feat: SHAP value support for XGBoost's binary classification models (#…

…1660)

* dispatch SHAP settings for XGBoost clsf

* enable XGBoost Classification SHAP check

* Update checks for SHAP binary classification

* add pred_contribs/pred_interactions keyword support

* fix classification SHAP value tests

* fix some test failures

* include daal_check_version

* remove circular import

* forgotten evaluation

* disable tests for older onedal versions

* change tolerances

* change correct tolerances

* return to original design

* fix for 2024.7

* modify tests

* forgotten formatting

---------

Co-authored-by: icfaust <[email protected]>

daal4py/mb/model_builders.py

@@ -190,7 +190,9 @@ def _convert_model(self, model):
         else:
             raise TypeError(f"Unknown model format {submodule_name}.{class_name}")
 
-    def _predict_classification(self, X, fptype, resultsToEvaluate):
+    def _predict_classification(
+        self, X, fptype, resultsToEvaluate, pred_contribs=False, pred_interactions=False
+    ):
         if X.shape[1] != self.n_features_in_:
             raise ValueError("Shape of input is different from what was seen in `fit`")
 
@@ -203,8 +205,34 @@ def _predict_classification(self, X, fptype, resultsToEvaluate):
             )
 
         # Prediction
+        try:
+            return self._predict_classification_with_results_to_compute(
+                X, fptype, resultsToEvaluate, pred_contribs, pred_interactions
+            )
+        except TypeError as e:
+            if "unexpected keyword argument 'resultsToCompute'" in str(e):
+                if pred_contribs or pred_interactions:
+                    # SHAP values requested, but not supported by this version
+                    raise TypeError(
+                        f"{'pred_contribs' if pred_contribs else 'pred_interactions'} not supported by this version of daal4py"
+                    ) from e
+            else:
+                # unknown type error
+                raise
+        except RuntimeError as e:
+            if "Method is not implemented" in str(e):
+                if pred_contribs or pred_interactions:
+                    raise NotImplementedError(
+                        f"{'pred_contribs' if pred_contribs else 'pred_interactions'} is not implemented for classification models"
+                    )
+            else:
+                raise
+
+        # fallback to calculation without `resultsToCompute`
         predict_algo = d4p.gbt_classification_prediction(
-            fptype=fptype, nClasses=self.n_classes_, resultsToEvaluate=resultsToEvaluate
+            nClasses=self.n_classes_,
+            fptype=fptype,
+            resultsToEvaluate=resultsToEvaluate,
         )
         predict_result = predict_algo.compute(X, self.daal_model_)
 
@@ -213,6 +241,40 @@ def _predict_classification(self, X, fptype, resultsToEvaluate):
         else:
             return predict_result.probabilities
 
+    def _predict_classification_with_results_to_compute(
+        self,
+        X,
+        fptype,
+        resultsToEvaluate,
+        pred_contribs=False,
+        pred_interactions=False,
+    ):
+        """Assume daal4py supports the resultsToCompute kwarg"""
+        resultsToCompute = ""
+        if pred_contribs:
+            resultsToCompute = "shapContributions"
+        elif pred_interactions:
+            resultsToCompute = "shapInteractions"
+
+        predict_algo = d4p.gbt_classification_prediction(
+            nClasses=self.n_classes_,
+            fptype=fptype,
+            resultsToCompute=resultsToCompute,
+            resultsToEvaluate=resultsToEvaluate,
+        )
+        predict_result = predict_algo.compute(X, self.daal_model_)
+
+        if pred_contribs:
+            return predict_result.prediction.ravel().reshape((-1, X.shape[1] + 1))
+        elif pred_interactions:
+            return predict_result.prediction.ravel().reshape(
+                (-1, X.shape[1] + 1, X.shape[1] + 1)
+            )
+        elif resultsToEvaluate == "computeClassLabels":
+            return predict_result.prediction.ravel().astype(np.int64, copy=False)
+        else:
+            return predict_result.probabilities
+
     def _predict_regression(
         self, X, fptype, pred_contribs=False, pred_interactions=False
     ):
@@ -278,11 +340,13 @@ def predict(self, X, pred_contribs=False, pred_interactions=False):
         if self._is_regression:
             return self._predict_regression(X, fptype, pred_contribs, pred_interactions)
         else:
-            if pred_contribs or pred_interactions:
+            if (pred_contribs or pred_interactions) and self.model_type != "xgboost":
                 raise NotImplementedError(
                     f"{'pred_contribs' if pred_contribs else 'pred_interactions'} is not implemented for classification models"
                 )
-            return self._predict_classification(X, fptype, "computeClassLabels")
+            return self._predict_classification(
+                X, fptype, "computeClassLabels", pred_contribs, pred_interactions
+            )
 
     def _check_proba(self):
         return not self._is_regression

daal4py/sklearn/ensemble/GBTDAAL.py

@@ -193,7 +193,9 @@ def fit(self, X, y):
         # Return the classifier
         return self
 
-    def _predict(self, X, resultsToEvaluate):
+    def _predict(
+        self, X, resultsToEvaluate, pred_contribs=False, pred_interactions=False
+    ):
         # Input validation
         if not self.allow_nan_:
             X = check_array(X, dtype=[np.single, np.double])
@@ -208,17 +210,21 @@ def _predict(self, X, resultsToEvaluate):
             return np.full(X.shape[0], self.classes_[0])
 
         fptype = getFPType(X)
-        predict_result = self._predict_classification(X, fptype, resultsToEvaluate)
+        predict_result = self._predict_classification(
+            X, fptype, resultsToEvaluate, pred_contribs, pred_interactions
+        )
 
-        if resultsToEvaluate == "computeClassLabels":
+        if resultsToEvaluate == "computeClassLabels" and not (
+            pred_contribs or pred_interactions
+        ):
             # Decode labels
             le = preprocessing.LabelEncoder()
             le.classes_ = self.classes_
             return le.inverse_transform(predict_result)
         return predict_result
 
-    def predict(self, X):
-        return self._predict(X, "computeClassLabels")
+    def predict(self, X, pred_contribs=False, pred_interactions=False):
+        return self._predict(X, "computeClassLabels", pred_contribs, pred_interactions)
 
     def predict_proba(self, X):
         return self._predict(X, "computeClassProbabilities")

tests/test_model_builders.py

@@ -48,11 +48,14 @@
 
 
 shap_required_version = (2024, "P", 1)
+shap_api_change_version = (2025, "P", 0)
 shap_supported = daal_check_version(shap_required_version)
+shap_api_changed = daal_check_version(shap_api_change_version)
 shap_not_supported_str = (
     f"SHAP value calculation only supported for version {shap_required_version} or later"
 )
 shap_unavailable_str = "SHAP Python package not available"
+shap_api_change_str = "SHAP calculation requires 2025.0 API"
 cb_unavailable_str = "CatBoost not available"
 
 # CatBoost's SHAP value calculation seems to be buggy
@@ -208,15 +211,15 @@ def test_model_predict_shap_contribs_missing_values(self):
         np.testing.assert_allclose(d4p_pred, xgboost_pred, rtol=5e-6)
 
 
-# duplicate all tests for bae_score=0.0
+# duplicate all tests for base_score=0.0
 @unittest.skipUnless(shap_supported, reason=shap_not_supported_str)
 class XGBoostRegressionModelBuilder_base_score0(XGBoostRegressionModelBuilder):
     @classmethod
     def setUpClass(cls):
         XGBoostRegressionModelBuilder.setUpClass(0)
 
 
-# duplicate all tests for bae_score=100
+# duplicate all tests for base_score=100
 @unittest.skipUnless(shap_supported, reason=shap_not_supported_str)
 class XGBoostRegressionModelBuilder_base_score100(XGBoostRegressionModelBuilder):
     @classmethod
@@ -235,7 +238,7 @@ def setUpClass(cls, base_score=0.5, n_classes=2, objective="binary:logistic"):
             n_samples=500,
             n_classes=n_classes,
             n_features=n_features,
-            n_informative=10,
+            n_informative=(2 * n_features) // 3,
             random_state=42,
         )
         cls.X_test = X[:2, :]
@@ -282,25 +285,59 @@ def test_missing_value_support(self):
     def test_model_predict_shap_contribs(self):
         booster = self.xgb_model.get_booster()
         m = d4p.mb.convert_model(booster)
-        with self.assertRaises(NotImplementedError):
-            m.predict(self.X_test, pred_contribs=True)
+        if not shap_api_changed:
+            with self.assertRaises(NotImplementedError):
+                m.predict(self.X_test, pred_contribs=True)
+        elif self.n_classes > 2:
+            with self.assertRaisesRegex(
+                RuntimeError, "Multiclass classification SHAP values not supported"
+            ):
+                m.predict(self.X_test, pred_contribs=True)
+        else:
+            d4p_pred = m.predict(self.X_test, pred_contribs=True)
+            xgboost_pred = booster.predict(
+                xgb.DMatrix(self.X_test),
+                pred_contribs=True,
+                approx_contribs=False,
+                validate_features=False,
+            )
+            np.testing.assert_allclose(d4p_pred, xgboost_pred, rtol=1e-5)
 
     def test_model_predict_shap_interactions(self):
         booster = self.xgb_model.get_booster()
         m = d4p.mb.convert_model(booster)
-        with self.assertRaises(NotImplementedError):
-            m.predict(self.X_test, pred_contribs=True)
-
-
-# duplicate all tests for bae_score=0.3
+        if not shap_api_changed:
+            with self.assertRaises(NotImplementedError):
+                m.predict(self.X_test, pred_contribs=True)
+        elif self.n_classes > 2:
+            with self.assertRaisesRegex(
+                RuntimeError, "Multiclass classification SHAP values not supported"
+            ):
+                m.predict(self.X_test, pred_interactions=True)
+        else:
+            d4p_pred = m.predict(self.X_test, pred_interactions=True)
+            xgboost_pred = booster.predict(
+                xgb.DMatrix(self.X_test),
+                pred_interactions=True,
+                approx_contribs=False,
+                validate_features=False,
+            )
+            # hitting floating precision limits for classification where class probabilities
+            # are between 0 and 1
+            # we need to accept large relative differences, as long as the absolute difference
+            # remains small (<1e-6)
+            np.testing.assert_allclose(d4p_pred, xgboost_pred, rtol=5e-2, atol=1e-6)
+
+
+# duplicate all tests for base_score=0.3
 @unittest.skipUnless(shap_supported, reason=shap_not_supported_str)
 class XGBoostClassificationModelBuilder_base_score03(XGBoostClassificationModelBuilder):
     @classmethod
     def setUpClass(cls):
         XGBoostClassificationModelBuilder.setUpClass(base_score=0.3)
 
 
-# duplicate all tests for bae_score=0.7
+# duplicate all tests for base_score=0.7
 @unittest.skipUnless(shap_supported, reason=shap_not_supported_str)
 class XGBoostClassificationModelBuilder_base_score07(XGBoostClassificationModelBuilder):
     @classmethod
@@ -328,6 +365,16 @@ def setUpClass(cls):
 class XGBoostClassificationModelBuilder_objective_logitraw(
     XGBoostClassificationModelBuilder
 ):
+    """
+    Caveat: logitraw is not per se supported in daal4py because we always
+
+                 1. apply the bias
+                 2. normalize to probabilities ("activation") using sigmoid
+                   (exception: SHAP values, the scores defining phi_ij are the raw class scores)
+
+    However, by undoing the activation and bias we can still compare if the original probas and SHAP values are aligned.
+    """
+
     @classmethod
     def setUpClass(cls):
         XGBoostClassificationModelBuilder.setUpClass(
@@ -352,6 +399,42 @@ def test_model_predict_proba(self):
         # accept an rtol of 1e-5
         np.testing.assert_allclose(d4p_pred, xgboost_pred, rtol=1e-5)
 
+    @unittest.skipUnless(shap_api_changed, reason=shap_api_change_str)
+    def test_model_predict_shap_contribs(self):
+        booster = self.xgb_model.get_booster()
+        with self.assertWarns(UserWarning):
+            # expect a warning that logitraw behaves differently and/or
+            # that base_score is ignored / fixed to 0.5
+            m = d4p.mb.convert_model(self.xgb_model.get_booster())
+        d4p_pred = m.predict(self.X_test, pred_contribs=True)
+        xgboost_pred = booster.predict(
+            xgb.DMatrix(self.X_test),
+            pred_contribs=True,
+            approx_contribs=False,
+            validate_features=False,
+        )
+        # undo bias
+        d4p_pred[:, -1] += 0.5
+        np.testing.assert_allclose(d4p_pred, xgboost_pred, rtol=5e-6)
+
+    @unittest.skipUnless(shap_api_changed, reason=shap_api_change_str)
+    def test_model_predict_shap_interactions(self):
+        booster = self.xgb_model.get_booster()
+        with self.assertWarns(UserWarning):
+            # expect a warning that logitraw behaves differently and/or
+            # that base_score is ignored / fixed to 0.5
+            m = d4p.mb.convert_model(self.xgb_model.get_booster())
+        d4p_pred = m.predict(self.X_test, pred_interactions=True)
+        xgboost_pred = booster.predict(
+            xgb.DMatrix(self.X_test),
+            pred_interactions=True,
+            approx_contribs=False,
+            validate_features=False,
+        )
+        # undo bias
+        d4p_pred[:, -1, -1] += 0.5
+        np.testing.assert_allclose(d4p_pred, xgboost_pred, rtol=5e-5)
+
 
 @unittest.skipUnless(shap_supported, reason=shap_not_supported_str)
 class LightGBMRegressionModelBuilder(unittest.TestCase):