Lightstar/venv/Lib/site-packages/sklearn/utils/tests/test_multiclass.py

from itertools import product

import numpy as np
import pytest
import scipy.sparse as sp
from scipy.sparse import (
    coo_matrix,
    csc_matrix,
    csr_matrix,
    dok_matrix,
    issparse,
    lil_matrix,
)

from sklearn import datasets
from sklearn.model_selection import ShuffleSplit
from sklearn.svm import SVC
from sklearn.utils._testing import (
    assert_allclose,
    assert_array_almost_equal,
    assert_array_equal,
)
from sklearn.utils.estimator_checks import _NotAnArray
from sklearn.utils.metaestimators import _safe_split
from sklearn.utils.multiclass import (
    _ovr_decision_function,
    check_classification_targets,
    class_distribution,
    is_multilabel,
    type_of_target,
    unique_labels,
)

sparse_multilable_explicit_zero = csc_matrix(np.array([[0, 1], [1, 0]]))
sparse_multilable_explicit_zero[:, 0] = 0


def _generate_sparse(
    matrix,
    matrix_types=(csr_matrix, csc_matrix, coo_matrix, dok_matrix, lil_matrix),
    dtypes=(bool, int, np.int8, np.uint8, float, np.float32),
):
    return [
        matrix_type(matrix, dtype=dtype)
        for matrix_type in matrix_types
        for dtype in dtypes
    ]


EXAMPLES = {
    "multilabel-indicator": [
        # valid when the data is formatted as sparse or dense, identified
        # by CSR format when the testing takes place
        csr_matrix(np.random.RandomState(42).randint(2, size=(10, 10))),
        [[0, 1], [1, 0]],
        [[0, 1]],
        sparse_multilable_explicit_zero,
        *_generate_sparse([[0, 1], [1, 0]]),
        *_generate_sparse([[0, 0], [0, 0]]),
        *_generate_sparse([[0, 1]]),
        # Only valid when data is dense
        [[-1, 1], [1, -1]],
        np.array([[-1, 1], [1, -1]]),
        np.array([[-3, 3], [3, -3]]),
        _NotAnArray(np.array([[-3, 3], [3, -3]])),
    ],
    "multiclass": [
        [1, 0, 2, 2, 1, 4, 2, 4, 4, 4],
        np.array([1, 0, 2]),
        np.array([1, 0, 2], dtype=np.int8),
        np.array([1, 0, 2], dtype=np.uint8),
        np.array([1, 0, 2], dtype=float),
        np.array([1, 0, 2], dtype=np.float32),
        np.array([[1], [0], [2]]),
        _NotAnArray(np.array([1, 0, 2])),
        [0, 1, 2],
        ["a", "b", "c"],
        np.array(["a", "b", "c"]),
        np.array(["a", "b", "c"], dtype=object),
        np.array(["a", "b", "c"], dtype=object),
    ],
    "multiclass-multioutput": [
        [[1, 0, 2, 2], [1, 4, 2, 4]],
        [["a", "b"], ["c", "d"]],
        np.array([[1, 0, 2, 2], [1, 4, 2, 4]]),
        np.array([[1, 0, 2, 2], [1, 4, 2, 4]], dtype=np.int8),
        np.array([[1, 0, 2, 2], [1, 4, 2, 4]], dtype=np.uint8),
        np.array([[1, 0, 2, 2], [1, 4, 2, 4]], dtype=float),
        np.array([[1, 0, 2, 2], [1, 4, 2, 4]], dtype=np.float32),
        *_generate_sparse(
            [[1, 0, 2, 2], [1, 4, 2, 4]],
            matrix_types=(csr_matrix, csc_matrix),
            dtypes=(int, np.int8, np.uint8, float, np.float32),
        ),
        np.array([["a", "b"], ["c", "d"]]),
        np.array([["a", "b"], ["c", "d"]]),
        np.array([["a", "b"], ["c", "d"]], dtype=object),
        np.array([[1, 0, 2]]),
        _NotAnArray(np.array([[1, 0, 2]])),
    ],
    "binary": [
        [0, 1],
        [1, 1],
        [],
        [0],
        np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1]),
        np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1], dtype=bool),
        np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1], dtype=np.int8),
        np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1], dtype=np.uint8),
        np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1], dtype=float),
        np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1], dtype=np.float32),
        np.array([[0], [1]]),
        _NotAnArray(np.array([[0], [1]])),
        [1, -1],
        [3, 5],
        ["a"],
        ["a", "b"],
        ["abc", "def"],
        np.array(["abc", "def"]),
        ["a", "b"],
        np.array(["abc", "def"], dtype=object),
    ],
    "continuous": [
        [1e-5],
        [0, 0.5],
        np.array([[0], [0.5]]),
        np.array([[0], [0.5]], dtype=np.float32),
    ],
    "continuous-multioutput": [
        np.array([[0, 0.5], [0.5, 0]]),
        np.array([[0, 0.5], [0.5, 0]], dtype=np.float32),
        np.array([[0, 0.5]]),
        *_generate_sparse(
            [[0, 0.5], [0.5, 0]],
            matrix_types=(csr_matrix, csc_matrix),
            dtypes=(float, np.float32),
        ),
        *_generate_sparse(
            [[0, 0.5]],
            matrix_types=(csr_matrix, csc_matrix),
            dtypes=(float, np.float32),
        ),
    ],
    "unknown": [
        [[]],
        np.array([[]], dtype=object),
        [()],
        # sequence of sequences that weren't supported even before deprecation
        np.array([np.array([]), np.array([1, 2, 3])], dtype=object),
        [np.array([]), np.array([1, 2, 3])],
        [{1, 2, 3}, {1, 2}],
        [frozenset([1, 2, 3]), frozenset([1, 2])],
        # and also confusable as sequences of sequences
        [{0: "a", 1: "b"}, {0: "a"}],
        # ndim 0
        np.array(0),
        # empty second dimension
        np.array([[], []]),
        # 3d
        np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]),
    ],
}

NON_ARRAY_LIKE_EXAMPLES = [
    {1, 2, 3},
    {0: "a", 1: "b"},
    {0: [5], 1: [5]},
    "abc",
    frozenset([1, 2, 3]),
    None,
]

MULTILABEL_SEQUENCES = [
    [[1], [2], [0, 1]],
    [(), (2), (0, 1)],
    np.array([[], [1, 2]], dtype="object"),
    _NotAnArray(np.array([[], [1, 2]], dtype="object")),
]


def test_unique_labels():
    # Empty iterable
    with pytest.raises(ValueError):
        unique_labels()

    # Multiclass problem
    assert_array_equal(unique_labels(range(10)), np.arange(10))
    assert_array_equal(unique_labels(np.arange(10)), np.arange(10))
    assert_array_equal(unique_labels([4, 0, 2]), np.array([0, 2, 4]))

    # Multilabel indicator
    assert_array_equal(
        unique_labels(np.array([[0, 0, 1], [1, 0, 1], [0, 0, 0]])), np.arange(3)
    )

    assert_array_equal(unique_labels(np.array([[0, 0, 1], [0, 0, 0]])), np.arange(3))

    # Several arrays passed
    assert_array_equal(unique_labels([4, 0, 2], range(5)), np.arange(5))
    assert_array_equal(unique_labels((0, 1, 2), (0,), (2, 1)), np.arange(3))

    # Border line case with binary indicator matrix
    with pytest.raises(ValueError):
        unique_labels([4, 0, 2], np.ones((5, 5)))
    with pytest.raises(ValueError):
        unique_labels(np.ones((5, 4)), np.ones((5, 5)))

    assert_array_equal(unique_labels(np.ones((4, 5)), np.ones((5, 5))), np.arange(5))


def test_unique_labels_non_specific():
    # Test unique_labels with a variety of collected examples

    # Smoke test for all supported format
    for format in ["binary", "multiclass", "multilabel-indicator"]:
        for y in EXAMPLES[format]:
            unique_labels(y)

    # We don't support those format at the moment
    for example in NON_ARRAY_LIKE_EXAMPLES:
        with pytest.raises(ValueError):
            unique_labels(example)

    for y_type in [
        "unknown",
        "continuous",
        "continuous-multioutput",
        "multiclass-multioutput",
    ]:
        for example in EXAMPLES[y_type]:
            with pytest.raises(ValueError):
                unique_labels(example)


def test_unique_labels_mixed_types():
    # Mix with binary or multiclass and multilabel
    mix_clf_format = product(
        EXAMPLES["multilabel-indicator"], EXAMPLES["multiclass"] + EXAMPLES["binary"]
    )

    for y_multilabel, y_multiclass in mix_clf_format:
        with pytest.raises(ValueError):
            unique_labels(y_multiclass, y_multilabel)
        with pytest.raises(ValueError):
            unique_labels(y_multilabel, y_multiclass)

    with pytest.raises(ValueError):
        unique_labels([[1, 2]], [["a", "d"]])

    with pytest.raises(ValueError):
        unique_labels(["1", 2])

    with pytest.raises(ValueError):
        unique_labels([["1", 2], [1, 3]])

    with pytest.raises(ValueError):
        unique_labels([["1", "2"], [2, 3]])


def test_is_multilabel():
    for group, group_examples in EXAMPLES.items():
        if group in ["multilabel-indicator"]:
            dense_exp = True
        else:
            dense_exp = False

        for example in group_examples:
            # Only mark explicitly defined sparse examples as valid sparse
            # multilabel-indicators
            if group == "multilabel-indicator" and issparse(example):
                sparse_exp = True
            else:
                sparse_exp = False

            if issparse(example) or (
                hasattr(example, "__array__")
                and np.asarray(example).ndim == 2
                and np.asarray(example).dtype.kind in "biuf"
                and np.asarray(example).shape[1] > 0
            ):
                examples_sparse = [
                    sparse_matrix(example)
                    for sparse_matrix in [
                        coo_matrix,
                        csc_matrix,
                        csr_matrix,
                        dok_matrix,
                        lil_matrix,
                    ]
                ]
                for exmpl_sparse in examples_sparse:
                    assert sparse_exp == is_multilabel(
                        exmpl_sparse
                    ), "is_multilabel(%r) should be %s" % (exmpl_sparse, sparse_exp)

            # Densify sparse examples before testing
            if issparse(example):
                example = example.toarray()

            assert dense_exp == is_multilabel(
                example
            ), "is_multilabel(%r) should be %s" % (example, dense_exp)


def test_check_classification_targets():
    for y_type in EXAMPLES.keys():
        if y_type in ["unknown", "continuous", "continuous-multioutput"]:
            for example in EXAMPLES[y_type]:
                msg = "Unknown label type: "
                with pytest.raises(ValueError, match=msg):
                    check_classification_targets(example)
        else:
            for example in EXAMPLES[y_type]:
                check_classification_targets(example)


# @ignore_warnings
def test_type_of_target():
    for group, group_examples in EXAMPLES.items():
        for example in group_examples:
            assert (
                type_of_target(example) == group
            ), "type_of_target(%r) should be %r, got %r" % (
                example,
                group,
                type_of_target(example),
            )

    for example in NON_ARRAY_LIKE_EXAMPLES:
        msg_regex = r"Expected array-like \(array or non-string sequence\).*"
        with pytest.raises(ValueError, match=msg_regex):
            type_of_target(example)

    for example in MULTILABEL_SEQUENCES:
        msg = (
            "You appear to be using a legacy multi-label data "
            "representation. Sequence of sequences are no longer supported;"
            " use a binary array or sparse matrix instead."
        )
        with pytest.raises(ValueError, match=msg):
            type_of_target(example)


def test_type_of_target_pandas_sparse():
    pd = pytest.importorskip("pandas")

    y = pd.arrays.SparseArray([1, np.nan, np.nan, 1, np.nan])
    msg = "y cannot be class 'SparseSeries' or 'SparseArray'"
    with pytest.raises(ValueError, match=msg):
        type_of_target(y)


def test_type_of_target_pandas_nullable():
    """Check that type_of_target works with pandas nullable dtypes."""
    pd = pytest.importorskip("pandas")

    for dtype in ["Int32", "Float32"]:
        y_true = pd.Series([1, 0, 2, 3, 4], dtype=dtype)
        assert type_of_target(y_true) == "multiclass"

        y_true = pd.Series([1, 0, 1, 0], dtype=dtype)
        assert type_of_target(y_true) == "binary"

    y_true = pd.DataFrame([[1.4, 3.1], [3.1, 1.4]], dtype="Float32")
    assert type_of_target(y_true) == "continuous-multioutput"

    y_true = pd.DataFrame([[0, 1], [1, 1]], dtype="Int32")
    assert type_of_target(y_true) == "multilabel-indicator"

    y_true = pd.DataFrame([[1, 2], [3, 1]], dtype="Int32")
    assert type_of_target(y_true) == "multiclass-multioutput"


@pytest.mark.parametrize("dtype", ["Int64", "Float64", "boolean"])
def test_unique_labels_pandas_nullable(dtype):
    """Checks that unique_labels work with pandas nullable dtypes.

    Non-regression test for gh-25634.
    """
    pd = pytest.importorskip("pandas")

    y_true = pd.Series([1, 0, 0, 1, 0, 1, 1, 0, 1], dtype=dtype)
    y_predicted = pd.Series([0, 0, 1, 1, 0, 1, 1, 1, 1], dtype="int64")

    labels = unique_labels(y_true, y_predicted)
    assert_array_equal(labels, [0, 1])


def test_class_distribution():
    y = np.array(
        [
            [1, 0, 0, 1],
            [2, 2, 0, 1],
            [1, 3, 0, 1],
            [4, 2, 0, 1],
            [2, 0, 0, 1],
            [1, 3, 0, 1],
        ]
    )
    # Define the sparse matrix with a mix of implicit and explicit zeros
    data = np.array([1, 2, 1, 4, 2, 1, 0, 2, 3, 2, 3, 1, 1, 1, 1, 1, 1])
    indices = np.array([0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 5, 0, 1, 2, 3, 4, 5])
    indptr = np.array([0, 6, 11, 11, 17])
    y_sp = sp.csc_matrix((data, indices, indptr), shape=(6, 4))

    classes, n_classes, class_prior = class_distribution(y)
    classes_sp, n_classes_sp, class_prior_sp = class_distribution(y_sp)
    classes_expected = [[1, 2, 4], [0, 2, 3], [0], [1]]
    n_classes_expected = [3, 3, 1, 1]
    class_prior_expected = [[3 / 6, 2 / 6, 1 / 6], [1 / 3, 1 / 3, 1 / 3], [1.0], [1.0]]

    for k in range(y.shape[1]):
        assert_array_almost_equal(classes[k], classes_expected[k])
        assert_array_almost_equal(n_classes[k], n_classes_expected[k])
        assert_array_almost_equal(class_prior[k], class_prior_expected[k])

        assert_array_almost_equal(classes_sp[k], classes_expected[k])
        assert_array_almost_equal(n_classes_sp[k], n_classes_expected[k])
        assert_array_almost_equal(class_prior_sp[k], class_prior_expected[k])

    # Test again with explicit sample weights
    (classes, n_classes, class_prior) = class_distribution(
        y, [1.0, 2.0, 1.0, 2.0, 1.0, 2.0]
    )
    (classes_sp, n_classes_sp, class_prior_sp) = class_distribution(
        y, [1.0, 2.0, 1.0, 2.0, 1.0, 2.0]
    )
    class_prior_expected = [[4 / 9, 3 / 9, 2 / 9], [2 / 9, 4 / 9, 3 / 9], [1.0], [1.0]]

    for k in range(y.shape[1]):
        assert_array_almost_equal(classes[k], classes_expected[k])
        assert_array_almost_equal(n_classes[k], n_classes_expected[k])
        assert_array_almost_equal(class_prior[k], class_prior_expected[k])

        assert_array_almost_equal(classes_sp[k], classes_expected[k])
        assert_array_almost_equal(n_classes_sp[k], n_classes_expected[k])
        assert_array_almost_equal(class_prior_sp[k], class_prior_expected[k])


def test_safe_split_with_precomputed_kernel():
    clf = SVC()
    clfp = SVC(kernel="precomputed")

    iris = datasets.load_iris()
    X, y = iris.data, iris.target
    K = np.dot(X, X.T)

    cv = ShuffleSplit(test_size=0.25, random_state=0)
    train, test = list(cv.split(X))[0]

    X_train, y_train = _safe_split(clf, X, y, train)
    K_train, y_train2 = _safe_split(clfp, K, y, train)
    assert_array_almost_equal(K_train, np.dot(X_train, X_train.T))
    assert_array_almost_equal(y_train, y_train2)

    X_test, y_test = _safe_split(clf, X, y, test, train)
    K_test, y_test2 = _safe_split(clfp, K, y, test, train)
    assert_array_almost_equal(K_test, np.dot(X_test, X_train.T))
    assert_array_almost_equal(y_test, y_test2)


def test_ovr_decision_function():
    # test properties for ovr decision function

    predictions = np.array([[0, 1, 1], [0, 1, 0], [0, 1, 1], [0, 1, 1]])

    confidences = np.array(
        [[-1e16, 0, -1e16], [1.0, 2.0, -3.0], [-5.0, 2.0, 5.0], [-0.5, 0.2, 0.5]]
    )

    n_classes = 3

    dec_values = _ovr_decision_function(predictions, confidences, n_classes)

    # check that the decision values are within 0.5 range of the votes
    votes = np.array([[1, 0, 2], [1, 1, 1], [1, 0, 2], [1, 0, 2]])

    assert_allclose(votes, dec_values, atol=0.5)

    # check that the prediction are what we expect
    # highest vote or highest confidence if there is a tie.
    # for the second sample we have a tie (should be won by 1)
    expected_prediction = np.array([2, 1, 2, 2])
    assert_array_equal(np.argmax(dec_values, axis=1), expected_prediction)

    # third and fourth sample have the same vote but third sample
    # has higher confidence, this should reflect on the decision values
    assert dec_values[2, 2] > dec_values[3, 2]

    # assert subset invariance.
    dec_values_one = [
        _ovr_decision_function(
            np.array([predictions[i]]), np.array([confidences[i]]), n_classes
        )[0]
        for i in range(4)
    ]

    assert_allclose(dec_values, dec_values_one, atol=1e-6)