PR comments

evalml/model_understanding/_partial_dependence_fast_mode_utils.py

@@ -35,8 +35,8 @@ def _get_cloned_feature_pipelines(
     new_parameters = pipeline.parameters
     for component in pipeline.component_graph.component_instances.values():
         new_parameters = component._handle_partial_dependence_fast_mode(
-            X_train,
             new_parameters,
+            X=X_train,
             target=pipeline.input_target_name,
         )
 

evalml/pipelines/components/component_base.py

@@ -103,14 +103,19 @@ def default_parameters(cls):
     def _supported_by_list_API(cls):
         return not cls.modifies_target
 
-    def _handle_partial_dependence_fast_mode(self, X, pipeline_parameters, target):
+    def _handle_partial_dependence_fast_mode(
+        self,
+        pipeline_parameters,
+        X=None,
+        target=None,
+    ):
         """Determines whether or not a component can be used with partial dependence's fast mode.
 
         Args:
-            X (pd.DataFrame): Holdout data being used for partial dependence calculations.
             pipeline_parameters (dict): Pipeline parameters that will be used to create the pipelines
                 used in partial dependence fast mode.
-            target (str): The target whose values we are trying to predict.
+            X (pd.DataFrame, optional): Holdout data being used for partial dependence calculations.
+            target (str, optional): The target whose values we are trying to predict.
         """
         if self._can_be_used_for_fast_partial_dependence:
             return pipeline_parameters

evalml/pipelines/components/transformers/feature_selection/feature_selector.py

@@ -71,7 +71,12 @@ def fit_transform(self, X, y=None):
         """
         return self.fit(X, y).transform(X, y)
 
-    def _handle_partial_dependence_fast_mode(self, X, pipeline_parameters, target):
+    def _handle_partial_dependence_fast_mode(
+        self,
+        pipeline_parameters,
+        X=None,
+        target=None,
+    ):
         """Updates pipeline parameters to not drop any features based off of feature importance.
 
             This is needed, because fast mode refits cloned pipelines on single columns,
@@ -81,10 +86,10 @@ def _handle_partial_dependence_fast_mode(self, X, pipeline_parameters, target):
             pipeline to determine if that feature gets dropped or not.
 
         Args:
-            X (pd.DataFrame): Holdout data being used for partial dependence calculations.
             pipeline_parameters (dict): Pipeline parameters that will be used to create the pipelines
                 used in partial dependence fast mode.
-            target (str): The target whose values we are trying to predict.
+            X (pd.DataFrame, optional): Holdout data being used for partial dependence calculations.
+            target (str, optional): The target whose values we are trying to predict.
 
         Return:
             pipeline_parameters (dict): Pipeline parameters updated to allow the FeatureSelector component

evalml/pipelines/components/transformers/preprocessing/featuretools.py

@@ -129,21 +129,21 @@ def transform(self, X, y=None):
         feature_matrix.ww.init(schema=partial_schema)
         return feature_matrix
 
-    def _handle_partial_dependence_fast_mode(self, X, pipeline_parameters, target):
-        """Determines whether or not a DFSTransformer component can be used with partial dependence's fast mode.
+    def _handle_partial_dependence_fast_mode(self, pipeline_parameters, X, target):
+        """Determines whether or not a DFS Transformer component can be used with partial dependence's fast mode.
 
         Note:
             This component can be used with partial dependence fast mode only when
             all of the features present in the ``features`` parameter are present
             in the DataFrame.
 
         Args:
-            X (pd.DataFrame): Holdout data being used for partial dependence calculations.
             pipeline_parameters (dict): Pipeline parameters that will be used to create the pipelines
                 used in partial dependence fast mode.
-            target (str): The target whose values we are trying to predict. May be present in the
-                list of features in the DFS Transformer's parameters, in which case we should ignore it.
-
+            X (pd.DataFrame): Holdout data being used for partial dependence calculations.
+            target (str): The target whose values we are trying to predict. This is used
+                to know which column to ignore if the target column is present in the list of features
+                in the DFS Transformer's parameters
         """
         dfs_transformer = pipeline_parameters.get("DFS Transformer")
         if dfs_transformer is not None:

evalml/tests/model_understanding_tests/test_partial_dependence.py

@@ -2871,7 +2871,7 @@ def test_partial_dependence_dfs_transformer_handling_with_multi_output_primitive
     pipeline = RegressionPipeline(
         [dfs_transformer, "Standard Scaler", "Random Forest Regressor"],
     )
-    # Confirm that the LSA primitive was actually used
+    # Confirm that a multi-output feature is present
     assert any(len(f.get_feature_names()) > 1 for f in features)
 
     pipeline.fit(X_fm, y)