File size: 8,701 Bytes
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import argparse
import json
import signal
import sys
from collections import Counter
from contextlib import contextmanager
from pathlib import Path
from typing import Iterable, Optional
from val_math import (
MATH_VERIFY_IMPORT_ERROR,
MatchResult,
SYMPY_IMPORT_ERROR,
cleanup_candidate,
compare_candidates,
extract_final_marked_candidates,
extract_llm_final_answer_candidates,
extract_answer_candidates,
read_jsonl_to_list,
tqdm,
)
try:
from datasets import load_dataset
except Exception as exc: # pragma: no cover - depends on local env
load_dataset = None
DATASETS_IMPORT_ERROR = exc
else:
DATASETS_IMPORT_ERROR = None
class EvaluationTimeoutError(TimeoutError):
pass
@contextmanager
def time_limit(seconds: Optional[float]):
if seconds is None or seconds <= 0:
yield
return
def _handle_timeout(signum, frame):
raise EvaluationTimeoutError(f"example evaluation exceeded {seconds} seconds")
previous_handler = signal.getsignal(signal.SIGALRM)
signal.signal(signal.SIGALRM, _handle_timeout)
signal.setitimer(signal.ITIMER_REAL, seconds)
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL, 0)
signal.signal(signal.SIGALRM, previous_handler)
def load_ground_truth_examples(dataset_name: str, split: str, gt_jsonl_path: Optional[str]) -> list[dict[str, Any]]:
if gt_jsonl_path:
return read_jsonl_to_list(gt_jsonl_path)
if load_dataset is None:
raise RuntimeError(
"datasets is not installed, so ground truth cannot be loaded from Hugging Face. "
"Please install datasets or pass --gt-jsonl-path."
) from DATASETS_IMPORT_ERROR
return list(load_dataset(dataset_name, "algebra", split=split))
def extract_ground_truth_solution_candidates(example: dict) -> list[str]:
solution_text = str(example.get("solution", "") or "").strip()
if not solution_text:
return []
candidates = extract_final_marked_candidates(solution_text)
if not candidates:
candidates.extend(extract_answer_candidates(solution_text))
if not candidates:
candidates.append(cleanup_candidate(solution_text))
return [item for item in candidates if item]
def evaluate_example(example: dict, prediction_row: dict) -> tuple:
pred_raw = str(prediction_row.get("answer", ""))
pred_candidates = extract_llm_final_answer_candidates(pred_raw)
gold_candidates = extract_ground_truth_solution_candidates(example)
if not pred_candidates and pred_raw.strip():
pred_candidates = [cleanup_candidate(pred_raw)]
result = compare_candidates(
gold_candidates=gold_candidates,
pred_candidates=pred_candidates,
)
return result, gold_candidates, pred_candidates
def build_argparser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
description="High-precision MATH evaluator using final answers extracted from ground-truth solutions"
)
parser.add_argument(
"--pred-path",
default="outputs/minerva_math_algebra/rank_0.jsonl",
help="Path to the model prediction jsonl file.",
)
parser.add_argument(
"--dataset-name",
default="EleutherAI/hendrycks_math",
help="Hugging Face dataset name for ground truth.",
)
parser.add_argument("--split", default="test", help="Dataset split.")
parser.add_argument(
"--gt-jsonl-path",
default=None,
help="Optional local jsonl ground truth path. If set, datasets will not be used.",
)
parser.add_argument("--limit", type=int, default=None, help="Evaluate only the first N examples.")
parser.add_argument(
"--print-wrong",
type=int,
default=0,
help="How many mismatched examples to print for debugging.",
)
parser.add_argument(
"--details-path",
default=None,
help="Optional jsonl path for saving per-example evaluation details. "
"If not set, a file will be created next to the prediction file automatically.",
)
parser.add_argument(
"--per-example-timeout",
type=float,
default=8.0,
help="Maximum seconds allowed for one example before it is skipped as a timeout.",
)
return parser
def main(argv: Optional[list[str]] = None) -> int:
args = build_argparser().parse_args(argv)
pred_path = Path(args.pred_path)
if not pred_path.exists():
print(f"Prediction file not found: {pred_path}", file=sys.stderr)
return 1
gt_examples = load_ground_truth_examples(args.dataset_name, args.split, args.gt_jsonl_path)
pred_examples = read_jsonl_to_list(str(pred_path))
if args.limit is not None:
gt_examples = gt_examples[: args.limit]
pred_examples = pred_examples[: args.limit]
total = min(len(gt_examples), len(pred_examples))
if total == 0:
print("No examples to evaluate.", file=sys.stderr)
return 1
if len(gt_examples) != len(pred_examples):
print(
f"[warning] ground truth count = {len(gt_examples)}, prediction count = {len(pred_examples)}; "
f"evaluating the first {total} pairs only.",
file=sys.stderr,
)
method_counter: Counter[str] = Counter()
wrong_printed = 0
correct = 0
details_path = Path(args.details_path) if args.details_path else pred_path.with_name(f"{pred_path.stem}_eval_details_solution.jsonl")
details_fh = open(details_path, "w", encoding="utf-8")
indices: Iterable[int] = range(total)
if tqdm is not None:
indices = tqdm(indices, total=total, desc="Evaluating MATH (solution GT)", unit="sample")
try:
for idx in indices:
example = gt_examples[idx]
prediction_row = pred_examples[idx]
try:
with time_limit(args.per_example_timeout):
result, gold_candidates, pred_candidates = evaluate_example(example, prediction_row)
except EvaluationTimeoutError:
result = MatchResult(False, "example_timeout")
gold_candidates = extract_ground_truth_solution_candidates(example)
pred_candidates = []
except Exception as exc:
result = MatchResult(False, f"example_error:{type(exc).__name__}")
gold_candidates = extract_ground_truth_solution_candidates(example)
pred_candidates = []
if result.correct:
correct += 1
method_counter[result.method] += 1
detail_row = {
"index": idx,
"correct": result.correct,
"method": result.method,
"problem": example.get("problem"),
"ground_truth_answer": example.get("answer"),
"ground_truth_solution": example.get("solution"),
"llm_response": prediction_row.get("answer", ""),
"gold_candidate": result.gold_candidate,
"pred_candidate": result.pred_candidate,
"ground_truth_solution_candidates": gold_candidates,
"llm_final_answer_candidates": pred_candidates,
}
details_fh.write(json.dumps(detail_row, ensure_ascii=False) + "\n")
if not result.correct and wrong_printed < args.print_wrong:
wrong_printed += 1
print("=" * 80)
print(f"Index: {idx}")
print(f"Problem: {example.get('problem', '')}")
print(f"Gold candidates: {gold_candidates[:5]}")
print(f"Pred candidates: {pred_candidates[:5]}")
print("Raw prediction tail:")
print(str(prediction_row.get("answer", ""))[-800:])
finally:
details_fh.close()
accuracy = correct / total
print("=" * 80)
print(f"Total: {total}")
print(f"Correct: {correct}")
print(f"Accuracy: {accuracy:.4%}")
print(f"Saved details: {details_path}")
print("Match breakdown:")
for method, count in method_counter.most_common():
print(f" {method}: {count}")
if MATH_VERIFY_IMPORT_ERROR is not None:
print(
f"[warning] math_verify unavailable, matching quality is reduced: {MATH_VERIFY_IMPORT_ERROR}",
file=sys.stderr,
)
if SYMPY_IMPORT_ERROR is not None:
print(
f"[warning] sympy unavailable, symbolic fallback is reduced: {SYMPY_IMPORT_ERROR}",
file=sys.stderr,
)
return 0
if __name__ == "__main__":
raise SystemExit(main())
|