Fix sklearn 1.0.1 tests (#836)

Author: itearsl

daal4py/sklearn/neighbors/_base.py

@@ -415,7 +415,7 @@ def stock_fit(self, X, y):
 class KNeighborsMixin(BaseKNeighborsMixin):
     def kneighbors(self, X=None, n_neighbors=None, return_distance=True):
         daal_model = getattr(self, '_daal_model', None)
-        if X is not None:
+        if X is not None and self.metric != "precomputed":
             X = check_array(
                 X, accept_sparse='csr', dtype=[
                     np.float64, np.float32])

daal4py/sklearn/neighbors/_classification.py

@@ -169,4 +169,6 @@ def predict(self, X):
 
     @support_usm_ndarray()
     def predict_proba(self, X):
+        if sklearn_check_version('1.0'):
+            self._check_feature_names(X, reset=False)
         return BaseKNeighborsClassifier.predict_proba(self, X)

daal4py/sklearn/neighbors/_regression.py

@@ -93,4 +93,6 @@ def fit(self, X, y):
 
     @support_usm_ndarray()
     def predict(self, X):
+        if sklearn_check_version('1.0'):
+            self._check_feature_names(X, reset=False)
         return BaseKNeighborsRegressor.predict(self, X)

deselected_tests.yaml

@@ -128,13 +128,13 @@ deselected_tests:
   - tests/test_common.py::test_estimators[NuSVR()-check_sample_weights_invariance(kind=zeros)] <1.0
   - tests/test_common.py::test_estimators[NuSVC()-check_class_weight_classifiers] <1.0
   - tests/test_multioutput.py::test_multi_output_classification
-  - tests/test_multioutput.py::test_classifier_chain_tuple_order >=0.24,<=1.0
+  - tests/test_multioutput.py::test_classifier_chain_tuple_order >=0.24,<=1.1
 
   # Different results sklearnex and sklearn linear regression with weights. Need to investigate.
   - inspection/tests/test_permutation_importance.py::test_permutation_importance_sample_weight >=1.0
 
   # Patched and unpatched kmeans belong same values to different clusters. Need to investigate.
-  - /preprocessing/tests/test_discretization.py::test_nonuniform_strategies[kmeans-expected_2bins1-expected_3bins1-expected_5bins1] >=1.0
+  - preprocessing/tests/test_discretization.py::test_nonuniform_strategies[kmeans-expected_2bins1-expected_3bins1-expected_5bins1] >=1.0,<=1.1
 
   # OOB scores in sklearn and oneDAL are different because of different random nuber generators
   - ensemble/tests/test_forest.py::test_forest_classifier_oob[X2-y2-0.65-array-RandomForestClassifier]
@@ -198,6 +198,8 @@ deselected_tests:
   # test checks that only exact number of options is used
   - tests/test_config.py::test_config_context
 
+  # HalvingGridSearchCV with Ridge and PCA didn't have feature_name_in. Need to fix.
+  - tests/test_common.py::test_pandas_column_name_consistency >=1.0.1
   # --------------------------------------------------------
   # Not need of testing for daal4py patching
 reduced_tests: