Rebuild dev docs at master=99530a3

Author: sklearn-ci

dev/_downloads/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

dev/_downloads/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

dev/_downloads/grid_search_digits.py

@@ -4,7 +4,7 @@
 ============================================================
 
 This examples shows how a classifier is optimized by cross-validation,
-which is done using the :class:`sklearn.grid_search.GridSearchCV` object
+which is done using the :class:`sklearn.model_selection.GridSearchCV` object
 on a development set that comprises only half of the available labeled data.
 
 The performance of the selected hyper-parameters and trained model is
@@ -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
 

dev/_downloads/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__)

dev/_downloads/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)
 

dev/_downloads/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

dev/_downloads/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

dev/_downloads/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.neural_network import MLPClassifier

dev/_downloads/plot_compare_gpr_krr.py

@@ -57,7 +57,7 @@
 import matplotlib.pyplot as plt
 
 from sklearn.kernel_ridge import KernelRidge
-from sklearn.grid_search import GridSearchCV
+from sklearn.model_selection import GridSearchCV
 from sklearn.gaussian_process import GaussianProcessRegressor
 from sklearn.gaussian_process.kernels import WhiteKernel, ExpSineSquared
 

dev/_downloads/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