add feature_names to explainability code

molpipeline/explainability/explainer.py

@@ -301,11 +301,6 @@ def explain(self, X: Any, **kwargs: Any) -> _SHAPExplainer_return_type_:
                 explanation_results.append(self.return_element_type_())
                 continue
 
-            # Feature names should also be extracted from the Pipeline.
-            # But first, we need to add the names to the pipelines.
-            # Therefore, feature_names is just None currently.
-            feature_names = None
-
             # compute the shap values for the features
             feature_weights = self.explainer.shap_values(feature_vector, **kwargs)
             feature_weights = np.asarray(feature_weights).squeeze()
@@ -331,7 +326,12 @@ def explain(self, X: Any, **kwargs: Any) -> _SHAPExplainer_return_type_:
             }
             if issubclass(self.return_element_type_, FeatureInfoMixin):
                 explanation_data["feature_vector"] = feature_vector
-                explanation_data["feature_names"] = feature_names
+                if not hasattr(featurization_element, "feature_names"):
+                    raise ValueError(
+                        "Featurization element does not have a get_feature_names method."
+                    )
+                explanation_data["feature_names"] = featurization_element.feature_names
+
             if issubclass(self.return_element_type_, FeatureExplanationMixin):
                 explanation_data["feature_weights"] = feature_weights
             if issubclass(self.return_element_type_, AtomExplanationMixin):

molpipeline/explainability/explanation.py

@@ -78,7 +78,7 @@ def is_valid(self) -> bool:
         return all(
             [
                 self.feature_vector is not None,
-                # self.feature_names is not None,
+                self.feature_names is not None,
                 self.molecule is not None,
                 self.prediction is not None,
                 self.feature_weights is not None,
@@ -107,7 +107,7 @@ def is_valid(self) -> bool:
         return all(
             [
                 self.feature_vector is not None,
-                # self.feature_names is not None,  # TODO uncomment when PR is merged
+                self.feature_names is not None,
                 self.molecule is not None,
                 self.prediction is not None,
                 self.feature_weights is not None,

tests/test_explainability/test_shap_explainers.py

@@ -124,9 +124,16 @@ def _test_valid_explanation(
             (nof_features,), explanation.feature_vector.shape  # type: ignore[union-attr]
         )
 
-        # feature names are not implemented yet
-        self.assertIsNone(explanation.feature_names)
-        # self.assertEqual(len(explanation.feature_names), explanation.feature_vector.shape[0])
+        # feature names should be a list of not empty strings
+        self.assertTrue(
+            all(
+                isinstance(name, str) and len(name) > 0
+                for name in explanation.feature_names
+            )
+        )
+        self.assertEqual(
+            len(explanation.feature_names), explanation.feature_vector.shape[0]
+        )
 
         self.assertIsInstance(explanation.molecule, RDKitMol)
         self.assertEqual(
@@ -176,9 +183,9 @@ def _test_valid_explanation(
                 (explanation.molecule.GetNumAtoms(),),  # type: ignore[union-attr]
             )
 
-    def test_explanations_fingerprint_pipeline(
+    def test_explanations_fingerprint_pipeline(  # pylint: disable=too-many-locals
         self,
-    ) -> None:  # pylint: disable=too-many-locals
+    ) -> None:
         """Test SHAP's TreeExplainer wrapper on MolPipeline's pipelines with fingerprints."""
 
         tree_estimators = [
@@ -371,6 +378,11 @@ def test_explanations_pipeline_with_physchem(self) -> None:
                     explainer=explainer,
                 )
 
+                self.assertEqual(
+                    explanation.feature_names,
+                    pipeline.named_steps["physchem"].feature_names,
+                )
+
     def test_explanations_pipeline_with_concatenated_features(self) -> None:
         """Test SHAP's TreeExplainer wrapper on concatenated feature vector."""
 
@@ -428,3 +440,8 @@ def test_explanations_pipeline_with_concatenated_features(self) -> None:
                     TEST_SMILES[i],
                     explainer=explainer,
                 )
+
+                self.assertEqual(
+                    explanation.feature_names,
+                    pipeline.named_steps["features"].feature_names,
+                )