File size: 9,066 Bytes
cadf371 | 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 | """シャードごとの shard_summary.json を集約し、全件の件数・母数・内訳を出す。
ローカルのディレクトリ（``--root``）か、HF データセット（``--repo`` と ``--prefix``、
``outputs/**/shard_summary.json`` を取得）を入力にできる。``--expected-inputs`` に
入力データセット名を渡すと、入力シャード一覧と突き合わせて未処理シャードを列挙する。
uv run python abeja-cc-ja/src/aggregate_summaries.py --repo Podtech/abeja-cc-ja-cleaned --prefix outputs \\
--expected-inputs Podtech/abeja-cc-ja-filtered --output dataset_summary.json
"""
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
def _load_local(root: Path, marker: str = "shard_summary.json") -> list[dict[str, Any]]:
return [json.loads(p.read_text(encoding="utf-8")) for p in sorted(root.rglob(marker))]
def _load_repo(repo_id: str, prefix: str, marker: str = "shard_summary.json") -> list[dict[str, Any]]:
from huggingface_hub import snapshot_download
local = snapshot_download(repo_id, repo_type="dataset", allow_patterns=[f"{prefix.strip('/')}/**/{marker}"])
return _load_local(Path(local) / prefix.strip("/"), marker)
def aggregate_noise(summaries: list[dict[str, Any]]) -> dict[str, Any]:
"""第2パス（noise_summary.json）の集約。パターン別・項目別・理由別に合算する。"""
total = Counter()
by_reason = Counter()
by_item: dict[str, Counter] = defaultdict(Counter)
by_pattern: dict[str, Counter] = defaultdict(Counter)
pattern_meta: dict[str, dict[str, Any]] = {}
thresholds: Counter = Counter()
shards = []
for s in summaries:
shards.append(s["shard"])
n = s["noise"]
total["input_rows"] += s["input_rows"]
for k in ("records_deleted", "records_retained", "records_modified", "records_newline_joined", "chars_before", "chars_after_retained"):
total[k] += n.get(k) or 0
if n.get("tokens_after_retained"):
total["tokens_after_retained"] += n["tokens_after_retained"]
for k, v in n["records_deleted_by_reason"].items():
by_reason[k] += v
for item, v in n["by_item"].items():
for k, x in v.items():
by_item[item][k] += x
for p in n["patterns"]:
pattern_meta[p["id"]] = {"item": p["item"], "action": p["action"]}
for k in ("records_detected", "occurrences", "lines_removed", "replacements"):
by_pattern[p["id"]][k] += p[k]
thresholds[json.dumps(n["thresholds"], sort_keys=True, ensure_ascii=False)] += 1
return {
"aggregated_at_utc": datetime.now(timezone.utc).isoformat(),
"stage": "noise",
"shards": len(shards),
"totals": dict(total),
"deletion_rate": round(total["records_deleted"] / max(1, total["input_rows"]), 5),
"records_deleted_by_reason": dict(by_reason),
"by_item": {k: dict(v) for k, v in sorted(by_item.items(), key=lambda kv: int(kv[0]))},
"by_pattern": {k: {**pattern_meta[k], **dict(v)} for k, v in sorted(by_pattern.items(), key=lambda kv: (pattern_meta[kv[0]]["item"], kv[0]))},
"thresholds": [json.loads(k) for k in thresholds],
"shard_list": sorted(shards),
}
def _expected_shards(repo_id: str) -> set[str]:
from huggingface_hub import HfApi
return {Path(p).stem for p in HfApi().list_repo_files(repo_id, repo_type="dataset") if p.endswith(".parquet")}
def aggregate(summaries: list[dict[str, Any]]) -> dict[str, Any]:
total = Counter()
by_reason = Counter()
by_category_match = Counter()
by_category_deleted = Counter()
by_source_matched = Counter()
by_source_deleted = Counter()
by_rule = Counter()
pii_records = Counter()
pii_replacements = Counter()
seconds = Counter()
policies = Counter()
versions = Counter()
shards = []
for s in summaries:
shards.append(s["shard"])
versions[s.get("pipeline_version", "?")] += 1
total["input_rows"] += s["input_rows"]
total["toxic_deleted"] += s["toxic"]["records_deleted"]
total["toxic_retained"] += s["toxic"]["records_retained"]
total["pii_records_with_replacement"] += s["pii"]["records_with_replacement"]
total["pii_total_replacements"] += s["pii"]["total_replacements"]
total["final_rows"] += s["output"]["final_rows"]
for k, v in s["toxic"]["records_deleted_by_reason"].items():
by_reason[k] += v
for c, v in s["toxic"]["by_category"].items():
by_category_match[c] += v["records_with_match"]
by_category_deleted[c] += v["records_deleted"]
for name, v in (s["toxic"].get("domain_blocklist_by_source") or {}).items():
by_source_matched[name] += v.get("records_matched", 0)
by_source_deleted[name] += v.get("records_deleted", 0)
for name, v in (s["toxic"].get("cooccurrence_by_rule") or {}).items():
by_rule[name] += v.get("records_matched", 0)
for t, v in s["pii"]["by_type"].items():
pii_records[t] += v["records_with_match"]
pii_replacements[t] += v["replacement_count"]
for k, v in s["seconds"].items():
seconds[k] += v
p = s["pii"].get("policy") or {}
policies[f"{p.get('action')}|{p.get('approved_by')}|{p.get('approved_on')}|{p.get('status')}"] += 1
n = total["input_rows"] or 1
return {
"aggregated_at_utc": datetime.now(timezone.utc).isoformat(),
"shards": len(shards),
"pipeline_versions": dict(versions),
"totals": dict(total),
"toxic": {
"deletion_rate": round(total["toxic_deleted"] / n, 5),
"records_deleted_by_reason": dict(by_reason),
"by_category": {c: {"records_with_match": by_category_match[c], "records_deleted": by_category_deleted[c]} for c in sorted(set(by_category_match) | set(by_category_deleted))},
"domain_blocklist_by_source": {k: {"records_matched": by_source_matched[k], "records_deleted": by_source_deleted[k]} for k in sorted(by_source_matched)},
"cooccurrence_by_rule": dict(by_rule),
},
"pii": {
"denominator_records": total["toxic_retained"],
"records_with_replacement_rate": round(total["pii_records_with_replacement"] / max(1, total["toxic_retained"]), 5),
"by_type": {t: {"records_with_match": pii_records[t], "replacement_count": pii_replacements[t]} for t in sorted(pii_records)},
"policies": dict(policies),
},
"seconds": dict(seconds),
"shard_list": sorted(shards),
}
def main(argv: list[str] | None = None) -> None:
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
source = parser.add_mutually_exclusive_group(required=True)
source.add_argument("--root", type=Path)
source.add_argument("--repo")
parser.add_argument("--prefix", default="outputs")
parser.add_argument("--expected-inputs", default=None, help="入力データセット（未処理シャードの検出用）")
parser.add_argument("--stage", choices=("full", "noise"), default="full", help="noise: 第2パスの noise_summary.json を集約")
parser.add_argument("--output", type=Path, required=True)
args = parser.parse_args(argv)
marker = "noise_summary.json" if args.stage == "noise" else "shard_summary.json"
summaries = _load_local(args.root, marker) if args.root else _load_repo(args.repo, args.prefix, marker)
result = aggregate_noise(summaries) if args.stage == "noise" else aggregate(summaries)
if args.expected_inputs:
expected = _expected_shards(args.expected_inputs)
done = set(result["shard_list"])
result["completeness"] = {"expected": len(expected), "done": len(done & expected), "missing": sorted(expected - done)[:200], "missing_count": len(expected - done), "unexpected": sorted(done - expected)[:50]}
args.output.write_text(json.dumps(result, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
t = result["totals"]
if args.stage == "noise":
print(json.dumps({"shards": result["shards"], "input_rows": t.get("input_rows"), "noise_deleted": t.get("records_deleted"), "retained": t.get("records_retained"),
"modified": t.get("records_modified"), "completeness": result.get("completeness", {}).get("missing_count")}, ensure_ascii=False))
else:
print(json.dumps({"shards": result["shards"], "input_rows": t.get("input_rows"), "toxic_deleted": t.get("toxic_deleted"), "final_rows": t.get("final_rows"),
"pii_replacements": t.get("pii_total_replacements"), "completeness": result.get("completeness", {}).get("missing_count")}, ensure_ascii=False))
if __name__ == "__main__":
main()
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