MNT Make modules private in feature_selection (scikit-learn#15376)

Author: thomasjpfan

.gitignore

@@ -223,3 +223,10 @@ sklearn/semi_supervised/label_propagation.py
 
 sklearn/preprocessing/data.py
 sklearn/preprocessing/label.py
+
+sklearn/feature_selection/base.py
+sklearn/feature_selection/from_model.py
+sklearn/feature_selection/mutual_info.py
+sklearn/feature_selection/rfe.py
+sklearn/feature_selection/univariate_selection.py
+sklearn/feature_selection/variance_threshold.py

sklearn/_build_utils/deprecated_modules.py

@@ -248,6 +248,21 @@
     ('_label', 'sklearn.preprocessing.label', 'sklearn.preprocessing',
      'LabelEncoder'),
 
+    ('_base', 'sklearn.feature_selection.base', 'sklearn.feature_selection',
+     'SelectorMixin'),
+    ('_from_model', 'sklearn.feature_selection.from_model',
+     'sklearn.feature_selection', 'SelectFromModel'),
+    ('_mutual_info', 'sklearn.feature_selection.mutual_info',
+     'sklearn.feature_selection', 'mutual_info_regression'),
+    ('_rfe', 'sklearn.feature_selection.rfe',
+     'sklearn.feature_selection.rfe', 'RFE'),
+    ('_univariate_selection',
+     'sklearn.feature_selection.univariate_selection',
+     'sklearn.feature_selection', 'chi2'),
+    ('_variance_threshold',
+     'sklearn.feature_selection.variance_threshold',
+     'sklearn.feature_selection', 'VarianceThreshold'),
+
     ('_testing', 'sklearn.utils.testing', 'sklearn.utils',
      'all_estimators'),
 ]

sklearn/feature_selection/__init__.py

@@ -4,25 +4,25 @@
 recursive feature elimination algorithm.
 """
 
-from .univariate_selection import chi2
-from .univariate_selection import f_classif
-from .univariate_selection import f_oneway
-from .univariate_selection import f_regression
-from .univariate_selection import SelectPercentile
-from .univariate_selection import SelectKBest
-from .univariate_selection import SelectFpr
-from .univariate_selection import SelectFdr
-from .univariate_selection import SelectFwe
-from .univariate_selection import GenericUnivariateSelect
+from ._univariate_selection import chi2
+from ._univariate_selection import f_classif
+from ._univariate_selection import f_oneway
+from ._univariate_selection import f_regression
+from ._univariate_selection import SelectPercentile
+from ._univariate_selection import SelectKBest
+from ._univariate_selection import SelectFpr
+from ._univariate_selection import SelectFdr
+from ._univariate_selection import SelectFwe
+from ._univariate_selection import GenericUnivariateSelect
 
-from .variance_threshold import VarianceThreshold
+from ._variance_threshold import VarianceThreshold
 
-from .rfe import RFE
-from .rfe import RFECV
+from ._rfe import RFE
+from ._rfe import RFECV
 
-from .from_model import SelectFromModel
+from ._from_model import SelectFromModel
 
-from .mutual_info_ import mutual_info_regression, mutual_info_classif
+from ._mutual_info import mutual_info_regression, mutual_info_classif
 
 
 __all__ = ['GenericUnivariateSelect',

sklearn/feature_selection/base.py renamed to sklearn/feature_selection/_base.py

@@ -4,7 +4,7 @@
 import numpy as np
 import numbers
 
-from .base import SelectorMixin
+from ._base import SelectorMixin
 from ..base import BaseEstimator, clone, MetaEstimatorMixin
 
 from ..exceptions import NotFittedError

sklearn/feature_selection/from_model.py renamed to sklearn/feature_selection/_from_model.py

@@ -20,7 +20,7 @@
 from ..model_selection import check_cv
 from ..model_selection._validation import _score
 from ..metrics import check_scoring
-from .base import SelectorMixin
+from ._base import SelectorMixin
 
 
 def _rfe_single_fit(rfe, estimator, X, y, train, test, scorer):

sklearn/feature_selection/mutual_info_.py renamed to sklearn/feature_selection/_mutual_info.py

@@ -17,7 +17,7 @@
                      safe_mask)
 from ..utils.extmath import safe_sparse_dot, row_norms
 from ..utils.validation import check_is_fitted
-from .base import SelectorMixin
+from ._base import SelectorMixin
 
 
 def _clean_nans(scores):

sklearn/feature_selection/rfe.py renamed to sklearn/feature_selection/_rfe.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 from ..base import BaseEstimator
-from .base import SelectorMixin
+from ._base import SelectorMixin
 from ..utils import check_array
 from ..utils.sparsefuncs import mean_variance_axis, min_max_axis
 from ..utils.validation import check_is_fitted

sklearn/feature_selection/univariate_selection.py renamed to sklearn/feature_selection/_univariate_selection.py

@@ -5,7 +5,7 @@
 from numpy.testing import assert_array_equal
 
 from sklearn.base import BaseEstimator
-from sklearn.feature_selection.base import SelectorMixin
+from sklearn.feature_selection._base import SelectorMixin
 from sklearn.utils import check_array
 
 

sklearn/feature_selection/variance_threshold.py renamed to sklearn/feature_selection/_variance_threshold.py

@@ -11,7 +11,7 @@
 import scipy.stats
 
 from sklearn.feature_selection import SelectKBest, chi2
-from sklearn.feature_selection.univariate_selection import _chisquare
+from sklearn.feature_selection._univariate_selection import _chisquare
 from sklearn.utils._testing import assert_array_almost_equal
 from sklearn.utils._testing import assert_array_equal
 

sklearn/feature_selection/tests/test_base.py

@@ -5,8 +5,9 @@
 
 from sklearn.utils import check_random_state
 from sklearn.utils._testing import assert_array_equal, assert_almost_equal
-from sklearn.feature_selection.mutual_info_ import (
-    mutual_info_regression, mutual_info_classif, _compute_mi)
+from sklearn.feature_selection._mutual_info import _compute_mi
+from sklearn.feature_selection import (mutual_info_regression,
+                                       mutual_info_classif)
 
 
 def test_compute_mi_dd():

sklearn/feature_selection/tests/test_chi2.py

@@ -6,7 +6,7 @@
 from numpy.testing import assert_array_almost_equal, assert_array_equal
 from scipy import sparse
 
-from sklearn.feature_selection.rfe import RFE, RFECV
+from sklearn.feature_selection import RFE, RFECV
 from sklearn.datasets import load_iris, make_friedman1
 from sklearn.metrics import zero_one_loss
 from sklearn.svm import SVC, SVR