"""Metrics and experiment result containers."""
from __future__ import annotations

import numpy as np
from sklearn.metrics import roc_auc_score


def auroc(scores, labels):
    scores = np.asarray(scores, dtype=np.float64)
    labels = np.asarray(labels)
    if len(np.unique(labels)) < 2:
        return float("nan")
    return float(roc_auc_score(labels, scores))


def sample_auroc_table(view_scores, labels, cats):
    """Fixed-strategy S-AUROC per category + macro. Returns dict."""
    from .fusion import fuse_all
    fused = fuse_all(view_scores)
    res = {}
    for name, s in fused.items():
        per = {}
        for cat in np.unique(cats):
            m = cats == cat
            per[cat] = auroc(s[m], labels[m])
        per["macro"] = float(np.nanmean(list(per.values())))
        res[name] = per
    return res