File size: 2,261 Bytes
ae7c16d | 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 | from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
import joblib
import numpy as np
import shap
@dataclass(frozen=True)
class ScoreResult:
probability: float
decision: str
top_factors: List[Dict[str, Any]]
class FraudModel:
def __init__(self, artifacts_path: Path):
payload = joblib.load(artifacts_path)
self.model = payload["model"]
self.imputer = payload["imputer"]
self.feature_names = list(payload["feature_names"])
self.freq_mappings = payload["freq_mappings"]
self.categorical_cols = list(payload.get("categorical_cols") or [])
self.explainer = shap.TreeExplainer(self.model)
def _decision(self, p: float) -> str:
return "ALLOW" if p < 0.2 else "REVIEW" if p < 0.6 else "BLOCK"
def _freq_encode_one(self, row: Dict[str, Any]) -> np.ndarray:
vals: List[float] = []
for col in self.feature_names:
v = row.get(col, None)
if col in self.categorical_cols:
mapping = self.freq_mappings.get(col, {})
key = "NA" if v is None else str(v)
vals.append(float(mapping.get(key, 0)))
else:
vals.append(np.nan if v is None else float(v))
X = np.array(vals, dtype=float).reshape(1, -1)
X = self.imputer.transform(X)
return X
def score(self, row: Dict[str, Any], top_k: int = 8) -> ScoreResult:
X = self._freq_encode_one(row)
p = float(self.model.predict_proba(X)[:, 1][0])
shap_vals = self.explainer.shap_values(X)
if isinstance(shap_vals, list):
shap_vals = shap_vals[0]
shap_vals = np.array(shap_vals).reshape(-1)
order = np.argsort(np.abs(shap_vals))[::-1][:top_k]
top = []
for idx in order:
top.append(
{
"feature": self.feature_names[int(idx)],
"contribution": float(shap_vals[int(idx)]),
"abs_contribution": float(abs(shap_vals[int(idx)])),
}
)
return ScoreResult(probability=p, decision=self._decision(p), top_factors=top)
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