Fix group selection in sample_posterior_predictive when predictions=True is passed in kwargs

pymc_extras/model_builder.py

@@ -530,6 +530,7 @@ def predict(
         self,
         X_pred: np.ndarray | pd.DataFrame | pd.Series,
         extend_idata: bool = True,
+        predictions: bool = True,
         **kwargs,
     ) -> np.ndarray:
         """
@@ -542,6 +543,9 @@ def predict(
             The input data used for prediction.
         extend_idata : Boolean determining whether the predictions should be added to inference data object.
             Defaults to True.
+        predictions : bool
+            Whether to use the predictions group for posterior predictive sampling.
+            Defaults to True.
         **kwargs: Additional arguments to pass to pymc.sample_posterior_predictive
 
         Returns
@@ -559,7 +563,7 @@ def predict(
         """
 
         posterior_predictive_samples = self.sample_posterior_predictive(
-            X_pred, extend_idata, combined=False, **kwargs
+            X_pred, extend_idata, combined=False, predictions=predictions, **kwargs
         )
 
         if self.output_var not in posterior_predictive_samples:
@@ -624,7 +628,9 @@ def sample_prior_predictive(
 
         return prior_predictive_samples
 
-    def sample_posterior_predictive(self, X_pred, extend_idata, combined, **kwargs):
+    def sample_posterior_predictive(
+        self, X_pred, extend_idata, combined, predictions=True, **kwargs
+    ):
         """
         Sample from the model's posterior predictive distribution.
 
@@ -634,6 +640,8 @@ def sample_posterior_predictive(self, X_pred, extend_idata, combined, **kwargs):
             The input data used for prediction using prior distribution..
         extend_idata : Boolean determining whether the predictions should be added to inference data object.
             Defaults to False.
+        predictions : Boolean determing whether to use the predictions group for posterior predictive sampling.
+            Defaults to True.
         combined: Combine chain and draw dims into sample. Won't work if a dim named sample already exists.
             Defaults to True.
         **kwargs: Additional arguments to pass to pymc.sample_posterior_predictive
@@ -646,13 +654,15 @@ def sample_posterior_predictive(self, X_pred, extend_idata, combined, **kwargs):
         self._data_setter(X_pred)
 
         with self.model:  # sample with new input data
-            post_pred = pm.sample_posterior_predictive(self.idata, **kwargs)
+            post_pred = pm.sample_posterior_predictive(
+                self.idata, predictions=predictions, **kwargs
+            )
             if extend_idata:
                 self.idata.extend(post_pred, join="right")
 
-        posterior_predictive_samples = az.extract(
-            post_pred, "posterior_predictive", combined=combined
-        )
+        group_name = "predictions" if predictions else "posterior_predictive"
+
+        posterior_predictive_samples = az.extract(post_pred, group_name, combined=combined)
 
         return posterior_predictive_samples
 
@@ -700,6 +710,7 @@ def predict_posterior(
         X_pred: np.ndarray | pd.DataFrame | pd.Series,
         extend_idata: bool = True,
         combined: bool = True,
+        predictions: bool = True,
         **kwargs,
     ) -> xr.DataArray:
         """
@@ -713,6 +724,8 @@ def predict_posterior(
             Defaults to True.
         combined: Combine chain and draw dims into sample. Won't work if a dim named sample already exists.
             Defaults to True.
+        predictions : Boolean determing whether to use the predictions group for posterior predictive sampling.
+            Defaults to True.
         **kwargs: Additional arguments to pass to pymc.sample_posterior_predictive
 
         Returns
@@ -723,7 +736,7 @@ def predict_posterior(
 
         X_pred = self._validate_data(X_pred)
         posterior_predictive_samples = self.sample_posterior_predictive(
-            X_pred, extend_idata, combined, **kwargs
+            X_pred, extend_idata, combined, predictions=predictions, **kwargs
         )
 
         if self.output_var not in posterior_predictive_samples:

tests/test_model_builder.py

@@ -304,3 +304,36 @@ def test_id():
     ).hexdigest()[:16]
 
     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)
+    prediction_data = pd.DataFrame({"input": x_pred})
+    output_var = fitted_model_instance.output_var
+
+    # Snapshot the original posterior_predictive values
+    pp_before = fitted_model_instance.idata.posterior_predictive[output_var].values.copy()
+
+    # Ensure 'predictions' group is not present initially
+    assert "predictions" not in fitted_model_instance.idata.groups()
+
+    # Run prediction with predictions=True or False
+    fitted_model_instance.predict(
+        prediction_data["input"],
+        extend_idata=True,
+        combined=False,
+        predictions=predictions,
+    )
+
+    pp_after = fitted_model_instance.idata.posterior_predictive[output_var].values
+
+    # Check predictions group presence
+    if predictions:
+        assert "predictions" in fitted_model_instance.idata.groups()
+        # Posterior predictive should remain unchanged
+        np.testing.assert_array_equal(pp_before, pp_after)
+    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)