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explore_dataset.py

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  1. """Feature analysis for the LEASH merchant-trust dataset (v2).
  3. Answers: which features are actually usable for a risk-score model, and
  4. which ones separate malicious from legitimate entities?
  6. Usage:
  7. python3 explore_dataset.py
  8. """
  10. import numpy as np
  11. import pandas as pd
  13. CSV_PATH = "data/processed/dataset_features.csv"
  15. # Features worth analyzing, by type (from schemas/canonical_schema.py).
  16. NUMERIC = [
  17. "company_age_days", "domain_age_days", "domain_typo_score",
  18. "brand_name_similarity", "name_registry_similarity",
  19. "label_confidence",
  20. ]
  21. BOOL = [
  22. "registry_found", "website_reachable", "https_enabled", "tls_valid",
  23. "dns_a_exists", "dns_mx_exists", "dns_txt_exists", "domain_privacy_proxy",
  24. "openphish_hit", "urlhaus_hit", "known_bad_domain",
  25. "possible_brand_impersonation", "homoglyph_detected",
  26. "punycode_domain", "suspicious_subdomain_pattern",
  27. "impressum_present", "privacy_policy_present", "terms_present",
  28. "contact_page_present",
  29. ]
  30. CATEGORICAL = ["entity_type", "legal_form", "registrar", "company_status",
  31. "label"]
  34. def header(title: str) -> None:
  35. print(f"\n{'=' * 70}\n{title}\n{'=' * 70}")
  38. def main() -> None:
  39. df = pd.read_csv(CSV_PATH, low_memory=False)
  40. print(f"Shape: {df.shape[0]} rows x {df.shape[1]} columns")
  42. # ---- 1. Overall label distribution ---------------------------------
  43. header("1. Label distribution")
  44. print(df["label"].value_counts(dropna=False).to_string())
  46. # ---- 2. Feature availability by entity type ------------------------
  47. # A feature is only useful if it's populated where it matters.
  48. header("2. Feature fill rate (%) by entity_type")
  49. et = df["entity_type"].fillna("<missing>")
  50. fill = df.drop(columns=["entity_type"]).notna().groupby(et).mean().T * 100
  51. fill["ALL"] = df.drop(columns=["entity_type"]).notna().mean() * 100
  52. interesting = [c for c in fill.index
  53. if fill.loc[c].max() > 0 and c not in
  54. ("merchant_id", "entity_key", "raw_json", "sources",
  55. "source_urls", "collected_at", "last_verified_at",
  56. "collector_version", "data_license", "label_source",
  57. "label_reason")]
  58. # Show columns sorted by ALL fill rate, top 45
  59. print(fill.loc[interesting].sort_values("ALL", ascending=False)
  60. .head(45).round(1).to_string())
  62. # ---- 3. Label-conditional stats for numeric features ----------------
  63. # Compare malicious vs likely_legitimate to see separation power.
  64. header("3. Numeric features: malicious vs likely_legitimate")
  65. num_rows = []
  66. for c in NUMERIC:
  67. if c not in df.columns:
  68. continue
  69. mal = pd.to_numeric(df.loc[df.label == "confirmed_malicious", c],
  70. errors="coerce")
  71. legit = pd.to_numeric(
  72. df.loc[df.label == "likely_legitimate", c], errors="coerce")
  73. if mal.notna().sum() == 0 and legit.notna().sum() == 0:
  74. continue
  75. num_rows.append({
  76. "feature": c,
  77. "mal_fill%": 100 * mal.notna().mean(),
  78. "mal_mean": mal.mean(),
  79. "mal_median": mal.median(),
  80. "legit_fill%": 100 * legit.notna().mean(),
  81. "legit_mean": legit.mean(),
  82. "legit_median": legit.median(),
  83. })
  84. num_df = pd.DataFrame(num_rows).set_index("feature")
  85. if not num_df.empty:
  86. print(num_df.round(2).to_string())
  87. print("\nInterpretation: large gap between mal_* and legit_* stats "
  88. "= strong signal. Different fill rates are themselves a signal "
  89. "(e.g. registry fields exist only for legitimate entities).")
  91. # ---- 4. Boolean features: positive rate by label --------------------
  92. header("4. Boolean features: % True by label")
  93. bool_rows = []
  94. for c in BOOL:
  95. if c not in df.columns:
  96. continue
  97. col = df[c]
  98. if col.dtype == object: # mixed types from CSV
  99. col = col.map(lambda v: v if isinstance(v, bool) else np.nan)
  100. mal = col[df.label == "confirmed_malicious"]
  101. legit = col[df.label == "likely_legitimate"]
  102. if mal.notna().sum() == 0 and legit.notna().sum() == 0:
  103. continue
  104. bool_rows.append({
  105. "feature": c,
  106. "mal_fill%": 100 * mal.notna().mean(),
  107. "mal_True%": 100 * mal.mean() if mal.notna().any() else np.nan,
  108. "legit_fill%": 100 * legit.notna().mean(),
  109. "legit_True%": 100 * legit.mean() if legit.notna().any() else np.nan,
  110. })
  111. bool_df = pd.DataFrame(bool_rows).set_index("feature")
  112. if not bool_df.empty:
  113. print(bool_df.round(1).to_string())
  114. print("\nInterpretation: a feature is useful when True% differs a lot "
  115. "between labels, OR when fill% differs (feature itself only "
  116. "exists for one class). All-zero columns can be dropped.")
  118. # ---- 5. Categorical highlights --------------------------------------
  119. header("5. Categorical: registrar / legal_form / entity_type by label")
  120. for c in ["entity_type", "legal_form", "registrar"]:
  121. if c not in df.columns:
  122. continue
  123. ct = pd.crosstab(df[c].fillna("<missing>"), df["label"],
  124. normalize="columns") * 100
  125. print(f"\n{c} (% of each label):")
  126. print(ct.round(1).to_string())
  128. # ---- 6. Usability verdict -------------------------------------------
  129. header("6. Verdict: features usable for a risk-score model")
  130. filled = (df.notna().mean() * 100).round(1)
  131. empty = filled[filled == 0].index.tolist()
  132. print(f"EMPTY (drop or collect later, {len(empty)} cols): "
  133. f"{', '.join(empty)}")
  134. usable = filled[(filled > 50) & ~filled.index.isin(
  135. ["merchant_id", "entity_key", "raw_json"])].index.tolist()
  136. print(f"WELL-FILLED >50% ({len(usable)} cols): {', '.join(usable)}")
  139. if __name__ == "__main__":
  140. main()