Support RF Classifier

python/cuml/cuml/ensemble/randomforest_common.pyx

@@ -278,6 +278,7 @@ class BaseRandomForestModel(UniversalBase):
         self.treelite_serialized_model = treelite_serialize(self._temp.handle)
         self._obtain_treelite_handle()
         self.dtype = np.float64
+        self.update_labels = False
         super().cpu_to_gpu()
 
     def gpu_to_cpu(self):

python/cuml/cuml/ensemble/randomforestclassifier.pyx

@@ -16,6 +16,8 @@
 #
 
 # distutils: language = c++
+from cuml.internals.api_decorators import device_interop_preparation
+from cuml.internals.api_decorators import enable_device_interop
 from cuml.internals.safe_imports import (
     cpu_only_import,
     gpu_only_import,
@@ -247,6 +249,9 @@ class RandomForestClassifier(BaseRandomForestModel,
     <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html>`_.
     """
 
+    _cpu_estimator_import_path = 'sklearn.ensemble.RandomForestClassifier'
+
+    @device_interop_preparation
     def __init__(self, *, split_criterion=0, handle=None, verbose=False,
                  output_type=None,
                  **kwargs):
@@ -337,6 +342,9 @@ class RandomForestClassifier(BaseRandomForestModel,
         self.treelite_serialized_model = None
         self.n_cols = None
 
+    def get_attr_names(self):
+        return []
+
     def convert_to_treelite_model(self):
         """
         Converts the cuML RF model to a Treelite model
@@ -417,6 +425,7 @@ class RandomForestClassifier(BaseRandomForestModel,
     @cuml.internals.api_base_return_any(set_output_type=False,
                                         set_output_dtype=True,
                                         set_n_features_in=False)
+    @enable_device_interop
     def fit(self, X, y, convert_dtype=True):
         """
         Perform Random Forest Classification on the input data
@@ -555,6 +564,7 @@ class RandomForestClassifier(BaseRandomForestModel,
     @insert_into_docstring(parameters=[('dense', '(n_samples, n_features)')],
                            return_values=[('dense', '(n_samples, 1)')])
     @cuml.internals.api_base_return_array(get_output_dtype=True)
+    @enable_device_interop
     def predict(self, X, predict_model="GPU", threshold=0.5,
                 algo='auto', convert_dtype=True,
                 fil_sparse_format='auto') -> CumlArray:

python/cuml/cuml/ensemble/randomforestregressor.pyx

@@ -13,10 +13,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-from cuml.internals.api_decorators import device_interop_preparation
-from cuml.internals.api_decorators import enable_device_interop
 # distutils: language = c++
 
+from cuml.internals.api_decorators import device_interop_preparation
+from cuml.internals.api_decorators import enable_device_interop
 from cuml.internals.safe_imports import (
     cpu_only_import,
     gpu_only_import,

python/cuml/cuml/tests/test_device_selection.py

@@ -37,7 +37,7 @@
 from cuml.decomposition import PCA, TruncatedSVD
 from cuml.cluster import KMeans
 from cuml.cluster import DBSCAN
-from cuml.ensemble import RandomForestRegressor
+from cuml.ensemble import RandomForestClassifier, RandomForestRegressor
 from cuml.common.device_selection import DeviceType, using_device_type
 from cuml.testing.utils import assert_dbscan_equal
 from hdbscan import HDBSCAN as refHDBSCAN
@@ -1016,9 +1016,19 @@ def test_dbscan_methods(train_device, infer_device):
 
 @pytest.mark.parametrize("train_device", ["cpu", "gpu"])
 @pytest.mark.parametrize("infer_device", ["cpu", "gpu"])
-def test_random_forest_methods(train_device, infer_device):
+def test_random_forest_regressor(train_device, infer_device):
     model = RandomForestRegressor()
     with using_device_type(train_device):
         model.fit(X_train_reg, y_train_reg)
     with using_device_type(infer_device):
-        output = model.predict(X_test_reg)
+        _ = model.predict(X_test_reg)
+
+
+@pytest.mark.parametrize("train_device", ["cpu", "gpu"])
+@pytest.mark.parametrize("infer_device", ["cpu", "gpu"])
+def test_random_forest_classifier(train_device, infer_device):
+    model = RandomForestClassifier()
+    with using_device_type(train_device):
+        model.fit(X_train_blob, y_train_blob)
+    with using_device_type(infer_device):
+        _ = model.predict(X_test_blob)