Support CalibratedClassifierCV for SHAP

RELEASE_NOTES.md

@@ -7,6 +7,7 @@
 - Fix FeatureInputComponent range calculation for boolean columns (avoid np.round on bools) and add a regression test.
 - Ensure save_html includes custom tabs by providing a static-export fallback for tabs without a to_html implementation.
 - Support string class labels in ClassifierExplainer by preserving label mappings and avoiding float casts.
+- Support CalibratedClassifierCV by using its fitted base estimator for SHAP (avoids falling back to kernel).
 
 ### Improvements
 - Replace print statements with standard logging and warnings; progress messages are now INFO-level and user-actionable guidance uses warnings. A one-time warning is emitted if logging is not configured, with instructions to call `enable_default_logging()`.

TODO.md

@@ -6,11 +6,9 @@
 - Rules: link an issue when possible; include size S/M/L; mark blockers.
 
 **Now**
-- [M/L][Explainers][#279] support CalibratedClassifierCV (unwrap estimator for SHAP; update logic and tests).
+- [S/M][Components][#277] whatif input range/rounding customization.
 
 **Next**
-- [S/M][Components][#277] whatif input range/rounding customization.
-- [S/M][Explainers][#274] support string labels without float casts.
 - [M][Explainers][#273] categorical columns with NaNs: sorting and column preservation.
 - [S][Explainers][#270] Autogluon integration (coerce predict_proba to ndarray).
 - [M][Hub][#269] add_dashboard endpoint fails after first request (Flask blueprint lifecycle).

explainerdashboard/explainer_methods.py

@@ -2,6 +2,7 @@
     "IndexNotFoundError",
     "append_dict_to_df",
     "safe_isinstance",
+    "unwrap_calibrated_classifier",
     "align_categorical_dtypes",
     "guess_shap",
     "mape_score",
@@ -296,6 +297,27 @@ def safe_isinstance(obj, *instance_str):
     return False
 
 
+def unwrap_calibrated_classifier(model):
+    """Return the fitted base estimator for a CalibratedClassifierCV model."""
+    if not safe_isinstance(model, "CalibratedClassifierCV"):
+        return model
+
+    calibrated_classifiers = getattr(model, "calibrated_classifiers_", None)
+    if calibrated_classifiers:
+        calibrated = calibrated_classifiers[0]
+        for attr in ("estimator", "base_estimator"):
+            estimator = getattr(calibrated, attr, None)
+            if estimator is not None:
+                return estimator
+
+    for attr in ("estimator", "base_estimator"):
+        estimator = getattr(model, attr, None)
+        if estimator is not None:
+            return estimator
+
+    return model
+
+
 def align_categorical_dtypes(
     df_target: pd.DataFrame,
     df_reference: pd.DataFrame,
@@ -332,6 +354,8 @@ def guess_shap(model):
     Returns:
         str: {'tree', 'linear', None}
     """
+    model = unwrap_calibrated_classifier(model)
+
     tree_models = [
         "RandomForestClassifier",
         "RandomForestRegressor",

explainerdashboard/explainers.py

@@ -250,6 +250,7 @@ def __init__(
                 self.X_background = None
         if not hasattr(self, "model"):
             self.model = model
+        self.model_for_shap = unwrap_calibrated_classifier(self.model)
 
         if safe_isinstance(model, "xgboost.core.Booster"):
             raise ValueError(
@@ -337,7 +338,7 @@ def __init__(
         self.shap_kwargs = shap_kwargs or {}
 
         if shap == "guess":
-            shap_guess = guess_shap(self.model)
+            shap_guess = guess_shap(self.model_for_shap)
             model_str = (
                 str(type(self.model))
                 .replace("'", "")
@@ -1230,13 +1231,14 @@ def shap_explainer(self):
         if not hasattr(self, "_shap_explainer"):
             X_str = ", X_background" if self.X_background is not None else "X"
             NoX_str = ", X_background" if self.X_background is not None else ""
+            model_for_shap = self.model_for_shap
             if self.shap == "tree":
                 logger.info(
                     "Generating self.shap_explainer = "
                     f"shap.TreeExplainer(model{NoX_str})"
                 )
                 # Fix XGBoost 3.1+ base_score string format before shap accesses it
-                model_for_shap = self._fix_xgboost_model_for_shap(self.model)
+                model_for_shap = self._fix_xgboost_model_for_shap(model_for_shap)
                 self._shap_explainer = shap.TreeExplainer(model_for_shap)
             elif self.shap == "linear":
                 if self.X_background is None:
@@ -1250,7 +1252,7 @@ def shap_explainer(self):
                     X_str,
                 )
                 self._shap_explainer = shap.LinearExplainer(
-                    self.model,
+                    model_for_shap,
                     self.X_background if self.X_background is not None else self.X,
                 )
             elif self.shap == "deep":
@@ -1263,7 +1265,7 @@ def shap_explainer(self):
                     UserWarning,
                 )
                 self._shap_explainer = shap.DeepExplainer(
-                    self.model,
+                    model_for_shap,
                     self.X_background
                     if self.X_background is not None
                     else shap.sample(self.X, 5),
@@ -1280,7 +1282,7 @@ def shap_explainer(self):
                 import torch
 
                 self._shap_explainer = shap.DeepExplainer(
-                    self.model.module_,
+                    model_for_shap.module_,
                     torch.tensor(self.X_background.values)
                     if self.X_background is not None
                     else torch.tensor(shap.sample(self.X, 5).values),
@@ -1327,7 +1329,7 @@ def model_predict(data_asarray):
                         "Please install a CUDA-enabled SHAP build that includes "
                         "GPUTree support."
                     )
-                self._shap_explainer = explainer_cls(self.model, X_data)
+                self._shap_explainer = explainer_cls(model_for_shap, X_data)
         return self._shap_explainer
 
     @insert_pos_label
@@ -2861,16 +2863,17 @@ def shap_explainer(self):
         Taking into account model type and model_output
         """
         if not hasattr(self, "_shap_explainer"):
+            model_for_shap = self.model_for_shap
             model_str = (
-                str(type(self.model))
+                str(type(model_for_shap))
                 .replace("'", "")
                 .replace("<", "")
                 .replace(">", "")
                 .split(".")[-1]
             )
             if self.shap == "tree":
                 if safe_isinstance(
-                    self.model,
+                    model_for_shap,
                     "XGBClassifier",
                     "LGBMClassifier",
                     "CatBoostClassifier",
@@ -2897,7 +2900,9 @@ def shap_explainer(self):
                             UserWarning,
                         )
                         # Fix XGBoost 3.1+ base_score string format before shap accesses it
-                        model_for_shap = self._fix_xgboost_model_for_shap(self.model)
+                        model_for_shap = self._fix_xgboost_model_for_shap(
+                            model_for_shap
+                        )
                         self._shap_explainer = shap.TreeExplainer(
                             model_for_shap,
                             self.X_background
@@ -2914,7 +2919,9 @@ def shap_explainer(self):
                             ", X_background" if self.X_background is not None else "",
                         )
                         # Fix XGBoost 3.1+ base_score string format before shap accesses it
-                        model_for_shap = self._fix_xgboost_model_for_shap(self.model)
+                        model_for_shap = self._fix_xgboost_model_for_shap(
+                            model_for_shap
+                        )
                         self._shap_explainer = shap.TreeExplainer(
                             model_for_shap, self.X_background
                         )
@@ -2929,7 +2936,7 @@ def shap_explainer(self):
                         ", X_background" if self.X_background is not None else "",
                     )
                     # Fix XGBoost 3.1+ base_score string format before shap accesses it
-                    model_for_shap = self._fix_xgboost_model_for_shap(self.model)
+                    model_for_shap = self._fix_xgboost_model_for_shap(model_for_shap)
                     self._shap_explainer = shap.TreeExplainer(
                         model_for_shap, self.X_background
                     )
@@ -2955,7 +2962,7 @@ def shap_explainer(self):
                 )
 
                 self._shap_explainer = shap.LinearExplainer(
-                    self.model,
+                    model_for_shap,
                     self.X_background if self.X_background is not None else self.X,
                 )
             elif self.shap == "deep":
@@ -2968,7 +2975,7 @@ def shap_explainer(self):
                     UserWarning,
                 )
                 self._shap_explainer = shap.DeepExplainer(
-                    self.model,
+                    model_for_shap,
                     self.X_background
                     if self.X_background is not None
                     else shap.sample(self.X, 5),
@@ -2985,7 +2992,7 @@ def shap_explainer(self):
                     UserWarning,
                 )
                 self._shap_explainer = shap.DeepExplainer(
-                    self.model.module_,
+                    model_for_shap.module_,
                     torch.tensor(
                         self.X_background.values
                         if self.X_background is not None

tests/test_classifier_base.py

@@ -7,6 +7,7 @@
 
 import plotly.graph_objects as go
 
+from sklearn.calibration import CalibratedClassifierCV
 from sklearn.ensemble import RandomForestClassifier
 
 from explainerdashboard import ClassifierExplainer, ExplainerDashboard
@@ -110,6 +111,17 @@ def test_string_labels_supported(classifier_data):
     assert {"pred_proba", "y"}.issubset(lift_df.columns)
 
 
+def test_calibrated_classifiercv_uses_tree_shap(classifier_data):
+    X_train, y_train, X_test, y_test = classifier_data
+    base_estimator = RandomForestClassifier(n_estimators=25, random_state=0)
+    model = CalibratedClassifierCV(estimator=base_estimator, cv=2)
+    model.fit(X_train, y_train)
+
+    explainer = ClassifierExplainer(model, X_test, y_test)
+
+    assert explainer.shap == "tree"
+
+
 def test_row_from_input(precalculated_rf_classifier_explainer):
     input_row = precalculated_rf_classifier_explainer.get_row_from_input(
         precalculated_rf_classifier_explainer.X.iloc[[0]].values.tolist()