DOC Fix versionadded/versionchanged for 0.24 (scikit-learn#18312)

Author: albertvillanova

doc/whats_new/v0.24.rst

@@ -184,8 +184,8 @@ Changelog
 .......................
 
 - |Feature| :class:`ensemble.HistGradientBoostingRegressor` and
-  :class:`ensemble.HistGradientClassifier` now support `staged_predict`,
-  which allows monitoring of each stage.
+  :class:`ensemble.HistGradientBoostingClassifier` now support the
+  method `staged_predict`, which allows monitoring of each stage.
   :pr:`16985` by :user:`Hao Chun Chang <haochunchang>`.
 
 - |API|: The parameter ``n_classes_`` is now deprecated in
@@ -219,8 +219,9 @@ Changelog
   attribute name/path or a `callable` for extracting feature importance from
   the estimator.  :pr:`15361` by :user:`Venkatachalam N <venkyyuvy>`.
 
-- |Enhancement| Added the option for the number of n_features_to_select to be
-  given as a float representing the percentage of features to select.
+- |Enhancement| :class:`feature_selection.RFE` supports the option for the
+  number of `n_features_to_select` to be given as a float representing the
+  percentage of features to select.
   :pr:`17090` by :user:`Lisa Schwetlick <lschwetlick>` and
   :user:`Marija Vlajic Wheeler <marijavlajic>`.
 
@@ -234,7 +235,7 @@ Changelog
 ...............................
 
 - |Enhancement| A new method
-  :class:`gaussian_process.Kernel._check_bounds_params` is called after
+  :meth:`gaussian_process.Kernel._check_bounds_params` is called after
   fitting a Gaussian Process and raises a ``ConvergenceWarning`` if the bounds
   of the hyperparameters are too tight.
   :issue:`12638` by :user:`Sylvain Lannuzel <SylvainLan>`.
@@ -253,10 +254,9 @@ Changelog
   :pr:`17526` by :user:`Ayako YAGI <yagi-3>` and
   :user:`Juan Carlos Alfaro Jiménez <alfaro96>`.
 
-- |Feature| :class:`impute.SimpleImputer` now supports ``inverse_transform``
-  functionality to revert imputed data to original when instantiated
-  with `add_indicator=True`.
-  :pr:`17612` by :user:`Srimukh Sripada <d3b0unce>`.
+- |Feature| Added method :meth:`impute.SimpleImputer.inverse_transform` to
+  revert imputed data to original when instantiated with
+  ``add_indicator=True``. :pr:`17612` by :user:`Srimukh Sripada <d3b0unce>`.
 
 - |Fix| :class:`impute.IterativeImputer` will not attempt to set the
   estimator's `random_state` attribute, allowing to use it with more external classes.
@@ -284,7 +284,7 @@ Changelog
   :pr:`16289` by :user:`Masashi Kishimoto <kishimoto-banana>` and
   :user:`Olivier Grisel <ogrisel>`.
 
-- |Enhancement| :class:`isotonic.IsotonicRegression` now accepts 2darray with
+- |Enhancement| :class:`isotonic.IsotonicRegression` now accepts 2d array with
   1 feature as input array. :pr:`17379` by :user:`Jiaxiang <fujiaxiang>`.
 
 :mod:`sklearn.kernel_approximation`
@@ -335,8 +335,8 @@ Changelog
   :pr:`10591` by :user:`Jeremy Karnowski <jkarnows>` and
   :user:`Daniel Mohns <dmohns>`.
 
-- |Feature| Added :func:`metrics.plot_det_curve` and :class:`DetCurveDisplay`
-  to ease the plot of DET curves.
+- |Feature| Added :func:`metrics.plot_det_curve` and
+  :class:`metrics.DetCurveDisplay` to ease the plot of DET curves.
   :pr:`18176` by :user:`Guillaume Lemaitre <glemaitre>`.
 
 - |Feature| Added :func:`metrics.mean_absolute_percentage_error` metric and

sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py

@@ -968,6 +968,8 @@ def staged_predict(self, X):
         This method allows monitoring (i.e. determine error on testing set)
         after each stage.
 
+        .. versionadded:: 0.24
+
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)
@@ -1193,6 +1195,8 @@ def staged_predict(self, X):
         This method allows monitoring (i.e. determine error on testing set)
         after each stage.
 
+        .. versionadded:: 0.24
+
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)

sklearn/feature_selection/_rfe.py

@@ -64,6 +64,9 @@ class RFE(SelectorMixin, MetaEstimatorMixin, BaseEstimator):
         to select. If float between 0 and 1, it is the fraction of features to
         select.
 
+        .. versionchanged:: 0.24
+           Added float values for fractions.
+
     step : int or float, default=1
         If greater than or equal to 1, then ``step`` corresponds to the
         (integer) number of features to remove at each iteration.

sklearn/impute/_base.py

@@ -501,6 +501,8 @@ def inverse_transform(self, X):
         indicator, and the imputation done at ``transform`` time won't be
         inverted.
 
+        .. versionadded:: 0.24
+
         Parameters
         ----------
         X : array-like of shape \

sklearn/inspection/_permutation_importance.py

@@ -98,6 +98,8 @@ def permutation_importance(estimator, X, y, *, scoring=None, n_repeats=5,
     sample_weight : array-like of shape (n_samples,), default=None
         Sample weights used in scoring.
 
+        .. versionadded:: 0.24
+
     Returns
     -------
     result : :class:`~sklearn.utils.Bunch`

sklearn/isotonic.py

@@ -302,6 +302,9 @@ def fit(self, X, y, sample_weight=None):
         X : array-like of shape (n_samples,) or (n_samples, 1)
             Training data.
 
+            .. versionchanged:: 0.24
+               Also accepts 2d array with 1 feature.
+
         y : array-like of shape (n_samples,)
             Training target.
 
@@ -346,6 +349,9 @@ def transform(self, T):
         T : array-like of shape (n_samples,) or (n_samples, 1)
             Data to transform.
 
+            .. versionchanged:: 0.24
+               Also accepts 2d array with 1 feature.
+
         Returns
         -------
         y_pred : ndarray of shape (n_samples,)

sklearn/kernel_approximation.py

@@ -39,6 +39,8 @@ class PolynomialCountSketch(BaseEstimator, TransformerMixin):
     vector with itself using Fast Fourier Transforms (FFT). Read more in the
     :ref:`User Guide <polynomial_kernel_approx>`.
 
+    .. versionadded:: 0.24
+
     Parameters
     ----------
     gamma : float, default=1.0

sklearn/linear_model/_ridge.py

@@ -1749,6 +1749,8 @@ class RidgeCV(MultiOutputMixin, RegressorMixin, _BaseRidgeCV):
         fitting, the `alpha_` attribute will contain a value for each target.
         When set to `False`, a single alpha is used for all targets.
 
+        .. versionadded:: 0.24
+
     Attributes
     ----------
     cv_values_ : ndarray of shape (n_samples, n_alphas) or \

sklearn/metrics/_regression.py

@@ -52,7 +52,7 @@
 
 
 def _check_reg_targets(y_true, y_pred, multioutput, dtype="numeric"):
-    """Check that y_true and y_pred belong to the same regression task
+    """Check that y_true and y_pred belong to the same regression task.
 
     Parameters
     ----------
@@ -81,9 +81,9 @@ def _check_reg_targets(y_true, y_pred, multioutput, dtype="numeric"):
         Custom output weights if ``multioutput`` is array-like or
         just the corresponding argument if ``multioutput`` is a
         correct keyword.
+
     dtype: str or list, default="numeric"
         the dtype argument passed to check_array
-
     """
     check_consistent_length(y_true, y_pred)
     y_true = check_array(y_true, ensure_2d=False, dtype=dtype)
@@ -126,7 +126,7 @@ def _check_reg_targets(y_true, y_pred, multioutput, dtype="numeric"):
 def mean_absolute_error(y_true, y_pred, *,
                         sample_weight=None,
                         multioutput='uniform_average'):
-    """Mean absolute error regression loss
+    """Mean absolute error regression loss.
 
     Read more in the :ref:`User Guide <mean_absolute_error>`.
 
@@ -197,12 +197,14 @@ def mean_absolute_error(y_true, y_pred, *,
 def mean_absolute_percentage_error(y_true, y_pred,
                                    sample_weight=None,
                                    multioutput='uniform_average'):
-    """Mean absolute percentage error regression loss
+    """Mean absolute percentage error regression loss.
 
     Note here that we do not represent the output as a percentage in range
     [0, 100]. Instead, we represent it in range [0, 1/eps]. Read more in the
     :ref:`User Guide <mean_absolute_percentage_error>`.
 
+    .. versionadded:: 0.24
+
     Parameters
     ----------
     y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
@@ -273,7 +275,7 @@ def mean_absolute_percentage_error(y_true, y_pred,
 def mean_squared_error(y_true, y_pred, *,
                        sample_weight=None,
                        multioutput='uniform_average', squared=True):
-    """Mean squared error regression loss
+    """Mean squared error regression loss.
 
     Read more in the :ref:`User Guide <mean_squared_error>`.
 
@@ -329,7 +331,6 @@ def mean_squared_error(y_true, y_pred, *,
     array([0.41666667, 1.        ])
     >>> mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7])
     0.825...
-
     """
     y_type, y_true, y_pred, multioutput = _check_reg_targets(
         y_true, y_pred, multioutput)
@@ -354,7 +355,7 @@ def mean_squared_error(y_true, y_pred, *,
 def mean_squared_log_error(y_true, y_pred, *,
                            sample_weight=None,
                            multioutput='uniform_average'):
-    """Mean squared logarithmic error regression loss
+    """Mean squared logarithmic error regression loss.
 
     Read more in the :ref:`User Guide <mean_squared_log_error>`.
 
@@ -403,7 +404,6 @@ def mean_squared_log_error(y_true, y_pred, *,
     array([0.00462428, 0.08377444])
     >>> mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7])
     0.060...
-
     """
     y_type, y_true, y_pred, multioutput = _check_reg_targets(
         y_true, y_pred, multioutput)
@@ -421,7 +421,7 @@ def mean_squared_log_error(y_true, y_pred, *,
 @_deprecate_positional_args
 def median_absolute_error(y_true, y_pred, *, multioutput='uniform_average',
                           sample_weight=None):
-    """Median absolute error regression loss
+    """Median absolute error regression loss.
 
     Median absolute error output is non-negative floating point. The best value
     is 0.0. Read more in the :ref:`User Guide <median_absolute_error>`.
@@ -473,7 +473,6 @@ def median_absolute_error(y_true, y_pred, *, multioutput='uniform_average',
     array([0.5, 1. ])
     >>> median_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
     0.85
-
     """
     y_type, y_true, y_pred, multioutput = _check_reg_targets(
         y_true, y_pred, multioutput)
@@ -497,7 +496,7 @@ def median_absolute_error(y_true, y_pred, *, multioutput='uniform_average',
 def explained_variance_score(y_true, y_pred, *,
                              sample_weight=None,
                              multioutput='uniform_average'):
-    """Explained variance regression score function
+    """Explained variance regression score function.
 
     Best possible score is 1.0, lower values are worse.
 
@@ -549,7 +548,6 @@ def explained_variance_score(y_true, y_pred, *,
     >>> y_pred = [[0, 2], [-1, 2], [8, -5]]
     >>> explained_variance_score(y_true, y_pred, multioutput='uniform_average')
     0.983...
-
     """
     y_type, y_true, y_pred, multioutput = _check_reg_targets(
         y_true, y_pred, multioutput)

sklearn/model_selection/_search.py

@@ -482,6 +482,8 @@ def score_samples(self, X):
         Only available if ``refit=True`` and the underlying estimator supports
         ``score_samples``.
 
+        .. versionadded:: 0.24
+
         Parameters
         ----------
         X : iterable