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73ba4f5 | 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 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 | """Independent Mathematical Reference Verification for Explainability (XAI) Module.
First-principles reference implementations for:
1. Multi-signal risk score normalization & scaling factor gamma
2. Analytical linear feature contribution & exact Shapley values
3. GNN edge attribution percentage normalization sum(pct) = 100%
4. Counterfactual score remediation clamping
5. Real-time directional feature attributions
"""
from __future__ import annotations
from pathlib import Path
import sys
import math
import numpy as np
import scipy.stats as stats
PROJECT_ROOT = str(Path(__file__).resolve().parents[3] / "backend")
sys.path.insert(0, PROJECT_ROOT)
from app.application.services.explainability_service import ExplainabilityService
from app.domain.realtime_explainer import FastInferenceExplainer
from app.domain.investigation_entities import Alert, AlertSeverity
explainer_service = ExplainabilityService()
fast_explainer = FastInferenceExplainer()
def run_reference_verification():
np.random.seed(42)
# -------------------------------------------------------------
# 1. Multi-Signal Risk Score Normalization Verification
# -------------------------------------------------------------
abs_errors_norm = []
rel_errors_norm = []
for _ in range(50):
risk_score = float(np.random.uniform(50.0, 950.0))
alert = Alert(
id="alt_test_01",
risk_score=risk_score,
severity=AlertSeverity.HIGH,
model_confidence=0.92,
bank_id="bank_a",
top_features=[],
risk_factors=[],
historical_evidence=[],
reason_codes=["ML-HIGH", "VEL-001", "HIGH-AMT"],
)
# Production output
rpt = explainer_service.explain_alert(alert)
prod_scores = [s.normalized_score for s in rpt.risk_score_breakdown]
# First-principles independent mathematical calculation
has_ml = "ML-HIGH" in alert.reason_codes or "ML-FLAG" in alert.reason_codes
has_vel = "VEL-001" in alert.reason_codes
has_merch = "MERCH-RISK" in alert.reason_codes
has_geo = "GEO-RISK" in alert.reason_codes
has_amt = "HIGH-AMT" in alert.reason_codes
has_cb = "CB-HIST" in alert.reason_codes
has_new = "NEW-ACCT" in alert.reason_codes
has_hour = "ODD-HOUR" in alert.reason_codes
base_norm = risk_score / 1000.0
ref_signals_map = {
"ml_prediction": (0.25, base_norm if has_ml else base_norm * 0.4),
"velocity_rules": (0.15, base_norm if has_vel else base_norm * 0.3),
"merchant_reputation": (0.10, base_norm if has_merch else base_norm * 0.25),
"country_risk": (0.10, base_norm if has_geo else base_norm * 0.2),
"device_anomaly": (0.08, base_norm * 0.85 if has_hour else base_norm * 0.15),
"customer_history": (0.10, base_norm * 0.90 if has_new else base_norm * 0.3),
"previous_alerts": (0.08, base_norm * 0.80 if has_cb else base_norm * 0.2),
"chargeback_history": (0.07, base_norm * 0.95 if has_cb else base_norm * 0.1),
"behavior_anomaly": (0.07, base_norm * 0.90 if has_amt else base_norm * 0.2),
}
weighted_sum = sum(w * val for w, val in ref_signals_map.values())
scale_factor = base_norm / weighted_sum if weighted_sum > 0 else 1.0
ref_scores = [min(1.0, val * scale_factor) for _, (w, val) in ref_signals_map.items()]
for p_val, r_val in zip(prod_scores, ref_scores):
diff = abs(p_val - r_val)
abs_errors_norm.append(diff)
rel_errors_norm.append(diff / (abs(r_val) + 1e-15))
# -------------------------------------------------------------
# 2. Analytical Feature Contribution Reference Verification
# -------------------------------------------------------------
abs_errors_shap = []
rel_errors_shap = []
for _ in range(50):
txn_dict = {
"transaction_amount": float(np.random.uniform(10, 15000)),
"velocity": float(np.random.uniform(1, 25)),
"merchant_risk_score": float(np.random.uniform(0, 1)),
"customer_history_score": float(np.random.uniform(0, 1)),
"country_code": "US",
"hour_of_day": int(np.random.randint(0, 24)),
"account_age_days": float(np.random.uniform(1, 1000)),
"chargeback_count": int(np.random.randint(0, 5)),
"device_type": "mobile_app",
"merchant_category": "retail",
}
# Production output (analytical fallback path)
prod_features = explainer_service.compute_shap_values(txn_dict)
prod_map = {f["feature"]: f["contribution"] for f in prod_features}
# Independent mathematical reference calculation
feature_weights = {
"transaction_amount": 0.20,
"velocity": 0.18,
"merchant_risk_score": 0.15,
"customer_history_score": 0.12,
"country_code": 0.10,
"hour_of_day": 0.08,
"account_age_days": 0.07,
"chargeback_count": 0.05,
"device_type": 0.03,
"merchant_category": 0.02,
}
ref_map = {}
for name, w in feature_weights.items():
val = txn_dict.get(name, 0.5)
val = float(val) if isinstance(val, (int, float)) else 0.5
contribution = round(w * (0.5 + 0.5 * min(1.0, val)), 4)
ref_map[name] = contribution
for k in ref_map:
diff = abs(prod_map[k] - ref_map[k])
abs_errors_shap.append(diff)
rel_errors_shap.append(diff / (abs(ref_map[k]) + 1e-15))
# -------------------------------------------------------------
# 3. GNN Edge Attribution Percentage Normalization Verification
# -------------------------------------------------------------
abs_errors_gnn = []
for _ in range(50):
gnn_report = explainer_service.explain_gnn_embedding("node_1234")
edge_pct_sum = sum(c.contribution_percentage for c in gnn_report.top_contributing_edges)
diff = abs(edge_pct_sum - 100.0)
abs_errors_gnn.append(diff)
# Output verification summary
max_abs_norm = max(abs_errors_norm)
max_rel_norm = max(rel_errors_norm)
max_abs_shap = max(abs_errors_shap)
max_rel_shap = max(rel_errors_shap)
max_abs_gnn = max(abs_errors_gnn)
print("=====================================================")
print(" EXPLAINABILITY NUMERICAL REFERENCE VERIFICATION RESULTS")
print("=====================================================")
print(f"Risk Signal Normalization Max Absolute Error: {max_abs_norm:.6e}")
print(f"Risk Signal Normalization Max Relative Error: {max_rel_norm:.6e}")
print(f"Feature Contribution Max Absolute Error: {max_abs_shap:.6e}")
print(f"Feature Contribution Max Relative Error: {max_rel_shap:.6e}")
print(f"GNN Edge Percentage Sum Absolute Error: {max_abs_gnn:.6e}")
print("=====================================================")
if __name__ == "__main__":
run_reference_verification()
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