pb_predictor.py

# import json
# from pathlib import Path
# from lotto_predictor import predict_for_game, NumpyEncoder

# def main():
#     csv_path = Path("pb_results.csv")
#     try:
#         # Run prediction
#         print("Generating prediction...")
#         res = predict_for_game(csv_path, "pb", allow_sequences=True, include_wheel=False, wheel_template_path=Path("wheel.txt"), run_backtest=False)
#         print("Prediction:")
#         print(json.dumps(res, indent=2, cls=NumpyEncoder))
#     except Exception as e:
#         print(f"Prediction failed: {str(e)}")
    
#     try:
#         # Run backtest
#         print("Starting backtest...")
#         backtest_res = predict_for_game(csv_path, "pb", allow_sequences=True, include_wheel=False, wheel_template_path=Path("wheel.txt"), run_backtest=True)
#         print("Backtest Results:")
#         print(json.dumps(backtest_res, indent=2, cls=NumpyEncoder))
#     except Exception as e:
#         print(f"Backtest failed: {str(e)}")

# if __name__ == "__main__":
#     main()


import json
from pathlib import Path
from lotto_predictor import predict_for_game_v3, NumpyEncoder

def main():
    csv_path = Path("pb_results.csv")
    
    try:
        # Run prediction
        print("Generating prediction...")
        res = predict_for_game_v3(csv_path, "pb", run_backtest=False)
        print("Prediction:")
        print(json.dumps(res, indent=2, cls=NumpyEncoder))
        print(f"\nPredicted Numbers: {res['numbers']}")
        if res.get('star'):
            print(f"Star Ball: {res['star']}")
        
        # Print model info
        model_info = res.get('model_info', {})
        print(f"\nModel built for {model_info.get('numbers_modeled', 0)} out of {model_info.get('total_possible', 0)} numbers")
        
    except Exception as e:
        print(f"Prediction failed: {str(e)}")
        import traceback
        traceback.print_exc()
    
    # try:
    #     # Run backtest
    #     print("\n" + "="*50)
    #     print("Starting backtest...")
    #     backtest_res = predict_for_game_v3(csv_path, "pb", run_backtest=True)
    #     print("\nBacktest Results:")
    #     print(json.dumps(backtest_res, indent=2, cls=NumpyEncoder))
        
    #     # Print summary
    #     if 'error' not in backtest_res:
    #         print(f"\nBacktest Summary:")
    #         print(f"Model 3+ matches: {backtest_res.get('model_3plus_rate', 0)}%")
    #         print(f"Random 3+ matches: {backtest_res.get('random_3plus_rate', 0)}%")
    #         print(f"Average sum error: {backtest_res.get('avg_sum_error', 0)}")
    #         print(f"Even count accuracy: {backtest_res.get('even_count_accuracy', 0)}%")
            
    #         # Show hit rates comparison
    #         print("\nHit Rate Comparison:")
    #         for i in range(6):
    #             model_rate = backtest_res.get(f'model_hit_{i}_rate', 0)
    #             random_rate = backtest_res.get(f'random_hit_{i}_rate', 0)
    #             print(f"{i} matches: Model {model_rate}% vs Random {random_rate}%")
        
    # except Exception as e:
    #     print(f"Backtest failed: {str(e)}")
    #     import traceback
    #     traceback.print_exc()

if __name__ == "__main__":
    main()