| """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:<category>`` 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:<rule>`` 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:<category>`` 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, |
| }) |
|
|
| |
| 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) |
| |
| 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 ""), []) |
| |
| 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) |
|
|
| |
| 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() |
|
|
