# Authors: The jax-sklearn developers
# SPDX-License-Identifier: BSD-3-Clause

import re
import warnings

import numpy as np
import numpy.linalg as la
import pytest
from scipy import sparse, stats

from xlearn import config_context, datasets
from xlearn.base import clone
from xlearn.exceptions import NotFittedError
from xlearn.externals._packaging.version import parse as parse_version
from xlearn.metrics.pairwise import linear_kernel
from xlearn.model_selection import cross_val_predict
from xlearn.pipeline import Pipeline
from xlearn.preprocessing import (
    Binarizer,
    KernelCenterer,
    MaxAbsScaler,
    MinMaxScaler,
    Normalizer,
    PowerTransformer,
    QuantileTransformer,
    RobustScaler,
    StandardScaler,
    add_dummy_feature,
    maxabs_scale,
    minmax_scale,
    normalize,
    power_transform,
    quantile_transform,
    robust_scale,
    scale,
)
from xlearn.preprocessing._data import BOUNDS_THRESHOLD, _handle_zeros_in_scale
from xlearn.svm import SVR
from xlearn.utils import gen_batches, shuffle
from xlearn.utils._array_api import (
    _convert_to_numpy,
    _get_namespace_device_dtype_ids,
    yield_namespace_device_dtype_combinations,
)
from xlearn.utils._test_common.instance_generator import _get_check_estimator_ids
from xlearn.utils._testing import (
    _array_api_for_tests,
    _convert_container,
    assert_allclose,
    assert_allclose_dense_sparse,
    assert_almost_equal,
    assert_array_almost_equal,
    assert_array_equal,
    assert_array_less,
    skip_if_32bit,
)
from xlearn.utils.estimator_checks import (
    check_array_api_input_and_values,
)
from xlearn.utils.fixes import (
    COO_CONTAINERS,
    CSC_CONTAINERS,
    CSR_CONTAINERS,
    LIL_CONTAINERS,
    sp_version,
)
from xlearn.utils.sparsefuncs import mean_variance_axis

iris = datasets.load_iris()

# Make some data to be used many times
rng = np.random.RandomState(0)
n_features = 30
n_samples = 1000
offsets = rng.uniform(-1, 1, size=n_features)
scales = rng.uniform(1, 10, size=n_features)
X_2d = rng.randn(n_samples, n_features) * scales + offsets
X_1row = X_2d[0, :].reshape(1, n_features)
X_1col = X_2d[:, 0].reshape(n_samples, 1)
X_list_1row = X_1row.tolist()
X_list_1col = X_1col.tolist()


def toarray(a):
    if hasattr(a, "toarray"):
        a = a.toarray()
    return a


def _check_dim_1axis(a):
    return np.asarray(a).shape[0]


def assert_correct_incr(i, batch_start, batch_stop, n, chunk_size, n_samples_seen):
    if batch_stop != n:
        assert (i + 1) * chunk_size == n_samples_seen
    else:
        assert i * chunk_size + (batch_stop - batch_start) == n_samples_seen


def test_raises_value_error_if_sample_weights_greater_than_1d():
    # Sample weights must be either scalar or 1D

    n_sampless = [2, 3]
    n_featuress = [3, 2]

    for n_samples, n_features in zip(n_sampless, n_featuress):
        X = rng.randn(n_samples, n_features)
        y = rng.randn(n_samples)

        scaler = StandardScaler()

        # make sure Error is raised the sample weights greater than 1d
        sample_weight_notOK = rng.randn(n_samples, 1) ** 2
        with pytest.raises(ValueError):
            scaler.fit(X, y, sample_weight=sample_weight_notOK)


@pytest.mark.parametrize(
    ["Xw", "X", "sample_weight"],
    [
        ([[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [1, 2, 3], [4, 5, 6]], [2.0, 1.0]),
        (
            [[1, 0, 1], [0, 0, 1]],
            [[1, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1]],
            np.array([1, 3]),
        ),
        (
            [[1, np.nan, 1], [np.nan, np.nan, 1]],
            [
                [1, np.nan, 1],
                [np.nan, np.nan, 1],
                [np.nan, np.nan, 1],
                [np.nan, np.nan, 1],
            ],
            np.array([1, 3]),
        ),
    ],
)
@pytest.mark.parametrize("array_constructor", ["array", "sparse_csr", "sparse_csc"])
def test_standard_scaler_sample_weight(Xw, X, sample_weight, array_constructor):
    with_mean = not array_constructor.startswith("sparse")
    X = _convert_container(X, array_constructor)
    Xw = _convert_container(Xw, array_constructor)

    # weighted StandardScaler
    yw = np.ones(Xw.shape[0])
    scaler_w = StandardScaler(with_mean=with_mean)
    scaler_w.fit(Xw, yw, sample_weight=sample_weight)

    # unweighted, but with repeated samples
    y = np.ones(X.shape[0])
    scaler = StandardScaler(with_mean=with_mean)
    scaler.fit(X, y)

    X_test = [[1.5, 2.5, 3.5], [3.5, 4.5, 5.5]]

    assert_almost_equal(scaler.mean_, scaler_w.mean_)
    assert_almost_equal(scaler.var_, scaler_w.var_)
    assert_almost_equal(scaler.transform(X_test), scaler_w.transform(X_test))


def test_standard_scaler_1d():
    # Test scaling of dataset along single axis
    for X in [X_1row, X_1col, X_list_1row, X_list_1row]:
        scaler = StandardScaler()
        X_scaled = scaler.fit(X).transform(X, copy=True)

        if isinstance(X, list):
            X = np.array(X)  # cast only after scaling done

        if _check_dim_1axis(X) == 1:
            assert_almost_equal(scaler.mean_, X.ravel())
            assert_almost_equal(scaler.scale_, np.ones(n_features))
            assert_array_almost_equal(X_scaled.mean(axis=0), np.zeros_like(n_features))
            assert_array_almost_equal(X_scaled.std(axis=0), np.zeros_like(n_features))
        else:
            assert_almost_equal(scaler.mean_, X.mean())
            assert_almost_equal(scaler.scale_, X.std())
            assert_array_almost_equal(X_scaled.mean(axis=0), np.zeros_like(n_features))
            assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
            assert_array_almost_equal(X_scaled.std(axis=0), 1.0)
        assert scaler.n_samples_seen_ == X.shape[0]

        # check inverse transform
        X_scaled_back = scaler.inverse_transform(X_scaled)
        assert_array_almost_equal(X_scaled_back, X)

    # Constant feature
    X = np.ones((5, 1))
    scaler = StandardScaler()
    X_scaled = scaler.fit(X).transform(X, copy=True)
    assert_almost_equal(scaler.mean_, 1.0)
    assert_almost_equal(scaler.scale_, 1.0)
    assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
    assert_array_almost_equal(X_scaled.std(axis=0), 0.0)
    assert scaler.n_samples_seen_ == X.shape[0]


@pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS + CSR_CONTAINERS)
@pytest.mark.parametrize("add_sample_weight", [False, True])
def test_standard_scaler_dtype(add_sample_weight, sparse_container):
    # Ensure scaling does not affect dtype
    rng = np.random.RandomState(0)
    n_samples = 10
    n_features = 3
    if add_sample_weight:
        sample_weight = np.ones(n_samples)
    else:
        sample_weight = None
    with_mean = True
    if sparse_container is not None:
        # scipy sparse containers do not support float16, see
        # https://github.com/scipy/scipy/issues/7408 for more details.
        supported_dtype = [np.float64, np.float32]
    else:
        supported_dtype = [np.float64, np.float32, np.float16]
    for dtype in supported_dtype:
        X = rng.randn(n_samples, n_features).astype(dtype)
        if sparse_container is not None:
            X = sparse_container(X)
            with_mean = False

        scaler = StandardScaler(with_mean=with_mean)
        X_scaled = scaler.fit(X, sample_weight=sample_weight).transform(X)
        assert X.dtype == X_scaled.dtype
        assert scaler.mean_.dtype == np.float64
        assert scaler.scale_.dtype == np.float64


@pytest.mark.parametrize(
    "scaler",
    [
        StandardScaler(with_mean=False),
        RobustScaler(with_centering=False),
    ],
)
@pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS + CSR_CONTAINERS)
@pytest.mark.parametrize("add_sample_weight", [False, True])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize("constant", [0, 1.0, 100.0])
def test_standard_scaler_constant_features(
    scaler, add_sample_weight, sparse_container, dtype, constant
):
    if isinstance(scaler, RobustScaler) and add_sample_weight:
        pytest.skip(f"{scaler.__class__.__name__} does not yet support sample_weight")

    rng = np.random.RandomState(0)
    n_samples = 100
    n_features = 1
    if add_sample_weight:
        fit_params = dict(sample_weight=rng.uniform(size=n_samples) * 2)
    else:
        fit_params = {}
    X_array = np.full(shape=(n_samples, n_features), fill_value=constant, dtype=dtype)
    X = X_array if sparse_container is None else sparse_container(X_array)
    X_scaled = scaler.fit(X, **fit_params).transform(X)

    if isinstance(scaler, StandardScaler):
        # The variance info should be close to zero for constant features.
        assert_allclose(scaler.var_, np.zeros(X.shape[1]), atol=1e-7)

    # Constant features should not be scaled (scale of 1.):
    assert_allclose(scaler.scale_, np.ones(X.shape[1]))

    assert X_scaled is not X  # make sure we make a copy
    assert_allclose_dense_sparse(X_scaled, X)

    if isinstance(scaler, StandardScaler) and not add_sample_weight:
        # Also check consistency with the standard scale function.
        X_scaled_2 = scale(X, with_mean=scaler.with_mean)
        assert X_scaled_2 is not X  # make sure we did a copy
        assert_allclose_dense_sparse(X_scaled_2, X)


@pytest.mark.parametrize("n_samples", [10, 100, 10_000])
@pytest.mark.parametrize("average", [1e-10, 1, 1e10])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS + CSR_CONTAINERS)
def test_standard_scaler_near_constant_features(
    n_samples, sparse_container, average, dtype
):
    # Check that when the variance is too small (var << mean**2) the feature
    # is considered constant and not scaled.

    scale_min, scale_max = -30, 19
    scales = np.array([10**i for i in range(scale_min, scale_max + 1)], dtype=dtype)

    n_features = scales.shape[0]
    X = np.empty((n_samples, n_features), dtype=dtype)
    # Make a dataset of known var = scales**2 and mean = average
    X[: n_samples // 2, :] = average + scales
    X[n_samples // 2 :, :] = average - scales
    X_array = X if sparse_container is None else sparse_container(X)

    scaler = StandardScaler(with_mean=False).fit(X_array)

    # StandardScaler uses float64 accumulators even if the data has a float32
    # dtype.
    eps = np.finfo(np.float64).eps

    # if var < bound = N.eps.var + N².eps².mean², the feature is considered
    # constant and the scale_ attribute is set to 1.
    bounds = n_samples * eps * scales**2 + n_samples**2 * eps**2 * average**2
    within_bounds = scales**2 <= bounds

    # Check that scale_min is small enough to have some scales below the
    # bound and therefore detected as constant:
    assert np.any(within_bounds)

    # Check that such features are actually treated as constant by the scaler:
    assert all(scaler.var_[within_bounds] <= bounds[within_bounds])
    assert_allclose(scaler.scale_[within_bounds], 1.0)

    # Depending the on the dtype of X, some features might not actually be
    # representable as non constant for small scales (even if above the
    # precision bound of the float64 variance estimate). Such feature should
    # be correctly detected as constants with 0 variance by StandardScaler.
    representable_diff = X[0, :] - X[-1, :] != 0
    assert_allclose(scaler.var_[np.logical_not(representable_diff)], 0)
    assert_allclose(scaler.scale_[np.logical_not(representable_diff)], 1)

    # The other features are scaled and scale_ is equal to sqrt(var_) assuming
    # that scales are large enough for average + scale and average - scale to
    # be distinct in X (depending on X's dtype).
    common_mask = np.logical_and(scales**2 > bounds, representable_diff)
    assert_allclose(scaler.scale_[common_mask], np.sqrt(scaler.var_)[common_mask])


def test_scale_1d():
    # 1-d inputs
    X_list = [1.0, 3.0, 5.0, 0.0]
    X_arr = np.array(X_list)

    for X in [X_list, X_arr]:
        X_scaled = scale(X)
        assert_array_almost_equal(X_scaled.mean(), 0.0)
        assert_array_almost_equal(X_scaled.std(), 1.0)
        assert_array_equal(scale(X, with_mean=False, with_std=False), X)


@skip_if_32bit
def test_standard_scaler_numerical_stability():
    # Test numerical stability of scaling
    # np.log(1e-5) is taken because of its floating point representation
    # was empirically found to cause numerical problems with np.mean & np.std.
    x = np.full(8, np.log(1e-5), dtype=np.float64)
    # This does not raise a warning as the number of samples is too low
    # to trigger the problem in recent numpy
    with warnings.catch_warnings():
        warnings.simplefilter("error", UserWarning)
        scale(x)
    assert_array_almost_equal(scale(x), np.zeros(8))

    # with 2 more samples, the std computation run into numerical issues:
    x = np.full(10, np.log(1e-5), dtype=np.float64)
    warning_message = "standard deviation of the data is probably very close to 0"
    with pytest.warns(UserWarning, match=warning_message):
        x_scaled = scale(x)
    assert_array_almost_equal(x_scaled, np.zeros(10))

    x = np.full(10, 1e-100, dtype=np.float64)
    with warnings.catch_warnings():
        warnings.simplefilter("error", UserWarning)
        x_small_scaled = scale(x)
    assert_array_almost_equal(x_small_scaled, np.zeros(10))

    # Large values can cause (often recoverable) numerical stability issues:
    x_big = np.full(10, 1e100, dtype=np.float64)
    warning_message = "Dataset may contain too large values"
    with pytest.warns(UserWarning, match=warning_message):
        x_big_scaled = scale(x_big)
    assert_array_almost_equal(x_big_scaled, np.zeros(10))
    assert_array_almost_equal(x_big_scaled, x_small_scaled)
    with pytest.warns(UserWarning, match=warning_message):
        x_big_centered = scale(x_big, with_std=False)
    assert_array_almost_equal(x_big_centered, np.zeros(10))
    assert_array_almost_equal(x_big_centered, x_small_scaled)


def test_scaler_2d_arrays():
    # Test scaling of 2d array along first axis
    rng = np.random.RandomState(0)
    n_features = 5
    n_samples = 4
    X = rng.randn(n_samples, n_features)
    X[:, 0] = 0.0  # first feature is always of zero

    scaler = StandardScaler()
    X_scaled = scaler.fit(X).transform(X, copy=True)
    assert not np.any(np.isnan(X_scaled))
    assert scaler.n_samples_seen_ == n_samples

    assert_array_almost_equal(X_scaled.mean(axis=0), n_features * [0.0])
    assert_array_almost_equal(X_scaled.std(axis=0), [0.0, 1.0, 1.0, 1.0, 1.0])
    # Check that X has been copied
    assert X_scaled is not X

    # check inverse transform
    X_scaled_back = scaler.inverse_transform(X_scaled)
    assert X_scaled_back is not X
    assert X_scaled_back is not X_scaled
    assert_array_almost_equal(X_scaled_back, X)

    X_scaled = scale(X, axis=1, with_std=False)
    assert not np.any(np.isnan(X_scaled))
    assert_array_almost_equal(X_scaled.mean(axis=1), n_samples * [0.0])
    X_scaled = scale(X, axis=1, with_std=True)
    assert not np.any(np.isnan(X_scaled))
    assert_array_almost_equal(X_scaled.mean(axis=1), n_samples * [0.0])
    assert_array_almost_equal(X_scaled.std(axis=1), n_samples * [1.0])
    # Check that the data hasn't been modified
    assert X_scaled is not X

    X_scaled = scaler.fit(X).transform(X, copy=False)
    assert not np.any(np.isnan(X_scaled))
    assert_array_almost_equal(X_scaled.mean(axis=0), n_features * [0.0])
    assert_array_almost_equal(X_scaled.std(axis=0), [0.0, 1.0, 1.0, 1.0, 1.0])
    # Check that X has not been copied
    assert X_scaled is X

    X = rng.randn(4, 5)
    X[:, 0] = 1.0  # first feature is a constant, non zero feature
    scaler = StandardScaler()
    X_scaled = scaler.fit(X).transform(X, copy=True)
    assert not np.any(np.isnan(X_scaled))
    assert_array_almost_equal(X_scaled.mean(axis=0), n_features * [0.0])
    assert_array_almost_equal(X_scaled.std(axis=0), [0.0, 1.0, 1.0, 1.0, 1.0])
    # Check that X has not been copied
    assert X_scaled is not X


def test_scaler_float16_overflow():
    # Test if the scaler will not overflow on float16 numpy arrays
    rng = np.random.RandomState(0)
    # float16 has a maximum of 65500.0. On the worst case 5 * 200000 is 100000
    # which is enough to overflow the data type
    X = rng.uniform(5, 10, [200000, 1]).astype(np.float16)

    with np.errstate(over="raise"):
        scaler = StandardScaler().fit(X)
        X_scaled = scaler.transform(X)

    # Calculate the float64 equivalent to verify result
    X_scaled_f64 = StandardScaler().fit_transform(X.astype(np.float64))

    # Overflow calculations may cause -inf, inf, or nan. Since there is no nan
    # input, all of the outputs should be finite. This may be redundant since a
    # FloatingPointError exception will be thrown on overflow above.
    assert np.all(np.isfinite(X_scaled))

    # The normal distribution is very unlikely to go above 4. At 4.0-8.0 the
    # float16 precision is 2^-8 which is around 0.004. Thus only 2 decimals are
    # checked to account for precision differences.
    assert_array_almost_equal(X_scaled, X_scaled_f64, decimal=2)


def test_handle_zeros_in_scale():
    s1 = np.array([0, 1e-16, 1, 2, 3])
    s2 = _handle_zeros_in_scale(s1, copy=True)

    assert_allclose(s1, np.array([0, 1e-16, 1, 2, 3]))
    assert_allclose(s2, np.array([1, 1, 1, 2, 3]))


def test_minmax_scaler_partial_fit():
    # Test if partial_fit run over many batches of size 1 and 50
    # gives the same results as fit
    X = X_2d
    n = X.shape[0]

    for chunk_size in [1, 2, 50, n, n + 42]:
        # Test mean at the end of the process
        scaler_batch = MinMaxScaler().fit(X)

        scaler_incr = MinMaxScaler()
        for batch in gen_batches(n_samples, chunk_size):
            scaler_incr = scaler_incr.partial_fit(X[batch])

        assert_array_almost_equal(scaler_batch.data_min_, scaler_incr.data_min_)
        assert_array_almost_equal(scaler_batch.data_max_, scaler_incr.data_max_)
        assert scaler_batch.n_samples_seen_ == scaler_incr.n_samples_seen_
        assert_array_almost_equal(scaler_batch.data_range_, scaler_incr.data_range_)
        assert_array_almost_equal(scaler_batch.scale_, scaler_incr.scale_)
        assert_array_almost_equal(scaler_batch.min_, scaler_incr.min_)

        # Test std after 1 step
        batch0 = slice(0, chunk_size)
        scaler_batch = MinMaxScaler().fit(X[batch0])
        scaler_incr = MinMaxScaler().partial_fit(X[batch0])

        assert_array_almost_equal(scaler_batch.data_min_, scaler_incr.data_min_)
        assert_array_almost_equal(scaler_batch.data_max_, scaler_incr.data_max_)
        assert scaler_batch.n_samples_seen_ == scaler_incr.n_samples_seen_
        assert_array_almost_equal(scaler_batch.data_range_, scaler_incr.data_range_)
        assert_array_almost_equal(scaler_batch.scale_, scaler_incr.scale_)
        assert_array_almost_equal(scaler_batch.min_, scaler_incr.min_)

        # Test std until the end of partial fits, and
        scaler_batch = MinMaxScaler().fit(X)
        scaler_incr = MinMaxScaler()  # Clean estimator
        for i, batch in enumerate(gen_batches(n_samples, chunk_size)):
            scaler_incr = scaler_incr.partial_fit(X[batch])
            assert_correct_incr(
                i,
                batch_start=batch.start,
                batch_stop=batch.stop,
                n=n,
                chunk_size=chunk_size,
                n_samples_seen=scaler_incr.n_samples_seen_,
            )


def test_standard_scaler_partial_fit():
    # Test if partial_fit run over many batches of size 1 and 50
    # gives the same results as fit
    X = X_2d
    n = X.shape[0]

    for chunk_size in [1, 2, 50, n, n + 42]:
        # Test mean at the end of the process
        scaler_batch = StandardScaler(with_std=False).fit(X)

        scaler_incr = StandardScaler(with_std=False)
        for batch in gen_batches(n_samples, chunk_size):
            scaler_incr = scaler_incr.partial_fit(X[batch])
        assert_array_almost_equal(scaler_batch.mean_, scaler_incr.mean_)
        assert scaler_batch.var_ == scaler_incr.var_  # Nones
        assert scaler_batch.n_samples_seen_ == scaler_incr.n_samples_seen_

        # Test std after 1 step
        batch0 = slice(0, chunk_size)
        scaler_incr = StandardScaler().partial_fit(X[batch0])
        if chunk_size == 1:
            assert_array_almost_equal(
                np.zeros(n_features, dtype=np.float64), scaler_incr.var_
            )
            assert_array_almost_equal(
                np.ones(n_features, dtype=np.float64), scaler_incr.scale_
            )
        else:
            assert_array_almost_equal(np.var(X[batch0], axis=0), scaler_incr.var_)
            assert_array_almost_equal(
                np.std(X[batch0], axis=0), scaler_incr.scale_
            )  # no constants

        # Test std until the end of partial fits, and
        scaler_batch = StandardScaler().fit(X)
        scaler_incr = StandardScaler()  # Clean estimator
        for i, batch in enumerate(gen_batches(n_samples, chunk_size)):
            scaler_incr = scaler_incr.partial_fit(X[batch])
            assert_correct_incr(
                i,
                batch_start=batch.start,
                batch_stop=batch.stop,
                n=n,
                chunk_size=chunk_size,
                n_samples_seen=scaler_incr.n_samples_seen_,
            )

        assert_array_almost_equal(scaler_batch.var_, scaler_incr.var_)
        assert scaler_batch.n_samples_seen_ == scaler_incr.n_samples_seen_


@pytest.mark.parametrize("sparse_container", CSC_CONTAINERS + CSR_CONTAINERS)
def test_standard_scaler_partial_fit_numerical_stability(sparse_container):
    # Test if the incremental computation introduces significative errors
    # for large datasets with values of large magniture
    rng = np.random.RandomState(0)
    n_features = 2
    n_samples = 100
    offsets = rng.uniform(-1e15, 1e15, size=n_features)
    scales = rng.uniform(1e3, 1e6, size=n_features)
    X = rng.randn(n_samples, n_features) * scales + offsets

    scaler_batch = StandardScaler().fit(X)
    scaler_incr = StandardScaler()
    for chunk in X:
        scaler_incr = scaler_incr.partial_fit(chunk.reshape(1, n_features))

    # Regardless of abs values, they must not be more diff 6 significant digits
    tol = 10 ** (-6)
    assert_allclose(scaler_incr.mean_, scaler_batch.mean_, rtol=tol)
    assert_allclose(scaler_incr.var_, scaler_batch.var_, rtol=tol)
    assert_allclose(scaler_incr.scale_, scaler_batch.scale_, rtol=tol)
    # NOTE Be aware that for much larger offsets std is very unstable (last
    # assert) while mean is OK.

    # Sparse input
    size = (100, 3)
    scale = 1e20
    X = sparse_container(rng.randint(0, 2, size).astype(np.float64) * scale)

    # with_mean=False is required with sparse input
    scaler = StandardScaler(with_mean=False).fit(X)
    scaler_incr = StandardScaler(with_mean=False)

    for chunk in X:
        if chunk.ndim == 1:
            # Sparse arrays can be 1D (in scipy 1.14 and later) while old
            # sparse matrix instances are always 2D.
            chunk = chunk.reshape(1, -1)
        scaler_incr = scaler_incr.partial_fit(chunk)

    # Regardless of magnitude, they must not differ more than of 6 digits
    tol = 10 ** (-6)
    assert scaler.mean_ is not None
    assert_allclose(scaler_incr.var_, scaler.var_, rtol=tol)
    assert_allclose(scaler_incr.scale_, scaler.scale_, rtol=tol)


@pytest.mark.parametrize("sample_weight", [True, None])
@pytest.mark.parametrize("sparse_container", CSC_CONTAINERS + CSR_CONTAINERS)
def test_partial_fit_sparse_input(sample_weight, sparse_container):
    # Check that sparsity is not destroyed
    X = sparse_container(np.array([[1.0], [0.0], [0.0], [5.0]]))

    if sample_weight:
        sample_weight = rng.rand(X.shape[0])

    null_transform = StandardScaler(with_mean=False, with_std=False, copy=True)
    X_null = null_transform.partial_fit(X, sample_weight=sample_weight).transform(X)
    assert_array_equal(X_null.toarray(), X.toarray())
    X_orig = null_transform.inverse_transform(X_null)
    assert_array_equal(X_orig.toarray(), X_null.toarray())
    assert_array_equal(X_orig.toarray(), X.toarray())


@pytest.mark.parametrize("sample_weight", [True, None])
def test_standard_scaler_trasform_with_partial_fit(sample_weight):
    # Check some postconditions after applying partial_fit and transform
    X = X_2d[:100, :]

    if sample_weight:
        sample_weight = rng.rand(X.shape[0])

    scaler_incr = StandardScaler()
    for i, batch in enumerate(gen_batches(X.shape[0], 1)):
        X_sofar = X[: (i + 1), :]
        chunks_copy = X_sofar.copy()
        if sample_weight is None:
            scaled_batch = StandardScaler().fit_transform(X_sofar)
            scaler_incr = scaler_incr.partial_fit(X[batch])
        else:
            scaled_batch = StandardScaler().fit_transform(
                X_sofar, sample_weight=sample_weight[: i + 1]
            )
            scaler_incr = scaler_incr.partial_fit(
                X[batch], sample_weight=sample_weight[batch]
            )
        scaled_incr = scaler_incr.transform(X_sofar)

        assert_array_almost_equal(scaled_batch, scaled_incr)
        assert_array_almost_equal(X_sofar, chunks_copy)  # No change
        right_input = scaler_incr.inverse_transform(scaled_incr)
        assert_array_almost_equal(X_sofar, right_input)

        zero = np.zeros(X.shape[1])
        epsilon = np.finfo(float).eps
        assert_array_less(zero, scaler_incr.var_ + epsilon)  # as less or equal
        assert_array_less(zero, scaler_incr.scale_ + epsilon)
        if sample_weight is None:
            # (i+1) because the Scaler has been already fitted
            assert (i + 1) == scaler_incr.n_samples_seen_
        else:
            assert np.sum(sample_weight[: i + 1]) == pytest.approx(
                scaler_incr.n_samples_seen_
            )


def test_standard_check_array_of_inverse_transform():
    # Check if StandardScaler inverse_transform is
    # converting the integer array to float
    x = np.array(
        [
            [1, 1, 1, 0, 1, 0],
            [1, 1, 1, 0, 1, 0],
            [0, 8, 0, 1, 0, 0],
            [1, 4, 1, 1, 0, 0],
            [0, 1, 0, 0, 1, 0],
            [0, 4, 0, 1, 0, 1],
        ],
        dtype=np.int32,
    )

    scaler = StandardScaler()
    scaler.fit(x)

    # The of inverse_transform should be converted
    # to a float array.
    # If not X *= self.scale_ will fail.
    scaler.inverse_transform(x)


@pytest.mark.parametrize(
    "array_namespace, device, dtype_name",
    yield_namespace_device_dtype_combinations(),
    ids=_get_namespace_device_dtype_ids,
)
@pytest.mark.parametrize(
    "check",
    [check_array_api_input_and_values],
    ids=_get_check_estimator_ids,
)
@pytest.mark.parametrize(
    "estimator",
    [
        MaxAbsScaler(),
        MinMaxScaler(),
        MinMaxScaler(clip=True),
        KernelCenterer(),
        Normalizer(norm="l1"),
        Normalizer(norm="l2"),
        Normalizer(norm="max"),
        Binarizer(),
    ],
    ids=_get_check_estimator_ids,
)
def test_preprocessing_array_api_compliance(
    estimator, check, array_namespace, device, dtype_name
):
    name = estimator.__class__.__name__
    check(name, estimator, array_namespace, device=device, dtype_name=dtype_name)


def test_min_max_scaler_iris():
    X = iris.data
    scaler = MinMaxScaler()
    # default params
    X_trans = scaler.fit_transform(X)
    assert_array_almost_equal(X_trans.min(axis=0), 0)
    assert_array_almost_equal(X_trans.max(axis=0), 1)
    X_trans_inv = scaler.inverse_transform(X_trans)
    assert_array_almost_equal(X, X_trans_inv)

    # not default params: min=1, max=2
    scaler = MinMaxScaler(feature_range=(1, 2))
    X_trans = scaler.fit_transform(X)
    assert_array_almost_equal(X_trans.min(axis=0), 1)
    assert_array_almost_equal(X_trans.max(axis=0), 2)
    X_trans_inv = scaler.inverse_transform(X_trans)
    assert_array_almost_equal(X, X_trans_inv)

    # min=-.5, max=.6
    scaler = MinMaxScaler(feature_range=(-0.5, 0.6))
    X_trans = scaler.fit_transform(X)
    assert_array_almost_equal(X_trans.min(axis=0), -0.5)
    assert_array_almost_equal(X_trans.max(axis=0), 0.6)
    X_trans_inv = scaler.inverse_transform(X_trans)
    assert_array_almost_equal(X, X_trans_inv)

    # raises on invalid range
    scaler = MinMaxScaler(feature_range=(2, 1))
    with pytest.raises(ValueError):
        scaler.fit(X)


def test_min_max_scaler_zero_variance_features():
    # Check min max scaler on toy data with zero variance features
    X = [[0.0, 1.0, +0.5], [0.0, 1.0, -0.1], [0.0, 1.0, +1.1]]

    X_new = [[+0.0, 2.0, 0.5], [-1.0, 1.0, 0.0], [+0.0, 1.0, 1.5]]

    # default params
    scaler = MinMaxScaler()
    X_trans = scaler.fit_transform(X)
    X_expected_0_1 = [[0.0, 0.0, 0.5], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]]
    assert_array_almost_equal(X_trans, X_expected_0_1)
    X_trans_inv = scaler.inverse_transform(X_trans)
    assert_array_almost_equal(X, X_trans_inv)

    X_trans_new = scaler.transform(X_new)
    X_expected_0_1_new = [[+0.0, 1.0, 0.500], [-1.0, 0.0, 0.083], [+0.0, 0.0, 1.333]]
    assert_array_almost_equal(X_trans_new, X_expected_0_1_new, decimal=2)

    # not default params
    scaler = MinMaxScaler(feature_range=(1, 2))
    X_trans = scaler.fit_transform(X)
    X_expected_1_2 = [[1.0, 1.0, 1.5], [1.0, 1.0, 1.0], [1.0, 1.0, 2.0]]
    assert_array_almost_equal(X_trans, X_expected_1_2)

    # function interface
    X_trans = minmax_scale(X)
    assert_array_almost_equal(X_trans, X_expected_0_1)
    X_trans = minmax_scale(X, feature_range=(1, 2))
    assert_array_almost_equal(X_trans, X_expected_1_2)


def test_minmax_scale_axis1():
    X = iris.data
    X_trans = minmax_scale(X, axis=1)
    assert_array_almost_equal(np.min(X_trans, axis=1), 0)
    assert_array_almost_equal(np.max(X_trans, axis=1), 1)


def test_min_max_scaler_1d():
    # Test scaling of dataset along single axis
    for X in [X_1row, X_1col, X_list_1row, X_list_1row]:
        scaler = MinMaxScaler(copy=True)
        X_scaled = scaler.fit(X).transform(X)

        if isinstance(X, list):
            X = np.array(X)  # cast only after scaling done

        if _check_dim_1axis(X) == 1:
            assert_array_almost_equal(X_scaled.min(axis=0), np.zeros(n_features))
            assert_array_almost_equal(X_scaled.max(axis=0), np.zeros(n_features))
        else:
            assert_array_almost_equal(X_scaled.min(axis=0), 0.0)
            assert_array_almost_equal(X_scaled.max(axis=0), 1.0)
        assert scaler.n_samples_seen_ == X.shape[0]

        # check inverse transform
        X_scaled_back = scaler.inverse_transform(X_scaled)
        assert_array_almost_equal(X_scaled_back, X)

    # Constant feature
    X = np.ones((5, 1))
    scaler = MinMaxScaler()
    X_scaled = scaler.fit(X).transform(X)
    assert X_scaled.min() >= 0.0
    assert X_scaled.max() <= 1.0
    assert scaler.n_samples_seen_ == X.shape[0]

    # Function interface
    X_1d = X_1row.ravel()
    min_ = X_1d.min()
    max_ = X_1d.max()
    assert_array_almost_equal(
        (X_1d - min_) / (max_ - min_), minmax_scale(X_1d, copy=True)
    )


@pytest.mark.parametrize("sample_weight", [True, None])
@pytest.mark.parametrize("sparse_container", CSC_CONTAINERS + CSR_CONTAINERS)
def test_scaler_without_centering(sample_weight, sparse_container):
    rng = np.random.RandomState(42)
    X = rng.randn(4, 5)
    X[:, 0] = 0.0  # first feature is always of zero
    X_sparse = sparse_container(X)

    if sample_weight:
        sample_weight = rng.rand(X.shape[0])

    with pytest.raises(ValueError):
        StandardScaler().fit(X_sparse)

    scaler = StandardScaler(with_mean=False).fit(X, sample_weight=sample_weight)
    X_scaled = scaler.transform(X, copy=True)
    assert not np.any(np.isnan(X_scaled))

    scaler_sparse = StandardScaler(with_mean=False).fit(
        X_sparse, sample_weight=sample_weight
    )
    X_sparse_scaled = scaler_sparse.transform(X_sparse, copy=True)
    assert not np.any(np.isnan(X_sparse_scaled.data))

    assert_array_almost_equal(scaler.mean_, scaler_sparse.mean_)
    assert_array_almost_equal(scaler.var_, scaler_sparse.var_)
    assert_array_almost_equal(scaler.scale_, scaler_sparse.scale_)
    assert_array_almost_equal(scaler.n_samples_seen_, scaler_sparse.n_samples_seen_)

    if sample_weight is None:
        assert_array_almost_equal(
            X_scaled.mean(axis=0), [0.0, -0.01, 2.24, -0.35, -0.78], 2
        )
        assert_array_almost_equal(X_scaled.std(axis=0), [0.0, 1.0, 1.0, 1.0, 1.0])

    X_sparse_scaled_mean, X_sparse_scaled_var = mean_variance_axis(X_sparse_scaled, 0)
    assert_array_almost_equal(X_sparse_scaled_mean, X_scaled.mean(axis=0))
    assert_array_almost_equal(X_sparse_scaled_var, X_scaled.var(axis=0))

    # Check that X has not been modified (copy)
    assert X_scaled is not X
    assert X_sparse_scaled is not X_sparse

    X_scaled_back = scaler.inverse_transform(X_scaled)
    assert X_scaled_back is not X
    assert X_scaled_back is not X_scaled
    assert_array_almost_equal(X_scaled_back, X)

    X_sparse_scaled_back = scaler_sparse.inverse_transform(X_sparse_scaled)
    assert X_sparse_scaled_back is not X_sparse
    assert X_sparse_scaled_back is not X_sparse_scaled
    assert_array_almost_equal(X_sparse_scaled_back.toarray(), X)

    if sparse_container in CSR_CONTAINERS:
        null_transform = StandardScaler(with_mean=False, with_std=False, copy=True)
        X_null = null_transform.fit_transform(X_sparse)
        assert_array_equal(X_null.data, X_sparse.data)
        X_orig = null_transform.inverse_transform(X_null)
        assert_array_equal(X_orig.data, X_sparse.data)


@pytest.mark.parametrize("with_mean", [True, False])
@pytest.mark.parametrize("with_std", [True, False])
@pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS + CSR_CONTAINERS)
def test_scaler_n_samples_seen_with_nan(with_mean, with_std, sparse_container):
    X = np.array(
        [[0, 1, 3], [np.nan, 6, 10], [5, 4, np.nan], [8, 0, np.nan]], dtype=np.float64
    )
    if sparse_container is not None:
        X = sparse_container(X)

    if sparse.issparse(X) and with_mean:
        pytest.skip("'with_mean=True' cannot be used with sparse matrix.")

    transformer = StandardScaler(with_mean=with_mean, with_std=with_std)
    transformer.fit(X)

    assert_array_equal(transformer.n_samples_seen_, np.array([3, 4, 2]))


def _check_identity_scalers_attributes(scaler_1, scaler_2):
    assert scaler_1.mean_ is scaler_2.mean_ is None
    assert scaler_1.var_ is scaler_2.var_ is None
    assert scaler_1.scale_ is scaler_2.scale_ is None
    assert scaler_1.n_samples_seen_ == scaler_2.n_samples_seen_


@pytest.mark.parametrize("sparse_container", CSC_CONTAINERS + CSR_CONTAINERS)
def test_scaler_return_identity(sparse_container):
    # test that the scaler return identity when with_mean and with_std are
    # False
    X_dense = np.array([[0, 1, 3], [5, 6, 0], [8, 0, 10]], dtype=np.float64)
    X_sparse = sparse_container(X_dense)

    transformer_dense = StandardScaler(with_mean=False, with_std=False)
    X_trans_dense = transformer_dense.fit_transform(X_dense)
    assert_allclose(X_trans_dense, X_dense)

    transformer_sparse = clone(transformer_dense)
    X_trans_sparse = transformer_sparse.fit_transform(X_sparse)
    assert_allclose_dense_sparse(X_trans_sparse, X_sparse)

    _check_identity_scalers_attributes(transformer_dense, transformer_sparse)

    transformer_dense.partial_fit(X_dense)
    transformer_sparse.partial_fit(X_sparse)
    _check_identity_scalers_attributes(transformer_dense, transformer_sparse)

    transformer_dense.fit(X_dense)
    transformer_sparse.fit(X_sparse)
    _check_identity_scalers_attributes(transformer_dense, transformer_sparse)


@pytest.mark.parametrize("sparse_container", CSC_CONTAINERS + CSR_CONTAINERS)
def test_scaler_int(sparse_container):
    # test that scaler converts integer input to floating
    # for both sparse and dense matrices
    rng = np.random.RandomState(42)
    X = rng.randint(20, size=(4, 5))
    X[:, 0] = 0  # first feature is always of zero
    X_sparse = sparse_container(X)

    with warnings.catch_warnings(record=True):
        scaler = StandardScaler(with_mean=False).fit(X)
        X_scaled = scaler.transform(X, copy=True)
    assert not np.any(np.isnan(X_scaled))

    with warnings.catch_warnings(record=True):
        scaler_sparse = StandardScaler(with_mean=False).fit(X_sparse)
        X_sparse_scaled = scaler_sparse.transform(X_sparse, copy=True)
    assert not np.any(np.isnan(X_sparse_scaled.data))

    assert_array_almost_equal(scaler.mean_, scaler_sparse.mean_)
    assert_array_almost_equal(scaler.var_, scaler_sparse.var_)
    assert_array_almost_equal(scaler.scale_, scaler_sparse.scale_)

    assert_array_almost_equal(
        X_scaled.mean(axis=0), [0.0, 1.109, 1.856, 21.0, 1.559], 2
    )
    assert_array_almost_equal(X_scaled.std(axis=0), [0.0, 1.0, 1.0, 1.0, 1.0])

    X_sparse_scaled_mean, X_sparse_scaled_std = mean_variance_axis(
        X_sparse_scaled.astype(float), 0
    )
    assert_array_almost_equal(X_sparse_scaled_mean, X_scaled.mean(axis=0))
    assert_array_almost_equal(X_sparse_scaled_std, X_scaled.std(axis=0))

    # Check that X has not been modified (copy)
    assert X_scaled is not X
    assert X_sparse_scaled is not X_sparse

    X_scaled_back = scaler.inverse_transform(X_scaled)
    assert X_scaled_back is not X
    assert X_scaled_back is not X_scaled
    assert_array_almost_equal(X_scaled_back, X)

    X_sparse_scaled_back = scaler_sparse.inverse_transform(X_sparse_scaled)
    assert X_sparse_scaled_back is not X_sparse
    assert X_sparse_scaled_back is not X_sparse_scaled
    assert_array_almost_equal(X_sparse_scaled_back.toarray(), X)

    if sparse_container in CSR_CONTAINERS:
        null_transform = StandardScaler(with_mean=False, with_std=False, copy=True)
        with warnings.catch_warnings(record=True):
            X_null = null_transform.fit_transform(X_sparse)
        assert_array_equal(X_null.data, X_sparse.data)
        X_orig = null_transform.inverse_transform(X_null)
        assert_array_equal(X_orig.data, X_sparse.data)


@pytest.mark.parametrize("sparse_container", CSR_CONTAINERS + CSC_CONTAINERS)
def test_scaler_without_copy(sparse_container):
    # Check that StandardScaler.fit does not change input
    rng = np.random.RandomState(42)
    X = rng.randn(4, 5)
    X[:, 0] = 0.0  # first feature is always of zero
    X_sparse = sparse_container(X)

    X_copy = X.copy()
    StandardScaler(copy=False).fit(X)
    assert_array_equal(X, X_copy)

    X_sparse_copy = X_sparse.copy()
    StandardScaler(with_mean=False, copy=False).fit(X_sparse)
    assert_array_equal(X_sparse.toarray(), X_sparse_copy.toarray())


@pytest.mark.parametrize("sparse_container", CSR_CONTAINERS + CSC_CONTAINERS)
def test_scale_sparse_with_mean_raise_exception(sparse_container):
    rng = np.random.RandomState(42)
    X = rng.randn(4, 5)
    X_sparse = sparse_container(X)

    # check scaling and fit with direct calls on sparse data
    with pytest.raises(ValueError):
        scale(X_sparse, with_mean=True)
    with pytest.raises(ValueError):
        StandardScaler(with_mean=True).fit(X_sparse)

    # check transform and inverse_transform after a fit on a dense array
    scaler = StandardScaler(with_mean=True).fit(X)
    with pytest.raises(ValueError):
        scaler.transform(X_sparse)

    X_transformed_sparse = sparse_container(scaler.transform(X))
    with pytest.raises(ValueError):
        scaler.inverse_transform(X_transformed_sparse)


def test_scale_input_finiteness_validation():
    # Check if non finite inputs raise ValueError
    X = [[np.inf, 5, 6, 7, 8]]
    with pytest.raises(
        ValueError, match="Input contains infinity or a value too large"
    ):
        scale(X)


def test_robust_scaler_error_sparse():
    X_sparse = sparse.rand(1000, 10)
    scaler = RobustScaler(with_centering=True)
    err_msg = "Cannot center sparse matrices"
    with pytest.raises(ValueError, match=err_msg):
        scaler.fit(X_sparse)


@pytest.mark.parametrize("with_centering", [True, False])
@pytest.mark.parametrize("with_scaling", [True, False])
@pytest.mark.parametrize("X", [np.random.randn(10, 3), sparse.rand(10, 3, density=0.5)])
def test_robust_scaler_attributes(X, with_centering, with_scaling):
    # check consistent type of attributes
    if with_centering and sparse.issparse(X):
        pytest.skip("RobustScaler cannot center sparse matrix")

    scaler = RobustScaler(with_centering=with_centering, with_scaling=with_scaling)
    scaler.fit(X)

    if with_centering:
        assert isinstance(scaler.center_, np.ndarray)
    else:
        assert scaler.center_ is None
    if with_scaling:
        assert isinstance(scaler.scale_, np.ndarray)
    else:
        assert scaler.scale_ is None


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_robust_scaler_col_zero_sparse(csr_container):
    # check that the scaler is working when there is not data materialized in a
    # column of a sparse matrix
    X = np.random.randn(10, 5)
    X[:, 0] = 0
    X = csr_container(X)

    scaler = RobustScaler(with_centering=False)
    scaler.fit(X)
    assert scaler.scale_[0] == pytest.approx(1)

    X_trans = scaler.transform(X)
    assert_allclose(X[:, [0]].toarray(), X_trans[:, [0]].toarray())


def test_robust_scaler_2d_arrays():
    # Test robust scaling of 2d array along first axis
    rng = np.random.RandomState(0)
    X = rng.randn(4, 5)
    X[:, 0] = 0.0  # first feature is always of zero

    scaler = RobustScaler()
    X_scaled = scaler.fit(X).transform(X)

    assert_array_almost_equal(np.median(X_scaled, axis=0), 5 * [0.0])
    assert_array_almost_equal(X_scaled.std(axis=0)[0], 0)


@pytest.mark.parametrize("density", [0, 0.05, 0.1, 0.5, 1])
@pytest.mark.parametrize("strictly_signed", ["positive", "negative", "zeros", None])
def test_robust_scaler_equivalence_dense_sparse(density, strictly_signed):
    # Check the equivalence of the fitting with dense and sparse matrices
    X_sparse = sparse.rand(1000, 5, density=density).tocsc()
    if strictly_signed == "positive":
        X_sparse.data = np.abs(X_sparse.data)
    elif strictly_signed == "negative":
        X_sparse.data = -np.abs(X_sparse.data)
    elif strictly_signed == "zeros":
        X_sparse.data = np.zeros(X_sparse.data.shape, dtype=np.float64)
    X_dense = X_sparse.toarray()

    scaler_sparse = RobustScaler(with_centering=False)
    scaler_dense = RobustScaler(with_centering=False)

    scaler_sparse.fit(X_sparse)
    scaler_dense.fit(X_dense)

    assert_allclose(scaler_sparse.scale_, scaler_dense.scale_)


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_robust_scaler_transform_one_row_csr(csr_container):
    # Check RobustScaler on transforming csr matrix with one row
    rng = np.random.RandomState(0)
    X = rng.randn(4, 5)
    single_row = np.array([[0.1, 1.0, 2.0, 0.0, -1.0]])
    scaler = RobustScaler(with_centering=False)
    scaler = scaler.fit(X)
    row_trans = scaler.transform(csr_container(single_row))
    row_expected = single_row / scaler.scale_
    assert_array_almost_equal(row_trans.toarray(), row_expected)
    row_scaled_back = scaler.inverse_transform(row_trans)
    assert_array_almost_equal(single_row, row_scaled_back.toarray())


def test_robust_scaler_iris():
    X = iris.data
    scaler = RobustScaler()
    X_trans = scaler.fit_transform(X)
    assert_array_almost_equal(np.median(X_trans, axis=0), 0)
    X_trans_inv = scaler.inverse_transform(X_trans)
    assert_array_almost_equal(X, X_trans_inv)
    q = np.percentile(X_trans, q=(25, 75), axis=0)
    iqr = q[1] - q[0]
    assert_array_almost_equal(iqr, 1)


def test_robust_scaler_iris_quantiles():
    X = iris.data
    scaler = RobustScaler(quantile_range=(10, 90))
    X_trans = scaler.fit_transform(X)
    assert_array_almost_equal(np.median(X_trans, axis=0), 0)
    X_trans_inv = scaler.inverse_transform(X_trans)
    assert_array_almost_equal(X, X_trans_inv)
    q = np.percentile(X_trans, q=(10, 90), axis=0)
    q_range = q[1] - q[0]
    assert_array_almost_equal(q_range, 1)


@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_quantile_transform_iris(csc_container):
    X = iris.data
    # uniform output distribution
    transformer = QuantileTransformer(n_quantiles=30)
    X_trans = transformer.fit_transform(X)
    X_trans_inv = transformer.inverse_transform(X_trans)
    assert_array_almost_equal(X, X_trans_inv)
    # normal output distribution
    transformer = QuantileTransformer(n_quantiles=30, output_distribution="normal")
    X_trans = transformer.fit_transform(X)
    X_trans_inv = transformer.inverse_transform(X_trans)
    assert_array_almost_equal(X, X_trans_inv)
    # make sure it is possible to take the inverse of a sparse matrix
    # which contain negative value; this is the case in the iris dataset
    X_sparse = csc_container(X)
    X_sparse_tran = transformer.fit_transform(X_sparse)
    X_sparse_tran_inv = transformer.inverse_transform(X_sparse_tran)
    assert_array_almost_equal(X_sparse.toarray(), X_sparse_tran_inv.toarray())


@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_quantile_transform_check_error(csc_container):
    X = np.transpose(
        [
            [0, 25, 50, 0, 0, 0, 75, 0, 0, 100],
            [2, 4, 0, 0, 6, 8, 0, 10, 0, 0],
            [0, 0, 2.6, 4.1, 0, 0, 2.3, 0, 9.5, 0.1],
        ]
    )
    X = csc_container(X)
    X_neg = np.transpose(
        [
            [0, 25, 50, 0, 0, 0, 75, 0, 0, 100],
            [-2, 4, 0, 0, 6, 8, 0, 10, 0, 0],
            [0, 0, 2.6, 4.1, 0, 0, 2.3, 0, 9.5, 0.1],
        ]
    )
    X_neg = csc_container(X_neg)

    err_msg = (
        "The number of quantiles cannot be greater than "
        "the number of samples used. Got 1000 quantiles "
        "and 10 samples."
    )
    with pytest.raises(ValueError, match=err_msg):
        QuantileTransformer(subsample=10).fit(X)

    transformer = QuantileTransformer(n_quantiles=10)
    err_msg = "QuantileTransformer only accepts non-negative sparse matrices."
    with pytest.raises(ValueError, match=err_msg):
        transformer.fit(X_neg)
    transformer.fit(X)
    err_msg = "QuantileTransformer only accepts non-negative sparse matrices."
    with pytest.raises(ValueError, match=err_msg):
        transformer.transform(X_neg)

    X_bad_feat = np.transpose(
        [[0, 25, 50, 0, 0, 0, 75, 0, 0, 100], [0, 0, 2.6, 4.1, 0, 0, 2.3, 0, 9.5, 0.1]]
    )
    err_msg = (
        "X has 2 features, but QuantileTransformer is expecting 3 features as input."
    )
    with pytest.raises(ValueError, match=err_msg):
        transformer.inverse_transform(X_bad_feat)

    transformer = QuantileTransformer(n_quantiles=10).fit(X)
    # check that an error is raised if input is scalar
    with pytest.raises(ValueError, match="Expected 2D array, got scalar array instead"):
        transformer.transform(10)
    # check that a warning is raised is n_quantiles > n_samples
    transformer = QuantileTransformer(n_quantiles=100)
    warn_msg = "n_quantiles is set to n_samples"
    with pytest.warns(UserWarning, match=warn_msg) as record:
        transformer.fit(X)
    assert len(record) == 1
    assert transformer.n_quantiles_ == X.shape[0]


@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_quantile_transform_sparse_ignore_zeros(csc_container):
    X = np.array([[0, 1], [0, 0], [0, 2], [0, 2], [0, 1]])
    X_sparse = csc_container(X)
    transformer = QuantileTransformer(ignore_implicit_zeros=True, n_quantiles=5)

    # dense case -> warning raise
    warning_message = (
        "'ignore_implicit_zeros' takes effect"
        " only with sparse matrix. This parameter has no"
        " effect."
    )
    with pytest.warns(UserWarning, match=warning_message):
        transformer.fit(X)

    X_expected = np.array([[0, 0], [0, 0], [0, 1], [0, 1], [0, 0]])
    X_trans = transformer.fit_transform(X_sparse)
    assert_almost_equal(X_expected, X_trans.toarray())

    # consider the case where sparse entries are missing values and user-given
    # zeros are to be considered
    X_data = np.array([0, 0, 1, 0, 2, 2, 1, 0, 1, 2, 0])
    X_col = np.array([0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1])
    X_row = np.array([0, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8])
    X_sparse = csc_container((X_data, (X_row, X_col)))
    X_trans = transformer.fit_transform(X_sparse)
    X_expected = np.array(
        [
            [0.0, 0.5],
            [0.0, 0.0],
            [0.0, 1.0],
            [0.0, 1.0],
            [0.0, 0.5],
            [0.0, 0.0],
            [0.0, 0.5],
            [0.0, 1.0],
            [0.0, 0.0],
        ]
    )
    assert_almost_equal(X_expected, X_trans.toarray())

    transformer = QuantileTransformer(ignore_implicit_zeros=True, n_quantiles=5)
    X_data = np.array([-1, -1, 1, 0, 0, 0, 1, -1, 1])
    X_col = np.array([0, 0, 1, 1, 1, 1, 1, 1, 1])
    X_row = np.array([0, 4, 0, 1, 2, 3, 4, 5, 6])
    X_sparse = csc_container((X_data, (X_row, X_col)))
    X_trans = transformer.fit_transform(X_sparse)
    X_expected = np.array(
        [[0, 1], [0, 0.375], [0, 0.375], [0, 0.375], [0, 1], [0, 0], [0, 1]]
    )
    assert_almost_equal(X_expected, X_trans.toarray())
    assert_almost_equal(
        X_sparse.toarray(), transformer.inverse_transform(X_trans).toarray()
    )

    # check in conjunction with subsampling
    transformer = QuantileTransformer(
        ignore_implicit_zeros=True, n_quantiles=5, subsample=8, random_state=0
    )
    X_trans = transformer.fit_transform(X_sparse)
    assert_almost_equal(X_expected, X_trans.toarray())
    assert_almost_equal(
        X_sparse.toarray(), transformer.inverse_transform(X_trans).toarray()
    )


def test_quantile_transform_dense_toy():
    X = np.array(
        [[0, 2, 2.6], [25, 4, 4.1], [50, 6, 2.3], [75, 8, 9.5], [100, 10, 0.1]]
    )

    transformer = QuantileTransformer(n_quantiles=5)
    transformer.fit(X)

    # using a uniform output, each entry of X should be map between 0 and 1
    # and equally spaced
    X_trans = transformer.fit_transform(X)
    X_expected = np.tile(np.linspace(0, 1, num=5), (3, 1)).T
    assert_almost_equal(np.sort(X_trans, axis=0), X_expected)

    X_test = np.array(
        [
            [-1, 1, 0],
            [101, 11, 10],
        ]
    )
    X_expected = np.array(
        [
            [0, 0, 0],
            [1, 1, 1],
        ]
    )
    assert_array_almost_equal(transformer.transform(X_test), X_expected)

    X_trans_inv = transformer.inverse_transform(X_trans)
    assert_array_almost_equal(X, X_trans_inv)


def test_quantile_transform_subsampling():
    # Test that subsampling the input yield to a consistent results We check
    # that the computed quantiles are almost mapped to a [0, 1] vector where
    # values are equally spaced. The infinite norm is checked to be smaller
    # than a given threshold. This is repeated 5 times.

    # dense support
    n_samples = 1000000
    n_quantiles = 1000
    X = np.sort(np.random.sample((n_samples, 1)), axis=0)
    ROUND = 5
    inf_norm_arr = []
    for random_state in range(ROUND):
        transformer = QuantileTransformer(
            random_state=random_state,
            n_quantiles=n_quantiles,
            subsample=n_samples // 10,
        )
        transformer.fit(X)
        diff = np.linspace(0, 1, n_quantiles) - np.ravel(transformer.quantiles_)
        inf_norm = np.max(np.abs(diff))
        assert inf_norm < 1e-2
        inf_norm_arr.append(inf_norm)
    # each random subsampling yield a unique approximation to the expected
    # linspace CDF
    assert len(np.unique(inf_norm_arr)) == len(inf_norm_arr)

    # sparse support

    X = sparse.rand(n_samples, 1, density=0.99, format="csc", random_state=0)
    inf_norm_arr = []
    for random_state in range(ROUND):
        transformer = QuantileTransformer(
            random_state=random_state,
            n_quantiles=n_quantiles,
            subsample=n_samples // 10,
        )
        transformer.fit(X)
        diff = np.linspace(0, 1, n_quantiles) - np.ravel(transformer.quantiles_)
        inf_norm = np.max(np.abs(diff))
        assert inf_norm < 1e-1
        inf_norm_arr.append(inf_norm)
    # each random subsampling yield a unique approximation to the expected
    # linspace CDF
    assert len(np.unique(inf_norm_arr)) == len(inf_norm_arr)


def test_quantile_transform_subsampling_disabled():
    """Check the behaviour of `QuantileTransformer` when `subsample=None`."""
    X = np.random.RandomState(0).normal(size=(200, 1))

    n_quantiles = 5
    transformer = QuantileTransformer(n_quantiles=n_quantiles, subsample=None).fit(X)

    expected_references = np.linspace(0, 1, n_quantiles)
    assert_allclose(transformer.references_, expected_references)
    expected_quantiles = np.quantile(X.ravel(), expected_references)
    assert_allclose(transformer.quantiles_.ravel(), expected_quantiles)


@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_quantile_transform_sparse_toy(csc_container):
    X = np.array(
        [
            [0.0, 2.0, 0.0],
            [25.0, 4.0, 0.0],
            [50.0, 0.0, 2.6],
            [0.0, 0.0, 4.1],
            [0.0, 6.0, 0.0],
            [0.0, 8.0, 0.0],
            [75.0, 0.0, 2.3],
            [0.0, 10.0, 0.0],
            [0.0, 0.0, 9.5],
            [100.0, 0.0, 0.1],
        ]
    )

    X = csc_container(X)

    transformer = QuantileTransformer(n_quantiles=10)
    transformer.fit(X)

    X_trans = transformer.fit_transform(X)
    assert_array_almost_equal(np.min(X_trans.toarray(), axis=0), 0.0)
    assert_array_almost_equal(np.max(X_trans.toarray(), axis=0), 1.0)

    X_trans_inv = transformer.inverse_transform(X_trans)
    assert_array_almost_equal(X.toarray(), X_trans_inv.toarray())

    transformer_dense = QuantileTransformer(n_quantiles=10).fit(X.toarray())

    X_trans = transformer_dense.transform(X)
    assert_array_almost_equal(np.min(X_trans.toarray(), axis=0), 0.0)
    assert_array_almost_equal(np.max(X_trans.toarray(), axis=0), 1.0)

    X_trans_inv = transformer_dense.inverse_transform(X_trans)
    assert_array_almost_equal(X.toarray(), X_trans_inv.toarray())


def test_quantile_transform_axis1():
    X = np.array([[0, 25, 50, 75, 100], [2, 4, 6, 8, 10], [2.6, 4.1, 2.3, 9.5, 0.1]])

    X_trans_a0 = quantile_transform(X.T, axis=0, n_quantiles=5)
    X_trans_a1 = quantile_transform(X, axis=1, n_quantiles=5)
    assert_array_almost_equal(X_trans_a0, X_trans_a1.T)


@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_quantile_transform_bounds(csc_container):
    # Lower and upper bounds are manually mapped. We checked that in the case
    # of a constant feature and binary feature, the bounds are properly mapped.
    X_dense = np.array([[0, 0], [0, 0], [1, 0]])
    X_sparse = csc_container(X_dense)

    # check sparse and dense are consistent
    X_trans = QuantileTransformer(n_quantiles=3, random_state=0).fit_transform(X_dense)
    assert_array_almost_equal(X_trans, X_dense)
    X_trans_sp = QuantileTransformer(n_quantiles=3, random_state=0).fit_transform(
        X_sparse
    )
    assert_array_almost_equal(X_trans_sp.toarray(), X_dense)
    assert_array_almost_equal(X_trans, X_trans_sp.toarray())

    # check the consistency of the bounds by learning on 1 matrix
    # and transforming another
    X = np.array([[0, 1], [0, 0.5], [1, 0]])
    X1 = np.array([[0, 0.1], [0, 0.5], [1, 0.1]])
    transformer = QuantileTransformer(n_quantiles=3).fit(X)
    X_trans = transformer.transform(X1)
    assert_array_almost_equal(X_trans, X1)

    # check that values outside of the range learned will be mapped properly.
    X = np.random.random((1000, 1))
    transformer = QuantileTransformer()
    transformer.fit(X)
    assert transformer.transform([[-10]]) == transformer.transform([[np.min(X)]])
    assert transformer.transform([[10]]) == transformer.transform([[np.max(X)]])
    assert transformer.inverse_transform([[-10]]) == transformer.inverse_transform(
        [[np.min(transformer.references_)]]
    )
    assert transformer.inverse_transform([[10]]) == transformer.inverse_transform(
        [[np.max(transformer.references_)]]
    )


def test_quantile_transform_and_inverse():
    X_1 = iris.data
    X_2 = np.array([[0.0], [BOUNDS_THRESHOLD / 10], [1.5], [2], [3], [3], [4]])
    for X in [X_1, X_2]:
        transformer = QuantileTransformer(n_quantiles=1000, random_state=0)
        X_trans = transformer.fit_transform(X)
        X_trans_inv = transformer.inverse_transform(X_trans)
        assert_array_almost_equal(X, X_trans_inv, decimal=9)


def test_quantile_transform_nan():
    X = np.array([[np.nan, 0, 0, 1], [np.nan, np.nan, 0, 0.5], [np.nan, 1, 1, 0]])

    transformer = QuantileTransformer(n_quantiles=10, random_state=42)
    transformer.fit_transform(X)

    # check that the quantile of the first column is all NaN
    assert np.isnan(transformer.quantiles_[:, 0]).all()
    # all other column should not contain NaN
    assert not np.isnan(transformer.quantiles_[:, 1:]).any()


@pytest.mark.parametrize("array_type", ["array", "sparse"])
def test_quantile_transformer_sorted_quantiles(array_type):
    # Non-regression test for:
    # https://github.com/chenxingqiang/jax-sklearn/issues/15733
    # Taken from upstream bug report:
    # https://github.com/numpy/numpy/issues/14685
    X = np.array([0, 1, 1, 2, 2, 3, 3, 4, 5, 5, 1, 1, 9, 9, 9, 8, 8, 7] * 10)
    X = 0.1 * X.reshape(-1, 1)
    X = _convert_container(X, array_type)

    n_quantiles = 100
    qt = QuantileTransformer(n_quantiles=n_quantiles).fit(X)

    # Check that the estimated quantile thresholds are monotically
    # increasing:
    quantiles = qt.quantiles_[:, 0]
    assert len(quantiles) == 100
    assert all(np.diff(quantiles) >= 0)


def test_robust_scaler_invalid_range():
    for range_ in [
        (-1, 90),
        (-2, -3),
        (10, 101),
        (100.5, 101),
        (90, 50),
    ]:
        scaler = RobustScaler(quantile_range=range_)

        with pytest.raises(ValueError, match=r"Invalid quantile range: \("):
            scaler.fit(iris.data)


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_scale_function_without_centering(csr_container):
    rng = np.random.RandomState(42)
    X = rng.randn(4, 5)
    X[:, 0] = 0.0  # first feature is always of zero
    X_csr = csr_container(X)

    X_scaled = scale(X, with_mean=False)
    assert not np.any(np.isnan(X_scaled))

    X_csr_scaled = scale(X_csr, with_mean=False)
    assert not np.any(np.isnan(X_csr_scaled.data))

    # test csc has same outcome
    X_csc_scaled = scale(X_csr.tocsc(), with_mean=False)
    assert_array_almost_equal(X_scaled, X_csc_scaled.toarray())

    # raises value error on axis != 0
    with pytest.raises(ValueError):
        scale(X_csr, with_mean=False, axis=1)

    assert_array_almost_equal(
        X_scaled.mean(axis=0), [0.0, -0.01, 2.24, -0.35, -0.78], 2
    )
    assert_array_almost_equal(X_scaled.std(axis=0), [0.0, 1.0, 1.0, 1.0, 1.0])
    # Check that X has not been copied
    assert X_scaled is not X

    X_csr_scaled_mean, X_csr_scaled_std = mean_variance_axis(X_csr_scaled, 0)
    assert_array_almost_equal(X_csr_scaled_mean, X_scaled.mean(axis=0))
    assert_array_almost_equal(X_csr_scaled_std, X_scaled.std(axis=0))

    # null scale
    X_csr_scaled = scale(X_csr, with_mean=False, with_std=False, copy=True)
    assert_array_almost_equal(X_csr.toarray(), X_csr_scaled.toarray())


def test_robust_scale_axis1():
    X = iris.data
    X_trans = robust_scale(X, axis=1)
    assert_array_almost_equal(np.median(X_trans, axis=1), 0)
    q = np.percentile(X_trans, q=(25, 75), axis=1)
    iqr = q[1] - q[0]
    assert_array_almost_equal(iqr, 1)


def test_robust_scale_1d_array():
    X = iris.data[:, 1]
    X_trans = robust_scale(X)
    assert_array_almost_equal(np.median(X_trans), 0)
    q = np.percentile(X_trans, q=(25, 75))
    iqr = q[1] - q[0]
    assert_array_almost_equal(iqr, 1)


def test_robust_scaler_zero_variance_features():
    # Check RobustScaler on toy data with zero variance features
    X = [[0.0, 1.0, +0.5], [0.0, 1.0, -0.1], [0.0, 1.0, +1.1]]

    scaler = RobustScaler()
    X_trans = scaler.fit_transform(X)

    # NOTE: for such a small sample size, what we expect in the third column
    # depends HEAVILY on the method used to calculate quantiles. The values
    # here were calculated to fit the quantiles produces by np.percentile
    # using numpy 1.9 Calculating quantiles with
    # scipy.stats.mstats.scoreatquantile or scipy.stats.mstats.mquantiles
    # would yield very different results!
    X_expected = [[0.0, 0.0, +0.0], [0.0, 0.0, -1.0], [0.0, 0.0, +1.0]]
    assert_array_almost_equal(X_trans, X_expected)
    X_trans_inv = scaler.inverse_transform(X_trans)
    assert_array_almost_equal(X, X_trans_inv)

    # make sure new data gets transformed correctly
    X_new = [[+0.0, 2.0, 0.5], [-1.0, 1.0, 0.0], [+0.0, 1.0, 1.5]]
    X_trans_new = scaler.transform(X_new)
    X_expected_new = [[+0.0, 1.0, +0.0], [-1.0, 0.0, -0.83333], [+0.0, 0.0, +1.66667]]
    assert_array_almost_equal(X_trans_new, X_expected_new, decimal=3)


def test_robust_scaler_unit_variance():
    # Check RobustScaler with unit_variance=True on standard normal data with
    # outliers
    rng = np.random.RandomState(42)
    X = rng.randn(1000000, 1)
    X_with_outliers = np.vstack([X, np.ones((100, 1)) * 100, np.ones((100, 1)) * -100])

    quantile_range = (1, 99)
    robust_scaler = RobustScaler(quantile_range=quantile_range, unit_variance=True).fit(
        X_with_outliers
    )
    X_trans = robust_scaler.transform(X)

    assert robust_scaler.center_ == pytest.approx(0, abs=1e-3)
    assert robust_scaler.scale_ == pytest.approx(1, abs=1e-2)
    assert X_trans.std() == pytest.approx(1, abs=1e-2)


@pytest.mark.parametrize("sparse_container", CSC_CONTAINERS + CSR_CONTAINERS)
def test_maxabs_scaler_zero_variance_features(sparse_container):
    # Check MaxAbsScaler on toy data with zero variance features
    X = [[0.0, 1.0, +0.5], [0.0, 1.0, -0.3], [0.0, 1.0, +1.5], [0.0, 0.0, +0.0]]

    scaler = MaxAbsScaler()
    X_trans = scaler.fit_transform(X)
    X_expected = [
        [0.0, 1.0, 1.0 / 3.0],
        [0.0, 1.0, -0.2],
        [0.0, 1.0, 1.0],
        [0.0, 0.0, 0.0],
    ]
    assert_array_almost_equal(X_trans, X_expected)
    X_trans_inv = scaler.inverse_transform(X_trans)
    assert_array_almost_equal(X, X_trans_inv)

    # make sure new data gets transformed correctly
    X_new = [[+0.0, 2.0, 0.5], [-1.0, 1.0, 0.0], [+0.0, 1.0, 1.5]]
    X_trans_new = scaler.transform(X_new)
    X_expected_new = [[+0.0, 2.0, 1.0 / 3.0], [-1.0, 1.0, 0.0], [+0.0, 1.0, 1.0]]

    assert_array_almost_equal(X_trans_new, X_expected_new, decimal=2)

    # function interface
    X_trans = maxabs_scale(X)
    assert_array_almost_equal(X_trans, X_expected)

    # sparse data
    X_sparse = sparse_container(X)
    X_trans_sparse = scaler.fit_transform(X_sparse)
    X_expected = [
        [0.0, 1.0, 1.0 / 3.0],
        [0.0, 1.0, -0.2],
        [0.0, 1.0, 1.0],
        [0.0, 0.0, 0.0],
    ]
    assert_array_almost_equal(X_trans_sparse.toarray(), X_expected)
    X_trans_sparse_inv = scaler.inverse_transform(X_trans_sparse)
    assert_array_almost_equal(X, X_trans_sparse_inv.toarray())


def test_maxabs_scaler_large_negative_value():
    # Check MaxAbsScaler on toy data with a large negative value
    X = [
        [0.0, 1.0, +0.5, -1.0],
        [0.0, 1.0, -0.3, -0.5],
        [0.0, 1.0, -100.0, 0.0],
        [0.0, 0.0, +0.0, -2.0],
    ]

    scaler = MaxAbsScaler()
    X_trans = scaler.fit_transform(X)
    X_expected = [
        [0.0, 1.0, 0.005, -0.5],
        [0.0, 1.0, -0.003, -0.25],
        [0.0, 1.0, -1.0, 0.0],
        [0.0, 0.0, 0.0, -1.0],
    ]
    assert_array_almost_equal(X_trans, X_expected)


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_maxabs_scaler_transform_one_row_csr(csr_container):
    # Check MaxAbsScaler on transforming csr matrix with one row
    X = csr_container([[0.5, 1.0, 1.0]])
    scaler = MaxAbsScaler()
    scaler = scaler.fit(X)
    X_trans = scaler.transform(X)
    X_expected = csr_container([[1.0, 1.0, 1.0]])
    assert_array_almost_equal(X_trans.toarray(), X_expected.toarray())
    X_scaled_back = scaler.inverse_transform(X_trans)
    assert_array_almost_equal(X.toarray(), X_scaled_back.toarray())


def test_maxabs_scaler_1d():
    # Test scaling of dataset along single axis
    for X in [X_1row, X_1col, X_list_1row, X_list_1row]:
        scaler = MaxAbsScaler(copy=True)
        X_scaled = scaler.fit(X).transform(X)

        if isinstance(X, list):
            X = np.array(X)  # cast only after scaling done

        if _check_dim_1axis(X) == 1:
            assert_array_almost_equal(np.abs(X_scaled.max(axis=0)), np.ones(n_features))
        else:
            assert_array_almost_equal(np.abs(X_scaled.max(axis=0)), 1.0)
        assert scaler.n_samples_seen_ == X.shape[0]

        # check inverse transform
        X_scaled_back = scaler.inverse_transform(X_scaled)
        assert_array_almost_equal(X_scaled_back, X)

    # Constant feature
    X = np.ones((5, 1))
    scaler = MaxAbsScaler()
    X_scaled = scaler.fit(X).transform(X)
    assert_array_almost_equal(np.abs(X_scaled.max(axis=0)), 1.0)
    assert scaler.n_samples_seen_ == X.shape[0]

    # function interface
    X_1d = X_1row.ravel()
    max_abs = np.abs(X_1d).max()
    assert_array_almost_equal(X_1d / max_abs, maxabs_scale(X_1d, copy=True))


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_maxabs_scaler_partial_fit(csr_container):
    # Test if partial_fit run over many batches of size 1 and 50
    # gives the same results as fit
    X = X_2d[:100, :]
    n = X.shape[0]

    for chunk_size in [1, 2, 50, n, n + 42]:
        # Test mean at the end of the process
        scaler_batch = MaxAbsScaler().fit(X)

        scaler_incr = MaxAbsScaler()
        scaler_incr_csr = MaxAbsScaler()
        scaler_incr_csc = MaxAbsScaler()
        for batch in gen_batches(n, chunk_size):
            scaler_incr = scaler_incr.partial_fit(X[batch])
            X_csr = csr_container(X[batch])
            scaler_incr_csr = scaler_incr_csr.partial_fit(X_csr)
            X_csc = csr_container(X[batch])
            scaler_incr_csc = scaler_incr_csc.partial_fit(X_csc)

        assert_array_almost_equal(scaler_batch.max_abs_, scaler_incr.max_abs_)
        assert_array_almost_equal(scaler_batch.max_abs_, scaler_incr_csr.max_abs_)
        assert_array_almost_equal(scaler_batch.max_abs_, scaler_incr_csc.max_abs_)
        assert scaler_batch.n_samples_seen_ == scaler_incr.n_samples_seen_
        assert scaler_batch.n_samples_seen_ == scaler_incr_csr.n_samples_seen_
        assert scaler_batch.n_samples_seen_ == scaler_incr_csc.n_samples_seen_
        assert_array_almost_equal(scaler_batch.scale_, scaler_incr.scale_)
        assert_array_almost_equal(scaler_batch.scale_, scaler_incr_csr.scale_)
        assert_array_almost_equal(scaler_batch.scale_, scaler_incr_csc.scale_)
        assert_array_almost_equal(scaler_batch.transform(X), scaler_incr.transform(X))

        # Test std after 1 step
        batch0 = slice(0, chunk_size)
        scaler_batch = MaxAbsScaler().fit(X[batch0])
        scaler_incr = MaxAbsScaler().partial_fit(X[batch0])

        assert_array_almost_equal(scaler_batch.max_abs_, scaler_incr.max_abs_)
        assert scaler_batch.n_samples_seen_ == scaler_incr.n_samples_seen_
        assert_array_almost_equal(scaler_batch.scale_, scaler_incr.scale_)
        assert_array_almost_equal(scaler_batch.transform(X), scaler_incr.transform(X))

        # Test std until the end of partial fits, and
        scaler_batch = MaxAbsScaler().fit(X)
        scaler_incr = MaxAbsScaler()  # Clean estimator
        for i, batch in enumerate(gen_batches(n, chunk_size)):
            scaler_incr = scaler_incr.partial_fit(X[batch])
            assert_correct_incr(
                i,
                batch_start=batch.start,
                batch_stop=batch.stop,
                n=n,
                chunk_size=chunk_size,
                n_samples_seen=scaler_incr.n_samples_seen_,
            )


def check_normalizer(norm, X_norm):
    """
    Convenient checking function for `test_normalizer_l1_l2_max` and
    `test_normalizer_l1_l2_max_non_csr`
    """
    if norm == "l1":
        row_sums = np.abs(X_norm).sum(axis=1)
        for i in range(3):
            assert_almost_equal(row_sums[i], 1.0)
        assert_almost_equal(row_sums[3], 0.0)
    elif norm == "l2":
        for i in range(3):
            assert_almost_equal(la.norm(X_norm[i]), 1.0)
        assert_almost_equal(la.norm(X_norm[3]), 0.0)
    elif norm == "max":
        row_maxs = abs(X_norm).max(axis=1)
        for i in range(3):
            assert_almost_equal(row_maxs[i], 1.0)
        assert_almost_equal(row_maxs[3], 0.0)


@pytest.mark.parametrize("norm", ["l1", "l2", "max"])
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_normalizer_l1_l2_max(norm, csr_container):
    rng = np.random.RandomState(0)
    X_dense = rng.randn(4, 5)
    X_sparse_unpruned = csr_container(X_dense)

    # set the row number 3 to zero
    X_dense[3, :] = 0.0

    # set the row number 3 to zero without pruning (can happen in real life)
    indptr_3 = X_sparse_unpruned.indptr[3]
    indptr_4 = X_sparse_unpruned.indptr[4]
    X_sparse_unpruned.data[indptr_3:indptr_4] = 0.0

    # build the pruned variant using the regular constructor
    X_sparse_pruned = csr_container(X_dense)

    # check inputs that support the no-copy optim
    for X in (X_dense, X_sparse_pruned, X_sparse_unpruned):
        normalizer = Normalizer(norm=norm, copy=True)
        X_norm1 = normalizer.transform(X)
        assert X_norm1 is not X
        X_norm1 = toarray(X_norm1)

        normalizer = Normalizer(norm=norm, copy=False)
        X_norm2 = normalizer.transform(X)
        assert X_norm2 is X
        X_norm2 = toarray(X_norm2)

        for X_norm in (X_norm1, X_norm2):
            check_normalizer(norm, X_norm)


@pytest.mark.parametrize("norm", ["l1", "l2", "max"])
@pytest.mark.parametrize(
    "sparse_container", COO_CONTAINERS + CSC_CONTAINERS + LIL_CONTAINERS
)
def test_normalizer_l1_l2_max_non_csr(norm, sparse_container):
    rng = np.random.RandomState(0)
    X_dense = rng.randn(4, 5)

    # set the row number 3 to zero
    X_dense[3, :] = 0.0

    X = sparse_container(X_dense)
    X_norm = Normalizer(norm=norm, copy=False).transform(X)

    assert X_norm is not X
    assert sparse.issparse(X_norm) and X_norm.format == "csr"

    X_norm = toarray(X_norm)
    check_normalizer(norm, X_norm)


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_normalizer_max_sign(csr_container):
    # check that we normalize by a positive number even for negative data
    rng = np.random.RandomState(0)
    X_dense = rng.randn(4, 5)
    # set the row number 3 to zero
    X_dense[3, :] = 0.0
    # check for mixed data where the value with
    # largest magnitude is negative
    X_dense[2, abs(X_dense[2, :]).argmax()] *= -1
    X_all_neg = -np.abs(X_dense)
    X_all_neg_sparse = csr_container(X_all_neg)

    for X in (X_dense, X_all_neg, X_all_neg_sparse):
        normalizer = Normalizer(norm="max")
        X_norm = normalizer.transform(X)
        assert X_norm is not X
        X_norm = toarray(X_norm)
        assert_array_equal(np.sign(X_norm), np.sign(toarray(X)))


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_normalize(csr_container):
    # Test normalize function
    # Only tests functionality not used by the tests for Normalizer.
    X = np.random.RandomState(37).randn(3, 2)
    assert_array_equal(normalize(X, copy=False), normalize(X.T, axis=0, copy=False).T)

    rs = np.random.RandomState(0)
    X_dense = rs.randn(10, 5)
    X_sparse = csr_container(X_dense)
    ones = np.ones((10))
    for X in (X_dense, X_sparse):
        for dtype in (np.float32, np.float64):
            for norm in ("l1", "l2"):
                X = X.astype(dtype)
                X_norm = normalize(X, norm=norm)
                assert X_norm.dtype == dtype

                X_norm = toarray(X_norm)
                if norm == "l1":
                    row_sums = np.abs(X_norm).sum(axis=1)
                else:
                    X_norm_squared = X_norm**2
                    row_sums = X_norm_squared.sum(axis=1)

                assert_array_almost_equal(row_sums, ones)

    # Test return_norm
    X_dense = np.array([[3.0, 0, 4.0], [1.0, 0.0, 0.0], [2.0, 3.0, 0.0]])
    for norm in ("l1", "l2", "max"):
        _, norms = normalize(X_dense, norm=norm, return_norm=True)
        if norm == "l1":
            assert_array_almost_equal(norms, np.array([7.0, 1.0, 5.0]))
        elif norm == "l2":
            assert_array_almost_equal(norms, np.array([5.0, 1.0, 3.60555127]))
        else:
            assert_array_almost_equal(norms, np.array([4.0, 1.0, 3.0]))

    X_sparse = csr_container(X_dense)
    for norm in ("l1", "l2"):
        with pytest.raises(NotImplementedError):
            normalize(X_sparse, norm=norm, return_norm=True)
    _, norms = normalize(X_sparse, norm="max", return_norm=True)
    assert_array_almost_equal(norms, np.array([4.0, 1.0, 3.0]))


@pytest.mark.parametrize(
    "constructor", [np.array, list] + CSC_CONTAINERS + CSR_CONTAINERS
)
def test_binarizer(constructor):
    X_ = np.array([[1, 0, 5], [2, 3, -1]])
    X = constructor(X_.copy())

    binarizer = Binarizer(threshold=2.0, copy=True)
    X_bin = toarray(binarizer.transform(X))
    assert np.sum(X_bin == 0) == 4
    assert np.sum(X_bin == 1) == 2
    X_bin = binarizer.transform(X)
    assert sparse.issparse(X) == sparse.issparse(X_bin)

    binarizer = Binarizer(copy=True).fit(X)
    X_bin = toarray(binarizer.transform(X))
    assert X_bin is not X
    assert np.sum(X_bin == 0) == 2
    assert np.sum(X_bin == 1) == 4

    binarizer = Binarizer(copy=True)
    X_bin = binarizer.transform(X)
    assert X_bin is not X
    X_bin = toarray(X_bin)
    assert np.sum(X_bin == 0) == 2
    assert np.sum(X_bin == 1) == 4

    binarizer = Binarizer(copy=False)
    X_bin = binarizer.transform(X)
    if constructor is not list:
        assert X_bin is X

    binarizer = Binarizer(copy=False)
    X_float = np.array([[1, 0, 5], [2, 3, -1]], dtype=np.float64)
    X_bin = binarizer.transform(X_float)
    if constructor is not list:
        assert X_bin is X_float

    X_bin = toarray(X_bin)
    assert np.sum(X_bin == 0) == 2
    assert np.sum(X_bin == 1) == 4

    binarizer = Binarizer(threshold=-0.5, copy=True)
    if constructor in (np.array, list):
        X = constructor(X_.copy())

        X_bin = toarray(binarizer.transform(X))
        assert np.sum(X_bin == 0) == 1
        assert np.sum(X_bin == 1) == 5
        X_bin = binarizer.transform(X)

    # Cannot use threshold < 0 for sparse
    if constructor in CSC_CONTAINERS:
        with pytest.raises(ValueError):
            binarizer.transform(constructor(X))


@pytest.mark.parametrize(
    "array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations()
)
def test_binarizer_array_api_int(array_namespace, device, dtype_name):
    # Checks that Binarizer works with integer elements and float threshold
    xp = _array_api_for_tests(array_namespace, device)
    for dtype_name_ in [dtype_name, "int32", "int64"]:
        X_np = np.reshape(np.asarray([0, 1, 2, 3, 4], dtype=dtype_name_), (-1, 1))
        X_xp = xp.asarray(X_np, device=device)
        binarized_np = Binarizer(threshold=2.5).fit_transform(X_np)
        with config_context(array_api_dispatch=True):
            binarized_xp = Binarizer(threshold=2.5).fit_transform(X_xp)
        assert_array_equal(_convert_to_numpy(binarized_xp, xp), binarized_np)


def test_center_kernel():
    # Test that KernelCenterer is equivalent to StandardScaler
    # in feature space
    rng = np.random.RandomState(0)
    X_fit = rng.random_sample((5, 4))
    scaler = StandardScaler(with_std=False)
    scaler.fit(X_fit)
    X_fit_centered = scaler.transform(X_fit)
    K_fit = np.dot(X_fit, X_fit.T)

    # center fit time matrix
    centerer = KernelCenterer()
    K_fit_centered = np.dot(X_fit_centered, X_fit_centered.T)
    K_fit_centered2 = centerer.fit_transform(K_fit)
    assert_array_almost_equal(K_fit_centered, K_fit_centered2)

    # center predict time matrix
    X_pred = rng.random_sample((2, 4))
    K_pred = np.dot(X_pred, X_fit.T)
    X_pred_centered = scaler.transform(X_pred)
    K_pred_centered = np.dot(X_pred_centered, X_fit_centered.T)
    K_pred_centered2 = centerer.transform(K_pred)
    assert_array_almost_equal(K_pred_centered, K_pred_centered2)

    # check the results coherence with the method proposed in:
    # B. Schölkopf, A. Smola, and K.R. Müller,
    # "Nonlinear component analysis as a kernel eigenvalue problem"
    # equation (B.3)

    # K_centered3 = (I - 1_M) K (I - 1_M)
    #             =  K - 1_M K - K 1_M + 1_M K 1_M
    ones_M = np.ones_like(K_fit) / K_fit.shape[0]
    K_fit_centered3 = K_fit - ones_M @ K_fit - K_fit @ ones_M + ones_M @ K_fit @ ones_M
    assert_allclose(K_fit_centered, K_fit_centered3)

    # K_test_centered3 = (K_test - 1'_M K)(I - 1_M)
    #                  = K_test - 1'_M K - K_test 1_M + 1'_M K 1_M
    ones_prime_M = np.ones_like(K_pred) / K_fit.shape[0]
    K_pred_centered3 = (
        K_pred - ones_prime_M @ K_fit - K_pred @ ones_M + ones_prime_M @ K_fit @ ones_M
    )
    assert_allclose(K_pred_centered, K_pred_centered3)


def test_kernelcenterer_non_linear_kernel():
    """Check kernel centering for non-linear kernel."""
    rng = np.random.RandomState(0)
    X, X_test = rng.randn(100, 50), rng.randn(20, 50)

    def phi(X):
        """Our mapping function phi."""
        return np.vstack(
            [
                np.clip(X, a_min=0, a_max=None),
                -np.clip(X, a_min=None, a_max=0),
            ]
        )

    phi_X = phi(X)
    phi_X_test = phi(X_test)

    # centered the projection
    scaler = StandardScaler(with_std=False)
    phi_X_center = scaler.fit_transform(phi_X)
    phi_X_test_center = scaler.transform(phi_X_test)

    # create the different kernel
    K = phi_X @ phi_X.T
    K_test = phi_X_test @ phi_X.T
    K_center = phi_X_center @ phi_X_center.T
    K_test_center = phi_X_test_center @ phi_X_center.T

    kernel_centerer = KernelCenterer()
    kernel_centerer.fit(K)

    assert_allclose(kernel_centerer.transform(K), K_center)
    assert_allclose(kernel_centerer.transform(K_test), K_test_center)

    # check the results coherence with the method proposed in:
    # B. Schölkopf, A. Smola, and K.R. Müller,
    # "Nonlinear component analysis as a kernel eigenvalue problem"
    # equation (B.3)

    # K_centered = (I - 1_M) K (I - 1_M)
    #            =  K - 1_M K - K 1_M + 1_M K 1_M
    ones_M = np.ones_like(K) / K.shape[0]
    K_centered = K - ones_M @ K - K @ ones_M + ones_M @ K @ ones_M
    assert_allclose(kernel_centerer.transform(K), K_centered)

    # K_test_centered = (K_test - 1'_M K)(I - 1_M)
    #                 = K_test - 1'_M K - K_test 1_M + 1'_M K 1_M
    ones_prime_M = np.ones_like(K_test) / K.shape[0]
    K_test_centered = (
        K_test - ones_prime_M @ K - K_test @ ones_M + ones_prime_M @ K @ ones_M
    )
    assert_allclose(kernel_centerer.transform(K_test), K_test_centered)


def test_cv_pipeline_precomputed():
    # Cross-validate a regression on four coplanar points with the same
    # value. Use precomputed kernel to ensure Pipeline with KernelCenterer
    # is treated as a pairwise operation.
    X = np.array([[3, 0, 0], [0, 3, 0], [0, 0, 3], [1, 1, 1]])
    y_true = np.ones((4,))
    K = X.dot(X.T)
    kcent = KernelCenterer()
    pipeline = Pipeline([("kernel_centerer", kcent), ("svr", SVR())])

    # did the pipeline set the pairwise attribute?
    assert pipeline.__xlearn_tags__().input_tags.pairwise

    # test cross-validation, score should be almost perfect
    # NB: this test is pretty vacuous -- it's mainly to test integration
    #     of Pipeline and KernelCenterer
    y_pred = cross_val_predict(pipeline, K, y_true, cv=2)
    assert_array_almost_equal(y_true, y_pred)


def test_fit_transform():
    rng = np.random.RandomState(0)
    X = rng.random_sample((5, 4))
    for obj in (StandardScaler(), Normalizer(), Binarizer()):
        X_transformed = obj.fit(X).transform(X)
        X_transformed2 = obj.fit_transform(X)
        assert_array_equal(X_transformed, X_transformed2)


def test_add_dummy_feature():
    X = [[1, 0], [0, 1], [0, 1]]
    X = add_dummy_feature(X)
    assert_array_equal(X, [[1, 1, 0], [1, 0, 1], [1, 0, 1]])


@pytest.mark.parametrize(
    "sparse_container", COO_CONTAINERS + CSC_CONTAINERS + CSR_CONTAINERS
)
def test_add_dummy_feature_sparse(sparse_container):
    X = sparse_container([[1, 0], [0, 1], [0, 1]])
    desired_format = X.format
    X = add_dummy_feature(X)
    assert sparse.issparse(X) and X.format == desired_format, X
    assert_array_equal(X.toarray(), [[1, 1, 0], [1, 0, 1], [1, 0, 1]])


def test_fit_cold_start():
    X = iris.data
    X_2d = X[:, :2]

    # Scalers that have a partial_fit method
    scalers = [
        StandardScaler(with_mean=False, with_std=False),
        MinMaxScaler(),
        MaxAbsScaler(),
    ]

    for scaler in scalers:
        scaler.fit_transform(X)
        # with a different shape, this may break the scaler unless the internal
        # state is reset
        scaler.fit_transform(X_2d)


@pytest.mark.parametrize("method", ["box-cox", "yeo-johnson"])
def test_power_transformer_notfitted(method):
    pt = PowerTransformer(method=method)
    X = np.abs(X_1col)
    with pytest.raises(NotFittedError):
        pt.transform(X)
    with pytest.raises(NotFittedError):
        pt.inverse_transform(X)


@pytest.mark.parametrize("method", ["box-cox", "yeo-johnson"])
@pytest.mark.parametrize("standardize", [True, False])
@pytest.mark.parametrize("X", [X_1col, X_2d])
def test_power_transformer_inverse(method, standardize, X):
    # Make sure we get the original input when applying transform and then
    # inverse transform
    X = np.abs(X) if method == "box-cox" else X
    pt = PowerTransformer(method=method, standardize=standardize)
    X_trans = pt.fit_transform(X)
    assert_almost_equal(X, pt.inverse_transform(X_trans))


def test_power_transformer_1d():
    X = np.abs(X_1col)

    for standardize in [True, False]:
        pt = PowerTransformer(method="box-cox", standardize=standardize)

        X_trans = pt.fit_transform(X)
        X_trans_func = power_transform(X, method="box-cox", standardize=standardize)

        X_expected, lambda_expected = stats.boxcox(X.flatten())

        if standardize:
            X_expected = scale(X_expected)

        assert_almost_equal(X_expected.reshape(-1, 1), X_trans)
        assert_almost_equal(X_expected.reshape(-1, 1), X_trans_func)

        assert_almost_equal(X, pt.inverse_transform(X_trans))
        assert_almost_equal(lambda_expected, pt.lambdas_[0])

        assert len(pt.lambdas_) == X.shape[1]
        assert isinstance(pt.lambdas_, np.ndarray)


def test_power_transformer_2d():
    X = np.abs(X_2d)

    for standardize in [True, False]:
        pt = PowerTransformer(method="box-cox", standardize=standardize)

        X_trans_class = pt.fit_transform(X)
        X_trans_func = power_transform(X, method="box-cox", standardize=standardize)

        for X_trans in [X_trans_class, X_trans_func]:
            for j in range(X_trans.shape[1]):
                X_expected, lmbda = stats.boxcox(X[:, j].flatten())

                if standardize:
                    X_expected = scale(X_expected)

                assert_almost_equal(X_trans[:, j], X_expected)
                assert_almost_equal(lmbda, pt.lambdas_[j])

            # Test inverse transformation
            X_inv = pt.inverse_transform(X_trans)
            assert_array_almost_equal(X_inv, X)

        assert len(pt.lambdas_) == X.shape[1]
        assert isinstance(pt.lambdas_, np.ndarray)


def test_power_transformer_boxcox_strictly_positive_exception():
    # Exceptions should be raised for negative arrays and zero arrays when
    # method is boxcox

    pt = PowerTransformer(method="box-cox")
    pt.fit(np.abs(X_2d))
    X_with_negatives = X_2d
    not_positive_message = "strictly positive"

    with pytest.raises(ValueError, match=not_positive_message):
        pt.transform(X_with_negatives)

    with pytest.raises(ValueError, match=not_positive_message):
        pt.fit(X_with_negatives)

    with pytest.raises(ValueError, match=not_positive_message):
        power_transform(X_with_negatives, method="box-cox")

    with pytest.raises(ValueError, match=not_positive_message):
        pt.transform(np.zeros(X_2d.shape))

    with pytest.raises(ValueError, match=not_positive_message):
        pt.fit(np.zeros(X_2d.shape))

    with pytest.raises(ValueError, match=not_positive_message):
        power_transform(np.zeros(X_2d.shape), method="box-cox")


@pytest.mark.parametrize("X", [X_2d, np.abs(X_2d), -np.abs(X_2d), np.zeros(X_2d.shape)])
def test_power_transformer_yeojohnson_any_input(X):
    # Yeo-Johnson method should support any kind of input
    power_transform(X, method="yeo-johnson")


@pytest.mark.parametrize("method", ["box-cox", "yeo-johnson"])
def test_power_transformer_shape_exception(method):
    pt = PowerTransformer(method=method)
    X = np.abs(X_2d)
    pt.fit(X)

    # Exceptions should be raised for arrays with different num_columns
    # than during fitting
    wrong_shape_message = (
        r"X has \d+ features, but PowerTransformer is expecting \d+ features"
    )

    with pytest.raises(ValueError, match=wrong_shape_message):
        pt.transform(X[:, 0:1])

    with pytest.raises(ValueError, match=wrong_shape_message):
        pt.inverse_transform(X[:, 0:1])


def test_power_transformer_lambda_zero():
    pt = PowerTransformer(method="box-cox", standardize=False)
    X = np.abs(X_2d)[:, 0:1]

    # Test the lambda = 0 case
    pt.lambdas_ = np.array([0])
    X_trans = pt.transform(X)
    assert_array_almost_equal(pt.inverse_transform(X_trans), X)


def test_power_transformer_lambda_one():
    # Make sure lambda = 1 corresponds to the identity for yeo-johnson
    pt = PowerTransformer(method="yeo-johnson", standardize=False)
    X = np.abs(X_2d)[:, 0:1]

    pt.lambdas_ = np.array([1])
    X_trans = pt.transform(X)
    assert_array_almost_equal(X_trans, X)


@pytest.mark.parametrize(
    "method, lmbda",
    [
        ("box-cox", 0.1),
        ("box-cox", 0.5),
        ("yeo-johnson", 0.1),
        ("yeo-johnson", 0.5),
        ("yeo-johnson", 1.0),
    ],
)
def test_optimization_power_transformer(method, lmbda):
    # Test the optimization procedure:
    # - set a predefined value for lambda
    # - apply inverse_transform to a normal dist (we get X_inv)
    # - apply fit_transform to X_inv (we get X_inv_trans)
    # - check that X_inv_trans is roughly equal to X

    rng = np.random.RandomState(0)
    n_samples = 20000
    X = rng.normal(loc=0, scale=1, size=(n_samples, 1))

    if method == "box-cox":
        # For box-cox, means that lmbda * y + 1 > 0 or y > - 1 / lmbda
        # Clip the data here to make sure the inequality is valid.
        X = np.clip(X, -1 / lmbda + 1e-5, None)

    pt = PowerTransformer(method=method, standardize=False)
    pt.lambdas_ = [lmbda]
    X_inv = pt.inverse_transform(X)

    pt = PowerTransformer(method=method, standardize=False)
    X_inv_trans = pt.fit_transform(X_inv)

    assert_almost_equal(0, np.linalg.norm(X - X_inv_trans) / n_samples, decimal=2)
    assert_almost_equal(0, X_inv_trans.mean(), decimal=1)
    assert_almost_equal(1, X_inv_trans.std(), decimal=1)


def test_invserse_box_cox():
    # output nan if the input is invalid
    pt = PowerTransformer(method="box-cox", standardize=False)
    pt.lambdas_ = [0.5]
    X_inv = pt.inverse_transform([[-2.1]])
    assert np.isnan(X_inv)


def test_yeo_johnson_darwin_example():
    # test from original paper "A new family of power transformations to
    # improve normality or symmetry" by Yeo and Johnson.
    X = [6.1, -8.4, 1.0, 2.0, 0.7, 2.9, 3.5, 5.1, 1.8, 3.6, 7.0, 3.0, 9.3, 7.5, -6.0]
    X = np.array(X).reshape(-1, 1)
    lmbda = PowerTransformer(method="yeo-johnson").fit(X).lambdas_
    assert np.allclose(lmbda, 1.305, atol=1e-3)


@pytest.mark.parametrize("method", ["box-cox", "yeo-johnson"])
def test_power_transformer_nans(method):
    # Make sure lambda estimation is not influenced by NaN values
    # and that transform() supports NaN silently

    X = np.abs(X_1col)
    pt = PowerTransformer(method=method)
    pt.fit(X)
    lmbda_no_nans = pt.lambdas_[0]

    # concat nans at the end and check lambda stays the same
    X = np.concatenate([X, np.full_like(X, np.nan)])
    X = shuffle(X, random_state=0)

    pt.fit(X)
    lmbda_nans = pt.lambdas_[0]

    assert_almost_equal(lmbda_no_nans, lmbda_nans, decimal=5)

    X_trans = pt.transform(X)
    assert_array_equal(np.isnan(X_trans), np.isnan(X))


@pytest.mark.parametrize("method", ["box-cox", "yeo-johnson"])
@pytest.mark.parametrize("standardize", [True, False])
def test_power_transformer_fit_transform(method, standardize):
    # check that fit_transform() and fit().transform() return the same values
    X = X_1col
    if method == "box-cox":
        X = np.abs(X)

    pt = PowerTransformer(method, standardize=standardize)
    assert_array_almost_equal(pt.fit(X).transform(X), pt.fit_transform(X))


@pytest.mark.parametrize("method", ["box-cox", "yeo-johnson"])
@pytest.mark.parametrize("standardize", [True, False])
def test_power_transformer_copy_True(method, standardize):
    # Check that neither fit, transform, fit_transform nor inverse_transform
    # modify X inplace when copy=True
    X = X_1col
    if method == "box-cox":
        X = np.abs(X)

    X_original = X.copy()
    assert X is not X_original  # sanity checks
    assert_array_almost_equal(X, X_original)

    pt = PowerTransformer(method, standardize=standardize, copy=True)

    pt.fit(X)
    assert_array_almost_equal(X, X_original)
    X_trans = pt.transform(X)
    assert X_trans is not X

    X_trans = pt.fit_transform(X)
    assert_array_almost_equal(X, X_original)
    assert X_trans is not X

    X_inv_trans = pt.inverse_transform(X_trans)
    assert X_trans is not X_inv_trans


@pytest.mark.parametrize("method", ["box-cox", "yeo-johnson"])
@pytest.mark.parametrize("standardize", [True, False])
def test_power_transformer_copy_False(method, standardize):
    # check that when copy=False fit doesn't change X inplace but transform,
    # fit_transform and inverse_transform do.
    X = X_1col
    if method == "box-cox":
        X = np.abs(X)

    X_original = X.copy()
    assert X is not X_original  # sanity checks
    assert_array_almost_equal(X, X_original)

    pt = PowerTransformer(method, standardize=standardize, copy=False)

    pt.fit(X)
    assert_array_almost_equal(X, X_original)  # fit didn't change X

    X_trans = pt.transform(X)
    assert X_trans is X

    if method == "box-cox":
        X = np.abs(X)
    X_trans = pt.fit_transform(X)
    assert X_trans is X

    X_inv_trans = pt.inverse_transform(X_trans)
    assert X_trans is X_inv_trans


def test_power_transformer_box_cox_raise_all_nans_col():
    """Check that box-cox raises informative when a column contains all nans.

    Non-regression test for gh-26303
    """
    X = rng.random_sample((4, 5))
    X[:, 0] = np.nan

    err_msg = "Column must not be all nan."

    pt = PowerTransformer(method="box-cox")
    with pytest.raises(ValueError, match=err_msg):
        pt.fit_transform(X)


@pytest.mark.parametrize(
    "X_2",
    [sparse.random(10, 1, density=0.8, random_state=0)]
    + [
        csr_container(np.full((10, 1), fill_value=np.nan))
        for csr_container in CSR_CONTAINERS
    ],
)
def test_standard_scaler_sparse_partial_fit_finite_variance(X_2):
    # non-regression test for:
    # https://github.com/chenxingqiang/jax-sklearn/issues/16448
    X_1 = sparse.random(5, 1, density=0.8)
    scaler = StandardScaler(with_mean=False)
    scaler.fit(X_1).partial_fit(X_2)
    assert np.isfinite(scaler.var_[0])


@pytest.mark.parametrize("feature_range", [(0, 1), (-10, 10)])
def test_minmax_scaler_clip(feature_range):
    # test behaviour of the parameter 'clip' in MinMaxScaler
    X = iris.data
    scaler = MinMaxScaler(feature_range=feature_range, clip=True).fit(X)
    X_min, X_max = np.min(X, axis=0), np.max(X, axis=0)
    X_test = [np.r_[X_min[:2] - 10, X_max[2:] + 10]]
    X_transformed = scaler.transform(X_test)
    assert_allclose(
        X_transformed,
        [[feature_range[0], feature_range[0], feature_range[1], feature_range[1]]],
    )


def test_standard_scaler_raise_error_for_1d_input():
    """Check that `inverse_transform` from `StandardScaler` raises an error
    with 1D array.
    Non-regression test for:
    https://github.com/chenxingqiang/jax-sklearn/issues/19518
    """
    scaler = StandardScaler().fit(X_2d)
    err_msg = "Expected 2D array, got 1D array instead"
    with pytest.raises(ValueError, match=err_msg):
        scaler.inverse_transform(X_2d[:, 0])


def test_power_transformer_significantly_non_gaussian():
    """Check that significantly non-Gaussian data before transforms correctly.

    For some explored lambdas, the transformed data may be constant and will
    be rejected. Non-regression test for
    https://github.com/chenxingqiang/jax-sklearn/issues/14959
    """

    X_non_gaussian = 1e6 * np.array(
        [0.6, 2.0, 3.0, 4.0] * 4 + [11, 12, 12, 16, 17, 20, 85, 90], dtype=np.float64
    ).reshape(-1, 1)
    pt = PowerTransformer()

    with warnings.catch_warnings():
        warnings.simplefilter("error", RuntimeWarning)
        X_trans = pt.fit_transform(X_non_gaussian)

    assert not np.any(np.isnan(X_trans))
    assert X_trans.mean() == pytest.approx(0.0)
    assert X_trans.std() == pytest.approx(1.0)
    assert X_trans.min() > -2
    assert X_trans.max() < 2


@pytest.mark.parametrize(
    "Transformer",
    [
        MinMaxScaler,
        MaxAbsScaler,
        RobustScaler,
        StandardScaler,
        QuantileTransformer,
        PowerTransformer,
    ],
)
def test_one_to_one_features(Transformer):
    """Check one-to-one transformers give correct feature names."""
    tr = Transformer().fit(iris.data)
    names_out = tr.get_feature_names_out(iris.feature_names)
    assert_array_equal(names_out, iris.feature_names)


@pytest.mark.parametrize(
    "Transformer",
    [
        MinMaxScaler,
        MaxAbsScaler,
        RobustScaler,
        StandardScaler,
        QuantileTransformer,
        PowerTransformer,
        Normalizer,
        Binarizer,
    ],
)
def test_one_to_one_features_pandas(Transformer):
    """Check one-to-one transformers give correct feature names."""
    pd = pytest.importorskip("pandas")

    df = pd.DataFrame(iris.data, columns=iris.feature_names)
    tr = Transformer().fit(df)

    names_out_df_default = tr.get_feature_names_out()
    assert_array_equal(names_out_df_default, iris.feature_names)

    names_out_df_valid_in = tr.get_feature_names_out(iris.feature_names)
    assert_array_equal(names_out_df_valid_in, iris.feature_names)

    msg = re.escape("input_features is not equal to feature_names_in_")
    with pytest.raises(ValueError, match=msg):
        invalid_names = list("abcd")
        tr.get_feature_names_out(invalid_names)


def test_kernel_centerer_feature_names_out():
    """Test that kernel centerer `feature_names_out`."""

    rng = np.random.RandomState(0)
    X = rng.random_sample((6, 4))
    X_pairwise = linear_kernel(X)
    centerer = KernelCenterer().fit(X_pairwise)

    names_out = centerer.get_feature_names_out()
    samples_out2 = X_pairwise.shape[1]
    assert_array_equal(names_out, [f"kernelcenterer{i}" for i in range(samples_out2)])


@pytest.mark.parametrize("standardize", [True, False])
def test_power_transformer_constant_feature(standardize):
    """Check that PowerTransfomer leaves constant features unchanged."""
    X = [[-2, 0, 2], [-2, 0, 2], [-2, 0, 2]]

    pt = PowerTransformer(method="yeo-johnson", standardize=standardize).fit(X)

    assert_allclose(pt.lambdas_, [1, 1, 1])

    Xft = pt.fit_transform(X)
    Xt = pt.transform(X)

    for Xt_ in [Xft, Xt]:
        if standardize:
            assert_allclose(Xt_, np.zeros_like(X))
        else:
            assert_allclose(Xt_, X)


@pytest.mark.skipif(
    sp_version < parse_version("1.12"),
    reason="scipy version 1.12 required for stable yeo-johnson",
)
def test_power_transformer_no_warnings():
    """Verify that PowerTransformer operates without raising any warnings on valid data.

    This test addresses numerical issues with floating point numbers (mostly
    overflows) with the Yeo-Johnson transform, see
    https://github.com/chenxingqiang/jax-sklearn/issues/23319#issuecomment-1464933635
    """
    x = np.array(
        [
            2003.0,
            1950.0,
            1997.0,
            2000.0,
            2009.0,
            2009.0,
            1980.0,
            1999.0,
            2007.0,
            1991.0,
        ]
    )

    def _test_no_warnings(data):
        """Internal helper to test for unexpected warnings."""
        with warnings.catch_warnings(record=True) as caught_warnings:
            warnings.simplefilter("always")  # Ensure all warnings are captured
            PowerTransformer(method="yeo-johnson", standardize=True).fit_transform(data)

        assert not caught_warnings, "Unexpected warnings were raised:\n" + "\n".join(
            str(w.message) for w in caught_warnings
        )

    # Full dataset: Should not trigger overflow in variance calculation.
    _test_no_warnings(x.reshape(-1, 1))

    # Subset of data: Should not trigger overflow in power calculation.
    _test_no_warnings(x[:5].reshape(-1, 1))


def test_yeojohnson_for_different_scipy_version():
    """Check that the results are consistent across different SciPy versions."""
    pt = PowerTransformer(method="yeo-johnson").fit(X_1col)
    pt.lambdas_[0] == pytest.approx(0.99546157, rel=1e-7)
