Datasets:
Download scripts/audit_downloaded_task_sources.py from arpandeepk/code-atlas-provenance: direct link, hf CLI and curl.
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- Download file 7.17 kB
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https://huggingface.co/datasets/arpandeepk/code-atlas-provenance/resolve/main/scripts/audit_downloaded_task_sources.py
- Command line
-
hf download hf://datasets/arpandeepk/code-atlas-provenance/scripts/audit_downloaded_task_sources.py
-
curl -L -o audit_downloaded_task_sources.py https://huggingface.co/datasets/arpandeepk/code-atlas-provenance/resolve/main/scripts/audit_downloaded_task_sources.py
7.17 kB
| #!/usr/bin/env python3 | |
| """Audit pinned task datasets without assigning train/evaluation roles.""" | |
| from __future__ import annotations | |
| import argparse | |
| import csv | |
| import hashlib | |
| import json | |
| import re | |
| from pathlib import Path | |
| import pyarrow.parquet as pq | |
| def sha256(path: Path) -> str: | |
| digest = hashlib.sha256() | |
| with path.open("rb") as handle: | |
| for chunk in iter(lambda: handle.read(1024 * 1024), b""): | |
| digest.update(chunk) | |
| return digest.hexdigest() | |
| def write_csv(path: Path, rows: list[dict[str, object]]) -> None: | |
| path.parent.mkdir(parents=True, exist_ok=True) | |
| with path.open("w", newline="") as handle: | |
| writer = csv.DictWriter(handle, fieldnames=list(rows[0])) | |
| writer.writeheader() | |
| writer.writerows(rows) | |
| def audit_swe(path: Path, output: Path, revision: str) -> None: | |
| table = pq.read_table(path, columns=["language", "repo", "base_commit", "license"]) | |
| data = table.to_pydict() | |
| languages = sorted(set(data["language"])) | |
| rows = [] | |
| for language in languages: | |
| indices = [i for i, value in enumerate(data["language"]) if value == language] | |
| rows.append( | |
| { | |
| "language": language, | |
| "tasks": len(indices), | |
| "unique_repositories": len({data["repo"][i] for i in indices}), | |
| "unique_base_commits": len({data["base_commit"][i] for i in indices}), | |
| "license_values": len({data["license"][i] for i in indices}), | |
| "source_revision": revision, | |
| "source_sha256": sha256(path), | |
| } | |
| ) | |
| rows.sort(key=lambda row: (-int(row["tasks"]), str(row["language"]))) | |
| write_csv(output, rows) | |
| def audit_mceval_instruct(path: Path, output: Path, revision: str) -> None: | |
| records = json.loads(path.read_text()) | |
| grouped: dict[str, list[dict[str, str]]] = {} | |
| for record in records: | |
| grouped.setdefault(record["language"].casefold(), []).append(record) | |
| rows = [] | |
| for language, group in grouped.items(): | |
| rows.append( | |
| { | |
| "normalized_language": language, | |
| "rows": len(group), | |
| "unique_instructions": len({record["instruction"] for record in group}), | |
| "unique_outputs": len({record["output"] for record in group}), | |
| "output_utf8_bytes": sum(len(record["output"].encode()) for record in group), | |
| "original_labels": ";".join(sorted({record["language"] for record in group})), | |
| "source_revision": revision, | |
| "source_sha256": sha256(path), | |
| } | |
| ) | |
| rows.sort(key=lambda row: (-int(row["rows"]), str(row["normalized_language"]))) | |
| write_csv(output, rows) | |
| def audit_mceval_eval(root: Path, output: Path, revision: str) -> None: | |
| grouped: dict[tuple[str, str], dict[str, object]] = {} | |
| source_files = sorted(root.glob("generation/*.jsonl")) | |
| source_files += sorted(root.glob("explanation/*.jsonl")) | |
| source_files += sorted(root.glob("completion/*/*.jsonl")) | |
| for path in source_files: | |
| relative = path.relative_to(root) | |
| surface = "/".join(relative.parts[:-1]) | |
| language = path.stem | |
| group = grouped.setdefault( | |
| (surface, language), | |
| {"rows": 0, "base_ids": set(), "solutions": set(), "tests": set()}, | |
| ) | |
| with path.open() as handle: | |
| for line in handle: | |
| record = json.loads(line) | |
| group["rows"] = int(group["rows"]) + 1 | |
| match = re.match(r"^([^/]+)/(\d+)", record["task_id"]) | |
| group["base_ids"].add(match.group(2) if match else record["task_id"]) | |
| group["solutions"].add(record.get("canonical_solution", "")) | |
| group["tests"].add(record.get("test", "")) | |
| tree_hash = hashlib.sha256() | |
| for path in source_files: | |
| tree_hash.update(str(path.relative_to(root)).encode()) | |
| tree_hash.update(bytes.fromhex(sha256(path))) | |
| rows = [] | |
| for (surface, language), group in grouped.items(): | |
| rows.append( | |
| { | |
| "surface": surface, | |
| "language": language, | |
| "rows": group["rows"], | |
| "unique_base_problem_ids": len(group["base_ids"]), | |
| "unique_canonical_solutions": len(group["solutions"]), | |
| "unique_tests": len(group["tests"]), | |
| "source_revision": revision, | |
| "audited_tree_sha256": tree_hash.hexdigest(), | |
| } | |
| ) | |
| rows.sort(key=lambda row: (str(row["surface"]), str(row["language"]))) | |
| write_csv(output, rows) | |
| def audit_swe_leaderboard(root: Path, output: Path, revision: str) -> None: | |
| """Inventory every frozen leaderboard split without treating it as training data.""" | |
| source_files = sorted((root / "data").glob("*.parquet")) | |
| if not source_files: | |
| raise FileNotFoundError(f"no parquet files found under {root / 'data'}") | |
| rows = [] | |
| for path in source_files: | |
| table = pq.read_table(path, columns=["repo", "instance_id", "base_commit"]) | |
| data = table.to_pydict() | |
| rows.append( | |
| { | |
| "split": path.stem.split("-00000", 1)[0], | |
| "rows": table.num_rows, | |
| "unique_instances": len(set(data["instance_id"])), | |
| "unique_repositories": len(set(data["repo"])), | |
| "unique_base_commits": len(set(data["base_commit"])), | |
| "source_revision": revision, | |
| "file_sha256": sha256(path), | |
| } | |
| ) | |
| write_csv(output, rows) | |
| def main() -> None: | |
| parser = argparse.ArgumentParser() | |
| subparsers = parser.add_subparsers(dest="source", required=True) | |
| swe = subparsers.add_parser("swe-rebench-v2") | |
| swe.add_argument("--input", type=Path, required=True) | |
| swe.add_argument("--output", type=Path, required=True) | |
| swe.add_argument("--revision", required=True) | |
| mceval = subparsers.add_parser("mceval-instruct") | |
| mceval.add_argument("--input", type=Path, required=True) | |
| mceval.add_argument("--output", type=Path, required=True) | |
| mceval.add_argument("--revision", required=True) | |
| mceval_eval = subparsers.add_parser("mceval-eval") | |
| mceval_eval.add_argument("--input", type=Path, required=True) | |
| mceval_eval.add_argument("--output", type=Path, required=True) | |
| mceval_eval.add_argument("--revision", required=True) | |
| leaderboard = subparsers.add_parser("swe-rebench-leaderboard") | |
| leaderboard.add_argument("--input", type=Path, required=True) | |
| leaderboard.add_argument("--output", type=Path, required=True) | |
| leaderboard.add_argument("--revision", required=True) | |
| args = parser.parse_args() | |
| if args.source == "swe-rebench-v2": | |
| audit_swe(args.input, args.output, args.revision) | |
| elif args.source == "mceval-instruct": | |
| audit_mceval_instruct(args.input, args.output, args.revision) | |
| elif args.source == "mceval-eval": | |
| audit_mceval_eval(args.input, args.output, args.revision) | |
| else: | |
| audit_swe_leaderboard(args.input, args.output, args.revision) | |
| if __name__ == "__main__": | |
| main() | |