example_usage.py

#!/usr/bin/env python3
"""
Example usage of the refactored Powerball prediction system.

This script demonstrates how to use the new modular components.
"""

from main_predictor import AdvancedPowerballPredictor, quick_prediction

def main():
    print("=" * 60)
    print("POWERBALL PREDICTION SYSTEM - EXAMPLE USAGE")
    print("=" * 60)
    
    # Method 1: Quick prediction (simplest way)
    print("\n1. QUICK PREDICTION:")
    print("-" * 30)
    predictions = quick_prediction('powerball.csv', num_predictions=5)
    
    # Method 2: Full system with testing and analysis
    print("\n2. COMPREHENSIVE ANALYSIS:")
    print("-" * 30)
    predictor = AdvancedPowerballPredictor('powerball.csv')
    
    # Generate predictions using different methods
    ml_predictions = predictor.generate_predictions(2, method='ml')
    pattern_predictions = predictor.generate_predictions(2, method='pattern')
    hybrid_predictions = predictor.generate_predictions(3, method='hybrid')
    lstm_predictions = predictor.generate_predictions(3, method='lstm')
    
    print(f"Generated predictions using different methods:")
    print(f"- ML-based: {len(ml_predictions)}")
    print(f"- Pattern-based: {len(pattern_predictions)}")
    print(f"- Hybrid: {len(hybrid_predictions)}")
    print(f"- LSTM Deep Learning: {len(lstm_predictions)}")
    
    # Test predictions
    all_predictions = ml_predictions + pattern_predictions + hybrid_predictions + lstm_predictions
    test_results = predictor.test_predictions(all_predictions, test_size=50)
    
    print(f"\nTest Results:")
    print(f"- Historical performance score: {test_results['historical']['statistics']['performance_score']:.4f}")
    print(f"- Matches found: {sum(test_results['historical']['matches'].values())}")
    
    # Display final predictions
    predictor.display_predictions(all_predictions)
    
    # Method 3: Individual component usage
    print("\n3. INDIVIDUAL COMPONENT USAGE:")
    print("-" * 30)
    
    # Access individual components
    data_handler = predictor.data_handler
    print(f"Data handler loaded {len(data_handler.data)} draws")
    
    # Access features
    features = predictor.prepare_features()
    print(f"Feature engineering created {len(features)} feature groups")
    
    # Access models
    models = predictor.build_models()
    print(f"Built {len(models['white'])} white ball models and {len(models['powerball'])} powerball models")
    
    print("\n" + "=" * 60)
    print("EXAMPLE COMPLETED SUCCESSFULLY")
    print("=" * 60)

if __name__ == "__main__":
    main()