/sklearn/utils/tests/test_validation.py

"""Tests for input validation functions"""

import numbers
import warnings
import re

from tempfile import NamedTemporaryFile
from itertools import product
from operator import itemgetter

import pytest
from pytest import importorskip
import numpy as np
import scipy.sparse as sp

from sklearn.utils._testing import assert_no_warnings
from sklearn.utils._testing import ignore_warnings
from sklearn.utils._testing import SkipTest
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_allclose_dense_sparse
from sklearn.utils._testing import assert_allclose
from sklearn.utils._testing import _convert_container
from sklearn.utils import as_float_array, check_array, check_symmetric
from sklearn.utils import check_X_y
from sklearn.utils import deprecated
from sklearn.utils._mocking import MockDataFrame
from sklearn.utils.fixes import parse_version
from sklearn.utils.estimator_checks import _NotAnArray
from sklearn.random_projection import _sparse_random_matrix
from sklearn.linear_model import ARDRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR
from sklearn.datasets import make_blobs
from sklearn.utils import _safe_indexing
from sklearn.utils.validation import (
    has_fit_parameter,
    check_is_fitted,
    check_consistent_length,
    assert_all_finite,
    check_memory,
    check_non_negative,
    _num_samples,
    check_scalar,
    _check_psd_eigenvalues,
    _check_y,
    _deprecate_positional_args,
    _check_sample_weight,
    _allclose_dense_sparse,
    _num_features,
    FLOAT_DTYPES,
    _get_feature_names,
    _check_feature_names_in,
    _check_fit_params,
)
from sklearn.base import BaseEstimator
import sklearn

from sklearn.exceptions import NotFittedError, PositiveSpectrumWarning

from sklearn.utils._testing import TempMemmap


# TODO: Remove np.matrix usage in 1.2
@pytest.mark.filterwarnings("ignore:np.matrix usage is deprecated in 1.0:FutureWarning")
@pytest.mark.filterwarnings("ignore:the matrix subclass:PendingDeprecationWarning")
def test_as_float_array():
    # Test function for as_float_array
    X = np.ones((3, 10), dtype=np.int32)
    X = X + np.arange(10, dtype=np.int32)
    X2 = as_float_array(X, copy=False)
    assert X2.dtype == np.float32
    # Another test
    X = X.astype(np.int64)
    X2 = as_float_array(X, copy=True)
    # Checking that the array wasn't overwritten
    assert as_float_array(X, copy=False) is not X
    assert X2.dtype == np.float64
    # Test int dtypes <= 32bit
    tested_dtypes = [bool, np.int8, np.int16, np.int32, np.uint8, np.uint16, np.uint32]
    for dtype in tested_dtypes:
        X = X.astype(dtype)
        X2 = as_float_array(X)
        assert X2.dtype == np.float32

    # Test object dtype
    X = X.astype(object)
    X2 = as_float_array(X, copy=True)
    assert X2.dtype == np.float64

    # Here, X is of the right type, it shouldn't be modified
    X = np.ones((3, 2), dtype=np.float32)
    assert as_float_array(X, copy=False) is X
    # Test that if X is fortran ordered it stays
    X = np.asfortranarray(X)
    assert np.isfortran(as_float_array(X, copy=True))

    # Test the copy parameter with some matrices
    matrices = [
        np.matrix(np.arange(5)),
        sp.csc_matrix(np.arange(5)).toarray(),
        _sparse_random_matrix(10, 10, density=0.10).toarray(),
    ]
    for M in matrices:
        N = as_float_array(M, copy=True)
        N[0, 0] = np.nan
        assert not np.isnan(M).any()


@pytest.mark.parametrize("X", [(np.random.random((10, 2))), (sp.rand(10, 2).tocsr())])
def test_as_float_array_nan(X):
    X[5, 0] = np.nan
    X[6, 1] = np.nan
    X_converted = as_float_array(X, force_all_finite="allow-nan")
    assert_allclose_dense_sparse(X_converted, X)


# TODO: Remove np.matrix usage in 1.2
@pytest.mark.filterwarnings("ignore:np.matrix usage is deprecated in 1.0:FutureWarning")
@pytest.mark.filterwarnings("ignore:the matrix subclass:PendingDeprecationWarning")
def test_np_matrix():
    # Confirm that input validation code does not return np.matrix
    X = np.arange(12).reshape(3, 4)

    assert not isinstance(as_float_array(X), np.matrix)
    assert not isinstance(as_float_array(np.matrix(X)), np.matrix)
    assert not isinstance(as_float_array(sp.csc_matrix(X)), np.matrix)


def test_memmap():
    # Confirm that input validation code doesn't copy memory mapped arrays

    asflt = lambda x: as_float_array(x, copy=False)

    with NamedTemporaryFile(prefix="sklearn-test") as tmp:
        M = np.memmap(tmp, shape=(10, 10), dtype=np.float32)
        M[:] = 0

        for f in (check_array, np.asarray, asflt):
            X = f(M)
            X[:] = 1
            assert_array_equal(X.ravel(), M.ravel())
            X[:] = 0


def test_ordering():
    # Check that ordering is enforced correctly by validation utilities.
    # We need to check each validation utility, because a 'copy' without
    # 'order=K' will kill the ordering.
    X = np.ones((10, 5))
    for A in X, X.T:
        for copy in (True, False):
            B = check_array(A, order="C", copy=copy)
            assert B.flags["C_CONTIGUOUS"]
            B = check_array(A, order="F", copy=copy)
            assert B.flags["F_CONTIGUOUS"]
            if copy:
                assert A is not B

    X = sp.csr_matrix(X)
    X.data = X.data[::-1]
    assert not X.data.flags["C_CONTIGUOUS"]


@pytest.mark.parametrize(
    "value, force_all_finite", [(np.inf, False), (np.nan, "allow-nan"), (np.nan, False)]
)
@pytest.mark.parametrize("retype", [np.asarray, sp.csr_matrix])
def test_check_array_force_all_finite_valid(value, force_all_finite, retype):
    X = retype(np.arange(4).reshape(2, 2).astype(float))
    X[0, 0] = value
    X_checked = check_array(X, force_all_finite=force_all_finite, accept_sparse=True)
    assert_allclose_dense_sparse(X, X_checked)


@pytest.mark.parametrize(
    "value, input_name, force_all_finite, match_msg",
    [
        (np.inf, "", True, "Input contains infinity"),
        (np.inf, "X", True, "Input X contains infinity"),
        (np.inf, "sample_weight", True, "Input sample_weight contains infinity"),
        (np.inf, "X", "allow-nan", "Input X contains infinity"),
        (np.nan, "", True, "Input contains NaN"),
        (np.nan, "X", True, "Input X contains NaN"),
        (np.nan, "y", True, "Input y contains NaN"),
        (
            np.nan,
            "",
            "allow-inf",
            'force_all_finite should be a bool or "allow-nan"',
        ),
        (np.nan, "", 1, "Input contains NaN"),
    ],
)
@pytest.mark.parametrize("retype", [np.asarray, sp.csr_matrix])
def test_check_array_force_all_finiteinvalid(
    value, input_name, force_all_finite, match_msg, retype
):
    X = retype(np.arange(4).reshape(2, 2).astype(np.float64))
    X[0, 0] = value
    with pytest.raises(ValueError, match=match_msg):
        check_array(
            X,
            input_name=input_name,
            force_all_finite=force_all_finite,
            accept_sparse=True,
        )


@pytest.mark.parametrize("input_name", ["X", "y", "sample_weight"])
@pytest.mark.parametrize("retype", [np.asarray, sp.csr_matrix])
def test_check_array_links_to_imputer_doc_only_for_X(input_name, retype):
    data = retype(np.arange(4).reshape(2, 2).astype(np.float64))
    data[0, 0] = np.nan
    estimator = SVR()
    extended_msg = (
        f"\n{estimator.__class__.__name__} does not accept missing values"
        " encoded as NaN natively. For supervised learning, you might want"
        " to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor"
        " which accept missing values encoded as NaNs natively."
        " Alternatively, it is possible to preprocess the"
        " data, for instance by using an imputer transformer in a pipeline"
        " or drop samples with missing values. See"
        " https://scikit-learn.org/stable/modules/impute.html"
        " You can find a list of all estimators that handle NaN values"
        " at the following page:"
        " https://scikit-learn.org/stable/modules/impute.html"
        "#estimators-that-handle-nan-values"
    )

    with pytest.raises(ValueError, match=f"Input {input_name} contains NaN") as ctx:
        check_array(
            data,
            estimator=estimator,
            input_name=input_name,
            accept_sparse=True,
        )

    if input_name == "X":
        assert extended_msg in ctx.value.args[0]
    else:
        assert extended_msg not in ctx.value.args[0]

    if input_name == "X":
        # Veriy that _validate_data is automatically called with the right argument
        # to generate the same exception:
        with pytest.raises(ValueError, match=f"Input {input_name} contains NaN") as ctx:
            SVR().fit(data, np.ones(data.shape[0]))
        assert extended_msg in ctx.value.args[0]


def test_check_array_force_all_finite_object():
    X = np.array([["a", "b", np.nan]], dtype=object).T

    X_checked = check_array(X, dtype=None, force_all_finite="allow-nan")
    assert X is X_checked

    X_checked = check_array(X, dtype=None, force_all_finite=False)
    assert X is X_checked

    with pytest.raises(ValueError, match="Input contains NaN"):
        check_array(X, dtype=None, force_all_finite=True)


@pytest.mark.parametrize(
    "X, err_msg",
    [
        (
            np.array([[1, np.nan]]),
            "Input contains NaN.",
        ),
        (
            np.array([[1, np.nan]]),
            "Input contains NaN.",
        ),
        (
            np.array([[1, np.inf]]),
            "Input contains infinity or a value too large for.*int",
        ),
        (np.array([[1, np.nan]], dtype=object), "cannot convert float NaN to integer"),
    ],
)
@pytest.mark.parametrize("force_all_finite", [True, False])
def test_check_array_force_all_finite_object_unsafe_casting(
    X, err_msg, force_all_finite
):
    # casting a float array containing NaN or inf to int dtype should
    # raise an error irrespective of the force_all_finite parameter.
    with pytest.raises(ValueError, match=err_msg):
        check_array(X, dtype=int, force_all_finite=force_all_finite)


@ignore_warnings
def test_check_array():
    # accept_sparse == False
    # raise error on sparse inputs
    X = [[1, 2], [3, 4]]
    X_csr = sp.csr_matrix(X)
    with pytest.raises(TypeError):
        check_array(X_csr)

    # ensure_2d=False
    X_array = check_array([0, 1, 2], ensure_2d=False)
    assert X_array.ndim == 1
    # ensure_2d=True with 1d array
    with pytest.raises(ValueError, match="Expected 2D array, got 1D array instead"):
        check_array([0, 1, 2], ensure_2d=True)

    # ensure_2d=True with scalar array
    with pytest.raises(ValueError, match="Expected 2D array, got scalar array instead"):
        check_array(10, ensure_2d=True)

    # don't allow ndim > 3
    X_ndim = np.arange(8).reshape(2, 2, 2)
    with pytest.raises(ValueError):
        check_array(X_ndim)
    check_array(X_ndim, allow_nd=True)  # doesn't raise

    # dtype and order enforcement.
    X_C = np.arange(4).reshape(2, 2).copy("C")
    X_F = X_C.copy("F")
    X_int = X_C.astype(int)
    X_float = X_C.astype(float)
    Xs = [X_C, X_F, X_int, X_float]
    dtypes = [np.int32, int, float, np.float32, None, bool, object]
    orders = ["C", "F", None]
    copys = [True, False]

    for X, dtype, order, copy in product(Xs, dtypes, orders, copys):
        X_checked = check_array(X, dtype=dtype, order=order, copy=copy)
        if dtype is not None:
            assert X_checked.dtype == dtype
        else:
            assert X_checked.dtype == X.dtype
        if order == "C":
            assert X_checked.flags["C_CONTIGUOUS"]
            assert not X_checked.flags["F_CONTIGUOUS"]
        elif order == "F":
            assert X_checked.flags["F_CONTIGUOUS"]
            assert not X_checked.flags["C_CONTIGUOUS"]
        if copy:
            assert X is not X_checked
        else:
            # doesn't copy if it was already good
            if (
                X.dtype == X_checked.dtype
                and X_checked.flags["C_CONTIGUOUS"] == X.flags["C_CONTIGUOUS"]
                and X_checked.flags["F_CONTIGUOUS"] == X.flags["F_CONTIGUOUS"]
            ):
                assert X is X_checked

    # allowed sparse != None
    X_csc = sp.csc_matrix(X_C)
    X_coo = X_csc.tocoo()
    X_dok = X_csc.todok()
    X_int = X_csc.astype(int)
    X_float = X_csc.astype(float)

    Xs = [X_csc, X_coo, X_dok, X_int, X_float]
    accept_sparses = [["csr", "coo"], ["coo", "dok"]]
    # scipy sparse matrices do not support the object dtype so
    # this dtype is skipped in this loop
    non_object_dtypes = [dt for dt in dtypes if dt is not object]
    for X, dtype, accept_sparse, copy in product(
        Xs, non_object_dtypes, accept_sparses, copys
    ):
        X_checked = check_array(X, dtype=dtype, accept_sparse=accept_sparse, copy=copy)
        if dtype is not None:
            assert X_checked.dtype == dtype
        else:
            assert X_checked.dtype == X.dtype
        if X.format in accept_sparse:
            # no change if allowed
            assert X.format == X_checked.format
        else:
            # got converted
            assert X_checked.format == accept_sparse[0]
        if copy:
            assert X is not X_checked
        else:
            # doesn't copy if it was already good
            if X.dtype == X_checked.dtype and X.format == X_checked.format:
                assert X is X_checked

    # other input formats
    # convert lists to arrays
    X_dense = check_array([[1, 2], [3, 4]])
    assert isinstance(X_dense, np.ndarray)
    # raise on too deep lists
    with pytest.raises(ValueError):
        check_array(X_ndim.tolist())
    check_array(X_ndim.tolist(), allow_nd=True)  # doesn't raise

    # convert weird stuff to arrays
    X_no_array = _NotAnArray(X_dense)
    result = check_array(X_no_array)
    assert isinstance(result, np.ndarray)


@pytest.mark.parametrize(
    "X",
    [
        [["1", "2"], ["3", "4"]],
        np.array([["1", "2"], ["3", "4"]], dtype="U"),
        np.array([["1", "2"], ["3", "4"]], dtype="S"),
        [[b"1", b"2"], [b"3", b"4"]],
        np.array([[b"1", b"2"], [b"3", b"4"]], dtype="V1"),
    ],
)
def test_check_array_numeric_error(X):
    """Test that check_array errors when it receives an array of bytes/string
    while a numeric dtype is required."""
    expected_msg = r"dtype='numeric' is not compatible with arrays of bytes/strings"
    with pytest.raises(ValueError, match=expected_msg):
        check_array(X, dtype="numeric")


@pytest.mark.parametrize(
    "pd_dtype", ["Int8", "Int16", "UInt8", "UInt16", "Float32", "Float64"]
)
@pytest.mark.parametrize(
    "dtype, expected_dtype",
    [
        ([np.float32, np.float64], np.float32),
        (np.float64, np.float64),
        ("numeric", np.float64),
    ],
)
def test_check_array_pandas_na_support(pd_dtype, dtype, expected_dtype):
    # Test pandas numerical extension arrays with pd.NA
    pd = pytest.importorskip("pandas")

    if pd_dtype in {"Float32", "Float64"}:
        # Extension dtypes with Floats was added in 1.2
        pd = pytest.importorskip("pandas", minversion="1.2")

    X_np = np.array(
        [[1, 2, 3, np.nan, np.nan], [np.nan, np.nan, 8, 4, 6], [1, 2, 3, 4, 5]]
    ).T

    # Creates dataframe with numerical extension arrays with pd.NA
    X = pd.DataFrame(X_np, dtype=pd_dtype, columns=["a", "b", "c"])
    # column c has no nans
    X["c"] = X["c"].astype("float")
    X_checked = check_array(X, force_all_finite="allow-nan", dtype=dtype)
    assert_allclose(X_checked, X_np)
    assert X_checked.dtype == expected_dtype

    X_checked = check_array(X, force_all_finite=False, dtype=dtype)
    assert_allclose(X_checked, X_np)
    assert X_checked.dtype == expected_dtype

    msg = "Input contains NaN"
    with pytest.raises(ValueError, match=msg):
        check_array(X, force_all_finite=True)


def test_check_array_pandas_dtype_casting():
    # test that data-frames with homogeneous dtype are not upcast
    pd = pytest.importorskip("pandas")
    X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
    X_df = pd.DataFrame(X)
    assert check_array(X_df).dtype == np.float32
    assert check_array(X_df, dtype=FLOAT_DTYPES).dtype == np.float32

    X_df = X_df.astype({0: np.float16})
    assert_array_equal(X_df.dtypes, (np.float16, np.float32, np.float32))
    assert check_array(X_df).dtype == np.float32
    assert check_array(X_df, dtype=FLOAT_DTYPES).dtype == np.float32

    X_df = X_df.astype({0: np.int16})
    # float16, int16, float32 casts to float32
    assert check_array(X_df).dtype == np.float32
    assert check_array(X_df, dtype=FLOAT_DTYPES).dtype == np.float32

    X_df = X_df.astype({2: np.float16})
    # float16, int16, float16 casts to float32
    assert check_array(X_df).dtype == np.float32
    assert check_array(X_df, dtype=FLOAT_DTYPES).dtype == np.float32

    X_df = X_df.astype(np.int16)
    assert check_array(X_df).dtype == np.int16
    # we're not using upcasting rules for determining
    # the target type yet, so we cast to the default of float64
    assert check_array(X_df, dtype=FLOAT_DTYPES).dtype == np.float64

    # check that we handle pandas dtypes in a semi-reasonable way
    # this is actually tricky because we can't really know that this
    # should be integer ahead of converting it.
    cat_df = pd.DataFrame({"cat_col": pd.Categorical([1, 2, 3])})
    assert check_array(cat_df).dtype == np.int64
    assert check_array(cat_df, dtype=FLOAT_DTYPES).dtype == np.float64


def test_check_array_on_mock_dataframe():
    arr = np.array([[0.2, 0.7], [0.6, 0.5], [0.4, 0.1], [0.7, 0.2]])
    mock_df = MockDataFrame(arr)
    checked_arr = check_array(mock_df)
    assert checked_arr.dtype == arr.dtype
    checked_arr = check_array(mock_df, dtype=np.float32)
    assert checked_arr.dtype == np.dtype(np.float32)


def test_check_array_dtype_stability():
    # test that lists with ints don't get converted to floats
    X = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
    assert check_array(X).dtype.kind == "i"
    assert check_array(X, ensure_2d=False).dtype.kind == "i"


def test_check_array_dtype_warning():
    X_int_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
    X_float32 = np.asarray(X_int_list, dtype=np.float32)
    X_int64 = np.asarray(X_int_list, dtype=np.int64)
    X_csr_float32 = sp.csr_matrix(X_float32)
    X_csc_float32 = sp.csc_matrix(X_float32)
    X_csc_int32 = sp.csc_matrix(X_int64, dtype=np.int32)
    integer_data = [X_int64, X_csc_int32]
    float32_data = [X_float32, X_csr_float32, X_csc_float32]
    for X in integer_data:
        X_checked = assert_no_warnings(
            check_array, X, dtype=np.float64, accept_sparse=True
        )
        assert X_checked.dtype == np.float64

    for X in float32_data:
        X_checked = assert_no_warnings(
            check_array, X, dtype=[np.float64, np.float32], accept_sparse=True
        )
        assert X_checked.dtype == np.float32
        assert X_checked is X

        X_checked = assert_no_warnings(
            check_array,
            X,
            dtype=[np.float64, np.float32],
            accept_sparse=["csr", "dok"],
            copy=True,
        )
        assert X_checked.dtype == np.float32
        assert X_checked is not X

    X_checked = assert_no_warnings(
        check_array,
        X_csc_float32,
        dtype=[np.float64, np.float32],
        accept_sparse=["csr", "dok"],
        copy=False,
    )
    assert X_checked.dtype == np.float32
    assert X_checked is not X_csc_float32
    assert X_checked.format == "csr"


def test_check_array_accept_sparse_type_exception():
    X = [[1, 2], [3, 4]]
    X_csr = sp.csr_matrix(X)
    invalid_type = SVR()

    msg = (
        "A sparse matrix was passed, but dense data is required. "
        r"Use X.toarray\(\) to convert to a dense numpy array."
    )
    with pytest.raises(TypeError, match=msg):
        check_array(X_csr, accept_sparse=False)

    msg = (
        "Parameter 'accept_sparse' should be a string, "
        "boolean or list of strings. You provided 'accept_sparse=.*'."
    )
    with pytest.raises(ValueError, match=msg):
        check_array(X_csr, accept_sparse=invalid_type)

    msg = (
        "When providing 'accept_sparse' as a tuple or list, "
        "it must contain at least one string value."
    )
    with pytest.raises(ValueError, match=msg):
        check_array(X_csr, accept_sparse=[])
    with pytest.raises(ValueError, match=msg):
        check_array(X_csr, accept_sparse=())
    with pytest.raises(TypeError, match="SVR"):
        check_array(X_csr, accept_sparse=[invalid_type])


def test_check_array_accept_sparse_no_exception():
    X = [[1, 2], [3, 4]]
    X_csr = sp.csr_matrix(X)

    check_array(X_csr, accept_sparse=True)
    check_array(X_csr, accept_sparse="csr")
    check_array(X_csr, accept_sparse=["csr"])
    check_array(X_csr, accept_sparse=("csr",))


@pytest.fixture(params=["csr", "csc", "coo", "bsr"])
def X_64bit(request):
    X = sp.rand(20, 10, format=request.param)
    for attr in ["indices", "indptr", "row", "col"]:
        if hasattr(X, attr):
            setattr(X, attr, getattr(X, attr).astype("int64"))
    yield X


def test_check_array_accept_large_sparse_no_exception(X_64bit):
    # When large sparse are allowed
    check_array(X_64bit, accept_large_sparse=True, accept_sparse=True)


def test_check_array_accept_large_sparse_raise_exception(X_64bit):
    # When large sparse are not allowed
    msg = (
        "Only sparse matrices with 32-bit integer indices "
        "are accepted. Got int64 indices."
    )
    with pytest.raises(ValueError, match=msg):
        check_array(X_64bit, accept_sparse=True, accept_large_sparse=False)


def test_check_array_min_samples_and_features_messages():
    # empty list is considered 2D by default:
    msg = r"0 feature\(s\) \(shape=\(1, 0\)\) while a minimum of 1 is" " required."
    with pytest.raises(ValueError, match=msg):
        check_array([[]])

    # If considered a 1D collection when ensure_2d=False, then the minimum
    # number of samples will break:
    msg = r"0 sample\(s\) \(shape=\(0,\)\) while a minimum of 1 is required."
    with pytest.raises(ValueError, match=msg):
        check_array([], ensure_2d=False)

    # Invalid edge case when checking the default minimum sample of a scalar
    msg = r"Singleton array array\(42\) cannot be considered a valid" " collection."
    with pytest.raises(TypeError, match=msg):
        check_array(42, ensure_2d=False)

    # Simulate a model that would need at least 2 samples to be well defined
    X = np.ones((1, 10))
    y = np.ones(1)
    msg = r"1 sample\(s\) \(shape=\(1, 10\)\) while a minimum of 2 is" " required."
    with pytest.raises(ValueError, match=msg):
        check_X_y(X, y, ensure_min_samples=2)

    # The same message is raised if the data has 2 dimensions even if this is
    # not mandatory
    with pytest.raises(ValueError, match=msg):
        check_X_y(X, y, ensure_min_samples=2, ensure_2d=False)

    # Simulate a model that would require at least 3 features (e.g. SelectKBest
    # with k=3)
    X = np.ones((10, 2))
    y = np.ones(2)
    msg = r"2 feature\(s\) \(shape=\(10, 2\)\) while a minimum of 3 is" " required."
    with pytest.raises(ValueError, match=msg):
        check_X_y(X, y, ensure_min_features=3)

    # Only the feature check is enabled whenever the number of dimensions is 2
    # even if allow_nd is enabled:
    with pytest.raises(ValueError, match=msg):
        check_X_y(X, y, ensure_min_features=3, allow_nd=True)

    # Simulate a case where a pipeline stage as trimmed all the features of a
    # 2D dataset.
    X = np.empty(0).reshape(10, 0)
    y = np.ones(10)
    msg = r"0 feature\(s\) \(shape=\(10, 0\)\) while a minimum of 1 is" " required."
    with pytest.raises(ValueError, match=msg):
        check_X_y(X, y)

    # nd-data is not checked for any minimum number of features by default:
    X = np.ones((10, 0, 28, 28))
    y = np.ones(10)
    X_checked, y_checked = check_X_y(X, y, allow_nd=True)
    assert_array_equal(X, X_checked)
    assert_array_equal(y, y_checked)


def test_check_array_complex_data_error():
    X = np.array([[1 + 2j, 3 + 4j, 5 + 7j], [2 + 3j, 4 + 5j, 6 + 7j]])
    with pytest.raises(ValueError, match="Complex data not supported"):
        check_array(X)

    # list of lists
    X = [[1 + 2j, 3 + 4j, 5 + 7j], [2 + 3j, 4 + 5j, 6 + 7j]]
    with pytest.raises(ValueError, match="Complex data not supported"):
        check_array(X)

    # tuple of tuples
    X = ((1 + 2j, 3 + 4j, 5 + 7j), (2 + 3j, 4 + 5j, 6 + 7j))
    with pytest.raises(ValueError, match="Complex data not supported"):
        check_array(X)

    # list of np arrays
    X = [np.array([1 + 2j, 3 + 4j, 5 + 7j]), np.array([2 + 3j, 4 + 5j, 6 + 7j])]
    with pytest.raises(ValueError, match="Complex data not supported"):
        check_array(X)

    # tuple of np arrays
    X = (np.array([1 + 2j, 3 + 4j, 5 + 7j]), np.array([2 + 3j, 4 + 5j, 6 + 7j]))
    with pytest.raises(ValueError, match="Complex data not supported"):
        check_array(X)

    # dataframe
    X = MockDataFrame(np.array([[1 + 2j, 3 + 4j, 5 + 7j], [2 + 3j, 4 + 5j, 6 + 7j]]))
    with pytest.raises(ValueError, match="Complex data not supported"):
        check_array(X)

    # sparse matrix
    X = sp.coo_matrix([[0, 1 + 2j], [0, 0]])
    with pytest.raises(ValueError, match="Complex data not supported"):
        check_array(X)

    # target variable does not always go through check_array but should
    # never accept complex data either.
    y = np.array([1 + 2j, 3 + 4j, 5 + 7j, 2 + 3j, 4 + 5j, 6 + 7j])
    with pytest.raises(ValueError, match="Complex data not supported"):
        _check_y(y)


def test_has_fit_parameter():
    assert not has_fit_parameter(KNeighborsClassifier, "sample_weight")
    assert has_fit_parameter(RandomForestRegressor, "sample_weight")
    assert has_fit_parameter(SVR, "sample_weight")
    assert has_fit_parameter(SVR(), "sample_weight")

    class TestClassWithDeprecatedFitMethod:
        @deprecated("Deprecated for the purpose of testing has_fit_parameter")
        def fit(self, X, y, sample_weight=None):
            pass

    assert has_fit_parameter(
        TestClassWithDeprecatedFitMethod, "sample_weight"
    ), "has_fit_parameter fails for class with deprecated fit method."


def test_check_symmetric():
    arr_sym = np.array([[0, 1], [1, 2]])
    arr_bad = np.ones(2)
    arr_asym = np.array([[0, 2], [0, 2]])

    test_arrays = {
        "dense": arr_asym,
        "dok": sp.dok_matrix(arr_asym),
        "csr": sp.csr_matrix(arr_asym),
        "csc": sp.csc_matrix(arr_asym),
        "coo": sp.coo_matrix(arr_asym),
        "lil": sp.lil_matrix(arr_asym),
        "bsr": sp.bsr_matrix(arr_asym),
    }

    # check error for bad inputs
    with pytest.raises(ValueError):
        check_symmetric(arr_bad)

    # check that asymmetric arrays are properly symmetrized
    for arr_format, arr in test_arrays.items():
        # Check for warnings and errors
        with pytest.warns(UserWarning):
            check_symmetric(arr)
        with pytest.raises(ValueError):
            check_symmetric(arr, raise_exception=True)

        output = check_symmetric(arr, raise_warning=False)
        if sp.issparse(output):
            assert output.format == arr_format
            assert_array_equal(output.toarray(), arr_sym)
        else:
            assert_array_equal(output, arr_sym)


def test_check_is_fitted_with_is_fitted():
    class Estimator(BaseEstimator):
        def fit(self, **kwargs):
            self._is_fitted = True
            return self

        def __sklearn_is_fitted__(self):
            return hasattr(self, "_is_fitted") and self._is_fitted

    with pytest.raises(NotFittedError):
        check_is_fitted(Estimator())
    check_is_fitted(Estimator().fit())


def test_check_is_fitted():
    # Check is TypeError raised when non estimator instance passed
    with pytest.raises(TypeError):
        check_is_fitted(ARDRegression)
    with pytest.raises(TypeError):
        check_is_fitted("SVR")

    ard = ARDRegression()
    svr = SVR()

    try:
        with pytest.raises(NotFittedError):
            check_is_fitted(ard)
        with pytest.raises(NotFittedError):
            check_is_fitted(svr)
    except ValueError:
        assert False, "check_is_fitted failed with ValueError"

    # NotFittedError is a subclass of both ValueError and AttributeError
    try:
        check_is_fitted(ard, msg="Random message %(name)s, %(name)s")
    except ValueError as e:
        assert str(e) == "Random message ARDRegression, ARDRegression"

    try:
        check_is_fitted(svr, msg="Another message %(name)s, %(name)s")
    except AttributeError as e:
        assert str(e) == "Another message SVR, SVR"

    ard.fit(*make_blobs())
    svr.fit(*make_blobs())

    assert check_is_fitted(ard) is None
    assert check_is_fitted(svr) is None


def test_check_is_fitted_attributes():
    class MyEstimator:
        def fit(self, X, y):
            return self

    msg = "not fitted"
    est = MyEstimator()

    with pytest.raises(NotFittedError, match=msg):
        check_is_fitted(est, attributes=["a_", "b_"])
    with pytest.raises(NotFittedError, match=msg):
        check_is_fitted(est, attributes=["a_", "b_"], all_or_any=all)
    with pytest.raises(NotFittedError, match=msg):
        check_is_fitted(est, attributes=["a_", "b_"], all_or_any=any)

    est.a_ = "a"
    with pytest.raises(NotFittedError, match=msg):
        check_is_fitted(est, attributes=["a_", "b_"])
    with pytest.raises(NotFittedError, match=msg):
        check_is_fitted(est, attributes=["a_", "b_"], all_or_any=all)
    check_is_fitted(est, attributes=["a_", "b_"], all_or_any=any)

    est.b_ = "b"
    check_is_fitted(est, attributes=["a_", "b_"])
    check_is_fitted(est, attributes=["a_", "b_"], all_or_any=all)
    check_is_fitted(est, attributes=["a_", "b_"], all_or_any=any)


@pytest.mark.parametrize(
    "wrap", [itemgetter(0), list, tuple], ids=["single", "list", "tuple"]
)
def test_check_is_fitted_with_attributes(wrap):
    ard = ARDRegression()
    with pytest.raises(NotFittedError, match="is not fitted yet"):
        check_is_fitted(ard, wrap(["coef_"]))

    ard.fit(*make_blobs())

    # Does not raise
    check_is_fitted(ard, wrap(["coef_"]))

    # Raises when using attribute that is not defined
    with pytest.raises(NotFittedError, match="is not fitted yet"):
        check_is_fitted(ard, wrap(["coef_bad_"]))


def test_check_consistent_length():
    check_consistent_length([1], [2], [3], [4], [5])
    check_consistent_length([[1, 2], [[1, 2]]], [1, 2], ["a", "b"])
    check_consistent_length([1], (2,), np.array([3]), sp.csr_matrix((1, 2)))
    with pytest.raises(ValueError, match="inconsistent numbers of samples"):
        check_consistent_length([1, 2], [1])
    with pytest.raises(TypeError, match=r"got <\w+ 'int'>"):
        check_consistent_length([1, 2], 1)
    with pytest.raises(TypeError, match=r"got <\w+ 'object'>"):
        check_consistent_length([1, 2], object())

    with pytest.raises(TypeError):
        check_consistent_length([1, 2], np.array(1))

    # Despite ensembles having __len__ they must raise TypeError
    with pytest.raises(TypeError, match="Expected sequence or array-like"):
        check_consistent_length([1, 2], RandomForestRegressor())
    # XXX: We should have a test with a string, but what is correct behaviour?


def test_check_dataframe_fit_attribute():
    # check pandas dataframe with 'fit' column does not raise error
    # https://github.com/scikit-learn/scikit-learn/issues/8415
    try:
        import pandas as pd

        X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
        X_df = pd.DataFrame(X, columns=["a", "b", "fit"])
        check_consistent_length(X_df)
    except ImportError:
        raise SkipTest("Pandas not found")


def test_suppress_validation():
    X = np.array([0, np.inf])
    with pytest.raises(ValueError):
        assert_all_finite(X)
    sklearn.set_config(assume_finite=True)
    assert_all_finite(X)
    sklearn.set_config(assume_finite=False)
    with pytest.raises(ValueError):
        assert_all_finite(X)


def test_check_array_series():
    # regression test that check_array works on pandas Series
    pd = importorskip("pandas")
    res = check_array(pd.Series([1, 2, 3]), ensure_2d=False)
    assert_array_equal(res, np.array([1, 2, 3]))

    # with categorical dtype (not a numpy dtype) (GH12699)
    s = pd.Series(["a", "b", "c"]).astype("category")
    res = check_array(s, dtype=None, ensure_2d=False)
    assert_array_equal(res, np.array(["a", "b", "c"], dtype=object))


@pytest.mark.parametrize(
    "dtype", ((np.float64, np.float32), np.float64, None, "numeric")
)
@pytest.mark.parametrize("bool_dtype", ("bool", "boolean"))
def test_check_dataframe_mixed_float_dtypes(dtype, bool_dtype):
    # pandas dataframe will coerce a boolean into a object, this is a mismatch
    # with np.result_type which will return a float
    # check_array needs to explicitly check for bool dtype in a dataframe for
    # this situation
    # https://github.com/scikit-learn/scikit-learn/issues/15787

    if bool_dtype == "boolean":
        # boolean extension arrays was introduced in 1.0
        pd = importorskip("pandas", minversion="1.0")
    else:
        pd = importorskip("pandas")

    df = pd.DataFrame(
        {
            "int": [1, 2, 3],
            "float": [0, 0.1, 2.1],
            "bool": pd.Series([True, False, True], dtype=bool_dtype),
        },
        columns=["int", "float", "bool"],
    )

    array = check_array(df, dtype=dtype)
    assert array.dtype == np.float64
    expected_array = np.array(
        [[1.0, 0.0, 1.0], [2.0, 0.1, 0.0], [3.0, 2.1, 1.0]], dtype=float
    )
    assert_allclose_dense_sparse(array, expected_array)


def test_check_dataframe_with_only_bool():
    """Check that dataframe with bool return a boolean arrays."""
    pd = importorskip("pandas")
    df = pd.DataFrame({"bool": [True, False, True]})

    array = check_array(df, dtype=None)
    assert array.dtype == np.bool_
    assert_array_equal(array, [[True], [False], [True]])

    # common dtype is int for bool + int
    df = pd.DataFrame(
        {"bool": [True, False, True], "int": [1, 2, 3]},
        columns=["bool", "int"],
    )
    array = check_array(df, dtype="numeric")
    assert array.dtype == np.int64
    assert_array_equal(array, [[1, 1], [0, 2], [1, 3]])


def test_check_dataframe_with_only_boolean():
    """Check that dataframe with boolean return a float array with dtype=None"""
    pd = importorskip("pandas", minversion="1.0")
    df = pd.DataFrame({"bool": pd.Series([True, False, True], dtype="boolean")})

    array = check_array(df, dtype=None)
    assert array.dtype == np.float64
    assert_array_equal(array, [[True], [False], [True]])


class DummyMemory:
    def cache(self, func):
        return func


class WrongDummyMemory:
    pass


def test_check_memory():
    memory = check_memory("cache_directory")
    assert memory.location == "cache_directory"

    memory = check_memory(None)
    assert memory.location is None

    dummy = DummyMemory()
    memory = check_memory(dummy)
    assert memory is dummy

    msg = (
        "'memory' should be None, a string or have the same interface as"
        " joblib.Memory. Got memory='1' instead."
    )
    with pytest.raises(ValueError, match=msg):
        check_memory(1)
    dummy = WrongDummyMemory()
    msg = (
        "'memory' should be None, a string or have the same interface as"
        " joblib.Memory. Got memory='{}' instead.".format(dummy)
    )
    with pytest.raises(ValueError, match=msg):
        check_memory(dummy)


@pytest.mark.parametrize("copy", [True, False])
def test_check_array_memmap(copy):
    X = np.ones((4, 4))
    with TempMemmap(X, mmap_mode="r") as X_memmap:
        X_checked = check_array(X_memmap, copy=copy)
        assert np.may_share_memory(X_memmap, X_checked) == (not copy)
        assert X_checked.flags["WRITEABLE"] == copy


@pytest.mark.parametrize(
    "retype",
    [
        np.asarray,
        sp.csr_matrix,
        sp.csc_matrix,
        sp.coo_matrix,
        sp.lil_matrix,
        sp.bsr_matrix,
        sp.dok_matrix,
        sp.dia_matrix,
    ],
)
def test_check_non_negative(retype):
    A = np.array([[1, 1, 0, 0], [1, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]])
    X = retype(A)
    check_non_negative(X, "")
    X = retype([[0, 0], [0, 0]])
    check_non_negative(X, "")

    A[0, 0] = -1
    X = retype(A)
    with pytest.raises(ValueError, match="Negative "):
        check_non_negative(X, "")


def test_check_X_y_informative_error():
    X = np.ones((2, 2))
    y = None
    msg = "estimator requires y to be passed, but the target y is None"
    with pytest.raises(ValueError, match=msg):
        check_X_y(X, y)

    msg = "RandomForestRegressor requires y to be passed, but the target y is None"
    with pytest.raises(ValueError, match=msg):
        check_X_y(X, y, estimator=RandomForestRegressor())


def test_retrieve_samples_from_non_standard_shape():
    class TestNonNumericShape:
        def __init__(self):
            self.shape = ("not numeric",)

        def __len__(self):
            return len([1, 2, 3])

    X = TestNonNumericShape()
    assert _num_samples(X) == len(X)

    # check that it gives a good error if there's no __len__
    class TestNoLenWeirdShape:
        def __init__(self):
            self.shape = ("not numeric",)

    with pytest.raises(TypeError, match="Expected sequence or array-like"):
        _num_samples(TestNoLenWeirdShape())


@pytest.mark.parametrize("x", [2, 3, 2.5, 5])
def test_check_scalar_valid(x):
    """Test that check_scalar returns no error/warning if valid inputs are
    provided"""
    with warnings.catch_warnings():
        warnings.simplefilter("error")
        scalar = check_scalar(
            x,
            "test_name",
            target_type=numbers.Real,
            min_val=2,
            max_val=5,
            include_boundaries="both",
        )
    assert scalar == x


@pytest.mark.parametrize(
    "x, target_name, target_type, min_val, max_val, include_boundaries, err_msg",
    [
        (
            1,
            "test_name1",
            float,
            2,
            4,
            "neither",
            TypeError("test_name1 must be an instance of float, not int."),
        ),
        (
            None,
            "test_name1",
            numbers.Real,
            2,
            4,
            "neither",
            TypeError("test_name1 must be an instance of float, not NoneType."),
        ),
        (
            None,
            "test_name1",
            numbers.Integral,
            2,
            4,
            "neither",
            TypeError("test_name1 must be an instance of int, not NoneType."),
        ),
        (
            1,
            "test_name1",
            (float, bool),
            2,
            4,
            "neither",
            TypeError("test_name1 must be an instance of {float, bool}, not int."),
        ),
        (
            1,
            "test_name2",
            int,
            2,
            4,
            "neither",
            ValueError("test_name2 == 1, must be > 2."),
        ),
        (
            5,
            "test_name3",
            int,
            2,
            4,
            "neither",
            ValueError("test_name3 == 5, must be < 4."),
        ),
        (
            2,
            "test_name4",
            int,
            2,
            4,
            "right",
            ValueError("test_name4 == 2, must be > 2."),
        ),
        (
            4,
            "test_name5",
            int,
            2,
            4,
            "left",
            ValueError("test_name5 == 4, must be < 4."),
        ),
        (
            4,
            "test_name6",
            int,
            2,
            4,
            "bad parameter value",
            ValueError(
                "Unknown value for `include_boundaries`: 'bad parameter value'. "
                "Possible values are: ('left', 'right', 'both', 'neither')."
            ),
        ),
        (
            4,
            "test_name7",
            int,
            None,
            4,
            "left",
            ValueError(
                "`include_boundaries`='left' without specifying explicitly `min_val` "
                "is inconsistent."
            ),
        ),
        (
            4,
            "test_name8",
            int,
            2,
            None,
            "right",
            ValueError(
                "`include_boundaries`='right' without specifying explicitly `max_val` "
                "is inconsistent."
            ),
        ),
    ],
)
def test_check_scalar_invalid(
    x, target_name, target_type, min_val, max_val, include_boundaries, err_msg
):
    """Test that check_scalar returns the right error if a wrong input is
    given"""
    with pytest.raises(Exception) as raised_error:
        check_scalar(
            x,
            target_name,
            target_type=target_type,
            min_val=min_val,
            max_val=max_val,
            include_boundaries=include_boundaries,
        )
    assert str(raised_error.value) == str(err_msg)
    assert type(raised_error.value) == type(err_msg)


_psd_cases_valid = {
    "nominal": ((1, 2), np.array([1, 2]), None, ""),
    "nominal_np_array": (np.array([1, 2]), np.array([1, 2]), None, ""),
    "insignificant_imag": (
        (5, 5e-5j),
        np.array([5, 0]),
        PositiveSpectrumWarning,
        "There are imaginary parts in eigenvalues \\(1e\\-05 of the maximum real part",
    ),
    "insignificant neg": ((5, -5e-5), np.array([5, 0]), PositiveSpectrumWarning, ""),
    "insignificant neg float32": (
        np.array([1, -1e-6], dtype=np.float32),
        np.array([1, 0], dtype=np.float32),
        PositiveSpectrumWarning,
        "There are negative eigenvalues \\(1e\\-06 of the maximum positive",
    ),
    "insignificant neg float64": (
        np.array([1, -1e-10], dtype=np.float64),
        np.array([1, 0], dtype=np.float64),
        PositiveSpectrumWarning,
        "There are negative eigenvalues \\(1e\\-10 of the maximum positive",
    ),
    "insignificant pos": (
        (5, 4e-12),
        np.array([5, 0]),
        PositiveSpectrumWarning,
        "the largest eigenvalue is more than 1e\\+12 times the smallest",
    ),
}


@pytest.mark.parametrize(
    "lambdas, expected_lambdas, w_type, w_msg",
    list(_psd_cases_valid.values()),
    ids=list(_psd_cases_valid.keys()),
)
@pytest.mark.parametrize("enable_warnings", [True, False])
def test_check_psd_eigenvalues_valid(
    lambdas, expected_lambdas, w_type, w_msg, enable_warnings
):
    # Test that ``_check_psd_eigenvalues`` returns the right output for valid
    # input, possibly raising the right warning

    if not enable_warnings:
        w_type = None

    if w_type is None:
        with warnings.catch_warnings():
            warnings.simplefilter("error", PositiveSpectrumWarning)
            lambdas_fixed = _check_psd_eigenvalues(
                lambdas, enable_warnings=enable_warnings
            )
    else:
        with pytest.warns(w_type, match=w_msg):
            lambdas_fixed = _check_psd_eigenvalues(
                lambdas, enable_warnings=enable_warnings
            )

    assert_allclose(expected_lambdas, lambdas_fixed)


_psd_cases_invalid = {
    "significant_imag": (
        (5, 5j),
        ValueError,
        "There are significant imaginary parts in eigenv",
    ),
    "all negative": (
        (-5, -1),
        ValueError,
        "All eigenvalues are negative \\(maximum is -1",
    ),
    "significant neg": (
        (5, -1),
        ValueError,
        "There are significant negative eigenvalues",
    ),
    "significant neg float32": (
        np.array([3e-4, -2e-6], dtype=np.float32),
        ValueError,
        "There are significant negative eigenvalues",
    ),
    "significant neg float64": (
        np.array([1e-5, -2e-10], dtype=np.float64),
        ValueError,
        "There are significant negative eigenvalues",
    ),
}


@pytest.mark.parametrize(
    "lambdas, err_type, err_msg",
    list(_psd_cases_invalid.values()),
    ids=list(_psd_cases_invalid.keys()),
)
def test_check_psd_eigenvalues_invalid(lambdas, err_type, err_msg):
    # Test that ``_check_psd_eigenvalues`` raises the right error for invalid
    # input

    with pytest.raises(err_type, match=err_msg):
        _check_psd_eigenvalues(lambdas)


def test_check_sample_weight():
    # check array order
    sample_weight = np.ones(10)[::2]
    assert not sample_weight.flags["C_CONTIGUOUS"]
    sample_weight = _check_sample_weight(sample_weight, X=np.ones((5, 1)))
    assert sample_weight.flags["C_CONTIGUOUS"]

    # check None input
    sample_weight = _check_sample_weight(None, X=np.ones((5, 2)))
    assert_allclose(sample_weight, np.ones(5))

    # check numbers input
    sample_weight = _check_sample_weight(2.0, X=np.ones((5, 2)))
    assert_allclose(sample_weight, 2 * np.ones(5))

    # check wrong number of dimensions
    with pytest.raises(ValueError, match="Sample weights must be 1D array or scalar"):
        _check_sample_weight(np.ones((2, 4)), X=np.ones((2, 2)))

    # check incorrect n_samples
    msg = r"sample_weight.shape == \(4,\), expected \(2,\)!"
    with pytest.raises(ValueError, match=msg):
        _check_sample_weight(np.ones(4), X=np.ones((2, 2)))

    # float32 dtype is preserved
    X = np.ones((5, 2))
    sample_weight = np.ones(5, dtype=np.float32)
    sample_weight = _check_sample_weight(sample_weight, X)
    assert sample_weight.dtype == np.float32

    # int dtype will be converted to float64 instead
    X = np.ones((5, 2), dtype=int)
    sample_weight = _check_sample_weight(None, X, dtype=X.dtype)
    assert sample_weight.dtype == np.float64

    # check negative weight when only_non_negative=True
    X = np.ones((5, 2))
    sample_weight = np.ones(_num_samples(X))
    sample_weight[-1] = -10
    err_msg = "Negative values in data passed to `sample_weight`"
    with pytest.raises(ValueError, match=err_msg):
        _check_sample_weight(sample_weight, X, only_non_negative=True)


@pytest.mark.parametrize("toarray", [np.array, sp.csr_matrix, sp.csc_matrix])
def test_allclose_dense_sparse_equals(toarray):
    base = np.arange(9).reshape(3, 3)
    x, y = toarray(base), toarray(base)
    assert _allclose_dense_sparse(x, y)


@pytest.mark.parametrize("toarray", [np.array, sp.csr_matrix, sp.csc_matrix])
def test_allclose_dense_sparse_not_equals(toarray):
    base = np.arange(9).reshape(3, 3)
    x, y = toarray(base), toarray(base + 1)
    assert not _allclose_dense_sparse(x, y)


@pytest.mark.parametrize("toarray", [sp.csr_matrix, sp.csc_matrix])
def test_allclose_dense_sparse_raise(toarray):
    x = np.arange(9).reshape(3, 3)
    y = toarray(x + 1)

    msg = "Can only compare two sparse matrices, not a sparse matrix and an array"
    with pytest.raises(ValueError, match=msg):
        _allclose_dense_sparse(x, y)


def test_deprecate_positional_args_warns_for_function():
    @_deprecate_positional_args
    def f1(a, b, *, c=1, d=1):
        pass

    with pytest.warns(FutureWarning, match=r"Pass c=3 as keyword args"):
        f1(1, 2, 3)

    with pytest.warns(FutureWarning, match=r"Pass c=3, d=4 as keyword args"):
        f1(1, 2, 3, 4)

    @_deprecate_positional_args
    def f2(a=1, *, b=1, c=1, d=1):
        pass

    with pytest.warns(FutureWarning, match=r"Pass b=2 as keyword args"):
        f2(1, 2)

    # The * is place before a keyword only argument without a default value
    @_deprecate_positional_args
    def f3(a, *, b, c=1, d=1):
        pass

    with pytest.warns(FutureWarning, match=r"Pass b=2 as keyword args"):
        f3(1, 2)


def test_deprecate_positional_args_warns_for_function_version():
    @_deprecate_positional_args(version="1.1")
    def f1(a, *, b):
        pass

    with pytest.warns(
        FutureWarning, match=r"From version 1.1 passing these as positional"
    ):
        f1(1, 2)


def test_deprecate_positional_args_warns_for_class():
    class A1:
        @_deprecate_positional_args
        def __init__(self, a, b, *, c=1, d=1):
            pass

    with pytest.warns(FutureWarning, match=r"Pass c=3 as keyword args"):
        A1(1, 2, 3)

    with pytest.warns(FutureWarning, match=r"Pass c=3, d=4 as keyword args"):
        A1(1, 2, 3, 4)

    class A2:
        @_deprecate_positional_args
        def __init__(self, a=1, b=1, *, c=1, d=1):
            pass

    with pytest.warns(FutureWarning, match=r"Pass c=3 as keyword args"):
        A2(1, 2, 3)

    with pytest.warns(FutureWarning, match=r"Pass c=3, d=4 as keyword args"):
        A2(1, 2, 3, 4)


@pytest.mark.parametrize("indices", [None, [1, 3]])
def test_check_fit_params(indices):
    X = np.random.randn(4, 2)
    fit_params = {
        "list": [1, 2, 3, 4],
        "array": np.array([1, 2, 3, 4]),
        "sparse-col": sp.csc_matrix([1, 2, 3, 4]).T,
        "sparse-row": sp.csc_matrix([1, 2, 3, 4]),
        "scalar-int": 1,
        "scalar-str": "xxx",
        "None": None,
    }
    result = _check_fit_params(X, fit_params, indices)
    indices_ = indices if indices is not None else list(range(X.shape[0]))

    for key in ["sparse-row", "scalar-int", "scalar-str", "None"]:
        assert result[key] is fit_params[key]

    assert result["list"] == _safe_indexing(fit_params["list"], indices_)
    assert_array_equal(result["array"], _safe_indexing(fit_params["array"], indices_))
    assert_allclose_dense_sparse(
        result["sparse-col"], _safe_indexing(fit_params["sparse-col"], indices_)
    )


@pytest.mark.parametrize("sp_format", [True, "csr", "csc", "coo", "bsr"])
def test_c