"""Subgroup robustness analysis (P10, Table 5 part 2).

Evaluates any model's saved OOF predictions sliced by demographic subgroup:
age bands (60-69, 70-79, 80+) and sex. Tests whether a model's performance is
stable across groups or driven by an age->label correlation.

Consumes the per-subject OOF prediction CSVs written by eval/runner.py (columns:
subject_id, seed, fold, y_true, y_pred, prob_0, prob_1, prob_2) joined to the
cohort table for age/sex, so no re-training is needed.
"""
from __future__ import annotations

from pathlib import Path

import numpy as np
import pandas as pd

from trifuse.eval.metrics import compute_metrics

AGE_BANDS = [("60-69", 60, 70), ("70-79", 70, 80), ("80+", 80, 200)]


def _band(age: float) -> str:
    for name, lo, hi in AGE_BANDS:
        if lo <= age < hi:
            return name
    return "other"


def subgroup_table(oof_csv: str | Path, subjects_csv: str | Path) -> pd.DataFrame:
    """Return per-subgroup metrics (pooled over all seeds/folds) for one model."""
    oof = pd.read_csv(oof_csv)
    sub = pd.read_csv(subjects_csv)[["subject_id", "age", "sex"]]
    df = oof.merge(sub, on="subject_id", how="left")
    df["age_band"] = df["age"].apply(_band)
    df["sex_str"] = df["sex"].map({0: "Male", 1: "Female"}).fillna("Unknown")

    prob_cols = ["prob_0", "prob_1", "prob_2"]

    def _row(name: str, sl: pd.DataFrame) -> dict:
        if len(sl) == 0:
            return {"subgroup": name, "n": 0}
        m = compute_metrics(sl["y_true"].to_numpy(), sl["y_pred"].to_numpy(),
                            sl[prob_cols].to_numpy())
        return {"subgroup": name, "n": int(sl["subject_id"].nunique()),
                "macro_f1": m["macro_f1"], "balanced_accuracy": m["balanced_accuracy"],
                "ad_recall": m["recall_AD"]}

    rows = [_row("Overall", df)]
    for name, _, _ in AGE_BANDS:
        rows.append(_row(name, df[df["age_band"] == name]))
    for sx in ["Male", "Female"]:
        rows.append(_row(sx, df[df["sex_str"] == sx]))
    return pd.DataFrame(rows)


def run_subgroup(oof_csvs: dict[str, str | Path], subjects_csv: str | Path,
                 out_dir: str | Path) -> pd.DataFrame:
    """oof_csvs: {model_name: path}. Builds a combined subgroup table across models."""
    out_dir = Path(out_dir); out_dir.mkdir(parents=True, exist_ok=True)
    frames = []
    for name, path in oof_csvs.items():
        t = subgroup_table(path, subjects_csv)
        t.insert(0, "model", name)
        frames.append(t)
    res = pd.concat(frames, ignore_index=True)
    res.to_csv(out_dir / "subgroup_summary.csv", index=False)
    return res


if __name__ == "__main__":
    import sys, glob
    subj = sys.argv[1] if len(sys.argv) > 1 else "data/metadata/subjects_clean.csv"
    oof_dir = sys.argv[2] if len(sys.argv) > 2 else "results/oof"
    csvs = {Path(p).stem.replace("oof_", ""): p for p in glob.glob(f"{oof_dir}/oof_*.csv")}
    print(run_subgroup(csvs, subj, "results/tables/subgroup").to_string(index=False))