add incremental ridge to nonsklearn docs (#2300)

Author: david-cortes-intel

doc/sources/non-scikit-algorithms.rst

@@ -39,4 +39,11 @@ IncrementalLinearRegression
 .. autoclass:: sklearnex.linear_model.IncrementalLinearRegression
 .. automethod:: sklearnex.linear_model.IncrementalLinearRegression.fit
 .. automethod:: sklearnex.linear_model.IncrementalLinearRegression.partial_fit
-.. automethod:: sklearnex.linear_model.IncrementalLinearRegression.predict
+.. automethod:: sklearnex.linear_model.IncrementalLinearRegression.predict
+
+IncrementalRidge
+----------------
+.. autoclass:: sklearnex.linear_model.IncrementalRidge
+.. automethod:: sklearnex.linear_model.IncrementalRidge.fit
+.. automethod:: sklearnex.linear_model.IncrementalRidge.partial_fit
+.. automethod:: sklearnex.linear_model.IncrementalRidge.predict

sklearnex/linear_model/incremental_ridge.py

@@ -32,7 +32,7 @@
 from onedal.linear_model import IncrementalRidge as onedal_IncrementalRidge
 
 from .._device_offload import dispatch, wrap_output_data
-from .._utils import PatchingConditionsChain
+from .._utils import IntelEstimator, PatchingConditionsChain
 
 if sklearn_check_version("1.6"):
     from sklearn.utils.validation import validate_data
@@ -43,22 +43,22 @@
 @control_n_jobs(
     decorated_methods=["fit", "partial_fit", "predict", "score", "_onedal_finalize_fit"]
 )
-class IncrementalRidge(MultiOutputMixin, RegressorMixin, BaseEstimator):
+class IncrementalRidge(IntelEstimator, MultiOutputMixin, RegressorMixin, BaseEstimator):
     """
     Incremental estimator for Ridge Regression.
     Allows to train Ridge Regression if data is splitted into batches.
 
     Parameters
     ----------
     fit_intercept : bool, default=True
-    Whether to calculate the intercept for this model. If set
-    to False, no intercept will be used in calculations
-    (i.e. data is expected to be centered).
+        Whether to calculate the intercept for this model. If set
+        to False, no intercept will be used in calculations
+        (i.e. data is expected to be centered).
 
     alpha : float, default=1.0
-    Regularization strength; must be a positive float. Regularization
-    improves the conditioning of the problem and reduces the variance of
-    the estimates. Larger values specify stronger regularization.
+        Regularization strength; must be a positive float. Regularization
+        improves the conditioning of the problem and reduces the variance of
+        the estimates. Larger values specify stronger regularization.
 
     copy_X : bool, default=True
         If True, X will be copied; else, it may be overwritten.