File size: 10,216 Bytes
b5cbcc7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | """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,
})
# 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()
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