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import json
import logging
import numpy as np
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
from model import JobRecommendationModel
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
import os
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler('job_recommendation_testing.log'),
logging.StreamHandler()
]
)
logger = logging.getLogger(__name__)
class JobRecommendationTester:
def __init__(self, model_path, test_data_path):
self.model = JobRecommendationModel()
self.model.load_model(model_path)
self.test_data_path = test_data_path
# Create results directory
self.results_dir = 'models/job_recommendation/test_results'
os.makedirs(self.results_dir, exist_ok=True)
def load_test_data(self):
"""Load test data from JSON file"""
with open(self.test_data_path, 'r') as f:
return json.load(f)
def evaluate_model(self):
"""Evaluate model performance on test data"""
test_data = self.load_test_data()
X_test, y_test = self.model.preprocess_data(test_data)
# Get predictions
y_pred = self.model.model.predict(X_test)
y_pred_binary = (y_pred > 0.5).astype(int)
# Calculate metrics
metrics = {
'accuracy': accuracy_score(y_test, y_pred_binary),
'precision': precision_score(y_test, y_pred_binary),
'recall': recall_score(y_test, y_pred_binary),
'f1': f1_score(y_test, y_pred_binary)
}
cm = confusion_matrix(y_test, y_pred_binary)
return metrics, cm, y_test, y_pred
def run_full_test(self):
"""Run complete test suite"""
logger.info("Starting full model testing")
metrics, cm, y_test, y_pred = self.evaluate_model()
# Save results
report = {
'metrics': metrics,
'test_timestamp': datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ")
}
with open(f'{self.results_dir}/test_report.json', 'w') as f:
json.dump(report, f, indent=2)
logger.info("Testing completed. Results saved in test_results directory")
return report
def main():
try:
model_path = 'models/job_recommendation/saved_model/model.keras'
test_data_path = 'models/job_recommendation/test_data/test_data.json'
tester = JobRecommendationTester(model_path, test_data_path)
report = tester.run_full_test()
logger.info("\nTest Results Summary:")
logger.info(f"Accuracy: {report['metrics']['accuracy']:.4f}")
logger.info(f"Precision: {report['metrics']['precision']:.4f}")
logger.info(f"Recall: {report['metrics']['recall']:.4f}")
logger.info(f"F1 Score: {report['metrics']['f1']:.4f}")
except Exception as e:
logger.error(f"Error during testing: {str(e)}", exc_info=True)
raise
if __name__ == "__main__":
main() |