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processing/clean_external.py

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  1. """External dataset cleaning pipeline (Phase-2 sources).
  3. Cleans and normalizes the 11 external datasets collected by
  4. collectors/{tranco,majestic,google_taxonomy,viseca,tabformer,ieee_cis,
  5. ulb_creditcard,hackaprompt,bipia,agentdojo,tensortrust}.py.
  7. Usage:
  8. python -m processing.clean_external # all collected sources
  9. python -m processing.clean_external tabformer # a single source
  11. Memory contract (host has ~2GB available): every large file is processed
  12. chunk-streamed (CSV chunks, bz2 line streaming, ARFF direct read); nothing
  13. big is fully materialized in RAM.
  15. Outputs:
  16. data/processed/external/<source>/... full cleaned parquet (gitignored,
  17. reproducible via `make clean-external`)
  18. data/exports/external/<source>/... compact tracked exports (csv.gz/json)
  19. data/exports/external/stats/*.json per-source cleaning stats
  20. data/exports/external/EXTERNAL_QUALITY_REPORT.md generated summary
  21. """
  23. from __future__ import annotations
  25. import bz2
  26. import csv
  27. import datetime as dt
  28. import io
  29. import json
  30. import pathlib
  31. import re
  32. import sys
  33. import zipfile
  35. import numpy as np
  36. import pandas as pd
  38. from collectors import common
  40. ROOT = common.ROOT
  41. RAW = ROOT / "data" / "raw"
  42. PEXT = ROOT / "data" / "processed" / "external"
  43. EXPORTS = ROOT / "data" / "exports" / "external"
  44. STATS = EXPORTS / "stats"
  46. SEED = 42
  47. DOMAIN_RE = re.compile(r"^(?=.{1,253}$)([a-z0-9]([a-z0-9-]{0,61}[a-z0-9])?\.)+[a-z]{2,63}$")
  50. def _fresh(path: pathlib.Path) -> pathlib.Path:
  51. path.mkdir(parents=True, exist_ok=True)
  52. return path
  55. def _newest(pattern: str) -> pathlib.Path | None:
  56. hits = sorted(RAW.glob(pattern))
  57. return hits[-1] if hits else None
  60. def _bool_series(s: pd.Series) -> pd.Series:
  61. return s.map({"True": True, "False": False, True: True, False: False}).astype("boolean")
  64. # ----------------------------------------------------------------- domains
  66. def clean_tranco() -> dict:
  67. src = _newest("tranco/tranco_top1m_*.zip")
  68. if not src:
  69. return {"status": "missing_raw"}
  70. out = _fresh(PEXT / "tranco")
  71. with zipfile.ZipFile(src) as zf:
  72. with zf.open(zf.namelist()[0]) as f:
  73. df = pd.read_csv(f, header=None, names=["rank", "domain"])
  74. n_raw = len(df)
  75. df["domain"] = df["domain"].str.strip().str.lower()
  76. bad = ~df["domain"].str.match(DOMAIN_RE)
  77. n_bad = int(bad.sum())
  78. df = df[~bad]
  79. dup = int(df["domain"].duplicated().sum())
  80. df = df.drop_duplicates("domain").sort_values("rank")
  81. df.to_parquet(out / "tranco_top1m.parquet", index=False)
  82. df.to_csv(_fresh(EXPORTS / "tranco") / "tranco_top1m.csv.gz", index=False, compression="gzip")
  83. return {"status": "ok", "rows_raw": n_raw, "rows_clean": len(df),
  84. "invalid_domains": n_bad, "dup_domains": dup,
  85. "list_date": src.stem.removeprefix("tranco_top1m_")}
  88. def clean_majestic() -> dict:
  89. src = _newest("majestic/majestic_million_*.csv")
  90. if not src:
  91. return {"status": "missing_raw"}
  92. out = _fresh(PEXT / "majestic")
  93. df = pd.read_csv(src)
  94. n_raw = len(df)
  95. df["Domain"] = df["Domain"].str.strip().str.lower()
  96. bad = ~df["Domain"].str.match(DOMAIN_RE)
  97. n_bad = int(bad.sum())
  98. df = df[~bad].drop_duplicates("Domain")
  99. keep = ["GlobalRank", "TldRank", "Domain", "TLD", "RefSubNets", "RefIPs",
  100. "PrevGlobalRank", "PrevTldRank", "PrevRefSubNets", "PrevRefIPs"]
  101. df = df[keep].astype({c: "int64" for c in keep if c not in ("Domain", "TLD")})
  102. df.to_parquet(out / "majestic_million.parquet", index=False)
  103. df.to_csv(_fresh(EXPORTS / "majestic") / "majestic_million.csv.gz",
  104. index=False, compression="gzip")
  105. return {"status": "ok", "rows_raw": n_raw, "rows_clean": len(df),
  106. "invalid_domains": n_bad}
  109. def clean_google_taxonomy() -> dict:
  110. src = _newest("google_taxonomy/taxonomy_en-US_*.txt")
  111. if not src:
  112. return {"status": "missing_raw"}
  113. rows = []
  114. for line in src.read_text(encoding="utf-8").splitlines():
  115. line = line.strip()
  116. if not line or line.startswith("#"):
  117. continue
  118. tid, _, path = line.partition(" - ")
  119. if not tid.isdigit():
  120. continue
  121. parts = [p.strip() for p in path.split(" > ")]
  122. rows.append({"taxonomy_id": int(tid), "path": path, "depth": len(parts), **{
  123. f"level_{i+1}": parts[i] if i < len(parts) else None for i in range(5)}})
  124. df = pd.DataFrame(rows)
  125. df.to_parquet(_fresh(PEXT / "google_taxonomy") / "google_product_taxonomy.parquet", index=False)
  126. df.to_csv(_fresh(EXPORTS / "google_taxonomy") / "google_product_taxonomy.csv", index=False)
  127. return {"status": "ok", "rows": len(df), "max_depth": int(df["depth"].max()),
  128. "distinct_l1": int(df["level_1"].nunique())}
  131. # ------------------------------------------------------------------ viseca
  133. def _viseca_clean_table(df: pd.DataFrame) -> pd.DataFrame:
  134. for c in df.columns:
  135. if c == "timestamp":
  136. df[c] = pd.to_datetime(df[c], utc=True, errors="coerce")
  137. continue
  138. vals = df[c].dropna().unique()[:8]
  139. if all(isinstance(v, str) and v in ("true", "false") for v in vals) and len(vals):
  140. df[c] = df[c].map({"true": True, "false": False}).astype("boolean")
  141. elif all(isinstance(v, str) and re.fullmatch(r"-?\d+(\.\d+)?", v or "x") for v in vals) and len(vals):
  142. df[c] = pd.to_numeric(df[c], errors="coerce")
  143. return df.convert_dtypes()
  146. def clean_viseca() -> dict:
  147. data_dir = next((RAW / "viseca" / "extracted").glob("*/data"), None)
  148. if not data_dir:
  149. return {"status": "missing_raw"}
  150. out = _fresh(PEXT / "viseca")
  151. exp = _fresh(EXPORTS / "viseca")
  152. # integrity: verify extracted CSVs against the pack's own sha256 manifest
  153. meta = json.loads((data_dir / "metadata.json").read_text())
  154. hashes = {f["path"]: f.get("sha256") for f in meta.get("files", []) if f.get("format") == "csv"}
  155. import hashlib
  156. verified, mismatched = 0, []
  157. for rel, want in hashes.items():
  158. p = data_dir / rel
  159. if not p.exists():
  160. mismatched.append(f"{rel}: missing")
  161. continue
  162. got = hashlib.sha256(p.read_bytes()).hexdigest()
  163. if got == want:
  164. verified += 1
  165. else:
  166. mismatched.append(rel)
  167. stats: dict = {"status": "ok", "sha256_verified": verified,
  168. "sha256_mismatch": mismatched, "tables": {}}
  169. for p in sorted(data_dir.glob("*.csv")):
  170. df = _viseca_clean_table(pd.read_csv(p))
  171. name = p.stem
  172. df.to_parquet(out / f"{name}.parquet", index=False)
  173. df.to_csv(exp / f"{name}.csv.gz", index=False, compression="gzip")
  174. stats["tables"][name] = {"rows": len(df), "cols": int(df.shape[1])}
  175. return stats
  178. # --------------------------------------------------------------- tabformer
  180. _TAB_SCHEMA = {
  181. "User": "int16", "Card": "int16", "Year": "int16", "Month": "int8", "Day": "int8",
  182. "Time": "string", "Amount": "string", "Use Chip": "string",
  183. "Merchant Name": "string", "Merchant City": "string", "Merchant State": "string",
  184. "Zip": "string", "MCC": "string", "Errors?": "string", "Is Fraud?": "string",
  185. }
  188. def clean_tabformer() -> dict:
  189. import tarfile
  190. import pyarrow as pa
  191. import pyarrow.parquet as pq
  193. src = RAW / "tabformer" / "tabformer_transactions.tgz"
  194. if not src.exists():
  195. return {"status": "missing_raw"}
  196. out_dir = _fresh(PEXT / "tabformer")
  197. csv_path = out_dir / "card_transaction.v1.csv"
  198. if not csv_path.exists():
  199. with tarfile.open(src, "r:gz") as tf:
  200. tf.extractall(out_dir, filter="data")
  202. cols = list(_TAB_SCHEMA)
  203. fraud_rate_warn = []
  204. stats = {"status": "ok", "rows": 0, "frauds": 0, "nulls_errors": 0,
  205. "users": set(), "years": set(), "use_chip": {}}
  206. rng = np.random.default_rng(SEED)
  207. samples: list[pd.DataFrame] = []
  208. writer = None
  209. target = out_dir / "tabformer_clean.parquet"
  210. try:
  211. for chunk in pd.read_csv(csv_path, chunksize=500_000, dtype=_TAB_SCHEMA,
  212. na_values=[""], keep_default_na=True):
  213. chunk["Amount"] = (chunk["Amount"].astype("string").str.replace("$", "", regex=False)
  214. .astype("float32"))
  215. fraud = (chunk["Is Fraud?"] == "Yes")
  216. stats["rows"] += len(chunk)
  217. stats["frauds"] += int(fraud.sum())
  218. stats["nulls_errors"] += int(chunk["Errors?"].isna().sum())
  219. stats["users"].update(pd.unique(chunk["User"]).tolist())
  220. stats["years"].update(pd.unique(chunk["Year"]).tolist())
  221. for k, v in chunk["Use Chip"].value_counts().items():
  222. stats["use_chip"][k] = stats["use_chip"].get(k, 0) + int(v)
  223. clean = chunk.copy()
  224. clean["Is Fraud?"] = fraud.astype("int8")
  225. table = pa.Table.from_pandas(clean, preserve_index=False)
  226. if writer is None:
  227. writer = pq.ParquetWriter(target, table.schema, compression="zstd")
  228. writer.write_table(table)
  229. # stratified sample: all frauds + ~0.5% of legit rows (cap 120k)
  230. legit = clean[~fraud]
  231. take = legit.loc[rng.random(len(legit)) < 0.005]
  232. samples.append(clean[fraud])
  233. samples.append(take)
  234. finally:
  235. if writer:
  236. writer.close()
  238. sample = pd.concat(samples, ignore_index=True)
  239. if len(sample) > 130_000:
  240. sample = sample.iloc[rng.choice(len(sample), 130_000, replace=False)]
  241. sample.to_csv(_fresh(EXPORTS / "tabformer") / "tabformer_sample.csv.gz",
  242. index=False, compression="gzip")
  243. csv_path.unlink() # 2.7GB intermediate; tgz stays cached in raw/
  244. if stats["rows"] and stats["frauds"] / stats["rows"] > 0.02:
  245. fraud_rate_warn.append("fraud rate implausible — verify parse")
  246. return {"status": "ok", "rows": stats["rows"], "frauds": stats["frauds"],
  247. "fraud_rate": round(stats["frauds"] / max(stats["rows"], 1), 6),
  248. "unique_users": len(stats["users"]),
  249. "years": f"{min(stats['years'])}-{max(stats['years'])}" if stats["years"] else None,
  250. "nulls_errors": stats["nulls_errors"], "use_chip": stats["use_chip"],
  251. "sample_rows": len(sample), "warnings": fraud_rate_warn}
  254. # ---------------------------------------------------------------- ieee-cis
  256. def clean_ieee_cis() -> dict:
  257. import pyarrow as pa
  258. import pyarrow.parquet as pq
  260. tx = RAW / "ieee_cis" / "ieee_cis_train_transaction.csv"
  261. ident = RAW / "ieee_cis" / "ieee_cis_train_identity.csv"
  262. if not tx.exists():
  263. return {"status": "missing_raw"}
  265. # identity is small (144k x 41): load once, downcast, merge per chunk
  266. idf = pd.read_csv(ident)
  267. id_obj = {c: "float32" for c in idf.columns if c.startswith("id_") and
  268. pd.api.types.is_numeric_dtype(idf[c])}
  269. idf = idf.astype(id_obj)
  270. for c in idf.columns:
  271. if c not in ("TransactionID",) and not pd.api.types.is_numeric_dtype(idf[c]):
  272. mcol = idf[c].map({"T": True, "F": False, "True": True, "False": False})
  273. if mcol.notna().mean() > 0.9: # T/F-typed strings -> boolean
  274. idf[c] = mcol.astype("boolean")
  275. for c in ("id_01", "id_02", "id_03", "id_05"):
  276. if c in idf: # spot null-rate stats
  277. pass
  279. vcols = [f"V{i}" for i in range(1, 340)]
  280. ccols = [f"C{i}" for i in range(1, 15)]
  281. dcols = [f"D{i}" for i in range(1, 16)]
  282. mcols = [f"M{i}" for i in range(1, 10)]
  283. out_dir = _fresh(PEXT / "ieee_cis")
  284. writer = None
  285. stats = {"rows": 0, "frauds": 0, "with_identity": 0, "null_V": 0}
  286. rng = np.random.default_rng(SEED)
  287. samples: list[pd.DataFrame] = []
  288. target = out_dir / "ieee_cis_train_clean.parquet"
  289. try:
  290. for chunk in pd.read_csv(tx, chunksize=100_000):
  291. stats["rows"] += len(chunk)
  292. chunk["isFraud"] = chunk["isFraud"].astype("int8")
  293. stats["frauds"] += int(chunk["isFraud"].sum())
  294. for c in vcols + ccols + dcols + ["addr1", "addr2", "dist1", "dist2",
  295. "TransactionAmt"]:
  296. if c in chunk:
  297. chunk[c] = chunk[c].astype("float32")
  298. stats["null_V"] += int(chunk[vcols].isna().any(axis=1).sum())
  299. for c in mcols:
  300. if c in chunk:
  301. chunk[c] = chunk[c].map({"T": True, "F": False}).astype("boolean")
  302. merged = chunk.merge(idf, on="TransactionID", how="left")
  303. stats["with_identity"] += int(merged["id_01"].notna().sum() if "id_01" in merged else 0)
  304. table = pa.Table.from_pandas(merged, preserve_index=False)
  305. if writer is None:
  306. writer = pq.ParquetWriter(target, table.schema, compression="zstd")
  307. writer.write_table(table)
  308. fraud = merged["isFraud"] == 1
  309. samples.append(merged[fraud])
  310. legit = merged[~fraud]
  311. samples.append(legit.loc[rng.random(len(legit)) < 0.05])
  312. finally:
  313. if writer:
  314. writer.close()
  316. sample = pd.concat(samples, ignore_index=True)
  317. sample.to_csv(_fresh(EXPORTS / "ieee_cis") / "ieee_cis_train_sample.csv.gz",
  318. index=False, compression="gzip")
  319. expected = {"rows": 590540, "frauds": 20663} # verified vs Kaggle kernels: isFraud 0:569877, 1:20663
  320. checks = {
  321. "rows_ok": stats["rows"] == expected["rows"],
  322. "frauds_ok": stats["frauds"] == expected["frauds"],
  323. }
  324. return {"status": "ok", **stats, "expected": expected,
  325. "integrity_checks": checks,
  326. "fraud_rate": round(stats["frauds"] / max(stats["rows"], 1), 6),
  327. "sample_rows": len(sample)}
  330. # ------------------------------------------------------------- ulb (OpenML)
  332. def clean_ulb_creditcard() -> dict:
  333. src = _newest("ulb_creditcard/ulb_creditcard_*.arff")
  334. if not src:
  335. return {"status": "missing_raw"}
  336. names: list[str] = []
  337. with src.open(encoding="utf-8", errors="replace") as f:
  338. header_lines = 0
  339. for line in f:
  340. header_lines += 1
  341. s = line.strip().lower()
  342. if s.startswith("@attribute"):
  343. names.append(line.split()[1])
  344. elif s.startswith("@data"):
  345. break
  346. df = pd.read_csv(src, skiprows=header_lines, header=None, names=names, quotechar="'")
  347. n_raw = len(df)
  348. dup = int(df.duplicated().sum())
  349. df["Class"] = df["Class"].astype("int8")
  350. df["Amount"] = df["Amount"].astype("float32")
  351. df.to_parquet(_fresh(PEXT / "ulb_creditcard") / "ulb_creditcard_clean.parquet", index=False)
  352. fraud = df[df["Class"] == 1]
  353. legit = df[df["Class"] == 0].sample(min(20_000, (df["Class"] == 0).sum()),
  354. random_state=SEED)
  355. pd.concat([fraud, legit]).to_csv(
  356. _fresh(EXPORTS / "ulb_creditcard") / "ulb_creditcard_fraud_plus_sample.csv.gz",
  357. index=False, compression="gzip")
  358. checks = {"rows_ok": n_raw == 284807, "frauds_ok": int((df["Class"] == 1).sum()) == 492}
  359. return {"status": "ok", "rows": n_raw, "duplicates_kept": dup,
  360. "frauds": int((df["Class"] == 1).sum()),
  361. "fraud_rate": round(float((df["Class"] == 1).mean()), 6),
  362. "integrity_checks": checks}
  365. # ------------------------------------------------------------------ bipia
  367. def clean_bipia() -> dict:
  368. bench = next((RAW / "bipia" / "extracted").glob("*/benchmark"), None)
  369. if not bench:
  370. return {"status": "missing_raw"}
  371. rows: list[dict] = []
  373. def add_text_attacks(path: pathlib.Path, domain: str):
  374. data = json.loads(path.read_text())
  375. split = "train" if "train" in path.name else "test"
  376. for attack_type, texts in data.items():
  377. for t in texts:
  378. rows.append({"domain": domain, "split": split, "attack_type": attack_type,
  379. "kind": "attack_text", "text": t, "ideal": None})
  381. for name in ("text_attack_train.json", "text_attack_test.json"):
  382. add_text_attacks(bench / name, "text")
  383. for name in ("code_attack_train.json", "code_attack_test.json"):
  384. add_text_attacks(bench / name, "code")
  386. per_domain_counts = {}
  387. for sub in ("qa", "email", "table", "code"):
  388. d = bench / sub
  389. if not d.exists():
  390. continue
  391. for split in ("train", "test"):
  392. p = d / f"{split}.jsonl"
  393. if not p.exists():
  394. continue
  395. n = 0
  396. with p.open() as f:
  397. for line in f:
  398. if not line.strip():
  399. continue
  400. rec = json.loads(line)
  401. n += 1
  402. rows.append({"domain": sub, "split": split, "attack_type": None,
  403. "kind": "context_pair",
  404. "text": str(rec.get("context", ""))[:8000],
  405. "ideal": str(rec.get("ideal", ""))[:500]})
  406. per_domain_counts[f"{sub}.{split}"] = n
  408. df = pd.DataFrame(rows)
  409. df.to_parquet(_fresh(PEXT / "bipia") / "bipia_benchmark.parquet", index=False)
  410. attacks = df[df["kind"] == "attack_text"][["domain", "split", "attack_type", "text"]]
  411. attacks.to_csv(_fresh(EXPORTS / "bipia") / "bipia_attack_texts.csv.gz",
  412. index=False, compression="gzip")
  413. return {"status": "ok", "rows": len(df), "attack_texts": len(attacks),
  414. "context_pairs": per_domain_counts}
  417. # --------------------------------------------------------------- agentdojo
  419. def clean_agentdojo() -> dict:
  420. root = RAW / "agentdojo" / "extracted" / "agentdojo-main"
  421. if not root.exists():
  422. return {"status": "missing_raw"}
  423. out = _fresh(PEXT / "agentdojo")
  424. runs = root / "runs"
  425. results: list[dict] = []
  426. files = sorted(runs.glob("*/*/*/*/*.json"))
  427. for p in files:
  428. try:
  429. d = json.loads(p.read_text())
  430. except Exception:
  431. continue
  432. results.append({
  433. "model": p.relative_to(runs).parts[0],
  434. "suite": d.get("suite_name") or p.relative_to(runs).parts[1],
  435. "task_id": d.get("user_task_id") or p.relative_to(runs).parts[2],
  436. "injection_task_id": d.get("injection_task_id"),
  437. "attack_type": d.get("attack_type") or p.relative_to(runs).parts[3],
  438. "attack_technique": p.stem,
  439. "utility": d.get("utility"),
  440. "security": d.get("security"),
  441. "error": d.get("error"),
  442. "duration_s": d.get("duration"),
  443. })
  444. rdf = pd.DataFrame(results)
  445. rdf.to_parquet(out / "agentdojo_benchmark_results.parquet", index=False)
  446. rdf.to_csv(_fresh(EXPORTS / "agentdojo") / "agentdojo_benchmark_results.csv.gz",
  447. index=False, compression="gzip")
  449. # suites inventory: user tasks + injection vectors per suite version
  450. inv = []
  451. for p in sorted((root / "src" / "agentdojo" / "default_suites").rglob("*.py")):
  452. rel = p.relative_to(root / "src" / "agentdojo" / "default_suites")
  453. inv.append({"suite_version": rel.parts[0], "suite": rel.parts[1] if len(rel.parts) > 1 else None,
  454. "file": str(rel), "kind": "injections" if "injection" in p.name else "tasks"})
  455. (out / "suites_inventory.jsonl").write_text(
  456. "\n".join(json.dumps(r) for r in inv))
  457. (_fresh(EXPORTS / "agentdojo") / "suites_inventory.jsonl").write_text(
  458. "\n".join(json.dumps(r) for r in inv))
  460. util = pd.to_numeric(rdf["utility"], errors="coerce")
  461. sec = pd.to_numeric(rdf["security"], errors="coerce")
  462. return {"status": "ok", "result_files": len(rdf), "models": int(rdf["model"].nunique()),
  463. "suites": sorted(rdf["suite"].dropna().unique().tolist()),
  464. "utility_true_rate": round(float(util.mean()), 4) if len(rdf) else None,
  465. "security_true_rate": round(float(sec.mean()), 4) if len(rdf) else None,
  466. "suite_files": len(inv)}
  469. # -------------------------------------------------------------- tensortrust
  471. import pyarrow as pa
  472. import pyarrow.parquet as pq
  474. _TT_ATTACKS_SCHEMA = pa.schema([
  475. ("attack_id", pa.int64()), ("attacker_id_anonymized", pa.int64()),
  476. ("defender_id_anonymized", pa.int64()), ("defense_id", pa.int64()),
  477. ("attacker_balance_before", pa.float64()), ("defender_balance_before", pa.float64()),
  478. ("attacker_balance_gain", pa.int64()), ("defender_balance_gain", pa.int64()),
  479. ("opening_defense", pa.string()), ("attacker_input", pa.string()),
  480. ("closing_defense", pa.string()), ("access_code", pa.string()),
  481. ("llm_choice", pa.string()), ("llm_output", pa.string()),
  482. ("output_is_access_granted", pa.bool_()), ("is_self_attack", pa.bool_()),
  483. ("timestamp", pa.string()),
  484. ])
  485. _TT_DEFENSES_SCHEMA = pa.schema([
  486. ("defense_id", pa.int64()), ("defender_id_anonymized", pa.int64()),
  487. ("opening_defense", pa.string()), ("closing_defense", pa.string()),
  488. ("access_code", pa.string()), ("llm_choice", pa.string()),
  489. ("llm_output", pa.string()), ("output_is_access_granted", pa.bool_()),
  490. ("timestamp", pa.string()),
  491. ])
  494. def _tt_coerce(v, dtype):
  495. if v is None or (isinstance(v, str) and v.strip() in ("", "nan", "None")):
  496. return None
  497. try:
  498. if pa.types.is_boolean(dtype):
  499. return v if isinstance(v, bool) else str(v).strip().lower() == "true"
  500. if pa.types.is_integer(dtype):
  501. return int(float(v))
  502. if pa.types.is_floating(dtype):
  503. return float(v)
  504. return str(v)
  505. except (TypeError, ValueError):
  506. return None
  509. def _tt_clean_stream(bz2_path: pathlib.Path, target: pathlib.Path,
  510. schema: pa.Schema) -> tuple[int, pd.DataFrame]:
  511. """Stream a Tensor Trust raw jsonl.bz2 dump into a typed parquet file.
  513. Raw values arrive as JSON strings ("True"/"nan"/"1234"); every field is
  514. coerced to an explicit schema so leading nulls can't poison inference.
  515. """
  516. writer = pq.ParquetWriter(target, schema, compression="zstd")
  517. n = 0
  518. samples: list[dict] = []
  519. sample_rng = np.random.default_rng(SEED)
  520. batch: list[dict] = []
  521. names = [f.name for f in schema]
  522. with bz2.open(bz2_path, "rt", encoding="utf-8") as f:
  523. for line in f:
  524. if not line.strip():
  525. continue
  526. rec = json.loads(line)
  527. clean = {name: _tt_coerce(rec.get(name), schema.field(name).type)
  528. for name in names}
  529. batch.append(clean)
  530. n += 1
  531. if len(batch) >= 5_000:
  532. writer.write_table(pa.Table.from_pylist(batch, schema=schema))
  533. batch = []
  534. if sample_rng.random() < 0.05 and len(samples) < 15_000:
  535. samples.append({k: (str(v)[:200] if isinstance(v, str) else v)
  536. for k, v in clean.items()})
  537. if batch:
  538. writer.write_table(pa.Table.from_pylist(batch, schema=schema))
  539. writer.close()
  540. return n, pd.DataFrame(samples)
  543. def clean_tensortrust() -> dict:
  544. out = _fresh(PEXT / "tensortrust")
  545. exp = _fresh(EXPORTS / "tensortrust")
  546. attacks_raw = RAW / "tensortrust" / "raw-data__v2__raw_dump_attacks.jsonl.bz2"
  547. defenses_raw = RAW / "tensortrust" / "raw-data__v2__raw_dump_defenses.jsonl.bz2"
  548. stats: dict = {"status": "ok"}
  549. if attacks_raw.exists():
  550. n, sample = _tt_clean_stream(attacks_raw, out / "attacks_v2_clean.parquet",
  551. _TT_ATTACKS_SCHEMA)
  552. sample.to_csv(exp / "tensortrust_attacks_sample.csv.gz", index=False, compression="gzip")
  553. granted = sample["output_is_access_granted"]
  554. stats["attacks"] = {"rows": n, "sample_rows": len(sample),
  555. "granted_rate_sample": round(float(granted.mean()), 4)}
  556. if defenses_raw.exists():
  557. n, sample = _tt_clean_stream(defenses_raw, out / "defenses_v2_clean.parquet",
  558. _TT_DEFENSES_SCHEMA)
  559. sample.to_csv(exp / "tensortrust_defenses_sample.csv.gz", index=False, compression="gzip")
  560. stats["defenses"] = {"rows": n, "sample_rows": len(sample)}
  561. for rel in ("benchmarks__hijacking-robustness__v1__hijacking_robustness_dataset.jsonl",
  562. "benchmarks__extraction-robustness__v1__extraction_robustness_dataset.jsonl",
  563. "detecting-extractions__v1__prompt_extraction_detection.jsonl"):
  564. src = RAW / "tensortrust" / rel
  565. if src.exists():
  566. n = sum(1 for line in src.open() if line.strip())
  567. import shutil
  568. shutil.copy(src, exp / rel.replace("__", "/").replace("/", "_"))
  569. stats.setdefault("benchmarks", {})[rel] = n
  570. return stats
  573. # -------------------------------------------------------------- hackaprompt
  575. def clean_hackaprompt() -> dict:
  576. blocked = _newest("hackaprompt/blocked_*.json")
  577. if blocked:
  578. return {"status": "blocked", "reason": json.loads(blocked.read_text())["reason"]}
  579. src = RAW / "hackaprompt" / "hackaprompt.parquet"
  580. if not src.exists():
  581. return {"status": "missing_raw"}
  582. df = pd.read_parquet(src)
  583. df.to_parquet(_fresh(PEXT / "hackaprompt") / "hackaprompt_clean.parquet", index=False)
  584. return {"status": "ok", "rows": len(df), "cols": int(df.shape[1])}
  587. # --------------------------------------------------------------- threatfox
  589. def _read_comment_csv(path: pathlib.Path) -> pd.DataFrame:
  590. """abuse.ch-style CSV: header (and notes) live in `#` comment lines.
  592. The column header is the comment line starting with `# "col1","col2"...`.
  593. """
  594. header: str | None = None
  595. lines = []
  596. with path.open(encoding="utf-8", errors="replace") as f:
  597. for line in f:
  598. if line.startswith("# ") and '"' in line and header is None \
  599. and "," in line:
  600. header = line[1:].lstrip()
  601. continue
  602. if not line.startswith("#"):
  603. lines.append(line)
  604. if header:
  605. lines.insert(0, header)
  606. return pd.read_csv(io.StringIO("".join(lines)), skipinitialspace=True)
  609. def _newest_data(pattern: str) -> pathlib.Path | None:
  610. """_newest but excluding .meta.json sidecars that share the glob."""
  611. hits = sorted(p for p in RAW.glob(pattern) if not p.name.endswith(".meta.json"))
  612. return hits[-1] if hits else None
  615. def _host_from_ioc(value: str, ioc_type: str) -> str | None:
  616. v = (value or "").strip()
  617. if not v:
  618. return None
  619. if ioc_type == "url":
  620. from urllib.parse import urlparse
  621. try:
  622. return (urlparse(v).hostname or "").lower() or None
  623. except ValueError:
  624. return None
  625. if ioc_type == "domain":
  626. return v.lower()
  627. if ioc_type == "ip:port":
  628. return v.rsplit(":", 1)[0].strip("[]").lower()
  629. if ioc_type in ("ip", "ipv4", "ipv6"):
  630. return v.lower()
  631. return None
  634. def clean_threatfox() -> dict:
  635. src = _newest("threatfox/threatfox_csv_recent_*.csv")
  636. if not src:
  637. return {"status": "missing_raw"}
  638. df = _read_comment_csv(src)
  639. n_raw = len(df)
  640. df.columns = [c.strip().lower() for c in df.columns]
  641. for c in df.select_dtypes("object"):
  642. df[c] = df[c].astype("string").str.strip()
  643. df["entity"] = [_host_from_ioc(v, t) for v, t in zip(df["ioc_value"], df["ioc_type"])]
  644. n_no_entity = int(df["entity"].isna().sum())
  645. df = df.dropna(subset=["entity"])
  646. df["confidence_level"] = pd.to_numeric(df["confidence_level"], errors="coerce").astype("Int64")
  647. out = _fresh(PEXT / "threatfox")
  648. df.to_parquet(out / "threatfox_recent.parquet", index=False)
  649. df.to_csv(_fresh(EXPORTS / "threatfox") / "threatfox_recent.csv.gz",
  650. index=False, compression="gzip")
  651. return {"status": "ok", "rows_raw": n_raw, "rows_clean": len(df),
  652. "no_host_extracted": n_no_entity,
  653. "unique_domains_ips": int(df["entity"].nunique()),
  654. "ioc_types": {k: int(v) for k, v in df["ioc_type"].value_counts().items()}}
  657. # ------------------------------------------------------------ feodotracker
  659. def clean_feodotracker() -> dict:
  660. src = _newest_data("feodotracker/feodotracker_ipblocklist_*.json")
  661. if not src:
  662. return {"status": "missing_raw"}
  663. rows = json.loads(src.read_text())
  664. df = pd.DataFrame(rows)
  665. n_raw = len(df)
  666. if "ip_address" not in df.columns:
  667. return {"status": "error", "error": "unexpected feodotracker json schema"}
  668. df["ip_address"] = df["ip_address"].astype("string").str.strip().str.lower()
  669. df = df.drop_duplicates(["ip_address", "port", "malware"])
  670. out = _fresh(PEXT / "feodotracker")
  671. df.to_parquet(out / "feodotracker_ipblocklist.parquet", index=False)
  672. df.to_csv(_fresh(EXPORTS / "feodotracker") / "feodotracker_ipblocklist.csv.gz",
  673. index=False, compression="gzip")
  674. return {"status": "ok", "rows_raw": n_raw, "rows_clean": len(df),
  675. "unique_ips": int(df["ip_address"].nunique()),
  676. "malware_families": {k: int(v) for k, v in df["malware"].value_counts().items()}}
  679. # ----------------------------------------------------------- malwarebazaar
  681. def clean_malwarebazaar() -> dict:
  682. probe = RAW / "malwarebazaar" / "probe_status.json"
  683. src = _newest("malwarebazaar/malwarebazaar_daily_*.json") \
  684. or _newest("malwarebazaar/malwarebazaar_get_recent_*.json")
  685. if not src:
  686. return {"status": "blocked",
  687. "reason": "no raw data; blob + API unreachable (see probe_status.json)",
  688. "probe": json.loads(probe.read_text()) if probe.exists() else None}
  689. rows = json.loads(src.read_text())
  690. if isinstance(rows, dict):
  691. rows = rows.get("data", [])
  692. df = pd.DataFrame(rows)
  693. out = _fresh(PEXT / "malwarebazaar")
  694. df.to_parquet(out / "malwarebazaar_sample.parquet", index=False)
  695. return {"status": "ok", "rows": len(df), "file": src.name}
  698. # --------------------------------------------------------------- sanctions
  700. def _xml_first(el, *path: str) -> str | None:
  701. cur = el
  702. for p in path:
  703. cur = cur.find(p)
  704. if cur is None:
  705. return None
  706. return (cur.text or "").strip() or None
  709. def clean_sanctions_un() -> dict:
  710. import xml.etree.ElementTree as ET
  711. src = _newest("sanctions_un/un_consolidated_*.xml")
  712. if not src:
  713. return {"status": "missing_raw"}
  714. rows: list[dict] = []
  715. counts = {"individual": 0, "entity": 0}
  716. for ev, el in ET.iterparse(str(src), events=("end",)):
  717. if el.tag not in ("INDIVIDUAL", "ENTITY"):
  718. continue
  719. kind = el.tag.lower()
  720. counts[kind] += 1
  721. aliases = [a.text.strip() for a in
  722. el.findall("INDIVIDUAL_ALIAS/ALIAS_NAME")
  723. + el.findall("ENTITY_ALIAS/ALIAS_NAME")
  724. if a.text and a.text.strip()]
  725. name = " ".join(p for p in [_xml_first(el, "FIRST_NAME"),
  726. _xml_first(el, "SECOND_NAME"),
  727. _xml_first(el, "THIRD_NAME"),
  728. _xml_first(el, "FOURTH_NAME")] if p)
  729. rows.append({
  730. "dataid": _xml_first(el, "DATAID"),
  731. "list_type": kind,
  732. "name": name or None,
  733. "n_aliases": len(aliases),
  734. "aliases": aliases[:10],
  735. "listed_on": _xml_first(el, "LISTED_ON"),
  736. "un_ref": _xml_first(el, "REFERENCE_NUMBER"),
  737. "comments": (_xml_first(el, "COMMENTS1") or "")[:500],
  738. })
  739. el.clear()
  740. df = pd.DataFrame(rows)
  741. out = _fresh(PEXT / "sanctions_un")
  742. df.to_parquet(out / "un_consolidated.parquet", index=False)
  743. keep = ["dataid", "list_type", "name", "n_aliases", "listed_on", "un_ref"]
  744. df[keep].to_csv(_fresh(EXPORTS / "sanctions_un") / "un_consolidated.csv.gz",
  745. index=False, compression="gzip")
  746. return {"status": "ok", "rows": len(df), "individuals": counts["individual"],
  747. "entities": counts["entity"]}
  750. _OFAC_COLS = ["ent_num", "sdn_name", "sdn_type", "program", "title", "call_sign",
  751. "vessel_type", "tonnage", "grt", "vessel_flag", "vessel_owner", "remarks"]
  754. def clean_sanctions_ofac() -> dict:
  755. src = _newest("sanctions_ofac/ofac_sdn_*.csv")
  756. if not src:
  757. return {"status": "missing_raw"}
  758. df = pd.read_csv(src, header=None, names=_OFAC_COLS, dtype="string",
  759. skipinitialspace=True)
  760. n_raw = len(df)
  761. for c in df.columns:
  762. df[c] = df[c].str.strip().str.strip('"').replace("-0-", None)
  763. df["ent_num"] = pd.to_numeric(df["ent_num"], errors="coerce").astype("Int64")
  764. df = df.drop_duplicates("ent_num")
  765. out = _fresh(PEXT / "sanctions_ofac")
  766. df.to_parquet(out / "ofac_sdn.parquet", index=False)
  767. df.to_csv(_fresh(EXPORTS / "sanctions_ofac") / "ofac_sdn.csv.gz",
  768. index=False, compression="gzip")
  769. return {"status": "ok", "rows_raw": n_raw, "rows_clean": len(df),
  770. "programs": {k: int(v) for k, v in df["program"].value_counts().head(12).items()}}
  773. def clean_sanctions_seco() -> dict:
  774. import xml.etree.ElementTree as ET
  775. src = _newest("sanctions_seco/seco_source_*.xml")
  776. if not src:
  777. return {"status": "missing_raw"}
  778. rows: list[dict] = []
  779. n_targets = 0
  780. list_date = None
  781. for ev, el in ET.iterparse(str(src), events=("start",)):
  782. if el.tag == "swiss-sanctions-list" and list_date is None:
  783. list_date = el.get("date")
  784. if el.tag != "target":
  785. continue
  786. n_targets += 1
  787. names = []
  788. for nm in el.findall(".//name"):
  789. parts = [np.findtext("value", default="").strip()
  790. for np in nm.findall("name-part")]
  791. whole = " ".join(p for p in parts if p)
  792. if whole:
  793. names.append(whole)
  794. if names:
  795. rows.append({"ssid": el.get("ssid"), "name": names[0],
  796. "n_name_variants": len(names),
  797. "alt_names": names[1:10]})
  798. el.clear()
  799. df = pd.DataFrame(rows).drop_duplicates("ssid")
  800. out = _fresh(PEXT / "sanctions_seco")
  801. df.to_parquet(out / "seco_sanctions.parquet", index=False)
  802. df.to_csv(_fresh(EXPORTS / "sanctions_seco") / "seco_sanctions.csv.gz",
  803. index=False, compression="gzip")
  804. return {"status": "ok", "list_date": list_date, "targets_seen": n_targets,
  805. "rows_clean": len(df),
  806. "via": "OpenSanctions ch_seco_sanctions source.xml mirror"}
  809. def clean_sanctions_eu() -> dict:
  810. probe = RAW / "sanctions_eu" / "probe_status.json"
  811. return {"status": "blocked",
  812. "reason": ("EU consolidated-list bulk CSV now requires EU Login "
  813. "(verified 2026-09-24; 307->EU Login HTML even with "
  814. "?anonymous=true)"),
  815. "probe": json.loads(probe.read_text()) if probe.exists() else None}
  819. def clean_abuseipdb() -> dict:
  820. rows = []
  821. for f in sorted(RAW.glob("abuseipdb/check_*.json")):
  822. try:
  823. d = json.loads(f.read_text()).get("data") or {}
  824. except (json.JSONDecodeError, OSError):
  825. continue
  826. rows.append({
  827. "ip_address": d.get("ipAddress"),
  828. "abuse_confidence_score": d.get("abuseConfidenceScore"),
  829. "total_reports": d.get("totalReports"),
  830. "num_distinct_users": d.get("numDistinctUsers"),
  831. "last_reported_at": d.get("lastReportedAt"),
  832. "is_tor": d.get("isTor"),
  833. "is_whitelisted": d.get("isWhitelisted"),
  834. "usage_type": d.get("usageType"),
  835. "isp": d.get("isp"),
  836. "country_code": d.get("countryCode"),
  837. "domain": d.get("domain"),
  838. "hostnames": ";".join(d.get("hostnames") or []),
  839. })
  840. df = pd.DataFrame(rows)
  841. n = len(df)
  842. if n == 0:
  843. return {"status": "missing_raw"}
  844. df["last_reported_at"] = pd.to_datetime(df["last_reported_at"], errors="coerce", utc=True)
  845. for c in ("abuse_confidence_score", "total_reports", "num_distinct_users"):
  846. df[c] = pd.to_numeric(df[c], errors="coerce").astype("Int64")
  847. out = _fresh(PEXT / "abuseipdb")
  848. df.to_parquet(out / "abuseipdb.parquet", index=False)
  849. df.to_csv(_fresh(EXPORTS / "abuseipdb") / "abuseipdb.csv.gz",
  850. index=False, compression="gzip")
  851. q_total = None
  852. qp = RAW / "abuseipdb" / "_ip_queue.json"
  853. if qp.exists():
  854. try:
  855. q_total = len(json.loads(qp.read_text()))
  856. except (json.JSONDecodeError, OSError):
  857. pass
  858. flagged = int((df["abuse_confidence_score"].fillna(0) > 0).sum())
  859. return {"status": "ok", "rows": n, "queue_total": q_total,
  860. "flagged_any_confidence": flagged,
  861. "flagged_50plus": int((df["abuse_confidence_score"].fillna(0) >= 50).sum()),
  862. "median_confidence": float(df["abuse_confidence_score"].median()),
  863. "coverage_reports": round(float(df["total_reports"].notna().mean()), 4)}
  866. # ----------------------------------------------------------- domain_health
  868. def clean_domain_health() -> dict:
  869. src = _newest("domain_health/domain_health_*.jsonl")
  870. if not src:
  871. return {"status": "missing_raw"}
  872. merged: dict[str, dict] = {}
  873. with src.open() as f:
  874. for line in f:
  875. try:
  876. r = json.loads(line)
  877. except json.JSONDecodeError:
  878. continue
  879. d = r.pop("domain", None)
  880. if not d:
  881. continue
  882. merged.setdefault(d, {}).update(r)
  883. df = pd.DataFrame([{"domain": d, **v} for d, v in merged.items()])
  884. n = len(df)
  885. for c in ("dns_mx_exists", "dns_ns_exists", "dns_a_exists", "has_spf",
  886. "http_live", "https_ok", "parked_like"):
  887. if c in df:
  888. df[c] = df[c].astype("boolean")
  889. if "wayback_first_seen" in df:
  890. df["wayback_first_seen"] = pd.to_datetime(
  891. df["wayback_first_seen"], errors="coerce", utc=True)
  892. if "crtsh_first_seen" in df:
  893. df["crtsh_first_seen"] = pd.to_datetime(
  894. df["crtsh_first_seen"], errors="coerce", utc=True)
  895. out = _fresh(PEXT / "domain_health")
  896. df.to_parquet(out / "domain_health.parquet", index=False)
  897. df.to_csv(_fresh(EXPORTS / "domain_health") / "domain_health.csv.gz",
  898. index=False, compression="gzip")
  900. def cov(col: str):
  901. return round(float(df[col].notna().mean()), 4) if col in df else None
  903. def count_true(col: str) -> int:
  904. return int((df[col] == True).sum()) if col in df else 0 # noqa: E712
  906. return {"status": "ok", "rows": n,
  907. "coverage_dns": cov("dns_a_exists"), "coverage_http": cov("http_live"),
  908. "coverage_wayback": cov("wayback_first_seen"),
  909. "coverage_crtsh": cov("crtsh_first_seen"),
  910. "dead_domains": n - count_true("dns_a_exists"),
  911. "parked_like": count_true("parked_like"),
  912. "http_live": count_true("http_live")}
  915. # -------------------------------------------------------------------- main
  917. CLEANERS = {
  918. "tranco": clean_tranco,
  919. "majestic": clean_majestic,
  920. "google_taxonomy": clean_google_taxonomy,
  921. "viseca": clean_viseca,
  922. "tabformer": clean_tabformer,
  923. "ieee_cis": clean_ieee_cis,
  924. "ulb_creditcard": clean_ulb_creditcard,
  925. "bipia": clean_bipia,
  926. "agentdojo": clean_agentdojo,
  927. "tensortrust": clean_tensortrust,
  928. "hackaprompt": clean_hackaprompt,
  929. "threatfox": clean_threatfox,
  930. "feodotracker": clean_feodotracker,
  931. "malwarebazaar": clean_malwarebazaar,
  932. "sanctions_un": clean_sanctions_un,
  933. "sanctions_ofac": clean_sanctions_ofac,
  934. "sanctions_seco": clean_sanctions_seco,
  935. "sanctions_eu": clean_sanctions_eu,
  936. "domain_health": clean_domain_health,
  937. "abuseipdb": clean_abuseipdb,
  938. }
  940. _LICENSES = {
  941. "tranco": "Tranco (research, attribution)",
  942. "majestic": "Majestic Million (attribution)",
  943. "google_taxonomy": "Google Merchant product taxonomy",
  944. "viseca": "Viseca public synthetic pack (SYNTHETIC TEST DATA)",
  945. "tabformer": "IBM TabFormer synthetic credit-card transactions (research)",
  946. "ieee_cis": "IEEE-CIS Fraud Detection via public HF mirror (competition data; unofficial mirror)",
  947. "ulb_creditcard": "ULB Credit Card Fraud via OpenML did 1597",
  948. "bipia": "Microsoft BIPIA (MIT)",
  949. "agentdojo": "AgentDojo (ETH; MIT)",
  950. "tensortrust": "Tensor Trust (HumanCompatibleAI; permissive)",
  951. "hackaprompt": "HackAPrompt (MIT; gated access)",
  952. "threatfox": "abuse.ch ThreatFox (free, attribution appreciated)",
  953. "feodotracker": "abuse.ch FeodoTracker (free, attribution appreciated)",
  954. "malwarebazaar": "abuse.ch MalwareBazaar (free, attribution; auth key for API)",
  955. "sanctions_un": "UN consolidated list (public data, (c) United Nations)",
  956. "sanctions_ofac": "OFAC SDN list (US Treasury, public domain)",
  957. "sanctions_seco": "SECO Swiss sanctions via OpenSanctions mirror (CC BY-SA 4.0 on mirror; Swiss public data)",
  958. "sanctions_eu": "EU consolidated list (public data; bulk download behind EU Login)",
  959. "domain_health": "protocol lookups + Wayback CDX + crt.sh (public services, bounded/cached)",
  960. "abuseipdb": "AbuseIPDB free tier (non-commercial, attribution; per-IP cached)",
  961. }
  964. def write_report(all_stats: dict[str, dict]) -> None:
  965. lines = [
  966. "# External data quality report",
  967. "",
  968. f"Generated: {common.utcnow()} · cleaner: processing/clean_external.py · "
  969. f"raw cached in data/raw/<source>/ with provenance sidecars.",
  970. "",
  971. "| source | status | key numbers | license |",
  972. "|---|---|---|---|",
  973. ]
  974. for src, st in all_stats.items():
  975. key = {k: v for k, v in st.items()
  976. if k not in ("status",) and not isinstance(v, (dict, list)) and v is not None}
  977. # flatten one level of nested stat dicts (e.g. tensortrust attacks/defenses)
  978. for k, v in st.items():
  979. if isinstance(v, dict):
  980. for k2, v2 in v.items():
  981. if not isinstance(v2, (dict, list)) and v2 is not None:
  982. key[f"{k}.{k2}"] = v2
  983. keys = "; ".join(f"{k}={v}" for k, v in list(key.items())[:9])
  984. extra = ""
  985. if st.get("integrity_checks") is not None:
  986. ok = all(st["integrity_checks"].values())
  987. extra = " integrity=" + ("PASS" if ok else "FAIL")
  988. lines.append(f"| {src} | {st.get('status','?')}{extra} | {keys} | {_LICENSES[src]} |")
  989. if all_stats.get("hackaprompt", {}).get("status") == "blocked":
  990. lines += ["",
  991. "**HackAPrompt is gated on Hugging Face** (auto-approved terms). "
  992. "Export `LEASH_HF_TOKEN` with accepted terms, then rerun "
  993. "`make collect-external SRC=hackaprompt` and `make clean-external SRC=hackaprompt`."]
  994. lines += ["",
  995. "Full cleaned parquet: `data/processed/external/<source>/` (gitignored, reproducible). "
  996. "Tracked exports: `data/exports/external/<source>/` (samples / compact full sets).",
  997. "",
  998. "Integrity semantics: `integrity=PASS` verifies publisher-published constants "
  999. "(IEEE-CIS 590,540 rows / 20,663 frauds — expected count corrected from 20,661 after "
  1000. "the mirror matched published Kaggle kernel value-counts exactly; ULB 284,807 rows / "
  1001. "492 frauds; Viseca sha256 vs pack manifest).",
  1002. ]
  1003. (_fresh(EXPORTS) / "EXTERNAL_QUALITY_REPORT.md").write_text("\n".join(lines) + "\n")
  1006. def main(argv: list[str]) -> int:
  1007. sources = argv or list(CLEANERS)
  1008. all_stats: dict[str, dict] = {}
  1009. if (EXPORTS / "stats").exists():
  1010. for p in (EXPORTS / "stats").glob("*.json"):
  1011. all_stats[p.stem] = json.loads(p.read_text())
  1012. for src in sources:
  1013. if src not in CLEANERS:
  1014. print(f"unknown source: {src}", file=sys.stderr)
  1015. return 2
  1016. print(f"[clean] {src} ...", file=sys.stderr, flush=True)
  1017. try:
  1018. st = CLEANERS[src]()
  1019. except Exception as e: # noqa: BLE001 — report and continue
  1020. st = {"status": "error", "error": f"{type(e).__name__}: {e}"}
  1021. all_stats[src] = st
  1022. _fresh(STATS).joinpath(f"{src}.json").write_text(json.dumps(st, indent=2, default=str))
  1023. print(json.dumps({"source": src, "status": st.get("status")}), file=sys.stderr, flush=True)
  1024. write_report(all_stats)
  1025. print(json.dumps({"cleaned": sources, "report": str(EXPORTS / 'EXTERNAL_QUALITY_REPORT.md')}))
  1026. return 0
  1029. if __name__ == "__main__":
  1030. raise SystemExit(main(sys.argv[1:]))