def test_stratified_kfold_no_shuffle():
# Manually check that StratifiedKFold preserves the data ordering as much
# as possible on toy datasets in order to avoid hiding sample dependencies
# when possible
splits = iter(cval.StratifiedKFold([1, 1, 0, 0], 2))
train, test = next(splits)
assert_array_equal(test, [0, 2])
assert_array_equal(train, [1, 3])
train, test = next(splits)
assert_array_equal(test, [1, 3])
assert_array_equal(train, [0, 2])
splits = iter(cval.StratifiedKFold([1, 1, 1, 0, 0, 0, 0], 2))
train, test = next(splits)
assert_array_equal(test, [0, 1, 3, 4])
assert_array_equal(train, [2, 5, 6])
train, test = next(splits)
assert_array_equal(test, [2, 5, 6])
assert_array_equal(train, [0, 1, 3, 4])
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