eval_metric = gmean_metric,
eval_set = [(dataset.X_validate, dataset.y_validate)],
verbose = True)
elapsed_time_training = time.time() - start_time
# Predicting
print('Predicting...')
start_time = time.time()
y_pred = model.predict(dataset.X_test, prediction_type='Class')
elapsed_time_testing = time.time() - start_time
# Analytics
title = "LightGBM (hyper weights dart)"
eval_results = {
'multi_logloss': model.evals_result_['valid_0']['multi_logloss'],
'gmean': model.evals_result_['valid_0']['gmean']}
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: {model.best_iteration_}', "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.append_to_file(dataset_parameters, "dataset_parameters.txt")
evaluator.append_to_file(model_parameters, "model_parameters.txt")
evaluator.save_advanced_metrics(dataset.y_test, y_pred, dataset.class_labels, dataset.class_descriptions)
evaluator.append_to_file(eval_results, "metric_results.txt")
evaluator.create_evaluation_metric_results(eval_results, xlabel='number of trees', ylabel='metric score')
evaluator.create_confusion_matrix(dataset.y_test, y_pred, dataset.class_labels, normalize = True)
plt.show()
monitor_score = "gmean",
sample_weight=dataset.weight_per_sample,
patience = 25,
higher_is_better = True)
start_time = time.time()
early.fit(dataset.X_train, dataset.y_train)
elapsed_time_training = time.time() - start_time
# Predicting
print('Predicting...')
start_time = time.time()
y_pred = early.estimator.predict(dataset.X_test)
elapsed_time_testing = time.time() - start_time
# Analytics
title = "GradientBoostingClassifier (hyper weights)"
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: {early.best_iteration_}', "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.append_to_file(classifier_parameters, "classifier_parameters.txt")
evaluator.append_to_file(dataset_parameters, "dataset_parameters.txt")
evaluator.append_to_file(model_parameters, "model_parameters.txt")
evaluator.save_advanced_metrics(dataset.y_test, y_pred, dataset.class_labels, dataset.class_descriptions)
evaluator.append_to_file(early.scores_, "metric_results.txt")
evaluator.create_evaluation_metric_results(early.scores_, xlabel='boosting stages', ylabel='metric score')
evaluator.create_confusion_matrix(dataset.y_test, y_pred, dataset.class_labels, normalize = True)
plt.show()
