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Add task_c runs (part 3)

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+ {
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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.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']\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",
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+ "requirements": [
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+ "numpy",
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+ "sklearn"
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+ "one_row_per_id": true
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+ "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.",
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+ "revision_summary": "Deterministic non-LLM reference baseline (rolling_mlp)."
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