File size: 2,750 Bytes
32030e4 | 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 | #!/usr/bin/env python3
"""Score predictions produced by your own harness.
Input: JSON lines with ``task_id`` and either ``response`` (the answer object) or ``text``
(raw model output; the final JSON object is extracted). An optional ``sample`` field allows
several samples per task. Multi-step tasks must be run interactively (``run_model.py`` or
``StoredDataEnv``); here only their final submitted answer is scored.
python eval/score_predictions.py --dataset . --split test --predictions preds.jsonl
"""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent))
from ejbench_eval.data import load_tasks # noqa: E402
from ejbench_eval.parsing import extract_json # noqa: E402
from ejbench_eval.report import summarise # noqa: E402
from ejbench_eval.scoring import score_task # noqa: E402
def main(argv: list[str] | None = None) -> int:
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("--dataset", default=str(Path(__file__).resolve().parents[1]))
ap.add_argument("--split", default="test", choices=("train", "test", "test_unseen_cell"))
ap.add_argument("--predictions", required=True)
ap.add_argument("--out", default=None)
args = ap.parse_args(argv)
tasks = {task["task_id"]: task for task in load_tasks(args.dataset, args.split)}
rows, seen = [], set()
for line in Path(args.predictions).read_text().splitlines():
if not line.strip():
continue
pred = json.loads(line)
task = tasks.get(pred["task_id"])
if task is None:
raise KeyError(f"{pred['task_id']} is not in the {args.split} split")
answer = pred.get("response")
if answer is None:
answer = extract_json(pred.get("text"))
result = score_task(task, answer if isinstance(answer, dict) else {})
seen.add(task["task_id"])
rows.append({"task_id": task["task_id"], "family": task["family"], "tags": task.get("tags"),
"parsed": answer, **result})
missing = sorted(set(tasks) - seen)
for task_id in missing: # unanswered tasks score 0
task = tasks[task_id]
rows.append({"task_id": task_id, "family": task["family"], "tags": task.get("tags"),
"parsed": None, "score": 0.0, "calibration_sq_error": None})
summary = {"split": args.split, "missing_tasks_scored_zero": len(missing), **summarise(rows)}
if args.out:
Path(args.out).write_text(json.dumps(summary, indent=2) + "\n")
print(json.dumps(summary, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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