def test_builds_without_errors(self):
batch_shape = [4, 3]
num_timesteps = 10
num_features = 2
design_matrix = self._build_placeholder(
np.random.randn(*(batch_shape + [num_timesteps, num_features])))
weights_batch_shape = []
if not self.use_static_shape:
weights_batch_shape = tf1.placeholder_with_default(np.array(
weights_batch_shape, dtype=np.int32),
shape=None)
sparse_regression = SparseLinearRegression(
design_matrix=design_matrix,
weights_batch_shape=weights_batch_shape)
prior_params = [
param.prior.sample() for param in sparse_regression.parameters
]
ssm = sparse_regression.make_state_space_model(
num_timesteps=num_timesteps, param_vals=prior_params)
if self.use_static_shape:
output_shape = ssm.sample().shape.as_list()
else:
output_shape = self.evaluate(tf.shape(ssm.sample()))
self.assertAllEqual(output_shape, batch_shape + [num_timesteps, 1])
def _build_sts(self, observed_time_series=None):
max_timesteps = 100
num_features = 3
# LinearRegression components don't currently take an `observed_time_series`
# argument, so they can't infer a prior batch shape. This means we have to
# manually set the batch shape expected by the tests.
batch_shape = None
if observed_time_series is not None:
observed_time_series_tensor, _ = (
sts_util.canonicalize_observed_time_series_with_mask(
observed_time_series))
batch_shape = tf.shape(observed_time_series_tensor)[:-2]
regression = SparseLinearRegression(design_matrix=np.random.randn(
max_timesteps, num_features).astype(np.float32),
weights_batch_shape=batch_shape)
return Sum(components=[regression],
observed_time_series=observed_time_series)
