Revert changes in covariance; remove unneeded imports

Author: Vika-F

sklearnex/decomposition/pca.py

@@ -44,7 +44,6 @@
 
     from sklearn.decomposition import PCA as _sklearn_PCA
 
-    from onedal.common.hyperparameters import get_hyperparameters
     from onedal.decomposition import PCA as onedal_PCA
     from onedal.utils._array_api import _is_numpy_namespace
 

sklearnex/ensemble/_forest.py

@@ -58,7 +58,6 @@
 from onedal.ensemble import RandomForestRegressor as onedal_RandomForestRegressor
 from onedal.primitives import get_tree_state_cls, get_tree_state_reg
 from onedal.utils.validation import _num_features, _num_samples
-from sklearnex import get_hyperparameters
 from sklearnex._utils import register_hyperparameters
 
 from .._config import get_config

sklearnex/linear_model/incremental_linear.py

@@ -36,8 +36,6 @@
 if sklearn_check_version("1.2"):
     from sklearn.utils._param_validation import Interval
 
-from onedal.common.hyperparameters import get_hyperparameters
-
 from .._device_offload import dispatch, wrap_output_data
 from .._utils import (
     PatchingConditionsChain,

sklearnex/preview/covariance/covariance.py

@@ -20,11 +20,9 @@
 from scipy import sparse as sp
 from sklearn.covariance import EmpiricalCovariance as _sklearn_EmpiricalCovariance
 from sklearn.utils import check_array
-from sklearn.utils.validation import _num_features
 
 from daal4py.sklearn._n_jobs_support import control_n_jobs
 from daal4py.sklearn._utils import daal_check_version, sklearn_check_version
-from onedal.common.hyperparameters import get_hyperparameters
 from onedal.covariance import EmpiricalCovariance as onedal_EmpiricalCovariance
 from sklearnex import config_context
 from sklearnex.metrics import pairwise_distances
@@ -91,15 +89,11 @@ def _onedal_supported(self, method_name, *data):
     _onedal_gpu_supported = _onedal_supported
 
     def fit(self, X, y=None):
-        self._fit(X)
-        return self
-
-    @wrap_output_data
-    def _fit(self, X):
         if sklearn_check_version("1.2"):
             self._validate_params()
+        X = validate_data(self, X, ensure_all_finite=False)
 
-        return dispatch(
+        dispatch(
             self,
             "fit",
             {
@@ -109,6 +103,8 @@ def _fit(self, X):
             X,
         )
 
+        return self
+
     # expose sklearnex pairwise_distances if mahalanobis distance eventually supported
     @wrap_output_data
     def mahalanobis(self, X):
@@ -117,20 +113,9 @@ def mahalanobis(self, X):
         precision = self.get_precision()
         with config_context(assume_finite=True):
             # compute mahalanobis distances
-            try:
-                dist = pairwise_distances(
-                    X, self.location_[np.newaxis, :], metric="mahalanobis", VI=precision
-                )
-
-            except ValueError as e:
-                # Throw the expected sklearn error in an n_feature length violation
-                if "Incompatible dimension for X and Y matrices:" in str(e):
-                    raise ValueError(
-                        f"X has {_num_features(X)} features, but {self.__class__.__name__} "
-                        f"is expecting {self.n_features_in_} features as input."
-                    )
-                else:
-                    raise e
+            dist = pairwise_distances(
+                X, self.location_[np.newaxis, :], metric="mahalanobis", VI=precision
+            )
 
         return np.reshape(dist, (len(X),)) ** 2