update predict calls to handle validate data and predictions group

Author: butterman0

tests/test_model_builder.py

@@ -124,11 +124,18 @@ def _save_input_params(self, idata):
     def output_var(self):
         return "output"
 
-    def _data_setter(self, x: pd.Series, y: pd.Series = None):
+    def _data_setter(self, X: pd.Series | np.ndarray, y: pd.Series | np.ndarray = None):
+
         with self.model:
-            pm.set_data({"x": x.values})
+            
+            X = X.values if isinstance(X, pd.Series) else X.ravel()
+            
+            pm.set_data({"x": X})
+            
             if y is not None:
-                pm.set_data({"y_data": y.values})
+                y = y.values if isinstance(y, pd.Series) else y.ravel()
+                
+                pm.set_data({"y_data": y})
 
     @property
     def _serializable_model_config(self):
@@ -177,8 +184,8 @@ def test_save_load(fitted_model_instance):
     assert fitted_model_instance.id == test_builder2.id
     x_pred = np.random.uniform(low=0, high=1, size=100)
     prediction_data = pd.DataFrame({"input": x_pred})
-    pred1 = fitted_model_instance.predict(prediction_data["input"])
-    pred2 = test_builder2.predict(prediction_data["input"])
+    pred1 = fitted_model_instance.predict(prediction_data[["input"]])
+    pred2 = test_builder2.predict(prediction_data[["input"]])
     assert pred1.shape == pred2.shape
     temp.close()
 
@@ -205,7 +212,7 @@ def test_empty_sampler_config_fit(toy_X, toy_y):
 
 def test_fit(fitted_model_instance):
     prediction_data = pd.DataFrame({"input": np.random.uniform(low=0, high=1, size=100)})
-    pred = fitted_model_instance.predict(prediction_data["input"])
+    pred = fitted_model_instance.predict(prediction_data[["input"]])
     post_pred = fitted_model_instance.sample_posterior_predictive(
         prediction_data["input"], extend_idata=True, combined=True
     )
@@ -223,7 +230,7 @@ def test_fit_no_y(toy_X):
 def test_predict(fitted_model_instance):
     x_pred = np.random.uniform(low=0, high=1, size=100)
     prediction_data = pd.DataFrame({"input": x_pred})
-    pred = fitted_model_instance.predict(prediction_data["input"])
+    pred = fitted_model_instance.predict(prediction_data[["input"]])
     # Perform elementwise comparison using numpy
     assert isinstance(pred, np.ndarray)
     assert len(pred) > 0
@@ -256,13 +263,12 @@ def test_sample_xxx_extend_idata_param(fitted_model_instance, group, extend_idat
 
     prediction_data = pd.DataFrame({"input": x_pred})
     if group == "prior_predictive":
-        prediction_method = fitted_model_instance.sample_prior_predictive
+        pred = fitted_model_instance.sample_prior_predictive(prediction_data["input"], combined=False, extend_idata=extend_idata)
     else:  # group == "posterior_predictive":
-        prediction_method = fitted_model_instance.sample_posterior_predictive
-
-    pred = prediction_method(prediction_data["input"], combined=False, extend_idata=extend_idata)
+        pred = fitted_model_instance.sample_posterior_predictive(prediction_data["input"], combined=False, predictions=False, extend_idata=extend_idata)
 
     pred_unstacked = pred[output_var].values
+    
     idata_now = fitted_model_instance.idata[group][output_var].values
 
     if extend_idata:
@@ -320,9 +326,40 @@ def test_predict_respects_predictions_flag(fitted_model_instance, predictions):
 
     # Run prediction with predictions=True or False
     fitted_model_instance.predict(
-        prediction_data["input"],
+        X_pred=prediction_data[["input"]],
+        extend_idata=True,
+        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
+        assert not np.array_equal(pp_before, pp_after)
+        
+@pytest.mark.parametrize("predictions", [True, False])
+def test_predict_posterior_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_posterior(
+        X_pred=prediction_data[["input"]],
         extend_idata=True,
-        combined=False,
+        combined=True,
         predictions=predictions,
     )
 
@@ -336,4 +373,4 @@ 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)
+        assert not np.array_equal(pp_before, pp_after)