diff --git "a/Source Code/Our Proposed/ANN.ipynb" "b/Source Code/Our Proposed/ANN.ipynb" new file mode 100644--- /dev/null +++ "b/Source Code/Our Proposed/ANN.ipynb" @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XdwkZ0q5StF9","outputId":"b4b0da36-0cd1-4bed-9baa-92b2e19b5c9e","executionInfo":{"status":"ok","timestamp":1736583588395,"user_tz":-420,"elapsed":16150,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":2,"metadata":{"id":"sT4b3MlpQUkp","executionInfo":{"status":"ok","timestamp":1736583894130,"user_tz":-420,"elapsed":363,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rrq-gfhdNyLM","outputId":"4f04fd18-0aeb-4ec5-c04d-9d0699724e56","executionInfo":{"status":"ok","timestamp":1736583896309,"user_tz":-420,"elapsed":1352,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I-dDe4UNQaw7","outputId":"2b7b63ca-cef2-4a43-a7c0-59db06f5d3c7","executionInfo":{"status":"ok","timestamp":1736583900660,"user_tz":-420,"elapsed":4005,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QyPVHFFbSpOe","outputId":"d97877e3-987c-4ea1-d90c-ac2b74b7bc43","executionInfo":{"status":"ok","timestamp":1736583924720,"user_tz":-420,"elapsed":24062,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Successfully loaded 9675 items from the JSON file.\n"]}],"source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data_bundle_interactions = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data_bundle_interactions)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"],"metadata":{"id":"amTibdP9S850","executionInfo":{"status":"ok","timestamp":1736583924721,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["bundle_tags = {}\n","bundle_specs = {}\n","for bundle_id, bundle_info in bundle_data.items():\n"," bundle_tags[bundle_id] = {}\n"," bundle_specs[bundle_id] = {}\n"," for item_id in bundle_info[\"items\"]:\n"," if item_id in items_dictionary:\n"," if 'tags' in items_dictionary[item_id]:\n"," for tag in items_dictionary[item_id]['tags']:\n"," if tag in input_default_tags:\n"," if tag not in bundle_tags[bundle_id]:\n"," bundle_tags[bundle_id][tag] = 1\n"," else:\n"," bundle_tags[bundle_id][tag] += 1\n"," if 'specs' in items_dictionary[item_id]:\n"," for spec in items_dictionary[item_id]['specs']:\n"," if spec in input_default_specs:\n"," if spec not in bundle_specs[bundle_id]:\n"," bundle_specs[bundle_id][spec] = 1\n"," else:\n"," bundle_specs[bundle_id][spec] += 1"],"metadata":{"id":"Fhghu_CXXEM8","executionInfo":{"status":"ok","timestamp":1736583924721,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# prompt: Get the percent value from string on unique_ratings\n","\n","import re\n","\n","def extract_percent(rating_str):\n"," \"\"\"\n"," Extracts the numeric percentage value from a string.\n","\n"," Args:\n"," rating_str: The input string potentially containing a percentage value.\n","\n"," Returns:\n"," The numeric percentage value as a float, or None if no percentage is found.\n"," \"\"\"\n"," match = re.search(r\"(\\d+(\\.\\d+)?)%\", rating_str) # Match digits (optional decimal) followed by %\n"," if match:\n"," return float(match.group(1))\n"," return None"],"metadata":{"id":"DY0CG_yqVPk0","executionInfo":{"status":"ok","timestamp":1736583924721,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_user_reviews_process.json.gz\"\n","\n","data_reviews = []\n","with gzip.open(f\"{file_path_write}\", 'rt', encoding='utf-8') as f:\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," percent = extract_percent(str(data_dict[\"rating\"])) # Convert to string before processing\n"," if percent is None:\n"," percent = 0.0\n"," data_dict[\"rating\"] = percent / 20\n"," data_reviews.append(data_dict)\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")"],"metadata":{"id":"3o9OG-TGVOfx","executionInfo":{"status":"ok","timestamp":1736583926788,"user_tz":-420,"elapsed":2070,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["# prompt: data_reviews convert to dictionary with structure user_id -> item_id\n","\n","user_item_dict = {}\n","for review in data_reviews:\n"," user_id = review['user_id']\n"," item_id = review['item_id']\n"," if user_id not in user_item_dict:\n"," user_item_dict[user_id] = {}\n"," user_item_dict[user_id][item_id] = review['rating']"],"metadata":{"id":"4lLCuDGbVJ_Z","executionInfo":{"status":"ok","timestamp":1736583926788,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["user_bundle_histories = []\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," rating = []\n"," if user_id in user_item_dict:\n"," if bundle_id in bundle_data:\n"," for item_id in bundle_data[bundle_id][\"items\"]:\n"," if item_id in user_item_dict[user_id]:\n"," rating.append(user_item_dict[user_id][item_id])\n"," if len(rating) > 0:\n"," rating = sum(rating) / len(rating)\n"," else:\n"," rating = 0.0\n"," user_bundle_histories.append({ \"user_id\": user_id, \"bundle_id\": bundle_id, \"label\": recommendation[\"label\"], \"rating\": rating / 20 })"],"metadata":{"id":"FMhe80ITCl4A","executionInfo":{"status":"ok","timestamp":1736583927255,"user_tz":-420,"elapsed":468,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"L9qLjFCOB99Y","outputId":"c4816cae-56a9-43d8-eba9-48103e43594e","executionInfo":{"status":"ok","timestamp":1736583927255,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of data entries with rating > 0.0: 23025\n"]}],"source":["# prompt: Check how many data that has rating more than 0.0 in user_bundle_histories\n","\n","count = 0\n","for entry in data_reviews:\n"," if entry[\"rating\"] > 0.0:\n"," count += 1\n","print(f\"Number of data entries with rating > 0.0: {count}\")"]},{"cell_type":"code","source":["# prompt: distinct value of rating in data_reviews\n","\n","distinct_ratings = set()\n","for review in data_reviews:\n"," distinct_ratings.add(review['rating'])\n","print(f\"Distinct rating values: {distinct_ratings}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oyIDC0pK596j","outputId":"b14de3d1-e8a5-4583-d5f7-bceecba899bd","executionInfo":{"status":"ok","timestamp":1736583927256,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Distinct rating values: {0.0, 1.25, 2.5, 3.75, 4.05, 5.0, 4.4, 1.65, 4.5, 3.35, 3.0, 4.0, 1.0, 2.0, 2.75, 0.25, 3.5, 3.25, 2.25, 4.75, 4.25, 1.5, 0.5, 0.75, 1.75, 1.9, 1.15, 1.4, 2.4, 2.15, 2.9, 2.65, 3.4, 3.15, 3.65, 3.9, 0.45, 0.2, 4.15, 4.65, 4.9, 0.15, 2.05, 2.8, 2.3, 2.55, 3.8, 3.3, 3.55, 0.55, 3.05, 4.3, 4.8, 4.55, 1.8, 0.8, 1.05, 1.55, 1.3, 0.85, 0.4, 0.9, 0.65, 0.35, 0.6, 0.95, 0.7, 1.45, 1.95, 1.2, 1.7, 0.1, 2.95, 2.7, 2.2, 2.45, 3.45, 3.7, 3.2, 3.95, 4.2, 4.45, 4.7, 0.3, 4.35, 4.1, 4.85, 4.6, 1.35, 1.1, 1.6, 1.85, 2.85, 2.1, 2.35, 2.6, 3.6, 3.1, 3.85}\n"]}]},{"cell_type":"code","execution_count":14,"metadata":{"id":"flYvFh9kThB6","executionInfo":{"status":"ok","timestamp":1736583927256,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: get top rec_bundles from data_dict with variation TOP 5 until top 20 with space 5\n","\n","def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results\n","\n","# Example usage (assuming data_dict is defined as in the provided code)\n","# Assuming data_dict contains the following:\n","# data_dict = {\"user_id\": 123, \"rec_bundles\": {\"bundle_1\": 0.9, \"bundle_2\": 0.8, \"bundle_3\": 0.7, \"bundle_4\": 0.6, \"bundle_5\": 0.5, \"bundle_6\": 0.4, \"bundle_7\": 0.3, \"bundle_8\":0.2, \"bundle_9\":0.1, \"bundle_10\": 0.0}}"]},{"cell_type":"markdown","metadata":{"id":"fbCasxBUTgRk"},"source":["### Evaluation"]},{"cell_type":"code","source":["!pip install surprise"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cQr5la-EVvSI","outputId":"5d186803-5dc1-4b29-9d63-433076ea06db","executionInfo":{"status":"ok","timestamp":1736584014062,"user_tz":-420,"elapsed":86809,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting surprise\n"," Downloading surprise-0.1-py2.py3-none-any.whl.metadata (327 bytes)\n","Collecting scikit-surprise (from surprise)\n"," Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/154.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n"," Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n"," Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.4.2)\n","Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.26.4)\n","Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.13.1)\n","Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n","Building wheels for collected packages: scikit-surprise\n"," Building wheel for scikit-surprise (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for scikit-surprise: filename=scikit_surprise-1.1.4-cp310-cp310-linux_x86_64.whl size=2357284 sha256=7cc8521c609d9d0ab276ade75fa5bcaeb51d5b9b3d05ea082c0083393d2e0f48\n"," Stored in directory: /root/.cache/pip/wheels/4b/3f/df/6acbf0a40397d9bf3ff97f582cc22fb9ce66adde75bc71fd54\n","Successfully built scikit-surprise\n","Installing collected packages: scikit-surprise, surprise\n","Successfully installed scikit-surprise-1.1.4 surprise-0.1\n"]}]},{"cell_type":"code","source":["# prompt: Apply train test split # Create a pandas DataFrame from user_bundle_histories\n","import pandas as pd\n","from surprise.model_selection import train_test_split\n","from surprise import Dataset, Reader\n","\n","df = pd.DataFrame(user_bundle_histories)\n","# Define the rating scale for the Surprise library\n","reader = Reader(rating_scale=(0, 5))\n","# Load the data into Surprise Dataset format\n","data = Dataset.load_from_df(df[['user_id', 'bundle_id', 'rating']], reader)\n","\n","# Split the data into training and testing sets\n","trainset, testset = train_test_split(data, test_size=0.2)"],"metadata":{"id":"x2VXxeekBgO3","executionInfo":{"status":"ok","timestamp":1736584015094,"user_tz":-420,"elapsed":1035,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":16,"outputs":[]},{"cell_type":"code","source":["# prompt: Convert to dictionary users bundles for variable testset\n","\n","testset = [{'user_id': entry['user_id'], 'bundle_id': entry['bundle_id'], 'rating': entry['rating']} for entry in user_bundle_histories]"],"metadata":{"id":"lXAq5lJTBo-W","executionInfo":{"status":"ok","timestamp":1736584015095,"user_tz":-420,"elapsed":7,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["# prompt: group testset by user_id as dictionary\n","\n","from collections import defaultdict\n","\n","def group_testset_by_user(testset):\n"," \"\"\"Groups the testset by user_id.\n","\n"," Args:\n"," testset: A list of dictionaries, where each dictionary represents a data point\n"," and contains 'user_id', 'bundle_id', and 'rating' keys.\n","\n"," Returns:\n"," A dictionary where keys are user IDs and values are lists of dictionaries\n"," containing bundle IDs and ratings for that user.\n"," \"\"\"\n"," grouped_data = {}\n"," for entry in testset:\n"," user_id = entry['user_id']\n"," if user_id not in grouped_data:\n"," grouped_data[user_id] = set()\n"," grouped_data[user_id].add(entry['bundle_id'])\n"," return grouped_data\n","\n","# Example usage (assuming user_bundle_histories is defined as in the provided code)\n","grouped_testset = group_testset_by_user(user_bundle_histories)\n","\n","# Accessing data for a specific user:\n","# user_id = 'some_user_id'\n","# if user_id in grouped_testset:\n","# print(grouped_testset[user_id])"],"metadata":{"id":"W4OsYmbFBwm-","executionInfo":{"status":"ok","timestamp":1736584015095,"user_tz":-420,"elapsed":7,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["# prompt: Check how many data that has rating more than 0.0 in user_bundle_histories\n","\n","count = 0\n","for entry in user_bundle_histories:\n"," if entry[\"rating\"] > 0.0:\n"," count += 1\n","print(f\"Number of data entries with rating > 0.0: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9Dy88qfjB8RC","outputId":"bba35cce-fd78-4738-c3f7-c9d22c303c97","executionInfo":{"status":"ok","timestamp":1736584015095,"user_tz":-420,"elapsed":7,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of data entries with rating > 0.0: 1870\n"]}]},{"cell_type":"code","source":["len(user_bundle_histories)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DfRuISneC_A3","outputId":"35a30a03-e084-484e-c8e0-086dca5cebe3","executionInfo":{"status":"ok","timestamp":1736584015095,"user_tz":-420,"elapsed":6,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["103453"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","execution_count":21,"metadata":{"id":"hfTbC2xdTWNg","executionInfo":{"status":"ok","timestamp":1736584015095,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: Calculate hit_ratio from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_hit_ratio(top_bundles_results, grouped_testset):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit = {}\n"," for user_id2 in grouped_testset:\n"," data_test = grouped_testset[user_id2]\n"," temp_hit = {}\n"," for bundle_id_test in data_test:\n"," for result in top_bundles_results:\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in temp_hit:\n"," temp_hit[top_n] = 0\n","\n"," if bundle_id_test in top_bundles:\n"," temp_hit[top_n] += 1\n","\n"," for top_n, count in temp_hit.items():\n"," if top_n not in hit:\n"," hit[top_n] = 0\n"," hit[top_n] += (count / len(data_test))\n","\n"," hit_ratios = {}\n"," for top_n, count in hit.items():\n"," hit_ratios[top_n] = count / len(grouped_testset)\n","\n"," return hit_ratios\n","\n","# Example usage (assuming the necessary data structures are defined):\n","# hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n","# hit_ratios"]},{"cell_type":"code","execution_count":22,"metadata":{"id":"xUuraXZlT3f-","executionInfo":{"status":"ok","timestamp":1736584015095,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: Calculate Gini-Simpson Diversity Index using top_bundles_results and grouped_testset based on varible bundle_tags bundle_specs for each bundle_id that recommended\n","\n","import pandas as pd\n","\n","def calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs):\n"," \"\"\"\n"," Calculates the Gini-Simpson diversity index for recommended bundles.\n","\n"," Args:\n"," top_bundles_results: Results from get_top_rec_bundles function.\n"," grouped_testset: User's interaction data grouped by user.\n"," bundle_tags: Dictionary of bundle tags.\n"," bundle_specs: Dictionary of bundle specs.\n","\n"," Returns:\n"," A dictionary containing Gini-Simpson index for each user and top_n.\n"," \"\"\"\n","\n"," gini_simpson_indices = {}\n","\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in gini_simpson_indices:\n"," gini_simpson_indices[top_n] = 0.0\n","\n"," # Combine tags and specs for each bundle\n"," combined_features = {}\n"," for bundle_id in top_bundles:\n"," combined_features[bundle_id] = {}\n"," if bundle_id in bundle_tags:\n"," combined_features[bundle_id].update(bundle_tags[bundle_id])\n"," if bundle_id in bundle_specs:\n"," combined_features[bundle_id].update(bundle_specs[bundle_id])\n","\n"," # Calculate probabilities for each feature\n"," total_features = {}\n"," for bundle_id in combined_features:\n"," total_features[bundle_id] = {}\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in total_features:\n"," total_features[bundle_id][feature] = 1\n","\n"," all_features = set()\n"," for bundle_id in combined_features:\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in all_features:\n"," all_features.add(feature)\n","\n"," probabilities = {}\n"," for feature in all_features:\n"," probabilities[feature] = 0\n"," for bundle_id in combined_features:\n"," if feature in combined_features[bundle_id]:\n"," probabilities[feature] += 1\n","\n"," probabilities[feature] = probabilities[feature] / len(combined_features)\n","\n"," # Calculate Gini-Simpson index\n"," gini_simpson_index = 1 - (sum(p**2 for p in probabilities.values()) / len(probabilities))\n"," gini_simpson_indices[top_n] = gini_simpson_index\n","\n"," return gini_simpson_indices\n","\n","# Example usage (assuming necessary data structures are defined)\n","# gini_simpson_result = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","# gini_simpson_result"]},{"cell_type":"code","source":["import numpy as np\n","\n","def calculate_mean(average):\n"," \"\"\"Calculates the mean hit ratio across all users and top_n values.\n","\n"," Args:\n"," average_hit_ratio: A dictionary containing hit ratios for different users and top_n values.\n","\n"," Returns:\n"," The mean hit ratio as a float. Returns 0 if input dictionary is empty or contains no valid hit ratios.\n"," \"\"\"\n"," all_hit_ratios = {}\n"," for user_id, hit_ratios in average.items():\n"," for top_n, hit_ratio in hit_ratios.items():\n"," if top_n not in all_hit_ratios:\n"," all_hit_ratios[top_n] = []\n"," all_hit_ratios[top_n].append(hit_ratio)\n","\n"," if not all_hit_ratios:\n"," return 0\n","\n"," new_hit_ratios = {}\n"," for top_n, hit_ratios in all_hit_ratios.items():\n"," new_hit_ratios[top_n] = np.mean(hit_ratios)\n","\n"," return new_hit_ratios"],"metadata":{"id":"XYJbHB_0a-n8","executionInfo":{"status":"ok","timestamp":1736584015095,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":23,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","\n","def combine_metrics(mean_hit_ratio, mean_diversity):\n"," \"\"\"Combines the three dataframes into a single columnar dataframe.\n"," \"\"\"\n"," combined_data = []\n"," for top_n in mean_hit_ratio:\n"," row = {\n"," 'top_n': top_n,\n"," 'hit_ratio': mean_hit_ratio[top_n],\n"," 'diversity': mean_diversity[top_n],\n"," }\n"," combined_data.append(row)\n"," return pd.DataFrame(combined_data)"],"metadata":{"id":"eC-WiqV-bGT0","executionInfo":{"status":"ok","timestamp":1736584015096,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":24,"outputs":[]},{"cell_type":"markdown","source":["### Tags and Specs"],"metadata":{"id":"qarAR1Xhamk_"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz'\n","\n","average_hit_ratio = {}\n","average_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict['rec_bundles']) > 0:\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_diversity[data_dict['user_id']] = diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_rVWLDRqV4mt","outputId":"481ae679-af9d-4265-807d-8b108bd68789","executionInfo":{"status":"ok","timestamp":1736585279502,"user_tz":-420,"elapsed":990134,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":30,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 9881\n","4888 / 9881\n","4889 / 9881\n","4890 / 9881\n","4891 / 9881\n","4892 / 9881\n","4893 / 9881\n","4894 / 9881\n","4895 / 9881\n","4896 / 9881\n","4897 / 9881\n","4898 / 9881\n","4899 / 9881\n","4900 / 9881\n","4901 / 9881\n","4902 / 9881\n","4903 / 9881\n","4904 / 9881\n","4905 / 9881\n","4906 / 9881\n","4907 / 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0.069771657067377, 10: 0.18081310105743048, 15: 0.23031802692321748, 20: 0.24765561679400952}\n","Mean Diversity: {5: 0.7239472522597888, 10: 0.7783845717782079, 15: 0.8393916735422627, 20: 0.8521412561853942}\n"]}],"source":["# Example usage:\n","mean_hit_ratio = calculate_mean(average_hit_ratio)\n","print(f\"Mean Hit Ratio: {mean_hit_ratio}\")\n","mean_diversity = calculate_mean(average_diversity)\n","print(f\"Mean Diversity: {mean_diversity}\")"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"kpWLqx-_asIk","outputId":"c8f6e11c-816a-496b-8186-aaeb0cf233a3","executionInfo":{"status":"ok","timestamp":1736585279502,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio diversity\n","0 5 0.069772 0.723947\n","1 10 0.180813 0.778385\n","2 15 0.230318 0.839392\n","3 20 0.247656 0.852141"],"text/html":["\n","
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top_nhit_ratiodiversity
050.0697720.723947
1100.1808130.778385
2150.2303180.839392
3200.2476560.852141
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0.7783845717782079,\n 0.8521412561853942,\n 0.7239472522597888\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":32}],"source":["# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(mean_hit_ratio, mean_diversity)\n","combined_df"]},{"cell_type":"code","execution_count":33,"metadata":{"id":"JImcjAVFawOX","executionInfo":{"status":"ok","timestamp":1736585279502,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/our_proposed/bundle_recommendation_ann_tags_specs.csv', index=False)"]},{"cell_type":"markdown","source":["### Price Patterns"],"metadata":{"id":"IefsuBOVbiMa"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz'\n","\n","average_hit_ratio = {}\n","average_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict['rec_bundles']) > 0:\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_diversity[data_dict['user_id']] = diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"outputId":"54f42785-b246-40d8-86d9-4c57aa7a8b19","id":"gt-Dl92KbiMa","executionInfo":{"status":"ok","timestamp":1736586253487,"user_tz":-420,"elapsed":973988,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":34,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 9881\n","4879 / 9881\n","4880 / 9881\n","4881 / 9881\n","4882 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9875\n"]}]},{"cell_type":"code","execution_count":35,"metadata":{"id":"2_46IbCBbiMa","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1736586253488,"user_tz":-420,"elapsed":25,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"c95c9b28-0a35-4060-be91-9dc1145b1a37"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Hit Ratio: {5: 0.06662603203656858, 10: 0.16892391849437932, 15: 0.2161954496113333, 20: 0.23619830502793274}\n","Mean Diversity: {5: 0.7699626770784954, 10: 0.8129473208641467, 15: 0.8547486336174472, 20: 0.8567684295943925}\n"]}],"source":["# Example usage:\n","mean_hit_ratio = calculate_mean(average_hit_ratio)\n","print(f\"Mean Hit Ratio: {mean_hit_ratio}\")\n","mean_diversity = calculate_mean(average_diversity)\n","print(f\"Mean Diversity: {mean_diversity}\")"]},{"cell_type":"code","execution_count":36,"metadata":{"id":"GXzwwOcgbiMa","colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"status":"ok","timestamp":1736586253488,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"536a6a48-d8a8-4d3f-f168-f43d53cf4cd9"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio diversity\n","0 5 0.066626 0.769963\n","1 10 0.168924 0.812947\n","2 15 0.216195 0.854749\n","3 20 0.236198 0.856768"],"text/html":["\n","
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top_nhit_ratiodiversity
050.0666260.769963
1100.1689240.812947
2150.2161950.854749
3200.2361980.856768
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"combined_df","summary":"{\n \"name\": \"combined_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"top_n\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6,\n \"min\": 5,\n \"max\": 20,\n \"num_unique_values\": 4,\n \"samples\": [\n 10,\n 20,\n 5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hit_ratio\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.07569190785528292,\n \"min\": 0.06662603203656858,\n \"max\": 0.23619830502793274,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.16892391849437932,\n 0.23619830502793274,\n 0.06662603203656858\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"diversity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.04107239096145902,\n \"min\": 0.7699626770784954,\n \"max\": 0.8567684295943925,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.8129473208641467,\n 0.8567684295943925,\n 0.7699626770784954\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":36}],"source":["# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(mean_hit_ratio, mean_diversity)\n","combined_df"]},{"cell_type":"code","execution_count":37,"metadata":{"id":"g6EVJI0AbiMb","executionInfo":{"status":"ok","timestamp":1736586253488,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/our_proposed/bundle_recommendation_ann_price_patterns.csv', index=False)"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file