scikit-learn · amueller · Oct 23, 2015 · Jun 4, 2015 · Jun 25, 2015 · Oct 20, 2015
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
@@ -25,6 +25,16 @@ New features
 Enhancements
 ............
 
+   - The cross-validation iterators are now modified as cross-validation splitters
+     which expose a ``split`` method that takes in the data and yields a generator
+     for the different splits. This change makes it possible to do nested cross-validation
+     with ease. (`#4294 https://github.com/scikit-learn/scikit-learn/pull/4294>`_) by `Raghav R V`_.
+
+   - The :mod:`cross_validation`, :mod:`grid_search` and :mod:`learning_curve`
+     have been deprecated and the classes and functions have been reorganized into
+     the :mod:`model_selection` module. (`#4294 https://github.com/scikit-learn/scikit-learn/pull/4294>`_) by `Raghav R V`_.
+
+
 Bug fixes
 .........
 

diff --git a/sklearn/__init__.py b/sklearn/__init__.py
@@ -62,8 +62,8 @@
                'ensemble', 'exceptions', 'externals', 'feature_extraction',
                'feature_selection', 'gaussian_process', 'grid_search',
                'isotonic', 'kernel_approximation', 'kernel_ridge',
-               'lda', 'learning_curve',
-               'linear_model', 'manifold', 'metrics', 'mixture', 'multiclass',
+               'lda', 'learning_curve', 'linear_model', 'manifold', 'metrics',
+               'mixture', 'model_selection', 'multiclass',
                'naive_bayes', 'neighbors', 'neural_network', 'pipeline',
                'preprocessing', 'qda', 'random_projection', 'semi_supervised',
                'svm', 'tree', 'discriminant_analysis',

diff --git a/sklearn/calibration.py b/sklearn/calibration.py
@@ -22,7 +22,7 @@
 from .utils.fixes import signature
 from .isotonic import IsotonicRegression
 from .svm import LinearSVC
-from .cross_validation import check_cv
+from .model_selection import check_cv
 from .metrics.classification import _check_binary_probabilistic_predictions
 
 
@@ -152,7 +152,7 @@ def fit(self, X, y, sample_weight=None):
                 calibrated_classifier.fit(X, y)
             self.calibrated_classifiers_.append(calibrated_classifier)
         else:
-            cv = check_cv(self.cv, X, y, classifier=True)
+            cv = check_cv(self.cv, y, classifier=True)
             fit_parameters = signature(base_estimator.fit).parameters
             estimator_name = type(base_estimator).__name__
             if (sample_weight is not None
@@ -163,7 +163,7 @@ def fit(self, X, y, sample_weight=None):
                 base_estimator_sample_weight = None
             else:
                 base_estimator_sample_weight = sample_weight
-            for train, test in cv:
+            for train, test in cv.split(X, y):
                 this_estimator = clone(base_estimator)
                 if base_estimator_sample_weight is not None:
                     this_estimator.fit(

diff --git a/sklearn/cluster/tests/test_bicluster.py b/sklearn/cluster/tests/test_bicluster.py
@@ -3,7 +3,7 @@
 import numpy as np
 from scipy.sparse import csr_matrix, issparse
 
-from sklearn.grid_search import ParameterGrid
+from sklearn.model_selection import ParameterGrid
 
 from sklearn.utils.testing import assert_equal
 from sklearn.utils.testing import assert_almost_equal

diff --git a/sklearn/covariance/graph_lasso_.py b/sklearn/covariance/graph_lasso_.py
@@ -21,7 +21,7 @@
 from ..utils.validation import check_random_state, check_array
 from ..linear_model import lars_path
 from ..linear_model import cd_fast
-from ..cross_validation import check_cv, cross_val_score
+from ..model_selection import check_cv, cross_val_score
 from ..externals.joblib import Parallel, delayed
 import collections
 
@@ -580,7 +580,7 @@ def fit(self, X, y=None):
         emp_cov = empirical_covariance(
             X, assume_centered=self.assume_centered)
 
-        cv = check_cv(self.cv, X, y, classifier=False)
+        cv = check_cv(self.cv, y, classifier=False)
 
         # List of (alpha, scores, covs)
         path = list()
@@ -612,14 +612,13 @@ def fit(self, X, y=None):
                 this_path = Parallel(
                     n_jobs=self.n_jobs,
                     verbose=self.verbose
-                )(
-                    delayed(graph_lasso_path)(
-                        X[train], alphas=alphas,
-                        X_test=X[test], mode=self.mode,
-                        tol=self.tol, enet_tol=self.enet_tol,
-                        max_iter=int(.1 * self.max_iter),
-                        verbose=inner_verbose)
-                    for train, test in cv)
+                )(delayed(graph_lasso_path)(X[train], alphas=alphas,
+                                            X_test=X[test], mode=self.mode,
+                                            tol=self.tol,
+                                            enet_tol=self.enet_tol,
+                                            max_iter=int(.1 * self.max_iter),
+                                            verbose=inner_verbose)
+                  for train, test in cv.split(X, y))
 
             # Little danse to transform the list in what we need
             covs, _, scores = zip(*this_path)

diff --git a/sklearn/cross_validation.py b/sklearn/cross_validation.py
@@ -34,6 +34,14 @@
 from .gaussian_process.kernels import Kernel as GPKernel
 from .exceptions import FitFailedWarning
 
+
+warnings.warn("This module has been deprecated in favor of the "
+              "model_selection module into which all the refactored classes "
+              "and functions are moved. Also note that the interface of the "
+              "new CV iterators are different from that of this module. "
+              "This module will be removed in 0.19.", DeprecationWarning)
+
+
 __all__ = ['KFold',
            'LabelKFold',
            'LeaveOneLabelOut',
@@ -304,7 +312,7 @@ class KFold(_BaseKFold):
 
     See also
     --------
-    StratifiedKFold: take label information into account to avoid building
+    StratifiedKFold take label information into account to avoid building
     folds with imbalanced class distributions (for binary or multiclass
     classification tasks).
 

diff --git a/sklearn/decomposition/tests/test_kernel_pca.py b/sklearn/decomposition/tests/test_kernel_pca.py
@@ -9,7 +9,7 @@
 from sklearn.datasets import make_circles
 from sklearn.linear_model import Perceptron
 from sklearn.pipeline import Pipeline
-from sklearn.grid_search import GridSearchCV
+from sklearn.model_selection import GridSearchCV
 from sklearn.metrics.pairwise import rbf_kernel
 
 

diff --git a/sklearn/ensemble/tests/test_bagging.py b/sklearn/ensemble/tests/test_bagging.py
@@ -21,15 +21,15 @@
 from sklearn.utils.testing import assert_warns_message
 
 from sklearn.dummy import DummyClassifier, DummyRegressor
-from sklearn.grid_search import GridSearchCV, ParameterGrid
+from sklearn.model_selection import GridSearchCV, ParameterGrid
 from sklearn.ensemble import BaggingClassifier, BaggingRegressor
 from sklearn.linear_model import Perceptron, LogisticRegression
 from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
 from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
 from sklearn.svm import SVC, SVR
 from sklearn.pipeline import make_pipeline
 from sklearn.feature_selection import SelectKBest
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.datasets import load_boston, load_iris, make_hastie_10_2
 from sklearn.utils import check_random_state
 

diff --git a/sklearn/ensemble/tests/test_forest.py b/sklearn/ensemble/tests/test_forest.py
@@ -38,7 +38,7 @@
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.ensemble import RandomTreesEmbedding
-from sklearn.grid_search import GridSearchCV
+from sklearn.model_selection import GridSearchCV
 from sklearn.svm import LinearSVC
 from sklearn.utils.fixes import bincount
 from sklearn.utils.validation import check_random_state

diff --git a/sklearn/ensemble/tests/test_voting_classifier.py b/sklearn/ensemble/tests/test_voting_classifier.py
@@ -7,9 +7,9 @@
 from sklearn.naive_bayes import GaussianNB
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.ensemble import VotingClassifier
-from sklearn.grid_search import GridSearchCV
+from sklearn.model_selection import GridSearchCV
 from sklearn import datasets
-from sklearn import cross_validation
+from sklearn.model_selection import cross_val_score
 from sklearn.datasets import make_multilabel_classification
 from sklearn.svm import SVC
 from sklearn.multiclass import OneVsRestClassifier
@@ -27,11 +27,7 @@ def test_majority_label_iris():
     eclf = VotingClassifier(estimators=[
                 ('lr', clf1), ('rf', clf2), ('gnb', clf3)],
                 voting='hard')
-    scores = cross_validation.cross_val_score(eclf,
-                                              X,
-                                              y,
-                                              cv=5,
-                                              scoring='accuracy')
+    scores = cross_val_score(eclf, X, y, cv=5, scoring='accuracy')
     assert_almost_equal(scores.mean(), 0.95, decimal=2)
 
 
@@ -55,11 +51,7 @@ def test_weights_iris():
                             ('lr', clf1), ('rf', clf2), ('gnb', clf3)],
                             voting='soft',
                             weights=[1, 2, 10])
-    scores = cross_validation.cross_val_score(eclf,
-                                              X,
-                                              y,
-                                              cv=5,
-                                              scoring='accuracy')
+    scores = cross_val_score(eclf, X, y, cv=5, scoring='accuracy')
     assert_almost_equal(scores.mean(), 0.93, decimal=2)
 
 

diff --git a/sklearn/ensemble/tests/test_weight_boosting.py b/sklearn/ensemble/tests/test_weight_boosting.py
@@ -7,8 +7,8 @@
 from sklearn.utils.testing import assert_raises, assert_raises_regexp
 
 from sklearn.base import BaseEstimator
-from sklearn.cross_validation import train_test_split
-from sklearn.grid_search import GridSearchCV
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import GridSearchCV
 from sklearn.ensemble import AdaBoostClassifier
 from sklearn.ensemble import AdaBoostRegressor
 from sklearn.ensemble import weight_boosting

diff --git a/sklearn/exceptions.py b/sklearn/exceptions.py
@@ -85,7 +85,7 @@ class FitFailedWarning(RuntimeWarning):
 
     Examples
     --------
-    >>> from sklearn.grid_search import GridSearchCV
+    >>> from sklearn.model_selection import GridSearchCV
     >>> from sklearn.svm import LinearSVC
     >>> from sklearn.exceptions import FitFailedWarning
     >>> import warnings

diff --git a/sklearn/feature_extraction/tests/test_text.py b/sklearn/feature_extraction/tests/test_text.py
@@ -12,9 +12,9 @@
 
 from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS
 
-from sklearn.cross_validation import train_test_split
-from sklearn.cross_validation import cross_val_score
-from sklearn.grid_search import GridSearchCV
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import cross_val_score
+from sklearn.model_selection import GridSearchCV
 from sklearn.pipeline import Pipeline
 from sklearn.svm import LinearSVC
 

diff --git a/sklearn/feature_selection/rfe.py b/sklearn/feature_selection/rfe.py
@@ -14,8 +14,8 @@
 from ..base import MetaEstimatorMixin
 from ..base import clone
 from ..base import is_classifier
-from ..cross_validation import check_cv
-from ..cross_validation import _safe_split, _score
+from ..model_selection import check_cv
+from ..model_selection._validation import _safe_split, _score
 from ..metrics.scorer import check_scoring
 from .base import SelectorMixin
 
@@ -373,7 +373,7 @@ def fit(self, X, y):
         X, y = check_X_y(X, y, "csr")
 
         # Initialization
-        cv = check_cv(self.cv, X, y, is_classifier(self.estimator))
+        cv = check_cv(self.cv, y, is_classifier(self.estimator))
         scorer = check_scoring(self.estimator, scoring=self.scoring)
         n_features = X.shape[1]
         n_features_to_select = 1
@@ -382,7 +382,7 @@ def fit(self, X, y):
         scores = []
 
         # Cross-validation
-        for n, (train, test) in enumerate(cv):
+        for n, (train, test) in enumerate(cv.split(X, y)):
             X_train, y_train = _safe_split(self.estimator, X, y, train)
             X_test, y_test = _safe_split(self.estimator, X, y, test, train)
 
@@ -414,7 +414,7 @@ def fit(self, X, y):
         self.estimator_ = clone(self.estimator)
         self.estimator_.fit(self.transform(X), y)
 
-        # Fixing a normalization error, n is equal to len(cv) - 1
-        # here, the scores are normalized by len(cv)
-        self.grid_scores_ = scores / len(cv)
+        # Fixing a normalization error, n is equal to get_n_splits(X, y) - 1
+        # here, the scores are normalized by get_n_splits(X, y)
+        self.grid_scores_ = scores / cv.get_n_splits(X, y)
         return self
diff --git a/sklearn/feature_selection/tests/test_rfe.py b/sklearn/feature_selection/tests/test_rfe.py
@@ -12,7 +12,7 @@
 from sklearn.metrics import zero_one_loss
 from sklearn.svm import SVC, SVR
 from sklearn.ensemble import RandomForestClassifier
-from sklearn.cross_validation import cross_val_score
+from sklearn.model_selection import cross_val_score
 
 from sklearn.utils import check_random_state
 from sklearn.utils.testing import ignore_warnings

diff --git a/sklearn/grid_search.py b/sklearn/grid_search.py
@@ -37,6 +37,12 @@
            'ParameterSampler', 'RandomizedSearchCV']
 
 
+warnings.warn("This module has been deprecated in favor of the "
+              "model_selection module into which all the refactored classes "
+              "and functions are moved. This module will be removed in 0.19.",
+              DeprecationWarning)
+
+
 class ParameterGrid(object):
     """Grid of parameters with a discrete number of values for each.
 

diff --git a/sklearn/learning_curve.py b/sklearn/learning_curve.py
@@ -17,6 +17,12 @@
 from .utils.fixes import astype
 
 
+warnings.warn("This module has been deprecated in favor of the "
+              "model_selection module into which all the functions are moved."
+              " This module will be removed in 0.19",
+              DeprecationWarning)
+
+
 __all__ = ['learning_curve', 'validation_curve']
 
 

diff --git a/sklearn/linear_model/coordinate_descent.py b/sklearn/linear_model/coordinate_descent.py
@@ -17,7 +17,7 @@
 from .base import center_data, sparse_center_data
 from ..utils import check_array, check_X_y, deprecated
 from ..utils.validation import check_random_state
-from ..cross_validation import check_cv
+from ..model_selection import check_cv
 from ..externals.joblib import Parallel, delayed
 from ..externals import six
 from ..externals.six.moves import xrange
@@ -1120,10 +1120,10 @@ def fit(self, X, y):
             path_params['copy_X'] = False
 
         # init cross-validation generator
-        cv = check_cv(self.cv, X)
+        cv = check_cv(self.cv)
 
         # Compute path for all folds and compute MSE to get the best alpha
-        folds = list(cv)
+        folds = list(cv.split(X))
         best_mse = np.inf
 
         # We do a double for loop folded in one, in order to be able to

diff --git a/sklearn/linear_model/least_angle.py b/sklearn/linear_model/least_angle.py
@@ -22,7 +22,7 @@
 from .base import LinearModel
 from ..base import RegressorMixin
 from ..utils import arrayfuncs, as_float_array, check_X_y
-from ..cross_validation import check_cv
+from ..model_selection import check_cv
 from ..exceptions import ConvergenceWarning
 from ..externals.joblib import Parallel, delayed
 from ..externals.six.moves import xrange
@@ -1079,7 +1079,7 @@ def fit(self, X, y):
         y = as_float_array(y, copy=self.copy_X)
 
         # init cross-validation generator
-        cv = check_cv(self.cv, X, y, classifier=False)
+        cv = check_cv(self.cv, classifier=False)
 
         Gram = 'auto' if self.precompute else None
 
@@ -1089,7 +1089,7 @@ def fit(self, X, y):
                 method=self.method, verbose=max(0, self.verbose - 1),
                 normalize=self.normalize, fit_intercept=self.fit_intercept,
                 max_iter=self.max_iter, eps=self.eps, positive=self.positive)
-            for train, test in cv)
+            for train, test in cv.split(X, y))
         all_alphas = np.concatenate(list(zip(*cv_paths))[0])
         # Unique also sorts
         all_alphas = np.unique(all_alphas)

diff --git a/sklearn/linear_model/logistic.py b/sklearn/linear_model/logistic.py
@@ -1,3 +1,4 @@
+
 """
 Logistic Regression
 """
@@ -32,7 +33,7 @@
 from ..utils.fixes import expit
 from ..utils.multiclass import check_classification_targets
 from ..externals.joblib import Parallel, delayed
-from ..cross_validation import check_cv
+from ..model_selection import check_cv
 from ..externals import six
 from ..metrics import SCORERS
 
@@ -1309,7 +1310,7 @@ class LogisticRegressionCV(LogisticRegression, BaseEstimator,
     cv : integer or cross-validation generator
         The default cross-validation generator used is Stratified K-Folds.
         If an integer is provided, then it is the number of folds used.
-        See the module :mod:`sklearn.cross_validation` module for the
+        See the module :mod:`sklearn.model_selection` module for the
         list of possible cross-validation objects.
 
     penalty : str, 'l1' or 'l2'
@@ -1506,8 +1507,8 @@ def fit(self, X, y, sample_weight=None):
         check_consistent_length(X, y)
 
         # init cross-validation generator
-        cv = check_cv(self.cv, X, y, classifier=True)
-        folds = list(cv)
+        cv = check_cv(self.cv, y, classifier=True)
+        folds = list(cv.split(X, y))
 
         self._enc = LabelEncoder()
         self._enc.fit(y)