def test_train_split(self):
np.random.seed(42)
y = np.random.randint(1, 3, 10)
X = np.random.randint(1, 255, [10, 3], dtype=np.uint8)
cv = cross_validation.LeaveOneOut(random_state=42)
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
cv, X, y)
assert (X_train.shape[0] + X_test.shape[0] == X.shape[0])
assert (y_train.shape[0] + y_test.shape[0] == y.shape[0])
assert (np.all(np.equal(y_test, np.array([1, 2]))))
def test_compare_loo_kf(self):
cv_loo = cross_validation.LeaveOneOut(random_state=12, verbose=2)
cv_kf_as_loo = cross_validation.RandomStratifiedKFold(n_splits=False,
valid_size=1,
random_state=12,
verbose=2)
for trvl_loo, trvl_kf in zip(cv_loo.split(X, y),
cv_kf_as_loo.split(X, y)):
assert (np.all(trvl_loo[0] == trvl_kf[0]))
assert (np.all(trvl_loo[1] == trvl_kf[1]))
assert (len(trvl_kf[1]) == n_class)
assert (np.unique(y[trvl_kf[1]]).size == n_class)
#to print extensions
cv_loo.get_supported_extensions()
def test_loo(self):
for split in [False, 1, 2, 5]:
cv = cross_validation.LeaveOneOut(n_repeats=split,
random_state=split,
verbose=split)
if split == False:
assert (cv.get_n_splits(X, y) == np.min(
np.unique(y, return_counts=True)[-1]))
else:
assert (cv.get_n_splits(X, y) == split)
assert (cv.verbose == split)
for tr, vl in cv.split(X, y):
assert (tr.size == y.size - 5)
assert (vl.size == 5)
assert (len(vl) == 5)