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processing/quality_report.py

108 lines3,316 bytessha256 3794415fd748
  1. """Data-quality report generator.
  3. Usage: python -m processing.quality_report
  4. Writes reports/data_quality_report.md and prints a JSON summary.
  5. """
  7. from __future__ import annotations
  9. import json
  10. import pathlib
  11. import sys
  13. import pandas as pd
  15. ROOT = pathlib.Path(__file__).resolve().parents[1]
  16. REPORTS = ROOT / "reports"
  17. PROCESSED = ROOT / "data" / "processed"
  19. KEY_MISSING_COLS = [
  20. "registry_id", "legal_company_name", "domain", "domain_creation_date",
  21. "registrar", "dns_a_exists", "dns_mx_exists", "company_status",
  22. "incorporation_date", "label_confidence",
  23. ]
  26. def _dist(series: pd.Series, n: int = 12) -> pd.Series:
  27. return series.value_counts().head(n)
  30. def main() -> None:
  31. df = pd.read_parquet(PROCESSED / "dataset_features.parquet")
  32. lines: list[str] = []
  33. summary: dict = {}
  35. def emit(s: str) -> None:
  36. lines.append(s)
  38. emit("# LEASH merchant-trust-data — Data Quality Report")
  39. emit("")
  40. emit(f"_Generated from dataset_features.parquet at build time._")
  41. emit("")
  43. summary["row_count"] = int(len(df))
  44. emit(f"## Row counts")
  45. emit(f"- rows: **{len(df)}**")
  46. emit(f"- unique merchant_id: **{df['merchant_id'].nunique()}**")
  47. emit(f"- unique entity_key: **{df['entity_key'].nunique()}**")
  48. emit(f"- unique companies (registry_id): **{df['registry_id'].nunique()}**")
  49. emit(f"- unique root domains: **{df['domain'].nunique()}**")
  50. emit("")
  52. emit("## Entity type distribution")
  53. for k, v in _dist(df["entity_type"]).items():
  54. emit(f"- {k}: {v}")
  55. summary.setdefault("entity_types", {})[str(k)] = int(v)
  56. emit("")
  58. emit("## Label distribution")
  59. for k, v in _dist(df["label"]).items():
  60. emit(f"- {k}: {v}")
  61. summary.setdefault("labels", {})[str(k)] = int(v)
  62. emit("")
  64. emit("## Country distribution (top)")
  65. for k, v in _dist(df["country"].fillna("(unknown)")).items():
  66. emit(f"- {k}: {v}")
  67. emit("")
  69. emit("## Source distribution")
  70. src = df["sources"].fillna("(none)").str.split(";").explode()
  71. for k, v in _dist(src).items():
  72. emit(f"- {k}: {v}")
  73. summary.setdefault("sources", {})[str(k)] = int(v)
  74. emit("")
  76. emit("## Missingness (key columns)")
  77. emit("| column | missing % |")
  78. emit("|---|---|")
  79. miss_summary = {}
  80. for c in KEY_MISSING_COLS:
  81. if c in df.columns:
  82. pct = round(100 * df[c].isna().mean(), 1)
  83. miss_summary[c] = pct
  84. emit(f"| {c} | {pct}% |")
  85. summary["missingness_pct"] = miss_summary
  86. emit("")
  87. emit("_Note: missing = unknown/not-collected (three-state convention), never negative evidence._")
  88. emit("")
  90. dupes = int(df["entity_key"].duplicated().sum())
  91. emit(f"## Duplicates: {dupes} duplicate entity_key rows")
  92. summary["duplicate_entity_keys"] = dupes
  93. emit("")
  95. emit("## Enrichment coverage")
  96. for c in ["rdap_available", "dns_a_exists", "dns_mx_exists"]:
  97. if c in df.columns:
  98. known = df[c].notna().sum()
  99. emit(f"- {c}: known for {known}/{len(df)} rows ({round(100*known/len(df),1)}%)")
  100. emit("")
  102. REPORTS.mkdir(parents=True, exist_ok=True)
  103. (REPORTS / "data_quality_report.md").write_text("\n".join(lines))
  104. print(json.dumps(summary))
  107. if __name__ == "__main__":
  108. sys.exit(main())