FIX: initialize ShapPlotData with preprocessed feature names

src/facet/inspection/_inspection.py

@@ -11,8 +11,6 @@
 
 from pytools.api import AllTracker
 
-from ..data import Sample
-
 log = logging.getLogger(__name__)
 
 __all__ = [
@@ -46,15 +44,22 @@ class ShapPlotData:
     """
 
     def __init__(
-        self, shap_values: Union[FloatArray, List[FloatArray]], sample: Sample
+        self,
+        *,
+        shap_values: Union[FloatArray, List[FloatArray]],
+        features: pd.DataFrame,
+        target: Union[pd.Series, pd.DataFrame],
     ) -> None:
         """
         :param shap_values: the shap values for all observations and outputs
-        :param sample: (sub)sample of all observations for which SHAP values are
-            available; aligned with param ``shap_values``
+        :param features: features for which SHAP values are available;
+            aligned with param ``shap_values``
+        :param target: target values for all observations;
+            aligned with param ``shap_values``
         """
         self._shap_values = shap_values
-        self._sample = sample
+        self._features = features
+        self._target = target
 
     @property
     def shap_values(self) -> Union[FloatArray, List[FloatArray]]:
@@ -69,7 +74,7 @@ def features(self) -> pd.DataFrame:
         """
         Matrix of feature values (number of observations by number of features).
         """
-        return self._sample.features
+        return self._features
 
     @property
     def target(self) -> Union[pd.Series, pd.DataFrame]:
@@ -78,7 +83,7 @@ def target(self) -> Union[pd.Series, pd.DataFrame]:
         or matrix of target values for multi-output models
         (number of observations by number of outputs).
         """
-        return self._sample.target
+        return self._target
 
 
 __tracker.validate()

src/facet/inspection/base/_model_inspector.py

@@ -736,7 +736,8 @@ def shap_plot_data(self) -> ShapPlotData:
 
         return ShapPlotData(
             shap_values=shap_values_numpy,
-            sample=sample,
+            features=self.preprocess_features(sample.features),
+            target=sample.target,
         )
 
     @property

test/test/conftest.py

@@ -453,7 +453,13 @@ def fit_classifier_selector(
     parameter_space = ParameterSpace(
         ClassifierPipelineDF(
             classifier=RandomForestClassifierDF(random_state=42),
-            preprocessing=None,
+            # this column transformer is a no-op, but we need it to
+            # run tests where preprocessing changes feature names
+            preprocessing=ColumnTransformerDF(
+                # we prefix all feature names with "pass__" except the last one
+                [("pass", "passthrough", sample.feature_names[:-1])],
+                remainder="passthrough",
+            ),
         )
     )
     parameter_space.classifier.n_estimators = [10, 50]

test/test/facet/test_inspection.py

@@ -45,6 +45,13 @@
 
 T = TypeVar("T")
 
+IRIS_FEATURE_NAMES_PREPROCESSED = [
+    "pass__sepal length (cm)",
+    "pass__sepal width (cm)",
+    "pass__petal length (cm)",
+    "remainder__petal width (cm)",
+]
+
 
 def test_regressor_selector(
     regressor_selector: LearnerSelector[
@@ -343,7 +350,7 @@ def test_model_inspection_classifier_multi_class(
 
     feature_importance: pd.DataFrame = iris_inspector_multi_class.feature_importance()
     assert feature_importance.index.equals(
-        pd.Index(iris_sample.feature_names, name="feature")
+        pd.Index(IRIS_FEATURE_NAMES_PREPROCESSED, name="feature")
     )
     assert feature_importance.columns.equals(
         pd.Index(iris_inspector_multi_class.output_names, name="class")
@@ -515,30 +522,46 @@ def test_model_inspection_classifier_interaction(
 ) -> None:
     warnings.filterwarnings("ignore", message="You are accessing a training score")
 
+    assert (
+        iris_classifier_binary.preprocessing is not None
+    ), "preprocessing step must be defined"
+
     cls_inspector: Type[
         Union[
             LearnerInspector[RandomForestClassifierDF],
             NativeLearnerInspector[RandomForestClassifier],
         ]
     ]
-    learner: Union[RandomForestClassifierDF, RandomForestClassifier]
+    classifier: Union[ClassifierPipelineDF[RandomForestClassifierDF], Pipeline]
     if native:
         cls_inspector = NativeLearnerInspector[RandomForestClassifier]
-        learner = iris_classifier_binary.final_estimator.native_estimator
+        # create a native pipeline from the classifier pipeline
+        classifier = Pipeline(
+            steps=[
+                (
+                    "preprocessing",
+                    iris_classifier_binary.preprocessing.native_estimator,
+                ),
+                (
+                    "classifier",
+                    iris_classifier_binary.classifier.native_estimator,
+                ),
+            ]
+        )
     else:
         cls_inspector = LearnerInspector[RandomForestClassifierDF]
-        learner = iris_classifier_binary.final_estimator
+        classifier = iris_classifier_binary
 
     model_inspector = cls_inspector(
-        model=learner,
+        model=classifier,
         explainer_factory=TreeExplainerFactory(
             feature_perturbation="tree_path_dependent", uses_background_dataset=True
         ),
         n_jobs=n_jobs,
     ).fit(iris_sample_binary)
 
     model_inspector_no_interaction = cls_inspector(
-        model=learner,
+        model=classifier,
         shap_interaction=False,
         explainer_factory=TreeExplainerFactory(
             feature_perturbation="tree_path_dependent", uses_background_dataset=True
@@ -560,17 +583,16 @@ def test_model_inspection_classifier_interaction(
     ).abs().max().max() < 0.015
 
     # the column names of the shap value data frames are the feature names
-    feature_columns = iris_sample_binary.feature_names
-    assert shap_values.columns.to_list() == feature_columns
-    assert shap_interaction_values.columns.to_list() == feature_columns
+    assert shap_values.columns.to_list() == IRIS_FEATURE_NAMES_PREPROCESSED
+    assert shap_interaction_values.columns.to_list() == IRIS_FEATURE_NAMES_PREPROCESSED
 
     # the length of rows in shap_values should be equal to the number of observations
     assert len(shap_values) == len(iris_sample_binary)
 
     # the length of rows in shap_interaction_values should be equal to the number of
     # observations, times the number of features
     assert len(shap_interaction_values) == (
-        len(iris_sample_binary) * len(feature_columns)
+        len(iris_sample_binary) * len(IRIS_FEATURE_NAMES_PREPROCESSED)
     )
 
     # do the shap values add up to predictions minus a constant value?
@@ -840,11 +862,20 @@ def test_shap_plot_data(
     assert all(shap.shape == features_shape for shap in shap_values)
 
     shap_index = shap_plot_data.features.index
+    preprocessing = iris_inspector_multi_class.model.preprocessing
+    assert preprocessing is not None, "preprocessing step must be defined"
+
     assert_frame_equal(
-        shap_plot_data.features, iris_sample_multi_class.features.loc[shap_index]
+        # the shap plot data should contain the same observations as the
+        # preprocessed features in the sample
+        shap_plot_data.features,
+        preprocessing.transform(iris_sample_multi_class.features).loc[shap_index],
     )
     assert_series_equal(
-        shap_plot_data.target, iris_sample_multi_class.target.loc[shap_index]
+        # the shap plot data should contain the same target values as the
+        # sample
+        shap_plot_data.target,
+        iris_sample_multi_class.target.loc[shap_index],
     )