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))
