Blame for code/e5cb5ca/src/audit_report.py

"""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 <toxic出力> \
        [--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 <output-root>/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()