MAINT: refactor some sklearnex examples (uxlfoundation#1948)

* MAINT: refactor some sklearnex examples

* fixed random_forest_regressor_dpnp.py

examples/sklearnex/incremental_pca_dpctl.py

@@ -13,14 +13,29 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
+
+# sklearnex IncrementalPCA example for GPU offloading with DPCtl usm ndarray:
+#    SKLEARNEX_PREVIEW=YES python ./incremental_pca_dpctl.py
+
 import dpctl
 import dpctl.tensor as dpt
 
-from sklearnex.preview.decomposition import IncrementalPCA
+# Import estimator via sklearnex's patch mechanism from sklearn
+from sklearnex import patch_sklearn, sklearn_is_patched
+
+patch_sklearn()
+
+# Function that can validate current state of patching
+sklearn_is_patched()
+
+# Import estimator from the patched sklearn namespace.
+from sklearn.decomposition import IncrementalPCA
+
+# Or just directly import estimator from sklearnex namespace.
+# from sklearnex.preview.decomposition import IncrementalPCA
 
 # We create GPU SyclQueue and then put data to dpctl tensor using
 # the queue. It allows us to do computation on GPU.
-
 queue = dpctl.SyclQueue("gpu")
 
 incpca = IncrementalPCA()

examples/sklearnex/random_forest_regressor_dpnp.py

@@ -15,26 +15,40 @@
 # ==============================================================================
 
 # sklearnex RF example for GPU offloading with DPNP ndarray:
-#    python ./random_forest_regressor_dpnp_batch.py
+#    python ./random_forest_regressor_dpnp.py
 
+import dpctl
 import dpnp
-import numpy as np
 from sklearn.datasets import make_regression
 from sklearn.model_selection import train_test_split
 
+# Import estimator via sklearnex's patch mechanism from sklearn
+from sklearnex import patch_sklearn, sklearn_is_patched
+
+patch_sklearn()
+
+# Function that can validate current state of patching
+sklearn_is_patched()
+
+# Import estimator from the patched sklearn namespace.
+from sklearn.ensemble import RandomForestRegressor
+
+# Or just directly import estimator from sklearnex namespace.
 from sklearnex.ensemble import RandomForestRegressor
 
-sycl_device = "gpu:0"
+# We create GPU SyclQueue and then put data to dpctl tensor using
+# the queue. It allows us to do computation on GPU.
+queue = dpctl.SyclQueue("gpu")
 
 X, y = make_regression(
     n_samples=1000, n_features=4, n_informative=2, random_state=0, shuffle=False
 )
 
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
 
-dpnp_X_train = dpnp.asarray(X_train, device=sycl_device)
-dpnp_y_train = dpnp.asarray(y_train, device=sycl_device)
-dpnp_X_test = dpnp.asarray(X_test, device=sycl_device)
+dpnp_X_train = dpnp.asarray(X_train, usm_type="device", sycl_queue=queue)
+dpnp_y_train = dpnp.asarray(y_train, usm_type="device", sycl_queue=queue)
+dpnp_X_test = dpnp.asarray(X_test, usm_type="device", sycl_queue=queue)
 
 rf = RandomForestRegressor(max_depth=2, random_state=0).fit(dpnp_X_train, dpnp_y_train)