f.write("Training time (s): \n\t{0}\n\n".format(train_time)) # Predictions f.write("===================\n") f.write("TEST SET\n") f.write("===================\n") print('Start predicting on test...') start = time.time() test_pred = model.predict(test_X, model_file) end = time.time() predict_test_time = end - start f.write("Testing prediction time (s):\n\t{0}\n".format(predict_test_time)) print('Finished predicting on testing...') # Calculate CE/class rate on testing set test_ce, test_class_rate, test_precision, test_recall, test_f1_score = model.evaluate(test_y, test_pred, cross_entropy_flag = True) f.write("testing CE:\n\t{0}\n".format(test_ce)) f.write("testing classification rate:\n\t{0}\n".format(test_class_rate)) f.write("testing precision:\n\t{0}\n".format(test_precision)) f.write("testing recall:\n\t{0}\n".format(test_recall)) f.write("testing f1 score:\n\t{0}\n".format(test_f1_score)) # Also write to the summary file fieldnames = ['algorithm', 'configuration', 'model_file', 'data_set', 'train_time', 'predict_time', 'ce', 'classification_rate', 'precision', 'recall', 'f1'] if os.path.isfile(summary_file): report = open(summary_file, 'a') writer = csv.DictWriter(report, fieldnames=fieldnames) else: report = open(summary_file, 'w') writer = csv.DictWriter(report, fieldnames=fieldnames)
f.write("Training time (s): \n\t{0}\n\n".format(train_time)) # Predictions f.write("===================\n") f.write("TRAINING\n") f.write("===================\n") print('Start predicting on training...') start = time.time() train_pred = model.predict(train_X, model_file) end = time.time() predict_train_time = end - start f.write("Training prediction time (s):\n\t{0}\n".format(predict_train_time)) print('Finished predicting on training...') # Calculate CE/class rate on training set train_ce, train_class_rate, train_precision, train_recall, train_f1_score = model.evaluate(train_y, train_pred, cross_entropy_flag = True) f.write("Training CE:\n\t{0}\n".format(train_ce)) f.write("Training classification rate:\n\t{0}\n".format(train_class_rate)) f.write("Training precision:\n\t{0}\n".format(train_precision)) f.write("Training recall:\n\t{0}\n".format(train_recall)) f.write("Training f1 score:\n\t{0}\n".format(train_f1_score)) # Evaluate on valid set f.write("\n===================\n") f.write("VALID\n") f.write("===================\n") print('Start predicting on validation...') start = time.time() valid_pred = model.predict(valid_X, model_file) end = time.time() predict_valid_time = end - start