ENH add metadata routing to cross_val*

doc/whats_new/v1.4.rst

@@ -157,6 +157,16 @@ Changelog
   object in the parameter grid if it's an estimator. :pr:`26786` by `Adrin
   Jalali`_.
 
+- |Feature| :func:`~model_selection.cross_validate`,
+  :func:`~model_selection.cross_val_score`, and
+  :func:`~model_selection.cross_val_predict` now support metadata routing. The
+  metadata are routed to the estimator's `fit`, the scorer, and the CV
+  splitter's `split`. The metadata is accepted via the new `params` parameter.
+  `fit_params` is deprecated and will be removed in version 1.6. `groups`
+  parameter is also not accepted as a separate argument when metadata routing
+  is enabled and should be passed via the `params` parameter. :pr:`26896` by
+  `Adrin Jalali`_.
+
 :mod:`sklearn.neighbors`
 ........................
 

sklearn/calibration.py

@@ -450,7 +450,7 @@ def fit(self, X, y, sample_weight=None, **fit_params):
                     cv=cv,
                     method=method_name,
                     n_jobs=self.n_jobs,
-                    fit_params=routed_params.estimator.fit,
+                    params=routed_params.estimator.fit,
                 )
                 predictions = _compute_predictions(
                     pred_method, method_name, X, n_classes

sklearn/ensemble/_stacking.py

@@ -254,7 +254,7 @@ def fit(self, X, y, sample_weight=None):
                     cv=deepcopy(cv),
                     method=meth,
                     n_jobs=self.n_jobs,
-                    fit_params=fit_params,
+                    params=fit_params,
                     verbose=self.verbose,
                 )
                 for est, meth in zip(all_estimators, self.stack_method_)

sklearn/feature_selection/_rfe.py

@@ -33,7 +33,12 @@ def _rfe_single_fit(rfe, estimator, X, y, train, test, scorer):
         X_train,
         y_train,
         lambda estimator, features: _score(
-            estimator, X_test[:, features], y_test, scorer
+            # TODO(SLEP6): pass score_params here
+            estimator,
+            X_test[:, features],
+            y_test,
+            scorer,
+            score_params=None,
         ),
     ).scores_
 

sklearn/metrics/tests/test_score_objects.py

@@ -55,7 +55,7 @@
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.pipeline import make_pipeline
 from sklearn.svm import LinearSVC
-from sklearn.tests.test_metadata_routing import assert_request_is_empty
+from sklearn.tests.metadata_routing_common import assert_request_is_empty
 from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
 from sklearn.utils._testing import (
     assert_almost_equal,

sklearn/model_selection/_search.py

@@ -816,6 +816,8 @@ def fit(self, X, y=None, *, groups=None, **fit_params):
         fit_and_score_kwargs = dict(
             scorer=scorers,
             fit_params=fit_params,
+            # TODO(SLEP6): pass score params along
+            score_params=None,
             return_train_score=self.return_train_score,
             return_n_test_samples=True,
             return_times=True,