def test_tcn_forecaster_fit_eva_pred(self):
    (train_data, val_data, test_data) = create_data()
    forecaster = LSTMForecaster(past_seq_len=24, input_feature_num=2, output_feature_num=2, loss='mae', lr=0.01)
    train_loss = forecaster.fit(train_data, epochs=2)
    test_pred = forecaster.predict(test_data[0])
    assert (test_pred.shape == test_data[1].shape)
    test_mse = forecaster.evaluate(test_data)
def test_tcn_forecaster_fit_eva_pred(self):
    (train_data, val_data, test_data) = create_data()
    forecaster = LSTMForecaster(past_seq_len=24, input_feature_num=2, output_feature_num=2, loss='mae', lr=0.01)
    train_loss = forecaster.fit(train_data, epochs=2)
    test_pred = forecaster.predict(test_data[0])
    assert (test_pred.shape == test_data[1].shape)
    test_mse = forecaster.evaluate(test_data)