"""Evaluation metrics for error prediction.

We report:
- ROC-AUC of wrongness score vs binary `correct/incorrect` label.
- PR-AUC (positive class = wrong).
- AURC (area under risk-coverage).
- Accuracy-Rejection Curve area *above baseline* (the paper's headline metric).
- ECE of the underlying classifier (constant across feature variants, included for context).
"""
from __future__ import annotations

from typing import Dict

import numpy as np
from sklearn.metrics import average_precision_score, roc_auc_score


def accuracy_rejection_curve(
    wrongness_scores: np.ndarray,
    y_true: np.ndarray,
    y_pred: np.ndarray,
    step: int = 1,
) -> Dict[str, np.ndarray]:
    """ARC à la Nadeem et al. 2009 — sort by ascending confidence (= descending wrongness),
    drop the least confident first, recompute accuracy on the remaining set.
    """
    N = len(wrongness_scores)
    order = np.argsort(-wrongness_scores)  # most-likely-wrong first
    r_rates, accs = [0.0], [float((y_pred == y_true).mean())]
    for i in range(step, N, step):
        keep = order[i:]
        if len(keep) == 0:
            break
        r_rates.append(i / N)
        accs.append(float((y_pred[keep] == y_true[keep]).mean()))
    return {"rejection_rate": np.asarray(r_rates), "accuracy": np.asarray(accs)}


def arc_auc_above_baseline(arc: Dict[str, np.ndarray]) -> float:
    """Trapezoid-integrate (accuracy - baseline_accuracy) wrt rejection_rate."""
    r = arc["rejection_rate"]
    a = arc["accuracy"]
    baseline = a[0]
    return float(np.trapz(np.maximum(a - baseline, 0), r))


def aurc(wrongness_scores: np.ndarray, y_true: np.ndarray, y_pred: np.ndarray) -> float:
    N = len(wrongness_scores)
    order = np.argsort(wrongness_scores)  # ascending wrongness → most confident first
    cum_errors = np.cumsum((y_pred[order] != y_true[order]).astype(np.float64))
    coverage = (np.arange(1, N + 1)) / N
    risk = cum_errors / np.arange(1, N + 1)
    return float(np.trapz(risk, coverage))


def expected_calibration_error(probs_max: np.ndarray, correct: np.ndarray, n_bins: int = 15) -> float:
    bins = np.linspace(0.0, 1.0, n_bins + 1)
    ece = 0.0
    N = len(probs_max)
    for i in range(n_bins):
        lo, hi = bins[i], bins[i + 1]
        mask = (probs_max > lo) & (probs_max <= hi)
        if mask.sum() == 0:
            continue
        acc = correct[mask].mean()
        conf = probs_max[mask].mean()
        ece += (mask.sum() / N) * abs(acc - conf)
    return float(ece)


def evaluate_all(
    wrongness_scores: np.ndarray,
    y_true: np.ndarray,
    y_pred: np.ndarray,
    probs_max: np.ndarray,
) -> Dict[str, float]:
    wrong = (y_pred != y_true).astype(int)
    metrics: Dict[str, float] = {}
    if len(np.unique(wrong)) > 1:
        metrics["roc_auc_wrong"] = float(roc_auc_score(wrong, wrongness_scores))
        metrics["pr_auc_wrong"] = float(average_precision_score(wrong, wrongness_scores))
    else:
        metrics["roc_auc_wrong"] = float("nan")
        metrics["pr_auc_wrong"] = float("nan")
    arc = accuracy_rejection_curve(wrongness_scores, y_true, y_pred, step=1)
    metrics["arc_auc_above_baseline"] = arc_auc_above_baseline(arc)
    metrics["aurc"] = aurc(wrongness_scores, y_true, y_pred)
    metrics["ece"] = expected_calibration_error(probs_max, (y_true == y_pred).astype(float))
    metrics["base_accuracy"] = float((y_pred == y_true).mean())
    return metrics