def test_subsample_splitter_determinism(subsample_test):
# Make sure _SubsampleMetaSplitter is consistent across calls to split():
# - we're OK having training sets differ (they're always sampled with a
# different fraction anyway)
# - when we don't subsample the test set, we want it to be always the same.
# This check is the most important. This is ensured by the determinism
# of the base_cv.
# Note: we could force both train and test splits to be always the same if
# we drew an int seed in _SubsampleMetaSplitter.__init__
n_samples = 100
X, y = make_classification(n_samples)
cv = _SubsampleMetaSplitter(base_cv=KFold(5),
fraction=.5,
subsample_test=subsample_test,
random_state=None)
folds_a = list(cv.split(X, y, groups=None))
folds_b = list(cv.split(X, y, groups=None))
for (train_a, test_a), (train_b, test_b) in zip(folds_a, folds_b):
assert not np.all(train_a == train_b)
if subsample_test:
assert not np.all(test_a == test_b)
else:
assert np.all(test_a == test_b)
assert np.all(X[test_a] == X[test_b])
def test_subsample_splitter_shapes(fraction, subsample_test,
expected_train_size, expected_test_size):
# Make sure splits returned by SubsampleMetaSplitter are of appropriate
# size
n_samples = 100
X, y = make_classification(n_samples)
cv = _SubsampleMetaSplitter(base_cv=KFold(5), fraction=fraction,
subsample_test=subsample_test,
random_state=None)
for train, test in cv.split(X, y):
assert train.shape[0] == expected_train_size
assert test.shape[0] == expected_test_size
if subsample_test:
assert train.shape[0] + test.shape[0] == int(n_samples * fraction)
else:
assert test.shape[0] == n_samples // cv.base_cv.get_n_splits()
