with open(results_file, 'w') as fout: csv_writer = csv.writer(fout, delimiter=';', quotechar='"', quoting=csv.QUOTE_MINIMAL) csv_writer.writerow( ["dataset", "cls", "params", "nr_events", "metric", "score"]) total = 0 total_acc = 0 total_mae = 0 total_auc_outcome = 0 for nr_events in range(2, max_len - 1): # encode only prefixes of this length X, y_a, y_t, y_o = dataset_manager.generate_3d_data_for_prefix_length_with_label( dt_test, max_len, nr_events) #X, y_a, y_t = dataset_manager.generate_3d_data_for_prefix_length(dt_test, max_len, nr_events) if X.shape[0] == 0: break y_t = y_t * dataset_manager.divisors["timesincelastevent"] pred_y = model.predict(X, verbose=0) pred_y_a = pred_y[0] pred_y_t = pred_y[1] pred_y_t = pred_y_t.flatten() pred_y_t[pred_y_t < 0] = 0 pred_y_t = pred_y_t * dataset_manager.divisors["timesincelastevent"] pred_y_o = pred_y[2] acc = accuracy_score(np.argmax(y_a, axis=1), np.argmax(pred_y_a, axis=1))