import io
import re
import warnings
from itertools import product

import numpy as np
import pytest
from scipy import sparse
from scipy.stats import kstest

from sklearn import tree
from sklearn.datasets import load_diabetes
from sklearn.dummy import DummyRegressor
from sklearn.exceptions import ConvergenceWarning

# make IterativeImputer available
from sklearn.experimental import enable_iterative_imputer  # noqa: F401
from sklearn.impute import IterativeImputer, KNNImputer, MissingIndicator, SimpleImputer
from sklearn.impute._base import _most_frequent
from sklearn.linear_model import ARDRegression, BayesianRidge, RidgeCV
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline, make_union
from sklearn.random_projection import _sparse_random_matrix
from sklearn.utils._testing import (
    _convert_container,
    assert_allclose,
    assert_allclose_dense_sparse,
    assert_array_almost_equal,
    assert_array_equal,
)
from sklearn.utils.fixes import (
    BSR_CONTAINERS,
    COO_CONTAINERS,
    CSC_CONTAINERS,
    CSR_CONTAINERS,
    LIL_CONTAINERS,
)


def _assert_array_equal_and_same_dtype(x, y):
    assert_array_equal(x, y)
    assert x.dtype == y.dtype


def _assert_allclose_and_same_dtype(x, y):
    assert_allclose(x, y)
    assert x.dtype == y.dtype


def _check_statistics(
    X, X_true, strategy, statistics, missing_values, sparse_container
):
    """Utility function for testing imputation for a given strategy.

    Test with dense and sparse arrays

    Check that:
        - the statistics (mean, median, mode) are correct
        - the missing values are imputed correctly"""

    err_msg = "Parameters: strategy = %s, missing_values = %s, sparse = {0}" % (
        strategy,
        missing_values,
    )

    assert_ae = assert_array_equal

    if X.dtype.kind == "f" or X_true.dtype.kind == "f":
        assert_ae = assert_array_almost_equal

    # Normal matrix
    imputer = SimpleImputer(missing_values=missing_values, strategy=strategy)
    X_trans = imputer.fit(X).transform(X.copy())
    assert_ae(imputer.statistics_, statistics, err_msg=err_msg.format(False))
    assert_ae(X_trans, X_true, err_msg=err_msg.format(False))

    # Sparse matrix
    imputer = SimpleImputer(missing_values=missing_values, strategy=strategy)
    imputer.fit(sparse_container(X))
    X_trans = imputer.transform(sparse_container(X.copy()))

    if sparse.issparse(X_trans):
        X_trans = X_trans.toarray()

    assert_ae(imputer.statistics_, statistics, err_msg=err_msg.format(True))
    assert_ae(X_trans, X_true, err_msg=err_msg.format(True))


@pytest.mark.parametrize("strategy", ["mean", "median", "most_frequent", "constant"])
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_imputation_shape(strategy, csr_container):
    # Verify the shapes of the imputed matrix for different strategies.
    X = np.random.randn(10, 2)
    X[::2] = np.nan

    imputer = SimpleImputer(strategy=strategy)
    X_imputed = imputer.fit_transform(csr_container(X))
    assert X_imputed.shape == (10, 2)
    X_imputed = imputer.fit_transform(X)
    assert X_imputed.shape == (10, 2)

    iterative_imputer = IterativeImputer(initial_strategy=strategy)
    X_imputed = iterative_imputer.fit_transform(X)
    assert X_imputed.shape == (10, 2)


@pytest.mark.parametrize("strategy", ["mean", "median", "most_frequent"])
def test_imputation_deletion_warning(strategy):
    X = np.ones((3, 5))
    X[:, 0] = np.nan
    imputer = SimpleImputer(strategy=strategy).fit(X)

    with pytest.warns(UserWarning, match="Skipping"):
        imputer.transform(X)


@pytest.mark.parametrize("strategy", ["mean", "median", "most_frequent"])
def test_imputation_deletion_warning_feature_names(strategy):
    pd = pytest.importorskip("pandas")

    missing_values = np.nan
    feature_names = np.array(["a", "b", "c", "d"], dtype=object)
    X = pd.DataFrame(
        [
            [missing_values, missing_values, 1, missing_values],
            [4, missing_values, 2, 10],
        ],
        columns=feature_names,
    )

    imputer = SimpleImputer(strategy=strategy).fit(X)

    # check SimpleImputer returning feature name attribute correctly
    assert_array_equal(imputer.feature_names_in_, feature_names)

    # ensure that skipped feature warning includes feature name
    with pytest.warns(
        UserWarning, match=r"Skipping features without any observed values: \['b'\]"
    ):
        imputer.transform(X)


@pytest.mark.parametrize("strategy", ["mean", "median", "most_frequent", "constant"])
@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_imputation_error_sparse_0(strategy, csc_container):
    # check that error are raised when missing_values = 0 and input is sparse
    X = np.ones((3, 5))
    X[0] = 0
    X = csc_container(X)

    imputer = SimpleImputer(strategy=strategy, missing_values=0)
    with pytest.raises(ValueError, match="Provide a dense array"):
        imputer.fit(X)

    imputer.fit(X.toarray())
    with pytest.raises(ValueError, match="Provide a dense array"):
        imputer.transform(X)


def safe_median(arr, *args, **kwargs):
    # np.median([]) raises a TypeError for numpy >= 1.10.1
    length = arr.size if hasattr(arr, "size") else len(arr)
    return np.nan if length == 0 else np.median(arr, *args, **kwargs)


def safe_mean(arr, *args, **kwargs):
    # np.mean([]) raises a RuntimeWarning for numpy >= 1.10.1
    length = arr.size if hasattr(arr, "size") else len(arr)
    return np.nan if length == 0 else np.mean(arr, *args, **kwargs)


@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_imputation_mean_median(csc_container):
    # Test imputation using the mean and median strategies, when
    # missing_values != 0.
    rng = np.random.RandomState(0)

    dim = 10
    dec = 10
    shape = (dim * dim, dim + dec)

    zeros = np.zeros(shape[0])
    values = np.arange(1, shape[0] + 1)
    values[4::2] = -values[4::2]

    tests = [
        ("mean", np.nan, lambda z, v, p: safe_mean(np.hstack((z, v)))),
        ("median", np.nan, lambda z, v, p: safe_median(np.hstack((z, v)))),
    ]

    for strategy, test_missing_values, true_value_fun in tests:
        X = np.empty(shape)
        X_true = np.empty(shape)
        true_statistics = np.empty(shape[1])

        # Create a matrix X with columns
        #    - with only zeros,
        #    - with only missing values
        #    - with zeros, missing values and values
        # And a matrix X_true containing all true values
        for j in range(shape[1]):
            nb_zeros = (j - dec + 1 > 0) * (j - dec + 1) * (j - dec + 1)
            nb_missing_values = max(shape[0] + dec * dec - (j + dec) * (j + dec), 0)
            nb_values = shape[0] - nb_zeros - nb_missing_values

            z = zeros[:nb_zeros]
            p = np.repeat(test_missing_values, nb_missing_values)
            v = values[rng.permutation(len(values))[:nb_values]]

            true_statistics[j] = true_value_fun(z, v, p)

            # Create the columns
            X[:, j] = np.hstack((v, z, p))

            if 0 == test_missing_values:
                # XXX unreached code as of v0.22
                X_true[:, j] = np.hstack(
                    (v, np.repeat(true_statistics[j], nb_missing_values + nb_zeros))
                )
            else:
                X_true[:, j] = np.hstack(
                    (v, z, np.repeat(true_statistics[j], nb_missing_values))
                )

            # Shuffle them the same way
            np.random.RandomState(j).shuffle(X[:, j])
            np.random.RandomState(j).shuffle(X_true[:, j])

        # Mean doesn't support columns containing NaNs, median does
        if strategy == "median":
            cols_to_keep = ~np.isnan(X_true).any(axis=0)
        else:
            cols_to_keep = ~np.isnan(X_true).all(axis=0)

        X_true = X_true[:, cols_to_keep]

        _check_statistics(
            X, X_true, strategy, true_statistics, test_missing_values, csc_container
        )


@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_imputation_median_special_cases(csc_container):
    # Test median imputation with sparse boundary cases
    X = np.array(
        [
            [0, np.nan, np.nan],  # odd: implicit zero
            [5, np.nan, np.nan],  # odd: explicit nonzero
            [0, 0, np.nan],  # even: average two zeros
            [-5, 0, np.nan],  # even: avg zero and neg
            [0, 5, np.nan],  # even: avg zero and pos
            [4, 5, np.nan],  # even: avg nonzeros
            [-4, -5, np.nan],  # even: avg negatives
            [-1, 2, np.nan],  # even: crossing neg and pos
        ]
    ).transpose()

    X_imputed_median = np.array(
        [
            [0, 0, 0],
            [5, 5, 5],
            [0, 0, 0],
            [-5, 0, -2.5],
            [0, 5, 2.5],
            [4, 5, 4.5],
            [-4, -5, -4.5],
            [-1, 2, 0.5],
        ]
    ).transpose()
    statistics_median = [0, 5, 0, -2.5, 2.5, 4.5, -4.5, 0.5]

    _check_statistics(
        X, X_imputed_median, "median", statistics_median, np.nan, csc_container
    )


@pytest.mark.parametrize("strategy", ["mean", "median"])
@pytest.mark.parametrize("dtype", [None, object, str])
def test_imputation_mean_median_error_invalid_type(strategy, dtype):
    X = np.array([["a", "b", 3], [4, "e", 6], ["g", "h", 9]], dtype=dtype)
    msg = "non-numeric data:\ncould not convert string to float:"
    with pytest.raises(ValueError, match=msg):
        imputer = SimpleImputer(strategy=strategy)
        imputer.fit_transform(X)


@pytest.mark.parametrize("strategy", ["mean", "median"])
@pytest.mark.parametrize("type", ["list", "dataframe"])
def test_imputation_mean_median_error_invalid_type_list_pandas(strategy, type):
    X = [["a", "b", 3], [4, "e", 6], ["g", "h", 9]]
    if type == "dataframe":
        pd = pytest.importorskip("pandas")
        X = pd.DataFrame(X)
    msg = "non-numeric data:\ncould not convert string to float:"
    with pytest.raises(ValueError, match=msg):
        imputer = SimpleImputer(strategy=strategy)
        imputer.fit_transform(X)


@pytest.mark.parametrize("strategy", ["constant", "most_frequent"])
@pytest.mark.parametrize("dtype", [str, np.dtype("U"), np.dtype("S")])
def test_imputation_const_mostf_error_invalid_types(strategy, dtype):
    # Test imputation on non-numeric data using "most_frequent" and "constant"
    # strategy
    X = np.array(
        [
            [np.nan, np.nan, "a", "f"],
            [np.nan, "c", np.nan, "d"],
            [np.nan, "b", "d", np.nan],
            [np.nan, "c", "d", "h"],
        ],
        dtype=dtype,
    )

    err_msg = "SimpleImputer does not support data"
    with pytest.raises(ValueError, match=err_msg):
        imputer = SimpleImputer(strategy=strategy)
        imputer.fit(X).transform(X)


@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_imputation_most_frequent(csc_container):
    # Test imputation using the most-frequent strategy.
    X = np.array(
        [
            [-1, -1, 0, 5],
            [-1, 2, -1, 3],
            [-1, 1, 3, -1],
            [-1, 2, 3, 7],
        ]
    )

    X_true = np.array(
        [
            [2, 0, 5],
            [2, 3, 3],
            [1, 3, 3],
            [2, 3, 7],
        ]
    )

    # scipy.stats.mode, used in SimpleImputer, doesn't return the first most
    # frequent as promised in the doc but the lowest most frequent. When this
    # test will fail after an update of scipy, SimpleImputer will need to be
    # updated to be consistent with the new (correct) behaviour
    _check_statistics(X, X_true, "most_frequent", [np.nan, 2, 3, 3], -1, csc_container)


@pytest.mark.parametrize("marker", [None, np.nan, "NAN", "", 0])
def test_imputation_most_frequent_objects(marker):
    # Test imputation using the most-frequent strategy.
    X = np.array(
        [
            [marker, marker, "a", "f"],
            [marker, "c", marker, "d"],
            [marker, "b", "d", marker],
            [marker, "c", "d", "h"],
        ],
        dtype=object,
    )

    X_true = np.array(
        [
            ["c", "a", "f"],
            ["c", "d", "d"],
            ["b", "d", "d"],
            ["c", "d", "h"],
        ],
        dtype=object,
    )

    imputer = SimpleImputer(missing_values=marker, strategy="most_frequent")
    X_trans = imputer.fit(X).transform(X)

    assert_array_equal(X_trans, X_true)


@pytest.mark.parametrize("dtype", [object, "category"])
def test_imputation_most_frequent_pandas(dtype):
    # Test imputation using the most frequent strategy on pandas df
    pd = pytest.importorskip("pandas")

    f = io.StringIO("Cat1,Cat2,Cat3,Cat4\n,i,x,\na,,y,\na,j,,\nb,j,x,")

    df = pd.read_csv(f, dtype=dtype)

    X_true = np.array(
        [["a", "i", "x"], ["a", "j", "y"], ["a", "j", "x"], ["b", "j", "x"]],
        dtype=object,
    )

    imputer = SimpleImputer(strategy="most_frequent")
    X_trans = imputer.fit_transform(df)

    assert_array_equal(X_trans, X_true)


@pytest.mark.parametrize("X_data, missing_value", [(1, 0), (1.0, np.nan)])
def test_imputation_constant_error_invalid_type(X_data, missing_value):
    # Verify that exceptions are raised on invalid fill_value type
    X = np.full((3, 5), X_data, dtype=float)
    X[0, 0] = missing_value

    fill_value = "x"
    err_msg = f"fill_value={fill_value!r} (of type {type(fill_value)!r}) cannot be cast"
    with pytest.raises(ValueError, match=re.escape(err_msg)):
        imputer = SimpleImputer(
            missing_values=missing_value, strategy="constant", fill_value=fill_value
        )
        imputer.fit_transform(X)


# TODO (1.8): check that `keep_empty_features=False` drop the
# empty features due to the behaviour change.
def test_imputation_constant_integer():
    # Test imputation using the constant strategy on integers
    X = np.array([[-1, 2, 3, -1], [4, -1, 5, -1], [6, 7, -1, -1], [8, 9, 0, -1]])

    X_true = np.array([[0, 2, 3, 0], [4, 0, 5, 0], [6, 7, 0, 0], [8, 9, 0, 0]])

    imputer = SimpleImputer(
        missing_values=-1, strategy="constant", fill_value=0, keep_empty_features=True
    )
    X_trans = imputer.fit_transform(X)

    assert_array_equal(X_trans, X_true)


# TODO (1.8): check that `keep_empty_features=False` drop the
# empty features due to the behaviour change.
@pytest.mark.parametrize("array_constructor", CSR_CONTAINERS + [np.asarray])
def test_imputation_constant_float(array_constructor):
    # Test imputation using the constant strategy on floats
    X = np.array(
        [
            [np.nan, 1.1, 0, np.nan],
            [1.2, np.nan, 1.3, np.nan],
            [0, 0, np.nan, np.nan],
            [1.4, 1.5, 0, np.nan],
        ]
    )

    X_true = np.array(
        [[-1, 1.1, 0, -1], [1.2, -1, 1.3, -1], [0, 0, -1, -1], [1.4, 1.5, 0, -1]]
    )

    X = array_constructor(X)

    X_true = array_constructor(X_true)

    imputer = SimpleImputer(
        strategy="constant", fill_value=-1, keep_empty_features=True
    )
    X_trans = imputer.fit_transform(X)

    assert_allclose_dense_sparse(X_trans, X_true)


# TODO (1.8): check that `keep_empty_features=False` drop the
# empty features due to the behaviour change.
@pytest.mark.parametrize("marker", [None, np.nan, "NAN", "", 0])
def test_imputation_constant_object(marker):
    # Test imputation using the constant strategy on objects
    X = np.array(
        [
            [marker, "a", "b", marker],
            ["c", marker, "d", marker],
            ["e", "f", marker, marker],
            ["g", "h", "i", marker],
        ],
        dtype=object,
    )

    X_true = np.array(
        [
            ["missing", "a", "b", "missing"],
            ["c", "missing", "d", "missing"],
            ["e", "f", "missing", "missing"],
            ["g", "h", "i", "missing"],
        ],
        dtype=object,
    )

    imputer = SimpleImputer(
        missing_values=marker,
        strategy="constant",
        fill_value="missing",
        keep_empty_features=True,
    )
    X_trans = imputer.fit_transform(X)

    assert_array_equal(X_trans, X_true)


# TODO (1.8): check that `keep_empty_features=False` drop the
# empty features due to the behaviour change.
@pytest.mark.parametrize("dtype", [object, "category"])
def test_imputation_constant_pandas(dtype):
    # Test imputation using the constant strategy on pandas df
    pd = pytest.importorskip("pandas")

    f = io.StringIO("Cat1,Cat2,Cat3,Cat4\n,i,x,\na,,y,\na,j,,\nb,j,x,")

    df = pd.read_csv(f, dtype=dtype)

    X_true = np.array(
        [
            ["missing_value", "i", "x", "missing_value"],
            ["a", "missing_value", "y", "missing_value"],
            ["a", "j", "missing_value", "missing_value"],
            ["b", "j", "x", "missing_value"],
        ],
        dtype=object,
    )

    imputer = SimpleImputer(strategy="constant", keep_empty_features=True)
    X_trans = imputer.fit_transform(df)

    assert_array_equal(X_trans, X_true)


@pytest.mark.parametrize("X", [[[1], [2]], [[1], [np.nan]]])
def test_iterative_imputer_one_feature(X):
    # check we exit early when there is a single feature
    imputer = IterativeImputer().fit(X)
    assert imputer.n_iter_ == 0
    imputer = IterativeImputer()
    imputer.fit([[1], [2]])
    assert imputer.n_iter_ == 0
    imputer.fit([[1], [np.nan]])
    assert imputer.n_iter_ == 0


def test_imputation_pipeline_grid_search():
    # Test imputation within a pipeline + gridsearch.
    X = _sparse_random_matrix(100, 100, density=0.10)
    missing_values = X.data[0]

    pipeline = Pipeline(
        [
            ("imputer", SimpleImputer(missing_values=missing_values)),
            ("tree", tree.DecisionTreeRegressor(random_state=0)),
        ]
    )

    parameters = {"imputer__strategy": ["mean", "median", "most_frequent"]}

    Y = _sparse_random_matrix(100, 1, density=0.10).toarray()
    gs = GridSearchCV(pipeline, parameters)
    gs.fit(X, Y)


def test_imputation_copy():
    # Test imputation with copy
    X_orig = _sparse_random_matrix(5, 5, density=0.75, random_state=0)

    # copy=True, dense => copy
    X = X_orig.copy().toarray()
    imputer = SimpleImputer(missing_values=0, strategy="mean", copy=True)
    Xt = imputer.fit(X).transform(X)
    Xt[0, 0] = -1
    assert not np.all(X == Xt)

    # copy=True, sparse csr => copy
    X = X_orig.copy()
    imputer = SimpleImputer(missing_values=X.data[0], strategy="mean", copy=True)
    Xt = imputer.fit(X).transform(X)
    Xt.data[0] = -1
    assert not np.all(X.data == Xt.data)

    # copy=False, dense => no copy
    X = X_orig.copy().toarray()
    imputer = SimpleImputer(missing_values=0, strategy="mean", copy=False)
    Xt = imputer.fit(X).transform(X)
    Xt[0, 0] = -1
    assert_array_almost_equal(X, Xt)

    # copy=False, sparse csc => no copy
    X = X_orig.copy().tocsc()
    imputer = SimpleImputer(missing_values=X.data[0], strategy="mean", copy=False)
    Xt = imputer.fit(X).transform(X)
    Xt.data[0] = -1
    assert_array_almost_equal(X.data, Xt.data)

    # copy=False, sparse csr => copy
    X = X_orig.copy()
    imputer = SimpleImputer(missing_values=X.data[0], strategy="mean", copy=False)
    Xt = imputer.fit(X).transform(X)
    Xt.data[0] = -1
    assert not np.all(X.data == Xt.data)

    # Note: If X is sparse and if missing_values=0, then a (dense) copy of X is
    # made, even if copy=False.


def test_iterative_imputer_zero_iters():
    rng = np.random.RandomState(0)

    n = 100
    d = 10
    X = _sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
    missing_flag = X == 0
    X[missing_flag] = np.nan

    imputer = IterativeImputer(max_iter=0)
    X_imputed = imputer.fit_transform(X)
    # with max_iter=0, only initial imputation is performed
    assert_allclose(X_imputed, imputer.initial_imputer_.transform(X))

    # repeat but force n_iter_ to 0
    imputer = IterativeImputer(max_iter=5).fit(X)
    # transformed should not be equal to initial imputation
    assert not np.all(imputer.transform(X) == imputer.initial_imputer_.transform(X))

    imputer.n_iter_ = 0
    # now they should be equal as only initial imputation is done
    assert_allclose(imputer.transform(X), imputer.initial_imputer_.transform(X))


def test_iterative_imputer_verbose():
    rng = np.random.RandomState(0)

    n = 100
    d = 3
    X = _sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
    imputer = IterativeImputer(missing_values=0, max_iter=1, verbose=1)
    imputer.fit(X)
    imputer.transform(X)
    imputer = IterativeImputer(missing_values=0, max_iter=1, verbose=2)
    imputer.fit(X)
    imputer.transform(X)


def test_iterative_imputer_all_missing():
    n = 100
    d = 3
    X = np.zeros((n, d))
    imputer = IterativeImputer(missing_values=0, max_iter=1)
    X_imputed = imputer.fit_transform(X)
    assert_allclose(X_imputed, imputer.initial_imputer_.transform(X))


@pytest.mark.parametrize(
    "imputation_order", ["random", "roman", "ascending", "descending", "arabic"]
)
def test_iterative_imputer_imputation_order(imputation_order):
    rng = np.random.RandomState(0)
    n = 100
    d = 10
    max_iter = 2
    X = _sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
    X[:, 0] = 1  # this column should not be discarded by IterativeImputer

    imputer = IterativeImputer(
        missing_values=0,
        max_iter=max_iter,
        n_nearest_features=5,
        sample_posterior=False,
        skip_complete=True,
        min_value=0,
        max_value=1,
        verbose=1,
        imputation_order=imputation_order,
        random_state=rng,
    )
    imputer.fit_transform(X)
    ordered_idx = [i.feat_idx for i in imputer.imputation_sequence_]

    assert len(ordered_idx) // imputer.n_iter_ == imputer.n_features_with_missing_

    if imputation_order == "roman":
        assert np.all(ordered_idx[: d - 1] == np.arange(1, d))
    elif imputation_order == "arabic":
        assert np.all(ordered_idx[: d - 1] == np.arange(d - 1, 0, -1))
    elif imputation_order == "random":
        ordered_idx_round_1 = ordered_idx[: d - 1]
        ordered_idx_round_2 = ordered_idx[d - 1 :]
        assert ordered_idx_round_1 != ordered_idx_round_2
    elif "ending" in imputation_order:
        assert len(ordered_idx) == max_iter * (d - 1)


@pytest.mark.parametrize(
    "estimator", [None, DummyRegressor(), BayesianRidge(), ARDRegression(), RidgeCV()]
)
def test_iterative_imputer_estimators(estimator):
    rng = np.random.RandomState(0)

    n = 100
    d = 10
    X = _sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()

    imputer = IterativeImputer(
        missing_values=0, max_iter=1, estimator=estimator, random_state=rng
    )
    imputer.fit_transform(X)

    # check that types are correct for estimators
    hashes = []
    for triplet in imputer.imputation_sequence_:
        expected_type = (
            type(estimator) if estimator is not None else type(BayesianRidge())
        )
        assert isinstance(triplet.estimator, expected_type)
        hashes.append(id(triplet.estimator))

    # check that each estimator is unique
    assert len(set(hashes)) == len(hashes)


def test_iterative_imputer_clip():
    rng = np.random.RandomState(0)
    n = 100
    d = 10
    X = _sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()

    imputer = IterativeImputer(
        missing_values=0, max_iter=1, min_value=0.1, max_value=0.2, random_state=rng
    )

    Xt = imputer.fit_transform(X)
    assert_allclose(np.min(Xt[X == 0]), 0.1)
    assert_allclose(np.max(Xt[X == 0]), 0.2)
    assert_allclose(Xt[X != 0], X[X != 0])


def test_iterative_imputer_clip_truncnorm():
    rng = np.random.RandomState(0)
    n = 100
    d = 10
    X = _sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
    X[:, 0] = 1

    imputer = IterativeImputer(
        missing_values=0,
        max_iter=2,
        n_nearest_features=5,
        sample_posterior=True,
        min_value=0.1,
        max_value=0.2,
        verbose=1,
        imputation_order="random",
        random_state=rng,
    )
    Xt = imputer.fit_transform(X)
    assert_allclose(np.min(Xt[X == 0]), 0.1)
    assert_allclose(np.max(Xt[X == 0]), 0.2)
    assert_allclose(Xt[X != 0], X[X != 0])


def test_iterative_imputer_truncated_normal_posterior():
    #  test that the values that are imputed using `sample_posterior=True`
    #  with boundaries (`min_value` and `max_value` are not None) are drawn
    #  from a distribution that looks gaussian via the Kolmogorov Smirnov test.
    #  note that starting from the wrong random seed will make this test fail
    #  because random sampling doesn't occur at all when the imputation
    #  is outside of the (min_value, max_value) range
    rng = np.random.RandomState(42)

    X = rng.normal(size=(5, 5))
    X[0][0] = np.nan

    imputer = IterativeImputer(
        min_value=0, max_value=0.5, sample_posterior=True, random_state=rng
    )

    imputer.fit_transform(X)
    # generate multiple imputations for the single missing value
    imputations = np.array([imputer.transform(X)[0][0] for _ in range(100)])

    assert all(imputations >= 0)
    assert all(imputations <= 0.5)

    mu, sigma = imputations.mean(), imputations.std()
    ks_statistic, p_value = kstest((imputations - mu) / sigma, "norm")
    if sigma == 0:
        sigma += 1e-12
    ks_statistic, p_value = kstest((imputations - mu) / sigma, "norm")
    # we want to fail to reject null hypothesis
    # null hypothesis: distributions are the same
    assert ks_statistic < 0.2 or p_value > 0.1, "The posterior does appear to be normal"


@pytest.mark.parametrize("strategy", ["mean", "median", "most_frequent"])
def test_iterative_imputer_missing_at_transform(strategy):
    rng = np.random.RandomState(0)
    n = 100
    d = 10
    X_train = rng.randint(low=0, high=3, size=(n, d))
    X_test = rng.randint(low=0, high=3, size=(n, d))

    X_train[:, 0] = 1  # definitely no missing values in 0th column
    X_test[0, 0] = 0  # definitely missing value in 0th column

    imputer = IterativeImputer(
        missing_values=0, max_iter=1, initial_strategy=strategy, random_state=rng
    ).fit(X_train)
    initial_imputer = SimpleImputer(missing_values=0, strategy=strategy).fit(X_train)

    # if there were no missing values at time of fit, then imputer will
    # only use the initial imputer for that feature at transform
    assert_allclose(
        imputer.transform(X_test)[:, 0], initial_imputer.transform(X_test)[:, 0]
    )


def test_iterative_imputer_transform_stochasticity():
    rng1 = np.random.RandomState(0)
    rng2 = np.random.RandomState(1)
    n = 100
    d = 10
    X = _sparse_random_matrix(n, d, density=0.10, random_state=rng1).toarray()

    # when sample_posterior=True, two transforms shouldn't be equal
    imputer = IterativeImputer(
        missing_values=0, max_iter=1, sample_posterior=True, random_state=rng1
    )
    imputer.fit(X)

    X_fitted_1 = imputer.transform(X)
    X_fitted_2 = imputer.transform(X)

    # sufficient to assert that the means are not the same
    assert np.mean(X_fitted_1) != pytest.approx(np.mean(X_fitted_2))

    # when sample_posterior=False, and n_nearest_features=None
    # and imputation_order is not random
    # the two transforms should be identical even if rng are different
    imputer1 = IterativeImputer(
        missing_values=0,
        max_iter=1,
        sample_posterior=False,
        n_nearest_features=None,
        imputation_order="ascending",
        random_state=rng1,
    )

    imputer2 = IterativeImputer(
        missing_values=0,
        max_iter=1,
        sample_posterior=False,
        n_nearest_features=None,
        imputation_order="ascending",
        random_state=rng2,
    )
    imputer1.fit(X)
    imputer2.fit(X)

    X_fitted_1a = imputer1.transform(X)
    X_fitted_1b = imputer1.transform(X)
    X_fitted_2 = imputer2.transform(X)

    assert_allclose(X_fitted_1a, X_fitted_1b)
    assert_allclose(X_fitted_1a, X_fitted_2)


def test_iterative_imputer_no_missing():
    rng = np.random.RandomState(0)
    X = rng.rand(100, 100)
    X[:, 0] = np.nan
    m1 = IterativeImputer(max_iter=10, random_state=rng)
    m2 = IterativeImputer(max_iter=10, random_state=rng)
    pred1 = m1.fit(X).transform(X)
    pred2 = m2.fit_transform(X)
    # should exclude the first column entirely
    assert_allclose(X[:, 1:], pred1)
    # fit and fit_transform should both be identical
    assert_allclose(pred1, pred2)


def test_iterative_imputer_rank_one():
    rng = np.random.RandomState(0)
    d = 50
    A = rng.rand(d, 1)
    B = rng.rand(1, d)
    X = np.dot(A, B)
    nan_mask = rng.rand(d, d) < 0.5
    X_missing = X.copy()
    X_missing[nan_mask] = np.nan

    imputer = IterativeImputer(max_iter=5, verbose=1, random_state=rng)
    X_filled = imputer.fit_transform(X_missing)
    assert_allclose(X_filled, X, atol=0.02)


@pytest.mark.parametrize("rank", [3, 5])
def test_iterative_imputer_transform_recovery(rank):
    rng = np.random.RandomState(0)
    n = 70
    d = 70
    A = rng.rand(n, rank)
    B = rng.rand(rank, d)
    X_filled = np.dot(A, B)
    nan_mask = rng.rand(n, d) < 0.5
    X_missing = X_filled.copy()
    X_missing[nan_mask] = np.nan

    # split up data in half
    n = n // 2
    X_train = X_missing[:n]
    X_test_filled = X_filled[n:]
    X_test = X_missing[n:]

    imputer = IterativeImputer(
        max_iter=5, imputation_order="descending", verbose=1, random_state=rng
    ).fit(X_train)
    X_test_est = imputer.transform(X_test)
    assert_allclose(X_test_filled, X_test_est, atol=0.1)


def test_iterative_imputer_additive_matrix():
    rng = np.random.RandomState(0)
    n = 100
    d = 10
    A = rng.randn(n, d)
    B = rng.randn(n, d)
    X_filled = np.zeros(A.shape)
    for i in range(d):
        for j in range(d):
            X_filled[:, (i + j) % d] += (A[:, i] + B[:, j]) / 2
    # a quarter is randomly missing
    nan_mask = rng.rand(n, d) < 0.25
    X_missing = X_filled.copy()
    X_missing[nan_mask] = np.nan

    # split up data
    n = n // 2
    X_train = X_missing[:n]
    X_test_filled = X_filled[n:]
    X_test = X_missing[n:]

    imputer = IterativeImputer(max_iter=10, verbose=1, random_state=rng).fit(X_train)
    X_test_est = imputer.transform(X_test)
    assert_allclose(X_test_filled, X_test_est, rtol=1e-3, atol=0.01)


def test_iterative_imputer_early_stopping():
    rng = np.random.RandomState(0)
    n = 50
    d = 5
    A = rng.rand(n, 1)
    B = rng.rand(1, d)
    X = np.dot(A, B)
    nan_mask = rng.rand(n, d) < 0.5
    X_missing = X.copy()
    X_missing[nan_mask] = np.nan

    imputer = IterativeImputer(
        max_iter=100, tol=1e-2, sample_posterior=False, verbose=1, random_state=rng
    )
    X_filled_100 = imputer.fit_transform(X_missing)
    assert len(imputer.imputation_sequence_) == d * imputer.n_iter_

    imputer = IterativeImputer(
        max_iter=imputer.n_iter_, sample_posterior=False, verbose=1, random_state=rng
    )
    X_filled_early = imputer.fit_transform(X_missing)
    assert_allclose(X_filled_100, X_filled_early, atol=1e-7)

    imputer = IterativeImputer(
        max_iter=100, tol=0, sample_posterior=False, verbose=1, random_state=rng
    )
    imputer.fit(X_missing)
    assert imputer.n_iter_ == imputer.max_iter


def test_iterative_imputer_catch_warning():
    # check that we catch a RuntimeWarning due to a division by zero when a
    # feature is constant in the dataset
    X, y = load_diabetes(return_X_y=True)
    n_samples, n_features = X.shape

    # simulate that a feature only contain one category during fit
    X[:, 3] = 1

    # add some missing values
    rng = np.random.RandomState(0)
    missing_rate = 0.15
    for feat in range(n_features):
        sample_idx = rng.choice(
            np.arange(n_samples), size=int(n_samples * missing_rate), replace=False
        )
        X[sample_idx, feat] = np.nan

    imputer = IterativeImputer(n_nearest_features=5, sample_posterior=True)
    with warnings.catch_warnings():
        warnings.simplefilter("error", RuntimeWarning)
        X_fill = imputer.fit_transform(X, y)
    assert not np.any(np.isnan(X_fill))


@pytest.mark.parametrize(
    "min_value, max_value, correct_output",
    [
        (0, 100, np.array([[0] * 3, [100] * 3])),
        (None, None, np.array([[-np.inf] * 3, [np.inf] * 3])),
        (-np.inf, np.inf, np.array([[-np.inf] * 3, [np.inf] * 3])),
        ([-5, 5, 10], [100, 200, 300], np.array([[-5, 5, 10], [100, 200, 300]])),
        (
            [-5, -np.inf, 10],
            [100, 200, np.inf],
            np.array([[-5, -np.inf, 10], [100, 200, np.inf]]),
        ),
    ],
    ids=["scalars", "None-default", "inf", "lists", "lists-with-inf"],
)
def test_iterative_imputer_min_max_array_like(min_value, max_value, correct_output):
    # check that passing scalar or array-like
    # for min_value and max_value in IterativeImputer works
    X = np.random.RandomState(0).randn(10, 3)
    imputer = IterativeImputer(min_value=min_value, max_value=max_value)
    imputer.fit(X)

    assert isinstance(imputer._min_value, np.ndarray) and isinstance(
        imputer._max_value, np.ndarray
    )
    assert (imputer._min_value.shape[0] == X.shape[1]) and (
        imputer._max_value.shape[0] == X.shape[1]
    )

    assert_allclose(correct_output[0, :], imputer._min_value)
    assert_allclose(correct_output[1, :], imputer._max_value)


@pytest.mark.parametrize(
    "min_value, max_value, err_msg",
    [
        (100, 0, "min_value >= max_value."),
        (np.inf, -np.inf, "min_value >= max_value."),
        ([-5, 5], [100, 200, 0], "_value' should be of shape"),
        ([-5, 5, 5], [100, 200], "_value' should be of shape"),
    ],
)
def test_iterative_imputer_catch_min_max_error(min_value, max_value, err_msg):
    # check that passing scalar or array-like
    # for min_value and max_value in IterativeImputer works
    X = np.random.random((10, 3))
    imputer = IterativeImputer(min_value=min_value, max_value=max_value)
    with pytest.raises(ValueError, match=err_msg):
        imputer.fit(X)


@pytest.mark.parametrize(
    "min_max_1, min_max_2",
    [([None, None], [-np.inf, np.inf]), ([-10, 10], [[-10] * 4, [10] * 4])],
    ids=["None-vs-inf", "Scalar-vs-vector"],
)
def test_iterative_imputer_min_max_array_like_imputation(min_max_1, min_max_2):
    # Test that None/inf and scalar/vector give the same imputation
    X_train = np.array(
        [
            [np.nan, 2, 2, 1],
            [10, np.nan, np.nan, 7],
            [3, 1, np.nan, 1],
            [np.nan, 4, 2, np.nan],
        ]
    )
    X_test = np.array(
        [[np.nan, 2, np.nan, 5], [2, 4, np.nan, np.nan], [np.nan, 1, 10, 1]]
    )
    imputer1 = IterativeImputer(
        min_value=min_max_1[0], max_value=min_max_1[1], random_state=0
    )
    imputer2 = IterativeImputer(
        min_value=min_max_2[0], max_value=min_max_2[1], random_state=0
    )
    X_test_imputed1 = imputer1.fit(X_train).transform(X_test)
    X_test_imputed2 = imputer2.fit(X_train).transform(X_test)
    assert_allclose(X_test_imputed1[:, 0], X_test_imputed2[:, 0])


@pytest.mark.parametrize("skip_complete", [True, False])
def test_iterative_imputer_skip_non_missing(skip_complete):
    # check the imputing strategy when missing data are present in the
    # testing set only.
    # taken from: https://github.com/scikit-learn/scikit-learn/issues/14383
    rng = np.random.RandomState(0)
    X_train = np.array([[5, 2, 2, 1], [10, 1, 2, 7], [3, 1, 1, 1], [8, 4, 2, 2]])
    X_test = np.array([[np.nan, 2, 4, 5], [np.nan, 4, 1, 2], [np.nan, 1, 10, 1]])
    imputer = IterativeImputer(
        initial_strategy="mean", skip_complete=skip_complete, random_state=rng
    )
    X_test_est = imputer.fit(X_train).transform(X_test)
    if skip_complete:
        # impute with the initial strategy: 'mean'
        assert_allclose(X_test_est[:, 0], np.mean(X_train[:, 0]))
    else:
        assert_allclose(X_test_est[:, 0], [11, 7, 12], rtol=1e-4)


@pytest.mark.parametrize("rs_imputer", [None, 1, np.random.RandomState(seed=1)])
@pytest.mark.parametrize("rs_estimator", [None, 1, np.random.RandomState(seed=1)])
def test_iterative_imputer_dont_set_random_state(rs_imputer, rs_estimator):
    class ZeroEstimator:
        def __init__(self, random_state):
            self.random_state = random_state

        def fit(self, *args, **kgards):
            return self

        def predict(self, X):
            return np.zeros(X.shape[0])

    estimator = ZeroEstimator(random_state=rs_estimator)
    imputer = IterativeImputer(random_state=rs_imputer)
    X_train = np.zeros((10, 3))
    imputer.fit(X_train)
    assert estimator.random_state == rs_estimator


@pytest.mark.parametrize(
    "X_fit, X_trans, params, msg_err",
    [
        (
            np.array([[-1, 1], [1, 2]]),
            np.array([[-1, 1], [1, -1]]),
            {"features": "missing-only", "sparse": "auto"},
            "have missing values in transform but have no missing values in fit",
        ),
        (
            np.array([["a", "b"], ["c", "a"]], dtype=str),
            np.array([["a", "b"], ["c", "a"]], dtype=str),
            {},
            "MissingIndicator does not support data with dtype",
        ),
    ],
)
def test_missing_indicator_error(X_fit, X_trans, params, msg_err):
    indicator = MissingIndicator(missing_values=-1)
    indicator.set_params(**params)
    with pytest.raises(ValueError, match=msg_err):
        indicator.fit(X_fit).transform(X_trans)


def _generate_missing_indicator_cases():
    missing_values_dtypes = [(0, np.int32), (np.nan, np.float64), (-1, np.int32)]
    arr_types = (
        [np.array]
        + CSC_CONTAINERS
        + CSR_CONTAINERS
        + COO_CONTAINERS
        + LIL_CONTAINERS
        + BSR_CONTAINERS
    )
    return [
        (arr_type, missing_values, dtype)
        for arr_type, (missing_values, dtype) in product(
            arr_types, missing_values_dtypes
        )
        if not (missing_values == 0 and arr_type is not np.array)
    ]


@pytest.mark.parametrize(
    "arr_type, missing_values, dtype", _generate_missing_indicator_cases()
)
@pytest.mark.parametrize(
    "param_features, n_features, features_indices",
    [("missing-only", 3, np.array([0, 1, 2])), ("all", 3, np.array([0, 1, 2]))],
)
def test_missing_indicator_new(
    missing_values, arr_type, dtype, param_features, n_features, features_indices
):
    X_fit = np.array([[missing_values, missing_values, 1], [4, 2, missing_values]])
    X_trans = np.array([[missing_values, missing_values, 1], [4, 12, 10]])
    X_fit_expected = np.array([[1, 1, 0], [0, 0, 1]])
    X_trans_expected = np.array([[1, 1, 0], [0, 0, 0]])

    # convert the input to the right array format and right dtype
    X_fit = arr_type(X_fit).astype(dtype)
    X_trans = arr_type(X_trans).astype(dtype)
    X_fit_expected = X_fit_expected.astype(dtype)
    X_trans_expected = X_trans_expected.astype(dtype)

    indicator = MissingIndicator(
        missing_values=missing_values, features=param_features, sparse=False
    )
    X_fit_mask = indicator.fit_transform(X_fit)
    X_trans_mask = indicator.transform(X_trans)

    assert X_fit_mask.shape[1] == n_features
    assert X_trans_mask.shape[1] == n_features

    assert_array_equal(indicator.features_, features_indices)
    assert_allclose(X_fit_mask, X_fit_expected[:, features_indices])
    assert_allclose(X_trans_mask, X_trans_expected[:, features_indices])

    assert X_fit_mask.dtype == bool
    assert X_trans_mask.dtype == bool
    assert isinstance(X_fit_mask, np.ndarray)
    assert isinstance(X_trans_mask, np.ndarray)

    indicator.set_params(sparse=True)
    X_fit_mask_sparse = indicator.fit_transform(X_fit)
    X_trans_mask_sparse = indicator.transform(X_trans)

    assert X_fit_mask_sparse.dtype == bool
    assert X_trans_mask_sparse.dtype == bool
    assert X_fit_mask_sparse.format == "csc"
    assert X_trans_mask_sparse.format == "csc"
    assert_allclose(X_fit_mask_sparse.toarray(), X_fit_mask)
    assert_allclose(X_trans_mask_sparse.toarray(), X_trans_mask)


@pytest.mark.parametrize(
    "arr_type",
    CSC_CONTAINERS + CSR_CONTAINERS + COO_CONTAINERS + LIL_CONTAINERS + BSR_CONTAINERS,
)
def test_missing_indicator_raise_on_sparse_with_missing_0(arr_type):
    # test for sparse input and missing_value == 0

    missing_values = 0
    X_fit = np.array([[missing_values, missing_values, 1], [4, missing_values, 2]])
    X_trans = np.array([[missing_values, missing_values, 1], [4, 12, 10]])

    # convert the input to the right array format
    X_fit_sparse = arr_type(X_fit)
    X_trans_sparse = arr_type(X_trans)

    indicator = MissingIndicator(missing_values=missing_values)

    with pytest.raises(ValueError, match="Sparse input with missing_values=0"):
        indicator.fit_transform(X_fit_sparse)

    indicator.fit_transform(X_fit)
    with pytest.raises(ValueError, match="Sparse input with missing_values=0"):
        indicator.transform(X_trans_sparse)


@pytest.mark.parametrize("param_sparse", [True, False, "auto"])
@pytest.mark.parametrize(
    "arr_type, missing_values",
    [(np.array, 0)]
    + list(
        product(
            CSC_CONTAINERS
            + CSR_CONTAINERS
            + COO_CONTAINERS
            + LIL_CONTAINERS
            + BSR_CONTAINERS,
            [np.nan],
        )
    ),
)
def test_missing_indicator_sparse_param(arr_type, missing_values, param_sparse):
    # check the format of the output with different sparse parameter
    X_fit = np.array([[missing_values, missing_values, 1], [4, missing_values, 2]])
    X_trans = np.array([[missing_values, missing_values, 1], [4, 12, 10]])
    X_fit = arr_type(X_fit).astype(np.float64)
    X_trans = arr_type(X_trans).astype(np.float64)

    indicator = MissingIndicator(missing_values=missing_values, sparse=param_sparse)
    X_fit_mask = indicator.fit_transform(X_fit)
    X_trans_mask = indicator.transform(X_trans)

    if param_sparse is True:
        assert X_fit_mask.format == "csc"
        assert X_trans_mask.format == "csc"
    elif param_sparse == "auto" and missing_values == 0:
        assert isinstance(X_fit_mask, np.ndarray)
        assert isinstance(X_trans_mask, np.ndarray)
    elif param_sparse is False:
        assert isinstance(X_fit_mask, np.ndarray)
        assert isinstance(X_trans_mask, np.ndarray)
    else:
        if sparse.issparse(X_fit):
            assert X_fit_mask.format == "csc"
            assert X_trans_mask.format == "csc"
        else:
            assert isinstance(X_fit_mask, np.ndarray)
            assert isinstance(X_trans_mask, np.ndarray)


def test_missing_indicator_string():
    X = np.array([["a", "b", "c"], ["b", "c", "a"]], dtype=object)
    indicator = MissingIndicator(missing_values="a", features="all")
    X_trans = indicator.fit_transform(X)
    assert_array_equal(X_trans, np.array([[True, False, False], [False, False, True]]))


@pytest.mark.parametrize(
    "X, missing_values, X_trans_exp",
    [
        (
            np.array([["a", "b"], ["b", "a"]], dtype=object),
            "a",
            np.array([["b", "b", True, False], ["b", "b", False, True]], dtype=object),
        ),
        (
            np.array([[np.nan, 1.0], [1.0, np.nan]]),
            np.nan,
            np.array([[1.0, 1.0, True, False], [1.0, 1.0, False, True]]),
        ),
        (
            np.array([[np.nan, "b"], ["b", np.nan]], dtype=object),
            np.nan,
            np.array([["b", "b", True, False], ["b", "b", False, True]], dtype=object),
        ),
        (
            np.array([[None, "b"], ["b", None]], dtype=object),
            None,
            np.array([["b", "b", True, False], ["b", "b", False, True]], dtype=object),
        ),
    ],
)
def test_missing_indicator_with_imputer(X, missing_values, X_trans_exp):
    trans = make_union(
        SimpleImputer(missing_values=missing_values, strategy="most_frequent"),
        MissingIndicator(missing_values=missing_values),
    )
    X_trans = trans.fit_transform(X)
    assert_array_equal(X_trans, X_trans_exp)


@pytest.mark.parametrize("imputer_constructor", [SimpleImputer, IterativeImputer])
@pytest.mark.parametrize(
    "imputer_missing_values, missing_value, err_msg",
    [
        ("NaN", np.nan, "Input X contains NaN"),
        ("-1", -1, "types are expected to be both numerical."),
    ],
)
def test_inconsistent_dtype_X_missing_values(
    imputer_constructor, imputer_missing_values, missing_value, err_msg
):
    # regression test for issue #11390. Comparison between incoherent dtype
    # for X and missing_values was not raising a proper error.
    rng = np.random.RandomState(42)
    X = rng.randn(10, 10)
    X[0, 0] = missing_value

    imputer = imputer_constructor(missing_values=imputer_missing_values)

    with pytest.raises(ValueError, match=err_msg):
        imputer.fit_transform(X)


def test_missing_indicator_no_missing():
    # check that all features are dropped if there are no missing values when
    # features='missing-only' (#13491)
    X = np.array([[1, 1], [1, 1]])

    mi = MissingIndicator(features="missing-only", missing_values=-1)
    Xt = mi.fit_transform(X)

    assert Xt.shape[1] == 0


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_missing_indicator_sparse_no_explicit_zeros(csr_container):
    # Check that non missing values don't become explicit zeros in the mask
    # generated by missing indicator when X is sparse. (#13491)
    X = csr_container([[0, 1, 2], [1, 2, 0], [2, 0, 1]])

    mi = MissingIndicator(features="all", missing_values=1)
    Xt = mi.fit_transform(X)

    assert Xt.nnz == Xt.sum()


@pytest.mark.parametrize("imputer_constructor", [SimpleImputer, IterativeImputer])
def test_imputer_without_indicator(imputer_constructor):
    X = np.array([[1, 1], [1, 1]])
    imputer = imputer_constructor()
    imputer.fit(X)

    assert imputer.indicator_ is None


@pytest.mark.parametrize(
    "arr_type",
    CSC_CONTAINERS + CSR_CONTAINERS + COO_CONTAINERS + LIL_CONTAINERS + BSR_CONTAINERS,
)
def test_simple_imputation_add_indicator_sparse_matrix(arr_type):
    X_sparse = arr_type([[np.nan, 1, 5], [2, np.nan, 1], [6, 3, np.nan], [1, 2, 9]])
    X_true = np.array(
        [
            [3.0, 1.0, 5.0, 1.0, 0.0, 0.0],
            [2.0, 2.0, 1.0, 0.0, 1.0, 0.0],
            [6.0, 3.0, 5.0, 0.0, 0.0, 1.0],
            [1.0, 2.0, 9.0, 0.0, 0.0, 0.0],
        ]
    )

    imputer = SimpleImputer(missing_values=np.nan, add_indicator=True)
    X_trans = imputer.fit_transform(X_sparse)

    assert sparse.issparse(X_trans)
    assert X_trans.shape == X_true.shape
    assert_allclose(X_trans.toarray(), X_true)


@pytest.mark.parametrize(
    "strategy, expected", [("most_frequent", "b"), ("constant", "missing_value")]
)
def test_simple_imputation_string_list(strategy, expected):
    X = [["a", "b"], ["c", np.nan]]

    X_true = np.array([["a", "b"], ["c", expected]], dtype=object)

    imputer = SimpleImputer(strategy=strategy)
    X_trans = imputer.fit_transform(X)

    assert_array_equal(X_trans, X_true)


@pytest.mark.parametrize(
    "order, idx_order",
    [("ascending", [3, 4, 2, 0, 1]), ("descending", [1, 0, 2, 4, 3])],
)
def test_imputation_order(order, idx_order):
    # regression test for #15393
    rng = np.random.RandomState(42)
    X = rng.rand(100, 5)
    X[:50, 1] = np.nan
    X[:30, 0] = np.nan
    X[:20, 2] = np.nan
    X[:10, 4] = np.nan

    with pytest.warns(ConvergenceWarning):
        trs = IterativeImputer(max_iter=1, imputation_order=order, random_state=0).fit(
            X
        )
        idx = [x.feat_idx for x in trs.imputation_sequence_]
        assert idx == idx_order


@pytest.mark.parametrize("missing_value", [-1, np.nan])
def test_simple_imputation_inverse_transform(missing_value):
    # Test inverse_transform feature for np.nan
    X_1 = np.array(
        [
            [9, missing_value, 3, -1],
            [4, -1, 5, 4],
            [6, 7, missing_value, -1],
            [8, 9, 0, missing_value],
        ]
    )

    X_2 = np.array(
        [
            [5, 4, 2, 1],
            [2, 1, missing_value, 3],
            [9, missing_value, 7, 1],
            [6, 4, 2, missing_value],
        ]
    )

    X_3 = np.array(
        [
            [1, missing_value, 5, 9],
            [missing_value, 4, missing_value, missing_value],
            [2, missing_value, 7, missing_value],
            [missing_value, 3, missing_value, 8],
        ]
    )

    X_4 = np.array(
        [
            [1, 1, 1, 3],
            [missing_value, 2, missing_value, 1],
            [2, 3, 3, 4],
            [missing_value, 4, missing_value, 2],
        ]
    )

    imputer = SimpleImputer(
        missing_values=missing_value, strategy="mean", add_indicator=True
    )

    X_1_trans = imputer.fit_transform(X_1)
    X_1_inv_trans = imputer.inverse_transform(X_1_trans)

    X_2_trans = imputer.transform(X_2)  # test on new data
    X_2_inv_trans = imputer.inverse_transform(X_2_trans)

    assert_array_equal(X_1_inv_trans, X_1)
    assert_array_equal(X_2_inv_trans, X_2)

    for X in [X_3, X_4]:
        X_trans = imputer.fit_transform(X)
        X_inv_trans = imputer.inverse_transform(X_trans)
        assert_array_equal(X_inv_trans, X)


@pytest.mark.parametrize("missing_value", [-1, np.nan])
def test_simple_imputation_inverse_transform_exceptions(missing_value):
    X_1 = np.array(
        [
            [9, missing_value, 3, -1],
            [4, -1, 5, 4],
            [6, 7, missing_value, -1],
            [8, 9, 0, missing_value],
        ]
    )

    imputer = SimpleImputer(missing_values=missing_value, strategy="mean")
    X_1_trans = imputer.fit_transform(X_1)
    with pytest.raises(
        ValueError, match=f"Got 'add_indicator={imputer.add_indicator}'"
    ):
        imputer.inverse_transform(X_1_trans)


@pytest.mark.parametrize(
    "expected,array,dtype,extra_value,n_repeat",
    [
        # array of object dtype
        ("extra_value", ["a", "b", "c"], object, "extra_value", 2),
        (
            "most_frequent_value",
            ["most_frequent_value", "most_frequent_value", "value"],
            object,
            "extra_value",
            1,
        ),
        ("a", ["min_value", "min_valuevalue"], object, "a", 2),
        ("min_value", ["min_value", "min_value", "value"], object, "z", 2),
        # array of numeric dtype
        (10, [1, 2, 3], int, 10, 2),
        (1, [1, 1, 2], int, 10, 1),
        (10, [20, 20, 1], int, 10, 2),
        (1, [1, 1, 20], int, 10, 2),
    ],
)
def test_most_frequent(expected, array, dtype, extra_value, n_repeat):
    assert expected == _most_frequent(
        np.array(array, dtype=dtype), extra_value, n_repeat
    )


@pytest.mark.parametrize(
    "expected,array",
    [
        ("a", ["a", "b"]),
        (1, [1, 2]),
        (None, [None, "a"]),
        (None, [None, 1]),
        (None, [None, "a", 1]),
        (1, [1, "1"]),
        (1, ["1", 1]),
    ],
)
def test_most_frequent_tie_object(expected, array):
    """Check the tie breaking behavior of the most frequent strategy.

    Non-regression test for issue #31717.
    """
    assert expected == _most_frequent(np.array(array, dtype=object), None, 0)


@pytest.mark.parametrize(
    "initial_strategy", ["mean", "median", "most_frequent", "constant"]
)
def test_iterative_imputer_keep_empty_features(initial_strategy):
    """Check the behaviour of the iterative imputer with different initial strategy
    and keeping empty features (i.e. features containing only missing values).
    """
    X = np.array([[1, np.nan, 2], [3, np.nan, np.nan]])

    imputer = IterativeImputer(
        initial_strategy=initial_strategy, keep_empty_features=True
    )
    X_imputed = imputer.fit_transform(X)
    assert_allclose(X_imputed[:, 1], 0)
    X_imputed = imputer.transform(X)
    assert_allclose(X_imputed[:, 1], 0)


# TODO (1.8): check that `keep_empty_features=False` drop the
# empty features due to the behaviour change.
def test_iterative_imputer_constant_fill_value():
    """Check that we propagate properly the parameter `fill_value`."""
    X = np.array([[-1, 2, 3, -1], [4, -1, 5, -1], [6, 7, -1, -1], [8, 9, 0, -1]])

    fill_value = 100
    imputer = IterativeImputer(
        missing_values=-1,
        initial_strategy="constant",
        fill_value=fill_value,
        max_iter=0,
        keep_empty_features=True,
    )
    imputer.fit_transform(X)
    assert_array_equal(imputer.initial_imputer_.statistics_, fill_value)


def test_iterative_imputer_min_max_value_remove_empty():
    """Check that we properly apply the empty feature mask to `min_value` and
    `max_value`.

    Non-regression test for https://github.com/scikit-learn/scikit-learn/issues/29355
    """
    # Intentionally make column 2 as a missing column, then the bound of the imputed
    # value of column 3 should be (4, 5)
    X = np.array(
        [
            [1, 2, np.nan, np.nan],
            [4, 5, np.nan, 6],
            [7, 8, np.nan, np.nan],
            [10, 11, np.nan, 12],
        ]
    )
    min_value = [-np.inf, -np.inf, -np.inf, 4]
    max_value = [np.inf, np.inf, np.inf, 5]

    X_imputed = IterativeImputer(
        min_value=min_value,
        max_value=max_value,
        keep_empty_features=False,
    ).fit_transform(X)

    X_without_missing_column = np.delete(X, 2, axis=1)
    assert X_imputed.shape == X_without_missing_column.shape
    assert np.min(X_imputed[np.isnan(X_without_missing_column)]) == pytest.approx(4)
    assert np.max(X_imputed[np.isnan(X_without_missing_column)]) == pytest.approx(5)

    # Intentionally make column 3 as a missing column, then the bound of the imputed
    # value of column 2 should be (3.5, 6)
    X = np.array(
        [
            [1, 2, np.nan, np.nan],
            [4, 5, 6, np.nan],
            [7, 8, np.nan, np.nan],
            [10, 11, 12, np.nan],
        ]
    )
    min_value = [-np.inf, -np.inf, 3.5, -np.inf]
    max_value = [np.inf, np.inf, 6, np.inf]

    X_imputed = IterativeImputer(
        min_value=min_value,
        max_value=max_value,
        keep_empty_features=False,
    ).fit_transform(X)

    X_without_missing_column = X[:, :3]
    assert X_imputed.shape == X_without_missing_column.shape
    assert np.min(X_imputed[np.isnan(X_without_missing_column)]) == pytest.approx(3.5)
    assert np.max(X_imputed[np.isnan(X_without_missing_column)]) == pytest.approx(6)


@pytest.mark.parametrize("keep_empty_features", [True, False])
def test_knn_imputer_keep_empty_features(keep_empty_features):
    """Check the behaviour of `keep_empty_features` for `KNNImputer`."""
    X = np.array([[1, np.nan, 2], [3, np.nan, np.nan]])

    imputer = KNNImputer(keep_empty_features=keep_empty_features)

    for method in ["fit_transform", "transform"]:
        X_imputed = getattr(imputer, method)(X)
        if keep_empty_features:
            assert X_imputed.shape == X.shape
            assert_array_equal(X_imputed[:, 1], 0)
        else:
            assert X_imputed.shape == (X.shape[0], X.shape[1] - 1)


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

    # Impute pandas array of string types.
    df = pd.DataFrame({"feature": pd.Series(["abc", None, "de"], dtype="string")})
    imputer = SimpleImputer(missing_values=pd.NA, strategy="constant", fill_value="na")
    _assert_array_equal_and_same_dtype(
        imputer.fit_transform(df), np.array([["abc"], ["na"], ["de"]], dtype=object)
    )

    # Impute pandas array of string types without any missing values.
    df = pd.DataFrame({"feature": pd.Series(["abc", "de", "fgh"], dtype="string")})
    imputer = SimpleImputer(fill_value="ok", strategy="constant")
    _assert_array_equal_and_same_dtype(
        imputer.fit_transform(df), np.array([["abc"], ["de"], ["fgh"]], dtype=object)
    )

    # Impute pandas array of integer types.
    df = pd.DataFrame({"feature": pd.Series([1, None, 3], dtype="Int64")})
    imputer = SimpleImputer(missing_values=pd.NA, strategy="constant", fill_value=-1)
    _assert_allclose_and_same_dtype(
        imputer.fit_transform(df), np.array([[1], [-1], [3]], dtype="float64")
    )

    # Use `np.nan` also works.
    imputer = SimpleImputer(missing_values=np.nan, strategy="constant", fill_value=-1)
    _assert_allclose_and_same_dtype(
        imputer.fit_transform(df), np.array([[1], [-1], [3]], dtype="float64")
    )

    # Impute pandas array of integer types with 'median' strategy.
    df = pd.DataFrame({"feature": pd.Series([1, None, 2, 3], dtype="Int64")})
    imputer = SimpleImputer(missing_values=pd.NA, strategy="median")
    _assert_allclose_and_same_dtype(
        imputer.fit_transform(df), np.array([[1], [2], [2], [3]], dtype="float64")
    )

    # Impute pandas array of integer types with 'mean' strategy.
    df = pd.DataFrame({"feature": pd.Series([1, None, 2], dtype="Int64")})
    imputer = SimpleImputer(missing_values=pd.NA, strategy="mean")
    _assert_allclose_and_same_dtype(
        imputer.fit_transform(df), np.array([[1], [1.5], [2]], dtype="float64")
    )

    # Impute pandas array of float types.
    df = pd.DataFrame({"feature": pd.Series([1.0, None, 3.0], dtype="float64")})
    imputer = SimpleImputer(missing_values=pd.NA, strategy="constant", fill_value=-2.0)
    _assert_allclose_and_same_dtype(
        imputer.fit_transform(df), np.array([[1.0], [-2.0], [3.0]], dtype="float64")
    )

    # Impute pandas array of float types with 'median' strategy.
    df = pd.DataFrame({"feature": pd.Series([1.0, None, 2.0, 3.0], dtype="float64")})
    imputer = SimpleImputer(missing_values=pd.NA, strategy="median")
    _assert_allclose_and_same_dtype(
        imputer.fit_transform(df),
        np.array([[1.0], [2.0], [2.0], [3.0]], dtype="float64"),
    )


def test_missing_indicator_feature_names_out():
    """Check that missing indicator return the feature names with a prefix."""
    pd = pytest.importorskip("pandas")

    missing_values = np.nan
    X = pd.DataFrame(
        [
            [missing_values, missing_values, 1, missing_values],
            [4, missing_values, 2, 10],
        ],
        columns=["a", "b", "c", "d"],
    )

    indicator = MissingIndicator(missing_values=missing_values).fit(X)
    feature_names = indicator.get_feature_names_out()
    expected_names = ["missingindicator_a", "missingindicator_b", "missingindicator_d"]
    assert_array_equal(expected_names, feature_names)


def test_imputer_lists_fit_transform():
    """Check transform uses object dtype when fitted on an object dtype.

    Non-regression test for #19572.
    """

    X = [["a", "b"], ["c", "b"], ["a", "a"]]
    imp_frequent = SimpleImputer(strategy="most_frequent").fit(X)
    X_trans = imp_frequent.transform([[np.nan, np.nan]])
    assert X_trans.dtype == object
    assert_array_equal(X_trans, [["a", "b"]])


@pytest.mark.parametrize("dtype_test", [np.float32, np.float64])
def test_imputer_transform_preserves_numeric_dtype(dtype_test):
    """Check transform preserves numeric dtype independent of fit dtype."""
    X = np.asarray(
        [[1.2, 3.4, np.nan], [np.nan, 1.2, 1.3], [4.2, 2, 1]], dtype=np.float64
    )
    imp = SimpleImputer().fit(X)

    X_test = np.asarray([[np.nan, np.nan, np.nan]], dtype=dtype_test)
    X_trans = imp.transform(X_test)
    assert X_trans.dtype == dtype_test


@pytest.mark.parametrize("array_type", ["array", "sparse"])
@pytest.mark.parametrize("keep_empty_features", [True, False])
def test_simple_imputer_constant_keep_empty_features(array_type, keep_empty_features):
    """Check the behaviour of `keep_empty_features` with `strategy='constant'.
    For backward compatibility, a column full of missing values will always be
    fill and never dropped.
    """
    X = np.array([[np.nan, 2], [np.nan, 3], [np.nan, 6]])
    X = _convert_container(X, array_type)
    fill_value = 10
    imputer = SimpleImputer(
        strategy="constant",
        fill_value=fill_value,
        keep_empty_features=keep_empty_features,
    )

    for method in ["fit_transform", "transform"]:
        # TODO(1.8): Remove the condition and still call getattr(imputer, method)(X)
        if method.startswith("fit") and not keep_empty_features:
            warn_msg = '`strategy="constant"`, empty features are not dropped. '
            with pytest.warns(FutureWarning, match=warn_msg):
                X_imputed = getattr(imputer, method)(X)
        else:
            X_imputed = getattr(imputer, method)(X)
        assert X_imputed.shape == X.shape
        constant_feature = (
            X_imputed[:, 0].toarray() if array_type == "sparse" else X_imputed[:, 0]
        )
        assert_array_equal(constant_feature, fill_value)


@pytest.mark.parametrize("array_type", ["array", "sparse"])
@pytest.mark.parametrize("strategy", ["mean", "median", "most_frequent"])
@pytest.mark.parametrize("keep_empty_features", [True, False])
def test_simple_imputer_keep_empty_features(strategy, array_type, keep_empty_features):
    """Check the behaviour of `keep_empty_features` with all strategies but
    'constant'.
    """
    X = np.array([[np.nan, 2], [np.nan, 3], [np.nan, 6]])
    X = _convert_container(X, array_type)
    imputer = SimpleImputer(strategy=strategy, keep_empty_features=keep_empty_features)

    for method in ["fit_transform", "transform"]:
        X_imputed = getattr(imputer, method)(X)
        if keep_empty_features:
            assert X_imputed.shape == X.shape
            constant_feature = (
                X_imputed[:, 0].toarray() if array_type == "sparse" else X_imputed[:, 0]
            )
            assert_array_equal(constant_feature, 0)
        else:
            assert X_imputed.shape == (X.shape[0], X.shape[1] - 1)


@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_imputation_custom(csc_container):
    X = np.array(
        [
            [1.1, 1.1, 1.1],
            [3.9, 1.2, np.nan],
            [np.nan, 1.3, np.nan],
            [0.1, 1.4, 1.4],
            [4.9, 1.5, 1.5],
            [np.nan, 1.6, 1.6],
        ]
    )

    X_true = np.array(
        [
            [1.1, 1.1, 1.1],
            [3.9, 1.2, 1.1],
            [0.1, 1.3, 1.1],
            [0.1, 1.4, 1.4],
            [4.9, 1.5, 1.5],
            [0.1, 1.6, 1.6],
        ]
    )

    imputer = SimpleImputer(missing_values=np.nan, strategy=np.min)
    X_trans = imputer.fit_transform(X)
    assert_array_equal(X_trans, X_true)

    # Sparse matrix
    imputer = SimpleImputer(missing_values=np.nan, strategy=np.min)
    X_trans = imputer.fit_transform(csc_container(X))
    assert_array_equal(X_trans.toarray(), X_true)


def test_simple_imputer_constant_fill_value_casting():
    """Check that we raise a proper error message when we cannot cast the fill value
    to the input data type. Otherwise, check that the casting is done properly.

    Non-regression test for:
    https://github.com/scikit-learn/scikit-learn/issues/28309
    """
    # cannot cast fill_value at fit
    fill_value = 1.5
    X_int64 = np.array([[1, 2, 3], [2, 3, 4]], dtype=np.int64)
    imputer = SimpleImputer(
        strategy="constant", fill_value=fill_value, missing_values=2
    )
    err_msg = f"fill_value={fill_value!r} (of type {type(fill_value)!r}) cannot be cast"
    with pytest.raises(ValueError, match=re.escape(err_msg)):
        imputer.fit(X_int64)

    # cannot cast fill_value at transform
    X_float64 = np.array([[1, 2, 3], [2, 3, 4]], dtype=np.float64)
    imputer.fit(X_float64)
    err_msg = (
        f"The dtype of the filling value (i.e. {imputer.statistics_.dtype!r}) "
        "cannot be cast"
    )
    with pytest.raises(ValueError, match=re.escape(err_msg)):
        imputer.transform(X_int64)

    # check that no error is raised when having the same kind of dtype
    fill_value_list = [np.float64(1.5), 1.5, 1]
    X_float32 = X_float64.astype(np.float32)

    for fill_value in fill_value_list:
        imputer = SimpleImputer(
            strategy="constant", fill_value=fill_value, missing_values=2
        )
        X_trans = imputer.fit_transform(X_float32)
        assert X_trans.dtype == X_float32.dtype


@pytest.mark.parametrize("strategy", ["mean", "median", "most_frequent", "constant"])
def test_iterative_imputer_no_empty_features(strategy):
    """Check the behaviour of `keep_empty_features` with no empty features.

    With no-empty features, we should get the same imputation whatever the
    parameter `keep_empty_features`.

    Non-regression test for:
    https://github.com/scikit-learn/scikit-learn/issues/29375
    """
    X = np.array([[np.nan, 0, 1], [2, np.nan, 3], [4, 5, np.nan]])

    imputer_drop_empty_features = IterativeImputer(
        initial_strategy=strategy, fill_value=1, keep_empty_features=False
    )

    imputer_keep_empty_features = IterativeImputer(
        initial_strategy=strategy, fill_value=1, keep_empty_features=True
    )

    assert_allclose(
        imputer_drop_empty_features.fit_transform(X),
        imputer_keep_empty_features.fit_transform(X),
    )


@pytest.mark.parametrize("strategy", ["mean", "median", "most_frequent", "constant"])
@pytest.mark.parametrize(
    "X_test",
    [
        np.array([[1, 2, 3, 4], [5, 6, 7, 8]]),  # without empty feature
        np.array([[np.nan, 2, 3, 4], [np.nan, 6, 7, 8]]),  # empty feature at column 0
        np.array([[1, 2, 3, np.nan], [5, 6, 7, np.nan]]),  # empty feature at column 3
    ],
)
def test_iterative_imputer_with_empty_features(strategy, X_test):
    """Check the behaviour of `keep_empty_features` in the presence of empty features.

    With `keep_empty_features=True`, the empty feature will be imputed with the value
    defined by the initial imputation.

    Non-regression test for:
    https://github.com/scikit-learn/scikit-learn/issues/29375
    """
    X_train = np.array(
        [[np.nan, np.nan, 0, 1], [np.nan, 2, np.nan, 3], [np.nan, 4, 5, np.nan]]
    )

    imputer_drop_empty_features = IterativeImputer(
        initial_strategy=strategy, fill_value=0, keep_empty_features=False
    )
    X_train_drop_empty_features = imputer_drop_empty_features.fit_transform(X_train)
    X_test_drop_empty_features = imputer_drop_empty_features.transform(X_test)

    imputer_keep_empty_features = IterativeImputer(
        initial_strategy=strategy, fill_value=0, keep_empty_features=True
    )
    X_train_keep_empty_features = imputer_keep_empty_features.fit_transform(X_train)
    X_test_keep_empty_features = imputer_keep_empty_features.transform(X_test)

    assert_allclose(X_train_drop_empty_features, X_train_keep_empty_features[:, 1:])
    assert_allclose(X_train_keep_empty_features[:, 0], 0)

    assert X_train_drop_empty_features.shape[1] == X_test_drop_empty_features.shape[1]
    assert X_train_keep_empty_features.shape[1] == X_test_keep_empty_features.shape[1]
