rebase: rebase to main

Author: yuejiaointel

onedal/neighbors/neighbors.py

@@ -170,15 +170,6 @@ def _kneighbors(self, X=None, n_neighbors=None, return_distance=True):
 
         if X is not None:
             query_is_train = False
-<<<<<<< HEAD
-<<<<<<< HEAD
-            if not use_raw_input:
-                X = _check_array(X, accept_sparse="csr", dtype=[np.float64, np.float32])
-=======
-            X = _check_array(X, accept_sparse="csr", dtype=[np.float64, np.float32])
->>>>>>> e003b37f (fix: try it again)
-=======
->>>>>>> 8cd6f2b2 (fix: first round of refactor move preprocssing function to sklearnex)
         else:
             query_is_train = True
             X = self._fit_X
@@ -517,4 +508,4 @@ def fit(self, X, y, queue=None):
 
     @supports_queue
     def kneighbors(self, X=None, n_neighbors=None, return_distance=True, queue=None):
-        return self._kneighbors(X, n_neighbors, return_distance)
+        return self._kneighbors(X, n_neighbors, return_distance)

sklearnex/neighbors/common.py

@@ -476,4 +476,4 @@ def kneighbors_graph(self, X=None, n_neighbors=None, mode="connectivity"):
 
         return kneighbors_graph
 
-    kneighbors_graph.__doc__ = KNeighborsMixin.kneighbors_graph.__doc__
+    kneighbors_graph.__doc__ = KNeighborsMixin.kneighbors_graph.__doc__

sklearnex/neighbors/knn_classification.py

@@ -174,6 +174,26 @@ def _onedal_fit(self, X, y, queue=None):
         # print("=" * 50, file=sys.stderr, flush=True)
         # print("DEBUG: _onedal_fit called!", file=sys.stderr, flush=True)
         # print("=" * 50, file=sys.stderr, flush=True)
+
+        # Perform preprocessing at sklearnex level
+        X, y = self._validate_data(
+            X, y, dtype=[np.float64, np.float32], accept_sparse="csr"
+        )
+
+        # Validate n_neighbors
+        self._validate_n_neighbors(self.n_neighbors)
+
+        # Parse auto method
+        self._fit_method = self._parse_auto_method(self.algorithm, X.shape[0], X.shape[1])
+
+        # Validate classification targets
+        from onedal.utils.validation import _check_classification_targets
+
+        _check_classification_targets(y)
+
+        # Handle shape and class processing at sklearnex level
+        y = self._process_classification_targets(y)
+
         onedal_params = {
             "n_neighbors": self.n_neighbors,
             "weights": self.weights,
@@ -230,4 +250,4 @@ def _save_attributes(self):
     predict.__doc__ = _sklearn_KNeighborsClassifier.predict.__doc__
     predict_proba.__doc__ = _sklearn_KNeighborsClassifier.predict_proba.__doc__
     score.__doc__ = _sklearn_KNeighborsClassifier.score.__doc__
-    kneighbors.__doc__ = _sklearn_KNeighborsClassifier.kneighbors.__doc__
+    kneighbors.__doc__ = _sklearn_KNeighborsClassifier.kneighbors.__doc__

sklearnex/neighbors/knn_regression.py

@@ -209,4 +209,4 @@ def _save_attributes(self):
     fit.__doc__ = _sklearn_KNeighborsRegressor.__doc__
     predict.__doc__ = _sklearn_KNeighborsRegressor.predict.__doc__
     kneighbors.__doc__ = _sklearn_KNeighborsRegressor.kneighbors.__doc__
-    score.__doc__ = _sklearn_KNeighborsRegressor.score.__doc__
+    score.__doc__ = _sklearn_KNeighborsRegressor.score.__doc__

sklearnex/neighbors/knn_unsupervised.py

@@ -195,4 +195,4 @@ def _save_attributes(self):
     radius_neighbors.__doc__ = _sklearn_NearestNeighbors.radius_neighbors.__doc__
     radius_neighbors_graph.__doc__ = (
         _sklearn_NearestNeighbors.radius_neighbors_graph.__doc__
-    )
+    )