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_cross_validator_with_default_params():
n_samples = 4
n_unique_labels = 4
n_splits = 2
p = 2
n_shuffle_splits = 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_splits)
skf = StratifiedKFold(n_splits)
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_splits=2, random_state=None, shuffle=False)"
skf_repr = "StratifiedKFold(n_splits=2, random_state=None, shuffle=False)"
lolo_repr = "LeaveOneLabelOut()"
lopo_repr = "LeavePLabelOut(n_labels=2)"
ss_repr = ("ShuffleSplit(n_splits=10, random_state=0, test_size=0.1, "
"train_size=None)")
ps_repr = "PredefinedSplit(test_fold=array([1, 1, 2, 2]))"
n_splits_expected = [
n_samples,
comb(n_samples, p), n_splits, n_splits, n_unique_labels,
comb(n_unique_labels, p), n_shuffle_splits, 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_expected[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))
def test_leave_label_out_changing_labels():
# Check that LeaveOneLabelOut and LeavePLabelOut work normally if
# the labels variable is changed before calling split
labels = np.array([0, 1, 2, 1, 1, 2, 0, 0])
X = np.ones(len(labels))
labels_changing = np.array(labels, copy=True)
lolo = LeaveOneLabelOut().split(X, labels=labels)
lolo_changing = LeaveOneLabelOut().split(X, labels=labels)
lplo = LeavePLabelOut(n_labels=2).split(X, labels=labels)
lplo_changing = LeavePLabelOut(n_labels=2).split(X, labels=labels)
labels_changing[:] = 0
for llo, llo_changing in [(lolo, lolo_changing), (lplo, lplo_changing)]:
for (train, test), (train_chan, test_chan) in zip(llo, llo_changing):
assert_array_equal(train, train_chan)
assert_array_equal(test, test_chan)
# n_splits = no of 2 (p) label combinations of the unique labels = 3C2 = 3
assert_equal(3, LeavePLabelOut(n_labels=2).get_n_splits(X, y, labels))
# n_splits = no of unique labels (C(uniq_lbls, 1) = n_unique_labels)
assert_equal(3, LeaveOneLabelOut().get_n_splits(X, y, labels))
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])
cvs = [
(LeaveOneOut(), "LeaveOneOut()", n_samples),
(LeavePOut(p), "LeavePOut(p=%u)" % p, comb(n_samples, p)),
(KFold(n_folds), "KFold(n_folds=2, random_state=None, shuffle=False)",
n_folds),
(StratifiedKFold(n_folds), ("StratifiedKFold(n_folds=2, "
"random_state=None, shuffle=False)"),
n_folds),
(LeaveOneLabelOut(), "LeaveOneLabelOut()", n_unique_labels),
(LeavePLabelOut(p), "LeavePLabelOut(n_labels=%u)" % p,
comb(n_unique_labels, p)),
(ShuffleSplit(random_state=0),
("ShuffleSplit(n_iter=10, random_state=0, test_size=0.1, "
"train_size=None)"), n_iter),
(PredefinedSplit([1, 1, 2, 2]),
"PredefinedSplit(test_fold=array([1, 1, 2, 2]))", 2),
]
# n_splits = np of unique folds = 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, n_splits_) in enumerate(cvs):
print(cv, cv_repr, n_splits_)
# Test if get_n_splits works correctly
assert_equal(n_splits_, 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))
def test_cross_validator_with_default_indices():
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
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 in enumerate([loo, lpo, kf, skf, lolo, lopo, ss, ps]):
# 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')
