"""
Test the ColumnTransformer.
"""

import pickle
import re
import warnings

import joblib
import numpy as np
import pytest
from numpy.testing import assert_allclose
from scipy import sparse

from sklearn import config_context
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.compose import (
    ColumnTransformer,
    make_column_selector,
    make_column_transformer,
)
from sklearn.exceptions import NotFittedError
from sklearn.feature_extraction import DictVectorizer
from sklearn.feature_selection import VarianceThreshold
from sklearn.preprocessing import (
    FunctionTransformer,
    Normalizer,
    OneHotEncoder,
    StandardScaler,
)
from sklearn.tests.metadata_routing_common import (
    ConsumingTransformer,
    _Registry,
    check_recorded_metadata,
)
from sklearn.utils._indexing import _safe_indexing
from sklearn.utils._testing import (
    _convert_container,
    assert_allclose_dense_sparse,
    assert_almost_equal,
    assert_array_equal,
)
from sklearn.utils.fixes import CSR_CONTAINERS, _sparse_eye_array, parse_version


class Trans(TransformerMixin, BaseEstimator):
    def fit(self, X, y=None):
        return self

    def transform(self, X, y=None):
        # 1D Series -> 2D DataFrame
        if hasattr(X, "to_frame"):
            return X.to_frame()
        # 1D array -> 2D array
        if getattr(X, "ndim", 2) == 1:
            return np.atleast_2d(X).T
        return X


class DoubleTrans(BaseEstimator):
    def fit(self, X, y=None):
        return self

    def transform(self, X):
        return 2 * X


class SparseMatrixTrans(BaseEstimator):
    def __init__(self, csr_container):
        self.csr_container = csr_container

    def fit(self, X, y=None):
        return self

    def transform(self, X, y=None):
        n_samples = len(X)
        return self.csr_container(_sparse_eye_array(n_samples))


class TransNo2D(BaseEstimator):
    def fit(self, X, y=None):
        return self

    def transform(self, X, y=None):
        return X


class TransRaise(BaseEstimator):
    def fit(self, X, y=None):
        raise ValueError("specific message")

    def transform(self, X, y=None):
        raise ValueError("specific message")


@pytest.mark.parametrize(
    "transformers, class_name",
    [
        ([("trans1", Trans, [0]), ("trans2", Trans(), [1])], "Trans"),
        ([("trans1", Trans(), [0]), ("trans2", Trans, [1])], "Trans"),
        ([("drop", "drop", [0]), ("trans2", Trans, [1])], "Trans"),
        ([("trans1", Trans, [0]), ("passthrough", "passthrough", [1])], "Trans"),
    ],
)
def test_column_transformer_raises_class_not_instance_error(transformers, class_name):
    # non-regression tests for https://github.com/scikit-learn/scikit-learn/issues/32719
    ct = ColumnTransformer(transformers)
    msg = re.escape(
        f"Expected an estimator instance ({class_name}()), "
        f"got estimator class instead ({class_name})."
    )
    with pytest.raises(TypeError, match=msg):
        ct.fit([[1]])


def test_column_transformer():
    X_array = np.array([[0, 1, 2], [2, 4, 6]]).T

    X_res_first1D = np.array([0, 1, 2])
    X_res_second1D = np.array([2, 4, 6])
    X_res_first = X_res_first1D.reshape(-1, 1)
    X_res_both = X_array

    cases = [
        # single column 1D / 2D
        (0, X_res_first),
        ([0], X_res_first),
        # list-like
        ([0, 1], X_res_both),
        (np.array([0, 1]), X_res_both),
        # slice
        (slice(0, 1), X_res_first),
        (slice(0, 2), X_res_both),
        # boolean mask
        (np.array([True, False]), X_res_first),
        ([True, False], X_res_first),
        (np.array([True, True]), X_res_both),
        ([True, True], X_res_both),
    ]

    for selection, res in cases:
        ct = ColumnTransformer([("trans", Trans(), selection)], remainder="drop")
        assert_array_equal(ct.fit_transform(X_array), res)
        assert_array_equal(ct.fit(X_array).transform(X_array), res)

        # callable that returns any of the allowed specifiers
        ct = ColumnTransformer(
            [("trans", Trans(), lambda x: selection)], remainder="drop"
        )
        assert_array_equal(ct.fit_transform(X_array), res)
        assert_array_equal(ct.fit(X_array).transform(X_array), res)

    ct = ColumnTransformer([("trans1", Trans(), [0]), ("trans2", Trans(), [1])])
    assert_array_equal(ct.fit_transform(X_array), X_res_both)
    assert_array_equal(ct.fit(X_array).transform(X_array), X_res_both)
    assert len(ct.transformers_) == 2

    # test with transformer_weights
    transformer_weights = {"trans1": 0.1, "trans2": 10}
    both = ColumnTransformer(
        [("trans1", Trans(), [0]), ("trans2", Trans(), [1])],
        transformer_weights=transformer_weights,
    )
    res = np.vstack(
        [
            transformer_weights["trans1"] * X_res_first1D,
            transformer_weights["trans2"] * X_res_second1D,
        ]
    ).T
    assert_array_equal(both.fit_transform(X_array), res)
    assert_array_equal(both.fit(X_array).transform(X_array), res)
    assert len(both.transformers_) == 2

    both = ColumnTransformer(
        [("trans", Trans(), [0, 1])], transformer_weights={"trans": 0.1}
    )
    assert_array_equal(both.fit_transform(X_array), 0.1 * X_res_both)
    assert_array_equal(both.fit(X_array).transform(X_array), 0.1 * X_res_both)
    assert len(both.transformers_) == 1


def test_column_transformer_tuple_transformers_parameter():
    X_array = np.array([[0, 1, 2], [2, 4, 6]]).T

    transformers = [("trans1", Trans(), [0]), ("trans2", Trans(), [1])]

    ct_with_list = ColumnTransformer(transformers)
    ct_with_tuple = ColumnTransformer(tuple(transformers))

    assert_array_equal(
        ct_with_list.fit_transform(X_array), ct_with_tuple.fit_transform(X_array)
    )
    assert_array_equal(
        ct_with_list.fit(X_array).transform(X_array),
        ct_with_tuple.fit(X_array).transform(X_array),
    )


@pytest.mark.parametrize("constructor_name", ["pandas", "polars"])
def test_column_transformer_dataframe(constructor_name):
    df_lib = pytest.importorskip(constructor_name)

    X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
    X_df = _convert_container(
        X_array, constructor_name, column_names=["first", "second"]
    )

    X_res_first = np.array([0, 1, 2]).reshape(-1, 1)
    X_res_both = X_array

    cases = [
        # String keys: label based
        # list
        (["first"], X_res_first),
        (["first", "second"], X_res_both),
        # slice
        (slice("first", "second"), X_res_both),
        # int keys: positional
        # list
        ([0], X_res_first),
        ([0, 1], X_res_both),
        (np.array([0, 1]), X_res_both),
        # slice
        (slice(0, 1), X_res_first),
        (slice(0, 2), X_res_both),
        # boolean mask
        (np.array([True, False]), X_res_first),
        ([True, False], X_res_first),
        # scalar
        (0, X_res_first),
        ("first", X_res_first),
    ]
    if constructor_name == "pandas":
        cases.extend(
            [
                (
                    df_lib.Series([True, False], index=["first", "second"]),
                    X_res_first,
                ),
            ]
        )

    for selection, res in cases:
        ct = ColumnTransformer([("trans", Trans(), selection)], remainder="drop")
        assert_array_equal(ct.fit_transform(X_df), res)
        assert_array_equal(ct.fit(X_df).transform(X_df), res)

        # callable that returns any of the allowed specifiers
        ct = ColumnTransformer(
            [("trans", Trans(), lambda X: selection)], remainder="drop"
        )
        assert_array_equal(ct.fit_transform(X_df), res)
        assert_array_equal(ct.fit(X_df).transform(X_df), res)

    ct = ColumnTransformer(
        [("trans1", Trans(), ["first"]), ("trans2", Trans(), ["second"])]
    )
    assert_array_equal(ct.fit_transform(X_df), X_res_both)
    assert_array_equal(ct.fit(X_df).transform(X_df), X_res_both)
    assert len(ct.transformers_) == 2
    assert ct.transformers_[-1][0] != "remainder"

    ct = ColumnTransformer([("trans1", Trans(), [0]), ("trans2", Trans(), [1])])
    assert_array_equal(ct.fit_transform(X_df), X_res_both)
    assert_array_equal(ct.fit(X_df).transform(X_df), X_res_both)
    assert len(ct.transformers_) == 2
    assert ct.transformers_[-1][0] != "remainder"

    # test with transformer_weights
    transformer_weights = {"trans1": 0.1, "trans2": 10}
    both = ColumnTransformer(
        [("trans1", Trans(), ["first"]), ("trans2", Trans(), ["second"])],
        transformer_weights=transformer_weights,
    )
    res = np.vstack(
        [
            transformer_weights["trans1"] * X_df["first"],
            transformer_weights["trans2"] * X_df["second"],
        ]
    ).T
    assert_array_equal(both.fit_transform(X_df), res)
    assert_array_equal(both.fit(X_df).transform(X_df), res)
    assert len(both.transformers_) == 2
    assert both.transformers_[-1][0] != "remainder"

    # test multiple columns
    both = ColumnTransformer(
        [("trans", Trans(), ["first", "second"])], transformer_weights={"trans": 0.1}
    )
    assert_array_equal(both.fit_transform(X_df), 0.1 * X_res_both)
    assert_array_equal(both.fit(X_df).transform(X_df), 0.1 * X_res_both)
    assert len(both.transformers_) == 1
    assert both.transformers_[-1][0] != "remainder"

    both = ColumnTransformer(
        [("trans", Trans(), [0, 1])], transformer_weights={"trans": 0.1}
    )
    assert_array_equal(both.fit_transform(X_df), 0.1 * X_res_both)
    assert_array_equal(both.fit(X_df).transform(X_df), 0.1 * X_res_both)
    assert len(both.transformers_) == 1
    assert both.transformers_[-1][0] != "remainder"

    # ensure pandas object is passed through

    class TransAssert(BaseEstimator):
        def __init__(self, expected_type_transform):
            self.expected_type_transform = expected_type_transform

        def fit(self, X, y=None):
            return self

        def transform(self, X, y=None):
            assert isinstance(X, self.expected_type_transform)
            if len(X.shape) < 2:
                X = _convert_container(X, constructor_name)
            return X

    ct = ColumnTransformer(
        [
            (
                "trans",
                TransAssert(expected_type_transform=df_lib.DataFrame),
                ["first", "second"],
            )
        ]
    )
    ct.fit_transform(X_df)

    ct = ColumnTransformer(
        [
            (
                "trans",
                TransAssert(expected_type_transform=df_lib.Series),
                "first",
            )
        ],
        remainder="drop",
    )
    ct.fit_transform(X_df)

    if constructor_name == "pandas":
        # Only pandas (but not polars, nor pyarrow) allows for column names that are
        # not strings.
        # integer column spec + integer column names -> still use positional
        X_df2 = X_df.copy()
        X_df2.columns = [1, 0]
        ct = ColumnTransformer([("trans", Trans(), 0)], remainder="drop")
        assert_array_equal(ct.fit_transform(X_df2), X_res_first)
        assert_array_equal(ct.fit(X_df2).transform(X_df2), X_res_first)

        assert len(ct.transformers_) == 2
        assert ct.transformers_[-1][0] == "remainder"
        assert ct.transformers_[-1][1] == "drop"
        assert_array_equal(ct.transformers_[-1][2], [1])


@pytest.mark.parametrize("pandas", [True, False], ids=["pandas", "numpy"])
@pytest.mark.parametrize(
    "column_selection",
    [[], np.array([False, False]), [False, False]],
    ids=["list", "bool", "bool_int"],
)
@pytest.mark.parametrize("callable_column", [False, True])
def test_column_transformer_empty_columns(pandas, column_selection, callable_column):
    # test case that ensures that the column transformer does also work when
    # a given transformer doesn't have any columns to work on
    X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
    X_res_both = X_array

    if pandas:
        pd = pytest.importorskip("pandas")
        X = pd.DataFrame(X_array, columns=["first", "second"])
    else:
        X = X_array

    if callable_column:
        column = lambda X: column_selection
    else:
        column = column_selection

    ct = ColumnTransformer(
        [("trans1", Trans(), [0, 1]), ("trans2", TransRaise(), column)]
    )
    assert_array_equal(ct.fit_transform(X), X_res_both)
    assert_array_equal(ct.fit(X).transform(X), X_res_both)
    assert len(ct.transformers_) == 2
    assert isinstance(ct.transformers_[1][1], TransRaise)

    ct = ColumnTransformer(
        [("trans1", TransRaise(), column), ("trans2", Trans(), [0, 1])]
    )
    assert_array_equal(ct.fit_transform(X), X_res_both)
    assert_array_equal(ct.fit(X).transform(X), X_res_both)
    assert len(ct.transformers_) == 2
    assert isinstance(ct.transformers_[0][1], TransRaise)

    ct = ColumnTransformer([("trans", TransRaise(), column)], remainder="passthrough")
    assert_array_equal(ct.fit_transform(X), X_res_both)
    assert_array_equal(ct.fit(X).transform(X), X_res_both)
    assert len(ct.transformers_) == 2  # including remainder
    assert isinstance(ct.transformers_[0][1], TransRaise)

    fixture = np.array([[], [], []])
    ct = ColumnTransformer([("trans", TransRaise(), column)], remainder="drop")
    assert_array_equal(ct.fit_transform(X), fixture)
    assert_array_equal(ct.fit(X).transform(X), fixture)
    assert len(ct.transformers_) == 2  # including remainder
    assert isinstance(ct.transformers_[0][1], TransRaise)


def test_column_transformer_output_indices():
    # Checks for the output_indices_ attribute
    X_array = np.arange(6).reshape(3, 2)

    ct = ColumnTransformer([("trans1", Trans(), [0]), ("trans2", Trans(), [1])])
    X_trans = ct.fit_transform(X_array)
    assert ct.output_indices_ == {
        "trans1": slice(0, 1),
        "trans2": slice(1, 2),
        "remainder": slice(0, 0),
    }
    assert_array_equal(X_trans[:, [0]], X_trans[:, ct.output_indices_["trans1"]])
    assert_array_equal(X_trans[:, [1]], X_trans[:, ct.output_indices_["trans2"]])

    # test with transformer_weights and multiple columns
    ct = ColumnTransformer(
        [("trans", Trans(), [0, 1])], transformer_weights={"trans": 0.1}
    )
    X_trans = ct.fit_transform(X_array)
    assert ct.output_indices_ == {"trans": slice(0, 2), "remainder": slice(0, 0)}
    assert_array_equal(X_trans[:, [0, 1]], X_trans[:, ct.output_indices_["trans"]])
    assert_array_equal(X_trans[:, []], X_trans[:, ct.output_indices_["remainder"]])

    # test case that ensures that the attribute does also work when
    # a given transformer doesn't have any columns to work on
    ct = ColumnTransformer([("trans1", Trans(), [0, 1]), ("trans2", TransRaise(), [])])
    X_trans = ct.fit_transform(X_array)
    assert ct.output_indices_ == {
        "trans1": slice(0, 2),
        "trans2": slice(0, 0),
        "remainder": slice(0, 0),
    }
    assert_array_equal(X_trans[:, [0, 1]], X_trans[:, ct.output_indices_["trans1"]])
    assert_array_equal(X_trans[:, []], X_trans[:, ct.output_indices_["trans2"]])
    assert_array_equal(X_trans[:, []], X_trans[:, ct.output_indices_["remainder"]])

    ct = ColumnTransformer([("trans", TransRaise(), [])], remainder="passthrough")
    X_trans = ct.fit_transform(X_array)
    assert ct.output_indices_ == {"trans": slice(0, 0), "remainder": slice(0, 2)}
    assert_array_equal(X_trans[:, []], X_trans[:, ct.output_indices_["trans"]])
    assert_array_equal(X_trans[:, [0, 1]], X_trans[:, ct.output_indices_["remainder"]])


def test_column_transformer_output_indices_df():
    # Checks for the output_indices_ attribute with data frames
    pd = pytest.importorskip("pandas")

    X_df = pd.DataFrame(np.arange(6).reshape(3, 2), columns=["first", "second"])

    ct = ColumnTransformer(
        [("trans1", Trans(), ["first"]), ("trans2", Trans(), ["second"])]
    )
    X_trans = ct.fit_transform(X_df)
    assert ct.output_indices_ == {
        "trans1": slice(0, 1),
        "trans2": slice(1, 2),
        "remainder": slice(0, 0),
    }
    assert_array_equal(X_trans[:, [0]], X_trans[:, ct.output_indices_["trans1"]])
    assert_array_equal(X_trans[:, [1]], X_trans[:, ct.output_indices_["trans2"]])
    assert_array_equal(X_trans[:, []], X_trans[:, ct.output_indices_["remainder"]])

    ct = ColumnTransformer([("trans1", Trans(), [0]), ("trans2", Trans(), [1])])
    X_trans = ct.fit_transform(X_df)
    assert ct.output_indices_ == {
        "trans1": slice(0, 1),
        "trans2": slice(1, 2),
        "remainder": slice(0, 0),
    }
    assert_array_equal(X_trans[:, [0]], X_trans[:, ct.output_indices_["trans1"]])
    assert_array_equal(X_trans[:, [1]], X_trans[:, ct.output_indices_["trans2"]])
    assert_array_equal(X_trans[:, []], X_trans[:, ct.output_indices_["remainder"]])


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_column_transformer_sparse_array(csr_container):
    X_sparse = csr_container(_sparse_eye_array(3, 2))

    # no distinction between 1D and 2D
    X_res_first = X_sparse[:, [0]]
    X_res_both = X_sparse

    for col in [(0,), [0], slice(0, 1)]:
        for remainder, res in [("drop", X_res_first), ("passthrough", X_res_both)]:
            ct = ColumnTransformer(
                [("trans", Trans(), col)], remainder=remainder, sparse_threshold=0.8
            )
            assert sparse.issparse(ct.fit_transform(X_sparse))
            assert_allclose_dense_sparse(ct.fit_transform(X_sparse), res)
            assert_allclose_dense_sparse(ct.fit(X_sparse).transform(X_sparse), res)

    for col in [[0, 1], slice(0, 2)]:
        ct = ColumnTransformer([("trans", Trans(), col)], sparse_threshold=0.8)
        assert sparse.issparse(ct.fit_transform(X_sparse))
        assert_allclose_dense_sparse(ct.fit_transform(X_sparse), X_res_both)
        assert_allclose_dense_sparse(ct.fit(X_sparse).transform(X_sparse), X_res_both)


def test_column_transformer_list():
    X_list = [[1, float("nan"), "a"], [0, 0, "b"]]
    expected_result = np.array(
        [
            [1, float("nan"), 1, 0],
            [-1, 0, 0, 1],
        ]
    )

    ct = ColumnTransformer(
        [
            ("numerical", StandardScaler(), [0, 1]),
            ("categorical", OneHotEncoder(), [2]),
        ]
    )

    assert_array_equal(ct.fit_transform(X_list), expected_result)
    assert_array_equal(ct.fit(X_list).transform(X_list), expected_result)


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
@pytest.mark.parametrize("constructor_name", ["array", "pandas", "polars"])
def test_column_transformer_sparse_stacking(csr_container, constructor_name):
    X = np.array([[0, 1, 2], [2, 4, 6]]).T
    X = _convert_container(X, constructor_name, column_names=["first", "second"])

    col_trans = ColumnTransformer(
        [("trans1", Trans(), [0]), ("trans2", SparseMatrixTrans(csr_container), 1)],
        sparse_threshold=0.8,
    )
    col_trans.fit(X)
    X_trans = col_trans.transform(X)
    assert sparse.issparse(X_trans)
    assert X_trans.shape == (X_trans.shape[0], X_trans.shape[0] + 1)
    assert_array_equal(X_trans.toarray()[:, 1:], np.eye(X_trans.shape[0]))
    assert len(col_trans.transformers_) == 2
    assert col_trans.transformers_[-1][0] != "remainder"

    col_trans = ColumnTransformer(
        [("trans1", Trans(), [0]), ("trans2", SparseMatrixTrans(csr_container), 1)],
        sparse_threshold=0.1,
    )
    col_trans.fit(X)
    X_trans = col_trans.transform(X)
    assert not sparse.issparse(X_trans)
    assert X_trans.shape == (X_trans.shape[0], X_trans.shape[0] + 1)
    assert_array_equal(X_trans[:, 1:], np.eye(X_trans.shape[0]))


def test_column_transformer_mixed_cols_sparse():
    df = np.array([["a", 1, True], ["b", 2, False]], dtype="O")

    ct = make_column_transformer(
        (OneHotEncoder(), [0]), ("passthrough", [1, 2]), sparse_threshold=1.0
    )

    # this shouldn't fail, since boolean can be coerced into a numeric
    # See: https://github.com/scikit-learn/scikit-learn/issues/11912
    X_trans = ct.fit_transform(df)
    assert X_trans.format == "csr"
    assert_array_equal(X_trans.toarray(), np.array([[1, 0, 1, 1], [0, 1, 2, 0]]))

    ct = make_column_transformer(
        (OneHotEncoder(), [0]), ("passthrough", [0]), sparse_threshold=1.0
    )
    with pytest.raises(ValueError, match="For a sparse output, all columns should"):
        # this fails since strings `a` and `b` cannot be
        # coerced into a numeric.
        ct.fit_transform(df)


def test_column_transformer_sparse_threshold():
    X_array = np.array([["a", "b"], ["A", "B"]], dtype=object).T
    # above data has sparsity of 4 / 8 = 0.5

    # apply threshold even if all sparse
    col_trans = ColumnTransformer(
        [("trans1", OneHotEncoder(), [0]), ("trans2", OneHotEncoder(), [1])],
        sparse_threshold=0.2,
    )
    res = col_trans.fit_transform(X_array)
    assert not sparse.issparse(res)
    assert not col_trans.sparse_output_

    # mixed -> sparsity of (4 + 2) / 8 = 0.75
    for thres in [0.75001, 1]:
        col_trans = ColumnTransformer(
            [
                ("trans1", OneHotEncoder(sparse_output=True), [0]),
                ("trans2", OneHotEncoder(sparse_output=False), [1]),
            ],
            sparse_threshold=thres,
        )
        res = col_trans.fit_transform(X_array)
        assert sparse.issparse(res)
        assert col_trans.sparse_output_

    for thres in [0.75, 0]:
        col_trans = ColumnTransformer(
            [
                ("trans1", OneHotEncoder(sparse_output=True), [0]),
                ("trans2", OneHotEncoder(sparse_output=False), [1]),
            ],
            sparse_threshold=thres,
        )
        res = col_trans.fit_transform(X_array)
        assert not sparse.issparse(res)
        assert not col_trans.sparse_output_

    # if nothing is sparse -> no sparse
    for thres in [0.33, 0, 1]:
        col_trans = ColumnTransformer(
            [
                ("trans1", OneHotEncoder(sparse_output=False), [0]),
                ("trans2", OneHotEncoder(sparse_output=False), [1]),
            ],
            sparse_threshold=thres,
        )
        res = col_trans.fit_transform(X_array)
        assert not sparse.issparse(res)
        assert not col_trans.sparse_output_


def test_column_transformer_error_msg_1D():
    X_array = np.array([[0.0, 1.0, 2.0], [2.0, 4.0, 6.0]]).T

    col_trans = ColumnTransformer([("trans", StandardScaler(), 0)])
    msg = "1D data passed to a transformer"
    with pytest.raises(ValueError, match=msg):
        col_trans.fit(X_array)

    with pytest.raises(ValueError, match=msg):
        col_trans.fit_transform(X_array)

    col_trans = ColumnTransformer([("trans", TransRaise(), 0)])
    for func in [col_trans.fit, col_trans.fit_transform]:
        with pytest.raises(ValueError, match="specific message"):
            func(X_array)


def test_2D_transformer_output():
    X_array = np.array([[0, 1, 2], [2, 4, 6]]).T

    # if one transformer is dropped, test that name is still correct
    ct = ColumnTransformer([("trans1", "drop", 0), ("trans2", TransNo2D(), 1)])

    msg = "the 'trans2' transformer should be 2D"
    with pytest.raises(ValueError, match=msg):
        ct.fit_transform(X_array)
    # because fit is also doing transform, this raises already on fit
    with pytest.raises(ValueError, match=msg):
        ct.fit(X_array)


def test_2D_transformer_output_pandas():
    pd = pytest.importorskip("pandas")

    X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
    X_df = pd.DataFrame(X_array, columns=["col1", "col2"])

    # if one transformer is dropped, test that name is still correct
    ct = ColumnTransformer([("trans1", TransNo2D(), "col1")])
    msg = "the 'trans1' transformer should be 2D"
    with pytest.raises(ValueError, match=msg):
        ct.fit_transform(X_df)
    # because fit is also doing transform, this raises already on fit
    with pytest.raises(ValueError, match=msg):
        ct.fit(X_df)


@pytest.mark.parametrize("remainder", ["drop", "passthrough"])
def test_column_transformer_invalid_columns(remainder):
    X_array = np.array([[0, 1, 2], [2, 4, 6]]).T

    # general invalid
    for col in [1.5, ["string", 1], slice(1, "s"), np.array([1.0])]:
        ct = ColumnTransformer([("trans", Trans(), col)], remainder=remainder)
        with pytest.raises(ValueError, match="No valid specification"):
            ct.fit(X_array)

    # invalid for arrays
    for col in ["string", ["string", "other"], slice("a", "b")]:
        ct = ColumnTransformer([("trans", Trans(), col)], remainder=remainder)
        with pytest.raises(ValueError, match="Specifying the columns"):
            ct.fit(X_array)

    # transformed n_features does not match fitted n_features
    col = [0, 1]
    ct = ColumnTransformer([("trans", Trans(), col)], remainder=remainder)
    ct.fit(X_array)
    X_array_more = np.array([[0, 1, 2], [2, 4, 6], [3, 6, 9]]).T
    msg = "X has 3 features, but ColumnTransformer is expecting 2 features as input."
    with pytest.raises(ValueError, match=msg):
        ct.transform(X_array_more)
    X_array_fewer = np.array(
        [
            [0, 1, 2],
        ]
    ).T
    err_msg = (
        "X has 1 features, but ColumnTransformer is expecting 2 features as input."
    )
    with pytest.raises(ValueError, match=err_msg):
        ct.transform(X_array_fewer)


def test_column_transformer_invalid_transformer():
    class NoTrans(BaseEstimator):
        def fit(self, X, y=None):
            return self

        def predict(self, X):
            return X

    X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
    ct = ColumnTransformer([("trans", NoTrans(), [0])])
    msg = "All estimators should implement fit and transform"
    with pytest.raises(TypeError, match=msg):
        ct.fit(X_array)


def test_make_column_transformer():
    scaler = StandardScaler()
    norm = Normalizer()
    ct = make_column_transformer((scaler, "first"), (norm, ["second"]))
    names, transformers, columns = zip(*ct.transformers)
    assert names == ("standardscaler", "normalizer")
    assert transformers == (scaler, norm)
    assert columns == ("first", ["second"])


def test_make_column_transformer_pandas():
    pd = pytest.importorskip("pandas")
    X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
    X_df = pd.DataFrame(X_array, columns=["first", "second"])
    norm = Normalizer()
    ct1 = ColumnTransformer([("norm", Normalizer(), X_df.columns)])
    ct2 = make_column_transformer((norm, X_df.columns))
    assert_almost_equal(ct1.fit_transform(X_df), ct2.fit_transform(X_df))


def test_make_column_transformer_kwargs():
    scaler = StandardScaler()
    norm = Normalizer()
    ct = make_column_transformer(
        (scaler, "first"),
        (norm, ["second"]),
        n_jobs=3,
        remainder="drop",
        sparse_threshold=0.5,
    )
    assert (
        ct.transformers
        == make_column_transformer((scaler, "first"), (norm, ["second"])).transformers
    )
    assert ct.n_jobs == 3
    assert ct.remainder == "drop"
    assert ct.sparse_threshold == 0.5
    # invalid keyword parameters should raise an error message
    msg = re.escape(
        "make_column_transformer() got an unexpected "
        "keyword argument 'transformer_weights'"
    )
    with pytest.raises(TypeError, match=msg):
        make_column_transformer(
            (scaler, "first"),
            (norm, ["second"]),
            transformer_weights={"pca": 10, "Transf": 1},
        )


def test_make_column_transformer_remainder_transformer():
    scaler = StandardScaler()
    norm = Normalizer()
    remainder = StandardScaler()
    ct = make_column_transformer(
        (scaler, "first"), (norm, ["second"]), remainder=remainder
    )
    assert ct.remainder == remainder


def test_column_transformer_get_set_params():
    ct = ColumnTransformer(
        [("trans1", StandardScaler(), [0]), ("trans2", StandardScaler(), [1])]
    )

    exp = {
        "n_jobs": None,
        "remainder": "drop",
        "sparse_threshold": 0.3,
        "trans1": ct.transformers[0][1],
        "trans1__copy": True,
        "trans1__with_mean": True,
        "trans1__with_std": True,
        "trans2": ct.transformers[1][1],
        "trans2__copy": True,
        "trans2__with_mean": True,
        "trans2__with_std": True,
        "transformers": ct.transformers,
        "transformer_weights": None,
        "verbose_feature_names_out": True,
        "verbose": False,
    }

    assert ct.get_params() == exp

    ct.set_params(trans1__with_mean=False)
    assert not ct.get_params()["trans1__with_mean"]

    ct.set_params(trans1="passthrough")
    exp = {
        "n_jobs": None,
        "remainder": "drop",
        "sparse_threshold": 0.3,
        "trans1": "passthrough",
        "trans2": ct.transformers[1][1],
        "trans2__copy": True,
        "trans2__with_mean": True,
        "trans2__with_std": True,
        "transformers": ct.transformers,
        "transformer_weights": None,
        "verbose_feature_names_out": True,
        "verbose": False,
    }

    assert ct.get_params() == exp


def test_column_transformer_named_estimators():
    X_array = np.array([[0.0, 1.0, 2.0], [2.0, 4.0, 6.0]]).T
    ct = ColumnTransformer(
        [
            ("trans1", StandardScaler(), [0]),
            ("trans2", StandardScaler(with_std=False), [1]),
        ]
    )
    assert not hasattr(ct, "transformers_")
    ct.fit(X_array)
    assert hasattr(ct, "transformers_")
    assert isinstance(ct.named_transformers_["trans1"], StandardScaler)
    assert isinstance(ct.named_transformers_.trans1, StandardScaler)
    assert isinstance(ct.named_transformers_["trans2"], StandardScaler)
    assert isinstance(ct.named_transformers_.trans2, StandardScaler)
    assert not ct.named_transformers_.trans2.with_std
    # check it are fitted transformers
    assert ct.named_transformers_.trans1.mean_ == 1.0


def test_column_transformer_cloning():
    X_array = np.array([[0.0, 1.0, 2.0], [2.0, 4.0, 6.0]]).T

    ct = ColumnTransformer([("trans", StandardScaler(), [0])])
    ct.fit(X_array)
    assert not hasattr(ct.transformers[0][1], "mean_")
    assert hasattr(ct.transformers_[0][1], "mean_")

    ct = ColumnTransformer([("trans", StandardScaler(), [0])])
    ct.fit_transform(X_array)
    assert not hasattr(ct.transformers[0][1], "mean_")
    assert hasattr(ct.transformers_[0][1], "mean_")


def test_column_transformer_get_feature_names():
    X_array = np.array([[0.0, 1.0, 2.0], [2.0, 4.0, 6.0]]).T
    ct = ColumnTransformer([("trans", Trans(), [0, 1])])
    # raise correct error when not fitted
    with pytest.raises(NotFittedError):
        ct.get_feature_names_out()
    # raise correct error when no feature names are available
    ct.fit(X_array)
    msg = re.escape(
        "Transformer trans (type Trans) does not provide get_feature_names_out"
    )
    with pytest.raises(AttributeError, match=msg):
        ct.get_feature_names_out()


def test_column_transformer_special_strings():
    # one 'drop' -> ignore
    X_array = np.array([[0.0, 1.0, 2.0], [2.0, 4.0, 6.0]]).T
    ct = ColumnTransformer([("trans1", Trans(), [0]), ("trans2", "drop", [1])])
    exp = np.array([[0.0], [1.0], [2.0]])
    assert_array_equal(ct.fit_transform(X_array), exp)
    assert_array_equal(ct.fit(X_array).transform(X_array), exp)
    assert len(ct.transformers_) == 2
    assert ct.transformers_[-1][0] != "remainder"

    # all 'drop' -> return shape 0 array
    ct = ColumnTransformer([("trans1", "drop", [0]), ("trans2", "drop", [1])])
    assert_array_equal(ct.fit(X_array).transform(X_array).shape, (3, 0))
    assert_array_equal(ct.fit_transform(X_array).shape, (3, 0))
    assert len(ct.transformers_) == 2
    assert ct.transformers_[-1][0] != "remainder"

    # 'passthrough'
    X_array = np.array([[0.0, 1.0, 2.0], [2.0, 4.0, 6.0]]).T
    ct = ColumnTransformer([("trans1", Trans(), [0]), ("trans2", "passthrough", [1])])
    exp = X_array
    assert_array_equal(ct.fit_transform(X_array), exp)
    assert_array_equal(ct.fit(X_array).transform(X_array), exp)
    assert len(ct.transformers_) == 2
    assert ct.transformers_[-1][0] != "remainder"


def test_column_transformer_remainder():
    X_array = np.array([[0, 1, 2], [2, 4, 6]]).T

    X_res_first = np.array([0, 1, 2]).reshape(-1, 1)
    X_res_second = np.array([2, 4, 6]).reshape(-1, 1)
    X_res_both = X_array

    # default drop
    ct = ColumnTransformer([("trans1", Trans(), [0])])
    assert_array_equal(ct.fit_transform(X_array), X_res_first)
    assert_array_equal(ct.fit(X_array).transform(X_array), X_res_first)
    assert len(ct.transformers_) == 2
    assert ct.transformers_[-1][0] == "remainder"
    assert ct.transformers_[-1][1] == "drop"
    assert_array_equal(ct.transformers_[-1][2], [1])

    # specify passthrough
    ct = ColumnTransformer([("trans", Trans(), [0])], remainder="passthrough")
    assert_array_equal(ct.fit_transform(X_array), X_res_both)
    assert_array_equal(ct.fit(X_array).transform(X_array), X_res_both)
    assert len(ct.transformers_) == 2
    assert ct.transformers_[-1][0] == "remainder"
    assert isinstance(ct.transformers_[-1][1], FunctionTransformer)
    assert_array_equal(ct.transformers_[-1][2], [1])

    # column order is not preserved (passed through added to end)
    ct = ColumnTransformer([("trans1", Trans(), [1])], remainder="passthrough")
    assert_array_equal(ct.fit_transform(X_array), X_res_both[:, ::-1])
    assert_array_equal(ct.fit(X_array).transform(X_array), X_res_both[:, ::-1])
    assert len(ct.transformers_) == 2
    assert ct.transformers_[-1][0] == "remainder"
    assert isinstance(ct.transformers_[-1][1], FunctionTransformer)
    assert_array_equal(ct.transformers_[-1][2], [0])

    # passthrough when all actual transformers are skipped
    ct = ColumnTransformer([("trans1", "drop", [0])], remainder="passthrough")
    assert_array_equal(ct.fit_transform(X_array), X_res_second)
    assert_array_equal(ct.fit(X_array).transform(X_array), X_res_second)
    assert len(ct.transformers_) == 2
    assert ct.transformers_[-1][0] == "remainder"
    assert isinstance(ct.transformers_[-1][1], FunctionTransformer)
    assert_array_equal(ct.transformers_[-1][2], [1])

    # check default for make_column_transformer
    ct = make_column_transformer((Trans(), [0]))
    assert ct.remainder == "drop"


@pytest.mark.parametrize(
    "cols1, cols2, expected_remainder_cols",
    [
        ([0], [False, True, False], [2]),  # mix types
        ([0], [1], [2]),  # ints
        (lambda x: [0], lambda x: [1], [2]),  # callables
        (["A"], ["B"], ["C"]),  # all strings
        ([True, False, False], [False, True, False], [False, False, True]),  # all bools
    ],
)
def test_column_transformer_remainder_dtypes(cols1, cols2, expected_remainder_cols):
    """Check that the remainder columns format matches the format of the other
    columns when they're all strings or masks.
    """
    X = np.ones((1, 3))

    if isinstance(cols1, list) and isinstance(cols1[0], str):
        pd = pytest.importorskip("pandas")
        X = pd.DataFrame(X, columns=["A", "B", "C"])

    # if inputs are column names store remainder columns as column names
    ct = make_column_transformer(
        (Trans(), cols1),
        (Trans(), cols2),
        remainder="passthrough",
    )
    ct.fit_transform(X)
    assert ct.transformers_[-1][-1] == expected_remainder_cols


@pytest.mark.parametrize(
    "key, expected_cols",
    [
        ([0], [1]),
        (np.array([0]), [1]),
        (slice(0, 1), [1]),
        (np.array([True, False]), [False, True]),
    ],
)
def test_column_transformer_remainder_numpy(key, expected_cols):
    # test different ways that columns are specified with passthrough
    X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
    X_res_both = X_array

    ct = ColumnTransformer(
        [("trans1", Trans(), key)],
        remainder="passthrough",
    )
    assert_array_equal(ct.fit_transform(X_array), X_res_both)
    assert_array_equal(ct.fit(X_array).transform(X_array), X_res_both)
    assert len(ct.transformers_) == 2
    assert ct.transformers_[-1][0] == "remainder"
    assert isinstance(ct.transformers_[-1][1], FunctionTransformer)
    assert ct.transformers_[-1][2] == expected_cols


@pytest.mark.parametrize(
    "key, expected_cols",
    [
        ([0], [1]),
        (slice(0, 1), [1]),
        (np.array([True, False]), [False, True]),
        (["first"], ["second"]),
        ("pd-index", ["second"]),
        (np.array(["first"]), ["second"]),
        (np.array(["first"], dtype=object), ["second"]),
        (slice(None, "first"), ["second"]),
        (slice("first", "first"), ["second"]),
    ],
)
def test_column_transformer_remainder_pandas(key, expected_cols):
    # test different ways that columns are specified with passthrough
    pd = pytest.importorskip("pandas")
    if isinstance(key, str) and key == "pd-index":
        key = pd.Index(["first"])

    X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
    X_df = pd.DataFrame(X_array, columns=["first", "second"])
    X_res_both = X_array

    ct = ColumnTransformer(
        [("trans1", Trans(), key)],
        remainder="passthrough",
    )
    assert_array_equal(ct.fit_transform(X_df), X_res_both)
    assert_array_equal(ct.fit(X_df).transform(X_df), X_res_both)
    assert len(ct.transformers_) == 2
    assert ct.transformers_[-1][0] == "remainder"
    assert isinstance(ct.transformers_[-1][1], FunctionTransformer)
    assert ct.transformers_[-1][2] == expected_cols


@pytest.mark.parametrize(
    "key, expected_cols",
    [
        ([0], [1, 2]),
        (np.array([0]), [1, 2]),
        (slice(0, 1), [1, 2]),
        (np.array([True, False, False]), [False, True, True]),
    ],
)
def test_column_transformer_remainder_transformer(key, expected_cols):
    X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).T
    X_res_both = X_array.copy()

    # second and third columns are doubled when remainder = DoubleTrans
    X_res_both[:, 1:3] *= 2

    ct = ColumnTransformer(
        [("trans1", Trans(), key)],
        remainder=DoubleTrans(),
    )

    assert_array_equal(ct.fit_transform(X_array), X_res_both)
    assert_array_equal(ct.fit(X_array).transform(X_array), X_res_both)
    assert len(ct.transformers_) == 2
    assert ct.transformers_[-1][0] == "remainder"
    assert isinstance(ct.transformers_[-1][1], DoubleTrans)
    assert ct.transformers_[-1][2] == expected_cols


def test_column_transformer_no_remaining_remainder_transformer():
    X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).T

    ct = ColumnTransformer([("trans1", Trans(), [0, 1, 2])], remainder=DoubleTrans())

    assert_array_equal(ct.fit_transform(X_array), X_array)
    assert_array_equal(ct.fit(X_array).transform(X_array), X_array)
    assert len(ct.transformers_) == 1
    assert ct.transformers_[-1][0] != "remainder"


def test_column_transformer_drops_all_remainder_transformer():
    X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).T

    # columns are doubled when remainder = DoubleTrans
    X_res_both = 2 * X_array.copy()[:, 1:3]

    ct = ColumnTransformer([("trans1", "drop", [0])], remainder=DoubleTrans())

    assert_array_equal(ct.fit_transform(X_array), X_res_both)
    assert_array_equal(ct.fit(X_array).transform(X_array), X_res_both)
    assert len(ct.transformers_) == 2
    assert ct.transformers_[-1][0] == "remainder"
    assert isinstance(ct.transformers_[-1][1], DoubleTrans)
    assert_array_equal(ct.transformers_[-1][2], [1, 2])


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_column_transformer_sparse_remainder_transformer(csr_container):
    X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).T

    ct = ColumnTransformer(
        [("trans1", Trans(), [0])],
        remainder=SparseMatrixTrans(csr_container),
        sparse_threshold=0.8,
    )

    X_trans = ct.fit_transform(X_array)
    assert sparse.issparse(X_trans)
    # SparseMatrixTrans creates 3 features for each column. There is
    # one column in ``transformers``, thus:
    assert X_trans.shape == (3, 3 + 1)

    exp_array = np.hstack((X_array[:, 0].reshape(-1, 1), np.eye(3)))
    assert_array_equal(X_trans.toarray(), exp_array)
    assert len(ct.transformers_) == 2
    assert ct.transformers_[-1][0] == "remainder"
    assert isinstance(ct.transformers_[-1][1], SparseMatrixTrans)
    assert_array_equal(ct.transformers_[-1][2], [1, 2])


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_column_transformer_drop_all_sparse_remainder_transformer(csr_container):
    X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).T
    ct = ColumnTransformer(
        [("trans1", "drop", [0])],
        remainder=SparseMatrixTrans(csr_container),
        sparse_threshold=0.8,
    )

    X_trans = ct.fit_transform(X_array)
    assert sparse.issparse(X_trans)

    #  SparseMatrixTrans creates 3 features for each column, thus:
    assert X_trans.shape == (3, 3)
    assert_array_equal(X_trans.toarray(), np.eye(3))
    assert len(ct.transformers_) == 2
    assert ct.transformers_[-1][0] == "remainder"
    assert isinstance(ct.transformers_[-1][1], SparseMatrixTrans)
    assert_array_equal(ct.transformers_[-1][2], [1, 2])


def test_column_transformer_get_set_params_with_remainder():
    ct = ColumnTransformer(
        [("trans1", StandardScaler(), [0])], remainder=StandardScaler()
    )

    exp = {
        "n_jobs": None,
        "remainder": ct.remainder,
        "remainder__copy": True,
        "remainder__with_mean": True,
        "remainder__with_std": True,
        "sparse_threshold": 0.3,
        "trans1": ct.transformers[0][1],
        "trans1__copy": True,
        "trans1__with_mean": True,
        "trans1__with_std": True,
        "transformers": ct.transformers,
        "transformer_weights": None,
        "verbose_feature_names_out": True,
        "verbose": False,
    }

    assert ct.get_params() == exp

    ct.set_params(remainder__with_std=False)
    assert not ct.get_params()["remainder__with_std"]

    ct.set_params(trans1="passthrough")
    exp = {
        "n_jobs": None,
        "remainder": ct.remainder,
        "remainder__copy": True,
        "remainder__with_mean": True,
        "remainder__with_std": False,
        "sparse_threshold": 0.3,
        "trans1": "passthrough",
        "transformers": ct.transformers,
        "transformer_weights": None,
        "verbose_feature_names_out": True,
        "verbose": False,
    }
    assert ct.get_params() == exp


def test_column_transformer_no_estimators():
    X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).astype("float").T
    ct = ColumnTransformer([], remainder=StandardScaler())

    params = ct.get_params()
    assert params["remainder__with_mean"]

    X_trans = ct.fit_transform(X_array)
    assert X_trans.shape == X_array.shape
    assert len(ct.transformers_) == 1
    assert ct.transformers_[-1][0] == "remainder"
    assert ct.transformers_[-1][2] == [0, 1, 2]


@pytest.mark.parametrize(
    ["est", "pattern"],
    [
        (
            ColumnTransformer(
                [("trans1", Trans(), [0]), ("trans2", Trans(), [1])],
                remainder=DoubleTrans(),
            ),
            (
                r"\[ColumnTransformer\].*\(1 of 3\) Processing trans1.* total=.*\n"
                r"\[ColumnTransformer\].*\(2 of 3\) Processing trans2.* total=.*\n"
                r"\[ColumnTransformer\].*\(3 of 3\) Processing remainder.* total=.*\n$"
            ),
        ),
        (
            ColumnTransformer(
                [("trans1", Trans(), [0]), ("trans2", Trans(), [1])],
                remainder="passthrough",
            ),
            (
                r"\[ColumnTransformer\].*\(1 of 3\) Processing trans1.* total=.*\n"
                r"\[ColumnTransformer\].*\(2 of 3\) Processing trans2.* total=.*\n"
                r"\[ColumnTransformer\].*\(3 of 3\) Processing remainder.* total=.*\n$"
            ),
        ),
        (
            ColumnTransformer(
                [("trans1", Trans(), [0]), ("trans2", "drop", [1])],
                remainder="passthrough",
            ),
            (
                r"\[ColumnTransformer\].*\(1 of 2\) Processing trans1.* total=.*\n"
                r"\[ColumnTransformer\].*\(2 of 2\) Processing remainder.* total=.*\n$"
            ),
        ),
        (
            ColumnTransformer(
                [("trans1", Trans(), [0]), ("trans2", "passthrough", [1])],
                remainder="passthrough",
            ),
            (
                r"\[ColumnTransformer\].*\(1 of 3\) Processing trans1.* total=.*\n"
                r"\[ColumnTransformer\].*\(2 of 3\) Processing trans2.* total=.*\n"
                r"\[ColumnTransformer\].*\(3 of 3\) Processing remainder.* total=.*\n$"
            ),
        ),
        (
            ColumnTransformer([("trans1", Trans(), [0])], remainder="passthrough"),
            (
                r"\[ColumnTransformer\].*\(1 of 2\) Processing trans1.* total=.*\n"
                r"\[ColumnTransformer\].*\(2 of 2\) Processing remainder.* total=.*\n$"
            ),
        ),
        (
            ColumnTransformer(
                [("trans1", Trans(), [0]), ("trans2", Trans(), [1])], remainder="drop"
            ),
            (
                r"\[ColumnTransformer\].*\(1 of 2\) Processing trans1.* total=.*\n"
                r"\[ColumnTransformer\].*\(2 of 2\) Processing trans2.* total=.*\n$"
            ),
        ),
        (
            ColumnTransformer([("trans1", Trans(), [0])], remainder="drop"),
            r"\[ColumnTransformer\].*\(1 of 1\) Processing trans1.* total=.*\n$",
        ),
    ],
)
@pytest.mark.parametrize("method", ["fit", "fit_transform"])
def test_column_transformer_verbose(est, pattern, method, capsys):
    X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).T

    func = getattr(est, method)
    est.set_params(verbose=False)
    func(X_array)
    assert not capsys.readouterr().out, "Got output for verbose=False"

    est.set_params(verbose=True)
    func(X_array)
    assert re.match(pattern, capsys.readouterr()[0])


def test_column_transformer_no_estimators_set_params():
    ct = ColumnTransformer([]).set_params(n_jobs=2)
    assert ct.n_jobs == 2


def test_column_transformer_callable_specifier():
    # assert that function gets the full array
    X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
    X_res_first = np.array([[0, 1, 2]]).T

    def func(X):
        assert_array_equal(X, X_array)
        return [0]

    ct = ColumnTransformer([("trans", Trans(), func)], remainder="drop")
    assert_array_equal(ct.fit_transform(X_array), X_res_first)
    assert_array_equal(ct.fit(X_array).transform(X_array), X_res_first)
    assert callable(ct.transformers[0][2])
    assert ct.transformers_[0][2] == [0]


def test_column_transformer_callable_specifier_dataframe():
    # assert that function gets the full dataframe
    pd = pytest.importorskip("pandas")
    X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
    X_res_first = np.array([[0, 1, 2]]).T

    X_df = pd.DataFrame(X_array, columns=["first", "second"])

    def func(X):
        assert_array_equal(X.columns, X_df.columns)
        assert_array_equal(X.values, X_df.values)
        return ["first"]

    ct = ColumnTransformer([("trans", Trans(), func)], remainder="drop")
    assert_array_equal(ct.fit_transform(X_df), X_res_first)
    assert_array_equal(ct.fit(X_df).transform(X_df), X_res_first)
    assert callable(ct.transformers[0][2])
    assert ct.transformers_[0][2] == ["first"]


def test_column_transformer_negative_column_indexes():
    X = np.random.randn(2, 2)
    X_categories = np.array([[1], [2]])
    X = np.concatenate([X, X_categories], axis=1)

    ohe = OneHotEncoder()

    tf_1 = ColumnTransformer([("ohe", ohe, [-1])], remainder="passthrough")
    tf_2 = ColumnTransformer([("ohe", ohe, [2])], remainder="passthrough")
    assert_array_equal(tf_1.fit_transform(X), tf_2.fit_transform(X))


@pytest.mark.parametrize("array_type", [np.asarray, *CSR_CONTAINERS])
def test_column_transformer_mask_indexing(array_type):
    # Regression test for #14510
    # Boolean array-like does not behave as boolean array with sparse matrices.
    X = np.transpose([[1, 2, 3], [4, 5, 6], [5, 6, 7], [8, 9, 10]])
    X = array_type(X)
    column_transformer = ColumnTransformer(
        [("identity", FunctionTransformer(), [False, True, False, True])]
    )
    X_trans = column_transformer.fit_transform(X)
    assert X_trans.shape == (3, 2)


def test_n_features_in():
    # make sure n_features_in is what is passed as input to the column
    # transformer.

    X = [[1, 2], [3, 4], [5, 6]]
    ct = ColumnTransformer([("a", DoubleTrans(), [0]), ("b", DoubleTrans(), [1])])
    assert not hasattr(ct, "n_features_in_")
    ct.fit(X)
    assert ct.n_features_in_ == 2


@pytest.mark.parametrize(
    "cols, pattern, include, exclude",
    [
        (["col_int", "col_float"], None, np.number, None),
        (["col_int", "col_float"], None, None, [object, "string"]),
        (["col_int", "col_float"], None, [int, float], None),
        (["col_str"], None, [object, "string"], None),
        (["col_float"], None, [float], None),
        (["col_float"], None, float, None),
        (["col_float"], "at$", [np.number], None),
        (["col_int"], None, [int], None),
        (["col_int"], "^col_int", [np.number], None),
        (["col_float", "col_str"], "float|str", None, None),
        (["col_str"], "^col_s", None, [int]),
        ([], "str$", float, None),
        (
            ["col_int", "col_float", "col_str"],
            None,
            [np.number, object, "string"],
            None,
        ),
    ],
)
def test_make_column_selector_with_select_dtypes(cols, pattern, include, exclude):
    pd = pytest.importorskip("pandas")

    X_df = pd.DataFrame(
        {
            "col_int": np.array([0, 1, 2], dtype=int),
            "col_float": np.array([0.0, 1.0, 2.0], dtype=float),
            "col_str": ["one", "two", "three"],
        },
        columns=["col_int", "col_float", "col_str"],
    )

    selector = make_column_selector(
        dtype_include=include, dtype_exclude=exclude, pattern=pattern
    )

    assert_array_equal(selector(X_df), cols)


def test_column_transformer_with_make_column_selector():
    # Functional test for column transformer + column selector
    pd = pytest.importorskip("pandas")
    X_df = pd.DataFrame(
        {
            "col_int": np.array([0, 1, 2], dtype=int),
            "col_float": np.array([0.0, 1.0, 2.0], dtype=float),
            "col_cat": ["one", "two", "one"],
            "col_str": ["low", "middle", "high"],
        },
        columns=["col_int", "col_float", "col_cat", "col_str"],
    )
    X_df["col_str"] = X_df["col_str"].astype("category")

    cat_selector = make_column_selector(dtype_include=["category", object, "string"])
    num_selector = make_column_selector(dtype_include=np.number)

    ohe = OneHotEncoder()
    scaler = StandardScaler()

    ct_selector = make_column_transformer((ohe, cat_selector), (scaler, num_selector))
    ct_direct = make_column_transformer(
        (ohe, ["col_cat", "col_str"]), (scaler, ["col_float", "col_int"])
    )

    X_selector = ct_selector.fit_transform(X_df)
    X_direct = ct_direct.fit_transform(X_df)

    assert_allclose(X_selector, X_direct)


def test_make_column_selector_error():
    selector = make_column_selector(dtype_include=np.number)
    X = np.array([[0.1, 0.2]])
    msg = "make_column_selector can only be applied to pandas dataframes"
    with pytest.raises(ValueError, match=msg):
        selector(X)


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

    X_df = pd.DataFrame(
        {
            "col_int": np.array([0, 1, 2], dtype=int),
            "col_float": np.array([0.0, 1.0, 2.0], dtype=float),
            "col_str": ["one", "two", "three"],
        },
        columns=["col_int", "col_float", "col_str"],
    )
    selector = make_column_selector(dtype_include=[object, "string"])
    selector_picked = pickle.loads(pickle.dumps(selector))

    assert_array_equal(selector(X_df), selector_picked(X_df))


@pytest.mark.parametrize(
    "empty_col",
    [[], np.array([], dtype=int), lambda x: []],
    ids=["list", "array", "callable"],
)
def test_feature_names_empty_columns(empty_col):
    pd = pytest.importorskip("pandas")

    df = pd.DataFrame({"col1": ["a", "a", "b"], "col2": ["z", "z", "z"]})

    ct = ColumnTransformer(
        transformers=[
            ("ohe", OneHotEncoder(), ["col1", "col2"]),
            ("empty_features", OneHotEncoder(), empty_col),
        ],
    )

    ct.fit(df)
    assert_array_equal(
        ct.get_feature_names_out(), ["ohe__col1_a", "ohe__col1_b", "ohe__col2_z"]
    )


@pytest.mark.parametrize(
    "selector",
    [
        [1],
        lambda x: [1],
        ["col2"],
        lambda x: ["col2"],
        [False, True],
        lambda x: [False, True],
    ],
)
def test_feature_names_out_pandas(selector):
    """Checks name when selecting only the second column"""
    pd = pytest.importorskip("pandas")
    df = pd.DataFrame({"col1": ["a", "a", "b"], "col2": ["z", "z", "z"]})
    ct = ColumnTransformer([("ohe", OneHotEncoder(), selector)])
    ct.fit(df)

    assert_array_equal(ct.get_feature_names_out(), ["ohe__col2_z"])


@pytest.mark.parametrize(
    "selector", [[1], lambda x: [1], [False, True], lambda x: [False, True]]
)
def test_feature_names_out_non_pandas(selector):
    """Checks name when selecting the second column with numpy array"""
    X = [["a", "z"], ["a", "z"], ["b", "z"]]
    ct = ColumnTransformer([("ohe", OneHotEncoder(), selector)])
    ct.fit(X)

    assert_array_equal(ct.get_feature_names_out(), ["ohe__x1_z"])


@pytest.mark.parametrize("remainder", ["passthrough", StandardScaler()])
def test_sk_visual_block_remainder(remainder):
    # remainder='passthrough' or an estimator will be shown in repr_html
    ohe = OneHotEncoder()
    ct = ColumnTransformer(
        transformers=[("ohe", ohe, ["col1", "col2"])], remainder=remainder
    )
    visual_block = ct._sk_visual_block_()
    assert visual_block.names == ("ohe", "remainder")
    assert visual_block.name_details == (["col1", "col2"], [])
    assert visual_block.estimators == (ohe, remainder)


def test_sk_visual_block_remainder_drop():
    # remainder='drop' is not shown in repr_html
    ohe = OneHotEncoder()
    ct = ColumnTransformer(transformers=[("ohe", ohe, ["col1", "col2"])])
    visual_block = ct._sk_visual_block_()
    assert visual_block.names == ("ohe",)
    assert visual_block.name_details == (["col1", "col2"],)
    assert visual_block.estimators == (ohe,)


@pytest.mark.parametrize("remainder", ["passthrough", StandardScaler()])
def test_sk_visual_block_remainder_fitted_pandas(remainder):
    # Remainder shows the columns after fitting
    pd = pytest.importorskip("pandas")
    ohe = OneHotEncoder()
    ct = ColumnTransformer(
        transformers=[("ohe", ohe, ["col1", "col2"])],
        remainder=remainder,
    )
    df = pd.DataFrame(
        {
            "col1": ["a", "b", "c"],
            "col2": ["z", "z", "z"],
            "col3": [1, 2, 3],
            "col4": [3, 4, 5],
        }
    )
    ct.fit(df)
    visual_block = ct._sk_visual_block_()
    assert visual_block.names == ("ohe", "remainder")
    assert visual_block.name_details == (["col1", "col2"], ["col3", "col4"])
    assert isinstance(visual_block.estimators[0], OneHotEncoder)
    if remainder == "passthrough":
        # comparing visual_block.estimators[1] to FunctionTransformer because
        # _column_transformer.py::sk_visual_block needs to send the remainder
        # as a transformer (not as a string) to estimator.py in order to
        # display output names.
        assert isinstance(visual_block.estimators[1], FunctionTransformer)
    else:
        assert isinstance(visual_block.estimators[1], StandardScaler)


@pytest.mark.parametrize("remainder", ["passthrough", StandardScaler()])
def test_sk_visual_block_remainder_fitted_numpy(remainder):
    # Remainder shows the indices after fitting
    X = np.array([[1, 2, 3], [4, 5, 6]], dtype=float)
    scaler = StandardScaler()
    ct = ColumnTransformer(
        transformers=[("scale", scaler, [0, 2])], remainder=remainder
    )
    ct.fit(X)
    visual_block = ct._sk_visual_block_()
    assert visual_block.names == ("scale", "remainder")
    assert visual_block.name_details == ([0, 2], [1])
    assert isinstance(visual_block.estimators[0], StandardScaler)
    if remainder == "passthrough":
        # comparing visual_block.estimators[1] to FunctionTransformer because
        # _column_transformer.py::sk_visual_block needs to send the remainder
        # as a transformer (not as a string) to estimator.py in order to
        # display output names.
        assert isinstance(visual_block.estimators[1], FunctionTransformer)
    else:
        assert isinstance(visual_block.estimators[1], StandardScaler)


def test_sk_visual_block_remainder_col_names_pandas():
    """Check that the visual block `name_details` matches the `feature_names_in_`
    Non-regression test - when remainder_columns logic is removed it should fail
    https://github.com/scikit-learn/scikit-learn/pull/31442#discussion_r2841235711
    """
    pd = pytest.importorskip("pandas")
    ohe = OneHotEncoder()
    ct = ColumnTransformer(
        transformers=[("ohe", ohe, ["col1"])],
        remainder="passthrough",
    )
    df = pd.DataFrame(
        {
            "col1": ["a", "b", "c"],
            "col2": ["z", "z", "z"],
        }
    )
    # It is not possible to guess the remainder columns when not fitted.
    visual_block = ct._sk_visual_block_()
    assert visual_block.name_details == (["col1"], [])

    ct.fit(df)
    # Once fitted, the remainder columns are the columns seen during fit not
    # specified for specific transformers.
    visual_block = ct._sk_visual_block_()
    assert visual_block.name_details == (["col1"], ["col2"])


def test_sk_visual_block_full_transform():
    """Check that visual_block doesn't return remainder when it has no columns
    Non-regression test - https://github.com/scikit-learn/scikit-learn/issues/33513
    """
    ct = ColumnTransformer([("norm1", Normalizer(), [0, 1])], remainder="passthrough")
    X = np.array([[0, 4], [3, 3]])
    ct.fit(X)
    visual_block = ct._sk_visual_block_()
    assert visual_block.names == ("norm1",)
    assert visual_block.name_details == ([0, 1],)
    assert isinstance(visual_block.estimators[0], Normalizer)
    assert len(visual_block.estimators) == 1


def test_sk_visual_block_int_remainder_cols_pandas():
    """Check that remainder still uses available string column names in visual block
    even when transformer columns are specified by integer index.
    """
    pd = pytest.importorskip("pandas")
    X = pd.DataFrame({"a": [1, 2], "b": [3, 4], "c": [5, 6]})
    ct = ColumnTransformer([("scaler", StandardScaler(), [0])], remainder="passthrough")
    ct.fit(X)
    visual_block = ct._sk_visual_block_()
    assert visual_block.name_details == ([0], ["b", "c"])


@pytest.mark.parametrize("explicit_colname", ["first", "second", 0, 1])
@pytest.mark.parametrize("remainder", [Trans(), "passthrough", "drop"])
def test_column_transformer_reordered_column_names_remainder(
    explicit_colname, remainder
):
    """Test the interaction between remainder and column transformer"""
    pd = pytest.importorskip("pandas")

    X_fit_array = np.array([[0, 1, 2], [2, 4, 6]]).T
    X_fit_df = pd.DataFrame(X_fit_array, columns=["first", "second"])

    X_trans_array = np.array([[2, 4, 6], [0, 1, 2]]).T
    X_trans_df = pd.DataFrame(X_trans_array, columns=["second", "first"])

    tf = ColumnTransformer([("bycol", Trans(), explicit_colname)], remainder=remainder)

    tf.fit(X_fit_df)
    X_fit_trans = tf.transform(X_fit_df)

    # Changing the order still works
    X_trans = tf.transform(X_trans_df)
    assert_allclose(X_trans, X_fit_trans)

    # extra columns are ignored
    X_extended_df = X_fit_df.copy()
    X_extended_df["third"] = [3, 6, 9]
    X_trans = tf.transform(X_extended_df)
    assert_allclose(X_trans, X_fit_trans)

    if isinstance(explicit_colname, str):
        # Raise error if columns are specified by names but input only allows
        # to specify by position, e.g. numpy array instead of a pandas df.
        X_array = X_fit_array.copy()
        err_msg = "Specifying the columns"
        with pytest.raises(ValueError, match=err_msg):
            tf.transform(X_array)


def test_feature_name_validation_missing_columns_drop_passthrough():
    """Test the interaction between {'drop', 'passthrough'} and
    missing column names."""
    pd = pytest.importorskip("pandas")

    X = np.ones(shape=(3, 4))
    df = pd.DataFrame(X, columns=["a", "b", "c", "d"])

    df_dropped = df.drop("c", axis=1)

    # with remainder='passthrough', all columns seen during `fit` must be
    # present
    tf = ColumnTransformer([("bycol", Trans(), [1])], remainder="passthrough")
    tf.fit(df)
    msg = r"columns are missing: {'c'}"
    with pytest.raises(ValueError, match=msg):
        tf.transform(df_dropped)

    # with remainder='drop', it is allowed to have column 'c' missing
    tf = ColumnTransformer([("bycol", Trans(), [1])], remainder="drop")
    tf.fit(df)

    df_dropped_trans = tf.transform(df_dropped)
    df_fit_trans = tf.transform(df)
    assert_allclose(df_dropped_trans, df_fit_trans)

    # bycol drops 'c', thus it is allowed for 'c' to be missing
    tf = ColumnTransformer([("bycol", "drop", ["c"])], remainder="passthrough")
    tf.fit(df)
    df_dropped_trans = tf.transform(df_dropped)
    df_fit_trans = tf.transform(df)
    assert_allclose(df_dropped_trans, df_fit_trans)


def test_feature_names_in_():
    """Feature names are stored in column transformer.

    Column transformer deliberately does not check for column name consistency.
    It only checks that the non-dropped names seen in `fit` are seen
    in `transform`. This behavior is already tested in
    `test_feature_name_validation_missing_columns_drop_passthrough`"""

    pd = pytest.importorskip("pandas")

    feature_names = ["a", "c", "d"]
    df = pd.DataFrame([[1, 2, 3]], columns=feature_names)
    ct = ColumnTransformer([("bycol", Trans(), ["a", "d"])], remainder="passthrough")

    ct.fit(df)
    assert_array_equal(ct.feature_names_in_, feature_names)
    assert isinstance(ct.feature_names_in_, np.ndarray)
    assert ct.feature_names_in_.dtype == object


class TransWithNames(Trans):
    def __init__(self, feature_names_out=None):
        self.feature_names_out = feature_names_out

    def get_feature_names_out(self, input_features=None):
        if self.feature_names_out is not None:
            return np.asarray(self.feature_names_out, dtype=object)
        return input_features


@pytest.mark.parametrize(
    "transformers, remainder, expected_names",
    [
        (
            [
                ("bycol1", TransWithNames(), ["d", "c"]),
                ("bycol2", "passthrough", ["d"]),
            ],
            "passthrough",
            ["bycol1__d", "bycol1__c", "bycol2__d", "remainder__a", "remainder__b"],
        ),
        (
            [
                ("bycol1", TransWithNames(), ["d", "c"]),
                ("bycol2", "passthrough", ["d"]),
            ],
            "drop",
            ["bycol1__d", "bycol1__c", "bycol2__d"],
        ),
        (
            [
                ("bycol1", TransWithNames(), ["b"]),
                ("bycol2", "drop", ["d"]),
            ],
            "passthrough",
            ["bycol1__b", "remainder__a", "remainder__c"],
        ),
        (
            [
                ("bycol1", TransWithNames(["pca1", "pca2"]), ["a", "b", "d"]),
            ],
            "passthrough",
            ["bycol1__pca1", "bycol1__pca2", "remainder__c"],
        ),
        (
            [
                ("bycol1", TransWithNames(["a", "b"]), ["d"]),
                ("bycol2", "passthrough", ["b"]),
            ],
            "drop",
            ["bycol1__a", "bycol1__b", "bycol2__b"],
        ),
        (
            [
                ("bycol1", TransWithNames([f"pca{i}" for i in range(2)]), ["b"]),
                ("bycol2", TransWithNames([f"pca{i}" for i in range(2)]), ["b"]),
            ],
            "passthrough",
            [
                "bycol1__pca0",
                "bycol1__pca1",
                "bycol2__pca0",
                "bycol2__pca1",
                "remainder__a",
                "remainder__c",
                "remainder__d",
            ],
        ),
        (
            [
                ("bycol1", "drop", ["d"]),
            ],
            "drop",
            [],
        ),
        (
            [
                ("bycol1", TransWithNames(), slice(1, 3)),
            ],
            "drop",
            ["bycol1__b", "bycol1__c"],
        ),
        (
            [
                ("bycol1", TransWithNames(), ["b"]),
                ("bycol2", "drop", slice(3, 4)),
            ],
            "passthrough",
            ["bycol1__b", "remainder__a", "remainder__c"],
        ),
        (
            [
                ("bycol1", TransWithNames(), ["d", "c"]),
                ("bycol2", "passthrough", slice(3, 4)),
            ],
            "passthrough",
            ["bycol1__d", "bycol1__c", "bycol2__d", "remainder__a", "remainder__b"],
        ),
        (
            [
                ("bycol1", TransWithNames(), slice("b", "c")),
            ],
            "drop",
            ["bycol1__b", "bycol1__c"],
        ),
        (
            [
                ("bycol1", TransWithNames(), ["b"]),
                ("bycol2", "drop", slice("c", "d")),
            ],
            "passthrough",
            ["bycol1__b", "remainder__a"],
        ),
        (
            [
                ("bycol1", TransWithNames(), ["d", "c"]),
                ("bycol2", "passthrough", slice("c", "d")),
            ],
            "passthrough",
            [
                "bycol1__d",
                "bycol1__c",
                "bycol2__c",
                "bycol2__d",
                "remainder__a",
                "remainder__b",
            ],
        ),
    ],
)
def test_verbose_feature_names_out_true(transformers, remainder, expected_names):
    """Check feature_names_out for verbose_feature_names_out=True (default)"""
    pd = pytest.importorskip("pandas")
    df = pd.DataFrame([[1, 2, 3, 4]], columns=["a", "b", "c", "d"])
    ct = ColumnTransformer(
        transformers,
        remainder=remainder,
    )
    ct.fit(df)

    names = ct.get_feature_names_out()
    assert isinstance(names, np.ndarray)
    assert names.dtype == object
    assert_array_equal(names, expected_names)


def _feature_names_out_callable_name_clash(trans_name: str, feat_name: str):
    return f"{trans_name[:2]}++{feat_name}"


def _feature_names_out_callable_upper(trans_name: str, feat_name: str):
    return f"{trans_name.upper()}={feat_name.upper()}"


@pytest.mark.parametrize(
    "transformers, remainder, verbose_feature_names_out, expected_names",
    [
        (
            [
                ("bycol1", TransWithNames(), ["d", "c"]),
                ("bycol2", "passthrough", ["d"]),
            ],
            "passthrough",
            _feature_names_out_callable_name_clash,
            ["by++d", "by++c", "by++d", "re++a", "re++b"],
        ),
        (
            [
                ("bycol1", TransWithNames(), ["d", "c"]),
                ("bycol2", "passthrough", ["d"]),
            ],
            "drop",
            "{feature_name}-{transformer_name}",
            ["d-bycol1", "c-bycol1", "d-bycol2"],
        ),
        (
            [
                ("bycol1", TransWithNames(), ["d", "c"]),
                ("bycol2", "passthrough", slice("c", "d")),
            ],
            "passthrough",
            _feature_names_out_callable_upper,
            [
                "BYCOL1=D",
                "BYCOL1=C",
                "BYCOL2=C",
                "BYCOL2=D",
                "REMAINDER=A",
                "REMAINDER=B",
            ],
        ),
    ],
)
def test_verbose_feature_names_out_callable_or_str(
    transformers, remainder, verbose_feature_names_out, expected_names
):
    """Check feature_names_out for verbose_feature_names_out=True (default)"""
    pd = pytest.importorskip("pandas")
    df = pd.DataFrame([[1, 2, 3, 4]], columns=["a", "b", "c", "d"])
    ct = ColumnTransformer(
        transformers,
        remainder=remainder,
        verbose_feature_names_out=verbose_feature_names_out,
    )
    ct.fit(df)

    names = ct.get_feature_names_out()
    assert isinstance(names, np.ndarray)
    assert names.dtype == object
    assert_array_equal(names, expected_names)


@pytest.mark.parametrize(
    "transformers, remainder, expected_names",
    [
        (
            [
                ("bycol1", TransWithNames(), ["d", "c"]),
                ("bycol2", "passthrough", ["a"]),
            ],
            "passthrough",
            ["d", "c", "a", "b"],
        ),
        (
            [
                ("bycol1", TransWithNames(["a"]), ["d", "c"]),
                ("bycol2", "passthrough", ["d"]),
            ],
            "drop",
            ["a", "d"],
        ),
        (
            [
                ("bycol1", TransWithNames(), ["b"]),
                ("bycol2", "drop", ["d"]),
            ],
            "passthrough",
            ["b", "a", "c"],
        ),
        (
            [
                ("bycol1", TransWithNames(["pca1", "pca2"]), ["a", "b", "d"]),
            ],
            "passthrough",
            ["pca1", "pca2", "c"],
        ),
        (
            [
                ("bycol1", TransWithNames(["a", "c"]), ["d"]),
                ("bycol2", "passthrough", ["d"]),
            ],
            "drop",
            ["a", "c", "d"],
        ),
        (
            [
                ("bycol1", TransWithNames([f"pca{i}" for i in range(2)]), ["b"]),
                ("bycol2", TransWithNames([f"kpca{i}" for i in range(2)]), ["b"]),
            ],
            "passthrough",
            ["pca0", "pca1", "kpca0", "kpca1", "a", "c", "d"],
        ),
        (
            [
                ("bycol1", "drop", ["d"]),
            ],
            "drop",
            [],
        ),
        (
            [
                ("bycol1", TransWithNames(), slice(1, 2)),
                ("bycol2", "drop", ["d"]),
            ],
            "passthrough",
            ["b", "a", "c"],
        ),
        (
            [
                ("bycol1", TransWithNames(), ["b"]),
                ("bycol2", "drop", slice(3, 4)),
            ],
            "passthrough",
            ["b", "a", "c"],
        ),
        (
            [
                ("bycol1", TransWithNames(), ["d", "c"]),
                ("bycol2", "passthrough", slice(0, 2)),
            ],
            "drop",
            ["d", "c", "a", "b"],
        ),
        (
            [
                ("bycol1", TransWithNames(), slice("a", "b")),
                ("bycol2", "drop", ["d"]),
            ],
            "passthrough",
            ["a", "b", "c"],
        ),
        (
            [
                ("bycol1", TransWithNames(), ["b"]),
                ("bycol2", "drop", slice("c", "d")),
            ],
            "passthrough",
            ["b", "a"],
        ),
        (
            [
                ("bycol1", TransWithNames(), ["d", "c"]),
                ("bycol2", "passthrough", slice("a", "b")),
            ],
            "drop",
            ["d", "c", "a", "b"],
        ),
        (
            [
                ("bycol1", TransWithNames(), ["d", "c"]),
                ("bycol2", "passthrough", slice("b", "b")),
            ],
            "drop",
            ["d", "c", "b"],
        ),
    ],
)
def test_verbose_feature_names_out_false(transformers, remainder, expected_names):
    """Check feature_names_out for verbose_feature_names_out=False"""
    pd = pytest.importorskip("pandas")
    df = pd.DataFrame([[1, 2, 3, 4]], columns=["a", "b", "c", "d"])
    ct = ColumnTransformer(
        transformers,
        remainder=remainder,
        verbose_feature_names_out=False,
    )
    ct.fit(df)

    names = ct.get_feature_names_out()
    assert isinstance(names, np.ndarray)
    assert names.dtype == object
    assert_array_equal(names, expected_names)


@pytest.mark.parametrize(
    "transformers, remainder, colliding_columns",
    [
        (
            [
                ("bycol1", TransWithNames(), ["b"]),
                ("bycol2", "passthrough", ["b"]),
            ],
            "drop",
            "['b']",
        ),
        (
            [
                ("bycol1", TransWithNames(["c", "d"]), ["c"]),
                ("bycol2", "passthrough", ["c"]),
            ],
            "drop",
            "['c']",
        ),
        (
            [
                ("bycol1", TransWithNames(["a"]), ["b"]),
                ("bycol2", "passthrough", ["b"]),
            ],
            "passthrough",
            "['a']",
        ),
        (
            [
                ("bycol1", TransWithNames(["a"]), ["b"]),
                ("bycol2", "drop", ["b"]),
            ],
            "passthrough",
            "['a']",
        ),
        (
            [
                ("bycol1", TransWithNames(["c", "b"]), ["b"]),
                ("bycol2", "passthrough", ["c", "b"]),
            ],
            "drop",
            "['b', 'c']",
        ),
        (
            [
                ("bycol1", TransWithNames(["a"]), ["b"]),
                ("bycol2", "passthrough", ["a"]),
                ("bycol3", TransWithNames(["a"]), ["b"]),
            ],
            "passthrough",
            "['a']",
        ),
        (
            [
                ("bycol1", TransWithNames(["a", "b"]), ["b"]),
                ("bycol2", "passthrough", ["a"]),
                ("bycol3", TransWithNames(["b"]), ["c"]),
            ],
            "passthrough",
            "['a', 'b']",
        ),
        (
            [
                ("bycol1", TransWithNames([f"pca{i}" for i in range(6)]), ["b"]),
                ("bycol2", TransWithNames([f"pca{i}" for i in range(6)]), ["b"]),
            ],
            "passthrough",
            "['pca0', 'pca1', 'pca2', 'pca3', 'pca4', ...]",
        ),
        (
            [
                ("bycol1", TransWithNames(["a", "b"]), slice(1, 2)),
                ("bycol2", "passthrough", ["a"]),
                ("bycol3", TransWithNames(["b"]), ["c"]),
            ],
            "passthrough",
            "['a', 'b']",
        ),
        (
            [
                ("bycol1", TransWithNames(["a", "b"]), ["b"]),
                ("bycol2", "passthrough", slice(0, 1)),
                ("bycol3", TransWithNames(["b"]), ["c"]),
            ],
            "passthrough",
            "['a', 'b']",
        ),
        (
            [
                ("bycol1", TransWithNames(["a", "b"]), slice("b", "c")),
                ("bycol2", "passthrough", ["a"]),
                ("bycol3", TransWithNames(["b"]), ["c"]),
            ],
            "passthrough",
            "['a', 'b']",
        ),
        (
            [
                ("bycol1", TransWithNames(["a", "b"]), ["b"]),
                ("bycol2", "passthrough", slice("a", "a")),
                ("bycol3", TransWithNames(["b"]), ["c"]),
            ],
            "passthrough",
            "['a', 'b']",
        ),
    ],
)
def test_verbose_feature_names_out_false_errors(
    transformers, remainder, colliding_columns
):
    """Check feature_names_out for verbose_feature_names_out=False"""

    pd = pytest.importorskip("pandas")
    df = pd.DataFrame([[1, 2, 3, 4]], columns=["a", "b", "c", "d"])
    ct = ColumnTransformer(
        transformers,
        remainder=remainder,
        verbose_feature_names_out=False,
    )
    ct.fit(df)

    msg = re.escape(
        f"Output feature names: {colliding_columns} are not unique. Please set "
        "verbose_feature_names_out=True to add prefixes to feature names"
    )
    with pytest.raises(ValueError, match=msg):
        ct.get_feature_names_out()


@pytest.mark.parametrize("verbose_feature_names_out", [True, False])
@pytest.mark.parametrize("remainder", ["drop", "passthrough"])
def test_column_transformer_set_output(verbose_feature_names_out, remainder):
    """Check column transformer behavior with set_output."""
    pd = pytest.importorskip("pandas")
    df = pd.DataFrame([[1, 2, 3, 4]], columns=["a", "b", "c", "d"], index=[10])
    ct = ColumnTransformer(
        [("first", TransWithNames(), ["a", "c"]), ("second", TransWithNames(), ["d"])],
        remainder=remainder,
        verbose_feature_names_out=verbose_feature_names_out,
    )
    X_trans = ct.fit_transform(df)
    assert isinstance(X_trans, np.ndarray)

    ct.set_output(transform="pandas")

    df_test = pd.DataFrame([[1, 2, 3, 4]], columns=df.columns, index=[20])
    X_trans = ct.transform(df_test)
    assert isinstance(X_trans, pd.DataFrame)

    feature_names_out = ct.get_feature_names_out()
    assert_array_equal(X_trans.columns, feature_names_out)
    assert_array_equal(X_trans.index, df_test.index)


@pytest.mark.parametrize("remainder", ["drop", "passthrough"])
@pytest.mark.parametrize("fit_transform", [True, False])
def test_column_transform_set_output_mixed(remainder, fit_transform):
    """Check ColumnTransformer outputs mixed types correctly."""
    pd = pytest.importorskip("pandas")
    df = pd.DataFrame(
        {
            "pet": pd.Series(["dog", "cat", "snake"], dtype="category"),
            "color": pd.Series(["green", "blue", "red"], dtype="object"),
            "age": [1.4, 2.1, 4.4],
            "height": [20, 40, 10],
            "distance": pd.Series([20, pd.NA, 100], dtype="Int32"),
        }
    )
    ct = ColumnTransformer(
        [
            (
                "color_encode",
                OneHotEncoder(sparse_output=False, dtype="int8"),
                ["color"],
            ),
            ("age", StandardScaler(), ["age"]),
        ],
        remainder=remainder,
        verbose_feature_names_out=False,
    ).set_output(transform="pandas")
    if fit_transform:
        X_trans = ct.fit_transform(df)
    else:
        X_trans = ct.fit(df).transform(df)

    assert isinstance(X_trans, pd.DataFrame)
    assert_array_equal(X_trans.columns, ct.get_feature_names_out())

    expected_dtypes = {
        "color_blue": "int8",
        "color_green": "int8",
        "color_red": "int8",
        "age": "float64",
        "pet": "category",
        "height": "int64",
        "distance": "Int32",
    }
    for col, dtype in X_trans.dtypes.items():
        assert dtype == expected_dtypes[col]


@pytest.mark.parametrize("remainder", ["drop", "passthrough"])
def test_column_transform_set_output_after_fitting(remainder):
    pd = pytest.importorskip("pandas")
    df = pd.DataFrame(
        {
            "pet": pd.Series(["dog", "cat", "snake"], dtype="category"),
            "age": [1.4, 2.1, 4.4],
            "height": [20, 40, 10],
        }
    )
    ct = ColumnTransformer(
        [
            (
                "color_encode",
                OneHotEncoder(sparse_output=False, dtype="int16"),
                ["pet"],
            ),
            ("age", StandardScaler(), ["age"]),
        ],
        remainder=remainder,
        verbose_feature_names_out=False,
    )

    # fit without calling set_output
    X_trans = ct.fit_transform(df)
    assert isinstance(X_trans, np.ndarray)
    assert X_trans.dtype == "float64"

    ct.set_output(transform="pandas")
    X_trans_df = ct.transform(df)
    expected_dtypes = {
        "pet_cat": "int16",
        "pet_dog": "int16",
        "pet_snake": "int16",
        "height": "int64",
        "age": "float64",
    }
    for col, dtype in X_trans_df.dtypes.items():
        assert dtype == expected_dtypes[col]


# PandasOutTransformer that does not define get_feature_names_out and always expects
# the input to be a DataFrame.
class PandasOutTransformer(BaseEstimator):
    def __init__(self, offset=1.0):
        self.offset = offset

    def fit(self, X, y=None):
        pd = pytest.importorskip("pandas")
        assert isinstance(X, pd.DataFrame)
        return self

    def transform(self, X, y=None):
        pd = pytest.importorskip("pandas")
        assert isinstance(X, pd.DataFrame)
        return X - self.offset

    def set_output(self, transform=None):
        # This transformer will always output a DataFrame regardless of the
        # configuration.
        return self


@pytest.mark.parametrize(
    "trans_1, expected_verbose_names, expected_non_verbose_names",
    [
        (
            PandasOutTransformer(offset=2.0),
            ["trans_0__feat1", "trans_1__feat0"],
            ["feat1", "feat0"],
        ),
        (
            "drop",
            ["trans_0__feat1"],
            ["feat1"],
        ),
        (
            "passthrough",
            ["trans_0__feat1", "trans_1__feat0"],
            ["feat1", "feat0"],
        ),
    ],
)
def test_transformers_with_pandas_out_but_not_feature_names_out(
    trans_1, expected_verbose_names, expected_non_verbose_names
):
    """Check that set_config(transform="pandas") is compatible with more transformers.

    Specifically, if transformers returns a DataFrame, but does not define
    `get_feature_names_out`.
    """
    pd = pytest.importorskip("pandas")

    X_df = pd.DataFrame({"feat0": [1.0, 2.0, 3.0], "feat1": [2.0, 3.0, 4.0]})
    ct = ColumnTransformer(
        [
            ("trans_0", PandasOutTransformer(offset=3.0), ["feat1"]),
            ("trans_1", trans_1, ["feat0"]),
        ]
    )
    X_trans_np = ct.fit_transform(X_df)
    assert isinstance(X_trans_np, np.ndarray)

    # `ct` does not have `get_feature_names_out` because `PandasOutTransformer` does
    # not define the method.
    with pytest.raises(AttributeError, match="not provide get_feature_names_out"):
        ct.get_feature_names_out()

    # The feature names are prefixed because verbose_feature_names_out=True is default
    ct.set_output(transform="pandas")
    X_trans_df0 = ct.fit_transform(X_df)
    assert_array_equal(X_trans_df0.columns, expected_verbose_names)

    ct.set_params(verbose_feature_names_out=False)
    X_trans_df1 = ct.fit_transform(X_df)
    assert_array_equal(X_trans_df1.columns, expected_non_verbose_names)


@pytest.mark.parametrize(
    "empty_selection",
    [[], np.array([False, False]), [False, False]],
    ids=["list", "bool", "bool_int"],
)
def test_empty_selection_pandas_output(empty_selection):
    """Check that pandas output works when there is an empty selection.

    Non-regression test for gh-25487
    """
    pd = pytest.importorskip("pandas")

    X = pd.DataFrame([[1.0, 2.2], [3.0, 1.0]], columns=["a", "b"])
    ct = ColumnTransformer(
        [
            ("categorical", "passthrough", empty_selection),
            ("numerical", StandardScaler(), ["a", "b"]),
        ],
        verbose_feature_names_out=True,
    )
    ct.set_output(transform="pandas")
    X_out = ct.fit_transform(X)
    assert_array_equal(X_out.columns, ["numerical__a", "numerical__b"])

    ct.set_params(verbose_feature_names_out=False)
    X_out = ct.fit_transform(X)
    assert_array_equal(X_out.columns, ["a", "b"])


def test_raise_error_if_index_not_aligned():
    """Check column transformer raises error if indices are not aligned.

    Non-regression test for gh-26210.
    """
    pd = pytest.importorskip("pandas")

    X = pd.DataFrame([[1.0, 2.2], [3.0, 1.0]], columns=["a", "b"], index=[8, 3])
    reset_index_transformer = FunctionTransformer(
        lambda x: x.reset_index(drop=True), feature_names_out="one-to-one"
    )

    ct = ColumnTransformer(
        [
            ("num1", "passthrough", ["a"]),
            ("num2", reset_index_transformer, ["b"]),
        ],
    )
    ct.set_output(transform="pandas")
    msg = (
        "Concatenating DataFrames from the transformer's output lead to"
        " an inconsistent number of samples. The output may have Pandas"
        " Indexes that do not match."
    )
    with pytest.raises(ValueError, match=msg):
        ct.fit_transform(X)


def test_remainder_set_output():
    """Check that the output is set for the remainder.

    Non-regression test for #26306.
    """

    pd = pytest.importorskip("pandas")
    df = pd.DataFrame({"a": [True, False, True], "b": [1, 2, 3]})

    ct = make_column_transformer(
        (VarianceThreshold(), make_column_selector(dtype_include=bool)),
        remainder=VarianceThreshold(),
        verbose_feature_names_out=False,
    )
    ct.set_output(transform="pandas")

    out = ct.fit_transform(df)
    pd.testing.assert_frame_equal(out, df)

    ct.set_output(transform="default")
    out = ct.fit_transform(df)
    assert isinstance(out, np.ndarray)


def test_transform_pd_na():
    """Check behavior when a transformer's output contains pandas.NA

    It should raise an error unless the output config is set to 'pandas'.
    """
    pd = pytest.importorskip("pandas")
    if not hasattr(pd, "Float64Dtype"):
        pytest.skip(
            "The issue with pd.NA tested here does not happen in old versions that do"
            " not have the extension dtypes"
        )
    df = pd.DataFrame({"a": [1.5, None]})
    ct = make_column_transformer(("passthrough", ["a"]))
    # No warning with non-extension dtypes and np.nan
    with warnings.catch_warnings():
        warnings.simplefilter("error")
        ct.fit_transform(df)
    df = df.convert_dtypes()

    # Error with extension dtype and pd.NA
    with pytest.raises(ValueError, match=r"set_output\(transform='pandas'\)"):
        ct.fit_transform(df)

    # No error when output is set to pandas
    ct.set_output(transform="pandas")
    ct.fit_transform(df)
    ct.set_output(transform="default")

    # No error when there are no pd.NA
    ct.fit_transform(df.fillna(-1.0))


def test_dataframe_different_dataframe_libraries():
    """Check fitting and transforming on pandas and polars dataframes."""
    pd = pytest.importorskip("pandas")
    pl = pytest.importorskip("polars")
    X_train_np = np.array([[0, 1], [2, 4], [4, 5]])
    X_test_np = np.array([[1, 2], [1, 3], [2, 3]])

    # Fit on pandas and transform on polars
    X_train_pd = pd.DataFrame(X_train_np, columns=["a", "b"])
    X_test_pl = pl.DataFrame(X_test_np, schema=["a", "b"])

    ct = make_column_transformer((Trans(), [0, 1]))
    ct.fit(X_train_pd)

    out_pl_in = ct.transform(X_test_pl)
    assert_array_equal(out_pl_in, X_test_np)

    # Fit on polars and transform on pandas
    X_train_pl = pl.DataFrame(X_train_np, schema=["a", "b"])
    X_test_pd = pd.DataFrame(X_test_np, columns=["a", "b"])
    ct.fit(X_train_pl)

    out_pd_in = ct.transform(X_test_pd)
    assert_array_equal(out_pd_in, X_test_np)


def test_column_transformer__getitem__():
    """Check __getitem__ for ColumnTransformer."""
    X = np.array([[0, 1, 2], [3, 4, 5]])
    ct = ColumnTransformer([("t1", Trans(), [0, 1]), ("t2", Trans(), [1, 2])])

    msg = "ColumnTransformer is subscriptable after it is fitted"
    with pytest.raises(TypeError, match=msg):
        ct["t1"]

    ct.fit(X)
    assert ct["t1"] is ct.named_transformers_["t1"]
    assert ct["t2"] is ct.named_transformers_["t2"]

    msg = "'does_not_exist' is not a valid transformer name"
    with pytest.raises(KeyError, match=msg):
        ct["does_not_exist"]


@pytest.mark.parametrize("transform_output", ["default", "pandas"])
def test_column_transformer_remainder_passthrough_naming_consistency(transform_output):
    """Check that when `remainder="passthrough"`, inconsistent naming is handled
    correctly by the underlying `FunctionTransformer`.

    Non-regression test for:
    https://github.com/scikit-learn/scikit-learn/issues/28232
    """
    pd = pytest.importorskip("pandas")
    X = pd.DataFrame(np.random.randn(10, 4))

    preprocessor = ColumnTransformer(
        transformers=[("scaler", StandardScaler(), [0, 1])],
        remainder="passthrough",
    ).set_output(transform=transform_output)
    X_trans = preprocessor.fit_transform(X)
    assert X_trans.shape == X.shape

    expected_column_names = [
        "scaler__x0",
        "scaler__x1",
        "remainder__x2",
        "remainder__x3",
    ]
    if hasattr(X_trans, "columns"):
        assert X_trans.columns.tolist() == expected_column_names
    assert preprocessor.get_feature_names_out().tolist() == expected_column_names


# DfOutTransformer that does not define get_feature_names_out
class DfOutTransformer(BaseEstimator):
    def __init__(self, offset=1.0):
        self.offset = offset

    def fit(self, X, y=None):
        return self

    def transform(self, X, y=None):
        return X - self.offset

    def set_output(self, transform=None):
        # This transformer will always output a DataFrame regardless of the
        # configuration.
        return self


@pytest.mark.parametrize("dataframe_lib", ["pandas", "polars"])
def test_column_transformer_column_renaming(dataframe_lib):
    """Check that we properly rename columns when using `ColumnTransformer` and
    selected columns are redundant between transformers.

    Non-regression test for:
    https://github.com/scikit-learn/scikit-learn/issues/28260
    """
    lib = pytest.importorskip(dataframe_lib)

    df = lib.DataFrame({"x1": [1, 2, 3], "x2": [10, 20, 30], "x3": [100, 200, 300]})

    transformer = ColumnTransformer(
        transformers=[
            ("A", "passthrough", ["x1", "x2", "x3"]),
            ("B", FunctionTransformer(), ["x1", "x2"]),
            ("C", StandardScaler(), ["x1", "x3"]),
            ("D", DfOutTransformer(), ["x2", "x3"]),
            # special case of a transformer returning 0-columns, e.g feature selector
            (
                "E",
                FunctionTransformer(lambda x: _safe_indexing(x, [], axis=1)),
                ["x1", "x2", "x3"],
            ),
        ],
    ).set_output(transform=dataframe_lib)

    # by default, verbose_feature_names_out is True
    df_trans = transformer.fit_transform(df)
    assert list(df_trans.columns) == [
        "A__x1",
        "A__x2",
        "A__x3",
        "B__x1",
        "B__x2",
        "C__x1",
        "C__x3",
        "D__x2",
        "D__x3",
    ]

    transformer.set_params(verbose_feature_names_out=False)
    with pytest.raises(ValueError, match=r"Output feature names:.*are not unique"):
        transformer.fit_transform(df)


# TODO: remove mark once loky bug is fixed:
# https://github.com/joblib/loky/issues/458
@pytest.mark.thread_unsafe
@pytest.mark.skipif(
    parse_version(joblib.__version__) < parse_version("1.3"),
    reason="requires joblib >= 1.3",
)
def test_column_transformer_auto_memmap(global_random_seed):
    """Check that ColumnTransformer works in parallel with joblib's auto-memmapping.

    non-regression test for issue #28781
    """
    X = np.random.RandomState(global_random_seed).uniform(size=(3, 4))

    scaler = StandardScaler(copy=False)

    transformer = ColumnTransformer(
        transformers=[("scaler", scaler, [0])],
        n_jobs=2,
    )

    with joblib.parallel_backend("loky", max_nbytes=1):
        Xt = transformer.fit_transform(X)

    assert_allclose(Xt, StandardScaler().fit_transform(X[:, [0]]))


def test_column_transformer_non_default_index():
    """Check index handling when both pd.Series and pd.DataFrame slices are used in
    ColumnTransformer.

    Non-regression test for issue #31546.
    """
    pd = pytest.importorskip("pandas")
    df = pd.DataFrame(
        {
            "dict_col": [{"foo": 1, "bar": 2}, {"foo": 3, "baz": 1}],
            "dummy_col": [1, 2],
        },
        index=[1, 2],
    )
    t = make_column_transformer(
        (DictVectorizer(sparse=False), "dict_col"),
        (FunctionTransformer(), ["dummy_col"]),
    )
    t.set_output(transform="pandas")
    X = t.fit_transform(df)
    assert list(X.index) == [1, 2]


# Metadata Routing Tests
# ======================


@pytest.mark.parametrize("method", ["transform", "fit_transform", "fit"])
def test_routing_passed_metadata_not_supported(method):
    """Test that the right error message is raised when metadata is passed while
    not supported when `enable_metadata_routing=False`."""

    X = np.array([[0, 1, 2], [2, 4, 6]]).T
    y = [1, 2, 3]
    trs = ColumnTransformer([("trans", Trans(), [0])]).fit(X, y)

    with pytest.raises(
        ValueError, match="is only supported if enable_metadata_routing=True"
    ):
        getattr(trs, method)([[1]], sample_weight=[1], prop="a")


@pytest.mark.parametrize("method", ["transform", "fit_transform", "fit"])
@config_context(enable_metadata_routing=True)
def test_metadata_routing_for_column_transformer(method):
    """Test that metadata is routed correctly for column transformer."""
    X = np.array([[0, 1, 2], [2, 4, 6]]).T
    y = [1, 2, 3]
    registry = _Registry()
    sample_weight, metadata = [1], "a"
    trs = ColumnTransformer(
        [
            (
                "trans",
                ConsumingTransformer(registry=registry)
                .set_fit_request(sample_weight=True, metadata=True)
                .set_transform_request(sample_weight=True, metadata=True),
                [0],
            )
        ]
    )

    if method == "transform":
        trs.fit(X, y, sample_weight=sample_weight, metadata=metadata)
        trs.transform(X, sample_weight=sample_weight, metadata=metadata)
    else:
        getattr(trs, method)(X, y, sample_weight=sample_weight, metadata=metadata)

    assert len(registry)
    for _trs in registry:
        check_recorded_metadata(
            obj=_trs,
            method=method,
            parent=method,
            sample_weight=sample_weight,
            metadata=metadata,
        )


@config_context(enable_metadata_routing=True)
def test_metadata_routing_no_fit_transform():
    """Test metadata routing when the sub-estimator doesn't implement
    ``fit_transform``."""

    class NoFitTransform(BaseEstimator):
        def fit(self, X, y=None, sample_weight=None, metadata=None):
            assert sample_weight
            assert metadata
            return self

        def transform(self, X, sample_weight=None, metadata=None):
            assert sample_weight
            assert metadata
            return X

    X = np.array([[0, 1, 2], [2, 4, 6]]).T
    y = [1, 2, 3]
    sample_weight, metadata = [1], "a"
    trs = ColumnTransformer(
        [
            (
                "trans",
                NoFitTransform()
                .set_fit_request(sample_weight=True, metadata=True)
                .set_transform_request(sample_weight=True, metadata=True),
                [0],
            )
        ]
    )

    trs.fit(X, y, sample_weight=sample_weight, metadata=metadata)
    trs.fit_transform(X, y, sample_weight=sample_weight, metadata=metadata)


@pytest.mark.parametrize("method", ["transform", "fit_transform", "fit"])
@config_context(enable_metadata_routing=True)
def test_metadata_routing_error_for_column_transformer(method):
    """Test that the right error is raised when metadata is not requested."""
    X = np.array([[0, 1, 2], [2, 4, 6]]).T
    y = [1, 2, 3]
    sample_weight, metadata = [1], "a"
    trs = ColumnTransformer([("trans", ConsumingTransformer(), [0])])

    error_message = (
        "[sample_weight, metadata] are passed but are not explicitly set as requested"
        f" or not requested for ConsumingTransformer.{method}"
    )
    with pytest.raises(ValueError, match=re.escape(error_message)):
        if method == "transform":
            trs.fit(X, y)
            trs.transform(X, sample_weight=sample_weight, metadata=metadata)
        else:
            getattr(trs, method)(X, y, sample_weight=sample_weight, metadata=metadata)


@config_context(enable_metadata_routing=True)
def test_get_metadata_routing_works_without_fit():
    # Regression test for https://github.com/scikit-learn/scikit-learn/issues/28186
    # Make sure ct.get_metadata_routing() works w/o having called fit.
    ct = ColumnTransformer([("trans", ConsumingTransformer(), [0])])
    ct.get_metadata_routing()


@config_context(enable_metadata_routing=True)
def test_remainder_request_always_present():
    # Test that remainder request is always present.
    ct = ColumnTransformer(
        [("trans", StandardScaler(), [0])],
        remainder=ConsumingTransformer()
        .set_fit_request(metadata=True)
        .set_transform_request(metadata=True),
    )
    router = ct.get_metadata_routing()
    assert router.consumes("fit", ["metadata"]) == set(["metadata"])


@config_context(enable_metadata_routing=True)
def test_unused_transformer_request_present():
    # Test that the request of a transformer is always present even when not
    # used due to no selected columns.
    ct = ColumnTransformer(
        [
            (
                "trans",
                ConsumingTransformer()
                .set_fit_request(metadata=True)
                .set_transform_request(metadata=True),
                lambda X: [],
            )
        ]
    )
    router = ct.get_metadata_routing()
    assert router.consumes("fit", ["metadata"]) == set(["metadata"])


@config_context(enable_metadata_routing=True)
@pytest.mark.parametrize(
    "remainder",
    [
        "drop",
        "passthrough",  # consumes no metadata
        Trans(),  # consumes no metadata
        ConsumingTransformer(),  # consumes metadata
    ],
)
def test_metadata_routing_with_remainder_no_error(remainder):
    # Make sure that metadata routing works with all possible remainder types.
    # Non-regression test for https://github.com/scikit-learn/scikit-learn/issues/33614

    X = np.array([[1, 2], [3, 4]])
    y = [0, 1]
    sample_weight = [1, 1]

    # This can only be set here because metadata routing has to be enabled first.
    if isinstance(remainder, ConsumingTransformer):
        remainder.set_fit_request(sample_weight=True).set_transform_request(
            sample_weight=True
        )

    transformer = (
        ConsumingTransformer()
        .set_fit_request(sample_weight=True)
        .set_transform_request(sample_weight=True)
    )
    ct = ColumnTransformer([("trans", transformer, [0])], remainder=remainder)

    # Check that no error is raised
    ct.fit_transform(X, y=y, sample_weight=sample_weight)


# End of Metadata Routing Tests
# =============================
