Refactor Feature Importances, fix shap multiclass, fix T2E FIs

octopus/models/classification_models.py

@@ -152,7 +152,12 @@ def catboost_classifier() -> ModelConfig:
     """CatBoost classification model config."""
     return ModelConfig(
         model_class=CatBoostClassifier,
-        ml_types=[MLType.BINARY, MLType.MULTICLASS],
+        # KNOWN ISSUE: CatBoost multiclass is disabled because shap.Explainer(model, bg)
+        # segfaults in SHAP <=0.51 when using TreeExplainer's interventional mode with
+        # CatBoost multiclass models.  Re-enable once SHAP fixes this upstream.
+        # See datasets_local/specifications_refactorfi/03_shap_catboost_segfault_proposal.md
+        # Original: ml_types=[MLType.BINARY, MLType.MULTICLASS],
+        ml_types=[MLType.BINARY],
         feature_method="internal",
         chpo_compatible=True,
         scaler=None,

octopus/modules/octo/bag.py

@@ -91,7 +91,7 @@ def fit(self):
             elif self._fi_type == "shap":
                 self._training.calculate_fi_shap(partition=self._partition)
             elif self._fi_type == "permutation":
-                self._training.calculate_fi_group_permutation(partition=self._partition)
+                self._training.calculate_fi_permutation(partition=self._partition)
             elif self._fi_type == "lofo":
                 self._training.calculate_fi_lofo()
             elif self._fi_type == "constant":
@@ -576,7 +576,7 @@ def _calculate_fi_sequential(self, fi_type="internal", partition="dev"):
                 elif fi_type == "shap":
                     training.calculate_fi_shap(partition=partition)
                 elif fi_type == "permutation":
-                    training.calculate_fi_group_permutation(partition=partition)
+                    training.calculate_fi_permutation(partition=partition)
                 elif fi_type == "lofo":
                     training.calculate_fi_lofo()
                 elif fi_type == "constant":