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'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 # Fit from scratch using labels available before this formation month.\n # shuffle=False preserves the chronological row order in each origin.\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 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 mean = x_train.mean(axis=0)\n scale = x_train.std(axis=0)\n scale = np.where(np.isfinite(scale) & (scale > 1e-12), scale, 1.0)\n x_train = (x_train - mean) / scale\n x_predict = (x_predict - mean) / scale\n base_seed = int(metadata.get(\"seed\", 1))\n scores = []\n for offset in (1,):\n model = MLPRegressor(\n hidden_layer_sizes=(64, 32), activation=\"relu\", solver=\"adam\",\n alpha=1e-4, batch_size=256, learning_rate_init=1e-3,\n max_iter=300, tol=1e-4, shuffle=False,\n early_stopping=False, random_state=base_seed + int(offset),\n )\n model.fit(x_train, y_train)\n scores.append(model.predict(x_predict))\n score = np.mean(np.asarray(scores, dtype=float), axis=0)\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 neural-network reference: two-hidden-layer MLP (64,32), ReLU/Adam, fixed seed(s), no row shuffling, fit from scratch on the labelled training window after median imputation and z-scoring.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_mlp)." +} diff --git a/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_84d38688c13c1d57/predictions.parquet 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'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 # Fit from scratch using labels available before this formation month.\n # shuffle=False preserves the chronological row order in each origin.\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 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 mean = x_train.mean(axis=0)\n scale = x_train.std(axis=0)\n scale = np.where(np.isfinite(scale) & (scale > 1e-12), scale, 1.0)\n x_train = (x_train - mean) / scale\n x_predict = (x_predict - mean) / scale\n base_seed = int(metadata.get(\"seed\", 1))\n scores = []\n for offset in (1,):\n model = MLPRegressor(\n hidden_layer_sizes=(64, 32), activation=\"relu\", solver=\"adam\",\n alpha=1e-4, batch_size=256, learning_rate_init=1e-3,\n max_iter=300, tol=1e-4, shuffle=False,\n early_stopping=False, random_state=base_seed + int(offset),\n )\n model.fit(x_train, y_train)\n scores.append(model.predict(x_predict))\n score = np.mean(np.asarray(scores, dtype=float), axis=0)\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 neural-network reference: two-hidden-layer MLP (64,32), ReLU/Adam, fixed seed(s), no row shuffling, fit from scratch on the labelled training window after median imputation and z-scoring.", + "revision_summary": "Deterministic non-LLM reference 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fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Fit from scratch using labels available before this formation month.\n # shuffle=False preserves the chronological row order in each origin.\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 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 mean = x_train.mean(axis=0)\n scale = x_train.std(axis=0)\n scale = np.where(np.isfinite(scale) & (scale > 1e-12), scale, 1.0)\n x_train = (x_train - mean) / scale\n x_predict = (x_predict - mean) / scale\n base_seed = int(metadata.get(\"seed\", 1))\n scores = []\n for offset in (1,):\n model = MLPRegressor(\n hidden_layer_sizes=(64, 32), activation=\"relu\", solver=\"adam\",\n alpha=1e-4, batch_size=256, learning_rate_init=1e-3,\n max_iter=300, tol=1e-4, shuffle=False,\n early_stopping=False, random_state=base_seed + int(offset),\n )\n model.fit(x_train, y_train)\n scores.append(model.predict(x_predict))\n score = np.mean(np.asarray(scores, dtype=float), axis=0)\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", 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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 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 mean = x_train.mean(axis=0)\n scale = x_train.std(axis=0)\n scale = np.where(np.isfinite(scale) & (scale > 1e-12), scale, 1.0)\n x_train = (x_train - mean) / scale\n x_predict = (x_predict - mean) / scale\n base_seed = int(metadata.get(\"seed\", 1))\n scores = []\n for offset in (1,):\n model = MLPRegressor(\n hidden_layer_sizes=(64, 32), activation=\"relu\", solver=\"adam\",\n alpha=1e-4, batch_size=256, learning_rate_init=1e-3,\n max_iter=300, tol=1e-4, shuffle=False,\n early_stopping=False, random_state=base_seed + int(offset),\n )\n model.fit(x_train, y_train)\n scores.append(model.predict(x_predict))\n score = np.mean(np.asarray(scores, dtype=float), axis=0)\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 neural-network reference: two-hidden-layer MLP (64,32), ReLU/Adam, fixed seed(s), no row shuffling, fit from scratch on the labelled training window after median imputation and z-scoring.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_mlp)." +} diff --git a/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_8e4ce247e267b28d/predictions.parquet b/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_8e4ce247e267b28d/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..56b60c825a052175fe75d0547bc9dd7b91a652f8 --- /dev/null +++ b/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_8e4ce247e267b28d/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19708a803d7356d1b73a8c0441da3c8d727a603ce8372deb4aafb157cbe4981f +size 330643 diff --git 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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 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 mean = x_train.mean(axis=0)\n scale = x_train.std(axis=0)\n scale = np.where(np.isfinite(scale) & (scale > 1e-12), scale, 1.0)\n x_train = (x_train - mean) / scale\n x_predict = (x_predict - mean) / scale\n base_seed = int(metadata.get(\"seed\", 1))\n scores = []\n for offset in (1,):\n model = MLPRegressor(\n hidden_layer_sizes=(64, 32), activation=\"relu\", solver=\"adam\",\n alpha=1e-4, batch_size=256, learning_rate_init=1e-3,\n max_iter=300, tol=1e-4, shuffle=False,\n early_stopping=False, random_state=base_seed + int(offset),\n )\n model.fit(x_train, y_train)\n scores.append(model.predict(x_predict))\n score = np.mean(np.asarray(scores, dtype=float), axis=0)\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 neural-network reference: two-hidden-layer MLP (64,32), ReLU/Adam, fixed seed(s), no row shuffling, fit from scratch on the labelled training window after median imputation and z-scoring.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_mlp)." +} diff --git a/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_8e9b35d038e88b48/predictions.parquet b/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_8e9b35d038e88b48/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..ef853b6ebe70891b06f7d0c121a7a4f2e01d2262 --- /dev/null +++ b/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_8e9b35d038e88b48/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c27905dcbdb126b1c200f2d02254743582dd843d67b77889ce31b5ffaab8b7cb +size 443624 diff --git 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scratch using labels available before this formation month.\n # shuffle=False preserves the chronological row order in each origin.\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 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 mean = x_train.mean(axis=0)\n scale = x_train.std(axis=0)\n scale = np.where(np.isfinite(scale) & (scale > 1e-12), scale, 1.0)\n x_train = (x_train - mean) / scale\n x_predict = (x_predict - mean) / scale\n base_seed = int(metadata.get(\"seed\", 1))\n scores = []\n for offset in (1,):\n model = MLPRegressor(\n hidden_layer_sizes=(64, 32), activation=\"relu\", solver=\"adam\",\n alpha=1e-4, batch_size=256, learning_rate_init=1e-3,\n max_iter=300, tol=1e-4, shuffle=False,\n early_stopping=False, random_state=base_seed + int(offset),\n )\n model.fit(x_train, y_train)\n scores.append(model.predict(x_predict))\n score = np.mean(np.asarray(scores, dtype=float), axis=0)\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", 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"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 neural-network reference: two-hidden-layer MLP (64,32), ReLU/Adam, fixed seed(s), no row shuffling, fit from scratch on the labelled training window after median imputation and z-scoring.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_mlp)." +} diff --git a/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_a64ff7d892218d20/predictions.parquet b/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_a64ff7d892218d20/predictions.parquet new file mode 100644 index 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fit_and_predict(train_df, valid_df, predict_df, metadata):\n # Fit from scratch using labels available before this formation month.\n # shuffle=False preserves the chronological row order in each origin.\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 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 mean = x_train.mean(axis=0)\n scale = x_train.std(axis=0)\n scale = np.where(np.isfinite(scale) & (scale > 1e-12), scale, 1.0)\n x_train = (x_train - mean) / scale\n x_predict = (x_predict - mean) / scale\n base_seed = int(metadata.get(\"seed\", 1))\n scores = []\n for offset in (1,):\n model = MLPRegressor(\n hidden_layer_sizes=(64, 32), activation=\"relu\", solver=\"adam\",\n alpha=1e-4, batch_size=256, learning_rate_init=1e-3,\n max_iter=300, tol=1e-4, shuffle=False,\n early_stopping=False, random_state=base_seed + int(offset),\n )\n model.fit(x_train, y_train)\n scores.append(model.predict(x_predict))\n score = np.mean(np.asarray(scores, dtype=float), axis=0)\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", 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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 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 mean = x_train.mean(axis=0)\n scale = x_train.std(axis=0)\n scale = np.where(np.isfinite(scale) & (scale > 1e-12), scale, 1.0)\n x_train = (x_train - mean) / scale\n x_predict = (x_predict - mean) / scale\n base_seed = int(metadata.get(\"seed\", 1))\n scores = []\n for offset in (1,):\n model = MLPRegressor(\n hidden_layer_sizes=(64, 32), activation=\"relu\", solver=\"adam\",\n alpha=1e-4, batch_size=256, learning_rate_init=1e-3,\n max_iter=300, tol=1e-4, shuffle=False,\n early_stopping=False, random_state=base_seed + int(offset),\n )\n model.fit(x_train, y_train)\n scores.append(model.predict(x_predict))\n score = np.mean(np.asarray(scores, dtype=float), axis=0)\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 neural-network reference: two-hidden-layer MLP (64,32), ReLU/Adam, fixed seed(s), no row shuffling, fit from scratch on the labelled training window after median imputation and z-scoring.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_mlp)." +} diff --git a/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_ae2fae9ab36ffceb/predictions.parquet b/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_ae2fae9ab36ffceb/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..2b12fd91fe199ab5cb7a91e99d8778139972e019 --- /dev/null +++ b/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_ae2fae9ab36ffceb/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3ee56fa885d7269910bbe166a3a07cdc485b381ad01642312c68d26febd8cc61 +size 670368 diff --git 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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 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 mean = x_train.mean(axis=0)\n scale = x_train.std(axis=0)\n scale = np.where(np.isfinite(scale) & (scale > 1e-12), scale, 1.0)\n x_train = (x_train - mean) / scale\n x_predict = (x_predict - mean) / scale\n base_seed = int(metadata.get(\"seed\", 1))\n scores = []\n for offset in (1,):\n model = MLPRegressor(\n hidden_layer_sizes=(64, 32), activation=\"relu\", solver=\"adam\",\n alpha=1e-4, batch_size=256, learning_rate_init=1e-3,\n max_iter=300, tol=1e-4, shuffle=False,\n early_stopping=False, random_state=base_seed + int(offset),\n )\n model.fit(x_train, y_train)\n scores.append(model.predict(x_predict))\n score = np.mean(np.asarray(scores, dtype=float), axis=0)\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 neural-network reference: two-hidden-layer MLP (64,32), ReLU/Adam, fixed seed(s), no row shuffling, fit from scratch on the labelled training window after median imputation and z-scoring.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_mlp)." +} diff --git a/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_bd44d3c656943445/predictions.parquet b/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_bd44d3c656943445/predictions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..4962cb26dcc51395a4b726889f79f7ed20331b8c --- /dev/null +++ b/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_bd44d3c656943445/predictions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:600bb6a8770593a28fb1b548c4b20ba8a4d2ac750892ee99f7dddde3489491bf +size 684644 diff --git 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b/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_bea2e131b792888b/baseline_response.json @@ -0,0 +1,265 @@ +{ + "revision": 0, + "status": "final", + "language": "python", + "code": "import numpy as np\nimport pandas as pd\nfrom sklearn.neural_network import MLPRegressor\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 # Fit from scratch using labels available before this formation month.\n # shuffle=False preserves the chronological row order in each origin.\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 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 mean = x_train.mean(axis=0)\n scale = x_train.std(axis=0)\n scale = np.where(np.isfinite(scale) & (scale > 1e-12), scale, 1.0)\n x_train = (x_train - mean) / scale\n x_predict = (x_predict - mean) / scale\n base_seed = int(metadata.get(\"seed\", 1))\n scores = []\n for offset in (1,):\n model = MLPRegressor(\n hidden_layer_sizes=(64, 32), activation=\"relu\", solver=\"adam\",\n alpha=1e-4, batch_size=256, learning_rate_init=1e-3,\n max_iter=300, tol=1e-4, shuffle=False,\n early_stopping=False, random_state=base_seed + int(offset),\n )\n model.fit(x_train, y_train)\n scores.append(model.predict(x_predict))\n score = np.mean(np.asarray(scores, dtype=float), axis=0)\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 neural-network reference: two-hidden-layer MLP (64,32), ReLU/Adam, fixed seed(s), no row shuffling, fit from scratch on the labelled training window after median imputation and z-scoring.", + "revision_summary": "Deterministic non-LLM reference baseline (rolling_mlp)." +} diff --git a/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_bea2e131b792888b/predictions.parquet b/task_c/runs/dev/baselines/20260910_dev_mlp/rolling_mlp/block_01/c_case_bea2e131b792888b/predictions.parquet new file mode 100644 index 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