Datasets:
File size: 3,086 Bytes
029e02e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | """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()
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