def test_nested_cv():
# Test if nested cross validation works with different combinations of cv
rng = np.random.RandomState(0)
X, y = make_classification(n_samples=15, n_classes=2, random_state=0)
labels = rng.randint(0, 5, 15)
cvs = [
LeaveOneLabelOut(),
LeaveOneOut(),
LabelKFold(),
StratifiedKFold(),
StratifiedShuffleSplit(n_iter=10, random_state=0)
]
for inner_cv, outer_cv in combinations_with_replacement(cvs, 2):
gs = GridSearchCV(LinearSVC(random_state=0),
param_grid={'C': [1, 10]},
cv=inner_cv)
cross_val_score(gs,
X=X,
y=y,
labels=labels,
cv=outer_cv,
fit_params={'labels': labels})
def test_nested_cv():
# Test if nested cross validation works with different combinations of cv
rng = np.random.RandomState(0)
X, y = make_classification(n_samples=15, n_classes=2, random_state=0)
labels = rng.randint(0, 5, 15)
cvs = [LeaveOneLabelOut(), LeaveOneOut(), LabelKFold(), StratifiedKFold(),
StratifiedShuffleSplit(n_iter=10, random_state=0)]
for inner_cv, outer_cv in combinations_with_replacement(cvs, 2):
gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
cv=inner_cv)
cross_val_score(gs, X=X, y=y, labels=labels, cv=outer_cv,
fit_params={'labels': labels})
def test_nested_cv():
# Test if nested cross validation works with different combinations of cv
rng = np.random.RandomState(0)
X, y = make_classification(n_samples=15, n_classes=2, random_state=0)
groups = rng.randint(0, 5, 15)
cvs = [LeaveOneGroupOut(), LeaveOneOut(), GroupKFold(), StratifiedKFold(),
StratifiedShuffleSplit(n_splits=3, random_state=0)]
for inner_cv, outer_cv in combinations_with_replacement(cvs, 2):
gs = GridSearchCV(Ridge(), param_grid={'alpha': [1, .1]},
cv=inner_cv)
cross_val_score(gs, X=X, y=y, groups=groups, cv=outer_cv,
fit_params={'groups': groups})
def test_nested_cv():
# Test if nested cross validation works with different combinations of cv
rng = np.random.RandomState(0)
X, y = make_classification(n_samples=15, n_classes=2, random_state=0)
groups = rng.randint(0, 5, 15)
cvs = [LeaveOneGroupOut(), LeaveOneOut(), GroupKFold(), StratifiedKFold(),
StratifiedShuffleSplit(n_splits=3, random_state=0)]
for inner_cv, outer_cv in combinations_with_replacement(cvs, 2):
gs = GridSearchCV(Ridge(), param_grid={'alpha': [1, .1]},
cv=inner_cv)
cross_val_score(gs, X=X, y=y, groups=groups, cv=outer_cv,
fit_params={'groups': groups})
