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__
- )
+ )