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Vehicle-Anti-Theft-Face-Rec.../venv/Lib/site-packages/sklearn/model_selection/tests/test_split.py

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2020-11-12 16:05:57 +00:00
"""Test the split module"""
import warnings
import pytest
import numpy as np
from scipy.sparse import coo_matrix, csc_matrix, csr_matrix
from scipy import stats
from scipy.special import comb
from itertools import combinations
from itertools import combinations_with_replacement
from itertools import permutations
from sklearn.utils._testing import assert_allclose
from sklearn.utils._testing import assert_raises
from sklearn.utils._testing import assert_raises_regexp
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_warns_message
from sklearn.utils._testing import assert_raise_message
from sklearn.utils._testing import ignore_warnings
from sklearn.utils.validation import _num_samples
from sklearn.utils._mocking import MockDataFrame
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import GroupKFold
from sklearn.model_selection import TimeSeriesSplit
from sklearn.model_selection import LeaveOneOut
from sklearn.model_selection import LeaveOneGroupOut
from sklearn.model_selection import LeavePOut
from sklearn.model_selection import LeavePGroupsOut
from sklearn.model_selection import ShuffleSplit
from sklearn.model_selection import GroupShuffleSplit
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.model_selection import PredefinedSplit
from sklearn.model_selection import check_cv
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RepeatedKFold
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.linear_model import Ridge
from sklearn.model_selection._split import _validate_shuffle_split
from sklearn.model_selection._split import _build_repr
from sklearn.datasets import load_digits
from sklearn.datasets import make_classification
from sklearn.svm import SVC
X = np.ones(10)
y = np.arange(10) // 2
P_sparse = coo_matrix(np.eye(5))
test_groups = (
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]),
[1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3],
['1', '1', '1', '1', '2', '2', '2', '3', '3', '3', '3', '3'])
digits = load_digits()
@ignore_warnings
def test_cross_validator_with_default_params():
n_samples = 4
n_unique_groups = 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])
groups = np.array([1, 2, 3, 4])
loo = LeaveOneOut()
lpo = LeavePOut(p)
kf = KFold(n_splits)
skf = StratifiedKFold(n_splits)
lolo = LeaveOneGroupOut()
lopo = LeavePGroupsOut(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 = "LeaveOneGroupOut()"
lopo_repr = "LeavePGroupsOut(n_groups=2)"
ss_repr = ("ShuffleSplit(n_splits=10, random_state=0, "
"test_size=None, 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_groups, comb(n_unique_groups, 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 n_splits_expected[i] == cv.get_n_splits(X, y, groups)
# Test if the cross-validator works as expected even if
# the data is 1d
np.testing.assert_equal(list(cv.split(X, y, groups)),
list(cv.split(X_1d, y, groups)))
# Test that train, test indices returned are integers
for train, test in cv.split(X, y, groups):
assert np.asarray(train).dtype.kind == 'i'
assert np.asarray(test).dtype.kind == 'i'
# Test if the repr works without any errors
assert 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 test_2d_y():
# smoke test for 2d y and multi-label
n_samples = 30
rng = np.random.RandomState(1)
X = rng.randint(0, 3, size=(n_samples, 2))
y = rng.randint(0, 3, size=(n_samples,))
y_2d = y.reshape(-1, 1)
y_multilabel = rng.randint(0, 2, size=(n_samples, 3))
groups = rng.randint(0, 3, size=(n_samples,))
splitters = [LeaveOneOut(), LeavePOut(p=2), KFold(), StratifiedKFold(),
RepeatedKFold(), RepeatedStratifiedKFold(),
ShuffleSplit(), StratifiedShuffleSplit(test_size=.5),
GroupShuffleSplit(), LeaveOneGroupOut(),
LeavePGroupsOut(n_groups=2), GroupKFold(n_splits=3),
TimeSeriesSplit(), PredefinedSplit(test_fold=groups)]
for splitter in splitters:
list(splitter.split(X, y, groups))
list(splitter.split(X, y_2d, groups))
try:
list(splitter.split(X, y_multilabel, groups))
except ValueError as e:
allowed_target_types = ('binary', 'multiclass')
msg = "Supported target types are: {}. Got 'multilabel".format(
allowed_target_types)
assert msg in str(e)
def check_valid_split(train, test, n_samples=None):
# Use python sets to get more informative assertion failure messages
train, test = set(train), set(test)
# Train and test split should not overlap
assert train.intersection(test) == set()
if n_samples is not None:
# Check that the union of train an test split cover all the indices
assert train.union(test) == set(range(n_samples))
def check_cv_coverage(cv, X, y, groups, expected_n_splits):
n_samples = _num_samples(X)
# Check that a all the samples appear at least once in a test fold
assert cv.get_n_splits(X, y, groups) == expected_n_splits
collected_test_samples = set()
iterations = 0
for train, test in cv.split(X, y, groups):
check_valid_split(train, test, n_samples=n_samples)
iterations += 1
collected_test_samples.update(test)
# Check that the accumulated test samples cover the whole dataset
assert iterations == expected_n_splits
if n_samples is not None:
assert collected_test_samples == set(range(n_samples))
def test_kfold_valueerrors():
X1 = np.array([[1, 2], [3, 4], [5, 6]])
X2 = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])
# Check that errors are raised if there is not enough samples
(ValueError, next, KFold(4).split(X1))
# Check that a warning is raised if the least populated class has too few
# members.
y = np.array([3, 3, -1, -1, 3])
skf_3 = StratifiedKFold(3)
assert_warns_message(Warning, "The least populated class",
next, skf_3.split(X2, y))
# Check that despite the warning the folds are still computed even
# though all the classes are not necessarily represented at on each
# side of the split at each split
with warnings.catch_warnings():
warnings.simplefilter("ignore")
check_cv_coverage(skf_3, X2, y, groups=None, expected_n_splits=3)
# Check that errors are raised if all n_groups for individual
# classes are less than n_splits.
y = np.array([3, 3, -1, -1, 2])
assert_raises(ValueError, next, skf_3.split(X2, y))
# Error when number of folds is <= 1
assert_raises(ValueError, KFold, 0)
assert_raises(ValueError, KFold, 1)
error_string = ("k-fold cross-validation requires at least one"
" train/test split")
assert_raise_message(ValueError, error_string,
StratifiedKFold, 0)
assert_raise_message(ValueError, error_string,
StratifiedKFold, 1)
# When n_splits is not integer:
assert_raises(ValueError, KFold, 1.5)
assert_raises(ValueError, KFold, 2.0)
assert_raises(ValueError, StratifiedKFold, 1.5)
assert_raises(ValueError, StratifiedKFold, 2.0)
# When shuffle is not a bool:
assert_raises(TypeError, KFold, n_splits=4, shuffle=None)
def test_kfold_indices():
# Check all indices are returned in the test folds
X1 = np.ones(18)
kf = KFold(3)
check_cv_coverage(kf, X1, y=None, groups=None, expected_n_splits=3)
# Check all indices are returned in the test folds even when equal-sized
# folds are not possible
X2 = np.ones(17)
kf = KFold(3)
check_cv_coverage(kf, X2, y=None, groups=None, expected_n_splits=3)
# Check if get_n_splits returns the number of folds
assert 5 == KFold(5).get_n_splits(X2)
def test_kfold_no_shuffle():
# Manually check that KFold preserves the data ordering on toy datasets
X2 = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
splits = KFold(2).split(X2[:-1])
train, test = next(splits)
assert_array_equal(test, [0, 1])
assert_array_equal(train, [2, 3])
train, test = next(splits)
assert_array_equal(test, [2, 3])
assert_array_equal(train, [0, 1])
splits = KFold(2).split(X2)
train, test = next(splits)
assert_array_equal(test, [0, 1, 2])
assert_array_equal(train, [3, 4])
train, test = next(splits)
assert_array_equal(test, [3, 4])
assert_array_equal(train, [0, 1, 2])
def test_stratified_kfold_no_shuffle():
# Manually check that StratifiedKFold preserves the data ordering as much
# as possible on toy datasets in order to avoid hiding sample dependencies
# when possible
X, y = np.ones(4), [1, 1, 0, 0]
splits = StratifiedKFold(2).split(X, y)
train, test = next(splits)
assert_array_equal(test, [0, 2])
assert_array_equal(train, [1, 3])
train, test = next(splits)
assert_array_equal(test, [1, 3])
assert_array_equal(train, [0, 2])
X, y = np.ones(7), [1, 1, 1, 0, 0, 0, 0]
splits = StratifiedKFold(2).split(X, y)
train, test = next(splits)
assert_array_equal(test, [0, 1, 3, 4])
assert_array_equal(train, [2, 5, 6])
train, test = next(splits)
assert_array_equal(test, [2, 5, 6])
assert_array_equal(train, [0, 1, 3, 4])
# Check if get_n_splits returns the number of folds
assert 5 == StratifiedKFold(5).get_n_splits(X, y)
# Make sure string labels are also supported
X = np.ones(7)
y1 = ['1', '1', '1', '0', '0', '0', '0']
y2 = [1, 1, 1, 0, 0, 0, 0]
np.testing.assert_equal(
list(StratifiedKFold(2).split(X, y1)),
list(StratifiedKFold(2).split(X, y2)))
# Check equivalence to KFold
y = [0, 1, 0, 1, 0, 1, 0, 1]
X = np.ones_like(y)
np.testing.assert_equal(
list(StratifiedKFold(3).split(X, y)),
list(KFold(3).split(X, y)))
@pytest.mark.parametrize('shuffle', [False, True])
@pytest.mark.parametrize('k', [4, 5, 6, 7, 8, 9, 10])
def test_stratified_kfold_ratios(k, shuffle):
# Check that stratified kfold preserves class ratios in individual splits
# Repeat with shuffling turned off and on
n_samples = 1000
X = np.ones(n_samples)
y = np.array([4] * int(0.10 * n_samples) +
[0] * int(0.89 * n_samples) +
[1] * int(0.01 * n_samples))
distr = np.bincount(y) / len(y)
test_sizes = []
random_state = None if not shuffle else 0
skf = StratifiedKFold(k, random_state=random_state, shuffle=shuffle)
for train, test in skf.split(X, y):
assert_allclose(np.bincount(y[train]) / len(train), distr, atol=0.02)
assert_allclose(np.bincount(y[test]) / len(test), distr, atol=0.02)
test_sizes.append(len(test))
assert np.ptp(test_sizes) <= 1
@pytest.mark.parametrize('shuffle', [False, True])
@pytest.mark.parametrize('k', [4, 6, 7])
def test_stratified_kfold_label_invariance(k, shuffle):
# Check that stratified kfold gives the same indices regardless of labels
n_samples = 100
y = np.array([2] * int(0.10 * n_samples) +
[0] * int(0.89 * n_samples) +
[1] * int(0.01 * n_samples))
X = np.ones(len(y))
def get_splits(y):
random_state = None if not shuffle else 0
return [(list(train), list(test))
for train, test
in StratifiedKFold(k, random_state=random_state,
shuffle=shuffle).split(X, y)]
splits_base = get_splits(y)
for perm in permutations([0, 1, 2]):
y_perm = np.take(perm, y)
splits_perm = get_splits(y_perm)
assert splits_perm == splits_base
def test_kfold_balance():
# Check that KFold returns folds with balanced sizes
for i in range(11, 17):
kf = KFold(5).split(X=np.ones(i))
sizes = [len(test) for _, test in kf]
assert (np.max(sizes) - np.min(sizes)) <= 1
assert np.sum(sizes) == i
def test_stratifiedkfold_balance():
# Check that KFold returns folds with balanced sizes (only when
# stratification is possible)
# Repeat with shuffling turned off and on
X = np.ones(17)
y = [0] * 3 + [1] * 14
for shuffle in (True, False):
cv = StratifiedKFold(3, shuffle=shuffle)
for i in range(11, 17):
skf = cv.split(X[:i], y[:i])
sizes = [len(test) for _, test in skf]
assert (np.max(sizes) - np.min(sizes)) <= 1
assert np.sum(sizes) == i
def test_shuffle_kfold():
# Check the indices are shuffled properly
kf = KFold(3)
kf2 = KFold(3, shuffle=True, random_state=0)
kf3 = KFold(3, shuffle=True, random_state=1)
X = np.ones(300)
all_folds = np.zeros(300)
for (tr1, te1), (tr2, te2), (tr3, te3) in zip(
kf.split(X), kf2.split(X), kf3.split(X)):
for tr_a, tr_b in combinations((tr1, tr2, tr3), 2):
# Assert that there is no complete overlap
assert len(np.intersect1d(tr_a, tr_b)) != len(tr1)
# Set all test indices in successive iterations of kf2 to 1
all_folds[te2] = 1
# Check that all indices are returned in the different test folds
assert sum(all_folds) == 300
def test_shuffle_kfold_stratifiedkfold_reproducibility():
X = np.ones(15) # Divisible by 3
y = [0] * 7 + [1] * 8
X2 = np.ones(16) # Not divisible by 3
y2 = [0] * 8 + [1] * 8
# Check that when the shuffle is True, multiple split calls produce the
# same split when random_state is int
kf = KFold(3, shuffle=True, random_state=0)
skf = StratifiedKFold(3, shuffle=True, random_state=0)
for cv in (kf, skf):
np.testing.assert_equal(list(cv.split(X, y)), list(cv.split(X, y)))
np.testing.assert_equal(list(cv.split(X2, y2)), list(cv.split(X2, y2)))
# Check that when the shuffle is True, multiple split calls often
# (not always) produce different splits when random_state is
# RandomState instance or None
kf = KFold(3, shuffle=True, random_state=np.random.RandomState(0))
skf = StratifiedKFold(3, shuffle=True,
random_state=np.random.RandomState(0))
for cv in (kf, skf):
for data in zip((X, X2), (y, y2)):
# Test if the two splits are different cv
for (_, test_a), (_, test_b) in zip(cv.split(*data),
cv.split(*data)):
# cv.split(...) returns an array of tuples, each tuple
# consisting of an array with train indices and test indices
# Ensure that the splits for data are not same
# when random state is not set
with pytest.raises(AssertionError):
np.testing.assert_array_equal(test_a, test_b)
def test_shuffle_stratifiedkfold():
# Check that shuffling is happening when requested, and for proper
# sample coverage
X_40 = np.ones(40)
y = [0] * 20 + [1] * 20
kf0 = StratifiedKFold(5, shuffle=True, random_state=0)
kf1 = StratifiedKFold(5, shuffle=True, random_state=1)
for (_, test0), (_, test1) in zip(kf0.split(X_40, y),
kf1.split(X_40, y)):
assert set(test0) != set(test1)
check_cv_coverage(kf0, X_40, y, groups=None, expected_n_splits=5)
# Ensure that we shuffle each class's samples with different
# random_state in StratifiedKFold
# See https://github.com/scikit-learn/scikit-learn/pull/13124
X = np.arange(10)
y = [0] * 5 + [1] * 5
kf1 = StratifiedKFold(5, shuffle=True, random_state=0)
kf2 = StratifiedKFold(5, shuffle=True, random_state=1)
test_set1 = sorted([tuple(s[1]) for s in kf1.split(X, y)])
test_set2 = sorted([tuple(s[1]) for s in kf2.split(X, y)])
assert test_set1 != test_set2
def test_kfold_can_detect_dependent_samples_on_digits(): # see #2372
# The digits samples are dependent: they are apparently grouped by authors
# although we don't have any information on the groups segment locations
# for this data. We can highlight this fact by computing k-fold cross-
# validation with and without shuffling: we observe that the shuffling case
# wrongly makes the IID assumption and is therefore too optimistic: it
# estimates a much higher accuracy (around 0.93) than that the non
# shuffling variant (around 0.81).
X, y = digits.data[:600], digits.target[:600]
model = SVC(C=10, gamma=0.005)
n_splits = 3
cv = KFold(n_splits=n_splits, shuffle=False)
mean_score = cross_val_score(model, X, y, cv=cv).mean()
assert 0.92 > mean_score
assert mean_score > 0.80
# Shuffling the data artificially breaks the dependency and hides the
# overfitting of the model with regards to the writing style of the authors
# by yielding a seriously overestimated score:
cv = KFold(n_splits, shuffle=True, random_state=0)
mean_score = cross_val_score(model, X, y, cv=cv).mean()
assert mean_score > 0.92
cv = KFold(n_splits, shuffle=True, random_state=1)
mean_score = cross_val_score(model, X, y, cv=cv).mean()
assert mean_score > 0.92
# Similarly, StratifiedKFold should try to shuffle the data as little
# as possible (while respecting the balanced class constraints)
# and thus be able to detect the dependency by not overestimating
# the CV score either. As the digits dataset is approximately balanced
# the estimated mean score is close to the score measured with
# non-shuffled KFold
cv = StratifiedKFold(n_splits)
mean_score = cross_val_score(model, X, y, cv=cv).mean()
assert 0.94 > mean_score
assert mean_score > 0.80
def test_shuffle_split():
ss1 = ShuffleSplit(test_size=0.2, random_state=0).split(X)
ss2 = ShuffleSplit(test_size=2, random_state=0).split(X)
ss3 = ShuffleSplit(test_size=np.int32(2), random_state=0).split(X)
ss4 = ShuffleSplit(test_size=int(2), random_state=0).split(X)
for t1, t2, t3, t4 in zip(ss1, ss2, ss3, ss4):
assert_array_equal(t1[0], t2[0])
assert_array_equal(t2[0], t3[0])
assert_array_equal(t3[0], t4[0])
assert_array_equal(t1[1], t2[1])
assert_array_equal(t2[1], t3[1])
assert_array_equal(t3[1], t4[1])
@pytest.mark.parametrize("split_class", [ShuffleSplit,
StratifiedShuffleSplit])
@pytest.mark.parametrize("train_size, exp_train, exp_test",
[(None, 9, 1),
(8, 8, 2),
(0.8, 8, 2)])
def test_shuffle_split_default_test_size(split_class, train_size, exp_train,
exp_test):
# Check that the default value has the expected behavior, i.e. 0.1 if both
# unspecified or complement train_size unless both are specified.
X = np.ones(10)
y = np.ones(10)
X_train, X_test = next(split_class(train_size=train_size).split(X, y))
assert len(X_train) == exp_train
assert len(X_test) == exp_test
@pytest.mark.parametrize("train_size, exp_train, exp_test",
[(None, 8, 2),
(7, 7, 3),
(0.7, 7, 3)])
def test_group_shuffle_split_default_test_size(train_size, exp_train,
exp_test):
# Check that the default value has the expected behavior, i.e. 0.2 if both
# unspecified or complement train_size unless both are specified.
X = np.ones(10)
y = np.ones(10)
groups = range(10)
X_train, X_test = next(GroupShuffleSplit(train_size=train_size)
.split(X, y, groups))
assert len(X_train) == exp_train
assert len(X_test) == exp_test
@ignore_warnings
def test_stratified_shuffle_split_init():
X = np.arange(7)
y = np.asarray([0, 1, 1, 1, 2, 2, 2])
# Check that error is raised if there is a class with only one sample
assert_raises(ValueError, next,
StratifiedShuffleSplit(3, 0.2).split(X, y))
# Check that error is raised if the test set size is smaller than n_classes
assert_raises(ValueError, next, StratifiedShuffleSplit(3, 2).split(X, y))
# Check that error is raised if the train set size is smaller than
# n_classes
assert_raises(ValueError, next,
StratifiedShuffleSplit(3, 3, 2).split(X, y))
X = np.arange(9)
y = np.asarray([0, 0, 0, 1, 1, 1, 2, 2, 2])
# Train size or test size too small
assert_raises(ValueError, next,
StratifiedShuffleSplit(train_size=2).split(X, y))
assert_raises(ValueError, next,
StratifiedShuffleSplit(test_size=2).split(X, y))
def test_stratified_shuffle_split_respects_test_size():
y = np.array([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2])
test_size = 5
train_size = 10
sss = StratifiedShuffleSplit(6, test_size=test_size, train_size=train_size,
random_state=0).split(np.ones(len(y)), y)
for train, test in sss:
assert len(train) == train_size
assert len(test) == test_size
def test_stratified_shuffle_split_iter():
ys = [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] * 2),
np.array([1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4]),
np.array([-1] * 800 + [1] * 50),
np.concatenate([[i] * (100 + i) for i in range(11)]),
[1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3],
['1', '1', '1', '1', '2', '2', '2', '3', '3', '3', '3', '3'],
]
for y in ys:
sss = StratifiedShuffleSplit(6, test_size=0.33,
random_state=0).split(np.ones(len(y)), y)
y = np.asanyarray(y) # To make it indexable for y[train]
# this is how test-size is computed internally
# in _validate_shuffle_split
test_size = np.ceil(0.33 * len(y))
train_size = len(y) - test_size
for train, test in sss:
assert_array_equal(np.unique(y[train]), np.unique(y[test]))
# Checks if folds keep classes proportions
p_train = (np.bincount(np.unique(y[train],
return_inverse=True)[1]) /
float(len(y[train])))
p_test = (np.bincount(np.unique(y[test],
return_inverse=True)[1]) /
float(len(y[test])))
assert_array_almost_equal(p_train, p_test, 1)
assert len(train) + len(test) == y.size
assert len(train) == train_size
assert len(test) == test_size
assert_array_equal(np.lib.arraysetops.intersect1d(train, test), [])
def test_stratified_shuffle_split_even():
# Test the StratifiedShuffleSplit, indices are drawn with a
# equal chance
n_folds = 5
n_splits = 1000
def assert_counts_are_ok(idx_counts, p):
# Here we test that the distribution of the counts
# per index is close enough to a binomial
threshold = 0.05 / n_splits
bf = stats.binom(n_splits, p)
for count in idx_counts:
prob = bf.pmf(count)
assert prob > threshold, \
"An index is not drawn with chance corresponding to even draws"
for n_samples in (6, 22):
groups = np.array((n_samples // 2) * [0, 1])
splits = StratifiedShuffleSplit(n_splits=n_splits,
test_size=1. / n_folds,
random_state=0)
train_counts = [0] * n_samples
test_counts = [0] * n_samples
n_splits_actual = 0
for train, test in splits.split(X=np.ones(n_samples), y=groups):
n_splits_actual += 1
for counter, ids in [(train_counts, train), (test_counts, test)]:
for id in ids:
counter[id] += 1
assert n_splits_actual == n_splits
n_train, n_test = _validate_shuffle_split(
n_samples, test_size=1. / n_folds, train_size=1. - (1. / n_folds))
assert len(train) == n_train
assert len(test) == n_test
assert len(set(train).intersection(test)) == 0
group_counts = np.unique(groups)
assert splits.test_size == 1.0 / n_folds
assert n_train + n_test == len(groups)
assert len(group_counts) == 2
ex_test_p = float(n_test) / n_samples
ex_train_p = float(n_train) / n_samples
assert_counts_are_ok(train_counts, ex_train_p)
assert_counts_are_ok(test_counts, ex_test_p)
def test_stratified_shuffle_split_overlap_train_test_bug():
# See https://github.com/scikit-learn/scikit-learn/issues/6121 for
# the original bug report
y = [0, 1, 2, 3] * 3 + [4, 5] * 5
X = np.ones_like(y)
sss = StratifiedShuffleSplit(n_splits=1,
test_size=0.5, random_state=0)
train, test = next(sss.split(X=X, y=y))
# no overlap
assert_array_equal(np.intersect1d(train, test), [])
# complete partition
assert_array_equal(np.union1d(train, test), np.arange(len(y)))
def test_stratified_shuffle_split_multilabel():
# fix for issue 9037
for y in [np.array([[0, 1], [1, 0], [1, 0], [0, 1]]),
np.array([[0, 1], [1, 1], [1, 1], [0, 1]])]:
X = np.ones_like(y)
sss = StratifiedShuffleSplit(n_splits=1, test_size=0.5, random_state=0)
train, test = next(sss.split(X=X, y=y))
y_train = y[train]
y_test = y[test]
# no overlap
assert_array_equal(np.intersect1d(train, test), [])
# complete partition
assert_array_equal(np.union1d(train, test), np.arange(len(y)))
# correct stratification of entire rows
# (by design, here y[:, 0] uniquely determines the entire row of y)
expected_ratio = np.mean(y[:, 0])
assert expected_ratio == np.mean(y_train[:, 0])
assert expected_ratio == np.mean(y_test[:, 0])
def test_stratified_shuffle_split_multilabel_many_labels():
# fix in PR #9922: for multilabel data with > 1000 labels, str(row)
# truncates with an ellipsis for elements in positions 4 through
# len(row) - 4, so labels were not being correctly split using the powerset
# method for transforming a multilabel problem to a multiclass one; this
# test checks that this problem is fixed.
row_with_many_zeros = [1, 0, 1] + [0] * 1000 + [1, 0, 1]
row_with_many_ones = [1, 0, 1] + [1] * 1000 + [1, 0, 1]
y = np.array([row_with_many_zeros] * 10 + [row_with_many_ones] * 100)
X = np.ones_like(y)
sss = StratifiedShuffleSplit(n_splits=1, test_size=0.5, random_state=0)
train, test = next(sss.split(X=X, y=y))
y_train = y[train]
y_test = y[test]
# correct stratification of entire rows
# (by design, here y[:, 4] uniquely determines the entire row of y)
expected_ratio = np.mean(y[:, 4])
assert expected_ratio == np.mean(y_train[:, 4])
assert expected_ratio == np.mean(y_test[:, 4])
def test_predefinedsplit_with_kfold_split():
# Check that PredefinedSplit can reproduce a split generated by Kfold.
folds = np.full(10, -1.)
kf_train = []
kf_test = []
for i, (train_ind, test_ind) in enumerate(KFold(5, shuffle=True).split(X)):
kf_train.append(train_ind)
kf_test.append(test_ind)
folds[test_ind] = i
ps = PredefinedSplit(folds)
# n_splits is simply the no of unique folds
assert len(np.unique(folds)) == ps.get_n_splits()
ps_train, ps_test = zip(*ps.split())
assert_array_equal(ps_train, kf_train)
assert_array_equal(ps_test, kf_test)
def test_group_shuffle_split():
for groups_i in test_groups:
X = y = np.ones(len(groups_i))
n_splits = 6
test_size = 1. / 3
slo = GroupShuffleSplit(n_splits, test_size=test_size, random_state=0)
# Make sure the repr works
repr(slo)
# Test that the length is correct
assert slo.get_n_splits(X, y, groups=groups_i) == n_splits
l_unique = np.unique(groups_i)
l = np.asarray(groups_i)
for train, test in slo.split(X, y, groups=groups_i):
# First test: no train group is in the test set and vice versa
l_train_unique = np.unique(l[train])
l_test_unique = np.unique(l[test])
assert not np.any(np.in1d(l[train], l_test_unique))
assert not np.any(np.in1d(l[test], l_train_unique))
# Second test: train and test add up to all the data
assert 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 groups are correct, +- 1 for rounding error
assert abs(len(l_test_unique) -
round(test_size * len(l_unique))) <= 1
assert abs(len(l_train_unique) -
round((1.0 - test_size) * len(l_unique))) <= 1
def test_leave_one_p_group_out():
logo = LeaveOneGroupOut()
lpgo_1 = LeavePGroupsOut(n_groups=1)
lpgo_2 = LeavePGroupsOut(n_groups=2)
# Make sure the repr works
assert repr(logo) == 'LeaveOneGroupOut()'
assert repr(lpgo_1) == 'LeavePGroupsOut(n_groups=1)'
assert repr(lpgo_2) == 'LeavePGroupsOut(n_groups=2)'
assert (repr(LeavePGroupsOut(n_groups=3)) ==
'LeavePGroupsOut(n_groups=3)')
for j, (cv, p_groups_out) in enumerate(((logo, 1), (lpgo_1, 1),
(lpgo_2, 2))):
for i, groups_i in enumerate(test_groups):
n_groups = len(np.unique(groups_i))
n_splits = (n_groups if p_groups_out == 1
else n_groups * (n_groups - 1) / 2)
X = y = np.ones(len(groups_i))
# Test that the length is correct
assert cv.get_n_splits(X, y, groups=groups_i) == n_splits
groups_arr = np.asarray(groups_i)
# Split using the original list / array / list of string groups_i
for train, test in cv.split(X, y, groups=groups_i):
# First test: no train group is in the test set and vice versa
assert_array_equal(np.intersect1d(groups_arr[train],
groups_arr[test]).tolist(),
[])
# Second test: train and test add up to all the data
assert len(train) + len(test) == len(groups_i)
# Third test:
# The number of groups in test must be equal to p_groups_out
assert np.unique(groups_arr[test]).shape[0], p_groups_out
# check get_n_splits() with dummy parameters
assert logo.get_n_splits(None, None, ['a', 'b', 'c', 'b', 'c']) == 3
assert logo.get_n_splits(groups=[1.0, 1.1, 1.0, 1.2]) == 3
assert lpgo_2.get_n_splits(None, None, np.arange(4)) == 6
assert 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
# the groups variable is changed before calling split
groups = np.array([0, 1, 2, 1, 1, 2, 0, 0])
X = np.ones(len(groups))
groups_changing = np.array(groups, copy=True)
lolo = LeaveOneGroupOut().split(X, groups=groups)
lolo_changing = LeaveOneGroupOut().split(X, groups=groups)
lplo = LeavePGroupsOut(n_groups=2).split(X, groups=groups)
lplo_changing = LeavePGroupsOut(n_groups=2).split(X, groups=groups)
groups_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) group combinations of the unique groups = 3C2 = 3
assert (
3 == LeavePGroupsOut(n_groups=2).get_n_splits(X, y=X,
groups=groups))
# n_splits = no of unique groups (C(uniq_lbls, 1) = n_unique_groups)
assert 3 == LeaveOneGroupOut().get_n_splits(X, y=X,
groups=groups)
def test_leave_one_p_group_out_error_on_fewer_number_of_groups():
X = y = groups = np.ones(0)
assert_raise_message(ValueError, "Found array with 0 sample(s)", next,
LeaveOneGroupOut().split(X, y, groups))
X = y = groups = np.ones(1)
msg = ("The groups parameter contains fewer than 2 unique groups ({}). "
"LeaveOneGroupOut expects at least 2.").format(groups)
assert_raise_message(ValueError, msg, next,
LeaveOneGroupOut().split(X, y, groups))
X = y = groups = np.ones(1)
msg = ("The groups parameter contains fewer than (or equal to) n_groups "
"(3) numbers of unique groups ({}). LeavePGroupsOut expects "
"that at least n_groups + 1 (4) unique groups "
"be present").format(groups)
assert_raise_message(ValueError, msg, next,
LeavePGroupsOut(n_groups=3).split(X, y, groups))
X = y = groups = np.arange(3)
msg = ("The groups parameter contains fewer than (or equal to) n_groups "
"(3) numbers of unique groups ({}). LeavePGroupsOut expects "
"that at least n_groups + 1 (4) unique groups "
"be present").format(groups)
assert_raise_message(ValueError, msg, next,
LeavePGroupsOut(n_groups=3).split(X, y, groups))
@ignore_warnings
def test_repeated_cv_value_errors():
# n_repeats is not integer or <= 0
for cv in (RepeatedKFold, RepeatedStratifiedKFold):
assert_raises(ValueError, cv, n_repeats=0)
assert_raises(ValueError, cv, n_repeats=1.5)
@pytest.mark.parametrize(
"RepeatedCV", [RepeatedKFold, RepeatedStratifiedKFold]
)
def test_repeated_cv_repr(RepeatedCV):
n_splits, n_repeats = 2, 6
repeated_cv = RepeatedCV(n_splits=n_splits, n_repeats=n_repeats)
repeated_cv_repr = ('{}(n_repeats=6, n_splits=2, random_state=None)'
.format(repeated_cv.__class__.__name__))
assert repeated_cv_repr == repr(repeated_cv)
def test_repeated_kfold_determinstic_split():
X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
random_state = 258173307
rkf = RepeatedKFold(
n_splits=2,
n_repeats=2,
random_state=random_state)
# split should produce same and deterministic splits on
# each call
for _ in range(3):
splits = rkf.split(X)
train, test = next(splits)
assert_array_equal(train, [2, 4])
assert_array_equal(test, [0, 1, 3])
train, test = next(splits)
assert_array_equal(train, [0, 1, 3])
assert_array_equal(test, [2, 4])
train, test = next(splits)
assert_array_equal(train, [0, 1])
assert_array_equal(test, [2, 3, 4])
train, test = next(splits)
assert_array_equal(train, [2, 3, 4])
assert_array_equal(test, [0, 1])
assert_raises(StopIteration, next, splits)
def test_get_n_splits_for_repeated_kfold():
n_splits = 3
n_repeats = 4
rkf = RepeatedKFold(n_splits=n_splits, n_repeats=n_repeats)
expected_n_splits = n_splits * n_repeats
assert expected_n_splits == rkf.get_n_splits()
def test_get_n_splits_for_repeated_stratified_kfold():
n_splits = 3
n_repeats = 4
rskf = RepeatedStratifiedKFold(n_splits=n_splits, n_repeats=n_repeats)
expected_n_splits = n_splits * n_repeats
assert expected_n_splits == rskf.get_n_splits()
def test_repeated_stratified_kfold_determinstic_split():
X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
y = [1, 1, 1, 0, 0]
random_state = 1944695409
rskf = RepeatedStratifiedKFold(
n_splits=2,
n_repeats=2,
random_state=random_state)
# split should produce same and deterministic splits on
# each call
for _ in range(3):
splits = rskf.split(X, y)
train, test = next(splits)
assert_array_equal(train, [1, 4])
assert_array_equal(test, [0, 2, 3])
train, test = next(splits)
assert_array_equal(train, [0, 2, 3])
assert_array_equal(test, [1, 4])
train, test = next(splits)
assert_array_equal(train, [2, 3])
assert_array_equal(test, [0, 1, 4])
train, test = next(splits)
assert_array_equal(train, [0, 1, 4])
assert_array_equal(test, [2, 3])
assert_raises(StopIteration, next, splits)
def test_train_test_split_errors():
pytest.raises(ValueError, train_test_split)
pytest.raises(ValueError, train_test_split, range(3), train_size=1.1)
pytest.raises(ValueError, train_test_split, range(3), test_size=0.6,
train_size=0.6)
pytest.raises(ValueError, train_test_split, range(3),
test_size=np.float32(0.6), train_size=np.float32(0.6))
pytest.raises(ValueError, train_test_split, range(3),
test_size="wrong_type")
pytest.raises(ValueError, train_test_split, range(3), test_size=2,
train_size=4)
pytest.raises(TypeError, train_test_split, range(3),
some_argument=1.1)
pytest.raises(ValueError, train_test_split, range(3), range(42))
pytest.raises(ValueError, train_test_split, range(10),
shuffle=False, stratify=True)
with pytest.raises(ValueError,
match=r'train_size=11 should be either positive and '
r'smaller than the number of samples 10 or a '
r'float in the \(0, 1\) range'):
train_test_split(range(10), train_size=11, test_size=1)
@pytest.mark.parametrize("train_size,test_size", [
(1.2, 0.8),
(1., 0.8),
(0.0, 0.8),
(-.2, 0.8),
(0.8, 1.2),
(0.8, 1.),
(0.8, 0.),
(0.8, -.2)])
def test_train_test_split_invalid_sizes1(train_size, test_size):
with pytest.raises(ValueError,
match=r'should be .* in the \(0, 1\) range'):
train_test_split(range(10), train_size=train_size, test_size=test_size)
@pytest.mark.parametrize("train_size,test_size", [
(-10, 0.8),
(0, 0.8),
(11, 0.8),
(0.8, -10),
(0.8, 0),
(0.8, 11)])
def test_train_test_split_invalid_sizes2(train_size, test_size):
with pytest.raises(ValueError,
match=r'should be either positive and smaller'):
train_test_split(range(10), train_size=train_size, test_size=test_size)
@pytest.mark.parametrize("train_size, exp_train, exp_test",
[(None, 7, 3),
(8, 8, 2),
(0.8, 8, 2)])
def test_train_test_split_default_test_size(train_size, exp_train, exp_test):
# Check that the default value has the expected behavior, i.e. complement
# train_size unless both are specified.
X_train, X_test = train_test_split(X, train_size=train_size)
assert len(X_train) == exp_train
assert len(X_test) == exp_test
def test_train_test_split():
X = np.arange(100).reshape((10, 10))
X_s = coo_matrix(X)
y = np.arange(10)
# simple test
split = train_test_split(X, y, test_size=None, train_size=.5)
X_train, X_test, y_train, y_test = split
assert len(y_test) == len(y_train)
# test correspondence of X and y
assert_array_equal(X_train[:, 0], y_train * 10)
assert_array_equal(X_test[:, 0], y_test * 10)
# don't convert lists to anything else by default
split = train_test_split(X, X_s, y.tolist())
X_train, X_test, X_s_train, X_s_test, y_train, y_test = split
assert isinstance(y_train, list)
assert isinstance(y_test, list)
# allow nd-arrays
X_4d = np.arange(10 * 5 * 3 * 2).reshape(10, 5, 3, 2)
y_3d = np.arange(10 * 7 * 11).reshape(10, 7, 11)
split = train_test_split(X_4d, y_3d)
assert split[0].shape == (7, 5, 3, 2)
assert split[1].shape == (3, 5, 3, 2)
assert split[2].shape == (7, 7, 11)
assert split[3].shape == (3, 7, 11)
# test stratification option
y = np.array([1, 1, 1, 1, 2, 2, 2, 2])
for test_size, exp_test_size in zip([2, 4, 0.25, 0.5, 0.75],
[2, 4, 2, 4, 6]):
train, test = train_test_split(y, test_size=test_size,
stratify=y,
random_state=0)
assert len(test) == exp_test_size
assert len(test) + len(train) == len(y)
# check the 1:1 ratio of ones and twos in the data is preserved
assert np.sum(train == 1) == np.sum(train == 2)
# test unshuffled split
y = np.arange(10)
for test_size in [2, 0.2]:
train, test = train_test_split(y, shuffle=False, test_size=test_size)
assert_array_equal(test, [8, 9])
assert_array_equal(train, [0, 1, 2, 3, 4, 5, 6, 7])
@ignore_warnings
def test_train_test_split_pandas():
# check train_test_split doesn't destroy pandas dataframe
types = [MockDataFrame]
try:
from pandas import DataFrame
types.append(DataFrame)
except ImportError:
pass
for InputFeatureType in types:
# X dataframe
X_df = InputFeatureType(X)
X_train, X_test = train_test_split(X_df)
assert isinstance(X_train, InputFeatureType)
assert isinstance(X_test, InputFeatureType)
def test_train_test_split_sparse():
# check that train_test_split converts scipy sparse matrices
# to csr, as stated in the documentation
X = np.arange(100).reshape((10, 10))
sparse_types = [csr_matrix, csc_matrix, coo_matrix]
for InputFeatureType in sparse_types:
X_s = InputFeatureType(X)
X_train, X_test = train_test_split(X_s)
assert isinstance(X_train, csr_matrix)
assert isinstance(X_test, csr_matrix)
def test_train_test_split_mock_pandas():
# X mock dataframe
X_df = MockDataFrame(X)
X_train, X_test = train_test_split(X_df)
assert isinstance(X_train, MockDataFrame)
assert isinstance(X_test, MockDataFrame)
X_train_arr, X_test_arr = train_test_split(X_df)
def test_train_test_split_list_input():
# Check that when y is a list / list of string labels, it works.
X = np.ones(7)
y1 = ['1'] * 4 + ['0'] * 3
y2 = np.hstack((np.ones(4), np.zeros(3)))
y3 = y2.tolist()
for stratify in (True, False):
X_train1, X_test1, y_train1, y_test1 = train_test_split(
X, y1, stratify=y1 if stratify else None, random_state=0)
X_train2, X_test2, y_train2, y_test2 = train_test_split(
X, y2, stratify=y2 if stratify else None, random_state=0)
X_train3, X_test3, y_train3, y_test3 = train_test_split(
X, y3, stratify=y3 if stratify else None, random_state=0)
np.testing.assert_equal(X_train1, X_train2)
np.testing.assert_equal(y_train2, y_train3)
np.testing.assert_equal(X_test1, X_test3)
np.testing.assert_equal(y_test3, y_test2)
@pytest.mark.parametrize("test_size, train_size",
[(2.0, None),
(1.0, None),
(0.1, 0.95),
(None, 1j),
(11, None),
(10, None),
(8, 3)])
def test_shufflesplit_errors(test_size, train_size):
with pytest.raises(ValueError):
next(ShuffleSplit(test_size=test_size, train_size=train_size).split(X))
def test_shufflesplit_reproducible():
# Check that iterating twice on the ShuffleSplit gives the same
# sequence of train-test when the random_state is given
ss = ShuffleSplit(random_state=21)
assert_array_equal(list(a for a, b in ss.split(X)),
list(a for a, b in ss.split(X)))
def test_stratifiedshufflesplit_list_input():
# Check that when y is a list / list of string labels, it works.
sss = StratifiedShuffleSplit(test_size=2, random_state=42)
X = np.ones(7)
y1 = ['1'] * 4 + ['0'] * 3
y2 = np.hstack((np.ones(4), np.zeros(3)))
y3 = y2.tolist()
np.testing.assert_equal(list(sss.split(X, y1)),
list(sss.split(X, y2)))
np.testing.assert_equal(list(sss.split(X, y3)),
list(sss.split(X, y2)))
def test_train_test_split_allow_nans():
# Check that train_test_split allows input data with NaNs
X = np.arange(200, dtype=np.float64).reshape(10, -1)
X[2, :] = np.nan
y = np.repeat([0, 1], X.shape[0] / 2)
train_test_split(X, y, test_size=0.2, random_state=42)
def test_check_cv():
X = np.ones(9)
cv = check_cv(3, classifier=False)
# Use numpy.testing.assert_equal which recursively compares
# lists of lists
np.testing.assert_equal(list(KFold(3).split(X)), list(cv.split(X)))
y_binary = np.array([0, 1, 0, 1, 0, 0, 1, 1, 1])
cv = check_cv(3, y_binary, classifier=True)
np.testing.assert_equal(list(StratifiedKFold(3).split(X, y_binary)),
list(cv.split(X, y_binary)))
y_multiclass = np.array([0, 1, 0, 1, 2, 1, 2, 0, 2])
cv = check_cv(3, y_multiclass, classifier=True)
np.testing.assert_equal(list(StratifiedKFold(3).split(X, y_multiclass)),
list(cv.split(X, y_multiclass)))
# also works with 2d multiclass
y_multiclass_2d = y_multiclass.reshape(-1, 1)
cv = check_cv(3, y_multiclass_2d, classifier=True)
np.testing.assert_equal(list(StratifiedKFold(3).split(X, y_multiclass_2d)),
list(cv.split(X, y_multiclass_2d)))
assert not np.all(
next(StratifiedKFold(3).split(X, y_multiclass_2d))[0] ==
next(KFold(3).split(X, y_multiclass_2d))[0])
X = np.ones(5)
y_multilabel = np.array([[0, 0, 0, 0], [0, 1, 1, 0], [0, 0, 0, 1],
[1, 1, 0, 1], [0, 0, 1, 0]])
cv = check_cv(3, y_multilabel, classifier=True)
np.testing.assert_equal(list(KFold(3).split(X)), list(cv.split(X)))
y_multioutput = np.array([[1, 2], [0, 3], [0, 0], [3, 1], [2, 0]])
cv = check_cv(3, y_multioutput, classifier=True)
np.testing.assert_equal(list(KFold(3).split(X)), list(cv.split(X)))
assert_raises(ValueError, check_cv, cv="lolo")
def test_cv_iterable_wrapper():
kf_iter = KFold().split(X, y)
kf_iter_wrapped = check_cv(kf_iter)
# Since the wrapped iterable is enlisted and stored,
# split can be called any number of times to produce
# consistent results.
np.testing.assert_equal(list(kf_iter_wrapped.split(X, y)),
list(kf_iter_wrapped.split(X, y)))
# If the splits are randomized, successive calls to split yields different
# results
kf_randomized_iter = KFold(shuffle=True, random_state=0).split(X, y)
kf_randomized_iter_wrapped = check_cv(kf_randomized_iter)
# numpy's assert_array_equal properly compares nested lists
np.testing.assert_equal(list(kf_randomized_iter_wrapped.split(X, y)),
list(kf_randomized_iter_wrapped.split(X, y)))
try:
splits_are_equal = True
np.testing.assert_equal(list(kf_iter_wrapped.split(X, y)),
list(kf_randomized_iter_wrapped.split(X, y)))
except AssertionError:
splits_are_equal = False
assert not splits_are_equal, (
"If the splits are randomized, "
"successive calls to split should yield different results")
def test_group_kfold():
rng = np.random.RandomState(0)
# Parameters of the test
n_groups = 15
n_samples = 1000
n_splits = 5
X = y = np.ones(n_samples)
# Construct the test data
tolerance = 0.05 * n_samples # 5 percent error allowed
groups = rng.randint(0, n_groups, n_samples)
ideal_n_groups_per_fold = n_samples // n_splits
len(np.unique(groups))
# Get the test fold indices from the test set indices of each fold
folds = np.zeros(n_samples)
lkf = GroupKFold(n_splits=n_splits)
for i, (_, test) in enumerate(lkf.split(X, y, groups)):
folds[test] = i
# Check that folds have approximately the same size
assert len(folds) == len(groups)
for i in np.unique(folds):
assert (tolerance >=
abs(sum(folds == i) - ideal_n_groups_per_fold))
# Check that each group appears only in 1 fold
for group in np.unique(groups):
assert len(np.unique(folds[groups == group])) == 1
# Check that no group is on both sides of the split
groups = np.asarray(groups, dtype=object)
for train, test in lkf.split(X, y, groups):
assert len(np.intersect1d(groups[train], groups[test])) == 0
# Construct the test data
groups = np.array(['Albert', 'Jean', 'Bertrand', 'Michel', 'Jean',
'Francis', 'Robert', 'Michel', 'Rachel', 'Lois',
'Michelle', 'Bernard', 'Marion', 'Laura', 'Jean',
'Rachel', 'Franck', 'John', 'Gael', 'Anna', 'Alix',
'Robert', 'Marion', 'David', 'Tony', 'Abel', 'Becky',
'Madmood', 'Cary', 'Mary', 'Alexandre', 'David',
'Francis', 'Barack', 'Abdoul', 'Rasha', 'Xi', 'Silvia'])
n_groups = len(np.unique(groups))
n_samples = len(groups)
n_splits = 5
tolerance = 0.05 * n_samples # 5 percent error allowed
ideal_n_groups_per_fold = n_samples // n_splits
X = y = np.ones(n_samples)
# Get the test fold indices from the test set indices of each fold
folds = np.zeros(n_samples)
for i, (_, test) in enumerate(lkf.split(X, y, groups)):
folds[test] = i
# Check that folds have approximately the same size
assert len(folds) == len(groups)
for i in np.unique(folds):
assert (tolerance >=
abs(sum(folds == i) - ideal_n_groups_per_fold))
# Check that each group appears only in 1 fold
with warnings.catch_warnings():
warnings.simplefilter("ignore", FutureWarning)
for group in np.unique(groups):
assert len(np.unique(folds[groups == group])) == 1
# Check that no group is on both sides of the split
groups = np.asarray(groups, dtype=object)
for train, test in lkf.split(X, y, groups):
assert len(np.intersect1d(groups[train], groups[test])) == 0
# groups can also be a list
cv_iter = list(lkf.split(X, y, groups.tolist()))
for (train1, test1), (train2, test2) in zip(lkf.split(X, y, groups),
cv_iter):
assert_array_equal(train1, train2)
assert_array_equal(test1, test2)
# Should fail if there are more folds than groups
groups = np.array([1, 1, 1, 2, 2])
X = y = np.ones(len(groups))
assert_raises_regexp(ValueError, "Cannot have number of splits.*greater",
next, GroupKFold(n_splits=3).split(X, y, groups))
def test_time_series_cv():
X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14]]
# Should fail if there are more folds than samples
assert_raises_regexp(ValueError, "Cannot have number of folds.*greater",
next,
TimeSeriesSplit(n_splits=7).split(X))
tscv = TimeSeriesSplit(2)
# Manually check that Time Series CV preserves the data
# ordering on toy datasets
splits = tscv.split(X[:-1])
train, test = next(splits)
assert_array_equal(train, [0, 1])
assert_array_equal(test, [2, 3])
train, test = next(splits)
assert_array_equal(train, [0, 1, 2, 3])
assert_array_equal(test, [4, 5])
splits = TimeSeriesSplit(2).split(X)
train, test = next(splits)
assert_array_equal(train, [0, 1, 2])
assert_array_equal(test, [3, 4])
train, test = next(splits)
assert_array_equal(train, [0, 1, 2, 3, 4])
assert_array_equal(test, [5, 6])
# Check get_n_splits returns the correct number of splits
splits = TimeSeriesSplit(2).split(X)
n_splits_actual = len(list(splits))
assert n_splits_actual == tscv.get_n_splits()
assert n_splits_actual == 2
def _check_time_series_max_train_size(splits, check_splits, max_train_size):
for (train, test), (check_train, check_test) in zip(splits, check_splits):
assert_array_equal(test, check_test)
assert len(check_train) <= max_train_size
suffix_start = max(len(train) - max_train_size, 0)
assert_array_equal(check_train, train[suffix_start:])
def test_time_series_max_train_size():
X = np.zeros((6, 1))
splits = TimeSeriesSplit(n_splits=3).split(X)
check_splits = TimeSeriesSplit(n_splits=3, max_train_size=3).split(X)
_check_time_series_max_train_size(splits, check_splits, max_train_size=3)
# Test for the case where the size of a fold is greater than max_train_size
check_splits = TimeSeriesSplit(n_splits=3, max_train_size=2).split(X)
_check_time_series_max_train_size(splits, check_splits, max_train_size=2)
# Test for the case where the size of each fold is less than max_train_size
check_splits = TimeSeriesSplit(n_splits=3, max_train_size=5).split(X)
_check_time_series_max_train_size(splits, check_splits, max_train_size=2)
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(n_splits=3),
StratifiedKFold(),
StratifiedShuffleSplit(n_splits=3, random_state=0)]
for inner_cv, outer_cv in combinations_with_replacement(cvs, 2):
gs = GridSearchCV(Ridge(solver="eigen"), param_grid={'alpha': [1, .1]},
cv=inner_cv, error_score='raise')
cross_val_score(gs, X=X, y=y, groups=groups, cv=outer_cv,
fit_params={'groups': groups})
def test_build_repr():
class MockSplitter:
def __init__(self, a, b=0, c=None):
self.a = a
self.b = b
self.c = c
def __repr__(self):
return _build_repr(self)
assert repr(MockSplitter(5, 6)) == "MockSplitter(a=5, b=6, c=None)"
@pytest.mark.parametrize('CVSplitter', (ShuffleSplit, GroupShuffleSplit,
StratifiedShuffleSplit))
def test_shuffle_split_empty_trainset(CVSplitter):
cv = CVSplitter(test_size=.99)
X, y = [[1]], [0] # 1 sample
with pytest.raises(
ValueError,
match='With n_samples=1, test_size=0.99 and train_size=None, '
'the resulting train set will be empty'):
next(cv.split(X, y, groups=[1]))
def test_train_test_split_empty_trainset():
X, = [[1]] # 1 sample
with pytest.raises(
ValueError,
match='With n_samples=1, test_size=0.99 and train_size=None, '
'the resulting train set will be empty'):
train_test_split(X, test_size=.99)
X = [[1], [1], [1]] # 3 samples, ask for more than 2 thirds
with pytest.raises(
ValueError,
match='With n_samples=3, test_size=0.67 and train_size=None, '
'the resulting train set will be empty'):
train_test_split(X, test_size=.67)
def test_leave_one_out_empty_trainset():
# LeaveOneGroup out expect at least 2 groups so no need to check
cv = LeaveOneOut()
X, y = [[1]], [0] # 1 sample
with pytest.raises(
ValueError,
match='Cannot perform LeaveOneOut with n_samples=1'):
next(cv.split(X, y))
def test_leave_p_out_empty_trainset():
# No need to check LeavePGroupsOut
cv = LeavePOut(p=2)
X, y = [[1], [2]], [0, 3] # 2 samples
with pytest.raises(
ValueError,
match='p=2 must be strictly less than the number of samples=2'):
next(cv.split(X, y, groups=[1, 2]))
@pytest.mark.parametrize('Klass', (KFold, StratifiedKFold))
def test_random_state_shuffle_false(Klass):
# passing a non-default random_state when shuffle=False makes no sense
# TODO 0.24: raise a ValueError instead of a warning
with pytest.warns(FutureWarning,
match='has no effect since shuffle is False'):
Klass(3, shuffle=False, random_state=0)
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