Fix for n_iter_ SVM attribute and oneDAL kNN classifier result option (#1071)

* Add workaround for n_iter_ SVM attribute

* Pytest onedal dir

* Exclude test_policy

* fix result_option fail for kneighbors method in knn classifier

Author: Alexsandruss

.ci/pipeline/ci.yml

@@ -141,9 +141,15 @@ jobs:
       pip install -r requirements-test.txt
       conda install -y /usr/share/miniconda/envs/CB/conda-bld/linux-64/daal4py*.tar.bz2
       python setup_sklearnex.py install --single-version-externally-managed --record=record1.txt
+    displayName: install sklearnex
+  - script: |
+      . /usr/share/miniconda/etc/profile.d/conda.sh
+      conda activate CB
       cd ..
       pytest --pyargs s/sklearnex/tests/
-    displayName: install sklearnex
+      # TODO: remove ignore of test_policy when device names are fixed
+      pytest --pyargs s/onedal/ --ignore=s/onedal/common/tests/test_policy.py
+    displayName: test sklearnex
   - script: |
       . /usr/share/miniconda/etc/profile.d/conda.sh
       conda activate CB

onedal/neighbors/neighbors.py

@@ -425,6 +425,8 @@ def _onedal_predict(self, model, X, params, queue):
             model = self._onedal_model
         else:
             model = self._create_model(_backend.neighbors.classification)
+        if 'responses' not in params['result_option']:
+            params['result_option'] += '|responses'
         result = _backend.neighbors.classification.infer(
             policy, params, model, to_table(X))
 

onedal/svm/svm.py

@@ -165,6 +165,9 @@ def _get_sample_weight(self, X, y, sample_weight):
 
     def _get_onedal_params(self, data):
         max_iter = 10000 if self.max_iter == -1 else self.max_iter
+        # TODO: remove this workaround
+        # when oneDAL SVM starts support of 'n_iterations' result
+        self.n_iter_ = 1 if max_iter < 1 else max_iter
         class_count = 0 if self.classes_ is None else len(self.classes_)
         return {
             'fptype': 'float' if data.dtype is np.dtype('float32') else 'double',