10000 P3K fix broken doctest and add forgotten print_function import · amueller/scikit-learn@c481ff8 · GitHub
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P3K fix broken doctest and add forgotten print_function import
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‎benchmarks/bench_plot_nmf.py

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@@ -2,6 +2,8 @@
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Benchmarks of Non-Negative Matrix Factorization
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"""
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from __future__ import print_function
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import gc
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from time import time
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import numpy as np

‎doc/modules/cross_validation.rst

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@@ -162,7 +162,7 @@ Example of 2-fold::
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sklearn.cross_validation.KFold(n=4, n_folds=2)
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>>> for train, test in kf:
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... print(train, test)
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... print("%s %s" % (train, test))
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[False False True True] [ True True False False]
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[ True True False False] [False False True True]
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@@ -181,7 +181,7 @@ when creating the cross-validation procedure::
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>>> kf = KFold(len(Y), n_folds=2, indices=True)
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>>> for train, test in kf:
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... print(train, test)
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... print("%s %s" % (train, test))
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[2 3] [0 1]
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[0 1] [2 3]
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@@ -210,7 +210,7 @@ Example of stratified 2-fold::
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sklearn.cross_validation.StratifiedKFold(labels=[0 0 0 1 1 1 0], n_folds=2)
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>>> for train, test in skf:
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... print(train, test)
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... print("%s %s" % (train, test))
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[1 4 6] [0 2 3 5]
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[0 2 3 5] [1 4 6]
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@@ -233,7 +233,7 @@ not waste much data as only one sample is removed from the learning set::
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sklearn.cross_validation.LeaveOneOut(n=4)
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>>> for train, test in loo:
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... print(train, test)
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... print("%s %s" % (train, test))
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[1 2 3] [0]
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[0 2 3] [1]
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[0 1 3] [2]
@@ -257,7 +257,7 @@ Example of Leave-2-Out::
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sklearn.cross_validation.LeavePOut(n=4, p=2)
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>>> for train, test in lpo:
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... print(train, test)
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... print("%s %s" % (train, test))
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[2 3] [0 1]
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[1 3] [0 2]
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[1 2] [0 3]
@@ -291,7 +291,7 @@ a training set using the samples of all the experiments except one::
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sklearn.cross_validation.LeaveOneLabelOut(labels=[1, 1, 2, 2])
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>>> for train, test in lolo:
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... print(train, test)
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... print("%s %s" % (train, test))
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[2 3] [0 1]
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[0 1] [2 3]
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@@ -318,7 +318,7 @@ Example of Leave-2-Label Out::
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sklearn.cross_validation.LeavePLabelOut(labels=[1, 1, 2, 2, 3, 3], p=2)
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>>> for train, test in lplo:
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... print(train, test)
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... print("%s %s" % (train, test))
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[4 5] [0 1 2 3]
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[2 3] [0 1 4 5]
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[0 1] [2 3 4 5]
@@ -348,7 +348,7 @@ Here is a usage example::
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ShuffleSplit(5, n_iter=3, test_size=0.25, indices=True, ...)
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>>> for train_index, test_index in ss:
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... print(train_index, test_index)
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... print("%s %s" % (train_index, test_index))
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...
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[1 3 4] [2 0]
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[1 4 3] [0 2]
@@ -394,7 +394,7 @@ smaller than the total dataset if it is very large.
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Bootstrap(9, n_iter=3, train_size=5, test_size=4, random_state=0)
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>>> for train_index, test_index in bs:
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... print(train_index, test_index)
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... print("%s %s" % (train_index, test_index))
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...
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[1 8 7 7 8] [0 3 0 5]
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[5 4 2 4 2] [6 7 1 0]

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