[bug] fix ensemble algo _onedal_gpu_supported logic (#1696) (#1710)

* Update _forest.py

* Update deselected_tests.yaml

* this is definitely going to fail CI

* Update deselected_tests.yaml

* Update deselected_tests.yaml

* Update deselected_tests.yaml

* Update test_forest.py

* Update test_forest.py

(cherry picked from commit e0a405c)

Co-authored-by: Ian Faust <[email protected]>

Author: mergify[bot]

deselected_tests.yaml

@@ -441,8 +441,6 @@ gpu:
   - ensemble/tests/test_bagging.py::test_gridsearch
   - ensemble/tests/test_bagging.py::test_estimators_samples
   - ensemble/tests/test_common.py::test_ensemble_heterogeneous_estimators_behavior
-  - ensemble/tests/test_forest.py::test_min_samples_split[RandomForestClassifier]
-  - ensemble/tests/test_forest.py::test_min_weight_fraction_leaf
   - ensemble/tests/test_voting.py::test_parallel_fit
   - ensemble/tests/test_voting.py::test_sample_weight
 
@@ -640,8 +638,6 @@ gpu:
   - model_selection/tests/test_search.py::test_random_search_cv_results
 
   # Segmentation faults on GPU
-  - ensemble/tests/test_forest.py::test_forest_classifier_oob
-  - ensemble/tests/test_forest.py::test_forest_regressor_oob
   - tests/test_common.py::test_search_cv
   - manifold/tests/test_t_sne.py::test_n_iter_without_progress
 
@@ -736,15 +732,25 @@ gpu:
   - tests/test_common.py::test_f_contiguous_array_estimator[TSNE]
   - manifold/tests/test_t_sne.py::test_tsne_works_with_pandas_output
 
-  # GPU ensemble (Random Forest and Extra Trees) algorithms have a different
-  # implementation compared to CPU and require further validation
-  - ensemble/tests/test_forest.py::test_importances[ExtraTreesClassifier-gini-float64]
-  - ensemble/tests/test_forest.py::test_importances[ExtraTreesClassifier-gini-float32]
-  - ensemble/tests/test_forest.py::test_importances[ExtraTreesRegressor-squared_error-float64]
-  - ensemble/tests/test_forest.py::test_importances[ExtraTreesRegressor-squared_error-float32]
-  - ensemble/tests/test_forest.py::test_importances[RandomForestClassifier-gini-float32]
-  - ensemble/tests/test_forest.py::test_importances[RandomForestRegressor-squared_error-float64]
-  - ensemble/tests/test_forest.py::test_importances[RandomForestRegressor-squared_error-float32]
+  # GPU Forest algorithm implementation does not follow certain Scikit-learn standards
+  - ensemble/tests/test_forest.py::test_max_leaf_nodes_max_depth
+  - ensemble/tests/test_forest.py::test_min_samples_split[ExtraTreesClassifier]
+  - ensemble/tests/test_forest.py::test_min_samples_split[RandomForestClassifier]
+  - ensemble/tests/test_forest.py::test_min_samples_split[ExtraTreesRegressor]
+  - ensemble/tests/test_forest.py::test_max_samples_boundary_regressors
+
+  # numerical issues in GPU Forest algorithms which require further investigation
+  - ensemble/tests/test_forest.py::test_forest_classifier_oob[X0-y0-0.9-array-ExtraTreesClassifier]
+  - ensemble/tests/test_forest.py::test_forest_classifier_oob[X0-y0-0.9-array-RandomForestClassifier]
+  - ensemble/tests/test_forest.py::test_forest_classifier_oob[X1-y1-0.65-array-RandomForestClassifier]
+  - ensemble/tests/test_forest.py::test_forest_classifier_oob[X2-y2-0.65-array-ExtraTreesClassifier]
+  - ensemble/tests/test_forest.py::test_forest_classifier_oob[X2-y2-0.65-array-RandomForestClassifier]
+  - ensemble/tests/test_forest.py::test_forest_regressor_oob[X0-y0-0.7-array-RandomForestRegressor]
+  - ensemble/tests/test_stacking.py::test_stacking_regressor_drop_estimator
+  - ensemble/tests/test_voting.py::test_predict_on_toy_problem[42]
+  - tests/test_common.py::test_estimators[ExtraTreesClassifier()-check_class_weight_classifiers]
+  - tests/test_common.py::test_estimators[ExtraTreesRegressor()-check_sample_weights_invariance(kind=zeros)]
+  - tests/test_common.py::test_estimators[RandomForestRegressor()-check_regressor_data_not_an_array]
 
   # GPU implementation of Extra Trees doesn't support sample_weights
   # comparisons to GPU with sample weights will use different algorithms

sklearnex/ensemble/_forest.py

@@ -745,7 +745,7 @@ def _onedal_gpu_supported(self, method_name, *data):
                         or self.estimator.__class__ == DecisionTreeClassifier,
                         "ExtraTrees only supported starting from oneDAL version 2023.1",
                     ),
-                    (sample_weight is not None, "sample_weight is not supported."),
+                    (sample_weight is None, "sample_weight is not supported."),
                 ]
             )
 
@@ -1052,7 +1052,7 @@ def _onedal_gpu_supported(self, method_name, *data):
                         or self.estimator.__class__ == DecisionTreeClassifier,
                         "ExtraTrees only supported starting from oneDAL version 2023.1",
                     ),
-                    (sample_weight is not None, "sample_weight is not supported."),
+                    (sample_weight is None, "sample_weight is not supported."),
                 ]
             )
 

sklearnex/ensemble/tests/test_forest.py

@@ -45,11 +45,7 @@ def test_sklearnex_import_rf_classifier(dataframe, queue):
     assert_allclose([1], _as_numpy(rf.predict([[0, 0, 0, 0]])))
 
 
-# TODO:
-# investigate failure for `dpnp.ndarrays` and `dpctl.tensors` on `GPU`
-@pytest.mark.parametrize(
-    "dataframe,queue", get_dataframes_and_queues(device_filter_="cpu")
-)
+@pytest.mark.parametrize("dataframe,queue", get_dataframes_and_queues())
 def test_sklearnex_import_rf_regression(dataframe, queue):
     from sklearnex.ensemble import RandomForestRegressor
 
@@ -59,17 +55,17 @@ def test_sklearnex_import_rf_regression(dataframe, queue):
     rf = RandomForestRegressor(max_depth=2, random_state=0).fit(X, y)
     assert "sklearnex" in rf.__module__
     pred = _as_numpy(rf.predict([[0, 0, 0, 0]]))
-    if daal_check_version((2024, "P", 0)):
-        assert_allclose([-6.971], pred, atol=1e-2)
+
+    if queue is not None and queue.sycl_device.is_gpu:
+        assert_allclose([-0.011208], pred, atol=1e-2)
     else:
-        assert_allclose([-6.839], pred, atol=1e-2)
+        if daal_check_version((2024, "P", 0)):
+            assert_allclose([-6.971], pred, atol=1e-2)
+        else:
+            assert_allclose([-6.839], pred, atol=1e-2)
 
 
-# TODO:
-# investigate failure for `dpnp.ndarrays` and `dpctl.tensors` on `GPU`
-@pytest.mark.parametrize(
-    "dataframe,queue", get_dataframes_and_queues(device_filter_="cpu")
-)
+@pytest.mark.parametrize("dataframe,queue", get_dataframes_and_queues())
 def test_sklearnex_import_et_classifier(dataframe, queue):
     from sklearnex.ensemble import ExtraTreesClassifier
 
@@ -90,11 +86,7 @@ def test_sklearnex_import_et_classifier(dataframe, queue):
     assert_allclose([1], _as_numpy(rf.predict([[0, 0, 0, 0]])))
 
 
-# TODO:
-# investigate failure for `dpnp.ndarrays` and `dpctl.tensors` on `GPU`
-@pytest.mark.parametrize(
-    "dataframe,queue", get_dataframes_and_queues(device_filter_="cpu")
-)
+@pytest.mark.parametrize("dataframe,queue", get_dataframes_and_queues())
 def test_sklearnex_import_et_regression(dataframe, queue):
     from sklearnex.ensemble import ExtraTreesRegressor
 
@@ -114,4 +106,8 @@ def test_sklearnex_import_et_regression(dataframe, queue):
             ]
         )
     )
-    assert_allclose([0.445], pred, atol=1e-2)
+
+    if queue is not None and queue.sycl_device.is_gpu:
+        assert_allclose([1.909769], pred, atol=1e-2)
+    else:
+        assert_allclose([0.445], pred, atol=1e-2)