"""目視確認と分類器用のサンプルを層別に抽出する（監査の第3手法の入口）。 有害フィルタの出力（summary.json, removed.jsonl, borderline.jsonl, cooccurrence_review.jsonl）と元の入力parquetから、次の層でサンプルを取る。 * ``deleted:`` ワードリストで削除した文書（カテゴリ別、既定30件） * ``deleted:domain_blocklist`` ドメインブロックリストで削除した文書（既定30件） * ``cooccurrence:`` 近接共起規則の一致（規則ごとに既定100件。一致文書は全件削除される） * ``borderline:`` しきい値に1語足りず保持された文書（カテゴリ別、既定30件） * ``retained:random`` 保持文書の無作為抽出（既定100件。見逃し率の推定に使う） 出力は ``review_samples.csv``（人が判定を記入する）と ``review_samples.jsonl`` （``classify_qwen3guard.py`` の入力）。抽出条件は ``sampling_manifest.json`` に残す。 """ from __future__ import annotations import argparse import csv import json import random from datetime import datetime, timezone from pathlib import Path from typing import Any import pyarrow.parquet as pq from filter_toxic import CATEGORIES, KeywordMatcher, load_wordlists from textmatch import normalize CSV_COLUMNS = [ "sample_id", "stratum", "category", "record_index", "url", "matched_terms", "excerpt", "reviewer", "verdict", "reason", "reviewed_at", ] def _read_jsonl(path: Path) -> list[dict[str, Any]]: if not path.exists(): return [] with path.open(encoding="utf-8") as handle: return [json.loads(line) for line in handle if line.strip()] def _excerpt(text: str, positions: list[int], length: int = 300) -> str: if not text: return "" if positions: start = max(0, positions[0] - length // 3) else: start = 0 return text[start:start + length].replace("\n", " ") def main(argv: list[str] | None = None) -> None: source_dir = Path(__file__).resolve().parent parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument("--input", type=Path, required=True, help="元の入力parquet") parser.add_argument("--output-root", type=Path, required=True, help="main_toxic.py の --output-root") parser.add_argument("--mode", default="distinct_terms_3plus") parser.add_argument("--dest", type=Path, required=True, help="サンプルの出力先ディレクトリ") parser.add_argument("--seed", type=int, default=20260904) parser.add_argument("--deleted-per-category", type=int, default=30) parser.add_argument("--deleted-domain", type=int, default=30) parser.add_argument("--borderline-per-category", type=int, default=30) parser.add_argument("--random-retained", type=int, default=100) parser.add_argument("--cooccurrence-limit", type=int, default=100, help="共起規則ごとの抽出上限（既定100。全件にするには 0）") parser.add_argument("--text-chars", type=int, default=4000, help="JSONLに含める本文の最大文字数") parser.add_argument("--reference-dir", type=Path, default=source_dir / "wordlists" / "reference") parser.add_argument("--additional-dir", type=Path, default=source_dir / "wordlists") parser.add_argument("--exclude-dir", type=Path, default=source_dir / "wordlists" / "exclude") args = parser.parse_args(argv) rng = random.Random(args.seed) mode_dir = args.output_root / args.mode summary = json.loads((mode_dir / "summary.json").read_text(encoding="utf-8")) threshold = summary["threshold"] removed = _read_jsonl(mode_dir / "removed.jsonl") borderline = _read_jsonl(mode_dir / "borderline.jsonl") cooccurrence = _read_jsonl(mode_dir / "cooccurrence_review.jsonl") removed_indices = {record["_filter_record_index"] for record in removed} row_range = summary["row_range"] samples: list[dict[str, Any]] = [] def add(stratum: str, category: str, record_index: int, url: str, terms: list[str], text: str, positions: list[int]) -> None: samples.append({ "stratum": stratum, "category": category, "record_index": record_index, "url": url, "matched_terms": terms, "text": text, "match_positions": positions, }) # 削除文書（ワードリスト） for category in CATEGORIES: pool = [r for r in removed if category in r["_filter_deleted_categories"]] for record in rng.sample(pool, min(args.deleted_per_category, len(pool))): terms = record["_filter_distinct_matched_terms_by_category"].get(category, []) text = normalize(record.get("content") or "") positions = sorted(p for t in terms for p in [text.find(t)] if p >= 0) add(f"deleted:{category}", category, record["_filter_record_index"], record.get("url") or "", terms, text, positions) # 削除文書（ドメインブロックリスト） pool = [r for r in removed if "domain_blocklist" in r.get("_filter_deleted_by", [])] for record in rng.sample(pool, min(args.deleted_domain, len(pool))): sources = sorted({m["source"] + "=" + m["matched"] for m in record.get("_filter_domain_matches", [])}) add("deleted:domain_blocklist", "domain_blocklist", record["_filter_record_index"], record.get("url") or "", sources, normalize(record.get("content") or ""), []) # 共起規則（既定は全件） by_rule: dict[str, list[dict[str, Any]]] = {} for item in cooccurrence: by_rule.setdefault(item["rule"], []).append(item) removed_by_index = {r["_filter_record_index"]: r for r in removed} for rule, items in by_rule.items(): chosen = items if not args.cooccurrence_limit else rng.sample(items, min(args.cooccurrence_limit, len(items))) for item in chosen: record = removed_by_index.get(item["record_index"], {}) text = normalize(record.get("content") or "") positions = [pair["position"] for pair in item["pairs"][:4]] pairs = [f"{pair['a']}×{pair['b']}" for pair in item["pairs"][:5]] add(f"cooccurrence:{rule}", item.get("label_ja", rule), item["record_index"], item.get("url") or "", pairs, text, positions) # 保持文書は入力parquetから本文を取る needed: dict[int, list[tuple[str, str, list[str]]]] = {} for category in CATEGORIES: pool = [b for b in borderline if category in b["categories"]] for record in rng.sample(pool, min(args.borderline_per_category, len(pool))): needed.setdefault(record["record_index"], []).append((f"borderline:{category}", category, record["categories"][category])) retained_pool = [i for i in range(row_range["start_row_inclusive"], row_range["end_row_exclusive"]) if i not in removed_indices] for record_index in rng.sample(retained_pool, min(args.random_retained, len(retained_pool))): needed.setdefault(record_index, []).append(("retained:random", "random", [])) if needed: table = pq.read_table(args.input, columns=["url", "content"]) wordlists = load_wordlists(args.reference_dir, args.additional_dir, args.exclude_dir) matcher = KeywordMatcher(wordlists.terms, wordlists.sources) for record_index, entries in sorted(needed.items()): row = table.slice(record_index, 1).to_pylist()[0] text = normalize(row.get("content") or "") positions = sorted({found.start() for found in matcher.pattern.finditer(text)})[:8] for stratum, category, terms in entries: add(stratum, category, record_index, row.get("url") or "", terms, text, positions) args.dest.mkdir(parents=True, exist_ok=True) with (args.dest / "review_samples.csv").open("w", encoding="utf-8", newline="") as handle: writer = csv.DictWriter(handle, fieldnames=CSV_COLUMNS) writer.writeheader() for number, sample in enumerate(samples, start=1): sample_id = f"S{number:05d}" sample["sample_id"] = sample_id writer.writerow({ "sample_id": sample_id, "stratum": sample["stratum"], "category": sample["category"], "record_index": sample["record_index"], "url": sample["url"], "matched_terms": " | ".join(sample["matched_terms"]), "excerpt": _excerpt(sample["text"], sample["match_positions"]), "reviewer": "", "verdict": "", "reason": "", "reviewed_at": "", }) with (args.dest / "review_samples.jsonl").open("w", encoding="utf-8") as handle: for sample in samples: handle.write(json.dumps({ "id": sample["sample_id"], "stratum": sample["stratum"], "category": sample["category"], "record_index": sample["record_index"], "url": sample["url"], "matched_terms": sample["matched_terms"], "match_positions": sample["match_positions"], "text": sample["text"][:args.text_chars], }, ensure_ascii=False, separators=(",", ":")) + "\n") strata: dict[str, int] = {} for sample in samples: strata[sample["stratum"]] = strata.get(sample["stratum"], 0) + 1 manifest = { "created_at_utc": datetime.now(timezone.utc).isoformat(), "input": str(args.input), "output_root": str(args.output_root), "mode": args.mode, "seed": args.seed, "sizes": {"deleted_per_category": args.deleted_per_category, "deleted_domain": args.deleted_domain, "borderline_per_category": args.borderline_per_category, "random_retained": args.random_retained, "cooccurrence_limit": args.cooccurrence_limit}, "threshold": threshold, "strata": strata, "total_samples": len(samples), "verdict_values": ["harmful", "not_harmful", "unsure"], } (args.dest / "sampling_manifest.json").write_text(json.dumps(manifest, ensure_ascii=False, indent=2) + "\n", encoding="utf-8") print(json.dumps({"total_samples": len(samples), "strata": strata}, ensure_ascii=False)) if __name__ == "__main__": main()