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src/models/meta.py

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+# src/models/meta.py
+import numpy as np
+from sklearn.linear_model import RidgeCV
+
+
+def fit_meta(oof_matrix: np.ndarray, y_train: np.ndarray,
+             test_matrix: np.ndarray) -> tuple:
+    """RidgeCV meta-learner on OOF predictions."""
+    meta  = RidgeCV(alphas=np.logspace(-3, 3, 50), cv=5)
+    meta.fit(oof_matrix, y_train)
+    preds = meta.predict(test_matrix)
+    print(f"  Meta alpha: {meta.alpha_:.4f}  "
+          f"coef range: [{meta.coef_.min():.3f}, {meta.coef_.max():.3f}]")
+    return meta, preds
+
+
+# src/models/calibration.py
+import numpy as np
+from sklearn.isotonic import IsotonicRegression
+from sklearn.model_selection import KFold
+
+
+def fit_isotonic(oof_preds: np.ndarray, y_train: np.ndarray,
+                 test_preds: np.ndarray) -> tuple:
+    """
+    Fits isotonic regression on OOF meta-predictions.
+    OOF predictions are unbiased — no test leakage.
+    Includes CV check: if test improves >> CV estimate, flag it.
+    """
+    # CV estimate of benefit
+    kf = KFold(n_splits=5, shuffle=True, random_state=42)
+    cv_raw, cv_cal = [], []
+    for tri, vali in kf.split(oof_preds):
+        iso = IsotonicRegression(out_of_bounds='clip')
+        iso.fit(oof_preds[tri], y_train[tri])
+        p = iso.predict(oof_preds[vali])
+        cv_raw.append(np.sqrt(np.mean((oof_preds[vali] - y_train[vali])**2)))
+        cv_cal.append(np.sqrt(np.mean((p              - y_train[vali])**2)))
+
+    cv_gain = np.mean(cv_raw) - np.mean(cv_cal)
+    print(f"  Isotonic CV RMSE: {np.mean(cv_raw):.4f} → {np.mean(cv_cal):.4f}  "
+          f"(gain={cv_gain:+.4f})")
+
+    # Fit on full OOF
+    iso_full = IsotonicRegression(out_of_bounds='clip')
+    iso_full.fit(oof_preds, y_train)
+    preds_cal = iso_full.predict(test_preds)
+
+    return iso_full, preds_cal