Add predictor to skl constructor. (#7000)

python-package/xgboost/sklearn.py

@@ -374,6 +374,7 @@ class XGBModel(XGBModelBase):
         importance_type: str = "gain",
         gpu_id: Optional[int] = None,
         validate_parameters: Optional[bool] = None,
+        predictor: Optional[str] = None,
         **kwargs: Any
     ) -> None:
         if not SKLEARN_INSTALLED:
@@ -409,6 +410,7 @@ class XGBModel(XGBModelBase):
         self.importance_type = importance_type
         self.gpu_id = gpu_id
         self.validate_parameters = validate_parameters
+        self.predictor = predictor
 
     def _more_tags(self) -> Dict[str, bool]:
         '''Tags used for scikit-learn data validation.'''

src/gbm/gbtree.cc

@@ -803,7 +803,11 @@ class Dart : public GBTree {
         bool success = predictor->InplacePredict(x, nullptr, model_, missing,
                                                  &predts, i, i + 1);
         device = predts.predictions.DeviceIdx();
-        CHECK(success) << msg;
+        CHECK(success) << msg << std::endl
+                       << "Current Predictor: "
+                       << (tparam_.predictor == PredictorType::kCPUPredictor
+                               ? "cpu_predictor"
+                               : "gpu_predictor");
       }
 
       auto w = this->weight_drop_.at(i);

src/gbm/gbtree.h

@@ -291,7 +291,11 @@ class GBTree : public GradientBooster {
     } else {
       bool success = this->GetPredictor()->InplacePredict(
           x, p_m, model_, missing, out_preds, tree_begin, tree_end);
-      CHECK(success) << msg;
+      CHECK(success) << msg << std::endl
+                     << "Current Predictor: "
+                     << (tparam_.predictor == PredictorType::kCPUPredictor
+                             ? "cpu_predictor"
+                             : "gpu_predictor");
     }
   }