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evaluate.py
-----------
Evaluate the trained LightGBM model on the held-out split.
Outputs:
eval/eval_results.json — precision, recall, F1, false-positive cost,
latency, and policy-action breakdown.
Usage:
python eval/evaluate.py
"""
import json
import os
import sys
import time
import lightgbm as lgb
import numpy as np
from sklearn.metrics import precision_score, recall_score, f1_score
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, BASE_DIR)
SCORING_DIR = os.path.join(BASE_DIR, "scoring")
EVAL_DIR = os.path.join(BASE_DIR, "eval")
MODEL_PATH = os.path.join(SCORING_DIR, "model.lgb")
HOLDOUT_PATH = os.path.join(SCORING_DIR, "holdout_split.json")
RESULTS_PATH = os.path.join(EVAL_DIR, "eval_results.json")
CLASSIFICATION_THRESHOLD = 0.5
# Policy thresholds (must match agent/policy.py exactly)
POLICY_HIGH_THRESHOLD = 0.85
POLICY_MEDIUM_THRESHOLD = 0.50
POLICY_LOW_EXPOSURE = 25000.0 # updated Phase 3 v3.0 — matches agent/policy.py
def policy_decide(risk_probability: float, exposure_rupees: float) -> str:
"""Deterministic policy gate — mirrors agent/policy.py exactly."""
if risk_probability >= POLICY_HIGH_THRESHOLD and exposure_rupees <= POLICY_LOW_EXPOSURE:
return "auto_hold"
if risk_probability >= POLICY_HIGH_THRESHOLD and exposure_rupees > POLICY_LOW_EXPOSURE:
return "escalate"
if POLICY_MEDIUM_THRESHOLD <= risk_probability < POLICY_HIGH_THRESHOLD:
return "escalate"
return "log_only"
def evaluate():
print(f"Loading model from {MODEL_PATH}...")
model = lgb.Booster(model_file=MODEL_PATH)
print(f"Loading holdout split from {HOLDOUT_PATH}...")
with open(HOLDOUT_PATH, encoding="utf-8") as f:
holdout = json.load(f)
print(f" {len(holdout)} held-out clusters")
# 6.1 — reconstruct feature matrix
from scoring.features import FEATURE_COLUMNS
feature_rows = [row["features"] for row in holdout]
y_true = np.array([row["label"] for row in holdout])
# 6.2 — time the feature extraction + inference pass
start = time.perf_counter()
X = np.array([[fr[f] for f in FEATURE_COLUMNS] for fr in feature_rows])
probs = model.predict(X)
elapsed = time.perf_counter() - start
y_pred = (probs >= CLASSIFICATION_THRESHOLD).astype(int)
precision = float(precision_score(y_true, y_pred, zero_division=0))
recall = float(recall_score(y_true, y_pred, zero_division=0))
f1 = float(f1_score(y_true, y_pred, zero_division=0))
latency_total_ms = elapsed * 1000
latency_per_cluster_ms = latency_total_ms / len(holdout)
print(f"\n--- Classification at threshold {CLASSIFICATION_THRESHOLD} ---")
print(f" Precision : {precision:.4f}")
print(f" Recall : {recall:.4f}")
print(f" F1 : {f1:.4f}")
print(f" Latency : {latency_total_ms:.2f} ms total, "
f"{latency_per_cluster_ms:.3f} ms/cluster")
# 6.3 — policy simulation on holdout
policy_counts = {"auto_hold": 0, "escalate": 0, "log_only": 0}
for i, row in enumerate(holdout):
prob = float(probs[i])
exposure = row["features"].get("total_amount", 0.0)
decision = policy_decide(prob, exposure)
policy_counts[decision] += 1
print(f"\n--- Policy simulation on holdout ---")
for action, count in policy_counts.items():
print(f" {action:12s}: {count}")
# 6.4 — false-positive cost
fp_cost = 0.0
fp_clusters = []
for i, row in enumerate(holdout):
if y_pred[i] == 1 and y_true[i] == 0:
amt = row["features"].get("total_amount", 0.0)
fp_cost += amt
fp_clusters.append({
"cluster_id": row["cluster_id"],
"total_amount": amt,
"risk_probability": float(probs[i]),
})
print(f"\n--- False-positive cost ---")
print(f" FP clusters: {len(fp_clusters)}")
print(f" FP cost (total_amount): Rs.{fp_cost:.2f}")
# 6.5 — latency already computed above
print(f"\n--- Latency ---")
print(f" Feature extraction + inference: {latency_per_cluster_ms:.3f} ms/cluster")
# 6.6 — write eval_results.json
os.makedirs(EVAL_DIR, exist_ok=True)
results = {
"precision": precision,
"recall": recall,
"f1": f1,
"classification_threshold": CLASSIFICATION_THRESHOLD,
"holdout_size": len(holdout),
"holdout_positives": int(y_true.sum()),
"holdout_negatives": int((len(y_true) - y_true.sum())),
"false_positive_cost_inr": fp_cost,
"false_positive_clusters": fp_clusters,
"latency_total_ms": latency_total_ms,
"latency_per_cluster_ms": latency_per_cluster_ms,
"policy_action_breakdown": policy_counts,
"confusion_matrix": {
"true_positive": int(((y_pred == 1) & (y_true == 1)).sum()),
"false_positive": int(((y_pred == 1) & (y_true == 0)).sum()),
"true_negative": int(((y_pred == 0) & (y_true == 0)).sum()),
"false_negative": int(((y_pred == 0) & (y_true == 1)).sum()),
},
}
with open(RESULTS_PATH, "w", encoding="utf-8") as f:
json.dump(results, f, indent=2, ensure_ascii=False)
print(f"\nEvaluation results saved to {RESULTS_PATH}")
return results
if __name__ == "__main__":
evaluate()
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