scikit-learn · amueller · Nov 12, 2018 · Oct 30, 2018 · Oct 31, 2018 · Nov 3, 2018
diff --git a/doc/modules/cross_validation.rst b/doc/modules/cross_validation.rst
@@ -142,6 +142,21 @@ validation iterator instead, for instance::
   >>> cross_val_score(clf, iris.data, iris.target, cv=cv)  # doctest: +ELLIPSIS
   array([0.977..., 0.977..., 1.  ..., 0.955..., 1.        ])
 
+Another option is to use an iterable yielding (train, test) splits as arrays of
+indices, for example::
+
+  >>> def custom_cv_2folds(X):
+  ...     n = X.shape[0]
+  ...     i = 1
+  ...     while i <= 2:
+  ...         idx = np.arange(n * (i - 1) / 2, n * i / 2, dtype=int)
+  ...         yield idx, idx
+  ...         i += 1
+  ...
+  >>> custom_cv = custom_cv_2folds(iris.data)
+  >>> cross_val_score(clf, iris.data, iris.target, cv=custom_cv)
+  array([1.        , 0.973...])
+
 .. topic:: Data transformation with held out data
 
     Just as it is important to test a predictor on data held-out from

diff --git a/examples/model_selection/plot_learning_curve.py b/examples/model_selection/plot_learning_curve.py
@@ -53,8 +53,8 @@ def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
         Possible inputs for cv are:
           - None, to use the default 3-fold cross-validation,
           - integer, to specify the number of folds.
-          - An object to be used as a cross-validation generator.
-          - An iterable yielding train/test splits.
+          - :term:`CV splitter`,
+          - An iterable yielding (train, test) splits as arrays of indices.
 
         For integer/None inputs, if ``y`` is binary or multiclass,
         :class:`StratifiedKFold` used. If the estimator is not a classifier

diff --git a/sklearn/calibration.py b/sklearn/calibration.py
@@ -63,8 +63,8 @@ class CalibratedClassifierCV(BaseEstimator, ClassifierMixin):
 
         - None, to use the default 3-fold cross-validation,
         - integer, to specify the number of folds.
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train/test splits.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
 
         For integer/None inputs, if ``y`` is binary or multiclass,
         :class:`sklearn.model_selection.StratifiedKFold` is used. If ``y`` is

diff --git a/sklearn/covariance/graph_lasso_.py b/sklearn/covariance/graph_lasso_.py
@@ -491,8 +491,8 @@ class GraphicalLassoCV(GraphicalLasso):
 
         - None, to use the default 3-fold cross-validation,
         - integer, to specify the number of folds.
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train/test splits.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
 
         For integer/None inputs :class:`KFold` is used.
 
@@ -897,8 +897,8 @@ class GraphLassoCV(GraphicalLassoCV):
 
         - None, to use the default 3-fold cross-validation,
         - integer, to specify the number of folds.
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train/test splits.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
 
         For integer/None inputs :class:`KFold` is used.
 

diff --git a/sklearn/feature_selection/rfe.py b/sklearn/feature_selection/rfe.py
@@ -356,8 +356,8 @@ class RFECV(RFE, MetaEstimatorMixin):
 
         - None, to use the default 3-fold cross-validation,
         - integer, to specify the number of folds.
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train/test splits.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
 
         For integer/None inputs, if ``y`` is binary or multiclass,
         :class:`sklearn.model_selection.StratifiedKFold` is used. If the

diff --git a/sklearn/linear_model/coordinate_descent.py b/sklearn/linear_model/coordinate_descent.py
@@ -1307,8 +1307,8 @@ class LassoCV(LinearModelCV, RegressorMixin):
 
         - None, to use the default 3-fold cross-validation,
         - integer, to specify the number of folds.
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train/test splits.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
 
         For integer/None inputs, :class:`KFold` is used.
 
@@ -1477,8 +1477,8 @@ class ElasticNetCV(LinearModelCV, RegressorMixin):
 
         - None, to use the default 3-fold cross-validation,
         - integer, to specify the number of folds.
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train/test splits.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
 
         For integer/None inputs, :class:`KFold` is used.
 
@@ -2015,8 +2015,8 @@ class MultiTaskElasticNetCV(LinearModelCV, RegressorMixin):
 
         - None, to use the default 3-fold cross-validation,
         - integer, to specify the number of folds.
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train/test splits.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
 
         For integer/None inputs, :class:`KFold` is used.
 
@@ -2194,8 +2194,8 @@ class MultiTaskLassoCV(LinearModelCV, RegressorMixin):
 
         - None, to use the default 3-fold cross-validation,
         - integer, to specify the number of folds.
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train/test splits.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
 
         For integer/None inputs, :class:`KFold` is used.
 

diff --git a/sklearn/linear_model/least_angle.py b/sklearn/linear_model/least_angle.py
@@ -1007,8 +1007,8 @@ class LarsCV(Lars):
 
         - None, to use the default 3-fold cross-validation,
         - integer, to specify the number of folds.
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train/test splits.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
 
         For integer/None inputs, :class:`KFold` is used.
 
@@ -1230,8 +1230,8 @@ class LassoLarsCV(LarsCV):
 
         - None, to use the default 3-fold cross-validation,
         - integer, to specify the number of folds.
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train/test splits.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
 
         For integer/None inputs, :class:`KFold` is used.
 

diff --git a/sklearn/linear_model/omp.py b/sklearn/linear_model/omp.py
@@ -790,8 +790,8 @@ class OrthogonalMatchingPursuitCV(LinearModel, RegressorMixin):
 
         - None, to use the default 3-fold cross-validation,
         - integer, to specify the number of folds.
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train/test splits.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
 
         For integer/None inputs, :class:`KFold` is used.
 

diff --git a/sklearn/linear_model/ridge.py b/sklearn/linear_model/ridge.py
@@ -1211,8 +1211,8 @@ class RidgeCV(_BaseRidgeCV, RegressorMixin):
 
         - None, to use the efficient Leave-One-Out cross-validation
         - integer, to specify the number of folds.
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train/test splits.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
 
         For integer/None inputs, if ``y`` is binary or multiclass,
         :class:`sklearn.model_selection.StratifiedKFold` is used, else,
@@ -1323,8 +1323,8 @@ class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV):
 
         - None, to use the efficient Leave-One-Out cross-validation
         - integer, to specify the number of folds.
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train/test splits.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
 
         Refer :ref:`User Guide <cross_validation>` for the various
         cross-validation strategies that can be used here.

diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py
@@ -901,8 +901,8 @@ class GridSearchCV(BaseSearchCV):
 
         - None, to use the default 3-fold cross validation,
         - integer, to specify the number of folds in a `(Stratified)KFold`,
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train, test splits.
+        - :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
@@ -1235,8 +1235,8 @@ class RandomizedSearchCV(BaseSearchCV):
 
         - None, to use the default 3-fold cross validation,
         - integer, to specify the number of folds in a `(Stratified)KFold`,
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train, test splits.
+        - :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

diff --git a/sklearn/model_selection/_split.py b/sklearn/model_selection/_split.py
@@ -1913,8 +1913,8 @@ def check_cv(cv='warn', y=None, classifier=False):
 
         - None, to use the default 3-fold cross-validation,
         - integer, to specify the number of folds.
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train/test splits.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
 
         For integer/None inputs, if classifier is True and ``y`` is either
         binary or multiclass, :class:`StratifiedKFold` is used. In all other

diff --git a/sklearn/model_selection/_validation.py b/sklearn/model_selection/_validation.py
@@ -82,8 +82,8 @@ def cross_validate(estimator, X, y=None, groups=None, scoring=None, cv='warn',
 
         - None, to use the default 3-fold cross validation,
         - integer, to specify the number of folds in a `(Stratified)KFold`,
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train, test splits.
+        - :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
@@ -292,8 +292,8 @@ def cross_val_score(estimator, X, y=None, groups=None, scoring=None, cv='warn',
 
         - None, to use the default 3-fold cross validation,
         - integer, to specify the number of folds in a `(Stratified)KFold`,
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train, test splits.
+        - :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
@@ -666,8 +666,8 @@ def cross_val_predict(estimator, X, y=None, groups=None, cv='warn',
 
         - None, to use the default 3-fold cross validation,
         - integer, to specify the number of folds in a `(Stratified)KFold`,
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train, test splits.
+        - :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
@@ -957,8 +957,8 @@ def permutation_test_score(estimator, X, y, groups=None, cv='warn',
 
         - None, to use the default 3-fold cross validation,
         - integer, to specify the number of folds in a `(Stratified)KFold`,
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train, test splits.
+        - :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
@@ -1109,8 +1109,8 @@ def learning_curve(estimator, X, y, groups=None,
 
         - None, to use the default 3-fold cross validation,
         - integer, to specify the number of folds in a `(Stratified)KFold`,
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train, test splits.
+        - :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
@@ -1360,8 +1360,8 @@ def validation_curve(estimator, X, y, param_name, param_range, groups=None,
 
         - None, to use the default 3-fold cross validation,
         - integer, to specify the number of folds in a `(Stratified)KFold`,
-        - An object to be used as a cross-validation generator.
-        - An iterable yielding train, test splits.
+        - :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

diff --git a/sklearn/multioutput.py b/sklearn/multioutput.py
@@ -511,9 +511,9 @@ class ClassifierChain(_BaseChain, ClassifierMixin, MetaEstimatorMixin):
         If cv is None the true labels are used when fitting. Otherwise
         possible inputs for cv are:
 
-        * integer, to specify the number of folds in a (Stratified)KFold,
-        * An object to be used as a cross-validation generator.
-        * An iterable yielding train, test splits.
+        - integer, to specify the number of folds in a (Stratified)KFold,
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
 
     random_state : int, RandomState instance or None, optional (default=None)
         If int, random_state is the seed used by the random number generator;
@@ -667,9 +667,9 @@ class RegressorChain(_BaseChain, RegressorMixin, MetaEstimatorMixin):
         If cv is None the true labels are used when fitting. Otherwise
         possible inputs for cv are:
 
-        * integer, to specify the number of folds in a (Stratified)KFold,
-        * An object to be used as a cross-validation generator.
-        * An iterable yielding train, test splits.
+        - integer, to specify the number of folds in a (Stratified)KFold,
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
 
     random_state : int, RandomState instance or None, optional (default=None)
         If int, random_state is the seed used by the random number generator;