def test_grid_search_labels():
# Check if ValueError (when labels is None) propagates to GridSearchCV
# And also check if labels is correctly passed to the cv object
rng = np.random.RandomState(0)
X, y = make_classification(n_samples=15, n_classes=2, random_state=0)
labels = rng.randint(0, 3, 15)
clf = LinearSVC(random_state=0)
grid = {'C': [1]}
label_cvs = [
LeaveOneLabelOut(),
LeavePLabelOut(2),
LabelKFold(),
LabelShuffleSplit()
]
for cv in label_cvs:
gs = GridSearchCV(clf, grid, cv=cv)
assert_raise_message(ValueError,
"The labels parameter should not be None", gs.fit,
X, y)
gs.fit(X, y, labels)
non_label_cvs = [StratifiedKFold(), StratifiedShuffleSplit()]
for cv in non_label_cvs:
gs = GridSearchCV(clf, grid, cv=cv)
# Should not raise an error
gs.fit(X, y)
def test_cross_val_score_predict_labels():
# Check if ValueError (when labels is None) propagates to cross_val_score
# and cross_val_predict
# And also check if labels is correctly passed to the cv object
X, y = make_classification(n_samples=20, n_classes=2, random_state=0)
clf = SVC(kernel="linear")
label_cvs = [
LeaveOneLabelOut(),
LeavePLabelOut(2),
LabelKFold(),
LabelShuffleSplit()
]
for cv in label_cvs:
assert_raise_message(ValueError,
"The labels parameter should not be None",
cross_val_score,
estimator=clf,
X=X,
y=y,
cv=cv)
assert_raise_message(ValueError,
"The labels parameter should not be None",
cross_val_predict,
estimator=clf,
X=X,
y=y,
cv=cv)
def test_label_shuffle_split():
labels = [
np.array([1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3]),
np.array([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3]),
np.array([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2]),
np.array([1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4])
]
for l in labels:
X = y = np.ones(len(l))
n_iter = 6
test_size = 1. / 3
slo = LabelShuffleSplit(n_iter, test_size=test_size, random_state=0)
# Make sure the repr works
repr(slo)
# Test that the length is correct
assert_equal(slo.get_n_splits(X, y, labels=l), n_iter)
l_unique = np.unique(l)
for train, test in slo.split(X, y, labels=l):
# First test: no train label is in the test set and vice versa
l_train_unique = np.unique(l[train])
l_test_unique = np.unique(l[test])
assert_false(np.any(np.in1d(l[train], l_test_unique)))
assert_false(np.any(np.in1d(l[test], l_train_unique)))
# Second test: train and test add up to all the data
assert_equal(l[train].size + l[test].size, l.size)
# Third test: train and test are disjoint
assert_array_equal(np.intersect1d(train, test), [])
# Fourth test:
# unique train and test labels are correct, +- 1 for rounding error
assert_true(
abs(len(l_test_unique) -
round(test_size * len(l_unique))) <= 1)
assert_true(
abs(
len(l_train_unique) -
round((1.0 - test_size) * len(l_unique))) <= 1)
def test_label_shuffle_split():
labels = [np.array([1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3]),
np.array([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3]),
np.array([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2]),
np.array([1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4])]
for l in labels:
X = y = np.ones(len(l))
n_iter = 6
test_size = 1./3
slo = LabelShuffleSplit(n_iter, test_size=test_size, random_state=0)
# Make sure the repr works
repr(slo)
# Test that the length is correct
assert_equal(slo.get_n_splits(X, y, labels=l), n_iter)
l_unique = np.unique(l)
for train, test in slo.split(X, y, labels=l):
# First test: no train label is in the test set and vice versa
l_train_unique = np.unique(l[train])
l_test_unique = np.unique(l[test])
assert_false(np.any(np.in1d(l[train], l_test_unique)))
assert_false(np.any(np.in1d(l[test], l_train_unique)))
# Second test: train and test add up to all the data
assert_equal(l[train].size + l[test].size, l.size)
# Third test: train and test are disjoint
assert_array_equal(np.intersect1d(train, test), [])
# Fourth test:
# unique train and test labels are correct, +- 1 for rounding error
assert_true(abs(len(l_test_unique) -
round(test_size * len(l_unique))) <= 1)
assert_true(abs(len(l_train_unique) -
round((1.0 - test_size) * len(l_unique))) <= 1)