scikit-learn · jeremiedbb · Jun 9, 2021 · Jun 9, 2021 · Jun 9, 2021 · Jun 9, 2021
diff --git a/sklearn/cluster/_affinity_propagation.py b/sklearn/cluster/_affinity_propagation.py
@@ -312,6 +312,8 @@ class AffinityPropagation(ClusterMixin, BaseEstimator):
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
+        .. versionadded:: 0.24
+
     Notes
     -----
     For an example, see :ref:`examples/cluster/plot_affinity_propagation.py

diff --git a/sklearn/cluster/_agglomerative.py b/sklearn/cluster/_agglomerative.py
@@ -776,6 +776,8 @@ class AgglomerativeClustering(ClusterMixin, BaseEstimator):
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
+        .. versionadded:: 0.24
+
     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.
@@ -1045,6 +1047,8 @@ class FeatureAgglomeration(AgglomerativeClustering, AgglomerationTransform):
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
+        .. versionadded:: 0.24
+
     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
@@ -261,6 +261,8 @@ class SpectralCoclustering(BaseSpectral):
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> from sklearn.cluster import SpectralCoclustering
@@ -401,6 +403,8 @@ class SpectralBiclustering(BaseSpectral):
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> from sklearn.cluster import SpectralBiclustering

diff --git a/sklearn/cluster/_birch.py b/sklearn/cluster/_birch.py
@@ -404,6 +404,8 @@ class Birch(ClusterMixin, TransformerMixin, BaseEstimator):
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
+        .. versionadded:: 0.24
+
     See Also
     --------
     MiniBatchKMeans : Alternative implementation that does incremental updates

diff --git a/sklearn/cluster/_dbscan.py b/sklearn/cluster/_dbscan.py
@@ -220,6 +220,8 @@ class DBSCAN(ClusterMixin, BaseEstimator):
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> from sklearn.cluster import DBSCAN

diff --git a/sklearn/cluster/_kmeans.py b/sklearn/cluster/_kmeans.py
@@ -769,6 +769,8 @@ class KMeans(TransformerMixin, ClusterMixin, BaseEstimator):
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
+        .. versionadded:: 0.24
+
     See Also
     --------
     MiniBatchKMeans : Alternative online implementation that does incremental
@@ -1471,6 +1473,8 @@ class MiniBatchKMeans(KMeans):
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
+        .. versionadded:: 0.24
+
     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
@@ -314,6 +314,8 @@ class MeanShift(ClusterMixin, BaseEstimator):
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> from sklearn.cluster import MeanShift

diff --git a/sklearn/cluster/_optics.py b/sklearn/cluster/_optics.py
@@ -181,6 +181,8 @@ class OPTICS(ClusterMixin, BaseEstimator):
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
+        .. versionadded:: 0.24
+
     See Also
     --------
     DBSCAN : A similar clustering for a specified neighborhood radius (eps).

diff --git a/sklearn/cluster/_spectral.py b/sklearn/cluster/_spectral.py
@@ -421,6 +421,8 @@ class SpectralClustering(ClusterMixin, BaseEstimator):
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> from sklearn.cluster import SpectralClustering

diff --git a/sklearn/covariance/_elliptic_envelope.py b/sklearn/covariance/_elliptic_envelope.py
@@ -83,6 +83,11 @@ class EllipticEnvelope(OutlierMixin, MinCovDet):
         Mahalanobis distances of the training set (on which :meth:`fit` is
         called) observations.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> import numpy as np

diff --git a/sklearn/covariance/_empirical_covariance.py b/sklearn/covariance/_empirical_covariance.py
@@ -125,6 +125,11 @@ class EmpiricalCovariance(BaseEstimator):
         Estimated pseudo-inverse matrix.
         (stored only if store_precision is True)
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> import numpy as np

diff --git a/sklearn/covariance/_graph_lasso.py b/sklearn/covariance/_graph_lasso.py
@@ -339,6 +339,11 @@ class GraphicalLasso(EmpiricalCovariance):
     n_iter_ : int
         Number of iterations run.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> import numpy as np
@@ -635,6 +640,11 @@ class GraphicalLassoCV(GraphicalLasso):
     n_iter_ : int
         Number of iterations run for the optimal alpha.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> import numpy as np

diff --git a/sklearn/covariance/_robust_covariance.py b/sklearn/covariance/_robust_covariance.py
@@ -582,6 +582,11 @@ class MinCovDet(EmpiricalCovariance):
         Mahalanobis distances of the training set (on which :meth:`fit` is
         called) observations.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> import numpy as np

diff --git a/sklearn/covariance/_shrunk_covariance.py b/sklearn/covariance/_shrunk_covariance.py
@@ -91,6 +91,11 @@ class ShrunkCovariance(EmpiricalCovariance):
         Estimated pseudo inverse matrix.
         (stored only if store_precision is True)
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> import numpy as np
@@ -356,6 +361,11 @@ class LedoitWolf(EmpiricalCovariance):
         Coefficient in the convex combination used for the computation
         of the shrunk estimate. Range is [0, 1].
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> import numpy as np
@@ -533,6 +543,11 @@ class OAS(EmpiricalCovariance):
       coefficient in the convex combination used for the computation
       of the shrunk estimate. Range is [0, 1].
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> import numpy as np

diff --git a/sklearn/decomposition/_dict_learning.py b/sklearn/decomposition/_dict_learning.py
@@ -1259,6 +1259,11 @@ class DictionaryLearning(_BaseSparseCoding, BaseEstimator):
     error_ : array
         vector of errors at each iteration
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     n_iter_ : int
         Number of iterations run.
 
@@ -1492,6 +1497,11 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator):
         `A` `(n_components, n_components)` is the dictionary covariance matrix.
         `B` `(n_features, n_components)` is the data approximation matrix.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     n_iter_ : int
         Number of iterations run.
 

diff --git a/sklearn/decomposition/_factor_analysis.py b/sklearn/decomposition/_factor_analysis.py
@@ -120,6 +120,11 @@ class FactorAnalysis(TransformerMixin, BaseEstimator):
     mean_ : ndarray of shape (n_features,)
         Per-feature empirical mean, estimated from the training set.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> from sklearn.datasets import load_digits

diff --git a/sklearn/decomposition/_fastica.py b/sklearn/decomposition/_fastica.py
@@ -362,6 +362,11 @@ def my_g(x):
     mean_ : ndarray of shape(n_features,)
         The mean over features. Only set if `self.whiten` is True.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     n_iter_ : int
         If the algorithm is "deflation", n_iter is the
         maximum number of iterations run across all components. Else

diff --git a/sklearn/decomposition/_incremental_pca.py b/sklearn/decomposition/_incremental_pca.py
@@ -107,6 +107,11 @@ class IncrementalPCA(_BasePCA):
     batch_size_ : int
         Inferred batch size from ``batch_size``.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> from sklearn.datasets import load_digits

diff --git a/sklearn/decomposition/_kernel_pca.py b/sklearn/decomposition/_kernel_pca.py
@@ -164,6 +164,11 @@ class KernelPCA(TransformerMixin, BaseEstimator):
         The data used to fit the model. If `copy_X=False`, then `X_fit_` is
         a reference. This attribute is used for the calls to transform.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> from sklearn.datasets import load_digits

diff --git a/sklearn/decomposition/_lda.py b/sklearn/decomposition/_lda.py
@@ -248,6 +248,11 @@ class LatentDirichletAllocation(TransformerMixin, BaseEstimator):
     n_batch_iter_ : int
         Number of iterations of the EM step.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     n_iter_ : int
         Number of passes over the dataset.
 

diff --git a/sklearn/decomposition/_nmf.py b/sklearn/decomposition/_nmf.py
@@ -1179,6 +1179,11 @@ class NMF(TransformerMixin, BaseEstimator):
     n_iter_ : int
         Actual number of iterations.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> import numpy as np

diff --git a/sklearn/decomposition/_pca.py b/sklearn/decomposition/_pca.py
@@ -264,6 +264,11 @@ class PCA(_BasePCA):
         Equal to the average of (min(n_features, n_samples) - n_components)
         smallest eigenvalues of the covariance matrix of X.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     See Also
     --------
     KernelPCA : Kernel Principal Component Analysis.

diff --git a/sklearn/decomposition/_sparse_pca.py b/sklearn/decomposition/_sparse_pca.py
@@ -88,6 +88,11 @@ class SparsePCA(TransformerMixin, BaseEstimator):
         Per-feature empirical mean, estimated from the training set.
         Equal to ``X.mean(axis=0)``.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> import numpy as np
@@ -279,6 +284,11 @@ class MiniBatchSparsePCA(SparsePCA):
         Per-feature empirical mean, estimated from the training set.
         Equal to ``X.mean(axis=0)``.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> import numpy as np

diff --git a/sklearn/decomposition/_truncated_svd.py b/sklearn/decomposition/_truncated_svd.py
@@ -84,6 +84,11 @@ class TruncatedSVD(TransformerMixin, BaseEstimator):
         The singular values are equal to the 2-norms of the ``n_components``
         variables in the lower-dimensional space.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> from sklearn.decomposition import TruncatedSVD

diff --git a/sklearn/discriminant_analysis.py b/sklearn/discriminant_analysis.py
@@ -278,6 +278,11 @@ class LinearDiscriminantAnalysis(LinearClassifierMixin,
     classes_ : array-like of shape (n_classes,)
         Unique class labels.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     See Also
     --------
     QuadraticDiscriminantAnalysis : Quadratic Discriminant Analysis.
@@ -732,6 +737,11 @@ class QuadraticDiscriminantAnalysis(ClassifierMixin, BaseEstimator):
     classes_ : ndarray of shape (n_classes,)
         Unique class labels.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis

diff --git a/sklearn/dummy.py b/sklearn/dummy.py
@@ -75,6 +75,11 @@ class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):
     n_outputs_ : int
         Number of outputs.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     sparse_output_ : bool
         True if the array returned from predict is to be in sparse CSC format.
         Is automatically set to True if the input y is passed in sparse format.
@@ -425,6 +430,11 @@ class DummyRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
         Mean or median or quantile of the training targets or constant value
         given by the user.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     n_outputs_ : int
         Number of outputs.