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[MRG + 1] Fix sklearn.model_selection.tests.test_split:test_cv_iterable_wrapper on numpy master #7946

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Merged
merged 1 commit into from
Nov 29, 2016
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[MRG + 1] Fix sklearn.model_selection.tests.test_split:test_cv_iterable_wrapper on numpy master #7946

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19 changes: 13 additions & 6 deletions sklearn/model_selection/tests/test_split.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1028,16 +1028,23 @@ def test_cv_iterable_wrapper():
# Since the wrapped iterable is enlisted and stored,
# split can be called any number of times to produce
# consistent results.
assert_array_equal(list(kf_iter_wrapped.split(X, y)),
list(kf_iter_wrapped.split(X, y)))
np.testing.assert_equal(list(kf_iter_wrapped.split(X, y)),
list(kf_iter_wrapped.split(X, y)))
# If the splits are randomized, successive calls to split yields different
# results
kf_randomized_iter = KFold(n_splits=5, shuffle=True).split(X, y)
kf_randomized_iter_wrapped = check_cv(kf_randomized_iter)
assert_array_equal(list(kf_randomized_iter_wrapped.split(X, y)),
list(kf_randomized_iter_wrapped.split(X, y)))
assert_true(np.any(np.array(list(kf_iter_wrapped.split(X, y))) !=
np.array(list(kf_randomized_iter_wrapped.split(X, y)))))
np.testing.assert_equal(list(kf_randomized_iter_wrapped.split(X, y)),
list(kf_randomized_iter_wrapped.split(X, y)))

try:
np.testing.assert_equal(list(kf_iter_wrapped.split(X, y)),
list(kf_randomized_iter_wrapped.split(X, y)))
splits_are_equal = True
except AssertionError:
splits_are_equal = False
assert_false(splits_are_equal, "If the splits are randomized, "
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@lesteve lesteve Nov 27, 2016 •
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I am afraid I did not find a better way of emulating numpy.testing.assert_not_equal. Better suggestions welcome!

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@raghavrv just reimplemented that in #7935 where I suggested

assert_false(all([(x == y).all() for x, y in zip(cvs[1].split(*data), cv[2].split(*data))]))

which is also not the prettiest solution.

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we could also implement our own version of that?

"successive calls to split should yield different results")


def test_group_kfold():
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