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'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_6ab9a8dd6527aa1d/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_6ab9a8dd6527aa1d/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..1a63ff7bdf2480d2182e2e639fa8b7f1cec70f6b --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_6ab9a8dd6527aa1d/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b068f818d7b00b37edcdc4cff8d03a893ffca76e20a648c9a56aa7831cba0f52 +size 728444 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_6ab9a8dd6527aa1d/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038', 'f039', 'f040', 'f041', 'f042', 'f043', 'f044', 'f045', 'f046', 'f047', 'f048', 'f049', 'f050', 'f051', 'f052', 'f053', 'f054', 'f055', 'f056', 'f057', 'f058', 'f059', 'f060', 'f061', 'f062', 'f063', 'f064', 'f065', 'f066', 'f067', 'f068', 'f069', 'f070', 'f071', 'f072', 'f073', 'f074', 'f075', 'f076']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038', 'f039', 'f040', 'f041', 'f042', 'f043', 'f044', 'f045', 'f046', 'f047', 'f048', 'f049', 'f050', 'f051', 'f052', 'f053', 'f054', 'f055', 'f056', 'f057', 'f058', 'f059', 'f060', 'f061', 'f062', 'f063', 'f064', 'f065', 'f066', 'f067', 'f068', 'f069', 'f070', 'f071', 'f072', 'f073', 'f074', 'f075', 'f076']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038', 'f039', 'f040', 'f041', 'f042', 'f043', 'f044', 'f045', 'f046', 'f047', 'f048', 'f049', 'f050', 'f051', 'f052', 'f053', 'f054', 'f055', 'f056', 'f057', 'f058', 'f059', 'f060', 'f061', 'f062', 'f063', 'f064', 'f065', 'f066', 'f067', 'f068', 'f069', 'f070', 'f071', 'f072', 'f073', 'f074', 'f075', 'f076', 'f077', 'f078', 'f079', 'f080', 'f081', 'f082', 'f083', 'f084', 'f085', 'f086', 'f087', 'f088', 'f089', 'f090', 'f091', 'f092', 'f093', 'f094', 'f095', 'f096', 'f097', 'f098', 'f099', 'f100', 'f101', 'f102', 'f103', 'f104', 'f105', 'f106', 'f107', 'f108', 'f109', 'f110', 'f111', 'f112', 'f113', 'f114', 'f115', 'f116', 'f117', 'f118', 'f119', 'f120', 'f121', 'f122', 'f123', 'f124', 'f125', 'f126', 'f127', 'f128', 'f129', 'f130', 'f131', 'f132', 'f133', 'f134', 'f135', 'f136', 'f137', 'f138', 'f139', 'f140', 'f141', 'f142', 'f143', 'f144', 'f145', 'f146', 'f147', 'f148', 'f149', 'f150', 'f151', 'f152', 'f153']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038', 'f039', 'f040', 'f041', 'f042', 'f043', 'f044', 'f045', 'f046', 'f047', 'f048', 'f049', 'f050', 'f051', 'f052', 'f053', 'f054', 'f055', 'f056', 'f057', 'f058', 'f059', 'f060', 'f061', 'f062', 'f063', 'f064', 'f065', 'f066', 'f067', 'f068', 'f069', 'f070', 'f071', 'f072', 'f073', 'f074', 'f075', 'f076']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_84d38688c13c1d57/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_84d38688c13c1d57/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..c8cf3f05fd683d1312d624dbb1308211c3c76bfd --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_84d38688c13c1d57/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d9576e7d09657c2eff90c0d184c130ac428e445cb6f64877cc262946c74f133d +size 738404 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_84d38688c13c1d57/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038', 'f039', 'f040', 'f041', 'f042', 'f043', 'f044', 'f045', 'f046', 'f047', 'f048', 'f049', 'f050', 'f051', 'f052', 'f053', 'f054', 'f055', 'f056', 'f057', 'f058', 'f059', 'f060', 'f061', 'f062', 'f063', 'f064', 'f065', 'f066', 'f067', 'f068', 'f069', 'f070', 'f071', 'f072', 'f073', 'f074', 'f075', 'f076']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_860d0dc401da0b55/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_860d0dc401da0b55/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..bb5c0775fd5ffd00df38dff6a8e3de62dac3068f --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_860d0dc401da0b55/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e30954dee3dbced965c6ab5036c1e493e49dbd1b403e0238ae75f07ce3379880 +size 720628 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_860d0dc401da0b55/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_87acd03738754c37/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_87acd03738754c37/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..927a4338bc05e1e7464aa38afce9d87697735fa6 --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_87acd03738754c37/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9acd15f227c0876d66b3275ae1010fd30035a2884450cb5e94ae2c17530a23f2 +size 726436 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_87acd03738754c37/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_8e4ce247e267b28d/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_8e4ce247e267b28d/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..b8e70eb4b4083e870082bcb959dc840426acea3f --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_8e4ce247e267b28d/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5c8ad58788fad52461eefbbd911e817a3695fd6483b69a48e7f5f520d4b207af +size 697601 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_8e4ce247e267b28d/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_8e9b35d038e88b48/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_8e9b35d038e88b48/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..7831ed9642fcf77837d8841a15bcf3d3635d5316 --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_8e9b35d038e88b48/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e8e2bcb97fa7e778f3dd14b88b4ece530005a420f60d77a777ffada5a17a8e01 +size 735876 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_8e9b35d038e88b48/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038', 'f039', 'f040', 'f041', 'f042', 'f043', 'f044', 'f045', 'f046', 'f047', 'f048', 'f049', 'f050', 'f051', 'f052', 'f053', 'f054', 'f055', 'f056', 'f057', 'f058', 'f059', 'f060', 'f061', 'f062', 'f063', 'f064', 'f065', 'f066', 'f067', 'f068', 'f069', 'f070', 'f071', 'f072', 'f073', 'f074', 'f075', 'f076']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_a64ff7d892218d20/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_a64ff7d892218d20/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..5cc7eeab9c64309676e32355d44934436a995af4 --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_a64ff7d892218d20/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:09b17467176ddfd4d076e5b148be98b66cd970c9134bd40baf58d3103a898517 +size 730900 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_a64ff7d892218d20/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_a704736153fe48b1/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_a704736153fe48b1/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..95b458ebcf6d14bfc13856e0b27ef95a6ebe47b8 --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_a704736153fe48b1/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:66c7fc066d7735a73fd680f7be4dfa985a4a5d2dfc0f253f440988990545f5d2 +size 737188 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_a704736153fe48b1/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_ae2fae9ab36ffceb/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_ae2fae9ab36ffceb/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..930ae97b0f166c62fce928a944fb8c29d56e63ad --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_ae2fae9ab36ffceb/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bf6bf533f6c341a9eb872f511ba041dd239771819719d1edb30e46e9f29c9fcf +size 729622 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_ae2fae9ab36ffceb/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038', 'f039', 'f040', 'f041', 'f042', 'f043', 'f044', 'f045', 'f046', 'f047', 'f048', 'f049', 'f050', 'f051', 'f052', 'f053', 'f054', 'f055', 'f056', 'f057', 'f058', 'f059', 'f060', 'f061', 'f062', 'f063', 'f064', 'f065', 'f066', 'f067', 'f068', 'f069', 'f070', 'f071', 'f072', 'f073', 'f074', 'f075', 'f076']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_bfb50feeac3e923c/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_bfb50feeac3e923c/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..e2b40ec1970430919618c9446e1d974ae0ebdddf --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_bfb50feeac3e923c/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fefd76252e059a5baaf159935ca9a45d2963a4e1846c99aef8b05d90d8a491ff +size 731092 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_bfb50feeac3e923c/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038', 'f039', 'f040', 'f041', 'f042', 'f043', 'f044', 'f045', 'f046', 'f047', 'f048', 'f049', 'f050', 'f051', 'f052', 'f053', 'f054', 'f055', 'f056', 'f057', 'f058', 'f059', 'f060', 'f061', 'f062', 'f063', 'f064', 'f065', 'f066', 'f067', 'f068', 'f069', 'f070', 'f071', 'f072', 'f073', 'f074', 'f075', 'f076']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_c0c5a5936e54c142/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_c0c5a5936e54c142/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..4c153b559bed2bb1fb005c067a5bb2454afa9988 --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_c0c5a5936e54c142/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e6bb0444b8d4ff75c0607280d400708d6b8f4f5b95f4a72be4b910e645280a6d +size 736396 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_c0c5a5936e54c142/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_c256b2cd6db3e9ff/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_c256b2cd6db3e9ff/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..db3e7f0d2240cd8f82bfa123bd716be2b0981ce4 --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_c256b2cd6db3e9ff/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a5541df25402b8c8a6d142e3451950b1597bfe9846f43f799bb3fd8ae308ecd0 +size 734436 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_c256b2cd6db3e9ff/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_ce49a6caee3fde85/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_ce49a6caee3fde85/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..f975e594dc39d911652b53dc09feefc641489a4b --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_ce49a6caee3fde85/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:703fabdd81be8d82f44d6b163faa8939dbbf98f2d3133dd82e95b46de3593177 +size 738804 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_ce49a6caee3fde85/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038', 'f039', 'f040', 'f041', 'f042', 'f043', 'f044', 'f045', 'f046', 'f047', 'f048', 'f049', 'f050', 'f051', 'f052', 'f053', 'f054', 'f055', 'f056', 'f057', 'f058', 'f059', 'f060', 'f061', 'f062', 'f063', 'f064', 'f065', 'f066', 'f067', 'f068', 'f069', 'f070', 'f071', 'f072', 'f073', 'f074', 'f075', 'f076', 'f077', 'f078', 'f079', 'f080', 'f081', 'f082', 'f083', 'f084', 'f085', 'f086', 'f087', 'f088', 'f089', 'f090', 'f091', 'f092', 'f093', 'f094', 'f095', 'f096', 'f097', 'f098', 'f099', 'f100', 'f101', 'f102', 'f103', 'f104', 'f105', 'f106', 'f107', 'f108', 'f109', 'f110', 'f111', 'f112', 'f113', 'f114', 'f115', 'f116', 'f117', 'f118', 'f119', 'f120', 'f121', 'f122', 'f123', 'f124', 'f125', 'f126', 'f127', 'f128', 'f129', 'f130', 'f131', 'f132', 'f133', 'f134', 'f135', 'f136', 'f137', 'f138', 'f139', 'f140', 'f141', 'f142', 'f143', 'f144', 'f145', 'f146', 'f147', 'f148', 'f149', 'f150', 'f151', 'f152', 'f153']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + 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"f144", + "f145", + "f146", + "f147", + "f148", + "f149", + "f150", + "f151", + "f152", + "f153", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "f077", + "f078", + "f079", + "f080", + "f081", + "f082", + "f083", + "f084", 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No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_cfce3735f39f1b16/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_cfce3735f39f1b16/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..f722d8fbb3ed64ad6cb2ba9c110c8aa525b72bf8 --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_cfce3735f39f1b16/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:74220db3206c1779cfe771e81c1420c3ec7982ae8486d78c5ad8481dea49d232 +size 738700 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_cfce3735f39f1b16/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038', 'f039', 'f040', 'f041', 'f042', 'f043', 'f044', 'f045', 'f046', 'f047', 'f048', 'f049', 'f050', 'f051', 'f052', 'f053', 'f054', 'f055', 'f056', 'f057', 'f058', 'f059', 'f060', 'f061', 'f062', 'f063', 'f064', 'f065', 'f066', 'f067', 'f068', 'f069', 'f070', 'f071', 'f072', 'f073', 'f074', 'f075', 'f076']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_ef409a4ab28a98ac/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_ef409a4ab28a98ac/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..fe9cca9666ae0fac73a1c129919866b9a0a58a3f --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_ef409a4ab28a98ac/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:825abe8539193974689d30975517d1ed6172eed4672a0bfd18c55ef5afa916c3 +size 727276 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_ef409a4ab28a98ac/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_f5022e43ff07378c/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_f5022e43ff07378c/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..cfc11c7a4063265585786430c68c388a7b4186bd --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_f5022e43ff07378c/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:81b4801c929aa4bf26dcb38ab1616aee29f306096732543b3ed326a2e613bce8 +size 711305 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_01/c_case_f5022e43ff07378c/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_02/c_case_01843ff0421e37d8/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_02/c_case_01843ff0421e37d8/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..486aed97d1169e22e739713b3d2a9578028f4510 --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_02/c_case_01843ff0421e37d8/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:84a90da59dedab0a205f11e91862496b7ae81b225874ab2763b7c5bea17342a1 +size 1057818 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_02/c_case_01843ff0421e37d8/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_02/c_case_028db880681e020a/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_02/c_case_028db880681e020a/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..ffc16f8faf8d5efc95b9dbc6dd6df0e9c6f3a8a2 --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_02/c_case_028db880681e020a/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b13ba1e93d4a3d5640f5634d9e67b2d9ef770737d94dbdcd82bffb50b13147aa +size 1057962 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_02/c_case_028db880681e020a/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038', 'f039', 'f040', 'f041', 'f042', 'f043', 'f044', 'f045', 'f046', 'f047', 'f048', 'f049', 'f050', 'f051', 'f052', 'f053', 'f054', 'f055', 'f056', 'f057', 'f058', 'f059', 'f060', 'f061', 'f062', 'f063', 'f064', 'f065', 'f066', 'f067', 'f068', 'f069', 'f070', 'f071', 'f072', 'f073', 'f074', 'f075', 'f076']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038', 'f039', 'f040', 'f041', 'f042', 'f043', 'f044', 'f045', 'f046', 'f047', 'f048', 'f049', 'f050', 'f051', 'f052', 'f053', 'f054', 'f055', 'f056', 'f057', 'f058', 'f059', 'f060', 'f061', 'f062', 'f063', 'f064', 'f065', 'f066', 'f067', 'f068', 'f069', 'f070', 'f071', 'f072', 'f073', 'f074', 'f075', 'f076', 'f077', 'f078', 'f079', 'f080', 'f081', 'f082', 'f083', 'f084', 'f085', 'f086', 'f087', 'f088', 'f089', 'f090', 'f091', 'f092', 'f093', 'f094', 'f095', 'f096', 'f097', 'f098', 'f099', 'f100', 'f101', 'f102', 'f103', 'f104', 'f105', 'f106', 'f107', 'f108', 'f109', 'f110', 'f111', 'f112', 'f113', 'f114', 'f115', 'f116', 'f117', 'f118', 'f119', 'f120', 'f121', 'f122', 'f123', 'f124', 'f125', 'f126', 'f127', 'f128', 'f129', 'f130', 'f131', 'f132', 'f133', 'f134', 'f135', 'f136', 'f137', 'f138', 'f139', 'f140', 'f141', 'f142', 'f143', 'f144', 'f145', 'f146', 'f147', 'f148', 'f149', 'f150', 'f151', 'f152', 'f153']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + 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No labels from the prediction month are used.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)." +} diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_02/c_case_07f9f2d4ace02933/predictions.parquet b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_02/c_case_07f9f2d4ace02933/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..e634bacd35d1977d5390241fc6d91a6ff7cfd549 --- /dev/null +++ b/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_02/c_case_07f9f2d4ace02933/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a7dc8a491d8d010ff84981070d397bf9d8469380d56db16cd61d9573df6d6f81 +size 1061386 diff --git a/task_c/runs/dev/baselines/20260911_dev_84/rolling_elastic_net/block_02/c_case_07f9f2d4ace02933/rolling_audit.parquet 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as pd\nfrom sklearn.linear_model import Ridge, ElasticNet\nfrom sklearn.preprocessing import StandardScaler\n\n_FEATURES = ['f001', 'f002', 'f003', 'f004', 'f005', 'f006', 'f007', 'f008', 'f009', 'f010', 'f011', 'f012', 'f013', 'f014', 'f015', 'f016', 'f017', 'f018', 'f019', 'f020', 'f021', 'f022', 'f023', 'f024', 'f025', 'f026', 'f027', 'f028', 'f029', 'f030', 'f031', 'f032', 'f033', 'f034', 'f035', 'f036', 'f037', 'f038', 'f039', 'f040', 'f041', 'f042', 'f043', 'f044', 'f045', 'f046', 'f047', 'f048', 'f049', 'f050', 'f051', 'f052', 'f053', 'f054', 'f055', 'f056', 'f057', 'f058', 'f059', 'f060', 'f061', 'f062', 'f063', 'f064', 'f065', 'f066', 'f067', 'f068', 'f069', 'f070', 'f071', 'f072', 'f073', 'f074', 'f075', 'f076']\n\n\ndef fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Rolling ridge / elastic-net reference: fit on the labelled training\n # window only, no cross-validation, no hyperparameter search, no label\n # data from the prediction month. Anonymous characteristic columns are\n # imputed by training median, centred and scaled by training std.\n columns = list(_FEATURES)\n x_train = train_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n y_train = train_df[\"label\"].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n x_predict = predict_df[columns].apply(pd.to_numeric, errors=\"coerce\").to_numpy(dtype=float)\n\n # Impute missing features with the training medians (per column), falling\n # back to 0 for columns whose training median is undefined, then standardize.\n med = np.nanmedian(x_train, axis=0)\n med = np.where(np.isfinite(med), med, 0.0)\n x_train = np.where(np.isfinite(x_train), x_train, med)\n x_predict = np.where(np.isfinite(x_predict), x_predict, med)\n x_train = np.nan_to_num(x_train, nan=0.0, posinf=0.0, neginf=0.0)\n x_predict = np.nan_to_num(x_predict, nan=0.0, posinf=0.0, neginf=0.0)\n y_train = np.nan_to_num(y_train, nan=0.0, posinf=0.0, neginf=0.0)\n scaler = StandardScaler().fit(x_train)\n x_train = scaler.transform(x_train)\n x_predict = scaler.transform(x_predict)\n\n model = ElasticNet(alpha=0.005, l1_ratio=0.5, max_iter=2000)\n model.fit(x_train, y_train)\n score = model.predict(x_predict)\n np.nan_to_num(score, copy=False, nan=0.0, posinf=0.0, neginf=0.0)\n return pd.DataFrame({\"id\": predict_df[\"id\"].astype(str), \"score\": score.astype(float)})\n", + "entrypoint": "fit_and_predict", + "requirements": [ + "numpy", + "pandas", + "sklearn" + ], + "input_contract": { + "train_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "validation_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076", + "label" + ], + "predict_columns": [ + "id", + "time_key", + "f001", + "f002", + "f003", + "f004", + "f005", + "f006", + "f007", + "f008", + "f009", + "f010", + "f011", + "f012", + "f013", + "f014", + "f015", + "f016", + "f017", + "f018", + "f019", + "f020", + "f021", + "f022", + "f023", + "f024", + "f025", + "f026", + "f027", + "f028", + "f029", + "f030", + "f031", + "f032", + "f033", + "f034", + "f035", + "f036", + "f037", + "f038", + "f039", + "f040", + "f041", + "f042", + "f043", + "f044", + "f045", + "f046", + "f047", + "f048", + "f049", + "f050", + "f051", + "f052", + "f053", + "f054", + "f055", + "f056", + "f057", + "f058", + "f059", + "f060", + "f061", + "f062", + "f063", + "f064", + "f065", + "f066", + "f067", + "f068", + "f069", + "f070", + "f071", + "f072", + "f073", + "f074", + "f075", + "f076" + ] + }, + "output_contract": { + "columns": [ + "id", + "score" + ], + "one_row_per_id": true + }, + "explanation": "Rolling linear model reference: elastic-net (alpha=0.005, l1_ratio=0.5) fit on the labelled training window only, anonymous characteristic columns median-imputed and z-scored. 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