[TESTING] Reproduce SVM segfaults on Windows in CI

deselected_tests.yaml

@@ -153,8 +153,6 @@ deselected_tests:
   # scikit-learn expects an exception for sparse matrices with 64-bit integer indices,
   # scikit-learn-intelex works correctly with 64-bit integer indices
   - tests/test_common.py::test_estimators[NuSVC()-check_estimator_sparse_data]
-  - tests/test_common.py::test_estimators[NuSVC()-check_estimator_sparse_array]
-  - tests/test_common.py::test_estimators[NuSVC()-check_estimator_sparse_matrix]
   - utils/tests/test_estimator_checks.py::test_xfail_ignored_in_check_estimator
 
   # SVC._dual_coef_ is changing after fitting, but the result of prediction is still the same

sklearnex/svm/nusvc.py

@@ -88,17 +88,8 @@ def __init__(
     def fit(self, X, y, sample_weight=None):
         if sklearn_check_version("1.2"):
             self._validate_params()
-        elif self.nu <= 0 or self.nu > 1:
-            # else if added to correct issues with
-            # sklearn tests:
-            # svm/tests/test_sparse.py::test_error
-            # svm/tests/test_svm.py::test_bad_input
-            # for sklearn versions < 1.2 (i.e. without
-            # validate_params parameter checking)
-            # Without this, a segmentation fault with
-            # Windows fatal exception: access violation
-            # occurs
-            raise ValueError("nu <= 0 or nu > 1")
+        if sklearn_check_version("1.0"):
+            self._check_feature_names(X, reset=True)
         dispatch(
             self,
             "fit",

sklearnex/svm/nusvr.py

@@ -71,17 +71,8 @@ def __init__(
     def fit(self, X, y, sample_weight=None):
         if sklearn_check_version("1.2"):
             self._validate_params()
-        elif self.nu <= 0 or self.nu > 1:
-            # else if added to correct issues with
-            # sklearn tests:
-            # svm/tests/test_sparse.py::test_error
-            # svm/tests/test_svm.py::test_bad_input
-            # for sklearn versions < 1.2 (i.e. without
-            # validate_params parameter checking)
-            # Without this, a segmentation fault with
-            # Windows fatal exception: access violation
-            # occurs
-            raise ValueError("nu <= 0 or nu > 1")
+        if sklearn_check_version("1.0"):
+            self._check_feature_names(X, reset=True)
         dispatch(
             self,
             "fit",

sklearnex/svm/svc.py

@@ -90,17 +90,8 @@ def __init__(
     def fit(self, X, y, sample_weight=None):
         if sklearn_check_version("1.2"):
             self._validate_params()
-        elif self.C <= 0:
-            # else if added to correct issues with
-            # sklearn tests:
-            # svm/tests/test_sparse.py::test_error
-            # svm/tests/test_svm.py::test_bad_input
-            # for sklearn versions < 1.2 (i.e. without
-            # validate_params parameter checking)
-            # Without this, a segmentation fault with
-            # Windows fatal exception: access violation
-            # occurs
-            raise ValueError("C <= 0")
+        if sklearn_check_version("1.0"):
+            self._check_feature_names(X, reset=True)
         dispatch(
             self,
             "fit",

sklearnex/svm/svr.py

@@ -71,17 +71,8 @@ def __init__(
     def fit(self, X, y, sample_weight=None):
         if sklearn_check_version("1.2"):
             self._validate_params()
-        elif self.C <= 0:
-            # else if added to correct issues with
-            # sklearn tests:
-            # svm/tests/test_sparse.py::test_error
-            # svm/tests/test_svm.py::test_bad_input
-            # for sklearn versions < 1.2 (i.e. without
-            # validate_params parameter checking)
-            # Without this, a segmentation fault with
-            # Windows fatal exception: access violation
-            # occurs
-            raise ValueError("C <= 0")
+        if sklearn_check_version("1.0"):
+            self._check_feature_names(X, reset=True)
         dispatch(
             self,
             "fit",