def test_tcn_forecaster_runtime_error(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)
with pytest.raises(RuntimeError):
with tempfile.TemporaryDirectory() as tmp_dir_name:
ckpt_name = os.path.join(tmp_dir_name, "ckpt")
forecaster.save(ckpt_name)
with pytest.raises(RuntimeError):
forecaster.predict(test_data[0])
with pytest.raises(RuntimeError):
forecaster.evaluate(test_data)
def test_tcn_forecaster_save_load(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_mse = forecaster.fit(train_data, epochs=2)
with tempfile.TemporaryDirectory() as tmp_dir_name:
ckpt_name = os.path.join(tmp_dir_name, "ckpt")
test_pred_save = forecaster.predict(test_data[0])
forecaster.save(ckpt_name)
forecaster.load(ckpt_name)
test_pred_load = forecaster.predict(test_data[0])
np.testing.assert_almost_equal(test_pred_save, test_pred_load)