| """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"] |
| |
| 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 |
| |
| 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() |
|
|
