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Add task_c runs (part 9)

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+ "status": "final",
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+ "language": "python",
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+ "code": "import numpy as np\nimport pandas 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",
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+ "requirements": [
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+ "revision_summary": "Deterministic non-LLM reference baseline (rolling_elastic_net)."
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