final_output = {"error": error, "micro_f1": micro_f1, "macro_f1": macro_f1}
parameters = {
"n_cv_folds": n_cv_folds,
"max_n_layers": max_n_layers,
"concat_type": concat_type,
"input_type": input_type,
"n_vlpso_generations": n_vlpso_generations,
"n_vlpso_population": n_vlpso_population,
"n_trees": n_trees,
"classifiers": classifiers_str
}
layer_errors = {
"test_layer_errors": test_layer_errors,
"val_layer_errors": val_layer_errors
}
time_output = {
"train_time": train_time_end - train_time_start,
"test_time": test_time_end - test_time_start
}
output_writer.write_output(best_candidate_output, 'best_candidate')
output_writer.write_output(vlpso_output, 'vlpso_output', indent=2)
output_writer.write_output(final_output, 'performance', indent=2)
output_writer.write_output(parameters, 'parameters', indent=2)
output_writer.write_output(layer_errors, 'layer_errors', indent=2)
output_writer.write_output(time_output, 'runtime', indent=2)
# ------------------ Testing phase ------------------------- #
test_time_start = time.time()
prediction = classifier.predict(X_test)
# --------------------------------------------------------- #
error = 1 - accuracy_score(Y_test, prediction)
print('error =', error)
micro_f1 = f1_score(Y_test - 1, prediction - 1, average='micro')
print('micro_f1 =', micro_f1)
macro_f1 = f1_score(Y_test - 1, prediction - 1, average='macro')
print('macro_f1 =', macro_f1)
test_time_end = time.time()
# ----------------- Writing Output ------------------------- #
final_output = {"error": error, "micro_f1": micro_f1, "macro_f1": macro_f1}
parameters = {"n_trees": n_trees, "classifiers": classifier_str}
time_output = {
"train_time": train_time_end - train_time_start,
"test_time": test_time_end - test_time_start
}
output_writer.write_output(final_output, 'performance', indent=2)
output_writer.write_output(parameters, 'parameters', indent=2)
output_writer.write_output(time_output, 'runtime', indent=2)
