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Test Server Predictions Against Dataset
Validates server API predictions vs actual labels
"""
import pandas as pd
import requests
from pathlib import Path
import logging
from tqdm import tqdm
import time
from sklearn.metrics import (
accuracy_score, precision_score, recall_score, f1_score,
confusion_matrix, classification_report
)
# Setup logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
datefmt='%H:%M:%S'
)
logger = logging.getLogger(__name__)
class ServerTester:
"""Test phishing detection server against dataset"""
def __init__(self, server_url='http://localhost:8000', batch_size=100):
self.server_url = server_url
self.batch_size = batch_size
self.results = []
def check_server_health(self):
"""Check if server is running"""
try:
response = requests.get(f"{self.server_url}/api/health", timeout=5)
if response.status_code == 200:
health = response.json()
logger.info(f"✓ Server is healthy")
logger.info(f" URL models: {health.get('url_models', 0)}")
logger.info(f" HTML models: {health.get('html_models', 0)}")
return True
else:
logger.error(f"Server health check failed: {response.status_code}")
return False
except Exception as e:
logger.error(f"Cannot connect to server: {e}")
logger.error(f"Make sure server is running: python server/app.py")
return False
def predict_url(self, url):
"""Get prediction from server for a URL"""
try:
response = requests.post(
f"{self.server_url}/api/predict/url",
json={"url": url},
timeout=10
)
if response.status_code == 200:
result = response.json()
return {
'predicted': 1 if result['is_phishing'] else 0,
'consensus': result['consensus'],
'predictions': result['predictions']
}
else:
logger.warning(f"Server error for {url}: {response.status_code}")
return None
except Exception as e:
logger.warning(f"Request error for {url}: {e}")
return None
def test_dataset(self, dataset_path, limit=None, sample_frac=None):
"""
Test server predictions against dataset.
Args:
dataset_path: Path to CSV with 'url' and 'label' columns
limit: Maximum number of URLs to test (None = all)
sample_frac: Random sample fraction (e.g., 0.1 = 10%)
"""
logger.info("="*80)
logger.info("SERVER PREDICTION TESTING")
logger.info("="*80)
# Load dataset
logger.info(f"\n1. Loading dataset: {dataset_path}")
df = pd.read_csv(dataset_path)
# Ensure we have required columns
if 'label' not in df.columns:
# Assume first column is URL, second is label
df.columns = ['url', 'label']
logger.info(f" Total URLs: {len(df):,}")
logger.info(f" Phishing: {(df['label']==1).sum():,}")
logger.info(f" Legitimate: {(df['label']==0).sum():,}")
# Sample if requested
if sample_frac:
df = df.sample(frac=sample_frac, random_state=42)
logger.info(f"\n Sampled {sample_frac*100:.1f}%: {len(df):,} URLs")
# Limit if requested
if limit and limit < len(df):
df = df.head(limit)
logger.info(f" Limited to: {limit:,} URLs")
# Check server
logger.info("\n2. Checking server health...")
if not self.check_server_health():
return None
# Test predictions
logger.info("\n3. Testing predictions...")
y_true = []
y_pred = []
errors = 0
for idx, row in tqdm(df.iterrows(), total=len(df), desc="Testing URLs"):
url = row['url'] if 'url' in row else row.iloc[0]
true_label = int(row['label']) if 'label' in row else int(row.iloc[1])
# Get prediction
result = self.predict_url(url)
if result:
y_true.append(true_label)
y_pred.append(result['predicted'])
self.results.append({
'url': url,
'true_label': true_label,
'predicted_label': result['predicted'],
'consensus': result['consensus'],
'correct': true_label == result['predicted']
})
else:
errors += 1
# Rate limiting
time.sleep(0.01) # 10ms delay between requests
logger.info(f"\n Processed: {len(y_pred):,} URLs")
if errors > 0:
logger.warning(f" Errors: {errors:,}")
# Calculate metrics
self._display_results(y_true, y_pred)
return {
'y_true': y_true,
'y_pred': y_pred,
'results': self.results
}
def _display_results(self, y_true, y_pred):
"""Display test results and metrics"""
logger.info("\n" + "="*80)
logger.info("TEST RESULTS")
logger.info("="*80)
# Calculate metrics
accuracy = accuracy_score(y_true, y_pred)
precision = precision_score(y_true, y_pred)
recall = recall_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred)
logger.info(f"\nOverall Metrics:")
logger.info(f" Accuracy: {accuracy:.4f} ({accuracy*100:.2f}%)")
logger.info(f" Precision: {precision:.4f} ({precision*100:.2f}%)")
logger.info(f" Recall: {recall:.4f} ({recall*100:.2f}%)")
logger.info(f" F1-Score: {f1:.4f} ({f1*100:.2f}%)")
# Confusion matrix
cm = confusion_matrix(y_true, y_pred)
tn, fp, fn, tp = cm.ravel()
logger.info(f"\nConfusion Matrix:")
logger.info(f" Predicted")
logger.info(f" Legit Phish")
logger.info(f"Actual Legit {tn:6,} {fp:6,}")
logger.info(f" Phish {fn:6,} {tp:6,}")
logger.info(f"\nError Analysis:")
logger.info(f" True Negatives: {tn:,} (correctly identified legitimate)")
logger.info(f" True Positives: {tp:,} (correctly identified phishing)")
logger.info(f" False Positives: {fp:,} ({fp/(tn+fp)*100:.2f}% of legitimate marked as phishing)")
logger.info(f" False Negatives: {fn:,} ({fn/(tp+fn)*100:.2f}% of phishing marked as legitimate) ⚠️")
# Classification report
logger.info(f"\nDetailed Classification Report:")
logger.info(classification_report(
y_true, y_pred,
target_names=['Legitimate', 'Phishing'],
digits=4
))
def save_results(self, output_path):
"""Save test results to CSV"""
if not self.results:
logger.warning("No results to save")
return
df = pd.DataFrame(self.results)
df.to_csv(output_path, index=False)
logger.info(f"\n✓ Results saved: {output_path}")
logger.info(f" Total: {len(df):,} predictions")
logger.info(f" Correct: {df['correct'].sum():,} ({df['correct'].mean()*100:.2f}%)")
logger.info(f" Incorrect: {(~df['correct']).sum():,}")
def main():
"""Main testing function"""
# Paths
dataset_path = Path('data/processed/mega_dataset_full_912357.csv')
output_path = Path('results/server_test_results.csv')
output_path.parent.mkdir(parents=True, exist_ok=True)
# Check dataset exists
if not dataset_path.exists():
logger.error(f"Dataset not found: {dataset_path}")
logger.info("Available datasets:")
for csv_file in Path('data/processed').glob('*.csv'):
logger.info(f" - {csv_file}")
return
# Create tester
tester = ServerTester(server_url='http://localhost:8000')
# Test with sample (10% of dataset for quick test)
logger.info("\nTesting with 10% sample for quick validation...")
logger.info("(Use sample_frac=1.0 or remove it to test full dataset)")
results = tester.test_dataset(
dataset_path,
# sample_frac=0.1 # 0.1 for 10% sample (91k URLs) 1.0 for full dataset
limit=1000 # Or use limit for exact number
)
if results:
# Save results
tester.save_results(output_path)
logger.info("\n" + "="*80)
logger.info("✓ SERVER TESTING COMPLETE!")
logger.info("="*80)
logger.info(f"\nResults saved to: {output_path}")
logger.info("\nTo test full dataset, change sample_frac=1.0")
if __name__ == '__main__':
main()
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