abeja-cc-ja-cleaned / code /e5cb5ca /src /pii_residual_check.py

code e5cb5ca

b5cbcc7 verified 2 months ago

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"""置換後データの PII 残存を、独立した検出器（GLiNER-ja）で確認する（監査項目12）。
``pii_detector.py`` と同じ検出器で再走査しても残存は必ず 0 になるため、
学習済み日本語 PII NER ``DataSign/gliner-ja-pii-v1``（Apache-2.0）で
サンプルを走査し、プレースホルダー以外に残っている氏名・電話・住所・
メール・口座番号を数える。``--original`` に置換前の parquet を渡すと、
同じ文書で GLiNER が置換前に検出した実体のうち置換後に消えている割合
（決定的検出器の再現率の目安）も出す。
    uv sync --group pii-check
    uv run python abeja-cc-ja/src/pii_residual_check.py --input <replaced.parquet> --sample 500 --output pii_residual.json
モデルは最大160トークンのため、文書を文字数で分割して重なり付きで走査する。
"""

from __future__ import annotations

import argparse
import json
import random
import time
import unicodedata
from collections import Counter, defaultdict
from datetime import datetime, timezone
from pathlib import Path
from typing import Any

import pyarrow.parquet as pq

from pii_detector import NAME_CONTEXT_WINDOW, TITLE_CONTEXT_RE, _ORG_RE, _same_sentence_window


MODEL_NAME = "DataSign/gliner-ja-pii-v1"
LABELS = ["person name", "phone number", "address", "email address", "account number"]
LABEL_TO_TYPE = {
    "person name": "person_name",
    "phone number": "phone",
    "address": "address",
    "email address": "email",
    "account number": "bank_account",
}
PLACEHOLDER_MARKERS = ("<EMAIL>", "<PHONE>", "<ADDRESS>", "<PERSON_NAME>", "<PERSON_AFFILIATION>", "<MY_NUMBER>", "<CREDIT_CARD>", "<BANK_ACCOUNT>")


def load_model(device: str | None):
    from gliner import GLiNER
    from gliner.data_processing import WordsSplitter

    class JapaneseSplitterNoWS:
        def __init__(self) -> None:
            self._base = WordsSplitter(splitter_type="janome")

        def __call__(self, text: str):
            for token, start, end in self._base(text):
                if token.strip() == "":
                    continue
                yield token, start, end

    model = GLiNER.from_pretrained(MODEL_NAME, max_width=25)
    model.data_processor.words_splitter = JapaneseSplitterNoWS()
    if device:
        try:
            model = model.to(device)
        except Exception:  # noqa: BLE001 - デバイス非対応時はCPUのまま
            pass
    model.eval()
    return model


def chunks(text: str, size: int, overlap: int):
    start = 0
    while start < len(text):
        end = min(len(text), start + size)
        yield start, text[start:end]
        if end == len(text):
            break
        start = end - overlap


def predict(model, text: str, threshold: float, chunk_chars: int, overlap: int) -> list[dict[str, Any]]:
    found: dict[tuple[str, int, int], dict[str, Any]] = {}
    for offset, piece in chunks(text, chunk_chars, overlap):
        for entity in model.predict_entities(piece, LABELS, threshold=threshold):
            key = (entity["label"], offset + entity["start"], offset + entity["end"])
            if key not in found:
                found[key] = {"label": entity["label"], "start": key[1], "end": key[2], "text": entity["text"], "score": round(float(entity["score"]), 3)}
    return sorted(found.values(), key=lambda item: (item["start"], item["end"]))


def is_placeholder(text: str) -> bool:
    return any(marker in text for marker in PLACEHOLDER_MARKERS) or "<" in text


def mask(text: str) -> str:
    return text[:1] + "*" * max(1, len(text) - 1)


def main(argv: list[str] | None = None) -> None:
    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
    parser.add_argument("--input", type=Path, required=True, help="置換後の parquet（url, content）")
    parser.add_argument("--original", type=Path, default=None, help="置換前の parquet（同じ行順）。再現率の目安を出す")
    parser.add_argument("--sample", type=int, default=500)
    parser.add_argument("--seed", type=int, default=20260904)
    parser.add_argument("--threshold", type=float, default=0.5)
    parser.add_argument("--chunk-chars", type=int, default=200)
    parser.add_argument("--overlap", type=int, default=40)
    parser.add_argument("--max-chars", type=int, default=3000, help="1文書あたり走査する最大文字数")
    parser.add_argument("--device", default=None, help="cpu / mps / cuda（既定はGLiNERの既定）")
    parser.add_argument("--output", type=Path, required=True)
    args = parser.parse_args(argv)

    table = pq.read_table(args.input, columns=["url", "content"])
    total = table.num_rows
    rng = random.Random(args.seed)
    indices = sorted(rng.sample(range(total), min(args.sample, total)))
    original = pq.read_table(args.original, columns=["content"]) if args.original else None
    model = load_model(args.device)

    residual_counts: Counter = Counter()
    residual_docs: Counter = Counter()
    examples: dict[str, list[dict[str, Any]]] = defaultdict(list)
    recall_found: Counter = Counter()
    recall_removed: Counter = Counter()
    # 「個人名＋所属」の定義（同一文・前後25文字に所属か役職）に該当する人名だけの再現率
    ctx_found = 0
    ctx_removed = 0
    started = time.perf_counter()
    docs_with_residual = 0
    for index in indices:
        row = table.slice(index, 1).to_pylist()[0]
        text = unicodedata.normalize("NFKC", row.get("content") or "")[: args.max_chars]
        entities = [e for e in predict(model, text, args.threshold, args.chunk_chars, args.overlap) if not is_placeholder(e["text"])]
        if entities:
            docs_with_residual += 1
        seen_types = set()
        for entity in entities:
            pii_type = LABEL_TO_TYPE[entity["label"]]
            residual_counts[pii_type] += 1
            seen_types.add(pii_type)
            if len(examples[pii_type]) < 5:
                context = text[max(0, entity["start"] - 25): entity["start"]] + "[" + mask(entity["text"]) + "]" + text[entity["end"]: entity["end"] + 25]
                examples[pii_type].append({"record_index": index, "url": row.get("url"), "score": entity["score"], "context_masked": context.replace("\n", " ")})
        for pii_type in seen_types:
            residual_docs[pii_type] += 1
        if original is not None:
            original_text = unicodedata.normalize("NFKC", original.slice(index, 1).to_pylist()[0].get("content") or "")[: args.max_chars]
            for entity in predict(model, original_text, args.threshold, args.chunk_chars, args.overlap):
                pii_type = LABEL_TO_TYPE[entity["label"]]
                recall_found[pii_type] += 1
                removed = entity["text"] not in text
                if removed:
                    recall_removed[pii_type] += 1
                if pii_type == "person_name":
                    before, after = _same_sentence_window(original_text, entity["start"], entity["end"], NAME_CONTEXT_WINDOW)
                    if _ORG_RE.search(before) or _ORG_RE.search(after) or TITLE_CONTEXT_RE.search(before) or TITLE_CONTEXT_RE.search(after):
                        ctx_found += 1
                        ctx_removed += removed
    elapsed = time.perf_counter() - started
    result = {
        "checked_at_utc": datetime.now(timezone.utc).isoformat(),
        "detector": {"model": MODEL_NAME, "license": "Apache-2.0", "labels": LABELS, "threshold": args.threshold,
                     "chunk_chars": args.chunk_chars, "overlap": args.overlap, "max_chars": args.max_chars},
        "input": str(args.input), "total_records": total, "sampled_records": len(indices), "seed": args.seed,
        "elapsed_s": round(elapsed, 1),
        "residual": {
            "records_with_any_residual": docs_with_residual,
            "records_with_residual_rate": round(docs_with_residual / max(1, len(indices)), 4),
            "by_type_entities": dict(residual_counts),
            "by_type_records": dict(residual_docs),
            "examples_masked": examples,
        },
        "note_ja": "GLiNER-ja が置換後テキストで検出した実体（プレースホルダーを除く）。氏名は公人・著者名・作品中の人名も含むため、残存の要否は目視で判断する。",
    }
    if original is not None:
        result["recall_estimate_person_name_with_affiliation_context"] = {
            "gliner_found_in_original": ctx_found,
            "removed_after_replacement": ctx_removed,
            "removed_rate": round(ctx_removed / ctx_found, 3) if ctx_found else None,
            "note_ja": "GLiNER-ja が検出した人名のうち、同一文・前後25文字に所属か役職がある（＝個人名＋所属の定義に該当する）ものの置換率。",
        }
        result["recall_estimate"] = {
            pii_type: {"gliner_found_in_original": recall_found[pii_type], "removed_after_replacement": recall_removed[pii_type],
                       "removed_rate": round(recall_removed[pii_type] / recall_found[pii_type], 3) if recall_found[pii_type] else None}
            for pii_type in sorted(recall_found)
        }
    args.output.parent.mkdir(parents=True, exist_ok=True)
    args.output.write_text(json.dumps(result, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
    print(json.dumps({"sampled": len(indices), "records_with_residual": docs_with_residual, "by_type": dict(residual_counts),
                      "recall_estimate": result.get("recall_estimate"),
                      "recall_person_name_with_affiliation_context": result.get("recall_estimate_person_name_with_affiliation_context"),
                      "elapsed_s": round(elapsed, 1)}, ensure_ascii=False))


if __name__ == "__main__":
    main()

	"""置換後データの PII 残存を、独立した検出器（GLiNER-ja）で確認する（監査項目12）。

	``pii_detector.py`` と同じ検出器で再走査しても残存は必ず 0 になるため、
	学習済み日本語 PII NER ``DataSign/gliner-ja-pii-v1``（Apache-2.0）で
	サンプルを走査し、プレースホルダー以外に残っている氏名・電話・住所・
	メール・口座番号を数える。``--original`` に置換前の parquet を渡すと、
	同じ文書で GLiNER が置換前に検出した実体のうち置換後に消えている割合
	（決定的検出器の再現率の目安）も出す。

	uv sync --group pii-check
	uv run python abeja-cc-ja/src/pii_residual_check.py --input <replaced.parquet> --sample 500 --output pii_residual.json

	モデルは最大160トークンのため、文書を文字数で分割して重なり付きで走査する。
	"""

	from __future__ import annotations

	import argparse
	import json
	import random
	import time
	import unicodedata
	from collections import Counter, defaultdict
	from datetime import datetime, timezone
	from pathlib import Path
	from typing import Any

	import pyarrow.parquet as pq

	from pii_detector import NAME_CONTEXT_WINDOW, TITLE_CONTEXT_RE, _ORG_RE, _same_sentence_window


	MODEL_NAME = "DataSign/gliner-ja-pii-v1"
	LABELS = ["person name", "phone number", "address", "email address", "account number"]
	LABEL_TO_TYPE = {
	"person name": "person_name",
	"phone number": "phone",
	"address": "address",
	"email address": "email",
	"account number": "bank_account",
	}
	PLACEHOLDER_MARKERS = ("<EMAIL>", "<PHONE>", "<ADDRESS>", "<PERSON_NAME>", "<PERSON_AFFILIATION>", "<MY_NUMBER>", "<CREDIT_CARD>", "<BANK_ACCOUNT>")


	def load_model(device: str \| None):
	from gliner import GLiNER
	from gliner.data_processing import WordsSplitter

	class JapaneseSplitterNoWS:
	def __init__(self) -> None:
	self._base = WordsSplitter(splitter_type="janome")

	def __call__(self, text: str):
	for token, start, end in self._base(text):
	if token.strip() == "":
	continue
	yield token, start, end

	model = GLiNER.from_pretrained(MODEL_NAME, max_width=25)
	model.data_processor.words_splitter = JapaneseSplitterNoWS()
	if device:
	try:
	model = model.to(device)
	except Exception: # noqa: BLE001 - デバイス非対応時はCPUのまま
	pass
	model.eval()
	return model


	def chunks(text: str, size: int, overlap: int):
	start = 0
	while start < len(text):
	end = min(len(text), start + size)
	yield start, text[start:end]
	if end == len(text):
	break
	start = end - overlap


	def predict(model, text: str, threshold: float, chunk_chars: int, overlap: int) -> list[dict[str, Any]]:
	found: dict[tuple[str, int, int], dict[str, Any]] = {}
	for offset, piece in chunks(text, chunk_chars, overlap):
	for entity in model.predict_entities(piece, LABELS, threshold=threshold):
	key = (entity["label"], offset + entity["start"], offset + entity["end"])
	if key not in found:
	found[key] = {"label": entity["label"], "start": key[1], "end": key[2], "text": entity["text"], "score": round(float(entity["score"]), 3)}
	return sorted(found.values(), key=lambda item: (item["start"], item["end"]))


	def is_placeholder(text: str) -> bool:
	return any(marker in text for marker in PLACEHOLDER_MARKERS) or "<" in text


	def mask(text: str) -> str:
	return text[:1] + "" max(1, len(text) - 1)


	def main(argv: list[str] \| None = None) -> None:
	parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
	parser.add_argument("--input", type=Path, required=True, help="置換後の parquet（url, content）")
	parser.add_argument("--original", type=Path, default=None, help="置換前の parquet（同じ行順）。再現率の目安を出す")
	parser.add_argument("--sample", type=int, default=500)
	parser.add_argument("--seed", type=int, default=20260904)
	parser.add_argument("--threshold", type=float, default=0.5)
	parser.add_argument("--chunk-chars", type=int, default=200)
	parser.add_argument("--overlap", type=int, default=40)
	parser.add_argument("--max-chars", type=int, default=3000, help="1文書あたり走査する最大文字数")
	parser.add_argument("--device", default=None, help="cpu / mps / cuda（既定はGLiNERの既定）")
	parser.add_argument("--output", type=Path, required=True)
	args = parser.parse_args(argv)

	table = pq.read_table(args.input, columns=["url", "content"])
	total = table.num_rows
	rng = random.Random(args.seed)
	indices = sorted(rng.sample(range(total), min(args.sample, total)))
	original = pq.read_table(args.original, columns=["content"]) if args.original else None
	model = load_model(args.device)

	residual_counts: Counter = Counter()
	residual_docs: Counter = Counter()
	examples: dict[str, list[dict[str, Any]]] = defaultdict(list)
	recall_found: Counter = Counter()
	recall_removed: Counter = Counter()
	# 「個人名＋所属」の定義（同一文・前後25文字に所属か役職）に該当する人名だけの再現率
	ctx_found = 0
	ctx_removed = 0
	started = time.perf_counter()
	docs_with_residual = 0
	for index in indices:
	row = table.slice(index, 1).to_pylist()[0]
	text = unicodedata.normalize("NFKC", row.get("content") or "")[: args.max_chars]
	entities = [e for e in predict(model, text, args.threshold, args.chunk_chars, args.overlap) if not is_placeholder(e["text"])]
	if entities:
	docs_with_residual += 1
	seen_types = set()
	for entity in entities:
	pii_type = LABEL_TO_TYPE[entity["label"]]
	residual_counts[pii_type] += 1
	seen_types.add(pii_type)
	if len(examples[pii_type]) < 5:
	context = text[max(0, entity["start"] - 25): entity["start"]] + "[" + mask(entity["text"]) + "]" + text[entity["end"]: entity["end"] + 25]
	examples[pii_type].append({"record_index": index, "url": row.get("url"), "score": entity["score"], "context_masked": context.replace("\n", " ")})
	for pii_type in seen_types:
	residual_docs[pii_type] += 1
	if original is not None:
	original_text = unicodedata.normalize("NFKC", original.slice(index, 1).to_pylist()[0].get("content") or "")[: args.max_chars]
	for entity in predict(model, original_text, args.threshold, args.chunk_chars, args.overlap):
	pii_type = LABEL_TO_TYPE[entity["label"]]
	recall_found[pii_type] += 1
	removed = entity["text"] not in text
	if removed:
	recall_removed[pii_type] += 1
	if pii_type == "person_name":
	before, after = _same_sentence_window(original_text, entity["start"], entity["end"], NAME_CONTEXT_WINDOW)
	if _ORG_RE.search(before) or _ORG_RE.search(after) or TITLE_CONTEXT_RE.search(before) or TITLE_CONTEXT_RE.search(after):
	ctx_found += 1
	ctx_removed += removed
	elapsed = time.perf_counter() - started
	result = {
	"checked_at_utc": datetime.now(timezone.utc).isoformat(),
	"detector": {"model": MODEL_NAME, "license": "Apache-2.0", "labels": LABELS, "threshold": args.threshold,
	"chunk_chars": args.chunk_chars, "overlap": args.overlap, "max_chars": args.max_chars},
	"input": str(args.input), "total_records": total, "sampled_records": len(indices), "seed": args.seed,
	"elapsed_s": round(elapsed, 1),
	"residual": {
	"records_with_any_residual": docs_with_residual,
	"records_with_residual_rate": round(docs_with_residual / max(1, len(indices)), 4),
	"by_type_entities": dict(residual_counts),
	"by_type_records": dict(residual_docs),
	"examples_masked": examples,
	},
	"note_ja": "GLiNER-ja が置換後テキストで検出した実体（プレースホルダーを除く）。氏名は公人・著者名・作品中の人名も含むため、残存の要否は目視で判断する。",
	}
	if original is not None:
	result["recall_estimate_person_name_with_affiliation_context"] = {
	"gliner_found_in_original": ctx_found,
	"removed_after_replacement": ctx_removed,
	"removed_rate": round(ctx_removed / ctx_found, 3) if ctx_found else None,
	"note_ja": "GLiNER-ja が検出した人名のうち、同一文・前後25文字に所属か役職がある（＝個人名＋所属の定義に該当する）ものの置換率。",
	}
	result["recall_estimate"] = {
	pii_type: {"gliner_found_in_original": recall_found[pii_type], "removed_after_replacement": recall_removed[pii_type],
	"removed_rate": round(recall_removed[pii_type] / recall_found[pii_type], 3) if recall_found[pii_type] else None}
	for pii_type in sorted(recall_found)
	}
	args.output.parent.mkdir(parents=True, exist_ok=True)
	args.output.write_text(json.dumps(result, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
	print(json.dumps({"sampled": len(indices), "records_with_residual": docs_with_residual, "by_type": dict(residual_counts),
	"recall_estimate": result.get("recall_estimate"),
	"recall_person_name_with_affiliation_context": result.get("recall_estimate_person_name_with_affiliation_context"),
	"elapsed_s": round(elapsed, 1)}, ensure_ascii=False))


	if __name__ == "__main__":
	main()