diff --git a/sklearn/model_selection/_split.py b/sklearn/model_selection/_split.py
index 151bbafd622206288a9be69846ffdb860483c7de..de889fab0bda5cfffb645145edfefc9d622f7ddc 100644
--- a/sklearn/model_selection/_split.py
+++ b/sklearn/model_selection/_split.py
@@ -188,7 +188,7 @@ class LeaveOneOut(BaseCrossValidator):
Returns the number of splitting iterations in the cross-validator.
"""
if X is None:
- raise ValueError("The X parameter should not be None")
+ raise ValueError("The 'X' parameter should not be None.")
return _num_samples(X)
@@ -259,7 +259,7 @@ class LeavePOut(BaseCrossValidator):
Always ignored, exists for compatibility.
"""
if X is None:
- raise ValueError("The X parameter should not be None")
+ raise ValueError("The 'X' parameter should not be None.")
return int(comb(_num_samples(X), self.p, exact=True))
@@ -477,7 +477,7 @@ class GroupKFold(_BaseKFold):
def _iter_test_indices(self, X, y, groups):
if groups is None:
- raise ValueError("The groups parameter should not be None")
+ raise ValueError("The 'groups' parameter should not be None.")
groups = check_array(groups, ensure_2d=False, dtype=None)
unique_groups, groups = np.unique(groups, return_inverse=True)
@@ -765,6 +765,8 @@ class LeaveOneGroupOut(BaseCrossValidator):
>>> logo = LeaveOneGroupOut()
>>> logo.get_n_splits(X, y, groups)
2
+ >>> logo.get_n_splits(groups=groups) # 'groups' is always required
+ 2
>>> print(logo)
LeaveOneGroupOut()
>>> for train_index, test_index in logo.split(X, y, groups):
@@ -785,7 +787,7 @@ class LeaveOneGroupOut(BaseCrossValidator):
def _iter_test_masks(self, X, y, groups):
if groups is None:
- raise ValueError("The groups parameter should not be None")
+ raise ValueError("The 'groups' parameter should not be None.")
# We make a copy of groups to avoid side-effects during iteration
groups = check_array(groups, copy=True, ensure_2d=False, dtype=None)
unique_groups = np.unique(groups)
@@ -796,20 +798,22 @@ class LeaveOneGroupOut(BaseCrossValidator):
for i in unique_groups:
yield groups == i
- def get_n_splits(self, X, y, groups):
+ def get_n_splits(self, X=None, y=None, groups=None):
"""Returns the number of splitting iterations in the cross-validator
Parameters
----------
- X : object
+ X : object, optional
Always ignored, exists for compatibility.
- y : object
+ y : object, optional
Always ignored, exists for compatibility.
groups : array-like, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into
- train/test set.
+ train/test set. This 'groups' parameter must always be specified to
+ calculate the number of splits, though the other parameters can be
+ omitted.
Returns
-------
@@ -817,7 +821,8 @@ class LeaveOneGroupOut(BaseCrossValidator):
Returns the number of splitting iterations in the cross-validator.
"""
if groups is None:
- raise ValueError("The groups parameter should not be None")
+ raise ValueError("The 'groups' parameter should not be None.")
+ groups = check_array(groups, ensure_2d=False, dtype=None)
return len(np.unique(groups))
@@ -852,6 +857,8 @@ class LeavePGroupsOut(BaseCrossValidator):
>>> lpgo = LeavePGroupsOut(n_groups=2)
>>> lpgo.get_n_splits(X, y, groups)
3
+ >>> lpgo.get_n_splits(groups=groups) # 'groups' is always required
+ 3
>>> print(lpgo)
LeavePGroupsOut(n_groups=2)
>>> for train_index, test_index in lpgo.split(X, y, groups):
@@ -879,7 +886,7 @@ class LeavePGroupsOut(BaseCrossValidator):
def _iter_test_masks(self, X, y, groups):
if groups is None:
- raise ValueError("The groups parameter should not be None")
+ raise ValueError("The 'groups' parameter should not be None.")
groups = check_array(groups, copy=True, ensure_2d=False, dtype=None)
unique_groups = np.unique(groups)
if self.n_groups >= len(unique_groups):
@@ -895,22 +902,22 @@ class LeavePGroupsOut(BaseCrossValidator):
test_index[groups == l] = True
yield test_index
- def get_n_splits(self, X, y, groups):
+ def get_n_splits(self, X=None, y=None, groups=None):
"""Returns the number of splitting iterations in the cross-validator
Parameters
----------
- X : object
+ X : object, optional
Always ignored, exists for compatibility.
- ``np.zeros(n_samples)`` may be used as a placeholder.
- y : object
+ y : object, optional
Always ignored, exists for compatibility.
- ``np.zeros(n_samples)`` may be used as a placeholder.
groups : array-like, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into
- train/test set.
+ train/test set. This 'groups' parameter must always be specified to
+ calculate the number of splits, though the other parameters can be
+ omitted.
Returns
-------
@@ -918,9 +925,8 @@ class LeavePGroupsOut(BaseCrossValidator):
Returns the number of splitting iterations in the cross-validator.
"""
if groups is None:
- raise ValueError("The groups parameter should not be None")
+ raise ValueError("The 'groups' parameter should not be None.")
groups = check_array(groups, ensure_2d=False, dtype=None)
- X, y, groups = indexable(X, y, groups)
return int(comb(len(np.unique(groups)), self.n_groups, exact=True))
@@ -1318,7 +1324,7 @@ class GroupShuffleSplit(ShuffleSplit):
def _iter_indices(self, X, y, groups):
if groups is None:
- raise ValueError("The groups parameter should not be None")
+ raise ValueError("The 'groups' parameter should not be None.")
groups = check_array(groups, ensure_2d=False, dtype=None)
classes, group_indices = np.unique(groups, return_inverse=True)
for group_train, group_test in super(
diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
index 055a4c061a7c031412d2f82db8bb06ea9b918b48..3f804d414b75010d1bb7013c07d55e70bc1490f2 100644
--- a/sklearn/model_selection/tests/test_search.py
+++ b/sklearn/model_selection/tests/test_search.py
@@ -317,7 +317,7 @@ def test_grid_search_groups():
for cv in group_cvs:
gs = GridSearchCV(clf, grid, cv=cv)
assert_raise_message(ValueError,
- "The groups parameter should not be None",
+ "The 'groups' parameter should not be None.",
gs.fit, X, y)
gs.fit(X, y, groups=groups)
diff --git a/sklearn/model_selection/tests/test_split.py b/sklearn/model_selection/tests/test_split.py
index fcd0160ca74eeb3aeca0419721105bcab72b2665..e97fdce5e1e5aaec7d72540998a819c3fd3f11c8 100644
--- a/sklearn/model_selection/tests/test_split.py
+++ b/sklearn/model_selection/tests/test_split.py
@@ -189,6 +189,13 @@ def test_cross_validator_with_default_params():
# Test if the repr works without any errors
assert_equal(cv_repr, repr(cv))
+ # ValueError for get_n_splits methods
+ msg = "The 'X' parameter should not be None."
+ assert_raise_message(ValueError, msg,
+ loo.get_n_splits, None, y, groups)
+ assert_raise_message(ValueError, msg,
+ lpo.get_n_splits, None, y, groups)
+
def check_valid_split(train, test, n_samples=None):
# Use python sets to get more informative assertion failure messages
@@ -757,6 +764,24 @@ def test_leave_one_p_group_out():
# The number of groups in test must be equal to p_groups_out
assert_true(np.unique(groups_arr[test]).shape[0], p_groups_out)
+ # check get_n_splits() with dummy parameters
+ assert_equal(logo.get_n_splits(None, None, ['a', 'b', 'c', 'b', 'c']), 3)
+ assert_equal(logo.get_n_splits(groups=[1.0, 1.1, 1.0, 1.2]), 3)
+ assert_equal(lpgo_2.get_n_splits(None, None, np.arange(4)), 6)
+ assert_equal(lpgo_1.get_n_splits(groups=np.arange(4)), 4)
+
+ # raise ValueError if a `groups` parameter is illegal
+ with assert_raises(ValueError):
+ logo.get_n_splits(None, None, [0.0, np.nan, 0.0])
+ with assert_raises(ValueError):
+ lpgo_2.get_n_splits(None, None, [0.0, np.inf, 0.0])
+
+ msg = "The 'groups' parameter should not be None."
+ assert_raise_message(ValueError, msg,
+ logo.get_n_splits, None, None, None)
+ assert_raise_message(ValueError, msg,
+ lpgo_1.get_n_splits, None, None, None)
+
def test_leave_group_out_changing_groups():
# Check that LeaveOneGroupOut and LeavePGroupsOut work normally if
diff --git a/sklearn/model_selection/tests/test_validation.py b/sklearn/model_selection/tests/test_validation.py
index cc6f5973a0b096c81fe1c9487242636ef4e8fa75..9228837e1be1a900bac71d81500be215b6556872 100644
--- a/sklearn/model_selection/tests/test_validation.py
+++ b/sklearn/model_selection/tests/test_validation.py
@@ -259,10 +259,10 @@ def test_cross_val_score_predict_groups():
GroupShuffleSplit()]
for cv in group_cvs:
assert_raise_message(ValueError,
- "The groups parameter should not be None",
+ "The 'groups' parameter should not be None.",
cross_val_score, estimator=clf, X=X, y=y, cv=cv)
assert_raise_message(ValueError,
- "The groups parameter should not be None",
+ "The 'groups' parameter should not be None.",
cross_val_predict, estimator=clf, X=X, y=y, cv=cv)