"""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