| from __future__ import annotations |
|
|
| import argparse |
| import json |
| from collections import Counter |
| from pathlib import Path |
|
|
| from src.data.io_utils import read_jsonl, write_csv, write_json |
| from src.data.normalize_text import stable_hash |
|
|
|
|
| def label_distribution(dataset: str, split: str, rows: list[dict], unit: str) -> list[dict]: |
| counts = Counter(row.get("label") if row.get("label") is not None else "UNLABELED" for row in rows) |
| return [ |
| { |
| "dataset": dataset, |
| "split": split, |
| "unit": unit, |
| "label": label, |
| "count": count, |
| "percent": round(count / max(1, len(rows)) * 100, 4), |
| } |
| for label, count in sorted(counts.items()) |
| ] |
|
|
|
|
| def claim_hashes(rows: list[dict]) -> set[str]: |
| hashes = set() |
| for row in rows: |
| metadata = row.get("metadata") or {} |
| hashes.add(metadata.get("claim_norm_hash") or stable_hash(row.get("claim"))) |
| return hashes |
|
|
|
|
| def split_overlap_rows(dataset: str, split_rows: dict[str, list[dict]], unit: str) -> list[dict]: |
| rows: list[dict] = [] |
| names = list(split_rows) |
| for i, split_a in enumerate(names): |
| for split_b in names[i + 1 :]: |
| hashes_a = claim_hashes(split_rows[split_a]) |
| hashes_b = claim_hashes(split_rows[split_b]) |
| overlap = sorted(hashes_a & hashes_b) |
| rows.append( |
| { |
| "dataset": dataset, |
| "split_a": split_a, |
| "split_b": split_b, |
| "unit": unit, |
| "overlap_type": "claim_norm_hash", |
| "overlap_count": len(overlap), |
| "examples": " | ".join(overlap[:5]), |
| } |
| ) |
| return rows |
|
|
|
|
| def evidence_hashes(rows: list[dict]) -> set[str]: |
| hashes = set() |
| for row in rows: |
| metadata = row.get("metadata") or {} |
| if metadata.get("evidence_hash"): |
| hashes.add(metadata["evidence_hash"]) |
| return hashes |
|
|
|
|
| def file_exists(path: Path) -> bool: |
| return path.exists() and path.stat().st_size > 0 |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--data-root", type=Path, default=Path("data_processed")) |
| parser.add_argument("--stats-dir", type=Path, default=Path("outputs/stats")) |
| parser.add_argument("--tables-dir", type=Path, default=Path("outputs/tables")) |
| args = parser.parse_args() |
|
|
| dataset_stats_rows: list[dict] = [] |
| label_rows: list[dict] = [] |
| overlap_rows: list[dict] = [] |
|
|
| vifactcheck = { |
| split: read_jsonl(args.data_root / "vifactcheck" / f"claims_{split}.jsonl") |
| for split in ["train", "dev", "test"] |
| } |
| dataset_stats_rows.append( |
| { |
| "Dataset": "ViFactCheck", |
| "Language": "Vietnamese", |
| "Domain": "News", |
| "Train": len(vifactcheck["train"]), |
| "Dev": len(vifactcheck["dev"]), |
| "Test": len(vifactcheck["test"]), |
| "Evidence source": "Context chunks; gold Evidence diagnostic only", |
| "Labels": "SUPPORTS/REFUTES/NEI", |
| "Unit": "claim", |
| } |
| ) |
| for split, rows in vifactcheck.items(): |
| label_rows.extend(label_distribution("vifactcheck", split, rows, "claim")) |
| overlap_rows.extend(split_overlap_rows("vifactcheck", vifactcheck, "claim")) |
|
|
| averitec = { |
| split: read_jsonl(args.data_root / "averitec" / f"claims_{split}.jsonl") |
| for split in ["train_inner", "dev_inner", "local_test", "hidden_test"] |
| } |
| dataset_stats_rows.append( |
| { |
| "Dataset": "AVeriTeC", |
| "Language": "English", |
| "Domain": "Web fact-checking", |
| "Train": len(averitec["train_inner"]), |
| "Dev": len(averitec["dev_inner"]), |
| "Test": f"local_test={len(averitec['local_test'])}; hidden_test={len(averitec['hidden_test'])}", |
| "Evidence source": "QA evidence store; hidden test has no local labels", |
| "Labels": "SUPPORTS/REFUTES/NEI/CONFLICTING", |
| "Unit": "claim", |
| } |
| ) |
| for split, rows in averitec.items(): |
| label_rows.extend(label_distribution("averitec", split, rows, "claim")) |
| overlap_rows.extend(split_overlap_rows("averitec", averitec, "claim")) |
|
|
| healthver_pairs = { |
| split: read_jsonl(args.data_root / "healthver" / f"pairs_{split}.jsonl") |
| for split in ["train", "dev", "test"] |
| } |
| dataset_stats_rows.append( |
| { |
| "Dataset": "HealthVer", |
| "Language": "English", |
| "Domain": "Health/Biomedical", |
| "Train": len(healthver_pairs["train"]), |
| "Dev": len(healthver_pairs["dev"]), |
| "Test": len(healthver_pairs["test"]), |
| "Evidence source": "claim-evidence pairs", |
| "Labels": "SUPPORTS/REFUTES/NEI", |
| "Unit": "pair", |
| } |
| ) |
| for split, rows in healthver_pairs.items(): |
| label_rows.extend(label_distribution("healthver", split, rows, "pair")) |
| healthver_grouped = { |
| split: read_jsonl(args.data_root / "healthver" / f"claims_grouped_{split}.jsonl") |
| for split in ["train", "dev", "test"] |
| } |
| overlap_rows.extend(split_overlap_rows("healthver", healthver_grouped, "claim_grouped")) |
| for split_a, split_b in [("train", "dev"), ("train", "test"), ("dev", "test")]: |
| hashes_a = evidence_hashes(healthver_pairs[split_a]) |
| hashes_b = evidence_hashes(healthver_pairs[split_b]) |
| overlap = sorted(hashes_a & hashes_b) |
| overlap_rows.append( |
| { |
| "dataset": "healthver", |
| "split_a": split_a, |
| "split_b": split_b, |
| "unit": "pair", |
| "overlap_type": "evidence_text_hash", |
| "overlap_count": len(overlap), |
| "examples": " | ".join(overlap[:5]), |
| } |
| ) |
|
|
| write_csv(args.stats_dir / "dataset_statistics.csv", dataset_stats_rows) |
| write_csv(args.stats_dir / "label_distribution.csv", label_rows) |
| write_csv(args.stats_dir / "split_overlap_report.csv", overlap_rows) |
| write_csv(args.tables_dir / "T1_dataset_statistics.csv", dataset_stats_rows) |
|
|
| label_mapping_rows = [ |
| {"Dataset": "ViFactCheck", "Raw label": "0", "Canonical label": "SUPPORTS", "Main?": "yes", "Note": "verified local numeric label"}, |
| {"Dataset": "ViFactCheck", "Raw label": "1", "Canonical label": "REFUTES", "Main?": "yes", "Note": "verified local numeric label"}, |
| {"Dataset": "ViFactCheck", "Raw label": "2", "Canonical label": "NEI", "Main?": "yes", "Note": "verified local numeric label"}, |
| {"Dataset": "AVeriTeC", "Raw label": "Supported", "Canonical label": "SUPPORTS", "Main?": "yes", "Note": "4-class"}, |
| {"Dataset": "AVeriTeC", "Raw label": "Refuted", "Canonical label": "REFUTES", "Main?": "yes", "Note": "4-class"}, |
| {"Dataset": "AVeriTeC", "Raw label": "Not Enough Evidence", "Canonical label": "NEI", "Main?": "yes", "Note": "4-class"}, |
| {"Dataset": "AVeriTeC", "Raw label": "Conflicting Evidence/Cherrypicking", "Canonical label": "CONFLICTING", "Main?": "yes", "Note": "not merged in main"}, |
| {"Dataset": "HealthVer", "Raw label": "Supports", "Canonical label": "SUPPORTS", "Main?": "yes", "Note": "pair-level"}, |
| {"Dataset": "HealthVer", "Raw label": "Refutes", "Canonical label": "REFUTES", "Main?": "yes", "Note": "pair-level"}, |
| {"Dataset": "HealthVer", "Raw label": "Neutral", "Canonical label": "NEI", "Main?": "yes", "Note": "pair-level"}, |
| ] |
| write_csv(args.tables_dir / "T2_label_mapping.csv", label_mapping_rows) |
|
|
| protocol_rows = [ |
| {"Protocol": "P1", "Dataset": "ViFactCheck", "Input allowed": "Statement + Context chunks", "Forbidden": "Evidence", "Role": "main"}, |
| {"Protocol": "P2", "Dataset": "ViFactCheck", "Input allowed": "Statement + Context-derived WikiKG", "Forbidden": "Evidence", "Role": "proposed"}, |
| {"Protocol": "P3", "Dataset": "ViFactCheck", "Input allowed": "Statement + Evidence", "Forbidden": "report as main", "Role": "upper-bound"}, |
| {"Protocol": "P4", "Dataset": "AVeriTeC", "Input allowed": "Claim + evidence store", "Forbidden": "hidden test label/questions", "Role": "main"}, |
| {"Protocol": "P5", "Dataset": "AVeriTeC", "Input allowed": "Claim + WikiKG evidence", "Forbidden": "hidden test label", "Role": "proposed"}, |
| {"Protocol": "P6", "Dataset": "HealthVer", "Input allowed": "Claim + evidence pair", "Forbidden": "test tuning", "Role": "main"}, |
| {"Protocol": "P7", "Dataset": "HealthVer", "Input allowed": "Claim + evidence-derived WikiKG", "Forbidden": "test tuning", "Role": "proposed"}, |
| {"Protocol": "P8", "Dataset": "All", "Input allowed": "same evidence without KG/path", "Forbidden": "KG features", "Role": "ablation"}, |
| ] |
| write_csv(args.tables_dir / "T3_protocol_matrix.csv", protocol_rows) |
|
|
| averitec_split_report = json.loads((args.stats_dir / "averitec_split_report.json").read_text(encoding="utf-8")) |
| averitec_local_overlap = [ |
| row |
| for row in overlap_rows |
| if row["dataset"] == "averitec" |
| and {row["split_a"], row["split_b"]} in [{"train_inner", "local_test"}, {"dev_inner", "local_test"}] |
| ] |
| leakage_checks = { |
| "raw_manifest_exists": file_exists(args.stats_dir / "raw_file_manifest.csv"), |
| "vifactcheck_context_evidence_separated": file_exists(args.data_root / "vifactcheck" / "context_chunks.jsonl") |
| and file_exists(args.data_root / "vifactcheck" / "gold_evidence.jsonl"), |
| "averitec_local_test_is_official_dev_labeled": all(row.get("label") not in (None, "UNLABELED") for row in averitec["local_test"]), |
| "averitec_hidden_test_has_no_local_labels": all(row.get("label") is None for row in averitec["hidden_test"]) |
| and not averitec_split_report["official"]["hidden_test_has_labels"], |
| "averitec_no_train_or_dev_inner_overlap_with_local_test": all(int(row["overlap_count"]) == 0 for row in averitec_local_overlap), |
| "healthver_pair_files_exist": all(file_exists(args.data_root / "healthver" / f"pairs_{split}.jsonl") for split in ["train", "dev", "test"]), |
| "healthver_grouped_claim_files_exist": all( |
| file_exists(args.data_root / "healthver" / f"claims_grouped_{split}.jsonl") for split in ["train", "dev", "test"] |
| ), |
| "tables_t1_t2_t3_exist": all(file_exists(args.tables_dir / name) for name in ["T1_dataset_statistics.csv", "T2_label_mapping.csv", "T3_protocol_matrix.csv"]), |
| } |
| warnings = { |
| "vifactcheck_official_split_claim_overlap": [ |
| row for row in overlap_rows if row["dataset"] == "vifactcheck" and int(row["overlap_count"]) > 0 |
| ], |
| "averitec_hidden_prediction_only_overlap": [ |
| row |
| for row in overlap_rows |
| if row["dataset"] == "averitec" and "hidden_test" in {row["split_a"], row["split_b"]} and int(row["overlap_count"]) > 0 |
| ], |
| "healthver_official_split_claim_overlap": [ |
| row |
| for row in overlap_rows |
| if row["dataset"] == "healthver" and row["overlap_type"] == "claim_norm_hash" and int(row["overlap_count"]) > 0 |
| ], |
| "healthver_official_split_evidence_text_overlap": [ |
| row |
| for row in overlap_rows |
| if row["dataset"] == "healthver" and row["overlap_type"] == "evidence_text_hash" and int(row["overlap_count"]) > 0 |
| ], |
| } |
| leakage_report = { |
| "stage": "Stage 0-1-2 protocol lock", |
| "checks": leakage_checks, |
| "pass": all(leakage_checks.values()), |
| "warnings": warnings, |
| "notes": { |
| "averitec_hidden_test": "Hidden-style local file is prediction-only; no metric is computed without official labels.", |
| "vifactcheck_evidence": "Gold Evidence is diagnostic/upper-bound only, not main input.", |
| "healthver_unit": "Main unit is pair-level; grouped claims are analysis/robustness files.", |
| "healthver_overlap": "The official HealthVer split has substantial repeated evidence text across splits; report this risk and consider evidence-disjoint robustness.", |
| }, |
| } |
| write_json(args.stats_dir / "leakage_report.json", leakage_report) |
| print(f"Wrote validation tables and leakage report. PASS={leakage_report['pass']}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
