- msg = f"Done training; best test set score was: {best_test_score:.1f}%"
- print(msg)
- logger.info(msg)
- scaler_filename, model_filename, model_info_filename = (
- self.maybe_persist_scaler_and_model(
- best_training_score,
- best_test_score,
- best_params,
- num_examples,
- scaler,
- best_model,
- )
+ time.sleep(1.0)
+ print('Done training...')
+ for params in all_models:
+ msg = f'{bold()}{params}{reset()}: score={all_models[params][0]:.2f}% '
+ msg += f'({all_models[params][2]:.2f}% test, '
+ msg += f'{all_models[params][1]:.2f}% train)'
+ if params == best_params:
+ msg += f'{bold()} <-- winner{reset()}'
+ print(msg)
+
+ (
+ scaler_filename,
+ model_filename,
+ model_info_filename,
+ ) = self.maybe_persist_scaler_and_model(
+ best_training_score,
+ best_test_score,
+ best_params,
+ num_examples,
+ scaler,
+ best_model,