Python sklearn.model_selection.LeavePOut() Examples

The following are 7 code examples of sklearn.model_selection.LeavePOut(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module sklearn.model_selection , or try the search function .
Example #1
Source File: test_split.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up 
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(), 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)
Example #2
Source File: test_split.py    From twitter-stock-recommendation with MIT License 6 votes vote down vote up 
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(), 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)
Example #3
Source File: test_split.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
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]))
Example #4
Source File: test_model_selection.py    From pandas-ml with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def test_objectmapper(self):
        df = pdml.ModelFrame([])

        # Splitter Classes
        self.assertIs(df.model_selection.KFold, ms.KFold)
        self.assertIs(df.model_selection.GroupKFold, ms.GroupKFold)
        self.assertIs(df.model_selection.StratifiedKFold, ms.StratifiedKFold)

        self.assertIs(df.model_selection.LeaveOneGroupOut, ms.LeaveOneGroupOut)
        self.assertIs(df.model_selection.LeavePGroupsOut, ms.LeavePGroupsOut)
        self.assertIs(df.model_selection.LeaveOneOut, ms.LeaveOneOut)
        self.assertIs(df.model_selection.LeavePOut, ms.LeavePOut)

        self.assertIs(df.model_selection.ShuffleSplit, ms.ShuffleSplit)
        self.assertIs(df.model_selection.GroupShuffleSplit,
                      ms.GroupShuffleSplit)
        # self.assertIs(df.model_selection.StratifiedShuffleSplit,
        #               ms.StratifiedShuffleSplit)
        self.assertIs(df.model_selection.PredefinedSplit, ms.PredefinedSplit)
        self.assertIs(df.model_selection.TimeSeriesSplit, ms.TimeSeriesSplit)

        # Splitter Functions

        # Hyper-parameter optimizers
        self.assertIs(df.model_selection.GridSearchCV, ms.GridSearchCV)
        self.assertIs(df.model_selection.RandomizedSearchCV, ms.RandomizedSearchCV)
        self.assertIs(df.model_selection.ParameterGrid, ms.ParameterGrid)
        self.assertIs(df.model_selection.ParameterSampler, ms.ParameterSampler)

        # Model validation
Example #5
Source File: test_model_selection.py    From pandas-ml with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def test_objectmapper_abbr(self):
        df = pdml.ModelFrame([])

        # Splitter Classes
        self.assertIs(df.ms.KFold, ms.KFold)
        self.assertIs(df.ms.GroupKFold, ms.GroupKFold)
        self.assertIs(df.ms.StratifiedKFold, ms.StratifiedKFold)

        self.assertIs(df.ms.LeaveOneGroupOut, ms.LeaveOneGroupOut)
        self.assertIs(df.ms.LeavePGroupsOut, ms.LeavePGroupsOut)
        self.assertIs(df.ms.LeaveOneOut, ms.LeaveOneOut)
        self.assertIs(df.ms.LeavePOut, ms.LeavePOut)

        self.assertIs(df.ms.ShuffleSplit, ms.ShuffleSplit)
        self.assertIs(df.ms.GroupShuffleSplit,
                      ms.GroupShuffleSplit)
        # self.assertIs(df.ms.StratifiedShuffleSplit,
        #               ms.StratifiedShuffleSplit)
        self.assertIs(df.ms.PredefinedSplit, ms.PredefinedSplit)
        self.assertIs(df.ms.TimeSeriesSplit, ms.TimeSeriesSplit)

        # Splitter Functions

        # Hyper-parameter optimizers
        self.assertIs(df.ms.GridSearchCV, ms.GridSearchCV)
        self.assertIs(df.ms.RandomizedSearchCV, ms.RandomizedSearchCV)
        self.assertIs(df.ms.ParameterGrid, ms.ParameterGrid)
        self.assertIs(df.ms.ParameterSampler, ms.ParameterSampler)

        # Model validation
Example #6
Source File: test_split.py    From Mastering-Elasticsearch-7.0 with MIT License 4 votes vote down vote up 
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_equal(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_equal(np.asarray(train).dtype.kind, 'i')
            assert_equal(np.asarray(train).dtype.kind, 'i')

        # Test if the repr works without any errors
        assert_equal(cv_repr, repr(cv))

    # 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)
Example #7
Source File: test_split.py    From twitter-stock-recommendation with MIT License 4 votes vote down vote up 
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='default',\n       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_equal(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_equal(np.asarray(train).dtype.kind, 'i')
            assert_equal(np.asarray(train).dtype.kind, 'i')

        # Test if the repr works without any errors
        assert_equal(cv_repr, repr(cv))

    # 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)