"""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.model_selection._split import _yields_constant_splits

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_time_series_test_size():
    X = np.zeros((10, 1))

    # Test alone
    splits = TimeSeriesSplit(n_splits=3, test_size=3).split(X)

    train, test = next(splits)
    assert_array_equal(train, [0])
    assert_array_equal(test, [1, 2, 3])

    train, test = next(splits)
    assert_array_equal(train, [0, 1, 2, 3])
    assert_array_equal(test, [4, 5, 6])

    train, test = next(splits)
    assert_array_equal(train, [0, 1, 2, 3, 4, 5, 6])
    assert_array_equal(test, [7, 8, 9])

    # Test with max_train_size
    splits = TimeSeriesSplit(n_splits=2, test_size=2,
                             max_train_size=4).split(X)

    train, test = next(splits)
    assert_array_equal(train, [2, 3, 4, 5])
    assert_array_equal(test, [6, 7])

    train, test = next(splits)
    assert_array_equal(train, [4, 5, 6, 7])
    assert_array_equal(test, [8, 9])

    # Should fail with not enough data points for configuration
    with pytest.raises(ValueError, match="Too many splits.*with test_size"):
        splits = TimeSeriesSplit(n_splits=5, test_size=2).split(X)
        next(splits)


def test_time_series_gap():
    X = np.zeros((10, 1))

    # Test alone
    splits = TimeSeriesSplit(n_splits=2, gap=2).split(X)

    train, test = next(splits)
    assert_array_equal(train, [0, 1])
    assert_array_equal(test, [4, 5, 6])

    train, test = next(splits)
    assert_array_equal(train, [0, 1, 2, 3, 4])
    assert_array_equal(test, [7, 8, 9])

    # Test with max_train_size
    splits = TimeSeriesSplit(n_splits=3, gap=2, max_train_size=2).split(X)

    train, test = next(splits)
    assert_array_equal(train, [0, 1])
    assert_array_equal(test, [4, 5])

    train, test = next(splits)
    assert_array_equal(train, [2, 3])
    assert_array_equal(test, [6, 7])

    train, test = next(splits)
    assert_array_equal(train, [4, 5])
    assert_array_equal(test, [8, 9])

    # Test with test_size
    splits = TimeSeriesSplit(n_splits=2, gap=2,
                             max_train_size=4, test_size=2).split(X)

    train, test = next(splits)
    assert_array_equal(train, [0, 1, 2, 3])
    assert_array_equal(test, [6, 7])

    train, test = next(splits)
    assert_array_equal(train, [2, 3, 4, 5])
    assert_array_equal(test, [8, 9])

    # Test with additional test_size
    splits = TimeSeriesSplit(n_splits=2, gap=2, test_size=3).split(X)

    train, test = next(splits)
    assert_array_equal(train, [0, 1])
    assert_array_equal(test, [4, 5, 6])

    train, test = next(splits)
    assert_array_equal(train, [0, 1, 2, 3, 4])
    assert_array_equal(test, [7, 8, 9])

    # Verify proper error is thrown
    with pytest.raises(ValueError, match="Too many splits.*and gap"):
        splits = TimeSeriesSplit(n_splits=4, gap=2).split(X)
        next(splits)


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(), 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
    with pytest.raises(ValueError,
                       match='has no effect since shuffle is False'):
        Klass(3, shuffle=False, random_state=0)


@pytest.mark.parametrize('cv, expected', [
    (KFold(), True),
    (KFold(shuffle=True, random_state=123), True),
    (StratifiedKFold(), True),
    (StratifiedKFold(shuffle=True, random_state=123), True),
    (RepeatedKFold(random_state=123), True),
    (RepeatedStratifiedKFold(random_state=123), True),
    (ShuffleSplit(random_state=123), True),
    (GroupShuffleSplit(random_state=123), True),
    (StratifiedShuffleSplit(random_state=123), True),
    (GroupKFold(), True),
    (TimeSeriesSplit(), True),
    (LeaveOneOut(), True),
    (LeaveOneGroupOut(), True),
    (LeavePGroupsOut(n_groups=2), True),
    (LeavePOut(p=2), True),

    (KFold(shuffle=True, random_state=None), False),
    (KFold(shuffle=True, random_state=None), False),
    (StratifiedKFold(shuffle=True, random_state=np.random.RandomState(0)),
     False),
    (StratifiedKFold(shuffle=True, random_state=np.random.RandomState(0)),
     False),
    (RepeatedKFold(random_state=None), False),
    (RepeatedKFold(random_state=np.random.RandomState(0)), False),
    (RepeatedStratifiedKFold(random_state=None), False),
    (RepeatedStratifiedKFold(random_state=np.random.RandomState(0)), False),
    (ShuffleSplit(random_state=None), False),
    (ShuffleSplit(random_state=np.random.RandomState(0)), False),
    (GroupShuffleSplit(random_state=None), False),
    (GroupShuffleSplit(random_state=np.random.RandomState(0)), False),
    (StratifiedShuffleSplit(random_state=None), False),
    (StratifiedShuffleSplit(random_state=np.random.RandomState(0)), False),
])
def test_yields_constant_splits(cv, expected):
    assert _yields_constant_splits(cv) == expected
