PaPP v2: Fase 3 - architettura classificatore extra-rendimento (walk-forward + OOS)

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

PaPP v2/Modello/src/evaluate.py

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+"""Metriche di valutazione del classificatore."""
+from __future__ import annotations
+import numpy as np
+from sklearn.metrics import roc_auc_score, accuracy_score, brier_score_loss
+
+
+def metrics(y_true, p_pred) -> dict:
+    y_true = np.asarray(y_true)
+    p = np.asarray(p_pred)
+    out = {"n": int(len(y_true)), "base_rate": float(y_true.mean())}
+    if len(np.unique(y_true)) < 2:
+        out.update({"auc": np.nan, "acc": np.nan, "brier": np.nan,
+                    "prec_top_decile": np.nan, "lift_top_decile": np.nan})
+        return out
+    out["auc"] = float(roc_auc_score(y_true, p))
+    out["acc"] = float(accuracy_score(y_true, (p >= 0.5).astype(int)))
+    out["brier"] = float(brier_score_loss(y_true, p))
+    # precisione sul decile a piu' alta probabilita' (uso pratico: prendo solo i segnali piu' forti)
+    k = max(1, int(0.1 * len(p)))
+    top = np.argsort(p)[-k:]
+    out["prec_top_decile"] = float(y_true[top].mean())
+    out["lift_top_decile"] = float(y_true[top].mean() / max(y_true.mean(), 1e-9))
+    return out