"""Re-score saved predictions with the current eval.py (no re-running models).

Reads every ``experiments/predictions/<method>_<task>_seed<seed>[_setX].pkl``,
calls ``ml.score`` with the current ``eval.py``, writes a fresh
``RunRecord`` JSON to ``experiments/results/canon_reeval_<timestamp>.json``.

Use this whenever ``eval.py`` is patched: regenerates metrics from cached
predictions in ~seconds, no LLM/GPU spend.
"""
from __future__ import annotations
import argparse, json, pickle, pathlib, sys
import numpy as np
import pandas as pd

def main():
    sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[2]))
    from whatif_bench import config
    from whatif_bench import macrolens as ml

    pred_dir = pathlib.Path(__file__).parent / "predictions"
    out_dir = pathlib.Path(__file__).parent / "results"
    out_path = out_dir / f"canon_reeval_{pd.Timestamp.utcnow().strftime('%Y%m%dT%H%M%SZ')}.json"
    records = []
    pkls = sorted(pred_dir.glob("*.pkl"))
    print(f"re-evaluating {len(pkls)} prediction files")
    for p in pkls:
        with open(p, "rb") as f:
            d = pickle.load(f)
        task = d["task"]
        meta_test = d["meta_test"]
        y_test = d["y_test"]
        y_pred = d["y_pred"]
        # cluster keys
        if task == "T4":
            ck = meta_test["scenario_id"].values if "scenario_id" in meta_test.columns else None
        elif task == "T7":
            ck = meta_test["address"].values if "address" in meta_test.columns else None
        elif "ticker" in meta_test.columns:
            ck = meta_test["ticker"].values
        else:
            ck = None
        kw = {"cluster_keys": ck}
        if task == "T1" and "close_last" in meta_test.columns:
            kw["close_last"] = meta_test["close_last"].values
        try:
            metrics = ml.score(task, y_test, y_pred, **kw)
        except Exception as exc:
            print(f"  {p.name}: score raised {type(exc).__name__}: {exc}")
            continue
        # Build a record (mirroring RunRecord essentials)
        rec = {
            "method_id": d["method_id"],
            "task": task,
            "granularity": d.get("granularity", "daily"),
            "seed": d.get("seed", 42),
            "status": "ok",
            "ablation_setting": d.get("ablation_setting"),
            "timestamp": pd.Timestamp.utcnow().isoformat(),
            "metrics": {k: (v.model_dump() if hasattr(v,"model_dump") else v) for k,v in metrics.items()},
        }
        records.append(rec)
        primary = {"T1":"mse","T2":"median_ape","T3":"overall_mape","T4":"return_mae_pct",
                   "T5":"median_ape","T6":"overall_mape","T7":"rent_MAPE"}.get(task)
        pv = (metrics or {}).get(primary)
        pv = pv.value if pv is not None and hasattr(pv, "value") else None
        print(f"  {d['method_id']:18s} {task} setting={d.get('ablation_setting')}  {primary}={pv}")
    out_path.write_text(json.dumps(records, indent=2, default=str))
    print(f"\nwrote {len(records)} re-evaluated records to {out_path}")

if __name__ == "__main__":
    main()