scikit-learn · lorentzenchr · Aug 26, 2021 · Aug 20, 2021 · Aug 20, 2021 · Aug 21, 2021
diff --git a/sklearn/calibration.py b/sklearn/calibration.py
@@ -148,6 +148,12 @@ class CalibratedClassifierCV(ClassifierMixin, MetaEstimatorMixin, BaseEstimator)
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Only defined if the
+        underlying base_estimator exposes such an attribute when fit.
+
+        .. versionadded:: 1.0
+
     calibrated_classifiers_ : list (len() equal to cv or 1 if `cv="prefit"` \
             or `ensemble=False`)
         The list of classifier and calibrator pairs.

diff --git a/sklearn/cluster/_affinity_propagation.py b/sklearn/cluster/_affinity_propagation.py
@@ -332,6 +332,12 @@ class AffinityPropagation(ClusterMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     AgglomerativeClustering : Recursively merges the pair of

diff --git a/sklearn/cluster/_agglomerative.py b/sklearn/cluster/_agglomerative.py
@@ -812,6 +812,12 @@ class AgglomerativeClustering(ClusterMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     children_ : array-like of shape (n_samples-1, 2)
         The children of each non-leaf node. Values less than `n_samples`
         correspond to leaves of the tree which are the original samples.
@@ -1098,6 +1104,12 @@ class FeatureAgglomeration(AgglomerativeClustering, AgglomerationTransform):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     children_ : array-like of shape (n_nodes-1, 2)
         The children of each non-leaf node. Values less than `n_features`
         correspond to leaves of the tree which are the original samples.

diff --git a/sklearn/cluster/_bicluster.py b/sklearn/cluster/_bicluster.py
@@ -278,6 +278,12 @@ class SpectralCoclustering(BaseSpectral):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     Examples
     --------
     >>> from sklearn.cluster import SpectralCoclustering
@@ -427,6 +433,12 @@ class SpectralBiclustering(BaseSpectral):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     Examples
     --------
     >>> from sklearn.cluster import SpectralBiclustering

diff --git a/sklearn/cluster/_birch.py b/sklearn/cluster/_birch.py
@@ -416,6 +416,12 @@ class Birch(ClusterMixin, TransformerMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     MiniBatchKMeans : Alternative implementation that does incremental updates

diff --git a/sklearn/cluster/_dbscan.py b/sklearn/cluster/_dbscan.py
@@ -239,6 +239,12 @@ class DBSCAN(ClusterMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     OPTICS : A similar clustering at multiple values of eps. Our implementation

diff --git a/sklearn/cluster/_kmeans.py b/sklearn/cluster/_kmeans.py
@@ -856,6 +856,12 @@ class KMeans(TransformerMixin, ClusterMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     MiniBatchKMeans : Alternative online implementation that does incremental
@@ -1620,6 +1626,12 @@ class MiniBatchKMeans(KMeans):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     KMeans : The classic implementation of the clustering method based on the

diff --git a/sklearn/cluster/_mean_shift.py b/sklearn/cluster/_mean_shift.py
@@ -331,6 +331,12 @@ class MeanShift(ClusterMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     Examples
     --------
     >>> from sklearn.cluster import MeanShift

diff --git a/sklearn/cluster/_optics.py b/sklearn/cluster/_optics.py
@@ -189,6 +189,12 @@ class OPTICS(ClusterMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     DBSCAN : A similar clustering for a specified neighborhood radius (eps).

diff --git a/sklearn/cluster/_spectral.py b/sklearn/cluster/_spectral.py
@@ -447,6 +447,12 @@ class SpectralClustering(ClusterMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     Examples
     --------
     >>> from sklearn.cluster import SpectralClustering

diff --git a/sklearn/covariance/_elliptic_envelope.py b/sklearn/covariance/_elliptic_envelope.py
@@ -88,6 +88,12 @@ class EllipticEnvelope(OutlierMixin, MinCovDet):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     EmpiricalCovariance : Maximum likelihood covariance estimator.

diff --git a/sklearn/covariance/_empirical_covariance.py b/sklearn/covariance/_empirical_covariance.py
@@ -131,6 +131,12 @@ class EmpiricalCovariance(BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     EllipticEnvelope : An object for detecting outliers in

diff --git a/sklearn/covariance/_graph_lasso.py b/sklearn/covariance/_graph_lasso.py
@@ -395,6 +395,12 @@ class GraphicalLasso(EmpiricalCovariance):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     graphical_lasso : L1-penalized covariance estimator.
@@ -760,6 +766,12 @@ class GraphicalLassoCV(GraphicalLasso):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     graphical_lasso : L1-penalized covariance estimator.

diff --git a/sklearn/covariance/_robust_covariance.py b/sklearn/covariance/_robust_covariance.py
@@ -648,6 +648,12 @@ class MinCovDet(EmpiricalCovariance):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     EllipticEnvelope : An object for detecting outliers in

diff --git a/sklearn/covariance/_shrunk_covariance.py b/sklearn/covariance/_shrunk_covariance.py
@@ -98,6 +98,12 @@ class ShrunkCovariance(EmpiricalCovariance):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     EllipticEnvelope : An object for detecting outliers in
@@ -386,6 +392,12 @@ class LedoitWolf(EmpiricalCovariance):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     EllipticEnvelope : An object for detecting outliers in
@@ -585,6 +597,12 @@ class OAS(EmpiricalCovariance):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     EllipticEnvelope : An object for detecting outliers in

diff --git a/sklearn/cross_decomposition/_pls.py b/sklearn/cross_decomposition/_pls.py
@@ -620,6 +620,12 @@ class PLSRegression(_PLS):
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     Examples
     --------
     >>> from sklearn.cross_decomposition import PLSRegression
@@ -736,6 +742,12 @@ class PLSCanonical(_PLS):
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     Examples
     --------
     >>> from sklearn.cross_decomposition import PLSCanonical
@@ -858,6 +870,12 @@ class CCA(_PLS):
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     PLSCanonical : Partial Least Squares transformer and regressor.
@@ -944,6 +962,12 @@ class PLSSVD(TransformerMixin, BaseEstimator):
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     Examples
     --------
     >>> import numpy as np

diff --git a/sklearn/decomposition/_dict_learning.py b/sklearn/decomposition/_dict_learning.py
@@ -1186,6 +1186,12 @@ class SparseCoder(_BaseSparseCoding, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     Examples
     --------
     >>> import numpy as np
@@ -1432,6 +1438,12 @@ class DictionaryLearning(_BaseSparseCoding, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     n_iter_ : int
         Number of iterations run.
 
@@ -1694,6 +1706,12 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     n_iter_ : int
         Number of iterations run.
 

diff --git a/sklearn/decomposition/_factor_analysis.py b/sklearn/decomposition/_factor_analysis.py
@@ -125,6 +125,12 @@ class FactorAnalysis(TransformerMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     Examples
     --------
     >>> from sklearn.datasets import load_digits

diff --git a/sklearn/decomposition/_fastica.py b/sklearn/decomposition/_fastica.py
@@ -389,6 +389,12 @@ def my_g(x):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     n_iter_ : int
         If the algorithm is "deflation", n_iter is the
         maximum number of iterations run across all components. Else