03:43:29.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 21, "forks": 3, "issues": 7, "pull_requests": 0}, "file_meta": {"content_id": "e8af90d18a934bf6a5994872625d0fc724a61882", "size_bytes": 5708, "file_timestamp": 1439940122, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2359e3bb20df587f182fba48f755cadff9ecd6aa"}}, "parsed_source_meta": {"github_metadata": {"branch": "sandstorm", "commit_count": 305, "repo_created_at": "2014-08-13T03:43:29.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 21, "forks": 3, "issues": 7, "pull_requests": 0}, "file_meta": {"content_id": "e8af90d18a934bf6a5994872625d0fc724a61882", "size_bytes": 5708, "file_timestamp": 1439940122, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2359e3bb20df587f182fba48f755cadff9ecd6aa", "url": "https://github.com/jparyani/wave/blob/2359e3bb20df587f182fba48f755cadff9ecd6aa/src/python/api/util.py"}, "gpt4o_tokens": 1397, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.9656278915162158, "noise_severity": 0.6336860422928662, "missing_content_severity": 0.01524466903857038, "factual_density": 3.3823860979558042, "pretraining_utility": 0.39628995470155876, "math_density": 0.18241704454544608, "code_density": 3.5998228713184144, "reasoning_density": 0.5878835744212572, "education_level": 2.0989269007339546, "creative_writing_quality": -0.2160787040715915}, "score_applicability": {"education_level": 0.9988492516069933, "creative_writing_quality": 0.0006710047457154434}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/jfvalenzuelas/Drift-Detector/blob/67f245cf2fbe1715865c6f60759d134491311bd7/engine/utils.py", "id": "python-0-d1603ec50222b327158c03935aaae3af", "text": "import pandas as pd\nimport numpy as np\nimport collections, re\nimport datetime\nimport pickle\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom nltk import word_tokenize\nfrom nltk.corpus import stopwords\nfrom nltk.stem.snowball import SnowballStemmer\nimport spacy\nfrom datetime import datetime\n\nglobal nlp\nnlp = spacy.load('es_core_news_sm')\n\ndef load_data(paths):\n    \"\"\"Load a list with dataframes containing the info of the csv files given as argument\n    \n    Key arguments:\n    list paths -- list of paths to the data files\n    \n    Returns:\n    list dataframes -- list of dataframes with the loaded data\n    dataframe -- full combination of all dataframes\n    \"\"\"\n    class_label = len(paths)-1\n    index = 0\n    dataframes = []\n    tmp_column = []\n    for path in paths:\n        dataframes.append(pd.read_csv(path,\n                                     delimiter = '|', \n                                     names = ['date', 'source', 'title', 'text']))\n        x, y = dataframes[index].shape\n        for i in range (0, x): tmp_column.append(class_label)\n\n        dataframes[index]['class'] = tmp_column\n        tmp_column = []\n        index += 1\n        class_label -= 1\n        \n    return dataframes, pd.concat(dataframes)\n\n    \ndef preprocessing(dataset):\n    \"\"\" Function that cleans the dataset given. Transforms the text to lowercase, removes punctuations,\n    the 10 most frequent words and the 10 less frequent words.\n    \n    Arguments:\n    DataFrame dataset -- DataFrame containing the instances and features.\n    \n    Returns:\n    DataFrame dataset -- clean DataFrame containing the instances and features.\n    \"\"\"\n    \n    print (str(datetime.now())+\" -- INFO: PREPROCESSING RUNNING\\n\")\n    \n    # Text to lowercase\n    dataset['text'] = dataset['text'].apply(lambda x: \" \".join(x.lower() for x in x.split()))\n    \n    # Remove punctuations\n    dataset['text'] = dataset['text'].str.replace('[^\\w\\s]','')\n    \n    # Remove numerics\n    dataset['text'] = dataset['text'].str.replace('[0-9]*','')\n    \n    # Remove stop-words\n    stop_words = stopwords.words('spanish')\n    newStopWords = ['si', 'sin', 'han', 'qué', 'que', 'cómo', 'como', 'cuando', 'cuándo', 'porque', 'no', 'loading', \n                    'día', 'dia', 'año']\n    stop_words.extend(newStopWords)\n    stop_words = set(stop_words)\n    dataset['text'] = dataset['text'].apply(lambda x: \" \".join(x for x in x.split() if x not in stop_words))\n    \n    # 10 most frequent words\n    freq = pd.Series(' '.join(dataset['text']).split()).value_counts()[:10]\n    freq = list(freq.index)\n    dataset['text'] = dataset['text'].apply(lambda x: \" \".join(x for x in x.split() if x not in freq))\n    \n    # 10 less frequent words\n    freq = pd.Series(' '.join(dataset['text']).split()).value_counts()[-10:]\n    freq = list(freq.index)\n    dataset['text'] = dataset['text'].apply(lambda x: \" \".join(x for x in x.split() if x not in freq))\n    \n    return dataset\n\ndef tokenize_2(text):\n    tokens = word_tokenize(text)\n    return tokens\n    \ndef tokenize(text):\n    lemmas = []\n    for token in nlp(text):\n        # Discard verbs and auxiliary verbs\n        if (token.pos_ != 'VERB' and token.pos_ != 'AUX'):\n            lemmas.append(token.lemma_)\n    return lemmas\n\ndef calculateTfidf(instances):\n    #removes features with frequency less than 5\n    vectorizer = TfidfVectorizer(min_df=5, max_df=0.5)\n    response = vectorizer.fit_transform(instances)\n    \n    return response\n\ndef serialize(filename, data):\n    outfile = open(filename, 'wb')\n    pickle.dump(data, outfile)\n    outfile.close()\n    print (str(datetime.now())+\" -- INFO: DATA WAS SERIALIZED SUCCESSFULLY TO FILENAME: \"+filename+\"\\n\")\n            \n    \ndef deserialize(filename):\n    infile = open(filename, 'rb')\n    data = pickle.load(infile)\n    infile.close()\n    print (str(datetime.now())+\" -- INFO: DATA WAS DESERIALIZED SUCCESSFULLY FROM FILENAME: \"+filename+\"\\n\")\n    \n    return data\n\ndef generateTfidfFiles(filename, dataset):\n    print (str(datetime.now())+\" -- INFO: GENERATING TFIDF FILE\\n\")\n    dataset = preprocessing(dataset)\n    class_label = list(dataset['class'])\n    print (str(datetime.now())+\" -- INFO: TOKENIZER RUNNING\\n\")\n    #text = [\" \".join(tokenize(txt)) for txt in text]\n    dataset['text'] = [\" \".join(tokenize(txt)) for txt in dataset['text']]\n    serialize('processed-dataset', dataset)\n    print (str(datetime.now())+\" -- INFO: CALCULATING TF-IDF\\n\")\n    text = dataset['text']\n    dataset_tfidf = calculateTfidf(text).toarray()\n    dataset_tfidf = np.c_[dataset_tfidf, class_label]\n    serialize(filename, dataset_tfidf)\n    \ndef generateStream(original_stream, stream1, stream2, instances_per_stream, drift_instances):\n    original_stream = pd.DataFrame(original_data, columns=['date', 'source', 'title', 'text', 'class'])\n    aux_date = []\n    for row in original_stream.iterrows():\n        aux_date.append(datetime.strptime(row[1]['date'].strip(), '%Y-%m-%d %H:%M:%S'))\n    original_stream['date'] = aux_date\n\n    original_stream  = original_stream.sort_values(by=['date'])\n    original_stream = original_stream.values\n    \n    shuffle_seed = np.arange(original_stream.shape[0])\n    #np.random.shuffle(shuffle_seed)\n    final_stream = np.r_[stream1, stream2]\n    final_stream = final_stream[shuffle_seed]\n    #original_stream = original_stream[shuffle_seed]\n    \n    for row in final_stream[instances_per_stream: instances_per_stream+drift_instances]:\n        if (row[-1] == 1.0):\n            row[-1] = 0.0\n        else:\n            row[-1] = 1.0\n            \n    for row in original_stream[instances_per_stream: instances_per_stream+drift_instances]:\n        if (row[-1] == 1.0):\n            row[-1] = 0.0\n        else:\n            row[-1] = 1.0\n            \n    print(\"Drift generated between \"+str(instances_per_stream)+\" and \"+str(instances_per_stream+drift_instances))\n    print(\"Stream's shape: \"+str(final_stream.shape))\n    \n    return final_stream, original_stream\n\ndef numOfTokensPerCategory(data):\n    c1 = data[data['class'] == 1]\n    c0 = data[data['class'] == 0]\n    \n    t1 = \" \".join(c1['text'])\n    t0 = \" \".join(c0['text'])\n            \n    return len(tokenize_2(t1)), len(tokenize_2(t0))\n    \ndef totalDocuments(data):\n    return len(data)\n\ndef documentsPerCategory(data):\n    documents_c1 = 0\n    documents_c0 = 0\n    \n    for row in data.iterrows():\n        if (row[1]['class'] == 1):\n            documents_c1 += 1\n        else:\n            documents_c0 += 1\n            \n    return documents_c1, documents_c0\n\ndef getKeywords(dataset, num):\n    text = tokenize_2(\" \".join(dataset['text']))\n    return Counter(text).most_common(num)", "text_preview": "import pandas as pd import numpy as np import collections, re import datetime import pickle from sklearn.feature_extraction.text import TfidfVectorizer from nltk import word_tokenize from nltk.corpus import stopwords from nltk.stem.snowball", "input_sha256": "03884cda30394d57d25a9c17909091f0e3018302a5acd5c3abed0145dc6e5ba3", "text_sha256": "d1603ec50222b327158c03935aaae3af536ccf7279ee49c2a0c48dcd03f733dd", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12056, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.3583952538882595, "noise_severity": 0.022374078163749575, "missing_content_severity": 0.04474943971687427, "factual_density": 3.0802192036683165, "pretraining_utility": 0.500943167177614, "math_density": 0.6839532414550299, "code_density": 3.849987653052727, "reasoning_density": 0.25054383773195094, "education_level": 2.3241056136044573, "creative_writing_quality": -0.5405766886474969}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.016886440018299753, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9966025767206377, "secondary": 0.9679895135640475, "tertiary": 0.9856101466266212, "structured_records": 0.004636873985956595, "procedural_steps": 0.0650710140130441, "worked_solution": 0.002495510859962821, "contextual_dialogue": 6.207506033463911e-06, "content_quality": 0.32020647845160344, "noise_severity": 0.981354934863542, "missing_content_severity": 0.9627088002359381, "factual_density": 0.2335160030569302, "pretraining_utility": 0.4454837710731253, "math_density": 0.4300389654541418, "code_density": 0.8749897108772726, "reasoning_density": 0.7912134685567076, "education_level": 0.6435741670076447, "creative_writing_quality": 0.9994980009626724, "primary_topic": 0.9499105681138343, "secondary_topic": 0.5114951308631996}, "probability_margin": {"primary": 0.995426148858274, "secondary": 0.9533973789504945, "tertiary": 0.978927216830586, "education_level": 0.4612397156757314, "creative_writing_quality": 0.9994409604788668, "primary_topic": 0.910516436358167, "secondary_topic": 0.18179407873792863}, "raw_probabilities": {"primary": {"Academic Writing": 5.48328173726802e-05, "Content Listing": 1.5854104244714312e-05, "Creative Writing": 1.3854186265734978e-05, "Customer Support Page": 8.530299772471955e-06, "Q&A Forum": 1.1538226402446782e-05, "Discussion Thread": 6.891981320577763e-06, "Comment Section": 1.1297810048326233e-05, "Missing Content": 2.1617779270758172e-05, "Knowledge Article": 0.00010190765174714776, "Legal Notices": 2.522485088449453e-05, "News Article": 4.371631323921296e-06, "Organizational Announcement": 8.42928673668953e-06, "Nonfiction Writing": 1.1124971694578384e-05, "Organizational About Page": 1.1067230777592343e-05, "Personal About Page": 1.169107008588301e-05, "Personal Blog": 7.3682777734280735e-06, "Product Page": 6.04578844145371e-06, "Spam / Ads": 9.07367874950927e-06, "Structured Data": 0.0007112727000797634, "Technical Writing": 0.9967441360239444, "Transcript / Interview": 1.4169777117838978e-05, "Tutorial / How-To Guide": 0.0008660444552579839, "User Reviews": 5.66823501722257e-06, "Other / Unknown": 0.001317987165670471}, "secondary": {"Academic Writing": 0.0003847805468641486, "Content Listing": 0.00011732021638318793, "Creative Writing": 0.00014098727510819897, "Customer Support Page": 0.00013560540523395554, "Q&A Forum": 8.238621805998388e-05, "Discussion Thread": 5.7533391920167645e-05, "Comment Section": 0.000255816284506917, "Missing Content": 0.0004058269406982169, "Knowledge Article": 0.002296191595343277, "Legal Notices": 0.0002498021460460855, "News Article": 7.97205860401435e-05, "Organizational Announcement": 8.039857679538944e-05, "Nonfiction Writing": 0.00010667106514801987, "Organizational About Page": 0.00010465905032139788, "Personal About Page": 9.209175269995115e-05, "Personal Blog": 8.207809564800142e-05, "Product Page": 5.522847265819398e-05, "Spam / Ads": 8.210588825136425e-05, "Structured Data": 0.0040233904956493375, "Technical Writing": 0.015925904881717717, "Transcript / Interview": 0.00012550124140529563, "Tutorial / How-To Guide": 0.0057488256851734925, "User Reviews": 4.389035611540658e-05, "NONE": 0.9693232838322122}, "tertiary": {"Academic Writing": 0.0001920303151430653, "Content Listing": 5.501235979342502e-05, "Creative Writing": 6.17231597400348e-05, "Customer Support Page": 5.4711883360039724e-05, "Q&A Forum": 2.8126773369383847e-05, "Discussion Thread": 2.5177836005333373e-05, "Comment Section": 0.00010325127298962337, "Missing Content": 0.0003487611478794449, "Knowledge Article": 0.0011236930770839677, "Legal Notices": 0.00016064331203834175, "News Article": 3.2419474379628775e-05, "Organizational Announcement": 3.32755292246144e-05, "Nonfiction Writing": 5.6562490060501414e-05, "Organizational About Page": 5.497769210033734e-05, "Personal About Page": 4.2694304538120546e-05, "Personal Blog": 3.610565992419426e-05, "Product Page": 2.640278409233151e-05, "Spam / Ads": 4.075959235384596e-05, "Structured Data": 0.001145875607231777, "Technical Writing": 0.007282507019925963, "Transcript / Interview": 5.256707798434472e-05, "Tutorial / How-To Guide": 0.00281478531436137, "User Reviews": 1.821246590840128e-05, "NONE": 0.986209723850512}, "structured_records": {"false": 0.9953631260140434, "true": 0.004636873985956595}, "procedural_steps": {"false": 0.9349289859869558, "true": 0.0650710140130441}, "worked_solution": {"false": 0.9975044891400372, "true": 0.002495510859962821}, "contextual_dialogue": {"false": 0.9999937924939666, "true": 6.207506033463911e-06}, "content_quality": {"-2": 0.026609303798487725, "-1": 0.13768351839729737, "0": 0.3734835377948502, "1": 0.375149900136197, "2": 0.08707373987316763}, "noise_severity": {"0": 0.9815167371720753, "1": 0.015663515202064177, "2": 0.0020453939716492614, "3": 0.0004776395984580018, "4": 0.00029671405575321737}, "missing_content_severity": {"0": 0.9699995398793093, "1": 0.018712677384475983, "2": 0.008411803633220938, "3": 0.0022907613460193222, "4": 0.0005852177569746138}, "factual_density": {"0": 0.02330544252349419, "1": 0.05229417002985802, "2": 0.15601796109226124, "3": 0.3576405939636104, "4": 0.41074183239077616}, "pretraining_utility": {"-2": 0.009820920220376991, "-1": 0.08071722788023525, "0": 0.3913409373457163, "1": 0.43493959360873985, "2": 0.08318132094493176}, "math_density": {"0": 0.6800894960364012, "1": 0.14527106786371546, "2": 0.06375443872521924, "3": 0.03236669335778056, "4": 0.07851830401688356}, "code_density": {"0": 0.0002944855747532421, "1": 0.0006020496065760223, "2": 0.018471995186284382, "3": 0.11008426545596303, "4": 0.8705472041764233}, "reasoning_density": {"0": 0.8647695170969436, "1": 0.048865791327699734, "2": 0.0635963365285647, "3": 0.016588046840046205, "4": 0.006180308206745801}, "education_level": {"none": 0.0010619684396174641, "0": 0.0014800802757789027, "1": 0.006832872167520782, "2": 0.7029784725063706, "3": 0.2417387568306392, "4": 0.04590784978007305}, "creative_writing_quality": {"not_applicable": 0.9995816674688937, "-2": 9.301698667605385e-05, "-1": 0.00012111697274170141, "0": 0.00014070699002692846, "1": 4.597303164839942e-05, "2": 1.7518550013219364e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.833464315371148e-05, "Art & Design": 0.00010662254274075186, "Law & Crime": 7.694043353350373e-05, "Education & Careers": 0.00010044690503712412, "Entertainment & Media": 0.00015016212409778336, "Fashion & Beauty": 6.291537161309491e-05, "Business, Economics & Finance": 0.00018003119508972986, "Food & Dining": 7.277661268206823e-05, "Games & Gaming": 0.0001058278148045817, "Electronics & Hardware": 0.0001240299304143686, "Health, Healthcare & Wellness": 7.508292360385912e-05, "History & Geography": 8.254007644840348e-05, "Home, Pets & Hobbies": 5.814488204530185e-05, "Industry & Production": 0.00011173786697000043, "Language, Literature & Philosophy": 0.00298982277061375, "Politics & Government": 6.433173715446202e-05, "Religion & Spirituality": 6.78149192483209e-05, "Science, Mathematics & Engineering": 0.04132064836667367, "Relationships & Community": 3.760524399516918e-05, "Software Usage & Applications": 0.0008037354278078472, "Programming & Software Engineering": 0.9518370847248407, "Sports & Fitness": 9.044934360803853e-05, "Vehicles & Transportation": 4.221004660334133e-05, "Travel & Tourism": 0.0001014629193231247, "Other / Uncovered Subject": 0.0011260060289078125, "Unknown / Insufficient Content": 0.00014323514898942162}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007900025268550578, "Art & Design": 0.0007464760779978825, "Law & Crime": 0.0008296607289276896, "Education & Careers": 0.0005894415929134629, "Entertainment & Media": 0.0010572621909580534, "Fashion & Beauty": 0.0004196135926188663, "Business, Economics & Finance": 0.0009162293328208494, "Food & Dining": 0.0003631586302302428, "Games & Gaming": 0.0004694938362296278, "Electronics & Hardware": 0.0007078019193979718, "Health, Healthcare & Wellness": 0.000624574625987904, "History & Geography": 0.0004979706907458607, "Home, Pets & Hobbies": 0.0003711434911059163, "Industry & Production": 0.0006085641122242194, "Language, Literature & Philosophy": 0.03607287930814657, "Politics & Government": 0.00044231314341149075, "Religion & Spirituality": 0.0005524350092224278, "Science, Mathematics & Engineering": 0.34848970093822484, "Relationships & Community": 0.00022101936330903502, "Software Usage & Applications": 0.0017799739212794874, "Programming & Software Engineering": 0.06983100321915632, "Sports & Fitness": 0.00047774625947360577, "Vehicles & Transportation": 0.00024623141673992335, "Travel & Tourism": 0.0006306100419438396, "Other / Uncovered Subject": 0.0019809143539253633, "NONE": 0.5302837796761535}}, "probabilities": {"primary": {"Academic Writing": 5.48328173726802e-05, "Content Listing": 1.5854104244714312e-05, "Creative Writing": 1.3854186265734978e-05, "Customer Support Page": 8.530299772471955e-06, "Q&A Forum": 1.1538226402446782e-05, "Discussion Thread": 6.891981320577763e-06, "Comment Section": 1.1297810048326233e-05, "Missing Content": 2.1617779270758172e-05, "Knowledge Article": 0.00010190765174714776, "Legal Notices": 2.522485088449453e-05, "News Article": 4.371631323921296e-06, "Organizational Announcement": 8.42928673668953e-06, "Nonfiction Writing": 1.1124971694578384e-05, "Organizational About Page": 1.1067230777592343e-05, "Personal About Page": 1.169107008588301e-05, "Personal Blog": 7.3682777734280735e-06, "Product Page": 6.04578844145371e-06, "Spam / Ads": 9.07367874950927e-06, "Structured Data": 0.0007112727000797634, "Technical Writing": 0.9967441360239444, "Transcript / Interview": 1.4169777117838978e-05, "Tutorial / How-To Guide": 0.0008660444552579839, "User Reviews": 5.66823501722257e-06, "Other / Unknown": 0.001317987165670471}, "secondary": {"Academic Writing": 0.0003847805468641486, "Content Listing": 0.00011732021638318793, "Creative Writing": 0.00014098727510819897, "Customer Support Page": 0.00013560540523395554, "Q&A Forum": 8.238621805998388e-05, "Discussion Thread": 5.7533391920167645e-05, "Comment Section": 0.000255816284506917, "Missing Content": 0.0004058269406982169, "Knowledge Article": 0.002296191595343277, "Legal Notices": 0.0002498021460460855, "News Article": 7.97205860401435e-05, "Organizational Announcement": 8.039857679538944e-05, "Nonfiction Writing": 0.00010667106514801987, "Organizational About Page": 0.00010465905032139788, "Personal About Page": 9.209175269995115e-05, "Personal Blog": 8.207809564800142e-05, "Product Page": 5.522847265819398e-05, "Spam / Ads": 8.210588825136425e-05, "Structured Data": 0.0040233904956493375, "Technical Writing": 0.015925904881717717, "Transcript / Interview": 0.00012550124140529563, "Tutorial / How-To Guide": 0.0057488256851734925, "User Reviews": 4.389035611540658e-05, "NONE": 0.9693232838322122}, "tertiary": {"Academic Writing": 0.0001920303151430653, "Content Listing": 5.501235979342502e-05, "Creative Writing": 6.17231597400348e-05, "Customer Support Page": 5.4711883360039724e-05, "Q&A Forum": 2.8126773369383847e-05, "Discussion Thread": 2.5177836005333373e-05, "Comment Section": 0.00010325127298962337, "Missing Content": 0.0003487611478794449, "Knowledge Article": 0.0011236930770839677, "Legal Notices": 0.00016064331203834175, "News Article": 3.2419474379628775e-05, "Organizational Announcement": 3.32755292246144e-05, "Nonfiction Writing": 5.6562490060501414e-05, "Organizational About Page": 5.497769210033734e-05, "Personal About Page": 4.2694304538120546e-05, "Personal Blog": 3.610565992419426e-05, "Product Page": 2.640278409233151e-05, "Spam / Ads": 4.075959235384596e-05, "Structured Data": 0.001145875607231777, "Technical Writing": 0.007282507019925963, "Transcript / Interview": 5.256707798434472e-05, "Tutorial / How-To Guide": 0.00281478531436137, "User Reviews": 1.821246590840128e-05, "NONE": 0.986209723850512}, "structured_records": {"false": 0.9953631260140434, "true": 0.004636873985956595}, "procedural_steps": {"false": 0.9349289859869558, "true": 0.0650710140130441}, "worked_solution": {"false": 0.9975044891400372, "true": 0.002495510859962821}, "contextual_dialogue": {"false": 0.9999937924939666, "true": 6.207506033463911e-06}, "content_quality": {"-2": 0.026609303798487728, "-1": 0.1376835183972974, "0": 0.3734835377948503, "1": 0.37514990013619703, "2": 0.08707373987316765}, "noise_severity": {"0": 0.9815167371720753, "1": 0.015663515202064177, "2": 0.0020453939716492614, "3": 0.0004776395984580018, "4": 0.00029671405575321737}, "missing_content_severity": {"0": 0.9699995398793091, "1": 0.01871267738447598, "2": 0.008411803633220936, "3": 0.002290761346019322, "4": 0.0005852177569746137}, "factual_density": {"0": 0.02330544252349419, "1": 0.05229417002985802, "2": 0.15601796109226124, "3": 0.3576405939636104, "4": 0.41074183239077616}, "pretraining_utility": {"-2": 0.00982092022037699, "-1": 0.08071722788023523, "0": 0.3913409373457162, "1": 0.43493959360873974, "2": 0.08318132094493175}, "math_density": {"0": 0.6800894960364012, "1": 0.14527106786371546, "2": 0.06375443872521924, "3": 0.03236669335778056, "4": 0.07851830401688356}, "code_density": {"0": 0.0002944855747532421, "1": 0.0006020496065760223, "2": 0.018471995186284382, "3": 0.11008426545596303, "4": 0.8705472041764233}, "reasoning_density": {"0": 0.8647695170969436, "1": 0.048865791327699734, "2": 0.0635963365285647, "3": 0.016588046840046205, "4": 0.006180308206745801}, "education_level": {"none": 0.0010619684396174641, "0": 0.0014800802757789027, "1": 0.006832872167520782, "2": 0.7029784725063706, "3": 0.2417387568306392, "4": 0.04590784978007305}, "creative_writing_quality": {"not_applicable": 0.9995816674688937, "-2": 9.301698667605385e-05, "-1": 0.00012111697274170141, "0": 0.00014070699002692846, "1": 4.597303164839942e-05, "2": 1.7518550013219364e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.833464315371148e-05, "Art & Design": 0.00010662254274075186, "Law & Crime": 7.694043353350373e-05, "Education & Careers": 0.00010044690503712412, "Entertainment & Media": 0.00015016212409778336, "Fashion & Beauty": 6.291537161309491e-05, "Business, Economics & Finance": 0.00018003119508972986, "Food & Dining": 7.277661268206823e-05, "Games & Gaming": 0.0001058278148045817, "Electronics & Hardware": 0.0001240299304143686, "Health, Healthcare & Wellness": 7.508292360385912e-05, "History & Geography": 8.254007644840348e-05, "Home, Pets & Hobbies": 5.814488204530185e-05, "Industry & Production": 0.00011173786697000043, "Language, Literature & Philosophy": 0.00298982277061375, "Politics & Government": 6.433173715446202e-05, "Religion & Spirituality": 6.78149192483209e-05, "Science, Mathematics & Engineering": 0.04132064836667367, "Relationships & Community": 3.760524399516918e-05, "Software Usage & Applications": 0.0008037354278078472, "Programming & Software Engineering": 0.9518370847248407, "Sports & Fitness": 9.044934360803853e-05, "Vehicles & Transportation": 4.221004660334133e-05, "Travel & Tourism": 0.0001014629193231247, "Other / Uncovered Subject": 0.0011260060289078125, "Unknown / Insufficient Content": 0.00014323514898942162}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007900025268550578, "Art & Design": 0.0007464760779978825, "Law & Crime": 0.0008296607289276896, "Education & Careers": 0.0005894415929134629, "Entertainment & Media": 0.0010572621909580534, "Fashion & Beauty": 0.0004196135926188663, "Business, Economics & Finance": 0.0009162293328208494, "Food & Dining": 0.0003631586302302428, "Games & Gaming": 0.0004694938362296278, "Electronics & Hardware": 0.0007078019193979718, "Health, Healthcare & Wellness": 0.000624574625987904, "History & Geography": 0.0004979706907458607, "Home, Pets & Hobbies": 0.0003711434911059163, "Industry & Production": 0.0006085641122242194, "Language, Literature & Philosophy": 0.03607287930814657, "Politics & Government": 0.00044231314341149075, "Religion & Spirituality": 0.0005524350092224278, "Science, Mathematics & Engineering": 0.34848970093822484, "Relationships & Community": 0.00022101936330903502, "Software Usage & Applications": 0.0017799739212794874, "Programming & Software Engineering": 0.06983100321915632, "Sports & Fitness": 0.00047774625947360577, "Vehicles & Transportation": 0.00024623141673992335, "Travel & Tourism": 0.0006306100419438396, "Other / Uncovered Subject": 0.0019809143539253633, "NONE": 0.5302837796761535}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10253, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10253, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:17.329356+00:00", "scoring_key_slot": "primary", "dataset_uid": "1a77e042-d300-4002-9b41-1343470696d3", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "8ffba9fe44bb05d6a483de686fcc349598c74a4b4df9c79af72842ce9348f80b", "source_metadata": {"uuid": "1a77e042-d300-4002-9b41-1343470696d3", "repo_name": "jfvalenzuelas/Drift-Detector", "relative_path": "engine/utils.py", "category": "DATA", "algo_rel_score": 0.9561583995819092, "quality_score": 4.142253875732422, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 3, "repo_created_at": "2020-05-10T09:40:52.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 5}, "file_meta": {"content_id": "af88320e6f3a99a5565897b29c075179286f5094", "size_bytes": 6645, "file_timestamp": 1589108134, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "67f245cf2fbe1715865c6f60759d134491311bd7"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 3, "repo_created_at": "2020-05-10T09:40:52.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 5}, "file_meta": {"content_id": "af88320e6f3a99a5565897b29c075179286f5094", "size_bytes": 6645, "file_timestamp": 1589108134, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "67f245cf2fbe1715865c6f60759d134491311bd7", "url": "https://github.com/jfvalenzuelas/Drift-Detector/blob/67f245cf2fbe1715865c6f60759d134491311bd7/engine/utils.py"}, "gpt4o_tokens": 1668, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.3583952538882595, "noise_severity": 0.022374078163749575, "missing_content_severity": 0.04474943971687428, "factual_density": 3.0802192036683165, "pretraining_utility": 0.5009431671776141, "math_density": 0.6839532414550299, "code_density": 3.849987653052727, "reasoning_density": 0.25054383773195094, "education_level": 2.3241056136044573, "creative_writing_quality": -0.5405766886474969}, "score_applicability": {"education_level": 0.9989380315603825, "creative_writing_quality": 0.0004183325311063025}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/kalaspuff/utcnow/blob/27b97ee22a2f734d2d0c358e50245536fb1aa108/tests/test_rfc3799.py", "id": "python-0-9bdaa3ad0f5dd6f6e501e6bb15b21679", "text": "import datetime\n\nimport pytest\nfrom google.protobuf.timestamp_pb2 import Timestamp\n\n\n@pytest.mark.parametrize(\n    \"value, expected_output, expect_error\",\n    [\n        # This represents 20 minutes and 50.52 seconds after the 23rd hour of April 12th, 1985 in UTC.\n        (\"1985-04-12T23:20:50.52Z\", \"1985-04-12T23:20:50.520000Z\", False),\n        # This represents 39 minutes and 57 seconds after the 16th hour of December 19th, 1996 with an offset of\n        # -08:00 from UTC (Pacific Standard Time).  Note that this is equivalent to 1996-12-20T00:39:57Z in UTC.\n        (\"1996-12-19T16:39:57-08:00\", \"1996-12-20T00:39:57.000000Z\", False),\n        # This represents the same instant of time as noon, January 1, 1937, Netherlands time. Standard time in the\n        # Netherlands was exactly 19 minutes and 32.13 seconds ahead of UTC by law from 1909-05-01 through 1937-06-30.\n        (\"1937-01-01T12:00:27.87+00:20\", \"1937-01-01T11:40:27.870000Z\", False),\n        # This represents the leap second inserted at the end of 1990. However this will fail since we can't handle\n        # second number 60.\n        (\"1990-12-31T15:59:60-08:00\", \"1990-12-31T23:59:60.000000Z\", True),\n        # Other tests for allowed input formats\n        (\"2021-02-18\", \"2021-02-18T00:00:00.000000Z\", False),\n        (\"2021-02-18 01:00\", \"2021-02-18T01:00:00.000000Z\", False),\n        (\"2021-02-18 03:00+01:00\", \"2021-02-18T02:00:00.000000Z\", False),\n        (\"2021-02-18-01:00\", \"2021-02-18T01:00:00.000000Z\", False),\n        (\"2021-02-18+01:00\", \"2021-02-17T23:00:00.000000Z\", False),\n        (\"2021-02-18 -01:00\", \"2021-02-18T01:00:00.000000Z\", False),\n        (\"2021-02-18 +01:00\", \"2021-02-17T23:00:00.000000Z\", False),\n        (\"2021-02-18T23:55\", \"2021-02-18T23:55:00.000000Z\", False),\n        (\"2021-02-18T23:55:10\", \"2021-02-18T23:55:10.000000Z\", False),\n        (\"2021-02-18T23:55:10.0\", \"2021-02-18T23:55:10.000000Z\", False),\n        (\"2021-02-18T23:55:10.0+05:00\", \"2021-02-18T18:55:10.000000Z\", False),\n        (\"2021-02-18T23:55:10.0-05:00\", \"2021-02-19T04:55:10.000000Z\", False),\n        (\"2021-02-18T23:55:10.550-05:00\", \"2021-02-19T04:55:10.550000Z\", False),\n        (\"2021-02-18 23:55:10.550+05:00\", \"2021-02-18T18:55:10.550000Z\", False),\n        (\"2021-02-18 23:55:10.550-01:00\", \"2021-02-19T00:55:10.550000Z\", False),\n        (\"2021-02-28 10:10:59.123987+00:00\", \"2021-02-28T10:10:59.123987Z\", False),\n        (\"2021-02-28 10:10:59.123987 +00:00\", \"2021-02-28T10:10:59.123987Z\", False),\n        (\"2021-02-28 10:10:59.123987 +01:00\", \"2021-02-28T09:10:59.123987Z\", False),\n        (\"2021-02-28 10:10:59.123987 -01:00\", \"2021-02-28T11:10:59.123987Z\", False),\n        (\"2021-02-28 10:10:59.123987Z\", \"2021-02-28T10:10:59.123987Z\", False),\n        (\"2021-02-28 10:10:59.123987 UTC\", \"2021-02-28T10:10:59.123987Z\", False),\n        # Not allowed input formats\n        (\"2021-02-28 10:10:59.123987+00:00 UTC\", \"\", True),\n        (\"2021-02-28 10:10:59.123987 Europe/Stockholm\", \"\", True),\n        (\"2021/02/28\", \"\", True),\n        (\"21-02-28 10:10:59.123987+00:00\", \"\", True),\n        (\"2021-02\", \"\", True),\n        (\"2021-02-30\", \"\", True),\n        (\"1900-01-01 20:30.123\", \"\", True),\n    ],\n)\ndef test_to_string_values(value: str, expected_output: str, expect_error: bool) -> None:\n    import utcnow\n\n    try:\n        assert isinstance(utcnow.as_string(value), str)\n        assert isinstance(utcnow.as_datetime(value), datetime.datetime)\n        assert isinstance(utcnow.as_unixtime(value), (float, int))\n        assert isinstance(utcnow.as_protobuf(value), Timestamp)\n        if expect_error:\n            assert False\n    except Exception:\n        if not expect_error:\n            raise\n        if not expect_error:\n            # unreachable\n            assert False\n\n        assert True\n        return\n\n    assert utcnow.as_string(value) == expected_output\n    assert utcnow.as_string(expected_output) == expected_output\n    assert utcnow.as_string(expected_output) == utcnow.as_string(expected_output)\n    assert utcnow.as_datetime(value) == utcnow.as_datetime(expected_output)\n    assert utcnow.utcnow(utcnow.as_datetime(value)) == utcnow.utcnow(utcnow.as_datetime(expected_output))\n    assert utcnow.utcnow(utcnow.as_datetime(value).replace(tzinfo=None)) == expected_output\n    assert utcnow.as_string(utcnow.utcnow(utcnow.as_datetime(value))) == expected_output\n", "text_preview": "import datetime import pytest from google.protobuf.timestamp_pb2 import Timestamp @pytest.mark.parametrize( \"value, expected_output, expect_error\", [ # This represents 20 minutes and 50.52 seconds after the 23rd hour of April 12th, 1985 in ", "input_sha256": "646b324fae47bc918e0fb65bf7b8ea6df25acbe7fb2aaa116c61b768c9f5af0a", "text_sha256": "9bdaa3ad0f5dd6f6e501e6bb15b2167974a530aee0e43e0d03afdd9137fe5db6", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12057, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": ["structured_records"]}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.8575734021315955, "noise_severity": 0.033547696683434526, "missing_content_severity": 0.00477334268943952, "factual_density": 3.263614754632929, "pretraining_utility": 0.36336986336830507, "math_density": 0.6337298582914392, "code_density": 3.5559130670548242, "reasoning_density": 0.7627774636858378, "education_level": 2.306371597622493, "creative_writing_quality": -0.31424518091584414}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.12303172175767252, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9795460783519653, "secondary": 0.7440647868358029, "tertiary": 0.8724457138872874, "structured_records": 0.8933939066123433, "procedural_steps": 0.002564627437092036, "worked_solution": 0.12730946692444584, "contextual_dialogue": 7.125328169634556e-06, "content_quality": 0.42523374883821774, "noise_severity": 0.9720435860971379, "missing_content_severity": 0.9960222144254671, "factual_density": 0.3863456288607743, "pretraining_utility": 0.4164392694381228, "math_density": 0.4718917847571339, "code_density": 0.6299275558790203, "reasoning_density": 0.3643521135951353, "education_level": 0.6423810260965773, "creative_writing_quality": 0.9995427475181953, "primary_topic": 0.9867855216773248, "secondary_topic": 0.8756254505355303}, "probability_margin": {"primary": 0.9672997992568079, "secondary": 0.5600974332815769, "tertiary": 0.7972745780050015, "education_level": 0.4843443160977796, "creative_writing_quality": 0.9994655000931022, "primary_topic": 0.979233670042787, "secondary_topic": 0.8187136330607472}, "raw_probabilities": {"primary": {"Academic Writing": 7.803962253294199e-05, "Content Listing": 3.3882430664108106e-05, "Creative Writing": 2.8002314162882626e-05, "Customer Support Page": 1.7323938076994566e-05, "Q&A Forum": 1.8122645363447694e-05, "Discussion Thread": 1.6813546207824657e-05, "Comment Section": 2.2669570051629164e-05, "Missing Content": 3.130927117412278e-05, "Knowledge Article": 7.857927041813794e-05, "Legal Notices": 4.1620951560163874e-05, "News Article": 9.573315964754685e-06, "Organizational Announcement": 2.01260300385911e-05, "Nonfiction Writing": 2.4647630337817573e-05, "Organizational About Page": 2.4088714810615867e-05, "Personal About Page": 1.9624716058456067e-05, "Personal Blog": 1.0409778708637037e-05, "Product Page": 1.2335232502668893e-05, "Spam / Ads": 1.7649026973887103e-05, "Structured Data": 0.013098525830492154, "Technical Writing": 0.9803983250873001, "Transcript / Interview": 2.878151743569084e-05, "Tutorial / How-To Guide": 0.0001343774631748457, "User Reviews": 1.1372985830837737e-05, "Other / Unknown": 0.005823799110158444}, "secondary": {"Academic Writing": 0.0009806745270018805, "Content Listing": 0.0004914969315695256, "Creative Writing": 0.0005984233023629264, "Customer Support Page": 0.00032731310161909533, "Q&A Forum": 0.00030034005130610125, "Discussion Thread": 0.0002816970598649185, "Comment Section": 0.0004401040388219661, "Missing Content": 0.0005485290822297382, "Knowledge Article": 0.002779596782452701, "Legal Notices": 0.0003909676743278592, "News Article": 0.00033991970090425233, "Organizational Announcement": 0.00036409540260224027, "Nonfiction Writing": 0.0008257268769895173, "Organizational About Page": 0.0004173880742587251, "Personal About Page": 0.0003305039008441612, "Personal Blog": 0.00031691222540452326, "Product Page": 0.0002485134630148783, "Spam / Ads": 0.0003363785350474392, "Structured Data": 0.19463132076940085, "Technical Writing": 0.03876278353299548, "Transcript / Interview": 0.000511260457698953, "Tutorial / How-To Guide": 0.0008529320875296001, "User Reviews": 0.00019436837077497647, "NONE": 0.7547287540509777}, "tertiary": {"Academic Writing": 0.0007605578215416336, "Content Listing": 0.00020317558679323327, "Creative Writing": 0.00029645370161336513, "Customer Support Page": 0.0001544621068934488, "Q&A Forum": 0.000125075655350618, "Discussion Thread": 0.00012303095212079775, "Comment Section": 0.0002739785578027503, "Missing Content": 0.0003025745884020411, "Knowledge Article": 0.006952402963614512, "Legal Notices": 0.0002520996880752338, "News Article": 0.0001550860283812045, "Organizational Announcement": 0.00016820074437931805, "Nonfiction Writing": 0.0018672448349298608, "Organizational About Page": 0.00024702731960054544, "Personal About Page": 0.00015677233407708526, "Personal Blog": 0.00015960268194453782, "Product Page": 0.00011537430662671994, "Spam / Ads": 0.00015944963314103817, "Structured Data": 0.08048589780364902, "Technical Writing": 0.028115481264696946, "Transcript / Interview": 0.00022034056375554544, "Tutorial / How-To Guide": 0.0008605092399765315, "User Reviews": 8.472581398365899e-05, "NONE": 0.8777604758086505}, "structured_records": {"false": 0.1066060933876567, "true": 0.8933939066123433}, "procedural_steps": {"false": 0.9974353725629079, "true": 0.002564627437092036}, "worked_solution": {"false": 0.8726905330755541, "true": 0.12730946692444584}, "contextual_dialogue": {"false": 0.9999928746718304, "true": 7.125328169634556e-06}, "content_quality": {"-2": 0.008824557287623613, "-1": 0.07901769877637682, "0": 0.23156398021564709, "1": 0.40694731195748535, "2": 0.2736464517628671}, "noise_severity": {"0": 0.9706735316438126, "1": 0.026234116073377008, "2": 0.0022866390095531513, "3": 0.00048255050207737294, "4": 0.000323162771179774}, "missing_content_severity": {"0": 0.9971276519215003, "1": 0.0014761014816755196, "2": 0.0010092870056981479, "3": 0.0002691711681366023, "4": 0.00011778842298947442}, "factual_density": {"0": 0.005421236698721883, "1": 0.026916847694363432, "2": 0.1304186429316326, "3": 0.3731124696258278, "4": 0.4641308030494542}, "pretraining_utility": {"-2": 0.012876608505135733, "-1": 0.14269828964270223, "0": 0.3975588836670013, "1": 0.36191106634904274, "2": 0.084955151836118}, "math_density": {"0": 0.7244316742130272, "1": 0.1085881416600004, "2": 0.0528133854008233, "3": 0.03715224907480425, "4": 0.07701454965134485}, "code_density": {"0": 0.0004583111553318456, "1": 0.0011921041608088765, "2": 0.0742519363390266, "3": 0.29017350316336843, "4": 0.6339241451814642}, "reasoning_density": {"0": 0.5936164028196304, "1": 0.18744209247094623, "2": 0.13254508222769334, "3": 0.03534048316741482, "4": 0.05105593931431507}, "education_level": {"none": 0.001217544520675863, "0": 0.0017487937304494767, "1": 0.020987636776889935, "2": 0.7019841884138144, "3": 0.21763987231603474, "4": 0.05642196424213565}, "creative_writing_quality": {"not_applicable": 0.9996189562651627, "-2": 6.971506244751467e-05, "-1": 8.136754533226227e-05, "0": 0.0001534561720605097, "1": 5.195339715765274e-05, "2": 2.455155783941305e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.115904400785201e-05, "Art & Design": 4.057209997024696e-05, "Law & Crime": 8.911742942712418e-05, "Education & Careers": 3.4456729043274043e-05, "Entertainment & Media": 2.4571533892297196e-05, "Fashion & Beauty": 1.9457314735826513e-05, "Business, Economics & Finance": 4.765640282814039e-05, "Food & Dining": 2.549215808455998e-05, "Games & Gaming": 3.916564396460144e-05, "Electronics & Hardware": 5.5413747437378604e-05, "Health, Healthcare & Wellness": 3.143596849259782e-05, "History & Geography": 0.001501937593491379, "Home, Pets & Hobbies": 2.4744491254381723e-05, "Industry & Production": 3.2646628831697866e-05, "Language, Literature & Philosophy": 6.934474896713551e-05, "Politics & Government": 0.00013171600394160863, "Religion & Spirituality": 5.2847734211994835e-05, "Science, Mathematics & Engineering": 0.008060100800794412, "Relationships & Community": 1.9587201870990682e-05, "Software Usage & Applications": 0.0002806264389109005, "Programming & Software Engineering": 0.9872937708435815, "Sports & Fitness": 4.937327458592602e-05, "Vehicles & Transportation": 2.5904221785651433e-05, "Travel & Tourism": 0.0001013472911645686, "Other / Uncovered Subject": 0.0018497701024550405, "Unknown / Insufficient Content": 6.77845522688301e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006140126081328824, "Art & Design": 0.00041682414835919813, "Law & Crime": 0.0014849218186983757, "Education & Careers": 0.0003866156189694288, "Entertainment & Media": 0.00040372982352193985, "Fashion & Beauty": 0.00021391123424529485, "Business, Economics & Finance": 0.0005350329084289243, "Food & Dining": 0.00023618636942252864, "Games & Gaming": 0.000307840845438899, "Electronics & Hardware": 0.000378068325087428, "Health, Healthcare & Wellness": 0.0003192305642812294, "History & Geography": 0.018286305960955256, "Home, Pets & Hobbies": 0.00021401632089922807, "Industry & Production": 0.0002240436822643204, "Language, Literature & Philosophy": 0.00026714716233993926, "Politics & Government": 0.0011047548183189342, "Religion & Spirituality": 0.0004894299011234755, "Science, Mathematics & Engineering": 0.06169545399264735, "Relationships & Community": 0.0001407228358946067, "Software Usage & Applications": 0.0011737717441378734, "Programming & Software Engineering": 0.02465455136735611, "Sports & Fitness": 0.00025587470094512997, "Vehicles & Transportation": 0.0001653163138702557, "Travel & Tourism": 0.0005801303194694663, "Other / Uncovered Subject": 0.0050430195617974394, "NONE": 0.8804090870533945}}, "probabilities": {"primary": {"Academic Writing": 7.8039622532942e-05, "Content Listing": 3.388243066410811e-05, "Creative Writing": 2.8002314162882633e-05, "Customer Support Page": 1.732393807699457e-05, "Q&A Forum": 1.8122645363447697e-05, "Discussion Thread": 1.681354620782466e-05, "Comment Section": 2.2669570051629167e-05, "Missing Content": 3.1309271174122783e-05, "Knowledge Article": 7.857927041813795e-05, "Legal Notices": 4.162095156016388e-05, "News Article": 9.573315964754687e-06, "Organizational Announcement": 2.0126030038591104e-05, "Nonfiction Writing": 2.464763033781758e-05, "Organizational About Page": 2.4088714810615874e-05, "Personal About Page": 1.962471605845607e-05, "Personal Blog": 1.0409778708637038e-05, "Product Page": 1.2335232502668896e-05, "Spam / Ads": 1.7649026973887106e-05, "Structured Data": 0.013098525830492157, "Technical Writing": 0.9803983250873003, "Transcript / Interview": 2.8781517435690846e-05, "Tutorial / How-To Guide": 0.00013437746317484573, "User Reviews": 1.1372985830837738e-05, "Other / Unknown": 0.005823799110158445}, "secondary": {"Academic Writing": 0.0009806745270018805, "Content Listing": 0.0004914969315695256, "Creative Writing": 0.0005984233023629264, "Customer Support Page": 0.00032731310161909533, "Q&A Forum": 0.00030034005130610125, "Discussion Thread": 0.0002816970598649185, "Comment Section": 0.0004401040388219661, "Missing Content": 0.0005485290822297382, "Knowledge Article": 0.002779596782452701, "Legal Notices": 0.0003909676743278592, "News Article": 0.00033991970090425233, "Organizational Announcement": 0.00036409540260224027, "Nonfiction Writing": 0.0008257268769895173, "Organizational About Page": 0.0004173880742587251, "Personal About Page": 0.0003305039008441612, "Personal Blog": 0.00031691222540452326, "Product Page": 0.0002485134630148783, "Spam / Ads": 0.0003363785350474392, "Structured Data": 0.19463132076940085, "Technical Writing": 0.03876278353299548, "Transcript / Interview": 0.000511260457698953, "Tutorial / How-To Guide": 0.0008529320875296001, "User Reviews": 0.00019436837077497647, "NONE": 0.7547287540509777}, "tertiary": {"Academic Writing": 0.0007605578215416334, "Content Listing": 0.00020317558679323322, "Creative Writing": 0.0002964537016133651, "Customer Support Page": 0.00015446210689344878, "Q&A Forum": 0.00012507565535061797, "Discussion Thread": 0.00012303095212079772, "Comment Section": 0.00027397855780275025, "Missing Content": 0.000302574588402041, "Knowledge Article": 0.00695240296361451, "Legal Notices": 0.00025209968807523377, "News Article": 0.00015508602838120448, "Organizational Announcement": 0.00016820074437931803, "Nonfiction Writing": 0.0018672448349298604, "Organizational About Page": 0.0002470273196005454, "Personal About Page": 0.00015677233407708523, "Personal Blog": 0.0001596026819445378, "Product Page": 0.00011537430662671992, "Spam / Ads": 0.00015944963314103814, "Structured Data": 0.08048589780364901, "Technical Writing": 0.02811548126469694, "Transcript / Interview": 0.0002203405637555454, "Tutorial / How-To Guide": 0.0008605092399765312, "User Reviews": 8.472581398365897e-05, "NONE": 0.8777604758086502}, "structured_records": {"false": 0.1066060933876567, "true": 0.8933939066123433}, "procedural_steps": {"false": 0.9974353725629079, "true": 0.002564627437092036}, "worked_solution": {"false": 0.8726905330755541, "true": 0.12730946692444584}, "contextual_dialogue": {"false": 0.9999928746718304, "true": 7.125328169634556e-06}, "content_quality": {"-2": 0.008824557287623613, "-1": 0.07901769877637682, "0": 0.23156398021564709, "1": 0.40694731195748535, "2": 0.2736464517628671}, "noise_severity": {"0": 0.9706735316438126, "1": 0.026234116073377008, "2": 0.0022866390095531513, "3": 0.00048255050207737294, "4": 0.000323162771179774}, "missing_content_severity": {"0": 0.9971276519215003, "1": 0.0014761014816755196, "2": 0.0010092870056981479, "3": 0.0002691711681366023, "4": 0.00011778842298947442}, "factual_density": {"0": 0.005421236698721884, "1": 0.026916847694363435, "2": 0.13041864293163263, "3": 0.37311246962582784, "4": 0.46413080304945425}, "pretraining_utility": {"-2": 0.012876608505135733, "-1": 0.14269828964270223, "0": 0.3975588836670013, "1": 0.36191106634904274, "2": 0.084955151836118}, "math_density": {"0": 0.7244316742130272, "1": 0.1085881416600004, "2": 0.0528133854008233, "3": 0.03715224907480425, "4": 0.07701454965134485}, "code_density": {"0": 0.0004583111553318456, "1": 0.0011921041608088765, "2": 0.0742519363390266, "3": 0.29017350316336843, "4": 0.6339241451814642}, "reasoning_density": {"0": 0.5936164028196305, "1": 0.18744209247094626, "2": 0.13254508222769337, "3": 0.035340483167414824, "4": 0.05105593931431508}, "education_level": {"none": 0.001217544520675863, "0": 0.0017487937304494767, "1": 0.020987636776889935, "2": 0.7019841884138144, "3": 0.21763987231603474, "4": 0.05642196424213565}, "creative_writing_quality": {"not_applicable": 0.9996189562651627, "-2": 6.971506244751467e-05, "-1": 8.136754533226227e-05, "0": 0.0001534561720605097, "1": 5.195339715765274e-05, "2": 2.455155783941305e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.1159044007852014e-05, "Art & Design": 4.057209997024697e-05, "Law & Crime": 8.91174294271242e-05, "Education & Careers": 3.445672904327405e-05, "Entertainment & Media": 2.45715338922972e-05, "Fashion & Beauty": 1.9457314735826516e-05, "Business, Economics & Finance": 4.76564028281404e-05, "Food & Dining": 2.5492158084559985e-05, "Games & Gaming": 3.9165643964601446e-05, "Electronics & Hardware": 5.541374743737861e-05, "Health, Healthcare & Wellness": 3.1435968492597826e-05, "History & Geography": 0.0015019375934913792, "Home, Pets & Hobbies": 2.4744491254381726e-05, "Industry & Production": 3.264662883169787e-05, "Language, Literature & Philosophy": 6.934474896713552e-05, "Politics & Government": 0.00013171600394160866, "Religion & Spirituality": 5.284773421199484e-05, "Science, Mathematics & Engineering": 0.008060100800794414, "Relationships & Community": 1.9587201870990685e-05, "Software Usage & Applications": 0.00028062643891090054, "Programming & Software Engineering": 0.9872937708435816, "Sports & Fitness": 4.937327458592603e-05, "Vehicles & Transportation": 2.5904221785651437e-05, "Travel & Tourism": 0.00010134729116456861, "Other / Uncovered Subject": 0.0018497701024550407, "Unknown / Insufficient Content": 6.778455226883011e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006140126081328824, "Art & Design": 0.00041682414835919813, "Law & Crime": 0.0014849218186983757, "Education & Careers": 0.0003866156189694288, "Entertainment & Media": 0.00040372982352193985, "Fashion & Beauty": 0.00021391123424529485, "Business, Economics & Finance": 0.0005350329084289243, "Food & Dining": 0.00023618636942252864, "Games & Gaming": 0.000307840845438899, "Electronics & Hardware": 0.000378068325087428, "Health, Healthcare & Wellness": 0.0003192305642812294, "History & Geography": 0.018286305960955256, "Home, Pets & Hobbies": 0.00021401632089922807, "Industry & Production": 0.0002240436822643204, "Language, Literature & Philosophy": 0.00026714716233993926, "Politics & Government": 0.0011047548183189342, "Religion & Spirituality": 0.0004894299011234755, "Science, Mathematics & Engineering": 0.06169545399264735, "Relationships & Community": 0.0001407228358946067, "Software Usage & Applications": 0.0011737717441378734, "Programming & Software Engineering": 0.02465455136735611, "Sports & Fitness": 0.00025587470094512997, "Vehicles & Transportation": 0.0001653163138702557, "Travel & Tourism": 0.0005801303194694663, "Other / Uncovered Subject": 0.0050430195617974394, "NONE": 0.8804090870533945}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10718, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10718, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:02.371787+00:00", "scoring_key_slot": "primary", "dataset_uid": "a11e9acd-4eb6-4b08-9374-1bdc0682f4fa", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "ea58fafd9ea5a0125083b69b34d7e796497d94de576bc7dc10dbee5d561af61f", "source_metadata": {"uuid": "a11e9acd-4eb6-4b08-9374-1bdc0682f4fa", "repo_name": "kalaspuff/utcnow", "relative_path": "tests/test_rfc3799.py", "category": "TEST", "algo_rel_score": 0.9938579201698303, "quality_score": 7.417626857757568, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 364, "repo_created_at": "2021-02-18T07:47:37.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 3, "forks": 0, "issues": 0, "pull_requests": 8}, "file_meta": {"content_id": "e86288fb952fbda538d987dcbce3f15fa40bf262", "size_bytes": 4360, "file_timestamp": 1709212820, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "27b97ee22a2f734d2d0c358e50245536fb1aa108"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 364, "repo_created_at": "2021-02-18T07:47:37.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 3, "forks": 0, "issues": 0, "pull_requests": 8}, "file_meta": {"content_id": "e86288fb952fbda538d987dcbce3f15fa40bf262", "size_bytes": 4360, "file_timestamp": 1709212820, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "27b97ee22a2f734d2d0c358e50245536fb1aa108", "url": "https://github.com/kalaspuff/utcnow/blob/27b97ee22a2f734d2d0c358e50245536fb1aa108/tests/test_rfc3799.py"}, "gpt4o_tokens": 1569, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.8575734021315955, "noise_severity": 0.033547696683434526, "missing_content_severity": 0.00477334268943952, "factual_density": 3.263614754632929, "pretraining_utility": 0.36336986336830507, "math_density": 0.6337298582914392, "code_density": 3.5559130670548242, "reasoning_density": 0.7627774636858378, "education_level": 2.306371597622493, "creative_writing_quality": -0.31424518091584414}, "score_applicability": {"education_level": 0.9987824554793242, "creative_writing_quality": 0.00038104373483735244}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/Cheikh27/back_signalement_citoyen/blob/cf2809ebd991c4090c168aa886b4c037907e4150/model_verification_categorisation/models_service.py", "id": "python-0-cad1dba0b06d8fff5e6cc9bcc59ee8bc", "text": "from flask import Flask, request, jsonify\nfrom transformers import BertTokenizer, BertModel\nimport torch\nimport torchvision.models as models\nfrom torchvision import transforms\nfrom PIL import Image\nimport cv2\nimport numpy as np\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom sklearn.ensemble import RandomForestClassifier\nimport os\nimport base64\nimport io\nimport logging\nimport tempfile\nfrom werkzeug.utils import secure_filename\nimport mimetypes\n\n# Configuration du logging\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\napp = Flask(__name__)\napp.config['MAX_CONTENT_LENGTH'] = 50 * 1024 * 1024  # 50MB max\n\n# ========== CONFIGURATION DES MODÈLES ==========\ndef load_models():\n    \"\"\"Charge les modèles avec gestion d'erreur\"\"\"\n    try:\n        # Modèle de texte\n        print(\"📚 Chargement du modèle BERT...\")\n        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n        text_model = BertModel.from_pretrained('bert-base-uncased')\n        text_model.eval()\n        \n        # Modèle d'image\n        print(\"🖼️ Chargement du modèle ResNet50...\")\n        image_model = models.resnet50(pretrained=True)\n        # Supprimer la dernière couche pour obtenir les features\n        image_model = torch.nn.Sequential(*list(image_model.children())[:-1])\n        image_model.eval()\n        \n        print(\"✅ Modèles chargés avec succès\")\n        return tokenizer, text_model, image_model\n        \n    except Exception as e:\n        logger.error(f\"❌ Erreur chargement modèles: {e}\")\n        raise\n\n# Charger les modèles\ntokenizer, text_model, image_model = load_models()\n\n# Transformation pour les images\npreprocess = transforms.Compose([\n    transforms.Resize(256),\n    transforms.CenterCrop(224),\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n])\n\n# ========== CATÉGORIES AMÉLIORÉES ==========\nCATEGORIES = {\n    \"Voirie & Transports\": {\n        \"keywords\": [\"route\", \"trou\", \"nid\", \"poule\", \"asphalte\", \"circulation\", \"feu\", \"signalisation\", \"transport\", \"bus\", \"taxi\", \"embouteillage\", \"accident\"],\n        \"weight\": 1.2\n    },\n    \"Propreté\": {\n        \"keywords\": [\"déchet\", \"ordure\", \"poubelle\", \"sale\", \"nettoyer\", \"balayer\", \"détritus\", \"caniveau\", \"égout\", \"smell\", \"odeur\"],\n        \"weight\": 1.1\n    },\n    \"Espaces Verts\": {\n        \"keywords\": [\"arbre\", \"parc\", \"jardin\", \"fleur\", \"herbe\", \"entretien\", \"élagage\", \"plantation\", \"vert\", \"nature\"],\n        \"weight\": 1.0\n    },\n    \"Sécurité\": {\n        \"keywords\": [\"danger\", \"sécurité\", \"vol\", \"agression\", \"éclairage\", \"lampadaire\", \"police\", \"criminalité\", \"violence\"],\n        \"weight\": 1.3\n    },\n    \"Environnement\": {\n        \"keywords\": [\"pollution\", \"bruit\", \"eau\", \"air\", \"environnement\", \"écologie\", \"nuisance\", \"fumée\", \"chimique\"],\n        \"weight\": 1.1\n    },\n    \"Événements\": {\n        \"keywords\": [\"événement\", \"manifestation\", \"fête\", \"concert\", \"rassemblement\", \"célébration\", \"festival\"],\n        \"weight\": 0.9\n    },\n    \"Services Publics\": {\n        \"keywords\": [\"administration\", \"mairie\", \"service\", \"public\", \"bureau\", \"document\", \"carte\", \"identité\"],\n        \"weight\": 1.0\n    },\n    \"Animalier\": {\n        \"keywords\": [\"animal\", \"chien\", \"chat\", \"errant\", \"abandon\", \"maltraitance\", \"vétérinaire\", \"refuge\"],\n        \"weight\": 1.0\n    },\n    \"Urbanisme\": {\n        \"keywords\": [\"construction\", \"bâtiment\", \"permis\", \"urbanisme\", \"architecture\", \"rénovation\", \"démolition\"],\n        \"weight\": 1.0\n    },\n    \"Social & Solidarité\": {\n        \"keywords\": [\"aide\", \"social\", \"solidarité\", \"pauvreté\", \"mendicité\", \"association\", \"humanitaire\"],\n        \"weight\": 1.0\n    },\n    \"Autres\": {\n        \"keywords\": [],\n        \"weight\": 0.5\n    }\n}\n\n# ========== UTILITAIRES ==========\ndef save_temp_file(file_data, filename):\n    \"\"\"Sauvegarde temporaire sécurisée d'un fichier\"\"\"\n    try:\n        # Créer un fichier temporaire\n        suffix = os.path.splitext(filename)[1] if filename else '.tmp'\n        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=suffix)\n        temp_file.write(file_data)\n        temp_file.close()\n        return temp_file.name\n    except Exception as e:\n        logger.error(f\"Erreur sauvegarde temporaire: {e}\")\n        return None\n\ndef cleanup_temp_file(filepath):\n    \"\"\"Supprime un fichier temporaire\"\"\"\n    try:\n        if filepath and os.path.exists(filepath):\n            os.unlink(filepath)\n    except:\n        pass\n\ndef decode_base64_file(base64_data):\n    \"\"\"Décode un fichier base64\"\"\"\n    try:\n        if ',' in base64_data:\n            header, data = base64_data.split(',', 1)\n        else:\n            data = base64_data\n        \n        file_data = base64.b64decode(data)\n        return file_data\n    except Exception as e:\n        logger.error(f\"Erreur décodage base64: {e}\")\n        return None\n\n# ========== ENDPOINTS AMÉLIORÉS ==========\n\n@app.route('/process_text', methods=['POST'])\ndef process_text():\n    \"\"\"Traitement de texte amélioré avec gestion d'erreur\"\"\"\n    try:\n        data = request.get_json()\n        if not data or 'text' not in data:\n            return jsonify({'error': 'Texte requis'}), 400\n        \n        text = data['text'].strip()\n        if not text:\n            return jsonify({'error': 'Texte vide'}), 400\n        \n        # Limitation de longueur\n        if len(text) > 5000:\n            text = text[:5000]\n            logger.warning(\"Texte tronqué à 5000 caractères\")\n        \n        # Tokenisation\n        inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True, max_length=512)\n        \n        with torch.no_grad():\n            outputs = text_model(**inputs)\n        \n        # Extraction des features (moyenne des embeddings)\n        features = outputs.last_hidden_state.mean(dim=1).squeeze().numpy()\n        \n        # Normalisation\n        features = features / np.linalg.norm(features)\n        \n        logger.info(f\"✅ Texte traité: {len(text)} caractères -> {len(features)} features\")\n        return jsonify({\n            'features': features.tolist(),\n            'text_length': len(text),\n            'status': 'success'\n        })\n        \n    except Exception as e:\n        logger.error(f\"❌ Erreur process_text: {e}\")\n        return jsonify({'error': str(e)}), 500\n\n@app.route('/process_image', methods=['POST'])\ndef process_image():\n    \"\"\"Traitement d'image amélioré avec plusieurs sources possibles\"\"\"\n    temp_file = None\n    try:\n        data = request.get_json()\n        if not data:\n            return jsonify({'error': 'Données requises'}), 400\n        \n        img = None\n        \n        # ========== MÉTHODE 1: Chemin de fichier ==========\n        if 'path' in data and data['path']:\n            image_path = data['path']\n            if os.path.exists(image_path):\n                img = Image.open(image_path)\n                logger.info(f\"📁 Image chargée depuis: {image_path}\")\n            else:\n                logger.warning(f\"⚠️ Fichier introuvable: {image_path}\")\n        \n        # ========== MÉTHODE 2: Données base64 ==========\n        elif 'base64' in data and data['base64']:\n            try:\n                file_data = decode_base64_file(data['base64'])\n                if file_data:\n                    img = Image.open(io.BytesIO(file_data))\n                    logger.info(\"📊 Image chargée depuis base64\")\n            except Exception as b64_error:\n                logger.error(f\"Erreur base64: {b64_error}\")\n        \n        # ========== MÉTHODE 3: URL ou nom de fichier ==========\n        elif 'filename' in data and data['filename']:\n            # Essayer de trouver le fichier dans différents dossiers\n            filename = data['filename']\n            possible_paths = [\n                filename,\n                os.path.join('uploads', filename),\n                os.path.join('temp', filename),\n                os.path.join('media', filename)\n            ]\n            \n            for path in possible_paths:\n                if os.path.exists(path):\n                    img = Image.open(path)\n                    logger.info(f\"📁 Image trouvée: {path}\")\n                    break\n        \n        if img is None:\n            return jsonify({'error': 'Image non trouvée ou format invalide'}), 404\n        \n        # ========== TRAITEMENT DE L'IMAGE ==========\n        \n        # Convertir en RGB si nécessaire\n        if img.mode != 'RGB':\n            img = img.convert('RGB')\n        \n        # Préprocessing\n        img_tensor = preprocess(img)\n        batch_tensor = torch.unsqueeze(img_tensor, 0)\n        \n        # Extraction des features\n        with torch.no_grad():\n            features = image_model(batch_tensor)\n        \n        # Aplatir et normaliser\n        features = features.squeeze().numpy()\n        features = features / np.linalg.norm(features)\n        \n        logger.info(f\"✅ Image traitée: {img.size} -> {len(features)} features\")\n        \n        return jsonify({\n            'features': features.tolist(),\n            'image_size': img.size,\n            'image_mode': img.mode,\n            'status': 'success'\n        })\n        \n    except Exception as e:\n        logger.error(f\"❌ Erreur process_image: {e}\")\n        return jsonify({'error': str(e)}), 500\n    \n    finally:\n        if temp_file:\n            cleanup_temp_file(temp_file)\n\n@app.route('/process_video', methods=['POST'])\ndef process_video():\n    \"\"\"Traitement vidéo amélioré avec sampling intelligent\"\"\"\n    temp_file = None\n    try:\n        data = request.get_json()\n        if not data:\n            return jsonify({'error': 'Données requises'}), 400\n        \n        video_path = None\n        \n        # ========== GESTION DES SOURCES VIDÉO ==========\n        if 'path' in data and data['path']:\n            if os.path.exists(data['path']):\n                video_path = data['path']\n            else:\n                return jsonify({'error': f'Fichier vidéo non trouvé: {data[\"path\"]}'}), 404\n        \n        elif 'base64' in data and data['base64']:\n            try:\n                file_data = decode_base64_file(data['base64'])\n                if file_data:\n                    temp_file = save_temp_file(file_data, 'video.mp4')\n                    video_path = temp_file\n            except Exception as e:\n                return jsonify({'error': f'Erreur décodage vidéo: {e}'}), 400\n        \n        if not video_path:\n            return jsonify({'error': 'Source vidéo requise'}), 400\n        \n        # ========== TRAITEMENT VIDÉO ==========\n        features_list = []\n        cap = cv2.VideoCapture(video_path)\n        \n        if not cap.isOpened():\n            return jsonify({'error': 'Impossible d\\'ouvrir la vidéo'}), 400\n        \n        # Informations sur la vidéo\n        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n        fps = cap.get(cv2.CAP_PROP_FPS)\n        duration = total_frames / fps if fps > 0 else 0\n        \n        # Sampling intelligent: prendre 1 frame par seconde, max 30 frames\n        max_frames = min(30, int(duration) if duration > 0 else 10)\n        frame_interval = max(1, total_frames // max_frames) if total_frames > 0 else 1\n        \n        frame_count = 0\n        processed_frames = 0\n        \n        logger.info(f\"🎬 Traitement vidéo: {total_frames} frames, {duration:.1f}s, sampling chaque {frame_interval} frames\")\n        \n        while cap.isOpened() and processed_frames < max_frames:\n            ret, frame = cap.read()\n            if not ret:\n                break\n            \n            # Prendre seulement certaines frames\n            if frame_count % frame_interval == 0:\n                try:\n                    # Convertir BGR -> RGB\n                    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n                    img = Image.fromarray(frame_rgb)\n                    \n                    # Préprocessing\n                    img_tensor = preprocess(img)\n                    batch_tensor = torch.unsqueeze(img_tensor, 0)\n                    \n                    # Extraction features\n                    with torch.no_grad():\n                        frame_features = image_model(batch_tensor)\n                    \n                    features_list.append(frame_features.squeeze().numpy())\n                    processed_frames += 1\n                    \n                except Exception as frame_error:\n                    logger.warning(f\"Erreur traitement frame {frame_count}: {frame_error}\")\n                    continue\n            \n            frame_count += 1\n        \n        cap.release()\n        \n        if not features_list:\n            return jsonify({'error': 'Aucune frame traitée avec succès'}), 500\n        \n        # Moyenne des features de toutes les frames\n        video_features = np.mean(features_list, axis=0)\n        video_features = video_features / np.linalg.norm(video_features)\n        \n        logger.info(f\"✅ Vidéo traitée: {processed_frames} frames -> {len(video_features)} features\")\n        \n        return jsonify({\n            'features': video_features.tolist(),\n            'video_info': {\n                'total_frames': total_frames,\n                'processed_frames': processed_frames,\n                'duration_seconds': duration,\n                'fps': fps\n            },\n            'status': 'success'\n        })\n        \n    except Exception as e:\n        logger.error(f\"❌ Erreur process_video: {e}\")\n        return jsonify({'error': str(e)}), 500\n    \n    finally:\n        if temp_file:\n            cleanup_temp_file(temp_file)\n\n@app.route('/validate', methods=['POST'])\ndef validate():\n    \"\"\"Validation améliorée de cohérence texte-média\"\"\"\n    try:\n        data = request.get_json()\n        if not data:\n            return jsonify({'error': 'Données requises'}), 400\n        \n        text_features = np.array(data.get('text_features', []))\n        media_features = np.array(data.get('media_features', []))\n        \n        if len(text_features) == 0 or len(media_features) == 0:\n            return jsonify({'error': 'Features texte et média requis'}), 400\n        \n        # Calcul de similarité cosinus\n        similarity = cosine_similarity([text_features], [media_features])[0][0]\n        \n        # Seuils adaptatifs selon le type de contenu\n        thresholds = {\n            'strict': 0.7,    # Pour contenu très spécifique\n            'normal': 0.5,    # Seuil par défaut\n            'relaxed': 0.3    # Pour contenu général\n        }\n        \n        validation_mode = data.get('mode', 'normal')\n        threshold = thresholds.get(validation_mode, thresholds['normal'])\n        \n        is_valid = similarity > threshold\n        \n        # Niveau de confiance\n        confidence = min(1.0, similarity / threshold) if threshold > 0 else 0.0\n        \n        logger.info(f\"🔍 Validation: similarité={similarity:.3f}, seuil={threshold}, valide={is_valid}\")\n        \n        return jsonify({\n            'is_valid': is_valid,\n            'similarity_score': round(similarity, 3),\n            'confidence': round(confidence, 3),\n            'threshold_used': threshold,\n            'validation_mode': validation_mode,\n            'status': 'success'\n        })\n        \n    except Exception as e:\n        logger.error(f\"❌ Erreur validation: {e}\")\n        return jsonify({'error': str(e)}), 500\n\n@app.route('/categorize', methods=['POST'])\ndef categorize():\n    \"\"\"Catégorisation intelligente basée sur les features et mots-clés\"\"\"\n    try:\n        data = request.get_json()\n        if not data:\n            return jsonify({'error': 'Données requises'}), 400\n        \n        features = np.array(data.get('features', []))\n        text = data.get('text', '').lower()\n        \n        if len(features) == 0:\n            return jsonify({'error': 'Features requis'}), 400\n        \n        # ========== CATÉGORISATION PAR MOTS-CLÉS ==========\n        category_scores = {}\n        \n        for category, info in CATEGORIES.items():\n            score = 0.0\n            keywords = info['keywords']\n            weight = info['weight']\n            \n            # Compter les occurrences de mots-clés\n            keyword_matches = 0\n            for keyword in keywords:\n                if keyword in text:\n                    keyword_matches += text.count(keyword)\n            \n            # Calculer le score basé sur les mots-clés\n            if keyword_matches > 0:\n                score = (keyword_matches / len(keywords)) * weight if keywords else 0\n            \n            category_scores[category] = score\n        \n        # ========== CATÉGORISATION PAR FEATURES (SIMPLIFIÉE) ==========\n        # Pour une vraie implémentation, il faudrait un modèle entraîné\n        # Ici on utilise une heuristique basée sur la magnitude des features\n        \n        feature_magnitude = np.linalg.norm(features)\n        \n        # Ajustement basé sur la complexité du contenu\n        if feature_magnitude > 100:  # Contenu complexe/riche\n            category_scores[\"Urbanisme\"] += 0.2\n            category_scores[\"Événements\"] += 0.2\n        elif feature_magnitude > 50:  # Contenu moyen\n            category_scores[\"Voirie & Transports\"] += 0.1\n            category_scores[\"Propreté\"] += 0.1\n        \n        # ========== SÉLECTION DE LA MEILLEURE CATÉGORIE ==========\n        if not any(score > 0 for score in category_scores.values()):\n            # Aucune catégorie détectée -> \"Autres\"\n            predicted_category = \"Autres\"\n            confidence = 0.5\n        else:\n            # Prendre la catégorie avec le meilleur score\n            predicted_category = max(category_scores, key=category_scores.get)\n            max_score = category_scores[predicted_category]\n            confidence = min(1.0, max_score)\n        \n        logger.info(f\"🎯 Catégorie prédite: {predicted_category} (confiance: {confidence:.2f})\")\n        \n        return jsonify({\n            'category': predicted_category,\n            'confidence': round(confidence, 3),\n            'all_scores': {k: round(v, 3) for k, v in category_scores.items()},\n            'feature_magnitude': round(feature_magnitude, 2),\n            'status': 'success'\n        })\n        \n    except Exception as e:\n        logger.error(f\"❌ Erreur catégorisation: {e}\")\n        return jsonify({'error': str(e)}), 500\n\n# ========== ENDPOINTS DE SANTÉ ET DEBUG ==========\n\n@app.route('/health', methods=['GET'])\ndef health_check():\n    \"\"\"Vérification de l'état du service\"\"\"\n    try:\n        # Test rapide des modèles\n        test_text = \"test\"\n        test_inputs = tokenizer(test_text, return_tensors=\"pt\", max_length=10)\n        \n        with torch.no_grad():\n            _ = text_model(**test_inputs)\n        \n        return jsonify({\n            'status': 'healthy',\n            'models_loaded': True,\n            'endpoints': ['/process_text', '/process_image', '/process_video', '/validate', '/categorize'],\n            'timestamp': str(torch.cuda.get_device_name(0)) if torch.cuda.is_available() else 'CPU'\n        })\n    except Exception as e:\n        return jsonify({\n            'status': 'unhealthy',\n            'error': str(e)\n        }), 500\n\n@app.route('/info', methods=['GET'])\ndef service_info():\n    \"\"\"Informations sur le service\"\"\"\n    return jsonify({\n        'service': 'AI Validation & Categorization Service',\n        'version': '2.0',\n        'models': {\n            'text': 'bert-base-uncased',\n            'image': 'resnet50',\n            'video': 'resnet50 (frame sampling)'\n        },\n        'categories': list(CATEGORIES.keys()),\n        'max_file_size': '50MB',\n        'supported_formats': {\n            'images': ['jpg', 'jpeg', 'png', 'gif', 'bmp'],\n            'videos': ['mp4', 'avi', 'mov', 'mkv'],\n            'text': 'max 5000 characters'\n        }\n    })\n\n# ========== GESTION D'ERREURS ==========\n\n@app.errorhandler(413)\ndef too_large(e):\n    return jsonify({'error': 'Fichier trop volumineux (max 50MB)'}), 413\n\n@app.errorhandler(404)\ndef not_found(e):\n    return jsonify({'error': 'Endpoint non trouvé'}), 404\n\n@app.errorhandler(500)\ndef server_error(e):\n    return jsonify({'error': 'Erreur interne du serveur'}), 500\n\nif __name__ == '__main__':\n    print(\"🚀 Démarrage du modèle de validation et catégorisation amélioré\")\n    print(\"📡 Accessible sur:\")\n    print(\"   - http://127.0.0.1:5001\")\n    print(\"   - Endpoints disponibles:\")\n    print(\"     * POST /process_text\")\n    print(\"     * POST /process_image\") \n    print(\"     * POST /process_video\")\n    print(\"     * POST /validate\")\n    print(\"     * POST /categorize\")\n    print(\"     * GET /health\")\n    print(\"     * GET /info\")\n    print(\"🔥 Serveur prêt pour les requêtes !\")\n    \n    app.run(host='0.0.0.0', port=5001, debug=True)", "text_preview": "from flask import Flask, request, jsonify from transformers import BertTokenizer, BertModel import torch import torchvision.models as models from torchvision import transforms from PIL import Image import cv2 import numpy as np from sklearn", "input_sha256": "5c6be4381a0919f76ca4e30e4f9ecfe6c4215790aa65b35f76e4f91f7c900b09", "text_sha256": "cad1dba0b06d8fff5e6cc9bcc59ee8bc97eb9e1c185225f67898747061def91d", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12058, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 1, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7133507804026995, "noise_severity": 0.1576459092980793, "missing_content_severity": 0.03866725299723768, "factual_density": 3.0844107956481017, "pretraining_utility": 0.6818720071747553, "math_density": 1.1020506513210548, "code_density": 3.601618103372659, "reasoning_density": 1.1020096937416732, "education_level": 2.5738029871731376, "creative_writing_quality": -0.3063383763377529}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.4268 below 0.70", "secondary_topic vs NONE margin 0.0862 below 0.10"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.11533192677220172, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9969269531581405, "secondary": 0.7480604059846389, "tertiary": 0.8684067643556319, "structured_records": 0.47467089130963, "procedural_steps": 0.07813653535430731, "worked_solution": 0.010519879853356328, "contextual_dialogue": 4.575084259180744e-06, "content_quality": 0.49184215152783684, "noise_severity": 0.8686284089182672, "missing_content_severity": 0.9677772891689685, "factual_density": 0.4880551623966283, "pretraining_utility": 0.5677018813568152, "math_density": 0.4077928894908456, "code_density": 0.6680150861438823, "reasoning_density": 0.08165858854860564, "education_level": 0.38059595835821786, "creative_writing_quality": 0.9992194629191087, "primary_topic": 0.943937497226493, "secondary_topic": 0.40390039810663847}, "probability_margin": {"primary": 0.9957815616337227, "secondary": 0.5961113216358072, "tertiary": 0.7876207395696535, "education_level": 0.0371363896864908, "creative_writing_quality": 0.9990319318246406, "primary_topic": 0.9064017916338981, "secondary_topic": 0.08622441217263538}, "raw_probabilities": {"primary": {"Academic Writing": 3.128564223986716e-05, "Content Listing": 1.5123569317185853e-05, "Creative Writing": 1.2275948810040632e-05, "Customer Support Page": 5.785258789090034e-06, "Q&A Forum": 6.612387609613551e-06, "Discussion Thread": 4.1207212029252275e-06, "Comment Section": 6.7820644230791095e-06, "Missing Content": 1.1035638475057848e-05, "Knowledge Article": 5.39316918374244e-05, "Legal Notices": 1.1829015808842626e-05, "News Article": 3.03715746708768e-06, "Organizational Announcement": 8.174445463620533e-06, "Nonfiction Writing": 6.615762214793591e-06, "Organizational About Page": 8.922944446843679e-06, "Personal About Page": 6.986358141510245e-06, "Personal Blog": 5.2433302858882546e-06, "Product Page": 6.236610720617152e-06, "Spam / Ads": 7.1981339192259575e-06, "Structured Data": 0.0012734351428286446, "Technical Writing": 0.9970549967765513, "Transcript / Interview": 9.018994817926921e-06, "Tutorial / How-To Guide": 0.0005400580835107896, "User Reviews": 4.438542701850239e-06, "Other / Unknown": 0.0009068557784169658}, "secondary": {"Academic Writing": 0.0013319422221323744, "Content Listing": 0.001590829005491975, "Creative Writing": 0.0014177247777057552, "Customer Support Page": 0.0005868241379945065, "Q&A Forum": 0.00033815755161511855, "Discussion Thread": 0.0002959861613342948, "Comment Section": 0.0005991208347479298, "Missing Content": 0.0006627717831528383, "Knowledge Article": 0.0021490738766600736, "Legal Notices": 0.0004682904383269104, "News Article": 0.0004422346318869506, "Organizational Announcement": 0.0005334250897776891, "Nonfiction Writing": 0.0005226798554323513, "Organizational About Page": 0.0006677368559159193, "Personal About Page": 0.00041192230908109807, "Personal Blog": 0.00037259623322668996, "Product Page": 0.00039858721071023306, "Spam / Ads": 0.0004701734925458079, "Structured Data": 0.162446567432805, "Technical Writing": 0.060895855984105525, "Transcript / Interview": 0.0005713439000230811, "Tutorial / How-To Guide": 0.00402063116104213, "User Reviews": 0.00024763598567345877, "NONE": 0.7585578890686122}, "tertiary": {"Academic Writing": 0.0012309883574897055, "Content Listing": 0.0008430095359629199, "Creative Writing": 0.0007305031377504087, "Customer Support Page": 0.00034405841996085006, "Q&A Forum": 0.0001733423270034277, "Discussion Thread": 0.00015220905568396373, "Comment Section": 0.0004182774420277109, "Missing Content": 0.00035607618027185326, "Knowledge Article": 0.0026793087216861224, "Legal Notices": 0.000302215509840476, "News Article": 0.00023428826162285126, "Organizational Announcement": 0.00027625879970303923, "Nonfiction Writing": 0.00027549647542115396, "Organizational About Page": 0.00041820484844532694, "Personal About Page": 0.00022219854847612278, "Personal Blog": 0.00020824579879128542, "Product Page": 0.00022608403344419724, "Spam / Ads": 0.0002818035776306204, "Structured Data": 0.08626907627116046, "Technical Writing": 0.026366344375459056, "Transcript / Interview": 0.00029496170029715365, "Tutorial / How-To Guide": 0.003689402716432286, "User Reviews": 0.00011783006462499771, "NONE": 0.8738898158408139}, "structured_records": {"false": 0.52532910869037, "true": 0.47467089130963}, "procedural_steps": {"false": 0.9218634646456927, "true": 0.07813653535430731}, "worked_solution": {"false": 0.9894801201466437, "true": 0.010519879853356328}, "contextual_dialogue": {"false": 0.9999954249157408, "true": 4.575084259180744e-06}, "content_quality": {"-2": 0.011196453302960638, "-1": 0.06457456163145261, "0": 0.2854808357101609, "1": 0.4771780500707782, "2": 0.16157009928464763}, "noise_severity": {"0": 0.8680135101496429, "1": 0.11108993340208985, "2": 0.017512461547341044, "3": 0.002005326802396852, "4": 0.0013787680985291966}, "missing_content_severity": {"0": 0.9698460258736229, "1": 0.023386900124440755, "2": 0.005359503248808735, "3": 0.0010689366373319662, "4": 0.00033863411579589055}, "factual_density": {"0": 0.006043714143579441, "1": 0.027298434058281144, "2": 0.19223349419067157, "3": 0.42505205722139394, "4": 0.3493723003860738}, "pretraining_utility": {"-2": 0.006272693883055658, "-1": 0.04751066604463463, "0": 0.30460345386009696, "1": 0.5412983114389243, "2": 0.10031487477328846}, "math_density": {"0": 0.30429894064496515, "1": 0.4023762351464562, "2": 0.22313361975473825, "3": 0.02735764115023943, "4": 0.04283356330360098}, "code_density": {"0": 0.0004095305848735813, "1": 0.0007275096807094799, "2": 0.03857287860060961, "3": 0.3174154880444992, "4": 0.6428745930893082}, "reasoning_density": {"0": 0.4402886275528976, "1": 0.1950961165867736, "2": 0.23561513604416603, "3": 0.0803171741980833, "4": 0.04868294561807943}, "education_level": {"none": 0.0006813540125470566, "0": 0.001113169681880333, "1": 0.002139694277638376, "2": 0.4838299652985149, "3": 0.4466935756120241, "4": 0.06554224111739539}, "creative_writing_quality": {"not_applicable": 0.9993495524325906, "-2": 8.251126354081087e-05, "-1": 0.000153453883768708, "0": 0.00031762060795000675, "1": 7.450426514265762e-05, "2": 2.2357547007304323e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001054776074784022, "Art & Design": 0.00021418084477311668, "Law & Crime": 0.0007655955303544038, "Education & Careers": 0.00013294327504549027, "Entertainment & Media": 0.00019629874903231078, "Fashion & Beauty": 0.00011954738008779831, "Business, Economics & Finance": 0.00021856875025563234, "Food & Dining": 0.00013354190096889136, "Games & Gaming": 0.0001423980329393289, "Electronics & Hardware": 0.0001774068586160714, "Health, Healthcare & Wellness": 0.00012611087192469065, "History & Geography": 0.00013747174936605961, "Home, Pets & Hobbies": 0.0002764591512321635, "Industry & Production": 0.00019163147782923074, "Language, Literature & Philosophy": 0.00015686805765002487, "Politics & Government": 0.005074231488393348, "Religion & Spirituality": 0.00011964792151061428, "Science, Mathematics & Engineering": 0.03969195569926825, "Relationships & Community": 0.0002606007719063432, "Software Usage & Applications": 0.0006453696641927435, "Programming & Software Engineering": 0.9460937473331663, "Sports & Fitness": 0.00014513140742213596, "Vehicles & Transportation": 0.00015538160319160033, "Travel & Tourism": 0.0001662917368920909, "Other / Uncovered Subject": 0.004412413156978768, "Unknown / Insufficient Content": 0.0001407289795243036}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.001129473915221547, "Art & Design": 0.002378099677335944, "Law & Crime": 0.004474148205390711, "Education & Careers": 0.0006581669630038162, "Entertainment & Media": 0.0021731590126127364, "Fashion & Beauty": 0.0006728483636837658, "Business, Economics & Finance": 0.0010581918474802017, "Food & Dining": 0.0005618631856358533, "Games & Gaming": 0.0006012330358582069, "Electronics & Hardware": 0.0009248264863336285, "Health, Healthcare & Wellness": 0.0009077776545982974, "History & Geography": 0.0007414062513995429, "Home, Pets & Hobbies": 0.0017010219141902947, "Industry & Production": 0.0010297530197079852, "Language, Literature & Philosophy": 0.0007318348765309737, "Politics & Government": 0.031248845971067304, "Religion & Spirituality": 0.0009400746320546702, "Science, Mathematics & Engineering": 0.4268273058717678, "Relationships & Community": 0.001059740037120622, "Software Usage & Applications": 0.0022556885772667392, "Programming & Software Engineering": 0.1581167507008476, "Sports & Fitness": 0.0005189572964334368, "Vehicles & Transportation": 0.001143221637905902, "Travel & Tourism": 0.0007716275260447734, "Other / Uncovered Subject": 0.016771089641375227, "NONE": 0.34060289369913244}}, "probabilities": {"primary": {"Academic Writing": 3.128564223986715e-05, "Content Listing": 1.512356931718585e-05, "Creative Writing": 1.2275948810040628e-05, "Customer Support Page": 5.785258789090033e-06, "Q&A Forum": 6.612387609613549e-06, "Discussion Thread": 4.120721202925227e-06, "Comment Section": 6.782064423079108e-06, "Missing Content": 1.1035638475057846e-05, "Knowledge Article": 5.393169183742439e-05, "Legal Notices": 1.1829015808842623e-05, "News Article": 3.0371574670876792e-06, "Organizational Announcement": 8.174445463620532e-06, "Nonfiction Writing": 6.615762214793589e-06, "Organizational About Page": 8.922944446843677e-06, "Personal About Page": 6.9863581415102436e-06, "Personal Blog": 5.243330285888254e-06, "Product Page": 6.23661072061715e-06, "Spam / Ads": 7.198133919225956e-06, "Structured Data": 0.0012734351428286444, "Technical Writing": 0.9970549967765511, "Transcript / Interview": 9.01899481792692e-06, "Tutorial / How-To Guide": 0.0005400580835107895, "User Reviews": 4.438542701850238e-06, "Other / Unknown": 0.0009068557784169656}, "secondary": {"Academic Writing": 0.0013319422221323746, "Content Listing": 0.0015908290054919751, "Creative Writing": 0.0014177247777057555, "Customer Support Page": 0.0005868241379945066, "Q&A Forum": 0.0003381575516151186, "Discussion Thread": 0.00029598616133429486, "Comment Section": 0.0005991208347479299, "Missing Content": 0.0006627717831528384, "Knowledge Article": 0.002149073876660074, "Legal Notices": 0.00046829043832691047, "News Article": 0.00044223463188695065, "Organizational Announcement": 0.0005334250897776892, "Nonfiction Writing": 0.0005226798554323514, "Organizational About Page": 0.0006677368559159195, "Personal About Page": 0.0004119223090810981, "Personal Blog": 0.00037259623322669, "Product Page": 0.0003985872107102331, "Spam / Ads": 0.00047017349254580796, "Structured Data": 0.16244656743280503, "Technical Writing": 0.06089585598410553, "Transcript / Interview": 0.0005713439000230812, "Tutorial / How-To Guide": 0.004020631161042131, "User Reviews": 0.0002476359856734588, "NONE": 0.7585578890686123}, "tertiary": {"Academic Writing": 0.0012309883574897057, "Content Listing": 0.00084300953596292, "Creative Writing": 0.0007305031377504088, "Customer Support Page": 0.0003440584199608501, "Q&A Forum": 0.00017334232700342772, "Discussion Thread": 0.00015220905568396375, "Comment Section": 0.000418277442027711, "Missing Content": 0.0003560761802718533, "Knowledge Article": 0.002679308721686123, "Legal Notices": 0.00030221550984047604, "News Article": 0.0002342882616228513, "Organizational Announcement": 0.0002762587997030393, "Nonfiction Writing": 0.000275496475421154, "Organizational About Page": 0.000418204848445327, "Personal About Page": 0.0002221985484761228, "Personal Blog": 0.00020824579879128545, "Product Page": 0.00022608403344419727, "Spam / Ads": 0.00028180357763062043, "Structured Data": 0.08626907627116047, "Technical Writing": 0.02636634437545906, "Transcript / Interview": 0.0002949617002971537, "Tutorial / How-To Guide": 0.0036894027164322864, "User Reviews": 0.00011783006462499772, "NONE": 0.873889815840814}, "structured_records": {"false": 0.52532910869037, "true": 0.47467089130963}, "procedural_steps": {"false": 0.9218634646456927, "true": 0.07813653535430731}, "worked_solution": {"false": 0.9894801201466437, "true": 0.010519879853356328}, "contextual_dialogue": {"false": 0.9999954249157408, "true": 4.575084259180744e-06}, "content_quality": {"-2": 0.011196453302960638, "-1": 0.06457456163145261, "0": 0.2854808357101609, "1": 0.4771780500707782, "2": 0.16157009928464763}, "noise_severity": {"0": 0.868013510149643, "1": 0.11108993340208986, "2": 0.017512461547341047, "3": 0.0020053268023968525, "4": 0.0013787680985291968}, "missing_content_severity": {"0": 0.9698460258736227, "1": 0.02338690012444075, "2": 0.005359503248808734, "3": 0.001068936637331966, "4": 0.0003386341157958905}, "factual_density": {"0": 0.006043714143579442, "1": 0.027298434058281147, "2": 0.1922334941906716, "3": 0.425052057221394, "4": 0.34937230038607386}, "pretraining_utility": {"-2": 0.006272693883055658, "-1": 0.04751066604463463, "0": 0.30460345386009696, "1": 0.5412983114389243, "2": 0.10031487477328846}, "math_density": {"0": 0.30429894064496515, "1": 0.4023762351464562, "2": 0.22313361975473825, "3": 0.02735764115023943, "4": 0.04283356330360098}, "code_density": {"0": 0.0004095305848735813, "1": 0.0007275096807094799, "2": 0.03857287860060961, "3": 0.3174154880444992, "4": 0.6428745930893082}, "reasoning_density": {"0": 0.4402886275528976, "1": 0.1950961165867736, "2": 0.23561513604416603, "3": 0.0803171741980833, "4": 0.04868294561807943}, "education_level": {"none": 0.0006813540125470565, "0": 0.0011131696818803328, "1": 0.0021396942776383755, "2": 0.4838299652985148, "3": 0.446693575612024, "4": 0.06554224111739537}, "creative_writing_quality": {"not_applicable": 0.9993495524325906, "-2": 8.251126354081087e-05, "-1": 0.000153453883768708, "0": 0.00031762060795000675, "1": 7.450426514265762e-05, "2": 2.2357547007304323e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00010547760747840217, "Art & Design": 0.00021418084477311663, "Law & Crime": 0.0007655955303544036, "Education & Careers": 0.00013294327504549024, "Entertainment & Media": 0.00019629874903231073, "Fashion & Beauty": 0.00011954738008779828, "Business, Economics & Finance": 0.0002185687502556323, "Food & Dining": 0.00013354190096889133, "Games & Gaming": 0.00014239803293932888, "Electronics & Hardware": 0.00017740685861607138, "Health, Healthcare & Wellness": 0.00012611087192469062, "History & Geography": 0.0001374717493660596, "Home, Pets & Hobbies": 0.00027645915123216346, "Industry & Production": 0.0001916314778292307, "Language, Literature & Philosophy": 0.00015686805765002484, "Politics & Government": 0.005074231488393347, "Religion & Spirituality": 0.00011964792151061425, "Science, Mathematics & Engineering": 0.03969195569926824, "Relationships & Community": 0.00026060077190634314, "Software Usage & Applications": 0.0006453696641927434, "Programming & Software Engineering": 0.9460937473331661, "Sports & Fitness": 0.00014513140742213594, "Vehicles & Transportation": 0.0001553816031916003, "Travel & Tourism": 0.00016629173689209088, "Other / Uncovered Subject": 0.004412413156978767, "Unknown / Insufficient Content": 0.00014072897952430359}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.001129473915221547, "Art & Design": 0.002378099677335944, "Law & Crime": 0.004474148205390711, "Education & Careers": 0.0006581669630038162, "Entertainment & Media": 0.0021731590126127364, "Fashion & Beauty": 0.0006728483636837658, "Business, Economics & Finance": 0.0010581918474802017, "Food & Dining": 0.0005618631856358533, "Games & Gaming": 0.0006012330358582069, "Electronics & Hardware": 0.0009248264863336285, "Health, Healthcare & Wellness": 0.0009077776545982974, "History & Geography": 0.0007414062513995429, "Home, Pets & Hobbies": 0.0017010219141902947, "Industry & Production": 0.0010297530197079852, "Language, Literature & Philosophy": 0.0007318348765309737, "Politics & Government": 0.031248845971067304, "Religion & Spirituality": 0.0009400746320546702, "Science, Mathematics & Engineering": 0.4268273058717678, "Relationships & Community": 0.001059740037120622, "Software Usage & Applications": 0.0022556885772667392, "Programming & Software Engineering": 0.1581167507008476, "Sports & Fitness": 0.0005189572964334368, "Vehicles & Transportation": 0.001143221637905902, "Travel & Tourism": 0.0007716275260447734, "Other / Uncovered Subject": 0.016771089641375227, "NONE": 0.34060289369913244}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 13697, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 13697, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:07.052880+00:00", "scoring_key_slot": "primary", "dataset_uid": "cc5373f0-65dd-4bbe-b356-e329f09bfc9d", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "c298aea9269631dd3bc5f42308c3bb9aac68b18727f4a8e2a865ab88c4de3e05", "source_metadata": {"uuid": "cc5373f0-65dd-4bbe-b356-e329f09bfc9d", "repo_name": "Cheikh27/back_signalement_citoyen", "relative_path": "model_verification_categorisation/models_service.py", "category": "WEB", "algo_rel_score": 0.9953290224075317, "quality_score": 6.297353267669678, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 4, "repo_created_at": "2025-07-08T15:44:55.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "3fd56dde3f61d40cb5f6d227d3529a3e0f45cc9b", "size_bytes": 20849, "file_timestamp": 1751988734, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "cf2809ebd991c4090c168aa886b4c037907e4150"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 4, "repo_created_at": "2025-07-08T15:44:55.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "3fd56dde3f61d40cb5f6d227d3529a3e0f45cc9b", "size_bytes": 20849, "file_timestamp": 1751988734, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "cf2809ebd991c4090c168aa886b4c037907e4150", "url": "https://github.com/Cheikh27/back_signalement_citoyen/blob/cf2809ebd991c4090c168aa886b4c037907e4150/model_verification_categorisation/models_service.py"}, "gpt4o_tokens": 4762, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.7133507804026995, "noise_severity": 0.1576459092980793, "missing_content_severity": 0.03866725299723768, "factual_density": 3.0844107956481017, "pretraining_utility": 0.6818720071747553, "math_density": 1.1020506513210548, "code_density": 3.601618103372659, "reasoning_density": 1.1020096937416732, "education_level": 2.5738029871731376, "creative_writing_quality": -0.3063383763377529}, "score_applicability": {"education_level": 0.9993186459874529, "creative_writing_quality": 0.0006504475674094875}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/tad-mctc/tad-libcint/blob/7645fc4f84229024547931f1f773c1b530cb10bf/src/tad_libcint/interface/integrals/utils.py", "id": "python-0-15fb78c3e58c07f83abbf8a0c7c31a37", "text": "# This file is part of tad-libcint, modified from diffqc/dqc.\n#\n# SPDX-License-Identifier: Apache-2.0\n# Copyright (C) 2024 Grimme Group\n#\n# Original file licensed under the Apache License, Version 2.0 by diffqc/dqc.\n# Modifications made by Grimme Group.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nIntegrals: Utility\n==================\n\nThis module contains utility functions for the integral interface.\n\"\"\"\nfrom __future__ import annotations\n\nimport copy\n\nimport torch\n\nfrom tad_libcint.typing import Callable, Tensor\n\nfrom ..namemanager import IntorNameManager\nfrom ..wrapper import LibcintWrapper\n\n__all__ = [\"get_integrals\", \"gather_at_dims\"]\n\n\ndef get_integrals(\n    int_nmgrs: list[IntorNameManager],\n    wrappers: list[LibcintWrapper],\n    int_fcn: Callable[[list[LibcintWrapper], IntorNameManager], Tensor],\n    new_axes_pos: list[int | None],\n) -> list[Tensor]:\n    # Return the list of tensors of the integrals given by the list of integral\n    # names. Int_fcn is the integral function that receives the name and\n    # returns the results. If new_axes_pos is specified, then move the new axes\n    # to 0, otherwise, just leave it as it is.\n\n    res: list[Tensor] = []\n    # indicating if the integral is available in the libcint-generated file\n    int_avail: list[bool] = [False] * len(int_nmgrs)\n\n    for i in range(len(int_nmgrs)):\n        res_i: Tensor | None = None\n\n        # check if the integral can be calculated from the previous results\n        for j in range(i - 1, -1, -1):\n            # check the integral names equivalence\n            transpose_path = int_nmgrs[j].get_transpose_path_to(int_nmgrs[i])\n            if transpose_path is not None:\n                # if the swapped wrappers remain unchanged, then just use the\n                # transposed version of the previous version\n                # TODO: think more about this (do we need to use different\n                # transpose path? e.g. transpose_path[::-1])\n                twrappers = _swap_list(wrappers, transpose_path)\n                if twrappers == wrappers:\n                    res_i = _transpose(res[j], transpose_path)\n                    permute_path = int_nmgrs[j].get_comp_permute_path(\n                        transpose_path\n                    )\n                    res_i = res_i.permute(*permute_path)\n                    break\n\n                # otherwise, use the swapped integral with the swapped wrappers,\n                # only if the integral is available in the libcint-generated\n                # files\n                elif int_avail[j]:\n                    res_i = int_fcn(twrappers, int_nmgrs[j])\n                    res_i = _transpose(res_i, transpose_path)\n                    permute_path = int_nmgrs[j].get_comp_permute_path(\n                        transpose_path\n                    )\n                    res_i = res_i.permute(*permute_path)\n                    break\n\n                # if the integral is not available, then continue the searching\n                else:\n                    continue\n\n        if res_i is None:\n            try:\n                # successfully executing the line below indicates that the integral\n                # is available in the libcint-generated files\n                res_i = int_fcn(wrappers, int_nmgrs[i])\n            except AttributeError as e:\n                msg = f\"The integral {int_nmgrs[i].fullname} is not available from libcint, please add it\"\n\n                raise AttributeError(msg) from e\n\n            int_avail[i] = True\n\n        res.append(res_i)\n\n    # move the new axes (if any) to dimension 0\n    assert res_i is not None\n    for i in range(len(res)):\n        new_axes_pos_i = new_axes_pos[i]\n        if new_axes_pos_i is not None:\n            res[i] = torch.movedim(res[i], new_axes_pos_i, 0)\n\n    return res\n\n\ndef _transpose(a: Tensor, axes: list[tuple[int, int]]) -> Tensor:\n    # perform the transpose of two axes for tensor a\n    for axis2 in axes:\n        a = a.transpose(*axis2)\n    return a\n\n\ndef _swap_list(a: list, swaps: list[tuple[int, int]]) -> list:\n    # swap the elements according to the swaps input\n    res = copy.copy(a)  # shallow copy\n    for idxs in swaps:\n        res[idxs[0]], res[idxs[1]] = (\n            res[idxs[1]],\n            res[idxs[0]],\n        )  # swap the elements\n    return res\n\n\ndef gather_at_dims(\n    inp: Tensor, mapidxs: list[Tensor], dims: list[int]\n) -> Tensor:\n    # expand inp in the dimension dim by gathering values based on the given\n    # mapping indices\n\n    # mapidx: (nnew,) with value from 0 to nold - 1\n    # inp: (..., nold, ...)\n    # out: (..., nnew, ...)\n    out = inp\n    for dim, mapidx in zip(dims, mapidxs):\n        if dim < 0:\n            dim = out.ndim + dim\n        map2 = mapidx[(...,) + (None,) * (out.ndim - 1 - dim)]\n        map2 = map2.expand(*out.shape[:dim], -1, *out.shape[dim + 1 :])\n        out = torch.gather(out, dim=dim, index=map2)\n    return out\n", "text_preview": "# This file is part of tad-libcint, modified from diffqc/dqc. # # SPDX-License-Identifier: Apache-2.0 # Copyright (C) 2024 Grimme Group # # Original file licensed under the Apache License, Version 2.0 by diffqc/dqc. # Modifications made by ", "input_sha256": "9f7dc758fbb0bb7e16322493103b6181ed86110ff3d135c0f0b9456873de7a13", "text_sha256": "15fb78c3e58c07f83abbf8a0c7c31a37e9fe91f5a54f00374333986bdf0a3e73", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12061, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Legal Notices", "sub_format": null}, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 1, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9535672251821806, "noise_severity": 0.8464902217648658, "missing_content_severity": 0.005117055042915996, "factual_density": 3.368408547534909, "pretraining_utility": 0.5166654982190292, "math_density": 2.1459406915668335, "code_density": 3.4810128695639535, "reasoning_density": 1.460360372669966, "education_level": 2.827709251461311, "creative_writing_quality": -0.11373334368060248}, "api_choices": {"primary": "Technical Writing", "secondary": "Legal Notices", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.6983522757676983, "secondary_choice": "Legal Notices", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9858943900096858, "secondary": 0.7893779514747181, "tertiary": 0.8295307790340911, "structured_records": 0.00040122297810904825, "procedural_steps": 0.009941514397171418, "worked_solution": 0.0037097828206971327, "contextual_dialogue": 5.074468450540508e-06, "content_quality": 0.5667192552635371, "noise_severity": 0.6804124377882907, "missing_content_severity": 0.9957357874642366, "factual_density": 0.473673789612424, "pretraining_utility": 0.47619957311267735, "math_density": 0.0, "code_density": 0.5675107246366278, "reasoning_density": 0.0, "education_level": 0.4610325816343005, "creative_writing_quality": 0.9993811828706967, "primary_topic": 0.9453701105771357, "secondary_topic": 0.6344762604908724}, "probability_margin": {"primary": 0.9743337600973636, "secondary": 0.6598724826899659, "tertiary": 0.6935381420871165, "education_level": 0.2506490693234035, "creative_writing_quality": 0.9991990173583011, "primary_topic": 0.8991807042678224, "secondary_topic": 0.4381375655790424}, "raw_probabilities": {"primary": {"Academic Writing": 5.01259081538199e-05, "Content Listing": 2.5155685247304248e-05, "Creative Writing": 1.0844282707657045e-05, "Customer Support Page": 9.2741205485409e-06, "Q&A Forum": 1.0839087140935895e-05, "Discussion Thread": 6.999064933141224e-06, "Comment Section": 1.343601933821481e-05, "Missing Content": 2.722154023994594e-05, "Knowledge Article": 5.797497682899524e-05, "Legal Notices": 0.012148363661918597, "News Article": 5.345206874554526e-06, "Organizational Announcement": 1.8046075188809053e-05, "Nonfiction Writing": 1.0359575132310263e-05, "Organizational About Page": 1.6963465388691106e-05, "Personal About Page": 6.985501491298088e-06, "Personal Blog": 7.192472228788846e-06, "Product Page": 6.554443798880446e-06, "Spam / Ads": 8.683931157750425e-06, "Structured Data": 7.966859154799197e-05, "Technical Writing": 0.9864821237592822, "Transcript / Interview": 1.4865936541450256e-05, "Tutorial / How-To Guide": 8.663033052961006e-05, "User Reviews": 6.106329105922992e-06, "Other / Unknown": 0.0008902400346744285}, "secondary": {"Academic Writing": 0.0008106977594558496, "Content Listing": 0.0004770015916248461, "Creative Writing": 0.0003522857396632399, "Customer Support Page": 0.0002394057199913591, "Q&A Forum": 0.00022911125096228117, "Discussion Thread": 0.00021295409226470963, "Comment Section": 0.0004150570323580267, "Missing Content": 0.00046792590740205183, "Knowledge Article": 0.0011537864712970174, "Legal Notices": 0.7981538701632716, "News Article": 0.00033388321423415433, "Organizational Announcement": 0.00047930714104835176, "Nonfiction Writing": 0.0002910617969300048, "Organizational About Page": 0.0004114776347180118, "Personal About Page": 0.00022448472176012243, "Personal Blog": 0.0001970947370586587, "Product Page": 0.0002763726499871748, "Spam / Ads": 0.0002658527925255307, "Structured Data": 0.0005725018108523755, "Technical Writing": 0.055136253826828345, "Transcript / Interview": 0.000376313676316632, "Tutorial / How-To Guide": 0.0004965251964792686, "User Reviews": 0.00014538759966487805, "NONE": 0.13828138747330568}, "tertiary": {"Academic Writing": 0.000853381669018962, "Content Listing": 0.00031081417252831365, "Creative Writing": 0.000275573897741938, "Customer Support Page": 0.00016820948816467703, "Q&A Forum": 0.000128476700716576, "Discussion Thread": 0.0001157401844060915, "Comment Section": 0.00022125717625275684, "Missing Content": 0.000366147925156532, "Knowledge Article": 0.00244427658468805, "Legal Notices": 0.14309552115388746, "News Article": 0.00021927525247118865, "Organizational Announcement": 0.000291938344125754, "Nonfiction Writing": 0.00017028271796260994, "Organizational About Page": 0.0002507857392492315, "Personal About Page": 0.00015124481092364088, "Personal Blog": 0.00013261106557660354, "Product Page": 0.0001521667681000776, "Spam / Ads": 0.00019225714175569736, "Structured Data": 0.00043273723482639454, "Technical Writing": 0.012537003894723713, "Transcript / Interview": 0.0002369866050175113, "Tutorial / How-To Guide": 0.0005321144923584437, "User Reviews": 8.753373934379431e-05, "NONE": 0.836633663241004}, "structured_records": {"false": 0.9995987770218909, "true": 0.00040122297810904825}, "procedural_steps": {"false": 0.9900584856028286, "true": 0.009941514397171418}, "worked_solution": {"false": 0.9962902171793029, "true": 0.0037097828206971327}, "contextual_dialogue": {"false": 0.9999949255315495, "true": 5.074468450540508e-06}, "content_quality": {"-2": 0.005095901744228929, "-1": 0.022981244701848765, "0": 0.22193463961440316, "1": 0.513236154506551, "2": 0.2367520594329681}, "noise_severity": {"0": 0.2685074264445927, "1": 0.6271747956884389, "2": 0.09686800028365973, "3": 0.004219684824127323, "4": 0.0032300927591813557}, "missing_content_severity": {"0": 0.9958077918596611, "1": 0.003605613657170109, "2": 0.0003402504872577762, "3": 0.00015443557241292887, "4": 9.190842349788681e-05}, "factual_density": {"0": 0.0027735563737139356, "1": 0.010851805319328394, "2": 0.1090504737073815, "3": 0.36984086359748725, "4": 0.5074833010020889}, "pretraining_utility": {"-2": 0.006674079009273819, "-1": 0.07192967009108475, "0": 0.392065929812888, "1": 0.45671731584484515, "2": 0.07261300524190821}, "math_density": {"0": 0.2090253252703955, "1": 0.16017665294050415, "2": 0.1911726979047357, "3": 0.15508265272060107, "4": 0.28454267116376364}, "code_density": {"0": 0.0006015996756768236, "1": 0.0012768981411763946, "2": 0.05684519923057263, "3": 0.39905963884866497, "4": 0.5422166641039092}, "reasoning_density": {"0": 0.39318118553132625, "1": 0.14185901343755322, "2": 0.18018579571280266, "3": 0.18096625346646417, "4": 0.1038077518518538}, "education_level": {"none": 0.0013963310206126876, "0": 0.0015941370144623706, "1": 0.004331326368177908, "2": 0.3002114153718469, "3": 0.5508604846952504, "4": 0.14160630552964976}, "creative_writing_quality": {"not_applicable": 0.9994843190589139, "-2": 4.377313361731143e-05, "-1": 9.273305661276475e-05, "0": 0.00028530170061279923, "1": 6.611689434126192e-05, "2": 2.775615590201869e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 9.698990701100983e-05, "Art & Design": 0.0001329436701897807, "Law & Crime": 0.0012876774434960865, "Education & Careers": 9.53926512399578e-05, "Entertainment & Media": 7.093274167030145e-05, "Fashion & Beauty": 6.972885011123871e-05, "Business, Economics & Finance": 0.0001056611566442378, "Food & Dining": 8.230629751024794e-05, "Games & Gaming": 0.0001193237456287686, "Electronics & Hardware": 0.00014226773913388302, "Health, Healthcare & Wellness": 0.00013630702332980362, "History & Geography": 0.00012730481721416105, "Home, Pets & Hobbies": 5.817405095378152e-05, "Industry & Production": 0.00010935632372545401, "Language, Literature & Philosophy": 8.29497769929553e-05, "Politics & Government": 5.333921074267912e-05, "Religion & Spirituality": 7.052506087678324e-05, "Science, Mathematics & Engineering": 0.048290555902500455, "Relationships & Community": 3.8206052795780205e-05, "Software Usage & Applications": 0.00047641819595854284, "Programming & Software Engineering": 0.9474712601703228, "Sports & Fitness": 9.483986646329878e-05, "Vehicles & Transportation": 4.465777493365247e-05, "Travel & Tourism": 9.716020417494212e-05, "Other / Uncovered Subject": 0.0004828419785499297, "Unknown / Insufficient Content": 0.00016287938782942423}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007301999661908605, "Art & Design": 0.0006326737883943749, "Law & Crime": 0.0669973669635239, "Education & Careers": 0.0004736485911503644, "Entertainment & Media": 0.0005423837291625042, "Fashion & Beauty": 0.0003656498342911361, "Business, Economics & Finance": 0.000932175883971131, "Food & Dining": 0.00032071613281503504, "Games & Gaming": 0.0004233933520900513, "Electronics & Hardware": 0.0006009615288752359, "Health, Healthcare & Wellness": 0.0005489294942815498, "History & Geography": 0.0005141021928308187, "Home, Pets & Hobbies": 0.00029701815276698154, "Industry & Production": 0.0003981404859225702, "Language, Literature & Philosophy": 0.00027020170563970224, "Politics & Government": 0.0006137367651884483, "Religion & Spirituality": 0.00048684198504644243, "Science, Mathematics & Engineering": 0.21039730027756562, "Relationships & Community": 0.00017556908404432154, "Software Usage & Applications": 0.0007464977817828629, "Programming & Software Engineering": 0.06284163955029483, "Sports & Fitness": 0.0003203189155705247, "Vehicles & Transportation": 0.00019086217842590608, "Travel & Tourism": 0.0005689813815091539, "Other / Uncovered Subject": 0.0010758244220575334, "NONE": 0.648534865856608}}, "probabilities": {"primary": {"Academic Writing": 5.0125908153819905e-05, "Content Listing": 2.515568524730425e-05, "Creative Writing": 1.0844282707657047e-05, "Customer Support Page": 9.274120548540901e-06, "Q&A Forum": 1.0839087140935897e-05, "Discussion Thread": 6.999064933141225e-06, "Comment Section": 1.3436019338214811e-05, "Missing Content": 2.7221540239945945e-05, "Knowledge Article": 5.7974976828995246e-05, "Legal Notices": 0.012148363661918599, "News Article": 5.3452068745545265e-06, "Organizational Announcement": 1.8046075188809056e-05, "Nonfiction Writing": 1.0359575132310265e-05, "Organizational About Page": 1.696346538869111e-05, "Personal About Page": 6.9855014912980886e-06, "Personal Blog": 7.192472228788847e-06, "Product Page": 6.554443798880447e-06, "Spam / Ads": 8.683931157750427e-06, "Structured Data": 7.966859154799198e-05, "Technical Writing": 0.9864821237592823, "Transcript / Interview": 1.4865936541450258e-05, "Tutorial / How-To Guide": 8.663033052961007e-05, "User Reviews": 6.106329105922993e-06, "Other / Unknown": 0.0008902400346744286}, "secondary": {"Academic Writing": 0.0008106977594558494, "Content Listing": 0.00047700159162484597, "Creative Writing": 0.00035228573966323987, "Customer Support Page": 0.00023940571999135906, "Q&A Forum": 0.00022911125096228112, "Discussion Thread": 0.00021295409226470958, "Comment Section": 0.0004150570323580266, "Missing Content": 0.0004679259074020517, "Knowledge Article": 0.0011537864712970172, "Legal Notices": 0.7981538701632713, "News Article": 0.0003338832142341543, "Organizational Announcement": 0.00047930714104835165, "Nonfiction Writing": 0.00029106179693000475, "Organizational About Page": 0.0004114776347180117, "Personal About Page": 0.00022448472176012237, "Personal Blog": 0.00019709473705865863, "Product Page": 0.00027637264998717475, "Spam / Ads": 0.0002658527925255306, "Structured Data": 0.0005725018108523754, "Technical Writing": 0.05513625382682833, "Transcript / Interview": 0.0003763136763166319, "Tutorial / How-To Guide": 0.0004965251964792684, "User Reviews": 0.00014538759966487802, "NONE": 0.13828138747330565}, "tertiary": {"Academic Writing": 0.000853381669018962, "Content Listing": 0.00031081417252831365, "Creative Writing": 0.000275573897741938, "Customer Support Page": 0.00016820948816467703, "Q&A Forum": 0.000128476700716576, "Discussion Thread": 0.0001157401844060915, "Comment Section": 0.00022125717625275684, "Missing Content": 0.000366147925156532, "Knowledge Article": 0.00244427658468805, "Legal Notices": 0.14309552115388746, "News Article": 0.00021927525247118865, "Organizational Announcement": 0.000291938344125754, "Nonfiction Writing": 0.00017028271796260994, "Organizational About Page": 0.0002507857392492315, "Personal About Page": 0.00015124481092364088, "Personal Blog": 0.00013261106557660354, "Product Page": 0.0001521667681000776, "Spam / Ads": 0.00019225714175569736, "Structured Data": 0.00043273723482639454, "Technical Writing": 0.012537003894723713, "Transcript / Interview": 0.0002369866050175113, "Tutorial / How-To Guide": 0.0005321144923584437, "User Reviews": 8.753373934379431e-05, "NONE": 0.836633663241004}, "structured_records": {"false": 0.9995987770218909, "true": 0.00040122297810904825}, "procedural_steps": {"false": 0.9900584856028286, "true": 0.009941514397171418}, "worked_solution": {"false": 0.9962902171793029, "true": 0.0037097828206971327}, "contextual_dialogue": {"false": 0.9999949255315495, "true": 5.074468450540508e-06}, "content_quality": {"-2": 0.005095901744228929, "-1": 0.022981244701848765, "0": 0.22193463961440316, "1": 0.513236154506551, "2": 0.2367520594329681}, "noise_severity": {"0": 0.2685074264445927, "1": 0.6271747956884389, "2": 0.09686800028365973, "3": 0.004219684824127323, "4": 0.0032300927591813557}, "missing_content_severity": {"0": 0.9958077918596613, "1": 0.0036056136571701097, "2": 0.00034025048725777623, "3": 0.0001544355724129289, "4": 9.190842349788684e-05}, "factual_density": {"0": 0.0027735563737139356, "1": 0.010851805319328394, "2": 0.1090504737073815, "3": 0.36984086359748725, "4": 0.5074833010020889}, "pretraining_utility": {"-2": 0.006674079009273819, "-1": 0.07192967009108475, "0": 0.392065929812888, "1": 0.45671731584484515, "2": 0.07261300524190821}, "math_density": {"0": 0.2090253252703955, "1": 0.16017665294050415, "2": 0.1911726979047357, "3": 0.15508265272060107, "4": 0.28454267116376364}, "code_density": {"0": 0.0006015996756768236, "1": 0.0012768981411763946, "2": 0.05684519923057263, "3": 0.39905963884866497, "4": 0.5422166641039092}, "reasoning_density": {"0": 0.39318118553132625, "1": 0.14185901343755322, "2": 0.18018579571280266, "3": 0.18096625346646417, "4": 0.1038077518518538}, "education_level": {"none": 0.0013963310206126876, "0": 0.0015941370144623706, "1": 0.004331326368177908, "2": 0.3002114153718469, "3": 0.5508604846952504, "4": 0.14160630552964976}, "creative_writing_quality": {"not_applicable": 0.9994843190589139, "-2": 4.377313361731143e-05, "-1": 9.273305661276475e-05, "0": 0.00028530170061279923, "1": 6.611689434126192e-05, "2": 2.775615590201869e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 9.698990701100983e-05, "Art & Design": 0.0001329436701897807, "Law & Crime": 0.0012876774434960865, "Education & Careers": 9.53926512399578e-05, "Entertainment & Media": 7.093274167030145e-05, "Fashion & Beauty": 6.972885011123871e-05, "Business, Economics & Finance": 0.0001056611566442378, "Food & Dining": 8.230629751024794e-05, "Games & Gaming": 0.0001193237456287686, "Electronics & Hardware": 0.00014226773913388302, "Health, Healthcare & Wellness": 0.00013630702332980362, "History & Geography": 0.00012730481721416105, "Home, Pets & Hobbies": 5.817405095378152e-05, "Industry & Production": 0.00010935632372545401, "Language, Literature & Philosophy": 8.29497769929553e-05, "Politics & Government": 5.333921074267912e-05, "Religion & Spirituality": 7.052506087678324e-05, "Science, Mathematics & Engineering": 0.048290555902500455, "Relationships & Community": 3.8206052795780205e-05, "Software Usage & Applications": 0.00047641819595854284, "Programming & Software Engineering": 0.9474712601703228, "Sports & Fitness": 9.483986646329878e-05, "Vehicles & Transportation": 4.465777493365247e-05, "Travel & Tourism": 9.716020417494212e-05, "Other / Uncovered Subject": 0.0004828419785499297, "Unknown / Insufficient Content": 0.00016287938782942423}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007301999661908606, "Art & Design": 0.000632673788394375, "Law & Crime": 0.06699736696352392, "Education & Careers": 0.00047364859115036446, "Entertainment & Media": 0.0005423837291625043, "Fashion & Beauty": 0.00036564983429113613, "Business, Economics & Finance": 0.0009321758839711311, "Food & Dining": 0.0003207161328150351, "Games & Gaming": 0.00042339335209005133, "Electronics & Hardware": 0.000600961528875236, "Health, Healthcare & Wellness": 0.0005489294942815499, "History & Geography": 0.0005141021928308188, "Home, Pets & Hobbies": 0.0002970181527669816, "Industry & Production": 0.00039814048592257025, "Language, Literature & Philosophy": 0.0002702017056397023, "Politics & Government": 0.0006137367651884484, "Religion & Spirituality": 0.0004868419850464425, "Science, Mathematics & Engineering": 0.21039730027756565, "Relationships & Community": 0.00017556908404432156, "Software Usage & Applications": 0.000746497781782863, "Programming & Software Engineering": 0.06284163955029484, "Sports & Fitness": 0.00032031891557052477, "Vehicles & Transportation": 0.0001908621784259061, "Travel & Tourism": 0.000568981381509154, "Other / Uncovered Subject": 0.0010758244220575336, "NONE": 0.6485348658566081}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9889, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9889, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:08.387200+00:00", "scoring_key_slot": "primary", "dataset_uid": "f25f21ab-cb03-438a-a3c9-0c720ac4d779", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "37dcdeef4b0c442453cb03f9b09731bdd0dbbb0d17b8bce5fd9b6bdec6958112", "source_metadata": {"uuid": "f25f21ab-cb03-438a-a3c9-0c720ac4d779", "repo_name": "tad-mctc/tad-libcint", "relative_path": "src/tad_libcint/interface/integrals/utils.py", "category": "DATA", "algo_rel_score": 0.9926264882087708, "quality_score": 7.855957984924316, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 62, "repo_created_at": "2023-07-07T15:36:19.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 4, "forks": 0, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "f5ffa9b5cb5534667323c5f50f787a1fa8f7a481", "size_bytes": 5410, "file_timestamp": 1748889971, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "7645fc4f84229024547931f1f773c1b530cb10bf"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 62, "repo_created_at": "2023-07-07T15:36:19.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 4, "forks": 0, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "f5ffa9b5cb5534667323c5f50f787a1fa8f7a481", "size_bytes": 5410, "file_timestamp": 1748889971, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "7645fc4f84229024547931f1f773c1b530cb10bf", "url": "https://github.com/tad-mctc/tad-libcint/blob/7645fc4f84229024547931f1f773c1b530cb10bf/src/tad_libcint/interface/integrals/utils.py"}, "gpt4o_tokens": 1313, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.9535672251821806, "noise_severity": 0.8464902217648658, "missing_content_severity": 0.005117055042915996, "factual_density": 3.368408547534909, "pretraining_utility": 0.5166654982190292, "math_density": 2.1459406915668335, "code_density": 3.4810128695639535, "reasoning_density": 1.460360372669966, "education_level": 2.827709251461311, "creative_writing_quality": -0.11373334368060248}, "score_applicability": {"education_level": 0.9986036689793873, "creative_writing_quality": 0.0005156809410861561}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/nilshellerhoff/abokistde/blob/e959f68ef3a79c65aa4c2c0d64655f7b31c8d1b7/abokistde/legacy_views.py", "id": "python-0-7433b92f4945d6af2dfc44059f6a884c", "text": "from django.shortcuts import render, get_object_or_404, redirect\nfrom django.template import loader\nfrom django.urls import reverse\nfrom django.http import HttpResponse, HttpResponseRedirect, JsonResponse\n\nfrom django.core.exceptions import ValidationError\n\nfrom .models import Episode, PublishingChannel, UserSubscription, Provider\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth import password_validation, authenticate, login, logout\n\nfrom django.forms.models import model_to_dict\nimport json\n\nfrom datetime import datetime, timedelta, timezone\nimport time\nimport uuid\n\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom . import sites_wrapper \n\n@csrf_exempt\ndef videos(request):\n\n    print(request.user)\n    print(request.user.id)\n\n    # TODO get this to work\n    # videos = Episode.objects.filter(publishing_channel__usersusbscription__in=UserSubscription.objects.filter(user=request.user)).order_by('-published')\n    videos_ids = Episode.objects.raw(\"\"\"\nSELECT e.id FROM abokistde_episode e\nINNER JOIN abokistde_publishingchannel c ON e.publishing_channel_id = c.id\nINNER JOIN abokistde_usersubscription us ON c.id = us.publishing_channel_id\nINNER JOIN auth_user u ON us.user_id = u.id\nWHERE u.id = %s\n    \"\"\" % request.user.id)\n\n    videos = Episode.objects.filter(id__in=[v.id for v in videos_ids]).order_by('-published')\n    videos_list = list(videos.values())\n\n    for v1, v2 in zip(videos_list, videos):\n        v1['rowid'] = v1['id']\n        v1['channelName'] = v2.publishing_channel.name\n        v1['channelThumbnail'] = v2.publishing_channel.thumbnail_url\n        v1['channelid'] = v2.publishing_channel.channel_id\n        v1['provider'] = v2.publishing_channel.provider.extractor.name\n        v1['runtime'] = v2.duration\n        v1['video_page_url'] = v2.thumbnail_url\n        v1['timeSincePublished'] = int((datetime.now(timezone.utc) - v2.published).total_seconds())\n        v1['thumbnail'] = v2.thumbnail_url\n\n    channels_ids = PublishingChannel.objects.raw(\"\"\"\nSELECT c.id FROM abokistde_publishingchannel c\nINNER JOIN abokistde_usersubscription us ON c.id = us.publishing_channel_id\nINNER JOIN auth_user u ON us.user_id = u.id\nWHERE u.id = %s\n\"\"\" % request.user.id)\n    channels = PublishingChannel.objects.filter(id__in=[c.id for c in channels_ids])\n    channels_list = list(channels.values())\n\n    for c1,c2 in zip(channels_list, channels):\n        c1['rowid'] = c2.id\n        c1['thumbnail'] = c2.thumbnail_url\n        c1['video_page_url'] = c2.url\n        \n    # provider_favicons = { 'youtube': 'https://www.youtube.com/s/desktop/9528aa7e/img/favicon_32.png' }\n    providers = Provider.objects.all()\n    provider_names = [p.extractor.name for p in providers]\n    provider_icons = [p.icon_url for p in providers]\n    provider_favicons = { k: v for (k,v) in zip(provider_names, provider_icons)}\n    results = {\n        'videos': videos_list,\n        'channels': channels_list,\n        'providerFavicons': provider_favicons\n    }\n    return JsonResponse(results)\n\n@csrf_exempt\ndef fetch_youtube(request):\n    sites_wrapper.getNewEpisodes()\n    return HttpResponseRedirect('/')\n\n@csrf_exempt\ndef user_login(request):\n    request_body = json.loads(request.body)\n    username = request_body['username']\n    password = request_body['<PASSWORD>']\n\n    user = authenticate(username=username, password=<PASSWORD>)\n    if user is not None:\n        login(request, user)\n        response = {\n            \"status\": \"success\",\n            \"token\": \"<PASSWORD>\"\n        }\n        return JsonResponse(response, status=200)\n    else:\n        response = {\n            \"status\": \"error\",\n            \"message\": \"Wrong username or password\"\n        }\n        return JsonResponse(response, status=400)\n\n@csrf_exempt\ndef user_logout(request):\n    logout(request)\n    response = {\n        \"status\": \"success\",\n        \"message\": \"Logged out\"\n    }\n    return JsonResponse(response, status=200)\n\n@csrf_exempt\ndef user_checktoken(request):\n    # check if user is logged in\n    if not request.user.is_authenticated:\n        return JsonResponse({\"status\": \"error\", \"message\": \"Not logged in\"}, status=401)\n    else:\n        return JsonResponse({\"status\": \"success\", \"message\": \"Logged in\"}, status=200)\n\n@csrf_exempt\ndef user_add(request):\n    request_body = json.loads(request.body)\n    username = request_body['username']\n    password = request_body['<PASSWORD>']\n\n    # check if uername is taken\n    if User.objects.filter(username=username).exists():\n        response = {\n            \"status\": \"error\",\n            \"message\": \"Username already taken\"\n        }\n        return JsonResponse(response, status=400)\n    \n    # check if password is valid    \n    try:\n        # create user\n        password_validation.validate_password(password)\n        user = User.objects.create_user(username=username, password=<PASSWORD>)\n        response = {\n            \"status\": \"success\",\n            \"message\": \"User created\"\n        }\n        return JsonResponse(response, status=200)\n    except ValidationError as e:\n        response = {\n            \"status\": \"error\",\n            \"message\": e.messages[0]\n        }\n        return JsonResponse(response, status=400)\n\n    except:\n        response = {\n            \"status\": \"error\",\n            \"message\": \"Something went wrong\"\n        }\n        return JsonResponse(response, status=400)\n\n@csrf_exempt\ndef search_online(request):\n    searchterm = request.GET.get('query')\n    channels_episodes = sites_wrapper.search(searchterm)\n\n    channel_ids = [channel.id for channel in channels_episodes['channel']]\n    channels = list(PublishingChannel.objects.filter(id__in=channel_ids).values())\n\n    episode_ids = [episode.id for episode in channels_episodes['episode']]\n    episodes = list(Episode.objects.filter(id__in=episode_ids).values())\n\n    for r in channels:\n        r['provider'] = {\n            \"id\": 82,\n            \"name\": \"Youtube\",\n            \"url\": \"https://www.youtube.com/\",\n            \"icon_url\": \"https://www.youtube.com/s/desktop/5191a190/img/favicon_144x144.png\"\n        }\n\n    return JsonResponse({\n        \"status\": \"success\",\n        \"data\": {\n            \"channels\": channels,\n            \"episodes\": episodes\n        }\n    })", "text_preview": "from django.shortcuts import render, get_object_or_404, redirect from django.template import loader from django.urls import reverse from django.http import HttpResponse, HttpResponseRedirect, JsonResponse from django.core.exceptions import ", "input_sha256": "aec2d0407fc2597b625f2230955a10d87353fe8d18a190c4b3c2e85674f08e21", "text_sha256": "7433b92f4945d6af2dfc44059f6a884c5a859a59980a8581a5e31d0e9cc8eb6a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12059, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": -0.12686709062983736, "noise_severity": 0.3426468636734601, "missing_content_severity": 0.18857896797009738, "factual_density": 3.268282096697159, "pretraining_utility": 0.3763781358899261, "math_density": 0.18374294448448988, "code_density": 3.8906762269663853, "reasoning_density": 0.3718125272990398, "education_level": 2.278376079534159, "creative_writing_quality": -0.48705744543394897}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.03564351425236212, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9974823426168548, "secondary": 0.912932952255699, "tertiary": 0.9501261845190333, "structured_records": 0.04321278855638276, "procedural_steps": 0.0030727949856435687, "worked_solution": 0.002536529699240046, "contextual_dialogue": 6.313824910771164e-06, "content_quality": 0.3510685965566033, "noise_severity": 0.7144609469387833, "missing_content_severity": 0.8428508600249188, "factual_density": 0.3902350805809659, "pretraining_utility": 0.44893485397504873, "math_density": 0.8468808795962585, "code_density": 0.9088968558053206, "reasoning_density": 0.6901562272508002, "education_level": 0.6662941926340019, "creative_writing_quality": 0.9993703430259256, "primary_topic": 0.9867383748044777, "secondary_topic": 0.8228627765709492}, "probability_margin": {"primary": 0.9963853926388533, "secondary": 0.8611030546277586, "tertiary": 0.9220074167719038, "education_level": 0.4734889473119053, "creative_writing_quality": 0.9992515758139231, "primary_topic": 0.9770897495967579, "secondary_topic": 0.7039476442469371}, "raw_probabilities": {"primary": {"Academic Writing": 1.8227022742654067e-05, "Content Listing": 1.909916920974188e-05, "Creative Writing": 9.300303376601788e-06, "Customer Support Page": 4.812952840066365e-06, "Q&A Forum": 6.115599400585023e-06, "Discussion Thread": 4.6576168814715354e-06, "Comment Section": 6.7268484062341125e-06, "Missing Content": 1.7028334916091327e-05, "Knowledge Article": 2.9441326595560842e-05, "Legal Notices": 1.5281520849277836e-05, "News Article": 2.739846618547242e-06, "Organizational Announcement": 6.7191257070503704e-06, "Nonfiction Writing": 6.925121959131502e-06, "Organizational About Page": 7.489513357599999e-06, "Personal About Page": 1.0585720020915675e-05, "Personal Blog": 4.4872607267531845e-06, "Product Page": 4.39687421840929e-06, "Spam / Ads": 6.106041920878557e-06, "Structured Data": 0.001201852368965816, "Technical Writing": 0.9975872450078191, "Transcript / Interview": 9.228948748777151e-06, "Tutorial / How-To Guide": 0.00017984451828812297, "User Reviews": 3.903309504306729e-06, "Other / Unknown": 0.0008377856469262275}, "secondary": {"Academic Writing": 0.00040984971583938055, "Content Listing": 0.0008581324354759883, "Creative Writing": 0.0003568013659699183, "Customer Support Page": 0.0001782749113177657, "Q&A Forum": 0.00018508113271804874, "Discussion Thread": 0.00014782864204610684, "Comment Section": 0.0009868833413637413, "Missing Content": 0.0032398276127266266, "Knowledge Article": 0.0006148505146380131, "Legal Notices": 0.0005454499388485523, "News Article": 0.00022418409409984534, "Organizational Announcement": 0.00022475480641064515, "Nonfiction Writing": 0.00024476897238234714, "Organizational About Page": 0.0002329596708220494, "Personal About Page": 0.00019214298902777823, "Personal Blog": 0.0002139593746398147, "Product Page": 0.0001371584000274787, "Spam / Ads": 0.00022894444877514456, "Structured Data": 0.055457691283952934, "Technical Writing": 0.017084003779719005, "Transcript / Interview": 0.0002875042951998491, "Tutorial / How-To Guide": 0.0012963086298603984, "User Reviews": 9.189373242699294e-05, "NONE": 0.9165607459117115}, "tertiary": {"Academic Writing": 0.00021445495877991775, "Content Listing": 0.0008173762822731324, "Creative Writing": 0.00018882397032426207, "Customer Support Page": 0.00010866820222831653, "Q&A Forum": 7.700895438709938e-05, "Discussion Thread": 7.132655526156255e-05, "Comment Section": 0.001113898949309626, "Missing Content": 0.002560792593673495, "Knowledge Article": 0.0007943965622491962, "Legal Notices": 0.0005076250316229014, "News Article": 0.00010849320170703498, "Organizational Announcement": 0.00010744689788642965, "Nonfiction Writing": 0.0001464156194539191, "Organizational About Page": 0.00014604452759522666, "Personal About Page": 0.00010720876722160056, "Personal Blog": 9.496974755217852e-05, "Product Page": 8.054744920183441e-05, "Spam / Ads": 0.00014743441783386923, "Structured Data": 0.0301968433921698, "Technical Writing": 0.009104983617687142, "Transcript / Interview": 0.00013991783287961018, "Tutorial / How-To Guide": 0.0009165580001117506, "User Reviews": 4.450430451640603e-05, "NONE": 0.9522042601640736}, "structured_records": {"false": 0.9567872114436172, "true": 0.04321278855638276}, "procedural_steps": {"false": 0.9969272050143564, "true": 0.0030727949856435687}, "worked_solution": {"false": 0.99746347030076, "true": 0.002536529699240046}, "contextual_dialogue": {"false": 0.9999936861750892, "true": 6.313824910771164e-06}, "content_quality": {"-2": 0.06094576437126067, "-1": 0.3309008586384353, "0": 0.34071977718849733, "1": 0.20894190285249428, "2": 0.058491696949312506}, "noise_severity": {"0": 0.765063740525641, "1": 0.16251906743450512, "2": 0.04398004660972947, "3": 0.021580878701001276, "4": 0.006856266729123039}, "missing_content_severity": {"0": 0.8509488266378193, "1": 0.11775104470977411, "2": 0.024513881002738276, "3": 0.005344829343826345, "4": 0.0014414183058419224}, "factual_density": {"0": 0.007051539532625537, "1": 0.02076429495248623, "2": 0.13017753793177514, "3": 0.3808637844513298, "4": 0.4611428431317833}, "pretraining_utility": {"-2": 0.013697024317315561, "-1": 0.11505597103537658, "0": 0.409810469889668, "1": 0.4040449139553461, "2": 0.057391620802293854}, "math_density": {"0": 0.8343453952644507, "1": 0.15611136283800053, "2": 0.00448332670920169, "3": 0.0015747325253020292, "4": 0.0034851826630449612}, "code_density": {"0": 0.0001776363812029929, "1": 0.000322923178998997, "2": 0.009300054675982385, "3": 0.08904434861984147, "4": 0.9011550371439743}, "reasoning_density": {"0": 0.7967863426125238, "1": 0.07312506754318475, "2": 0.10041779825451444, "3": 0.02083130311228218, "4": 0.00883948847749491}, "education_level": {"none": 0.000917465659437298, "0": 0.0011698060523519607, "1": 0.007706210385946627, "2": 0.7219118271950016, "3": 0.2484228798830963, "4": 0.01987181082416603}, "creative_writing_quality": {"not_applicable": 0.9994752858549381, "-2": 8.83795221481283e-05, "-1": 0.0001538008084455039, "0": 0.00022371004101490372, "1": 4.265362524185557e-05, "2": 1.6170148211500604e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00015089543283140373, "Art & Design": 6.483906134863031e-05, "Law & Crime": 3.345366147026692e-05, "Education & Careers": 5.389062007086083e-05, "Entertainment & Media": 0.010158687715239869, "Fashion & Beauty": 3.94696563387993e-05, "Business, Economics & Finance": 7.226611125111173e-05, "Food & Dining": 4.27369078212259e-05, "Games & Gaming": 0.0002936686360936286, "Electronics & Hardware": 6.566699107464781e-05, "Health, Healthcare & Wellness": 2.552581123539266e-05, "History & Geography": 2.6088869916265465e-05, "Home, Pets & Hobbies": 6.205866887436675e-05, "Industry & Production": 2.2453782756781166e-05, "Language, Literature & Philosophy": 3.471101228532232e-05, "Politics & Government": 2.275374965704464e-05, "Religion & Spirituality": 3.077879078651199e-05, "Science, Mathematics & Engineering": 8.81385746546503e-05, "Relationships & Community": 3.199380077252342e-05, "Software Usage & Applications": 0.0007865413502590989, "Programming & Software Engineering": 0.9872484373119977, "Sports & Fitness": 5.69059438769901e-05, "Vehicles & Transportation": 1.5621035641552408e-05, "Travel & Tourism": 4.301959240431932e-05, "Other / Uncovered Subject": 0.0003745862560742424, "Unknown / Insufficient Content": 0.00015481065526700352}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0013925049293480353, "Art & Design": 0.0011612139794890844, "Law & Crime": 0.001000811383174843, "Education & Careers": 0.0004640370688438601, "Entertainment & Media": 0.12572810245589863, "Fashion & Beauty": 0.0003927189914365005, "Business, Economics & Finance": 0.0010446865222192366, "Food & Dining": 0.0003026793514642718, "Games & Gaming": 0.001205963342292462, "Electronics & Hardware": 0.0006760994049577026, "Health, Healthcare & Wellness": 0.0003539243758571764, "History & Geography": 0.00030245356121054033, "Home, Pets & Hobbies": 0.0003927920308007883, "Industry & Production": 0.00023399878545734204, "Language, Literature & Philosophy": 0.0002863529068674881, "Politics & Government": 0.0002906227445514139, "Religion & Spirituality": 0.0003770258592672265, "Science, Mathematics & Engineering": 0.0013077593715706327, "Relationships & Community": 0.00025446030493559704, "Software Usage & Applications": 0.00981810646136936, "Programming & Software Engineering": 0.020811708550055735, "Sports & Fitness": 0.0003984648379285852, "Vehicles & Transportation": 0.0001553765191799777, "Travel & Tourism": 0.000462173301940369, "Other / Uncovered Subject": 0.0015102162570473695, "NONE": 0.8296757467028357}}, "probabilities": {"primary": {"Academic Writing": 1.8227022742654067e-05, "Content Listing": 1.909916920974188e-05, "Creative Writing": 9.300303376601788e-06, "Customer Support Page": 4.812952840066365e-06, "Q&A Forum": 6.115599400585023e-06, "Discussion Thread": 4.6576168814715354e-06, "Comment Section": 6.7268484062341125e-06, "Missing Content": 1.7028334916091327e-05, "Knowledge Article": 2.9441326595560842e-05, "Legal Notices": 1.5281520849277836e-05, "News Article": 2.739846618547242e-06, "Organizational Announcement": 6.7191257070503704e-06, "Nonfiction Writing": 6.925121959131502e-06, "Organizational About Page": 7.489513357599999e-06, "Personal About Page": 1.0585720020915675e-05, "Personal Blog": 4.4872607267531845e-06, "Product Page": 4.39687421840929e-06, "Spam / Ads": 6.106041920878557e-06, "Structured Data": 0.001201852368965816, "Technical Writing": 0.9975872450078191, "Transcript / Interview": 9.228948748777151e-06, "Tutorial / How-To Guide": 0.00017984451828812297, "User Reviews": 3.903309504306729e-06, "Other / Unknown": 0.0008377856469262275}, "secondary": {"Academic Writing": 0.00040984971583938055, "Content Listing": 0.0008581324354759883, "Creative Writing": 0.0003568013659699183, "Customer Support Page": 0.0001782749113177657, "Q&A Forum": 0.00018508113271804874, "Discussion Thread": 0.00014782864204610684, "Comment Section": 0.0009868833413637413, "Missing Content": 0.0032398276127266266, "Knowledge Article": 0.0006148505146380131, "Legal Notices": 0.0005454499388485523, "News Article": 0.00022418409409984534, "Organizational Announcement": 0.00022475480641064515, "Nonfiction Writing": 0.00024476897238234714, "Organizational About Page": 0.0002329596708220494, "Personal About Page": 0.00019214298902777823, "Personal Blog": 0.0002139593746398147, "Product Page": 0.0001371584000274787, "Spam / Ads": 0.00022894444877514456, "Structured Data": 0.055457691283952934, "Technical Writing": 0.017084003779719005, "Transcript / Interview": 0.0002875042951998491, "Tutorial / How-To Guide": 0.0012963086298603984, "User Reviews": 9.189373242699294e-05, "NONE": 0.9165607459117115}, "tertiary": {"Academic Writing": 0.00021445495877991778, "Content Listing": 0.0008173762822731325, "Creative Writing": 0.0001888239703242621, "Customer Support Page": 0.00010866820222831654, "Q&A Forum": 7.70089543870994e-05, "Discussion Thread": 7.132655526156256e-05, "Comment Section": 0.0011138989493096262, "Missing Content": 0.0025607925936734954, "Knowledge Article": 0.0007943965622491963, "Legal Notices": 0.0005076250316229015, "News Article": 0.00010849320170703499, "Organizational Announcement": 0.00010744689788642966, "Nonfiction Writing": 0.00014641561945391912, "Organizational About Page": 0.0001460445275952267, "Personal About Page": 0.00010720876722160058, "Personal Blog": 9.496974755217853e-05, "Product Page": 8.054744920183442e-05, "Spam / Ads": 0.00014743441783386925, "Structured Data": 0.030196843392169805, "Technical Writing": 0.009104983617687143, "Transcript / Interview": 0.0001399178328796102, "Tutorial / How-To Guide": 0.0009165580001117507, "User Reviews": 4.4504304516406035e-05, "NONE": 0.9522042601640737}, "structured_records": {"false": 0.9567872114436172, "true": 0.04321278855638276}, "procedural_steps": {"false": 0.9969272050143564, "true": 0.0030727949856435687}, "worked_solution": {"false": 0.99746347030076, "true": 0.002536529699240046}, "contextual_dialogue": {"false": 0.9999936861750892, "true": 6.313824910771164e-06}, "content_quality": {"-2": 0.06094576437126067, "-1": 0.3309008586384353, "0": 0.34071977718849733, "1": 0.20894190285249428, "2": 0.058491696949312506}, "noise_severity": {"0": 0.7650637405256411, "1": 0.16251906743450514, "2": 0.04398004660972948, "3": 0.02158087870100128, "4": 0.0068562667291230395}, "missing_content_severity": {"0": 0.8509488266378193, "1": 0.11775104470977411, "2": 0.024513881002738276, "3": 0.005344829343826345, "4": 0.0014414183058419224}, "factual_density": {"0": 0.007051539532625537, "1": 0.02076429495248623, "2": 0.13017753793177514, "3": 0.3808637844513298, "4": 0.4611428431317833}, "pretraining_utility": {"-2": 0.013697024317315561, "-1": 0.11505597103537658, "0": 0.409810469889668, "1": 0.4040449139553461, "2": 0.057391620802293854}, "math_density": {"0": 0.8343453952644508, "1": 0.15611136283800056, "2": 0.004483326709201691, "3": 0.0015747325253020294, "4": 0.0034851826630449617}, "code_density": {"0": 0.0001776363812029929, "1": 0.000322923178998997, "2": 0.009300054675982385, "3": 0.08904434861984147, "4": 0.9011550371439743}, "reasoning_density": {"0": 0.7967863426125238, "1": 0.07312506754318475, "2": 0.10041779825451444, "3": 0.02083130311228218, "4": 0.00883948847749491}, "education_level": {"none": 0.0009174656594372982, "0": 0.0011698060523519609, "1": 0.007706210385946629, "2": 0.7219118271950017, "3": 0.24842287988309636, "4": 0.019871810824166034}, "creative_writing_quality": {"not_applicable": 0.9994752858549381, "-2": 8.83795221481283e-05, "-1": 0.0001538008084455039, "0": 0.00022371004101490372, "1": 4.265362524185557e-05, "2": 1.6170148211500604e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001508954328314037, "Art & Design": 6.48390613486303e-05, "Law & Crime": 3.3453661470266913e-05, "Education & Careers": 5.3890620070860815e-05, "Entertainment & Media": 0.010158687715239867, "Fashion & Beauty": 3.946965633879929e-05, "Business, Economics & Finance": 7.226611125111172e-05, "Food & Dining": 4.273690782122589e-05, "Games & Gaming": 0.00029366863609362853, "Electronics & Hardware": 6.56669910746478e-05, "Health, Healthcare & Wellness": 2.5525811235392654e-05, "History & Geography": 2.608886991626546e-05, "Home, Pets & Hobbies": 6.205866887436674e-05, "Industry & Production": 2.2453782756781162e-05, "Language, Literature & Philosophy": 3.4711012285322316e-05, "Politics & Government": 2.2753749657044636e-05, "Religion & Spirituality": 3.0778790786511986e-05, "Science, Mathematics & Engineering": 8.813857465465028e-05, "Relationships & Community": 3.1993800772523415e-05, "Software Usage & Applications": 0.0007865413502590987, "Programming & Software Engineering": 0.9872484373119975, "Sports & Fitness": 5.690594387699009e-05, "Vehicles & Transportation": 1.5621035641552404e-05, "Travel & Tourism": 4.3019592404319314e-05, "Other / Uncovered Subject": 0.0003745862560742423, "Unknown / Insufficient Content": 0.0001548106552670035}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0013925049293480353, "Art & Design": 0.0011612139794890844, "Law & Crime": 0.001000811383174843, "Education & Careers": 0.0004640370688438601, "Entertainment & Media": 0.12572810245589863, "Fashion & Beauty": 0.0003927189914365005, "Business, Economics & Finance": 0.0010446865222192366, "Food & Dining": 0.0003026793514642718, "Games & Gaming": 0.001205963342292462, "Electronics & Hardware": 0.0006760994049577026, "Health, Healthcare & Wellness": 0.0003539243758571764, "History & Geography": 0.00030245356121054033, "Home, Pets & Hobbies": 0.0003927920308007883, "Industry & Production": 0.00023399878545734204, "Language, Literature & Philosophy": 0.0002863529068674881, "Politics & Government": 0.0002906227445514139, "Religion & Spirituality": 0.0003770258592672265, "Science, Mathematics & Engineering": 0.0013077593715706327, "Relationships & Community": 0.00025446030493559704, "Software Usage & Applications": 0.00981810646136936, "Programming & Software Engineering": 0.020811708550055735, "Sports & Fitness": 0.0003984648379285852, "Vehicles & Transportation": 0.0001553765191799777, "Travel & Tourism": 0.000462173301940369, "Other / Uncovered Subject": 0.0015102162570473695, "NONE": 0.8296757467028357}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10051, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10051, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:08.527205+00:00", "scoring_key_slot": "primary", "dataset_uid": "5160a48e-b6a0-4de0-b303-1226b2500326", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "a3f5c13fcd09e65ba74e4b3917c4699201abc275e3659fbfaaa864aa9446e08c", "source_metadata": {"uuid": "5160a48e-b6a0-4de0-b303-1226b2500326", "repo_name": "nilshellerhoff/abokistde", "relative_path": "abokistde/legacy_views.py", "category": "WEB", "algo_rel_score": 0.9130615592002869, "quality_score": 4.868037223815918, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 136, "repo_created_at": "2023-01-31T16:19:52.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "87bdcf79167b629c614e1ad23e0de42b5e947c95", "size_bytes": 6215, "file_timestamp": 1743952083, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "e959f68ef3a79c65aa4c2c0d64655f7b31c8d1b7"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 136, "repo_created_at": "2023-01-31T16:19:52.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "87bdcf79167b629c614e1ad23e0de42b5e947c95", "size_bytes": 6215, "file_timestamp": 1743952083, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "e959f68ef3a79c65aa4c2c0d64655f7b31c8d1b7", "url": "https://github.com/nilshellerhoff/abokistde/blob/e959f68ef3a79c65aa4c2c0d64655f7b31c8d1b7/abokistde/legacy_views.py"}, "gpt4o_tokens": 1435, "dataset_split": "python", "probability_weighted_scores": {"content_quality": -0.12686709062983736, "noise_severity": 0.3426468636734601, "missing_content_severity": 0.18857896797009738, "factual_density": 3.268282096697159, "pretraining_utility": 0.3763781358899261, "math_density": 0.18374294448448988, "code_density": 3.8906762269663853, "reasoning_density": 0.3718125272990398, "education_level": 2.278376079534159, "creative_writing_quality": -0.48705744543394897}, "score_applicability": {"education_level": 0.9990825343405627, "creative_writing_quality": 0.0005247141450618921}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/guilhermefay/helio-job-robot/blob/8aa3f41c00e6c4d669cdf56a19df27fec84d880b/core/services/batch_keyword_extractor.py", "id": "python-0-8a4841a2df3b9cb1f677461c31515ed6", "text": "\"\"\"\nExtrator de palavras-chave com processamento em lotes\n\"\"\"\nimport os\nimport json\nimport asyncio\nimport google.generativeai as genai\nfrom typing import List, Dict, Any\nfrom collections import Counter\nfrom datetime import datetime\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nclass BatchKeywordExtractor:\n    def __init__(self):\n        # Configurar Gemini\n        if os.getenv('GOOGLE_API_KEY'):\n            genai.configure(api_key=os.getenv('GOOGLE_API_KEY'))\n            self.model = genai.GenerativeModel('gemini-1.5-flash')\n            print(\"✅ Gemini configurado para processamento em lotes\")\n        else:\n            raise ValueError(\"GOOGLE_API_KEY não configurada\")\n        \n        # Configuração de segurança\n        self.safety_settings = [\n            {\"category\": \"HARM_CATEGORY_HARASSMENT\", \"threshold\": \"BLOCK_NONE\"},\n            {\"category\": \"HARM_CATEGORY_HATE_SPEECH\", \"threshold\": \"BLOCK_NONE\"},\n            {\"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\", \"threshold\": \"BLOCK_NONE\"},\n            {\"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\", \"threshold\": \"BLOCK_NONE\"}\n        ]\n    \n    async def extract_keywords_batch(\n        self, \n        vagas: List[Dict[str, Any]], \n        cargo: str,\n        batch_size: int = 10,\n        callback: callable = None\n    ) -> Dict[str, Any]:\n        \"\"\"\n        Extrai palavras-chave processando vagas em lotes\n        \n        Args:\n            vagas: Lista completa de vagas\n            cargo: Cargo objetivo\n            batch_size: Tamanho de cada lote (padrão 10)\n            callback: Função para reportar progresso\n        \"\"\"\n        total_vagas = len(vagas)\n        \n        print(f\"\\n🚀 PROCESSAMENTO EM LOTES\")\n        print(f\"📊 Total de vagas: {total_vagas}\")\n        print(f\"📦 Tamanho do lote: {batch_size}\")\n        print(f\"🔢 Total de lotes: {(total_vagas + batch_size - 1) // batch_size}\")\n        \n        # Dividir em lotes\n        lotes = []\n        for i in range(0, total_vagas, batch_size):\n            lote = vagas[i:i + batch_size]\n            lotes.append(lote)\n        \n        # Processar cada lote\n        todos_resultados = []\n        palavras_consolidadas = Counter()\n        categorias_consolidadas = {}  # Para rastrear categorias\n        \n        for idx, lote in enumerate(lotes, 1):\n            if callback:\n                await callback(f\"🔄 Iniciando lote {idx} de {len(lotes)} ({len(lote)} vagas)...\")\n            \n            print(f\"\\n📦 Processando lote {idx}/{len(lotes)} ({len(lote)} vagas)\")\n            \n            try:\n                # Processar lote\n                resultado_lote = await self._processar_lote(lote, cargo, idx)\n                \n                if resultado_lote:\n                    todos_resultados.append(resultado_lote)\n                    \n                    # Consolidar palavras e categorias\n                    palavras_lote = []\n                    for palavra in resultado_lote.get('palavras', []):\n                        termo = palavra['termo']\n                        freq = palavra.get('frequencia', 1)\n                        palavras_consolidadas[termo] += freq\n                        \n                        # Guardar categoria se fornecida\n                        if 'categoria' in palavra:\n                            categorias_consolidadas[termo] = palavra['categoria']\n                        \n                        palavras_lote.append(f\"{termo} ({freq}x)\")\n                    \n                    # Enviar callback com detalhes do lote processado\n                    if callback and palavras_lote:\n                        top_5_lote = palavras_lote[:5]\n                        msg = f\"✅ Lote {idx} concluído: {len(palavras_lote)} palavras encontradas\\n\"\n                        msg += f\"   Top 5 do lote: {', '.join(top_5_lote)}\"\n                        if len(palavras_lote) > 5:\n                            msg += f\" (+{len(palavras_lote) - 5} outras)\"\n                        await callback(msg)\n                \n                # Delay entre lotes para evitar rate limit\n                if idx < len(lotes):\n                    await asyncio.sleep(1)\n                    \n            except Exception as e:\n                print(f\"❌ Erro no lote {idx}: {e}\")\n                continue\n        \n        # Verificar se temos resultados válidos\n        if not todos_resultados:\n            print(\"⚠️ AVISO: Nenhum lote foi processado com sucesso!\")\n            # Retornar resultado vazio mas válido\n            return {\n                'top_10_palavras_chave': [],\n                'categorias': {},\n                'total_palavras_unicas': 0,\n                'total_vagas_analisadas': total_vagas,\n                'modelo_usado': 'gemini-1.5-flash-batch',\n                'timestamp': datetime.now().isoformat(),\n                'erro': 'Falha no processamento de todos os lotes'\n            }\n        \n        # Consolidar resultados finais\n        resultado_final = self._consolidar_resultados(\n            palavras_consolidadas,\n            categorias_consolidadas,\n            todos_resultados,\n            total_vagas\n        )\n        \n        print(f\"\\n✅ Processamento concluído!\")\n        print(f\"📊 Total de palavras únicas: {len(palavras_consolidadas)}\")\n        print(f\"🏆 Top 10 palavras mais frequentes:\")\n        \n        # Preparar resumo final para callback\n        if callback:\n            top_10_final = []\n            for termo, freq in palavras_consolidadas.most_common(10):\n                print(f\"   • {termo}: {freq} menções\")\n                percentual = (freq / total_vagas) * 100\n                top_10_final.append(f\"{termo} ({percentual:.0f}%)\")\n            \n            await callback(f\"🎯 Análise concluída! Top 10 palavras-chave:\\n   {', '.join(top_10_final)}\")\n        else:\n            for termo, freq in palavras_consolidadas.most_common(10):\n                print(f\"   • {termo}: {freq} menções\")\n        \n        return resultado_final\n    \n    async def _processar_lote(self, lote: List[Dict], cargo: str, numero_lote: int) -> Dict:\n        \"\"\"Processa um lote de vagas\"\"\"\n        \n        # Preparar texto do lote\n        texto_lote = self._preparar_texto_lote(lote)\n        \n        # Criar prompt completo estilo <NAME>\n        # Usar string normal e depois formatar para evitar conflito com {}\n        prompt_template = \"\"\"Você é especialista em análise de vagas seguindo a metodologia <NAME>.\nAnalise estas %d vagas de %s e extraia palavras-chave estratégicas.\n\nINSTRUÇÕES IMPORTANTES:\n1. Extraia APENAS as 15-20 palavras-chave MAIS RELEVANTES\n2. Conte quantas vezes cada termo aparece nas vagas\n3. Ignore palavras genéricas (dinâmico, proativo, inovador)\n4. Foque em termos ESPECÍFICOS e MENSURÁVEIS\n5. Preserve termos em inglês quando forem padrão do mercado\n\nCATEGORIAS:\n- Técnicas: linguagens, frameworks, metodologias técnicas\n- Ferramentas: softwares, plataformas, IDEs\n- Comportamentais: soft skills específicas (não adjetivos genéricos)\n\n%s\n\nRETORNE APENAS JSON VÁLIDO (sem explicações):\n{\n  \"palavras\": [\n    {\"termo\": \"React\", \"frequencia\": 3, \"categoria\": \"framework\"},\n    {\"termo\": \"JavaScript\", \"frequencia\": 3, \"categoria\": \"linguagem\"},\n    {\"termo\": \"Git\", \"frequencia\": 2, \"categoria\": \"ferramenta\"}\n  ]\n}\"\"\"\n        \n        prompt = prompt_template % (len(lote), cargo, texto_lote)\n        \n        # Tentar até 3 vezes com delay exponencial\n        for tentativa in range(3):\n            try:\n                response = self.model.generate_content(\n                    prompt,\n                    generation_config={\n                        \"temperature\": 0.1,\n                        \"max_output_tokens\": 2048,  # Aumentado para evitar truncamento\n                        \"candidate_count\": 1\n                    },\n                    safety_settings=self.safety_settings\n                )\n                \n                # Extrair JSON\n                texto = response.text\n                \n                # Debug: mostrar primeiros caracteres\n                print(f\"   📝 Resposta do Gemini (primeiros 100 chars): {texto[:100]}...\")\n                \n                # Limpar resposta\n                texto = texto.replace('```json', '').replace('```', '').strip()\n                \n                # Tentar várias limpezas\n                if texto.startswith('JSON:'):\n                    texto = texto[5:].strip()\n                \n                # Verificar se começa e termina com {}\n                if not (texto.startswith('{') and texto.endswith('}')):\n                    # Tentar encontrar JSON no texto\n                    inicio = texto.find('{')\n                    fim = texto.rfind('}')\n                    if inicio >= 0 and fim > inicio:\n                        texto = texto[inicio:fim+1]\n                        print(f\"   🔧 JSON extraído de posição {inicio} até {fim}\")\n                \n                try:\n                    resultado = json.loads(texto)\n                    print(f\"   ✅ Lote {numero_lote}: {len(resultado.get('palavras', []))} palavras extraídas\")\n                    return resultado\n                except json.JSONDecodeError as json_err:\n                    print(f\"   ❌ Erro JSON no lote {numero_lote}: {json_err}\")\n                    print(f\"   📄 Texto recebido: {texto[:200]}...\")\n                    \n                    # Se o erro é de vírgula faltando, provavelmente está truncado\n                    if \"Expecting ',' delimiter\" in str(json_err):\n                        print(f\"   🔧 JSON truncado detectado, tentando recuperar...\")\n                        # Tentar encontrar o último objeto completo\n                        try:\n                            # Encontrar o último } antes do erro\n                            pos_erro = json_err.pos\n                            ultimo_fecha = texto.rfind('}', 0, pos_erro)\n                            if ultimo_fecha > 0:\n                                # Encontrar o ] para fechar o array\n                                texto_truncado = texto[:ultimo_fecha+1] + ']}'\n                                resultado = json.loads(texto_truncado)\n                                print(f\"   ✅ JSON recuperado! {len(resultado.get('palavras', []))} palavras extraídas\")\n                                return resultado\n                        except:\n                            pass\n                    \n                    # Tentar parse manual se for array simples\n                    if '\"palavras\"' in texto:\n                        print(f\"   🔄 Tentando extração manual de palavras...\")\n                        palavras = []\n                        # Regex para encontrar objetos de palavra\n                        import re\n                        pattern = r'\\{\"termo\":\\s*\"([^\"]+)\",\\s*\"frequencia\":\\s*(\\d+),\\s*\"categoria\":\\s*\"([^\"]+)\"\\}'\n                        matches = re.findall(pattern, texto)\n                        for termo, freq, categoria in matches:\n                            palavras.append({\n                                \"termo\": termo,\n                                \"frequencia\": int(freq),\n                                \"categoria\": categoria\n                            })\n                        if palavras:\n                            print(f\"   ✅ Extraídas {len(palavras)} palavras manualmente\")\n                            return {\"palavras\": palavras}\n                    \n                    # Forçar formato vazio se tudo falhar\n                    return {\"palavras\": []}\n                \n            except Exception as e:\n                erro_str = str(e)\n                if \"overloaded\" in erro_str.lower() or \"503\" in erro_str:\n                    wait_time = (tentativa + 1) * 2  # 2, 4, 6 segundos\n                    print(f\"   ⏳ Modelo sobrecarregado. Aguardando {wait_time}s antes de tentar novamente...\")\n                    await asyncio.sleep(wait_time)\n                    continue\n                else:\n                    print(f\"   ❌ Erro ao processar lote {numero_lote}: {e}\")\n                    return None\n        \n        print(f\"   ❌ Falha após 3 tentativas no lote {numero_lote}\")\n        return None\n    \n    def _preparar_texto_lote(self, lote: List[Dict]) -> str:\n        \"\"\"Prepara texto estruturado do lote\"\"\"\n        textos = []\n        \n        for i, vaga in enumerate(lote, 1):\n            titulo = vaga.get('titulo', 'Sem título')\n            empresa = vaga.get('empresa', 'Empresa não informada')\n            desc = vaga.get('descricao', '')\n            \n            # Usar descrição mais completa para análise mais precisa\n            # Limite aumentado para 2500 caracteres para melhor extração\n            if len(desc) > 2500:\n                desc = desc[:2500] + \"...\"\n            \n            texto_vaga = f\"\"\"\n--- VAGA {i} ---\nTítulo: {titulo}\nEmpresa: {empresa}\nDescrição: {desc}\n\"\"\"\n            textos.append(texto_vaga)\n        \n        return \"\\n\".join(textos)\n    \n    def _consolidar_resultados(\n        self, \n        palavras_consolidadas: Counter,\n        categorias_consolidadas: Dict[str, str],\n        todos_resultados: List[Dict],\n        total_vagas: int\n    ) -> Dict:\n        \"\"\"Consolida resultados de todos os lotes\"\"\"\n        \n        # Top 10 palavras mais frequentes\n        top_10 = []\n        for termo, freq in palavras_consolidadas.most_common(10):\n            top_10.append({\n                \"termo\": termo,\n                \"frequencia\": freq,\n                \"percentual\": round((freq / total_vagas) * 100, 1),\n                \"categoria\": categorias_consolidadas.get(termo, self._categorizar_termo(termo))\n            })\n        \n        # Categorizar todas as palavras\n        categorias = {\n            \"linguagens\": [],\n            \"frameworks\": [],\n            \"ferramentas\": [],\n            \"metodologias\": [],\n            \"outros\": []\n        }\n        \n        for termo, freq in palavras_consolidadas.items():\n            categoria = self._categorizar_termo(termo)\n            categorias[categoria].append({\n                \"termo\": termo,\n                \"frequencia\": freq\n            })\n        \n        # Ordenar cada categoria por frequência\n        for cat in categorias:\n            categorias[cat].sort(key=lambda x: x['frequencia'], reverse=True)\n        \n        return {\n            \"success\": True,\n            \"total_vagas_analisadas\": total_vagas,\n            \"total_lotes_processados\": len(todos_resultados),\n            \"total_palavras_unicas\": len(palavras_consolidadas),\n            \"top_10_palavras_chave\": top_10,\n            \"categorias\": categorias,\n            \"timestamp\": datetime.now().isoformat(),\n            \"modelo_usado\": \"gemini-2.0-flash-exp-batch\"\n        }\n    \n    def _categorizar_termo(self, termo: str) -> str:\n        \"\"\"Categoriza um termo\"\"\"\n        termo_lower = termo.lower()\n        \n        # Linguagens\n        linguagens = ['javascript', 'typescript', 'python', 'java', 'c#', 'php', 'ruby', 'go', 'rust', 'swift', 'kotlin', 'css', 'html', 'sql']\n        if any(lang in termo_lower for lang in linguagens):\n            return \"linguagens\"\n        \n        # Frameworks\n        frameworks = ['react', 'angular', 'vue', 'django', 'flask', 'spring', 'express', 'nest', 'next', 'nuxt', 'rails', 'laravel']\n        if any(fw in termo_lower for fw in frameworks):\n            return \"frameworks\"\n        \n        # Ferramentas\n        ferramentas = ['git', 'docker', 'kubernetes', 'jenkins', 'jira', 'figma', 'vscode', 'postman', 'aws', 'azure', 'gcp']\n        if any(tool in termo_lower for tool in ferramentas):\n            return \"ferramentas\"\n        \n        # Metodologias\n        metodologias = ['agile', 'scrum', 'kanban', 'devops', 'ci/cd', 'tdd', 'solid', 'rest', 'api', 'microservices']\n        if any(met in termo_lower for met in metodologias):\n            return \"metodologias\"\n        \n        return \"outros\"", "text_preview": "\"\"\" Extrator de palavras-chave com processamento em lotes \"\"\" import os import json import asyncio import google.generativeai as genai from typing import List, Dict, Any from collections import Counter from datetime import datetime from dot", "input_sha256": "3159e60119fea177d53c1b3e59947cb222255e1b7965c037dc08c6d41f703af2", "text_sha256": "8a4841a2df3b9cb1f677461c31515ed61655dca4c2899a0b20023e017963a262", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12060, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.3713906241628565, "noise_severity": 0.2635986387979542, "missing_content_severity": 0.13289429169183004, "factual_density": 2.7777240313626326, "pretraining_utility": 0.6423573827702788, "math_density": 0.4646931546586112, "code_density": 3.5733734192437057, "reasoning_density": 1.3642481762444607, "education_level": 2.23674572628307, "creative_writing_quality": -0.30468650423071}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.02102869374018413, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9966627552276937, "secondary": 0.8696520663224974, "tertiary": 0.8671800197023709, "structured_records": 0.13857195080879978, "procedural_steps": 0.12823732687589995, "worked_solution": 0.007826071647264714, "contextual_dialogue": 1.0826538571707511e-05, "content_quality": 0.29006776922037447, "noise_severity": 0.7803344676683716, "missing_content_severity": 0.889254756923475, "factual_density": 0.3703987626893823, "pretraining_utility": 0.5528486975694271, "math_density": 0.6127557044511573, "code_density": 0.6444778493697545, "reasoning_density": 0.0, "education_level": 0.7122535947801906, "creative_writing_quality": 0.9980744479846099, "primary_topic": 0.9681723576387733, "secondary_topic": 0.6277055013153977}, "probability_margin": {"primary": 0.9956797547142053, "secondary": 0.8232731639908184, "tertiary": 0.8027990093944121, "education_level": 0.5482405961828999, "creative_writing_quality": 0.9975945989840151, "primary_topic": 0.9511371280225697, "secondary_topic": 0.45954367770084414}, "raw_probabilities": {"primary": {"Academic Writing": 3.85883772131e-05, "Content Listing": 2.0746728451407255e-05, "Creative Writing": 1.8583471642868784e-05, "Customer Support Page": 9.449799235098409e-06, "Q&A Forum": 1.1396079387283455e-05, "Discussion Thread": 6.887048091156634e-06, "Comment Section": 1.2858965441257597e-05, "Missing Content": 3.5691818544237485e-05, "Knowledge Article": 9.246516563385622e-05, "Legal Notices": 2.4311542303111165e-05, "News Article": 5.231204253597778e-06, "Organizational Announcement": 1.2108541933364661e-05, "Nonfiction Writing": 1.1912525898432499e-05, "Organizational About Page": 1.3099230876469502e-05, "Personal About Page": 1.357370074482521e-05, "Personal Blog": 8.632248903192043e-06, "Product Page": 9.624681089559905e-06, "Spam / Ads": 1.4801552461239063e-05, "Structured Data": 0.0007086954601284672, "Technical Writing": 0.9968018070932064, "Transcript / Interview": 1.6214002204927746e-05, "Tutorial / How-To Guide": 0.0011220523790010832, "User Reviews": 7.4482050929449215e-06, "Other / Unknown": 0.0009838201782620118}, "secondary": {"Academic Writing": 0.0007578263536566997, "Content Listing": 0.000523621003990234, "Creative Writing": 0.0006659284886743148, "Customer Support Page": 0.00035779656215844183, "Q&A Forum": 0.0002746199461109407, "Discussion Thread": 0.00021496198492424595, "Comment Section": 0.000698662868877472, "Missing Content": 0.0017096367807639434, "Knowledge Article": 0.0030432842589339045, "Legal Notices": 0.00078731988396899, "News Article": 0.00032390200568003976, "Organizational Announcement": 0.0003515700039232192, "Nonfiction Writing": 0.0004529252762932219, "Organizational About Page": 0.00036911340835272047, "Personal About Page": 0.00033207584424593226, "Personal Blog": 0.00031874947889962374, "Product Page": 0.0002468665542822048, "Spam / Ads": 0.0005229976697879492, "Structured Data": 0.04888648241350927, "Technical Writing": 0.05181006623490826, "Transcript / Interview": 0.0005394746213638678, "Tutorial / How-To Guide": 0.011536203149639436, "User Reviews": 0.00019268498132842674, "NONE": 0.8750832302257266}, "tertiary": {"Academic Writing": 0.0005331526479921251, "Content Listing": 0.0004022030104728377, "Creative Writing": 0.0005613011399408578, "Customer Support Page": 0.00031655197120625223, "Q&A Forum": 0.00018842571885430427, "Discussion Thread": 0.00017149232949448503, "Comment Section": 0.0006336285394028265, "Missing Content": 0.001666178385505214, "Knowledge Article": 0.0038600130745959187, "Legal Notices": 0.0008839475595924167, "News Article": 0.0002521078113635233, "Organizational Announcement": 0.0002721439103772809, "Nonfiction Writing": 0.00048053597683280554, "Organizational About Page": 0.0003277541041990058, "Personal About Page": 0.00023254404562148288, "Personal Blog": 0.00022034850424591763, "Product Page": 0.00021673814135497342, "Spam / Ads": 0.0005410883512394323, "Structured Data": 0.0699151761536934, "Technical Writing": 0.03389645435009714, "Transcript / Interview": 0.0004512371230520758, "Tutorial / How-To Guide": 0.011131408662386169, "User Reviews": 0.0001313829403742219, "NONE": 0.8727141855481054}, "structured_records": {"false": 0.8614280491912002, "true": 0.13857195080879978}, "procedural_steps": {"false": 0.8717626731241, "true": 0.12823732687589995}, "worked_solution": {"false": 0.9921739283527353, "true": 0.007826071647264714}, "contextual_dialogue": {"false": 0.9999891734614282, "true": 1.0826538571707511e-05}, "content_quality": {"-2": 0.01691599102491605, "-1": 0.18194644478349553, "0": 0.3256231637446078, "1": 0.36385974989777703, "2": 0.11165465054920355}, "noise_severity": {"0": 0.8004137102603959, "1": 0.14395078893074015, "2": 0.04927832580484696, "3": 0.004337501758548361, "4": 0.0020196732454687595}, "missing_content_severity": {"0": 0.8899568971238832, "1": 0.09338892648232959, "2": 0.011902653889628386, "3": 0.0033060325863917476, "4": 0.0014454899177671102}, "factual_density": {"0": 0.023977655626258282, "1": 0.08302668495379059, "2": 0.2509123899186983, "3": 0.375460511433566, "4": 0.2666227580676868}, "pretraining_utility": {"-2": 0.007374048005238273, "-1": 0.05121807106236352, "0": 0.3225538039327625, "1": 0.5293846041561525, "2": 0.08946947284348307}, "math_density": {"0": 0.6020434951350793, "1": 0.3542006689757422, "2": 0.02986119523223056, "3": 0.0048084674093835785, "4": 0.009086173247564272}, "code_density": {"0": 0.0006160057721604957, "1": 0.0012025100510786233, "2": 0.0548187433587768, "3": 0.3109175407968632, "4": 0.6324452000211209}, "reasoning_density": {"0": 0.3547803193693749, "1": 0.22551123144481386, "2": 0.22650486034091874, "3": 0.08708713126176112, "4": 0.10611645758313157}, "education_level": {"none": 0.001089121636259403, "0": 0.0014719753901927147, "1": 0.00768419433628354, "2": 0.7602113289834921, "3": 0.21197073280059223, "4": 0.017572646853179993}, "creative_writing_quality": {"not_applicable": 0.9983953733205082, "-2": 0.00018457354019884006, "-1": 0.00039600565973954175, "0": 0.0008007743364931134, "1": 0.0001703016395532061, "2": 5.297150350715509e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.184438481227715e-05, "Art & Design": 0.00010360023129281709, "Law & Crime": 0.0001352405837445526, "Education & Careers": 0.018259369707020006, "Entertainment & Media": 8.949356893946023e-05, "Fashion & Beauty": 0.00010144441811388629, "Business, Economics & Finance": 0.0012844577684184336, "Food & Dining": 0.00011164469438148554, "Games & Gaming": 8.101317143083894e-05, "Electronics & Hardware": 0.00011964997557294084, "Health, Healthcare & Wellness": 8.232348030661905e-05, "History & Geography": 9.283140514021634e-05, "Home, Pets & Hobbies": 6.943222634814561e-05, "Industry & Production": 8.782908716579469e-05, "Language, Literature & Philosophy": 0.00044636482041522744, "Politics & Government": 5.25092023873766e-05, "Religion & Spirituality": 7.781682252605078e-05, "Science, Mathematics & Engineering": 0.005909069158934854, "Relationships & Community": 8.858364025257081e-05, "Software Usage & Applications": 0.0013025249501738003, "Programming & Software Engineering": 0.9693964977295897, "Sports & Fitness": 0.00010566709040382022, "Vehicles & Transportation": 5.6311537377041584e-05, "Travel & Tourism": 0.00012691997848278354, "Other / Uncovered Subject": 0.0016629467632750846, "Unknown / Insufficient Content": 9.461360349425329e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0011252035757597046, "Art & Design": 0.0008376065424738004, "Law & Crime": 0.0010284134024849674, "Education & Careers": 0.18248084279473056, "Entertainment & Media": 0.000971342259233427, "Fashion & Beauty": 0.0005355597220320139, "Business, Economics & Finance": 0.0065272723462978825, "Food & Dining": 0.0005051994305446865, "Games & Gaming": 0.0005041708815361617, "Electronics & Hardware": 0.0006669214575146207, "Health, Healthcare & Wellness": 0.0007310494321182716, "History & Geography": 0.0006121220666153867, "Home, Pets & Hobbies": 0.0004512315069274958, "Industry & Production": 0.000488023468473864, "Language, Literature & Philosophy": 0.002355917953077524, "Politics & Government": 0.0004277286223376287, "Religion & Spirituality": 0.0006330531979301853, "Science, Mathematics & Engineering": 0.05174186970296639, "Relationships & Community": 0.0003855483872285475, "Software Usage & Applications": 0.010412621919941452, "Programming & Software Engineering": 0.09083925917048331, "Sports & Fitness": 0.0005088734092189028, "Vehicles & Transportation": 0.00022201498037155488, "Travel & Tourism": 0.00078307883083918, "Other / Uncovered Subject": 0.002200554443287637, "NONE": 0.6420245204955747}}, "probabilities": {"primary": {"Academic Writing": 3.8588377213100007e-05, "Content Listing": 2.074672845140726e-05, "Creative Writing": 1.8583471642868788e-05, "Customer Support Page": 9.44979923509841e-06, "Q&A Forum": 1.1396079387283457e-05, "Discussion Thread": 6.8870480911566345e-06, "Comment Section": 1.2858965441257598e-05, "Missing Content": 3.569181854423749e-05, "Knowledge Article": 9.246516563385623e-05, "Legal Notices": 2.4311542303111168e-05, "News Article": 5.231204253597779e-06, "Organizational Announcement": 1.2108541933364663e-05, "Nonfiction Writing": 1.19125258984325e-05, "Organizational About Page": 1.3099230876469504e-05, "Personal About Page": 1.3573700744825212e-05, "Personal Blog": 8.632248903192045e-06, "Product Page": 9.624681089559906e-06, "Spam / Ads": 1.4801552461239065e-05, "Structured Data": 0.0007086954601284673, "Technical Writing": 0.9968018070932065, "Transcript / Interview": 1.621400220492775e-05, "Tutorial / How-To Guide": 0.0011220523790010835, "User Reviews": 7.448205092944922e-06, "Other / Unknown": 0.000983820178262012}, "secondary": {"Academic Writing": 0.0007578263536566997, "Content Listing": 0.000523621003990234, "Creative Writing": 0.0006659284886743148, "Customer Support Page": 0.00035779656215844183, "Q&A Forum": 0.0002746199461109407, "Discussion Thread": 0.00021496198492424595, "Comment Section": 0.000698662868877472, "Missing Content": 0.0017096367807639434, "Knowledge Article": 0.0030432842589339045, "Legal Notices": 0.00078731988396899, "News Article": 0.00032390200568003976, "Organizational Announcement": 0.0003515700039232192, "Nonfiction Writing": 0.0004529252762932219, "Organizational About Page": 0.00036911340835272047, "Personal About Page": 0.00033207584424593226, "Personal Blog": 0.00031874947889962374, "Product Page": 0.0002468665542822048, "Spam / Ads": 0.0005229976697879492, "Structured Data": 0.04888648241350927, "Technical Writing": 0.05181006623490826, "Transcript / Interview": 0.0005394746213638678, "Tutorial / How-To Guide": 0.011536203149639436, "User Reviews": 0.00019268498132842674, "NONE": 0.8750832302257266}, "tertiary": {"Academic Writing": 0.0005331526479921251, "Content Listing": 0.0004022030104728377, "Creative Writing": 0.0005613011399408578, "Customer Support Page": 0.00031655197120625223, "Q&A Forum": 0.00018842571885430427, "Discussion Thread": 0.00017149232949448503, "Comment Section": 0.0006336285394028265, "Missing Content": 0.001666178385505214, "Knowledge Article": 0.0038600130745959187, "Legal Notices": 0.0008839475595924167, "News Article": 0.0002521078113635233, "Organizational Announcement": 0.0002721439103772809, "Nonfiction Writing": 0.00048053597683280554, "Organizational About Page": 0.0003277541041990058, "Personal About Page": 0.00023254404562148288, "Personal Blog": 0.00022034850424591763, "Product Page": 0.00021673814135497342, "Spam / Ads": 0.0005410883512394323, "Structured Data": 0.0699151761536934, "Technical Writing": 0.03389645435009714, "Transcript / Interview": 0.0004512371230520758, "Tutorial / How-To Guide": 0.011131408662386169, "User Reviews": 0.0001313829403742219, "NONE": 0.8727141855481054}, "structured_records": {"false": 0.8614280491912002, "true": 0.13857195080879978}, "procedural_steps": {"false": 0.8717626731241, "true": 0.12823732687589995}, "worked_solution": {"false": 0.9921739283527353, "true": 0.007826071647264714}, "contextual_dialogue": {"false": 0.9999891734614282, "true": 1.0826538571707511e-05}, "content_quality": {"-2": 0.01691599102491605, "-1": 0.18194644478349553, "0": 0.3256231637446078, "1": 0.36385974989777703, "2": 0.11165465054920355}, "noise_severity": {"0": 0.8004137102603959, "1": 0.14395078893074015, "2": 0.04927832580484696, "3": 0.004337501758548361, "4": 0.0020196732454687595}, "missing_content_severity": {"0": 0.8899568971238832, "1": 0.09338892648232959, "2": 0.011902653889628386, "3": 0.0033060325863917476, "4": 0.0014454899177671102}, "factual_density": {"0": 0.023977655626258282, "1": 0.08302668495379059, "2": 0.2509123899186983, "3": 0.375460511433566, "4": 0.2666227580676868}, "pretraining_utility": {"-2": 0.007374048005238274, "-1": 0.05121807106236353, "0": 0.32255380393276256, "1": 0.5293846041561526, "2": 0.08946947284348308}, "math_density": {"0": 0.6020434951350794, "1": 0.35420066897574226, "2": 0.029861195232230565, "3": 0.004808467409383579, "4": 0.009086173247564274}, "code_density": {"0": 0.0006160057721604957, "1": 0.0012025100510786233, "2": 0.0548187433587768, "3": 0.3109175407968632, "4": 0.6324452000211209}, "reasoning_density": {"0": 0.3547803193693748, "1": 0.2255112314448138, "2": 0.22650486034091868, "3": 0.08708713126176111, "4": 0.10611645758313154}, "education_level": {"none": 0.001089121636259403, "0": 0.0014719753901927147, "1": 0.00768419433628354, "2": 0.7602113289834921, "3": 0.21197073280059223, "4": 0.017572646853179993}, "creative_writing_quality": {"not_applicable": 0.9983953733205082, "-2": 0.00018457354019884006, "-1": 0.00039600565973954175, "0": 0.0008007743364931134, "1": 0.0001703016395532061, "2": 5.297150350715509e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.184438481227715e-05, "Art & Design": 0.00010360023129281709, "Law & Crime": 0.0001352405837445526, "Education & Careers": 0.018259369707020006, "Entertainment & Media": 8.949356893946023e-05, "Fashion & Beauty": 0.00010144441811388629, "Business, Economics & Finance": 0.0012844577684184336, "Food & Dining": 0.00011164469438148554, "Games & Gaming": 8.101317143083894e-05, "Electronics & Hardware": 0.00011964997557294084, "Health, Healthcare & Wellness": 8.232348030661905e-05, "History & Geography": 9.283140514021634e-05, "Home, Pets & Hobbies": 6.943222634814561e-05, "Industry & Production": 8.782908716579469e-05, "Language, Literature & Philosophy": 0.00044636482041522744, "Politics & Government": 5.25092023873766e-05, "Religion & Spirituality": 7.781682252605078e-05, "Science, Mathematics & Engineering": 0.005909069158934854, "Relationships & Community": 8.858364025257081e-05, "Software Usage & Applications": 0.0013025249501738003, "Programming & Software Engineering": 0.9693964977295897, "Sports & Fitness": 0.00010566709040382022, "Vehicles & Transportation": 5.6311537377041584e-05, "Travel & Tourism": 0.00012691997848278354, "Other / Uncovered Subject": 0.0016629467632750846, "Unknown / Insufficient Content": 9.461360349425329e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0011252035757597049, "Art & Design": 0.0008376065424738005, "Law & Crime": 0.0010284134024849676, "Education & Careers": 0.1824808427947306, "Entertainment & Media": 0.0009713422592334271, "Fashion & Beauty": 0.000535559722032014, "Business, Economics & Finance": 0.006527272346297883, "Food & Dining": 0.0005051994305446866, "Games & Gaming": 0.0005041708815361618, "Electronics & Hardware": 0.0006669214575146208, "Health, Healthcare & Wellness": 0.0007310494321182717, "History & Geography": 0.0006121220666153868, "Home, Pets & Hobbies": 0.0004512315069274959, "Industry & Production": 0.00048802346847386406, "Language, Literature & Philosophy": 0.0023559179530775244, "Politics & Government": 0.00042772862233762875, "Religion & Spirituality": 0.0006330531979301854, "Science, Mathematics & Engineering": 0.051741869702966396, "Relationships & Community": 0.00038554838722854756, "Software Usage & Applications": 0.010412621919941453, "Programming & Software Engineering": 0.09083925917048333, "Sports & Fitness": 0.0005088734092189029, "Vehicles & Transportation": 0.0002220149803715549, "Travel & Tourism": 0.0007830788308391801, "Other / Uncovered Subject": 0.0022005544432876375, "NONE": 0.6420245204955748}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 12269, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 12269, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:09.326434+00:00", "scoring_key_slot": "primary", "dataset_uid": "3eb5ce62-1a96-40a5-b3ce-876166051450", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "bb73012c4be0251c112da56b46b613615db33e4d445f764b0923f2b826416d45", "source_metadata": {"uuid": "3eb5ce62-1a96-40a5-b3ce-876166051450", "repo_name": "guilhermefay/helio-job-robot", "relative_path": "core/services/batch_keyword_extractor.py", "category": "DATA", "algo_rel_score": 0.9254891872406006, "quality_score": 6.639676570892334, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 254, "repo_created_at": "2025-07-08T14:25:26.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "e3b6f2f950f3c7ddb14125c120a90f28ee2f1bba", "size_bytes": 15938, "file_timestamp": 1753303560, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "8aa3f41c00e6c4d669cdf56a19df27fec84d880b"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 254, "repo_created_at": "2025-07-08T14:25:26.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "e3b6f2f950f3c7ddb14125c120a90f28ee2f1bba", "size_bytes": 15938, "file_timestamp": 1753303560, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "8aa3f41c00e6c4d669cdf56a19df27fec84d880b", "url": "https://github.com/guilhermefay/helio-job-robot/blob/8aa3f41c00e6c4d669cdf56a19df27fec84d880b/core/services/batch_keyword_extractor.py"}, "gpt4o_tokens": 3491, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.3713906241628565, "noise_severity": 0.2635986387979542, "missing_content_severity": 0.13289429169183004, "factual_density": 2.7777240313626326, "pretraining_utility": 0.6423573827702788, "math_density": 0.4646931546586112, "code_density": 3.5733734192437057, "reasoning_density": 1.3642481762444607, "education_level": 2.23674572628307, "creative_writing_quality": -0.30468650423071}, "score_applicability": {"education_level": 0.9989108783637406, "creative_writing_quality": 0.0016046266794918565}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/Gaurav11170/Hotel-Booking-Application/blob/2cf0f6c6fed4a4f1538696c34e59c0725af2a3a5/app.py", "id": "python-0-a420a9a3516bd8ec820a4d3edcd52eaa", "text": "import streamlit as st\nimport pandas as pd\nimport datetime\nimport smtplib\nfrom email.mime.text import MIMEText\nimport random\nimport string\nimport os\n\n# Email configuration\nSENDER_EMAIL = \"<EMAIL>\"\nEMAIL_APP_PASSWORD = \"<PASSWORD>@@\"\n\n# File paths\nBOOKING_FILE = 'booking.csv'\nHOTELS_FILE = 'hotels.csv'\nNORTH_FILE = 'north.csv'\nSOUTH_FILE = 'south.csv'\n\ndef ensure_file_exists(filename, columns):\n    \"\"\"Ensure file exists and create with headers if it doesn't\"\"\"\n    try:\n        if not os.path.exists(filename):\n            df = pd.DataFrame(columns=columns)\n            df.to_csv(filename, index=False)\n    except Exception as e:\n        st.error(f\"Error creating file {filename}: {str(e)}\")\n\ndef load_data():\n    \"\"\"Load and process hotel and location data\"\"\"\n    try:\n        hotels_df = pd.read_csv(HOTELS_FILE)\n        north_df = pd.read_csv(NORTH_FILE)\n        south_df = pd.read_csv(SOUTH_FILE)\n        \n        # Clean price column\n        hotels_df['Price'] = hotels_df['Price'].astype(str)\n        hotels_df['Price'] = hotels_df['Price'].apply(lambda x: ''.join(filter(str.isdigit, x)))\n        hotels_df['Price'] = pd.to_numeric(hotels_df['Price'], errors='coerce')\n        \n        return hotels_df, north_df, south_df\n    except Exception as e:\n        st.error(f\"Error loading data: {str(e)}\")\n        return None, None, None\n\ndef load_bookings():\n    \"\"\"Load booking data\"\"\"\n    booking_columns = ['OTP', 'Firstname', 'Secondname', 'Place_ID', 'Duration', \n                      'Total_Members', 'Date_time', 'Hotel']\n    ensure_file_exists(BOOKING_FILE, booking_columns)\n    \n    try:\n        return pd.read_csv(BOOKING_FILE)\n    except Exception as e:\n        st.error(f\"Error loading bookings: {str(e)}\")\n        return pd.DataFrame(columns=booking_columns)\n\ndef save_booking(booking_data):\n    \"\"\"Save booking to CSV file\"\"\"\n    try:\n        bookings_df = load_bookings()\n        new_booking = pd.DataFrame([booking_data])\n        updated_bookings = pd.concat([bookings_df, new_booking], ignore_index=True)\n        updated_bookings.to_csv(BOOKING_FILE, index=False)\n        return True\n    except Exception as e:\n        st.error(f\"Error saving booking: {str(e)}\")\n        return False\n\ndef generate_otp():\n    \"\"\"Generate a 6-digit OTP\"\"\"\n    return ''.join(random.choices(string.digits, k=6))\n\ndef send_email(receiver_email, subject, message):\n    \"\"\"Helper function to send emails\"\"\"\n    try:\n        msg = MIMEText(message)\n        msg['From'] = SENDER_EMAIL\n        msg['To'] = receiver_email\n        msg['Subject'] = subject\n        \n        server = smtplib.SMTP('smtp.gmail.com', 587)\n        server.starttls()\n        server.login(SENDER_EMAIL, EMAIL_APP_PASSWORD)\n        server.send_message(msg)\n        server.quit()\n        return True\n    except Exception as e:\n        st.error(f\"Failed to send email: {str(e)}\")\n        return False\n\ndef send_otp_email(email, otp):\n    \"\"\"Send OTP via email\"\"\"\n    subject = \"Email Verification OTP\"\n    message = f\"\"\"\n    Your OTP for email verification is: {otp}\n    \n    This OTP will expire in 10 minutes.\n    Please do not share this OTP with anyone.\n    \"\"\"\n    return send_email(email, subject, message)\n\ndef reset_session_state():\n    \"\"\"Reset all session state variables\"\"\"\n    for key in ['show_booking_form', 'selected_hotel', 'booking_data', 'show_review',\n                'otp', 'email_verified', 'verification_email', 'otp_sent', 'view_bookings']:\n        if key in st.session_state:\n            del st.session_state[key]\n    st.rerun()\n\ndef view_bookings():\n    \"\"\"Display the view bookings page\"\"\"\n    st.title('View Your Booking')\n    \n    with st.form(key='view_booking_form'):\n        otp = st.text_input('Enter your OTP')\n        submit_view = st.form_submit_button('View Booking')\n        \n        if submit_view and otp:\n            bookings = load_bookings()\n            user_booking = bookings[bookings['OTP'].astype(str) == otp]\n            \n            if not user_booking.empty:\n                st.success(\"Booking found!\")\n                st.dataframe(user_booking)\n            else:\n                st.warning('No booking found with this OTP.')\n\ndef display_dashboard():\n    \"\"\"Display the hotel search and filtering dashboard\"\"\"\n    st.header(\"🏨 Hotel Search Dashboard\")\n    \n    # Load data\n    hotels_df, north_df, south_df = load_data()\n    if hotels_df is None:\n        return\n    \n    # Combine location data\n    all_locations = pd.concat([\n        north_df[['State_UT', 'Places']],\n        south_df[['State_UT', 'Places']]\n    ]).drop_duplicates()\n    \n    # Create filters\n    col1, col2, col3 = st.columns(3)\n    \n    with col1:\n        selected_state = st.selectbox(\n            \"Select State/UT\",\n            options=['All'] + sorted(all_locations['State_UT'].unique().tolist())\n        )\n    \n    # Filter locations based on selected state\n    if selected_state != 'All':\n        available_places = all_locations[all_locations['State_UT'] == selected_state]['Places'].tolist()\n    else:\n        available_places = all_locations['Places'].tolist()\n    \n    with col2:\n        selected_place = st.selectbox(\n            \"Select Destination\",\n            options=['All'] + sorted(available_places)\n        )\n    \n    with col3:\n        price_range = st.slider(\n            \"Price Range (₹)\",\n            min_value=int(hotels_df['Price'].min()),\n            max_value=int(hotels_df['Price'].max()),\n            value=(int(hotels_df['Price'].min()), int(hotels_df['Price'].max()))\n        )\n    \n    # Filter hotels\n    filtered_hotels = hotels_df.copy()\n    if selected_place != 'All':\n        filtered_hotels = filtered_hotels[filtered_hotels['Places'] == selected_place]\n    filtered_hotels = filtered_hotels[\n        (filtered_hotels['Price'] >= price_range[0]) &\n        (filtered_hotels['Price'] <= price_range[1])\n    ]\n    \n    # Display results\n    st.subheader(f\"Available Hotels ({len(filtered_hotels)} results)\")\n    \n    # Display hotels in a grid\n    cols = st.columns(3)\n    for idx, hotel in filtered_hotels.iterrows():\n        col_idx = idx % 3\n        with cols[col_idx]:\n            st.markdown(f\"\"\"\n            **{hotel['Name']}**  \n            📍 {hotel['Places']}  \n            ⭐ {hotel['Category']}  \n            💰 ₹{hotel['Price']:,.0f} per night\n            \"\"\")\n            if st.button(f\"Book Now 🏷️\", key=f\"book_{idx}\"):\n                st.session_state.selected_hotel = hotel\n                st.session_state.show_booking_form = True\n                st.rerun()\n\ndef verify_email():\n    \"\"\"Handle email verification process\"\"\"\n    if 'otp_sent' not in st.session_state:\n        st.session_state.otp_sent = False\n        \n    if not st.session_state.otp_sent:\n        email = st.text_input('Email*')\n        if st.button('Send OTP'):\n            if email:\n                otp = generate_otp()\n                if send_otp_email(email, otp):\n                    st.session_state.verification_email = email\n                    st.session_state.otp = otp\n                    st.session_state.otp_sent = True\n                    st.success('OTP sent successfully!')\n                    st.rerun()\n            else:\n                st.error('Please enter your email address.')\n    else:\n        entered_otp = st.text_input('Enter OTP*')\n        if st.button('Verify OTP'):\n            if entered_otp == st.session_state.otp:\n                st.session_state.email_verified = True\n                st.success('Email verified successfully!')\n                st.rerun()\n            else:\n                st.error('Invalid OTP. Please try again.')\n\ndef display_booking_form():\n    \"\"\"Display the booking form for the selected hotel\"\"\"\n    hotel = st.session_state.selected_hotel\n    \n    st.header(f\"Book Your Stay at {hotel['Name']}\")\n    st.subheader(f\"📍 {hotel['Places']} | ⭐ {hotel['Category']} | 💰 ₹{hotel['Price']:,.0f} per night\")\n    \n    # First verify email if not already verified\n    if not st.session_state.get('email_verified', False):\n        verify_email()\n        return\n    \n    with st.form(key='booking_form', clear_on_submit=False):\n        col1, col2 = st.columns(2)\n        \n        with col1:\n            first_name = st.text_input('First Name*')\n            phone = st.text_input('Phone Number*')\n            check_in = st.date_input('Check-in Date*')\n        \n        with col2:\n            last_name = st.text_input('Last Name*')\n            check_out = st.date_input('Check-out Date*', min_value=check_in)\n        \n        guests = st.number_input('Number of Guests*', min_value=1, max_value=10, value=1)\n        special_requests = st.text_area('Special Requests', height=100)\n        terms = st.checkbox('I agree to the terms and conditions*')\n        \n        submitted = st.form_submit_button('Submit Booking', use_container_width=True)\n        \n        if submitted:\n            process_booking(\n                first_name, last_name, \n                st.session_state.verification_email, \n                phone, check_in, check_out, \n                guests, special_requests, terms, hotel\n            )\n\ndef process_booking(first_name, last_name, email, phone, check_in, check_out, guests, special_requests, terms, hotel):\n    \"\"\"Process the booking submission\"\"\"\n    if all([first_name, last_name, phone, terms]):\n        duration = (check_out - check_in).days\n        otp = generate_otp()\n        \n        booking_data = {\n            'OTP': otp,\n            'Firstname': first_name,\n            'Secondname': last_name,\n            'Place_ID': hotel['Places'],\n            'Duration': f\"{duration} days\",\n            'Total_Members': guests,\n            'Date_time': datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"),\n            'Hotel': hotel['Name']\n        }\n        \n        # Save booking data\n        if save_booking(booking_data):\n            st.session_state.booking_data = {\n                **booking_data,\n                'Email': email,\n                'Phone': phone,\n                'CheckIn': check_in.strftime('%Y-%m-%d'),\n                'CheckOut': check_out.strftime('%Y-%m-%d'),\n                'SpecialRequests': special_requests,\n                'Category': hotel['Category'],\n                'Price': hotel['Price']\n            }\n            \n            # Send booking confirmation email\n            subject = \"Booking Confirmation\"\n            message = f\"\"\"\n            Thank you for your booking!\n            \n            Booking Details:\n            Hotel: {hotel['Name']}\n            Check-in: {check_in}\n            Check-out: {check_out}\n            Duration: {duration} days\n            Guests: {guests}\n            \n            Your booking OTP is: {otp}\n            Please save this OTP to view your booking later.\n            \"\"\"\n            \n            if send_email(email, subject, message):\n                st.success(f'Booking confirmed! Your OTP is: {otp}')\n                st.info('Please save this OTP to view your booking later.')\n                st.session_state.show_review = True\n                st.rerun()\n            else:\n                st.error('Failed to send confirmation email. Please try again.')\n        else:\n            st.error('Failed to save booking. Please try again.')\n    else:\n        st.error('Please fill in all required fields and accept the terms.')\n\ndef main():\n    st.set_page_config(page_title='Hotel Booking', layout='wide')\n    \n    # Initialize session state\n    if 'show_booking_form' not in st.session_state:\n        st.session_state.show_booking_form = False\n    if 'selected_hotel' not in st.session_state:\n        st.session_state.selected_hotel = None\n    if 'booking_data' not in st.session_state:\n        st.session_state.booking_data = None\n    if 'show_review' not in st.session_state:\n        st.session_state.show_review = False\n    \n    # Sidebar\n    with st.sidebar:\n        st.title('Navigation')\n        if st.button('Reset'):\n            reset_session_state()\n        if st.button('View Bookings'):\n            st.session_state.show_booking_form = False\n            st.session_state.show_review = False\n            st.session_state.view_bookings = True\n            st.rerun()\n    \n    # Main content\n    st.title('🏨 Luxury Hotels & Resorts Booking')\n    \n    if st.session_state.get('view_bookings', False):\n        view_bookings()\n    elif not st.session_state.show_booking_form:\n        display_dashboard()\n    elif st.session_state.show_review:\n        st.success('Booking confirmed! Check your email for details.')\n        if st.button('Make Another Booking'):\n            reset_session_state()\n    else:\n        if st.button('← Back to Search'):\n            st.session_state.show_booking_form = False\n            st.rerun()\n        display_booking_form()\n\nif __name__ == \"__main__\":\n    main()\n", "text_preview": "import streamlit as st import pandas as pd import datetime import smtplib from email.mime.text import MIMEText import random import string import os # Email configuration SENDER_EMAIL = \"<EMAIL>\" EMAIL_APP_PASSWORD = \"<PASSWORD>@@\" # File p", "input_sha256": "2c14b6ede049abe9df696377a40e9480f3abbb2a297ddac1a237e6bd9e2a902b", "text_sha256": "a420a9a3516bd8ec820a4d3edcd52eaaf0ad012eadb5bb65a473cce8b573eea9", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12062, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Travel & Tourism", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5827339248692686, "noise_severity": 0.13518330502865838, "missing_content_severity": 0.1829140441597378, "factual_density": 3.139564380715525, "pretraining_utility": 0.6158349938244029, "math_density": 0.35323295114356745, "code_density": 3.7414121239684044, "reasoning_density": 0.6868292317685192, "education_level": 2.021975471221683, "creative_writing_quality": -0.27264316598737776}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Travel & Tourism"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.027160507780285448, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.995304521882949, "secondary": 0.9441646140790142, "tertiary": 0.9158232146561076, "structured_records": 0.028430237153850322, "procedural_steps": 0.04092308884305563, "worked_solution": 0.007405165793363262, "contextual_dialogue": 2.429097293006794e-05, "content_quality": 0.4465712176318367, "noise_severity": 0.8873472458094513, "missing_content_severity": 0.8475716298668852, "factual_density": 0.282970317262937, "pretraining_utility": 0.532531352894315, "math_density": 0.7056392073803605, "code_density": 0.7845101033070038, "reasoning_density": 0.4276423068595674, "education_level": 0.8768193060119528, "creative_writing_quality": 0.9984909941989192, "primary_topic": 0.9451071233210528, "secondary_topic": 0.6921710030245666}, "probability_margin": {"primary": 0.9937187973828064, "secondary": 0.9318469330489633, "tertiary": 0.898123061361657, "education_level": 0.8378409952488641, "creative_writing_quality": 0.9980072045144462, "primary_topic": 0.9013124671399073, "secondary_topic": 0.5721676825463765}, "raw_probabilities": {"primary": {"Academic Writing": 4.2404362949242766e-05, "Content Listing": 0.00010957337993286952, "Creative Writing": 2.7105204446382735e-05, "Customer Support Page": 1.5540476441590963e-05, "Q&A Forum": 1.6817481529645684e-05, "Discussion Thread": 1.0075588925610477e-05, "Comment Section": 1.9089116583527388e-05, "Missing Content": 4.4545001687868705e-05, "Knowledge Article": 8.746459366674207e-05, "Legal Notices": 3.33784974932889e-05, "News Article": 8.023247975806812e-06, "Organizational Announcement": 1.7491141606309987e-05, "Nonfiction Writing": 1.812772553459723e-05, "Organizational About Page": 2.1405236153767517e-05, "Personal About Page": 2.506232380255733e-05, "Personal Blog": 1.2923291585435364e-05, "Product Page": 0.00010739379982831817, "Spam / Ads": 2.2870104637015025e-05, "Structured Data": 0.00034068721156046695, "Technical Writing": 0.9955001668044928, "Transcript / Interview": 2.0168317719318737e-05, "Tutorial / How-To Guide": 0.0017813694216863977, "User Reviews": 1.1660338287870037e-05, "Other / Unknown": 0.0017066573314726025}, "secondary": {"Academic Writing": 0.0004040243094962695, "Content Listing": 0.011422644062058989, "Creative Writing": 0.000490928410276725, "Customer Support Page": 0.0004876559785515536, "Q&A Forum": 0.00016133938261105027, "Discussion Thread": 0.0001225584111382547, "Comment Section": 0.0003429011153205105, "Missing Content": 0.009690186597820245, "Knowledge Article": 0.0011237645413343376, "Legal Notices": 0.0009243473538346821, "News Article": 0.0002326984621854463, "Organizational Announcement": 0.0002508384419957598, "Nonfiction Writing": 0.0002717288534221551, "Organizational About Page": 0.0003285910024646754, "Personal About Page": 0.0003023365769034038, "Personal Blog": 0.00025677906783045274, "Product Page": 0.0022307863479514136, "Spam / Ads": 0.0007740399133493555, "Structured Data": 0.004030671978624085, "Technical Writing": 0.014644155443425328, "Transcript / Interview": 0.0003061166726690584, "Tutorial / How-To Guide": 0.004548608396770824, "User Reviews": 0.00016121018757679986, "NONE": 0.9464910884923886}, "tertiary": {"Academic Writing": 0.0003982873163237714, "Content Listing": 0.021207519350446128, "Creative Writing": 0.0006626088947362747, "Customer Support Page": 0.0010168377744873625, "Q&A Forum": 0.00016707178156271749, "Discussion Thread": 0.00014721503382116673, "Comment Section": 0.00044164321489200114, "Missing Content": 0.01674408139491554, "Knowledge Article": 0.001889806260855402, "Legal Notices": 0.003648821408398424, "News Article": 0.00029957106194176557, "Organizational Announcement": 0.00032845828375234167, "Nonfiction Writing": 0.000455485212051575, "Organizational About Page": 0.00048325407806994137, "Personal About Page": 0.00036433221563855403, "Personal Blog": 0.0002740056638443618, "Product Page": 0.005597242190146825, "Spam / Ads": 0.004775859802508585, "Structured Data": 0.005433934061746657, "Technical Writing": 0.011597213339073927, "Transcript / Interview": 0.0004105342993336098, "Tutorial / How-To Guide": 0.004115034780219436, "User Reviews": 0.0002106018691304271, "NONE": 0.9193305807121032}, "structured_records": {"false": 0.9715697628461497, "true": 0.028430237153850322}, "procedural_steps": {"false": 0.9590769111569444, "true": 0.04092308884305563}, "worked_solution": {"false": 0.9925948342066367, "true": 0.007405165793363262}, "contextual_dialogue": {"false": 0.9999757090270699, "true": 2.429097293006794e-05}, "content_quality": {"-2": 0.014695416587819594, "-1": 0.09060472179702328, "0": 0.31539461362875837, "1": 0.45588101613086646, "2": 0.12342423185553224}, "noise_severity": {"0": 0.8860660155384719, "1": 0.09740300651561137, "2": 0.013069878724412695, "3": 0.0022038558217943203, "4": 0.0012572433997096602}, "missing_content_severity": {"0": 0.8614864680991314, "1": 0.10254565734623851, "2": 0.029051497725372705, "3": 0.005400115954275178, "4": 0.001516260874982082}, "factual_density": {"0": 0.013718489408318317, "1": 0.034895464420005545, "2": 0.16812100675723238, "3": 0.3646332548767208, "4": 0.4186317845377229}, "pretraining_utility": {"-2": 0.008115029596214464, "-1": 0.05906344019217513, "0": 0.330091722178216, "1": 0.514331122857782, "2": 0.08839868517561243}, "math_density": {"0": 0.713932182500668, "1": 0.24460317305964305, "2": 0.025551585254704908, "3": 0.0061256291654214826, "4": 0.009787430019562539}, "code_density": {"0": 0.0003212413550398519, "1": 0.0006161731211494388, "2": 0.03203756994607387, "3": 0.19137925135584, "4": 0.7756457642218968}, "reasoning_density": {"0": 0.6476197027167396, "1": 0.1114843479955563, "2": 0.16832028924809908, "3": 0.051598334881654766, "4": 0.020977325157950078}, "education_level": {"none": 0.0010671638559235572, "0": 0.002045837292679834, "1": 0.03784101118111262, "2": 0.8973494216766273, "3": 0.059508426427763265, "4": 0.0021881395658935624}, "creative_writing_quality": {"not_applicable": 0.998742495165766, "-2": 0.00012343653275275528, "-1": 0.0002649559280625194, "0": 0.0007352906513196739, "1": 9.866454988074096e-05, "2": 3.515717221860868e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 8.746437679408983e-05, "Art & Design": 0.00024144783738176784, "Law & Crime": 0.00017543797644896893, "Education & Careers": 0.00012146052157189203, "Entertainment & Media": 0.00017626265902094226, "Fashion & Beauty": 0.00012571475570312028, "Business, Economics & Finance": 0.001953207548468752, "Food & Dining": 0.00018117060860927764, "Games & Gaming": 0.00022835657452776117, "Electronics & Hardware": 0.00011101172896710154, "Health, Healthcare & Wellness": 9.752276929171825e-05, "History & Geography": 0.0001577532251545446, "Home, Pets & Hobbies": 0.00012499102724001634, "Industry & Production": 0.0001254576730357614, "Language, Literature & Philosophy": 0.00011309322562458009, "Politics & Government": 6.758529824976359e-05, "Religion & Spirituality": 0.00010918793143670425, "Science, Mathematics & Engineering": 0.0001483708642516702, "Relationships & Community": 7.242012150421617e-05, "Software Usage & Applications": 0.0016301515024298417, "Programming & Software Engineering": 0.9472183878087047, "Sports & Fitness": 0.0001154601370458869, "Vehicles & Transportation": 9.056513457488502e-05, "Travel & Tourism": 0.04590592066879742, "Other / Uncovered Subject": 0.0005015491269535938, "Unknown / Insufficient Content": 0.00012004889821123891}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010057640380990353, "Art & Design": 0.002671326354499919, "Law & Crime": 0.0012510509223504136, "Education & Careers": 0.0006332014217354423, "Entertainment & Media": 0.0010734095237411965, "Fashion & Beauty": 0.0005763316807715813, "Business, Economics & Finance": 0.019427531091282882, "Food & Dining": 0.0008709052890332189, "Games & Gaming": 0.0007927679128912244, "Electronics & Hardware": 0.0005930375958186179, "Health, Healthcare & Wellness": 0.0007033290389464211, "History & Geography": 0.0008043012648949714, "Home, Pets & Hobbies": 0.0006663184508851201, "Industry & Production": 0.0006698516779373398, "Language, Literature & Philosophy": 0.0004375010394115755, "Politics & Government": 0.00044479962527151903, "Religion & Spirituality": 0.0007581598592463011, "Science, Mathematics & Engineering": 0.0010263455523891708, "Relationships & Community": 0.00035286148675927, "Software Usage & Applications": 0.018620341063698975, "Programming & Software Engineering": 0.10824641694708628, "Sports & Fitness": 0.0004931920849017985, "Vehicles & Transportation": 0.0004403503863690028, "Travel & Tourism": 0.704010579831314, "Other / Uncovered Subject": 0.0015874285757273407, "NONE": 0.13184289728493748}}, "probabilities": {"primary": {"Academic Writing": 4.2404362949242766e-05, "Content Listing": 0.00010957337993286952, "Creative Writing": 2.7105204446382735e-05, "Customer Support Page": 1.5540476441590963e-05, "Q&A Forum": 1.6817481529645684e-05, "Discussion Thread": 1.0075588925610477e-05, "Comment Section": 1.9089116583527388e-05, "Missing Content": 4.4545001687868705e-05, "Knowledge Article": 8.746459366674207e-05, "Legal Notices": 3.33784974932889e-05, "News Article": 8.023247975806812e-06, "Organizational Announcement": 1.7491141606309987e-05, "Nonfiction Writing": 1.812772553459723e-05, "Organizational About Page": 2.1405236153767517e-05, "Personal About Page": 2.506232380255733e-05, "Personal Blog": 1.2923291585435364e-05, "Product Page": 0.00010739379982831817, "Spam / Ads": 2.2870104637015025e-05, "Structured Data": 0.00034068721156046695, "Technical Writing": 0.9955001668044928, "Transcript / Interview": 2.0168317719318737e-05, "Tutorial / How-To Guide": 0.0017813694216863977, "User Reviews": 1.1660338287870037e-05, "Other / Unknown": 0.0017066573314726025}, "secondary": {"Academic Writing": 0.0004040243094962695, "Content Listing": 0.011422644062058989, "Creative Writing": 0.000490928410276725, "Customer Support Page": 0.0004876559785515536, "Q&A Forum": 0.00016133938261105027, "Discussion Thread": 0.0001225584111382547, "Comment Section": 0.0003429011153205105, "Missing Content": 0.009690186597820245, "Knowledge Article": 0.0011237645413343376, "Legal Notices": 0.0009243473538346821, "News Article": 0.0002326984621854463, "Organizational Announcement": 0.0002508384419957598, "Nonfiction Writing": 0.0002717288534221551, "Organizational About Page": 0.0003285910024646754, "Personal About Page": 0.0003023365769034038, "Personal Blog": 0.00025677906783045274, "Product Page": 0.0022307863479514136, "Spam / Ads": 0.0007740399133493555, "Structured Data": 0.004030671978624085, "Technical Writing": 0.014644155443425328, "Transcript / Interview": 0.0003061166726690584, "Tutorial / How-To Guide": 0.004548608396770824, "User Reviews": 0.00016121018757679986, "NONE": 0.9464910884923886}, "tertiary": {"Academic Writing": 0.0003982873163237714, "Content Listing": 0.021207519350446128, "Creative Writing": 0.0006626088947362747, "Customer Support Page": 0.0010168377744873625, "Q&A Forum": 0.00016707178156271749, "Discussion Thread": 0.00014721503382116673, "Comment Section": 0.00044164321489200114, "Missing Content": 0.01674408139491554, "Knowledge Article": 0.001889806260855402, "Legal Notices": 0.003648821408398424, "News Article": 0.00029957106194176557, "Organizational Announcement": 0.00032845828375234167, "Nonfiction Writing": 0.000455485212051575, "Organizational About Page": 0.00048325407806994137, "Personal About Page": 0.00036433221563855403, "Personal Blog": 0.0002740056638443618, "Product Page": 0.005597242190146825, "Spam / Ads": 0.004775859802508585, "Structured Data": 0.005433934061746657, "Technical Writing": 0.011597213339073927, "Transcript / Interview": 0.0004105342993336098, "Tutorial / How-To Guide": 0.004115034780219436, "User Reviews": 0.0002106018691304271, "NONE": 0.9193305807121032}, "structured_records": {"false": 0.9715697628461497, "true": 0.028430237153850322}, "procedural_steps": {"false": 0.9590769111569444, "true": 0.04092308884305563}, "worked_solution": {"false": 0.9925948342066367, "true": 0.007405165793363262}, "contextual_dialogue": {"false": 0.9999757090270699, "true": 2.429097293006794e-05}, "content_quality": {"-2": 0.014695416587819596, "-1": 0.0906047217970233, "0": 0.3153946136287584, "1": 0.4558810161308665, "2": 0.12342423185553225}, "noise_severity": {"0": 0.886066015538472, "1": 0.09740300651561139, "2": 0.013069878724412697, "3": 0.0022038558217943208, "4": 0.0012572433997096605}, "missing_content_severity": {"0": 0.8614864680991315, "1": 0.10254565734623852, "2": 0.02905149772537271, "3": 0.0054001159542751784, "4": 0.0015162608749820822}, "factual_density": {"0": 0.013718489408318319, "1": 0.03489546442000555, "2": 0.1681210067572324, "3": 0.3646332548767209, "4": 0.41863178453772293}, "pretraining_utility": {"-2": 0.008115029596214464, "-1": 0.05906344019217513, "0": 0.330091722178216, "1": 0.514331122857782, "2": 0.08839868517561243}, "math_density": {"0": 0.713932182500668, "1": 0.24460317305964305, "2": 0.025551585254704908, "3": 0.0061256291654214826, "4": 0.009787430019562539}, "code_density": {"0": 0.0003212413550398519, "1": 0.0006161731211494388, "2": 0.03203756994607387, "3": 0.19137925135584, "4": 0.7756457642218968}, "reasoning_density": {"0": 0.6476197027167397, "1": 0.11148434799555632, "2": 0.1683202892480991, "3": 0.05159833488165477, "4": 0.02097732515795008}, "education_level": {"none": 0.001067163855923557, "0": 0.0020458372926798335, "1": 0.03784101118111261, "2": 0.8973494216766271, "3": 0.05950842642776325, "4": 0.002188139565893562}, "creative_writing_quality": {"not_applicable": 0.9987424951657657, "-2": 0.00012343653275275526, "-1": 0.00026495592806251934, "0": 0.0007352906513196736, "1": 9.866454988074093e-05, "2": 3.515717221860867e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 8.746437679408981e-05, "Art & Design": 0.0002414478373817678, "Law & Crime": 0.0001754379764489689, "Education & Careers": 0.000121460521571892, "Entertainment & Media": 0.00017626265902094223, "Fashion & Beauty": 0.00012571475570312025, "Business, Economics & Finance": 0.0019532075484687515, "Food & Dining": 0.00018117060860927761, "Games & Gaming": 0.00022835657452776112, "Electronics & Hardware": 0.00011101172896710151, "Health, Healthcare & Wellness": 9.752276929171822e-05, "History & Geography": 0.00015775322515454456, "Home, Pets & Hobbies": 0.0001249910272400163, "Industry & Production": 0.00012545767303576136, "Language, Literature & Philosophy": 0.00011309322562458006, "Politics & Government": 6.758529824976357e-05, "Religion & Spirituality": 0.00010918793143670422, "Science, Mathematics & Engineering": 0.00014837086425167017, "Relationships & Community": 7.242012150421615e-05, "Software Usage & Applications": 0.0016301515024298413, "Programming & Software Engineering": 0.9472183878087045, "Sports & Fitness": 0.00011546013704588687, "Vehicles & Transportation": 9.0565134574885e-05, "Travel & Tourism": 0.045905920668797416, "Other / Uncovered Subject": 0.0005015491269535937, "Unknown / Insufficient Content": 0.00012004889821123888}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010057640380990353, "Art & Design": 0.002671326354499919, "Law & Crime": 0.0012510509223504136, "Education & Careers": 0.0006332014217354423, "Entertainment & Media": 0.0010734095237411965, "Fashion & Beauty": 0.0005763316807715813, "Business, Economics & Finance": 0.019427531091282882, "Food & Dining": 0.0008709052890332189, "Games & Gaming": 0.0007927679128912244, "Electronics & Hardware": 0.0005930375958186179, "Health, Healthcare & Wellness": 0.0007033290389464211, "History & Geography": 0.0008043012648949714, "Home, Pets & Hobbies": 0.0006663184508851201, "Industry & Production": 0.0006698516779373398, "Language, Literature & Philosophy": 0.0004375010394115755, "Politics & Government": 0.00044479962527151903, "Religion & Spirituality": 0.0007581598592463011, "Science, Mathematics & Engineering": 0.0010263455523891708, "Relationships & Community": 0.00035286148675927, "Software Usage & Applications": 0.018620341063698975, "Programming & Software Engineering": 0.10824641694708628, "Sports & Fitness": 0.0004931920849017985, "Vehicles & Transportation": 0.0004403503863690028, "Travel & Tourism": 0.704010579831314, "Other / Uncovered Subject": 0.0015874285757273407, "NONE": 0.13184289728493748}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11579, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11579, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:09.363331+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "e5dc449b-2538-4e2f-8fc7-3ef71feadf95", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "0a67e3d7679005d1f48cd0ff6e937de5c31b52e7f210d083858e2a28600503d2", "source_metadata": {"uuid": "e5dc449b-2538-4e2f-8fc7-3ef71feadf95", "repo_name": "Gaurav11170/Hotel-Booking-Application", "relative_path": "app.py", "category": "TOOL", "algo_rel_score": 0.9578156471252441, "quality_score": 6.392649173736572, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 9, "repo_created_at": "2025-03-23T12:48:15.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "426b5cdd4828fcd3e68d32f8e823c7cc8d08bc21", "size_bytes": 13156, "file_timestamp": 1742739690, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2cf0f6c6fed4a4f1538696c34e59c0725af2a3a5"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 9, "repo_created_at": "2025-03-23T12:48:15.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "426b5cdd4828fcd3e68d32f8e823c7cc8d08bc21", "size_bytes": 13156, "file_timestamp": 1742739690, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2cf0f6c6fed4a4f1538696c34e59c0725af2a3a5", "url": "https://github.com/Gaurav11170/Hotel-Booking-Application/blob/2cf0f6c6fed4a4f1538696c34e59c0725af2a3a5/app.py"}, "gpt4o_tokens": 2825, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.5827339248692686, "noise_severity": 0.13518330502865838, "missing_content_severity": 0.1829140441597378, "factual_density": 3.139564380715525, "pretraining_utility": 0.6158349938244029, "math_density": 0.35323295114356745, "code_density": 3.7414121239684044, "reasoning_density": 0.6868292317685192, "education_level": 2.021975471221683, "creative_writing_quality": -0.27264316598737776}, "score_applicability": {"education_level": 0.9989328361440764, "creative_writing_quality": 0.0012575048342342977}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/InformationManagement1/pos_gui/blob/02292f7af7a17322524b755bec8b2efbf57808e7/cashier_gui.py", "id": "python-0-6ede80f3c2a6722c1dba920bc59f3c60", "text": "import tkinter as tk\nfrom tkinter import ttk, messagebox\nimport datetime\nimport mysql.connector\nimport subprocess\n\ndef logout():\n    root.destroy()\n    subprocess.Popen([\"python\", \"login.py\"])\n\n# Connect to MySQL\ndef connect_db():\n    return mysql.connector.connect(\n        host=\"localhost\",\n        user=\"your_mysql_user\",\n        password=\"<PASSWORD>\",\n        database=\"pos_system\"\n    )\n\ncart = []\ngift_card_code = \"\"\n\n# Temporary products dictionary for testing without DB\ntemp_products = {\n    \"123456\": {\"product_id\": 1, \"barcode\": \"123456\", \"name\": \"Rice\", \"price\": 100.0, \"stock\": 5},\n    \"789012\": {\"product_id\": 2, \"barcode\": \"789012\", \"name\": \"Kwek2x\", \"price\": 50.0, \"stock\": 3},\n    \"345678\": {\"product_id\": 3, \"barcode\": \"345678\", \"name\": \"Siomai\", \"price\": 75.0, \"stock\": 0},  # out of stock example\n}\n\n# Add product to cart by barcode\ndef add_product(barcode):\n    # Uncomment this section when ready to use DB\n    \"\"\"\n    try:\n        conn = connect_db()\n        cursor = conn.cursor(dictionary=True)\n        cursor.execute(\"SELECT * FROM products WHERE barcode = %s\", (barcode,))\n        product = cursor.fetchone()\n\n        if not product:\n            messagebox.showerror(\"Error\", \"Product not found.\")\n            return\n\n        if product[\"stock\"] <= 0:\n            messagebox.showwarning(\"Out of Stock\", \"Product is out of stock.\")\n            return\n\n        cart.append(product)\n        cursor.execute(\"UPDATE products SET stock = stock - 1 WHERE product_id = %s\", (product[\"product_id\"],))\n        conn.commit()\n        refresh_cart()\n\n    except mysql.connector.Error as err:\n        messagebox.showerror(\"Database Error\", str(err))\n    finally:\n        conn.close()\n    \"\"\"\n\n    # Temporary non-DB code for testing\n    product = temp_products.get(barcode)\n    if not product:\n        messagebox.showerror(\"Error\", \"Product not found.\")\n        return\n    if product[\"stock\"] <= 0:\n        messagebox.showwarning(\"Out of Stock\", \"Product is out of stock.\")\n        return\n\n    # Decrease stock locally for temp testing\n    product[\"stock\"] -= 1\n\n    cart.append(product)\n    refresh_cart()\n\n# Refresh cart UI\ndef refresh_cart():\n    for i in cart_tree.get_children():\n        cart_tree.delete(i)\n    total = 0\n    for idx, item in enumerate(cart):\n        cart_tree.insert(\"\", \"end\", iid=idx, values=(item[\"name\"], item[\"price\"]))\n        total += item[\"price\"]\n    total_label.config(text=f\"Total: ₱{total:.2f}\")\n\n# Load customer list\ndef load_customers():\n    conn = None\n    try:\n        conn = connect_db()\n        cursor = conn.cursor(dictionary=True)\n        cursor.execute(\"SELECT * FROM customers\")\n        return cursor.fetchall()\n    except:\n        return []\n    finally:\n        if conn:\n            conn.close()\n\n# Apply gift card\ndef apply_gift_card():\n    global gift_card_code\n    code = gift_card_entry.get()\n    try:\n        conn = connect_db()\n        cursor = conn.cursor(dictionary=True)\n        cursor.execute(\"SELECT * FROM gift_cards WHERE card_code = %s AND status = 'active'\", (code,))\n        card = cursor.fetchone()\n        if not card:\n            messagebox.showerror(\"Invalid\", \"Gift card not valid or already used.\")\n            return\n        gift_card_code = code\n        gift_card_balance_label.config(text=f\"Gift Card Balance: ₱{card['balance']:.2f}\")\n    except mysql.connector.Error as err:\n        messagebox.showerror(\"DB Error\", str(err))\n    finally:\n        conn.close()\n\n# Process checkout\ndef checkout():\n    if not cart:\n        messagebox.showwarning(\"Empty Cart\", \"Cart is empty.\")\n        return\n\n    try:\n        conn = connect_db()\n        cursor = conn.cursor()\n\n        subtotal = sum(item[\"price\"] for item in cart)\n        discount = 0\n\n        # Loyalty points\n        customer_id = customer_var.get()\n        if customer_id != \"None\":\n            cursor.execute(\"SELECT * FROM customers WHERE customer_id = %s\", (customer_id,))\n            customer = cursor.fetchone()\n            points_to_redeem = min(customer[4], int(subtotal))  # loyalty_points\n            discount += points_to_redeem\n            new_points = int(subtotal / 100)\n            cursor.execute(\n                \"UPDATE customers SET loyalty_points = loyalty_points - %s + %s WHERE customer_id = %s\",\n                (points_to_redeem, new_points, customer_id)\n            )\n        else:\n            customer_id = None\n\n        # Gift card\n        if gift_card_code:\n            cursor.execute(\"SELECT balance FROM gift_cards WHERE card_code = %s\", (gift_card_code,))\n            card = cursor.fetchone()\n            if card:\n                usable = min(card[0], subtotal - discount)\n                discount += usable\n                cursor.execute(\"UPDATE gift_cards SET balance = balance - %s WHERE card_code = %s\", (usable, gift_card_code))\n                cursor.execute(\"UPDATE gift_cards SET status = 'used' WHERE card_code = %s AND balance <= 0\", (gift_card_code,))\n\n        total = max(0, subtotal - discount)\n\n        # Insert transaction\n        cursor.execute(\n            \"INSERT INTO sales_transactions (cashier_id, total_amount, payment_method, customer_id) VALUES (%s, %s, %s, %s)\",\n            (1, total, \"Cash\", customer_id)\n        )\n        transaction_id = cursor.lastrowid\n\n        for item in cart:\n            cursor.execute(\n                \"INSERT INTO sales_items (transaction_id, product_id, quantity, price) VALUES (%s, %s, %s, %s)\",\n                (transaction_id, item[\"product_id\"], 1, item[\"price\"])\n            )\n\n        conn.commit()\n        messagebox.showinfo(\"Success\", f\"Transaction complete!\\nTotal Paid: ₱{total:.2f}\")\n        cart.clear()\n        refresh_cart()\n        gift_card_entry.delete(0, tk.END)\n        gift_card_balance_label.config(text=\"Gift Card Balance: ₱0.00\")\n\n    except mysql.connector.Error as err:\n        messagebox.showerror(\"DB Error\", str(err))\n    finally:\n        conn.close()\n\n# GUI SETUP\nroot = tk.Tk()\nroot.title(\"Retail POS System\")\n\nframe = tk.Frame(root)\nframe.pack(pady=10, padx=10)\n\ntk.Label(frame, text=\"Scan Barcode:\").grid(row=0, column=0)\nbarcode_entry = tk.Entry(frame)\nbarcode_entry.grid(row=0, column=1)\nbarcode_entry.focus()\n\ndef scan_product(event=None):\n    add_product(barcode_entry.get())\n    barcode_entry.delete(0, tk.END)\n\nbarcode_entry.bind(\"<Return>\", scan_product)\n\ncart_tree = ttk.Treeview(frame, columns=(\"Product\", \"Price\"), show=\"headings\", height=10)\ncart_tree.heading(\"Product\", text=\"Product\")\ncart_tree.heading(\"Price\", text=\"Price (₱)\")\ncart_tree.grid(row=1, column=0, columnspan=2, pady=10)\n\ntotal_label = tk.Label(frame, text=\"Total: ₱0.00\", font=(\"Arial\", 14))\ntotal_label.grid(row=2, column=0, columnspan=2)\n\n# Loyalty customer dropdown\ntk.Label(frame, text=\"Customer:\").grid(row=3, column=0)\ncustomers = load_customers()\ncustomer_options = [\"None\"] + [str(c[\"customer_id\"]) for c in customers]\ncustomer_var = tk.StringVar(value=\"None\")\ncustomer_dropdown = ttk.Combobox(frame, textvariable=customer_var, values=customer_options)\ncustomer_dropdown.grid(row=3, column=1)\n\n# Gift card input\ntk.Label(frame, text=\"Gift Card Code:\").grid(row=4, column=0)\ngift_card_entry = tk.Entry(frame)\ngift_card_entry.grid(row=4, column=1)\n\ngift_card_balance_label = tk.Label(frame, text=\"Gift Card Balance: ₱0.00\")\ngift_card_balance_label.grid(row=5, column=0, columnspan=2)\n\napply_card_btn = tk.Button(frame, text=\"Apply Gift Card\", command=apply_gift_card)\napply_card_btn.grid(row=6, column=0, columnspan=2, pady=5)\n\ncheckout_btn = tk.Button(frame, text=\"Checkout\", command=checkout, bg=\"green\", fg=\"white\")\ncheckout_btn.grid(row=7, column=0, columnspan=2, pady=10, ipadx=20)\n\ntk.Button(root, text=\"Logout\", command=lambda: logout(), bg=\"red\", fg=\"white\").pack(pady=10)\n\nroot.mainloop()\n", "text_preview": "import tkinter as tk from tkinter import ttk, messagebox import datetime import mysql.connector import subprocess def logout(): root.destroy() subprocess.Popen([\"python\", \"login.py\"]) # Connect to MySQL def connect_db(): return mysql.connec", "input_sha256": "b32e8afc53842a907447c5df42684c352b5a84cb6bd07be4aee94353f7eecb42", "text_sha256": "6ede80f3c2a6722c1dba920bc59f3c60aa3947af05af8f9f4ec38928b96e2631", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12063, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 1, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.3145843933552558, "noise_severity": 0.14043476500059468, "missing_content_severity": 0.30854910753598064, "factual_density": 3.2031516359549137, "pretraining_utility": 0.6008937779583879, "math_density": 0.8626689984832563, "code_density": 3.7014497229721104, "reasoning_density": 0.6965395928417824, "education_level": 1.9571289166197283, "creative_writing_quality": -0.2600330906088425}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.007137160897338624, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9955211550300511, "secondary": 0.9364428983864399, "tertiary": 0.9289954261457387, "structured_records": 0.42693209763094003, "procedural_steps": 0.013622975433010734, "worked_solution": 0.014949894267332375, "contextual_dialogue": 5.374600108138294e-06, "content_quality": 0.3092453175449724, "noise_severity": 0.8829710291661711, "missing_content_severity": 0.7428757437200162, "factual_density": 0.33595969662909475, "pretraining_utility": 0.5392242085067085, "math_density": 0.5685766543818458, "code_density": 0.7512081024767586, "reasoning_density": 0.4195503392985148, "education_level": 0.8608328210176621, "creative_writing_quality": 0.9983744689787497, "primary_topic": 0.9882536804616712, "secondary_topic": 0.6634473520479488}, "probability_margin": {"primary": 0.9942245023931823, "secondary": 0.9108382399243389, "tertiary": 0.8984158458895483, "education_level": 0.8076479749623585, "creative_writing_quality": 0.997790520900359, "primary_topic": 0.981039341285517, "secondary_topic": 0.4201285925845563}, "raw_probabilities": {"primary": {"Academic Writing": 3.989256716698298e-05, "Content Listing": 5.3890031733060204e-05, "Creative Writing": 2.2791398278048334e-05, "Customer Support Page": 9.465335305642896e-06, "Q&A Forum": 1.1841433748409547e-05, "Discussion Thread": 6.74816848093607e-06, "Comment Section": 1.4160222053379748e-05, "Missing Content": 2.616293279563504e-05, "Knowledge Article": 6.216212254229446e-05, "Legal Notices": 2.6378040846549478e-05, "News Article": 4.600375630288905e-06, "Organizational Announcement": 9.716713768339905e-06, "Nonfiction Writing": 1.339833352272296e-05, "Organizational About Page": 1.2806744535846803e-05, "Personal About Page": 1.3277684041255295e-05, "Personal Blog": 9.020497599190544e-06, "Product Page": 1.6678328311205576e-05, "Spam / Ads": 1.124852440486526e-05, "Structured Data": 0.0010068247761889893, "Technical Writing": 0.9957077735704657, "Transcript / Interview": 1.6873382292631974e-05, "Tutorial / How-To Guide": 0.001413446681543505, "User Reviews": 7.570957460908759e-06, "Other / Unknown": 0.0014832711772834362}, "secondary": {"Academic Writing": 0.0003190037564649733, "Content Listing": 0.0025868819151421557, "Creative Writing": 0.0003178932925120816, "Customer Support Page": 0.00015673354363675914, "Q&A Forum": 0.00013005841522413086, "Discussion Thread": 9.786131136724898e-05, "Comment Section": 0.00025384006912839205, "Missing Content": 0.002452689802503752, "Knowledge Article": 0.0007479707022566644, "Legal Notices": 0.00027927748081940103, "News Article": 0.0001646401246453427, "Organizational Announcement": 0.00013832643054131446, "Nonfiction Writing": 0.00018519886265454298, "Organizational About Page": 0.00016678034386266024, "Personal About Page": 0.0001429993320991977, "Personal Blog": 0.00014636742989065083, "Product Page": 0.00081529516889631, "Spam / Ads": 0.00022093267612862414, "Structured Data": 0.028252871029332688, "Technical Writing": 0.019656420351301815, "Transcript / Interview": 0.00017489643284312068, "Tutorial / How-To Guide": 0.0034153779109160343, "User Reviews": 8.657266416059684e-05, "NONE": 0.9390911109536716}, "tertiary": {"Academic Writing": 0.0002725895197882567, "Content Listing": 0.003322071371680686, "Creative Writing": 0.000336611794868902, "Customer Support Page": 0.00016399400086020288, "Q&A Forum": 0.00011321835680433095, "Discussion Thread": 0.00010010436242804163, "Comment Section": 0.00029281312547944064, "Missing Content": 0.0015837814248733971, "Knowledge Article": 0.0010036556929331319, "Legal Notices": 0.0003056642098769799, "News Article": 0.00014390317864315205, "Organizational Announcement": 0.00013504471776138246, "Nonfiction Writing": 0.0002236379905478256, "Organizational About Page": 0.00019216078160049356, "Personal About Page": 0.0001406827211633804, "Personal Blog": 0.00014006657766337297, "Product Page": 0.001330392608111149, "Spam / Ads": 0.00023118779032654978, "Structured Data": 0.03353810416678469, "Technical Writing": 0.01838662081102581, "Transcript / Interview": 0.00017730973232304206, "Tutorial / How-To Guide": 0.005835172842927112, "User Reviews": 7.726216519573129e-05, "NONE": 0.931953950056333}, "structured_records": {"false": 0.5730679023690599, "true": 0.42693209763094003}, "procedural_steps": {"false": 0.9863770245669893, "true": 0.013622975433010734}, "worked_solution": {"false": 0.9850501057326676, "true": 0.014949894267332375}, "contextual_dialogue": {"false": 0.9999946253998918, "true": 5.374600108138294e-06}, "content_quality": {"-2": 0.016028975969637444, "-1": 0.17129905952682545, "0": 0.3664755658328254, "1": 0.37445139252006726, "2": 0.07174500615064443}, "noise_severity": {"0": 0.8767348283458251, "1": 0.11022478706401127, "2": 0.010055552982548805, "3": 0.0018404544589748666, "4": 0.001144377148640306}, "missing_content_severity": {"0": 0.7612061977427759, "1": 0.17838312551261515, "2": 0.05261301243946629, "3": 0.006250700076137174, "4": 0.0015469642290053308}, "factual_density": {"0": 0.0074500959350557215, "1": 0.02358941538576028, "2": 0.14434753027856403, "3": 0.4075846735904546, "4": 0.4170282848101654}, "pretraining_utility": {"-2": 0.007934228183566244, "-1": 0.051717564340599716, "0": 0.3487807726848827, "1": 0.5146550709157824, "2": 0.07691236387516885}, "math_density": {"0": 0.32751950812926434, "1": 0.5127922897605544, "2": 0.13960897502895944, "3": 0.009658149660104344, "4": 0.010421077421117513}, "code_density": {"0": 0.0003888147118386788, "1": 0.0007909943484686031, "2": 0.03004197917477968, "3": 0.2345380767855697, "4": 0.7342401349793434}, "reasoning_density": {"0": 0.6590692782985614, "1": 0.09497211160223808, "2": 0.15421818206108212, "3": 0.07383059503509386, "4": 0.017909833003024605}, "education_level": {"none": 0.0010835745266680361, "0": 0.0022357871623343204, "1": 0.07637937588569323, "2": 0.8840273508480517, "3": 0.03452150231053338, "4": 0.0017524092667192356}, "creative_writing_quality": {"not_applicable": 0.9986453908156248, "-2": 0.0001168408982002487, "-1": 0.0002663187334159836, "0": 0.0008548699152657087, "1": 8.540195795068246e-05, "2": 3.117767954277074e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.442638736839374e-05, "Art & Design": 0.00013982514971601852, "Law & Crime": 5.5929284900049854e-05, "Education & Careers": 5.07666496111384e-05, "Entertainment & Media": 3.2466184716604436e-05, "Fashion & Beauty": 3.892599654217227e-05, "Business, Economics & Finance": 0.007666120696859176, "Food & Dining": 0.00037049553460795885, "Games & Gaming": 0.00011805389641770846, "Electronics & Hardware": 6.872639099661953e-05, "Health, Healthcare & Wellness": 3.1123294260398444e-05, "History & Geography": 3.5437132389162736e-05, "Home, Pets & Hobbies": 2.7012563132251962e-05, "Industry & Production": 0.00010961283415138683, "Language, Literature & Philosophy": 3.3772725455654304e-05, "Politics & Government": 2.0755621053702105e-05, "Religion & Spirituality": 3.705266172876298e-05, "Science, Mathematics & Engineering": 9.210580696288697e-05, "Relationships & Community": 2.1095104758878778e-05, "Software Usage & Applications": 0.0013960390562853411, "Programming & Software Engineering": 0.9887054619823761, "Sports & Fitness": 4.1747879616290496e-05, "Vehicles & Transportation": 2.5677012830369435e-05, "Travel & Tourism": 4.979966792552711e-05, "Other / Uncovered Subject": 0.0007452517380889614, "Unknown / Insufficient Content": 5.2318747248609645e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007642868315238598, "Art & Design": 0.0029440841910904326, "Law & Crime": 0.0008595595378845592, "Education & Careers": 0.0005449850260313602, "Entertainment & Media": 0.0004983526504097759, "Fashion & Beauty": 0.00043349108808899296, "Business, Economics & Finance": 0.25626309207693293, "Food & Dining": 0.005979889764483495, "Games & Gaming": 0.0005629983548123098, "Electronics & Hardware": 0.0008339312349241303, "Health, Healthcare & Wellness": 0.0004236718481394535, "History & Geography": 0.0003796578504864239, "Home, Pets & Hobbies": 0.0003852748740602481, "Industry & Production": 0.0007042420991134838, "Language, Literature & Philosophy": 0.0002754341433120056, "Politics & Government": 0.0002895706449501409, "Religion & Spirituality": 0.00046143433911592137, "Science, Mathematics & Engineering": 0.0008701443105907143, "Relationships & Community": 0.00020750886071651213, "Software Usage & Applications": 0.02202576116199132, "Programming & Software Engineering": 0.025557299768794707, "Sports & Fitness": 0.0002637810631124815, "Vehicles & Transportation": 0.00022729202242161484, "Travel & Tourism": 0.00048474907088556275, "Other / Uncovered Subject": 0.001367822524638379, "NONE": 0.6763916846614892}}, "probabilities": {"primary": {"Academic Writing": 3.9892567166982986e-05, "Content Listing": 5.389003173306022e-05, "Creative Writing": 2.2791398278048338e-05, "Customer Support Page": 9.465335305642897e-06, "Q&A Forum": 1.184143374840955e-05, "Discussion Thread": 6.748168480936071e-06, "Comment Section": 1.4160222053379751e-05, "Missing Content": 2.6162932795635046e-05, "Knowledge Article": 6.216212254229447e-05, "Legal Notices": 2.6378040846549485e-05, "News Article": 4.600375630288906e-06, "Organizational Announcement": 9.716713768339907e-06, "Nonfiction Writing": 1.3398333522722963e-05, "Organizational About Page": 1.2806744535846806e-05, "Personal About Page": 1.3277684041255298e-05, "Personal Blog": 9.020497599190546e-06, "Product Page": 1.667832831120558e-05, "Spam / Ads": 1.1248524404865262e-05, "Structured Data": 0.0010068247761889895, "Technical Writing": 0.9957077735704659, "Transcript / Interview": 1.6873382292631978e-05, "Tutorial / How-To Guide": 0.0014134466815435053, "User Reviews": 7.570957460908761e-06, "Other / Unknown": 0.0014832711772834366}, "secondary": {"Academic Writing": 0.0003190037564649733, "Content Listing": 0.0025868819151421557, "Creative Writing": 0.0003178932925120816, "Customer Support Page": 0.00015673354363675914, "Q&A Forum": 0.00013005841522413086, "Discussion Thread": 9.786131136724898e-05, "Comment Section": 0.00025384006912839205, "Missing Content": 0.002452689802503752, "Knowledge Article": 0.0007479707022566644, "Legal Notices": 0.00027927748081940103, "News Article": 0.0001646401246453427, "Organizational Announcement": 0.00013832643054131446, "Nonfiction Writing": 0.00018519886265454298, "Organizational About Page": 0.00016678034386266024, "Personal About Page": 0.0001429993320991977, "Personal Blog": 0.00014636742989065083, "Product Page": 0.00081529516889631, "Spam / Ads": 0.00022093267612862414, "Structured Data": 0.028252871029332688, "Technical Writing": 0.019656420351301815, "Transcript / Interview": 0.00017489643284312068, "Tutorial / How-To Guide": 0.0034153779109160343, "User Reviews": 8.657266416059684e-05, "NONE": 0.9390911109536716}, "tertiary": {"Academic Writing": 0.0002725895197882567, "Content Listing": 0.003322071371680686, "Creative Writing": 0.000336611794868902, "Customer Support Page": 0.00016399400086020288, "Q&A Forum": 0.00011321835680433095, "Discussion Thread": 0.00010010436242804163, "Comment Section": 0.00029281312547944064, "Missing Content": 0.0015837814248733971, "Knowledge Article": 0.0010036556929331319, "Legal Notices": 0.0003056642098769799, "News Article": 0.00014390317864315205, "Organizational Announcement": 0.00013504471776138246, "Nonfiction Writing": 0.0002236379905478256, "Organizational About Page": 0.00019216078160049356, "Personal About Page": 0.0001406827211633804, "Personal Blog": 0.00014006657766337297, "Product Page": 0.001330392608111149, "Spam / Ads": 0.00023118779032654978, "Structured Data": 0.03353810416678469, "Technical Writing": 0.01838662081102581, "Transcript / Interview": 0.00017730973232304206, "Tutorial / How-To Guide": 0.005835172842927112, "User Reviews": 7.726216519573129e-05, "NONE": 0.931953950056333}, "structured_records": {"false": 0.5730679023690599, "true": 0.42693209763094003}, "procedural_steps": {"false": 0.9863770245669893, "true": 0.013622975433010734}, "worked_solution": {"false": 0.9850501057326676, "true": 0.014949894267332375}, "contextual_dialogue": {"false": 0.9999946253998918, "true": 5.374600108138294e-06}, "content_quality": {"-2": 0.016028975969637444, "-1": 0.17129905952682545, "0": 0.3664755658328254, "1": 0.37445139252006726, "2": 0.07174500615064443}, "noise_severity": {"0": 0.8767348283458248, "1": 0.11022478706401125, "2": 0.010055552982548803, "3": 0.0018404544589748662, "4": 0.0011443771486403058}, "missing_content_severity": {"0": 0.7612061977427761, "1": 0.17838312551261518, "2": 0.052613012439466304, "3": 0.006250700076137176, "4": 0.0015469642290053313}, "factual_density": {"0": 0.0074500959350557215, "1": 0.02358941538576028, "2": 0.14434753027856403, "3": 0.4075846735904546, "4": 0.4170282848101654}, "pretraining_utility": {"-2": 0.007934228183566244, "-1": 0.051717564340599716, "0": 0.3487807726848827, "1": 0.5146550709157824, "2": 0.07691236387516885}, "math_density": {"0": 0.32751950812926434, "1": 0.5127922897605544, "2": 0.13960897502895944, "3": 0.009658149660104344, "4": 0.010421077421117513}, "code_density": {"0": 0.0003888147118386788, "1": 0.0007909943484686031, "2": 0.03004197917477968, "3": 0.2345380767855697, "4": 0.7342401349793434}, "reasoning_density": {"0": 0.6590692782985614, "1": 0.09497211160223808, "2": 0.15421818206108212, "3": 0.07383059503509386, "4": 0.017909833003024605}, "education_level": {"none": 0.0010835745266680363, "0": 0.002235787162334321, "1": 0.07637937588569324, "2": 0.8840273508480518, "3": 0.034521502310533386, "4": 0.0017524092667192358}, "creative_writing_quality": {"not_applicable": 0.9986453908156245, "-2": 0.00011684089820024867, "-1": 0.0002663187334159835, "0": 0.0008548699152657085, "1": 8.540195795068245e-05, "2": 3.117767954277073e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.442638736839374e-05, "Art & Design": 0.00013982514971601852, "Law & Crime": 5.5929284900049854e-05, "Education & Careers": 5.07666496111384e-05, "Entertainment & Media": 3.2466184716604436e-05, "Fashion & Beauty": 3.892599654217227e-05, "Business, Economics & Finance": 0.007666120696859176, "Food & Dining": 0.00037049553460795885, "Games & Gaming": 0.00011805389641770846, "Electronics & Hardware": 6.872639099661953e-05, "Health, Healthcare & Wellness": 3.1123294260398444e-05, "History & Geography": 3.5437132389162736e-05, "Home, Pets & Hobbies": 2.7012563132251962e-05, "Industry & Production": 0.00010961283415138683, "Language, Literature & Philosophy": 3.3772725455654304e-05, "Politics & Government": 2.0755621053702105e-05, "Religion & Spirituality": 3.705266172876298e-05, "Science, Mathematics & Engineering": 9.210580696288697e-05, "Relationships & Community": 2.1095104758878778e-05, "Software Usage & Applications": 0.0013960390562853411, "Programming & Software Engineering": 0.9887054619823761, "Sports & Fitness": 4.1747879616290496e-05, "Vehicles & Transportation": 2.5677012830369435e-05, "Travel & Tourism": 4.979966792552711e-05, "Other / Uncovered Subject": 0.0007452517380889614, "Unknown / Insufficient Content": 5.2318747248609645e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007642868315238598, "Art & Design": 0.0029440841910904326, "Law & Crime": 0.0008595595378845592, "Education & Careers": 0.0005449850260313602, "Entertainment & Media": 0.0004983526504097759, "Fashion & Beauty": 0.00043349108808899296, "Business, Economics & Finance": 0.25626309207693293, "Food & Dining": 0.005979889764483495, "Games & Gaming": 0.0005629983548123098, "Electronics & Hardware": 0.0008339312349241303, "Health, Healthcare & Wellness": 0.0004236718481394535, "History & Geography": 0.0003796578504864239, "Home, Pets & Hobbies": 0.0003852748740602481, "Industry & Production": 0.0007042420991134838, "Language, Literature & Philosophy": 0.0002754341433120056, "Politics & Government": 0.0002895706449501409, "Religion & Spirituality": 0.00046143433911592137, "Science, Mathematics & Engineering": 0.0008701443105907143, "Relationships & Community": 0.00020750886071651213, "Software Usage & Applications": 0.02202576116199132, "Programming & Software Engineering": 0.025557299768794707, "Sports & Fitness": 0.0002637810631124815, "Vehicles & Transportation": 0.00022729202242161484, "Travel & Tourism": 0.00048474907088556275, "Other / Uncovered Subject": 0.001367822524638379, "NONE": 0.6763916846614892}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10562, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10562, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:10.727557+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "64cfe53a-942c-4aeb-b236-65145e6f4108", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "b79114c63fdee7d4fe7af608e08c6783b050a8139d036b36481c61eff0a842d8", "source_metadata": {"uuid": "64cfe53a-942c-4aeb-b236-65145e6f4108", "repo_name": "InformationManagement1/pos_gui", "relative_path": "cashier_gui.py", "category": "TOOL", "algo_rel_score": 0.9535613656044006, "quality_score": 5.389917373657227, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2025-05-25T18:31:42.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "b56bfb3fefbe6c719c3d60bffbf1261893a5ddb5", "size_bytes": 7985, "file_timestamp": 1748200083, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "02292f7af7a17322524b755bec8b2efbf57808e7"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2025-05-25T18:31:42.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "b56bfb3fefbe6c719c3d60bffbf1261893a5ddb5", "size_bytes": 7985, "file_timestamp": 1748200083, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "02292f7af7a17322524b755bec8b2efbf57808e7", "url": "https://github.com/InformationManagement1/pos_gui/blob/02292f7af7a17322524b755bec8b2efbf57808e7/cashier_gui.py"}, "gpt4o_tokens": 1877, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.3145843933552558, "noise_severity": 0.14043476500059468, "missing_content_severity": 0.30854910753598064, "factual_density": 3.2031516359549137, "pretraining_utility": 0.6008937779583879, "math_density": 0.8626689984832563, "code_density": 3.7014497229721104, "reasoning_density": 0.6965395928417824, "education_level": 1.9571289166197283, "creative_writing_quality": -0.2600330906088425}, "score_applicability": {"education_level": 0.998916425473332, "creative_writing_quality": 0.0013546091843753937}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/Kashydo/AskMate-sprint1-/blob/82eb3e752efe966d505858ab51b0c5ced631c5b6/server.py", "id": "python-0-c5cfe9740df1a66377d1627b4087db43", "text": "from flask import (\n    Flask,\n    render_template,\n    request,\n    redirect,\n    url_for,\n    make_response,\n)\nimport util\nimport datetime\nimport data_hendler\nimport csv\nfrom config import *\nfrom errors import *\nfrom messages import *\nfrom jinja2 import Environment\n\napp = Flask(__name__)\n\n@app.route(\"/\")\n@app.route(\"/\")\n@app.route(\"/list\")\ndef question_list(messages_msg=None):\n    user_id = util.get_user_id(request)\n    with open(QUESTIONS_FILE, \"r\", newline=\"\") as csvfile:\n        questions = list(csv.DictReader(csvfile))\n    order_by = request.args.get(\"order_by\")\n    order_direction = request.args.get(\"order_direction\")\n    if order_by != None:\n        if order_direction == \"desc\":\n            questions = sorted(\n                questions,\n                key=lambda i: int(i[order_by])\n                if i[order_by].isnumeric()\n                else i[order_by],\n                reverse=True,\n            )\n        else:\n            questions = sorted(\n                questions,\n                key=lambda i: int(i[order_by])\n                if i[order_by].isnumeric()\n                else i[order_by],\n            )\n    response = make_response(\n        render_template(\"question_list.html\", request=request, user_questions=questions, messages_msg=messages_msg, user_id=user_id)\n    )\n    if not request.cookies.get(\"userID\"):\n        response.set_cookie(\"user_id\", user_id)\n    return response\n\n\n@app.route(\"/question/<question_id>/\")\n@app.route(\"/question/<question_id>/<string:messages_msg>\")\ndef question_detail(question_id, messages_msg=None):\n    user_id = util.get_user_id(request)\n    with open(QUESTIONS_FILE, \"r\", newline=\"\") as csvfile:\n        questions = list(csv.DictReader(csvfile))\n    question = next((q for q in questions if q[\"id\"] == question_id), None)\n    answers_to_question = data_hendler.read_specific_data(\n        ANSWER_FILE, \"question_id\", question_id\n    )\n    response = make_response(\n        render_template(\n            \"question_detail.html\",\n            question=question,\n            answers=answers_to_question,\n            messages_msg=messages_msg,\n            user_id=user_id,\n        )\n    )\n    if not request.cookies.get(\"userID\"):\n        response.set_cookie(\"user_id\", user_id)\n    return response\n\n\n@app.route(\"/add-question\", methods=[\"GET\", \"POST\"])\ndef question():\n    user_id = util.get_user_id(request)\n    errors_msg = []\n    if request.method == \"POST\":\n        question_id = util.generate_id(QUESTIONS_FILE)\n        submision_time = round(datetime.datetime.now().timestamp())\n        views = 0\n        vote = 0\n        title = request.form.get(\"title\")\n        message = str(request.form.get(\"message\"))\n        imagename = \"\"\n        if len(title) == 0:\n            errors_msg.append(errors[\"empty_title\"])\n        if len(message) == 0:\n            errors_msg.append(errors[\"empty_message\"])\n        if \"image\" in request.files:\n            image = request.files[\"image\"]\n            if image.filename != \"\":\n                if not util.is_allowed_file_extension(image.filename):\n                    errors_msg.append(errors[\"wrong_file_extension\"])\n                else:\n                    imagename = (\n                        IMAGES_FOLDER\n                        + str(question_id)\n                        + \".\"\n                        + util.get_file_extension(image.filename)\n                    )\n                    image.save(imagename)\n        question = [\n            question_id,\n            submision_time,\n            views,\n            vote,\n            title,\n            message,\n            imagename,\n            user_id,\n        ]\n        if len(errors_msg) == 0:\n            messages_msg = messages[\"added_question\"]\n            data_hendler.addtofile(question, QUESTIONS_FILE)\n            return redirect(\n                url_for(\n                    \"question_detail\",\n                    question_id=question_id,\n                    messages_msg=messages_msg,\n                )\n            )\n    response = make_response(\n        render_template(\"add_question.html\", form=request.form, errors_msg=errors_msg)\n    )\n    if not request.cookies.get(\"userID\"):\n        response.set_cookie(\"user_id\", user_id)\n    return response\n\n\n@app.route(\"/question/<int:question_id>/new-answer\", methods=[\"GET\", \"POST\"])\ndef answer(question_id):\n    user_id = util.get_user_id(request)\n    errors_msg = []\n    if request.method == \"POST\":\n        id = util.generate_id(ANSWER_FILE)\n        submission_time = round(datetime.datetime.now().timestamp())\n        vote_number = 0\n        message = str(request.form.get(\"message\"))\n        imagename = \"\"\n        if len(message) == 0:\n            errors_msg.append(errors[\"empty_message\"])\n        if \"image\" in request.files:\n            image = request.files[\"image\"]\n            if image.filename != \"\":\n                if not util.is_allowed_file_extension(image.filename):\n                    errors_msg.append(errors[\"wrong_file_extension\"])\n                else:\n                    imagename = (\n                        IMAGES_FOLDER\n                        + str(question_id)\n                        + \"-\"\n                        + str(id)\n                        + \".\"\n                        + util.get_file_extension(image.filename)\n                    )\n                    image.save(imagename)\n        answer = [\n            id,\n            submission_time,\n            vote_number,\n            question_id,\n            message,\n            imagename,\n            user_id,\n        ]\n        if len(errors_msg) == 0:\n            messages_msg = messages[\"added_answer\"]\n            data_hendler.addtofile(answer, ANSWER_FILE)\n            return redirect(\n                url_for(\n                    \"question_detail\",\n                    question_id=question_id,\n                    messages_msg=messages_msg,\n                )\n            )\n    response = make_response(\n        render_template(\n            \"add_answer.html\",\n            question_id=question_id,\n            form=request.form,\n            errors_msg=errors_msg,\n        )\n    )\n    if not request.cookies.get(\"userID\"):\n        response.set_cookie(\"user_id\", user_id)\n    return response\n\n\n@app.route(\"/question/<int:question_id>/delete/<int:answer_id>\")\ndef delete_answer(question_id, answer_id):\n    user_id = util.get_user_id(request)\n    with open(ANSWER_FILE, \"r\", newline=\"\") as csvfile:\n        answers = list(csv.DictReader(csvfile))\n    answer = next((a for a in answers if a[\"id\"] == str(answer_id)), None)\n    if user_id == answer[\"user_id\"]:\n        messages_msg = messages[\"delete_answer\"]\n        data_hendler.delete_data(ANSWER_FILE, answer_id, \"id\", ANSWER_HEADER)\n    else:\n        messages_msg = messages[\"cant_delete\"]\n    response = make_response(\n        redirect(\n            url_for(\n                \"question_detail\",\n                question_id=question_id,\n                messages_msg=messages_msg,\n            )\n        )\n    )\n    if not request.cookies.get(\"userID\"):\n        response.set_cookie(\"user_id\", user_id)\n    return response\n\n\n@app.route(\"/question/<int:question_id>/delete\")\ndef delete_question(question_id):\n    user_id = util.get_user_id(request)\n    with open(QUESTIONS_FILE, \"r\", newline=\"\") as csvfile:\n        questions = list(csv.DictReader(csvfile))\n    question = next((q for q in questions if q[\"id\"] == str(question_id)), None)\n    if user_id == question[\"user_id\"]:\n        messages_msg = messages[\"delete_question\"]\n        data_hendler.delete_data(QUESTIONS_FILE, question_id, \"id\", QUESTION_HEADER)\n\n        with open(ANSWER_FILE, \"r\", newline=\"\") as csvfile:\n            answers = list(csv.DictReader(csvfile))\n        for a in answers:\n            if a[\"question_id\"] == str(question_id):\n                messages_msg = messages[\"delete_answer\"]\n                data_hendler.delete_data(\n                    ANSWER_FILE, question_id, \"question_id\", ANSWER_HEADER\n                )\n    else:\n        messages_msg = messages[\"cant_delete\"]\n    response = make_response(\n        redirect(\n            url_for(\"question_list\", question_id=question_id, messages_msg=messages_msg)\n        )\n    )\n    if not request.cookies.get(\"userID\"):\n        response.set_cookie(\"user_id\", user_id)\n    return response\n\n\n@app.route(\"/question/<int:question_id>/vote\")\ndef vote_question(question_id):\n    messages_msg = \"\"\n    user_id = util.get_user_id(request)\n    with open(QUESTIONS_FILE, \"r\", newline=\"\") as csvfile:\n        questions = list(csv.DictReader(csvfile))\n    question = next((q for q in questions if q[\"id\"] == str(question_id)), None)\n    response = make_response(\n        redirect(\n            url_for(\"question_list\", messages_msg=messages_msg)\n        )\n    )\n    if user_id != question[\"user_id\"]:\n        messages_msg = messages[\"vote_added\"]\n        data_hendler.add_vote(QUESTIONS_FILE, question_id, \"id\", QUESTION_HEADER)\n        response.set_cookie(\"vote_question_\"+str(question_id), \"1\")\n    else:\n        messages_msg = messages[\"cant_vote\"]\n    if not request.cookies.get(\"userID\"):\n        response.set_cookie(\"user_id\", user_id)\n    return response\n\n\n@app.route(\"/question/<int:question_id>/vote/<int:answer_id>\")\ndef vote_answer(question_id, answer_id):\n    messages_msg = \"\"\n    user_id = util.get_user_id(request)\n    with open(ANSWER_FILE, \"r\", newline=\"\") as csvfile:\n        answers = list(csv.DictReader(csvfile))\n    answer = next((a for a in answers if a[\"id\"] == str(answer_id)), None)\n    response = make_response(\n        redirect(\n            url_for(\"question_detail\", question_id=question_id,  messages_msg=messages_msg)\n        )\n    )\n    if user_id != answer[\"user_id\"]:\n        messages_msg = messages[\"vote_added\"]\n        data_hendler.add_vote(ANSWER_FILE, answer_id, \"id\", ANSWER_HEADER)\n        response.set_cookie(\"vote_answer_\"+str(question_id)+\"-\"+str(answer_id), \"1\")\n    else:\n        messages_msg = messages[\"cant_vote\"]\n    if not request.cookies.get(\"userID\"):\n        response.set_cookie(\"user_id\", user_id)\n    return response\n\n\n@app.template_filter(\"post_time\")\ndef show_post_date(timestamp):\n    return util.translate_timestamp(timestamp)\n\n\n@app.route(\"/question/<question_id>/edit\", methods=[\"GET\", \"POST\"])\ndef question_edit(question_id):\n    user_id = util.get_user_id(request)\n    errors_msg = []\n    if request.method == \"POST\":\n        with open(QUESTIONS_FILE, \"r\", newline=\"\") as csvfile:\n            questions = list(csv.DictReader(csvfile))\n        question = next((q for q in questions if q[\"id\"] == question_id), None)\n        if question:\n            title = request.form.get(\"title\")\n            message = str(request.form.get(\"message\"))\n            imagename = question[\"image\"]\n            if len(title) == 0:\n                errors_msg.append(errors[\"empty_title\"])\n            if len(message) == 0:\n                errors_msg.append(errors[\"empty_message\"])\n            if \"image\" in request.files:\n                image = request.files[\"image\"]\n                if image.filename != \"\":\n                    if not util.is_allowed_file_extension(image.filename):\n                        errors_msg.append(errors[\"wrong_file_extension\"])\n                    else:\n                        imagename = (\n                            IMAGES_FOLDER\n                            + str(question_id)\n                            + \".\"\n                            + util.get_file_extension(image.filename)\n                        )\n                        image.save(imagename)\n\n            if len(errors_msg) == 0:\n                question[\"title\"] = title\n                question[\"message\"] = message\n                question[\"image\"] = imagename\n\n                with open(QUESTIONS_FILE, \"w\", newline=\"\") as csvfile:\n                    fieldnames = [\n                        \"id\",\n                        \"submission_time\",\n                        \"view_number\",\n                        \"vote_number\",\n                        \"title\",\n                        \"message\",\n                        \"image\",\n                        \"user_id\",\n                    ]\n                    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n                    writer.writeheader()\n                    writer.writerows(questions)\n\n                messages_msg = messages[\"edited_question\"]\n                return redirect(\n                    url_for(\n                        \"question_detail\",\n                        question_id=question_id,\n                        messages_msg=messages_msg,\n                    )\n                )\n\n    with open(QUESTIONS_FILE, \"r\", newline=\"\") as csvfile:\n        questions = list(csv.DictReader(csvfile))\n    question = next((q for q in questions if q[\"id\"] == question_id), None)\n    if question:\n        response = make_response(\n            render_template(\n                \"question_edit.html\",\n                question=question,\n                form=request.form,\n                errors_msg=errors_msg,\n            )\n        )\n        if not request.cookies.get(\"userID\"):\n            response.set_cookie(\"user_id\", user_id)\n        return response\n    else:\n        return redirect(url_for(\"question_list\"))\n\n\nif __name__ == \"__main__\":\n    app.run()\n", "text_preview": "from flask import ( Flask, render_template, request, redirect, url_for, make_response, ) import util import datetime import data_hendler import csv from config import * from errors import * from messages import * from jinja2 import Environm", "input_sha256": "87fc307653f8ab7dfdded61554efc0eb6e1de2daa3e915361d6d357b50eeb0f9", "text_sha256": "c5cfe9740df1a66377d1627b4087db43f3f2c271cc6d6e7761e6190438a9a430", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12064, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.33375481152074493, "noise_severity": 0.10003885738552537, "missing_content_severity": 0.026782514052941478, "factual_density": 3.17366217313385, "pretraining_utility": 0.5037951144900764, "math_density": 0.07047710373884905, "code_density": 3.906465281784759, "reasoning_density": 0.4392839938552071, "education_level": 2.0250054862134643, "creative_writing_quality": -0.45018843803316777}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.004634813528172632, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9967237473229779, "secondary": 0.9898491808600502, "tertiary": 0.9946855080198828, "structured_records": 0.03349657184552441, "procedural_steps": 0.0025885381448629476, "worked_solution": 0.0023620326304405726, "contextual_dialogue": 1.10090235845267e-05, "content_quality": 0.42973499458104414, "noise_severity": 0.9166342855120622, "missing_content_severity": 0.9776812382892155, "factual_density": 0.31138514427820807, "pretraining_utility": 0.46394484441492323, "math_density": 0.9412690802176258, "code_density": 0.9220544014872994, "reasoning_density": 0.6339300051206607, "education_level": 0.8683263837638663, "creative_writing_quality": 0.9993578358674099, "primary_topic": 0.9917984019089052, "secondary_topic": 0.9745805099522399}, "probability_margin": {"primary": 0.9951089735098909, "secondary": 0.9846849964908373, "tertiary": 0.9923453254173504, "education_level": 0.8267188900696736, "creative_writing_quality": 0.9992235926494282, "primary_topic": 0.9884418642962849, "secondary_topic": 0.9677971969368957}, "raw_probabilities": {"primary": {"Academic Writing": 3.5922917655778425e-05, "Content Listing": 2.903840632140169e-05, "Creative Writing": 1.8475007946590304e-05, "Customer Support Page": 8.471480326391995e-06, "Q&A Forum": 5.9541758852110034e-05, "Discussion Thread": 9.345278618779138e-06, "Comment Section": 1.2558069254496681e-05, "Missing Content": 3.56787326763909e-05, "Knowledge Article": 6.63086942291969e-05, "Legal Notices": 3.321922827421344e-05, "News Article": 5.897248773833868e-06, "Organizational Announcement": 1.2066385720689913e-05, "Nonfiction Writing": 1.2262857462088016e-05, "Organizational About Page": 1.2813915539032591e-05, "Personal About Page": 1.7612498979686983e-05, "Personal Blog": 8.755698817670307e-06, "Product Page": 1.0657020868748352e-05, "Spam / Ads": 1.16098359573983e-05, "Structured Data": 0.00044660154329890237, "Technical Writing": 0.9968602578511871, "Transcript / Interview": 1.548319158080283e-05, "Tutorial / How-To Guide": 0.0005194089931015978, "User Reviews": 6.729043260796461e-06, "Other / Unknown": 0.0017512843412962511}, "secondary": {"Academic Writing": 7.613027761412185e-05, "Content Listing": 0.00021557185914048292, "Creative Writing": 5.8248797312232596e-05, "Customer Support Page": 3.1287843291422444e-05, "Q&A Forum": 0.00012262981702562917, "Discussion Thread": 2.0843956060616258e-05, "Comment Section": 3.398696566324987e-05, "Missing Content": 0.00013986865762347631, "Knowledge Article": 0.00013829663046677586, "Legal Notices": 6.284696711973571e-05, "News Article": 3.082817610198214e-05, "Organizational Announcement": 3.016773705595742e-05, "Nonfiction Writing": 2.677632169145292e-05, "Organizational About Page": 3.860426276001809e-05, "Personal About Page": 3.4334638059515346e-05, "Personal Blog": 2.9174436880223857e-05, "Product Page": 2.124086144738599e-05, "Spam / Ads": 3.260345765369158e-05, "Structured Data": 0.0024329903684815507, "Technical Writing": 0.005587135166710781, "Transcript / Interview": 3.6041033163687985e-05, "Tutorial / How-To Guide": 0.0005129982713804368, "User Reviews": 1.526183974753379e-05, "NONE": 0.9902721316575481}, "tertiary": {"Academic Writing": 3.942084196765561e-05, "Content Listing": 0.0001614912417257485, "Creative Writing": 4.136853385612444e-05, "Customer Support Page": 1.923625605890737e-05, "Q&A Forum": 6.902364302286925e-05, "Discussion Thread": 1.2536940918239988e-05, "Comment Section": 2.269245190919329e-05, "Missing Content": 0.00019625481448218177, "Knowledge Article": 0.00010079159215533569, "Legal Notices": 6.812476782686921e-05, "News Article": 1.914829113748871e-05, "Organizational Announcement": 1.8696776215329824e-05, "Nonfiction Writing": 2.1636279810084033e-05, "Organizational About Page": 3.199994530647123e-05, "Personal About Page": 2.071177341832033e-05, "Personal Blog": 1.758235538647104e-05, "Product Page": 1.4209616188474622e-05, "Spam / Ads": 2.259686978628864e-05, "Structured Data": 0.001112166789706887, "Technical Writing": 0.002561619768370581, "Transcript / Interview": 2.153597404854867e-05, "Tutorial / How-To Guide": 0.0004897934362210284, "User Reviews": 1.041585476004009e-05, "NONE": 0.994906945185721}, "structured_records": {"false": 0.9665034281544755, "true": 0.03349657184552441}, "procedural_steps": {"false": 0.9974114618551371, "true": 0.0025885381448629476}, "worked_solution": {"false": 0.9976379673695595, "true": 0.0023620326304405726}, "contextual_dialogue": {"false": 0.9999889909764155, "true": 1.10090235845267e-05}, "content_quality": {"-2": 0.015968836110680572, "-1": 0.14334392526963993, "0": 0.40902857639032864, "1": 0.3542809154469556, "2": 0.07737774678239519}, "noise_severity": {"0": 0.914058927933105, "1": 0.07486213225162573, "2": 0.008953289435358886, "3": 0.001232455256459644, "4": 0.0008931951234507283}, "missing_content_severity": {"0": 0.9877144794652496, "1": 0.0017800481688588468, "2": 0.006879724948808008, "3": 0.003259973681866987, "4": 0.00036577373521641223}, "factual_density": {"0": 0.011151377796545377, "1": 0.022554239838224868, "2": 0.1612010319669719, "3": 0.3916675322313503, "4": 0.4134258181669076}, "pretraining_utility": {"-2": 0.007695752161397898, "-1": 0.07871924514952608, "0": 0.38920978932276196, "1": 0.45084456277022955, "2": 0.07353065059608432}, "math_density": {"0": 0.9457810183952803, "1": 0.04552347384757898, "2": 0.004257829028652743, "3": 0.0013127430799878732, "4": 0.003124935648500245}, "code_density": {"0": 0.0001888252511796025, "1": 0.00036369140263977696, "2": 0.010880448142306388, "3": 0.06992744671799019, "4": 0.918639588485884}, "reasoning_density": {"0": 0.7682848933243506, "1": 0.08387290053469117, "2": 0.10525879267164516, "3": 0.025440145900026977, "4": 0.01714326756928617}, "education_level": {"none": 0.0011097854763555317, "0": 0.0015882456137212423, "1": 0.04078421392145892, "2": 0.8902719864698886, "3": 0.06355309640021505, "4": 0.0026926721183608256}, "creative_writing_quality": {"not_applicable": 0.9994648632228416, "-2": 8.419997746070692e-05, "-1": 0.0001492843971489584, "0": 0.0002412705734133383, "1": 4.399169604370433e-05, "2": 1.6390133091790146e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.4140958910734366e-05, "Art & Design": 6.407180553667379e-05, "Law & Crime": 4.37529277794279e-05, "Education & Careers": 0.00011165522271410326, "Entertainment & Media": 5.071441570431299e-05, "Fashion & Beauty": 3.749863647247925e-05, "Business, Economics & Finance": 9.266351466858929e-05, "Food & Dining": 3.2473293251815476e-05, "Games & Gaming": 0.00019740682016530775, "Electronics & Hardware": 9.35135848723457e-05, "Health, Healthcare & Wellness": 4.7171958671526806e-05, "History & Geography": 3.666022854980954e-05, "Home, Pets & Hobbies": 5.128101006052698e-05, "Industry & Production": 3.796385894691754e-05, "Language, Literature & Philosophy": 5.158745845142538e-05, "Politics & Government": 3.1556998224469244e-05, "Religion & Spirituality": 6.839303726895968e-05, "Science, Mathematics & Engineering": 0.00011727615027868998, "Relationships & Community": 0.003671983693047158, "Software Usage & Applications": 0.001662807016078912, "Programming & Software Engineering": 0.992113847989332, "Sports & Fitness": 6.28980732932646e-05, "Vehicles & Transportation": 2.240612298380397e-05, "Travel & Tourism": 7.343903896925653e-05, "Other / Uncovered Subject": 0.0009326576927029043, "Unknown / Insufficient Content": 0.0002501784930644607}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0002475419995890303, "Art & Design": 0.00030042283463643815, "Law & Crime": 0.00022330906058633785, "Education & Careers": 0.0002741123505709923, "Entertainment & Media": 0.00019809318862451012, "Fashion & Beauty": 9.671638807708955e-05, "Business, Economics & Finance": 0.0002869299571315469, "Food & Dining": 9.07377489881911e-05, "Games & Gaming": 0.00034565976923836133, "Electronics & Hardware": 0.00020947008208005847, "Health, Healthcare & Wellness": 0.00012017589219069232, "History & Geography": 8.929347109042359e-05, "Home, Pets & Hobbies": 0.0001026176892865845, "Industry & Production": 7.640571206765129e-05, "Language, Literature & Philosophy": 0.00012744679215182428, "Politics & Government": 0.00010614055432761109, "Religion & Spirituality": 0.00014161240753872618, "Science, Mathematics & Engineering": 0.00040516588479369006, "Relationships & Community": 0.0068968151250524985, "Software Usage & Applications": 0.004747906877149515, "Programming & Software Engineering": 0.007760985709488787, "Sports & Fitness": 0.0001055133611590767, "Vehicles & Transportation": 4.425928076393727e-05, "Travel & Tourism": 0.00015905216966972417, "Other / Uncovered Subject": 0.0012854330473621638, "NONE": 0.9755581826463845}}, "probabilities": {"primary": {"Academic Writing": 3.5922917655778425e-05, "Content Listing": 2.903840632140169e-05, "Creative Writing": 1.8475007946590304e-05, "Customer Support Page": 8.471480326391995e-06, "Q&A Forum": 5.9541758852110034e-05, "Discussion Thread": 9.345278618779138e-06, "Comment Section": 1.2558069254496681e-05, "Missing Content": 3.56787326763909e-05, "Knowledge Article": 6.63086942291969e-05, "Legal Notices": 3.321922827421344e-05, "News Article": 5.897248773833868e-06, "Organizational Announcement": 1.2066385720689913e-05, "Nonfiction Writing": 1.2262857462088016e-05, "Organizational About Page": 1.2813915539032591e-05, "Personal About Page": 1.7612498979686983e-05, "Personal Blog": 8.755698817670307e-06, "Product Page": 1.0657020868748352e-05, "Spam / Ads": 1.16098359573983e-05, "Structured Data": 0.00044660154329890237, "Technical Writing": 0.9968602578511871, "Transcript / Interview": 1.548319158080283e-05, "Tutorial / How-To Guide": 0.0005194089931015978, "User Reviews": 6.729043260796461e-06, "Other / Unknown": 0.0017512843412962511}, "secondary": {"Academic Writing": 7.613027761412185e-05, "Content Listing": 0.00021557185914048292, "Creative Writing": 5.8248797312232596e-05, "Customer Support Page": 3.1287843291422444e-05, "Q&A Forum": 0.00012262981702562917, "Discussion Thread": 2.0843956060616258e-05, "Comment Section": 3.398696566324987e-05, "Missing Content": 0.00013986865762347631, "Knowledge Article": 0.00013829663046677586, "Legal Notices": 6.284696711973571e-05, "News Article": 3.082817610198214e-05, "Organizational Announcement": 3.016773705595742e-05, "Nonfiction Writing": 2.677632169145292e-05, "Organizational About Page": 3.860426276001809e-05, "Personal About Page": 3.4334638059515346e-05, "Personal Blog": 2.9174436880223857e-05, "Product Page": 2.124086144738599e-05, "Spam / Ads": 3.260345765369158e-05, "Structured Data": 0.0024329903684815507, "Technical Writing": 0.005587135166710781, "Transcript / Interview": 3.6041033163687985e-05, "Tutorial / How-To Guide": 0.0005129982713804368, "User Reviews": 1.526183974753379e-05, "NONE": 0.9902721316575481}, "tertiary": {"Academic Writing": 3.94208419676556e-05, "Content Listing": 0.00016149124172574848, "Creative Writing": 4.1368533856124435e-05, "Customer Support Page": 1.9236256058907368e-05, "Q&A Forum": 6.902364302286924e-05, "Discussion Thread": 1.2536940918239985e-05, "Comment Section": 2.2692451909193286e-05, "Missing Content": 0.0001962548144821817, "Knowledge Article": 0.00010079159215533566, "Legal Notices": 6.81247678268692e-05, "News Article": 1.9148291137488707e-05, "Organizational Announcement": 1.869677621532982e-05, "Nonfiction Writing": 2.163627981008403e-05, "Organizational About Page": 3.1999945306471226e-05, "Personal About Page": 2.0711773418320326e-05, "Personal Blog": 1.7582355386471037e-05, "Product Page": 1.4209616188474618e-05, "Spam / Ads": 2.2596869786288636e-05, "Structured Data": 0.0011121667897068868, "Technical Writing": 0.0025616197683705805, "Transcript / Interview": 2.1535974048548666e-05, "Tutorial / How-To Guide": 0.0004897934362210282, "User Reviews": 1.0415854760040089e-05, "NONE": 0.9949069451857208}, "structured_records": {"false": 0.9665034281544755, "true": 0.03349657184552441}, "procedural_steps": {"false": 0.9974114618551371, "true": 0.0025885381448629476}, "worked_solution": {"false": 0.9976379673695595, "true": 0.0023620326304405726}, "contextual_dialogue": {"false": 0.9999889909764155, "true": 1.10090235845267e-05}, "content_quality": {"-2": 0.015968836110680576, "-1": 0.14334392526963996, "0": 0.4090285763903287, "1": 0.35428091544695567, "2": 0.0773777467823952}, "noise_severity": {"0": 0.9140589279331051, "1": 0.07486213225162575, "2": 0.008953289435358888, "3": 0.0012324552564596442, "4": 0.0008931951234507284}, "missing_content_severity": {"0": 0.9877144794652497, "1": 0.001780048168858847, "2": 0.006879724948808009, "3": 0.0032599736818669876, "4": 0.0003657737352164123}, "factual_density": {"0": 0.011151377796545377, "1": 0.022554239838224868, "2": 0.1612010319669719, "3": 0.3916675322313503, "4": 0.4134258181669076}, "pretraining_utility": {"-2": 0.0076957521613978995, "-1": 0.0787192451495261, "0": 0.38920978932276207, "1": 0.45084456277022966, "2": 0.07353065059608434}, "math_density": {"0": 0.9457810183952801, "1": 0.045523473847578974, "2": 0.004257829028652742, "3": 0.001312743079987873, "4": 0.003124935648500244}, "code_density": {"0": 0.0001888252511796025, "1": 0.00036369140263977696, "2": 0.010880448142306388, "3": 0.06992744671799019, "4": 0.918639588485884}, "reasoning_density": {"0": 0.7682848933243506, "1": 0.08387290053469117, "2": 0.10525879267164516, "3": 0.025440145900026977, "4": 0.01714326756928617}, "education_level": {"none": 0.0011097854763555315, "0": 0.0015882456137212419, "1": 0.040784213921458916, "2": 0.8902719864698884, "3": 0.06355309640021503, "4": 0.002692672118360825}, "creative_writing_quality": {"not_applicable": 0.9994648632228416, "-2": 8.419997746070692e-05, "-1": 0.0001492843971489584, "0": 0.0002412705734133383, "1": 4.399169604370433e-05, "2": 1.6390133091790146e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.414095891073437e-05, "Art & Design": 6.40718055366738e-05, "Law & Crime": 4.375292777942791e-05, "Education & Careers": 0.00011165522271410328, "Entertainment & Media": 5.0714415704312996e-05, "Fashion & Beauty": 3.749863647247926e-05, "Business, Economics & Finance": 9.26635146685893e-05, "Food & Dining": 3.247329325181548e-05, "Games & Gaming": 0.00019740682016530778, "Electronics & Hardware": 9.351358487234572e-05, "Health, Healthcare & Wellness": 4.717195867152681e-05, "History & Geography": 3.666022854980955e-05, "Home, Pets & Hobbies": 5.128101006052699e-05, "Industry & Production": 3.796385894691755e-05, "Language, Literature & Philosophy": 5.158745845142539e-05, "Politics & Government": 3.155699822446925e-05, "Religion & Spirituality": 6.83930372689597e-05, "Science, Mathematics & Engineering": 0.00011727615027869, "Relationships & Community": 0.0036719836930471585, "Software Usage & Applications": 0.0016628070160789122, "Programming & Software Engineering": 0.9921138479893321, "Sports & Fitness": 6.289807329326461e-05, "Vehicles & Transportation": 2.2406122983803973e-05, "Travel & Tourism": 7.343903896925655e-05, "Other / Uncovered Subject": 0.0009326576927029044, "Unknown / Insufficient Content": 0.0002501784930644608}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0002475419995890303, "Art & Design": 0.00030042283463643815, "Law & Crime": 0.00022330906058633785, "Education & Careers": 0.0002741123505709923, "Entertainment & Media": 0.00019809318862451012, "Fashion & Beauty": 9.671638807708955e-05, "Business, Economics & Finance": 0.0002869299571315469, "Food & Dining": 9.07377489881911e-05, "Games & Gaming": 0.00034565976923836133, "Electronics & Hardware": 0.00020947008208005847, "Health, Healthcare & Wellness": 0.00012017589219069232, "History & Geography": 8.929347109042359e-05, "Home, Pets & Hobbies": 0.0001026176892865845, "Industry & Production": 7.640571206765129e-05, "Language, Literature & Philosophy": 0.00012744679215182428, "Politics & Government": 0.00010614055432761109, "Religion & Spirituality": 0.00014161240753872618, "Science, Mathematics & Engineering": 0.00040516588479369006, "Relationships & Community": 0.0068968151250524985, "Software Usage & Applications": 0.004747906877149515, "Programming & Software Engineering": 0.007760985709488787, "Sports & Fitness": 0.0001055133611590767, "Vehicles & Transportation": 4.425928076393727e-05, "Travel & Tourism": 0.00015905216966972417, "Other / Uncovered Subject": 0.0012854330473621638, "NONE": 0.9755581826463845}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11490, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11490, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:10.957200+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "6961cca5-48d8-42a7-9e26-bf1d6e62d7e7", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "8c58de82f3e066b0ce863fec111baf4c1252774cd9110346294476b4a689bfcc", "source_metadata": {"uuid": "6961cca5-48d8-42a7-9e26-bf1d6e62d7e7", "repo_name": "Kashydo/AskMate-sprint1-", "relative_path": "server.py", "category": "WEB", "algo_rel_score": 0.991904079914093, "quality_score": 4.030932903289795, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 46, "repo_created_at": "2023-04-04T19:16:40.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "632090187339f2d20c26a518fd998ecf66568b18", "size_bytes": 13221, "file_timestamp": 1681649910, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "82eb3e752efe966d505858ab51b0c5ced631c5b6"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 46, "repo_created_at": "2023-04-04T19:16:40.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "632090187339f2d20c26a518fd998ecf66568b18", "size_bytes": 13221, "file_timestamp": 1681649910, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "82eb3e752efe966d505858ab51b0c5ced631c5b6", "url": "https://github.com/Kashydo/AskMate-sprint1-/blob/82eb3e752efe966d505858ab51b0c5ced631c5b6/server.py"}, "gpt4o_tokens": 2681, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.33375481152074493, "noise_severity": 0.10003885738552537, "missing_content_severity": 0.026782514052941478, "factual_density": 3.17366217313385, "pretraining_utility": 0.5037951144900764, "math_density": 0.07047710373884906, "code_density": 3.906465281784759, "reasoning_density": 0.4392839938552071, "education_level": 2.0250054862134643, "creative_writing_quality": -0.45018843803316777}, "score_applicability": {"education_level": 0.9988902145236443, "creative_writing_quality": 0.0005351367771584981}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/RobertoSanchezFormos/OptimizationAPI/blob/79d8b33e658547642195224ce211898fc52c7936/modeling/models/utils.py", "id": "python-0-7e5b62e2e7b6962e61896f14b9bd072a", "text": "import random\nfrom random import uniform, randint\nfrom typing import List\nimport pandas as pd\nimport datetime as dt\n\nimport pyomo.environ as pm\n\nfrom modeling.classes.Aircraft import Aircraft\nfrom modeling.classes.ProcessedAircraftData import ProcessedAircraftData\n\nNON_SUCCESSFUL_SOLVER_SOLUTION_MSG = 'There is no optimal solution for this problem.'\nSUCCESSFUL_SOLVER_SOLUTION_MSG = 'Optimal solution found'\n\n\ndef dataToDict(data: List[ProcessedAircraftData]) -> dict:\n    resp = dict()\n    for p in data:\n        resp[p.processedAircraft.aircraftCode] = p\n    return resp\n\n\ndef generateAircraftsAndAirports(n_aircrafts, n_airports, seed=None):\n    aircrafts = []\n    if seed is not None:\n        random.seed(seed)\n\n    for i in range(n_aircrafts):\n        code = f'aircraft{i}'\n        aircraft = Aircraft(aircraftCode=code, seats=randint(3, 10))\n        aircrafts.append(aircraft)\n\n    airports = [f'airport{i}' for i in range(n_airports)]\n    return aircrafts, airports\n\n\ndef generateSpeedKmH(aircrafts, seed=None):\n    if seed is not None:\n        random.seed(seed)\n    speed = {}\n    # km/h\n    for ac in aircrafts:\n        speed[ac] = 400 + 400 * uniform(0, 1)\n    return speed\n\n\ndef generateDistanceMatrixKm(airports, seed=None):\n    if seed is not None:\n        random.seed(seed)\n    # distance matrix: km\n    df_dist = pd.DataFrame(index=airports, columns=airports)\n    for ap_i in airports:\n        for ap_j in airports:\n            dist = 0 if ap_i == ap_j else randint(400, 1500)\n            df_dist[ap_i].loc[ap_j] = dist\n            df_dist[ap_j].loc[ap_i] = dist\n    return df_dist\n\n\ndef generateTimeTripInMinutes(speed: dict, df_dist: pd.DataFrame, airports: list):\n    aircrafts = speed.keys()\n    time = {}\n    for ac in aircrafts:\n        df_time = pd.DataFrame(index=airports, columns=airports)\n        for a_i in airports:\n            for a_j in airports:\n                t = round(df_dist[a_i][a_j] / (speed[ac] * (1 / 60)), 2)\n                df_time[a_i][a_j] = t\n                df_time[a_j][a_i] = t\n            time[ac] = df_time\n    return time\n\n\ndef generateCostTrip(aircrafts: list, airports: list, seed=None):\n    if seed is not None:\n        random.seed(seed)\n    cost = {}\n    for ac in aircrafts:\n        df_cost = pd.DataFrame(index=airports, columns=airports)\n        for a_i in airports:\n            for a_j in airports:\n                c = 0 if a_i == a_j else round(50 + 50 * uniform(0, 1), 2)\n                df_cost[a_i].loc[a_j] = c\n                df_cost[a_j].loc[a_i] = c\n\n        cost[ac] = df_cost\n    return cost\n\n\ndef generateParameters(aircrafts, airports, seed=None):\n    if seed is not None:\n        random.seed(seed)\n    speed_dict = generateSpeedKmH(aircrafts, seed)\n    df_dist = generateDistanceMatrixKm(airports, seed)\n    time_dict = generateTimeTripInMinutes(speed_dict, df_dist, airports)\n    cost_dict = generateCostTrip(aircrafts, airports, seed)\n    return speed_dict, df_dist, time_dict, cost_dict\n\n\ndef generateDayValidPeriodsPerDay(n_days,\n                                  initial_time: dt.timedelta = dt.timedelta(hours=6),\n                                  final_time: dt.timedelta = dt.timedelta(hours=20)):\n    one_day = dt.timedelta(days=1)\n\n    t_i = initial_time  # time to start flights\n    t_f = final_time  # time to finish flights\n    days = [f'd{i}' for i in range(1, n_days + 1)]\n    t_per_day = dt.timedelta(seconds=0, days=0, minutes=0)\n\n    df_days = pd.DataFrame(index=days, columns=['ini', 'end'])\n    for day in days:\n        df_days['ini'].loc[day] = (t_per_day + t_i).total_seconds() / 60\n        df_days['end'].loc[day] = (t_per_day + t_f).total_seconds() / 60\n        t_per_day += one_day\n    return df_days\n\n\ndef is_valid_solution(solver_results):\n    return solver_results['Solver'][0]['Termination condition'] == 'optimal'\n\n\ndef print_results(solver_results, model, print_model: bool = False):\n    print(solver_results['Solver'][0])\n    if is_valid_solution(solver_results):\n        if print_model:\n            model.display()\n        n_boolean = 0\n        print('# ==========================================================\\nSelected solution:')\n        print('Variables:')\n        for v in model.component_data_objects(pm.Var):\n            if v.domain == pm.Boolean:\n                if v.value < 1 or print(str(v), v.value):\n                    n_boolean += 1\n\n        print(f'\\nA total of {n_boolean} boolean variables are False')\n\n    else:\n        for c in model.component_objects(ctype=pm.Constraint):\n            print(c.display())\n", "text_preview": "import random from random import uniform, randint from typing import List import pandas as pd import datetime as dt import pyomo.environ as pm from modeling.classes.Aircraft import Aircraft from modeling.classes.ProcessedAircraftData import", "input_sha256": "b706b613b06858488fdacd25da801238e3db693e48b906a8abb1abc23be2745c", "text_sha256": "7e5b62e2e7b6962e61896f14b9bd072af69958694a06a3ebbdb70d81178a7f27", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12066, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Vehicles & Transportation", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 3, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.6352426435555184, "noise_severity": 0.010596783935071104, "missing_content_severity": 0.01482332317171625, "factual_density": 3.2981073702929318, "pretraining_utility": 0.5116633616502015, "math_density": 2.672106051097755, "code_density": 3.8403106558691973, "reasoning_density": 0.3800952059785598, "education_level": 2.368716938285921, "creative_writing_quality": -0.3827668981652211}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Vehicles & Transportation"}, "review_reasons": ["secondary_topic probability 0.3826 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.002668528906265233, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9974972209274183, "secondary": 0.989706615846753, "tertiary": 0.9924911677489429, "structured_records": 0.031121479297764154, "procedural_steps": 0.012538932390483853, "worked_solution": 0.0019812841686923637, "contextual_dialogue": 4.972247262739609e-06, "content_quality": 0.5019630390770593, "noise_severity": 0.9911693467207741, "missing_content_severity": 0.9876472306902364, "factual_density": 0.4150894752441098, "pretraining_utility": 0.46391061209984297, "math_density": 0.3103232180858855, "code_density": 0.8669255465576644, "reasoning_density": 0.6832539950178668, "education_level": 0.5837343694376369, "creative_writing_quality": 0.99958261651597, "primary_topic": 0.8785021185203497, "secondary_topic": 0.3579455695599729}, "probability_margin": {"primary": 0.996131264393817, "secondary": 0.9848105802768035, "tertiary": 0.9891491495740906, "education_level": 0.3615048534983804, "creative_writing_quality": 0.9995103613454289, "primary_topic": 0.8260913166494633, "secondary_topic": 0.11426711449054722}, "raw_probabilities": {"primary": {"Academic Writing": 3.425530325963123e-05, "Content Listing": 1.3880213492955823e-05, "Creative Writing": 9.817337017014106e-06, "Customer Support Page": 4.6528775623674595e-06, "Q&A Forum": 7.091733415427268e-06, "Discussion Thread": 4.173800736226006e-06, "Comment Section": 5.2917268556081165e-06, "Missing Content": 1.4001156194876381e-05, "Knowledge Article": 5.7460768082016076e-05, "Legal Notices": 1.521259772361932e-05, "News Article": 2.6530500384086573e-06, "Organizational Announcement": 6.113639989547054e-06, "Nonfiction Writing": 7.713362599719799e-06, "Organizational About Page": 7.9774205193756e-06, "Personal About Page": 8.212186528250792e-06, "Personal Blog": 4.779749109677685e-06, "Product Page": 4.726309730887718e-06, "Spam / Ads": 6.3698299442759555e-06, "Structured Data": 0.0003806564166654083, "Technical Writing": 0.9976015033887758, "Transcript / Interview": 8.60832921047589e-06, "Tutorial / How-To Guide": 0.000320951367296739, "User Reviews": 3.6584402928748203e-06, "Other / Unknown": 0.001470238994958837}, "secondary": {"Academic Writing": 0.00016598077680195228, "Content Listing": 5.937495937597825e-05, "Creative Writing": 7.266683692119307e-05, "Customer Support Page": 3.1567502622129157e-05, "Q&A Forum": 2.3404942286753407e-05, "Discussion Thread": 2.06576651729655e-05, "Comment Section": 3.984053287804782e-05, "Missing Content": 0.00015280391746392866, "Knowledge Article": 0.0003895169685179782, "Legal Notices": 0.00011160326932879188, "News Article": 3.358089430989859e-05, "Organizational Announcement": 3.0470973221668112e-05, "Nonfiction Writing": 3.479540947692454e-05, "Organizational About Page": 4.6024805471360595e-05, "Personal About Page": 3.854839171227665e-05, "Personal Blog": 3.203878943624174e-05, "Product Page": 2.1726826420686887e-05, "Spam / Ads": 3.611185004397551e-05, "Structured Data": 0.002194273211406715, "Technical Writing": 0.0053249265763348885, "Transcript / Interview": 4.1513777679453685e-05, "Tutorial / How-To Guide": 0.0009463823006770466, "User Reviews": 1.6681969300774714e-05, "NONE": 0.9901355068531383}, "tertiary": {"Academic Writing": 9.272174399589333e-05, "Content Listing": 5.315761456134887e-05, "Creative Writing": 6.91794186142493e-05, "Customer Support Page": 2.3122423568398658e-05, "Q&A Forum": 1.803836504429115e-05, "Discussion Thread": 1.4567228179979494e-05, "Comment Section": 3.5564237366568865e-05, "Missing Content": 0.00017574951254053207, "Knowledge Article": 0.0003412025000239374, "Legal Notices": 0.00012575377068267446, "News Article": 2.8188347293552286e-05, "Organizational Announcement": 2.6496732188692484e-05, "Nonfiction Writing": 3.7205177408429744e-05, "Organizational About Page": 4.836722090885627e-05, "Personal About Page": 2.686670630766338e-05, "Personal Blog": 2.539593453551882e-05, "Product Page": 2.1070299469201654e-05, "Spam / Ads": 3.352632706030329e-05, "Structured Data": 0.0015805875946299404, "Technical Writing": 0.003654886185312986, "Transcript / Interview": 3.028546516636196e-05, "Tutorial / How-To Guide": 0.0007190119673542624, "User Reviews": 1.501946838273446e-05, "NONE": 0.9928040357594036}, "structured_records": {"false": 0.9688785207022359, "true": 0.031121479297764154}, "procedural_steps": {"false": 0.9874610676095161, "true": 0.012538932390483853}, "worked_solution": {"false": 0.9980187158313076, "true": 0.0019812841686923637}, "contextual_dialogue": {"false": 0.9999950277527373, "true": 4.972247262739609e-06}, "content_quality": {"-2": 0.008368813543196074, "-1": 0.06084355263985671, "0": 0.3344073088667035, "1": 0.4799368266187201, "2": 0.11644349833152359}, "noise_severity": {"0": 0.9908506942634843, "1": 0.008195582226674136, "2": 0.0006095695233865118, "3": 0.00019455328419546285, "4": 0.00014960070225938829}, "missing_content_severity": {"0": 0.9920410256527736, "1": 0.002952224735809878, "2": 0.0034081479973196616, "3": 0.001339604015120854, "4": 0.00025899759897612257}, "factual_density": {"0": 0.007747801615056942, "1": 0.019005147447552887, "2": 0.13160872345818708, "3": 0.3506685339878077, "4": 0.49096979349139536}, "pretraining_utility": {"-2": 0.008587273219272384, "-1": 0.07227172671902678, "0": 0.39551667881912267, "1": 0.4461390076773831, "2": 0.07748531356519495}, "math_density": {"0": 0.027970643850599934, "1": 0.11712447119230476, "2": 0.2595921696631819, "3": 0.3454536205965673, "4": 0.2498590946973461}, "code_density": {"0": 0.00025523375686411413, "1": 0.000562011449379795, "2": 0.02067350219429998, "3": 0.11563537036660693, "4": 0.8628738822328492}, "reasoning_density": {"0": 0.7976634269029526, "1": 0.08297128307344497, "2": 0.07982497446970571, "3": 0.020687288249883487, "4": 0.018853027304013236}, "education_level": {"none": 0.0011793087326143177, "0": 0.0015298730183773736, "1": 0.008469571061604068, "2": 0.653111974531364, "3": 0.29160712103298364, "4": 0.044102151623056404}, "creative_writing_quality": {"not_applicable": 0.9996521804299751, "-2": 6.314727424783436e-05, "-1": 8.287509342281494e-05, "0": 0.00014181908454609553, "1": 4.3920411637251086e-05, "2": 1.605770617083381e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00013466354679560747, "Art & Design": 0.00020508346225628986, "Law & Crime": 0.00017158929024211662, "Education & Careers": 0.00016307846217613486, "Entertainment & Media": 0.00014030891746657508, "Fashion & Beauty": 0.00013700867769373795, "Business, Economics & Finance": 0.002209190854603741, "Food & Dining": 0.00014627902293201883, "Games & Gaming": 0.0003913606866655179, "Electronics & Hardware": 0.00020757454871944515, "Health, Healthcare & Wellness": 0.00015144059249719603, "History & Geography": 0.00017180461222285982, "Home, Pets & Hobbies": 0.0001087705671132009, "Industry & Production": 0.0003527606327715964, "Language, Literature & Philosophy": 0.00013070342771036026, "Politics & Government": 8.736306436150526e-05, "Religion & Spirituality": 0.00011205210007644696, "Science, Mathematics & Engineering": 0.057083797312411304, "Relationships & Community": 7.519139006807503e-05, "Software Usage & Applications": 0.0004091497887347281, "Programming & Software Engineering": 0.8831751139618746, "Sports & Fitness": 0.00019127934674411948, "Vehicles & Transportation": 0.04849779351544737, "Travel & Tourism": 0.00034226048022200094, "Other / Uncovered Subject": 0.005013584279559798, "Unknown / Insufficient Content": 0.0001907974586337819}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009559409633241022, "Art & Design": 0.0009709369689627067, "Law & Crime": 0.000922929877034654, "Education & Careers": 0.0005764193026119383, "Entertainment & Media": 0.0006232650039745948, "Fashion & Beauty": 0.0005217155432898909, "Business, Economics & Finance": 0.015642357193287548, "Food & Dining": 0.0005358889465323097, "Games & Gaming": 0.0008281903639512965, "Electronics & Hardware": 0.000964400512255253, "Health, Healthcare & Wellness": 0.000670732811244773, "History & Geography": 0.0006773411847453356, "Home, Pets & Hobbies": 0.0004447156032556364, "Industry & Production": 0.001135906486003177, "Language, Literature & Philosophy": 0.00035335007651296865, "Politics & Government": 0.00038024523085398055, "Religion & Spirituality": 0.0006060753649113485, "Science, Mathematics & Engineering": 0.268372856240196, "Relationships & Community": 0.00021451523714388575, "Software Usage & Applications": 0.000729270918766399, "Programming & Software Engineering": 0.10491886711700209, "Sports & Fitness": 0.0007990671328289579, "Vehicles & Transportation": 0.38263997073074324, "Travel & Tourism": 0.0016279001644325319, "Other / Uncovered Subject": 0.006224432250487438, "NONE": 0.207662708775648}}, "probabilities": {"primary": {"Academic Writing": 3.425530325963123e-05, "Content Listing": 1.3880213492955823e-05, "Creative Writing": 9.817337017014106e-06, "Customer Support Page": 4.6528775623674595e-06, "Q&A Forum": 7.091733415427268e-06, "Discussion Thread": 4.173800736226006e-06, "Comment Section": 5.2917268556081165e-06, "Missing Content": 1.4001156194876381e-05, "Knowledge Article": 5.7460768082016076e-05, "Legal Notices": 1.521259772361932e-05, "News Article": 2.6530500384086573e-06, "Organizational Announcement": 6.113639989547054e-06, "Nonfiction Writing": 7.713362599719799e-06, "Organizational About Page": 7.9774205193756e-06, "Personal About Page": 8.212186528250792e-06, "Personal Blog": 4.779749109677685e-06, "Product Page": 4.726309730887718e-06, "Spam / Ads": 6.3698299442759555e-06, "Structured Data": 0.0003806564166654083, "Technical Writing": 0.9976015033887758, "Transcript / Interview": 8.60832921047589e-06, "Tutorial / How-To Guide": 0.000320951367296739, "User Reviews": 3.6584402928748203e-06, "Other / Unknown": 0.001470238994958837}, "secondary": {"Academic Writing": 0.00016598077680195228, "Content Listing": 5.937495937597825e-05, "Creative Writing": 7.266683692119307e-05, "Customer Support Page": 3.1567502622129157e-05, "Q&A Forum": 2.3404942286753407e-05, "Discussion Thread": 2.06576651729655e-05, "Comment Section": 3.984053287804782e-05, "Missing Content": 0.00015280391746392866, "Knowledge Article": 0.0003895169685179782, "Legal Notices": 0.00011160326932879188, "News Article": 3.358089430989859e-05, "Organizational Announcement": 3.0470973221668112e-05, "Nonfiction Writing": 3.479540947692454e-05, "Organizational About Page": 4.6024805471360595e-05, "Personal About Page": 3.854839171227665e-05, "Personal Blog": 3.203878943624174e-05, "Product Page": 2.1726826420686887e-05, "Spam / Ads": 3.611185004397551e-05, "Structured Data": 0.002194273211406715, "Technical Writing": 0.0053249265763348885, "Transcript / Interview": 4.1513777679453685e-05, "Tutorial / How-To Guide": 0.0009463823006770466, "User Reviews": 1.6681969300774714e-05, "NONE": 0.9901355068531383}, "tertiary": {"Academic Writing": 9.272174399589333e-05, "Content Listing": 5.315761456134887e-05, "Creative Writing": 6.91794186142493e-05, "Customer Support Page": 2.3122423568398658e-05, "Q&A Forum": 1.803836504429115e-05, "Discussion Thread": 1.4567228179979494e-05, "Comment Section": 3.5564237366568865e-05, "Missing Content": 0.00017574951254053207, "Knowledge Article": 0.0003412025000239374, "Legal Notices": 0.00012575377068267446, "News Article": 2.8188347293552286e-05, "Organizational Announcement": 2.6496732188692484e-05, "Nonfiction Writing": 3.7205177408429744e-05, "Organizational About Page": 4.836722090885627e-05, "Personal About Page": 2.686670630766338e-05, "Personal Blog": 2.539593453551882e-05, "Product Page": 2.1070299469201654e-05, "Spam / Ads": 3.352632706030329e-05, "Structured Data": 0.0015805875946299404, "Technical Writing": 0.003654886185312986, "Transcript / Interview": 3.028546516636196e-05, "Tutorial / How-To Guide": 0.0007190119673542624, "User Reviews": 1.501946838273446e-05, "NONE": 0.9928040357594036}, "structured_records": {"false": 0.9688785207022359, "true": 0.031121479297764154}, "procedural_steps": {"false": 0.9874610676095161, "true": 0.012538932390483853}, "worked_solution": {"false": 0.9980187158313076, "true": 0.0019812841686923637}, "contextual_dialogue": {"false": 0.9999950277527373, "true": 4.972247262739609e-06}, "content_quality": {"-2": 0.008368813543196074, "-1": 0.06084355263985671, "0": 0.3344073088667035, "1": 0.4799368266187201, "2": 0.11644349833152359}, "noise_severity": {"0": 0.9908506942634845, "1": 0.008195582226674138, "2": 0.0006095695233865119, "3": 0.0001945532841954629, "4": 0.0001496007022593883}, "missing_content_severity": {"0": 0.9920410256527734, "1": 0.0029522247358098773, "2": 0.0034081479973196607, "3": 0.0013396040151208539, "4": 0.0002589975989761225}, "factual_density": {"0": 0.007747801615056943, "1": 0.01900514744755289, "2": 0.1316087234581871, "3": 0.35066853398780773, "4": 0.4909697934913954}, "pretraining_utility": {"-2": 0.008587273219272386, "-1": 0.07227172671902679, "0": 0.3955166788191227, "1": 0.4461390076773832, "2": 0.07748531356519496}, "math_density": {"0": 0.027970643850599934, "1": 0.11712447119230476, "2": 0.2595921696631819, "3": 0.3454536205965673, "4": 0.2498590946973461}, "code_density": {"0": 0.00025523375686411413, "1": 0.000562011449379795, "2": 0.02067350219429998, "3": 0.11563537036660693, "4": 0.8628738822328492}, "reasoning_density": {"0": 0.7976634269029526, "1": 0.08297128307344497, "2": 0.07982497446970571, "3": 0.020687288249883487, "4": 0.018853027304013236}, "education_level": {"none": 0.001179308732614318, "0": 0.0015298730183773738, "1": 0.00846957106160407, "2": 0.6531119745313642, "3": 0.2916071210329837, "4": 0.04410215162305641}, "creative_writing_quality": {"not_applicable": 0.9996521804299752, "-2": 6.314727424783437e-05, "-1": 8.287509342281496e-05, "0": 0.00014181908454609555, "1": 4.392041163725109e-05, "2": 1.6057706170833813e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00013466354679560744, "Art & Design": 0.0002050834622562898, "Law & Crime": 0.0001715892902421166, "Education & Careers": 0.00016307846217613483, "Entertainment & Media": 0.00014030891746657506, "Fashion & Beauty": 0.00013700867769373792, "Business, Economics & Finance": 0.0022091908546037404, "Food & Dining": 0.0001462790229320188, "Games & Gaming": 0.0003913606866655178, "Electronics & Hardware": 0.0002075745487194451, "Health, Healthcare & Wellness": 0.000151440592497196, "History & Geography": 0.0001718046122228598, "Home, Pets & Hobbies": 0.00010877056711320087, "Industry & Production": 0.00035276063277159633, "Language, Literature & Philosophy": 0.00013070342771036024, "Politics & Government": 8.736306436150524e-05, "Religion & Spirituality": 0.00011205210007644693, "Science, Mathematics & Engineering": 0.05708379731241129, "Relationships & Community": 7.519139006807501e-05, "Software Usage & Applications": 0.000409149788734728, "Programming & Software Engineering": 0.8831751139618744, "Sports & Fitness": 0.00019127934674411943, "Vehicles & Transportation": 0.048497793515447356, "Travel & Tourism": 0.0003422604802220009, "Other / Uncovered Subject": 0.005013584279559797, "Unknown / Insufficient Content": 0.00019079745863378184}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009559409633241022, "Art & Design": 0.0009709369689627067, "Law & Crime": 0.000922929877034654, "Education & Careers": 0.0005764193026119383, "Entertainment & Media": 0.0006232650039745948, "Fashion & Beauty": 0.0005217155432898909, "Business, Economics & Finance": 0.015642357193287548, "Food & Dining": 0.0005358889465323097, "Games & Gaming": 0.0008281903639512965, "Electronics & Hardware": 0.000964400512255253, "Health, Healthcare & Wellness": 0.000670732811244773, "History & Geography": 0.0006773411847453356, "Home, Pets & Hobbies": 0.0004447156032556364, "Industry & Production": 0.001135906486003177, "Language, Literature & Philosophy": 0.00035335007651296865, "Politics & Government": 0.00038024523085398055, "Religion & Spirituality": 0.0006060753649113485, "Science, Mathematics & Engineering": 0.268372856240196, "Relationships & Community": 0.00021451523714388575, "Software Usage & Applications": 0.000729270918766399, "Programming & Software Engineering": 0.10491886711700209, "Sports & Fitness": 0.0007990671328289579, "Vehicles & Transportation": 0.38263997073074324, "Travel & Tourism": 0.0016279001644325319, "Other / Uncovered Subject": 0.006224432250487438, "NONE": 0.207662708775648}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9761, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9761, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:11.407974+00:00", "scoring_key_slot": "primary", "dataset_uid": "d740269c-5a01-4008-b0cd-4de66d803e83", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "ee16f20c1353d271652b591a909c560e087363f2248b9474598e45825ee21a33", "source_metadata": {"uuid": "d740269c-5a01-4008-b0cd-4de66d803e83", "repo_name": "RobertoSanchezFormos/OptimizationAPI", "relative_path": "modeling/models/utils.py", "category": "DATA", "algo_rel_score": 0.9696612358093262, "quality_score": 6.195309638977051, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 21, "repo_created_at": "2022-11-21T19:07:01.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "63aca879edb612ee83564bf44f746eb9cb1ae1ee", "size_bytes": 4534, "file_timestamp": 1669737343, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "79d8b33e658547642195224ce211898fc52c7936"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 21, "repo_created_at": "2022-11-21T19:07:01.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "63aca879edb612ee83564bf44f746eb9cb1ae1ee", "size_bytes": 4534, "file_timestamp": 1669737343, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "79d8b33e658547642195224ce211898fc52c7936", "url": "https://github.com/RobertoSanchezFormos/OptimizationAPI/blob/79d8b33e658547642195224ce211898fc52c7936/modeling/models/utils.py"}, "gpt4o_tokens": 1163, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.6352426435555184, "noise_severity": 0.010596783935071104, "missing_content_severity": 0.014823323171716252, "factual_density": 3.2981073702929318, "pretraining_utility": 0.5116633616502015, "math_density": 2.672106051097755, "code_density": 3.8403106558691973, "reasoning_density": 0.3800952059785598, "education_level": 2.368716938285921, "creative_writing_quality": -0.3827668981652211}, "score_applicability": {"education_level": 0.9988206912673857, "creative_writing_quality": 0.00034781957002482977}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/yekingyan/web_practice/blob/6f6994401a7715255666faecb50d7e15c8095b0b/getStatuscode_headers_body.py", "id": "python-0-46bc319cf4dc82b3482359ad8c66f820", "text": "import socket\nimport ssl\nimport re\n\n'''\ndef protocol_of_url(url):\n\n    #返回代表协议的字符串, 'http' 或者 'https'\n\n    #创建一个socket对象\n    s = socket.socket()\n    host = url\n    port = 80\n    #连接上主机\n    s.connect((host, port))\n    #构建一个请求发送给服务器\n    http_request = 'GET / HTTP/1.1\\r\\nhost:{}\\r\\n\\r\\n'.format(host)\n    #发送前将str格式转化为bytes，编码为utf-8，使send函数可以支持\n    request = http_request.encode('utf-8')\n    s.send(request)\n    #接收响应\n    responed = s.recv(1024)\n    #转码使可用正则表达式\n    responed = str(responed)\n    print(responed)\n    #判断协议类型\n    if bool(re.findall('https',responed)) == True:\n        print('https')\n        return 'https'\n    else:\n        print('http')\n        return 'http'\nprotocol_of_url('zhihu.com')\n'''\n'''\n# 创建一个socket对象\ns = socket.socket()\nhost = 'huya.com'\nport = 80\n# 连接上主机\ns.connect((host, port))\n# 构建一个请求发送给服务器\nhttp_request = 'GET / HTTP/1.1\\r\\nhost:{}\\r\\n\\r\\n'.format(host)\n# 发送前将str格式转化为bytes，编码为utf-8，使send函数可以支持\nrequest = http_request.encode('utf-8')\ns.send(request)\n# 接收响应\nresponed = s.recv(1024)\n# 转码使可用正则表达式\nresponed = str(responed)\nprint(responed)\nif responed[:7] == 'http://':\n    print('http')\nelif responed[:8] == 'https://':\n    print('https')\nelse:\n    print('aa\\n',responed)\n# 判断协议类型\nif responed[:7] == 'http://':\n    print('https')\n    return 'https'\nelse:\n    print('http')\n    return 'http'\n'''\n\n\ndef log(*args, **kwargs):\n    print(\"log\", *args, **kwargs)\n\n\ndef parsed_url(url):\n    # 检查协议\n    protocol = 'http'\n    if url[:7] == 'http://':\n        u = url.split('://')[1]\n    elif url[:8] == 'https://':\n        protocol = 'https'\n        u = url.split('://')[1]\n    else:\n        u = url\n\n    # 检查默认 path\n    i = u.find('/')\n    if i == -1:\n        host = u\n        path = '/'\n    else:\n        host = u[:i]\n        path = u[i:]\n\n    # 检查端口\n    port_dict = {\n        'http': 80,\n        'https': 443,\n    }\n    # 默认端口\n    port = port_dict[protocol]\n    if ':' in host:\n        u = host.split(':')\n        host = u[0]\n        port = int(u[1])\n    return protocol, host, port, path\n\n\ndef socket_by_protocol(protocol):\n    # 根据协议返回一个socket实例\n    if protocol == 'http':\n        s = socket.socket()\n    else:\n        s = ssl.wrap_socket(socket.socket())\n    return s\n\n\ndef response_by_socket(s):\n    # 参数是一个socket 实例\n    # 返回这个socket读取的所有数据\n    respons = b''\n    buffer_size = 1024\n    while True:\n        r = s.recv(buffer_size)\n        if len(r) == 0:\n            break\n        respons += r\n    return respons\n\n\ndef parsed_response(r):\n    # 把response解析出状态码headers body返回状态码是int\n    # headers是dict\n    # body是str\n    header, body = r.split('\\r\\n\\r\\n', 1)\n    h = header.split('\\r\\n')\n    status_code = h[0].split()[1]\n    statu_code = int(status_code)\n\n    headers = {}\n    for line in h[1:]:\n        # log(line)\n        k, v = line.split(': ')\n        headers[k] = v\n    return statu_code, headers, body\n\n\ndef get(url):\n    # 用get请求url并返回响应\n    protocol, host, port, path = parsed_url(url)\n    s = socket_by_protocol(protocol)\n    s.connect((host, port))\n\n    request = 'GET {} HTTP/1.1\\r\\nhost:{}\\r\\nConnection: close\\r\\n\\r\\n'.format(path, host)\n    s.send(request.encode('utf-8'))\n    response = response_by_socket(s)\n    r = response.decode('utf-8')\n    status_code, headers, body = parsed_response(r)\n\n    # 获取重定向的新地址\n    if status_code in [301, 302]:\n        url = headers['Location']\n        return get(url)\n    return status_code, headers, body\n\n\n# test 单元测试\ndef test_parsed_url():\n    http = 'http'\n    https = 'https'\n    host = 'g.cn'\n    path = '/'\n    test_items = {\n        ('http://g.cn', (http, host, 80, path)),\n        ('http://g.cn/', (http, host, 80, path)),\n        ('http://g.cn:90', (http, host, 90, path)),\n        ('http://g.cn:90/', (http, host, 90, path)),\n        ('https://g.cn', (https, host, 443, path)),\n        ('https://g.cn:233/', (https, host, 233, path)),\n    }\n\n    for t in test_items:\n        url = t[0]\n        expected = t[1]\n        u = parsed_url(url)\n        e = \"parsed_url ERROR,({}) ({}) ({})\".format(url, u, expected)\n        # TURE则断言成功，FALSE则输出e\n        assert u == expected, e\n\n\ndef test():\n    # 用于测试主函数\n    test_parsed_url()\n\n\nif __name__ == '__main__':\n    s, h, b = get('http://movie.douban.com/top250')\n    log(b)\n", "text_preview": "import socket import ssl import re ''' def protocol_of_url(url): #返回代表协议的字符串, 'http' 或者 'https' #创建一个socket对象 s = socket.socket() host = url port = 80 #连接上主机 s.connect((host, port)) #构建一个请求发送给服务器 http_request = 'GET / HTTP/1.1\\r\\nhost:{}\\r\\", "input_sha256": "06b3dd1d9720f2bbd7923d0416112452e781f1c8a8b281df5df4a3b13d210a3f", "text_sha256": "46bc319cf4dc82b3482359ad8c66f82090625c9f6cee815485ffc7d7904dbba1", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12065, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.41610303156912903, "noise_severity": 0.1522851598632252, "missing_content_severity": 0.01295921334038888, "factual_density": 3.2331660928285006, "pretraining_utility": 0.5706420542219196, "math_density": 0.07461720131956852, "code_density": 3.8796118511315796, "reasoning_density": 1.4291266002503915, "education_level": 1.958744697646298, "creative_writing_quality": -0.4389422542013033}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.014407045905865057, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9912287811753477, "secondary": 0.957629752662504, "tertiary": 0.9726631918686242, "structured_records": 0.2290545596852253, "procedural_steps": 0.03902375146647691, "worked_solution": 0.09572788191828809, "contextual_dialogue": 7.478590876179658e-06, "content_quality": 0.3465338590425313, "noise_severity": 0.873095700113979, "missing_content_severity": 0.989200655549676, "factual_density": 0.360971744023751, "pretraining_utility": 0.5066763101218565, "math_density": 0.9378189989003596, "code_density": 0.8996765426096496, "reasoning_density": 0.0, "education_level": 0.8569942766286897, "creative_writing_quality": 0.9979021813960246, "primary_topic": 0.996101606901095, "secondary_topic": 0.9662216350187203}, "probability_margin": {"primary": 0.9857545899301158, "secondary": 0.9382804835712365, "tertiary": 0.9602538482262547, "education_level": 0.8031903641433322, "creative_writing_quality": 0.9974405796020877, "primary_topic": 0.9949414882535292, "secondary_topic": 0.955585542280271}, "raw_probabilities": {"primary": {"Academic Writing": 5.865841646083098e-05, "Content Listing": 3.1479288778359754e-05, "Creative Writing": 3.6389463435161496e-05, "Customer Support Page": 1.957330890398372e-05, "Q&A Forum": 3.143207118898375e-05, "Discussion Thread": 1.8251708744006025e-05, "Comment Section": 3.552713397330135e-05, "Missing Content": 4.1994906147031526e-05, "Knowledge Article": 0.000166162944301567, "Legal Notices": 3.9267651927651405e-05, "News Article": 1.0776449834916903e-05, "Organizational Announcement": 1.977124458512156e-05, "Nonfiction Writing": 2.5275486445736744e-05, "Organizational About Page": 2.5450364781397338e-05, "Personal About Page": 3.4221629042867454e-05, "Personal Blog": 2.025493586546934e-05, "Product Page": 1.6826108272429398e-05, "Spam / Ads": 2.429472950591655e-05, "Structured Data": 0.00037319693024678803, "Technical Writing": 0.9915942486263749, "Transcript / Interview": 2.8681577907136498e-05, "Tutorial / How-To Guide": 0.005839658696259133, "User Reviews": 1.5333062438215097e-05, "Other / Unknown": 0.0014932732645788782}, "secondary": {"Academic Writing": 0.00022600885726357117, "Content Listing": 0.00011621242109892018, "Creative Writing": 0.0001563440731001926, "Customer Support Page": 0.00010369434130409665, "Q&A Forum": 0.00010255387411200548, "Discussion Thread": 6.240417528644108e-05, "Comment Section": 0.0010044732687602659, "Missing Content": 0.0003307879163630216, "Knowledge Article": 0.0010713614755524098, "Legal Notices": 0.00019009959233892698, "News Article": 9.2947372432391e-05, "Organizational Announcement": 9.173865135445692e-05, "Nonfiction Writing": 0.00010511881558524751, "Organizational About Page": 0.00010938839501999874, "Personal About Page": 0.00012088260083217087, "Personal Blog": 0.0001020935248020931, "Product Page": 6.961393384819139e-05, "Spam / Ads": 9.849261370918878e-05, "Structured Data": 0.0024201134821010676, "Technical Writing": 0.0127480012051703, "Transcript / Interview": 0.00010898983461718471, "Tutorial / How-To Guide": 0.021114696063663212, "User Reviews": 5.880387678500728e-05, "NONE": 0.9593951796348997}, "tertiary": {"Academic Writing": 0.00011825136335814657, "Content Listing": 8.061358828322549e-05, "Creative Writing": 0.0001143981890536188, "Customer Support Page": 6.37439651391948e-05, "Q&A Forum": 5.976150863663806e-05, "Discussion Thread": 3.9220222913602853e-05, "Comment Section": 0.001734736356529537, "Missing Content": 0.000315272314446337, "Knowledge Article": 0.0006183532157430057, "Legal Notices": 0.00018875090091210317, "News Article": 5.92003846355917e-05, "Organizational Announcement": 4.883494020467228e-05, "Nonfiction Writing": 7.456366524386901e-05, "Organizational About Page": 8.720265078014312e-05, "Personal About Page": 6.714692600717676e-05, "Personal Blog": 7.078948708437015e-05, "Product Page": 4.961022340225016e-05, "Spam / Ads": 6.995438982884561e-05, "Structured Data": 0.002690147907350236, "Technical Writing": 0.006005328576097042, "Transcript / Interview": 5.949875174468654e-05, "Tutorial / How-To Guide": 0.013548377314510092, "User Reviews": 3.401761733086255e-05, "NONE": 0.9738022255407648}, "structured_records": {"false": 0.7709454403147746, "true": 0.2290545596852253}, "procedural_steps": {"false": 0.9609762485335231, "true": 0.03902375146647691}, "worked_solution": {"false": 0.9042721180817119, "true": 0.09572788191828809}, "contextual_dialogue": {"false": 0.9999925214091239, "true": 7.478590876179658e-06}, "content_quality": {"-2": 0.025668946417099568, "-1": 0.11961139699546043, "0": 0.36779853554769715, "1": 0.386789920680697, "2": 0.1001312003590458}, "noise_severity": {"0": 0.8897701462904248, "1": 0.07619637853826013, "2": 0.027687578432608443, "3": 0.0046699624950777814, "4": 0.001675934243628705}, "missing_content_severity": {"0": 0.9906845201666266, "1": 0.006727687856450879, "2": 0.0017154539568726287, "3": 0.0006887345100071506, "4": 0.0001836035100428227}, "factual_density": {"0": 0.01016705349960136, "1": 0.025789026360752837, "2": 0.1325842227216618, "3": 0.38363016864751126, "4": 0.4478295287704729}, "pretraining_utility": {"-2": 0.008684865466350932, "-1": 0.06954909603086516, "0": 0.3455203983551431, "1": 0.49493039910979514, "2": 0.0813152410378458}, "math_density": {"0": 0.9495659790649132, "1": 0.03946429214269764, "2": 0.003502611625309748, "3": 0.00172078274206633, "4": 0.005746334425013096}, "code_density": {"0": 0.00018552564300008996, "1": 0.00045148928031700416, "2": 0.013628037458028073, "3": 0.091035503539413, "4": 0.8946994440792418}, "reasoning_density": {"0": 0.46349318837333575, "1": 0.07797072092575508, "2": 0.16652815723461506, "3": 0.14993216900976994, "4": 0.14207576445652406}, "education_level": {"none": 0.0011179366771077932, "0": 0.002038304540927024, "1": 0.07763819971390917, "2": 0.8808285638572414, "3": 0.036248363163941526, "4": 0.0021286320468731656}, "creative_writing_quality": {"not_applicable": 0.9982518178300205, "-2": 0.0002237572256583748, "-1": 0.0005388868215097502, "0": 0.0008112382279328368, "1": 0.00012954953937616246, "2": 4.475035550248158e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.768277898291716e-05, "Art & Design": 4.4873083243563086e-05, "Law & Crime": 3.504406159199011e-05, "Education & Careers": 6.499067555735821e-05, "Entertainment & Media": 0.00020781610728246383, "Fashion & Beauty": 2.1270516350970663e-05, "Business, Economics & Finance": 4.223598830347657e-05, "Food & Dining": 2.4913554667931296e-05, "Games & Gaming": 0.00010704171376388448, "Electronics & Hardware": 0.00014586925662785793, "Health, Healthcare & Wellness": 2.6049602076468855e-05, "History & Geography": 3.0216582965020038e-05, "Home, Pets & Hobbies": 2.7672634112816724e-05, "Industry & Production": 2.846397828016424e-05, "Language, Literature & Philosophy": 6.517495475992358e-05, "Politics & Government": 1.95539220395026e-05, "Religion & Spirituality": 3.4559864311703546e-05, "Science, Mathematics & Engineering": 0.0013100568436774514, "Relationships & Community": 2.200432126407432e-05, "Software Usage & Applications": 0.0009509893055050965, "Programming & Software Engineering": 0.9962515450972067, "Sports & Fitness": 4.929787174656674e-05, "Vehicles & Transportation": 2.6693367456962004e-05, "Travel & Tourism": 5.6978442275372415e-05, "Other / Uncovered Subject": 0.0002916728604473175, "Unknown / Insufficient Content": 7.733261550251937e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0003711084782497513, "Art & Design": 0.00020850499626542702, "Law & Crime": 0.0002824982937778982, "Education & Careers": 0.00038993910578948346, "Entertainment & Media": 0.005988345056288837, "Fashion & Beauty": 0.00011714193205825921, "Business, Economics & Finance": 0.00026416625577648585, "Food & Dining": 9.452721422052931e-05, "Games & Gaming": 0.0002704101465576784, "Electronics & Hardware": 0.00028495557876793623, "Health, Healthcare & Wellness": 0.00010240605810632747, "History & Geography": 0.00010133023966700748, "Home, Pets & Hobbies": 7.672006481973803e-05, "Industry & Production": 7.618344885657274e-05, "Language, Literature & Philosophy": 0.00038400497372918297, "Politics & Government": 0.00012512017794045455, "Religion & Spirituality": 0.0001308032733637582, "Science, Mathematics & Engineering": 0.008231800063347073, "Relationships & Community": 6.814457638701959e-05, "Software Usage & Applications": 0.002257305694636721, "Programming & Software Engineering": 0.011935260622344594, "Sports & Fitness": 0.00010291879347915743, "Vehicles & Transportation": 5.2976185293974875e-05, "Travel & Tourism": 0.00015293990662267485, "Other / Uncovered Subject": 0.0004096859610377011, "NONE": 0.9675208029026157}}, "probabilities": {"primary": {"Academic Writing": 5.8658416460831e-05, "Content Listing": 3.147928877835976e-05, "Creative Writing": 3.63894634351615e-05, "Customer Support Page": 1.9573308903983722e-05, "Q&A Forum": 3.143207118898376e-05, "Discussion Thread": 1.8251708744006028e-05, "Comment Section": 3.552713397330136e-05, "Missing Content": 4.199490614703153e-05, "Knowledge Article": 0.00016616294430156703, "Legal Notices": 3.926765192765141e-05, "News Article": 1.0776449834916905e-05, "Organizational Announcement": 1.9771244585121563e-05, "Nonfiction Writing": 2.527548644573675e-05, "Organizational About Page": 2.5450364781397344e-05, "Personal About Page": 3.422162904286746e-05, "Personal Blog": 2.0254935865469345e-05, "Product Page": 1.68261082724294e-05, "Spam / Ads": 2.4294729505916556e-05, "Structured Data": 0.00037319693024678814, "Technical Writing": 0.9915942486263751, "Transcript / Interview": 2.8681577907136505e-05, "Tutorial / How-To Guide": 0.005839658696259134, "User Reviews": 1.53330624382151e-05, "Other / Unknown": 0.0014932732645788786}, "secondary": {"Academic Writing": 0.00022600885726357117, "Content Listing": 0.00011621242109892018, "Creative Writing": 0.0001563440731001926, "Customer Support Page": 0.00010369434130409665, "Q&A Forum": 0.00010255387411200548, "Discussion Thread": 6.240417528644108e-05, "Comment Section": 0.0010044732687602659, "Missing Content": 0.0003307879163630216, "Knowledge Article": 0.0010713614755524098, "Legal Notices": 0.00019009959233892698, "News Article": 9.2947372432391e-05, "Organizational Announcement": 9.173865135445692e-05, "Nonfiction Writing": 0.00010511881558524751, "Organizational About Page": 0.00010938839501999874, "Personal About Page": 0.00012088260083217087, "Personal Blog": 0.0001020935248020931, "Product Page": 6.961393384819139e-05, "Spam / Ads": 9.849261370918878e-05, "Structured Data": 0.0024201134821010676, "Technical Writing": 0.0127480012051703, "Transcript / Interview": 0.00010898983461718471, "Tutorial / How-To Guide": 0.021114696063663212, "User Reviews": 5.880387678500728e-05, "NONE": 0.9593951796348997}, "tertiary": {"Academic Writing": 0.00011825136335814657, "Content Listing": 8.061358828322549e-05, "Creative Writing": 0.0001143981890536188, "Customer Support Page": 6.37439651391948e-05, "Q&A Forum": 5.976150863663806e-05, "Discussion Thread": 3.9220222913602853e-05, "Comment Section": 0.001734736356529537, "Missing Content": 0.000315272314446337, "Knowledge Article": 0.0006183532157430057, "Legal Notices": 0.00018875090091210317, "News Article": 5.92003846355917e-05, "Organizational Announcement": 4.883494020467228e-05, "Nonfiction Writing": 7.456366524386901e-05, "Organizational About Page": 8.720265078014312e-05, "Personal About Page": 6.714692600717676e-05, "Personal Blog": 7.078948708437015e-05, "Product Page": 4.961022340225016e-05, "Spam / Ads": 6.995438982884561e-05, "Structured Data": 0.002690147907350236, "Technical Writing": 0.006005328576097042, "Transcript / Interview": 5.949875174468654e-05, "Tutorial / How-To Guide": 0.013548377314510092, "User Reviews": 3.401761733086255e-05, "NONE": 0.9738022255407648}, "structured_records": {"false": 0.7709454403147746, "true": 0.2290545596852253}, "procedural_steps": {"false": 0.9609762485335231, "true": 0.03902375146647691}, "worked_solution": {"false": 0.9042721180817119, "true": 0.09572788191828809}, "contextual_dialogue": {"false": 0.9999925214091239, "true": 7.478590876179658e-06}, "content_quality": {"-2": 0.025668946417099568, "-1": 0.11961139699546043, "0": 0.36779853554769715, "1": 0.386789920680697, "2": 0.1001312003590458}, "noise_severity": {"0": 0.889770146290425, "1": 0.07619637853826014, "2": 0.027687578432608446, "3": 0.004669962495077782, "4": 0.0016759342436287051}, "missing_content_severity": {"0": 0.9906845201666266, "1": 0.006727687856450879, "2": 0.0017154539568726287, "3": 0.0006887345100071506, "4": 0.0001836035100428227}, "factual_density": {"0": 0.010167053499601359, "1": 0.02578902636075283, "2": 0.13258422272166176, "3": 0.38363016864751115, "4": 0.4478295287704728}, "pretraining_utility": {"-2": 0.00868486546635093, "-1": 0.06954909603086515, "0": 0.34552039835514303, "1": 0.494930399109795, "2": 0.08131524103784579}, "math_density": {"0": 0.9495659790649132, "1": 0.03946429214269764, "2": 0.003502611625309748, "3": 0.00172078274206633, "4": 0.005746334425013096}, "code_density": {"0": 0.00018552564300008996, "1": 0.00045148928031700416, "2": 0.013628037458028073, "3": 0.091035503539413, "4": 0.8946994440792418}, "reasoning_density": {"0": 0.4634931883733358, "1": 0.07797072092575509, "2": 0.1665281572346151, "3": 0.14993216900976997, "4": 0.1420757644565241}, "education_level": {"none": 0.0011179366771077932, "0": 0.002038304540927024, "1": 0.07763819971390917, "2": 0.8808285638572414, "3": 0.036248363163941526, "4": 0.0021286320468731656}, "creative_writing_quality": {"not_applicable": 0.9982518178300205, "-2": 0.0002237572256583748, "-1": 0.0005388868215097502, "0": 0.0008112382279328368, "1": 0.00012954953937616246, "2": 4.475035550248158e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.768277898291716e-05, "Art & Design": 4.4873083243563086e-05, "Law & Crime": 3.504406159199011e-05, "Education & Careers": 6.499067555735821e-05, "Entertainment & Media": 0.00020781610728246383, "Fashion & Beauty": 2.1270516350970663e-05, "Business, Economics & Finance": 4.223598830347657e-05, "Food & Dining": 2.4913554667931296e-05, "Games & Gaming": 0.00010704171376388448, "Electronics & Hardware": 0.00014586925662785793, "Health, Healthcare & Wellness": 2.6049602076468855e-05, "History & Geography": 3.0216582965020038e-05, "Home, Pets & Hobbies": 2.7672634112816724e-05, "Industry & Production": 2.846397828016424e-05, "Language, Literature & Philosophy": 6.517495475992358e-05, "Politics & Government": 1.95539220395026e-05, "Religion & Spirituality": 3.4559864311703546e-05, "Science, Mathematics & Engineering": 0.0013100568436774514, "Relationships & Community": 2.200432126407432e-05, "Software Usage & Applications": 0.0009509893055050965, "Programming & Software Engineering": 0.9962515450972067, "Sports & Fitness": 4.929787174656674e-05, "Vehicles & Transportation": 2.6693367456962004e-05, "Travel & Tourism": 5.6978442275372415e-05, "Other / Uncovered Subject": 0.0002916728604473175, "Unknown / Insufficient Content": 7.733261550251937e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00037110847824975136, "Art & Design": 0.00020850499626542704, "Law & Crime": 0.0002824982937778983, "Education & Careers": 0.0003899391057894835, "Entertainment & Media": 0.005988345056288838, "Fashion & Beauty": 0.00011714193205825922, "Business, Economics & Finance": 0.0002641662557764859, "Food & Dining": 9.452721422052932e-05, "Games & Gaming": 0.00027041014655767844, "Electronics & Hardware": 0.0002849555787679363, "Health, Healthcare & Wellness": 0.00010240605810632748, "History & Geography": 0.00010133023966700749, "Home, Pets & Hobbies": 7.672006481973805e-05, "Industry & Production": 7.618344885657276e-05, "Language, Literature & Philosophy": 0.000384004973729183, "Politics & Government": 0.00012512017794045457, "Religion & Spirituality": 0.00013080327336375823, "Science, Mathematics & Engineering": 0.008231800063347075, "Relationships & Community": 6.81445763870196e-05, "Software Usage & Applications": 0.0022573056946367212, "Programming & Software Engineering": 0.011935260622344596, "Sports & Fitness": 0.00010291879347915744, "Vehicles & Transportation": 5.297618529397488e-05, "Travel & Tourism": 0.00015293990662267488, "Other / Uncovered Subject": 0.00040968596103770115, "NONE": 0.9675208029026158}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9988, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9988, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:11.925186+00:00", "scoring_key_slot": "primary", "dataset_uid": "671518ad-e6bf-4d88-a2c1-88f793576ede", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "cefd703e11843dae53c1f7776f359dfaeb152a1887a47ed977047b6985520b3c", "source_metadata": {"uuid": "671518ad-e6bf-4d88-a2c1-88f793576ede", "repo_name": "yekingyan/web_practice", "relative_path": "getStatuscode_headers_body.py", "category": "TOOL", "algo_rel_score": 0.986884593963623, "quality_score": 3.961167812347412, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 4, "repo_created_at": "2018-07-03T13:12:37.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "32a279c726ff60d707458c4209ec29de8173ceae", "size_bytes": 4668, "file_timestamp": 1533553048, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "6f6994401a7715255666faecb50d7e15c8095b0b"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 4, "repo_created_at": "2018-07-03T13:12:37.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "32a279c726ff60d707458c4209ec29de8173ceae", "size_bytes": 4668, "file_timestamp": 1533553048, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "6f6994401a7715255666faecb50d7e15c8095b0b", "url": "https://github.com/yekingyan/web_practice/blob/6f6994401a7715255666faecb50d7e15c8095b0b/getStatuscode_headers_body.py"}, "gpt4o_tokens": 1356, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.41610303156912903, "noise_severity": 0.1522851598632252, "missing_content_severity": 0.01295921334038888, "factual_density": 3.233166092828501, "pretraining_utility": 0.5706420542219197, "math_density": 0.07461720131956852, "code_density": 3.8796118511315796, "reasoning_density": 1.4291266002503915, "education_level": 1.958744697646298, "creative_writing_quality": -0.4389422542013033}, "score_applicability": {"education_level": 0.9988820633228923, "creative_writing_quality": 0.001748182169979606}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/CaptainSandwich/BioinformaticsH1/blob/7d8dd0ba421a6b2ca78aefd0bb34dec5ab2a4b91/SmithWaterman/SmithWaterman.py", "id": "python-0-7d2b3ddc6d53f6aa5b609358fd5227bb", "text": "#: Match score\nm = 1\n\n#: Deletion penalty\nd = -1\n\n#: Mismatch penalty\ns = -1\n\ndef set_alignment_scores(new_m, new_d, new_s):\n    \"\"\"\n    Sets the scoring variables for the Needleman-Wunsch algorithm\n\n    :param new_m: New match score\n    :param new_d: New deletion score\n    :param new_s: New mismatch score\n    \"\"\"\n    global m, d, s\n    m = new_m\n    d = new_d\n    s = new_s\n\n\ndef get_file_data(prompt):\n    filename = input(prompt)\n    file = open(filename, 'r')\n    return file.read()\n\n\ndef create_matrix(vertical_length, horizontal_length):\n    \"\"\"\n    Creates and initializes a matrix of size vertical_length * horizontal_length for the Smith-Waterman algorithm\n\n    :param int vertical_length: Length of the list representing the vertical sequence of the alignment matrix\n    :param int horizontal_length: Length of the list representing the horizontal sequence of the alignment matrix\n\n    :return matrix: A two dimensional list representing the alignment table initialized such that each element = 0\n    \"\"\"\n    matrix = [[0 for x in range(horizontal_length + 1)] for y in range(vertical_length + 1)]\n    return matrix\n\n\ndef f(row, column, matrix, vertical_list, horizontal_list):\n    \"\"\"\n    Finds the optimal alignment for the subsequences vertical_list[0:row-1] and horizontal_list[0:column-1]\n    according to the Smith-Waterman algorithm\n\n    :param int row: Index for the row of matrix\n    :param int column: Index for the column of matrix\n    :param list matrix: The alignment scoring matrix\n    :param list vertical_list: List representing the vertical sequence of the alignment matrix\n    :param list horizontal_list: List representing the horizontal sequence of the alignment matrix\n    :return local_max: The maximum score for the current subsequence\n    \"\"\"\n    up = matrix[row - 1][column] + d\n    left = matrix[row][column - 1] + d\n    diag = matrix[row - 1][column - 1] + (m if vertical_list[row - 1] == horizontal_list[column - 1] else s)\n\n    return max(0, up, left, diag)\n\n\ndef fill_matrix(vertical_list, horizontal_list, matrix):\n    \"\"\"\n    Fills the alignment scoring matrix according to the Smith-Waterman algorithm\n\n    :param list vertical_list:\n    :param list horizontal_list:\n    :param list matrix: The alignment scoring matrix\n    \"\"\"\n    end1 = len(vertical_list) + 1\n    end2 = len(horizontal_list) + 1\n    r1 = range(1, end1)\n    r2 = range(1, end2)\n    for row in r1:\n        for col in r2:\n            matrix[row][col] = f(row, col, matrix, vertical_list, horizontal_list)\n\n\ndef get_max(matrix):\n    \"\"\"\n    Finds the location of the optimal local alignment score of the alignment matrix\n\n    :param matrix: The alignment scoring matrix\n    :return max_coords: The row and column of the optimal local alignment score of the matrix\n    \"\"\"\n    max_row = 0\n    max_col = 0\n    max_val = 0\n\n    for row in range(1, len(matrix)):\n        for col in range(1, len(matrix[0])):\n            if matrix[row][col] > max_val:\n                max_val = matrix[row][col]\n                max_row = row\n                max_col = col\n\n    max_coords = [max_row, max_col]\n    return max_coords\n\n\ndef do_alignment(vertical_list, horizontal_list, matrix):\n    \"\"\"\n    Generates three lists representing the optimal local alignment of vertical_list and horizontal_list,\n    such that matches line up together with '|', mismatches line up together with '.', and gaps insert '-' into the\n    string with the deletion and a ' ' into the alignment string\n\n    Example:\n    The filled alignment matrix for\n    vertical_list = \"GATTACA\"\n    and\n    horizontal_list = \"GCATGCU\"\n    will yield\n    [['G', '-', 'A', 'T', 'T', 'A', 'C', 'A'],\n     ['|', ' ', '|', '|', '.', ' ', '|', '.'],\n     ['G', 'C', 'A', 'T', 'G', '-', 'C', 'U']]\n\n    :param list vertical_list: List representing the vertical sequence of the alignment matrix\n    :param list horizontal_list: List representing the horizontal sequence of the alignment matrix\n    :param list matrix: The alignment scoring matrix\n    :return alignment:\n    \"\"\"\n\n    max_coords = get_max(matrix)\n    row = max_coords[0]\n    col = max_coords[1]\n    new_list_1 = []\n    new_list_2 = []\n    aligner = []\n\n    while matrix[row][col] != 0:\n        if matrix[row][col] == (matrix[row - 1][col] + d):\n            new_list_2 = ['-'] + new_list_2\n            new_list_1 = [vertical_list[row - 1]] + new_list_1\n            row -= 1\n            aligner = [' '] + aligner\n        elif matrix[row][col] == (matrix[row][col - 1] + d):\n            new_list_2 = [horizontal_list[col - 1]] + new_list_2\n            new_list_1 = ['-'] + new_list_1\n            col -= 1\n            aligner = [' '] + aligner\n        else:\n            if vertical_list[row - 1] == horizontal_list[col - 1]:\n                aligner = ['|'] + aligner\n            else:\n                aligner = ['.'] + aligner\n            new_list_1 = [vertical_list[row - 1]] + new_list_1\n            new_list_2 = [horizontal_list[col - 1]] + new_list_2\n            col -= 1\n            row -= 1\n\n    return [new_list_1, aligner, new_list_2]\n", "text_preview": "#: Match score m = 1 #: Deletion penalty d = -1 #: Mismatch penalty s = -1 def set_alignment_scores(new_m, new_d, new_s): \"\"\" Sets the scoring variables for the Needleman-Wunsch algorithm :param new_m: New match score :param new_d: New dele", "input_sha256": "909dc5f349e0295746891ba425a61e553eaf71c0b39948fb69d09ddf4693ed5b", "text_sha256": "7d2b3ddc6d53f6aa5b609358fd5227bb922127f950b29fdb8e5d7daa076b9718", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12069, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7142843893100264, "noise_severity": 0.013584568254187076, "missing_content_severity": 0.0824932230562478, "factual_density": 3.282106063208744, "pretraining_utility": 0.7830649476290523, "math_density": 3.061418432692973, "code_density": 3.733303346849862, "reasoning_density": 1.8482395142392183, "education_level": 2.222708022446088, "creative_writing_quality": -0.3507183256035204}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.4360 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.03224526361262092, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9928162059691205, "secondary": 0.9298624646029637, "tertiary": 0.9635096961987422, "structured_records": 0.008435466378131964, "procedural_steps": 0.047063995201216804, "worked_solution": 0.1444434752729898, "contextual_dialogue": 7.755582903004815e-06, "content_quality": 0.5017256754928692, "noise_severity": 0.9886795264548441, "missing_content_severity": 0.9312556474531268, "factual_density": 0.4017550526739537, "pretraining_utility": 0.5750084964338151, "math_density": 0.2178486939108113, "code_density": 0.7777527890415514, "reasoning_density": 0.0, "education_level": 0.7003363451824051, "creative_writing_quality": 0.9992848444721475, "primary_topic": 0.7561934581104022, "secondary_topic": 0.41346999488649877}, "probability_margin": {"primary": 0.990611177999632, "secondary": 0.9027039631818432, "tertiary": 0.955144914723576, "education_level": 0.5714315695182766, "creative_writing_quality": 0.999159277281721, "primary_topic": 0.5525346720175531, "secondary_topic": 0.14439498196525974}, "raw_probabilities": {"primary": {"Academic Writing": 0.0004719538855585222, "Content Listing": 2.5774049156901003e-05, "Creative Writing": 2.0450341822281467e-05, "Customer Support Page": 1.6022743410600887e-05, "Q&A Forum": 2.1095119203916944e-05, "Discussion Thread": 1.0766705982661237e-05, "Comment Section": 1.798521726416029e-05, "Missing Content": 3.486916062166926e-05, "Knowledge Article": 0.0005892726808889724, "Legal Notices": 2.731705624489538e-05, "News Article": 6.614183586038414e-06, "Organizational Announcement": 1.2996908468528027e-05, "Nonfiction Writing": 2.7658573354341355e-05, "Organizational About Page": 1.9492072505878697e-05, "Personal About Page": 1.9060796598608668e-05, "Personal Blog": 1.1424388733307414e-05, "Product Page": 1.0205584318652755e-05, "Spam / Ads": 1.2787730813991061e-05, "Structured Data": 0.0011124333065483472, "Technical Writing": 0.9931155307204071, "Transcript / Interview": 2.604834933263756e-05, "Tutorial / How-To Guide": 0.002504352720775143, "User Reviews": 9.403690014261462e-06, "Other / Unknown": 0.0018764840143885785}, "secondary": {"Academic Writing": 0.010664355156980502, "Content Listing": 0.00016118658296297714, "Creative Writing": 0.00020402211453581512, "Customer Support Page": 0.00016084024124718655, "Q&A Forum": 0.00011304303296849284, "Discussion Thread": 8.227103228866759e-05, "Comment Section": 0.00013483636633881938, "Missing Content": 0.0002920443808097917, "Knowledge Article": 0.006514713659718422, "Legal Notices": 0.00018072315302758633, "News Article": 0.00010192173962700197, "Organizational Announcement": 0.00010070871659685775, "Nonfiction Writing": 0.00016480370063766124, "Organizational About Page": 0.00014829433685813824, "Personal About Page": 0.00012511261401493525, "Personal Blog": 9.837992978910945e-05, "Product Page": 7.599276053254325e-05, "Spam / Ads": 0.00010807941103254273, "Structured Data": 0.00518187087559307, "Technical Writing": 0.030080898729330383, "Transcript / Interview": 0.00017194177627784019, "Tutorial / How-To Guide": 0.012289965388213446, "User Reviews": 5.9132389444664216e-05, "NONE": 0.9327848619111736}, "tertiary": {"Academic Writing": 0.007083788116810911, "Content Listing": 7.972933076527148e-05, "Creative Writing": 0.00010163269547338215, "Customer Support Page": 7.514686497342062e-05, "Q&A Forum": 5.1727933420241235e-05, "Discussion Thread": 3.4458199480873607e-05, "Comment Section": 7.336025043553387e-05, "Missing Content": 0.000257353601541674, "Knowledge Article": 0.0040265246844859515, "Legal Notices": 0.000154774767728702, "News Article": 5.4557329235352664e-05, "Organizational Announcement": 5.2081189142070866e-05, "Nonfiction Writing": 0.00011973339609412346, "Organizational About Page": 8.698947660473759e-05, "Personal About Page": 6.632604669371813e-05, "Personal Blog": 5.201608963411088e-05, "Product Page": 4.1242716789655865e-05, "Spam / Ads": 5.942815103937585e-05, "Structured Data": 0.003767305647680811, "Technical Writing": 0.00988521080021849, "Transcript / Interview": 8.350316523787421e-05, "Tutorial / How-To Guide": 0.00873390694848918, "User Reviews": 2.9077074230000984e-05, "NONE": 0.9650301255237945}, "structured_records": {"false": 0.991564533621868, "true": 0.008435466378131964}, "procedural_steps": {"false": 0.9529360047987832, "true": 0.047063995201216804}, "worked_solution": {"false": 0.8555565247270102, "true": 0.1444434752729898}, "contextual_dialogue": {"false": 0.999992244417097, "true": 7.755582903004815e-06}, "content_quality": {"-2": 0.010139155315581894, "-1": 0.05785665520611531, "0": 0.295691623690289, "1": 0.48020577642872203, "2": 0.15610678935929168}, "noise_severity": {"0": 0.9887756170798289, "1": 0.009613153469772723, "2": 0.001084597471705165, "3": 0.0003043080737686347, "4": 0.00022232390492452883}, "missing_content_severity": {"0": 0.9561656716535892, "1": 0.011088996945387626, "2": 0.02757624064956409, "3": 0.004424618194104918, "4": 0.0007444725573543173}, "factual_density": {"0": 0.004337771773398627, "1": 0.014198069991230678, "2": 0.13005450137654706, "3": 0.39783963697087477, "4": 0.4535700198879488}, "pretraining_utility": {"-2": 0.005961853446141359, "-1": 0.033604627905904946, "0": 0.2783676121749507, "1": 0.5355385305187661, "2": 0.14652737595423704}, "math_density": {"0": 0.020436491668092967, "1": 0.07278691433568955, "2": 0.16705082382447048, "3": 0.30437320997864503, "4": 0.4353525601931019}, "code_density": {"0": 0.00046489299028769387, "1": 0.0011086880040670675, "2": 0.04291315528482564, "3": 0.175684706607135, "4": 0.7798285571136845}, "reasoning_density": {"0": 0.3689084634008348, "1": 0.06489483104471747, "2": 0.12974207877887114, "3": 0.22195798146554774, "4": 0.21449664531002877}, "education_level": {"none": 0.0009725579070300591, "0": 0.0017136006528709444, "1": 0.02976658737960714, "2": 0.7502802876520042, "3": 0.17884871813372766, "4": 0.038418248274760036}, "creative_writing_quality": {"not_applicable": 0.9994040370601229, "-2": 0.0001040953446762469, "-1": 0.0001425093370037113, "0": 0.0002447597784018591, "1": 6.751205762990775e-05, "2": 3.708642216541725e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.000362153483305708, "Art & Design": 0.0005072865891176269, "Law & Crime": 0.0005046875972542675, "Education & Careers": 0.0003875751009225182, "Entertainment & Media": 0.00032779601189537736, "Fashion & Beauty": 0.00029045784215289575, "Business, Economics & Finance": 0.0003463690466826464, "Food & Dining": 0.0003053485363932173, "Games & Gaming": 0.0003638690470573971, "Electronics & Hardware": 0.00038363387005775813, "Health, Healthcare & Wellness": 0.0017053077355972611, "History & Geography": 0.0005339813420989833, "Home, Pets & Hobbies": 0.0002658208332440165, "Industry & Production": 0.0003323237446155673, "Language, Literature & Philosophy": 0.0011467159382009648, "Politics & Government": 0.00016019241464431772, "Religion & Spirituality": 0.0003147624980012155, "Science, Mathematics & Engineering": 0.21303596078091044, "Relationships & Community": 0.00016450959430378744, "Software Usage & Applications": 0.0007142373660219631, "Programming & Software Engineering": 0.7655706327984636, "Sports & Fitness": 0.00040380359215155473, "Vehicles & Transportation": 0.00018627304524812638, "Travel & Tourism": 0.00038920948743250187, "Other / Uncovered Subject": 0.010847413747507454, "Unknown / Insufficient Content": 0.0004496779567188022}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008797924476038695, "Art & Design": 0.0007487219771137797, "Law & Crime": 0.0006984402490327267, "Education & Careers": 0.0006036212909463225, "Entertainment & Media": 0.0005555349690303059, "Fashion & Beauty": 0.00046303853556858176, "Business, Economics & Finance": 0.000513703546026631, "Food & Dining": 0.00037173420110343884, "Games & Gaming": 0.00046523311290898274, "Electronics & Hardware": 0.0006240227593388302, "Health, Healthcare & Wellness": 0.001410007712717159, "History & Geography": 0.0005461448056551402, "Home, Pets & Hobbies": 0.0003924897367084108, "Industry & Production": 0.0005068706279262162, "Language, Literature & Philosophy": 0.0011670816924761037, "Politics & Government": 0.0003033566351985059, "Religion & Spirituality": 0.0005446377425417699, "Science, Mathematics & Engineering": 0.43602884123701807, "Relationships & Community": 0.0002254607182998576, "Software Usage & Applications": 0.0005708840584847301, "Programming & Software Engineering": 0.29163385927175833, "Sports & Fitness": 0.000577931114406703, "Vehicles & Transportation": 0.0002964562309773295, "Travel & Tourism": 0.0006739381524493667, "Other / Uncovered Subject": 0.005413853739107583, "NONE": 0.25378434343560113}}, "probabilities": {"primary": {"Academic Writing": 0.0004719538855585222, "Content Listing": 2.5774049156901003e-05, "Creative Writing": 2.0450341822281467e-05, "Customer Support Page": 1.6022743410600887e-05, "Q&A Forum": 2.1095119203916944e-05, "Discussion Thread": 1.0766705982661237e-05, "Comment Section": 1.798521726416029e-05, "Missing Content": 3.486916062166926e-05, "Knowledge Article": 0.0005892726808889724, "Legal Notices": 2.731705624489538e-05, "News Article": 6.614183586038414e-06, "Organizational Announcement": 1.2996908468528027e-05, "Nonfiction Writing": 2.7658573354341355e-05, "Organizational About Page": 1.9492072505878697e-05, "Personal About Page": 1.9060796598608668e-05, "Personal Blog": 1.1424388733307414e-05, "Product Page": 1.0205584318652755e-05, "Spam / Ads": 1.2787730813991061e-05, "Structured Data": 0.0011124333065483472, "Technical Writing": 0.9931155307204071, "Transcript / Interview": 2.604834933263756e-05, "Tutorial / How-To Guide": 0.002504352720775143, "User Reviews": 9.403690014261462e-06, "Other / Unknown": 0.0018764840143885785}, "secondary": {"Academic Writing": 0.010664355156980502, "Content Listing": 0.00016118658296297714, "Creative Writing": 0.00020402211453581512, "Customer Support Page": 0.00016084024124718655, "Q&A Forum": 0.00011304303296849284, "Discussion Thread": 8.227103228866759e-05, "Comment Section": 0.00013483636633881938, "Missing Content": 0.0002920443808097917, "Knowledge Article": 0.006514713659718422, "Legal Notices": 0.00018072315302758633, "News Article": 0.00010192173962700197, "Organizational Announcement": 0.00010070871659685775, "Nonfiction Writing": 0.00016480370063766124, "Organizational About Page": 0.00014829433685813824, "Personal About Page": 0.00012511261401493525, "Personal Blog": 9.837992978910945e-05, "Product Page": 7.599276053254325e-05, "Spam / Ads": 0.00010807941103254273, "Structured Data": 0.00518187087559307, "Technical Writing": 0.030080898729330383, "Transcript / Interview": 0.00017194177627784019, "Tutorial / How-To Guide": 0.012289965388213446, "User Reviews": 5.9132389444664216e-05, "NONE": 0.9327848619111736}, "tertiary": {"Academic Writing": 0.007083788116810911, "Content Listing": 7.972933076527148e-05, "Creative Writing": 0.00010163269547338215, "Customer Support Page": 7.514686497342062e-05, "Q&A Forum": 5.1727933420241235e-05, "Discussion Thread": 3.4458199480873607e-05, "Comment Section": 7.336025043553387e-05, "Missing Content": 0.000257353601541674, "Knowledge Article": 0.0040265246844859515, "Legal Notices": 0.000154774767728702, "News Article": 5.4557329235352664e-05, "Organizational Announcement": 5.2081189142070866e-05, "Nonfiction Writing": 0.00011973339609412346, "Organizational About Page": 8.698947660473759e-05, "Personal About Page": 6.632604669371813e-05, "Personal Blog": 5.201608963411088e-05, "Product Page": 4.1242716789655865e-05, "Spam / Ads": 5.942815103937585e-05, "Structured Data": 0.003767305647680811, "Technical Writing": 0.00988521080021849, "Transcript / Interview": 8.350316523787421e-05, "Tutorial / How-To Guide": 0.00873390694848918, "User Reviews": 2.9077074230000984e-05, "NONE": 0.9650301255237945}, "structured_records": {"false": 0.991564533621868, "true": 0.008435466378131964}, "procedural_steps": {"false": 0.9529360047987832, "true": 0.047063995201216804}, "worked_solution": {"false": 0.8555565247270102, "true": 0.1444434752729898}, "contextual_dialogue": {"false": 0.999992244417097, "true": 7.755582903004815e-06}, "content_quality": {"-2": 0.010139155315581896, "-1": 0.05785665520611532, "0": 0.29569162369028906, "1": 0.4802057764287221, "2": 0.1561067893592917}, "noise_severity": {"0": 0.988775617079829, "1": 0.009613153469772725, "2": 0.0010845974717051652, "3": 0.00030430807376863474, "4": 0.00022232390492452886}, "missing_content_severity": {"0": 0.956165671653589, "1": 0.011088996945387624, "2": 0.02757624064956408, "3": 0.004424618194104917, "4": 0.000744472557354317}, "factual_density": {"0": 0.004337771773398627, "1": 0.014198069991230678, "2": 0.13005450137654706, "3": 0.39783963697087477, "4": 0.4535700198879488}, "pretraining_utility": {"-2": 0.005961853446141357, "-1": 0.03360462790590494, "0": 0.27836761217495065, "1": 0.535538530518766, "2": 0.146527375954237}, "math_density": {"0": 0.020436491668092967, "1": 0.07278691433568955, "2": 0.16705082382447048, "3": 0.30437320997864503, "4": 0.4353525601931019}, "code_density": {"0": 0.0004648929902876939, "1": 0.0011086880040670677, "2": 0.042913155284825644, "3": 0.17568470660713503, "4": 0.7798285571136846}, "reasoning_density": {"0": 0.36890846340083483, "1": 0.06489483104471748, "2": 0.12974207877887117, "3": 0.22195798146554777, "4": 0.2144966453100288}, "education_level": {"none": 0.0009725579070300591, "0": 0.0017136006528709444, "1": 0.02976658737960714, "2": 0.7502802876520042, "3": 0.17884871813372766, "4": 0.038418248274760036}, "creative_writing_quality": {"not_applicable": 0.9994040370601229, "-2": 0.0001040953446762469, "-1": 0.0001425093370037113, "0": 0.0002447597784018591, "1": 6.751205762990775e-05, "2": 3.708642216541725e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.000362153483305708, "Art & Design": 0.0005072865891176269, "Law & Crime": 0.0005046875972542675, "Education & Careers": 0.0003875751009225182, "Entertainment & Media": 0.00032779601189537736, "Fashion & Beauty": 0.00029045784215289575, "Business, Economics & Finance": 0.0003463690466826464, "Food & Dining": 0.0003053485363932173, "Games & Gaming": 0.0003638690470573971, "Electronics & Hardware": 0.00038363387005775813, "Health, Healthcare & Wellness": 0.0017053077355972611, "History & Geography": 0.0005339813420989833, "Home, Pets & Hobbies": 0.0002658208332440165, "Industry & Production": 0.0003323237446155673, "Language, Literature & Philosophy": 0.0011467159382009648, "Politics & Government": 0.00016019241464431772, "Religion & Spirituality": 0.0003147624980012155, "Science, Mathematics & Engineering": 0.21303596078091044, "Relationships & Community": 0.00016450959430378744, "Software Usage & Applications": 0.0007142373660219631, "Programming & Software Engineering": 0.7655706327984636, "Sports & Fitness": 0.00040380359215155473, "Vehicles & Transportation": 0.00018627304524812638, "Travel & Tourism": 0.00038920948743250187, "Other / Uncovered Subject": 0.010847413747507454, "Unknown / Insufficient Content": 0.0004496779567188022}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008797924476038696, "Art & Design": 0.0007487219771137798, "Law & Crime": 0.0006984402490327268, "Education & Careers": 0.0006036212909463226, "Entertainment & Media": 0.000555534969030306, "Fashion & Beauty": 0.0004630385355685818, "Business, Economics & Finance": 0.0005137035460266311, "Food & Dining": 0.0003717342011034389, "Games & Gaming": 0.0004652331129089828, "Electronics & Hardware": 0.0006240227593388303, "Health, Healthcare & Wellness": 0.0014100077127171592, "History & Geography": 0.0005461448056551403, "Home, Pets & Hobbies": 0.00039248973670841084, "Industry & Production": 0.0005068706279262163, "Language, Literature & Philosophy": 0.001167081692476104, "Politics & Government": 0.00030335663519850595, "Religion & Spirituality": 0.00054463774254177, "Science, Mathematics & Engineering": 0.4360288412370181, "Relationships & Community": 0.00022546071829985764, "Software Usage & Applications": 0.0005708840584847302, "Programming & Software Engineering": 0.2916338592717584, "Sports & Fitness": 0.0005779311144067031, "Vehicles & Transportation": 0.00029645623097732956, "Travel & Tourism": 0.0006739381524493668, "Other / Uncovered Subject": 0.005413853739107584, "NONE": 0.2537843434356012}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9852, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9852, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:15.927963+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "3db7843c-7c0c-4115-aab7-51e9908e43c0", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "dd5163a5d070dd66e69a971a6be15b488778548eb5ef1aace5ab28e69ce1d5e7", "source_metadata": {"uuid": "3db7843c-7c0c-4115-aab7-51e9908e43c0", "repo_name": "CaptainSandwich/BioinformaticsH1", "relative_path": "SmithWaterman/SmithWaterman.py", "category": "TOOL", "algo_rel_score": 0.9992650151252747, "quality_score": 6.093926429748535, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 10, "repo_created_at": "2017-01-30T22:17:22.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "72604806e8cdf7c5fafa003e0e1b123f248de449", "size_bytes": 5054, "file_timestamp": 1486231573, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "7d8dd0ba421a6b2ca78aefd0bb34dec5ab2a4b91"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 10, "repo_created_at": "2017-01-30T22:17:22.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "72604806e8cdf7c5fafa003e0e1b123f248de449", "size_bytes": 5054, "file_timestamp": 1486231573, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "7d8dd0ba421a6b2ca78aefd0bb34dec5ab2a4b91", "url": "https://github.com/CaptainSandwich/BioinformaticsH1/blob/7d8dd0ba421a6b2ca78aefd0bb34dec5ab2a4b91/SmithWaterman/SmithWaterman.py"}, "gpt4o_tokens": 1295, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.7142843893100264, "noise_severity": 0.013584568254187076, "missing_content_severity": 0.0824932230562478, "factual_density": 3.282106063208744, "pretraining_utility": 0.7830649476290523, "math_density": 3.061418432692973, "code_density": 3.733303346849862, "reasoning_density": 1.8482395142392183, "education_level": 2.222708022446088, "creative_writing_quality": -0.3507183256035204}, "score_applicability": {"education_level": 0.9990274420929699, "creative_writing_quality": 0.0005959629398771423}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/antespo/social-media-app/blob/2d886552e8dfafba503f98c95cb8a3b927cee6f6/appl/main/views.py", "id": "python-0-f6b973ccfbfc83a1a666909705c84a42", "text": "#Flask\nfrom flask import render_template, redirect, request, url_for, flash\n#Logins\nfrom flask_login import login_user, logout_user, login_required, current_user\n#Operating system dependent functionality\nimport os\n#local imports\nfrom .. import db, photos\nfrom ..models import User, Post, Pic, Group, ScoreTable\nfrom . import mainBlue\nfrom .forms import LoginForm, RegistrationForm, ChangePasswordForm, PhotoForm, photos, PostForm, GroupForm\nimport pprint\nimport json\nimport requests\nfrom sqlalchemy import func\n\n\n@mainBlue.route('/group/<group_name>')\n@login_required\ndef group_page(group_name):\n    \n    user = current_user._get_current_object()\n    group = Group.query.filter_by(group_name=group_name).first()\n    group2 = Group.query.filter(Group.grouped.any(username=user.username)).all()\n    for x in group2:\n        if (x.group_name == group_name):\n            flash('Already a member of ' + group_name)\n            return redirect(url_for('mainBlue.group'))\n    group.grouped.append(user)\n    db.session.commit()\n    flash('Added to group: ' + group_name)\n    return redirect(url_for('mainBlue.group'))\n\n\n@mainBlue.route('/group/remove/<group_name>')\n@login_required\ndef group_page_remove(group_name):\n    \n    user = current_user._get_current_object()\n    group = Group.query.filter_by(group_name=group_name).first()\n    user1 = Group.query.filter(Group.grouped.any(username=user.username))\\\n            .filter_by(group_name=group_name).all()\n\n    if user1 == []:\n        flash('Not a member of: ' + group_name)\n        return redirect(url_for('mainBlue.group'))\n    else:\n        group.grouped.remove(user)\n        db.session.commit()\n        flash('Removed: ' + group_name)\n        return redirect(url_for('mainBlue.group'))\n\n\n@mainBlue.route('/groups', methods=['GET', 'POST'])\n@login_required\ndef group():\n    list1=[]\n    list2=[]\n    list3=[]\n    form = GroupForm()\n    if form.validate_on_submit():\n        group = Group(group_name=form.name.data)\n        db.session.add(group)\n        db.session.commit()\n        flash('Group Added.') \n        return redirect(url_for('mainBlue.group'))\n    group = Group.query.order_by(Group.group_name).all()\n    user = current_user._get_current_object()\n    group2 = Group.query.filter(Group.grouped.any(username=user.username)).all()\n    \n    for x in group2:\n        list1.append(x.group_name)\n    for x in group:\n        list2.append(x.group_name)\n    list1.sort()\n    list2.sort() \n    for x in list2:\n        if x in list1:\n            list3.append(\"(Following)\")\n        else:\n            list3.append(\"(Not following)\")\n    \n    return render_template('groups.html', form=form, group=group, list1=list1, list2=list2, list3=list3)\n\n@mainBlue.route('/', methods=['GET', 'POST'])\ndef index():\n    if User.query.filter_by(username=\"tony\").first():\n        return render_template('index.html')\n    else:  \n        user = User(username=\"tony\",password=\"<PASSWORD>\")\n        db.session.add(user)\n        db.session.commit()\n        return render_template('index.html')\n\n@mainBlue.route('/post', methods=['GET', 'POST'])\n@login_required\ndef indexPost():\n    form = PostForm()\n    if form.validate_on_submit():\n        send_url = 'http://freegeoip.net/json/'\n        ip = request.remote_addr\n        #ip = '172.16.31.10'\n        send_url_ip = send_url + ip\n        r = requests.get(send_url_ip)\n        j = json.loads(r.text)\n        location = (\"%s, %s\" % (j['city'], j['region_code']))\n        post = Post(body=form.body.data,location=location,author=current_user._get_current_object())\n        db.session.add(post)\n        db.session.commit()\n        return redirect(url_for('mainBlue.indexPost'))   \n    posts = Post.query.order_by(Post.timestamp.desc()).all()\n    return render_template('indexPost.html', form=form, posts=posts)\n\n@mainBlue.route('/user/<username>')\ndef user(username):\n    user = User.query.filter_by(username=username).first_or_404()\n    pic = Pic.query.join(User).filter_by(id=user.id).all()\n    posts = user.posts.order_by(Post.timestamp.desc()).all()\n    group = Group.query.filter(Group.grouped.any(username=username)).all()\n    score = ScoreTable.query.with_entities(func.sum(ScoreTable.score)).join(Pic).group_by(Pic.pic_id).filter_by(pic_id=user.id).all()\n    return render_template('user.html', user=user, posts=posts, pic=pic, group=group, score=score)\n\n@mainBlue.route('/login', methods=['GET', 'POST'])\ndef login():\n    form = LoginForm()\n    if form.validate_on_submit():\n        user = User.query.filter_by(username=form.username.data).first()\n        if user is not None and user.verify_password(form.password.data):\n            login_user(user, form.remember_me.data)\n            return redirect(request.args.get('next') or url_for('mainBlue.index'))\n        flash('Invalid username or password.')\n    return render_template('auth/login.html', form=form)\n\n@mainBlue.route('/logout')\n@login_required\ndef logout():\n    logout_user()\n    flash('You have been logged out.')\n    return redirect(url_for('mainBlue.index'))\n\n@mainBlue.route('/register', methods=['GET', 'POST'])\ndef register():\n    form = RegistrationForm()\n    if form.validate_on_submit():\n        user = User(username=form.username.data,password=form.password.data)\n        db.session.add(user)\n        db.session.commit()\n        return redirect(url_for('mainBlue.login'))\n    return render_template('auth/register.html', form=form)\n\n@mainBlue.route('/change-password', methods=['GET', 'POST'])\n@login_required\ndef change_password():\n    form = ChangePasswordForm()\n    if form.validate_on_submit():\n        if current_user.verify_password(form.old_password.data):\n            current_user.password = form.password.data\n            db.session.add(current_user)\n            db.session.commit()\n            flash('Your password has been updated.')\n            return redirect(url_for('mainBlue.index'))\n        else:\n            flash('Invalid password.')\n    return render_template(\"auth/change_password.html\", form=form)\n\n@mainBlue.route('/upload', methods=['GET', 'POST'])\n@login_required\ndef upload():\n    form = PhotoForm()\n    if form.validate_on_submit():\n        filename = photos.save(form.photo.data)\n        pic = Pic(pic_location='static/img/%s' % filename,Pics=current_user._get_current_object())\n        db.session.add(pic)\n        db.session.commit()\n        flash('File Uploaded. (%s)' % filename)\n        return redirect(url_for('mainBlue.upload'))\n    return render_template('upload.html', form=form)\n\n\n@mainBlue.route('/show', methods=['GET', 'POST'])\n@login_required\ndef show():\n    path = \"appl/static/img\"    \n    return render_template('show.html',path=path,os=os)\n\n@mainBlue.route('/show2', methods=['GET', 'POST'])\n@login_required\ndef show2():\n    path = \"appl/static/img\"\n    for x in os.listdir(path):\n        pic = Pic(pic_location='static/img/%s' % x)\n        #db.session.add(pic)\n        #db.session.commit()\n        print('static/img/%s' % x)\n    return \"show2\"\n\n\n@mainBlue.route('/test')\n@login_required\ndef test():\n    x = ScoreTable.query.with_entities(func.sum(ScoreTable.score), Pic.pic_id).join(Pic).group_by(Pic.pic_id).filter_by(pic_id=3).all()\n    for y in x:\n        print('UserId: %s UserScore: %s'  % (y[1],y[0]))   \n    return \"test\"\n\n@mainBlue.route('/test2/<pic_location>', methods=['POST','GET'])\ndef test2(pic_location):\n    user = current_user._get_current_object()\n    pic_location = 'static/img/' + pic_location\n    print(pic_location)#static/img/A.jpg\n    pic = Pic.query.filter_by(pic_location=pic_location).first()\n    #print(pic)\n    #f = ScoreTable(test2=pic,test=user, score=5)\n    #db.session.add(f)\n    #db.session.commit()\n    return render_template('test.html')\n\n\n@mainBlue.route('/api/register', methods=['POST'])\ndef register2():\n    if not request.json or not 'username' in request.json:\n        return \"Data Format Error!\"\n    else:\n        if User.query.filter_by(username=username).first():\n            return \"Username already in use.\"\n        user = User(username=username,password=<PASSWORD>)\n        db.session.add(user)\n        db.session.commit()\n        return \"User Added\"\n        \n@mainBlue.route('/test3')\ndef test3():\n    p = Pic.query.get_or_404(1)\n    #print(p)\n    for q in p.child2.all():\n        pprint.pprint(vars(q))\n\n    return \"test3\"\n\n\n@mainBlue.route('/test4')\ndef test4():\n    return render_template('test4.html')\n\n\n#voting system--------------------------------------------------------------------------------------------\n@mainBlue.route('/picscore', methods=['POST', 'GET'])\n@login_required\ndef picscore():\n    if request.method == 'GET':\n        #print(json.dumps(Pic().score_get()))\n        return json.dumps(Pic().score_get())\n#---------------------------------------------\n    elif request.method == 'POST':\n  \n        info = request.json['VOTE'].split( )#request.data\n        if info[1] == 'upvote':\n            formatinfo  = info[2].replace('up','',1)\n            \n        elif info[1] == 'downvote':\n            formatinfo = info[2].replace('down','',1)\n            \n        state = int(info[0])\n        user = current_user._get_current_object()\n        pic_location = 'static/img/' + formatinfo\n        pic = Pic.query.filter_by(pic_location=pic_location).first()\n        scoreacc = ScoreTable.query.filter_by(users_id=user.id,pics_id=pic.id).first()\n\n        if scoreacc:\n            scoreacc.score = state\n            \n            if scoreacc.score ==  0:\n                db.session.delete(scoreacc)\n                db.session.commit()\n            \n            db.session.commit()\n        else:\n            f = ScoreTable(test2=pic,test=user, score=state)\n            db.session.add(f)\n            db.session.commit()\n\n        return '1Success1'\n\n\n@mainBlue.route('/show3', methods=['POST', 'GET'])\n@login_required\ndef new_show():\n    togglelist = []\n    img = []\n    \n    path = \"appl/static/img\"\n\n    for piclinks in os.listdir(path):\n        img.append(piclinks)\n    \n    user = current_user._get_current_object()\n    scoreall = ScoreTable.query.filter_by(users_id=user.id).all()\n    for data in scoreall:\n        pic = Pic.query.filter_by(id=data.pics_id).first()\n    \n        if data.score == 1:\n            direction = 'up'\n        elif data.score == -1:\n            direction = 'down'\n        pic_location = Pic.str_format(pic.pic_location)\n        toggle = '.'+direction+pic_location\n        togglelist.append(toggle)\n   \n    togglelist = ', '.join(togglelist)\n    \n    return render_template('show454.html',img=img, togglelist=togglelist)\n\n#voting system--------------------------------------------------------------------------------------------\n\n", "text_preview": "#Flask from flask import render_template, redirect, request, url_for, flash #Logins from flask_login import login_user, logout_user, login_required, current_user #Operating system dependent functionality import os #local imports from .. imp", "input_sha256": "ae77f5b2f0c2ad901128b1b16d4fbfbb72047d9aea94dd24894ad26af88ac8d4", "text_sha256": "f6b973ccfbfc83a1a666909705c84a42f608fdace4f08b1a09fc8f3f2f3d87de", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12068, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.162312590242046, "noise_severity": 0.170185567174524, "missing_content_severity": 0.1306068786101965, "factual_density": 3.383809705717972, "pretraining_utility": 0.4304629688115825, "math_density": 0.09747213608045877, "code_density": 3.8975529116389858, "reasoning_density": 0.6352517871424396, "education_level": 2.1802786904729214, "creative_writing_quality": -0.5565977569423367}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.01112087786323257, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9970593551394711, "secondary": 0.9720366663101908, "tertiary": 0.9836410606022596, "structured_records": 0.04967174515042282, "procedural_steps": 0.0032999495936808596, "worked_solution": 0.0032737930771698414, "contextual_dialogue": 7.694773957402874e-06, "content_quality": 0.42896652267782187, "noise_severity": 0.85817869402123, "missing_content_severity": 0.8911609344915029, "factual_density": 0.48650808809831003, "pretraining_utility": 0.41632607697944046, "math_density": 0.918773219932951, "code_density": 0.9146274263658213, "reasoning_density": 0.4706235107146337, "education_level": 0.7483028295157634, "creative_writing_quality": 0.9992243105921232, "primary_topic": 0.9859422856981244, "secondary_topic": 0.9114979298025044}, "probability_margin": {"primary": 0.9954890293357164, "secondary": 0.9603140907894611, "tertiary": 0.9784068572058547, "education_level": 0.6111858953774872, "creative_writing_quality": 0.9991087347754563, "primary_topic": 0.9775948402203509, "secondary_topic": 0.8727091547196764}, "raw_probabilities": {"primary": {"Academic Writing": 3.295976348809699e-05, "Content Listing": 4.729902911988408e-05, "Creative Writing": 1.8157119530660857e-05, "Customer Support Page": 8.29949638089856e-06, "Q&A Forum": 1.1825180128761482e-05, "Discussion Thread": 7.95212155503994e-06, "Comment Section": 1.228863939738555e-05, "Missing Content": 3.0194337184627417e-05, "Knowledge Article": 5.7581278844202594e-05, "Legal Notices": 3.13690093603193e-05, "News Article": 5.00399342588865e-06, "Organizational Announcement": 1.1708419353488816e-05, "Nonfiction Writing": 1.284274818702938e-05, "Organizational About Page": 1.3090373511711701e-05, "Personal About Page": 2.1057234501931154e-05, "Personal Blog": 9.852254252914265e-06, "Product Page": 7.55563887550509e-06, "Spam / Ads": 1.161322177124444e-05, "Structured Data": 0.0004355614761161712, "Technical Writing": 0.9971818820086598, "Transcript / Interview": 1.6066461879202624e-05, "Tutorial / How-To Guide": 0.00031650019346177443, "User Reviews": 6.4873280703027805e-06, "Other / Unknown": 0.0016928526729433986}, "secondary": {"Academic Writing": 0.00020829143183068044, "Content Listing": 0.0007369119896210539, "Creative Writing": 0.00019410406787980346, "Customer Support Page": 8.988866509991144e-05, "Q&A Forum": 8.250295780424797e-05, "Discussion Thread": 6.644442012965287e-05, "Comment Section": 0.000674464694374984, "Missing Content": 0.004213768704712676, "Knowledge Article": 0.0005765968830734456, "Legal Notices": 0.000379937116699339, "News Article": 9.210916673152324e-05, "Organizational Announcement": 0.00010038158014269524, "Nonfiction Writing": 0.00013694024656139584, "Organizational About Page": 0.00011509046330335683, "Personal About Page": 0.00012195385398755824, "Personal Blog": 0.00011531282969369759, "Product Page": 5.508785255267782e-05, "Spam / Ads": 0.00010682140108485482, "Structured Data": 0.003939443477156249, "Technical Writing": 0.012887714424471808, "Transcript / Interview": 0.00011798279211592882, "Tutorial / How-To Guide": 0.001735023044719902, "User Reviews": 5.142272231964554e-05, "NONE": 0.9732018052139328}, "tertiary": {"Academic Writing": 0.00011916859376616306, "Content Listing": 0.000772055658272391, "Creative Writing": 0.0001507194511083461, "Customer Support Page": 5.633776696943913e-05, "Q&A Forum": 4.408693760220179e-05, "Discussion Thread": 3.9835363880455756e-05, "Comment Section": 0.0004016347976202623, "Missing Content": 0.0032944621659566787, "Knowledge Article": 0.0005034099063857012, "Legal Notices": 0.0004589298651262718, "News Article": 5.94554741307986e-05, "Organizational Announcement": 6.550576638368632e-05, "Nonfiction Writing": 0.00011101644928253439, "Organizational About Page": 8.404708210354906e-05, "Personal About Page": 8.637299911165356e-05, "Personal Blog": 7.929819823552828e-05, "Product Page": 3.6993432385212436e-05, "Spam / Ads": 8.717512982755058e-05, "Structured Data": 0.0021344165293736396, "Technical Writing": 0.0059158258713107766, "Transcript / Interview": 7.223313343666018e-05, "Tutorial / How-To Guide": 0.0010709964913811886, "User Reviews": 3.333985918385189e-05, "NONE": 0.9843226830771655}, "structured_records": {"false": 0.9503282548495772, "true": 0.04967174515042282}, "procedural_steps": {"false": 0.9967000504063191, "true": 0.0032999495936808596}, "worked_solution": {"false": 0.9967262069228302, "true": 0.0032737930771698414}, "contextual_dialogue": {"false": 0.9999923052260425, "true": 7.694773957402874e-06}, "content_quality": {"-2": 0.030067135499760804, "-1": 0.2013295202727623, "0": 0.40206983224961473, "1": 0.30929064244139426, "2": 0.0572428695364678}, "noise_severity": {"0": 0.855384060685987, "1": 0.12350965770646861, "2": 0.01762305074343635, "3": 0.0025031154752486744, "4": 0.000980115388859156}, "missing_content_severity": {"0": 0.8994822803738437, "1": 0.0766227050062633, "2": 0.01874166216159047, "3": 0.004112560552457753, "4": 0.0010407919058447498}, "factual_density": {"0": 0.0085681601550288, "1": 0.017137946495473007, "2": 0.10522521838936376, "3": 0.3200533773967662, "4": 0.5490152975633682}, "pretraining_utility": {"-2": 0.011273448922227503, "-1": 0.11219885448836585, "0": 0.37675481366313024, "1": 0.43433704470814954, "2": 0.06543583821812698}, "math_density": {"0": 0.9192434303662603, "1": 0.07025893750041826, "2": 0.00672897218432315, "3": 0.0013193855845986325, "4": 0.002449274364399574}, "code_density": {"0": 0.00019252315329925632, "1": 0.00037126782788728016, "2": 0.009084476645904192, "3": 0.0823942389723472, "4": 0.9079574934005621}, "reasoning_density": {"0": 0.6920965730490908, "1": 0.08858134210260707, "2": 0.14440972059188736, "3": 0.0417984531696017, "4": 0.033113911086813164}, "education_level": {"none": 0.0012671685913070894, "0": 0.0015162153794264883, "1": 0.017259791969695477, "2": 0.7902523579298029, "3": 0.17906646255231562, "4": 0.0106380035774524}, "creative_writing_quality": {"not_applicable": 0.9993535921601027, "-2": 0.00013379804946412032, "-1": 0.00019074241263672962, "0": 0.00024485738464641906, "1": 5.547062849259323e-05, "2": 2.153936465772065e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.262410609460562e-05, "Art & Design": 7.159957045611959e-05, "Law & Crime": 4.578766178253724e-05, "Education & Careers": 5.176147834237398e-05, "Entertainment & Media": 0.0001512531240558392, "Fashion & Beauty": 5.34920579437214e-05, "Business, Economics & Finance": 9.249300182520757e-05, "Food & Dining": 3.524301315358099e-05, "Games & Gaming": 0.00022327769569936983, "Electronics & Hardware": 7.114322974093838e-05, "Health, Healthcare & Wellness": 3.0870719249234486e-05, "History & Geography": 3.340406103085218e-05, "Home, Pets & Hobbies": 3.226125379584907e-05, "Industry & Production": 3.349860158331886e-05, "Language, Literature & Philosophy": 4.438692330400998e-05, "Politics & Government": 3.65079441878898e-05, "Religion & Spirituality": 3.397121834064061e-05, "Science, Mathematics & Engineering": 0.0001001011944971123, "Relationships & Community": 0.00888812679707635, "Software Usage & Applications": 0.0013252499005075456, "Programming & Software Engineering": 0.9864829670174273, "Sports & Fitness": 5.00119253400047e-05, "Vehicles & Transportation": 2.271927687682525e-05, "Travel & Tourism": 5.554732037341477e-05, "Other / Uncovered Subject": 0.0017665356432898791, "Unknown / Insufficient Content": 0.00020516526402522585}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010375538207906371, "Art & Design": 0.000993198875664319, "Law & Crime": 0.0011805179647670313, "Education & Careers": 0.00038482048351942195, "Entertainment & Media": 0.0016730592197773456, "Fashion & Beauty": 0.00031353694982002173, "Business, Economics & Finance": 0.001078120032344685, "Food & Dining": 0.00019651389644292786, "Games & Gaming": 0.0008220726299434368, "Electronics & Hardware": 0.00042822470823856914, "Health, Healthcare & Wellness": 0.00027245095917054793, "History & Geography": 0.00034220147362829683, "Home, Pets & Hobbies": 0.00024047072218245778, "Industry & Production": 0.00017479024527751444, "Language, Literature & Philosophy": 0.00020727064915540354, "Politics & Government": 0.00029818351635504676, "Religion & Spirituality": 0.0002672889569463636, "Science, Mathematics & Engineering": 0.0006297212455895849, "Relationships & Community": 0.042192700859654916, "Software Usage & Applications": 0.008063230356740265, "Programming & Software Engineering": 0.018236531055753784, "Sports & Fitness": 0.0001773544401692436, "Vehicles & Transportation": 0.00010626908537001512, "Travel & Tourism": 0.0004933934160749219, "Other / Uncovered Subject": 0.005288668857292082, "NONE": 0.9149018555793312}}, "probabilities": {"primary": {"Academic Writing": 3.2959763488096984e-05, "Content Listing": 4.7299029119884064e-05, "Creative Writing": 1.8157119530660854e-05, "Customer Support Page": 8.299496380898558e-06, "Q&A Forum": 1.1825180128761478e-05, "Discussion Thread": 7.952121555039938e-06, "Comment Section": 1.2288639397385547e-05, "Missing Content": 3.019433718462741e-05, "Knowledge Article": 5.758127884420258e-05, "Legal Notices": 3.136900936031929e-05, "News Article": 5.003993425888649e-06, "Organizational Announcement": 1.1708419353488812e-05, "Nonfiction Writing": 1.2842748187029377e-05, "Organizational About Page": 1.3090373511711698e-05, "Personal About Page": 2.105723450193115e-05, "Personal Blog": 9.852254252914263e-06, "Product Page": 7.555638875505088e-06, "Spam / Ads": 1.1613221771244437e-05, "Structured Data": 0.00043556147611617106, "Technical Writing": 0.9971818820086595, "Transcript / Interview": 1.606646187920262e-05, "Tutorial / How-To Guide": 0.0003165001934617744, "User Reviews": 6.487328070302779e-06, "Other / Unknown": 0.0016928526729433982}, "secondary": {"Academic Writing": 0.00020829143183068047, "Content Listing": 0.000736911989621054, "Creative Writing": 0.0001941040678798035, "Customer Support Page": 8.988866509991146e-05, "Q&A Forum": 8.250295780424799e-05, "Discussion Thread": 6.644442012965289e-05, "Comment Section": 0.0006744646943749841, "Missing Content": 0.004213768704712677, "Knowledge Article": 0.0005765968830734457, "Legal Notices": 0.00037993711669933905, "News Article": 9.210916673152325e-05, "Organizational Announcement": 0.00010038158014269525, "Nonfiction Writing": 0.00013694024656139587, "Organizational About Page": 0.00011509046330335684, "Personal About Page": 0.00012195385398755826, "Personal Blog": 0.0001153128296936976, "Product Page": 5.5087852552677824e-05, "Spam / Ads": 0.00010682140108485484, "Structured Data": 0.00393944347715625, "Technical Writing": 0.01288771442447181, "Transcript / Interview": 0.00011798279211592884, "Tutorial / How-To Guide": 0.0017350230447199023, "User Reviews": 5.142272231964555e-05, "NONE": 0.973201805213933}, "tertiary": {"Academic Writing": 0.00011916859376616306, "Content Listing": 0.000772055658272391, "Creative Writing": 0.0001507194511083461, "Customer Support Page": 5.633776696943913e-05, "Q&A Forum": 4.408693760220179e-05, "Discussion Thread": 3.9835363880455756e-05, "Comment Section": 0.0004016347976202623, "Missing Content": 0.0032944621659566787, "Knowledge Article": 0.0005034099063857012, "Legal Notices": 0.0004589298651262718, "News Article": 5.94554741307986e-05, "Organizational Announcement": 6.550576638368632e-05, "Nonfiction Writing": 0.00011101644928253439, "Organizational About Page": 8.404708210354906e-05, "Personal About Page": 8.637299911165356e-05, "Personal Blog": 7.929819823552828e-05, "Product Page": 3.6993432385212436e-05, "Spam / Ads": 8.717512982755058e-05, "Structured Data": 0.0021344165293736396, "Technical Writing": 0.0059158258713107766, "Transcript / Interview": 7.223313343666018e-05, "Tutorial / How-To Guide": 0.0010709964913811886, "User Reviews": 3.333985918385189e-05, "NONE": 0.9843226830771655}, "structured_records": {"false": 0.9503282548495772, "true": 0.04967174515042282}, "procedural_steps": {"false": 0.9967000504063191, "true": 0.0032999495936808596}, "worked_solution": {"false": 0.9967262069228302, "true": 0.0032737930771698414}, "contextual_dialogue": {"false": 0.9999923052260425, "true": 7.694773957402874e-06}, "content_quality": {"-2": 0.030067135499760807, "-1": 0.20132952027276232, "0": 0.4020698322496148, "1": 0.3092906424413943, "2": 0.05724286953646781}, "noise_severity": {"0": 0.8553840606859872, "1": 0.12350965770646863, "2": 0.017623050743436352, "3": 0.002503115475248675, "4": 0.0009801153888591562}, "missing_content_severity": {"0": 0.8994822803738437, "1": 0.0766227050062633, "2": 0.01874166216159047, "3": 0.004112560552457753, "4": 0.0010407919058447498}, "factual_density": {"0": 0.0085681601550288, "1": 0.017137946495473007, "2": 0.10522521838936376, "3": 0.3200533773967662, "4": 0.5490152975633682}, "pretraining_utility": {"-2": 0.011273448922227501, "-1": 0.11219885448836582, "0": 0.37675481366313013, "1": 0.43433704470814943, "2": 0.06543583821812697}, "math_density": {"0": 0.9192434303662604, "1": 0.07025893750041827, "2": 0.006728972184323151, "3": 0.0013193855845986327, "4": 0.0024492743643995743}, "code_density": {"0": 0.00019252315329925632, "1": 0.00037126782788728016, "2": 0.009084476645904192, "3": 0.0823942389723472, "4": 0.9079574934005621}, "reasoning_density": {"0": 0.6920965730490908, "1": 0.08858134210260707, "2": 0.14440972059188736, "3": 0.0417984531696017, "4": 0.033113911086813164}, "education_level": {"none": 0.0012671685913070896, "0": 0.0015162153794264885, "1": 0.01725979196969548, "2": 0.790252357929803, "3": 0.17906646255231565, "4": 0.010638003577452402}, "creative_writing_quality": {"not_applicable": 0.9993535921601024, "-2": 0.0001337980494641203, "-1": 0.00019074241263672957, "0": 0.000244857384646419, "1": 5.547062849259322e-05, "2": 2.1539364657720647e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.262410609460564e-05, "Art & Design": 7.15995704561196e-05, "Law & Crime": 4.578766178253725e-05, "Education & Careers": 5.176147834237399e-05, "Entertainment & Media": 0.00015125312405583923, "Fashion & Beauty": 5.349205794372141e-05, "Business, Economics & Finance": 9.24930018252076e-05, "Food & Dining": 3.5243013153580995e-05, "Games & Gaming": 0.00022327769569936988, "Electronics & Hardware": 7.11432297409384e-05, "Health, Healthcare & Wellness": 3.087071924923449e-05, "History & Geography": 3.340406103085219e-05, "Home, Pets & Hobbies": 3.226125379584908e-05, "Industry & Production": 3.349860158331887e-05, "Language, Literature & Philosophy": 4.438692330400999e-05, "Politics & Government": 3.650794418788981e-05, "Religion & Spirituality": 3.3971218340640615e-05, "Science, Mathematics & Engineering": 0.00010010119449711233, "Relationships & Community": 0.008888126797076353, "Software Usage & Applications": 0.0013252499005075458, "Programming & Software Engineering": 0.9864829670174275, "Sports & Fitness": 5.0011925340004713e-05, "Vehicles & Transportation": 2.2719276876825253e-05, "Travel & Tourism": 5.5547320373414784e-05, "Other / Uncovered Subject": 0.0017665356432898795, "Unknown / Insufficient Content": 0.0002051652640252259}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010375538207906371, "Art & Design": 0.000993198875664319, "Law & Crime": 0.0011805179647670313, "Education & Careers": 0.00038482048351942195, "Entertainment & Media": 0.0016730592197773456, "Fashion & Beauty": 0.00031353694982002173, "Business, Economics & Finance": 0.001078120032344685, "Food & Dining": 0.00019651389644292786, "Games & Gaming": 0.0008220726299434368, "Electronics & Hardware": 0.00042822470823856914, "Health, Healthcare & Wellness": 0.00027245095917054793, "History & Geography": 0.00034220147362829683, "Home, Pets & Hobbies": 0.00024047072218245778, "Industry & Production": 0.00017479024527751444, "Language, Literature & Philosophy": 0.00020727064915540354, "Politics & Government": 0.00029818351635504676, "Religion & Spirituality": 0.0002672889569463636, "Science, Mathematics & Engineering": 0.0006297212455895849, "Relationships & Community": 0.042192700859654916, "Software Usage & Applications": 0.008063230356740265, "Programming & Software Engineering": 0.018236531055753784, "Sports & Fitness": 0.0001773544401692436, "Vehicles & Transportation": 0.00010626908537001512, "Travel & Tourism": 0.0004933934160749219, "Other / Uncovered Subject": 0.005288668857292082, "NONE": 0.9149018555793312}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11143, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11143, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:15.941755+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "4bd136ea-6866-4df4-808f-cf02a8a56a5d", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "51a0e41daca6421da6840ae15549cee4b30481ca304d752eb97b2b4db873f087", "source_metadata": {"uuid": "4bd136ea-6866-4df4-808f-cf02a8a56a5d", "repo_name": "antespo/social-media-app", "relative_path": "appl/main/views.py", "category": "WEB", "algo_rel_score": 0.9665563106536865, "quality_score": 3.9987189769744873, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 3, "repo_created_at": "2017-08-23T01:19:11.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 1, "pull_requests": 0}, "file_meta": {"content_id": "827a379702f89e7ea0e82b93e5f8f7cbdda473f3", "size_bytes": 10610, "file_timestamp": 1503453131, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2d886552e8dfafba503f98c95cb8a3b927cee6f6"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 3, "repo_created_at": "2017-08-23T01:19:11.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 1, "pull_requests": 0}, "file_meta": {"content_id": "827a379702f89e7ea0e82b93e5f8f7cbdda473f3", "size_bytes": 10610, "file_timestamp": 1503453131, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2d886552e8dfafba503f98c95cb8a3b927cee6f6", "url": "https://github.com/antespo/social-media-app/blob/2d886552e8dfafba503f98c95cb8a3b927cee6f6/appl/main/views.py"}, "gpt4o_tokens": 2425, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.162312590242046, "noise_severity": 0.170185567174524, "missing_content_severity": 0.1306068786101965, "factual_density": 3.383809705717972, "pretraining_utility": 0.4304629688115826, "math_density": 0.09747213608045877, "code_density": 3.8975529116389858, "reasoning_density": 0.6352517871424396, "education_level": 2.1802786904729214, "creative_writing_quality": -0.5565977569423367}, "score_applicability": {"education_level": 0.998732831408693, "creative_writing_quality": 0.0006464078398975827}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/kyeser/scTools/blob/f83776f03deca2931516502f38ba3a79df2ab230/scTools.py", "id": "python-0-6215244c23c1b63ecaebd7a94295f0b6", "text": "from tto import *\n\ndef unique(pcset):\n    \"\"\"Removes duplicates from pcset.\"\"\"\n    l = []\n    for x in pcset:\n        if x not in l:\n            l.append(x)\n    return l\n\ndef fsv(pcset):\n    \"\"\"Finds smallest values of pcset and returns their index numbers.\"\"\"\n    l = []\n    for x in range(len(pcset)):\n        if len(l) == 0 or pcset[x] < pcset[l[0]]:\n            l = [x]\n        elif pcset[x] == pcset[l[0]]:\n            l.append(x)\n    return l\n\ndef normalForm(p):\n    \"\"\"Returns normal form of pcset.\"\"\"\n    # Remove duplicates (optional, though fail-safe).\n    # p = unique(p)\n    # number of elements in p\n    n = len(p)\n    # Test if p is null set.\n    if n == 0:\n        return p\n    else:\n        # Arrange p in ascending order.\n        p.sort()\n        q = 1\n        # Produce the set s comprised of all rotations of p.\n        s = [p[y:]+p[0:y] for y in range(n)]\n        # Find the subset subset_s of all pccycs s[z] in s where\n        # i<s[z][0],s[z][n-q]> is minimal.\n        while 1:\n            r = [(z[n-q]-z[0])%12 for z in s]\n            t = fsv(r)\n            subset_s = [s[xx] for xx in t]\n            # Delete all members of s not in subset_s.\n            s = subset_s\n            # If s has only one member, the resulting pccyc is normal_form\n            # If q = n, take any member of s and\n            # the resulting pccyc is normal_form\n            if len(s) == 1 or q == n:\n                normal_form = s[0]\n                return normal_form\n                break\n            # Otherwise, increment the value of q by 1 and continue.\n            else:\n                q += 1\n                continue\n\ndef primeForm(p):\n    \"\"\"Returns prime form of pcset.\"\"\"\n    # Remove duplicates.\n    # p = unique(p)\n    # number of elements in p\n    n = len(p)\n    # Test if p is null set.\n    if n == 0:\n        return p\n    else:\n        # Produce inversion of p, ip.\n        ip = [(12-x)%12 for x in p]\n        # Arrange p and ip in ascending order.\n        p.sort(), ip.sort()\n        q = 1\n        # Produce the set s comprised of all rotations of p and ip.\n        s = []\n        for y in range(n):\n            rotation_p = p[y:]+p[0:y]; s.append(rotation_p)\n            rotation_ip = ip[y:]+ip[0:y]; s.append(rotation_ip)\n        # Find the subset subset_s of all pccycs s[z] in s where\n        # i<s[z][0],s[z][n-q]> is minimal.\n        while 1:\n            r = [(z[n-q]-z[0])%12 for z in s]\n            t = fsv(r)\n            subset_s = [s[xx] for xx in t]\n            # Delete all members of s not in subset_s.\n            s = subset_s\n            # If s has only one member, transpose the sole member of s\n            # so it begins on 0; if q = n, take any member of s and\n            # transpose it so it begins on 0.\n            # The resulting pccyc is prime form.\n            if len(s) == 1 or q == n:\n                s = s[0]\n                prime_form = [(yy-s[0])%12 for yy in s]\n                return prime_form\n                break\n            # Otherwise, increment the value of q by 1 and continue.\n            else:\n                q += 1\n                continue\n\ndef interval(p):\n    return [(p[x]-p[x-1])%12 for x in range(1, len(p))]\n\ndef icv(p):\n    \"\"\"Returns interval-class vector of pcset.\"\"\"\n    l = []\n    c = 1 \n    n = len(p)\n    while c < n:\n        for x in p:\n            for y in range(c, n):\n                k = (x-p[y])%12\n                if k > 6:\n                    k = 12 - k\n                l.append(k)\n            c += 1\n    return [l.count(z) for z in range(1, 7)]\n\ndef sim(a, b):\n    l = []\n    va, vb = icv(a), icv(b)\n    for x in range(6):\n        k = abs(va[x]-vb[x])\n        l.append(k)\n    s = sum(l)\n    return s\n\ndef asim(a, b):\n    va, vb = icv(a), icv(b)\n    s = sim(a, b)\n    sv = sum(va) + sum(vb)\n    return float(s) / sv\n\ndef uniqueCombinations(items, n):\n    if n==0:\n        yield []\n    else:\n        for i in xrange(len(items)):\n            for cc in uniqueCombinations(items[i+1:], n-1):\n                yield [items[i]]+cc\n\ndef permutations(k):\n    if k:\n        for i, ith in enumerate(k):\n            for x in permutations(k[:i] + k[i+1:]):\n                x.insert(0, ith)\n                yield x\n    else:\n        yield []\n\ndef partitions(n):\n    # Base case of recursion: zero is the sum of the empty list.\n    if n == 0:\n        yield []\n        return\n    # Modify partitions of n-1 to form partitions of n.\n    for p in partitions(n-1):\n        yield [1] + p\n        if p and (len(p) < 2 or p[1] > p[0]):\n            yield [p[0] + 1] + p[1:]\n", "text_preview": "from tto import * def unique(pcset): \"\"\"Removes duplicates from pcset.\"\"\" l = [] for x in pcset: if x not in l: l.append(x) return l def fsv(pcset): \"\"\"Finds smallest values of pcset and returns their index numbers.\"\"\" l = [] for x in range", "input_sha256": "702d2d82dd062078b7c466aa4ddbcf2a4a40c37a29435be022335e6f3a846087", "text_sha256": "6215244c23c1b63ecaebd7a94295f0b67554855688131b237dca9058c10c9333", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12067, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.6317022182581166, "noise_severity": 0.013016962627393013, "missing_content_severity": 0.03612315188814074, "factual_density": 3.3465631561241462, "pretraining_utility": 0.4567986011553911, "math_density": 3.322233941528993, "code_density": 3.8468008624965457, "reasoning_density": 1.4015387926635026, "education_level": 2.2403429572824276, "creative_writing_quality": -0.3691958145926097}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.4246 below 0.70", "secondary_topic vs NONE margin 0.0405 below 0.10"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.005874728812233054, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9969736681640505, "secondary": 0.9901068681291785, "tertiary": 0.9962370199332479, "structured_records": 0.00216459894732922, "procedural_steps": 0.013584047481700293, "worked_solution": 0.003129748226653232, "contextual_dialogue": 5.057590258097198e-06, "content_quality": 0.46859619062951474, "noise_severity": 0.9891525311438392, "missing_content_severity": 0.9698973734265494, "factual_density": 0.4554692967701216, "pretraining_utility": 0.4328291244099973, "math_density": 0.43519495127416097, "code_density": 0.8723340520804549, "reasoning_density": 0.0, "education_level": 0.614422555309645, "creative_writing_quality": 0.9992424378245621, "primary_topic": 0.8295012696867677, "secondary_topic": 0.4015993208089347}, "probability_margin": {"primary": 0.995452463480849, "secondary": 0.9871385491831305, "tertiary": 0.99463977312096, "education_level": 0.5135524293433412, "creative_writing_quality": 0.9991027579477267, "primary_topic": 0.7352696596705309, "secondary_topic": 0.04048891483245509}, "raw_probabilities": {"primary": {"Academic Writing": 4.4331346896299907e-05, "Content Listing": 1.7767824438449568e-05, "Creative Writing": 1.40787885641147e-05, "Customer Support Page": 5.653416213388423e-06, "Q&A Forum": 7.261248809093401e-06, "Discussion Thread": 5.4437012866970606e-06, "Comment Section": 7.685298947928654e-06, "Missing Content": 1.7757084734446486e-05, "Knowledge Article": 0.00010897780538472263, "Legal Notices": 1.8995383937372953e-05, "News Article": 3.214216024864227e-06, "Organizational Announcement": 6.867014587178754e-06, "Nonfiction Writing": 1.2852012669913189e-05, "Organizational About Page": 8.255598644588104e-06, "Personal About Page": 1.0227280530670156e-05, "Personal Blog": 5.599789349411749e-06, "Product Page": 5.2246723600948065e-06, "Spam / Ads": 6.7529542233768586e-06, "Structured Data": 0.0004797489027418286, "Technical Writing": 0.9970997653238818, "Transcript / Interview": 1.146739436825155e-05, "Tutorial / How-To Guide": 0.0004499295687665972, "User Reviews": 4.8415296060123304e-06, "Other / Unknown": 0.001647301843032747}, "secondary": {"Academic Writing": 0.0011911254698983216, "Content Listing": 5.79880712055341e-05, "Creative Writing": 7.151641027352083e-05, "Customer Support Page": 4.015559611502066e-05, "Q&A Forum": 2.7773869672900606e-05, "Discussion Thread": 2.3457200517391e-05, "Comment Section": 4.324237081305476e-05, "Missing Content": 0.00020857775907764646, "Knowledge Article": 0.0008109054811581501, "Legal Notices": 7.663826223450572e-05, "News Article": 3.372874745842429e-05, "Organizational Announcement": 3.0282834224902867e-05, "Nonfiction Writing": 5.032470173519557e-05, "Organizational About Page": 4.7750912704130374e-05, "Personal About Page": 4.056524910160711e-05, "Personal Blog": 3.314053416304554e-05, "Product Page": 2.0985740477221538e-05, "Spam / Ads": 3.355642429474855e-05, "Structured Data": 0.002345506572906324, "Technical Writing": 0.0033805327739988185, "Transcript / Interview": 4.14748046084564e-05, "Tutorial / How-To Guide": 0.0008553995819634953, "User Reviews": 1.6288674268129764e-05, "NONE": 0.9905190819571293}, "tertiary": {"Academic Writing": 0.00019477284706825775, "Content Listing": 2.4891485462731703e-05, "Creative Writing": 3.4140546543387074e-05, "Customer Support Page": 1.6835684806441924e-05, "Q&A Forum": 1.0463762835858976e-05, "Discussion Thread": 8.498249909416341e-06, "Comment Section": 2.0825758415172628e-05, "Missing Content": 0.00010284790909884435, "Knowledge Article": 0.00023210523260843916, "Legal Notices": 4.574059615346352e-05, "News Article": 1.4642623481078367e-05, "Organizational Announcement": 1.4846583003032015e-05, "Nonfiction Writing": 2.6578949098133603e-05, "Organizational About Page": 3.05652516572092e-05, "Personal About Page": 1.6363739539436054e-05, "Personal Blog": 1.3259553658913846e-05, "Product Page": 1.0237915775334492e-05, "Spam / Ads": 1.72648884583996e-05, "Structured Data": 0.00048618112073602055, "Technical Writing": 0.0017540376484025636, "Transcript / Interview": 1.6356726290436793e-05, "Tutorial / How-To Guide": 0.000506250841524579, "User Reviews": 8.481316110361004e-06, "NONE": 0.9963938107693625}, "structured_records": {"false": 0.9978354010526708, "true": 0.00216459894732922}, "procedural_steps": {"false": 0.9864159525182997, "true": 0.013584047481700293}, "worked_solution": {"false": 0.9968702517733468, "true": 0.003129748226653232}, "contextual_dialogue": {"false": 0.9999949424097418, "true": 5.057590258097198e-06}, "content_quality": {"-2": 0.014838848979961856, "-1": 0.06783067502430842, "0": 0.32281327950473043, "1": 0.45982380173964976, "2": 0.13469339475134948}, "noise_severity": {"0": 0.9890774582089462, "1": 0.009515774849654948, "2": 0.0009157560423273952, "3": 0.0002943679032025223, "4": 0.0001966429958689267}, "missing_content_severity": {"0": 0.9768385618771418, "1": 0.013849133292619716, "2": 0.006045306954260943, "3": 0.0028845868169119567, "4": 0.0003824110590658184}, "factual_density": {"0": 0.00798522910498136, "1": 0.017861910652212203, "2": 0.12073048342511473, "3": 0.32644922864906245, "4": 0.5269731481686292}, "pretraining_utility": {"-2": 0.009361070553051049, "-1": 0.09318108367020393, "0": 0.4183991462967268, "1": 0.3894155730283392, "2": 0.08964312645167899}, "math_density": {"0": 0.01581474725876387, "1": 0.026768853110826153, "2": 0.11031863881566722, "3": 0.31356323247213835, "4": 0.5335345283426044}, "code_density": {"0": 0.0002848756919044319, "1": 0.000640047355475034, "2": 0.0219278126049804, "3": 0.10628386745945054, "4": 0.8708633968881895}, "reasoning_density": {"0": 0.4973462739324593, "1": 0.06420074295988877, "2": 0.11844279447081792, "3": 0.17958829378535784, "4": 0.14042189485147613}, "education_level": {"none": 0.0018171217071598828, "0": 0.0024705571970603203, "1": 0.07469111466302693, "2": 0.6786854627580374, "3": 0.16513303341469618, "4": 0.07720271026001911}, "creative_writing_quality": {"not_applicable": 0.9993686981871351, "-2": 0.0001039501521418045, "-1": 0.0001591271702070017, "0": 0.00026594023940838146, "1": 7.061501477895543e-05, "2": 3.166923632862751e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.000270618524264416, "Art & Design": 0.012217402543927398, "Law & Crime": 0.00035226339732745735, "Education & Careers": 0.00025646863712370453, "Entertainment & Media": 0.021714793217249843, "Fashion & Beauty": 0.0003713321218650472, "Business, Economics & Finance": 0.0003995195750528166, "Food & Dining": 0.00021429226749742204, "Games & Gaming": 0.0006430521843587827, "Electronics & Hardware": 0.00031441659683955394, "Health, Healthcare & Wellness": 0.00028199903668323285, "History & Geography": 0.0003263127758363823, "Home, Pets & Hobbies": 0.00045879291584867454, "Industry & Production": 0.0001704910188401295, "Language, Literature & Philosophy": 0.00038761190111474093, "Politics & Government": 0.00015257631786785126, "Religion & Spirituality": 0.0003388769689995638, "Science, Mathematics & Engineering": 0.1007892534898227, "Relationships & Community": 0.00011699737466633405, "Software Usage & Applications": 0.0008111931049151282, "Programming & Software Engineering": 0.8360589131603536, "Sports & Fitness": 0.0002777270139307037, "Vehicles & Transportation": 0.0001459431983884644, "Travel & Tourism": 0.00029437635080291645, "Other / Uncovered Subject": 0.022187427554493124, "Unknown / Insufficient Content": 0.0004473487519300338}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008904950006205016, "Art & Design": 0.015277057428868191, "Law & Crime": 0.0007816435087518096, "Education & Careers": 0.0005459533346209938, "Entertainment & Media": 0.04356280766244574, "Fashion & Beauty": 0.0004642125783057489, "Business, Economics & Finance": 0.0006627730732867939, "Food & Dining": 0.0003922507464423556, "Games & Gaming": 0.0008278511140864126, "Electronics & Hardware": 0.0005755041398920709, "Health, Healthcare & Wellness": 0.0005294150613777056, "History & Geography": 0.0004580576110300578, "Home, Pets & Hobbies": 0.0007493565383429387, "Industry & Production": 0.00032935578135215434, "Language, Literature & Philosophy": 0.0003952474768822009, "Politics & Government": 0.00028233233803217554, "Religion & Spirituality": 0.0006682898563986937, "Science, Mathematics & Engineering": 0.4246147315470526, "Relationships & Community": 0.00022960823766081574, "Software Usage & Applications": 0.0007527264842332617, "Programming & Software Engineering": 0.09345658690989596, "Sports & Fitness": 0.0005287157467421764, "Vehicles & Transportation": 0.00024929194022178867, "Travel & Tourism": 0.0006397756846961978, "Other / Uncovered Subject": 0.028010143484163037, "NONE": 0.3841258167145975}}, "probabilities": {"primary": {"Academic Writing": 4.433134689629991e-05, "Content Listing": 1.776782443844957e-05, "Creative Writing": 1.4078788564114704e-05, "Customer Support Page": 5.653416213388424e-06, "Q&A Forum": 7.261248809093403e-06, "Discussion Thread": 5.443701286697061e-06, "Comment Section": 7.685298947928656e-06, "Missing Content": 1.775708473444649e-05, "Knowledge Article": 0.00010897780538472266, "Legal Notices": 1.8995383937372956e-05, "News Article": 3.214216024864228e-06, "Organizational Announcement": 6.8670145871787554e-06, "Nonfiction Writing": 1.2852012669913192e-05, "Organizational About Page": 8.255598644588106e-06, "Personal About Page": 1.0227280530670158e-05, "Personal Blog": 5.59978934941175e-06, "Product Page": 5.224672360094807e-06, "Spam / Ads": 6.75295422337686e-06, "Structured Data": 0.0004797489027418287, "Technical Writing": 0.997099765323882, "Transcript / Interview": 1.1467394368251553e-05, "Tutorial / How-To Guide": 0.0004499295687665973, "User Reviews": 4.841529606012331e-06, "Other / Unknown": 0.0016473018430327474}, "secondary": {"Academic Writing": 0.0011911254698983218, "Content Listing": 5.7988071205534105e-05, "Creative Writing": 7.151641027352084e-05, "Customer Support Page": 4.0155596115020665e-05, "Q&A Forum": 2.777386967290061e-05, "Discussion Thread": 2.3457200517391004e-05, "Comment Section": 4.3242370813054765e-05, "Missing Content": 0.0002085777590776465, "Knowledge Article": 0.0008109054811581502, "Legal Notices": 7.663826223450574e-05, "News Article": 3.37287474584243e-05, "Organizational Announcement": 3.028283422490287e-05, "Nonfiction Writing": 5.0324701735195574e-05, "Organizational About Page": 4.775091270413038e-05, "Personal About Page": 4.0565249101607114e-05, "Personal Blog": 3.314053416304555e-05, "Product Page": 2.098574047722154e-05, "Spam / Ads": 3.3556424294748555e-05, "Structured Data": 0.0023455065729063246, "Technical Writing": 0.003380532773998819, "Transcript / Interview": 4.147480460845641e-05, "Tutorial / How-To Guide": 0.0008553995819634954, "User Reviews": 1.6288674268129768e-05, "NONE": 0.9905190819571295}, "tertiary": {"Academic Writing": 0.00019477284706825775, "Content Listing": 2.4891485462731703e-05, "Creative Writing": 3.4140546543387074e-05, "Customer Support Page": 1.6835684806441924e-05, "Q&A Forum": 1.0463762835858976e-05, "Discussion Thread": 8.498249909416341e-06, "Comment Section": 2.0825758415172628e-05, "Missing Content": 0.00010284790909884435, "Knowledge Article": 0.00023210523260843916, "Legal Notices": 4.574059615346352e-05, "News Article": 1.4642623481078367e-05, "Organizational Announcement": 1.4846583003032015e-05, "Nonfiction Writing": 2.6578949098133603e-05, "Organizational About Page": 3.05652516572092e-05, "Personal About Page": 1.6363739539436054e-05, "Personal Blog": 1.3259553658913846e-05, "Product Page": 1.0237915775334492e-05, "Spam / Ads": 1.72648884583996e-05, "Structured Data": 0.00048618112073602055, "Technical Writing": 0.0017540376484025636, "Transcript / Interview": 1.6356726290436793e-05, "Tutorial / How-To Guide": 0.000506250841524579, "User Reviews": 8.481316110361004e-06, "NONE": 0.9963938107693625}, "structured_records": {"false": 0.9978354010526708, "true": 0.00216459894732922}, "procedural_steps": {"false": 0.9864159525182997, "true": 0.013584047481700293}, "worked_solution": {"false": 0.9968702517733468, "true": 0.003129748226653232}, "contextual_dialogue": {"false": 0.9999949424097418, "true": 5.057590258097198e-06}, "content_quality": {"-2": 0.014838848979961856, "-1": 0.06783067502430842, "0": 0.32281327950473043, "1": 0.45982380173964976, "2": 0.13469339475134948}, "noise_severity": {"0": 0.9890774582089462, "1": 0.009515774849654948, "2": 0.0009157560423273952, "3": 0.0002943679032025223, "4": 0.0001966429958689267}, "missing_content_severity": {"0": 0.9768385618771416, "1": 0.013849133292619713, "2": 0.006045306954260941, "3": 0.0028845868169119562, "4": 0.0003824110590658183}, "factual_density": {"0": 0.007985229104981362, "1": 0.017861910652212206, "2": 0.12073048342511475, "3": 0.3264492286490625, "4": 0.5269731481686293}, "pretraining_utility": {"-2": 0.009361070553051049, "-1": 0.09318108367020393, "0": 0.4183991462967268, "1": 0.3894155730283392, "2": 0.08964312645167899}, "math_density": {"0": 0.01581474725876387, "1": 0.026768853110826153, "2": 0.11031863881566722, "3": 0.31356323247213835, "4": 0.5335345283426044}, "code_density": {"0": 0.00028487569190443194, "1": 0.0006400473554750341, "2": 0.021927812604980404, "3": 0.10628386745945055, "4": 0.8708633968881896}, "reasoning_density": {"0": 0.4973462739324593, "1": 0.06420074295988877, "2": 0.11844279447081792, "3": 0.17958829378535784, "4": 0.14042189485147613}, "education_level": {"none": 0.001817121707159883, "0": 0.0024705571970603207, "1": 0.07469111466302694, "2": 0.6786854627580375, "3": 0.1651330334146962, "4": 0.07720271026001913}, "creative_writing_quality": {"not_applicable": 0.9993686981871353, "-2": 0.00010395015214180452, "-1": 0.00015912717020700171, "0": 0.0002659402394083815, "1": 7.061501477895544e-05, "2": 3.1669236328627516e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.000270618524264416, "Art & Design": 0.012217402543927398, "Law & Crime": 0.00035226339732745735, "Education & Careers": 0.00025646863712370453, "Entertainment & Media": 0.021714793217249843, "Fashion & Beauty": 0.0003713321218650472, "Business, Economics & Finance": 0.0003995195750528166, "Food & Dining": 0.00021429226749742204, "Games & Gaming": 0.0006430521843587827, "Electronics & Hardware": 0.00031441659683955394, "Health, Healthcare & Wellness": 0.00028199903668323285, "History & Geography": 0.0003263127758363823, "Home, Pets & Hobbies": 0.00045879291584867454, "Industry & Production": 0.0001704910188401295, "Language, Literature & Philosophy": 0.00038761190111474093, "Politics & Government": 0.00015257631786785126, "Religion & Spirituality": 0.0003388769689995638, "Science, Mathematics & Engineering": 0.1007892534898227, "Relationships & Community": 0.00011699737466633405, "Software Usage & Applications": 0.0008111931049151282, "Programming & Software Engineering": 0.8360589131603536, "Sports & Fitness": 0.0002777270139307037, "Vehicles & Transportation": 0.0001459431983884644, "Travel & Tourism": 0.00029437635080291645, "Other / Uncovered Subject": 0.022187427554493124, "Unknown / Insufficient Content": 0.0004473487519300338}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008904950006205017, "Art & Design": 0.015277057428868193, "Law & Crime": 0.0007816435087518097, "Education & Careers": 0.000545953334620994, "Entertainment & Media": 0.043562807662445746, "Fashion & Beauty": 0.00046421257830574897, "Business, Economics & Finance": 0.000662773073286794, "Food & Dining": 0.00039225074644235565, "Games & Gaming": 0.0008278511140864127, "Electronics & Hardware": 0.000575504139892071, "Health, Healthcare & Wellness": 0.0005294150613777057, "History & Geography": 0.00045805761103005787, "Home, Pets & Hobbies": 0.0007493565383429388, "Industry & Production": 0.0003293557813521544, "Language, Literature & Philosophy": 0.00039524747688220093, "Politics & Government": 0.0002823323380321756, "Religion & Spirituality": 0.0006682898563986938, "Science, Mathematics & Engineering": 0.42461473154705265, "Relationships & Community": 0.00022960823766081577, "Software Usage & Applications": 0.0007527264842332618, "Programming & Software Engineering": 0.09345658690989597, "Sports & Fitness": 0.0005287157467421765, "Vehicles & Transportation": 0.0002492919402217887, "Travel & Tourism": 0.0006397756846961979, "Other / Uncovered Subject": 0.02801014348416304, "NONE": 0.38412581671459756}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9902, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9902, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:16.608252+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "4f757ab0-de4a-4986-93e9-7f6de2bd9b59", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "dd637b2202bf723b31e67d39397453fffa99af30051f41f2aa6dd4b948043e8c", "source_metadata": {"uuid": "4f757ab0-de4a-4986-93e9-7f6de2bd9b59", "repo_name": "kyeser/scTools", "relative_path": "scTools.py", "category": "ALGO", "algo_rel_score": 0.9998824596405029, "quality_score": 5.403520107269287, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 7, "repo_created_at": "2015-04-01T16:47:25.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "091c2df64a12164fe07af64249704641f112fb6d", "size_bytes": 4539, "file_timestamp": 1705114174, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "f83776f03deca2931516502f38ba3a79df2ab230"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 7, "repo_created_at": "2015-04-01T16:47:25.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "091c2df64a12164fe07af64249704641f112fb6d", "size_bytes": 4539, "file_timestamp": 1705114174, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "f83776f03deca2931516502f38ba3a79df2ab230", "url": "https://github.com/kyeser/scTools/blob/f83776f03deca2931516502f38ba3a79df2ab230/scTools.py"}, "gpt4o_tokens": 1291, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.6317022182581166, "noise_severity": 0.013016962627393013, "missing_content_severity": 0.03612315188814074, "factual_density": 3.3465631561241462, "pretraining_utility": 0.4567986011553911, "math_density": 3.322233941528993, "code_density": 3.8468008624965457, "reasoning_density": 1.4015387926635026, "education_level": 2.2403429572824276, "creative_writing_quality": -0.3691958145926097}, "score_applicability": {"education_level": 0.9981828782928401, "creative_writing_quality": 0.0006313018128647707}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/Sacha-Demos/DataMiningTextCSCI420/blob/2e191e2e82290ff83554185ab4f50587ab690fd0/text_proccessing.py", "id": "python-0-25a7a3cf83f97ec4bd68271f82c23ee8", "text": "import nltk\nfrom nltk.corpus import wordnet as wn\nfrom nltk.corpus import sentiwordnet as swn\n\nfrom frequency_vector import freqdict, normalize\n\ncharacter_ngrams=[2,3,4]\nword_ngrams=[2,3]\n\ntest=None\ntry:\n    test=wn.synsets(\"test\")\nexcept:\n    print(\"Problem with WordNet, make sure it is downloaded. (run 'nltk.download()' )\")\n    exit(1)\ntry:\n    swn.senti_synsets(\"test\")\nexcept BaseException as e:\n    print(\"Problem with SentiWordNet, make sure it is downloaded. (run 'nltk.download()' )\")\n    exit(1)\n\ndef proccess_book(bookdata):\n    for x in bookdata.chapters:\n        proccess_chapter(x)\n\ndef proccess_chapter(chapter):\n    chapter_register={}\n    chapter_register[\"validwords\"]=0\n    chapter_register[\"word_freq_dict\"]=freqdict()\n    chapter_register[\"region_freq_dict\"]=freqdict()\n    chapter_register[\"topic_freq_dict\"]=freqdict()\n    chapter_register[\"usage_freq_dict\"]=freqdict()\n    chapter_register[\"sense_dist_dict\"]=freqdict()\n    chapter_register[\"character_ngrams\"]={}\n    for n in character_ngrams:\n        chapter_register[\"character_ngrams\"][n]=freqdict()\n    chapter_register[\"word_ngrams\"]={}\n    for n in word_ngrams:\n        chapter_register[\"word_ngrams\"][n]=freqdict()\n    for x in chapter.paragraphs:\n        proccess_paragraph(x,chapter_register)\n    normalize(chapter_register[\"word_freq_dict\"])\n    normalize(chapter_register[\"region_freq_dict\"])\n    normalize(chapter_register[\"topic_freq_dict\"])\n    normalize(chapter_register[\"usage_freq_dict\"])\n    normalize(chapter_register[\"sense_dist_dict\"],cachetotal=\"total\")\n    for ngrams in chapter_register[\"character_ngrams\"]:\n        normalize(chapter_register[\"character_ngrams\"][ngrams])\n    for ngrams in chapter_register[\"word_ngrams\"]:\n        normalize(chapter_register[\"word_ngrams\"][ngrams])\n    chapter.chapter_register=chapter_register\n\ndef proccess_paragraph(paragraphdata,chapter_register):\n    for x in paragraphdata.sentences:\n        proccess_sentence(x,chapter_register)\n\n\ndef proccess_sentence(sentencedata,chapter_register):\n    wordngrams=chapter_register[\"word_ngrams\"]\n    for n in wordngrams:\n        if len(sentencedata.words)>n:\n            ngrams(wordngrams[n],n,sentencedata.words)\n    for x in sentencedata.words:\n        words=chapter_register[\"word_freq_dict\"]\n        words.plusplus(x,1)\n        try:\n            sets=wn.synsets(x)\n            if len(sets)<=0:\n                break\n            chapter_register[\"validwords\"]=chapter_register[\"validwords\"]+1\n            usage=chapter_register[\"usage_freq_dict\"]\n            usages = [inner for outer in sets for inner in outer.usage_domains()]\n            for use in usages:\n                usage.plusplus(use._name,float(1)/len(usages))\n            topic=chapter_register[\"topic_freq_dict\"]\n            topics = [inner for outer in sets for inner in outer.topic_domains()]\n            for top in topics:\n                topic.plusplus(top._name,float(1)/len(topics))\n            region=chapter_register[\"region_freq_dict\"]\n            regions = [inner for outer in sets for inner in outer.region_domains()]\n            for reg in regions:\n                region.plusplus(reg._name,float(1)/len(regions))\n            sense=chapter_register[\"sense_dist_dict\"]\n            sentis = [swn.senti_synset(synset._name) for synset in sets]\n            if None in sentis:\n                continue\n            for sen in sentis:\n                sense.plusplus(\"pos\",float(sen.pos_score())/len(sentis))\n                sense.plusplus(\"neg\",float(sen.neg_score())/len(sentis))\n                sense.plusplus(\"obj\",float(sen.obj_score())/len(sentis))\n            sense.plusplus(\"total\",1)\n            charngrams=chapter_register[\"character_ngrams\"]\n            for n in character_ngrams:\n                if len(x)>n:\n                    ngrams(charngrams[n],n,\"^\"+x+\"*\")\n        except BaseException as e:\n            print(e)\n            print(\"problem finding word:\"+x)\n\ndef ngrams(ngramfreq,n,seq,comb=tuple):\n    for i in range(len(seq)+1-n):\n        ngramfreq.plusplus(comb(seq[i:i+n]))\n    return ngramfreq\n\nclass dictreducer(dict):\n    def __init__(self,red=lambda x,y:x+y):\n        self.red=red\n    def append(self,x):\n        if x in self:\n            self[x]=self[x]+1\n        else:\n            self[x]=1\n    def __add__(self,other):\n        res=dictreducer()\n        for x in self.keys():\n            res[x]=self[x]\n        for x in other.keys():\n            if x in res:\n                res[x]=self.red(res[x],other[x])\n            else:\n                res[x]=other[x]\n        return res\n", "text_preview": "import nltk from nltk.corpus import wordnet as wn from nltk.corpus import sentiwordnet as swn from frequency_vector import freqdict, normalize character_ngrams=[2,3,4] word_ngrams=[2,3] test=None try: test=wn.synsets(\"test\") except: print(\"", "input_sha256": "13ff4d6adc7b4b1d45a0bc77c1ecc4ac31121d64610b147cc80e69016cbca143", "text_sha256": "25a7a3cf83f97ec4bd68271f82c23ee8d30136a2bbb50e81220f17c27b3cebc4", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12070, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.22241810940059115, "noise_severity": 0.01723747576904679, "missing_content_severity": 0.024483625751059123, "factual_density": 2.9617629815681243, "pretraining_utility": 0.38097963841855714, "math_density": 0.9903081997053927, "code_density": 3.8749870308424406, "reasoning_density": 0.3191602053880352, "education_level": 2.249751954425715, "creative_writing_quality": -0.7193261353910028}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.008664756295619802, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9963894038683108, "secondary": 0.9847433600512673, "tertiary": 0.9937848448814792, "structured_records": 0.0032146945691987148, "procedural_steps": 0.016884571096570766, "worked_solution": 0.001030507598879252, "contextual_dialogue": 6.439234304864828e-06, "content_quality": 0.3964342311208555, "noise_severity": 0.9856354368591277, "missing_content_severity": 0.9795969785407841, "factual_density": 0.13480248464010336, "pretraining_utility": 0.4433571516009768, "math_density": 0.17474316691217273, "code_density": 0.8958225257020337, "reasoning_density": 0.7340331621766374, "education_level": 0.689705148856555, "creative_writing_quality": 0.9995318295755801, "primary_topic": 0.91465189087758, "secondary_topic": 0.48853120425258656}, "probability_margin": {"primary": 0.9948176186149075, "secondary": 0.9791675041754284, "tertiary": 0.9913987625774322, "education_level": 0.5347327411160087, "creative_writing_quality": 0.9994883778815524, "primary_topic": 0.8607467642190502, "secondary_topic": 0.23087012612037777}, "raw_probabilities": {"primary": {"Academic Writing": 7.84993837640439e-05, "Content Listing": 1.4306963217633934e-05, "Creative Writing": 1.650693862446278e-05, "Customer Support Page": 8.782785810826457e-06, "Q&A Forum": 1.0336084959464135e-05, "Discussion Thread": 7.4407497657518635e-06, "Comment Section": 1.147973708408763e-05, "Missing Content": 3.4094170847163134e-05, "Knowledge Article": 0.0001053617760089249, "Legal Notices": 2.702150488494612e-05, "News Article": 4.10351546241318e-06, "Organizational Announcement": 8.072910795733189e-06, "Nonfiction Writing": 1.710509260329108e-05, "Organizational About Page": 1.1302773453114435e-05, "Personal About Page": 1.1350620752890247e-05, "Personal Blog": 6.947152500650759e-06, "Product Page": 5.990413482958087e-06, "Spam / Ads": 9.744124778207552e-06, "Structured Data": 0.0006887589727863869, "Technical Writing": 0.9965398453737978, "Transcript / Interview": 1.6042663592439675e-05, "Tutorial / How-To Guide": 0.0006389021984736048, "User Reviews": 5.777333662979751e-06, "Other / Unknown": 0.001722226758890314}, "secondary": {"Academic Writing": 0.0005952819566202176, "Content Listing": 6.823125333132059e-05, "Creative Writing": 9.354423729189254e-05, "Customer Support Page": 0.00014458186743584943, "Q&A Forum": 4.24124803744612e-05, "Discussion Thread": 3.0025087255886908e-05, "Comment Section": 6.338338843363381e-05, "Missing Content": 0.00021752528657883235, "Knowledge Article": 0.0006199165827716557, "Legal Notices": 9.906063695449764e-05, "News Article": 4.569589892577969e-05, "Organizational Announcement": 4.09179668670969e-05, "Nonfiction Writing": 4.7402634585753355e-05, "Organizational About Page": 6.166296716084453e-05, "Personal About Page": 4.688512874843782e-05, "Personal Blog": 4.1534376637968574e-05, "Product Page": 2.7290405377605864e-05, "Spam / Ads": 5.2440243559825926e-05, "Structured Data": 0.0036357845908608142, "Technical Writing": 0.006211549207036002, "Transcript / Interview": 5.7621947307439695e-05, "Tutorial / How-To Guide": 0.0023531366226515536, "User Reviews": 2.5061850768266636e-05, "NONE": 0.9853790533824645}, "tertiary": {"Academic Writing": 0.00018378212283467538, "Content Listing": 2.8390177186445076e-05, "Creative Writing": 4.116938349685974e-05, "Customer Support Page": 9.429321462223104e-05, "Q&A Forum": 1.75852800896037e-05, "Discussion Thread": 1.214096741696033e-05, "Comment Section": 3.3296800713505585e-05, "Missing Content": 0.00014594811714717482, "Knowledge Article": 0.00022189009771007585, "Legal Notices": 9.414895047878116e-05, "News Article": 2.2473904972137822e-05, "Organizational Announcement": 2.191676271769391e-05, "Nonfiction Writing": 3.0874240813530144e-05, "Organizational About Page": 3.6809339957430255e-05, "Personal About Page": 2.3697772502102803e-05, "Personal Blog": 1.9593061186030662e-05, "Product Page": 1.620138523771715e-05, "Spam / Ads": 2.8466930026843344e-05, "Structured Data": 0.0009228745340539859, "Technical Writing": 0.0026450471006520455, "Transcript / Interview": 2.7212481678323997e-05, "Tutorial / How-To Guide": 0.0012764120865387165, "User Reviews": 1.196560988289234e-05, "NONE": 0.9940438096780843}, "structured_records": {"false": 0.9967853054308012, "true": 0.0032146945691987148}, "procedural_steps": {"false": 0.9831154289034292, "true": 0.016884571096570766}, "worked_solution": {"false": 0.9989694924011208, "true": 0.001030507598879252}, "contextual_dialogue": {"false": 0.9999935607656951, "true": 6.439234304864828e-06}, "content_quality": {"-2": 0.03317746767783635, "-1": 0.1845754712715184, "0": 0.3877391123373479, "1": 0.3156673813988123, "2": 0.07884056731448497}, "noise_severity": {"0": 0.9860073426457141, "1": 0.011773174177314534, "2": 0.0014717625547274475, "3": 0.0004701060066981365, "4": 0.0002776146155457383}, "missing_content_severity": {"0": 0.9872868999816953, "1": 0.003970784741372346, "2": 0.006086774386611787, "3": 0.0022828713248194076, "4": 0.0003726695655012452}, "factual_density": {"0": 0.03729473104930495, "1": 0.061138596670868316, "2": 0.17273581710785652, "3": 0.360170670006338, "4": 0.3686601851656322}, "pretraining_utility": {"-2": 0.011532866437029356, "-1": 0.12043015695607666, "0": 0.42538665547604165, "1": 0.3608251140130121, "2": 0.08182520711784022}, "math_density": {"0": 0.5095656558945814, "1": 0.25171859634591404, "2": 0.08197904583467491, "3": 0.0523152960091896, "4": 0.10442140591563999}, "code_density": {"0": 0.0002201543919806775, "1": 0.00044388555068509614, "2": 0.011790648540973527, "3": 0.09921939785563454, "4": 0.8883259136607262}, "reasoning_density": {"0": 0.8484292451839391, "1": 0.047137316113665464, "2": 0.060881194657581884, "3": 0.023948476220047844, "4": 0.0196037678247656}, "education_level": {"none": 0.0014588643001597545, "0": 0.001867281210149519, "1": 0.016898471772731887, "2": 0.7414209573804624, "3": 0.2066882162644538, "4": 0.031666209072042595}, "creative_writing_quality": {"not_applicable": 0.9996098579796501, "-2": 0.00012148009809766838, "-1": 0.00010710622425701637, "0": 0.00010880379647451036, "1": 3.607673434079987e-05, "2": 1.6675167179835763e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00011138957230310673, "Art & Design": 0.00020457708068686092, "Law & Crime": 0.00015461341860653188, "Education & Careers": 0.00025556751881035666, "Entertainment & Media": 0.00014566228972600118, "Fashion & Beauty": 0.00010695672971379713, "Business, Economics & Finance": 0.00018984831251543964, "Food & Dining": 0.00011877129893043129, "Games & Gaming": 0.0001854560522462917, "Electronics & Hardware": 0.0001914201721814604, "Health, Healthcare & Wellness": 0.00013813695015671098, "History & Geography": 0.0001421638505474951, "Home, Pets & Hobbies": 0.00011249395400453411, "Industry & Production": 0.00028675454257311953, "Language, Literature & Philosophy": 0.0183032805807094, "Politics & Government": 8.091361576232876e-05, "Religion & Spirituality": 0.0001220672529424492, "Science, Mathematics & Engineering": 0.05718774624016138, "Relationships & Community": 6.834780278087857e-05, "Software Usage & Applications": 0.001026090082313286, "Programming & Software Engineering": 0.9179345104592116, "Sports & Fitness": 0.0001760997489025377, "Vehicles & Transportation": 8.826745938368798e-05, "Travel & Tourism": 0.00015226739372333072, "Other / Uncovered Subject": 0.002300478500984436, "Unknown / Insufficient Content": 0.0002161191201226182}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007981610583601897, "Art & Design": 0.0008288432755426535, "Law & Crime": 0.0007374989260812661, "Education & Careers": 0.0015058901271316164, "Entertainment & Media": 0.0006481813434546761, "Fashion & Beauty": 0.00038085796293194584, "Business, Economics & Finance": 0.0007824552916612352, "Food & Dining": 0.00029255497179187765, "Games & Gaming": 0.0005194971493264984, "Electronics & Hardware": 0.0006507396636347055, "Health, Healthcare & Wellness": 0.0006004825078288414, "History & Geography": 0.0005401300042006137, "Home, Pets & Hobbies": 0.0003673297099504849, "Industry & Production": 0.0008283817832858506, "Language, Literature & Philosophy": 0.11380788091099435, "Politics & Government": 0.00029996306723215256, "Religion & Spirituality": 0.0005759510406288117, "Science, Mathematics & Engineering": 0.27733295489172466, "Relationships & Community": 0.0002577678758197864, "Software Usage & Applications": 0.00135930413018991, "Programming & Software Engineering": 0.0843086040372005, "Sports & Fitness": 0.00044438865918660763, "Vehicles & Transportation": 0.00026012208786005186, "Travel & Tourism": 0.000568956885968026, "Other / Uncovered Subject": 0.003100021625910264, "NONE": 0.5082030810121024}}, "probabilities": {"primary": {"Academic Writing": 7.84993837640439e-05, "Content Listing": 1.4306963217633934e-05, "Creative Writing": 1.650693862446278e-05, "Customer Support Page": 8.782785810826457e-06, "Q&A Forum": 1.0336084959464135e-05, "Discussion Thread": 7.4407497657518635e-06, "Comment Section": 1.147973708408763e-05, "Missing Content": 3.4094170847163134e-05, "Knowledge Article": 0.0001053617760089249, "Legal Notices": 2.702150488494612e-05, "News Article": 4.10351546241318e-06, "Organizational Announcement": 8.072910795733189e-06, "Nonfiction Writing": 1.710509260329108e-05, "Organizational About Page": 1.1302773453114435e-05, "Personal About Page": 1.1350620752890247e-05, "Personal Blog": 6.947152500650759e-06, "Product Page": 5.990413482958087e-06, "Spam / Ads": 9.744124778207552e-06, "Structured Data": 0.0006887589727863869, "Technical Writing": 0.9965398453737978, "Transcript / Interview": 1.6042663592439675e-05, "Tutorial / How-To Guide": 0.0006389021984736048, "User Reviews": 5.777333662979751e-06, "Other / Unknown": 0.001722226758890314}, "secondary": {"Academic Writing": 0.0005952819566202176, "Content Listing": 6.823125333132059e-05, "Creative Writing": 9.354423729189254e-05, "Customer Support Page": 0.00014458186743584943, "Q&A Forum": 4.24124803744612e-05, "Discussion Thread": 3.0025087255886908e-05, "Comment Section": 6.338338843363381e-05, "Missing Content": 0.00021752528657883235, "Knowledge Article": 0.0006199165827716557, "Legal Notices": 9.906063695449764e-05, "News Article": 4.569589892577969e-05, "Organizational Announcement": 4.09179668670969e-05, "Nonfiction Writing": 4.7402634585753355e-05, "Organizational About Page": 6.166296716084453e-05, "Personal About Page": 4.688512874843782e-05, "Personal Blog": 4.1534376637968574e-05, "Product Page": 2.7290405377605864e-05, "Spam / Ads": 5.2440243559825926e-05, "Structured Data": 0.0036357845908608142, "Technical Writing": 0.006211549207036002, "Transcript / Interview": 5.7621947307439695e-05, "Tutorial / How-To Guide": 0.0023531366226515536, "User Reviews": 2.5061850768266636e-05, "NONE": 0.9853790533824645}, "tertiary": {"Academic Writing": 0.00018378212283467538, "Content Listing": 2.8390177186445076e-05, "Creative Writing": 4.116938349685974e-05, "Customer Support Page": 9.429321462223104e-05, "Q&A Forum": 1.75852800896037e-05, "Discussion Thread": 1.214096741696033e-05, "Comment Section": 3.3296800713505585e-05, "Missing Content": 0.00014594811714717482, "Knowledge Article": 0.00022189009771007585, "Legal Notices": 9.414895047878116e-05, "News Article": 2.2473904972137822e-05, "Organizational Announcement": 2.191676271769391e-05, "Nonfiction Writing": 3.0874240813530144e-05, "Organizational About Page": 3.6809339957430255e-05, "Personal About Page": 2.3697772502102803e-05, "Personal Blog": 1.9593061186030662e-05, "Product Page": 1.620138523771715e-05, "Spam / Ads": 2.8466930026843344e-05, "Structured Data": 0.0009228745340539859, "Technical Writing": 0.0026450471006520455, "Transcript / Interview": 2.7212481678323997e-05, "Tutorial / How-To Guide": 0.0012764120865387165, "User Reviews": 1.196560988289234e-05, "NONE": 0.9940438096780843}, "structured_records": {"false": 0.9967853054308012, "true": 0.0032146945691987148}, "procedural_steps": {"false": 0.9831154289034292, "true": 0.016884571096570766}, "worked_solution": {"false": 0.9989694924011208, "true": 0.001030507598879252}, "contextual_dialogue": {"false": 0.9999935607656951, "true": 6.439234304864828e-06}, "content_quality": {"-2": 0.03317746767783636, "-1": 0.18457547127151844, "0": 0.387739112337348, "1": 0.31566738139881234, "2": 0.07884056731448498}, "noise_severity": {"0": 0.9860073426457141, "1": 0.011773174177314534, "2": 0.0014717625547274475, "3": 0.0004701060066981365, "4": 0.0002776146155457383}, "missing_content_severity": {"0": 0.9872868999816953, "1": 0.003970784741372346, "2": 0.006086774386611787, "3": 0.0022828713248194076, "4": 0.0003726695655012452}, "factual_density": {"0": 0.03729473104930495, "1": 0.061138596670868316, "2": 0.17273581710785652, "3": 0.360170670006338, "4": 0.3686601851656322}, "pretraining_utility": {"-2": 0.011532866437029356, "-1": 0.12043015695607666, "0": 0.42538665547604165, "1": 0.3608251140130121, "2": 0.08182520711784022}, "math_density": {"0": 0.5095656558945815, "1": 0.2517185963459141, "2": 0.08197904583467493, "3": 0.052315296009189605, "4": 0.10442140591564}, "code_density": {"0": 0.0002201543919806775, "1": 0.00044388555068509614, "2": 0.011790648540973527, "3": 0.09921939785563454, "4": 0.8883259136607262}, "reasoning_density": {"0": 0.8484292451839393, "1": 0.04713731611366547, "2": 0.06088119465758189, "3": 0.023948476220047848, "4": 0.019603767824765604}, "education_level": {"none": 0.0014588643001597545, "0": 0.001867281210149519, "1": 0.016898471772731887, "2": 0.7414209573804624, "3": 0.2066882162644538, "4": 0.031666209072042595}, "creative_writing_quality": {"not_applicable": 0.9996098579796502, "-2": 0.0001214800980976684, "-1": 0.00010710622425701639, "0": 0.00010880379647451038, "1": 3.607673434079988e-05, "2": 1.6675167179835766e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00011138957230310673, "Art & Design": 0.00020457708068686092, "Law & Crime": 0.00015461341860653188, "Education & Careers": 0.00025556751881035666, "Entertainment & Media": 0.00014566228972600118, "Fashion & Beauty": 0.00010695672971379713, "Business, Economics & Finance": 0.00018984831251543964, "Food & Dining": 0.00011877129893043129, "Games & Gaming": 0.0001854560522462917, "Electronics & Hardware": 0.0001914201721814604, "Health, Healthcare & Wellness": 0.00013813695015671098, "History & Geography": 0.0001421638505474951, "Home, Pets & Hobbies": 0.00011249395400453411, "Industry & Production": 0.00028675454257311953, "Language, Literature & Philosophy": 0.0183032805807094, "Politics & Government": 8.091361576232876e-05, "Religion & Spirituality": 0.0001220672529424492, "Science, Mathematics & Engineering": 0.05718774624016138, "Relationships & Community": 6.834780278087857e-05, "Software Usage & Applications": 0.001026090082313286, "Programming & Software Engineering": 0.9179345104592116, "Sports & Fitness": 0.0001760997489025377, "Vehicles & Transportation": 8.826745938368798e-05, "Travel & Tourism": 0.00015226739372333072, "Other / Uncovered Subject": 0.002300478500984436, "Unknown / Insufficient Content": 0.0002161191201226182}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007981610583601897, "Art & Design": 0.0008288432755426535, "Law & Crime": 0.0007374989260812661, "Education & Careers": 0.0015058901271316164, "Entertainment & Media": 0.0006481813434546761, "Fashion & Beauty": 0.00038085796293194584, "Business, Economics & Finance": 0.0007824552916612352, "Food & Dining": 0.00029255497179187765, "Games & Gaming": 0.0005194971493264984, "Electronics & Hardware": 0.0006507396636347055, "Health, Healthcare & Wellness": 0.0006004825078288414, "History & Geography": 0.0005401300042006137, "Home, Pets & Hobbies": 0.0003673297099504849, "Industry & Production": 0.0008283817832858506, "Language, Literature & Philosophy": 0.11380788091099435, "Politics & Government": 0.00029996306723215256, "Religion & Spirituality": 0.0005759510406288117, "Science, Mathematics & Engineering": 0.27733295489172466, "Relationships & Community": 0.0002577678758197864, "Software Usage & Applications": 0.00135930413018991, "Programming & Software Engineering": 0.0843086040372005, "Sports & Fitness": 0.00044438865918660763, "Vehicles & Transportation": 0.00026012208786005186, "Travel & Tourism": 0.000568956885968026, "Other / Uncovered Subject": 0.003100021625910264, "NONE": 0.5082030810121024}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9668, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9668, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:18.335126+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "5660af6c-60ea-4cd1-aff2-2f3895e1ca9f", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "fbe8ebf446e9718579348909dd4007424a850409ac0f7f74d07949af089e6643", "source_metadata": {"uuid": "5660af6c-60ea-4cd1-aff2-2f3895e1ca9f", "repo_name": "Sacha-Demos/DataMiningTextCSCI420", "relative_path": "text_proccessing.py", "category": "DATA", "algo_rel_score": 0.9270222783088684, "quality_score": 3.9264793395996094, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 60, "repo_created_at": "2015-10-15T19:05:58.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "04ea3cfab5f6a3f58df5dd4620bd07d3c316cd5e", "size_bytes": 4551, "file_timestamp": 1448943366, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2e191e2e82290ff83554185ab4f50587ab690fd0"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 60, "repo_created_at": "2015-10-15T19:05:58.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "04ea3cfab5f6a3f58df5dd4620bd07d3c316cd5e", "size_bytes": 4551, "file_timestamp": 1448943366, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2e191e2e82290ff83554185ab4f50587ab690fd0", "url": "https://github.com/Sacha-Demos/DataMiningTextCSCI420/blob/2e191e2e82290ff83554185ab4f50587ab690fd0/text_proccessing.py"}, "gpt4o_tokens": 1089, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.22241810940059115, "noise_severity": 0.01723747576904679, "missing_content_severity": 0.024483625751059123, "factual_density": 2.9617629815681243, "pretraining_utility": 0.38097963841855714, "math_density": 0.9903081997053927, "code_density": 3.8749870308424406, "reasoning_density": 0.3191602053880352, "education_level": 2.249751954425715, "creative_writing_quality": -0.7193261353910028}, "score_applicability": {"education_level": 0.9985411356998403, "creative_writing_quality": 0.0003901420203498308}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/windjackz/DecisionTree/blob/84aadc792c8a9fc6c0991fee060c4e53eb5b15c8/DecisionTree.py", "id": "python-0-4681b871eb5447d0fe56cf2ee2ec49ee", "text": "import numpy as np\nfrom math import log\nfrom itertools import islice\nimport csv\n\ndef get_entropy(data_set):\n    labels_count = {}\n    record_nums = len(data_set)\n    for data in data_set:\n        label = data[-1]\n        labels_count[label] = labels_count.get(label, 0) + 1\n    entropy = 0.0\n    for key in labels_count:\n        p = float(labels_count[key])/record_nums\n        entropy -= p*log(p,2)\n    return  entropy\n\ndef get_conditional_entropy(branch_dict,total_record_nums):\n    entropy = 0.0\n    for feature_value in branch_dict.keys():\n        p = float(len(branch_dict[feature_value]))/total_record_nums\n        entropy += p*get_entropy(branch_dict[feature_value])\n    return entropy\n\ndef create_tree(date_sets, feature_labels):\n    labels = [list[-1] for list in date_sets]\n    labels_set = set(labels)\n    if len(labels_set) == 1:\n        return labels_set.pop()\n    feature_num = len(date_sets[0])-1\n    if feature_num == 1:\n        return get_most_common_value(labels)\n    entropy_oringal = get_entropy(date_sets)\n    record_nums = len(date_sets)\n    information_gain = 0.0\n    best_information_gain = 0.0\n    best_feature_index = 0.0\n    best_brahcn_dict = {}\n    for index in range(0,feature_num):\n        branch_dict = get_branch_sets(date_sets,index)\n        entropy_condition = get_conditional_entropy(branch_dict,record_nums)\n        information_gain = entropy_oringal - entropy_condition\n        if(information_gain>best_information_gain):\n            best_information_gain = information_gain\n            best_feature_index = index\n            best_brahcn_dict = branch_dict\n    if 0 == information_gain:\n        return get_most_common_value(labels)\n    feature = feature_labels[best_feature_index]\n    del(feature_labels[best_feature_index])\n    tree_node = {}\n    for feature_value in best_brahcn_dict.keys():\n        tree_node[feature_value] = create_tree(best_brahcn_dict[feature_value], feature_labels[:])\n    tree = {}\n    tree[feature] = tree_node\n    return tree\n\ndef search_tree(tree, feature_labels, test_data):\n    feature = tree.keys()[0]\n    feature_index = feature_labels.index(feature)\n    sub_tree = tree[feature][test_data[feature_index]]\n    if isinstance(sub_tree, dict):\n        return search_tree(sub_tree, feature_labels, test_data)\n    else :\n        return sub_tree\n\ndef get_most_common_value(list):\n    count_dict = {}\n    for label in list:\n        count_dict[label] = count_dict.get(label,0) + 1\n    keys = count_dict.keys()\n    temp = keys[0]\n    for key in keys:\n        if count_dict[key] > count_dict[temp]:\n            temp = key\n    return temp\n\ndef get_branch_sets(data_set,feature_index):\n    branch_sets = []\n    branch_dict = {}\n    for record in data_set:\n        value = record[feature_index]\n        if value not in branch_dict:\n            branch_dict[value] = []\n        new_record = record[0:feature_index]\n        new_record.extend(record[feature_index+1:])\n        branch_dict[value].append(new_record)\n    for key in branch_dict.keys():\n        branch_sets.append(branch_dict[key])\n    return branch_dict\n\ndef classcify_bench(train_set, feature_labels, test_data_set):\n    result = []\n    print \"creating tree...\"\n    tree = create_tree(train_set, feature_labels[:])\n    print \"classifing tree...\"\n    for test_data in test_data_set:\n        result.append(search_tree(tree,feature_labels, test_data))\n    return result\n\ndef discretize_dataset(data_set,splite_value_dict):\n    num_features = len(data_set[0]) - 1\n    for i in range(num_features):\n        for j in range(len(data_set)):\n            if(data_set[j][i]<splite_value_dict[i]):\n                data_set[j][i] = 0\n            else:\n                data_set[j][i] = 1\n\ndef split_data_set(data_set,feature_index,value):\n    samller_set = []\n    bigger_set = []\n    for data in data_set:\n        new_record = data[:feature_index]\n        new_record.extend(data[feature_index+1:])\n        if data[feature_index] >= value:\n            bigger_set.append(new_record)\n        else:\n            samller_set.append(new_record)\n    return samller_set,bigger_set\n\ndef get_discretize_splite_value_dict(data_set,clip_count = 2):\n    best_splite_value_dict = {}\n    features_num = len(data_set[0]) - 1\n    record_num = len(data_set)\n    entropy_oringinal = get_entropy(data_set)\n    for index in range(0,features_num):\n        print \"caluting the \" + str(index) + \" th feathre splite point\"\n        best_information_gain = 0.0\n        values = [record[index] for record in data_set]\n        values_set = set(values)\n        values_list = list(values_set)\n        values_num = len(values_list)\n        distance = values_num/clip_count\n        if clip_count > values_num:\n            distance = 1\n            clip_count = values_num\n        splite_index = -1\n        for clip_times in range(0,clip_count):\n            splite_index += distance\n            splite_value = values_list[splite_index]\n            set1,set2 = split_data_set(data_set, index, splite_value)\n            entropy_conditional = get_conditional_entropy({\"set1\":set1,\"set2\":set2},record_num)\n            infomation_gain = entropy_oringinal - entropy_conditional\n            if infomation_gain > best_information_gain:\n                best_information_gain = infomation_gain\n                best_splite_value_dict[index] = splite_value\n    return best_splite_value_dict\n\n#///////////////////////////////////////////////////////////////////////////\n\ndef lod_csv_file_dt(path):\n    data_set = []\n    labels = []\n    csvfile = file(path,'rb')\n    reader = csv.reader(csvfile)\n    index = 0\n    for line in reader:\n        if(0==index):\n            labels.extend(line[:-1])\n        else:\n            line =  map(float,line)\n            data_set.append(line)\n        index = index+1\n    return data_set, labels\n\ndef write_list(list,path):\n    f=file(path,\"w+\")\n    for value in list:\n        f.writelines(str(value)+\"\\n\")\n    f.close()\n\ndef run():\n    data__set_path = \"lab5/Datac_all.csv\"\n    result_path = \"lab5/dt_result.txt\"\n    train_num = 27751\n    data_list,feature_labels = lod_csv_file_dt(data__set_path)\n    print \"calculating splite points...\"\n    splite_value_dict = get_discretize_splite_value_dict(data_list[:train_num],20)\n    discretize_dataset(data_list,splite_value_dict)\n    result = classcify_bench(data_list[:train_num], feature_labels, data_list[train_num:])\n    print \"finish\"\n    write_list(result,result_path)\n\nrun()", "text_preview": "import numpy as np from math import log from itertools import islice import csv def get_entropy(data_set): labels_count = {} record_nums = len(data_set) for data in data_set: label = data[-1] labels_count[label] = labels_count.get(label, 0)", "input_sha256": "2470b531a4445cdda00eee680d8dcede78556b93c272c1cb377b606daf37c068", "text_sha256": "4681b871eb5447d0fe56cf2ee2ec49ee60e64cde10eb085e7c94286722e7c028", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12072, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.07320474076899997, "noise_severity": 0.034591533329751525, "missing_content_severity": 0.00417550685220121, "factual_density": 3.231506604175974, "pretraining_utility": 0.6900455643243842, "math_density": 3.1513380694565862, "code_density": 3.860725157085877, "reasoning_density": 1.5064180119782158, "education_level": 2.1745590444685563, "creative_writing_quality": -0.4781991343323486}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.4636 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.009003192298753682, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9953809173108045, "secondary": 0.9861571685546023, "tertiary": 0.9955518039967801, "structured_records": 0.0011765329040041965, "procedural_steps": 0.04010732670185159, "worked_solution": 0.010855220079834242, "contextual_dialogue": 5.907945531506411e-06, "content_quality": 0.43013837867655635, "noise_severity": 0.971173722225207, "missing_content_severity": 0.996520410956499, "factual_density": 0.35958883681331155, "pretraining_utility": 0.5365366599538063, "math_density": 0.2927817245471551, "code_density": 0.8839376309048975, "reasoning_density": 0.0, "education_level": 0.753547165276276, "creative_writing_quality": 0.9987115385481089, "primary_topic": 0.8832770366110653, "secondary_topic": 0.4421032552600857}, "probability_margin": {"primary": 0.9937844881387439, "secondary": 0.9805867162675954, "tertiary": 0.9939603984321967, "education_level": 0.6334541525274391, "creative_writing_quality": 0.9984379454573473, "primary_topic": 0.7804250377437928, "secondary_topic": 0.11052075098085651}, "raw_probabilities": {"primary": {"Academic Writing": 8.008376773888288e-05, "Content Listing": 2.630033425450555e-05, "Creative Writing": 2.5681338147760684e-05, "Customer Support Page": 1.1563316114817464e-05, "Q&A Forum": 1.5595425900470397e-05, "Discussion Thread": 1.0610128962444766e-05, "Comment Section": 1.6262736512634583e-05, "Missing Content": 2.950420460367934e-05, "Knowledge Article": 0.00015500425890939198, "Legal Notices": 3.3244898783589846e-05, "News Article": 6.619022794283656e-06, "Organizational Announcement": 1.2939047263375684e-05, "Nonfiction Writing": 2.142197396225332e-05, "Organizational About Page": 1.6454562042321076e-05, "Personal About Page": 1.6794195679501068e-05, "Personal Blog": 1.0807602598388033e-05, "Product Page": 8.627705594111112e-06, "Spam / Ads": 1.4613289425532486e-05, "Structured Data": 0.0005170976534449185, "Technical Writing": 0.9955733790895209, "Transcript / Interview": 2.118638768963395e-05, "Tutorial / How-To Guide": 0.0017888909507770934, "User Reviews": 9.376975426343221e-06, "Other / Unknown": 0.001577941133853128}, "secondary": {"Academic Writing": 0.0003840143860195816, "Content Listing": 5.307022453470456e-05, "Creative Writing": 6.67209080407942e-05, "Customer Support Page": 3.3639582954557376e-05, "Q&A Forum": 2.5772385192772417e-05, "Discussion Thread": 2.089788203127147e-05, "Comment Section": 4.8127141508848146e-05, "Missing Content": 0.00012065354933242068, "Knowledge Article": 0.0003249316562951522, "Legal Notices": 8.070970971063491e-05, "News Article": 3.6623430642870984e-05, "Organizational Announcement": 3.2308553833023425e-05, "Nonfiction Writing": 3.3690903870898725e-05, "Organizational About Page": 4.036840390927246e-05, "Personal About Page": 3.488126053096145e-05, "Personal Blog": 3.20924721570806e-05, "Product Page": 2.120181776441286e-05, "Spam / Ads": 3.527066011712833e-05, "Structured Data": 0.002299709017673188, "Technical Writing": 0.006147236930565209, "Transcript / Interview": 4.19982813142306e-05, "Tutorial / How-To Guide": 0.0033326717436591526, "User Reviews": 1.9455900181192302e-05, "NONE": 0.9867339531981606}, "tertiary": {"Academic Writing": 0.0001495069143023455, "Content Listing": 3.0000225213250376e-05, "Creative Writing": 4.1013561330111924e-05, "Customer Support Page": 1.8749801469019352e-05, "Q&A Forum": 1.3192699117065392e-05, "Discussion Thread": 1.0716386541862505e-05, "Comment Section": 2.995601177345562e-05, "Missing Content": 9.694175496180502e-05, "Knowledge Article": 0.00016614216146075577, "Legal Notices": 8.307563600552528e-05, "News Article": 2.3330355032763326e-05, "Organizational Announcement": 1.687699415810636e-05, "Nonfiction Writing": 2.3479064668282834e-05, "Organizational About Page": 3.470298518534932e-05, "Personal About Page": 2.1455268795717184e-05, "Personal Blog": 1.7597001813917297e-05, "Product Page": 1.4826388124085898e-05, "Spam / Ads": 2.312082079639485e-05, "Structured Data": 0.0008595139305680343, "Technical Writing": 0.0017767470647175833, "Transcript / Interview": 2.3809411995219294e-05, "Tutorial / How-To Guide": 0.000776449107480584, "User Reviews": 1.1650957574455546e-05, "NONE": 0.9957371454969143}, "structured_records": {"false": 0.9988234670959958, "true": 0.0011765329040041965}, "procedural_steps": {"false": 0.9598926732981484, "true": 0.04010732670185159}, "worked_solution": {"false": 0.9891447799201658, "true": 0.010855220079834242}, "contextual_dialogue": {"false": 0.9999940920544685, "true": 5.907945531506411e-06}, "content_quality": {"-2": 0.030656470533221212, "-1": 0.24400166134312382, "0": 0.4067572927632141, "1": 0.2586498075423155, "2": 0.05993476781812536}, "noise_severity": {"0": 0.9688162936782331, "1": 0.028865146919071785, "2": 0.0015267244115622284, "3": 0.0004944023769757536, "4": 0.0002974326141570052}, "missing_content_severity": {"0": 0.9978393725869569, "1": 0.0007727617488639952, "2": 0.0008535044885291347, "3": 0.00044170857632044354, "4": 9.265259932940337e-05}, "factual_density": {"0": 0.015415399450080898, "1": 0.030114522038397373, "2": 0.14502829534996275, "3": 0.3264316412085848, "4": 0.48301014195297404}, "pretraining_utility": {"-2": 0.006596261830999894, "-1": 0.04386837235508741, "0": 0.3255296916623496, "1": 0.5009048879616548, "2": 0.12310078618990832}, "math_density": {"0": 0.014265478673070011, "1": 0.04240372260178639, "2": 0.1672987937567429, "3": 0.32979126053228874, "4": 0.4462407444361119}, "code_density": {"0": 0.00026462040214153044, "1": 0.0005441339561029874, "2": 0.017443391986422828, "3": 0.10169717546440218, "4": 0.8800506781909304}, "reasoning_density": {"0": 0.4886901473278693, "1": 0.04849613397199813, "2": 0.10904036695125581, "3": 0.17525226289180038, "4": 0.17852108885707618}, "education_level": {"none": 0.0012103473665551207, "0": 0.0016125057279237991, "1": 0.022122584767722726, "2": 0.79462263773023, "3": 0.16116848520279087, "4": 0.019263439204777424}, "creative_writing_quality": {"not_applicable": 0.998926282123424, "-2": 0.00017684703800734583, "-1": 0.0003010928672248667, "0": 0.0004883366660766913, "1": 7.354662639083039e-05, "2": 3.3894678876421425e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00015071084360693046, "Art & Design": 0.0002333565214146323, "Law & Crime": 0.00022942436695826333, "Education & Careers": 0.0001948284375093778, "Entertainment & Media": 0.00015033151773125372, "Fashion & Beauty": 0.00011988302887009409, "Business, Economics & Finance": 0.0002411633315115559, "Food & Dining": 0.0001322199058280937, "Games & Gaming": 0.0002625197112908921, "Electronics & Hardware": 0.0002344083008287989, "Health, Healthcare & Wellness": 0.00019688405976022732, "History & Geography": 0.00023108863011613262, "Home, Pets & Hobbies": 0.00013807304461913904, "Industry & Production": 0.0001749804272951648, "Language, Literature & Philosophy": 0.00015658271758538459, "Politics & Government": 9.565139418790821e-05, "Religion & Spirituality": 0.00013346747263780524, "Science, Mathematics & Engineering": 0.10734134361300078, "Relationships & Community": 8.842596559932198e-05, "Software Usage & Applications": 0.0006222152111941541, "Programming & Software Engineering": 0.8877663813567935, "Sports & Fitness": 0.00018235613227218515, "Vehicles & Transportation": 9.943904501743944e-05, "Travel & Tourism": 0.00016853994575585204, "Other / Uncovered Subject": 0.0004595673858688915, "Unknown / Insufficient Content": 0.0001961576327461058}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006660906513166093, "Art & Design": 0.0006367111031913744, "Law & Crime": 0.0006013714930693244, "Education & Careers": 0.0006081330839976033, "Entertainment & Media": 0.00043732394033405144, "Fashion & Beauty": 0.0003422321487622219, "Business, Economics & Finance": 0.0005262343388970408, "Food & Dining": 0.00029529545325187966, "Games & Gaming": 0.00046831352884795516, "Electronics & Hardware": 0.000577756963564361, "Health, Healthcare & Wellness": 0.0005815416725000124, "History & Geography": 0.00041044384586106373, "Home, Pets & Hobbies": 0.00033949310738660385, "Industry & Production": 0.0004295501291905556, "Language, Literature & Philosophy": 0.00028700993808491277, "Politics & Government": 0.0002715477957539583, "Religion & Spirituality": 0.0004231465429122897, "Science, Mathematics & Engineering": 0.4635608223654671, "Relationships & Community": 0.00019663134231325487, "Software Usage & Applications": 0.0007302825426902844, "Programming & Software Engineering": 0.1725720206621379, "Sports & Fitness": 0.0004147710501046349, "Vehicles & Transportation": 0.00023961969245472298, "Travel & Tourism": 0.0004813252644526515, "Other / Uncovered Subject": 0.0008622599588471891, "NONE": 0.3530400713846106}}, "probabilities": {"primary": {"Academic Writing": 8.008376773888288e-05, "Content Listing": 2.630033425450555e-05, "Creative Writing": 2.5681338147760684e-05, "Customer Support Page": 1.1563316114817464e-05, "Q&A Forum": 1.5595425900470397e-05, "Discussion Thread": 1.0610128962444766e-05, "Comment Section": 1.6262736512634583e-05, "Missing Content": 2.950420460367934e-05, "Knowledge Article": 0.00015500425890939198, "Legal Notices": 3.3244898783589846e-05, "News Article": 6.619022794283656e-06, "Organizational Announcement": 1.2939047263375684e-05, "Nonfiction Writing": 2.142197396225332e-05, "Organizational About Page": 1.6454562042321076e-05, "Personal About Page": 1.6794195679501068e-05, "Personal Blog": 1.0807602598388033e-05, "Product Page": 8.627705594111112e-06, "Spam / Ads": 1.4613289425532486e-05, "Structured Data": 0.0005170976534449185, "Technical Writing": 0.9955733790895209, "Transcript / Interview": 2.118638768963395e-05, "Tutorial / How-To Guide": 0.0017888909507770934, "User Reviews": 9.376975426343221e-06, "Other / Unknown": 0.001577941133853128}, "secondary": {"Academic Writing": 0.0003840143860195816, "Content Listing": 5.307022453470456e-05, "Creative Writing": 6.67209080407942e-05, "Customer Support Page": 3.3639582954557376e-05, "Q&A Forum": 2.5772385192772417e-05, "Discussion Thread": 2.089788203127147e-05, "Comment Section": 4.8127141508848146e-05, "Missing Content": 0.00012065354933242068, "Knowledge Article": 0.0003249316562951522, "Legal Notices": 8.070970971063491e-05, "News Article": 3.6623430642870984e-05, "Organizational Announcement": 3.2308553833023425e-05, "Nonfiction Writing": 3.3690903870898725e-05, "Organizational About Page": 4.036840390927246e-05, "Personal About Page": 3.488126053096145e-05, "Personal Blog": 3.20924721570806e-05, "Product Page": 2.120181776441286e-05, "Spam / Ads": 3.527066011712833e-05, "Structured Data": 0.002299709017673188, "Technical Writing": 0.006147236930565209, "Transcript / Interview": 4.19982813142306e-05, "Tutorial / How-To Guide": 0.0033326717436591526, "User Reviews": 1.9455900181192302e-05, "NONE": 0.9867339531981606}, "tertiary": {"Academic Writing": 0.0001495069143023455, "Content Listing": 3.0000225213250376e-05, "Creative Writing": 4.1013561330111924e-05, "Customer Support Page": 1.8749801469019352e-05, "Q&A Forum": 1.3192699117065392e-05, "Discussion Thread": 1.0716386541862505e-05, "Comment Section": 2.995601177345562e-05, "Missing Content": 9.694175496180502e-05, "Knowledge Article": 0.00016614216146075577, "Legal Notices": 8.307563600552528e-05, "News Article": 2.3330355032763326e-05, "Organizational Announcement": 1.687699415810636e-05, "Nonfiction Writing": 2.3479064668282834e-05, "Organizational About Page": 3.470298518534932e-05, "Personal About Page": 2.1455268795717184e-05, "Personal Blog": 1.7597001813917297e-05, "Product Page": 1.4826388124085898e-05, "Spam / Ads": 2.312082079639485e-05, "Structured Data": 0.0008595139305680343, "Technical Writing": 0.0017767470647175833, "Transcript / Interview": 2.3809411995219294e-05, "Tutorial / How-To Guide": 0.000776449107480584, "User Reviews": 1.1650957574455546e-05, "NONE": 0.9957371454969143}, "structured_records": {"false": 0.9988234670959958, "true": 0.0011765329040041965}, "procedural_steps": {"false": 0.9598926732981484, "true": 0.04010732670185159}, "worked_solution": {"false": 0.9891447799201658, "true": 0.010855220079834242}, "contextual_dialogue": {"false": 0.9999940920544685, "true": 5.907945531506411e-06}, "content_quality": {"-2": 0.030656470533221212, "-1": 0.24400166134312382, "0": 0.4067572927632141, "1": 0.2586498075423155, "2": 0.05993476781812536}, "noise_severity": {"0": 0.9688162936782332, "1": 0.02886514691907179, "2": 0.0015267244115622287, "3": 0.0004944023769757537, "4": 0.00029743261415700523}, "missing_content_severity": {"0": 0.997839372586957, "1": 0.0007727617488639953, "2": 0.0008535044885291348, "3": 0.0004417085763204436, "4": 9.265259932940339e-05}, "factual_density": {"0": 0.0154153994500809, "1": 0.030114522038397377, "2": 0.14502829534996278, "3": 0.32643164120858487, "4": 0.4830101419529741}, "pretraining_utility": {"-2": 0.006596261830999894, "-1": 0.04386837235508741, "0": 0.3255296916623496, "1": 0.5009048879616548, "2": 0.12310078618990832}, "math_density": {"0": 0.014265478673070013, "1": 0.042403722601786396, "2": 0.16729879375674292, "3": 0.3297912605322888, "4": 0.44624074443611195}, "code_density": {"0": 0.0002646204021415305, "1": 0.0005441339561029875, "2": 0.01744339198642283, "3": 0.1016971754644022, "4": 0.8800506781909305}, "reasoning_density": {"0": 0.4886901473278694, "1": 0.048496133971998145, "2": 0.10904036695125584, "3": 0.1752522628918004, "4": 0.1785210888570762}, "education_level": {"none": 0.0012103473665551207, "0": 0.0016125057279237991, "1": 0.022122584767722726, "2": 0.79462263773023, "3": 0.16116848520279087, "4": 0.019263439204777424}, "creative_writing_quality": {"not_applicable": 0.9989262821234238, "-2": 0.0001768470380073458, "-1": 0.00030109286722486666, "0": 0.0004883366660766912, "1": 7.354662639083038e-05, "2": 3.389467887642142e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00015071084360693049, "Art & Design": 0.00023335652141463233, "Law & Crime": 0.00022942436695826335, "Education & Careers": 0.00019482843750937782, "Entertainment & Media": 0.00015033151773125375, "Fashion & Beauty": 0.0001198830288700941, "Business, Economics & Finance": 0.00024116333151155594, "Food & Dining": 0.00013221990582809373, "Games & Gaming": 0.00026251971129089214, "Electronics & Hardware": 0.00023440830082879893, "Health, Healthcare & Wellness": 0.00019688405976022735, "History & Geography": 0.00023108863011613265, "Home, Pets & Hobbies": 0.00013807304461913906, "Industry & Production": 0.00017498042729516484, "Language, Literature & Philosophy": 0.0001565827175853846, "Politics & Government": 9.565139418790822e-05, "Religion & Spirituality": 0.00013346747263780526, "Science, Mathematics & Engineering": 0.1073413436130008, "Relationships & Community": 8.8425965599322e-05, "Software Usage & Applications": 0.0006222152111941543, "Programming & Software Engineering": 0.8877663813567936, "Sports & Fitness": 0.00018235613227218518, "Vehicles & Transportation": 9.943904501743945e-05, "Travel & Tourism": 0.00016853994575585207, "Other / Uncovered Subject": 0.00045956738586889155, "Unknown / Insufficient Content": 0.00019615763274610584}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006660906513166093, "Art & Design": 0.0006367111031913744, "Law & Crime": 0.0006013714930693244, "Education & Careers": 0.0006081330839976033, "Entertainment & Media": 0.00043732394033405144, "Fashion & Beauty": 0.0003422321487622219, "Business, Economics & Finance": 0.0005262343388970408, "Food & Dining": 0.00029529545325187966, "Games & Gaming": 0.00046831352884795516, "Electronics & Hardware": 0.000577756963564361, "Health, Healthcare & Wellness": 0.0005815416725000124, "History & Geography": 0.00041044384586106373, "Home, Pets & Hobbies": 0.00033949310738660385, "Industry & Production": 0.0004295501291905556, "Language, Literature & Philosophy": 0.00028700993808491277, "Politics & Government": 0.0002715477957539583, "Religion & Spirituality": 0.0004231465429122897, "Science, Mathematics & Engineering": 0.4635608223654671, "Relationships & Community": 0.00019663134231325487, "Software Usage & Applications": 0.0007302825426902844, "Programming & Software Engineering": 0.1725720206621379, "Sports & Fitness": 0.0004147710501046349, "Vehicles & Transportation": 0.00023961969245472298, "Travel & Tourism": 0.0004813252644526515, "Other / Uncovered Subject": 0.0008622599588471891, "NONE": 0.3530400713846106}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10156, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10156, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:04.719854+00:00", "scoring_key_slot": "primary", "dataset_uid": "a58e5d7c-d179-4106-b9ad-a3942a4f44f1", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "a302d124ace7da2ec88f0a940e36da2a4a172b0726d91554b8f44b14cea7626a", "source_metadata": {"uuid": "a58e5d7c-d179-4106-b9ad-a3942a4f44f1", "repo_name": "windjackz/DecisionTree", "relative_path": "DecisionTree.py", "category": "DATA", "algo_rel_score": 0.9920016527175903, "quality_score": 3.9979660511016846, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 2, "repo_created_at": "2016-01-29T05:50:40.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c810a3fc4df5df95063e6446eee65473e58b4ca9", "size_bytes": 6413, "file_timestamp": 1454046938, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "84aadc792c8a9fc6c0991fee060c4e53eb5b15c8"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 2, "repo_created_at": "2016-01-29T05:50:40.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c810a3fc4df5df95063e6446eee65473e58b4ca9", "size_bytes": 6413, "file_timestamp": 1454046938, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "84aadc792c8a9fc6c0991fee060c4e53eb5b15c8", "url": "https://github.com/windjackz/DecisionTree/blob/84aadc792c8a9fc6c0991fee060c4e53eb5b15c8/DecisionTree.py"}, "gpt4o_tokens": 1538, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.07320474076899997, "noise_severity": 0.034591533329751525, "missing_content_severity": 0.00417550685220121, "factual_density": 3.231506604175974, "pretraining_utility": 0.6900455643243842, "math_density": 3.1513380694565862, "code_density": 3.860725157085877, "reasoning_density": 1.5064180119782158, "education_level": 2.1745590444685563, "creative_writing_quality": -0.4781991343323486}, "score_applicability": {"education_level": 0.9987896526334449, "creative_writing_quality": 0.0010737178765761555}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/artemisia47/Flask-Finance-Project/blob/df8acafdfb09744be9197c6ad8649dfb9159d446/app.py", "id": "python-0-6c34d7e225fd28306a24f98ba24a7363", "text": "import os\nimport sqlite3\nfrom flask import Flask, flash, redirect, render_template, request, session\nfrom flask_session import Session\nfrom werkzeug.security import check_password_hash, generate_password_hash\nfrom helpers import apology, login_required, lookup, usd\n\napp = Flask(__name__)\napp.jinja_env.filters[\"usd\"] = usd\napp.config[\"SESSION_PERMANENT\"] = False\napp.config[\"SESSION_TYPE\"] = \"filesystem\"\napp.config[\"SESSION_COOKIE_HTTPONLY\"] = True  # Enhanced security\n# Uncomment the next line if using HTTPS\n# app.config[\"SESSION_COOKIE_SECURE\"] = True\nSession(app)\n\n# Create a function to get a connection to the SQLite database\ndef get_db():\n    conn = sqlite3.connect('finance.db')\n    conn.row_factory = sqlite3.Row  # Allows us to access columns by name\n    return conn\n\n# Initialize tables if they don't exist\nwith get_db() as conn:\n    conn.execute(\"\"\"\n        CREATE TABLE IF NOT EXISTS users (\n            id INTEGER PRIMARY KEY AUTOINCREMENT,\n            username TEXT NOT NULL UNIQUE,\n            hash TEXT NOT NULL,\n            cash NUMERIC DEFAULT 10000.00\n        )\n    \"\"\")\n    conn.execute(\"\"\"\n        CREATE TABLE IF NOT EXISTS history (\n            id INTEGER PRIMARY KEY AUTOINCREMENT,\n            user_id INTEGER NOT NULL,\n            symbol TEXT NOT NULL,\n            shares INTEGER NOT NULL,\n            price NUMERIC NOT NULL,\n            timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,\n            FOREIGN KEY(user_id) REFERENCES users(id)\n        )\n    \"\"\")\n\n@app.after_request\ndef after_request(response):\n    response.headers[\"Cache-Control\"] = \"no-cache, no-store, must-revalidate\"\n    response.headers[\"Expires\"] = 0\n    response.headers[\"Pragma\"] = \"no-cache\"\n    return response\n\n@app.route(\"/\")\n@login_required\ndef index():\n    # Use the get_db function to connect to the database\n    with get_db() as conn:\n        user_cash = conn.execute(\"SELECT cash FROM users WHERE id = ?\", (session[\"user_id\"],)).fetchone()[\"cash\"]\n        holdings = conn.execute(\"\"\"\n            SELECT symbol, SUM(shares) as total_shares\n            FROM history\n            WHERE user_id = ?\n            GROUP BY symbol\n            HAVING total_shares > 0\n        \"\"\", (session[\"user_id\"],)).fetchall()\n\n    total_portfolio_value = user_cash\n    # Convert the sqlite3.Row objects to dictionaries for item assignment\n    holdings_list = []\n    for holding in holdings:\n        holding_dict = dict(holding)  # Convert the Row object to a dictionary\n        quote_result = lookup(holding_dict[\"symbol\"])\n        if quote_result:  # Check if the quote_result is not None\n            holding_dict[\"name\"] = quote_result[\"name\"]\n            holding_dict[\"price\"] = quote_result[\"price\"]\n            holding_dict[\"total_value\"] = holding_dict[\"total_shares\"] * quote_result[\"price\"]\n            total_portfolio_value += holding_dict[\"total_value\"]\n        holdings_list.append(holding_dict)\n\n    return render_template(\"index.html\", user_cash=user_cash, holdings=holdings_list, total_portfolio_value=total_portfolio_value)\n\n\n@app.route(\"/login\", methods=[\"GET\", \"POST\"])\ndef login():\n    session.clear()\n    if request.method == \"POST\":\n        username = request.form.get(\"username\")\n        password = request.form.get(\"password\")\n\n        if not username or not password:\n            return apology(\"must provide username and password\", 403)\n\n        with get_db() as conn:\n            rows = conn.execute(\"SELECT * FROM users WHERE username = ?\", (username,)).fetchall()\n\n        if len(rows) != 1 or not check_password_hash(rows[0][\"hash\"], password):\n            return apology(\"invalid username and/or password\", 403)\n\n        session[\"user_id\"] = rows[0][\"id\"]\n        flash('Logged in successfully!', 'success')  # Provide user feedback\n        return redirect(\"/\")\n    else:\n        return render_template(\"login.html\")\n\n@app.route(\"/register\", methods=[\"GET\", \"POST\"])\ndef register():\n    if request.method == \"POST\":\n        username = request.form.get(\"username\")\n        password = request.form.get(\"password\")\n        confirmation = request.form.get(\"confirmation\")\n\n        if not username:\n            return apology(\"must provide username\", 400)\n        elif not password:\n            return apology(\"must provide password\", 400)\n        elif not confirmation:\n            return apology(\"must confirm password\", 400)\n        elif password != confirmation:\n            return apology(\"passwords do not match\", 400)\n\n        hashed_password = generate_password_hash(password)\n\n        try:\n            with get_db() as conn:\n                conn.execute(\"INSERT INTO users (username, hash) VALUES (?, ?)\", (username, hashed_password))\n                session[\"user_id\"] = conn.execute(\"SELECT id FROM users WHERE username = ?\", (username,)).fetchone()[\"id\"]\n            flash('Registered successfully!', 'success')\n            return redirect(\"/\")\n        except sqlite3.IntegrityError:\n            return apology(\"username already exists\", 400)\n    else:\n        return render_template(\"register.html\")\n\n@app.route(\"/quote\", methods=[\"GET\", \"POST\"])\n@login_required\ndef quote():\n    if request.method == \"POST\":\n        symbol = request.form.get(\"symbol\")\n        if not symbol:\n            return apology(\"must provide stock symbol\", 400)\n\n        quote_result = lookup(symbol)\n        if not quote_result:\n            return apology(\"stock symbol not found\", 400)\n\n        return render_template(\"quoted.html\", quote=quote_result)\n    else:\n        return render_template(\"quote.html\")\n\n@app.route(\"/buy\", methods=[\"GET\", \"POST\"])\n@login_required\ndef buy():\n    if request.method == \"POST\":\n        symbol = request.form.get(\"symbol\")\n        shares_str = request.form.get(\"shares\")\n\n        if not symbol:\n            return apology(\"must provide stock symbol\", 400)\n        if not shares_str:\n            return apology(\"must provide number of shares\", 400)\n\n        try:\n            shares = int(shares_str)\n            if shares <= 0:\n                raise ValueError\n        except ValueError:\n            return apology(\"number of shares must be a positive integer\", 400)\n\n        quote_result = lookup(symbol)\n        if not quote_result:\n            return apology(\"stock symbol not found\", 400)\n\n        total_cost = shares * quote_result[\"price\"]\n\n        with get_db() as conn:\n            user_cash = conn.execute(\"SELECT cash FROM users WHERE id = ?\", (session[\"user_id\"],)).fetchone()[\"cash\"]\n\n        if total_cost > user_cash:\n            return apology(\"insufficient funds\", 400)\n\n        with get_db() as conn:\n            conn.execute(\"UPDATE users SET cash = cash - ? WHERE id = ?\", (total_cost, session[\"user_id\"]))\n            conn.execute(\"INSERT INTO history (user_id, symbol, shares, price) VALUES (?, ?, ?, ?)\",\n                        (session[\"user_id\"], quote_result[\"symbol\"], shares, quote_result[\"price\"]))\n        flash('Purchased successfully!', 'success')  # Provide user feedback\n        return redirect(\"/\")\n    else:\n        return render_template(\"buy.html\")\n\n@app.route(\"/sell\", methods=[\"GET\", \"POST\"])\n@login_required\ndef sell():\n    if request.method == \"POST\":\n        symbol = request.form.get(\"symbol\")\n        shares_str = request.form.get(\"shares\")\n\n        if not symbol:\n            return apology(\"must provide stock symbol\", 400)\n        if not shares_str:\n            return apology(\"must provide number of shares\", 400)\n\n        try:\n            shares = int(shares_str)\n            if shares <= 0:\n                raise ValueError\n        except ValueError:\n            return apology(\"number of shares must be a positive integer\", 400)\n\n        quote_result = lookup(symbol)\n        if not quote_result:\n            return apology(\"stock symbol not found\", 400)\n\n        with get_db() as conn:\n            user_holdings = conn.execute(\"\"\"\n                SELECT SUM(shares) as total_shares\n                FROM history\n                WHERE user_id = ? AND symbol = ?\n                GROUP BY symbol\n            \"\"\", (session[\"user_id\"], quote_result[\"symbol\"])).fetchone()\n\n        if not user_holdings or user_holdings[\"total_shares\"] < shares:\n            return apology(\"insufficient shares to sell\", 400)\n\n        total_sale_value = shares * quote_result[\"price\"]\n\n        with get_db() as conn:\n            conn.execute(\"UPDATE users SET cash = cash + ? WHERE id = ?\", (total_sale_value, session[\"user_id\"]))\n            conn.execute(\"INSERT INTO history (user_id, symbol, shares, price) VALUES (?, ?, ?, ?)\",\n                        (session[\"user_id\"], quote_result[\"symbol\"], -shares, quote_result[\"price\"]))\n        flash('Sold successfully!', 'success')  # Provide user feedback\n        return redirect(\"/\")\n    else:\n        with get_db() as conn:\n            symbols = conn.execute(\"\"\"\n                SELECT symbol\n                FROM history\n                WHERE user_id = ?\n                GROUP BY symbol\n                HAVING SUM(shares) > 0\n            \"\"\", (session[\"user_id\"],)).fetchall()\n        return render_template(\"sell.html\", holdings=symbols)\n\n@app.route(\"/history\")\n@login_required\ndef history():\n    # Fetch the transactions from the database\n    with get_db() as conn:\n        transactions = conn.execute(\"\"\"\n            SELECT symbol, shares, price, timestamp\n            FROM history\n            WHERE user_id = ?\n        \"\"\", (session[\"user_id\"],)).fetchall()\n\n    # Convert sqlite3.Row to a dictionary for each transaction\n    for transaction in transactions:\n        transaction_dict = dict(transaction)  # Convert Row object to dictionary\n        quote_result = lookup(transaction_dict[\"symbol\"])\n        transaction_dict[\"name\"] = quote_result[\"name\"] if quote_result else \"Unknown\"\n        # Replace the original transaction with the updated dictionary\n        transaction = transaction_dict\n\n    return render_template(\"history.html\", transactions=transactions)\n\n\n@app.route(\"/logout\")\ndef logout():\n    session.clear()\n    flash('Logged out successfully!', 'success')  # Provide user feedback\n    return redirect(\"/login\")\n", "text_preview": "import os import sqlite3 from flask import Flask, flash, redirect, render_template, request, session from flask_session import Session from werkzeug.security import check_password_hash, generate_password_hash from helpers import apology, lo", "input_sha256": "019392940df79ccfb9f8d14d1b50974c9b8df2e7ce1b6ac2f7c3ef08af11d116", "text_sha256": "6c34d7e225fd28306a24f98ba24a73638860fa277b65d28abdcae0b205b73510", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12073, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Business, Economics & Finance", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 1, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7543294076109006, "noise_severity": 0.033027255510793115, "missing_content_severity": 0.09872490352895788, "factual_density": 3.3535681511821616, "pretraining_utility": 0.8420437062797673, "math_density": 0.8375118521527363, "code_density": 3.9073549109223737, "reasoning_density": 0.8422510058859888, "education_level": 2.050622881454771, "creative_writing_quality": -0.27965947633051474}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Business, Economics & Finance"}, "review_reasons": ["secondary_topic probability 0.6378 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.001912824425889326, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.996366286584099, "secondary": 0.9623964099824498, "tertiary": 0.9638135462208324, "structured_records": 0.13350080194785907, "procedural_steps": 0.011641434089955636, "worked_solution": 0.006274607380397077, "contextual_dialogue": 6.14105813591665e-06, "content_quality": 0.5288183427149684, "noise_severity": 0.9724772870743391, "missing_content_severity": 0.9177292470592018, "factual_density": 0.4613067926518012, "pretraining_utility": 0.6304467844573329, "math_density": 0.5187926906813174, "code_density": 0.9227957591019779, "reasoning_density": 0.298124161761676, "education_level": 0.8821335353454662, "creative_writing_quality": 0.999502448316518, "primary_topic": 0.9574997509543153, "secondary_topic": 0.6233247378626227}, "probability_margin": {"primary": 0.9945865297920763, "secondary": 0.9495235392819679, "tertiary": 0.951822129530275, "education_level": 0.8301700578266329, "creative_writing_quality": 0.9993668486092087, "primary_topic": 0.9216804958805876, "secondary_topic": 0.3716448505152475}, "raw_probabilities": {"primary": {"Academic Writing": 2.997366198391513e-05, "Content Listing": 1.9426887849944033e-05, "Creative Writing": 1.6117878534020158e-05, "Customer Support Page": 8.573825607751446e-06, "Q&A Forum": 1.0762810163217684e-05, "Discussion Thread": 7.585524363740629e-06, "Comment Section": 1.2677348510535463e-05, "Missing Content": 1.8402543709374484e-05, "Knowledge Article": 6.612219312162331e-05, "Legal Notices": 2.2113199213175964e-05, "News Article": 4.873083240468255e-06, "Organizational Announcement": 1.079835211271619e-05, "Nonfiction Writing": 1.2609103114542272e-05, "Organizational About Page": 1.233246390619602e-05, "Personal About Page": 1.5413198837782115e-05, "Personal Blog": 8.407892578437767e-06, "Product Page": 8.89471846071939e-06, "Spam / Ads": 1.0688039675782818e-05, "Structured Data": 0.00034438874381318633, "Technical Writing": 0.9965176913097615, "Transcript / Interview": 1.287167136236604e-05, "Tutorial / How-To Guide": 0.0019311615176852038, "User Reviews": 7.027687290642619e-06, "Other / Unknown": 0.0008910863451030613}, "secondary": {"Academic Writing": 0.00017810340273355824, "Content Listing": 0.00031821692093313137, "Creative Writing": 0.00013043216147049565, "Customer Support Page": 8.914997266958972e-05, "Q&A Forum": 5.0866680936183196e-05, "Discussion Thread": 5.2118508232366014e-05, "Comment Section": 0.0004760858640106064, "Missing Content": 0.0003580502145111855, "Knowledge Article": 0.0007294206989545779, "Legal Notices": 0.00019998519817777306, "News Article": 7.81940932468817e-05, "Organizational Announcement": 7.562067154984077e-05, "Nonfiction Writing": 9.661705841484557e-05, "Organizational About Page": 9.208429737519074e-05, "Personal About Page": 8.613462406532403e-05, "Personal Blog": 7.509754613926812e-05, "Product Page": 5.619809235153819e-05, "Spam / Ads": 8.614682336938081e-05, "Structured Data": 0.012963812868031608, "Technical Writing": 0.014439686951213228, "Transcript / Interview": 9.069834448989552e-05, "Tutorial / How-To Guide": 0.005270614573497017, "User Reviews": 4.3438200445453455e-05, "NONE": 0.9639632262331811}, "tertiary": {"Academic Writing": 0.00015514641712244414, "Content Listing": 0.0003302294111603046, "Creative Writing": 0.00015863162082297826, "Customer Support Page": 9.965590188289719e-05, "Q&A Forum": 4.7972821544337314e-05, "Discussion Thread": 5.610131715337744e-05, "Comment Section": 0.000742026953011183, "Missing Content": 0.0004892250131663555, "Knowledge Article": 0.001154962019951782, "Legal Notices": 0.0003737796879178635, "News Article": 7.464284362060493e-05, "Organizational Announcement": 8.227573417856973e-05, "Nonfiction Writing": 0.0001342636793382211, "Organizational About Page": 0.00011750237994799318, "Personal About Page": 9.189500258212867e-05, "Personal Blog": 7.474545602628552e-05, "Product Page": 6.540566178111508e-05, "Spam / Ads": 0.00010381160074808847, "Structured Data": 0.011050988442142282, "Technical Writing": 0.013499185598022635, "Transcript / Interview": 8.900587458383784e-05, "Tutorial / How-To Guide": 0.0056443033680666456, "User Reviews": 4.292806693035222e-05, "NONE": 0.9653213151282977}, "structured_records": {"false": 0.8664991980521409, "true": 0.13350080194785907}, "procedural_steps": {"false": 0.9883585659100443, "true": 0.011641434089955636}, "worked_solution": {"false": 0.9937253926196029, "true": 0.006274607380397077}, "contextual_dialogue": {"false": 0.9999938589418641, "true": 6.14105813591665e-06}, "content_quality": {"-2": 0.007599100030827174, "-1": 0.05504250249675345, "0": 0.27266198547958026, "1": 0.50482271381637, "2": 0.15987369817646926}, "noise_severity": {"0": 0.9700520291642956, "1": 0.02789823834402499, "2": 0.0013281089800269517, "3": 0.00041369483989561046, "4": 0.0003079286717568475}, "missing_content_severity": {"0": 0.9443368740379918, "1": 0.020757299524384692, "2": 0.02748828251965756, "3": 0.0066791367066063026, "4": 0.0007384072113597946}, "factual_density": {"0": 0.003845087613728352, "1": 0.009540778219967395, "2": 0.10369739397560529, "3": 0.3950343757518124, "4": 0.4878823644388866}, "pretraining_utility": {"-2": 0.005360043176867217, "-1": 0.025514310194595023, "0": 0.23360132626592484, "1": 0.5927705378971291, "2": 0.14275378246548384}, "math_density": {"0": 0.3699684595148414, "1": 0.46813688238507695, "2": 0.1330607963790614, "3": 0.012082069874544412, "4": 0.016751791846475815}, "code_density": {"0": 0.00016079466732019963, "1": 0.00030716880249115683, "2": 0.008823017823149966, "3": 0.07343436835457241, "4": 0.9172746503524664}, "reasoning_density": {"0": 0.6044336362235573, "1": 0.12052836874042133, "2": 0.14798671986039796, "3": 0.082455903277722, "4": 0.04459537189790139}, "education_level": {"none": 0.000632654327834514, "0": 0.0010097588669440512, "1": 0.022980340223263677, "2": 0.9017779461212219, "3": 0.07160788829458899, "4": 0.0019914121661468802}, "creative_writing_quality": {"not_applicable": 0.9995853735970983, "-2": 4.8130019075601757e-05, "-1": 9.184342994800037e-05, "0": 0.0002185249878896822, "1": 4.0106666585837545e-05, "2": 1.6021299402541128e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.2796725979986875e-05, "Art & Design": 0.00010358170818726763, "Law & Crime": 0.00010372855464567044, "Education & Careers": 8.208981444741414e-05, "Entertainment & Media": 6.580243686233298e-05, "Fashion & Beauty": 6.732969161911948e-05, "Business, Economics & Finance": 0.037453880037023185, "Food & Dining": 6.5683813168121e-05, "Games & Gaming": 0.000818675877642152, "Electronics & Hardware": 9.314906554696042e-05, "Health, Healthcare & Wellness": 5.1154239245508914e-05, "History & Geography": 7.27242850387995e-05, "Home, Pets & Hobbies": 6.853263812611433e-05, "Industry & Production": 7.051503267510028e-05, "Language, Literature & Philosophy": 6.399489938698662e-05, "Politics & Government": 5.688828627828712e-05, "Religion & Spirituality": 6.387009224151503e-05, "Science, Mathematics & Engineering": 0.00015821384981419952, "Relationships & Community": 4.1512655052057476e-05, "Software Usage & Applications": 0.0007406255952568798, "Programming & Software Engineering": 0.9591343759176109, "Sports & Fitness": 7.518451224168247e-05, "Vehicles & Transportation": 3.681607387998326e-05, "Travel & Tourism": 6.25735064433309e-05, "Other / Uncovered Subject": 0.00031193799915532096, "Unknown / Insufficient Content": 8.436269243157149e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007110011464503722, "Art & Design": 0.0009175237344688741, "Law & Crime": 0.0011141674813464851, "Education & Careers": 0.0006625556319048604, "Entertainment & Media": 0.00055257165169602, "Fashion & Beauty": 0.00048252724269065323, "Business, Economics & Finance": 0.6378122479448295, "Food & Dining": 0.0005129040288649865, "Games & Gaming": 0.0009611339683115723, "Electronics & Hardware": 0.0006674958500550574, "Health, Healthcare & Wellness": 0.0004768121751943443, "History & Geography": 0.0003923650176702368, "Home, Pets & Hobbies": 0.0005413844517928497, "Industry & Production": 0.000454338886315059, "Language, Literature & Philosophy": 0.0003450904488977152, "Politics & Government": 0.00036724388830780205, "Religion & Spirituality": 0.0006083400305447584, "Science, Mathematics & Engineering": 0.0011748078294318881, "Relationships & Community": 0.0002753614827722461, "Software Usage & Applications": 0.00530654479999144, "Programming & Software Engineering": 0.07762832949104045, "Sports & Fitness": 0.00042410352541999067, "Vehicles & Transportation": 0.00027751759669658203, "Travel & Tourism": 0.0004724809449401622, "Other / Uncovered Subject": 0.0006937533207841657, "NONE": 0.266167397429582}}, "probabilities": {"primary": {"Academic Writing": 2.9973661983915132e-05, "Content Listing": 1.9426887849944037e-05, "Creative Writing": 1.611787853402016e-05, "Customer Support Page": 8.573825607751448e-06, "Q&A Forum": 1.0762810163217686e-05, "Discussion Thread": 7.5855243637406295e-06, "Comment Section": 1.2677348510535464e-05, "Missing Content": 1.8402543709374487e-05, "Knowledge Article": 6.612219312162332e-05, "Legal Notices": 2.2113199213175968e-05, "News Article": 4.873083240468256e-06, "Organizational Announcement": 1.0798352112716192e-05, "Nonfiction Writing": 1.2609103114542274e-05, "Organizational About Page": 1.2332463906196021e-05, "Personal About Page": 1.541319883778212e-05, "Personal Blog": 8.407892578437769e-06, "Product Page": 8.894718460719391e-06, "Spam / Ads": 1.068803967578282e-05, "Structured Data": 0.0003443887438131864, "Technical Writing": 0.9965176913097616, "Transcript / Interview": 1.2871671362366041e-05, "Tutorial / How-To Guide": 0.001931161517685204, "User Reviews": 7.02768729064262e-06, "Other / Unknown": 0.0008910863451030614}, "secondary": {"Academic Writing": 0.00017810340273355824, "Content Listing": 0.00031821692093313137, "Creative Writing": 0.00013043216147049565, "Customer Support Page": 8.914997266958972e-05, "Q&A Forum": 5.0866680936183196e-05, "Discussion Thread": 5.2118508232366014e-05, "Comment Section": 0.0004760858640106064, "Missing Content": 0.0003580502145111855, "Knowledge Article": 0.0007294206989545779, "Legal Notices": 0.00019998519817777306, "News Article": 7.81940932468817e-05, "Organizational Announcement": 7.562067154984077e-05, "Nonfiction Writing": 9.661705841484557e-05, "Organizational About Page": 9.208429737519074e-05, "Personal About Page": 8.613462406532403e-05, "Personal Blog": 7.509754613926812e-05, "Product Page": 5.619809235153819e-05, "Spam / Ads": 8.614682336938081e-05, "Structured Data": 0.012963812868031608, "Technical Writing": 0.014439686951213228, "Transcript / Interview": 9.069834448989552e-05, "Tutorial / How-To Guide": 0.005270614573497017, "User Reviews": 4.3438200445453455e-05, "NONE": 0.9639632262331811}, "tertiary": {"Academic Writing": 0.00015514641712244414, "Content Listing": 0.0003302294111603046, "Creative Writing": 0.00015863162082297826, "Customer Support Page": 9.965590188289719e-05, "Q&A Forum": 4.7972821544337314e-05, "Discussion Thread": 5.610131715337744e-05, "Comment Section": 0.000742026953011183, "Missing Content": 0.0004892250131663555, "Knowledge Article": 0.001154962019951782, "Legal Notices": 0.0003737796879178635, "News Article": 7.464284362060493e-05, "Organizational Announcement": 8.227573417856973e-05, "Nonfiction Writing": 0.0001342636793382211, "Organizational About Page": 0.00011750237994799318, "Personal About Page": 9.189500258212867e-05, "Personal Blog": 7.474545602628552e-05, "Product Page": 6.540566178111508e-05, "Spam / Ads": 0.00010381160074808847, "Structured Data": 0.011050988442142282, "Technical Writing": 0.013499185598022635, "Transcript / Interview": 8.900587458383784e-05, "Tutorial / How-To Guide": 0.0056443033680666456, "User Reviews": 4.292806693035222e-05, "NONE": 0.9653213151282977}, "structured_records": {"false": 0.8664991980521409, "true": 0.13350080194785907}, "procedural_steps": {"false": 0.9883585659100443, "true": 0.011641434089955636}, "worked_solution": {"false": 0.9937253926196029, "true": 0.006274607380397077}, "contextual_dialogue": {"false": 0.9999938589418641, "true": 6.14105813591665e-06}, "content_quality": {"-2": 0.0075991000308271725, "-1": 0.05504250249675344, "0": 0.2726619854795802, "1": 0.5048227138163699, "2": 0.15987369817646924}, "noise_severity": {"0": 0.9700520291642956, "1": 0.02789823834402499, "2": 0.0013281089800269517, "3": 0.00041369483989561046, "4": 0.0003079286717568475}, "missing_content_severity": {"0": 0.9443368740379916, "1": 0.02075729952438469, "2": 0.027488282519657552, "3": 0.006679136706606301, "4": 0.0007384072113597944}, "factual_density": {"0": 0.003845087613728352, "1": 0.009540778219967395, "2": 0.10369739397560529, "3": 0.3950343757518124, "4": 0.4878823644388866}, "pretraining_utility": {"-2": 0.005360043176867217, "-1": 0.025514310194595023, "0": 0.23360132626592484, "1": 0.5927705378971291, "2": 0.14275378246548384}, "math_density": {"0": 0.3699684595148414, "1": 0.46813688238507695, "2": 0.1330607963790614, "3": 0.012082069874544412, "4": 0.016751791846475815}, "code_density": {"0": 0.00016079466732019963, "1": 0.00030716880249115683, "2": 0.008823017823149966, "3": 0.07343436835457241, "4": 0.9172746503524664}, "reasoning_density": {"0": 0.6044336362235573, "1": 0.12052836874042133, "2": 0.14798671986039796, "3": 0.082455903277722, "4": 0.04459537189790139}, "education_level": {"none": 0.000632654327834514, "0": 0.0010097588669440512, "1": 0.022980340223263677, "2": 0.9017779461212219, "3": 0.07160788829458899, "4": 0.0019914121661468802}, "creative_writing_quality": {"not_applicable": 0.9995853735970983, "-2": 4.8130019075601757e-05, "-1": 9.184342994800037e-05, "0": 0.0002185249878896822, "1": 4.0106666585837545e-05, "2": 1.6021299402541128e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.2796725979986854e-05, "Art & Design": 0.00010358170818726759, "Law & Crime": 0.0001037285546456704, "Education & Careers": 8.20898144474141e-05, "Entertainment & Media": 6.580243686233295e-05, "Fashion & Beauty": 6.732969161911946e-05, "Business, Economics & Finance": 0.03745388003702317, "Food & Dining": 6.568381316812097e-05, "Games & Gaming": 0.0008186758776421517, "Electronics & Hardware": 9.314906554696038e-05, "Health, Healthcare & Wellness": 5.1154239245508894e-05, "History & Geography": 7.272428503879948e-05, "Home, Pets & Hobbies": 6.85326381261143e-05, "Industry & Production": 7.051503267510025e-05, "Language, Literature & Philosophy": 6.39948993869866e-05, "Politics & Government": 5.6888286278287095e-05, "Religion & Spirituality": 6.3870092241515e-05, "Science, Mathematics & Engineering": 0.00015821384981419944, "Relationships & Community": 4.1512655052057456e-05, "Software Usage & Applications": 0.0007406255952568795, "Programming & Software Engineering": 0.9591343759176104, "Sports & Fitness": 7.518451224168244e-05, "Vehicles & Transportation": 3.681607387998325e-05, "Travel & Tourism": 6.257350644333087e-05, "Other / Uncovered Subject": 0.0003119379991553208, "Unknown / Insufficient Content": 8.436269243157145e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007110011464503722, "Art & Design": 0.0009175237344688741, "Law & Crime": 0.0011141674813464851, "Education & Careers": 0.0006625556319048604, "Entertainment & Media": 0.00055257165169602, "Fashion & Beauty": 0.00048252724269065323, "Business, Economics & Finance": 0.6378122479448295, "Food & Dining": 0.0005129040288649865, "Games & Gaming": 0.0009611339683115723, "Electronics & Hardware": 0.0006674958500550574, "Health, Healthcare & Wellness": 0.0004768121751943443, "History & Geography": 0.0003923650176702368, "Home, Pets & Hobbies": 0.0005413844517928497, "Industry & Production": 0.000454338886315059, "Language, Literature & Philosophy": 0.0003450904488977152, "Politics & Government": 0.00036724388830780205, "Religion & Spirituality": 0.0006083400305447584, "Science, Mathematics & Engineering": 0.0011748078294318881, "Relationships & Community": 0.0002753614827722461, "Software Usage & Applications": 0.00530654479999144, "Programming & Software Engineering": 0.07762832949104045, "Sports & Fitness": 0.00042410352541999067, "Vehicles & Transportation": 0.00027751759669658203, "Travel & Tourism": 0.0004724809449401622, "Other / Uncovered Subject": 0.0006937533207841657, "NONE": 0.266167397429582}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000004, "mass_delta": 4.440892098500626e-16, "missing_mass": 0.0, "excess_mass": 4.440892098500626e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10804, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10804, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:06.560384+00:00", "scoring_key_slot": "primary", "dataset_uid": "708f1348-b5d4-4407-a545-948c39ea18f2", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "59859f89766cba504c9e5e117cbe09e4afe2433658cc8ffb76b3f92829f9b7fd", "source_metadata": {"uuid": "708f1348-b5d4-4407-a545-948c39ea18f2", "repo_name": "artemisia47/Flask-Finance-Project", "relative_path": "app.py", "category": "WEB", "algo_rel_score": 0.9728330373764038, "quality_score": 6.571577548980713, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 4, "repo_created_at": "2025-03-08T22:32:03.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "3126fca121fec666ad19cb42846bfed550aa6d18", "size_bytes": 10049, "file_timestamp": 1741524713, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "df8acafdfb09744be9197c6ad8649dfb9159d446"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 4, "repo_created_at": "2025-03-08T22:32:03.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "3126fca121fec666ad19cb42846bfed550aa6d18", "size_bytes": 10049, "file_timestamp": 1741524713, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "df8acafdfb09744be9197c6ad8649dfb9159d446", "url": "https://github.com/artemisia47/Flask-Finance-Project/blob/df8acafdfb09744be9197c6ad8649dfb9159d446/app.py"}, "gpt4o_tokens": 2099, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.7543294076109006, "noise_severity": 0.033027255510793115, "missing_content_severity": 0.0987249035289579, "factual_density": 3.3535681511821616, "pretraining_utility": 0.8420437062797673, "math_density": 0.8375118521527363, "code_density": 3.9073549109223737, "reasoning_density": 0.8422510058859888, "education_level": 2.050622881454771, "creative_writing_quality": -0.27965947633051474}, "score_applicability": {"education_level": 0.9993673456721655, "creative_writing_quality": 0.000414626402901663}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/vllm-project/vllm/blob/e6b90a2805e809022580f2c1f4928c64b5f531f1/tests/v1/kv_connector/unit/test_multi_connector.py", "id": "python-0-cef4b596031e25e462d8881232102624", "text": "# SPDX-License-Identifier: Apache-2.0\n# SPDX-FileCopyrightText: Copyright contributors to the vLLM project\nimport filecmp\nimport shutil\nimport tempfile\nfrom pathlib import Path\n\nfrom vllm import LLM, SamplingParams\nfrom vllm.config import KVTransferConfig\n\nMODEL_NAME = \"meta-llama/Llama-3.2-1B-Instruct\"\n\nPROMPT_CONTEXT = \"Hi \" * 100\nPROMPTS = [\n    PROMPT_CONTEXT + \"Hello, my name is\",\n    PROMPT_CONTEXT + \"The capital of France is\",\n]\n\nSAMPLING_PARAMS = SamplingParams(temperature=0, max_tokens=20)\n\n\n# Helper function to compare directories recursively\ndef _compare_directories(dir1: Path, dir2: Path) -> bool:\n    \"\"\"Compares two directories recursively for identical content.\"\"\"\n    dcmp = filecmp.dircmp(dir1, dir2)\n    if dcmp.left_only or dcmp.right_only or dcmp.diff_files:\n        print(f\"Differences found between {dir1} and {dir2}:\")\n        print(f\"  Left only: {dcmp.left_only}\")\n        print(f\"  Right only: {dcmp.right_only}\")\n        print(f\"  Different files: {dcmp.diff_files}\")\n        return False\n    for sub_dir in dcmp.common_dirs:\n        if not _compare_directories(dir1 / sub_dir, dir2 / sub_dir):\n            return False\n    return True\n\n\ndef test_multi_shared_storage_connector_consistency():\n    \"\"\"\n    Tests that MultiConnector with two SharedStorageConnectors saves\n    identical KV cache data to separate storage locations.\n    \"\"\"\n    storage_1_path = Path(\"storage_1/\")\n    storage_2_path = Path(\"storage_2/\")\n    shutil.rmtree(storage_1_path, ignore_errors=True)\n    shutil.rmtree(storage_2_path, ignore_errors=True)\n    storage_1_path.mkdir()\n    storage_2_path.mkdir()\n\n    # Configure MultiConnector with two SharedStorageConnectors\n    kv_transfer_config = KVTransferConfig(\n        kv_connector=\"MultiConnector\",\n        kv_role=\"kv_both\",\n        kv_connector_extra_config={\n            \"connectors\": [{\n                \"kv_connector\":\n                \"TestSharedStorageConnector\",\n                \"kv_role\":\n                \"kv_both\",\n                \"kv_connector_extra_config\": {\n                    \"shared_storage_path\": str(storage_1_path),\n                    \"name\": \"storage1\",\n                },\n                \"kv_connector_module_path\":\n                \"tests.v1.kv_connector.unit.utils\",\n            }, {\n                \"kv_connector\":\n                \"TestSharedStorageConnector\",\n                \"kv_role\":\n                \"kv_both\",\n                \"kv_connector_extra_config\": {\n                    \"shared_storage_path\": str(storage_2_path),\n                    \"name\": \"storage2\",\n                },\n                \"kv_connector_module_path\":\n                \"tests.v1.kv_connector.unit.utils\",\n            }]\n        },\n    )\n\n    llm = LLM(\n        model=MODEL_NAME,\n        enforce_eager=True,\n        gpu_memory_utilization=0.5,\n        kv_transfer_config=kv_transfer_config,\n    )\n    # Run generation - this should trigger saving KV cache\n    _ = llm.generate(PROMPTS, SAMPLING_PARAMS)\n\n    # --- Verification ---\n\n    # Check that both storage directories were populated\n    local_subdirs = list(storage_1_path.iterdir())\n    external_subdirs = list(storage_2_path.iterdir())\n\n    assert len(\n        local_subdirs\n    ) > 0, f\"Local storage path {storage_1_path} is empty after generation.\"\n    assert len(external_subdirs) > 0, (\n        f\"External storage path {storage_2_path} is empty after generation.\")\n    assert len(local_subdirs) == len(external_subdirs), (\n        f\"Mismatch in number of cache entries: \"\n        f\"Local={len(local_subdirs)}, External={len(external_subdirs)}\")\n\n    # The subdirectories should correspond to the prompt hashes\n    # Since prompts are the same, the hash directories should be the same name\n    local_subdir_names = sorted([d.name for d in local_subdirs])\n    external_subdir_names = sorted([d.name for d in external_subdirs])\n    assert local_subdir_names == external_subdir_names, (\n        \"Cache directory names do not match between local and external storage\"\n    )\n\n    # Compare the contents of each corresponding cache directory\n    for subdir_name in local_subdir_names:\n        print(f\"Comparing contents of cache directory: {subdir_name}\")\n        assert _compare_directories(storage_1_path / subdir_name,\n                                    storage_2_path / subdir_name), \\\n            (f\"Contents differ for cache directory '{subdir_name}' between \"\n             f\"{storage_1_path} and {storage_2_path}\")\n\n    events = get_connector_events()\n    # get_num_new_matched_tokens and update_state_after_alloc will be called\n    # on each connector in turn.\n    assert events[\"storage1-SCHEDULER\"][:3] == [\n        'get_num_new_matched_tokens 0',\n        'update_state_after_alloc num_blocks=[0] 0', 'build_connector_meta'\n    ]\n    assert events[\"storage1-WORKER\"][:5] == [\n        'register_kv_caches', 'bind_connector_metadata', 'start_load_kv',\n        'wait_for_layer_load', 'save_kv_layer'\n    ]\n    assert events[\"storage2-SCHEDULER\"][:3] == [\n        'get_num_new_matched_tokens 0',\n        'update_state_after_alloc num_blocks=[0] 0', 'build_connector_meta'\n    ]\n    assert events[\"storage2-WORKER\"][:5] == [\n        'register_kv_caches', 'bind_connector_metadata', 'start_load_kv',\n        'wait_for_layer_load', 'save_kv_layer'\n    ]\n\n    # Reset prefix cache or else we'll just get the tokens back from there.\n    llm.reset_prefix_cache()\n\n    # Run generation again - this should trigger loading from the first\n    # connector.\n    _ = llm.generate(PROMPTS, SAMPLING_PARAMS)\n\n    events = get_connector_events()\n    # get_num_new_matched_tokens will return new tokens from the first\n    # connector so update_state_after_alloc will be with allocated blocks\n    # on that one but with zero blocks for others (first nonzero match is\n    # chosen).\n    assert events[\"storage1-SCHEDULER\"][:3] == [\n        'get_num_new_matched_tokens 0',\n        'update_state_after_alloc num_blocks=[7] 96', 'build_connector_meta'\n    ]\n    assert events[\"storage2-SCHEDULER\"][:3] == [\n        'get_num_new_matched_tokens 0',\n        'update_state_after_alloc num_blocks=[0] 0', 'build_connector_meta'\n    ]\n\n    # Delete storage1 connector state\n    shutil.rmtree(storage_1_path)\n\n    # Reset prefix cache or else we'll just get the tokens back from there.\n    llm.reset_prefix_cache()\n\n    # Run generation again - this should trigger loading from the first\n    # connector.\n    _ = llm.generate(PROMPTS, SAMPLING_PARAMS)\n\n    events = get_connector_events()\n    # get_num_new_matched_tokens will be called for both connectors but will\n    # return 0 from the first connector, but the second connector should have\n    # a hit, so update_state_after_alloc will only be called with allocated\n    # blocks for the second connector.\n    assert events[\"storage1-SCHEDULER\"][:3] == [\n        'get_num_new_matched_tokens 0',\n        'update_state_after_alloc num_blocks=[0] 0', 'build_connector_meta'\n    ]\n    assert events[\"storage2-SCHEDULER\"][:3] == [\n        'get_num_new_matched_tokens 0',\n        'update_state_after_alloc num_blocks=[7] 96', 'build_connector_meta'\n    ]\n\n    # Clean up\n    shutil.rmtree(storage_1_path)\n    shutil.rmtree(storage_2_path)\n\n\ndef get_connector_events() -> dict[str, list[str]]:\n    # Read in connector events and reset the files.\n    import glob\n    event_files = glob.glob(tempfile.gettempdir() + \"/connector_*_events.log\")\n    connector_events = {}\n    for fname in event_files:\n        name = fname.split(\"connector_\")[1].split(\"_events.log\")[0]\n        try:\n            with open(fname, \"r+\") as f:\n                connector_events[name] = [\n                    line.strip() for line in f if line.strip()\n                ]\n                f.truncate(0)\n        except Exception as e:\n            print(f\"[ERROR] Could not read connector events for {name}: {e}\")\n\n    return connector_events\n\n\ndef test_engine_id_conflict():\n    configs = [KVTransferConfig() for _ in range(2)]\n    ids = [config.engine_id for config in configs]\n    assert ids[0] != ids[1], (\n        \"Engine IDs should be different for different configs. \"\n        f\"Got {ids}\")\n", "text_preview": "# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import filecmp import shutil import tempfile from pathlib import Path from vllm import LLM, SamplingParams from vllm.config import KV", "input_sha256": "e57ce8d50a8439ef7410ed21514dede88e2bbbb854bd3ec319507341132ba9ee", "text_sha256": "cef4b596031e25e462d8881232102624b40d44513ceaee93c2ca658a7426350e", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12071, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Legal Notices", "sub_format": null}, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.2928389488493641, "noise_severity": 0.08404303630682423, "missing_content_severity": 0.0016618292969079553, "factual_density": 3.1389780062278714, "pretraining_utility": 0.5533217822387188, "math_density": 0.10676108858246303, "code_density": 3.8860550846064115, "reasoning_density": 1.652607698359862, "education_level": 3.27275640352032, "creative_writing_quality": -0.20842607937439162}, "api_choices": {"primary": "Technical Writing", "secondary": "Legal Notices", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": ["secondary probability 0.4897 below 0.70", "secondary vs NONE margin 0.0189 below 0.10"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.1019953460436328, "secondary_choice": "Legal Notices", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9932105033475213, "secondary": 0.4674786157521387, "tertiary": 0.5541405900549555, "structured_records": 0.10144157188495238, "procedural_steps": 0.10496515120548935, "worked_solution": 0.14721746776538613, "contextual_dialogue": 8.323425888731789e-06, "content_quality": 0.5654618796654931, "noise_severity": 0.9299641364109799, "missing_content_severity": 0.9986151422525767, "factual_density": 0.4943539125805494, "pretraining_utility": 0.4799426673005518, "math_density": 0.9110324261812808, "code_density": 0.9050459038386763, "reasoning_density": 0.0, "education_level": 0.4399609987334392, "creative_writing_quality": 0.9996131105179585, "primary_topic": 0.9821390459743344, "secondary_topic": 0.7383615634310919}, "probability_margin": {"primary": 0.9886393813681024, "secondary": 0.018944287336766785, "tertiary": 0.16967372023999822, "education_level": 0.16194789503927198, "creative_writing_quality": 0.9995259066028912, "primary_topic": 0.9675164495484088, "secondary_topic": 0.5731731670564768}, "raw_probabilities": {"primary": {"Academic Writing": 9.103522585290082e-05, "Content Listing": 1.9421337593732763e-05, "Creative Writing": 1.5994383436523763e-05, "Customer Support Page": 1.2859106026778645e-05, "Q&A Forum": 1.3279234714543863e-05, "Discussion Thread": 8.043662314575102e-06, "Comment Section": 1.6042496342506456e-05, "Missing Content": 2.0264854745740662e-05, "Knowledge Article": 5.658759628407791e-05, "Legal Notices": 0.0003139641696185066, "News Article": 6.9400244538687984e-06, "Organizational Announcement": 1.460568208892935e-05, "Nonfiction Writing": 1.457533437261802e-05, "Organizational About Page": 1.2751341752852597e-05, "Personal About Page": 9.25532508040688e-06, "Personal Blog": 7.386522100359789e-06, "Product Page": 8.489855620019896e-06, "Spam / Ads": 1.071696932815281e-05, "Structured Data": 0.0005955311733527954, "Technical Writing": 0.9934933990413746, "Transcript / Interview": 2.033618690671413e-05, "Tutorial / How-To Guide": 0.00037574727589922703, "User Reviews": 8.75552746714088e-06, "Other / Unknown": 0.004854017673272176}, "secondary": {"Academic Writing": 0.0008380370504474909, "Content Listing": 0.0005393365898290701, "Creative Writing": 0.0006355589620696739, "Customer Support Page": 0.00035358381954311645, "Q&A Forum": 0.00035268067862363343, "Discussion Thread": 0.00032304500203300543, "Comment Section": 0.0005052516128403637, "Missing Content": 0.0006965031969633299, "Knowledge Article": 0.0010315958219676512, "Legal Notices": 0.48966700676246633, "News Article": 0.0006143038578608317, "Organizational Announcement": 0.0006283264281374261, "Nonfiction Writing": 0.0005078547200357466, "Organizational About Page": 0.0005589391683539902, "Personal About Page": 0.0003546712846593196, "Personal Blog": 0.0002994526092994959, "Product Page": 0.0003645586709337255, "Spam / Ads": 0.0003739224129747933, "Structured Data": 0.009468509763181306, "Technical Writing": 0.019171548113127587, "Transcript / Interview": 0.000691036832857235, "Tutorial / How-To Guide": 0.0010202893714541443, "User Reviews": 0.00028126784464121984, "NONE": 0.47072271942569954}, "tertiary": {"Academic Writing": 0.0006265766246726216, "Content Listing": 0.0003303940352166875, "Creative Writing": 0.0004538538071272741, "Customer Support Page": 0.0002379500677908371, "Q&A Forum": 0.0002106430642390676, "Discussion Thread": 0.0002049791640400187, "Comment Section": 0.0003432367914818976, "Missing Content": 0.00042708810152812304, "Knowledge Article": 0.001001290467612749, "Legal Notices": 0.40304434522933413, "News Article": 0.00041715463251529094, "Organizational Announcement": 0.00039029408565317397, "Nonfiction Writing": 0.0003435926264885441, "Organizational About Page": 0.0003966568301871407, "Personal About Page": 0.00020855563527087721, "Personal Blog": 0.00020232207905921897, "Product Page": 0.00025602727108356085, "Spam / Ads": 0.00028059370955705573, "Structured Data": 0.007205377034034824, "Technical Writing": 0.00920362226288201, "Transcript / Interview": 0.00043837208814518963, "Tutorial / How-To Guide": 0.0008925481211799315, "User Reviews": 0.0001664608015674274, "NONE": 0.5727180654693323}, "structured_records": {"false": 0.8985584281150476, "true": 0.10144157188495238}, "procedural_steps": {"false": 0.8950348487945107, "true": 0.10496515120548935}, "worked_solution": {"false": 0.8527825322346139, "true": 0.14721746776538613}, "contextual_dialogue": {"false": 0.9999916765741113, "true": 8.323425888731789e-06}, "content_quality": {"-2": 0.0029930328906176045, "-1": 0.0090344840660059, "0": 0.08725533097215754, "1": 0.4935748054458328, "2": 0.40714234662538623}, "noise_severity": {"0": 0.9202079361102018, "1": 0.07732011891905617, "2": 0.001304263804886164, "3": 0.000556334885427989, "4": 0.000611346280427941}, "missing_content_severity": {"0": 0.9990687428756182, "1": 0.00042547646649010424, "2": 0.0003275465312179624, "3": 0.0001316767387122262, "4": 4.65573879613118e-05}, "factual_density": {"0": 0.0063702092526364065, "1": 0.024723786087727295, "2": 0.16534044940437076, "3": 0.4306888996896594, "4": 0.3728766555656061}, "pretraining_utility": {"-2": 0.00879337037359584, "-1": 0.076083916250173, "0": 0.35682556487917594, "1": 0.4696018577580269, "2": 0.08869529073902827}, "math_density": {"0": 0.9306437851560008, "1": 0.05122931084680929, "2": 0.0066405500529913754, "3": 0.003694738147122133, "4": 0.007791615797076138}, "code_density": {"0": 0.0001803913889664015, "1": 0.0003252071642525157, "2": 0.006020468701427354, "3": 0.1002067909421105, "4": 0.8932671418032432}, "reasoning_density": {"0": 0.35086423042657194, "1": 0.12189450939167207, "2": 0.17917776768107005, "3": 0.2198963163966942, "4": 0.12816717610399184}, "education_level": {"none": 0.000921323600463262, "0": 0.0010698615457355458, "1": 0.0022832006554670005, "2": 0.09107184468187429, "3": 0.533300832277866, "4": 0.37135293723859397}, "creative_writing_quality": {"not_applicable": 0.9996775920982988, "-2": 4.71026799241081e-05, "-1": 5.814180698864721e-05, "0": 0.00015168549540758196, "1": 4.580688683592533e-05, "2": 1.967103254500891e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.809233536047508e-05, "Art & Design": 5.082656939532771e-05, "Law & Crime": 3.416697933150257e-05, "Education & Careers": 3.5391399546513354e-05, "Entertainment & Media": 3.070660264997661e-05, "Fashion & Beauty": 2.171986256447123e-05, "Business, Economics & Finance": 4.122497791874966e-05, "Food & Dining": 2.5820810442525105e-05, "Games & Gaming": 6.13654616206126e-05, "Electronics & Hardware": 0.0001845463036849415, "Health, Healthcare & Wellness": 5.314056482305954e-05, "History & Geography": 3.988415047424148e-05, "Home, Pets & Hobbies": 2.3554247700320363e-05, "Industry & Production": 3.436315946742893e-05, "Language, Literature & Philosophy": 7.928000587569976e-05, "Politics & Government": 2.128611640671792e-05, "Religion & Spirituality": 3.286793519669788e-05, "Science, Mathematics & Engineering": 0.015309556196143654, "Relationships & Community": 1.796185738218986e-05, "Software Usage & Applications": 0.000670874923389267, "Programming & Software Engineering": 0.9828260057445524, "Sports & Fitness": 6.054956849380487e-05, "Vehicles & Transportation": 1.750522418701067e-05, "Travel & Tourism": 4.2957391582815e-05, "Other / Uncovered Subject": 0.00019112377001389284, "Unknown / Insufficient Content": 4.5227841795901105e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007923856667864771, "Art & Design": 0.000527381996742846, "Law & Crime": 0.002180741516676393, "Education & Careers": 0.0004109165093928476, "Entertainment & Media": 0.0004920381716828087, "Fashion & Beauty": 0.0003155011056998795, "Business, Economics & Finance": 0.0006385626604204444, "Food & Dining": 0.00027527839873889683, "Games & Gaming": 0.0004364947006144882, "Electronics & Hardware": 0.0019062005281705076, "Health, Healthcare & Wellness": 0.0005290415734396909, "History & Geography": 0.00047627002959005224, "Home, Pets & Hobbies": 0.00025691334048315735, "Industry & Production": 0.0002983941174229111, "Language, Literature & Philosophy": 0.0005326056925380822, "Politics & Government": 0.000275465906090989, "Religion & Spirituality": 0.0004217844931784929, "Science, Mathematics & Engineering": 0.17525141316572695, "Relationships & Community": 0.00017370215342096663, "Software Usage & Applications": 0.0026540165975918843, "Programming & Software Engineering": 0.06116895579000207, "Sports & Fitness": 0.00034652934322656787, "Vehicles & Transportation": 0.0001532278866565297, "Travel & Tourism": 0.0004598624567609882, "Other / Uncovered Subject": 0.0006017359767411853, "NONE": 0.7484245802222038}}, "probabilities": {"primary": {"Academic Writing": 9.103522585290085e-05, "Content Listing": 1.942133759373277e-05, "Creative Writing": 1.599438343652377e-05, "Customer Support Page": 1.285910602677865e-05, "Q&A Forum": 1.3279234714543868e-05, "Discussion Thread": 8.043662314575106e-06, "Comment Section": 1.6042496342506463e-05, "Missing Content": 2.026485474574067e-05, "Knowledge Article": 5.658759628407793e-05, "Legal Notices": 0.0003139641696185067, "News Article": 6.940024453868801e-06, "Organizational Announcement": 1.4605682088929354e-05, "Nonfiction Writing": 1.4575334372618026e-05, "Organizational About Page": 1.2751341752852602e-05, "Personal About Page": 9.255325080406884e-06, "Personal Blog": 7.386522100359791e-06, "Product Page": 8.4898556200199e-06, "Spam / Ads": 1.0716969328152813e-05, "Structured Data": 0.0005955311733527956, "Technical Writing": 0.9934933990413749, "Transcript / Interview": 2.0336186906714135e-05, "Tutorial / How-To Guide": 0.00037574727589922714, "User Reviews": 8.755527467140883e-06, "Other / Unknown": 0.004854017673272178}, "secondary": {"Academic Writing": 0.0008380370504474909, "Content Listing": 0.0005393365898290701, "Creative Writing": 0.0006355589620696739, "Customer Support Page": 0.00035358381954311645, "Q&A Forum": 0.00035268067862363343, "Discussion Thread": 0.00032304500203300543, "Comment Section": 0.0005052516128403637, "Missing Content": 0.0006965031969633299, "Knowledge Article": 0.0010315958219676512, "Legal Notices": 0.48966700676246633, "News Article": 0.0006143038578608317, "Organizational Announcement": 0.0006283264281374261, "Nonfiction Writing": 0.0005078547200357466, "Organizational About Page": 0.0005589391683539902, "Personal About Page": 0.0003546712846593196, "Personal Blog": 0.0002994526092994959, "Product Page": 0.0003645586709337255, "Spam / Ads": 0.0003739224129747933, "Structured Data": 0.009468509763181306, "Technical Writing": 0.019171548113127587, "Transcript / Interview": 0.000691036832857235, "Tutorial / How-To Guide": 0.0010202893714541443, "User Reviews": 0.00028126784464121984, "NONE": 0.47072271942569954}, "tertiary": {"Academic Writing": 0.0006265766246726216, "Content Listing": 0.0003303940352166875, "Creative Writing": 0.0004538538071272741, "Customer Support Page": 0.0002379500677908371, "Q&A Forum": 0.0002106430642390676, "Discussion Thread": 0.0002049791640400187, "Comment Section": 0.0003432367914818976, "Missing Content": 0.00042708810152812304, "Knowledge Article": 0.001001290467612749, "Legal Notices": 0.40304434522933413, "News Article": 0.00041715463251529094, "Organizational Announcement": 0.00039029408565317397, "Nonfiction Writing": 0.0003435926264885441, "Organizational About Page": 0.0003966568301871407, "Personal About Page": 0.00020855563527087721, "Personal Blog": 0.00020232207905921897, "Product Page": 0.00025602727108356085, "Spam / Ads": 0.00028059370955705573, "Structured Data": 0.007205377034034824, "Technical Writing": 0.00920362226288201, "Transcript / Interview": 0.00043837208814518963, "Tutorial / How-To Guide": 0.0008925481211799315, "User Reviews": 0.0001664608015674274, "NONE": 0.5727180654693323}, "structured_records": {"false": 0.8985584281150476, "true": 0.10144157188495238}, "procedural_steps": {"false": 0.8950348487945107, "true": 0.10496515120548935}, "worked_solution": {"false": 0.8527825322346139, "true": 0.14721746776538613}, "contextual_dialogue": {"false": 0.9999916765741113, "true": 8.323425888731789e-06}, "content_quality": {"-2": 0.0029930328906176045, "-1": 0.0090344840660059, "0": 0.08725533097215754, "1": 0.4935748054458328, "2": 0.40714234662538623}, "noise_severity": {"0": 0.9202079361102018, "1": 0.07732011891905617, "2": 0.001304263804886164, "3": 0.000556334885427989, "4": 0.000611346280427941}, "missing_content_severity": {"0": 0.9990687428756184, "1": 0.00042547646649010435, "2": 0.0003275465312179625, "3": 0.00013167673871222624, "4": 4.6557387961311814e-05}, "factual_density": {"0": 0.0063702092526364065, "1": 0.024723786087727295, "2": 0.16534044940437076, "3": 0.4306888996896594, "4": 0.3728766555656061}, "pretraining_utility": {"-2": 0.008793370373595842, "-1": 0.07608391625017301, "0": 0.356825564879176, "1": 0.46960185775802693, "2": 0.08869529073902828}, "math_density": {"0": 0.9306437851560011, "1": 0.0512293108468093, "2": 0.006640550052991378, "3": 0.0036947381471221343, "4": 0.00779161579707614}, "code_density": {"0": 0.0001803913889664015, "1": 0.0003252071642525157, "2": 0.006020468701427354, "3": 0.1002067909421105, "4": 0.8932671418032432}, "reasoning_density": {"0": 0.35086423042657194, "1": 0.12189450939167207, "2": 0.17917776768107005, "3": 0.2198963163966942, "4": 0.12816717610399184}, "education_level": {"none": 0.000921323600463262, "0": 0.0010698615457355458, "1": 0.0022832006554670005, "2": 0.09107184468187429, "3": 0.533300832277866, "4": 0.37135293723859397}, "creative_writing_quality": {"not_applicable": 0.9996775920982988, "-2": 4.71026799241081e-05, "-1": 5.814180698864721e-05, "0": 0.00015168549540758196, "1": 4.580688683592533e-05, "2": 1.967103254500891e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.809233536047507e-05, "Art & Design": 5.08265693953277e-05, "Law & Crime": 3.416697933150256e-05, "Education & Careers": 3.539139954651335e-05, "Entertainment & Media": 3.07066026499766e-05, "Fashion & Beauty": 2.1719862564471228e-05, "Business, Economics & Finance": 4.122497791874965e-05, "Food & Dining": 2.58208104425251e-05, "Games & Gaming": 6.136546162061259e-05, "Electronics & Hardware": 0.00018454630368494146, "Health, Healthcare & Wellness": 5.314056482305953e-05, "History & Geography": 3.988415047424147e-05, "Home, Pets & Hobbies": 2.3554247700320357e-05, "Industry & Production": 3.436315946742892e-05, "Language, Literature & Philosophy": 7.928000587569975e-05, "Politics & Government": 2.1286116406717916e-05, "Religion & Spirituality": 3.2867935196697876e-05, "Science, Mathematics & Engineering": 0.01530955619614365, "Relationships & Community": 1.7961857382189855e-05, "Software Usage & Applications": 0.0006708749233892669, "Programming & Software Engineering": 0.9828260057445521, "Sports & Fitness": 6.054956849380486e-05, "Vehicles & Transportation": 1.7505224187010667e-05, "Travel & Tourism": 4.295739158281499e-05, "Other / Uncovered Subject": 0.00019112377001389278, "Unknown / Insufficient Content": 4.52278417959011e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007923856667864772, "Art & Design": 0.0005273819967428461, "Law & Crime": 0.0021807415166763934, "Education & Careers": 0.00041091650939284767, "Entertainment & Media": 0.0004920381716828088, "Fashion & Beauty": 0.00031550110569987955, "Business, Economics & Finance": 0.0006385626604204445, "Food & Dining": 0.0002752783987388969, "Games & Gaming": 0.00043649470061448824, "Electronics & Hardware": 0.0019062005281705078, "Health, Healthcare & Wellness": 0.000529041573439691, "History & Geography": 0.0004762700295900523, "Home, Pets & Hobbies": 0.0002569133404831574, "Industry & Production": 0.00029839411742291113, "Language, Literature & Philosophy": 0.0005326056925380823, "Politics & Government": 0.00027546590609098904, "Religion & Spirituality": 0.00042178449317849297, "Science, Mathematics & Engineering": 0.17525141316572698, "Relationships & Community": 0.00017370215342096666, "Software Usage & Applications": 0.0026540165975918848, "Programming & Software Engineering": 0.061168955790002075, "Sports & Fitness": 0.0003465293432265679, "Vehicles & Transportation": 0.00015322788665652972, "Travel & Tourism": 0.00045986245676098825, "Other / Uncovered Subject": 0.0006017359767411854, "NONE": 0.7484245802222039}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10554, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10554, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:07.693296+00:00", "scoring_key_slot": "primary", "dataset_uid": "6155618a-ef05-41f1-b5f4-2413a6abdd9a", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "8b371e26e53c0adf1fee85aa2d0e15df35d6521213452b9050e6966109b65265", "source_metadata": {"uuid": "6155618a-ef05-41f1-b5f4-2413a6abdd9a", "repo_name": "vllm-project/vllm", "relative_path": "tests/v1/kv_connector/unit/test_multi_connector.py", "category": "TEST", "algo_rel_score": 0.9784964323043823, "quality_score": 7.766260623931885, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 7842, "repo_created_at": "2023-02-09T11:23:20.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 52761, "forks": 8817, "issues": 1824, "pull_requests": 832}, "file_meta": {"content_id": "2c482b81ea9fcd789c6bc4a86b6b02eb9ef9cfb5", "size_bytes": 8106, "file_timestamp": 1753089902, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "e6b90a2805e809022580f2c1f4928c64b5f531f1"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 7842, "repo_created_at": "2023-02-09T11:23:20.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 52761, "forks": 8817, "issues": 1824, "pull_requests": 832}, "file_meta": {"content_id": "2c482b81ea9fcd789c6bc4a86b6b02eb9ef9cfb5", "size_bytes": 8106, "file_timestamp": 1753089902, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "e6b90a2805e809022580f2c1f4928c64b5f531f1", "url": "https://github.com/vllm-project/vllm/blob/e6b90a2805e809022580f2c1f4928c64b5f531f1/tests/v1/kv_connector/unit/test_multi_connector.py"}, "gpt4o_tokens": 1918, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.2928389488493641, "noise_severity": 0.08404303630682423, "missing_content_severity": 0.0016618292969079553, "factual_density": 3.1389780062278714, "pretraining_utility": 0.5533217822387188, "math_density": 0.10676108858246303, "code_density": 3.8860550846064115, "reasoning_density": 1.652607698359862, "education_level": 3.27275640352032, "creative_writing_quality": -0.20842607937439162}, "score_applicability": {"education_level": 0.9990786763995367, "creative_writing_quality": 0.0003224079017012715}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/Shayantan1012/express_falsk_react_for_pizza_app/blob/38ceca809fdc9544788805cca3ded0f8ea756254/FLASK_BACKEND/VoiceAssistance/routes.py", "id": "python-0-f84c728f8a82d56489a3a0feb28a9f3e", "text": "from VoiceAssistance.utils import PromptManager\nfrom VoiceAssistance.llm_engine import llm_output\nfrom VoiceAssistance import voiceBlueprint\nfrom VoiceAssistance.service import IntentService\nfrom VoiceAssistance.utils import PredefinedResponseManager, speak\nimport json\nfrom flask import request, jsonify\nfrom flask import session\nimport random\n\n\n@voiceBlueprint.route('/', methods=['POST'])\ndef voiceAssistanceInput():\n    try:\n        input_data = request.form['text']\n        if( not input_data):\n            return jsonify({'error': 'No input data provided'}), 400\n        if len(input_data) > 1000:\n            return jsonify({'error': 'Input data exceeds maximum length of 1000 characters'}), 400\n        # Create a PromptManager instance\n        \n        prompt_manager = PromptManager(input_data)\n        \n        system_prompt = prompt_manager.intentPrompt()\n        \n        response = llm_output(system_prompt=system_prompt)\n        \n        if not response:\n            return jsonify({'error': 'No response from the model'}), 500\n        # Return the response as JSON\n        \n        try:\n            response = json.loads(response)\n        except json.JSONDecodeError as e:\n            return jsonify({'error': f'LLM response is not valid JSON: {str(e)}', 'raw_response': response}), 500\n        \n        if not isinstance(response, dict):\n            return jsonify({'error': 'Invalid response format from the model'}), 500\n        \n        active_intent = session.get('active_intent', None) \n        \n        \n        if( response['intent'] == 'confirm_intent' and active_intent is None):\n            try:\n                \n                response =  random.choice([\n                \"Hmm, there's nothing to confirm right now. Would you like to place an order or see the menu?\",\n                \"I'm not sure what you're confirming — let me know what you want to do next.\",\n                \"There's no current action to proceed with. Want to browse the menu or check your cart?\",\n                \"You said yes, but I don't have any action to confirm. Try telling me what you'd like to do!\",         \n                ])\n        \n                speak(response)\n\n                return jsonify({\n                    'message': 'No active intent to confirm.',\n                    'response':response,\n                    'intent': 'confirm_intent'\n                }), 200\n            except Exception as e:\n                return jsonify({'error': f'An unexpected error occurred: {str(e)}'}), 500\n                \n               \n        if ( response['intent'] == 'cancel_intent'):\n            try:\n                session.pop('active_intent', None)\n\n                response = random.choice([\n                    \"Okay, I have cancelled that for you.\",\n                    \"Sure, I have cancelled.\",\n                    \"No problem, I have cancelled that.\",\n                    \"Alright, I have cancelled that for you.\",\n                    \"Got it, I have cancelled that for you.\",\n                ])\n                speak(response)\n            \n                return jsonify({\n                    'message': 'Intent cancelled successfully.',\n                    'response': response ,\n                    'intent': 'cancel_intent'\n                }), 200\n            except Exception as e:\n                return jsonify({'error': f'An unexpected error occurred: {str(e)}'}),\n \n        \n        if(response['intent'] == 'add_cart'  or (response['intent'] == 'confirm_intent' and active_intent['intent']!=None and active_intent['intent'] == 'add_cart')):\n            \n            order_intent = IntentService(response)\n        \n            response_data = order_intent.order_service()\n            \n            speak(response_data)\n            \n            if not response_data:\n                return jsonify({'error': 'Failed to process order service'}), 500\n            \n            return jsonify({\n                'message': 'Successfully processed order service.',\n                'response': response_data,\n                'intent': 'add_cart'\n            }), 200\n            \n            \n        if(response['intent'] == 'log_in'):\n            \n            login_intent = IntentService(response)\n        \n            response_data = login_intent.login_service()\n            speak(response_data)\n            \n            if not response_data:\n                return jsonify({'error': 'Failed to process order service'}), 500\n            \n            return jsonify({\n                'message': 'Successfully processed order service.',\n                'response': response_data,\n                'intent': 'log_in'\n            }), 200\n            \n            \n        if(response['intent'] == 'new_user'):\n            \n            newuser_intent = IntentService(response)\n        \n            response_data = newuser_intent.new_user_service()\n            \n            speak(response_data)\n            if not response_data:\n                return jsonify({'error': 'Failed to process order service'}), 500\n            \n            return jsonify({\n                'message': 'Successfully processed order service.',\n                'response': response_data,\n                'intent': 'new_user'\n            }), 200\n            \n                           \n        if(response['intent'] == 'send_menu'):\n            \n            sendmenu_intent = IntentService(response)\n        \n            response_data = sendmenu_intent.send_menu_service()\n            speak(response_data)\n            \n            if not response_data:\n                return jsonify({'error': 'Failed to process order service'}), 500\n            \n            return jsonify({\n                'message': 'Successfully processed order service.',\n                'response': response_data,\n                'intent': 'send_menu'\n            }), 200\n            \n            \n        if(response['intent'] == 'home_page'):\n            \n            homepage_intent = IntentService(response)\n        \n            response_data = homepage_intent.home_page_service()\n            \n            speak(response_data)\n            if not response_data:\n                return jsonify({'error': 'Failed to process order service'}), 500\n            \n            return jsonify({\n                'message': 'Successfully processed order service.',\n                'response': response_data,\n                'intent': 'home_page'\n            }), 200\n            \n            \n        if(response['intent'] == 'payment'):\n            \n            payment_intent = IntentService(response)\n        \n            response_data = payment_intent.payment_service()\n            \n            speak(response_data)\n            if not response_data:\n                return jsonify({'error': 'Failed to process order service'}), 500\n            \n            return jsonify({\n                'message': 'Successfully processed order service.',\n                'response': response_data,\n                'intent': 'payment'\n            }), 200\n \n            \n        if(response['intent'] == 'other_queries'):\n            \n            payment_intent = IntentService(response)\n        \n            response_data = payment_intent.other_service()\n            \n            speak(response_data)\n            if not response_data:\n                return jsonify({'error': 'Failed to process order service'}), 500\n            \n            return jsonify({\n                'message': 'Successfully processed order service.',\n                'response': response_data,\n                'intent': 'other_queries'\n            }), 200\n            \n            \n        if(response['intent'] == 'search_in_product'):\n            \n            product_query_intent = IntentService(response)\n        \n            response_data = product_query_intent.product_query()\n            speak(response_data)\n            \n            if not response_data:\n                return jsonify({'error': 'Failed to process order service'}), 500\n            \n            return jsonify({\n                'message': 'Successfully processed order service.',\n                'response': response_data,\n                'intent': 'search_in_product'\n            }), 200\n            \n            \n        if(response['intent'] == 'price_intent'):\n            \n            price_intent = IntentService(response)\n        \n            response_data = price_intent.price_query()\n            speak(response_data)\n            \n            if not response_data:\n                return jsonify({'error': 'Failed to process order service'}), 500\n            \n            return jsonify({\n                'message': 'Successfully processed order service.',\n                'response': response_data,\n                'intent': 'price_intent'\n            }), 200\n            \n\n        if(response['intent'] == 'product_query'):\n            \n            product_description_intent = IntentService(response)\n        \n            response_data = product_description_intent.product_description_query()\n            \n            speak(response_data)\n            if not response_data:\n                return jsonify({'error': 'Failed to process order service'}), 500\n            \n            return jsonify({\n                'message': 'Successfully processed order service.',\n                'response': response_data,\n                'intent': 'product_query'\n            }), 200\n            \n\n        if(response['intent'] == 'remove_from_cart'):\n            \n            remove_cart_intent = IntentService(response)\n        \n            response_data = remove_cart_intent.remove_from_cart_query()\n            speak(response_data)\n            if not response_data:\n                return jsonify({'error': 'Failed to process order service'}), 500\n            \n            return jsonify({\n                'message': 'Successfully processed order service.',\n                'response': response_data,\n                'intent': 'remove_from_cart'\n            }), 200\n\n\n        if(response['intent'] == 'watch_cart'):\n            \n            watchcart_intent = IntentService(response)\n        \n            response_data = watchcart_intent.watch_cart_service()\n            \n            speak(response_data)\n            if not response_data:\n                return jsonify({'error': 'Failed to process order service'}), 500\n            \n            return jsonify({\n                'message': 'Successfully processed order service.',\n                'response': response_data,\n                'intent': 'watch_cart'\n            }), 200\n\n\n        return jsonify({\n            'message': 'Successfully got the response.',\n            'response': response,\n            'intent' : response['intent']\n        }), 200        \n            \n    except Exception as e:\n        return jsonify({'error': f'An unexpected error occurred: {str(e)}'}), 500\n\n\n\n@voiceBlueprint.route('/welcome', methods=['POST'])\ndef welcome():\n    try:\n        response = request.form.get('user_info', None)\n        print(\"This is the response from the welcome route:\", response)\n        session['user_info']=response    \n        predefined_response_manager = PredefinedResponseManager()\n        response = predefined_response_manager.welcome_messeges()\n        response = random.choice(response)\n        \n        speak(response)\n                \n        return jsonify({\n            'message': 'Welcome message sent successfully.',\n            'response': response,\n        }), 200\n        \n    except Exception as e:\n        return jsonify({'error': f'An unexpected error occurred: {str(e)}'}), 500\n    \n    \n\n\n@voiceBlueprint.route('/clear', methods=['POST'])\ndef clear():\n    try:\n        session.pop('user_info',None)  \n        session.pop('active_intent',None)\n        return jsonify({\n            'message': 'Welcome message sent successfully.',\n            'response': \"Successfully removed the session!!\",\n        }), 200\n        \n    except Exception as e:\n        return jsonify({'error': f'An unexpected error occurred: {str(e)}'}), 500\n    \n    ", "text_preview": "from VoiceAssistance.utils import PromptManager from VoiceAssistance.llm_engine import llm_output from VoiceAssistance import voiceBlueprint from VoiceAssistance.service import IntentService from VoiceAssistance.utils import PredefinedRespo", "input_sha256": "28fbeb3bb6de0b361c8d23c11e04c297103fa5a85fd5034d0b2e1321e2bbdde6", "text_sha256": "f84c728f8a82d56489a3a0feb28a9f3e7da156749d09f36249355e355f0dc5ec", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12074, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": -0.05790954586233311, "noise_severity": 0.2071532522930839, "missing_content_severity": 0.09031074155342494, "factual_density": 2.76747943152478, "pretraining_utility": 0.15316549114091207, "math_density": 0.05436640745822069, "code_density": 3.8874928183667765, "reasoning_density": 0.38975191431400497, "education_level": 2.097928331132985, "creative_writing_quality": -0.39996892169855547}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.01231774342034464, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9961810341152664, "secondary": 0.9610203571491542, "tertiary": 0.9738736546312531, "structured_records": 0.07285516788192464, "procedural_steps": 0.003910761002793948, "worked_solution": 0.0035301566670037556, "contextual_dialogue": 2.8323691102463895e-05, "content_quality": 0.47278770920885627, "noise_severity": 0.8273722897557634, "missing_content_severity": 0.9247410487054792, "factual_density": 0.41047201723833715, "pretraining_utility": 0.5069865336423851, "math_density": 0.9546946604514828, "code_density": 0.906244015305647, "reasoning_density": 0.6752067380716624, "education_level": 0.8131012865152117, "creative_writing_quality": 0.9893415767937742, "primary_topic": 0.9860570203150375, "secondary_topic": 0.8041064232138327}, "probability_margin": {"primary": 0.9947404915039727, "secondary": 0.9399962750152205, "tertiary": 0.9607946987049681, "education_level": 0.726247305298856, "creative_writing_quality": 0.9857779454966359, "primary_topic": 0.9776082589132727, "secondary_topic": 0.685745870424111}, "raw_probabilities": {"primary": {"Academic Writing": 3.7941828921824235e-05, "Content Listing": 2.347679919248145e-05, "Creative Writing": 2.0527018562775434e-05, "Customer Support Page": 1.0174314015169135e-05, "Q&A Forum": 1.4012942961989692e-05, "Discussion Thread": 7.930620605128489e-06, "Comment Section": 1.470619438084545e-05, "Missing Content": 2.245052389204766e-05, "Knowledge Article": 7.667768341192884e-05, "Legal Notices": 2.1021880582622612e-05, "News Article": 4.341812507866121e-06, "Organizational Announcement": 1.0146949659161761e-05, "Nonfiction Writing": 1.0907235336519861e-05, "Organizational About Page": 1.1925037834546719e-05, "Personal About Page": 1.4256587250328102e-05, "Personal Blog": 9.479990440946062e-06, "Product Page": 8.507973126205903e-06, "Spam / Ads": 1.0862764287847474e-05, "Structured Data": 0.0007988272378147419, "Technical Writing": 0.9963401576937969, "Transcript / Interview": 1.9887558095270553e-05, "Tutorial / How-To Guide": 0.0009056535361584074, "User Reviews": 6.459627340169362e-06, "Other / Unknown": 0.0015996661898242377}, "secondary": {"Academic Writing": 0.0001885848588357558, "Content Listing": 0.00026270705588394785, "Creative Writing": 0.0007279679675238107, "Customer Support Page": 0.00011077850545980131, "Q&A Forum": 7.35865152929906e-05, "Discussion Thread": 6.709716733134645e-05, "Comment Section": 0.0001494054390576273, "Missing Content": 0.00033790402019585745, "Knowledge Article": 0.00033725032391283594, "Legal Notices": 0.00020657805715771252, "News Article": 9.841223044574185e-05, "Organizational Announcement": 9.23811663844592e-05, "Nonfiction Writing": 9.596429225519917e-05, "Organizational About Page": 0.00010572590326169494, "Personal About Page": 9.895209480848913e-05, "Personal Blog": 9.916091546002813e-05, "Product Page": 6.43446370638986e-05, "Spam / Ads": 0.00011583067581936224, "Structured Data": 0.022648233919385675, "Technical Writing": 0.010248889856333288, "Transcript / Interview": 0.0003124648343435896, "Tutorial / How-To Guide": 0.000867534186834544, "User Reviews": 4.573644234621801e-05, "NONE": 0.9626445089346062}, "tertiary": {"Academic Writing": 0.00013493295927564415, "Content Listing": 0.00023543792541519707, "Creative Writing": 0.0005951052072407318, "Customer Support Page": 9.585898308196305e-05, "Q&A Forum": 4.966891818288298e-05, "Discussion Thread": 4.569113222597695e-05, "Comment Section": 0.00021094719742165342, "Missing Content": 0.0004732440789605554, "Knowledge Article": 0.0004497928364076324, "Legal Notices": 0.00028583620194341385, "News Article": 6.482939856357736e-05, "Organizational Announcement": 6.963608171078612e-05, "Nonfiction Writing": 0.00010116536878956974, "Organizational About Page": 9.665163894431458e-05, "Personal About Page": 7.773309304068922e-05, "Personal Blog": 7.053195707323862e-05, "Product Page": 6.187105773772705e-05, "Spam / Ads": 0.00010774755390035519, "Structured Data": 0.014167553649982694, "Technical Writing": 0.006397770377958582, "Transcript / Interview": 0.0004853858813665741, "Tutorial / How-To Guide": 0.0007213406428326001, "User Reviews": 3.9015502992813665e-05, "NONE": 0.9749622523549508}, "structured_records": {"false": 0.9271448321180753, "true": 0.07285516788192464}, "procedural_steps": {"false": 0.996089238997206, "true": 0.003910761002793948}, "worked_solution": {"false": 0.9964698433329963, "true": 0.0035301566670037556}, "contextual_dialogue": {"false": 0.9999716763088975, "true": 2.8323691102463895e-05}, "content_quality": {"-2": 0.028079995233913222, "-1": 0.28912215693802634, "0": 0.4353204299246824, "1": 0.20758223426323638, "2": 0.03989518364014165}, "noise_severity": {"0": 0.846723619338657, "1": 0.10592047487546609, "2": 0.042568364106690425, "3": 0.003054117512508325, "4": 0.0017334241666779873}, "missing_content_severity": {"0": 0.9410569045285038, "1": 0.03275358401251298, "2": 0.021827331756467228, "3": 0.0035462247820871296, "4": 0.0008159549204290343}, "factual_density": {"0": 0.02001602762930239, "1": 0.07021383486435588, "2": 0.2695013212779888, "3": 0.4028123108089652, "4": 0.23745650541938773}, "pretraining_utility": {"-2": 0.015526421989836975, "-1": 0.18817249026443897, "0": 0.45582433382429993, "1": 0.30856268245782315, "2": 0.03191407146360091}, "math_density": {"0": 0.9510543657432191, "1": 0.046046003283405613, "2": 0.0013896972666944567, "3": 0.0004987251852970029, "4": 0.0010112085213837886}, "code_density": {"0": 0.00020139093436712327, "1": 0.00038643980785354744, "2": 0.010159393587616928, "3": 0.09022351129696056, "4": 0.8990292643732019}, "reasoning_density": {"0": 0.759692902722912, "1": 0.11746166918714983, "2": 0.10302211719228727, "3": 0.013047232848323051, "4": 0.006776078049327868}, "education_level": {"none": 0.0010865973142940473, "0": 0.0016299636180297295, "1": 0.028993039146245562, "2": 0.8442510720960098, "3": 0.11800376679715383, "4": 0.006035561028266896}, "creative_writing_quality": {"not_applicable": 0.9911179806614785, "-2": 0.0008713993130349352, "-1": 0.002282899700494901, "0": 0.005340035164842617, "1": 0.0003022036910672025, "2": 8.54814690817271e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.6567555775852366e-05, "Art & Design": 5.4215968128754574e-05, "Law & Crime": 4.526217228575946e-05, "Education & Careers": 4.836006042821914e-05, "Entertainment & Media": 5.410117684772866e-05, "Fashion & Beauty": 4.217355646848796e-05, "Business, Economics & Finance": 0.0008049878127349324, "Food & Dining": 0.008985029851186464, "Games & Gaming": 0.00023631369488792242, "Electronics & Hardware": 7.308093828552943e-05, "Health, Healthcare & Wellness": 3.864733580204207e-05, "History & Geography": 4.89296935487264e-05, "Home, Pets & Hobbies": 3.4566947338338154e-05, "Industry & Production": 3.28542597837218e-05, "Language, Literature & Philosophy": 0.00010731614569805594, "Politics & Government": 2.619312510590186e-05, "Religion & Spirituality": 3.24288821718327e-05, "Science, Mathematics & Engineering": 0.0014509540911700287, "Relationships & Community": 2.830013756912827e-05, "Software Usage & Applications": 0.0007090188543642539, "Programming & Software Engineering": 0.9865932887644592, "Sports & Fitness": 6.25600417277793e-05, "Vehicles & Transportation": 3.552153656243486e-05, "Travel & Tourism": 6.71996202022237e-05, "Other / Uncovered Subject": 0.00027655865717669185, "Unknown / Insufficient Content": 7.556912029020066e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00110673897370614, "Art & Design": 0.0007888188582172311, "Law & Crime": 0.0006662251241037392, "Education & Careers": 0.0003974992185212776, "Entertainment & Media": 0.0007015150820002752, "Fashion & Beauty": 0.0004124733879887275, "Business, Economics & Finance": 0.00679873397196067, "Food & Dining": 0.12589492112765135, "Games & Gaming": 0.0007263122261420496, "Electronics & Hardware": 0.0004892295020147226, "Health, Healthcare & Wellness": 0.0004386786452467306, "History & Geography": 0.00034060744640998855, "Home, Pets & Hobbies": 0.00037868558074402386, "Industry & Production": 0.00027991524688257346, "Language, Literature & Philosophy": 0.001123379866688062, "Politics & Government": 0.0002506258675451525, "Religion & Spirituality": 0.0003728853649010513, "Science, Mathematics & Engineering": 0.01439582330681777, "Relationships & Community": 0.00020787573658818633, "Software Usage & Applications": 0.003078326551973729, "Programming & Software Engineering": 0.02713893498601066, "Sports & Fitness": 0.00030429024758993426, "Vehicles & Transportation": 0.00019375114447115572, "Travel & Tourism": 0.00040945088346408423, "Other / Uncovered Subject": 0.0014635101005983978, "NONE": 0.8116407915517623}}, "probabilities": {"primary": {"Academic Writing": 3.7941828921824235e-05, "Content Listing": 2.347679919248145e-05, "Creative Writing": 2.0527018562775434e-05, "Customer Support Page": 1.0174314015169135e-05, "Q&A Forum": 1.4012942961989692e-05, "Discussion Thread": 7.930620605128489e-06, "Comment Section": 1.470619438084545e-05, "Missing Content": 2.245052389204766e-05, "Knowledge Article": 7.667768341192884e-05, "Legal Notices": 2.1021880582622612e-05, "News Article": 4.341812507866121e-06, "Organizational Announcement": 1.0146949659161761e-05, "Nonfiction Writing": 1.0907235336519861e-05, "Organizational About Page": 1.1925037834546719e-05, "Personal About Page": 1.4256587250328102e-05, "Personal Blog": 9.479990440946062e-06, "Product Page": 8.507973126205903e-06, "Spam / Ads": 1.0862764287847474e-05, "Structured Data": 0.0007988272378147419, "Technical Writing": 0.9963401576937969, "Transcript / Interview": 1.9887558095270553e-05, "Tutorial / How-To Guide": 0.0009056535361584074, "User Reviews": 6.459627340169362e-06, "Other / Unknown": 0.0015996661898242377}, "secondary": {"Academic Writing": 0.0001885848588357558, "Content Listing": 0.00026270705588394785, "Creative Writing": 0.0007279679675238107, "Customer Support Page": 0.00011077850545980131, "Q&A Forum": 7.35865152929906e-05, "Discussion Thread": 6.709716733134645e-05, "Comment Section": 0.0001494054390576273, "Missing Content": 0.00033790402019585745, "Knowledge Article": 0.00033725032391283594, "Legal Notices": 0.00020657805715771252, "News Article": 9.841223044574185e-05, "Organizational Announcement": 9.23811663844592e-05, "Nonfiction Writing": 9.596429225519917e-05, "Organizational About Page": 0.00010572590326169494, "Personal About Page": 9.895209480848913e-05, "Personal Blog": 9.916091546002813e-05, "Product Page": 6.43446370638986e-05, "Spam / Ads": 0.00011583067581936224, "Structured Data": 0.022648233919385675, "Technical Writing": 0.010248889856333288, "Transcript / Interview": 0.0003124648343435896, "Tutorial / How-To Guide": 0.000867534186834544, "User Reviews": 4.573644234621801e-05, "NONE": 0.9626445089346062}, "tertiary": {"Academic Writing": 0.00013493295927564415, "Content Listing": 0.00023543792541519707, "Creative Writing": 0.0005951052072407318, "Customer Support Page": 9.585898308196305e-05, "Q&A Forum": 4.966891818288298e-05, "Discussion Thread": 4.569113222597695e-05, "Comment Section": 0.00021094719742165342, "Missing Content": 0.0004732440789605554, "Knowledge Article": 0.0004497928364076324, "Legal Notices": 0.00028583620194341385, "News Article": 6.482939856357736e-05, "Organizational Announcement": 6.963608171078612e-05, "Nonfiction Writing": 0.00010116536878956974, "Organizational About Page": 9.665163894431458e-05, "Personal About Page": 7.773309304068922e-05, "Personal Blog": 7.053195707323862e-05, "Product Page": 6.187105773772705e-05, "Spam / Ads": 0.00010774755390035519, "Structured Data": 0.014167553649982694, "Technical Writing": 0.006397770377958582, "Transcript / Interview": 0.0004853858813665741, "Tutorial / How-To Guide": 0.0007213406428326001, "User Reviews": 3.9015502992813665e-05, "NONE": 0.9749622523549508}, "structured_records": {"false": 0.9271448321180753, "true": 0.07285516788192464}, "procedural_steps": {"false": 0.996089238997206, "true": 0.003910761002793948}, "worked_solution": {"false": 0.9964698433329963, "true": 0.0035301566670037556}, "contextual_dialogue": {"false": 0.9999716763088975, "true": 2.8323691102463895e-05}, "content_quality": {"-2": 0.028079995233913222, "-1": 0.28912215693802634, "0": 0.4353204299246824, "1": 0.20758223426323638, "2": 0.03989518364014165}, "noise_severity": {"0": 0.8467236193386571, "1": 0.1059204748754661, "2": 0.04256836410669043, "3": 0.0030541175125083256, "4": 0.0017334241666779875}, "missing_content_severity": {"0": 0.9410569045285035, "1": 0.03275358401251297, "2": 0.021827331756467224, "3": 0.0035462247820871287, "4": 0.0008159549204290341}, "factual_density": {"0": 0.02001602762930239, "1": 0.07021383486435588, "2": 0.2695013212779888, "3": 0.4028123108089652, "4": 0.23745650541938773}, "pretraining_utility": {"-2": 0.015526421989836977, "-1": 0.188172490264439, "0": 0.4558243338243, "1": 0.3085626824578232, "2": 0.03191407146360092}, "math_density": {"0": 0.9510543657432191, "1": 0.046046003283405613, "2": 0.0013896972666944567, "3": 0.0004987251852970029, "4": 0.0010112085213837886}, "code_density": {"0": 0.00020139093436712327, "1": 0.00038643980785354744, "2": 0.010159393587616928, "3": 0.09022351129696056, "4": 0.8990292643732019}, "reasoning_density": {"0": 0.759692902722912, "1": 0.11746166918714983, "2": 0.10302211719228727, "3": 0.013047232848323051, "4": 0.006776078049327868}, "education_level": {"none": 0.0010865973142940475, "0": 0.0016299636180297297, "1": 0.028993039146245566, "2": 0.8442510720960099, "3": 0.11800376679715384, "4": 0.006035561028266897}, "creative_writing_quality": {"not_applicable": 0.9911179806614786, "-2": 0.0008713993130349353, "-1": 0.0022828997004949014, "0": 0.005340035164842618, "1": 0.00030220369106720256, "2": 8.548146908172712e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.656755577585236e-05, "Art & Design": 5.421596812875456e-05, "Law & Crime": 4.526217228575945e-05, "Education & Careers": 4.836006042821913e-05, "Entertainment & Media": 5.410117684772864e-05, "Fashion & Beauty": 4.217355646848795e-05, "Business, Economics & Finance": 0.0008049878127349322, "Food & Dining": 0.008985029851186462, "Games & Gaming": 0.00023631369488792236, "Electronics & Hardware": 7.308093828552942e-05, "Health, Healthcare & Wellness": 3.864733580204206e-05, "History & Geography": 4.892969354872639e-05, "Home, Pets & Hobbies": 3.456694733833815e-05, "Industry & Production": 3.285425978372179e-05, "Language, Literature & Philosophy": 0.00010731614569805592, "Politics & Government": 2.6193125105901853e-05, "Religion & Spirituality": 3.2428882171832696e-05, "Science, Mathematics & Engineering": 0.0014509540911700285, "Relationships & Community": 2.8300137569128263e-05, "Software Usage & Applications": 0.0007090188543642538, "Programming & Software Engineering": 0.986593288764459, "Sports & Fitness": 6.256004172777928e-05, "Vehicles & Transportation": 3.552153656243485e-05, "Travel & Tourism": 6.719962020222369e-05, "Other / Uncovered Subject": 0.0002765586571766918, "Unknown / Insufficient Content": 7.556912029020065e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00110673897370614, "Art & Design": 0.0007888188582172311, "Law & Crime": 0.0006662251241037392, "Education & Careers": 0.0003974992185212776, "Entertainment & Media": 0.0007015150820002752, "Fashion & Beauty": 0.0004124733879887275, "Business, Economics & Finance": 0.00679873397196067, "Food & Dining": 0.12589492112765135, "Games & Gaming": 0.0007263122261420496, "Electronics & Hardware": 0.0004892295020147226, "Health, Healthcare & Wellness": 0.0004386786452467306, "History & Geography": 0.00034060744640998855, "Home, Pets & Hobbies": 0.00037868558074402386, "Industry & Production": 0.00027991524688257346, "Language, Literature & Philosophy": 0.001123379866688062, "Politics & Government": 0.0002506258675451525, "Religion & Spirituality": 0.0003728853649010513, "Science, Mathematics & Engineering": 0.01439582330681777, "Relationships & Community": 0.00020787573658818633, "Software Usage & Applications": 0.003078326551973729, "Programming & Software Engineering": 0.02713893498601066, "Sports & Fitness": 0.00030429024758993426, "Vehicles & Transportation": 0.00019375114447115572, "Travel & Tourism": 0.00040945088346408423, "Other / Uncovered Subject": 0.0014635101005983978, "NONE": 0.8116407915517623}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10895, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10895, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:08.231297+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "ec79b8fa-fa29-4bca-a79f-82975350ca37", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "22f8db95552812925ea855fb6e730599d2188a9cbef5a9de5cf01d6ba7d7ac13", "source_metadata": {"uuid": "ec79b8fa-fa29-4bca-a79f-82975350ca37", "repo_name": "Shayantan1012/express_falsk_react_for_pizza_app", "relative_path": "FLASK_BACKEND/VoiceAssistance/routes.py", "category": "WEB", "algo_rel_score": 0.9760229587554932, "quality_score": 4.579627990722656, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 38, "repo_created_at": "2025-06-09T18:53:22.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "cc61baad4cbd205fc5ed264be91597039d9c045e", "size_bytes": 12026, "file_timestamp": 1751534024, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "38ceca809fdc9544788805cca3ded0f8ea756254"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 38, "repo_created_at": "2025-06-09T18:53:22.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "cc61baad4cbd205fc5ed264be91597039d9c045e", "size_bytes": 12026, "file_timestamp": 1751534024, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "38ceca809fdc9544788805cca3ded0f8ea756254", "url": "https://github.com/Shayantan1012/express_falsk_react_for_pizza_app/blob/38ceca809fdc9544788805cca3ded0f8ea756254/FLASK_BACKEND/VoiceAssistance/routes.py"}, "gpt4o_tokens": 2158, "dataset_split": "python", "probability_weighted_scores": {"content_quality": -0.05790954586233311, "noise_severity": 0.2071532522930839, "missing_content_severity": 0.09031074155342496, "factual_density": 2.76747943152478, "pretraining_utility": 0.15316549114091207, "math_density": 0.05436640745822069, "code_density": 3.8874928183667765, "reasoning_density": 0.38975191431400497, "education_level": 2.097928331132985, "creative_writing_quality": -0.39996892169855547}, "score_applicability": {"education_level": 0.9989134026857059, "creative_writing_quality": 0.008882019338521384}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/TiepiNL/py4e/blob/ceb52e1aded94ad8606cfc973b4f1765cba3846c/toronto1/a1.py", "id": "python-0-9341ed3c320c33104ca189a3957db67e", "text": "\"\"\"\nProgramming Assignment: Assignment 1: Time Zones\n\"\"\"\n\ndef seconds_difference(time_1, time_2):\n    \"\"\" (number, number) -> number\n\n    Return the number of seconds later that a time in seconds\n    time_2 is than a time in seconds time_1.\n\n    >>> seconds_difference(1800.0, 3600.0)\n    1800.0\n    >>> seconds_difference(3600.0, 1800.0)\n    -1800.0\n    >>> seconds_difference(1800.0, 2160.0)\n    360.0\n    >>> seconds_difference(1800.0, 1800.0)\n    0.0\n    \"\"\"\n    return time_2 - time_1\n\n\ndef hours_difference(time_1, time_2):\n    \"\"\" (number, number) -> float\n\n    Return the number of hours later that a time in seconds\n    time_2 is than a time in seconds time_1.\n\n    >>> hours_difference(1800.0, 3600.0)\n    0.5\n    >>> hours_difference(3600.0, 1800.0)\n    -0.5\n    >>> hours_difference(1800.0, 2160.0)\n    0.1\n    >>> hours_difference(1800.0, 1800.0)\n    0.0\n    \"\"\"\n    # Retrieve the differnce in seconds and convert it to hours.\n    return seconds_difference(time_1, time_2) / 60 / 60\n\n\ndef to_float_hours(hours, minutes, seconds):\n    \"\"\" (int, int, int) -> float\n\n    Return the total number of hours in the specified number\n    of hours, minutes, and seconds.\n\n    Precondition: 0 <= minutes < 60  and  0 <= seconds < 60\n\n    >>> to_float_hours(0, 15, 0)\n    0.25\n    >>> to_float_hours(2, 45, 9)\n    2.7525\n    >>> to_float_hours(1, 0, 36)\n    1.01\n    \"\"\"\n    try:\n        assert 0 <= minutes and minutes < 60\n        assert 0 <= seconds and seconds < 60\n    except AssertionError:\n        # Preconditions not met\n        quit()\n\n    return hours + (minutes / 60) + (seconds / (60 * 60))\n\n\ndef to_24_hour_clock(hours):\n    \"\"\" (number) -> number\n\n    hours is a number of hours since midnight. Return the\n    hour as seen on a 24-hour clock.\n\n    Precondition: hours >= 0\n\n    >>> to_24_hour_clock(24)\n    0\n    >>> to_24_hour_clock(48)\n    0\n    >>> to_24_hour_clock(25)\n    1\n    >>> to_24_hour_clock(4)\n    4\n    >>> to_24_hour_clock(28.5)\n    4.5\n    \"\"\"\n\n    return hours % 24\n\n\ndef get_hours(seconds):\n    \"\"\" (int) -> int\n    seconds is the number of seconds since midnight. Return the number of hours\n    that have elapsed since midnight, as seen on a 24-hour clock.\n\n    >>> get_hours(3800)\n    1\n    >>> get_hours(2400)\n    0\n    >>> get_hours(50485)\n    14\n    \"\"\"\n\n    return to_24_hour_clock(seconds // 3600)\n\n\ndef get_minutes(seconds):\n    \"\"\" (int) -> int\n    seconds is the number of seconds since midnight. Return the number of minutes that\n    have elapsed since midnight as seen on a clock.\n\n    >>> get_minutes(3800)\n    3\n    >>> get_minutes(2400)\n    40\n    >>> get_minutes(50485)\n    1\n    \"\"\"\n    return seconds % 3600 // 60\n\n\ndef get_seconds(seconds):\n    \"\"\" (int) -> int\n    seconds is the number of seconds since midnight. Return the number of seconds that\n    have elapsed since midnight as seen on a clock.\n\n    >>> get_seconds(3800)\n    20\n    >>> get_seconds(2400)\n    0\n    >>> get_seconds(50485)\n    25\n    \"\"\"\n    return seconds % 60\n\n\ndef time_to_utc(utc_offset, time):\n    \"\"\" (number, float) -> float\n\n    Return time at UTC+0, where utc_offset is the number of hours away from\n    UTC+0.\n\n    >>> time_to_utc(+0, 12.0)\n    12.0\n    >>> time_to_utc(+1, 12.0)\n    11.0\n    >>> time_to_utc(-1, 12.0)\n    13.0\n    >>> time_to_utc(-11, 18.0)\n    5.0\n    >>> time_to_utc(-1, 0.0)\n    1.0\n    >>> time_to_utc(-1, 23.0)\n    0.0\n    \"\"\"\n    totalseconds = (time - utc_offset) * 3600\n    hours = get_hours(totalseconds)\n    minutes = get_minutes(totalseconds)\n    seconds = get_seconds(totalseconds)\n    return to_float_hours(hours, minutes, seconds)\n\n\ndef time_from_utc(utc_offset, time):\n    \"\"\" (number, float) -> float\n\n    Return UTC time in time zone utc_offset.\n\n    >>> time_from_utc(+0, 12.0)\n    12.0\n    >>> time_from_utc(+1, 12.0)\n    13.0\n    >>> time_from_utc(-1, 12.0)\n    11.0\n    >>> time_from_utc(+6, 6.0)\n    12.0\n    >>> time_from_utc(-7, 6.0)\n    23.0\n    >>> time_from_utc(-1, 0.0)\n    23.0\n    >>> time_from_utc(-1, 23.0)\n    22.0\n    >>> time_from_utc(+1, 23.0)\n    0.0\n    \"\"\"\n    return time_to_utc(-utc_offset, time)\n", "text_preview": "\"\"\" Programming Assignment: Assignment 1: Time Zones \"\"\" def seconds_difference(time_1, time_2): \"\"\" (number, number) -> number Return the number of seconds later that a time in seconds time_2 is than a time in seconds time_1. >>> seconds_d", "input_sha256": "f9617ff221f57be14547fbd7a51798fdd3e4e71075d0e9039470e4a498352b54", "text_sha256": "9341ed3c320c33104ca189a3957db67e4f2e62b60c6831e973cb0cdaad44c9f0", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12075, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.025474809967249, "noise_severity": 0.020696698277346888, "missing_content_severity": 0.007499156312481182, "factual_density": 3.3354951070087133, "pretraining_utility": 0.6935699816669592, "math_density": 3.3246149249098442, "code_density": 3.755358115529982, "reasoning_density": 1.3313532019986145, "education_level": 1.621529155475444, "creative_writing_quality": -0.15284611743775584}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0519827068276435, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9568302353239355, "secondary": 0.8655717526109967, "tertiary": 0.9198145771267986, "structured_records": 0.05177425493073746, "procedural_steps": 0.01055520903933856, "worked_solution": 0.2974838938187191, "contextual_dialogue": 6.60160795017526e-06, "content_quality": 0.5484989998424321, "noise_severity": 0.9827527514355443, "missing_content_severity": 0.9937507030729323, "factual_density": 0.4462459225072607, "pretraining_utility": 0.5538692658473781, "math_density": 0.4371791040915366, "code_density": 0.796131762941652, "reasoning_density": 0.0, "education_level": 0.5373974142782242, "creative_writing_quality": 0.9991598468289715, "primary_topic": 0.9676195439000981, "secondary_topic": 0.69949741614749}, "probability_margin": {"primary": 0.930252840430542, "secondary": 0.818072378824932, "tertiary": 0.8887059399233687, "education_level": 0.24274204879125555, "creative_writing_quality": 0.9989689407467238, "primary_topic": 0.9497084778239214, "secondary_topic": 0.553431929902683}, "raw_probabilities": {"primary": {"Academic Writing": 0.0021002326239414858, "Content Listing": 8.091208769941178e-05, "Creative Writing": 7.87477942199415e-05, "Customer Support Page": 4.336528653444519e-05, "Q&A Forum": 7.088288413725793e-05, "Discussion Thread": 3.092212830265029e-05, "Comment Section": 4.799733447878962e-05, "Missing Content": 0.00011981558283728733, "Knowledge Article": 0.0001796889298685798, "Legal Notices": 4.867260419339708e-05, "News Article": 2.4175510474897685e-05, "Organizational Announcement": 3.791918817006513e-05, "Nonfiction Writing": 5.4725149754446096e-05, "Organizational About Page": 4.76768253809072e-05, "Personal About Page": 3.9910846188717785e-05, "Personal Blog": 2.6528805291970864e-05, "Product Page": 2.769900744169676e-05, "Spam / Ads": 3.9625285233523264e-05, "Structured Data": 0.00042290840100529, "Technical Writing": 0.9586289755187716, "Transcript / Interview": 6.82062577358067e-05, "Tutorial / How-To Guide": 0.02837613508822954, "User Reviews": 2.4734611671368836e-05, "Other / Unknown": 0.009379542248436866}, "secondary": {"Academic Writing": 0.02838789388155588, "Content Listing": 0.0004213604590712339, "Creative Writing": 0.00044763634829648265, "Customer Support Page": 0.0003001465939664268, "Q&A Forum": 0.00027742772417296136, "Discussion Thread": 0.00012776689757304727, "Comment Section": 0.00022086995956578827, "Missing Content": 0.0014254963974550676, "Knowledge Article": 0.00104166697155293, "Legal Notices": 0.00023853853248003232, "News Article": 0.00021784264978747062, "Organizational Announcement": 0.0002149231297132806, "Nonfiction Writing": 0.0002778201025710254, "Organizational About Page": 0.00021901616425341368, "Personal About Page": 0.00020902126620447634, "Personal Blog": 0.00016343637781872827, "Product Page": 0.00015852161419846613, "Spam / Ads": 0.00021744610297917886, "Structured Data": 0.0015897951717900871, "Technical Writing": 0.03912671670849872, "Transcript / Interview": 0.0003213694115395111, "Tutorial / How-To Guide": 0.053100550760606435, "User Reviews": 0.00012180718881089474, "NONE": 0.8711729295855385}, "tertiary": {"Academic Writing": 0.017135120837659546, "Content Listing": 0.0002331556518619114, "Creative Writing": 0.00028178493284395685, "Customer Support Page": 0.00019039648216636037, "Q&A Forum": 0.0001499278385640342, "Discussion Thread": 7.260027486298222e-05, "Comment Section": 0.00012721698750016642, "Missing Content": 0.0015361536500741743, "Knowledge Article": 0.0010570407708545787, "Legal Notices": 0.00018025379554513274, "News Article": 0.00012102827274206021, "Organizational Announcement": 0.00012842194169560155, "Nonfiction Writing": 0.00020929317632190265, "Organizational About Page": 0.00014125376723987063, "Personal About Page": 0.00012243016041855616, "Personal Blog": 9.261360367019401e-05, "Product Page": 0.00010335667110912062, "Spam / Ads": 0.00014235837590517722, "Structured Data": 0.0012798710944732401, "Technical Writing": 0.01883925265910839, "Transcript / Interview": 0.00018461992340888404, "Tutorial / How-To Guide": 0.034449696489813365, "User Reviews": 6.651622897886127e-05, "NONE": 0.923155636413182}, "structured_records": {"false": 0.9482257450692625, "true": 0.05177425493073746}, "procedural_steps": {"false": 0.9894447909606614, "true": 0.01055520903933856}, "worked_solution": {"false": 0.7025161061812809, "true": 0.2974838938187191}, "contextual_dialogue": {"false": 0.9999933983920498, "true": 6.60160795017526e-06}, "content_quality": {"-2": 0.007733969616896217, "-1": 0.026811046454233, "0": 0.18133919335176155, "1": 0.500477785498944, "2": 0.2836380050781653}, "noise_severity": {"0": 0.9820984325120674, "1": 0.01612935315537974, "2": 0.0010670096905776847, "3": 0.00038749282708979414, "4": 0.00031771181488559995}, "missing_content_severity": {"0": 0.9964250767998256, "1": 0.0010684621620844778, "2": 0.0015521500492429717, "3": 0.0004908499034762171, "4": 0.00046346108537052697}, "factual_density": {"0": 0.004095800303859095, "1": 0.011326298431996963, "2": 0.12321760325774951, "3": 0.3677075899643608, "4": 0.49365270804203365}, "pretraining_utility": {"-2": 0.00667832156008466, "-1": 0.049778291942814724, "0": 0.30130190109221017, "1": 0.5277780540798377, "2": 0.11446343132505277}, "math_density": {"0": 0.005034420300845858, "1": 0.01620762223456477, "2": 0.12726101716576652, "3": 0.35210249285154516, "4": 0.4993944474472778}, "code_density": {"0": 0.0004373817859159395, "1": 0.0009207779290917222, "2": 0.029230982406523564, "3": 0.18166805872603148, "4": 0.7877427991524373}, "reasoning_density": {"0": 0.41052893570881066, "1": 0.14164071818908933, "2": 0.23737758839199005, "3": 0.12685372381489463, "4": 0.0835990338952153}, "education_level": {"none": 0.0015948111984842247, "0": 0.00655634660352922, "1": 0.3717557964405979, "2": 0.6144978452318535, "3": 0.004189166386822528, "4": 0.0014060341387127637}, "creative_writing_quality": {"not_applicable": 0.9992998723574763, "-2": 6.33448024238325e-05, "-1": 0.00016073010712976268, "0": 0.0003309316107524623, "1": 0.0001098343243283315, "2": 3.528679788925414e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.533575763193017e-05, "Art & Design": 7.83124506263938e-05, "Law & Crime": 6.389165053989111e-05, "Education & Careers": 0.003991734057101068, "Entertainment & Media": 6.62613419520279e-05, "Fashion & Beauty": 6.160188840248842e-05, "Business, Economics & Finance": 8.036148054631324e-05, "Food & Dining": 6.683834745555036e-05, "Games & Gaming": 9.470422697823793e-05, "Electronics & Hardware": 8.256957314427226e-05, "Health, Healthcare & Wellness": 6.127494447595483e-05, "History & Geography": 0.0036206287136502156, "Home, Pets & Hobbies": 6.728125379806321e-05, "Industry & Production": 4.65977849721943e-05, "Language, Literature & Philosophy": 8.497344020501234e-05, "Politics & Government": 4.404554594803209e-05, "Religion & Spirituality": 6.576431662894805e-05, "Science, Mathematics & Engineering": 0.019156468233865184, "Relationships & Community": 3.9103253726097466e-05, "Software Usage & Applications": 0.00031209403151569756, "Programming & Software Engineering": 0.9688649460577866, "Sports & Fitness": 9.692492746976076e-05, "Vehicles & Transportation": 9.283661745046122e-05, "Travel & Tourism": 0.0009463656160793653, "Other / Uncovered Subject": 0.0017580798650870836, "Unknown / Insufficient Content": 9.100462296330464e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.000848856150635579, "Art & Design": 0.0006062594507976227, "Law & Crime": 0.0007997940321348361, "Education & Careers": 0.03363548574968376, "Entertainment & Media": 0.0006167552461051712, "Fashion & Beauty": 0.0003604053506400824, "Business, Economics & Finance": 0.0006490963069623269, "Food & Dining": 0.0004223081617327348, "Games & Gaming": 0.0004961170239739507, "Electronics & Hardware": 0.0005325351235900853, "Health, Healthcare & Wellness": 0.0005554713294593189, "History & Geography": 0.029510977491618837, "Home, Pets & Hobbies": 0.0004415566001397266, "Industry & Production": 0.000274974969819064, "Language, Literature & Philosophy": 0.0003155918528309755, "Politics & Government": 0.0003756580904059708, "Religion & Spirituality": 0.0005452865423848593, "Science, Mathematics & Engineering": 0.1576232779314419, "Relationships & Community": 0.00022120417999533428, "Software Usage & Applications": 0.0006276012114130585, "Programming & Software Engineering": 0.050980769091317574, "Sports & Fitness": 0.0004445605912851752, "Vehicles & Transportation": 0.0003939816996057557, "Travel & Tourism": 0.005136559815664226, "Other / Uncovered Subject": 0.0025297081722370565, "NONE": 0.7110552078341249}}, "probabilities": {"primary": {"Academic Writing": 0.002100232623941486, "Content Listing": 8.091208769941179e-05, "Creative Writing": 7.874779421994152e-05, "Customer Support Page": 4.3365286534445194e-05, "Q&A Forum": 7.088288413725794e-05, "Discussion Thread": 3.0922128302650296e-05, "Comment Section": 4.7997334478789624e-05, "Missing Content": 0.00011981558283728735, "Knowledge Article": 0.00017968892986857982, "Legal Notices": 4.8672604193397086e-05, "News Article": 2.417551047489769e-05, "Organizational Announcement": 3.7919188170065135e-05, "Nonfiction Writing": 5.47251497544461e-05, "Organizational About Page": 4.7676825380907205e-05, "Personal About Page": 3.991084618871779e-05, "Personal Blog": 2.6528805291970867e-05, "Product Page": 2.7699007441696765e-05, "Spam / Ads": 3.962528523352327e-05, "Structured Data": 0.00042290840100529006, "Technical Writing": 0.9586289755187717, "Transcript / Interview": 6.820625773580671e-05, "Tutorial / How-To Guide": 0.028376135088229542, "User Reviews": 2.473461167136884e-05, "Other / Unknown": 0.009379542248436868}, "secondary": {"Academic Writing": 0.02838789388155588, "Content Listing": 0.0004213604590712339, "Creative Writing": 0.00044763634829648265, "Customer Support Page": 0.0003001465939664268, "Q&A Forum": 0.00027742772417296136, "Discussion Thread": 0.00012776689757304727, "Comment Section": 0.00022086995956578827, "Missing Content": 0.0014254963974550676, "Knowledge Article": 0.00104166697155293, "Legal Notices": 0.00023853853248003232, "News Article": 0.00021784264978747062, "Organizational Announcement": 0.0002149231297132806, "Nonfiction Writing": 0.0002778201025710254, "Organizational About Page": 0.00021901616425341368, "Personal About Page": 0.00020902126620447634, "Personal Blog": 0.00016343637781872827, "Product Page": 0.00015852161419846613, "Spam / Ads": 0.00021744610297917886, "Structured Data": 0.0015897951717900871, "Technical Writing": 0.03912671670849872, "Transcript / Interview": 0.0003213694115395111, "Tutorial / How-To Guide": 0.053100550760606435, "User Reviews": 0.00012180718881089474, "NONE": 0.8711729295855385}, "tertiary": {"Academic Writing": 0.017135120837659546, "Content Listing": 0.0002331556518619114, "Creative Writing": 0.00028178493284395685, "Customer Support Page": 0.00019039648216636037, "Q&A Forum": 0.0001499278385640342, "Discussion Thread": 7.260027486298222e-05, "Comment Section": 0.00012721698750016642, "Missing Content": 0.0015361536500741743, "Knowledge Article": 0.0010570407708545787, "Legal Notices": 0.00018025379554513274, "News Article": 0.00012102827274206021, "Organizational Announcement": 0.00012842194169560155, "Nonfiction Writing": 0.00020929317632190265, "Organizational About Page": 0.00014125376723987063, "Personal About Page": 0.00012243016041855616, "Personal Blog": 9.261360367019401e-05, "Product Page": 0.00010335667110912062, "Spam / Ads": 0.00014235837590517722, "Structured Data": 0.0012798710944732401, "Technical Writing": 0.01883925265910839, "Transcript / Interview": 0.00018461992340888404, "Tutorial / How-To Guide": 0.034449696489813365, "User Reviews": 6.651622897886127e-05, "NONE": 0.923155636413182}, "structured_records": {"false": 0.9482257450692625, "true": 0.05177425493073746}, "procedural_steps": {"false": 0.9894447909606614, "true": 0.01055520903933856}, "worked_solution": {"false": 0.7025161061812809, "true": 0.2974838938187191}, "contextual_dialogue": {"false": 0.9999933983920498, "true": 6.60160795017526e-06}, "content_quality": {"-2": 0.007733969616896217, "-1": 0.026811046454233, "0": 0.18133919335176155, "1": 0.500477785498944, "2": 0.2836380050781653}, "noise_severity": {"0": 0.9820984325120672, "1": 0.016129353155379736, "2": 0.0010670096905776845, "3": 0.00038749282708979403, "4": 0.0003177118148855999}, "missing_content_severity": {"0": 0.9964250767998258, "1": 0.001068462162084478, "2": 0.001552150049242972, "3": 0.0004908499034762173, "4": 0.0004634610853705271}, "factual_density": {"0": 0.004095800303859095, "1": 0.011326298431996963, "2": 0.12321760325774951, "3": 0.3677075899643608, "4": 0.49365270804203365}, "pretraining_utility": {"-2": 0.00667832156008466, "-1": 0.049778291942814724, "0": 0.30130190109221017, "1": 0.5277780540798377, "2": 0.11446343132505277}, "math_density": {"0": 0.005034420300845858, "1": 0.01620762223456477, "2": 0.12726101716576652, "3": 0.35210249285154516, "4": 0.4993944474472778}, "code_density": {"0": 0.0004373817859159395, "1": 0.0009207779290917222, "2": 0.029230982406523564, "3": 0.18166805872603148, "4": 0.7877427991524373}, "reasoning_density": {"0": 0.41052893570881066, "1": 0.14164071818908933, "2": 0.23737758839199005, "3": 0.12685372381489463, "4": 0.0835990338952153}, "education_level": {"none": 0.0015948111984842243, "0": 0.006556346603529218, "1": 0.37175579644059786, "2": 0.6144978452318534, "3": 0.004189166386822527, "4": 0.0014060341387127635}, "creative_writing_quality": {"not_applicable": 0.9992998723574763, "-2": 6.33448024238325e-05, "-1": 0.00016073010712976268, "0": 0.0003309316107524623, "1": 0.0001098343243283315, "2": 3.528679788925414e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.533575763193016e-05, "Art & Design": 7.831245062639379e-05, "Law & Crime": 6.38916505398911e-05, "Education & Careers": 0.003991734057101067, "Entertainment & Media": 6.626134195202789e-05, "Fashion & Beauty": 6.160188840248841e-05, "Business, Economics & Finance": 8.036148054631323e-05, "Food & Dining": 6.683834745555035e-05, "Games & Gaming": 9.47042269782379e-05, "Electronics & Hardware": 8.256957314427225e-05, "Health, Healthcare & Wellness": 6.127494447595481e-05, "History & Geography": 0.0036206287136502147, "Home, Pets & Hobbies": 6.72812537980632e-05, "Industry & Production": 4.659778497219429e-05, "Language, Literature & Philosophy": 8.497344020501232e-05, "Politics & Government": 4.4045545948032085e-05, "Religion & Spirituality": 6.576431662894804e-05, "Science, Mathematics & Engineering": 0.01915646823386518, "Relationships & Community": 3.910325372609746e-05, "Software Usage & Applications": 0.0003120940315156975, "Programming & Software Engineering": 0.9688649460577864, "Sports & Fitness": 9.692492746976074e-05, "Vehicles & Transportation": 9.283661745046119e-05, "Travel & Tourism": 0.000946365616079365, "Other / Uncovered Subject": 0.0017580798650870832, "Unknown / Insufficient Content": 9.100462296330462e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008488561506355791, "Art & Design": 0.0006062594507976228, "Law & Crime": 0.0007997940321348362, "Education & Careers": 0.033635485749683766, "Entertainment & Media": 0.0006167552461051713, "Fashion & Beauty": 0.00036040535064008246, "Business, Economics & Finance": 0.000649096306962327, "Food & Dining": 0.0004223081617327349, "Games & Gaming": 0.0004961170239739508, "Electronics & Hardware": 0.0005325351235900854, "Health, Healthcare & Wellness": 0.000555471329459319, "History & Geography": 0.02951097749161884, "Home, Pets & Hobbies": 0.00044155660013972666, "Industry & Production": 0.00027497496981906407, "Language, Literature & Philosophy": 0.00031559185283097554, "Politics & Government": 0.00037565809040597086, "Religion & Spirituality": 0.0005452865423848594, "Science, Mathematics & Engineering": 0.15762327793144193, "Relationships & Community": 0.0002212041799953343, "Software Usage & Applications": 0.0006276012114130586, "Programming & Software Engineering": 0.05098076909131758, "Sports & Fitness": 0.00044456059128517527, "Vehicles & Transportation": 0.00039398169960575577, "Travel & Tourism": 0.005136559815664227, "Other / Uncovered Subject": 0.002529708172237057, "NONE": 0.711055207834125}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10034, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10034, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:08.982892+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "7275dd3f-e71f-46c7-ac59-61e2107e71dc", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "183edaeceb2aa64196b9b2ac6c5441ca289f258a113f2ddaa23adf17347d0abc", "source_metadata": {"uuid": "7275dd3f-e71f-46c7-ac59-61e2107e71dc", "repo_name": "TiepiNL/py4e", "relative_path": "toronto1/a1.py", "category": "ALGO", "algo_rel_score": 0.9966239929199219, "quality_score": 5.864041805267334, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 57, "repo_created_at": "2021-12-16T11:34:13.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "4596aa21aededfd5ebcb3265a4cdedfca39e2495", "size_bytes": 4090, "file_timestamp": 1646339441, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "ceb52e1aded94ad8606cfc973b4f1765cba3846c"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 57, "repo_created_at": "2021-12-16T11:34:13.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "4596aa21aededfd5ebcb3265a4cdedfca39e2495", "size_bytes": 4090, "file_timestamp": 1646339441, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "ceb52e1aded94ad8606cfc973b4f1765cba3846c", "url": "https://github.com/TiepiNL/py4e/blob/ceb52e1aded94ad8606cfc973b4f1765cba3846c/toronto1/a1.py"}, "gpt4o_tokens": 1356, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.025474809967249, "noise_severity": 0.020696698277346888, "missing_content_severity": 0.007499156312481182, "factual_density": 3.3354951070087133, "pretraining_utility": 0.6935699816669592, "math_density": 3.3246149249098442, "code_density": 3.755358115529982, "reasoning_density": 1.3313532019986145, "education_level": 1.621529155475444, "creative_writing_quality": -0.15284611743775584}, "score_applicability": {"education_level": 0.9984051888015157, "creative_writing_quality": 0.0007001276425236431}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/peterldowns/ngram/blob/bfc3dc9cfd0da2dd183e691bdf8f28181414e6d6/ngram.py", "id": "python-0-c963a3f471e997e79686e64a330250c2", "text": "sep = \" \" # separating symbol\nkeyjoin = lambda x: sep.join(map(str, x))\ndef calc_ngram(n, source):\n\t# calculates ngrams on lists. By default, tries to coerce everything\n\t# to a string and then sep.join the strings, but any suitable\n\t# joining function may be supplied.\n\n\t# n is the length of the gram. n=5 -> 5-gram. \n\tsource_len = len(source)\n\tprint \"Calculating n-gram level %d\" % n\n\tgrams = {}\n\tif source_len < n:\n\t\treturn grams\n\tfor i in xrange(source_len+1-n):\n\t\tgramkey = keyjoin(source[i:i+n]) # can't use lists as hashkeys.\n\t\tgrams.setdefault(gramkey, 0)\n\t\tgrams[gramkey] += 1\n\treturn grams\n\ndef calc_up_to_ngram(source, level=1):\n\t# Calculates 1 through n-grams on a given source and combines them\n\t# into one big dictionary\n\tprint \"Creating ngram-count dictionary ... \"\n\tcounts = {}\n\tfor n in xrange(1, level+2): # +2: need 1 to offset xrange, 1 to look ahead for possibilities\n\t\tcounts.update(calc_ngram(n, source))\n\treturn counts\n\ndef get_subgrams(ngram, grams):\n\t# keylist should be a sep separated string. If it's characters, have sep='\\0'. If it's words, same thing works, but ' ' allows for pretty printing\n\tkey = keyjoin(ngram)\n\tchildren = {}\n\tkeylen = len(key)\n\tfor gramkey in grams:\n\t\tif gramkey.count(sep) == (key.count(sep)+1): # only look at the next possible word\n\t\t\tif gramkey[0:keylen] == key and gramkey[keylen] == sep: # don't let annointment be chosen from an\n\t\t\t\tchildren[gramkey] = grams[gramkey]\n\t#DEBUGGING\n\t#print \"key = %s\" % key\n\t#print \"children =\"\n\t#for i in children:\n\t\t#print \"\\t%s\" % i\n\treturn children\n\nfrom random import choice\ndef rand_selection(elements):\n\t# elements is a (value, count) tuple\n\t# given those (value, count) tuples, randomly pick a value\n\t# (but bias by count). Higher count = greater probability\n\t# note: there *must* be a better way to do this instead of\n\t#\tfilling a list (slow!)\n\tpopulation = []\n\tfor val, count in elements:\n\t\tfor i in xrange(count):\n\t\t\tpopulation.append(val)\n\treturn choice(population)\n\nfrom random import random\ndef rand_selection2(elements):\n\t#slow????\n\ttotals = []\n\trunning_total = 0\n\tfor w in map(lambda x: x[1], elements):\n\t\trunning_total += w\n\t\ttotals.append(running_total)\n\trnd = random()*running_total\n\tfor i, total in enumerate(totals):\n\t\tif rnd < total:\n\t\t\treturn elements[i][0]\n\ndef choose_child(existing, grams, level):\n\t# Given some existing amount of text to look at, look at most level elements\n\t# back and use that (and the count frequency dictionary, grams) to randomly\n\t# select the next element\n\tif level < 1:\n\t\tprint \"Level < 1; choosing a new word\"\n\t\treturn choice(list(set([i for i in grams if len(i.split(sep)) == 1]))) # choose a new word\n\t_slice = existing[-level:] # take at most the last level words\n\tpos_subgrams = get_subgrams(_slice, grams)\n\t\n\t#DEBUGGING\n\t#num_subgrams = len(pos_subgrams)\n\t#next_probs = {}\n\t#for key in pos_subgrams:\n\t\t#next_probs[\" \".join(key.split(sep)[-1:])] = float(pos_subgrams[key])/num_subgrams\n\t#for a in next_probs:\n\t\t#print \"P(%s | %s) = %f\" % (a, _slice, next_probs[a])\n\t\n\tnext_pop = [] # hold possible next words\n\tfor gram in pos_subgrams:\n\t\tnext_subgram = gram.split(sep)[-1] # for each whole child subgram, only look at the part that is different (the last element)\n\t\tcount = pos_subgrams[gram]\n\t\tnext_pop.append((next_subgram, count))\n\n\tif len(next_pop) == 0: # couldn't find anything; no valid next elements based on past history\n\t\tprint \"No children. Trying shallower level ( %d -> %d)\" % (level, level-1)\n\t\treturn choose_child(existing, grams, level-1) # ooh... recursive!\n\telse:\t\n\t\treturn rand_selection(next_pop)\n\ndef stochastic_walk(source, length, level, ngrams=None, sep='\\0'):\n\t# Use a source to figure out probabilities of elements (characters, words, whatever)\n\t# appearing after other elements. Randomly choose a starting element from the source,\n\t# and then use those probabilities to randomly generate a new set of elements.\n\tif not ngrams:\n\t\tngrams = calc_up_to_ngram(source, level)\n\tseed = choice(list(set(source)))\n\tprint \"Seed: %s\" % seed\n\tout = [seed] # this is the output list\n\t\n\twhile len(out) < length:\n\t\t_next = choose_child(out, ngrams, level)\n\t\tout.append(_next)\n\treturn out\n\n", "text_preview": "sep = \" \" # separating symbol keyjoin = lambda x: sep.join(map(str, x)) def calc_ngram(n, source): # calculates ngrams on lists. By default, tries to coerce everything # to a string and then sep.join the strings, but any suitable # joining ", "input_sha256": "b47691ddd234efc3e0066b9fc15fea1b937a6b7c1004dd683712608d82d00db6", "text_sha256": "c963a3f471e997e79686e64a330250c2e6d035cf6fa99d7e8ec09d2d8fb4132e", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12076, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5671192521661458, "noise_severity": 0.04072018325804032, "missing_content_severity": 0.03356333898531431, "factual_density": 3.3699183675023914, "pretraining_utility": 0.5103424770255129, "math_density": 1.7846288279225164, "code_density": 3.84899887601152, "reasoning_density": 0.6529095720537951, "education_level": 2.0645957397269252, "creative_writing_quality": -0.5110814668811404}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.4467 below 0.70", "secondary_topic vs NONE margin 0.0337 below 0.10"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.005009435874893553, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9969402899550668, "secondary": 0.9908265308346781, "tertiary": 0.9960537682693497, "structured_records": 0.00045967383443516486, "procedural_steps": 0.018950145364861795, "worked_solution": 0.002113161971541099, "contextual_dialogue": 6.800010528281007e-06, "content_quality": 0.42700905577058845, "noise_severity": 0.9660665139516331, "missing_content_severity": 0.9720305508455714, "factual_density": 0.47493197291865963, "pretraining_utility": 0.4218150574930353, "math_density": 0.0, "code_density": 0.8741657300095996, "reasoning_density": 0.45590868995517086, "education_level": 0.7496215731215853, "creative_writing_quality": 0.9994477779111476, "primary_topic": 0.9066535128364414, "secondary_topic": 0.42452551924959653}, "probability_margin": {"primary": 0.9957953983217906, "secondary": 0.9870820237472845, "tertiary": 0.9943996592885473, "education_level": 0.6846765854228521, "creative_writing_quality": 0.9993786629271261, "primary_topic": 0.8414199649622491, "secondary_topic": 0.033723336139288795}, "raw_probabilities": {"primary": {"Academic Writing": 4.290134362071354e-05, "Content Listing": 1.2728837528508129e-05, "Creative Writing": 1.5358470723203327e-05, "Customer Support Page": 6.855049455312246e-06, "Q&A Forum": 9.16760385525904e-06, "Discussion Thread": 6.671946639231481e-06, "Comment Section": 1.1899925042103342e-05, "Missing Content": 2.3631106578775663e-05, "Knowledge Article": 9.212367484345174e-05, "Legal Notices": 1.935923745524486e-05, "News Article": 3.3356695003344954e-06, "Organizational Announcement": 7.498220474559614e-06, "Nonfiction Writing": 1.224446677234029e-05, "Organizational About Page": 1.0095021232393484e-05, "Personal About Page": 1.1307359326476026e-05, "Personal Blog": 6.049006082978261e-06, "Product Page": 5.452551500654365e-06, "Spam / Ads": 7.524628515444203e-06, "Structured Data": 0.0005136962325022255, "Technical Writing": 0.9970677778736057, "Transcript / Interview": 1.4494518542410866e-05, "Tutorial / How-To Guide": 0.0008221189512414219, "User Reviews": 5.3287531460559954e-06, "Other / Unknown": 0.001272379551815007}, "secondary": {"Academic Writing": 0.00013756818921683306, "Content Listing": 3.1993508503170225e-05, "Creative Writing": 5.392588389702325e-05, "Customer Support Page": 3.196253918794934e-05, "Q&A Forum": 2.0882641457377362e-05, "Discussion Thread": 1.942298451031269e-05, "Comment Section": 5.968334005544819e-05, "Missing Content": 0.00010233627389119639, "Knowledge Article": 0.0006596532010409148, "Legal Notices": 5.198680656353355e-05, "News Article": 2.749753095590104e-05, "Organizational Announcement": 2.46164089951676e-05, "Nonfiction Writing": 3.798297880197094e-05, "Organizational About Page": 3.345152145774828e-05, "Personal About Page": 3.1911051131267216e-05, "Personal Blog": 2.7354043478145505e-05, "Product Page": 1.682004821165682e-05, "Spam / Ads": 2.9653948146880445e-05, "Structured Data": 0.0013810734470187573, "Technical Writing": 0.004126734969282065, "Transcript / Interview": 3.433811905291146e-05, "Tutorial / How-To Guide": 0.0018362885890879546, "User Reviews": 1.4103259489270114e-05, "NONE": 0.9912087587165666}, "tertiary": {"Academic Writing": 6.487783980975724e-05, "Content Listing": 2.074835874571285e-05, "Creative Writing": 3.143324910906786e-05, "Customer Support Page": 1.7853857174758504e-05, "Q&A Forum": 1.1745405401329548e-05, "Discussion Thread": 1.0809127354203529e-05, "Comment Section": 4.022256777726233e-05, "Missing Content": 9.653890336217576e-05, "Knowledge Article": 0.00022396039792002068, "Legal Notices": 5.463754240539813e-05, "News Article": 1.9383459519211396e-05, "Organizational Announcement": 1.619794260547541e-05, "Nonfiction Writing": 3.478827571084629e-05, "Organizational About Page": 2.8303457808047785e-05, "Personal About Page": 2.0193299637750458e-05, "Personal Blog": 1.979330431888775e-05, "Product Page": 1.2282946921254892e-05, "Spam / Ads": 2.1933587828612543e-05, "Structured Data": 0.00047503116126931973, "Technical Writing": 0.0018185353029127574, "Transcript / Interview": 2.1350842595473754e-05, "Tutorial / How-To Guide": 0.000711133361169012, "User Reviews": 1.0051217183663234e-05, "NONE": 0.9962181945914601}, "structured_records": {"false": 0.9995403261655649, "true": 0.00045967383443516486}, "procedural_steps": {"false": 0.9810498546351382, "true": 0.018950145364861795}, "worked_solution": {"false": 0.9978868380284589, "true": 0.002113161971541099}, "contextual_dialogue": {"false": 0.9999931999894717, "true": 6.800010528281007e-06}, "content_quality": {"-2": 0.016603624111032826, "-1": 0.08031438874228194, "0": 0.3497952906369116, "1": 0.42593250388905374, "2": 0.12735419262071979}, "noise_severity": {"0": 0.9630655295988856, "1": 0.034128888387312206, "2": 0.0020799327163342984, "3": 0.0004711677518118619, "4": 0.0002544815456559809}, "missing_content_severity": {"0": 0.9782067578246225, "1": 0.012562566938883373, "2": 0.007082683742097784, "3": 0.0017565614153497331, "4": 0.00039143007904654173}, "factual_density": {"0": 0.007744902030082514, "1": 0.014943275313697615, "2": 0.107294484702843, "3": 0.33968322903049947, "4": 0.5303341089228774}, "pretraining_utility": {"-2": 0.009583774284521476, "-1": 0.08720173790422915, "0": 0.3885849283293996, "1": 0.4125473554649146, "2": 0.10208220401693528}, "math_density": {"0": 0.3312731352896788, "1": 0.16204196203854387, "2": 0.13930164373572387, "3": 0.12554945733168912, "4": 0.24183380160436427}, "code_density": {"0": 0.00024802069877353903, "1": 0.0005315511049004592, "2": 0.01813063777130392, "3": 0.11215311233607703, "4": 0.8689366780889451}, "reasoning_density": {"0": 0.6972454822564798, "1": 0.10195291119637076, "2": 0.09405518674181812, "3": 0.06413939184753725, "4": 0.04260702795779405}, "education_level": {"none": 0.0012791154704169987, "0": 0.002246710228751911, "1": 0.07818815548204339, "2": 0.7913513109346544, "3": 0.1066747255118022, "4": 0.020259982372331148}, "creative_writing_quality": {"not_applicable": 0.9995398149259563, "-2": 9.292066387793789e-05, "-1": 0.00013873820210272408, "0": 0.00016115199883031574, "1": 4.536095128578029e-05, "2": 2.2013257946892298e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00014069453456746028, "Art & Design": 0.00026274610650191493, "Law & Crime": 0.00022042607773247797, "Education & Careers": 0.00016438319375754342, "Entertainment & Media": 0.00016197579459193063, "Fashion & Beauty": 0.00011254140286004088, "Business, Economics & Finance": 0.0002170430455018312, "Food & Dining": 0.00012422253964574872, "Games & Gaming": 0.00025924840197374564, "Electronics & Hardware": 0.00021044168277990286, "Health, Healthcare & Wellness": 0.0001624370861713909, "History & Geography": 0.00017910347368085228, "Home, Pets & Hobbies": 0.00012263774380541915, "Industry & Production": 0.00018528469404044225, "Language, Literature & Philosophy": 0.016071529923595354, "Politics & Government": 8.255370315842779e-05, "Religion & Spirituality": 0.0001336591599792799, "Science, Mathematics & Engineering": 0.06882379738048296, "Relationships & Community": 6.760048509651297e-05, "Software Usage & Applications": 0.0005037965312127616, "Programming & Software Engineering": 0.9102437623427321, "Sports & Fitness": 0.00018422031074350782, "Vehicles & Transportation": 0.00010027147799182542, "Travel & Tourism": 0.00016407840607890795, "Other / Uncovered Subject": 0.0009024054704897394, "Unknown / Insufficient Content": 0.00019913903082780331}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006289239047215367, "Art & Design": 0.0007308953159174776, "Law & Crime": 0.0006657169094188386, "Education & Careers": 0.0004587844921071112, "Entertainment & Media": 0.0005180320986131141, "Fashion & Beauty": 0.0003546791044663316, "Business, Economics & Finance": 0.0006136057033501139, "Food & Dining": 0.00030306601330664165, "Games & Gaming": 0.0005100202106949928, "Electronics & Hardware": 0.000562778830168219, "Health, Healthcare & Wellness": 0.00048794036218385297, "History & Geography": 0.0004731684698662264, "Home, Pets & Hobbies": 0.00033041351886656906, "Industry & Production": 0.00047864970394689984, "Language, Literature & Philosophy": 0.04471503361024758, "Politics & Government": 0.0002805385459059872, "Religion & Spirituality": 0.0005234983210477106, "Science, Mathematics & Engineering": 0.446659153124612, "Relationships & Community": 0.00021635002602172752, "Software Usage & Applications": 0.0005840157178682906, "Programming & Software Engineering": 0.08471797732876082, "Sports & Fitness": 0.00041210607467263816, "Vehicles & Transportation": 0.0002464452098211373, "Travel & Tourism": 0.0005143989287094832, "Other / Uncovered Subject": 0.0010779914893816213, "NONE": 0.4129358169853232}}, "probabilities": {"primary": {"Academic Writing": 4.290134362071355e-05, "Content Listing": 1.2728837528508132e-05, "Creative Writing": 1.535847072320333e-05, "Customer Support Page": 6.855049455312248e-06, "Q&A Forum": 9.167603855259042e-06, "Discussion Thread": 6.671946639231483e-06, "Comment Section": 1.1899925042103346e-05, "Missing Content": 2.363110657877567e-05, "Knowledge Article": 9.212367484345176e-05, "Legal Notices": 1.9359237455244865e-05, "News Article": 3.3356695003344963e-06, "Organizational Announcement": 7.498220474559616e-06, "Nonfiction Writing": 1.2244466772340294e-05, "Organizational About Page": 1.0095021232393486e-05, "Personal About Page": 1.1307359326476028e-05, "Personal Blog": 6.049006082978263e-06, "Product Page": 5.452551500654366e-06, "Spam / Ads": 7.5246285154442045e-06, "Structured Data": 0.0005136962325022256, "Technical Writing": 0.9970677778736059, "Transcript / Interview": 1.4494518542410869e-05, "Tutorial / How-To Guide": 0.0008221189512414222, "User Reviews": 5.328753146055996e-06, "Other / Unknown": 0.0012723795518150072}, "secondary": {"Academic Writing": 0.00013756818921683306, "Content Listing": 3.1993508503170225e-05, "Creative Writing": 5.392588389702325e-05, "Customer Support Page": 3.196253918794934e-05, "Q&A Forum": 2.0882641457377362e-05, "Discussion Thread": 1.942298451031269e-05, "Comment Section": 5.968334005544819e-05, "Missing Content": 0.00010233627389119639, "Knowledge Article": 0.0006596532010409148, "Legal Notices": 5.198680656353355e-05, "News Article": 2.749753095590104e-05, "Organizational Announcement": 2.46164089951676e-05, "Nonfiction Writing": 3.798297880197094e-05, "Organizational About Page": 3.345152145774828e-05, "Personal About Page": 3.1911051131267216e-05, "Personal Blog": 2.7354043478145505e-05, "Product Page": 1.682004821165682e-05, "Spam / Ads": 2.9653948146880445e-05, "Structured Data": 0.0013810734470187573, "Technical Writing": 0.004126734969282065, "Transcript / Interview": 3.433811905291146e-05, "Tutorial / How-To Guide": 0.0018362885890879546, "User Reviews": 1.4103259489270114e-05, "NONE": 0.9912087587165666}, "tertiary": {"Academic Writing": 6.487783980975724e-05, "Content Listing": 2.074835874571285e-05, "Creative Writing": 3.143324910906786e-05, "Customer Support Page": 1.7853857174758504e-05, "Q&A Forum": 1.1745405401329548e-05, "Discussion Thread": 1.0809127354203529e-05, "Comment Section": 4.022256777726233e-05, "Missing Content": 9.653890336217576e-05, "Knowledge Article": 0.00022396039792002068, "Legal Notices": 5.463754240539813e-05, "News Article": 1.9383459519211396e-05, "Organizational Announcement": 1.619794260547541e-05, "Nonfiction Writing": 3.478827571084629e-05, "Organizational About Page": 2.8303457808047785e-05, "Personal About Page": 2.0193299637750458e-05, "Personal Blog": 1.979330431888775e-05, "Product Page": 1.2282946921254892e-05, "Spam / Ads": 2.1933587828612543e-05, "Structured Data": 0.00047503116126931973, "Technical Writing": 0.0018185353029127574, "Transcript / Interview": 2.1350842595473754e-05, "Tutorial / How-To Guide": 0.000711133361169012, "User Reviews": 1.0051217183663234e-05, "NONE": 0.9962181945914601}, "structured_records": {"false": 0.9995403261655649, "true": 0.00045967383443516486}, "procedural_steps": {"false": 0.9810498546351382, "true": 0.018950145364861795}, "worked_solution": {"false": 0.9978868380284589, "true": 0.002113161971541099}, "contextual_dialogue": {"false": 0.9999931999894717, "true": 6.800010528281007e-06}, "content_quality": {"-2": 0.01660362411103283, "-1": 0.08031438874228196, "0": 0.34979529063691167, "1": 0.4259325038890538, "2": 0.1273541926207198}, "noise_severity": {"0": 0.9630655295988857, "1": 0.03412888838731221, "2": 0.002079932716334299, "3": 0.00047116775181186193, "4": 0.00025448154565598093}, "missing_content_severity": {"0": 0.9782067578246225, "1": 0.012562566938883373, "2": 0.007082683742097784, "3": 0.0017565614153497331, "4": 0.00039143007904654173}, "factual_density": {"0": 0.007744902030082514, "1": 0.014943275313697615, "2": 0.107294484702843, "3": 0.33968322903049947, "4": 0.5303341089228774}, "pretraining_utility": {"-2": 0.009583774284521474, "-1": 0.08720173790422914, "0": 0.3885849283293995, "1": 0.4125473554649145, "2": 0.10208220401693525}, "math_density": {"0": 0.33127313528967883, "1": 0.1620419620385439, "2": 0.1393016437357239, "3": 0.12554945733168915, "4": 0.2418338016043643}, "code_density": {"0": 0.00024802069877353903, "1": 0.0005315511049004592, "2": 0.01813063777130392, "3": 0.11215311233607703, "4": 0.8689366780889451}, "reasoning_density": {"0": 0.6972454822564799, "1": 0.10195291119637077, "2": 0.09405518674181813, "3": 0.06413939184753727, "4": 0.04260702795779406}, "education_level": {"none": 0.0012791154704169987, "0": 0.002246710228751911, "1": 0.07818815548204339, "2": 0.7913513109346544, "3": 0.1066747255118022, "4": 0.020259982372331148}, "creative_writing_quality": {"not_applicable": 0.9995398149259563, "-2": 9.292066387793789e-05, "-1": 0.00013873820210272408, "0": 0.00016115199883031574, "1": 4.536095128578029e-05, "2": 2.2013257946892298e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001406945345674603, "Art & Design": 0.000262746106501915, "Law & Crime": 0.000220426077732478, "Education & Careers": 0.00016438319375754344, "Entertainment & Media": 0.00016197579459193066, "Fashion & Beauty": 0.00011254140286004089, "Business, Economics & Finance": 0.00021704304550183121, "Food & Dining": 0.00012422253964574875, "Games & Gaming": 0.0002592484019737457, "Electronics & Hardware": 0.0002104416827799029, "Health, Healthcare & Wellness": 0.00016243708617139094, "History & Geography": 0.0001791034736808523, "Home, Pets & Hobbies": 0.00012263774380541918, "Industry & Production": 0.00018528469404044227, "Language, Literature & Philosophy": 0.016071529923595358, "Politics & Government": 8.25537031584278e-05, "Religion & Spirituality": 0.00013365915997927993, "Science, Mathematics & Engineering": 0.06882379738048297, "Relationships & Community": 6.760048509651298e-05, "Software Usage & Applications": 0.0005037965312127618, "Programming & Software Engineering": 0.9102437623427322, "Sports & Fitness": 0.00018422031074350785, "Vehicles & Transportation": 0.00010027147799182543, "Travel & Tourism": 0.00016407840607890798, "Other / Uncovered Subject": 0.0009024054704897395, "Unknown / Insufficient Content": 0.00019913903082780334}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006289239047215366, "Art & Design": 0.0007308953159174775, "Law & Crime": 0.0006657169094188385, "Education & Careers": 0.0004587844921071111, "Entertainment & Media": 0.000518032098613114, "Fashion & Beauty": 0.0003546791044663315, "Business, Economics & Finance": 0.0006136057033501138, "Food & Dining": 0.0003030660133066416, "Games & Gaming": 0.0005100202106949927, "Electronics & Hardware": 0.0005627788301682189, "Health, Healthcare & Wellness": 0.00048794036218385286, "History & Geography": 0.00047316846986622627, "Home, Pets & Hobbies": 0.000330413518866569, "Industry & Production": 0.00047864970394689973, "Language, Literature & Philosophy": 0.044715033610247576, "Politics & Government": 0.0002805385459059871, "Religion & Spirituality": 0.0005234983210477105, "Science, Mathematics & Engineering": 0.4466591531246119, "Relationships & Community": 0.00021635002602172747, "Software Usage & Applications": 0.0005840157178682905, "Programming & Software Engineering": 0.08471797732876081, "Sports & Fitness": 0.00041210607467263806, "Vehicles & Transportation": 0.0002464452098211372, "Travel & Tourism": 0.0005143989287094831, "Other / Uncovered Subject": 0.001077991489381621, "NONE": 0.4129358169853231}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9711, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9711, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:09.184628+00:00", "scoring_key_slot": "primary", "dataset_uid": "45bd121d-f438-4971-a679-2fbbc298f1c1", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "954f2e7d51c795dc716aae2b4bc2243336dd1e1541091577da74719ae0bcc52a", "source_metadata": {"uuid": "45bd121d-f438-4971-a679-2fbbc298f1c1", "repo_name": "peterldowns/ngram", "relative_path": "ngram.py", "category": "TOOL", "algo_rel_score": 0.9645441770553589, "quality_score": 4.034825801849365, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 7, "repo_created_at": "2012-01-05T23:16:33.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "a47e77ebf743eaaba146e4d50f4902c223ae29f2", "size_bytes": 4146, "file_timestamp": 1331173295, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "bfc3dc9cfd0da2dd183e691bdf8f28181414e6d6"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 7, "repo_created_at": "2012-01-05T23:16:33.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "a47e77ebf743eaaba146e4d50f4902c223ae29f2", "size_bytes": 4146, "file_timestamp": 1331173295, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "bfc3dc9cfd0da2dd183e691bdf8f28181414e6d6", "url": "https://github.com/peterldowns/ngram/blob/bfc3dc9cfd0da2dd183e691bdf8f28181414e6d6/ngram.py"}, "gpt4o_tokens": 1162, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.5671192521661458, "noise_severity": 0.04072018325804032, "missing_content_severity": 0.03356333898531431, "factual_density": 3.3699183675023914, "pretraining_utility": 0.510342477025513, "math_density": 1.7846288279225164, "code_density": 3.84899887601152, "reasoning_density": 0.6529095720537951, "education_level": 2.0645957397269252, "creative_writing_quality": -0.5110814668811404}, "score_applicability": {"education_level": 0.9987208845295831, "creative_writing_quality": 0.0004601850740436503}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/pabloem/dagwhat/blob/d3639237f5780078672a2aac26b8ba0056316dfa/dagcheck/api.py", "id": "python-0-0d5bd55a3f0b48dc034dae07fa3b27ef", "text": "#\n# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements.  See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership.  The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License.  You may obtain a copy of the License at\n#\n#   http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied.  See the License for the\n# specific language governing permissions and limitations\n# under the License.\n\n\n\"\"\"The public API definition for Dagcheck.\"\"\"\n\nfrom airflow.models import DAG\nfrom dagcheck.base import DagSelector\nfrom dagcheck.base import DagTest\nfrom dagcheck.base import TaskTestCheckBuilder\nfrom dagcheck.base import TaskGroupSelector\nfrom dagcheck.base import TaskSelectorEnum\nfrom dagcheck.base import TaskOutcome\nfrom dagcheck.base import TaskOutcomes\n\n\n__all__ = [\n    \"assert_that\",\n    \"given\",\n    \"task\",\n    \"tasks\",\n    \"any_task\",\n    \"succeeds\",\n    \"fails\",\n    \"runs\",\n    \"returns\",\n    \"the_dag\",\n    \"does_not_run\",\n    \"may_run\",\n    \"will_run\",\n]\n\nfrom dagcheck.execution import run_check\n\n# TODO(pabloem): Document or improve how special configurations are passed.\nOPTIONS = {\"max_simulation_time\": 40}\n\n\ndef assert_that(test_case: TaskTestCheckBuilder):\n    \"\"\"Execution call for DAG checks. All checks must finish with assert_that.\n\n    A DAG check contains the following four things:\n    - A DAG\n    - A series of assumptions\n    - A series of expectations.\n    - An `assert_that` function call that executes the check.\n    \"\"\"\n    run_check(test_case.build())\n\n\ndef given(dag: DAG) -> \"DagTest\":\n    \"\"\"Entry point for DAG checks. All DAG checks initialize with their DAG.\"\"\"\n    return DagTest(dag)\n\n\ndef task(task_id: str):\n    \"\"\"Select an individual task via its ID.\"\"\"\n    return TaskGroupSelector(ids=[task_id], group_is=TaskSelectorEnum.ALL)\n\n\ndef tasks(*ids: str):\n    \"\"\"Select a group of tasks via their IDs.\"\"\"\n    return TaskGroupSelector(ids=ids, group_is=TaskSelectorEnum.ALL)\n\n\ndef any_task(with_id: str = None, with_operator=None):\n    \"\"\"Select any task matching the input ID or input operator.\n\n    This selector defines the family of task groups with a single task matching\n    the ID or the operator.\n    \"\"\"\n    return TaskGroupSelector(\n        ids=[with_id] if with_id else [],\n        operators=[with_operator] if with_operator else [],\n        group_is=TaskSelectorEnum.ANY,\n    )\n\n\ndef all_tasks(with_id=None, with_operator=None):\n    \"\"\"Select all tasks matching the input ID or input operator.\n\n    This selector defines the family of task groups with every task matching\n    the ID or the operator.\n    \"\"\"\n    return TaskGroupSelector(\n        ids=[with_id], operators=[with_operator], group_is=TaskSelectorEnum.ALL\n    )\n\n\ndef the_dag():\n    \"\"\"A selector representing the whole DAG execution.\"\"\"\n    return DagSelector\n\n\n##############################################################################\n# END TASK SELECTORS\n##############################################################################\n\n##############################################################################\n# OUTCOME CHECKERS\n##############################################################################\n\n\ndef succeeds() -> TaskOutcome:\n    \"\"\"Assumption: The task(s) in question succeed upon execution.\"\"\"\n    return TaskOutcomes.SUCCESS\n\n\ndef fails() -> TaskOutcome:\n    \"\"\"Assumption: The task(s) in question fail upon execution.\"\"\"\n    return TaskOutcomes.FAILURE\n\n\ndef runs() -> TaskOutcome:\n    \"\"\"Assumption: The task(s) in question will run. May fail or succeed.\"\"\"\n    return TaskOutcomes.RUNS\n\n\ndef returns(value) -> TaskOutcome:\n    \"\"\"Assumption: The task(s) have a function that returns `value`.\"\"\"\n    return TaskOutcome(\"RETURNS\", value)\n\n\ndef does_not_run() -> TaskOutcome:\n    \"\"\"Expectation: The task(s) in question will not run given assumptions.\"\"\"\n    return TaskOutcomes.WILL_NOT_RUN\n\n\ndef may_run() -> TaskOutcome:\n    \"\"\"Expectation: The task(s) may run despite the given assumptions.\"\"\"\n    return TaskOutcomes.MAY_RUN\n\n\ndef may_not_run() -> TaskOutcome:\n    \"\"\"Expectation: The task(s) may not run despite the given assumptions.\"\"\"\n    return TaskOutcomes.MAY_NOT_RUN\n\n\ndef will_run() -> TaskOutcome:\n    \"\"\"Expectation: The task(s) in question will run in assumed conditions.\"\"\"\n    return TaskOutcomes.WILL_RUN\n\n\n##############################################################################\n# END OUTCOME CHECKERS\n##############################################################################\n", "text_preview": "# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file", "input_sha256": "a2c98c50dc64b20e9ff809068d489832a5b1034b45a133b087b320f415f5c97f", "text_sha256": "0d5bd55a3f0b48dc034dae07fa3b27efee902a82ddde547e363cd38c9d2cdca7", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12079, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Legal Notices", "sub_format": null}, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 1, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.0338718908003848, "noise_severity": 0.9279369740853484, "missing_content_severity": 0.06347567313567752, "factual_density": 3.347778820618245, "pretraining_utility": 0.39696754199190853, "math_density": 0.0299838260284664, "code_density": 3.6827366490641906, "reasoning_density": 0.0669627731069084, "education_level": 2.364394428271771, "creative_writing_quality": -0.10543662988292578}, "api_choices": {"primary": "Technical Writing", "secondary": "Legal Notices", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.555769872645101, "secondary_choice": "Legal Notices", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9920087444897436, "secondary": 0.7482619897127808, "tertiary": 0.7210112921186335, "structured_records": 0.07142112917025703, "procedural_steps": 0.045546086062839254, "worked_solution": 0.00012094140617390631, "contextual_dialogue": 5.924555989643984e-06, "content_quality": 0.5796895441480958, "noise_severity": 0.7047699272598212, "missing_content_severity": 0.9471036057202687, "factual_density": 0.456482350515204, "pretraining_utility": 0.4751070136828436, "math_density": 0.9750134783096114, "code_density": 0.7356138742201586, "reasoning_density": 0.9441976890775763, "education_level": 0.5814532731644472, "creative_writing_quality": 0.9995799067343055, "primary_topic": 0.9956296577866409, "secondary_topic": 0.9145265617840612}, "probability_margin": {"primary": 0.9866084130841289, "secondary": 0.5818851245061589, "tertiary": 0.49025005267051314, "education_level": 0.36387793171610006, "creative_writing_quality": 0.9994803431721927, "primary_topic": 0.9941577101347915, "secondary_topic": 0.8697310976968856}, "raw_probabilities": {"primary": {"Academic Writing": 3.3098926285568214e-05, "Content Listing": 2.938033018887161e-05, "Creative Writing": 1.2527145461547733e-05, "Customer Support Page": 1.4001047030570993e-05, "Q&A Forum": 1.1484356115480743e-05, "Discussion Thread": 7.731316185441633e-06, "Comment Section": 1.459623784012203e-05, "Missing Content": 2.4071956480426926e-05, "Knowledge Article": 7.206186887577729e-05, "Legal Notices": 0.005733300385208649, "News Article": 4.756457508736034e-06, "Organizational Announcement": 1.538889288312136e-05, "Nonfiction Writing": 1.088692843704495e-05, "Organizational About Page": 1.642010258842434e-05, "Personal About Page": 1.0579788773988687e-05, "Personal Blog": 8.768209787414318e-06, "Product Page": 7.756617935192634e-06, "Spam / Ads": 9.539411003967112e-06, "Structured Data": 0.0006017762787828639, "Technical Writing": 0.9923417134693376, "Transcript / Interview": 1.9631777971821213e-05, "Tutorial / How-To Guide": 0.00019661972819120288, "User Reviews": 7.114174153348493e-06, "Other / Unknown": 0.0007967945929728682}, "secondary": {"Academic Writing": 0.000595863512989476, "Content Listing": 0.0006988978577478143, "Creative Writing": 0.0004312113166276552, "Customer Support Page": 0.00035279411009729264, "Q&A Forum": 0.0002877814255534699, "Discussion Thread": 0.0002629777672154287, "Comment Section": 0.0004986991855589488, "Missing Content": 0.0005627441206750393, "Knowledge Article": 0.001053299889198484, "Legal Notices": 0.7587510734747482, "News Article": 0.00039101709446358546, "Organizational Announcement": 0.0004896573613658526, "Nonfiction Writing": 0.0003713123173778076, "Organizational About Page": 0.0004927507520866217, "Personal About Page": 0.00032948237753588697, "Personal Blog": 0.00023255207327764504, "Product Page": 0.0003463320959004727, "Spam / Ads": 0.00031959357634351183, "Structured Data": 0.0034498994665195753, "Technical Writing": 0.0516950994612936, "Transcript / Interview": 0.00046901001566533433, "Tutorial / How-To Guide": 0.0008636382795193358, "User Reviews": 0.00018836349964966226, "NONE": 0.17686594896858934}, "tertiary": {"Academic Writing": 0.0004919596197420861, "Content Listing": 0.0004899098787505516, "Creative Writing": 0.00036497987641679686, "Customer Support Page": 0.00027501929886991644, "Q&A Forum": 0.0001633076504043887, "Discussion Thread": 0.0001715169960570287, "Comment Section": 0.0006969524054675656, "Missing Content": 0.0005392941879749486, "Knowledge Article": 0.0016270456963855741, "Legal Notices": 0.2423857689431772, "News Article": 0.0002701092117224406, "Organizational Announcement": 0.00032133165046710125, "Nonfiction Writing": 0.00023993329362804065, "Organizational About Page": 0.00031361048927642865, "Personal About Page": 0.00026296400232754075, "Personal Blog": 0.00021591999101958317, "Product Page": 0.00021727100253772621, "Spam / Ads": 0.000266479305464441, "Structured Data": 0.005220768884156523, "Technical Writing": 0.009600394164831383, "Transcript / Interview": 0.0002975213613947275, "Tutorial / How-To Guide": 0.0028070379503163973, "User Reviews": 0.0001250825259212731, "NONE": 0.7326358216136903}, "structured_records": {"false": 0.928578870829743, "true": 0.07142112917025703}, "procedural_steps": {"false": 0.9544539139371607, "true": 0.045546086062839254}, "worked_solution": {"false": 0.9998790585938261, "true": 0.00012094140617390631}, "contextual_dialogue": {"false": 0.9999940754440103, "true": 5.924555989643984e-06}, "content_quality": {"-2": 0.003642596840877594, "-1": 0.015105309402575672, "0": 0.19411191878316597, "1": 0.5180179560620459, "2": 0.26912221891133487}, "noise_severity": {"0": 0.21316955660143305, "1": 0.6569479429688043, "2": 0.12198830752858124, "3": 0.00456435554534383, "4": 0.0033298373558375135}, "missing_content_severity": {"0": 0.9424213568284627, "1": 0.053810097842856064, "2": 0.002221495209342849, "3": 0.0009656156032177911, "4": 0.0005814345161205952}, "factual_density": {"0": 0.0027874938351452774, "1": 0.00748794560956996, "2": 0.10001591188816034, "3": 0.41857554343614356, "4": 0.471133105230981}, "pretraining_utility": {"-2": 0.009180261639383638, "-1": 0.09809149751557233, "0": 0.4315820243298957, "1": 0.4088728702440485, "2": 0.05227334627109979}, "math_density": {"0": 0.9812523102902058, "1": 0.014032565033658207, "2": 0.0010719780717246745, "3": 0.0007652815662869077, "4": 0.0028778650381245307}, "code_density": {"0": 0.0005195628400143757, "1": 0.001040298681753011, "2": 0.028703003862065302, "3": 0.25465819580636256, "4": 0.7150789388098048}, "reasoning_density": {"0": 0.945244611548541, "1": 0.04481624989413987, "2": 0.008137369680958333, "3": 0.0013352916545912314, "4": 0.0004664772217695414}, "education_level": {"none": 0.0013133908387827126, "0": 0.00182254203481995, "1": 0.012137321006720632, "2": 0.6512110609703726, "3": 0.28733312925427257, "4": 0.046182555895031514}, "creative_writing_quality": {"not_applicable": 0.999649922278588, "-2": 3.442297054939454e-05, "-1": 6.828448273487643e-05, "0": 0.00016957910639529584, "1": 5.536291477399136e-05, "2": 2.2428246958449163e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.9507307961255066e-05, "Art & Design": 3.0515637104838234e-05, "Law & Crime": 0.0006406055592737207, "Education & Careers": 5.100851901821984e-05, "Entertainment & Media": 1.9157751622233534e-05, "Fashion & Beauty": 1.5148916540788609e-05, "Business, Economics & Finance": 0.00011712289587268586, "Food & Dining": 1.8376976570006578e-05, "Games & Gaming": 3.699115390148425e-05, "Electronics & Hardware": 5.3918986781594656e-05, "Health, Healthcare & Wellness": 2.1449137742204806e-05, "History & Geography": 2.7648530394194104e-05, "Home, Pets & Hobbies": 1.8461169287401573e-05, "Industry & Production": 3.7582639144755886e-05, "Language, Literature & Philosophy": 3.6406442964966286e-05, "Politics & Government": 3.375195453883181e-05, "Religion & Spirituality": 2.52216137311821e-05, "Science, Mathematics & Engineering": 0.001640037736978578, "Relationships & Community": 1.1394854149058118e-05, "Software Usage & Applications": 0.0009284639065935637, "Programming & Software Engineering": 0.9957977478717701, "Sports & Fitness": 3.135891491681307e-05, "Vehicles & Transportation": 1.599913243484234e-05, "Travel & Tourism": 3.2964495023244975e-05, "Other / Uncovered Subject": 0.0002677510151074974, "Unknown / Insufficient Content": 6.140688057598813e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00041941211406373136, "Art & Design": 0.00030059137252704085, "Law & Crime": 0.04808290401855791, "Education & Careers": 0.0003998037611469922, "Entertainment & Media": 0.0002783281321575323, "Fashion & Beauty": 0.00018316084454264155, "Business, Economics & Finance": 0.0006783168957894992, "Food & Dining": 0.00014218457261845402, "Games & Gaming": 0.00020752992168894015, "Electronics & Hardware": 0.0002807511514682335, "Health, Healthcare & Wellness": 0.00020509236919157502, "History & Geography": 0.00021997041403890055, "Home, Pets & Hobbies": 0.00015859127785077806, "Industry & Production": 0.00016654042451941703, "Language, Literature & Philosophy": 0.00016785950346850378, "Politics & Government": 0.00045760658088280583, "Religion & Spirituality": 0.00031347007759325896, "Science, Mathematics & Engineering": 0.005690635672724948, "Relationships & Community": 9.686930583436047e-05, "Software Usage & Applications": 0.005975553845357863, "Programming & Software Engineering": 0.01659649751260347, "Sports & Fitness": 0.00016191689663102636, "Vehicles & Transportation": 9.445483741671567e-05, "Travel & Tourism": 0.0002455953725180733, "Other / Uncovered Subject": 0.0006623614093638229, "NONE": 0.9178140017154435}}, "probabilities": {"primary": {"Academic Writing": 3.3098926285568214e-05, "Content Listing": 2.938033018887161e-05, "Creative Writing": 1.2527145461547733e-05, "Customer Support Page": 1.4001047030570993e-05, "Q&A Forum": 1.1484356115480743e-05, "Discussion Thread": 7.731316185441633e-06, "Comment Section": 1.459623784012203e-05, "Missing Content": 2.4071956480426926e-05, "Knowledge Article": 7.206186887577729e-05, "Legal Notices": 0.005733300385208649, "News Article": 4.756457508736034e-06, "Organizational Announcement": 1.538889288312136e-05, "Nonfiction Writing": 1.088692843704495e-05, "Organizational About Page": 1.642010258842434e-05, "Personal About Page": 1.0579788773988687e-05, "Personal Blog": 8.768209787414318e-06, "Product Page": 7.756617935192634e-06, "Spam / Ads": 9.539411003967112e-06, "Structured Data": 0.0006017762787828639, "Technical Writing": 0.9923417134693376, "Transcript / Interview": 1.9631777971821213e-05, "Tutorial / How-To Guide": 0.00019661972819120288, "User Reviews": 7.114174153348493e-06, "Other / Unknown": 0.0007967945929728682}, "secondary": {"Academic Writing": 0.000595863512989476, "Content Listing": 0.0006988978577478143, "Creative Writing": 0.0004312113166276552, "Customer Support Page": 0.00035279411009729264, "Q&A Forum": 0.0002877814255534699, "Discussion Thread": 0.0002629777672154287, "Comment Section": 0.0004986991855589488, "Missing Content": 0.0005627441206750393, "Knowledge Article": 0.001053299889198484, "Legal Notices": 0.7587510734747482, "News Article": 0.00039101709446358546, "Organizational Announcement": 0.0004896573613658526, "Nonfiction Writing": 0.0003713123173778076, "Organizational About Page": 0.0004927507520866217, "Personal About Page": 0.00032948237753588697, "Personal Blog": 0.00023255207327764504, "Product Page": 0.0003463320959004727, "Spam / Ads": 0.00031959357634351183, "Structured Data": 0.0034498994665195753, "Technical Writing": 0.0516950994612936, "Transcript / Interview": 0.00046901001566533433, "Tutorial / How-To Guide": 0.0008636382795193358, "User Reviews": 0.00018836349964966226, "NONE": 0.17686594896858934}, "tertiary": {"Academic Writing": 0.0004919596197420861, "Content Listing": 0.0004899098787505516, "Creative Writing": 0.00036497987641679686, "Customer Support Page": 0.00027501929886991644, "Q&A Forum": 0.0001633076504043887, "Discussion Thread": 0.0001715169960570287, "Comment Section": 0.0006969524054675656, "Missing Content": 0.0005392941879749486, "Knowledge Article": 0.0016270456963855741, "Legal Notices": 0.2423857689431772, "News Article": 0.0002701092117224406, "Organizational Announcement": 0.00032133165046710125, "Nonfiction Writing": 0.00023993329362804065, "Organizational About Page": 0.00031361048927642865, "Personal About Page": 0.00026296400232754075, "Personal Blog": 0.00021591999101958317, "Product Page": 0.00021727100253772621, "Spam / Ads": 0.000266479305464441, "Structured Data": 0.005220768884156523, "Technical Writing": 0.009600394164831383, "Transcript / Interview": 0.0002975213613947275, "Tutorial / How-To Guide": 0.0028070379503163973, "User Reviews": 0.0001250825259212731, "NONE": 0.7326358216136903}, "structured_records": {"false": 0.928578870829743, "true": 0.07142112917025703}, "procedural_steps": {"false": 0.9544539139371607, "true": 0.045546086062839254}, "worked_solution": {"false": 0.9998790585938261, "true": 0.00012094140617390631}, "contextual_dialogue": {"false": 0.9999940754440103, "true": 5.924555989643984e-06}, "content_quality": {"-2": 0.003642596840877594, "-1": 0.015105309402575672, "0": 0.19411191878316597, "1": 0.5180179560620459, "2": 0.26912221891133487}, "noise_severity": {"0": 0.21316955660143305, "1": 0.6569479429688043, "2": 0.12198830752858124, "3": 0.00456435554534383, "4": 0.0033298373558375135}, "missing_content_severity": {"0": 0.9424213568284627, "1": 0.053810097842856064, "2": 0.002221495209342849, "3": 0.0009656156032177911, "4": 0.0005814345161205952}, "factual_density": {"0": 0.002787493835145277, "1": 0.007487945609569958, "2": 0.10001591188816031, "3": 0.41857554343614345, "4": 0.4711331052309809}, "pretraining_utility": {"-2": 0.00918026163938364, "-1": 0.09809149751557235, "0": 0.43158202432989573, "1": 0.40887287024404856, "2": 0.052273346271099794}, "math_density": {"0": 0.9812523102902058, "1": 0.014032565033658207, "2": 0.0010719780717246745, "3": 0.0007652815662869077, "4": 0.0028778650381245307}, "code_density": {"0": 0.0005195628400143757, "1": 0.001040298681753011, "2": 0.028703003862065302, "3": 0.25465819580636256, "4": 0.7150789388098048}, "reasoning_density": {"0": 0.945244611548541, "1": 0.04481624989413987, "2": 0.008137369680958333, "3": 0.0013352916545912314, "4": 0.0004664772217695414}, "education_level": {"none": 0.0013133908387827126, "0": 0.00182254203481995, "1": 0.012137321006720632, "2": 0.6512110609703726, "3": 0.28733312925427257, "4": 0.046182555895031514}, "creative_writing_quality": {"not_applicable": 0.999649922278588, "-2": 3.442297054939454e-05, "-1": 6.828448273487643e-05, "0": 0.00016957910639529584, "1": 5.536291477399136e-05, "2": 2.2428246958449163e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.9507307961255066e-05, "Art & Design": 3.0515637104838234e-05, "Law & Crime": 0.0006406055592737207, "Education & Careers": 5.100851901821984e-05, "Entertainment & Media": 1.9157751622233534e-05, "Fashion & Beauty": 1.5148916540788609e-05, "Business, Economics & Finance": 0.00011712289587268586, "Food & Dining": 1.8376976570006578e-05, "Games & Gaming": 3.699115390148425e-05, "Electronics & Hardware": 5.3918986781594656e-05, "Health, Healthcare & Wellness": 2.1449137742204806e-05, "History & Geography": 2.7648530394194104e-05, "Home, Pets & Hobbies": 1.8461169287401573e-05, "Industry & Production": 3.7582639144755886e-05, "Language, Literature & Philosophy": 3.6406442964966286e-05, "Politics & Government": 3.375195453883181e-05, "Religion & Spirituality": 2.52216137311821e-05, "Science, Mathematics & Engineering": 0.001640037736978578, "Relationships & Community": 1.1394854149058118e-05, "Software Usage & Applications": 0.0009284639065935637, "Programming & Software Engineering": 0.9957977478717701, "Sports & Fitness": 3.135891491681307e-05, "Vehicles & Transportation": 1.599913243484234e-05, "Travel & Tourism": 3.2964495023244975e-05, "Other / Uncovered Subject": 0.0002677510151074974, "Unknown / Insufficient Content": 6.140688057598813e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00041941211406373136, "Art & Design": 0.00030059137252704085, "Law & Crime": 0.04808290401855791, "Education & Careers": 0.0003998037611469922, "Entertainment & Media": 0.0002783281321575323, "Fashion & Beauty": 0.00018316084454264155, "Business, Economics & Finance": 0.0006783168957894992, "Food & Dining": 0.00014218457261845402, "Games & Gaming": 0.00020752992168894015, "Electronics & Hardware": 0.0002807511514682335, "Health, Healthcare & Wellness": 0.00020509236919157502, "History & Geography": 0.00021997041403890055, "Home, Pets & Hobbies": 0.00015859127785077806, "Industry & Production": 0.00016654042451941703, "Language, Literature & Philosophy": 0.00016785950346850378, "Politics & Government": 0.00045760658088280583, "Religion & Spirituality": 0.00031347007759325896, "Science, Mathematics & Engineering": 0.005690635672724948, "Relationships & Community": 9.686930583436047e-05, "Software Usage & Applications": 0.005975553845357863, "Programming & Software Engineering": 0.01659649751260347, "Sports & Fitness": 0.00016191689663102636, "Vehicles & Transportation": 9.445483741671567e-05, "Travel & Tourism": 0.0002455953725180733, "Other / Uncovered Subject": 0.0006623614093638229, "NONE": 0.9178140017154435}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9597, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9597, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:10.469618+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "4b905245-5604-41fd-b904-9053b54fc9a9", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "162dc10e965e3c8b6e2bccf2cb4a3c9b5419eb556e2c59362fbd7851d634762d", "source_metadata": {"uuid": "4b905245-5604-41fd-b904-9053b54fc9a9", "repo_name": "pabloem/dagwhat", "relative_path": "dagcheck/api.py", "category": "TEST", "algo_rel_score": 0.9839141368865967, "quality_score": 7.457329750061035, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 30, "repo_created_at": "2022-09-20T15:08:16.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 5, "forks": 1, "issues": 1, "pull_requests": 0}, "file_meta": {"content_id": "594423a4f4974988017f1b0c63c087e60f7d3833", "size_bytes": 4847, "file_timestamp": 1665084535, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "d3639237f5780078672a2aac26b8ba0056316dfa"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 30, "repo_created_at": "2022-09-20T15:08:16.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 5, "forks": 1, "issues": 1, "pull_requests": 0}, "file_meta": {"content_id": "594423a4f4974988017f1b0c63c087e60f7d3833", "size_bytes": 4847, "file_timestamp": 1665084535, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "d3639237f5780078672a2aac26b8ba0056316dfa", "url": "https://github.com/pabloem/dagwhat/blob/d3639237f5780078672a2aac26b8ba0056316dfa/dagcheck/api.py"}, "gpt4o_tokens": 1018, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.0338718908003848, "noise_severity": 0.9279369740853484, "missing_content_severity": 0.06347567313567752, "factual_density": 3.3477788206182453, "pretraining_utility": 0.39696754199190853, "math_density": 0.0299838260284664, "code_density": 3.6827366490641906, "reasoning_density": 0.0669627731069084, "education_level": 2.364394428271771, "creative_writing_quality": -0.10543662988292578}, "score_applicability": {"education_level": 0.9986866091612173, "creative_writing_quality": 0.00035007772141200735}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/jdhenshaw/scousepy/blob/2cc17efd2397b8e0ae4a76df5c988571d69e7d1b/scousepy/noisy.py", "id": "python-0-5cd660976e444f53a4ea678c4ddbc622", "text": "# Licensed under an MIT open source license - see LICENSE\n\nimport numpy as np\nfrom astropy.stats import sigma_clip, mad_std, median_absolute_deviation\n\nclass getnoise(object):\n    \"\"\"\n    Autonomous calculation of the spectral rms\n\n    Notes\n    -----\n    The method for identifying noise channels is described in Riener+ 2019,\n    and has been adapted from the method in https://github.com/mriener/gausspyplus\n    credit for the method design goes to <NAME>.\n\n    Most of the technical parts of the code below is lifted directly from\n    https://github.com/mriener/gausspyplus/blob/master/gausspyplus/utils/noise_estimation.py\n    JDH wrapped it up and tinkered a bit.\n\n    \"\"\"\n    def __init__(self, spectral_axis, spectrum, p_limit=0.02, pad_channels=2,\n                    n_mad=5.0, remove_broad=True, mad=False):\n\n        \"\"\"\n        Parameters\n        ----------\n\n        spectral_axis : array\n            An array of values corresponding to the spectral axis (e.g. velocity)\n        spectrum : numpy.ndarray\n            Array of the data values of the spectrum\n        p_limit : float\n            Maximum probability for consecutive positive/negative channels being\n            due to chance\n        pad_channels : int\n            Number of channels by which an interval (low, upp) gets extended on\n            both sides, resulting in (low - pad_channels, upp + pad_channels).\n            Used for masking of spectral features\n        n_mad : float\n            Multiple of the median absolute deviation. Used to identify spikes\n            in the data which are not noise. All channels below nmad*MAD are\n            considered \"noise\"\n        remove_broad : boolean\n            Determines method by which the noise channels are identified.\n            Default=True. If True, broad components are removed using a simple\n            Markov chain to estimate the probability that consecutive +ve or -ve\n            channels are due to noise (assuming Gaussian noise). See\n            Riener+ 2019 Appendix A for details. If False, a simple method of\n            reflected noise is imposed.\n\n        \"\"\"\n\n        # Set the global quantities\n        self.spectral_axis=spectral_axis\n        self.spectrum=spectrum\n        self.p_limit=p_limit\n        self.pad_channels=pad_channels\n        self.n_mad=n_mad\n        self.n_channels=np.size(spectral_axis)\n        self.mask=np.zeros(self.n_channels, dtype='bool')\n        self.consecutive_channels=None\n        self.ranges=None\n        self.max_consecutive_channels=None\n        self.noise=None\n        self.spectrum_masked=np.copy(self.spectrum)\n        self.flag=''\n        self.mad=mad\n\n        # fail safe checks for dealing with troublesome spectra\n        if np.isfinite(self.spectrum).any():\n            if not ((self.spectrum>0.0).all() or (self.spectrum<0.0).all()):\n                # So long as not all of the channels are nan, all positive or all\n                # negative, we can go ahead and calculate the noise\n                self.calculate_noise(remove_broad=remove_broad)\n            else:\n                # if all channels are either positive or negative then return\n                # nan - indicates poor baselining\n                self.rms=np.nan\n                self.flag='Spectrum is all positive/negative'\n        else:\n            # if all channels are nan values then return nan\n            self.rms=np.nan\n            self.flag='Spectrum is all NaNs'\n\n    def calculate_noise(self, remove_broad=True):\n        \"\"\"\n        Routine for calculating the noise\n\n        \"\"\"\n        # find peaks within the spectrum\n        self.consecutive_channels, self.ranges=self.determine_peaks(self.spectrum)\n\n        # start by masking any nan values\n        if np.isnan(self.spectrum).any():\n            mask=np.isnan(self.spectrum)\n            self.mask+=mask\n\n        self.spectrum_masked[self.mask] = 0\n\n        if remove_broad:\n            # start by masking broad features...\n\n            # determine the maximum number of consecutive positive/negative\n            # channels being due to chance\n            self.max_consecutive_channels=self.get_max_consecutive_channels(self.n_channels,self.p_limit)\n            # create the mask\n            mask = self.mask_broadpeaks(self.n_channels, self.pad_channels, self.consecutive_channels, self.ranges, max_consecutive_channels=self.max_consecutive_channels)\n            # update the mask accordingly\n            self.mask+=mask\n            self.spectrum_masked[self.mask] = 0\n\n        # remove features with high positive or negative data values\n        MAD=self.compute_MAD(self.spectrum[~self.mask])\n        # Mask outlier peaks in the spectrum\n        mask=self.mask_outlierpeaks(self.spectrum_masked, self.n_channels, self.pad_channels, self.n_mad*MAD, self.ranges)\n        # update the mask accordingly\n        self.mask+=mask\n        self.spectrum_masked[self.mask] = 0\n\n        # determine the channels with noise\n        self.noise = self.spectrum[~self.mask]\n\n        if np.size(self.noise)==0:\n            self.rms=np.nan\n            self.flag='All flagged'\n        else:\n            #  determine the noise from the remaining channels\n            if not self.mad:\n                self.rms = np.sqrt(np.sum(self.noise**2) / np.size(self.noise))\n            else:\n                self.rms = median_absolute_deviation(self.noise)\n\n    def get_max_consecutive_channels(self, n_channels, p_limit):\n        \"\"\"\n\n        Determine the maximum number of random consecutive positive/negative channels.\n        Calculate the number of consecutive positive or negative channels,\n        whose probability of occurring due to random chance in a spectrum\n        is less than p_limit.\n\n        Parameters\n        ----------\n        n_channels : int\n            Number of spectral channels.\n        p_limit : float\n            Maximum probability for consecutive positive/negative channels being\n            due to chance.\n\n        Returns\n        -------\n        consec_channels : int\n            Number of consecutive positive/negative channels that have a probability\n            less than p_limit to be due to chance.\n\n        \"\"\"\n        for consec_channels in range(2, 30):\n            a = np.zeros((consec_channels, consec_channels))\n            for i in range(consec_channels - 1):\n                a[i, 0] = a[i, i + 1] = 0.5\n            a[consec_channels - 1, consec_channels - 1] = 1.0\n            if np.linalg.matrix_power(\n                    a, n_channels - 1)[0, consec_channels - 1] < p_limit:\n                return consec_channels\n\n    def mask_broadpeaks(self, n_channels, pad_channels, consecutive_channels, ranges, max_consecutive_channels=14 ):\n        \"\"\"\n        Method used to mask out broad peaks within a spectrum\n\n        Parameters\n        ----------\n        n_channels : int\n            Number of spectral channels\n        pad_channels : int\n            Number of channels by which an interval (low, upp) gets extended on\n            both sides, resulting in (low - pad_channels, upp + pad_channels).\n        consecutive_channels: int\n            number of consecutive channels in a peak\n        ranges : array\n            List of intervals [(low, upp), ...] determined to contain peaks.\n        max_consecutive_channels : int\n            Number of consecutive positive/negative channels that have a\n            probability less than p_limit to be due to chance.\n\n        Returns\n        -------\n        mask : array\n            A mask that can be applied to the spectrum to mask broad peaks\n\n        \"\"\"\n        mask_consec = consecutive_channels >= max_consecutive_channels\n        if mask_consec.any():\n            mask = self.mask_channels(n_channels, ranges[mask_consec], pad_channels=pad_channels)\n            return mask\n        else:\n            return np.zeros(n_channels, dtype='bool')\n\n    def determine_peaks(self, spectrum, peak='both'):\n        \"\"\"\n        Find peaks in a spectrum.\n\n        Parameters\n        ----------\n        spectrum : numpy.ndarray\n            Array of the data values of the spectrum.\n        peak : 'both' (default), 'positive', 'negative'\n            Description of parameter `peak`.\n        amp_threshold : float\n            Required minimum threshold that at least one data point in a peak\n            feature has to exceed.\n\n        Returns\n        -------\n        consecutive_channels or amp_vals : numpy.ndarray\n            If the 'amp_threshold' value is supplied an array with the maximum\n            data values of the ranges is returned. Otherwise, the number of\n            spectral channels of the ranges is returned.\n        ranges : list\n            List of intervals [(low, upp), ...] determined to contain peaks.\n\n        \"\"\"\n\n        if (peak == 'both') or (peak == 'positive'):\n            clipped_spectrum = spectrum.clip(max=0)\n            # Create an array that is 1 where a is 0, and pad each end with an extra 0.\n            iszero = np.concatenate(\n                ([0], np.equal(clipped_spectrum, 0).view(np.int8), [0]))\n            absdiff = np.abs(np.diff(iszero))\n            # Runs start and end where absdiff is 1.\n            ranges = np.where(absdiff == 1)[0].reshape(-1, 2)\n\n        if (peak == 'both') or (peak == 'negative'):\n            clipped_spectrum = spectrum.clip(min=0)\n            # Create an array that is 1 where a is 0, and pad each end with an extra 0.\n            iszero = np.concatenate(\n                ([0], np.equal(clipped_spectrum, 0).view(np.int8), [0]))\n            absdiff = np.abs(np.diff(iszero))\n            if peak == 'both':\n                # Runs start and end where absdiff is 1.\n                ranges = np.append(\n                    ranges, np.where(absdiff == 1)[0].reshape(-1, 2), axis=0)\n            else:\n                ranges = np.where(absdiff == 1)[0].reshape(-1, 2)\n\n        sort_indices = np.argsort(ranges[:, 0])\n        ranges = ranges[sort_indices]\n\n        consecutive_channels = ranges[:, 1] - ranges[:, 0]\n        return consecutive_channels, ranges\n\n    def compute_MAD(self, noise_spectrum):\n        \"\"\"\n        Computes the median absolute deviation of a spectrum that is assumed to\n        be pure noise. If remove_broad=True then broad peaks will have already\n        been masked at this point.\n\n        We restrict the calculation of the MAD to spectral channels with\n        negative values, since the positive channels can still contain\n        multiple narrow high signal peaks that were not identified during\n        remove_broad. The assumption is that negative spikes will be\n        sufficiently uncommon that they shouldn't affect the computation of MAD.\n\n        Parameters\n        ----------\n        noise_spectrum : array\n            A spectrum which is assumed to contain mostly noise\n\n        Returns\n        -------\n        MAD : float\n            The median absolute deviation of the spectrum computed from spectral\n            channels with negative values (reflected noise)\n\n        \"\"\"\n        negative_indices = (noise_spectrum < 0.0)\n        spectrum_negative_values = noise_spectrum[negative_indices]\n        reflected_noise = np.concatenate((spectrum_negative_values,np.abs(spectrum_negative_values)))\n        if np.size(reflected_noise)!=0:\n            return median_absolute_deviation(reflected_noise)\n        else:\n            return median_absolute_deviation(noise_spectrum)\n\n    def mask_outlierpeaks(self, spectrum, n_channels, pad_channels, MADthresh, ranges):\n        \"\"\"\n        Method used to mask out outlier high-amplitude peaks within a spectrum\n\n        Parameters\n        ----------\n        spectrum : numpy.ndarray\n            Array of the data values of the spectrum\n        n_channels : int\n            Number of spectral channels\n        pad_channels : int\n            Number of channels by which an interval (low, upp) gets extended on\n            both sides, resulting in (low - pad_channels, upp + pad_channels).\n        MADthresh : float\n            n_mad*MAD. The threshold used to determine where the peaks lie\n        ranges : array\n            List of intervals [(low, upp), ...] determined to contain peaks.\n\n        Returns\n        -------\n        mask : array\n            A mask that can be applied to the spectrum to mask high-amplitude\n            peaks\n\n        \"\"\"\n        # determine where there are spikes above MADthresh\n        inds_high_amps = np.where(np.abs(spectrum) > MADthresh)[0]\n\n        if inds_high_amps.size > 0:\n            # Find the ranges of these spikes and mask them\n            inds_ranges = np.digitize(inds_high_amps, ranges[:, 0]) - 1\n            ranges = ranges[inds_ranges]\n            mask = self.mask_channels(n_channels, ranges, pad_channels=pad_channels)\n            return mask\n        else:\n            # if there aren't any, the spectrum is considered to be noise\n            return np.zeros(n_channels, dtype='bool')\n\n    def mask_channels(self, n_channels, ranges, pad_channels=None, remove_intervals=None):\n        \"\"\"\n        Determine the 1D boolean mask for a given list of spectral ranges.\n\n        Parameters\n        ----------\n        n_channels : int\n            Number of spectral channels.\n        ranges : list\n            List of intervals [(low, upp), ...].\n        pad_channels : int\n            Number of channels by which an interval (low, upp) gets extended on both sides, resulting in (low - pad_channels, upp + pad_channels).\n        remove_intervals : type\n            Nested list containing info about ranges of the spectrum that should be masked out.\n\n        Returns\n        -------\n        mask : numpy.ndarray\n            1D boolean mask that has 'True' values at the position of the channels contained in ranges.\n\n        \"\"\"\n        mask = np.zeros(n_channels)\n\n        for (lower, upper) in ranges:\n            if pad_channels is not None:\n                lower = max(0, lower - pad_channels)\n                upper = min(n_channels, upper + pad_channels)\n            mask[lower:upper] = 1\n\n        if remove_intervals is not None:\n            for (low, upp) in remove_intervals:\n                mask[low:upp] = 0\n\n        return mask.astype('bool')\n", "text_preview": "# Licensed under an MIT open source license - see LICENSE import numpy as np from astropy.stats import sigma_clip, mad_std, median_absolute_deviation class getnoise(object): \"\"\" Autonomous calculation of the spectral rms Notes ----- The met", "input_sha256": "a37a701d8c77de6dc4305b58d6dae0a8cacc49c75b10cc1c33a7a1ac3fd433cb", "text_sha256": "5cd660976e444f53a4ea678c4ddbc6227e2f1f38ce39ce8003217b3063318b11", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12077, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9087327340526177, "noise_severity": 0.15240416658173508, "missing_content_severity": 0.40017731668434164, "factual_density": 3.2328510553696743, "pretraining_utility": 0.687250590645092, "math_density": 3.193548074333764, "code_density": 3.683182517115837, "reasoning_density": 1.8358786322312146, "education_level": 2.8888587762872766, "creative_writing_quality": -0.18577902076497166}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.5717 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.02949111079356645, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9976121356883946, "secondary": 0.7684242686473874, "tertiary": 0.767058687271254, "structured_records": 0.001997750618962182, "procedural_steps": 0.06522301326727965, "worked_solution": 0.0045059709455570396, "contextual_dialogue": 7.478045291448673e-06, "content_quality": 0.5108197628300254, "noise_severity": 0.8729965278485541, "missing_content_severity": 0.6665189027630487, "factual_density": 0.49525668118849986, "pretraining_utility": 0.5294535897108061, "math_density": 0.3279567286114695, "code_density": 0.735985430929864, "reasoning_density": 0.0, "education_level": 0.36865931459015633, "creative_writing_quality": 0.999038843650911, "primary_topic": 0.8216678317670512, "secondary_topic": 0.5545867949064356}, "probability_margin": {"primary": 0.9966781887888438, "secondary": 0.6270883512517332, "tertiary": 0.5962885583060389, "education_level": 0.16609898009088447, "creative_writing_quality": 0.9987835126676244, "primary_topic": 0.665903971269113, "secondary_topic": 0.36156737563718067}, "raw_probabilities": {"primary": {"Academic Writing": 0.00017571809147739215, "Content Listing": 1.705214620254355e-05, "Creative Writing": 9.982244564399868e-06, "Customer Support Page": 9.43337686515369e-06, "Q&A Forum": 1.1410653570394003e-05, "Discussion Thread": 6.419300881949384e-06, "Comment Section": 1.0420453134805715e-05, "Missing Content": 2.3424654102196826e-05, "Knowledge Article": 0.00013225259178402344, "Legal Notices": 0.00026928322364664606, "News Article": 4.165143289840241e-06, "Organizational Announcement": 1.0183306664562078e-05, "Nonfiction Writing": 1.3152646345212309e-05, "Organizational About Page": 1.1386417820425487e-05, "Personal About Page": 1.038383254589712e-05, "Personal Blog": 6.841953636237223e-06, "Product Page": 7.67112077903895e-06, "Spam / Ads": 8.58335716152478e-06, "Structured Data": 0.00025309583094298635, "Technical Writing": 0.9977116300347114, "Transcript / Interview": 1.2781806152458341e-05, "Tutorial / How-To Guide": 0.00024476510494187884, "User Reviews": 6.521462911493251e-06, "Other / Unknown": 0.001033441245867603}, "secondary": {"Academic Writing": 0.01458938808031365, "Content Listing": 0.000492711966485119, "Creative Writing": 0.0005486135709058218, "Customer Support Page": 0.00044996326959785007, "Q&A Forum": 0.00031677851556945706, "Discussion Thread": 0.0002982633666877542, "Comment Section": 0.0005364822699186591, "Missing Content": 0.0017351981574253476, "Knowledge Article": 0.0020804136422636634, "Legal Notices": 0.15098490620201313, "News Article": 0.0004949086354442719, "Organizational Announcement": 0.0006163591586244277, "Nonfiction Writing": 0.0007773366973845676, "Organizational About Page": 0.0005339864815939899, "Personal About Page": 0.0004699961738291034, "Personal Blog": 0.0004357071695499858, "Product Page": 0.0003005304413291876, "Spam / Ads": 0.0003971088014360884, "Structured Data": 0.0011204782576169409, "Technical Writing": 0.04269151095685532, "Transcript / Interview": 0.0006253561962618263, "Tutorial / How-To Guide": 0.0011871323916447778, "User Reviews": 0.00024361214350272835, "NONE": 0.7780732574537463}, "tertiary": {"Academic Writing": 0.017233980935856038, "Content Listing": 0.0003256106632441903, "Creative Writing": 0.0004380826665945904, "Customer Support Page": 0.00026976006175616184, "Q&A Forum": 0.00019171423780464485, "Discussion Thread": 0.00018103606961299186, "Comment Section": 0.0003390607117741555, "Missing Content": 0.0011000852857458213, "Knowledge Article": 0.0022187361149282334, "Legal Notices": 0.18047601699557955, "News Article": 0.00031512861706814546, "Organizational Announcement": 0.0003559439163433394, "Nonfiction Writing": 0.0006769945052664437, "Organizational About Page": 0.00042347177469529454, "Personal About Page": 0.00027311475259607316, "Personal Blog": 0.00033945170974217963, "Product Page": 0.00022106994383788188, "Spam / Ads": 0.00027117355466038456, "Structured Data": 0.0005754798761428039, "Technical Writing": 0.01569405289061493, "Transcript / Interview": 0.0003856988261937082, "Tutorial / How-To Guide": 0.0007888553146307215, "User Reviews": 0.00014090527369312145, "NONE": 0.7767645753016185}, "structured_records": {"false": 0.9980022493810378, "true": 0.001997750618962182}, "procedural_steps": {"false": 0.9347769867327204, "true": 0.06522301326727965}, "worked_solution": {"false": 0.995494029054443, "true": 0.0045059709455570396}, "contextual_dialogue": {"false": 0.9999925219547086, "true": 7.478045291448673e-06}, "content_quality": {"-2": 0.006680310845124143, "-1": 0.039706657745289184, "0": 0.2396875272497251, "1": 0.46605099483156787, "2": 0.24787450932829358}, "noise_severity": {"0": 0.860506721037078, "1": 0.13073913842408882, "2": 0.0057225778196558046, "3": 0.001906378358374996, "4": 0.001125184360802416}, "missing_content_severity": {"0": 0.6217441378993739, "1": 0.36188555536222605, "2": 0.012381070737605254, "3": 0.0024273241562736605, "4": 0.0015619118445210044}, "factual_density": {"0": 0.004963024416751266, "1": 0.022031722389094496, "2": 0.12746794557362026, "3": 0.4262657886487968, "4": 0.4192715189717371}, "pretraining_utility": {"-2": 0.007178985868177747, "-1": 0.05101406357809652, "0": 0.315137466090244, "1": 0.5007163429674194, "2": 0.1259531414960623}, "math_density": {"0": 0.007263252009496156, "1": 0.028196655116273405, "2": 0.16660930955073253, "3": 0.3595903331779666, "4": 0.43834045014553136}, "code_density": {"0": 0.0004119455910609907, "1": 0.0008465720324459244, "2": 0.027612115872131013, "3": 0.2574057526783194, "4": 0.7137236138260428}, "reasoning_density": {"0": 0.26653185321283634, "1": 0.18734732273474167, "2": 0.16958170274791493, "3": 0.19678858121738485, "4": 0.17975054008712216}, "education_level": {"none": 0.0012989891986699103, "0": 0.001581007663095341, "1": 0.004641168772973441, "2": 0.30778378206757917, "3": 0.47388276215846364, "4": 0.21081229013921848}, "creative_writing_quality": {"not_applicable": 0.9991990363757591, "-2": 8.12546735251022e-05, "-1": 0.0001656361888646996, "0": 0.0004155237081346498, "1": 9.775480929796164e-05, "2": 4.079424441854703e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00026142988657190495, "Art & Design": 0.0003516115787664118, "Law & Crime": 0.00030113519358418884, "Education & Careers": 0.0002429928895752723, "Entertainment & Media": 0.00028833823810038816, "Fashion & Beauty": 0.00024239794151881497, "Business, Economics & Finance": 0.0002856427680609166, "Food & Dining": 0.00019750369008029317, "Games & Gaming": 0.00031018177661044157, "Electronics & Hardware": 0.0003418323019598677, "Health, Healthcare & Wellness": 0.00032213454640530573, "History & Geography": 0.0003475241281502724, "Home, Pets & Hobbies": 0.0002288884792858628, "Industry & Production": 0.0003095524833341466, "Language, Literature & Philosophy": 0.00021120499091734811, "Politics & Government": 0.00015691336373627654, "Religion & Spirituality": 0.00022079573122768659, "Science, Mathematics & Engineering": 0.16262279004535918, "Relationships & Community": 0.00012319272431733918, "Software Usage & Applications": 0.0010195042662430212, "Programming & Software Engineering": 0.8285267613144722, "Sports & Fitness": 0.0002548478711735195, "Vehicles & Transportation": 0.00014084730849052733, "Travel & Tourism": 0.0002580157631647607, "Other / Uncovered Subject": 0.0020838904172422994, "Unknown / Insufficient Content": 0.00035007030165167555}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006799949662636556, "Art & Design": 0.0006916370164249548, "Law & Crime": 0.0016710805858597972, "Education & Careers": 0.0004508431408873458, "Entertainment & Media": 0.0006282861741651826, "Fashion & Beauty": 0.00045271690922884313, "Business, Economics & Finance": 0.0005420797999829705, "Food & Dining": 0.00035891495153663307, "Games & Gaming": 0.0004235515650898693, "Electronics & Hardware": 0.0006468588566239525, "Health, Healthcare & Wellness": 0.000575184441695186, "History & Geography": 0.0005637240685331075, "Home, Pets & Hobbies": 0.00038452963146759294, "Industry & Production": 0.0005209808015084413, "Language, Literature & Philosophy": 0.0002916614121173713, "Politics & Government": 0.0003099131482054736, "Religion & Spirituality": 0.0005272423693780786, "Science, Mathematics & Engineering": 0.5717180720254188, "Relationships & Community": 0.00018705922027649797, "Software Usage & Applications": 0.0008863225803720425, "Programming & Software Engineering": 0.20170522367494356, "Sports & Fitness": 0.0005007574636382412, "Vehicles & Transportation": 0.00028463445570964415, "Travel & Tourism": 0.0005367521782573152, "Other / Uncovered Subject": 0.004311282174177279, "NONE": 0.21015069638823813}}, "probabilities": {"primary": {"Academic Writing": 0.00017571809147739215, "Content Listing": 1.705214620254355e-05, "Creative Writing": 9.982244564399868e-06, "Customer Support Page": 9.43337686515369e-06, "Q&A Forum": 1.1410653570394003e-05, "Discussion Thread": 6.419300881949384e-06, "Comment Section": 1.0420453134805715e-05, "Missing Content": 2.3424654102196826e-05, "Knowledge Article": 0.00013225259178402344, "Legal Notices": 0.00026928322364664606, "News Article": 4.165143289840241e-06, "Organizational Announcement": 1.0183306664562078e-05, "Nonfiction Writing": 1.3152646345212309e-05, "Organizational About Page": 1.1386417820425487e-05, "Personal About Page": 1.038383254589712e-05, "Personal Blog": 6.841953636237223e-06, "Product Page": 7.67112077903895e-06, "Spam / Ads": 8.58335716152478e-06, "Structured Data": 0.00025309583094298635, "Technical Writing": 0.9977116300347114, "Transcript / Interview": 1.2781806152458341e-05, "Tutorial / How-To Guide": 0.00024476510494187884, "User Reviews": 6.521462911493251e-06, "Other / Unknown": 0.001033441245867603}, "secondary": {"Academic Writing": 0.01458938808031365, "Content Listing": 0.000492711966485119, "Creative Writing": 0.0005486135709058218, "Customer Support Page": 0.00044996326959785007, "Q&A Forum": 0.00031677851556945706, "Discussion Thread": 0.0002982633666877542, "Comment Section": 0.0005364822699186591, "Missing Content": 0.0017351981574253476, "Knowledge Article": 0.0020804136422636634, "Legal Notices": 0.15098490620201313, "News Article": 0.0004949086354442719, "Organizational Announcement": 0.0006163591586244277, "Nonfiction Writing": 0.0007773366973845676, "Organizational About Page": 0.0005339864815939899, "Personal About Page": 0.0004699961738291034, "Personal Blog": 0.0004357071695499858, "Product Page": 0.0003005304413291876, "Spam / Ads": 0.0003971088014360884, "Structured Data": 0.0011204782576169409, "Technical Writing": 0.04269151095685532, "Transcript / Interview": 0.0006253561962618263, "Tutorial / How-To Guide": 0.0011871323916447778, "User Reviews": 0.00024361214350272835, "NONE": 0.7780732574537463}, "tertiary": {"Academic Writing": 0.01723398093585604, "Content Listing": 0.00032561066324419036, "Creative Writing": 0.00043808266659459046, "Customer Support Page": 0.0002697600617561619, "Q&A Forum": 0.00019171423780464487, "Discussion Thread": 0.0001810360696129919, "Comment Section": 0.00033906071177415553, "Missing Content": 0.0011000852857458215, "Knowledge Article": 0.002218736114928234, "Legal Notices": 0.18047601699557958, "News Article": 0.0003151286170681455, "Organizational Announcement": 0.00035594391634333943, "Nonfiction Writing": 0.0006769945052664438, "Organizational About Page": 0.0004234717746952946, "Personal About Page": 0.0002731147525960732, "Personal Blog": 0.0003394517097421797, "Product Page": 0.0002210699438378819, "Spam / Ads": 0.0002711735546603846, "Structured Data": 0.000575479876142804, "Technical Writing": 0.015694052890614932, "Transcript / Interview": 0.00038569882619370825, "Tutorial / How-To Guide": 0.0007888553146307216, "User Reviews": 0.00014090527369312148, "NONE": 0.7767645753016186}, "structured_records": {"false": 0.9980022493810378, "true": 0.001997750618962182}, "procedural_steps": {"false": 0.9347769867327204, "true": 0.06522301326727965}, "worked_solution": {"false": 0.995494029054443, "true": 0.0045059709455570396}, "contextual_dialogue": {"false": 0.9999925219547086, "true": 7.478045291448673e-06}, "content_quality": {"-2": 0.0066803108451241435, "-1": 0.03970665774528919, "0": 0.23968752724972514, "1": 0.4660509948315679, "2": 0.2478745093282936}, "noise_severity": {"0": 0.860506721037078, "1": 0.13073913842408882, "2": 0.0057225778196558046, "3": 0.001906378358374996, "4": 0.001125184360802416}, "missing_content_severity": {"0": 0.621744137899374, "1": 0.3618855553622261, "2": 0.012381070737605256, "3": 0.002427324156273661, "4": 0.0015619118445210046}, "factual_density": {"0": 0.004963024416751267, "1": 0.0220317223890945, "2": 0.1274679455736203, "3": 0.42626578864879683, "4": 0.41927151897173714}, "pretraining_utility": {"-2": 0.007178985868177747, "-1": 0.05101406357809652, "0": 0.315137466090244, "1": 0.5007163429674194, "2": 0.1259531414960623}, "math_density": {"0": 0.007263252009496156, "1": 0.028196655116273405, "2": 0.16660930955073253, "3": 0.3595903331779666, "4": 0.43834045014553136}, "code_density": {"0": 0.0004119455910609907, "1": 0.0008465720324459244, "2": 0.027612115872131013, "3": 0.2574057526783194, "4": 0.7137236138260428}, "reasoning_density": {"0": 0.26653185321283634, "1": 0.18734732273474167, "2": 0.16958170274791493, "3": 0.19678858121738485, "4": 0.17975054008712216}, "education_level": {"none": 0.0012989891986699103, "0": 0.001581007663095341, "1": 0.004641168772973441, "2": 0.30778378206757917, "3": 0.47388276215846364, "4": 0.21081229013921848}, "creative_writing_quality": {"not_applicable": 0.9991990363757591, "-2": 8.12546735251022e-05, "-1": 0.0001656361888646996, "0": 0.0004155237081346498, "1": 9.775480929796164e-05, "2": 4.079424441854703e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00026142988657190495, "Art & Design": 0.0003516115787664118, "Law & Crime": 0.00030113519358418884, "Education & Careers": 0.0002429928895752723, "Entertainment & Media": 0.00028833823810038816, "Fashion & Beauty": 0.00024239794151881497, "Business, Economics & Finance": 0.0002856427680609166, "Food & Dining": 0.00019750369008029317, "Games & Gaming": 0.00031018177661044157, "Electronics & Hardware": 0.0003418323019598677, "Health, Healthcare & Wellness": 0.00032213454640530573, "History & Geography": 0.0003475241281502724, "Home, Pets & Hobbies": 0.0002288884792858628, "Industry & Production": 0.0003095524833341466, "Language, Literature & Philosophy": 0.00021120499091734811, "Politics & Government": 0.00015691336373627654, "Religion & Spirituality": 0.00022079573122768659, "Science, Mathematics & Engineering": 0.16262279004535918, "Relationships & Community": 0.00012319272431733918, "Software Usage & Applications": 0.0010195042662430212, "Programming & Software Engineering": 0.8285267613144722, "Sports & Fitness": 0.0002548478711735195, "Vehicles & Transportation": 0.00014084730849052733, "Travel & Tourism": 0.0002580157631647607, "Other / Uncovered Subject": 0.0020838904172422994, "Unknown / Insufficient Content": 0.00035007030165167555}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006799949662636556, "Art & Design": 0.0006916370164249548, "Law & Crime": 0.0016710805858597972, "Education & Careers": 0.0004508431408873458, "Entertainment & Media": 0.0006282861741651826, "Fashion & Beauty": 0.00045271690922884313, "Business, Economics & Finance": 0.0005420797999829705, "Food & Dining": 0.00035891495153663307, "Games & Gaming": 0.0004235515650898693, "Electronics & Hardware": 0.0006468588566239525, "Health, Healthcare & Wellness": 0.000575184441695186, "History & Geography": 0.0005637240685331075, "Home, Pets & Hobbies": 0.00038452963146759294, "Industry & Production": 0.0005209808015084413, "Language, Literature & Philosophy": 0.0002916614121173713, "Politics & Government": 0.0003099131482054736, "Religion & Spirituality": 0.0005272423693780786, "Science, Mathematics & Engineering": 0.5717180720254188, "Relationships & Community": 0.00018705922027649797, "Software Usage & Applications": 0.0008863225803720425, "Programming & Software Engineering": 0.20170522367494356, "Sports & Fitness": 0.0005007574636382412, "Vehicles & Transportation": 0.00028463445570964415, "Travel & Tourism": 0.0005367521782573152, "Other / Uncovered Subject": 0.004311282174177279, "NONE": 0.21015069638823813}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11638, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11638, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:11.229550+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "2b94e87c-e97d-4c24-8042-fc90fba7d935", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "bbf30154ba3319d897d8153da59156435ccaadee07a0bb4ee9782d7b2a53bd02", "source_metadata": {"uuid": "2b94e87c-e97d-4c24-8042-fc90fba7d935", "repo_name": "jdhenshaw/scousepy", "relative_path": "scousepy/noisy.py", "category": "DATA", "algo_rel_score": 0.9808531403541565, "quality_score": 5.782818794250488, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 454, "repo_created_at": "2017-11-23T14:01:06.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 22, "forks": 8, "issues": 10, "pull_requests": 2}, "file_meta": {"content_id": "e627e94d52778071f3c0b0d0ec1401db48f51bf5", "size_bytes": 14140, "file_timestamp": 1729761054, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2cc17efd2397b8e0ae4a76df5c988571d69e7d1b"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 454, "repo_created_at": "2017-11-23T14:01:06.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 22, "forks": 8, "issues": 10, "pull_requests": 2}, "file_meta": {"content_id": "e627e94d52778071f3c0b0d0ec1401db48f51bf5", "size_bytes": 14140, "file_timestamp": 1729761054, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2cc17efd2397b8e0ae4a76df5c988571d69e7d1b", "url": "https://github.com/jdhenshaw/scousepy/blob/2cc17efd2397b8e0ae4a76df5c988571d69e7d1b/scousepy/noisy.py"}, "gpt4o_tokens": 3005, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.9087327340526177, "noise_severity": 0.15240416658173508, "missing_content_severity": 0.40017731668434164, "factual_density": 3.2328510553696743, "pretraining_utility": 0.687250590645092, "math_density": 3.193548074333764, "code_density": 3.683182517115837, "reasoning_density": 1.8358786322312146, "education_level": 2.8888587762872766, "creative_writing_quality": -0.18577902076497166}, "score_applicability": {"education_level": 0.9987010108013301, "creative_writing_quality": 0.0008009636242409603}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/Pattama-Dev/523480/blob/2a3a088d66d3087243c3254658d02bc4465f8a2a/mem.py", "id": "python-0-626ee37ed852b4f5ab787b8391aa723b", "text": "import os\nimport csv\n\ndef determine_label_and_type(file_name):\n    if \"normal\" in file_name.lower():\n        return 0, \"normal\"\n    elif \"brute\" in file_name.lower():\n        return 1, \"brute force\"\n    elif \"nmap\" in file_name.lower():\n        return 1, \"scanning\"\n    elif any(attack in file_name.lower() for attack in [\"tcp\", \"udp\", \"icmp\", \"ack\", \"hping\"]):\n        return 1, \"dos\"\n    elif \"ddos\" in file_name.lower():\n        return 1, \"ddos\"\n    elif \"injection\" in file_name.lower():\n        return 1, \"injection\"\n    elif \"arp\" in file_name.lower():\n        return 1, \"arp spoofing\"\n    elif \"xss\" in file_name.lower():\n        return 1, \"xss\"\n    else:\n    \treturn \"-\", \"unknow\"\n\ndef extract_memory_snapshots_to_csv(file_path, output_file, label, type_):\n    with open(file_path, 'r') as file:\n        lines = file.readlines()\n\n    snapshots = []\n    current_snapshot = None\n    extracting_process = False\n    process_count = 0\n\n    for line in lines:\n        # ตรวจจับ ATOP Header เพื่อดึง Timestamp\n        if \"ATOP - Linux\" in line:\n            if current_snapshot:\n                snapshots.append(current_snapshot)\n            timestamp = \" \".join(line.split()[3:5])\n            current_snapshot = {\"timestamp\": timestamp, \"processes\": []}\n            extracting_process = False\n            process_count = 0\n\n        # เริ่มดึงข้อมูล process เมื่อเจอ \"PID\"\n        elif \"PID\" in line:\n            extracting_process = True\n            process_count = 0\n            continue\n\n        # หยุดดึงข้อมูลเมื่อเจอบรรทัดว่าง\n        elif line.strip() == \"\":\n            extracting_process = False\n\n        # ดึงข้อมูล process สูงสุด 10 แถว\n        elif extracting_process and process_count < 10:\n            process_details = line.strip().split()\n            # ดึงเฉพาะคอลัมน์ที่ต้องการ\n            if len(process_details) >= 10:  # ตรวจสอบว่ามีข้อมูลเพียงพอ\n                selected_data = {\n                    \"PID\": process_details[0],\n                    \"MINFLT\": process_details[2],\n                    \"MAJFLT\": process_details[3],\n                    \"VSTEXT\": process_details[4],\n                    \"VSIZE\": process_details[8],\n                    \"RSIZE\": process_details[9],\n                    \"VGROW\": process_details[11],\n                    \"RGROW\": process_details[12],\n                    \"MEM\": process_details[16],\n                    \"CMD\": process_details[17],\n                }\n                current_snapshot[\"processes\"].append(selected_data)\n                process_count += 1\n\n    # เพิ่ม Snapshot ชุดสุดท้าย\n    if current_snapshot:\n        snapshots.append(current_snapshot)\n\n    # บันทึกข้อมูลลงไฟล์ CSV\n    with open(output_file, 'w', newline='') as csvfile:\n        writer = csv.writer(csvfile)\n        # เขียนหัวข้อ\n        writer.writerow([\"PID\", \"MINFLT\", \"MAJFLT\", \"VSTEXT\", \"VSIZE\", \"RSIZE\", \"VGROW\", \"RGROW\", \"MEM\", \"CMD\", \"label\", \"type\", \"Timestamp\"])\n\n        for snapshot in snapshots:\n            timestamp = snapshot[\"timestamp\"]\n            for process in snapshot[\"processes\"]:\n                # เพิ่มข้อมูลพร้อม Timestamp\n                writer.writerow([\n                    process[\"PID\"], process[\"MINFLT\"], process[\"MAJFLT\"], process[\"VSTEXT\"],\n                    process[\"VSIZE\"], process[\"RSIZE\"], process[\"VGROW\"], process[\"RGROW\"],\n                    process[\"MEM\"], process[\"CMD\"], label, type_, timestamp\n                ])\n\n    print(f\"Extracted memory snapshots saved to {output_file}\")\n\ndef process_multiple_files(input_dir, output_dir):\n    if not os.path.exists(output_dir):\n        os.makedirs(output_dir)  # สร้างโฟลเดอร์ output หากยังไม่มี\n\n    for file_name in os.listdir(input_dir):\n        if file_name.endswith(\".txt\"):\n            input_file = os.path.join(input_dir, file_name)\n            output_file = os.path.join(output_dir, f\"{os.path.splitext(file_name)[0]}.csv\")\n            \n            # ตรวจสอบ label และ type จากชื่อไฟล์\n            label, type_ = determine_label_and_type(file_name)\n\n            print(f\"Processing file: {file_name} -> label: {label}, type: {type_}\")\n            extract_memory_snapshots_to_csv(input_file, output_file, label, type_)\n            print(\"\")\n\n# ใช้งานฟังก์ชัน\nprocess_multiple_files(\"/home/os/Desktop/project/New_Files/host/attck/text_log/MEM\", \"/home/os/Desktop/project/New_Files/csv/attack/host/mem\")\n", "text_preview": "import os import csv def determine_label_and_type(file_name): if \"normal\" in file_name.lower(): return 0, \"normal\" elif \"brute\" in file_name.lower(): return 1, \"brute force\" elif \"nmap\" in file_name.lower(): return 1, \"scanning\" elif any(at", "input_sha256": "e1adea1f8dd837584e82d68c13de4c586934909c88c5745374cb675835707ecb", "text_sha256": "626ee37ed852b4f5ab787b8391aa723b87a3492ef769699bcaf93f0f3f6a2f8f", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12078, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5020699906581485, "noise_severity": 0.019892224527746465, "missing_content_severity": 0.0069461364215677905, "factual_density": 2.9898251376803153, "pretraining_utility": 0.5109825697679465, "math_density": 0.034529484188406456, "code_density": 3.6978764130515644, "reasoning_density": 0.6250239363384964, "education_level": 2.1445794260512274, "creative_writing_quality": -0.5433343704790955}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.028140768439063768, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9889635684365536, "secondary": 0.9497643413274374, "tertiary": 0.9791286214377647, "structured_records": 0.05859827324391094, "procedural_steps": 0.061838684758836936, "worked_solution": 0.008858233405091974, "contextual_dialogue": 7.600030825740277e-06, "content_quality": 0.4098428016735777, "noise_severity": 0.983423146226878, "missing_content_severity": 0.9942115529820268, "factual_density": 0.15818761473359566, "pretraining_utility": 0.46395309725114164, "math_density": 0.9712254298429946, "code_density": 0.7482303442096372, "reasoning_density": 0.47914671971791967, "education_level": 0.7258067186200886, "creative_writing_quality": 0.9991503752961135, "primary_topic": 0.972592247591281, "secondary_topic": 0.7329588477413174}, "probability_margin": {"primary": 0.9847514215436608, "secondary": 0.9332314065588179, "tertiary": 0.9741520706733753, "education_level": 0.6287956322645554, "creative_writing_quality": 0.9990690816679828, "primary_topic": 0.9644505982127162, "secondary_topic": 0.6449839817299295}, "raw_probabilities": {"primary": {"Academic Writing": 0.00016060277539674397, "Content Listing": 5.023960837031386e-05, "Creative Writing": 5.501641904909776e-05, "Customer Support Page": 2.749454501936378e-05, "Q&A Forum": 4.227957336623204e-05, "Discussion Thread": 2.7530162862284394e-05, "Comment Section": 4.54229298812385e-05, "Missing Content": 7.175870138424001e-05, "Knowledge Article": 0.0003267079595078688, "Legal Notices": 6.3292960196695e-05, "News Article": 1.8085301382140283e-05, "Organizational Announcement": 2.8196678899883758e-05, "Nonfiction Writing": 5.140028668596172e-05, "Organizational About Page": 3.6125907735244736e-05, "Personal About Page": 3.498797695280928e-05, "Personal Blog": 2.257822993574997e-05, "Product Page": 2.087580427588247e-05, "Spam / Ads": 3.285548577230847e-05, "Structured Data": 0.0022158121534245506, "Technical Writing": 0.9894234197516972, "Transcript / Interview": 4.8785932462924e-05, "Tutorial / How-To Guide": 0.004671998208036471, "User Reviews": 1.7712900527459103e-05, "Other / Unknown": 0.002506819747177192}, "secondary": {"Academic Writing": 0.002541947894912374, "Content Listing": 0.00020767826131353395, "Creative Writing": 0.00026427223013108305, "Customer Support Page": 0.00014628401440548513, "Q&A Forum": 0.00013768150454998294, "Discussion Thread": 9.832824345105506e-05, "Comment Section": 0.0014835409980611593, "Missing Content": 0.00033747819055339435, "Knowledge Article": 0.0018890684203581958, "Legal Notices": 0.00024187823156532467, "News Article": 0.00014423490940130047, "Organizational Announcement": 0.00011921268549129678, "Nonfiction Writing": 0.00026608217607172605, "Organizational About Page": 0.00014410003288412344, "Personal About Page": 0.00014155857640887881, "Personal Blog": 0.00014675651983559758, "Product Page": 8.526883216470434e-05, "Spam / Ads": 0.00015547168095300155, "Structured Data": 0.010655256194245915, "Technical Writing": 0.018626087213309626, "Transcript / Interview": 0.0002125448241566443, "Tutorial / How-To Guide": 0.01002860575583633, "User Reviews": 6.916883781176013e-05, "NONE": 0.9518574937721275}, "tertiary": {"Academic Writing": 0.0011385868514534009, "Content Listing": 9.903644790963335e-05, "Creative Writing": 0.0001292416869862441, "Customer Support Page": 5.804926480002148e-05, "Q&A Forum": 5.380912093338016e-05, "Discussion Thread": 3.369807343220151e-05, "Comment Section": 0.0006987489224516421, "Missing Content": 0.00018232787983686025, "Knowledge Article": 0.0013722981319639076, "Legal Notices": 0.00015839274131821052, "News Article": 5.9275615437481736e-05, "Organizational Announcement": 4.797235419517209e-05, "Nonfiction Writing": 0.0003283428032744438, "Organizational About Page": 7.479681759550673e-05, "Personal About Page": 6.580237091807812e-05, "Personal Blog": 6.679002167121859e-05, "Product Page": 4.4914227064412185e-05, "Spam / Ads": 8.164626495006484e-05, "Structured Data": 0.003980571286331661, "Technical Writing": 0.0058461915378158836, "Transcript / Interview": 0.00011293045423426266, "Tutorial / How-To Guide": 0.005338060327117496, "User Reviews": 3.0254587117577922e-05, "NONE": 0.9799982622111911}, "structured_records": {"false": 0.941401726756089, "true": 0.05859827324391094}, "procedural_steps": {"false": 0.9381613152411631, "true": 0.061838684758836936}, "worked_solution": {"false": 0.991141766594908, "true": 0.008858233405091974}, "contextual_dialogue": {"false": 0.9999923999691742, "true": 7.600030825740277e-06}, "content_quality": {"-2": 0.01929778335607042, "-1": 0.10162697646505338, "0": 0.34191202066846105, "1": 0.4320339051854875, "2": 0.10512931432492763}, "noise_severity": {"0": 0.9836465540117476, "1": 0.013835742172940915, "2": 0.0017617873244607479, "3": 0.0004907582575177006, "4": 0.0002651582333327365}, "missing_content_severity": {"0": 0.9962614533325854, "1": 0.0014013600918397864, "2": 0.0016411551214053504, "3": 0.0005216597297605724, "4": 0.00017437172440889657}, "factual_density": {"0": 0.021806567659702746, "1": 0.05422203997124933, "2": 0.20469547054783505, "3": 0.35089153067145595, "4": 0.36838439114975696}, "pretraining_utility": {"-2": 0.010099639231259358, "-1": 0.08371913174293609, "0": 0.3683996755856915, "1": 0.46066212690682484, "2": 0.07711942653328825}, "math_density": {"0": 0.9816312868855365, "1": 0.011333363348561816, "2": 0.0017594223978601782, "3": 0.0014264334280415503, "4": 0.0038494939399999067}, "code_density": {"0": 0.0003730824827167804, "1": 0.0008088752676399918, "2": 0.0499390272291438, "3": 0.19832657675636076, "4": 0.7505524382641386}, "reasoning_density": {"0": 0.6964686517465578, "1": 0.08100868849720794, "2": 0.1494734214202009, "3": 0.047128548343246694, "4": 0.02592068999278665}, "education_level": {"none": 0.0013139486573482486, "0": 0.0025157286622722688, "1": 0.052399522547353555, "2": 0.7715055988500739, "3": 0.14270996658551854, "4": 0.029555234697433472}, "creative_writing_quality": {"not_applicable": 0.999291979413428, "-2": 0.00015949453849994512, "-1": 0.000213320648001155, "0": 0.0002228977454451605, "1": 7.699750394245764e-05, "2": 3.5310150683551675e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00012938474392328584, "Art & Design": 0.00010914693836482207, "Law & Crime": 0.009195793701977048, "Education & Careers": 0.00012492801609639937, "Entertainment & Media": 0.00010400630373728687, "Fashion & Beauty": 7.410275533414937e-05, "Business, Economics & Finance": 0.0001615588183607575, "Food & Dining": 8.749680967562068e-05, "Games & Gaming": 0.0001492724201039238, "Electronics & Hardware": 0.002240938535325265, "Health, Healthcare & Wellness": 0.00011136337323577861, "History & Geography": 0.00013042464391885966, "Home, Pets & Hobbies": 8.935481370847453e-05, "Industry & Production": 0.0001026582228047224, "Language, Literature & Philosophy": 9.884792145439571e-05, "Politics & Government": 7.951968394937814e-05, "Religion & Spirituality": 8.802118976516562e-05, "Science, Mathematics & Engineering": 0.003989991299786448, "Relationships & Community": 7.536358252402223e-05, "Software Usage & Applications": 0.003284870494543345, "Programming & Software Engineering": 0.9736463919146933, "Sports & Fitness": 0.00011793896474669792, "Vehicles & Transportation": 6.442581205497393e-05, "Travel & Tourism": 0.000130205106972534, "Other / Uncovered Subject": 0.005441566555848105, "Unknown / Insufficient Content": 0.0001724273770951272}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010791663358790701, "Art & Design": 0.0007810196870431541, "Law & Crime": 0.09558436591417294, "Education & Careers": 0.0006739594440584127, "Entertainment & Media": 0.000717821665054919, "Fashion & Beauty": 0.00044499229384863516, "Business, Economics & Finance": 0.001059842213017899, "Food & Dining": 0.0004024946437618681, "Games & Gaming": 0.000659714493325054, "Electronics & Hardware": 0.0068189770856383934, "Health, Healthcare & Wellness": 0.0006588217010477793, "History & Geography": 0.0005581336690637931, "Home, Pets & Hobbies": 0.000461855765914211, "Industry & Production": 0.00046911864592476477, "Language, Literature & Philosophy": 0.0005026531164313591, "Politics & Government": 0.0004917798319247975, "Religion & Spirituality": 0.0006070736347599388, "Science, Mathematics & Engineering": 0.004178414666296171, "Relationships & Community": 0.0002917487543286778, "Software Usage & Applications": 0.030276390980338463, "Programming & Software Engineering": 0.09824567955979864, "Sports & Fitness": 0.0004483571112682479, "Vehicles & Transportation": 0.0002571860142396134, "Travel & Tourism": 0.0007337970126881226, "Other / Uncovered Subject": 0.010366974470446898, "NONE": 0.7432296612897282}}, "probabilities": {"primary": {"Academic Writing": 0.000160602775396744, "Content Listing": 5.023960837031387e-05, "Creative Writing": 5.5016419049097766e-05, "Customer Support Page": 2.7494545019363782e-05, "Q&A Forum": 4.227957336623205e-05, "Discussion Thread": 2.7530162862284398e-05, "Comment Section": 4.5422929881238504e-05, "Missing Content": 7.175870138424002e-05, "Knowledge Article": 0.00032670795950786886, "Legal Notices": 6.329296019669501e-05, "News Article": 1.8085301382140287e-05, "Organizational Announcement": 2.819667889988376e-05, "Nonfiction Writing": 5.140028668596173e-05, "Organizational About Page": 3.612590773524474e-05, "Personal About Page": 3.498797695280929e-05, "Personal Blog": 2.2578229935749975e-05, "Product Page": 2.0875804275882475e-05, "Spam / Ads": 3.2855485772308476e-05, "Structured Data": 0.002215812153424551, "Technical Writing": 0.9894234197516973, "Transcript / Interview": 4.878593246292401e-05, "Tutorial / How-To Guide": 0.004671998208036472, "User Reviews": 1.7712900527459106e-05, "Other / Unknown": 0.0025068197471771925}, "secondary": {"Academic Writing": 0.002541947894912374, "Content Listing": 0.00020767826131353395, "Creative Writing": 0.00026427223013108305, "Customer Support Page": 0.00014628401440548513, "Q&A Forum": 0.00013768150454998294, "Discussion Thread": 9.832824345105506e-05, "Comment Section": 0.0014835409980611593, "Missing Content": 0.00033747819055339435, "Knowledge Article": 0.0018890684203581958, "Legal Notices": 0.00024187823156532467, "News Article": 0.00014423490940130047, "Organizational Announcement": 0.00011921268549129678, "Nonfiction Writing": 0.00026608217607172605, "Organizational About Page": 0.00014410003288412344, "Personal About Page": 0.00014155857640887881, "Personal Blog": 0.00014675651983559758, "Product Page": 8.526883216470434e-05, "Spam / Ads": 0.00015547168095300155, "Structured Data": 0.010655256194245915, "Technical Writing": 0.018626087213309626, "Transcript / Interview": 0.0002125448241566443, "Tutorial / How-To Guide": 0.01002860575583633, "User Reviews": 6.916883781176013e-05, "NONE": 0.9518574937721275}, "tertiary": {"Academic Writing": 0.001138586851453401, "Content Listing": 9.903644790963337e-05, "Creative Writing": 0.00012924168698624411, "Customer Support Page": 5.804926480002149e-05, "Q&A Forum": 5.3809120933380164e-05, "Discussion Thread": 3.369807343220152e-05, "Comment Section": 0.0006987489224516422, "Missing Content": 0.00018232787983686027, "Knowledge Article": 0.0013722981319639078, "Legal Notices": 0.00015839274131821055, "News Article": 5.927561543748174e-05, "Organizational Announcement": 4.7972354195172094e-05, "Nonfiction Writing": 0.00032834280327444384, "Organizational About Page": 7.479681759550674e-05, "Personal About Page": 6.580237091807813e-05, "Personal Blog": 6.67900216712186e-05, "Product Page": 4.491422706441219e-05, "Spam / Ads": 8.164626495006486e-05, "Structured Data": 0.003980571286331662, "Technical Writing": 0.005846191537815884, "Transcript / Interview": 0.00011293045423426268, "Tutorial / How-To Guide": 0.005338060327117497, "User Reviews": 3.0254587117577925e-05, "NONE": 0.9799982622111912}, "structured_records": {"false": 0.941401726756089, "true": 0.05859827324391094}, "procedural_steps": {"false": 0.9381613152411631, "true": 0.061838684758836936}, "worked_solution": {"false": 0.991141766594908, "true": 0.008858233405091974}, "contextual_dialogue": {"false": 0.9999923999691742, "true": 7.600030825740277e-06}, "content_quality": {"-2": 0.01929778335607042, "-1": 0.10162697646505338, "0": 0.34191202066846105, "1": 0.4320339051854875, "2": 0.10512931432492763}, "noise_severity": {"0": 0.9836465540117479, "1": 0.01383574217294092, "2": 0.0017617873244607485, "3": 0.0004907582575177008, "4": 0.00026515823333273663}, "missing_content_severity": {"0": 0.9962614533325854, "1": 0.0014013600918397864, "2": 0.0016411551214053504, "3": 0.0005216597297605724, "4": 0.00017437172440889657}, "factual_density": {"0": 0.021806567659702746, "1": 0.05422203997124933, "2": 0.20469547054783505, "3": 0.35089153067145595, "4": 0.36838439114975696}, "pretraining_utility": {"-2": 0.010099639231259358, "-1": 0.08371913174293609, "0": 0.3683996755856915, "1": 0.46066212690682484, "2": 0.07711942653328825}, "math_density": {"0": 0.9816312868855366, "1": 0.011333363348561817, "2": 0.0017594223978601785, "3": 0.0014264334280415505, "4": 0.003849493939999907}, "code_density": {"0": 0.0003730824827167804, "1": 0.0008088752676399918, "2": 0.0499390272291438, "3": 0.19832657675636076, "4": 0.7505524382641386}, "reasoning_density": {"0": 0.6964686517465578, "1": 0.08100868849720794, "2": 0.1494734214202009, "3": 0.047128548343246694, "4": 0.02592068999278665}, "education_level": {"none": 0.0013139486573482486, "0": 0.0025157286622722688, "1": 0.052399522547353555, "2": 0.7715055988500739, "3": 0.14270996658551854, "4": 0.029555234697433472}, "creative_writing_quality": {"not_applicable": 0.9992919794134277, "-2": 0.0001594945384999451, "-1": 0.00021332064800115496, "0": 0.00022289774544516044, "1": 7.699750394245762e-05, "2": 3.531015068355167e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00012938474392328587, "Art & Design": 0.00010914693836482209, "Law & Crime": 0.00919579370197705, "Education & Careers": 0.0001249280160963994, "Entertainment & Media": 0.00010400630373728689, "Fashion & Beauty": 7.410275533414938e-05, "Business, Economics & Finance": 0.00016155881836075754, "Food & Dining": 8.749680967562069e-05, "Games & Gaming": 0.00014927242010392383, "Electronics & Hardware": 0.0022409385353252655, "Health, Healthcare & Wellness": 0.00011136337323577863, "History & Geography": 0.0001304246439188597, "Home, Pets & Hobbies": 8.935481370847454e-05, "Industry & Production": 0.00010265822280472242, "Language, Literature & Philosophy": 9.884792145439572e-05, "Politics & Government": 7.951968394937815e-05, "Religion & Spirituality": 8.802118976516563e-05, "Science, Mathematics & Engineering": 0.003989991299786449, "Relationships & Community": 7.536358252402224e-05, "Software Usage & Applications": 0.0032848704945433456, "Programming & Software Engineering": 0.9736463919146934, "Sports & Fitness": 0.00011793896474669793, "Vehicles & Transportation": 6.442581205497394e-05, "Travel & Tourism": 0.00013020510697253404, "Other / Uncovered Subject": 0.0054415665558481055, "Unknown / Insufficient Content": 0.00017242737709512724}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010791663358790701, "Art & Design": 0.0007810196870431541, "Law & Crime": 0.09558436591417294, "Education & Careers": 0.0006739594440584127, "Entertainment & Media": 0.000717821665054919, "Fashion & Beauty": 0.00044499229384863516, "Business, Economics & Finance": 0.001059842213017899, "Food & Dining": 0.0004024946437618681, "Games & Gaming": 0.000659714493325054, "Electronics & Hardware": 0.0068189770856383934, "Health, Healthcare & Wellness": 0.0006588217010477793, "History & Geography": 0.0005581336690637931, "Home, Pets & Hobbies": 0.000461855765914211, "Industry & Production": 0.00046911864592476477, "Language, Literature & Philosophy": 0.0005026531164313591, "Politics & Government": 0.0004917798319247975, "Religion & Spirituality": 0.0006070736347599388, "Science, Mathematics & Engineering": 0.004178414666296171, "Relationships & Community": 0.0002917487543286778, "Software Usage & Applications": 0.030276390980338463, "Programming & Software Engineering": 0.09824567955979864, "Sports & Fitness": 0.0004483571112682479, "Vehicles & Transportation": 0.0002571860142396134, "Travel & Tourism": 0.0007337970126881226, "Other / Uncovered Subject": 0.010366974470446898, "NONE": 0.7432296612897282}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9578, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9578, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:11.420059+00:00", "scoring_key_slot": "primary", "dataset_uid": "e20b28d7-57ce-431b-8581-18660ef61990", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "18f2f8bfac66274f5d7ccec4a12ab4057d02d40156c1343ee346e2f4308bc668", "source_metadata": {"uuid": "e20b28d7-57ce-431b-8581-18660ef61990", "repo_name": "Pattama-Dev/523480", "relative_path": "mem.py", "category": "TOOL", "algo_rel_score": 0.9780958890914917, "quality_score": 3.99680757522583, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 4, "repo_created_at": "2025-01-08T08:34:51.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "f8541291c89bf9d2d5008464ff694e83a7988843", "size_bytes": 4839, "file_timestamp": 1736326053, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2a3a088d66d3087243c3254658d02bc4465f8a2a"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 4, "repo_created_at": "2025-01-08T08:34:51.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "f8541291c89bf9d2d5008464ff694e83a7988843", "size_bytes": 4839, "file_timestamp": 1736326053, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2a3a088d66d3087243c3254658d02bc4465f8a2a", "url": "https://github.com/Pattama-Dev/523480/blob/2a3a088d66d3087243c3254658d02bc4465f8a2a/mem.py"}, "gpt4o_tokens": 1036, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.5020699906581485, "noise_severity": 0.019892224527746465, "missing_content_severity": 0.0069461364215677905, "factual_density": 2.9898251376803153, "pretraining_utility": 0.5109825697679465, "math_density": 0.034529484188406456, "code_density": 3.6978764130515644, "reasoning_density": 0.6250239363384964, "education_level": 2.1445794260512274, "creative_writing_quality": -0.5433343704790955}, "score_applicability": {"education_level": 0.9986860513426516, "creative_writing_quality": 0.0007080205865722698}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/aditjain588/CPT_S-355-Programming-Language-Design/blob/893b53f6b4e87305700db21e575678e51d7a0c53/Hw%203%20Python/HW3.py", "id": "python-0-3fbfd5b934c2d76984809cf2706b3b21", "text": "# CptS 355 - Fall 2020 - Assignment 3\n# <NAME>\n\ndebugging = False\ndef debug(*s): \n     if debugging: \n          print(*s)\n\n## problem 1-a) getNumCases - 10%\n\ndef getNumCases(data,counties,months):\n    count=0\n    for x1,y1 in data.items():\n        for i in range(0,len(counties)):\n            if(x1==counties[i]):\n               for x2,y2 in y1.items():\n                   for j in range(0,len(months)):\n                       if(x2==months[j]):\n                           count=count+y2\n                        \n    #print(count)\n    return count\n\n\n## problem 1-b) getMonthlyCases - 15%\n\n\ndef getMonthlyCases(data):\n    d={}\n    for k1,v1 in data.items():\n        for k2,v2 in v1.items():\n            if k2 in d:\n                t=d[k2]\n            else:\n                t={}\n            t[k1] = v2\n            d[k2] = t\n\n    #print(d)\n    return d\n\n\n\nCDCdata = { 'King':{'Mar':2706,'Apr':3620,'May':1860,'Jun':2157,'July':5014,'Aug':4327,'Sep':2843},\n            'Pierce':{'Mar':460,'Apr':965,'May':522,'Jun':647,'July':2470,'Aug':1776,'Sep':1266}, \n            'Snohomish':{'Mar':1301,'Apr':1145,'May':532,'Jun':568,'July':1540,'Aug':1134,'Sep':811}, \n            'Spokane':{'Mar':147,'Apr':222,'May':233,'Jun':794,'July':2412,'Aug':1530,'Sep':1751}, \n            'Whitman' : {'Apr':7,'May':5,'Jun':19,'July':51,'Aug':514,'Sep':732, 'Oct':278} }\n\n\nfrom functools import reduce\n## problem 1-c) mostCases - 15%\n\ndef helper(x,d):\n    a=sum(d[1].values())\n    #print(x,a)\n    return (x,a)\n    \ndef mostCases(data):\n   data=getMonthlyCases(data)\n   d1=data.items()\n   #print(d1)\n   a=max(list(map(helper,data.keys(), d1)),key = lambda x :x[1])\n   #print (a)\n   return (a)\n\n## problem 2a) searchDicts(L,k) - 5%\n\ndef searchDicts(L,k): \n    L.reverse()\n    #print(L)\n    for i in range(0,len(L)):\n        for x1,y1 in L[i].items():\n            if(x1==k):\n                print(y1)\n                return y1\n    return 'None'\n\n## problem 2b) searchDicts2(L,k) - 10%\n\ndef searchDicts2(L,k):\n    if not L:\n        return None\n    val=L[-1][0]\n    #print(\"val is \",val)\n    i=val+1-len(L)\n\n    for a,b in L[::-1]:   \n        for x1,y1 in b.items():\n            if(x1==k):\n                print(y1)\n                return y1\n        return searchDicts2(L[:i],k)\n\n\n## problem 3 - adduptoZero - 10%\nfrom itertools import combinations\ndef adduptoZero(L,n):\n    x=list(combinations(L,n))\n    #print(x)\n    a=[]\n    for i in range(0,len(x)):\n        if (sum(x[i]) == 0):\n            a.append(list(x[i]))\n    print(a)\n    return a\n\n## problem 4 - getLongest - 10%\n\ndef getLongest(L):\n    if isinstance (L,str):\n        return L\n    else:\n        return (max([getLongest(x) for x in L]))\n        #for x in L:\n        #    return (max(getLongest([x])))\n\nprint (getLongest(['1',['22',['333',['4444','55555',['666666']],'7777777'],'4444'],'22']))\nprint(getLongest([['cat',['dog','horse'],['bird',['bunny','fish']]]]))\nprint(getLongest(['222',[['44444',[['555555555555555'],'3333333'],'1111111']]]))\n\n\n\n## problem 5 - apply2nextN - 20% \n\nclass apply2nextN (): \n    def __init__(self,op,n,L):\n        self.op=op\n        self.n=n\n        self.L=L\n       \n    def __iter__(self):\n        return self\n        \n    def __next__(self):\n        self.j=0\n        self.res=0\n        self.str1=\"\"\n        if self.L is None:\n            raise StopIteration\n        else:\n            try:\n                for self.i in self.L:\n                    self.j+=1\n                    self.res=self.op (self.res,self.i)\n                    if(self.j%self.n==0):\n                        break\n                if((self.res==0)):\n                    raise StopIteration\n            except TypeError:\n                for self.i in self.L:\n                    self.str1 = self.op(self.str1,self.i)\n                    print(self.j)\n                    self.j+=1\n                    \n                    if(self.j%self.n==0):\n                        break\n                if((self.str1==\"\")):\n                    raise StopIteration\n                return self.str1\n            \n            return self.res\n\n\n\"\"\"iSequence = apply2nextN(lambda a,b:a+b, 3, iter(range(1,7)))\nprint(iSequence.__next__())\nprint(iSequence.__next__())\nprint(iSequence.__next__())\nprint(iSequence.__next__())\n\nstrIter =iter('aaaabbbbccccddddeeeeffffgggghhhhjjjjkkkkllllmmmm')\niSequence = apply2nextN(lambda a,b:a+b, 4, strIter)\nprint(iSequence.__next__()) # returns 'aaaa'\nprint(iSequence.__next__()) # returns 'bbbb'\nprint(iSequence.__next__()) # returns 'cccc'\n\niSequence2 = apply2nextN(lambda  a,b: a-b, 4, iter(range(1,10)))\nprint(iSequence2.__next__())\nprint(iSequence2.__next__())\nprint(iSequence2.__next__())\n\niSequence2 = apply2nextN(lambda  a,b: (a-b)*2, 4, iter(range(1,15)))\nprint(iSequence2.__next__())\nprint(iSequence2.__next__())\nprint(iSequence2.__next__())\nprint(iSequence2.__next__())\"\"\"\n", "text_preview": "# CptS 355 - Fall 2020 - Assignment 3 # <NAME> debugging = False def debug(*s): if debugging: print(*s) ## problem 1-a) getNumCases - 10% def getNumCases(data,counties,months): count=0 for x1,y1 in data.items(): for i in range(0,len(countie", "input_sha256": "9f318804686cf7bea4ae8c96bb3a58e2c6c92e381710b213366cd92298b6fdb2", "text_sha256": "3fbfd5b934c2d76984809cf2706b3b21c6381da8bee94fa5c4bba702ec985473", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12081, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.342244723676323, "noise_severity": 0.15061986283135054, "missing_content_severity": 0.08738023030738934, "factual_density": 3.0530491447706596, "pretraining_utility": 0.3929782370303691, "math_density": 2.885250670665433, "code_density": 3.678497653497139, "reasoning_density": 1.4265891004744706, "education_level": 1.9257882568046765, "creative_writing_quality": -0.603416272370923}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.06160254209756666, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9493927855500861, "secondary": 0.748732598569938, "tertiary": 0.8130135120630511, "structured_records": 0.6454591289316565, "procedural_steps": 0.009615671454354529, "worked_solution": 0.16202234995783082, "contextual_dialogue": 7.843673124626409e-06, "content_quality": 0.42227479228365483, "noise_severity": 0.8744834476405412, "missing_content_severity": 0.927183141410509, "factual_density": 0.2108742873088829, "pretraining_utility": 0.4487669612568407, "math_density": 0.07104222555452733, "code_density": 0.7320813779142825, "reasoning_density": 0.0, "education_level": 0.794826878028561, "creative_writing_quality": 0.998532916848501, "primary_topic": 0.9702735874047728, "secondary_topic": 0.729401154919398}, "probability_margin": {"primary": 0.9276141889869498, "secondary": 0.625189835433773, "tertiary": 0.7197312602578755, "education_level": 0.7092143956702008, "creative_writing_quality": 0.9983333249546311, "primary_topic": 0.9555087759678472, "secondary_topic": 0.6173605414419883}, "raw_probabilities": {"primary": {"Academic Writing": 0.004436990353115929, "Content Listing": 9.72149124742501e-05, "Creative Writing": 0.00011925056980504517, "Customer Support Page": 3.658482327801393e-05, "Q&A Forum": 9.246987552581267e-05, "Discussion Thread": 3.826996995121161e-05, "Comment Section": 6.338933331621321e-05, "Missing Content": 0.0001266367427942216, "Knowledge Article": 0.00013816372753780445, "Legal Notices": 6.381495298803875e-05, "News Article": 3.1232134946957555e-05, "Organizational Announcement": 5.037160723797584e-05, "Nonfiction Writing": 6.370592661254815e-05, "Organizational About Page": 5.1356477432018996e-05, "Personal About Page": 6.315280082447246e-05, "Personal Blog": 3.934818549250776e-05, "Product Page": 2.9837814881939384e-05, "Spam / Ads": 5.076999317729806e-05, "Structured Data": 0.0029114481825010534, "Technical Writing": 0.9515014194854992, "Transcript / Interview": 8.575995625467178e-05, "Tutorial / How-To Guide": 0.023887230498549294, "User Reviews": 3.449463157755725e-05, "Other / Unknown": 0.015987087044226085}, "secondary": {"Academic Writing": 0.03551472038968337, "Content Listing": 0.0006779780426176099, "Creative Writing": 0.000919215063756119, "Customer Support Page": 0.00029222694119135676, "Q&A Forum": 0.0004989432674026839, "Discussion Thread": 0.00026362387719200983, "Comment Section": 0.0003934796689367522, "Missing Content": 0.001045810021265003, "Knowledge Article": 0.0008525967179181849, "Legal Notices": 0.00040343492143077677, "News Article": 0.0003948622671786609, "Organizational Announcement": 0.0003549619634737362, "Nonfiction Writing": 0.0004155149253265565, "Organizational About Page": 0.00034156157580283043, "Personal About Page": 0.00035412697663052284, "Personal Blog": 0.0003160238928149728, "Product Page": 0.00021929105453475178, "Spam / Ads": 0.0002995665866573957, "Structured Data": 0.13401223819575087, "Technical Writing": 0.036399218272802084, "Transcript / Interview": 0.0005661040808281546, "Tutorial / How-To Guide": 0.02604966801350913, "User Reviews": 0.0002127596537724717, "NONE": 0.759202073629524}, "tertiary": {"Academic Writing": 0.03277112002773552, "Content Listing": 0.0004517433334291038, "Creative Writing": 0.0007345805990696543, "Customer Support Page": 0.00021133012900892374, "Q&A Forum": 0.0003488973064748427, "Discussion Thread": 0.00019168866924401716, "Comment Section": 0.00029769473399173196, "Missing Content": 0.0012287130703546607, "Knowledge Article": 0.0009888275545214657, "Legal Notices": 0.0003263529348423531, "News Article": 0.00036520910208089194, "Organizational Announcement": 0.0002737521030572015, "Nonfiction Writing": 0.0003757286304316924, "Organizational About Page": 0.0002960139597330763, "Personal About Page": 0.00027012939555001324, "Personal Blog": 0.0002131934725612083, "Product Page": 0.000172578879205396, "Spam / Ads": 0.00023647625098252784, "Structured Data": 0.10107335546921514, "Technical Writing": 0.017093777539240088, "Transcript / Interview": 0.00041923453214801174, "Tutorial / How-To Guide": 0.020710355429445267, "User Reviews": 0.0001446311505865841, "NONE": 0.8208046157270906}, "structured_records": {"false": 0.35454087106834353, "true": 0.6454591289316565}, "procedural_steps": {"false": 0.9903843285456455, "true": 0.009615671454354529}, "worked_solution": {"false": 0.8379776500421692, "true": 0.16202234995783082}, "contextual_dialogue": {"false": 0.9999921563268753, "true": 7.843673124626409e-06}, "content_quality": {"-2": 0.026279867044112795, "-1": 0.12295302870341997, "0": 0.398431651169623, "1": 0.38691341969772003, "2": 0.06542203338512427}, "noise_severity": {"0": 0.8670644371840956, "1": 0.1191239066897762, "2": 0.010992418104517204, "3": 0.0017658321539039017, "4": 0.001053405867707055}, "missing_content_severity": {"0": 0.9322918681257797, "1": 0.052385558909458896, "2": 0.012055493776746253, "3": 0.0021846329076221946, "4": 0.001082446280392835}, "factual_density": {"0": 0.01285850216429125, "1": 0.05798000819198925, "2": 0.17846483733860044, "3": 0.36464714731900694, "4": 0.3860495049861122}, "pretraining_utility": {"-2": 0.012158547498985534, "-1": 0.10993360973273991, "0": 0.42325685004632263, "1": 0.3820730436828238, "2": 0.07257794903912815}, "math_density": {"0": 0.05749143153089665, "1": 0.08268934367857382, "2": 0.19053159348151047, "3": 0.25565238521223815, "4": 0.41363524609678093}, "code_density": {"0": 0.0003432672329440326, "1": 0.0007571653194449439, "2": 0.030234350868793797, "3": 0.2573890798751624, "4": 0.7112761367036549}, "reasoning_density": {"0": 0.4650862449407242, "1": 0.07211104929667607, "2": 0.18509752950385752, "3": 0.12653771286488927, "4": 0.15116746339385284}, "education_level": {"none": 0.0012395723077288086, "0": 0.0021685540971403275, "1": 0.1198080026869334, "2": 0.8290223983571342, "3": 0.04549758659446261, "4": 0.0022638859566005156}, "creative_writing_quality": {"not_applicable": 0.9987774307070841, "-2": 0.00024571266958151283, "-1": 0.00040851396116058124, "0": 0.0004441057524530665, "1": 8.62527245642233e-05, "2": 3.798418515647155e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.5040018434686843e-05, "Art & Design": 8.314322513798443e-05, "Law & Crime": 0.00013978239819382343, "Education & Careers": 0.005155786735220315, "Entertainment & Media": 6.075702086938383e-05, "Fashion & Beauty": 6.710377155625102e-05, "Business, Economics & Finance": 0.00014279487575382315, "Food & Dining": 8.934068526774075e-05, "Games & Gaming": 8.530795234080852e-05, "Electronics & Hardware": 9.632984461693789e-05, "Health, Healthcare & Wellness": 0.003331071318486347, "History & Geography": 0.0006025233729982214, "Home, Pets & Hobbies": 4.532244187813106e-05, "Industry & Production": 5.5813439472846036e-05, "Language, Literature & Philosophy": 0.0008070912184160834, "Politics & Government": 0.000533138123218399, "Religion & Spirituality": 6.396748194408947e-05, "Science, Mathematics & Engineering": 0.01590813499828053, "Relationships & Community": 4.84521485853566e-05, "Software Usage & Applications": 0.00029734652268817994, "Programming & Software Engineering": 0.9714169109661277, "Sports & Fitness": 9.392644850164085e-05, "Vehicles & Transportation": 4.559195651467914e-05, "Travel & Tourism": 0.0001040783581279847, "Other / Uncovered Subject": 0.0005549276339162865, "Unknown / Insufficient Content": 0.00011631704345161316}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0011046100992954733, "Art & Design": 0.0006206064943963745, "Law & Crime": 0.0014142096730626744, "Education & Careers": 0.042338946495916234, "Entertainment & Media": 0.0005950746794517083, "Fashion & Beauty": 0.0003924785868748718, "Business, Economics & Finance": 0.0010457794398867964, "Food & Dining": 0.0005785745148252129, "Games & Gaming": 0.0004931169227866078, "Electronics & Hardware": 0.0005672172365564037, "Health, Healthcare & Wellness": 0.031934100396217066, "History & Geography": 0.008363451476511267, "Home, Pets & Hobbies": 0.00035926324402284476, "Industry & Production": 0.00027620704458408544, "Language, Literature & Philosophy": 0.0024040783785637, "Politics & Government": 0.0027132414340845937, "Religion & Spirituality": 0.00048538829244485464, "Science, Mathematics & Engineering": 0.12244826136512521, "Relationships & Community": 0.0002490183035886105, "Software Usage & Applications": 0.0005640066792535882, "Programming & Software Engineering": 0.03773109111108047, "Sports & Fitness": 0.00037100434154700306, "Vehicles & Transportation": 0.00022395324155525322, "Travel & Tourism": 0.000756940126690742, "Other / Uncovered Subject": 0.0021605776145648286, "NONE": 0.7398088028071135}}, "probabilities": {"primary": {"Academic Writing": 0.004436990353115929, "Content Listing": 9.72149124742501e-05, "Creative Writing": 0.00011925056980504517, "Customer Support Page": 3.658482327801393e-05, "Q&A Forum": 9.246987552581267e-05, "Discussion Thread": 3.826996995121161e-05, "Comment Section": 6.338933331621321e-05, "Missing Content": 0.0001266367427942216, "Knowledge Article": 0.00013816372753780445, "Legal Notices": 6.381495298803875e-05, "News Article": 3.1232134946957555e-05, "Organizational Announcement": 5.037160723797584e-05, "Nonfiction Writing": 6.370592661254815e-05, "Organizational About Page": 5.1356477432018996e-05, "Personal About Page": 6.315280082447246e-05, "Personal Blog": 3.934818549250776e-05, "Product Page": 2.9837814881939384e-05, "Spam / Ads": 5.076999317729806e-05, "Structured Data": 0.0029114481825010534, "Technical Writing": 0.9515014194854992, "Transcript / Interview": 8.575995625467178e-05, "Tutorial / How-To Guide": 0.023887230498549294, "User Reviews": 3.449463157755725e-05, "Other / Unknown": 0.015987087044226085}, "secondary": {"Academic Writing": 0.03551472038968337, "Content Listing": 0.0006779780426176099, "Creative Writing": 0.000919215063756119, "Customer Support Page": 0.00029222694119135676, "Q&A Forum": 0.0004989432674026839, "Discussion Thread": 0.00026362387719200983, "Comment Section": 0.0003934796689367522, "Missing Content": 0.001045810021265003, "Knowledge Article": 0.0008525967179181849, "Legal Notices": 0.00040343492143077677, "News Article": 0.0003948622671786609, "Organizational Announcement": 0.0003549619634737362, "Nonfiction Writing": 0.0004155149253265565, "Organizational About Page": 0.00034156157580283043, "Personal About Page": 0.00035412697663052284, "Personal Blog": 0.0003160238928149728, "Product Page": 0.00021929105453475178, "Spam / Ads": 0.0002995665866573957, "Structured Data": 0.13401223819575087, "Technical Writing": 0.036399218272802084, "Transcript / Interview": 0.0005661040808281546, "Tutorial / How-To Guide": 0.02604966801350913, "User Reviews": 0.0002127596537724717, "NONE": 0.759202073629524}, "tertiary": {"Academic Writing": 0.03277112002773552, "Content Listing": 0.0004517433334291038, "Creative Writing": 0.0007345805990696543, "Customer Support Page": 0.00021133012900892374, "Q&A Forum": 0.0003488973064748427, "Discussion Thread": 0.00019168866924401716, "Comment Section": 0.00029769473399173196, "Missing Content": 0.0012287130703546607, "Knowledge Article": 0.0009888275545214657, "Legal Notices": 0.0003263529348423531, "News Article": 0.00036520910208089194, "Organizational Announcement": 0.0002737521030572015, "Nonfiction Writing": 0.0003757286304316924, "Organizational About Page": 0.0002960139597330763, "Personal About Page": 0.00027012939555001324, "Personal Blog": 0.0002131934725612083, "Product Page": 0.000172578879205396, "Spam / Ads": 0.00023647625098252784, "Structured Data": 0.10107335546921514, "Technical Writing": 0.017093777539240088, "Transcript / Interview": 0.00041923453214801174, "Tutorial / How-To Guide": 0.020710355429445267, "User Reviews": 0.0001446311505865841, "NONE": 0.8208046157270906}, "structured_records": {"false": 0.35454087106834353, "true": 0.6454591289316565}, "procedural_steps": {"false": 0.9903843285456455, "true": 0.009615671454354529}, "worked_solution": {"false": 0.8379776500421692, "true": 0.16202234995783082}, "contextual_dialogue": {"false": 0.9999921563268753, "true": 7.843673124626409e-06}, "content_quality": {"-2": 0.026279867044112795, "-1": 0.12295302870341997, "0": 0.398431651169623, "1": 0.38691341969772003, "2": 0.06542203338512427}, "noise_severity": {"0": 0.8670644371840956, "1": 0.1191239066897762, "2": 0.010992418104517204, "3": 0.0017658321539039017, "4": 0.001053405867707055}, "missing_content_severity": {"0": 0.9322918681257798, "1": 0.0523855589094589, "2": 0.012055493776746255, "3": 0.002184632907622195, "4": 0.0010824462803928352}, "factual_density": {"0": 0.01285850216429125, "1": 0.05798000819198925, "2": 0.17846483733860044, "3": 0.36464714731900694, "4": 0.3860495049861122}, "pretraining_utility": {"-2": 0.012158547498985534, "-1": 0.10993360973273991, "0": 0.42325685004632263, "1": 0.3820730436828238, "2": 0.07257794903912815}, "math_density": {"0": 0.05749143153089665, "1": 0.08268934367857382, "2": 0.19053159348151047, "3": 0.25565238521223815, "4": 0.41363524609678093}, "code_density": {"0": 0.0003432672329440326, "1": 0.0007571653194449439, "2": 0.030234350868793797, "3": 0.2573890798751624, "4": 0.7112761367036549}, "reasoning_density": {"0": 0.46508624494072426, "1": 0.07211104929667608, "2": 0.18509752950385755, "3": 0.1265377128648893, "4": 0.15116746339385287}, "education_level": {"none": 0.0012395723077288088, "0": 0.002168554097140328, "1": 0.11980800268693341, "2": 0.8290223983571343, "3": 0.04549758659446262, "4": 0.002263885956600516}, "creative_writing_quality": {"not_applicable": 0.9987774307070841, "-2": 0.00024571266958151283, "-1": 0.00040851396116058124, "0": 0.0004441057524530665, "1": 8.62527245642233e-05, "2": 3.798418515647155e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.504001843468685e-05, "Art & Design": 8.314322513798444e-05, "Law & Crime": 0.00013978239819382345, "Education & Careers": 0.005155786735220316, "Entertainment & Media": 6.075702086938384e-05, "Fashion & Beauty": 6.710377155625103e-05, "Business, Economics & Finance": 0.00014279487575382318, "Food & Dining": 8.934068526774077e-05, "Games & Gaming": 8.530795234080854e-05, "Electronics & Hardware": 9.63298446169379e-05, "Health, Healthcare & Wellness": 0.0033310713184863472, "History & Geography": 0.0006025233729982215, "Home, Pets & Hobbies": 4.532244187813107e-05, "Industry & Production": 5.581343947284604e-05, "Language, Literature & Philosophy": 0.0008070912184160835, "Politics & Government": 0.0005331381232183991, "Religion & Spirituality": 6.396748194408949e-05, "Science, Mathematics & Engineering": 0.015908134998280532, "Relationships & Community": 4.845214858535661e-05, "Software Usage & Applications": 0.00029734652268818, "Programming & Software Engineering": 0.9714169109661278, "Sports & Fitness": 9.392644850164086e-05, "Vehicles & Transportation": 4.5591956514679146e-05, "Travel & Tourism": 0.00010407835812798471, "Other / Uncovered Subject": 0.0005549276339162866, "Unknown / Insufficient Content": 0.00011631704345161318}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0011046100992954733, "Art & Design": 0.0006206064943963745, "Law & Crime": 0.0014142096730626744, "Education & Careers": 0.042338946495916234, "Entertainment & Media": 0.0005950746794517083, "Fashion & Beauty": 0.0003924785868748718, "Business, Economics & Finance": 0.0010457794398867964, "Food & Dining": 0.0005785745148252129, "Games & Gaming": 0.0004931169227866078, "Electronics & Hardware": 0.0005672172365564037, "Health, Healthcare & Wellness": 0.031934100396217066, "History & Geography": 0.008363451476511267, "Home, Pets & Hobbies": 0.00035926324402284476, "Industry & Production": 0.00027620704458408544, "Language, Literature & Philosophy": 0.0024040783785637, "Politics & Government": 0.0027132414340845937, "Religion & Spirituality": 0.00048538829244485464, "Science, Mathematics & Engineering": 0.12244826136512521, "Relationships & Community": 0.0002490183035886105, "Software Usage & Applications": 0.0005640066792535882, "Programming & Software Engineering": 0.03773109111108047, "Sports & Fitness": 0.00037100434154700306, "Vehicles & Transportation": 0.00022395324155525322, "Travel & Tourism": 0.000756940126690742, "Other / Uncovered Subject": 0.0021605776145648286, "NONE": 0.7398088028071135}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10158, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10158, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:12.189544+00:00", "scoring_key_slot": "primary", "dataset_uid": "a30510e1-685d-43b0-99d9-ba55caf7b95f", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "28d9957405d25e68e7ee804a08fc876a36c17e8baf8630cbc704d8ec4f3ddd75", "source_metadata": {"uuid": "a30510e1-685d-43b0-99d9-ba55caf7b95f", "repo_name": "aditjain588/CPT_S-355-Programming-Language-Design", "relative_path": "Hw 3 Python/HW3.py", "category": "TOOL", "algo_rel_score": 0.9970376491546631, "quality_score": 4.682318687438965, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 35, "repo_created_at": "2020-11-23T12:47:35.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "633415925f90206bfd614889514a187c7d243233", "size_bytes": 5020, "file_timestamp": 1618589647, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "893b53f6b4e87305700db21e575678e51d7a0c53"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 35, "repo_created_at": "2020-11-23T12:47:35.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "633415925f90206bfd614889514a187c7d243233", "size_bytes": 5020, "file_timestamp": 1618589647, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "893b53f6b4e87305700db21e575678e51d7a0c53", "url": "https://github.com/aditjain588/CPT_S-355-Programming-Language-Design/blob/893b53f6b4e87305700db21e575678e51d7a0c53/Hw%203%20Python/HW3.py"}, "gpt4o_tokens": 1407, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.342244723676323, "noise_severity": 0.15061986283135054, "missing_content_severity": 0.08738023030738934, "factual_density": 3.0530491447706596, "pretraining_utility": 0.3929782370303691, "math_density": 2.885250670665433, "code_density": 3.678497653497139, "reasoning_density": 1.4265891004744706, "education_level": 1.9257882568046765, "creative_writing_quality": -0.603416272370923}, "score_applicability": {"education_level": 0.9987604276922711, "creative_writing_quality": 0.0012225692929158554}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/claude89757/crawler_on_airflow/blob/d4a41a49c48cbc7fb7afb792a6375a83431bffae/scf/get_templates.py", "id": "python-0-e660458b2d9a3fe58699911fc1492720", "text": "#!/usr/bin/env python3\n# -*- coding: utf8 -*-\n\"\"\"\n获取回复模板数据\n\nAuthor: by cursor\nDate: 2025-05-22\n\"\"\"\n\nimport json\nimport os\nimport pymysql\nimport logging\nfrom datetime import datetime\n\n# 配置日志\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\ndef get_db_connection():\n    \"\"\"\n    获取数据库连接\n    \"\"\"\n    try:\n        # 从环境变量获取数据库连接信息\n        db_name = os.environ.get('DB_NAME')\n        db_ip = os.environ.get('DB_IP')\n        db_port = int(os.environ.get('DB_PORT', 3306))\n        db_user = os.environ.get('DB_USER')\n        db_password = <PASSWORD>.get('DB_PASSWORD')\n        \n        # 创建数据库连接\n        connection = pymysql.connect(\n            host=db_ip,\n            port=db_port,\n            user=db_user,\n            password=db_password,\n            database=db_name,\n            charset='utf8mb4',\n            cursorclass=pymysql.cursors.DictCursor\n        )\n        \n        return connection\n    except Exception as e:\n        logger.error(f\"数据库连接失败: {str(e)}\")\n        raise e\n\n\ndef get_reply_templates(email=None):\n    \"\"\"\n    获取用户的回复模板\n    \n    Args:\n        email: 用户邮箱，如果为空，返回所有模板\n        \n    Returns:\n        list: 回复模板列表\n    \"\"\"\n    # 打印email参数\n    print(f\"get_reply_templates called with email: {email}\")\n    logger.info(f\"get_reply_templates called with email: {email}\")\n    \n    try:\n        conn = get_db_connection()\n        cursor = conn.cursor()\n        \n        # 查询用户的回复模板\n        if email:\n            # 如果指定了email，只查询特定用户的模板\n            query = \"SELECT id, userInfo, content, image_urls, created_at FROM reply_template WHERE userInfo = %s ORDER BY created_at DESC\"\n            cursor.execute(query, (email,))\n        else:\n            # 如果没有指定email，返回所有模板\n            query = \"SELECT id, userInfo, content, image_urls, created_at FROM reply_template ORDER BY created_at DESC\"\n            cursor.execute(query)\n        templates = cursor.fetchall()\n        \n        # 关闭连接\n        cursor.close()\n        conn.close()\n        \n        # 处理日期时间格式，使其可JSON序列化\n        for template in templates:\n            for key, value in template.items():\n                if isinstance(value, datetime):\n                    template[key] = value.strftime('%Y-%m-%d %H:%M:%S')\n        \n        return templates\n    except Exception as e:\n        logger.error(f\"获取回复模板失败: {str(e)}\")\n        return []\n\n\ndef get_template_by_id(template_id):\n    \"\"\"\n    获取指定ID的回复模板\n    \n    Args:\n        template_id: 模板ID\n        \n    Returns:\n        dict: 回复模板信息\n    \"\"\"\n    try:\n        conn = get_db_connection()\n        cursor = conn.cursor()\n        \n        # 查询指定ID的回复模板\n        query = \"SELECT id, userInfo, content, image_urls, created_at FROM reply_template WHERE id = %s\"\n        cursor.execute(query, (template_id,))\n        template = cursor.fetchone()\n        \n        # 关闭连接\n        cursor.close()\n        conn.close()\n        \n        # 处理日期时间格式，使其可JSON序列化\n        if template:\n            for key, value in template.items():\n                if isinstance(value, datetime):\n                    template[key] = value.strftime('%Y-%m-%d %H:%M:%S')\n        \n        return template\n    except Exception as e:\n        logger.error(f\"获取回复模板失败: {str(e)}\")\n        return None\n\n\ndef main_handler(event, context):\n    \"\"\"\n    云函数入口函数，获取回复模板数据\n    \n    Args:\n        event: 触发事件，包含查询参数\n        context: 函数上下文\n        \n    Returns:\n        JSON格式的回复模板列表\n    \"\"\"\n    logger.info(f\"收到请求: {json.dumps(event, ensure_ascii=False)}\")\n    \n    # 解析查询参数\n    query_params = {}\n    if 'queryString' in event:\n        query_params = event['queryString']\n    elif 'body' in event:\n        try:\n            # 尝试解析body为JSON\n            if isinstance(event['body'], str):\n                query_params = json.loads(event['body'])\n            else:\n                query_params = event['body']\n        except:\n            pass\n    \n    # 打印参数信息\n    print(f\"Query parameters: {json.dumps(query_params, ensure_ascii=False)}\")\n    logger.info(f\"Query parameters: {json.dumps(query_params, ensure_ascii=False)}\")\n    \n    try:\n        # 根据参数决定使用哪种查询方式\n        if 'template_id' in query_params:\n            # 按模板ID查询\n            template_id = query_params.get('template_id')\n            template = get_template_by_id(template_id)\n            \n            if template:\n                result = {\n                    \"code\": 0,\n                    \"message\": \"success\",\n                    \"data\": template\n                }\n            else:\n                result = {\n                    \"code\": 1,\n                    \"message\": \"模板不存在\",\n                    \"data\": None\n                }\n        else:\n            # 使用默认查询，带有可选的email参数\n            email = query_params.get('email')\n            templates = get_reply_templates(email)\n            total_count = len(templates)\n            \n            result = {\n                \"code\": 0,\n                \"message\": \"success\",\n                \"data\": {\n                    \"total\": total_count,\n                    \"records\": templates\n                }\n            }\n        \n        return result\n    \n    except Exception as e:\n        logger.error(f\"查询失败: {str(e)}\")\n        return {\n            \"code\": 1,\n            \"message\": f\"查询失败: {str(e)}\",\n            \"data\": None\n        }\n\n\nif __name__ == \"__main__\":\n    # 本地测试用\n    test_event = {\n        'queryString': {\n            'email': '<EMAIL>'\n        }\n    }\n    \n    # 测试获取所有模板\n    # test_event = {\n    #     'queryString': {}\n    # }\n    result = main_handler(test_event, {})\n    print(json.dumps(result, ensure_ascii=False, indent=2))", "text_preview": "#!/usr/bin/env python3 # -*- coding: utf8 -*- \"\"\" 获取回复模板数据 Author: by cursor Date: 2025-05-22 \"\"\" import json import os import pymysql import logging from datetime import datetime # 配置日志 logger = logging.getLogger() logger.setLevel(logging.", "input_sha256": "5ca1e43b81e38fa067a66620188dc99eba6ee3ee6bd50a92d7a1e275b34a9a42", "text_sha256": "e660458b2d9a3fe58699911fc149272041916fedbe22565d3264e8677791eb1f", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12080, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.22682948990426235, "noise_severity": 0.38855187335302077, "missing_content_severity": 0.5688637137167083, "factual_density": 3.060237508998109, "pretraining_utility": 0.46476656875488703, "math_density": 0.028500850091318027, "code_density": 3.7128446789315084, "reasoning_density": 0.4610810727350961, "education_level": 2.0756946183994582, "creative_writing_quality": -0.27791450795859113}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.010661208568804984, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9927296796059922, "secondary": 0.9095372656051743, "tertiary": 0.9041986396255599, "structured_records": 0.013903431432717842, "procedural_steps": 0.0195456347027622, "worked_solution": 0.009473463088048875, "contextual_dialogue": 7.974554125924748e-06, "content_quality": 0.1983281218603592, "noise_severity": 0.676206772205816, "missing_content_severity": 0.5259469052360763, "factual_density": 0.48337835085099246, "pretraining_utility": 0.4583257786598952, "math_density": 0.9762492915905683, "code_density": 0.7607038991095906, "reasoning_density": 0.6157657727207533, "education_level": 0.8249940734504106, "creative_writing_quality": 0.9960168538331821, "primary_topic": 0.9901683734240389, "secondary_topic": 0.9512769159041811}, "probability_margin": {"primary": 0.9907811671434389, "secondary": 0.8837454266041178, "tertiary": 0.8730355605479279, "education_level": 0.7490285348875184, "creative_writing_quality": 0.9948029707581003, "primary_topic": 0.987655359380868, "secondary_topic": 0.9290590937442793}, "raw_probabilities": {"primary": {"Academic Writing": 8.00346014337319e-05, "Content Listing": 5.284936757303881e-05, "Creative Writing": 4.725118369286692e-05, "Customer Support Page": 2.495010006317893e-05, "Q&A Forum": 3.417845936004914e-05, "Discussion Thread": 2.1077472039458145e-05, "Comment Section": 3.155451848246677e-05, "Missing Content": 0.000145000322150607, "Knowledge Article": 0.00016067278687515084, "Legal Notices": 5.258672708762643e-05, "News Article": 1.518496532616034e-05, "Organizational Announcement": 2.8926645365263607e-05, "Nonfiction Writing": 3.117131621755887e-05, "Organizational About Page": 3.096892392106268e-05, "Personal About Page": 3.759639611241177e-05, "Personal Blog": 2.3231655600004553e-05, "Product Page": 2.224142941892507e-05, "Spam / Ads": 3.17729316593243e-05, "Structured Data": 0.0017109492461293094, "Technical Writing": 0.9930326096224092, "Transcript / Interview": 4.091057075703172e-05, "Tutorial / How-To Guide": 0.0022514424789703306, "User Reviews": 1.796246138796075e-05, "Other / Unknown": 0.002074875817967025}, "secondary": {"Academic Writing": 0.0004151464119439401, "Content Listing": 0.00034144339307637535, "Creative Writing": 0.0003982191254720243, "Customer Support Page": 0.00019681229394645283, "Q&A Forum": 0.00016168785535255957, "Discussion Thread": 0.00013062457580363278, "Comment Section": 0.0003735911550068447, "Missing Content": 0.024493593857762102, "Knowledge Article": 0.0026413767266479965, "Legal Notices": 0.0006670709128945239, "News Article": 0.00024289565790316155, "Organizational Announcement": 0.0002324885885970631, "Nonfiction Writing": 0.0002587947015723669, "Organizational About Page": 0.00024284863885450233, "Personal About Page": 0.0002972803084223315, "Personal Blog": 0.00020824139462371626, "Product Page": 0.00013711530183569827, "Spam / Ads": 0.00025669639613165184, "Structured Data": 0.029561119600840968, "Technical Writing": 0.018652323034570727, "Transcript / Interview": 0.0002779861114156837, "Tutorial / How-To Guide": 0.006396205525312152, "User Reviews": 0.00010989222705481593, "NONE": 0.9133065462049588}, "tertiary": {"Academic Writing": 0.0003458935055349817, "Content Listing": 0.0003022610139242966, "Creative Writing": 0.0003486660630531283, "Customer Support Page": 0.00019796897935067833, "Q&A Forum": 0.0001263722681640626, "Discussion Thread": 0.00011222858790378759, "Comment Section": 0.00034853042378471045, "Missing Content": 0.035154802426567086, "Knowledge Article": 0.0035741098820633683, "Legal Notices": 0.0008733414148600524, "News Article": 0.00020437964783756715, "Organizational Announcement": 0.000225460136417522, "Nonfiction Writing": 0.00028637127132825706, "Organizational About Page": 0.00025532696360529116, "Personal About Page": 0.0003097602004483641, "Personal Blog": 0.00018335146647163286, "Product Page": 0.00014426028771023257, "Spam / Ads": 0.0002309974673565635, "Structured Data": 0.024747257464857033, "Technical Writing": 0.01595170580414991, "Transcript / Interview": 0.00024496034514807853, "Tutorial / How-To Guide": 0.007542421517711388, "User Reviews": 9.92098872570925e-05, "NONE": 0.908190362974495}, "structured_records": {"false": 0.9860965685672821, "true": 0.013903431432717842}, "procedural_steps": {"false": 0.9804543652972378, "true": 0.0195456347027622}, "worked_solution": {"false": 0.9905265369119511, "true": 0.009473463088048875}, "contextual_dialogue": {"false": 0.9999920254458741, "true": 7.974554125924748e-06}, "content_quality": {"-2": 0.04628746646661877, "-1": 0.2083340349961147, "0": 0.31205791253956755, "1": 0.33890271416178336, "2": 0.09441787183591561}, "noise_severity": {"0": 0.7014613901302712, "1": 0.2318547617346172, "2": 0.04837782841348589, "3": 0.013282624095071532, "4": 0.005023395626554318}, "missing_content_severity": {"0": 0.5476599583858031, "1": 0.3535803335529052, "2": 0.08488542060071226, "3": 0.00998461087994004, "4": 0.0038896765806396387}, "factual_density": {"0": 0.008508031937505873, "1": 0.02960427684850502, "2": 0.1951215854808226, "3": 0.4266743617447078, "4": 0.34009174398845876}, "pretraining_utility": {"-2": 0.011690066335941775, "-1": 0.07724337802769395, "0": 0.4030642933634061, "1": 0.4506144450914518, "2": 0.05738781718150635}, "math_density": {"0": 0.9765059172283127, "1": 0.02086546777955712, "2": 0.0011642329695764956, "3": 0.0005506117176067158, "4": 0.0009137703049469424}, "code_density": {"0": 0.0005138222443766581, "1": 0.0009870205029164332, "2": 0.032393109235321904, "3": 0.21735275211159155, "4": 0.7487532959057934}, "reasoning_density": {"0": 0.7345508884276093, "1": 0.10329425140196245, "2": 0.1358795474866598, "3": 0.01907352437525981, "4": 0.007201788308508645}, "education_level": {"none": 0.0012799223115082133, "0": 0.0020479264273295834, "1": 0.033398021920216535, "2": 0.8541617278753422, "3": 0.10513319298782373, "4": 0.003979208477779815}, "creative_writing_quality": {"not_applicable": 0.9966807115276517, "-2": 0.00020572306455012316, "-1": 0.0009130094390256323, "0": 0.001877740769551378, "1": 0.00024365325288142937, "2": 7.916194633959379e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00014958587214454673, "Art & Design": 0.00015800185302026514, "Law & Crime": 0.0001380645007873031, "Education & Careers": 0.00015152562666652936, "Entertainment & Media": 0.00016634043631524452, "Fashion & Beauty": 0.00011866718273387172, "Business, Economics & Finance": 0.0005262054398874415, "Food & Dining": 9.689351959183029e-05, "Games & Gaming": 0.00016087533929970787, "Electronics & Hardware": 0.0002850392644225951, "Health, Healthcare & Wellness": 0.00011167621844824953, "History & Geography": 0.00010262313664886366, "Home, Pets & Hobbies": 9.481309084205697e-05, "Industry & Production": 0.0001360341071913053, "Language, Literature & Philosophy": 0.0005830281893928191, "Politics & Government": 8.535742056475615e-05, "Religion & Spirituality": 0.00014106430332778335, "Science, Mathematics & Engineering": 0.0004320175169574063, "Relationships & Community": 0.0003115604281648176, "Software Usage & Applications": 0.0028911535268616438, "Programming & Software Engineering": 0.9905465129077297, "Sports & Fitness": 0.0001558600128646683, "Vehicles & Transportation": 7.480809468745634e-05, "Travel & Tourism": 0.00015189145122750921, "Other / Uncovered Subject": 0.0018689989613272465, "Unknown / Insufficient Content": 0.00036140159889433356}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0003945018767195604, "Art & Design": 0.00041301629568021506, "Law & Crime": 0.0003926282185326199, "Education & Careers": 0.0002867725683604958, "Entertainment & Media": 0.00031700008146907796, "Fashion & Beauty": 0.00016443889142182772, "Business, Economics & Finance": 0.0009785520940817547, "Food & Dining": 0.00013629661502517163, "Games & Gaming": 0.00026262027996771435, "Electronics & Hardware": 0.0003167015350829158, "Health, Healthcare & Wellness": 0.00020468556799105506, "History & Geography": 0.00013845399930289954, "Home, Pets & Hobbies": 0.0001349716852610138, "Industry & Production": 0.0001503802880242537, "Language, Literature & Philosophy": 0.0006593168477245275, "Politics & Government": 0.0001454330411052305, "Religion & Spirituality": 0.00018533828237217025, "Science, Mathematics & Engineering": 0.0007194625826714449, "Relationships & Community": 0.00020270443633849165, "Software Usage & Applications": 0.014684617741732185, "Programming & Software Engineering": 0.024091786932817924, "Sports & Fitness": 0.00012977568904502953, "Vehicles & Transportation": 7.947188904459653e-05, "Travel & Tourism": 0.00019094009854823165, "Other / Uncovered Subject": 0.0014692517845823615, "NONE": 0.9531508806770972}}, "probabilities": {"primary": {"Academic Writing": 8.003460143373192e-05, "Content Listing": 5.284936757303882e-05, "Creative Writing": 4.725118369286693e-05, "Customer Support Page": 2.4950100063178938e-05, "Q&A Forum": 3.417845936004915e-05, "Discussion Thread": 2.107747203945815e-05, "Comment Section": 3.1554518482466775e-05, "Missing Content": 0.00014500032215060703, "Knowledge Article": 0.00016067278687515086, "Legal Notices": 5.258672708762644e-05, "News Article": 1.5184965326160343e-05, "Organizational Announcement": 2.8926645365263614e-05, "Nonfiction Writing": 3.1171316217558876e-05, "Organizational About Page": 3.096892392106269e-05, "Personal About Page": 3.7596396112411776e-05, "Personal Blog": 2.323165560000456e-05, "Product Page": 2.2241429418925073e-05, "Spam / Ads": 3.177293165932431e-05, "Structured Data": 0.0017109492461293099, "Technical Writing": 0.9930326096224095, "Transcript / Interview": 4.0910570757031725e-05, "Tutorial / How-To Guide": 0.002251442478970331, "User Reviews": 1.7962461387960754e-05, "Other / Unknown": 0.0020748758179670256}, "secondary": {"Academic Writing": 0.0004151464119439401, "Content Listing": 0.00034144339307637535, "Creative Writing": 0.0003982191254720243, "Customer Support Page": 0.00019681229394645283, "Q&A Forum": 0.00016168785535255957, "Discussion Thread": 0.00013062457580363278, "Comment Section": 0.0003735911550068447, "Missing Content": 0.024493593857762102, "Knowledge Article": 0.0026413767266479965, "Legal Notices": 0.0006670709128945239, "News Article": 0.00024289565790316155, "Organizational Announcement": 0.0002324885885970631, "Nonfiction Writing": 0.0002587947015723669, "Organizational About Page": 0.00024284863885450233, "Personal About Page": 0.0002972803084223315, "Personal Blog": 0.00020824139462371626, "Product Page": 0.00013711530183569827, "Spam / Ads": 0.00025669639613165184, "Structured Data": 0.029561119600840968, "Technical Writing": 0.018652323034570727, "Transcript / Interview": 0.0002779861114156837, "Tutorial / How-To Guide": 0.006396205525312152, "User Reviews": 0.00010989222705481593, "NONE": 0.9133065462049588}, "tertiary": {"Academic Writing": 0.0003458935055349817, "Content Listing": 0.0003022610139242966, "Creative Writing": 0.0003486660630531283, "Customer Support Page": 0.00019796897935067833, "Q&A Forum": 0.0001263722681640626, "Discussion Thread": 0.00011222858790378759, "Comment Section": 0.00034853042378471045, "Missing Content": 0.035154802426567086, "Knowledge Article": 0.0035741098820633683, "Legal Notices": 0.0008733414148600524, "News Article": 0.00020437964783756715, "Organizational Announcement": 0.000225460136417522, "Nonfiction Writing": 0.00028637127132825706, "Organizational About Page": 0.00025532696360529116, "Personal About Page": 0.0003097602004483641, "Personal Blog": 0.00018335146647163286, "Product Page": 0.00014426028771023257, "Spam / Ads": 0.0002309974673565635, "Structured Data": 0.024747257464857033, "Technical Writing": 0.01595170580414991, "Transcript / Interview": 0.00024496034514807853, "Tutorial / How-To Guide": 0.007542421517711388, "User Reviews": 9.92098872570925e-05, "NONE": 0.908190362974495}, "structured_records": {"false": 0.9860965685672821, "true": 0.013903431432717842}, "procedural_steps": {"false": 0.9804543652972378, "true": 0.0195456347027622}, "worked_solution": {"false": 0.9905265369119511, "true": 0.009473463088048875}, "contextual_dialogue": {"false": 0.9999920254458741, "true": 7.974554125924748e-06}, "content_quality": {"-2": 0.04628746646661877, "-1": 0.2083340349961147, "0": 0.31205791253956755, "1": 0.33890271416178336, "2": 0.09441787183591561}, "noise_severity": {"0": 0.7014613901302711, "1": 0.23185476173461714, "2": 0.048377828413485875, "3": 0.013282624095071529, "4": 0.005023395626554317}, "missing_content_severity": {"0": 0.547659958385803, "1": 0.35358033355290513, "2": 0.08488542060071225, "3": 0.009984610879940039, "4": 0.003889676580639638}, "factual_density": {"0": 0.008508031937505873, "1": 0.02960427684850502, "2": 0.1951215854808226, "3": 0.4266743617447078, "4": 0.34009174398845876}, "pretraining_utility": {"-2": 0.011690066335941775, "-1": 0.07724337802769395, "0": 0.4030642933634061, "1": 0.4506144450914518, "2": 0.05738781718150635}, "math_density": {"0": 0.9765059172283127, "1": 0.02086546777955712, "2": 0.0011642329695764956, "3": 0.0005506117176067158, "4": 0.0009137703049469424}, "code_density": {"0": 0.0005138222443766581, "1": 0.0009870205029164332, "2": 0.032393109235321904, "3": 0.21735275211159155, "4": 0.7487532959057934}, "reasoning_density": {"0": 0.7345508884276093, "1": 0.10329425140196245, "2": 0.1358795474866598, "3": 0.01907352437525981, "4": 0.007201788308508645}, "education_level": {"none": 0.0012799223115082133, "0": 0.0020479264273295834, "1": 0.033398021920216535, "2": 0.8541617278753422, "3": 0.10513319298782373, "4": 0.003979208477779815}, "creative_writing_quality": {"not_applicable": 0.9966807115276518, "-2": 0.0002057230645501232, "-1": 0.0009130094390256324, "0": 0.0018777407695513783, "1": 0.0002436532528814294, "2": 7.91619463395938e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00014958587214454675, "Art & Design": 0.00015800185302026516, "Law & Crime": 0.00013806450078730312, "Education & Careers": 0.0001515256266665294, "Entertainment & Media": 0.00016634043631524455, "Fashion & Beauty": 0.00011866718273387173, "Business, Economics & Finance": 0.0005262054398874417, "Food & Dining": 9.68935195918303e-05, "Games & Gaming": 0.0001608753392997079, "Electronics & Hardware": 0.00028503926442259515, "Health, Healthcare & Wellness": 0.00011167621844824954, "History & Geography": 0.00010262313664886368, "Home, Pets & Hobbies": 9.481309084205698e-05, "Industry & Production": 0.00013603410719130533, "Language, Literature & Philosophy": 0.0005830281893928192, "Politics & Government": 8.535742056475616e-05, "Religion & Spirituality": 0.00014106430332778337, "Science, Mathematics & Engineering": 0.00043201751695740633, "Relationships & Community": 0.0003115604281648177, "Software Usage & Applications": 0.002891153526861644, "Programming & Software Engineering": 0.9905465129077298, "Sports & Fitness": 0.00015586001286466833, "Vehicles & Transportation": 7.480809468745635e-05, "Travel & Tourism": 0.00015189145122750924, "Other / Uncovered Subject": 0.0018689989613272467, "Unknown / Insufficient Content": 0.0003614015988943336}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0003945018767195604, "Art & Design": 0.00041301629568021506, "Law & Crime": 0.0003926282185326199, "Education & Careers": 0.0002867725683604958, "Entertainment & Media": 0.00031700008146907796, "Fashion & Beauty": 0.00016443889142182772, "Business, Economics & Finance": 0.0009785520940817547, "Food & Dining": 0.00013629661502517163, "Games & Gaming": 0.00026262027996771435, "Electronics & Hardware": 0.0003167015350829158, "Health, Healthcare & Wellness": 0.00020468556799105506, "History & Geography": 0.00013845399930289954, "Home, Pets & Hobbies": 0.0001349716852610138, "Industry & Production": 0.0001503802880242537, "Language, Literature & Philosophy": 0.0006593168477245275, "Politics & Government": 0.0001454330411052305, "Religion & Spirituality": 0.00018533828237217025, "Science, Mathematics & Engineering": 0.0007194625826714449, "Relationships & Community": 0.00020270443633849165, "Software Usage & Applications": 0.014684617741732185, "Programming & Software Engineering": 0.024091786932817924, "Sports & Fitness": 0.00012977568904502953, "Vehicles & Transportation": 7.947188904459653e-05, "Travel & Tourism": 0.00019094009854823165, "Other / Uncovered Subject": 0.0014692517845823615, "NONE": 0.9531508806770972}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9945, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9945, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:13.554929+00:00", "scoring_key_slot": "primary", "dataset_uid": "57c63ae1-5a65-4b94-bd09-3420051c3df0", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "00dfa266d06ad297fab510ae3a9900051cc6f298cb5eed59f25a4fd699bae7c9", "source_metadata": {"uuid": "57c63ae1-5a65-4b94-bd09-3420051c3df0", "repo_name": "claude89757/crawler_on_airflow", "relative_path": "scf/get_templates.py", "category": "TOOL", "algo_rel_score": 0.9949742555618286, "quality_score": 5.758975505828857, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 796, "repo_created_at": "2025-02-22T16:18:04.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 7, "forks": 2, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "0771379cd6f90befdca98c0c0c3852fe32c66a26", "size_bytes": 6169, "file_timestamp": 1753428738, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "d4a41a49c48cbc7fb7afb792a6375a83431bffae"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 796, "repo_created_at": "2025-02-22T16:18:04.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 7, "forks": 2, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "0771379cd6f90befdca98c0c0c3852fe32c66a26", "size_bytes": 6169, "file_timestamp": 1753428738, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "d4a41a49c48cbc7fb7afb792a6375a83431bffae", "url": "https://github.com/claude89757/crawler_on_airflow/blob/d4a41a49c48cbc7fb7afb792a6375a83431bffae/scf/get_templates.py"}, "gpt4o_tokens": 1322, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.22682948990426235, "noise_severity": 0.38855187335302077, "missing_content_severity": 0.5688637137167083, "factual_density": 3.060237508998109, "pretraining_utility": 0.46476656875488703, "math_density": 0.028500850091318027, "code_density": 3.7128446789315084, "reasoning_density": 0.4610810727350961, "education_level": 2.0756946183994582, "creative_writing_quality": -0.27791450795859113}, "score_applicability": {"education_level": 0.9987200776884918, "creative_writing_quality": 0.003319288472348157}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/medragneel/bioinf/blob/aa82b184598a737e4014d219baae8184009502c8/motif_finder.py", "id": "python-0-d4bd5b67051baaf089b48acb3571c216", "text": "from myseq import MySeq\nclass MotifFinder:\n    def __init__(self,size=8,seqs=None):\n        self.motif_size = size\n        if seqs != None:\n            self.seqs = seqs\n            self.alphabet = seqs[0].alphabet()\n        else:\n            self.seqs = []\n            self.alphabet = \"ACGT\" \n    def __len__(self):\n        return len(self.seqs)\n    def __getItem__(self,n):\n        return self.seqs[n]\n    def seq_size(self,i):\n        return len(self.seqs[i])\n    def read_file(self,file,t):\n        with open(file,\"r\") as f:\n            self.seqs = [ MySeq( s.strip().upper(),t ) for s in f.readlines() ] \n            self.alphabet = self.seqs[0].alphabet()\n            print(self.alphabet)\n    def create_motif_from_indexes(self,indexes):\n        res = [[0]*self.motif_size for _ in range(len(self.alphabet))]\n        for i,val in enumerate(indexes):\n            subseq = self.seqs[i][val:(val + self.motif_size)]\n            for ms in range(self.motif_size):\n                for k in range(len( self.alphabet )):\n                    if subseq[ms] == self.alphabet[k]:\n                        res[k][ms] = res[k][ms] + 1\n        return res\n\n    def score(self,s):\n        score = 0\n        mat = self.create_motif_from_indexes(s)\n        for j in range(len(mat[0])):\n            maxcol = mat[0][j]\n            for i in range(1,len(mat)):\n                if mat[i][j] > maxcol:\n                    maxcol = mat[i][j]\n            score += maxcol\n        return score\n\n    def dup_score(self,s):\n        score = 1.0\n        mat = self.create_motif_from_indexes(s)\n        for j in range(len(mat[0])):\n            maxcol = mat[0][j]\n            for i in range(1,len(mat)):\n                if mat[i][j] > maxcol:\n                    maxcol = mat[i][j]\n            score *= maxcol\n        return score\n         \n    def next_sol(self,s):\n        next_sol = [0] * len(s)\n        pos = len(s) - 1\n        while pos >= 0 and s[pos] == self.seq_size(pos) - self.motif_size:\n             pos -= 1 \n        if pos < 0:\n            next_sol = None\n        else:\n            for i in range(pos):\n                next_sol[i] = s[i]\n            next_sol[pos] = s[pos] + 1\n            for i in range(pos+1,len(s)):\n                next_sol[i] = 0\n                \n        return next_sol\n\n    def exhaustive_search(self):\n        best_score = -1\n        res = []\n        s = [0] * len(self.seqs)\n        while s != None:\n            sc = self.score(s)\n            if sc > best_score:\n                best_score = sc\n                res = s\n            s = self.next_sol(s) \n        return res\n    def next_vertex(self,s):\n        res = []\n        if len(s) < len(self.seqs):\n            for i in range(len(s)):\n                res.append(s[i])\n            res.append(0)\n            #bypass\n        else:\n            pos = len(s) - 1\n            while pos >= 0 and s[pos] == self.seq_size(pos) - self.motif_size :\n                pos -= 1\n            if pos < 0: res = None\n            else:\n                for i in range(pos): res.append(s[i])\n                res.append(s[pos] + 1)\n        return res\n\n    def bypass(self,s):\n        res = []\n        pos = len(s) - 1\n        while pos >= 0 and s[pos] == self.seq_size(pos) - self.motif_size :\n            pos -= 1\n        if pos < 0: res = None\n        else:\n            for i in range(pos): res.append(s[i])\n            res.append(s[pos] + 1)\n        return res\n    def branch_and_bound(self):\n        best_score = -1\n        best_motif = None\n        size = len(self.seqs)\n        s = [0] * size\n        while s != None:\n            if len(s) < size:\n                opt_score = self.score(s) + (size-len(s)) * self.motif_size\n                if opt_score < best_score: s = self.bypass(s)\n                else: s = self.next_vertex(s)\n            else:\n                sc = self.score(s)\n                if sc > best_score:\n                    best_score = sc\n                    best_motif = s\n                s = self.next_vertex(s)\n        return best_motif\n\n    def heuristic_consensus(self):\n        res = [0] * len(self.seqs)\n        max_score = -1\n        partial = [0,0]\n        for i in range(self.seq_size(0) - self.motif_size):\n            for j in range(self.seq_size(1) - self.motif_size):\n                partial[0] = i \n                partial[1] = j\n                sc = self.score(partial)\n                if (sc > max_score):\n                    max_score = sc\n                    res[0] = i\n                    res[1] = j\n        for k in range(2,len(self.seqs)):\n            partial = [0] * (k+1)\n            for j in range(k):\n                partial[j] = res[j]\n            max_score  = -1\n            for i in range(self.seq_size(k)-self.motif_size):\n                partial[k] = i\n                sc = self.score(partial)\n                if (sc > max_score):\n                    max_score = sc \n                    res[k] = i\n            return res\n\n\n\n         \n         \n    \ndef test():\n    m = MotifFinder()\n    m.read_file(\"./seqBlast.txt\",\"DNA\")\n    sm = m.create_motif_from_indexes([25,20,2,55,59])\n    print(m.score([25,20,2,55,59]))\n    print(m.dup_score([25,20,2,55,59]))\n    print(sm)\n\n\n    for i in range(len(sm)):\n        print(\"ACGT\"[i],end=\"\\t\")\n        for j in range(len(sm[i])):\n            print(sm[i][j],end=\"\\t\")\n        print()\ndef test2():\n    seq1 = MySeq(\"ATAGAGCTGA\",\"DNA\")\n    seq2 = MySeq(\"ACGTAGATGA\",\"DNA\")\n    seq3 = MySeq(\"AAGATAGGGG\",\"DNA\")\n    mf = MotifFinder(3, [seq1,seq2,seq3])\n    \n    print (\"Exhaustive:\")\n    sol = mf.exhaustive_search()\n    print (\"Solution: \" , sol)\n    print (\"Score: \", mf.score(sol))\n    \n\n    print (\"\\nBranch and Bound:\")\n    sol2 = mf.branch_and_bound()\n    print (\"Solution: \" , sol2)\n    print (\"Score:\" , mf.score(sol2))\n    \n    print (\"\\nHeuristic consensus: \")\n    sol3 = mf.heuristic_consensus()\n    print (\"Solution: \" , sol3)\n    print (\"Score:\" , mf.score(sol3))\ndef test3():\n    mf = MotifFinder()\n    mf.read_file(\"./example_motifs.txt\",\"DNA\")\n    print (\"Branch and Bound:\")\n    sol = mf.branch_and_bound()\n    print (\"Solution: \", sol)\n    print (\"Score:\", mf.score(sol))\n    \n    print (\"\\nHeuristic consensus: \")\n    sol2 = mf.heuristic_consensus()\n    print (\"Solution: \" , sol2)\n    print (\"Score:\" , mf.score(sol2)) \n\n\nif __name__ == \"__main__\":\n    test()\n    test2()\n    test3()\n\n        \n", "text_preview": "from myseq import MySeq class MotifFinder: def __init__(self,size=8,seqs=None): self.motif_size = size if seqs != None: self.seqs = seqs self.alphabet = seqs[0].alphabet() else: self.seqs = [] self.alphabet = \"ACGT\" def __len__(self): retur", "input_sha256": "0e869d66ff3bc19482b1cefcb17edc173815e6db8a3af87dd5108c423eecdd37", "text_sha256": "d4bd5b67051baaf089b48acb3571c2163c0a788cb786e7130b867cc51c0ccb76", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12083, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.19050345759494564, "noise_severity": 0.07621155048001195, "missing_content_severity": 0.07648335626564524, "factual_density": 3.2251188908307333, "pretraining_utility": 0.5430337553790487, "math_density": 2.7884564070734874, "code_density": 3.8651764292584674, "reasoning_density": 1.2874800976320835, "education_level": 2.364517186393129, "creative_writing_quality": -0.38715090223451437}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.5094 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.005199688104227551, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9957226946503089, "secondary": 0.9797703032655622, "tertiary": 0.9851960647656259, "structured_records": 0.006059431585535896, "procedural_steps": 0.02734917861771054, "worked_solution": 0.04607940590379355, "contextual_dialogue": 8.727614904615978e-06, "content_quality": 0.4155201435266489, "noise_severity": 0.9364903745999901, "missing_content_severity": 0.936263869778629, "factual_density": 0.35426574235894404, "pretraining_utility": 0.4490822247720675, "math_density": 0.0, "code_density": 0.8876470243820559, "reasoning_density": 0.0, "education_level": 0.6130811618860218, "creative_writing_quality": 0.9992385179674539, "primary_topic": 0.8304959334303722, "secondary_topic": 0.4898201350990422}, "probability_margin": {"primary": 0.9938366536582768, "secondary": 0.9712558903627784, "tertiary": 0.9793304716595691, "education_level": 0.4258991014499956, "creative_writing_quality": 0.9990815176508265, "primary_topic": 0.6825510274928989, "secondary_topic": 0.2515031259344293}, "raw_probabilities": {"primary": {"Academic Writing": 0.0003263017276485331, "Content Listing": 1.888002348029315e-05, "Creative Writing": 1.8559311769427864e-05, "Customer Support Page": 8.116972168838138e-06, "Q&A Forum": 1.2041186723639809e-05, "Discussion Thread": 7.327250097785066e-06, "Comment Section": 1.1650706980331513e-05, "Missing Content": 3.199720086428417e-05, "Knowledge Article": 0.00015296852853186412, "Legal Notices": 2.4659207967338868e-05, "News Article": 4.988279013940499e-06, "Organizational Announcement": 9.464115654170627e-06, "Nonfiction Writing": 1.971506567470591e-05, "Organizational About Page": 1.3069587367379615e-05, "Personal About Page": 1.2816688657677478e-05, "Personal Blog": 7.99020428945958e-06, "Product Page": 6.6210141318711475e-06, "Spam / Ads": 9.454629904311899e-06, "Structured Data": 0.0005713779193764035, "Technical Writing": 0.995900915706546, "Transcript / Interview": 1.7385339813546128e-05, "Tutorial / How-To Guide": 0.0007431137699112593, "User Reviews": 6.323515157890442e-06, "Other / Unknown": 0.0020642620482692175}, "secondary": {"Academic Writing": 0.0035921730340620598, "Content Listing": 0.00010049040521336382, "Creative Writing": 0.0001096417845995339, "Customer Support Page": 5.515496384871269e-05, "Q&A Forum": 4.74130646422568e-05, "Discussion Thread": 3.3520912278769174e-05, "Comment Section": 5.4346413604130604e-05, "Missing Content": 0.00020735716591161499, "Knowledge Article": 0.0007112146572280903, "Legal Notices": 0.00010134179699911344, "News Article": 5.449229975832302e-05, "Organizational Announcement": 4.92461487059664e-05, "Nonfiction Writing": 8.221444158082225e-05, "Organizational About Page": 6.40953253391632e-05, "Personal About Page": 5.578630304438217e-05, "Personal Blog": 4.437535324875948e-05, "Product Page": 3.111834997294222e-05, "Spam / Ads": 4.8575682765732896e-05, "Structured Data": 0.001756354365058395, "Technical Writing": 0.009357316933385429, "Transcript / Interview": 7.124667991973595e-05, "Tutorial / How-To Guide": 0.002730197023639627, "User Reviews": 2.9119599029144542e-05, "NONE": 0.9806132072961639}, "tertiary": {"Academic Writing": 0.002104312092177615, "Content Listing": 7.781553087521908e-05, "Creative Writing": 9.559683022384613e-05, "Customer Support Page": 3.454922463383859e-05, "Q&A Forum": 3.1773420017950835e-05, "Discussion Thread": 2.5500766304767037e-05, "Comment Section": 4.8548283044120155e-05, "Missing Content": 0.0002329366597872149, "Knowledge Article": 0.0008389011361188691, "Legal Notices": 0.00016746434470841267, "News Article": 4.458362440943922e-05, "Organizational Announcement": 3.599255247620338e-05, "Nonfiction Writing": 8.36127969508818e-05, "Organizational About Page": 5.796040473742165e-05, "Personal About Page": 4.060033697071476e-05, "Personal Blog": 3.5015235913254524e-05, "Product Page": 2.722088879084238e-05, "Spam / Ads": 4.2234483223818445e-05, "Structured Data": 0.0015438997066128653, "Technical Writing": 0.006482423740822374, "Transcript / Interview": 5.283347430673409e-05, "Tutorial / How-To Guide": 0.0020608379787203264, "User Reviews": 2.2491087781725948e-05, "NONE": 0.9858128954003915}, "structured_records": {"false": 0.9939405684144641, "true": 0.006059431585535896}, "procedural_steps": {"false": 0.9726508213822894, "true": 0.02734917861771054}, "worked_solution": {"false": 0.9539205940962064, "true": 0.04607940590379355}, "contextual_dialogue": {"false": 0.9999912723850953, "true": 8.727614904615978e-06}, "content_quality": {"-2": 0.03135975405387891, "-1": 0.19271667697878003, "0": 0.39926301778016265, "1": 0.3073814596928735, "2": 0.069279091494305}, "noise_severity": {"0": 0.9340267661481401, "1": 0.058375670346113, "2": 0.005709283092604586, "3": 0.0011358077038795592, "4": 0.0007524727092627771}, "missing_content_severity": {"0": 0.957384763461424, "1": 0.014621237454492422, "2": 0.022816116661876665, "3": 0.00448164420142776, "4": 0.000696238220779051}, "factual_density": {"0": 0.012032911202877427, "1": 0.025539033795277173, "2": 0.1367538671362634, "3": 0.3766246286993991, "4": 0.449049559166183}, "pretraining_utility": {"-2": 0.009623494685002745, "-1": 0.07593541733008914, "0": 0.3782924687320486, "1": 0.4340810764265758, "2": 0.10206754282628382}, "math_density": {"0": 0.07977484956730882, "1": 0.09869841333819464, "2": 0.15487701460725825, "3": 0.286594925428177, "4": 0.38005479705906137}, "code_density": {"0": 0.0003142293990271158, "1": 0.0006085579060401511, "2": 0.01720224208214357, "3": 0.09733649526301684, "4": 0.8845384753497724}, "reasoning_density": {"0": 0.5597803825751443, "1": 0.04818282861007459, "2": 0.09760252595574903, "3": 0.1336448343256176, "4": 0.1607894285334145}, "education_level": {"none": 0.00082666023408765, "0": 0.0012042119117301288, "1": 0.007503391363116371, "2": 0.6775676349050181, "3": 0.25166853345502255, "4": 0.06122956813102506}, "creative_writing_quality": {"not_applicable": 0.9993654316395449, "-2": 9.851080891910144e-05, "-1": 0.0001623911558901836, "0": 0.0002839139887183796, "1": 6.576575340594515e-05, "2": 2.398665352140326e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0002762764181065825, "Art & Design": 0.0003831164837481346, "Law & Crime": 0.00032129492275275183, "Education & Careers": 0.0002236156860913099, "Entertainment & Media": 0.0002108342761986054, "Fashion & Beauty": 0.0001964889827773941, "Business, Economics & Finance": 0.00025285567890661204, "Food & Dining": 0.0002235741049331495, "Games & Gaming": 0.0002896510567168867, "Electronics & Hardware": 0.0002732515127296867, "Health, Healthcare & Wellness": 0.000899100691446221, "History & Geography": 0.0003957690214892511, "Home, Pets & Hobbies": 0.00018814568166777203, "Industry & Production": 0.00022814139464094832, "Language, Literature & Philosophy": 0.00034476455851771, "Politics & Government": 0.00011295665305314529, "Religion & Spirituality": 0.00020796970556674204, "Science, Mathematics & Engineering": 0.15446429311322832, "Relationships & Community": 0.0001113771233460055, "Software Usage & Applications": 0.0006438145102362972, "Programming & Software Engineering": 0.8370153206061272, "Sports & Fitness": 0.00023747828239528633, "Vehicles & Transportation": 0.00013454169594284045, "Travel & Tourism": 0.00023067132883750654, "Other / Uncovered Subject": 0.0017999213941244758, "Unknown / Insufficient Content": 0.000334775116419353}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009132840897472449, "Art & Design": 0.0006582961405061526, "Law & Crime": 0.0006354216747865753, "Education & Careers": 0.0004120718262250815, "Entertainment & Media": 0.00042173562271815977, "Fashion & Beauty": 0.0004186031279085596, "Business, Economics & Finance": 0.0004503964867447057, "Food & Dining": 0.0003228153247377604, "Games & Gaming": 0.00041352034463269983, "Electronics & Hardware": 0.0005318970900904852, "Health, Healthcare & Wellness": 0.0011517840545515023, "History & Geography": 0.00047980042448272193, "Home, Pets & Hobbies": 0.0003293524820127461, "Industry & Production": 0.00040016423304163113, "Language, Literature & Philosophy": 0.00033648347182504024, "Politics & Government": 0.00024114626303316206, "Religion & Spirituality": 0.00048743427088703554, "Science, Mathematics & Engineering": 0.5094424375952329, "Relationships & Community": 0.00019898623522196846, "Software Usage & Applications": 0.0005394170775968909, "Programming & Software Engineering": 0.21941061474167442, "Sports & Fitness": 0.00047682562025240906, "Vehicles & Transportation": 0.00023111894225172686, "Travel & Tourism": 0.0005263025128818472, "Other / Uncovered Subject": 0.0026307786861529767, "NONE": 0.2579393116608036}}, "probabilities": {"primary": {"Academic Writing": 0.00032630172764853304, "Content Listing": 1.8880023480293148e-05, "Creative Writing": 1.855931176942786e-05, "Customer Support Page": 8.116972168838136e-06, "Q&A Forum": 1.2041186723639806e-05, "Discussion Thread": 7.327250097785065e-06, "Comment Section": 1.165070698033151e-05, "Missing Content": 3.199720086428416e-05, "Knowledge Article": 0.0001529685285318641, "Legal Notices": 2.465920796733886e-05, "News Article": 4.988279013940498e-06, "Organizational Announcement": 9.464115654170625e-06, "Nonfiction Writing": 1.9715065674705908e-05, "Organizational About Page": 1.3069587367379611e-05, "Personal About Page": 1.2816688657677474e-05, "Personal Blog": 7.990204289459579e-06, "Product Page": 6.621014131871146e-06, "Spam / Ads": 9.454629904311897e-06, "Structured Data": 0.0005713779193764034, "Technical Writing": 0.9959009157065458, "Transcript / Interview": 1.7385339813546124e-05, "Tutorial / How-To Guide": 0.0007431137699112591, "User Reviews": 6.32351515789044e-06, "Other / Unknown": 0.002064262048269217}, "secondary": {"Academic Writing": 0.00359217303406206, "Content Listing": 0.00010049040521336383, "Creative Writing": 0.00010964178459953392, "Customer Support Page": 5.51549638487127e-05, "Q&A Forum": 4.7413064642256806e-05, "Discussion Thread": 3.352091227876918e-05, "Comment Section": 5.434641360413061e-05, "Missing Content": 0.000207357165911615, "Knowledge Article": 0.0007112146572280904, "Legal Notices": 0.00010134179699911345, "News Article": 5.449229975832303e-05, "Organizational Announcement": 4.924614870596641e-05, "Nonfiction Writing": 8.221444158082226e-05, "Organizational About Page": 6.409532533916322e-05, "Personal About Page": 5.5786303044382175e-05, "Personal Blog": 4.437535324875949e-05, "Product Page": 3.1118349972942224e-05, "Spam / Ads": 4.85756827657329e-05, "Structured Data": 0.0017563543650583953, "Technical Writing": 0.00935731693338543, "Transcript / Interview": 7.124667991973597e-05, "Tutorial / How-To Guide": 0.0027301970236396273, "User Reviews": 2.9119599029144546e-05, "NONE": 0.980613207296164}, "tertiary": {"Academic Writing": 0.002104312092177615, "Content Listing": 7.781553087521908e-05, "Creative Writing": 9.559683022384613e-05, "Customer Support Page": 3.454922463383859e-05, "Q&A Forum": 3.1773420017950835e-05, "Discussion Thread": 2.5500766304767037e-05, "Comment Section": 4.8548283044120155e-05, "Missing Content": 0.0002329366597872149, "Knowledge Article": 0.0008389011361188691, "Legal Notices": 0.00016746434470841267, "News Article": 4.458362440943922e-05, "Organizational Announcement": 3.599255247620338e-05, "Nonfiction Writing": 8.36127969508818e-05, "Organizational About Page": 5.796040473742165e-05, "Personal About Page": 4.060033697071476e-05, "Personal Blog": 3.5015235913254524e-05, "Product Page": 2.722088879084238e-05, "Spam / Ads": 4.2234483223818445e-05, "Structured Data": 0.0015438997066128653, "Technical Writing": 0.006482423740822374, "Transcript / Interview": 5.283347430673409e-05, "Tutorial / How-To Guide": 0.0020608379787203264, "User Reviews": 2.2491087781725948e-05, "NONE": 0.9858128954003915}, "structured_records": {"false": 0.9939405684144641, "true": 0.006059431585535896}, "procedural_steps": {"false": 0.9726508213822894, "true": 0.02734917861771054}, "worked_solution": {"false": 0.9539205940962064, "true": 0.04607940590379355}, "contextual_dialogue": {"false": 0.9999912723850953, "true": 8.727614904615978e-06}, "content_quality": {"-2": 0.03135975405387891, "-1": 0.19271667697878003, "0": 0.39926301778016265, "1": 0.3073814596928735, "2": 0.069279091494305}, "noise_severity": {"0": 0.9340267661481401, "1": 0.058375670346113, "2": 0.005709283092604586, "3": 0.0011358077038795592, "4": 0.0007524727092627771}, "missing_content_severity": {"0": 0.9573847634614241, "1": 0.014621237454492423, "2": 0.02281611666187667, "3": 0.004481644201427761, "4": 0.0006962382207790511}, "factual_density": {"0": 0.012032911202877427, "1": 0.025539033795277173, "2": 0.1367538671362634, "3": 0.3766246286993991, "4": 0.449049559166183}, "pretraining_utility": {"-2": 0.009623494685002745, "-1": 0.07593541733008914, "0": 0.3782924687320486, "1": 0.4340810764265758, "2": 0.10206754282628382}, "math_density": {"0": 0.07977484956730882, "1": 0.09869841333819464, "2": 0.15487701460725825, "3": 0.286594925428177, "4": 0.38005479705906137}, "code_density": {"0": 0.0003142293990271158, "1": 0.0006085579060401511, "2": 0.01720224208214357, "3": 0.09733649526301684, "4": 0.8845384753497724}, "reasoning_density": {"0": 0.5597803825751443, "1": 0.04818282861007459, "2": 0.09760252595574903, "3": 0.1336448343256176, "4": 0.1607894285334145}, "education_level": {"none": 0.0008266602340876501, "0": 0.001204211911730129, "1": 0.007503391363116372, "2": 0.6775676349050183, "3": 0.2516685334550226, "4": 0.061229568131025064}, "creative_writing_quality": {"not_applicable": 0.9993654316395449, "-2": 9.851080891910144e-05, "-1": 0.0001623911558901836, "0": 0.0002839139887183796, "1": 6.576575340594515e-05, "2": 2.398665352140326e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00027627641810658245, "Art & Design": 0.0003831164837481345, "Law & Crime": 0.0003212949227527518, "Education & Careers": 0.00022361568609130986, "Entertainment & Media": 0.00021083427619860535, "Fashion & Beauty": 0.00019648898277739406, "Business, Economics & Finance": 0.000252855678906612, "Food & Dining": 0.00022357410493314945, "Games & Gaming": 0.00028965105671688665, "Electronics & Hardware": 0.00027325151272968663, "Health, Healthcare & Wellness": 0.0008991006914462208, "History & Geography": 0.000395769021489251, "Home, Pets & Hobbies": 0.00018814568166777197, "Industry & Production": 0.00022814139464094827, "Language, Literature & Philosophy": 0.00034476455851770995, "Politics & Government": 0.00011295665305314526, "Religion & Spirituality": 0.00020796970556674198, "Science, Mathematics & Engineering": 0.1544642931132283, "Relationships & Community": 0.00011137712334600548, "Software Usage & Applications": 0.000643814510236297, "Programming & Software Engineering": 0.8370153206061269, "Sports & Fitness": 0.00023747828239528628, "Vehicles & Transportation": 0.00013454169594284042, "Travel & Tourism": 0.0002306713288375065, "Other / Uncovered Subject": 0.0017999213941244754, "Unknown / Insufficient Content": 0.00033477511641935294}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009132840897472449, "Art & Design": 0.0006582961405061526, "Law & Crime": 0.0006354216747865753, "Education & Careers": 0.0004120718262250815, "Entertainment & Media": 0.00042173562271815977, "Fashion & Beauty": 0.0004186031279085596, "Business, Economics & Finance": 0.0004503964867447057, "Food & Dining": 0.0003228153247377604, "Games & Gaming": 0.00041352034463269983, "Electronics & Hardware": 0.0005318970900904852, "Health, Healthcare & Wellness": 0.0011517840545515023, "History & Geography": 0.00047980042448272193, "Home, Pets & Hobbies": 0.0003293524820127461, "Industry & Production": 0.00040016423304163113, "Language, Literature & Philosophy": 0.00033648347182504024, "Politics & Government": 0.00024114626303316206, "Religion & Spirituality": 0.00048743427088703554, "Science, Mathematics & Engineering": 0.5094424375952329, "Relationships & Community": 0.00019898623522196846, "Software Usage & Applications": 0.0005394170775968909, "Programming & Software Engineering": 0.21941061474167442, "Sports & Fitness": 0.00047682562025240906, "Vehicles & Transportation": 0.00023111894225172686, "Travel & Tourism": 0.0005263025128818472, "Other / Uncovered Subject": 0.0026307786861529767, "NONE": 0.2579393116608036}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10335, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10335, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:14.531670+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "0ddb0c96-a119-43f1-8690-2aa89bf3a140", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "9e8956647f0e0d5c3634616c7bd6fa09d86eb3c97796456fcaca9fc3ea5cacd6", "source_metadata": {"uuid": "0ddb0c96-a119-43f1-8690-2aa89bf3a140", "repo_name": "medragneel/bioinf", "relative_path": "motif_finder.py", "category": "ALGO", "algo_rel_score": 0.9991031885147095, "quality_score": 3.5330324172973633, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 6, "repo_created_at": "2022-11-23T07:16:18.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "b983155e4a46bcf4fbe664abc536bbc59515f9b6", "size_bytes": 6342, "file_timestamp": 1670089819, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "aa82b184598a737e4014d219baae8184009502c8"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 6, "repo_created_at": "2022-11-23T07:16:18.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "b983155e4a46bcf4fbe664abc536bbc59515f9b6", "size_bytes": 6342, "file_timestamp": 1670089819, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "aa82b184598a737e4014d219baae8184009502c8", "url": "https://github.com/medragneel/bioinf/blob/aa82b184598a737e4014d219baae8184009502c8/motif_finder.py"}, "gpt4o_tokens": 1677, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.19050345759494564, "noise_severity": 0.07621155048001195, "missing_content_severity": 0.07648335626564524, "factual_density": 3.2251188908307333, "pretraining_utility": 0.5430337553790487, "math_density": 2.7884564070734874, "code_density": 3.8651764292584674, "reasoning_density": 1.2874800976320835, "education_level": 2.364517186393129, "creative_writing_quality": -0.38715090223451437}, "score_applicability": {"education_level": 0.9991733397659124, "creative_writing_quality": 0.000634568360455013}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/lei0310/bigdata/blob/4ccc077d2693797e3cc45f28886d114f739c5ef0/clustering_agglo.py", "id": "python-0-ed0e77ac2ac3cebfb13a812630df74bc", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jan 10 17:57:02 2022\n\n@author: lmene\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport time\nimport scipy.cluster.hierarchy as shc\n\nfrom sklearn import preprocessing\nfrom scipy.io import arff\nfrom sklearn import cluster\nfrom sklearn import metrics\n\n\npath = './artificial/'\ndatabrut = arff.loadarff(open(path+\"banana.arff\", 'r'))\ndatanp = np.array([[x[0],x[1]] for x in databrut[0]])\n\nscaler = preprocessing.StandardScaler().fit(datanp)\n\ndata_scaled = scaler.transform(datanp)\n\nprint(\"-------------------------------------------\")\nprint(\"Affichage données standardisées            \")\nf0_scaled = data_scaled[:,0] # tous les élements de la première colonne\nf1_scaled = data_scaled[:,1] # tous les éléments de la deuxième colonne\n#print(f0)\n#print(f1)\n\nplt.scatter(f0_scaled, f1_scaled, s=8)\nplt.title(\"Donnees standardisées\")\nplt.show()\n\n# print(\"-----------------------------------------\")\n# print(\"Dendrogramme 'complete' données standardisées\")\n\n# distance = shc.linkage(data_scaled, 'complete')\n\n\n# plt.figure(figsize=(12, 12))\n# shc.dendrogram(distance,\n#             orientation='top',\n#             distance_sort='descending',\n#             show_leaf_counts=False)\n# plt.show()\n\n# Run clustering method for a given number of clusters\nsum_err=[] #initialization du vecteur\nprint(\"-----------------------------------------------------------\")\nprint(\"Appel Aglo Clustering 'ward' pour une valeur de k fixée\")\ntps3 = time.time()\nk=3\nmodel_scaled = cluster.AgglomerativeClustering(n_clusters=k, affinity='euclidean', linkage='ward')\nmodel_scaled.fit(data_scaled)\n#cluster.fit_predict(X)\n\ntps4 = time.time()\nlabels_scaled = model_scaled.labels_\n\nplt.scatter(f0_scaled, f1_scaled, c=labels_scaled, s=8)\nplt.title(\"Clustering ward\")\nplt.show()\nprint(\"nb clusters =\",k,\", runtime = \", round((tps4 - tps3)*1000,2),\"ms\")\nsilh = metrics.silhouette_score(datanp, labels_scaled, metric='euclidean')\nsum_err.append(silh)\n#print(\"labels\", labels)\n\n# Run clustering method for a given number of clusters\nprint(\"-----------------------------------------------------------\")\nprint(\"Appel Aglo Clustering 'complete' pour une valeur de k fixée\")\ntps3 = time.time()\nk=3\nmodel_scaled = cluster.AgglomerativeClustering(n_clusters=k, affinity='euclidean', linkage='complete')\nmodel_scaled.fit(data_scaled)\n#cluster.fit_predict(X)\n\ntps4 = time.time()\nlabels_scaled = model_scaled.labels_\n\nplt.scatter(f0_scaled, f1_scaled, c=labels_scaled, s=8)\nplt.title(\"Clustering complete\")\nplt.show()\nprint(\"nb clusters =\",k,\", runtime = \", round((tps4 - tps3)*1000,2),\"ms\")\nsilh = metrics.silhouette_score(datanp, labels_scaled, metric='euclidean')\nsum_err.append(silh)\n#print(\"labels\", labels)\n\n# Run clustering method for a given number of clusters\nprint(\"-----------------------------------------------------------\")\nprint(\"Appel Aglo Clustering 'single' pour une valeur de k fixée\")\ntps3 = time.time()\nk=3\nmodel_scaled = cluster.AgglomerativeClustering(n_clusters=k, affinity='euclidean', linkage='single')\nmodel_scaled.fit(data_scaled)\n#cluster.fit_predict(X)\n\ntps4 = time.time()\nlabels_scaled = model_scaled.labels_\n\nplt.scatter(f0_scaled, f1_scaled, c=labels_scaled, s=8)\nplt.title(\"Clustering single\")\nplt.show()\nprint(\"nb clusters =\",k,\", runtime = \", round((tps4 - tps3)*1000,2),\"ms\")\n#print(\"labels\", labels)\nsilh = metrics.silhouette_score(datanp, labels_scaled, metric='euclidean')\nsum_err.append(silh)\n\n# Run clustering method for a given number of clusters\nprint(\"-----------------------------------------------------------\")\nprint(\"Appel Aglo Clustering 'average' pour une valeur de k fixée\")\ntps3 = time.time()\nk=3\nmodel_scaled = cluster.AgglomerativeClustering(n_clusters=k, affinity='euclidean', linkage='average')\nmodel_scaled.fit(data_scaled)\n#cluster.fit_predict(X)\n\ntps4 = time.time()\nlabels_scaled = model_scaled.labels_\n\nplt.scatter(f0_scaled, f1_scaled, c=labels_scaled, s=8)\nplt.title(\"Clustering average\")\nplt.show()\nprint(\"nb clusters =\",k,\", runtime = \", round((tps4 - tps3)*1000,2),\"ms\")\n#print(\"labels\", labels)\nsilh = metrics.silhouette_score(datanp, labels_scaled, metric='euclidean')\nsum_err.append(silh)\n\n# Calcul de l'inertie des différentes méthodes \nplt.plot([\"ward\",\"complete\",\"single\",\"average\"],sum_err)\nplt.title('Evaluation de l inertie des méthodes de clustering agglomératifs')\nplt.ylabel('Inertie')\nplt.show()\n\n# Calcul des coeff de silhouette des différentes méthodes \n\nsum_err_21=[]\nsum_err_22=[]\nsum_err_23=[]\nsum_err_24=[]\n\nlink=['ward','complete','single','average']\nfor j in link :\n  for i in range (2,10):\n    sil=cluster.AgglomerativeClustering(n_clusters=i, affinity='euclidean', linkage=j)\n    sil.fit(data_scaled)\n    if (j==\"ward\"):\n      sum_err_21.append(metrics.silhouette_score(datanp, sil.labels_, metric='euclidean'))\n    if (j==\"complete\"):\n      sum_err_22.append(metrics.silhouette_score(datanp, sil.labels_, metric='euclidean'))\n    if (j==\"single\"):\n      sum_err_23.append(metrics.silhouette_score(datanp, sil.labels_, metric='euclidean'))\n    if (j==\"average\"):\n      sum_err_24.append(metrics.silhouette_score(datanp, sil.labels_, metric='euclidean'))\n      \nplt.figure(1)\nplt.plot(range(2,10),sum_err_21,label='ward')\nplt.plot(range(2,10),sum_err_22,label='complete')\nplt.plot(range(2,10),sum_err_23,label='single')\nplt.plot(range(2,10),sum_err_24,label='average')\nplt.legend()\nplt.title('Evaluation du coefficient de silhouette des méthodes de clustering agglomératifs')\nplt.show()\n\n##############################  DEUXIEME JEU DE DONNEES ########################################################\n\n\n", "text_preview": "# -*- coding: utf-8 -*- \"\"\" Created on Mon Jan 10 17:57:02 2022 @author: lmene \"\"\" import numpy as np import matplotlib.pyplot as plt import time import scipy.cluster.hierarchy as shc from sklearn import preprocessing from scipy.io import a", "input_sha256": "cb74ee38ce80d8f421298dbf9c056ee14cf8c0f5e931fc40c72db0ad49a4fc9e", "text_sha256": "ed0e77ac2ac3cebfb13a812630df74bc6a0e7eaba9c556b529b25a138f3f7915", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12084, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.3976290011217828, "noise_severity": 0.3648792578424915, "missing_content_severity": 0.6003596911859806, "factual_density": 2.3810876439623447, "pretraining_utility": 0.4689854422393935, "math_density": 2.8585219614525803, "code_density": 3.808958762474049, "reasoning_density": 0.38649406959492905, "education_level": 2.282930699832036, "creative_writing_quality": -0.468273357786593}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.4537 below 0.70", "secondary_topic vs NONE margin 0.0930 below 0.10"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.04039970441842733, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9682303824539144, "secondary": 0.9025384996173786, "tertiary": 0.944694712923564, "structured_records": 0.0036571176315847256, "procedural_steps": 0.10883511217441344, "worked_solution": 0.07130641949197916, "contextual_dialogue": 8.70905721391528e-06, "content_quality": 0.3490456381112157, "noise_severity": 0.6959339517979237, "missing_content_severity": 0.49970025734501633, "factual_density": 0.1518982458914122, "pretraining_utility": 0.42807427164517975, "math_density": 0.04876830121048359, "code_density": 0.8407989687283738, "reasoning_density": 0.6779216086708925, "education_level": 0.6625446580914207, "creative_writing_quality": 0.9995839461679544, "primary_topic": 0.7752935900958017, "secondary_topic": 0.43185569488353925}, "probability_margin": {"primary": 0.9447284678739692, "secondary": 0.8657257633269483, "tertiary": 0.9300338653491618, "education_level": 0.48551060747524866, "creative_writing_quality": 0.9995325747379221, "primary_topic": 0.5769629898163916, "secondary_topic": 0.09302613630921064}, "raw_probabilities": {"primary": {"Academic Writing": 0.00022662946590590308, "Content Listing": 7.842875395285878e-05, "Creative Writing": 7.258657023877245e-05, "Customer Support Page": 4.2324757210128716e-05, "Q&A Forum": 5.873468088339253e-05, "Discussion Thread": 4.0233678918552005e-05, "Comment Section": 7.041208171053759e-05, "Missing Content": 0.00012914109206148044, "Knowledge Article": 0.00032119330060132974, "Legal Notices": 5.377346919438668e-05, "News Article": 2.8588521912526243e-05, "Organizational Announcement": 4.4101957311399975e-05, "Nonfiction Writing": 7.419602789754723e-05, "Organizational About Page": 5.670125778502499e-05, "Personal About Page": 5.188168940414355e-05, "Personal Blog": 3.336496064563749e-05, "Product Page": 2.564836407776415e-05, "Spam / Ads": 5.1583109603839206e-05, "Structured Data": 0.000484220235952612, "Technical Writing": 0.9695541165183346, "Transcript / Interview": 6.35038801442583e-05, "Tutorial / How-To Guide": 0.024825648644365327, "User Reviews": 3.006782401966138e-05, "Other / Unknown": 0.003582919157868575}, "secondary": {"Academic Writing": 0.0015820128287606088, "Content Listing": 0.0002657747957532241, "Creative Writing": 0.00034159273853044274, "Customer Support Page": 0.00018886071750063394, "Q&A Forum": 0.00019369375970947343, "Discussion Thread": 0.00012308502264181688, "Comment Section": 0.0003000469756060007, "Missing Content": 0.020442771113718627, "Knowledge Article": 0.0018393519888557138, "Legal Notices": 0.0004023856113055402, "News Article": 0.00020953317879008848, "Organizational Announcement": 0.00020101616836346752, "Nonfiction Writing": 0.00032711628351842913, "Organizational About Page": 0.00021183211269022088, "Personal About Page": 0.00029390741979261673, "Personal Blog": 0.0003067974606952724, "Product Page": 0.0001054098488425908, "Spam / Ads": 0.00020204392370548128, "Structured Data": 0.0023812337683759696, "Technical Writing": 0.022205512059156076, "Transcript / Interview": 0.00028414989188556473, "Tutorial / How-To Guide": 0.04087363213970631, "User Reviews": 0.00011884472544129619, "NONE": 0.9065993954666546}, "tertiary": {"Academic Writing": 0.0012047787517934657, "Content Listing": 0.0001302582597507743, "Creative Writing": 0.00018501932413350253, "Customer Support Page": 9.144968144893498e-05, "Q&A Forum": 8.425763893470025e-05, "Discussion Thread": 7.756368575874147e-05, "Comment Section": 0.00020532339053246578, "Missing Content": 0.016419841969861845, "Knowledge Article": 0.0018242128169136073, "Legal Notices": 0.0005091092027626958, "News Article": 0.00010564879587827001, "Organizational Announcement": 0.00010659984143743407, "Nonfiction Writing": 0.0002599558802837428, "Organizational About Page": 0.00014031387087404056, "Personal About Page": 0.00024779462582713525, "Personal Blog": 0.000401749773858706, "Product Page": 6.122363353764119e-05, "Spam / Ads": 0.00011638736856042778, "Structured Data": 0.003215221419855697, "Technical Writing": 0.01043552397886009, "Transcript / Interview": 0.00015245204431020177, "Tutorial / How-To Guide": 0.016965234535920408, "User Reviews": 6.097962382346165e-05, "NONE": 0.9469990998850821}, "structured_records": {"false": 0.9963428823684153, "true": 0.0036571176315847256}, "procedural_steps": {"false": 0.8911648878255866, "true": 0.10883511217441344}, "worked_solution": {"false": 0.9286935805080209, "true": 0.07130641949197916}, "contextual_dialogue": {"false": 0.999991290942786, "true": 8.70905721391528e-06}, "content_quality": {"-2": 0.023064081314088797, "-1": 0.12140241827910067, "0": 0.3797610360719114, "1": 0.38638534664073715, "2": 0.08938711769416197}, "noise_severity": {"0": 0.6923788689284526, "1": 0.26012736063278746, "2": 0.040137846732051105, "3": 0.004947491081233667, "4": 0.0024084326254751962}, "missing_content_severity": {"0": 0.6462297153773277, "1": 0.1461051010770011, "2": 0.1745242096274258, "3": 0.027357724818853655, "4": 0.005783249099391715}, "factual_density": {"0": 0.06851549635120104, "1": 0.1764211574333696, "2": 0.2599284265636382, "3": 0.2957300452054665, "4": 0.1994048744463248}, "pretraining_utility": {"-2": 0.010436914895288326, "-1": 0.09281036310627688, "0": 0.3917312449947767, "1": 0.4273733188710692, "2": 0.0776481581325889}, "math_density": {"0": 0.061364360463116344, "1": 0.10893853241411244, "2": 0.16737817445627282, "3": 0.23444865054007136, "4": 0.427870282126427}, "code_density": {"0": 0.0003897116137537866, "1": 0.0007490169837219225, "2": 0.021896437514546837, "3": 0.14344246509067685, "4": 0.8335223687973007}, "reasoning_density": {"0": 0.8332226949162077, "1": 0.035238229664494816, "2": 0.07158111387574208, "3": 0.031738233995271745, "4": 0.02821972754828371}, "education_level": {"none": 0.0016143348577043104, "0": 0.0019022129975599586, "1": 0.011945828533355237, "2": 0.7187872150761839, "3": 0.2332766076009353, "4": 0.032473800934261166}, "creative_writing_quality": {"not_applicable": 0.9996532884732954, "-2": 6.956274553870314e-05, "-1": 9.728209543146944e-05, "0": 0.00012071373537331032, "1": 4.42540850062801e-05, "2": 1.4898865354692195e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00024992227342762713, "Art & Design": 0.0005533318290810516, "Law & Crime": 0.00037817459904687406, "Education & Careers": 0.000285335826221565, "Entertainment & Media": 0.00024437531950955463, "Fashion & Beauty": 0.00021589824503086353, "Business, Economics & Finance": 0.00048639085228905623, "Food & Dining": 0.0002144144261431056, "Games & Gaming": 0.00032903805857015657, "Electronics & Hardware": 0.00033309195794472505, "Health, Healthcare & Wellness": 0.00035397618774982213, "History & Geography": 0.00034091231813739736, "Home, Pets & Hobbies": 0.00023041579182539896, "Industry & Production": 0.000313628401603501, "Language, Literature & Philosophy": 0.00025533798145203467, "Politics & Government": 0.0001459987534047949, "Religion & Spirituality": 0.00019736381707751395, "Science, Mathematics & Engineering": 0.20697315450649467, "Relationships & Community": 0.00011972450292066396, "Software Usage & Applications": 0.0016496908178314953, "Programming & Software Engineering": 0.7839361443228863, "Sports & Fitness": 0.00033891561588649216, "Vehicles & Transportation": 0.00015622161079743925, "Travel & Tourism": 0.0002464202580449959, "Other / Uncovered Subject": 0.0011119240833969218, "Unknown / Insufficient Content": 0.0003401976432259304}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.000915122300926725, "Art & Design": 0.002302226004305058, "Law & Crime": 0.000662791020782362, "Education & Careers": 0.0005917716243419675, "Entertainment & Media": 0.0005083137656579715, "Fashion & Beauty": 0.0004235012991672802, "Business, Economics & Finance": 0.0008038255999966353, "Food & Dining": 0.00041775562304984117, "Games & Gaming": 0.0005090793019460846, "Electronics & Hardware": 0.0006360958264216594, "Health, Healthcare & Wellness": 0.0006433199287555636, "History & Geography": 0.0005037480252416515, "Home, Pets & Hobbies": 0.00045322148959176407, "Industry & Production": 0.0005074954133227072, "Language, Literature & Philosophy": 0.00047125443316148823, "Politics & Government": 0.0003282286554358387, "Religion & Spirituality": 0.00046896413800370996, "Science, Mathematics & Engineering": 0.45370739892648004, "Relationships & Community": 0.00023278343170616417, "Software Usage & Applications": 0.0019887429069391977, "Programming & Software Engineering": 0.1695636165101476, "Sports & Fitness": 0.0005200275070901443, "Vehicles & Transportation": 0.0002971193804755676, "Travel & Tourism": 0.0005673722380706065, "Other / Uncovered Subject": 0.0012949620317128433, "NONE": 0.3606812626172694}}, "probabilities": {"primary": {"Academic Writing": 0.00022662946590590302, "Content Listing": 7.842875395285877e-05, "Creative Writing": 7.258657023877243e-05, "Customer Support Page": 4.232475721012871e-05, "Q&A Forum": 5.873468088339252e-05, "Discussion Thread": 4.0233678918552e-05, "Comment Section": 7.041208171053758e-05, "Missing Content": 0.00012914109206148042, "Knowledge Article": 0.0003211933006013297, "Legal Notices": 5.3773469194386666e-05, "News Article": 2.8588521912526236e-05, "Organizational Announcement": 4.410195731139997e-05, "Nonfiction Writing": 7.419602789754722e-05, "Organizational About Page": 5.670125778502497e-05, "Personal About Page": 5.1881689404143534e-05, "Personal Blog": 3.336496064563748e-05, "Product Page": 2.5648364077764145e-05, "Spam / Ads": 5.158310960383919e-05, "Structured Data": 0.0004842202359526119, "Technical Writing": 0.9695541165183343, "Transcript / Interview": 6.350388014425829e-05, "Tutorial / How-To Guide": 0.02482564864436532, "User Reviews": 3.0067824019661372e-05, "Other / Unknown": 0.003582919157868574}, "secondary": {"Academic Writing": 0.0015820128287606088, "Content Listing": 0.0002657747957532241, "Creative Writing": 0.00034159273853044274, "Customer Support Page": 0.00018886071750063394, "Q&A Forum": 0.00019369375970947343, "Discussion Thread": 0.00012308502264181688, "Comment Section": 0.0003000469756060007, "Missing Content": 0.020442771113718627, "Knowledge Article": 0.0018393519888557138, "Legal Notices": 0.0004023856113055402, "News Article": 0.00020953317879008848, "Organizational Announcement": 0.00020101616836346752, "Nonfiction Writing": 0.00032711628351842913, "Organizational About Page": 0.00021183211269022088, "Personal About Page": 0.00029390741979261673, "Personal Blog": 0.0003067974606952724, "Product Page": 0.0001054098488425908, "Spam / Ads": 0.00020204392370548128, "Structured Data": 0.0023812337683759696, "Technical Writing": 0.022205512059156076, "Transcript / Interview": 0.00028414989188556473, "Tutorial / How-To Guide": 0.04087363213970631, "User Reviews": 0.00011884472544129619, "NONE": 0.9065993954666546}, "tertiary": {"Academic Writing": 0.0012047787517934655, "Content Listing": 0.00013025825975077426, "Creative Writing": 0.00018501932413350248, "Customer Support Page": 9.144968144893497e-05, "Q&A Forum": 8.425763893470024e-05, "Discussion Thread": 7.756368575874146e-05, "Comment Section": 0.00020532339053246573, "Missing Content": 0.01641984196986184, "Knowledge Article": 0.0018242128169136068, "Legal Notices": 0.0005091092027626957, "News Article": 0.00010564879587826999, "Organizational Announcement": 0.00010659984143743404, "Nonfiction Writing": 0.00025995588028374274, "Organizational About Page": 0.00014031387087404053, "Personal About Page": 0.0002477946258271352, "Personal Blog": 0.00040174977385870587, "Product Page": 6.122363353764118e-05, "Spam / Ads": 0.00011638736856042775, "Structured Data": 0.003215221419855696, "Technical Writing": 0.010435523978860089, "Transcript / Interview": 0.00015245204431020174, "Tutorial / How-To Guide": 0.016965234535920404, "User Reviews": 6.097962382346164e-05, "NONE": 0.9469990998850819}, "structured_records": {"false": 0.9963428823684153, "true": 0.0036571176315847256}, "procedural_steps": {"false": 0.8911648878255866, "true": 0.10883511217441344}, "worked_solution": {"false": 0.9286935805080209, "true": 0.07130641949197916}, "contextual_dialogue": {"false": 0.999991290942786, "true": 8.70905721391528e-06}, "content_quality": {"-2": 0.023064081314088797, "-1": 0.12140241827910067, "0": 0.3797610360719114, "1": 0.38638534664073715, "2": 0.08938711769416197}, "noise_severity": {"0": 0.6923788689284526, "1": 0.26012736063278746, "2": 0.040137846732051105, "3": 0.004947491081233667, "4": 0.0024084326254751962}, "missing_content_severity": {"0": 0.6462297153773278, "1": 0.14610510107700111, "2": 0.17452420962742582, "3": 0.02735772481885366, "4": 0.005783249099391716}, "factual_density": {"0": 0.06851549635120104, "1": 0.1764211574333696, "2": 0.2599284265636382, "3": 0.2957300452054665, "4": 0.1994048744463248}, "pretraining_utility": {"-2": 0.010436914895288326, "-1": 0.09281036310627688, "0": 0.3917312449947767, "1": 0.4273733188710692, "2": 0.0776481581325889}, "math_density": {"0": 0.061364360463116344, "1": 0.10893853241411244, "2": 0.16737817445627282, "3": 0.23444865054007136, "4": 0.427870282126427}, "code_density": {"0": 0.0003897116137537866, "1": 0.0007490169837219225, "2": 0.021896437514546837, "3": 0.14344246509067685, "4": 0.8335223687973007}, "reasoning_density": {"0": 0.8332226949162077, "1": 0.035238229664494816, "2": 0.07158111387574208, "3": 0.031738233995271745, "4": 0.02821972754828371}, "education_level": {"none": 0.0016143348577043106, "0": 0.0019022129975599588, "1": 0.011945828533355239, "2": 0.718787215076184, "3": 0.23327660760093533, "4": 0.03247380093426117}, "creative_writing_quality": {"not_applicable": 0.9996532884732956, "-2": 6.956274553870316e-05, "-1": 9.728209543146947e-05, "0": 0.00012071373537331035, "1": 4.4254085006280105e-05, "2": 1.4898865354692199e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0002499222734276272, "Art & Design": 0.0005533318290810518, "Law & Crime": 0.0003781745990468741, "Education & Careers": 0.0002853358262215651, "Entertainment & Media": 0.0002443753195095547, "Fashion & Beauty": 0.00021589824503086356, "Business, Economics & Finance": 0.0004863908522890563, "Food & Dining": 0.00021441442614310562, "Games & Gaming": 0.0003290380585701566, "Electronics & Hardware": 0.0003330919579447251, "Health, Healthcare & Wellness": 0.0003539761877498222, "History & Geography": 0.0003409123181373974, "Home, Pets & Hobbies": 0.00023041579182539899, "Industry & Production": 0.00031362840160350104, "Language, Literature & Philosophy": 0.0002553379814520347, "Politics & Government": 0.00014599875340479492, "Religion & Spirituality": 0.00019736381707751398, "Science, Mathematics & Engineering": 0.2069731545064947, "Relationships & Community": 0.00011972450292066397, "Software Usage & Applications": 0.0016496908178314955, "Programming & Software Engineering": 0.7839361443228864, "Sports & Fitness": 0.0003389156158864922, "Vehicles & Transportation": 0.00015622161079743927, "Travel & Tourism": 0.000246420258044996, "Other / Uncovered Subject": 0.001111924083396922, "Unknown / Insufficient Content": 0.00034019764322593045}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009151223009267251, "Art & Design": 0.0023022260043050586, "Law & Crime": 0.0006627910207823621, "Education & Careers": 0.0005917716243419676, "Entertainment & Media": 0.0005083137656579716, "Fashion & Beauty": 0.00042350129916728026, "Business, Economics & Finance": 0.0008038255999966354, "Food & Dining": 0.0004177556230498412, "Games & Gaming": 0.0005090793019460847, "Electronics & Hardware": 0.0006360958264216596, "Health, Healthcare & Wellness": 0.0006433199287555637, "History & Geography": 0.0005037480252416516, "Home, Pets & Hobbies": 0.0004532214895917641, "Industry & Production": 0.0005074954133227073, "Language, Literature & Philosophy": 0.0004712544331614883, "Politics & Government": 0.00032822865543583876, "Religion & Spirituality": 0.00046896413800371, "Science, Mathematics & Engineering": 0.4537073989264801, "Relationships & Community": 0.0002327834317061642, "Software Usage & Applications": 0.001988742906939198, "Programming & Software Engineering": 0.16956361651014762, "Sports & Fitness": 0.0005200275070901445, "Vehicles & Transportation": 0.00029711938047556765, "Travel & Tourism": 0.0005673722380706066, "Other / Uncovered Subject": 0.0012949620317128435, "NONE": 0.36068126261726946}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10037, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10037, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:15.744673+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "0517927a-90dd-49b6-b416-e876338cd26e", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "cb6680b95152ab3c0acec1513094ab2166dffbfa724be21b1b6be3f61abba00e", "source_metadata": {"uuid": "0517927a-90dd-49b6-b416-e876338cd26e", "repo_name": "lei0310/bigdata", "relative_path": "clustering_agglo.py", "category": "DATA", "algo_rel_score": 0.9550601840019226, "quality_score": 4.048821926116943, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2022-01-10T18:57:41.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "f80202d5194cf414689fb8b075403b0556e1b9cd", "size_bytes": 5788, "file_timestamp": 1641841091, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "4ccc077d2693797e3cc45f28886d114f739c5ef0"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2022-01-10T18:57:41.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "f80202d5194cf414689fb8b075403b0556e1b9cd", "size_bytes": 5788, "file_timestamp": 1641841091, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "4ccc077d2693797e3cc45f28886d114f739c5ef0", "url": "https://github.com/lei0310/bigdata/blob/4ccc077d2693797e3cc45f28886d114f739c5ef0/clustering_agglo.py"}, "gpt4o_tokens": 1414, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.3976290011217828, "noise_severity": 0.3648792578424915, "missing_content_severity": 0.6003596911859806, "factual_density": 2.3810876439623447, "pretraining_utility": 0.4689854422393935, "math_density": 2.8585219614525803, "code_density": 3.808958762474049, "reasoning_density": 0.38649406959492905, "education_level": 2.282930699832036, "creative_writing_quality": -0.468273357786593}, "score_applicability": {"education_level": 0.9983856651422958, "creative_writing_quality": 0.00034671152670445526}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/techuila/better-bites-be/blob/7b5e465b3d5d6bec91f0418396ecb1d58197dd0a/libs/model.py", "id": "python-0-3c66956b08695ce678c96833126ba8c8", "text": "from flask import Flask, jsonify, request\nfrom groq import Groq\nfrom dotenv import load_dotenv\nimport os\nimport json\nimport logging\nimport re\n\n# Load environment variables\nload_dotenv()\n\n# Configure logging\nlogging.basicConfig(level=logging.INFO)\n\ndef get_api_key():\n    \"\"\"Fetch API key from environment variables.\"\"\"\n    api_key = os.getenv(\"GROQ_API_KEY\")\n    if not api_key:\n        logging.error(\"Error: GROQ_API_KEY is not set. Check your .env file.\")\n        raise ValueError(\"Error: GROQ_API_KEY is not set. Check your .env file.\")\n    return api_key\n\nclass Nutritionist:\n    \"\"\"\n    Nutritionist class to analyze food ingredients for education and health awareness.\n    - Uses the Groq API to generate analysis.\n    - Bases analysis on user profile: age, sex, height, weight, and health conditions.\n    \"\"\"\n    MODEL = \"llama3-70b-8192\"\n    model_response_error = \"Model returned an invalid JSON response.\"\n\n    def __init__(self):\n        \"\"\"Initialize Groq API client.\"\"\"\n        self.client = Groq(api_key=get_api_key())\n\n    def extract_amounts(self, ingredient):\n        \"\"\"Extract %, g, or ml values from an ingredient name.\"\"\"\n        match = re.search(r\"(\\d+(\\.\\d+)?\\s*(%|g|ml))\", ingredient)\n        return match.group(0) if match else None\n\n    def get_advice_from_ingredients(self, ingredients, user_profile):\n        \"\"\"\n        Analyze ingredients and return educational nutritional advice.\n        - Uses age, sex, height, weight, and health conditions for context.\n        - Highlights nutritional benefits, health risks (e.g., allergies), and allergens.\n        \"\"\"\n        # Ensure all ingredients are strings\n        if not all(isinstance(ing, str) for ing in ingredients):\n            return jsonify({\"error\": \"Invalid input. Ingredients must be a list of strings.\"}), 400\n\n        # Identify ingredients with amounts (%, g, ml)\n        ingredients_with_amounts = {\n            ing: self.extract_amounts(ing) for ing in ingredients if self.extract_amounts(ing)\n        }\n\n        # Prompt using complete user profile for education and health awareness\n        prompt = f\"\"\"\n        You are a professional nutritionist. Analyze the given ingredients with amounts (e.g., %, g, ml) and the user’s health profile to educate about nutrition and health awareness. Use the user’s age, sex, height, weight, and health conditions (if any) to provide context, such as general nutritional needs or health risks.\n\n        1. **List suitable ingredients** with their nutritional benefits and general recommended intake, adjusted for the user’s profile (e.g., age, sex, weight considerations).\n        2. **Identify unsuitable ingredients** based on health conditions (e.g., allergies) if provided, or general health concerns if not, with alternatives to broaden understanding.\n        3. **Categorize all allergens separately**, including common allergens (e.g., nuts, shellfish, dairy) regardless of user profile, explaining why they’re allergens and potential reactions.\n        4. **Ensure all provided ingredients are accounted for** in the response (suitable, unsuitable, or allergens).\n        5. **For ingredients with amounts (e.g., %, g, ml)**, explain their nutritional impact based on the user’s profile (e.g., calorie density relative to weight, sodium for blood pressure).\n        6. **In the Health Tips section**, provide general educational advice about all scanned ingredients, tailored to the user’s profile where applicable (e.g., allergy awareness, weight management).\n\n        Ingredients with amounts: {ingredients_with_amounts}\n\n        User Profile:\n        {user_profile}\n\n        Ingredients: {ingredients}\n\n        **Response Format (ONLY return valid JSON)**:\n        {{\n            \"title\": \"Ingredient Analysis\",\n            \"suitable_ingredients\": [\n                {{\n                    \"name\": \"Ingredient Name (if applicable, include volume: e.g., 30g, 250ml, 10%)\",\n                    \"description\": \"Provide a clear explanation of the ingredient's nutritional benefits and why it’s generally healthy, considering age, sex, height, and weight.\",\n                    \"Recommended Intake\": \"Specify a general recommended intake amount, adjusted for the user’s profile (e.g., 30g per meal, consume in moderation).\"\n                }}\n            ],\n            \"unsuitable_ingredients\": [\n                {{\n                    \"name\": \"Ingredient Name (if applicable, include volume: e.g., 30g, 250ml, 10%)\",\n                    \"description\": \"Provide a clear explanation of why this ingredient might be a concern, based on health conditions or general nutrition relative to the user’s profile.\",\n                    \"alternatives\": \"Suggest healthier or safer options to explore (e.g., 10g of a substitute).\"\n                }}\n            ],\n            \"allergens\": [\n                {{\n                    \"name\": \"Ingredient Name (if applicable, include volume: e.g., 30g, 250ml, 10%)\",\n                    \"description\": \"Provide a clear explanation of why this ingredient is considered a common allergen.\",\n                    \"Potential Reaction\": \"Explain how it may affect sensitive individuals (e.g., allergic reactions, digestive issues).\"\n                }}\n            ],\n            \"health_tips\": [\n                {{\n                    \"name\": \"Educational Health Tip\",\n                    \"description\": \"Provide a clear explanation of a health concept related to these ingredients, tailored to the user’s age, sex, height, weight, and health conditions.\",\n                    \"suggestion\": \"Give actionable advice for better health awareness (e.g., monitor portions, avoid allergens).\"\n                }}\n            ]\n        }}\n\n        If the ingredients are invalid or unrelated to food, return:\n        {{\n            \"error\": \"Invalid input. Provide valid food ingredients.\"\n        }}\n\n        JSON Response:\n        \"\"\"\n\n        try:\n            response = self.client.chat.completions.create(\n                model=self.MODEL,\n                messages=[\n                    {\"role\": \"system\", \"content\": \"You are a nutritionist dietitian. Always respond in valid JSON.\"},\n                    {\"role\": \"user\", \"content\": prompt}\n                ],\n                temperature=0.7,\n                max_tokens=1024,\n                top_p=1,\n                stream=False\n            )\n\n            response_text = response.choices[0].message.content.strip()\n            logging.info(f\"Model Response: {response_text}\")\n\n            # Validate JSON format\n            try:\n                parsed_response = json.loads(response_text)\n                return jsonify(parsed_response)\n            except json.JSONDecodeError:\n                logging.error(\"Invalid JSON received from model.\")\n                return jsonify({\"error\": self.model_response_error}), 500\n\n        except Exception as e:\n            logging.error(f\"Error encountered: {e}\")\n            return jsonify({\"error\": \"Error encountered from Groq API\"}), 500\n\n", "text_preview": "from flask import Flask, jsonify, request from groq import Groq from dotenv import load_dotenv import os import json import logging import re # Load environment variables load_dotenv() # Configure logging logging.basicConfig(level=logging.I", "input_sha256": "750794d04fe8d34a681585861aa3a1785b899b596730a5fae2e47b96781fc911", "text_sha256": "3c66956b08695ce678c96833126ba8c834e7b4e25f8632a17034bd3969943c43", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12086, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Health, Healthcare & Wellness", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9268807952406616, "noise_severity": 0.07021549331802938, "missing_content_severity": 0.09498327642638094, "factual_density": 2.6186391025316462, "pretraining_utility": 0.6492961130007888, "math_density": 0.13313127163234117, "code_density": 3.453865289473221, "reasoning_density": 0.4637350544351477, "education_level": 2.0353075573576134, "creative_writing_quality": -0.1088006709003904}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Health, Healthcare & Wellness"}, "review_reasons": ["secondary_topic probability 0.5895 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.044571611937587075, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9964589500938477, "secondary": 0.8400999445968593, "tertiary": 0.7935904364880728, "structured_records": 0.04763846933472568, "procedural_steps": 0.05678610725329213, "worked_solution": 0.0068550623384499455, "contextual_dialogue": 3.4941753133955424e-05, "content_quality": 0.5770846895862962, "noise_severity": 0.9414870889016422, "missing_content_severity": 0.9208472696446826, "factual_density": 0.3093201482518676, "pretraining_utility": 0.55076105917345, "math_density": 0.8890572736397158, "code_density": 0.5448877412276839, "reasoning_density": 0.6135541213040436, "education_level": 0.8304803311423629, "creative_writing_quality": 0.996353040199762, "primary_topic": 0.8716564357136045, "secondary_topic": 0.5730862388785003}, "probability_margin": {"primary": 0.9953335283381348, "secondary": 0.7924299588415814, "tertiary": 0.741125971230803, "education_level": 0.7791478801753379, "creative_writing_quality": 0.9949243565401338, "primary_topic": 0.782223861582832, "secondary_topic": 0.4169396715656074}, "raw_probabilities": {"primary": {"Academic Writing": 3.4673793506932625e-05, "Content Listing": 1.7951261777481966e-05, "Creative Writing": 1.9723747122012766e-05, "Customer Support Page": 1.1733835994200651e-05, "Q&A Forum": 1.3560076902142915e-05, "Discussion Thread": 7.837996310397794e-06, "Comment Section": 1.3392214580593911e-05, "Missing Content": 1.9015069903575512e-05, "Knowledge Article": 0.00023379168486094265, "Legal Notices": 2.8941076976943397e-05, "News Article": 5.717112648175417e-06, "Organizational Announcement": 1.2196576279429392e-05, "Nonfiction Writing": 3.13580107059496e-05, "Organizational About Page": 2.1637976000070093e-05, "Personal About Page": 1.7277048868865417e-05, "Personal Blog": 9.937164492620818e-06, "Product Page": 1.1678713975443008e-05, "Spam / Ads": 1.3793466002584284e-05, "Structured Data": 0.0007122869654406157, "Technical Writing": 0.9966064938399374, "Transcript / Interview": 2.1773333682338535e-05, "Tutorial / How-To Guide": 0.0012729655018025973, "User Reviews": 7.951099955676573e-06, "Other / Unknown": 0.0008543124322728027}, "secondary": {"Academic Writing": 0.0008060514369728497, "Content Listing": 0.00043592829039450513, "Creative Writing": 0.0008023757977707547, "Customer Support Page": 0.0004917590266327525, "Q&A Forum": 0.0003386729598352575, "Discussion Thread": 0.00024102732634879862, "Comment Section": 0.000446850966133664, "Missing Content": 0.0007543595419292018, "Knowledge Article": 0.027154471459691087, "Legal Notices": 0.0006649896131926961, "News Article": 0.0003626946114804476, "Organizational Announcement": 0.00038542660069681555, "Nonfiction Writing": 0.001970450419456612, "Organizational About Page": 0.0005418762057643517, "Personal About Page": 0.0004161662586425389, "Personal Blog": 0.0003813684405332792, "Product Page": 0.00028959463358256684, "Spam / Ads": 0.0004698879475516803, "Structured Data": 0.04051189786580316, "Technical Writing": 0.05433248806374209, "Transcript / Interview": 0.0009013906089740285, "Tutorial / How-To Guide": 0.020337255125564467, "User Reviews": 0.00020056989398288465, "NONE": 0.8467624469053235}, "tertiary": {"Academic Writing": 0.0011359194713475545, "Content Listing": 0.0004922076467289257, "Creative Writing": 0.000997888959066374, "Customer Support Page": 0.0006888546831881415, "Q&A Forum": 0.0003761998214217043, "Discussion Thread": 0.00025233796556295874, "Comment Section": 0.0005267193251950676, "Missing Content": 0.0007493223084142137, "Knowledge Article": 0.033697662336942616, "Legal Notices": 0.0010999256320250242, "News Article": 0.00041779893426215775, "Organizational Announcement": 0.0004103860905483847, "Nonfiction Writing": 0.00324530690692707, "Organizational About Page": 0.00073415070352343, "Personal About Page": 0.00041996809740157666, "Personal Blog": 0.0003833757548559884, "Product Page": 0.00038202044259985326, "Spam / Ads": 0.0005642553459475605, "Structured Data": 0.061064863736933386, "Technical Writing": 0.03835456454563525, "Transcript / Interview": 0.0009903518617032508, "Tutorial / How-To Guide": 0.05062266727665476, "User Reviews": 0.00020241718537836835, "NONE": 0.8021908349677365}, "structured_records": {"false": 0.9523615306652743, "true": 0.04763846933472568}, "procedural_steps": {"false": 0.9432138927467079, "true": 0.05678610725329213}, "worked_solution": {"false": 0.9931449376615501, "true": 0.0068550623384499455}, "contextual_dialogue": {"false": 0.999965058246866, "true": 3.4941753133955424e-05}, "content_quality": {"-2": 0.005895300829442399, "-1": 0.019255734618317664, "0": 0.234111416902929, "1": 0.5235479637807579, "2": 0.21718958386855303}, "noise_severity": {"0": 0.9440479516608836, "1": 0.04446246985718113, "2": 0.00938169212992408, "3": 0.0014419062070443651, "4": 0.000665980144966748}, "missing_content_severity": {"0": 0.9509840892557913, "1": 0.01162996302926183, "2": 0.02984906650860152, "3": 0.006492344445465361, "4": 0.0010445367608799968}, "factual_density": {"0": 0.016809462191619117, "1": 0.11925953399657166, "2": 0.3161409052150558, "3": 0.32406263628205095, "4": 0.22372746231470242}, "pretraining_utility": {"-2": 0.005915485637625337, "-1": 0.05230052795405874, "0": 0.3225477951745421, "1": 0.5250447702374496, "2": 0.09419142099632435}, "math_density": {"0": 0.8841621775066734, "1": 0.10537257117530698, "2": 0.0063054759774582815, "3": 0.0014913528601280452, "4": 0.0026684224804333734}, "code_density": {"0": 0.0007697571299759958, "1": 0.0015728621238603182, "2": 0.08173129974306824, "3": 0.3748744961491579, "4": 0.5410515848539376}, "reasoning_density": {"0": 0.724440515977778, "1": 0.12449594394140959, "2": 0.1229442948826507, "3": 0.01912646006420984, "4": 0.008992785133951792}, "education_level": {"none": 0.0011398139210112696, "0": 0.002780836364570191, "1": 0.051425588568968984, "2": 0.8587336092853024, "3": 0.07958572910996453, "4": 0.006334422750182619}, "creative_writing_quality": {"not_applicable": 0.996960866833135, "-2": 0.00015661659659323238, "-1": 0.0004775502546105027, "0": 0.002036510293001204, "1": 0.00027678832503367465, "2": 9.166769762637683e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001424786564794525, "Art & Design": 0.0002410274670995837, "Law & Crime": 0.00025521530327667373, "Education & Careers": 0.0004119867788960824, "Entertainment & Media": 0.00017196838765075394, "Fashion & Beauty": 0.00029359305539563375, "Business, Economics & Finance": 0.00026179719598662095, "Food & Dining": 0.02063387569140658, "Games & Gaming": 0.00021136713984892902, "Electronics & Hardware": 0.00023846785333579313, "Health, Healthcare & Wellness": 0.09436886506486465, "History & Geography": 0.00022562679920999447, "Home, Pets & Hobbies": 0.00021005654509362263, "Industry & Production": 0.00022328438886178067, "Language, Literature & Philosophy": 0.00015277195710458778, "Politics & Government": 9.974921228654258e-05, "Religion & Spirituality": 0.00017331285499377525, "Science, Mathematics & Engineering": 0.002307593103908368, "Relationships & Community": 0.00017493530967268423, "Software Usage & Applications": 0.001024590942064229, "Programming & Software Engineering": 0.8765927266476966, "Sports & Fitness": 0.0004517076305259344, "Vehicles & Transportation": 0.00012678587835605542, "Travel & Tourism": 0.00020696328267647265, "Other / Uncovered Subject": 0.0005922180096201606, "Unknown / Insufficient Content": 0.00020703484368841295}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.001158923425348663, "Art & Design": 0.0008317152875338148, "Law & Crime": 0.0008187149336506645, "Education & Careers": 0.0024791390389013387, "Entertainment & Media": 0.0006673240852607065, "Fashion & Beauty": 0.0008580304317789404, "Business, Economics & Finance": 0.0008832142827725217, "Food & Dining": 0.10769182143190749, "Games & Gaming": 0.000610592674511908, "Electronics & Hardware": 0.0007918308684606133, "Health, Healthcare & Wellness": 0.5895059989216349, "History & Geography": 0.0007856002363444323, "Home, Pets & Hobbies": 0.0006244510231068079, "Industry & Production": 0.0006869439691256517, "Language, Literature & Philosophy": 0.00037065658689761105, "Politics & Government": 0.0003832039415881392, "Religion & Spirituality": 0.0006806549490658541, "Science, Mathematics & Engineering": 0.015590268704440748, "Relationships & Community": 0.0004839679562597131, "Software Usage & Applications": 0.00340632709442825, "Programming & Software Engineering": 0.1725663273560275, "Sports & Fitness": 0.0015258209543778792, "Vehicles & Transportation": 0.0003090258968507075, "Travel & Tourism": 0.0006865451163817581, "Other / Uncovered Subject": 0.0014677826496158931, "NONE": 0.09413511818372745}}, "probabilities": {"primary": {"Academic Writing": 3.467379350693263e-05, "Content Listing": 1.795126177748197e-05, "Creative Writing": 1.972374712201277e-05, "Customer Support Page": 1.1733835994200655e-05, "Q&A Forum": 1.3560076902142918e-05, "Discussion Thread": 7.837996310397796e-06, "Comment Section": 1.3392214580593914e-05, "Missing Content": 1.9015069903575516e-05, "Knowledge Article": 0.0002337916848609427, "Legal Notices": 2.8941076976943404e-05, "News Article": 5.717112648175418e-06, "Organizational Announcement": 1.2196576279429395e-05, "Nonfiction Writing": 3.135801070594961e-05, "Organizational About Page": 2.1637976000070097e-05, "Personal About Page": 1.727704886886542e-05, "Personal Blog": 9.93716449262082e-06, "Product Page": 1.1678713975443011e-05, "Spam / Ads": 1.3793466002584287e-05, "Structured Data": 0.0007122869654406158, "Technical Writing": 0.9966064938399376, "Transcript / Interview": 2.1773333682338538e-05, "Tutorial / How-To Guide": 0.0012729655018025976, "User Reviews": 7.951099955676574e-06, "Other / Unknown": 0.0008543124322728029}, "secondary": {"Academic Writing": 0.0008060514369728497, "Content Listing": 0.00043592829039450513, "Creative Writing": 0.0008023757977707547, "Customer Support Page": 0.0004917590266327525, "Q&A Forum": 0.0003386729598352575, "Discussion Thread": 0.00024102732634879862, "Comment Section": 0.000446850966133664, "Missing Content": 0.0007543595419292018, "Knowledge Article": 0.027154471459691087, "Legal Notices": 0.0006649896131926961, "News Article": 0.0003626946114804476, "Organizational Announcement": 0.00038542660069681555, "Nonfiction Writing": 0.001970450419456612, "Organizational About Page": 0.0005418762057643517, "Personal About Page": 0.0004161662586425389, "Personal Blog": 0.0003813684405332792, "Product Page": 0.00028959463358256684, "Spam / Ads": 0.0004698879475516803, "Structured Data": 0.04051189786580316, "Technical Writing": 0.05433248806374209, "Transcript / Interview": 0.0009013906089740285, "Tutorial / How-To Guide": 0.020337255125564467, "User Reviews": 0.00020056989398288465, "NONE": 0.8467624469053235}, "tertiary": {"Academic Writing": 0.0011359194713475545, "Content Listing": 0.0004922076467289257, "Creative Writing": 0.000997888959066374, "Customer Support Page": 0.0006888546831881415, "Q&A Forum": 0.0003761998214217043, "Discussion Thread": 0.00025233796556295874, "Comment Section": 0.0005267193251950676, "Missing Content": 0.0007493223084142137, "Knowledge Article": 0.033697662336942616, "Legal Notices": 0.0010999256320250242, "News Article": 0.00041779893426215775, "Organizational Announcement": 0.0004103860905483847, "Nonfiction Writing": 0.00324530690692707, "Organizational About Page": 0.00073415070352343, "Personal About Page": 0.00041996809740157666, "Personal Blog": 0.0003833757548559884, "Product Page": 0.00038202044259985326, "Spam / Ads": 0.0005642553459475605, "Structured Data": 0.061064863736933386, "Technical Writing": 0.03835456454563525, "Transcript / Interview": 0.0009903518617032508, "Tutorial / How-To Guide": 0.05062266727665476, "User Reviews": 0.00020241718537836835, "NONE": 0.8021908349677365}, "structured_records": {"false": 0.9523615306652743, "true": 0.04763846933472568}, "procedural_steps": {"false": 0.9432138927467079, "true": 0.05678610725329213}, "worked_solution": {"false": 0.9931449376615501, "true": 0.0068550623384499455}, "contextual_dialogue": {"false": 0.999965058246866, "true": 3.4941753133955424e-05}, "content_quality": {"-2": 0.0058953008294424, "-1": 0.019255734618317668, "0": 0.23411141690292903, "1": 0.523547963780758, "2": 0.21718958386855305}, "noise_severity": {"0": 0.9440479516608836, "1": 0.04446246985718113, "2": 0.00938169212992408, "3": 0.0014419062070443651, "4": 0.000665980144966748}, "missing_content_severity": {"0": 0.9509840892557913, "1": 0.01162996302926183, "2": 0.02984906650860152, "3": 0.006492344445465361, "4": 0.0010445367608799968}, "factual_density": {"0": 0.01680946219161912, "1": 0.11925953399657167, "2": 0.31614090521505583, "3": 0.324062636282051, "4": 0.22372746231470245}, "pretraining_utility": {"-2": 0.005915485637625337, "-1": 0.05230052795405874, "0": 0.3225477951745421, "1": 0.5250447702374496, "2": 0.09419142099632435}, "math_density": {"0": 0.8841621775066734, "1": 0.10537257117530698, "2": 0.0063054759774582815, "3": 0.0014913528601280452, "4": 0.0026684224804333734}, "code_density": {"0": 0.0007697571299759958, "1": 0.0015728621238603182, "2": 0.08173129974306824, "3": 0.3748744961491579, "4": 0.5410515848539376}, "reasoning_density": {"0": 0.724440515977778, "1": 0.12449594394140959, "2": 0.1229442948826507, "3": 0.01912646006420984, "4": 0.008992785133951792}, "education_level": {"none": 0.0011398139210112696, "0": 0.002780836364570191, "1": 0.051425588568968984, "2": 0.8587336092853024, "3": 0.07958572910996453, "4": 0.006334422750182619}, "creative_writing_quality": {"not_applicable": 0.996960866833135, "-2": 0.00015661659659323238, "-1": 0.0004775502546105027, "0": 0.002036510293001204, "1": 0.00027678832503367465, "2": 9.166769762637683e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001424786564794525, "Art & Design": 0.0002410274670995837, "Law & Crime": 0.00025521530327667373, "Education & Careers": 0.0004119867788960824, "Entertainment & Media": 0.00017196838765075394, "Fashion & Beauty": 0.00029359305539563375, "Business, Economics & Finance": 0.00026179719598662095, "Food & Dining": 0.02063387569140658, "Games & Gaming": 0.00021136713984892902, "Electronics & Hardware": 0.00023846785333579313, "Health, Healthcare & Wellness": 0.09436886506486465, "History & Geography": 0.00022562679920999447, "Home, Pets & Hobbies": 0.00021005654509362263, "Industry & Production": 0.00022328438886178067, "Language, Literature & Philosophy": 0.00015277195710458778, "Politics & Government": 9.974921228654258e-05, "Religion & Spirituality": 0.00017331285499377525, "Science, Mathematics & Engineering": 0.002307593103908368, "Relationships & Community": 0.00017493530967268423, "Software Usage & Applications": 0.001024590942064229, "Programming & Software Engineering": 0.8765927266476966, "Sports & Fitness": 0.0004517076305259344, "Vehicles & Transportation": 0.00012678587835605542, "Travel & Tourism": 0.00020696328267647265, "Other / Uncovered Subject": 0.0005922180096201606, "Unknown / Insufficient Content": 0.00020703484368841295}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.001158923425348663, "Art & Design": 0.0008317152875338148, "Law & Crime": 0.0008187149336506645, "Education & Careers": 0.0024791390389013387, "Entertainment & Media": 0.0006673240852607065, "Fashion & Beauty": 0.0008580304317789404, "Business, Economics & Finance": 0.0008832142827725217, "Food & Dining": 0.10769182143190749, "Games & Gaming": 0.000610592674511908, "Electronics & Hardware": 0.0007918308684606133, "Health, Healthcare & Wellness": 0.5895059989216349, "History & Geography": 0.0007856002363444323, "Home, Pets & Hobbies": 0.0006244510231068079, "Industry & Production": 0.0006869439691256517, "Language, Literature & Philosophy": 0.00037065658689761105, "Politics & Government": 0.0003832039415881392, "Religion & Spirituality": 0.0006806549490658541, "Science, Mathematics & Engineering": 0.015590268704440748, "Relationships & Community": 0.0004839679562597131, "Software Usage & Applications": 0.00340632709442825, "Programming & Software Engineering": 0.1725663273560275, "Sports & Fitness": 0.0015258209543778792, "Vehicles & Transportation": 0.0003090258968507075, "Travel & Tourism": 0.0006865451163817581, "Other / Uncovered Subject": 0.0014677826496158931, "NONE": 0.09413511818372745}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10056, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10056, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:35:01.931120+00:00", "scoring_key_slot": "primary", "dataset_uid": "1886a63f-9ebc-4ca3-816a-46878462a787", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "10b6f151473b4a5155fd34bb70ae77caff020101794ba331b75a789e5eb836ed", "source_metadata": {"uuid": "1886a63f-9ebc-4ca3-816a-46878462a787", "repo_name": "techuila/better-bites-be", "relative_path": "libs/model.py", "category": "WEB", "algo_rel_score": 0.9935302138328552, "quality_score": 6.302380084991455, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 11, "repo_created_at": "2024-10-05T10:57:17.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "9a045280b01cfb7e8e02a7d1e9b3cb8f6931576e", "size_bytes": 7022, "file_timestamp": 1741234717, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "7b5e465b3d5d6bec91f0418396ecb1d58197dd0a"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 11, "repo_created_at": "2024-10-05T10:57:17.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "9a045280b01cfb7e8e02a7d1e9b3cb8f6931576e", "size_bytes": 7022, "file_timestamp": 1741234717, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "7b5e465b3d5d6bec91f0418396ecb1d58197dd0a", "url": "https://github.com/techuila/better-bites-be/blob/7b5e465b3d5d6bec91f0418396ecb1d58197dd0a/libs/model.py"}, "gpt4o_tokens": 1422, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.9268807952406614, "noise_severity": 0.07021549331802938, "missing_content_severity": 0.09498327642638094, "factual_density": 2.6186391025316462, "pretraining_utility": 0.6492961130007888, "math_density": 0.13313127163234117, "code_density": 3.453865289473221, "reasoning_density": 0.4637350544351477, "education_level": 2.0353075573576134, "creative_writing_quality": -0.1088006709003904}, "score_applicability": {"education_level": 0.9988601860789887, "creative_writing_quality": 0.003039133166864991}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/googleapis/python-aiplatform/blob/0e9f3df0a655f13ef4e43640bac4436cf7d9b769/tests/unit/vertexai/test_caching.py", "id": "python-0-b7eb938eaf84bcb5c61c7a05fc4b3d1b", "text": "# -*- coding: utf-8 -*-\n\n# Copyright 2024 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\n\nfrom contextlib import redirect_stdout\nimport datetime\nimport io\nimport json\nimport mock\nimport pytest\nfrom vertexai.caching import CachedContent\nfrom google.cloud.aiplatform import initializer\nimport vertexai\nfrom google.cloud.aiplatform_v1beta1.types.cached_content import (\n    CachedContent as GapicCachedContent,\n)\nfrom google.cloud.aiplatform_v1beta1.types.content import (\n    Content as GapicContent,\n    Part as GapicPart,\n)\nfrom google.cloud.aiplatform_v1beta1.types.tool import (\n    ToolConfig as GapicToolConfig,\n)\nfrom google.cloud.aiplatform_v1.services import (\n    gen_ai_cache_service,\n)\n\n\n_TEST_PROJECT = \"test-project\"\n_TEST_LOCATION = \"us-central1\"\n_CREATED_CONTENT_ID = \"contents-id-mocked\"\n_TEST_DISPLAY_NAME = \"test-display-name\"\n\n\n@pytest.fixture\ndef mock_create_cached_content():\n    \"\"\"Mocks GenAiCacheServiceClient.create_cached_content().\"\"\"\n\n    def create_cached_content(self, request):\n        response = GapicCachedContent(\n            name=f\"{request.parent}/cachedContents/{_CREATED_CONTENT_ID}\",\n            model=f\"{request.cached_content.model}\",\n            display_name=f\"{request.cached_content.display_name}\",\n            create_time=datetime.datetime(\n                year=2024,\n                month=2,\n                day=1,\n                hour=1,\n                minute=1,\n                second=1,\n                tzinfo=datetime.timezone.utc,\n            ),\n            update_time=datetime.datetime(\n                year=2024,\n                month=2,\n                day=1,\n                hour=1,\n                minute=1,\n                second=1,\n                tzinfo=datetime.timezone.utc,\n            ),\n            expire_time=datetime.datetime(\n                year=2024,\n                month=2,\n                day=1,\n                hour=2,\n                minute=1,\n                second=1,\n                tzinfo=datetime.timezone.utc,\n            ),\n        )\n        return response\n\n    with mock.patch.object(\n        gen_ai_cache_service.client.GenAiCacheServiceClient,\n        \"create_cached_content\",\n        new=create_cached_content,\n    ) as create_cached_content:\n        yield create_cached_content\n\n\n@pytest.fixture\ndef mock_get_cached_content():\n    \"\"\"Mocks GenAiCacheServiceClient.get_cached_content().\"\"\"\n\n    def get_cached_content(self, name, retry=None):\n        del self, retry\n        response = GapicCachedContent(\n            name=f\"{name}\",\n            model=\"model-name\",\n        )\n        return response\n\n    with mock.patch.object(\n        gen_ai_cache_service.client.GenAiCacheServiceClient,\n        \"get_cached_content\",\n        new=get_cached_content,\n    ) as get_cached_content:\n        yield get_cached_content\n\n\n@pytest.fixture\ndef mock_list_cached_contents():\n    \"\"\"Mocks GenAiCacheServiceClient.get_cached_content().\"\"\"\n\n    def list_cached_contents(self, request):\n        del self, request\n        response = [\n            GapicCachedContent(\n                name=\"cached_content1_from_list_request\",\n                model=\"model-name1\",\n            ),\n            GapicCachedContent(\n                name=\"cached_content2_from_list_request\",\n                model=\"model-name2\",\n            ),\n        ]\n        return response\n\n    with mock.patch.object(\n        gen_ai_cache_service.client.GenAiCacheServiceClient,\n        \"list_cached_contents\",\n        new=list_cached_contents,\n    ) as list_cached_contents:\n        yield list_cached_contents\n\n\n@pytest.mark.usefixtures(\"google_auth_mock\")\nclass TestCaching:\n    \"\"\"Unit tests for CachedContent.\"\"\"\n\n    def setup_method(self):\n        vertexai.init(\n            project=_TEST_PROJECT,\n            location=_TEST_LOCATION,\n        )\n\n    def teardown_method(self):\n        initializer.global_pool.shutdown(wait=True)\n\n    def test_constructor_with_full_resource_name(self, mock_get_cached_content):\n        full_resource_name = (\n            \"projects/123/locations/europe-west1/cachedContents/contents-id\"\n        )\n        cache = CachedContent(\n            cached_content_name=full_resource_name,\n        )\n\n        assert cache.name == \"contents-id\"\n        assert cache.resource_name == full_resource_name\n\n    def test_constructor_with_only_content_id(self, mock_get_cached_content):\n        partial_resource_name = \"contents-id\"\n\n        cache = CachedContent(\n            cached_content_name=partial_resource_name,\n        )\n\n        assert cache.name == \"contents-id\"\n        assert cache.resource_name == (\n            f\"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/cachedContents/contents-id\"\n        )\n        assert cache.model_name == \"model-name\"\n\n    def test_get_with_content_id(self, mock_get_cached_content):\n        partial_resource_name = \"contents-id\"\n\n        cache = CachedContent.get(\n            cached_content_name=partial_resource_name,\n        )\n\n        assert cache.name == \"contents-id\"\n        assert cache.resource_name == (\n            f\"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/cachedContents/contents-id\"\n        )\n        assert cache.model_name == \"model-name\"\n\n    def test_create_with_real_payload(\n        self, mock_create_cached_content, mock_get_cached_content\n    ):\n        cache = CachedContent.create(\n            model_name=\"model-name\",\n            system_instruction=GapicContent(\n                role=\"system\", parts=[GapicPart(text=\"system instruction\")]\n            ),\n            tools=[],\n            tool_config=GapicToolConfig(),\n            contents=[GapicContent(role=\"user\")],\n            ttl=datetime.timedelta(days=1),\n            display_name=_TEST_DISPLAY_NAME,\n        )\n\n        # parent is automantically set to align with the current project and location.\n        # _CREATED_CONTENT_ID is from the mock\n        assert cache.resource_name == (\n            f\"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/cachedContents/{_CREATED_CONTENT_ID}\"\n        )\n        assert cache.name == _CREATED_CONTENT_ID\n        assert (\n            cache.model_name\n            == f\"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/publishers/google/models/model-name\"\n        )\n        assert cache.display_name == _TEST_DISPLAY_NAME\n\n    def test_create_with_encrypted_real_payload(\n        self, mock_create_cached_content, mock_get_cached_content\n    ):\n        cache = CachedContent.create(\n            model_name=\"model-name\",\n            system_instruction=GapicContent(\n                role=\"system\", parts=[GapicPart(text=\"system instruction\")]\n            ),\n            tools=[],\n            tool_config=GapicToolConfig(),\n            contents=[GapicContent(role=\"user\")],\n            ttl=datetime.timedelta(days=1),\n            display_name=_TEST_DISPLAY_NAME,\n            kms_key_name=f\"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/keyRings/test-key-ring/cryptoKeys/test-key\",\n        )\n\n        # parent is automantically set to align with the current project and location.\n        # _CREATED_CONTENT_ID is from the mock\n        assert cache.resource_name == (\n            f\"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/cachedContents/{_CREATED_CONTENT_ID}\"\n        )\n        assert cache.name == _CREATED_CONTENT_ID\n        assert (\n            cache.model_name\n            == f\"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/publishers/google/models/model-name\"\n        )\n        assert cache.display_name == _TEST_DISPLAY_NAME\n\n    def test_create_with_real_payload_and_wrapped_type(\n        self, mock_create_cached_content, mock_get_cached_content\n    ):\n        cache = CachedContent.create(\n            model_name=\"model-name\",\n            system_instruction=\"Please answer my questions with cool\",\n            tools=[],\n            tool_config=GapicToolConfig(),\n            contents=[\"user content\"],\n            ttl=datetime.timedelta(days=1),\n            display_name=_TEST_DISPLAY_NAME,\n        )\n\n        # parent is automantically set to align with the current project and location.\n        # _CREATED_CONTENT_ID is from the mock\n        assert (\n            cache.model_name\n            == f\"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/publishers/google/models/model-name\"\n        )\n        assert cache.name == _CREATED_CONTENT_ID\n        assert cache.display_name == _TEST_DISPLAY_NAME\n\n    def test_list(self, mock_list_cached_contents):\n        cached_contents = CachedContent.list()\n        for i, cached_content in enumerate(cached_contents):\n            assert cached_content.name == f\"cached_content{i + 1}_from_list_request\"\n            assert cached_content.model_name == f\"model-name{i + 1}\"\n\n    def test_print_a_cached_content(\n        self, mock_create_cached_content, mock_get_cached_content\n    ):\n        cached_content = CachedContent.create(\n            model_name=\"model-name\",\n            system_instruction=\"Please answer my questions with cool\",\n            tools=[],\n            tool_config=GapicToolConfig(),\n            contents=[\"user content\"],\n            ttl=datetime.timedelta(days=1),\n            display_name=_TEST_DISPLAY_NAME,\n        )\n        f = io.StringIO()\n        with redirect_stdout(f):\n            print(cached_content)\n        output = f.getvalue()\n        assert (\n            json.dumps(\n                {\n                    \"name\": f\"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/cachedContents/{_CREATED_CONTENT_ID}\",\n                    \"model\": \"projects/test-project/locations/us-central1/publishers/google/models/model-name\",\n                    \"createTime\": \"2024-02-01T01:01:01Z\",\n                    \"updateTime\": \"2024-02-01T01:01:01Z\",\n                    \"expireTime\": \"2024-02-01T02:01:01Z\",\n                    \"displayName\": \"test-display-name\",\n                },\n                indent=2,\n            )\n            in output\n        )\n", "text_preview": "# -*- coding: utf-8 -*- # Copyright 2024 Google LLC # # Licensed under the Apache License, Version 2.0 (the \"License\"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://", "input_sha256": "2a15829e105568d44cd5a04651bd243207f855bea4420ded5ba88822d5218c99", "text_sha256": "b7eb938eaf84bcb5c61c7a05fc4b3d1b64a7fb1d593b451e8d214638af159f91", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12087, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Legal Notices", "sub_format": null}, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 1, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.094966750179173, "noise_severity": 0.6297296548612153, "missing_content_severity": 0.0023797837787412858, "factual_density": 3.163009791093218, "pretraining_utility": 0.39909147404435386, "math_density": 0.02890456456004588, "code_density": 3.76464556367236, "reasoning_density": 0.5718262202503062, "education_level": 2.8626532458762965, "creative_writing_quality": -0.3021136391377907}, "api_choices": {"primary": "Technical Writing", "secondary": "Legal Notices", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.513309209642828, "secondary_choice": "Legal Notices", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9904104262387609, "secondary": 0.7710486414067156, "tertiary": 0.6578039216193464, "structured_records": 0.22078659206437348, "procedural_steps": 0.012044311605669112, "worked_solution": 0.15941245048130356, "contextual_dialogue": 1.3718080779924907e-05, "content_quality": 0.5574774303485677, "noise_severity": 0.6271903882913201, "missing_content_severity": 0.998016846851049, "factual_density": 0.5241996575732997, "pretraining_utility": 0.45047610959331874, "math_density": 0.9759128628666285, "code_density": 0.8038713030602997, "reasoning_density": 0.5234781497914117, "education_level": 0.5080730783980782, "creative_writing_quality": 0.9994367998326203, "primary_topic": 0.9950880414199874, "secondary_topic": 0.8690205333587221}, "probability_margin": {"primary": 0.9866938335885038, "secondary": 0.6218353994390569, "tertiary": 0.37742235567973553, "education_level": 0.32315694185580207, "creative_writing_quality": 0.9992932833563829, "primary_topic": 0.9924305764864679, "secondary_topic": 0.8289021339746216}, "raw_probabilities": {"primary": {"Academic Writing": 7.680471162994112e-05, "Content Listing": 2.0154283520295322e-05, "Creative Writing": 2.0072952625416034e-05, "Customer Support Page": 1.6295750272146147e-05, "Q&A Forum": 1.4999569086976882e-05, "Discussion Thread": 8.133357385684489e-06, "Comment Section": 1.5430691411882337e-05, "Missing Content": 2.24232033597299e-05, "Knowledge Article": 6.45728510440291e-05, "Legal Notices": 0.004116158223641932, "News Article": 8.31183086386342e-06, "Organizational Announcement": 2.373343305547223e-05, "Nonfiction Writing": 1.269129436380017e-05, "Organizational About Page": 1.68558102536393e-05, "Personal About Page": 7.810536354541533e-06, "Personal Blog": 7.500696509513469e-06, "Product Page": 1.0374239240623447e-05, "Spam / Ads": 9.906369299824738e-06, "Structured Data": 0.0006745086585480472, "Technical Writing": 0.9908099918121458, "Transcript / Interview": 2.4541120955641446e-05, "Tutorial / How-To Guide": 0.00021072908941695826, "User Reviews": 8.784578876921628e-06, "Other / Unknown": 0.003799214936137474}, "secondary": {"Academic Writing": 0.0008211689390216484, "Content Listing": 0.00043026851668247364, "Creative Writing": 0.0004372237282255, "Customer Support Page": 0.0002758795446394684, "Q&A Forum": 0.000280569104000005, "Discussion Thread": 0.00022273196986550935, "Comment Section": 0.0004569597810048935, "Missing Content": 0.00044425387279778855, "Knowledge Article": 0.0008300198285856046, "Legal Notices": 0.7805882813481024, "News Article": 0.0003609072747310817, "Organizational Announcement": 0.000487073147518577, "Nonfiction Writing": 0.0003187330655423716, "Organizational About Page": 0.0004169687065924803, "Personal About Page": 0.00021615677561310028, "Personal Blog": 0.0002212842630526315, "Product Page": 0.0002982841103169531, "Spam / Ads": 0.00030186553102583535, "Structured Data": 0.006792800818855381, "Technical Writing": 0.04585479707265734, "Transcript / Interview": 0.0004762209127444029, "Tutorial / How-To Guide": 0.0005371649226728274, "User Reviews": 0.00017750485670607846, "NONE": 0.15875288190904563}, "tertiary": {"Academic Writing": 0.0008897217271875141, "Content Listing": 0.0003676879450436903, "Creative Writing": 0.00047915548016236987, "Customer Support Page": 0.0003027215214124267, "Q&A Forum": 0.00019577219720666943, "Discussion Thread": 0.00017983733996291732, "Comment Section": 0.000404174696440906, "Missing Content": 0.00041880060966407507, "Knowledge Article": 0.0007516838459383704, "Legal Notices": 0.29463973587213815, "News Article": 0.00031954072741781917, "Organizational Announcement": 0.0003864892834063126, "Nonfiction Writing": 0.0002619128186100428, "Organizational About Page": 0.00038554501851898766, "Personal About Page": 0.0001972097408327515, "Personal Blog": 0.00022194201710055308, "Product Page": 0.00022742243727193952, "Spam / Ads": 0.000263868489050795, "Structured Data": 0.01767142183659727, "Technical Writing": 0.008110930810535053, "Transcript / Interview": 0.00040161572780894817, "Tutorial / How-To Guide": 0.0007178040384235481, "User Reviews": 0.00014291426739518936, "NONE": 0.6720620915518737}, "structured_records": {"false": 0.7792134079356265, "true": 0.22078659206437348}, "procedural_steps": {"false": 0.9879556883943309, "true": 0.012044311605669112}, "worked_solution": {"false": 0.8405875495186964, "true": 0.15941245048130356}, "contextual_dialogue": {"false": 0.9999862819192201, "true": 1.3718080779924907e-05}, "content_quality": {"-2": 0.003965965671483541, "-1": 0.01392184931717007, "0": 0.17828857105248208, "1": 0.4908266970784185, "2": 0.31299691688044595}, "noise_severity": {"0": 0.4088209395946002, "1": 0.5603272458746842, "2": 0.02559464651425456, "3": 0.0028155561078220676, "4": 0.002441611908638954}, "missing_content_severity": {"0": 0.9987331343687894, "1": 0.0005356409163015681, "2": 0.00042757077200801177, "3": 0.00022561445318070264, "4": 7.803948972039658e-05}, "factual_density": {"0": 0.0057719441899258365, "1": 0.02082167807277956, "2": 0.14501612119407467, "3": 0.461405155540591, "4": 0.36698510100262893}, "pretraining_utility": {"-2": 0.008713646737180277, "-1": 0.11274130374747121, "0": 0.41453638136506243, "1": 0.39875726503438647, "2": 0.06525140311589958}, "math_density": {"0": 0.9796703505952665, "1": 0.015598664119645972, "2": 0.0023559513307091517, "3": 0.000906138038531784, "4": 0.0014688959158465624}, "code_density": {"0": 0.00030557209176148745, "1": 0.0005676496721369394, "2": 0.01528860802970295, "3": 0.2018519828847776, "4": 0.781986187321621}, "reasoning_density": {"0": 0.6853681775651393, "1": 0.13483503875424913, "2": 0.12763149396569035, "3": 0.026932965295008052, "4": 0.025232324419913008}, "education_level": {"none": 0.0009054409693060033, "0": 0.0011563621756266289, "1": 0.0026075642633714056, "2": 0.2669039568092631, "3": 0.5900608986650652, "4": 0.13836577711736764}, "creative_writing_quality": {"not_applicable": 0.9995306665271836, "-2": 6.690676267452175e-05, "-1": 9.613051303902309e-05, "0": 0.00023738317080071266, "1": 4.967405765821961e-05, "2": 1.9238968644036367e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.7336089525817345e-05, "Art & Design": 3.0198498052435236e-05, "Law & Crime": 0.00046438603314079985, "Education & Careers": 2.814639075046506e-05, "Entertainment & Media": 1.84423136178679e-05, "Fashion & Beauty": 1.4373939584687428e-05, "Business, Economics & Finance": 5.5915865275912e-05, "Food & Dining": 1.535684468311684e-05, "Games & Gaming": 3.567891103688907e-05, "Electronics & Hardware": 9.378051192573683e-05, "Health, Healthcare & Wellness": 2.1883961482090648e-05, "History & Geography": 2.1662943038180135e-05, "Home, Pets & Hobbies": 1.3247817729336983e-05, "Industry & Production": 2.2666343952995974e-05, "Language, Literature & Philosophy": 2.9492748058342936e-05, "Politics & Government": 1.854266113198913e-05, "Religion & Spirituality": 2.1413369863006907e-05, "Science, Mathematics & Engineering": 0.0028463864173661657, "Relationships & Community": 1.1565568802410583e-05, "Software Usage & Applications": 0.0006813668668253888, "Programming & Software Engineering": 0.995276962903834, "Sports & Fitness": 2.779626555785514e-05, "Vehicles & Transportation": 1.0908717370781513e-05, "Travel & Tourism": 2.463681455873439e-05, "Other / Uncovered Subject": 0.00012897477657936425, "Unknown / Insufficient Content": 4.8876426255460315e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005308728554626928, "Art & Design": 0.00034884987846348114, "Law & Crime": 0.04515607117799566, "Education & Careers": 0.00037394108538380303, "Entertainment & Media": 0.0003404663553642663, "Fashion & Beauty": 0.00022628000697755748, "Business, Economics & Finance": 0.0009334269103803439, "Food & Dining": 0.0001700652311030906, "Games & Gaming": 0.00030973794490702944, "Electronics & Hardware": 0.000633569089126379, "Health, Healthcare & Wellness": 0.0002885962973450605, "History & Geography": 0.0002191006865334886, "Home, Pets & Hobbies": 0.00017642236019761699, "Industry & Production": 0.00018282069704467232, "Language, Literature & Philosophy": 0.00020097527543705415, "Politics & Government": 0.0004699688126232522, "Religion & Spirituality": 0.0002926004363605122, "Science, Mathematics & Engineering": 0.03895872020189369, "Relationships & Community": 0.00010013382544696386, "Software Usage & Applications": 0.002345926797629297, "Programming & Software Engineering": 0.03245139217004424, "Sports & Fitness": 0.000193277784613506, "Vehicles & Transportation": 9.014876710496518e-05, "Travel & Tourism": 0.00026972998788345057, "Other / Uncovered Subject": 0.0006787002120605538, "NONE": 0.8740582051526173}}, "probabilities": {"primary": {"Academic Writing": 7.680471162994111e-05, "Content Listing": 2.015428352029532e-05, "Creative Writing": 2.007295262541603e-05, "Customer Support Page": 1.6295750272146144e-05, "Q&A Forum": 1.4999569086976878e-05, "Discussion Thread": 8.133357385684487e-06, "Comment Section": 1.5430691411882334e-05, "Missing Content": 2.2423203359729896e-05, "Knowledge Article": 6.457285104402909e-05, "Legal Notices": 0.004116158223641931, "News Article": 8.311830863863418e-06, "Organizational Announcement": 2.3733433055472224e-05, "Nonfiction Writing": 1.2691294363800167e-05, "Organizational About Page": 1.6855810253639296e-05, "Personal About Page": 7.810536354541531e-06, "Personal Blog": 7.500696509513467e-06, "Product Page": 1.0374239240623446e-05, "Spam / Ads": 9.906369299824736e-06, "Structured Data": 0.0006745086585480471, "Technical Writing": 0.9908099918121456, "Transcript / Interview": 2.454112095564144e-05, "Tutorial / How-To Guide": 0.0002107290894169582, "User Reviews": 8.784578876921627e-06, "Other / Unknown": 0.003799214936137473}, "secondary": {"Academic Writing": 0.0008211689390216484, "Content Listing": 0.00043026851668247364, "Creative Writing": 0.0004372237282255, "Customer Support Page": 0.0002758795446394684, "Q&A Forum": 0.000280569104000005, "Discussion Thread": 0.00022273196986550935, "Comment Section": 0.0004569597810048935, "Missing Content": 0.00044425387279778855, "Knowledge Article": 0.0008300198285856046, "Legal Notices": 0.7805882813481024, "News Article": 0.0003609072747310817, "Organizational Announcement": 0.000487073147518577, "Nonfiction Writing": 0.0003187330655423716, "Organizational About Page": 0.0004169687065924803, "Personal About Page": 0.00021615677561310028, "Personal Blog": 0.0002212842630526315, "Product Page": 0.0002982841103169531, "Spam / Ads": 0.00030186553102583535, "Structured Data": 0.006792800818855381, "Technical Writing": 0.04585479707265734, "Transcript / Interview": 0.0004762209127444029, "Tutorial / How-To Guide": 0.0005371649226728274, "User Reviews": 0.00017750485670607846, "NONE": 0.15875288190904563}, "tertiary": {"Academic Writing": 0.0008897217271875141, "Content Listing": 0.0003676879450436903, "Creative Writing": 0.00047915548016236987, "Customer Support Page": 0.0003027215214124267, "Q&A Forum": 0.00019577219720666943, "Discussion Thread": 0.00017983733996291732, "Comment Section": 0.000404174696440906, "Missing Content": 0.00041880060966407507, "Knowledge Article": 0.0007516838459383704, "Legal Notices": 0.29463973587213815, "News Article": 0.00031954072741781917, "Organizational Announcement": 0.0003864892834063126, "Nonfiction Writing": 0.0002619128186100428, "Organizational About Page": 0.00038554501851898766, "Personal About Page": 0.0001972097408327515, "Personal Blog": 0.00022194201710055308, "Product Page": 0.00022742243727193952, "Spam / Ads": 0.000263868489050795, "Structured Data": 0.01767142183659727, "Technical Writing": 0.008110930810535053, "Transcript / Interview": 0.00040161572780894817, "Tutorial / How-To Guide": 0.0007178040384235481, "User Reviews": 0.00014291426739518936, "NONE": 0.6720620915518737}, "structured_records": {"false": 0.7792134079356265, "true": 0.22078659206437348}, "procedural_steps": {"false": 0.9879556883943309, "true": 0.012044311605669112}, "worked_solution": {"false": 0.8405875495186964, "true": 0.15941245048130356}, "contextual_dialogue": {"false": 0.9999862819192201, "true": 1.3718080779924907e-05}, "content_quality": {"-2": 0.00396596567148354, "-1": 0.013921849317170066, "0": 0.17828857105248205, "1": 0.49082669707841836, "2": 0.3129969168804459}, "noise_severity": {"0": 0.4088209395946002, "1": 0.5603272458746842, "2": 0.02559464651425456, "3": 0.0028155561078220676, "4": 0.002441611908638954}, "missing_content_severity": {"0": 0.9987331343687894, "1": 0.0005356409163015681, "2": 0.00042757077200801177, "3": 0.00022561445318070264, "4": 7.803948972039658e-05}, "factual_density": {"0": 0.0057719441899258365, "1": 0.02082167807277956, "2": 0.14501612119407467, "3": 0.461405155540591, "4": 0.36698510100262893}, "pretraining_utility": {"-2": 0.008713646737180277, "-1": 0.11274130374747121, "0": 0.41453638136506243, "1": 0.39875726503438647, "2": 0.06525140311589958}, "math_density": {"0": 0.9796703505952665, "1": 0.015598664119645972, "2": 0.0023559513307091517, "3": 0.000906138038531784, "4": 0.0014688959158465624}, "code_density": {"0": 0.0003055720917614875, "1": 0.0005676496721369395, "2": 0.015288608029702952, "3": 0.20185198288477763, "4": 0.7819861873216211}, "reasoning_density": {"0": 0.6853681775651395, "1": 0.13483503875424915, "2": 0.12763149396569037, "3": 0.026932965295008056, "4": 0.02523232441991301}, "education_level": {"none": 0.0009054409693060033, "0": 0.0011563621756266289, "1": 0.0026075642633714056, "2": 0.2669039568092631, "3": 0.5900608986650652, "4": 0.13836577711736764}, "creative_writing_quality": {"not_applicable": 0.9995306665271836, "-2": 6.690676267452175e-05, "-1": 9.613051303902309e-05, "0": 0.00023738317080071266, "1": 4.967405765821961e-05, "2": 1.9238968644036367e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.733608952581735e-05, "Art & Design": 3.019849805243524e-05, "Law & Crime": 0.0004643860331407999, "Education & Careers": 2.8146390750465064e-05, "Entertainment & Media": 1.8442313617867902e-05, "Fashion & Beauty": 1.437393958468743e-05, "Business, Economics & Finance": 5.5915865275912006e-05, "Food & Dining": 1.5356844683116844e-05, "Games & Gaming": 3.5678911036889076e-05, "Electronics & Hardware": 9.378051192573684e-05, "Health, Healthcare & Wellness": 2.188396148209065e-05, "History & Geography": 2.166294303818014e-05, "Home, Pets & Hobbies": 1.3247817729336984e-05, "Industry & Production": 2.2666343952995977e-05, "Language, Literature & Philosophy": 2.949274805834294e-05, "Politics & Government": 1.8542661131989134e-05, "Religion & Spirituality": 2.141336986300691e-05, "Science, Mathematics & Engineering": 0.002846386417366166, "Relationships & Community": 1.1565568802410585e-05, "Software Usage & Applications": 0.0006813668668253889, "Programming & Software Engineering": 0.9952769629038342, "Sports & Fitness": 2.7796265557855145e-05, "Vehicles & Transportation": 1.0908717370781514e-05, "Travel & Tourism": 2.4636814558734393e-05, "Other / Uncovered Subject": 0.00012897477657936428, "Unknown / Insufficient Content": 4.887642625546032e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005308728554626928, "Art & Design": 0.00034884987846348114, "Law & Crime": 0.04515607117799566, "Education & Careers": 0.00037394108538380303, "Entertainment & Media": 0.0003404663553642663, "Fashion & Beauty": 0.00022628000697755748, "Business, Economics & Finance": 0.0009334269103803439, "Food & Dining": 0.0001700652311030906, "Games & Gaming": 0.00030973794490702944, "Electronics & Hardware": 0.000633569089126379, "Health, Healthcare & Wellness": 0.0002885962973450605, "History & Geography": 0.0002191006865334886, "Home, Pets & Hobbies": 0.00017642236019761699, "Industry & Production": 0.00018282069704467232, "Language, Literature & Philosophy": 0.00020097527543705415, "Politics & Government": 0.0004699688126232522, "Religion & Spirituality": 0.0002926004363605122, "Science, Mathematics & Engineering": 0.03895872020189369, "Relationships & Community": 0.00010013382544696386, "Software Usage & Applications": 0.002345926797629297, "Programming & Software Engineering": 0.03245139217004424, "Sports & Fitness": 0.000193277784613506, "Vehicles & Transportation": 9.014876710496518e-05, "Travel & Tourism": 0.00026972998788345057, "Other / Uncovered Subject": 0.0006787002120605538, "NONE": 0.8740582051526173}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10850, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10850, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:35:06.944710+00:00", "scoring_key_slot": "primary", "dataset_uid": "115e088b-dae3-4bdd-8c4e-210d44bd6756", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "694c9139ab1f4102fb65b52c4095122a4142ffc06b2987317a88e9c19f79dc91", "source_metadata": {"uuid": "115e088b-dae3-4bdd-8c4e-210d44bd6756", "repo_name": "googleapis/python-aiplatform", "relative_path": "tests/unit/vertexai/test_caching.py", "category": "TEST", "algo_rel_score": 0.9469121694564819, "quality_score": 7.113887786865234, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 2766, "repo_created_at": "2020-09-23T15:43:39.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 774, "forks": 374, "issues": 157, "pull_requests": 55}, "file_meta": {"content_id": "0797597d68ea7a71c02dd24dc8a0dcf3e7afe031", "size_bytes": 10409, "file_timestamp": 1752791221, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0e9f3df0a655f13ef4e43640bac4436cf7d9b769"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 2766, "repo_created_at": "2020-09-23T15:43:39.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 774, "forks": 374, "issues": 157, "pull_requests": 55}, "file_meta": {"content_id": "0797597d68ea7a71c02dd24dc8a0dcf3e7afe031", "size_bytes": 10409, "file_timestamp": 1752791221, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0e9f3df0a655f13ef4e43640bac4436cf7d9b769", "url": "https://github.com/googleapis/python-aiplatform/blob/0e9f3df0a655f13ef4e43640bac4436cf7d9b769/tests/unit/vertexai/test_caching.py"}, "gpt4o_tokens": 2107, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.0949667501791733, "noise_severity": 0.6297296548612153, "missing_content_severity": 0.0023797837787412858, "factual_density": 3.163009791093218, "pretraining_utility": 0.39909147404435386, "math_density": 0.02890456456004588, "code_density": 3.76464556367236, "reasoning_density": 0.5718262202503062, "education_level": 2.8626532458762965, "creative_writing_quality": -0.3021136391377907}, "score_applicability": {"education_level": 0.999094559030694, "creative_writing_quality": 0.0004693334728165135}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/konrad-c/TimeSeriesClassification/blob/2a6bf8fddd714516047abc3429dc1258a7049456/Python/cricket_normalization.py", "id": "python-0-a5f38ae68b45c0f7ff8ebd35b47a4224", "text": "import os\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\nDATA_PATH = \"NormalizationData\\\\CricketDataRaw\\\\\"\nDATA_PATH_OUT = \"NormalizationData\\\\CricketClean\\\\\"\ncricket_data = [os.path.join(dp, f) for dp, dn, filenames in os.walk(DATA_PATH) for f in filenames]\n\ndef get_max_length(cricket_data):\n    lengths = []\n    for i in range(len(cricket_data)):\n        filename = cricket_data[i]\n        f = open(filename)\n        t_len = 0\n        for line in f:\n            t_len = t_len + 1\n        f.close()\n        lengths.append(t_len)\n    return max(lengths)\n\ndef clean_data_truncate(cricket_data):\n    classification = np.zeros(len(cricket_data), dtype='<U10')\n    max_len = get_max_length(cricket_data)\n    x = np.zeros((len(cricket_data),max_len))\n    y = np.zeros((len(cricket_data),max_len))\n    z = np.zeros((len(cricket_data),max_len))\n    for i in range(len(cricket_data)):\n        filename = cricket_data[i]\n        t_class = filename.split(\"\\\\\")[-1].split(\"-\")[0]\n        t_x = []\n        t_y = []\n        t_z = []\n        f = open(filename)\n        for line in f:\n            vals = line.split(\" \")\n            t_x.append(float(vals[0]))\n            t_y.append(float(vals[1]))\n            t_z.append(float(vals[2]))\n        f.close()\n        t_len = len(t_x)\n        classification[i] = t_class\n        x[i, 0:t_len] = np.array(t_x)\n        x[i, t_len:] = np.ones(x.shape[1] - t_len, dtype=float)*np.array(t_x).astype(float)[-1]\n        y[i, 0:t_len] = np.array(t_y)\n        y[i, t_len:] = np.ones(y.shape[1] - t_len, dtype=float)*np.array(t_y).astype(float)[-1]\n        z[i, 0:t_len] = np.array(t_z)\n        z[i, t_len:] = np.ones(z.shape[1] - t_len, dtype=float)*np.array(t_z).astype(float)[-1]\n    return classification,x,y,z\n\ndef clean_data_stretched(gestures_data, size=315, rolling_average=True):\n    classification = np.zeros(len(cricket_data), dtype='<U10')\n    max_len = get_max_length(cricket_data)\n    x = np.zeros((len(cricket_data),size))\n    y = np.zeros((len(cricket_data),size))\n    z = np.zeros((len(cricket_data),size))\n    for i in range(len(cricket_data)):\n        filename = cricket_data[i]\n        t_class = filename.split(\"\\\\\")[-1].split(\"-\")[0]\n        t_x = []\n        t_y = []\n        t_z = []\n        f = open(filename)\n        for line in f:\n            vals = line.split(\" \")\n            t_x.append(float(vals[0]))\n            t_y.append(float(vals[1]))\n            t_z.append(float(vals[2]))\n        f.close()\n        t_len = len(t_x)\n        classification[i] = t_class\n        for t in range(size):\n            prop = float(t)/float(size)\n            x[i,t] = t_x[int(t_len*prop)]\n            y[i,t] = t_y[int(t_len*prop)]\n            z[i,t] = t_z[int(t_len*prop)]\n        if rolling_average:\n            window = int(size/t_len)\n            if window - 1 > 0:\n                half_w = max(int(window/2), 1)\n                for t in range(size):\n                    x_copy = x[i]\n                    y_copy = y[i]\n                    z_copy = z[i]\n                    #### Make sure to copy before smoothing\n                    x[i,t] = np.mean(x_copy[max(0,t-half_w):min(size,t+half_w+1)])\n                    y[i,t] = np.mean(y_copy[max(0,t-half_w):min(size,t+half_w+1)])\n                    z[i,t] = np.mean(z_copy[max(0,t-half_w):min(size,t+half_w+1)])\n    return classification,x,y,z\n\ndef out_data(classification,x,y,z,path_out):\n    ### Write data to files ###\n    out_x = open(path_out + \"CricketX.csv\", \"w+\")\n    out_y = open(path_out + \"CricketY.csv\", \"w+\")\n    out_z = open(path_out + \"CricketZ.csv\", \"w+\")\n    classification = pd.factorize(classification)[0]\n    for i in range(len(cricket_data)):\n        str_x = str(classification[i]) + \",\" + np.array2string(x[i], separator=\",\").replace(\"\\n\", \"\").replace(\" \", \"\").replace(\"[\", \" \").replace(\"]\", \"\").replace(\".,\", \".0,\") + \"\\n\"\n        str_y = str(classification[i]) + \",\" + np.array2string(y[i], separator=\",\").replace(\"\\n\", \"\").replace(\" \", \"\").replace(\"[\", \" \").replace(\"]\", \"\").replace(\".,\", \".0,\") + \"\\n\"\n        str_z = str(classification[i]) + \",\" + np.array2string(z[i], separator=\",\").replace(\"\\n\", \"\").replace(\" \", \"\").replace(\"[\", \" \").replace(\"]\", \"\").replace(\".,\", \".0,\") + \"\\n\"\n        out_x.write(str_x)\n        out_y.write(str_y)\n        out_z.write(str_z)\n    out_x.close()\n    out_y.close()\n    out_z.close()\n    \ndef main():  ### Stretched\n    classification,x,y,z = clean_data_stretched(cricket_data, size=300, rolling_average=True)\n    out_data(classification,x,y,z,DATA_PATH_OUT + \"StretchedAvg\")\n    #classification,x,y,z = clean_data_stretched(cricket_data, size=300, rolling_average=False)\n    #out_data(classification,x,y,z,DATA_PATH_OUT + \"Stretched\")\n    ### Truncated\n    #classification,x,y,z = clean_data_truncate(cricket_data)\n    #out_data(classification,x,y,z,DATA_PATH_OUT + \"Truncated\")\n\n#main()\n\n\n\n", "text_preview": "import os import matplotlib.pyplot as plt import pandas as pd import numpy as np DATA_PATH = \"NormalizationData\\\\CricketDataRaw\\\\\" DATA_PATH_OUT = \"NormalizationData\\\\CricketClean\\\\\" cricket_data = [os.path.join(dp, f) for dp, dn, filenames", "input_sha256": "94685e7e72daefbe2c5e6adf2c875f424101bc8b8022eafb0a2a0f230a379d45", "text_sha256": "a5f38ae68b45c0f7ff8ebd35b47a42248ecbaca06ad5cccddddd90616584e5db", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12085, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.38426631754027485, "noise_severity": 0.04427353067863444, "missing_content_severity": 0.023111958078297197, "factual_density": 3.001202437602613, "pretraining_utility": 0.4427116258561205, "math_density": 2.782142932789867, "code_density": 3.872833237547989, "reasoning_density": 0.34873566366943887, "education_level": 2.1434560352016727, "creative_writing_quality": -0.3429418925625247}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.009699270026276352, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9926769388418942, "secondary": 0.9817353936801363, "tertiary": 0.9918563710988596, "structured_records": 0.002630592833471955, "procedural_steps": 0.03676987139855327, "worked_solution": 0.005228844045346007, "contextual_dialogue": 8.97394894194464e-06, "content_quality": 0.34507238176040866, "noise_severity": 0.963105391101138, "missing_content_severity": 0.9807400349347524, "factual_density": 0.4110086612633509, "pretraining_utility": 0.4376758995079669, "math_density": 0.0, "code_density": 0.8940276979566572, "reasoning_density": 0.7093869469421343, "education_level": 0.7516633192662735, "creative_writing_quality": 0.9990590930554226, "primary_topic": 0.8775524606589048, "secondary_topic": 0.44005626354195876}, "probability_margin": {"primary": 0.9900355593853573, "secondary": 0.9749513151143694, "tertiary": 0.9895214001653122, "education_level": 0.6507749521780204, "creative_writing_quality": 0.9988377646069272, "primary_topic": 0.7828734790209378, "secondary_topic": 0.0952806295495896}, "raw_probabilities": {"primary": {"Academic Writing": 0.00010870862414109078, "Content Listing": 2.81614780042896e-05, "Creative Writing": 3.08481030480599e-05, "Customer Support Page": 1.603818524432169e-05, "Q&A Forum": 2.4179425126473314e-05, "Discussion Thread": 1.6075096747231734e-05, "Comment Section": 2.169020817508488e-05, "Missing Content": 5.1288797770724035e-05, "Knowledge Article": 0.00015289010121223112, "Legal Notices": 3.774383682852162e-05, "News Article": 9.410621045525509e-06, "Organizational Announcement": 1.6177722755354624e-05, "Nonfiction Writing": 2.7898030045182992e-05, "Organizational About Page": 2.1869303334418375e-05, "Personal About Page": 2.3275667538746285e-05, "Personal Blog": 1.4597754124760313e-05, "Product Page": 1.1530150422650149e-05, "Spam / Ads": 2.068631496665885e-05, "Structured Data": 0.0008140304513322792, "Technical Writing": 0.9929820663901486, "Transcript / Interview": 2.5948183991938977e-05, "Tutorial / How-To Guide": 0.0029465070047913866, "User Reviews": 1.0933728798792844e-05, "Other / Unknown": 0.0025874448204055538}, "secondary": {"Academic Writing": 0.0002661116947780613, "Content Listing": 5.934335231080625e-05, "Creative Writing": 7.713783355747072e-05, "Customer Support Page": 4.850077680856132e-05, "Q&A Forum": 3.4475150729525606e-05, "Discussion Thread": 2.6574027920038152e-05, "Comment Section": 4.61928104833509e-05, "Missing Content": 0.0001379267110988254, "Knowledge Article": 0.0003512108356529512, "Legal Notices": 7.680922458724557e-05, "News Article": 3.933200223279308e-05, "Organizational Announcement": 3.9150723219159686e-05, "Nonfiction Writing": 4.647525660580043e-05, "Organizational About Page": 5.2304639219328754e-05, "Personal About Page": 4.643629152277906e-05, "Personal Blog": 3.897827099496511e-05, "Product Page": 2.6225410746493188e-05, "Spam / Ads": 4.1273651535891505e-05, "Structured Data": 0.002566935611183966, "Technical Writing": 0.007545103829094552, "Transcript / Interview": 5.308820423379068e-05, "Tutorial / How-To Guide": 0.005861983131978322, "User Reviews": 2.2011616041271465e-05, "NONE": 0.982496418943464}, "tertiary": {"Academic Writing": 0.00013658037986836528, "Content Listing": 3.7213721713296355e-05, "Creative Writing": 5.925269258875481e-05, "Customer Support Page": 2.7373237880811346e-05, "Q&A Forum": 2.1545672112773316e-05, "Discussion Thread": 1.6565864654974184e-05, "Comment Section": 4.040992787672889e-05, "Missing Content": 0.00016315771309909088, "Knowledge Article": 0.00031040196054240427, "Legal Notices": 9.627921228739006e-05, "News Article": 2.8494055255024042e-05, "Organizational Announcement": 2.5372906996352308e-05, "Nonfiction Writing": 4.6768855481186816e-05, "Organizational About Page": 4.0029129064088036e-05, "Personal About Page": 3.032703479671691e-05, "Personal Blog": 2.669969196831232e-05, "Product Page": 2.1959300337824622e-05, "Spam / Ads": 3.3634066645725034e-05, "Structured Data": 0.0016872882912387364, "Technical Writing": 0.002230736535267566, "Transcript / Interview": 3.369635157981574e-05, "Tutorial / How-To Guide": 0.0026742888044281985, "User Reviews": 1.6235624575475048e-05, "NONE": 0.9921956889697404}, "structured_records": {"false": 0.997369407166528, "true": 0.002630592833471955}, "procedural_steps": {"false": 0.9632301286014467, "true": 0.03676987139855327}, "worked_solution": {"false": 0.994771155954654, "true": 0.005228844045346007}, "contextual_dialogue": {"false": 0.999991026051058, "true": 8.97394894194464e-06}, "content_quality": {"-2": 0.025448370879706476, "-1": 0.1260390813559112, "0": 0.3724001368226756, "1": 0.3910226812278146, "2": 0.08508972971389221}, "noise_severity": {"0": 0.9612352795330762, "1": 0.03481680448579331, "2": 0.0028383346524845894, "3": 0.0006582684267111204, "4": 0.0004513129019346456}, "missing_content_severity": {"0": 0.9838869354905714, "1": 0.010982291670315118, "2": 0.003608237267415937, "3": 0.001176950413639958, "4": 0.00034558515805758257}, "factual_density": {"0": 0.02163856821881067, "1": 0.051310474548980634, "2": 0.18525693068628962, "3": 0.38779800450262303, "4": 0.353996022043296}, "pretraining_utility": {"-2": 0.009127297056879773, "-1": 0.09778405325340003, "0": 0.41423253312895947, "1": 0.3989619598982412, "2": 0.07989415666251942}, "math_density": {"0": 0.06601741118107034, "1": 0.10317921748010132, "2": 0.165919564410635, "3": 0.3124106412242779, "4": 0.3524731657039155}, "code_density": {"0": 0.0002572504957642748, "1": 0.0005737473297141264, "2": 0.017529222219118047, "3": 0.08935807404157588, "4": 0.8922817059138277}, "reasoning_density": {"0": 0.8291262231016113, "1": 0.058437014444772145, "2": 0.06672443737010264, "3": 0.025999525849593398, "4": 0.01971279923392029}, "education_level": {"none": 0.0014182370919361203, "0": 0.0019818888415196285, "1": 0.03920001464363289, "2": 0.7930527660552279, "3": 0.1422778138772075, "4": 0.022069279490475976}, "creative_writing_quality": {"not_applicable": 0.9992159108795189, "-2": 9.912809910900323e-05, "-1": 0.00020445864178211668, "0": 0.00037814627259167687, "1": 7.08943809126115e-05, "2": 3.146172608597098e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001691500289176024, "Art & Design": 0.000263262771938208, "Law & Crime": 0.00019051341363714953, "Education & Careers": 0.00019692279843238304, "Entertainment & Media": 0.0002485994679001349, "Fashion & Beauty": 0.00026760308133375727, "Business, Economics & Finance": 0.0002287827264843828, "Food & Dining": 0.0002276057152735674, "Games & Gaming": 0.0004152527603815153, "Electronics & Hardware": 0.00030094511334908896, "Health, Healthcare & Wellness": 0.00078527117229728, "History & Geography": 0.0002664808748891859, "Home, Pets & Hobbies": 0.0007192315124982596, "Industry & Production": 0.00024635911419484155, "Language, Literature & Philosophy": 0.00017778721534974354, "Politics & Government": 0.00011045440843404312, "Religion & Spirituality": 0.00015999483828721362, "Science, Mathematics & Engineering": 0.09938850238185529, "Relationships & Community": 0.00025601444037636075, "Software Usage & Applications": 0.0007269947723974347, "Programming & Software Engineering": 0.8822619814027931, "Sports & Fitness": 0.009183640173175323, "Vehicles & Transportation": 0.00015219597807701157, "Travel & Tourism": 0.00017381317978158142, "Other / Uncovered Subject": 0.002455730734767013, "Unknown / Insufficient Content": 0.0004269099231782999}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009769849667838094, "Art & Design": 0.0008641853427599598, "Law & Crime": 0.0006266242276344226, "Education & Careers": 0.0005551678270632354, "Entertainment & Media": 0.0009763367010258282, "Fashion & Beauty": 0.0006217054050918615, "Business, Economics & Finance": 0.0006933194639977604, "Food & Dining": 0.0005105069949959585, "Games & Gaming": 0.0009718409900805802, "Electronics & Hardware": 0.0007113847222269649, "Health, Healthcare & Wellness": 0.0020271321657337866, "History & Geography": 0.0005747003545848083, "Home, Pets & Hobbies": 0.0011503706685507317, "Industry & Production": 0.0005028464158251082, "Language, Literature & Philosophy": 0.00030434035054935116, "Politics & Government": 0.00029776729013477796, "Religion & Spirituality": 0.000503042590732061, "Science, Mathematics & Engineering": 0.36631193154844766, "Relationships & Community": 0.0004914669509868165, "Software Usage & Applications": 0.0009014409187951577, "Programming & Software Engineering": 0.08691332912388723, "Sports & Fitness": 0.06670164324885755, "Vehicles & Transportation": 0.00033983365577061966, "Travel & Tourism": 0.0004961387483865775, "Other / Uncovered Subject": 0.0033833982290601026, "NONE": 0.46159256109803726}}, "probabilities": {"primary": {"Academic Writing": 0.0001087086241410908, "Content Listing": 2.8161478004289603e-05, "Creative Writing": 3.0848103048059906e-05, "Customer Support Page": 1.6038185244321694e-05, "Q&A Forum": 2.4179425126473318e-05, "Discussion Thread": 1.6075096747231738e-05, "Comment Section": 2.1690208175084885e-05, "Missing Content": 5.128879777072404e-05, "Knowledge Article": 0.00015289010121223115, "Legal Notices": 3.7743836828521626e-05, "News Article": 9.41062104552551e-06, "Organizational Announcement": 1.6177722755354627e-05, "Nonfiction Writing": 2.7898030045182996e-05, "Organizational About Page": 2.1869303334418378e-05, "Personal About Page": 2.3275667538746288e-05, "Personal Blog": 1.4597754124760315e-05, "Product Page": 1.153015042265015e-05, "Spam / Ads": 2.0686314966658852e-05, "Structured Data": 0.0008140304513322793, "Technical Writing": 0.9929820663901487, "Transcript / Interview": 2.594818399193898e-05, "Tutorial / How-To Guide": 0.002946507004791387, "User Reviews": 1.0933728798792845e-05, "Other / Unknown": 0.002587444820405554}, "secondary": {"Academic Writing": 0.00026611169477806134, "Content Listing": 5.934335231080626e-05, "Creative Writing": 7.713783355747073e-05, "Customer Support Page": 4.850077680856133e-05, "Q&A Forum": 3.447515072952561e-05, "Discussion Thread": 2.6574027920038155e-05, "Comment Section": 4.6192810483350905e-05, "Missing Content": 0.00013792671109882543, "Knowledge Article": 0.00035121083565295126, "Legal Notices": 7.680922458724559e-05, "News Article": 3.933200223279309e-05, "Organizational Announcement": 3.915072321915969e-05, "Nonfiction Writing": 4.6475256605800436e-05, "Organizational About Page": 5.230463921932876e-05, "Personal About Page": 4.643629152277907e-05, "Personal Blog": 3.8978270994965114e-05, "Product Page": 2.622541074649319e-05, "Spam / Ads": 4.127365153589151e-05, "Structured Data": 0.0025669356111839665, "Technical Writing": 0.007545103829094553, "Transcript / Interview": 5.3088204233790684e-05, "Tutorial / How-To Guide": 0.005861983131978323, "User Reviews": 2.201161604127147e-05, "NONE": 0.9824964189434641}, "tertiary": {"Academic Writing": 0.00013658037986836528, "Content Listing": 3.7213721713296355e-05, "Creative Writing": 5.925269258875481e-05, "Customer Support Page": 2.7373237880811346e-05, "Q&A Forum": 2.1545672112773316e-05, "Discussion Thread": 1.6565864654974184e-05, "Comment Section": 4.040992787672889e-05, "Missing Content": 0.00016315771309909088, "Knowledge Article": 0.00031040196054240427, "Legal Notices": 9.627921228739006e-05, "News Article": 2.8494055255024042e-05, "Organizational Announcement": 2.5372906996352308e-05, "Nonfiction Writing": 4.6768855481186816e-05, "Organizational About Page": 4.0029129064088036e-05, "Personal About Page": 3.032703479671691e-05, "Personal Blog": 2.669969196831232e-05, "Product Page": 2.1959300337824622e-05, "Spam / Ads": 3.3634066645725034e-05, "Structured Data": 0.0016872882912387364, "Technical Writing": 0.002230736535267566, "Transcript / Interview": 3.369635157981574e-05, "Tutorial / How-To Guide": 0.0026742888044281985, "User Reviews": 1.6235624575475048e-05, "NONE": 0.9921956889697404}, "structured_records": {"false": 0.997369407166528, "true": 0.002630592833471955}, "procedural_steps": {"false": 0.9632301286014467, "true": 0.03676987139855327}, "worked_solution": {"false": 0.994771155954654, "true": 0.005228844045346007}, "contextual_dialogue": {"false": 0.999991026051058, "true": 8.97394894194464e-06}, "content_quality": {"-2": 0.025448370879706476, "-1": 0.1260390813559112, "0": 0.3724001368226756, "1": 0.3910226812278146, "2": 0.08508972971389221}, "noise_severity": {"0": 0.9612352795330763, "1": 0.034816804485793315, "2": 0.00283833465248459, "3": 0.0006582684267111205, "4": 0.00045131290193464567}, "missing_content_severity": {"0": 0.9838869354905714, "1": 0.010982291670315118, "2": 0.003608237267415937, "3": 0.001176950413639958, "4": 0.00034558515805758257}, "factual_density": {"0": 0.02163856821881067, "1": 0.051310474548980634, "2": 0.18525693068628962, "3": 0.38779800450262303, "4": 0.353996022043296}, "pretraining_utility": {"-2": 0.009127297056879775, "-1": 0.09778405325340005, "0": 0.4142325331289595, "1": 0.39896195989824124, "2": 0.07989415666251944}, "math_density": {"0": 0.06601741118107034, "1": 0.10317921748010132, "2": 0.165919564410635, "3": 0.3124106412242779, "4": 0.3524731657039155}, "code_density": {"0": 0.0002572504957642748, "1": 0.0005737473297141264, "2": 0.017529222219118047, "3": 0.08935807404157588, "4": 0.8922817059138277}, "reasoning_density": {"0": 0.8291262231016115, "1": 0.05843701444477216, "2": 0.06672443737010265, "3": 0.025999525849593405, "4": 0.019712799233920292}, "education_level": {"none": 0.0014182370919361203, "0": 0.0019818888415196285, "1": 0.03920001464363289, "2": 0.7930527660552279, "3": 0.1422778138772075, "4": 0.022069279490475976}, "creative_writing_quality": {"not_applicable": 0.9992159108795187, "-2": 9.912809910900321e-05, "-1": 0.00020445864178211663, "0": 0.00037814627259167676, "1": 7.089438091261149e-05, "2": 3.146172608597097e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00016915002891760244, "Art & Design": 0.00026326277193820807, "Law & Crime": 0.00019051341363714958, "Education & Careers": 0.0001969227984323831, "Entertainment & Media": 0.00024859946790013493, "Fashion & Beauty": 0.0002676030813337573, "Business, Economics & Finance": 0.00022878272648438284, "Food & Dining": 0.00022760571527356745, "Games & Gaming": 0.0004152527603815154, "Electronics & Hardware": 0.000300945113349089, "Health, Healthcare & Wellness": 0.0007852711722972802, "History & Geography": 0.00026648087488918593, "Home, Pets & Hobbies": 0.0007192315124982597, "Industry & Production": 0.0002463591141948416, "Language, Literature & Philosophy": 0.00017778721534974357, "Politics & Government": 0.00011045440843404315, "Religion & Spirituality": 0.00015999483828721365, "Science, Mathematics & Engineering": 0.09938850238185531, "Relationships & Community": 0.0002560144403763608, "Software Usage & Applications": 0.0007269947723974348, "Programming & Software Engineering": 0.8822619814027933, "Sports & Fitness": 0.009183640173175325, "Vehicles & Transportation": 0.0001521959780770116, "Travel & Tourism": 0.00017381317978158145, "Other / Uncovered Subject": 0.0024557307347670134, "Unknown / Insufficient Content": 0.00042690992317830003}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009769849667838094, "Art & Design": 0.0008641853427599598, "Law & Crime": 0.0006266242276344226, "Education & Careers": 0.0005551678270632354, "Entertainment & Media": 0.0009763367010258282, "Fashion & Beauty": 0.0006217054050918615, "Business, Economics & Finance": 0.0006933194639977604, "Food & Dining": 0.0005105069949959585, "Games & Gaming": 0.0009718409900805802, "Electronics & Hardware": 0.0007113847222269649, "Health, Healthcare & Wellness": 0.0020271321657337866, "History & Geography": 0.0005747003545848083, "Home, Pets & Hobbies": 0.0011503706685507317, "Industry & Production": 0.0005028464158251082, "Language, Literature & Philosophy": 0.00030434035054935116, "Politics & Government": 0.00029776729013477796, "Religion & Spirituality": 0.000503042590732061, "Science, Mathematics & Engineering": 0.36631193154844766, "Relationships & Community": 0.0004914669509868165, "Software Usage & Applications": 0.0009014409187951577, "Programming & Software Engineering": 0.08691332912388723, "Sports & Fitness": 0.06670164324885755, "Vehicles & Transportation": 0.00033983365577061966, "Travel & Tourism": 0.0004961387483865775, "Other / Uncovered Subject": 0.0033833982290601026, "NONE": 0.46159256109803726}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9920, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9920, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:35:07.672660+00:00", "scoring_key_slot": "primary", "dataset_uid": "7589cdcd-daff-4ccb-8967-f27f0f66e5b3", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "85dae751a322f65f6c85b904752e08a2f44edba3babaea32b269089652daeee7", "source_metadata": {"uuid": "7589cdcd-daff-4ccb-8967-f27f0f66e5b3", "repo_name": "konrad-c/TimeSeriesClassification", "relative_path": "Python/cricket_normalization.py", "category": "DATA", "algo_rel_score": 0.9760074019432068, "quality_score": 3.8537092208862305, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 25, "repo_created_at": "2017-08-10T00:12:18.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "f21f8942e1f39b90d6f5b5d57b0d2e773bee9520", "size_bytes": 4862, "file_timestamp": 1507461080, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2a6bf8fddd714516047abc3429dc1258a7049456"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 25, "repo_created_at": "2017-08-10T00:12:18.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "f21f8942e1f39b90d6f5b5d57b0d2e773bee9520", "size_bytes": 4862, "file_timestamp": 1507461080, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2a6bf8fddd714516047abc3429dc1258a7049456", "url": "https://github.com/konrad-c/TimeSeriesClassification/blob/2a6bf8fddd714516047abc3429dc1258a7049456/Python/cricket_normalization.py"}, "gpt4o_tokens": 1319, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.38426631754027485, "noise_severity": 0.04427353067863444, "missing_content_severity": 0.023111958078297197, "factual_density": 3.001202437602613, "pretraining_utility": 0.4427116258561205, "math_density": 2.782142932789867, "code_density": 3.872833237547989, "reasoning_density": 0.34873566366943887, "education_level": 2.1434560352016727, "creative_writing_quality": -0.3429418925625247}, "score_applicability": {"education_level": 0.9985817629080639, "creative_writing_quality": 0.0007840891204813791}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/sidequestboy/celery/blob/25022a67c76fbc01a062c9363009bd7d20b4abea/celery.py", "id": "python-0-e3eae38f5c01965c0b58ac15c8f8ba02", "text": "\"\"\"Module with function decorator to register functions as commandline\ncommands and to generate a commandline help text.\n\nAdapted from @uniphil's code for Qcumber API\nhttps://github.com/Queens-Hacks/qcumber-api/blob/master/manage.py\nCopyright 2014 jameh and other contributors\n * Released under the MIT license\n * https://github.com/jameh/celery/blob/master/LICENSE\n\nUsage:\n.. code-block::\n    from celery import command, parse_args, help\n\n    @command\n    def my_func(arg_1, arg_2):\n        \\\"\\\"\\\"does something cool\\\"\\\"\\\"\n        return int(arg_1) ^ int(arg_2)\n\n    if __name__ == '__main__':\n        parse_args()\n\"\"\"\n\nfrom functools import wraps\nimport sys\nimport inspect\nimport __main__\n\n\ndef command(func, _funcs={}):\n    \"\"\"Decorate functions with this to register them as commands\"\"\"\n\n    # register the command\n    func_name = func.__name__.lower()\n    if func_name in _funcs:\n        raise Exception('Duplicate definition for command {}'.format(func_name))\n    if func.__module__ not in _funcs:\n        _funcs[func.__module__] = {}\n\n    _funcs[func.__module__][func_name] = func\n\n    # play nice and leave the command where it was in this script\n    @wraps(func)\n    def wrapped(*args, **kwargs):\n        return func(*args, **kwargs)\n    return wrapped\n\n\ndef parse_args():\n    \"\"\"Get the command, or run 'help' if no command is provided\n    .. note::\n        The \"=\" sign is reserved to indicate keyword arguments e.g.\n\n    .. code::\n        python my_file.py my_func kw_1=3 lala\n        python my_file.py my_func --kw_1 3 lala\n        python my_file.py my_func -kw_1 3 lala\n        python my_file.py my_func --kw_1=3 lala\n        python my_file.py my_func -kw_1=3 lala\n\n    Here, \"lala\" is the first positional argument, kw_1 is a\n    keyword argument with value \"3\"\n    \"\"\"\n    if len(sys.argv) < 2:\n        cmd, raw_args = 'help', []\n    else:\n        cmd, raw_args = sys.argv[1].lower(), sys.argv[2:]\n\n    # Parse raw_args into *args and **kwargs\n    args = []\n    kwargs = {}\n    iter_args = iter(raw_args)\n    for arg in iter_args:\n        if arg[:2] == \"--\":\n            if len(arg.split(\"=\")) == 1:\n                # eat the next arg for value\n                kwargs[arg[2:]] = next(iter_args, \"\")\n            elif len(arg[2:].split(\"=\")) == 2:\n                # use the rhs of \"=\" as value\n                kwargs[arg[2:].split(\"=\")[0]] = arg[2:].split(\"=\")[1]\n            else:\n                raise Exception('Invalid kwarg format \"{}\"'.format(arg))\n        elif arg[:1] == \"-\":\n            if len(arg.split(\"=\")) == 1:\n                # eat the next arg for value\n                kwargs[arg[1:]] = next(iter_args, \"\")\n            elif len(arg[1:].split(\"=\")) == 2:\n                # use the rhs of \"=\" as value\n                kwargs[arg[1:].split(\"=\")[0]] = arg[1:].split(\"=\")[1]\n            else:\n                raise Exception('Invalid kwarg format \"{}\"'.format(arg))\n        elif len(arg.split(\"=\")) == 2:\n            kwargs[arg.split(\"=\")[0]] = arg.split(\"=\")[1]\n        else:\n            # positional arguments\n            args.append(arg)\n\n    # Map the command to a function, falling back to 'help' if it's not found\n    funcs = command.__defaults__[0]['__main__']  # _funcs={}\n    if cmd == 'help':\n        output = command.__defaults__[0][__name__]['help']()\n        print(output)\n        return\n    elif cmd not in funcs:\n        output = command.__defaults__[0][__name__]['help'](cmd)\n        print(output)\n        return\n\n    # do it!\n    try:\n        output = funcs[cmd](*args, **kwargs)\n        # (and print the output)\n        if output:\n            print(output)\n    except Exception as e:\n        help()\n        raise e\n\n\ndef _signature(name, func):\n    \"\"\"Return string repr of function signature\"\"\"\n    defaults = inspect.getfullargspec(func).defaults or []\n    args = inspect.getfullargspec(func).args or []\n    arg_str_list = []\n\n    n_positional_args = len(args) - len(defaults)\n\n    for i in range(n_positional_args):\n        # positional arguments\n        arg_str_list.append('<' + args[i] + '>')\n\n    for i in range(len(defaults)):\n        # keyword arguments\n        arg_str_list.append('[' + args[i + n_positional_args] + '=' + str(defaults[i]) + ']')\n\n    return '{} {}'.format(name, ' '.join(arg_str_list))\n\n\ndef _indent(string, spaces=4, bullet='|'):\n    lines = string.splitlines()\n    for i, line in enumerate(lines):\n        if line[:4] == ' ' * len(line[:4]):\n            # starts with 4 spaces, indent and add a '|'\n            lines[i] = ' ' * spaces + bullet + ' ' + line[4:]\n        else:\n            lines[i] = ' ' * spaces + bullet + ' ' + line\n\n    return '\\n'.join(lines)\n\n\n@command\ndef help(*args, **kwargs):\n    \"\"\"Get usage information about this script\n    Multiple lines!\"\"\"\n\n    text = \"\"\n    for i, f_name in enumerate(args):\n        # Find f_name in available commands\n        try:\n            func = command.__defaults__[0]['__main__'][f_name]\n            text += \"Usage: {} {}\\n\".format(sys.argv[0],\n                                            _signature(f_name, func))\n            if func.__doc__:\n                text += _indent(func.__doc__.strip(), spaces=2) + '\\n'\n            return text\n        except KeyError:\n            text += help() + '\\n'\n            text += 'Command \"{}\" not found :('.format(f_name)\n            return text.strip()\n\n    text += 'Usage: {} [command]\\n'.format(sys.argv[0])\n\n    # print module help here\n    if __main__.__doc__ is not None:\n        text += _indent(__main__.__doc__.strip(), spaces=2)\n\n    text += '\\n'\n    text += 'Available commands:\\n'\n    for name, func in sorted(command.__defaults__[0]['__main__'].items()):  # _funcs={}\n        text += _indent(_signature(name, func), 2, '*') + '\\n'\n        if func.__doc__:\n            text += _indent(func.__doc__.strip(), 4, '|') + '\\n'\n    return text.strip()\n\nif __name__ == '__main__':\n    parse_args()\n", "text_preview": "\"\"\"Module with function decorator to register functions as commandline commands and to generate a commandline help text. Adapted from @uniphil's code for Qcumber API https://github.com/Queens-Hacks/qcumber-api/blob/master/manage.py Copyrigh", "input_sha256": "f6030665a6baff94a56ad6d5d786ba3fe229842963f7f4e60452ebbccf016330", "text_sha256": "e3eae38f5c01965c0b58ac15c8f8ba02a859273914386b5d7a40602c8180a3a0", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12082, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7772003371401899, "noise_severity": 0.11204060168295818, "missing_content_severity": 0.010794985702855822, "factual_density": 3.1081943976901094, "pretraining_utility": 0.5882008601529706, "math_density": 0.35293308163215664, "code_density": 3.558628049067478, "reasoning_density": 0.5372627749210108, "education_level": 2.048370041676942, "creative_writing_quality": -0.1479707328733484}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.05929404848455877, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9931933718793212, "secondary": 0.4179546490840445, "tertiary": 0.41608607319312163, "structured_records": 0.0032140345588596805, "procedural_steps": 0.11681979108127656, "worked_solution": 0.013912940158391372, "contextual_dialogue": 8.805507970571777e-06, "content_quality": 0.5260008119011008, "noise_severity": 0.9066328319308682, "missing_content_severity": 0.9910041785809535, "factual_density": 0.46329199817077527, "pretraining_utility": 0.5162272250308126, "math_density": 0.7058890986398694, "code_density": 0.6321900408895649, "reasoning_density": 0.5522810208991576, "education_level": 0.7915347141146133, "creative_writing_quality": 0.9973347409525916, "primary_topic": 0.9902872431576686, "secondary_topic": 0.9364895120020823}, "probability_margin": {"primary": 0.9893262100736268, "secondary": 0.06258208332903059, "tertiary": 0.001497316282337513, "education_level": 0.7242360584054227, "creative_writing_quality": 0.9963676122654173, "primary_topic": 0.9838170036445576, "secondary_topic": 0.90797889945813}, "raw_probabilities": {"primary": {"Academic Writing": 4.598973716651917e-05, "Content Listing": 3.5497010538018336e-05, "Creative Writing": 1.969827812964152e-05, "Customer Support Page": 4.018094045941621e-05, "Q&A Forum": 1.916500406662176e-05, "Discussion Thread": 1.1551799799200623e-05, "Comment Section": 2.4068086639282283e-05, "Missing Content": 4.875970484251976e-05, "Knowledge Article": 0.00020481806733529527, "Legal Notices": 0.0003802093417402159, "News Article": 7.490397139606565e-06, "Organizational Announcement": 2.4404766316244238e-05, "Nonfiction Writing": 1.745751665333845e-05, "Organizational About Page": 2.232723683793427e-05, "Personal About Page": 2.3635679377094285e-05, "Personal Blog": 1.2686244490776307e-05, "Product Page": 1.2969762043436737e-05, "Spam / Ads": 1.5767987685358163e-05, "Structured Data": 0.00014222229250085146, "Technical Writing": 0.9934769813843495, "Transcript / Interview": 2.1588211051749835e-05, "Tutorial / How-To Guide": 0.00415077131072268, "User Reviews": 1.121246442529217e-05, "Other / Unknown": 0.0012305467756893625}, "secondary": {"Academic Writing": 0.0010225670549923328, "Content Listing": 0.0008923829493875207, "Creative Writing": 0.0007753375549489132, "Customer Support Page": 0.0008957987471759178, "Q&A Forum": 0.0005129667977227524, "Discussion Thread": 0.000381990348490577, "Comment Section": 0.0006371457648058782, "Missing Content": 0.0009355344499367807, "Knowledge Article": 0.0016469662398573047, "Legal Notices": 0.37962445537651207, "News Article": 0.0006689292423147119, "Organizational Announcement": 0.000905941608510527, "Nonfiction Writing": 0.0006545739602742, "Organizational About Page": 0.0007820541601527687, "Personal About Page": 0.0005600208156992087, "Personal Blog": 0.0004202917939387822, "Product Page": 0.0004887703441097821, "Spam / Ads": 0.0006021299158811364, "Structured Data": 0.0007974411507520346, "Technical Writing": 0.032399745731429244, "Transcript / Interview": 0.000902036251641614, "Tutorial / How-To Guide": 0.13091661101502403, "User Reviews": 0.0003697700208992892, "NONE": 0.44220653870554266}, "tertiary": {"Academic Writing": 0.0008244470231664971, "Content Listing": 0.000689158459542368, "Creative Writing": 0.0007198454258758283, "Customer Support Page": 0.0006190034625112408, "Q&A Forum": 0.0003905875371101463, "Discussion Thread": 0.0003227754322074925, "Comment Section": 0.000528900530209213, "Missing Content": 0.0007184518125021821, "Knowledge Article": 0.0013498054881380319, "Legal Notices": 0.43891850386107073, "News Article": 0.0005939141334875095, "Organizational Announcement": 0.0007363554647692482, "Nonfiction Writing": 0.0005316470268418635, "Organizational About Page": 0.0006774767174393384, "Personal About Page": 0.0004633244313723588, "Personal Blog": 0.0002959766909220177, "Product Page": 0.000450244420526455, "Spam / Ads": 0.00047656576932416625, "Structured Data": 0.0004892579997881345, "Technical Writing": 0.014227666959961605, "Transcript / Interview": 0.0006816113510408649, "Tutorial / How-To Guide": 0.09457462905620996, "User Reviews": 0.00030403080257432487, "NONE": 0.44041582014340824}, "structured_records": {"false": 0.9967859654411403, "true": 0.0032140345588596805}, "procedural_steps": {"false": 0.8831802089187234, "true": 0.11681979108127656}, "worked_solution": {"false": 0.9860870598416086, "true": 0.013912940158391372}, "contextual_dialogue": {"false": 0.9999911944920294, "true": 8.805507970571777e-06}, "content_quality": {"-2": 0.010468467558406531, "-1": 0.04184139665431022, "0": 0.2807111483054045, "1": 0.49397930605244444, "2": 0.17299968142943437}, "noise_severity": {"0": 0.8962616454961878, "1": 0.09836577927315839, "2": 0.003408344319772724, "3": 0.000998789873270052, "4": 0.0009654410376110484}, "missing_content_severity": {"0": 0.9920919724050749, "1": 0.0058889810781425625, "2": 0.0013389441319945643, "3": 0.0004922931784276218, "4": 0.00018780920636031614}, "factual_density": {"0": 0.005990514988603501, "1": 0.04074560201108295, "2": 0.16846485326450375, "3": 0.4086770297932201, "4": 0.3761219999425896}, "pretraining_utility": {"-2": 0.007541451620363528, "-1": 0.06434626181489088, "0": 0.3448463564141548, "1": 0.4989018350925931, "2": 0.08436409505799772}, "math_density": {"0": 0.6741570647897625, "1": 0.31299114818121027, "2": 0.004319693514154147, "3": 0.00282582763685441, "4": 0.005706265878018715}, "code_density": {"0": 0.0005548624331408003, "1": 0.0014259868359362033, "2": 0.04870511810742114, "3": 0.3374643044773081, "4": 0.6118497281461939}, "reasoning_density": {"0": 0.7150864698119269, "1": 0.12128277850089983, "2": 0.09918438701971666, "3": 0.040174236289148506, "4": 0.024272128378308014}, "education_level": {"none": 0.0010920182425102668, "0": 0.0022098655182684907, "1": 0.062019517950585044, "2": 0.8262789284288444, "3": 0.10204287002342172, "4": 0.0063567998363699635}, "creative_writing_quality": {"not_applicable": 0.9977789507938263, "-2": 0.0001419990462283804, "-1": 0.0003896505346890898, "0": 0.0014113385284090275, "1": 0.00021112384533386846, "2": 6.693725151334474e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.569764850540367e-05, "Art & Design": 7.255892347835494e-05, "Law & Crime": 0.00011656883025463307, "Education & Careers": 7.088485761080427e-05, "Entertainment & Media": 4.289980157371275e-05, "Fashion & Beauty": 3.9413633324540845e-05, "Business, Economics & Finance": 9.917231722153016e-05, "Food & Dining": 4.2269637394934066e-05, "Games & Gaming": 0.00010279239249650333, "Electronics & Hardware": 0.00013162891318442725, "Health, Healthcare & Wellness": 3.8191168120661565e-05, "History & Geography": 4.730472737300123e-05, "Home, Pets & Hobbies": 4.4212818267502905e-05, "Industry & Production": 5.21726446828207e-05, "Language, Literature & Philosophy": 0.00010211089912396817, "Politics & Government": 3.6756452453418385e-05, "Religion & Spirituality": 5.179048442337259e-05, "Science, Mathematics & Engineering": 0.0007515125374465454, "Relationships & Community": 3.4684791365821406e-05, "Software Usage & Applications": 0.006843807083969875, "Programming & Software Engineering": 0.9906608107285275, "Sports & Fitness": 6.355856346970613e-05, "Vehicles & Transportation": 3.289243451705765e-05, "Travel & Tourism": 5.783453738808861e-05, "Other / Uncovered Subject": 0.00029480483642327495, "Unknown / Insufficient Content": 0.00011366833740263559}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0003921907391967128, "Art & Design": 0.0003014274693829963, "Law & Crime": 0.012241593606595155, "Education & Careers": 0.00040119215600140215, "Entertainment & Media": 0.0002650431344704934, "Fashion & Beauty": 0.00015023550845435593, "Business, Economics & Finance": 0.000519083446706621, "Food & Dining": 0.00016926384462335583, "Games & Gaming": 0.00024908128756215633, "Electronics & Hardware": 0.0003085547226065461, "Health, Healthcare & Wellness": 0.00015488096396867656, "History & Geography": 0.00017043309352096322, "Home, Pets & Hobbies": 0.00013237700121878932, "Industry & Production": 0.00012310408190502217, "Language, Literature & Philosophy": 0.00024893168078085577, "Politics & Government": 0.00018214280422585168, "Religion & Spirituality": 0.00026009613630573734, "Science, Mathematics & Engineering": 0.0010216861790996305, "Relationships & Community": 9.798617706873997e-05, "Software Usage & Applications": 0.030953323620795246, "Programming & Software Engineering": 0.011771873318377406, "Sports & Fitness": 0.0001420275113935045, "Vehicles & Transportation": 8.724559396143518e-05, "Travel & Tourism": 0.0001902021312084237, "Other / Uncovered Subject": 0.0005338007116445038, "NONE": 0.9389322230789253}}, "probabilities": {"primary": {"Academic Writing": 4.5989737166519175e-05, "Content Listing": 3.549701053801834e-05, "Creative Writing": 1.9698278129641524e-05, "Customer Support Page": 4.018094045941622e-05, "Q&A Forum": 1.9165004066621765e-05, "Discussion Thread": 1.1551799799200625e-05, "Comment Section": 2.4068086639282287e-05, "Missing Content": 4.8759704842519764e-05, "Knowledge Article": 0.0002048180673352953, "Legal Notices": 0.00038020934174021594, "News Article": 7.490397139606566e-06, "Organizational Announcement": 2.440476631624424e-05, "Nonfiction Writing": 1.7457516653338454e-05, "Organizational About Page": 2.2327236837934272e-05, "Personal About Page": 2.3635679377094288e-05, "Personal Blog": 1.2686244490776308e-05, "Product Page": 1.2969762043436739e-05, "Spam / Ads": 1.5767987685358166e-05, "Structured Data": 0.00014222229250085148, "Technical Writing": 0.9934769813843496, "Transcript / Interview": 2.1588211051749838e-05, "Tutorial / How-To Guide": 0.004150771310722681, "User Reviews": 1.1212464425292171e-05, "Other / Unknown": 0.0012305467756893627}, "secondary": {"Academic Writing": 0.0010225670549923328, "Content Listing": 0.0008923829493875207, "Creative Writing": 0.0007753375549489132, "Customer Support Page": 0.0008957987471759178, "Q&A Forum": 0.0005129667977227524, "Discussion Thread": 0.000381990348490577, "Comment Section": 0.0006371457648058782, "Missing Content": 0.0009355344499367807, "Knowledge Article": 0.0016469662398573047, "Legal Notices": 0.37962445537651207, "News Article": 0.0006689292423147119, "Organizational Announcement": 0.000905941608510527, "Nonfiction Writing": 0.0006545739602742, "Organizational About Page": 0.0007820541601527687, "Personal About Page": 0.0005600208156992087, "Personal Blog": 0.0004202917939387822, "Product Page": 0.0004887703441097821, "Spam / Ads": 0.0006021299158811364, "Structured Data": 0.0007974411507520346, "Technical Writing": 0.032399745731429244, "Transcript / Interview": 0.000902036251641614, "Tutorial / How-To Guide": 0.13091661101502403, "User Reviews": 0.0003697700208992892, "NONE": 0.44220653870554266}, "tertiary": {"Academic Writing": 0.0008244470231664973, "Content Listing": 0.0006891584595423681, "Creative Writing": 0.0007198454258758284, "Customer Support Page": 0.0006190034625112409, "Q&A Forum": 0.0003905875371101464, "Discussion Thread": 0.00032277543220749253, "Comment Section": 0.0005289005302092131, "Missing Content": 0.0007184518125021822, "Knowledge Article": 0.001349805488138032, "Legal Notices": 0.43891850386107084, "News Article": 0.0005939141334875096, "Organizational Announcement": 0.0007363554647692484, "Nonfiction Writing": 0.0005316470268418636, "Organizational About Page": 0.0006774767174393385, "Personal About Page": 0.00046332443137235893, "Personal Blog": 0.0002959766909220178, "Product Page": 0.0004502444205264551, "Spam / Ads": 0.00047656576932416636, "Structured Data": 0.0004892579997881347, "Technical Writing": 0.014227666959961608, "Transcript / Interview": 0.000681611351040865, "Tutorial / How-To Guide": 0.09457462905620999, "User Reviews": 0.0003040308025743249, "NONE": 0.44041582014340835}, "structured_records": {"false": 0.9967859654411403, "true": 0.0032140345588596805}, "procedural_steps": {"false": 0.8831802089187234, "true": 0.11681979108127656}, "worked_solution": {"false": 0.9860870598416086, "true": 0.013912940158391372}, "contextual_dialogue": {"false": 0.9999911944920294, "true": 8.805507970571777e-06}, "content_quality": {"-2": 0.010468467558406531, "-1": 0.04184139665431022, "0": 0.2807111483054045, "1": 0.49397930605244444, "2": 0.17299968142943437}, "noise_severity": {"0": 0.8962616454961878, "1": 0.09836577927315839, "2": 0.003408344319772724, "3": 0.000998789873270052, "4": 0.0009654410376110484}, "missing_content_severity": {"0": 0.992091972405075, "1": 0.005888981078142563, "2": 0.0013389441319945645, "3": 0.0004922931784276219, "4": 0.00018780920636031617}, "factual_density": {"0": 0.005990514988603502, "1": 0.04074560201108296, "2": 0.16846485326450378, "3": 0.4086770297932202, "4": 0.37612199994258966}, "pretraining_utility": {"-2": 0.007541451620363528, "-1": 0.06434626181489088, "0": 0.3448463564141548, "1": 0.4989018350925931, "2": 0.08436409505799772}, "math_density": {"0": 0.6741570647897625, "1": 0.31299114818121027, "2": 0.004319693514154147, "3": 0.00282582763685441, "4": 0.005706265878018715}, "code_density": {"0": 0.0005548624331408003, "1": 0.0014259868359362033, "2": 0.04870511810742114, "3": 0.3374643044773081, "4": 0.6118497281461939}, "reasoning_density": {"0": 0.715086469811927, "1": 0.12128277850089984, "2": 0.09918438701971667, "3": 0.04017423628914851, "4": 0.024272128378308017}, "education_level": {"none": 0.001092018242510267, "0": 0.002209865518268491, "1": 0.06201951795058505, "2": 0.8262789284288445, "3": 0.10204287002342173, "4": 0.006356799836369964}, "creative_writing_quality": {"not_applicable": 0.9977789507938263, "-2": 0.0001419990462283804, "-1": 0.0003896505346890898, "0": 0.0014113385284090275, "1": 0.00021112384533386846, "2": 6.693725151334474e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.569764850540366e-05, "Art & Design": 7.255892347835492e-05, "Law & Crime": 0.00011656883025463304, "Education & Careers": 7.088485761080425e-05, "Entertainment & Media": 4.2899801573712744e-05, "Fashion & Beauty": 3.941363332454084e-05, "Business, Economics & Finance": 9.917231722153014e-05, "Food & Dining": 4.226963739493406e-05, "Games & Gaming": 0.0001027923924965033, "Electronics & Hardware": 0.00013162891318442723, "Health, Healthcare & Wellness": 3.819116812066156e-05, "History & Geography": 4.730472737300122e-05, "Home, Pets & Hobbies": 4.42128182675029e-05, "Industry & Production": 5.2172644682820685e-05, "Language, Literature & Philosophy": 0.00010211089912396815, "Politics & Government": 3.675645245341838e-05, "Religion & Spirituality": 5.1790484423372574e-05, "Science, Mathematics & Engineering": 0.0007515125374465451, "Relationships & Community": 3.46847913658214e-05, "Software Usage & Applications": 0.006843807083969873, "Programming & Software Engineering": 0.9906608107285273, "Sports & Fitness": 6.355856346970612e-05, "Vehicles & Transportation": 3.2892434517057646e-05, "Travel & Tourism": 5.783453738808859e-05, "Other / Uncovered Subject": 0.0002948048364232749, "Unknown / Insufficient Content": 0.00011366833740263556}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00039219073919671284, "Art & Design": 0.00030142746938299634, "Law & Crime": 0.012241593606595156, "Education & Careers": 0.0004011921560014022, "Entertainment & Media": 0.00026504313447049346, "Fashion & Beauty": 0.00015023550845435596, "Business, Economics & Finance": 0.0005190834467066211, "Food & Dining": 0.00016926384462335586, "Games & Gaming": 0.0002490812875621564, "Electronics & Hardware": 0.00030855472260654616, "Health, Healthcare & Wellness": 0.0001548809639686766, "History & Geography": 0.00017043309352096324, "Home, Pets & Hobbies": 0.00013237700121878935, "Industry & Production": 0.0001231040819050222, "Language, Literature & Philosophy": 0.0002489316807808558, "Politics & Government": 0.0001821428042258517, "Religion & Spirituality": 0.0002600961363057374, "Science, Mathematics & Engineering": 0.0010216861790996307, "Relationships & Community": 9.798617706873998e-05, "Software Usage & Applications": 0.03095332362079525, "Programming & Software Engineering": 0.011771873318377408, "Sports & Fitness": 0.00014202751139350453, "Vehicles & Transportation": 8.72455939614352e-05, "Travel & Tourism": 0.00019020213120842373, "Other / Uncovered Subject": 0.0005338007116445039, "NONE": 0.9389322230789254}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10106, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10106, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:35:07.699067+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "012e0cb1-db8a-4d37-8687-a52ad7f68938", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "b4388991f717a13465d05757d6e510170564f876a08a27d3bacbe89cf53b4119", "source_metadata": {"uuid": "012e0cb1-db8a-4d37-8687-a52ad7f68938", "repo_name": "sidequestboy/celery", "relative_path": "celery.py", "category": "TOOL", "algo_rel_score": 0.933054506778717, "quality_score": 7.645164489746094, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 11, "repo_created_at": "2014-03-09T05:49:25.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 3, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "50f0d3494ecb9677f038c44bbdeabe2e7007aa71", "size_bytes": 5870, "file_timestamp": 1411747895, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "25022a67c76fbc01a062c9363009bd7d20b4abea"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 11, "repo_created_at": "2014-03-09T05:49:25.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 3, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "50f0d3494ecb9677f038c44bbdeabe2e7007aa71", "size_bytes": 5870, "file_timestamp": 1411747895, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "25022a67c76fbc01a062c9363009bd7d20b4abea", "url": "https://github.com/sidequestboy/celery/blob/25022a67c76fbc01a062c9363009bd7d20b4abea/celery.py"}, "gpt4o_tokens": 1497, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.7772003371401899, "noise_severity": 0.11204060168295818, "missing_content_severity": 0.010794985702855822, "factual_density": 3.1081943976901094, "pretraining_utility": 0.5882008601529706, "math_density": 0.35293308163215664, "code_density": 3.558628049067478, "reasoning_density": 0.5372627749210108, "education_level": 2.048370041676942, "creative_writing_quality": -0.1479707328733484}, "score_applicability": {"education_level": 0.9989079817574897, "creative_writing_quality": 0.002221049206173711}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/angelosmath/A-deep-graph-analysis-of-protein-protein-interaction-networks/blob/aca1fca2056d7c5ec48876e88d897e6aa903ff06/model/validation_predictions.py", "id": "python-0-9efe2db827e7c316b31fa57464567e5d", "text": "import os\nimport torch\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport dgl\nfrom model import SAGE  \n\ndef load_pairs_from_csv(csv_path):\n    \"\"\"\n    Load pairs and their corresponding confidence scores from a CSV file.\n    \"\"\"\n    df = pd.read_csv(csv_path)\n    pairs = set(zip(df['gene1'], df['gene2']))\n    pairs |= set((b, a) for a, b in pairs)\n    confidence_scores = df['combined_score'].values\n    return pairs, confidence_scores\n\ndef load_model(model_path, in_size, hidden_size):\n    \"\"\"\n    Load the trained GraphSAGE model from a file.\n    \"\"\"\n    model = SAGE(in_size, hidden_size)\n    model.load_state_dict(torch.load(model_path))\n    model.eval()  # Set the model to evaluation mode idiot!\n    return model\n\ndef load_ucsc_pairs(ucsc_path):\n    \"\"\"\n    Load UCSC dataset and return a set of pairs.\n    \"\"\"\n    ucsc_df = pd.read_csv(ucsc_path)\n    ucsc_pairs = set(zip(ucsc_df['gene1'], ucsc_df['gene2']))\n    ucsc_pairs |= set((b, a) for a, b in ucsc_pairs)\n    return ucsc_pairs\n\ndef filter_pairs_with_scores(pairs, confidence_scores, ucsc_pairs):\n    \"\"\"\n    Filter out pairs that exist in the UCSC dataset and return corresponding confidence scores.\n    \"\"\"\n    num_edges_before = len(pairs)\n    common_pairs = pairs.intersection(ucsc_pairs)\n    num_common_pairs = len(common_pairs)\n\n    # Filter pairs that are not in UCSC dataset\n    filtered_pairs = pairs - ucsc_pairs\n\n    # Filter confidence scores to match filtered pairs\n    filtered_scores = [score for pair, score in zip(pairs, confidence_scores) if pair in filtered_pairs]\n    num_edges_after = len(filtered_pairs)\n\n    print(f\"Number of edges in STRING before filtering: {num_edges_before}\")\n    print(f\"Number of edges common in UCSC and STRING: {num_common_pairs}\")\n    print(f\"Number of edges in STRING after filtering: {num_edges_after}\")\n\n    return filtered_pairs, np.array(filtered_scores)\n\ndef create_dgl_graph_from_pairs(pairs, node_features):\n    \"\"\"\n    Create a DGL graph from filtered pairs.\n    \"\"\"\n    src, dst = zip(*pairs)\n    graph = dgl.graph((src, dst))\n    graph.ndata['feat'] = node_features\n    return graph\n\ndef predict(graph, model):\n    \"\"\"\n    Perform predictions on a given graph using the trained model.\n    \"\"\"\n    with torch.no_grad():\n        node_features = graph.ndata['feat']\n        pair_graph = graph\n        neg_pair_graph = dgl.graph(([], []), num_nodes=graph.number_of_nodes())\n        blocks = [graph]  \n        pos_score, neg_score = model(pair_graph, neg_pair_graph, blocks, node_features)\n        return pos_score\n\ndef plot_violin_box_plots(predictions, label, title, output_path, file_name_prefix, y_axis_label, show_0_5_line=True):\n    \"\"\"\n    Plot and save both a violin plot and a box plot for predictions.\n    \"\"\"\n    if not os.path.exists(output_path):\n        os.makedirs(output_path)\n        \n    predictions = predictions.cpu().numpy()\n    mean_score, max_score, min_score = np.mean(predictions), np.max(predictions), np.min(predictions)\n    print(f\"{label} - Mean: {mean_score}, Max: {max_score}, Min: {min_score}\")\n\n    plt.figure(figsize=(8, 6))\n    sns.violinplot(y=predictions)\n    if show_0_5_line:\n        plt.axhline(0.5, linestyle='--', color='red')\n        plt.legend()\n    plt.ylabel(y_axis_label)\n    plt.xlabel(label)\n    plt.title(f'Violin Plot of {title}')\n    plt.tight_layout()\n    plt.savefig(os.path.join(output_path, f\"{file_name_prefix}_violin_plot.png\"))\n    plt.close()\n\n    plt.figure(figsize=(8, 6))\n    sns.boxplot(y=predictions)\n    if show_0_5_line:\n        plt.axhline(0.5, linestyle='--', color='red')\n    plt.ylabel(y_axis_label)\n    plt.xlabel(label)\n    plt.title(f'Box Plot of {title}')\n    plt.tight_layout()\n    plt.savefig(os.path.join(output_path, f\"{file_name_prefix}_box_plot.png\"))\n    plt.close()\n\ndef categorize_and_summarize(predictions, confidence_scores):\n    \"\"\"\n    Categorize predictions by confidence score ranges and summarize them.\n    \"\"\"\n    print(\"Type of confidence_scores:\", type(confidence_scores))\n    print(\"Shape of confidence_scores:\", confidence_scores.shape)\n    print(\"First few confidence_scores:\", confidence_scores[:10])\n\n    categories = {\n        \"Very high (900-1000)\": (900, 1000),\n        \"High (700-900)\": (700, 900),\n        \"Medium (400-700)\": (400, 700),\n        \"Low (200-400)\": (200, 400),\n        \"Very low (0-200)\": (0, 200)\n    }\n\n    summary = []\n\n    for category, (low, high) in categories.items():\n        print(f\"Processing category: {category} with range {low}-{high}\")\n        indices = (confidence_scores >= low) & (confidence_scores < high)\n        print(\"Type of indices:\", type(indices))\n        print(\"Indices shape:\", indices.shape)\n        category_probs = predictions[indices]\n        mean_prob = category_probs.mean().item()\n        above_0_5 = (category_probs > 0.5).sum().item()\n        total = indices.sum().item()\n        summary.append({\n            \"Confidence Level\": category,\n            \"Number of Samples\": total,\n            \"Mean Probability\": mean_prob,\n            \"Probability > 0.5\": f\"{above_0_5} ({(above_0_5 / total) * 100:.2f}%)\"\n        })\n\n    return pd.DataFrame(summary)\n\ndef main(csv_path, model_path, ucsc_path, in_size, hidden_size, plot_path, output_csv_path):\n    string_pairs, confidence_scores = load_pairs_from_csv(csv_path)\n    model = load_model(model_path, in_size, hidden_size)\n    ucsc_pairs = load_ucsc_pairs(ucsc_path)\n    filtered_pairs, filtered_confidence_scores = filter_pairs_with_scores(string_pairs, confidence_scores, ucsc_pairs)\n    \n    unique_nodes = set([gene for pair in filtered_pairs for gene in pair])\n    node_to_id = {gene: i for i, gene in enumerate(unique_nodes)}\n    node_features = torch.randn(len(unique_nodes), in_size)\n    \n    filtered_pairs_id = [(node_to_id[pair[0]], node_to_id[pair[1]]) for pair in filtered_pairs]\n\n    graph = create_dgl_graph_from_pairs(filtered_pairs_id, node_features)\n\n    pos_score = predict(graph, model).flatten()\n    pos_score_sigmoid = torch.sigmoid(pos_score)\n    \n    print(\"Length of filtered_pairs_id:\", len(filtered_pairs_id))\n    print(\"Length of filtered_confidence_scores:\", len(filtered_confidence_scores))\n    \n    summary_df = categorize_and_summarize(pos_score_sigmoid.cpu().numpy(), filtered_confidence_scores)\n    \n    summary_df.to_csv(output_csv_path, index=False)\n    print(f\"Summary saved to {output_csv_path}\")\n\n    plot_violin_box_plots(pos_score, \"STRING scores\", \"String Scores (Raw)\", plot_path, \"positive_scores\", \"Scores\", show_0_5_line=False)\n    plot_violin_box_plots(pos_score_sigmoid, \"physical STRING samples\", \"String Probabilities\", plot_path, \"positive_probabilities\", \"Probability\")\n\nif __name__ == \"__main__\":\n    csv_path = '/home/angelosmath/MSc/thesis_final_last/mapping/STRING_gene.csv'\n    model_path = '/home/angelosmath/MSc/thesis_final_last/model/run_kantale_last/model.pth'\n    ucsc_path = '/home/angelosmath/MSc/thesis_final_last/data/ucsc/gg_ppi.csv'\n    plot_path = '/home/angelosmath/MSc/thesis_final_last/model/validation'\n    output_csv_path = '/home/angelosmath/MSc/thesis_final_last/model/validation/summary_table.csv'\n    in_size = 200 \n    hidden_size = 200\n    main(csv_path, model_path, ucsc_path, in_size, hidden_size, plot_path, output_csv_path)\n", "text_preview": "import os import torch import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import dgl from model import SAGE def load_pairs_from_csv(csv_path): \"\"\" Load pairs and their corresponding confidence score", "input_sha256": "8158a9019e22fd8ab1ab1d6a13ec58121e828c7641b059c7946d746e680444d5", "text_sha256": "9efe2db827e7c316b31fa57464567e5d4f141744bdc207570125ac7b00e1a47a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12088, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.330333871913438, "noise_severity": 0.19844375321455934, "missing_content_severity": 0.06927904153731396, "factual_density": 2.996162486951061, "pretraining_utility": 0.6319545746640653, "math_density": 2.3250315704474454, "code_density": 3.768172956263938, "reasoning_density": 0.5788380940238841, "education_level": 3.0849844612990878, "creative_writing_quality": -0.5850048105796892}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.5690 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0028780379021768453, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9925245450007427, "secondary": 0.9515028680235608, "tertiary": 0.9540951671702875, "structured_records": 0.052815166323489086, "procedural_steps": 0.13718938721204552, "worked_solution": 0.015459045293349665, "contextual_dialogue": 8.076291780102133e-06, "content_quality": 0.39491118740728304, "noise_severity": 0.8346302056545338, "missing_content_severity": 0.9422674653855717, "factual_density": 0.39723419438388174, "pretraining_utility": 0.527075784515749, "math_density": 0.0, "code_density": 0.806810796886615, "reasoning_density": 0.5176349216467633, "education_level": 0.521404787965868, "creative_writing_quality": 0.9976491044006304, "primary_topic": 0.7099171753931321, "secondary_topic": 0.5517875254171707}, "probability_margin": {"primary": 0.9898906004027271, "secondary": 0.937256554205258, "tertiary": 0.9426188787897147, "education_level": 0.3566464996410761, "creative_writing_quality": 0.9972041565468835, "primary_topic": 0.4569614470345334, "secondary_topic": 0.30105386942930307}, "raw_probabilities": {"primary": {"Academic Writing": 0.001059984845484928, "Content Listing": 3.000103974375808e-05, "Creative Writing": 3.0076754582901183e-05, "Customer Support Page": 1.5945372318508052e-05, "Q&A Forum": 2.246256303393302e-05, "Discussion Thread": 1.3414441758956246e-05, "Comment Section": 2.205825380552371e-05, "Missing Content": 3.074854138815796e-05, "Knowledge Article": 0.00011869381908785377, "Legal Notices": 2.938654244793692e-05, "News Article": 9.212017198090368e-06, "Organizational Announcement": 1.7224213133264967e-05, "Nonfiction Writing": 3.241656118865422e-05, "Organizational About Page": 2.2046098122593254e-05, "Personal About Page": 2.184210851499818e-05, "Personal Blog": 1.520627382490374e-05, "Product Page": 1.1315985803959438e-05, "Spam / Ads": 1.8529512398815152e-05, "Structured Data": 0.00044613006257693825, "Technical Writing": 0.9928360222923784, "Transcript / Interview": 2.907718673708632e-05, "Tutorial / How-To Guide": 0.0029454218896512964, "User Reviews": 1.3081901777520457e-05, "Other / Unknown": 0.002209701723041081}, "secondary": {"Academic Writing": 0.010646183428030358, "Content Listing": 0.00015459695616058363, "Creative Writing": 0.0002591039711324282, "Customer Support Page": 0.0001208269192747474, "Q&A Forum": 0.00011714431646345936, "Discussion Thread": 8.230503084263573e-05, "Comment Section": 0.000233700286825998, "Missing Content": 0.00026695570828172595, "Knowledge Article": 0.0013124059045678746, "Legal Notices": 0.0002051242561405665, "News Article": 0.00010724980933640683, "Organizational Announcement": 0.00010099222516732943, "Nonfiction Writing": 0.00018040832424239916, "Organizational About Page": 0.00012133487872978238, "Personal About Page": 0.00012931757543333674, "Personal Blog": 0.00011870945276433799, "Product Page": 6.2456740974797e-05, "Spam / Ads": 0.00015009794703734646, "Structured Data": 0.004300934200672012, "Technical Writing": 0.016267027650654305, "Transcript / Interview": 0.0001790809561774067, "Tutorial / How-To Guide": 0.011280549491008637, "User Reviews": 7.991211416907916e-05, "NONE": 0.9535235818559123}, "tertiary": {"Academic Writing": 0.009573060658119357, "Content Listing": 0.00014749773500783374, "Creative Writing": 0.0003574129109759903, "Customer Support Page": 0.00011266495586250322, "Q&A Forum": 0.00011495606478637412, "Discussion Thread": 8.797770806321742e-05, "Comment Section": 0.00042059929822008845, "Missing Content": 0.0003184884794413401, "Knowledge Article": 0.0016677182376574442, "Legal Notices": 0.00032181430202036636, "News Article": 0.00010310424243331662, "Organizational Announcement": 9.300423292457411e-05, "Nonfiction Writing": 0.0002627233158476997, "Organizational About Page": 0.00013443693068560843, "Personal About Page": 0.00011883732240106628, "Personal Blog": 0.00013040329968972032, "Product Page": 6.736712749519949e-05, "Spam / Ads": 0.00028600572845111603, "Structured Data": 0.00581183384026042, "Technical Writing": 0.013388989748477463, "Transcript / Interview": 0.00015344557717320522, "Tutorial / How-To Guide": 0.010233301262503833, "User Reviews": 8.648848331003417e-05, "NONE": 0.9560078685381922}, "structured_records": {"false": 0.947184833676511, "true": 0.052815166323489086}, "procedural_steps": {"false": 0.8628106127879545, "true": 0.13718938721204552}, "worked_solution": {"false": 0.9845409547066504, "true": 0.015459045293349665}, "contextual_dialogue": {"false": 0.9999919237082199, "true": 8.076291780102133e-06}, "content_quality": {"-2": 0.02294874589202137, "-1": 0.15198885981486848, "0": 0.3808048910209404, "1": 0.3602947830319902, "2": 0.08396272024017948}, "noise_severity": {"0": 0.8177798519834348, "1": 0.1701592255795928, "2": 0.009165724464105903, "3": 0.0016277131847109022, "4": 0.0012674847881555054}, "missing_content_severity": {"0": 0.9640873731529266, "1": 0.008626542339487895, "2": 0.0216944478770422, "3": 0.005102943078431048, "4": 0.0004886935521121258}, "factual_density": {"0": 0.01573964412920923, "1": 0.056985973446682645, "2": 0.2023873606131476, "3": 0.3651462949657594, "4": 0.35974072684520114}, "pretraining_utility": {"-2": 0.008667408023662462, "-1": 0.05141706200854312, "0": 0.33894089387044435, "1": 0.5012428194747668, "2": 0.09973181662258322}, "math_density": {"0": 0.11861696138939315, "1": 0.1787906342450162, "2": 0.23069717553464986, "3": 0.20273433019063353, "4": 0.26916089864030723}, "code_density": {"0": 0.00028903565856089845, "1": 0.0005915668749352138, "2": 0.02675333781582348, "3": 0.17538952484536588, "4": 0.7969765348053146}, "reasoning_density": {"0": 0.7390065000837416, "1": 0.06736984673910698, "2": 0.10792325984404463, "3": 0.047179845735739245, "4": 0.0385205475973675}, "education_level": {"none": 0.001403339266919729, "0": 0.001552387178079183, "1": 0.0036523362758105822, "2": 0.1476971236438199, "3": 0.6011706566382233, "4": 0.24452415699714722}, "creative_writing_quality": {"not_applicable": 0.9980409203338586, "-2": 0.0002505824513610144, "-1": 0.0008367637869750492, "0": 0.0007249460792600041, "1": 0.00010171703639544538, "2": 4.5070312149998416e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0004950529781478526, "Art & Design": 0.0006897372644920823, "Law & Crime": 0.000628114750569513, "Education & Careers": 0.000447648476615395, "Entertainment & Media": 0.00036476017876564725, "Fashion & Beauty": 0.00033280752996727456, "Business, Economics & Finance": 0.0004754330777895228, "Food & Dining": 0.0003784275882099795, "Games & Gaming": 0.0005348849299102594, "Electronics & Hardware": 0.0004999166455664948, "Health, Healthcare & Wellness": 0.00278397349890126, "History & Geography": 0.0006078736341936276, "Home, Pets & Hobbies": 0.00033118313777977975, "Industry & Production": 0.000453214277369753, "Language, Literature & Philosophy": 0.0003542158601370348, "Politics & Government": 0.00021990249888888112, "Religion & Spirituality": 0.0003395684065601764, "Science, Mathematics & Engineering": 0.2641127600742475, "Relationships & Community": 0.00028250180508949995, "Software Usage & Applications": 0.0011788793747590908, "Programming & Software Engineering": 0.7210742071087809, "Sports & Fitness": 0.0004656190653768521, "Vehicles & Transportation": 0.00024405816033441297, "Travel & Tourism": 0.00044421525843743977, "Other / Uncovered Subject": 0.0017600565448782429, "Unknown / Insufficient Content": 0.0005009878742313305}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0012205286903780746, "Art & Design": 0.0013290379575788388, "Law & Crime": 0.0007212115904155687, "Education & Careers": 0.000537347948950768, "Entertainment & Media": 0.0005037500314737468, "Fashion & Beauty": 0.000516392248423151, "Business, Economics & Finance": 0.0005699317556312574, "Food & Dining": 0.00043259738751806246, "Games & Gaming": 0.0005136464209671774, "Electronics & Hardware": 0.0006515799481183627, "Health, Healthcare & Wellness": 0.0027848056017556197, "History & Geography": 0.0005491677299254163, "Home, Pets & Hobbies": 0.0003698234764227592, "Industry & Production": 0.0005313303172944503, "Language, Literature & Philosophy": 0.0003151546982311014, "Politics & Government": 0.00033607684601206177, "Religion & Spirituality": 0.000541097019772365, "Science, Mathematics & Engineering": 0.5690264667472795, "Relationships & Community": 0.00031339103605190294, "Software Usage & Applications": 0.0012613222925134807, "Programming & Software Engineering": 0.26797259731797646, "Sports & Fitness": 0.0005315477429657852, "Vehicles & Transportation": 0.0002516881437235126, "Travel & Tourism": 0.0005978258702336617, "Other / Uncovered Subject": 0.00169806775288877, "NONE": 0.14592361342749813}}, "probabilities": {"primary": {"Academic Writing": 0.001059984845484928, "Content Listing": 3.000103974375808e-05, "Creative Writing": 3.0076754582901183e-05, "Customer Support Page": 1.5945372318508052e-05, "Q&A Forum": 2.246256303393302e-05, "Discussion Thread": 1.3414441758956246e-05, "Comment Section": 2.205825380552371e-05, "Missing Content": 3.074854138815796e-05, "Knowledge Article": 0.00011869381908785377, "Legal Notices": 2.938654244793692e-05, "News Article": 9.212017198090368e-06, "Organizational Announcement": 1.7224213133264967e-05, "Nonfiction Writing": 3.241656118865422e-05, "Organizational About Page": 2.2046098122593254e-05, "Personal About Page": 2.184210851499818e-05, "Personal Blog": 1.520627382490374e-05, "Product Page": 1.1315985803959438e-05, "Spam / Ads": 1.8529512398815152e-05, "Structured Data": 0.00044613006257693825, "Technical Writing": 0.9928360222923784, "Transcript / Interview": 2.907718673708632e-05, "Tutorial / How-To Guide": 0.0029454218896512964, "User Reviews": 1.3081901777520457e-05, "Other / Unknown": 0.002209701723041081}, "secondary": {"Academic Writing": 0.01064618342803036, "Content Listing": 0.00015459695616058366, "Creative Writing": 0.0002591039711324283, "Customer Support Page": 0.00012082691927474741, "Q&A Forum": 0.00011714431646345938, "Discussion Thread": 8.230503084263574e-05, "Comment Section": 0.00023370028682599803, "Missing Content": 0.000266955708281726, "Knowledge Article": 0.0013124059045678748, "Legal Notices": 0.00020512425614056652, "News Article": 0.00010724980933640684, "Organizational Announcement": 0.00010099222516732944, "Nonfiction Writing": 0.0001804083242423992, "Organizational About Page": 0.0001213348787297824, "Personal About Page": 0.00012931757543333677, "Personal Blog": 0.000118709452764338, "Product Page": 6.245674097479701e-05, "Spam / Ads": 0.0001500979470373465, "Structured Data": 0.004300934200672013, "Technical Writing": 0.016267027650654308, "Transcript / Interview": 0.00017908095617740673, "Tutorial / How-To Guide": 0.011280549491008639, "User Reviews": 7.991211416907917e-05, "NONE": 0.9535235818559125}, "tertiary": {"Academic Writing": 0.009573060658119357, "Content Listing": 0.00014749773500783374, "Creative Writing": 0.0003574129109759903, "Customer Support Page": 0.00011266495586250322, "Q&A Forum": 0.00011495606478637412, "Discussion Thread": 8.797770806321742e-05, "Comment Section": 0.00042059929822008845, "Missing Content": 0.0003184884794413401, "Knowledge Article": 0.0016677182376574442, "Legal Notices": 0.00032181430202036636, "News Article": 0.00010310424243331662, "Organizational Announcement": 9.300423292457411e-05, "Nonfiction Writing": 0.0002627233158476997, "Organizational About Page": 0.00013443693068560843, "Personal About Page": 0.00011883732240106628, "Personal Blog": 0.00013040329968972032, "Product Page": 6.736712749519949e-05, "Spam / Ads": 0.00028600572845111603, "Structured Data": 0.00581183384026042, "Technical Writing": 0.013388989748477463, "Transcript / Interview": 0.00015344557717320522, "Tutorial / How-To Guide": 0.010233301262503833, "User Reviews": 8.648848331003417e-05, "NONE": 0.9560078685381922}, "structured_records": {"false": 0.947184833676511, "true": 0.052815166323489086}, "procedural_steps": {"false": 0.8628106127879545, "true": 0.13718938721204552}, "worked_solution": {"false": 0.9845409547066504, "true": 0.015459045293349665}, "contextual_dialogue": {"false": 0.9999919237082199, "true": 8.076291780102133e-06}, "content_quality": {"-2": 0.022948745892021373, "-1": 0.1519888598148685, "0": 0.38080489102094045, "1": 0.36029478303199025, "2": 0.08396272024017949}, "noise_severity": {"0": 0.817779851983435, "1": 0.17015922557959282, "2": 0.009165724464105905, "3": 0.0016277131847109024, "4": 0.0012674847881555056}, "missing_content_severity": {"0": 0.9640873731529267, "1": 0.008626542339487897, "2": 0.021694447877042205, "3": 0.0051029430784310485, "4": 0.0004886935521121259}, "factual_density": {"0": 0.01573964412920923, "1": 0.056985973446682645, "2": 0.2023873606131476, "3": 0.3651462949657594, "4": 0.35974072684520114}, "pretraining_utility": {"-2": 0.008667408023662464, "-1": 0.051417062008543124, "0": 0.3389408938704444, "1": 0.5012428194747669, "2": 0.09973181662258324}, "math_density": {"0": 0.11861696138939315, "1": 0.1787906342450162, "2": 0.23069717553464986, "3": 0.20273433019063353, "4": 0.26916089864030723}, "code_density": {"0": 0.00028903565856089845, "1": 0.0005915668749352138, "2": 0.02675333781582348, "3": 0.17538952484536588, "4": 0.7969765348053146}, "reasoning_density": {"0": 0.7390065000837417, "1": 0.067369846739107, "2": 0.10792325984404465, "3": 0.04717984573573925, "4": 0.038520547597367505}, "education_level": {"none": 0.001403339266919729, "0": 0.001552387178079183, "1": 0.0036523362758105822, "2": 0.1476971236438199, "3": 0.6011706566382233, "4": 0.24452415699714722}, "creative_writing_quality": {"not_applicable": 0.9980409203338584, "-2": 0.0002505824513610143, "-1": 0.000836763786975049, "0": 0.000724946079260004, "1": 0.00010171703639544535, "2": 4.507031214999841e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0004950529781478527, "Art & Design": 0.0006897372644920824, "Law & Crime": 0.0006281147505695132, "Education & Careers": 0.00044764847661539513, "Entertainment & Media": 0.0003647601787656473, "Fashion & Beauty": 0.0003328075299672746, "Business, Economics & Finance": 0.0004754330777895229, "Food & Dining": 0.0003784275882099796, "Games & Gaming": 0.0005348849299102595, "Electronics & Hardware": 0.0004999166455664949, "Health, Healthcare & Wellness": 0.0027839734989012605, "History & Geography": 0.0006078736341936277, "Home, Pets & Hobbies": 0.0003311831377797798, "Industry & Production": 0.0004532142773697531, "Language, Literature & Philosophy": 0.00035421586013703485, "Politics & Government": 0.00021990249888888117, "Religion & Spirituality": 0.0003395684065601765, "Science, Mathematics & Engineering": 0.26411276007424755, "Relationships & Community": 0.0002825018050895, "Software Usage & Applications": 0.001178879374759091, "Programming & Software Engineering": 0.721074207108781, "Sports & Fitness": 0.0004656190653768522, "Vehicles & Transportation": 0.00024405816033441302, "Travel & Tourism": 0.0004442152584374399, "Other / Uncovered Subject": 0.0017600565448782433, "Unknown / Insufficient Content": 0.0005009878742313306}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0012205286903780746, "Art & Design": 0.0013290379575788388, "Law & Crime": 0.0007212115904155687, "Education & Careers": 0.000537347948950768, "Entertainment & Media": 0.0005037500314737468, "Fashion & Beauty": 0.000516392248423151, "Business, Economics & Finance": 0.0005699317556312574, "Food & Dining": 0.00043259738751806246, "Games & Gaming": 0.0005136464209671774, "Electronics & Hardware": 0.0006515799481183627, "Health, Healthcare & Wellness": 0.0027848056017556197, "History & Geography": 0.0005491677299254163, "Home, Pets & Hobbies": 0.0003698234764227592, "Industry & Production": 0.0005313303172944503, "Language, Literature & Philosophy": 0.0003151546982311014, "Politics & Government": 0.00033607684601206177, "Religion & Spirituality": 0.000541097019772365, "Science, Mathematics & Engineering": 0.5690264667472795, "Relationships & Community": 0.00031339103605190294, "Software Usage & Applications": 0.0012613222925134807, "Programming & Software Engineering": 0.26797259731797646, "Sports & Fitness": 0.0005315477429657852, "Vehicles & Transportation": 0.0002516881437235126, "Travel & Tourism": 0.0005978258702336617, "Other / Uncovered Subject": 0.00169806775288877, "NONE": 0.14592361342749813}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10489, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10489, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:35:08.283860+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "a146a70f-905a-4b21-b113-64f82f344fb1", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "a8025ce8a0db1adfe03e90febc7af53534ce6b32d874f6c369b426ee76f6863b", "source_metadata": {"uuid": "a146a70f-905a-4b21-b113-64f82f344fb1", "repo_name": "angelosmath/A-deep-graph-analysis-of-protein-protein-interaction-networks", "relative_path": "model/validation_predictions.py", "category": "DATA", "algo_rel_score": 0.9759638905525208, "quality_score": 7.987972736358643, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 11, "repo_created_at": "2024-09-25T19:25:58.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "218399df6441553439d89b0b142746f3e04e0071", "size_bytes": 7278, "file_timestamp": 1732482749, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "aca1fca2056d7c5ec48876e88d897e6aa903ff06"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 11, "repo_created_at": "2024-09-25T19:25:58.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "218399df6441553439d89b0b142746f3e04e0071", "size_bytes": 7278, "file_timestamp": 1732482749, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "aca1fca2056d7c5ec48876e88d897e6aa903ff06", "url": "https://github.com/angelosmath/A-deep-graph-analysis-of-protein-protein-interaction-networks/blob/aca1fca2056d7c5ec48876e88d897e6aa903ff06/model/validation_predictions.py"}, "gpt4o_tokens": 1812, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.330333871913438, "noise_severity": 0.19844375321455934, "missing_content_severity": 0.06927904153731396, "factual_density": 2.996162486951061, "pretraining_utility": 0.6319545746640652, "math_density": 2.3250315704474454, "code_density": 3.768172956263938, "reasoning_density": 0.5788380940238841, "education_level": 3.0849844612990878, "creative_writing_quality": -0.5850048105796892}, "score_applicability": {"education_level": 0.9985966607330802, "creative_writing_quality": 0.001959079666141511}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/Xingyetan89/Data-processing-for-investigation-on-population-dynamics/blob/de55fb6ac218c6a9af7537dcde7cb951d6c29a71/Preprocessing/Chinese%20cities/data_process.py", "id": "python-0-008b0192e0e1a1363baeb9f5b082cbc6", "text": "# -*- coding: utf-8 -*-\n\nimport pandas as pd     # Imports the Pandas library and aliases it as 'pd' for easier usage.\n# Pandas is commonly used for data manipulation and analysis.\nimport os   # Imports the 'os' module, which provides functions for interacting with the operating system\n# (e.g., file and directory operations).\n\n\n# data_preprocess() is a function to preprocess data from a file containing check-in data. The function reads the file\n# screen data for specific cities, and saves the results into region-specific CSV files.\ndef data_preprocess(file_path):\n\n    # Open the file specified by 'file_path' for reading. Reads the entire content of the file into a string.\n    # Close the file to release the file resource.\n    f = open(file_path)\n    test_str = f.read()\n    f.close()\n\n    # Clean the format of raw data by removing {}, replacing spaces with commas. Separate data by colon marks and\n    # replace quotation marks with commas to distinguish and obtain date and time information.\n    data = test_str.replace('{', '').replace('}', '').replace(' ', ',')\n    data1 = data.split('\":\"')\n    time1 = data1[1]\n    time2 = time1.replace('\"', '')\n    time3 = time2.split(',')\n    date = time3[0]\n    time = time3[1]\n\n    # Extract and remove unnecessary symbols from the check-in data. Establish an empty set to store the subsequent\n    # filtering results for specific areas.\n    data2 = data1[2]\n    data3 = data2.replace('{', '').replace('}', '').replace(' ', '').replace(':', '').replace('\"', '')\n    data4 = data3.split(',')\n    tempBJ = []\n    tempSH = []\n    tempGZ = []\n    tempSZ = []\n\n    # Screen check-in data based on the geographical coordinates of specific areas (Here shows examples of Beijing,\n    # Shanghai, Guangzhou, and Shenzhen) and arrange the results by date, time, latitude, longitude, and check-in count.\n    for i in range(int(len(data4)/3)):\n        lat = data4[0 + i * 3]\n        lon = data4[1 + i * 3]\n        count = data4[2 + i * 3]\n        if(3932 < int(lat) < 4114 and 11536 < int(lon) < 11753):\n            tempBJ.append([date, time, int(lat)/100, int(lon)/100, count])\n        if(3067 < int(lat) < 3192 and 12081 < int(lon) < 12209):\n            tempSH.append([date, time, int(lat)/100, int(lon)/100, count])\n        if(2254 < int(lat) < 2399 and 11289 < int(lon) < 11408):\n            tempGZ.append([date, time, int(lat)/100, int(lon)/100, count])\n        if(2242 < int(lat) < 2289 and 11370 < int(lon) < 11466):\n            tempSZ.append([date, time, int(lat)/100, int(lon)/100, count])\n\n    # Convert the lists for each city into Pandas DataFrames and sets column names\n    result1 = pd.DataFrame(tempBJ)\n    result2 = pd.DataFrame(tempSH)\n    result3 = pd.DataFrame(tempGZ)\n    result4 = pd.DataFrame(tempSZ)\n    result1.columns = ['date', 'time', 'lat', 'lon', 'count']\n    result2.columns = ['date', 'time', 'lat', 'lon', 'count']\n    result3.columns = ['date', 'time', 'lat', 'lon', 'count']\n    result4.columns = ['date', 'time', 'lat', 'lon', 'count']\n\n    # Write the DataFrames to CSV files in append mode. For the first file (c == 1), includes the header.\n    # For subsequent files, excludes the header.\n    if c == 1:\n        result1.to_csv('TecentData_BJ.txt', mode='a', index=False)\n        result2.to_csv('TecentData_SH.txt', mode='a', index=False)\n        result3.to_csv('TecentData_GZ.txt', mode='a', index=False)\n        result4.to_csv('TecentData_SZ.txt', mode='a', index=False)\n    else:\n        result1.to_csv('TecentData_BJ.txt', mode='a', header=False, index=False)\n        result2.to_csv('TecentData_SH.txt', mode='a', header=False, index=False)\n        result3.to_csv('TecentData_GZ.txt', mode='a', header=False, index=False)\n        result4.to_csv('TecentData_SZ.txt', mode='a', header=False, index=False)\n\n\n# Iterate through all files in the specified directory and its subdirectories.\n# For each file, increments the counter c, constructs the full file path, and calls data_preprocess().\nif __name__ == '__main__':\n    c = 0\n    for root, dirs, files in os.walk(r'G:/GRMD PhD/Research Project/POP Time series analysis and DFA/Tecent_Data/2018-04-29'):\n        for file in files:\n            c = c + 1\n            dir_file_path = os.path.join(root, file)\n            data_preprocess(dir_file_path)\n            print(c)\n\n", "text_preview": "# -*- coding: utf-8 -*- import pandas as pd # Imports the Pandas library and aliases it as 'pd' for easier usage. # Pandas is commonly used for data manipulation and analysis. import os # Imports the 'os' module, which provides functions fo", "input_sha256": "6b2e32fdb773fa40247838075397a0971a110f0431229f553e292ae8038ff052", "text_sha256": "008b0192e0e1a1363baeb9f5b082cbc641fcc17523709198566af573e3c8a84e", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12089, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.32376568161773833, "noise_severity": 0.04879767560159787, "missing_content_severity": 0.012148819561731439, "factual_density": 2.983431597728025, "pretraining_utility": 0.49213198252151397, "math_density": 1.433113777921485, "code_density": 3.623655699157656, "reasoning_density": 0.47805416823228813, "education_level": 2.054396971749721, "creative_writing_quality": -0.360280158316524}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.3599 below 0.70", "secondary_topic vs NONE margin 0.0195 below 0.10"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0170302632437368, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9852226173737557, "secondary": 0.9426372198842189, "tertiary": 0.9604079293559442, "structured_records": 0.00892263525562788, "procedural_steps": 0.12747824259440607, "worked_solution": 0.013817373981907348, "contextual_dialogue": 7.937429218526175e-06, "content_quality": 0.3414543657107838, "noise_severity": 0.9593352703320017, "missing_content_severity": 0.9898759836985571, "factual_density": 0.4201711088053576, "pretraining_utility": 0.4642456504300011, "math_density": 0.0, "code_density": 0.6863797492980467, "reasoning_density": 0.6016215264730933, "education_level": 0.7508234459917618, "creative_writing_quality": 0.9986490338685318, "primary_topic": 0.8356861141958938, "secondary_topic": 0.33426276718204734}, "probability_margin": {"primary": 0.9772457816300861, "secondary": 0.9193413297463835, "tertiary": 0.946579572882249, "education_level": 0.6878116059807617, "creative_writing_quality": 0.9983982909138029, "primary_topic": 0.7492066782111048, "secondary_topic": 0.019493809997701728}, "raw_probabilities": {"primary": {"Academic Writing": 0.0013617385638831953, "Content Listing": 5.01027034376492e-05, "Creative Writing": 4.509701354564213e-05, "Customer Support Page": 3.040437807710209e-05, "Q&A Forum": 4.715213642402317e-05, "Discussion Thread": 2.482966390378125e-05, "Comment Section": 5.085316046269743e-05, "Missing Content": 6.0725826677316444e-05, "Knowledge Article": 0.0004036574217398466, "Legal Notices": 4.633713857383985e-05, "News Article": 1.8698093504744206e-05, "Organizational Announcement": 2.862504322776216e-05, "Nonfiction Writing": 6.727164823785989e-05, "Organizational About Page": 3.920462410329434e-05, "Personal About Page": 3.981712732446726e-05, "Personal Blog": 3.183121401322559e-05, "Product Page": 1.6542164810254184e-05, "Spam / Ads": 3.40185702734907e-05, "Structured Data": 0.000982762592383479, "Technical Writing": 0.9858383416498492, "Transcript / Interview": 5.108938029328763e-05, "Tutorial / How-To Guide": 0.008592560019763051, "User Reviews": 1.9292324641278836e-05, "Other / Unknown": 0.0021190475408492757}, "secondary": {"Academic Writing": 0.0046958356339898664, "Content Listing": 0.0001704969617930569, "Creative Writing": 0.0002156262157744205, "Customer Support Page": 0.0001592324012098686, "Q&A Forum": 0.00012178966635888208, "Discussion Thread": 7.397071550561858e-05, "Comment Section": 0.000351861685852179, "Missing Content": 0.00033664891804561565, "Knowledge Article": 0.003673966384417659, "Legal Notices": 0.00015589574554013207, "News Article": 0.0001081668686659499, "Organizational Announcement": 0.0001011095963851402, "Nonfiction Writing": 0.000173842387231601, "Organizational About Page": 0.00013179935454818225, "Personal About Page": 0.000127433426988869, "Personal Blog": 0.0001368624777399942, "Product Page": 7.2224702049147e-05, "Spam / Ads": 0.00013423003833627325, "Structured Data": 0.004857110825692367, "Technical Writing": 0.013268747663020898, "Transcript / Interview": 0.0001517562705222635, "Tutorial / How-To Guide": 0.025686005975992878, "User Reviews": 6.805036196269997e-05, "NONE": 0.9450273357223764}, "tertiary": {"Academic Writing": 0.004661554879234562, "Content Listing": 9.618794994176443e-05, "Creative Writing": 0.00013655766446319458, "Customer Support Page": 0.00010214912259331328, "Q&A Forum": 7.211080156492844e-05, "Discussion Thread": 4.794237496088277e-05, "Comment Section": 0.000209778266403951, "Missing Content": 0.0003144754061047228, "Knowledge Article": 0.004454700728577429, "Legal Notices": 0.00016210253415145244, "News Article": 6.707234450305396e-05, "Organizational Announcement": 6.531847481233488e-05, "Nonfiction Writing": 0.00015707006911950007, "Organizational About Page": 0.00010229384053844935, "Personal About Page": 8.734728091639552e-05, "Personal Blog": 0.0001007279003712922, "Product Page": 5.839551955909372e-05, "Spam / Ads": 8.287824358737918e-05, "Structured Data": 0.0030996493236928766, "Technical Writing": 0.008250578781423126, "Transcript / Interview": 9.79592615441995e-05, "Tutorial / How-To Guide": 0.015478026083864171, "User Reviews": 3.752418195877116e-05, "NONE": 0.9620575989661132}, "structured_records": {"false": 0.9910773647443721, "true": 0.00892263525562788}, "procedural_steps": {"false": 0.8725217574055939, "true": 0.12747824259440607}, "worked_solution": {"false": 0.9861826260180927, "true": 0.013817373981907348}, "contextual_dialogue": {"false": 0.9999920625707814, "true": 7.937429218526175e-06}, "content_quality": {"-2": 0.038299975025083, "-1": 0.15664458971449458, "0": 0.35202674335502315, "1": 0.34904716242839956, "2": 0.10398152947699965}, "noise_severity": {"0": 0.9560649952464111, "1": 0.04058080849774844, "2": 0.002300223768545082, "3": 0.0005994703824226321, "4": 0.0004545021048728414}, "missing_content_severity": {"0": 0.9931846244437216, "1": 0.002688562100638768, "2": 0.0031600641354480076, "3": 0.0007268680905706471, "4": 0.00023988122962117914}, "factual_density": {"0": 0.011698704149419717, "1": 0.05493291718606474, "2": 0.21121958903238444, "3": 0.38253565605133316, "4": 0.3396131335807978}, "pretraining_utility": {"-2": 0.008177854025886933, "-1": 0.08058239103371757, "0": 0.3896882743361465, "1": 0.45403287960149274, "2": 0.06751860100275632}, "math_density": {"0": 0.31204519427252014, "1": 0.29627531159398846, "2": 0.16898896250276702, "3": 0.09190158520093515, "4": 0.13078894642978936}, "code_density": {"0": 0.00043462299887693767, "1": 0.0010129119309526301, "2": 0.04142754813440822, "3": 0.28871197678516186, "4": 0.6684129401506004}, "reasoning_density": {"0": 0.7563524134020787, "1": 0.07930652345529264, "2": 0.109023911494553, "3": 0.04056878480441262, "4": 0.014748366843662894}, "education_level": {"none": 0.0014726141514248257, "0": 0.003376728942384557, "1": 0.0799946603098808, "2": 0.7923528716598015, "3": 0.10454126567903978, "4": 0.018261859257468687}, "creative_writing_quality": {"not_applicable": 0.9988741948904432, "-2": 0.00014415116755838993, "-1": 0.0003348045044278235, "0": 0.00047590397664019435, "1": 0.00012438932542104244, "2": 4.655613550942853e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00019077783543051553, "Art & Design": 0.0003327197530835094, "Law & Crime": 0.0003697374448292437, "Education & Careers": 0.0002697337766303795, "Entertainment & Media": 0.0003677428454834624, "Fashion & Beauty": 0.0003069124370880919, "Business, Economics & Finance": 0.0012825673081459164, "Food & Dining": 0.0003205935149707998, "Games & Gaming": 0.0003485001409608562, "Electronics & Hardware": 0.0003377529546767584, "Health, Healthcare & Wellness": 0.0006545417299481552, "History & Geography": 0.008823615132621762, "Home, Pets & Hobbies": 0.00023754428420236497, "Industry & Production": 0.0003237381042826328, "Language, Literature & Philosophy": 0.00023284448264696494, "Politics & Government": 0.0011060914045341625, "Religion & Spirituality": 0.0002942286399444318, "Science, Mathematics & Engineering": 0.09279920082340853, "Relationships & Community": 0.005368890244448655, "Software Usage & Applications": 0.0009137563827931947, "Programming & Software Engineering": 0.8420058790345133, "Sports & Fitness": 0.0003194027274379359, "Vehicles & Transportation": 0.00026122123301682525, "Travel & Tourism": 0.0009192681520302613, "Other / Uncovered Subject": 0.04120595199322399, "Unknown / Insufficient Content": 0.0004067876196473201}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007856938668238171, "Art & Design": 0.0008824737624419303, "Law & Crime": 0.0010755550784890316, "Education & Careers": 0.0005351682791481981, "Entertainment & Media": 0.0010628516469836844, "Fashion & Beauty": 0.0006183536312488613, "Business, Economics & Finance": 0.002675980557350333, "Food & Dining": 0.0006191269266800344, "Games & Gaming": 0.0007190142989295894, "Electronics & Hardware": 0.0008212007960582353, "Health, Healthcare & Wellness": 0.0015748210098885194, "History & Geography": 0.11365945643104264, "Home, Pets & Hobbies": 0.0004919244096285501, "Industry & Production": 0.0007182158956643678, "Language, Literature & Philosophy": 0.0003624210570683642, "Politics & Government": 0.0023066909391501957, "Religion & Spirituality": 0.0008126175232528118, "Science, Mathematics & Engineering": 0.35986804536735323, "Relationships & Community": 0.011679667471004166, "Software Usage & Applications": 0.0025546697502583132, "Programming & Software Engineering": 0.09243956372279608, "Sports & Fitness": 0.0004845034009413971, "Vehicles & Transportation": 0.0005462921469143438, "Travel & Tourism": 0.0017160886319490568, "Other / Uncovered Subject": 0.06061536802928283, "NONE": 0.3403742353696515}}, "probabilities": {"primary": {"Academic Writing": 0.0013617385638831955, "Content Listing": 5.010270343764921e-05, "Creative Writing": 4.509701354564214e-05, "Customer Support Page": 3.0404378077102095e-05, "Q&A Forum": 4.7152136424023186e-05, "Discussion Thread": 2.4829663903781257e-05, "Comment Section": 5.0853160462697446e-05, "Missing Content": 6.072582667731646e-05, "Knowledge Article": 0.0004036574217398467, "Legal Notices": 4.633713857383986e-05, "News Article": 1.869809350474421e-05, "Organizational Announcement": 2.8625043227762168e-05, "Nonfiction Writing": 6.72716482378599e-05, "Organizational About Page": 3.9204624103294346e-05, "Personal About Page": 3.981712732446727e-05, "Personal Blog": 3.18312140132256e-05, "Product Page": 1.6542164810254188e-05, "Spam / Ads": 3.401857027349071e-05, "Structured Data": 0.0009827625923834792, "Technical Writing": 0.9858383416498494, "Transcript / Interview": 5.108938029328764e-05, "Tutorial / How-To Guide": 0.008592560019763053, "User Reviews": 1.929232464127884e-05, "Other / Unknown": 0.002119047540849276}, "secondary": {"Academic Writing": 0.0046958356339898664, "Content Listing": 0.0001704969617930569, "Creative Writing": 0.0002156262157744205, "Customer Support Page": 0.0001592324012098686, "Q&A Forum": 0.00012178966635888208, "Discussion Thread": 7.397071550561858e-05, "Comment Section": 0.000351861685852179, "Missing Content": 0.00033664891804561565, "Knowledge Article": 0.003673966384417659, "Legal Notices": 0.00015589574554013207, "News Article": 0.0001081668686659499, "Organizational Announcement": 0.0001011095963851402, "Nonfiction Writing": 0.000173842387231601, "Organizational About Page": 0.00013179935454818225, "Personal About Page": 0.000127433426988869, "Personal Blog": 0.0001368624777399942, "Product Page": 7.2224702049147e-05, "Spam / Ads": 0.00013423003833627325, "Structured Data": 0.004857110825692367, "Technical Writing": 0.013268747663020898, "Transcript / Interview": 0.0001517562705222635, "Tutorial / How-To Guide": 0.025686005975992878, "User Reviews": 6.805036196269997e-05, "NONE": 0.9450273357223764}, "tertiary": {"Academic Writing": 0.004661554879234562, "Content Listing": 9.618794994176443e-05, "Creative Writing": 0.00013655766446319458, "Customer Support Page": 0.00010214912259331328, "Q&A Forum": 7.211080156492844e-05, "Discussion Thread": 4.794237496088277e-05, "Comment Section": 0.000209778266403951, "Missing Content": 0.0003144754061047228, "Knowledge Article": 0.004454700728577429, "Legal Notices": 0.00016210253415145244, "News Article": 6.707234450305396e-05, "Organizational Announcement": 6.531847481233488e-05, "Nonfiction Writing": 0.00015707006911950007, "Organizational About Page": 0.00010229384053844935, "Personal About Page": 8.734728091639552e-05, "Personal Blog": 0.0001007279003712922, "Product Page": 5.839551955909372e-05, "Spam / Ads": 8.287824358737918e-05, "Structured Data": 0.0030996493236928766, "Technical Writing": 0.008250578781423126, "Transcript / Interview": 9.79592615441995e-05, "Tutorial / How-To Guide": 0.015478026083864171, "User Reviews": 3.752418195877116e-05, "NONE": 0.9620575989661132}, "structured_records": {"false": 0.9910773647443721, "true": 0.00892263525562788}, "procedural_steps": {"false": 0.8725217574055939, "true": 0.12747824259440607}, "worked_solution": {"false": 0.9861826260180927, "true": 0.013817373981907348}, "contextual_dialogue": {"false": 0.9999920625707814, "true": 7.937429218526175e-06}, "content_quality": {"-2": 0.038299975025083005, "-1": 0.1566445897144946, "0": 0.3520267433550232, "1": 0.3490471624283996, "2": 0.10398152947699967}, "noise_severity": {"0": 0.9560649952464111, "1": 0.04058080849774844, "2": 0.002300223768545082, "3": 0.0005994703824226321, "4": 0.0004545021048728414}, "missing_content_severity": {"0": 0.9931846244437214, "1": 0.0026885621006387675, "2": 0.0031600641354480067, "3": 0.000726868090570647, "4": 0.0002398812296211791}, "factual_density": {"0": 0.011698704149419719, "1": 0.054932917186064745, "2": 0.21121958903238447, "3": 0.3825356560513332, "4": 0.33961313358079787}, "pretraining_utility": {"-2": 0.008177854025886933, "-1": 0.08058239103371757, "0": 0.3896882743361465, "1": 0.45403287960149274, "2": 0.06751860100275632}, "math_density": {"0": 0.3120451942725201, "1": 0.2962753115939884, "2": 0.168988962502767, "3": 0.09190158520093514, "4": 0.13078894642978933}, "code_density": {"0": 0.00043462299887693767, "1": 0.0010129119309526301, "2": 0.04142754813440822, "3": 0.28871197678516186, "4": 0.6684129401506004}, "reasoning_density": {"0": 0.7563524134020788, "1": 0.07930652345529265, "2": 0.10902391149455301, "3": 0.04056878480441263, "4": 0.014748366843662895}, "education_level": {"none": 0.0014726141514248253, "0": 0.003376728942384556, "1": 0.07999466030988078, "2": 0.7923528716598013, "3": 0.10454126567903975, "4": 0.018261859257468684}, "creative_writing_quality": {"not_applicable": 0.9988741948904432, "-2": 0.00014415116755838993, "-1": 0.0003348045044278235, "0": 0.00047590397664019435, "1": 0.00012438932542104244, "2": 4.655613550942853e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00019077783543051553, "Art & Design": 0.0003327197530835094, "Law & Crime": 0.0003697374448292437, "Education & Careers": 0.0002697337766303795, "Entertainment & Media": 0.0003677428454834624, "Fashion & Beauty": 0.0003069124370880919, "Business, Economics & Finance": 0.0012825673081459164, "Food & Dining": 0.0003205935149707998, "Games & Gaming": 0.0003485001409608562, "Electronics & Hardware": 0.0003377529546767584, "Health, Healthcare & Wellness": 0.0006545417299481552, "History & Geography": 0.008823615132621762, "Home, Pets & Hobbies": 0.00023754428420236497, "Industry & Production": 0.0003237381042826328, "Language, Literature & Philosophy": 0.00023284448264696494, "Politics & Government": 0.0011060914045341625, "Religion & Spirituality": 0.0002942286399444318, "Science, Mathematics & Engineering": 0.09279920082340853, "Relationships & Community": 0.005368890244448655, "Software Usage & Applications": 0.0009137563827931947, "Programming & Software Engineering": 0.8420058790345133, "Sports & Fitness": 0.0003194027274379359, "Vehicles & Transportation": 0.00026122123301682525, "Travel & Tourism": 0.0009192681520302613, "Other / Uncovered Subject": 0.04120595199322399, "Unknown / Insufficient Content": 0.0004067876196473201}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007856938668238171, "Art & Design": 0.0008824737624419303, "Law & Crime": 0.0010755550784890316, "Education & Careers": 0.0005351682791481981, "Entertainment & Media": 0.0010628516469836844, "Fashion & Beauty": 0.0006183536312488613, "Business, Economics & Finance": 0.002675980557350333, "Food & Dining": 0.0006191269266800344, "Games & Gaming": 0.0007190142989295894, "Electronics & Hardware": 0.0008212007960582353, "Health, Healthcare & Wellness": 0.0015748210098885194, "History & Geography": 0.11365945643104264, "Home, Pets & Hobbies": 0.0004919244096285501, "Industry & Production": 0.0007182158956643678, "Language, Literature & Philosophy": 0.0003624210570683642, "Politics & Government": 0.0023066909391501957, "Religion & Spirituality": 0.0008126175232528118, "Science, Mathematics & Engineering": 0.35986804536735323, "Relationships & Community": 0.011679667471004166, "Software Usage & Applications": 0.0025546697502583132, "Programming & Software Engineering": 0.09243956372279608, "Sports & Fitness": 0.0004845034009413971, "Vehicles & Transportation": 0.0005462921469143438, "Travel & Tourism": 0.0017160886319490568, "Other / Uncovered Subject": 0.06061536802928283, "NONE": 0.3403742353696515}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9786, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9786, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:35:08.834887+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "74c2caee-04a6-49aa-be12-08b7b2a4033b", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "de04cf73e3fe6892eb73dbe20d3fb44f0a3cb4ac80a503a99ab5f701d4ba1592", "source_metadata": {"uuid": "74c2caee-04a6-49aa-be12-08b7b2a4033b", "repo_name": "Xingyetan89/Data-processing-for-investigation-on-population-dynamics", "relative_path": "Preprocessing/Chinese cities/data_process.py", "category": "DATA", "algo_rel_score": 0.9770484566688538, "quality_score": 4.209651947021484, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 81, "repo_created_at": "2024-06-30T17:14:41.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 4, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c0b0611a294dfa8f77fdc26c59ea281ed96e0eca", "size_bytes": 4389, "file_timestamp": 1733473174, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "de55fb6ac218c6a9af7537dcde7cb951d6c29a71"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 81, "repo_created_at": "2024-06-30T17:14:41.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 4, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c0b0611a294dfa8f77fdc26c59ea281ed96e0eca", "size_bytes": 4389, "file_timestamp": 1733473174, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "de55fb6ac218c6a9af7537dcde7cb951d6c29a71", "url": "https://github.com/Xingyetan89/Data-processing-for-investigation-on-population-dynamics/blob/de55fb6ac218c6a9af7537dcde7cb951d6c29a71/Preprocessing/Chinese%20cities/data_process.py"}, "gpt4o_tokens": 1157, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.32376568161773833, "noise_severity": 0.04879767560159787, "missing_content_severity": 0.012148819561731439, "factual_density": 2.983431597728025, "pretraining_utility": 0.49213198252151397, "math_density": 1.433113777921485, "code_density": 3.623655699157656, "reasoning_density": 0.47805416823228813, "education_level": 2.054396971749721, "creative_writing_quality": -0.360280158316524}, "score_applicability": {"education_level": 0.998527385848575, "creative_writing_quality": 0.0011258051095568787}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/OpenISS/kg-recommendation-framework/blob/928b7d813383b88cbee021f58ada5daf16c42752/src/OpenISS_RS/recommendation_system/data_loader.py", "id": "python-0-28c720fa0d6ad431d36fe326d5ba3be2", "text": "import numpy as np\n\ndata_path = \"../../../data/\"\n\n\ndef load_ratings_final(dataset):\n    print('loading RATING_FINAL file ...')\n    rating_final_path = data_path + dataset + '/ratings_final'\n    rating_file = open(rating_final_path + '.txt', encoding=\"UTF-8\")\n\n    # user_set = set()\n    item_set = set()\n    for row in rating_file:\n        user_id = row.split(\"\\t\")[0]\n        item_id = row.split(\"\\t\")[4]\n        # if user_id not in user_set:\n        #     user_set.add(user_id)\n        if item_id not in item_set:\n            item_set.add(item_id)\n\n    # user_number = len(user_set)\n    item_number = len(item_set)\n\n    train_data, eval_data, test_data = dataset_split(rating_final_path)\n\n    return item_number, train_data, eval_data, test_data\n\ndef dataset_split(rating_final_path):\n    # train:eval:test = 6:2:2\n    print('splitting dataset ...')\n    rating_final_np = np.loadtxt(rating_final_path + '.txt', dtype=np.int32)\n\n    eval_ratio = 0.2\n    test_ratio = 0.2\n    ratings_number = rating_final_np.shape[0]\n\n    current_ratings_number = range(ratings_number)\n    eval_id = np.random.choice(list(current_ratings_number), size=int(ratings_number * eval_ratio), replace=False)\n    current_ratings_number = set(range(ratings_number)) - set(eval_id)\n    test_id = np.random.choice(list(current_ratings_number), size=int(ratings_number * test_ratio), replace=False)\n    train_id = list(current_ratings_number - set(test_id))\n\n    train_data = rating_final_np[train_id]\n    eval_data = rating_final_np[eval_id]\n    test_data = rating_final_np[test_id]\n\n    return train_data, eval_data, test_data\n\ndef load_kg_final(dataset):\n    print('loading KG_FINAL file ...')\n\n    # reading kg file\n    kg_final_file = data_path + dataset + '/kg_final'\n\n    kg = np.loadtxt(kg_final_file + '.txt', dtype=np.int32)\n    \n    head_set = set(kg[:, 0])\n    tail_set = set(kg[:, 2])\n    relation_set = set(kg[:, 1])\n    \n    entity_number = len(head_set | tail_set)\n    relation_number = len(relation_set)\n\n    return entity_number, relation_number, kg\n\n\ndef load_user_infos(dataset):\n    print('loading users.dat file ...')\n    \n    # reading users_info_final file\n    users_final_path = data_path + dataset + '/users'\n    user_file = open(users_final_path + '.dat', encoding=\"UTF-8\")\n    \n    user_set = set()\n    user_gender_set = set()\n    user_age_set = set()\n    use_job_set = set()\n    \n    for row in user_file:\n        user_id = row.split(\"::\")[0]\n        user_gender = row.split(\"::\")[1]\n        user_age = row.split(\"::\")[2]\n        user_job = row.split(\"::\")[3]\n        \n        if user_id not in user_set:\n            user_set.add(user_id)\n        if user_gender not in user_gender_set:\n            user_gender_set.add(user_gender)\n        if user_age not in user_age_set:\n            user_age_set.add(user_age)\n        if user_job not in use_job_set:\n            use_job_set.add(user_job)\n            \n            \n    user_numbers = len(user_set)\n    user_gender_numbers = len(user_gender_set)\n    user_age_numbers = len(user_age_set)\n    user_job_numbers = len(use_job_set)\n\n    return user_numbers, user_gender_numbers, user_age_numbers, user_job_numbers\n\n        \ndef get_usr_info(dataset):\n    # convert m/f to num，M-0， F-1\n    def gender2num(gender):\n        return 1 if gender == 'F' else 0\n    user_info_path = data_path + dataset + '/users.dat'\n    with open(user_info_path, 'r') as f:\n        data = f.readlines()\n\n    use_info = {}\n    \n    max_usr_id = 0\n    for item in data:\n        item = item.strip().split(\"::\")\n        usr_id = item[0]\n        use_info[usr_id] = {'usr_id': int(usr_id),\n                            'gender': gender2num(item[1]),\n                            'age': int(item[2]),\n                            'job': int(item[3])}\n        max_usr_id = max(max_usr_id, int(usr_id))\n    \n    return use_info\n\ndef load_datas(args):\n    \n    user_number, user_gender_number, user_age_number, user_job_number = load_user_infos(args.dataset)\n    \n    item_number, train_data, eval_data, test_data = load_ratings_final(args.dataset)\n\n    entity_number, relation_number, kg = load_kg_final(args.dataset)\n\n    print(\"n_user\")\n    print(user_number)\n    print(item_number)\n    print(entity_number)\n    print(relation_number)\n    print(user_number, user_gender_number, user_age_number, user_job_number)\n    return user_number, item_number, entity_number, relation_number, train_data, eval_data, test_data, kg, \\\n           user_gender_number, user_age_number, user_job_number\n", "text_preview": "import numpy as np data_path = \"../../../data/\" def load_ratings_final(dataset): print('loading RATING_FINAL file ...') rating_final_path = data_path + dataset + '/ratings_final' rating_file = open(rating_final_path + '.txt', encoding=\"UTF-", "input_sha256": "b5513a70f0812d703378c61c592aae28d2835343d201021b79a5b073ca2f47d5", "text_sha256": "28c720fa0d6ad431d36fe326d5ba3be26d0d34145668c11f2a11ed05f5f5e51a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12090, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.47368810082424656, "noise_severity": 0.01938846015467657, "missing_content_severity": 0.036867534371549146, "factual_density": 3.0677315599724877, "pretraining_utility": 0.4173877482998699, "math_density": 1.2197188584819028, "code_density": 3.900770702619322, "reasoning_density": 0.28445706734577003, "education_level": 2.158157817608746, "creative_writing_quality": -0.421649231304243}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.008606306417044629, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9970179934176894, "secondary": 0.9829224575310436, "tertiary": 0.9919029511836118, "structured_records": 0.01720679284740767, "procedural_steps": 0.03790431898015009, "worked_solution": 0.0031076623807591817, "contextual_dialogue": 4.081918565228499e-06, "content_quality": 0.4128804911087076, "noise_severity": 0.9838429498711029, "missing_content_severity": 0.9692770546903757, "factual_density": 0.22310963331040679, "pretraining_utility": 0.47148756686815263, "math_density": 0.0, "code_density": 0.9173089188494351, "reasoning_density": 0.7629524438785249, "education_level": 0.7585598535585825, "creative_writing_quality": 0.9994719994783788, "primary_topic": 0.9507871035585551, "secondary_topic": 0.5519575585595785}, "probability_margin": {"primary": 0.9958746852947417, "secondary": 0.9765902948897273, "tertiary": 0.9887242332848929, "education_level": 0.6692298120294954, "creative_writing_quality": 0.999382614122527, "primary_topic": 0.9103451421429772, "secondary_topic": 0.245991765361522}, "raw_probabilities": {"primary": {"Academic Writing": 7.145948488050972e-05, "Content Listing": 1.4402375306671742e-05, "Creative Writing": 9.987661996122331e-06, "Customer Support Page": 5.937618407123831e-06, "Q&A Forum": 8.372262178217408e-06, "Discussion Thread": 5.387081471119082e-06, "Comment Section": 8.406140528334992e-06, "Missing Content": 1.8459105995254997e-05, "Knowledge Article": 5.6844169637219304e-05, "Legal Notices": 1.3156346084041464e-05, "News Article": 3.386361576295885e-06, "Organizational Announcement": 6.972774446235918e-06, "Nonfiction Writing": 8.114539323762037e-06, "Organizational About Page": 8.597344979723776e-06, "Personal About Page": 6.640551980082453e-06, "Personal Blog": 5.0609842879684405e-06, "Product Page": 4.350721179348206e-06, "Spam / Ads": 6.907814896545403e-06, "Structured Data": 0.0007779200672786648, "Technical Writing": 0.9971422436919524, "Transcript / Interview": 9.787783432574429e-06, "Tutorial / How-To Guide": 0.0005358679669665515, "User Reviews": 4.1787540045311855e-06, "Other / Unknown": 0.0012675583972107198}, "secondary": {"Academic Writing": 0.0006054860132662221, "Content Listing": 8.138234295389364e-05, "Creative Writing": 8.44876447890391e-05, "Customer Support Page": 4.482438724589454e-05, "Q&A Forum": 4.025793428206819e-05, "Discussion Thread": 3.203121509254849e-05, "Comment Section": 7.798821306632869e-05, "Missing Content": 0.00021653230714071737, "Knowledge Article": 0.0004591146551211828, "Legal Notices": 8.593459065025097e-05, "News Article": 4.841571703001979e-05, "Organizational Announcement": 4.118101620609123e-05, "Nonfiction Writing": 4.725379544931835e-05, "Organizational About Page": 5.916799503330819e-05, "Personal About Page": 4.286090796188621e-05, "Personal Blog": 4.1315372784667095e-05, "Product Page": 2.4327602753938744e-05, "Spam / Ads": 4.7245932308041226e-05, "Structured Data": 0.00502758205312844, "Technical Writing": 0.007043726910856227, "Transcript / Interview": 5.283240817601823e-05, "Tutorial / How-To Guide": 0.002140389259017794, "User Reviews": 2.163992510264849e-05, "NONE": 0.9836340218005835}, "tertiary": {"Academic Writing": 0.00020112179993907493, "Content Listing": 3.9305455099266775e-05, "Creative Writing": 4.0185326293718094e-05, "Customer Support Page": 2.156338469821579e-05, "Q&A Forum": 1.8510908286361095e-05, "Discussion Thread": 1.53534818324087e-05, "Comment Section": 6.069241373400797e-05, "Missing Content": 0.00020375899238291205, "Knowledge Article": 0.00030279342411299666, "Legal Notices": 7.226949702771232e-05, "News Article": 2.6167251503507167e-05, "Organizational Announcement": 2.1082941638558217e-05, "Nonfiction Writing": 3.0125374201724105e-05, "Organizational About Page": 4.1739488986447194e-05, "Personal About Page": 2.196006839478961e-05, "Personal Blog": 1.9999418186938484e-05, "Product Page": 1.6073930782671536e-05, "Spam / Ads": 2.7448149077998e-05, "Structured Data": 0.0016341632291579158, "Technical Writing": 0.0035160949327351265, "Transcript / Interview": 2.8323592818451646e-05, "Tutorial / How-To Guide": 0.001388381124096667, "User Reviews": 1.2557597384489336e-05, "NONE": 0.992240328217628}, "structured_records": {"false": 0.9827932071525923, "true": 0.01720679284740767}, "procedural_steps": {"false": 0.9620956810198499, "true": 0.03790431898015009}, "worked_solution": {"false": 0.9968923376192408, "true": 0.0031076623807591817}, "contextual_dialogue": {"false": 0.9999959180814347, "true": 4.081918565228499e-06}, "content_quality": {"-2": 0.013901899951204581, "-1": 0.09742596718664098, "0": 0.3788700206957564, "1": 0.42068635641949936, "2": 0.08911575574689867}, "noise_severity": {"0": 0.9828974385670759, "1": 0.015503013901621504, "2": 0.0011108403341739052, "3": 0.00029106320380811095, "4": 0.00019764399332073126}, "missing_content_severity": {"0": 0.9729327733238268, "1": 0.01970531758255306, "2": 0.005329863879542872, "3": 0.0016256918263976967, "4": 0.0004063533876793103}, "factual_density": {"0": 0.01879638245533903, "1": 0.04013439387450817, "2": 0.18721210290499493, "3": 0.36225552277264145, "4": 0.39160159799251637}, "pretraining_utility": {"-2": 0.008117249880082172, "-1": 0.09217908596900909, "0": 0.43810333792404116, "1": 0.39739931842469184, "2": 0.06420100780217575}, "math_density": {"0": 0.4045669551730717, "1": 0.28576057871033916, "2": 0.11753006386602198, "3": 0.06967145696274918, "4": 0.12247094528781803}, "code_density": {"0": 0.00020616697463370632, "1": 0.00040524273919016795, "2": 0.012259143842561849, "3": 0.07267061357944887, "4": 0.9144588328641654}, "reasoning_density": {"0": 0.8480828315016984, "1": 0.04767902989453942, "2": 0.08227849172637074, "3": 0.015617533511077301, "4": 0.006342113366314325}, "education_level": {"none": 0.0012043815835211561, "0": 0.002093877684178436, "1": 0.034692856327784925, "2": 0.7987998779654853, "3": 0.12957006593598994, "4": 0.033638940503040206}, "creative_writing_quality": {"not_applicable": 0.9995599995653157, "-2": 7.765072169036657e-05, "-1": 0.00011805052968012987, "0": 0.00017738544278868748, "1": 4.60013530472547e-05, "2": 2.09123874777448e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.945830832663978e-05, "Art & Design": 0.0001077829228704069, "Law & Crime": 8.897821751462445e-05, "Education & Careers": 8.467012789474105e-05, "Entertainment & Media": 0.0002870833493509617, "Fashion & Beauty": 0.0001226038865172017, "Business, Economics & Finance": 0.00021319619633342136, "Food & Dining": 7.373564123646028e-05, "Games & Gaming": 0.00016303614466651995, "Electronics & Hardware": 0.00012934246674707264, "Health, Healthcare & Wellness": 0.00010678852429307221, "History & Geography": 8.672391200277545e-05, "Home, Pets & Hobbies": 5.6313029831619076e-05, "Industry & Production": 8.471285421864964e-05, "Language, Literature & Philosophy": 6.936649139850555e-05, "Politics & Government": 4.41402816868945e-05, "Religion & Spirituality": 5.975281801838178e-05, "Science, Mathematics & Engineering": 0.0423347651248642, "Relationships & Community": 0.00010795111529562783, "Software Usage & Applications": 0.0006072989015102255, "Programming & Software Engineering": 0.9526799072678415, "Sports & Fitness": 9.107568006680458e-05, "Vehicles & Transportation": 3.670619423724929e-05, "Travel & Tourism": 8.145683473759822e-05, "Other / Uncovered Subject": 0.0020730093307311914, "Unknown / Insufficient Content": 0.0001501443778078473}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005698294372300095, "Art & Design": 0.0006223195637750858, "Law & Crime": 0.0006398844847797263, "Education & Careers": 0.00048240893580039554, "Entertainment & Media": 0.004788974960300631, "Fashion & Beauty": 0.0004933123187988299, "Business, Economics & Finance": 0.0013240026562507364, "Food & Dining": 0.0004438073131665884, "Games & Gaming": 0.0007275038773743724, "Electronics & Hardware": 0.0006311363063472049, "Health, Healthcare & Wellness": 0.0006754454812192513, "History & Geography": 0.00038609795093837987, "Home, Pets & Hobbies": 0.0002996608073771021, "Industry & Production": 0.00036708102381870726, "Language, Literature & Philosophy": 0.00024568610259030247, "Politics & Government": 0.00026550932150984195, "Religion & Spirituality": 0.00038461837286878265, "Science, Mathematics & Engineering": 0.3231981947919188, "Relationships & Community": 0.0003333663184456362, "Software Usage & Applications": 0.0009052257100174513, "Programming & Software Engineering": 0.08970542751587125, "Sports & Fitness": 0.00038797744473232233, "Vehicles & Transportation": 0.0002002061945360696, "Travel & Tourism": 0.0004977993782566511, "Other / Uncovered Subject": 0.002234563578635102, "NONE": 0.5691899601534408}}, "probabilities": {"primary": {"Academic Writing": 7.145948488050972e-05, "Content Listing": 1.4402375306671742e-05, "Creative Writing": 9.987661996122331e-06, "Customer Support Page": 5.937618407123831e-06, "Q&A Forum": 8.372262178217408e-06, "Discussion Thread": 5.387081471119082e-06, "Comment Section": 8.406140528334992e-06, "Missing Content": 1.8459105995254997e-05, "Knowledge Article": 5.6844169637219304e-05, "Legal Notices": 1.3156346084041464e-05, "News Article": 3.386361576295885e-06, "Organizational Announcement": 6.972774446235918e-06, "Nonfiction Writing": 8.114539323762037e-06, "Organizational About Page": 8.597344979723776e-06, "Personal About Page": 6.640551980082453e-06, "Personal Blog": 5.0609842879684405e-06, "Product Page": 4.350721179348206e-06, "Spam / Ads": 6.907814896545403e-06, "Structured Data": 0.0007779200672786648, "Technical Writing": 0.9971422436919524, "Transcript / Interview": 9.787783432574429e-06, "Tutorial / How-To Guide": 0.0005358679669665515, "User Reviews": 4.1787540045311855e-06, "Other / Unknown": 0.0012675583972107198}, "secondary": {"Academic Writing": 0.0006054860132662221, "Content Listing": 8.138234295389364e-05, "Creative Writing": 8.44876447890391e-05, "Customer Support Page": 4.482438724589454e-05, "Q&A Forum": 4.025793428206819e-05, "Discussion Thread": 3.203121509254849e-05, "Comment Section": 7.798821306632869e-05, "Missing Content": 0.00021653230714071737, "Knowledge Article": 0.0004591146551211828, "Legal Notices": 8.593459065025097e-05, "News Article": 4.841571703001979e-05, "Organizational Announcement": 4.118101620609123e-05, "Nonfiction Writing": 4.725379544931835e-05, "Organizational About Page": 5.916799503330819e-05, "Personal About Page": 4.286090796188621e-05, "Personal Blog": 4.1315372784667095e-05, "Product Page": 2.4327602753938744e-05, "Spam / Ads": 4.7245932308041226e-05, "Structured Data": 0.00502758205312844, "Technical Writing": 0.007043726910856227, "Transcript / Interview": 5.283240817601823e-05, "Tutorial / How-To Guide": 0.002140389259017794, "User Reviews": 2.163992510264849e-05, "NONE": 0.9836340218005835}, "tertiary": {"Academic Writing": 0.00020112179993907496, "Content Listing": 3.930545509926678e-05, "Creative Writing": 4.01853262937181e-05, "Customer Support Page": 2.1563384698215795e-05, "Q&A Forum": 1.85109082863611e-05, "Discussion Thread": 1.5353481832408702e-05, "Comment Section": 6.069241373400798e-05, "Missing Content": 0.00020375899238291208, "Knowledge Article": 0.0003027934241129967, "Legal Notices": 7.226949702771233e-05, "News Article": 2.616725150350717e-05, "Organizational Announcement": 2.108294163855822e-05, "Nonfiction Writing": 3.012537420172411e-05, "Organizational About Page": 4.17394889864472e-05, "Personal About Page": 2.1960068394789615e-05, "Personal Blog": 1.9999418186938487e-05, "Product Page": 1.607393078267154e-05, "Spam / Ads": 2.7448149077998002e-05, "Structured Data": 0.001634163229157916, "Technical Writing": 0.003516094932735127, "Transcript / Interview": 2.832359281845165e-05, "Tutorial / How-To Guide": 0.0013883811240966671, "User Reviews": 1.2557597384489337e-05, "NONE": 0.9922403282176281}, "structured_records": {"false": 0.9827932071525923, "true": 0.01720679284740767}, "procedural_steps": {"false": 0.9620956810198499, "true": 0.03790431898015009}, "worked_solution": {"false": 0.9968923376192408, "true": 0.0031076623807591817}, "contextual_dialogue": {"false": 0.9999959180814347, "true": 4.081918565228499e-06}, "content_quality": {"-2": 0.013901899951204581, "-1": 0.09742596718664098, "0": 0.3788700206957564, "1": 0.42068635641949936, "2": 0.08911575574689867}, "noise_severity": {"0": 0.9828974385670757, "1": 0.015503013901621501, "2": 0.001110840334173905, "3": 0.0002910632038081109, "4": 0.0001976439933207312}, "missing_content_severity": {"0": 0.9729327733238271, "1": 0.019705317582553068, "2": 0.005329863879542874, "3": 0.001625691826397697, "4": 0.0004063533876793104}, "factual_density": {"0": 0.01879638245533903, "1": 0.04013439387450817, "2": 0.18721210290499493, "3": 0.36225552277264145, "4": 0.39160159799251637}, "pretraining_utility": {"-2": 0.008117249880082172, "-1": 0.09217908596900909, "0": 0.43810333792404116, "1": 0.39739931842469184, "2": 0.06420100780217575}, "math_density": {"0": 0.4045669551730717, "1": 0.28576057871033916, "2": 0.11753006386602198, "3": 0.06967145696274918, "4": 0.12247094528781803}, "code_density": {"0": 0.00020616697463370632, "1": 0.00040524273919016795, "2": 0.012259143842561849, "3": 0.07267061357944887, "4": 0.9144588328641654}, "reasoning_density": {"0": 0.8480828315016982, "1": 0.047679029894539406, "2": 0.08227849172637072, "3": 0.015617533511077298, "4": 0.006342113366314323}, "education_level": {"none": 0.0012043815835211561, "0": 0.002093877684178436, "1": 0.034692856327784925, "2": 0.7987998779654853, "3": 0.12957006593598994, "4": 0.033638940503040206}, "creative_writing_quality": {"not_applicable": 0.9995599995653158, "-2": 7.765072169036658e-05, "-1": 0.00011805052968012989, "0": 0.0001773854427886875, "1": 4.600135304725471e-05, "2": 2.0912387477744805e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.9458308326639765e-05, "Art & Design": 0.00010778292287040688, "Law & Crime": 8.897821751462444e-05, "Education & Careers": 8.467012789474104e-05, "Entertainment & Media": 0.00028708334935096167, "Fashion & Beauty": 0.00012260388651720168, "Business, Economics & Finance": 0.0002131961963334213, "Food & Dining": 7.373564123646026e-05, "Games & Gaming": 0.00016303614466651992, "Electronics & Hardware": 0.00012934246674707261, "Health, Healthcare & Wellness": 0.00010678852429307218, "History & Geography": 8.672391200277544e-05, "Home, Pets & Hobbies": 5.631302983161906e-05, "Industry & Production": 8.471285421864963e-05, "Language, Literature & Philosophy": 6.936649139850553e-05, "Politics & Government": 4.4140281686894494e-05, "Religion & Spirituality": 5.9752818018381764e-05, "Science, Mathematics & Engineering": 0.04233476512486419, "Relationships & Community": 0.0001079511152956278, "Software Usage & Applications": 0.0006072989015102253, "Programming & Software Engineering": 0.9526799072678412, "Sports & Fitness": 9.107568006680457e-05, "Vehicles & Transportation": 3.670619423724928e-05, "Travel & Tourism": 8.14568347375982e-05, "Other / Uncovered Subject": 0.002073009330731191, "Unknown / Insufficient Content": 0.00015014437780784727}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005698294372300095, "Art & Design": 0.0006223195637750858, "Law & Crime": 0.0006398844847797263, "Education & Careers": 0.00048240893580039554, "Entertainment & Media": 0.004788974960300631, "Fashion & Beauty": 0.0004933123187988299, "Business, Economics & Finance": 0.0013240026562507364, "Food & Dining": 0.0004438073131665884, "Games & Gaming": 0.0007275038773743724, "Electronics & Hardware": 0.0006311363063472049, "Health, Healthcare & Wellness": 0.0006754454812192513, "History & Geography": 0.00038609795093837987, "Home, Pets & Hobbies": 0.0002996608073771021, "Industry & Production": 0.00036708102381870726, "Language, Literature & Philosophy": 0.00024568610259030247, "Politics & Government": 0.00026550932150984195, "Religion & Spirituality": 0.00038461837286878265, "Science, Mathematics & Engineering": 0.3231981947919188, "Relationships & Community": 0.0003333663184456362, "Software Usage & Applications": 0.0009052257100174513, "Programming & Software Engineering": 0.08970542751587125, "Sports & Fitness": 0.00038797744473232233, "Vehicles & Transportation": 0.0002002061945360696, "Travel & Tourism": 0.0004977993782566511, "Other / Uncovered Subject": 0.002234563578635102, "NONE": 0.5691899601534408}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9698, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9698, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:35:09.263000+00:00", "scoring_key_slot": "primary", "dataset_uid": "c66ad5b7-01d3-43d1-91b0-2559977cdc92", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "e4d4017dd8db34b56344a77d6ddf26445ab8d8c04aa34d7a87d11b0ebc46528b", "source_metadata": {"uuid": "c66ad5b7-01d3-43d1-91b0-2559977cdc92", "repo_name": "OpenISS/kg-recommendation-framework", "relative_path": "src/OpenISS_RS/recommendation_system/data_loader.py", "category": "DATA", "algo_rel_score": 0.9648869037628174, "quality_score": 4.448725700378418, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 109, "repo_created_at": "2021-02-02T18:36:30.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 21, "forks": 10, "issues": 2, "pull_requests": 0}, "file_meta": {"content_id": "3981aa20ab977fdc68f6854f3a26af8b87cc07d2", "size_bytes": 4501, "file_timestamp": 1723956378, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "928b7d813383b88cbee021f58ada5daf16c42752"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 109, "repo_created_at": "2021-02-02T18:36:30.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 21, "forks": 10, "issues": 2, "pull_requests": 0}, "file_meta": {"content_id": "3981aa20ab977fdc68f6854f3a26af8b87cc07d2", "size_bytes": 4501, "file_timestamp": 1723956378, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "928b7d813383b88cbee021f58ada5daf16c42752", "url": "https://github.com/OpenISS/kg-recommendation-framework/blob/928b7d813383b88cbee021f58ada5daf16c42752/src/OpenISS_RS/recommendation_system/data_loader.py"}, "gpt4o_tokens": 1116, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.47368810082424656, "noise_severity": 0.019388460154676573, "missing_content_severity": 0.036867534371549146, "factual_density": 3.0677315599724877, "pretraining_utility": 0.4173877482998699, "math_density": 1.2197188584819028, "code_density": 3.900770702619322, "reasoning_density": 0.2844570673457701, "education_level": 2.158157817608746, "creative_writing_quality": -0.421649231304243}, "score_applicability": {"education_level": 0.9987956184164789, "creative_writing_quality": 0.00044000043468418347}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/Chushihyun/3dcv/blob/feb2b6020d1474c892b85326d5bcd12d3afdfd15/r09922115_hw3/vo.py", "id": "python-0-b7e4810749d6f44595f07ab932eceb12", "text": "import open3d as o3d\nimport numpy as np\nimport cv2 as cv\nimport sys, os, argparse, glob\nimport multiprocessing as mp\nimport matplotlib.pyplot as plt\nimport ipdb\n\nos.chdir(\"/Users/yun/Desktop/3d_cv/hw/hw3/homework3-Chushihyun-master\") \n\ndef draw_match(img1,img2,points1,points2):\n    num=points1.shape[0]\n\n    keypoints1=[]\n    keypoints2=[]\n    matches=[]\n    for point in points1:\n        keypoints1.append(cv.KeyPoint(point[0],point[1],1))\n    for point in points2:\n        keypoints2.append(cv.KeyPoint(point[0],point[1],1))\n    for i in range(num):\n        matches.append(cv.DMatch(i,i,1))\n\n    img_draw_match = cv.drawMatches(img1, keypoints1, img2, keypoints2, matches, None, flags=cv.DrawMatchesFlags_DEFAULT)\n    cv.namedWindow('match',0)\n    cv.resizeWindow('match', 1000, 400)\n    cv.imshow('match', img_draw_match)\n    cv.waitKey(0)\n    cv.destroyAllWindows()\n\n    return 0\n\n\ndef draw_camera(position, rotation):\n    p = position\n    r = rotation\n    model = o3d.geometry.LineSet()\n    model.points = o3d.utility.Vector3dVector(\n        [[0, 0, 0], [1, 1, -1], [1, -1, -1], [-1, -1, -1], [-1, 1, -1]])\n    model.lines = o3d.utility.Vector2iVector(\n        [[0, 1], [0, 2], [0, 3], [0, 4], [1, 2], [2, 3], [3, 4], [4, 1]])\n    color = np.array([1, 0, 0])\n    model.colors = o3d.utility.Vector3dVector(np.tile(color, (8, 1)))\n    model.scale(0.5, np.zeros(3))\n    model.rotate(r)\n    model.translate(p)\n    return model\n\ndef sort_inliers(points1,points2,inlier):\n    new_points1=[]\n    new_points2=[]\n    for i in range(len(inlier)):\n        if inlier[i][0]!=0:\n            new_points1.append(points1[i])\n            new_points2.append(points2[i])\n    return np.array(new_points1),np.array(new_points2)\n\n\ndef sort_inliers_3d(points1,points2,points3d,inlier):\n    new_points1=[]\n    new_points2=[]\n    new_points3d=[]\n    for i in range(len(inlier)):\n        if inlier[i][0]!=0:\n            new_points1.append(points1[i])\n            new_points2.append(points2[i])\n            new_points3d.append(points3d[:,i])\n\n    for i in range(len(new_points3d)):\n        point=new_points3d[i]\n        point=point/point[3]\n        point=point[:3]\n        new_points3d[i]=point\n\n    return np.array(new_points1),np.array(new_points2),np.array(new_points3d)\n\n\ndef rescale_ratio(points2_prev,points3d_prev,points1,points3d):\n    same_points=[]\n    for i in range(points2_prev.shape[0]):\n        for j in range(points1.shape[0]):\n            if np.all(points2_prev[i]==points1[j]):\n                same_points.append([i,j])\n\n    # print(len(same_points))\n    if len(same_points)<=1:\n        return 1\n\n    # find pairs\n    sample_times=13\n    sample_times=min(sample_times,len(same_points))\n    ratios=[]\n    for t in range(sample_times):\n        index1,index2=np.random.choice(len(same_points), 2, replace=False)\n        idx1_prev=same_points[index1][0]\n        idx1=same_points[index1][1]\n        idx2_prev=same_points[index2][0]\n        idx2=same_points[index2][1]\n\n        norm=np.linalg.norm(points3d[idx1]-points3d[idx2])\n        norm_prev=np.linalg.norm(points3d_prev[idx1_prev]-points3d_prev[idx2_prev])\n        ratio=norm/(norm_prev+0.000001)\n        ratios.append(ratio)\n\n    scale=np.median(np.array(ratios))\n\n    return scale\n\nclass SimpleVO:\n    def __init__(self, args):\n        camera_params = np.load(args.camera_parameters, allow_pickle=True)[()]\n        self.K = camera_params['K']\n        self.dist = camera_params['dist']\n        \n        self.frame_paths = sorted(list(glob.glob(os.path.join(args.input, '*.png'))))\n\n    def run(self):\n        vis = o3d.visualization.Visualizer()\n        vis.create_window()\n        queue = mp.Queue()\n\n        p = mp.Process(target=self.process_frames, args=(queue, ))\n        p.start()\n        \n        keep_running = True\n        Rt_prev=np.eye(4, dtype=np.float64)\n        while keep_running:\n            try:\n                R, t = queue.get(block=False)\n                if R is not None:\n                    \n                    # build Rt\n                    Rt = np.concatenate([R, t], -1)\n                    Rt = np.concatenate([Rt, np.zeros((1, 4))], 0)\n                    Rt[-1, -1] = 1.\n\n                    # upate Rt_prev\n                    Rt_prev=Rt_prev@Rt\n\n                    R=Rt_prev[:3,:3]\n                    t=Rt_prev[:3,3]\n                    \n                    model=draw_camera(t,R)\n                    vis.add_geometry(model)\n\n                    pass\n            except: pass\n            \n            keep_running = keep_running and vis.poll_events()\n        vis.destroy_window()\n        p.join()\n\n    def get_correspond_points(self,img1,img2):\n        \n        orb = cv.ORB_create()\n        kp1, des1 = orb.detectAndCompute(img1,None)\n        kp2, des2 = orb.detectAndCompute(img2,None)\n        \n        bf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True)\n        matches = bf.match(des1,des2)\n        matches = sorted(matches, key = lambda x:x.distance)\n\n        points1 = np.array([kp1[m.queryIdx].pt for m in matches])\n        points2 = np.array([kp2[m.trainIdx].pt for m in matches])\n\n        return points1,points2\n\n    def process_frames(self, queue):\n        R, t = np.eye(3, dtype=np.float64), np.zeros((3, 1), dtype=np.float64)\n        frame_path_prev=self.frame_paths[0]\n        for frame_path in self.frame_paths[1:]:\n\n            img1 = cv.imread(frame_path_prev)\n            img2 = cv.imread(frame_path)\n\n            points1,points2= self.get_correspond_points(img1,img2)\n            # draw_match(img1,img2,points1,points2)\n\n            essential,inlier=cv.findEssentialMat(points1,points2,self.K)\n            points1,points2=sort_inliers(points1,points2,inlier)\n            # draw_match(img1,img2,points1,points2)\n\n            val,R,t,inlier,traingularpoints=cv.recoverPose(essential,points1,points2,self.K,distanceThresh=400)\n            points1,points2,points3d=sort_inliers_3d(points1,points2,traingularpoints,inlier)\n            # draw_match(img1,img2,points1,points2)\n\n            # rescale t\n            if frame_path_prev==self.frame_paths[0]:\n                # first time\n                ratio=1\n            else:\n                ratio=rescale_ratio(points2_prev,points3d_prev,points1,points3d)\n                ratio*=ratio_prev\n                ratio=min(max(ratio,0.5),2)\n            t*=ratio\n\n            queue.put((R, t))\n            \n            ratio_prev=ratio\n            points2_prev=points2\n            points3d_prev=points3d\n            frame_path_prev=frame_path\n            \n            cv.imshow('frame', img1)\n            if cv.waitKey(30) == 27: break\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('input', help='directory of sequential frames')\n    parser.add_argument('--camera_parameters', default='camera_parameters.npy', help='npy file of camera parameters')\n    args = parser.parse_args()\n\n    vo = SimpleVO(args)\n    vo.run()\n", "text_preview": "import open3d as o3d import numpy as np import cv2 as cv import sys, os, argparse, glob import multiprocessing as mp import matplotlib.pyplot as plt import ipdb os.chdir(\"/Users/yun/Desktop/3d_cv/hw/hw3/homework3-Chushihyun-master\") def dra", "input_sha256": "a399b31c1e8e87b60cafefc830fa666b5a4bda1127b89b58129a668fd024b86d", "text_sha256": "b7e4810749d6f44595f07ab932eceb12480a76186eb95e9bbc285a662e3d44db", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12091, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 3, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.2613759538933575, "noise_severity": 0.05774456035438332, "missing_content_severity": 0.01680618323367651, "factual_density": 3.3179736178508845, "pretraining_utility": 0.5880410765611576, "math_density": 2.9901449936005347, "code_density": 3.912071123673693, "reasoning_density": 0.8155536201897325, "education_level": 2.766280285699795, "creative_writing_quality": -0.4289404087273526}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.4565 below 0.70", "secondary_topic vs NONE margin 0.0834 below 0.10"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.010518520179062074, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9948939397193097, "secondary": 0.9818514112945962, "tertiary": 0.9928272584379653, "structured_records": 0.011057550472707489, "procedural_steps": 0.03184403385856344, "worked_solution": 0.013439453261620693, "contextual_dialogue": 5.468011745468566e-06, "content_quality": 0.403878106758522, "noise_severity": 0.9518795330380139, "missing_content_severity": 0.9859948473052695, "factual_density": 0.43164468154240376, "pretraining_utility": 0.4794750806131366, "math_density": 0.41233373781417926, "code_density": 0.9267259363947443, "reasoning_density": 0.320371983175223, "education_level": 0.4400495684392929, "creative_writing_quality": 0.9986859256539162, "primary_topic": 0.9187454322175332, "secondary_topic": 0.4347257212355487}, "probability_margin": {"primary": 0.9930170982982839, "secondary": 0.9729507528050384, "tertiary": 0.9903737894187442, "education_level": 0.1913051123281614, "creative_writing_quality": 0.998381940029681, "primary_topic": 0.8508707092188027, "secondary_topic": 0.08338826789538673}, "raw_probabilities": {"primary": {"Academic Writing": 9.497211619435778e-05, "Content Listing": 2.8709073136253763e-05, "Creative Writing": 2.716754666393889e-05, "Customer Support Page": 1.0706930947175462e-05, "Q&A Forum": 1.8482151026277896e-05, "Discussion Thread": 1.1377166289124336e-05, "Comment Section": 1.9482060262785424e-05, "Missing Content": 3.8338240638972966e-05, "Knowledge Article": 0.00012151208608779786, "Legal Notices": 3.055511224367657e-05, "News Article": 6.370294235268371e-06, "Organizational Announcement": 1.2591501679402876e-05, "Nonfiction Writing": 2.2618138940509017e-05, "Organizational About Page": 1.6853477531178428e-05, "Personal About Page": 2.0170202068875404e-05, "Personal Blog": 1.1243797953606246e-05, "Product Page": 8.648880449476847e-06, "Spam / Ads": 1.4202056562941179e-05, "Structured Data": 0.000327555542936046, "Technical Writing": 0.9951066922310051, "Transcript / Interview": 2.288328129178596e-05, "Tutorial / How-To Guide": 0.002089593932721194, "User Reviews": 9.734079200982566e-06, "Other / Unknown": 0.0019295400999334452}, "secondary": {"Academic Writing": 0.0006266455916028877, "Content Listing": 6.546394723837263e-05, "Creative Writing": 9.081820130856618e-05, "Customer Support Page": 4.399167813734251e-05, "Q&A Forum": 3.8760221251110947e-05, "Discussion Thread": 2.9475483092981876e-05, "Comment Section": 6.979651618530965e-05, "Missing Content": 0.00016830010421159443, "Knowledge Article": 0.0006530223386071609, "Legal Notices": 0.00012157733311777805, "News Article": 4.400963800042541e-05, "Organizational Announcement": 3.885591121961604e-05, "Nonfiction Writing": 5.8982036045353116e-05, "Organizational About Page": 5.794965927286453e-05, "Personal About Page": 5.7146063853593364e-05, "Personal Blog": 4.823488461170474e-05, "Product Page": 2.7593107244857365e-05, "Spam / Ads": 5.101673865727947e-05, "Structured Data": 0.001076859248471123, "Technical Writing": 0.009656849685616274, "Transcript / Interview": 5.978761090853944e-05, "Tutorial / How-To Guide": 0.004281773713479039, "User Reviews": 2.5487797211596713e-05, "NONE": 0.9826076024906547}, "tertiary": {"Academic Writing": 0.000182350705602164, "Content Listing": 3.837234750265784e-05, "Creative Writing": 6.353678825526986e-05, "Customer Support Page": 2.5727614671134503e-05, "Q&A Forum": 2.0747643612400063e-05, "Discussion Thread": 1.4988705810431225e-05, "Comment Section": 4.937846122534757e-05, "Missing Content": 0.00020979162132869197, "Knowledge Article": 0.000346062285783301, "Legal Notices": 0.0001538343535688811, "News Article": 2.73053793876851e-05, "Organizational Announcement": 2.420359435939327e-05, "Nonfiction Writing": 4.938178125035859e-05, "Organizational About Page": 4.062826805998492e-05, "Personal About Page": 3.2054509239432715e-05, "Personal Blog": 2.8856625496330705e-05, "Product Page": 1.7239288260011274e-05, "Spam / Ads": 3.166644297623575e-05, "Structured Data": 0.0008567983227716068, "Technical Writing": 0.0027523332509724837, "Transcript / Interview": 3.1732058820493276e-05, "Tutorial / How-To Guide": 0.0018598476668833896, "User Reviews": 1.703961444557894e-05, "NONE": 0.9931261226697167}, "structured_records": {"false": 0.9889424495272925, "true": 0.011057550472707489}, "procedural_steps": {"false": 0.9681559661414365, "true": 0.03184403385856344}, "worked_solution": {"false": 0.9865605467383793, "true": 0.013439453261620693}, "contextual_dialogue": {"false": 0.9999945319882545, "true": 5.468011745468566e-06}, "content_quality": {"-2": 0.03366404814376807, "-1": 0.15965706271067193, "0": 0.38770758190923144, "1": 0.34958150158109175, "2": 0.0693898056552369}, "noise_severity": {"0": 0.9489688190452762, "1": 0.046057601056178434, "2": 0.0037211587565778898, "3": 0.0007650427828203251, "4": 0.00048737835914703157}, "missing_content_severity": {"0": 0.9890249790414418, "1": 0.006457243728699778, "2": 0.0034041694554994215, "3": 0.0009138305034576541, "4": 0.00019977727090123108}, "factual_density": {"0": 0.009945957878647806, "1": 0.024209590571242307, "2": 0.11705212789828354, "3": 0.33550952312423027, "4": 0.5132828005275961}, "pretraining_utility": {"-2": 0.008814743835662494, "-1": 0.07220169987129864, "0": 0.34744678210195445, "1": 0.46520128427838753, "2": 0.1063354899126968}, "math_density": {"0": 0.016800823017488427, "1": 0.04770158078065902, "2": 0.21172162989744175, "3": 0.37610371219265104, "4": 0.34767225411175967}, "code_density": {"0": 0.00022504694267454502, "1": 0.0004096891236324627, "2": 0.00933673451644788, "3": 0.06712615215181544, "4": 0.9229023772654296}, "reasoning_density": {"0": 0.6776325774016339, "1": 0.0572551323942968, "2": 0.10797132468690991, "3": 0.08620802364702176, "4": 0.07093294187013764}, "education_level": {"none": 0.0010158897826414023, "0": 0.0013524325050884238, "1": 0.003180988195180308, "2": 0.342069528037916, "3": 0.5333746403660774, "4": 0.1190065211130963}, "creative_writing_quality": {"not_applicable": 0.9989049380449302, "-2": 0.00014493493226964694, "-1": 0.00031861596481257224, "0": 0.0005229980152491965, "1": 7.825657871492206e-05, "2": 3.0256464023711643e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001214455896739202, "Art & Design": 0.0004501576661852531, "Law & Crime": 0.00015611056353805684, "Education & Careers": 0.00017256224247973767, "Entertainment & Media": 0.00012417569312953215, "Fashion & Beauty": 0.00010540312652756414, "Business, Economics & Finance": 0.00012982616499827333, "Food & Dining": 9.170906007280456e-05, "Games & Gaming": 0.0003978170246650656, "Electronics & Hardware": 0.0006761038323287396, "Health, Healthcare & Wellness": 0.0001563587924874268, "History & Geography": 0.00015902250791535033, "Home, Pets & Hobbies": 0.0001356203432467613, "Industry & Production": 0.00016557011456637058, "Language, Literature & Philosophy": 0.0001173830570828009, "Politics & Government": 5.680757738241309e-05, "Religion & Spirituality": 0.00010097641436766453, "Science, Mathematics & Engineering": 0.07099989868267158, "Relationships & Community": 6.43823466684729e-05, "Software Usage & Applications": 0.000862878713937671, "Programming & Software Engineering": 0.9218706079014742, "Sports & Fitness": 0.00017475177273977422, "Vehicles & Transportation": 0.0001282450134743865, "Travel & Tourism": 0.00013864192221642457, "Other / Uncovered Subject": 0.00227286667613522, "Unknown / Insufficient Content": 0.0001706772000346116}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007649324379722768, "Art & Design": 0.0033474694816161966, "Law & Crime": 0.0006640852576783359, "Education & Careers": 0.000710444695363296, "Entertainment & Media": 0.0006508759087574307, "Fashion & Beauty": 0.0003867257663491588, "Business, Economics & Finance": 0.0005479257502831008, "Food & Dining": 0.000360002716011368, "Games & Gaming": 0.0007575818323825648, "Electronics & Hardware": 0.004616717719589271, "Health, Healthcare & Wellness": 0.000668591993975978, "History & Geography": 0.0005601274891886717, "Home, Pets & Hobbies": 0.00047566539379569354, "Industry & Production": 0.0005650015916650708, "Language, Literature & Philosophy": 0.0003467571865001419, "Politics & Government": 0.0002926633889564133, "Religion & Spirituality": 0.00048156920713539255, "Science, Mathematics & Engineering": 0.45646703964956603, "Relationships & Community": 0.0002216667966907786, "Software Usage & Applications": 0.0020856651206247307, "Programming & Software Engineering": 0.14714763713176668, "Sports & Fitness": 0.0005711289874493657, "Vehicles & Transportation": 0.000550391937426033, "Travel & Tourism": 0.000549496147119447, "Other / Uncovered Subject": 0.003131064657957255, "NONE": 0.3730787717541793}}, "probabilities": {"primary": {"Academic Writing": 9.497211619435775e-05, "Content Listing": 2.8709073136253756e-05, "Creative Writing": 2.7167546663938884e-05, "Customer Support Page": 1.070693094717546e-05, "Q&A Forum": 1.8482151026277893e-05, "Discussion Thread": 1.1377166289124334e-05, "Comment Section": 1.948206026278542e-05, "Missing Content": 3.833824063897296e-05, "Knowledge Article": 0.00012151208608779783, "Legal Notices": 3.055511224367656e-05, "News Article": 6.370294235268369e-06, "Organizational Announcement": 1.2591501679402873e-05, "Nonfiction Writing": 2.2618138940509013e-05, "Organizational About Page": 1.6853477531178425e-05, "Personal About Page": 2.01702020688754e-05, "Personal Blog": 1.1243797953606244e-05, "Product Page": 8.648880449476846e-06, "Spam / Ads": 1.4202056562941175e-05, "Structured Data": 0.00032755554293604594, "Technical Writing": 0.9951066922310049, "Transcript / Interview": 2.2883281291785957e-05, "Tutorial / How-To Guide": 0.0020895939327211935, "User Reviews": 9.734079200982565e-06, "Other / Unknown": 0.0019295400999334448}, "secondary": {"Academic Writing": 0.0006266455916028877, "Content Listing": 6.546394723837263e-05, "Creative Writing": 9.081820130856618e-05, "Customer Support Page": 4.399167813734251e-05, "Q&A Forum": 3.8760221251110947e-05, "Discussion Thread": 2.9475483092981876e-05, "Comment Section": 6.979651618530965e-05, "Missing Content": 0.00016830010421159443, "Knowledge Article": 0.0006530223386071609, "Legal Notices": 0.00012157733311777805, "News Article": 4.400963800042541e-05, "Organizational Announcement": 3.885591121961604e-05, "Nonfiction Writing": 5.8982036045353116e-05, "Organizational About Page": 5.794965927286453e-05, "Personal About Page": 5.7146063853593364e-05, "Personal Blog": 4.823488461170474e-05, "Product Page": 2.7593107244857365e-05, "Spam / Ads": 5.101673865727947e-05, "Structured Data": 0.001076859248471123, "Technical Writing": 0.009656849685616274, "Transcript / Interview": 5.978761090853944e-05, "Tutorial / How-To Guide": 0.004281773713479039, "User Reviews": 2.5487797211596713e-05, "NONE": 0.9826076024906547}, "tertiary": {"Academic Writing": 0.00018235070560216403, "Content Listing": 3.8372347502657846e-05, "Creative Writing": 6.353678825526988e-05, "Customer Support Page": 2.5727614671134507e-05, "Q&A Forum": 2.0747643612400066e-05, "Discussion Thread": 1.4988705810431227e-05, "Comment Section": 4.9378461225347574e-05, "Missing Content": 0.000209791621328692, "Knowledge Article": 0.00034606228578330106, "Legal Notices": 0.00015383435356888111, "News Article": 2.7305379387685103e-05, "Organizational Announcement": 2.4203594359393274e-05, "Nonfiction Writing": 4.9381781250358596e-05, "Organizational About Page": 4.0628268059984926e-05, "Personal About Page": 3.205450923943272e-05, "Personal Blog": 2.8856625496330708e-05, "Product Page": 1.7239288260011278e-05, "Spam / Ads": 3.1666442976235755e-05, "Structured Data": 0.000856798322771607, "Technical Writing": 0.002752333250972484, "Transcript / Interview": 3.173205882049328e-05, "Tutorial / How-To Guide": 0.0018598476668833898, "User Reviews": 1.7039614445578945e-05, "NONE": 0.9931261226697168}, "structured_records": {"false": 0.9889424495272925, "true": 0.011057550472707489}, "procedural_steps": {"false": 0.9681559661414365, "true": 0.03184403385856344}, "worked_solution": {"false": 0.9865605467383793, "true": 0.013439453261620693}, "contextual_dialogue": {"false": 0.9999945319882545, "true": 5.468011745468566e-06}, "content_quality": {"-2": 0.03366404814376807, "-1": 0.15965706271067193, "0": 0.38770758190923144, "1": 0.34958150158109175, "2": 0.0693898056552369}, "noise_severity": {"0": 0.9489688190452763, "1": 0.04605760105617844, "2": 0.00372115875657789, "3": 0.0007650427828203252, "4": 0.0004873783591470316}, "missing_content_severity": {"0": 0.9890249790414419, "1": 0.006457243728699779, "2": 0.003404169455499422, "3": 0.0009138305034576542, "4": 0.0001997772709012311}, "factual_density": {"0": 0.009945957878647806, "1": 0.024209590571242307, "2": 0.11705212789828354, "3": 0.33550952312423027, "4": 0.5132828005275961}, "pretraining_utility": {"-2": 0.008814743835662496, "-1": 0.07220169987129865, "0": 0.3474467821019545, "1": 0.4652012842783876, "2": 0.10633548991269681}, "math_density": {"0": 0.01680082301748843, "1": 0.047701580780659024, "2": 0.21172162989744178, "3": 0.3761037121926511, "4": 0.3476722541117597}, "code_density": {"0": 0.00022504694267454505, "1": 0.00040968912363246275, "2": 0.009336734516447882, "3": 0.06712615215181546, "4": 0.9229023772654297}, "reasoning_density": {"0": 0.6776325774016339, "1": 0.0572551323942968, "2": 0.10797132468690991, "3": 0.08620802364702176, "4": 0.07093294187013764}, "education_level": {"none": 0.0010158897826414025, "0": 0.001352432505088424, "1": 0.0031809881951803086, "2": 0.34206952803791607, "3": 0.5333746403660775, "4": 0.11900652111309631}, "creative_writing_quality": {"not_applicable": 0.99890493804493, "-2": 0.00014493493226964692, "-1": 0.0003186159648125722, "0": 0.0005229980152491964, "1": 7.825657871492204e-05, "2": 3.0256464023711636e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001214455896739202, "Art & Design": 0.0004501576661852531, "Law & Crime": 0.00015611056353805684, "Education & Careers": 0.00017256224247973767, "Entertainment & Media": 0.00012417569312953215, "Fashion & Beauty": 0.00010540312652756414, "Business, Economics & Finance": 0.00012982616499827333, "Food & Dining": 9.170906007280456e-05, "Games & Gaming": 0.0003978170246650656, "Electronics & Hardware": 0.0006761038323287396, "Health, Healthcare & Wellness": 0.0001563587924874268, "History & Geography": 0.00015902250791535033, "Home, Pets & Hobbies": 0.0001356203432467613, "Industry & Production": 0.00016557011456637058, "Language, Literature & Philosophy": 0.0001173830570828009, "Politics & Government": 5.680757738241309e-05, "Religion & Spirituality": 0.00010097641436766453, "Science, Mathematics & Engineering": 0.07099989868267158, "Relationships & Community": 6.43823466684729e-05, "Software Usage & Applications": 0.000862878713937671, "Programming & Software Engineering": 0.9218706079014742, "Sports & Fitness": 0.00017475177273977422, "Vehicles & Transportation": 0.0001282450134743865, "Travel & Tourism": 0.00013864192221642457, "Other / Uncovered Subject": 0.00227286667613522, "Unknown / Insufficient Content": 0.0001706772000346116}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007649324379722768, "Art & Design": 0.0033474694816161966, "Law & Crime": 0.0006640852576783359, "Education & Careers": 0.000710444695363296, "Entertainment & Media": 0.0006508759087574307, "Fashion & Beauty": 0.0003867257663491588, "Business, Economics & Finance": 0.0005479257502831008, "Food & Dining": 0.000360002716011368, "Games & Gaming": 0.0007575818323825648, "Electronics & Hardware": 0.004616717719589271, "Health, Healthcare & Wellness": 0.000668591993975978, "History & Geography": 0.0005601274891886717, "Home, Pets & Hobbies": 0.00047566539379569354, "Industry & Production": 0.0005650015916650708, "Language, Literature & Philosophy": 0.0003467571865001419, "Politics & Government": 0.0002926633889564133, "Religion & Spirituality": 0.00048156920713539255, "Science, Mathematics & Engineering": 0.45646703964956603, "Relationships & Community": 0.0002216667966907786, "Software Usage & Applications": 0.0020856651206247307, "Programming & Software Engineering": 0.14714763713176668, "Sports & Fitness": 0.0005711289874493657, "Vehicles & Transportation": 0.000550391937426033, "Travel & Tourism": 0.000549496147119447, "Other / Uncovered Subject": 0.003131064657957255, "NONE": 0.3730787717541793}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10472, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10472, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:35:10.061471+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "7cbf9b58-9b50-4b14-80ba-d926182208b8", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "d6b2a2e221fe03dd06c5f9f41a712186f949ee662d9a3d2f18f975c45bb8917b", "source_metadata": {"uuid": "7cbf9b58-9b50-4b14-80ba-d926182208b8", "repo_name": "Chushihyun/3dcv", "relative_path": "r09922115_hw3/vo.py", "category": "DATA", "algo_rel_score": 0.9259764552116394, "quality_score": 4.021060943603516, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 3, "repo_created_at": "2021-05-24T20:30:09.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "7252758aa752b62006c11d42c70c69d93c43bf2d", "size_bytes": 6896, "file_timestamp": 1621888456, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "feb2b6020d1474c892b85326d5bcd12d3afdfd15"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 3, "repo_created_at": "2021-05-24T20:30:09.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "7252758aa752b62006c11d42c70c69d93c43bf2d", "size_bytes": 6896, "file_timestamp": 1621888456, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "feb2b6020d1474c892b85326d5bcd12d3afdfd15", "url": "https://github.com/Chushihyun/3dcv/blob/feb2b6020d1474c892b85326d5bcd12d3afdfd15/r09922115_hw3/vo.py"}, "gpt4o_tokens": 1823, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.2613759538933575, "noise_severity": 0.05774456035438332, "missing_content_severity": 0.01680618323367651, "factual_density": 3.3179736178508845, "pretraining_utility": 0.5880410765611576, "math_density": 2.9901449936005347, "code_density": 3.912071123673693, "reasoning_density": 0.8155536201897325, "education_level": 2.766280285699795, "creative_writing_quality": -0.4289404087273526}, "score_applicability": {"education_level": 0.9989841102173587, "creative_writing_quality": 0.0010950619550700491}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/bothe/EDAs/blob/0f8bf6cdcb2e5f069d6690dacbf08bbd18ad6072/utils/da_names.py", "id": "python-0-958f6ee1f258224e68614ff2bf7867fb", "text": "EDAs = {\n    # 'Statement': ['sd', 'sv'],\n    'sd': 'Statement-non-opinion', 'sv': 'Statement-opinion',\n    # 'Questions': ['qw', 'qy^d', 'qy', 'qh', 'qo', 'qrr', '^q', 'qw^d', '^g'],\n    'qw': 'Wh-Question', 'qw^d': 'Declarative Wh-Question',\n    'qy': 'Yes-No-Question', 'qy^d': 'Declarative Yes-No-Question',\n    'qh': 'Rhetorical-Question', 'qo': 'Open-question', 'qrr': 'Or-Clause-Question',\n    '^q': 'Quotation', '^g': 'Tag-Question',\n    # 'Answers': ['nn', 'na', 'ny', 'no', 'ng'],\n    'nn': 'No-Answer', 'ng': 'Negative non-no answers',\n    'ny': 'Yes-Answer', 'na': 'Affirmative non-yes answers',\n    'no': 'Other Answer', 'nd': 'Dispreferred answer',\n    # 'Agreement': ['aa', 'ar', 'ad', '^h', 'aap_am', 'arp_nd'],\n    'aa': 'Agree or Accept', 'ar': 'Reject', 'aap': 'Accept-part', 'am': 'Maybe',\n    'ad': 'Action-directive', '^h': 'Hold before answer or agreement',\n    # 'BackwardFunction': ['b', 'bk', 'bh', 'ba', 'bf', 'br', 'bd', '^2'],\n    'b': 'Acknowledge (Backchannel)', 'bk': 'Response Acknowledgement',\n    'bh': 'Rhetorical Backchannel', 'ba': 'Appreciation or Assessment',\n    'bf': 'Reformulate', 'br': 'Signal-non-understanding',\n    'bd': 'Downplaying-response', '^2': 'Collaborative Completion',\n    # 'ForwardFunction': ['fo_o_fw_\"_by_bc', 'fc', 'ft', 'fp', 'fa', 'oo_co_cc', 'fo_o', 'fw'],\n    'fo': 'Other Forward Function', 'fc': 'Conventional-closing', 'fp': 'Conventional-opening',\n    'ft': 'Thanking', 'fa': 'Apology', 'oo_co_cc': 'Offers, Options, Commits',\n    # 'CommInfoStatus': ['%', 'b^m', 't1', 't3'],\n    '%': 'Uninterpretable', 'b^m': 'Repeat-phrase',\n    't1': 'Self-talk', 't3': '3rd-party-talk',\n    # 'Other': ['h', '^q'],\n    'h': 'Hedge',\n    # 'NonSpeechVerbal': ['x']\n    'x': 'Non-verbal',\n}\n\n\nDAs = {\n    'Statement': ['sd', 'sv'],\n    'Questions': ['qw', 'qy^d', 'qy', 'qh', 'qo', 'qrr', '^q', 'qw^d', '^g'],\n    'Answers': ['nn', 'na', 'ny', 'no', 'ng'],\n    'Agreement': ['aa', 'ar', 'ad', '^h', 'aap_am', 'arp_nd'],\n    'BackwardFunction': ['b', 'bk', 'bh', 'ba', 'bf', 'br', 'bd', '^2'],\n    'ForwardFunction': ['fo_o_fw_\"_by_bc', 'fc', 'ft', 'fp', 'fa', 'oo_co_cc', 'fo_o', 'fw'],\n    'CommInfoStatus': ['%', 'b^m', 't1', 't3'],\n    'Other': ['h', '^q'],\n    'NonSpeechVerbal': ['x']\n}\n\n\ndef highDAClass(da, DAs):\n    if da in DAs['Statement']:\n        return 'Statement'\n    elif da in DAs['Questions']:\n        return 'Questions'\n    elif da in DAs['Answers']:\n        return 'Answers'\n    elif da in DAs['Agreement']:\n        return 'Agreement'\n    elif da in DAs['BackwardFunction']:\n        return 'Backward Function'\n    elif da in DAs['ForwardFunction']:\n        return 'Forward Function'\n    elif da in DAs['CommInfoStatus']:\n        return 'CommInfoStatus'\n    elif da in DAs['Other']:\n        return 'Other'\n    elif da in DAs['NonSpeechVerbal']:\n        return 'NonSpeechVerbal'\n\n\nDA_names = ['Other Functions', 'Wh-Question', 'Declarative Yes-No-Question ',\n            'Yes-No-Question', 'Statement-non-opinion', 'Action-Directive', 'Hedge',\n            'Agree/Accept', 'Backchannel', 'Statement-opinion', 'Acknowledge Ans',\n            'No Answer', 'Affirmative Answer', 'Backchannel-Que', 'Yes Answers', 'NonSpeech',\n            'Uninterpretable', 'Appreciation', 'Reformulate', 'Repeat Phrase',\n            'Rhetorical-Question', 'Other Answers', 'Self-talk', 'Open-Question',\n            'Hold', 'Or-Clause', 'Open Option', 'Quotation', 'Completion',\n            'Signal-non-understand', 'Maybe/Accept-part', 'Downplayer',\n            'Tag-Question', 'Conv. Closing', 'Thanking', 'Reject', '3rd-party-talk ',\n            'Negative Answer', 'Info-Request', 'Conv. Opening', 'Apology', 'Reject-part']\n\n\ndef returnDAname(da, tag, DA_names):\n    return DA_names[tag.index(da)]\n", "text_preview": "EDAs = { # 'Statement': ['sd', 'sv'], 'sd': 'Statement-non-opinion', 'sv': 'Statement-opinion', # 'Questions': ['qw', 'qy^d', 'qy', 'qh', 'qo', 'qrr', '^q', 'qw^d', '^g'], 'qw': 'Wh-Question', 'qw^d': 'Declarative Wh-Question', 'qy': 'Yes-N", "input_sha256": "f1e6893eade44f92efb11dcc550488cc1e0c165dfd53fc04293031caadd8b4a4", "text_sha256": "958f6ee1f258224e68614ff2bf7867fbe6c8d1f6916bd7a5765ed0838abc58f2", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12094, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": ["structured_records"]}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7696927664737007, "noise_severity": 0.03290601694114231, "missing_content_severity": 0.013922090512385249, "factual_density": 3.420459198837394, "pretraining_utility": -0.15675403623210274, "math_density": 0.032039374461491105, "code_density": 3.5744499688457068, "reasoning_density": 0.06698305968022934, "education_level": 2.039164822251446, "creative_writing_quality": -0.47509196391949415}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.3859616183337734, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9213048783397616, "secondary": 0.5134682641172307, "tertiary": 0.9162108223785596, "structured_records": 0.7240890314748721, "procedural_steps": 0.002056395704014541, "worked_solution": 0.00037171378283786324, "contextual_dialogue": 1.3121450738119155e-05, "content_quality": 0.49066636301134736, "noise_severity": 0.9725783192157147, "missing_content_severity": 0.9883982579063456, "factual_density": 0.5170493323644945, "pretraining_utility": 0.5211315067435811, "math_density": 0.9733005212820908, "code_density": 0.6453749740380887, "reasoning_density": 0.9441807835998088, "education_level": 0.550188409048432, "creative_writing_quality": 0.9997286816415326, "primary_topic": 0.7882671547630002, "secondary_topic": 0.5720264702529011}, "probability_margin": {"primary": 0.8546801201440292, "secondary": 0.15638086590826034, "tertiary": 0.867942569382368, "education_level": 0.450706449793925, "creative_writing_quality": 0.9996954650369049, "primary_topic": 0.6517595045340069, "secondary_topic": 0.33082047164953643}, "raw_probabilities": {"primary": {"Academic Writing": 0.00018135569665311276, "Content Listing": 0.00014323264984657138, "Creative Writing": 9.633248669901561e-05, "Customer Support Page": 6.941752298078203e-05, "Q&A Forum": 6.16437342252375e-05, "Discussion Thread": 6.871211129757013e-05, "Comment Section": 7.359016204118501e-05, "Missing Content": 0.00011886039507280251, "Knowledge Article": 0.0002485657347178013, "Legal Notices": 0.00010606709658357306, "News Article": 3.611149062881724e-05, "Organizational Announcement": 7.115305734819828e-05, "Nonfiction Writing": 8.247344670126216e-05, "Organizational About Page": 9.868562505886233e-05, "Personal About Page": 6.409744643427272e-05, "Personal Blog": 3.7558622286623315e-05, "Product Page": 4.03791882739817e-05, "Spam / Ads": 5.96960957712421e-05, "Structured Data": 0.06990372159824232, "Technical Writing": 0.9245838417422715, "Transcript / Interview": 0.00023889337409318102, "Tutorial / How-To Guide": 0.00018411740622685142, "User Reviews": 3.227340397363967e-05, "Other / Unknown": 0.003399219912571569}, "secondary": {"Academic Writing": 0.0023416578699351764, "Content Listing": 0.0008289931495098126, "Creative Writing": 0.0006414915730054239, "Customer Support Page": 0.0004283375858732337, "Q&A Forum": 0.0003471321714316342, "Discussion Thread": 0.00035681168217617244, "Comment Section": 0.0004389882138819151, "Missing Content": 0.0006796255475369949, "Knowledge Article": 0.001189007675232351, "Legal Notices": 0.00042801418033725213, "News Article": 0.00038144103732768526, "Organizational Announcement": 0.00042994311148528304, "Nonfiction Writing": 0.0005445953353329884, "Organizational About Page": 0.0005394842335548094, "Personal About Page": 0.0004188119820372119, "Personal Blog": 0.0003473066791766937, "Product Page": 0.0003137014084489454, "Spam / Ads": 0.0004284846691598921, "Structured Data": 0.37735955387075243, "Technical Writing": 0.07611543554348459, "Transcript / Interview": 0.000992124741444929, "Tutorial / How-To Guide": 0.0004917616466091628, "User Reviews": 0.0002168763132525725, "NONE": 0.5337404197790128}, "tertiary": {"Academic Writing": 0.0010681490618462668, "Content Listing": 0.0005920518487139375, "Creative Writing": 0.00021328089238341206, "Customer Support Page": 0.00013796765815523204, "Q&A Forum": 7.746014690683694e-05, "Discussion Thread": 9.178443551844412e-05, "Comment Section": 0.00012881363458464463, "Missing Content": 0.00028047358656360286, "Knowledge Article": 0.0010936500017807893, "Legal Notices": 0.000156849549355671, "News Article": 9.527710833208496e-05, "Organizational Announcement": 0.00010467771364409215, "Nonfiction Writing": 0.00013784304600293772, "Organizational About Page": 0.0001695340780128148, "Personal About Page": 0.00011531213792954204, "Personal Blog": 0.00010031709800320518, "Product Page": 8.358941096637672e-05, "Spam / Ads": 0.0001263531782410918, "Structured Data": 0.05175946873041823, "Technical Writing": 0.022846529747483937, "Transcript / Interview": 0.0006545755640546876, "Tutorial / How-To Guide": 0.00021236998167855923, "User Reviews": 5.163327663741527e-05, "NONE": 0.9197020381127863}, "structured_records": {"false": 0.27591096852512786, "true": 0.7240890314748721}, "procedural_steps": {"false": 0.9979436042959855, "true": 0.002056395704014541}, "worked_solution": {"false": 0.9996282862171622, "true": 0.00037171378283786324}, "contextual_dialogue": {"false": 0.9999868785492618, "true": 1.3121450738119155e-05}, "content_quality": {"-2": 0.009374885688135695, "-1": 0.06481342345083851, "0": 0.2630022949902571, "1": 0.47236283044072674, "2": 0.1904465654300419}, "noise_severity": {"0": 0.972247558491585, "1": 0.02412247590171656, "2": 0.0025070766022893954, "3": 0.0007221681827886613, "4": 0.0004007208216202421}, "missing_content_severity": {"0": 0.9887805879918435, "1": 0.009291701459101723, "2": 0.001326417462880773, "3": 0.0004276182171742032, "4": 0.0001736748689998423}, "factual_density": {"0": 0.0034292284922330966, "1": 0.006431993181870773, "2": 0.08925004657842253, "3": 0.36802781449121674, "4": 0.532860917256257}, "pretraining_utility": {"-2": 0.030397745610259357, "-1": 0.30490262284938396, "0": 0.4899196227239728, "1": 0.14061593979496784, "2": 0.03416406902141605}, "math_density": {"0": 0.9882056676278238, "1": 0.003896777733099468, "2": 0.0009266203691871068, "3": 0.0015943810895409358, "4": 0.005376553180348654}, "code_density": {"0": 0.0008380514957913927, "1": 0.0018403949094612745, "2": 0.08247727985884482, "3": 0.25172208072505453, "4": 0.6631221930108481}, "reasoning_density": {"0": 0.9580660567742812, "1": 0.018188077547440878, "2": 0.022626493047225915, "3": 0.0009354944858722189, "4": 0.00018387814517999593}, "education_level": {"none": 0.005237458435292587, "0": 0.016072126556403787, "1": 0.17445055774643498, "2": 0.62515700754036, "3": 0.11261119046099637, "4": 0.06647165926051239}, "creative_writing_quality": {"not_applicable": 0.9997739013679439, "-2": 5.8139811730302766e-05, "-1": 4.709713324158133e-05, "0": 7.843633103898922e-05, "1": 2.889159853131941e-05, "2": 1.3533757513911013e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00020726030450438887, "Art & Design": 0.000292510862823045, "Law & Crime": 0.0003204830763528255, "Education & Careers": 0.00030500641841799293, "Entertainment & Media": 0.0003304427284647667, "Fashion & Beauty": 0.0002236258395408161, "Business, Economics & Finance": 0.00031112715000552327, "Food & Dining": 0.00018466885189205408, "Games & Gaming": 0.00036600804810430913, "Electronics & Hardware": 0.0002678114303809554, "Health, Healthcare & Wellness": 0.00031384064358423735, "History & Geography": 0.0003731308194160657, "Home, Pets & Hobbies": 0.00016631819096694293, "Industry & Production": 0.0002280691959779126, "Language, Literature & Philosophy": 0.1446512211996472, "Politics & Government": 0.00017296928577478828, "Religion & Spirituality": 0.000288314451674258, "Science, Mathematics & Engineering": 0.021783197257966406, "Relationships & Community": 0.004224808999106367, "Software Usage & Applications": 0.0005272861121405399, "Programming & Software Engineering": 0.7964107257336541, "Sports & Fitness": 0.00033211970461376563, "Vehicles & Transportation": 0.00011865408523647437, "Travel & Tourism": 0.00033233974926186313, "Other / Uncovered Subject": 0.026370216501211505, "Unknown / Insufficient Content": 0.0008978433592805828}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007438763972264902, "Art & Design": 0.0007957403662299996, "Law & Crime": 0.0010344862806250996, "Education & Careers": 0.0006345732291309365, "Entertainment & Media": 0.0007279790407882324, "Fashion & Beauty": 0.0003730559625527564, "Business, Economics & Finance": 0.0007154369325936275, "Food & Dining": 0.00034853116370072926, "Games & Gaming": 0.0006342894649623837, "Electronics & Hardware": 0.0005514298394947888, "Health, Healthcare & Wellness": 0.0007513795222247553, "History & Geography": 0.0006167784191241537, "Home, Pets & Hobbies": 0.00032994365351954053, "Industry & Production": 0.00036791011383928397, "Language, Literature & Philosophy": 0.2576665189782531, "Politics & Government": 0.00035449068549706043, "Religion & Spirituality": 0.0007015068003670426, "Science, Mathematics & Engineering": 0.04398047091998497, "Relationships & Community": 0.004480509254588981, "Software Usage & Applications": 0.0006002423276105331, "Programming & Software Engineering": 0.06096546891083861, "Sports & Fitness": 0.00039533435201059456, "Vehicles & Transportation": 0.00019752466625539734, "Travel & Tourism": 0.0006770391591630591, "Other / Uncovered Subject": 0.032868492931628406, "NONE": 0.5884869906277895}}, "probabilities": {"primary": {"Academic Writing": 0.00018135569665311276, "Content Listing": 0.00014323264984657138, "Creative Writing": 9.633248669901561e-05, "Customer Support Page": 6.941752298078203e-05, "Q&A Forum": 6.16437342252375e-05, "Discussion Thread": 6.871211129757013e-05, "Comment Section": 7.359016204118501e-05, "Missing Content": 0.00011886039507280251, "Knowledge Article": 0.0002485657347178013, "Legal Notices": 0.00010606709658357306, "News Article": 3.611149062881724e-05, "Organizational Announcement": 7.115305734819828e-05, "Nonfiction Writing": 8.247344670126216e-05, "Organizational About Page": 9.868562505886233e-05, "Personal About Page": 6.409744643427272e-05, "Personal Blog": 3.7558622286623315e-05, "Product Page": 4.03791882739817e-05, "Spam / Ads": 5.96960957712421e-05, "Structured Data": 0.06990372159824232, "Technical Writing": 0.9245838417422715, "Transcript / Interview": 0.00023889337409318102, "Tutorial / How-To Guide": 0.00018411740622685142, "User Reviews": 3.227340397363967e-05, "Other / Unknown": 0.003399219912571569}, "secondary": {"Academic Writing": 0.002341657869935177, "Content Listing": 0.0008289931495098127, "Creative Writing": 0.000641491573005424, "Customer Support Page": 0.00042833758587323376, "Q&A Forum": 0.00034713217143163424, "Discussion Thread": 0.0003568116821761725, "Comment Section": 0.00043898821388191513, "Missing Content": 0.000679625547536995, "Knowledge Article": 0.0011890076752323513, "Legal Notices": 0.0004280141803372522, "News Article": 0.0003814410373276853, "Organizational Announcement": 0.0004299431114852831, "Nonfiction Writing": 0.0005445953353329885, "Organizational About Page": 0.0005394842335548095, "Personal About Page": 0.00041881198203721197, "Personal Blog": 0.00034730667917669375, "Product Page": 0.00031370140844894545, "Spam / Ads": 0.00042848466915989213, "Structured Data": 0.3773595538707525, "Technical Writing": 0.0761154355434846, "Transcript / Interview": 0.0009921247414449292, "Tutorial / How-To Guide": 0.0004917616466091629, "User Reviews": 0.00021687631325257252, "NONE": 0.5337404197790129}, "tertiary": {"Academic Writing": 0.0010681490618462668, "Content Listing": 0.0005920518487139375, "Creative Writing": 0.00021328089238341206, "Customer Support Page": 0.00013796765815523204, "Q&A Forum": 7.746014690683694e-05, "Discussion Thread": 9.178443551844412e-05, "Comment Section": 0.00012881363458464463, "Missing Content": 0.00028047358656360286, "Knowledge Article": 0.0010936500017807893, "Legal Notices": 0.000156849549355671, "News Article": 9.527710833208496e-05, "Organizational Announcement": 0.00010467771364409215, "Nonfiction Writing": 0.00013784304600293772, "Organizational About Page": 0.0001695340780128148, "Personal About Page": 0.00011531213792954204, "Personal Blog": 0.00010031709800320518, "Product Page": 8.358941096637672e-05, "Spam / Ads": 0.0001263531782410918, "Structured Data": 0.05175946873041823, "Technical Writing": 0.022846529747483937, "Transcript / Interview": 0.0006545755640546876, "Tutorial / How-To Guide": 0.00021236998167855923, "User Reviews": 5.163327663741527e-05, "NONE": 0.9197020381127863}, "structured_records": {"false": 0.27591096852512786, "true": 0.7240890314748721}, "procedural_steps": {"false": 0.9979436042959855, "true": 0.002056395704014541}, "worked_solution": {"false": 0.9996282862171622, "true": 0.00037171378283786324}, "contextual_dialogue": {"false": 0.9999868785492618, "true": 1.3121450738119155e-05}, "content_quality": {"-2": 0.009374885688135695, "-1": 0.06481342345083851, "0": 0.2630022949902571, "1": 0.47236283044072674, "2": 0.1904465654300419}, "noise_severity": {"0": 0.9722475584915852, "1": 0.024122475901716567, "2": 0.002507076602289396, "3": 0.0007221681827886614, "4": 0.0004007208216202422}, "missing_content_severity": {"0": 0.9887805879918435, "1": 0.009291701459101723, "2": 0.001326417462880773, "3": 0.0004276182171742032, "4": 0.0001736748689998423}, "factual_density": {"0": 0.0034292284922330966, "1": 0.006431993181870773, "2": 0.08925004657842253, "3": 0.36802781449121674, "4": 0.532860917256257}, "pretraining_utility": {"-2": 0.030397745610259357, "-1": 0.30490262284938396, "0": 0.4899196227239728, "1": 0.14061593979496784, "2": 0.03416406902141605}, "math_density": {"0": 0.9882056676278238, "1": 0.003896777733099468, "2": 0.0009266203691871068, "3": 0.0015943810895409358, "4": 0.005376553180348654}, "code_density": {"0": 0.0008380514957913927, "1": 0.0018403949094612745, "2": 0.08247727985884482, "3": 0.25172208072505453, "4": 0.6631221930108481}, "reasoning_density": {"0": 0.958066056774281, "1": 0.018188077547440874, "2": 0.02262649304722591, "3": 0.0009354944858722187, "4": 0.00018387814517999588}, "education_level": {"none": 0.005237458435292587, "0": 0.016072126556403787, "1": 0.17445055774643498, "2": 0.62515700754036, "3": 0.11261119046099637, "4": 0.06647165926051239}, "creative_writing_quality": {"not_applicable": 0.9997739013679439, "-2": 5.8139811730302766e-05, "-1": 4.709713324158133e-05, "0": 7.843633103898922e-05, "1": 2.889159853131941e-05, "2": 1.3533757513911013e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00020726030450438895, "Art & Design": 0.0002925108628230451, "Law & Crime": 0.00032048307635282563, "Education & Careers": 0.00030500641841799304, "Entertainment & Media": 0.0003304427284647668, "Fashion & Beauty": 0.00022362583954081618, "Business, Economics & Finance": 0.0003111271500055234, "Food & Dining": 0.00018466885189205413, "Games & Gaming": 0.00036600804810430924, "Electronics & Hardware": 0.0002678114303809555, "Health, Healthcare & Wellness": 0.00031384064358423746, "History & Geography": 0.0003731308194160658, "Home, Pets & Hobbies": 0.00016631819096694299, "Industry & Production": 0.00022806919597791268, "Language, Literature & Philosophy": 0.14465122119964727, "Politics & Government": 0.00017296928577478833, "Religion & Spirituality": 0.0002883144516742581, "Science, Mathematics & Engineering": 0.021783197257966413, "Relationships & Community": 0.004224808999106369, "Software Usage & Applications": 0.0005272861121405401, "Programming & Software Engineering": 0.7964107257336543, "Sports & Fitness": 0.00033211970461376574, "Vehicles & Transportation": 0.0001186540852364744, "Travel & Tourism": 0.00033233974926186324, "Other / Uncovered Subject": 0.026370216501211515, "Unknown / Insufficient Content": 0.0008978433592805831}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007438763972264902, "Art & Design": 0.0007957403662299996, "Law & Crime": 0.0010344862806250996, "Education & Careers": 0.0006345732291309365, "Entertainment & Media": 0.0007279790407882324, "Fashion & Beauty": 0.0003730559625527564, "Business, Economics & Finance": 0.0007154369325936275, "Food & Dining": 0.00034853116370072926, "Games & Gaming": 0.0006342894649623837, "Electronics & Hardware": 0.0005514298394947888, "Health, Healthcare & Wellness": 0.0007513795222247553, "History & Geography": 0.0006167784191241537, "Home, Pets & Hobbies": 0.00032994365351954053, "Industry & Production": 0.00036791011383928397, "Language, Literature & Philosophy": 0.2576665189782531, "Politics & Government": 0.00035449068549706043, "Religion & Spirituality": 0.0007015068003670426, "Science, Mathematics & Engineering": 0.04398047091998497, "Relationships & Community": 0.004480509254588981, "Software Usage & Applications": 0.0006002423276105331, "Programming & Software Engineering": 0.06096546891083861, "Sports & Fitness": 0.00039533435201059456, "Vehicles & Transportation": 0.00019752466625539734, "Travel & Tourism": 0.0006770391591630591, "Other / Uncovered Subject": 0.032868492931628406, "NONE": 0.5884869906277895}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9780, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9780, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:35:11.561653+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "e31a0b6d-90cd-4553-a5ac-5a254be5cb14", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "0280192f7411b81d358fd5f67e76a8fe275fdde6fc55195927a8e33c75c1fa72", "source_metadata": {"uuid": "e31a0b6d-90cd-4553-a5ac-5a254be5cb14", "repo_name": "bothe/EDAs", "relative_path": "utils/da_names.py", "category": "CONFIG", "algo_rel_score": 0.9049743413925171, "quality_score": 6.445736408233643, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 58, "repo_created_at": "2019-11-21T23:53:02.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 51, "forks": 9, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "2353385ef1f1f38dd45b277bb2f8e6fd8595acf4", "size_bytes": 3754, "file_timestamp": 1621510997, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0f8bf6cdcb2e5f069d6690dacbf08bbd18ad6072"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 58, "repo_created_at": "2019-11-21T23:53:02.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 51, "forks": 9, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "2353385ef1f1f38dd45b277bb2f8e6fd8595acf4", "size_bytes": 3754, "file_timestamp": 1621510997, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0f8bf6cdcb2e5f069d6690dacbf08bbd18ad6072", "url": "https://github.com/bothe/EDAs/blob/0f8bf6cdcb2e5f069d6690dacbf08bbd18ad6072/utils/da_names.py"}, "gpt4o_tokens": 1194, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.7696927664737007, "noise_severity": 0.03290601694114231, "missing_content_severity": 0.013922090512385249, "factual_density": 3.420459198837394, "pretraining_utility": -0.15675403623210274, "math_density": 0.032039374461491105, "code_density": 3.5744499688457068, "reasoning_density": 0.06698305968022934, "education_level": 2.039164822251446, "creative_writing_quality": -0.47509196391949415}, "score_applicability": {"education_level": 0.9947625415647074, "creative_writing_quality": 0.00022609863205610373}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/SudeepNB/HRF_Vizzly/blob/d0a19030760e4445e2daa5056d587d1de9608762/app/vizzly/utils.py", "id": "python-0-53d940ada090ad573038ee2e72cd57c7", "text": "import json\nimport collections\n\nfrom bokeh.models import ColumnDataSource, FactorRange, HoverTool\nfrom bokeh.transform import factor_cmap\nfrom bokeh.palettes import viridis\nfrom bokeh.plotting import figure\n\nfrom .load_data import *\n\n\ndef columnsToDate(df, column_dic, date_format):\n    for column in column_dic:\n        df[column] = pd.to_datetime(df[column], format=date_format, errors='coerce')\n\n\ndef ewt_date_convert(time_scale):\n    ts = time_scale.upper()\n    dt = \"`REPAIR-DT`\"\n    dt_obj = \"STR_TO_DATE({0}, '%Y-%m-%d')\".format(dt)\n\n    if ts == \"QUARTER\":\n        yr = \"YEAR({0})\".format(dt_obj)\n        qt = \"QUARTER({0})\".format(dt_obj)\n        dt = \"CONCAT(CAST({0} AS CHAR(4)),'-', CAST({1} AS CHAR(4)))\".format(yr, qt)\n\n    elif ts == \"MONTH\":\n        yr = \"YEAR({0})\".format(dt_obj)\n        mt = \"MONTH({0})\".format(dt_obj)\n        dt = \"CONCAT(CAST({0} AS CHAR(4)),'-', CAST({1} AS CHAR(4)))\".format(yr, mt)\n\n    return dt\n\n\ndef json_to_sql(json_data, filters):\n    query_params = json.loads(json_data)\n    time_scale = query_params.get('plot_parameters').get('time_scale')\n    compare_parameter = query_params.get('plot_parameters').get('compare_parameter')\n    aggregation_method = query_params.get('plot_parameters').get('aggregation_method')\n    aggregation_parameter = query_params.get('plot_parameters').get('aggregation_parameter', \"1\")\n\n    where_clause = \"1\"\n\n    for ft in filters:\n        where_clause += \" AND `{0}`{1}'{2}'\".format(ft.get('parameter'), ft.get('operator'), ft.get('value'))\n\n    query = \"SELECT {0} x, `{1}` z, {2}(`{3}`) y FROM ewt WHERE {4} GROUP BY {0}, `{1}`\".format(\n        ewt_date_convert(time_scale), compare_parameter, aggregation_method, aggregation_parameter, where_clause)\n\n    return query\n\n\ndef get_plot_labels(json_data):\n    query_params = json.loads(json_data)\n    time_adj_maps = {'DAY': 'Daily', 'MONTH': 'Monthly', 'QUARTER': 'Quarterly'}\n    time_adj = time_adj_maps[query_params.get('plot_parameters').get('time_scale')]\n\n    if query_params.get('plot_parameters').get('aggregation_method') == 'COUNT':\n        aggr_str = 'Count'\n    else:\n        aggr_str = 'Sum of {0}'.format(query_params.get('plot_parameters').get('aggregation_parameter'))\n    plot_title = '{0} {1} by {2}'.format(time_adj, aggr_str,\n                                         query_params.get('plot_parameters').get('compare_parameter'))\n\n    x_label = '{0}/{1}'.format(query_params.get('plot_parameters').get('compare_parameter'),\n                               query_params.get('plot_parameters').get('time_scale').capitalize())\n    y_label = aggr_str\n\n    PlotLabels = collections.namedtuple(\"PlotLabels\", \"title x_label y_label\")\n\n    return PlotLabels(title=plot_title, x_label=x_label, y_label=y_label)\n\n\ndef get_layout(json_data, filters):\n    sql_data = json_to_sql(json_data, filters)\n    data = get_dataframe(sql_data)\n\n    labels = get_plot_labels(json_data)\n\n    columns = data.columns.tolist()\n    columns.remove('x')\n\n    x_axis_data = [(x, z) for x in data['x'] for z in columns]\n\n    to_zip = [data[c].tolist() for c in columns]\n\n    y_axis_data = sum(zip(*to_zip), ())\n\n    source = ColumnDataSource(data=dict(x_axis_data=x_axis_data, y_axis_data=y_axis_data))\n\n    hover = HoverTool(tooltips=[\n        (','.join(labels.x_label.rsplit('-', 1)[::-1]), \"@x_axis_data\"),\n        (labels.y_label, \"@y_axis_data\"),\n    ])\n\n    p = figure(x_range=FactorRange(*x_axis_data), plot_width=1200, plot_height=600,\n               title=labels.title, tools=[hover, 'pan', 'box_zoom', 'wheel_zoom', 'save', 'reset'])\n\n    p.vbar(x='x_axis_data', top='y_axis_data', width=1, source=source, line_color=\"white\",\n           fill_color=factor_cmap('x_axis_data', palette=viridis(len(columns)), factors=columns, start=1,\n                                  end=len(columns)))\n\n    p.y_range.start = 0\n    p.x_range.range_padding = 0.1\n    p.xaxis.major_label_orientation = 1\n    p.xgrid.grid_line_color = None\n    p.xaxis.axis_label = labels.x_label\n    p.yaxis.axis_label = labels.y_label\n    p.title.align = 'center'\n\n    # eng = TextInput(title=\"ENG\")\n    # tran = TextInput(title=\"TRAN\")\n    # miles = TextInput(title=\"MILES\")\n    #\n    # controls = [eng, tran, miles]\n    #\n    # sizing_mode = 'fixed'\n    #\n    # ly = layout([controls, [p]], sizing_mode=sizing_mode)\n\n    # return ly\n    return p\n", "text_preview": "import json import collections from bokeh.models import ColumnDataSource, FactorRange, HoverTool from bokeh.transform import factor_cmap from bokeh.palettes import viridis from bokeh.plotting import figure from .load_data import * def colum", "input_sha256": "81caa6d6d341e2e0d3e91d199cc62cacbdcffdcf95a669d6550ad998a7487caf", "text_sha256": "53d940ada090ad573038ee2e72cd57c7b835167965972801230b81def9b908a0", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12093, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7275581173799294, "noise_severity": 0.08626657798372171, "missing_content_severity": 0.08024009116911565, "factual_density": 3.4203589402659382, "pretraining_utility": 0.48597418180375435, "math_density": 0.5439112351216646, "code_density": 3.8727806050156066, "reasoning_density": 0.31806139624655305, "education_level": 2.2376768375299956, "creative_writing_quality": -0.28431623506689674}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0025097797774844555, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9975876380212999, "secondary": 0.9904851527921456, "tertiary": 0.9931040534295206, "structured_records": 0.002200620854647798, "procedural_steps": 0.004983953106418865, "worked_solution": 0.002473533643230716, "contextual_dialogue": 4.740920590742282e-06, "content_quality": 0.5219707271956766, "noise_severity": 0.9281111850135653, "missing_content_severity": 0.9331332573590703, "factual_density": 0.5169657835549482, "pretraining_utility": 0.44923908822887215, "math_density": 0.5467406373986128, "code_density": 0.8939838375130051, "reasoning_density": 0.7349488364612058, "education_level": 0.7029487028266473, "creative_writing_quality": 0.9996904565842192, "primary_topic": 0.9785337037562146, "secondary_topic": 0.758567504771794}, "probability_margin": {"primary": 0.9965913590374508, "secondary": 0.9856208439345647, "tertiary": 0.9898859911352966, "education_level": 0.5322216464757024, "creative_writing_quality": 0.9996174894254298, "primary_topic": 0.9688501346551613, "secondary_topic": 0.6323111852531804}, "raw_probabilities": {"primary": {"Academic Writing": 2.8201679902118428e-05, "Content Listing": 1.4360919294615337e-05, "Creative Writing": 8.487273250876147e-06, "Customer Support Page": 6.0513239662435735e-06, "Q&A Forum": 7.919583775890285e-06, "Discussion Thread": 4.8452598412619055e-06, "Comment Section": 8.35257513079321e-06, "Missing Content": 2.2518942569640008e-05, "Knowledge Article": 6.810282146980762e-05, "Legal Notices": 2.2474810279786632e-05, "News Article": 2.984184233774531e-06, "Organizational Announcement": 6.2747604323003554e-06, "Nonfiction Writing": 8.010931619638392e-06, "Organizational About Page": 7.310802378983567e-06, "Personal About Page": 8.105427650794305e-06, "Personal Blog": 5.410236637000298e-06, "Product Page": 4.743688510067096e-06, "Spam / Ads": 7.391521782966848e-06, "Structured Data": 0.0004325666130331651, "Technical Writing": 0.9976881531037457, "Transcript / Interview": 9.76496340475476e-06, "Tutorial / How-To Guide": 0.0005266632555625204, "User Reviews": 4.511255232195682e-06, "Other / Unknown": 0.0010967940662949436}, "secondary": {"Academic Writing": 6.199683938431564e-05, "Content Listing": 3.5386876385754226e-05, "Creative Writing": 4.327362904177588e-05, "Customer Support Page": 2.4005920447435866e-05, "Q&A Forum": 1.7870948959893152e-05, "Discussion Thread": 1.4540643061899488e-05, "Comment Section": 3.688835636783692e-05, "Missing Content": 0.00016308558139260766, "Knowledge Article": 0.00021443707689195417, "Legal Notices": 6.434023354612753e-05, "News Article": 2.365093530419916e-05, "Organizational Announcement": 2.1202987738727833e-05, "Nonfiction Writing": 1.9988171169329327e-05, "Organizational About Page": 2.7864383339083244e-05, "Personal About Page": 2.2928323048246036e-05, "Personal Blog": 2.0493980639258926e-05, "Product Page": 1.540458971346749e-05, "Spam / Ads": 2.6107406726855774e-05, "Structured Data": 0.0017701768460296537, "Technical Writing": 0.0052607608245747025, "Transcript / Interview": 2.898032951593266e-05, "Tutorial / How-To Guide": 0.0011929542694855831, "User Reviews": 1.2056088096002692e-05, "NONE": 0.9908816047591394}, "tertiary": {"Academic Writing": 4.324130915013067e-05, "Content Listing": 3.204347659691123e-05, "Creative Writing": 4.0640825104746e-05, "Customer Support Page": 1.8561035789980826e-05, "Q&A Forum": 1.4808580832969042e-05, "Discussion Thread": 1.2255840302527663e-05, "Comment Section": 4.705349459736307e-05, "Missing Content": 0.00024508747902858254, "Knowledge Article": 0.00021583438954372327, "Legal Notices": 7.394312715757012e-05, "News Article": 2.153311872358058e-05, "Organizational Announcement": 1.8478343602578934e-05, "Nonfiction Writing": 2.2954020428745878e-05, "Organizational About Page": 3.638277275759916e-05, "Personal About Page": 1.999798212224645e-05, "Personal Blog": 1.7216944022262385e-05, "Product Page": 1.459969136804964e-05, "Spam / Ads": 2.465077627275301e-05, "Structured Data": 0.00104075293217001, "Technical Writing": 0.003505393401327249, "Transcript / Interview": 2.5286714150975615e-05, "Tutorial / How-To Guide": 0.0011055906527721517, "User Reviews": 1.2308555553420328e-05, "NONE": 0.9933913845366239}, "structured_records": {"false": 0.9977993791453522, "true": 0.002200620854647798}, "procedural_steps": {"false": 0.9950160468935811, "true": 0.004983953106418865}, "worked_solution": {"false": 0.9975264663567693, "true": 0.002473533643230716}, "contextual_dialogue": {"false": 0.9999952590794092, "true": 4.740920590742282e-06}, "content_quality": {"-2": 0.009488562284413846, "-1": 0.04404365778423563, "0": 0.3064855025709165, "1": 0.48938565498787545, "2": 0.15059662237255864}, "noise_severity": {"0": 0.9208447194602819, "1": 0.07394549757614635, "2": 0.003847785271276893, "3": 0.0008224809041585116, "4": 0.0005395167881365156}, "missing_content_severity": {"0": 0.940381403116916, "1": 0.042584174111493164, "2": 0.013950161595607124, "3": 0.002581450837527156, "4": 0.0005028103384566983}, "factual_density": {"0": 0.008473289992348074, "1": 0.013775593220640923, "2": 0.0913618545983337, "3": 0.3216974109060797, "4": 0.5646918512825977}, "pretraining_utility": {"-2": 0.007710194136494776, "-1": 0.08303833921228777, "0": 0.3982621953267396, "1": 0.43754563335992397, "2": 0.07344363796455385}, "math_density": {"0": 0.7299909699599099, "1": 0.1302805526367468, "2": 0.058971782867555944, "3": 0.02733966139334393, "4": 0.05341703314244354}, "code_density": {"0": 0.0001784057502025356, "1": 0.0003878073522581316, "2": 0.014942454261233229, "3": 0.09545744140434288, "4": 0.8890338912319633}, "reasoning_density": {"0": 0.8463524145641584, "1": 0.04015789885172241, "2": 0.07531200672071356, "3": 0.02543123550021932, "4": 0.012746444363186409}, "education_level": {"none": 0.0010344436928900786, "0": 0.0013229209632952975, "1": 0.01001944798188545, "2": 0.7524572523555394, "3": 0.22023560587983704, "4": 0.014930329126552599}, "creative_writing_quality": {"not_applicable": 0.999742047153516, "-2": 3.8607220274327734e-05, "-1": 5.124969525499215e-05, "0": 0.0001245577280861672, "1": 3.195245207026072e-05, "2": 1.1585750798148203e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.9472443860748344e-05, "Art & Design": 0.0008488432581226596, "Law & Crime": 4.7546363239014566e-05, "Education & Careers": 6.807906473204624e-05, "Entertainment & Media": 4.820954091651534e-05, "Fashion & Beauty": 4.0482915704260515e-05, "Business, Economics & Finance": 0.0007798952847359902, "Food & Dining": 8.704119826364366e-05, "Games & Gaming": 8.47905111329476e-05, "Electronics & Hardware": 0.00011356129327638394, "Health, Healthcare & Wellness": 0.00025276355604221166, "History & Geography": 7.013383264113336e-05, "Home, Pets & Hobbies": 5.769684450849702e-05, "Industry & Production": 0.0008177033161024768, "Language, Literature & Philosophy": 4.483277312629545e-05, "Politics & Government": 5.590418270349833e-05, "Religion & Spirituality": 4.6527611354981576e-05, "Science, Mathematics & Engineering": 0.010509195879660408, "Relationships & Community": 2.7814599552177587e-05, "Software Usage & Applications": 0.001452262649352105, "Programming & Software Engineering": 0.9793593305348217, "Sports & Fitness": 0.000605179848649712, "Vehicles & Transportation": 0.003630066423511008, "Travel & Tourism": 0.00011775387595420859, "Other / Uncovered Subject": 0.000623351205164566, "Unknown / Insufficient Content": 0.00016156099287072026}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007430403397586153, "Art & Design": 0.02840549597258401, "Law & Crime": 0.0008128909334473134, "Education & Careers": 0.0005172339258075444, "Entertainment & Media": 0.0006008026152701892, "Fashion & Beauty": 0.0003821637110519433, "Business, Economics & Finance": 0.004849341263710151, "Food & Dining": 0.0005714661363674222, "Games & Gaming": 0.0006052647783414612, "Electronics & Hardware": 0.0007098225272232074, "Health, Healthcare & Wellness": 0.0018007919713946034, "History & Geography": 0.0005828170503387025, "Home, Pets & Hobbies": 0.0004734314541430713, "Industry & Production": 0.0023374671342653273, "Language, Literature & Philosophy": 0.0002835338101238034, "Politics & Government": 0.0004532171147826996, "Religion & Spirituality": 0.00044685579309027804, "Science, Mathematics & Engineering": 0.1355421847196985, "Relationships & Community": 0.00021825604513246312, "Software Usage & Applications": 0.007803641307880572, "Programming & Software Engineering": 0.030780901664142817, "Sports & Fitness": 0.0021270705772995007, "Vehicles & Transportation": 0.007634375912868127, "Travel & Tourism": 0.0007253085160731627, "Other / Uncovered Subject": 0.0027392547523256163, "NONE": 0.7678533699728789}}, "probabilities": {"primary": {"Academic Writing": 2.820167990211843e-05, "Content Listing": 1.4360919294615339e-05, "Creative Writing": 8.487273250876148e-06, "Customer Support Page": 6.051323966243574e-06, "Q&A Forum": 7.919583775890287e-06, "Discussion Thread": 4.845259841261906e-06, "Comment Section": 8.352575130793212e-06, "Missing Content": 2.251894256964001e-05, "Knowledge Article": 6.810282146980763e-05, "Legal Notices": 2.2474810279786635e-05, "News Article": 2.9841842337745316e-06, "Organizational Announcement": 6.274760432300356e-06, "Nonfiction Writing": 8.010931619638394e-06, "Organizational About Page": 7.310802378983568e-06, "Personal About Page": 8.105427650794306e-06, "Personal Blog": 5.410236637000299e-06, "Product Page": 4.743688510067097e-06, "Spam / Ads": 7.391521782966849e-06, "Structured Data": 0.00043256661303316514, "Technical Writing": 0.9976881531037458, "Transcript / Interview": 9.764963404754761e-06, "Tutorial / How-To Guide": 0.0005266632555625205, "User Reviews": 4.511255232195683e-06, "Other / Unknown": 0.0010967940662949438}, "secondary": {"Academic Writing": 6.199683938431564e-05, "Content Listing": 3.5386876385754226e-05, "Creative Writing": 4.327362904177588e-05, "Customer Support Page": 2.4005920447435866e-05, "Q&A Forum": 1.7870948959893152e-05, "Discussion Thread": 1.4540643061899488e-05, "Comment Section": 3.688835636783692e-05, "Missing Content": 0.00016308558139260766, "Knowledge Article": 0.00021443707689195417, "Legal Notices": 6.434023354612753e-05, "News Article": 2.365093530419916e-05, "Organizational Announcement": 2.1202987738727833e-05, "Nonfiction Writing": 1.9988171169329327e-05, "Organizational About Page": 2.7864383339083244e-05, "Personal About Page": 2.2928323048246036e-05, "Personal Blog": 2.0493980639258926e-05, "Product Page": 1.540458971346749e-05, "Spam / Ads": 2.6107406726855774e-05, "Structured Data": 0.0017701768460296537, "Technical Writing": 0.0052607608245747025, "Transcript / Interview": 2.898032951593266e-05, "Tutorial / How-To Guide": 0.0011929542694855831, "User Reviews": 1.2056088096002692e-05, "NONE": 0.9908816047591394}, "tertiary": {"Academic Writing": 4.324130915013067e-05, "Content Listing": 3.204347659691123e-05, "Creative Writing": 4.0640825104746e-05, "Customer Support Page": 1.8561035789980826e-05, "Q&A Forum": 1.4808580832969042e-05, "Discussion Thread": 1.2255840302527663e-05, "Comment Section": 4.705349459736307e-05, "Missing Content": 0.00024508747902858254, "Knowledge Article": 0.00021583438954372327, "Legal Notices": 7.394312715757012e-05, "News Article": 2.153311872358058e-05, "Organizational Announcement": 1.8478343602578934e-05, "Nonfiction Writing": 2.2954020428745878e-05, "Organizational About Page": 3.638277275759916e-05, "Personal About Page": 1.999798212224645e-05, "Personal Blog": 1.7216944022262385e-05, "Product Page": 1.459969136804964e-05, "Spam / Ads": 2.465077627275301e-05, "Structured Data": 0.00104075293217001, "Technical Writing": 0.003505393401327249, "Transcript / Interview": 2.5286714150975615e-05, "Tutorial / How-To Guide": 0.0011055906527721517, "User Reviews": 1.2308555553420328e-05, "NONE": 0.9933913845366239}, "structured_records": {"false": 0.9977993791453522, "true": 0.002200620854647798}, "procedural_steps": {"false": 0.9950160468935811, "true": 0.004983953106418865}, "worked_solution": {"false": 0.9975264663567693, "true": 0.002473533643230716}, "contextual_dialogue": {"false": 0.9999952590794092, "true": 4.740920590742282e-06}, "content_quality": {"-2": 0.009488562284413846, "-1": 0.04404365778423563, "0": 0.3064855025709165, "1": 0.48938565498787545, "2": 0.15059662237255864}, "noise_severity": {"0": 0.9208447194602817, "1": 0.07394549757614634, "2": 0.0038477852712768923, "3": 0.0008224809041585114, "4": 0.0005395167881365154}, "missing_content_severity": {"0": 0.9403814031169158, "1": 0.04258417411149316, "2": 0.01395016159560712, "3": 0.0025814508375271556, "4": 0.0005028103384566982}, "factual_density": {"0": 0.008473289992348074, "1": 0.013775593220640923, "2": 0.0913618545983337, "3": 0.3216974109060797, "4": 0.5646918512825977}, "pretraining_utility": {"-2": 0.007710194136494776, "-1": 0.08303833921228777, "0": 0.3982621953267396, "1": 0.43754563335992397, "2": 0.07344363796455385}, "math_density": {"0": 0.7299909699599099, "1": 0.1302805526367468, "2": 0.058971782867555944, "3": 0.02733966139334393, "4": 0.05341703314244354}, "code_density": {"0": 0.0001784057502025356, "1": 0.0003878073522581316, "2": 0.014942454261233229, "3": 0.09545744140434288, "4": 0.8890338912319633}, "reasoning_density": {"0": 0.8463524145641582, "1": 0.040157898851722405, "2": 0.07531200672071355, "3": 0.025431235500219314, "4": 0.012746444363186405}, "education_level": {"none": 0.0010344436928900788, "0": 0.0013229209632952977, "1": 0.010019447981885452, "2": 0.7524572523555395, "3": 0.22023560587983707, "4": 0.0149303291265526}, "creative_writing_quality": {"not_applicable": 0.9997420471535161, "-2": 3.860722027432774e-05, "-1": 5.124969525499216e-05, "0": 0.00012455772808616722, "1": 3.1952452070260724e-05, "2": 1.1585750798148204e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.947244386074835e-05, "Art & Design": 0.0008488432581226597, "Law & Crime": 4.754636323901457e-05, "Education & Careers": 6.807906473204626e-05, "Entertainment & Media": 4.820954091651535e-05, "Fashion & Beauty": 4.048291570426052e-05, "Business, Economics & Finance": 0.0007798952847359903, "Food & Dining": 8.704119826364367e-05, "Games & Gaming": 8.479051113294762e-05, "Electronics & Hardware": 0.00011356129327638395, "Health, Healthcare & Wellness": 0.0002527635560422117, "History & Geography": 7.013383264113337e-05, "Home, Pets & Hobbies": 5.7696844508497026e-05, "Industry & Production": 0.0008177033161024769, "Language, Literature & Philosophy": 4.483277312629546e-05, "Politics & Government": 5.590418270349834e-05, "Religion & Spirituality": 4.652761135498158e-05, "Science, Mathematics & Engineering": 0.01050919587966041, "Relationships & Community": 2.781459955217759e-05, "Software Usage & Applications": 0.0014522626493521052, "Programming & Software Engineering": 0.9793593305348218, "Sports & Fitness": 0.0006051798486497121, "Vehicles & Transportation": 0.0036300664235110083, "Travel & Tourism": 0.0001177538759542086, "Other / Uncovered Subject": 0.0006233512051645661, "Unknown / Insufficient Content": 0.00016156099287072028}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007430403397586153, "Art & Design": 0.02840549597258401, "Law & Crime": 0.0008128909334473134, "Education & Careers": 0.0005172339258075444, "Entertainment & Media": 0.0006008026152701892, "Fashion & Beauty": 0.0003821637110519433, "Business, Economics & Finance": 0.004849341263710151, "Food & Dining": 0.0005714661363674222, "Games & Gaming": 0.0006052647783414612, "Electronics & Hardware": 0.0007098225272232074, "Health, Healthcare & Wellness": 0.0018007919713946034, "History & Geography": 0.0005828170503387025, "Home, Pets & Hobbies": 0.0004734314541430713, "Industry & Production": 0.0023374671342653273, "Language, Literature & Philosophy": 0.0002835338101238034, "Politics & Government": 0.0004532171147826996, "Religion & Spirituality": 0.00044685579309027804, "Science, Mathematics & Engineering": 0.1355421847196985, "Relationships & Community": 0.00021825604513246312, "Software Usage & Applications": 0.007803641307880572, "Programming & Software Engineering": 0.030780901664142817, "Sports & Fitness": 0.0021270705772995007, "Vehicles & Transportation": 0.007634375912868127, "Travel & Tourism": 0.0007253085160731627, "Other / Uncovered Subject": 0.0027392547523256163, "NONE": 0.7678533699728789}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9712, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9712, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:35:11.994802+00:00", "scoring_key_slot": "primary", "dataset_uid": "1db3840f-7ffe-4451-8242-08932f062e39", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "a8844a075427f3784ddf5dbad528724d0cbac3f7df8c8b8422430ec80031f23e", "source_metadata": {"uuid": "1db3840f-7ffe-4451-8242-08932f062e39", "repo_name": "SudeepNB/HRF_Vizzly", "relative_path": "app/vizzly/utils.py", "category": "DATA", "algo_rel_score": 0.9880098700523376, "quality_score": 4.992566108703613, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 8, "repo_created_at": "2017-09-23T13:49:22.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "67440652e79e9575f4e3aa4e197aa60778e91ef1", "size_bytes": 4335, "file_timestamp": 1506241605, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "d0a19030760e4445e2daa5056d587d1de9608762"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 8, "repo_created_at": "2017-09-23T13:49:22.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "67440652e79e9575f4e3aa4e197aa60778e91ef1", "size_bytes": 4335, "file_timestamp": 1506241605, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "d0a19030760e4445e2daa5056d587d1de9608762", "url": "https://github.com/SudeepNB/HRF_Vizzly/blob/d0a19030760e4445e2daa5056d587d1de9608762/app/vizzly/utils.py"}, "gpt4o_tokens": 1142, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.7275581173799294, "noise_severity": 0.08626657798372173, "missing_content_severity": 0.08024009116911567, "factual_density": 3.4203589402659382, "pretraining_utility": 0.48597418180375435, "math_density": 0.5439112351216646, "code_density": 3.8727806050156066, "reasoning_density": 0.3180613962465531, "education_level": 2.2376768375299956, "creative_writing_quality": -0.28431623506689674}, "score_applicability": {"education_level": 0.9989655563071099, "creative_writing_quality": 0.00025795284648389604}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/MarianHolly/blog_platform/blob/82f2ef70e1027c44faec4658a7d9af2b38bcc6a4/blog_platform/test_connections.py", "id": "python-0-f82e5fcce7ffb6cc9e3bafba0d6737de", "text": "# test_connections.py (UPDATED VERSION)\nimport os\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\n\ndef test_redis_connection():\n    \"\"\"Test Redis connection with proper Upstash handling\"\"\"\n    try:\n        import redis\n\n        redis_url = os.getenv('REDIS_URL')\n        if not redis_url:\n            print(\"❌ REDIS_URL not found in environment variables\")\n            return False\n\n        print(f\"🔍 Testing Redis connection...\")\n        print(f\"🔗 Redis URL: {redis_url[:50]}...\")\n\n        # Handle Upstash Redis (requires SSL)\n        if 'upstash.io' in redis_url:\n            print(\"🔒 Detected Upstash Redis - using SSL connection\")\n\n            # Convert to SSL URL if needed\n            if redis_url.startswith('redis://'):\n                ssl_url = redis_url.replace('redis://', 'rediss://', 1)\n            else:\n                ssl_url = redis_url\n\n            # Connect with SSL settings\n            r = redis.from_url(\n                ssl_url,\n                ssl_check_hostname=False,\n                ssl_cert_reqs=None\n            )\n        else:\n            # Regular Redis connection\n            print(\"🔓 Using regular Redis connection\")\n            r = redis.from_url(redis_url)\n\n        # Test connection\n        print(\"🏓 Testing ping...\")\n        r.ping()\n\n        print(\"📝 Testing read/write...\")\n        r.set('test_key', 'test_value', ex=10)  # Set with 10 second expiry\n        value = r.get('test_key')\n\n        if value == b'test_value':\n            print(\"✅ Redis connection successful!\")\n            print(\"🎯 Redis read/write test passed\")\n\n            # Clean up\n            r.delete('test_key')\n            return True\n        else:\n            print(\"❌ Redis read/write test failed\")\n            return False\n\n    except Exception as e:\n        print(f\"❌ Redis connection failed: {e}\")\n        print(f\"🔍 Error type: {type(e).__name__}\")\n        return False\n\n\ndef test_database_connection():\n    \"\"\"Test PostgreSQL connection\"\"\"\n    try:\n        import psycopg2\n\n        database_url = os.getenv('DATABASE_URL')\n        if not database_url:\n            print(\"❌ DATABASE_URL not found in environment variables\")\n            return False\n\n        print(f\"🔍 Testing database connection...\")\n        print(f\"🔗 Database URL: {database_url[:50]}...\")\n\n        # Parse the URL and connect\n        conn = psycopg2.connect(database_url)\n        cursor = conn.cursor()\n        cursor.execute(\"SELECT version();\")\n        version = cursor.fetchone()\n\n        print(f\"✅ Database connection successful!\")\n        print(f\"📊 PostgreSQL version: {version[0][:50]}...\")\n\n        cursor.close()\n        conn.close()\n        return True\n\n    except Exception as e:\n        print(f\"❌ Database connection failed: {e}\")\n        return False\n\n\ndef test_cloudinary_connection():\n    \"\"\"Test Cloudinary connection\"\"\"\n    try:\n        import cloudinary\n        import cloudinary.api\n\n        cloud_name = os.getenv('CLOUDINARY_CLOUD_NAME')\n        api_key = os.getenv('CLOUDINARY_API_KEY')\n        api_secret = os.getenv('CLOUDINARY_API_SECRET')\n\n        if not all([cloud_name, api_key, api_secret]):\n            print(\"❌ Cloudinary credentials not found\")\n            return False\n\n        print(f\"🔍 Testing Cloudinary connection...\")\n\n        cloudinary.config(\n            cloud_name=cloud_name,\n            api_key=api_key,\n            api_secret=api_secret,\n            secure=True\n        )\n\n        # Test API connection\n        result = cloudinary.api.ping()\n\n        if result.get('status') == 'ok':\n            print(\"✅ Cloudinary connection successful!\")\n            return True\n        else:\n            print(f\"❌ Cloudinary connection failed: {result}\")\n            return False\n\n    except Exception as e:\n        print(f\"❌ Cloudinary connection failed: {e}\")\n        return False\n\n\nif __name__ == \"__main__\":\n    print(\"🧪 Testing external service connections...\\n\")\n\n    # Test Redis first (the problematic one)\n    redis_ok = test_redis_connection()\n    print()\n\n    db_ok = test_database_connection()\n    print()\n\n    cloudinary_ok = test_cloudinary_connection()\n    print()\n\n    print(\"📋 Summary:\")\n    print(f\"   Redis: {'✅' if redis_ok else '❌'}\")\n    print(f\"   Database: {'✅' if db_ok else '❌'}\")\n    print(f\"   Cloudinary: {'✅' if cloudinary_ok else '❌'}\")\n\n    if all([redis_ok, db_ok, cloudinary_ok]):\n        print(\"\\n🎉 All connections successful! Your app should work.\")\n    else:\n        print(\"\\n⚠️  Some connections failed. Check the errors above.\")\n\n        if not redis_ok:\n            print(\"\\n💡 Redis troubleshooting:\")\n            print(\"   1. Check if REDIS_URL is correct in .env\")\n            print(\"   2. Try updating redis library: pip install redis --upgrade\")\n            print(\"   3. Consider skipping Redis for now (app will use database sessions)\")", "text_preview": "# test_connections.py (UPDATED VERSION) import os from dotenv import load_dotenv load_dotenv() def test_redis_connection(): \"\"\"Test Redis connection with proper Upstash handling\"\"\" try: import redis redis_url = os.getenv('REDIS_URL') if not", "input_sha256": "da3a3ea1d38e1f9d88a52e7ebfe74f2e4de399389914c92df146b4607c3a85dd", "text_sha256": "f82e5fcce7ffb6cc9e3bafba0d6737de6b37d8d4bbd707c01be9c71fc7972192", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12095, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.0556981831720322, "noise_severity": 0.061097128119267846, "missing_content_severity": 0.012763505497715042, "factual_density": 2.9375061379465364, "pretraining_utility": 0.6926530743747502, "math_density": 0.021354112623888206, "code_density": 3.7918271999206885, "reasoning_density": 1.3653387035944302, "education_level": 2.1216839348955574, "creative_writing_quality": -0.13107619252052743}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.01301088666294041, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9882958133536589, "secondary": 0.8643592610029261, "tertiary": 0.8714352887273429, "structured_records": 0.001493593728048906, "procedural_steps": 0.34440101897937636, "worked_solution": 0.028273394830662376, "contextual_dialogue": 5.460473920060913e-06, "content_quality": 0.5928811824288066, "noise_severity": 0.9490857265672767, "missing_content_severity": 0.9893637454185708, "factual_density": 0.46110742744395483, "pretraining_utility": 0.5875673021261809, "math_density": 0.9822049061467598, "code_density": 0.8265226666005736, "reasoning_density": 0.0, "education_level": 0.7768865726529459, "creative_writing_quality": 0.9988750527801598, "primary_topic": 0.9931945419841447, "secondary_topic": 0.8743680987416375}, "probability_margin": {"primary": 0.9803001899964565, "secondary": 0.797740718087315, "tertiary": 0.8105469362854564, "education_level": 0.6685169622463577, "creative_writing_quality": 0.9985145092658735, "primary_topic": 0.9877537011074278, "secondary_topic": 0.78374421478006}, "raw_probabilities": {"primary": {"Academic Writing": 5.814052060278538e-05, "Content Listing": 3.7050038017680996e-05, "Creative Writing": 3.798599343552436e-05, "Customer Support Page": 0.0001635877624984711, "Q&A Forum": 3.5789423997075594e-05, "Discussion Thread": 1.8852615979247822e-05, "Comment Section": 3.270694459347221e-05, "Missing Content": 4.1185911029376876e-05, "Knowledge Article": 0.00020417351166458352, "Legal Notices": 4.272562534767274e-05, "News Article": 1.153364445469562e-05, "Organizational Announcement": 2.615969159151421e-05, "Nonfiction Writing": 3.02922958120491e-05, "Organizational About Page": 2.730023287439094e-05, "Personal About Page": 2.7503883593946705e-05, "Personal Blog": 1.753135929217131e-05, "Product Page": 1.5737955751564044e-05, "Spam / Ads": 2.4964311306054496e-05, "Structured Data": 0.0002862744387963948, "Technical Writing": 0.9887834877972564, "Transcript / Interview": 3.3345908992226365e-05, "Tutorial / How-To Guide": 0.008483297800799877, "User Reviews": 1.7792830585897916e-05, "Other / Unknown": 0.001542579501727018}, "secondary": {"Academic Writing": 0.0005029129839542078, "Content Listing": 0.00032235425654199464, "Creative Writing": 0.0004367342386663129, "Customer Support Page": 0.01654125255848311, "Q&A Forum": 0.0003641719191378188, "Discussion Thread": 0.00018363645173502207, "Comment Section": 0.00032682131703085354, "Missing Content": 0.0004532376459253465, "Knowledge Article": 0.0018014541394942873, "Legal Notices": 0.00031139993112941074, "News Article": 0.00026238100216727873, "Organizational Announcement": 0.0002869247195473574, "Nonfiction Writing": 0.0002971182126936501, "Organizational About Page": 0.0002797637797608924, "Personal About Page": 0.00028411955767854684, "Personal Blog": 0.0002702246885605548, "Product Page": 0.00017806082935374506, "Spam / Ads": 0.00027527768363894655, "Structured Data": 0.000864695069161372, "Technical Writing": 0.0329520045442289, "Transcript / Interview": 0.00035321165812667976, "Tutorial / How-To Guide": 0.07227024037382261, "User Reviews": 0.00017104397802355178, "NONE": 0.8700109584611375}, "tertiary": {"Academic Writing": 0.00026911864302768795, "Content Listing": 0.0002028214247517374, "Creative Writing": 0.00026777663292294957, "Customer Support Page": 0.02955213922142352, "Q&A Forum": 0.00019864041557274558, "Discussion Thread": 0.00011888381839623506, "Comment Section": 0.00022717066759719197, "Missing Content": 0.00026502898199258046, "Knowledge Article": 0.002784267886427618, "Legal Notices": 0.00022934782743717146, "News Article": 0.00014452948264725288, "Organizational Announcement": 0.00018577581793900765, "Nonfiction Writing": 0.00019993497444226505, "Organizational About Page": 0.00020978148290089237, "Personal About Page": 0.00017631079527174426, "Personal Blog": 0.0001590923965729687, "Product Page": 0.00014077932296826888, "Spam / Ads": 0.00019303733573684858, "Structured Data": 0.000497562434318182, "Technical Writing": 0.020603545169726005, "Transcript / Interview": 0.00024064540084307685, "Tutorial / How-To Guide": 0.06624521541158053, "User Reviews": 9.644275846658218e-05, "NONE": 0.876792151697037}, "structured_records": {"false": 0.9985064062719511, "true": 0.001493593728048906}, "procedural_steps": {"false": 0.6555989810206236, "true": 0.34440101897937636}, "worked_solution": {"false": 0.9717266051693376, "true": 0.028273394830662376}, "contextual_dialogue": {"false": 0.99999453952608, "true": 5.460473920060913e-06}, "content_quality": {"-2": 0.004558936408623521, "-1": 0.011379941935804005, "0": 0.17998550585922135, "1": 0.531955233667619, "2": 0.2721203821287321}, "noise_severity": {"0": 0.9471880914402759, "1": 0.04659507024133212, "2": 0.00477708060811487, "3": 0.000811134179402738, "4": 0.000628623530874442}, "missing_content_severity": {"0": 0.9896882913680117, "1": 0.00852067684642791, "2": 0.0012728315515829803, "3": 0.00037563538778854057, "4": 0.00014256484618888765}, "factual_density": {"0": 0.009560735296508477, "1": 0.045092621896852494, "2": 0.2357150248771284, "3": 0.41754300542261524, "4": 0.2920886125068954}, "pretraining_utility": {"-2": 0.005807577774878463, "-1": 0.0380180212592915, "0": 0.307674305693698, "1": 0.5547139393604654, "2": 0.09378615591166652}, "math_density": {"0": 0.9820937165221783, "1": 0.016107094633117623, "2": 0.000789764177007185, "3": 0.0003702090340310741, "4": 0.0006392156336657471}, "code_density": {"0": 0.0002737910316937796, "1": 0.0005418222518350201, "2": 0.015382332814745213, "3": 0.17468750356754104, "4": 0.809114550334185}, "reasoning_density": {"0": 0.338347786302731, "1": 0.21280127877870172, "2": 0.27616590000708946, "3": 0.0905345148443616, "4": 0.08215052006711619}, "education_level": {"none": 0.0008760591896972488, "0": 0.0017680198542295593, "1": 0.03196633341717555, "2": 0.814072143877455, "3": 0.14555518163109726, "4": 0.005762262030345307}, "creative_writing_quality": {"not_applicable": 0.9990625439834665, "-2": 7.121938490465991e-05, "-1": 0.00016336589117379622, "0": 0.0005480347175930028, "1": 0.00012674555004401673, "2": 2.809047281820211e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.440060605113038e-05, "Art & Design": 3.6933997018736695e-05, "Law & Crime": 2.781155382221877e-05, "Education & Careers": 3.2166176555814046e-05, "Entertainment & Media": 2.2757272640714888e-05, "Fashion & Beauty": 1.8757453611190514e-05, "Business, Economics & Finance": 5.424117555631279e-05, "Food & Dining": 1.571106329659277e-05, "Games & Gaming": 3.87302576018697e-05, "Electronics & Hardware": 7.694828824228786e-05, "Health, Healthcare & Wellness": 1.6807114933488196e-05, "History & Geography": 2.254314429495227e-05, "Home, Pets & Hobbies": 1.9914594588977713e-05, "Industry & Production": 2.0825849233663458e-05, "Language, Literature & Philosophy": 2.892091573037478e-05, "Politics & Government": 1.2677731922490215e-05, "Religion & Spirituality": 2.1130157950467405e-05, "Science, Mathematics & Engineering": 0.00014614437271822324, "Relationships & Community": 1.8551046324534292e-05, "Software Usage & Applications": 0.005702589261942141, "Programming & Software Engineering": 0.9934562903693699, "Sports & Fitness": 3.5171753221170574e-05, "Vehicles & Transportation": 1.3742072414690877e-05, "Travel & Tourism": 3.0470069515428415e-05, "Other / Uncovered Subject": 6.918833695420417e-05, "Unknown / Insufficient Content": 2.6575364488527765e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0004627730712705279, "Art & Design": 0.00046071222683924514, "Law & Crime": 0.00043865418064807486, "Education & Careers": 0.0003178039766202362, "Entertainment & Media": 0.00045899530274620703, "Fashion & Beauty": 0.00016615458481590914, "Business, Economics & Finance": 0.0006526930581886235, "Food & Dining": 0.00015381294935509345, "Games & Gaming": 0.0002409853582906254, "Electronics & Hardware": 0.0004028902185209596, "Health, Healthcare & Wellness": 0.0001860798712754518, "History & Geography": 0.00018474371808949302, "Home, Pets & Hobbies": 0.00015546198554751398, "Industry & Production": 0.00015837618002431203, "Language, Literature & Philosophy": 0.00016527944002564274, "Politics & Government": 0.00015347147384283828, "Religion & Spirituality": 0.00019644413227713734, "Science, Mathematics & Engineering": 0.0006033831129098739, "Relationships & Community": 0.00012750373991816884, "Software Usage & Applications": 0.09545588016382224, "Programming & Software Engineering": 0.01870162900664785, "Sports & Fitness": 0.00017438160584531688, "Vehicles & Transportation": 8.059512193881597e-05, "Travel & Tourism": 0.00024512220449028036, "Other / Uncovered Subject": 0.0004560783721673156, "NONE": 0.8792000949438822}}, "probabilities": {"primary": {"Academic Writing": 5.8140520602785364e-05, "Content Listing": 3.705003801768099e-05, "Creative Writing": 3.798599343552435e-05, "Customer Support Page": 0.00016358776249847108, "Q&A Forum": 3.578942399707559e-05, "Discussion Thread": 1.885261597924782e-05, "Comment Section": 3.27069445934722e-05, "Missing Content": 4.118591102937687e-05, "Knowledge Article": 0.00020417351166458346, "Legal Notices": 4.2725625347672735e-05, "News Article": 1.1533644454695617e-05, "Organizational Announcement": 2.61596915915142e-05, "Nonfiction Writing": 3.029229581204909e-05, "Organizational About Page": 2.7300232874390933e-05, "Personal About Page": 2.75038835939467e-05, "Personal Blog": 1.7531359292171307e-05, "Product Page": 1.573795575156404e-05, "Spam / Ads": 2.496431130605449e-05, "Structured Data": 0.00028627443879639477, "Technical Writing": 0.9887834877972562, "Transcript / Interview": 3.334590899222636e-05, "Tutorial / How-To Guide": 0.008483297800799875, "User Reviews": 1.7792830585897913e-05, "Other / Unknown": 0.0015425795017270177}, "secondary": {"Academic Writing": 0.0005029129839542078, "Content Listing": 0.00032235425654199464, "Creative Writing": 0.0004367342386663129, "Customer Support Page": 0.01654125255848311, "Q&A Forum": 0.0003641719191378188, "Discussion Thread": 0.00018363645173502207, "Comment Section": 0.00032682131703085354, "Missing Content": 0.0004532376459253465, "Knowledge Article": 0.0018014541394942873, "Legal Notices": 0.00031139993112941074, "News Article": 0.00026238100216727873, "Organizational Announcement": 0.0002869247195473574, "Nonfiction Writing": 0.0002971182126936501, "Organizational About Page": 0.0002797637797608924, "Personal About Page": 0.00028411955767854684, "Personal Blog": 0.0002702246885605548, "Product Page": 0.00017806082935374506, "Spam / Ads": 0.00027527768363894655, "Structured Data": 0.000864695069161372, "Technical Writing": 0.0329520045442289, "Transcript / Interview": 0.00035321165812667976, "Tutorial / How-To Guide": 0.07227024037382261, "User Reviews": 0.00017104397802355178, "NONE": 0.8700109584611375}, "tertiary": {"Academic Writing": 0.00026911864302768795, "Content Listing": 0.0002028214247517374, "Creative Writing": 0.00026777663292294957, "Customer Support Page": 0.02955213922142352, "Q&A Forum": 0.00019864041557274558, "Discussion Thread": 0.00011888381839623506, "Comment Section": 0.00022717066759719197, "Missing Content": 0.00026502898199258046, "Knowledge Article": 0.002784267886427618, "Legal Notices": 0.00022934782743717146, "News Article": 0.00014452948264725288, "Organizational Announcement": 0.00018577581793900765, "Nonfiction Writing": 0.00019993497444226505, "Organizational About Page": 0.00020978148290089237, "Personal About Page": 0.00017631079527174426, "Personal Blog": 0.0001590923965729687, "Product Page": 0.00014077932296826888, "Spam / Ads": 0.00019303733573684858, "Structured Data": 0.000497562434318182, "Technical Writing": 0.020603545169726005, "Transcript / Interview": 0.00024064540084307685, "Tutorial / How-To Guide": 0.06624521541158053, "User Reviews": 9.644275846658218e-05, "NONE": 0.876792151697037}, "structured_records": {"false": 0.9985064062719511, "true": 0.001493593728048906}, "procedural_steps": {"false": 0.6555989810206236, "true": 0.34440101897937636}, "worked_solution": {"false": 0.9717266051693376, "true": 0.028273394830662376}, "contextual_dialogue": {"false": 0.99999453952608, "true": 5.460473920060913e-06}, "content_quality": {"-2": 0.004558936408623522, "-1": 0.011379941935804007, "0": 0.17998550585922138, "1": 0.5319552336676191, "2": 0.27212038212873213}, "noise_severity": {"0": 0.9471880914402759, "1": 0.04659507024133212, "2": 0.00477708060811487, "3": 0.000811134179402738, "4": 0.000628623530874442}, "missing_content_severity": {"0": 0.9896882913680117, "1": 0.00852067684642791, "2": 0.0012728315515829803, "3": 0.00037563538778854057, "4": 0.00014256484618888765}, "factual_density": {"0": 0.009560735296508477, "1": 0.045092621896852494, "2": 0.2357150248771284, "3": 0.41754300542261524, "4": 0.2920886125068954}, "pretraining_utility": {"-2": 0.005807577774878464, "-1": 0.038018021259291504, "0": 0.3076743056936981, "1": 0.5547139393604655, "2": 0.09378615591166653}, "math_density": {"0": 0.9820937165221784, "1": 0.016107094633117627, "2": 0.0007897641770071852, "3": 0.00037020903403107416, "4": 0.0006392156336657472}, "code_density": {"0": 0.0002737910316937796, "1": 0.0005418222518350201, "2": 0.015382332814745213, "3": 0.17468750356754104, "4": 0.809114550334185}, "reasoning_density": {"0": 0.338347786302731, "1": 0.21280127877870172, "2": 0.27616590000708946, "3": 0.0905345148443616, "4": 0.08215052006711619}, "education_level": {"none": 0.0008760591896972489, "0": 0.0017680198542295595, "1": 0.03196633341717556, "2": 0.8140721438774551, "3": 0.1455551816310973, "4": 0.005762262030345308}, "creative_writing_quality": {"not_applicable": 0.9990625439834663, "-2": 7.12193849046599e-05, "-1": 0.0001633658911737962, "0": 0.0005480347175930027, "1": 0.0001267455500440167, "2": 2.8090472818202102e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.440060605113038e-05, "Art & Design": 3.6933997018736695e-05, "Law & Crime": 2.781155382221877e-05, "Education & Careers": 3.2166176555814046e-05, "Entertainment & Media": 2.2757272640714888e-05, "Fashion & Beauty": 1.8757453611190514e-05, "Business, Economics & Finance": 5.424117555631279e-05, "Food & Dining": 1.571106329659277e-05, "Games & Gaming": 3.87302576018697e-05, "Electronics & Hardware": 7.694828824228786e-05, "Health, Healthcare & Wellness": 1.6807114933488196e-05, "History & Geography": 2.254314429495227e-05, "Home, Pets & Hobbies": 1.9914594588977713e-05, "Industry & Production": 2.0825849233663458e-05, "Language, Literature & Philosophy": 2.892091573037478e-05, "Politics & Government": 1.2677731922490215e-05, "Religion & Spirituality": 2.1130157950467405e-05, "Science, Mathematics & Engineering": 0.00014614437271822324, "Relationships & Community": 1.8551046324534292e-05, "Software Usage & Applications": 0.005702589261942141, "Programming & Software Engineering": 0.9934562903693699, "Sports & Fitness": 3.5171753221170574e-05, "Vehicles & Transportation": 1.3742072414690877e-05, "Travel & Tourism": 3.0470069515428415e-05, "Other / Uncovered Subject": 6.918833695420417e-05, "Unknown / Insufficient Content": 2.6575364488527765e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00046277307127052793, "Art & Design": 0.0004607122268392452, "Law & Crime": 0.0004386541806480749, "Education & Careers": 0.00031780397662023625, "Entertainment & Media": 0.0004589953027462071, "Fashion & Beauty": 0.00016615458481590917, "Business, Economics & Finance": 0.0006526930581886236, "Food & Dining": 0.00015381294935509348, "Games & Gaming": 0.00024098535829062542, "Electronics & Hardware": 0.00040289021852095967, "Health, Healthcare & Wellness": 0.00018607987127545182, "History & Geography": 0.00018474371808949305, "Home, Pets & Hobbies": 0.000155461985547514, "Industry & Production": 0.00015837618002431206, "Language, Literature & Philosophy": 0.00016527944002564277, "Politics & Government": 0.0001534714738428383, "Religion & Spirituality": 0.00019644413227713737, "Science, Mathematics & Engineering": 0.000603383112909874, "Relationships & Community": 0.00012750373991816886, "Software Usage & Applications": 0.09545588016382225, "Programming & Software Engineering": 0.018701629006647853, "Sports & Fitness": 0.0001743816058453169, "Vehicles & Transportation": 8.059512193881599e-05, "Travel & Tourism": 0.0002451222044902804, "Other / Uncovered Subject": 0.00045607837216731563, "NONE": 0.8792000949438823}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9679, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9679, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:35:12.450219+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "4869901f-756f-40fa-a689-6122de108ab7", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "dbe887bd379ee8b89ca624039db2508df436952a3921b9ab67ac0a396dfb394d", "source_metadata": {"uuid": "4869901f-756f-40fa-a689-6122de108ab7", "repo_name": "MarianHolly/blog_platform", "relative_path": "blog_platform/test_connections.py", "category": "TEST", "algo_rel_score": 0.980512261390686, "quality_score": 5.97832727432251, "source_meta": {"github_metadata": {"branch": "dev", "commit_count": 264, "repo_created_at": "2025-04-07T16:55:10.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "06a3c64435905d453d31f43139a211512f1ddde9", "size_bytes": 4881, "file_timestamp": 1752833408, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "82f2ef70e1027c44faec4658a7d9af2b38bcc6a4"}}, "parsed_source_meta": {"github_metadata": {"branch": "dev", "commit_count": 264, "repo_created_at": "2025-04-07T16:55:10.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "06a3c64435905d453d31f43139a211512f1ddde9", "size_bytes": 4881, "file_timestamp": 1752833408, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "82f2ef70e1027c44faec4658a7d9af2b38bcc6a4", "url": "https://github.com/MarianHolly/blog_platform/blob/82f2ef70e1027c44faec4658a7d9af2b38bcc6a4/blog_platform/test_connections.py"}, "gpt4o_tokens": 1073, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.055698183172032, "noise_severity": 0.061097128119267846, "missing_content_severity": 0.012763505497715042, "factual_density": 2.9375061379465364, "pretraining_utility": 0.6926530743747501, "math_density": 0.021354112623888206, "code_density": 3.7918271999206885, "reasoning_density": 1.3653387035944302, "education_level": 2.1216839348955574, "creative_writing_quality": -0.13107619252052743}, "score_applicability": {"education_level": 0.9991239408103028, "creative_writing_quality": 0.0009374560165336776}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/akaiang/face_recognition_django/blob/6b659d0728ca6149eb8eab827ad0adf14a9f7830/face_recognize/views.py", "id": "python-0-35c88f9b77690212e3d2f75e4df7f5a7", "text": "from django.shortcuts import render\nfrom django.http import StreamingHttpResponse\nimport cv2\nimport pickle\nimport face_recognition\nimport numpy as np\nimport imutils\nimport os\nfrom imutils import paths\nfrom django.conf import settings\nfrom openpyxl import Workbook\nfrom .models import RecognitionHistory, IdNameMapping\nfrom django.http import HttpResponse\nfrom django.utils.timezone import localtime\nfrom django.db.models import Max\nfrom .apps import *\nfrom rest_framework.decorators import api_view\nfrom django.utils import timezone\nfrom django.utils.timezone import localtime\nimport time\n\nlast_recognition_time = {}\n\n# Đường dẫn tệp\nENCODINGS_PATH = \"encodings.pickle\"\nDATASET_PATH = \"dataset\"\nPROCESSED_DATASET_PATH = \"processed_dataset\"\n\n\n# Camera\ncamera = None\n\n# Tải dữ liệu ban đầu\ndef load_data():\n    print(\"[INFO] loading encodings...\")\n    try:\n        with open(ENCODINGS_PATH, \"rb\") as f:\n            data = pickle.load(f)\n    except FileNotFoundError:\n        data = {\"encodings\": [], \"ids\": []}\n\n    print(\"[INFO] loading ID-to-Name mappings from database...\")\n    id_to_name = {entry['id_code']: entry['name'] for entry in IdNameMapping.objects.all().values('id_code', 'name')}\n    return data, id_to_name\n\ndata, id_to_name = load_data()\n\n# Căn chỉnh khuôn mặt\ndef align_face(image, landmarks):\n    left_eye = landmarks[\"left_eye\"]\n    right_eye = landmarks[\"right_eye\"]\n\n    # Tính toán vị trí mắt trái và mắt phải\n    left_eye_center = np.mean(left_eye, axis=0).astype(\"int\")\n    right_eye_center = np.mean(right_eye, axis=0).astype(\"int\")\n\n    # Tính góc giữa hai mắt\n    dy = right_eye_center[1] - left_eye_center[1]\n    dx = right_eye_center[0] - left_eye_center[0]\n    angle = np.degrees(np.arctan2(dy, dx))\n\n    # Tính toán vị trí trung tâm giữa hai mắt\n    eyes_center = (\n        int((left_eye_center[0] + right_eye_center[0]) // 2),\n        int((left_eye_center[1] + right_eye_center[1]) // 2)\n    )\n\n    # Tạo ma trận quay và căn chỉnh khuôn mặt\n    M = cv2.getRotationMatrix2D(eyes_center, angle, 1.0)\n    (h, w) = image.shape[:2]\n    rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC)\n\n    return rotated\n\n# Cắt và lưu khuôn mặt đã căn chỉnh từ dataset\ndef save_cropped_faces():\n    if os.path.exists(ENCODINGS_PATH):\n        with open(ENCODINGS_PATH, \"rb\") as f:\n            data = pickle.load(f)\n            existing_ids = set(data[\"ids\"])\n    else:\n        existing_ids = set()\n\n    # Lấy paths của images trong dataset\n    imagePaths = list(paths.list_images(DATASET_PATH))\n    \n    # Duyệt qua các image paths\n    for (i, imagePath) in enumerate(imagePaths):\n        id = imagePath.split(os.path.sep)[-2]\n        \n        # Kiểm tra nếu ID đã tồn tại trong encodings thì bỏ qua\n        if id in existing_ids:\n            print(f\"[INFO] ID '{id}' already exists. Skipping images in this folder.\")\n            continue\n\n        print(f\"[INFO] processing image {i+1}/{len(imagePaths)}: {imagePath}\")\n\n        # Load image bằng OpenCV\n        image = cv2.imread(imagePath)\n        if image is None:\n            print(f\"[WARNING] Cannot open image file: {imagePath}\")\n            continue\n\n        rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n\n        # Detect face và landmarks bằng face_recognition\n        face_locations = face_recognition.face_locations(rgb)\n        face_landmarks = face_recognition.face_landmarks(rgb)\n\n        if len(face_locations) == 0:\n            print(f\"[WARNING] No face detected in the image: {imagePath}\")\n            continue\n\n        # Duyệt qua các khuôn mặt phát hiện được và lưu lại theo yêu cầu\n        for face_location, landmarks in zip(face_locations, face_landmarks):\n            # Căn chỉnh khuôn mặt dựa trên landmarks\n            aligned_face = align_face(rgb, landmarks)\n            \n            # Cắt khuôn mặt dựa trên bounding box từ face_location\n            top, right, bottom, left = face_location\n            face_cropped = aligned_face[top:bottom, left:right]\n\n            # Resize khuôn mặt\n            aligned_face_resized = cv2.resize(face_cropped, (256, 256))\n\n            # Kiểm tra nếu thư mục của người dùng chưa tồn tại trong processed_dataset\n            user_dir = os.path.join(PROCESSED_DATASET_PATH, id)\n            if not os.path.exists(user_dir):\n                os.makedirs(user_dir)\n\n            # Lưu ảnh khuôn mặt đã cắt và căn chỉnh\n            face_fileid = os.path.join(user_dir, f\"face_{i}.jpg\")\n            face_bgr = cv2.cvtColor(aligned_face_resized, cv2.COLOR_RGB2BGR)\n            cv2.imwrite(face_fileid, face_bgr)\n            print(f\"[INFO] Saved cropped and aligned face to {face_fileid}\")\n\n# Tính toán encoding của khuôn mặt từ ảnh đã căn chỉnh\ndef compute_embeddings():\n    if os.path.exists(ENCODINGS_PATH):\n        with open(ENCODINGS_PATH, \"rb\") as f:\n            data = pickle.loads(f.read())\n            knownEncodings = data[\"encodings\"]\n            knownIds = data[\"ids\"]\n    else:\n        knownEncodings = []\n        knownIds = []\n\n    existing_ids = set(knownIds)\n\n    # Duyệt qua tất cả các thư mục con của processed_dataset\n    for user_dir in os.listdir(PROCESSED_DATASET_PATH):\n        user_path = os.path.join(PROCESSED_DATASET_PATH, user_dir)\n\n        if not os.path.isdir(user_path):\n            continue\n\n        # Bỏ qua ID nếu đã tồn tại\n        if user_dir in existing_ids:\n            print(f\"[INFO] ID '{user_dir}' already exists. Skipping images in this folder.\")\n            continue\n\n        # Duyệt qua từng ảnh khuôn mặt đã cắt trong thư mục của mỗi ID\n        for face_image in os.listdir(user_path):\n            face_path = os.path.join(user_path, face_image)\n            image = cv2.imread(face_path)\n            if image is None:\n                print(f\"[WARNING] Cannot open image file: {face_path}\")\n                continue\n\n            rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n\n            # Detect và tính toán encoding của khuôn mặt\n            encodings = face_recognition.face_encodings(rgb)\n            if len(encodings) == 0:\n                print(f\"[WARNING] No face detected in the image: {face_path}\")\n                continue\n\n            # Lưu encoding và ID\n            knownEncodings.append(np.array(encodings[0]))\n            knownIds.append(user_dir)\n            print(f\"[INFO] Computed encoding for {face_path}\")\n\n    # Lưu lại facial encodings + ids vào ổ cứng\n    print(\"[INFO] serializing encodings...\")\n    data = {\"encodings\": knownEncodings, \"ids\": knownIds}\n    \n    with open(ENCODINGS_PATH, \"wb\") as f:\n        f.write(pickle.dumps(data))\n\n    print(\"[INFO] Done!\")\n\ndef detect_faces(frame):\n    rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n    rgb = imutils.resize(rgb, width=750)\n    r = frame.shape[1] / float(rgb.shape[1])\n    \n    # Phát hiện vị trí khuôn mặt\n    face_locations = face_recognition.face_locations(rgb)\n    \n    return face_locations, r\n\n# def recognize_faces(frame):\n#     global data, id_to_name, last_recognition_time\n#     rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n#     rgb = imutils.resize(rgb, width=750)\n#     face_locations, r = detect_faces(frame)\n    \n#     # Trích xuất encoding từ vị trí khuôn mặt\n#     encodings = face_recognition.face_encodings(rgb, face_locations)\n#     ids = []\n    \n#     for face_encoding in encodings:\n#         # Tính khoảng cách giữa encoding mới và encoding đã lưu\n#         distances = np.linalg.norm(data[\"encodings\"] - face_encoding, axis=1)\n#         min_distance = np.min(distances)\n        \n#         id_detected = \"Unknown\"\n#         accuracy = (1 - min_distance) * 100  # Tính độ chính xác\n#         threshold = 0.4  # Ngưỡng nhận diện\n#         if min_distance < threshold:\n#             id_detected = data[\"ids\"][np.argmin(distances)]\n        \n#         ids.append((id_detected, accuracy))\n    \n#     # Vẽ bounding boxes và thông tin nhận diện lên ảnh\n#     for (box, (id_detected, accuracy)) in zip(face_locations, ids):\n#         top, right, bottom, left = box\n#         left, top, right, bottom = int(left * r), int(top * r), int(right * r), int(bottom * r)\n        \n#         name = id_to_name.get(id_detected.strip(), \"Unknown\")\n#         label = f\"{id_detected}_{name}\"\n\n#         cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)\n#         y = top - 15 if top - 15 > 15 else top + 15\n#         cv2.putText(frame, label, (left, y), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)\n        \n#         # Lưu lịch sử nhận diện chỉ khi đủ 1 phút đã trôi qua\n#         if id_detected != \"Unknown\":\n#             current_time = time.time()  # Lấy thời gian hiện tại (số giây từ epoch)\n\n#             # Kiểm tra nếu chưa nhận diện người này hoặc nếu đã qua 1 phút kể từ lần nhận diện cuối\n#             if id_detected not in last_recognition_time or (current_time - last_recognition_time[id_detected]) >= 60:\n#                 # Lưu vào database\n#                 RecognitionHistory.objects.create(\n#                     recognized_id=id_detected,\n#                     recognized_name=name,\n#                     prediction_accuracy=round(accuracy, 2)\n#                 )\n#                 # Cập nhật thời gian nhận diện lần này\n#                 last_recognition_time[id_detected] = current_time\n\n#     return frame\n\nfrom deepface import DeepFace\nimport cv2\n\ndef detect_liveness_with_deepface(face_image):\n    \"\"\"\n    Kiểm tra liveness của khuôn mặt bằng DeepFace.\n    :param face_image: Khuôn mặt (numpy array)\n    :return: True nếu là thật, False nếu là giả\n    \"\"\"\n    try:\n        # Kiểm tra liveness qua DeepFace\n        analysis = DeepFace.analyze(face_image, actions=['emotion'], enforce_detection=False)\n        if analysis:  # Nếu nhận diện thành công\n            return True  # Khuôn mặt thật\n    except Exception as e:\n        print(f\"Lỗi khi kiểm tra liveness: {e}\")\n    return False  # Khuôn mặt giả\n\ndef recognize_faces_with_liveness(frame):\n    \"\"\"\n    Nhận diện khuôn mặt và kiểm tra liveness.\n    :param frame: Frame video\n    :return: Frame đã xử lý\n    \"\"\"\n    global data, id_to_name, last_recognition_time\n    rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n    face_locations, r = detect_faces(frame)\n\n    ids = []\n    for (top, right, bottom, left) in face_locations:\n        face = rgb[top:bottom, left:right]\n\n        # Kiểm tra liveness\n        is_real = detect_liveness_with_deepface(face)\n        if not is_real:\n            ids.append((\"Fake\", 0))  # Gắn nhãn \"Fake\" nếu khuôn mặt giả\n            continue\n\n        # Trích xuất encoding khuôn mặt\n        encodings = face_recognition.face_encodings(rgb, [(top, right, bottom, left)])\n        if len(encodings) > 0:\n            face_encoding = encodings[0]\n            distances = np.linalg.norm(data[\"encodings\"] - face_encoding, axis=1)\n            min_distance = np.min(distances)\n\n            id_detected = \"Unknown\"\n            accuracy = (1 - min_distance) * 100  # Tính độ chính xác\n            threshold = 0.4  # Ngưỡng nhận diện\n            if min_distance < threshold:\n                id_detected = data[\"ids\"][np.argmin(distances)]\n\n            ids.append((id_detected, accuracy))\n        else:\n            ids.append((\"Unknown\", 0))\n\n    # Vẽ bounding boxes\n    for (box, (id_detected, accuracy)) in zip(face_locations, ids):\n        top, right, bottom, left = box\n        left, top, right, bottom = int(left * r), int(top * r), int(right * r), int(bottom * r)\n        \n        color = (0, 255, 0) if id_detected != \"Fake\" else (0, 0, 255)\n        label = f\"{id_detected} ({accuracy:.2f}%)\" if id_detected != \"Fake\" else \"Fake\"\n        cv2.rectangle(frame, (left, top), (right, bottom), color, 2)\n        y = top - 15 if top - 15 > 15 else top + 15\n        cv2.putText(frame, label, (left, y), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)\n\n    return frame\n\n\ndef start_camera():\n    global camera\n    if camera is None or not camera.isOpened():\n        camera = cv2.VideoCapture(0)\n\n@api_view([\"POST\"])\ndef stop_camera(request):\n    global camera\n    if camera:\n        camera.release()\n        camera = None\n        return JsonResponse({\"success\": True, \"message\": \"Camera đã được dừng\"})\n    return JsonResponse({\"success\": False, \"message\": \"Camera không đang hoạt động\"})\n\n# Lấy khung hình từ camera, nhận diện khuôn mặt và stream ảnh JPEG theo thời gian thực.\n# def generate_frames():\n#     global camera\n#     while camera and camera.isOpened():\n#         success, frame = camera.read()\n#         if not success:\n#             break\n#         else:\n#             frame = recognize_faces(frame)\n#             frame = cv2.resize(frame, (320, 240))\n#             ret, buffer = cv2.imencode('.jpg', frame, [int(cv2.IMWRITE_JPEG_QUALITY), 50])\n#             frame = buffer.tobytes()\n#             yield (b'--frame\\r\\n'\n#                    b'Content-Type: image/jpeg\\r\\n\\r\\n' + frame + b'\\r\\n')\n\ndef generate_frames():\n    global camera\n    while camera and camera.isOpened():\n        success, frame = camera.read()\n        if not success:\n            break\n        else:\n            # Nhận diện và kiểm tra liveness\n            frame = recognize_faces_with_liveness(frame)\n            frame = cv2.resize(frame, (320, 240))\n            ret, buffer = cv2.imencode('.jpg', frame, [int(cv2.IMWRITE_JPEG_QUALITY), 50])\n            frame = buffer.tobytes()\n            yield (b'--frame\\r\\n'\n                   b'Content-Type: image/jpeg\\r\\n\\r\\n' + frame + b'\\r\\n')\n\n\n# Nhận diện qua camera           \ndef recognize_video(request):\n    start_camera()  \n    return StreamingHttpResponse(generate_frames(), content_type='multipart/x-mixed-replace; boundary=frame')\n\n# Nhận diện hình ảnh\n# def recognize_image(request):\n#     if request.method == \"POST\" and request.FILES.get('image'):\n#         stop_camera()\n#         image_file = request.FILES['image']\n#         img = cv2.imdecode(np.frombuffer(image_file.read(), np.uint8), cv2.IMREAD_COLOR)\n\n#         img = recognize_faces(img)\n#         img_resized = cv2.resize(img, (680, 500))\n#         cv2.imwrite(\"static/recognized_image.jpg\", img_resized)\n        \n#         return render(request, 'index.html', {'recognized_image': 'recognized_image.jpg'})\n#     return render(request, 'index.html')\n\n\n@api_view([\"POST\"]) \ndef GenerateID(request):\n    try:\n        person = IdNameMapping.objects.order_by('-id_code').first()\n        id = person.id_code\n        id = int(id)\n        new_id = id + 1\n        new_id = str(new_id).zfill(5)\n    except:\n        new_id = \"00001\"\n    return ObjResponse({\"id\": new_id})\n\n@api_view([\"POST\"])\ndef save_new_face(request):\n    if request.method == \"POST\":\n        id_code = request.POST.get(\"id\")\n        name = request.POST.get(\"name\")\n        images = request.FILES.getlist(\"images[]\")\n\n        if not id_code or not name:\n            return Response({\"success\": False, \"error\": \"ID and name are required.\"}, status=400)\n\n        # Lưu ảnh vào thư mục\n        dataset_path = os.path.join(\"dataset\", id_code)\n        os.makedirs(dataset_path, exist_ok=True)\n\n        # Lưu từng ảnh\n        for image in images:\n            image_path = os.path.join(dataset_path, image.name)\n            with open(image_path, \"wb\") as f:\n                for chunk in image.chunks():\n                    f.write(chunk)\n        \n        # Cập nhật thông tin vào cơ sở dữ liệu\n        IdNameMapping.objects.update_or_create(id_code=id_code, defaults={\"name\": name})\n\n        # Gọi các hàm xử lý (lưu ảnh cắt và tính toán embeddings)\n        save_cropped_faces()\n        compute_embeddings()\n\n        global data, id_to_name\n        data, id_to_name = load_data()\n        \n        return Response({\"success\": True})\n    else:\n        return Response({\"success\": False, \"error\": \"Invalid request method\"}, status=400)\n    \n# Xuất lịch sử nhận diện\ndef export_recognition_history(request):\n    workbook = Workbook()\n    worksheet = workbook.active\n    worksheet.title = \"Recognition History\"\n\n    worksheet.append([\"ID\", \"Name\", \"Recognition Time\", \"Prediction Accuracy\"])\n    \n    recognition_histories = RecognitionHistory.objects.values(\"recognized_id\", \"recognized_name\", \"prediction_accuracy\").annotate(\n        latest_recognition_time=Max(\"recognition_time\")\n    )\n    \n    for history in recognition_histories:\n        local_time = localtime(history[\"latest_recognition_time\"])\n        \n        worksheet.append([\n            history[\"recognized_id\"],\n            history[\"recognized_name\"],\n            local_time.strftime('%Y-%m-%d %H:%M:%S'),\n            f\"{history['prediction_accuracy']}%\"\n        ])\n    \n    for column in worksheet.columns:\n        max_length = 0\n        column_letter = column[0].column_letter\n        for cell in column:\n            try:\n                max_length = max(max_length, len(str(cell.value)))\n            except:\n                pass\n        adjusted_width = max_length + 2\n        worksheet.column_dimensions[column_letter].width = adjusted_width\n    \n    response = HttpResponse(content_type=\"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet\")\n    response[\"Content-Disposition\"] = 'attachment; filename=\"recognition_history.xlsx\"'\n    workbook.save(response)\n    \n    return response\n\n@api_view(['GET'])\ndef get_recognition_history(request):\n    try:\n        # Lấy lịch sử nhận diện từ database, sắp xếp theo thời gian nhận diện mới nhất\n        recognition_history = RecognitionHistory.objects.order_by('-recognition_time').values(\n            'recognized_id', 'recognized_name', 'recognition_time', 'prediction_accuracy'\n        )\n        data = [\n            {\n                \"recognized_id\": record['recognized_id'],\n                \"recognized_name\": record['recognized_name'],\n                \"recognition_time\": record['recognition_time'].strftime('%Y-%m-%d %H:%M:%S'),\n                \"prediction_accuracy\": f\"{record['prediction_accuracy']}%\"\n            }\n            for record in recognition_history\n        ]\n        return Response({\"history\": data}, status=200)\n    except Exception as e:\n        return Response({\"error\": f\"Lỗi: {str(e)}\"}, status=500)\n\n@api_view([\"POST\"]) \ndef clear_recognition_history(request):\n    if request.method == \"POST\":\n        try:\n            # Xóa toàn bộ dữ liệu\n            RecognitionHistory.objects.all().delete()\n            return JsonResponse({\"success\": True})\n        except Exception as e:\n            # Trả về lỗi nếu có\n            return JsonResponse({\"success\": False, \"error\": str(e)}, status=500)\n    # Trường hợp không phải POST\n    return JsonResponse({\"success\": False, \"error\": \"Invalid request method\"}, status=400)\n\ndef home(request):\n    # Lấy tất cả các bản ghi, không giới hạn số lượng\n    recognition_history = RecognitionHistory.objects.all().order_by('-recognition_time')\n    return render(request, 'index.html', {'recognition_history': recognition_history})", "text_preview": "from django.shortcuts import render from django.http import StreamingHttpResponse import cv2 import pickle import face_recognition import numpy as np import imutils import os from imutils import paths from django.conf import settings from o", "input_sha256": "2eff2cd77c2dcc2d26740bd16a1271c5c5de831e39b643e89853dcd44901fe89", "text_sha256": "35c88f9b77690212e3d2f75e4df7f5a742ffbaea02b1363ba587ff58a00b9191", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12096, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 2, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": -0.04092923248462718, "noise_severity": 0.2998608510033203, "missing_content_severity": 0.06750562414237757, "factual_density": 3.3166732435841153, "pretraining_utility": 0.6431447028727728, "math_density": 1.5438533404817223, "code_density": 3.7043323854680614, "reasoning_density": 1.005906993082672, "education_level": 2.4620176995961685, "creative_writing_quality": -0.5803073984884434}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0045943790683737554, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9960033320250583, "secondary": 0.9579734549527522, "tertiary": 0.9538465659371018, "structured_records": 0.03211122735322776, "procedural_steps": 0.04923916514905121, "worked_solution": 0.005599214938746769, "contextual_dialogue": 6.654196883468003e-06, "content_quality": 0.45549772908435593, "noise_severity": 0.7501159574972331, "missing_content_severity": 0.9437453132146854, "factual_density": 0.430561036320096, "pretraining_utility": 0.5379967876350141, "math_density": 0.372032989885387, "code_density": 0.7536103212233844, "reasoning_density": 0.16174417243110673, "education_level": 0.4805069915730177, "creative_writing_quality": 0.9991952034286748, "primary_topic": 0.9741487342919132, "secondary_topic": 0.6250357532055207}, "probability_margin": {"primary": 0.9946528779554297, "secondary": 0.9440957290166176, "tertiary": 0.9417333382674105, "education_level": 0.18350988641019955, "creative_writing_quality": 0.9990958385351256, "primary_topic": 0.9547915733228658, "secondary_topic": 0.4128139876080043}, "raw_probabilities": {"primary": {"Academic Writing": 5.844948154241847e-05, "Content Listing": 3.19982423821572e-05, "Creative Writing": 2.059037153472862e-05, "Customer Support Page": 1.3632903723237172e-05, "Q&A Forum": 1.6466493266028122e-05, "Discussion Thread": 1.0607595042203264e-05, "Comment Section": 1.7228979192046326e-05, "Missing Content": 3.5613650679034856e-05, "Knowledge Article": 7.647526597352442e-05, "Legal Notices": 2.9052174023781715e-05, "News Article": 7.440924491939617e-06, "Organizational Announcement": 1.457625256749023e-05, "Nonfiction Writing": 1.420531695619192e-05, "Organizational About Page": 1.578119439406197e-05, "Personal About Page": 1.7086508244617377e-05, "Personal Blog": 1.0799777814125309e-05, "Product Page": 1.0883043951580838e-05, "Spam / Ads": 1.4134526683414677e-05, "Structured Data": 0.000402776377892361, "Technical Writing": 0.9961698598573475, "Transcript / Interview": 2.0961057992609505e-05, "Tutorial / How-To Guide": 0.0015169819019177988, "User Reviews": 8.665468617470162e-06, "Other / Unknown": 0.001465732633769764}, "secondary": {"Academic Writing": 0.00040431117222694703, "Content Listing": 0.0002875604680638642, "Creative Writing": 0.00026441172106309225, "Customer Support Page": 0.0002027788726429419, "Q&A Forum": 0.0001422658955124159, "Discussion Thread": 8.586250921681686e-05, "Comment Section": 0.002206162450139501, "Missing Content": 0.002004592517697013, "Knowledge Article": 0.0022987402584160477, "Legal Notices": 0.00033921831111470997, "News Article": 0.00013940689304884715, "Organizational Announcement": 0.0001522614975926353, "Nonfiction Writing": 0.0002108660154382615, "Organizational About Page": 0.0001844843294675046, "Personal About Page": 0.00019069733565145743, "Personal Blog": 0.00017051202262286191, "Product Page": 9.280675493462137e-05, "Spam / Ads": 0.0001907174325885662, "Structured Data": 0.003034931938936979, "Technical Writing": 0.015628831979769907, "Transcript / Interview": 0.00022598966160864757, "Tutorial / How-To Guide": 0.011735991898071898, "User Reviews": 8.203706778681699e-05, "NONE": 0.9597245609963876}, "tertiary": {"Academic Writing": 0.00037203295406145425, "Content Listing": 0.0002913417737561966, "Creative Writing": 0.0002880951790111027, "Customer Support Page": 0.0001984521034336426, "Q&A Forum": 0.00012187048665275406, "Discussion Thread": 9.653831931936777e-05, "Comment Section": 0.0028375043267317526, "Missing Content": 0.004424479317216764, "Knowledge Article": 0.0040413134701809995, "Legal Notices": 0.000690996654668148, "News Article": 0.00013502762159137063, "Organizational Announcement": 0.0001700825326216402, "Nonfiction Writing": 0.0002877751369806786, "Organizational About Page": 0.00019950774720983925, "Personal About Page": 0.00018271536179871668, "Personal Blog": 0.00017746075609489334, "Product Page": 0.0001012571493362146, "Spam / Ads": 0.00022213767671995654, "Structured Data": 0.004028280703979937, "Technical Writing": 0.011034452911396155, "Transcript / Interview": 0.0002104493490649598, "Tutorial / How-To Guide": 0.014036287422312154, "User Reviews": 8.231535613869301e-05, "NONE": 0.9557696256897226}, "structured_records": {"false": 0.9678887726467722, "true": 0.03211122735322776}, "procedural_steps": {"false": 0.9507608348509488, "true": 0.04923916514905121}, "worked_solution": {"false": 0.9944007850612532, "true": 0.005599214938746769}, "contextual_dialogue": {"false": 0.9999933458031165, "true": 6.654196883468003e-06}, "content_quality": {"-2": 0.0341723152872808, "-1": 0.2788213482171384, "0": 0.4240180222632326, "1": 0.21973988215762372, "2": 0.04324843207472454}, "noise_severity": {"0": 0.7792025226555939, "1": 0.1539701229529004, "2": 0.05727761536420406, "3": 0.006863458787195332, "4": 0.0026862802401064366}, "missing_content_severity": {"0": 0.9579210066838351, "1": 0.022348734837044366, "2": 0.014600352657891606, "3": 0.004563439295365221, "4": 0.0005664665258635782}, "factual_density": {"0": 0.006052654029763767, "1": 0.015626056266581805, "2": 0.11889479822546206, "3": 0.37444837504616, "4": 0.4849781164320323}, "pretraining_utility": {"-2": 0.009964381198288918, "-1": 0.05721230764507269, "0": 0.311311817097593, "1": 0.5227372152036676, "2": 0.09877427885537787}, "math_density": {"0": 0.10374831965836837, "1": 0.39735689651116995, "2": 0.40072975641482245, "3": 0.0476231785216498, "4": 0.050541848893989536}, "code_density": {"0": 0.00028796292719924604, "1": 0.0005584358353953384, "2": 0.026969784126729592, "3": 0.23890088706349652, "4": 0.7332829300471794}, "reasoning_density": {"0": 0.5358750547704803, "1": 0.12448107703580541, "2": 0.18854879016349857, "3": 0.10005197640099348, "4": 0.05104310162922226}, "education_level": {"none": 0.0009086609365747993, "0": 0.0012441088917340434, "1": 0.004616922625676769, "2": 0.5670891596441814, "3": 0.38357927323398183, "4": 0.042561874667851135}, "creative_writing_quality": {"not_applicable": 0.9993293361905624, "-2": 0.00013785358075326206, "-1": 0.0002172295376452838, "0": 0.00023349765543679237, "1": 6.042054256769627e-05, "2": 2.1662493034525936e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.893317504075052e-05, "Art & Design": 0.0001167669358176638, "Law & Crime": 0.00018395674508761594, "Education & Careers": 0.00011202481241418091, "Entertainment & Media": 6.555177558707965e-05, "Fashion & Beauty": 8.275934156956732e-05, "Business, Economics & Finance": 0.0002765439380543955, "Food & Dining": 6.373598670850045e-05, "Games & Gaming": 0.00010749526415966554, "Electronics & Hardware": 0.0006586434557841978, "Health, Healthcare & Wellness": 0.0002405765599602598, "History & Geography": 9.11469081074835e-05, "Home, Pets & Hobbies": 6.887702326542103e-05, "Industry & Production": 7.36389969802604e-05, "Language, Literature & Philosophy": 6.145842600613913e-05, "Politics & Government": 4.553869744114182e-05, "Religion & Spirituality": 6.171588651095471e-05, "Science, Mathematics & Engineering": 0.020351440419358414, "Relationships & Community": 5.4886063505461275e-05, "Software Usage & Applications": 0.0008240376184901169, "Programming & Software Engineering": 0.9751430137422242, "Sports & Fitness": 8.199193833013567e-05, "Vehicles & Transportation": 3.9551275722992724e-05, "Travel & Tourism": 7.740094373751527e-05, "Other / Uncovered Subject": 0.0009562751089474571, "Unknown / Insufficient Content": 9.203896118823412e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008644624917969027, "Art & Design": 0.0010033211153078643, "Law & Crime": 0.0035518026553207987, "Education & Careers": 0.0006829646303501675, "Entertainment & Media": 0.0007911255962359325, "Fashion & Beauty": 0.0005743109300446833, "Business, Economics & Finance": 0.0009914717880721517, "Food & Dining": 0.0003708793966279785, "Games & Gaming": 0.00048661482100567297, "Electronics & Hardware": 0.019429179966367713, "Health, Healthcare & Wellness": 0.0020437789519240288, "History & Geography": 0.00046411129755392954, "Home, Pets & Hobbies": 0.00044920255832848344, "Industry & Production": 0.0004034899991357577, "Language, Literature & Philosophy": 0.00027937516407729456, "Politics & Government": 0.00034557202358197843, "Religion & Spirituality": 0.0004633716206722042, "Science, Mathematics & Engineering": 0.22664346739730407, "Relationships & Community": 0.00030264521092947017, "Software Usage & Applications": 0.007491259108517364, "Programming & Software Engineering": 0.08906374863096964, "Sports & Fitness": 0.0003922078042172384, "Vehicles & Transportation": 0.0002183766312386054, "Travel & Tourism": 0.000490378539761039, "Other / Uncovered Subject": 0.0027454266653506396, "NONE": 0.6394574550053084}}, "probabilities": {"primary": {"Academic Writing": 5.844948154241847e-05, "Content Listing": 3.19982423821572e-05, "Creative Writing": 2.059037153472862e-05, "Customer Support Page": 1.3632903723237172e-05, "Q&A Forum": 1.6466493266028122e-05, "Discussion Thread": 1.0607595042203264e-05, "Comment Section": 1.7228979192046326e-05, "Missing Content": 3.5613650679034856e-05, "Knowledge Article": 7.647526597352442e-05, "Legal Notices": 2.9052174023781715e-05, "News Article": 7.440924491939617e-06, "Organizational Announcement": 1.457625256749023e-05, "Nonfiction Writing": 1.420531695619192e-05, "Organizational About Page": 1.578119439406197e-05, "Personal About Page": 1.7086508244617377e-05, "Personal Blog": 1.0799777814125309e-05, "Product Page": 1.0883043951580838e-05, "Spam / Ads": 1.4134526683414677e-05, "Structured Data": 0.000402776377892361, "Technical Writing": 0.9961698598573475, "Transcript / Interview": 2.0961057992609505e-05, "Tutorial / How-To Guide": 0.0015169819019177988, "User Reviews": 8.665468617470162e-06, "Other / Unknown": 0.001465732633769764}, "secondary": {"Academic Writing": 0.0004043111722269471, "Content Listing": 0.00028756046806386427, "Creative Writing": 0.0002644117210630923, "Customer Support Page": 0.00020277887264294193, "Q&A Forum": 0.00014226589551241594, "Discussion Thread": 8.586250921681688e-05, "Comment Section": 0.0022061624501395014, "Missing Content": 0.0020045925176970136, "Knowledge Article": 0.002298740258416048, "Legal Notices": 0.00033921831111471, "News Article": 0.00013940689304884718, "Organizational Announcement": 0.00015226149759263533, "Nonfiction Writing": 0.00021086601543826152, "Organizational About Page": 0.00018448432946750463, "Personal About Page": 0.00019069733565145745, "Personal Blog": 0.00017051202262286194, "Product Page": 9.280675493462139e-05, "Spam / Ads": 0.00019071743258856623, "Structured Data": 0.0030349319389369796, "Technical Writing": 0.01562883197976991, "Transcript / Interview": 0.0002259896616086476, "Tutorial / How-To Guide": 0.0117359918980719, "User Reviews": 8.2037067786817e-05, "NONE": 0.9597245609963877}, "tertiary": {"Academic Writing": 0.00037203295406145425, "Content Listing": 0.0002913417737561966, "Creative Writing": 0.0002880951790111027, "Customer Support Page": 0.0001984521034336426, "Q&A Forum": 0.00012187048665275406, "Discussion Thread": 9.653831931936777e-05, "Comment Section": 0.0028375043267317526, "Missing Content": 0.004424479317216764, "Knowledge Article": 0.0040413134701809995, "Legal Notices": 0.000690996654668148, "News Article": 0.00013502762159137063, "Organizational Announcement": 0.0001700825326216402, "Nonfiction Writing": 0.0002877751369806786, "Organizational About Page": 0.00019950774720983925, "Personal About Page": 0.00018271536179871668, "Personal Blog": 0.00017746075609489334, "Product Page": 0.0001012571493362146, "Spam / Ads": 0.00022213767671995654, "Structured Data": 0.004028280703979937, "Technical Writing": 0.011034452911396155, "Transcript / Interview": 0.0002104493490649598, "Tutorial / How-To Guide": 0.014036287422312154, "User Reviews": 8.231535613869301e-05, "NONE": 0.9557696256897226}, "structured_records": {"false": 0.9678887726467722, "true": 0.03211122735322776}, "procedural_steps": {"false": 0.9507608348509488, "true": 0.04923916514905121}, "worked_solution": {"false": 0.9944007850612532, "true": 0.005599214938746769}, "contextual_dialogue": {"false": 0.9999933458031165, "true": 6.654196883468003e-06}, "content_quality": {"-2": 0.0341723152872808, "-1": 0.2788213482171384, "0": 0.4240180222632326, "1": 0.21973988215762372, "2": 0.04324843207472454}, "noise_severity": {"0": 0.7792025226555939, "1": 0.1539701229529004, "2": 0.05727761536420406, "3": 0.006863458787195332, "4": 0.0026862802401064366}, "missing_content_severity": {"0": 0.9579210066838352, "1": 0.02234873483704437, "2": 0.014600352657891608, "3": 0.004563439295365222, "4": 0.0005664665258635783}, "factual_density": {"0": 0.006052654029763767, "1": 0.015626056266581805, "2": 0.11889479822546206, "3": 0.37444837504616, "4": 0.4849781164320323}, "pretraining_utility": {"-2": 0.009964381198288918, "-1": 0.05721230764507269, "0": 0.311311817097593, "1": 0.5227372152036676, "2": 0.09877427885537787}, "math_density": {"0": 0.10374831965836837, "1": 0.39735689651116995, "2": 0.40072975641482245, "3": 0.0476231785216498, "4": 0.050541848893989536}, "code_density": {"0": 0.00028796292719924604, "1": 0.0005584358353953384, "2": 0.026969784126729592, "3": 0.23890088706349652, "4": 0.7332829300471794}, "reasoning_density": {"0": 0.5358750547704803, "1": 0.12448107703580541, "2": 0.18854879016349857, "3": 0.10005197640099348, "4": 0.05104310162922226}, "education_level": {"none": 0.0009086609365747993, "0": 0.0012441088917340434, "1": 0.004616922625676769, "2": 0.5670891596441814, "3": 0.38357927323398183, "4": 0.042561874667851135}, "creative_writing_quality": {"not_applicable": 0.9993293361905624, "-2": 0.00013785358075326206, "-1": 0.0002172295376452838, "0": 0.00023349765543679237, "1": 6.042054256769627e-05, "2": 2.1662493034525936e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.893317504075053e-05, "Art & Design": 0.00011676693581766381, "Law & Crime": 0.00018395674508761597, "Education & Careers": 0.00011202481241418093, "Entertainment & Media": 6.555177558707966e-05, "Fashion & Beauty": 8.275934156956733e-05, "Business, Economics & Finance": 0.0002765439380543956, "Food & Dining": 6.373598670850047e-05, "Games & Gaming": 0.00010749526415966555, "Electronics & Hardware": 0.0006586434557841979, "Health, Healthcare & Wellness": 0.00024057655996025982, "History & Geography": 9.114690810748351e-05, "Home, Pets & Hobbies": 6.887702326542104e-05, "Industry & Production": 7.363899698026041e-05, "Language, Literature & Philosophy": 6.145842600613914e-05, "Politics & Government": 4.553869744114183e-05, "Religion & Spirituality": 6.171588651095472e-05, "Science, Mathematics & Engineering": 0.020351440419358418, "Relationships & Community": 5.488606350546128e-05, "Software Usage & Applications": 0.000824037618490117, "Programming & Software Engineering": 0.9751430137422243, "Sports & Fitness": 8.199193833013569e-05, "Vehicles & Transportation": 3.955127572299273e-05, "Travel & Tourism": 7.740094373751528e-05, "Other / Uncovered Subject": 0.0009562751089474572, "Unknown / Insufficient Content": 9.203896118823414e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008644624917969027, "Art & Design": 0.0010033211153078643, "Law & Crime": 0.0035518026553207987, "Education & Careers": 0.0006829646303501675, "Entertainment & Media": 0.0007911255962359325, "Fashion & Beauty": 0.0005743109300446833, "Business, Economics & Finance": 0.0009914717880721517, "Food & Dining": 0.0003708793966279785, "Games & Gaming": 0.00048661482100567297, "Electronics & Hardware": 0.019429179966367713, "Health, Healthcare & Wellness": 0.0020437789519240288, "History & Geography": 0.00046411129755392954, "Home, Pets & Hobbies": 0.00044920255832848344, "Industry & Production": 0.0004034899991357577, "Language, Literature & Philosophy": 0.00027937516407729456, "Politics & Government": 0.00034557202358197843, "Religion & Spirituality": 0.0004633716206722042, "Science, Mathematics & Engineering": 0.22664346739730407, "Relationships & Community": 0.00030264521092947017, "Software Usage & Applications": 0.007491259108517364, "Programming & Software Engineering": 0.08906374863096964, "Sports & Fitness": 0.0003922078042172384, "Vehicles & Transportation": 0.0002183766312386054, "Travel & Tourism": 0.000490378539761039, "Other / Uncovered Subject": 0.0027454266653506396, "NONE": 0.6394574550053084}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 13284, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 13284, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:35:14.371263+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "0f50bbc4-2082-4a3c-9ed1-ba50c3f5dffd", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "cee2ac891b033054703a5eac2f449cb7830e0bca36ae3716fe61ded610d0beae", "source_metadata": {"uuid": "0f50bbc4-2082-4a3c-9ed1-ba50c3f5dffd", "repo_name": "akaiang/face_recognition_django", "relative_path": "face_recognize/views.py", "category": "WEB", "algo_rel_score": 0.9345343112945557, "quality_score": 4.471981525421143, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 3, "repo_created_at": "2024-11-24T22:31:20.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "1007264057aff5811e92a9d2eb63d8c2b751bc99", "size_bytes": 19883, "file_timestamp": 1732487969, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "6b659d0728ca6149eb8eab827ad0adf14a9f7830"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 3, "repo_created_at": "2024-11-24T22:31:20.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "1007264057aff5811e92a9d2eb63d8c2b751bc99", "size_bytes": 19883, "file_timestamp": 1732487969, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "6b659d0728ca6149eb8eab827ad0adf14a9f7830", "url": "https://github.com/akaiang/face_recognition_django/blob/6b659d0728ca6149eb8eab827ad0adf14a9f7830/face_recognize/views.py"}, "gpt4o_tokens": 4583, "dataset_split": "python", "probability_weighted_scores": {"content_quality": -0.04092923248462718, "noise_severity": 0.2998608510033203, "missing_content_severity": 0.06750562414237757, "factual_density": 3.3166732435841153, "pretraining_utility": 0.6431447028727728, "math_density": 1.5438533404817223, "code_density": 3.7043323854680614, "reasoning_density": 1.005906993082672, "education_level": 2.4620176995961685, "creative_writing_quality": -0.5803073984884434}, "score_applicability": {"education_level": 0.9990913390634252, "creative_writing_quality": 0.0006706638094375604}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/MaxParks/AsanaClone/blob/5a19bbf2a54312cf34cca26bb79f862e286ca86d/app/api/team_routes.py", "id": "python-0-0636911604ebb3c09671557a75610979", "text": "from flask import Blueprint, request, jsonify\nfrom flask_login import login_required, current_user\nfrom app.models import db, Team, User, Project, UserTeam\nfrom datetime import datetime\n\nteam_routes = Blueprint('teams', __name__)\n\ndef find_user_by_email(email):\n    return User.query.filter(User.email == email).first()\n\n@team_routes.route('/', methods=['GET'])\n@login_required\ndef get_teams():\n    \"\"\"\n    Retrieves the teams associated with the current user.\n    \"\"\"\n    user_id = current_user.id\n    teams = Team.query.join(UserTeam).filter(UserTeam.user_id == user_id).all()\n    teams_data = []\n    for team in teams:\n        members_data = [\n            {\n                'id': member.id,\n                'team_id': member.team_id,\n                'user_id': member.user_id,\n                'name': member.user.firstName\n            }\n            for member in team.members\n        ]\n        team_dict = {\n            'id': team.id,\n            'name': team.name,\n            'members': members_data,\n            'projects': [project.to_dict() for project in team.projects],\n        }\n        teams_data.append(team_dict)\n    return jsonify(teams_data), 200\n\n\n@team_routes.route('/<int:id>/members', methods=['POST'])\n@login_required\ndef invite_team_member(id):\n    \"\"\"\n    Invites a user to join a team.\n    \"\"\"\n    team = Team.query.get(id)\n    if not team:\n        return {\"message\": \"Team not found\", \"statusCode\": 404}, 404\n\n    if current_user.id != team.owner_id:\n        return {\"message\": \"Unauthorized\", \"statusCode\": 403}, 403\n\n    data = request.get_json()\n    email = data.get('email')\n\n    if not email:\n        return {\"message\": \"Invalid request body\", \"statusCode\": 400}, 400\n\n    user = User.query.filter(User.email == email).first()\n    if not user:\n        return {\"message\": \"User not found\", \"statusCode\": 404}, 404\n\n    if user in team.members:\n        return {\"message\": \"User is already a member of the team\", \"statusCode\": 400}, 400\n\n    user_team = UserTeam(user_id=user.id, team_id=team.id)\n    db.session.add(user_team)\n    db.session.commit()\n\n    return jsonify(user_team.to_dict()), 201\n\n\n@team_routes.route('/', methods=['POST'])\n@login_required\ndef create_team():\n    data = request.get_json()\n    name = data.get('name')\n    members_emails = data.get('members')\n\n    if not name or not members_emails:\n        return {\"message\": \"Invalid request body\", \"statusCode\": 400}, 400\n\n    new_team = Team(name=name, owner_id=current_user.id, created_at=datetime.utcnow(), updated_at=datetime.utcnow())\n    db.session.add(new_team)\n    db.session.flush()\n\n    # Add owner as a member of the team\n    owner_user_team = UserTeam(user_id=current_user.id, team_id=new_team.id)\n    db.session.add(owner_user_team)\n\n    # Add members to the team\n    for email in members_emails:\n        user = find_user_by_email(email)\n        if user:\n            new_user_team = UserTeam(user_id=user.id, team_id=new_team.id)\n            db.session.add(new_user_team)\n        else:\n            return {\"message\": f\"User with email {email} not found\", \"statusCode\": 400}, 400\n\n    db.session.commit()\n\n    return jsonify(new_team.to_dict()), 201\n\n@team_routes.route('/<int:id>', methods=['GET'])\n@login_required\ndef retrieve_team(id):\n    team = Team.query.get(id)\n    if team:\n        team_dict = team.to_dict()\n        team_dict['members'] = [user_team.user.to_dict() for user_team in team.members]\n        team_dict['projects'] = [project.to_dict() for project in team.projects]\n        return jsonify(team_dict)\n    else:\n        return {\"message\": \"Team not found\", \"statusCode\": 404}, 404\n\n@team_routes.route('/<int:id>', methods=['PUT'])\n@login_required\ndef update_team(id):\n    data = request.get_json()\n    name = data.get('name')\n    new_member_email = data.get('new_member')\n\n    team = Team.query.get(id)\n    if not team:\n        return {\"message\": \"Team not found\", \"statusCode\": 404}, 404\n\n    # Check if the current user is the owner of the project\n    if current_user.id != team.owner_id:\n        return {\"message\": \"Unauthorized\", \"statusCode\": 403}, 403\n\n    if not name and not new_member_email:\n        return {\"message\": \"Invalid request body\", \"statusCode\": 400}, 400\n\n    if name:\n        team.name = name\n\n    if new_member_email:\n        new_member = find_user_by_email(new_member_email)\n        if new_member:\n            new_user_team = UserTeam(user_id=new_member.id, team_id=team.id)\n            db.session.add(new_user_team)\n        else:\n            return {\"message\": \"User not found\", \"statusCode\": 404}, 404\n\n    team.updated_at = datetime.utcnow()\n    db.session.commit()\n\n    return jsonify(team.to_dict()), 200\n\n@team_routes.route('/<int:id>', methods=['DELETE'])\n@login_required\ndef delete_team(id):\n    team = Team.query.get(id)\n    if team:\n        if team.owner_id != current_user.id:\n            return {\"message\": \"Unauthorized\", \"statusCode\": 403}, 403\n        db.session.delete(team)\n        db.session.commit()\n        return {\"message\": \"Successfully Deleted.\"}, 204\n    else:\n        return {\"message\": \"Team not found\", \"statusCode\": 404}, 404\n", "text_preview": "from flask import Blueprint, request, jsonify from flask_login import login_required, current_user from app.models import db, Team, User, Project, UserTeam from datetime import datetime team_routes = Blueprint('teams', __name__) def find_us", "input_sha256": "803d9e434983cc0272cb865fb73eb17a9b0b908956a76d163ed9b10e31f4a956", "text_sha256": "0636911604ebb3c09671557a7561097950b99e935054f818f953542e815bd180", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12097, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7768062770521124, "noise_severity": 0.013302901190403124, "missing_content_severity": 0.013698233476085878, "factual_density": 3.3640332999170037, "pretraining_utility": 0.651232835413184, "math_density": 0.0288089997198037, "code_density": 3.854123748557407, "reasoning_density": 0.7560807055740529, "education_level": 2.1766333274482124, "creative_writing_quality": -0.18174773410863038}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.010902876711018306, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9966326828486822, "secondary": 0.9678451180987209, "tertiary": 0.9792220329276097, "structured_records": 0.05587483630228917, "procedural_steps": 0.012539983356334456, "worked_solution": 0.009501442738746438, "contextual_dialogue": 6.631634318612246e-06, "content_quality": 0.5360751634698654, "noise_severity": 0.9889142490079974, "missing_content_severity": 0.9885848054365951, "factual_density": 0.4700277499308364, "pretraining_utility": 0.5426603184271639, "math_density": 0.9759925002334969, "code_density": 0.8784364571311725, "reasoning_density": 0.3699327453549559, "education_level": 0.7571555470640052, "creative_writing_quality": 0.9995899863965918, "primary_topic": 0.9963248155896871, "secondary_topic": 0.9594235309077529}, "probability_margin": {"primary": 0.9954483884774945, "secondary": 0.9518271181509087, "tertiary": 0.9687291533333174, "education_level": 0.6162201156993637, "creative_writing_quality": 0.9994756408663876, "primary_topic": 0.9951123733912126, "secondary_topic": 0.9466841159155779}, "raw_probabilities": {"primary": {"Academic Writing": 2.790525550123212e-05, "Content Listing": 1.8484572300937355e-05, "Creative Writing": 1.3344509691913634e-05, "Customer Support Page": 7.298198692081355e-06, "Q&A Forum": 9.900496951754035e-06, "Discussion Thread": 6.174055708508713e-06, "Comment Section": 1.0437869018999963e-05, "Missing Content": 1.940400389028604e-05, "Knowledge Article": 7.620746579676669e-05, "Legal Notices": 1.854738250111278e-05, "News Article": 3.46407129493889e-06, "Organizational Announcement": 8.251417452733034e-06, "Nonfiction Writing": 9.566112718490396e-06, "Organizational About Page": 9.524675030920923e-06, "Personal About Page": 9.74716199781071e-06, "Personal Blog": 6.222710866426296e-06, "Product Page": 6.1454122827189175e-06, "Spam / Ads": 8.125393234083399e-06, "Structured Data": 0.0013245992524926562, "Technical Writing": 0.9967729877299871, "Transcript / Interview": 1.1943002133741157e-05, "Tutorial / How-To Guide": 0.00048030342014867725, "User Reviews": 5.0671980367684304e-06, "Other / Unknown": 0.0011363486322694802}, "secondary": {"Academic Writing": 0.00014987439612018175, "Content Listing": 0.00016246850268153206, "Creative Writing": 0.00011657361925868979, "Customer Support Page": 9.601662213704294e-05, "Q&A Forum": 5.436289118299665e-05, "Discussion Thread": 4.6879565385100285e-05, "Comment Section": 0.00012731610666392892, "Missing Content": 0.00021387490017162384, "Knowledge Article": 0.0009727739764617883, "Legal Notices": 0.0001324708423758942, "News Article": 7.309304919257897e-05, "Organizational Announcement": 6.795649967450559e-05, "Nonfiction Writing": 6.83953345934039e-05, "Organizational About Page": 8.208007576240916e-05, "Personal About Page": 6.60670741713474e-05, "Personal Blog": 6.535836261497478e-05, "Product Page": 4.598002334327007e-05, "Spam / Ads": 6.582729816056668e-05, "Structured Data": 0.01735778669369876, "Technical Writing": 0.009239210975121507, "Transcript / Interview": 0.00010083661762842439, "Tutorial / How-To Guide": 0.0014770342808492693, "User Reviews": 3.2857448142586016e-05, "NONE": 0.9691849048446075}, "tertiary": {"Academic Writing": 7.899039419921693e-05, "Content Listing": 0.00014012542614720618, "Creative Writing": 7.628531254668662e-05, "Customer Support Page": 5.673670138242479e-05, "Q&A Forum": 2.9829261184657637e-05, "Discussion Thread": 2.523769869006481e-05, "Comment Section": 0.00010868640169768115, "Missing Content": 0.00018227203408125028, "Knowledge Article": 0.0007756537419425673, "Legal Notices": 0.0001266264606463365, "News Article": 4.220125240484681e-05, "Organizational Announcement": 3.5821134879790064e-05, "Nonfiction Writing": 5.1283448260016464e-05, "Organizational About Page": 6.84518204497395e-05, "Personal About Page": 3.826914360873731e-05, "Personal Blog": 3.807799874495326e-05, "Product Page": 3.4148021074359454e-05, "Spam / Ads": 4.535206213991696e-05, "Structured Data": 0.011358628222308488, "Technical Writing": 0.004958394912803423, "Transcript / Interview": 5.369733053410192e-05, "Tutorial / How-To Guide": 0.0015655656396884234, "User Reviews": 2.188402495915412e-05, "NONE": 0.9800877815556259}, "structured_records": {"false": 0.9441251636977108, "true": 0.05587483630228917}, "procedural_steps": {"false": 0.9874600166436656, "true": 0.012539983356334456}, "worked_solution": {"false": 0.9904985572612536, "true": 0.009501442738746438}, "contextual_dialogue": {"false": 0.9999933683656814, "true": 6.631634318612246e-06}, "content_quality": {"-2": 0.007641528141376765, "-1": 0.03846212808694024, "0": 0.29010292279401384, "1": 0.4970353805335324, "2": 0.16675804044413683}, "noise_severity": {"0": 0.9887124689941537, "1": 0.010014867314588737, "2": 0.0007590124959882443, "3": 0.00028459589723966837, "4": 0.0002290552980297234}, "missing_content_severity": {"0": 0.9925813701687902, "1": 0.0026965786395801872, "2": 0.0034088890809365236, "3": 0.0010687717681403287, "4": 0.00024439034255291516}, "factual_density": {"0": 0.0032730153782958888, "1": 0.008645121123578969, "2": 0.1044164039062803, "3": 0.3881064673865152, "4": 0.4955589922053295}, "pretraining_utility": {"-2": 0.0068173757123258325, "-1": 0.049415812554912696, "0": 0.3295036389903068, "1": 0.514242946092161, "2": 0.10002022665029368}, "math_density": {"0": 0.98215970646462, "1": 0.012941321279564435, "2": 0.0014506057886274377, "3": 0.0008269990057682731, "4": 0.0026213674614198926}, "code_density": {"0": 0.00028071412766553396, "1": 0.0005388788518750674, "2": 0.014712241058403824, "3": 0.11371227625949802, "4": 0.8707558897025576}, "reasoning_density": {"0": 0.62445591354888, "1": 0.10930185845563574, "2": 0.18155951916146154, "3": 0.05507102654059694, "4": 0.02961168229342581}, "education_level": {"none": 0.0008569590496978466, "0": 0.0012088045025818522, "1": 0.013433384003283493, "2": 0.7976296225533377, "3": 0.18140950685397395, "4": 0.0054617230371252}, "creative_writing_quality": {"not_applicable": 0.9996583219971599, "-2": 3.2295283261293465e-05, "-1": 6.982173965460374e-05, "0": 0.0001826811307722076, "1": 4.1446594937556904e-05, "2": 1.5433254214349755e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.1088467266348642e-05, "Art & Design": 2.3521858351700332e-05, "Law & Crime": 1.4432463531342731e-05, "Education & Careers": 3.917284456706354e-05, "Entertainment & Media": 1.3700049553220823e-05, "Fashion & Beauty": 9.054109058096387e-06, "Business, Economics & Finance": 0.0013537954450250503, "Food & Dining": 1.5520585354043845e-05, "Games & Gaming": 5.32798662069167e-05, "Electronics & Hardware": 3.3234878372301307e-05, "Health, Healthcare & Wellness": 1.128078891036504e-05, "History & Geography": 1.5674629993744705e-05, "Home, Pets & Hobbies": 1.2023234175514054e-05, "Industry & Production": 1.4156830992677633e-05, "Language, Literature & Philosophy": 1.9242018165748792e-05, "Politics & Government": 1.2000947268512823e-05, "Religion & Spirituality": 1.578053748078248e-05, "Science, Mathematics & Engineering": 5.074953870437642e-05, "Relationships & Community": 0.0004731462379992989, "Software Usage & Applications": 0.0009306212559757737, "Programming & Software Engineering": 0.9964661688362376, "Sports & Fitness": 2.457523106753822e-05, "Vehicles & Transportation": 9.259950519914477e-06, "Travel & Tourism": 1.9728061620812722e-05, "Other / Uncovered Subject": 0.0003255485897701671, "Unknown / Insufficient Content": 2.3242743831251237e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0003191100648103993, "Art & Design": 0.00026069916878329857, "Law & Crime": 0.0002756431273328085, "Education & Careers": 0.00027396572838473305, "Entertainment & Media": 0.0001889293819403535, "Fashion & Beauty": 0.000109454685220194, "Business, Economics & Finance": 0.014300048418799987, "Food & Dining": 9.274467140527737e-05, "Games & Gaming": 0.00033743237578296917, "Electronics & Hardware": 0.0002003223770418103, "Health, Healthcare & Wellness": 9.83344457634168e-05, "History & Geography": 0.00011114256703741625, "Home, Pets & Hobbies": 0.00010347076870667672, "Industry & Production": 9.215263935484443e-05, "Language, Literature & Philosophy": 0.00010532018338028261, "Politics & Government": 0.00012222313678647464, "Religion & Spirituality": 0.00014338994847378972, "Science, Mathematics & Engineering": 0.00034276005468506323, "Relationships & Community": 0.0009311854471126406, "Software Usage & Applications": 0.007161622133180416, "Programming & Software Engineering": 0.012381846382864542, "Sports & Fitness": 0.000119197979404795, "Vehicles & Transportation": 5.021687291656299e-05, "Travel & Tourism": 0.0001569409816931331, "Other / Uncovered Subject": 0.0007376821247603015, "NONE": 0.9609841643343778}}, "probabilities": {"primary": {"Academic Writing": 2.7905255501232115e-05, "Content Listing": 1.848457230093735e-05, "Creative Writing": 1.334450969191363e-05, "Customer Support Page": 7.298198692081353e-06, "Q&A Forum": 9.900496951754033e-06, "Discussion Thread": 6.174055708508712e-06, "Comment Section": 1.0437869018999961e-05, "Missing Content": 1.9404003890286035e-05, "Knowledge Article": 7.620746579676667e-05, "Legal Notices": 1.8547382501112778e-05, "News Article": 3.4640712949388893e-06, "Organizational Announcement": 8.251417452733032e-06, "Nonfiction Writing": 9.566112718490394e-06, "Organizational About Page": 9.524675030920921e-06, "Personal About Page": 9.747161997810708e-06, "Personal Blog": 6.222710866426295e-06, "Product Page": 6.145412282718916e-06, "Spam / Ads": 8.125393234083397e-06, "Structured Data": 0.001324599252492656, "Technical Writing": 0.9967729877299869, "Transcript / Interview": 1.1943002133741154e-05, "Tutorial / How-To Guide": 0.00048030342014867715, "User Reviews": 5.06719803676843e-06, "Other / Unknown": 0.00113634863226948}, "secondary": {"Academic Writing": 0.00014987439612018178, "Content Listing": 0.0001624685026815321, "Creative Writing": 0.0001165736192586898, "Customer Support Page": 9.601662213704296e-05, "Q&A Forum": 5.436289118299666e-05, "Discussion Thread": 4.687956538510029e-05, "Comment Section": 0.00012731610666392895, "Missing Content": 0.00021387490017162387, "Knowledge Article": 0.0009727739764617884, "Legal Notices": 0.00013247084237589423, "News Article": 7.309304919257898e-05, "Organizational Announcement": 6.79564996745056e-05, "Nonfiction Writing": 6.839533459340391e-05, "Organizational About Page": 8.208007576240918e-05, "Personal About Page": 6.606707417134741e-05, "Personal Blog": 6.53583626149748e-05, "Product Page": 4.5980023343270076e-05, "Spam / Ads": 6.582729816056669e-05, "Structured Data": 0.017357786693698765, "Technical Writing": 0.009239210975121509, "Transcript / Interview": 0.0001008366176284244, "Tutorial / How-To Guide": 0.0014770342808492695, "User Reviews": 3.285744814258602e-05, "NONE": 0.9691849048446076}, "tertiary": {"Academic Writing": 7.899039419921693e-05, "Content Listing": 0.00014012542614720618, "Creative Writing": 7.628531254668662e-05, "Customer Support Page": 5.673670138242479e-05, "Q&A Forum": 2.9829261184657637e-05, "Discussion Thread": 2.523769869006481e-05, "Comment Section": 0.00010868640169768115, "Missing Content": 0.00018227203408125028, "Knowledge Article": 0.0007756537419425673, "Legal Notices": 0.0001266264606463365, "News Article": 4.220125240484681e-05, "Organizational Announcement": 3.5821134879790064e-05, "Nonfiction Writing": 5.1283448260016464e-05, "Organizational About Page": 6.84518204497395e-05, "Personal About Page": 3.826914360873731e-05, "Personal Blog": 3.807799874495326e-05, "Product Page": 3.4148021074359454e-05, "Spam / Ads": 4.535206213991696e-05, "Structured Data": 0.011358628222308488, "Technical Writing": 0.004958394912803423, "Transcript / Interview": 5.369733053410192e-05, "Tutorial / How-To Guide": 0.0015655656396884234, "User Reviews": 2.188402495915412e-05, "NONE": 0.9800877815556259}, "structured_records": {"false": 0.9441251636977108, "true": 0.05587483630228917}, "procedural_steps": {"false": 0.9874600166436656, "true": 0.012539983356334456}, "worked_solution": {"false": 0.9904985572612536, "true": 0.009501442738746438}, "contextual_dialogue": {"false": 0.9999933683656814, "true": 6.631634318612246e-06}, "content_quality": {"-2": 0.007641528141376765, "-1": 0.03846212808694024, "0": 0.29010292279401384, "1": 0.4970353805335324, "2": 0.16675804044413683}, "noise_severity": {"0": 0.9887124689941537, "1": 0.010014867314588737, "2": 0.0007590124959882443, "3": 0.00028459589723966837, "4": 0.0002290552980297234}, "missing_content_severity": {"0": 0.99258137016879, "1": 0.002696578639580187, "2": 0.0034088890809365227, "3": 0.0010687717681403285, "4": 0.0002443903425529151}, "factual_density": {"0": 0.003273015378295889, "1": 0.00864512112357897, "2": 0.10441640390628032, "3": 0.3881064673865153, "4": 0.49555899220532956}, "pretraining_utility": {"-2": 0.0068173757123258325, "-1": 0.049415812554912696, "0": 0.3295036389903068, "1": 0.514242946092161, "2": 0.10002022665029368}, "math_density": {"0": 0.98215970646462, "1": 0.012941321279564435, "2": 0.0014506057886274377, "3": 0.0008269990057682731, "4": 0.0026213674614198926}, "code_density": {"0": 0.00028071412766553396, "1": 0.0005388788518750674, "2": 0.014712241058403824, "3": 0.11371227625949802, "4": 0.8707558897025576}, "reasoning_density": {"0": 0.62445591354888, "1": 0.10930185845563574, "2": 0.18155951916146154, "3": 0.05507102654059694, "4": 0.02961168229342581}, "education_level": {"none": 0.0008569590496978466, "0": 0.0012088045025818522, "1": 0.013433384003283493, "2": 0.7976296225533377, "3": 0.18140950685397395, "4": 0.0054617230371252}, "creative_writing_quality": {"not_applicable": 0.99965832199716, "-2": 3.229528326129347e-05, "-1": 6.982173965460375e-05, "0": 0.00018268113077220762, "1": 4.144659493755691e-05, "2": 1.543325421434976e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.108846726634864e-05, "Art & Design": 2.3521858351700325e-05, "Law & Crime": 1.4432463531342728e-05, "Education & Careers": 3.917284456706353e-05, "Entertainment & Media": 1.370004955322082e-05, "Fashion & Beauty": 9.054109058096385e-06, "Business, Economics & Finance": 0.00135379544502505, "Food & Dining": 1.5520585354043842e-05, "Games & Gaming": 5.327986620691669e-05, "Electronics & Hardware": 3.32348783723013e-05, "Health, Healthcare & Wellness": 1.1280788910365038e-05, "History & Geography": 1.5674629993744702e-05, "Home, Pets & Hobbies": 1.2023234175514051e-05, "Industry & Production": 1.415683099267763e-05, "Language, Literature & Philosophy": 1.924201816574879e-05, "Politics & Government": 1.200094726851282e-05, "Religion & Spirituality": 1.5780537480782478e-05, "Science, Mathematics & Engineering": 5.074953870437641e-05, "Relationships & Community": 0.0004731462379992988, "Software Usage & Applications": 0.0009306212559757735, "Programming & Software Engineering": 0.9964661688362374, "Sports & Fitness": 2.4575231067538212e-05, "Vehicles & Transportation": 9.259950519914475e-06, "Travel & Tourism": 1.972806162081272e-05, "Other / Uncovered Subject": 0.00032554858977016705, "Unknown / Insufficient Content": 2.324274383125123e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0003191100648103993, "Art & Design": 0.00026069916878329857, "Law & Crime": 0.0002756431273328085, "Education & Careers": 0.00027396572838473305, "Entertainment & Media": 0.0001889293819403535, "Fashion & Beauty": 0.000109454685220194, "Business, Economics & Finance": 0.014300048418799987, "Food & Dining": 9.274467140527737e-05, "Games & Gaming": 0.00033743237578296917, "Electronics & Hardware": 0.0002003223770418103, "Health, Healthcare & Wellness": 9.83344457634168e-05, "History & Geography": 0.00011114256703741625, "Home, Pets & Hobbies": 0.00010347076870667672, "Industry & Production": 9.215263935484443e-05, "Language, Literature & Philosophy": 0.00010532018338028261, "Politics & Government": 0.00012222313678647464, "Religion & Spirituality": 0.00014338994847378972, "Science, Mathematics & Engineering": 0.00034276005468506323, "Relationships & Community": 0.0009311854471126406, "Software Usage & Applications": 0.007161622133180416, "Programming & Software Engineering": 0.012381846382864542, "Sports & Fitness": 0.000119197979404795, "Vehicles & Transportation": 5.021687291656299e-05, "Travel & Tourism": 0.0001569409816931331, "Other / Uncovered Subject": 0.0007376821247603015, "NONE": 0.9609841643343778}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9859, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9859, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:35:14.714114+00:00", "scoring_key_slot": "primary", "dataset_uid": "e0ff3ffc-4837-4b58-aade-e445680990d4", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "578e1e3015d115dd8805f7c0445f3da91d4432262037d33be8cefcb5fa5c9062", "source_metadata": {"uuid": "e0ff3ffc-4837-4b58-aade-e445680990d4", "repo_name": "MaxParks/AsanaClone", "relative_path": "app/api/team_routes.py", "category": "WEB", "algo_rel_score": 0.914341151714325, "quality_score": 6.206806659698486, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 365, "repo_created_at": "2023-05-18T00:03:34.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 2, "issues": 18, "pull_requests": 0}, "file_meta": {"content_id": "4d5fd26608836a464949f5af152115c005261e2c", "size_bytes": 5104, "file_timestamp": 1686691593, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "5a19bbf2a54312cf34cca26bb79f862e286ca86d"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 365, "repo_created_at": "2023-05-18T00:03:34.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 2, "issues": 18, "pull_requests": 0}, "file_meta": {"content_id": "4d5fd26608836a464949f5af152115c005261e2c", "size_bytes": 5104, "file_timestamp": 1686691593, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "5a19bbf2a54312cf34cca26bb79f862e286ca86d", "url": "https://github.com/MaxParks/AsanaClone/blob/5a19bbf2a54312cf34cca26bb79f862e286ca86d/app/api/team_routes.py"}, "gpt4o_tokens": 1219, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.7768062770521124, "noise_severity": 0.013302901190403124, "missing_content_severity": 0.013698233476085878, "factual_density": 3.3640332999170037, "pretraining_utility": 0.651232835413184, "math_density": 0.0288089997198037, "code_density": 3.854123748557407, "reasoning_density": 0.7560807055740529, "education_level": 2.1766333274482124, "creative_writing_quality": -0.18174773410863038}, "score_applicability": {"education_level": 0.9991430409503022, "creative_writing_quality": 0.00034167800284001153}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/apifabric/GolfShoes/blob/4185027f895e633682aec880b32d17af2f0829e4/database/models.py", "id": "python-0-f647897e981fb2b034c198c3b2b29afe", "text": "# coding: utf-8\nfrom sqlalchemy import DECIMAL, DateTime  # API Logic Server GenAI assist\nfrom sqlalchemy import Column, DateTime, Float, ForeignKey, Integer, String\nfrom sqlalchemy.orm import relationship\nfrom sqlalchemy.ext.declarative import declarative_base\n\n########################################################################################################################\n# Classes describing database for SqlAlchemy ORM, initially created by schema introspection.\n#\n# Alter this file per your database maintenance policy\n#    See https://apilogicserver.github.io/Docs/Project-Rebuild/#rebuilding\n#\n# Created:  October 31, 2024 18:40:08\n# Database: sqlite:////tmp/tmp.G9Pge8XdEz/GolfShoes/database/db.sqlite\n# Dialect:  sqlite\n#\n# mypy: ignore-errors\n########################################################################################################################\n \nfrom database.system.SAFRSBaseX import SAFRSBaseX\nfrom flask_login import UserMixin\nimport safrs, flask_sqlalchemy\nfrom safrs import jsonapi_attr\nfrom flask_sqlalchemy import SQLAlchemy\nfrom sqlalchemy.orm import relationship\nfrom sqlalchemy.orm import Mapped\nfrom sqlalchemy.sql.sqltypes import NullType\nfrom typing import List\n\ndb = SQLAlchemy() \nBase = declarative_base()  # type: flask_sqlalchemy.model.DefaultMeta\nmetadata = Base.metadata\n\n#NullType = db.String  # datatype fixup\n#TIMESTAMP= db.TIMESTAMP\n\nfrom sqlalchemy.dialects.sqlite import *\n\n\n\nclass Brand(SAFRSBaseX, Base):\n    \"\"\"\n    description: Brands of products\n    \"\"\"\n    __tablename__ = 'brands'\n    _s_collection_name = 'Brand'  # type: ignore\n    __bind_key__ = 'None'\n\n    id = Column(Integer, primary_key=True)\n    name = Column(String)\n    created_date = Column(DateTime)\n    created_by = Column(String)\n    updated_date = Column(DateTime)\n\n    # parent relationships (access parent)\n\n    # child relationships (access children)\n    ProductList : Mapped[List[\"Product\"]] = relationship(back_populates=\"brand\")\n\n\n\nclass Category(SAFRSBaseX, Base):\n    \"\"\"\n    description: Product categories\n    \"\"\"\n    __tablename__ = 'categories'\n    _s_collection_name = 'Category'  # type: ignore\n    __bind_key__ = 'None'\n\n    id = Column(Integer, primary_key=True)\n    name = Column(String)\n    created_date = Column(DateTime)\n    created_by = Column(String)\n    updated_date = Column(DateTime)\n\n    # parent relationships (access parent)\n\n    # child relationships (access children)\n    ProductList : Mapped[List[\"Product\"]] = relationship(back_populates=\"category\")\n\n\n\nclass Customer(SAFRSBaseX, Base):\n    \"\"\"\n    description: Stores details of customers\n    \"\"\"\n    __tablename__ = 'customers'\n    _s_collection_name = 'Customer'  # type: ignore\n    __bind_key__ = 'None'\n\n    id = Column(Integer, primary_key=True)\n    name = Column(String)\n    email = Column(String, unique=True)\n    created_date = Column(DateTime)\n    created_by = Column(String)\n    updated_date = Column(DateTime)\n\n    # parent relationships (access parent)\n\n    # child relationships (access children)\n    OrderList : Mapped[List[\"Order\"]] = relationship(back_populates=\"customer\")\n    AddressList : Mapped[List[\"Address\"]] = relationship(back_populates=\"customer\")\n    CartList : Mapped[List[\"Cart\"]] = relationship(back_populates=\"customer\")\n    ReviewList : Mapped[List[\"Review\"]] = relationship(back_populates=\"customer\")\n\n\n\nclass Order(SAFRSBaseX, Base):\n    \"\"\"\n    description: Represents an order placed by a customer\n    \"\"\"\n    __tablename__ = 'orders'\n    _s_collection_name = 'Order'  # type: ignore\n    __bind_key__ = 'None'\n\n    id = Column(Integer, primary_key=True)\n    customer_id = Column(ForeignKey('customers.id'))\n    total_amount = Column(Float)\n    status = Column(String)\n    payment_id = Column(ForeignKey('payments.id'))\n    created_date = Column(DateTime)\n    created_by = Column(String)\n    updated_date = Column(DateTime)\n\n    # parent relationships (access parent)\n    customer : Mapped[\"Customer\"] = relationship(back_populates=(\"OrderList\"))\n    payment : Mapped[\"Payment\"] = relationship(foreign_keys='[Order.payment_id]', back_populates=(\"OrderList\"))\n\n    # child relationships (access children)\n    PaymentList : Mapped[List[\"Payment\"]] = relationship(foreign_keys='[Payment.order_id]', back_populates=\"order\")\n    OrderDetailList : Mapped[List[\"OrderDetail\"]] = relationship(back_populates=\"order\")\n\n\n\nclass Payment(SAFRSBaseX, Base):\n    \"\"\"\n    description: Payment records for orders\n    \"\"\"\n    __tablename__ = 'payments'\n    _s_collection_name = 'Payment'  # type: ignore\n    __bind_key__ = 'None'\n\n    id = Column(Integer, primary_key=True)\n    order_id = Column(ForeignKey('orders.id'))\n    amount_paid = Column(Float)\n    payment_date = Column(DateTime)\n    created_date = Column(DateTime)\n    created_by = Column(String)\n    updated_date = Column(DateTime)\n\n    # parent relationships (access parent)\n    order : Mapped[\"Order\"] = relationship(foreign_keys='[Payment.order_id]', back_populates=(\"PaymentList\"))\n\n    # child relationships (access children)\n    OrderList : Mapped[List[\"Order\"]] = relationship(foreign_keys='[Order.payment_id]', back_populates=\"payment\")\n\n\n\nclass Address(SAFRSBaseX, Base):\n    \"\"\"\n    description: Addresses used by customers for orders\n    \"\"\"\n    __tablename__ = 'addresses'\n    _s_collection_name = 'Address'  # type: ignore\n    __bind_key__ = 'None'\n\n    id = Column(Integer, primary_key=True)\n    customer_id = Column(ForeignKey('customers.id'))\n    street = Column(String)\n    city = Column(String)\n    state = Column(String)\n    postal_code = Column(String)\n    created_date = Column(DateTime)\n    created_by = Column(String)\n    updated_date = Column(DateTime)\n\n    # parent relationships (access parent)\n    customer : Mapped[\"Customer\"] = relationship(back_populates=(\"AddressList\"))\n\n    # child relationships (access children)\n\n\n\nclass Cart(SAFRSBaseX, Base):\n    \"\"\"\n    description: Shopping cart details for customers\n    \"\"\"\n    __tablename__ = 'carts'\n    _s_collection_name = 'Cart'  # type: ignore\n    __bind_key__ = 'None'\n\n    id = Column(Integer, primary_key=True)\n    customer_id = Column(ForeignKey('customers.id'))\n    created_date = Column(DateTime)\n    created_by = Column(String)\n    updated_date = Column(DateTime)\n\n    # parent relationships (access parent)\n    customer : Mapped[\"Customer\"] = relationship(back_populates=(\"CartList\"))\n\n    # child relationships (access children)\n    CartItemList : Mapped[List[\"CartItem\"]] = relationship(back_populates=\"cart\")\n\n\n\nclass Product(SAFRSBaseX, Base):\n    \"\"\"\n    description: Stores details of men's golf shoes\n    \"\"\"\n    __tablename__ = 'products'\n    _s_collection_name = 'Product'  # type: ignore\n    __bind_key__ = 'None'\n\n    id = Column(Integer, primary_key=True)\n    name = Column(String)\n    price = Column(Float)\n    stock_quantity = Column(Integer)\n    category_id = Column(ForeignKey('categories.id'))\n    brand_id = Column(ForeignKey('brands.id'))\n    created_date = Column(DateTime)\n    created_by = Column(String)\n    updated_date = Column(DateTime)\n\n    # parent relationships (access parent)\n    brand : Mapped[\"Brand\"] = relationship(back_populates=(\"ProductList\"))\n    category : Mapped[\"Category\"] = relationship(back_populates=(\"ProductList\"))\n\n    # child relationships (access children)\n    CartItemList : Mapped[List[\"CartItem\"]] = relationship(back_populates=\"product\")\n    OrderDetailList : Mapped[List[\"OrderDetail\"]] = relationship(back_populates=\"product\")\n    PromotionList : Mapped[List[\"Promotion\"]] = relationship(back_populates=\"product\")\n    ReviewList : Mapped[List[\"Review\"]] = relationship(back_populates=\"product\")\n\n\n\nclass CartItem(SAFRSBaseX, Base):\n    \"\"\"\n    description: Items within a shopping cart\n    \"\"\"\n    __tablename__ = 'cart_items'\n    _s_collection_name = 'CartItem'  # type: ignore\n    __bind_key__ = 'None'\n\n    id = Column(Integer, primary_key=True)\n    cart_id = Column(ForeignKey('carts.id'))\n    product_id = Column(ForeignKey('products.id'))\n    quantity = Column(Integer)\n    created_date = Column(DateTime)\n    created_by = Column(String)\n    updated_date = Column(DateTime)\n\n    # parent relationships (access parent)\n    cart : Mapped[\"Cart\"] = relationship(back_populates=(\"CartItemList\"))\n    product : Mapped[\"Product\"] = relationship(back_populates=(\"CartItemList\"))\n\n    # child relationships (access children)\n\n\n\nclass OrderDetail(SAFRSBaseX, Base):\n    \"\"\"\n    description: Details of products ordered in an order\n    \"\"\"\n    __tablename__ = 'order_details'\n    _s_collection_name = 'OrderDetail'  # type: ignore\n    __bind_key__ = 'None'\n\n    id = Column(Integer, primary_key=True)\n    order_id = Column(ForeignKey('orders.id'))\n    product_id = Column(ForeignKey('products.id'))\n    quantity = Column(Integer)\n    unit_price = Column(Float)\n    amount = Column(Float)\n    created_date = Column(DateTime)\n    created_by = Column(String)\n    updated_date = Column(DateTime)\n\n    # parent relationships (access parent)\n    order : Mapped[\"Order\"] = relationship(back_populates=(\"OrderDetailList\"))\n    product : Mapped[\"Product\"] = relationship(back_populates=(\"OrderDetailList\"))\n\n    # child relationships (access children)\n\n\n\nclass Promotion(SAFRSBaseX, Base):\n    \"\"\"\n    description: Promotional offers on products\n    \"\"\"\n    __tablename__ = 'promotions'\n    _s_collection_name = 'Promotion'  # type: ignore\n    __bind_key__ = 'None'\n\n    id = Column(Integer, primary_key=True)\n    product_id = Column(ForeignKey('products.id'))\n    discount_percentage = Column(Float)\n    start_date = Column(DateTime)\n    end_date = Column(DateTime)\n    created_date = Column(DateTime)\n    created_by = Column(String)\n    updated_date = Column(DateTime)\n\n    # parent relationships (access parent)\n    product : Mapped[\"Product\"] = relationship(back_populates=(\"PromotionList\"))\n\n    # child relationships (access children)\n\n\n\nclass Review(SAFRSBaseX, Base):\n    \"\"\"\n    description: Customer reviews and ratings for products\n    \"\"\"\n    __tablename__ = 'reviews'\n    _s_collection_name = 'Review'  # type: ignore\n    __bind_key__ = 'None'\n\n    id = Column(Integer, primary_key=True)\n    product_id = Column(ForeignKey('products.id'))\n    customer_id = Column(ForeignKey('customers.id'))\n    rating = Column(Integer)\n    comment = Column(String)\n    created_date = Column(DateTime)\n    created_by = Column(String)\n    updated_date = Column(DateTime)\n\n    # parent relationships (access parent)\n    customer : Mapped[\"Customer\"] = relationship(back_populates=(\"ReviewList\"))\n    product : Mapped[\"Product\"] = relationship(back_populates=(\"ReviewList\"))\n\n    # child relationships (access children)\n", "text_preview": "# coding: utf-8 from sqlalchemy import DECIMAL, DateTime # API Logic Server GenAI assist from sqlalchemy import Column, DateTime, Float, ForeignKey, Integer, String from sqlalchemy.orm import relationship from sqlalchemy.ext.declarative imp", "input_sha256": "7fec6c8c45409b84d5f2217245e65e52a2c5da7ccf5298f68ceba46f911ccb2b", "text_sha256": "f647897e981fb2b034c198c3b2b29afe5461f5f9a64d72343e27f5fc5d2aa83e", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12098, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9401094704088241, "noise_severity": 0.505735847182155, "missing_content_severity": 0.01587812913053131, "factual_density": 3.40738898984053, "pretraining_utility": 0.25431481382360605, "math_density": 0.026538284825008075, "code_density": 3.860723897329151, "reasoning_density": 0.01049126023867705, "education_level": 2.1890350569809414, "creative_writing_quality": -0.4202979501448271}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.03451858747524709, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9900649303014935, "secondary": 0.8948052108622385, "tertiary": 0.9266878179364886, "structured_records": 0.3997239959778005, "procedural_steps": 0.00033414460647215833, "worked_solution": 0.000152800462529755, "contextual_dialogue": 6.158247648213127e-06, "content_quality": 0.5652900464292604, "noise_severity": 0.5785534606815375, "missing_content_severity": 0.9867682257245572, "factual_density": 0.5061574915337749, "pretraining_utility": 0.49250310509346706, "math_density": 0.9778847626458266, "code_density": 0.8839365811076259, "reasoning_density": 0.9912572831344358, "education_level": 0.7437794107208957, "creative_writing_quality": 0.9995633263914914, "primary_topic": 0.9511286653463069, "secondary_topic": 0.42932600301683943}, "probability_margin": {"primary": 0.9822205864608314, "secondary": 0.8309163044178401, "tertiary": 0.8959890570059101, "education_level": 0.6040152881050344, "creative_writing_quality": 0.9994833133015817, "primary_topic": 0.9274137707607087, "secondary_topic": 0.16600126150960898}, "raw_probabilities": {"primary": {"Academic Writing": 3.2021579283377835e-05, "Content Listing": 3.315994400068091e-05, "Creative Writing": 1.6235842524918843e-05, "Customer Support Page": 9.913765042309693e-06, "Q&A Forum": 1.020713113254041e-05, "Discussion Thread": 8.41666045234444e-06, "Comment Section": 1.5997472880974483e-05, "Missing Content": 2.0951644143784288e-05, "Knowledge Article": 6.165567085283866e-05, "Legal Notices": 2.4520394435107205e-05, "News Article": 5.302813635010164e-06, "Organizational Announcement": 1.2091013649721524e-05, "Nonfiction Writing": 1.0039032575700831e-05, "Organizational About Page": 1.5663894915943033e-05, "Personal About Page": 1.0920539480414577e-05, "Personal Blog": 9.914322873686119e-06, "Product Page": 2.222990357602357e-05, "Spam / Ads": 1.2536835272193576e-05, "Structured Data": 0.0082583050780998, "Technical Writing": 0.9904788915389312, "Transcript / Interview": 1.4813922913412111e-05, "Tutorial / How-To Guide": 7.223396990072696e-05, "User Reviews": 7.712384969203417e-06, "Other / Unknown": 0.0008362646444580633}, "secondary": {"Academic Writing": 0.0003968819776209133, "Content Listing": 0.0005725279779967799, "Creative Writing": 0.0003658037311303704, "Customer Support Page": 0.0003263672178269661, "Q&A Forum": 0.0001485297891216842, "Discussion Thread": 0.00014929730763304743, "Comment Section": 0.0004579647158475106, "Missing Content": 0.0007766171250403913, "Knowledge Article": 0.001896909856215669, "Legal Notices": 0.0006076879320244036, "News Article": 0.00020193202559645493, "Organizational Announcement": 0.0002886097673289451, "Nonfiction Writing": 0.0002503807563969328, "Organizational About Page": 0.00028288345060867683, "Personal About Page": 0.0001818446668893995, "Personal Blog": 0.00020966552676459933, "Product Page": 0.0002138107998951413, "Spam / Ads": 0.000315669745102428, "Structured Data": 0.06827202265847175, "Technical Writing": 0.02273416249314318, "Transcript / Interview": 0.00025289778567153556, "Tutorial / How-To Guide": 0.001803762981107586, "User Reviews": 0.00010544263625369187, "NONE": 0.8991883270763119}, "tertiary": {"Academic Writing": 0.000362854695995087, "Content Listing": 0.0004921658989403231, "Creative Writing": 0.000304290343282411, "Customer Support Page": 0.00045311572126015623, "Q&A Forum": 0.0001157047216023301, "Discussion Thread": 0.00010179374686760581, "Comment Section": 0.0007106837011744522, "Missing Content": 0.0008438451039975473, "Knowledge Article": 0.003911135853129089, "Legal Notices": 0.001522400992184209, "News Article": 0.00018140234760865111, "Organizational Announcement": 0.0003255075442161676, "Nonfiction Writing": 0.00026734746172246503, "Organizational About Page": 0.0002904798153559116, "Personal About Page": 0.00014615369129493817, "Personal Blog": 0.000161782634684537, "Product Page": 0.0002562828826092454, "Spam / Ads": 0.0005890319722521531, "Structured Data": 0.03375343518322466, "Technical Writing": 0.018137485449029576, "Transcript / Interview": 0.00019156581106276777, "Tutorial / How-To Guide": 0.007047406895219516, "User Reviews": 9.163534415135429e-05, "NONE": 0.9297424921891349}, "structured_records": {"false": 0.6002760040221995, "true": 0.3997239959778005}, "procedural_steps": {"false": 0.9996658553935278, "true": 0.00033414460647215833}, "worked_solution": {"false": 0.9998471995374703, "true": 0.000152800462529755}, "contextual_dialogue": {"false": 0.9999938417523518, "true": 6.158247648213127e-06}, "content_quality": {"-2": 0.007218714519538875, "-1": 0.027210622103662773, "0": 0.21469384917208956, "1": 0.5199961068578529, "2": 0.23088070734685578}, "noise_severity": {"0": 0.5663917917284473, "1": 0.36933365458842604, "2": 0.05854779867481426, "3": 0.0036004247891494473, "4": 0.0021263302191630523}, "missing_content_severity": {"0": 0.990427756021351, "1": 0.0050922205414272595, "2": 0.002962816886472349, "3": 0.001208551386838446, "4": 0.000308655163911003}, "factual_density": {"0": 0.0018901674328621074, "1": 0.004003360545949862, "2": 0.06649019251874887, "3": 0.4400598737526744, "4": 0.4875564057497648}, "pretraining_utility": {"-2": 0.011822733290462673, "-1": 0.1536952634511914, "0": 0.4455848838623765, "1": 0.346138694936216, "2": 0.04275842445975335}, "math_density": {"0": 0.9854248202437326, "1": 0.009100626539708861, "2": 0.0016867965469425266, "3": 0.0010869614870497764, "4": 0.0027007951825662063}, "code_density": {"0": 0.0002991361580496908, "1": 0.0005153071331819047, "2": 0.01163217247902106, "3": 0.11326929168106244, "4": 0.8742840925486849}, "reasoning_density": {"0": 0.9919904784370225, "1": 0.006118654577227861, "2": 0.0014465765219133983, "3": 0.0002977092377233219, "4": 0.00014658122611310733}, "education_level": {"none": 0.0009459126072492469, "0": 0.0013837985338528425, "1": 0.016094498538518083, "2": 0.786482842267413, "3": 0.18246755416237867, "4": 0.01262539389058818}, "creative_writing_quality": {"not_applicable": 0.9996361053262428, "-2": 6.294652131112472e-05, "-1": 9.670535474205416e-05, "0": 0.0001527920246611089, "1": 3.324733417003578e-05, "2": 1.8203438872770318e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 8.246198684792241e-05, "Art & Design": 0.00017968496862378315, "Law & Crime": 0.00011218129376732026, "Education & Careers": 0.00011024682484037085, "Entertainment & Media": 0.00012365164343047712, "Fashion & Beauty": 0.02559456130304792, "Business, Economics & Finance": 0.008954357782199903, "Food & Dining": 0.00017512404659462974, "Games & Gaming": 0.0002942452347919001, "Electronics & Hardware": 0.00017783249026316551, "Health, Healthcare & Wellness": 9.207009826296447e-05, "History & Geography": 9.267507728115508e-05, "Home, Pets & Hobbies": 0.0015764990677104235, "Industry & Production": 0.00023807955204253076, "Language, Literature & Philosophy": 0.00010071394814670061, "Politics & Government": 5.2342073215292595e-05, "Religion & Spirituality": 9.500073465107393e-05, "Science, Mathematics & Engineering": 0.00026158299106820326, "Relationships & Community": 7.031519919039613e-05, "Software Usage & Applications": 0.0007049030585355878, "Programming & Software Engineering": 0.9530083320637567, "Sports & Fitness": 0.005486828237539729, "Vehicles & Transportation": 9.431754406029699e-05, "Travel & Tourism": 0.00010280275391072654, "Other / Uncovered Subject": 0.0020283558467262077, "Unknown / Insufficient Content": 0.00019083417949441426}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010276888392004141, "Art & Design": 0.0011439021399464848, "Law & Crime": 0.0009909943978441676, "Education & Careers": 0.0006526752198640144, "Entertainment & Media": 0.001113440149954242, "Fashion & Beauty": 0.2852737413911982, "Business, Economics & Finance": 0.12700095029845762, "Food & Dining": 0.0005874561613178925, "Games & Gaming": 0.0009834108958219879, "Electronics & Hardware": 0.0008077068200256425, "Health, Healthcare & Wellness": 0.0007177527898244403, "History & Geography": 0.0004844441038886802, "Home, Pets & Hobbies": 0.002983573327901177, "Industry & Production": 0.0008652084914598925, "Language, Literature & Philosophy": 0.0004036687746151274, "Politics & Government": 0.00030359063037819757, "Religion & Spirituality": 0.0006669774116248602, "Science, Mathematics & Engineering": 0.000947114355717387, "Relationships & Community": 0.00034030301098492756, "Software Usage & Applications": 0.002316524542182606, "Programming & Software Engineering": 0.06091329446498141, "Sports & Fitness": 0.05416960436098542, "Vehicles & Transportation": 0.0003901327211159976, "Travel & Tourism": 0.0005607243420105532, "Other / Uncovered Subject": 0.0030801174578915583, "NONE": 0.4512750029008072}}, "probabilities": {"primary": {"Academic Writing": 3.2021579283377835e-05, "Content Listing": 3.315994400068091e-05, "Creative Writing": 1.6235842524918843e-05, "Customer Support Page": 9.913765042309693e-06, "Q&A Forum": 1.020713113254041e-05, "Discussion Thread": 8.41666045234444e-06, "Comment Section": 1.5997472880974483e-05, "Missing Content": 2.0951644143784288e-05, "Knowledge Article": 6.165567085283866e-05, "Legal Notices": 2.4520394435107205e-05, "News Article": 5.302813635010164e-06, "Organizational Announcement": 1.2091013649721524e-05, "Nonfiction Writing": 1.0039032575700831e-05, "Organizational About Page": 1.5663894915943033e-05, "Personal About Page": 1.0920539480414577e-05, "Personal Blog": 9.914322873686119e-06, "Product Page": 2.222990357602357e-05, "Spam / Ads": 1.2536835272193576e-05, "Structured Data": 0.0082583050780998, "Technical Writing": 0.9904788915389312, "Transcript / Interview": 1.4813922913412111e-05, "Tutorial / How-To Guide": 7.223396990072696e-05, "User Reviews": 7.712384969203417e-06, "Other / Unknown": 0.0008362646444580633}, "secondary": {"Academic Writing": 0.0003968819776209133, "Content Listing": 0.0005725279779967799, "Creative Writing": 0.0003658037311303704, "Customer Support Page": 0.0003263672178269661, "Q&A Forum": 0.0001485297891216842, "Discussion Thread": 0.00014929730763304743, "Comment Section": 0.0004579647158475106, "Missing Content": 0.0007766171250403913, "Knowledge Article": 0.001896909856215669, "Legal Notices": 0.0006076879320244036, "News Article": 0.00020193202559645493, "Organizational Announcement": 0.0002886097673289451, "Nonfiction Writing": 0.0002503807563969328, "Organizational About Page": 0.00028288345060867683, "Personal About Page": 0.0001818446668893995, "Personal Blog": 0.00020966552676459933, "Product Page": 0.0002138107998951413, "Spam / Ads": 0.000315669745102428, "Structured Data": 0.06827202265847175, "Technical Writing": 0.02273416249314318, "Transcript / Interview": 0.00025289778567153556, "Tutorial / How-To Guide": 0.001803762981107586, "User Reviews": 0.00010544263625369187, "NONE": 0.8991883270763119}, "tertiary": {"Academic Writing": 0.000362854695995087, "Content Listing": 0.0004921658989403231, "Creative Writing": 0.000304290343282411, "Customer Support Page": 0.00045311572126015623, "Q&A Forum": 0.0001157047216023301, "Discussion Thread": 0.00010179374686760581, "Comment Section": 0.0007106837011744522, "Missing Content": 0.0008438451039975473, "Knowledge Article": 0.003911135853129089, "Legal Notices": 0.001522400992184209, "News Article": 0.00018140234760865111, "Organizational Announcement": 0.0003255075442161676, "Nonfiction Writing": 0.00026734746172246503, "Organizational About Page": 0.0002904798153559116, "Personal About Page": 0.00014615369129493817, "Personal Blog": 0.000161782634684537, "Product Page": 0.0002562828826092454, "Spam / Ads": 0.0005890319722521531, "Structured Data": 0.03375343518322466, "Technical Writing": 0.018137485449029576, "Transcript / Interview": 0.00019156581106276777, "Tutorial / How-To Guide": 0.007047406895219516, "User Reviews": 9.163534415135429e-05, "NONE": 0.9297424921891349}, "structured_records": {"false": 0.6002760040221995, "true": 0.3997239959778005}, "procedural_steps": {"false": 0.9996658553935278, "true": 0.00033414460647215833}, "worked_solution": {"false": 0.9998471995374703, "true": 0.000152800462529755}, "contextual_dialogue": {"false": 0.9999938417523518, "true": 6.158247648213127e-06}, "content_quality": {"-2": 0.007218714519538876, "-1": 0.027210622103662777, "0": 0.21469384917208958, "1": 0.519996106857853, "2": 0.2308807073468558}, "noise_severity": {"0": 0.5663917917284472, "1": 0.369333654588426, "2": 0.058547798674814244, "3": 0.0036004247891494464, "4": 0.002126330219163052}, "missing_content_severity": {"0": 0.990427756021351, "1": 0.0050922205414272595, "2": 0.002962816886472349, "3": 0.001208551386838446, "4": 0.000308655163911003}, "factual_density": {"0": 0.0018901674328621074, "1": 0.004003360545949862, "2": 0.06649019251874887, "3": 0.4400598737526744, "4": 0.4875564057497648}, "pretraining_utility": {"-2": 0.011822733290462675, "-1": 0.15369526345119142, "0": 0.44558488386237655, "1": 0.3461386949362161, "2": 0.04275842445975336}, "math_density": {"0": 0.9854248202437327, "1": 0.009100626539708863, "2": 0.0016867965469425268, "3": 0.0010869614870497766, "4": 0.0027007951825662067}, "code_density": {"0": 0.0002991361580496908, "1": 0.0005153071331819047, "2": 0.01163217247902106, "3": 0.11326929168106244, "4": 0.8742840925486849}, "reasoning_density": {"0": 0.9919904784370223, "1": 0.0061186545772278595, "2": 0.0014465765219133981, "3": 0.00029770923772332183, "4": 0.0001465812261131073}, "education_level": {"none": 0.0009459126072492469, "0": 0.0013837985338528425, "1": 0.016094498538518083, "2": 0.786482842267413, "3": 0.18246755416237867, "4": 0.01262539389058818}, "creative_writing_quality": {"not_applicable": 0.9996361053262429, "-2": 6.294652131112474e-05, "-1": 9.670535474205418e-05, "0": 0.00015279202466110894, "1": 3.324733417003579e-05, "2": 1.820343887277032e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 8.246198684792243e-05, "Art & Design": 0.00017968496862378317, "Law & Crime": 0.00011218129376732029, "Education & Careers": 0.00011024682484037088, "Entertainment & Media": 0.00012365164343047715, "Fashion & Beauty": 0.025594561303047925, "Business, Economics & Finance": 0.008954357782199905, "Food & Dining": 0.00017512404659462976, "Games & Gaming": 0.00029424523479190017, "Electronics & Hardware": 0.00017783249026316554, "Health, Healthcare & Wellness": 9.20700982629645e-05, "History & Geography": 9.267507728115511e-05, "Home, Pets & Hobbies": 0.001576499067710424, "Industry & Production": 0.0002380795520425308, "Language, Literature & Philosophy": 0.00010071394814670063, "Politics & Government": 5.234207321529261e-05, "Religion & Spirituality": 9.500073465107396e-05, "Science, Mathematics & Engineering": 0.0002615829910682033, "Relationships & Community": 7.031519919039615e-05, "Software Usage & Applications": 0.0007049030585355879, "Programming & Software Engineering": 0.9530083320637569, "Sports & Fitness": 0.00548682823753973, "Vehicles & Transportation": 9.431754406029702e-05, "Travel & Tourism": 0.00010280275391072657, "Other / Uncovered Subject": 0.002028355846726208, "Unknown / Insufficient Content": 0.0001908341794944143}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010276888392004141, "Art & Design": 0.0011439021399464848, "Law & Crime": 0.0009909943978441676, "Education & Careers": 0.0006526752198640144, "Entertainment & Media": 0.001113440149954242, "Fashion & Beauty": 0.2852737413911982, "Business, Economics & Finance": 0.12700095029845762, "Food & Dining": 0.0005874561613178925, "Games & Gaming": 0.0009834108958219879, "Electronics & Hardware": 0.0008077068200256425, "Health, Healthcare & Wellness": 0.0007177527898244403, "History & Geography": 0.0004844441038886802, "Home, Pets & Hobbies": 0.002983573327901177, "Industry & Production": 0.0008652084914598925, "Language, Literature & Philosophy": 0.0004036687746151274, "Politics & Government": 0.00030359063037819757, "Religion & Spirituality": 0.0006669774116248602, "Science, Mathematics & Engineering": 0.000947114355717387, "Relationships & Community": 0.00034030301098492756, "Software Usage & Applications": 0.002316524542182606, "Programming & Software Engineering": 0.06091329446498141, "Sports & Fitness": 0.05416960436098542, "Vehicles & Transportation": 0.0003901327211159976, "Travel & Tourism": 0.0005607243420105532, "Other / Uncovered Subject": 0.0030801174578915583, "NONE": 0.4512750029008072}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11095, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11095, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:35:16.695458+00:00", "scoring_key_slot": "primary", "dataset_uid": "baec092a-6831-458b-a623-bdac138c0ae6", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "d09499ac2aa071646cb691620005500a4e3c337572f110f4c83e7797e01eb3bb", "source_metadata": {"uuid": "baec092a-6831-458b-a623-bdac138c0ae6", "repo_name": "apifabric/GolfShoes", "relative_path": "database/models.py", "category": "CONFIG", "algo_rel_score": 0.9428808093070984, "quality_score": 6.901590347290039, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 1, "repo_created_at": "2024-10-31T18:40:09.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "4df56ad256c1bbb6487726b3dd5c6ad82da50290", "size_bytes": 10645, "file_timestamp": 1730400011, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "4185027f895e633682aec880b32d17af2f0829e4"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 1, "repo_created_at": "2024-10-31T18:40:09.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "4df56ad256c1bbb6487726b3dd5c6ad82da50290", "size_bytes": 10645, "file_timestamp": 1730400011, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "4185027f895e633682aec880b32d17af2f0829e4", "url": "https://github.com/apifabric/GolfShoes/blob/4185027f895e633682aec880b32d17af2f0829e4/database/models.py"}, "gpt4o_tokens": 2449, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.9401094704088241, "noise_severity": 0.505735847182155, "missing_content_severity": 0.01587812913053131, "factual_density": 3.40738898984053, "pretraining_utility": 0.25431481382360605, "math_density": 0.026538284825008075, "code_density": 3.860723897329151, "reasoning_density": 0.01049126023867705, "education_level": 2.1890350569809414, "creative_writing_quality": -0.4202979501448271}, "score_applicability": {"education_level": 0.9990540873927508, "creative_writing_quality": 0.000363894673757094}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/coralen/sentiment-order-study/blob/0d5a230c70af2e93e5872fe27cd617eac9e62ae5/src/preprocess.py", "id": "python-0-63bdf75eb649a2a7212f1ab17580f622", "text": "import os\nos.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n\nfrom tensorflow.keras.preprocessing.text import Tokenizer # type: ignore\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences # type: ignore\nfrom torch.utils.data import DataLoader, TensorDataset\nfrom transformers import BertTokenizerFast\nfrom sklearn.model_selection import train_test_split\nfrom gensim.models import Word2Vec\nfrom sklearn.utils import shuffle\nimport pandas as pd\nimport numpy as np\nimport torch\nimport spacy\n\nfrom config import TEST_SIZE, RANDOM_STATE\n\n    \ndef load_and_split(file_path):\n    try:\n        imdb_df = pd.read_csv(file_path)\n        imdb_df['label'] = imdb_df['sentiment'].map({'positive': 1, 'negative': 0})\n        return train_test_split(imdb_df, test_size=TEST_SIZE, random_state=RANDOM_STATE)\n    except Exception as e:\n        raise\n\ndef extract_features(docs):\n    tokens = []\n    lemmas = []\n    pos_tags = []\n    for doc in docs:\n        tokens.append([t.text for t in doc if t.is_alpha])\n        lemmas.append([t.lemma_ for t in doc if t.is_alpha])\n        pos_tags.append([t.pos_ for t in doc if t.is_alpha])\n    return tokens, lemmas, pos_tags\n\ndef nlp_pipeline(df):\n    nlp = spacy.load(\"en_core_web_sm\", disable=[\"parser\", \"ner\"])\n    data_docs = nlp.pipe(df[\"review\"])\n    df[\"tokens\"], df[\"lemmas\"], df[\"pos\"] = extract_features(data_docs)\n    return df\n\ndef process(file_path):\n    '''Process text for tokens, lemmas and pos tags, and create a shuffled train set'''\n    train_df, test_df = load_and_split(file_path)\n    train_df = nlp_pipeline(train_df)\n    test_df = nlp_pipeline(test_df)\n    train_df_shuffled = shuffle(train_df, random_state=RANDOM_STATE)\n    return train_df, train_df_shuffled, test_df\n\ndef tinybert_tokenizer(tokenizer, df, max_len):\n    return tokenizer.batch_encode_plus(\n            df.tolist(),\n            add_special_tokens=True,\n            padding='max_length',\n            truncation=True,\n            max_length=max_len,\n            return_tensors='pt'\n        )\n\ndef prepare_data_tinybert(train_df, train_labels, test_df, config):\n    '''Accept raw data since TinyBERT works better with it.'''\n    tokenizer = BertTokenizerFast.from_pretrained(\"huawei-noah/TinyBERT_General_4L_312D\")\n    X_train, X_val, y_train, y_val = train_test_split(train_df, train_labels, test_size=TEST_SIZE, \n                                                      random_state=RANDOM_STATE)\n\n    train_tokens = tinybert_tokenizer(tokenizer, X_train['review'], config.max_len)\n    train_dataset = TensorDataset(train_tokens['input_ids'], train_tokens['attention_mask'], \n                                    torch.tensor(y_train.tolist()))\n    train_loader = DataLoader(train_dataset, batch_size=config.batch_size)\n    \n    val_tokens = tinybert_tokenizer(tokenizer, X_val['review'], config.max_len)\n    val_dataset = TensorDataset(val_tokens['input_ids'], val_tokens['attention_mask'], \n                                torch.tensor(y_val.tolist()))\n    val_loader = DataLoader(val_dataset, batch_size=config.batch_size)\n    \n    test_tokens = tinybert_tokenizer(tokenizer, test_df['review'], config.max_len)\n    test_dataset = TensorDataset(test_tokens['input_ids'], test_tokens['attention_mask'], \n                                    torch.tensor(test_df['label'].tolist()))\n    test_loader = DataLoader(test_dataset, batch_size=config.batch_size)\n\n    return train_loader, val_loader, test_loader\n\ndef prepare_data_lstm(train_texts, test_texts, max_len, w2v_config):\n    '''Accept lemmas since LSTM works better with it.''' \n    tokenizer = Tokenizer()\n    tokenizer.fit_on_texts(train_texts)\n    train_sequences = tokenizer.texts_to_sequences(train_texts)\n    test_sequences = tokenizer.texts_to_sequences(test_texts)\n\n    # Pad sequences\n    train_padded = pad_sequences(train_sequences, maxlen=max_len, padding='post')\n    test_padded = pad_sequences(test_sequences, maxlen=max_len, padding='post')\n\n    w2v_model = Word2Vec(sentences=train_texts, vector_size=w2v_config.vector_size, \n                         window=w2v_config.window, min_count=w2v_config.min_count, workers=w2v_config.workers)\n\n    # Create embedding matrix\n    word_index = tokenizer.word_index\n    embedding_dim = w2v_model.vector_size\n    embedding_matrix = np.zeros((len(word_index) + 1, embedding_dim))\n    for word, i in word_index.items():\n        if word in w2v_model.wv:\n            embedding_matrix[i] = w2v_model.wv[word]\n\n    return train_padded, test_padded, embedding_matrix\n", "text_preview": "import os os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\" from tensorflow.keras.preprocessing.text import Tokenizer # type: ignore from tensorflow.keras.preprocessing.sequence import pad_sequences # type: ignore from torch.utils.data import DataL", "input_sha256": "dc7fdc90688cd5d6eb71d3f90133c0827584e382d8602cbc9725a15119383b07", "text_sha256": "63bdf75eb649a2a7212f1ab17580f622ca99cbe124744ce9396f69b801a6bcfb", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12092, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.764549907006177, "noise_severity": 0.017311500878815334, "missing_content_severity": 0.026690288372734773, "factual_density": 3.2509659995108295, "pretraining_utility": 0.6838520356337106, "math_density": 0.7052883798100359, "code_density": 3.8582249726126427, "reasoning_density": 0.26978186787544123, "education_level": 2.716992730113654, "creative_writing_quality": -0.35754176122944875}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0054713266630180835, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9958056791554997, "secondary": 0.98514831280782, "tertiary": 0.9908575232387957, "structured_records": 0.0008163008361773448, "procedural_steps": 0.09601787299301721, "worked_solution": 0.0017943546924221912, "contextual_dialogue": 7.007479267204815e-06, "content_quality": 0.5336436879834865, "noise_severity": 0.9855737492676538, "missing_content_severity": 0.977758093022721, "factual_density": 0.3758049995923577, "pretraining_utility": 0.5228282239145119, "math_density": 0.4122596834916368, "code_density": 0.8818541438438685, "reasoning_density": 0.7751817767704656, "education_level": 0.40625122613539744, "creative_writing_quality": 0.9997148982724824, "primary_topic": 0.9276385540446764, "secondary_topic": 0.4544702301627}, "probability_margin": {"primary": 0.9936009229048628, "secondary": 0.9805005557078252, "tertiary": 0.9881946952783042, "education_level": 0.12515057698901327, "creative_writing_quality": 0.9996659022487362, "primary_topic": 0.8690214686596938, "secondary_topic": 0.12852227673870104}, "raw_probabilities": {"primary": {"Academic Writing": 0.00014739413273569816, "Content Listing": 1.691780724704458e-05, "Creative Writing": 1.18164927248989e-05, "Customer Support Page": 8.036873657177153e-06, "Q&A Forum": 1.0939851027614939e-05, "Discussion Thread": 6.1567746052755984e-06, "Comment Section": 1.0728633717939022e-05, "Missing Content": 1.841500024714265e-05, "Knowledge Article": 0.00010329894042356728, "Legal Notices": 1.925426647944282e-05, "News Article": 4.071829087996748e-06, "Organizational Announcement": 8.221097798272515e-06, "Nonfiction Writing": 1.4429611226367807e-05, "Organizational About Page": 1.0223538115800327e-05, "Personal About Page": 1.0631350128090829e-05, "Personal Blog": 7.544344429261786e-06, "Product Page": 6.423039247084302e-06, "Spam / Ads": 9.264877291566129e-06, "Structured Data": 0.0002393286128154723, "Technical Writing": 0.9959804425240205, "Transcript / Interview": 1.1897523560680177e-05, "Tutorial / How-To Guide": 0.0023795196191577117, "User Reviews": 6.111947779687046e-06, "Other / Unknown": 0.0009589313124757672}, "secondary": {"Academic Writing": 0.0018289735927478144, "Content Listing": 5.9512974837271014e-05, "Creative Writing": 6.232298233969395e-05, "Customer Support Page": 4.143011042339574e-05, "Q&A Forum": 3.698872676316211e-05, "Discussion Thread": 2.2984833937937615e-05, "Comment Section": 5.8052916730618235e-05, "Missing Content": 0.0001598142362926178, "Knowledge Article": 0.0007114837724864307, "Legal Notices": 9.791344479314891e-05, "News Article": 3.695064916128154e-05, "Organizational Announcement": 3.4053180058632556e-05, "Nonfiction Writing": 4.793338941439446e-05, "Organizational About Page": 4.505352163004963e-05, "Personal About Page": 4.269356411257623e-05, "Personal Blog": 3.611267857382025e-05, "Product Page": 2.2559535719712117e-05, "Spam / Ads": 4.1022637183725875e-05, "Structured Data": 0.0007352197960132618, "Technical Writing": 0.004773226768089939, "Transcript / Interview": 4.9096567766006484e-05, "Tutorial / How-To Guide": 0.005266577399669007, "User Reviews": 2.288961376121683e-05, "NONE": 0.9857671331074942}, "tertiary": {"Academic Writing": 0.001095507559267695, "Content Listing": 3.3887030326951775e-05, "Creative Writing": 4.3128670265922034e-05, "Customer Support Page": 2.9903944455551057e-05, "Q&A Forum": 2.1682703900603265e-05, "Discussion Thread": 1.5169048128736417e-05, "Comment Section": 5.879314818532402e-05, "Missing Content": 0.000171011932726812, "Knowledge Article": 0.0006511412246087091, "Legal Notices": 0.00011585567688247628, "News Article": 2.664080776668143e-05, "Organizational Announcement": 2.201660720502e-05, "Nonfiction Writing": 5.312336440267906e-05, "Organizational About Page": 4.02863100325069e-05, "Personal About Page": 2.6189126602542888e-05, "Personal Blog": 2.4336451382309946e-05, "Product Page": 1.8259877524298342e-05, "Spam / Ads": 3.066338178032422e-05, "Structured Data": 0.0003618082594468795, "Technical Writing": 0.002830282270302656, "Transcript / Interview": 3.205053683088402e-05, "Tutorial / How-To Guide": 0.0030437644922084233, "User Reviews": 1.6037805253512456e-05, "NONE": 0.9912384597705126}, "structured_records": {"false": 0.9991836991638227, "true": 0.0008163008361773448}, "procedural_steps": {"false": 0.9039821270069828, "true": 0.09601787299301721}, "worked_solution": {"false": 0.9982056453075778, "true": 0.0017943546924221912}, "contextual_dialogue": {"false": 0.9999929925207328, "true": 7.007479267204815e-06}, "content_quality": {"-2": 0.009090485577675264, "-1": 0.04059652876446994, "0": 0.289074319444854, "1": 0.4991499255000042, "2": 0.16208874071299662}, "noise_severity": {"0": 0.9848284707705901, "1": 0.013791678251941527, "2": 0.0008573649735946291, "3": 0.0002848513358095529, "4": 0.00023763466806397133}, "missing_content_severity": {"0": 0.9856913187609665, "1": 0.004956815957608357, "2": 0.006694117625217433, "3": 0.0022857534601399463, "4": 0.0003719941960679294}, "factual_density": {"0": 0.010751110348830844, "1": 0.029767319691931894, "2": 0.1363393886797251, "3": 0.34404882265860154, "4": 0.47909335862091057}, "pretraining_utility": {"-2": 0.008066690060935676, "-1": 0.05114387574947632, "0": 0.31788922615267784, "1": 0.49467112456876206, "2": 0.12822908346814804}, "math_density": {"0": 0.6798655766480547, "1": 0.13793430598961195, "2": 0.06290742451865809, "3": 0.035631546591593496, "4": 0.0836611462520818}, "code_density": {"0": 0.0003183276357083288, "1": 0.0006040742334513462, "2": 0.016478578364197492, "3": 0.10573233741577541, "4": 0.8768666823508675}, "reasoning_density": {"0": 0.7999917375382546, "1": 0.1493771193525053, "2": 0.03554372684328338, "3": 0.011032370227457476, "4": 0.004055046038499178}, "education_level": {"none": 0.0010769912237161745, "0": 0.001473555708487595, "1": 0.0034681160407657127, "2": 0.3800587781238179, "3": 0.5052093551128312, "4": 0.10871320379038148}, "creative_writing_quality": {"not_applicable": 0.9997624152270687, "-2": 4.1352951067646255e-05, "-1": 5.74260290769563e-05, "0": 9.65129783324806e-05, "1": 2.940017585200572e-05, "2": 1.2892638602493275e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 9.551324264756483e-05, "Art & Design": 0.0001768958596200335, "Law & Crime": 0.00015116815210377665, "Education & Careers": 0.00013389978314426505, "Entertainment & Media": 0.00020881188388436524, "Fashion & Beauty": 0.00011752470149742626, "Business, Economics & Finance": 0.00016344208044225317, "Food & Dining": 9.669309859255222e-05, "Games & Gaming": 0.00017134971176454677, "Electronics & Hardware": 0.0001435952164944346, "Health, Healthcare & Wellness": 0.0001317296681750435, "History & Geography": 0.0001508155035420609, "Home, Pets & Hobbies": 7.999971739654464e-05, "Industry & Production": 0.00014138643927333956, "Language, Literature & Philosophy": 0.003584947223276756, "Politics & Government": 7.32112453137874e-05, "Religion & Spirituality": 0.0001034309602242471, "Science, Mathematics & Engineering": 0.06140021792172587, "Relationships & Community": 7.987278922557893e-05, "Software Usage & Applications": 0.0009166844992888469, "Programming & Software Engineering": 0.9304216865814197, "Sports & Fitness": 0.00011476475258926504, "Vehicles & Transportation": 5.8870607713780466e-05, "Travel & Tourism": 0.00011942201805699908, "Other / Uncovered Subject": 0.0010345651430535025, "Unknown / Insufficient Content": 0.00012950119953341864}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008514734319577833, "Art & Design": 0.0007498348996694707, "Law & Crime": 0.0006700286233307781, "Education & Careers": 0.0005496356635339291, "Entertainment & Media": 0.0026189162134162467, "Fashion & Beauty": 0.0004673317740010126, "Business, Economics & Finance": 0.0008323934133968164, "Food & Dining": 0.00034629615886084186, "Games & Gaming": 0.0006101939780077952, "Electronics & Hardware": 0.0006566838322188789, "Health, Healthcare & Wellness": 0.0006894422784447656, "History & Geography": 0.0005203044444664293, "Home, Pets & Hobbies": 0.0003526350684404652, "Industry & Production": 0.00047913541971326506, "Language, Literature & Philosophy": 0.06632781156991531, "Politics & Government": 0.0003452174173916451, "Religion & Spirituality": 0.0005358554955923882, "Science, Mathematics & Engineering": 0.3469298676485105, "Relationships & Community": 0.0002953638930696504, "Software Usage & Applications": 0.0012272512207403734, "Programming & Software Engineering": 0.09565634729660709, "Sports & Fitness": 0.00041222242984822316, "Vehicles & Transportation": 0.00020417843899764686, "Travel & Tourism": 0.0006196713358263075, "Other / Uncovered Subject": 0.0015997636668309578, "NONE": 0.47545214438721156}}, "probabilities": {"primary": {"Academic Writing": 0.00014739413273569816, "Content Listing": 1.691780724704458e-05, "Creative Writing": 1.18164927248989e-05, "Customer Support Page": 8.036873657177153e-06, "Q&A Forum": 1.0939851027614939e-05, "Discussion Thread": 6.1567746052755984e-06, "Comment Section": 1.0728633717939022e-05, "Missing Content": 1.841500024714265e-05, "Knowledge Article": 0.00010329894042356728, "Legal Notices": 1.925426647944282e-05, "News Article": 4.071829087996748e-06, "Organizational Announcement": 8.221097798272515e-06, "Nonfiction Writing": 1.4429611226367807e-05, "Organizational About Page": 1.0223538115800327e-05, "Personal About Page": 1.0631350128090829e-05, "Personal Blog": 7.544344429261786e-06, "Product Page": 6.423039247084302e-06, "Spam / Ads": 9.264877291566129e-06, "Structured Data": 0.0002393286128154723, "Technical Writing": 0.9959804425240205, "Transcript / Interview": 1.1897523560680177e-05, "Tutorial / How-To Guide": 0.0023795196191577117, "User Reviews": 6.111947779687046e-06, "Other / Unknown": 0.0009589313124757672}, "secondary": {"Academic Writing": 0.0018289735927478146, "Content Listing": 5.951297483727102e-05, "Creative Writing": 6.232298233969397e-05, "Customer Support Page": 4.143011042339575e-05, "Q&A Forum": 3.6988726763162115e-05, "Discussion Thread": 2.298483393793762e-05, "Comment Section": 5.805291673061824e-05, "Missing Content": 0.00015981423629261784, "Knowledge Article": 0.0007114837724864308, "Legal Notices": 9.791344479314892e-05, "News Article": 3.6950649161281543e-05, "Organizational Announcement": 3.405318005863256e-05, "Nonfiction Writing": 4.7933389414394465e-05, "Organizational About Page": 4.505352163004964e-05, "Personal About Page": 4.269356411257624e-05, "Personal Blog": 3.611267857382026e-05, "Product Page": 2.255953571971212e-05, "Spam / Ads": 4.102263718372588e-05, "Structured Data": 0.0007352197960132619, "Technical Writing": 0.00477322676808994, "Transcript / Interview": 4.909656776600649e-05, "Tutorial / How-To Guide": 0.005266577399669008, "User Reviews": 2.2889613761216835e-05, "NONE": 0.9857671331074943}, "tertiary": {"Academic Writing": 0.0010955075592676948, "Content Listing": 3.388703032695177e-05, "Creative Writing": 4.312867026592203e-05, "Customer Support Page": 2.990394445555105e-05, "Q&A Forum": 2.168270390060326e-05, "Discussion Thread": 1.5169048128736414e-05, "Comment Section": 5.879314818532401e-05, "Missing Content": 0.00017101193272681198, "Knowledge Article": 0.000651141224608709, "Legal Notices": 0.00011585567688247625, "News Article": 2.6640807766681423e-05, "Organizational Announcement": 2.2016607205019996e-05, "Nonfiction Writing": 5.3123364402679045e-05, "Organizational About Page": 4.028631003250689e-05, "Personal About Page": 2.618912660254288e-05, "Personal Blog": 2.433645138230994e-05, "Product Page": 1.825987752429834e-05, "Spam / Ads": 3.0663381780324213e-05, "Structured Data": 0.00036180825944687943, "Technical Writing": 0.0028302822703026556, "Transcript / Interview": 3.205053683088401e-05, "Tutorial / How-To Guide": 0.0030437644922084225, "User Reviews": 1.6037805253512453e-05, "NONE": 0.9912384597705124}, "structured_records": {"false": 0.9991836991638227, "true": 0.0008163008361773448}, "procedural_steps": {"false": 0.9039821270069828, "true": 0.09601787299301721}, "worked_solution": {"false": 0.9982056453075778, "true": 0.0017943546924221912}, "contextual_dialogue": {"false": 0.9999929925207328, "true": 7.007479267204815e-06}, "content_quality": {"-2": 0.009090485577675264, "-1": 0.04059652876446994, "0": 0.289074319444854, "1": 0.4991499255000042, "2": 0.16208874071299662}, "noise_severity": {"0": 0.9848284707705903, "1": 0.01379167825194153, "2": 0.0008573649735946293, "3": 0.00028485133580955294, "4": 0.00023763466806397138}, "missing_content_severity": {"0": 0.9856913187609663, "1": 0.004956815957608356, "2": 0.006694117625217431, "3": 0.002285753460139946, "4": 0.0003719941960679293}, "factual_density": {"0": 0.010751110348830846, "1": 0.029767319691931898, "2": 0.13633938867972512, "3": 0.3440488226586016, "4": 0.4790933586209106}, "pretraining_utility": {"-2": 0.008066690060935678, "-1": 0.05114387574947633, "0": 0.3178892261526779, "1": 0.4946711245687621, "2": 0.12822908346814807}, "math_density": {"0": 0.6798655766480547, "1": 0.13793430598961195, "2": 0.06290742451865809, "3": 0.035631546591593496, "4": 0.0836611462520818}, "code_density": {"0": 0.0003183276357083288, "1": 0.0006040742334513462, "2": 0.016478578364197492, "3": 0.10573233741577541, "4": 0.8768666823508675}, "reasoning_density": {"0": 0.7999917375382547, "1": 0.14937711935250533, "2": 0.035543726843283385, "3": 0.011032370227457478, "4": 0.004055046038499179}, "education_level": {"none": 0.0010769912237161745, "0": 0.001473555708487595, "1": 0.0034681160407657127, "2": 0.3800587781238179, "3": 0.5052093551128312, "4": 0.10871320379038148}, "creative_writing_quality": {"not_applicable": 0.9997624152270684, "-2": 4.135295106764625e-05, "-1": 5.7426029076956285e-05, "0": 9.651297833248057e-05, "1": 2.9400175852005713e-05, "2": 1.2892638602493271e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 9.551324264756485e-05, "Art & Design": 0.00017689585962003351, "Law & Crime": 0.00015116815210377668, "Education & Careers": 0.00013389978314426508, "Entertainment & Media": 0.00020881188388436527, "Fashion & Beauty": 0.00011752470149742628, "Business, Economics & Finance": 0.0001634420804422532, "Food & Dining": 9.669309859255223e-05, "Games & Gaming": 0.0001713497117645468, "Electronics & Hardware": 0.00014359521649443463, "Health, Healthcare & Wellness": 0.00013172966817504353, "History & Geography": 0.00015081550354206094, "Home, Pets & Hobbies": 7.999971739654465e-05, "Industry & Production": 0.0001413864392733396, "Language, Literature & Philosophy": 0.0035849472232767562, "Politics & Government": 7.321124531378742e-05, "Religion & Spirituality": 0.00010343096022424711, "Science, Mathematics & Engineering": 0.061400217921725876, "Relationships & Community": 7.987278922557894e-05, "Software Usage & Applications": 0.000916684499288847, "Programming & Software Engineering": 0.9304216865814198, "Sports & Fitness": 0.00011476475258926505, "Vehicles & Transportation": 5.887060771378047e-05, "Travel & Tourism": 0.00011942201805699909, "Other / Uncovered Subject": 0.0010345651430535027, "Unknown / Insufficient Content": 0.00012950119953341867}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008514734319577831, "Art & Design": 0.0007498348996694704, "Law & Crime": 0.000670028623330778, "Education & Careers": 0.000549635663533929, "Entertainment & Media": 0.0026189162134162463, "Fashion & Beauty": 0.0004673317740010125, "Business, Economics & Finance": 0.0008323934133968162, "Food & Dining": 0.0003462961588608418, "Games & Gaming": 0.0006101939780077951, "Electronics & Hardware": 0.0006566838322188788, "Health, Healthcare & Wellness": 0.0006894422784447655, "History & Geography": 0.0005203044444664292, "Home, Pets & Hobbies": 0.00035263506844046517, "Industry & Production": 0.00047913541971326496, "Language, Literature & Philosophy": 0.0663278115699153, "Politics & Government": 0.00034521741739164505, "Religion & Spirituality": 0.0005358554955923881, "Science, Mathematics & Engineering": 0.34692986764851047, "Relationships & Community": 0.00029536389306965033, "Software Usage & Applications": 0.0012272512207403732, "Programming & Software Engineering": 0.09565634729660706, "Sports & Fitness": 0.00041222242984822305, "Vehicles & Transportation": 0.0002041784389976468, "Travel & Tourism": 0.0006196713358263074, "Other / Uncovered Subject": 0.0015997636668309573, "NONE": 0.47545214438721145}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9574, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9574, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:35:19.059196+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "b98b50cb-7f46-4539-9147-4774da8f6e33", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "21644d74dc8d5e6a4ac8f1655094ca33394f63a97e5c4a9986d74c9c8255a1de", "source_metadata": {"uuid": "b98b50cb-7f46-4539-9147-4774da8f6e33", "repo_name": "coralen/sentiment-order-study", "relative_path": "src/preprocess.py", "category": "DATA", "algo_rel_score": 0.9613538384437561, "quality_score": 5.626435279846191, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 35, "repo_created_at": "2025-06-17T13:59:32.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 5, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "40de1cdaf906b65719df5f172cf1a3bcb759b2de", "size_bytes": 4484, "file_timestamp": 1752085559, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0d5a230c70af2e93e5872fe27cd617eac9e62ae5"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 35, "repo_created_at": "2025-06-17T13:59:32.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 5, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "40de1cdaf906b65719df5f172cf1a3bcb759b2de", "size_bytes": 4484, "file_timestamp": 1752085559, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0d5a230c70af2e93e5872fe27cd617eac9e62ae5", "url": "https://github.com/coralen/sentiment-order-study/blob/0d5a230c70af2e93e5872fe27cd617eac9e62ae5/src/preprocess.py"}, "gpt4o_tokens": 1038, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.764549907006177, "noise_severity": 0.017311500878815334, "missing_content_severity": 0.026690288372734777, "factual_density": 3.2509659995108295, "pretraining_utility": 0.6838520356337106, "math_density": 0.7052883798100359, "code_density": 3.8582249726126427, "reasoning_density": 0.26978186787544123, "education_level": 2.716992730113654, "creative_writing_quality": -0.35754176122944875}, "score_applicability": {"education_level": 0.9989230087762838, "creative_writing_quality": 0.0002375847729315821}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/funnydog/AoC2023/blob/84a6167c0c24496c863e43e08072ea30c6c2aa71/day21/day21.py", "id": "python-0-644c2bf0824ff563037f1e62a652c262", "text": "#!/usr/bin/env python3\nimport sys\n\nif __name__ == \"__main__\":\n    if len(sys.argv) < 2:\n        print(f\"Usage: { sys.argv[0] } <filename>\", file=sys.stderr)\n        sys.exit(1)\n\n    try:\n        with open(sys.argv[1], \"rt\") as f:\n            lines = f.read().strip().splitlines()\n    except:\n        print(f\"cannot open { sys.argv[1] }\", file=sys.stderr)\n        sys.exit(1)\n\n    height, width = len(lines), len(lines[0])\n\n    def is_equal(ta: tuple[int,int], tb: tuple[int,int], distance) -> bool:\n        ax, ay = ta[0] * width, ta[1] * height\n        bx, by = tb[0] * width, tb[1] * height\n        for x in range(width):\n            a = distance.get((ax+x, ay), -1)\n            b = distance.get((bx+x, by), -1)\n            if a < 0 or b < 0 or a != b:\n                return False\n\n            a = distance.get((ax+x, ay+height-1), -1)\n            b = distance.get((bx+x, by+height-1), -1)\n            if a < 0 or b < 0 or a != b:\n                return False\n\n        for y in range(1, height-2):\n            a = distance.get((ax, ay+y), -1)\n            b = distance.get((bx, by+y), -1)\n            if a < 0 or b < 0 or a != b:\n                return False\n\n            a = distance.get((ax+width-1, ay+y), -1)\n            b = distance.get((bx+width-1, by+y), -1)\n            if a < 0 or b < 0 or a != b:\n                return False\n\n        return True\n\n    def bfs(sx, sy):\n        queue = [(sx, sy, 0)]\n        distance = {}\n        x0, x1, y0, y1 = 0, 0, 0, 0\n        while queue:\n            x, y, steps = queue.pop(0)\n\n            nsteps = steps + 1\n            for dx, dy in (-1,0), (0,-1), (1,0), (0,1):\n                nx = x + dx\n                ny = y + dy\n\n                # check against the boundaries\n                if x0 and nx < x0 or x1 and nx >= x1: continue\n                if y0 and ny < y0 or y1 and ny >= y1: continue\n\n                tx, rx = nx // width, nx % width\n                ty, ry = ny // height, ny % height\n\n                # find the boundaries\n                if tx == 0 or ty == 0:\n                    if x1 == 0 and dx > 0 and rx == 0:\n                        if is_equal((tx-1, ty), (tx-2, ty), distance):\n                            x1 = x+1\n                    if x0 == 0 and dx < 0 and rx == width-1:\n                        if is_equal((tx+1, ty), (tx+2, ty), distance):\n                            x0 = x\n                    if y1 == 0 and dy > 0 and ry == 0:\n                        if is_equal((tx, ty-1), (tx, ty-2), distance):\n                            y1 = y+1\n                    if y0 == 0 and dy < 0 and ry == height-1:\n                        if is_equal((tx, ty+1), (tx, ty+2), distance):\n                            y0 = y\n\n                if lines[ry][rx] != \"#\" and not (nx, ny) in distance:\n                    distance[nx, ny] = nsteps - abs(nx-sx) - abs(ny-sy)\n                    queue.append((nx, ny, nsteps))\n\n        return distance, ((x0, y0), (x1, y1))\n\n    # find the start position\n    sx, sy = 0, 0\n    for y, row in enumerate(lines):\n        for x, tile in enumerate(row):\n            if tile == \"S\":\n                sx, sy = x, y\n                break\n\n    ndistance, nrect = bfs(sx, sy)\n\n    def get_value(x, y):\n        (x0, y0), (x1, y1) = nrect\n        tx, rx = x // width, x % width\n        ty, ry = y // height, y % height\n        if x0 <= x < x1 and y0 <= y < y1:\n            rx, ry = x, y\n        else:\n            if tx < 0:\n                rx += x0\n            elif tx > 0:\n                rx += x1 - width\n            if ty < 0:\n                ry += y0\n            elif ty > 0:\n                ry += y1 - height\n\n        d = ndistance.get((rx, ry), -1)\n        if d < 0:\n            return -1\n\n        return d + abs(x-sx) + abs(y-sy)\n\n    def count_diamond(steps):\n        count = 0\n        for y in range(-steps, steps+1):\n            x0 = abs(y)-steps\n            x1 = 1 - x0\n            for x in range(x0, x1, 2):\n                d = get_value(sx+x, sy+y)\n                if 0 <= d <= steps:\n                    count += 1\n\n        return count\n\n    def tile_count(tx, ty, steps, rem):\n        x0 = tx * width\n        x1 = x0 + width\n        y0 = ty * height\n        y1 = y0 + height\n        count = 0\n        for y in range(y0, y1):\n            for x in range(x0, x1):\n                d = get_value(x, y)\n                if d < 0:\n                    continue\n                if d <= steps and d % 2 == rem:\n                    count += 1\n        return count\n\n    # assert tile_count(-1, 0) == tile_count(-3, 0)\n    # assert tile_count(-1, 0) != tile_count(-2, 0)\n    # assert tile_count(-2, 0) == tile_count(-4, 0)\n\n    def count_diamond_fast(steps):\n        rem = steps % 2\n\n        # center\n        value = tile_count(0, 0, steps, rem)\n\n        # arrows\n        #      2\n        #      |\n        #      |\n        #      |\n        #      |\n        # 1----S----3\n        #      |\n        #      |\n        #      |\n        #      |\n        #      4\n        count = (steps - sx + width - 1) // width\n        if count > 0:\n            value += tile_count(-count, 0, steps, rem)\n            value += tile_count(0, -count, steps, rem)\n            value += tile_count(count, 0, steps, rem)\n            value += tile_count(0, count, steps, rem)\n            if count - 1 > 0 and (steps + sx) % width <= width // 2:\n                count -= 1\n                value += tile_count(-count, 0, steps, rem)\n                value += tile_count(0, -count, steps, rem)\n                value += tile_count(count, 0, steps, rem)\n                value += tile_count(0, count, steps, rem)\n\n        # with borders\n        #     1^2\n        #    11|22\n        #   11 | 22\n        #  11  |  22\n        # 11   |   22\n        # <----S---->\n        # 44   |   33\n        #  44  |  33\n        #   44 | 33\n        #    44|33\n        #      v\n        # outer borders\n        if count > 0:\n            value += count * tile_count(-count, -1, steps, rem)\n            value += count * tile_count(1, -count, steps, rem)\n            value += count * tile_count(count, 1, steps, rem)\n            value += count * tile_count(-1, count, steps, rem)\n\n        # inner borders\n        count -= 1\n        if count > 0:\n            value += count * tile_count(-count, -1, steps, rem)\n            value += count * tile_count(1, -count, steps, rem)\n            value += count * tile_count(count, 1, steps, rem)\n            value += count * tile_count(-1, count, steps, rem)\n\n        # with axes\n        #      ^\n        #      +\n        #      *\n        #      +\n        #      *\n        # <+*+*S*+*+>\n        #      *\n        #      +\n        #      *\n        #      +\n        #      v\n        if count > 0:\n            even = count // 2\n            odd = count - even\n            value += odd * tile_count(-1, 0, steps, rem)\n            value += odd * tile_count(0, -1, steps, rem)\n            value += odd * tile_count(1, 0, steps, rem)\n            value += odd * tile_count(0, 1, steps, rem)\n            value += even * tile_count(-1, 0, steps, 1-rem)\n            value += even * tile_count(0, -1, steps, 1-rem)\n            value += even * tile_count(1, 0, steps, 1-rem)\n            value += even * tile_count(0, 1, steps, 1-rem)\n\n        # steps per quadrant\n        #      v\n        #     /|\\\n        #    /*|*\\\n        #   /*+|+*\\\n        #  /*+*|*+*\\\n        # <----S---->\n        #  \\*+*|*+*/\n        #   \\*+|+*/\n        #    \\*|*/\n        #     \\|/\n        #      v\n        if count > 0:\n            even, odd = 0, 0\n            for i in range(1, count):\n                even, odd = odd + i, even\n\n            if count % 2 == 1:\n                even, odd = odd, even\n\n            value += even * tile_count(-1, -1, steps, rem)\n            value += even * tile_count(1, -1, steps, rem)\n            value += even * tile_count(1, 1, steps, rem)\n            value += even * tile_count(-1, 1, steps, rem)\n            value += odd * tile_count(-1, -1, steps, 1-rem)\n            value += odd * tile_count(1, -1, steps, 1-rem)\n            value += odd * tile_count(1, 1, steps, 1-rem)\n            value += odd * tile_count(-1, 1, steps, 1-rem)\n\n        return value\n\n    print(\"Part1:\", count_diamond(64))\n\n    # assert count_diamond(6) == count_diamond_fast(6)\n    # assert count_diamond(10) == count_diamond_fast(10)\n    # assert count_diamond(50) == count_diamond_fast(50)\n    # assert count_diamond(100) == count_diamond_fast(100)\n    # assert count_diamond(500) == count_diamond_fast(500)\n    # assert count_diamond(1000) == count_diamond_fast(1000)\n    # assert count_diamond(5000) == count_diamond_fast(5000)\n\n    print(\"Part2:\", count_diamond_fast(26501365))\n", "text_preview": "#!/usr/bin/env python3 import sys if __name__ == \"__main__\": if len(sys.argv) < 2: print(f\"Usage: { sys.argv[0] } <filename>\", file=sys.stderr) sys.exit(1) try: with open(sys.argv[1], \"rt\") as f: lines = f.read().strip().splitlines() except", "input_sha256": "e8ffd7b92a9c430f62cd5efff0e077e06e1f16c123be1d9b59a32f9ba201ac91", "text_sha256": "644c2bf0824ff563037f1e62a652c262b6b4c66dc865a2d13cefe8336a563568", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12100, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 4, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9697200178763911, "noise_severity": 0.0372347179125596, "missing_content_severity": 0.009360542655915507, "factual_density": 3.080232114878865, "pretraining_utility": 0.5938087682529072, "math_density": 3.3183676665988617, "code_density": 3.796418791097147, "reasoning_density": 2.3161025045667203, "education_level": 2.0874418276843625, "creative_writing_quality": -0.22476742992922719}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.01225375516764704, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9903175173260628, "secondary": 0.9688521742678744, "tertiary": 0.9816387013993322, "structured_records": 0.0007302510377145383, "procedural_steps": 0.013623142027633359, "worked_solution": 0.08075174946504197, "contextual_dialogue": 6.643376421736751e-06, "content_quality": 0.49998864785872976, "noise_severity": 0.9689710684062003, "missing_content_severity": 0.992199547786737, "factual_density": 0.23352676239905423, "pretraining_utility": 0.42716007277335566, "math_density": 0.4319730554990512, "code_density": 0.8303489925809557, "reasoning_density": 0.0, "education_level": 0.719810184784078, "creative_writing_quality": 0.9991406362709863, "primary_topic": 0.8906385071080343, "secondary_topic": 0.5371140148372218}, "probability_margin": {"primary": 0.9858168387436201, "secondary": 0.9505613084798941, "tertiary": 0.9724879171206131, "education_level": 0.6571850055474543, "creative_writing_quality": 0.9989566048289924, "primary_topic": 0.8441133481773371, "secondary_topic": 0.2809560777689154}, "raw_probabilities": {"primary": {"Academic Writing": 7.663878719983127e-05, "Content Listing": 2.8649186186976135e-05, "Creative Writing": 5.368580683525599e-05, "Customer Support Page": 1.1459624972535217e-05, "Q&A Forum": 2.1561845112917614e-05, "Discussion Thread": 1.6567757506566475e-05, "Comment Section": 2.4517874410307324e-05, "Missing Content": 4.1628504002654886e-05, "Knowledge Article": 0.00014244464492766952, "Legal Notices": 4.0495217589745156e-05, "News Article": 1.0406423790340509e-05, "Organizational Announcement": 1.6717106944514943e-05, "Nonfiction Writing": 3.4435002247407364e-05, "Organizational About Page": 1.9781836854773445e-05, "Personal About Page": 2.8096212911144505e-05, "Personal Blog": 1.751830839228041e-05, "Product Page": 1.0673866976051942e-05, "Spam / Ads": 1.922992607782106e-05, "Structured Data": 0.0002897744192899243, "Technical Writing": 0.9907209541041435, "Transcript / Interview": 3.0507924818620757e-05, "Tutorial / How-To Guide": 0.0034276622312491307, "User Reviews": 1.2478027036736146e-05, "Other / Unknown": 0.004904115360523337}, "secondary": {"Academic Writing": 0.00024900687595686604, "Content Listing": 9.282381228404984e-05, "Creative Writing": 0.0005113001182504265, "Customer Support Page": 5.231689696562029e-05, "Q&A Forum": 4.8684321609968146e-05, "Discussion Thread": 4.806592531637347e-05, "Comment Section": 0.00011496701437515763, "Missing Content": 0.00016996501089767328, "Knowledge Article": 0.0005591328299243618, "Legal Notices": 0.00010854827025649758, "News Article": 6.720570144183344e-05, "Organizational Announcement": 5.7603167648146976e-05, "Nonfiction Writing": 8.471789887223756e-05, "Organizational About Page": 7.008663744214398e-05, "Personal About Page": 8.030412605360895e-05, "Personal Blog": 6.529625677370925e-05, "Product Page": 3.9548209133009055e-05, "Spam / Ads": 6.443396932136856e-05, "Structured Data": 0.0007937188769178948, "Technical Writing": 0.019588691860152163, "Transcript / Interview": 8.376620748460447e-05, "Tutorial / How-To Guide": 0.0068675482144954, "User Reviews": 3.226745838068585e-05, "NONE": 0.9701500003400463}, "tertiary": {"Academic Writing": 0.0002514810466417441, "Content Listing": 5.818073699786202e-05, "Creative Writing": 0.000385795899142344, "Customer Support Page": 3.7959378633284536e-05, "Q&A Forum": 3.450831103382617e-05, "Discussion Thread": 3.3670725347885035e-05, "Comment Section": 0.00013767407566762352, "Missing Content": 0.00022414367866355943, "Knowledge Article": 0.0006581926668095376, "Legal Notices": 0.00014701453774689557, "News Article": 5.091983766322084e-05, "Organizational Announcement": 3.9495852052628045e-05, "Nonfiction Writing": 9.769334071233991e-05, "Organizational About Page": 6.328035510924976e-05, "Personal About Page": 5.597089398930609e-05, "Personal Blog": 4.741740106061233e-05, "Product Page": 3.073221948781909e-05, "Spam / Ads": 4.859073615873419e-05, "Structured Data": 0.0009696216575987718, "Technical Writing": 0.009915838387080187, "Transcript / Interview": 5.7008019069362124e-05, "Tutorial / How-To Guide": 0.004223062055044384, "User Reviews": 2.7992680595600625e-05, "NONE": 0.9824037555076933}, "structured_records": {"false": 0.9992697489622855, "true": 0.0007302510377145383}, "procedural_steps": {"false": 0.9863768579723666, "true": 0.013623142027633359}, "worked_solution": {"false": 0.9192482505349581, "true": 0.08075174946504197}, "contextual_dialogue": {"false": 0.9999933566235782, "true": 6.643376421736751e-06}, "content_quality": {"-2": 0.009833450159683772, "-1": 0.03672668463938801, "0": 0.2121930825887393, "1": 0.45637996238923123, "2": 0.2848668202229577}, "noise_severity": {"0": 0.9688782224932025, "1": 0.026491457793890144, "2": 0.003528833976536369, "3": 0.0007203507798874916, "4": 0.0003811349564835603}, "missing_content_severity": {"0": 0.9930626241912792, "1": 0.005215972057390287, "2": 0.0011835447373925278, "3": 0.00037395493201220566, "4": 0.0001639040819258872}, "factual_density": {"0": 0.021005516021307288, "1": 0.05293187095109467, "2": 0.15576581916860302, "3": 0.3654185698454157, "4": 0.4048782240135793}, "pretraining_utility": {"-2": 0.01184296689462143, "-1": 0.0819971925049487, "0": 0.3472762865157713, "1": 0.41827521362221826, "2": 0.14060834046244022}, "math_density": {"0": 0.011219784493204323, "1": 0.019704973387184625, "2": 0.11379631564585899, "3": 0.3500456439750493, "4": 0.5052332824987027}, "code_density": {"0": 0.0003146176352154237, "1": 0.0006590333679787032, "2": 0.02151528992444263, "3": 0.15731505840917007, "4": 0.8201960006631931}, "reasoning_density": {"0": 0.29413229747987807, "1": 0.043352874860342956, "2": 0.09265121353840405, "3": 0.19200725385593037, "4": 0.3778563602654445}, "education_level": {"none": 0.0016237378210560674, "0": 0.0027132911310070187, "1": 0.08541968611317005, "2": 0.7665084873200649, "3": 0.10932348177261064, "4": 0.034411315842091304}, "creative_writing_quality": {"not_applicable": 0.9992838635591552, "-2": 0.00011028210292167228, "-1": 0.0001329401302549077, "0": 0.0003272587301628351, "1": 9.87707661993373e-05, "2": 4.688471130580939e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00023823060197036757, "Art & Design": 0.0005287809528282741, "Law & Crime": 0.00019058002361303658, "Education & Careers": 0.00016528755175977607, "Entertainment & Media": 0.00037532032071792374, "Fashion & Beauty": 0.000145025670768777, "Business, Economics & Finance": 0.0001720684174684768, "Food & Dining": 0.0001300436325813008, "Games & Gaming": 0.047441724728253226, "Electronics & Hardware": 0.00016610134560532064, "Health, Healthcare & Wellness": 0.00013014412700172539, "History & Geography": 0.00023187947035533454, "Home, Pets & Hobbies": 0.0002495372483899086, "Industry & Production": 0.00012507909397464987, "Language, Literature & Philosophy": 0.00016244133933191487, "Politics & Government": 6.953024653413034e-05, "Religion & Spirituality": 0.0001721405533176884, "Science, Mathematics & Engineering": 0.05073137019577274, "Relationships & Community": 9.535569189776778e-05, "Software Usage & Applications": 0.00038995589154296433, "Programming & Software Engineering": 0.8948447183731099, "Sports & Fitness": 0.0002542059496524434, "Vehicles & Transportation": 0.00010859160606899, "Travel & Tourism": 0.00021581792737006554, "Other / Uncovered Subject": 0.0022428259781855898, "Unknown / Insufficient Content": 0.0004232430619275589}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008018584357059103, "Art & Design": 0.0009271666143408132, "Law & Crime": 0.0005658088783158073, "Education & Careers": 0.0003523758639748698, "Entertainment & Media": 0.0008998245787305077, "Fashion & Beauty": 0.00031965656697577317, "Business, Economics & Finance": 0.0004953724278251797, "Food & Dining": 0.00027881473103006456, "Games & Gaming": 0.0781252765328854, "Electronics & Hardware": 0.00042500346074289606, "Health, Healthcare & Wellness": 0.0003317957751587867, "History & Geography": 0.0004103330697729183, "Home, Pets & Hobbies": 0.0004212920796945977, "Industry & Production": 0.0002333719125361728, "Language, Literature & Philosophy": 0.0002710870187213447, "Politics & Government": 0.00021789964764399702, "Religion & Spirituality": 0.00041835913066423354, "Science, Mathematics & Engineering": 0.2739612441899517, "Relationships & Community": 0.00017475335981402874, "Software Usage & Applications": 0.00041588760746518973, "Programming & Software Engineering": 0.08195591023771231, "Sports & Fitness": 0.0003868962529868127, "Vehicles & Transportation": 0.00019575923293857947, "Travel & Tourism": 0.0005257570183043904, "Other / Uncovered Subject": 0.0019711734172405742, "NONE": 0.5549173219588671}}, "probabilities": {"primary": {"Academic Writing": 7.663878719983127e-05, "Content Listing": 2.8649186186976135e-05, "Creative Writing": 5.368580683525599e-05, "Customer Support Page": 1.1459624972535217e-05, "Q&A Forum": 2.1561845112917614e-05, "Discussion Thread": 1.6567757506566475e-05, "Comment Section": 2.4517874410307324e-05, "Missing Content": 4.1628504002654886e-05, "Knowledge Article": 0.00014244464492766952, "Legal Notices": 4.0495217589745156e-05, "News Article": 1.0406423790340509e-05, "Organizational Announcement": 1.6717106944514943e-05, "Nonfiction Writing": 3.4435002247407364e-05, "Organizational About Page": 1.9781836854773445e-05, "Personal About Page": 2.8096212911144505e-05, "Personal Blog": 1.751830839228041e-05, "Product Page": 1.0673866976051942e-05, "Spam / Ads": 1.922992607782106e-05, "Structured Data": 0.0002897744192899243, "Technical Writing": 0.9907209541041435, "Transcript / Interview": 3.0507924818620757e-05, "Tutorial / How-To Guide": 0.0034276622312491307, "User Reviews": 1.2478027036736146e-05, "Other / Unknown": 0.004904115360523337}, "secondary": {"Academic Writing": 0.00024900687595686604, "Content Listing": 9.282381228404984e-05, "Creative Writing": 0.0005113001182504265, "Customer Support Page": 5.231689696562029e-05, "Q&A Forum": 4.8684321609968146e-05, "Discussion Thread": 4.806592531637347e-05, "Comment Section": 0.00011496701437515763, "Missing Content": 0.00016996501089767328, "Knowledge Article": 0.0005591328299243618, "Legal Notices": 0.00010854827025649758, "News Article": 6.720570144183344e-05, "Organizational Announcement": 5.7603167648146976e-05, "Nonfiction Writing": 8.471789887223756e-05, "Organizational About Page": 7.008663744214398e-05, "Personal About Page": 8.030412605360895e-05, "Personal Blog": 6.529625677370925e-05, "Product Page": 3.9548209133009055e-05, "Spam / Ads": 6.443396932136856e-05, "Structured Data": 0.0007937188769178948, "Technical Writing": 0.019588691860152163, "Transcript / Interview": 8.376620748460447e-05, "Tutorial / How-To Guide": 0.0068675482144954, "User Reviews": 3.226745838068585e-05, "NONE": 0.9701500003400463}, "tertiary": {"Academic Writing": 0.0002514810466417441, "Content Listing": 5.818073699786202e-05, "Creative Writing": 0.000385795899142344, "Customer Support Page": 3.7959378633284536e-05, "Q&A Forum": 3.450831103382617e-05, "Discussion Thread": 3.3670725347885035e-05, "Comment Section": 0.00013767407566762352, "Missing Content": 0.00022414367866355943, "Knowledge Article": 0.0006581926668095376, "Legal Notices": 0.00014701453774689557, "News Article": 5.091983766322084e-05, "Organizational Announcement": 3.9495852052628045e-05, "Nonfiction Writing": 9.769334071233991e-05, "Organizational About Page": 6.328035510924976e-05, "Personal About Page": 5.597089398930609e-05, "Personal Blog": 4.741740106061233e-05, "Product Page": 3.073221948781909e-05, "Spam / Ads": 4.859073615873419e-05, "Structured Data": 0.0009696216575987718, "Technical Writing": 0.009915838387080187, "Transcript / Interview": 5.7008019069362124e-05, "Tutorial / How-To Guide": 0.004223062055044384, "User Reviews": 2.7992680595600625e-05, "NONE": 0.9824037555076933}, "structured_records": {"false": 0.9992697489622855, "true": 0.0007302510377145383}, "procedural_steps": {"false": 0.9863768579723666, "true": 0.013623142027633359}, "worked_solution": {"false": 0.9192482505349581, "true": 0.08075174946504197}, "contextual_dialogue": {"false": 0.9999933566235782, "true": 6.643376421736751e-06}, "content_quality": {"-2": 0.009833450159683772, "-1": 0.03672668463938801, "0": 0.2121930825887393, "1": 0.45637996238923123, "2": 0.2848668202229577}, "noise_severity": {"0": 0.9688782224932025, "1": 0.026491457793890144, "2": 0.003528833976536369, "3": 0.0007203507798874916, "4": 0.0003811349564835603}, "missing_content_severity": {"0": 0.993062624191279, "1": 0.005215972057390286, "2": 0.0011835447373925276, "3": 0.00037395493201220555, "4": 0.00016390408192588717}, "factual_density": {"0": 0.021005516021307288, "1": 0.05293187095109467, "2": 0.15576581916860302, "3": 0.3654185698454157, "4": 0.4048782240135793}, "pretraining_utility": {"-2": 0.011842966894621432, "-1": 0.08199719250494872, "0": 0.34727628651577136, "1": 0.4182752136222183, "2": 0.14060834046244025}, "math_density": {"0": 0.011219784493204325, "1": 0.01970497338718463, "2": 0.113796315645859, "3": 0.35004564397504934, "4": 0.5052332824987028}, "code_density": {"0": 0.00031461763521542377, "1": 0.0006590333679787033, "2": 0.021515289924442633, "3": 0.1573150584091701, "4": 0.8201960006631932}, "reasoning_density": {"0": 0.29413229747987807, "1": 0.043352874860342956, "2": 0.09265121353840405, "3": 0.19200725385593037, "4": 0.3778563602654445}, "education_level": {"none": 0.0016237378210560674, "0": 0.0027132911310070187, "1": 0.08541968611317005, "2": 0.7665084873200649, "3": 0.10932348177261064, "4": 0.034411315842091304}, "creative_writing_quality": {"not_applicable": 0.9992838635591554, "-2": 0.00011028210292167231, "-1": 0.00013294013025490773, "0": 0.00032725873016283517, "1": 9.877076619933733e-05, "2": 4.6884711305809406e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00023823060197036762, "Art & Design": 0.0005287809528282743, "Law & Crime": 0.00019058002361303664, "Education & Careers": 0.0001652875517597761, "Entertainment & Media": 0.00037532032071792385, "Fashion & Beauty": 0.00014502567076877702, "Business, Economics & Finance": 0.00017206841746847683, "Food & Dining": 0.00013004363258130083, "Games & Gaming": 0.04744172472825324, "Electronics & Hardware": 0.00016610134560532066, "Health, Healthcare & Wellness": 0.0001301441270017254, "History & Geography": 0.0002318794703553346, "Home, Pets & Hobbies": 0.00024953724838990866, "Industry & Production": 0.0001250790939746499, "Language, Literature & Philosophy": 0.0001624413393319149, "Politics & Government": 6.953024653413036e-05, "Religion & Spirituality": 0.00017214055331768842, "Science, Mathematics & Engineering": 0.05073137019577276, "Relationships & Community": 9.535569189776781e-05, "Software Usage & Applications": 0.00038995589154296444, "Programming & Software Engineering": 0.8948447183731101, "Sports & Fitness": 0.00025420594965244346, "Vehicles & Transportation": 0.00010859160606899003, "Travel & Tourism": 0.0002158179273700656, "Other / Uncovered Subject": 0.00224282597818559, "Unknown / Insufficient Content": 0.000423243061927559}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008018584357059103, "Art & Design": 0.0009271666143408132, "Law & Crime": 0.0005658088783158073, "Education & Careers": 0.0003523758639748698, "Entertainment & Media": 0.0008998245787305077, "Fashion & Beauty": 0.00031965656697577317, "Business, Economics & Finance": 0.0004953724278251797, "Food & Dining": 0.00027881473103006456, "Games & Gaming": 0.0781252765328854, "Electronics & Hardware": 0.00042500346074289606, "Health, Healthcare & Wellness": 0.0003317957751587867, "History & Geography": 0.0004103330697729183, "Home, Pets & Hobbies": 0.0004212920796945977, "Industry & Production": 0.0002333719125361728, "Language, Literature & Philosophy": 0.0002710870187213447, "Politics & Government": 0.00021789964764399702, "Religion & Spirituality": 0.00041835913066423354, "Science, Mathematics & Engineering": 0.2739612441899517, "Relationships & Community": 0.00017475335981402874, "Software Usage & Applications": 0.00041588760746518973, "Programming & Software Engineering": 0.08195591023771231, "Sports & Fitness": 0.0003868962529868127, "Vehicles & Transportation": 0.00019575923293857947, "Travel & Tourism": 0.0005257570183043904, "Other / Uncovered Subject": 0.0019711734172405742, "NONE": 0.5549173219588671}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11280, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11280, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:36:02.307291+00:00", "scoring_key_slot": "primary", "dataset_uid": "7481beb5-ba02-4f2b-9f0b-df0ed71df469", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "6a1cd47227e61909ff021932a85494b493db4533f32ce1e92585b725d3a8846b", "source_metadata": {"uuid": "7481beb5-ba02-4f2b-9f0b-df0ed71df469", "repo_name": "funnydog/AoC2023", "relative_path": "day21/day21.py", "category": "ALGO", "algo_rel_score": 0.9774032235145569, "quality_score": 6.326574802398682, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2024-09-28T11:18:04.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "9b68f85f0f1190f398569b3c311671cc6d27d9f7", "size_bytes": 8623, "file_timestamp": 1727522213, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "84a6167c0c24496c863e43e08072ea30c6c2aa71"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2024-09-28T11:18:04.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "9b68f85f0f1190f398569b3c311671cc6d27d9f7", "size_bytes": 8623, "file_timestamp": 1727522213, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "84a6167c0c24496c863e43e08072ea30c6c2aa71", "url": "https://github.com/funnydog/AoC2023/blob/84a6167c0c24496c863e43e08072ea30c6c2aa71/day21/day21.py"}, "gpt4o_tokens": 2579, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.9697200178763911, "noise_severity": 0.0372347179125596, "missing_content_severity": 0.009360542655915509, "factual_density": 3.080232114878865, "pretraining_utility": 0.5938087682529072, "math_density": 3.3183676665988617, "code_density": 3.796418791097147, "reasoning_density": 2.3161025045667203, "education_level": 2.0874418276843625, "creative_writing_quality": -0.22476742992922719}, "score_applicability": {"education_level": 0.9983762621789439, "creative_writing_quality": 0.000716136440844562}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/abhiraj-zangoh/hybridSearch/blob/9fe9776a9233b19f43e7911be88a6a4693894ff5/hybridsearch.py", "id": "python-0-b46d4cbd2a58dbef61086419cb09f592", "text": "import pinecone\nfrom datasets import load_dataset\nimport requests\nfrom transformers import BertTokenizerFast\nimport pinecone_text\nfrom sentence_transformers import SentenceTransformer\nfrom tqdm.auto import tqdm\nfrom IPython.core.display import HTML\nfrom io import BytesIO\nimport numpy as np\nfrom typing import List, Callable, Optional, Dict\n\nfrom sklearn.base import BaseEstimator\nfrom sklearn.feature_extraction.text import HashingVectorizer\nfrom base64 import b64encode\nSparseVector = Dict[str, List]\n\n\nclass BM25(BaseEstimator):\n\n    def __init__(self, tokenizer: Callable[[str], List[str]], n_features=2 ** 16, b=0.75, k1=1.6):\n        \"\"\"OKapi BM25 with HashingVectorizer\n\n        Args:\n            tokenizer: A function to converts text to a list of tokens\n            n_features: The number of features to hash to\n            b: BM25 parameters\n            k1: BM25 parameters\n        \"\"\"\n        # Fixed params\n        self.n_features: int = n_features\n        self.b: float = b\n        self.k1: float = k1\n\n        self._tokenizer: Callable[[str], List[str]] = tokenizer\n        self._vectorizer = HashingVectorizer(\n            n_features=self.n_features,\n            token_pattern=None,\n            tokenizer=tokenizer, norm=None,\n            alternate_sign=False, binary=True)\n        # Learned Params\n        self.doc_freq: Optional[np.ndarray] = None\n        self.n_docs: Optional[int] = None\n        self.avgdl: Optional[float] = None\n\n    def fit(self, X: List[str], y=None) -> \"BM25\":\n        \"\"\"Fit BM25 by calculating document frequency over the corpus\"\"\"\n        X = self._vectorizer.transform(X)\n        self.avgdl = X.sum(1).mean()\n        self.n_docs = X.shape[0]\n        self.doc_freq = X.sum(axis=0).A1\n        return self\n\n    def vectorize(self, text) -> SparseVector:\n        sparse_array = self._vectorizer.transform(text)\n        return {'indices': [int(x) for x in sparse_array.indices], 'values': sparse_array.data.tolist()}\n\n    def get_params(self, deep=True):\n        return {\n            'avgdl': self.avgdl,\n            'ndocs': self.n_docs,\n            'doc_freq': list(self.doc_freq),\n            'b': self.b,\n            'k1': self.k1,\n            'n_features': self.n_features\n        }\n\n    def set_params(self, **params):\n        self.avgdl = params['avgdl']\n        self.n_docs = params['ndocs']\n        self.doc_freq = np.array(params['doc_freq'])\n        self.b = params['b']\n        self.k1 = params['k1']\n        self.n_features = params['n_features']\n\n    def transform_doc(self, doc: str) -> SparseVector:\n        \"\"\"Normalize document for BM25 scoring\"\"\"\n        doc_tf = self._vectorizer.transform([doc])\n        norm_doc_tf = self._norm_doc_tf(doc_tf)\n        return {'indices': [int(x) for x in doc_tf.indices], 'values': norm_doc_tf.tolist()}\n\n    def transform_query(self, query: str) -> SparseVector:\n        \"\"\"Normalize query for BM25 scoring\"\"\"\n        query_tf = self._vectorizer.transform([query])\n        indices, values = self._norm_query_tf(query_tf)\n        return {'indices': [int(x) for x in indices], 'values': values.tolist()}\n\n    def _norm_doc_tf(self, doc_tf) -> np.ndarray:\n        \"\"\"Calculate BM25 normalized document term-frequencies\"\"\"\n        b, k1, avgdl = self.b, self.k1, self.avgdl\n        tf = doc_tf.data\n        norm_tf = tf / (k1 * (1.0 - b + b * (tf.sum() / avgdl)) + tf)\n        return norm_tf\n\n    def _norm_query_tf(self, query_tf):\n        \"\"\"Calculate BM25 normalized query term-frequencies\"\"\"\n        idf = np.log((self.n_docs + 1) / (self.doc_freq[query_tf.indices] + 0.5))\n        return query_tf.indices, idf / idf.sum()\n\ndef display_result(image_batch):\n    figures = []\n    for img in image_batch:\n        b = BytesIO()\n        img.save(b, format='png')\n        figures.append(f'''\n            <figure style=\"margin: 5px !important;\">\n              <img src=\"data:image/png;base64,{b64encode(b.getvalue()).decode('utf-8')}\" style=\"width: 90px; height: 120px\" >\n            </figure>\n        ''')\n    return HTML(data=f'''\n        <div style=\"display: flex; flex-flow: row wrap; text-align: center;\">\n        {''.join(figures)}\n        </div>\n    ''')\n\n# init connection to pinecone\npinecone.init(\n    api_key='YOUR_API_KEY',  # app.pinecone.io\n    environment='gcp-starter'  # find next to api key\n)\nindex_name = \"hybrid-image-search\"\n\nif index_name not in pinecone.list_indexes():\n    # create the index\n    pinecone.create_index(\n      index_name,\n      dimension=512,\n      metric=\"dotproduct\",\n      pod_type=\"s1\"\n    )\nindex = pinecone.Index(index_name)\nfashion = load_dataset(\n    \"ashraq/fashion-product-images-small\",\n    split=\"train\"\n)\nimages = fashion[\"image\"]\nmetadata = fashion.remove_columns(\"image\")\nimages[9000]\n\n# convert metadata into a pandas dataframe\nmetadata = metadata.to_pandas()\nmetadata.head()\n\ndef hybrid_scale(dense, sparse, alpha: float):\n    \"\"\"Hybrid vector scaling using a convex combination\n\n    alpha * dense + (1 - alpha) * sparse\n\n    Args:\n        dense: Array of floats representing\n        sparse: a dict of `indices` and `values`\n        alpha: float between 0 and 1 where 0 == sparse only\n               and 1 == dense only\n    \"\"\"\n    if alpha < 0 or alpha > 1:\n        raise ValueError(\"Alpha must be between 0 and 1\")\n    # scale sparse and dense vectors to create hybrid search vecs\n    hsparse = {\n        'indices': sparse['indices'],\n        'values':  [v * (1 - alpha) for v in sparse['values']]\n    }\n    hdense = [v * alpha for v in dense]\n    return hdense, hsparse\n\n# load bert tokenizer from huggingface\ntokenizer = BertTokenizerFast.from_pretrained(\n    'bert-base-uncased'\n)\ndef tokenize_func(text):\n    token_ids = tokenizer(\n        text,\n        add_special_tokens=False\n    )['input_ids']\n    return tokenizer.convert_ids_to_tokens(token_ids)\n\nbm25 = BM25(tokenize_func)\ntokenize_func('Turtle Check Men Navy Blue Shirt')\nbm25.fit(metadata['productDisplayName'])\nbm25.transform_query(metadata['productDisplayName'][0])\nbm25.transform_doc(metadata['productDisplayName'][0])\nmodel = SentenceTransformer(\n    'sentence-transformers/clip-ViT-B-32',\n    device='cpu'\n)\n#insert the dataset in pinecone database\n# batch_size = 200\n\n# for i in tqdm(range(0, len(fashion), batch_size)):\n#     # find end of batch\n#     i_end = min(i+batch_size, len(fashion))\n#     # extract metadata batch\n#     meta_batch = metadata.iloc[i:i_end]\n#     meta_dict = meta_batch.to_dict(orient=\"records\")\n#     # concatinate all metadata field except for id and year to form a single string\n#     meta_batch = [\" \".join(x) for x in meta_batch.loc[:, ~meta_batch.columns.isin(['id', 'year'])].values.tolist()]\n#     # extract image batch\n#     img_batch = images[i:i_end]\n#     # create sparse BM25 vectors\n#     sparse_embeds = [bm25.transform_doc(text) for text in meta_batch]\n#     # create dense vectors\n#     dense_embeds = model.encode(img_batch).tolist()\n#     # create unique IDs\n#     ids = [str(x) for x in range(i, i_end)]\n\n#     upserts = []\n#     # loop through the data and create dictionaries for uploading documents to pinecone index\n#     for _id, sparse, dense, meta in zip(ids, sparse_embeds, dense_embeds, meta_dict):\n#         upserts.append({\n#             'id': _id,\n#             'sparse_values': sparse,\n#             'values': dense,\n#             'metadata': meta\n#         })\n#     # upload the documents to the new hybrid index\n#     index.upsert(upserts)\n\n# show index description after uploading the documents\nindex.describe_index_stats()\n\n# display the images\nquery = \"blue tshirt for mens\"\n# create sparse and dense vectors\nsparse = bm25.transform_query(query)\ndense = model.encode(query).tolist()\n# scale sparse and dense vectors - keyword search first\nhdense, hsparse = hybrid_scale(dense, sparse, alpha=0.05)\n# search\nresult = index.query(\n    top_k=14,\n    vector=hdense,\n    sparse_vector=hsparse,\n    include_metadata=True\n)\n# used returned product ids to get images\nimgs = [images[int(r[\"id\"])] for r in result[\"matches\"]]\n# display the images\ndisplay_result(imgs)\n\n\n\n\n", "text_preview": "import pinecone from datasets import load_dataset import requests from transformers import BertTokenizerFast import pinecone_text from sentence_transformers import SentenceTransformer from tqdm.auto import tqdm from IPython.core.display imp", "input_sha256": "5ecfbab48d5d3efc0bfbaa0a4b66059e937af84807c0bd05b04104e41ea241cb", "text_sha256": "b46d4cbd2a58dbef61086419cb09f592319a7dbba3c0961cdc140a3f9263baa2", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12101, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 2, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7407484627625138, "noise_severity": 0.046913694599692385, "missing_content_severity": 0.07884758214621315, "factual_density": 3.267215414149375, "pretraining_utility": 0.8030803014685981, "math_density": 2.3535630572774027, "code_density": 3.882518768579259, "reasoning_density": 0.6070570617038744, "education_level": 2.7119907390091385, "creative_writing_quality": -0.3993305871326321}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.06340753759661966, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9407664971421427, "secondary": 0.830550445703516, "tertiary": 0.8967148327608586, "structured_records": 0.013868642627747764, "procedural_steps": 0.17042668081991347, "worked_solution": 0.04004895348415925, "contextual_dialogue": 6.383198265059013e-06, "content_quality": 0.49433722490335175, "noise_severity": 0.9609052545002563, "missing_content_severity": 0.9342936815448224, "factual_density": 0.38934617845781216, "pretraining_utility": 0.5725878670670104, "math_density": 0.32820344184226025, "code_density": 0.902098973816049, "reasoning_density": 0.4941191152467712, "education_level": 0.4010049872875454, "creative_writing_quality": 0.999433875053058, "primary_topic": 0.9284032583711369, "secondary_topic": 0.4109748638644922}, "probability_margin": {"primary": 0.8896509903764476, "secondary": 0.7270805296605743, "tertiary": 0.8364682562779489, "education_level": 0.11462478759329653, "creative_writing_quality": 0.9993320231843807, "primary_topic": 0.8886229919490282, "secondary_topic": 0.12424706448129746}, "raw_probabilities": {"primary": {"Academic Writing": 0.00014750546369297817, "Content Listing": 0.00010272279288553255, "Creative Writing": 7.814342999248758e-05, "Customer Support Page": 7.699585848276456e-05, "Q&A Forum": 6.550045621927252e-05, "Discussion Thread": 4.354933850911953e-05, "Comment Section": 7.576153193006874e-05, "Missing Content": 8.948028143168445e-05, "Knowledge Article": 0.0002077874392703169, "Legal Notices": 7.524475114229916e-05, "News Article": 3.62175059918207e-05, "Organizational Announcement": 5.531188575076381e-05, "Nonfiction Writing": 6.779074104151797e-05, "Organizational About Page": 6.938468660545527e-05, "Personal About Page": 5.929511279984297e-05, "Personal Blog": 4.297608018576984e-05, "Product Page": 4.579686395133319e-05, "Spam / Ads": 6.739086699351966e-05, "Structured Data": 0.00037819042133746116, "Technical Writing": 0.9432345597612201, "Transcript / Interview": 7.615680157012779e-05, "Tutorial / How-To Guide": 0.05358356938477244, "User Reviews": 3.6899258489620354e-05, "Other / Unknown": 0.0012837692857336385}, "secondary": {"Academic Writing": 0.0011555506748326485, "Content Listing": 0.0005447101132770953, "Creative Writing": 0.00038525537223630806, "Customer Support Page": 0.0004260445229697058, "Q&A Forum": 0.00024403902821637882, "Discussion Thread": 0.00016390131428511593, "Comment Section": 0.000680106620984933, "Missing Content": 0.0007911043247591101, "Knowledge Article": 0.0014903238880407603, "Legal Notices": 0.0003787959892742049, "News Article": 0.000216962572608641, "Organizational Announcement": 0.00021317244469696466, "Nonfiction Writing": 0.0003040801256635909, "Organizational About Page": 0.00025922235624245365, "Personal About Page": 0.0002491709594826874, "Personal Blog": 0.00025647820318408493, "Product Page": 0.00017916935695971468, "Spam / Ads": 0.0002687981281319989, "Structured Data": 0.0021530506344113695, "Technical Writing": 0.041067521134626264, "Transcript / Interview": 0.0002784859783815059, "Tutorial / How-To Guide": 0.11053031413862846, "User Reviews": 0.00015289831890312946, "NONE": 0.8376108437992028}, "tertiary": {"Academic Writing": 0.0006790869713986255, "Content Listing": 0.00036472036474080805, "Creative Writing": 0.0002917061711493176, "Customer Support Page": 0.0003123510967223906, "Q&A Forum": 0.0001251604650207339, "Discussion Thread": 0.00010097756056537098, "Comment Section": 0.0011577670217710091, "Missing Content": 0.0011864779910164018, "Knowledge Article": 0.0032431632604897377, "Legal Notices": 0.000543535932677927, "News Article": 0.0001191417078072303, "Organizational Announcement": 0.00013283348811644918, "Nonfiction Writing": 0.0002556890650282511, "Organizational About Page": 0.00016170930109772995, "Personal About Page": 0.0001504226246330992, "Personal Blog": 0.0001438222233083089, "Product Page": 0.0001414330146525127, "Spam / Ads": 0.0002340889073325914, "Structured Data": 0.002176550242911536, "Technical Writing": 0.02267993642205643, "Transcript / Interview": 0.0001530783962816549, "Tutorial / How-To Guide": 0.06455012511787393, "User Reviews": 7.784125752527807e-05, "NONE": 0.9010183813958228}, "structured_records": {"false": 0.9861313573722522, "true": 0.013868642627747764}, "procedural_steps": {"false": 0.8295733191800865, "true": 0.17042668081991347}, "worked_solution": {"false": 0.9599510465158407, "true": 0.04004895348415925}, "contextual_dialogue": {"false": 0.9999936168017349, "true": 6.383198265059013e-06}, "content_quality": {"-2": 0.010367535723547127, "-1": 0.056839568037789376, "0": 0.28824169043051184, "1": 0.4707793093689057, "2": 0.17377189643924584}, "noise_severity": {"0": 0.9598586417759586, "1": 0.03484770446438268, "2": 0.0041270327752391065, "3": 0.0008545593528476262, "4": 0.00031206163157215604}, "missing_content_severity": {"0": 0.9470437234222091, "1": 0.033234771132377904, "2": 0.014103872912729116, "3": 0.005065464942359105, "4": 0.0005521675903249299}, "factual_density": {"0": 0.006119023711187129, "1": 0.021513456384022068, "2": 0.12018981387387398, "3": 0.40338849410606265, "4": 0.4487892119248542}, "pretraining_utility": {"-2": 0.006995218796057494, "-1": 0.03517509267086607, "0": 0.2635712872955901, "1": 0.5362709707433935, "2": 0.15798743049409286}, "math_density": {"0": 0.027273710316589997, "1": 0.17174898562276272, "2": 0.3803385426477176, "3": 0.26141805929251427, "4": 0.15922070212041534}, "code_density": {"0": 0.00016327055196008024, "1": 0.00033958205451535865, "2": 0.011024742059374901, "3": 0.09375991893060508, "4": 0.8947124864035446}, "reasoning_density": {"0": 0.7378442936592706, "1": 0.06768893063114889, "2": 0.09104554937335381, "3": 0.05640787301888943, "4": 0.04701335331733745}, "education_level": {"none": 0.0008913895337313845, "0": 0.0011864972878064552, "1": 0.002950753610923011, "2": 0.3862127018129913, "3": 0.5008374894062878, "4": 0.10792116834826004}, "creative_writing_quality": {"not_applicable": 0.9995282292108817, "-2": 7.899535489852467e-05, "-1": 0.0001234912974100622, "0": 0.00019620602650094568, "1": 5.3066719620690026e-05, "2": 2.0011390687923543e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 9.037923901511018e-05, "Art & Design": 0.0004997572034857285, "Law & Crime": 0.00014277989945091517, "Education & Careers": 0.00012726761968660813, "Entertainment & Media": 0.00013438409653566385, "Fashion & Beauty": 0.019833370754398588, "Business, Economics & Finance": 0.00018433636473997388, "Food & Dining": 9.480926156143326e-05, "Games & Gaming": 0.00016748871599729526, "Electronics & Hardware": 0.00015811304286971161, "Health, Healthcare & Wellness": 0.00011442561940939264, "History & Geography": 0.00011488436955015023, "Home, Pets & Hobbies": 9.893470653545065e-05, "Industry & Production": 0.00010021324580736152, "Language, Literature & Philosophy": 0.00017165038177042584, "Politics & Government": 5.9698131667216575e-05, "Religion & Spirituality": 9.768831550452476e-05, "Science, Mathematics & Engineering": 0.04253398725398809, "Relationships & Community": 5.209899173481654e-05, "Software Usage & Applications": 0.0025105106065519507, "Programming & Software Engineering": 0.9311569792030162, "Sports & Fitness": 0.00013849621109104866, "Vehicles & Transportation": 6.216011942967546e-05, "Travel & Tourism": 0.00010614438173639825, "Other / Uncovered Subject": 0.0011168518454154478, "Unknown / Insufficient Content": 0.00013259041905070072}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010468069679730675, "Art & Design": 0.0029747841035609656, "Law & Crime": 0.0009059916494685731, "Education & Careers": 0.0006281231097645677, "Entertainment & Media": 0.0008927357064786389, "Fashion & Beauty": 0.14903689784189017, "Business, Economics & Finance": 0.0017273963118143772, "Food & Dining": 0.000488307194490334, "Games & Gaming": 0.0005483372518569691, "Electronics & Hardware": 0.0007350751640465886, "Health, Healthcare & Wellness": 0.0006081174263590535, "History & Geography": 0.0005574574766736927, "Home, Pets & Hobbies": 0.000516644992846746, "Industry & Production": 0.00045972988445909204, "Language, Literature & Philosophy": 0.0005925081558300721, "Politics & Government": 0.00034945115895335414, "Religion & Spirituality": 0.0005925293459518717, "Science, Mathematics & Engineering": 0.30938261231148356, "Relationships & Community": 0.00022004813249721907, "Software Usage & Applications": 0.007210015843653309, "Programming & Software Engineering": 0.0839816955362163, "Sports & Fitness": 0.0005377104241075275, "Vehicles & Transportation": 0.00026615283610565446, "Travel & Tourism": 0.000510148644659694, "Other / Uncovered Subject": 0.0016010457360776966, "NONE": 0.433629676792781}}, "probabilities": {"primary": {"Academic Writing": 0.0001475054636929782, "Content Listing": 0.00010272279288553256, "Creative Writing": 7.81434299924876e-05, "Customer Support Page": 7.699585848276458e-05, "Q&A Forum": 6.550045621927253e-05, "Discussion Thread": 4.354933850911954e-05, "Comment Section": 7.576153193006875e-05, "Missing Content": 8.948028143168447e-05, "Knowledge Article": 0.00020778743927031692, "Legal Notices": 7.524475114229917e-05, "News Article": 3.6217505991820704e-05, "Organizational Announcement": 5.531188575076382e-05, "Nonfiction Writing": 6.779074104151798e-05, "Organizational About Page": 6.938468660545528e-05, "Personal About Page": 5.9295112799842974e-05, "Personal Blog": 4.2976080185769845e-05, "Product Page": 4.57968639513332e-05, "Spam / Ads": 6.739086699351968e-05, "Structured Data": 0.0003781904213374612, "Technical Writing": 0.9432345597612202, "Transcript / Interview": 7.61568015701278e-05, "Tutorial / How-To Guide": 0.05358356938477245, "User Reviews": 3.689925848962036e-05, "Other / Unknown": 0.0012837692857336387}, "secondary": {"Academic Writing": 0.0011555506748326487, "Content Listing": 0.0005447101132770954, "Creative Writing": 0.0003852553722363081, "Customer Support Page": 0.00042604452296970583, "Q&A Forum": 0.00024403902821637884, "Discussion Thread": 0.00016390131428511596, "Comment Section": 0.0006801066209849331, "Missing Content": 0.0007911043247591102, "Knowledge Article": 0.0014903238880407605, "Legal Notices": 0.00037879598927420495, "News Article": 0.00021696257260864102, "Organizational Announcement": 0.0002131724446969647, "Nonfiction Writing": 0.00030408012566359094, "Organizational About Page": 0.0002592223562424537, "Personal About Page": 0.0002491709594826875, "Personal Blog": 0.000256478203184085, "Product Page": 0.0001791693569597147, "Spam / Ads": 0.00026879812813199893, "Structured Data": 0.00215305063441137, "Technical Writing": 0.04106752113462627, "Transcript / Interview": 0.00027848597838150594, "Tutorial / How-To Guide": 0.11053031413862847, "User Reviews": 0.0001528983189031295, "NONE": 0.8376108437992029}, "tertiary": {"Academic Writing": 0.0006790869713986254, "Content Listing": 0.000364720364740808, "Creative Writing": 0.00029170617114931757, "Customer Support Page": 0.00031235109672239056, "Q&A Forum": 0.00012516046502073386, "Discussion Thread": 0.00010097756056537096, "Comment Section": 0.001157767021771009, "Missing Content": 0.0011864779910164016, "Knowledge Article": 0.003243163260489737, "Legal Notices": 0.0005435359326779269, "News Article": 0.00011914170780723027, "Organizational Announcement": 0.00013283348811644915, "Nonfiction Writing": 0.00025568906502825104, "Organizational About Page": 0.00016170930109772993, "Personal About Page": 0.00015042262463309917, "Personal Blog": 0.00014382222330830888, "Product Page": 0.00014143301465251268, "Spam / Ads": 0.00023408890733259136, "Structured Data": 0.0021765502429115357, "Technical Writing": 0.022679936422056426, "Transcript / Interview": 0.00015307839628165488, "Tutorial / How-To Guide": 0.06455012511787392, "User Reviews": 7.784125752527806e-05, "NONE": 0.9010183813958226}, "structured_records": {"false": 0.9861313573722522, "true": 0.013868642627747764}, "procedural_steps": {"false": 0.8295733191800865, "true": 0.17042668081991347}, "worked_solution": {"false": 0.9599510465158407, "true": 0.04004895348415925}, "contextual_dialogue": {"false": 0.9999936168017349, "true": 6.383198265059013e-06}, "content_quality": {"-2": 0.010367535723547129, "-1": 0.05683956803778938, "0": 0.2882416904305119, "1": 0.4707793093689058, "2": 0.17377189643924587}, "noise_severity": {"0": 0.9598586417759584, "1": 0.03484770446438267, "2": 0.004127032775239106, "3": 0.000854559352847626, "4": 0.000312061631572156}, "missing_content_severity": {"0": 0.9470437234222089, "1": 0.0332347711323779, "2": 0.014103872912729113, "3": 0.005065464942359104, "4": 0.0005521675903249298}, "factual_density": {"0": 0.006119023711187129, "1": 0.021513456384022068, "2": 0.12018981387387398, "3": 0.40338849410606265, "4": 0.4487892119248542}, "pretraining_utility": {"-2": 0.006995218796057494, "-1": 0.03517509267086607, "0": 0.2635712872955901, "1": 0.5362709707433935, "2": 0.15798743049409286}, "math_density": {"0": 0.02727371031659, "1": 0.17174898562276275, "2": 0.38033854264771766, "3": 0.2614180592925143, "4": 0.15922070212041536}, "code_density": {"0": 0.00016327055196008024, "1": 0.00033958205451535865, "2": 0.011024742059374901, "3": 0.09375991893060508, "4": 0.8947124864035446}, "reasoning_density": {"0": 0.7378442936592705, "1": 0.06768893063114888, "2": 0.0910455493733538, "3": 0.056407873018889415, "4": 0.047013353317337435}, "education_level": {"none": 0.0008913895337313845, "0": 0.0011864972878064552, "1": 0.002950753610923011, "2": 0.3862127018129913, "3": 0.5008374894062878, "4": 0.10792116834826004}, "creative_writing_quality": {"not_applicable": 0.9995282292108818, "-2": 7.899535489852468e-05, "-1": 0.00012349129741006222, "0": 0.0001962060265009457, "1": 5.306671962069003e-05, "2": 2.0011390687923546e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 9.037923901511019e-05, "Art & Design": 0.0004997572034857286, "Law & Crime": 0.0001427798994509152, "Education & Careers": 0.00012726761968660815, "Entertainment & Media": 0.00013438409653566388, "Fashion & Beauty": 0.01983337075439859, "Business, Economics & Finance": 0.0001843363647399739, "Food & Dining": 9.480926156143327e-05, "Games & Gaming": 0.0001674887159972953, "Electronics & Hardware": 0.00015811304286971164, "Health, Healthcare & Wellness": 0.00011442561940939265, "History & Geography": 0.00011488436955015024, "Home, Pets & Hobbies": 9.893470653545067e-05, "Industry & Production": 0.00010021324580736154, "Language, Literature & Philosophy": 0.00017165038177042587, "Politics & Government": 5.969813166721658e-05, "Religion & Spirituality": 9.768831550452477e-05, "Science, Mathematics & Engineering": 0.0425339872539881, "Relationships & Community": 5.2098991734816544e-05, "Software Usage & Applications": 0.002510510606551951, "Programming & Software Engineering": 0.9311569792030163, "Sports & Fitness": 0.0001384962110910487, "Vehicles & Transportation": 6.216011942967547e-05, "Travel & Tourism": 0.00010614438173639826, "Other / Uncovered Subject": 0.001116851845415448, "Unknown / Insufficient Content": 0.00013259041905070074}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010468069679730673, "Art & Design": 0.0029747841035609648, "Law & Crime": 0.0009059916494685729, "Education & Careers": 0.0006281231097645676, "Entertainment & Media": 0.0008927357064786387, "Fashion & Beauty": 0.14903689784189014, "Business, Economics & Finance": 0.0017273963118143768, "Food & Dining": 0.0004883071944903339, "Games & Gaming": 0.000548337251856969, "Electronics & Hardware": 0.0007350751640465883, "Health, Healthcare & Wellness": 0.0006081174263590533, "History & Geography": 0.0005574574766736926, "Home, Pets & Hobbies": 0.0005166449928467459, "Industry & Production": 0.00045972988445909193, "Language, Literature & Philosophy": 0.000592508155830072, "Politics & Government": 0.0003494511589533541, "Religion & Spirituality": 0.0005925293459518716, "Science, Mathematics & Engineering": 0.3093826123114835, "Relationships & Community": 0.000220048132497219, "Software Usage & Applications": 0.007210015843653307, "Programming & Software Engineering": 0.08398169553621629, "Sports & Fitness": 0.0005377104241075274, "Vehicles & Transportation": 0.0002661528361056544, "Travel & Tourism": 0.0005101486446596939, "Other / Uncovered Subject": 0.0016010457360776961, "NONE": 0.4336296767927809}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10679, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10679, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:36:06.654409+00:00", "scoring_key_slot": "primary", "dataset_uid": "13dd706d-0661-4638-be9c-1b12b6d99cc8", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "f87b0b9cc21a107bcbc47fb1579b1fbdc402e4f5b84864cdc56b99efb4fe4b97", "source_metadata": {"uuid": "13dd706d-0661-4638-be9c-1b12b6d99cc8", "repo_name": "abhiraj-zangoh/hybridSearch", "relative_path": "hybridsearch.py", "category": "DATA", "algo_rel_score": 0.9059292674064636, "quality_score": 7.422246932983398, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 4, "repo_created_at": "2023-09-13T12:04:37.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "adf4cd16cab0264aa4eaa39970eeb5409c521e08", "size_bytes": 8039, "file_timestamp": 1698680860, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "9fe9776a9233b19f43e7911be88a6a4693894ff5"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 4, "repo_created_at": "2023-09-13T12:04:37.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "adf4cd16cab0264aa4eaa39970eeb5409c521e08", "size_bytes": 8039, "file_timestamp": 1698680860, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "9fe9776a9233b19f43e7911be88a6a4693894ff5", "url": "https://github.com/abhiraj-zangoh/hybridSearch/blob/9fe9776a9233b19f43e7911be88a6a4693894ff5/hybridsearch.py"}, "gpt4o_tokens": 2011, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.7407484627625138, "noise_severity": 0.04691369459969239, "missing_content_severity": 0.07884758214621317, "factual_density": 3.267215414149375, "pretraining_utility": 0.8030803014685981, "math_density": 2.3535630572774027, "code_density": 3.882518768579259, "reasoning_density": 0.6070570617038744, "education_level": 2.7119907390091385, "creative_writing_quality": -0.3993305871326321}, "score_applicability": {"education_level": 0.9991086104662686, "creative_writing_quality": 0.0004717707891181462}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/jsqiaoliang/--CTR-/blob/549ff2b585e1302a12423da8f3d2afd87d340071/01_%E7%89%B9%E5%BE%81%E5%B7%A5%E7%A8%8B%E4%BB%A3%E7%A0%81%E5%8F%8A%E4%BD%BF%E7%94%A8%E8%AF%B4%E6%98%8E/utils.py", "id": "python-0-289879710d6b965a91eaf68f388f8336", "text": "#将字典存储磁盘\n#将字典存储磁盘\ndef write_dict(name,one_dict):\n    with open(name,'w')as file:\n        for key,value in one_dict.items():\n            file.write('|'+str(key))\n            file.write('>'+str(value))\n\n\n#读取时间特征的dict\ndef read_dict(name,key_int=True):\n    with open(name,'r')as file:\n        dict_hours_={}\n        f=file.readlines()\n        f=''.join(f)\n        f=f.split('|')\n        f.remove('')\n        for line in f:\n            feature=line.split('>')\n            if key_int:\n                dict_hours_[int(feature[0])]=int(feature[1])\n            else: \n                dict_hours_[feature[0]]=int(feature[1])                \n    \n        return dict_hours_\n\n\n#将双重字典存储磁盘\ndef write_double_dict(name,double_dict):\n    with open(name,'w')as file:\n        for k,v in double_dict.items():\n            file.write('!')        \n            file.write(k)\n            for key,value in v.items():\n                file.write('|'+str(key))\n                file.write('>'+str(value))\n                \n#从磁盘中将双重字典读取成双重字典               \ndef read_double_dict(name,key_int=True):\n    with open(name,'r')as file:\n        f=file.readlines()\n        f=''.join(f)\n\n    f=f.split('!')\n    f.remove('')\n    dict_21={}\n    for line in f:\n        feature=line\n        feature=feature.split('|')\n        k_=feature[0]\n        feature.remove(k_)\n        dict_={}\n        for word in feature:\n            key_value=word.split('>')\n            if key_int:\n                dict_[int(key_value[0])]=int(key_value[1])\n            else:\n                dict_[key_value[0]]=int(key_value[1])\n        dict_21[k_]=dict_\n    \n    return dict_21\n#————————————————————————————————————————\n\n#统计数据中所有特征中维度的名字\ndef count_name(data):\n    dict_name_all={}\n    for k,v in data.items():\n        list_1=[]\n        for key,value in v.items():\n            list_1.append(value)\n        dict_name_all[k]=set(list_1)\n    return dict_name_all\n\n#将两个value为set的字典合并起来\ndef combine_dict_list(dict1,dict2):\n    dict3={}\n    list_k=[]\n\n    list_k=list(dict1.keys())\n    \n    for k in list_k:\n        list_3=[]\n        list_1=list(dict1[k])\n        for x in list_1:\n            list_3.append(x)\n        list_2=list(dict2[k])\n        for x in list_2:\n            list_3.append(x)\n        dict3[k]=set(list_3)\n#提取dict2的取值       \n    return dict3\n\n\n#找到数据中前n%的数据\ndef count_head(dict1,name='0',other_rate=0.01):\n    dict2={}\n    list_=sorted(dict1.items(),key = lambda x:x[1],reverse = True)\n    for x in list_:\n        dict2[x[0]]=x[1]\n    \n    dict3={}\n#    print(name,':',dict1['other']/40428968)\n    threshold=40428968*(1-other_rate)\n    num=0\n    rate=0\n    for key,value in dict2.items():\n              \n        if num<threshold:\n            dict3[key]=round((value/40428968),5)\n            rate+=dict3[key]\n        else:\n            dict3['other']=1-rate\n        num+=value  \n    print(len(dict3.values()))\n    print(name,':',dict3['other'],'\\n')\n\n#    print(dict3)\n    return dict3\n\n\n#合并两个字典中value的分布。\ndef combine_dict(dict1,dict2):\n    dict3={}\n    list_k=[]\n\n#    for key in dict1.keys():\n#        list_k.append(str(key))\n#    for key in dict2.keys():\n#        list_k.append(key)\n#    set_k=set(list_k)              #获取两个字典的key\n    list_k=list(dict1.keys())\n    \n    for k in list_k:\n        dict3[k]={}\n        list_key=[]\n        for key in dict1[k].keys():\n            list_key.append(key)\n        for key in dict2[k].keys():\n            list_key.append(key)\n        set_key=set(list_key)\n        for key in set_key:\n            if key in dict1[k].keys():\n                value1=dict1[k][key]                \n            else:\n                value1=0\n                \n            if key in dict2[k].keys():\n                value2=dict2[k][key]                \n            else:\n                value2=0\n                \n            dict3[k][key]=value1+value2\n#提取dict2的取值       \n    return dict3\n#——————————————————————————————————————————————\n\n\n\ndef data_plt(data):\n    data=data.fillna(-1)\n    for col in data.columns:\n        fig=plt.figure()\n        sns.distplot(data[col].values,bins=30,kde=False)\n        plt.xlabel(col,fontsize=24)\n        plt.show()\n\n\n\n#输入dict绘制相应的饼图                \ndef plt_pie(plt_data,name='pie',label_n=3):\n    labels=[]\n    sizes=[]\n    colors=[]\n    colors_all=['lightskyblue','orange','blue','green','lightcoral'\\\n                ,'green','lightcyan','purple','red','brown',\\\n                'gray','olive','cyan','gold','ivory',\\\n                 'lightpink','pink','lightsalmon','lightslategray','lightseagreen',\\\n                'lightskyblue','orange','blue','green','lightcoral'\\\n                ,'green','lightcyan','purple','red','brown',\\\n                'gray','olive','cyan','gold','ivory',\\\n                 'lightpink','pink','lightsalmon','lightslategray','lightseagreen',\\\n                'lightskyblue','orange','blue','green','lightcoral'\\\n                ,'green','lightcyan','purple','red','brown',\\\n                'gray','olive','cyan','gold','ivory',\\\n                 'lightpink','pink','lightsalmon','lightslategray','lightseagreen',\\\n                'lightskyblue','orange','blue','green','lightcoral'\\\n                ,'green','lightcyan','purple','red','brown',\\\n                'gray','olive','cyan','gold','ivory',\\\n                 'lightpink','pink','lightsalmon','lightslategray','lightseagreen',\\\n                'lightskyblue','orange','blue','green','lightcoral'\\\n                ,'green','lightcyan','purple','red','brown',\\\n                'gray','olive','cyan','gold','ivory',\\\n                 'lightpink','pink','lightsalmon','lightslategray','lightseagreen',\\\n                'lightskyblue','orange','blue','green','lightcoral'\\\n                ,'green','lightcyan','purple','red','brown',\\\n                'gray','olive','cyan','gold','ivory',\\\n                 'lightpink','pink','lightsalmon','lightslategray','lightseagreen',\\\n               'tomato']\n    i=0\n    plt_list=sorted(plt_data.items(),key = lambda x:x[1],reverse = True)\n    \n    for key,value in plt_list:\n        if i<label_n or key=='other':\n            labels.append(key)\n            sizes.append(value)\n            colors.append(colors_all[i])\n        else:\n            labels.append('')\n            sizes.append(value)\n            colors.append(colors_all[i])\n        i+=1  \n        \n    plt.pie(sizes,labels=labels,\n        colors=colors,autopct='%1.2f%%',shadow=True,startangle=50)\n    plt.axis('equal')    \n    plt.title(name)\n    plt.show()\n#___________________________________________________________________________________________                \n                \n\n#对输入list反馈1、类别的数目，各种元素所占比例，\nclass Edit_List(object):\n    def __init__(self,data,head_num=20):\n        self.data = list(data)\n#list中，类别的比例\n        self.class_n=0\n#取特征中排名前几的数据\n        self.head_num=head_num\n#list中，各数据数量的dict\n        self.class_num = {}\n#list中，各数据数量所占比例   \n        self.class_num_rate = {}\n#list中，前1%各数据数量所占比例   \n        self.class_num_head = {}\n#list中，前1%各数据数量所占比例   \n        self.class_num_rate_head = {}\n\n        \n#在init中初始化如下方法，可以直接以.class_num,.count_num_rate的方式调用数据\n#        self.get_class_num()\n#        self.get_class_num_rate()\n    def get_class_set(self):\n        set1=set(self.data)\n        return set1\n\n#获得列表中，类别的数量        \n    def get_class_n(self):\n        set0=set(self.data)\n        self.class_n=len(set0)\n        return self.class_n\n\n#获得列表中，不同数据的数量\n    def get_class_num(self):\n        set0 = set(self.data)\n        for x in set0:\n            self.class_num.update({x: list(self.data).count(x)})\n        return self.class_num\n    \n##获取列表中，元素占比超过前的数据量\n    def get_class_num_head(self):\n        set0 = set(self.data)\n        n_all=0\n        other=0\n        count_num_all = (self.data).__len__()\n#        print(len(set0))\n        \n        if count_num_all<self.head_num:\n            self.head_num=count_num_all\n\n#初始化前n个元素，并获取一个初步阈值\n        threshold=count_num_all\n#        threshold_index\n        list_head_num=list(set0)[:self.head_num]\n        for x in list_head_num:\n            num=self.data.count(x)\n            self.class_num_head[x]=num\n            if num<threshold:\n                threshold_index=x\n                threshold=num \n\n        for x in set0:\n            num=self.data.count(x)\n            if num>threshold:\n                if x in list(self.class_num_head.keys()):\n                    continue\n                else:\n                    self.class_num_head[x]=num               \n#删除此前阈值对应的元素确定新的阈值\n                    self.class_num_head.pop(threshold_index)\n                    threshold=count_num_all\n                    for key,value in self.class_num_head.items():\n                        if value<threshold:\n                            threshold_index=key\n                            threshold=value \n        \n        for key,value in self.class_num_head.items():\n            n_all+=value\n        other=count_num_all-n_all\n        self.class_num_head['other']=other\n         \n        return self.class_num_head\n    \n#获得列表中，不同数据占总数量的比例\n    def get_class_num_rate(self):\n        set0 = set(self.data)\n        for x in set0:\n            self.class_num.update({x: list(self.data).count(x)})\n\n        count_num_all = list(self.data).__len__()\n        for key, value in self.class_num.items():\n            self.class_num_rate[key] = round(value/count_num_all, 4)\n        return self.class_num_rate\n\n\n##获取列表中，元素占比超过1%的数据\n    def get_class_num_rate_head(self):\n        set0 = set(self.data)\n        rate_all=0.0000\n        rate_other=0.0000\n        count_num_all = (self.data).__len__()\n#        print(len(set0))\n        \n        if count_num_all<self.head_num:\n            self.head_num=count_num_all\n\n#初始化前n个元素，并获取一个初步阈值\n        threshold=1\n#        threshold_index\n        list_head_num=list(set0)[:self.head_num]\n        for x in list_head_num:\n            rate=self.data.count(x)/count_num_all\n            self.class_num_rate_head[x]=rate\n            if rate<threshold:\n                threshold_index=x\n                threshold=rate \n\n        for x in set0:\n            rate=self.data.count(x)/count_num_all  \n            if rate>threshold:\n                if x in list(self.class_num_rate_head.keys()):\n                    continue\n                else:\n                    self.class_num_rate_head[x]=rate               \n#删除此前阈值对应的元素确定新的阈值\n                    self.class_num_rate_head.pop(threshold_index)\n                    threshold=1\n                    for key,value in self.class_num_rate_head.items():\n                        if value<threshold:\n                            threshold_index=key\n                            threshold=value \n        \n        for key,value in self.class_num_rate_head.items():\n            rate_all+=value\n        print('***'+str(rate_all)+'\\n'+'\\n')\n        rate_other=1.00-rate_all\n        self.class_num_rate_head['other']=rate_other\n        \n        for key,value in self.class_num_rate_head.items():\n            self.class_num_rate_head[key]=round(value,7)\n        \n        return self.class_num_rate_head\n    \n\n#当采用类调用方法时，有return或print都会触发此命令，故而一个类中仅有一个方法可以触发此命令\n    def __repr__(self):\n        return 'this is rate:%s' % str(self.class_num_rate)\n    \n############————————————————————————————————\n#---------------------------------------------\ndef count_class_num(data,list_cols,list_drop=True,head_num_=100):  \n    list_class_rate_21_s={}\n    cols=list(data.columns)\n#是指获取list_cols的元素，还是获取处list_cols外的元素\n    if list_drop:\n        for x in list_cols:\n            cols.remove(x)\n    else:\n        cols=list_cols    \n    \n    for col in cols:\n#            print(str(col)+'\\n')\n        start=time.time()\n        edit=Edit_List(data[col],head_num=head_num_)\n        list_class_rate_21_s[col]=edit.get_class_num_head()    #提取数据中的前n项分布\n#            print(list_class_rate_21_s[col])\n        end=time.time()\n#            print(end-start)\n#            print(\"\\n\")\n        times=end-start\n    return list_class_rate_21_s", "text_preview": "#将字典存储磁盘 #将字典存储磁盘 def write_dict(name,one_dict): with open(name,'w')as file: for key,value in one_dict.items(): file.write('|'+str(key)) file.write('>'+str(value)) #读取时间特征的dict def read_dict(name,key_int=True): with open(name,'r')as file: d", "input_sha256": "e6bda735d22b148d99eeba46db3a26fb52d8b7f815bee33c82243627ce7e79d4", "text_sha256": "289879710d6b965a91eaf68f388f83363882f7f6c031f51c51d1d4839cb8e647", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12099, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.015944771286635122, "noise_severity": 0.5085634382510561, "missing_content_severity": 0.03730157451372067, "factual_density": 3.088587825860035, "pretraining_utility": 0.24689819527883525, "math_density": 1.7951438369772017, "code_density": 3.8839831345026927, "reasoning_density": 0.3380055085644873, "education_level": 2.0415103594142927, "creative_writing_quality": -0.5941405852020106}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.013950375547576055, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9956836514146841, "secondary": 0.9678221285342566, "tertiary": 0.9823790421491184, "structured_records": 0.01980864318065531, "procedural_steps": 0.004824499098885565, "worked_solution": 0.0009703869157423811, "contextual_dialogue": 4.46547748718204e-06, "content_quality": 0.46963756083593766, "noise_severity": 0.5761971347907866, "missing_content_severity": 0.9689153545718995, "factual_density": 0.24048985488336228, "pretraining_utility": 0.4934207670899954, "math_density": 0.0, "code_density": 0.9033192787522435, "reasoning_density": 0.7183287428629272, "education_level": 0.7856242415010619, "creative_writing_quality": 0.999537351542334, "primary_topic": 0.96249584907335, "secondary_topic": 0.6363524816031649}, "probability_margin": {"primary": 0.9939125131847935, "secondary": 0.956906575732151, "tertiary": 0.9758765424438255, "education_level": 0.7269244076068095, "creative_writing_quality": 0.9994893293089752, "primary_topic": 0.9324294475329391, "secondary_topic": 0.38439722456974856}, "raw_probabilities": {"primary": {"Academic Writing": 4.85060931661048e-05, "Content Listing": 2.20043106442191e-05, "Creative Writing": 2.3357137734376988e-05, "Customer Support Page": 1.2238042981605861e-05, "Q&A Forum": 1.4194144507183526e-05, "Discussion Thread": 8.30610212203273e-06, "Comment Section": 1.5421482676083574e-05, "Missing Content": 3.3422777883891056e-05, "Knowledge Article": 0.0001261785961964248, "Legal Notices": 2.529061397832563e-05, "News Article": 4.9015059821162895e-06, "Organizational Announcement": 1.0971659301655532e-05, "Nonfiction Writing": 1.3837471590392772e-05, "Organizational About Page": 1.2380152165930872e-05, "Personal About Page": 1.342893230175557e-05, "Personal Blog": 1.053081391490848e-05, "Product Page": 7.601393839741451e-06, "Spam / Ads": 1.3962415357046937e-05, "Structured Data": 0.0005429930948189975, "Technical Writing": 0.9958634992724056, "Transcript / Interview": 1.8398830615883896e-05, "Tutorial / How-To Guide": 0.0019509860876121728, "User Reviews": 7.650910971768803e-06, "Other / Unknown": 0.00119993815723193}, "secondary": {"Academic Writing": 0.00027876904918102243, "Content Listing": 0.00012162790554693576, "Creative Writing": 0.0001636333547699882, "Customer Support Page": 0.00011209734783888646, "Q&A Forum": 7.13556834794115e-05, "Discussion Thread": 5.64191673535071e-05, "Comment Section": 0.00041322893654355784, "Missing Content": 0.0005037962650024576, "Knowledge Article": 0.001055661949249031, "Legal Notices": 0.0001716570245158408, "News Article": 6.909984431527774e-05, "Organizational Announcement": 7.337315569949822e-05, "Nonfiction Writing": 8.242932159980695e-05, "Organizational About Page": 8.687079639615417e-05, "Personal About Page": 7.494732266777565e-05, "Personal Blog": 8.236170481748784e-05, "Product Page": 4.976621807005308e-05, "Spam / Ads": 9.431066782566373e-05, "Structured Data": 0.0036462093115537755, "Technical Writing": 0.012256297446511594, "Transcript / Interview": 0.00010120074592619208, "Tutorial / How-To Guide": 0.011228459385313182, "User Reviews": 4.3554217160411076e-05, "NONE": 0.9691628731786626}, "tertiary": {"Academic Writing": 0.00014579410986909232, "Content Listing": 7.767999473275039e-05, "Creative Writing": 9.66472136470196e-05, "Customer Support Page": 7.362854062853172e-05, "Q&A Forum": 3.7869580466042806e-05, "Discussion Thread": 3.0238871478808525e-05, "Comment Section": 0.00017883394195340095, "Missing Content": 0.0005096604216173126, "Knowledge Article": 0.0007425589187435614, "Legal Notices": 0.00013336361212002917, "News Article": 4.035069075378467e-05, "Organizational Announcement": 4.0748011477019256e-05, "Nonfiction Writing": 5.505450597022742e-05, "Organizational About Page": 5.89805710154654e-05, "Personal About Page": 4.4989213143982464e-05, "Personal Blog": 4.726746092396756e-05, "Product Page": 3.195209445038096e-05, "Spam / Ads": 6.663111001523849e-05, "Structured Data": 0.0020628702622348906, "Technical Writing": 0.007236706282413068, "Transcript / Interview": 5.7892457997581626e-05, "Tutorial / How-To Guide": 0.005092367784360003, "User Reviews": 2.4665623749211656e-05, "NONE": 0.9831132487262385}, "structured_records": {"false": 0.9801913568193447, "true": 0.01980864318065531}, "procedural_steps": {"false": 0.9951755009011144, "true": 0.004824499098885565}, "worked_solution": {"false": 0.9990296130842576, "true": 0.0009703869157423811}, "contextual_dialogue": {"false": 0.9999955345225128, "true": 4.46547748718204e-06}, "content_quality": {"-2": 0.030842003418964595, "-1": 0.24856107101719063, "0": 0.4362199603257219, "1": 0.24256408133449076, "2": 0.041812883903632095}, "noise_severity": {"0": 0.640071293688327, "1": 0.2321682502994974, "2": 0.11142338094747196, "3": 0.011799874202199516, "4": 0.004537200862504069}, "missing_content_severity": {"0": 0.9733866370333077, "1": 0.018560434680479857, "2": 0.005729884134114199, "3": 0.0020108050433799386, "4": 0.00031223910871814713}, "factual_density": {"0": 0.024229210991609153, "1": 0.04304569335103831, "2": 0.16503618872475176, "3": 0.3552858726709101, "4": 0.41240303426169067}, "pretraining_utility": {"-2": 0.012310514179846535, "-1": 0.15587741374689207, "0": 0.4536812830286345, "1": 0.3288649407038332, "2": 0.049265848340793575}, "math_density": {"0": 0.30083181010634386, "1": 0.19274122974119867, "2": 0.15450725108166677, "3": 0.11429073121049314, "4": 0.23762897786029755}, "code_density": {"0": 0.00020563805946079, "1": 0.0004251883771346913, "2": 0.011471665267067525, "3": 0.09097541759392552, "4": 0.8969220907024116}, "reasoning_density": {"0": 0.8474469301161898, "1": 0.040880688322641176, "2": 0.061386449420225114, "3": 0.026791807162379784, "4": 0.023494124978564138}, "education_level": {"none": 0.0015022632496003142, "0": 0.002174429256063801, "1": 0.06990452013461071, "2": 0.8213535345842182, "3": 0.09442912697740866, "4": 0.010636125798098202}, "creative_writing_quality": {"not_applicable": 0.9996144596186117, "-2": 8.854301812381179e-05, "-1": 0.00011949176125749, "0": 0.00012513030963651206, "1": 3.7237975052384295e-05, "2": 1.5137317317899324e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.104391133675723e-05, "Art & Design": 0.00015701695383843093, "Law & Crime": 0.00013253607404435162, "Education & Careers": 9.046267988153885e-05, "Entertainment & Media": 6.519113921515701e-05, "Fashion & Beauty": 5.1482402515606125e-05, "Business, Economics & Finance": 0.00038110245289555116, "Food & Dining": 5.5803468151308736e-05, "Games & Gaming": 0.00014931290353455238, "Electronics & Hardware": 0.00011182614495483908, "Health, Healthcare & Wellness": 6.169401209424055e-05, "History & Geography": 7.124964181048137e-05, "Home, Pets & Hobbies": 5.571756012673705e-05, "Industry & Production": 6.913824445113846e-05, "Language, Literature & Philosophy": 7.7906471278757e-05, "Politics & Government": 4.1504156480721184e-05, "Religion & Spirituality": 5.243715721992927e-05, "Science, Mathematics & Engineering": 0.03150886888374344, "Relationships & Community": 2.8481189665073446e-05, "Software Usage & Applications": 0.0008285709422376199, "Programming & Software Engineering": 0.9639383164166826, "Sports & Fitness": 8.025658787973762e-05, "Vehicles & Transportation": 4.065464716378872e-05, "Travel & Tourism": 8.120452446444933e-05, "Other / Uncovered Subject": 0.0015359000511313934, "Unknown / Insufficient Content": 0.0002723213832016975}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006793588093478261, "Art & Design": 0.001857307088773596, "Law & Crime": 0.0009552909861499796, "Education & Careers": 0.0005243368611655766, "Entertainment & Media": 0.0005548237596261223, "Fashion & Beauty": 0.000366289168717208, "Business, Economics & Finance": 0.0017413732656461288, "Food & Dining": 0.0003051865455382775, "Games & Gaming": 0.0005774376129548327, "Electronics & Hardware": 0.0005389192960314869, "Health, Healthcare & Wellness": 0.00050285094255177, "History & Geography": 0.00042056460687562013, "Home, Pets & Hobbies": 0.00029815784976354557, "Industry & Production": 0.00034821635686201443, "Language, Literature & Philosophy": 0.000440017575732408, "Politics & Government": 0.0003223403108130746, "Religion & Spirituality": 0.0004328434809416592, "Science, Mathematics & Engineering": 0.26594170004867923, "Relationships & Community": 0.0001979759524712412, "Software Usage & Applications": 0.0018134851535024036, "Programming & Software Engineering": 0.06744353456615287, "Sports & Fitness": 0.00038063246104392444, "Vehicles & Transportation": 0.00022077016295059127, "Travel & Tourism": 0.0005673833381313833, "Other / Uncovered Subject": 0.0022302791811495046, "NONE": 0.6503389246184278}}, "probabilities": {"primary": {"Academic Writing": 4.850609316610479e-05, "Content Listing": 2.2004310644219097e-05, "Creative Writing": 2.335713773437698e-05, "Customer Support Page": 1.2238042981605858e-05, "Q&A Forum": 1.4194144507183522e-05, "Discussion Thread": 8.306102122032728e-06, "Comment Section": 1.542148267608357e-05, "Missing Content": 3.342277788389105e-05, "Knowledge Article": 0.00012617859619642479, "Legal Notices": 2.5290613978325623e-05, "News Article": 4.901505982116289e-06, "Organizational Announcement": 1.097165930165553e-05, "Nonfiction Writing": 1.3837471590392769e-05, "Organizational About Page": 1.2380152165930869e-05, "Personal About Page": 1.3428932301755567e-05, "Personal Blog": 1.0530813914908479e-05, "Product Page": 7.601393839741449e-06, "Spam / Ads": 1.3962415357046933e-05, "Structured Data": 0.0005429930948189973, "Technical Writing": 0.9958634992724054, "Transcript / Interview": 1.8398830615883893e-05, "Tutorial / How-To Guide": 0.0019509860876121723, "User Reviews": 7.650910971768802e-06, "Other / Unknown": 0.0011999381572319297}, "secondary": {"Academic Writing": 0.00027876904918102243, "Content Listing": 0.00012162790554693576, "Creative Writing": 0.0001636333547699882, "Customer Support Page": 0.00011209734783888646, "Q&A Forum": 7.13556834794115e-05, "Discussion Thread": 5.64191673535071e-05, "Comment Section": 0.00041322893654355784, "Missing Content": 0.0005037962650024576, "Knowledge Article": 0.001055661949249031, "Legal Notices": 0.0001716570245158408, "News Article": 6.909984431527774e-05, "Organizational Announcement": 7.337315569949822e-05, "Nonfiction Writing": 8.242932159980695e-05, "Organizational About Page": 8.687079639615417e-05, "Personal About Page": 7.494732266777565e-05, "Personal Blog": 8.236170481748784e-05, "Product Page": 4.976621807005308e-05, "Spam / Ads": 9.431066782566373e-05, "Structured Data": 0.0036462093115537755, "Technical Writing": 0.012256297446511594, "Transcript / Interview": 0.00010120074592619208, "Tutorial / How-To Guide": 0.011228459385313182, "User Reviews": 4.3554217160411076e-05, "NONE": 0.9691628731786626}, "tertiary": {"Academic Writing": 0.00014579410986909235, "Content Listing": 7.76799947327504e-05, "Creative Writing": 9.664721364701962e-05, "Customer Support Page": 7.362854062853174e-05, "Q&A Forum": 3.786958046604281e-05, "Discussion Thread": 3.0238871478808528e-05, "Comment Section": 0.00017883394195340098, "Missing Content": 0.0005096604216173127, "Knowledge Article": 0.0007425589187435615, "Legal Notices": 0.0001333636121200292, "News Article": 4.0350690753784675e-05, "Organizational Announcement": 4.074801147701926e-05, "Nonfiction Writing": 5.5054505970227425e-05, "Organizational About Page": 5.898057101546541e-05, "Personal About Page": 4.498921314398247e-05, "Personal Blog": 4.7267460923967566e-05, "Product Page": 3.195209445038097e-05, "Spam / Ads": 6.66311100152385e-05, "Structured Data": 0.002062870262234891, "Technical Writing": 0.0072367062824130685, "Transcript / Interview": 5.789245799758163e-05, "Tutorial / How-To Guide": 0.005092367784360004, "User Reviews": 2.466562374921166e-05, "NONE": 0.9831132487262386}, "structured_records": {"false": 0.9801913568193447, "true": 0.01980864318065531}, "procedural_steps": {"false": 0.9951755009011144, "true": 0.004824499098885565}, "worked_solution": {"false": 0.9990296130842576, "true": 0.0009703869157423811}, "contextual_dialogue": {"false": 0.9999955345225128, "true": 4.46547748718204e-06}, "content_quality": {"-2": 0.030842003418964595, "-1": 0.24856107101719063, "0": 0.4362199603257219, "1": 0.24256408133449076, "2": 0.041812883903632095}, "noise_severity": {"0": 0.640071293688327, "1": 0.2321682502994974, "2": 0.11142338094747196, "3": 0.011799874202199516, "4": 0.004537200862504069}, "missing_content_severity": {"0": 0.9733866370333079, "1": 0.01856043468047986, "2": 0.0057298841341142, "3": 0.002010805043379939, "4": 0.0003122391087181472}, "factual_density": {"0": 0.024229210991609153, "1": 0.04304569335103831, "2": 0.16503618872475176, "3": 0.3552858726709101, "4": 0.41240303426169067}, "pretraining_utility": {"-2": 0.012310514179846537, "-1": 0.1558774137468921, "0": 0.45368128302863453, "1": 0.32886494070383326, "2": 0.04926584834079358}, "math_density": {"0": 0.30083181010634386, "1": 0.19274122974119867, "2": 0.15450725108166677, "3": 0.11429073121049314, "4": 0.23762897786029755}, "code_density": {"0": 0.00020563805946079, "1": 0.0004251883771346913, "2": 0.011471665267067525, "3": 0.09097541759392552, "4": 0.8969220907024116}, "reasoning_density": {"0": 0.8474469301161898, "1": 0.040880688322641176, "2": 0.061386449420225114, "3": 0.026791807162379784, "4": 0.023494124978564138}, "education_level": {"none": 0.0015022632496003144, "0": 0.0021744292560638015, "1": 0.06990452013461072, "2": 0.8213535345842183, "3": 0.09442912697740867, "4": 0.010636125798098204}, "creative_writing_quality": {"not_applicable": 0.999614459618612, "-2": 8.85430181238118e-05, "-1": 0.00011949176125749003, "0": 0.00012513030963651208, "1": 3.72379750523843e-05, "2": 1.5137317317899327e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.104391133675724e-05, "Art & Design": 0.00015701695383843096, "Law & Crime": 0.00013253607404435164, "Education & Careers": 9.046267988153886e-05, "Entertainment & Media": 6.519113921515703e-05, "Fashion & Beauty": 5.148240251560613e-05, "Business, Economics & Finance": 0.0003811024528955512, "Food & Dining": 5.580346815130874e-05, "Games & Gaming": 0.0001493129035345524, "Electronics & Hardware": 0.00011182614495483909, "Health, Healthcare & Wellness": 6.169401209424056e-05, "History & Geography": 7.124964181048138e-05, "Home, Pets & Hobbies": 5.5717560126737055e-05, "Industry & Production": 6.913824445113848e-05, "Language, Literature & Philosophy": 7.790647127875701e-05, "Politics & Government": 4.150415648072119e-05, "Religion & Spirituality": 5.2437157219929275e-05, "Science, Mathematics & Engineering": 0.03150886888374345, "Relationships & Community": 2.848118966507345e-05, "Software Usage & Applications": 0.00082857094223762, "Programming & Software Engineering": 0.9639383164166827, "Sports & Fitness": 8.025658787973763e-05, "Vehicles & Transportation": 4.0654647163788725e-05, "Travel & Tourism": 8.120452446444934e-05, "Other / Uncovered Subject": 0.0015359000511313936, "Unknown / Insufficient Content": 0.00027232138320169754}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006793588093478261, "Art & Design": 0.001857307088773596, "Law & Crime": 0.0009552909861499796, "Education & Careers": 0.0005243368611655766, "Entertainment & Media": 0.0005548237596261223, "Fashion & Beauty": 0.000366289168717208, "Business, Economics & Finance": 0.0017413732656461288, "Food & Dining": 0.0003051865455382775, "Games & Gaming": 0.0005774376129548327, "Electronics & Hardware": 0.0005389192960314869, "Health, Healthcare & Wellness": 0.00050285094255177, "History & Geography": 0.00042056460687562013, "Home, Pets & Hobbies": 0.00029815784976354557, "Industry & Production": 0.00034821635686201443, "Language, Literature & Philosophy": 0.000440017575732408, "Politics & Government": 0.0003223403108130746, "Religion & Spirituality": 0.0004328434809416592, "Science, Mathematics & Engineering": 0.26594170004867923, "Relationships & Community": 0.0001979759524712412, "Software Usage & Applications": 0.0018134851535024036, "Programming & Software Engineering": 0.06744353456615287, "Sports & Fitness": 0.00038063246104392444, "Vehicles & Transportation": 0.00022077016295059127, "Travel & Tourism": 0.0005673833381313833, "Other / Uncovered Subject": 0.0022302791811495046, "NONE": 0.6503389246184278}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11745, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11745, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:36:06.762307+00:00", "scoring_key_slot": "primary", "dataset_uid": "9395a0c0-c19a-40ec-91b7-00cbdadd8bbc", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "8fa0cb296a172635d974b9ac55e07bf1840c6eb003439cf5d1e437520ae31634", "source_metadata": {"uuid": "9395a0c0-c19a-40ec-91b7-00cbdadd8bbc", "repo_name": "jsqiaoliang/--CTR-", "relative_path": "01_特征工程代码及使用说明/utils.py", "category": "DATA", "algo_rel_score": 0.9744487404823303, "quality_score": 3.9879186153411865, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 8, "repo_created_at": "2018-08-12T03:49:41.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "a0621d37c7b08c27c3679a92edfecd572f7f2c19", "size_bytes": 12965, "file_timestamp": 1534099462, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "549ff2b585e1302a12423da8f3d2afd87d340071"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 8, "repo_created_at": "2018-08-12T03:49:41.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "a0621d37c7b08c27c3679a92edfecd572f7f2c19", "size_bytes": 12965, "file_timestamp": 1534099462, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "549ff2b585e1302a12423da8f3d2afd87d340071", "url": "https://github.com/jsqiaoliang/--CTR-/blob/549ff2b585e1302a12423da8f3d2afd87d340071/01_%E7%89%B9%E5%BE%81%E5%B7%A5%E7%A8%8B%E4%BB%A3%E7%A0%81%E5%8F%8A%E4%BD%BF%E7%94%A8%E8%AF%B4%E6%98%8E/utils.py"}, "gpt4o_tokens": 2992, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.015944771286635122, "noise_severity": 0.5085634382510561, "missing_content_severity": 0.03730157451372067, "factual_density": 3.088587825860035, "pretraining_utility": 0.24689819527883525, "math_density": 1.7951438369772017, "code_density": 3.8839831345026927, "reasoning_density": 0.3380055085644873, "education_level": 2.0415103594142927, "creative_writing_quality": -0.5941405852020106}, "score_applicability": {"education_level": 0.9984977367503998, "creative_writing_quality": 0.00038554038138809756}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/MarcelBostelaar/pandoctool/blob/0c7266f5ca3666d88dd9ccd7baecfa270e4dbcbb/pandoctool.py", "id": "python-0-83d1ab4da00ee8e38716295ad5a12cb7", "text": "import time\n\nimport os\nfrom os.path import isfile, join, isdir\nimport traceback\n\nallowedOutputs = [\"html\", \"pdf\", \"docx\"]\npollTime = 1\ncommandString = 'pandoc -s --toc --toc-depth=2 {original} -o \"{output}\"'\nclearTheScreen = False\n\n\ndef clearScreen():\n    if clearTheScreen:\n        os.system(\"cls\")  # clears screen\n    else:\n        pass\n\n\nclass Menu:\n    def __init__(self):\n        self.actions = {}\n        self.choices = []\n\n    def addChoice(self, name, f):\n        self.actions[name] = f\n        self.choices.append(name)\n\n    def dialog(self, prePrint, error=False):\n        clearScreen()\n        print(prePrint)\n        self.printOptions()\n        if error:\n            print(\"Wrong input, please write a number that is in the list\")\n        choice = input(\"Please choose an option\\n\")\n        try:\n            val = int(choice)\n            self.actions[self.choices[val]]()\n        except Exception as e:\n            print(e)\n            traceback.print_exc()\n            self.dialog(prePrint, True)\n\n    def printOptions(self):\n        for i in range(len(self.choices)):\n            print(str(i) + \". \" + self.choices[i])\n\n\ndef getHash(path):\n    f = open(path, \"rb\")\n    all = f.read()\n    f.close()\n    return hash(all)\n\n\ndef getManyHash(paths):\n    return sum([getHash(x) for x in paths])\n\n\ndef command(fileToWatch, outputLocation):\n    fixedInputs = \" \".join(['\"' + x + '\"' for x in fileToWatch])\n    os.system(commandString.format(original=fixedInputs, output=outputLocation))\n\n\ndef isMD(path):\n    if not os.path.isfile(path):\n        return False\n    return path[-3:] == \".md\"\n\n\ndef isBookDef(path):\n    if not os.path.isfile(path):\n        return False\n    return path[-8:] == \".bookdef\"\n\n\ndef getCommand(mdFiles, outputFile):\n    files = \" \".join(mdFiles)\n    return f'pandoc -s --toc \"{files}\" -o \"{outputFile}\"'\n\n\ndef buildAndVerifyCommand(inputFile, outputType):\n    if isBookDef(inputFile):\n        f = open(inputFile)\n        lines = f.read().replace(\"\\n\", \"\\r\").split(\"\\r\")\n        f.close()\n        origin = os.path.dirname(inputFile)\n        lines = [os.path.join(origin, x) for x in lines if x != \"\"]\n        for x in lines:\n            if not isMD(x):\n                raise Exception(f\"Specified file is not an md file: {x}\")\n        input = lines\n    elif isMD(inputFile):\n        input = [inputFile]\n    else:\n        raise Exception(\"Input not a valid type\")\n    if outputType not in allowedOutputs:\n        raise Exception(\"Invalid output type\")\n    return input, \".\".join(inputFile.split(\".\")[:-1]) + \".\" + outputType\n\n\ndef defFileWatch(whattowatch: str, outputtype: str):\n    defFileHash = getHash(whattowatch)\n    inputs, outputFile = buildAndVerifyCommand(whattowatch, outputtype)\n    command(inputs, outputFile)\n    print(\"Updated!\" + str(time.ctime()))\n    oldHash = getManyHash(inputs)\n    while True:\n        if getHash(whattowatch) != defFileHash:\n            inputs, outputFile = buildAndVerifyCommand(whattowatch, outputtype)\n\n        newHash = getManyHash(inputs)\n        if oldHash != newHash:\n            command(inputs, outputFile)\n            oldHash = getManyHash(inputs)\n            print(\"Updated! - \" + str(time.ctime()))\n        time.sleep(pollTime)\n\n\ndef simpleWatch(whattowatch: str, outputtype: str):\n    inputs, outputFile = buildAndVerifyCommand(whattowatch, outputtype)\n    oldHash = getManyHash(inputs)\n    command(inputs, outputFile)\n    print(\"Updated!\" + str(time.ctime()))\n    while True:\n        newHash = getManyHash(inputs)\n        if oldHash != newHash:\n            command(inputs, outputFile)\n            oldHash = getManyHash(inputs)\n            print(\"Updated! - \" + str(time.ctime()))\n        time.sleep(pollTime)\n\n\ndef getFiles(folder):\n    items = [join(folder, f) for f in os.listdir(folder)]\n    onlyfiles = [f for f in items if isBookDef(f) or isMD(f)]\n    return onlyfiles\n\n\ndef getFolders(folder):\n    items = [join(folder, f) for f in os.listdir(folder)]\n    onlyFolders = [f for f in items if isdir(f)]\n    return onlyFolders\n\n\ndef goUp(fromFolder, chosenAction, cancel, preprint):\n    newFolder = join(fromFolder, \"..\")\n    folderSelection(newFolder, chosenAction, cancel, preprint)\n\n\ndef folderSelection(folder, chosenAction, cancel, preprint):\n    folder = os.path.normpath(folder)\n    currentFolderText = \"\\nCurrent folder: \"\n    folders = getFolders(folder)\n    items = getFiles(folder)\n    folders.sort()\n    items.sort()\n    x = Menu()\n    if len(folder) <= 3:\n        x.addChoice(\"*Select drive\", lambda: selectDrive(chosenAction, cancel, preprint))\n    else:\n        x.addChoice(\"*Folder up\", lambda: goUp(folder, chosenAction, cancel, preprint))\n\n    for i in folders:\n        x.addChoice(os.path.split(i)[-1] + \"\\\\\", lambda i=i: folderSelection(i, chosenAction, cancel, preprint))\n\n    for i in items:\n        x.addChoice(os.path.split(i)[-1], lambda i=i: chosenAction(i))\n\n    x.addChoice(\"*Cancel\", cancel)\n    x.dialog(preprint + currentFolderText + folder)\n\n\ndef selectFolder(action, message, cancelAction):\n    currPath = os.path.dirname(os.path.realpath(__file__))\n    folderSelection(currPath, action, cancelAction, message)\n\n\ndef combinator(simple, bookdef):\n    def internal(file):\n        if isBookDef(file):\n            bookdef(file)\n        else:\n            simple(file)\n    return internal\n\n\ndef selectDrive(chosenAction, cancel, preprint):\n    drives = [ chr(x) + \":\\\\\" for x in range(65,91) if os.path.exists(chr(x) + \":\") ]\n    print(drives)\n    x = Menu()\n    for i in drives:\n        x.addChoice(i, lambda i=i: folderSelection(i, chosenAction, cancel, preprint))\n    x.addChoice(\"*Cancel\", cancel)\n    x.dialog(preprint)\n\n\ndef buildChoice(filename, outputType):\n    inputs, outputFile = buildAndVerifyCommand(filename, outputType)\n    return command(inputs, outputFile)\n\n\ndef WatchBuildSelector(outputType):\n    menu = Menu()\n    menu.addChoice(\"Watch\", lambda: selectFolder(\n            combinator(\n                lambda filename: simpleWatch(filename, outputType), \n                lambda filename: defFileWatch(filename, outputType)\n                ), \n            \"Please select a file to watch\", \n            lambda: WatchBuildSelector(outputType)\n            ))\n\n    menu.addChoice(\"Build\", lambda: selectFolder(\n        lambda filename: buildChoice(filename, outputType), \n        \"Please select a file to build\", \n        lambda: WatchBuildSelector(outputType)\n        ))\n    menu.addChoice(\"Back\", main)\n    menu.dialog(\"Output type: \" + outputType + \"\\nPlease choose a mode.\")\n\n\ndef main():\n    menu = Menu()\n    menu.addChoice(\"HTML\", lambda: WatchBuildSelector(\"html\"))\n    menu.addChoice(\"DOCX\", lambda: WatchBuildSelector(\"docx\"))\n    menu.addChoice(\"*Exit\", exit)\n    menu.dialog(\"Please choose an output target\")\n\n\nif __name__ == \"__main__\":\n    main()", "text_preview": "import time import os from os.path import isfile, join, isdir import traceback allowedOutputs = [\"html\", \"pdf\", \"docx\"] pollTime = 1 commandString = 'pandoc -s --toc --toc-depth=2 {original} -o \"{output}\"' clearTheScreen = False def clearSc", "input_sha256": "80be308c5c78ee0c91331d509695b2450e6df27e6a4c7a5d9e4b0d4066cdb915", "text_sha256": "83d1ab4da00ee8e38716295ad5a12cb7205789e10b1e507582ca6f6b4f1c7db3", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12102, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5083446140069906, "noise_severity": 0.01665736132719348, "missing_content_severity": 0.0029928645939246465, "factual_density": 3.2569456431718224, "pretraining_utility": 0.3987886440929568, "math_density": 0.04977378503856954, "code_density": 3.8671265185691204, "reasoning_density": 0.5615645352706681, "education_level": 1.9445790614743488, "creative_writing_quality": -0.37798000576967283}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0029257264047377607, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9960491318842043, "secondary": 0.988013038874344, "tertiary": 0.99106597077494, "structured_records": 0.0034687136655116844, "procedural_steps": 0.020256810288019123, "worked_solution": 0.0035780068102393464, "contextual_dialogue": 1.4754198395498922e-05, "content_quality": 0.41744920799058427, "noise_severity": 0.9861188655606721, "missing_content_severity": 0.9975059461717295, "factual_density": 0.38078803597651845, "pretraining_utility": 0.47761512257930216, "math_density": 0.958521845801192, "code_density": 0.8892720988075999, "reasoning_density": 0.53202955394111, "education_level": 0.807108527176805, "creative_writing_quality": 0.9991981754892554, "primary_topic": 0.9821346933252986, "secondary_topic": 0.8131185231449382}, "probability_margin": {"primary": 0.9940276235741115, "secondary": 0.9815144147506827, "tertiary": 0.9868134967557253, "education_level": 0.7353854653567117, "creative_writing_quality": 0.9990013841355677, "primary_topic": 0.9675333977803162, "secondary_topic": 0.6710684845018882}, "raw_probabilities": {"primary": {"Academic Writing": 3.210021164937761e-05, "Content Listing": 2.4892945263330858e-05, "Creative Writing": 2.141104177168371e-05, "Customer Support Page": 8.87500654776681e-06, "Q&A Forum": 9.8876646866521e-06, "Discussion Thread": 7.5325260904732024e-06, "Comment Section": 1.5908619223825767e-05, "Missing Content": 3.41170468554755e-05, "Knowledge Article": 7.086492895543946e-05, "Legal Notices": 3.768708163814903e-05, "News Article": 4.557302426552057e-06, "Organizational Announcement": 1.0262358820513491e-05, "Nonfiction Writing": 1.580682596870999e-05, "Organizational About Page": 1.2383295805163424e-05, "Personal About Page": 2.0956994405822094e-05, "Personal Blog": 8.516167646589263e-06, "Product Page": 7.688050339897706e-06, "Spam / Ads": 1.0093230412012796e-05, "Structured Data": 0.00033951579168968026, "Technical Writing": 0.9962137513890291, "Transcript / Interview": 1.713022152668376e-05, "Tutorial / How-To Guide": 0.0008827996790369897, "User Reviews": 7.1338052924052234e-06, "Other / Unknown": 0.002186127814917709}, "secondary": {"Academic Writing": 7.632958816191397e-05, "Content Listing": 0.00010798198970917313, "Creative Writing": 5.791012871430925e-05, "Customer Support Page": 3.45983877293204e-05, "Q&A Forum": 2.4331366389133755e-05, "Discussion Thread": 1.960781792466635e-05, "Comment Section": 6.406873613017534e-05, "Missing Content": 0.00012566614006605925, "Knowledge Article": 0.00019623214258930136, "Legal Notices": 8.753433972038993e-05, "News Article": 3.163204717268041e-05, "Organizational Announcement": 2.826733242468528e-05, "Nonfiction Writing": 3.326736002542696e-05, "Organizational About Page": 4.321370459518821e-05, "Personal About Page": 4.113260929294629e-05, "Personal Blog": 3.5577927173049045e-05, "Product Page": 2.3119381854269508e-05, "Spam / Ads": 3.3724136673762224e-05, "Structured Data": 0.0014428333282055735, "Technical Writing": 0.006998080837230252, "Transcript / Interview": 4.356522081306052e-05, "Tutorial / How-To Guide": 0.0019202282046491635, "User Reviews": 1.8601684842525893e-05, "NONE": 0.988512495587913}, "tertiary": {"Academic Writing": 6.595407172959206e-05, "Content Listing": 0.00014313892485260106, "Creative Writing": 5.81739645836563e-05, "Customer Support Page": 3.703638391260419e-05, "Q&A Forum": 1.9998254970448072e-05, "Discussion Thread": 1.7373915120058905e-05, "Comment Section": 8.15506732924893e-05, "Missing Content": 0.00019761574692868015, "Knowledge Article": 0.00021855667334685065, "Legal Notices": 0.00018740589312102633, "News Article": 3.119793588449976e-05, "Organizational Announcement": 2.7669219287257202e-05, "Nonfiction Writing": 4.719741361547593e-05, "Organizational About Page": 5.0419220330830234e-05, "Personal About Page": 3.6898889237500405e-05, "Personal Blog": 3.05623767950808e-05, "Product Page": 2.6739129479839556e-05, "Spam / Ads": 3.701509168459471e-05, "Structured Data": 0.0009732272302123873, "Technical Writing": 0.004624725236925445, "Transcript / Interview": 4.948662227895569e-05, "Tutorial / How-To Guide": 0.001580671205637048, "User Reviews": 1.9163934122335364e-05, "NONE": 0.9914382219926507}, "structured_records": {"false": 0.9965312863344883, "true": 0.0034687136655116844}, "procedural_steps": {"false": 0.9797431897119808, "true": 0.020256810288019123}, "worked_solution": {"false": 0.9964219931897607, "true": 0.0035780068102393464}, "contextual_dialogue": {"false": 0.9999852458016045, "true": 1.4754198395498922e-05}, "content_quality": {"-2": 0.01922797788775301, "-1": 0.09354552299565404, "0": 0.3505831885475871, "1": 0.43294052835986097, "2": 0.10370278220914479}, "noise_severity": {"0": 0.9862805068517065, "1": 0.01180678702792321, "2": 0.0011987325083948571, "3": 0.0004027851654215483, "4": 0.00031118844655397907}, "missing_content_severity": {"0": 0.9985522919070019, "1": 0.0004238259028267645, "2": 0.0005967411687036933, "3": 0.00033300773217920324, "4": 9.413328928822123e-05}, "factual_density": {"0": 0.01172661446372322, "1": 0.02289341299015277, "2": 0.13831706272632077, "3": 0.350833534550185, "4": 0.4762293752696182}, "pretraining_utility": {"-2": 0.009714595789399217, "-1": 0.0946074128271419, "0": 0.4441325386101015, "1": 0.39026565704781774, "2": 0.061279795725539675}, "math_density": {"0": 0.9603776325930102, "1": 0.03524663762699296, "2": 0.0012118788070006972, "3": 0.0005520140944093567, "4": 0.0026118368785867777}, "code_density": {"0": 0.00022132340031555544, "1": 0.00042713677064540193, "2": 0.012540505168185884, "3": 0.10562576718130981, "4": 0.8811852674795434}, "reasoning_density": {"0": 0.7272697723837653, "1": 0.08159709576208483, "2": 0.12135690218201252, "3": 0.041851283543991036, "4": 0.027924946128146274}, "education_level": {"none": 0.0016325506968091976, "0": 0.0035023829455369674, "1": 0.10387164062395914, "2": 0.8392571059806708, "3": 0.04792669402485832, "4": 0.0038096257281655657}, "creative_writing_quality": {"not_applicable": 0.9993318129077128, "-2": 0.00010007469932155945, "-1": 0.0001567398285244122, "0": 0.0003304287721451119, "1": 5.75597184233083e-05, "2": 2.3384073873089786e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.7977506204934577e-05, "Art & Design": 7.343871502407079e-05, "Law & Crime": 5.046276405256821e-05, "Education & Careers": 5.3458535289074475e-05, "Entertainment & Media": 4.0942606116148064e-05, "Fashion & Beauty": 2.998955105881814e-05, "Business, Economics & Finance": 6.19941821061056e-05, "Food & Dining": 3.274972679893867e-05, "Games & Gaming": 7.843935871253376e-05, "Electronics & Hardware": 0.00012420120969738568, "Health, Healthcare & Wellness": 2.814091707637148e-05, "History & Geography": 3.533630996244493e-05, "Home, Pets & Hobbies": 4.804906041935559e-05, "Industry & Production": 4.8069341444380714e-05, "Language, Literature & Philosophy": 0.000209982985405803, "Politics & Government": 3.6109797285542866e-05, "Religion & Spirituality": 4.0615233466808294e-05, "Science, Mathematics & Engineering": 0.00021463872082373803, "Relationships & Community": 2.4369871908638788e-05, "Software Usage & Applications": 0.015288422724778542, "Programming & Software Engineering": 0.9828218205050948, "Sports & Fitness": 5.508886066220497e-05, "Vehicles & Transportation": 2.2965406413717754e-05, "Travel & Tourism": 4.939183856217377e-05, "Other / Uncovered Subject": 0.0003988765258263936, "Unknown / Insufficient Content": 9.446774580820164e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006369798206019738, "Art & Design": 0.0005646434648789198, "Law & Crime": 0.000517666872583971, "Education & Careers": 0.0003665665293384756, "Entertainment & Media": 0.00046814292814697806, "Fashion & Beauty": 0.0002593479967266454, "Business, Economics & Finance": 0.0004920161699052151, "Food & Dining": 0.00022973990097574244, "Games & Gaming": 0.00035946037851098485, "Electronics & Hardware": 0.0006120110954569845, "Health, Healthcare & Wellness": 0.00023631554815204238, "History & Geography": 0.00024565407593629397, "Home, Pets & Hobbies": 0.00030184127929662685, "Industry & Production": 0.00023908896204697438, "Language, Literature & Philosophy": 0.0009002378586971993, "Politics & Government": 0.00026063421834491115, "Religion & Spirituality": 0.00029418444768606483, "Science, Mathematics & Engineering": 0.0008247094381539737, "Relationships & Community": 0.00017057188137290958, "Software Usage & Applications": 0.14923778775285995, "Programming & Software Engineering": 0.020852537906731687, "Sports & Fitness": 0.00022798248779638347, "Vehicles & Transportation": 0.00011686895973535496, "Travel & Tourism": 0.00036624278180725015, "Other / Uncovered Subject": 0.0009124949895082137, "NONE": 0.8203062722547482}}, "probabilities": {"primary": {"Academic Writing": 3.210021164937761e-05, "Content Listing": 2.4892945263330858e-05, "Creative Writing": 2.141104177168371e-05, "Customer Support Page": 8.87500654776681e-06, "Q&A Forum": 9.8876646866521e-06, "Discussion Thread": 7.5325260904732024e-06, "Comment Section": 1.5908619223825767e-05, "Missing Content": 3.41170468554755e-05, "Knowledge Article": 7.086492895543946e-05, "Legal Notices": 3.768708163814903e-05, "News Article": 4.557302426552057e-06, "Organizational Announcement": 1.0262358820513491e-05, "Nonfiction Writing": 1.580682596870999e-05, "Organizational About Page": 1.2383295805163424e-05, "Personal About Page": 2.0956994405822094e-05, "Personal Blog": 8.516167646589263e-06, "Product Page": 7.688050339897706e-06, "Spam / Ads": 1.0093230412012796e-05, "Structured Data": 0.00033951579168968026, "Technical Writing": 0.9962137513890291, "Transcript / Interview": 1.713022152668376e-05, "Tutorial / How-To Guide": 0.0008827996790369897, "User Reviews": 7.1338052924052234e-06, "Other / Unknown": 0.002186127814917709}, "secondary": {"Academic Writing": 7.632958816191397e-05, "Content Listing": 0.00010798198970917313, "Creative Writing": 5.791012871430925e-05, "Customer Support Page": 3.45983877293204e-05, "Q&A Forum": 2.4331366389133755e-05, "Discussion Thread": 1.960781792466635e-05, "Comment Section": 6.406873613017534e-05, "Missing Content": 0.00012566614006605925, "Knowledge Article": 0.00019623214258930136, "Legal Notices": 8.753433972038993e-05, "News Article": 3.163204717268041e-05, "Organizational Announcement": 2.826733242468528e-05, "Nonfiction Writing": 3.326736002542696e-05, "Organizational About Page": 4.321370459518821e-05, "Personal About Page": 4.113260929294629e-05, "Personal Blog": 3.5577927173049045e-05, "Product Page": 2.3119381854269508e-05, "Spam / Ads": 3.3724136673762224e-05, "Structured Data": 0.0014428333282055735, "Technical Writing": 0.006998080837230252, "Transcript / Interview": 4.356522081306052e-05, "Tutorial / How-To Guide": 0.0019202282046491635, "User Reviews": 1.8601684842525893e-05, "NONE": 0.988512495587913}, "tertiary": {"Academic Writing": 6.595407172959206e-05, "Content Listing": 0.00014313892485260106, "Creative Writing": 5.81739645836563e-05, "Customer Support Page": 3.703638391260419e-05, "Q&A Forum": 1.9998254970448072e-05, "Discussion Thread": 1.7373915120058905e-05, "Comment Section": 8.15506732924893e-05, "Missing Content": 0.00019761574692868015, "Knowledge Article": 0.00021855667334685065, "Legal Notices": 0.00018740589312102633, "News Article": 3.119793588449976e-05, "Organizational Announcement": 2.7669219287257202e-05, "Nonfiction Writing": 4.719741361547593e-05, "Organizational About Page": 5.0419220330830234e-05, "Personal About Page": 3.6898889237500405e-05, "Personal Blog": 3.05623767950808e-05, "Product Page": 2.6739129479839556e-05, "Spam / Ads": 3.701509168459471e-05, "Structured Data": 0.0009732272302123873, "Technical Writing": 0.004624725236925445, "Transcript / Interview": 4.948662227895569e-05, "Tutorial / How-To Guide": 0.001580671205637048, "User Reviews": 1.9163934122335364e-05, "NONE": 0.9914382219926507}, "structured_records": {"false": 0.9965312863344883, "true": 0.0034687136655116844}, "procedural_steps": {"false": 0.9797431897119808, "true": 0.020256810288019123}, "worked_solution": {"false": 0.9964219931897607, "true": 0.0035780068102393464}, "contextual_dialogue": {"false": 0.9999852458016045, "true": 1.4754198395498922e-05}, "content_quality": {"-2": 0.019227977887753014, "-1": 0.09354552299565405, "0": 0.35058318854758713, "1": 0.432940528359861, "2": 0.1037027822091448}, "noise_severity": {"0": 0.9862805068517063, "1": 0.011806787027923206, "2": 0.001198732508394857, "3": 0.0004027851654215482, "4": 0.000311188446553979}, "missing_content_severity": {"0": 0.9985522919070021, "1": 0.0004238259028267646, "2": 0.0005967411687036934, "3": 0.0003330077321792033, "4": 9.413328928822125e-05}, "factual_density": {"0": 0.01172661446372322, "1": 0.02289341299015277, "2": 0.13831706272632077, "3": 0.350833534550185, "4": 0.4762293752696182}, "pretraining_utility": {"-2": 0.009714595789399217, "-1": 0.0946074128271419, "0": 0.4441325386101015, "1": 0.39026565704781774, "2": 0.061279795725539675}, "math_density": {"0": 0.9603776325930102, "1": 0.03524663762699296, "2": 0.0012118788070006972, "3": 0.0005520140944093567, "4": 0.0026118368785867777}, "code_density": {"0": 0.00022132340031555544, "1": 0.00042713677064540193, "2": 0.012540505168185884, "3": 0.10562576718130981, "4": 0.8811852674795434}, "reasoning_density": {"0": 0.7272697723837653, "1": 0.08159709576208483, "2": 0.12135690218201252, "3": 0.041851283543991036, "4": 0.027924946128146274}, "education_level": {"none": 0.0016325506968091976, "0": 0.0035023829455369674, "1": 0.10387164062395914, "2": 0.8392571059806708, "3": 0.04792669402485832, "4": 0.0038096257281655657}, "creative_writing_quality": {"not_applicable": 0.9993318129077126, "-2": 0.00010007469932155942, "-1": 0.00015673982852441217, "0": 0.00033042877214511186, "1": 5.7559718423308286e-05, "2": 2.338407387308978e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.797750620493459e-05, "Art & Design": 7.343871502407082e-05, "Law & Crime": 5.046276405256822e-05, "Education & Careers": 5.3458535289074495e-05, "Entertainment & Media": 4.094260611614808e-05, "Fashion & Beauty": 2.9989551058818152e-05, "Business, Economics & Finance": 6.199418210610563e-05, "Food & Dining": 3.274972679893868e-05, "Games & Gaming": 7.843935871253378e-05, "Electronics & Hardware": 0.00012420120969738573, "Health, Healthcare & Wellness": 2.814091707637149e-05, "History & Geography": 3.533630996244494e-05, "Home, Pets & Hobbies": 4.80490604193556e-05, "Industry & Production": 4.806934144438073e-05, "Language, Literature & Philosophy": 0.0002099829854058031, "Politics & Government": 3.610979728554288e-05, "Religion & Spirituality": 4.061523346680831e-05, "Science, Mathematics & Engineering": 0.00021463872082373811, "Relationships & Community": 2.4369871908638795e-05, "Software Usage & Applications": 0.015288422724778547, "Programming & Software Engineering": 0.9828218205050951, "Sports & Fitness": 5.508886066220499e-05, "Vehicles & Transportation": 2.296540641371776e-05, "Travel & Tourism": 4.939183856217378e-05, "Other / Uncovered Subject": 0.0003988765258263937, "Unknown / Insufficient Content": 9.446774580820167e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006369798206019739, "Art & Design": 0.00056464346487892, "Law & Crime": 0.0005176668725839711, "Education & Careers": 0.00036656652933847564, "Entertainment & Media": 0.0004681429281469781, "Fashion & Beauty": 0.00025934799672664544, "Business, Economics & Finance": 0.0004920161699052152, "Food & Dining": 0.00022973990097574246, "Games & Gaming": 0.0003594603785109849, "Electronics & Hardware": 0.0006120110954569846, "Health, Healthcare & Wellness": 0.0002363155481520424, "History & Geography": 0.000245654075936294, "Home, Pets & Hobbies": 0.0003018412792966269, "Industry & Production": 0.0002390889620469744, "Language, Literature & Philosophy": 0.0009002378586971994, "Politics & Government": 0.0002606342183449112, "Religion & Spirituality": 0.0002941844476860649, "Science, Mathematics & Engineering": 0.0008247094381539738, "Relationships & Community": 0.0001705718813729096, "Software Usage & Applications": 0.14923778775285998, "Programming & Software Engineering": 0.02085253790673169, "Sports & Fitness": 0.0002279824877963835, "Vehicles & Transportation": 0.00011686895973535497, "Travel & Tourism": 0.0003662427818072502, "Other / Uncovered Subject": 0.0009124949895082138, "NONE": 0.8203062722547483}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10302, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10302, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:36:08.144306+00:00", "scoring_key_slot": "primary", "dataset_uid": "19c602f1-b988-40e1-8088-4ed002ca16d3", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "db6d486832a487c50a66936ee4862a3285403ce692e6982d28b1a9b950d6a882", "source_metadata": {"uuid": "19c602f1-b988-40e1-8088-4ed002ca16d3", "repo_name": "MarcelBostelaar/pandoctool", "relative_path": "pandoctool.py", "category": "TOOL", "algo_rel_score": 0.9221822619438171, "quality_score": 5.763596057891846, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2023-03-20T10:56:24.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "b3a73a27bd49c19ae063af00748686b8ee1713cf", "size_bytes": 6783, "file_timestamp": 1679309942, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0c7266f5ca3666d88dd9ccd7baecfa270e4dbcbb"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2023-03-20T10:56:24.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "b3a73a27bd49c19ae063af00748686b8ee1713cf", "size_bytes": 6783, "file_timestamp": 1679309942, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0c7266f5ca3666d88dd9ccd7baecfa270e4dbcbb", "url": "https://github.com/MarcelBostelaar/pandoctool/blob/0c7266f5ca3666d88dd9ccd7baecfa270e4dbcbb/pandoctool.py"}, "gpt4o_tokens": 1663, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.5083446140069906, "noise_severity": 0.016657361327193483, "missing_content_severity": 0.0029928645939246465, "factual_density": 3.2569456431718224, "pretraining_utility": 0.3987886440929568, "math_density": 0.04977378503856954, "code_density": 3.8671265185691204, "reasoning_density": 0.5615645352706681, "education_level": 1.9445790614743488, "creative_writing_quality": -0.37798000576967283}, "score_applicability": {"education_level": 0.9983674493031908, "creative_writing_quality": 0.0006681870922874815}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/h20-studio-tech/aipatent-api/blob/24fc455d0938c9a17ac9bf2d26fdc39c0f94434e/tests/test_ocr_header_detection.py", "id": "python-0-a66559dcde3073c89a96652328ac4db2", "text": "import pytest\nimport json\nfrom pathlib import Path\nfrom typing import List, Dict, Any\n\nfrom pydantic import ValidationError\nfrom src.models.ocr_schemas import ProcessedPage, HeaderDetectionPage\nfrom src.utils.ocr import detect_description_headers\n\n\n\n\n\n# Define the path to the test data\nBASE_DIR = Path(__file__).resolve().parent.parent # This should point to aipatent-api root\nPARTITIONS_DIR = BASE_DIR / \"experiments\" / \"partitions\"\nTEST_PATENT_FILE = PARTITIONS_DIR / \"ald_gvhd_provisional_patent_processed.json\"\n\n# Define the target section name (case-sensitive)\nTARGET_SECTION_NAME = \"Detailed Description\" \n\ndef load_patent_data(file_path: Path) -> List[Dict[str, Any]]:\n    \"\"\"Loads patent data from a JSON file.\"\"\"\n    if not file_path.exists():\n        pytest.fail(f\"Test data file not found: {file_path}\")\n    with open(file_path, 'r', encoding='utf-8') as f:\n        # Assuming the JSON file contains a list of page data objects\n        data = json.load(f)\n    if not isinstance(data, list):\n        pytest.fail(f\"Test data file {file_path} is not a JSON list as expected.\")\n    return data\n\n@pytest.mark.asyncio\nasync def test_detect_headers_on_detailed_description():\n    \"\"\"Test header detection on pages from the 'Detailed Description' section of a patent.\"\"\"\n    raw_pages_data = load_patent_data(TEST_PATENT_FILE)\n\n    processed_pages_for_test: List[ProcessedPage] = [] # Uses test's local ProcessedPage model\n    for i, page_data_from_json in enumerate(raw_pages_data):\n        try:\n            # Transform page_data_from_json to match the test's local ProcessedPage model structure\n            transformed_page_dict = {\n                \"text\": page_data_from_json.get(\"text\"),         # Map 'text' to 'text_content'\n                \"filename\": page_data_from_json.get(\"filename\"),\n                \"page_number\": page_data_from_json.get(\"page_number\"),\n                \"section\": page_data_from_json.get(\"section\"),\n                \"image\": page_data_from_json.get(\"image\"),       # Map 'image' to 'image_base64'\n            }\n\n            json_section_name = page_data_from_json.get(\"section\")\n            if json_section_name:\n                # Create the PatentSectionWithConfidence object as expected by test's ProcessedPage model\n            \n                page_obj = ProcessedPage(**transformed_page_dict) # Instantiate with transformed data\n\n            # Original filtering logic based on the test's ProcessedPage model structure\n            if (\n    page_obj.section\n    and page_obj.section.strip().lower() == TARGET_SECTION_NAME.strip().lower()\n):\n                if page_obj.image:\n                    processed_pages_for_test.append(page_obj)\n                else:\n                    print(f\"Skipping page {page_obj.page_number} (idx {i}) from {page_obj.filename}: No image for section '{TARGET_SECTION_NAME}'.\")\n            else:\n                section_name_for_log = page_obj.section if page_obj.section else \"None\"\n                print(f\"Skipping page {page_obj.page_number} (idx {i}): section '{section_name_for_log}' ('{json_section_name}') != '{TARGET_SECTION_NAME}' or no image\")\n\n        except ValidationError as e:\n            print(f\"Warning: Skipping page data at index {i} ({page_data_from_json.get('filename')}, page {page_data_from_json.get('page_number')}) due to Pydantic validation error: {e}\")\n        except Exception as e: # Catch other potential errors during transformation\n            print(f\"Warning: Skipping page data at index {i} ({page_data_from_json.get('filename')}, page {page_data_from_json.get('page_number')}) due to error: {e}\")\n\n    if not processed_pages_for_test:\n        pytest.skip(f\"No suitable pages (with images and section '{TARGET_SECTION_NAME}') found in {TEST_PATENT_FILE.name}. Check TARGET_SECTION_NAME and data structure.\")\n\n    print(f\"Running header detection on {len(processed_pages_for_test)} filtered pages.\")\n    \n    # Ensure detect_description_headers is imported correctly\n    # from src.utils.ocr import detect_description_headers\n    try:\n        results: List[HeaderDetectionPage] = await detect_description_headers(processed_pages_for_test)\n    except ValidationError as e:\n        print(f\"ValidationError in detect_description_headers: {e}\")\n        raise\n\n    assert len(results) == len(processed_pages_for_test), \\\n        f\"Number of results ({len(results)}) should match number of input pages ({len(processed_pages_for_test)})\"\n\n    for i, result_page in enumerate(results):\n        original_page = processed_pages_for_test[i]\n        assert isinstance(result_page, HeaderDetectionPage), \\\n            f\"Result item {i} is not a HeaderDetectionPage object\"\n        assert result_page.filename == original_page.filename, \\\n            f\"Filename mismatch for page {i}\"\n        assert result_page.page_number == original_page.page_number, \\\n            f\"Page number mismatch for page {i}\"\n        \n        # Check the direct attributes of HeaderDetectionPage that detect_description_header populates\n        assert isinstance(result_page.has_header, bool), \\\n            f\"'has_header' should be a boolean for page {i}\"\n        if result_page.header is not None:\n            assert isinstance(result_page.header, str), \\\n                f\"'header' should be a string or None for page {i}\"\n\n    print(f\"Successfully asserted header detection results for {len(results)} pages.\")\n    # Optional: Log some specific results for manual inspection if needed\n    detected_count = sum(1 for r in results if r.has_header)\n    print(f\"{detected_count} headers detected out of {len(results)} pages.\")\n", "text_preview": "import pytest import json from pathlib import Path from typing import List, Dict, Any from pydantic import ValidationError from src.models.ocr_schemas import ProcessedPage, HeaderDetectionPage from src.utils.ocr import detect_description_he", "input_sha256": "1aa58da75c621244e49070fd86eb1f4fdb693afe4735103b0749b1b40f3d40b5", "text_sha256": "a66559dcde3073c89a96652328ac4db2fa18eae07b1ba200306179627441293a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12103, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.09669162325760555, "noise_severity": 0.09665698244176307, "missing_content_severity": 0.04990004803528837, "factual_density": 2.929800182391793, "pretraining_utility": 0.4254903927531862, "math_density": 0.006186181600349046, "code_density": 3.7615437370302227, "reasoning_density": 0.9663435701165637, "education_level": 2.4102237900379255, "creative_writing_quality": -0.31650025706380563}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.014851798615016176, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9958051807359791, "secondary": 0.9688513288477842, "tertiary": 0.9843488578373663, "structured_records": 0.0063613790947545205, "procedural_steps": 0.07190509031673202, "worked_solution": 0.02294677433913791, "contextual_dialogue": 5.997331447713813e-06, "content_quality": 0.359366289066805, "noise_severity": 0.9194525146318641, "missing_content_severity": 0.9584166266372597, "factual_density": 0.4261859628779686, "pretraining_utility": 0.45030178103486773, "math_density": 0.9948448486663758, "code_density": 0.8012864475251852, "reasoning_density": 0.19471369156953033, "education_level": 0.5490616103576028, "creative_writing_quality": 0.99890381798537, "primary_topic": 0.9629580923985592, "secondary_topic": 0.619314601596411}, "probability_margin": {"primary": 0.9938434746049172, "secondary": 0.9557504345052974, "tertiary": 0.9791079604942787, "education_level": 0.30982470282764246, "creative_writing_quality": 0.9986170683723972, "primary_topic": 0.944553353509769, "secondary_topic": 0.4266470619457652}, "raw_probabilities": {"primary": {"Academic Writing": 5.4713290892278604e-05, "Content Listing": 1.6188270991275963e-05, "Creative Writing": 1.428106572130645e-05, "Customer Support Page": 1.01559178467395e-05, "Q&A Forum": 1.2680952222487915e-05, "Discussion Thread": 7.060931338337747e-06, "Comment Section": 1.1643339696742085e-05, "Missing Content": 2.396747546493076e-05, "Knowledge Article": 6.411026350541116e-05, "Legal Notices": 2.797830736021132e-05, "News Article": 5.948098027512132e-06, "Organizational Announcement": 1.1007886975704147e-05, "Nonfiction Writing": 1.1998031956568118e-05, "Organizational About Page": 1.2280220452213267e-05, "Personal About Page": 9.037414180899574e-06, "Personal Blog": 7.610334691984946e-06, "Product Page": 7.707077361855517e-06, "Spam / Ads": 1.1182201038408637e-05, "Structured Data": 0.00044455023173405544, "Technical Writing": 0.99597996487198, "Transcript / Interview": 1.5335468281214486e-05, "Tutorial / How-To Guide": 0.0010963936797355012, "User Reviews": 7.714401481315123e-06, "Other / Unknown": 0.002136490267062913}, "secondary": {"Academic Writing": 0.0007190090627885009, "Content Listing": 0.0001097631057086713, "Creative Writing": 0.000154869490805398, "Customer Support Page": 0.00010418503359745145, "Q&A Forum": 7.819800418684882e-05, "Discussion Thread": 5.9200162667939315e-05, "Comment Section": 0.00019887969006906033, "Missing Content": 0.0005264766582956428, "Knowledge Article": 0.0007961225109476767, "Legal Notices": 0.00028224427802281773, "News Article": 9.240953019201319e-05, "Organizational Announcement": 8.923213510899923e-05, "Nonfiction Writing": 9.555005056086111e-05, "Organizational About Page": 9.332270656733526e-05, "Personal About Page": 7.967650630407499e-05, "Personal Blog": 8.85777146718152e-05, "Product Page": 6.391611824630834e-05, "Spam / Ads": 9.159565453460378e-05, "Structured Data": 0.005725687141114096, "Technical Writing": 0.014398755640495817, "Transcript / Interview": 0.00011455103659902601, "Tutorial / How-To Guide": 0.005834769884948482, "User Reviews": 5.381773777338109e-05, "NONE": 0.9701491901457933}, "tertiary": {"Academic Writing": 0.0009210131492377959, "Content Listing": 6.633833949697543e-05, "Creative Writing": 9.762000045326648e-05, "Customer Support Page": 6.57469942284067e-05, "Q&A Forum": 4.000589761527572e-05, "Discussion Thread": 3.1711802555989136e-05, "Comment Section": 0.00015033986475826647, "Missing Content": 0.0005635431048799906, "Knowledge Article": 0.0008273642789916811, "Legal Notices": 0.00032870551277403655, "News Article": 4.9361013539960606e-05, "Organizational Announcement": 5.2257777413576234e-05, "Nonfiction Writing": 8.632468797614908e-05, "Organizational About Page": 6.555806526970068e-05, "Personal About Page": 4.4038941872497355e-05, "Personal Blog": 4.565293329228661e-05, "Product Page": 4.018370114270193e-05, "Spam / Ads": 5.9201449102605424e-05, "Structured Data": 0.0026752422324927733, "Technical Writing": 0.005893028266530592, "Transcript / Interview": 6.486505097340106e-05, "Tutorial / How-To Guide": 0.002798532911969882, "User Reviews": 3.2375262622803354e-05, "NONE": 0.9850009887608093}, "structured_records": {"false": 0.9936386209052455, "true": 0.0063613790947545205}, "procedural_steps": {"false": 0.928094909683268, "true": 0.07190509031673202}, "worked_solution": {"false": 0.9770532256608621, "true": 0.02294677433913791}, "contextual_dialogue": {"false": 0.9999940026685523, "true": 5.997331447713813e-06}, "content_quality": {"-2": 0.055620417834760245, "-1": 0.2247935792615937, "0": 0.365068126397623, "1": 0.2763097148233263, "2": 0.07820816168269672}, "noise_severity": {"0": 0.9173958697093295, "1": 0.07237303631923434, "2": 0.007371106336736761, "3": 0.0018982170897424945, "4": 0.000961770544956934}, "missing_content_severity": {"0": 0.9679145794551006, "1": 0.01750758985681627, "2": 0.011896891952905967, "3": 0.0021250806680486335, "4": 0.0005558580671285662}, "factual_density": {"0": 0.013119668158204013, "1": 0.05409224247398227, "2": 0.23184484165474595, "3": 0.39175473424395274, "4": 0.30918851346911513}, "pretraining_utility": {"-2": 0.012942667341688776, "-1": 0.09118840031910877, "0": 0.41792175384768204, "1": 0.4133302292273683, "2": 0.06461694926415207}, "math_density": {"0": 0.99649800954534, "1": 0.0023317909734058345, "2": 0.00028341461796287733, "3": 0.00025957806214872014, "4": 0.0006272068011428244}, "code_density": {"0": 0.0003432351020900949, "1": 0.000662144239687101, "2": 0.023645861142276722, "3": 0.18780516755780263, "4": 0.7875435919581434}, "reasoning_density": {"0": 0.5374203794429002, "1": 0.12858516983722312, "2": 0.2054138598430615, "3": 0.0873916829140426, "4": 0.041188907962772414}, "education_level": {"none": 0.0010208929148284988, "0": 0.001521392210545035, "1": 0.0064127755948722, "2": 0.6242180086313357, "3": 0.31439330580369323, "4": 0.05243362484472536}, "creative_writing_quality": {"not_applicable": 0.9990865149878083, "-2": 0.00011206387335444053, "-1": 0.00021531771054663506, "0": 0.0004694466154112057, "1": 8.298640968588726e-05, "2": 3.3670403193513856e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.712329480614666e-05, "Art & Design": 0.0001163925581291245, "Law & Crime": 0.01982942764269176, "Education & Careers": 7.272630955522052e-05, "Entertainment & Media": 6.275698774018429e-05, "Fashion & Beauty": 5.818121257380264e-05, "Business, Economics & Finance": 0.0005096881571519793, "Food & Dining": 5.909500993703769e-05, "Games & Gaming": 7.731183790906127e-05, "Electronics & Hardware": 0.0001085337746067896, "Health, Healthcare & Wellness": 7.51166551563101e-05, "History & Geography": 7.213021316610212e-05, "Home, Pets & Hobbies": 5.458217277096033e-05, "Industry & Production": 8.84435730461144e-05, "Language, Literature & Philosophy": 0.00012548620851776464, "Politics & Government": 0.0006199069250046996, "Religion & Spirituality": 8.008573513764736e-05, "Science, Mathematics & Engineering": 0.0076810853549227525, "Relationships & Community": 2.6901474962910645e-05, "Software Usage & Applications": 0.00027749055116084925, "Programming & Software Engineering": 0.9643827811524608, "Sports & Fitness": 7.11523971168427e-05, "Vehicles & Transportation": 3.9421759608596436e-05, "Travel & Tourism": 7.89886210485909e-05, "Other / Uncovered Subject": 0.005262047839610741, "Unknown / Insufficient Content": 0.00010314258120742858}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008423972707686655, "Art & Design": 0.0011331522193389703, "Law & Crime": 0.20730928574309163, "Education & Careers": 0.0005833487877876115, "Entertainment & Media": 0.0006770550182472726, "Fashion & Beauty": 0.00044710867671504884, "Business, Economics & Finance": 0.0020175872672807565, "Food & Dining": 0.0003316274460941364, "Games & Gaming": 0.000423564963191273, "Electronics & Hardware": 0.0007467248647275229, "Health, Healthcare & Wellness": 0.0006907900653230439, "History & Geography": 0.0005280911384410447, "Home, Pets & Hobbies": 0.0003618889094101251, "Industry & Production": 0.00043311749793955917, "Language, Literature & Philosophy": 0.0006410011057083647, "Politics & Government": 0.0022423099162345033, "Religion & Spirituality": 0.0006218940031792683, "Science, Mathematics & Engineering": 0.0733529576781562, "Relationships & Community": 0.00019018225871301022, "Software Usage & Applications": 0.0007989744719922617, "Programming & Software Engineering": 0.06598011784641387, "Sports & Fitness": 0.00041327512980259625, "Vehicles & Transportation": 0.00022797877087984002, "Travel & Tourism": 0.0005065424313484457, "Other / Uncovered Subject": 0.0045426788303581855, "NONE": 0.6339563476888568}}, "probabilities": {"primary": {"Academic Writing": 5.471329089227861e-05, "Content Listing": 1.6188270991275967e-05, "Creative Writing": 1.4281065721306451e-05, "Customer Support Page": 1.0155917846739501e-05, "Q&A Forum": 1.2680952222487916e-05, "Discussion Thread": 7.060931338337748e-06, "Comment Section": 1.1643339696742086e-05, "Missing Content": 2.3967475464930763e-05, "Knowledge Article": 6.411026350541117e-05, "Legal Notices": 2.7978307360211324e-05, "News Article": 5.948098027512133e-06, "Organizational Announcement": 1.1007886975704149e-05, "Nonfiction Writing": 1.199803195656812e-05, "Organizational About Page": 1.2280220452213268e-05, "Personal About Page": 9.037414180899576e-06, "Personal Blog": 7.610334691984947e-06, "Product Page": 7.70707736185552e-06, "Spam / Ads": 1.1182201038408638e-05, "Structured Data": 0.0004445502317340555, "Technical Writing": 0.9959799648719802, "Transcript / Interview": 1.533546828121449e-05, "Tutorial / How-To Guide": 0.0010963936797355014, "User Reviews": 7.714401481315125e-06, "Other / Unknown": 0.0021364902670629135}, "secondary": {"Academic Writing": 0.0007190090627885009, "Content Listing": 0.0001097631057086713, "Creative Writing": 0.000154869490805398, "Customer Support Page": 0.00010418503359745145, "Q&A Forum": 7.819800418684882e-05, "Discussion Thread": 5.9200162667939315e-05, "Comment Section": 0.00019887969006906033, "Missing Content": 0.0005264766582956428, "Knowledge Article": 0.0007961225109476767, "Legal Notices": 0.00028224427802281773, "News Article": 9.240953019201319e-05, "Organizational Announcement": 8.923213510899923e-05, "Nonfiction Writing": 9.555005056086111e-05, "Organizational About Page": 9.332270656733526e-05, "Personal About Page": 7.967650630407499e-05, "Personal Blog": 8.85777146718152e-05, "Product Page": 6.391611824630834e-05, "Spam / Ads": 9.159565453460378e-05, "Structured Data": 0.005725687141114096, "Technical Writing": 0.014398755640495817, "Transcript / Interview": 0.00011455103659902601, "Tutorial / How-To Guide": 0.005834769884948482, "User Reviews": 5.381773777338109e-05, "NONE": 0.9701491901457933}, "tertiary": {"Academic Writing": 0.000921013149237796, "Content Listing": 6.633833949697544e-05, "Creative Writing": 9.76200004532665e-05, "Customer Support Page": 6.574699422840672e-05, "Q&A Forum": 4.000589761527573e-05, "Discussion Thread": 3.171180255598914e-05, "Comment Section": 0.0001503398647582665, "Missing Content": 0.0005635431048799907, "Knowledge Article": 0.0008273642789916812, "Legal Notices": 0.0003287055127740366, "News Article": 4.936101353996061e-05, "Organizational Announcement": 5.225777741357624e-05, "Nonfiction Writing": 8.63246879761491e-05, "Organizational About Page": 6.55580652697007e-05, "Personal About Page": 4.403894187249736e-05, "Personal Blog": 4.5652933292286616e-05, "Product Page": 4.018370114270194e-05, "Spam / Ads": 5.920144910260543e-05, "Structured Data": 0.0026752422324927737, "Technical Writing": 0.0058930282665305925, "Transcript / Interview": 6.486505097340107e-05, "Tutorial / How-To Guide": 0.0027985329119698823, "User Reviews": 3.237526262280336e-05, "NONE": 0.9850009887608094}, "structured_records": {"false": 0.9936386209052455, "true": 0.0063613790947545205}, "procedural_steps": {"false": 0.928094909683268, "true": 0.07190509031673202}, "worked_solution": {"false": 0.9770532256608621, "true": 0.02294677433913791}, "contextual_dialogue": {"false": 0.9999940026685523, "true": 5.997331447713813e-06}, "content_quality": {"-2": 0.05562041783476025, "-1": 0.22479357926159374, "0": 0.36506812639762304, "1": 0.27630971482332634, "2": 0.07820816168269673}, "noise_severity": {"0": 0.9173958697093295, "1": 0.07237303631923434, "2": 0.007371106336736761, "3": 0.0018982170897424945, "4": 0.000961770544956934}, "missing_content_severity": {"0": 0.9679145794551006, "1": 0.01750758985681627, "2": 0.011896891952905967, "3": 0.0021250806680486335, "4": 0.0005558580671285662}, "factual_density": {"0": 0.013119668158204013, "1": 0.05409224247398227, "2": 0.23184484165474595, "3": 0.39175473424395274, "4": 0.30918851346911513}, "pretraining_utility": {"-2": 0.012942667341688778, "-1": 0.09118840031910878, "0": 0.4179217538476821, "1": 0.41333022922736834, "2": 0.06461694926415208}, "math_density": {"0": 0.9964980095453397, "1": 0.002331790973405834, "2": 0.0002834146179628773, "3": 0.0002595780621487201, "4": 0.0006272068011428243}, "code_density": {"0": 0.00034323510209009495, "1": 0.0006621442396871011, "2": 0.023645861142276726, "3": 0.18780516755780266, "4": 0.7875435919581435}, "reasoning_density": {"0": 0.5374203794429003, "1": 0.12858516983722315, "2": 0.20541385984306154, "3": 0.08739168291404262, "4": 0.04118890796277242}, "education_level": {"none": 0.0010208929148284988, "0": 0.001521392210545035, "1": 0.0064127755948722, "2": 0.6242180086313357, "3": 0.31439330580369323, "4": 0.05243362484472536}, "creative_writing_quality": {"not_applicable": 0.9990865149878083, "-2": 0.00011206387335444053, "-1": 0.00021531771054663506, "0": 0.0004694466154112057, "1": 8.298640968588726e-05, "2": 3.3670403193513856e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.712329480614665e-05, "Art & Design": 0.00011639255812912447, "Law & Crime": 0.019829427642691758, "Education & Careers": 7.272630955522051e-05, "Entertainment & Media": 6.275698774018427e-05, "Fashion & Beauty": 5.8181212573802627e-05, "Business, Economics & Finance": 0.0005096881571519791, "Food & Dining": 5.909500993703768e-05, "Games & Gaming": 7.731183790906126e-05, "Electronics & Hardware": 0.00010853377460678957, "Health, Healthcare & Wellness": 7.511665515631008e-05, "History & Geography": 7.213021316610211e-05, "Home, Pets & Hobbies": 5.458217277096032e-05, "Industry & Production": 8.844357304611439e-05, "Language, Literature & Philosophy": 0.00012548620851776462, "Politics & Government": 0.0006199069250046995, "Religion & Spirituality": 8.008573513764735e-05, "Science, Mathematics & Engineering": 0.007681085354922751, "Relationships & Community": 2.6901474962910638e-05, "Software Usage & Applications": 0.0002774905511608492, "Programming & Software Engineering": 0.9643827811524606, "Sports & Fitness": 7.115239711684269e-05, "Vehicles & Transportation": 3.942175960859643e-05, "Travel & Tourism": 7.898862104859089e-05, "Other / Uncovered Subject": 0.00526204783961074, "Unknown / Insufficient Content": 0.00010314258120742855}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008423972707686655, "Art & Design": 0.0011331522193389703, "Law & Crime": 0.20730928574309163, "Education & Careers": 0.0005833487877876115, "Entertainment & Media": 0.0006770550182472726, "Fashion & Beauty": 0.00044710867671504884, "Business, Economics & Finance": 0.0020175872672807565, "Food & Dining": 0.0003316274460941364, "Games & Gaming": 0.000423564963191273, "Electronics & Hardware": 0.0007467248647275229, "Health, Healthcare & Wellness": 0.0006907900653230439, "History & Geography": 0.0005280911384410447, "Home, Pets & Hobbies": 0.0003618889094101251, "Industry & Production": 0.00043311749793955917, "Language, Literature & Philosophy": 0.0006410011057083647, "Politics & Government": 0.0022423099162345033, "Religion & Spirituality": 0.0006218940031792683, "Science, Mathematics & Engineering": 0.0733529576781562, "Relationships & Community": 0.00019018225871301022, "Software Usage & Applications": 0.0007989744719922617, "Programming & Software Engineering": 0.06598011784641387, "Sports & Fitness": 0.00041327512980259625, "Vehicles & Transportation": 0.00022797877087984002, "Travel & Tourism": 0.0005065424313484457, "Other / Uncovered Subject": 0.0045426788303581855, "NONE": 0.6339563476888568}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9758, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9758, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:36:08.628356+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "fa183de1-424c-4ac2-847e-de6971ffc9d1", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "f865b5b708afd4efaf17d2fcc1cc5dcc1ed1265a31d328c64ba4c9e8ba41ac4f", "source_metadata": {"uuid": "fa183de1-424c-4ac2-847e-de6971ffc9d1", "repo_name": "h20-studio-tech/aipatent-api", "relative_path": "tests/test_ocr_header_detection.py", "category": "TEST", "algo_rel_score": 0.9933079481124878, "quality_score": 7.847318649291992, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 145, "repo_created_at": "2025-03-13T00:26:02.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "fd5e465343553b74784bc3b5def18eb65c1a5e61", "size_bytes": 5593, "file_timestamp": 1753360968, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "24fc455d0938c9a17ac9bf2d26fdc39c0f94434e"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 145, "repo_created_at": "2025-03-13T00:26:02.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "fd5e465343553b74784bc3b5def18eb65c1a5e61", "size_bytes": 5593, "file_timestamp": 1753360968, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "24fc455d0938c9a17ac9bf2d26fdc39c0f94434e", "url": "https://github.com/h20-studio-tech/aipatent-api/blob/24fc455d0938c9a17ac9bf2d26fdc39c0f94434e/tests/test_ocr_header_detection.py"}, "gpt4o_tokens": 1192, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.09669162325760555, "noise_severity": 0.09665698244176307, "missing_content_severity": 0.04990004803528837, "factual_density": 2.929800182391793, "pretraining_utility": 0.4254903927531862, "math_density": 0.006186181600349046, "code_density": 3.7615437370302227, "reasoning_density": 0.9663435701165637, "education_level": 2.4102237900379255, "creative_writing_quality": -0.31650025706380563}, "score_applicability": {"education_level": 0.9989791070851716, "creative_writing_quality": 0.0009134850121916825}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/j-morano/ariavt-backend/blob/061ac8035c49904aeea02f00378d4a99cd80b0d0/tests/test_users.py", "id": "python-0-71e0ac1d6869caace9047e3f08f7c11d", "text": "import pytest\nfrom http import HTTPStatus as StC\nfrom httpx import AsyncClient\n\nfrom tests.models_test import AccessToken, TokenResponse\n\nfrom app.globals import USER_ROLE\n\n\n@pytest.mark.asyncio\nasync def test_user_methods(client: AsyncClient, token_r: TokenResponse):\n\n    user1 = {\n        \"username\": \"user1\",\n        \"full_name\": \"<NAME>\",\n        \"email\": \"<EMAIL>\",\n        \"role\": USER_ROLE,\n        \"password\": \"<PASSWORD>\"\n    }\n\n    user2 = {\n        \"username\": \"user2\",\n        \"full_name\": \"<NAME>\",\n        \"email\": \"<EMAIL>\",\n        \"role\": USER_ROLE,\n        \"password\": \"<PASSWORD>\"\n    }\n\n    user3 = {\n        \"username\": \"user3\",\n        \"full_name\": \"User One\",\n        \"email\": \"<EMAIL>\",\n        \"role\": USER_ROLE,\n        \"password\": \"<PASSWORD>\"\n    }\n\n    # -----------------------------------------------------------------\n\n    # -> Add users\n\n    response = await client.post(\n        \"/users/\",\n        data=user1,\n        headers=token_r.headers\n    )\n\n    assert response.status_code == StC.OK\n    user1_id = response.json()['id']\n\n    response = await client.post(\n        \"/users/\",\n        data=user2,\n        headers=token_r.headers\n    )\n\n    print(response.json())\n    assert response.status_code == StC.OK\n    assert response.json()['id']\n    user2_id = response.json()['id']\n\n    # -> Get added users\n\n    response = await client.get(\n        \"/users/{}\".format(user1_id),\n        headers=token_r.headers\n    )\n\n    print(response.json())\n    assert response.status_code == StC.OK\n    assert response.json()['id']\n    assert response.json()['username']\n    assert response.json()['full_name']\n    assert response.json()['role']\n    assert response.json()['email']\n    assert response.json()['username'] == user1['username']\n\n    response = await client.get(\n        \"/users/{}\".format(user2_id),\n        headers=token_r.headers\n    )\n\n    print(response.json())\n    assert response.status_code == StC.OK\n    assert response.json()['id']\n    assert response.json()['username']\n    assert response.json()['full_name']\n    assert response.json()['role']\n    assert response.json()['email']\n\n    # -> Get all users\n\n    response = await client.get(\"/users/\", headers=token_r.headers)\n    print(response.json())\n    assert response.status_code == StC.OK\n    # 4: 2 default users (admin, user) + 2 added users\n    assert len(response.json()) == 4\n\n    # -----------------------------------------------------------------\n    # => Update user\n\n    # -> Update user as admin\n\n    # - Update user\n\n    user1_updated = user1.copy()\n    del user1_updated['username']\n    user1_updated['full_name'] = 'User 1 Updated'\n    response = await client.put(\n        \"/users/{}\".format(user1_id),\n        data=user1_updated,\n        headers=token_r.headers\n    )\n    assert response.status_code == StC.OK\n\n    # - Get updated user\n\n    response = await client.get(\n        \"/users/{}\".format(user1_id),\n        headers=token_r.headers\n    )\n\n    print(response.json())\n    assert response.status_code == StC.OK\n    assert response.json()['full_name'] == 'User 1 Updated'\n\n    # -> Update user as user\n\n    user_tr = await AccessToken.get_certain_token(client, \"user1\", \"user1\")\n\n    # - Update user\n\n    user1_updated = user1.copy()\n    del user1_updated['username']\n    user1_updated['full_name'] = 'User 1 Updated (II)'\n    user1_updated['old_password'] = <PASSWORD>1['<PASSWORD>']\n    user1_updated['password'] = '<PASSWORD>'\n    print('user1_updated', user1_updated)\n\n    response = await client.put(\n        \"/users/{}\".format(user1_id),\n        data=user1_updated,\n        headers=user_tr.headers\n    )\n    if response.status_code != StC.OK:\n        print(response.json())\n    assert response.status_code == StC.OK\n\n    # - Get updated user\n\n    response = await client.get(\n        \"/users/{}\".format(user1_id),\n        headers=token_r.headers\n    )\n\n    print(response.json())\n    assert response.status_code == StC.OK\n    assert response.json()['full_name'] == 'User 1 Updated (II)'\n\n    # -----------------------------------------------------------------\n    # => Try to perform admin operations with user account\n\n    user_tr = await AccessToken.get_certain_token(\n        client, \"user1\", \"<PASSWORD>\")\n\n    response = await client.post(\n        \"/users/\",\n        data=user3,\n        headers=user_tr.headers\n    )\n    assert response.status_code == StC.UNAUTHORIZED\n\n    response = await client.get(\n        \"/users/{}\".format(user2_id),\n        headers=user_tr.headers\n    )\n    assert response.status_code == StC.UNAUTHORIZED\n\n    # - Delete added user\n    response = await client.delete(\n        \"/users/{}\".format(user2_id),\n        headers=user_tr.headers)\n    assert response.status_code == StC.UNAUTHORIZED\n\n    # -----------------------------------------------------------------\n\n    # -> Delete added users\n\n    response = await client.delete(\n        \"/users/{}\".format(user1_id),\n        headers=token_r.headers\n    )\n    assert response.status_code == StC.OK\n\n    response = await client.delete(\n        \"/users/{}\".format(user2_id),\n        headers=token_r.headers\n    )\n\n    print(response.json())\n    assert response.status_code == StC.OK\n    assert response.json()['removed']\n    assert response.json()['removed'] == user2_id\n\n    # -> Get deleted users\n\n    response = await client.get(\n        \"/users/{}\".format(user1_id),\n        headers=token_r.headers\n    )\n    assert response.status_code == StC.NOT_FOUND\n\n    response = await client.get(\n        \"/users/{}\".format(user2_id),\n        headers=token_r.headers\n    )\n    assert response.status_code == StC.NOT_FOUND\n", "text_preview": "import pytest from http import HTTPStatus as StC from httpx import AsyncClient from tests.models_test import AccessToken, TokenResponse from app.globals import USER_ROLE @pytest.mark.asyncio async def test_user_methods(client: AsyncClient, ", "input_sha256": "933a141c9a341649ddd90149a55ac9e71153fe4bc07c170a48db0cd85768d490", "text_sha256": "71e0ac1d6869caace9047e3f08f7c11d736b118c0cd7f567ac47589e2dbea816", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12105, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.535067951502757, "noise_severity": 0.3489168319491127, "missing_content_severity": 0.23934373587944882, "factual_density": 2.9481572208990583, "pretraining_utility": 0.5122416461640462, "math_density": 0.04934325642635788, "code_density": 3.883236002911708, "reasoning_density": 1.0242513153297597, "education_level": 2.3298903298247855, "creative_writing_quality": -0.26908634413697213}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.03473726287368151, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9905210793431113, "secondary": 0.9110314126516881, "tertiary": 0.9472789913024864, "structured_records": 0.2825846388977247, "procedural_steps": 0.04759102756996296, "worked_solution": 0.14763737136510846, "contextual_dialogue": 2.823091268787577e-05, "content_quality": 0.3099646881423196, "noise_severity": 0.7092359733757394, "missing_content_severity": 0.8005468867671259, "factual_density": 0.4544444360580717, "pretraining_utility": 0.44342196881235507, "math_density": 0.9588806196447017, "code_density": 0.90269666909309, "reasoning_density": 0.14645723722520065, "education_level": 0.6145452365580087, "creative_writing_quality": 0.9991535866215058, "primary_topic": 0.9974485579427302, "secondary_topic": 0.9673592198322634}, "probability_margin": {"primary": 0.986179710536822, "secondary": 0.8711882215818695, "tertiary": 0.9250403180838985, "education_level": 0.4019556227244458, "creative_writing_quality": 0.998927170020307, "primary_topic": 0.9969203965952559, "secondary_topic": 0.952148784217712}, "raw_probabilities": {"primary": {"Academic Writing": 8.409016157134513e-05, "Content Listing": 3.106270855218971e-05, "Creative Writing": 3.353612953796838e-05, "Customer Support Page": 1.877845518734678e-05, "Q&A Forum": 2.156777736147467e-05, "Discussion Thread": 1.592432125580691e-05, "Comment Section": 2.58414027075339e-05, "Missing Content": 6.892200646526101e-05, "Knowledge Article": 0.00010925416860371311, "Legal Notices": 4.588781313683351e-05, "News Article": 1.0176464976057227e-05, "Organizational Announcement": 1.9014882122878535e-05, "Nonfiction Writing": 2.325808328477049e-05, "Organizational About Page": 2.2915435123904864e-05, "Personal About Page": 2.5046776282349e-05, "Personal Blog": 1.4470533560210009e-05, "Product Page": 1.2929551656133419e-05, "Spam / Ads": 2.3109957810512928e-05, "Structured Data": 0.0019747418916336913, "Technical Writing": 0.9909160343704817, "Transcript / Interview": 3.189992345591687e-05, "Tutorial / How-To Guide": 0.001722400490822524, "User Reviews": 1.281286074992566e-05, "Other / Unknown": 0.004736323833659769}, "secondary": {"Academic Writing": 0.00040549752699283923, "Content Listing": 0.0002237117005180692, "Creative Writing": 0.00030673240820296827, "Customer Support Page": 0.00015180257730988448, "Q&A Forum": 0.00014642758417997227, "Discussion Thread": 0.0001378963681066635, "Comment Section": 0.00022881940619973797, "Missing Content": 0.009119075897064925, "Knowledge Article": 0.0005775697653905171, "Legal Notices": 0.00025378883952591777, "News Article": 0.00020548056551632877, "Organizational Announcement": 0.00017679280890149337, "Nonfiction Writing": 0.0001808330731887717, "Organizational About Page": 0.00019172778037253763, "Personal About Page": 0.000161446666953808, "Personal Blog": 0.00016542002171989533, "Product Page": 0.00010459903292462028, "Spam / Ads": 0.00019378200969362363, "Structured Data": 0.04355021554266496, "Technical Writing": 0.02165965281652556, "Transcript / Interview": 0.0002474559522876873, "Tutorial / How-To Guide": 0.006770681769711622, "User Reviews": 0.00010215276151317891, "NONE": 0.9147384371245344}, "tertiary": {"Academic Writing": 0.0001939310729651255, "Content Listing": 0.00011919636108748851, "Creative Writing": 0.0001570110826431467, "Customer Support Page": 8.086617112619235e-05, "Q&A Forum": 6.372398740069949e-05, "Discussion Thread": 6.548033299379176e-05, "Comment Section": 0.00011547895488382726, "Missing Content": 0.008963079991751745, "Knowledge Article": 0.0004042510564750296, "Legal Notices": 0.00018142146265414928, "News Article": 9.468898558265227e-05, "Organizational Announcement": 7.581467763232803e-05, "Nonfiction Writing": 9.851107418673231e-05, "Organizational About Page": 0.00012007745300520684, "Personal About Page": 7.549181332903509e-05, "Personal Blog": 7.792889677755227e-05, "Product Page": 5.4891092389703974e-05, "Spam / Ads": 0.00011358896937812806, "Structured Data": 0.024435381914317558, "Technical Writing": 0.009937019690270818, "Transcript / Interview": 0.0001099028615766531, "Tutorial / How-To Guide": 0.004937464492696043, "User Reviews": 4.90976066603996e-05, "NONE": 0.9494756999982161}, "structured_records": {"false": 0.7174153611022753, "true": 0.2825846388977247}, "procedural_steps": {"false": 0.9524089724300371, "true": 0.04759102756996296}, "worked_solution": {"false": 0.8523626286348915, "true": 0.14763737136510846}, "contextual_dialogue": {"false": 0.9999717690873121, "true": 2.823091268787577e-05}, "content_quality": {"-2": 0.040746260715404506, "-1": 0.15074133985766525, "0": 0.2227657495016857, "1": 0.4041914870592579, "2": 0.1815551628659867}, "noise_severity": {"0": 0.7712929476996635, "1": 0.15240559513472407, "2": 0.041321775051127385, "3": 0.026051041745806365, "4": 0.008928640368678697}, "missing_content_severity": {"0": 0.8166250242531109, "1": 0.1357856248334743, "2": 0.04094171519803863, "3": 0.004915862211607462, "4": 0.0017317735037687136}, "factual_density": {"0": 0.013528857216485606, "1": 0.04854038719086443, "2": 0.21558738188444207, "3": 0.42093142489352176, "4": 0.30141194881468614}, "pretraining_utility": {"-2": 0.012943359035555258, "-1": 0.08868911713330639, "0": 0.3616176842572853, "1": 0.44668219777924284, "2": 0.09006764179461009}, "math_density": {"0": 0.9674956752383954, "1": 0.024165291499999374, "2": 0.0032723102974735758, "3": 0.0016335475251154183, "4": 0.003433175439016275}, "code_density": {"0": 0.00016983292622642802, "1": 0.00035668258345708824, "2": 0.011063926022937902, "3": 0.0928867655871392, "4": 0.8955227928802394}, "reasoning_density": {"0": 0.5688874594546589, "1": 0.09902379166782148, "2": 0.1450922355403465, "3": 0.11294300076744718, "4": 0.07405351256972584}, "education_level": {"none": 0.0010664646245262526, "0": 0.0013508312301879638, "1": 0.009505921233855146, "2": 0.678787697131674, "3": 0.2768320744072282, "4": 0.032457011372528516}, "creative_writing_quality": {"not_applicable": 0.9992946555179215, "-2": 8.536853148823905e-05, "-1": 0.00014997511351512154, "0": 0.0003674854976145688, "1": 7.41170704691806e-05, "2": 2.8398268991367705e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.543782405839151e-05, "Art & Design": 3.717322760825814e-05, "Law & Crime": 4.615906600115619e-05, "Education & Careers": 4.492246432752396e-05, "Entertainment & Media": 2.572191140667587e-05, "Fashion & Beauty": 1.714904453052406e-05, "Business, Economics & Finance": 0.00011516223283575056, "Food & Dining": 2.4716358726574895e-05, "Games & Gaming": 0.0001363909761035411, "Electronics & Hardware": 7.630099292812663e-05, "Health, Healthcare & Wellness": 2.892897007454821e-05, "History & Geography": 2.6023127676059955e-05, "Home, Pets & Hobbies": 2.156607256068117e-05, "Industry & Production": 2.611055767612304e-05, "Language, Literature & Philosophy": 4.2490361589834804e-05, "Politics & Government": 2.7401050621141584e-05, "Religion & Spirituality": 3.3190610945205205e-05, "Science, Mathematics & Engineering": 0.0003341195259845862, "Relationships & Community": 0.00013853934993374468, "Software Usage & Applications": 0.0006262937342924978, "Programming & Software Engineering": 0.9975466903295483, "Sports & Fitness": 4.701769865199716e-05, "Vehicles & Transportation": 1.9436333235570843e-05, "Travel & Tourism": 3.96872676702694e-05, "Other / Uncovered Subject": 0.0003661492492610991, "Unknown / Insufficient Content": 0.0001072216617518}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0004441331930956823, "Art & Design": 0.00023837324590142168, "Law & Crime": 0.0026768826972307868, "Education & Careers": 0.0002772501649116409, "Entertainment & Media": 0.0002495747711272872, "Fashion & Beauty": 0.00012827518058983916, "Business, Economics & Finance": 0.0006007250980220791, "Food & Dining": 0.00012508030612427823, "Games & Gaming": 0.0005538848702766101, "Electronics & Hardware": 0.0002771152183161877, "Health, Healthcare & Wellness": 0.00016702625919502365, "History & Geography": 0.00012903564423239413, "Home, Pets & Hobbies": 0.00011144411346953391, "Industry & Production": 0.00010040962083776197, "Language, Literature & Philosophy": 0.00015722510249683642, "Politics & Government": 0.0001779749592514936, "Religion & Spirituality": 0.00016682034635250414, "Science, Mathematics & Engineering": 0.001087068472605252, "Relationships & Community": 0.0003159899643366912, "Software Usage & Applications": 0.005791705223133913, "Programming & Software Engineering": 0.016465850236387383, "Sports & Fitness": 0.0001430317209891268, "Vehicles & Transportation": 5.688784842960914e-05, "Travel & Tourism": 0.0001749705005274898, "Other / Uncovered Subject": 0.0007686307880597791, "NONE": 0.9686146344540993}}, "probabilities": {"primary": {"Academic Writing": 8.409016157134515e-05, "Content Listing": 3.1062708552189715e-05, "Creative Writing": 3.3536129537968384e-05, "Customer Support Page": 1.8778455187346785e-05, "Q&A Forum": 2.1567777361474672e-05, "Discussion Thread": 1.5924321255806915e-05, "Comment Section": 2.5841402707533907e-05, "Missing Content": 6.892200646526103e-05, "Knowledge Article": 0.00010925416860371314, "Legal Notices": 4.5887813136833524e-05, "News Article": 1.0176464976057229e-05, "Organizational Announcement": 1.901488212287854e-05, "Nonfiction Writing": 2.3258083284770495e-05, "Organizational About Page": 2.291543512390487e-05, "Personal About Page": 2.5046776282349007e-05, "Personal Blog": 1.4470533560210012e-05, "Product Page": 1.2929551656133422e-05, "Spam / Ads": 2.3109957810512935e-05, "Structured Data": 0.0019747418916336917, "Technical Writing": 0.9909160343704819, "Transcript / Interview": 3.189992345591688e-05, "Tutorial / How-To Guide": 0.0017224004908225244, "User Reviews": 1.2812860749925663e-05, "Other / Unknown": 0.0047363238336597694}, "secondary": {"Academic Writing": 0.00040549752699283923, "Content Listing": 0.0002237117005180692, "Creative Writing": 0.00030673240820296827, "Customer Support Page": 0.00015180257730988448, "Q&A Forum": 0.00014642758417997227, "Discussion Thread": 0.0001378963681066635, "Comment Section": 0.00022881940619973797, "Missing Content": 0.009119075897064925, "Knowledge Article": 0.0005775697653905171, "Legal Notices": 0.00025378883952591777, "News Article": 0.00020548056551632877, "Organizational Announcement": 0.00017679280890149337, "Nonfiction Writing": 0.0001808330731887717, "Organizational About Page": 0.00019172778037253763, "Personal About Page": 0.000161446666953808, "Personal Blog": 0.00016542002171989533, "Product Page": 0.00010459903292462028, "Spam / Ads": 0.00019378200969362363, "Structured Data": 0.04355021554266496, "Technical Writing": 0.02165965281652556, "Transcript / Interview": 0.0002474559522876873, "Tutorial / How-To Guide": 0.006770681769711622, "User Reviews": 0.00010215276151317891, "NONE": 0.9147384371245344}, "tertiary": {"Academic Writing": 0.00019393107296512545, "Content Listing": 0.00011919636108748848, "Creative Writing": 0.00015701108264314667, "Customer Support Page": 8.086617112619234e-05, "Q&A Forum": 6.372398740069948e-05, "Discussion Thread": 6.548033299379175e-05, "Comment Section": 0.00011547895488382724, "Missing Content": 0.008963079991751744, "Knowledge Article": 0.0004042510564750295, "Legal Notices": 0.00018142146265414925, "News Article": 9.468898558265225e-05, "Organizational Announcement": 7.581467763232802e-05, "Nonfiction Writing": 9.851107418673228e-05, "Organizational About Page": 0.00012007745300520681, "Personal About Page": 7.549181332903507e-05, "Personal Blog": 7.792889677755226e-05, "Product Page": 5.489109238970396e-05, "Spam / Ads": 0.00011358896937812803, "Structured Data": 0.02443538191431755, "Technical Writing": 0.009937019690270816, "Transcript / Interview": 0.00010990286157665307, "Tutorial / How-To Guide": 0.004937464492696042, "User Reviews": 4.9097606660399583e-05, "NONE": 0.9494756999982159}, "structured_records": {"false": 0.7174153611022753, "true": 0.2825846388977247}, "procedural_steps": {"false": 0.9524089724300371, "true": 0.04759102756996296}, "worked_solution": {"false": 0.8523626286348915, "true": 0.14763737136510846}, "contextual_dialogue": {"false": 0.9999717690873121, "true": 2.823091268787577e-05}, "content_quality": {"-2": 0.040746260715404506, "-1": 0.15074133985766525, "0": 0.2227657495016857, "1": 0.4041914870592579, "2": 0.1815551628659867}, "noise_severity": {"0": 0.7712929476996635, "1": 0.15240559513472407, "2": 0.041321775051127385, "3": 0.026051041745806365, "4": 0.008928640368678697}, "missing_content_severity": {"0": 0.8166250242531109, "1": 0.1357856248334743, "2": 0.04094171519803863, "3": 0.004915862211607462, "4": 0.0017317735037687136}, "factual_density": {"0": 0.013528857216485606, "1": 0.04854038719086443, "2": 0.21558738188444207, "3": 0.42093142489352176, "4": 0.30141194881468614}, "pretraining_utility": {"-2": 0.01294335903555526, "-1": 0.0886891171333064, "0": 0.36161768425728535, "1": 0.4466821977792429, "2": 0.0900676417946101}, "math_density": {"0": 0.9674956752383954, "1": 0.024165291499999374, "2": 0.0032723102974735758, "3": 0.0016335475251154183, "4": 0.003433175439016275}, "code_density": {"0": 0.00016983292622642802, "1": 0.00035668258345708824, "2": 0.011063926022937902, "3": 0.0928867655871392, "4": 0.8955227928802394}, "reasoning_density": {"0": 0.568887459454659, "1": 0.09902379166782149, "2": 0.14509223554034653, "3": 0.11294300076744719, "4": 0.07405351256972585}, "education_level": {"none": 0.0010664646245262526, "0": 0.0013508312301879638, "1": 0.009505921233855146, "2": 0.678787697131674, "3": 0.2768320744072282, "4": 0.032457011372528516}, "creative_writing_quality": {"not_applicable": 0.9992946555179215, "-2": 8.536853148823905e-05, "-1": 0.00014997511351512154, "0": 0.0003674854976145688, "1": 7.41170704691806e-05, "2": 2.8398268991367705e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.543782405839151e-05, "Art & Design": 3.717322760825814e-05, "Law & Crime": 4.615906600115619e-05, "Education & Careers": 4.492246432752396e-05, "Entertainment & Media": 2.572191140667587e-05, "Fashion & Beauty": 1.714904453052406e-05, "Business, Economics & Finance": 0.00011516223283575056, "Food & Dining": 2.4716358726574895e-05, "Games & Gaming": 0.0001363909761035411, "Electronics & Hardware": 7.630099292812663e-05, "Health, Healthcare & Wellness": 2.892897007454821e-05, "History & Geography": 2.6023127676059955e-05, "Home, Pets & Hobbies": 2.156607256068117e-05, "Industry & Production": 2.611055767612304e-05, "Language, Literature & Philosophy": 4.2490361589834804e-05, "Politics & Government": 2.7401050621141584e-05, "Religion & Spirituality": 3.3190610945205205e-05, "Science, Mathematics & Engineering": 0.0003341195259845862, "Relationships & Community": 0.00013853934993374468, "Software Usage & Applications": 0.0006262937342924978, "Programming & Software Engineering": 0.9975466903295483, "Sports & Fitness": 4.701769865199716e-05, "Vehicles & Transportation": 1.9436333235570843e-05, "Travel & Tourism": 3.96872676702694e-05, "Other / Uncovered Subject": 0.0003661492492610991, "Unknown / Insufficient Content": 0.0001072216617518}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0004441331930956823, "Art & Design": 0.00023837324590142168, "Law & Crime": 0.0026768826972307868, "Education & Careers": 0.0002772501649116409, "Entertainment & Media": 0.0002495747711272872, "Fashion & Beauty": 0.00012827518058983916, "Business, Economics & Finance": 0.0006007250980220791, "Food & Dining": 0.00012508030612427823, "Games & Gaming": 0.0005538848702766101, "Electronics & Hardware": 0.0002771152183161877, "Health, Healthcare & Wellness": 0.00016702625919502365, "History & Geography": 0.00012903564423239413, "Home, Pets & Hobbies": 0.00011144411346953391, "Industry & Production": 0.00010040962083776197, "Language, Literature & Philosophy": 0.00015722510249683642, "Politics & Government": 0.0001779749592514936, "Religion & Spirituality": 0.00016682034635250414, "Science, Mathematics & Engineering": 0.001087068472605252, "Relationships & Community": 0.0003159899643366912, "Software Usage & Applications": 0.005791705223133913, "Programming & Software Engineering": 0.016465850236387383, "Sports & Fitness": 0.0001430317209891268, "Vehicles & Transportation": 5.688784842960914e-05, "Travel & Tourism": 0.0001749705005274898, "Other / Uncovered Subject": 0.0007686307880597791, "NONE": 0.9686146344540993}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9886, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9886, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:36:09.378411+00:00", "scoring_key_slot": "primary", "dataset_uid": "c4784401-fe51-40ca-b37a-161add885033", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "56e1faa6a53f32e4702fed929fc70d7549c62ee016ed8bdc24d030082cc599ff", "source_metadata": {"uuid": "c4784401-fe51-40ca-b37a-161add885033", "repo_name": "j-morano/ariavt-backend", "relative_path": "tests/test_users.py", "category": "TEST", "algo_rel_score": 0.9817827343940735, "quality_score": 5.964035511016846, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 82, "repo_created_at": "2021-06-22T00:40:57.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "a38553bbcaa7ecc875111a26687e5ad194638d4f", "size_bytes": 5620, "file_timestamp": 1641576897, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "061ac8035c49904aeea02f00378d4a99cd80b0d0"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 82, "repo_created_at": "2021-06-22T00:40:57.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "a38553bbcaa7ecc875111a26687e5ad194638d4f", "size_bytes": 5620, "file_timestamp": 1641576897, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "061ac8035c49904aeea02f00378d4a99cd80b0d0", "url": "https://github.com/j-morano/ariavt-backend/blob/061ac8035c49904aeea02f00378d4a99cd80b0d0/tests/test_users.py"}, "gpt4o_tokens": 1276, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.535067951502757, "noise_severity": 0.3489168319491127, "missing_content_severity": 0.23934373587944882, "factual_density": 2.9481572208990583, "pretraining_utility": 0.5122416461640462, "math_density": 0.04934325642635788, "code_density": 3.883236002911708, "reasoning_density": 1.0242513153297597, "education_level": 2.3298903298247855, "creative_writing_quality": -0.26908634413697213}, "score_applicability": {"education_level": 0.9989335353754738, "creative_writing_quality": 0.0007053444820784777}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/ShakyaMajumdar/diffusion_denoising/blob/c58db33a33e81831afedfca858c806823d249aa1/gmm_denoiser_2.py", "id": "python-0-9db761a7778403fa5227ebab2583d78d", "text": "from typing import NamedTuple\nimport numpy as np\nfrom sklearn.mixture import GaussianMixture\nfrom scipy.stats import multivariate_normal\nfrom utils import snr_db_to_np\nfrom numba import njit, prange\nimport numpy.linalg as la\n\n\ndef get_patches(sig, P):\n    \"\"\"\n    return list of all overlapping patches of length P each from sig\n    \"\"\"\n    N = len(sig)\n    patches = []\n    sig2 = np.tile(sig, 2)\n    for i in range(N):\n        patches.append(sig2[i:i+P])\n    return patches\n\n@njit(fastmath=True)\ndef _G(u, alphas, Cov1inv, det_rts, means, Cs, K):\n    \"\"\"\n    denoise a single patch\n    \"\"\"\n    a_no_us = np.array([alphas[i] * mvn_pdf(means[i], Cov1inv[i], det_rts[i], u) for i in range(K)])\n    M = np.zeros_like(Cs[0])\n    for j in range(K):\n        M += a_no_us[j]*Cs[j]\n    return M @ u / a_no_us.sum()\n\n@njit(parallel=True, fastmath=True)\ndef _D(z, P, K, N, Cs, alphas, Cov1inv, means, det_rts):\n    \"\"\"\n    gmm-denoise the signal\n    \"\"\"\n    res = np.zeros((N+P,))\n    for i in prange(N):\n        res[i:i+P] += _G(z[i:i+P], alphas, Cov1inv, det_rts, means, Cs, K)\n    res[:P] += res[N:]\n    return res[:N] / P\n\n@njit(fastmath=True)\ndef mvn_pdf(mu, sigma_inv, det_rt, x):\n    return det_rt * np.exp(-1/2 * (x - mu) @ sigma_inv @ (x - mu))\n\nclass _DenoiseCtx(NamedTuple):\n    P: int  # size of each patch\n    K: int  # no. of gmm components\n    N: int  # size of signal\n    sigma2: float  # noise power\n    # Pis: list[np.ndarray]  # Pis[i] extracts [i:i+P] slice from vector of size N\n    Cov1inv: list[np.ndarray]  # (gmm.covariances[j] + sigma2 * I)^-1\n    Cs: list[np.ndarray]  # gmm.covariances[j] @ Cov1inv[j]\n    alphas: list[np.ndarray]  # gmm component weights\n    # Nos: list[np.ndarray]  # gmm component pdfs\n    means: list[np.ndarray]\n    det_rts: list[float]\n\n\nclass GMMDenoiser:\n    def __init__(self, gmm_n_components, patch_size, train_signals):\n        self.K = gmm_n_components\n        self.P = patch_size\n        self.gmm = GaussianMixture(gmm_n_components)\n        self.train_signals = train_signals\n\n        self._trained = False\n\n    def n_params(self):\n        return self.gmm.covariances_.size + self.gmm.means_.size + self.gmm.weights_.size\n\n    def fit(self):\n        self.gmm.fit(\n            [\n                patch\n                for train_signal in self.train_signals\n                for patch in get_patches(train_signal, self.P)\n            ]\n        )\n        self._trained = True\n\n    def compute_ctx(self, signal, snr_db_est):\n        N = len(signal)\n        sigma2 = snr_db_to_np(signal, snr_db_est)\n        Cov1inv = np.array([\n            la.inv(self.gmm.covariances_[j] + sigma2 * np.eye(self.P)) for j in range(self.K)\n        ])\n        Cs = np.array([self.gmm.covariances_[j] @ Cov1inv[j] for j in range(self.K)])\n        alphas = self.gmm.weights_\n        det_rts = np.array([(2*np.pi)**(-self.P/2) * la.det(Cov1inv[j])**(1/2) for j in range(self.K)])\n        return _DenoiseCtx(self.P, self.K, N, sigma2, Cov1inv, Cs, alphas, self.gmm.means_, det_rts)\n\n    def assert_trained(self):\n        if not self._trained:\n            raise RuntimeError(\"Model has not been fit\")\n\n    def denoise(self, signal, *, snr_db_est):\n        self.assert_trained()\n        ctx = self.compute_ctx(signal, snr_db_est)\n        return _D(np.tile(signal,2), self.P, self.K, ctx.N, ctx.Cs, ctx.alphas, ctx.Cov1inv, ctx.means, ctx.det_rts)\n\n\n", "text_preview": "from typing import NamedTuple import numpy as np from sklearn.mixture import GaussianMixture from scipy.stats import multivariate_normal from utils import snr_db_to_np from numba import njit, prange import numpy.linalg as la def get_patches", "input_sha256": "718f9fcf654e6991ac0284624d5e3c20b47376703b232b04c2b8a429017e3c76", "text_sha256": "9db761a7778403fa5227ebab2583d78d40f5a3852f95a9d21d920526fbc60ac5", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12104, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.002356474067769, "noise_severity": 0.005767661611588484, "missing_content_severity": 0.0724095342347309, "factual_density": 3.4852820830900715, "pretraining_utility": 0.7565399922838294, "math_density": 3.4176322690564276, "code_density": 3.86404640130341, "reasoning_density": 0.40273809943853656, "education_level": 2.9422665311493232, "creative_writing_quality": -0.14990005323908318}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.5629 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0056052278781099085, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9976531540258466, "secondary": 0.9904841126708813, "tertiary": 0.9963330461089093, "structured_records": 0.003382651269678969, "procedural_steps": 0.020808123284366764, "worked_solution": 0.00404324724509665, "contextual_dialogue": 3.8083232943598666e-06, "content_quality": 0.5533706856229881, "noise_severity": 0.9951936153236762, "missing_content_severity": 0.9396587214710576, "factual_density": 0.5710684025750594, "pretraining_utility": 0.5407416873803008, "math_density": 0.5146935575470233, "code_density": 0.8867053344195082, "reasoning_density": 0.6643849171345528, "education_level": 0.5455563255036747, "creative_writing_quality": 0.9994455848047419, "primary_topic": 0.7975121430141494, "secondary_topic": 0.5454005908371347}, "probability_margin": {"primary": 0.9967704106095049, "secondary": 0.9853502268953797, "tertiary": 0.9947451248168728, "education_level": 0.4102732534589939, "creative_writing_quality": 0.9992871484093456, "primary_topic": 0.6164229533336801, "secondary_topic": 0.3268743225492148}, "raw_probabilities": {"primary": {"Academic Writing": 4.872831846309735e-05, "Content Listing": 1.1994317712489078e-05, "Creative Writing": 9.352321766420879e-06, "Customer Support Page": 5.346058139690015e-06, "Q&A Forum": 7.335483340481063e-06, "Discussion Thread": 4.724937708683741e-06, "Comment Section": 9.296557200267484e-06, "Missing Content": 1.6077562265667004e-05, "Knowledge Article": 7.62641948127533e-05, "Legal Notices": 1.4650239226796696e-05, "News Article": 2.874560912334285e-06, "Organizational Announcement": 5.8889842317157496e-06, "Nonfiction Writing": 1.0568506825164717e-05, "Organizational About Page": 7.60521126949769e-06, "Personal About Page": 8.61856076263575e-06, "Personal Blog": 5.248163670884121e-06, "Product Page": 5.137526426754047e-06, "Spam / Ads": 7.149113786537548e-06, "Structured Data": 0.000430982110159174, "Technical Writing": 0.9977509392747697, "Transcript / Interview": 9.1166107447423e-06, "Tutorial / How-To Guide": 0.0005672186252018684, "User Reviews": 4.354095337919504e-06, "Other / Unknown": 0.0009805286652647444}, "secondary": {"Academic Writing": 0.0003388760925182496, "Content Listing": 3.108932237735715e-05, "Creative Writing": 4.335508571542047e-05, "Customer Support Page": 2.6544484803770898e-05, "Q&A Forum": 1.6861304451520404e-05, "Discussion Thread": 1.4199657502277146e-05, "Comment Section": 4.96183785441382e-05, "Missing Content": 0.00016587685644468304, "Knowledge Article": 0.00046301645748880537, "Legal Notices": 5.666121154662355e-05, "News Article": 2.3768059525108296e-05, "Organizational Announcement": 1.9634398494198338e-05, "Nonfiction Writing": 2.916019790465115e-05, "Organizational About Page": 3.352759165395272e-05, "Personal About Page": 2.8005164234523032e-05, "Personal Blog": 2.0537473260898983e-05, "Product Page": 1.7127207033175787e-05, "Spam / Ads": 2.7417646603450123e-05, "Structured Data": 0.0009394024513404021, "Technical Writing": 0.0055303810808817015, "Transcript / Interview": 3.1485115744801074e-05, "Tutorial / How-To Guide": 0.0011991524600505103, "User Reviews": 1.3694325618381603e-05, "NONE": 0.9908806079762613}, "tertiary": {"Academic Writing": 0.0001270499246959738, "Content Listing": 1.683223863072974e-05, "Creative Writing": 2.6519647076655863e-05, "Customer Support Page": 1.2896688072971475e-05, "Q&A Forum": 8.944450703910319e-06, "Discussion Thread": 7.302486548201862e-06, "Comment Section": 2.7275678505911807e-05, "Missing Content": 0.00012320561642533264, "Knowledge Article": 0.00025258052747905384, "Legal Notices": 5.3333941732195026e-05, "News Article": 1.5648009856059138e-05, "Organizational Announcement": 1.221678588902509e-05, "Nonfiction Writing": 2.0088418832962356e-05, "Organizational About Page": 2.531165626615317e-05, "Personal About Page": 1.5781465991001605e-05, "Personal Blog": 1.2325814530078853e-05, "Product Page": 1.0669219564162954e-05, "Spam / Ads": 1.621171485663526e-05, "Structured Data": 0.0002757900304810556, "Technical Writing": 0.0017407110374985165, "Transcript / Interview": 1.7552337144691878e-05, "Tutorial / How-To Guide": 0.0006872860286877658, "User Reviews": 8.630426159613651e-06, "NONE": 0.9964858358543713}, "structured_records": {"false": 0.996617348730321, "true": 0.003382651269678969}, "procedural_steps": {"false": 0.9791918767156332, "true": 0.020808123284366764}, "worked_solution": {"false": 0.9959567527549034, "true": 0.00404324724509665}, "contextual_dialogue": {"false": 0.9999961916767056, "true": 3.8083232943598666e-06}, "content_quality": {"-2": 0.007229609368250279, "-1": 0.018916944596808715, "0": 0.20727663429395432, "1": 0.4974209860808951, "2": 0.2691558256600916}, "noise_severity": {"0": 0.9951691179796568, "1": 0.004240999701827495, "2": 0.00034078688838646366, "3": 0.00015129358752831603, "4": 9.78018426007782e-05}, "missing_content_severity": {"0": 0.9388176587658944, "1": 0.05251301759590598, "2": 0.006618254306268464, "3": 0.001544269301436402, "4": 0.0005068000304946953}, "factual_density": {"0": 0.002684093144751758, "1": 0.006463392156618405, "2": 0.07477525899597263, "3": 0.3350408498691211, "4": 0.5810364058335361}, "pretraining_utility": {"-2": 0.0066690657453109415, "-1": 0.04575340783556701, "0": 0.2857709785228379, "1": 0.5079815641825499, "2": 0.1538249837137342}, "math_density": {"0": 0.003682459160506881, "1": 0.009185863171706651, "2": 0.09133996039902909, "3": 0.35740038398836643, "4": 0.5383913332803909}, "code_density": {"0": 0.0002843877029017047, "1": 0.0005753781542106657, "2": 0.018506309085151697, "3": 0.09607729525204796, "4": 0.884556629805688}, "reasoning_density": {"0": 0.8148376852907521, "1": 0.05519235863482079, "2": 0.06700463625332928, "3": 0.03832481098733404, "4": 0.02464050883376375}, "education_level": {"none": 0.0010508660245528133, "0": 0.0012390012120360578, "1": 0.0027738736652952773, "2": 0.21102368446073508, "3": 0.621296937919729, "4": 0.1626156367176519}, "creative_writing_quality": {"not_applicable": 0.9995379873372849, "-2": 4.546118974041458e-05, "-1": 8.344560926309783e-05, "0": 0.0002508389279392698, "1": 5.9421605538224875e-05, "2": 2.2845330233810206e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001949899815177799, "Art & Design": 0.00036973459795406716, "Law & Crime": 0.00028620371981954784, "Education & Careers": 0.00020061730666208344, "Entertainment & Media": 0.00023723433677557882, "Fashion & Beauty": 0.0001607158533380903, "Business, Economics & Finance": 0.00038893980859364266, "Food & Dining": 0.00015894445372954181, "Games & Gaming": 0.0002813361237388669, "Electronics & Hardware": 0.0003287793005161041, "Health, Healthcare & Wellness": 0.0002655543292357778, "History & Geography": 0.00027087883323045247, "Home, Pets & Hobbies": 0.00019557839850570594, "Industry & Production": 0.0002365311954980287, "Language, Literature & Philosophy": 0.00020375363913037068, "Politics & Government": 0.0001000611948525325, "Religion & Spirituality": 0.0001639485315759158, "Science, Mathematics & Engineering": 0.18887718417992502, "Relationships & Community": 9.616687998671098e-05, "Software Usage & Applications": 0.0004488462082894923, "Programming & Software Engineering": 0.8053001375136052, "Sports & Fitness": 0.00024332326790489747, "Vehicles & Transportation": 0.00011270215323755717, "Travel & Tourism": 0.00020619317419394407, "Other / Uncovered Subject": 0.00042127316468807774, "Unknown / Insufficient Content": 0.00025037185349498225}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006023139503247037, "Art & Design": 0.0007258031929726367, "Law & Crime": 0.0004856407736040373, "Education & Careers": 0.00035190656543793115, "Entertainment & Media": 0.0005397923218634123, "Fashion & Beauty": 0.0003448513565065642, "Business, Economics & Finance": 0.0004847015025329199, "Food & Dining": 0.0002694178337316982, "Games & Gaming": 0.00040750597415694907, "Electronics & Hardware": 0.0005067403714753552, "Health, Healthcare & Wellness": 0.0004213075735397079, "History & Geography": 0.00036809953597673607, "Home, Pets & Hobbies": 0.00030882279487567596, "Industry & Production": 0.00034242754854791474, "Language, Literature & Philosophy": 0.00022759880704926243, "Politics & Government": 0.0002078147555946417, "Religion & Spirituality": 0.0003732450084582067, "Science, Mathematics & Engineering": 0.5628851834972448, "Relationships & Community": 0.00016855505039091293, "Software Usage & Applications": 0.0004503783118773343, "Programming & Software Engineering": 0.19189468592552278, "Sports & Fitness": 0.00036018857912157074, "Vehicles & Transportation": 0.00020189858864852844, "Travel & Tourism": 0.0004489241816579379, "Other / Uncovered Subject": 0.0006113350508577287, "NONE": 0.23601086094803003}}, "probabilities": {"primary": {"Academic Writing": 4.872831846309735e-05, "Content Listing": 1.1994317712489078e-05, "Creative Writing": 9.352321766420879e-06, "Customer Support Page": 5.346058139690015e-06, "Q&A Forum": 7.335483340481063e-06, "Discussion Thread": 4.724937708683741e-06, "Comment Section": 9.296557200267484e-06, "Missing Content": 1.6077562265667004e-05, "Knowledge Article": 7.62641948127533e-05, "Legal Notices": 1.4650239226796696e-05, "News Article": 2.874560912334285e-06, "Organizational Announcement": 5.8889842317157496e-06, "Nonfiction Writing": 1.0568506825164717e-05, "Organizational About Page": 7.60521126949769e-06, "Personal About Page": 8.61856076263575e-06, "Personal Blog": 5.248163670884121e-06, "Product Page": 5.137526426754047e-06, "Spam / Ads": 7.149113786537548e-06, "Structured Data": 0.000430982110159174, "Technical Writing": 0.9977509392747697, "Transcript / Interview": 9.1166107447423e-06, "Tutorial / How-To Guide": 0.0005672186252018684, "User Reviews": 4.354095337919504e-06, "Other / Unknown": 0.0009805286652647444}, "secondary": {"Academic Writing": 0.0003388760925182497, "Content Listing": 3.1089322377357156e-05, "Creative Writing": 4.335508571542048e-05, "Customer Support Page": 2.65444848037709e-05, "Q&A Forum": 1.6861304451520408e-05, "Discussion Thread": 1.4199657502277147e-05, "Comment Section": 4.9618378544138206e-05, "Missing Content": 0.00016587685644468307, "Knowledge Article": 0.0004630164574888054, "Legal Notices": 5.666121154662356e-05, "News Article": 2.37680595251083e-05, "Organizational Announcement": 1.963439849419834e-05, "Nonfiction Writing": 2.9160197904651155e-05, "Organizational About Page": 3.3527591653952725e-05, "Personal About Page": 2.8005164234523036e-05, "Personal Blog": 2.0537473260898986e-05, "Product Page": 1.712720703317579e-05, "Spam / Ads": 2.7417646603450126e-05, "Structured Data": 0.0009394024513404022, "Technical Writing": 0.005530381080881702, "Transcript / Interview": 3.148511574480108e-05, "Tutorial / How-To Guide": 0.0011991524600505105, "User Reviews": 1.3694325618381604e-05, "NONE": 0.9908806079762614}, "tertiary": {"Academic Writing": 0.0001270499246959738, "Content Listing": 1.683223863072974e-05, "Creative Writing": 2.6519647076655863e-05, "Customer Support Page": 1.2896688072971475e-05, "Q&A Forum": 8.944450703910319e-06, "Discussion Thread": 7.302486548201862e-06, "Comment Section": 2.7275678505911807e-05, "Missing Content": 0.00012320561642533264, "Knowledge Article": 0.00025258052747905384, "Legal Notices": 5.3333941732195026e-05, "News Article": 1.5648009856059138e-05, "Organizational Announcement": 1.221678588902509e-05, "Nonfiction Writing": 2.0088418832962356e-05, "Organizational About Page": 2.531165626615317e-05, "Personal About Page": 1.5781465991001605e-05, "Personal Blog": 1.2325814530078853e-05, "Product Page": 1.0669219564162954e-05, "Spam / Ads": 1.621171485663526e-05, "Structured Data": 0.0002757900304810556, "Technical Writing": 0.0017407110374985165, "Transcript / Interview": 1.7552337144691878e-05, "Tutorial / How-To Guide": 0.0006872860286877658, "User Reviews": 8.630426159613651e-06, "NONE": 0.9964858358543713}, "structured_records": {"false": 0.996617348730321, "true": 0.003382651269678969}, "procedural_steps": {"false": 0.9791918767156332, "true": 0.020808123284366764}, "worked_solution": {"false": 0.9959567527549034, "true": 0.00404324724509665}, "contextual_dialogue": {"false": 0.9999961916767056, "true": 3.8083232943598666e-06}, "content_quality": {"-2": 0.007229609368250279, "-1": 0.018916944596808715, "0": 0.20727663429395432, "1": 0.4974209860808951, "2": 0.2691558256600916}, "noise_severity": {"0": 0.9951691179796569, "1": 0.004240999701827496, "2": 0.0003407868883864637, "3": 0.00015129358752831605, "4": 9.780184260077821e-05}, "missing_content_severity": {"0": 0.9388176587658945, "1": 0.052513017595905986, "2": 0.006618254306268465, "3": 0.0015442693014364021, "4": 0.0005068000304946954}, "factual_density": {"0": 0.002684093144751758, "1": 0.006463392156618405, "2": 0.07477525899597263, "3": 0.3350408498691211, "4": 0.5810364058335361}, "pretraining_utility": {"-2": 0.0066690657453109415, "-1": 0.04575340783556701, "0": 0.2857709785228379, "1": 0.5079815641825499, "2": 0.1538249837137342}, "math_density": {"0": 0.003682459160506881, "1": 0.009185863171706651, "2": 0.09133996039902909, "3": 0.35740038398836643, "4": 0.5383913332803909}, "code_density": {"0": 0.0002843877029017047, "1": 0.0005753781542106657, "2": 0.018506309085151697, "3": 0.09607729525204796, "4": 0.884556629805688}, "reasoning_density": {"0": 0.8148376852907522, "1": 0.0551923586348208, "2": 0.0670046362533293, "3": 0.03832481098733405, "4": 0.024640508833763754}, "education_level": {"none": 0.0010508660245528133, "0": 0.0012390012120360578, "1": 0.0027738736652952773, "2": 0.21102368446073508, "3": 0.621296937919729, "4": 0.1626156367176519}, "creative_writing_quality": {"not_applicable": 0.9995379873372852, "-2": 4.546118974041459e-05, "-1": 8.344560926309784e-05, "0": 0.00025083892793926983, "1": 5.942160553822489e-05, "2": 2.284533023381021e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001949899815177799, "Art & Design": 0.00036973459795406716, "Law & Crime": 0.00028620371981954784, "Education & Careers": 0.00020061730666208344, "Entertainment & Media": 0.00023723433677557882, "Fashion & Beauty": 0.0001607158533380903, "Business, Economics & Finance": 0.00038893980859364266, "Food & Dining": 0.00015894445372954181, "Games & Gaming": 0.0002813361237388669, "Electronics & Hardware": 0.0003287793005161041, "Health, Healthcare & Wellness": 0.0002655543292357778, "History & Geography": 0.00027087883323045247, "Home, Pets & Hobbies": 0.00019557839850570594, "Industry & Production": 0.0002365311954980287, "Language, Literature & Philosophy": 0.00020375363913037068, "Politics & Government": 0.0001000611948525325, "Religion & Spirituality": 0.0001639485315759158, "Science, Mathematics & Engineering": 0.18887718417992502, "Relationships & Community": 9.616687998671098e-05, "Software Usage & Applications": 0.0004488462082894923, "Programming & Software Engineering": 0.8053001375136052, "Sports & Fitness": 0.00024332326790489747, "Vehicles & Transportation": 0.00011270215323755717, "Travel & Tourism": 0.00020619317419394407, "Other / Uncovered Subject": 0.00042127316468807774, "Unknown / Insufficient Content": 0.00025037185349498225}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006023139503247037, "Art & Design": 0.0007258031929726367, "Law & Crime": 0.0004856407736040373, "Education & Careers": 0.00035190656543793115, "Entertainment & Media": 0.0005397923218634123, "Fashion & Beauty": 0.0003448513565065642, "Business, Economics & Finance": 0.0004847015025329199, "Food & Dining": 0.0002694178337316982, "Games & Gaming": 0.00040750597415694907, "Electronics & Hardware": 0.0005067403714753552, "Health, Healthcare & Wellness": 0.0004213075735397079, "History & Geography": 0.00036809953597673607, "Home, Pets & Hobbies": 0.00030882279487567596, "Industry & Production": 0.00034242754854791474, "Language, Literature & Philosophy": 0.00022759880704926243, "Politics & Government": 0.0002078147555946417, "Religion & Spirituality": 0.0003732450084582067, "Science, Mathematics & Engineering": 0.5628851834972448, "Relationships & Community": 0.00016855505039091293, "Software Usage & Applications": 0.0004503783118773343, "Programming & Software Engineering": 0.19189468592552278, "Sports & Fitness": 0.00036018857912157074, "Vehicles & Transportation": 0.00020189858864852844, "Travel & Tourism": 0.0004489241816579379, "Other / Uncovered Subject": 0.0006113350508577287, "NONE": 0.23601086094803003}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9586, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9586, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:36:09.528018+00:00", "scoring_key_slot": "primary", "dataset_uid": "52648719-d145-4f00-b174-a2f8ce67250e", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "5ea4674d9cec4e8b63037b63cb6cd72efabb030fffbe726f77f49256fc6ecb97", "source_metadata": {"uuid": "52648719-d145-4f00-b174-a2f8ce67250e", "repo_name": "ShakyaMajumdar/diffusion_denoising", "relative_path": "gmm_denoiser_2.py", "category": "DATA", "algo_rel_score": 0.9773005247116089, "quality_score": 6.317421913146973, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2024-06-10T04:58:46.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "353404bf8cca7b67911d7478157b903b2e09d188", "size_bytes": 3368, "file_timestamp": 1717995613, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "c58db33a33e81831afedfca858c806823d249aa1"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2024-06-10T04:58:46.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "353404bf8cca7b67911d7478157b903b2e09d188", "size_bytes": 3368, "file_timestamp": 1717995613, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "c58db33a33e81831afedfca858c806823d249aa1", "url": "https://github.com/ShakyaMajumdar/diffusion_denoising/blob/c58db33a33e81831afedfca858c806823d249aa1/gmm_denoiser_2.py"}, "gpt4o_tokens": 1028, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.002356474067769, "noise_severity": 0.005767661611588484, "missing_content_severity": 0.0724095342347309, "factual_density": 3.4852820830900715, "pretraining_utility": 0.7565399922838294, "math_density": 3.4176322690564276, "code_density": 3.86404640130341, "reasoning_density": 0.40273809943853656, "education_level": 2.9422665311493232, "creative_writing_quality": -0.14990005323908318}, "score_applicability": {"education_level": 0.9989491339754473, "creative_writing_quality": 0.00046201266271481736}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/Juanarchy/findneighsides2/blob/363b0522ad1fdb86e5f773193ec3b1a1230106ad/findneighsides2.py", "id": "python-0-ab0bccb4bf12e0a5aab7b04c78a1e147", "text": "import cupy as np\n\n\"\"\"\nThis script loads a shape (3,nElems) array from a text file whose i-th row contains the index of the (possibly 1-indexed) nodes at each vertex of the i-th triangle \nof a non-ramified triangulation, and saves 3 text files containing each:\n    - A shape (3,nElems) array whose i-th row contains, at position j, the index of the cell that neighbors cell i from side j.\n    - A shape (3,nElems) array whose i-th row contains, at position j, the limiting side of the cell that neighbors cell i from side j.\n    - A shape (3,nElems) array whose i-th row contains the index of the (now 0-indexed) nodes at each vertex of the i-th triangle.\n\nIts implemented algorithm compares the three sides of each cell to the three sides of each other cell and positions neighbors and sides accordingly through precise\narray manipulation, thus avoiding loops and conditional statements of any kind. This means that it can efficiently be parallelized or ran on GPUs through CUDA imp-\nlementations of numpy such as cupy. This algorithm can be easily extended to arbitrary (even higher-dimensional) non-ramified tilings by translating \"sides\" into \n\"faces\" and all their concerning instructions.\n\nIt also has the option to load a text file containing the indices of cells to remove from the tiling, but I haven't tested what happens if one removes enough cells\nto leave nodes isolated. I suspect that would be fine, but the resulting answers would assume the existence of said unused, isolated nodes.\n\n-<NAME>. Contact: juan[dot]fuenzalidaa[at]sansano[dot]usm[dot]cl\n\"\"\"\n\n\nelements=np.loadtxt(\"ElemNodes.txt\",dtype=int)#load nodes faces array.\nelements=elements-1 #turn into 0-index if 1-indexed.\nbad_cells=None\n\n#bad_cells=np.loadtxt(\"bad_cells.txt\",dtype=int) #Bad cells to further remove.\n\nif bad_cells is not None:\n    elements=np.delete(elements,bad_cells,0)\n\n#Get indices to compare cells to other cells but not to themselves.\nl,r=np.meshgrid(np.array(range(len(elements))),np.array(range(len(elements))))\nlr=np.vstack((l,r)).T\nii=np.vstack((np.array(range(len(elements))),np.array(range(len(elements))))).T\n\nlr=np.setdiff1d(lr,ii)\nl=lr[:,0]\nr=lr[:,1]\n\n#Get the nodes for sides 0,1,2 for each cell.\nside0=np.take(elements,(0,1)).sort(axis=1)\nside1=np.take(elements,(1,2)).sort(axis=1)\nside2=np.take(elements,(2,0)).sort(axis=1)\n\n#Compare sides of cells (cmpXY is vector with True at position i if side X of cell l[i] has the same nodes as side Y of cell r[i]).\ncmp00=(side0[l]==side0[r]).all(axis=1)\ncmp01=(side0[l]==side1[r]).all(axis=1)\ncmp02=(side0[l]==side2[r]).all(axis=1)\ncmp11=(side1[l]==side1[r]).all(axis=1)\ncmp12=(side1[l]==side2[r]).all(axis=1)\ncmp22=(side2[l]==side2[r]).all(axis=1)\n\n#Get cells sharing sides with other cells (tlXY is vector with indices of cells whose side X is equal to side Y of another cell).\n#                                         (Cell tlXY[i] has side X equal to side Y of cell trXY[i], i.e. cell tlXY[i] has trXY[i] as neighbor at side X)\ntl00=l[cmp00]\ntl01=l[cmp01]\ntl02=l[cmp02]\ntl11=l[cmp11]\ntl12=l[cmp12]\ntl22=l[cmp22]\n\n#Get cells sharing sides with other cells (trXY is vector with indices of cells whose side Y is equal to side X of another cell).\ntr00=r[cmp00]\ntr01=r[cmp01]\ntr02=r[cmp02]\ntr11=r[cmp11]\ntr12=r[cmp12]\ntr22=r[cmp22]\n\n#Initialize arrays of neighbors and neighoring sides. Will be -1 at end of computation if cell has no neighboring cells/sides at that side.\nElemNeighs=np.tile(np.array((-1,-1,-1)),(1,len(elements)))\nElemNeighSides=np.tile(np.array((-1,-1,-1)),(1,len(elements)))\n\nElemNeighs[tl00,0]=tr00     #Put cell tr00[i] at position 0 for neighbors of cell tl00[i] for all i\nElemNeighSides[tl00,0]=0    #Put side 0 at position 0 for neighboring sides of cell tl00[i] for all i\nElemNeighs[tr00,0]=tl00     #Put cell tl00[i] at position 0 for neighbors of cell tr00[i] for all i\nElemNeighSides[tr00,0]=0    #Put side 0 at position 0 for neighboring sides of cell tr00[i] for all i\n\nElemNeighs[tl01,0]=tr01     #Put cell tr01[i] at position 0 for neighbors of cell tl01[i] for all i\nElemNeighSides[tl01,0]=1    #Put side 1 at position 0 for neighboring sides of cell tl01[i] for all i\nElemNeighs[tr01,1]=tl01     #Put cell tl01[i] at position 1 for neighbors of cell tr01[i] for all i\nElemNeighSides[tr01,1]=0    #Put side 0 at position 1 for neighboring sides of cell tr01[i] for all i\n\nElemNeighs[tl02,0]=tr02     #Analogous...\nElemNeighSides[tl02,0]=2    \nElemNeighs[tr02,2]=tl02     \nElemNeighSides[tr02,2]=0    \n\nElemNeighs[tl11,1]=tr11     \nElemNeighSides[tl11,1]=1    \nElemNeighs[tr11,1]=tl11     \nElemNeighSides[tl11,1]=1    \n\nElemNeighs[tl12,1]=tr12     \nElemNeighSides[tl12,1]=2    \nElemNeighs[tr12,2]=tl12     \nElemNeighSides[tr12,2]=1    \n\nElemNeighs[tl22,2]=tr22     \nElemNeighSides[tl22,2]=2    \nElemNeighs[tr22,2]=tl22     \nElemNeighSides[tl22,2]=2    \n\n#save results\nnp.savetxt('ElemNeighsFoundGPU.txt',ElemNeighs,fmt=\"%1d\")\nnp.savetxt('ElemNeighSidesFoundGPU.txt',ElemNeighSides,fmt=\"%1d\")\nnp.savetxt('ElemNodesNewGPU.txt',elements,fmt='%1d')\n", "text_preview": "import cupy as np \"\"\" This script loads a shape (3,nElems) array from a text file whose i-th row contains the index of the (possibly 1-indexed) nodes at each vertex of the i-th triangle of a non-ramified triangulation, and saves 3 text file", "input_sha256": "f70b957b9c57d93435639d373816320657f2bcde5a0d29a4aa4016d9ae46e72b", "text_sha256": "ab0bccb4bf12e0a5aab7b04c78a1e1474d64905d8c47f6aa05425a8b6b333fc9", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12106, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9183370029283127, "noise_severity": 0.08085627985168126, "missing_content_severity": 0.12830791339455974, "factual_density": 3.2017420010749396, "pretraining_utility": 0.6710530000597571, "math_density": 3.2849435818719246, "code_density": 3.4468343768010703, "reasoning_density": 1.5051239973399118, "education_level": 2.5782974361901454, "creative_writing_quality": -0.052251134327423926}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.4546 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.07784026151329049, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9937339954823297, "secondary": 0.818150179483635, "tertiary": 0.8993748001931555, "structured_records": 0.007664394870669122, "procedural_steps": 0.12853443235875636, "worked_solution": 0.024629073815101363, "contextual_dialogue": 9.970706796055596e-06, "content_quality": 0.5262326759079776, "noise_severity": 0.9326197667902656, "missing_content_severity": 0.8930767388378669, "factual_density": 0.48495398111381893, "pretraining_utility": 0.5122824674424403, "math_density": 0.4041196515599369, "code_density": 0.5390286473342254, "reasoning_density": 0.0, "education_level": 0.40109663864327333, "creative_writing_quality": 0.9958350888968706, "primary_topic": 0.8578186900952101, "secondary_topic": 0.4328276096985171}, "probability_margin": {"primary": 0.9916941064733145, "secondary": 0.7432418565725583, "tertiary": 0.8821944814042206, "education_level": 0.10606393472174613, "creative_writing_quality": 0.9943314609740141, "primary_topic": 0.7334693593853439, "secondary_topic": 0.12747124393134635}, "raw_probabilities": {"primary": {"Academic Writing": 0.00020222543856269673, "Content Listing": 3.1349497905392196e-05, "Creative Writing": 3.0130236857870646e-05, "Customer Support Page": 1.9098858048156574e-05, "Q&A Forum": 2.6863739371501815e-05, "Discussion Thread": 1.7878306121272727e-05, "Comment Section": 2.661031252277318e-05, "Missing Content": 4.4510900791115445e-05, "Knowledge Article": 0.00041979019873268836, "Legal Notices": 5.573791371472944e-05, "News Article": 1.029720660770569e-05, "Organizational Announcement": 2.6166047229968237e-05, "Nonfiction Writing": 6.874572705278288e-05, "Organizational About Page": 2.8923812665992898e-05, "Personal About Page": 0.00015732482360028338, "Personal Blog": 3.038375822886519e-05, "Product Page": 1.7676776266420774e-05, "Spam / Ads": 2.6128518568233e-05, "Structured Data": 0.00045518215996961665, "Technical Writing": 0.9939950790038993, "Transcript / Interview": 3.1416209669490034e-05, "Tutorial / How-To Guide": 0.0023009725305847815, "User Reviews": 1.55885762498568e-05, "Other / Unknown": 0.001961919446778441}, "secondary": {"Academic Writing": 0.006967405843454669, "Content Listing": 0.0005585980106587085, "Creative Writing": 0.0008217133608371922, "Customer Support Page": 0.0005773354165881037, "Q&A Forum": 0.00046096140002001995, "Discussion Thread": 0.00044642985824304996, "Comment Section": 0.0007282205854223669, "Missing Content": 0.0010626056512425066, "Knowledge Article": 0.021562959525934386, "Legal Notices": 0.0014466893644241056, "News Article": 0.0004995437716151342, "Organizational Announcement": 0.0007575250128103677, "Nonfiction Writing": 0.009281944000593262, "Organizational About Page": 0.0008587829633512306, "Personal About Page": 0.010631657250628734, "Personal Blog": 0.0022227145398691895, "Product Page": 0.0004211184253739482, "Spam / Ads": 0.0005910420319337278, "Structured Data": 0.001677338600815729, "Technical Writing": 0.08248539876592527, "Transcript / Interview": 0.0008802978022609041, "Tutorial / How-To Guide": 0.028974848777898354, "User Reviews": 0.00035761370161550805, "NONE": 0.8257272553384836}, "tertiary": {"Academic Writing": 0.006299732409635508, "Content Listing": 0.0003173179969556323, "Creative Writing": 0.0005364665278954305, "Customer Support Page": 0.00037881446966801053, "Q&A Forum": 0.00029930446909637976, "Discussion Thread": 0.00024629848266212, "Comment Section": 0.0005288287859102499, "Missing Content": 0.0008104212246273952, "Knowledge Article": 0.008987466083855469, "Legal Notices": 0.0029589125775744763, "News Article": 0.00028636524565518295, "Organizational Announcement": 0.0004885207574132584, "Nonfiction Writing": 0.008015217087251543, "Organizational About Page": 0.0006552028300024332, "Personal About Page": 0.020785965913808234, "Personal Blog": 0.0035451696915703933, "Product Page": 0.00026873319224589076, "Spam / Ads": 0.0004638988759602153, "Structured Data": 0.0015027450170580746, "Technical Writing": 0.02137303544755348, "Transcript / Interview": 0.0004852731676049835, "Tutorial / How-To Guide": 0.01698037185494944, "User Reviews": 0.00021842103927209526, "NONE": 0.903567516851774}, "structured_records": {"false": 0.9923356051293308, "true": 0.007664394870669122}, "procedural_steps": {"false": 0.8714655676412436, "true": 0.12853443235875636}, "worked_solution": {"false": 0.9753709261848986, "true": 0.024629073815101363}, "contextual_dialogue": {"false": 0.9999900292932039, "true": 9.970706796055596e-06}, "content_quality": {"-2": 0.010547947455960407, "-1": 0.036610449153430195, "0": 0.22022715231631546, "1": 0.48918555515492423, "2": 0.24342889591936984}, "noise_severity": {"0": 0.9273775508862047, "1": 0.06692450154261743, "2": 0.003826176635391423, "3": 0.0012076587048650985, "4": 0.0006641122309214211}, "missing_content_severity": {"0": 0.8807233362580374, "1": 0.11285219244604218, "2": 0.0045402965348635815, "3": 0.0011615711654370132, "4": 0.0007226035956198414}, "factual_density": {"0": 0.004647666201256191, "1": 0.02119944066617005, "2": 0.1518147308581302, "3": 0.4124395504052652, "4": 0.40989861186917836}, "pretraining_utility": {"-2": 0.009868027315824634, "-1": 0.05994592577319094, "0": 0.3076080860108014, "1": 0.4944209413357686, "2": 0.1281570195644144}, "math_density": {"0": 0.007718580804400254, "1": 0.02297868792211661, "2": 0.11488938901279101, "3": 0.3854672531185427, "4": 0.4689460891421495}, "code_density": {"0": 0.0008082029997655136, "1": 0.001622686784503659, "2": 0.04005929192994105, "3": 0.4649461669864744, "4": 0.49256365129931556}, "reasoning_density": {"0": 0.3289067626664307, "1": 0.22213003344901433, "2": 0.1821561628052599, "3": 0.14854652603680313, "4": 0.1182605150424921}, "education_level": {"none": 0.0010452986382663062, "0": 0.001400211576308134, "1": 0.005979317288071648, "2": 0.5009138655360611, "3": 0.39484993081431496, "4": 0.09581137614697789}, "creative_writing_quality": {"not_applicable": 0.9965292407473921, "-2": 0.0001641260423056842, "-1": 0.000552739156144526, "0": 0.002197779773378034, "1": 0.00041258842872925704, "2": 0.0001435258520502404}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00018131256025727562, "Art & Design": 0.000517658192203012, "Law & Crime": 0.0002939892221356373, "Education & Careers": 0.00018890031131959685, "Entertainment & Media": 0.0001906235058968037, "Fashion & Beauty": 0.00017120566614636383, "Business, Economics & Finance": 0.00024622365015799786, "Food & Dining": 0.00016305623349947124, "Games & Gaming": 0.0003704675056388491, "Electronics & Hardware": 0.0006037314498686966, "Health, Healthcare & Wellness": 0.0002158342316708721, "History & Geography": 0.0002606739703813713, "Home, Pets & Hobbies": 0.00017343545099211325, "Industry & Production": 0.00032222017271849545, "Language, Literature & Philosophy": 0.00020503927749949193, "Politics & Government": 0.00011014444260285498, "Religion & Spirituality": 0.000161126911228232, "Science, Mathematics & Engineering": 0.12981784262928123, "Relationships & Community": 0.00010580599353356553, "Software Usage & Applications": 0.0008003277269390581, "Programming & Software Engineering": 0.863287202014625, "Sports & Fitness": 0.00023866897047931514, "Vehicles & Transportation": 0.00014023140491162072, "Travel & Tourism": 0.00022934070656266785, "Other / Uncovered Subject": 0.0007562230191857318, "Unknown / Insufficient Content": 0.0002487147802647892}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006144746418102273, "Art & Design": 0.0011689261008141476, "Law & Crime": 0.000612315778088802, "Education & Careers": 0.0004779139093863793, "Entertainment & Media": 0.0004800546756734845, "Fashion & Beauty": 0.000359970630222267, "Business, Economics & Finance": 0.0005571926997603639, "Food & Dining": 0.0003161765638369124, "Games & Gaming": 0.0006676853029127741, "Electronics & Hardware": 0.002903908695778989, "Health, Healthcare & Wellness": 0.0005177596402293731, "History & Geography": 0.0004930666564874921, "Home, Pets & Hobbies": 0.00038232856514147434, "Industry & Production": 0.0005250206659926841, "Language, Literature & Philosophy": 0.00030576434045309316, "Politics & Government": 0.00023750926070947737, "Religion & Spirituality": 0.0004878914380652122, "Science, Mathematics & Engineering": 0.45464193240242023, "Relationships & Community": 0.00023397340708076033, "Software Usage & Applications": 0.0009254827262112379, "Programming & Software Engineering": 0.20361932673205496, "Sports & Fitness": 0.0004530305316396136, "Vehicles & Transportation": 0.00029811856754819165, "Travel & Tourism": 0.000539853748676794, "Other / Uncovered Subject": 0.0010096338479310058, "NONE": 0.3271706884710739}}, "probabilities": {"primary": {"Academic Writing": 0.00020222543856269676, "Content Listing": 3.13494979053922e-05, "Creative Writing": 3.013023685787065e-05, "Customer Support Page": 1.9098858048156577e-05, "Q&A Forum": 2.686373937150182e-05, "Discussion Thread": 1.787830612127273e-05, "Comment Section": 2.6610312522773185e-05, "Missing Content": 4.451090079111545e-05, "Knowledge Article": 0.0004197901987326884, "Legal Notices": 5.5737913714729446e-05, "News Article": 1.0297206607705692e-05, "Organizational Announcement": 2.616604722996824e-05, "Nonfiction Writing": 6.87457270527829e-05, "Organizational About Page": 2.89238126659929e-05, "Personal About Page": 0.0001573248236002834, "Personal Blog": 3.0383758228865193e-05, "Product Page": 1.7676776266420777e-05, "Spam / Ads": 2.6128518568233003e-05, "Structured Data": 0.0004551821599696167, "Technical Writing": 0.9939950790038994, "Transcript / Interview": 3.141620966949004e-05, "Tutorial / How-To Guide": 0.002300972530584782, "User Reviews": 1.5588576249856802e-05, "Other / Unknown": 0.0019619194467784414}, "secondary": {"Academic Writing": 0.006967405843454669, "Content Listing": 0.0005585980106587085, "Creative Writing": 0.0008217133608371922, "Customer Support Page": 0.0005773354165881037, "Q&A Forum": 0.00046096140002001995, "Discussion Thread": 0.00044642985824304996, "Comment Section": 0.0007282205854223669, "Missing Content": 0.0010626056512425066, "Knowledge Article": 0.021562959525934386, "Legal Notices": 0.0014466893644241056, "News Article": 0.0004995437716151342, "Organizational Announcement": 0.0007575250128103677, "Nonfiction Writing": 0.009281944000593262, "Organizational About Page": 0.0008587829633512306, "Personal About Page": 0.010631657250628734, "Personal Blog": 0.0022227145398691895, "Product Page": 0.0004211184253739482, "Spam / Ads": 0.0005910420319337278, "Structured Data": 0.001677338600815729, "Technical Writing": 0.08248539876592527, "Transcript / Interview": 0.0008802978022609041, "Tutorial / How-To Guide": 0.028974848777898354, "User Reviews": 0.00035761370161550805, "NONE": 0.8257272553384836}, "tertiary": {"Academic Writing": 0.006299732409635508, "Content Listing": 0.0003173179969556323, "Creative Writing": 0.0005364665278954305, "Customer Support Page": 0.00037881446966801053, "Q&A Forum": 0.00029930446909637976, "Discussion Thread": 0.00024629848266212, "Comment Section": 0.0005288287859102499, "Missing Content": 0.0008104212246273952, "Knowledge Article": 0.008987466083855469, "Legal Notices": 0.0029589125775744763, "News Article": 0.00028636524565518295, "Organizational Announcement": 0.0004885207574132584, "Nonfiction Writing": 0.008015217087251543, "Organizational About Page": 0.0006552028300024332, "Personal About Page": 0.020785965913808234, "Personal Blog": 0.0035451696915703933, "Product Page": 0.00026873319224589076, "Spam / Ads": 0.0004638988759602153, "Structured Data": 0.0015027450170580746, "Technical Writing": 0.02137303544755348, "Transcript / Interview": 0.0004852731676049835, "Tutorial / How-To Guide": 0.01698037185494944, "User Reviews": 0.00021842103927209526, "NONE": 0.903567516851774}, "structured_records": {"false": 0.9923356051293308, "true": 0.007664394870669122}, "procedural_steps": {"false": 0.8714655676412436, "true": 0.12853443235875636}, "worked_solution": {"false": 0.9753709261848986, "true": 0.024629073815101363}, "contextual_dialogue": {"false": 0.9999900292932039, "true": 9.970706796055596e-06}, "content_quality": {"-2": 0.010547947455960405, "-1": 0.03661044915343019, "0": 0.2202271523163154, "1": 0.4891855551549241, "2": 0.24342889591936978}, "noise_severity": {"0": 0.9273775508862047, "1": 0.06692450154261743, "2": 0.003826176635391423, "3": 0.0012076587048650985, "4": 0.0006641122309214211}, "missing_content_severity": {"0": 0.8807233362580374, "1": 0.11285219244604218, "2": 0.0045402965348635815, "3": 0.0011615711654370132, "4": 0.0007226035956198414}, "factual_density": {"0": 0.004647666201256191, "1": 0.02119944066617005, "2": 0.1518147308581302, "3": 0.4124395504052652, "4": 0.40989861186917836}, "pretraining_utility": {"-2": 0.009868027315824634, "-1": 0.05994592577319094, "0": 0.3076080860108014, "1": 0.4944209413357686, "2": 0.1281570195644144}, "math_density": {"0": 0.007718580804400254, "1": 0.02297868792211661, "2": 0.11488938901279101, "3": 0.3854672531185427, "4": 0.4689460891421495}, "code_density": {"0": 0.0008082029997655134, "1": 0.0016226867845036586, "2": 0.040059291929941045, "3": 0.4649461669864743, "4": 0.49256365129931545}, "reasoning_density": {"0": 0.32890676266643065, "1": 0.22213003344901427, "2": 0.18215616280525987, "3": 0.1485465260368031, "4": 0.11826051504249208}, "education_level": {"none": 0.0010452986382663062, "0": 0.001400211576308134, "1": 0.005979317288071648, "2": 0.5009138655360611, "3": 0.39484993081431496, "4": 0.09581137614697789}, "creative_writing_quality": {"not_applicable": 0.9965292407473922, "-2": 0.00016412604230568422, "-1": 0.0005527391561445261, "0": 0.0021977797733780343, "1": 0.0004125884287292571, "2": 0.00014352585205024043}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001813125602572756, "Art & Design": 0.0005176581922030119, "Law & Crime": 0.00029398922213563723, "Education & Careers": 0.0001889003113195968, "Entertainment & Media": 0.00019062350589680365, "Fashion & Beauty": 0.0001712056661463638, "Business, Economics & Finance": 0.0002462236501579978, "Food & Dining": 0.00016305623349947122, "Games & Gaming": 0.000370467505638849, "Electronics & Hardware": 0.0006037314498686965, "Health, Healthcare & Wellness": 0.00021583423167087206, "History & Geography": 0.00026067397038137123, "Home, Pets & Hobbies": 0.00017343545099211322, "Industry & Production": 0.0003222201727184954, "Language, Literature & Philosophy": 0.00020503927749949188, "Politics & Government": 0.00011014444260285495, "Religion & Spirituality": 0.00016112691122823196, "Science, Mathematics & Engineering": 0.1298178426292812, "Relationships & Community": 0.0001058059935335655, "Software Usage & Applications": 0.0008003277269390579, "Programming & Software Engineering": 0.8632872020146248, "Sports & Fitness": 0.0002386689704793151, "Vehicles & Transportation": 0.0001402314049116207, "Travel & Tourism": 0.0002293407065626678, "Other / Uncovered Subject": 0.0007562230191857315, "Unknown / Insufficient Content": 0.00024871478026478917}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006144746418102274, "Art & Design": 0.0011689261008141479, "Law & Crime": 0.0006123157780888022, "Education & Careers": 0.00047791390938637935, "Entertainment & Media": 0.00048005467567348455, "Fashion & Beauty": 0.00035997063022226704, "Business, Economics & Finance": 0.000557192699760364, "Food & Dining": 0.0003161765638369125, "Games & Gaming": 0.0006676853029127742, "Electronics & Hardware": 0.0029039086957789893, "Health, Healthcare & Wellness": 0.0005177596402293732, "History & Geography": 0.0004930666564874922, "Home, Pets & Hobbies": 0.0003823285651414744, "Industry & Production": 0.0005250206659926842, "Language, Literature & Philosophy": 0.0003057643404530932, "Politics & Government": 0.0002375092607094774, "Religion & Spirituality": 0.00048789143806521226, "Science, Mathematics & Engineering": 0.4546419324024203, "Relationships & Community": 0.00023397340708076035, "Software Usage & Applications": 0.000925482726211238, "Programming & Software Engineering": 0.203619326732055, "Sports & Fitness": 0.00045303053163961367, "Vehicles & Transportation": 0.0002981185675481917, "Travel & Tourism": 0.0005398537486767941, "Other / Uncovered Subject": 0.001009633847931006, "NONE": 0.32717068847107394}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10162, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10162, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:36:10.514913+00:00", "scoring_key_slot": "primary", "dataset_uid": "ad98aae8-5276-46f6-8dc4-c9b960186262", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "bbdc455492298aed8ecf25392af72d69f4d0f1af467f669c7d854e29169f369c", "source_metadata": {"uuid": "ad98aae8-5276-46f6-8dc4-c9b960186262", "repo_name": "Juanarchy/findneighsides2", "relative_path": "findneighsides2.py", "category": "DATA", "algo_rel_score": 0.9380069971084595, "quality_score": 4.464105129241943, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 6, "repo_created_at": "2024-12-22T20:09:50.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "9214139ccefedf358e9706b99e39256bd3cf408e", "size_bytes": 5136, "file_timestamp": 1734907306, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "363b0522ad1fdb86e5f773193ec3b1a1230106ad"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 6, "repo_created_at": "2024-12-22T20:09:50.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "9214139ccefedf358e9706b99e39256bd3cf408e", "size_bytes": 5136, "file_timestamp": 1734907306, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "363b0522ad1fdb86e5f773193ec3b1a1230106ad", "url": "https://github.com/Juanarchy/findneighsides2/blob/363b0522ad1fdb86e5f773193ec3b1a1230106ad/findneighsides2.py"}, "gpt4o_tokens": 1518, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.9183370029283128, "noise_severity": 0.08085627985168126, "missing_content_severity": 0.12830791339455974, "factual_density": 3.2017420010749396, "pretraining_utility": 0.6710530000597571, "math_density": 3.2849435818719246, "code_density": 3.4468343768010703, "reasoning_density": 1.5051239973399118, "education_level": 2.5782974361901454, "creative_writing_quality": -0.052251134327423926}, "score_applicability": {"education_level": 0.9989547013617337, "creative_writing_quality": 0.0034707592526077424}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/st2eam/GraduationProject/blob/6b1bc46231b08e21980957d0a0d33285d9bdeb9b/feeling_server/src/services/post_service.py", "id": "python-0-777b25836e602393bb1cada679ab5d83", "text": "import re\nimport time\nimport jieba\nfrom bson import ObjectId\nfrom keybert import KeyBERT\nfrom ..models import ENoticeType, EPostType,  IPagination,  ELogType\nfrom ..utils.check import *\nfrom ..database import get_collection\nfrom ..services import session_service, notice_service, log_service, user_service\nfrom ..bert import NER_MAIN, TextClassifier_MAIN\nfrom ..word2vec import Word2VecModel\n\nkw_model = KeyBERT(model='paraphrase-multilingual-MiniLM-L12-v2')\n\nner_class_list = ['address', 'book', 'company', 'game', 'goverment',\n                  'movie', 'name', 'organization', 'position', 'scene', 'customize']\n\nclassify_class_list = ['财经', '房产', '股票',\n                       '教育', '科技', '社会', '时政', '体育', '游戏', '娱乐']\n\n\nfilterDeleted = [{'$match': {'type': {'$ne': EPostType.Delete.value}}}]\n\nrelatInfo = [\n    {'$lookup': {\n        'from': 'posts',\n        'localField': 'relationId',\n        'foreignField': '_id',\n        'as': 'relate.post'\n    }\n    },\n    {'$lookup': {\n        'from': 'users',\n        'localField': 'relate.post.userId',\n        'foreignField': 'userId',\n        'as': 'relate.user'\n    }\n    },\n    {'$project': {\n        'relate.user._id': 0,\n        'relate.user.createdAt': 0,\n        'relate.user.banner': 0,\n        'relate.user.bio': 0,\n        'relate.user.status': 0\n    }\n    }\n]\n\nuserInfo = [\n    {\n        '$lookup': {\n            'from': 'users',\n            'localField': 'userId',\n            'foreignField': 'userId',\n            'as': 'user'\n        }\n    },\n    {\n        '$unwind': '$user'\n    },\n    {\n        '$project': {\n            'user._id': 0,\n            'user.createdAt': 0,\n            'user.banner': 0,\n            'user.bio': 0,\n            'user.password': 0,\n            'user.status': 0\n        }\n    }\n]\n\n\ndef get_recommend(token: str):\n    userId = session_service.getSessionBySid(token)['userId']\n    user = get_collection('users').find_one({'userId': userId})\n    labels = user['labels']\n    recommend_option = [\n        *filterDeleted,\n        *userInfo,\n        *relatInfo,\n        {\n            '$lookup': {\n                'from': 'follows',\n                'localField': 'userId',\n                'foreignField': 'followId',\n                'as': 'follow'\n            }\n        },\n        {\n            '$lookup': {\n                'from': 'recommend',\n                'localField': '_id',\n                'foreignField': 'postId',\n                'as': 'recommended'\n            }\n        },\n        {\n            '$lookup': {\n                'from': 'likes',\n                'localField': '_id',\n                'foreignField': 'postId',\n                'as': 'hasLikes'\n            }\n        },\n        {\n            '$addFields': {\n                'isLike': {'$in': [userId, '$hasLikes.userId']},\n            }\n        },\n        {\n            '$addFields': {\n                'haveRecommended': {'$in': [userId, '$recommended.userId']},\n            }\n        },\n        {\n            '$project': {\n                'hasLikes': 0,\n                'recommended': 0\n            }\n        },\n        {\n            '$match': {\n                '$and': [\n                    {'userId': {'$not': {'$eq': userId}}},\n                    {'follow.userId': {'$not': {'$eq': userId}}},\n                    {'haveRecommended': False}\n                ]\n            }\n        },\n        {'$match': {'type': {'$ne': EPostType.Comment.value}}},\n        {\n            '$sort': {\n                '_id': -1\n            }\n        },\n        {\n            '$project': {\n                'follow': 0,\n                'haveRecommended': 0,\n                'user.labels': 0\n            }\n        }\n    ]\n    recommend_data_cursor = get_collection(\n        'posts').aggregate([*recommend_option])\n    temp = [x for x in recommend_data_cursor]\n    model = Word2VecModel.get_instance()\n    recommend_arr = sorted(temp, key=lambda x: model.wv.n_similarity(\n        x['keywords'], labels), reverse=True)\n    items = recommend_arr[:10]\n    if (len(items) != 0):\n        log_service.addRecommendedPosts(\n            userId=userId, postList=map(lambda post: post['_id'], items))\n    return {\n        'items': items\n    }\n\n\ndef get_historical_recommend(token: str, options: IPagination):\n    userId = session_service.getSessionBySid(token)['userId']\n    option = [\n        *filterDeleted,\n        *userInfo,\n        *relatInfo,\n        {\n            '$lookup': {\n                'from': 'follows',\n                'localField': 'userId',\n                'foreignField': 'followId',\n                'as': 'follow'\n            }\n        },\n        {\n            '$lookup': {\n                'from': 'recommend',\n                'localField': '_id',\n                'foreignField': 'postId',\n                'as': 'recommended'\n            }\n        },\n        {\n            '$lookup': {\n                'from': 'likes',\n                'localField': '_id',\n                'foreignField': 'postId',\n                'as': 'hasLikes'\n            }\n        },\n        {\n            '$addFields': {\n                'isLike': {'$in': [userId, '$hasLikes.userId']},\n            }\n        },\n        {\n            '$addFields': {\n                'haveRecommended': {'$in': [userId, '$recommended.userId']},\n            }\n        },\n        {\n            '$project': {\n                'hasLikes': 0,\n                'recommended': 0\n            }\n        },\n        {\n            '$match': {\n                '$and': [\n                    {'userId': {'$not': {'$eq': userId}}},\n                    {'follow.userId': {'$not': {'$eq': userId}}},\n                    {'haveRecommended': True}\n                ]\n            }\n        },\n        {'$match': {'type': {'$ne': EPostType.Comment.value}}},\n        {\n            '$sort': {\n                '_id': -1\n            }\n        },\n        {\n            '$project': {\n                'follow': 0,\n                'haveRecommended': 0,\n                'user.labels': 0\n            }\n        }\n    ]\n    if bool(options.next):\n        next = get_collection('posts').find_one(\n            {'_id': ObjectId(options.next)})\n        data_cursor = get_collection('posts').aggregate([\n            *option,\n            {\n                '$match': {\n                    'createdAt': {\n                        '$lt': next['createdAt']\n                    }\n                }\n            },\n            {\n                '$limit': options.limit\n            }\n        ])\n    else:\n        data_cursor = get_collection('posts').aggregate([\n            *option, {\n                '$limit': options.limit\n            }\n        ])\n    items = [x for x in data_cursor]\n    hasNext = len(items) == options.limit\n    return {\n        'items': items,\n        'hasNext': hasNext\n    }\n\n\ndef get_following(token: str, options: IPagination):\n    userId = session_service.getSessionBySid(token)['userId']\n    hasNext = False\n    option = [\n        *filterDeleted,\n        *userInfo,\n        *relatInfo,\n        {\n            '$lookup': {\n                'from': 'follows',\n                'localField': 'userId',\n                'foreignField': 'followId',\n                'as': 'follow'\n            }\n        },\n        {\n            '$lookup': {\n                'from': 'likes',\n                'localField': '_id',\n                'foreignField': 'postId',\n                'as': 'hasLikes'\n            }\n        },\n        {\n            '$addFields': {\n                'isLike': {'$in': [userId, '$hasLikes.userId']},\n            }\n        },\n        {\n            '$project': {\n                'hasLikes': 0\n            }\n        },\n        {\n            '$match': {\n                '$or': [{'userId': userId}, {'follow.userId': userId}]\n            }\n        },\n        {'$match': {'type': {'$ne': EPostType.Comment.value}}},\n        {\n            '$sort': {\n                '_id': -1\n            }\n        },\n        {\n            '$project': {\n                'follow': 0\n            }\n        }\n    ]\n    if bool(options.next):\n        data_cursor = get_collection('posts').aggregate([\n            *option,\n            {\n                '$match': {\n                    '_id': {\n                        '$lt': ObjectId(options.next)\n                    }\n                }\n            },\n            {\n                '$limit': options.limit\n            }\n        ])\n    else:\n        data_cursor = get_collection('posts').aggregate([\n            *option, {\n                '$limit': options.limit\n            }\n        ])\n    arr = [x for x in data_cursor]\n    hasNext = len(arr) == options.limit\n    return {\n        'items': arr,\n        'hasNext': hasNext\n    }\n\n\ndef create_post(token: str, content: str, imgs: list[str], labels: list[str]):\n    result_Classify = TextClassifier_MAIN.predict([content])[0]\n    result_NER = NER_MAIN.pos_predict([content])[0][\"label\"]\n    doc = \" \".join(jieba.cut(content))\n    extract_keywords = kw_model.extract_keywords(doc)\n    keywords = []\n    for item in extract_keywords:\n        keywords.append(item[0])\n\n    for key in result_NER:\n        keywords.extend(result_NER[key])\n\n    if labels:\n        result_NER.update({'customize': labels})\n    keywords = list(set(keywords))\n    increment_count = int(len(keywords)/53*47)\n    for i in range(increment_count):\n        keywords.append(result_Classify)\n    userId = session_service.getSessionBySid(token)['userId']\n    post = {\n        'userId': userId,\n        'relationId': None,\n        'type': EPostType.Post.value,\n        'imgs': imgs,\n        'content': content,\n        'classify': result_Classify,\n        'label': result_NER,\n        'keywords': keywords,\n        'likes': 0,\n        'comments': 0,\n        'forwards': 0,\n        'createdAt': time.time() * 1000\n    }\n    res = get_collection('posts').insert_one(post)\n    user_service.addLabels(userId=userId, newLabels=keywords)\n    log_service.addItem(userId=userId, postId=res.inserted_id,\n                        type=ELogType.Post.value)\n    return str(res.inserted_id)\n\n\ndef create_comment(token: str, relationId: str, content: str, imgs: list[str]):\n    userId = session_service.getSessionBySid(token)['userId']\n    post = get_collection('posts').find_one_and_update(\n        {'_id': ObjectId(relationId)}, {'$inc': {'comments': 1}})\n    check(post, PostErrorStat.ERR_POST_NOT_FOUND.value)\n    check(post['type'] != EPostType.Delete.value,\n          PostErrorStat.ERR_POST_HAS_BEEN_DELETED.value)\n    doc = \" \".join(jieba.cut(content))\n    extract_keywords = kw_model.extract_keywords(doc)\n    keywords = post['keywords']\n    for item in extract_keywords:\n        keywords.append(item[0])\n    keywords = list(set(keywords))\n    comment = {\n        'userId': userId,\n        'relationId': ObjectId(relationId),\n        'type': EPostType.Comment.value,\n        'imgs': imgs,\n        'content': content,\n        'classify': post['classify'],\n        'label': post['label'],\n        'keywords': keywords,\n        'likes': 0,\n        'comments': 0,\n        'forwards': 0,\n        'createdAt': time.time() * 1000\n    }\n    res = get_collection('posts').insert_one(comment)\n    if userId != post['userId']:\n        notice_service.create_notice(\n            type=ENoticeType.Comment.value,\n            senderId=userId,\n            receiverId=post['userId'],\n            relationId=ObjectId(relationId),\n            content=''\n        )\n    user_service.addLabels(userId=userId, newLabels=keywords)\n    log_service.addItem(userId=userId, postId=ObjectId(\n        relationId), type=ELogType.Comment.value)\n    return str(res.inserted_id)\n\n\ndef create_forward(token: str, relationId: str, content: str, imgs: list[str]):\n    userId = session_service.getSessionBySid(token)['userId']\n    post = get_collection('posts').find_one_and_update(\n        {'_id': ObjectId(relationId)}, {'$inc': {'forwards': 1}})\n    check(post, PostErrorStat.ERR_POST_NOT_FOUND.value)\n    check(post['type'] != EPostType.Delete.value,\n          PostErrorStat.ERR_POST_HAS_BEEN_DELETED.value)\n    doc = \" \".join(jieba.cut(content))\n    extract_keywords = kw_model.extract_keywords(doc)\n    keywords = post['keywords']\n    for item in extract_keywords:\n        keywords.append(item[0])\n    keywords = list(set(keywords))\n    forward = {\n        'userId': userId,\n        'relationId': ObjectId(relationId),\n        'type': EPostType.Forward.value,\n        'imgs': imgs,\n        'content': content,\n        'classify': post['classify'],\n        'label': post['label'],\n        'keywords': keywords,\n        'likes': 0,\n        'comments': 0,\n        'forwards': 0,\n        'createdAt': time.time() * 1000\n    }\n\n    res = get_collection('posts').insert_one(forward)\n    if userId != post['userId']:\n        notice_service.create_notice(\n            type=ENoticeType.Forward.value,\n            senderId=userId,\n            receiverId=post['userId'],\n            relationId=ObjectId(relationId),\n            content=''\n        )\n    user_service.addLabels(userId=userId, newLabels=keywords)\n    log_service.addItem(userId=userId, postId=ObjectId(\n        relationId), type=ELogType.Forward.value)\n    return str(res.inserted_id)\n\n\ndef get_detail(id: str, token: str):\n    pattern = r'^[A-Fa-f0-9]{1,24}$'\n    check(re.match(pattern, id) and len(id) == 24,\n          PostErrorStat.ERR_POST_NOT_FOUND.value)\n    userId = session_service.getSessionBySid(token)['userId']\n    likeInfo = [\n        {\n            '$lookup': {\n                'from': 'likes',\n                'localField': '_id',\n                'foreignField': 'postId',\n                'as': 'hasLikes'\n            }\n        },\n        {\n            '$addFields': {\n                'isLike': {'$in': [userId, '$hasLikes.userId']},\n            }\n        },\n        {\n            '$project': {\n                'hasLikes': 0\n            }\n        }\n    ]\n    data_cursor = get_collection('posts').aggregate([\n        {'$match': {'_id': ObjectId(id)}},\n        *relatInfo,\n        *userInfo,\n        *likeInfo\n    ])\n    post = [x for x in data_cursor][0]\n\n    check(bool(post), PostErrorStat.ERR_POST_NOT_FOUND.value)\n    if (post['type'] == EPostType.Delete.value):\n        post['content'] = '帖子已删除'\n        post['imgs'] = []\n    return post\n\n\ndef get_comments(id: str, token: str, options: IPagination):\n    hasNext = False\n    userId = session_service.getSessionBySid(token)['userId']\n    likeInfo = [\n        {\n            '$lookup': {\n                'from': 'likes',\n                'localField': '_id',\n                'foreignField': 'postId',\n                'as': 'hasLikes'\n            }\n        },\n        {\n            '$addFields': {\n                'isLike': {'$in': [userId, '$hasLikes.userId']},\n            }\n        },\n        {\n            '$project': {\n                'hasLikes': 0\n            }\n        }\n    ]\n    option = [\n        *filterDeleted,\n        *relatInfo,\n        *userInfo,\n        *likeInfo,\n        {'$sort': {'_id': -1}}\n    ]\n    if options.next:\n        data_cursor = get_collection('posts').aggregate([\n            *option,\n            {\n                \"$match\": {\n                    \"relationId\": ObjectId(id),\n                    \"_id\": {\"$lt\": ObjectId(options.next)},\n                    \"type\": EPostType.Comment.value\n                }\n            },\n            {\"$match\": {\"userId\": userId}},\n            {\"$limit\": options.limit}\n        ])\n    else:\n        data_cursor = get_collection('posts').aggregate([\n            *option,\n            {\n                \"$match\": {\n                    \"relationId\": ObjectId(id),\n                    \"type\": EPostType.Comment.value\n                }\n            },\n            {\"$limit\": options.limit}\n        ])\n    items = [x for x in data_cursor]\n    hasNext = len(items) == options.limit\n    return {\n        'items': items,\n        'hasNext': hasNext\n    }\n\n\ndef like(id: str, token: str):\n    userId = session_service.getSessionBySid(token)['userId']\n    exist = get_collection('likes').find_one(\n        {'postId': ObjectId(id), 'userId': userId})\n    check(not exist, PostErrorStat.ERR_POST_HAS_BEEN_LIKED.value)\n    post = get_collection('posts').find_one_and_update(\n        {'_id': ObjectId(id)}, {'$inc': {'likes': 1}})\n    check(post, PostErrorStat.ERR_POST_NOT_FOUND.value)\n    check(post['type'] != EPostType.Delete.value,\n          PostErrorStat.ERR_POST_HAS_BEEN_DELETED.value)\n    res = get_collection('likes').insert_one(\n        {\n            'postId': ObjectId(id),\n            'userId': userId,\n            'createdAt': time.time() * 1000\n        })\n    user_service.addLabels(userId=userId, newLabels=post['keywords'])\n    log_service.addItem(userId=userId, postId=ObjectId(id),\n                        type=ELogType.Like.value)\n    return str(res.inserted_id)\n\n\ndef unlike(id: str, token: str):\n    userId = session_service.getSessionBySid(token)['userId']\n    post = get_collection('posts').find_one_and_update(\n        {'_id': ObjectId(id)}, {'$inc': {'likes': -1}})\n    check(post, PostErrorStat.ERR_POST_NOT_FOUND.value)\n    check(post['type'] != EPostType.Delete.value,\n          PostErrorStat.ERR_POST_HAS_BEEN_DELETED.value)\n    like = get_collection('likes').find_one(\n        {'userId': userId, 'postId': ObjectId(id)})\n    check(like, PostErrorStat.ERR_LIKE_NOT_FOUND.value)\n    res = get_collection('likes').delete_one(\n        {'userId': userId, 'postId': ObjectId(id)})\n    user_service.delLabels(token=token, newLabels=post['keywords'])\n    log_service.addItem(userId=userId, postId=ObjectId(id),\n                        type=ELogType.Unlike.value)\n    return res.acknowledged\n\n\ndef delete(id: str, token: str):\n    # 获取用户ID和帖子\n    userId = session_service.getSessionBySid(token)['userId']\n    post = get_collection('posts').find_one({'_id': ObjectId(id)})\n    check(post, PostErrorStat.ERR_POST_NOT_FOUND.value)\n\n    # 将帖子类型设置为删除\n    get_collection('posts').update_one({'_id': ObjectId(id)}, {\n        '$set': {'type': EPostType.Delete.value}})\n    # 更新用户数据,仅添加属于自己的内容\n    if post['userId'] == userId:\n        type = ELogType.DeletePost.value if post['type'] == EPostType.Post.value else ELogType.DeleteForward.value if post[\n            'type'] == EPostType.Forward.value else ELogType.DeleteComment.value\n        user_service.delLabels(token=token, newLabels=post['keywords'])\n        log_service.addItem(userId=userId, postId=ObjectId(\n            id), type=type)\n\n    # 删除相关评论\n    comments = get_collection('posts').find({\n        'relationId': id,\n        'type': EPostType.Comment.value\n    })\n    for comment in comments:\n        delete(comment['_id'], token=token)\n\n\ndef get_user_post(token: str, relationId: str, options: IPagination):\n    userId = user_service.get_user_info(token, relationId)['userId']\n    option = [\n        *filterDeleted,\n        *userInfo,\n        *relatInfo,\n        {\n            '$lookup': {\n                'from': 'likes',\n                'localField': '_id',\n                'foreignField': 'postId',\n                'as': 'hasLikes'\n            }\n        },\n        {\n            '$addFields': {\n                'isLike': {'$in': [userId, '$hasLikes.userId']},\n            }\n        },\n        {\n            '$project': {\n                'hasLikes': 0\n            }\n        },\n        {'$sort': {'_id': -1}}\n    ]\n    if (options.next):\n        data_cursor = get_collection('posts').aggregate([\n            *option,\n            {'$match': {'userId': userId,\n                        'type': {'$ne': EPostType.Comment.value},\n                        '_id': {'$lt': ObjectId(options.next)}}},\n            {'$limit': options.limit}\n        ])\n    else:\n        data_cursor = get_collection('posts').aggregate([\n            *option,\n            {'$match': {'userId': userId, 'type': {'$ne': EPostType.Comment.value}}},\n            {'$limit': options.limit}\n        ])\n    items = [x for x in data_cursor]\n    hasNext = len(items) == options.limit\n    return {'items': items, 'hasNext': hasNext}\n\n\ndef get_user_img_post(token: str, relationId: str, options: IPagination):\n    userId = user_service.get_user_info(token, relationId)['userId']\n    option = [\n        *filterDeleted,\n        *userInfo,\n        *relatInfo,\n        {\n            '$lookup': {\n                'from': 'likes',\n                'localField': '_id',\n                'foreignField': 'postId',\n                'as': 'hasLikes'\n            }\n        },\n        {\n            '$addFields': {\n                'isLike': {'$in': [userId, '$hasLikes.userId']},\n            }\n        },\n        {\n            '$project': {\n                'hasLikes': 0\n            }\n        },\n        {'$sort': {'_id': -1}}\n    ]\n    if (options.next):\n        data_cursor = get_collection('posts').aggregate([\n            *option,\n            {'$match': {'userId': userId,\n                        'type': {'$ne': EPostType.Comment.value},\n                        'imgs': {'$ne': []},\n                        '_id': {'$lt': ObjectId(options.next)}}},\n            {'$limit': options.limit}\n        ])\n    else:\n        data_cursor = get_collection('posts').aggregate([\n            *option,\n            {'$match': {'userId': userId,\n                        'type': {'$ne': EPostType.Comment.value},\n                        'imgs': {'$ne': []}}},\n            {'$limit': options.limit}\n        ])\n    items = [x for x in data_cursor]\n    hasNext = len(items) == options.limit\n    return {'items': items, 'hasNext': hasNext}\n\n\ndef get_user_like_post(token: str, relationId: str, options: IPagination):\n    userId = user_service.get_user_info(token, relationId)['userId']\n    option = [\n        *filterDeleted,\n        *userInfo,\n        *relatInfo,\n        {\n            '$lookup': {\n                'from': 'likes',\n                'localField': '_id',\n                'foreignField': 'postId',\n                'as': 'likePosts'\n            }\n        },\n        {\n            '$lookup': {\n                'from': 'likes',\n                'localField': '_id',\n                'foreignField': 'postId',\n                'as': 'hasLikes'\n            }\n        },\n        {\n            '$addFields': {\n                'isLike': {'$in': [userId, '$hasLikes.userId']},\n            }\n        },\n        {\n            '$project': {\n                'hasLikes': 0\n            }\n        },\n        {'$unwind': '$likePosts'},\n        {'$match': {'likePosts.userId': userId}},\n        {'$sort': {'_id': -1}}\n    ]\n    if (options.next):\n        data_cursor = get_collection('posts').aggregate([\n            *option,\n            {'$match': {'type': {'$ne': EPostType.Comment.value},\n                        '_id': {'$lt': ObjectId(options.next)}}},\n            {'$limit': options.limit}\n        ])\n    else:\n        data_cursor = get_collection('posts').aggregate([\n            *option,\n            {'$match': {'type': {'$ne': EPostType.Comment.value}}},\n            {'$limit': options.limit}\n        ])\n    items = [x for x in data_cursor]\n    hasNext = len(items) == options.limit\n    return {'items': items, 'hasNext': hasNext}\n\n\ndef similar(id: str, token: str):\n    post = get_collection('posts').find_one({'_id': ObjectId(id)})\n    userId = session_service.getSessionBySid(token)['userId']\n    check(post, PostErrorStat.ERR_POST_NOT_FOUND.value)\n    recommend_option = [\n        *filterDeleted,\n        *userInfo,\n        *relatInfo,\n        {\n            '$lookup': {\n                'from': 'follows',\n                'localField': 'userId',\n                'foreignField': 'followId',\n                'as': 'follow'\n            }\n        },\n        {\n            '$lookup': {\n                'from': 'recommend',\n                'localField': '_id',\n                'foreignField': 'postId',\n                'as': 'recommended'\n            }\n        },\n        {\n            '$lookup': {\n                'from': 'likes',\n                'localField': '_id',\n                'foreignField': 'postId',\n                'as': 'hasLikes'\n            }\n        },\n        {\n            '$addFields': {\n                'isLike': {'$in': [userId, '$hasLikes.userId']},\n            }\n        },\n        {\n            '$addFields': {\n                'haveRecommended': {'$in': [userId, '$recommended.userId']},\n            }\n        },\n        {\n            '$project': {\n                'hasLikes': 0,\n                'recommended': 0\n            }\n        },\n        {\n            '$match': {\n                '$and': [\n                    {'_id': {'$not': {'$eq': ObjectId(id)}}},\n                    {'userId': {'$not': {'$eq': userId}}},\n                    {'follow.userId': {'$not': {'$eq': userId}}},\n                    {'haveRecommended': {'$eq': False}}\n                ]\n            }\n        },\n        {'$match': {'type': {'$ne': EPostType.Comment.value}}},\n        {\n            '$sort': {\n                '_id': -1\n            }\n        },\n        {\n            '$project': {\n                'follow': 0,\n                'haveRecommended': 0,\n                'user.labels': 0\n            }\n        }\n    ]\n    recommend_data_cursor = get_collection(\n        'posts').aggregate([*recommend_option])\n    temp = [x for x in recommend_data_cursor]\n    if len(temp) == 0:\n        return None\n    else:\n        model = Word2VecModel.get_instance()\n        recommend_arr = sorted(temp, key=lambda x: model.wv.n_similarity(\n            x['keywords'], post['keywords']), reverse=True)\n        most_recommended = recommend_arr[0]\n        log_service.addRecommendedPosts(userId=userId, postList=[\n                                        most_recommended['_id']])\n    return most_recommended\n\n\ndef create_daily_posts(data: list[str]):\n    user_array = [\"老王\",  \"Caphriel\",\n                  \"Rahmiel\", \"Naaririel\", \"小李\", \"Ophanim\", \"Yahoel\", \"Wish\"]\n    for i, content in enumerate(data):\n        userId = user_array[i % len(user_array)]\n        result_Classify = TextClassifier_MAIN.predict([content])[0]\n        result_NER = NER_MAIN.pos_predict([content])[0][\"label\"]\n        doc = \" \".join(jieba.cut(content))\n        extract_keywords = kw_model.extract_keywords(doc)\n        keywords = []\n        keywords.append(result_Classify)\n        for key in result_NER:\n            keywords.extend(result_NER[key])\n        for item in extract_keywords:\n            keywords.append(item[0])\n        keywords = list(set(keywords))\n        post = {\n            'userId': userId,\n            'relationId': None,\n            'type': EPostType.Post.value,\n            'imgs': [],\n            'content': content,\n            'classify': result_Classify,\n            'label': result_NER,\n            'keywords': keywords,\n            'likes': 0,\n            'comments': 0,\n            'forwards': 0,\n            'createdAt': time.time() * 1000\n        }\n        res = get_collection('posts').insert_one(post)\n        user_service.addLabels(userId=userId, newLabels=keywords)\n        log_service.addItem(userId=userId, postId=res.inserted_id,\n                            type=ELogType.Post.value)\n        print(userId+'成功发布一篇文章')\n", "text_preview": "import re import time import jieba from bson import ObjectId from keybert import KeyBERT from ..models import ENoticeType, EPostType, IPagination, ELogType from ..utils.check import * from ..database import get_collection from ..services im", "input_sha256": "95e9352b168b584313ac84b042bb08e41d191365508a1d43a94a6b810ef0508a", "text_sha256": "777b25836e602393bb1cada679ab5d831c9486e5ceecbcf01fe21daa82f629a3", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12107, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.6281991559395388, "noise_severity": 0.050658348652180975, "missing_content_severity": 0.0570162708897953, "factual_density": 3.17619470390646, "pretraining_utility": 0.4790603504341483, "math_density": 0.2522266995636918, "code_density": 3.8963199706848934, "reasoning_density": 0.5166689362314727, "education_level": 2.403452862418304, "creative_writing_quality": -0.5002678013890132}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.08420399632080333, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9953179810928884, "secondary": 0.8738246632173576, "tertiary": 0.9616897028564567, "structured_records": 0.22602247510673612, "procedural_steps": 0.014696513229107021, "worked_solution": 0.0045923157563521316, "contextual_dialogue": 1.0500862466663682e-05, "content_quality": 0.42825708104379634, "noise_severity": 0.9577847094565158, "missing_content_severity": 0.9524864409251705, "factual_density": 0.4702167282820974, "pretraining_utility": 0.4250591558341503, "math_density": 0.7898110836969235, "code_density": 0.9135999755707445, "reasoning_density": 0.5694425531404395, "education_level": 0.5452069529928456, "creative_writing_quality": 0.9989870607620283, "primary_topic": 0.9808451939100968, "secondary_topic": 0.7966908601903157}, "probability_margin": {"primary": 0.9924502832102617, "secondary": 0.7934359362944294, "tertiary": 0.9382334456534414, "education_level": 0.28331058204847276, "creative_writing_quality": 0.9988393651542804, "primary_topic": 0.9739165440720903, "secondary_topic": 0.7175715133359888}, "raw_probabilities": {"primary": {"Academic Writing": 3.77657571841551e-05, "Content Listing": 2.0998381068480977e-05, "Creative Writing": 1.5340461043355598e-05, "Customer Support Page": 7.414353597787965e-06, "Q&A Forum": 1.0302094469650832e-05, "Discussion Thread": 8.618544225352209e-06, "Comment Section": 1.6851462467428504e-05, "Missing Content": 1.6803559324401117e-05, "Knowledge Article": 4.0062852875074306e-05, "Legal Notices": 2.1705309652004057e-05, "News Article": 5.138724441931932e-06, "Organizational Announcement": 1.099776457418188e-05, "Nonfiction Writing": 9.416589305574717e-06, "Organizational About Page": 1.1005360715079892e-05, "Personal About Page": 1.1389011234035622e-05, "Personal Blog": 7.7907330084892e-06, "Product Page": 6.490884618987941e-06, "Spam / Ads": 1.0276610923114014e-05, "Structured Data": 0.003062782003756341, "Technical Writing": 0.9955130652140181, "Transcript / Interview": 1.2924790172623649e-05, "Tutorial / How-To Guide": 0.00011663731761227777, "User Reviews": 6.142100461233578e-06, "Other / Unknown": 0.0010200801192503316}, "secondary": {"Academic Writing": 0.0004582129149022443, "Content Listing": 0.0006315419071959339, "Creative Writing": 0.00034236331167593353, "Customer Support Page": 0.00017968791704241717, "Q&A Forum": 0.00017760524454890647, "Discussion Thread": 0.00016686465694279757, "Comment Section": 0.00027272984375349766, "Missing Content": 0.00037837228487441564, "Knowledge Article": 0.0006162621948641679, "Legal Notices": 0.00028871111006580183, "News Article": 0.00020917943310975892, "Organizational Announcement": 0.0002244634339670468, "Nonfiction Writing": 0.00023076896422327147, "Organizational About Page": 0.0002441447012542974, "Personal About Page": 0.00018974338247304353, "Personal Blog": 0.00019307376705357795, "Product Page": 0.00014084504072296632, "Spam / Ads": 0.0002651844382229184, "Structured Data": 0.08564603262220494, "Technical Writing": 0.02905138539652651, "Transcript / Interview": 0.0002900537337355189, "Tutorial / How-To Guide": 0.0006278977942364521, "User Reviews": 9.290698976917891e-05, "NONE": 0.8790819689166344}, "tertiary": {"Academic Writing": 0.00019161757914715075, "Content Listing": 0.0003847899562791344, "Creative Writing": 0.0001556381641180012, "Customer Support Page": 6.222336002651745e-05, "Q&A Forum": 4.728808002133561e-05, "Discussion Thread": 5.482511252137612e-05, "Comment Section": 0.00014273031116326736, "Missing Content": 0.00021855852549840335, "Knowledge Article": 0.0006648409384689103, "Legal Notices": 0.00021660469983584006, "News Article": 7.943657109934542e-05, "Organizational Announcement": 8.128484351679201e-05, "Nonfiction Writing": 0.00013759943057492922, "Organizational About Page": 0.00010227779204495805, "Personal About Page": 7.128519678201418e-05, "Personal Blog": 6.732114833247407e-05, "Product Page": 5.0410127888977295e-05, "Spam / Ads": 0.0001146139337042837, "Structured Data": 0.02505251958399637, "Technical Writing": 0.008272268693305822, "Transcript / Interview": 8.41532433388634e-05, "Tutorial / How-To Guide": 0.00042937394332891583, "User Reviews": 3.237352756858178e-05, "NONE": 0.9632859652374377}, "structured_records": {"false": 0.7739775248932639, "true": 0.22602247510673612}, "procedural_steps": {"false": 0.985303486770893, "true": 0.014696513229107021}, "worked_solution": {"false": 0.9954076842436479, "true": 0.0045923157563521316}, "contextual_dialogue": {"false": 0.9999894991375333, "true": 1.0500862466663682e-05}, "content_quality": {"-2": 0.014231592911887902, "-1": 0.09574629650085849, "0": 0.2947588016665721, "1": 0.43811797957718984, "2": 0.15714532934349157}, "noise_severity": {"0": 0.9618219824708213, "1": 0.028770178186088347, "2": 0.007142852959055626, "3": 0.0014574809881577177, "4": 0.0008075053958770554}, "missing_content_severity": {"0": 0.960404988468212, "1": 0.027193221617828143, "2": 0.007998201220041442, "3": 0.003787707943789936, "4": 0.00061588075012862}, "factual_density": {"0": 0.010149889104081584, "1": 0.026573417570926183, "2": 0.1461761086234144, "3": 0.4111332697176063, "4": 0.40596731498397154}, "pretraining_utility": {"-2": 0.012113916259668317, "-1": 0.10456581523536121, "0": 0.3599609520327082, "1": 0.4388646347556784, "2": 0.08449468171658389}, "math_density": {"0": 0.8218186607436521, "1": 0.13622372618027856, "2": 0.023147830504125902, "3": 0.005531817912611982, "4": 0.013277964659331358}, "code_density": {"0": 0.0002349369707039752, "1": 0.00042174495236710005, "2": 0.010088674369455208, "3": 0.08129769783627908, "4": 0.9079569458711947}, "reasoning_density": {"0": 0.7677770358308106, "1": 0.062165360170128955, "2": 0.09232767126853017, "3": 0.04107149739783781, "4": 0.03665843533269249}, "education_level": {"none": 0.0009302865990335706, "0": 0.0012490219507303934, "1": 0.0034530009794384886, "2": 0.6210057941607047, "3": 0.3376952121122319, "4": 0.03566668419786109}, "creative_writing_quality": {"not_applicable": 0.9991558839683569, "-2": 0.00016698755009262966, "-1": 0.0002397609068825959, "0": 0.0003165188140764265, "1": 9.024558538269333e-05, "2": 3.0603175208890715e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 9.36215730622462e-05, "Art & Design": 0.00013320496995832088, "Law & Crime": 7.018762685275048e-05, "Education & Careers": 0.0002261051096088814, "Entertainment & Media": 0.0003334424046696005, "Fashion & Beauty": 0.00010141342196911666, "Business, Economics & Finance": 0.00020949339351446997, "Food & Dining": 6.452229082296981e-05, "Games & Gaming": 0.0003202976897610112, "Electronics & Hardware": 0.00012638291952052732, "Health, Healthcare & Wellness": 0.00011986835433367666, "History & Geography": 6.44959292113578e-05, "Home, Pets & Hobbies": 5.8488573939642726e-05, "Industry & Production": 7.994100933677293e-05, "Language, Literature & Philosophy": 0.000365663439223888, "Politics & Government": 0.0001104817303336957, "Religion & Spirituality": 0.00013050086125526126, "Science, Mathematics & Engineering": 0.0049186511551285944, "Relationships & Community": 0.007665373149156738, "Software Usage & Applications": 0.0007011925368940328, "Programming & Software Engineering": 0.981581917221247, "Sports & Fitness": 8.729461700758063e-05, "Vehicles & Transportation": 3.5039436451545174e-05, "Travel & Tourism": 0.00010811464882036324, "Other / Uncovered Subject": 0.002101329984609389, "Unknown / Insufficient Content": 0.0001929759533104861}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0011716071701407165, "Art & Design": 0.0010565769599116313, "Law & Crime": 0.0009608020753178682, "Education & Careers": 0.0010244074736447905, "Entertainment & Media": 0.0024190016237337366, "Fashion & Beauty": 0.0005762613710879358, "Business, Economics & Finance": 0.0014609783058030994, "Food & Dining": 0.0004363570527140882, "Games & Gaming": 0.00105845685320953, "Electronics & Hardware": 0.0006418148214003345, "Health, Healthcare & Wellness": 0.0007946012177753555, "History & Geography": 0.00043708424280313005, "Home, Pets & Hobbies": 0.00041926365710009424, "Industry & Production": 0.0004446380668245118, "Language, Literature & Philosophy": 0.0017453000503163444, "Politics & Government": 0.0006460280871395019, "Religion & Spirituality": 0.0006798117488518491, "Science, Mathematics & Engineering": 0.08693892915469931, "Relationships & Community": 0.03002627010572428, "Software Usage & Applications": 0.0020136395004980647, "Programming & Software Engineering": 0.051618140337796554, "Sports & Fitness": 0.0003588059268846992, "Vehicles & Transportation": 0.0001583028565222135, "Travel & Tourism": 0.0005888772985117228, "Other / Uncovered Subject": 0.007813601550900554, "NONE": 0.8045104424906881}}, "probabilities": {"primary": {"Academic Writing": 3.77657571841551e-05, "Content Listing": 2.0998381068480977e-05, "Creative Writing": 1.5340461043355598e-05, "Customer Support Page": 7.414353597787965e-06, "Q&A Forum": 1.0302094469650832e-05, "Discussion Thread": 8.618544225352209e-06, "Comment Section": 1.6851462467428504e-05, "Missing Content": 1.6803559324401117e-05, "Knowledge Article": 4.0062852875074306e-05, "Legal Notices": 2.1705309652004057e-05, "News Article": 5.138724441931932e-06, "Organizational Announcement": 1.099776457418188e-05, "Nonfiction Writing": 9.416589305574717e-06, "Organizational About Page": 1.1005360715079892e-05, "Personal About Page": 1.1389011234035622e-05, "Personal Blog": 7.7907330084892e-06, "Product Page": 6.490884618987941e-06, "Spam / Ads": 1.0276610923114014e-05, "Structured Data": 0.003062782003756341, "Technical Writing": 0.9955130652140181, "Transcript / Interview": 1.2924790172623649e-05, "Tutorial / How-To Guide": 0.00011663731761227777, "User Reviews": 6.142100461233578e-06, "Other / Unknown": 0.0010200801192503316}, "secondary": {"Academic Writing": 0.0004582129149022443, "Content Listing": 0.0006315419071959339, "Creative Writing": 0.00034236331167593353, "Customer Support Page": 0.00017968791704241717, "Q&A Forum": 0.00017760524454890647, "Discussion Thread": 0.00016686465694279757, "Comment Section": 0.00027272984375349766, "Missing Content": 0.00037837228487441564, "Knowledge Article": 0.0006162621948641679, "Legal Notices": 0.00028871111006580183, "News Article": 0.00020917943310975892, "Organizational Announcement": 0.0002244634339670468, "Nonfiction Writing": 0.00023076896422327147, "Organizational About Page": 0.0002441447012542974, "Personal About Page": 0.00018974338247304353, "Personal Blog": 0.00019307376705357795, "Product Page": 0.00014084504072296632, "Spam / Ads": 0.0002651844382229184, "Structured Data": 0.08564603262220494, "Technical Writing": 0.02905138539652651, "Transcript / Interview": 0.0002900537337355189, "Tutorial / How-To Guide": 0.0006278977942364521, "User Reviews": 9.290698976917891e-05, "NONE": 0.8790819689166344}, "tertiary": {"Academic Writing": 0.00019161757914715075, "Content Listing": 0.0003847899562791344, "Creative Writing": 0.0001556381641180012, "Customer Support Page": 6.222336002651745e-05, "Q&A Forum": 4.728808002133561e-05, "Discussion Thread": 5.482511252137612e-05, "Comment Section": 0.00014273031116326736, "Missing Content": 0.00021855852549840335, "Knowledge Article": 0.0006648409384689103, "Legal Notices": 0.00021660469983584006, "News Article": 7.943657109934542e-05, "Organizational Announcement": 8.128484351679201e-05, "Nonfiction Writing": 0.00013759943057492922, "Organizational About Page": 0.00010227779204495805, "Personal About Page": 7.128519678201418e-05, "Personal Blog": 6.732114833247407e-05, "Product Page": 5.0410127888977295e-05, "Spam / Ads": 0.0001146139337042837, "Structured Data": 0.02505251958399637, "Technical Writing": 0.008272268693305822, "Transcript / Interview": 8.41532433388634e-05, "Tutorial / How-To Guide": 0.00042937394332891583, "User Reviews": 3.237352756858178e-05, "NONE": 0.9632859652374377}, "structured_records": {"false": 0.7739775248932639, "true": 0.22602247510673612}, "procedural_steps": {"false": 0.985303486770893, "true": 0.014696513229107021}, "worked_solution": {"false": 0.9954076842436479, "true": 0.0045923157563521316}, "contextual_dialogue": {"false": 0.9999894991375333, "true": 1.0500862466663682e-05}, "content_quality": {"-2": 0.014231592911887903, "-1": 0.0957462965008585, "0": 0.29475880166657215, "1": 0.4381179795771899, "2": 0.1571453293434916}, "noise_severity": {"0": 0.9618219824708213, "1": 0.028770178186088347, "2": 0.007142852959055626, "3": 0.0014574809881577177, "4": 0.0008075053958770554}, "missing_content_severity": {"0": 0.9604049884682118, "1": 0.027193221617828136, "2": 0.00799820122004144, "3": 0.003787707943789935, "4": 0.0006158807501286199}, "factual_density": {"0": 0.010149889104081584, "1": 0.026573417570926183, "2": 0.1461761086234144, "3": 0.4111332697176063, "4": 0.40596731498397154}, "pretraining_utility": {"-2": 0.012113916259668317, "-1": 0.10456581523536121, "0": 0.3599609520327082, "1": 0.4388646347556784, "2": 0.08449468171658389}, "math_density": {"0": 0.8218186607436522, "1": 0.1362237261802786, "2": 0.023147830504125905, "3": 0.005531817912611983, "4": 0.01327796465933136}, "code_density": {"0": 0.0002349369707039752, "1": 0.00042174495236710005, "2": 0.010088674369455208, "3": 0.08129769783627908, "4": 0.9079569458711947}, "reasoning_density": {"0": 0.7677770358308106, "1": 0.062165360170128955, "2": 0.09232767126853017, "3": 0.04107149739783781, "4": 0.03665843533269249}, "education_level": {"none": 0.0009302865990335704, "0": 0.0012490219507303932, "1": 0.0034530009794384877, "2": 0.6210057941607046, "3": 0.33769521211223186, "4": 0.035666684197861086}, "creative_writing_quality": {"not_applicable": 0.9991558839683569, "-2": 0.00016698755009262966, "-1": 0.0002397609068825959, "0": 0.0003165188140764265, "1": 9.024558538269333e-05, "2": 3.0603175208890715e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 9.362157306224622e-05, "Art & Design": 0.0001332049699583209, "Law & Crime": 7.018762685275049e-05, "Education & Careers": 0.00022610510960888142, "Entertainment & Media": 0.00033344240466960055, "Fashion & Beauty": 0.00010141342196911667, "Business, Economics & Finance": 0.00020949339351447, "Food & Dining": 6.452229082296982e-05, "Games & Gaming": 0.00032029768976101126, "Electronics & Hardware": 0.00012638291952052734, "Health, Healthcare & Wellness": 0.00011986835433367667, "History & Geography": 6.449592921135781e-05, "Home, Pets & Hobbies": 5.848857393964273e-05, "Industry & Production": 7.994100933677294e-05, "Language, Literature & Philosophy": 0.00036566343922388807, "Politics & Government": 0.00011048173033369572, "Religion & Spirituality": 0.00013050086125526128, "Science, Mathematics & Engineering": 0.004918651155128595, "Relationships & Community": 0.007665373149156739, "Software Usage & Applications": 0.0007011925368940329, "Programming & Software Engineering": 0.9815819172212471, "Sports & Fitness": 8.729461700758064e-05, "Vehicles & Transportation": 3.503943645154518e-05, "Travel & Tourism": 0.00010811464882036326, "Other / Uncovered Subject": 0.0021013299846093896, "Unknown / Insufficient Content": 0.00019297595331048612}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0011716071701407165, "Art & Design": 0.0010565769599116313, "Law & Crime": 0.0009608020753178682, "Education & Careers": 0.0010244074736447905, "Entertainment & Media": 0.0024190016237337366, "Fashion & Beauty": 0.0005762613710879358, "Business, Economics & Finance": 0.0014609783058030994, "Food & Dining": 0.0004363570527140882, "Games & Gaming": 0.00105845685320953, "Electronics & Hardware": 0.0006418148214003345, "Health, Healthcare & Wellness": 0.0007946012177753555, "History & Geography": 0.00043708424280313005, "Home, Pets & Hobbies": 0.00041926365710009424, "Industry & Production": 0.0004446380668245118, "Language, Literature & Philosophy": 0.0017453000503163444, "Politics & Government": 0.0006460280871395019, "Religion & Spirituality": 0.0006798117488518491, "Science, Mathematics & Engineering": 0.08693892915469931, "Relationships & Community": 0.03002627010572428, "Software Usage & Applications": 0.0020136395004980647, "Programming & Software Engineering": 0.051618140337796554, "Sports & Fitness": 0.0003588059268846992, "Vehicles & Transportation": 0.0001583028565222135, "Travel & Tourism": 0.0005888772985117228, "Other / Uncovered Subject": 0.007813601550900554, "NONE": 0.8045104424906881}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 15479, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 15479, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:36:11.842274+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "9fc947fd-af4c-4659-ab48-449a6df0c0a0", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "b532c6eb5189d9aa3df206af082b09f7835a449112df4640dda03493e3f80bc2", "source_metadata": {"uuid": "9fc947fd-af4c-4659-ab48-449a6df0c0a0", "repo_name": "st2eam/GraduationProject", "relative_path": "feeling_server/src/services/post_service.py", "category": "TOOL", "algo_rel_score": 0.9542399644851685, "quality_score": 4.332182884216309, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 47, "repo_created_at": "2023-02-19T13:29:29.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "0a3d1645d78fc7c2c3c810b36a899a7d51cd1ffd", "size_bytes": 27105, "file_timestamp": 1682154975, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "6b1bc46231b08e21980957d0a0d33285d9bdeb9b"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 47, "repo_created_at": "2023-02-19T13:29:29.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "0a3d1645d78fc7c2c3c810b36a899a7d51cd1ffd", "size_bytes": 27105, "file_timestamp": 1682154975, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "6b1bc46231b08e21980957d0a0d33285d9bdeb9b", "url": "https://github.com/st2eam/GraduationProject/blob/6b1bc46231b08e21980957d0a0d33285d9bdeb9b/feeling_server/src/services/post_service.py"}, "gpt4o_tokens": 6436, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.6281991559395388, "noise_severity": 0.050658348652180975, "missing_content_severity": 0.05701627088979531, "factual_density": 3.17619470390646, "pretraining_utility": 0.4790603504341483, "math_density": 0.2522266995636918, "code_density": 3.8963199706848934, "reasoning_density": 0.5166689362314727, "education_level": 2.403452862418304, "creative_writing_quality": -0.5002678013890132}, "score_applicability": {"education_level": 0.9990697134009664, "creative_writing_quality": 0.0008441160316432361}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/Ritik-912/mmr-question-extraction/blob/b5a31955a055f76cbb96c3d35cd9141744673730/mmr-pdfs-extraction/eng/extract.py", "id": "python-0-e1c2b3113f0057c49b6356d63ab892ad", "text": "from pdfplumber import open as op\nfrom pandas import DataFrame\nfrom json import dump\nfrom langdetect import detect\nfrom langcodes import Language\nfrom tqdm import tqdm\nfrom os import path, makedirs, walk\n\nnot_extracted = []  # recording non-English PDFs or error PDFs\n\n# Sort elements from top to bottom then left to right\ndef sort_elements(elements):\n    return sorted(elements, key=lambda element: (element['top'], element['x0']))\n\ndef group_elements_by_line(elements, tolerance=5):\n    lines = []; current_line = []; current_top = elements[0]['top']\n    for element in elements:\n        if abs(element['top'] - current_top) > tolerance:\n            lines.append(current_line)\n            current_line = [element]\n            current_top = element['top']\n        else:\n            current_line.append(element)\n    lines.append(current_line)\n    return lines\n\ndef reconstruct_text_with_spaces(lines, margin_unit=2):\n    result = []\n    for line in lines:\n        sorted_line = sorted(line, key=lambda e: e['x0'])\n        if sorted_line:\n            first_element = sorted_line[0]\n            line_text = ' ' * int(first_element['x0'] / margin_unit)  # Add leading spaces based on margin\n            last_x = first_element['x0']\n\n            for element in sorted_line:\n                if element != first_element:\n                    space_count = int((element['x0'] - last_x) / margin_unit)\n                    line_text += ' ' * space_count\n                line_text += element['content']\n                last_x = element['x0'] + len(element['content']) * margin_unit\n            result.append(line_text)\n    return \"\\n\".join(result).strip()\n\ndef extract_text_and_images_per_page(pdf_path: str, image_save_dir: str) -> list:\n    page_data = []\n    with op(pdf_path) as pdf:\n        pdf_name = pdf_path.replace(\"/\", '_').split('.')[-2][1:]\n        for page_num, page in enumerate(pdf.pages):\n            elements = []\n\n            # Extract words\n            elements.extend([{'x0': word['x0'], 'top': word['top'], 'content': word['text']} for word in page.extract_words()])\n\n            # Extract images and add as textual elements\n            if page.images:\n                for img_num, img in enumerate(page.images):\n                    img_bbox = img['x0'], img['top'], img['x1'], img['bottom']\n                    save_path = path.join(image_save_dir, f\"{pdf_name}_page_{page_num + 1}_img_{img_num + 1}.png\")\n                    page.within_bbox(img_bbox).to_image(resolution=512).save(save_path, format=\"PNG\")\n                    # Add the image path as a string element\n                    elements.append({'x0': img['x0'], 'top': img['top'], 'content': f\"<img>{save_path}</img>\"})\n\n            # Extract tables and add as textual elements\n            tables_loc = page.find_tables()\n            tables = page.extract_tables()\n            if tables:\n                elements.extend([{'x0': loc.bbox[0], 'top': loc.bbox[1], 'content': f\"<table>{str(table)}</table>\"} for table, loc in zip(tables, tables_loc)])\n\n            # Sort and group elements into lines\n            text = reconstruct_text_with_spaces(group_elements_by_line(sort_elements(elements)))\n\n            # Language detection\n            lang_code = detect(text) if text else \"unknown\"\n            if lang_code != \"unknown\":\n                detected_language = Language.get(lang_code).display_name()\n\n            # Append page data\n            page_data.append({'pdf_path': pdf_path, 'page_number': page_num + 1, 'text': text, 'language': detected_language})\n    return page_data\n\ndef process_pdf(pdf_path: str, output_dir: str) -> None:\n    try:\n        page_data = extract_text_and_images_per_page(pdf_path, path.join(output_dir, \"images\")) # extract data from a pdf\n        df = DataFrame(page_data)\n        pdf_name = pdf_path.replace(\"/\", \"_\").split('.')[-2][1:]\n        if (df['language'] != 'English').any():\n            not_extracted.append({'pdf_path': pdf_path, \"language\": ', '.join(df['language'].unique())})\n        else:\n            csv_file_path = path.join(output_dir, f\"csv/{pdf_name}.csv\")\n            print(csv_file_path)\n            printsaving(\"saving..\")\n            df.to_csv(csv_file_path, mode='a', index=False, header=not os.path.exists(csv_file_path), encoding='utf-8') # save as csv\n            json_file_path = path.join(output_dir, f\"json/{pdf_name}.json\")\n            with open(json_file_path, mode='w', encoding='utf-8') as json_file:\n                dump(page_data, json_file, ensure_ascii=False, indent=4)\n\n    except Exception as e:\n        print(f\"Failed to process {pdf_path}: {e}\")\n        not_extracted.append({\"pdf_path\": pdf_path, \"language\": \"unknown\"})\n\ndef process_all_pdfs(directory, output_dir):\n    if not path.exists(output_dir):\n        makedirs(output_dir)\n    images_dir = path.join(output_dir, 'images')\n    if not path.exists(images_dir):\n        makedirs(images_dir); del images_dir\n    pdf_paths = []\n    for root, dirs, files in walk(directory):\n        for file in files:\n            if file.endswith('.pdf'):\n                pdf_paths.append(path.join(root, file))\n    print(\"Number of pdfs files found in \", directory, \": \", len(pdf_paths))\n    try:\n        list(process_pdf(pdf_path, output_dir) for pdf_path in tqdm(pdf_paths[:1], total=len(pdf_paths[:1]), desc=f\"Processing {directory} PDFs\"))\n    except Exception as e:\n        print(e)\n    DataFrame(not_extracted).to_csv(path.join(output_dir, f\"notExtracted-eng_pdfs.csv\"), encoding = 'utf-8', index=False, mode = \"w\", header=True)\n\nif __name__ == \"__main__\":\n    pdf_directory = \"../../../pdfs/\"\n    output_directory = \"../../../extracted_data/eng/\"\n    process_all_pdfs(pdf_directory, output_directory)\n", "text_preview": "from pdfplumber import open as op from pandas import DataFrame from json import dump from langdetect import detect from langcodes import Language from tqdm import tqdm from os import path, makedirs, walk not_extracted = [] # recording non-E", "input_sha256": "22079eec31d80fefde65bcab0ef59e780b7ca5ec69b1603a7b8df4b9f68579d3", "text_sha256": "e1c2b3113f0057c49b6356d63ab892ad0cd0a3953813913ee55b41dc47220bfe", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12108, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": -0.17868375402180586, "noise_severity": 0.03270434391140902, "missing_content_severity": 0.009615640121155396, "factual_density": 3.1936940459953242, "pretraining_utility": 0.5420121810644287, "math_density": 0.26059899018474686, "code_density": 3.7125056461322954, "reasoning_density": 0.45775765724000445, "education_level": 2.0911687904228113, "creative_writing_quality": -0.44446447034000647}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.012574161707339826, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9936608795067509, "secondary": 0.9672911365241096, "tertiary": 0.9804120009143772, "structured_records": 0.02436915877672157, "procedural_steps": 0.08206918857779963, "worked_solution": 0.0031801685596620053, "contextual_dialogue": 7.186614473899163e-06, "content_quality": 0.38205615446619345, "noise_severity": 0.9727463800738259, "missing_content_severity": 0.9919869665657038, "factual_density": 0.32807837166277, "pretraining_utility": 0.4823949074933279, "math_density": 0.7828341748460443, "code_density": 0.7604213717769127, "reasoning_density": 0.6185352856333295, "education_level": 0.7942327426224212, "creative_writing_quality": 0.999387168537921, "primary_topic": 0.9748438115765873, "secondary_topic": 0.8575060318475897}, "probability_margin": {"primary": 0.9909211320451156, "secondary": 0.9578296340492825, "tertiary": 0.9747015026043422, "education_level": 0.7127599323894829, "creative_writing_quality": 0.9992752067838583, "primary_topic": 0.9664203124279362, "secondary_topic": 0.8146173521406398}, "raw_probabilities": {"primary": {"Academic Writing": 4.621822922173465e-05, "Content Listing": 2.1127792489337364e-05, "Creative Writing": 1.8903760817384535e-05, "Customer Support Page": 1.098144603404855e-05, "Q&A Forum": 1.6872881773292526e-05, "Discussion Thread": 1.0424128378202151e-05, "Comment Section": 1.5783999789594458e-05, "Missing Content": 3.397624952817169e-05, "Knowledge Article": 0.00010044524303870981, "Legal Notices": 3.055520207942555e-05, "News Article": 6.726546877849741e-06, "Organizational Announcement": 1.181363960235042e-05, "Nonfiction Writing": 1.6840938766125587e-05, "Organizational About Page": 1.5642964286782653e-05, "Personal About Page": 1.5215485205878064e-05, "Personal Blog": 1.1098956374086453e-05, "Product Page": 9.016854226805881e-06, "Spam / Ads": 1.4647779741049344e-05, "Structured Data": 0.0012911472947009616, "Technical Writing": 0.9939250095273029, "Transcript / Interview": 2.1140177698867862e-05, "Tutorial / How-To Guide": 0.0030038774821872847, "User Reviews": 8.290977592690439e-06, "Other / Unknown": 0.0013442424422864478}, "secondary": {"Academic Writing": 0.00018618872916800758, "Content Listing": 0.00011458094052095852, "Creative Writing": 0.000125389772428283, "Customer Support Page": 8.778751454083503e-05, "Q&A Forum": 6.637668375651193e-05, "Discussion Thread": 4.816861494690647e-05, "Comment Section": 0.0002220732928355311, "Missing Content": 0.00022558399134934488, "Knowledge Article": 0.0005460994489925158, "Legal Notices": 0.0001379367123446967, "News Article": 7.991566383410606e-05, "Organizational Announcement": 6.758203259852432e-05, "Nonfiction Writing": 8.42017399987099e-05, "Organizational About Page": 8.48303425676943e-05, "Personal About Page": 7.270348490863538e-05, "Personal Blog": 7.991253914103606e-05, "Product Page": 4.889653717725616e-05, "Spam / Ads": 8.111174227123963e-05, "Structured Data": 0.007219841000450971, "Technical Writing": 0.010824371786322604, "Transcript / Interview": 9.602133390058801e-05, "Tutorial / How-To Guide": 0.010804848117004964, "User Reviews": 4.1572143335131356e-05, "NONE": 0.968654005835605}, "tertiary": {"Academic Writing": 9.602957993663933e-05, "Content Listing": 7.325802232715495e-05, "Creative Writing": 6.941534994856565e-05, "Customer Support Page": 4.609483409498719e-05, "Q&A Forum": 3.4023645918259135e-05, "Discussion Thread": 2.631734945116737e-05, "Comment Section": 0.00023316769566791902, "Missing Content": 0.00028156309229413346, "Knowledge Article": 0.0004701944241894782, "Legal Notices": 0.0001768077782668181, "News Article": 4.5759811612359784e-05, "Organizational Announcement": 3.906630480101642e-05, "Nonfiction Writing": 5.574114620388404e-05, "Organizational About Page": 6.178617590767574e-05, "Personal About Page": 4.286013802568674e-05, "Personal Blog": 4.183555346380157e-05, "Product Page": 3.381353477792514e-05, "Spam / Ads": 5.1368195374047744e-05, "Structured Data": 0.005662061905321315, "Technical Writing": 0.004623730959097712, "Transcript / Interview": 5.713829400686507e-05, "Tutorial / How-To Guide": 0.006526664938602658, "User Reviews": 2.3133727765127774e-05, "NONE": 0.9812281675429448}, "structured_records": {"false": 0.9756308412232785, "true": 0.02436915877672157}, "procedural_steps": {"false": 0.9179308114222003, "true": 0.08206918857779963}, "worked_solution": {"false": 0.996819831440338, "true": 0.0031801685596620053}, "contextual_dialogue": {"false": 0.9999928133855261, "true": 7.186614473899163e-06}, "content_quality": {"-2": 0.0756606542067466, "-1": 0.3087868759176936, "0": 0.3802800817551416, "1": 0.18912034593145563, "2": 0.04615204218896266}, "noise_severity": {"0": 0.9751194248916418, "1": 0.019058056255439323, "2": 0.004330713339091567, "3": 0.000982361077523023, "4": 0.000509444436304375}, "missing_content_severity": {"0": 0.9944263995344639, "1": 0.0025046325576301223, "2": 0.0022773034514597546, "3": 0.0006102571651793654, "4": 0.00018140729126691723}, "factual_density": {"0": 0.019512553863339062, "1": 0.040067756734634294, "2": 0.1424498846934687, "3": 0.32315269896047943, "4": 0.4748171057480785}, "pretraining_utility": {"-2": 0.010626139754969937, "-1": 0.07600630035417304, "0": 0.355665944998533, "1": 0.4761324688561066, "2": 0.08156914603621752}, "math_density": {"0": 0.8290311162019878, "1": 0.131344264875855, "2": 0.01144538640022148, "3": 0.006352977579293579, "4": 0.021826254942642027}, "code_density": {"0": 0.0005483712995272246, "1": 0.0010429969754599416, "2": 0.047961003893169686, "3": 0.1862498699568767, "4": 0.7641977578749665}, "reasoning_density": {"0": 0.7889717609921636, "1": 0.0550599609266097, "2": 0.09559381519150614, "3": 0.029987785628500088, "4": 0.030386677261220553}, "education_level": {"none": 0.0013130750425563496, "0": 0.0019677773490590846, "1": 0.04187724579754464, "2": 0.8285272855186843, "3": 0.11576735312920133, "4": 0.010547263162954259}, "creative_writing_quality": {"not_applicable": 0.9994893071149341, "-2": 8.83907926491851e-05, "-1": 0.00013805116535236096, "0": 0.00021410033107588145, "1": 5.2453283993785845e-05, "2": 1.76973119948286e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 8.078892274728991e-05, "Art & Design": 0.0001249545262482661, "Law & Crime": 8.595799379867485e-05, "Education & Careers": 0.00015121632798940202, "Entertainment & Media": 7.724007767982793e-05, "Fashion & Beauty": 5.122443742043712e-05, "Business, Economics & Finance": 0.00011363781975661938, "Food & Dining": 6.626191626823803e-05, "Games & Gaming": 9.702370632504048e-05, "Electronics & Hardware": 0.0002139642284156771, "Health, Healthcare & Wellness": 6.878151125588354e-05, "History & Geography": 6.878334794490628e-05, "Home, Pets & Hobbies": 5.629301091506005e-05, "Industry & Production": 0.00010816439884996485, "Language, Literature & Philosophy": 0.008163564314378315, "Politics & Government": 4.85779869579039e-05, "Religion & Spirituality": 8.308411826271099e-05, "Science, Mathematics & Engineering": 0.0093910448572438, "Relationships & Community": 4.014740043919745e-05, "Software Usage & Applications": 0.003304738339142979, "Programming & Software Engineering": 0.9758113572851801, "Sports & Fitness": 9.835907711344867e-05, "Vehicles & Transportation": 4.139421239049794e-05, "Travel & Tourism": 8.729248595720845e-05, "Other / Uncovered Subject": 0.0013878210447385536, "Unknown / Insufficient Content": 0.00017832665258011673}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007815255926913525, "Art & Design": 0.0010387236923185587, "Law & Crime": 0.000709107045304095, "Education & Careers": 0.0006165242798668426, "Entertainment & Media": 0.000759987798842717, "Fashion & Beauty": 0.00030239294365825946, "Business, Economics & Finance": 0.0007132421956840187, "Food & Dining": 0.00022326218988631005, "Games & Gaming": 0.00040986753491057814, "Electronics & Hardware": 0.0005467371298790652, "Health, Healthcare & Wellness": 0.00042551595964596275, "History & Geography": 0.0003101708205594576, "Home, Pets & Hobbies": 0.00026282679035059147, "Industry & Production": 0.0003069940190675706, "Language, Literature & Philosophy": 0.04836921694358102, "Politics & Government": 0.00032564544819800215, "Religion & Spirituality": 0.00044325003377288066, "Science, Mathematics & Engineering": 0.03818999688953983, "Relationships & Community": 0.00016864643900219633, "Software Usage & Applications": 0.014254169241020121, "Programming & Software Engineering": 0.02399206604103435, "Sports & Fitness": 0.0002552619768444866, "Vehicles & Transportation": 0.00013549403373563638, "Travel & Tourism": 0.0003983521153085612, "Other / Uncovered Subject": 0.0030744537610766453, "NONE": 0.8629865690842208}}, "probabilities": {"primary": {"Academic Writing": 4.621822922173465e-05, "Content Listing": 2.1127792489337364e-05, "Creative Writing": 1.8903760817384535e-05, "Customer Support Page": 1.098144603404855e-05, "Q&A Forum": 1.6872881773292526e-05, "Discussion Thread": 1.0424128378202151e-05, "Comment Section": 1.5783999789594458e-05, "Missing Content": 3.397624952817169e-05, "Knowledge Article": 0.00010044524303870981, "Legal Notices": 3.055520207942555e-05, "News Article": 6.726546877849741e-06, "Organizational Announcement": 1.181363960235042e-05, "Nonfiction Writing": 1.6840938766125587e-05, "Organizational About Page": 1.5642964286782653e-05, "Personal About Page": 1.5215485205878064e-05, "Personal Blog": 1.1098956374086453e-05, "Product Page": 9.016854226805881e-06, "Spam / Ads": 1.4647779741049344e-05, "Structured Data": 0.0012911472947009616, "Technical Writing": 0.9939250095273029, "Transcript / Interview": 2.1140177698867862e-05, "Tutorial / How-To Guide": 0.0030038774821872847, "User Reviews": 8.290977592690439e-06, "Other / Unknown": 0.0013442424422864478}, "secondary": {"Academic Writing": 0.00018618872916800758, "Content Listing": 0.00011458094052095852, "Creative Writing": 0.000125389772428283, "Customer Support Page": 8.778751454083503e-05, "Q&A Forum": 6.637668375651193e-05, "Discussion Thread": 4.816861494690647e-05, "Comment Section": 0.0002220732928355311, "Missing Content": 0.00022558399134934488, "Knowledge Article": 0.0005460994489925158, "Legal Notices": 0.0001379367123446967, "News Article": 7.991566383410606e-05, "Organizational Announcement": 6.758203259852432e-05, "Nonfiction Writing": 8.42017399987099e-05, "Organizational About Page": 8.48303425676943e-05, "Personal About Page": 7.270348490863538e-05, "Personal Blog": 7.991253914103606e-05, "Product Page": 4.889653717725616e-05, "Spam / Ads": 8.111174227123963e-05, "Structured Data": 0.007219841000450971, "Technical Writing": 0.010824371786322604, "Transcript / Interview": 9.602133390058801e-05, "Tutorial / How-To Guide": 0.010804848117004964, "User Reviews": 4.1572143335131356e-05, "NONE": 0.968654005835605}, "tertiary": {"Academic Writing": 9.602957993663933e-05, "Content Listing": 7.325802232715495e-05, "Creative Writing": 6.941534994856565e-05, "Customer Support Page": 4.609483409498719e-05, "Q&A Forum": 3.4023645918259135e-05, "Discussion Thread": 2.631734945116737e-05, "Comment Section": 0.00023316769566791902, "Missing Content": 0.00028156309229413346, "Knowledge Article": 0.0004701944241894782, "Legal Notices": 0.0001768077782668181, "News Article": 4.5759811612359784e-05, "Organizational Announcement": 3.906630480101642e-05, "Nonfiction Writing": 5.574114620388404e-05, "Organizational About Page": 6.178617590767574e-05, "Personal About Page": 4.286013802568674e-05, "Personal Blog": 4.183555346380157e-05, "Product Page": 3.381353477792514e-05, "Spam / Ads": 5.1368195374047744e-05, "Structured Data": 0.005662061905321315, "Technical Writing": 0.004623730959097712, "Transcript / Interview": 5.713829400686507e-05, "Tutorial / How-To Guide": 0.006526664938602658, "User Reviews": 2.3133727765127774e-05, "NONE": 0.9812281675429448}, "structured_records": {"false": 0.9756308412232785, "true": 0.02436915877672157}, "procedural_steps": {"false": 0.9179308114222003, "true": 0.08206918857779963}, "worked_solution": {"false": 0.996819831440338, "true": 0.0031801685596620053}, "contextual_dialogue": {"false": 0.9999928133855261, "true": 7.186614473899163e-06}, "content_quality": {"-2": 0.0756606542067466, "-1": 0.3087868759176936, "0": 0.3802800817551416, "1": 0.18912034593145563, "2": 0.04615204218896266}, "noise_severity": {"0": 0.9751194248916416, "1": 0.01905805625543932, "2": 0.004330713339091566, "3": 0.0009823610775230227, "4": 0.0005094444363043749}, "missing_content_severity": {"0": 0.9944263995344639, "1": 0.0025046325576301223, "2": 0.0022773034514597546, "3": 0.0006102571651793654, "4": 0.00018140729126691723}, "factual_density": {"0": 0.019512553863339062, "1": 0.040067756734634294, "2": 0.1424498846934687, "3": 0.32315269896047943, "4": 0.4748171057480785}, "pretraining_utility": {"-2": 0.010626139754969937, "-1": 0.07600630035417304, "0": 0.355665944998533, "1": 0.4761324688561066, "2": 0.08156914603621752}, "math_density": {"0": 0.8290311162019879, "1": 0.13134426487585502, "2": 0.011445386400221481, "3": 0.00635297757929358, "4": 0.02182625494264203}, "code_density": {"0": 0.0005483712995272246, "1": 0.0010429969754599416, "2": 0.047961003893169686, "3": 0.1862498699568767, "4": 0.7641977578749665}, "reasoning_density": {"0": 0.7889717609921636, "1": 0.0550599609266097, "2": 0.09559381519150614, "3": 0.029987785628500088, "4": 0.030386677261220553}, "education_level": {"none": 0.0013130750425563499, "0": 0.001967777349059085, "1": 0.04187724579754465, "2": 0.8285272855186844, "3": 0.11576735312920135, "4": 0.01054726316295426}, "creative_writing_quality": {"not_applicable": 0.9994893071149339, "-2": 8.839079264918508e-05, "-1": 0.00013805116535236094, "0": 0.0002141003310758814, "1": 5.245328399378583e-05, "2": 1.7697311994828596e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 8.07889227472899e-05, "Art & Design": 0.00012495452624826608, "Law & Crime": 8.595799379867484e-05, "Education & Careers": 0.000151216327989402, "Entertainment & Media": 7.724007767982791e-05, "Fashion & Beauty": 5.122443742043711e-05, "Business, Economics & Finance": 0.00011363781975661935, "Food & Dining": 6.626191626823802e-05, "Games & Gaming": 9.702370632504046e-05, "Electronics & Hardware": 0.00021396422841567705, "Health, Healthcare & Wellness": 6.878151125588353e-05, "History & Geography": 6.878334794490627e-05, "Home, Pets & Hobbies": 5.629301091506004e-05, "Industry & Production": 0.00010816439884996482, "Language, Literature & Philosophy": 0.008163564314378313, "Politics & Government": 4.8577986957903885e-05, "Religion & Spirituality": 8.308411826271098e-05, "Science, Mathematics & Engineering": 0.009391044857243798, "Relationships & Community": 4.014740043919744e-05, "Software Usage & Applications": 0.0033047383391429782, "Programming & Software Engineering": 0.9758113572851799, "Sports & Fitness": 9.835907711344864e-05, "Vehicles & Transportation": 4.1394212390497934e-05, "Travel & Tourism": 8.729248595720844e-05, "Other / Uncovered Subject": 0.0013878210447385534, "Unknown / Insufficient Content": 0.0001783266525801167}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007815255926913526, "Art & Design": 0.001038723692318559, "Law & Crime": 0.0007091070453040951, "Education & Careers": 0.0006165242798668427, "Entertainment & Media": 0.0007599877988427171, "Fashion & Beauty": 0.0003023929436582595, "Business, Economics & Finance": 0.0007132421956840188, "Food & Dining": 0.00022326218988631008, "Games & Gaming": 0.0004098675349105782, "Electronics & Hardware": 0.0005467371298790653, "Health, Healthcare & Wellness": 0.0004255159596459628, "History & Geography": 0.00031017082055945764, "Home, Pets & Hobbies": 0.0002628267903505915, "Industry & Production": 0.00030699401906757065, "Language, Literature & Philosophy": 0.048369216943581025, "Politics & Government": 0.0003256454481980022, "Religion & Spirituality": 0.0004432500337728807, "Science, Mathematics & Engineering": 0.038189996889539835, "Relationships & Community": 0.00016864643900219636, "Software Usage & Applications": 0.014254169241020123, "Programming & Software Engineering": 0.023992066041034352, "Sports & Fitness": 0.00025526197684448666, "Vehicles & Transportation": 0.0001354940337356364, "Travel & Tourism": 0.00039835211530856124, "Other / Uncovered Subject": 0.0030744537610766457, "NONE": 0.8629865690842209}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9910, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9910, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:36:12.380129+00:00", "scoring_key_slot": "primary", "dataset_uid": "914aff3d-ab08-4002-893b-bb0276353141", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "7d2ee328155b9a9e7855f6a51c10409e5829c3c95155992f850b922e32e40c25", "source_metadata": {"uuid": "914aff3d-ab08-4002-893b-bb0276353141", "repo_name": "Ritik-912/mmr-question-extraction", "relative_path": "mmr-pdfs-extraction/eng/extract.py", "category": "DATA", "algo_rel_score": 0.9833021759986877, "quality_score": 5.616456508636475, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2024-08-11T11:25:31.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "2d5159e043276be48691d50a7c501909ef18d8cc", "size_bytes": 5695, "file_timestamp": 1724843097, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "b5a31955a055f76cbb96c3d35cd9141744673730"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2024-08-11T11:25:31.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "2d5159e043276be48691d50a7c501909ef18d8cc", "size_bytes": 5695, "file_timestamp": 1724843097, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "b5a31955a055f76cbb96c3d35cd9141744673730", "url": "https://github.com/Ritik-912/mmr-question-extraction/blob/b5a31955a055f76cbb96c3d35cd9141744673730/mmr-pdfs-extraction/eng/extract.py"}, "gpt4o_tokens": 1318, "dataset_split": "python", "probability_weighted_scores": {"content_quality": -0.17868375402180586, "noise_severity": 0.03270434391140903, "missing_content_severity": 0.009615640121155396, "factual_density": 3.1936940459953242, "pretraining_utility": 0.5420121810644287, "math_density": 0.26059899018474686, "code_density": 3.7125056461322954, "reasoning_density": 0.45775765724000445, "education_level": 2.0911687904228113, "creative_writing_quality": -0.44446447034000647}, "score_applicability": {"education_level": 0.9986869249574437, "creative_writing_quality": 0.0005106928850660418}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/nexcode-project/nexcode/blob/37f6f94bd3d252e578c8e711a391c7500086dab1/nexcode_server/app/api/v1/commit_message.py", "id": "python-0-039174e4f2e988de6b7741f14428eb00", "text": "from fastapi import APIRouter, Depends\nfrom time import time\nfrom typing import Optional\nfrom app.models.schemas import CommitMessageRequest, CommitMessageResponse\nfrom app.core.llm_client import get_llm_solution\nfrom app.core.dependencies import OptionalUser, DatabaseSession\nfrom app.services.commit_service import commit_service\nfrom app.models.user_schemas import CommitInfoCreate\n\nrouter = APIRouter()\n\n# 配置diff长度限制 - 增加到更大的值以保持更多上下文\nMAX_DIFF_LENGTH = 65536  # 64KB，足够包含大部分代码变更\n\ndef clean_commit_message(message: str) -> str:\n    \"\"\"\n    清理和优化AI生成的提交消息\n    \"\"\"\n    if not message:\n        return \"chore: update code\"\n    \n    # 移除可能的格式标记\n    message = message.replace('```', '').replace('`', '').strip()\n    \n    # 只取第一行\n    first_line = message.split('\\n')[0].strip()\n    \n    # 移除引号和多余空格\n    first_line = first_line.strip('\"\\'`').strip()\n    \n    # 确保不超过72字符\n    if len(first_line) > 72:\n        first_line = first_line[:69] + \"...\"\n    \n    # 验证conventional commits格式\n    valid_prefixes = ['feat:', 'fix:', 'docs:', 'style:', 'refactor:', 'test:', 'chore:', 'build:', 'ci:', 'perf:']\n    \n    if not any(first_line.startswith(prefix) for prefix in valid_prefixes):\n        # 智能判断类型并添加前缀\n        lower_msg = first_line.lower()\n        if any(word in lower_msg for word in ['fix', 'bug', 'error', 'issue']):\n            first_line = f\"fix: {first_line}\"\n        elif any(word in lower_msg for word in ['add', 'new', 'create', 'implement']):\n            first_line = f\"feat: {first_line}\"\n        elif any(word in lower_msg for word in ['update', 'modify', 'change', 'improve']):\n            first_line = f\"refactor: {first_line}\"\n        elif any(word in lower_msg for word in ['doc', 'readme', 'comment']):\n            first_line = f\"docs: {first_line}\"\n        elif any(word in lower_msg for word in ['test', 'spec']):\n            first_line = f\"test: {first_line}\"\n        elif any(word in lower_msg for word in ['config', 'setting', 'setup']):\n            first_line = f\"chore: {first_line}\"\n        else:\n            first_line = f\"chore: {first_line}\"\n    \n    # 确保描述部分不重复type\n    for prefix in valid_prefixes:\n        if first_line.startswith(prefix):\n            description = first_line[len(prefix):].strip()\n            # 移除描述中重复的type关键词\n            description = description.replace(prefix.replace(':', ''), '').strip()\n            if description.startswith(':'):\n                description = description[1:].strip()\n            first_line = prefix + ' ' + description\n            break\n    \n    return first_line\n\ndef truncate_diff(diff: str) -> str:\n    \"\"\"\n    智能截取diff内容，优先保持重要的代码变更信息\n    \"\"\"\n    if not diff or len(diff) <= MAX_DIFF_LENGTH:\n        return diff\n    \n    lines = diff.split('\\n')\n    important_lines = []\n    current_length = 0\n    \n    # 优先保留重要的diff行\n    for line in lines:\n        line_with_newline = line + '\\n'\n        \n        # 重要行：文件头、hunk头、实际的代码变更\n        is_important = (\n            line.startswith('diff --git') or \n            line.startswith('index ') or\n            line.startswith('+++') or line.startswith('---') or\n            line.startswith('@@') or\n            line.startswith('+') or line.startswith('-') or\n            line.strip() == ''  # 保留空行以维持结构\n        )\n        \n        if is_important or current_length + len(line_with_newline) <= MAX_DIFF_LENGTH:\n            important_lines.append(line)\n            current_length += len(line_with_newline)\n        else:\n            break\n    \n    # 如果截断了，添加提示\n    if len(important_lines) < len(lines):\n        important_lines.append('')\n        important_lines.append('[... diff truncated - showing most relevant changes ...]')\n    \n    return '\\n'.join(important_lines)\n\n@router.post(\"/commit-message\", response_model=CommitMessageResponse)\nasync def generate_commit_message(\n    request: CommitMessageRequest, \n    current_user: OptionalUser,\n    db: DatabaseSession\n) -> CommitMessageResponse:\n    \"\"\"\n    生成提交消息，并记录到数据库\n    \"\"\"\n    start_time = time()\n    \n    try:\n        # 截取diff长度\n        original_diff = request.diff\n        truncated_diff = truncate_diff(original_diff) if original_diff else None\n        \n        # 调试输出：显示接收到的数据\n        print(f\"\\n=== COMMIT MESSAGE DEBUG ===\")\n        print(f\"Style: {request.style}\")\n        print(f\"API Key: {request.api_key[:10] + '...' if request.api_key else 'None'}\")\n        print(f\"Model Name: {request.model_name}\")\n        print(f\"Original diff length: {len(original_diff) if original_diff else 0}\")\n        print(f\"Truncated diff length: {len(truncated_diff) if truncated_diff else 0}\")\n        print(f\"Diff preview (first 500 chars):\")\n        print(truncated_diff[:500] if truncated_diff else \"No diff\")\n        print(f\"Context: {request.context}\")\n        print(\"===========================\\n\")\n        \n        # 准备LLM请求数据，使用截取后的diff\n        llm_data = {\n            \"diff\": truncated_diff,\n            \"style\": request.style,\n            \"context\": request.context or {}\n        }\n        \n        # 调用LLM，传递CLI的API配置\n        message = get_llm_solution(\n            task_type=\"commit_message\",\n            data=llm_data,\n            api_key=request.api_key,\n            api_base_url=request.api_base_url,\n            model_name=request.model_name\n        )\n        \n        print(f\"Generated message: {message}\")\n        \n        # 清理和优化生成的消息\n        cleaned_message = clean_commit_message(message)\n        print(f\"Cleaned message: {cleaned_message}\")\n        print(\"=== END DEBUG ===\\n\")\n        \n        generation_time = int((time() - start_time) * 1000)  # 转换为毫秒\n        \n        # 如果用户已认证，记录到数据库\n        if current_user and db:\n            try:\n                # 解析diff信息（简化版）\n                files_changed = []\n                lines_added = 0\n                lines_deleted = 0\n                \n                if truncated_diff:\n                    # 简单解析diff（可以后续优化）\n                    for line in truncated_diff.split('\\n'):\n                        if line.startswith('+++') or line.startswith('---'):\n                            if not line.endswith('/dev/null'):\n                                file_path = line[4:].strip()\n                                if file_path and file_path not in files_changed:\n                                    files_changed.append(file_path)\n                        elif line.startswith('+') and not line.startswith('+++'):\n                            lines_added += 1\n                        elif line.startswith('-') and not line.startswith('---'):\n                            lines_deleted += 1\n                \n                # 创建commit信息记录 - 存储原始diff长度信息\n                commit_data = CommitInfoCreate(\n                    repository_url=request.context.get('repository_url') if request.context else None,\n                    repository_name=request.context.get('repository_name') if request.context else None,\n                    branch_name=request.context.get('branch_name') if request.context else None,\n                    ai_generated_message=message,\n                    final_commit_message=cleaned_message,  # 使用清理后的消息\n                    diff_content=truncated_diff,  # 存储截取后的diff\n                    files_changed=files_changed,\n                    lines_added=lines_added,\n                    lines_deleted=lines_deleted,\n                    ai_model_used=request.model_name,\n                    ai_parameters={\n                        \"api_key\": request.api_key[:10] + \"...\" if request.api_key else None,\n                        \"api_base_url\": request.api_base_url,\n                        \"style\": request.style,\n                        \"original_diff_length\": len(original_diff) if original_diff else 0,\n                        \"truncated_diff_length\": len(truncated_diff) if truncated_diff else 0,\n                        \"was_truncated\": len(original_diff) > MAX_DIFF_LENGTH if original_diff else False\n                    },\n                    generation_time_ms=generation_time,\n                    commit_style=request.style or \"conventional\"\n                )\n                \n                await commit_service.create_commit_info(\n                    db=db,\n                    user_id=current_user.id,\n                    commit_data=commit_data\n                )\n            except Exception as db_error:\n                # 记录数据库错误，但不影响主要功能\n                print(f\"Database error: {db_error}\")\n        \n        return CommitMessageResponse(message=cleaned_message)\n    except Exception as e:\n        return CommitMessageResponse(message=f\"Error generating commit message: {str(e)}\") ", "text_preview": "from fastapi import APIRouter, Depends from time import time from typing import Optional from app.models.schemas import CommitMessageRequest, CommitMessageResponse from app.core.llm_client import get_llm_solution from app.core.dependencies ", "input_sha256": "0ee88a44e69d3db495d13787cba28222d06c0344f2487fa56b29bb6dea4c4f55", "text_sha256": "039174e4f2e988de6b7741f14428eb000717570b23e84559e1b2cef1982c22d5", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12111, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.4611512167716321, "noise_severity": 0.19341853024419003, "missing_content_severity": 0.09446625725571826, "factual_density": 3.2183203228010213, "pretraining_utility": 0.6131543570558726, "math_density": 0.1389333066565493, "code_density": 3.8739745893483257, "reasoning_density": 0.8117245288932675, "education_level": 2.333136354515158, "creative_writing_quality": -0.21146805236515714}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.00182251202622063, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9984214523948572, "secondary": 0.9842124153349753, "tertiary": 0.9858423593298876, "structured_records": 0.007719892562943842, "procedural_steps": 0.034095243156740986, "worked_solution": 0.010560010830219919, "contextual_dialogue": 4.3693962466537e-06, "content_quality": 0.3868845347004888, "noise_severity": 0.838817891463175, "missing_content_severity": 0.9212781189535681, "factual_density": 0.3486002690008507, "pretraining_utility": 0.5467302474312528, "math_density": 0.8842222444528756, "code_density": 0.8949788244569379, "reasoning_density": 0.32356289258894355, "education_level": 0.6091816559895132, "creative_writing_quality": 0.9981522991842163, "primary_topic": 0.9963462788074331, "secondary_topic": 0.9391308595031859}, "probability_margin": {"primary": 0.9977243351806053, "secondary": 0.9762687037288662, "tertiary": 0.9796532454168779, "education_level": 0.3796743966412274, "creative_writing_quality": 0.9975474396657915, "primary_topic": 0.9944284288090985, "secondary_topic": 0.9143733712896657}, "raw_probabilities": {"primary": {"Academic Writing": 2.7423890911655645e-05, "Content Listing": 1.4491999012845189e-05, "Creative Writing": 1.2189191777225993e-05, "Customer Support Page": 7.442020538501816e-06, "Q&A Forum": 8.600624446568043e-06, "Discussion Thread": 5.397517674205886e-06, "Comment Section": 9.562339102205796e-06, "Missing Content": 1.6023215471832196e-05, "Knowledge Article": 5.458655753258151e-05, "Legal Notices": 1.934084313007868e-05, "News Article": 3.3049553790149655e-06, "Organizational Announcement": 8.110225444669518e-06, "Nonfiction Writing": 7.309596253903775e-06, "Organizational About Page": 7.841311679719706e-06, "Personal About Page": 6.048632358201496e-06, "Personal Blog": 6.102921808484798e-06, "Product Page": 5.934461830159464e-06, "Spam / Ads": 7.453594520671061e-06, "Structured Data": 0.00016562272265614836, "Technical Writing": 0.9984872252117382, "Transcript / Interview": 1.0712800157240454e-05, "Tutorial / How-To Guide": 0.0003414332299889527, "User Reviews": 4.952105454069737e-06, "Other / Unknown": 0.0007628900311328117}, "secondary": {"Academic Writing": 0.0001397791682865251, "Content Listing": 8.839713072905122e-05, "Creative Writing": 0.00011601042800466484, "Customer Support Page": 0.00010313940812297517, "Q&A Forum": 4.462949714278798e-05, "Discussion Thread": 3.541392653588408e-05, "Comment Section": 0.00013036749773142153, "Missing Content": 0.0002462670834614824, "Knowledge Article": 0.0012704743359674194, "Legal Notices": 0.00014956289205925454, "News Article": 5.966143572110219e-05, "Organizational Announcement": 6.401735827302984e-05, "Nonfiction Writing": 7.174532114181714e-05, "Organizational About Page": 6.93115826099448e-05, "Personal About Page": 5.343540374698358e-05, "Personal Blog": 5.460293263149159e-05, "Product Page": 4.1240381231774907e-05, "Spam / Ads": 6.657151950893093e-05, "Structured Data": 0.001856386377349524, "Technical Writing": 0.008601527633818428, "Transcript / Interview": 8.61243585606137e-05, "Tutorial / How-To Guide": 0.001751286823318531, "User Reviews": 2.9816141361628733e-05, "NONE": 0.9848702313626846}, "tertiary": {"Academic Writing": 0.0001364279792437102, "Content Listing": 7.526121336028552e-05, "Creative Writing": 0.00010050056062804735, "Customer Support Page": 8.311187296992777e-05, "Q&A Forum": 3.21750255193889e-05, "Discussion Thread": 3.1721089797971125e-05, "Comment Section": 0.00014572197339569597, "Missing Content": 0.0003316751150299565, "Knowledge Article": 0.0014287504760520388, "Legal Notices": 0.00020191043921812134, "News Article": 5.1075473119665354e-05, "Organizational Announcement": 6.149900728180248e-05, "Nonfiction Writing": 0.00011209776730283857, "Organizational About Page": 7.538408415088745e-05, "Personal About Page": 4.421501783503144e-05, "Personal Blog": 4.839294768610085e-05, "Product Page": 4.480725935466876e-05, "Spam / Ads": 7.227433123399396e-05, "Structured Data": 0.0017064976146945441, "Technical Writing": 0.0067790156075977995, "Transcript / Interview": 8.378993329458111e-05, "Tutorial / How-To Guide": 0.0018913325013436093, "User Reviews": 3.0101685413640248e-05, "NONE": 0.9864322610244757}, "structured_records": {"false": 0.9922801074370562, "true": 0.007719892562943842}, "procedural_steps": {"false": 0.965904756843259, "true": 0.034095243156740986}, "worked_solution": {"false": 0.98943998916978, "true": 0.010560010830219919}, "contextual_dialogue": {"false": 0.9999956306037533, "true": 4.3693962466537e-06}, "content_quality": {"-2": 0.013456622366951538, "-1": 0.11727995124522668, "0": 0.36236390120258266, "1": 0.4084546376197161, "2": 0.09844488756552282}, "noise_severity": {"0": 0.8598269782655416, "1": 0.09180850738241168, "2": 0.04464547383676751, "3": 0.002557086872873506, "4": 0.0011619536424057075}, "missing_content_severity": {"0": 0.9155260630572747, "1": 0.07710388166518672, "2": 0.005217211430866183, "3": 0.0016834226578907438, "4": 0.0004694211887817352}, "factual_density": {"0": 0.007649237717992723, "1": 0.022959720477021252, "2": 0.15188682639431994, "3": 0.37842991210730453, "4": 0.4390743033033616}, "pretraining_utility": {"-2": 0.007163168369692684, "-1": 0.0508380873518319, "0": 0.34221899320057025, "1": 0.5212407210087204, "2": 0.07853903006918465}, "math_density": {"0": 0.8774136363451963, "1": 0.11433305826701143, "2": 0.0035264480180889884, "3": 0.0013600771254531504, "4": 0.0033667802442501078}, "code_density": {"0": 0.00024524977912335975, "1": 0.0004397572846946685, "2": 0.009761798290936399, "3": 0.1042015430992243, "4": 0.8853516515460214}, "reasoning_density": {"0": 0.5520048033184624, "1": 0.1884621894637912, "2": 0.17907782919789147, "3": 0.05671403104572677, "4": 0.023741146974128317}, "education_level": {"none": 0.0009490264495299284, "0": 0.0013873505916835458, "1": 0.005484200699409052, "2": 0.6743180466579277, "3": 0.29464365001670034, "4": 0.02321772558474946}, "creative_writing_quality": {"not_applicable": 0.9984602493201803, "-2": 0.00012775140418499356, "-1": 0.00030642359494698627, "0": 0.0009128096543887645, "1": 0.00014921372666999998, "2": 4.355229962880349e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.7643200278948786e-05, "Art & Design": 3.424025265182832e-05, "Law & Crime": 2.6305918483857855e-05, "Education & Careers": 2.6441991386242156e-05, "Entertainment & Media": 1.9118890582868626e-05, "Fashion & Beauty": 1.1817084012064186e-05, "Business, Economics & Finance": 3.523555125557155e-05, "Food & Dining": 1.4756212534701671e-05, "Games & Gaming": 3.533765112560488e-05, "Electronics & Hardware": 4.29679145976444e-05, "Health, Healthcare & Wellness": 1.66930328565276e-05, "History & Geography": 2.5477684371426002e-05, "Home, Pets & Hobbies": 1.4443041040549759e-05, "Industry & Production": 2.1556805120232072e-05, "Language, Literature & Philosophy": 0.00019021737151031447, "Politics & Government": 1.4800452676957937e-05, "Religion & Spirituality": 2.2886899065382758e-05, "Science, Mathematics & Engineering": 0.002058377736510231, "Relationships & Community": 1.5184054059679051e-05, "Software Usage & Applications": 0.0006019041948180917, "Programming & Software Engineering": 0.9964868065456087, "Sports & Fitness": 2.7101604582067817e-05, "Vehicles & Transportation": 1.4322999886708218e-05, "Travel & Tourism": 3.9703387150168636e-05, "Other / Uncovered Subject": 0.00012776650671012034, "Unknown / Insufficient Content": 3.889301712347773e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00047488561916878317, "Art & Design": 0.00024488626801584643, "Law & Crime": 0.0003434826349847671, "Education & Careers": 0.00021105986505261479, "Entertainment & Media": 0.00023979752844648878, "Fashion & Beauty": 0.00012699765474854236, "Business, Economics & Finance": 0.0003921767166470233, "Food & Dining": 0.00010716516036722359, "Games & Gaming": 0.00016645985291268144, "Electronics & Hardware": 0.0002189737227398452, "Health, Healthcare & Wellness": 0.00014383578001726723, "History & Geography": 0.00017942765415658176, "Home, Pets & Hobbies": 0.00010419921170792896, "Industry & Production": 9.718540399034294e-05, "Language, Literature & Philosophy": 0.0023702663976574085, "Politics & Government": 0.0001249886218968014, "Religion & Spirituality": 0.00017451706793122557, "Science, Mathematics & Engineering": 0.02709860900185928, "Relationships & Community": 8.893149536848436e-05, "Software Usage & Applications": 0.004411668587149957, "Programming & Software Engineering": 0.020512985717277877, "Sports & Fitness": 0.00010736545620901471, "Vehicles & Transportation": 5.7558732270549895e-05, "Travel & Tourism": 0.00021109458911800454, "Other / Uncovered Subject": 0.00031950096878053834, "NONE": 0.9414719802915249}}, "probabilities": {"primary": {"Academic Writing": 2.7423890911655648e-05, "Content Listing": 1.449199901284519e-05, "Creative Writing": 1.2189191777225995e-05, "Customer Support Page": 7.442020538501817e-06, "Q&A Forum": 8.600624446568044e-06, "Discussion Thread": 5.3975176742058865e-06, "Comment Section": 9.562339102205797e-06, "Missing Content": 1.60232154718322e-05, "Knowledge Article": 5.458655753258152e-05, "Legal Notices": 1.9340843130078684e-05, "News Article": 3.304955379014966e-06, "Organizational Announcement": 8.11022544466952e-06, "Nonfiction Writing": 7.309596253903776e-06, "Organizational About Page": 7.841311679719707e-06, "Personal About Page": 6.048632358201497e-06, "Personal Blog": 6.102921808484799e-06, "Product Page": 5.9344618301594645e-06, "Spam / Ads": 7.4535945206710615e-06, "Structured Data": 0.0001656227226561484, "Technical Writing": 0.9984872252117383, "Transcript / Interview": 1.0712800157240455e-05, "Tutorial / How-To Guide": 0.0003414332299889528, "User Reviews": 4.952105454069738e-06, "Other / Unknown": 0.0007628900311328118}, "secondary": {"Academic Writing": 0.00013977916828652513, "Content Listing": 8.839713072905123e-05, "Creative Writing": 0.00011601042800466485, "Customer Support Page": 0.00010313940812297518, "Q&A Forum": 4.4629497142787985e-05, "Discussion Thread": 3.541392653588409e-05, "Comment Section": 0.00013036749773142156, "Missing Content": 0.00024626708346148245, "Knowledge Article": 0.0012704743359674196, "Legal Notices": 0.00014956289205925456, "News Article": 5.9661435721102194e-05, "Organizational Announcement": 6.401735827302985e-05, "Nonfiction Writing": 7.174532114181716e-05, "Organizational About Page": 6.931158260994481e-05, "Personal About Page": 5.3435403746983586e-05, "Personal Blog": 5.4602932631491597e-05, "Product Page": 4.124038123177491e-05, "Spam / Ads": 6.657151950893095e-05, "Structured Data": 0.0018563863773495243, "Technical Writing": 0.00860152763381843, "Transcript / Interview": 8.612435856061371e-05, "Tutorial / How-To Guide": 0.0017512868233185313, "User Reviews": 2.9816141361628736e-05, "NONE": 0.9848702313626847}, "tertiary": {"Academic Writing": 0.0001364279792437102, "Content Listing": 7.526121336028552e-05, "Creative Writing": 0.00010050056062804735, "Customer Support Page": 8.311187296992777e-05, "Q&A Forum": 3.21750255193889e-05, "Discussion Thread": 3.1721089797971125e-05, "Comment Section": 0.00014572197339569597, "Missing Content": 0.0003316751150299565, "Knowledge Article": 0.0014287504760520388, "Legal Notices": 0.00020191043921812134, "News Article": 5.1075473119665354e-05, "Organizational Announcement": 6.149900728180248e-05, "Nonfiction Writing": 0.00011209776730283857, "Organizational About Page": 7.538408415088745e-05, "Personal About Page": 4.421501783503144e-05, "Personal Blog": 4.839294768610085e-05, "Product Page": 4.480725935466876e-05, "Spam / Ads": 7.227433123399396e-05, "Structured Data": 0.0017064976146945441, "Technical Writing": 0.0067790156075977995, "Transcript / Interview": 8.378993329458111e-05, "Tutorial / How-To Guide": 0.0018913325013436093, "User Reviews": 3.0101685413640248e-05, "NONE": 0.9864322610244757}, "structured_records": {"false": 0.9922801074370562, "true": 0.007719892562943842}, "procedural_steps": {"false": 0.965904756843259, "true": 0.034095243156740986}, "worked_solution": {"false": 0.98943998916978, "true": 0.010560010830219919}, "contextual_dialogue": {"false": 0.9999956306037533, "true": 4.3693962466537e-06}, "content_quality": {"-2": 0.013456622366951542, "-1": 0.11727995124522671, "0": 0.3623639012025827, "1": 0.40845463761971623, "2": 0.09844488756552285}, "noise_severity": {"0": 0.8598269782655416, "1": 0.09180850738241168, "2": 0.04464547383676751, "3": 0.002557086872873506, "4": 0.0011619536424057075}, "missing_content_severity": {"0": 0.9155260630572747, "1": 0.07710388166518672, "2": 0.005217211430866183, "3": 0.0016834226578907438, "4": 0.0004694211887817352}, "factual_density": {"0": 0.007649237717992723, "1": 0.022959720477021252, "2": 0.15188682639431994, "3": 0.37842991210730453, "4": 0.4390743033033616}, "pretraining_utility": {"-2": 0.007163168369692685, "-1": 0.05083808735183191, "0": 0.3422189932005703, "1": 0.5212407210087205, "2": 0.07853903006918467}, "math_density": {"0": 0.8774136363451963, "1": 0.11433305826701143, "2": 0.0035264480180889884, "3": 0.0013600771254531504, "4": 0.0033667802442501078}, "code_density": {"0": 0.00024524977912335975, "1": 0.0004397572846946685, "2": 0.009761798290936399, "3": 0.1042015430992243, "4": 0.8853516515460214}, "reasoning_density": {"0": 0.5520048033184622, "1": 0.18846218946379115, "2": 0.17907782919789145, "3": 0.056714031045726754, "4": 0.02374114697412831}, "education_level": {"none": 0.0009490264495299284, "0": 0.0013873505916835458, "1": 0.005484200699409052, "2": 0.6743180466579277, "3": 0.29464365001670034, "4": 0.02321772558474946}, "creative_writing_quality": {"not_applicable": 0.9984602493201804, "-2": 0.00012775140418499358, "-1": 0.0003064235949469863, "0": 0.0009128096543887646, "1": 0.00014921372667, "2": 4.35522996288035e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.7643200278948786e-05, "Art & Design": 3.424025265182832e-05, "Law & Crime": 2.6305918483857855e-05, "Education & Careers": 2.6441991386242156e-05, "Entertainment & Media": 1.9118890582868626e-05, "Fashion & Beauty": 1.1817084012064186e-05, "Business, Economics & Finance": 3.523555125557155e-05, "Food & Dining": 1.4756212534701671e-05, "Games & Gaming": 3.533765112560488e-05, "Electronics & Hardware": 4.29679145976444e-05, "Health, Healthcare & Wellness": 1.66930328565276e-05, "History & Geography": 2.5477684371426002e-05, "Home, Pets & Hobbies": 1.4443041040549759e-05, "Industry & Production": 2.1556805120232072e-05, "Language, Literature & Philosophy": 0.00019021737151031447, "Politics & Government": 1.4800452676957937e-05, "Religion & Spirituality": 2.2886899065382758e-05, "Science, Mathematics & Engineering": 0.002058377736510231, "Relationships & Community": 1.5184054059679051e-05, "Software Usage & Applications": 0.0006019041948180917, "Programming & Software Engineering": 0.9964868065456087, "Sports & Fitness": 2.7101604582067817e-05, "Vehicles & Transportation": 1.4322999886708218e-05, "Travel & Tourism": 3.9703387150168636e-05, "Other / Uncovered Subject": 0.00012776650671012034, "Unknown / Insufficient Content": 3.889301712347773e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00047488561916878317, "Art & Design": 0.00024488626801584643, "Law & Crime": 0.0003434826349847671, "Education & Careers": 0.00021105986505261479, "Entertainment & Media": 0.00023979752844648878, "Fashion & Beauty": 0.00012699765474854236, "Business, Economics & Finance": 0.0003921767166470233, "Food & Dining": 0.00010716516036722359, "Games & Gaming": 0.00016645985291268144, "Electronics & Hardware": 0.0002189737227398452, "Health, Healthcare & Wellness": 0.00014383578001726723, "History & Geography": 0.00017942765415658176, "Home, Pets & Hobbies": 0.00010419921170792896, "Industry & Production": 9.718540399034294e-05, "Language, Literature & Philosophy": 0.0023702663976574085, "Politics & Government": 0.0001249886218968014, "Religion & Spirituality": 0.00017451706793122557, "Science, Mathematics & Engineering": 0.02709860900185928, "Relationships & Community": 8.893149536848436e-05, "Software Usage & Applications": 0.004411668587149957, "Programming & Software Engineering": 0.020512985717277877, "Sports & Fitness": 0.00010736545620901471, "Vehicles & Transportation": 5.7558732270549895e-05, "Travel & Tourism": 0.00021109458911800454, "Other / Uncovered Subject": 0.00031950096878053834, "NONE": 0.9414719802915249}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10539, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10539, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:36:14.764711+00:00", "scoring_key_slot": "primary", "dataset_uid": "7b132f40-8898-47ca-9852-a9a8507a2e43", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "bdb1951cc29f5a80be3f477a61262841ec2f943b58cad113e4ba3025cc0b6605", "source_metadata": {"uuid": "7b132f40-8898-47ca-9852-a9a8507a2e43", "repo_name": "nexcode-project/nexcode", "relative_path": "nexcode_server/app/api/v1/commit_message.py", "category": "TOOL", "algo_rel_score": 0.9080652594566345, "quality_score": 6.6121721267700195, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 93, "repo_created_at": "2025-06-19T08:49:31.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "ad72aebe2678663aac7f18686fc5965184864b76", "size_bytes": 9164, "file_timestamp": 1753076311, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "37f6f94bd3d252e578c8e711a391c7500086dab1"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 93, "repo_created_at": "2025-06-19T08:49:31.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "ad72aebe2678663aac7f18686fc5965184864b76", "size_bytes": 9164, "file_timestamp": 1753076311, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "37f6f94bd3d252e578c8e711a391c7500086dab1", "url": "https://github.com/nexcode-project/nexcode/blob/37f6f94bd3d252e578c8e711a391c7500086dab1/nexcode_server/app/api/v1/commit_message.py"}, "gpt4o_tokens": 1941, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.4611512167716321, "noise_severity": 0.19341853024419003, "missing_content_severity": 0.09446625725571826, "factual_density": 3.2183203228010213, "pretraining_utility": 0.6131543570558725, "math_density": 0.1389333066565493, "code_density": 3.8739745893483257, "reasoning_density": 0.8117245288932676, "education_level": 2.333136354515158, "creative_writing_quality": -0.21146805236515714}, "score_applicability": {"education_level": 0.9990509735504701, "creative_writing_quality": 0.001539750679819548}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/Astronecsys/learn_c_the_hard_way/blob/b98182e6f21d593b977cc56784a0797b914453ad/ex17_test.py", "id": "python-0-44761d5ffa5e6c89db80978bf3a4d3df", "text": "import subprocess\nimport os\nimport pytest\nimport tempfile\n\nEX17_BIN = \"./ex17\"\n\ndef run_cmd(args):\n    proc = subprocess.run(\n        [EX17_BIN] + args,\n        stdout=subprocess.PIPE,\n        stderr=subprocess.PIPE,\n        text=True\n    )\n    return proc.stdout, proc.stderr, proc.returncode\n\n@pytest.fixture(scope=\"module\", autouse=True)\ndef check_ex17_bin():\n    if not os.path.isfile(EX17_BIN) or not os.access(EX17_BIN, os.X_OK):\n        pytest.skip(\"ex17 binary not found or not executable\")\n\n@pytest.fixture\ndef db_file():\n    with tempfile.NamedTemporaryFile(delete=True) as tf:\n        yield tf.name\n\ndef test_create(db_file):\n    out, err, code = run_cmd([db_file, 'c', '5', '64'])\n    assert code == 0\n    assert err == \"\"\n\ndef test_set_and_get(db_file):\n    # 先创建数据库\n    run_cmd([db_file, 'c', '5', '64'])\n    \n    # 设置 id=1 的记录\n    out, err, code = run_cmd([db_file, 's', '1', 'Alice', '<EMAIL>'])\n    assert code == 0\n    assert err == \"\"\n    \n    # 获取 id=1 记录，检查输出包含 Alice 和邮箱\n    out, err, code = run_cmd([db_file, 'g', '1'])\n    assert code == 0\n    assert \"Alice\" in out\n    assert \"<EMAIL>\" in out\n\ndef test_get_unset_id(db_file):\n    run_cmd([db_file, 'c', '5', '64'])\n    # 试图 get 一个没设置的 id，应失败\n    out, err, code = run_cmd([db_file, 'g', '3'])\n    assert code != 0\n    assert \"ID is not set\" in out or \"ID is not set\" in err\n\ndef test_delete(db_file):\n    run_cmd([db_file, 'c', '5', '64'])\n    run_cmd([db_file, 's', '2', 'Bob', '<EMAIL>'])\n    \n    # 删除 id=2\n    out, err, code = run_cmd([db_file, 'd', '2'])\n    assert code == 0\n    \n    # 再 get id=2 应该失败\n    out, err, code = run_cmd([db_file, 'g', '2'])\n    assert code != 0\n    assert \"ID is not set\" in out or \"ID is not set\" in err\n\ndef test_list(db_file):\n    run_cmd([db_file, 'c', '5', '64'])\n    run_cmd([db_file, 's', '0', 'Alice', '<EMAIL>'])\n    run_cmd([db_file, 's', '1', 'Bob', '<EMAIL>'])\n    \n    out, err, code = run_cmd([db_file, 'l'])\n    assert code == 0\n    assert \"Alice\" in out\n    assert \"Bob\" in out\n\ndef test_set_existing(db_file):\n    run_cmd([db_file, 'c', '5', '64'])\n    run_cmd([db_file, 's', '0', 'Alice', '<EMAIL>'])\n    \n    # 再次设置同一个 id 应该失败\n    out, err, code = run_cmd([db_file, 's', '0', 'Alice2', '<EMAIL>'])\n    assert code != 0\n    assert \"Already set, delete it first\" in out or \"Already set, delete it first\" in err\n\ndef test_invalid_action(db_file):\n    run_cmd([db_file, 'c', '5', '64'])\n    out, err, code = run_cmd([db_file, 'x'])\n    assert code != 0\n    assert \"Invalid action\" in out or \"Invalid action\" in err   \n    \ndef test_find(db_file):\n    # 先创建数据库\n    run_cmd([db_file, 'c', '5', '64'])\n    \n    # 插入两条记录\n    run_cmd([db_file, 's', '0', 'Alice', '<EMAIL>'])\n    run_cmd([db_file, 's', '1', 'Bob', '<EMAIL>'])\n    \n    # 查找 Alice\n    out, err, code = run_cmd([db_file, 'f', 'Alice'])\n    assert code == 0\n    assert \"Alice\" in out\n    assert \"<EMAIL>\" in out\n    \n    # 查找 Bob\n    out, err, code = run_cmd([db_file, 'f', 'Bob'])\n    assert code == 0\n    assert \"Bob\" in out\n    assert \"<EMAIL>\" in out\n    \n    # 查找不存在的 Charlie\n    out, err, code = run_cmd([db_file, 'f', 'Charlie'])\n    assert code == 0  # 注意：你的实现即使找不到也不会报错，只会打印 Empty Address\n    assert \"Empty Address\" in out\n", "text_preview": "import subprocess import os import pytest import tempfile EX17_BIN = \"./ex17\" def run_cmd(args): proc = subprocess.run( [EX17_BIN] + args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True ) return proc.stdout, proc.stderr, proc.ret", "input_sha256": "8151d75c9a02dcbf2e7f4f84d3662cea830cd09f01bb95ba4c4397ef4e9b004f", "text_sha256": "44761d5ffa5e6c89db80978bf3a4d3df172f5054380d7ebe9b586c147e427b59", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12110, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.038320496000157, "noise_severity": 0.03156106498064295, "missing_content_severity": 0.0039794690265343305, "factual_density": 3.2163739602708645, "pretraining_utility": 0.7291624227615753, "math_density": 0.028867377523602147, "code_density": 3.7767819012474453, "reasoning_density": 1.116485857449762, "education_level": 2.020012849568662, "creative_writing_quality": -0.10413025451417386}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.021056729270252816, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9857657924018847, "secondary": 0.9593418583114276, "tertiary": 0.9813140975499522, "structured_records": 0.13122040121599152, "procedural_steps": 0.04614408939840268, "worked_solution": 0.11649597509322167, "contextual_dialogue": 8.9794877721835e-06, "content_quality": 0.5747402727382911, "noise_severity": 0.9736991125161308, "missing_content_severity": 0.9966837758112214, "factual_density": 0.5274389521629194, "pretraining_utility": 0.5898382979568753, "math_density": 0.9759438520636649, "code_density": 0.8139849177062046, "reasoning_density": 0.06959511879186475, "education_level": 0.8303487705066358, "creative_writing_quality": 0.998598109424813, "primary_topic": 0.9959072666932715, "secondary_topic": 0.9502320003329454}, "probability_margin": {"primary": 0.9785015881006663, "secondary": 0.9427725488930716, "tertiary": 0.9736245147919235, "education_level": 0.7840857742312369, "creative_writing_quality": 0.9981106788940258, "primary_topic": 0.9947065307533565, "secondary_topic": 0.931946115333485}, "raw_probabilities": {"primary": {"Academic Writing": 0.0001253443554245418, "Content Listing": 5.4530221459530064e-05, "Creative Writing": 6.31936366335543e-05, "Customer Support Page": 3.0005455058660802e-05, "Q&A Forum": 3.9095416168408096e-05, "Discussion Thread": 2.5402149827938872e-05, "Comment Section": 4.206726250238798e-05, "Missing Content": 5.25911007096768e-05, "Knowledge Article": 0.00020498095585400847, "Legal Notices": 5.0203816879469646e-05, "News Article": 1.7586156205621274e-05, "Organizational Announcement": 2.9093630097306543e-05, "Nonfiction Writing": 3.557446786371777e-05, "Organizational About Page": 3.593279359341139e-05, "Personal About Page": 3.0927919314367386e-05, "Personal Blog": 2.3720790198918292e-05, "Product Page": 2.3175088780854366e-05, "Spam / Ads": 3.248123174890369e-05, "Structured Data": 0.0008413364525356908, "Technical Writing": 0.9863588843851395, "Transcript / Interview": 4.765831688715963e-05, "Tutorial / How-To Guide": 0.007857296284473202, "User Reviews": 2.2471225814778157e-05, "Other / Unknown": 0.003956446886828356}, "secondary": {"Academic Writing": 0.0002513004517985856, "Content Listing": 0.00011362426103595531, "Creative Writing": 0.00016797342242744014, "Customer Support Page": 8.624015646623094e-05, "Q&A Forum": 7.311548046107816e-05, "Discussion Thread": 5.0886085441266936e-05, "Comment Section": 0.00012046895605632272, "Missing Content": 0.00020942523341238922, "Knowledge Article": 0.0005978739419986196, "Legal Notices": 0.00012458415816645352, "News Article": 7.818830909048547e-05, "Organizational Announcement": 7.53261010406456e-05, "Nonfiction Writing": 7.901315585645977e-05, "Organizational About Page": 8.144309009007257e-05, "Personal About Page": 7.867689561337969e-05, "Personal Blog": 7.234775125949452e-05, "Product Page": 6.104621258597017e-05, "Spam / Ads": 8.293441016770289e-05, "Structured Data": 0.001953419206821659, "Technical Writing": 0.018263398655379837, "Transcript / Interview": 0.00010365353264461649, "Tutorial / How-To Guide": 0.01619132599761591, "User Reviews": 4.778698611793796e-05, "NONE": 0.9610359475484515}, "tertiary": {"Academic Writing": 0.00012043514858314678, "Content Listing": 5.910035534553483e-05, "Creative Writing": 0.00010634052375130714, "Customer Support Page": 5.190388806914274e-05, "Q&A Forum": 3.934616533623232e-05, "Discussion Thread": 3.124515872137366e-05, "Comment Section": 9.306604593473284e-05, "Missing Content": 0.00023893315916524328, "Knowledge Article": 0.0006075032351290662, "Legal Notices": 0.000132401621694169, "News Article": 4.652510219617058e-05, "Organizational Announcement": 4.2088183626653644e-05, "Nonfiction Writing": 7.071922713028837e-05, "Organizational About Page": 5.877026370886715e-05, "Personal About Page": 4.084038621771607e-05, "Personal Blog": 4.418411418183965e-05, "Product Page": 4.007804799845606e-05, "Spam / Ads": 7.739282468874123e-05, "Structured Data": 0.0012580366830422173, "Technical Writing": 0.006187692672466975, "Transcript / Interview": 6.096270883783181e-05, "Tutorial / How-To Guide": 0.008468162026780772, "User Reviews": 3.159563868931142e-05, "NONE": 0.9820926768187043}, "structured_records": {"false": 0.8687795987840085, "true": 0.13122040121599152}, "procedural_steps": {"false": 0.9538559106015974, "true": 0.04614408939840268}, "worked_solution": {"false": 0.8835040249067784, "true": 0.11649597509322167}, "contextual_dialogue": {"false": 0.9999910205122278, "true": 8.9794877721835e-06}, "content_quality": {"-2": 0.004621161963363806, "-1": 0.017605865379929503, "0": 0.18692037170699635, "1": 0.5165365165926064, "2": 0.274316084357104}, "noise_severity": {"0": 0.971909542502571, "1": 0.02577285355574282, "2": 0.0015296677395684123, "3": 0.00042286886270847504, "4": 0.0003650673394094718}, "missing_content_severity": {"0": 0.9976342955654581, "1": 0.001250607956880377, "2": 0.0007211756858526341, "3": 0.00028917346928767083, "4": 0.00010474732252141839}, "factual_density": {"0": 0.0045931494762281875, "1": 0.011414999005079147, "2": 0.1387402021279733, "3": 0.4535280405530387, "4": 0.39172360883768065}, "pretraining_utility": {"-2": 0.005822882058040847, "-1": 0.038456907717409054, "0": 0.2871333482361465, "1": 0.5579086293817411, "2": 0.11067823260666247}, "math_density": {"0": 0.9806415320852441, "1": 0.014565769837759895, "2": 0.0019455719144690542, "3": 0.000978040793204361, "4": 0.0018690853693227672}, "code_density": {"0": 0.00040322374757596405, "1": 0.000784866714955456, "2": 0.024777114469073283, "3": 0.16969637467923765, "4": 0.8043384203891576}, "reasoning_density": {"0": 0.48012817802279356, "1": 0.15755705772115178, "2": 0.20028429735365835, "3": 0.08976166258829199, "4": 0.07226880431410444}, "education_level": {"none": 0.0009693710084629026, "0": 0.0017763813134515848, "1": 0.05972537700047408, "2": 0.8586239754221965, "3": 0.07453820119095955, "4": 0.004366694064455506}, "creative_writing_quality": {"not_applicable": 0.9988317578540108, "-2": 8.09316010413494e-05, "-1": 0.0001839077429152443, "0": 0.0007210789599849827, "1": 0.00014052609109347, "2": 4.17977509542134e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.8639464634207676e-05, "Art & Design": 4.1696231085452215e-05, "Law & Crime": 6.252779791425345e-05, "Education & Careers": 0.0006427989007730846, "Entertainment & Media": 2.8277985978978984e-05, "Fashion & Beauty": 1.9729725422202617e-05, "Business, Economics & Finance": 4.805650772833433e-05, "Food & Dining": 3.056420447431912e-05, "Games & Gaming": 7.272562077061993e-05, "Electronics & Hardware": 8.9665304882038e-05, "Health, Healthcare & Wellness": 2.6394845135096676e-05, "History & Geography": 3.596503998214779e-05, "Home, Pets & Hobbies": 2.7634072593038212e-05, "Industry & Production": 2.9202669196630407e-05, "Language, Literature & Philosophy": 0.0001524843711371546, "Politics & Government": 2.1813316940782927e-05, "Religion & Spirituality": 3.4600338446906806e-05, "Science, Mathematics & Engineering": 0.0013581487594045843, "Relationships & Community": 4.195882503461674e-05, "Software Usage & Applications": 0.0006892287183438479, "Programming & Software Engineering": 0.996064679512761, "Sports & Fitness": 5.599620836562171e-05, "Vehicles & Transportation": 2.4347060245480983e-05, "Travel & Tourism": 4.9520930353176294e-05, "Other / Uncovered Subject": 0.0002542683808768649, "Unknown / Insufficient Content": 5.907520751945223e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.000341116298516613, "Art & Design": 0.00021819827441318693, "Law & Crime": 0.0003265704828730874, "Education & Careers": 0.0176088930726758, "Entertainment & Media": 0.00022826391234378855, "Fashion & Beauty": 0.00012484430866639286, "Business, Economics & Finance": 0.000251700353179185, "Food & Dining": 0.0001076399708393074, "Games & Gaming": 0.00020230401543982945, "Electronics & Hardware": 0.00020588460922927479, "Health, Healthcare & Wellness": 0.0001309403150917289, "History & Geography": 0.0001289767390112984, "Home, Pets & Hobbies": 8.578198201156854e-05, "Industry & Production": 9.129110893210012e-05, "Language, Literature & Philosophy": 0.0005475855374642942, "Politics & Government": 0.0001314114376612049, "Religion & Spirituality": 0.0001563111335306247, "Science, Mathematics & Engineering": 0.0039845523909146545, "Relationships & Community": 0.00011408788235690613, "Software Usage & Applications": 0.0019849775105509767, "Programming & Software Engineering": 0.02020003883280861, "Sports & Fitness": 0.00011650949506838259, "Vehicles & Transportation": 6.146429387514363e-05, "Travel & Tourism": 0.00017948303623943826, "Other / Uncovered Subject": 0.0003250188400128614, "NONE": 0.9521461541662937}}, "probabilities": {"primary": {"Academic Writing": 0.0001253443554245418, "Content Listing": 5.4530221459530064e-05, "Creative Writing": 6.31936366335543e-05, "Customer Support Page": 3.0005455058660802e-05, "Q&A Forum": 3.9095416168408096e-05, "Discussion Thread": 2.5402149827938872e-05, "Comment Section": 4.206726250238798e-05, "Missing Content": 5.25911007096768e-05, "Knowledge Article": 0.00020498095585400847, "Legal Notices": 5.0203816879469646e-05, "News Article": 1.7586156205621274e-05, "Organizational Announcement": 2.9093630097306543e-05, "Nonfiction Writing": 3.557446786371777e-05, "Organizational About Page": 3.593279359341139e-05, "Personal About Page": 3.0927919314367386e-05, "Personal Blog": 2.3720790198918292e-05, "Product Page": 2.3175088780854366e-05, "Spam / Ads": 3.248123174890369e-05, "Structured Data": 0.0008413364525356908, "Technical Writing": 0.9863588843851395, "Transcript / Interview": 4.765831688715963e-05, "Tutorial / How-To Guide": 0.007857296284473202, "User Reviews": 2.2471225814778157e-05, "Other / Unknown": 0.003956446886828356}, "secondary": {"Academic Writing": 0.0002513004517985856, "Content Listing": 0.00011362426103595531, "Creative Writing": 0.00016797342242744014, "Customer Support Page": 8.624015646623094e-05, "Q&A Forum": 7.311548046107816e-05, "Discussion Thread": 5.0886085441266936e-05, "Comment Section": 0.00012046895605632272, "Missing Content": 0.00020942523341238922, "Knowledge Article": 0.0005978739419986196, "Legal Notices": 0.00012458415816645352, "News Article": 7.818830909048547e-05, "Organizational Announcement": 7.53261010406456e-05, "Nonfiction Writing": 7.901315585645977e-05, "Organizational About Page": 8.144309009007257e-05, "Personal About Page": 7.867689561337969e-05, "Personal Blog": 7.234775125949452e-05, "Product Page": 6.104621258597017e-05, "Spam / Ads": 8.293441016770289e-05, "Structured Data": 0.001953419206821659, "Technical Writing": 0.018263398655379837, "Transcript / Interview": 0.00010365353264461649, "Tutorial / How-To Guide": 0.01619132599761591, "User Reviews": 4.778698611793796e-05, "NONE": 0.9610359475484515}, "tertiary": {"Academic Writing": 0.00012043514858314678, "Content Listing": 5.910035534553483e-05, "Creative Writing": 0.00010634052375130714, "Customer Support Page": 5.190388806914274e-05, "Q&A Forum": 3.934616533623232e-05, "Discussion Thread": 3.124515872137366e-05, "Comment Section": 9.306604593473284e-05, "Missing Content": 0.00023893315916524328, "Knowledge Article": 0.0006075032351290662, "Legal Notices": 0.000132401621694169, "News Article": 4.652510219617058e-05, "Organizational Announcement": 4.2088183626653644e-05, "Nonfiction Writing": 7.071922713028837e-05, "Organizational About Page": 5.877026370886715e-05, "Personal About Page": 4.084038621771607e-05, "Personal Blog": 4.418411418183965e-05, "Product Page": 4.007804799845606e-05, "Spam / Ads": 7.739282468874123e-05, "Structured Data": 0.0012580366830422173, "Technical Writing": 0.006187692672466975, "Transcript / Interview": 6.096270883783181e-05, "Tutorial / How-To Guide": 0.008468162026780772, "User Reviews": 3.159563868931142e-05, "NONE": 0.9820926768187043}, "structured_records": {"false": 0.8687795987840085, "true": 0.13122040121599152}, "procedural_steps": {"false": 0.9538559106015974, "true": 0.04614408939840268}, "worked_solution": {"false": 0.8835040249067784, "true": 0.11649597509322167}, "contextual_dialogue": {"false": 0.9999910205122278, "true": 8.9794877721835e-06}, "content_quality": {"-2": 0.004621161963363806, "-1": 0.017605865379929503, "0": 0.18692037170699635, "1": 0.5165365165926064, "2": 0.274316084357104}, "noise_severity": {"0": 0.9719095425025708, "1": 0.025772853555742815, "2": 0.001529667739568412, "3": 0.00042286886270847493, "4": 0.0003650673394094717}, "missing_content_severity": {"0": 0.9976342955654579, "1": 0.0012506079568803767, "2": 0.000721175685852634, "3": 0.0002891734692876708, "4": 0.00010474732252141836}, "factual_density": {"0": 0.0045931494762281875, "1": 0.011414999005079147, "2": 0.1387402021279733, "3": 0.4535280405530387, "4": 0.39172360883768065}, "pretraining_utility": {"-2": 0.005822882058040847, "-1": 0.038456907717409054, "0": 0.2871333482361465, "1": 0.5579086293817411, "2": 0.11067823260666247}, "math_density": {"0": 0.9806415320852438, "1": 0.014565769837759892, "2": 0.0019455719144690538, "3": 0.0009780407932043608, "4": 0.0018690853693227667}, "code_density": {"0": 0.00040322374757596405, "1": 0.000784866714955456, "2": 0.024777114469073283, "3": 0.16969637467923765, "4": 0.8043384203891576}, "reasoning_density": {"0": 0.48012817802279345, "1": 0.15755705772115175, "2": 0.2002842973536583, "3": 0.08976166258829198, "4": 0.07226880431410443}, "education_level": {"none": 0.0009693710084629024, "0": 0.0017763813134515844, "1": 0.05972537700047407, "2": 0.8586239754221963, "3": 0.07453820119095954, "4": 0.004366694064455505}, "creative_writing_quality": {"not_applicable": 0.9988317578540108, "-2": 8.09316010413494e-05, "-1": 0.0001839077429152443, "0": 0.0007210789599849827, "1": 0.00014052609109347, "2": 4.17977509542134e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.863946463420768e-05, "Art & Design": 4.169623108545222e-05, "Law & Crime": 6.252779791425346e-05, "Education & Careers": 0.0006427989007730847, "Entertainment & Media": 2.8277985978978988e-05, "Fashion & Beauty": 1.972972542220262e-05, "Business, Economics & Finance": 4.805650772833434e-05, "Food & Dining": 3.056420447431913e-05, "Games & Gaming": 7.272562077061995e-05, "Electronics & Hardware": 8.966530488203801e-05, "Health, Healthcare & Wellness": 2.639484513509668e-05, "History & Geography": 3.59650399821478e-05, "Home, Pets & Hobbies": 2.7634072593038216e-05, "Industry & Production": 2.920266919663041e-05, "Language, Literature & Philosophy": 0.00015248437113715463, "Politics & Government": 2.181331694078293e-05, "Religion & Spirituality": 3.460033844690681e-05, "Science, Mathematics & Engineering": 0.0013581487594045846, "Relationships & Community": 4.195882503461675e-05, "Software Usage & Applications": 0.000689228718343848, "Programming & Software Engineering": 0.9960646795127611, "Sports & Fitness": 5.599620836562172e-05, "Vehicles & Transportation": 2.4347060245480987e-05, "Travel & Tourism": 4.95209303531763e-05, "Other / Uncovered Subject": 0.00025426838087686497, "Unknown / Insufficient Content": 5.907520751945224e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00034111629851661304, "Art & Design": 0.00021819827441318696, "Law & Crime": 0.0003265704828730875, "Education & Careers": 0.017608893072675803, "Entertainment & Media": 0.00022826391234378858, "Fashion & Beauty": 0.0001248443086663929, "Business, Economics & Finance": 0.00025170035317918505, "Food & Dining": 0.00010763997083930741, "Games & Gaming": 0.00020230401543982948, "Electronics & Hardware": 0.0002058846092292748, "Health, Healthcare & Wellness": 0.00013094031509172893, "History & Geography": 0.00012897673901129844, "Home, Pets & Hobbies": 8.578198201156855e-05, "Industry & Production": 9.129110893210013e-05, "Language, Literature & Philosophy": 0.0005475855374642943, "Politics & Government": 0.00013141143766120492, "Religion & Spirituality": 0.00015631113353062472, "Science, Mathematics & Engineering": 0.003984552390914655, "Relationships & Community": 0.00011408788235690615, "Software Usage & Applications": 0.001984977510550977, "Programming & Software Engineering": 0.020200038832808614, "Sports & Fitness": 0.0001165094950683826, "Vehicles & Transportation": 6.146429387514364e-05, "Travel & Tourism": 0.0001794830362394383, "Other / Uncovered Subject": 0.00032501884001286146, "NONE": 0.9521461541662938}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9620, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9620, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:36:15.875556+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "3fda4ad1-e82a-40ac-ad4c-c04aa0e47f9c", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "0d3d67edc38009665d28b48701743a48528398079d1ec241cd8e112f28646c14", "source_metadata": {"uuid": "3fda4ad1-e82a-40ac-ad4c-c04aa0e47f9c", "repo_name": "Astronecsys/learn_c_the_hard_way", "relative_path": "ex17_test.py", "category": "TEST", "algo_rel_score": 0.9728723168373108, "quality_score": 5.839431285858154, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 21, "repo_created_at": "2025-06-26T10:44:17.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "3a95ff96f47e1d09cd9df84722755e18b01a0d05", "size_bytes": 3411, "file_timestamp": 1753036789, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "b98182e6f21d593b977cc56784a0797b914453ad"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 21, "repo_created_at": "2025-06-26T10:44:17.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "3a95ff96f47e1d09cd9df84722755e18b01a0d05", "size_bytes": 3411, "file_timestamp": 1753036789, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "b98182e6f21d593b977cc56784a0797b914453ad", "url": "https://github.com/Astronecsys/learn_c_the_hard_way/blob/b98182e6f21d593b977cc56784a0797b914453ad/ex17_test.py"}, "gpt4o_tokens": 1056, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.038320496000157, "noise_severity": 0.03156106498064295, "missing_content_severity": 0.0039794690265343305, "factual_density": 3.2163739602708645, "pretraining_utility": 0.7291624227615753, "math_density": 0.02886737752360215, "code_density": 3.7767819012474453, "reasoning_density": 1.1164858574497623, "education_level": 2.020012849568662, "creative_writing_quality": -0.10413025451417386}, "score_applicability": {"education_level": 0.9990306289915369, "creative_writing_quality": 0.0011682421459892599}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/wuzhihui3723/iov/blob/9b77d0fe1feb1ec1a35e4f20fb4737e95908d6b2/%E4%B8%80%E6%9C%9F/test.py", "id": "python-0-6a02d4cd2f459a288cc4043a06ec389c", "text": "import pandas as pd\nimport numpy as np\nimport datetime\n\nimport os\nimport sys\nimport pathlib\nimport glob\nimport ast\n\n\n#1.load data\n#BASE_FILE = os.path.dirname(os.path.abspath(__file__))#the directory of the script being run\nDATA_FILE = '/sdb/trip/stat/'\noutputfolder = '/sdb/risk/'\nfolderlist = ['2020_1001-2000.csv','2020_2001-3000.csv']\n#RISK_FEATURE_DATA_FILE = DATA_FILE + '/risk_feature'\n\n#trip_data_files = glob.glob(TRIP_FEATURE_DATA_FILE + \"/*.csv\")\n\n# calculate risk features\ndef calculate_risk_features(trip_feature_df):\n    # calculate activeDay\n    trip_feature_df['activeDay'] = trip_feature_df['activeDay']. \\\n        apply(lambda x: ast.literal_eval(str(x)))\n\n    # calculate normL1\n    center_lat = np.mean([trip_feature_df['startLat'], trip_feature_df['endLat']])\n    center_lon = np.mean([trip_feature_df['startLon'], trip_feature_df['endLon']])\n    trip_feature_df['L1_distance'] = np.abs(trip_feature_df['startLat'] - center_lat) \\\n                                     + np.abs(trip_feature_df['startLon'] - center_lon) \\\n                                     + np.abs(trip_feature_df['endLat'] - center_lat) \\\n                                     + np.abs(trip_feature_df['endLon'] - center_lon)\n\n    risk_feature_df = trip_feature_df.groupby(['vin']). \\\n        agg({'brakeCount': 'median', 'warnCount': 'sum', 'bdCount': 'sum',\n             'TripCurve': 'mean', 'distance': ['sum', 'median'],\n             'duration': ['sum', 'median'], 'tripId': 'count',\n             })\n    risk_feature_df.columns = ['brakeMedian', 'warnCountPhk', 'bdPhk',\n                               'averageTripCurve',\n                               'distance', 'distanceMedian',\n                               'duration', 'durationMedian',\n                               'tripNum',\n                               ]\n\n    risk_feature_df['avgSpd'] = risk_feature_df['distance'] / risk_feature_df['duration']\n    risk_feature_df['disPerTrip'] = risk_feature_df['distance'] / risk_feature_df['tripNum']\n    risk_feature_df['activeDay'] = len(set(trip_feature_df['activeDay'].sum()))\n    risk_feature_df['tripNumPerDay'] = risk_feature_df['tripNum'] / risk_feature_df['activeDay']\n    risk_feature_df['normL1'] = np.sum(trip_feature_df['L1_distance']) / (2 * trip_feature_df.shape[0])\n\n    risk_feature_df['afternoonDisRatio'] = np.sum(trip_feature_df['afternoonDis']) / \\\n                                           risk_feature_df['distance']\n    risk_feature_df['afternoonDurationPerDay'] = np.sum(trip_feature_df['afternoonDuration']) / \\\n                                                 risk_feature_df['activeDay']\n    risk_feature_df['afternoonDurationPerTrip'] = np.sum(trip_feature_df['afternoonDuration']) / \\\n                                                  np.sum(trip_feature_df['afternoonFlag'])\n\n    risk_feature_df['duskDisRatio'] = np.sum(trip_feature_df['duskDis']) / \\\n                                      risk_feature_df['distance']\n    risk_feature_df['duskDurationPerDay'] = np.sum(trip_feature_df['duskDuration']) / \\\n                                            risk_feature_df['activeDay']\n    risk_feature_df['duskDurationPerTrip'] = np.sum(trip_feature_df['duskDuration']) / \\\n                                             np.sum(trip_feature_df['duskFlag'])\n\n    risk_feature_df['lateNightDisRatio'] = np.sum(trip_feature_df['lateNightDis']) / \\\n                                           risk_feature_df['distance']\n    risk_feature_df['lateNightDurationPerDay'] = np.sum(trip_feature_df['lateNightDuration']) / \\\n                                                 risk_feature_df['activeDay']\n    risk_feature_df['lateNightDurationPerTrip'] = np.sum(trip_feature_df['lateNightDuration']) / \\\n                                                  np.sum(trip_feature_df['lateNightFlag'])\n    risk_feature_df['lateNightTripRatio'] = np.sum(trip_feature_df['lateNightFlag']) / \\\n                                            risk_feature_df['tripNum']\n\n    risk_feature_df['highCurveTripRatio'] = np.sum(trip_feature_df['isHighCurveTrip']) / \\\n                                            risk_feature_df['tripNum']\n    risk_feature_df['longDisTripRatio'] = np.sum(trip_feature_df['isLongDistanceTrip']) / \\\n                                          risk_feature_df['tripNum']\n    risk_feature_df['longTripRatio'] = np.sum(trip_feature_df['isLongTimeTrip']) / \\\n                                       risk_feature_df['tripNum']\n    return risk_feature_df\n\n\ndef file_process(file_name):\n    print(file_name)\n    trip_feature_df = pd.read_csv(file_name)\n    trip_feature_df['vin'] = file_name\n    risk_feature_df = calculate_risk_features(trip_feature_df)\n    return risk_feature_df\n\n\n\ndef folder_process(basefolder, folderlist, outputfolder):\n    risk_feature_dfs = []\n    for foldername in folderlist:\n        savepath = outputfolder+'/'+foldername+'/'\n        if not os.path.exists(savepath):\n            os.makedirs(savepath)\n        os.chdir(path=basefolder+foldername)\n        for file_name in os.listdir():\n            risk_feature_df = file_process(file_name)\n            risk_feature_dfs.append(risk_feature_df)\n        RISK_FEATURE_DF = pd.concat(risk_feature_dfs)\n        RISK_FEATURE_DF.to_csv(savepath+'risk_feature.csv')\n\nfolder_process(DATA_FILE, folderlist, outputfolder)\nprint(\"finished\")\n\n#risk_feature_dfs = []\n#for i in trip_data_files:\n#    vin = os.path.splitext(os.path.basename(i))[0]\n#    trip_feature_df = pd.read_csv(i)\n#    trip_feature_df['vin'] = vin\n#    risk_feature_df = calculate_risk_features(trip_feature_df)\n#    risk_feature_dfs.append(risk_feature_df)\n\n# 2.Concatenate all data into one DataFrame\n#RISK_FEATURE_DF = pd.concat(risk_feature_dfs)\n\n# 3.save risk feature data\n#RISK_FEATURE_DF.to_csv(RISK_FEATURE_DATA_FILE + \"/risk_feature.csv\")\n\n", "text_preview": "import pandas as pd import numpy as np import datetime import os import sys import pathlib import glob import ast #1.load data #BASE_FILE = os.path.dirname(os.path.abspath(__file__))#the directory of the script being run DATA_FILE = '/sdb/t", "input_sha256": "11534085fbf7bfa62a74fecb81efc515837ffa2826df4561847bbb4863a57acd", "text_sha256": "6a02d4cd2f459a288cc4043a06ec389c8c5a289effcd2b19b1f8c37599885294", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12109, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 3, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.329799850572311, "noise_severity": 0.07476399304201152, "missing_content_severity": 0.022725255479756193, "factual_density": 3.141235286126943, "pretraining_utility": 0.4910914548795236, "math_density": 2.838246721270368, "code_density": 3.7938599698575497, "reasoning_density": 0.7691138130226374, "education_level": 2.161241265365267, "creative_writing_quality": -0.3528480213255729}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.04382509560027403, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9832196307657153, "secondary": 0.9323810904597778, "tertiary": 0.9781116249991939, "structured_records": 0.0059571989426467535, "procedural_steps": 0.1110219909490648, "worked_solution": 0.017930069314721152, "contextual_dialogue": 1.0178166730269863e-05, "content_quality": 0.3058641247900704, "noise_severity": 0.9376966724649904, "missing_content_severity": 0.9810622871002032, "factual_density": 0.2843627384391193, "pretraining_utility": 0.436114342264852, "math_density": 0.4336430935800998, "code_density": 0.8282166415479578, "reasoning_density": 0.35907182248113567, "education_level": 0.735781354812895, "creative_writing_quality": 0.9982691785288762, "primary_topic": 0.8585690602920444, "secondary_topic": 0.3254402351811487}, "probability_margin": {"primary": 0.9726913736459317, "secondary": 0.8946134394070279, "tertiary": 0.9674899147866636, "education_level": 0.6155621488690848, "creative_writing_quality": 0.9978246007896868, "primary_topic": 0.807549185288048, "secondary_topic": 0.06515958332004662}, "raw_probabilities": {"primary": {"Academic Writing": 0.00012415434083562501, "Content Listing": 4.727804873586937e-05, "Creative Writing": 5.407196346960063e-05, "Customer Support Page": 2.6281919282519696e-05, "Q&A Forum": 3.819644833915507e-05, "Discussion Thread": 2.5776887116152468e-05, "Comment Section": 4.012168032330923e-05, "Missing Content": 7.232992768924226e-05, "Knowledge Article": 0.00018974990908029164, "Legal Notices": 4.538134187930109e-05, "News Article": 1.726607886781854e-05, "Organizational Announcement": 2.6094781227243674e-05, "Nonfiction Writing": 5.038725140413134e-05, "Organizational About Page": 3.6722674953533214e-05, "Personal About Page": 3.574306674167358e-05, "Personal Blog": 2.3157984143410105e-05, "Product Page": 1.8312992769047065e-05, "Spam / Ads": 3.4904459168898016e-05, "Structured Data": 0.0006672158779838165, "Technical Writing": 0.9839188128171438, "Transcript / Interview": 4.0889839618760396e-05, "Tutorial / How-To Guide": 0.011227439171212095, "User Reviews": 1.9401077299810625e-05, "Other / Unknown": 0.0032203094607149447}, "secondary": {"Academic Writing": 0.0004337714705179137, "Content Listing": 0.0001508574641963619, "Creative Writing": 0.00019971544023458327, "Customer Support Page": 0.00010521308739857842, "Q&A Forum": 9.972305789127078e-05, "Discussion Thread": 7.71613424513701e-05, "Comment Section": 0.0001584850009770376, "Missing Content": 0.00035200017869129813, "Knowledge Article": 0.0008146338266577386, "Legal Notices": 0.00012391050015520165, "News Article": 0.0001111180005492612, "Organizational Announcement": 9.53967386228108e-05, "Nonfiction Writing": 0.00015162247798293556, "Organizational About Page": 0.00012895883095573194, "Personal About Page": 0.00011486167699024754, "Personal Blog": 0.00011396433835970878, "Product Page": 7.380235235449593e-05, "Spam / Ads": 0.00012010315720705194, "Structured Data": 0.0032515135462667624, "Technical Writing": 0.01733542789256531, "Transcript / Interview": 0.0001425258175262501, "Tutorial / How-To Guide": 0.04058510561692587, "User Reviews": 6.158316056863076e-05, "NONE": 0.9351985450239537}, "tertiary": {"Academic Writing": 0.00025517504773064014, "Content Listing": 6.546466103118148e-05, "Creative Writing": 8.523963632592862e-05, "Customer Support Page": 3.9314867229003965e-05, "Q&A Forum": 3.162930112879013e-05, "Discussion Thread": 2.7892004645324374e-05, "Comment Section": 7.970662164030297e-05, "Missing Content": 0.00028019777273647, "Knowledge Article": 0.0005686526821569124, "Legal Notices": 8.271224245940465e-05, "News Article": 4.668896256383579e-05, "Organizational Announcement": 3.392199450756429e-05, "Nonfiction Writing": 8.88704577493449e-05, "Organizational About Page": 6.569857348819567e-05, "Personal About Page": 4.160881475128065e-05, "Personal Blog": 4.3589759872259386e-05, "Product Page": 3.235189740849999e-05, "Spam / Ads": 5.0168180798219886e-05, "Structured Data": 0.00196607251712881, "Technical Writing": 0.005486573932393638, "Transcript / Interview": 4.715103249350694e-05, "Tutorial / How-To Guide": 0.011533725837563904, "User Reviews": 2.3952577969541195e-05, "NONE": 0.9790236406242275}, "structured_records": {"false": 0.9940428010573532, "true": 0.0059571989426467535}, "procedural_steps": {"false": 0.8889780090509352, "true": 0.1110219909490648}, "worked_solution": {"false": 0.9820699306852788, "true": 0.017930069314721152}, "contextual_dialogue": {"false": 0.9999898218332697, "true": 1.0178166730269863e-05}, "content_quality": {"-2": 0.03559203766018102, "-1": 0.13625540268925043, "0": 0.37229468148075834, "1": 0.3744764277576969, "2": 0.08138145041211324}, "noise_severity": {"0": 0.9363913032104885, "1": 0.05449572603082776, "2": 0.007597488924348417, "3": 0.0009886381748542658, "4": 0.0005268436594810304}, "missing_content_severity": {"0": 0.9869455773700243, "1": 0.005826956168674239, "2": 0.005070877606466242, "3": 0.0018698113211919052, "4": 0.0002867775336434397}, "factual_density": {"0": 0.017019106632241, "1": 0.037247615078142976, "2": 0.15392302580925268, "3": 0.3710993904911584, "4": 0.4207108619892049}, "pretraining_utility": {"-2": 0.010632132348710539, "-1": 0.08805437512796195, "0": 0.3847805198992714, "1": 0.43265585054320527, "2": 0.08387712208085062}, "math_density": {"0": 0.02113852886276594, "1": 0.06074641356674735, "2": 0.23578236949496353, "3": 0.42339518358839906, "4": 0.25893750448712427}, "code_density": {"0": 0.00034270605669858677, "1": 0.0007045464713020195, "2": 0.026009196184749735, "3": 0.1506371741322507, "4": 0.822306377154999}, "reasoning_density": {"0": 0.6906994911088747, "1": 0.043145011092314306, "2": 0.1291333946763836, "3": 0.08038639991215317, "4": 0.056635703210274044}, "education_level": {"none": 0.0018201297075619949, "0": 0.002489940764329817, "1": 0.03385365133877119, "2": 0.7798177956774125, "3": 0.16425564680832766, "4": 0.01776283570359687}, "creative_writing_quality": {"not_applicable": 0.9985576487740635, "-2": 0.000167333162944863, "-1": 0.0003822824148863086, "0": 0.0007330479843766933, "1": 0.00011135736280960689, "2": 4.833030091909744e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0002317281988636105, "Art & Design": 0.0003292168310441728, "Law & Crime": 0.0006488406832256267, "Education & Careers": 0.0002391392258878888, "Entertainment & Media": 0.00022083988879421752, "Fashion & Beauty": 0.0002702415056813163, "Business, Economics & Finance": 0.009788997592828232, "Food & Dining": 0.0002643515772266063, "Games & Gaming": 0.0003295023495462453, "Electronics & Hardware": 0.00040596036481165535, "Health, Healthcare & Wellness": 0.0004414052292284598, "History & Geography": 0.0003829405272535477, "Home, Pets & Hobbies": 0.00022163859125690706, "Industry & Production": 0.0007682962754039902, "Language, Literature & Philosophy": 0.00021721526000570494, "Politics & Government": 0.0002556364682924615, "Religion & Spirituality": 0.00020727200474340897, "Science, Mathematics & Engineering": 0.05072175369662471, "Relationships & Community": 0.0002007834421097571, "Software Usage & Applications": 0.0009512668915180781, "Programming & Software Engineering": 0.8640087118192734, "Sports & Fitness": 0.00047258508816470414, "Vehicles & Transportation": 0.056459526531225414, "Travel & Tourism": 0.0007276969343400525, "Other / Uncovered Subject": 0.010781934377086114, "Unknown / Insufficient Content": 0.00045251864556369967}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009566119342177568, "Art & Design": 0.0007877551179220791, "Law & Crime": 0.003701434385702903, "Education & Careers": 0.0006408540020174548, "Entertainment & Media": 0.0007480615457095219, "Fashion & Beauty": 0.0006290838914172379, "Business, Economics & Finance": 0.025454014070759236, "Food & Dining": 0.00070617683685737, "Games & Gaming": 0.0007904880549498475, "Electronics & Hardware": 0.0010111812413361322, "Health, Healthcare & Wellness": 0.0016825704053461145, "History & Geography": 0.000982253004934359, "Home, Pets & Hobbies": 0.0005557456920484911, "Industry & Production": 0.0017180943219862028, "Language, Literature & Philosophy": 0.0003931596313624012, "Politics & Government": 0.000592819406955014, "Religion & Spirituality": 0.0006366635514767222, "Science, Mathematics & Engineering": 0.28622525820028866, "Relationships & Community": 0.00037670315486739125, "Software Usage & Applications": 0.001412244579658995, "Programming & Software Engineering": 0.10020590841118752, "Sports & Fitness": 0.001065312317793409, "Vehicles & Transportation": 0.19893139063191997, "Travel & Tourism": 0.0018299164057168581, "Other / Uncovered Subject": 0.016581457683233068, "NONE": 0.3513848415203353}}, "probabilities": {"primary": {"Academic Writing": 0.00012415434083562501, "Content Listing": 4.727804873586937e-05, "Creative Writing": 5.407196346960063e-05, "Customer Support Page": 2.6281919282519696e-05, "Q&A Forum": 3.819644833915507e-05, "Discussion Thread": 2.5776887116152468e-05, "Comment Section": 4.012168032330923e-05, "Missing Content": 7.232992768924226e-05, "Knowledge Article": 0.00018974990908029164, "Legal Notices": 4.538134187930109e-05, "News Article": 1.726607886781854e-05, "Organizational Announcement": 2.6094781227243674e-05, "Nonfiction Writing": 5.038725140413134e-05, "Organizational About Page": 3.6722674953533214e-05, "Personal About Page": 3.574306674167358e-05, "Personal Blog": 2.3157984143410105e-05, "Product Page": 1.8312992769047065e-05, "Spam / Ads": 3.4904459168898016e-05, "Structured Data": 0.0006672158779838165, "Technical Writing": 0.9839188128171438, "Transcript / Interview": 4.0889839618760396e-05, "Tutorial / How-To Guide": 0.011227439171212095, "User Reviews": 1.9401077299810625e-05, "Other / Unknown": 0.0032203094607149447}, "secondary": {"Academic Writing": 0.0004337714705179136, "Content Listing": 0.00015085746419636186, "Creative Writing": 0.0001997154402345832, "Customer Support Page": 0.00010521308739857839, "Q&A Forum": 9.972305789127075e-05, "Discussion Thread": 7.716134245137009e-05, "Comment Section": 0.00015848500097703758, "Missing Content": 0.0003520001786912981, "Knowledge Article": 0.0008146338266577384, "Legal Notices": 0.00012391050015520162, "News Article": 0.00011111800054926117, "Organizational Announcement": 9.539673862281077e-05, "Nonfiction Writing": 0.00015162247798293553, "Organizational About Page": 0.0001289588309557319, "Personal About Page": 0.00011486167699024751, "Personal Blog": 0.00011396433835970875, "Product Page": 7.380235235449592e-05, "Spam / Ads": 0.00012010315720705191, "Structured Data": 0.0032515135462667616, "Technical Writing": 0.017335427892565307, "Transcript / Interview": 0.00014252581752625007, "Tutorial / How-To Guide": 0.04058510561692586, "User Reviews": 6.158316056863075e-05, "NONE": 0.9351985450239535}, "tertiary": {"Academic Writing": 0.00025517504773064014, "Content Listing": 6.546466103118148e-05, "Creative Writing": 8.523963632592862e-05, "Customer Support Page": 3.9314867229003965e-05, "Q&A Forum": 3.162930112879013e-05, "Discussion Thread": 2.7892004645324374e-05, "Comment Section": 7.970662164030297e-05, "Missing Content": 0.00028019777273647, "Knowledge Article": 0.0005686526821569124, "Legal Notices": 8.271224245940465e-05, "News Article": 4.668896256383579e-05, "Organizational Announcement": 3.392199450756429e-05, "Nonfiction Writing": 8.88704577493449e-05, "Organizational About Page": 6.569857348819567e-05, "Personal About Page": 4.160881475128065e-05, "Personal Blog": 4.3589759872259386e-05, "Product Page": 3.235189740849999e-05, "Spam / Ads": 5.0168180798219886e-05, "Structured Data": 0.00196607251712881, "Technical Writing": 0.005486573932393638, "Transcript / Interview": 4.715103249350694e-05, "Tutorial / How-To Guide": 0.011533725837563904, "User Reviews": 2.3952577969541195e-05, "NONE": 0.9790236406242275}, "structured_records": {"false": 0.9940428010573532, "true": 0.0059571989426467535}, "procedural_steps": {"false": 0.8889780090509352, "true": 0.1110219909490648}, "worked_solution": {"false": 0.9820699306852788, "true": 0.017930069314721152}, "contextual_dialogue": {"false": 0.9999898218332697, "true": 1.0178166730269863e-05}, "content_quality": {"-2": 0.03559203766018103, "-1": 0.13625540268925046, "0": 0.3722946814807584, "1": 0.37447642775769696, "2": 0.08138145041211325}, "noise_severity": {"0": 0.9363913032104886, "1": 0.054495726030827765, "2": 0.007597488924348418, "3": 0.000988638174854266, "4": 0.0005268436594810305}, "missing_content_severity": {"0": 0.9869455773700241, "1": 0.005826956168674238, "2": 0.0050708776064662415, "3": 0.0018698113211919048, "4": 0.00028677753364343965}, "factual_density": {"0": 0.017019106632241, "1": 0.037247615078142976, "2": 0.15392302580925268, "3": 0.3710993904911584, "4": 0.4207108619892049}, "pretraining_utility": {"-2": 0.01063213234871054, "-1": 0.08805437512796196, "0": 0.38478051989927153, "1": 0.4326558505432054, "2": 0.08387712208085063}, "math_density": {"0": 0.021138528862765935, "1": 0.060746413566747336, "2": 0.23578236949496348, "3": 0.42339518358839895, "4": 0.2589375044871242}, "code_density": {"0": 0.00034270605669858677, "1": 0.0007045464713020195, "2": 0.026009196184749735, "3": 0.1506371741322507, "4": 0.822306377154999}, "reasoning_density": {"0": 0.6906994911088749, "1": 0.04314501109231431, "2": 0.12913339467638363, "3": 0.08038639991215318, "4": 0.05663570321027405}, "education_level": {"none": 0.0018201297075619949, "0": 0.002489940764329817, "1": 0.03385365133877119, "2": 0.7798177956774125, "3": 0.16425564680832766, "4": 0.01776283570359687}, "creative_writing_quality": {"not_applicable": 0.9985576487740635, "-2": 0.000167333162944863, "-1": 0.0003822824148863086, "0": 0.0007330479843766933, "1": 0.00011135736280960689, "2": 4.833030091909744e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0002317281988636105, "Art & Design": 0.0003292168310441728, "Law & Crime": 0.0006488406832256267, "Education & Careers": 0.0002391392258878888, "Entertainment & Media": 0.00022083988879421752, "Fashion & Beauty": 0.0002702415056813163, "Business, Economics & Finance": 0.009788997592828232, "Food & Dining": 0.0002643515772266063, "Games & Gaming": 0.0003295023495462453, "Electronics & Hardware": 0.00040596036481165535, "Health, Healthcare & Wellness": 0.0004414052292284598, "History & Geography": 0.0003829405272535477, "Home, Pets & Hobbies": 0.00022163859125690706, "Industry & Production": 0.0007682962754039902, "Language, Literature & Philosophy": 0.00021721526000570494, "Politics & Government": 0.0002556364682924615, "Religion & Spirituality": 0.00020727200474340897, "Science, Mathematics & Engineering": 0.05072175369662471, "Relationships & Community": 0.0002007834421097571, "Software Usage & Applications": 0.0009512668915180781, "Programming & Software Engineering": 0.8640087118192734, "Sports & Fitness": 0.00047258508816470414, "Vehicles & Transportation": 0.056459526531225414, "Travel & Tourism": 0.0007276969343400525, "Other / Uncovered Subject": 0.010781934377086114, "Unknown / Insufficient Content": 0.00045251864556369967}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009566119342177568, "Art & Design": 0.0007877551179220791, "Law & Crime": 0.003701434385702903, "Education & Careers": 0.0006408540020174548, "Entertainment & Media": 0.0007480615457095219, "Fashion & Beauty": 0.0006290838914172379, "Business, Economics & Finance": 0.025454014070759236, "Food & Dining": 0.00070617683685737, "Games & Gaming": 0.0007904880549498475, "Electronics & Hardware": 0.0010111812413361322, "Health, Healthcare & Wellness": 0.0016825704053461145, "History & Geography": 0.000982253004934359, "Home, Pets & Hobbies": 0.0005557456920484911, "Industry & Production": 0.0017180943219862028, "Language, Literature & Philosophy": 0.0003931596313624012, "Politics & Government": 0.000592819406955014, "Religion & Spirituality": 0.0006366635514767222, "Science, Mathematics & Engineering": 0.28622525820028866, "Relationships & Community": 0.00037670315486739125, "Software Usage & Applications": 0.001412244579658995, "Programming & Software Engineering": 0.10020590841118752, "Sports & Fitness": 0.001065312317793409, "Vehicles & Transportation": 0.19893139063191997, "Travel & Tourism": 0.0018299164057168581, "Other / Uncovered Subject": 0.016581457683233068, "NONE": 0.3513848415203353}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9918, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9918, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:36:16.089283+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "2c8f9a61-b41b-4e89-bfe1-3ca235acdd51", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "00980f64fb7e5a57eeb86bd9673e30618ff6bc937553700fcec3852699615d67", "source_metadata": {"uuid": "2c8f9a61-b41b-4e89-bfe1-3ca235acdd51", "repo_name": "wuzhihui3723/iov", "relative_path": "一期/test.py", "category": "DATA", "algo_rel_score": 0.9556114673614502, "quality_score": 4.387537479400635, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 6, "repo_created_at": "2022-05-05T06:23:53.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "99da3dbbfddbff32aa337d548b4beda3b25ad8a9", "size_bytes": 5790, "file_timestamp": 1652944066, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "9b77d0fe1feb1ec1a35e4f20fb4737e95908d6b2"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 6, "repo_created_at": "2022-05-05T06:23:53.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "99da3dbbfddbff32aa337d548b4beda3b25ad8a9", "size_bytes": 5790, "file_timestamp": 1652944066, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "9b77d0fe1feb1ec1a35e4f20fb4737e95908d6b2", "url": "https://github.com/wuzhihui3723/iov/blob/9b77d0fe1feb1ec1a35e4f20fb4737e95908d6b2/%E4%B8%80%E6%9C%9F/test.py"}, "gpt4o_tokens": 1317, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.329799850572311, "noise_severity": 0.07476399304201152, "missing_content_severity": 0.022725255479756196, "factual_density": 3.141235286126943, "pretraining_utility": 0.4910914548795236, "math_density": 2.8382467212703686, "code_density": 3.7938599698575497, "reasoning_density": 0.7691138130226374, "education_level": 2.161241265365267, "creative_writing_quality": -0.3528480213255729}, "score_applicability": {"education_level": 0.998179870292438, "creative_writing_quality": 0.0014423512259365693}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/androguard/androguard/blob/70fd5fe2ae0a8c7cb5d17aa19c2d92be4dca3919/tests/test_strings.py", "id": "python-0-13d02364147c9dfdb910300e137e57ab", "text": "# -*- coding: utf8- -*-\nimport os\nimport unittest\n\nfrom androguard.core import dex, mutf8\n\ntest_dir = os.path.dirname(os.path.abspath(__file__))\n\n\nclass StringTest(unittest.TestCase):\n    def testDex(self):\n        with open(\n            os.path.join(test_dir, 'data/APK/StringTests.dex'), \"rb\"\n        ) as fd:\n            d = dex.DEX(fd.read())\n\n            stests = [\n                \"this is a quite normal string\",\n                \"\\u0000 \\u0001 \\u1234\",\n                \"使用在線工具將字符串翻譯為中文\",\n                \"перевод строки на русский с помощью онлайн-инструментов\",\n                \"온라인 도구를 사용하여 문자열을 한국어로 번역\",\n                \"オンラインツールを使用して文字列を日本語に翻訳\",\n                \"This is \\U0001f64f, an emoji.\",  # complete surrogate\n                \"\\u2713 check this string\",\n                \"\\uffff \\u0000 \\uff00\",\n                \"\\u0420\\u043e\\u0441\\u0441\\u0438\\u044f\",\n            ]\n\n            for s in stests:\n                self.assertIn(s, d.get_strings())\n\n    def testMUTF8(self):\n        self.assertEqual(\"\\x67\", mutf8.decode(b\"\\x67\"))\n        # # Null byte\n        self.assertEqual(\"\\x00\", mutf8.decode(b\"\\xc0\\x80\"))\n        self.assertEqual(\"\\uacf0\", mutf8.decode(b\"\\xea\\xb3\\xb0\"))\n        # # Surrogates\n        self.assertEqual(\"🙏\", mutf8.decode(b\"\\xed\\xa0\\xbd\\xed\\xb9\\x8f\"))\n        self.assertEqual(\n            \"\\U00014f5c\", mutf8.decode(b\"\\xed\\xa1\\x93\\xed\\xbd\\x9c\")\n        )\n        # # Lonely surrogates\n        # self.assertEqual(\"\\ud853\", mutf8.decode(b\"\\xed\\xa1\\x93\"))\n        # self.assertEqual(\"\\udf5c\", mutf8.decode(b\"\\xed\\xbd\\x9c\"))\n        # # Normal ASCII String\n        self.assertEqual(\n            \"hello world\",\n            mutf8.decode(b\"\\x68\\x65\\x6c\\x6c\\x6f\\x20\\x77\\x6f\\x72\\x6c\\x64\"),\n        )\n\n        # Testing decode\n\n        b = (\n            b\"\\xed\\xa1\\x93\\xed\\xbd\\x9c\"\n            + b\"\\xed\\xa0\\xbd\\xed\\xb9\\x8f\"\n            + b\"\\xed\\xa0\\xbd\"\n            + b\"\\xea\\xb3\\xb0\"\n            + b\"\\x68\\x65\\x6c\\x6c\\x6f\\x20\\x77\\x6f\\x72\\x6c\\x64\"\n            + b\"\\xc0\\x80\"\n        )\n\n        self.assertEqual(\n            \"hello world\",\n            mutf8.decode(b\"\\x68\\x65\\x6c\\x6c\\x6f\\x20\\x77\\x6f\\x72\\x6c\\x64\")\n            .encode('utf8', errors='backslashreplace')\n            .decode('utf8'),\n        )\n        self.assertEqual(\n            \"\\U00014f5c\",\n            mutf8.decode(b\"\\xed\\xa1\\x93\\xed\\xbd\\x9c\")\n            .encode('utf8', errors='backslashreplace')\n            .decode('utf8'),\n        )\n        self.assertEqual(\n            \"\\U0001f64f\",\n            mutf8.decode(b\"\\xed\\xa0\\xbd\\xed\\xb9\\x8f\")\n            .encode('utf8', errors='backslashreplace')\n            .decode('utf8'),\n        )\n        # self.assertEqual(\"\\\\ud853\",\n        # mutf8.decode(b\"\\xed\\xa1\\x93\").encode('utf8', errors='backslashreplace').decode('utf8'))\n        # self.assertEqual(\"\\U00024f5c\\U0001f64f\\\\ud83d\\uacf0hello world\\x00\",\n        #                  mutf8.decode(b).encode('utf8', errors='backslashreplace').decode('utf8'))\n\n        # Testing encode\n\n        self.assertEqual(\n            b\"\\x68\\x65\\x6c\\x6c\\x6f\\x20\\x77\\x6f\\x72\\x6c\\x64\",\n            mutf8.encode(\"hello world\"),\n        )\n        self.assertEqual(\n            b\"\\xed\\xa2\\x93\\xed\\xbd\\x9c\", mutf8.encode(\"\\U00024f5c\")\n        )\n        self.assertEqual(\n            b\"\\xed\\xa1\\xbd\\xed\\xb9\\x8f\", mutf8.encode(\"\\U0001f64f\")\n        )\n        # self.assertEqual(b\"\\xed\\xa1\\x93\", mutf8.encode(\"\\ud853\"))\n        # self.assertEqual(b, mutf8.encode(\"\\U00024f5c\\U0001f64f\\ud83d\\uacf0hello world\\x00\"))\n\n\nif __name__ == '__main__':\n    unittest.main()\n", "text_preview": "# -*- coding: utf8- -*- import os import unittest from androguard.core import dex, mutf8 test_dir = os.path.dirname(os.path.abspath(__file__)) class StringTest(unittest.TestCase): def testDex(self): with open( os.path.join(test_dir, 'data/A", "input_sha256": "1874fa5b912e0786ea974eff4ca3f218a576807b4274e30d96a60bd8f7a77fd9", "text_sha256": "13d02364147c9dfdb910300e137e57ab59d53d29c68b12e7c05ef892ba620bda", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12112, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.0419282445443245, "noise_severity": 0.05674738258806065, "missing_content_severity": 0.015430917870299871, "factual_density": 2.9806991569556414, "pretraining_utility": 0.06762599125077134, "math_density": 0.09345815874653782, "code_density": 3.8729053751196596, "reasoning_density": 0.20671170866497282, "education_level": 2.4613272480129247, "creative_writing_quality": -0.42801960539112743}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.02556360154051196, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9886546944867022, "secondary": 0.9324186477088902, "tertiary": 0.9590937101859459, "structured_records": 0.14794546479781773, "procedural_steps": 0.0036332462779822615, "worked_solution": 0.07781920938950385, "contextual_dialogue": 8.35775591274711e-06, "content_quality": 0.5458177902080841, "noise_severity": 0.9527105145099495, "missing_content_severity": 0.9871409017747501, "factual_density": 0.15058263079636758, "pretraining_utility": 0.47913743068665393, "math_density": 0.9221182010445518, "code_density": 0.8940878125997164, "reasoning_density": 0.8277402427791893, "education_level": 0.5254683786865364, "creative_writing_quality": 0.9993086395690012, "primary_topic": 0.9793516618874951, "secondary_topic": 0.8442628318917786}, "probability_margin": {"primary": 0.9820342720976751, "secondary": 0.9036224133956403, "tertiary": 0.9414505619628303, "education_level": 0.3064639724399573, "creative_writing_quality": 0.9992039378181312, "primary_topic": 0.9679532071463154, "secondary_topic": 0.7631332811911532}, "raw_probabilities": {"primary": {"Academic Writing": 8.723125832599877e-05, "Content Listing": 3.572593313877489e-05, "Creative Writing": 2.9154680562751186e-05, "Customer Support Page": 1.8010949843328833e-05, "Q&A Forum": 2.044863911377073e-05, "Discussion Thread": 1.2245547111426161e-05, "Comment Section": 2.305784051769588e-05, "Missing Content": 4.53054178793806e-05, "Knowledge Article": 0.00011166825127535771, "Legal Notices": 4.584624027719424e-05, "News Article": 9.430967205551369e-06, "Organizational Announcement": 1.894655102717894e-05, "Nonfiction Writing": 2.3800395157490187e-05, "Organizational About Page": 2.1370222862565113e-05, "Personal About Page": 1.4917731544305518e-05, "Personal Blog": 1.1975153876542828e-05, "Product Page": 1.1682242245578808e-05, "Spam / Ads": 2.1046754857245868e-05, "Structured Data": 0.0025720943646195694, "Technical Writing": 0.9891274155497563, "Transcript / Interview": 2.8005713947727984e-05, "Tutorial / How-To Guide": 0.0006056054328196131, "User Reviews": 1.1870709953765972e-05, "Other / Unknown": 0.0070931434520810745}, "secondary": {"Academic Writing": 0.00038309957262696876, "Content Listing": 0.0002059277615924176, "Creative Writing": 0.00026065207322092823, "Customer Support Page": 0.00010320183373365968, "Q&A Forum": 9.80242046681728e-05, "Discussion Thread": 8.522848293678281e-05, "Comment Section": 0.0001522960423802244, "Missing Content": 0.0003553592279451569, "Knowledge Article": 0.0005571828370265923, "Legal Notices": 0.00015158797421931255, "News Article": 0.0001246154163611154, "Organizational Announcement": 0.00012114946405278916, "Nonfiction Writing": 0.00014884239915716004, "Organizational About Page": 0.0001348646148587448, "Personal About Page": 0.0001021847770213569, "Personal Blog": 0.00010321074121295593, "Product Page": 7.081147256373751e-05, "Spam / Ads": 0.00014699600054856666, "Structured Data": 0.03161212399204599, "Technical Writing": 0.028553863506088486, "Transcript / Interview": 0.0001547265943087743, "Tutorial / How-To Guide": 0.001084084742739523, "User Reviews": 5.5428881004181383e-05, "NONE": 0.9352345373876864}, "tertiary": {"Academic Writing": 0.0003727394334625354, "Content Listing": 0.0001630174178762703, "Creative Writing": 0.00021848306401299193, "Customer Support Page": 7.156514759423886e-05, "Q&A Forum": 5.830972916213377e-05, "Discussion Thread": 5.1467406717187646e-05, "Comment Section": 0.00013013544786202516, "Missing Content": 0.0003613874014448197, "Knowledge Article": 0.0008845265598204262, "Legal Notices": 0.000188229163048046, "News Article": 7.932134535135348e-05, "Organizational Announcement": 6.774508355415025e-05, "Nonfiction Writing": 0.0001537919790579095, "Organizational About Page": 9.772653435982514e-05, "Personal About Page": 5.745306834753773e-05, "Personal Blog": 6.351662138129625e-05, "Product Page": 4.940998099070525e-05, "Spam / Ads": 0.00017712307653266118, "Structured Data": 0.01934757696536792, "Technical Writing": 0.015377101687491533, "Transcript / Interview": 8.82665932249194e-05, "Tutorial / How-To Guide": 0.0011052786963385473, "User Reviews": 3.76886688026188e-05, "NONE": 0.9607981389281982}, "structured_records": {"false": 0.8520545352021822, "true": 0.14794546479781773}, "procedural_steps": {"false": 0.9963667537220178, "true": 0.0036332462779822615}, "worked_solution": {"false": 0.9221807906104962, "true": 0.07781920938950385}, "contextual_dialogue": {"false": 0.9999916422440872, "true": 8.35775591274711e-06}, "content_quality": {"-2": 0.00667035635425439, "-1": 0.017061786603799124, "0": 0.19741056133262588, "1": 0.485383847562009, "2": 0.2934734481473117}, "noise_severity": {"0": 0.9517341212542543, "1": 0.0418901203884234, "2": 0.004848518382764921, "3": 0.0009487344641220179, "4": 0.0005785055104353379}, "missing_content_severity": {"0": 0.9870169484758368, "1": 0.01133563576613727, "2": 0.0010383678896514971, "3": 0.000417645148638465, "4": 0.00019140271973605303}, "factual_density": {"0": 0.024552839537024633, "1": 0.06851087689968152, "2": 0.18111148928764959, "3": 0.3533338756219166, "4": 0.3724909186537276}, "pretraining_utility": {"-2": 0.026104919318949107, "-1": 0.2264947073247237, "0": 0.45388173424430894, "1": 0.2407067410106433, "2": 0.05281189810137499}, "math_density": {"0": 0.9565534568622559, "1": 0.021737579716125163, "2": 0.0059137602036185176, "3": 0.0032877542488261298, "4": 0.012507448969174306}, "code_density": {"0": 0.00026339415934638306, "1": 0.0005674082483895006, "2": 0.014325143958958595, "3": 0.0956885355798691, "4": 0.8891555180534363}, "reasoning_density": {"0": 0.8853327351670227, "1": 0.049132337981421484, "2": 0.046975001858472816, "3": 0.01061033300572615, "4": 0.007949591987356812}, "education_level": {"none": 0.0012742976156179346, "0": 0.0017976321740138532, "1": 0.007438173998520975, "2": 0.6045569822387803, "3": 0.29809300979882297, "4": 0.08683990417424382}, "creative_writing_quality": {"not_applicable": 0.999423866307501, "-2": 0.00012653029462125518, "-1": 0.00012708943813329683, "0": 0.00021992848936981207, "1": 7.161742908937256e-05, "2": 3.096804128524392e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.747244356608537e-05, "Art & Design": 8.276362723191301e-05, "Law & Crime": 6.387175697585523e-05, "Education & Careers": 7.107424528304849e-05, "Entertainment & Media": 5.576750516828913e-05, "Fashion & Beauty": 3.405777295021554e-05, "Business, Economics & Finance": 6.422780672042635e-05, "Food & Dining": 4.5740602505342824e-05, "Games & Gaming": 0.00012815099968333063, "Electronics & Hardware": 0.0002486826069223241, "Health, Healthcare & Wellness": 5.1262647222220904e-05, "History & Geography": 5.775513038846292e-05, "Home, Pets & Hobbies": 5.336369511609481e-05, "Industry & Production": 5.317892304722364e-05, "Language, Literature & Philosophy": 0.012192621591660626, "Politics & Government": 3.933692799941713e-05, "Religion & Spirituality": 6.387200062773665e-05, "Science, Mathematics & Engineering": 0.0049864684361578555, "Relationships & Community": 2.8159845896228313e-05, "Software Usage & Applications": 0.000491090685133463, "Programming & Software Engineering": 0.9801458287379761, "Sports & Fitness": 8.419634233256645e-05, "Vehicles & Transportation": 3.535938742614674e-05, "Travel & Tourism": 6.70728798668599e-05, "Other / Uncovered Subject": 0.0006755595507981286, "Unknown / Insufficient Content": 0.00011306385134434758}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008191746564265234, "Art & Design": 0.0006277237067395888, "Law & Crime": 0.0009703409106931482, "Education & Careers": 0.00039838854614420734, "Entertainment & Media": 0.000610467857367737, "Fashion & Beauty": 0.0002886459280322038, "Business, Economics & Finance": 0.0005768326072789887, "Food & Dining": 0.00026891022339525734, "Games & Gaming": 0.0008430274973290528, "Electronics & Hardware": 0.0018844026714360776, "Health, Healthcare & Wellness": 0.0003441146449007448, "History & Geography": 0.00042826535497318447, "Home, Pets & Hobbies": 0.00029036003746971573, "Industry & Production": 0.0002503764274920219, "Language, Literature & Philosophy": 0.0871194417817108, "Politics & Government": 0.0002911397503485972, "Religion & Spirituality": 0.0004098757012325546, "Science, Mathematics & Engineering": 0.015372289673542185, "Relationships & Community": 0.00016402667316860672, "Software Usage & Applications": 0.0036340673554971846, "Programming & Software Engineering": 0.031730183798814836, "Sports & Fitness": 0.0003136939780482314, "Vehicles & Transportation": 0.00016198434304682725, "Travel & Tourism": 0.0004003570769873783, "Other / Uncovered Subject": 0.001549185825060312, "NONE": 0.850252722972864}}, "probabilities": {"primary": {"Academic Writing": 8.723125832599875e-05, "Content Listing": 3.572593313877488e-05, "Creative Writing": 2.915468056275118e-05, "Customer Support Page": 1.801094984332883e-05, "Q&A Forum": 2.0448639113770727e-05, "Discussion Thread": 1.2245547111426158e-05, "Comment Section": 2.3057840517695872e-05, "Missing Content": 4.5305417879380595e-05, "Knowledge Article": 0.00011166825127535769, "Legal Notices": 4.5846240277194227e-05, "News Article": 9.430967205551367e-06, "Organizational Announcement": 1.8946551027178938e-05, "Nonfiction Writing": 2.380039515749018e-05, "Organizational About Page": 2.137022286256511e-05, "Personal About Page": 1.4917731544305514e-05, "Personal Blog": 1.1975153876542825e-05, "Product Page": 1.1682242245578805e-05, "Spam / Ads": 2.1046754857245865e-05, "Structured Data": 0.002572094364619569, "Technical Writing": 0.989127415549756, "Transcript / Interview": 2.8005713947727977e-05, "Tutorial / How-To Guide": 0.000605605432819613, "User Reviews": 1.1870709953765969e-05, "Other / Unknown": 0.007093143452081073}, "secondary": {"Academic Writing": 0.00038309957262696876, "Content Listing": 0.0002059277615924176, "Creative Writing": 0.00026065207322092823, "Customer Support Page": 0.00010320183373365968, "Q&A Forum": 9.80242046681728e-05, "Discussion Thread": 8.522848293678281e-05, "Comment Section": 0.0001522960423802244, "Missing Content": 0.0003553592279451569, "Knowledge Article": 0.0005571828370265923, "Legal Notices": 0.00015158797421931255, "News Article": 0.0001246154163611154, "Organizational Announcement": 0.00012114946405278916, "Nonfiction Writing": 0.00014884239915716004, "Organizational About Page": 0.0001348646148587448, "Personal About Page": 0.0001021847770213569, "Personal Blog": 0.00010321074121295593, "Product Page": 7.081147256373751e-05, "Spam / Ads": 0.00014699600054856666, "Structured Data": 0.03161212399204599, "Technical Writing": 0.028553863506088486, "Transcript / Interview": 0.0001547265943087743, "Tutorial / How-To Guide": 0.001084084742739523, "User Reviews": 5.5428881004181383e-05, "NONE": 0.9352345373876864}, "tertiary": {"Academic Writing": 0.0003727394334625355, "Content Listing": 0.00016301741787627032, "Creative Writing": 0.00021848306401299196, "Customer Support Page": 7.156514759423887e-05, "Q&A Forum": 5.8309729162133776e-05, "Discussion Thread": 5.146740671718765e-05, "Comment Section": 0.0001301354478620252, "Missing Content": 0.00036138740144481973, "Knowledge Article": 0.0008845265598204263, "Legal Notices": 0.00018822916304804603, "News Article": 7.93213453513535e-05, "Organizational Announcement": 6.774508355415026e-05, "Nonfiction Writing": 0.00015379197905790952, "Organizational About Page": 9.772653435982515e-05, "Personal About Page": 5.745306834753774e-05, "Personal Blog": 6.351662138129626e-05, "Product Page": 4.9409980990705254e-05, "Spam / Ads": 0.0001771230765326612, "Structured Data": 0.019347576965367923, "Technical Writing": 0.015377101687491535, "Transcript / Interview": 8.826659322491941e-05, "Tutorial / How-To Guide": 0.0011052786963385475, "User Reviews": 3.7688668802618805e-05, "NONE": 0.9607981389281983}, "structured_records": {"false": 0.8520545352021822, "true": 0.14794546479781773}, "procedural_steps": {"false": 0.9963667537220178, "true": 0.0036332462779822615}, "worked_solution": {"false": 0.9221807906104962, "true": 0.07781920938950385}, "contextual_dialogue": {"false": 0.9999916422440872, "true": 8.35775591274711e-06}, "content_quality": {"-2": 0.00667035635425439, "-1": 0.017061786603799124, "0": 0.19741056133262588, "1": 0.485383847562009, "2": 0.2934734481473117}, "noise_severity": {"0": 0.9517341212542543, "1": 0.0418901203884234, "2": 0.004848518382764921, "3": 0.0009487344641220179, "4": 0.0005785055104353379}, "missing_content_severity": {"0": 0.9870169484758368, "1": 0.01133563576613727, "2": 0.0010383678896514971, "3": 0.000417645148638465, "4": 0.00019140271973605303}, "factual_density": {"0": 0.024552839537024636, "1": 0.06851087689968154, "2": 0.1811114892876496, "3": 0.35333387562191665, "4": 0.37249091865372763}, "pretraining_utility": {"-2": 0.026104919318949107, "-1": 0.2264947073247237, "0": 0.45388173424430894, "1": 0.2407067410106433, "2": 0.05281189810137499}, "math_density": {"0": 0.9565534568622559, "1": 0.021737579716125163, "2": 0.0059137602036185176, "3": 0.0032877542488261298, "4": 0.012507448969174306}, "code_density": {"0": 0.0002633941593463831, "1": 0.0005674082483895007, "2": 0.014325143958958597, "3": 0.09568853557986912, "4": 0.8891555180534364}, "reasoning_density": {"0": 0.8853327351670227, "1": 0.049132337981421484, "2": 0.046975001858472816, "3": 0.01061033300572615, "4": 0.007949591987356812}, "education_level": {"none": 0.0012742976156179349, "0": 0.0017976321740138534, "1": 0.007438173998520976, "2": 0.6045569822387804, "3": 0.298093009798823, "4": 0.08683990417424384}, "creative_writing_quality": {"not_applicable": 0.999423866307501, "-2": 0.00012653029462125518, "-1": 0.00012708943813329683, "0": 0.00021992848936981207, "1": 7.161742908937256e-05, "2": 3.096804128524392e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.747244356608536e-05, "Art & Design": 8.2763627231913e-05, "Law & Crime": 6.387175697585522e-05, "Education & Careers": 7.107424528304847e-05, "Entertainment & Media": 5.5767505168289114e-05, "Fashion & Beauty": 3.4057772950215534e-05, "Business, Economics & Finance": 6.422780672042633e-05, "Food & Dining": 4.574060250534282e-05, "Games & Gaming": 0.0001281509996833306, "Electronics & Hardware": 0.00024868260692232405, "Health, Healthcare & Wellness": 5.126264722222089e-05, "History & Geography": 5.7755130388462904e-05, "Home, Pets & Hobbies": 5.3363695116094795e-05, "Industry & Production": 5.317892304722363e-05, "Language, Literature & Philosophy": 0.012192621591660622, "Politics & Government": 3.933692799941712e-05, "Religion & Spirituality": 6.387200062773663e-05, "Science, Mathematics & Engineering": 0.004986468436157855, "Relationships & Community": 2.8159845896228306e-05, "Software Usage & Applications": 0.0004910906851334629, "Programming & Software Engineering": 0.9801458287379758, "Sports & Fitness": 8.419634233256643e-05, "Vehicles & Transportation": 3.535938742614673e-05, "Travel & Tourism": 6.707287986685988e-05, "Other / Uncovered Subject": 0.0006755595507981285, "Unknown / Insufficient Content": 0.00011306385134434756}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008191746564265234, "Art & Design": 0.0006277237067395888, "Law & Crime": 0.0009703409106931482, "Education & Careers": 0.00039838854614420734, "Entertainment & Media": 0.000610467857367737, "Fashion & Beauty": 0.0002886459280322038, "Business, Economics & Finance": 0.0005768326072789887, "Food & Dining": 0.00026891022339525734, "Games & Gaming": 0.0008430274973290528, "Electronics & Hardware": 0.0018844026714360776, "Health, Healthcare & Wellness": 0.0003441146449007448, "History & Geography": 0.00042826535497318447, "Home, Pets & Hobbies": 0.00029036003746971573, "Industry & Production": 0.0002503764274920219, "Language, Literature & Philosophy": 0.0871194417817108, "Politics & Government": 0.0002911397503485972, "Religion & Spirituality": 0.0004098757012325546, "Science, Mathematics & Engineering": 0.015372289673542185, "Relationships & Community": 0.00016402667316860672, "Software Usage & Applications": 0.0036340673554971846, "Programming & Software Engineering": 0.031730183798814836, "Sports & Fitness": 0.0003136939780482314, "Vehicles & Transportation": 0.00016198434304682725, "Travel & Tourism": 0.0004003570769873783, "Other / Uncovered Subject": 0.001549185825060312, "NONE": 0.850252722972864}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9724, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9724, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:36:16.165597+00:00", "scoring_key_slot": "primary", "dataset_uid": "1966538c-7e41-4a04-b5f8-0f9454c3feb5", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "dedddd24f32dc4ace15ced0f3894a5a366139e079c1bba6534b959bea5a14de8", "source_metadata": {"uuid": "1966538c-7e41-4a04-b5f8-0f9454c3feb5", "repo_name": "androguard/androguard", "relative_path": "tests/test_strings.py", "category": "TEST", "algo_rel_score": 0.9120294451713562, "quality_score": 5.035790920257568, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 2411, "repo_created_at": "2014-09-12T08:48:56.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 5689, "forks": 1101, "issues": 41, "pull_requests": 5}, "file_meta": {"content_id": "6f4979bf34dcd4df78dedaff7f5002bd290e6e38", "size_bytes": 3700, "file_timestamp": 1742067666, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "70fd5fe2ae0a8c7cb5d17aa19c2d92be4dca3919"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 2411, "repo_created_at": "2014-09-12T08:48:56.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 5689, "forks": 1101, "issues": 41, "pull_requests": 5}, "file_meta": {"content_id": "6f4979bf34dcd4df78dedaff7f5002bd290e6e38", "size_bytes": 3700, "file_timestamp": 1742067666, "language": "python", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "70fd5fe2ae0a8c7cb5d17aa19c2d92be4dca3919", "url": "https://github.com/androguard/androguard/blob/70fd5fe2ae0a8c7cb5d17aa19c2d92be4dca3919/tests/test_strings.py"}, "gpt4o_tokens": 1075, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.0419282445443245, "noise_severity": 0.05674738258806065, "missing_content_severity": 0.015430917870299871, "factual_density": 2.9806991569556414, "pretraining_utility": 0.06762599125077134, "math_density": 0.09345815874653782, "code_density": 3.8729053751196596, "reasoning_density": 0.20671170866497282, "education_level": 2.4613272480129247, "creative_writing_quality": -0.42801960539112743}, "score_applicability": {"education_level": 0.9987257023843821, "creative_writing_quality": 0.0005761336924989805}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/zhongxinghong/PKUAutoElective/blob/c18ad77abc948f81656905728b529ad5c145873a/autoelective/captcha/processor.py", "id": "python-0-5098ac2d993c4674a9564e137d14763e", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# filename: processor.py\n# modified: 2019-09-08\n\nimport cv2\nimport numpy as np\nfrom io import BytesIO\nfrom PIL import Image\n\n\ndef extract_c0(M0, M_merge):\n    _, M_mask = cv2.threshold(M_merge, 0, 0xff, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n    M_mask[:, 40:] = 0xff\n    M_darken = M0.copy()\n    M_darken[M_mask == 0x00] >>= 1\n\n    _, M_threshold = cv2.threshold(M_darken, 0, 0xff, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n    kernel1 = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 1))\n    M_close1 = cv2.morphologyEx(M_threshold, cv2.MORPH_CLOSE, kernel1, iterations=1)\n    M_blur1 = cv2.medianBlur(M_close1, 3)\n\n    kernel2 = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 2))\n    M_close2 = cv2.morphologyEx(M_threshold, cv2.MORPH_CLOSE, kernel2, iterations=1)\n    M_blur2 = cv2.medianBlur(M_close2, 3)\n\n    if np.sum(M_blur1[:, :40]) <= np.sum(M_blur2[:, :40]):\n        Mt = M_blur1\n    else:\n        Mt = M_blur2\n    return Mt\n\n\ndef extract_c123(M0, M0_last):\n    M_subtract = cv2.subtract(M0_last, M0)\n\n    _, M_threshold = cv2.threshold(M_subtract, 0, 0xff, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)\n    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))\n    M_opened = cv2.morphologyEx(M_threshold, cv2.MORPH_OPEN, kernel, iterations=1)\n\n    Mt = cv2.medianBlur(M_opened, 3)\n    return Mt\n\n\ndef crop(Mt, first):\n    char_width = 32\n    captcha_size = 52\n\n    assert Mt.shape[0] == captcha_size\n\n    M_blur = cv2.medianBlur(Mt, 5)\n    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (11, 11))\n    M_opened = cv2.morphologyEx(M_blur, cv2.MORPH_OPEN, kernel, iterations=3)\n\n    w = 50 if first else Mt.shape[1]\n\n    S0 = (0xff - M_opened).sum(axis=0).cumsum()\n    k = char_width\n\n    max_sum = -1\n    max_pos = -1\n\n    for i in range(k, w):\n        s = S0[i] - S0[i - k]\n        if s > max_sum:\n            max_sum = s\n            max_pos = i\n\n    w = max_pos - k - (captcha_size - k) // 2\n\n    if w >= 0 and w + captcha_size <= Mt.shape[1]:\n        return Mt[:, w : w+captcha_size]\n\n    M_cropped = 0xff - np.zeros((captcha_size, captcha_size), np.uint8)\n    if w < 0:\n        M_cropped[:, -w : captcha_size] = Mt[:, : captcha_size+w]\n    else:\n        M_cropped[:, : Mt.shape[1]-w] = Mt[:, w :]\n    return M_cropped\n\n\ndef split_captcha(im_data):\n    assert isinstance(im_data, bytes)\n\n    fp = BytesIO(im_data)\n    im = Image.open(fp)\n\n    assert im.format == \"GIF\", im.format\n    assert im.n_frames == 16, im.n_frames\n\n    N = 52\n    w, h = im.size\n\n    M0_list = []\n    M0_last = None\n    M_mask = np.zeros((h, w), dtype=np.uint8)\n    M_merge = np.full((h, w), 0xff, dtype=np.uint8)\n    Xlist = []\n\n    for ix in (3, 7, 11, 15):\n        im.seek(ix)\n        M0 = np.array(im.convert('RGB'))\n        M0 = cv2.cvtColor(M0, cv2.COLOR_RGB2GRAY)\n        M0_list.append(M0)\n        M_merge = np.min([M_merge, M0], axis=0)\n\n    for cix, M0 in enumerate(M0_list):\n        first = (M0_last is None)\n\n        if first:\n            Mt = extract_c0(M0, M_merge)\n        else:\n            Mt = extract_c123(M0, M0_last)\n\n        M0_last = M0\n\n        Mt_inv = 0xff - Mt\n        Mt = 0xff - np.bitwise_and(Mt_inv, 0xff - M_mask)\n        M_mask = np.bitwise_or(M_mask, Mt_inv)\n\n        Mt = crop(Mt, first)\n\n        Xlist.append(Mt)\n\n    im.close()\n    fp.close()\n\n    return Xlist\n", "text_preview": "#!/usr/bin/env python3 # -*- coding: utf-8 -*- # filename: processor.py # modified: 2019-09-08 import cv2 import numpy as np from io import BytesIO from PIL import Image def extract_c0(M0, M_merge): _, M_mask = cv2.threshold(M_merge, 0, 0xf", "input_sha256": "c9078a6f050e6767b4ac6383a931ad52c03bb2f88658a785b0fea194c47bf188", "text_sha256": "5098ac2d993c4674a9564e137d14763e395493721b5f390d92cd94661d237b53", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12114, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.0150054353973832, "noise_severity": 0.0241627757144011, "missing_content_severity": 0.007350682390866181, "factual_density": 3.5249712686882453, "pretraining_utility": 0.6110804606617543, "math_density": 2.4136747436069372, "code_density": 3.9234845960109213, "reasoning_density": 0.5315470107235936, "education_level": 2.3828358632600533, "creative_writing_quality": -0.24270507962872162}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0041422101306805015, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9969359892623818, "secondary": 0.9925065014116576, "tertiary": 0.9968288076349763, "structured_records": 0.00012399618139702153, "procedural_steps": 0.029790592324694873, "worked_solution": 0.003704879530732356, "contextual_dialogue": 6.954648018533172e-06, "content_quality": 0.5273953818551966, "noise_severity": 0.9798643535713324, "missing_content_severity": 0.9938744313409449, "factual_density": 0.6041427239068711, "pretraining_utility": 0.4600396814319294, "math_density": 0.0, "code_density": 0.9362371633424343, "reasoning_density": 0.5570441577303388, "education_level": 0.5897875147086019, "creative_writing_quality": 0.9997129965804827, "primary_topic": 0.9780421457636639, "secondary_topic": 0.6498186234789091}, "probability_margin": {"primary": 0.995513372110942, "secondary": 0.9880065884799191, "tertiary": 0.9952634180310994, "education_level": 0.39725058511165806, "creative_writing_quality": 0.9996561325732903, "primary_topic": 0.9629837579463113, "secondary_topic": 0.44460476702773377}, "raw_probabilities": {"primary": {"Academic Writing": 3.906806628477619e-05, "Content Listing": 1.3127067415382582e-05, "Creative Writing": 1.496190875591367e-05, "Customer Support Page": 5.501659595568065e-06, "Q&A Forum": 8.08223586748228e-06, "Discussion Thread": 5.005765047253933e-06, "Comment Section": 1.001965853755635e-05, "Missing Content": 2.6782561825439895e-05, "Knowledge Article": 6.433610297495087e-05, "Legal Notices": 2.1958452297118848e-05, "News Article": 3.532337486798838e-06, "Organizational Announcement": 7.192775337988256e-06, "Nonfiction Writing": 1.1228663000607809e-05, "Organizational About Page": 9.201248399907347e-06, "Personal About Page": 1.1280654912526717e-05, "Personal Blog": 6.067075838664903e-06, "Product Page": 5.027835419025617e-06, "Spam / Ads": 8.709270033978816e-06, "Structured Data": 0.0001775109195311018, "Technical Writing": 0.9970636563764492, "Transcript / Interview": 1.1057620636843565e-05, "Tutorial / How-To Guide": 0.0009215444706844081, "User Reviews": 4.863008160169757e-06, "Other / Unknown": 0.0015502842655072148}, "secondary": {"Academic Writing": 9.36095696346115e-05, "Content Listing": 3.140663332745916e-05, "Creative Writing": 4.515754257895289e-05, "Customer Support Page": 1.964179561518414e-05, "Q&A Forum": 1.5555167440960886e-05, "Discussion Thread": 1.1690800448749787e-05, "Comment Section": 3.2214000783865343e-05, "Missing Content": 0.00019374168831095542, "Knowledge Article": 0.00017897563573659523, "Legal Notices": 0.0001329070339416882, "News Article": 2.1802010401919234e-05, "Organizational Announcement": 2.19501032404955e-05, "Nonfiction Writing": 2.0832023975520943e-05, "Organizational About Page": 2.7261533748254062e-05, "Personal About Page": 2.614515226916935e-05, "Personal Blog": 1.9811240323902357e-05, "Product Page": 1.3524517255077503e-05, "Spam / Ads": 2.6199115103512307e-05, "Structured Data": 0.00025261607975082383, "Technical Writing": 0.00481214203958615, "Transcript / Interview": 2.6307663656507483e-05, "Tutorial / How-To Guide": 0.001146199675468883, "User Reviews": 1.1578457895524316e-05, "NONE": 0.9928187305195052}, "tertiary": {"Academic Writing": 4.910567232505406e-05, "Content Listing": 1.830268917422982e-05, "Creative Writing": 3.090564862515552e-05, "Customer Support Page": 1.0058485599133335e-05, "Q&A Forum": 8.215746753529157e-06, "Discussion Thread": 6.820557397698539e-06, "Comment Section": 1.8482381278932285e-05, "Missing Content": 0.00014278557296659454, "Knowledge Article": 9.235094273484662e-05, "Legal Notices": 0.00016405861805991604, "News Article": 1.5384104348971816e-05, "Organizational Announcement": 1.3725858586861846e-05, "Nonfiction Writing": 1.6419079522626832e-05, "Organizational About Page": 2.8169636783439706e-05, "Personal About Page": 1.5374116352125995e-05, "Personal Blog": 1.3141904135820157e-05, "Product Page": 9.210725637926694e-06, "Spam / Ads": 1.5727329851618574e-05, "Structured Data": 0.00013114216820051645, "Technical Writing": 0.0016975226190861453, "Transcript / Interview": 1.4350533769859107e-05, "Tutorial / How-To Guide": 0.0005198812241775409, "User Reviews": 7.923734445722359e-06, "NONE": 0.9969609406501856}, "structured_records": {"false": 0.999876003818603, "true": 0.00012399618139702153}, "procedural_steps": {"false": 0.9702094076753052, "true": 0.029790592324694873}, "worked_solution": {"false": 0.9962951204692676, "true": 0.003704879530732356}, "contextual_dialogue": {"false": 0.9999930453519814, "true": 6.954648018533172e-06}, "content_quality": {"-2": 0.0068328152954058976, "-1": 0.01997347886020769, "0": 0.21561464958155738, "1": 0.46651356767725544, "2": 0.2910654885855736}, "noise_severity": {"0": 0.9774617201934684, "1": 0.021545654017782204, "2": 0.000556113149111621, "3": 0.00024115516015550762, "4": 0.00019535747948228347}, "missing_content_severity": {"0": 0.9960909585899203, "1": 0.0013541170833893193, "2": 0.0018313796320450766, "3": 0.0005603727351945284, "4": 0.000163171959450781}, "factual_density": {"0": 0.005793811078766899, "1": 0.014898076159567493, "2": 0.07029763314228532, "3": 0.266563992233414, "4": 0.6424464873859663}, "pretraining_utility": {"-2": 0.008200644613101679, "-1": 0.07227515167165467, "0": 0.3492837236273509, "1": 0.4407240586161732, "2": 0.12951642147171952}, "math_density": {"0": 0.16868343059717344, "1": 0.14706012707267316, "2": 0.13122190846362297, "3": 0.2079673358591035, "4": 0.3450671980074268}, "code_density": {"0": 0.00016245610680708185, "1": 0.0003176448329226962, "2": 0.008100974579503236, "3": 0.05871069590407595, "4": 0.9327082285766909}, "reasoning_density": {"0": 0.7792922762850208, "1": 0.049729722806190715, "2": 0.07653392921448163, "3": 0.04902685728878759, "4": 0.04541721440551919}, "education_level": {"none": 0.0011193366806217953, "0": 0.001764568906572971, "1": 0.010359169438989617, "2": 0.6581562622571683, "3": 0.2609056771455102, "4": 0.06769498557113698}, "creative_writing_quality": {"not_applicable": 0.9997608304837357, "-2": 4.0599096970683e-05, "-1": 4.28945670516025e-05, "0": 0.00010469791044535802, "1": 3.591077909038225e-05, "2": 1.506716270642941e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 8.322574865197746e-05, "Art & Design": 0.00027767445832069276, "Law & Crime": 0.0003840504876241477, "Education & Careers": 0.0006107539261700263, "Entertainment & Media": 7.282713562447518e-05, "Fashion & Beauty": 4.9541839523563866e-05, "Business, Economics & Finance": 7.962610994454971e-05, "Food & Dining": 5.1198843053996906e-05, "Games & Gaming": 0.001805323233113309, "Electronics & Hardware": 0.00017572635354870801, "Health, Healthcare & Wellness": 6.179733178397232e-05, "History & Geography": 7.347599488453344e-05, "Home, Pets & Hobbies": 5.317271456298434e-05, "Industry & Production": 5.3231493928046e-05, "Language, Literature & Philosophy": 5.8468767263075655e-05, "Politics & Government": 6.245676664202229e-05, "Religion & Spirituality": 5.183440629607343e-05, "Science, Mathematics & Engineering": 0.01590292067259621, "Relationships & Community": 2.6671494978351254e-05, "Software Usage & Applications": 0.00040819978607058594, "Programming & Software Engineering": 0.9788866786189075, "Sports & Fitness": 7.225708261626251e-05, "Vehicles & Transportation": 2.9006202059514685e-05, "Travel & Tourism": 6.767215388409255e-05, "Other / Uncovered Subject": 0.00048639687190430483, "Unknown / Insufficient Content": 0.0001158115060472032}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010316571784202925, "Art & Design": 0.0034654947788947624, "Law & Crime": 0.004568885642742481, "Education & Careers": 0.005138467362726052, "Entertainment & Media": 0.0008495071296183125, "Fashion & Beauty": 0.00045721000831883146, "Business, Economics & Finance": 0.0008362226943162142, "Food & Dining": 0.0003851089448451035, "Games & Gaming": 0.0026154037821939026, "Electronics & Hardware": 0.0010100006572298593, "Health, Healthcare & Wellness": 0.000617547646261131, "History & Geography": 0.0005071013572187736, "Home, Pets & Hobbies": 0.0004255701721802832, "Industry & Production": 0.00036607404326448773, "Language, Literature & Philosophy": 0.00033606957617574225, "Politics & Government": 0.0005831098258804033, "Religion & Spirituality": 0.0005332195729452054, "Science, Mathematics & Engineering": 0.21868237093275567, "Relationships & Community": 0.00024038765408934283, "Software Usage & Applications": 0.0012081780534177772, "Programming & Software Engineering": 0.08958433514839548, "Sports & Fitness": 0.0004587257554685262, "Vehicles & Transportation": 0.00026299756042750914, "Travel & Tourism": 0.00061025114311165, "Other / Uncovered Subject": 0.001938965418612879, "NONE": 0.6632871379604894}}, "probabilities": {"primary": {"Academic Writing": 3.90680662847762e-05, "Content Listing": 1.3127067415382584e-05, "Creative Writing": 1.4961908755913671e-05, "Customer Support Page": 5.5016595955680655e-06, "Q&A Forum": 8.082235867482282e-06, "Discussion Thread": 5.005765047253934e-06, "Comment Section": 1.0019658537556351e-05, "Missing Content": 2.67825618254399e-05, "Knowledge Article": 6.433610297495089e-05, "Legal Notices": 2.195845229711885e-05, "News Article": 3.5323374867988383e-06, "Organizational Announcement": 7.192775337988257e-06, "Nonfiction Writing": 1.122866300060781e-05, "Organizational About Page": 9.201248399907348e-06, "Personal About Page": 1.1280654912526718e-05, "Personal Blog": 6.067075838664904e-06, "Product Page": 5.027835419025618e-06, "Spam / Ads": 8.709270033978818e-06, "Structured Data": 0.00017751091953110181, "Technical Writing": 0.9970636563764493, "Transcript / Interview": 1.1057620636843567e-05, "Tutorial / How-To Guide": 0.0009215444706844082, "User Reviews": 4.863008160169758e-06, "Other / Unknown": 0.001550284265507215}, "secondary": {"Academic Writing": 9.36095696346115e-05, "Content Listing": 3.140663332745916e-05, "Creative Writing": 4.515754257895289e-05, "Customer Support Page": 1.964179561518414e-05, "Q&A Forum": 1.5555167440960886e-05, "Discussion Thread": 1.1690800448749787e-05, "Comment Section": 3.2214000783865343e-05, "Missing Content": 0.00019374168831095542, "Knowledge Article": 0.00017897563573659523, "Legal Notices": 0.0001329070339416882, "News Article": 2.1802010401919234e-05, "Organizational Announcement": 2.19501032404955e-05, "Nonfiction Writing": 2.0832023975520943e-05, "Organizational About Page": 2.7261533748254062e-05, "Personal About Page": 2.614515226916935e-05, "Personal Blog": 1.9811240323902357e-05, "Product Page": 1.3524517255077503e-05, "Spam / Ads": 2.6199115103512307e-05, "Structured Data": 0.00025261607975082383, "Technical Writing": 0.00481214203958615, "Transcript / Interview": 2.6307663656507483e-05, "Tutorial / How-To Guide": 0.001146199675468883, "User Reviews": 1.1578457895524316e-05, "NONE": 0.9928187305195052}, "tertiary": {"Academic Writing": 4.910567232505407e-05, "Content Listing": 1.8302689174229822e-05, "Creative Writing": 3.090564862515553e-05, "Customer Support Page": 1.0058485599133337e-05, "Q&A Forum": 8.215746753529159e-06, "Discussion Thread": 6.8205573976985395e-06, "Comment Section": 1.848238127893229e-05, "Missing Content": 0.00014278557296659456, "Knowledge Article": 9.235094273484663e-05, "Legal Notices": 0.00016405861805991607, "News Article": 1.538410434897182e-05, "Organizational Announcement": 1.3725858586861848e-05, "Nonfiction Writing": 1.6419079522626836e-05, "Organizational About Page": 2.816963678343971e-05, "Personal About Page": 1.5374116352126e-05, "Personal Blog": 1.3141904135820159e-05, "Product Page": 9.210725637926696e-06, "Spam / Ads": 1.5727329851618577e-05, "Structured Data": 0.00013114216820051648, "Technical Writing": 0.0016975226190861455, "Transcript / Interview": 1.435053376985911e-05, "Tutorial / How-To Guide": 0.000519881224177541, "User Reviews": 7.92373444572236e-06, "NONE": 0.9969609406501857}, "structured_records": {"false": 0.999876003818603, "true": 0.00012399618139702153}, "procedural_steps": {"false": 0.9702094076753052, "true": 0.029790592324694873}, "worked_solution": {"false": 0.9962951204692676, "true": 0.003704879530732356}, "contextual_dialogue": {"false": 0.9999930453519814, "true": 6.954648018533172e-06}, "content_quality": {"-2": 0.0068328152954058976, "-1": 0.01997347886020769, "0": 0.21561464958155738, "1": 0.46651356767725544, "2": 0.2910654885855736}, "noise_severity": {"0": 0.9774617201934684, "1": 0.021545654017782204, "2": 0.000556113149111621, "3": 0.00024115516015550762, "4": 0.00019535747948228347}, "missing_content_severity": {"0": 0.9960909585899203, "1": 0.0013541170833893193, "2": 0.0018313796320450766, "3": 0.0005603727351945284, "4": 0.000163171959450781}, "factual_density": {"0": 0.005793811078766899, "1": 0.014898076159567493, "2": 0.07029763314228532, "3": 0.266563992233414, "4": 0.6424464873859663}, "pretraining_utility": {"-2": 0.008200644613101679, "-1": 0.07227515167165467, "0": 0.3492837236273509, "1": 0.4407240586161732, "2": 0.12951642147171952}, "math_density": {"0": 0.16868343059717347, "1": 0.14706012707267319, "2": 0.131221908463623, "3": 0.20796733585910354, "4": 0.34506719800742686}, "code_density": {"0": 0.00016245610680708187, "1": 0.00031764483292269627, "2": 0.008100974579503237, "3": 0.05871069590407596, "4": 0.932708228576691}, "reasoning_density": {"0": 0.7792922762850208, "1": 0.049729722806190715, "2": 0.07653392921448163, "3": 0.04902685728878759, "4": 0.04541721440551919}, "education_level": {"none": 0.0011193366806217955, "0": 0.0017645689065729712, "1": 0.010359169438989619, "2": 0.6581562622571684, "3": 0.2609056771455103, "4": 0.067694985571137}, "creative_writing_quality": {"not_applicable": 0.9997608304837357, "-2": 4.0599096970683e-05, "-1": 4.28945670516025e-05, "0": 0.00010469791044535802, "1": 3.591077909038225e-05, "2": 1.506716270642941e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 8.322574865197745e-05, "Art & Design": 0.0002776744583206927, "Law & Crime": 0.0003840504876241476, "Education & Careers": 0.0006107539261700262, "Entertainment & Media": 7.282713562447516e-05, "Fashion & Beauty": 4.954183952356385e-05, "Business, Economics & Finance": 7.96261099445497e-05, "Food & Dining": 5.119884305399689e-05, "Games & Gaming": 0.0018053232331133086, "Electronics & Hardware": 0.000175726353548708, "Health, Healthcare & Wellness": 6.17973317839723e-05, "History & Geography": 7.347599488453342e-05, "Home, Pets & Hobbies": 5.317271456298433e-05, "Industry & Production": 5.3231493928045985e-05, "Language, Literature & Philosophy": 5.846876726307564e-05, "Politics & Government": 6.245676664202227e-05, "Religion & Spirituality": 5.183440629607342e-05, "Science, Mathematics & Engineering": 0.015902920672596205, "Relationships & Community": 2.6671494978351247e-05, "Software Usage & Applications": 0.00040819978607058583, "Programming & Software Engineering": 0.9788866786189073, "Sports & Fitness": 7.22570826162625e-05, "Vehicles & Transportation": 2.900620205951468e-05, "Travel & Tourism": 6.767215388409253e-05, "Other / Uncovered Subject": 0.0004863968719043047, "Unknown / Insufficient Content": 0.00011581150604720317}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010316571784202925, "Art & Design": 0.0034654947788947624, "Law & Crime": 0.004568885642742481, "Education & Careers": 0.005138467362726052, "Entertainment & Media": 0.0008495071296183125, "Fashion & Beauty": 0.00045721000831883146, "Business, Economics & Finance": 0.0008362226943162142, "Food & Dining": 0.0003851089448451035, "Games & Gaming": 0.0026154037821939026, "Electronics & Hardware": 0.0010100006572298593, "Health, Healthcare & Wellness": 0.000617547646261131, "History & Geography": 0.0005071013572187736, "Home, Pets & Hobbies": 0.0004255701721802832, "Industry & Production": 0.00036607404326448773, "Language, Literature & Philosophy": 0.00033606957617574225, "Politics & Government": 0.0005831098258804033, "Religion & Spirituality": 0.0005332195729452054, "Science, Mathematics & Engineering": 0.21868237093275567, "Relationships & Community": 0.00024038765408934283, "Software Usage & Applications": 0.0012081780534177772, "Programming & Software Engineering": 0.08958433514839548, "Sports & Fitness": 0.0004587257554685262, "Vehicles & Transportation": 0.00026299756042750914, "Travel & Tourism": 0.00061025114311165, "Other / Uncovered Subject": 0.001938965418612879, "NONE": 0.6632871379604894}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9709, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9709, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:01.346936+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "d64113cf-24e0-433d-a15a-8d1a98e35c4d", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "1562f908d7c0c772b05b296239ab2cf8be8355a61f6a355bc58640d2f19c1ad1", "source_metadata": {"uuid": "d64113cf-24e0-433d-a15a-8d1a98e35c4d", "repo_name": "zhongxinghong/PKUAutoElective", "relative_path": "autoelective/captcha/processor.py", "category": "DATA", "algo_rel_score": 0.971529483795166, "quality_score": 4.8590240478515625, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 50, "repo_created_at": "2019-02-18T07:05:14.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 704, "forks": 230, "issues": 12, "pull_requests": 7}, "file_meta": {"content_id": "602948698c8b3c16d2d7ea44d18ca25a0ff84e5d", "size_bytes": 3329, "file_timestamp": 1631545783, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "c18ad77abc948f81656905728b529ad5c145873a"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 50, "repo_created_at": "2019-02-18T07:05:14.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 704, "forks": 230, "issues": 12, "pull_requests": 7}, "file_meta": {"content_id": "602948698c8b3c16d2d7ea44d18ca25a0ff84e5d", "size_bytes": 3329, "file_timestamp": 1631545783, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "c18ad77abc948f81656905728b529ad5c145873a", "url": "https://github.com/zhongxinghong/PKUAutoElective/blob/c18ad77abc948f81656905728b529ad5c145873a/autoelective/captcha/processor.py"}, "gpt4o_tokens": 1124, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.0150054353973832, "noise_severity": 0.0241627757144011, "missing_content_severity": 0.007350682390866181, "factual_density": 3.5249712686882453, "pretraining_utility": 0.6110804606617543, "math_density": 2.4136747436069372, "code_density": 3.9234845960109213, "reasoning_density": 0.5315470107235936, "education_level": 2.3828358632600533, "creative_writing_quality": -0.24270507962872162}, "score_applicability": {"education_level": 0.9988806633193783, "creative_writing_quality": 0.0002391695162644552}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/jiaminglin01/Customer-Segmentation-and-Movie-Recommendation/blob/ceed7df14a1f2f331245c1921d0c32a4efd197b2/Movie_Recommendation/Question4/popularity_baseline.py", "id": "python-0-a949d458491c7bbc0ae29a26989618ae", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n'''\nPopularity-based recommendation using regularized popularity score:\n    score = sum_rating / (count + beta)\nEvaluated on: MAP@100, Precision@100, Recall@100, NDCG@100\n\nRun:\n    $ spark-submit popularity_baseline.py\n'''\n\nimport os\nfrom pyspark.sql import SparkSession, functions as F\nfrom pyspark.sql.window import Window\nfrom pyspark.mllib.evaluation import RankingMetrics\nimport numpy as np\n\n# -------------------------------------------\n# Compute popularity score for each movie:\n# sum of ratings divided by (count + beta)\n# -------------------------------------------\ndef compute_popularity(train_df, beta):\n    stats = train_df.groupBy(\"movieId\").agg(\n        F.sum(\"rating\").alias(\"total_rating\"),\n        F.count(\"rating\").alias(\"count\")\n    )\n\n    return stats.withColumn(\n        \"score\",\n        F.col(\"total_rating\") / (F.col(\"count\") + F.lit(beta))\n    )\n\n# -------------------------------------------\n# Select top-K movies globally by popularity\n# -------------------------------------------\ndef get_top_movies(pop_df, k=100):\n    return pop_df.orderBy(F.desc(\"score\")).limit(k).select(\"movieId\").rdd.flatMap(lambda x: x).collect()\n\n# -------------------------------------------\n# Evaluate recommendation using Spark RankingMetrics\n# -------------------------------------------\ndef evaluate(top_movie_ids, eval_df):\n    from pyspark.sql.types import IntegerType\n    from pyspark.sql.window import Window\n\n    # Take top-100 movie IDs and convert to DataFrame with one movieId per row\n    top_k_ids = top_movie_ids[:100]\n    top_k_df = spark.createDataFrame(top_k_ids, IntegerType()).toDF(\"movieId\")\n\n    # Extract distinct users from evaluation set\n    user_df = eval_df.select(\"userId\").distinct()\n\n    # Generate predicted recommendation list by joining users with top-K movies\n    prediction_df = user_df.crossJoin(F.broadcast(top_k_df)) \\\n                           .groupBy(\"userId\") \\\n                           .agg(F.collect_list(\"movieId\").alias(\"recommendations\"))\n\n    # Define ground truth: collect movieIds with rating > 2.5 using window function\n    window_spec = Window.partitionBy(\"userId\").orderBy(F.desc(\"rating\"))\n    relevant_temp = eval_df.filter(F.col(\"rating\") > 2.5) \\\n                           .withColumn(\"relevant_movies\", F.collect_list(\"movieId\").over(window_spec))\n\n    relevance_df = relevant_temp.select(\"userId\", \"relevant_movies\").distinct() \\\n                                .withColumnRenamed(\"relevant_movies\", \"true_rank\")\n\n    # Join predicted and ground truth lists for evaluation\n    eval_data = prediction_df.join(relevance_df, on=\"userId\", how=\"inner\") \\\n                             .select(\"recommendations\", \"true_rank\")\n\n    # Convert to RDD format for RankingMetrics\n    prediction_and_labels_rdd = eval_data.rdd.map(lambda row: (row[\"recommendations\"], row[\"true_rank\"]))\n    metrics = RankingMetrics(prediction_and_labels_rdd)\n\n    # Return evaluation results as rounded floats\n    return {\n        \"MAP@100\": round(metrics.meanAveragePrecisionAt(100), 4),\n        \"Precision@100\": round(metrics.precisionAt(100), 4),\n        \"Recall@100\": round(metrics.recallAt(100), 4),\n        \"NDCG@100\": round(metrics.ndcgAt(100), 4)\n    }\n\n\n# -------------------------------------------\n# Main training/validation/testing workflow\n# -------------------------------------------\ndef main(spark, userID):\n    base = f\"/user/{userID}/ml-latest/ratings\"\n\n    # Load train/val/test data\n    train = spark.read.option(\"header\", True).csv(f\"{base}/train.csv\") \\\n                .selectExpr(\"cast(userId as int) userId\", \"cast(movieId as int) movieId\", \"cast(rating as float) rating\")\n    val = spark.read.option(\"header\", True).csv(f\"{base}/val.csv\") \\\n              .selectExpr(\"cast(userId as int) userId\", \"cast(movieId as int) movieId\", \"cast(rating as float) rating\")\n    test = spark.read.option(\"header\", True).csv(f\"{base}/test.csv\") \\\n               .selectExpr(\"cast(userId as int) userId\", \"cast(movieId as int) movieId\", \"cast(rating as float) rating\")\n\n    # Generate beta values based on rating count (data scale)\n    total_ratings = train.count()\n    log_scale = np.log(total_ratings)\n    beta_list = [round(log_scale * factor, 2) for factor in [10, 50, 100, 200]]  # dynamically scaled\n\n    best_beta, best_map, best_metrics = None, -1, None\n\n    # Grid search for best beta using validation set\n    for beta in beta_list:\n        pop_df = compute_popularity(train, beta)\n        top_movies = get_top_movies(pop_df)\n        metrics = evaluate(top_movies, val)\n        print(f\"Beta={round(beta, 3)} | MAP={metrics['MAP@100']}, \"\n              f\"Precision={metrics['Precision@100']}, Recall={metrics['Recall@100']}, NDCG={metrics['NDCG@100']}\")\n        if metrics[\"MAP@100\"] > best_map:\n            best_beta, best_map, best_metrics = beta, metrics[\"MAP@100\"], metrics\n\n    print(f\"\\nBest Beta: {round(best_beta, 3)} | Validation Metrics: {best_metrics}\")\n\n    # Final evaluation on test set using best beta\n    final_pop = compute_popularity(train, best_beta)\n    top_movies = get_top_movies(final_pop)\n    test_metrics = evaluate(top_movies, test)\n\n    print(\"\\nTest Metrics:\")\n    for metric, value in test_metrics.items():\n        print(f\"{metric}: {value}\")\n\n\n# -------------------------------------------\n# Entry point\n# -------------------------------------------\nif __name__ == \"__main__\":\n    spark = SparkSession.builder.appName(\"popularity_baseline\").getOrCreate()\n    userID = os.environ['USER']  # Get current Dataproc user\n    main(spark, userID)\n\n", "text_preview": "#!/usr/bin/env python # -*- coding: utf-8 -*- ''' Popularity-based recommendation using regularized popularity score: score = sum_rating / (count + beta) Evaluated on: MAP@100, Precision@100, Recall@100, NDCG@100 Run: $ spark-submit popular", "input_sha256": "764a7e834366a4151fefbb54a6676ab57c078ddae0de6d2ea25a8574e573e753", "text_sha256": "a949d458491c7bbc0ae29a26989618aea01580bb77d09e8dea33006912a33ec3", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12113, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 3, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7834628690760339, "noise_severity": 0.06075676967790637, "missing_content_severity": 0.007517768032927425, "factual_density": 3.2846803472237864, "pretraining_utility": 0.8615688338342624, "math_density": 2.7257095354315783, "code_density": 3.624803564595554, "reasoning_density": 1.574900617576047, "education_level": 2.4832724656679392, "creative_writing_quality": -0.0852915800477126}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.4364 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.022465919357123054, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9841599804253646, "secondary": 0.8902114497047312, "tertiary": 0.913654148164338, "structured_records": 0.005776665877194545, "procedural_steps": 0.2681583779750921, "worked_solution": 0.031864239071895645, "contextual_dialogue": 4.165524286572909e-06, "content_quality": 0.5174136767783857, "noise_severity": 0.9493693586017447, "missing_content_severity": 0.9937351933058938, "factual_density": 0.40390028935315536, "pretraining_utility": 0.6200791261471772, "math_density": 0.3170668669283717, "code_density": 0.6873363038296283, "reasoning_density": 0.0, "education_level": 0.4707044490759892, "creative_writing_quality": 0.9984756873888443, "primary_topic": 0.9043788417792603, "secondary_topic": 0.413811725759811}, "probability_margin": {"primary": 0.9720777007278473, "secondary": 0.8302753123096026, "tertiary": 0.8633911878277688, "education_level": 0.1759673434525843, "creative_writing_quality": 0.9979433634060032, "primary_topic": 0.830257950038968, "secondary_topic": 0.13202731982790472}, "raw_probabilities": {"primary": {"Academic Writing": 0.0001948768473355591, "Content Listing": 3.7970541380082695e-05, "Creative Writing": 2.9152656731337926e-05, "Customer Support Page": 2.2444309485052796e-05, "Q&A Forum": 2.9444051936448043e-05, "Discussion Thread": 1.5843654528056765e-05, "Comment Section": 2.8027615550815307e-05, "Missing Content": 3.31218745085083e-05, "Knowledge Article": 0.00022209868295379557, "Legal Notices": 3.1370048330013866e-05, "News Article": 1.2330583847525133e-05, "Organizational Announcement": 2.207133303482741e-05, "Nonfiction Writing": 3.367950273331164e-05, "Organizational About Page": 2.888229133072109e-05, "Personal About Page": 2.5467721544012356e-05, "Personal Blog": 1.6454585564491513e-05, "Product Page": 1.703817175000411e-05, "Spam / Ads": 2.3958342445898315e-05, "Structured Data": 0.00020390242849765663, "Technical Writing": 0.9848199812409744, "Transcript / Interview": 2.9097799183377376e-05, "Tutorial / How-To Guide": 0.0127422805131271, "User Reviews": 1.679349897293138e-05, "Other / Unknown": 0.0013637117042539107}, "secondary": {"Academic Writing": 0.0016564476427762642, "Content Listing": 0.0002355520794502284, "Creative Writing": 0.0002711141275321583, "Customer Support Page": 0.0003030353287912113, "Q&A Forum": 0.00019779842613531985, "Discussion Thread": 0.00011506570079134039, "Comment Section": 0.00020202441589818135, "Missing Content": 0.0003168069272042795, "Knowledge Article": 0.0025870520729816735, "Legal Notices": 0.00022607521887537562, "News Article": 0.00016679759737582545, "Organizational Announcement": 0.0001753350620296565, "Nonfiction Writing": 0.0002551793843777273, "Organizational About Page": 0.0002079804648453572, "Personal About Page": 0.00018409045674280353, "Personal Blog": 0.00015293747041120512, "Product Page": 0.0001287555607823805, "Spam / Ads": 0.00018449539709313083, "Structured Data": 0.0007489060717634822, "Technical Writing": 0.03204042939341242, "Transcript / Interview": 0.00023537804779586978, "Tutorial / How-To Guide": 0.06451066032409809, "User Reviews": 0.00011211019513519465, "NONE": 0.8947859726337007}, "tertiary": {"Academic Writing": 0.0031176687216933688, "Content Listing": 0.00015359523767875477, "Creative Writing": 0.0001768025465343101, "Customer Support Page": 0.0003234416941322905, "Q&A Forum": 0.00010190544323772093, "Discussion Thread": 7.23704357595522e-05, "Comment Section": 0.00014413464997347974, "Missing Content": 0.0002315340456620899, "Knowledge Article": 0.003600165128709529, "Legal Notices": 0.00023379995354146172, "News Article": 0.00010932222152025957, "Organizational Announcement": 0.00010111163736331186, "Nonfiction Writing": 0.00020917347913371905, "Organizational About Page": 0.00014939469765126072, "Personal About Page": 0.00012253974474999105, "Personal Blog": 9.907193083377272e-05, "Product Page": 8.924330203815759e-05, "Spam / Ads": 0.00012680269274534307, "Structured Data": 0.0011291237859681728, "Technical Writing": 0.01839222057191875, "Transcript / Interview": 0.00014397363976514907, "Tutorial / How-To Guide": 0.05386070416305509, "User Reviews": 6.000828551062563e-05, "NONE": 0.9172518919908239}, "structured_records": {"false": 0.9942233341228055, "true": 0.005776665877194545}, "procedural_steps": {"false": 0.7318416220249079, "true": 0.2681583779750921}, "worked_solution": {"false": 0.9681357609281044, "true": 0.031864239071895645}, "contextual_dialogue": {"false": 0.9999958344757134, "true": 4.165524286572909e-06}, "content_quality": {"-2": 0.012731686010842609, "-1": 0.057802518485913765, "0": 0.2440202643905963, "1": 0.5041623026416617, "2": 0.18128322847098557}, "noise_severity": {"0": 0.944041739730583, "1": 0.052758414868441024, "2": 0.002106941976395808, "3": 0.0005871428416469196, "4": 0.0005057605829332441}, "missing_content_severity": {"0": 0.9957732748859875, "1": 0.0015749050553859477, "2": 0.002117119063882877, "3": 0.0004301791291986626, "4": 0.00010452186554493375}, "factual_density": {"0": 0.003767707427769012, "1": 0.013438115197280687, "2": 0.12265317425048375, "3": 0.41462812897232804, "4": 0.44551287415213847}, "pretraining_utility": {"-2": 0.004742544935223912, "-1": 0.023123559844839308, "0": 0.23669335289921217, "1": 0.5767036010918998, "2": 0.1587369412288249}, "math_density": {"0": 0.016457253337123855, "1": 0.10956544919026322, "2": 0.27840245373528966, "3": 0.32296019617855704, "4": 0.2726146475587662}, "code_density": {"0": 0.0005573884437089058, "1": 0.0011194901304670247, "2": 0.03819573361636397, "3": 0.29321694400548143, "4": 0.6669104438039787}, "reasoning_density": {"0": 0.38863133895288915, "1": 0.09224352919242651, "2": 0.20025985813557734, "3": 0.19332372276396234, "4": 0.1255415509551447}, "education_level": {"none": 0.0006270081181045994, "0": 0.0011104009871387428, "1": 0.003513716889712583, "2": 0.558920374229991, "3": 0.38295303077740667, "4": 0.05287546899764631}, "creative_writing_quality": {"not_applicable": 0.9987297394907035, "-2": 7.555346366707926e-05, "-1": 0.00020783376937039063, "0": 0.0007863760847003806, "1": 0.0001503962123225244, "2": 5.0100979235963934e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00011973760868698463, "Art & Design": 0.0002212714738336564, "Law & Crime": 0.0001931258442373602, "Education & Careers": 0.00014093651446328212, "Entertainment & Media": 0.0073097906292090105, "Fashion & Beauty": 0.00018638720065515527, "Business, Economics & Finance": 0.0005317595162405017, "Food & Dining": 0.00012602174946930566, "Games & Gaming": 0.000274687497821892, "Electronics & Hardware": 0.00016724570791772908, "Health, Healthcare & Wellness": 0.00015078463594597464, "History & Geography": 0.00016647059519850894, "Home, Pets & Hobbies": 0.00012170552364451984, "Industry & Production": 0.00013867219092459393, "Language, Literature & Philosophy": 0.00012082602404927612, "Politics & Government": 8.543076979458908e-05, "Religion & Spirituality": 0.00011932187865337428, "Science, Mathematics & Engineering": 0.07779862859493615, "Relationships & Community": 0.0001035602452903026, "Software Usage & Applications": 0.0011366321233208057, "Programming & Software Engineering": 0.9080565786339041, "Sports & Fitness": 0.00017713344290577407, "Vehicles & Transportation": 7.890189031320682e-05, "Travel & Tourism": 0.0001536277797961516, "Other / Uncovered Subject": 0.002166526909833307, "Unknown / Insufficient Content": 0.00015423501895456629}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009120743097892429, "Art & Design": 0.0010076293143728575, "Law & Crime": 0.0007389588287580163, "Education & Careers": 0.0004986816419406934, "Entertainment & Media": 0.07990568711159331, "Fashion & Beauty": 0.0005901972950463522, "Business, Economics & Finance": 0.00393467870801014, "Food & Dining": 0.000416517308341301, "Games & Gaming": 0.000670301797348412, "Electronics & Hardware": 0.0006275063680457896, "Health, Healthcare & Wellness": 0.0005901925107897274, "History & Geography": 0.0005016193575394405, "Home, Pets & Hobbies": 0.00046556861208840283, "Industry & Production": 0.00041805600179192916, "Language, Literature & Philosophy": 0.0003121481811719208, "Politics & Government": 0.000294698095221943, "Religion & Spirituality": 0.0004675243138927503, "Science, Mathematics & Engineering": 0.4363574286152029, "Relationships & Community": 0.00032407989622979585, "Software Usage & Applications": 0.0027460508771342913, "Programming & Software Engineering": 0.1597479091993925, "Sports & Fitness": 0.0004791863357427079, "Vehicles & Transportation": 0.00024065744939735749, "Travel & Tourism": 0.0005881125086482277, "Other / Uncovered Subject": 0.0028344265752118752, "NONE": 0.30433010878729816}}, "probabilities": {"primary": {"Academic Writing": 0.00019487684733555916, "Content Listing": 3.79705413800827e-05, "Creative Writing": 2.9152656731337933e-05, "Customer Support Page": 2.24443094850528e-05, "Q&A Forum": 2.944405193644805e-05, "Discussion Thread": 1.5843654528056768e-05, "Comment Section": 2.8027615550815314e-05, "Missing Content": 3.312187450850831e-05, "Knowledge Article": 0.00022209868295379562, "Legal Notices": 3.137004833001387e-05, "News Article": 1.2330583847525136e-05, "Organizational Announcement": 2.2071333034827413e-05, "Nonfiction Writing": 3.3679502733311646e-05, "Organizational About Page": 2.8882291330721095e-05, "Personal About Page": 2.5467721544012363e-05, "Personal Blog": 1.6454585564491517e-05, "Product Page": 1.7038171750004113e-05, "Spam / Ads": 2.3958342445898322e-05, "Structured Data": 0.00020390242849765668, "Technical Writing": 0.9848199812409746, "Transcript / Interview": 2.9097799183377383e-05, "Tutorial / How-To Guide": 0.012742280513127103, "User Reviews": 1.6793498972931383e-05, "Other / Unknown": 0.001363711704253911}, "secondary": {"Academic Writing": 0.0016564476427762644, "Content Listing": 0.00023555207945022842, "Creative Writing": 0.00027111412753215837, "Customer Support Page": 0.0003030353287912114, "Q&A Forum": 0.00019779842613531988, "Discussion Thread": 0.0001150657007913404, "Comment Section": 0.00020202441589818137, "Missing Content": 0.00031680692720427954, "Knowledge Article": 0.002587052072981674, "Legal Notices": 0.00022607521887537565, "News Article": 0.00016679759737582548, "Organizational Announcement": 0.00017533506202965652, "Nonfiction Writing": 0.00025517938437772736, "Organizational About Page": 0.00020798046484535722, "Personal About Page": 0.00018409045674280355, "Personal Blog": 0.00015293747041120515, "Product Page": 0.00012875556078238053, "Spam / Ads": 0.00018449539709313085, "Structured Data": 0.0007489060717634823, "Technical Writing": 0.03204042939341243, "Transcript / Interview": 0.0002353780477958698, "Tutorial / How-To Guide": 0.0645106603240981, "User Reviews": 0.00011211019513519466, "NONE": 0.8947859726337009}, "tertiary": {"Academic Writing": 0.0031176687216933688, "Content Listing": 0.00015359523767875477, "Creative Writing": 0.0001768025465343101, "Customer Support Page": 0.0003234416941322905, "Q&A Forum": 0.00010190544323772093, "Discussion Thread": 7.23704357595522e-05, "Comment Section": 0.00014413464997347974, "Missing Content": 0.0002315340456620899, "Knowledge Article": 0.003600165128709529, "Legal Notices": 0.00023379995354146172, "News Article": 0.00010932222152025957, "Organizational Announcement": 0.00010111163736331186, "Nonfiction Writing": 0.00020917347913371905, "Organizational About Page": 0.00014939469765126072, "Personal About Page": 0.00012253974474999105, "Personal Blog": 9.907193083377272e-05, "Product Page": 8.924330203815759e-05, "Spam / Ads": 0.00012680269274534307, "Structured Data": 0.0011291237859681728, "Technical Writing": 0.01839222057191875, "Transcript / Interview": 0.00014397363976514907, "Tutorial / How-To Guide": 0.05386070416305509, "User Reviews": 6.000828551062563e-05, "NONE": 0.9172518919908239}, "structured_records": {"false": 0.9942233341228055, "true": 0.005776665877194545}, "procedural_steps": {"false": 0.7318416220249079, "true": 0.2681583779750921}, "worked_solution": {"false": 0.9681357609281044, "true": 0.031864239071895645}, "contextual_dialogue": {"false": 0.9999958344757134, "true": 4.165524286572909e-06}, "content_quality": {"-2": 0.012731686010842609, "-1": 0.057802518485913765, "0": 0.2440202643905963, "1": 0.5041623026416617, "2": 0.18128322847098557}, "noise_severity": {"0": 0.944041739730583, "1": 0.052758414868441024, "2": 0.002106941976395808, "3": 0.0005871428416469196, "4": 0.0005057605829332441}, "missing_content_severity": {"0": 0.9957732748859875, "1": 0.0015749050553859477, "2": 0.002117119063882877, "3": 0.0004301791291986626, "4": 0.00010452186554493375}, "factual_density": {"0": 0.003767707427769012, "1": 0.013438115197280687, "2": 0.12265317425048375, "3": 0.41462812897232804, "4": 0.44551287415213847}, "pretraining_utility": {"-2": 0.004742544935223912, "-1": 0.023123559844839308, "0": 0.23669335289921217, "1": 0.5767036010918998, "2": 0.1587369412288249}, "math_density": {"0": 0.016457253337123855, "1": 0.10956544919026322, "2": 0.27840245373528966, "3": 0.32296019617855704, "4": 0.2726146475587662}, "code_density": {"0": 0.0005573884437089058, "1": 0.0011194901304670247, "2": 0.03819573361636397, "3": 0.29321694400548143, "4": 0.6669104438039787}, "reasoning_density": {"0": 0.38863133895288915, "1": 0.09224352919242651, "2": 0.20025985813557734, "3": 0.19332372276396234, "4": 0.1255415509551447}, "education_level": {"none": 0.0006270081181045995, "0": 0.001110400987138743, "1": 0.0035137168897125836, "2": 0.5589203742299911, "3": 0.3829530307774067, "4": 0.052875468997646315}, "creative_writing_quality": {"not_applicable": 0.9987297394907036, "-2": 7.555346366707927e-05, "-1": 0.00020783376937039065, "0": 0.0007863760847003807, "1": 0.00015039621232252443, "2": 5.010097923596394e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00011973760868698463, "Art & Design": 0.0002212714738336564, "Law & Crime": 0.0001931258442373602, "Education & Careers": 0.00014093651446328212, "Entertainment & Media": 0.0073097906292090105, "Fashion & Beauty": 0.00018638720065515527, "Business, Economics & Finance": 0.0005317595162405017, "Food & Dining": 0.00012602174946930566, "Games & Gaming": 0.000274687497821892, "Electronics & Hardware": 0.00016724570791772908, "Health, Healthcare & Wellness": 0.00015078463594597464, "History & Geography": 0.00016647059519850894, "Home, Pets & Hobbies": 0.00012170552364451984, "Industry & Production": 0.00013867219092459393, "Language, Literature & Philosophy": 0.00012082602404927612, "Politics & Government": 8.543076979458908e-05, "Religion & Spirituality": 0.00011932187865337428, "Science, Mathematics & Engineering": 0.07779862859493615, "Relationships & Community": 0.0001035602452903026, "Software Usage & Applications": 0.0011366321233208057, "Programming & Software Engineering": 0.9080565786339041, "Sports & Fitness": 0.00017713344290577407, "Vehicles & Transportation": 7.890189031320682e-05, "Travel & Tourism": 0.0001536277797961516, "Other / Uncovered Subject": 0.002166526909833307, "Unknown / Insufficient Content": 0.00015423501895456629}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009120743097892429, "Art & Design": 0.0010076293143728575, "Law & Crime": 0.0007389588287580163, "Education & Careers": 0.0004986816419406934, "Entertainment & Media": 0.07990568711159331, "Fashion & Beauty": 0.0005901972950463522, "Business, Economics & Finance": 0.00393467870801014, "Food & Dining": 0.000416517308341301, "Games & Gaming": 0.000670301797348412, "Electronics & Hardware": 0.0006275063680457896, "Health, Healthcare & Wellness": 0.0005901925107897274, "History & Geography": 0.0005016193575394405, "Home, Pets & Hobbies": 0.00046556861208840283, "Industry & Production": 0.00041805600179192916, "Language, Literature & Philosophy": 0.0003121481811719208, "Politics & Government": 0.000294698095221943, "Religion & Spirituality": 0.0004675243138927503, "Science, Mathematics & Engineering": 0.4363574286152029, "Relationships & Community": 0.00032407989622979585, "Software Usage & Applications": 0.0027460508771342913, "Programming & Software Engineering": 0.1597479091993925, "Sports & Fitness": 0.0004791863357427079, "Vehicles & Transportation": 0.00024065744939735749, "Travel & Tourism": 0.0005881125086482277, "Other / Uncovered Subject": 0.0028344265752118752, "NONE": 0.30433010878729816}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9917, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9917, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:02.776911+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "4073e4d1-f8ba-4928-8baa-a47daad20043", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "f50e4b4b001a5ce7a65a8147b457b336071376b7976a10be4acb086f6de4594f", "source_metadata": {"uuid": "4073e4d1-f8ba-4928-8baa-a47daad20043", "repo_name": "jiaminglin01/Customer-Segmentation-and-Movie-Recommendation", "relative_path": "Movie_Recommendation/Question4/popularity_baseline.py", "category": "DATA", "algo_rel_score": 0.9863801598548889, "quality_score": 5.646038055419922, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 17, "repo_created_at": "2025-06-04T01:54:18.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "1703a854cc618c5c99e46d2136dea06c7cdd6097", "size_bytes": 5571, "file_timestamp": 1749002380, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "ceed7df14a1f2f331245c1921d0c32a4efd197b2"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 17, "repo_created_at": "2025-06-04T01:54:18.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "1703a854cc618c5c99e46d2136dea06c7cdd6097", "size_bytes": 5571, "file_timestamp": 1749002380, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "ceed7df14a1f2f331245c1921d0c32a4efd197b2", "url": "https://github.com/jiaminglin01/Customer-Segmentation-and-Movie-Recommendation/blob/ceed7df14a1f2f331245c1921d0c32a4efd197b2/Movie_Recommendation/Question4/popularity_baseline.py"}, "gpt4o_tokens": 1305, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.7834628690760339, "noise_severity": 0.06075676967790637, "missing_content_severity": 0.007517768032927425, "factual_density": 3.2846803472237864, "pretraining_utility": 0.8615688338342624, "math_density": 2.7257095354315783, "code_density": 3.624803564595554, "reasoning_density": 1.574900617576047, "education_level": 2.4832724656679392, "creative_writing_quality": -0.0852915800477126}, "score_applicability": {"education_level": 0.9993729918818954, "creative_writing_quality": 0.001270260509296339}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/davidwbritt/trademo_entities/blob/d29f012621e6d2ae3c5589dae015397ff2b24ceb/tokenized_index/prune_inverted_index.py", "id": "python-0-0a3efa8a0154dec897da16aa45d6beae", "text": "import os\nimport time\nfrom collections import defaultdict\nfrom typing import Any, Dict, List, Tuple\n\nfrom bson import ObjectId\nfrom pymongo import MongoClient, errors\n\n# MongoDB connection parameters\nMONGO_URI = \"mongodb://172.17.0.4:27017\"\nDATABASE = \"tradeverifyd\"\n\nclient = MongoClient(MONGO_URI, socketTimeoutMS=3 * 60 * 60 * 1000, serverSelectionTimeoutMS=3 * 60 * 60 * 1000)\ndb = client[DATABASE]\nsource_collection = db[\"entity_token_index\"]\ntarget_collection = db[\"entity_token_index_filtered\"]\n\nBATCH_SIZE = 1000\nMAX_RETRIES = 3\nCHECKPOINT_FILE = \"token_consolidation_checkpoint.txt\"\n\n\ndef load_checkpoint() -> Tuple[ObjectId, bool]:\n    \"\"\"\n    Load the last processed ObjectId from checkpoint file\n    Returns (last_id, is_new_start)\n    \"\"\"\n    try:\n        if os.path.exists(CHECKPOINT_FILE):\n            with open(CHECKPOINT_FILE, \"r\") as f:\n                last_id_str = f.read().strip()\n                if last_id_str:\n                    last_id = ObjectId(last_id_str)\n                    print(f\"Resuming from _id: {last_id}\")\n                    return last_id, False\n                return None, False\n    except (IOError, OSError) as e:\n        print(f\"Error reading checkpoint file: {e}\")\n        return None, True\n\n    print(\"Checkpoint file not found. Starting from scratch.\")\n    return None, True\n\n\ndef save_checkpoint(last_id: str):\n    \"\"\"\n    Save the last processed ObjectId to checkpoint file\n    \"\"\"\n    try:\n        with open(CHECKPOINT_FILE, \"w\") as f:\n            f.write(str(last_id))\n            print(f\"Saved checkpoint: {last_id}\")\n    except (IOError, OSError) as e:\n        print(f\"Warning: Could not save checkpoint: {e}\")\n\n\ndef identify_tokens_with_large_chunks():\n    \"\"\"\n    Identify tokens with any chunk >= 1\n    Returns a set of tokens to exclude\n    \"\"\"\n    print(\"Identifying tokens with large chunks...\")\n\n    # Step 1: Identify tokens with chunk >= 1\n    pipeline = [\n        {\"$match\": {\"chunk\": {\"$gte\": 1}}},\n        {\"$group\": {\"_id\": \"$token\"}}\n    ]\n\n    try:\n        # Retrieve tokens with chunk >= 1\n        tokens_to_exclude = {doc[\"_id\"] for doc in source_collection.aggregate(pipeline)}\n        print(f\"Identified {len(tokens_to_exclude)} tokens to exclude.\")\n        return tokens_to_exclude\n    except errors.PyMongoError as e:\n        print(f\"Error during token identification: {e}\")\n        raise\n\n\ndef process_tokens_batch(tokens_to_exclude, last_id: str = None) -> Tuple[List[Dict[str, Any]], str]:\n    \"\"\"\n    Process a batch of documents, excluding any tokens that are in the tokens_to_exclude set\n    Returns tuple of (processed_documents, last_processed_id)\n    \"\"\"\n    # Convert last_id to ObjectId if it's a string\n    last_id_obj = ObjectId(last_id) if isinstance(last_id, str) and last_id else last_id\n\n    # Get batch of documents and filter out excluded tokens\n    query = {\n        \"_id\": {\"$gt\": last_id_obj} if last_id_obj else {\"$exists\": True},\n        \"token\": {\"$nin\": list(tokens_to_exclude)}\n    }\n\n    try:\n        # Get a batch of documents, sorted by _id\n        docs = list(source_collection.find(query).sort(\"_id\", 1).limit(BATCH_SIZE))\n    except errors.OperationFailure as e:\n        print(f\"Error fetching document batch: {e}\")\n        raise\n\n    if not docs:\n        return [], None\n\n    result_docs = []\n    token_groups = defaultdict(list)\n\n    # Group documents by token\n    for doc in docs:\n        token_groups[doc[\"token\"]].append(doc)\n\n    # Merge entity_ids for each token group\n    for token, token_docs in token_groups.items():\n        # Create a set of ObjectIds for deduplication\n        all_entity_ids = set()\n        for doc in token_docs:\n            # Handle each entity_id in the array\n            for entity_id in doc[\"entity_ids\"]:\n                # If it's already an ObjectId, add it directly\n                if isinstance(entity_id, ObjectId):\n                    all_entity_ids.add(entity_id)\n                # If it's a dict with $oid, convert to ObjectId\n                elif isinstance(entity_id, dict) and \"$oid\" in entity_id:\n                    all_entity_ids.add(ObjectId(entity_id[\"$oid\"]))\n                # If it's a string, convert to ObjectId\n                else:\n                    all_entity_ids.add(ObjectId(str(entity_id)))\n\n        result_docs.append(\n            {\n                \"_id\": ObjectId(),  # Generate new _id for merged document\n                \"token\": token,\n                \"chunk\": 0,\n                \"entity_ids\": list(all_entity_ids),  # Convert set back to list for MongoDB storage\n            }\n        )\n\n    return result_docs, str(docs[-1][\"_id\"]) if docs else None\n\n\ndef main():\n    # Load checkpoint and determine if we need to drop the collection\n    last_id, is_new_start = load_checkpoint()\n\n    if is_new_start:\n        print(\"Dropping target collection for fresh start...\")\n        target_collection.drop()\n        # Create initial checkpoint file\n        save_checkpoint(\"\")  # Empty string for initial checkpoint\n        \n        # Step to identify tokens with large chunks\n        tokens_to_exclude = identify_tokens_with_large_chunks()\n    else:\n        print(\"Resuming previous operation, keeping existing target collection...\")\n        # Load previously identified tokens if not starting fresh\n        tokens_to_exclude = identify_tokens_with_large_chunks()\n\n    processed_count = 0\n    error_count = 0\n\n    while True:\n        try:\n            print(f\"Processing batch after _id: {last_id}\")\n            batch_docs, new_last_id = process_tokens_batch(tokens_to_exclude, last_id)\n\n            if not batch_docs:\n                print(\"No more documents to process.\")\n                break\n\n            # Insert the processed documents into the target collection\n            target_collection.insert_many(batch_docs)\n            processed_count += len(batch_docs)\n\n            # Update last_id and save checkpoint\n            last_id = new_last_id\n            if last_id:  # Check for valid last_id before saving\n                save_checkpoint(last_id)\n\n            print(\n                f\"Successfully processed {len(batch_docs)} documents. Total processed: {processed_count}\"\n            )\n            error_count = 0  # Reset error count on success\n\n        except errors.PyMongoError as e:\n            error_count += 1\n            print(f\"Error processing batch (attempt {error_count}): {e}\")\n\n            if error_count >= MAX_RETRIES:\n                print(\"Max retry attempts reached. Exiting.\")\n                raise\n\n            time.sleep(30)  # Wait before retrying\n            continue\n\n        except Exception as e:\n            print(f\"Unexpected error: {e}\")\n            raise\n\n    print(f\"Processing completed. Total documents processed: {processed_count}\")\n\n    # Create index if we processed any documents\n    if processed_count > 0:\n        print(\"Creating index on token field...\")\n        target_collection.create_index(\"token\")\n        print(\"Index created successfully.\")\n\n\nif __name__ == \"__main__\":\n    main()\n", "text_preview": "import os import time from collections import defaultdict from typing import Any, Dict, List, Tuple from bson import ObjectId from pymongo import MongoClient, errors # MongoDB connection parameters MONGO_URI = \"mongodb://172.17.0.4:27017\" D", "input_sha256": "c1708392b597106704ba172f4d99f804d1e9884fbd3c31c366c80d77069a8cd5", "text_sha256": "0a3efa8a0154dec897da16aa45d6beaeb00a377cc1f00d7280a669289e65cba3", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12115, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.6678172578917227, "noise_severity": 0.012456795087460627, "missing_content_severity": 0.002832405787627832, "factual_density": 3.1276440870578868, "pretraining_utility": 0.6110675563641672, "math_density": 0.210954657837168, "code_density": 3.817345131565106, "reasoning_density": 1.1544739487409326, "education_level": 2.3230949192064765, "creative_writing_quality": -0.17862405833741898}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.01277470394355218, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9963487695795286, "secondary": 0.9777276955956655, "tertiary": 0.9910578214498069, "structured_records": 0.022877600015808255, "procedural_steps": 0.09656651023808141, "worked_solution": 0.008447775541077653, "contextual_dialogue": 5.039948638375886e-06, "content_quality": 0.46195409532521325, "noise_severity": 0.9896193374271162, "missing_content_severity": 0.9976396618436435, "factual_density": 0.27303673921490546, "pretraining_utility": 0.5090553100084005, "math_density": 0.82420445180236, "code_density": 0.847787609637588, "reasoning_density": 0.03793837604922301, "education_level": 0.6075172090753012, "creative_writing_quality": 0.9994088429307192, "primary_topic": 0.9929038711947661, "secondary_topic": 0.926262604934072}, "probability_margin": {"primary": 0.9951095767581029, "secondary": 0.9660466410255025, "tertiary": 0.9874158027245031, "education_level": 0.37398374105450816, "creative_writing_quality": 0.9992473193125797, "primary_topic": 0.9901368307443003, "secondary_topic": 0.9005062958531469}, "raw_probabilities": {"primary": {"Academic Writing": 3.2972580548368997e-05, "Content Listing": 1.5502501718753157e-05, "Creative Writing": 1.4287159096603346e-05, "Customer Support Page": 7.740262789066516e-06, "Q&A Forum": 1.1458998832071753e-05, "Discussion Thread": 6.7780077770689755e-06, "Comment Section": 1.0856867792953266e-05, "Missing Content": 1.7844563417016144e-05, "Knowledge Article": 7.604670937025939e-05, "Legal Notices": 2.4673087586395136e-05, "News Article": 4.857373143050979e-06, "Organizational Announcement": 9.000062985364164e-06, "Nonfiction Writing": 1.1548355233819533e-05, "Organizational About Page": 1.1359917911843213e-05, "Personal About Page": 1.1489890572708302e-05, "Personal Blog": 6.996960645521003e-06, "Product Page": 6.168436585930012e-06, "Spam / Ads": 9.450217585642062e-06, "Structured Data": 0.0006112142290336636, "Technical Writing": 0.9965009041803815, "Transcript / Interview": 1.2465635865579312e-05, "Tutorial / How-To Guide": 0.001391327422278705, "User Reviews": 6.006502732565903e-06, "Other / Unknown": 0.0011890500761153131}, "secondary": {"Academic Writing": 0.0001061530655213827, "Content Listing": 6.261131954287637e-05, "Creative Writing": 8.011352328710914e-05, "Customer Support Page": 5.4417581776436e-05, "Q&A Forum": 3.5267973662770126e-05, "Discussion Thread": 2.679961868448657e-05, "Comment Section": 5.94279044269096e-05, "Missing Content": 0.00014000664025995654, "Knowledge Article": 0.0005031983079127135, "Legal Notices": 0.00014463936764389957, "News Article": 5.0316757997362276e-05, "Organizational Announcement": 4.301257922337988e-05, "Nonfiction Writing": 4.839854519540423e-05, "Organizational About Page": 5.312112028506589e-05, "Personal About Page": 4.989803441016284e-05, "Personal Blog": 3.956766541788143e-05, "Product Page": 3.0144468840822154e-05, "Spam / Ads": 4.2955906452769817e-05, "Structured Data": 0.003387679606789873, "Technical Writing": 0.012609067253676925, "Transcript / Interview": 5.928172167313628e-05, "Tutorial / How-To Guide": 0.0036948891312766417, "User Reviews": 2.3323626862560944e-05, "NONE": 0.9786557082791795}, "tertiary": {"Academic Writing": 5.551261801826025e-05, "Content Listing": 3.241507496282846e-05, "Creative Writing": 4.2941379625751686e-05, "Customer Support Page": 2.4577234178562008e-05, "Q&A Forum": 1.7999626715574004e-05, "Discussion Thread": 1.3997630751470712e-05, "Comment Section": 3.851456431041252e-05, "Missing Content": 0.00012891680837676434, "Knowledge Article": 0.00029300531952431043, "Legal Notices": 0.00012265282999084627, "News Article": 2.8106761783906184e-05, "Organizational Announcement": 2.320482581906216e-05, "Nonfiction Writing": 3.256754191783464e-05, "Organizational About Page": 3.927440202707292e-05, "Personal About Page": 2.763569434021398e-05, "Personal Blog": 2.274297564940943e-05, "Product Page": 1.949707853909585e-05, "Spam / Ads": 2.773641029222516e-05, "Structured Data": 0.001661154539562966, "Technical Writing": 0.004014609498228499, "Transcript / Interview": 3.1713591683645885e-05, "Tutorial / How-To Guide": 0.0018565198045805368, "User Reviews": 1.4291566389066384e-05, "NONE": 0.9914304122227316}, "structured_records": {"false": 0.9771223999841917, "true": 0.022877600015808255}, "procedural_steps": {"false": 0.9034334897619186, "true": 0.09656651023808141}, "worked_solution": {"false": 0.9915522244589223, "true": 0.008447775541077653}, "contextual_dialogue": {"false": 0.9999949600513616, "true": 5.039948638375886e-06}, "content_quality": {"-2": 0.012895673611109772, "-1": 0.0779303413893183, "0": 0.2943712102470447, "1": 0.45806660300179364, "2": 0.15673617175073343}, "noise_severity": {"0": 0.9898596025002296, "1": 0.008603830502275249, "2": 0.0009887900576146945, "3": 0.00031572328956588983, "4": 0.00023205365031457993}, "missing_content_severity": {"0": 0.9983453124394692, "1": 0.0008121235447894757, "2": 0.000576658649421612, "3": 0.0001966565212831306, "4": 6.924884503643503e-05}, "factual_density": {"0": 0.00982958925326413, "1": 0.03443260531962174, "2": 0.1743998798573944, "3": 0.3809399802554029, "4": 0.4003979453143168}, "pretraining_utility": {"-2": 0.007873956479011223, "-1": 0.059304979835363134, "0": 0.3468012067051161, "1": 0.4859192648034661, "2": 0.10010059217704333}, "math_density": {"0": 0.8573822089613554, "1": 0.11192758290202437, "2": 0.009438051135742581, "3": 0.0048576553398512875, "4": 0.01639450166102614}, "code_density": {"0": 0.0003619834556638557, "1": 0.0006462176565607138, "2": 0.020112897435228563, "3": 0.1390424867720996, "4": 0.8398364146804473}, "reasoning_density": {"0": 0.5230855426043748, "1": 0.09404030568904853, "2": 0.18081266515579072, "3": 0.10943763346284095, "4": 0.0926238530879449}, "education_level": {"none": 0.0009131803646278999, "0": 0.0015331941804723477, "1": 0.008143901881587971, "2": 0.6729310075627509, "3": 0.29894726650824277, "4": 0.017531449502318053}, "creative_writing_quality": {"not_applicable": 0.9995073691089327, "-2": 4.56760603278334e-05, "-1": 0.00010445736742102933, "0": 0.00026004979635299, "1": 5.708157487905292e-05, "2": 2.5366092086410146e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.346593379220219e-05, "Art & Design": 6.309379656607426e-05, "Law & Crime": 4.818599002263767e-05, "Education & Careers": 5.065572696225572e-05, "Entertainment & Media": 3.4555670883725184e-05, "Fashion & Beauty": 2.853124948699255e-05, "Business, Economics & Finance": 0.0006062110062969083, "Food & Dining": 3.714075002143769e-05, "Games & Gaming": 9.179204660794707e-05, "Electronics & Hardware": 0.000124556107014383, "Health, Healthcare & Wellness": 3.922899251651387e-05, "History & Geography": 3.968787239888731e-05, "Home, Pets & Hobbies": 3.373000921107214e-05, "Industry & Production": 6.049032290223098e-05, "Language, Literature & Philosophy": 0.0004467021860567057, "Politics & Government": 3.1108899281370535e-05, "Religion & Spirituality": 4.661317890118702e-05, "Science, Mathematics & Engineering": 0.003039968481436341, "Relationships & Community": 3.023231410588756e-05, "Software Usage & Applications": 0.0009908016851824513, "Programming & Software Engineering": 0.9931767992257367, "Sports & Fitness": 5.98330454151221e-05, "Vehicles & Transportation": 3.045618155706645e-05, "Travel & Tourism": 5.35701477532844e-05, "Other / Uncovered Subject": 0.0006680096565463317, "Unknown / Insufficient Content": 0.00010457952334441918}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0003760151647904745, "Art & Design": 0.0002977465082533834, "Law & Crime": 0.0009735824811144624, "Education & Careers": 0.00027720067513360567, "Entertainment & Media": 0.000293208241775075, "Fashion & Beauty": 0.00018196314466188075, "Business, Economics & Finance": 0.007277627198476713, "Food & Dining": 0.00016643813772741376, "Games & Gaming": 0.00035856237655010505, "Electronics & Hardware": 0.0003507050110809707, "Health, Healthcare & Wellness": 0.00021764641063476322, "History & Geography": 0.0001815931139887944, "Home, Pets & Hobbies": 0.0001527835002980287, "Industry & Production": 0.00018040578921375524, "Language, Literature & Philosophy": 0.0005105446376933398, "Politics & Government": 0.00019600809360477607, "Religion & Spirituality": 0.00025709012496866245, "Science, Mathematics & Engineering": 0.009851240870386626, "Relationships & Community": 0.00011205742413159943, "Software Usage & Applications": 0.017936790512751932, "Programming & Software Engineering": 0.028592362737306875, "Sports & Fitness": 0.00017899451986689454, "Vehicles & Transportation": 9.143915569293483e-05, "Travel & Tourism": 0.00023122791507538051, "Other / Uncovered Subject": 0.001658107664367711, "NONE": 0.9290986585904538}}, "probabilities": {"primary": {"Academic Writing": 3.2972580548369e-05, "Content Listing": 1.550250171875316e-05, "Creative Writing": 1.428715909660335e-05, "Customer Support Page": 7.740262789066517e-06, "Q&A Forum": 1.1458998832071756e-05, "Discussion Thread": 6.778007777068977e-06, "Comment Section": 1.0856867792953267e-05, "Missing Content": 1.7844563417016147e-05, "Knowledge Article": 7.60467093702594e-05, "Legal Notices": 2.4673087586395143e-05, "News Article": 4.8573731430509795e-06, "Organizational Announcement": 9.000062985364166e-06, "Nonfiction Writing": 1.1548355233819537e-05, "Organizational About Page": 1.1359917911843215e-05, "Personal About Page": 1.1489890572708306e-05, "Personal Blog": 6.996960645521004e-06, "Product Page": 6.1684365859300135e-06, "Spam / Ads": 9.450217585642064e-06, "Structured Data": 0.0006112142290336637, "Technical Writing": 0.9965009041803817, "Transcript / Interview": 1.2465635865579315e-05, "Tutorial / How-To Guide": 0.0013913274222787052, "User Reviews": 6.006502732565905e-06, "Other / Unknown": 0.0011890500761153134}, "secondary": {"Academic Writing": 0.0001061530655213827, "Content Listing": 6.261131954287637e-05, "Creative Writing": 8.011352328710914e-05, "Customer Support Page": 5.4417581776436e-05, "Q&A Forum": 3.5267973662770126e-05, "Discussion Thread": 2.679961868448657e-05, "Comment Section": 5.94279044269096e-05, "Missing Content": 0.00014000664025995654, "Knowledge Article": 0.0005031983079127135, "Legal Notices": 0.00014463936764389957, "News Article": 5.0316757997362276e-05, "Organizational Announcement": 4.301257922337988e-05, "Nonfiction Writing": 4.839854519540423e-05, "Organizational About Page": 5.312112028506589e-05, "Personal About Page": 4.989803441016284e-05, "Personal Blog": 3.956766541788143e-05, "Product Page": 3.0144468840822154e-05, "Spam / Ads": 4.2955906452769817e-05, "Structured Data": 0.003387679606789873, "Technical Writing": 0.012609067253676925, "Transcript / Interview": 5.928172167313628e-05, "Tutorial / How-To Guide": 0.0036948891312766417, "User Reviews": 2.3323626862560944e-05, "NONE": 0.9786557082791795}, "tertiary": {"Academic Writing": 5.551261801826025e-05, "Content Listing": 3.241507496282846e-05, "Creative Writing": 4.2941379625751686e-05, "Customer Support Page": 2.4577234178562008e-05, "Q&A Forum": 1.7999626715574004e-05, "Discussion Thread": 1.3997630751470712e-05, "Comment Section": 3.851456431041252e-05, "Missing Content": 0.00012891680837676434, "Knowledge Article": 0.00029300531952431043, "Legal Notices": 0.00012265282999084627, "News Article": 2.8106761783906184e-05, "Organizational Announcement": 2.320482581906216e-05, "Nonfiction Writing": 3.256754191783464e-05, "Organizational About Page": 3.927440202707292e-05, "Personal About Page": 2.763569434021398e-05, "Personal Blog": 2.274297564940943e-05, "Product Page": 1.949707853909585e-05, "Spam / Ads": 2.773641029222516e-05, "Structured Data": 0.001661154539562966, "Technical Writing": 0.004014609498228499, "Transcript / Interview": 3.1713591683645885e-05, "Tutorial / How-To Guide": 0.0018565198045805368, "User Reviews": 1.4291566389066384e-05, "NONE": 0.9914304122227316}, "structured_records": {"false": 0.9771223999841917, "true": 0.022877600015808255}, "procedural_steps": {"false": 0.9034334897619186, "true": 0.09656651023808141}, "worked_solution": {"false": 0.9915522244589223, "true": 0.008447775541077653}, "contextual_dialogue": {"false": 0.9999949600513616, "true": 5.039948638375886e-06}, "content_quality": {"-2": 0.012895673611109774, "-1": 0.07793034138931831, "0": 0.29437121024704477, "1": 0.4580666030017937, "2": 0.15673617175073346}, "noise_severity": {"0": 0.9898596025002296, "1": 0.008603830502275249, "2": 0.0009887900576146945, "3": 0.00031572328956588983, "4": 0.00023205365031457993}, "missing_content_severity": {"0": 0.9983453124394693, "1": 0.0008121235447894758, "2": 0.0005766586494216122, "3": 0.00019665652128313062, "4": 6.924884503643504e-05}, "factual_density": {"0": 0.00982958925326413, "1": 0.03443260531962174, "2": 0.1743998798573944, "3": 0.3809399802554029, "4": 0.4003979453143168}, "pretraining_utility": {"-2": 0.007873956479011224, "-1": 0.05930497983536314, "0": 0.34680120670511616, "1": 0.48591926480346614, "2": 0.10010059217704334}, "math_density": {"0": 0.8573822089613556, "1": 0.1119275829020244, "2": 0.009438051135742583, "3": 0.004857655339851288, "4": 0.016394501661026142}, "code_density": {"0": 0.0003619834556638557, "1": 0.0006462176565607138, "2": 0.020112897435228563, "3": 0.1390424867720996, "4": 0.8398364146804473}, "reasoning_density": {"0": 0.5230855426043749, "1": 0.09404030568904855, "2": 0.18081266515579075, "3": 0.10943763346284097, "4": 0.09262385308794491}, "education_level": {"none": 0.0009131803646278999, "0": 0.0015331941804723477, "1": 0.008143901881587971, "2": 0.6729310075627509, "3": 0.29894726650824277, "4": 0.017531449502318053}, "creative_writing_quality": {"not_applicable": 0.9995073691089327, "-2": 4.56760603278334e-05, "-1": 0.00010445736742102933, "0": 0.00026004979635299, "1": 5.708157487905292e-05, "2": 2.5366092086410146e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.346593379220219e-05, "Art & Design": 6.309379656607426e-05, "Law & Crime": 4.818599002263767e-05, "Education & Careers": 5.065572696225572e-05, "Entertainment & Media": 3.4555670883725184e-05, "Fashion & Beauty": 2.853124948699255e-05, "Business, Economics & Finance": 0.0006062110062969083, "Food & Dining": 3.714075002143769e-05, "Games & Gaming": 9.179204660794707e-05, "Electronics & Hardware": 0.000124556107014383, "Health, Healthcare & Wellness": 3.922899251651387e-05, "History & Geography": 3.968787239888731e-05, "Home, Pets & Hobbies": 3.373000921107214e-05, "Industry & Production": 6.049032290223098e-05, "Language, Literature & Philosophy": 0.0004467021860567057, "Politics & Government": 3.1108899281370535e-05, "Religion & Spirituality": 4.661317890118702e-05, "Science, Mathematics & Engineering": 0.003039968481436341, "Relationships & Community": 3.023231410588756e-05, "Software Usage & Applications": 0.0009908016851824513, "Programming & Software Engineering": 0.9931767992257367, "Sports & Fitness": 5.98330454151221e-05, "Vehicles & Transportation": 3.045618155706645e-05, "Travel & Tourism": 5.35701477532844e-05, "Other / Uncovered Subject": 0.0006680096565463317, "Unknown / Insufficient Content": 0.00010457952334441918}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0003760151647904745, "Art & Design": 0.0002977465082533834, "Law & Crime": 0.0009735824811144624, "Education & Careers": 0.00027720067513360567, "Entertainment & Media": 0.000293208241775075, "Fashion & Beauty": 0.00018196314466188075, "Business, Economics & Finance": 0.007277627198476713, "Food & Dining": 0.00016643813772741376, "Games & Gaming": 0.00035856237655010505, "Electronics & Hardware": 0.0003507050110809707, "Health, Healthcare & Wellness": 0.00021764641063476322, "History & Geography": 0.0001815931139887944, "Home, Pets & Hobbies": 0.0001527835002980287, "Industry & Production": 0.00018040578921375524, "Language, Literature & Philosophy": 0.0005105446376933398, "Politics & Government": 0.00019600809360477607, "Religion & Spirituality": 0.00025709012496866245, "Science, Mathematics & Engineering": 0.009851240870386626, "Relationships & Community": 0.00011205742413159943, "Software Usage & Applications": 0.017936790512751932, "Programming & Software Engineering": 0.028592362737306875, "Sports & Fitness": 0.00017899451986689454, "Vehicles & Transportation": 9.143915569293483e-05, "Travel & Tourism": 0.00023122791507538051, "Other / Uncovered Subject": 0.001658107664367711, "NONE": 0.9290986585904538}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10162, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10162, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:08.556929+00:00", "scoring_key_slot": "primary", "dataset_uid": "51598067-75ff-4558-addb-4e98fead9249", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "c2a421e3e4b43d5e1586e8cf37a6d9dc1995269a8a7c5e39c5e40b1fc707e8d2", "source_metadata": {"uuid": "51598067-75ff-4558-addb-4e98fead9249", "repo_name": "davidwbritt/trademo_entities", "relative_path": "tokenized_index/prune_inverted_index.py", "category": "DATA", "algo_rel_score": 0.9715931415557861, "quality_score": 6.052150726318359, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 4, "repo_created_at": "2024-11-16T15:15:16.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "0746bcdeb42185d83dd0ffe61ebeb835084d9d0d", "size_bytes": 7011, "file_timestamp": 1732461397, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "d29f012621e6d2ae3c5589dae015397ff2b24ceb"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 4, "repo_created_at": "2024-11-16T15:15:16.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "0746bcdeb42185d83dd0ffe61ebeb835084d9d0d", "size_bytes": 7011, "file_timestamp": 1732461397, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "d29f012621e6d2ae3c5589dae015397ff2b24ceb", "url": "https://github.com/davidwbritt/trademo_entities/blob/d29f012621e6d2ae3c5589dae015397ff2b24ceb/tokenized_index/prune_inverted_index.py"}, "gpt4o_tokens": 1560, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.6678172578917227, "noise_severity": 0.012456795087460627, "missing_content_severity": 0.002832405787627832, "factual_density": 3.1276440870578868, "pretraining_utility": 0.6110675563641672, "math_density": 0.210954657837168, "code_density": 3.817345131565106, "reasoning_density": 1.1544739487409326, "education_level": 2.3230949192064765, "creative_writing_quality": -0.17862405833741898}, "score_applicability": {"education_level": 0.999086819635372, "creative_writing_quality": 0.0004926308910673158}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/dianaruth/knowledgetechnologies/blob/b53cf6078429b3cd2cb4773e4197da38faf50bb4/project2/project2.py", "id": "python-0-a54f05369944135c8fc6a10ebafc0140", "text": "import sys\nimport numpy as np\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.naive_bayes import GaussianNB\n\ndef get_class(name) :\n    if name == \"B\" :\n        return 0\n    elif name == \"H\" :\n        return 1\n    elif name == \"SD\" :\n        return 2\n    elif name == \"Se\" :\n        return 3\n    elif name == \"W\" :\n        return 4\n    # indicate error\n    else :\n        return -1\n\ndef get_name(c) :\n    if c == 0 :\n        return \"B\"\n    elif c == 1 :\n        return \"H\"\n    elif c == 2 :\n        return \"SD\"\n    elif c == 3 :\n        return \"Se\"\n    elif c == 4 :\n        return \"W\"\n    # indicate error\n    else :\n        return -1\n\ndef parse_input(lines) :\n    features = []\n    ids = []\n    model = []\n    answers = []\n    for line in lines :\n        if '@ATTRIBUTE' in line:\n            if not (\"id NUMERIC\" in line or \"location {B,H,SD,Se,W}\" in line) :\n                tokens = line.split(' ')\n                features.append(tokens[1])\n        elif '@RELATION' in line or '@DATA' in line :\n            pass\n        else :\n            tokens = line.split(',')\n            id = tokens[0]\n            ids.append(int(id.strip()))\n            answer = tokens[len(features) + 1]\n            answers.append(int(get_class(answer.strip())))\n            tokens.remove(id)\n            tokens.remove(answer)\n            tokens = [int(t) for t in tokens]\n            model.append(tokens)\n    return features, ids, model, answers\n\ndef print_predictions(ids, results, f) :\n    for i in range(0, len(ids)) :\n        print(str(ids[i]) + \" : \" + str(get_name(results[i])), file=f)\n\ndef print_stats(predicted, actual, f) :\n    # 2D array representing confusion matrix\n    stats = [[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0]]\n    for i in range(0, len(actual)) :\n        p = int(predicted[i])\n        a = int(actual[i])\n        stats[p][a] = stats[p][a] + 1\n    # TP, FP, FN\n    stats2 = [[0,0,0],[0,0,0],[0,0,0],[0,0,0],[0,0,0]]\n    # for each class\n    for i in range(0, 5) :\n        for j in range(0, 5) :\n            if i == j :\n                # true positive\n                stats2[i][0] = stats2[i][0] + stats[i][j]\n            else :\n                # false positive for i\n                stats2[i][1] = stats2[i][1] + stats[i][j]\n                # false negative for j\n                stats2[j][2] = stats2[j][2] + stats[i][j]\n    print(\"Accuracy: \" + str(accuracy_score(actual, predicted)) + '\\n', file=f)\n    for i in range(0, 5) :\n        precision = stats2[i][0]/(stats2[i][0] + stats2[i][1])\n        print(\"Precision - \" + get_name(i) + \": \" + str(precision), file=f)\n        recall = stats2[i][0]/(stats2[i][0] + stats2[i][2])\n        print(\"Recall - \" + get_name(i) + \": \" + str(recall), file=f)\n        f1_score = (2 * precision * recall)/(precision + recall)\n        print(\"F1 Score - \" + get_name(i) + \": \" + str(f1_score) + '\\n', file=f)\n\ndef print_for_kaggle(ids, results, f) :\n    print(\"Id,Category\", file=f)\n    for i in range(0, len(ids)) :\n        print(str(ids[i]) + \",\" + str(get_name(results[i])), file=f)\n\ndef main() :\n    # open training data file\n    # NOTE: sys.argv[0] is the Python file itself, so we skip it\n    train_file = open(sys.argv[1], 'r')\n    dev_file = open(sys.argv[2], 'r')\n    test_file = open(sys.argv[3], 'r')\n\n    # create output files\n    stats = open(\"stats.txt\", \"w\")\n    predictions = open(\"predictions.txt\", \"w\")\n    kaggle = open(\"dianaruth_kaggle.csv\", \"w\")\n\n    # parse ARFF format\n    # 1. put features into an array\n    # 2. create 2D array representing the vector space model for the tweets\n    # 3. create array with classes for training data\n    train_lines = train_file.readlines()\n    dev_lines = dev_file.readlines()\n    test_lines = test_file.readlines()\n\n    # close files\n    train_file.close()\n    dev_file.close()\n    test_file.close()\n\n    # get parsed input\n    train_features, train_ids, train_model, train_answers = parse_input(train_lines)\n    dev_features, dev_ids, dev_model, dev_answers = parse_input(dev_lines)\n    test_features, test_ids, test_model, test_answers = parse_input(test_lines)\n\n    ###########################################################################\n    # 1-NN\n    ###########################################################################\n\n    # set up classifier\n    neighbors1 = KNeighborsClassifier(n_neighbors=1)\n    neighbors1.fit(train_model, train_answers)\n\n    # predict development data answers and calculate statistics\n    dev_predicted_answers_1nn = []\n    for m in dev_model :\n        result = neighbors1.predict([m])\n        dev_predicted_answers_1nn.append(result[0])\n    print(\"-------------------------------------------------------------------\", file=stats)\n    print(\"-------------------------------------------------------------------\", file=predictions)\n    print(\"1-NN\", file=stats)\n    print(\"1-NN\", file=predictions)\n    print(\"-------------------------------------------------------------------\", file=stats)\n    print(\"-------------------------------------------------------------------\", file=predictions)\n    print_stats(dev_predicted_answers_1nn, dev_answers, stats)\n\n    # predict test data answers\n    test_predicted_answers_1nn = []\n    for m in test_model :\n        result = neighbors1.kneighbors([m])\n        test_predicted_answers_1nn.append(result[0])\n    print_predictions(test_ids, test_predicted_answers_1nn, predictions)\n\n    ###########################################################################\n    # 3-NN\n    ###########################################################################\n\n    # set up classifier\n    neighbors3 = KNeighborsClassifier(n_neighbors=3)\n    neighbors3.fit(train_model, train_answers)\n\n    # predict development data answers and calculate statistics\n    dev_predicted_answers_3nn = []\n    for m in dev_model :\n        result = neighbors3.predict([m])\n        dev_predicted_answers_3nn.append(result[0])\n    print(\"-------------------------------------------------------------------\", file=stats)\n    print(\"-------------------------------------------------------------------\", file=predictions)\n    print(\"3-NN\", file=stats)\n    print(\"3-NN\", file=predictions)\n    print(\"-------------------------------------------------------------------\", file=stats)\n    print(\"-------------------------------------------------------------------\", file=predictions)\n    print_stats(dev_predicted_answers_3nn, dev_answers, stats)\n\n    # predict test data answers\n    test_predicted_answers_3nn = []\n    for m in test_model :\n        result = neighbors3.predict([m])\n        test_predicted_answers_3nn.append(result[0])\n    print_predictions(test_ids, test_predicted_answers_3nn, predictions)\n\n    ###########################################################################\n    # Decision Tree\n    ###########################################################################\n\n    # set up classifier\n    decision_tree = DecisionTreeClassifier()\n    decision_tree = decision_tree.fit(train_model, train_answers)\n\n    # predict development data answers and calculate statistics\n    dev_predicted_answers_decision_tree = []\n    for m in dev_model :\n        result = decision_tree.predict([m])\n        dev_predicted_answers_decision_tree.append(result[0])\n    print(\"-------------------------------------------------------------------\", file=stats)\n    print(\"-------------------------------------------------------------------\", file=predictions)\n    print(\"Decision Tree\", file=stats)\n    print(\"Decision Tree\", file=predictions)\n    print(\"-------------------------------------------------------------------\", file=stats)\n    print(\"-------------------------------------------------------------------\", file=predictions)\n    print_stats(dev_predicted_answers_decision_tree, dev_answers, stats)\n\n    # predict test data answers\n    test_predicted_answers_decision_tree = []\n    for m in test_model :\n        result = decision_tree.predict([m])\n        test_predicted_answers_decision_tree.append(result[0])\n    print_predictions(test_ids, test_predicted_answers_decision_tree, predictions)\n\n    ###########################################################################\n    # Naive Bayes\n    ###########################################################################\n\n    # set up classifier\n    gnb = GaussianNB()\n    nb = gnb.fit(train_model, train_answers)\n\n    # predict development data answers and calculate statistics\n    dev_predicted_answers_nb = []\n    for m in dev_model :\n        result = nb.predict([m])\n        dev_predicted_answers_nb.append(result[0])\n    print(\"-------------------------------------------------------------------\", file=stats)\n    print(\"-------------------------------------------------------------------\", file=predictions)\n    print(\"Naive Bayes\", file=stats)\n    print(\"Naive Bayes\", file=predictions)\n    print(\"-------------------------------------------------------------------\", file=stats)\n    print(\"-------------------------------------------------------------------\", file=predictions)\n    print_stats(dev_predicted_answers_nb, dev_answers, stats)\n\n    # predict test data answers\n    test_predicted_answers_nb = []\n    for m in test_model :\n        result = nb.predict([m])\n        test_predicted_answers_nb.append(result[0])\n    print_predictions(test_ids, test_predicted_answers_nb, predictions)\n    # print_for_kaggle(test_ids, test_predicted_answers_nb, kaggle)\n\n    # close output files\n    stats.close()\n    predictions.close()\n    kaggle.close()\n\nif __name__ == '__main__' :\n    main()\n", "text_preview": "import sys import numpy as np from sklearn.metrics import accuracy_score from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.naive_bayes import GaussianNB def get_class(name) : if ", "input_sha256": "9532a4da28f8ede77e50fe13b244f58f72702aa5c191aa975f3dde7c7068d9f3", "text_sha256": "a54f05369944135c8fc6a10ebafc0140d638da448f9b45d8810eb9f72b4a2217", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12117, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.6159740766035254, "noise_severity": 0.27073439483223677, "missing_content_severity": 0.009597402646641592, "factual_density": 2.9234295886919783, "pretraining_utility": 0.6947806045470949, "math_density": 2.5521534145913156, "code_density": 3.8658959981560144, "reasoning_density": 0.8928443956459536, "education_level": 2.131755672989406, "creative_writing_quality": -0.4196672596282423}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.4756 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.015853040316099354, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9885514731155708, "secondary": 0.9536924607228416, "tertiary": 0.9702347636613803, "structured_records": 0.021632684401787765, "procedural_steps": 0.07848428520482373, "worked_solution": 0.03271113314509221, "contextual_dialogue": 1.0083774085298898e-05, "content_quality": 0.45414255280590443, "noise_severity": 0.7743880043064694, "missing_content_severity": 0.992002164461132, "factual_density": 0.44817619355870164, "pretraining_utility": 0.5287331243034836, "math_density": 0.0, "code_density": 0.8882466651300122, "reasoning_density": 0.2559630036283721, "education_level": 0.7944148325444413, "creative_writing_quality": 0.9994183947168878, "primary_topic": 0.8818284776069041, "secondary_topic": 0.45464717415134404}, "probability_margin": {"primary": 0.9835405069358601, "secondary": 0.9371804740846893, "tertiary": 0.9595105158612398, "education_level": 0.6949825429044334, "creative_writing_quality": 0.9993061638341871, "primary_topic": 0.780673630732195, "secondary_topic": 0.13913947709146307}, "raw_probabilities": {"primary": {"Academic Writing": 0.00016703604304283936, "Content Listing": 3.453906998076335e-05, "Creative Writing": 3.7214306607589076e-05, "Customer Support Page": 2.0274973034232522e-05, "Q&A Forum": 2.985964770224398e-05, "Discussion Thread": 2.2059115879012562e-05, "Comment Section": 3.5619828256732824e-05, "Missing Content": 5.474845887619211e-05, "Knowledge Article": 0.00020143455135093283, "Legal Notices": 5.0825921605112147e-05, "News Article": 1.4762916899728957e-05, "Organizational Announcement": 2.1588370688336096e-05, "Nonfiction Writing": 4.729382620045213e-05, "Organizational About Page": 3.072994027210657e-05, "Personal About Page": 3.1822508713429686e-05, "Personal Blog": 1.959316693366365e-05, "Product Page": 1.5812135694716928e-05, "Spam / Ads": 2.8639083606913166e-05, "Structured Data": 0.0009709775084115857, "Technical Writing": 0.9890284950690886, "Transcript / Interview": 3.55469698934636e-05, "Tutorial / How-To Guide": 0.0054879881332285125, "User Reviews": 1.5181061260537444e-05, "Other / Unknown": 0.003597957392772271}, "secondary": {"Academic Writing": 0.0013866317227952285, "Content Listing": 0.0001445750351673777, "Creative Writing": 0.0001793572429375797, "Customer Support Page": 0.00010398522875604834, "Q&A Forum": 9.588251102428128e-05, "Discussion Thread": 7.51114820116566e-05, "Comment Section": 0.00016379404570914042, "Missing Content": 0.00024739924042480656, "Knowledge Article": 0.0011569593766856576, "Legal Notices": 0.0001972867644625924, "News Article": 0.00011198117262452939, "Organizational Announcement": 9.514641886589909e-05, "Nonfiction Writing": 0.00012235552109496415, "Organizational About Page": 0.0001226742585359354, "Personal About Page": 0.00011476712042128292, "Personal Blog": 9.730006843363873e-05, "Product Page": 6.096000196151687e-05, "Spam / Ads": 0.00010510752699780761, "Structured Data": 0.0037153072038465006, "Technical Writing": 0.01844146744136727, "Transcript / Interview": 0.00012681477491067672, "Tutorial / How-To Guide": 0.017454186859786655, "User Reviews": 5.900745512227137e-05, "NONE": 0.9556219415260566}, "tertiary": {"Academic Writing": 0.001842863037699662, "Content Listing": 7.783321366713821e-05, "Creative Writing": 0.0001054696960193285, "Customer Support Page": 5.086473738289305e-05, "Q&A Forum": 4.89047352827297e-05, "Discussion Thread": 4.161642372890622e-05, "Comment Section": 8.912586717375672e-05, "Missing Content": 0.0002321440074663396, "Knowledge Article": 0.001274158032630546, "Legal Notices": 0.00016417670789165806, "News Article": 6.063709323188479e-05, "Organizational Announcement": 4.9378137669915555e-05, "Nonfiction Writing": 0.00010789610815102901, "Organizational About Page": 7.89936420631068e-05, "Personal About Page": 6.575127089196572e-05, "Personal Blog": 5.4824584524370233e-05, "Product Page": 3.889989059794726e-05, "Spam / Ads": 6.764695275651036e-05, "Structured Data": 0.002753782877405017, "Technical Writing": 0.011964465980916274, "Transcript / Interview": 6.906585442281714e-05, "Tutorial / How-To Guide": 0.009253555687564691, "User Reviews": 3.296361870538993e-05, "NONE": 0.9714749818421561}, "structured_records": {"false": 0.9783673155982122, "true": 0.021632684401787765}, "procedural_steps": {"false": 0.9215157147951762, "true": 0.07848428520482373}, "worked_solution": {"false": 0.9672888668549078, "true": 0.03271113314509221}, "contextual_dialogue": {"false": 0.9999899162259147, "true": 1.0083774085298898e-05}, "content_quality": {"-2": 0.018033265189447645, "-1": 0.08386507335184448, "0": 0.29769748774266275, "1": 0.464902667097825, "2": 0.13550150661822002}, "noise_severity": {"0": 0.7886826539098409, "1": 0.1603028942795998, "2": 0.04465222177830531, "3": 0.004321863132989818, "4": 0.0020403668992642256}, "missing_content_severity": {"0": 0.9926786007302907, "1": 0.005684176526514579, "2": 0.0011037484589442746, "3": 0.00042816793476354697, "4": 0.00010530634948695565}, "factual_density": {"0": 0.018861224847414225, "1": 0.05331269772210995, "2": 0.20617041953232726, "3": 0.42884657968738044, "4": 0.2928090782107681}, "pretraining_utility": {"-2": 0.006570868620305731, "-1": 0.044980607333282706, "0": 0.3256960026168798, "1": 0.49260209373807445, "2": 0.1301504276914573}, "math_density": {"0": 0.05499425557427937, "1": 0.1499397824926767, "2": 0.2569671285703686, "3": 0.26411595849279984, "4": 0.27398287486987544}, "code_density": {"0": 0.00029801062088250375, "1": 0.0005835777527123139, "2": 0.013876834440404705, "3": 0.10340755722150906, "4": 0.8818340199644913}, "reasoning_density": {"0": 0.6175984408088719, "1": 0.10252072445500594, "2": 0.1249705705033695, "3": 0.0792585267468014, "4": 0.07565173748595108}, "education_level": {"none": 0.0010147552861187715, "0": 0.0013036924925635888, "1": 0.023359735925691544, "2": 0.8286790271203678, "3": 0.13369648421593433, "4": 0.011946304959324121}, "creative_writing_quality": {"not_applicable": 0.9995153289307398, "-2": 8.548678790743746e-05, "-1": 0.00012148036869834029, "0": 0.0002091650965526174, "1": 4.80242671474868e-05, "2": 2.051454895414205e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00012285924492790758, "Art & Design": 0.00024115529655872783, "Law & Crime": 0.00019524304445314855, "Education & Careers": 0.0002272964535604414, "Entertainment & Media": 0.00027178669910509265, "Fashion & Beauty": 0.00016874702580736345, "Business, Economics & Finance": 0.0003066382792125921, "Food & Dining": 0.00015379456687063318, "Games & Gaming": 0.0003169651483319557, "Electronics & Hardware": 0.00024348610303783204, "Health, Healthcare & Wellness": 0.0002482756499794055, "History & Geography": 0.0003141244170637253, "Home, Pets & Hobbies": 0.00016160150427741354, "Industry & Production": 0.0001805690925184485, "Language, Literature & Philosophy": 0.0003680545498723001, "Politics & Government": 0.00015568463508538533, "Religion & Spirituality": 0.00014156948254637956, "Science, Mathematics & Engineering": 0.1056999054282897, "Relationships & Community": 0.0001794723966567949, "Software Usage & Applications": 0.0013236942479486278, "Programming & Software Engineering": 0.8863735361604846, "Sports & Fitness": 0.00022242024197735347, "Vehicles & Transportation": 0.0001540375707148541, "Travel & Tourism": 0.0002967675573455471, "Other / Uncovered Subject": 0.0016602813782022417, "Unknown / Insufficient Content": 0.00027203382517128203}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006817936528026002, "Art & Design": 0.0007044478027427961, "Law & Crime": 0.000958176406792807, "Education & Careers": 0.0005633932540865938, "Entertainment & Media": 0.0018303396044637577, "Fashion & Beauty": 0.000463958675045632, "Business, Economics & Finance": 0.0008672573339381746, "Food & Dining": 0.0004300461579582235, "Games & Gaming": 0.000621838141173368, "Electronics & Hardware": 0.0006739562377260051, "Health, Healthcare & Wellness": 0.0007747769632159919, "History & Geography": 0.0008423617679045786, "Home, Pets & Hobbies": 0.00043980441621851166, "Industry & Production": 0.0005110624850445994, "Language, Literature & Philosophy": 0.0013363436936929176, "Politics & Government": 0.0004934751312121574, "Religion & Spirituality": 0.000532092584008285, "Science, Mathematics & Engineering": 0.47562228283783087, "Relationships & Community": 0.0004914643030371632, "Software Usage & Applications": 0.0009720545068567289, "Programming & Software Engineering": 0.16992330155765237, "Sports & Fitness": 0.0005025170000133454, "Vehicles & Transportation": 0.0003648946911565862, "Travel & Tourism": 0.0008278434713684181, "Other / Uncovered Subject": 0.002087711577689564, "NONE": 0.3364828057463678}}, "probabilities": {"primary": {"Academic Writing": 0.00016703604304283936, "Content Listing": 3.453906998076335e-05, "Creative Writing": 3.7214306607589076e-05, "Customer Support Page": 2.0274973034232522e-05, "Q&A Forum": 2.985964770224398e-05, "Discussion Thread": 2.2059115879012562e-05, "Comment Section": 3.5619828256732824e-05, "Missing Content": 5.474845887619211e-05, "Knowledge Article": 0.00020143455135093283, "Legal Notices": 5.0825921605112147e-05, "News Article": 1.4762916899728957e-05, "Organizational Announcement": 2.1588370688336096e-05, "Nonfiction Writing": 4.729382620045213e-05, "Organizational About Page": 3.072994027210657e-05, "Personal About Page": 3.1822508713429686e-05, "Personal Blog": 1.959316693366365e-05, "Product Page": 1.5812135694716928e-05, "Spam / Ads": 2.8639083606913166e-05, "Structured Data": 0.0009709775084115857, "Technical Writing": 0.9890284950690886, "Transcript / Interview": 3.55469698934636e-05, "Tutorial / How-To Guide": 0.0054879881332285125, "User Reviews": 1.5181061260537444e-05, "Other / Unknown": 0.003597957392772271}, "secondary": {"Academic Writing": 0.0013866317227952288, "Content Listing": 0.00014457503516737772, "Creative Writing": 0.00017935724293757972, "Customer Support Page": 0.00010398522875604835, "Q&A Forum": 9.58825110242813e-05, "Discussion Thread": 7.511148201165661e-05, "Comment Section": 0.00016379404570914045, "Missing Content": 0.0002473992404248066, "Knowledge Article": 0.0011569593766856579, "Legal Notices": 0.00019728676446259243, "News Article": 0.0001119811726245294, "Organizational Announcement": 9.51464188658991e-05, "Nonfiction Writing": 0.00012235552109496418, "Organizational About Page": 0.00012267425853593542, "Personal About Page": 0.00011476712042128294, "Personal Blog": 9.730006843363874e-05, "Product Page": 6.096000196151688e-05, "Spam / Ads": 0.00010510752699780762, "Structured Data": 0.003715307203846501, "Technical Writing": 0.018441467441367272, "Transcript / Interview": 0.00012681477491067675, "Tutorial / How-To Guide": 0.01745418685978666, "User Reviews": 5.900745512227138e-05, "NONE": 0.9556219415260567}, "tertiary": {"Academic Writing": 0.001842863037699662, "Content Listing": 7.783321366713821e-05, "Creative Writing": 0.0001054696960193285, "Customer Support Page": 5.086473738289305e-05, "Q&A Forum": 4.89047352827297e-05, "Discussion Thread": 4.161642372890622e-05, "Comment Section": 8.912586717375672e-05, "Missing Content": 0.0002321440074663396, "Knowledge Article": 0.001274158032630546, "Legal Notices": 0.00016417670789165806, "News Article": 6.063709323188479e-05, "Organizational Announcement": 4.9378137669915555e-05, "Nonfiction Writing": 0.00010789610815102901, "Organizational About Page": 7.89936420631068e-05, "Personal About Page": 6.575127089196572e-05, "Personal Blog": 5.4824584524370233e-05, "Product Page": 3.889989059794726e-05, "Spam / Ads": 6.764695275651036e-05, "Structured Data": 0.002753782877405017, "Technical Writing": 0.011964465980916274, "Transcript / Interview": 6.906585442281714e-05, "Tutorial / How-To Guide": 0.009253555687564691, "User Reviews": 3.296361870538993e-05, "NONE": 0.9714749818421561}, "structured_records": {"false": 0.9783673155982122, "true": 0.021632684401787765}, "procedural_steps": {"false": 0.9215157147951762, "true": 0.07848428520482373}, "worked_solution": {"false": 0.9672888668549078, "true": 0.03271113314509221}, "contextual_dialogue": {"false": 0.9999899162259147, "true": 1.0083774085298898e-05}, "content_quality": {"-2": 0.018033265189447648, "-1": 0.0838650733518445, "0": 0.2976974877426628, "1": 0.4649026670978251, "2": 0.13550150661822005}, "noise_severity": {"0": 0.7886826539098409, "1": 0.1603028942795998, "2": 0.04465222177830531, "3": 0.004321863132989818, "4": 0.0020403668992642256}, "missing_content_severity": {"0": 0.9926786007302907, "1": 0.005684176526514579, "2": 0.0011037484589442746, "3": 0.00042816793476354697, "4": 0.00010530634948695565}, "factual_density": {"0": 0.018861224847414225, "1": 0.05331269772210995, "2": 0.20617041953232726, "3": 0.42884657968738044, "4": 0.2928090782107681}, "pretraining_utility": {"-2": 0.006570868620305731, "-1": 0.044980607333282706, "0": 0.3256960026168798, "1": 0.49260209373807445, "2": 0.1301504276914573}, "math_density": {"0": 0.05499425557427938, "1": 0.14993978249267673, "2": 0.25696712857036863, "3": 0.2641159584927999, "4": 0.2739828748698755}, "code_density": {"0": 0.0002980106208825038, "1": 0.000583577752712314, "2": 0.013876834440404707, "3": 0.10340755722150907, "4": 0.8818340199644914}, "reasoning_density": {"0": 0.617598440808872, "1": 0.10252072445500596, "2": 0.12497057050336952, "3": 0.07925852674680141, "4": 0.0756517374859511}, "education_level": {"none": 0.0010147552861187713, "0": 0.0013036924925635886, "1": 0.02335973592569154, "2": 0.8286790271203676, "3": 0.1336964842159343, "4": 0.011946304959324118}, "creative_writing_quality": {"not_applicable": 0.99951532893074, "-2": 8.548678790743747e-05, "-1": 0.00012148036869834031, "0": 0.00020916509655261744, "1": 4.802426714748682e-05, "2": 2.0514548954142052e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001228592449279076, "Art & Design": 0.00024115529655872788, "Law & Crime": 0.0001952430444531486, "Education & Careers": 0.00022729645356044144, "Entertainment & Media": 0.0002717866991050927, "Fashion & Beauty": 0.00016874702580736348, "Business, Economics & Finance": 0.00030663827921259215, "Food & Dining": 0.0001537945668706332, "Games & Gaming": 0.00031696514833195577, "Electronics & Hardware": 0.0002434861030378321, "Health, Healthcare & Wellness": 0.00024827564997940553, "History & Geography": 0.00031412441706372534, "Home, Pets & Hobbies": 0.00016160150427741357, "Industry & Production": 0.00018056909251844852, "Language, Literature & Philosophy": 0.0003680545498723002, "Politics & Government": 0.00015568463508538536, "Religion & Spirituality": 0.0001415694825463796, "Science, Mathematics & Engineering": 0.10569990542828973, "Relationships & Community": 0.00017947239665679494, "Software Usage & Applications": 0.001323694247948628, "Programming & Software Engineering": 0.8863735361604849, "Sports & Fitness": 0.00022242024197735352, "Vehicles & Transportation": 0.00015403757071485414, "Travel & Tourism": 0.00029676755734554713, "Other / Uncovered Subject": 0.001660281378202242, "Unknown / Insufficient Content": 0.0002720338251712821}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006817936528026003, "Art & Design": 0.0007044478027427963, "Law & Crime": 0.0009581764067928072, "Education & Careers": 0.0005633932540865939, "Entertainment & Media": 0.001830339604463758, "Fashion & Beauty": 0.00046395867504563207, "Business, Economics & Finance": 0.0008672573339381747, "Food & Dining": 0.00043004615795822355, "Games & Gaming": 0.0006218381411733681, "Electronics & Hardware": 0.0006739562377260052, "Health, Healthcare & Wellness": 0.000774776963215992, "History & Geography": 0.0008423617679045787, "Home, Pets & Hobbies": 0.0004398044162185117, "Industry & Production": 0.0005110624850445995, "Language, Literature & Philosophy": 0.0013363436936929178, "Politics & Government": 0.0004934751312121575, "Religion & Spirituality": 0.0005320925840082852, "Science, Mathematics & Engineering": 0.4756222828378309, "Relationships & Community": 0.0004914643030371633, "Software Usage & Applications": 0.000972054506856729, "Programming & Software Engineering": 0.1699233015576524, "Sports & Fitness": 0.0005025170000133455, "Vehicles & Transportation": 0.0003648946911565863, "Travel & Tourism": 0.0008278434713684182, "Other / Uncovered Subject": 0.0020877115776895645, "NONE": 0.33648280574636785}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10798, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10798, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:08.626979+00:00", "scoring_key_slot": "primary", "dataset_uid": "a7e2e1cf-85f8-451f-b458-fe823bf56ca5", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "02ad09e304f1aba4ab4d84f73de0977b4f85532441fe6efe387c9c300dad3240", "source_metadata": {"uuid": "a7e2e1cf-85f8-451f-b458-fe823bf56ca5", "repo_name": "dianaruth/knowledgetechnologies", "relative_path": "project2/project2.py", "category": "DATA", "algo_rel_score": 0.972407341003418, "quality_score": 3.929582357406616, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 7, "repo_created_at": "2016-08-18T01:27:21.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "b9f2aab7ffa19fd0dac0d6d6ae4d6093c4f4d222", "size_bytes": 9615, "file_timestamp": 1476330177, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "b53cf6078429b3cd2cb4773e4197da38faf50bb4"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 7, "repo_created_at": "2016-08-18T01:27:21.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "b9f2aab7ffa19fd0dac0d6d6ae4d6093c4f4d222", "size_bytes": 9615, "file_timestamp": 1476330177, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "b53cf6078429b3cd2cb4773e4197da38faf50bb4", "url": "https://github.com/dianaruth/knowledgetechnologies/blob/b53cf6078429b3cd2cb4773e4197da38faf50bb4/project2/project2.py"}, "gpt4o_tokens": 2165, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.6159740766035254, "noise_severity": 0.27073439483223677, "missing_content_severity": 0.009597402646641592, "factual_density": 2.9234295886919783, "pretraining_utility": 0.6947806045470949, "math_density": 2.552153414591315, "code_density": 3.8658959981560144, "reasoning_density": 0.8928443956459536, "education_level": 2.131755672989406, "creative_writing_quality": -0.4196672596282423}, "score_applicability": {"education_level": 0.9989852447138811, "creative_writing_quality": 0.0004846710692600241}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/database-tools/mongosyncdb/blob/bfdd8108e8118b022d01cc0d37092deffb4f1f2e/src/change_stream.py", "id": "python-0-311adc5ffe0df4317122c25f9bdfc410", "text": "from src import logger\nimport pymongo\nfrom pymongo import MongoClient\nfrom pymongo.errors import OperationFailure\nfrom pymongo.write_concern import WriteConcern\nimport sys\nfrom bson.timestamp import Timestamp\nimport time\nfrom dateutil import tz\nfrom datetime import datetime\n\napplyCount = 0\nlast_batch_time = time.time()\n\ndef convert_timestamp_in_datetime(timestamp):\n    \"\"\"Converts a BSON timestamp to a local datetime.\"\"\"\n    to_zone = tz.tzlocal()\n    dt_naive_utc = datetime.fromtimestamp(timestamp)\n    return dt_naive_utc.astimezone(to_zone)\n\ndef getResumeTimeStamp(config): \n    \"\"\"Retrieves the last resume timestamp from the checkpoint collection.\"\"\"\n    doc = config[\"colCheckpoint\"].find_one(\n        { \"database\": config[\"database\"], \"parameter\": \"resumeTimestamp\" },\n        { \"_id\": 0, \"ts\": 1 }\n    )\n\n    if doc:\n        timestamp = doc['ts']\n        timestamp_ts = timestamp.time\n        timestamp_inc = timestamp.inc\n        config[\"resumeTimeStamp\"] = Timestamp(timestamp_ts, timestamp_inc + 1)\n\ndef saveResumeTimeStamp(timestamp, config):\n    \"\"\"Saves the current timestamp to the checkpoint collection to allow resuming later.\"\"\"\n    try:\n        checkpointCollection = config[\"clientTarget\"][\"mongosyncdb\"][\"checkpoints\"]\n        checkpointCollection.with_options(write_concern=WriteConcern(w=config[\"changeStream\"][\"writeConcern\"])).update_one(\n            {\"database\": config[\"database\"], \"parameter\": \"resumeTimestamp\"},\n            {\"$set\": {\"ts\": timestamp}},\n            upsert=True\n        )\n        config[\"resumeTimeStamp\"] = timestamp\n        logger.log(f\"Checkpoint saved: {timestamp}\",config)\n\n    except Exception as e:\n        logger.log(f\"Error saving checkpoint for database '{config['database']}': {str(e)}\",config)\n        sys.exit(1)  # Stop execution if checkpoint can't be saved\n\ndef start(config):\n    \"\"\"Starts the MongoDB Change Stream and applies changes to the target database.\"\"\"\n    config[\"dbCheckpoint\"] = config[\"clientTarget\"][\"mongosyncdb\"]\n    config[\"colCheckpoint\"] = config[\"dbCheckpoint\"][\"checkpoints\"]\n\n    if not config[\"changeStream\"][\"resume\"]:\n        start_timestamp = config[\"lastTimestampFromOplog\"]\n        start_position = convert_timestamp_in_datetime(start_timestamp.time)\n        logger.log(f\"ChangeStream started for database '{config['database']}' from '{start_timestamp}' - ({start_position})\",config)\n    else:\n        getResumeTimeStamp(config)\n        if \"resumeTimeStamp\" not in config:\n            logger.log(f\"Error: No checkpoint found for database '{config['database']}', but resume=True was used!\",config)\n            logger.log(\"Solution: Either start with `resume=False` to perform an initial load OR manually add a checkpoint.\",config)\n            sys.exit(1)  # Abort execution safely\n    \n        start_timestamp = config[\"resumeTimeStamp\"]\n        start_position = convert_timestamp_in_datetime(start_timestamp.time)\n        logger.log(f\"ChangeStream resumed for database '{config['database']}' from '{start_timestamp}' - ({start_position})\",config)\n    \n    # Start Change Stream\n    with config[\"dbSource\"].watch(start_at_operation_time=start_timestamp, full_document=\"updateLookup\") as stream:\n        for change in stream:\n            apply_change_to_target(change, config)\n\ndef apply_change_to_target(change, config):\n    \"\"\"Applies the changes received from the Change Stream to the target database.\"\"\"\n    try:\n        global applyCount\n        global last_batch_time\n\n        clusterTime = change[\"clusterTime\"]\n        operation = change[\"operationType\"]\n        collectionName = change[\"ns\"][\"coll\"]\n        coll = config[\"dbTarget\"][collectionName]\n\n        try:  # Make sure all database writes are inside this try block\n            if operation == \"insert\":\n                document = change[\"fullDocument\"]\n                coll.with_options(write_concern=WriteConcern(w=config[\"changeStream\"][\"writeConcern\"])).replace_one(\n                    {\"_id\": document[\"_id\"]}, document, upsert=True\n                )\n            \n            elif operation == \"update\":\n                documentId = change[\"documentKey\"][\"_id\"]\n                updateFields = change[\"updateDescription\"][\"updatedFields\"]\n                removeFields = change[\"updateDescription\"][\"removedFields\"]\n\n                coll.with_options(write_concern=WriteConcern(w=config[\"changeStream\"][\"writeConcern\"])).update_one(\n                    { \"_id\": documentId }, { \"$set\": updateFields }\n                )\n\n                for field in removeFields:\n                    coll.with_options(write_concern=WriteConcern(w=config[\"changeStream\"][\"writeConcern\"])).update_one(\n                        { \"_id\": documentId }, { \"$unset\": { field: 1 } }\n                    )\n            \n            elif operation == \"replace\":\n                document = change[\"fullDocument\"]\n                coll.with_options(write_concern=WriteConcern(w=config[\"changeStream\"][\"writeConcern\"])).replace_one(\n                    {\"_id\": document[\"_id\"]}, document\n                )\n            \n            elif operation == \"delete\":\n                documentId = change[\"documentKey\"][\"_id\"]\n                coll.with_options(write_concern=WriteConcern(w=config[\"changeStream\"][\"writeConcern\"])).delete_one(\n                    {\"_id\": documentId}\n                )\n            \n            elif operation == \"rename\":\n                collectionNameTo = change[\"to\"][\"coll\"]\n                coll.with_options(write_concern=WriteConcern(w=config[\"changeStream\"][\"writeConcern\"])).rename(collectionNameTo)\n            \n            elif operation in [\"dropDatabase\", \"drop\"]:\n                logger.log(f\"ChangeStream operation '{operation}' received, synchronization aborted!\",config)\n                sys.exit(1)  # 🔴 Stop execution immediately\n\n            else:\n                logger.log(f\"Unsupported ChangeStream operation '{operation}' detected. Synchronization failed!\",config)\n                sys.exit(1)  # 🔴 Stop execution immediately\n\n        except pymongo.errors.PyMongoError as e:\n            logger.log(f\"Error writing to target database: {str(e)}\",config)\n            logger.log(\"Sync aborted due to write failure.\",config)\n            sys.exit(1)  # Stop execution if any write operation fails\n        \n        applyCount += 1\n        \n        # Checkpoint when reaching checkpointBatchSize or checkpointTimeInterval\n        if applyCount == config[\"changeStream\"][\"checkpointBatchSize\"] or time.time() - last_batch_time >= config[\"changeStream\"][\"checkpointTimeInterval\"]:\n            saveResumeTimeStamp(clusterTime, config)\n            checkpointPosition = convert_timestamp_in_datetime(clusterTime.time)\n            logger.log(f\"Checkpoint completed, position '{checkpointPosition}' - {clusterTime}\",config)\n            applyCount = 0\n            last_batch_time = time.time()\n\n    except Exception as e:\n        logger.log(\"ChangeStream processing failed!\",config)\n        logger.log(str(e),config)\n        sys.exit(1)  # Stop execution on failure", "text_preview": "from src import logger import pymongo from pymongo import MongoClient from pymongo.errors import OperationFailure from pymongo.write_concern import WriteConcern import sys from bson.timestamp import Timestamp import time from dateutil impor", "input_sha256": "4bce67449978283af9d05ddcafe51f89a4ea7993c3862255174ab3ace59d9eb9", "text_sha256": "311adc5ffe0df4317122c25f9bdfc410cb0da7d76d30a52378725ab72bbbcb67", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12118, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9508744822500422, "noise_severity": 0.013904642725066077, "missing_content_severity": 0.01774745511414664, "factual_density": 3.3643988298456886, "pretraining_utility": 0.7365042559065388, "math_density": 0.13977345308800854, "code_density": 3.8285767517054192, "reasoning_density": 0.720870910498041, "education_level": 2.5342032012373306, "creative_writing_quality": -0.2207380805296992}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.001037917210469086, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9983654965574392, "secondary": 0.9921518134351387, "tertiary": 0.9932348574808456, "structured_records": 0.0010616664458269513, "procedural_steps": 0.03480026568088457, "worked_solution": 0.0020817282271255928, "contextual_dialogue": 4.2082480430396425e-06, "content_quality": 0.5673116138755258, "noise_severity": 0.9884127977291116, "missing_content_severity": 0.9852104540715445, "factual_density": 0.4703323582047406, "pretraining_utility": 0.5972669107556525, "math_density": 0.8835221224266596, "code_density": 0.8571472930878493, "reasoning_density": 0.39927424125163247, "education_level": 0.39419938968025314, "creative_writing_quality": 0.9996741912784629, "primary_topic": 0.9971616485848177, "secondary_topic": 0.9287717853890806}, "probability_margin": {"primary": 0.9977509283260354, "secondary": 0.9891140053282741, "tertiary": 0.9905472138419542, "education_level": 0.04067516998363724, "creative_writing_quality": 0.9996047070031221, "primary_topic": 0.9955339955989226, "secondary_topic": 0.8958229757184886}, "raw_probabilities": {"primary": {"Academic Writing": 2.4375179724211292e-05, "Content Listing": 1.1528358254806053e-05, "Creative Writing": 9.041108343250912e-06, "Customer Support Page": 6.922185233479523e-06, "Q&A Forum": 7.35034099872885e-06, "Discussion Thread": 4.139439455550605e-06, "Comment Section": 6.698442526071223e-06, "Missing Content": 1.1125436557893941e-05, "Knowledge Article": 7.3172314871501e-05, "Legal Notices": 1.1950270571957034e-05, "News Article": 2.4644061302734088e-06, "Organizational Announcement": 5.936005298436824e-06, "Nonfiction Writing": 5.555916088014246e-06, "Organizational About Page": 6.172618975567828e-06, "Personal About Page": 4.7427665194737785e-06, "Personal Blog": 3.875055246565322e-06, "Product Page": 4.543660349797646e-06, "Spam / Ads": 5.504786167898961e-06, "Structured Data": 0.0001310416230100935, "Technical Writing": 0.998433600867546, "Transcript / Interview": 8.909707743247153e-06, "Tutorial / How-To Guide": 0.0006826725415105515, "User Reviews": 3.505056635749376e-06, "Other / Unknown": 0.0005351719122407461}, "secondary": {"Academic Writing": 7.767961832087754e-05, "Content Listing": 3.675675146160892e-05, "Creative Writing": 5.4193569603136e-05, "Customer Support Page": 0.00018218302434254527, "Q&A Forum": 3.1273509588582415e-05, "Discussion Thread": 1.8530699529474355e-05, "Comment Section": 5.6410063593904675e-05, "Missing Content": 0.0002324676180539137, "Knowledge Article": 0.0005986682740568989, "Legal Notices": 6.656076662220318e-05, "News Article": 3.064158279791091e-05, "Organizational Announcement": 2.915777606064502e-05, "Nonfiction Writing": 3.0219712338737618e-05, "Organizational About Page": 3.826473945237821e-05, "Personal About Page": 2.7439446830090038e-05, "Personal Blog": 2.1736280903005866e-05, "Product Page": 2.105675952179543e-05, "Spam / Ads": 2.8581040860578318e-05, "Structured Data": 0.00045573977672027315, "Technical Writing": 0.003364815880400464, "Transcript / Interview": 3.6282767042184864e-05, "Tutorial / How-To Guide": 0.002065316914357282, "User Reviews": 1.7202218866985684e-05, "NONE": 0.9924788212086746}, "tertiary": {"Academic Writing": 5.228102750422444e-05, "Content Listing": 3.41667950052786e-05, "Creative Writing": 4.674899777261283e-05, "Customer Support Page": 0.00035050115696443204, "Q&A Forum": 2.901789602419637e-05, "Discussion Thread": 1.4476385053446656e-05, "Comment Section": 5.243021888018604e-05, "Missing Content": 0.00022071578495455374, "Knowledge Article": 0.0005321939716546447, "Legal Notices": 7.171122966173374e-05, "News Article": 2.639377025998596e-05, "Organizational Announcement": 2.5494431061155517e-05, "Nonfiction Writing": 3.4536277106916124e-05, "Organizational About Page": 4.2108037676835234e-05, "Personal About Page": 2.2134108345649776e-05, "Personal Blog": 1.8858136386982923e-05, "Product Page": 2.0109638350828753e-05, "Spam / Ads": 3.0983224259877505e-05, "Structured Data": 0.00026168575317960847, "Technical Writing": 0.0029695245771894133, "Transcript / Interview": 3.0649590979786894e-05, "Tutorial / How-To Guide": 0.001582384595803168, "User Reviews": 1.4155976780916795e-05, "NONE": 0.9935167384191437}, "structured_records": {"false": 0.998938333554173, "true": 0.0010616664458269513}, "procedural_steps": {"false": 0.9651997343191154, "true": 0.03480026568088457}, "worked_solution": {"false": 0.9979182717728744, "true": 0.0020817282271255928}, "contextual_dialogue": {"false": 0.999995791751957, "true": 4.2082480430396425e-06}, "content_quality": {"-2": 0.005685616053860034, "-1": 0.01919761307300196, "0": 0.2287237162420794, "1": 0.511342781831353, "2": 0.23505027279970567}, "noise_severity": {"0": 0.9879473608912122, "1": 0.010845409116313879, "2": 0.0007600021036404308, "3": 0.00024968215386265356, "4": 0.00019754573497084377}, "missing_content_severity": {"0": 0.9908764478040254, "1": 0.0024642315850275744, "2": 0.004929165901774715, "3": 0.001495727111119351, "4": 0.00023442759805289486}, "factual_density": {"0": 0.003632485836191021, "1": 0.008662657989192416, "2": 0.092194172102119, "3": 0.4106949086377318, "4": 0.48481577543476573}, "pretraining_utility": {"-2": 0.006324039495116704, "-1": 0.03387473690622399, "0": 0.286666133295541, "1": 0.5632431088032404, "2": 0.10989198149987792}, "math_density": {"0": 0.8844476163277932, "1": 0.10357447239378983, "2": 0.00471991312766269, "3": 0.002272838164123568, "4": 0.004985159986630651}, "code_density": {"0": 0.00026624864240606976, "1": 0.0005441649585707443, "2": 0.016695294534476702, "3": 0.13533516978029092, "4": 0.8471591220842555}, "reasoning_density": {"0": 0.6174216007186817, "1": 0.16074778361983666, "2": 0.1369287859620657, "3": 0.053341763843590864, "4": 0.03156006585582508}, "education_level": {"none": 0.0010069422065829448, "0": 0.001471805877757809, "1": 0.0045367661238980995, "2": 0.4951661580668776, "3": 0.45449098808324034, "4": 0.043327339641643224}, "creative_writing_quality": {"not_applicable": 0.9997284927320524, "-2": 3.447946225425405e-05, "-1": 5.943372442764336e-05, "0": 0.0001237857289303578, "1": 3.9156048911447716e-05, "2": 1.4652303424029303e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.671336502944406e-05, "Art & Design": 2.5484601992489422e-05, "Law & Crime": 1.846257774544363e-05, "Education & Careers": 2.0447761427995293e-05, "Entertainment & Media": 1.2491070728409613e-05, "Fashion & Beauty": 1.025874841765458e-05, "Business, Economics & Finance": 2.723181028512788e-05, "Food & Dining": 1.1403996901040085e-05, "Games & Gaming": 2.8755200985012563e-05, "Electronics & Hardware": 6.876349168442112e-05, "Health, Healthcare & Wellness": 1.402852033355352e-05, "History & Geography": 1.592721588166557e-05, "Home, Pets & Hobbies": 1.2154226544460716e-05, "Industry & Production": 1.7571609343308428e-05, "Language, Literature & Philosophy": 2.212572251328832e-05, "Politics & Government": 1.1228093748016982e-05, "Religion & Spirituality": 1.8770623386572565e-05, "Science, Mathematics & Engineering": 0.00041675776722627955, "Relationships & Community": 1.2516028309409252e-05, "Software Usage & Applications": 0.0017368203480175124, "Programming & Software Engineering": 0.9972708159469401, "Sports & Fitness": 2.8805195566212648e-05, "Vehicles & Transportation": 1.0164867634547554e-05, "Travel & Tourism": 2.4114591312589147e-05, "Other / Uncovered Subject": 0.00011241803285080651, "Unknown / Insufficient Content": 2.5768585194664273e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00036325339493013174, "Art & Design": 0.0002471747684856415, "Law & Crime": 0.0003170052515670635, "Education & Careers": 0.00022619527944181205, "Entertainment & Media": 0.00022276236448685066, "Fashion & Beauty": 0.00012974686786375355, "Business, Economics & Finance": 0.0003938635995615361, "Food & Dining": 0.00011083014536851719, "Games & Gaming": 0.0002057771373185274, "Electronics & Hardware": 0.00028404733296282815, "Health, Healthcare & Wellness": 0.00013621268533101845, "History & Geography": 0.000135652660767258, "Home, Pets & Hobbies": 0.00011650666170847112, "Industry & Production": 0.00013754782953889882, "Language, Literature & Philosophy": 0.00011504844226112719, "Politics & Government": 0.00012812726921955037, "Religion & Spirituality": 0.00017210187580761005, "Science, Mathematics & Engineering": 0.0007023463966906027, "Relationships & Community": 0.0001029479986037422, "Software Usage & Applications": 0.03568835638639659, "Programming & Software Engineering": 0.027814033440798674, "Sports & Fitness": 0.00011904340107417395, "Vehicles & Transportation": 6.743113345876896e-05, "Travel & Tourism": 0.00021655785489955285, "Other / Uncovered Subject": 0.0003360977165722755, "NONE": 0.9315113321048851}}, "probabilities": {"primary": {"Academic Writing": 2.43751797242113e-05, "Content Listing": 1.1528358254806057e-05, "Creative Writing": 9.041108343250914e-06, "Customer Support Page": 6.9221852334795245e-06, "Q&A Forum": 7.350340998728852e-06, "Discussion Thread": 4.1394394555506055e-06, "Comment Section": 6.698442526071225e-06, "Missing Content": 1.1125436557893943e-05, "Knowledge Article": 7.317231487150101e-05, "Legal Notices": 1.1950270571957037e-05, "News Article": 2.464406130273409e-06, "Organizational Announcement": 5.936005298436826e-06, "Nonfiction Writing": 5.555916088014247e-06, "Organizational About Page": 6.17261897556783e-06, "Personal About Page": 4.742766519473779e-06, "Personal Blog": 3.8750552465653225e-06, "Product Page": 4.543660349797647e-06, "Spam / Ads": 5.504786167898962e-06, "Structured Data": 0.00013104162301009353, "Technical Writing": 0.9984336008675462, "Transcript / Interview": 8.909707743247155e-06, "Tutorial / How-To Guide": 0.0006826725415105516, "User Reviews": 3.505056635749377e-06, "Other / Unknown": 0.0005351719122407462}, "secondary": {"Academic Writing": 7.767961832087754e-05, "Content Listing": 3.675675146160892e-05, "Creative Writing": 5.4193569603136e-05, "Customer Support Page": 0.00018218302434254527, "Q&A Forum": 3.1273509588582415e-05, "Discussion Thread": 1.8530699529474355e-05, "Comment Section": 5.6410063593904675e-05, "Missing Content": 0.0002324676180539137, "Knowledge Article": 0.0005986682740568989, "Legal Notices": 6.656076662220318e-05, "News Article": 3.064158279791091e-05, "Organizational Announcement": 2.915777606064502e-05, "Nonfiction Writing": 3.0219712338737618e-05, "Organizational About Page": 3.826473945237821e-05, "Personal About Page": 2.7439446830090038e-05, "Personal Blog": 2.1736280903005866e-05, "Product Page": 2.105675952179543e-05, "Spam / Ads": 2.8581040860578318e-05, "Structured Data": 0.00045573977672027315, "Technical Writing": 0.003364815880400464, "Transcript / Interview": 3.6282767042184864e-05, "Tutorial / How-To Guide": 0.002065316914357282, "User Reviews": 1.7202218866985684e-05, "NONE": 0.9924788212086746}, "tertiary": {"Academic Writing": 5.228102750422444e-05, "Content Listing": 3.41667950052786e-05, "Creative Writing": 4.674899777261283e-05, "Customer Support Page": 0.00035050115696443204, "Q&A Forum": 2.901789602419637e-05, "Discussion Thread": 1.4476385053446656e-05, "Comment Section": 5.243021888018604e-05, "Missing Content": 0.00022071578495455374, "Knowledge Article": 0.0005321939716546447, "Legal Notices": 7.171122966173374e-05, "News Article": 2.639377025998596e-05, "Organizational Announcement": 2.5494431061155517e-05, "Nonfiction Writing": 3.4536277106916124e-05, "Organizational About Page": 4.2108037676835234e-05, "Personal About Page": 2.2134108345649776e-05, "Personal Blog": 1.8858136386982923e-05, "Product Page": 2.0109638350828753e-05, "Spam / Ads": 3.0983224259877505e-05, "Structured Data": 0.00026168575317960847, "Technical Writing": 0.0029695245771894133, "Transcript / Interview": 3.0649590979786894e-05, "Tutorial / How-To Guide": 0.001582384595803168, "User Reviews": 1.4155976780916795e-05, "NONE": 0.9935167384191437}, "structured_records": {"false": 0.998938333554173, "true": 0.0010616664458269513}, "procedural_steps": {"false": 0.9651997343191154, "true": 0.03480026568088457}, "worked_solution": {"false": 0.9979182717728744, "true": 0.0020817282271255928}, "contextual_dialogue": {"false": 0.999995791751957, "true": 4.2082480430396425e-06}, "content_quality": {"-2": 0.005685616053860034, "-1": 0.01919761307300196, "0": 0.2287237162420794, "1": 0.511342781831353, "2": 0.23505027279970567}, "noise_severity": {"0": 0.9879473608912122, "1": 0.010845409116313879, "2": 0.0007600021036404308, "3": 0.00024968215386265356, "4": 0.00019754573497084377}, "missing_content_severity": {"0": 0.9908764478040255, "1": 0.002464231585027575, "2": 0.004929165901774716, "3": 0.0014957271111193512, "4": 0.00023442759805289488}, "factual_density": {"0": 0.003632485836191021, "1": 0.008662657989192416, "2": 0.092194172102119, "3": 0.4106949086377318, "4": 0.48481577543476573}, "pretraining_utility": {"-2": 0.006324039495116704, "-1": 0.03387473690622399, "0": 0.286666133295541, "1": 0.5632431088032404, "2": 0.10989198149987792}, "math_density": {"0": 0.8844476163277932, "1": 0.10357447239378983, "2": 0.00471991312766269, "3": 0.002272838164123568, "4": 0.004985159986630651}, "code_density": {"0": 0.0002662486424060698, "1": 0.0005441649585707445, "2": 0.016695294534476705, "3": 0.13533516978029095, "4": 0.8471591220842556}, "reasoning_density": {"0": 0.6174216007186817, "1": 0.16074778361983666, "2": 0.1369287859620657, "3": 0.053341763843590864, "4": 0.03156006585582508}, "education_level": {"none": 0.0010069422065829448, "0": 0.001471805877757809, "1": 0.0045367661238980995, "2": 0.4951661580668776, "3": 0.45449098808324034, "4": 0.043327339641643224}, "creative_writing_quality": {"not_applicable": 0.9997284927320522, "-2": 3.4479462254254046e-05, "-1": 5.9433724427643346e-05, "0": 0.00012378572893035776, "1": 3.915604891144771e-05, "2": 1.46523034240293e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.671336502944406e-05, "Art & Design": 2.5484601992489422e-05, "Law & Crime": 1.846257774544363e-05, "Education & Careers": 2.0447761427995293e-05, "Entertainment & Media": 1.2491070728409613e-05, "Fashion & Beauty": 1.025874841765458e-05, "Business, Economics & Finance": 2.723181028512788e-05, "Food & Dining": 1.1403996901040085e-05, "Games & Gaming": 2.8755200985012563e-05, "Electronics & Hardware": 6.876349168442112e-05, "Health, Healthcare & Wellness": 1.402852033355352e-05, "History & Geography": 1.592721588166557e-05, "Home, Pets & Hobbies": 1.2154226544460716e-05, "Industry & Production": 1.7571609343308428e-05, "Language, Literature & Philosophy": 2.212572251328832e-05, "Politics & Government": 1.1228093748016982e-05, "Religion & Spirituality": 1.8770623386572565e-05, "Science, Mathematics & Engineering": 0.00041675776722627955, "Relationships & Community": 1.2516028309409252e-05, "Software Usage & Applications": 0.0017368203480175124, "Programming & Software Engineering": 0.9972708159469401, "Sports & Fitness": 2.8805195566212648e-05, "Vehicles & Transportation": 1.0164867634547554e-05, "Travel & Tourism": 2.4114591312589147e-05, "Other / Uncovered Subject": 0.00011241803285080651, "Unknown / Insufficient Content": 2.5768585194664273e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00036325339493013174, "Art & Design": 0.0002471747684856415, "Law & Crime": 0.0003170052515670635, "Education & Careers": 0.00022619527944181205, "Entertainment & Media": 0.00022276236448685066, "Fashion & Beauty": 0.00012974686786375355, "Business, Economics & Finance": 0.0003938635995615361, "Food & Dining": 0.00011083014536851719, "Games & Gaming": 0.0002057771373185274, "Electronics & Hardware": 0.00028404733296282815, "Health, Healthcare & Wellness": 0.00013621268533101845, "History & Geography": 0.000135652660767258, "Home, Pets & Hobbies": 0.00011650666170847112, "Industry & Production": 0.00013754782953889882, "Language, Literature & Philosophy": 0.00011504844226112719, "Politics & Government": 0.00012812726921955037, "Religion & Spirituality": 0.00017210187580761005, "Science, Mathematics & Engineering": 0.0007023463966906027, "Relationships & Community": 0.0001029479986037422, "Software Usage & Applications": 0.03568835638639659, "Programming & Software Engineering": 0.027814033440798674, "Sports & Fitness": 0.00011904340107417395, "Vehicles & Transportation": 6.743113345876896e-05, "Travel & Tourism": 0.00021655785489955285, "Other / Uncovered Subject": 0.0003360977165722755, "NONE": 0.9315113321048851}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9989, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9989, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:09.856828+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "69d43890-4304-4085-8072-92cc316efdb0", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "fcf193caf01cc848a3421435eccc1f61bae175fb0b589ada235d83303acea11d", "source_metadata": {"uuid": "69d43890-4304-4085-8072-92cc316efdb0", "repo_name": "database-tools/mongosyncdb", "relative_path": "src/change_stream.py", "category": "TOOL", "algo_rel_score": 0.9014530777931213, "quality_score": 6.000790119171143, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 3, "repo_created_at": "2024-01-04T16:43:14.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "00f42d210ac7e7923c9218037d74422b51957e38", "size_bytes": 7029, "file_timestamp": 1743974255, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "bfdd8108e8118b022d01cc0d37092deffb4f1f2e"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 3, "repo_created_at": "2024-01-04T16:43:14.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "00f42d210ac7e7923c9218037d74422b51957e38", "size_bytes": 7029, "file_timestamp": 1743974255, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "bfdd8108e8118b022d01cc0d37092deffb4f1f2e", "url": "https://github.com/database-tools/mongosyncdb/blob/bfdd8108e8118b022d01cc0d37092deffb4f1f2e/src/change_stream.py"}, "gpt4o_tokens": 1420, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.9508744822500422, "noise_severity": 0.013904642725066077, "missing_content_severity": 0.01774745511414664, "factual_density": 3.3643988298456886, "pretraining_utility": 0.7365042559065388, "math_density": 0.13977345308800854, "code_density": 3.8285767517054192, "reasoning_density": 0.720870910498041, "education_level": 2.5342032012373306, "creative_writing_quality": -0.2207380805296992}, "score_applicability": {"education_level": 0.9989930577934171, "creative_writing_quality": 0.0002715072679477322}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/MuddasirKHuwaja998/dignosis/blob/a5e0ef7114f2157c3f9a4befae6a8539d87f6c3a/app.py", "id": "python-0-4ff984ed71db1528413568001a4c8944", "text": "import os\nfrom flask import Flask, render_template, request, flash, redirect, url_for\nfrom werkzeug.utils import secure_filename\nfrom tensorflow.keras.models import load_model\nfrom tensorflow.keras.preprocessing.image import load_img, img_to_array\nimport numpy as np\n\napp = Flask(__name__)\napp.secret_key = \"supersecretkey\"\n\n# Path to the trained model\nMODEL_PATH = \"saved_model/model.h5\"\n\n# Load the trained model\nmodel = load_model(MODEL_PATH)\n\n# Class details in Italian with alternating details\nclass_details_alternate = {\n    \"Chronic Otitis Media\": [\n        {\n            \"description\": \"L'otite media cronica è un'infezione persistente dell'orecchio che può causare secrezione.\",\n            \"causes\": [\"Infezioni ripetute dell'orecchio\", \"Funzione scarsa della tromba di Eustachio\", \"Allergie\"]\n        },\n        {\n            \"description\": \"L'otite media cronica può causare sordità progressiva e dolore costante.\",\n            \"causes\": [\"Uso di oggetti nell'orecchio\", \"Complicazioni di infezioni respiratorie\", \"Problemi immunologici\"]\n        },\n    ],\n    \"Earwax Plug\": [\n        {\n            \"description\": \"Il tappo di cerume si verifica quando il cerume si accumula e blocca il canale uditivo.\",\n            \"causes\": [\"Produzione eccessiva di cerume\", \"Pulizia impropria dell'orecchio\", \"Uso di auricolari o apparecchi acustici\"]\n        },\n        {\n            \"description\": \"Un tappo di cerume può causare prurito, irritazione e perdita temporanea dell'udito.\",\n            \"causes\": [\"Uso di cotton fioc\", \"Accumulo cronico di cera\", \"Anomalie del canale uditivo\"]\n        }\n    ],\n    \"Myringosclerosis\": [\n        {\n            \"description\": \"La miringosclerosi comporta la formazione di cicatrici sul timpano, spesso a causa di infezioni ripetute.\",\n            \"causes\": [\"Infezioni ripetute dell'orecchio\", \"Trauma all'orecchio\", \"Interventi chirurgici sull'orecchio\"]\n        },\n        {\n            \"description\": \"La miringosclerosi può causare rigidità del timpano e perdita parziale dell'udito.\",\n            \"causes\": [\"Esposizione a rumori forti\", \"Trattamenti medici invasivi\", \"Infiammazione cronica\"]\n        }\n    ],\n    \"Normal\": [\n        {\n            \"description\": \"L'orecchio appare normale senza segni di infezione o anomalie.\",\n            \"causes\": [\"Orecchio sano\", \"Nessun problema sottostante\"]\n        },\n        {\n            \"description\": \"L'orecchio è in condizioni ottimali, senza segni di malattie.\",\n            \"causes\": [\"Buona igiene auricolare\", \"Stile di vita sano\"]\n        }\n    ]\n}\n\n# Allowed file extensions\nALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}\n\n# Helper function to check allowed file extensions\ndef allowed_file(filename):\n    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\n# Image preprocessing function\ndef preprocess_image(image_path):\n    img = load_img(image_path, target_size=(224, 224))\n    img_array = img_to_array(img) / 255.0\n    img_array = np.expand_dims(img_array, axis=0)\n    return img_array\n\n# Prediction function\ndef predict_image(image_path):\n    img_array = preprocess_image(image_path)\n    predictions = model.predict(img_array)\n    predicted_class = list(class_details_alternate.keys())[np.argmax(predictions[0])]\n    confidence = np.max(predictions[0]) * 100\n    return predicted_class, confidence\n\n# Toggle flag for alternating details\ntoggle_flag = {key: 0 for key in class_details_alternate.keys()}\n\n@app.route(\"/\", methods=[\"GET\", \"POST\"])\ndef index():\n    global toggle_flag\n\n    if request.method == \"POST\":\n        # Check if a file is uploaded\n        if \"file\" not in request.files:\n            flash(\"Nessun file caricato!\")\n            return redirect(request.url)\n\n        file = request.files[\"file\"]\n\n        # Handle single image upload\n        if file and allowed_file(file.filename):\n            filename = secure_filename(file.filename)\n            save_dir = os.path.join(\"static\", \"uploads\")\n            os.makedirs(save_dir, exist_ok=True)\n            file_path = os.path.join(save_dir, filename)\n            file.save(file_path)\n\n            # Predict the uploaded image\n            predicted_class, confidence = predict_image(file_path)\n\n            # Toggle between the two sets of details\n            current_flag = toggle_flag[predicted_class]\n            details = class_details_alternate[predicted_class][current_flag]\n            toggle_flag[predicted_class] = 1 - current_flag  # Flip the toggle\n\n            return render_template(\n                \"index.html\",\n                uploaded_image=url_for(\"static\", filename=f\"uploads/{filename}\"),\n                prediction=predicted_class,\n                confidence=f\"{confidence:.2f}%\",\n                description=details[\"description\"],\n                causes=details[\"causes\"]\n            )\n\n        else:\n            flash(\"Tipo di file non valido! Carica un'immagine valida.\")\n            return redirect(request.url)\n\n    return render_template(\"index.html\")\n\nif __name__ == \"__main__\":\n    app.run(debug=True)", "text_preview": "import os from flask import Flask, render_template, request, flash, redirect, url_for from werkzeug.utils import secure_filename from tensorflow.keras.models import load_model from tensorflow.keras.preprocessing.image import load_img, img_t", "input_sha256": "764cba8b1214f95dbe45af3e34201dab06dc33c8caf36d25950cd33fa3bd557e", "text_sha256": "4ff984ed71db1528413568001a4c8944d5456d6335e799e9f88f0ed1b2cd7370", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12119, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": ["structured_records"]}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Health, Healthcare & Wellness", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 2, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.592985023262314, "noise_severity": 0.12604241169158717, "missing_content_severity": 0.0742724980869916, "factual_density": 2.376541183734163, "pretraining_utility": 0.6064373017262801, "math_density": 0.6588142483583814, "code_density": 3.541264987053233, "reasoning_density": 0.505953231652541, "education_level": 2.192572850716874, "creative_writing_quality": -0.304076203435299}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Health, Healthcare & Wellness"}, "review_reasons": ["secondary_topic probability 0.6445 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.1561973732518814, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9909478115572776, "secondary": 0.5416636037194283, "tertiary": 0.6710809116543471, "structured_records": 0.8328333844207967, "procedural_steps": 0.040399567333907475, "worked_solution": 0.009400104462869598, "contextual_dialogue": 8.56945918645344e-06, "content_quality": 0.4407569765714927, "noise_severity": 0.8949646569236773, "missing_content_severity": 0.9381062515941737, "factual_density": 0.32329540752842856, "pretraining_utility": 0.5219672756763443, "math_density": 0.45098812636801555, "code_density": 0.6177208225443607, "reasoning_density": 0.5783723069562158, "education_level": 0.7563114186014928, "creative_writing_quality": 0.9966900776899353, "primary_topic": 0.7549654078393883, "secondary_topic": 0.630239322189819}, "probability_margin": {"primary": 0.9867086893801786, "secondary": 0.3036870416451754, "tertiary": 0.49945490819287613, "education_level": 0.6375724117532544, "creative_writing_quality": 0.9955849121680668, "primary_topic": 0.5736883336324743, "secondary_topic": 0.4763101886493288}, "raw_probabilities": {"primary": {"Academic Writing": 7.90815263568944e-05, "Content Listing": 4.9259068797727305e-05, "Creative Writing": 3.0167399027911415e-05, "Customer Support Page": 2.101892502359714e-05, "Q&A Forum": 1.9836406868259723e-05, "Discussion Thread": 1.36860565750105e-05, "Comment Section": 2.0428787904956544e-05, "Missing Content": 3.63790789915143e-05, "Knowledge Article": 0.0009064697237160787, "Legal Notices": 3.22876379535638e-05, "News Article": 9.179401930227493e-06, "Organizational Announcement": 1.966489018732298e-05, "Nonfiction Writing": 3.4832924687973674e-05, "Organizational About Page": 2.6116336473860427e-05, "Personal About Page": 2.8178394410455014e-05, "Personal Blog": 1.4491852010895371e-05, "Product Page": 1.560063759263001e-05, "Spam / Ads": 2.4586224349036156e-05, "Structured Data": 0.004616296695545721, "Technical Writing": 0.9913249860757244, "Transcript / Interview": 2.759299176923269e-05, "Tutorial / How-To Guide": 0.0010866925815174336, "User Reviews": 1.2366916150769863e-05, "Other / Unknown": 0.0015507994664346254}, "secondary": {"Academic Writing": 0.0018742510497852468, "Content Listing": 0.0015075456165394494, "Creative Writing": 0.0008379136054867449, "Customer Support Page": 0.0006060522108771411, "Q&A Forum": 0.000439073636240488, "Discussion Thread": 0.00036559324898415073, "Comment Section": 0.0005382449721906807, "Missing Content": 0.0007657700621660102, "Knowledge Article": 0.14030111552348265, "Legal Notices": 0.000543316093418239, "News Article": 0.00050221935453577, "Organizational Announcement": 0.0004982250439473945, "Nonfiction Writing": 0.0011404120487308808, "Organizational About Page": 0.0007818567721862133, "Personal About Page": 0.0005815915295853369, "Personal Blog": 0.0003852305909756381, "Product Page": 0.0003608005751609265, "Spam / Ads": 0.0005848754129828624, "Structured Data": 0.2570739119192767, "Technical Writing": 0.02702640813623946, "Transcript / Interview": 0.0007581940936978611, "Tutorial / How-To Guide": 0.0014745247437419077, "User Reviews": 0.0002919201953160545, "NONE": 0.5607609535644521}, "tertiary": {"Academic Writing": 0.0015514706978666355, "Content Listing": 0.0012461104074915322, "Creative Writing": 0.0009310346722498446, "Customer Support Page": 0.000708730999211757, "Q&A Forum": 0.00037590806827659715, "Discussion Thread": 0.00032470682714566616, "Comment Section": 0.00048053360044755946, "Missing Content": 0.000669666049153124, "Knowledge Article": 0.1853309654758732, "Legal Notices": 0.0005124247249850481, "News Article": 0.00041247938940363, "Organizational Announcement": 0.00040409357759691583, "Nonfiction Writing": 0.0010209527639049186, "Organizational About Page": 0.0006971308934446, "Personal About Page": 0.0004966931776659469, "Personal Blog": 0.00033621742318148176, "Product Page": 0.0003656552392027031, "Spam / Ads": 0.0004824023942106609, "Structured Data": 0.10087653866739535, "Technical Writing": 0.015752740193381347, "Transcript / Interview": 0.0006810828683103043, "Tutorial / How-To Guide": 0.001335205809323464, "User Reviews": 0.0002213824115284798, "NONE": 0.6847858736687493}, "structured_records": {"false": 0.16716661557920331, "true": 0.8328333844207967}, "procedural_steps": {"false": 0.9596004326660925, "true": 0.040399567333907475}, "worked_solution": {"false": 0.9905998955371303, "true": 0.009400104462869598}, "contextual_dialogue": {"false": 0.9999914305408135, "true": 8.56945918645344e-06}, "content_quality": {"-2": 0.015939724456282046, "-1": 0.08828869775210575, "0": 0.3146567335528897, "1": 0.4490765185504613, "2": 0.1320383256882613}, "noise_severity": {"0": 0.8971073264274422, "1": 0.08451983262237557, "2": 0.014967452702292822, "3": 0.002033879326931582, "4": 0.0013715089209577998}, "missing_content_severity": {"0": 0.9616711405978411, "1": 0.011734409862329528, "2": 0.01812102676372099, "3": 0.007597656407212909, "4": 0.0008757663688953385}, "factual_density": {"0": 0.011545142288858421, "1": 0.19466187903814441, "2": 0.3423764356425104, "3": 0.30853973871094886, "4": 0.14287680431953784}, "pretraining_utility": {"-2": 0.008627582515174148, "-1": 0.06345626577368711, "0": 0.3308057046381568, "1": 0.5070721616156484, "2": 0.09003828545733353}, "math_density": {"0": 0.4926961857089443, "1": 0.4119214787429957, "2": 0.06208476747443868, "3": 0.010467037627976853, "4": 0.022830530445644422}, "code_density": {"0": 0.00048324749770923723, "1": 0.0009506804902123068, "2": 0.05565262211865597, "3": 0.34264473724798133, "4": 0.600268712645441}, "reasoning_density": {"0": 0.6756028791636085, "1": 0.18834206217108507, "2": 0.09905884714464956, "3": 0.028491370890470616, "4": 0.008504840630186247}, "education_level": {"none": 0.0011753209107950165, "0": 0.0021109473623824996, "1": 0.014550972719942412, "2": 0.7969261821679107, "3": 0.15935377041465626, "4": 0.02588280642431318}, "creative_writing_quality": {"not_applicable": 0.9972417314082794, "-2": 0.00027353086808874896, "-1": 0.0005913777110826058, "0": 0.0016568192402126967, "1": 0.00017336593883824215, "2": 6.317483349831498e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.000262918409279479, "Art & Design": 0.00038161577119545626, "Law & Crime": 0.00041219121884083685, "Education & Careers": 0.0002714137746287067, "Entertainment & Media": 0.0002547183438638945, "Fashion & Beauty": 0.00044535611526703777, "Business, Economics & Finance": 0.0003083688613978114, "Food & Dining": 0.0004835562907511856, "Games & Gaming": 0.00034762685922894306, "Electronics & Hardware": 0.00040280489598109253, "Health, Healthcare & Wellness": 0.19070148159770672, "History & Geography": 0.00039564393606400506, "Home, Pets & Hobbies": 0.00030143859121711363, "Industry & Production": 0.0002383937723653004, "Language, Literature & Philosophy": 0.0003148064553181697, "Politics & Government": 0.00015418398928067491, "Religion & Spirituality": 0.00026599211324863047, "Science, Mathematics & Engineering": 0.036008988676505135, "Relationships & Community": 0.00025701130657828736, "Software Usage & Applications": 0.0010966568314043975, "Programming & Software Engineering": 0.764389815230181, "Sports & Fitness": 0.0004615118751058351, "Vehicles & Transportation": 0.00016443954684053997, "Travel & Tourism": 0.0002854672324561594, "Other / Uncovered Subject": 0.0009951686984292266, "Unknown / Insufficient Content": 0.0003984296068643066}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0011970628224105616, "Art & Design": 0.0009918412286526513, "Law & Crime": 0.0007107180547144133, "Education & Careers": 0.0005846564085059632, "Entertainment & Media": 0.0006252434389131006, "Fashion & Beauty": 0.0008392730043601106, "Business, Economics & Finance": 0.0006916864887935074, "Food & Dining": 0.0007467486022496697, "Games & Gaming": 0.0005686316374923751, "Electronics & Hardware": 0.0007580193412504863, "Health, Healthcare & Wellness": 0.6444608867209798, "History & Geography": 0.0008294884536386495, "Home, Pets & Hobbies": 0.0006573170926486545, "Industry & Production": 0.0004860986504603493, "Language, Literature & Philosophy": 0.0010570285423094402, "Politics & Government": 0.00037182538038752334, "Religion & Spirituality": 0.0006887438292299152, "Science, Mathematics & Engineering": 0.09415485068519301, "Relationships & Community": 0.00045301080727583033, "Software Usage & Applications": 0.0011711386181200182, "Programming & Software Engineering": 0.168150698071651, "Sports & Fitness": 0.0008148919997333832, "Vehicles & Transportation": 0.0002962223459669322, "Travel & Tourism": 0.0005456729497365199, "Other / Uncovered Subject": 0.0010994916999852542, "NONE": 0.07704875312534097}}, "probabilities": {"primary": {"Academic Writing": 7.90815263568944e-05, "Content Listing": 4.9259068797727305e-05, "Creative Writing": 3.0167399027911415e-05, "Customer Support Page": 2.101892502359714e-05, "Q&A Forum": 1.9836406868259723e-05, "Discussion Thread": 1.36860565750105e-05, "Comment Section": 2.0428787904956544e-05, "Missing Content": 3.63790789915143e-05, "Knowledge Article": 0.0009064697237160787, "Legal Notices": 3.22876379535638e-05, "News Article": 9.179401930227493e-06, "Organizational Announcement": 1.966489018732298e-05, "Nonfiction Writing": 3.4832924687973674e-05, "Organizational About Page": 2.6116336473860427e-05, "Personal About Page": 2.8178394410455014e-05, "Personal Blog": 1.4491852010895371e-05, "Product Page": 1.560063759263001e-05, "Spam / Ads": 2.4586224349036156e-05, "Structured Data": 0.004616296695545721, "Technical Writing": 0.9913249860757244, "Transcript / Interview": 2.759299176923269e-05, "Tutorial / How-To Guide": 0.0010866925815174336, "User Reviews": 1.2366916150769863e-05, "Other / Unknown": 0.0015507994664346254}, "secondary": {"Academic Writing": 0.001874251049785247, "Content Listing": 0.0015075456165394496, "Creative Writing": 0.000837913605486745, "Customer Support Page": 0.0006060522108771412, "Q&A Forum": 0.00043907363624048803, "Discussion Thread": 0.0003655932489841508, "Comment Section": 0.0005382449721906808, "Missing Content": 0.0007657700621660103, "Knowledge Article": 0.14030111552348268, "Legal Notices": 0.0005433160934182391, "News Article": 0.0005022193545357701, "Organizational Announcement": 0.0004982250439473946, "Nonfiction Writing": 0.001140412048730881, "Organizational About Page": 0.0007818567721862135, "Personal About Page": 0.000581591529585337, "Personal Blog": 0.00038523059097563813, "Product Page": 0.0003608005751609266, "Spam / Ads": 0.0005848754129828625, "Structured Data": 0.25707391191927675, "Technical Writing": 0.027026408136239462, "Transcript / Interview": 0.0007581940936978612, "Tutorial / How-To Guide": 0.001474524743741908, "User Reviews": 0.00029192019531605455, "NONE": 0.5607609535644522}, "tertiary": {"Academic Writing": 0.0015514706978666355, "Content Listing": 0.0012461104074915322, "Creative Writing": 0.0009310346722498446, "Customer Support Page": 0.000708730999211757, "Q&A Forum": 0.00037590806827659715, "Discussion Thread": 0.00032470682714566616, "Comment Section": 0.00048053360044755946, "Missing Content": 0.000669666049153124, "Knowledge Article": 0.1853309654758732, "Legal Notices": 0.0005124247249850481, "News Article": 0.00041247938940363, "Organizational Announcement": 0.00040409357759691583, "Nonfiction Writing": 0.0010209527639049186, "Organizational About Page": 0.0006971308934446, "Personal About Page": 0.0004966931776659469, "Personal Blog": 0.00033621742318148176, "Product Page": 0.0003656552392027031, "Spam / Ads": 0.0004824023942106609, "Structured Data": 0.10087653866739535, "Technical Writing": 0.015752740193381347, "Transcript / Interview": 0.0006810828683103043, "Tutorial / How-To Guide": 0.001335205809323464, "User Reviews": 0.0002213824115284798, "NONE": 0.6847858736687493}, "structured_records": {"false": 0.16716661557920331, "true": 0.8328333844207967}, "procedural_steps": {"false": 0.9596004326660925, "true": 0.040399567333907475}, "worked_solution": {"false": 0.9905998955371303, "true": 0.009400104462869598}, "contextual_dialogue": {"false": 0.9999914305408135, "true": 8.56945918645344e-06}, "content_quality": {"-2": 0.015939724456282042, "-1": 0.08828869775210574, "0": 0.31465673355288964, "1": 0.4490765185504612, "2": 0.13203832568826127}, "noise_severity": {"0": 0.8971073264274422, "1": 0.08451983262237557, "2": 0.014967452702292822, "3": 0.002033879326931582, "4": 0.0013715089209577998}, "missing_content_severity": {"0": 0.9616711405978412, "1": 0.01173440986232953, "2": 0.018121026763720995, "3": 0.00759765640721291, "4": 0.0008757663688953386}, "factual_density": {"0": 0.011545142288858421, "1": 0.19466187903814441, "2": 0.3423764356425104, "3": 0.30853973871094886, "4": 0.14287680431953784}, "pretraining_utility": {"-2": 0.008627582515174148, "-1": 0.06345626577368711, "0": 0.3308057046381568, "1": 0.5070721616156484, "2": 0.09003828545733353}, "math_density": {"0": 0.49269618570894436, "1": 0.41192147874299573, "2": 0.06208476747443869, "3": 0.010467037627976855, "4": 0.022830530445644426}, "code_density": {"0": 0.0004832474977092373, "1": 0.0009506804902123069, "2": 0.05565262211865598, "3": 0.3426447372479814, "4": 0.6002687126454411}, "reasoning_density": {"0": 0.6756028791636085, "1": 0.18834206217108507, "2": 0.09905884714464956, "3": 0.028491370890470616, "4": 0.008504840630186247}, "education_level": {"none": 0.0011753209107950165, "0": 0.0021109473623824996, "1": 0.014550972719942412, "2": 0.7969261821679107, "3": 0.15935377041465626, "4": 0.02588280642431318}, "creative_writing_quality": {"not_applicable": 0.9972417314082794, "-2": 0.00027353086808874896, "-1": 0.0005913777110826058, "0": 0.0016568192402126967, "1": 0.00017336593883824215, "2": 6.317483349831498e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.000262918409279479, "Art & Design": 0.00038161577119545626, "Law & Crime": 0.00041219121884083685, "Education & Careers": 0.0002714137746287067, "Entertainment & Media": 0.0002547183438638945, "Fashion & Beauty": 0.00044535611526703777, "Business, Economics & Finance": 0.0003083688613978114, "Food & Dining": 0.0004835562907511856, "Games & Gaming": 0.00034762685922894306, "Electronics & Hardware": 0.00040280489598109253, "Health, Healthcare & Wellness": 0.19070148159770672, "History & Geography": 0.00039564393606400506, "Home, Pets & Hobbies": 0.00030143859121711363, "Industry & Production": 0.0002383937723653004, "Language, Literature & Philosophy": 0.0003148064553181697, "Politics & Government": 0.00015418398928067491, "Religion & Spirituality": 0.00026599211324863047, "Science, Mathematics & Engineering": 0.036008988676505135, "Relationships & Community": 0.00025701130657828736, "Software Usage & Applications": 0.0010966568314043975, "Programming & Software Engineering": 0.764389815230181, "Sports & Fitness": 0.0004615118751058351, "Vehicles & Transportation": 0.00016443954684053997, "Travel & Tourism": 0.0002854672324561594, "Other / Uncovered Subject": 0.0009951686984292266, "Unknown / Insufficient Content": 0.0003984296068643066}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0011970628224105616, "Art & Design": 0.0009918412286526513, "Law & Crime": 0.0007107180547144133, "Education & Careers": 0.0005846564085059632, "Entertainment & Media": 0.0006252434389131006, "Fashion & Beauty": 0.0008392730043601106, "Business, Economics & Finance": 0.0006916864887935074, "Food & Dining": 0.0007467486022496697, "Games & Gaming": 0.0005686316374923751, "Electronics & Hardware": 0.0007580193412504863, "Health, Healthcare & Wellness": 0.6444608867209798, "History & Geography": 0.0008294884536386495, "Home, Pets & Hobbies": 0.0006573170926486545, "Industry & Production": 0.0004860986504603493, "Language, Literature & Philosophy": 0.0010570285423094402, "Politics & Government": 0.00037182538038752334, "Religion & Spirituality": 0.0006887438292299152, "Science, Mathematics & Engineering": 0.09415485068519301, "Relationships & Community": 0.00045301080727583033, "Software Usage & Applications": 0.0011711386181200182, "Programming & Software Engineering": 0.168150698071651, "Sports & Fitness": 0.0008148919997333832, "Vehicles & Transportation": 0.0002962223459669322, "Travel & Tourism": 0.0005456729497365199, "Other / Uncovered Subject": 0.0010994916999852542, "NONE": 0.07704875312534097}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9764, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9764, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:10.016757+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "b9d66c53-9402-4b93-8317-5a08c511ba3b", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "12d4737b03d14b7f4bc1128d37f9571f9d1a5991bf45b9c38e4ce6c41c9e4559", "source_metadata": {"uuid": "b9d66c53-9402-4b93-8317-5a08c511ba3b", "repo_name": "MuddasirKHuwaja998/dignosis", "relative_path": "app.py", "category": "WEB", "algo_rel_score": 0.951751708984375, "quality_score": 6.90715217590332, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 5, "repo_created_at": "2025-04-28T13:56:57.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "2a605297216031deacc2a1ccbee082c388885f2d", "size_bytes": 5178, "file_timestamp": 1745849422, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a5e0ef7114f2157c3f9a4befae6a8539d87f6c3a"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 5, "repo_created_at": "2025-04-28T13:56:57.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "2a605297216031deacc2a1ccbee082c388885f2d", "size_bytes": 5178, "file_timestamp": 1745849422, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a5e0ef7114f2157c3f9a4befae6a8539d87f6c3a", "url": "https://github.com/MuddasirKHuwaja998/dignosis/blob/a5e0ef7114f2157c3f9a4befae6a8539d87f6c3a/app.py"}, "gpt4o_tokens": 1181, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.5929850232623141, "noise_severity": 0.12604241169158717, "missing_content_severity": 0.0742724980869916, "factual_density": 2.376541183734163, "pretraining_utility": 0.6064373017262801, "math_density": 0.6588142483583814, "code_density": 3.541264987053233, "reasoning_density": 0.505953231652541, "education_level": 2.192572850716874, "creative_writing_quality": -0.304076203435299}, "score_applicability": {"education_level": 0.998824679089205, "creative_writing_quality": 0.0027582685917206086}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/Ixooman/vs-s3-api-test/blob/8f98c071c6c1c9b8793bd212f33eba1f2e7920b8/checks/check_metadata.py", "id": "python-0-69ce7eaec536bf8c67e101efa7c89298", "text": "\"\"\"\nS3 Metadata Compatibility Checks\n\nThis module provides comprehensive testing for S3 metadata functionality including\nstandard headers, custom metadata, encoding, size limits, and copy behavior.\n\"\"\"\n\nimport time\nimport json\nimport urllib.parse\nfrom typing import Dict, Any\nfrom framework.base_check import BaseCheck, CheckResult\nfrom framework.s3_client import S3ClientError\n\n\nclass MetadataChecks(BaseCheck):\n    \"\"\"\n    Comprehensive S3 metadata compatibility testing.\n    \n    Tests all aspects of S3 metadata handling including:\n    - Standard S3 metadata headers (Content-Type, Content-Encoding, etc.)\n    - Custom metadata (x-amz-meta-* headers)\n    - Metadata encoding and special characters\n    - Metadata size limits and validation\n    - Metadata case sensitivity\n    - Metadata behavior during copy operations\n    - System vs user metadata distinction\n    \"\"\"\n    \n    def run_checks(self):\n        \"\"\"Run all metadata compatibility checks.\"\"\"\n        self.logger.info(\"Starting comprehensive S3 metadata compatibility checks...\")\n        \n        # Create a test bucket for metadata operations\n        self.test_bucket = self.generate_unique_name(\"metadata-test-bucket\")\n        try:\n            self.s3_client.create_bucket(Bucket=self.test_bucket)\n            self.add_cleanup_item('bucket', self.test_bucket)\n            self.logger.info(f\"Created test bucket: {self.test_bucket}\")\n        except S3ClientError as e:\n            self.add_result(\n                'metadata_test_bucket_creation',\n                False,\n                f\"Failed to create test bucket for metadata operations: {e}\",\n                {'error_code': e.error_code}\n            )\n            return self.results\n        \n        # Run metadata tests\n        self._check_standard_metadata_headers()\n        self._check_custom_metadata()\n        self._check_metadata_encoding()\n        self._check_metadata_size_limits()\n        self._check_metadata_case_sensitivity()\n        self._check_metadata_copy_behavior()\n        self._check_system_vs_user_metadata()\n        self._check_metadata_edge_cases()\n        \n        return self.results\n    \n    def _generate_test_data(self, size: int, content: str = None) -> bytes:\n        \"\"\"\n        Generate test data of specified size.\n        \n        Args:\n            size: Size of data in bytes\n            content: Base content to repeat (optional)\n            \n        Returns:\n            Test data as bytes\n        \"\"\"\n        if content is None:\n            content = self.config.get('test_data', {}).get('test_file_content', 'S3 metadata test data')\n        \n        # Repeat content to reach desired size\n        content_bytes = content.encode('utf-8')\n        if len(content_bytes) >= size:\n            return content_bytes[:size]\n        \n        # Repeat content and add sequence numbers to make it unique\n        data = b''\n        chunk_num = 0\n        while len(data) < size:\n            chunk = f\"{content} - chunk {chunk_num}\\n\".encode('utf-8')\n            data += chunk\n            chunk_num += 1\n        \n        return data[:size]\n    \n    def _check_standard_metadata_headers(self):\n        \"\"\"Test standard S3 metadata headers.\"\"\"\n        self.logger.info(\"Checking standard S3 metadata headers...\")\n        \n        test_key = self.generate_unique_name(\"standard-metadata-test\")\n        test_data = self._generate_test_data(1024)\n        \n        # Standard metadata to test\n        standard_metadata = {\n            'ContentType': 'application/json',\n            'ContentEncoding': 'gzip',\n            'ContentDisposition': 'attachment; filename=\"test.json\"',\n            'ContentLanguage': 'en-US',\n            'CacheControl': 'max-age=3600, no-cache',\n            'Expires': 'Wed, 21 Oct 2025 07:28:00 GMT'\n        }\n        \n        try:\n            # Upload with standard metadata\n            start_time = time.time()\n            put_response = self.s3_client.put_object(\n                Bucket=self.test_bucket,\n                Key=test_key,\n                Body=test_data,\n                **standard_metadata\n            )\n            duration = time.time() - start_time\n            \n            self.add_cleanup_item('object', test_key, bucket=self.test_bucket)\n            \n            # Verify upload succeeded\n            if 'ETag' not in put_response:\n                self.add_result(\n                    'standard_metadata_upload',\n                    False,\n                    \"Upload with standard metadata failed - no ETag returned\",\n                    {'object_key': test_key, 'response': put_response}\n                )\n                return\n            \n            # Retrieve and verify metadata\n            head_response = self.s3_client.head_object(Bucket=self.test_bucket, Key=test_key)\n            \n            verified_headers = []\n            for header, expected_value in standard_metadata.items():\n                actual_value = head_response.get(header)\n                if actual_value == expected_value:\n                    verified_headers.append(header)\n                else:\n                    self.logger.debug(f\"Standard metadata mismatch - {header}: expected '{expected_value}', got '{actual_value}'\")\n            \n            success_rate = len(verified_headers) / len(standard_metadata)\n            \n            self.add_result(\n                'standard_metadata_headers',\n                success_rate >= 0.8,  # At least 80% of headers should be preserved\n                f\"Standard metadata headers: {len(verified_headers)}/{len(standard_metadata)} preserved correctly\",\n                {\n                    'object_key': test_key,\n                    'verified_headers': verified_headers,\n                    'expected_headers': list(standard_metadata.keys()),\n                    'success_rate': success_rate,\n                    'head_response': dict(head_response)\n                },\n                duration\n            )\n            \n        except S3ClientError as e:\n            self.add_result(\n                'standard_metadata_headers',\n                False,\n                f\"Failed to test standard metadata headers: {e}\",\n                {'object_key': test_key, 'error_code': e.error_code}\n            )\n    \n    def _check_custom_metadata(self):\n        \"\"\"Test custom metadata (x-amz-meta-* headers).\"\"\"\n        self.logger.info(\"Checking custom metadata handling...\")\n        \n        test_key = self.generate_unique_name(\"custom-metadata-test\")\n        test_data = self._generate_test_data(512)\n        \n        # Custom metadata to test\n        custom_metadata = {\n            'author': 'S3CompatibilityChecker',\n            'project': 'metadata-testing',\n            'version': '1.0.0',\n            'environment': 'test',\n            'numeric-value': '42',\n            'boolean-value': 'true',\n            'special-chars': '<EMAIL>'\n        }\n        \n        try:\n            # Upload with custom metadata\n            start_time = time.time()\n            put_response = self.s3_client.put_object(\n                Bucket=self.test_bucket,\n                Key=test_key,\n                Body=test_data,\n                Metadata=custom_metadata,\n                ContentType='text/plain'\n            )\n            duration = time.time() - start_time\n            \n            self.add_cleanup_item('object', test_key, bucket=self.test_bucket)\n            \n            if 'ETag' not in put_response:\n                self.add_result(\n                    'custom_metadata_upload',\n                    False,\n                    \"Upload with custom metadata failed - no ETag returned\",\n                    {'object_key': test_key, 'metadata': custom_metadata}\n                )\n                return\n            \n            # Retrieve and verify custom metadata\n            head_response = self.s3_client.head_object(Bucket=self.test_bucket, Key=test_key)\n            returned_metadata = head_response.get('Metadata', {})\n            \n            verified_metadata = []\n            for key, expected_value in custom_metadata.items():\n                actual_value = returned_metadata.get(key)\n                if actual_value == expected_value:\n                    verified_metadata.append(key)\n                else:\n                    self.logger.debug(f\"Custom metadata mismatch - {key}: expected '{expected_value}', got '{actual_value}'\")\n            \n            success_rate = len(verified_metadata) / len(custom_metadata)\n            \n            self.add_result(\n                'custom_metadata_preservation',\n                success_rate >= 0.9,  # At least 90% should be preserved\n                f\"Custom metadata: {len(verified_metadata)}/{len(custom_metadata)} fields preserved correctly\",\n                {\n                    'object_key': test_key,\n                    'verified_fields': verified_metadata,\n                    'expected_metadata': custom_metadata,\n                    'returned_metadata': returned_metadata,\n                    'success_rate': success_rate\n                },\n                duration\n            )\n            \n        except S3ClientError as e:\n            self.add_result(\n                'custom_metadata_preservation',\n                False,\n                f\"Failed to test custom metadata: {e}\",\n                {'object_key': test_key, 'error_code': e.error_code}\n            )\n    \n    def _check_metadata_encoding(self):\n        \"\"\"Test metadata encoding and special characters.\"\"\"\n        self.logger.info(\"Checking metadata encoding and special characters...\")\n        \n        test_key = self.generate_unique_name(\"encoding-metadata-test\")\n        test_data = self._generate_test_data(256)\n        \n        # Metadata with various encodings and special characters\n        encoding_metadata = {\n            'ascii-text': 'simple-ascii-value',\n            'utf8-text': 'café-München-日本',\n            'spaces': 'value with spaces',\n            'url-encoded': urllib.parse.quote('<EMAIL>'),\n            'special-symbols': '!@#$%^&*()',\n            'numbers': '123456789',\n            'mixed': 'Test_123-Value@2024'\n        }\n        \n        try:\n            # Upload with encoding test metadata\n            start_time = time.time()\n            put_response = self.s3_client.put_object(\n                Bucket=self.test_bucket,\n                Key=test_key,\n                Body=test_data,\n                Metadata=encoding_metadata,\n                ContentType='application/octet-stream'\n            )\n            duration = time.time() - start_time\n            \n            self.add_cleanup_item('object', test_key, bucket=self.test_bucket)\n            \n            if 'ETag' not in put_response:\n                self.add_result(\n                    'metadata_encoding',\n                    False,\n                    \"Upload with encoded metadata failed - no ETag returned\",\n                    {'object_key': test_key, 'metadata': encoding_metadata}\n                )\n                return\n            \n            # Retrieve and verify encoded metadata\n            head_response = self.s3_client.head_object(Bucket=self.test_bucket, Key=test_key)\n            returned_metadata = head_response.get('Metadata', {})\n            \n            encoding_results = []\n            for key, expected_value in encoding_metadata.items():\n                actual_value = returned_metadata.get(key)\n                if actual_value == expected_value:\n                    encoding_results.append(f\"{key}:preserved\")\n                elif actual_value:\n                    encoding_results.append(f\"{key}:modified\")\n                else:\n                    encoding_results.append(f\"{key}:missing\")\n            \n            preserved_count = len([r for r in encoding_results if r.endswith(':preserved')])\n            success_rate = preserved_count / len(encoding_metadata)\n            \n            self.add_result(\n                'metadata_encoding_handling',\n                success_rate >= 0.7,  # At least 70% should handle encoding correctly\n                f\"Metadata encoding: {preserved_count}/{len(encoding_metadata)} values preserved correctly\",\n                {\n                    'object_key': test_key,\n                    'encoding_results': encoding_results,\n                    'expected_metadata': encoding_metadata,\n                    'returned_metadata': returned_metadata,\n                    'success_rate': success_rate\n                },\n                duration\n            )\n            \n        except S3ClientError as e:\n            self.add_result(\n                'metadata_encoding_handling',\n                False,\n                f\"Failed to test metadata encoding: {e}\",\n                {'object_key': test_key, 'error_code': e.error_code}\n            )\n    \n    def _check_metadata_size_limits(self):\n        \"\"\"Test metadata size limits and validation.\"\"\"\n        self.logger.info(\"Checking metadata size limits...\")\n        \n        # Test 1: Individual metadata value size limit (2KB)\n        large_value_key = self.generate_unique_name(\"large-value-metadata\")\n        large_value = 'x' * 2048  # 2KB value\n        \n        try:\n            start_time = time.time()\n            response = self.s3_client.put_object(\n                Bucket=self.test_bucket,\n                Key=large_value_key,\n                Body=b\"test data\",\n                Metadata={'large-field': large_value}\n            )\n            duration = time.time() - start_time\n            \n            self.add_cleanup_item('object', large_value_key, bucket=self.test_bucket)\n            \n            if 'ETag' in response:\n                self.add_result(\n                    'large_metadata_value_acceptance',\n                    False,  # This should ideally be rejected\n                    \"Large metadata value (2KB) was accepted - may indicate no size validation\",\n                    {\n                        'object_key': large_value_key,\n                        'value_size': len(large_value),\n                        'response': response\n                    },\n                    duration\n                )\n            \n        except S3ClientError as e:\n            if e.status_code in [400, 413]:  # Bad Request or Payload Too Large\n                self.add_result(\n                    'large_metadata_value_rejection',\n                    True,\n                    f\"Large metadata value correctly rejected: {e.error_code}\",\n                    {\n                        'object_key': large_value_key,\n                        'value_size': len(large_value),\n                        'error_code': e.error_code,\n                        'status_code': e.status_code\n                    }\n                )\n            else:\n                self.add_result(\n                    'large_metadata_value_rejection',\n                    False,\n                    f\"Unexpected error for large metadata value: {e}\",\n                    {'object_key': large_value_key, 'error_code': e.error_code}\n                )\n        \n        # Test 2: Total metadata size limit\n        many_fields_key = self.generate_unique_name(\"many-fields-metadata\")\n        many_fields_metadata = {f\"field{i}\": f\"value{i}\" * 50 for i in range(100)}  # Many fields\n        \n        try:\n            start_time = time.time()\n            response = self.s3_client.put_object(\n                Bucket=self.test_bucket,\n                Key=many_fields_key,\n                Body=b\"test data\",\n                Metadata=many_fields_metadata\n            )\n            duration = time.time() - start_time\n            \n            self.add_cleanup_item('object', many_fields_key, bucket=self.test_bucket)\n            \n            total_size = sum(len(k) + len(v) for k, v in many_fields_metadata.items())\n            \n            if 'ETag' in response:\n                self.add_result(\n                    'total_metadata_size_check',\n                    total_size < 8192,  # AWS limit is ~8KB total\n                    f\"Total metadata size {total_size} bytes accepted\",\n                    {\n                        'object_key': many_fields_key,\n                        'field_count': len(many_fields_metadata),\n                        'total_size': total_size,\n                        'response': response\n                    },\n                    duration\n                )\n            \n        except S3ClientError as e:\n            if e.status_code in [400, 413]:\n                total_size = sum(len(k) + len(v) for k, v in many_fields_metadata.items())\n                self.add_result(\n                    'total_metadata_size_rejection',\n                    True,\n                    f\"Large total metadata size ({total_size} bytes) correctly rejected\",\n                    {\n                        'object_key': many_fields_key,\n                        'field_count': len(many_fields_metadata),\n                        'total_size': total_size,\n                        'error_code': e.error_code\n                    }\n                )\n            else:\n                self.add_result(\n                    'total_metadata_size_rejection',\n                    False,\n                    f\"Unexpected error for large total metadata: {e}\",\n                    {'object_key': many_fields_key, 'error_code': e.error_code}\n                )\n    \n    def _check_metadata_case_sensitivity(self):\n        \"\"\"Test metadata case sensitivity behavior.\"\"\"\n        self.logger.info(\"Checking metadata case sensitivity...\")\n        \n        test_key = self.generate_unique_name(\"case-sensitivity-test\")\n        test_data = self._generate_test_data(256)\n        \n        # Metadata with different cases\n        case_metadata = {\n            'lowercase': 'value1',\n            'UPPERCASE': 'value2',\n            'MixedCase': 'value3',\n            'camelCase': 'value4'\n        }\n        \n        try:\n            # Upload with case-varied metadata\n            start_time = time.time()\n            put_response = self.s3_client.put_object(\n                Bucket=self.test_bucket,\n                Key=test_key,\n                Body=test_data,\n                Metadata=case_metadata\n            )\n            duration = time.time() - start_time\n            \n            self.add_cleanup_item('object', test_key, bucket=self.test_bucket)\n            \n            if 'ETag' not in put_response:\n                self.add_result(\n                    'metadata_case_sensitivity',\n                    False,\n                    \"Upload with case-varied metadata failed\",\n                    {'object_key': test_key}\n                )\n                return\n            \n            # Retrieve and check case preservation\n            head_response = self.s3_client.head_object(Bucket=self.test_bucket, Key=test_key)\n            returned_metadata = head_response.get('Metadata', {})\n            \n            case_results = {}\n            for original_key, expected_value in case_metadata.items():\n                # Check if key is preserved exactly\n                if original_key in returned_metadata:\n                    case_results[original_key] = 'exact_match'\n                else:\n                    # Check if key exists in different case\n                    found_key = None\n                    for ret_key in returned_metadata:\n                        if ret_key.lower() == original_key.lower():\n                            found_key = ret_key\n                            break\n                    \n                    if found_key:\n                        case_results[original_key] = f'case_changed_to_{found_key}'\n                    else:\n                        case_results[original_key] = 'missing'\n            \n            exact_matches = len([r for r in case_results.values() if r == 'exact_match'])\n            total_found = len([r for r in case_results.values() if r != 'missing'])\n            \n            self.add_result(\n                'metadata_case_preservation',\n                exact_matches >= len(case_metadata) * 0.5,  # At least 50% should preserve case\n                f\"Case sensitivity: {exact_matches}/{len(case_metadata)} keys preserved exactly, {total_found} found total\",\n                {\n                    'object_key': test_key,\n                    'case_results': case_results,\n                    'expected_metadata': case_metadata,\n                    'returned_metadata': returned_metadata,\n                    'exact_matches': exact_matches,\n                    'total_found': total_found\n                },\n                duration\n            )\n            \n        except S3ClientError as e:\n            self.add_result(\n                'metadata_case_preservation',\n                False,\n                f\"Failed to test metadata case sensitivity: {e}\",\n                {'object_key': test_key, 'error_code': e.error_code}\n            )\n    \n    def _check_metadata_copy_behavior(self):\n        \"\"\"Test metadata behavior during copy operations.\"\"\"\n        self.logger.info(\"Checking metadata behavior during copy operations...\")\n        \n        source_key = self.generate_unique_name(\"copy-source-metadata\")\n        dest_key = self.generate_unique_name(\"copy-dest-metadata\")\n        test_data = self._generate_test_data(512)\n        \n        source_metadata = {\n            'original-author': 'source-creator',\n            'creation-time': '2024-01-01',\n            'category': 'original'\n        }\n        \n        try:\n            # Upload source object with metadata\n            put_response = self.s3_client.put_object(\n                Bucket=self.test_bucket,\n                Key=source_key,\n                Body=test_data,\n                Metadata=source_metadata,\n                ContentType='text/plain'\n            )\n            \n            self.add_cleanup_item('object', source_key, bucket=self.test_bucket)\n            self.add_cleanup_item('object', dest_key, bucket=self.test_bucket)\n            \n            if 'ETag' not in put_response:\n                self.add_result(\n                    'metadata_copy_behavior',\n                    False,\n                    \"Failed to create source object for copy test\",\n                    {'source_key': source_key}\n                )\n                return\n            \n            # Test 1: Copy with metadata preservation (default behavior)\n            start_time = time.time()\n            copy_response = self.s3_client.copy_object(\n                Bucket=self.test_bucket,\n                Key=dest_key,\n                CopySource={'Bucket': self.test_bucket, 'Key': source_key}\n            )\n            copy_duration = time.time() - start_time\n            \n            # Check if metadata was preserved\n            dest_head = self.s3_client.head_object(Bucket=self.test_bucket, Key=dest_key)\n            dest_metadata = dest_head.get('Metadata', {})\n            \n            preserved_fields = []\n            for key, value in source_metadata.items():\n                if dest_metadata.get(key) == value:\n                    preserved_fields.append(key)\n            \n            preservation_rate = len(preserved_fields) / len(source_metadata)\n            \n            self.add_result(\n                'metadata_copy_preservation',\n                preservation_rate >= 0.8,  # At least 80% should be preserved\n                f\"Copy operation metadata preservation: {len(preserved_fields)}/{len(source_metadata)} fields preserved\",\n                {\n                    'source_key': source_key,\n                    'dest_key': dest_key,\n                    'source_metadata': source_metadata,\n                    'dest_metadata': dest_metadata,\n                    'preserved_fields': preserved_fields,\n                    'preservation_rate': preservation_rate\n                },\n                copy_duration\n            )\n            \n            # Test 2: Copy with metadata replacement\n            replacement_key = self.generate_unique_name(\"copy-replacement-metadata\")\n            self.add_cleanup_item('object', replacement_key, bucket=self.test_bucket)\n            \n            new_metadata = {\n                'new-author': 'copy-creator',\n                'modified-time': '2024-12-01',\n                'category': 'modified'\n            }\n            \n            start_time = time.time()\n            replace_response = self.s3_client.copy_object(\n                Bucket=self.test_bucket,\n                Key=replacement_key,\n                CopySource={'Bucket': self.test_bucket, 'Key': source_key},\n                Metadata=new_metadata,\n                MetadataDirective='REPLACE'\n            )\n            replace_duration = time.time() - start_time\n            \n            # Verify metadata replacement\n            replace_head = self.s3_client.head_object(Bucket=self.test_bucket, Key=replacement_key)\n            replace_metadata = replace_head.get('Metadata', {})\n            \n            replaced_correctly = all(\n                replace_metadata.get(key) == value \n                for key, value in new_metadata.items()\n            )\n            \n            old_metadata_absent = all(\n                key not in replace_metadata \n                for key in source_metadata.keys()\n            )\n            \n            self.add_result(\n                'metadata_copy_replacement',\n                replaced_correctly and old_metadata_absent,\n                f\"Copy with metadata replacement: new metadata set={replaced_correctly}, old metadata removed={old_metadata_absent}\",\n                {\n                    'source_key': source_key,\n                    'replacement_key': replacement_key,\n                    'new_metadata': new_metadata,\n                    'replace_metadata': replace_metadata,\n                    'source_metadata': source_metadata,\n                    'replaced_correctly': replaced_correctly,\n                    'old_metadata_absent': old_metadata_absent\n                },\n                replace_duration\n            )\n            \n        except S3ClientError as e:\n            self.add_result(\n                'metadata_copy_operations',\n                False,\n                f\"Failed to test metadata copy behavior: {e}\",\n                {'source_key': source_key, 'error_code': e.error_code}\n            )\n    \n    def _check_system_vs_user_metadata(self):\n        \"\"\"Test distinction between system and user metadata.\"\"\"\n        self.logger.info(\"Checking system vs user metadata handling...\")\n        \n        test_key = self.generate_unique_name(\"system-user-metadata-test\")\n        test_data = self._generate_test_data(1024)\n        \n        user_metadata = {\n            'user-field': 'user-value',\n            'application': 'test-app'\n        }\n        \n        try:\n            # Upload with user metadata and system metadata\n            start_time = time.time()\n            put_response = self.s3_client.put_object(\n                Bucket=self.test_bucket,\n                Key=test_key,\n                Body=test_data,\n                Metadata=user_metadata,\n                ContentType='application/json',\n                CacheControl='no-cache',\n                ContentEncoding='identity'\n            )\n            duration = time.time() - start_time\n            \n            self.add_cleanup_item('object', test_key, bucket=self.test_bucket)\n            \n            if 'ETag' not in put_response:\n                self.add_result(\n                    'system_user_metadata',\n                    False,\n                    \"Upload with system and user metadata failed\",\n                    {'object_key': test_key}\n                )\n                return\n            \n            # Retrieve and analyze metadata types\n            head_response = self.s3_client.head_object(Bucket=self.test_bucket, Key=test_key)\n            \n            # Check system metadata\n            system_metadata_present = []\n            expected_system = ['ContentType', 'ContentLength', 'ETag', 'LastModified']\n            for field in expected_system:\n                if field in head_response:\n                    system_metadata_present.append(field)\n            \n            # Check user metadata\n            returned_user_metadata = head_response.get('Metadata', {})\n            user_metadata_preserved = all(\n                returned_user_metadata.get(key) == value \n                for key, value in user_metadata.items()\n            )\n            \n            # Check that system metadata is separate from user metadata\n            metadata_separation = not any(\n                key in returned_user_metadata \n                for key in expected_system\n            )\n            \n            self.add_result(\n                'system_user_metadata_distinction',\n                user_metadata_preserved and len(system_metadata_present) >= 3 and metadata_separation,\n                f\"System/User metadata distinction: system fields={len(system_metadata_present)}, user preserved={user_metadata_preserved}, separated={metadata_separation}\",\n                {\n                    'object_key': test_key,\n                    'system_metadata_present': system_metadata_present,\n                    'user_metadata_preserved': user_metadata_preserved,\n                    'metadata_separation': metadata_separation,\n                    'returned_user_metadata': returned_user_metadata,\n                    'head_response_keys': list(head_response.keys())\n                },\n                duration\n            )\n            \n        except S3ClientError as e:\n            self.add_result(\n                'system_user_metadata_distinction',\n                False,\n                f\"Failed to test system vs user metadata: {e}\",\n                {'object_key': test_key, 'error_code': e.error_code}\n            )\n    \n    def _check_metadata_edge_cases(self):\n        \"\"\"Test metadata edge cases and boundary conditions.\"\"\"\n        self.logger.info(\"Checking metadata edge cases...\")\n        \n        # Test 1: Empty metadata values\n        empty_test_key = self.generate_unique_name(\"empty-metadata-test\")\n        \n        try:\n            start_time = time.time()\n            put_response = self.s3_client.put_object(\n                Bucket=self.test_bucket,\n                Key=empty_test_key,\n                Body=b\"test\",\n                Metadata={'empty-field': '', 'normal-field': 'value'}\n            )\n            duration = time.time() - start_time\n            \n            self.add_cleanup_item('object', empty_test_key, bucket=self.test_bucket)\n            \n            if 'ETag' in put_response:\n                head_response = self.s3_client.head_object(Bucket=self.test_bucket, Key=empty_test_key)\n                returned_metadata = head_response.get('Metadata', {})\n                \n                empty_field_handling = 'empty-field' in returned_metadata\n                normal_field_preserved = returned_metadata.get('normal-field') == 'value'\n                \n                self.add_result(\n                    'empty_metadata_values',\n                    normal_field_preserved,  # Normal field should be preserved\n                    f\"Empty metadata values: empty field present={empty_field_handling}, normal field preserved={normal_field_preserved}\",\n                    {\n                        'object_key': empty_test_key,\n                        'empty_field_handling': empty_field_handling,\n                        'normal_field_preserved': normal_field_preserved,\n                        'returned_metadata': returned_metadata\n                    },\n                    duration\n                )\n            \n        except S3ClientError as e:\n            self.add_result(\n                'empty_metadata_values',\n                False,\n                f\"Failed to test empty metadata values: {e}\",\n                {'object_key': empty_test_key, 'error_code': e.error_code}\n            )\n        \n        # Test 2: No metadata (baseline)\n        no_metadata_key = self.generate_unique_name(\"no-metadata-test\")\n        \n        try:\n            start_time = time.time()\n            put_response = self.s3_client.put_object(\n                Bucket=self.test_bucket,\n                Key=no_metadata_key,\n                Body=b\"test without metadata\"\n            )\n            duration = time.time() - start_time\n            \n            self.add_cleanup_item('object', no_metadata_key, bucket=self.test_bucket)\n            \n            if 'ETag' in put_response:\n                head_response = self.s3_client.head_object(Bucket=self.test_bucket, Key=no_metadata_key)\n                user_metadata = head_response.get('Metadata', {})\n                \n                self.add_result(\n                    'no_metadata_baseline',\n                    len(user_metadata) == 0,\n                    f\"No metadata baseline: user metadata count={len(user_metadata)}\",\n                    {\n                        'object_key': no_metadata_key,\n                        'user_metadata_count': len(user_metadata),\n                        'user_metadata': user_metadata\n                    },\n                    duration\n                )\n            \n        except S3ClientError as e:\n            self.add_result(\n                'no_metadata_baseline',\n                False,\n                f\"Failed to test no metadata baseline: {e}\",\n                {'object_key': no_metadata_key, 'error_code': e.error_code}\n            )", "text_preview": "\"\"\" S3 Metadata Compatibility Checks This module provides comprehensive testing for S3 metadata functionality including standard headers, custom metadata, encoding, size limits, and copy behavior. \"\"\" import time import json import urllib.p", "input_sha256": "b657b1bd9d920604d717a0358cceb582be51aa4700b14433d3cae9e9b165f5f2", "text_sha256": "69ce7eaec536bf8c67e101efa7c892989a1e067669442b2a56fd390854751092", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12116, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 1, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.1012280808466677, "noise_severity": 0.05099902139461347, "missing_content_severity": 0.015705484949146736, "factual_density": 2.9438836724821864, "pretraining_utility": 0.7151700948435842, "math_density": 0.2668764866902172, "code_density": 3.738100454288293, "reasoning_density": 1.627471581597833, "education_level": 2.5554466816614005, "creative_writing_quality": -0.08110888789930853}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.021727159185201517, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9952440221100907, "secondary": 0.9198085384982972, "tertiary": 0.9424803567785075, "structured_records": 0.11139168248928337, "procedural_steps": 0.28873005739403745, "worked_solution": 0.015523974015332344, "contextual_dialogue": 4.644855831033864e-06, "content_quality": 0.5658926003164261, "noise_severity": 0.9575008155044887, "missing_content_severity": 0.986912095875711, "factual_density": 0.48023576869558293, "pretraining_utility": 0.5541948308426248, "math_density": 0.7776029277581524, "code_density": 0.7817503785735774, "reasoning_density": 0.10686405023916357, "education_level": 0.39756519508077987, "creative_writing_quality": 0.9994217387683721, "primary_topic": 0.9943278884208964, "secondary_topic": 0.9535038442937352}, "probability_margin": {"primary": 0.9935703871284614, "secondary": 0.8916113667219259, "tertiary": 0.9175507634401263, "education_level": 0.06736961370329969, "creative_writing_quality": 0.9992916667890859, "primary_topic": 0.9906632086809618, "secondary_topic": 0.9340308451753859}, "raw_probabilities": {"primary": {"Academic Writing": 4.7809910653367014e-05, "Content Listing": 2.4490183959717703e-05, "Creative Writing": 1.5424185985960584e-05, "Customer Support Page": 1.872782663361391e-05, "Q&A Forum": 1.248037239192304e-05, "Discussion Thread": 7.831650925506841e-06, "Comment Section": 1.4147416042820165e-05, "Missing Content": 2.3797683483399576e-05, "Knowledge Article": 0.0001411193655025868, "Legal Notices": 2.6834356338976465e-05, "News Article": 6.999532590763577e-06, "Organizational Announcement": 1.3826190516869922e-05, "Nonfiction Writing": 1.3381876770269888e-05, "Organizational About Page": 1.4225004805078426e-05, "Personal About Page": 1.1492711026248453e-05, "Personal Blog": 8.670848247874597e-06, "Product Page": 1.1969451029301267e-05, "Spam / Ads": 1.1356363565549041e-05, "Structured Data": 0.000429489762780723, "Technical Writing": 0.9954421878555035, "Transcript / Interview": 1.8278301869915665e-05, "Tutorial / How-To Guide": 0.001802599579718714, "User Reviews": 1.105884261501284e-05, "Other / Unknown": 0.0018718007270421934}, "secondary": {"Academic Writing": 0.00043400787611847663, "Content Listing": 0.00039405585638344526, "Creative Writing": 0.0002842464292017034, "Customer Support Page": 0.00027238606992629956, "Q&A Forum": 0.00016452090087300513, "Discussion Thread": 0.00010706028433223268, "Comment Section": 0.00025739178001755515, "Missing Content": 0.0005674206719824857, "Knowledge Article": 0.0031297150985512673, "Legal Notices": 0.00033601189564851834, "News Article": 0.00019328958506109012, "Organizational Announcement": 0.00018843472740741974, "Nonfiction Writing": 0.0002026986019290093, "Organizational About Page": 0.0002131734403235491, "Personal About Page": 0.0001711292507230714, "Personal Blog": 0.00015683310930625715, "Product Page": 0.00014558094077070109, "Spam / Ads": 0.00016947323063730917, "Structured Data": 0.028691560677402504, "Technical Writing": 0.03153848267227568, "Transcript / Interview": 0.0002508882221109607, "Tutorial / How-To Guide": 0.00886896777904523, "User Reviews": 0.00011282150577066504, "NONE": 0.9231498493942015}, "tertiary": {"Academic Writing": 0.00020925498831591289, "Content Listing": 0.0003067594019582059, "Creative Writing": 0.00014434112444429618, "Customer Support Page": 0.00014360901402501634, "Q&A Forum": 6.272287661923964e-05, "Discussion Thread": 5.647538607339226e-05, "Comment Section": 0.00016542006511615243, "Missing Content": 0.0004806750198981165, "Knowledge Article": 0.0032535361051549217, "Legal Notices": 0.00034163408568759, "News Article": 0.00010079998470204516, "Organizational Announcement": 0.00010468832788479302, "Nonfiction Writing": 0.00012034990454793499, "Organizational About Page": 0.00013303809229352927, "Personal About Page": 8.128795585866481e-05, "Personal Blog": 7.542844776897603e-05, "Product Page": 8.475720273961244e-05, "Spam / Ads": 9.236990173758401e-05, "Structured Data": 0.027326245139276777, "Technical Writing": 0.015871772289490615, "Transcript / Interview": 0.00010893205920916735, "Tutorial / How-To Guide": 0.005806763103043081, "User Reviews": 5.213094475129925e-05, "NONE": 0.944877008579403}, "structured_records": {"false": 0.8886083175107167, "true": 0.11139168248928337}, "procedural_steps": {"false": 0.7112699426059625, "true": 0.28873005739403745}, "worked_solution": {"false": 0.9844760259846677, "true": 0.015523974015332344}, "contextual_dialogue": {"false": 0.9999953551441689, "true": 4.644855831033864e-06}, "content_quality": {"-2": 0.004007199729537105, "-1": 0.015933240150741313, "0": 0.16596231989671648, "1": 0.5030187599895269, "2": 0.3110784802334783}, "noise_severity": {"0": 0.9561578130171122, "1": 0.03890191485443981, "2": 0.003395469288030171, "3": 0.0008730433975582915, "4": 0.0006717594428596115}, "missing_content_severity": {"0": 0.9916422008598385, "1": 0.0031662973873308794, "2": 0.0033756400568578605, "3": 0.001475539335790906, "4": 0.0003403223601818546}, "factual_density": {"0": 0.0067520542033876006, "1": 0.03972355642676132, "2": 0.2402134270778716, "3": 0.429510587268236, "4": 0.2838003750237435}, "pretraining_utility": {"-2": 0.005657392023862395, "-1": 0.04766317250867032, "0": 0.2975995329837053, "1": 0.5240117535675447, "2": 0.12506814891621723}, "math_density": {"0": 0.7891813703527503, "1": 0.1715430097041231, "2": 0.028742214922310622, "3": 0.004284572941790728, "4": 0.006248832079025153}, "code_density": {"0": 0.0003437167397973864, "1": 0.0006674109241304993, "2": 0.015498151075843673, "3": 0.22752614382843864, "4": 0.7559645774317898}, "reasoning_density": {"0": 0.22214577905758545, "1": 0.29006447851253075, "2": 0.2350396339472849, "3": 0.1436725987396635, "4": 0.10907750974293547}, "education_level": {"none": 0.0007210070477183362, "0": 0.0011664457335450393, "1": 0.004100876094147409, "2": 0.49797099590064986, "3": 0.43060138219735017, "4": 0.06543929302658913}, "creative_writing_quality": {"not_applicable": 0.9995181156403101, "-2": 4.846778349213739e-05, "-1": 8.938068014789252e-05, "0": 0.00022644885122422901, "1": 8.794294703030047e-05, "2": 2.964409779566132e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.935636595662856e-05, "Art & Design": 3.2639415180171517e-05, "Law & Crime": 2.7072357407494606e-05, "Education & Careers": 3.121708138951552e-05, "Entertainment & Media": 2.2262223268029903e-05, "Fashion & Beauty": 1.298975015416626e-05, "Business, Economics & Finance": 6.837102562183511e-05, "Food & Dining": 1.7380391369385203e-05, "Games & Gaming": 4.40900928575723e-05, "Electronics & Hardware": 0.00012549191173356634, "Health, Healthcare & Wellness": 2.0658206740551664e-05, "History & Geography": 2.8054729850042513e-05, "Home, Pets & Hobbies": 1.845848683347032e-05, "Industry & Production": 3.257377139737997e-05, "Language, Literature & Philosophy": 3.101181879727192e-05, "Politics & Government": 1.799223924105329e-05, "Religion & Spirituality": 2.8461220123015817e-05, "Science, Mathematics & Engineering": 0.0004016618184475796, "Relationships & Community": 2.6801455960942413e-05, "Software Usage & Applications": 0.0038828378775923374, "Programming & Software Engineering": 0.9945460465585542, "Sports & Fitness": 3.4986504730648966e-05, "Vehicles & Transportation": 4.9133731994702894e-05, "Travel & Tourism": 3.99378230126488e-05, "Other / Uncovered Subject": 0.00039099431856216317, "Unknown / Insufficient Content": 2.951882322376927e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00035206833449811885, "Art & Design": 0.0002054945254880628, "Law & Crime": 0.00030541064368861293, "Education & Careers": 0.00020652360993476674, "Entertainment & Media": 0.00019454123691692958, "Fashion & Beauty": 0.00010801426934753261, "Business, Economics & Finance": 0.000443404897833837, "Food & Dining": 9.99685368589625e-05, "Games & Gaming": 0.00018105779090901706, "Electronics & Hardware": 0.0003452096595807185, "Health, Healthcare & Wellness": 0.00012846145971363892, "History & Geography": 0.00012782848172605179, "Home, Pets & Hobbies": 9.673706776838515e-05, "Industry & Production": 0.0001512666071285727, "Language, Literature & Philosophy": 0.00016998978388647442, "Politics & Government": 0.00014498643583989263, "Religion & Spirituality": 0.00015801067106418427, "Science, Mathematics & Engineering": 0.0007756451030673349, "Relationships & Community": 9.401686349322183e-05, "Software Usage & Applications": 0.02126131279935945, "Programming & Software Engineering": 0.018230913077413202, "Sports & Fitness": 0.00011722042930233783, "Vehicles & Transportation": 0.00012168734246692543, "Travel & Tourism": 0.00018163257061378047, "Other / Uncovered Subject": 0.000506439827354618, "NONE": 0.9552921579747454}}, "probabilities": {"primary": {"Academic Writing": 4.780991065336702e-05, "Content Listing": 2.4490183959717706e-05, "Creative Writing": 1.5424185985960587e-05, "Customer Support Page": 1.8727826633613914e-05, "Q&A Forum": 1.2480372391923041e-05, "Discussion Thread": 7.831650925506843e-06, "Comment Section": 1.4147416042820167e-05, "Missing Content": 2.379768348339958e-05, "Knowledge Article": 0.00014111936550258682, "Legal Notices": 2.6834356338976468e-05, "News Article": 6.999532590763578e-06, "Organizational Announcement": 1.3826190516869924e-05, "Nonfiction Writing": 1.338187677026989e-05, "Organizational About Page": 1.4225004805078427e-05, "Personal About Page": 1.1492711026248454e-05, "Personal Blog": 8.670848247874598e-06, "Product Page": 1.1969451029301269e-05, "Spam / Ads": 1.1356363565549043e-05, "Structured Data": 0.00042948976278072305, "Technical Writing": 0.9954421878555036, "Transcript / Interview": 1.8278301869915668e-05, "Tutorial / How-To Guide": 0.0018025995797187142, "User Reviews": 1.1058842615012842e-05, "Other / Unknown": 0.0018718007270421937}, "secondary": {"Academic Writing": 0.00043400787611847663, "Content Listing": 0.00039405585638344526, "Creative Writing": 0.0002842464292017034, "Customer Support Page": 0.00027238606992629956, "Q&A Forum": 0.00016452090087300513, "Discussion Thread": 0.00010706028433223268, "Comment Section": 0.00025739178001755515, "Missing Content": 0.0005674206719824857, "Knowledge Article": 0.0031297150985512673, "Legal Notices": 0.00033601189564851834, "News Article": 0.00019328958506109012, "Organizational Announcement": 0.00018843472740741974, "Nonfiction Writing": 0.0002026986019290093, "Organizational About Page": 0.0002131734403235491, "Personal About Page": 0.0001711292507230714, "Personal Blog": 0.00015683310930625715, "Product Page": 0.00014558094077070109, "Spam / Ads": 0.00016947323063730917, "Structured Data": 0.028691560677402504, "Technical Writing": 0.03153848267227568, "Transcript / Interview": 0.0002508882221109607, "Tutorial / How-To Guide": 0.00886896777904523, "User Reviews": 0.00011282150577066504, "NONE": 0.9231498493942015}, "tertiary": {"Academic Writing": 0.00020925498831591289, "Content Listing": 0.0003067594019582059, "Creative Writing": 0.00014434112444429618, "Customer Support Page": 0.00014360901402501634, "Q&A Forum": 6.272287661923964e-05, "Discussion Thread": 5.647538607339226e-05, "Comment Section": 0.00016542006511615243, "Missing Content": 0.0004806750198981165, "Knowledge Article": 0.0032535361051549217, "Legal Notices": 0.00034163408568759, "News Article": 0.00010079998470204516, "Organizational Announcement": 0.00010468832788479302, "Nonfiction Writing": 0.00012034990454793499, "Organizational About Page": 0.00013303809229352927, "Personal About Page": 8.128795585866481e-05, "Personal Blog": 7.542844776897603e-05, "Product Page": 8.475720273961244e-05, "Spam / Ads": 9.236990173758401e-05, "Structured Data": 0.027326245139276777, "Technical Writing": 0.015871772289490615, "Transcript / Interview": 0.00010893205920916735, "Tutorial / How-To Guide": 0.005806763103043081, "User Reviews": 5.213094475129925e-05, "NONE": 0.944877008579403}, "structured_records": {"false": 0.8886083175107167, "true": 0.11139168248928337}, "procedural_steps": {"false": 0.7112699426059625, "true": 0.28873005739403745}, "worked_solution": {"false": 0.9844760259846677, "true": 0.015523974015332344}, "contextual_dialogue": {"false": 0.9999953551441689, "true": 4.644855831033864e-06}, "content_quality": {"-2": 0.004007199729537104, "-1": 0.01593324015074131, "0": 0.16596231989671645, "1": 0.5030187599895268, "2": 0.3110784802334782}, "noise_severity": {"0": 0.9561578130171122, "1": 0.03890191485443981, "2": 0.003395469288030171, "3": 0.0008730433975582915, "4": 0.0006717594428596115}, "missing_content_severity": {"0": 0.9916422008598385, "1": 0.0031662973873308794, "2": 0.0033756400568578605, "3": 0.001475539335790906, "4": 0.0003403223601818546}, "factual_density": {"0": 0.0067520542033876006, "1": 0.03972355642676132, "2": 0.2402134270778716, "3": 0.429510587268236, "4": 0.2838003750237435}, "pretraining_utility": {"-2": 0.005657392023862396, "-1": 0.047663172508670325, "0": 0.29759953298370534, "1": 0.5240117535675448, "2": 0.12506814891621726}, "math_density": {"0": 0.7891813703527504, "1": 0.17154300970412312, "2": 0.028742214922310626, "3": 0.004284572941790729, "4": 0.006248832079025154}, "code_density": {"0": 0.0003437167397973864, "1": 0.0006674109241304993, "2": 0.015498151075843673, "3": 0.22752614382843864, "4": 0.7559645774317898}, "reasoning_density": {"0": 0.22214577905758545, "1": 0.29006447851253075, "2": 0.2350396339472849, "3": 0.1436725987396635, "4": 0.10907750974293547}, "education_level": {"none": 0.0007210070477183363, "0": 0.0011664457335450395, "1": 0.00410087609414741, "2": 0.4979709959006499, "3": 0.4306013821973502, "4": 0.06543929302658914}, "creative_writing_quality": {"not_applicable": 0.9995181156403097, "-2": 4.846778349213737e-05, "-1": 8.938068014789248e-05, "0": 0.0002264488512242289, "1": 8.794294703030043e-05, "2": 2.9644097795661305e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.935636595662856e-05, "Art & Design": 3.2639415180171517e-05, "Law & Crime": 2.7072357407494606e-05, "Education & Careers": 3.121708138951552e-05, "Entertainment & Media": 2.2262223268029903e-05, "Fashion & Beauty": 1.298975015416626e-05, "Business, Economics & Finance": 6.837102562183511e-05, "Food & Dining": 1.7380391369385203e-05, "Games & Gaming": 4.40900928575723e-05, "Electronics & Hardware": 0.00012549191173356634, "Health, Healthcare & Wellness": 2.0658206740551664e-05, "History & Geography": 2.8054729850042513e-05, "Home, Pets & Hobbies": 1.845848683347032e-05, "Industry & Production": 3.257377139737997e-05, "Language, Literature & Philosophy": 3.101181879727192e-05, "Politics & Government": 1.799223924105329e-05, "Religion & Spirituality": 2.8461220123015817e-05, "Science, Mathematics & Engineering": 0.0004016618184475796, "Relationships & Community": 2.6801455960942413e-05, "Software Usage & Applications": 0.0038828378775923374, "Programming & Software Engineering": 0.9945460465585542, "Sports & Fitness": 3.4986504730648966e-05, "Vehicles & Transportation": 4.9133731994702894e-05, "Travel & Tourism": 3.99378230126488e-05, "Other / Uncovered Subject": 0.00039099431856216317, "Unknown / Insufficient Content": 2.951882322376927e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00035206833449811885, "Art & Design": 0.0002054945254880628, "Law & Crime": 0.00030541064368861293, "Education & Careers": 0.00020652360993476674, "Entertainment & Media": 0.00019454123691692958, "Fashion & Beauty": 0.00010801426934753261, "Business, Economics & Finance": 0.000443404897833837, "Food & Dining": 9.99685368589625e-05, "Games & Gaming": 0.00018105779090901706, "Electronics & Hardware": 0.0003452096595807185, "Health, Healthcare & Wellness": 0.00012846145971363892, "History & Geography": 0.00012782848172605179, "Home, Pets & Hobbies": 9.673706776838515e-05, "Industry & Production": 0.0001512666071285727, "Language, Literature & Philosophy": 0.00016998978388647442, "Politics & Government": 0.00014498643583989263, "Religion & Spirituality": 0.00015801067106418427, "Science, Mathematics & Engineering": 0.0007756451030673349, "Relationships & Community": 9.401686349322183e-05, "Software Usage & Applications": 0.02126131279935945, "Programming & Software Engineering": 0.018230913077413202, "Sports & Fitness": 0.00011722042930233783, "Vehicles & Transportation": 0.00012168734246692543, "Travel & Tourism": 0.00018163257061378047, "Other / Uncovered Subject": 0.000506439827354618, "NONE": 0.9552921579747454}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000004, "mass_delta": 4.440892098500626e-16, "missing_mass": 0.0, "excess_mass": 4.440892098500626e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 15093, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 15093, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:10.386557+00:00", "scoring_key_slot": "primary", "dataset_uid": "b34afba6-eb7b-44fe-bfbf-8e8358c5ae42", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "3c7e7322d96442919caed0ac2a44ac076b0f1fdaa8df8f94eb623822346bd4d8", "source_metadata": {"uuid": "b34afba6-eb7b-44fe-bfbf-8e8358c5ae42", "repo_name": "Ixooman/vs-s3-api-test", "relative_path": "checks/check_metadata.py", "category": "TEST", "algo_rel_score": 0.959396481513977, "quality_score": 7.322190761566162, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 14, "repo_created_at": "2025-07-12T15:48:30.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "3083a0f1ab9ea1d9323b6fe8b34000e39cae3f62", "size_bytes": 33359, "file_timestamp": 1752591055, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "8f98c071c6c1c9b8793bd212f33eba1f2e7920b8"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 14, "repo_created_at": "2025-07-12T15:48:30.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "3083a0f1ab9ea1d9323b6fe8b34000e39cae3f62", "size_bytes": 33359, "file_timestamp": 1752591055, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "8f98c071c6c1c9b8793bd212f33eba1f2e7920b8", "url": "https://github.com/Ixooman/vs-s3-api-test/blob/8f98c071c6c1c9b8793bd212f33eba1f2e7920b8/checks/check_metadata.py"}, "gpt4o_tokens": 5970, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.101228080846668, "noise_severity": 0.05099902139461347, "missing_content_severity": 0.015705484949146736, "factual_density": 2.9438836724821864, "pretraining_utility": 0.7151700948435841, "math_density": 0.2668764866902172, "code_density": 3.738100454288293, "reasoning_density": 1.627471581597833, "education_level": 2.5554466816614005, "creative_writing_quality": -0.08110888789930853}, "score_applicability": {"education_level": 0.9992789929522817, "creative_writing_quality": 0.0004818843596902205}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/samboat79/Restaurant_true_review/blob/dfa6134e1e6214c924149a647d52284052bd4438/Restaurant_true_review-master/project2/app.py", "id": "python-0-86577849ec5b100099de021ca64e7815", "text": "import datetime as dt\nimport numpy as np\nimport pandas as pd\n\nfrom flask import (\n    Flask,\n    render_template,\n    jsonify,\n    request,\n    redirect)\n\nfrom flask_sqlalchemy import SQLAlchemy\n\n# Flask Setup\napp = Flask(__name__)\n\n# Database Setup\n# Import SQL Alchemy\nfrom sqlalchemy import create_engine\n\n# Import and establish Base for which classes will be constructed \nfrom sqlalchemy.ext.declarative import declarative_base\nBase = declarative_base()\n\n# Import modules to declare columns and column data types\nfrom sqlalchemy import Column, Integer, String, Float\n\n# Dependencies\nimport json\nimport pandas as pd\n\ntest_data = pd.read_json(\"Tweet.json\")\ntest_data2 = pd.read_json(\"tweets.JSON\")\ntest_data3 = pd.read_json(\"Resturants.json\")\n\ntest_data4 = test_data.groupby([\"Restaurants\"]).mean().reset_index(inplace=False)\ntest_data4[\"Restaurant_Name\"] = test_data4[\"Restaurants\"]\n\nthe_final_df = pd.merge(test_data4, test_data3, left_on='Restaurant_Name', right_on='Restaurant_Name')\n\n# Create the Garbage class\n### BEGIN SOLUTION\nclass Garbage(Base):\n    __tablename__ = 'tweets2'\n    id = Column(Integer, primary_key=True)\n    Restaurants = Column(String(255))\n    compound = Column(Float())\n    negative = Column(Float())\n    positive = Column(Float())\n    Restaurant_Name = Column(String(255))\n    Coordinates = Column(String(255))\n    Cuisine = Column(String(255))\n    Latitude = Column(Float())\n    Longitude = Column(Float())\n    Zomato_Avg_Rating = Column(Float())\n### END SOLUTION\n\n # Create a connection to a SQLite database\n### BEGIN SOLUTION\nengine = create_engine('sqlite:///tweet2.db')\n### END SOLUTION\n\n# Create the garbage_collection table within the database\nBase.metadata.create_all(engine)\n\n# To push the objects made and query the server we use a Session object\nfrom sqlalchemy.orm import Session\nsession = Session(bind=engine)\n\n# The database URI\napp.config['SQLALCHEMY_DATABASE_URI'] = \"sqlite:///tweets2.sqlite\"\n\ndb = SQLAlchemy(app)\n\n# Create database tables\n@app.before_first_request\ndef setup():\n    db.create_all()\n\n# Flask Routes\n@app.route(\"/\")\ndef home():\n    \"\"\"Render Home Page.\"\"\"\n    return render_template(\"index.html\")\n\n\n@app.route(\"/emoji_char\")\ndef emoji_char_data():\n    \"\"\"Return emoji score and emoji char\"\"\"\n\n    # query for the top 10 emoji data\n    results = session.query(Garbage.Restaurants, Garbage.positive).\\\n        order_by(Garbage.positive.desc()).\\\n        limit(10).all()\n    resturants_name = [result[0] for result in results]\n    scores = [float(result[1]) for result in results]\n\n    # Generate the plot trace\n    plot_trace = {\n        \"x\": resturants_name,\n        \"y\": scores,\n        \"type\": \"bar\"\n    }\n    return jsonify(plot_trace)\n\n\n@app.route(\"/emoji_id\")\ndef emoji_id_data():\n    \"\"\"Return emoji score and emoji id\"\"\"\n\n    # query for the emoji data using pandas\n    query_statement = session.query(Garbage).\\\n        order_by(Garbage.compound.desc()).\\\n        limit(10).statement\n    df = pd.read_sql_query(query_statement, session.bind)\n    print(df)\n    # Format the data for Plotly\n    plot_trace = {\n        \"x\": df[\"Restaurants\"].values.tolist(),\n        \"y\": df[\"compound\"].values.tolist(),\n        \"type\": \"bar\"\n    }\n    return jsonify(plot_trace)\n\n\n@app.route(\"/emoji_name\")\ndef emoji_name_data():\n    \"\"\"Return emoji score and emoji name\"\"\"\n\n    # query for the top 10 emoji data\n    query_statement = session.query(Garbage).\\\n        order_by(Garbage.negative.desc()).\\\n        limit(10).statement\n    df = pd.read_sql_query(query_statement, session.bind)\n\n    # Format the data for Plotly\n    plot_trace = {\n        \"x\": df[\"Restaurants\"].values.tolist(),\n        \"y\": df[\"negative\"].values.tolist(),\n        \"type\": \"bar\"\n    }\n    return jsonify(plot_trace)\n\n@app.route(\"/pie\")\ndef pie_data():\n    \"\"\"Return emoji score and emoji name\"\"\"\n\n    # query for the top 10 emoji data\n    query_statement = session.query(Garbage).\\\n        order_by(Garbage.negative.desc()).\\\n        limit(10).statement\n    df = pd.read_sql_query(query_statement, session.bind)\n    print(df[\"Restaurants\"][0])\n    print([df[\"positive\"][0],df[\"negative\"][0]])\n    # Format the data for Plotly\n    plot_trace = {\n        \"values\": [df[\"positive\"][0],df[\"negative\"][0]],\n        \"labels\": [\"positive\",\"negative\"],\n        \"type\": \"pie\",\n        \"title\": df[\"Restaurants\"][0]\n    }\n    return jsonify(plot_trace)\n\n@app.route(\"/pie2\")\ndef pie_data_2():\n    \"\"\"Return emoji score and emoji name\"\"\"\n\n    # query for the top 10 emoji data\n    query_statement = session.query(Garbage).\\\n        order_by(Garbage.compound.desc()).\\\n        limit(10).statement\n    df = pd.read_sql_query(query_statement, session.bind)\n    print(df[\"Restaurants\"][1])\n    print([df[\"positive\"][1],df[\"negative\"][1]])\n    # Format the data for Plotly\n    plot_trace = {\n        \"values\": [df[\"positive\"][1],df[\"negative\"][1]],\n        \"labels\": [\"positive\",\"negative\"],\n        \"type\": \"pie\",\n        \"title\": df[\"Restaurants\"][1]\n    }\n    return jsonify(plot_trace)\n\n@app.route(\"/pie3\")\ndef pie_data_3():\n    \"\"\"Return emoji score and emoji name\"\"\"\n\n    # query for the top 10 emoji data\n    query_statement = session.query(Garbage).\\\n        order_by(Garbage.compound.desc()).\\\n        limit(10).statement\n    df = pd.read_sql_query(query_statement, session.bind)\n    print(df[\"Restaurants\"][2])\n    print([df[\"positive\"][2],df[\"negative\"][2]])\n    # Format the data for Plotly\n    plot_trace = {\n        \"values\": [df[\"positive\"][2],df[\"negative\"][2]],\n        \"labels\": [\"positive\",\"negative\"],\n        \"type\": \"pie\",\n        \"title\": df[\"Restaurants\"][2]\n    }\n    return jsonify(plot_trace)\n\n@app.route(\"/pie4\")\ndef pie_data_4():\n    \"\"\"Return emoji score and emoji name\"\"\"\n\n    # query for the top 10 emoji data\n    query_statement = session.query(Garbage).\\\n        order_by(Garbage.compound.desc()).\\\n        limit(10).statement\n    df = pd.read_sql_query(query_statement, session.bind)\n    print(df[\"Restaurants\"][3])\n    print([df[\"positive\"][3],df[\"negative\"][3]])\n    # Format the data for Plotly\n    plot_trace = {\n        \"values\": [df[\"positive\"][3],df[\"negative\"][3]],\n        \"labels\": [\"positive\",\"negative\"],\n        \"type\": \"pie\",\n        \"title\": df[\"Restaurants\"][3]\n    }\n    return jsonify(plot_trace)\n\n@app.route(\"/pie5\")\ndef pie_data_5():\n    \"\"\"Return emoji score and emoji name\"\"\"\n\n    # query for the top 10 emoji data\n    query_statement = session.query(Garbage).\\\n        order_by(Garbage.compound.desc()).\\\n        limit(10).statement\n    df = pd.read_sql_query(query_statement, session.bind)\n    print(df[\"Restaurants\"][4])\n    print([df[\"positive\"][4],df[\"negative\"][4]])\n    # Format the data for Plotly\n    plot_trace = {\n        \"values\": [df[\"positive\"][4],df[\"negative\"][4]],\n        \"labels\": [\"positive\",\"negative\"],\n        \"type\": \"pie\",\n        \"title\": df[\"Restaurants\"][4]\n    }\n    return jsonify(plot_trace)\n\n\nif __name__ == '__main__':\n    app.run(debug=True)\n", "text_preview": "import datetime as dt import numpy as np import pandas as pd from flask import ( Flask, render_template, jsonify, request, redirect) from flask_sqlalchemy import SQLAlchemy # Flask Setup app = Flask(__name__) # Database Setup # Import SQL A", "input_sha256": "dc14c38522e23e9b196a914855d083d054192f25a23203573bf38b776c72fb88", "text_sha256": "86577849ec5b100099de021ca64e7815d63e8bb2692d5ec85a2a23cf1d957587", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12121, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.09093199186937695, "noise_severity": 0.2735842151890604, "missing_content_severity": 0.03272798896829124, "factual_density": 2.8357316441792153, "pretraining_utility": 0.24815749942848142, "math_density": 0.3723233471410686, "code_density": 3.864393683281995, "reasoning_density": 0.23352948487627168, "education_level": 2.103060442618565, "creative_writing_quality": -0.6036628026889183}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.021025251654929855, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9895038119623472, "secondary": 0.901774871581315, "tertiary": 0.9237142646125461, "structured_records": 0.07750245013935886, "procedural_steps": 0.028203495301319135, "worked_solution": 0.024378638262350254, "contextual_dialogue": 1.054880989854594e-05, "content_quality": 0.44116645279164557, "noise_severity": 0.7720131540091163, "missing_content_severity": 0.9727266758597573, "factual_density": 0.4839194885238838, "pretraining_utility": 0.4863831721832914, "math_density": 0.6897305440491095, "code_density": 0.8869947360683296, "reasoning_density": 0.8053920959364402, "education_level": 0.8182363199182787, "creative_writing_quality": 0.9986604747882453, "primary_topic": 0.9368901180269253, "secondary_topic": 0.431966584137524}, "probability_margin": {"primary": 0.984086659218267, "secondary": 0.8701829120514853, "tertiary": 0.8976105162060656, "education_level": 0.7272018213731206, "creative_writing_quality": 0.9984666366254672, "primary_topic": 0.8904642006411988, "secondary_topic": 0.029183179492311417}, "raw_probabilities": {"primary": {"Academic Writing": 6.120177571321109e-05, "Content Listing": 5.2365080309334565e-05, "Creative Writing": 3.719181826070859e-05, "Customer Support Page": 1.84646343492043e-05, "Q&A Forum": 2.9475127139901385e-05, "Discussion Thread": 1.8088941701544245e-05, "Comment Section": 3.122107587254043e-05, "Missing Content": 5.676166610644005e-05, "Knowledge Article": 0.00011812751610208648, "Legal Notices": 4.410105457012272e-05, "News Article": 1.2793909015076576e-05, "Organizational Announcement": 2.2337181033403244e-05, "Nonfiction Writing": 2.8213114895533635e-05, "Organizational About Page": 3.032161648535281e-05, "Personal About Page": 2.689550618706053e-05, "Personal Blog": 2.026415103808711e-05, "Product Page": 1.7720369233282766e-05, "Spam / Ads": 3.056218607894066e-05, "Structured Data": 0.0014447573825914393, "Technical Writing": 0.9899411531305827, "Transcript / Interview": 2.6022092079450275e-05, "Tutorial / How-To Guide": 0.005854493912315707, "User Reviews": 9.427948592729535e-05, "Other / Unknown": 0.00198318727241139}, "secondary": {"Academic Writing": 0.0005115682255696423, "Content Listing": 0.00059966616848237, "Creative Writing": 0.00032152641906993596, "Customer Support Page": 0.00022598511327290462, "Q&A Forum": 0.00019935956791750092, "Discussion Thread": 0.00014520331773133243, "Comment Section": 0.0004403565831502034, "Missing Content": 0.0007255677386755248, "Knowledge Article": 0.0008865141538809545, "Legal Notices": 0.00033062922344014815, "News Article": 0.00021242004312559267, "Organizational Announcement": 0.00019071241194468545, "Nonfiction Writing": 0.00021944616159059408, "Organizational About Page": 0.00024659680767142407, "Personal About Page": 0.00019310765075485428, "Personal Blog": 0.00020970264892558433, "Product Page": 0.00013708827376423632, "Spam / Ads": 0.00023170807499320835, "Structured Data": 0.021317417778482093, "Technical Writing": 0.03025086732903679, "Transcript / Interview": 0.0002223145138037847, "Tutorial / How-To Guide": 0.03568467321394149, "User Reviews": 0.0006299833153482303, "NONE": 0.9058675852654269}, "tertiary": {"Academic Writing": 0.0003167605152571404, "Content Listing": 0.00046400381999521913, "Creative Writing": 0.0002397488826520922, "Customer Support Page": 0.00015745113242896235, "Q&A Forum": 0.00012222505568819912, "Discussion Thread": 9.466683687483579e-05, "Comment Section": 0.0003518083019146427, "Missing Content": 0.0012951744032657538, "Knowledge Article": 0.0014004775362251823, "Legal Notices": 0.00031320467678497374, "News Article": 0.00013399085686948082, "Organizational Announcement": 0.00012944970457896788, "Nonfiction Writing": 0.0001906752910690545, "Organizational About Page": 0.00019377267480850352, "Personal About Page": 0.0001286133705651316, "Personal Blog": 0.00014765782037061084, "Product Page": 0.00011546086192497511, "Spam / Ads": 0.00018177444862938063, "Structured Data": 0.023249252059772154, "Technical Writing": 0.0140556943901833, "Transcript / Interview": 0.00014858702923577325, "Tutorial / How-To Guide": 0.029282320714291106, "User Reviews": 0.0003943926962578176, "NONE": 0.9268928369203567}, "structured_records": {"false": 0.9224975498606411, "true": 0.07750245013935886}, "procedural_steps": {"false": 0.9717965046986808, "true": 0.028203495301319135}, "worked_solution": {"false": 0.9756213617376498, "true": 0.024378638262350254}, "contextual_dialogue": {"false": 0.9999894511901014, "true": 1.054880989854594e-05}, "content_quality": {"-2": 0.037311541345541904, "-1": 0.21521104969924035, "0": 0.41870457112641074, "1": 0.27677955139791316, "2": 0.05199328643089398}, "noise_severity": {"0": 0.7979466363323771, "1": 0.14046673317138353, "2": 0.05424122256475101, "3": 0.004746594837778985, "4": 0.0025988130937094774}, "missing_content_severity": {"0": 0.9806786363529248, "1": 0.009163858930288076, "2": 0.007332604257547257, "3": 0.002400680314050963, "4": 0.0004242201451889401}, "factual_density": {"0": 0.016723214380944693, "1": 0.05450240700685123, "2": 0.23260802763952532, "3": 0.46865222199740125, "4": 0.22751412897527745}, "pretraining_utility": {"-2": 0.015050682003100523, "-1": 0.1539899829695834, "0": 0.445146889483451, "1": 0.33937604468346416, "2": 0.04643640086040086}, "math_density": {"0": 0.7858154864134087, "1": 0.12804715435373018, "2": 0.04191865027979211, "3": 0.016435943584522134, "4": 0.027782765368546944}, "code_density": {"0": 0.0002675495285066718, "1": 0.0004924761237819978, "2": 0.012448873807356407, "3": 0.10816094261791903, "4": 0.878630157922436}, "reasoning_density": {"0": 0.876594681894421, "1": 0.03644148301514058, "2": 0.06940848924904351, "3": 0.011950360002535186, "4": 0.005604985838859619}, "education_level": {"none": 0.0011122314823267154, "0": 0.0014492391777603784, "1": 0.023547928784008933, "2": 0.8485302665985656, "3": 0.12132844522544492, "4": 0.004031888731893668}, "creative_writing_quality": {"not_applicable": 0.9988837289902044, "-2": 0.0002156619652077929, "-1": 0.00037793332768871975, "0": 0.0004170923647371726, "1": 7.576073255388293e-05, "2": 2.9822619608323003e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 8.316019217718122e-05, "Art & Design": 0.00020435478664172015, "Law & Crime": 0.00011707559100262142, "Education & Careers": 0.0001312199548493593, "Entertainment & Media": 0.0002411108311413264, "Fashion & Beauty": 0.0002190342046535741, "Business, Economics & Finance": 0.0007194811647324293, "Food & Dining": 0.048853220538537176, "Games & Gaming": 0.00016315113952895683, "Electronics & Hardware": 0.00015882764673131187, "Health, Healthcare & Wellness": 0.00011062106295357922, "History & Geography": 0.0001459118538727192, "Home, Pets & Hobbies": 9.04411843872558e-05, "Industry & Production": 0.00010374345569918178, "Language, Literature & Philosophy": 0.00010827727302597509, "Politics & Government": 7.9487850716555e-05, "Religion & Spirituality": 8.606513447213367e-05, "Science, Mathematics & Engineering": 0.0029256522714033962, "Relationships & Community": 0.00039898928146541254, "Software Usage & Applications": 0.0007875405260677092, "Programming & Software Engineering": 0.9393174211797359, "Sports & Fitness": 0.00012783653657717084, "Vehicles & Transportation": 7.902301125492349e-05, "Travel & Tourism": 0.0007063770986341582, "Other / Uncovered Subject": 0.003831628789482807, "Unknown / Insufficient Content": 0.00021034744025568794}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0011900499324329925, "Art & Design": 0.0024122022872551377, "Law & Crime": 0.0010383671205823404, "Education & Careers": 0.0009550913800754673, "Entertainment & Media": 0.0019596062667095082, "Fashion & Beauty": 0.001011887808503572, "Business, Economics & Finance": 0.003458201491291961, "Food & Dining": 0.4246308437168463, "Games & Gaming": 0.0008773246258293538, "Electronics & Hardware": 0.000901931344398919, "Health, Healthcare & Wellness": 0.0010044274399779657, "History & Geography": 0.0010748596083391983, "Home, Pets & Hobbies": 0.0006164672408744711, "Industry & Production": 0.0005360472329406363, "Language, Literature & Philosophy": 0.0006165654294919466, "Politics & Government": 0.0005509456268378109, "Religion & Spirituality": 0.0007574993491131125, "Science, Mathematics & Engineering": 0.03177035223697228, "Relationships & Community": 0.0026342063067192043, "Software Usage & Applications": 0.004062703198340826, "Programming & Software Engineering": 0.050931383127418656, "Sports & Fitness": 0.0006138407833468403, "Vehicles & Transportation": 0.00044503419454615384, "Travel & Tourism": 0.003012157540059376, "Other / Uncovered Subject": 0.009123981501938227, "NONE": 0.4538140232091577}}, "probabilities": {"primary": {"Academic Writing": 6.12017757132111e-05, "Content Listing": 5.236508030933457e-05, "Creative Writing": 3.71918182607086e-05, "Customer Support Page": 1.8464634349204304e-05, "Q&A Forum": 2.9475127139901388e-05, "Discussion Thread": 1.808894170154425e-05, "Comment Section": 3.122107587254044e-05, "Missing Content": 5.6761666106440053e-05, "Knowledge Article": 0.0001181275161020865, "Legal Notices": 4.410105457012273e-05, "News Article": 1.2793909015076578e-05, "Organizational Announcement": 2.2337181033403247e-05, "Nonfiction Writing": 2.8213114895533638e-05, "Organizational About Page": 3.0321616485352815e-05, "Personal About Page": 2.6895506187060535e-05, "Personal Blog": 2.0264151038087112e-05, "Product Page": 1.772036923328277e-05, "Spam / Ads": 3.0562186078940664e-05, "Structured Data": 0.0014447573825914395, "Technical Writing": 0.9899411531305828, "Transcript / Interview": 2.602209207945028e-05, "Tutorial / How-To Guide": 0.005854493912315708, "User Reviews": 9.427948592729536e-05, "Other / Unknown": 0.0019831872724113903}, "secondary": {"Academic Writing": 0.0005115682255696423, "Content Listing": 0.00059966616848237, "Creative Writing": 0.00032152641906993596, "Customer Support Page": 0.00022598511327290462, "Q&A Forum": 0.00019935956791750092, "Discussion Thread": 0.00014520331773133243, "Comment Section": 0.0004403565831502034, "Missing Content": 0.0007255677386755248, "Knowledge Article": 0.0008865141538809545, "Legal Notices": 0.00033062922344014815, "News Article": 0.00021242004312559267, "Organizational Announcement": 0.00019071241194468545, "Nonfiction Writing": 0.00021944616159059408, "Organizational About Page": 0.00024659680767142407, "Personal About Page": 0.00019310765075485428, "Personal Blog": 0.00020970264892558433, "Product Page": 0.00013708827376423632, "Spam / Ads": 0.00023170807499320835, "Structured Data": 0.021317417778482093, "Technical Writing": 0.03025086732903679, "Transcript / Interview": 0.0002223145138037847, "Tutorial / How-To Guide": 0.03568467321394149, "User Reviews": 0.0006299833153482303, "NONE": 0.9058675852654269}, "tertiary": {"Academic Writing": 0.0003167605152571404, "Content Listing": 0.00046400381999521913, "Creative Writing": 0.0002397488826520922, "Customer Support Page": 0.00015745113242896235, "Q&A Forum": 0.00012222505568819912, "Discussion Thread": 9.466683687483579e-05, "Comment Section": 0.0003518083019146427, "Missing Content": 0.0012951744032657538, "Knowledge Article": 0.0014004775362251823, "Legal Notices": 0.00031320467678497374, "News Article": 0.00013399085686948082, "Organizational Announcement": 0.00012944970457896788, "Nonfiction Writing": 0.0001906752910690545, "Organizational About Page": 0.00019377267480850352, "Personal About Page": 0.0001286133705651316, "Personal Blog": 0.00014765782037061084, "Product Page": 0.00011546086192497511, "Spam / Ads": 0.00018177444862938063, "Structured Data": 0.023249252059772154, "Technical Writing": 0.0140556943901833, "Transcript / Interview": 0.00014858702923577325, "Tutorial / How-To Guide": 0.029282320714291106, "User Reviews": 0.0003943926962578176, "NONE": 0.9268928369203567}, "structured_records": {"false": 0.9224975498606411, "true": 0.07750245013935886}, "procedural_steps": {"false": 0.9717965046986808, "true": 0.028203495301319135}, "worked_solution": {"false": 0.9756213617376498, "true": 0.024378638262350254}, "contextual_dialogue": {"false": 0.9999894511901014, "true": 1.054880989854594e-05}, "content_quality": {"-2": 0.0373115413455419, "-1": 0.2152110496992403, "0": 0.41870457112641063, "1": 0.2767795513979131, "2": 0.051993286430893966}, "noise_severity": {"0": 0.7979466363323771, "1": 0.14046673317138353, "2": 0.05424122256475101, "3": 0.004746594837778985, "4": 0.0025988130937094774}, "missing_content_severity": {"0": 0.9806786363529248, "1": 0.009163858930288076, "2": 0.007332604257547257, "3": 0.002400680314050963, "4": 0.0004242201451889401}, "factual_density": {"0": 0.016723214380944693, "1": 0.05450240700685123, "2": 0.23260802763952532, "3": 0.46865222199740125, "4": 0.22751412897527745}, "pretraining_utility": {"-2": 0.015050682003100523, "-1": 0.1539899829695834, "0": 0.445146889483451, "1": 0.33937604468346416, "2": 0.04643640086040086}, "math_density": {"0": 0.7858154864134087, "1": 0.12804715435373018, "2": 0.04191865027979211, "3": 0.016435943584522134, "4": 0.027782765368546944}, "code_density": {"0": 0.00026754952850667176, "1": 0.0004924761237819977, "2": 0.012448873807356403, "3": 0.108160942617919, "4": 0.8786301579224358}, "reasoning_density": {"0": 0.8765946818944211, "1": 0.03644148301514059, "2": 0.06940848924904353, "3": 0.011950360002535187, "4": 0.00560498583885962}, "education_level": {"none": 0.0011122314823267152, "0": 0.0014492391777603782, "1": 0.023547928784008926, "2": 0.8485302665985653, "3": 0.12132844522544489, "4": 0.004031888731893667}, "creative_writing_quality": {"not_applicable": 0.9988837289902042, "-2": 0.00021566196520779284, "-1": 0.00037793332768871964, "0": 0.00041709236473717247, "1": 7.576073255388292e-05, "2": 2.9822619608322996e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 8.31601921771812e-05, "Art & Design": 0.0002043547866417201, "Law & Crime": 0.0001170755910026214, "Education & Careers": 0.00013121995484935927, "Entertainment & Media": 0.00024111083114132634, "Fashion & Beauty": 0.00021903420465357404, "Business, Economics & Finance": 0.0007194811647324292, "Food & Dining": 0.04885322053853716, "Games & Gaming": 0.0001631511395289568, "Electronics & Hardware": 0.00015882764673131184, "Health, Healthcare & Wellness": 0.0001106210629535792, "History & Geography": 0.00014591185387271917, "Home, Pets & Hobbies": 9.044118438725579e-05, "Industry & Production": 0.00010374345569918176, "Language, Literature & Philosophy": 0.00010827727302597506, "Politics & Government": 7.948785071655499e-05, "Religion & Spirituality": 8.606513447213366e-05, "Science, Mathematics & Engineering": 0.002925652271403396, "Relationships & Community": 0.00039898928146541243, "Software Usage & Applications": 0.000787540526067709, "Programming & Software Engineering": 0.9393174211797357, "Sports & Fitness": 0.0001278365365771708, "Vehicles & Transportation": 7.902301125492347e-05, "Travel & Tourism": 0.0007063770986341581, "Other / Uncovered Subject": 0.003831628789482806, "Unknown / Insufficient Content": 0.0002103474402556879}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0011900499324329927, "Art & Design": 0.002412202287255138, "Law & Crime": 0.0010383671205823407, "Education & Careers": 0.0009550913800754674, "Entertainment & Media": 0.0019596062667095087, "Fashion & Beauty": 0.0010118878085035722, "Business, Economics & Finance": 0.0034582014912919613, "Food & Dining": 0.42463084371684634, "Games & Gaming": 0.0008773246258293539, "Electronics & Hardware": 0.0009019313443989191, "Health, Healthcare & Wellness": 0.001004427439977966, "History & Geography": 0.0010748596083391985, "Home, Pets & Hobbies": 0.0006164672408744712, "Industry & Production": 0.0005360472329406364, "Language, Literature & Philosophy": 0.0006165654294919467, "Politics & Government": 0.000550945626837811, "Religion & Spirituality": 0.0007574993491131126, "Science, Mathematics & Engineering": 0.031770352236972284, "Relationships & Community": 0.0026342063067192048, "Software Usage & Applications": 0.004062703198340827, "Programming & Software Engineering": 0.05093138312741866, "Sports & Fitness": 0.0006138407833468405, "Vehicles & Transportation": 0.0004450341945461539, "Travel & Tourism": 0.0030121575400593762, "Other / Uncovered Subject": 0.009123981501938229, "NONE": 0.45381402320915776}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10376, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10376, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:11.216917+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "b12956da-a8df-4e4a-85f9-b839686058a4", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "c0af88214b253dc56f69e4922b0ad21350d4b2c268441e2feee8095194f2adae", "source_metadata": {"uuid": "b12956da-a8df-4e4a-85f9-b839686058a4", "repo_name": "samboat79/Restaurant_true_review", "relative_path": "Restaurant_true_review-master/project2/app.py", "category": "WEB", "algo_rel_score": 0.9735679626464844, "quality_score": 5.634400844573975, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 23, "repo_created_at": "2018-07-16T23:58:40.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "2c922d46a431c07fb8746562581011f354b972d5", "size_bytes": 6947, "file_timestamp": 1532411177, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "dfa6134e1e6214c924149a647d52284052bd4438"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 23, "repo_created_at": "2018-07-16T23:58:40.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "2c922d46a431c07fb8746562581011f354b972d5", "size_bytes": 6947, "file_timestamp": 1532411177, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "dfa6134e1e6214c924149a647d52284052bd4438", "url": "https://github.com/samboat79/Restaurant_true_review/blob/dfa6134e1e6214c924149a647d52284052bd4438/Restaurant_true_review-master/project2/app.py"}, "gpt4o_tokens": 1691, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.09093199186937696, "noise_severity": 0.2735842151890604, "missing_content_severity": 0.03272798896829124, "factual_density": 2.8357316441792153, "pretraining_utility": 0.24815749942848142, "math_density": 0.3723233471410686, "code_density": 3.8643936832819956, "reasoning_density": 0.23352948487627168, "education_level": 2.103060442618565, "creative_writing_quality": -0.6036628026889183}, "score_applicability": {"education_level": 0.9988877685176732, "creative_writing_quality": 0.001116271009795891}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/toshiro-sama/Medit-Waitlist/blob/acb701f34ee8e6615d68741dc50a157d55d7ae3b/join_waitlist/views.py", "id": "python-0-93c131861030065d8c054c687ccb8de7", "text": "from django.shortcuts import render\nfrom django.http import JsonResponse\nfrom django.core.mail import send_mail\nfrom django.conf import settings\nfrom .models import WaitlistEntry\n\ndef join_waitlist(request):\n    if request.method == 'POST':\n        email = request.POST.get('email')\n        \n        if not email:\n            return JsonResponse({'success': False, 'message': 'Email is required.'}, status=400)\n        \n        # Check if email already exists in the waitlist\n        if WaitlistEntry.objects.filter(email=email).exists():\n            return JsonResponse({'success': False, 'message': 'This email is already on the waitlist.'}, status=400)\n        \n        # Save the email to the database\n        WaitlistEntry.objects.create(email=email)\n        \n        # Email template\n        email_html_message = '''\n        <!DOCTYPE html>\n<html lang=\"en\">\n<head>\n    <meta charset=\"UTF-8\">\n    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n    <title>Welcome to the Medit AI Waitlist!</title>\n    <!--[if mso]>\n    <noscript>\n    <xml>\n        <o:OfficeDocumentSettings>\n            <o:PixelsPerInch>96</o:PixelsPerInch>\n        </o:OfficeDocumentSettings>\n    </xml>\n    </noscript>\n    <![endif]-->\n    <style>\n        @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700&display=swap');\n        body, table, td, a { -webkit-text-size-adjust: 100%; -ms-text-size-adjust: 100%; }\n        table, td { mso-table-lspace: 0pt; mso-table-rspace: 0pt; }\n        img { -ms-interpolation-mode: bicubic; }\n        img { border: 0; height: auto; line-height: 100%; outline: none; text-decoration: none; }\n        table { border-collapse: collapse !important; }\n        body { height: 100% !important; margin: 0 !important; padding: 0 !important; width: 100% !important; }\n        a[x-apple-data-detectors] { color: inherit !important; text-decoration: none !important; font-size: inherit !important; font-family: inherit !important; font-weight: inherit !important; line-height: inherit !important; }\n        @media screen and (max-width: 525px) {\n            .wrapper { width: 100% !important; max-width: 100% !important; }\n            .responsive-table { width: 100% !important; }\n            .padding { padding: 10px 5% 15px 5% !important; }\n            .section-padding { padding: 0 15px 50px 15px !important; }\n            .mobile-button-container { margin: 0 auto; width: 100% !important; }\n            .mobile-button { padding: 15px !important; border: 0 !important; font-size: 16px !important; display: block !important; }\n        }\n        .social-icon {\n            display: inline-block;\n            margin: 0 5px;\n            width: 35px;\n            height: 35px;\n        }\n    </style>\n</head>\n<body style=\"margin: 0 !important; padding: 0 !important; background-color: #F4F7FA;\">\n    <table border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\">\n        <tr>\n            <td bgcolor=\"#F4F7FA\" align=\"center\" style=\"padding: 50px 10px 0 10px;\">\n                <table border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\" style=\"max-width: 600px;\" class=\"responsive-table\">\n                    <tr>\n                        <td>\n                            <table width=\"100%\" border=\"0\" cellspacing=\"0\" cellpadding=\"0\">\n                                <tr>\n                                    <td>\n                                        <table width=\"100%\" border=\"0\" cellspacing=\"0\" cellpadding=\"0\">\n                                            <tr>\n                                                <td style=\"padding: 0 0 0 0; font-size: 16px; line-height: 25px; color: #232323; font-family: 'Inter', Helvetica, Arial, sans-serif;\" class=\"padding\">\n                                                    <h2 style=\"font-size: 28px; font-weight: 700; line-height: 36px; color: #19469D; margin: 0;\">Welcome to the Medit AI Waitlist!</h2>\n                                                </td>\n                                            </tr>\n                                            <tr>\n                                                <td style=\"padding: 20px 0 0 0; font-size: 16px; line-height: 25px; color: #232323; font-family: 'Inter', Helvetica, Arial, sans-serif;\" class=\"padding\">\n                                                    Thank you for joining our waitlist. We're thrilled to have you on board as we prepare to revolutionize medical writing with AI.\n                                                </td>\n                                            </tr>\n                                            <tr>\n                                                <td style=\"padding: 20px 0 0 0; font-size: 16px; line-height: 25px; color: #232323; font-family: 'Inter', Helvetica, Arial, sans-serif;\" class=\"padding\">\n                                                    Here's what you can expect:\n                                                    <ul>\n                                                        <li>Early access to our platform</li>\n                                                        <li>Exclusive updates on our progress</li>\n                                                        <li>Special launch offers for waitlist members</li>\n                                                    </ul>\n                                                </td>\n                                            </tr>\n                                            <tr>\n                                                <td align=\"center\">\n                                                    <table width=\"100%\" border=\"0\" cellspacing=\"0\" cellpadding=\"0\">\n                                                        <tr>\n                                                            <td align=\"center\" style=\"padding: 30px 0 0 0;\" class=\"padding\">\n                                                                <table border=\"0\" cellspacing=\"0\" cellpadding=\"0\" class=\"mobile-button-container\">\n                                                                    <tr>\n                                                                        <td align=\"center\" style=\"border-radius: 8px;\" bgcolor=\"#19469D\"><a href=\"https://meditai.netlify.app\" target=\"_blank\" style=\"font-size: 16px; font-family: 'Inter', Helvetica, Arial, sans-serif; color: #ffffff; text-decoration: none; border-radius: 8px; padding: 15px 25px; border: 1px solid #19469D; display: inline-block;\" class=\"mobile-button\">Visit Our Website</a></td>\n                                                                    </tr>\n                                                                </table>\n                                                            </td>\n                                                        </tr>\n                                                    </table>\n                                                </td>\n                                            </tr>\n                                            <tr>\n                                                <td style=\"padding: 30px 0 0 0; font-size: 16px; line-height: 25px; color: #232323; font-family: 'Inter', Helvetica, Arial, sans-serif;\" class=\"padding\">\n                                                    If you have any questions or feedback, don't hesitate to reach out. We're here to help!\n                                                </td>\n                                            </tr>\n                                            <tr>\n                                                <td style=\"padding: 20px 0 0 0; font-size: 16px; line-height: 25px; color: #232323; font-family: 'Inter', Helvetica, Arial, sans-serif;\" class=\"padding\">\n                                                    Best regards,<br>\n                                                    The Medit AI Team\n                                                </td>\n                                            </tr>\n                                        </table>\n                                    </td>\n                                </tr>\n                            </table>\n                        </td>\n                    </tr>\n                </table>\n            </td>\n        </tr>\n        <tr>\n            <td bgcolor=\"#F4F7FA\" align=\"center\" style=\"padding: 30px 10px 0 10px;\">\n                <table border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\" style=\"max-width: 600px;\" class=\"responsive-table\">\n                    <tr>\n                        <td style=\"padding: 30px 0 30px 0; border-top: 1px solid #DDDDDD;\" align=\"center\">\n                            <table width=\"100%\" border=\"0\" cellspacing=\"0\" cellpadding=\"0\">\n                                <tr>\n                                    <td align=\"center\" style=\"padding: 20px 0 0 0;\">\n                                        <table border=\"0\" cellpadding=\"0\" cellspacing=\"0\">\n                                            <tr>\n                                                <td align=\"center\" style=\"font-family: 'Inter', Helvetica, Arial, sans-serif; font-size: 12px; line-height: 18px; color: #666666;\">\n                                                    <span style=\"font-family: 'Inter', Helvetica, Arial, sans-serif; font-size: 12px; line-height: 18px; color: #666666;\">Copyright © 2025 Medit AI. All Rights Reserved.</span>\n                                                </td>\n                                            </tr>\n                                        </table>\n                                    </td>\n                                </tr>\n                            </table>\n                        </td>\n                    </tr>\n                </table>\n            </td>\n        </tr>\n    </table>\n</body>\n</html>\n        '''\n        \n        # Send confirmation email\n        send_mail(\n            'Thank you for joining the waitlist',  # Subject\n            '',  # Plain text body (optional)\n            settings.DEFAULT_FROM_EMAIL,  # From email\n            [email],  # To email\n            fail_silently=False,\n            html_message=email_html_message  # HTML content\n        )\n        \n        # Return JSON response\n        return JsonResponse({'success': True, 'message': 'Thank you for joining our waitlist! Confirmation email sent.'}, status=200)\n    \n    # For GET requests, render the index.html page\n    return render(request, 'join_waitlist/index.html')\n", "text_preview": "from django.shortcuts import render from django.http import JsonResponse from django.core.mail import send_mail from django.conf import settings from .models import WaitlistEntry def join_waitlist(request): if request.method == 'POST': emai", "input_sha256": "4771fb63e75000f045d1ed983c5488f188e5cffc723f4216b450e28fcbb37137", "text_sha256": "93c131861030065d8c054c687ccb8de7e9f4ec86fc5f2600ff883f1a47b7f006", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12120, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Spam / Ads", "sub_format": null}, "tertiary": {"main_format": "Spam / Ads", "sub_format": null}, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.931573454160787, "noise_severity": 0.9480833781218345, "missing_content_severity": 0.10235049465288358, "factual_density": 2.6165216059764487, "pretraining_utility": 0.10335288647238969, "math_density": 0.011607573904142958, "code_density": 3.461201877170539, "reasoning_density": 0.34844889003966184, "education_level": 1.9727299010822927, "creative_writing_quality": 0.095074097352189}, "api_choices": {"primary": "Technical Writing", "secondary": "Spam / Ads", "tertiary": "Spam / Ads", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": ["Duplicate Document Type slots", "secondary probability 0.6602 below 0.70", "tertiary probability 0.4047 below 0.70", "tertiary vs NONE margin 0.0398 below 0.10"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.25546178871535974, "secondary_choice": "Spam / Ads", "tertiary_choice": "Spam / Ads"}, "confidence": {"primary": 0.9740888751923896, "secondary": 0.6453960190159126, "tertiary": 0.37882719600858045, "structured_records": 0.18792167389041517, "procedural_steps": 0.025870237427670185, "worked_solution": 0.00917461043767503, "contextual_dialogue": 8.50603205797118e-06, "content_quality": 0.5449125917157494, "noise_severity": 0.20993051823180475, "missing_content_severity": 0.914707921122597, "factual_density": 0.3090888439756513, "pretraining_utility": 0.5015096658407758, "math_density": 0.9903270217465475, "code_density": 0.5510015643087822, "reasoning_density": 0.7096259249669484, "education_level": 0.808705261397871, "creative_writing_quality": 0.8217708469212786, "primary_topic": 0.9382130140282637, "secondary_topic": 0.4957618906468265}, "probability_margin": {"primary": 0.9638264201673633, "secondary": 0.5181406440709602, "tertiary": 0.03984009761232027, "education_level": 0.771656674770622, "creative_writing_quality": 0.7310329328190486, "primary_topic": 0.9096186294845282, "secondary_topic": 0.2827995372626298}, "raw_probabilities": {"primary": {"Academic Writing": 0.000111378186745219, "Content Listing": 9.425129657549806e-05, "Creative Writing": 0.00011617351083445148, "Customer Support Page": 0.00016119230198855517, "Q&A Forum": 6.257559165355029e-05, "Discussion Thread": 3.766510153164145e-05, "Comment Section": 7.27633732427873e-05, "Missing Content": 0.0001247429458375458, "Knowledge Article": 0.00011913309989071115, "Legal Notices": 0.00013474994347090338, "News Article": 4.607333622353522e-05, "Organizational Announcement": 0.004578554086235366, "Nonfiction Writing": 7.42029248934653e-05, "Organizational About Page": 0.0003663176667585055, "Personal About Page": 7.654881463293908e-05, "Personal Blog": 5.119098434037481e-05, "Product Page": 0.0002594731475967196, "Spam / Ads": 0.011342085225343445, "Structured Data": 0.0008765495306126866, "Technical Writing": 0.9751685053927067, "Transcript / Interview": 8.53574947746079e-05, "Tutorial / How-To Guide": 0.001070767358084003, "User Reviews": 3.954282420269788e-05, "Other / Unknown": 0.004930205861824043}, "secondary": {"Academic Writing": 0.000727237530118796, "Content Listing": 0.0012852993420410857, "Creative Writing": 0.001512639786201823, "Customer Support Page": 0.001756609108057653, "Q&A Forum": 0.00044055910208418034, "Discussion Thread": 0.0003708994307932354, "Comment Section": 0.0005550356767290291, "Missing Content": 0.0009075739087816917, "Knowledge Article": 0.0009805047080701423, "Legal Notices": 0.006233014170903492, "News Article": 0.0007425947998288497, "Organizational Announcement": 0.11766775316309167, "Nonfiction Writing": 0.0013512678925598261, "Organizational About Page": 0.01134849972578557, "Personal About Page": 0.0006110970279308024, "Personal Blog": 0.0006236254438257015, "Product Page": 0.002862668682630104, "Spam / Ads": 0.6601711848902495, "Structured Data": 0.013404058183213139, "Technical Writing": 0.03235347986954283, "Transcript / Interview": 0.0007826291003283775, "Tutorial / How-To Guide": 0.0009130527391625546, "User Reviews": 0.00036817489878072056, "NONE": 0.1420305408192894}, "tertiary": {"Academic Writing": 0.0011984059946190844, "Content Listing": 0.001685689312784786, "Creative Writing": 0.001955579276090931, "Customer Support Page": 0.002731742723015754, "Q&A Forum": 0.0006595719673267731, "Discussion Thread": 0.0005675737495936467, "Comment Section": 0.0007928488438144083, "Missing Content": 0.001144520913462809, "Knowledge Article": 0.001406774178251483, "Legal Notices": 0.06826749266826415, "News Article": 0.0010971756804280272, "Organizational Announcement": 0.09726707042823082, "Nonfiction Writing": 0.0015901893415436368, "Organizational About Page": 0.00980584138061573, "Personal About Page": 0.0008310454586880352, "Personal Blog": 0.0009371766385976647, "Product Page": 0.0027666625932967763, "Spam / Ads": 0.4047093961748896, "Structured Data": 0.02199656545560718, "Technical Writing": 0.010977749134724482, "Transcript / Interview": 0.0010623446718592492, "Tutorial / How-To Guide": 0.0011607553409911664, "User Reviews": 0.0005185295107344052, "NONE": 0.36486929856256933}, "structured_records": {"false": 0.8120783261095849, "true": 0.18792167389041517}, "procedural_steps": {"false": 0.9741297625723299, "true": 0.025870237427670185}, "worked_solution": {"false": 0.990825389562325, "true": 0.00917461043767503}, "contextual_dialogue": {"false": 0.9999914939679421, "true": 8.50603205797118e-06}, "content_quality": {"-2": 0.006920715743435853, "-1": 0.028233583684919, "0": 0.23003640329001138, "1": 0.49597012523068984, "2": 0.23883917205094385}, "noise_severity": {"0": 0.4345374141271506, "1": 0.2845722081032546, "2": 0.19919233637518047, "3": 0.06166566830943816, "4": 0.02003237308497607}, "missing_content_severity": {"0": 0.9284530969111039, "1": 0.047672559983117126, "2": 0.01823262325996303, "3": 0.004354191233423411, "4": 0.0012875286123925401}, "factual_density": {"0": 0.01090183643149689, "1": 0.13966092882723705, "2": 0.29425852367742034, "3": 0.3323712144610124, "4": 0.22280749660283342}, "pretraining_utility": {"-2": 0.01697216667036379, "-1": 0.2134734239186121, "0": 0.4557510158146475, "1": 0.27683614346102353, "2": 0.036967250135352916}, "math_density": {"0": 0.9910502098699133, "1": 0.007564929028472234, "2": 0.0005940805295047335, "3": 0.00030863847177757845, "4": 0.0004821421003321302}, "code_density": {"0": 0.0007454729887647255, "1": 0.001925002629551292, "2": 0.10584108248487689, "3": 0.31835905801599473, "4": 0.5731293838808124}, "reasoning_density": {"0": 0.7842559863162137, "1": 0.09744444753653149, "2": 0.10688979946561993, "3": 0.008414223154649129, "4": 0.002995543526985778}, "education_level": {"none": 0.002458589095580077, "0": 0.023340863622229237, "1": 0.05960531384858002, "2": 0.8405877178315592, "3": 0.06893104306093716, "4": 0.005076472541114252}, "creative_writing_quality": {"not_applicable": 0.8514757057677321, "-2": 0.0013351622252098466, "-1": 0.0057586423426308165, "0": 0.12044277294868361, "1": 0.019425653429433577, "2": 0.001562063286310381}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00014156896306452498, "Art & Design": 0.00107558959028726, "Law & Crime": 0.00019307353130484199, "Education & Careers": 0.0002138385029351797, "Entertainment & Media": 0.00019893086519651534, "Fashion & Beauty": 0.00024769414056996844, "Business, Economics & Finance": 0.008851766104402723, "Food & Dining": 0.0002993924019954405, "Games & Gaming": 0.00017599666392826912, "Electronics & Hardware": 0.00020223090580307683, "Health, Healthcare & Wellness": 0.030970807081109945, "History & Geography": 0.0001517419045857431, "Home, Pets & Hobbies": 0.00012309311749454015, "Industry & Production": 0.00017109812696418519, "Language, Literature & Philosophy": 0.003362254794066015, "Politics & Government": 0.00013583804194434242, "Religion & Spirituality": 0.00016660955572939917, "Science, Mathematics & Engineering": 0.002062917487776191, "Relationships & Community": 0.00012930070043577966, "Software Usage & Applications": 0.0029441223604708237, "Programming & Software Engineering": 0.9405894365656382, "Sports & Fitness": 0.00018570604310913578, "Vehicles & Transportation": 7.744399755426746e-05, "Travel & Tourism": 0.00014049392522777785, "Other / Uncovered Subject": 0.006895333835341625, "Unknown / Insufficient Content": 0.00029372079306416195}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.002727073594319681, "Art & Design": 0.05432435512506939, "Law & Crime": 0.0012348945849233832, "Education & Careers": 0.0007917734095875234, "Entertainment & Media": 0.0013817849815951368, "Fashion & Beauty": 0.0009144811400984152, "Business, Economics & Finance": 0.0408968613696585, "Food & Dining": 0.0010477126414910507, "Games & Gaming": 0.0006267993508766805, "Electronics & Hardware": 0.0009287930988391409, "Health, Healthcare & Wellness": 0.2323561268208572, "History & Geography": 0.0008277875417251337, "Home, Pets & Hobbies": 0.0006731424841701492, "Industry & Production": 0.0008231433190262785, "Language, Literature & Philosophy": 0.03244845502798918, "Politics & Government": 0.0006607727372014155, "Religion & Spirituality": 0.0009131351466537152, "Science, Mathematics & Engineering": 0.011184258947533285, "Relationships & Community": 0.0003916671399813051, "Software Usage & Applications": 0.007621694012663363, "Programming & Software Engineering": 0.0749656777945974, "Sports & Fitness": 0.000696249140142556, "Vehicles & Transportation": 0.0003146696468457701, "Travel & Tourism": 0.0007231958035974204, "Other / Uncovered Subject": 0.015369831057069993, "NONE": 0.515155664083487}}, "probabilities": {"primary": {"Academic Writing": 0.000111378186745219, "Content Listing": 9.425129657549806e-05, "Creative Writing": 0.00011617351083445148, "Customer Support Page": 0.00016119230198855517, "Q&A Forum": 6.257559165355029e-05, "Discussion Thread": 3.766510153164145e-05, "Comment Section": 7.27633732427873e-05, "Missing Content": 0.0001247429458375458, "Knowledge Article": 0.00011913309989071115, "Legal Notices": 0.00013474994347090338, "News Article": 4.607333622353522e-05, "Organizational Announcement": 0.004578554086235366, "Nonfiction Writing": 7.42029248934653e-05, "Organizational About Page": 0.0003663176667585055, "Personal About Page": 7.654881463293908e-05, "Personal Blog": 5.119098434037481e-05, "Product Page": 0.0002594731475967196, "Spam / Ads": 0.011342085225343445, "Structured Data": 0.0008765495306126866, "Technical Writing": 0.9751685053927067, "Transcript / Interview": 8.53574947746079e-05, "Tutorial / How-To Guide": 0.001070767358084003, "User Reviews": 3.954282420269788e-05, "Other / Unknown": 0.004930205861824043}, "secondary": {"Academic Writing": 0.0007272375301187959, "Content Listing": 0.0012852993420410855, "Creative Writing": 0.0015126397862018225, "Customer Support Page": 0.0017566091080576526, "Q&A Forum": 0.00044055910208418023, "Discussion Thread": 0.0003708994307932353, "Comment Section": 0.000555035676729029, "Missing Content": 0.0009075739087816915, "Knowledge Article": 0.0009805047080701421, "Legal Notices": 0.00623301417090349, "News Article": 0.0007425947998288495, "Organizational Announcement": 0.11766775316309164, "Nonfiction Writing": 0.001351267892559826, "Organizational About Page": 0.011348499725785569, "Personal About Page": 0.0006110970279308023, "Personal Blog": 0.0006236254438257014, "Product Page": 0.0028626686826301036, "Spam / Ads": 0.6601711848902494, "Structured Data": 0.013404058183213136, "Technical Writing": 0.032353479869542824, "Transcript / Interview": 0.0007826291003283773, "Tutorial / How-To Guide": 0.0009130527391625544, "User Reviews": 0.00036817489878072045, "NONE": 0.14203054081928937}, "tertiary": {"Academic Writing": 0.0011984059946190846, "Content Listing": 0.0016856893127847861, "Creative Writing": 0.0019555792760909316, "Customer Support Page": 0.0027317427230157546, "Q&A Forum": 0.0006595719673267732, "Discussion Thread": 0.0005675737495936468, "Comment Section": 0.0007928488438144084, "Missing Content": 0.0011445209134628093, "Knowledge Article": 0.0014067741782514832, "Legal Notices": 0.06826749266826417, "News Article": 0.0010971756804280274, "Organizational Announcement": 0.09726707042823084, "Nonfiction Writing": 0.001590189341543637, "Organizational About Page": 0.009805841380615733, "Personal About Page": 0.0008310454586880353, "Personal Blog": 0.0009371766385976648, "Product Page": 0.0027666625932967768, "Spam / Ads": 0.40470939617488966, "Structured Data": 0.021996565455607183, "Technical Writing": 0.010977749134724483, "Transcript / Interview": 0.0010623446718592494, "Tutorial / How-To Guide": 0.0011607553409911666, "User Reviews": 0.0005185295107344053, "NONE": 0.3648692985625694}, "structured_records": {"false": 0.8120783261095849, "true": 0.18792167389041517}, "procedural_steps": {"false": 0.9741297625723299, "true": 0.025870237427670185}, "worked_solution": {"false": 0.990825389562325, "true": 0.00917461043767503}, "contextual_dialogue": {"false": 0.9999914939679421, "true": 8.50603205797118e-06}, "content_quality": {"-2": 0.006920715743435854, "-1": 0.028233583684919003, "0": 0.2300364032900114, "1": 0.4959701252306899, "2": 0.23883917205094388}, "noise_severity": {"0": 0.43453741412715063, "1": 0.2845722081032547, "2": 0.1991923363751805, "3": 0.06166566830943817, "4": 0.020032373084976074}, "missing_content_severity": {"0": 0.9284530969111039, "1": 0.047672559983117126, "2": 0.01823262325996303, "3": 0.004354191233423411, "4": 0.0012875286123925401}, "factual_density": {"0": 0.01090183643149689, "1": 0.13966092882723705, "2": 0.29425852367742034, "3": 0.3323712144610124, "4": 0.22280749660283342}, "pretraining_utility": {"-2": 0.016972166670363793, "-1": 0.21347342391861215, "0": 0.4557510158146476, "1": 0.2768361434610236, "2": 0.03696725013535292}, "math_density": {"0": 0.9910502098699133, "1": 0.007564929028472234, "2": 0.0005940805295047335, "3": 0.00030863847177757845, "4": 0.0004821421003321302}, "code_density": {"0": 0.0007454729887647255, "1": 0.001925002629551292, "2": 0.10584108248487689, "3": 0.31835905801599473, "4": 0.5731293838808124}, "reasoning_density": {"0": 0.7842559863162137, "1": 0.09744444753653149, "2": 0.10688979946561993, "3": 0.008414223154649129, "4": 0.002995543526985778}, "education_level": {"none": 0.0024585890955800774, "0": 0.02334086362222924, "1": 0.059605313848580026, "2": 0.8405877178315593, "3": 0.06893104306093717, "4": 0.005076472541114253}, "creative_writing_quality": {"not_applicable": 0.8514757057677318, "-2": 0.001335162225209846, "-1": 0.005758642342630814, "0": 0.12044277294868355, "1": 0.01942565342943357, "2": 0.0015620632863103804}, "primary_topic": {"Adult & Explicit Sexual Content": 0.000141568963064525, "Art & Design": 0.0010755895902872603, "Law & Crime": 0.000193073531304842, "Education & Careers": 0.00021383850293517972, "Entertainment & Media": 0.00019893086519651536, "Fashion & Beauty": 0.0002476941405699685, "Business, Economics & Finance": 0.008851766104402725, "Food & Dining": 0.00029939240199544057, "Games & Gaming": 0.00017599666392826915, "Electronics & Hardware": 0.00020223090580307685, "Health, Healthcare & Wellness": 0.030970807081109948, "History & Geography": 0.00015174190458574314, "Home, Pets & Hobbies": 0.00012309311749454017, "Industry & Production": 0.0001710981269641852, "Language, Literature & Philosophy": 0.0033622547940660155, "Politics & Government": 0.00013583804194434245, "Religion & Spirituality": 0.0001666095557293992, "Science, Mathematics & Engineering": 0.0020629174877761914, "Relationships & Community": 0.0001293007004357797, "Software Usage & Applications": 0.002944122360470824, "Programming & Software Engineering": 0.9405894365656383, "Sports & Fitness": 0.0001857060431091358, "Vehicles & Transportation": 7.744399755426747e-05, "Travel & Tourism": 0.00014049392522777788, "Other / Uncovered Subject": 0.006895333835341626, "Unknown / Insufficient Content": 0.000293720793064162}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.002727073594319681, "Art & Design": 0.05432435512506939, "Law & Crime": 0.0012348945849233832, "Education & Careers": 0.0007917734095875234, "Entertainment & Media": 0.0013817849815951368, "Fashion & Beauty": 0.0009144811400984152, "Business, Economics & Finance": 0.0408968613696585, "Food & Dining": 0.0010477126414910507, "Games & Gaming": 0.0006267993508766805, "Electronics & Hardware": 0.0009287930988391409, "Health, Healthcare & Wellness": 0.2323561268208572, "History & Geography": 0.0008277875417251337, "Home, Pets & Hobbies": 0.0006731424841701492, "Industry & Production": 0.0008231433190262785, "Language, Literature & Philosophy": 0.03244845502798918, "Politics & Government": 0.0006607727372014155, "Religion & Spirituality": 0.0009131351466537152, "Science, Mathematics & Engineering": 0.011184258947533285, "Relationships & Community": 0.0003916671399813051, "Software Usage & Applications": 0.007621694012663363, "Programming & Software Engineering": 0.0749656777945974, "Sports & Fitness": 0.000696249140142556, "Vehicles & Transportation": 0.0003146696468457701, "Travel & Tourism": 0.0007231958035974204, "Other / Uncovered Subject": 0.015369831057069993, "NONE": 0.515155664083487}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000004, "mass_delta": 4.440892098500626e-16, "missing_mass": 0.0, "excess_mass": 4.440892098500626e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10868, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10868, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:11.473230+00:00", "scoring_key_slot": "primary", "dataset_uid": "4e9bdc2f-fef6-4e1c-b433-3af1e287f6ed", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "6ae46d953113070c569aa5966400d34df3fbf9d9f9f71a23d7fea6e31b3a5296", "source_metadata": {"uuid": "4e9bdc2f-fef6-4e1c-b433-3af1e287f6ed", "repo_name": "toshiro-sama/Medit-Waitlist", "relative_path": "join_waitlist/views.py", "category": "WEB", "algo_rel_score": 0.9004985690116882, "quality_score": 5.378695964813232, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2025-01-19T05:01:38.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "cbfa78362cf773e37ceb91d96f29b88187aadbdf", "size_bytes": 10378, "file_timestamp": 1737263177, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "acb701f34ee8e6615d68741dc50a157d55d7ae3b"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2025-01-19T05:01:38.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "cbfa78362cf773e37ceb91d96f29b88187aadbdf", "size_bytes": 10378, "file_timestamp": 1737263177, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "acb701f34ee8e6615d68741dc50a157d55d7ae3b", "url": "https://github.com/toshiro-sama/Medit-Waitlist/blob/acb701f34ee8e6615d68741dc50a157d55d7ae3b/join_waitlist/views.py"}, "gpt4o_tokens": 2087, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.931573454160787, "noise_severity": 0.9480833781218345, "missing_content_severity": 0.10235049465288358, "factual_density": 2.6165216059764487, "pretraining_utility": 0.10335288647238969, "math_density": 0.011607573904142958, "code_density": 3.461201877170539, "reasoning_density": 0.34844889003966184, "education_level": 1.9727299010822927, "creative_writing_quality": 0.095074097352189}, "score_applicability": {"education_level": 0.99754141090442, "creative_writing_quality": 0.14852429423226815}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/Kobbykk/BookNest/blob/a02cfdd3fda7882583a6e8e2603ff2d82bee4526/views/admin.py", "id": "python-0-6919b190bd33e6307e15f73fd695f3b2", "text": "from flask import Blueprint, render_template, redirect, url_for, flash, request, jsonify, current_app\nfrom flask_login import login_required, current_user\nfrom models import User, Book, Order, Category, Review, UserActivity, BookFormat, BookSeries\nfrom forms import UserForm, CategoryForm, BookForm\nfrom extensions import db\nfrom utils.activity_logger import log_user_activity\nfrom utils.mailer import send_order_status_email\nfrom werkzeug.security import generate_password_hash\nfrom functools import wraps\nfrom datetime import datetime\n\nadmin = Blueprint('admin', __name__, url_prefix='/admin')\n\ndef permission_required(permission):\n    def decorator(f):\n        @wraps(f)\n        def decorated_function(*args, **kwargs):\n            if not current_user.is_authenticated or not current_user.can(permission):\n                flash('You do not have permission to access this page.', 'danger')\n                return redirect(url_for('main.index'))\n            return f(*args, **kwargs)\n        return decorated_function\n    return decorator\n\n@admin.route('/dashboard')\n@permission_required('manage_books')\ndef dashboard():\n    books = Book.query.all()\n    orders = Order.query.order_by(Order.created_at.desc()).limit(10).all()\n    categories = Category.query.all()\n    return render_template('admin/dashboard.html', \n                         books=books,\n                         orders=orders,\n                         categories=categories)\n\n@admin.route('/books/add', methods=['GET', 'POST'])\n@permission_required('manage_books')\ndef add_book():\n    form = BookForm()\n    if form.validate_on_submit():\n        try:\n            # Validate required fields\n            if not form.title.data or not form.author.data or not form.price.data:\n                flash('Please fill in all required fields.', 'danger')\n                return render_template('admin/book_form.html', form=form)\n\n            # Validate price\n            if form.price.data <= 0:\n                flash('Price must be greater than zero.', 'danger')\n                return render_template('admin/book_form.html', form=form)\n\n            # Create new book\n            book = Book(\n                title=form.title.data,\n                author=form.author.data,\n                price=form.price.data,\n                description=form.description.data,\n                image_url=form.image_url.data,\n                stock=form.stock.data,\n                category=form.category_id.data,\n                is_featured=form.is_featured.data,\n                isbn=form.isbn.data,\n                publisher=form.publisher.data,\n                publication_date=form.publication_date.data,\n                page_count=form.page_count.data,\n                language=form.language.data,\n                tags=form.tags.data\n            )\n\n            # Validate and handle series information\n            if form.series_id.data:\n                series = BookSeries.query.get(form.series_id.data)\n                if not series:\n                    flash('Selected series does not exist.', 'danger')\n                    return render_template('admin/book_form.html', form=form)\n                book.series_id = series.id\n                book.series_order = form.series_order.data\n\n            # Validate and add book formats\n            if form.formats.data:\n                for format_data in form.formats.data:\n                    if not format_data.get('format_type') or not format_data.get('price'):\n                        flash('All book format fields are required.', 'danger')\n                        return render_template('admin/book_form.html', form=form)\n                    \n                    if float(format_data['price']) <= 0:\n                        flash('Format prices must be greater than zero.', 'danger')\n                        return render_template('admin/book_form.html', form=form)\n\n                    format = BookFormat(\n                        format_type=format_data['format_type'],\n                        price=float(format_data['price']),\n                        stock=int(format_data.get('stock', 0)),\n                        isbn=format_data.get('isbn')\n                    )\n                    book.formats.append(format)\n\n            db.session.add(book)\n            db.session.commit()\n            flash('Book added successfully!', 'success')\n            return redirect(url_for('admin.dashboard'))\n        except Exception as e:\n            db.session.rollback()\n            flash(f'Error adding book: {str(e)}', 'danger')\n            return render_template('admin/book_form.html', form=form)\n    \n    # If form validation fails, show errors\n    if form.errors:\n        for field, errors in form.errors.items():\n            for error in errors:\n                flash(f'{field}: {error}', 'danger')\n    \n    return render_template('admin/book_form.html', form=form)\n\n@admin.route('/delete_book/<int:book_id>', methods=['POST'])\n@permission_required('manage_books')\ndef delete_book(book_id):\n    try:\n        book = Book.query.get_or_404(book_id)\n        \n        # Delete associated formats\n        for format in book.formats:\n            db.session.delete(format)\n        \n        # Delete the book\n        db.session.delete(book)\n        db.session.commit()\n        \n        log_user_activity(current_user, 'book_deleted', f'Deleted book: {book.title}')\n        return jsonify({'success': True})\n    except Exception as e:\n        db.session.rollback()\n        return jsonify({'success': False, 'error': str(e)})\n\n@admin.route('/books/edit/<int:book_id>', methods=['GET', 'POST'])\n@permission_required('manage_books')\ndef edit_book(book_id):\n    book = Book.query.get_or_404(book_id)\n    form = BookForm(obj=book)\n    \n    if form.validate_on_submit():\n        try:\n            book.title = form.title.data\n            book.author = form.author.data\n            book.price = form.price.data\n            book.description = form.description.data\n            book.image_url = form.image_url.data\n            book.stock = form.stock.data\n            book.category = form.category_id.data\n            book.is_featured = form.is_featured.data\n            book.isbn = form.isbn.data\n            book.publisher = form.publisher.data\n            book.publication_date = form.publication_date.data\n            book.page_count = form.page_count.data\n            book.language = form.language.data\n            book.tags = form.tags.data\n            \n            # Update series information\n            book.series_id = form.series_id.data\n            book.series_order = form.series_order.data if form.series_id.data else None\n            \n            # Update formats\n            book.formats = []\n            for format_data in form.formats.data:\n                if not format_data.get('format_type') or not format_data.get('price'):\n                    continue\n                format = BookFormat(\n                    format_type=format_data['format_type'],\n                    price=format_data['price'],\n                    stock=format_data.get('stock', 0),\n                    isbn=format_data.get('isbn')\n                )\n                book.formats.append(format)\n            \n            db.session.commit()\n            flash('Book updated successfully!', 'success')\n            return redirect(url_for('admin.dashboard'))\n        except Exception as e:\n            db.session.rollback()\n            flash(f'Error updating book: {str(e)}', 'danger')\n        \n    return render_template('admin/book_form.html', form=form, book=book)\n\n@admin.route('/orders')\n@permission_required('manage_orders')\ndef manage_orders():\n    orders = Order.query.order_by(Order.created_at.desc()).all()\n    return render_template('admin/manage_orders.html', orders=orders)\n\n@admin.route('/users')\n@permission_required('manage_users')\ndef manage_users():\n    users = User.query.all()\n    roles = User.ROLES\n    return render_template('admin/users.html', users=users, roles=roles)\n\n@admin.route('/users/add', methods=['GET', 'POST'])\n@permission_required('manage_users')\ndef add_user():\n    form = UserForm()\n    if form.validate_on_submit():\n        user = User(\n            username=form.username.data,\n            email=form.email.data,\n            role=form.role.data\n        )\n        if form.password.data:\n            user.password_hash = generate_password_hash(form.password.data)\n        db.session.add(user)\n        db.session.commit()\n        flash('User added successfully!', 'success')\n        return redirect(url_for('admin.manage_users'))\n    return render_template('admin/user_form.html', form=form)\n\n@admin.route('/users/edit/<int:user_id>', methods=['GET', 'POST'])\n@permission_required('manage_users')\ndef edit_user(user_id):\n    user = User.query.get_or_404(user_id)\n    form = UserForm(obj=user)\n    if form.validate_on_submit():\n        user.username = form.username.data\n        user.email = form.email.data\n        user.role = form.role.data\n        if form.password.data:\n            user.password_hash = generate_password_hash(form.password.data)\n        db.session.commit()\n        flash('User updated successfully!', 'success')\n        return redirect(url_for('admin.manage_users'))\n    return render_template('admin/user_form.html', form=form, user=user)\n\n@admin.route('/users/delete/<int:user_id>', methods=['POST'])\n@permission_required('manage_users')\ndef delete_user(user_id):\n    if current_user.id == user_id:\n        return jsonify({'success': False, 'error': 'Cannot delete your own account'})\n    user = User.query.get_or_404(user_id)\n    db.session.delete(user)\n    db.session.commit()\n    return jsonify({'success': True})\n\n@admin.route('/users/<int:user_id>/activity')\n@permission_required('manage_users')\ndef user_activity(user_id):\n    user = User.query.get_or_404(user_id)\n    activities = UserActivity.query.filter_by(user_id=user_id).order_by(UserActivity.timestamp.desc()).all()\n    return render_template('admin/user_activities.html', user=user, activities=activities)\n\n@admin.route('/categories')\n@permission_required('manage_categories')\ndef manage_categories():\n    categories = Category.query.order_by(Category.display_order).all()\n    form = CategoryForm()\n    return render_template('admin/categories.html', categories=categories, form=form)\n\n@admin.route('/categories/add', methods=['GET', 'POST'])\n@permission_required('manage_categories')\ndef add_category():\n    form = CategoryForm()\n    if form.validate_on_submit():\n        category = Category(\n            name=form.name.data,\n            description=form.description.data,\n            display_order=form.display_order.data\n        )\n        db.session.add(category)\n        db.session.commit()\n        flash('Category added successfully!', 'success')\n        return redirect(url_for('admin.manage_categories'))\n    return render_template('admin/category_form.html', form=form)\n\n@admin.route('/categories/edit/<int:category_id>', methods=['GET', 'POST'])\n@permission_required('manage_categories')\ndef edit_category(category_id):\n    category = Category.query.get_or_404(category_id)\n    form = CategoryForm(obj=category)\n    if form.validate_on_submit():\n        category.name = form.name.data\n        category.description = form.description.data\n        category.display_order = form.display_order.data\n        db.session.commit()\n        flash('Category updated successfully!', 'success')\n        return redirect(url_for('admin.manage_categories'))\n    return render_template('admin/category_form.html', form=form, category=category)\n\n@admin.route('/categories/delete/<int:category_id>', methods=['POST'])\n@permission_required('manage_categories')\ndef delete_category(category_id):\n    try:\n        category = Category.query.get_or_404(category_id)\n        if category.books:\n            return jsonify({'success': False, 'error': 'Cannot delete category with books'})\n        db.session.delete(category)\n        db.session.commit()\n        return jsonify({'success': True})\n    except Exception as e:\n        db.session.rollback()\n        return jsonify({'success': False, 'error': str(e)})\n\n@admin.route('/update_order_status/<int:order_id>', methods=['POST'])\n@permission_required('manage_orders')\ndef update_order_status(order_id):\n    try:\n        data = request.get_json()\n        order = Order.query.get_or_404(order_id)\n        order.status = data['status']\n        db.session.commit()\n        \n        send_order_status_email(order.user.email, order.id, order.status, order.items)\n        \n        return jsonify({'success': True})\n    except Exception as e:\n        db.session.rollback()\n        return jsonify({'success': False, 'error': str(e)})\n\n@admin.route('/update_shipping_info/<int:order_id>', methods=['POST'])\n@permission_required('manage_orders')\ndef update_shipping_info(order_id):\n    try:\n        data = request.get_json()\n        order = Order.query.get_or_404(order_id)\n        \n        order.carrier = data.get('carrier')\n        order.tracking_number = data.get('tracking_number')\n        order.shipping_date = datetime.fromisoformat(data.get('shipping_date')) if data.get('shipping_date') else None\n        order.shipping_address = data.get('shipping_address')\n        \n        db.session.commit()\n        \n        log_user_activity(current_user, 'shipping_update', \n                         f'Updated shipping info for order #{order.id}')\n        \n        return jsonify({'success': True})\n    except Exception as e:\n        db.session.rollback()\n        return jsonify({'success': False, 'error': str(e)})\n\n@admin.route('/series')\n@permission_required('manage_books')\ndef manage_series():\n    series_list = BookSeries.query.order_by(BookSeries.title).all()\n    return render_template('admin/manage_series.html', series_list=series_list)\n\n@admin.route('/series/add', methods=['POST'])\n@permission_required('manage_books')\ndef add_series():\n    try:\n        data = request.get_json()\n        series = BookSeries(\n            title=data['title'],\n            total_books=int(data['total_books'])\n        )\n        db.session.add(series)\n        db.session.commit()\n        return jsonify({'success': True})\n    except Exception as e:\n        db.session.rollback()\n        return jsonify({'success': False, 'error': str(e)})\n\n@admin.route('/series/edit/<int:series_id>', methods=['POST'])\n@permission_required('manage_books')\ndef edit_series(series_id):\n    try:\n        data = request.get_json()\n        series = BookSeries.query.get_or_404(series_id)\n        series.title = data['title']\n        series.total_books = int(data['total_books'])\n        db.session.commit()\n        return jsonify({'success': True})\n    except Exception as e:\n        db.session.rollback()\n        return jsonify({'success': False, 'error': str(e)})\n\n@admin.route('/series/delete/<int:series_id>', methods=['POST'])\n@permission_required('manage_books')\ndef delete_series(series_id):\n    try:\n        series = BookSeries.query.get_or_404(series_id)\n        if series.books:\n            return jsonify({\n                'success': False, \n                'error': 'Cannot delete series with associated books'\n            })\n        db.session.delete(series)\n        db.session.commit()\n        return jsonify({'success': True})\n    except Exception as e:\n        db.session.rollback()\n        return jsonify({'success': False, 'error': str(e)})", "text_preview": "from flask import Blueprint, render_template, redirect, url_for, flash, request, jsonify, current_app from flask_login import login_required, current_user from models import User, Book, Order, Category, Review, UserActivity, BookFormat, Boo", "input_sha256": "63f7018ffd9c07297b970c039ef32661e7e3119608d0ada137889a73fd02cac7", "text_sha256": "6919b190bd33e6307e15f73fd695f3b24494ea5d806f206d9f6586ab266c0d4d", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12122, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9686987827093944, "noise_severity": 0.032140474884748096, "missing_content_severity": 0.00901355658501015, "factual_density": 3.241618017731368, "pretraining_utility": 0.6524803246577171, "math_density": 0.04501682684826338, "code_density": 3.8660062316108434, "reasoning_density": 0.5697517390952492, "education_level": 2.138431165051263, "creative_writing_quality": -0.26535048435378666}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0029117349813958526, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9971953321701539, "secondary": 0.9860594737307176, "tertiary": 0.9890978058852175, "structured_records": 0.026336124684490494, "procedural_steps": 0.014012319791177478, "worked_solution": 0.004650786050548582, "contextual_dialogue": 6.239115641148932e-06, "content_quality": 0.5771449295815425, "noise_severity": 0.9732162709293766, "missing_content_severity": 0.9924887028458249, "factual_density": 0.36801501477613985, "pretraining_utility": 0.5447811545878767, "math_density": 0.9624859776264472, "code_density": 0.8883385263423693, "reasoning_density": 0.5252068840872922, "education_level": 0.8019073629762892, "creative_writing_quality": 0.999516993289205, "primary_topic": 0.9926554258144614, "secondary_topic": 0.9079093869040183}, "probability_margin": {"primary": 0.9958637031335709, "secondary": 0.9801427553250157, "tertiary": 0.9856901746257157, "education_level": 0.6897223005287404, "creative_writing_quality": 0.9993877103835825, "primary_topic": 0.9899057824068545, "secondary_topic": 0.8679654807690963}, "raw_probabilities": {"primary": {"Academic Writing": 2.3893672546467007e-05, "Content Listing": 2.839445611896332e-05, "Creative Writing": 1.2574802595175868e-05, "Customer Support Page": 7.3243917390885144e-06, "Q&A Forum": 8.395138377426568e-06, "Discussion Thread": 4.719318865438475e-06, "Comment Section": 6.721031353671229e-06, "Missing Content": 1.5927580509319656e-05, "Knowledge Article": 4.8670510537297015e-05, "Legal Notices": 1.4741375812299513e-05, "News Article": 3.3273342380404384e-06, "Organizational Announcement": 7.292181206651992e-06, "Nonfiction Writing": 7.374878507449248e-06, "Organizational About Page": 8.967346527216981e-06, "Personal About Page": 9.331032163273285e-06, "Personal Blog": 5.22388950222815e-06, "Product Page": 6.459325479640596e-06, "Spam / Ads": 6.2301486393491154e-06, "Structured Data": 0.0004131284410971903, "Technical Writing": 0.9973121933297308, "Transcript / Interview": 9.801451004175144e-06, "Tutorial / How-To Guide": 0.0005864531049780996, "User Reviews": 4.365062310682276e-06, "Other / Unknown": 0.0014484901961599413}, "secondary": {"Academic Writing": 0.00011311562024025482, "Content Listing": 0.0007567997254543737, "Creative Writing": 8.71583405012574e-05, "Customer Support Page": 6.243341799133721e-05, "Q&A Forum": 3.388325190179928e-05, "Discussion Thread": 2.418301647551831e-05, "Comment Section": 0.00010526023676369474, "Missing Content": 0.00017801797500699359, "Knowledge Article": 0.00042951396609341953, "Legal Notices": 0.0001117329779833488, "News Article": 4.284958304768656e-05, "Organizational Announcement": 4.035079490193088e-05, "Nonfiction Writing": 4.528084491495882e-05, "Organizational About Page": 5.186595078460317e-05, "Personal About Page": 4.788479484677281e-05, "Personal Blog": 4.33998953804704e-05, "Product Page": 3.073354473145314e-05, "Spam / Ads": 4.5040651643860185e-05, "Structured Data": 0.002826585684114964, "Technical Writing": 0.006497573666921948, "Transcript / Interview": 5.544238550275733e-05, "Tutorial / How-To Guide": 0.0017075218319118374, "User Reviews": 2.3042850947128954e-05, "NONE": 0.9866403289919377}, "tertiary": {"Academic Writing": 7.041707248118831e-05, "Content Listing": 0.0007933848052129116, "Creative Writing": 7.377256895753428e-05, "Customer Support Page": 4.7290576426951773e-05, "Q&A Forum": 2.4355665298096345e-05, "Discussion Thread": 1.6493428480707156e-05, "Comment Section": 0.00020285958285599503, "Missing Content": 0.0002515332574993276, "Knowledge Article": 0.0004705347576994906, "Legal Notices": 0.00018756936125602927, "News Article": 3.8294135907229694e-05, "Organizational Announcement": 3.633906469355647e-05, "Nonfiction Writing": 5.643789672928005e-05, "Organizational About Page": 6.239329776711389e-05, "Personal About Page": 3.860368327580626e-05, "Personal Blog": 3.4952069875259746e-05, "Product Page": 3.2220858194270917e-05, "Spam / Ads": 4.8527624271316124e-05, "Structured Data": 0.0023515377074000868, "Technical Writing": 0.0038618893476176815, "Transcript / Interview": 4.424454914593412e-05, "Tutorial / How-To Guide": 0.001683757431313933, "User Reviews": 2.0527284306811802e-05, "NONE": 0.9895520639733334}, "structured_records": {"false": 0.9736638753155095, "true": 0.026336124684490494}, "procedural_steps": {"false": 0.9859876802088225, "true": 0.014012319791177478}, "worked_solution": {"false": 0.9953492139494514, "true": 0.004650786050548582}, "contextual_dialogue": {"false": 0.9999937608843589, "true": 6.239115641148932e-06}, "content_quality": {"-2": 0.005111175472773247, "-1": 0.020467068177908172, "0": 0.21309598812224126, "1": 0.5232633346213056, "2": 0.23806243360577165}, "noise_severity": {"0": 0.9719554434639247, "1": 0.025136949130583657, "2": 0.0020891406709198056, "3": 0.00044862252596271444, "4": 0.00036984420860917104}, "missing_content_severity": {"0": 0.9955402023985037, "1": 0.0012804669436920894, "2": 0.002004429958394554, "3": 0.000975373073109336, "4": 0.00019952762630023557}, "factual_density": {"0": 0.005784149283319546, "1": 0.013329094390139936, "2": 0.14248275137177413, "3": 0.4102925992213859, "4": 0.4281114057333804}, "pretraining_utility": {"-2": 0.006483437111951293, "-1": 0.05442054704414631, "0": 0.3185997394942689, "1": 0.521124806773501, "2": 0.09937146957613247}, "math_density": {"0": 0.960441202610508, "1": 0.036317845174133744, "2": 0.0018739922649717872, "3": 0.0005168426573590505, "4": 0.0008501172930272249}, "code_density": {"0": 0.0002350802150686278, "1": 0.00044164473475873046, "2": 0.014230284859132348, "3": 0.10326794360634135, "4": 0.881825046584699}, "reasoning_density": {"0": 0.6566383308029459, "1": 0.1564371952778445, "2": 0.1551675458781021, "3": 0.024048260103229597, "4": 0.007708667937877938}, "education_level": {"none": 0.0007264266623920507, "0": 0.0010926869306296665, "1": 0.013599895692883576, "2": 0.834922802480241, "3": 0.14520050195150055, "4": 0.004457686282353165}, "creative_writing_quality": {"not_applicable": 0.9995974944076708, "-2": 4.74657269083934e-05, "-1": 8.643652586211732e-05, "0": 0.0002097840240882021, "1": 4.307570514184619e-05, "2": 1.5743610328691728e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.051143957250993e-05, "Art & Design": 6.10882974540702e-05, "Law & Crime": 3.700757057545241e-05, "Education & Careers": 6.643066549612609e-05, "Entertainment & Media": 9.935572738843818e-05, "Fashion & Beauty": 5.268827961218868e-05, "Business, Economics & Finance": 0.003032127030127621, "Food & Dining": 8.912439080256046e-05, "Games & Gaming": 0.00012874420864043064, "Electronics & Hardware": 5.685292288063003e-05, "Health, Healthcare & Wellness": 2.5643221771928023e-05, "History & Geography": 3.263179380378674e-05, "Home, Pets & Hobbies": 0.00043469906887692893, "Industry & Production": 5.467510410564423e-05, "Language, Literature & Philosophy": 0.0006327005963855018, "Politics & Government": 1.9199695823286173e-05, "Religion & Spirituality": 3.9053132266050014e-05, "Science, Mathematics & Engineering": 6.416503177834008e-05, "Relationships & Community": 2.7633933265043646e-05, "Software Usage & Applications": 0.000740463493980246, "Programming & Software Engineering": 0.9929379094369821, "Sports & Fitness": 4.273941667573399e-05, "Vehicles & Transportation": 7.212163449428132e-05, "Travel & Tourism": 7.443809857305726e-05, "Other / Uncovered Subject": 0.0010780741216564293, "Unknown / Insufficient Content": 6.992168701152267e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005389987671959193, "Art & Design": 0.0010535500960787516, "Law & Crime": 0.0006370718080973142, "Education & Careers": 0.0004811735170668375, "Entertainment & Media": 0.0014234772998979572, "Fashion & Beauty": 0.0003089754929935813, "Business, Economics & Finance": 0.04348585279245979, "Food & Dining": 0.0003347611644670477, "Games & Gaming": 0.0006220856058009839, "Electronics & Hardware": 0.00038863205964516026, "Health, Healthcare & Wellness": 0.00022327714176082264, "History & Geography": 0.00026041779146708733, "Home, Pets & Hobbies": 0.0017513829414485512, "Industry & Production": 0.0002817740624534828, "Language, Literature & Philosophy": 0.010837747506962222, "Politics & Government": 0.00017686686900781408, "Religion & Spirituality": 0.0004061479828070774, "Science, Mathematics & Engineering": 0.00042809929225286376, "Relationships & Community": 0.0001515904441724296, "Software Usage & Applications": 0.0055261809537640256, "Programming & Software Engineering": 0.013793934288461311, "Sports & Fitness": 0.0002188130751034294, "Vehicles & Transportation": 0.0007755015603476777, "Travel & Tourism": 0.0004365380065688379, "Other / Uncovered Subject": 0.0040058159181631265, "NONE": 0.911451333561556}}, "probabilities": {"primary": {"Academic Writing": 2.389367254646701e-05, "Content Listing": 2.8394456118963325e-05, "Creative Writing": 1.257480259517587e-05, "Customer Support Page": 7.324391739088515e-06, "Q&A Forum": 8.39513837742657e-06, "Discussion Thread": 4.719318865438476e-06, "Comment Section": 6.72103135367123e-06, "Missing Content": 1.592758050931966e-05, "Knowledge Article": 4.867051053729702e-05, "Legal Notices": 1.4741375812299515e-05, "News Article": 3.327334238040439e-06, "Organizational Announcement": 7.292181206651993e-06, "Nonfiction Writing": 7.374878507449249e-06, "Organizational About Page": 8.967346527216983e-06, "Personal About Page": 9.331032163273287e-06, "Personal Blog": 5.223889502228151e-06, "Product Page": 6.4593254796405965e-06, "Spam / Ads": 6.230148639349116e-06, "Structured Data": 0.00041312844109719037, "Technical Writing": 0.9973121933297309, "Transcript / Interview": 9.801451004175146e-06, "Tutorial / How-To Guide": 0.0005864531049780997, "User Reviews": 4.365062310682277e-06, "Other / Unknown": 0.0014484901961599416}, "secondary": {"Academic Writing": 0.00011311562024025482, "Content Listing": 0.0007567997254543737, "Creative Writing": 8.71583405012574e-05, "Customer Support Page": 6.243341799133721e-05, "Q&A Forum": 3.388325190179928e-05, "Discussion Thread": 2.418301647551831e-05, "Comment Section": 0.00010526023676369474, "Missing Content": 0.00017801797500699359, "Knowledge Article": 0.00042951396609341953, "Legal Notices": 0.0001117329779833488, "News Article": 4.284958304768656e-05, "Organizational Announcement": 4.035079490193088e-05, "Nonfiction Writing": 4.528084491495882e-05, "Organizational About Page": 5.186595078460317e-05, "Personal About Page": 4.788479484677281e-05, "Personal Blog": 4.33998953804704e-05, "Product Page": 3.073354473145314e-05, "Spam / Ads": 4.5040651643860185e-05, "Structured Data": 0.002826585684114964, "Technical Writing": 0.006497573666921948, "Transcript / Interview": 5.544238550275733e-05, "Tutorial / How-To Guide": 0.0017075218319118374, "User Reviews": 2.3042850947128954e-05, "NONE": 0.9866403289919377}, "tertiary": {"Academic Writing": 7.041707248118833e-05, "Content Listing": 0.0007933848052129117, "Creative Writing": 7.37725689575343e-05, "Customer Support Page": 4.729057642695178e-05, "Q&A Forum": 2.4355665298096348e-05, "Discussion Thread": 1.649342848070716e-05, "Comment Section": 0.00020285958285599505, "Missing Content": 0.00025153325749932765, "Knowledge Article": 0.00047053475769949065, "Legal Notices": 0.0001875693612560293, "News Article": 3.82941359072297e-05, "Organizational Announcement": 3.633906469355648e-05, "Nonfiction Writing": 5.643789672928006e-05, "Organizational About Page": 6.23932977671139e-05, "Personal About Page": 3.8603683275806265e-05, "Personal Blog": 3.495206987525975e-05, "Product Page": 3.222085819427092e-05, "Spam / Ads": 4.852762427131613e-05, "Structured Data": 0.002351537707400087, "Technical Writing": 0.003861889347617682, "Transcript / Interview": 4.424454914593413e-05, "Tutorial / How-To Guide": 0.0016837574313139333, "User Reviews": 2.0527284306811805e-05, "NONE": 0.9895520639733335}, "structured_records": {"false": 0.9736638753155095, "true": 0.026336124684490494}, "procedural_steps": {"false": 0.9859876802088225, "true": 0.014012319791177478}, "worked_solution": {"false": 0.9953492139494514, "true": 0.004650786050548582}, "contextual_dialogue": {"false": 0.9999937608843589, "true": 6.239115641148932e-06}, "content_quality": {"-2": 0.0051111754727732476, "-1": 0.020467068177908176, "0": 0.2130959881222413, "1": 0.5232633346213057, "2": 0.23806243360577167}, "noise_severity": {"0": 0.9719554434639247, "1": 0.025136949130583657, "2": 0.0020891406709198056, "3": 0.00044862252596271444, "4": 0.00036984420860917104}, "missing_content_severity": {"0": 0.9955402023985038, "1": 0.0012804669436920896, "2": 0.0020044299583945545, "3": 0.0009753730731093362, "4": 0.0001995276263002356}, "factual_density": {"0": 0.005784149283319547, "1": 0.013329094390139937, "2": 0.14248275137177416, "3": 0.41029259922138595, "4": 0.42811140573338047}, "pretraining_utility": {"-2": 0.006483437111951293, "-1": 0.05442054704414631, "0": 0.3185997394942689, "1": 0.521124806773501, "2": 0.09937146957613247}, "math_density": {"0": 0.9604412026105082, "1": 0.03631784517413375, "2": 0.0018739922649717877, "3": 0.0005168426573590507, "4": 0.0008501172930272252}, "code_density": {"0": 0.0002350802150686278, "1": 0.00044164473475873046, "2": 0.014230284859132348, "3": 0.10326794360634135, "4": 0.881825046584699}, "reasoning_density": {"0": 0.6566383308029459, "1": 0.1564371952778445, "2": 0.1551675458781021, "3": 0.024048260103229597, "4": 0.007708667937877938}, "education_level": {"none": 0.0007264266623920507, "0": 0.0010926869306296665, "1": 0.013599895692883576, "2": 0.834922802480241, "3": 0.14520050195150055, "4": 0.004457686282353165}, "creative_writing_quality": {"not_applicable": 0.9995974944076708, "-2": 4.74657269083934e-05, "-1": 8.643652586211732e-05, "0": 0.0002097840240882021, "1": 4.307570514184619e-05, "2": 1.5743610328691728e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.0511439572509932e-05, "Art & Design": 6.108829745407021e-05, "Law & Crime": 3.700757057545242e-05, "Education & Careers": 6.64306654961261e-05, "Entertainment & Media": 9.935572738843819e-05, "Fashion & Beauty": 5.268827961218869e-05, "Business, Economics & Finance": 0.0030321270301276212, "Food & Dining": 8.912439080256047e-05, "Games & Gaming": 0.00012874420864043067, "Electronics & Hardware": 5.6852922880630034e-05, "Health, Healthcare & Wellness": 2.5643221771928026e-05, "History & Geography": 3.263179380378675e-05, "Home, Pets & Hobbies": 0.000434699068876929, "Industry & Production": 5.467510410564424e-05, "Language, Literature & Philosophy": 0.0006327005963855019, "Politics & Government": 1.9199695823286177e-05, "Religion & Spirituality": 3.905313226605002e-05, "Science, Mathematics & Engineering": 6.41650317783401e-05, "Relationships & Community": 2.763393326504365e-05, "Software Usage & Applications": 0.0007404634939802462, "Programming & Software Engineering": 0.9929379094369822, "Sports & Fitness": 4.2739416675733995e-05, "Vehicles & Transportation": 7.212163449428133e-05, "Travel & Tourism": 7.443809857305728e-05, "Other / Uncovered Subject": 0.0010780741216564296, "Unknown / Insufficient Content": 6.992168701152269e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005389987671959192, "Art & Design": 0.0010535500960787514, "Law & Crime": 0.0006370718080973141, "Education & Careers": 0.00048117351706683737, "Entertainment & Media": 0.001423477299897957, "Fashion & Beauty": 0.00030897549299358125, "Business, Economics & Finance": 0.043485852792459784, "Food & Dining": 0.00033476116446704766, "Games & Gaming": 0.0006220856058009838, "Electronics & Hardware": 0.00038863205964516015, "Health, Healthcare & Wellness": 0.0002232771417608226, "History & Geography": 0.0002604177914670873, "Home, Pets & Hobbies": 0.0017513829414485507, "Industry & Production": 0.00028177406245348275, "Language, Literature & Philosophy": 0.01083774750696222, "Politics & Government": 0.00017686686900781406, "Religion & Spirituality": 0.0004061479828070773, "Science, Mathematics & Engineering": 0.00042809929225286366, "Relationships & Community": 0.00015159044417242958, "Software Usage & Applications": 0.005526180953764025, "Programming & Software Engineering": 0.013793934288461308, "Sports & Fitness": 0.00021881307510342936, "Vehicles & Transportation": 0.0007755015603476775, "Travel & Tourism": 0.0004365380065688378, "Other / Uncovered Subject": 0.004005815918163126, "NONE": 0.9114513335615558}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11783, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11783, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:13.726953+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "a6fcebc6-26d8-4909-8c8a-7a78e1e8a8bf", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "f46d6faa763abcdfaca7623131748a9aa95b3f7c106e9c1b80b52a796d336361", "source_metadata": {"uuid": "a6fcebc6-26d8-4909-8c8a-7a78e1e8a8bf", "repo_name": "Kobbykk/BookNest", "relative_path": "views/admin.py", "category": "WEB", "algo_rel_score": 0.9949855804443359, "quality_score": 6.289872646331787, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 161, "repo_created_at": "2024-11-12T01:35:52.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "44c3446753b34e465e60e3cd83c5ab21ad614736", "size_bytes": 15336, "file_timestamp": 1731376652, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a02cfdd3fda7882583a6e8e2603ff2d82bee4526"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 161, "repo_created_at": "2024-11-12T01:35:52.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "44c3446753b34e465e60e3cd83c5ab21ad614736", "size_bytes": 15336, "file_timestamp": 1731376652, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a02cfdd3fda7882583a6e8e2603ff2d82bee4526", "url": "https://github.com/Kobbykk/BookNest/blob/a02cfdd3fda7882583a6e8e2603ff2d82bee4526/views/admin.py"}, "gpt4o_tokens": 3007, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.9686987827093944, "noise_severity": 0.032140474884748096, "missing_content_severity": 0.00901355658501015, "factual_density": 3.241618017731368, "pretraining_utility": 0.6524803246577171, "math_density": 0.04501682684826338, "code_density": 3.8660062316108434, "reasoning_density": 0.5697517390952492, "education_level": 2.138431165051263, "creative_writing_quality": -0.26535048435378666}, "score_applicability": {"education_level": 0.9992735733376079, "creative_writing_quality": 0.00040250559232925075}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/nevasv/skillfactory_News_Portal/blob/c45c10870e3a7b7052f143c9e7f6e2bb6531bd32/NewsPaper/news/models.py", "id": "python-0-d6ebfeada292ae4f9ad794c2eaba95d5", "text": "from django.db import models\nfrom django.contrib.auth.models import User\nfrom param import *\nfrom django.db.models import Sum\n\n\nclass Author(models.Model):\n    \"\"\"\n    Модель Author\n    имеет следующие поля:\n    -<portalUser> связь «один к одному» с встроенной моделью пользователей User;\n    - <ratingAuthor> рейтинг пользователя.\n    \"\"\"\n    authorUser = models.OneToOneField(User, on_delete=models.CASCADE)\n    ratingAuthor = models.SmallIntegerField(default=0)\n\n    def update_rating(self):\n        \"\"\"\n        - update_rating() модели Author, который обновляет рейтинг пользователя, переданный в аргумент этого метода.\n        Он состоит из следующего:\n        -суммарный рейтинг каждой статьи автора умножается на 3;\n        -суммарный рейтинг всех комментариев автора;\n        -суммарный рейтинг всех комментариев к статьям автора.\n        \"\"\"\n\n        postR = self.post_set.all().aggregate(postRating=Sum('rating'))\n        p_R = 0\n        p_R += postR.get('postRating')\n\n        commentR = self.authorUser.comment_set.all().aggregate(commentRating=Sum('rating'))\n        c_R = 0\n        c_R += commentR.get('commentRating')\n\n        self.ratingAuthor = p_R * 3 + c_R\n        self.save()\n\n    def __str__(self):\n        return f\"{self.authorUser}\"\n\n\nclass Category(models.Model):\n    \"\"\"\n    Модель Category\n    Темы, которые они отражают (спорт, политика, образование и т. д.).\n    Имеет единственное поле: название категории.\n    - <name> Поле должно быть уникальным (в определении поля необходимо написать параметр unique = True).\n    \"\"\"\n    name = models.CharField(max_length=64, unique=True)\n\n    def __str__(self):\n        return f\"{self.name}\"\n\n\nclass Post(models.Model):\n    \"\"\"\n    Модель Post\n    Эта модель должна содержать в себе статьи и новости, которые создают пользователи.\n    Каждый объект может иметь одну или несколько категорий.\n    Соответственно, модель должна включать следующие поля:\n    - <author>        связь «один ко многим» с моделью Author;\n    - <categoryFild>  поле с выбором — «статья» или «новость»;\n    - <dataCreations> автоматически добавляемая дата и время создания;\n    - <postCategory>  связь «многие ко многим» с моделью Category (с дополнительной моделью PostCategory);\n    - <title>         заголовок статьи/новости;\n    - <text>          текст статьи/новости;\n    - <rating>        рейтинг статьи/новости.\n    \"\"\"\n    author = models.ForeignKey(Author, on_delete=models.CASCADE)\n    categoryType = models.CharField(max_length=20, choices=CATEGORY_CHOISES, default=ARTICLE)\n    dataCreations = models.DateTimeField(auto_now_add=True)\n    postCategory = models.ManyToManyField(Category, through='PostCategory')\n    title = models.CharField(max_length=128)\n    text = models.TextField()\n    rating = models.SmallIntegerField(default=0)\n\n    def like(self):\n        \"\"\"\n        - like()    который увеличивает рейтинг на единицу.\n        \"\"\"\n        self.rating += 1\n        self.save()\n\n    def dislike(self):\n        \"\"\"\n        - dislike() который уменьшают рейтинг на единицу.\n        \"\"\"\n        self.rating -= 1\n        self.save()\n\n    def preview(self):\n        \"\"\"\n        - preview() модели Post, который возвращает начало статьи\n        (предварительный просмотр) длиной 124 символа и добавляет многоточие в конце.\n        \"\"\"\n        return self.text[0:128] + '...'\n\n    def __str__(self):\n        dataf = 'Post from {}'.format(self.dataCreations.strftime('%d.%m.%Y %H:%M'))\n        return f\"{dataf},{self.author},{self.title}\"\n\n\nclass PostCategory(models.Model):\n    \"\"\"\n    Модель PostCategory\n    Промежуточная модель для связи «многие ко многим»:\n    - <postThrough>     связь «один ко многим» с моделью Post;\n    - <categoryThrough> связь «один ко многим» с моделью Category.\n    \"\"\"\n    postThrough = models.ForeignKey(Post, on_delete=models.CASCADE)\n    categoryThrough = models.ForeignKey(Category, on_delete=models.CASCADE)\n\n    def __str__(self):\n        return f\"{self.postThrough},from the category:  {self.categoryThrough}\"\n\n\nclass Comment(models.Model):\n    \"\"\"\n    Модель Comment\n    Под каждой новостью/статьёй можно оставлять комментарии, поэтому необходимо организовать их способ хранения тоже.\n    Модель будет иметь следующие поля:\n    - <commentPost>  связь «один ко многим» с моделью Post;\n    - <userPost>     связь «один ко многим» со встроенной моделью User\n                    (комментарии может оставить любой пользователь, необязательно автор);\n    - <text>         текст комментария;\n    - <dataCreation> дата и время создания комментария;\n    - <rating>       рейтинг комментария.\n    \"\"\"\n    commentPost = models.ForeignKey(Post, on_delete=models.CASCADE)\n    userPost = models.ForeignKey(User, on_delete=models.CASCADE)\n    text = models.TextField()\n    dataCreation = models.DateTimeField(auto_now_add=True)\n    rating = models.SmallIntegerField(default=0)\n\n    def like(self):\n        \"\"\"\n        - like()    который увеличивает рейтинг на единицу.\n        \"\"\"\n        self.rating += 1\n        self.save()\n\n    def dislike(self):\n        \"\"\"\n        - dislike() который уменьшают рейтинг на единицу.\n        \"\"\"\n        self.rating -= 1\n        self.save()\n\n    def __str__(self):\n        return f\"{self.dataCreation}, {self.userPost}\"\n", "text_preview": "from django.db import models from django.contrib.auth.models import User from param import * from django.db.models import Sum class Author(models.Model): \"\"\" Модель Author имеет следующие поля: -<portalUser> связь «один к одному» с встроенн", "input_sha256": "e3ffde358fe72b5132b0a6e7e2d5f534bd57a1b5bb97eb8cecdba282bde55f9a", "text_sha256": "d6ebfeada292ae4f9ad794c2eaba95d583ef250f8fb0dc001a11a5bf3e9f76f2", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12123, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.509390619617342, "noise_severity": 0.04871090924957351, "missing_content_severity": 0.06766502471700442, "factual_density": 3.528708312593714, "pretraining_utility": 0.5893422453962653, "math_density": 0.7063620354043343, "code_density": 3.5650281467550875, "reasoning_density": 0.29774783597364574, "education_level": 2.047000834087073, "creative_writing_quality": -0.43888069265283647}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.019785265403334584, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9963070360559884, "secondary": 0.93287932666316, "tertiary": 0.9535248209970744, "structured_records": 0.042183183949344866, "procedural_steps": 0.024717567925668666, "worked_solution": 0.061720059234343115, "contextual_dialogue": 4.8778966436207745e-06, "content_quality": 0.4127468124102389, "noise_severity": 0.9594075756253554, "missing_content_severity": 0.9436124794024963, "factual_density": 0.6072569271614279, "pretraining_utility": 0.5265520842287024, "math_density": 0.4113649704963881, "code_density": 0.6375234556292393, "reasoning_density": 0.7518768033552953, "education_level": 0.8468643329922582, "creative_writing_quality": 0.9987246650802877, "primary_topic": 0.9885647570772857, "secondary_topic": 0.9332740942030805}, "probability_margin": {"primary": 0.9949835199792394, "secondary": 0.9042873761371607, "tertiary": 0.9387621757583114, "education_level": 0.7907449911292137, "creative_writing_quality": 0.9984663397740094, "primary_topic": 0.9837606046224984, "secondary_topic": 0.9036805133219898}, "raw_probabilities": {"primary": {"Academic Writing": 2.620145602254742e-05, "Content Listing": 1.5614763273817577e-05, "Creative Writing": 1.5252989486520637e-05, "Customer Support Page": 1.0421269966745182e-05, "Q&A Forum": 1.060424525455859e-05, "Discussion Thread": 7.5872842693052645e-06, "Comment Section": 1.542632711500626e-05, "Missing Content": 2.0501215397210145e-05, "Knowledge Article": 8.73766772709734e-05, "Legal Notices": 2.6521614048474186e-05, "News Article": 6.675718253783848e-06, "Organizational Announcement": 9.724480641541115e-06, "Nonfiction Writing": 8.180333600817458e-06, "Organizational About Page": 1.1420284891841421e-05, "Personal About Page": 1.1440500553633182e-05, "Personal Blog": 7.001432293722051e-06, "Product Page": 8.368193328819457e-06, "Spam / Ads": 8.424649653099312e-06, "Structured Data": 0.00147738957441617, "Technical Writing": 0.9964609095536555, "Transcript / Interview": 1.3074217954540628e-05, "Tutorial / How-To Guide": 0.000556185232125733, "User Reviews": 6.143148418657926e-06, "Other / Unknown": 0.0011795548381072325}, "secondary": {"Academic Writing": 0.00042165390960020844, "Content Listing": 0.00029881780603606303, "Creative Writing": 0.00029388362140614875, "Customer Support Page": 0.00033326873184041125, "Q&A Forum": 0.00015629177412267416, "Discussion Thread": 0.00013483013332004569, "Comment Section": 0.00029349038668393903, "Missing Content": 0.00041945843758292714, "Knowledge Article": 0.0032432237631901256, "Legal Notices": 0.00024101890527749738, "News Article": 0.00018780114881091723, "Organizational Announcement": 0.00017337397980516243, "Nonfiction Writing": 0.00025166425948335556, "Organizational About Page": 0.00020801822101743355, "Personal About Page": 0.00017801244574124596, "Personal Blog": 0.00016836401142409683, "Product Page": 0.00011008723950200142, "Spam / Ads": 0.00015601961982581326, "Structured Data": 0.01701469917046923, "Technical Writing": 0.03138864524836762, "Transcript / Interview": 0.00021431197224455905, "Tutorial / How-To Guide": 0.008348505182068132, "User Reviews": 8.853864665203601e-05, "NONE": 0.9356760213855283}, "tertiary": {"Academic Writing": 0.0002530540951889517, "Content Listing": 0.00019586952399355747, "Creative Writing": 0.00019219549617672401, "Customer Support Page": 0.00023044661264333833, "Q&A Forum": 8.828821607424636e-05, "Discussion Thread": 8.322235745108556e-05, "Comment Section": 0.00025422871721658376, "Missing Content": 0.0003816554273010671, "Knowledge Article": 0.0034007155229111305, "Legal Notices": 0.00023658808810677646, "News Article": 0.00013052340467084673, "Organizational Announcement": 0.00011083199890132233, "Nonfiction Writing": 0.00020606425383541417, "Organizational About Page": 0.00015723623576849747, "Personal About Page": 0.00011398886442582804, "Personal Blog": 9.720103527245943e-05, "Product Page": 8.150947370535158e-05, "Spam / Ads": 0.00011989752213055442, "Structured Data": 0.013535687269018753, "Technical Writing": 0.01669911103055148, "Transcript / Interview": 0.00013172895681114036, "Tutorial / How-To Guide": 0.007788332614602555, "User Reviews": 5.033649437938974e-05, "NONE": 0.9554612867888629}, "structured_records": {"false": 0.9578168160506552, "true": 0.042183183949344866}, "procedural_steps": {"false": 0.9752824320743313, "true": 0.024717567925668666}, "worked_solution": {"false": 0.9382799407656569, "true": 0.061720059234343115}, "contextual_dialogue": {"false": 0.9999951221033564, "true": 4.8778966436207745e-06}, "content_quality": {"-2": 0.012464857663699046, "-1": 0.11066868338841487, "0": 0.33892466297725876, "1": 0.4308945736080997, "2": 0.10704722236252764}, "noise_severity": {"0": 0.9594265783424688, "1": 0.03447283236589606, "2": 0.004624093910438592, "3": 0.000916092461985938, "4": 0.000560402919210613}, "missing_content_severity": {"0": 0.9585159436438901, "1": 0.020088011836164803, "2": 0.01727953369417829, "3": 0.003448097810583811, "4": 0.0006684130151828997}, "factual_density": {"0": 0.0015889652692854292, "1": 0.003206717025537808, "2": 0.04642612106166231, "3": 0.3624634331292067, "4": 0.5863147635143077}, "pretraining_utility": {"-2": 0.006956397496632729, "-1": 0.058069772832891796, "0": 0.35238888860896417, "1": 0.5038450689006, "2": 0.0787398721609113}, "math_density": {"0": 0.4840999044984616, "1": 0.39303256879970844, "2": 0.08269487150785211, "3": 0.012750897186990013, "4": 0.027421758006987897}, "code_density": {"0": 0.0005049714840544439, "1": 0.0011542064327419005, "2": 0.05595013022559934, "3": 0.31758908755927073, "4": 0.6248016042983336}, "reasoning_density": {"0": 0.835880718477895, "1": 0.05800905897935925, "2": 0.08275676912411316, "3": 0.019188574928470058, "4": 0.0041648784901624925}, "education_level": {"none": 0.0010189688511331057, "0": 0.0017720276717462483, "1": 0.03916838951938536, "2": 0.8723869441602151, "3": 0.08164195303100136, "4": 0.004011716766518601}, "creative_writing_quality": {"not_applicable": 0.9989372209002397, "-2": 0.0001408360377281307, "-1": 0.00033443469761124627, "0": 0.00047088112623028315, "1": 8.358093075338891e-05, "2": 3.304630743719289e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.780334386025468e-05, "Art & Design": 7.352072038619334e-05, "Law & Crime": 5.1905172795989464e-05, "Education & Careers": 0.0002788781320689754, "Entertainment & Media": 0.005243969490276336, "Fashion & Beauty": 3.829240830265404e-05, "Business, Economics & Finance": 0.0001864779269939993, "Food & Dining": 4.798134670317569e-05, "Games & Gaming": 8.944753503772119e-05, "Electronics & Hardware": 6.970121723785851e-05, "Health, Healthcare & Wellness": 2.913891345433435e-05, "History & Geography": 4.2077646767033515e-05, "Home, Pets & Hobbies": 3.720407397039645e-05, "Industry & Production": 3.518417385175815e-05, "Language, Literature & Philosophy": 0.00015901965479572235, "Politics & Government": 0.00021380870129627006, "Religion & Spirituality": 4.146976168821755e-05, "Science, Mathematics & Engineering": 0.00021514988796488279, "Relationships & Community": 0.0003028412026107872, "Software Usage & Applications": 0.0012103070704086673, "Programming & Software Engineering": 0.9890045741127748, "Sports & Fitness": 9.105654773530625e-05, "Vehicles & Transportation": 2.5815363410647366e-05, "Travel & Tourism": 6.482203230516471e-05, "Other / Uncovered Subject": 0.0022986853770636815, "Unknown / Insufficient Content": 0.00011086818623882224}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0004079075892959044, "Art & Design": 0.0005622627265013451, "Law & Crime": 0.0005970088724549297, "Education & Careers": 0.0010168109816247582, "Entertainment & Media": 0.03215996187327991, "Fashion & Beauty": 0.0002026967052214461, "Business, Economics & Finance": 0.0009999752650650807, "Food & Dining": 0.00017183789704936357, "Games & Gaming": 0.00025033199381816677, "Electronics & Hardware": 0.00025580878204874564, "Health, Healthcare & Wellness": 0.00019009686586395498, "History & Geography": 0.00018007821557022673, "Home, Pets & Hobbies": 0.00015852989784795085, "Industry & Production": 0.00013048984615353525, "Language, Literature & Philosophy": 0.0006848645094379149, "Politics & Government": 0.0008343735442078514, "Religion & Spirituality": 0.00021993781416635471, "Science, Mathematics & Engineering": 0.0007518932212896164, "Relationships & Community": 0.00047432663756490876, "Software Usage & Applications": 0.006523064864419105, "Programming & Software Engineering": 0.013478338157295497, "Sports & Fitness": 0.00046928097804719855, "Vehicles & Transportation": 8.470891666984604e-05, "Travel & Tourism": 0.00023104227246098903, "Other / Uncovered Subject": 0.0031238963773755546, "NONE": 0.9358404751952697}}, "probabilities": {"primary": {"Academic Writing": 2.6201456022547413e-05, "Content Listing": 1.5614763273817573e-05, "Creative Writing": 1.5252989486520634e-05, "Customer Support Page": 1.042126996674518e-05, "Q&A Forum": 1.0604245254558589e-05, "Discussion Thread": 7.587284269305263e-06, "Comment Section": 1.5426327115006256e-05, "Missing Content": 2.050121539721014e-05, "Knowledge Article": 8.737667727097339e-05, "Legal Notices": 2.652161404847418e-05, "News Article": 6.675718253783846e-06, "Organizational Announcement": 9.724480641541113e-06, "Nonfiction Writing": 8.180333600817456e-06, "Organizational About Page": 1.142028489184142e-05, "Personal About Page": 1.144050055363318e-05, "Personal Blog": 7.0014322937220496e-06, "Product Page": 8.368193328819455e-06, "Spam / Ads": 8.42464965309931e-06, "Structured Data": 0.0014773895744161695, "Technical Writing": 0.9964609095536553, "Transcript / Interview": 1.3074217954540625e-05, "Tutorial / How-To Guide": 0.0005561852321257329, "User Reviews": 6.143148418657924e-06, "Other / Unknown": 0.0011795548381072322}, "secondary": {"Academic Writing": 0.0004216539096002085, "Content Listing": 0.0002988178060360631, "Creative Writing": 0.0002938836214061488, "Customer Support Page": 0.0003332687318404113, "Q&A Forum": 0.0001562917741226742, "Discussion Thread": 0.0001348301333200457, "Comment Section": 0.0002934903866839391, "Missing Content": 0.0004194584375829272, "Knowledge Article": 0.003243223763190126, "Legal Notices": 0.0002410189052774974, "News Article": 0.00018780114881091726, "Organizational Announcement": 0.00017337397980516246, "Nonfiction Writing": 0.0002516642594833556, "Organizational About Page": 0.00020801822101743358, "Personal About Page": 0.00017801244574124599, "Personal Blog": 0.00016836401142409686, "Product Page": 0.00011008723950200143, "Spam / Ads": 0.00015601961982581329, "Structured Data": 0.017014699170469234, "Technical Writing": 0.031388645248367625, "Transcript / Interview": 0.00021431197224455907, "Tutorial / How-To Guide": 0.008348505182068134, "User Reviews": 8.853864665203602e-05, "NONE": 0.9356760213855284}, "tertiary": {"Academic Writing": 0.0002530540951889518, "Content Listing": 0.0001958695239935575, "Creative Writing": 0.00019219549617672404, "Customer Support Page": 0.00023044661264333836, "Q&A Forum": 8.828821607424638e-05, "Discussion Thread": 8.322235745108557e-05, "Comment Section": 0.0002542287172165838, "Missing Content": 0.00038165542730106715, "Knowledge Article": 0.003400715522911131, "Legal Notices": 0.0002365880881067765, "News Article": 0.00013052340467084676, "Organizational Announcement": 0.00011083199890132234, "Nonfiction Writing": 0.0002060642538354142, "Organizational About Page": 0.0001572362357684975, "Personal About Page": 0.00011398886442582806, "Personal Blog": 9.720103527245944e-05, "Product Page": 8.150947370535159e-05, "Spam / Ads": 0.00011989752213055443, "Structured Data": 0.013535687269018755, "Technical Writing": 0.016699111030551484, "Transcript / Interview": 0.0001317289568111404, "Tutorial / How-To Guide": 0.007788332614602556, "User Reviews": 5.033649437938975e-05, "NONE": 0.955461286788863}, "structured_records": {"false": 0.9578168160506552, "true": 0.042183183949344866}, "procedural_steps": {"false": 0.9752824320743313, "true": 0.024717567925668666}, "worked_solution": {"false": 0.9382799407656569, "true": 0.061720059234343115}, "contextual_dialogue": {"false": 0.9999951221033564, "true": 4.8778966436207745e-06}, "content_quality": {"-2": 0.012464857663699046, "-1": 0.11066868338841487, "0": 0.33892466297725876, "1": 0.4308945736080997, "2": 0.10704722236252764}, "noise_severity": {"0": 0.9594265783424688, "1": 0.03447283236589606, "2": 0.004624093910438592, "3": 0.000916092461985938, "4": 0.000560402919210613}, "missing_content_severity": {"0": 0.9585159436438901, "1": 0.020088011836164803, "2": 0.01727953369417829, "3": 0.003448097810583811, "4": 0.0006684130151828997}, "factual_density": {"0": 0.0015889652692854294, "1": 0.0032067170255378084, "2": 0.046426121061662315, "3": 0.36246343312920676, "4": 0.5863147635143078}, "pretraining_utility": {"-2": 0.006956397496632729, "-1": 0.058069772832891796, "0": 0.35238888860896417, "1": 0.5038450689006, "2": 0.0787398721609113}, "math_density": {"0": 0.4840999044984616, "1": 0.39303256879970844, "2": 0.08269487150785211, "3": 0.012750897186990013, "4": 0.027421758006987897}, "code_density": {"0": 0.0005049714840544439, "1": 0.0011542064327419005, "2": 0.05595013022559934, "3": 0.31758908755927073, "4": 0.6248016042983336}, "reasoning_density": {"0": 0.835880718477895, "1": 0.05800905897935925, "2": 0.08275676912411316, "3": 0.019188574928470058, "4": 0.0041648784901624925}, "education_level": {"none": 0.001018968851133106, "0": 0.0017720276717462487, "1": 0.039168389519385366, "2": 0.8723869441602153, "3": 0.08164195303100137, "4": 0.0040117167665186014}, "creative_writing_quality": {"not_applicable": 0.9989372209002397, "-2": 0.0001408360377281307, "-1": 0.00033443469761124627, "0": 0.00047088112623028315, "1": 8.358093075338891e-05, "2": 3.304630743719289e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.780334386025469e-05, "Art & Design": 7.352072038619337e-05, "Law & Crime": 5.1905172795989484e-05, "Education & Careers": 0.00027887813206897553, "Entertainment & Media": 0.005243969490276339, "Fashion & Beauty": 3.829240830265406e-05, "Business, Economics & Finance": 0.00018647792699399938, "Food & Dining": 4.798134670317571e-05, "Games & Gaming": 8.944753503772123e-05, "Electronics & Hardware": 6.970121723785854e-05, "Health, Healthcare & Wellness": 2.9138913454334363e-05, "History & Geography": 4.2077646767033536e-05, "Home, Pets & Hobbies": 3.7204073970396463e-05, "Industry & Production": 3.5184173851758165e-05, "Language, Literature & Philosophy": 0.00015901965479572243, "Politics & Government": 0.00021380870129627017, "Religion & Spirituality": 4.146976168821757e-05, "Science, Mathematics & Engineering": 0.0002151498879648829, "Relationships & Community": 0.0003028412026107873, "Software Usage & Applications": 0.0012103070704086677, "Programming & Software Engineering": 0.9890045741127752, "Sports & Fitness": 9.105654773530629e-05, "Vehicles & Transportation": 2.5815363410647376e-05, "Travel & Tourism": 6.482203230516474e-05, "Other / Uncovered Subject": 0.0022986853770636823, "Unknown / Insufficient Content": 0.00011086818623882229}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00040790758929590444, "Art & Design": 0.0005622627265013452, "Law & Crime": 0.0005970088724549298, "Education & Careers": 0.0010168109816247584, "Entertainment & Media": 0.032159961873279914, "Fashion & Beauty": 0.00020269670522144614, "Business, Economics & Finance": 0.000999975265065081, "Food & Dining": 0.0001718378970493636, "Games & Gaming": 0.0002503319938181668, "Electronics & Hardware": 0.0002558087820487457, "Health, Healthcare & Wellness": 0.000190096865863955, "History & Geography": 0.00018007821557022676, "Home, Pets & Hobbies": 0.00015852989784795088, "Industry & Production": 0.00013048984615353528, "Language, Literature & Philosophy": 0.000684864509437915, "Politics & Government": 0.0008343735442078515, "Religion & Spirituality": 0.00021993781416635474, "Science, Mathematics & Engineering": 0.0007518932212896165, "Relationships & Community": 0.0004743266375649088, "Software Usage & Applications": 0.006523064864419106, "Programming & Software Engineering": 0.013478338157295499, "Sports & Fitness": 0.0004692809780471986, "Vehicles & Transportation": 8.470891666984606e-05, "Travel & Tourism": 0.00023104227246098906, "Other / Uncovered Subject": 0.003123896377375555, "NONE": 0.9358404751952698}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999996, "mass_delta": -4.440892098500626e-16, "missing_mass": 4.440892098500626e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9802, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9802, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:14.316787+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "7ca52e4e-53b0-41ed-81f0-d806a0459652", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "cc8557750acab0202c45b674b6043684127e555dbacd84185dd85ceeac43a2d0", "source_metadata": {"uuid": "7ca52e4e-53b0-41ed-81f0-d806a0459652", "repo_name": "nevasv/skillfactory_News_Portal", "relative_path": "NewsPaper/news/models.py", "category": "TOOL", "algo_rel_score": 0.9759844541549683, "quality_score": 6.115514755249023, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 36, "repo_created_at": "2023-01-22T12:18:47.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 2, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "6816d59f9dee242271591839273451599df4add4", "size_bytes": 6740, "file_timestamp": 1677695503, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "c45c10870e3a7b7052f143c9e7f6e2bb6531bd32"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 36, "repo_created_at": "2023-01-22T12:18:47.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 2, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "6816d59f9dee242271591839273451599df4add4", "size_bytes": 6740, "file_timestamp": 1677695503, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "c45c10870e3a7b7052f143c9e7f6e2bb6531bd32", "url": "https://github.com/nevasv/skillfactory_News_Portal/blob/c45c10870e3a7b7052f143c9e7f6e2bb6531bd32/NewsPaper/news/models.py"}, "gpt4o_tokens": 1239, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.509390619617342, "noise_severity": 0.04871090924957351, "missing_content_severity": 0.06766502471700442, "factual_density": 3.528708312593714, "pretraining_utility": 0.5893422453962653, "math_density": 0.7063620354043343, "code_density": 3.5650281467550875, "reasoning_density": 0.29774783597364574, "education_level": 2.047000834087073, "creative_writing_quality": -0.43888069265283647}, "score_applicability": {"education_level": 0.9989810311488669, "creative_writing_quality": 0.001062779099760242}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/ITISFoundation/osparc-simcore/blob/ae5f894175940f0a9969c0ae2b8a82066fc5c331/services/director-v2/src/simcore_service_director_v2/core/settings.py", "id": "python-0-be315e05b64804f833b5544cd27d0c0f", "text": "# pylint: disable=no-self-argument\n# pylint: disable=no-self-use\n\n\nimport datetime\nfrom functools import cached_property\nfrom typing import Annotated, cast\n\nfrom common_library.pydantic_validators import validate_numeric_string_as_timedelta\nfrom fastapi import FastAPI\nfrom models_library.basic_types import LogLevel, PortInt\nfrom models_library.clusters import (\n    BaseCluster,\n    ClusterAuthentication,\n    ClusterTypeInModel,\n    NoAuthentication,\n)\nfrom pydantic import (\n    AliasChoices,\n    AnyUrl,\n    Field,\n    NonNegativeInt,\n    PositiveInt,\n    field_validator,\n)\nfrom servicelib.logging_utils_filtering import LoggerName, MessageSubstring\nfrom settings_library.application import BaseApplicationSettings\nfrom settings_library.base import BaseCustomSettings\nfrom settings_library.catalog import CatalogSettings\nfrom settings_library.director_v0 import DirectorV0Settings\nfrom settings_library.docker_registry import RegistrySettings\nfrom settings_library.http_client_request import ClientRequestSettings\nfrom settings_library.node_ports import (\n    NODE_PORTS_400_REQUEST_TIMEOUT_ATTEMPTS_DEFAULT_VALUE,\n    StorageAuthSettings,\n)\nfrom settings_library.postgres import PostgresSettings\nfrom settings_library.rabbit import RabbitSettings\nfrom settings_library.redis import RedisSettings\nfrom settings_library.resource_usage_tracker import (\n    DEFAULT_RESOURCE_USAGE_HEARTBEAT_INTERVAL,\n    ResourceUsageTrackerSettings,\n)\nfrom settings_library.storage import StorageSettings\nfrom settings_library.tracing import TracingSettings\nfrom settings_library.utils_logging import MixinLoggingSettings\nfrom simcore_sdk.node_ports_v2 import FileLinkType\n\nfrom .dynamic_services_settings import DynamicServicesSettings\n\n\nclass ComputationalBackendSettings(BaseCustomSettings):\n    COMPUTATIONAL_BACKEND_ENABLED: bool = Field(\n        default=True,\n    )\n    COMPUTATIONAL_BACKEND_SCHEDULING_CONCURRENCY: PositiveInt = Field(\n        default=50,\n        description=\"defines how many pipelines the application can schedule concurrently\",\n    )\n    COMPUTATIONAL_BACKEND_DASK_CLIENT_ENABLED: bool = Field(\n        default=True,\n    )\n    COMPUTATIONAL_BACKEND_DEFAULT_CLUSTER_URL: AnyUrl = Field(\n        ...,\n        description=\"This is the cluster that will be used by default\"\n        \" when submitting computational services (typically \"\n        \"tcp://dask-scheduler:8786, tls://dask-scheduler:8786 for the internal cluster\",\n    )\n    COMPUTATIONAL_BACKEND_DEFAULT_CLUSTER_AUTH: ClusterAuthentication = Field(\n        default=...,\n        description=\"this is the cluster authentication that will be used by default\",\n    )\n    COMPUTATIONAL_BACKEND_DEFAULT_CLUSTER_FILE_LINK_TYPE: FileLinkType = Field(\n        FileLinkType.S3,\n        description=f\"Default file link type to use with the internal cluster '{list(FileLinkType)}'\",\n    )\n    COMPUTATIONAL_BACKEND_DEFAULT_FILE_LINK_TYPE: FileLinkType = Field(\n        FileLinkType.PRESIGNED,\n        description=f\"Default file link type to use with computational backend '{list(FileLinkType)}'\",\n    )\n    COMPUTATIONAL_BACKEND_ON_DEMAND_CLUSTERS_FILE_LINK_TYPE: FileLinkType = Field(\n        FileLinkType.PRESIGNED,\n        description=f\"Default file link type to use with computational backend on-demand clusters '{list(FileLinkType)}'\",\n    )\n\n    @cached_property\n    def default_cluster(self) -> BaseCluster:\n        return BaseCluster(\n            name=\"Default cluster\",\n            endpoint=self.COMPUTATIONAL_BACKEND_DEFAULT_CLUSTER_URL,\n            authentication=self.COMPUTATIONAL_BACKEND_DEFAULT_CLUSTER_AUTH,\n            owner=1,  # NOTE: currently this is a soft hack (the group of everyone is the group 1)\n            type=ClusterTypeInModel.ON_PREMISE,\n        )\n\n    @field_validator(\"COMPUTATIONAL_BACKEND_DEFAULT_CLUSTER_AUTH\", mode=\"before\")\n    @classmethod\n    def _empty_auth_is_none(cls, v):\n        if not v:\n            return NoAuthentication()\n        return v\n\n\nclass AppSettings(BaseApplicationSettings, MixinLoggingSettings):\n    LOG_LEVEL: Annotated[\n        LogLevel,\n        Field(\n            validation_alias=AliasChoices(\n                \"DIRECTOR_V2_LOGLEVEL\", \"LOG_LEVEL\", \"LOGLEVEL\"\n            ),\n        ),\n    ] = LogLevel.INFO\n\n    DIRECTOR_V2_LOG_FORMAT_LOCAL_DEV_ENABLED: bool = Field(\n        default=False,\n        validation_alias=AliasChoices(\n            \"DIRECTOR_V2_LOG_FORMAT_LOCAL_DEV_ENABLED\",\n            \"LOG_FORMAT_LOCAL_DEV_ENABLED\",\n        ),\n        description=\"Enables local development log format. WARNING: make sure it is disabled if you want to have structured logs!\",\n    )\n    DIRECTOR_V2_LOG_FILTER_MAPPING: dict[LoggerName, list[MessageSubstring]] = Field(\n        default_factory=dict,\n        validation_alias=AliasChoices(\n            \"DIRECTOR_V2_LOG_FILTER_MAPPING\", \"LOG_FILTER_MAPPING\"\n        ),\n        description=\"is a dictionary that maps specific loggers (such as 'uvicorn.access' or 'gunicorn.access') to a list of log message patterns that should be filtered out.\",\n    )\n    DIRECTOR_V2_DEV_FEATURES_ENABLED: bool = False\n\n    DIRECTOR_V2_DEV_FEATURE_R_CLONE_MOUNTS_ENABLED: bool = Field(\n        default=False,\n        description=(\n            \"Under development feature. If enabled state \"\n            \"is saved using rclone docker volumes.\"\n        ),\n    )\n\n    # for passing self-signed certificate to spawned services\n    DIRECTOR_V2_SELF_SIGNED_SSL_SECRET_ID: str = Field(\n        default=\"\",\n        description=\"ID of the docker secret containing the self-signed certificate\",\n    )\n    DIRECTOR_V2_SELF_SIGNED_SSL_SECRET_NAME: str = Field(\n        default=\"\",\n        description=\"Name of the docker secret containing the self-signed certificate\",\n    )\n    DIRECTOR_V2_SELF_SIGNED_SSL_FILENAME: str = Field(\n        default=\"\",\n        description=\"Filepath to self-signed osparc.crt file *as mounted inside the container*, empty strings disables it\",\n    )\n    DIRECTOR_V2_PROMETHEUS_INSTRUMENTATION_ENABLED: bool = True\n    DIRECTOR_V2_PROFILING: bool = False\n\n    DIRECTOR_V2_REMOTE_DEBUGGING_PORT: PortInt | None = Field(default=None)\n\n    # extras\n    SWARM_STACK_NAME: str = Field(default=\"undefined-please-check\")\n    SERVICE_TRACKING_HEARTBEAT: datetime.timedelta = Field(\n        default=DEFAULT_RESOURCE_USAGE_HEARTBEAT_INTERVAL,\n        description=\"Service scheduler heartbeat (everytime a heartbeat is sent into RabbitMQ)\"\n        \" (default to seconds, or see https://pydantic-docs.helpmanual.io/usage/types/#datetime-types for string formating)\",\n    )\n\n    SIMCORE_SERVICES_NETWORK_NAME: str | None = Field(\n        default=None,\n        description=\"used to find the right network name\",\n    )\n    SIMCORE_SERVICES_PREFIX: str | None = Field(\n        \"simcore/services\",\n        description=\"useful when developing with an alternative registry namespace\",\n    )\n\n    DIRECTOR_V2_NODE_PORTS_400_REQUEST_TIMEOUT_ATTEMPTS: NonNegativeInt = Field(\n        default=NODE_PORTS_400_REQUEST_TIMEOUT_ATTEMPTS_DEFAULT_VALUE,\n        description=\"forwarded to sidecars which use nodeports\",\n    )\n\n    # debug settings\n    CLIENT_REQUEST: ClientRequestSettings = Field(\n        json_schema_extra={\"auto_default_from_env\": True}\n    )\n\n    # App modules settings ---------------------\n    DIRECTOR_V2_STORAGE: Annotated[\n        StorageSettings, Field(json_schema_extra={\"auto_default_from_env\": True})\n    ]\n    DIRECTOR_V2_NODE_PORTS_STORAGE_AUTH: StorageAuthSettings | None = Field(\n        json_schema_extra={\"auto_default_from_env\": True}\n    )\n\n    DIRECTOR_V2_CATALOG: Annotated[\n        CatalogSettings | None, Field(json_schema_extra={\"auto_default_from_env\": True})\n    ]\n\n    DIRECTOR_V0: DirectorV0Settings = Field(\n        json_schema_extra={\"auto_default_from_env\": True}\n    )\n\n    DYNAMIC_SERVICES: Annotated[\n        DynamicServicesSettings,\n        Field(json_schema_extra={\"auto_default_from_env\": True}),\n    ]\n\n    POSTGRES: Annotated[\n        PostgresSettings, Field(json_schema_extra={\"auto_default_from_env\": True})\n    ]\n\n    REDIS: RedisSettings = Field(json_schema_extra={\"auto_default_from_env\": True})\n\n    DIRECTOR_V2_RABBITMQ: RabbitSettings = Field(\n        json_schema_extra={\"auto_default_from_env\": True}\n    )\n\n    TRAEFIK_SIMCORE_ZONE: str = Field(\"internal_simcore_stack\")\n\n    DIRECTOR_V2_COMPUTATIONAL_BACKEND: ComputationalBackendSettings = Field(\n        json_schema_extra={\"auto_default_from_env\": True}\n    )\n\n    DIRECTOR_V2_DOCKER_REGISTRY: RegistrySettings = Field(\n        json_schema_extra={\"auto_default_from_env\": True},\n        description=\"settings for the private registry deployed with the platform\",\n    )\n    DIRECTOR_V2_DOCKER_HUB_REGISTRY: RegistrySettings | None = Field(\n        default=None, description=\"public DockerHub registry settings\"\n    )\n\n    DIRECTOR_V2_RESOURCE_USAGE_TRACKER: ResourceUsageTrackerSettings = Field(\n        json_schema_extra={\"auto_default_from_env\": True},\n        description=\"resource usage tracker service client's plugin\",\n    )\n\n    DIRECTOR_V2_TRACING: TracingSettings | None = Field(\n        json_schema_extra={\"auto_default_from_env\": True},\n        description=\"settings for opentelemetry tracing\",\n    )\n\n    @field_validator(\"LOG_LEVEL\", mode=\"before\")\n    @classmethod\n    def _validate_loglevel(cls, value: str) -> str:\n        log_level: str = cls.validate_log_level(value)\n        return log_level\n\n    _validate_service_tracking_heartbeat = validate_numeric_string_as_timedelta(\n        \"SERVICE_TRACKING_HEARTBEAT\"\n    )\n\n\ndef get_application_settings(app: FastAPI) -> AppSettings:\n    return cast(AppSettings, app.state.settings)\n", "text_preview": "# pylint: disable=no-self-argument # pylint: disable=no-self-use import datetime from functools import cached_property from typing import Annotated, cast from common_library.pydantic_validators import validate_numeric_string_as_timedelta fr", "input_sha256": "190d2a1814a8cb59ed5398cfc37b38fb4d93b06040ac0bad89997c2d29c175ff", "text_sha256": "be315e05b64804f833b5544cd27d0c0fd01432f32782f3cb88f9e394d500ab2e", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12125, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.175794262815128, "noise_severity": 0.04449887260624566, "missing_content_severity": 0.030304957360376653, "factual_density": 3.563063233748169, "pretraining_utility": 0.37690615960587015, "math_density": 0.012099254519878418, "code_density": 3.927026714232063, "reasoning_density": 0.1427412454593435, "education_level": 2.983336304868736, "creative_writing_quality": -0.1553035435164431}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.024094659813761576, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.997465427236677, "secondary": 0.9481444448355169, "tertiary": 0.9732866985542246, "structured_records": 0.396558266095636, "procedural_steps": 0.00021931675336421994, "worked_solution": 0.000464572716236177, "contextual_dialogue": 4.056422792758598e-06, "content_quality": 0.5892906911393381, "noise_severity": 0.9629176061614619, "missing_content_severity": 0.9747458688663528, "factual_density": 0.6358860281234741, "pretraining_utility": 0.4582395708815107, "math_density": 0.9899172879001014, "code_density": 0.939188928526719, "reasoning_density": 0.8810489621172137, "education_level": 0.5911973697837366, "creative_writing_quality": 0.9996186718236807, "primary_topic": 0.9916690889279316, "secondary_topic": 0.9289516753163639}, "probability_margin": {"primary": 0.9957336983336944, "secondary": 0.9176268414621788, "tertiary": 0.9582996481508577, "education_level": 0.48740715024163284, "creative_writing_quality": 0.9995337019234249, "primary_topic": 0.9881292185901709, "secondary_topic": 0.9082480670100704}, "raw_probabilities": {"primary": {"Academic Writing": 1.4698568335148263e-05, "Content Listing": 8.03726648972771e-06, "Creative Writing": 4.3931625550392485e-06, "Customer Support Page": 4.539033678955382e-06, "Q&A Forum": 3.9437500449197235e-06, "Discussion Thread": 2.8116507014130886e-06, "Comment Section": 4.182957727872079e-06, "Missing Content": 7.2720941900345535e-06, "Knowledge Article": 2.982811195664642e-05, "Legal Notices": 9.815651657715841e-06, "News Article": 1.5288968709257392e-06, "Organizational Announcement": 4.077602647246222e-06, "Nonfiction Writing": 3.0188019976145234e-06, "Organizational About Page": 4.584108460420361e-06, "Personal About Page": 2.8037877881156068e-06, "Personal Blog": 2.736338304189864e-06, "Product Page": 3.3104959517298452e-06, "Spam / Ads": 2.8177830353042834e-06, "Structured Data": 0.0018373361014543477, "Technical Writing": 0.9975710344351487, "Transcript / Interview": 5.238482424729673e-06, "Tutorial / How-To Guide": 2.554095511143871e-05, "User Reviews": 1.9969110479488614e-06, "Other / Unknown": 0.0004444530524201824}, "secondary": {"Academic Writing": 0.00021733558230604207, "Content Listing": 0.00016982298663654728, "Creative Writing": 0.0001456811186964164, "Customer Support Page": 0.00012858434883793166, "Q&A Forum": 7.21979999496535e-05, "Discussion Thread": 6.139215069588486e-05, "Comment Section": 0.00011409762281040981, "Missing Content": 0.00023777854968201915, "Knowledge Article": 0.0006901044047818975, "Legal Notices": 0.0003459809258784486, "News Article": 9.278849224496076e-05, "Organizational Announcement": 9.246020128237967e-05, "Nonfiction Writing": 8.06732207355594e-05, "Organizational About Page": 0.00010833196781314438, "Personal About Page": 7.329286262482297e-05, "Personal Blog": 8.953575480666643e-05, "Product Page": 6.337429999296429e-05, "Spam / Ads": 9.519803483505896e-05, "Structured Data": 0.032678251505191595, "Technical Writing": 0.01372243488698514, "Transcript / Interview": 0.00011105582627613651, "Tutorial / How-To Guide": 0.0002642650381216828, "User Reviews": 4.0269251444379014e-05, "NONE": 0.9503050929673703}, "tertiary": {"Academic Writing": 0.0001306537118821725, "Content Listing": 0.00010356733568883446, "Creative Writing": 8.559854816976498e-05, "Customer Support Page": 7.967473152042428e-05, "Q&A Forum": 3.624135303793141e-05, "Discussion Thread": 3.4740323350363096e-05, "Comment Section": 0.00010230696584675715, "Missing Content": 0.00026124376086180605, "Knowledge Article": 0.000767120826533625, "Legal Notices": 0.0005037050996572801, "News Article": 5.1424165823659e-05, "Organizational Announcement": 6.061377292069432e-05, "Nonfiction Writing": 5.764296876336276e-05, "Organizational About Page": 7.323580183494478e-05, "Personal About Page": 3.572938379276227e-05, "Personal Blog": 4.528480323948636e-05, "Product Page": 4.250463802579324e-05, "Spam / Ads": 6.456128009526571e-05, "Structured Data": 0.016100104630274222, "Technical Writing": 0.006561078696594755, "Transcript / Interview": 5.465990791613937e-05, "Tutorial / How-To Guide": 0.00032549291695674706, "User Reviews": 2.30615960813538e-05, "NONE": 0.9743997527811319}, "structured_records": {"false": 0.6034417339043641, "true": 0.396558266095636}, "procedural_steps": {"false": 0.9997806832466358, "true": 0.00021931675336421994}, "worked_solution": {"false": 0.9995354272837639, "true": 0.000464572716236177}, "contextual_dialogue": {"false": 0.9999959435772072, "true": 4.056422792758598e-06}, "content_quality": {"-2": 0.0026075183723993493, "-1": 0.006776361882210903, "0": 0.13715317502721333, "1": 0.5191402279942153, "2": 0.3343227167239612}, "noise_severity": {"0": 0.9593917353748679, "1": 0.03804214441165811, "2": 0.001641591748293239, "3": 0.0005245691627222907, "4": 0.00039995930245854895}, "missing_content_severity": {"0": 0.975158904708595, "1": 0.020978471352353978, "2": 0.0025735442107980284, "3": 0.0009769213265844086, "4": 0.00031215840166834636}, "factual_density": {"0": 0.0016192015331677437, "1": 0.003388040024328755, "2": 0.04518188151761824, "3": 0.32993207701093735, "4": 0.6198787999139478}, "pretraining_utility": {"-2": 0.008967813205118302, "-1": 0.1186675512579219, "0": 0.410544913936501, "1": 0.41013010592688887, "2": 0.0516896156735699}, "math_density": {"0": 0.9914224600492434, "1": 0.006892807511145527, "2": 0.0006011741037787638, "3": 0.00033013454215415723, "4": 0.0007534237936782231}, "code_density": {"0": 0.00010979142312157867, "1": 0.0002034361513408111, "2": 0.00455463360006349, "3": 0.06281454442130152, "4": 0.9323175944041725}, "reasoning_density": {"0": 0.880998240879021, "1": 0.09769172447588974, "2": 0.019453134621621765, "3": 0.001284348353659807, "4": 0.000572551669807704}, "education_level": {"none": 0.001103085061410922, "0": 0.0011005831294060543, "1": 0.002653590700995331, "2": 0.17192399124481428, "3": 0.6593311414864471, "4": 0.16388760837692623}, "creative_writing_quality": {"not_applicable": 0.9996822265197339, "-2": 3.619414128550566e-05, "-1": 6.337979760459231e-05, "0": 0.00014852459630902687, "1": 5.293315747945207e-05, "2": 1.6741787587628667e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.294442780805935e-05, "Art & Design": 2.4192788171127276e-05, "Law & Crime": 1.8897832053008848e-05, "Education & Careers": 1.979903592737517e-05, "Entertainment & Media": 1.3501093444818159e-05, "Fashion & Beauty": 9.985880339570115e-06, "Business, Economics & Finance": 3.1366174971212186e-05, "Food & Dining": 1.4317522176646085e-05, "Games & Gaming": 2.399206811378331e-05, "Electronics & Hardware": 8.917914293951746e-05, "Health, Healthcare & Wellness": 4.0321871609480746e-05, "History & Geography": 1.845478353370591e-05, "Home, Pets & Hobbies": 1.2212364332012805e-05, "Industry & Production": 4.025966272897216e-05, "Language, Literature & Philosophy": 1.6855714484169278e-05, "Politics & Government": 1.1037803384120447e-05, "Religion & Spirituality": 1.3316538693597441e-05, "Science, Mathematics & Engineering": 0.0038602899943787467, "Relationships & Community": 7.623608947364186e-06, "Software Usage & Applications": 0.0035410400307103324, "Programming & Software Engineering": 0.9919895085845496, "Sports & Fitness": 2.0742801182117532e-05, "Vehicles & Transportation": 9.541942900749875e-06, "Travel & Tourism": 2.2034780852985766e-05, "Other / Uncovered Subject": 0.00010135525407682075, "Unknown / Insufficient Content": 2.7228297690208795e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.000374931292918043, "Art & Design": 0.0002567755287893796, "Law & Crime": 0.0003626110639296852, "Education & Careers": 0.00023050225532900804, "Entertainment & Media": 0.00023361858614897774, "Fashion & Beauty": 0.0001438061254862219, "Business, Economics & Finance": 0.00038928838531881196, "Food & Dining": 0.00013373305013671173, "Games & Gaming": 0.00020151745738610988, "Electronics & Hardware": 0.000973360221046643, "Health, Healthcare & Wellness": 0.00034973878736467383, "History & Geography": 0.0001687729939247512, "Home, Pets & Hobbies": 0.0001230941386099791, "Industry & Production": 0.00021843139530579658, "Language, Literature & Philosophy": 0.00011560470618310913, "Politics & Government": 0.00016160001717949245, "Religion & Spirituality": 0.00017762564183616524, "Science, Mathematics & Engineering": 0.020210505701253045, "Relationships & Community": 9.119131997820976e-05, "Software Usage & Applications": 0.02343623617874101, "Programming & Software Engineering": 0.019023675619057875, "Sports & Fitness": 0.0001574121761688932, "Vehicles & Transportation": 7.631522673289729e-05, "Travel & Tourism": 0.00020516110636317517, "Other / Uncovered Subject": 0.0005001878359999221, "NONE": 0.9316843031888115}}, "probabilities": {"primary": {"Academic Writing": 1.4698568335148256e-05, "Content Listing": 8.037266489727706e-06, "Creative Writing": 4.393162555039247e-06, "Customer Support Page": 4.53903367895538e-06, "Q&A Forum": 3.943750044919722e-06, "Discussion Thread": 2.8116507014130873e-06, "Comment Section": 4.182957727872077e-06, "Missing Content": 7.27209419003455e-06, "Knowledge Article": 2.9828111956646407e-05, "Legal Notices": 9.815651657715836e-06, "News Article": 1.5288968709257386e-06, "Organizational Announcement": 4.07760264724622e-06, "Nonfiction Writing": 3.018801997614522e-06, "Organizational About Page": 4.584108460420359e-06, "Personal About Page": 2.8037877881156055e-06, "Personal Blog": 2.736338304189863e-06, "Product Page": 3.310495951729844e-06, "Spam / Ads": 2.817783035304282e-06, "Structured Data": 0.0018373361014543468, "Technical Writing": 0.9975710344351483, "Transcript / Interview": 5.23848242472967e-06, "Tutorial / How-To Guide": 2.55409551114387e-05, "User Reviews": 1.9969110479488605e-06, "Other / Unknown": 0.00044445305242018216}, "secondary": {"Academic Writing": 0.00021733558230604207, "Content Listing": 0.00016982298663654728, "Creative Writing": 0.0001456811186964164, "Customer Support Page": 0.00012858434883793166, "Q&A Forum": 7.21979999496535e-05, "Discussion Thread": 6.139215069588486e-05, "Comment Section": 0.00011409762281040981, "Missing Content": 0.00023777854968201915, "Knowledge Article": 0.0006901044047818975, "Legal Notices": 0.0003459809258784486, "News Article": 9.278849224496076e-05, "Organizational Announcement": 9.246020128237967e-05, "Nonfiction Writing": 8.06732207355594e-05, "Organizational About Page": 0.00010833196781314438, "Personal About Page": 7.329286262482297e-05, "Personal Blog": 8.953575480666643e-05, "Product Page": 6.337429999296429e-05, "Spam / Ads": 9.519803483505896e-05, "Structured Data": 0.032678251505191595, "Technical Writing": 0.01372243488698514, "Transcript / Interview": 0.00011105582627613651, "Tutorial / How-To Guide": 0.0002642650381216828, "User Reviews": 4.0269251444379014e-05, "NONE": 0.9503050929673703}, "tertiary": {"Academic Writing": 0.0001306537118821725, "Content Listing": 0.00010356733568883446, "Creative Writing": 8.559854816976498e-05, "Customer Support Page": 7.967473152042428e-05, "Q&A Forum": 3.624135303793141e-05, "Discussion Thread": 3.4740323350363096e-05, "Comment Section": 0.00010230696584675715, "Missing Content": 0.00026124376086180605, "Knowledge Article": 0.000767120826533625, "Legal Notices": 0.0005037050996572801, "News Article": 5.1424165823659e-05, "Organizational Announcement": 6.061377292069432e-05, "Nonfiction Writing": 5.764296876336276e-05, "Organizational About Page": 7.323580183494478e-05, "Personal About Page": 3.572938379276227e-05, "Personal Blog": 4.528480323948636e-05, "Product Page": 4.250463802579324e-05, "Spam / Ads": 6.456128009526571e-05, "Structured Data": 0.016100104630274222, "Technical Writing": 0.006561078696594755, "Transcript / Interview": 5.465990791613937e-05, "Tutorial / How-To Guide": 0.00032549291695674706, "User Reviews": 2.30615960813538e-05, "NONE": 0.9743997527811319}, "structured_records": {"false": 0.6034417339043641, "true": 0.396558266095636}, "procedural_steps": {"false": 0.9997806832466358, "true": 0.00021931675336421994}, "worked_solution": {"false": 0.9995354272837639, "true": 0.000464572716236177}, "contextual_dialogue": {"false": 0.9999959435772072, "true": 4.056422792758598e-06}, "content_quality": {"-2": 0.0026075183723993493, "-1": 0.006776361882210903, "0": 0.13715317502721333, "1": 0.5191402279942153, "2": 0.3343227167239612}, "noise_severity": {"0": 0.9593917353748679, "1": 0.03804214441165811, "2": 0.001641591748293239, "3": 0.0005245691627222907, "4": 0.00039995930245854895}, "missing_content_severity": {"0": 0.9751589047085952, "1": 0.02097847135235398, "2": 0.002573544210798029, "3": 0.0009769213265844088, "4": 0.0003121584016683464}, "factual_density": {"0": 0.001619201533167744, "1": 0.0033880400243287553, "2": 0.04518188151761825, "3": 0.3299320770109374, "4": 0.6198787999139479}, "pretraining_utility": {"-2": 0.008967813205118302, "-1": 0.1186675512579219, "0": 0.410544913936501, "1": 0.41013010592688887, "2": 0.0516896156735699}, "math_density": {"0": 0.9914224600492434, "1": 0.006892807511145527, "2": 0.0006011741037787638, "3": 0.00033013454215415723, "4": 0.0007534237936782231}, "code_density": {"0": 0.00010979142312157869, "1": 0.00020343615134081112, "2": 0.004554633600063491, "3": 0.06281454442130154, "4": 0.9323175944041726}, "reasoning_density": {"0": 0.880998240879021, "1": 0.09769172447588974, "2": 0.019453134621621765, "3": 0.001284348353659807, "4": 0.000572551669807704}, "education_level": {"none": 0.0011030850614109223, "0": 0.0011005831294060545, "1": 0.0026535907009953316, "2": 0.1719239912448143, "3": 0.6593311414864472, "4": 0.16388760837692626}, "creative_writing_quality": {"not_applicable": 0.9996822265197337, "-2": 3.6194141285505653e-05, "-1": 6.33797976045923e-05, "0": 0.00014852459630902685, "1": 5.2933157479452055e-05, "2": 1.6741787587628664e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.294442780805935e-05, "Art & Design": 2.4192788171127276e-05, "Law & Crime": 1.8897832053008848e-05, "Education & Careers": 1.979903592737517e-05, "Entertainment & Media": 1.3501093444818159e-05, "Fashion & Beauty": 9.985880339570115e-06, "Business, Economics & Finance": 3.1366174971212186e-05, "Food & Dining": 1.4317522176646085e-05, "Games & Gaming": 2.399206811378331e-05, "Electronics & Hardware": 8.917914293951746e-05, "Health, Healthcare & Wellness": 4.0321871609480746e-05, "History & Geography": 1.845478353370591e-05, "Home, Pets & Hobbies": 1.2212364332012805e-05, "Industry & Production": 4.025966272897216e-05, "Language, Literature & Philosophy": 1.6855714484169278e-05, "Politics & Government": 1.1037803384120447e-05, "Religion & Spirituality": 1.3316538693597441e-05, "Science, Mathematics & Engineering": 0.0038602899943787467, "Relationships & Community": 7.623608947364186e-06, "Software Usage & Applications": 0.0035410400307103324, "Programming & Software Engineering": 0.9919895085845496, "Sports & Fitness": 2.0742801182117532e-05, "Vehicles & Transportation": 9.541942900749875e-06, "Travel & Tourism": 2.2034780852985766e-05, "Other / Uncovered Subject": 0.00010135525407682075, "Unknown / Insufficient Content": 2.7228297690208795e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.000374931292918043, "Art & Design": 0.0002567755287893796, "Law & Crime": 0.0003626110639296852, "Education & Careers": 0.00023050225532900804, "Entertainment & Media": 0.00023361858614897774, "Fashion & Beauty": 0.0001438061254862219, "Business, Economics & Finance": 0.00038928838531881196, "Food & Dining": 0.00013373305013671173, "Games & Gaming": 0.00020151745738610988, "Electronics & Hardware": 0.000973360221046643, "Health, Healthcare & Wellness": 0.00034973878736467383, "History & Geography": 0.0001687729939247512, "Home, Pets & Hobbies": 0.0001230941386099791, "Industry & Production": 0.00021843139530579658, "Language, Literature & Philosophy": 0.00011560470618310913, "Politics & Government": 0.00016160001717949245, "Religion & Spirituality": 0.00017762564183616524, "Science, Mathematics & Engineering": 0.020210505701253045, "Relationships & Community": 9.119131997820976e-05, "Software Usage & Applications": 0.02343623617874101, "Programming & Software Engineering": 0.019023675619057875, "Sports & Fitness": 0.0001574121761688932, "Vehicles & Transportation": 7.631522673289729e-05, "Travel & Tourism": 0.00020516110636317517, "Other / Uncovered Subject": 0.0005001878359999221, "NONE": 0.9316843031888115}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000004, "mass_delta": 4.440892098500626e-16, "missing_mass": 0.0, "excess_mass": 4.440892098500626e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10727, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10727, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:16.836764+00:00", "scoring_key_slot": "primary", "dataset_uid": "0836b4c8-e298-4624-bd24-54cf8e5b070f", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "8a74bcb8958facc2646eb60c3c563139b2cead746dbe06e2c6f506a7f23a2b04", "source_metadata": {"uuid": "0836b4c8-e298-4624-bd24-54cf8e5b070f", "repo_name": "ITISFoundation/osparc-simcore", "relative_path": "services/director-v2/src/simcore_service_director_v2/core/settings.py", "category": "CONFIG", "algo_rel_score": 0.9936599135398865, "quality_score": 7.852862358093262, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 4943, "repo_created_at": "2018-01-23T10:48:05.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 49, "forks": 36, "issues": 485, "pull_requests": 21}, "file_meta": {"content_id": "0cdf55371388046589c370a393dd01a168477993", "size_bytes": 9605, "file_timestamp": 1752817672, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "ae5f894175940f0a9969c0ae2b8a82066fc5c331"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 4943, "repo_created_at": "2018-01-23T10:48:05.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 49, "forks": 36, "issues": 485, "pull_requests": 21}, "file_meta": {"content_id": "0cdf55371388046589c370a393dd01a168477993", "size_bytes": 9605, "file_timestamp": 1752817672, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "ae5f894175940f0a9969c0ae2b8a82066fc5c331", "url": "https://github.com/ITISFoundation/osparc-simcore/blob/ae5f894175940f0a9969c0ae2b8a82066fc5c331/services/director-v2/src/simcore_service_director_v2/core/settings.py"}, "gpt4o_tokens": 2055, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 1.175794262815128, "noise_severity": 0.04449887260624566, "missing_content_severity": 0.030304957360376653, "factual_density": 3.563063233748169, "pretraining_utility": 0.37690615960587015, "math_density": 0.012099254519878418, "code_density": 3.927026714232063, "reasoning_density": 0.1427412454593435, "education_level": 2.983336304868736, "creative_writing_quality": -0.1553035435164431}, "score_applicability": {"education_level": 0.9988969149385891, "creative_writing_quality": 0.00031777348026620553}}
{"source_group": "python", "source": "openbmb/UltraData-Stack-v3-L2/python", "domain": "github.com", "language": "python", "url": "https://github.com/rhombicosi/RNA_NNT_ILP/blob/cadcd2fd2888a2aed480654d87dea06565985f00/ILP/ilp_grb.py", "id": "python-0-75f272c22c287a105412183a354ff5c5", "text": "from binary_variables_grb import *\nfrom constraints_grb import *\nfrom objective_grb import *\nfrom pathlib import Path\nimport time\nfrom prepro_run import *\n\ndef optimize(seq_files, seq_number, lp_dir, sol_dir, start = solstart_dir):\n    \n    chain_file = seq_files[seq_number]\n    chain_name_with_ext = os.path.basename(chain_file)        \n    chain_name_without_ext = os.path.splitext(chain_name_with_ext)[0]\n    lp_file_name = chain_name_without_ext\n    seq_data = parse_seq_file(chain_file)\n    this_RNA = seq_data['sequence']\n\n    print(chain_name_without_ext)\n    print(this_RNA)\n\n    mip = gp.Model(f'MIP-{seq_number}')\n\n    # set the number of loops\n    numH = 1#len(this_RNA)//5\n    numI = 2#len(this_RNA)//4\n    numB = 0#len(this_RNA)//3\n    numM = 0#len(this_RNA)//3\n    \n    # start = 0\n\n    try:\n        \n        listP, listQ, listH, listI, listB, listM, listX, listY, listZ, listW = add_binary_vars(this_RNA, mip, 0)\n        mip.update()\n\n        mip.setObjective(objectiveTerm(this_RNA, listQ, listH, listI, listB, listM), GRB.MINIMIZE)\n        \n        # mip.addConstr(objectiveTerm(this_RNA, listQ, listH, listI, listB) >= MFE,\"CMFE\")\n        # mip.addConstr(mip.getVarByName(f'B(14,15,28,30)') == 1)\n        # mip.addConstr(mip.getVarByName(f'H(19,24)') == 1)\n        # mip.addConstr(mip.getVarByName(f'I(6,12,37,41)') == 1)\n        onePairConstraints(this_RNA, mip)\n        noCrossConstraints(this_RNA, mip)\n        stemConstraints(this_RNA, mip)\n        # firstPairConstraints(this_RNA, mip)\n        # lastPairConstraints(this_RNA, mip)\n        loopNTConstraints(this_RNA, mip)\n        # hairpinZeroConstraints(this_RNA, mip, maxH)\n        hairpinIfThenConstraints(this_RNA, mip)\n        # hairpinOnlyIfConstraints(this_RNA, mip)\n        # numHairpinConstraints(this_RNA, numH, mip)\n        # internalNTConstraints(this_RNA, mip)\n        # internalZeroConstraints(this_RNA, mip, maxI)\n        internalIfThenConstraints(this_RNA, mip)\n        internalOnlyIfConstraints(this_RNA, mip)\n        # numInternalConstraints(this_RNA, numI, mip)\n        # bulgeNTConstraints(this_RNA, mip)\n        # bulgeZeroConstraints(this_RNA, mip, maxB)\n        bulgeIfThenConstraints(this_RNA, mip)\n        # bulgeOnlyIfConstraints(this_RNA, mip)\n        # numBulgeConstraints(this_RNA, numB, mip)\n        # multiNTConstraints(this_RNA, mip)\n        # multiZeroConstraints(this_RNA,mip,maxM)\n        multiIfThenConstraints(this_RNA, mip)\n        # multiOnlyIfConstraints(this_RNA, mip)\n        # numMultiConstraints(this_RNA, numM, mip)\n        # numGreaterMultiConstraints(this_RNA, numM, mip)\n        consecutiveUnpairedConstraints(this_RNA, L, mip)\n        isolatedPairsConstraints(this_RNA, mip)\n\n        mip.update()   \n\n        start_time = time.time()\n        mip.write(f'{lp_dir}/{lp_file_name}-loopdeco.lp')\n        print(f\"--- {time.time() - start_time} seconds ---\")\n\n        # uncomment optimization tuning params if needed\n        # mip.setParam(\"MIPGap\", 0.2)\n        # mip.setParam(\"PoolSolutions\", 20)\n        # mip.setParam(\"PoolSearchMode\", 2)\n        # mip.setParam(\"SolFiles\", f\"{chain_f}-decomposition-grb\")\n        mip.setParam(\"LogFile\", f'{grb_log_dir}/log-{lp_file_name}')\n        # mip.setParam(\"MIPFocus\", 2)\n        # mip.setParam(\"ConcurrentMIP \", 5)\n        # mip.setParam(\"Presolve\", 2)\n        # mip.NumStart = 1\n\n        # mip.params.StartNumber = 1\n\n        if start:\n\n            solvars = read_sol(f'{solstart_dir}/{lp_file_name}-start.sol')\n            \n            for v in mip.getVars(): \n                if v.varName in solvars.keys():         \n                    v.Start = round(int(solvars[v.varName]), 1)\n                    # print(v.varName)           \n                    # print(v.Start)\n                # elif v.varName == \"I(8,12,25,27)\":\n                    # print(v.varName)           \n                    # print(v.Start)\n                #     v.Start = 1\n                # elif v.varName == \"B(13,14,21,24)\":\n                    # print(v.varName)           \n                    # print(v.Start)\n                #     v.Start = 1\n                # else:\n                #     v.Start = 0 \n            mip.update()\n\n            # for v in mip.getVars(): \n\n            #     if v.Start > 0:\n            #         print(f'{v.varName}::{v.Start}')\n        \n        opt_start_time = time.time()        \n        mip.optimize()\n        opt_time = time.time() - opt_start_time\n\n        # mip.write(f'{sol_dir}/{lp_file_name}-start.sol')\n        mip.write(f'{sol_dir}/{lp_file_name}-loopdeco.sol')\n\n        print(f'Obj: {mip.ObjVal:g}')\n        \n        obj_val = mip.ObjVal\n\n    except gp.GurobiError as e:\n        print(f'Error code {e.errno}: {e}')\n        obj_val = None\n\n    except AttributeError:\n        print('Encountered an attribute error')\n        obj_val = None\n\n    if obj_val is not None:\n        return obj_val, lp_file_name, opt_time\n    else:\n        print(\"Object value was not assigned due to an error.\")\n\n\ndef optimize_start(seq_files, seq_number, lpstart_dir, solstart_dir):\n    \n    chain_file = seq_files[seq_number]\n    chain_name_with_ext = os.path.basename(chain_file)        \n    chain_name_without_ext = os.path.splitext(chain_name_with_ext)[0]\n    lp_file_name = chain_name_without_ext\n    seq_data = parse_seq_file(chain_file)\n    this_RNA = seq_data['sequence']\n\n    print(chain_name_without_ext)\n    print(this_RNA)\n\n    mip = gp.Model(f'MIP-{seq_number}')\n\n    # set the number of loops\n    numH = 1#len(this_RNA)//5\n    numI = 2#len(this_RNA)//4\n    numB = 0#len(this_RNA)//3\n    numM = 0#len(this_RNA)//3\n\n    start = 1\n\n    try:\n        \n        listP, listQ, listX, listH, listI = add_binary_vars(this_RNA, mip, start)\n        mip.update()\n\n        mip.setObjective(objectiveStartTerm(this_RNA, listQ, listH, listI), GRB.MINIMIZE)\n        \n        # mip.addConstr(objectiveTerm(this_RNA, listQ, listH, listI, listB) >= MFE,\"CMFE\")\n        # mip.addConstr(mip.getVarByName(f'B(14,15,28,30)') == 1)\n        # mip.addConstr(mip.getVarByName(f'H(19,24)') == 1)\n        # mip.addConstr(mip.getVarByName(f'I(6,12,37,41)') == 1)\n        onePairConstraints(this_RNA, mip)\n        noCrossConstraints(this_RNA, mip)\n        stemConstraints(this_RNA, mip)\n        # firstPairConstraints(this_RNA, mip)\n        # lastPairConstraints(this_RNA, mip)\n        loopNTConstraints(this_RNA, mip)\n        # hairpinZeroConstraints(this_RNA, mip, maxH)\n        hairpinIfThenConstraints(this_RNA, mip)\n        # hairpinOnlyIfConstraints(this_RNA, mip)\n        # numHairpinConstraints(this_RNA, numH, mip)\n        # internalNTConstraints(this_RNA, mip)\n        # internalZeroConstraints(this_RNA, mip, maxI)\n        internalIfThenConstraints(this_RNA, mip)\n        internalOnlyIfConstraints(this_RNA, mip)\n        # numInternalConstraints(this_RNA, numI, mip)\n        # bulgeNTConstraints(this_RNA, mip)\n        # bulgeZeroConstraints(this_RNA, mip, maxB)\n        # bulgeIfThenConstraints(this_RNA, mip)\n        # bulgeOnlyIfConstraints(this_RNA, mip)\n        # numBulgeConstraints(this_RNA, numB, mip)\n        # multiNTConstraints(this_RNA, mip)\n        # multiZeroConstraints(this_RNA,mip,maxM)\n        # multiIfThenConstraints(this_RNA, mip)\n        # multiOnlyIfConstraints(this_RNA, mip)\n        # numMultiConstraints(this_RNA, numM, mip)\n        # numGreaterMultiConstraints(this_RNA, numM, mip)\n        consecutiveUnpairedConstraints(this_RNA, L, mip)\n        isolatedPairsConstraints(this_RNA, mip)\n\n        mip.update()   \n\n        start_time = time.time()\n        mip.write(f'{lpstart_dir}/{lp_file_name}-start.lp')\n        print(f\"--- {time.time() - start_time} seconds ---\")\n\n        # uncomment optimization tuning params if needed\n        # mip.setParam(\"MIPGap\", 0.2)\n        # mip.setParam(\"PoolSolutions\", 20)\n        # mip.setParam(\"PoolSearchMode\", 2)\n        # mip.setParam(\"SolFiles\", f\"{chain_f}-decomposition-grb\")\n        mip.setParam(\"LogFile\", f'{grb_log_dir}/log-{lp_file_name}')\n        # mip.setParam(\"MIPFocus\", 2)\n        # mip.setParam(\"ConcurrentMIP \", 5)\n        # mip.setParam(\"Presolve\", 2)\n        \n        opt_start_time = time.time()        \n        mip.optimize()\n        opt_time = time.time() - opt_start_time\n\n        mip.write(f'{solstart_dir}/{lp_file_name}-start.sol')\n\n        print(f'Obj: {mip.ObjVal:g}')\n        \n        obj_val = mip.ObjVal\n\n    except gp.GurobiError as e:\n        print(f'Error code {e.errno}: {e}')\n        obj_val = None\n\n    except AttributeError:\n        print('Encountered an attribute error')\n        obj_val = None\n\n    if obj_val is not None:\n        return obj_val, lp_file_name, opt_time\n    else:\n        print(\"Object value was not assigned due to an error.\")\n\n\n#### TEST #######\n# seq_number = 1\n# chain_file = seq_files[seq_number]\n# chain_name_with_ext = os.path.basename(chain_file)        \n# chain_name_without_ext = os.path.splitext(chain_name_with_ext)[0]\n# lp_file_name = chain_name_without_ext\n# seq_data = parse_seq_file(chain_file)\n# RNA = seq_data['sequence']\n\n# print(chain_name_without_ext)\n# print(RNA)\n\n# print(G_hairpin(RNA, 16,31))\n# print(G_bulge(RNA, 8,9,38,43))\n\n\n# mip = gp.Model(f'MIP-{seq_number}')\n\n# add_binary_vars(RNA, mip)\n# consecutiveUnpairedConstraints(RNA, L, mip)\n# bulgeNTConstraints(RNA, mip)\n# bulgeZeroConstraints(RNA, mip, maxB)\n# bulgeIfThenConstraints(RNA, mip)\n# bulgeOnlyIfConstraints(RNA, mip)\n# numBulgeConstraints(RNA, noB, mip)\n\n\n\n", "text_preview": "from binary_variables_grb import * from constraints_grb import * from objective_grb import * from pathlib import Path import time from prepro_run import * def optimize(seq_files, seq_number, lp_dir, sol_dir, start = solstart_dir): chain_fil", "input_sha256": "0ddc042ed5bd66df3ece81eff4898543111f634b73b36018dd96c7b4897bffa8", "text_sha256": "75f272c22c287a105412183a354ff5c5719432566fcecdfc91b7677b28bfadd4", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12124, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5183453499257733, "noise_severity": 0.19301019772446876, "missing_content_severity": 0.1313311799228979, "factual_density": 2.9935074581497108, "pretraining_utility": 0.40765532613252364, "math_density": 2.6908342801466283, "code_density": 3.8564429120317625, "reasoning_density": 0.6407147139246157, "education_level": 3.16777050941537, "creative_writing_quality": -0.42403829745763033}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.6039 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.008325014739853758, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9962777157547207, "secondary": 0.9722572828430637, "tertiary": 0.976960568580198, "structured_records": 0.0020204959921318287, "procedural_steps": 0.03446808223922935, "worked_solution": 0.00819055900144731, "contextual_dialogue": 5.587779330691275e-06, "content_quality": 0.41573459757317777, "noise_severity": 0.8391581685629427, "missing_content_severity": 0.8905573500642517, "factual_density": 0.42722751493744304, "pretraining_utility": 0.3840415355691026, "math_density": 0.0, "code_density": 0.880369093359802, "reasoning_density": 0.4660710717294869, "education_level": 0.38398599105078635, "creative_writing_quality": 0.9995158697529257, "primary_topic": 0.7837324016046439, "secondary_topic": 0.5880775755097094}, "probability_margin": {"primary": 0.9947703672530744, "secondary": 0.9549703134134836, "tertiary": 0.9678026436514244, "education_level": 0.14378643644319666, "creative_writing_quality": 0.999446104147093, "primary_topic": 0.595414719474598, "secondary_topic": 0.40011391410170677}, "raw_probabilities": {"primary": {"Academic Writing": 0.00015461394936159732, "Content Listing": 2.3200055818392956e-05, "Creative Writing": 1.6633744800138337e-05, "Customer Support Page": 9.471243019083597e-06, "Q&A Forum": 1.2147395617556234e-05, "Discussion Thread": 8.282263725690386e-06, "Comment Section": 1.3976094173493234e-05, "Missing Content": 2.7792886963205865e-05, "Knowledge Article": 0.00010932255088390584, "Legal Notices": 2.4247571819233602e-05, "News Article": 5.464256246333413e-06, "Organizational Announcement": 1.0485930150335593e-05, "Nonfiction Writing": 1.546191790292035e-05, "Organizational About Page": 1.2484741038861604e-05, "Personal About Page": 1.1556638559903384e-05, "Personal Blog": 7.597297483333621e-06, "Product Page": 7.01612011726408e-06, "Spam / Ads": 1.057673251154144e-05, "Structured Data": 0.000408722477682511, "Technical Writing": 0.9964328109316073, "Transcript / Interview": 1.4973047585472247e-05, "Tutorial / How-To Guide": 0.0009943163340201066, "User Reviews": 6.402140378883875e-06, "Other / Unknown": 0.001662443678532804}, "secondary": {"Academic Writing": 0.002350052541565703, "Content Listing": 9.420940973869292e-05, "Creative Writing": 0.00010177192789687706, "Customer Support Page": 5.663080782006848e-05, "Q&A Forum": 4.875848366257881e-05, "Discussion Thread": 3.688129092494739e-05, "Comment Section": 0.00019035198352285507, "Missing Content": 0.00017727567443454054, "Knowledge Article": 0.0004791553976283107, "Legal Notices": 9.048416614789654e-05, "News Article": 4.892292758300593e-05, "Organizational Announcement": 5.388794334860549e-05, "Nonfiction Writing": 5.855388735483158e-05, "Organizational About Page": 6.650881720469373e-05, "Personal About Page": 5.975259279862492e-05, "Personal Blog": 4.6174457944925506e-05, "Product Page": 3.094065364032759e-05, "Spam / Ads": 5.5533165012335954e-05, "Structured Data": 0.0007236217483355557, "Technical Writing": 0.018442915977785773, "Transcript / Interview": 6.625375712612613e-05, "Tutorial / How-To Guide": 0.0032787701135661624, "User Reviews": 2.9362883687131443e-05, "NONE": 0.9734132293912694}, "tertiary": {"Academic Writing": 0.0035479257592801973, "Content Listing": 0.00010069332586049265, "Creative Writing": 0.0001089851462659039, "Customer Support Page": 4.7040852258557716e-05, "Q&A Forum": 4.5080020975864324e-05, "Discussion Thread": 3.7574961265482425e-05, "Comment Section": 0.00033025171352313987, "Missing Content": 0.00042264598545483875, "Knowledge Article": 0.0009889568402492545, "Legal Notices": 0.00019530831738404647, "News Article": 4.959055475309847e-05, "Organizational Announcement": 4.5712786838045727e-05, "Nonfiction Writing": 9.519406901386046e-05, "Organizational About Page": 6.735650350654455e-05, "Personal About Page": 5.419302337488324e-05, "Personal Blog": 4.3086047857104077e-05, "Product Page": 3.43174335176696e-05, "Spam / Ads": 6.700726070996385e-05, "Structured Data": 0.0008717535506308244, "Technical Writing": 0.010117901237932015, "Transcript / Interview": 7.035693966968534e-05, "Tutorial / How-To Guide": 0.004710433243744376, "User Reviews": 2.8089536577739887e-05, "NONE": 0.9779205448893564}, "structured_records": {"false": 0.9979795040078682, "true": 0.0020204959921318287}, "procedural_steps": {"false": 0.9655319177607706, "true": 0.03446808223922935}, "worked_solution": {"false": 0.9918094409985527, "true": 0.00819055900144731}, "contextual_dialogue": {"false": 0.9999944122206693, "true": 5.587779330691275e-06}, "content_quality": {"-2": 0.01827508794843738, "-1": 0.0946231106272748, "0": 0.34731508139334494, "1": 0.4300548036119628, "2": 0.10973191641898}, "noise_severity": {"0": 0.8489722097716101, "1": 0.11366371392273979, "2": 0.0338102383375025, "3": 0.002489344745866697, "4": 0.0010644932222809713}, "missing_content_severity": {"0": 0.926069113311673, "1": 0.02920904374933773, "2": 0.033231591577827076, "3": 0.010302052426742602, "4": 0.001188198934419544}, "factual_density": {"0": 0.01764045481360677, "1": 0.04685486661063405, "2": 0.2002786643005904, "3": 0.39480879416277936, "4": 0.34041722011238934}, "pretraining_utility": {"-2": 0.011211639930498252, "-1": 0.1179341303097686, "0": 0.39624423518124463, "1": 0.4012072528536881, "2": 0.07340274172480031}, "math_density": {"0": 0.09510021971764579, "1": 0.1268138958500628, "2": 0.15833440799813325, "3": 0.2316543374363333, "4": 0.3880971389978247}, "code_density": {"0": 0.00018949502819439647, "1": 0.00037194658033953495, "2": 0.013966386464582925, "3": 0.11375049518527565, "4": 0.8717216767416074}, "reasoning_density": {"0": 0.7549407952877075, "1": 0.05140720260411574, "2": 0.06863343152072368, "3": 0.04803363407075947, "4": 0.07698493651669351}, "education_level": {"none": 0.0019447401239103472, "0": 0.0013333697183667208, "1": 0.004225866053648483, "2": 0.16297247546262725, "3": 0.4866549925423219, "4": 0.34286855609912525}, "creative_writing_quality": {"not_applicable": 0.9995965581274381, "-2": 7.9267421560396e-05, "-1": 0.00010426692791140553, "0": 0.0001504539803451069, "1": 4.718011922218371e-05, "2": 2.227342352286221e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00031474097067785085, "Art & Design": 0.0005058610796518836, "Law & Crime": 0.0003838528214096704, "Education & Careers": 0.00026943130756621397, "Entertainment & Media": 0.0002840851068385931, "Fashion & Beauty": 0.0002858464071082006, "Business, Economics & Finance": 0.00042610612993078086, "Food & Dining": 0.00031403922876987084, "Games & Gaming": 0.00037565148656924335, "Electronics & Hardware": 0.0003839932351514539, "Health, Healthcare & Wellness": 0.0011430302119321333, "History & Geography": 0.0005757523368786586, "Home, Pets & Hobbies": 0.0002761985438576705, "Industry & Production": 0.0003540488246581821, "Language, Literature & Philosophy": 0.000379948898688316, "Politics & Government": 0.00014580703390380492, "Religion & Spirituality": 0.0002882741140344884, "Science, Mathematics & Engineering": 0.19663566668371338, "Relationships & Community": 0.00013743577693297562, "Software Usage & Applications": 0.0006222790981072075, "Programming & Software Engineering": 0.7920503861583114, "Sports & Fitness": 0.0004540048081278525, "Vehicles & Transportation": 0.0001795834433814661, "Travel & Tourism": 0.00034424378077850553, "Other / Uncovered Subject": 0.002375229997051732, "Unknown / Insufficient Content": 0.0004945025159684518}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006729346322939644, "Art & Design": 0.0006806855105473111, "Law & Crime": 0.0006268708905987617, "Education & Careers": 0.0003401710971490173, "Entertainment & Media": 0.0004089103504980108, "Fashion & Beauty": 0.00040051573209032597, "Business, Economics & Finance": 0.0005021628856116454, "Food & Dining": 0.00034324197601249347, "Games & Gaming": 0.0004015452936554385, "Electronics & Hardware": 0.0005308937462584104, "Health, Healthcare & Wellness": 0.0011577796073604584, "History & Geography": 0.00047232741607856167, "Home, Pets & Hobbies": 0.0003440895246688974, "Industry & Production": 0.00047300650147715396, "Language, Literature & Philosophy": 0.00030434185339552213, "Politics & Government": 0.00025835957120161313, "Religion & Spirituality": 0.0004772417268579551, "Science, Mathematics & Engineering": 0.6039207456824128, "Relationships & Community": 0.00019086937932959985, "Software Usage & Applications": 0.0005274595322468162, "Programming & Software Engineering": 0.20380683158070606, "Sports & Fitness": 0.0005449006081864688, "Vehicles & Transportation": 0.00022346931671755042, "Travel & Tourism": 0.0004962831657848454, "Other / Uncovered Subject": 0.0019033406732969107, "NONE": 0.17999102174556322}}, "probabilities": {"primary": {"Academic Writing": 0.00015461394936159734, "Content Listing": 2.320005581839296e-05, "Creative Writing": 1.663374480013834e-05, "Customer Support Page": 9.471243019083599e-06, "Q&A Forum": 1.2147395617556236e-05, "Discussion Thread": 8.282263725690388e-06, "Comment Section": 1.3976094173493236e-05, "Missing Content": 2.779288696320587e-05, "Knowledge Article": 0.00010932255088390586, "Legal Notices": 2.4247571819233606e-05, "News Article": 5.464256246333414e-06, "Organizational Announcement": 1.0485930150335595e-05, "Nonfiction Writing": 1.5461917902920352e-05, "Organizational About Page": 1.2484741038861605e-05, "Personal About Page": 1.1556638559903385e-05, "Personal Blog": 7.597297483333622e-06, "Product Page": 7.016120117264081e-06, "Spam / Ads": 1.0576732511541441e-05, "Structured Data": 0.00040872247768251104, "Technical Writing": 0.9964328109316074, "Transcript / Interview": 1.4973047585472249e-05, "Tutorial / How-To Guide": 0.0009943163340201068, "User Reviews": 6.402140378883876e-06, "Other / Unknown": 0.0016624436785328042}, "secondary": {"Academic Writing": 0.002350052541565703, "Content Listing": 9.420940973869292e-05, "Creative Writing": 0.00010177192789687706, "Customer Support Page": 5.663080782006848e-05, "Q&A Forum": 4.875848366257881e-05, "Discussion Thread": 3.688129092494739e-05, "Comment Section": 0.00019035198352285507, "Missing Content": 0.00017727567443454054, "Knowledge Article": 0.0004791553976283107, "Legal Notices": 9.048416614789654e-05, "News Article": 4.892292758300593e-05, "Organizational Announcement": 5.388794334860549e-05, "Nonfiction Writing": 5.855388735483158e-05, "Organizational About Page": 6.650881720469373e-05, "Personal About Page": 5.975259279862492e-05, "Personal Blog": 4.6174457944925506e-05, "Product Page": 3.094065364032759e-05, "Spam / Ads": 5.5533165012335954e-05, "Structured Data": 0.0007236217483355557, "Technical Writing": 0.018442915977785773, "Transcript / Interview": 6.625375712612613e-05, "Tutorial / How-To Guide": 0.0032787701135661624, "User Reviews": 2.9362883687131443e-05, "NONE": 0.9734132293912694}, "tertiary": {"Academic Writing": 0.0035479257592801973, "Content Listing": 0.00010069332586049265, "Creative Writing": 0.0001089851462659039, "Customer Support Page": 4.7040852258557716e-05, "Q&A Forum": 4.5080020975864324e-05, "Discussion Thread": 3.7574961265482425e-05, "Comment Section": 0.00033025171352313987, "Missing Content": 0.00042264598545483875, "Knowledge Article": 0.0009889568402492545, "Legal Notices": 0.00019530831738404647, "News Article": 4.959055475309847e-05, "Organizational Announcement": 4.5712786838045727e-05, "Nonfiction Writing": 9.519406901386046e-05, "Organizational About Page": 6.735650350654455e-05, "Personal About Page": 5.419302337488324e-05, "Personal Blog": 4.3086047857104077e-05, "Product Page": 3.43174335176696e-05, "Spam / Ads": 6.700726070996385e-05, "Structured Data": 0.0008717535506308244, "Technical Writing": 0.010117901237932015, "Transcript / Interview": 7.035693966968534e-05, "Tutorial / How-To Guide": 0.004710433243744376, "User Reviews": 2.8089536577739887e-05, "NONE": 0.9779205448893564}, "structured_records": {"false": 0.9979795040078682, "true": 0.0020204959921318287}, "procedural_steps": {"false": 0.9655319177607706, "true": 0.03446808223922935}, "worked_solution": {"false": 0.9918094409985527, "true": 0.00819055900144731}, "contextual_dialogue": {"false": 0.9999944122206693, "true": 5.587779330691275e-06}, "content_quality": {"-2": 0.018275087948437383, "-1": 0.09462311062727481, "0": 0.347315081393345, "1": 0.43005480361196285, "2": 0.10973191641898002}, "noise_severity": {"0": 0.8489722097716101, "1": 0.11366371392273979, "2": 0.0338102383375025, "3": 0.002489344745866697, "4": 0.0010644932222809713}, "missing_content_severity": {"0": 0.9260691133116731, "1": 0.029209043749337735, "2": 0.03323159157782708, "3": 0.010302052426742604, "4": 0.0011881989344195442}, "factual_density": {"0": 0.017640454813606772, "1": 0.04685486661063406, "2": 0.20027866430059044, "3": 0.3948087941627794, "4": 0.3404172201123894}, "pretraining_utility": {"-2": 0.011211639930498254, "-1": 0.11793413030976861, "0": 0.3962442351812447, "1": 0.40120725285368813, "2": 0.07340274172480032}, "math_density": {"0": 0.0951002197176458, "1": 0.12681389585006284, "2": 0.15833440799813328, "3": 0.23165433743633332, "4": 0.3880971389978248}, "code_density": {"0": 0.0001894950281943965, "1": 0.000371946580339535, "2": 0.013966386464582927, "3": 0.11375049518527566, "4": 0.8717216767416075}, "reasoning_density": {"0": 0.7549407952877076, "1": 0.05140720260411575, "2": 0.0686334315207237, "3": 0.04803363407075948, "4": 0.07698493651669353}, "education_level": {"none": 0.0019447401239103472, "0": 0.0013333697183667208, "1": 0.004225866053648483, "2": 0.16297247546262725, "3": 0.4866549925423219, "4": 0.34286855609912525}, "creative_writing_quality": {"not_applicable": 0.9995965581274381, "-2": 7.9267421560396e-05, "-1": 0.00010426692791140553, "0": 0.0001504539803451069, "1": 4.718011922218371e-05, "2": 2.227342352286221e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0003147409706778509, "Art & Design": 0.0005058610796518837, "Law & Crime": 0.00038385282140967044, "Education & Careers": 0.000269431307566214, "Entertainment & Media": 0.00028408510683859315, "Fashion & Beauty": 0.00028584640710820064, "Business, Economics & Finance": 0.0004261061299307809, "Food & Dining": 0.0003140392287698709, "Games & Gaming": 0.0003756514865692434, "Electronics & Hardware": 0.000383993235151454, "Health, Healthcare & Wellness": 0.0011430302119321335, "History & Geography": 0.0005757523368786587, "Home, Pets & Hobbies": 0.00027619854385767056, "Industry & Production": 0.00035404882465818216, "Language, Literature & Philosophy": 0.00037994889868831605, "Politics & Government": 0.00014580703390380494, "Religion & Spirituality": 0.00028827411403448843, "Science, Mathematics & Engineering": 0.1966356666837134, "Relationships & Community": 0.00013743577693297565, "Software Usage & Applications": 0.0006222790981072076, "Programming & Software Engineering": 0.7920503861583115, "Sports & Fitness": 0.00045400480812785254, "Vehicles & Transportation": 0.00017958344338146612, "Travel & Tourism": 0.0003442437807785056, "Other / Uncovered Subject": 0.0023752299970517324, "Unknown / Insufficient Content": 0.000494502515968452}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006729346322939645, "Art & Design": 0.0006806855105473112, "Law & Crime": 0.0006268708905987618, "Education & Careers": 0.00034017109714901735, "Entertainment & Media": 0.00040891035049801084, "Fashion & Beauty": 0.000400515732090326, "Business, Economics & Finance": 0.0005021628856116455, "Food & Dining": 0.0003432419760124935, "Games & Gaming": 0.00040154529365543857, "Electronics & Hardware": 0.0005308937462584105, "Health, Healthcare & Wellness": 0.0011577796073604586, "History & Geography": 0.0004723274160785617, "Home, Pets & Hobbies": 0.00034408952466889747, "Industry & Production": 0.000473006501477154, "Language, Literature & Philosophy": 0.0003043418533955222, "Politics & Government": 0.0002583595712016132, "Religion & Spirituality": 0.00047724172685795517, "Science, Mathematics & Engineering": 0.6039207456824129, "Relationships & Community": 0.00019086937932959988, "Software Usage & Applications": 0.0005274595322468163, "Programming & Software Engineering": 0.2038068315807061, "Sports & Fitness": 0.0005449006081864689, "Vehicles & Transportation": 0.00022346931671755044, "Travel & Tourism": 0.0004962831657848455, "Other / Uncovered Subject": 0.001903340673296911, "NONE": 0.17999102174556325}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11252, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11252, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:17.128291+00:00", "scoring_key_slot": "primary", "dataset_uid": "27c3f42a-049b-4f27-bc67-10126ab36fdf", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "32137f8ca2caa0e85992215874ce22212ec80c7c73f8f89028bca232bf8286ca", "source_metadata": {"uuid": "27c3f42a-049b-4f27-bc67-10126ab36fdf", "repo_name": "rhombicosi/RNA_NNT_ILP", "relative_path": "ILP/ilp_grb.py", "category": "DATA", "algo_rel_score": 0.9759951233863831, "quality_score": 4.13144588470459, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 29, "repo_created_at": "2024-02-26T23:36:13.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "60b40b44f0431d1fd60ba4c10d677ec5634c080e", "size_bytes": 9416, "file_timestamp": 1746755702, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "cadcd2fd2888a2aed480654d87dea06565985f00"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 29, "repo_created_at": "2024-02-26T23:36:13.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "60b40b44f0431d1fd60ba4c10d677ec5634c080e", "size_bytes": 9416, "file_timestamp": 1746755702, "language": "python", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "cadcd2fd2888a2aed480654d87dea06565985f00", "url": "https://github.com/rhombicosi/RNA_NNT_ILP/blob/cadcd2fd2888a2aed480654d87dea06565985f00/ILP/ilp_grb.py"}, "gpt4o_tokens": 2556, "dataset_split": "python", "probability_weighted_scores": {"content_quality": 0.5183453499257733, "noise_severity": 0.19301019772446876, "missing_content_severity": 0.1313311799228979, "factual_density": 2.9935074581497108, "pretraining_utility": 0.40765532613252364, "math_density": 2.6908342801466283, "code_density": 3.8564429120317625, "reasoning_density": 0.6407147139246157, "education_level": 3.16777050941537, "creative_writing_quality": -0.42403829745763033}, "score_applicability": {"education_level": 0.9980552598760897, "creative_writing_quality": 0.0004034418725619544}}