Tests for dask skl categorical data support. (#7054)

python-package/xgboost/sklearn.py

@@ -632,6 +632,12 @@ class XGBModel(XGBModelBase):
                 eval_metric = None
             else:
                 params.update({"eval_metric": eval_metric})
+        if self.enable_categorical and params.get("tree_method", None) != "gpu_hist":
+            raise ValueError(
+                "Experimental support for categorical data is not implemented for"
+                " current tree method yet."
+            )
+
         return model, feval, params
 
     def _set_evaluation_result(self, evals_result: TrainingCallback.EvalsLog) -> None:

tests/python-gpu/test_gpu_with_dask.py

@@ -211,7 +211,7 @@ def test_categorical(local_cuda_cluster: LocalCUDACluster) -> None:
         )
         assert tm.non_increasing(by_builtin_results["Train"]["rmse"])
 
-        model = output["booster"]
+        def check_model_output(model: dxgb.Booster) -> None:
             with tempfile.TemporaryDirectory() as tempdir:
                 path = os.path.join(tempdir, "model.json")
                 model.save_model(path)
@@ -226,6 +226,20 @@ def test_categorical(local_cuda_cluster: LocalCUDACluster) -> None:
                 assert categories_sizes.shape[0] != 0
                 np.testing.assert_allclose(categories_sizes, 1)
 
+        check_model_output(output["booster"])
+        reg = dxgb.DaskXGBRegressor(
+            enable_categorical=True, n_estimators=10, tree_method="gpu_hist"
+        )
+        reg.fit(X, y)
+
+        check_model_output(reg.get_booster())
+
+        reg = dxgb.DaskXGBRegressor(
+            enable_categorical=True, n_estimators=10
+        )
+        with pytest.raises(ValueError):
+            reg.fit(X, y)
+
 
 def to_cp(x: Any, DMatrixT: Type) -> Any:
     import cupy