def test_forecast_features_num_threads(self):
output_time_points = 5
forecasts = feature_utils.forecast_features(
self.covariates,
self.feature_specs,
num_forecast_steps=output_time_points,
num_threads=1,
)
forecasts_parallel = feature_utils.forecast_features(
self.covariates,
self.feature_specs,
num_forecast_steps=output_time_points,
num_threads=2,
)
_, n_locations, n_features = self.covariates.shape
self.assertTupleEqual(forecasts.shape,
(output_time_points, n_locations, n_features))
np.testing.assert_allclose(
forecasts, self.expected_output[:output_time_points, :, :])
np.testing.assert_allclose(forecasts, forecasts_parallel)
def test_forecast_features_forecast_shorter(self):
output_time_points = 3
_, n_locations, n_features = self.covariates.shape
expected_output = np.zeros(
(output_time_points, n_locations, n_features))
expected_output[:, :, 1] = 27
expected_output[:, :, 2] = np.arange(21, 24).reshape(-1, 1)
expected_output[:, :, 3] = 1.0
forecasts = feature_utils.forecast_features(
self.covariates,
self.feature_specs,
num_forecast_steps=output_time_points,
)
_, n_locations, n_features = self.covariates.shape
self.assertTupleEqual(forecasts.shape,
(output_time_points, n_locations, n_features))
np.testing.assert_allclose(
forecasts, self.expected_output[:output_time_points, :, :])