[experiment] Verify diverging parameters do not impact SVM fit/predict quality.

onedal/svm/svm.py

@@ -354,7 +354,7 @@ def __init__(
     ):
         super().__init__(
             C=C,
-            nu=0.5,
+            nu=0.0,
             epsilon=epsilon,
             kernel=kernel,
             degree=degree,
@@ -406,8 +406,8 @@ def __init__(
     ):
         super().__init__(
             C=C,
-            nu=0.5,
-            epsilon=0.0,
+            nu=0.0,
+            epsilon=0.0,  #  unused in BaseSVC
             kernel=kernel,
             degree=degree,
             gamma=gamma,
@@ -518,9 +518,9 @@ def __init__(
         **kwargs,
     ):
         super().__init__(
-            C=1.0,
+            C=0.0,
             nu=nu,
-            epsilon=0.0,
+            epsilon=0.0,  #  unused in libSVM
             kernel=kernel,
             degree=degree,
             gamma=gamma,

sklearnex/ensemble/tests/test_forest.py

@@ -39,7 +39,8 @@
 @pytest.mark.parametrize("block, trees, rows, scale", hparam_values)
 def test_sklearnex_import_rf_classifier(dataframe, queue, block, trees, rows, scale):
     from sklearnex.ensemble import RandomForestClassifier
-
+    from sklearnex.utils.validation import validate_data
+
     X, y = make_classification(
         n_samples=1000,
         n_features=4,
@@ -51,6 +52,8 @@ def test_sklearnex_import_rf_classifier(dataframe, queue, block, trees, rows, sc
     X = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
     y = _convert_to_dataframe(y, sycl_queue=queue, target_df=dataframe)
     rf = RandomForestClassifier(max_depth=2, random_state=0).fit(X, y)
+    # Test to see if this changes validation coverage
+    validate_data(rf, X, reset=False)
     hparams = RandomForestClassifier.get_hyperparameters("infer")
     if hparams and block is not None:
         hparams.block_size = block