Adding validate_data calls and updated tags to Ridge2FoldCV

Author: cajchristian

src/skmatter/linear_model/_ridge.py

@@ -1,13 +1,12 @@
 import numpy as np
 from joblib import Parallel, delayed
-from sklearn.base import BaseEstimator, MultiOutputMixin, RegressorMixin
+from sklearn.base import RegressorMixin, MultiOutputMixin, BaseEstimator
 from sklearn.metrics import check_scoring
 from sklearn.model_selection import KFold, check_cv
-from sklearn.utils import check_array
-from sklearn.utils.validation import check_is_fitted
+from sklearn.utils.validation import check_is_fitted, validate_data
 
 
-class Ridge2FoldCV(BaseEstimator, MultiOutputMixin, RegressorMixin):
+class Ridge2FoldCV(RegressorMixin, MultiOutputMixin, BaseEstimator):
     r"""Ridge regression with an efficient 2-fold cross-validation method using the SVD
     solver.
 
@@ -20,7 +19,7 @@ class Ridge2FoldCV(BaseEstimator, MultiOutputMixin, RegressorMixin):
     while the alpha value is determined with a 2-fold cross-validation from a list of
     alpha values. It is more efficient version than doing 2-fold cross-validation
     naively The algorithmic trick is to reuse the matrices obtained by SVD for each
-    regularization paramater :param alpha: The 2-fold CV can be broken donw to
+    regularization paramater :param alpha: The 2-fold CV can be broken down to
 
     .. math::
 
@@ -136,6 +135,11 @@ def __init__(
         self.shuffle = shuffle
         self.n_jobs = n_jobs
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.target_tags.single_output = False
+        return tags
+
     def _more_tags(self):
         return {"multioutput_only": True}
 
@@ -195,7 +199,7 @@ def predict(self, X):
             Training data, where n_samples is the number of samples
             and n_features is the number of features.
         """
-        X = check_array(X)
+        X = validate_data(self, X, reset=False)
 
         check_is_fitted(self, ["coef_"])