"""監査項目B（有害・倫理的に問題のあるコンテンツ、項目5〜10）の報告書を生成する。 有害フィルタの出力（summary.json, removed.jsonl, cooccurrence_review.jsonl）と、 あれば分類器の出力（classify_qwen3guard.py）、目視集計（review_tally.py）を 統合し、項目ごとに「適用した手法」「検出・除外件数」「例3件」「適用リスト」を Markdown にまとめる。手法が1つしか適用されていない項目は「実施」と表示しない。 uv run python abeja-cc-ja/src/audit_report.py --output-root \ [--classified review/classified.jsonl] [--review-tally review/review_tally.json] \ --output audit_report.md """ from __future__ import annotations import argparse import json from collections import Counter, defaultdict from datetime import datetime, timezone from pathlib import Path from typing import Any from textmatch import normalize # 監査項目と、フィルタ内部のカテゴリ・規則・ブロックリストの対応。 AUDIT_ITEMS: list[dict[str, Any]] = [ {"item": 5, "title": "性的・わいせつな表現（未成年文脈は0件）", "wordlist": ["adult"], "cooccurrence": ["minor_sexual_context"], "blocklist": ["ut1:adult", "ut1:mixed_adult", "ut1:child", "local:domains", "local:rules"], "classifier": ["Sexual Content or Sexual Acts"]}, {"item": 6, "title": "暴力・自傷・自殺の助長、危害を加える具体的手順", "wordlist": ["violence"], "cooccurrence": [], "blocklist": [], "classifier": ["Violent", "Suicide & Self-Harm"]}, {"item": 7, "title": "差別・ヘイト、誹謗中傷", "wordlist": ["discrimination"], "cooccurrence": [], "blocklist": ["ut1:agressif"], "classifier": ["Unethical Acts"]}, {"item": 8, "title": "違法行為の手引き（薬物、武器、詐欺、不正アクセス等）", "wordlist": ["crime"], "cooccurrence": ["illegal_howto"], "blocklist": ["ut1:drogue", "ut1:dangerous_material", "ut1:hacking"], "classifier": ["Non-violent Illegal Acts"]}, {"item": 9, "title": "過激な政治・宗教プロパガンダ、陰謀論", "wordlist": ["propaganda_conspiracy"], "cooccurrence": [], "blocklist": ["ut1:sect", "ut1:fakenews"], "classifier": ["Politically Sensitive Topics"]}, {"item": 10, "title": "スパム・詐欺・アダルト・違法コピー配布サイト由来のページ", "wordlist": ["spam"], "cooccurrence": [], "blocklist": ["ut1:adult", "ut1:mixed_adult", "ut1:gambling", "ut1:phishing", "ut1:malware", "ut1:warez", "ut1:cryptojacking", "ut1:stalkerware", "ut1:ddos", "ut1:dating", "local:domains", "local:rules"], "classifier": ["Copyright Violation"]}, ] def _read_jsonl(path: Path) -> list[dict[str, Any]]: if not path or not path.exists(): return [] with path.open(encoding="utf-8") as handle: return [json.loads(line) for line in handle if line.strip()] def _snippet(record: dict[str, Any], terms: list[str], length: int = 90) -> str: text = normalize(record.get("content") or "") position = -1 for term in terms: position = text.find(term) if position >= 0: break start = max(0, position - length // 3) if position >= 0 else 0 return text[start:start + length].replace("\n", " ").replace("|", "／") def _examples_wordlist(removed: list[dict[str, Any]], category: str, limit: int = 3) -> list[str]: rows = [] for record in removed: if category not in record.get("_filter_deleted_categories", []): continue terms = record["_filter_distinct_matched_terms_by_category"].get(category, []) rows.append(f"| {record.get('_filter_record_index')} | {record.get('url', '')[:70]} | {', '.join(terms[:5])} | {_snippet(record, terms)} |") if len(rows) >= limit: break return rows def _examples_cooccurrence(review: list[dict[str, Any]], rule: str, limit: int = 3) -> list[str]: rows = [] for item in review: if item["rule"] != rule: continue pairs = ", ".join(f"{p['a']}×{p['b']}" for p in item["pairs"][:3]) rows.append(f"| {item['record_index']} | {item.get('url', '')[:70]} | {pairs} | {item['excerpt'][:90].replace('|', '／')} |") if len(rows) >= limit: break return rows def _examples_blocklist(removed: list[dict[str, Any]], sources: list[str], limit: int = 3) -> list[str]: rows = [] for record in removed: hits = [m for m in record.get("_filter_domain_matches", []) if m["source"] in sources and m["action"] == "delete"] if not hits: continue rows.append(f"| {record.get('_filter_record_index')} | {record.get('url', '')[:70]} | {hits[0]['source']} ({hits[0]['matched'][:40]}) | |") if len(rows) >= limit: break return rows def main(argv: list[str] | None = None) -> None: parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument("--output-root", type=Path, required=True) parser.add_argument("--mode", default="distinct_terms_3plus") parser.add_argument("--classified", type=Path, default=None, help="classify_qwen3guard.py の出力JSONL") parser.add_argument("--review-tally", type=Path, default=None, help="review_tally.py の出力JSON") parser.add_argument("--sampling-manifest", type=Path, default=None) parser.add_argument("--output", type=Path, default=None, help="既定は /audit_report.md") args = parser.parse_args(argv) mode_dir = args.output_root / args.mode summary = json.loads((mode_dir / "summary.json").read_text(encoding="utf-8")) removed = _read_jsonl(mode_dir / "removed.jsonl") review = _read_jsonl(mode_dir / "cooccurrence_review.jsonl") classified = _read_jsonl(args.classified) if args.classified else [] tally = json.loads(args.review_tally.read_text(encoding="utf-8")) if args.review_tally and args.review_tally.exists() else None sampling = json.loads(args.sampling_manifest.read_text(encoding="utf-8")) if args.sampling_manifest and args.sampling_manifest.exists() else None total = summary["total_records_scanned"] block = summary.get("domain_blocklist", {}) cooc = summary.get("cooccurrence", {}) by_reason = summary.get("records_deleted_by_reason", {}) # 分類器の集計（層別 × 判定） classifier_by_stratum: dict[str, Counter] = defaultdict(Counter) classifier_by_category: Counter = Counter() for record in classified: classifier_by_stratum[record.get("stratum", "?")][record.get("safety") or "unparsed"] += 1 for category in record.get("categories", []): classifier_by_category[category] += 1 lines: list[str] = [] lines.append(f"# 有害コンテンツ監査報告（項目B: 5〜10）") lines.append("") lines.append(f"- 生成日時: {datetime.now(timezone.utc).isoformat()}") lines.append(f"- 対象: `{summary['source_file']}`（行 {summary['row_range']['start_row_inclusive']}〜{summary['row_range']['end_row_exclusive']}、{total:,} 文書）") lines.append(f"- 削除合計: {summary['records_deleted']:,} 文書（{summary['deletion_rate']*100:.2f}%）。理由別: ワードリスト {by_reason.get('wordlist', 0):,}、ドメインブロックリスト {by_reason.get('domain_blocklist', 0):,}、近接共起 {by_reason.get('cooccurrence', 0):,}、複数理由 {by_reason.get('multiple_reasons', 0):,}") lines.append("") lines.append("## 適用した手法") lines.append("") lines.append("| 手法 | 状態 | 内容 |") lines.append("|---|---|---|") lines.append(f"| 辞書（ワードリスト） | 適用 | カテゴリ別語彙、同一カテゴリ{summary['threshold']}語以上で削除。除外語: " + ", ".join(f"{k} {len(v)}" for k, v in summary.get("wordlist_excluded_terms_by_category", {}).items()) + " |") lines.append(f"| 辞書（近接共起規則） | {'適用' if cooc.get('enabled') else '未適用'} | " + "; ".join(f"{r['name']}（{r['label_ja']}、{r['window']}文字以内）" for r in cooc.get("rules", [])) + " |") lines.append(f"| ドメインブロックリスト | {'適用' if block.get('enabled') else '未適用'} | " + ", ".join(f"{s['name']}={s['entries']:,}" for s in block.get("sources", [])) + " |") if classified: lines.append(f"| 分類器 | 適用（サンプル） | {classified[0].get('model', '')} を {len(classified)} 文書へ適用。判定: " + ", ".join(f"{k} {v}" for k, v in sorted(Counter(r.get('safety') or 'unparsed' for r in classified).items())) + " |") else: lines.append("| 分類器 | 未適用 | `classify_qwen3guard.py` の出力を `--classified` で渡す |") if tally: reviewed = sum(e.get("reviewed", 0) for e in tally["strata"].values()) lines.append(f"| 目視 | {'適用' if reviewed else '未記入'} | 判定済み {reviewed} 件、判定者 {', '.join(tally.get('reviewers', [])) or '未記入'} |") else: lines.append("| 目視 | 未適用 | `sample_for_review.py` → 記入 → `review_tally.py` の結果を `--review-tally` で渡す |") lines.append("") methods_applied = 1 + (1 if classified else 0) + (1 if tally and any(e.get("reviewed") for e in tally["strata"].values()) else 0) lines.append(f"手法数: {methods_applied}/3。" + ("3手法が揃っており「実施」と表示できます。" if methods_applied >= 3 else "3手法が揃うまでは「実施」としないこと。")) lines.append("") if block.get("enabled"): lines.append("## 適用したドメインブロックリスト") lines.append("") lines.append("| リスト | 動作 | 件数 | 一致文書 | 削除文書 | 取得元 | 取得日時 | SHA-256 |") lines.append("|---|---|---:|---:|---:|---|---|---|") for source in block["sources"]: stat = block["by_source"].get(source["name"], {}) lines.append(f"| {source['name']} | {source['action']} | {source['entries']:,} | {stat.get('records_matched', 0)} | {stat.get('records_deleted', 0)} | {source.get('source_url') or source.get('path')} | {source.get('fetched_at_utc') or ''} | {(source.get('sha256') or '')[:16]} |") lines.append("") for spec in AUDIT_ITEMS: lines.append(f"## 項目{spec['item']}: {spec['title']}") lines.append("") counts_lines = [] for category in spec["wordlist"]: cat = summary["by_category"].get(category, {}) counts_lines.append(f"- ワードリスト `{category}`: 一致 {cat.get('records_with_match', 0):,} 文書、削除 {cat.get('records_deleted', 0):,} 文書（{cat.get('distinct_terms_matched', 0)} 語が一致）") for rule in spec["cooccurrence"]: stat = cooc.get("by_rule", {}).get(rule) if stat is not None: counts_lines.append(f"- 近接共起 `{rule}`（{stat['label_ja']}）: 一致・削除 {stat['records_matched']:,} 文書。全件を `cooccurrence_review.jsonl` で目視確認する") for source_name in spec["blocklist"]: stat = block.get("by_source", {}).get(source_name) if stat is not None: counts_lines.append(f"- ブロックリスト `{source_name}`（{stat['action']}）: 一致 {stat['records_matched']:,} 文書、削除 {stat['records_deleted']:,} 文書") if classified: hit = sum(1 for r in classified if any(c in spec["classifier"] for c in r.get("categories", []))) counts_lines.append(f"- 分類器: サンプル {len(classified)} 件中 {hit} 件が {', '.join(spec['classifier'])} に該当") if tally: for stratum, entry in tally["strata"].items(): if any(stratum.endswith(":" + c) for c in spec["wordlist"] + spec["cooccurrence"]): rate = entry.get("precision", entry.get("harmful_rate")) counts_lines.append(f"- 目視 `{stratum}`: harmful {entry['harmful']}、not_harmful {entry['not_harmful']}、unsure {entry['unsure']}、未確認 {entry['unreviewed']}" + (f"、精度 {rate}" if rate is not None else "")) lines.extend(counts_lines or ["- 該当する検出手段なし"]) if spec["item"] == 5: minor = cooc.get("by_rule", {}).get("minor_sexual_context", {}) lines.append(f"- 未成年文脈: 共起規則で {minor.get('records_matched', 0):,} 文書を削除。保持データ側の残存は目視層 `cooccurrence:minor_sexual_context` と `retained:random` の確認結果で判断する") lines.append("") lines.append("例（最大3件）:") lines.append("") lines.append("| record_index | URL | 該当語・規則 | 抜粋 |") lines.append("|---|---|---|---|") examples: list[str] = [] for category in spec["wordlist"]: examples.extend(_examples_wordlist(removed, category)) for rule in spec["cooccurrence"]: examples.extend(_examples_cooccurrence(review, rule)) examples.extend(_examples_blocklist(removed, spec["blocklist"])) lines.extend(examples[:3] if examples else ["| | （該当なし） | | |"]) lines.append("") if sampling: lines.append("## 目視サンプル") lines.append("") lines.append(f"- 抽出日時 {sampling['created_at_utc']}、seed {sampling['seed']}、合計 {sampling['total_samples']} 件") lines.append("- 層別件数: " + ", ".join(f"{k} {v}" for k, v in sampling["strata"].items())) lines.append("") if classified: lines.append("## 分類器の層別結果") lines.append("") lines.append("| 層 | Safe | Controversial | Unsafe | 未解析 |") lines.append("|---|---:|---:|---:|---:|") for stratum, counter in sorted(classifier_by_stratum.items()): lines.append(f"| {stratum} | {counter.get('Safe', 0)} | {counter.get('Controversial', 0)} | {counter.get('Unsafe', 0)} | {counter.get('unparsed', 0)} |") lines.append("") lines.append("カテゴリ別: " + ", ".join(f"{k} {v}" for k, v in classifier_by_category.most_common())) lines.append("") output = args.output or (args.output_root / "audit_report.md") output.write_text("\n".join(lines) + "\n", encoding="utf-8") print(f"wrote {output} ({len(lines)} lines); methods_applied={methods_applied}/3") if __name__ == "__main__": main()