TST Fixes docstring ordering and test_docstring_parameters (scikit-learn#19048)

Author: thomasjpfan

sklearn/compose/_column_transformer.py

@@ -809,6 +809,14 @@ def __init__(self, pattern=None, *, dtype_include=None,
         self.dtype_exclude = dtype_exclude
 
     def __call__(self, df):
+        """Callable for column selection to be used by a
+        :class:`ColumnTransformer`.
+
+        Parameters
+        ----------
+        df : dataframe of shape (n_features, n_samples)
+            DataFrame to select columns from.
+        """
         if not hasattr(df, 'iloc'):
             raise ValueError("make_column_selector can only be applied to "
                              "pandas dataframes")

sklearn/covariance/_robust_covariance.py

@@ -632,7 +632,7 @@ def fit(self, X, y=None):
             Training data, where `n_samples` is the number of samples
             and `n_features` is the number of features.
 
-        y: Ignored
+        y : Ignored
             Not used, present for API consistency by convention.
 
         Returns

sklearn/covariance/_shrunk_covariance.py

@@ -135,7 +135,7 @@ def fit(self, X, y=None):
             Training data, where n_samples is the number of samples
             and n_features is the number of features.
 
-        y: Ignored
+        y : Ignored
             Not used, present for API consistency by convention.
 
         Returns

sklearn/cross_decomposition/_pls.py

@@ -537,7 +537,8 @@ class PLSRegression(_PLS):
         `Y = X @ coef_`.
 
     n_iter_ : list of shape (n_components,)
-        Number of iterations of the power method for each component.
+        Number of iterations of the power method, for each
+        component.
 
     n_features_in_ : int
         Number of features seen during :term:`fit`.

sklearn/decomposition/_dict_learning.py

@@ -1123,6 +1123,8 @@ def transform(self, X, y=None):
             Test data to be transformed, must have the same number of
             features as the data used to train the model.
 
+        y : Ignored
+
         Returns
         -------
         X_new : ndarray of shape (n_samples, n_components)

sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py

@@ -940,14 +940,6 @@ class HistGradientBoostingRegressor(RegressorMixin, BaseHistGradientBoosting):
         Features with a small number of unique values may use less than
         ``max_bins`` bins. In addition to the ``max_bins`` bins, one more bin
         is always reserved for missing values. Must be no larger than 255.
-    monotonic_cst : array-like of int of shape (n_features), default=None
-        Indicates the monotonic constraint to enforce on each feature. -1, 1
-        and 0 respectively correspond to a negative constraint, positive
-        constraint and no constraint. Read more in the :ref:`User Guide
-        <monotonic_cst_gbdt>`.
-
-        .. versionadded:: 0.23
-
     categorical_features : array-like of {bool, int} of shape (n_features) \
             or shape (n_categorical_features,), default=None.
         Indicates the categorical features.
@@ -964,6 +956,14 @@ class HistGradientBoostingRegressor(RegressorMixin, BaseHistGradientBoosting):
 
         .. versionadded:: 0.24
 
+    monotonic_cst : array-like of int of shape (n_features), default=None
+        Indicates the monotonic constraint to enforce on each feature. -1, 1
+        and 0 respectively correspond to a negative constraint, positive
+        constraint and no constraint. Read more in the :ref:`User Guide
+        <monotonic_cst_gbdt>`.
+
+        .. versionadded:: 0.23
+
     warm_start : bool, default=False
         When set to ``True``, reuse the solution of the previous call to fit
         and add more estimators to the ensemble. For results to be valid, the
@@ -1193,14 +1193,6 @@ class HistGradientBoostingClassifier(ClassifierMixin,
         Features with a small number of unique values may use less than
         ``max_bins`` bins. In addition to the ``max_bins`` bins, one more bin
         is always reserved for missing values. Must be no larger than 255.
-    monotonic_cst : array-like of int of shape (n_features), default=None
-        Indicates the monotonic constraint to enforce on each feature. -1, 1
-        and 0 respectively correspond to a negative constraint, positive
-        constraint and no constraint. Read more in the :ref:`User Guide
-        <monotonic_cst_gbdt>`.
-
-        .. versionadded:: 0.23
-
     categorical_features : array-like of {bool, int} of shape (n_features) \
             or shape (n_categorical_features,), default=None.
         Indicates the categorical features.
@@ -1217,6 +1209,14 @@ class HistGradientBoostingClassifier(ClassifierMixin,
 
         .. versionadded:: 0.24
 
+    monotonic_cst : array-like of int of shape (n_features), default=None
+        Indicates the monotonic constraint to enforce on each feature. -1, 1
+        and 0 respectively correspond to a negative constraint, positive
+        constraint and no constraint. Read more in the :ref:`User Guide
+        <monotonic_cst_gbdt>`.
+
+        .. versionadded:: 0.23
+
     warm_start : bool, default=False
         When set to ``True``, reuse the solution of the previous call to fit
         and add more estimators to the ensemble. For results to be valid, the

sklearn/feature_selection/_sequential.py

@@ -36,7 +36,7 @@ class SequentialFeatureSelector(SelectorMixin, MetaEstimatorMixin,
         to select. If float between 0 and 1, it is the fraction of features to
         select.
 
-    direction: {'forward', 'backward'}, default='forward'
+    direction : {'forward', 'backward'}, default='forward'
         Whether to perform forward selection or backward selection.
 
     scoring : str, callable, list/tuple or dict, default=None

sklearn/linear_model/_glm/glm.py

@@ -593,6 +593,10 @@ class TweedieRegressor(GeneralizedLinearRegressor):
         GLMs. In this case, the design matrix `X` must have full column rank
         (no collinearities).
 
+    fit_intercept : bool, default=True
+        Specifies if a constant (a.k.a. bias or intercept) should be
+        added to the linear predictor (X @ coef + intercept).
+
     link : {'auto', 'identity', 'log'}, default='auto'
         The link function of the GLM, i.e. mapping from linear predictor
         `X @ coeff + intercept` to prediction `y_pred`. Option 'auto' sets
@@ -601,10 +605,6 @@ class TweedieRegressor(GeneralizedLinearRegressor):
         - 'identity' for Normal distribution
         - 'log' for Poisson,  Gamma and Inverse Gaussian distributions
 
-    fit_intercept : bool, default=True
-        Specifies if a constant (a.k.a. bias or intercept) should be
-        added to the linear predictor (X @ coef + intercept).
-
     max_iter : int, default=100
         The maximal number of iterations for the solver.
 

sklearn/metrics/_plot/det_curve.py

@@ -22,8 +22,8 @@ class DetCurveDisplay:
     fpr : ndarray
         False positive rate.
 
-    tpr : ndarray
-        True positive rate.
+    fnr : ndarray
+        False negative rate.
 
     estimator_name : str, default=None
         Name of estimator. If None, the estimator name is not shown.

sklearn/model_selection/_search.py

@@ -1024,42 +1024,6 @@ class GridSearchCV(BaseSearchCV):
         .. versionchanged:: v0.20
            `n_jobs` default changed from 1 to None
 
-    pre_dispatch : int, or str, default=n_jobs
-        Controls the number of jobs that get dispatched during parallel
-        execution. Reducing this number can be useful to avoid an
-        explosion of memory consumption when more jobs get dispatched
-        than CPUs can process. This parameter can be:
-
-            - None, in which case all the jobs are immediately
-              created and spawned. Use this for lightweight and
-              fast-running jobs, to avoid delays due to on-demand
-              spawning of the jobs
-
-            - An int, giving the exact number of total jobs that are
-              spawned
-
-            - A str, giving an expression as a function of n_jobs,
-              as in '2*n_jobs'
-
-    cv : int, cross-validation generator or an iterable, default=None
-        Determines the cross-validation splitting strategy.
-        Possible inputs for cv are:
-
-        - None, to use the default 5-fold cross validation,
-        - integer, to specify the number of folds in a `(Stratified)KFold`,
-        - :term:`CV splitter`,
-        - An iterable yielding (train, test) splits as arrays of indices.
-
-        For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, :class:`StratifiedKFold` is used. In all
-        other cases, :class:`KFold` is used.
-
-        Refer :ref:`User Guide <cross_validation>` for the various
-        cross-validation strategies that can be used here.
-
-        .. versionchanged:: 0.22
-            ``cv`` default value if None changed from 3-fold to 5-fold.
-
     refit : bool, str, or callable, default=True
         Refit an estimator using the best found parameters on the whole
         dataset.
@@ -1090,6 +1054,25 @@ class GridSearchCV(BaseSearchCV):
         .. versionchanged:: 0.20
             Support for callable added.
 
+    cv : int, cross-validation generator or an iterable, default=None
+        Determines the cross-validation splitting strategy.
+        Possible inputs for cv are:
+
+        - None, to use the default 5-fold cross validation,
+        - integer, to specify the number of folds in a `(Stratified)KFold`,
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
+
+        For integer/None inputs, if the estimator is a classifier and ``y`` is
+        either binary or multiclass, :class:`StratifiedKFold` is used. In all
+        other cases, :class:`KFold` is used.
+
+        Refer :ref:`User Guide <cross_validation>` for the various
+        cross-validation strategies that can be used here.
+
+        .. versionchanged:: 0.22
+            ``cv`` default value if None changed from 3-fold to 5-fold.
+
     verbose : int
         Controls the verbosity: the higher, the more messages.
 
@@ -1099,6 +1082,23 @@ class GridSearchCV(BaseSearchCV):
         - >3 : the fold and candidate parameter indexes are also displayed
           together with the starting time of the computation.
 
+    pre_dispatch : int, or str, default=n_jobs
+        Controls the number of jobs that get dispatched during parallel
+        execution. Reducing this number can be useful to avoid an
+        explosion of memory consumption when more jobs get dispatched
+        than CPUs can process. This parameter can be:
+
+            - None, in which case all the jobs are immediately
+              created and spawned. Use this for lightweight and
+              fast-running jobs, to avoid delays due to on-demand
+              spawning of the jobs
+
+            - An int, giving the exact number of total jobs that are
+              spawned
+
+            - A str, giving an expression as a function of n_jobs,
+              as in '2*n_jobs'
+
     error_score : 'raise' or numeric, default=np.nan
         Value to assign to the score if an error occurs in estimator fitting.
         If set to 'raise', the error is raised. If a numeric value is given,
@@ -1366,42 +1366,6 @@ class RandomizedSearchCV(BaseSearchCV):
         .. versionchanged:: v0.20
            `n_jobs` default changed from 1 to None
 
-    pre_dispatch : int, or str, default=None
-        Controls the number of jobs that get dispatched during parallel
-        execution. Reducing this number can be useful to avoid an
-        explosion of memory consumption when more jobs get dispatched
-        than CPUs can process. This parameter can be:
-
-            - None, in which case all the jobs are immediately
-              created and spawned. Use this for lightweight and
-              fast-running jobs, to avoid delays due to on-demand
-              spawning of the jobs
-
-            - An int, giving the exact number of total jobs that are
-              spawned
-
-            - A str, giving an expression as a function of n_jobs,
-              as in '2*n_jobs'
-
-    cv : int, cross-validation generator or an iterable, default=None
-        Determines the cross-validation splitting strategy.
-        Possible inputs for cv are:
-
-        - None, to use the default 5-fold cross validation,
-        - integer, to specify the number of folds in a `(Stratified)KFold`,
-        - :term:`CV splitter`,
-        - An iterable yielding (train, test) splits as arrays of indices.
-
-        For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, :class:`StratifiedKFold` is used. In all
-        other cases, :class:`KFold` is used.
-
-        Refer :ref:`User Guide <cross_validation>` for the various
-        cross-validation strategies that can be used here.
-
-        .. versionchanged:: 0.22
-            ``cv`` default value if None changed from 3-fold to 5-fold.
-
     refit : bool, str, or callable, default=True
         Refit an estimator using the best found parameters on the whole
         dataset.
@@ -1432,9 +1396,45 @@ class RandomizedSearchCV(BaseSearchCV):
         .. versionchanged:: 0.20
             Support for callable added.
 
+    cv : int, cross-validation generator or an iterable, default=None
+        Determines the cross-validation splitting strategy.
+        Possible inputs for cv are:
+
+        - None, to use the default 5-fold cross validation,
+        - integer, to specify the number of folds in a `(Stratified)KFold`,
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
+
+        For integer/None inputs, if the estimator is a classifier and ``y`` is
+        either binary or multiclass, :class:`StratifiedKFold` is used. In all
+        other cases, :class:`KFold` is used.
+
+        Refer :ref:`User Guide <cross_validation>` for the various
+        cross-validation strategies that can be used here.
+
+        .. versionchanged:: 0.22
+            ``cv`` default value if None changed from 3-fold to 5-fold.
+
     verbose : int
         Controls the verbosity: the higher, the more messages.
 
+    pre_dispatch : int, or str, default=None
+        Controls the number of jobs that get dispatched during parallel
+        execution. Reducing this number can be useful to avoid an
+        explosion of memory consumption when more jobs get dispatched
+        than CPUs can process. This parameter can be:
+
+            - None, in which case all the jobs are immediately
+              created and spawned. Use this for lightweight and
+              fast-running jobs, to avoid delays due to on-demand
+              spawning of the jobs
+
+            - An int, giving the exact number of total jobs that are
+              spawned
+
+            - A str, giving an expression as a function of n_jobs,
+              as in '2*n_jobs'
+
     random_state : int, RandomState instance or None, default=None
         Pseudo random number generator state used for random uniform sampling
         from lists of possible values instead of scipy.stats distributions.