Thread STS name into its joint distribution

PiperOrigin-RevId: 427855341

tensorflow_probability/python/sts/structural_time_series.py

@@ -387,7 +387,8 @@ def state_space_model_likelihood(**param_vals):
                 # Likelihood.
                 [('observed_time_series', state_space_model_likelihood)]),
             use_vectorized_map=False,
-            batch_ndims=batch_ndims))
+            batch_ndims=batch_ndims,
+            name=self.name))
 
     if observed_time_series is not None:
       return joint_distribution.experimental_pin(

tensorflow_probability/python/sts/structural_time_series_test.py

@@ -307,6 +307,11 @@ def test_prior_sample(self):
           2,
       ] + param.prior.batch_shape.as_list() + param.prior.event_shape.as_list())
 
+  def test_joint_distribution_name(self):
+    model = self._build_sts(name='foo')
+    jd = model.joint_distribution(num_timesteps=5)
+    self.assertEqual('foo', jd.name)
+
   def test_joint_distribution_log_prob(self):
     model = self._build_sts(
         # Dummy series to build the model with float64 priors. Working in
@@ -402,14 +407,17 @@ def test_add_component(self):
 @test_util.test_all_tf_execution_regimes
 class AutoregressiveTest(test_util.TestCase, _StsTestHarness):
 
-  def _build_sts(self, observed_time_series=None):
-    return Autoregressive(order=3, observed_time_series=observed_time_series)
+  def _build_sts(self, observed_time_series=None, **kwargs):
+    return Autoregressive(
+        order=3,
+        observed_time_series=observed_time_series,
+        **kwargs)
 
 
 @test_util.test_all_tf_execution_regimes
 class ARMATest(test_util.TestCase, _StsTestHarness):
 
-  def _build_sts(self, observed_time_series=None):
+  def _build_sts(self, observed_time_series=None, **kwargs):
     one = 1.
     if observed_time_series is not None:
       observed_time_series = (
@@ -421,36 +429,42 @@ def _build_sts(self, observed_time_series=None):
         ma_order=1,
         integration_degree=0,
         level_drift_prior=tfd.Normal(loc=one, scale=one),
-        observed_time_series=observed_time_series)
+        observed_time_series=observed_time_series,
+        **kwargs)
 
 
 @test_util.test_all_tf_execution_regimes
 class ARIMATest(test_util.TestCase, _StsTestHarness):
 
-  def _build_sts(self, observed_time_series=None):
+  def _build_sts(self, observed_time_series=None, **kwargs):
     return AutoregressiveIntegratedMovingAverage(
         ar_order=1, ma_order=2, integration_degree=2,
-        observed_time_series=observed_time_series)
+        observed_time_series=observed_time_series,
+        **kwargs)
 
 
 @test_util.test_all_tf_execution_regimes
 class LocalLevelTest(test_util.TestCase, _StsTestHarness):
 
-  def _build_sts(self, observed_time_series=None):
-    return LocalLevel(observed_time_series=observed_time_series)
+  def _build_sts(self, observed_time_series=None, **kwargs):
+    return LocalLevel(
+        observed_time_series=observed_time_series,
+        **kwargs)
 
 
 @test_util.test_all_tf_execution_regimes
 class LocalLinearTrendTest(test_util.TestCase, _StsTestHarness):
 
-  def _build_sts(self, observed_time_series=None):
-    return LocalLinearTrend(observed_time_series=observed_time_series)
+  def _build_sts(self, observed_time_series=None, **kwargs):
+    return LocalLinearTrend(
+        observed_time_series=observed_time_series,
+        **kwargs)
 
 
 @test_util.test_all_tf_execution_regimes
 class SeasonalTest(test_util.TestCase, _StsTestHarness):
 
-  def _build_sts(self, observed_time_series=None):
+  def _build_sts(self, observed_time_series=None, **kwargs):
     # Note that a Seasonal model with `num_steps_per_season > 1` would have
     # deterministic dependence between timesteps, so evaluating `log_prob` of an
     # arbitrary time series leads to Cholesky decomposition errors unless the
@@ -461,79 +475,87 @@ def _build_sts(self, observed_time_series=None):
     return Seasonal(num_seasons=7,
                     num_steps_per_season=1,
                     observed_time_series=observed_time_series,
-                    constrain_mean_effect_to_zero=False)
+                    constrain_mean_effect_to_zero=False,
+                    **kwargs)
 
 
 @test_util.test_all_tf_execution_regimes
 class SeasonalWithZeroMeanConstraintTest(test_util.TestCase, _StsTestHarness):
 
-  def _build_sts(self, observed_time_series=None):
+  def _build_sts(self, observed_time_series=None, **kwargs):
     return Seasonal(num_seasons=7,
                     num_steps_per_season=1,
                     observed_time_series=observed_time_series,
-                    constrain_mean_effect_to_zero=True)
+                    constrain_mean_effect_to_zero=True,
+                    **kwargs)
 
 
 @test_util.test_all_tf_execution_regimes
 class SeasonalWithMultipleStepsAndNoiseTest(test_util.TestCase,
                                             _StsTestHarness):
 
-  def _build_sts(self, observed_time_series=None):
+  def _build_sts(self, observed_time_series=None, **kwargs):
     day_of_week = tfp.sts.Seasonal(num_seasons=7,
                                    num_steps_per_season=24,
                                    allow_drift=False,
                                    observed_time_series=observed_time_series,
                                    name='day_of_week')
     return tfp.sts.Sum(components=[day_of_week],
-                       observed_time_series=observed_time_series)
+                       observed_time_series=observed_time_series,
+                       **kwargs)
 
 
 @test_util.test_all_tf_execution_regimes
 class SemiLocalLinearTrendTest(test_util.TestCase, _StsTestHarness):
 
-  def _build_sts(self, observed_time_series=None):
-    return SemiLocalLinearTrend(observed_time_series=observed_time_series)
+  def _build_sts(self, observed_time_series=None, **kwargs):
+    return SemiLocalLinearTrend(
+        observed_time_series=observed_time_series,
+        **kwargs)
 
 
 @test_util.test_all_tf_execution_regimes
 class SmoothSeasonalTest(test_util.TestCase, _StsTestHarness):
 
-  def _build_sts(self, observed_time_series=None):
+  def _build_sts(self, observed_time_series=None, **kwargs):
     return SmoothSeasonal(period=42,
                           frequency_multipliers=[1, 2, 4],
-                          observed_time_series=observed_time_series)
+                          observed_time_series=observed_time_series,
+                          **kwargs)
 
 
 @test_util.test_all_tf_execution_regimes
 class SmoothSeasonalWithNoDriftTest(test_util.TestCase, _StsTestHarness):
 
-  def _build_sts(self, observed_time_series=None):
+  def _build_sts(self, observed_time_series=None, **kwargs):
     smooth_seasonal = SmoothSeasonal(period=42,
                                      frequency_multipliers=[1, 2, 4],
                                      allow_drift=False,
                                      observed_time_series=observed_time_series)
     # The test harness doesn't like models with no parameters, so wrap with Sum.
     return tfp.sts.Sum([smooth_seasonal],
-                       observed_time_series=observed_time_series)
+                       observed_time_series=observed_time_series,
+                       **kwargs)
 
 
 @test_util.test_all_tf_execution_regimes
 class SumTest(test_util.TestCase, _StsTestHarness):
 
-  def _build_sts(self, observed_time_series=None):
+  def _build_sts(self, observed_time_series=None, **kwargs):
     first_component = LocalLinearTrend(
         observed_time_series=observed_time_series, name='first_component')
     second_component = LocalLinearTrend(
         observed_time_series=observed_time_series, name='second_component')
     return Sum(
         components=[first_component, second_component],
-        observed_time_series=observed_time_series)
+        observed_time_series=observed_time_series,
+        **kwargs)
 
 
 @test_util.test_all_tf_execution_regimes
 class LinearRegressionTest(test_util.TestCase, _StsTestHarness):
 
-  def _build_sts(self, observed_time_series=None):
+  def _build_sts(self, observed_time_series=None, **kwargs):
     max_timesteps = 100
     num_features = 3
 
@@ -557,13 +579,14 @@ def _build_sts(self, observed_time_series=None):
             max_timesteps, num_features).astype(dtype),
         weights_prior=prior)
     return Sum(components=[regression],
-               observed_time_series=observed_time_series)
+               observed_time_series=observed_time_series,
+               **kwargs)
 
 
 @test_util.test_all_tf_execution_regimes
 class SparseLinearRegressionTest(test_util.TestCase, _StsTestHarness):
 
-  def _build_sts(self, observed_time_series=None):
+  def _build_sts(self, observed_time_series=None, **kwargs):
     max_timesteps = 100
     num_features = 3
 
@@ -584,13 +607,14 @@ def _build_sts(self, observed_time_series=None):
             max_timesteps, num_features).astype(dtype),
         weights_batch_shape=batch_shape)
     return Sum(components=[regression],
-               observed_time_series=observed_time_series)
+               observed_time_series=observed_time_series,
+               **kwargs)
 
 
 @test_util.test_all_tf_execution_regimes
 class DynamicLinearRegressionTest(test_util.TestCase, _StsTestHarness):
 
-  def _build_sts(self, observed_time_series=None):
+  def _build_sts(self, observed_time_series=None, **kwargs):
     max_timesteps = 100
     num_features = 3
 
@@ -604,7 +628,8 @@ def _build_sts(self, observed_time_series=None):
     return DynamicLinearRegression(
         design_matrix=np.random.randn(
             max_timesteps, num_features).astype(dtype),
-        observed_time_series=observed_time_series)
+        observed_time_series=observed_time_series,
+        **kwargs)
 
 
 if __name__ == '__main__':