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"""
Evaluate Scam Detector Accuracy.
Tests the current detector (keyword-based or fine-tuned) against the dataset.
Used to determine if fine-tuning is needed (Task 4.2 prerequisite).
Note: Task 4.2 states "Only if time permits and pre-trained model accuracy <85%"
"""
import json
import os
import sys
import time
# Add project root to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from typing import Dict, List, Tuple
# Ensure UTF-8 output on Windows
if sys.stdout.encoding != 'utf-8':
sys.stdout.reconfigure(encoding='utf-8')
# Dataset path
DATASET_PATH = os.path.join(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
"data",
"scam_detection_train.jsonl"
)
def load_dataset(filepath: str) -> List[Dict]:
"""Load dataset from JSONL file."""
samples = []
with open(filepath, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if not line:
continue
samples.append(json.loads(line))
return samples
def evaluate_detector(samples: List[Dict]) -> Dict[str, float]:
"""Evaluate the ScamDetector on samples."""
from app.models.detector import ScamDetector
# Initialize detector (may use BERT if available, fallback to keyword)
detector = ScamDetector(load_model=True)
correct = 0
total = 0
true_positives = 0
false_positives = 0
true_negatives = 0
false_negatives = 0
total_time = 0.0
for sample in samples:
message = sample["message"]
expected_label = sample["label"] # 'scam' or 'legitimate'
language = sample["language"]
start_time = time.perf_counter()
result = detector.detect(message, language)
detection_time = time.perf_counter() - start_time
total_time += detection_time
predicted_scam = result["scam_detected"]
actual_scam = (expected_label == "scam")
if predicted_scam == actual_scam:
correct += 1
if actual_scam:
true_positives += 1
else:
true_negatives += 1
else:
if predicted_scam:
false_positives += 1
else:
false_negatives += 1
total += 1
# Calculate metrics
accuracy = correct / total if total > 0 else 0
precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) > 0 else 0
recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) > 0 else 0
f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
fpr = false_positives / (false_positives + true_negatives) if (false_positives + true_negatives) > 0 else 0
avg_time = total_time / total if total > 0 else 0
return {
"accuracy": accuracy,
"precision": precision,
"recall": recall,
"f1": f1,
"false_positive_rate": fpr,
"true_positives": true_positives,
"false_positives": false_positives,
"true_negatives": true_negatives,
"false_negatives": false_negatives,
"total": total,
"correct": correct,
"avg_inference_time_ms": avg_time * 1000,
"model_loaded": detector._model_loaded,
}
def evaluate_by_language(samples: List[Dict]) -> Dict[str, Dict[str, float]]:
"""Evaluate detector accuracy by language."""
from app.models.detector import ScamDetector
detector = ScamDetector(load_model=True)
language_results = {}
for lang in ["en", "hi", "hinglish"]:
lang_samples = [s for s in samples if s["language"] == lang]
correct = 0
total = len(lang_samples)
for sample in lang_samples:
result = detector.detect(sample["message"], sample["language"])
predicted_scam = result["scam_detected"]
actual_scam = (sample["label"] == "scam")
if predicted_scam == actual_scam:
correct += 1
language_results[lang] = {
"accuracy": correct / total if total > 0 else 0,
"total": total,
"correct": correct,
}
return language_results
def evaluate_by_scam_type(samples: List[Dict]) -> Dict[str, Dict[str, float]]:
"""Evaluate detector accuracy by scam type."""
from app.models.detector import ScamDetector
detector = ScamDetector(load_model=True)
type_results = {}
# Get unique scam types
scam_types = set(s["scam_type"] for s in samples if s["scam_type"])
for scam_type in scam_types:
type_samples = [s for s in samples if s["scam_type"] == scam_type]
correct = 0
total = len(type_samples)
for sample in type_samples:
result = detector.detect(sample["message"], sample["language"])
if result["scam_detected"]: # All these samples are scams
correct += 1
type_results[scam_type] = {
"recall": correct / total if total > 0 else 0, # Recall for this scam type
"total": total,
"detected": correct,
}
return type_results
def main():
"""Main evaluation function."""
print("=" * 60)
print("Scam Detector Evaluation")
print("=" * 60)
# Load dataset
print(f"\nLoading dataset: {DATASET_PATH}")
if not os.path.exists(DATASET_PATH):
print("[ERROR] Dataset not found. Run scripts/generate_dataset.py first.")
return 1
samples = load_dataset(DATASET_PATH)
print(f"Loaded {len(samples)} samples")
# Overall evaluation
print(f"\n{'=' * 60}")
print("Overall Evaluation")
print(f"{'=' * 60}")
metrics = evaluate_detector(samples)
print(f"\nDetector Mode: {'BERT + Keyword' if metrics['model_loaded'] else 'Keyword-only'}")
print(f"\nResults:")
print(f" Accuracy: {metrics['accuracy']:.4f} ({metrics['accuracy']*100:.1f}%)")
print(f" Precision: {metrics['precision']:.4f}")
print(f" Recall: {metrics['recall']:.4f}")
print(f" F1 Score: {metrics['f1']:.4f}")
print(f" False Positive Rate: {metrics['false_positive_rate']:.4f}")
print(f" Avg Inference Time: {metrics['avg_inference_time_ms']:.2f}ms")
print(f"\nConfusion Matrix:")
print(f" True Positives: {metrics['true_positives']}")
print(f" False Positives: {metrics['false_positives']}")
print(f" True Negatives: {metrics['true_negatives']}")
print(f" False Negatives: {metrics['false_negatives']}")
# By language
print(f"\n{'=' * 60}")
print("Accuracy by Language")
print(f"{'=' * 60}")
lang_results = evaluate_by_language(samples)
for lang, result in lang_results.items():
print(f" {lang}: {result['accuracy']:.1%} ({result['correct']}/{result['total']})")
# By scam type
print(f"\n{'=' * 60}")
print("Recall by Scam Type")
print(f"{'=' * 60}")
type_results = evaluate_by_scam_type(samples)
for scam_type, result in sorted(type_results.items()):
print(f" {scam_type}: {result['recall']:.1%} ({result['detected']}/{result['total']})")
# Task 4.2 Prerequisite Check
print(f"\n{'=' * 60}")
print("Task 4.2 Prerequisite Check")
print(f"{'=' * 60}")
print(f"\nNote: Task 4.2 states 'Only if pre-trained model accuracy <85%'")
print(f"Current Accuracy: {metrics['accuracy']*100:.1f}%")
if metrics['accuracy'] < 0.85:
print("\n[RECOMMENDED] Fine-tuning is recommended (accuracy <85%)")
print("Run: python scripts/fine_tune_indicbert.py")
else:
print("\n[OK] Current accuracy is sufficient (>=85%)")
print("Fine-tuning is optional but may still improve results.")
# AC Check
print(f"\n{'=' * 60}")
print("Acceptance Criteria Status")
print(f"{'=' * 60}")
ac1_pass = metrics['accuracy'] >= 0.90
print(f"\nAC (Accuracy >90%): {metrics['accuracy']*100:.1f}% - {'PASS' if ac1_pass else 'NEEDS IMPROVEMENT'}")
return 0
if __name__ == "__main__":
sys.exit(main())
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