def test_cross_validator_with_default_params():
n_samples = 4
n_unique_labels = 4
n_folds = 2
p = 2
n_iter = 10 # (the default value)
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
X_1d = np.array([1, 2, 3, 4])
y = np.array([1, 1, 2, 2])
labels = np.array([1, 2, 3, 4])
loo = LeaveOneOut()
lpo = LeavePOut(p)
kf = KFold(n_folds)
skf = StratifiedKFold(n_folds)
lolo = LeaveOneLabelOut()
lopo = LeavePLabelOut(p)
ss = ShuffleSplit(random_state=0)
ps = PredefinedSplit([1, 1, 2, 2]) # n_splits = np of unique folds = 2
loo_repr = "LeaveOneOut()"
lpo_repr = "LeavePOut(p=2)"
kf_repr = "KFold(n_folds=2, random_state=None, shuffle=False)"
skf_repr = "StratifiedKFold(n_folds=2, random_state=None, shuffle=False)"
lolo_repr = "LeaveOneLabelOut()"
lopo_repr = "LeavePLabelOut(n_labels=2)"
ss_repr = ("ShuffleSplit(n_iter=10, random_state=0, test_size=0.1, "
"train_size=None)")
ps_repr = "PredefinedSplit(test_fold=array([1, 1, 2, 2]))"
n_splits = [n_samples, comb(n_samples, p), n_folds, n_folds,
n_unique_labels, comb(n_unique_labels, p), n_iter, 2]
for i, (cv, cv_repr) in enumerate(zip(
[loo, lpo, kf, skf, lolo, lopo, ss, ps],
[loo_repr, lpo_repr, kf_repr, skf_repr, lolo_repr, lopo_repr,
ss_repr, ps_repr])):
# Test if get_n_splits works correctly
assert_equal(n_splits[i], cv.get_n_splits(X, y, labels))
# Test if the cross-validator works as expected even if
# the data is 1d
np.testing.assert_equal(list(cv.split(X, y, labels)),
list(cv.split(X_1d, y, labels)))
# Test that train, test indices returned are integers
for train, test in cv.split(X, y, labels):
assert_equal(np.asarray(train).dtype.kind, 'i')
assert_equal(np.asarray(train).dtype.kind, 'i')
# Test if the repr works without any errors
assert_equal(cv_repr, repr(cv))
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