[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

pymc_extras/model_builder.py

@@ -628,7 +628,9 @@ def sample_prior_predictive(
 
         return prior_predictive_samples
 
-    def sample_posterior_predictive(self, X_pred, extend_idata, combined, predictions = True, **kwargs):
+    def sample_posterior_predictive(
+        self, X_pred, extend_idata, combined, predictions=True, **kwargs
+    ):
         """
         Sample from the model's posterior predictive distribution.
 
@@ -652,15 +654,15 @@ def sample_posterior_predictive(self, X_pred, extend_idata, combined, prediction
         self._data_setter(X_pred)
 
         with self.model:  # sample with new input data
-            post_pred = pm.sample_posterior_predictive(self.idata, predictions=predictions, **kwargs)
+            post_pred = pm.sample_posterior_predictive(
+                self.idata, predictions=predictions, **kwargs
+            )
             if extend_idata:
                 self.idata.extend(post_pred, join="right")
 
         group_name = "predictions" if predictions else "posterior_predictive"
 
-        posterior_predictive_samples = az.extract(
-            post_pred, group_name, combined=combined
-        )
+        posterior_predictive_samples = az.extract(post_pred, group_name, combined=combined)
 
         return posterior_predictive_samples
 

tests/test_model_builder.py

@@ -305,6 +305,7 @@ def test_id():
 
     assert model_builder.id == expected_id
 
+
 @pytest.mark.parametrize("predictions", [True, False])
 def test_predict_respects_predictions_flag(fitted_model_instance, predictions):
     x_pred = np.random.uniform(0, 1, 100)
@@ -324,7 +325,7 @@ def test_predict_respects_predictions_flag(fitted_model_instance, predictions):
         combined=False,
         predictions=predictions,
     )
-    
+
     pp_after = fitted_model_instance.idata.posterior_predictive[output_var].values
 
     # Check predictions group presence
@@ -335,4 +336,4 @@ def test_predict_respects_predictions_flag(fitted_model_instance, predictions):
     else:
         assert "predictions" not in fitted_model_instance.idata.groups()
         # Posterior predictive should be updated
-        np.testing.assert_array_not_equal(pp_before, pp_after)
+        np.testing.assert_array_not_equal(pp_before, pp_after)