Fix svm attributes: class_weight_, n_iter_ (#1100)

* Fix svm attributes: class_weight_, n_iter_

* Fix [nu]svc n_iter_

Author: Alexsandruss

sklearnex/svm/_common.py

@@ -107,6 +107,7 @@ def _save_attributes(self):
         self.dual_coef_ = self._onedal_estimator.dual_coef_
         self.shape_fit_ = self._onedal_estimator.class_weight_
         self.classes_ = self._onedal_estimator.classes_
+        self.class_weight_ = self._onedal_estimator.class_weight_
         self.support_ = self._onedal_estimator.support_
 
         self._intercept_ = self._onedal_estimator.intercept_
@@ -129,6 +130,10 @@ def _save_attributes(self):
         self.intercept_ = self._intercept_
         self._is_in_fit = False
 
+        if Version(sklearn_version) >= Version("1.1"):
+            length = int(len(self.classes_) * (len(self.classes_) - 1) / 2)
+            self.n_iter_ = np.full((length, ), self._onedal_estimator.n_iter_)
+
 
 class BaseSVR(ABC):
     def _save_attributes(self):
@@ -153,3 +158,6 @@ def _save_attributes(self):
         self._dual_coef_ = self.dual_coef_
         self.intercept_ = self._intercept_
         self._is_in_fit = False
+
+        if Version(sklearn_version) >= Version("1.1"):
+            self.n_iter_ = self._onedal_estimator.n_iter_

sklearnex/svm/nusvc.py

@@ -195,7 +195,6 @@ def _onedal_fit(self, X, y, sample_weight=None, queue=None):
 
         self._onedal_estimator = onedal_NuSVC(**onedal_params)
         self._onedal_estimator.fit(X, y, sample_weight, queue=queue)
-        self.class_weight_ = self._onedal_estimator.class_weight_
 
         if self.probability:
             self._fit_proba(X, y, sample_weight, queue=queue)

sklearnex/svm/svc.py

@@ -209,7 +209,6 @@ def _onedal_fit(self, X, y, sample_weight=None, queue=None):
 
         self._onedal_estimator = onedal_SVC(**onedal_params)
         self._onedal_estimator.fit(X, y, sample_weight, queue=queue)
-        self.class_weight_ = self._onedal_estimator.class_weight_
 
         if self.probability:
             self._fit_proba(X, y, sample_weight, queue=queue)