}
#eval_results = {
# 'accuracy': model.history.history["val_acc"],
# 'loss': model.history.history["val_loss"],
# }
title = "TensorFlow (4 layers weights smote)"
save_path = "C:/Users/thoma/source/repos/PythonMachineLearning/PythonMachineLearning/Library/Results"
print('Analyzing...')
evaluator = Evaluator(title, save_path)
evaluator.append_to_file(f'Best iteration: {callback[0].best_epoch}',
"info.txt")
#evaluator.append_to_file(f'Best iteration: {len(model.history.history["val_loss"])}', "info.txt")
evaluator.append_to_file(f'Training time (seconds): {elapsed_time_training}',
"info.txt")
evaluator.append_to_file(f'Testing time (seconds): {elapsed_time_testing}',
"info.txt")
evaluator.save_dict_to_file(dataset_parameters, "dataset_parameters.csv")
evaluator.save_dict_to_file(model_parameters, "model_parameters.csv")
evaluator.save_advanced_metrics(dataset.y_test, y_pred, dataset.class_labels,
dataset.class_descriptions)
evaluator.save_eval_scores_to_file(eval_results, "metric_results.csv")
evaluator.create_evaluation_metric_results(eval_results,
xlabel='epochs',
ylabel='metric score')
evaluator.create_confusion_matrix(dataset.y_test,
y_pred,
dataset.class_labels,
normalize=True)
plt.show()
