FIX all the examples to use the new cv classes

examples/applications/face_recognition.py

@@ -31,9 +31,9 @@
 import logging
 import matplotlib.pyplot as plt
 
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import GridSearchCV
 from sklearn.datasets import fetch_lfw_people
-from sklearn.grid_search import GridSearchCV
 from sklearn.metrics import classification_report
 from sklearn.metrics import confusion_matrix
 from sklearn.decomposition import RandomizedPCA

examples/calibration/plot_calibration.py

@@ -36,7 +36,7 @@
 from sklearn.naive_bayes import GaussianNB
 from sklearn.metrics import brier_score_loss
 from sklearn.calibration import CalibratedClassifierCV
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 
 
 n_samples = 50000

examples/calibration/plot_calibration_curve.py

@@ -56,7 +56,7 @@
 from sklearn.metrics import (brier_score_loss, precision_score, recall_score,
                              f1_score)
 from sklearn.calibration import CalibratedClassifierCV, calibration_curve
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 
 
 # Create dataset of classification task with many redundant and few

examples/classification/plot_classifier_comparison.py

@@ -31,7 +31,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.colors import ListedColormap
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
 from sklearn.datasets import make_moons, make_circles, make_classification
 from sklearn.neighbors import KNeighborsClassifier

examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py

@@ -30,9 +30,9 @@
 from sklearn.cluster import FeatureAgglomeration
 from sklearn.linear_model import BayesianRidge
 from sklearn.pipeline import Pipeline
-from sklearn.grid_search import GridSearchCV
 from sklearn.externals.joblib import Memory
-from sklearn.cross_validation import KFold
+from sklearn.model_selection import GridSearchCV
+from sklearn.model_selection import KFold
 
 ###############################################################################
 # Generate data
@@ -60,7 +60,7 @@
 
 ###############################################################################
 # Compute the coefs of a Bayesian Ridge with GridSearch
-cv = KFold(len(y), 2)  # cross-validation generator for model selection
+cv = KFold(2)  # cross-validation generator for model selection
 ridge = BayesianRidge()
 cachedir = tempfile.mkdtemp()
 mem = Memory(cachedir=cachedir, verbose=1)

examples/covariance/plot_covariance_estimation.py

@@ -49,7 +49,7 @@
 
 from sklearn.covariance import LedoitWolf, OAS, ShrunkCovariance, \
     log_likelihood, empirical_covariance
-from sklearn.grid_search import GridSearchCV
+from sklearn.model_selection import GridSearchCV
 
 
 ###############################################################################

examples/decomposition/plot_pca_vs_fa_model_selection.py

@@ -35,8 +35,8 @@
 
 from sklearn.decomposition import PCA, FactorAnalysis
 from sklearn.covariance import ShrunkCovariance, LedoitWolf
-from sklearn.cross_validation import cross_val_score
-from sklearn.grid_search import GridSearchCV
+from sklearn.model_selection import cross_val_score
+from sklearn.model_selection import GridSearchCV
 
 ###############################################################################
 # Create the data

examples/ensemble/plot_gradient_boosting_oob.py

@@ -33,8 +33,8 @@
 import matplotlib.pyplot as plt
 
 from sklearn import ensemble
-from sklearn.cross_validation import KFold
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import KFold
+from sklearn.model_selection import train_test_split
 
 
 # Generate data (adapted from G. Ridgeway's gbm example)
@@ -75,10 +75,10 @@ def heldout_score(clf, X_test, y_test):
 
 
 def cv_estimate(n_folds=3):
-    cv = KFold(n=X_train.shape[0], n_folds=n_folds)
+    cv = KFold(n_folds=n_folds)
     cv_clf = ensemble.GradientBoostingClassifier(**params)
     val_scores = np.zeros((n_estimators,), dtype=np.float64)
-    for train, test in cv:
+    for train, test in cv.split(X_train, y_train):
         cv_clf.fit(X_train[train], y_train[train])
         val_scores += heldout_score(cv_clf, X_train[test], y_train[test])
     val_scores /= n_folds

examples/ensemble/plot_partial_dependence.py

@@ -51,7 +51,7 @@
 
 from mpl_toolkits.mplot3d import Axes3D
 
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.ensemble import GradientBoostingRegressor
 from sklearn.ensemble.partial_dependence import plot_partial_dependence
 from sklearn.ensemble.partial_dependence import partial_dependence

examples/exercises/plot_cv_diabetes.py

@@ -14,21 +14,25 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-from sklearn import cross_validation, datasets, linear_model
+from sklearn import datasets
+from sklearn.linear_model import LassoCV
+from sklearn.linear_model import Lasso
+from sklearn.model_selection import KFold
+from sklearn.model_selection import cross_val_score
 
 diabetes = datasets.load_diabetes()
 X = diabetes.data[:150]
 y = diabetes.target[:150]
 
-lasso = linear_model.Lasso()
+lasso = Lasso()
 alphas = np.logspace(-4, -.5, 30)
 
 scores = list()
 scores_std = list()
 
 for alpha in alphas:
     lasso.alpha = alpha
-    this_scores = cross_validation.cross_val_score(lasso, X, y, n_jobs=1)
+    this_scores = cross_val_score(lasso, X, y, n_jobs=1)
     scores.append(np.mean(this_scores))
     scores_std.append(np.std(this_scores))
 
@@ -51,15 +55,15 @@
 # performs cross-validation on the training data it receives).
 # We use external cross-validation to see how much the automatically obtained
 # alphas differ across different cross-validation folds.
-lasso_cv = linear_model.LassoCV(alphas=alphas)
-k_fold = cross_validation.KFold(len(X), 3)
+lasso_cv = LassoCV(alphas=alphas)
+k_fold = KFold(3)
 
 print("Answer to the bonus question:",
       "how much can you trust the selection of alpha?")
 print()
 print("Alpha parameters maximising the generalization score on different")
 print("subsets of the data:")
-for k, (train, test) in enumerate(k_fold):
+for k, (train, test) in enumerate(k_fold.split(X, y)):
     lasso_cv.fit(X[train], y[train])
     print("[fold {0}] alpha: {1:.5f}, score: {2:.5f}".
           format(k, lasso_cv.alpha_, lasso_cv.score(X[test], y[test])))

examples/exercises/plot_cv_digits.py

@@ -12,7 +12,8 @@
 
 
 import numpy as np
-from sklearn import cross_validation, datasets, svm
+from sklearn.model_selection import cross_val_score
+from sklearn import datasets, svm
 
 digits = datasets.load_digits()
 X = digits.data
@@ -25,7 +26,7 @@
 scores_std = list()
 for C in C_s:
     svc.C = C
-    this_scores = cross_validation.cross_val_score(svc, X, y, n_jobs=1)
+    this_scores = cross_val_score(svc, X, y, n_jobs=1)
     scores.append(np.mean(this_scores))
     scores_std.append(np.std(this_scores))
 

examples/feature_selection/plot_permutation_test_for_classification.py

@@ -20,7 +20,8 @@
 import matplotlib.pyplot as plt
 
 from sklearn.svm import SVC
-from sklearn.cross_validation import StratifiedKFold, permutation_test_score
+from sklearn.model_selection import StratifiedKFold
+from sklearn.model_selection import permutation_test_score
 from sklearn import datasets
 
 
@@ -39,7 +40,7 @@
 X = np.c_[X, E]
 
 svm = SVC(kernel='linear')
-cv = StratifiedKFold(y, 2)
+cv = StratifiedKFold(2)
 
 score, permutation_scores, pvalue = permutation_test_score(
     svm, X, y, scoring="accuracy", cv=cv, n_permutations=100, n_jobs=1)

examples/feature_selection/plot_rfe_with_cross_validation.py

@@ -10,7 +10,7 @@
 
 import matplotlib.pyplot as plt
 from sklearn.svm import SVC
-from sklearn.cross_validation import StratifiedKFold
+from sklearn.model_selection import StratifiedKFold
 from sklearn.feature_selection import RFECV
 from sklearn.datasets import make_classification
 
@@ -23,7 +23,7 @@
 svc = SVC(kernel="linear")
 # The "accuracy" scoring is proportional to the number of correct
 # classifications
-rfecv = RFECV(estimator=svc, step=1, cv=StratifiedKFold(y, 2),
+rfecv = RFECV(estimator=svc, step=1, cv=StratifiedKFold(2),
               scoring='accuracy')
 rfecv.fit(X, y)
 

examples/feature_stacker.py

@@ -20,7 +20,7 @@
 # License: BSD 3 clause
 
 from sklearn.pipeline import Pipeline, FeatureUnion
-from sklearn.grid_search import GridSearchCV
+from sklearn.model_selection import GridSearchCV
 from sklearn.svm import SVC
 from sklearn.datasets import load_iris
 from sklearn.decomposition import PCA

examples/gaussian_process/gp_diabetes_dataset.py

@@ -25,7 +25,8 @@
 
 from sklearn import datasets
 from sklearn.gaussian_process import GaussianProcess
-from sklearn.cross_validation import cross_val_score, KFold
+from sklearn.model_selection import cross_val_score
+from sklearn.model_selection import KFold
 
 # Load the dataset from scikit's data sets
 diabetes = datasets.load_diabetes()
@@ -43,9 +44,9 @@
 gp.theta0 = gp.theta_  # Given correlation parameter = MLE
 gp.thetaL, gp.thetaU = None, None  # None bounds deactivate MLE
 
-# Perform a cross-validation estimate of the coefficient of determination using
-# the cross_validation module using all CPUs available on the machine
+# Perform a cross-validated estimate of the coefficient of determination using
+# the model_selection.cross_val_score using all CPUs available on the machine
 K = 20  # folds
-R2 = cross_val_score(gp, X, y=y, cv=KFold(y.size, K), n_jobs=1).mean()
+R2 = cross_val_score(gp, X, y=y, cv=KFold(K), n_jobs=1).mean()
 print("The %d-Folds estimate of the coefficient of determination is R2 = %s"
       % (K, R2))

examples/linear_model/plot_sgd_comparison.py

@@ -14,7 +14,7 @@
 import matplotlib.pyplot as plt
 from sklearn import datasets
 
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.linear_model import SGDClassifier, Perceptron
 from sklearn.linear_model import PassiveAggressiveClassifier
 

examples/missing_values.py

@@ -28,7 +28,7 @@
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import Imputer
-from sklearn.cross_validation import cross_val_score
+from sklearn.model_selection import cross_val_score
 
 rng = np.random.RandomState(0)
 

examples/mixture/plot_gmm_classifier.py

@@ -33,7 +33,7 @@
 import numpy as np
 
 from sklearn import datasets
-from sklearn.cross_validation import StratifiedKFold
+from sklearn.model_selection import StratifiedKFold
 from sklearn.externals.six.moves import xrange
 from sklearn.mixture import GMM
 
@@ -55,9 +55,9 @@ def make_ellipses(gmm, ax):
 
 # Break up the dataset into non-overlapping training (75%) and testing
 # (25%) sets.
-skf = StratifiedKFold(iris.target, n_folds=4)
+skf = StratifiedKFold(n_folds=4)
 # Only take the first fold.
-train_index, test_index = next(iter(skf))
+train_index, test_index = next(iter(skf.split(iris.data, iris.target)))
 
 
 X_train = iris.data[train_index]

examples/model_selection/README.txt

@@ -3,5 +3,4 @@
 Model Selection
 -----------------------
 
-Examples concerning model selection, mostly contained in the
-:mod:`sklearn.grid_search` and :mod:`sklearn.cross_validation` modules.
+Examples related to the :mod:`sklearn.model_selection` module.

examples/model_selection/grid_search_digits.py

@@ -19,8 +19,8 @@
 from __future__ import print_function
 
 from sklearn import datasets
-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.metrics import classification_report
 from sklearn.svm import SVC
 

examples/model_selection/grid_search_text_feature_extraction.py

@@ -1,3 +1,4 @@
+
 """
 ==========================================================
 Sample pipeline for text feature extraction and evaluation
@@ -56,7 +57,7 @@
 from sklearn.feature_extraction.text import CountVectorizer
 from sklearn.feature_extraction.text import TfidfTransformer
 from sklearn.linear_model import SGDClassifier
-from sklearn.grid_search import GridSearchCV
+from sklearn.model_selection import GridSearchCV
 from sklearn.pipeline import Pipeline
 
 print(__doc__)

examples/model_selection/plot_confusion_matrix.py

@@ -30,7 +30,7 @@
 import matplotlib.pyplot as plt
 
 from sklearn import svm, datasets
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.metrics import confusion_matrix
 
 # import some data to play with

examples/model_selection/plot_learning_curve.py

@@ -17,11 +17,11 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from sklearn import cross_validation
 from sklearn.naive_bayes import GaussianNB
 from sklearn.svm import SVC
 from sklearn.datasets import load_digits
-from sklearn.learning_curve import learning_curve
+from sklearn.model_selection import learning_curve
+from sklearn.model_selection import ShuffleSplit
 
 
 def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
@@ -48,10 +48,20 @@ def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
     ylim : tuple, shape (ymin, ymax), optional
         Defines minimum and maximum yvalues plotted.
 
-    cv : integer, cross-validation generator, optional
-        If an integer is passed, it is the number of folds (defaults to 3).
-        Specific cross-validation objects can be passed, see
-        sklearn.cross_validation module for the list of possible objects
+    cv : int, cross-validation generator or an iterable, optional
+        Determines the cross-validation splitting strategy.
+        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.
+
+        For integer/None inputs, if ``y`` is binary or multiclass,
+        :class:`StratifiedKFold` used. If classifier is False or if ``y`` is
+        neither binary nor multiclass, :class:`KFold` is used.
+
+        Refer :ref:`User Guide <cross_validation>` for the various
+        cross-validators that can be used here.
 
     n_jobs : integer, optional
         Number of jobs to run in parallel (default 1).
@@ -91,16 +101,14 @@ def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
 title = "Learning Curves (Naive Bayes)"
 # Cross validation with 100 iterations to get smoother mean test and train
 # score curves, each time with 20% data randomly selected as a validation set.
-cv = cross_validation.ShuffleSplit(digits.data.shape[0], n_iter=100,
-                                   test_size=0.2, random_state=0)
+cv = ShuffleSplit(n_iter=100, test_size=0.2, random_state=0)
 
 estimator = GaussianNB()
 plot_learning_curve(estimator, title, X, y, ylim=(0.7, 1.01), cv=cv, n_jobs=4)
 
 title = "Learning Curves (SVM, RBF kernel, $\gamma=0.001$)"
 # SVC is more expensive so we do a lower number of CV iterations:
-cv = cross_validation.ShuffleSplit(digits.data.shape[0], n_iter=10,
-                                   test_size=0.2, random_state=0)
+cv = ShuffleSplit(n_iter=10, test_size=0.2, random_state=0)
 estimator = SVC(gamma=0.001)
 plot_learning_curve(estimator, title, X, y, (0.7, 1.01), cv=cv, n_jobs=4)
 

examples/model_selection/plot_precision_recall.py

@@ -78,7 +78,7 @@
 from sklearn import svm, datasets
 from sklearn.metrics import precision_recall_curve
 from sklearn.metrics import average_precision_score
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import label_binarize
 from sklearn.multiclass import OneVsRestClassifier