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train_risk_model_linear.py

91 lines3,339 bytessha256 0301ab18f222
  1. """Plain linear risk-score model (0-100), optimized for interpretability.
  3. risk = clip(100 * (b0 + sum(c_i * x_i)), 0, 100)
  5. Trained with ordinary least squares on y = 1 (confirmed_malicious) /
  6. 0 (likely_legitimate). Coefficients read directly as risk points.
  8. Usage:
  9. .venv/bin/python train_risk_model_linear.py
  10. """
  12. import json
  14. import numpy as np
  15. import pandas as pd
  16. from sklearn.linear_model import LinearRegression
  17. from sklearn.metrics import roc_auc_score
  18. from sklearn.model_selection import train_test_split
  20. CSV_PATH = "data/processed/dataset_features.csv"
  21. MODEL_OUT = "models/risk_score_linear_points.json"
  23. NUMERIC = ["company_age_days", "domain_age_days",
  24. "domain_typo_score", "brand_name_similarity"]
  25. BOOL = ["registry_found", "website_reachable", "https_enabled", "tls_valid",
  26. "dns_a_exists", "dns_mx_exists", "dns_txt_exists",
  27. "domain_privacy_proxy", "urlhaus_hit", "possible_brand_impersonation"]
  30. def engineer(df: pd.DataFrame, medians: dict | None = None):
  31. X = pd.DataFrame(index=df.index)
  32. meds = medians or {}
  33. for c in NUMERIC:
  34. v = pd.to_numeric(df[c], errors="coerce")
  35. med = meds.get(c, float(v.median()))
  36. X[c] = v.fillna(med)
  37. X[f"{c}_missing"] = v.isna().astype(int)
  38. for c in BOOL:
  39. v = df[c]
  40. if v.dtype == object:
  41. v = v.map(lambda x: x if isinstance(x, (bool, np.bool_))
  42. else np.nan)
  43. X[c] = v.astype(float).fillna(0.5) # 0.5 = unknown
  44. return X, meds
  47. def main() -> None:
  48. df = pd.read_csv(CSV_PATH, low_memory=False)
  49. df = df[df["label"].isin(["confirmed_malicious",
  50. "likely_legitimate"])].copy()
  51. y = (df["label"] == "confirmed_malicious").astype(int)
  53. X, meds = engineer(df)
  54. Xtr, Xte, ytr, yte = train_test_split(
  55. X, y, test_size=0.2, random_state=42, stratify=y)
  57. lin = LinearRegression()
  58. lin.fit(Xtr, ytr)
  60. score = np.clip(100 * lin.predict(Xte), 0, 100)
  61. print(f"Test ROC-AUC (score vs label): {roc_auc_score(yte, score):.4f}")
  62. print(f"Malicious: median {np.median(score[yte == 1]):.1f}, "
  63. f"10th pct {np.percentile(score[yte == 1], 10):.1f}")
  64. print(f"Legit: median {np.median(score[yte == 0]):.1f}, "
  65. f"90th pct {np.percentile(score[yte == 0], 90):.1f}")
  67. coefs = pd.Series(lin.coef_, index=X.columns)
  68. print("\nFORMULA: risk = clip(100 * (b0 + sum(c_i * x_i)), 0, 100)")
  69. print("Each c_i is in label-probability units; multiply by 100 for "
  70. "risk points per unit of the feature.")
  71. print(f"\nb0 = {lin.intercept_:.4f}")
  72. pts = (100 * coefs).round(2)
  73. print("\nContribution per unit feature (risk points):")
  74. print(pts.sort_values(key=abs, ascending=False).to_string())
  76. out = {
  77. "formula": "risk = clip(100 * (b0 + sum(c_i * x_i)), 0, 100)",
  78. "intercept": float(lin.intercept_),
  79. "coefficients_risk_points": pts.to_dict(),
  80. "median_impute": meds,
  81. "encoding": ("booleans: 1=True, 0=False, 0.5=unknown; numeric "
  82. "missing -> median impute + _missing indicator = 1"),
  83. "test_auc": float(roc_auc_score(yte, score)),
  84. }
  85. with open(MODEL_OUT, "w") as f:
  86. json.dump(out, f, indent=2)
  87. print(f"\nSaved -> {MODEL_OUT}")
  90. if __name__ == "__main__":
  91. main()