From 6bd9df5a286077ace9685072211fa02becf1dbd8 Mon Sep 17 00:00:00 2001
From: "Thomas J. Fan" <thomasjpfan@gmail.com>
Date: Fri, 17 Sep 2021 16:10:21 -0400
Subject: [PATCH 01/12] ENH Adds feature_names_out to preprocessing module

---
 sklearn/preprocessing/_data.py               | 25 ++++++++++++++++++--
 sklearn/preprocessing/_encoders.py           |  4 ++--
 sklearn/preprocessing/tests/test_data.py     | 16 +++++++++++++
 sklearn/preprocessing/tests/test_encoders.py | 12 ++++++++++
 sklearn/tests/test_common.py                 |  1 -
 sklearn/utils/validation.py                  | 17 ++++++++++---
 6 files changed, 67 insertions(+), 8 deletions(-)

diff --git a/sklearn/preprocessing/_data.py b/sklearn/preprocessing/_data.py
index ecce5e6b6d096..7c718b33e9156 100644
--- a/sklearn/preprocessing/_data.py
+++ b/sklearn/preprocessing/_data.py
@@ -35,6 +35,7 @@
     check_random_state,
     _check_sample_weight,
     FLOAT_DTYPES,
+    _check_feature_names_in,
 )
 
 from ._encoders import OneHotEncoder
@@ -1833,7 +1834,7 @@ def normalize(X, norm="l2", *, axis=1, copy=True, return_norm=False):
         return X
 
 
-class Normalizer(TransformerMixin, BaseEstimator):
+class Normalizer(_OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
     """Normalize samples individually to unit norm.
 
     Each sample (i.e. each row of the data matrix) with at least one
@@ -2004,7 +2005,7 @@ def binarize(X, *, threshold=0.0, copy=True):
     return X
 
 
-class Binarizer(TransformerMixin, BaseEstimator):
+class Binarizer(_OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
     """Binarize data (set feature values to 0 or 1) according to a threshold.
 
     Values greater than the threshold map to 1, while values less than
@@ -2266,6 +2267,26 @@ def transform(self, K, copy=True):
 
         return K
 
+    def get_feature_names_out(self, input_features=None):
+        """Get output feature names for transformation.
+
+        Parameters
+        ----------
+        input_features : array-like of str or None, default=None
+            Not used, present here for API consistency by convention.
+
+        Returns
+        -------
+        feature_names_out : ndarray of str objects
+            Transformed feature names.
+        """
+        _check_feature_names_in(self, input_features, generate_names=False)
+        class_name = self.__class__.__name__.lower()
+        return np.asarray(
+            [f"{class_name}{i}" for i in range(self.n_features_in_)],
+            dtype=object,
+        )
+
     def _more_tags(self):
         return {"pairwise": True}
 
diff --git a/sklearn/preprocessing/_encoders.py b/sklearn/preprocessing/_encoders.py
index df35c7b811443..8893a153e3f20 100644
--- a/sklearn/preprocessing/_encoders.py
+++ b/sklearn/preprocessing/_encoders.py
@@ -7,7 +7,7 @@
 from scipy import sparse
 import numbers
 
-from ..base import BaseEstimator, TransformerMixin
+from ..base import BaseEstimator, TransformerMixin, _OneToOneFeatureMixin
 from ..utils import check_array, is_scalar_nan
 from ..utils.deprecation import deprecated
 from ..utils.validation import check_is_fitted
@@ -734,7 +734,7 @@ def get_feature_names_out(self, input_features=None):
         return np.asarray(feature_names, dtype=object)
 
 
-class OrdinalEncoder(_BaseEncoder):
+class OrdinalEncoder(_OneToOneFeatureMixin, _BaseEncoder):
     """
     Encode categorical features as an integer array.
 
diff --git a/sklearn/preprocessing/tests/test_data.py b/sklearn/preprocessing/tests/test_data.py
index 9e7a8a174c182..7868d86967bf3 100644
--- a/sklearn/preprocessing/tests/test_data.py
+++ b/sklearn/preprocessing/tests/test_data.py
@@ -45,6 +45,7 @@
 from sklearn.preprocessing import power_transform
 from sklearn.preprocessing._data import _handle_zeros_in_scale
 from sklearn.preprocessing._data import BOUNDS_THRESHOLD
+from sklearn.metrics.pairwise import linear_kernel
 
 from sklearn.exceptions import NotFittedError
 
@@ -2672,6 +2673,8 @@ def test_one_to_one_features(Transformer):
         StandardScaler,
         QuantileTransformer,
         PowerTransformer,
+        Normalizer,
+        Binarizer,
     ],
 )
 def test_one_to_one_features_pandas(Transformer):
@@ -2691,3 +2694,16 @@ def test_one_to_one_features_pandas(Transformer):
     with pytest.raises(ValueError, match=msg):
         invalid_names = list("abcd")
         tr.get_feature_names_out(invalid_names)
+
+
+def test_kernel_centerer_feature_names_out():
+    """Test that kernel centerer feature_names_out"""
+
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((6, 4))
+    X_pairwise = linear_kernel(X)
+    centerer = KernelCenterer().fit(X_pairwise)
+
+    names_out = centerer.get_feature_names_out()
+    samples_out2 = X_pairwise.shape[1]
+    assert_array_equal(names_out, [f"kernelcenterer{i}" for i in range(samples_out2)])
diff --git a/sklearn/preprocessing/tests/test_encoders.py b/sklearn/preprocessing/tests/test_encoders.py
index 0429dc00c2322..9e20a5f15e40e 100644
--- a/sklearn/preprocessing/tests/test_encoders.py
+++ b/sklearn/preprocessing/tests/test_encoders.py
@@ -1387,3 +1387,15 @@ def test_ordinal_encoder_python_integer():
     assert_array_equal(encoder.categories_, np.sort(X, axis=0).T)
     X_trans = encoder.transform(X)
     assert_array_equal(X_trans, [[0], [3], [2], [1]])
+
+
+def test_ordinal_encoder_features_names_out_pandas():
+    """Check feature names out is same as the input"""
+    pd = pytest.importorskip("pandas")
+
+    names = ["b", "c", "a"]
+    X = pd.DataFrame([[1, 2, 3]], columns=names)
+    enc = OrdinalEncoder().fit(X)
+
+    feature_names_out = enc.get_feature_names_out()
+    assert_array_equal(names, feature_names_out)
diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
index 4f6818081c67d..0b9f7e422c458 100644
--- a/sklearn/tests/test_common.py
+++ b/sklearn/tests/test_common.py
@@ -368,7 +368,6 @@ def test_pandas_column_name_consistency(estimator):
     "impute",
     "isotonic",
     "kernel_approximation",
-    "preprocessing",
     "manifold",
     "neighbors",
     "neural_network",
diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
index f81fc2fdba685..f6a8987478291 100644
--- a/sklearn/utils/validation.py
+++ b/sklearn/utils/validation.py
@@ -1693,8 +1693,10 @@ def _get_feature_names(X):
         return feature_names
 
 
-def _check_feature_names_in(estimator, input_features=None):
-    """Get output feature names for transformation.
+def _check_feature_names_in(estimator, input_features=None, *, generate_names=True):
+    """Check input_features and generate names if needed.
+
+    Commonly used in :term:`get_feature_names_out`.
 
     Parameters
     ----------
@@ -1707,9 +1709,15 @@ def _check_feature_names_in(estimator, input_features=None):
         - If `input_features` is an array-like, then `input_features` must
             match `feature_names_in_` if `feature_names_in_` is defined.
 
+    generate_names : bool, default=True
+        Wether to generate names when `input_features` is `None` and
+        `estimator.feature_names_in_` is not defined. This is useful for jransformers
+        validates `input_features` but does not require them in
+        :term:`get_feature_names_out` i.e. `PCA`.
+
     Returns
     -------
-    feature_names_in : ndarray of str
+    feature_names_in : ndarray of str or `None`
         Feature names in.
     """
 
@@ -1733,6 +1741,9 @@ def _check_feature_names_in(estimator, input_features=None):
     if feature_names_in_ is not None:
         return feature_names_in_
 
+    if not generate_names:
+        return
+
     # Generates feature names if `n_features_in_` is defined
     if n_features_in_ is None:
         raise ValueError("Unable to generate feature names without n_features_in_")

From fcd7e70c7e4a44d62cd7595aa386e4683eb60f34 Mon Sep 17 00:00:00 2001
From: "Thomas J. Fan" <thomasjpfan@gmail.com>
Date: Fri, 17 Sep 2021 16:57:56 -0400
Subject: [PATCH 02/12] DOC Adds whats new

---
 doc/whats_new/v1.1.rst | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/doc/whats_new/v1.1.rst b/doc/whats_new/v1.1.rst
index 9c1084e393e8d..5f4841d925228 100644
--- a/doc/whats_new/v1.1.rst
+++ b/doc/whats_new/v1.1.rst
@@ -38,6 +38,15 @@ Changelog
     :pr:`123456` by :user:`Joe Bloggs <joeongithub>`.
     where 123456 is the *pull request* number, not the issue number.
 
+:mod:`sklearn.preprocessing`
+............................
+
+- |API| Adds :meth:`get_feature_names_out` to
+  :class:`preprocessing.Normalizer`,
+  :class:`preprocessing.KernelCenterer`,
+  :class:`preprocessing.OrdinalEncoder`, and
+  :class:`preprocessing.Binarizer`. :pr:`xxxxx` by `Thomas Fan`_.
+
 :mod:`sklearn.utils`
 ....................
 

From 88ea6adbc365a8f560727276bb3f0d7b253dc2ac Mon Sep 17 00:00:00 2001
From: "Thomas J. Fan" <thomasjpfan@gmail.com>
Date: Fri, 17 Sep 2021 16:58:30 -0400
Subject: [PATCH 03/12] DOC Adds whats new

---
 doc/whats_new/v1.1.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/whats_new/v1.1.rst b/doc/whats_new/v1.1.rst
index 5f4841d925228..9965740f5cea9 100644
--- a/doc/whats_new/v1.1.rst
+++ b/doc/whats_new/v1.1.rst
@@ -45,7 +45,7 @@ Changelog
   :class:`preprocessing.Normalizer`,
   :class:`preprocessing.KernelCenterer`,
   :class:`preprocessing.OrdinalEncoder`, and
-  :class:`preprocessing.Binarizer`. :pr:`xxxxx` by `Thomas Fan`_.
+  :class:`preprocessing.Binarizer`. :pr:`21079` by `Thomas Fan`_.
 
 :mod:`sklearn.utils`
 ....................

From a704c828f8fa5a19186f7788ac1dc02ecb50376f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E8=B5=B5=E4=B8=B0=20=28Zhao=20Feng=29?= <616545598@qq.com>
Date: Fri, 24 Sep 2021 19:57:32 +0800
Subject: [PATCH 04/12] DOC remove redundant code in GPR example (#21133)

---
 examples/gaussian_process/plot_gpr_prior_posterior.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/examples/gaussian_process/plot_gpr_prior_posterior.py b/examples/gaussian_process/plot_gpr_prior_posterior.py
index c9f12c16819fd..bd82613a20a45 100644
--- a/examples/gaussian_process/plot_gpr_prior_posterior.py
+++ b/examples/gaussian_process/plot_gpr_prior_posterior.py
@@ -57,9 +57,6 @@ def plot_gpr_samples(gpr_model, n_samples, ax):
     y_mean, y_std = gpr_model.predict(X, return_std=True)
     y_samples = gpr_model.sample_y(X, n_samples)
 
-    y_mean, y_std = gpr_model.predict(X, return_std=True)
-    y_samples = gpr_model.sample_y(X, n_samples)
-
     for idx, single_prior in enumerate(y_samples.T):
         ax.plot(
             x,

From 7eba091cd9923d99bebe0395e2c746dc246b8607 Mon Sep 17 00:00:00 2001
From: Niket Jain <51831161+nikJ13@users.noreply.github.com>
Date: Fri, 24 Sep 2021 17:29:04 +0530
Subject: [PATCH 05/12] DOC Ensures that RandomizedSearchCV passes numpydoc
 validation (#21131)

---
 maint_tools/test_docstrings.py     |  1 -
 sklearn/model_selection/_search.py | 14 +++++++-------
 2 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
index afa328f68ba8f..90d70a7c18101 100644
--- a/maint_tools/test_docstrings.py
+++ b/maint_tools/test_docstrings.py
@@ -32,7 +32,6 @@
     "PatchExtractor",
     "PolynomialFeatures",
     "QuadraticDiscriminantAnalysis",
-    "RandomizedSearchCV",
     "RobustScaler",
     "SelfTrainingClassifier",
     "SparseRandomProjection",
diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py
index 746ec5e9a0813..c13e5b6643ce1 100644
--- a/sklearn/model_selection/_search.py
+++ b/sklearn/model_selection/_search.py
@@ -1420,7 +1420,7 @@ class RandomizedSearchCV(BaseSearchCV):
 
     Parameters
     ----------
-    estimator : estimator object.
+    estimator : estimator object
         A object of that type is instantiated for each grid point.
         This is assumed to implement the scikit-learn estimator interface.
         Either estimator needs to provide a ``score`` function,
@@ -1692,6 +1692,12 @@ class RandomizedSearchCV(BaseSearchCV):
 
         .. versionadded:: 1.0
 
+    See Also
+    --------
+    GridSearchCV : Does exhaustive search over a grid of parameters.
+    ParameterSampler : A generator over parameter settings, constructed from
+        param_distributions.
+
     Notes
     -----
     The parameters selected are those that maximize the score of the held-out
@@ -1705,12 +1711,6 @@ class RandomizedSearchCV(BaseSearchCV):
     `pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *
     n_jobs`.
 
-    See Also
-    --------
-    GridSearchCV : Does exhaustive search over a grid of parameters.
-    ParameterSampler : A generator over parameter settings, constructed from
-        param_distributions.
-
     Examples
     --------
     >>> from sklearn.datasets import load_iris

From 19e4ff1e0e34ec6dccadf69e825563ac7232242b Mon Sep 17 00:00:00 2001
From: "Thomas J. Fan" <thomasjpfan@gmail.com>
Date: Fri, 24 Sep 2021 09:38:11 -0400
Subject: [PATCH 06/12] CLN Address comments

---
 sklearn/utils/validation.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
index f6a8987478291..c27930600a9b4 100644
--- a/sklearn/utils/validation.py
+++ b/sklearn/utils/validation.py
@@ -1710,9 +1710,9 @@ def _check_feature_names_in(estimator, input_features=None, *, generate_names=Tr
             match `feature_names_in_` if `feature_names_in_` is defined.
 
     generate_names : bool, default=True
-        Wether to generate names when `input_features` is `None` and
-        `estimator.feature_names_in_` is not defined. This is useful for jransformers
-        validates `input_features` but does not require them in
+        Whether to generate names when `input_features` is `None` and
+        `estimator.feature_names_in_` is not defined. This is useful for transformers
+        that validates `input_features` but does not require them in
         :term:`get_feature_names_out` i.e. `PCA`.
 
     Returns

From 82d19c7f1be17eb7fb386e1e380ac1c5c06cbe8c Mon Sep 17 00:00:00 2001
From: "Thomas J. Fan" <thomasjpfan@gmail.com>
Date: Tue, 19 Oct 2021 13:04:31 -0400
Subject: [PATCH 07/12] DOC Fix grammar

---
 sklearn/utils/validation.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
index 95ee51689862b..5259186a313d2 100644
--- a/sklearn/utils/validation.py
+++ b/sklearn/utils/validation.py
@@ -1714,7 +1714,7 @@ def _check_feature_names_in(estimator, input_features=None, *, generate_names=Tr
     generate_names : bool, default=True
         Whether to generate names when `input_features` is `None` and
         `estimator.feature_names_in_` is not defined. This is useful for transformers
-        that validates `input_features` but does not require them in
+        that validates `input_features` but do not require them in
         :term:`get_feature_names_out` i.e. `PCA`.
 
     Returns

From ff31d1b786f5345f5194d0d43289ce5c8076d7f5 Mon Sep 17 00:00:00 2001
From: "Thomas J. Fan" <thomasjpfan@gmail.com>
Date: Wed, 20 Oct 2021 13:51:30 -0400
Subject: [PATCH 08/12] CLN Address comments

---
 sklearn/preprocessing/_data.py               | 2 +-
 sklearn/preprocessing/tests/test_data.py     | 2 +-
 sklearn/preprocessing/tests/test_encoders.py | 2 +-
 sklearn/utils/validation.py                  | 4 ++--
 4 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/sklearn/preprocessing/_data.py b/sklearn/preprocessing/_data.py
index 44c6d5ebc60ad..c55c62cf5caa0 100644
--- a/sklearn/preprocessing/_data.py
+++ b/sklearn/preprocessing/_data.py
@@ -2259,7 +2259,7 @@ def transform(self, K, copy=True):
         """
         check_is_fitted(self)
 
-        K = self._validate_data(K, copy=copy, dtype=FLOAT_DTYPES, reset=False)
+        K = check_array(K, copy=copy, dtype=FLOAT_DTYPES)
 
         K_pred_cols = (np.sum(K, axis=1) / self.K_fit_rows_.shape[0])[:, np.newaxis]
 
diff --git a/sklearn/preprocessing/tests/test_data.py b/sklearn/preprocessing/tests/test_data.py
index 7868d86967bf3..764ef2df653e0 100644
--- a/sklearn/preprocessing/tests/test_data.py
+++ b/sklearn/preprocessing/tests/test_data.py
@@ -2697,7 +2697,7 @@ def test_one_to_one_features_pandas(Transformer):
 
 
 def test_kernel_centerer_feature_names_out():
-    """Test that kernel centerer feature_names_out"""
+    """Test that kernel centerer `feature_names_out`."""
 
     rng = np.random.RandomState(0)
     X = rng.random_sample((6, 4))
diff --git a/sklearn/preprocessing/tests/test_encoders.py b/sklearn/preprocessing/tests/test_encoders.py
index 1a768301c4e5e..27c52088f80d9 100644
--- a/sklearn/preprocessing/tests/test_encoders.py
+++ b/sklearn/preprocessing/tests/test_encoders.py
@@ -1390,7 +1390,7 @@ def test_ordinal_encoder_python_integer():
 
 
 def test_ordinal_encoder_features_names_out_pandas():
-    """Check feature names out is same as the input"""
+    """Check feature names out is same as the input."""
     pd = pytest.importorskip("pandas")
 
     names = ["b", "c", "a"]
diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
index 5259186a313d2..1b1641d8f4bdb 100644
--- a/sklearn/utils/validation.py
+++ b/sklearn/utils/validation.py
@@ -1696,7 +1696,7 @@ def _get_feature_names(X):
 
 
 def _check_feature_names_in(estimator, input_features=None, *, generate_names=True):
-    """Check input_features and generate names if needed.
+    """Check `input_features` and generate names if needed.
 
     Commonly used in :term:`get_feature_names_out`.
 
@@ -1715,7 +1715,7 @@ def _check_feature_names_in(estimator, input_features=None, *, generate_names=Tr
         Whether to generate names when `input_features` is `None` and
         `estimator.feature_names_in_` is not defined. This is useful for transformers
         that validates `input_features` but do not require them in
-        :term:`get_feature_names_out` i.e. `PCA`.
+        :term:`get_feature_names_out` e.g. `PCA`.
 
     Returns
     -------

From 01a32d3df908c6c3612df3256a50b8815bb87f13 Mon Sep 17 00:00:00 2001
From: "Thomas J. Fan" <thomasjpfan@gmail.com>
Date: Wed, 20 Oct 2021 15:26:23 -0400
Subject: [PATCH 09/12] REV Revert unneeded change

---
 sklearn/preprocessing/_data.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklearn/preprocessing/_data.py b/sklearn/preprocessing/_data.py
index c55c62cf5caa0..44c6d5ebc60ad 100644
--- a/sklearn/preprocessing/_data.py
+++ b/sklearn/preprocessing/_data.py
@@ -2259,7 +2259,7 @@ def transform(self, K, copy=True):
         """
         check_is_fitted(self)
 
-        K = check_array(K, copy=copy, dtype=FLOAT_DTYPES)
+        K = self._validate_data(K, copy=copy, dtype=FLOAT_DTYPES, reset=False)
 
         K_pred_cols = (np.sum(K, axis=1) / self.K_fit_rows_.shape[0])[:, np.newaxis]
 

From 61cde09d9fd40d3922a6ece0dba466edc1d3bc23 Mon Sep 17 00:00:00 2001
From: "Thomas J. Fan" <thomasjpfan@gmail.com>
Date: Tue, 26 Oct 2021 11:40:49 -0400
Subject: [PATCH 10/12] CLN Merge conflict

---
 doc/whats_new/v1.1.rst | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

diff --git a/doc/whats_new/v1.1.rst b/doc/whats_new/v1.1.rst
index 3a0b1cf79d768..3d7ad4a57cf1d 100644
--- a/doc/whats_new/v1.1.rst
+++ b/doc/whats_new/v1.1.rst
@@ -58,13 +58,6 @@ Changelog
 - |Fix| :class:`decomposition.FastICA` now validates input parameters in `fit` instead of `__init__`.
   :pr:`21432` by :user:`Hannah Bohle <hhnnhh>` and :user:`Maren Westermann <marenwestermann>`.
 
-:mod:`sklearn.impute`
-.....................
-
-- |Fix| :class:`linear_model.LogisticRegression` now raises a better error
-  message when the solver does not support sparse matrices with int64 indices.
-  :pr:`21093` by `Tom Dupre la Tour`_.
-  
 :mod:`sklearn.preprocessing`
 ............................
 
@@ -73,6 +66,10 @@ Changelog
   :class:`preprocessing.KernelCenterer`,
   :class:`preprocessing.OrdinalEncoder`, and
   :class:`preprocessing.Binarizer`. :pr:`21079` by `Thomas Fan`_.
+
+:mod:`sklearn.impute`
+.....................
+
 - |API| Adds :meth:`get_feature_names_out` to :class:`impute.SimpleImputer`,
   :class:`impute.KNNImputer`, :class:`impute.IterativeImputer`, and
   :class:`impute.MissingIndicator`. :pr:`21078` by `Thomas Fan`_.

From 2cd55e9b27feb8564adc8e34a08495f8a4fdd4e3 Mon Sep 17 00:00:00 2001
From: Olivier Grisel <olivier.grisel@ensta.org>
Date: Mon, 6 Dec 2021 12:02:28 +0100
Subject: [PATCH 11/12] Make KernelCenterer inherit from
 _ClassNamePrefixFeaturesOutMixin

---
 sklearn/preprocessing/_data.py | 37 ++++++++++++++--------------------
 1 file changed, 15 insertions(+), 22 deletions(-)

diff --git a/sklearn/preprocessing/_data.py b/sklearn/preprocessing/_data.py
index 7437c4acfe9af..6802b4774d3ef 100644
--- a/sklearn/preprocessing/_data.py
+++ b/sklearn/preprocessing/_data.py
@@ -16,7 +16,12 @@
 from scipy import optimize
 from scipy.special import boxcox
 
-from ..base import BaseEstimator, TransformerMixin, _OneToOneFeatureMixin
+from ..base import (
+    BaseEstimator,
+    TransformerMixin,
+    _OneToOneFeatureMixin,
+    _ClassNamePrefixFeaturesOutMixin,
+)
 from ..utils import check_array
 from ..utils.deprecation import deprecated
 from ..utils.extmath import _incremental_mean_and_var, row_norms
@@ -35,7 +40,6 @@
     check_random_state,
     _check_sample_weight,
     FLOAT_DTYPES,
-    _check_feature_names_in,
 )
 
 from ._encoders import OneHotEncoder
@@ -2120,7 +2124,7 @@ def _more_tags(self):
         return {"stateless": True}
 
 
-class KernelCenterer(TransformerMixin, BaseEstimator):
+class KernelCenterer(_ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
     r"""Center an arbitrary kernel matrix :math:`K`.
 
     Let define a kernel :math:`K` such that:
@@ -2259,25 +2263,14 @@ def transform(self, K, copy=True):
 
         return K
 
-    def get_feature_names_out(self, input_features=None):
-        """Get output feature names for transformation.
-
-        Parameters
-        ----------
-        input_features : array-like of str or None, default=None
-            Not used, present here for API consistency by convention.
-
-        Returns
-        -------
-        feature_names_out : ndarray of str objects
-            Transformed feature names.
-        """
-        _check_feature_names_in(self, input_features, generate_names=False)
-        class_name = self.__class__.__name__.lower()
-        return np.asarray(
-            [f"{class_name}{i}" for i in range(self.n_features_in_)],
-            dtype=object,
-        )
+    @property
+    def _n_features_out(self):
+        """Number of transformed output features."""
+        # Used by _ClassNamePrefixFeaturesOutMixin. This model preserves the
+        # number of input features but this is not a one-to-one mapping in the
+        # usual sense. Hence the choice not to use _OneToOneFeatureMixin to
+        # implement get_feature_names_out for this class.
+        return self.n_features_in_
 
     def _more_tags(self):
         return {"pairwise": True}

From 8778438b262d521bf2bd5988e7d7f70a92d62d88 Mon Sep 17 00:00:00 2001
From: "Thomas J. Fan" <thomasjpfan@gmail.com>
Date: Tue, 4 Jan 2022 14:33:54 -0500
Subject: [PATCH 12/12] DOC Adjust merge

---
 doc/whats_new/v1.1.rst | 16 ++++++----------
 1 file changed, 6 insertions(+), 10 deletions(-)

diff --git a/doc/whats_new/v1.1.rst b/doc/whats_new/v1.1.rst
index 790478e9f4015..822e6f79a9ae2 100644
--- a/doc/whats_new/v1.1.rst
+++ b/doc/whats_new/v1.1.rst
@@ -387,16 +387,6 @@ Changelog
   the model. The option is only available when `strategy` is set to `quantile`.
   :pr:`21445` by :user:`Felipe Bidu <fbidu>` and :user:`Amanda Dsouza <amy12xx>`.
 
-- |Fix| :class:`preprocessing.LabelBinarizer` now validates input parameters in `fit`
-  instead of `__init__`.
-  :pr:`21434` by :user:`Krum Arnaudov <krumeto>`.
-
-- |API| Adds :meth:`get_feature_names_out` to
-  :class:`preprocessing.Normalizer`,
-  :class:`preprocessing.KernelCenterer`,
-  :class:`preprocessing.OrdinalEncoder`, and
-  :class:`preprocessing.Binarizer`. :pr:`21079` by `Thomas Fan`_.
-
 - |Enhancement| Added the `get_feature_names_out` method and a new parameter
   `feature_names_out` to :class:`preprocessing.FunctionTransformer`. You can set
   `feature_names_out` to 'one-to-one' to use the input features names as the
@@ -410,6 +400,12 @@ Changelog
   `fit` instead of `__init__`.
   :pr:`21434` by :user:`Krum Arnaudov <krumeto>`.
 
+- |API| Adds :meth:`get_feature_names_out` to
+  :class:`preprocessing.Normalizer`,
+  :class:`preprocessing.KernelCenterer`,
+  :class:`preprocessing.OrdinalEncoder`, and
+  :class:`preprocessing.Binarizer`. :pr:`21079` by `Thomas Fan`_.
+
 :mod:`sklearn.random_projection`
 ................................