Blame for code/3c1a8da/src/quality_report.py

"""監査項目 E（22 重複）・F（25〜27 言語・品質）・G（28〜29 ドメイン適合）の報告書を生成する。

第2パスが書く ``features.parquet``（文書ごとの言語・ジャンル・品質フラグ・文字数・perplexity・SHA-1）と
``noise_summary.json`` を DuckDB で集約し、JSON と Markdown を出力する。全件（約2.2億行）でも
DuckDB がファイルを逐次読みするため、1台のジョブ（cpu-xl 等）で処理できる。

    uv run python abeja-cc-ja/src/quality_report.py --features-glob "<root>/**/features.parquet" \\
        --noise-summaries-glob "<root>/**/noise_summary.json" --output-dir <report_dir> \\
        [--previous-low-quality-rate 0.35] [--exclude-genres none]

項目22の配布元重複除去の記録は本スクリプト内の定数（出典 URL 付き）に置き、完全一致（本文 SHA-1、URL）の
再確認結果を添える。
"""

from __future__ import annotations

import argparse
import json
from datetime import datetime, timezone
from pathlib import Path
from typing import Any


UPSTREAM_DEDUP = {
    "performed_by": "ABEJA（配布元、ABEJA-CC-JA 構築時）",
    "methods": [
        {"method": "URL 重複除去（同一 URL は最新のスナップショットを採用）", "threshold": "完全一致"},
        {"method": "MinHash による近似重複除去（text-dedup、Dataproc 上で実行）", "threshold": "非公開（記事に閾値の記載なし）"},
    ],
    "source_urls": [
        "https://tech-blog.abeja.asia/entry/abeja-nedo-project-part2-202405",
        "https://tech-blog.abeja.asia/entry/abeja-cc-ja-202409",
        "https://registry.opendata.aws/abeja-cc-ja/",
    ],
    "decision_ja": "配布元で URL 重複除去と MinHash 近似重複除去が実施済みのため、近似重複除去は再実行しない。完全一致（本文 SHA-1、URL）の残存だけを全件で再確認して記録する。",
}

GENRE_LABELS_JA = {
    "diary_blog": "日記ブログ", "affiliate": "投資・美容・健康のアフィリエイト", "game": "ゲーム",
    "entertainment": "芸能", "ec_product": "EC 商品ページ", "other": "その他",
}
LOW_QUALITY_FLAGS_JA = {
    "short_lt500": "最終本文が500字未満", "symbol_digit_ratio_gt30": "記号・数字比率30%超", "line_repetition_gt30": "同一行の反復30%超",
    "avg_line_len_lt20": "平均行長20字未満", "template_commerce": "販促・商品ページの定型", "low_lid_confidence": "言語判定の確信度0.5未満",
}


def q(con, sql: str) -> list[tuple]:
    return con.execute(sql).fetchall()


def q1(con, sql: str):
    return con.execute(sql).fetchone()


def build(features_glob: str, noise_glob: str | None, previous_low_quality_rate: float | None, exclude_genres: list[str], sample_examples: int = 5) -> dict[str, Any]:
    import duckdb

    con = duckdb.connect()
    con.execute(f"CREATE VIEW f AS SELECT * FROM read_parquet('{features_glob}', union_by_name=true)")
    total = q1(con, "SELECT count(*) FROM f")[0]
    retained = q1(con, "SELECT count(*) FROM f WHERE status = 'retained'")[0]
    report: dict[str, Any] = {"generated_at_utc": datetime.now(timezone.utc).isoformat(), "features_glob": features_glob,
                              "records_total": total, "records_retained": retained}

    # --- 22 重複 ---
    dup_sha = q1(con, "SELECT count(*) - count(DISTINCT content_sha1) FROM f WHERE status = 'retained' AND content_sha1 <> ''")[0]
    dup_url = q1(con, "SELECT count(*) - count(DISTINCT url) FROM f WHERE status = 'retained' AND url <> ''")[0]
    report["item22_dedup"] = {
        "upstream": UPSTREAM_DEDUP,
        "exact_duplicate_check": {
            "scope": "第2パス後の保持文書（全件）",
            "content_sha1_duplicates": dup_sha, "url_duplicates": dup_url,
            "content_sha1_duplicate_rate": round(dup_sha / max(1, retained), 6), "url_duplicate_rate": round(dup_url / max(1, retained), 6),
        },
    }

    # --- 25 言語 ---
    langs = q(con, "SELECT lang, count(*) FROM f WHERE status NOT LIKE 'deleted_toxic%' GROUP BY lang ORDER BY 2 DESC")
    lang_total = sum(n for _, n in langs)
    mixed = q1(con, "SELECT count(*) FROM f WHERE status = 'retained' AND mixed_language")[0]
    non_ja_deleted = q1(con, "SELECT count(*) FROM f WHERE status = 'deleted_noise:non_japanese'")[0]
    report["item25_language"] = {
        "tool": "fastText lid.176（文書先頭1,000字。行判定は30字以上の行を最大50行）",
        "scope": "第1パス後の文書（有害フィルタ削除分を除く）",
        "distribution": [{"lang": l, "records": n, "ratio": round(n / max(1, lang_total), 5)} for l, n in langs[:15]],
        "japanese_ratio": round(next((n for l, n in langs if l == "ja"), 0) / max(1, lang_total), 5),
        "mixed_language_records": mixed, "mixed_definition": "文書は日本語判定だが、30字以上の行のうち非日本語判定が3割以上",
        "processing": {"non_japanese": f"削除（{non_ja_deleted} 文書、確信度0.5以上）", "mixed": "保持し件数のみ記録（非日本語行は行単位で削除しない）"},
    }

    # --- 26 perplexity ---
    has_ppl = q1(con, "SELECT count(*) FROM f WHERE perplexity IS NOT NULL")[0] > 0
    if has_ppl:
        quant = q1(con, "SELECT min(perplexity), quantile_cont(perplexity, 0.05), quantile_cont(perplexity, 0.25), quantile_cont(perplexity, 0.5), quantile_cont(perplexity, 0.75), quantile_cont(perplexity, 0.95), max(perplexity) FROM f WHERE perplexity IS NOT NULL")
        hist = q(con, "SELECT floor(perplexity/5)*5 AS b, count(*) FROM f WHERE perplexity IS NOT NULL GROUP BY b ORDER BY b")
        near_threshold = q(con, f"SELECT url, perplexity, chars_final, genre FROM f WHERE status='retained' AND perplexity >= 38 ORDER BY perplexity DESC LIMIT {sample_examples}")
        low = q(con, f"SELECT url, perplexity, chars_final, genre FROM f WHERE status='retained' AND perplexity IS NOT NULL ORDER BY perplexity ASC LIMIT {sample_examples}")
        report["item26_perplexity"] = {
            "filter": {"applied_by": "Podtech（入力データセット abeja-cc-ja-filtered の作成時）", "model": "KenLM（lighttransport/japanese-scoring-model）", "threshold": "perplexity < 40 を保持",
                       "source_url": "https://huggingface.co/datasets/Podtech/abeja-cc-ja-filtered"},
            "distribution_retained": {"min": quant[0], "p5": quant[1], "p25": quant[2], "p50": quant[3], "p75": quant[4], "p95": quant[5], "max": quant[6]},
            "histogram_bin5": [{"bin_start": int(b), "records": n} for b, n in hist],
            "examples_near_threshold_retained": [{"url": u, "perplexity": p, "chars": c, "genre": g} for u, p, c, g in near_threshold],
            "examples_lowest_perplexity": [{"url": u, "perplexity": p, "chars": c, "genre": g} for u, p, c, g in low],
            "note_ja": "閾値以上（除外側）の文書は入力データに含まれないため、除外例は配布元データ（S3 の原本）からの抽出が必要。ここでは保持側の分布と閾値付近の残存例を示す。",
        }
    else:
        report["item26_perplexity"] = {"note_ja": "perplexity 列が無いため未集計"}

    # --- 27 文書長の分布（前後） ---
    def dist(col: str, where: str) -> dict[str, Any]:
        r = q1(con, f"SELECT count(*), min({col}), quantile_cont({col}, 0.05), quantile_cont({col}, 0.5), quantile_cont({col}, 0.95), max({col}), avg({col}), sum({col}) FROM f WHERE {where}")
        return {"records": r[0], "min": r[1], "p5": r[2], "p50": r[3], "p95": r[4], "max": r[5], "mean": round(r[6], 1) if r[6] else None, "sum": r[7]}
    by_genre_status = q(con, "SELECT genre, split_part(status, ':', 1) AS st, count(*) FROM f GROUP BY genre, st ORDER BY genre, st")
    genre_rates: dict[str, dict[str, Any]] = {}
    for g, st, n in by_genre_status:
        genre_rates.setdefault(g, {"label_ja": GENRE_LABELS_JA.get(g, g), "records": 0})
        genre_rates[g][st] = n
        genre_rates[g]["records"] += n
    for g, v in genre_rates.items():
        v["deleted_rate"] = round((v.get("deleted_toxic", 0) + v.get("deleted_noise", 0)) / max(1, v["records"]), 5)
    report["item27_length"] = {
        "chars_input_all": dist("chars_input", "chars_input IS NOT NULL"),
        "chars_final_retained": dist("chars_final", "status = 'retained'"),
        "chars_input_deleted_toxic": dist("chars_input", "status LIKE 'deleted_toxic%'"),
        "chars_input_deleted_noise": dist("chars_input", "status LIKE 'deleted_noise%'"),
        "deletion_rate_by_genre": genre_rates,
        "tokens_note_ja": "トークン数は方針によりトークナイザを指定しないため未集計（文字数のみ）。",
    }

    # --- 28 低品質 ---
    lq = q1(con, "SELECT count(*) FROM f WHERE status = 'retained' AND is_low_quality")[0]
    flags = q(con, "SELECT flag, count(*) FROM (SELECT unnest(string_split(low_quality_flags, '|')) AS flag FROM f WHERE status = 'retained' AND low_quality_flags <> '') GROUP BY flag ORDER BY 2 DESC")
    lq_by_genre = q(con, "SELECT genre, count(*), sum(CASE WHEN is_low_quality THEN 1 ELSE 0 END) FROM f WHERE status = 'retained' GROUP BY genre ORDER BY 2 DESC")
    report["item28_low_quality"] = {
        "definition_ja": "保持文書のうち次のいずれかに該当: " + "、".join(f"{k}（{v}）" for k, v in LOW_QUALITY_FLAGS_JA.items()),
        "method_ja": "第2パスで文書ごとに決定的規則で判定（doc_features.py）。分類の妥当性はサンプルの目視で確認する。",
        "records_retained": retained, "low_quality_records": lq, "low_quality_rate": round(lq / max(1, retained), 5),
        "by_flag": [{"flag": f_, "label_ja": LOW_QUALITY_FLAGS_JA.get(f_, f_), "records": n, "ratio": round(n / max(1, retained), 5)} for f_, n in flags],
        "by_genre": [{"genre": g, "label_ja": GENRE_LABELS_JA.get(g, g), "records": n, "low_quality": m, "rate": round(m / max(1, n), 4)} for g, n, m in lq_by_genre],
        "previous_review_rate": previous_low_quality_rate,
        "comparison_ja": (f"前回レビュー指摘値 {previous_low_quality_rate:.1%} に対し今回 {lq / max(1, retained):.1%}" if previous_low_quality_rate is not None else "前回レビューの指摘値が未入力のため比較なし"),
    }

    # --- 29 ジャンル ---
    genres = q(con, "SELECT genre, count(*) FROM f WHERE status = 'retained' GROUP BY genre ORDER BY 2 DESC")
    manufacturing = q1(con, "SELECT count(*) FROM f WHERE status = 'retained' AND manufacturing_score >= 2.0")[0]
    report["item29_genre"] = {
        "method_ja": "wordlists/genre/genre_rules.json の URL 規則と手がかり語で、1,000字あたりの異なり手がかり語数をスコア化（しきい値2.0）。製造業・重工の適合スコアは独立に算出。",
        "distribution_retained": [{"genre": g, "label_ja": GENRE_LABELS_JA.get(g, g), "records": n, "ratio": round(n / max(1, retained), 5)} for g, n in genres],
        "manufacturing_relevant_records": manufacturing, "manufacturing_relevant_ratio": round(manufacturing / max(1, retained), 5),
        "exclusion_decision": {"excluded_genres": exclude_genres, "note_ja": "除外するジャンルは方針として決める。除外する場合は第3パスで genre 列を条件に削除し、件数を記録する。"},
    }
    if noise_glob:
        report["noise_summaries_glob"] = noise_glob
    return report


def to_markdown(r: dict[str, Any]) -> str:
    L: list[str] = []
    L.append("# 品質監査報告（E 重複・F 言語/品質・G ドメイン適合）")
    L.append("")
    L.append(f"- 生成日時: {r['generated_at_utc']}")
    L.append(f"- 対象文書: {r['records_total']:,}（保持 {r['records_retained']:,}）")
    L.append("")
    d = r["item22_dedup"]
    L.append("## 項目22: 重複除去")
    L.append("")
    L.append(f"- 配布元の実施: {d['upstream']['performed_by']}")
    for m in d["upstream"]["methods"]:
        L.append(f"  - {m['method']}（閾値: {m['threshold']}）")
    L.append("- 出典: " + ", ".join(d["upstream"]["source_urls"]))
    L.append(f"- 判断: {d['upstream']['decision_ja']}")
    e = d["exact_duplicate_check"]
    L.append(f"- 完全一致の再確認（{e['scope']}）: 本文 SHA-1 重複 {e['content_sha1_duplicates']:,}（{e['content_sha1_duplicate_rate']:.4%}）、URL 重複 {e['url_duplicates']:,}")
    L.append("")
    l = r["item25_language"]
    L.append("## 項目25: 言語判定")
    L.append("")
    L.append(f"- ツール: {l['tool']}。対象: {l['scope']}")
    L.append(f"- 日本語比率: {l['japanese_ratio']:.4%}。混在文書（{l['mixed_definition']}）: {l['mixed_language_records']:,}")
    L.append("- 処理: " + "、".join(f"{k}: {v}" for k, v in l["processing"].items()))
    L.append("")
    L.append("| 言語 | 文書数 | 比率 |")
    L.append("|---|---:|---:|")
    for x in l["distribution"][:8]:
        L.append(f"| {x['lang']} | {x['records']:,} | {x['ratio']:.4%} |")
    L.append("")
    p = r["item26_perplexity"]
    L.append("## 項目26: perplexity フィルタ")
    L.append("")
    if "filter" in p:
        L.append(f"- 適用者: {p['filter']['applied_by']}、モデル: {p['filter']['model']}、閾値: {p['filter']['threshold']}（{p['filter']['source_url']}）")
        ds = p["distribution_retained"]
        L.append(f"- 保持側の分布: min {ds['min']:.1f} / p5 {ds['p5']:.1f} / p50 {ds['p50']:.1f} / p95 {ds['p95']:.1f} / max {ds['max']:.1f}")
        L.append("- ヒストグラム（5刻み）: " + ", ".join(f"{h['bin_start']}-{h['bin_start']+5}: {h['records']:,}" for h in p["histogram_bin5"]))
        L.append("- 閾値付近の残存例:")
        for x in p["examples_near_threshold_retained"]:
            L.append(f"  - {x['url'][:90]} (ppl {x['perplexity']:.1f}, {x['chars']}字, {x['genre']})")
        L.append(f"- 注記: {p['note_ja']}")
    else:
        L.append(f"- {p['note_ja']}")
    L.append("")
    n = r["item27_length"]
    L.append("## 項目27: 文書長の分布（フィルタ前後）")
    L.append("")
    L.append("| 区分 | 文書数 | min | p5 | p50 | p95 | max | 平均 |")
    L.append("|---|---:|---:|---:|---:|---:|---:|---:|")
    for key, label in (("chars_input_all", "入力（全件）"), ("chars_final_retained", "最終（保持）"), ("chars_input_deleted_toxic", "有害フィルタで削除"), ("chars_input_deleted_noise", "ノイズで削除")):
        x = n[key]
        if x["records"]:
            L.append(f"| {label} | {x['records']:,} | {x['min']:.0f} | {x['p5']:.0f} | {x['p50']:.0f} | {x['p95']:.0f} | {x['max']:.0f} | {x['mean']} |")
    L.append("")
    L.append("ジャンル別の削除率（特定ジャンルだけが大量に落ちていないかの確認）:")
    L.append("")
    L.append("| ジャンル | 文書数 | 有害で削除 | ノイズで削除 | 削除率 |")
    L.append("|---|---:|---:|---:|---:|")
    for g, v in sorted(n["deletion_rate_by_genre"].items(), key=lambda kv: -kv[1]["records"]):
        L.append(f"| {v['label_ja']} | {v['records']:,} | {v.get('deleted_toxic', 0):,} | {v.get('deleted_noise', 0):,} | {v['deleted_rate']:.2%} |")
    L.append(f"\n{n['tokens_note_ja']}")
    L.append("")
    lq = r["item28_low_quality"]
    L.append("## 項目28: 低品質ページ")
    L.append("")
    L.append(f"- 定義: {lq['definition_ja']}")
    L.append(f"- 方法: {lq['method_ja']}")
    L.append(f"- 比率: {lq['low_quality_records']:,} / {lq['records_retained']:,} = {lq['low_quality_rate']:.2%}。{lq['comparison_ja']}")
    L.append("")
    L.append("| 該当条件 | 文書数 | 比率 |")
    L.append("|---|---:|---:|")
    for x in lq["by_flag"]:
        L.append(f"| {x['label_ja']} | {x['records']:,} | {x['ratio']:.2%} |")
    L.append("")
    g = r["item29_genre"]
    L.append("## 項目29: 無関係ジャンルの比率")
    L.append("")
    L.append(f"- 方法: {g['method_ja']}")
    L.append("")
    L.append("| ジャンル | 文書数 | 比率 |")
    L.append("|---|---:|---:|")
    for x in g["distribution_retained"]:
        L.append(f"| {x['label_ja']} | {x['records']:,} | {x['ratio']:.2%} |")
    L.append("")
    L.append(f"- 製造業・重工に適合（スコア2.0以上）: {g['manufacturing_relevant_records']:,}（{g['manufacturing_relevant_ratio']:.2%}）")
    L.append(f"- 除外の判断: 除外ジャンル = {g['exclusion_decision']['excluded_genres'] or 'なし'}。{g['exclusion_decision']['note_ja']}")
    L.append("")
    return "\n".join(L) + "\n"


def main(argv: list[str] | None = None) -> None:
    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
    parser.add_argument("--features-glob", required=True, help="features.parquet の glob（DuckDB 形式、例: /cleaned/outputs_v2/**/features.parquet）")
    parser.add_argument("--noise-summaries-glob", default=None)
    parser.add_argument("--output-dir", type=Path, required=True)
    parser.add_argument("--previous-low-quality-rate", type=float, default=None, help="前回レビューの低品質比率（0〜1）")
    parser.add_argument("--exclude-genres", default="", help="除外を決めたジャンル（カンマ区切り、記録用）")
    args = parser.parse_args(argv)
    excluded = [x for x in args.exclude_genres.split(",") if x]
    report = build(args.features_glob, args.noise_summaries_glob, args.previous_low_quality_rate, excluded)
    args.output_dir.mkdir(parents=True, exist_ok=True)
    (args.output_dir / "quality_report.json").write_text(json.dumps(report, ensure_ascii=False, indent=2, default=str) + "\n", encoding="utf-8")
    (args.output_dir / "quality_report.md").write_text(to_markdown(report), encoding="utf-8")
    print(json.dumps({"records_total": report["records_total"], "retained": report["records_retained"],
                      "japanese_ratio": report["item25_language"]["japanese_ratio"], "low_quality_rate": report["item28_low_quality"]["low_quality_rate"],
                      "manufacturing_ratio": report["item29_genre"]["manufacturing_relevant_ratio"], "output": str(args.output_dir)}, ensure_ascii=False))


if __name__ == "__main__":
    main()