# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import warnings
from copy import deepcopy

import joblib
import numpy as np
import pytest
from scipy import interpolate, sparse

from sklearn.base import clone, config_context, is_classifier
from sklearn.datasets import load_diabetes, make_regression
from sklearn.exceptions import ConvergenceWarning
from sklearn.linear_model import (
    ElasticNet,
    ElasticNetCV,
    Lasso,
    LassoCV,
    LassoLars,
    LassoLarsCV,
    LinearRegression,
    MultiTaskElasticNet,
    MultiTaskElasticNetCV,
    MultiTaskLasso,
    MultiTaskLassoCV,
    Ridge,
    RidgeClassifier,
    RidgeClassifierCV,
    RidgeCV,
    enet_path,
    lars_path,
    lasso_path,
)
from sklearn.linear_model._coordinate_descent import _set_order
from sklearn.model_selection import (
    BaseCrossValidator,
    GridSearchCV,
    LeaveOneGroupOut,
)
from sklearn.model_selection._split import GroupsConsumerMixin
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.utils import check_array
from sklearn.utils._testing import (
    TempMemmap,
    assert_allclose,
    assert_almost_equal,
    assert_array_almost_equal,
    assert_array_equal,
    assert_array_less,
    ignore_warnings,
)
from sklearn.utils.fixes import COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS


@pytest.mark.parametrize("order", ["C", "F"])
@pytest.mark.parametrize("input_order", ["C", "F"])
def test_set_order_dense(order, input_order):
    """Check that _set_order returns arrays with promised order."""
    X = np.array([[0], [0], [0]], order=input_order)
    y = np.array([0, 0, 0], order=input_order)
    X2, y2 = _set_order(X, y, order=order)
    if order == "C":
        assert X2.flags["C_CONTIGUOUS"]
        assert y2.flags["C_CONTIGUOUS"]
    elif order == "F":
        assert X2.flags["F_CONTIGUOUS"]
        assert y2.flags["F_CONTIGUOUS"]

    if order == input_order:
        assert X is X2
        assert y is y2


@pytest.mark.parametrize("order", ["C", "F"])
@pytest.mark.parametrize("input_order", ["C", "F"])
@pytest.mark.parametrize("coo_container", COO_CONTAINERS)
def test_set_order_sparse(order, input_order, coo_container):
    """Check that _set_order returns sparse matrices in promised format."""
    X = coo_container(np.array([[0], [0], [0]]))
    y = coo_container(np.array([0, 0, 0]))
    sparse_format = "csc" if input_order == "F" else "csr"
    X = X.asformat(sparse_format)
    y = X.asformat(sparse_format)
    X2, y2 = _set_order(X, y, order=order)

    format = "csc" if order == "F" else "csr"
    assert sparse.issparse(X2) and X2.format == format
    assert sparse.issparse(y2) and y2.format == format


def test_lasso_zero():
    # Check that the lasso can handle zero data without crashing
    X = [[0], [0], [0]]
    y = [0, 0, 0]
    # _cd_fast.pyx tests for gap < tol, but here we get 0.0 < 0.0
    # should probably be changed to gap <= tol ?
    with ignore_warnings(category=ConvergenceWarning):
        clf = Lasso(alpha=0.1).fit(X, y)
    pred = clf.predict([[1], [2], [3]])
    assert_array_almost_equal(clf.coef_, [0])
    assert_array_almost_equal(pred, [0, 0, 0])
    assert_almost_equal(clf.dual_gap_, 0)


@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
def test_enet_nonfinite_params():
    # Check ElasticNet throws ValueError when dealing with non-finite parameter
    # values
    rng = np.random.RandomState(0)
    n_samples = 10
    fmax = np.finfo(np.float64).max
    X = fmax * rng.uniform(size=(n_samples, 2))
    y = rng.randint(0, 2, size=n_samples)

    clf = ElasticNet(alpha=0.1)
    msg = "Coordinate descent iterations resulted in non-finite parameter values"
    with pytest.raises(ValueError, match=msg):
        clf.fit(X, y)


def test_lasso_toy():
    # Test Lasso on a toy example for various values of alpha.
    # When validating this against glmnet notice that glmnet divides it
    # against nobs.

    X = [[-1], [0], [1]]
    Y = [-1, 0, 1]  # just a straight line
    T = [[2], [3], [4]]  # test sample

    clf = Lasso(alpha=1e-8)
    clf.fit(X, Y)
    pred = clf.predict(T)
    assert_array_almost_equal(clf.coef_, [1])
    assert_array_almost_equal(pred, [2, 3, 4])
    assert_almost_equal(clf.dual_gap_, 0)

    clf = Lasso(alpha=0.1)
    clf.fit(X, Y)
    pred = clf.predict(T)
    assert_array_almost_equal(clf.coef_, [0.85])
    assert_array_almost_equal(pred, [1.7, 2.55, 3.4])
    assert_almost_equal(clf.dual_gap_, 0)

    clf = Lasso(alpha=0.5)
    clf.fit(X, Y)
    pred = clf.predict(T)
    assert_array_almost_equal(clf.coef_, [0.25])
    assert_array_almost_equal(pred, [0.5, 0.75, 1.0])
    assert_almost_equal(clf.dual_gap_, 0)

    clf = Lasso(alpha=1)
    clf.fit(X, Y)
    pred = clf.predict(T)
    assert_array_almost_equal(clf.coef_, [0.0])
    assert_array_almost_equal(pred, [0, 0, 0])
    assert_almost_equal(clf.dual_gap_, 0)


def test_enet_toy():
    # Test ElasticNet for various parameters of alpha and l1_ratio.
    # Actually, the parameters alpha = 0 should not be allowed. However,
    # we test it as a border case.
    # ElasticNet is tested with and without precomputed Gram matrix

    X = np.array([[-1.0], [0.0], [1.0]])
    Y = [-1, 0, 1]  # just a straight line
    T = [[2.0], [3.0], [4.0]]  # test sample

    # this should be the same as lasso
    clf = ElasticNet(alpha=1e-8, l1_ratio=1.0)
    clf.fit(X, Y)
    pred = clf.predict(T)
    assert_array_almost_equal(clf.coef_, [1])
    assert_array_almost_equal(pred, [2, 3, 4])
    assert_almost_equal(clf.dual_gap_, 0)

    clf = ElasticNet(alpha=0.5, l1_ratio=0.3, max_iter=100, precompute=False)
    clf.fit(X, Y)
    pred = clf.predict(T)
    assert_array_almost_equal(clf.coef_, [0.50819], decimal=3)
    assert_array_almost_equal(pred, [1.0163, 1.5245, 2.0327], decimal=3)
    assert_almost_equal(clf.dual_gap_, 0)

    clf.set_params(max_iter=100, precompute=True)
    clf.fit(X, Y)  # with Gram
    pred = clf.predict(T)
    assert_array_almost_equal(clf.coef_, [0.50819], decimal=3)
    assert_array_almost_equal(pred, [1.0163, 1.5245, 2.0327], decimal=3)
    assert_almost_equal(clf.dual_gap_, 0)

    clf.set_params(max_iter=100, precompute=np.dot(X.T, X))
    clf.fit(X, Y)  # with Gram
    pred = clf.predict(T)
    assert_array_almost_equal(clf.coef_, [0.50819], decimal=3)
    assert_array_almost_equal(pred, [1.0163, 1.5245, 2.0327], decimal=3)
    assert_almost_equal(clf.dual_gap_, 0)

    clf = ElasticNet(alpha=0.5, l1_ratio=0.5)
    clf.fit(X, Y)
    pred = clf.predict(T)
    assert_array_almost_equal(clf.coef_, [0.45454], 3)
    assert_array_almost_equal(pred, [0.9090, 1.3636, 1.8181], 3)
    assert_almost_equal(clf.dual_gap_, 0)


def test_lasso_dual_gap():
    """
    Check that Lasso.dual_gap_ matches its objective formulation, with the
    datafit normalized by n_samples
    """
    X, y, _, _ = build_dataset(n_samples=10, n_features=30)
    n_samples = len(y)
    alpha = 0.01 * np.max(np.abs(X.T @ y)) / n_samples
    clf = Lasso(alpha=alpha, fit_intercept=False).fit(X, y)
    w = clf.coef_
    R = y - X @ w
    primal = 0.5 * np.mean(R**2) + clf.alpha * np.sum(np.abs(w))
    # dual pt: R / n_samples, dual constraint: norm(X.T @ theta, inf) <= alpha
    R /= np.max(np.abs(X.T @ R) / (n_samples * alpha))
    dual = 0.5 * (np.mean(y**2) - np.mean((y - R) ** 2))
    assert_allclose(clf.dual_gap_, primal - dual)


def build_dataset(n_samples=50, n_features=200, n_informative_features=10, n_targets=1):
    """
    build an ill-posed linear regression problem with many noisy features and
    comparatively few samples
    """
    random_state = np.random.RandomState(0)
    if n_targets > 1:
        w = random_state.randn(n_features, n_targets)
    else:
        w = random_state.randn(n_features)
    w[n_informative_features:] = 0.0
    X = random_state.randn(n_samples, n_features)
    y = np.dot(X, w)
    X_test = random_state.randn(n_samples, n_features)
    y_test = np.dot(X_test, w)
    return X, y, X_test, y_test


def test_lasso_cv():
    X, y, X_test, y_test = build_dataset()
    max_iter = 150
    clf = LassoCV(n_alphas=10, eps=1e-3, max_iter=max_iter, cv=3).fit(X, y)
    assert_almost_equal(clf.alpha_, 0.056, 2)

    clf = LassoCV(n_alphas=10, eps=1e-3, max_iter=max_iter, precompute=True, cv=3)
    clf.fit(X, y)
    assert_almost_equal(clf.alpha_, 0.056, 2)

    # Check that the lars and the coordinate descent implementation
    # select a similar alpha
    lars = LassoLarsCV(max_iter=30, cv=3).fit(X, y)
    # for this we check that they don't fall in the grid of
    # clf.alphas further than 1
    assert (
        np.abs(
            np.searchsorted(clf.alphas_[::-1], lars.alpha_)
            - np.searchsorted(clf.alphas_[::-1], clf.alpha_)
        )
        <= 1
    )
    # check that they also give a similar MSE
    mse_lars = interpolate.interp1d(lars.cv_alphas_, lars.mse_path_.T)
    assert_allclose(mse_lars(clf.alphas_[5]).mean(), clf.mse_path_[5].mean(), rtol=1e-2)

    # test set
    assert clf.score(X_test, y_test) > 0.99


def test_lasso_cv_with_some_model_selection():
    from sklearn import datasets
    from sklearn.model_selection import ShuffleSplit

    diabetes = datasets.load_diabetes()
    X = diabetes.data
    y = diabetes.target

    pipe = make_pipeline(StandardScaler(), LassoCV(cv=ShuffleSplit(random_state=0)))
    pipe.fit(X, y)


def test_lasso_cv_positive_constraint():
    X, y, X_test, y_test = build_dataset()
    max_iter = 500

    # Ensure the unconstrained fit has a negative coefficient
    clf_unconstrained = LassoCV(n_alphas=3, eps=1e-1, max_iter=max_iter, cv=2, n_jobs=1)
    clf_unconstrained.fit(X, y)
    assert min(clf_unconstrained.coef_) < 0

    # On same data, constrained fit has non-negative coefficients
    clf_constrained = LassoCV(
        n_alphas=3, eps=1e-1, max_iter=max_iter, positive=True, cv=2, n_jobs=1
    )
    clf_constrained.fit(X, y)
    assert min(clf_constrained.coef_) >= 0


@pytest.mark.parametrize(
    "alphas, err_type, err_msg",
    [
        ((1, -1, -100), ValueError, r"alphas\[1\] == -1, must be >= 0.0."),
        (
            (-0.1, -1.0, -10.0),
            ValueError,
            r"alphas\[0\] == -0.1, must be >= 0.0.",
        ),
        (
            (1, 1.0, "1"),
            TypeError,
            r"alphas\[2\] must be an instance of float, not str",
        ),
    ],
)
def test_lassocv_alphas_validation(alphas, err_type, err_msg):
    """Check the `alphas` validation in LassoCV."""

    n_samples, n_features = 5, 5
    rng = np.random.RandomState(0)
    X = rng.randn(n_samples, n_features)
    y = rng.randint(0, 2, n_samples)
    lassocv = LassoCV(alphas=alphas)
    with pytest.raises(err_type, match=err_msg):
        lassocv.fit(X, y)


def _scale_alpha_inplace(estimator, n_samples):
    """Rescale the parameter alpha from when the estimator is evoked with
    normalize set to True as if it were evoked in a Pipeline with normalize set
    to False and with a StandardScaler.
    """
    if ("alpha" not in estimator.get_params()) and (
        "alphas" not in estimator.get_params()
    ):
        return

    if isinstance(estimator, (RidgeCV, RidgeClassifierCV)):
        # alphas is not validated at this point and can be a list.
        # We convert it to a np.ndarray to make sure broadcasting
        # is used.
        alphas = np.asarray(estimator.alphas) * n_samples
        return estimator.set_params(alphas=alphas)
    if isinstance(estimator, (Lasso, LassoLars, MultiTaskLasso)):
        alpha = estimator.alpha * np.sqrt(n_samples)
    if isinstance(estimator, (Ridge, RidgeClassifier)):
        alpha = estimator.alpha * n_samples
    if isinstance(estimator, (ElasticNet, MultiTaskElasticNet)):
        if estimator.l1_ratio == 1:
            alpha = estimator.alpha * np.sqrt(n_samples)
        elif estimator.l1_ratio == 0:
            alpha = estimator.alpha * n_samples
        else:
            # To avoid silent errors in case of refactoring
            raise NotImplementedError

    estimator.set_params(alpha=alpha)


@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
@pytest.mark.parametrize(
    "LinearModel, params",
    [
        (Lasso, {"tol": 1e-16, "alpha": 0.1}),
        (LassoCV, {"tol": 1e-16}),
        (ElasticNetCV, {}),
        (RidgeClassifier, {"solver": "sparse_cg", "alpha": 0.1}),
        (ElasticNet, {"tol": 1e-16, "l1_ratio": 1, "alpha": 0.01}),
        (ElasticNet, {"tol": 1e-16, "l1_ratio": 0, "alpha": 0.01}),
        (Ridge, {"solver": "sparse_cg", "tol": 1e-12, "alpha": 0.1}),
        (LinearRegression, {}),
        (RidgeCV, {}),
        (RidgeClassifierCV, {}),
    ],
)
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_model_pipeline_same_dense_and_sparse(LinearModel, params, csr_container):
    # Test that linear model preceded by StandardScaler in the pipeline and
    # with normalize set to False gives the same y_pred and the same .coef_
    # given X sparse or dense

    model_dense = make_pipeline(StandardScaler(with_mean=False), LinearModel(**params))

    model_sparse = make_pipeline(StandardScaler(with_mean=False), LinearModel(**params))

    # prepare the data
    rng = np.random.RandomState(0)
    n_samples = 200
    n_features = 2
    X = rng.randn(n_samples, n_features)
    X[X < 0.1] = 0.0

    X_sparse = csr_container(X)
    y = rng.rand(n_samples)

    if is_classifier(model_dense):
        y = np.sign(y)

    model_dense.fit(X, y)
    model_sparse.fit(X_sparse, y)

    assert_allclose(model_sparse[1].coef_, model_dense[1].coef_)
    y_pred_dense = model_dense.predict(X)
    y_pred_sparse = model_sparse.predict(X_sparse)
    assert_allclose(y_pred_dense, y_pred_sparse)

    assert_allclose(model_dense[1].intercept_, model_sparse[1].intercept_)


def test_lasso_path_return_models_vs_new_return_gives_same_coefficients():
    # Test that lasso_path with lars_path style output gives the
    # same result

    # Some toy data
    X = np.array([[1, 2, 3.1], [2.3, 5.4, 4.3]]).T
    y = np.array([1, 2, 3.1])
    alphas = [5.0, 1.0, 0.5]

    # Use lars_path and lasso_path(new output) with 1D linear interpolation
    # to compute the same path
    alphas_lars, _, coef_path_lars = lars_path(X, y, method="lasso")
    coef_path_cont_lars = interpolate.interp1d(
        alphas_lars[::-1], coef_path_lars[:, ::-1]
    )
    alphas_lasso2, coef_path_lasso2, _ = lasso_path(X, y, alphas=alphas)
    coef_path_cont_lasso = interpolate.interp1d(
        alphas_lasso2[::-1], coef_path_lasso2[:, ::-1]
    )

    assert_array_almost_equal(
        coef_path_cont_lasso(alphas), coef_path_cont_lars(alphas), decimal=1
    )


def test_enet_path():
    # We use a large number of samples and of informative features so that
    # the l1_ratio selected is more toward ridge than lasso
    X, y, X_test, y_test = build_dataset(
        n_samples=200, n_features=100, n_informative_features=100
    )
    max_iter = 150

    # Here we have a small number of iterations, and thus the
    # ElasticNet might not converge. This is to speed up tests
    clf = ElasticNetCV(
        alphas=[0.01, 0.05, 0.1], eps=2e-3, l1_ratio=[0.5, 0.7], cv=3, max_iter=max_iter
    )
    ignore_warnings(clf.fit)(X, y)
    # Well-conditioned settings, we should have selected our
    # smallest penalty
    assert_almost_equal(clf.alpha_, min(clf.alphas_))
    # Non-sparse ground truth: we should have selected an elastic-net
    # that is closer to ridge than to lasso
    assert clf.l1_ratio_ == min(clf.l1_ratio)

    clf = ElasticNetCV(
        alphas=[0.01, 0.05, 0.1],
        eps=2e-3,
        l1_ratio=[0.5, 0.7],
        cv=3,
        max_iter=max_iter,
        precompute=True,
    )
    ignore_warnings(clf.fit)(X, y)

    # Well-conditioned settings, we should have selected our
    # smallest penalty
    assert_almost_equal(clf.alpha_, min(clf.alphas_))
    # Non-sparse ground truth: we should have selected an elastic-net
    # that is closer to ridge than to lasso
    assert clf.l1_ratio_ == min(clf.l1_ratio)

    # We are in well-conditioned settings with low noise: we should
    # have a good test-set performance
    assert clf.score(X_test, y_test) > 0.99

    # Multi-output/target case
    X, y, X_test, y_test = build_dataset(n_features=10, n_targets=3)
    clf = MultiTaskElasticNetCV(
        n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7], cv=3, max_iter=max_iter
    )
    ignore_warnings(clf.fit)(X, y)
    # We are in well-conditioned settings with low noise: we should
    # have a good test-set performance
    assert clf.score(X_test, y_test) > 0.99
    assert clf.coef_.shape == (3, 10)

    # Mono-output should have same cross-validated alpha_ and l1_ratio_
    # in both cases.
    X, y, _, _ = build_dataset(n_features=10)
    clf1 = ElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
    clf1.fit(X, y)
    clf2 = MultiTaskElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
    clf2.fit(X, y[:, np.newaxis])
    assert_almost_equal(clf1.l1_ratio_, clf2.l1_ratio_)
    assert_almost_equal(clf1.alpha_, clf2.alpha_)


def test_path_parameters():
    X, y, _, _ = build_dataset()
    max_iter = 100

    clf = ElasticNetCV(n_alphas=50, eps=1e-3, max_iter=max_iter, l1_ratio=0.5, tol=1e-3)
    clf.fit(X, y)  # new params
    assert_almost_equal(0.5, clf.l1_ratio)
    assert 50 == clf.n_alphas
    assert 50 == len(clf.alphas_)


def test_warm_start():
    X, y, _, _ = build_dataset()
    clf = ElasticNet(alpha=0.1, max_iter=5, warm_start=True)
    ignore_warnings(clf.fit)(X, y)
    ignore_warnings(clf.fit)(X, y)  # do a second round with 5 iterations

    clf2 = ElasticNet(alpha=0.1, max_iter=10)
    ignore_warnings(clf2.fit)(X, y)
    assert_array_almost_equal(clf2.coef_, clf.coef_)


def test_lasso_alpha_warning():
    X = [[-1], [0], [1]]
    Y = [-1, 0, 1]  # just a straight line

    clf = Lasso(alpha=0)
    warning_message = (
        "With alpha=0, this algorithm does not "
        "converge well. You are advised to use the "
        "LinearRegression estimator"
    )
    with pytest.warns(UserWarning, match=warning_message):
        clf.fit(X, Y)


def test_lasso_positive_constraint():
    X = [[-1], [0], [1]]
    y = [1, 0, -1]  # just a straight line with negative slope

    lasso = Lasso(alpha=0.1, positive=True)
    lasso.fit(X, y)
    assert min(lasso.coef_) >= 0

    lasso = Lasso(alpha=0.1, precompute=True, positive=True)
    lasso.fit(X, y)
    assert min(lasso.coef_) >= 0


def test_enet_positive_constraint():
    X = [[-1], [0], [1]]
    y = [1, 0, -1]  # just a straight line with negative slope

    enet = ElasticNet(alpha=0.1, positive=True)
    enet.fit(X, y)
    assert min(enet.coef_) >= 0


def test_enet_cv_positive_constraint():
    X, y, X_test, y_test = build_dataset()
    max_iter = 500

    # Ensure the unconstrained fit has a negative coefficient
    enetcv_unconstrained = ElasticNetCV(
        n_alphas=3, eps=1e-1, max_iter=max_iter, cv=2, n_jobs=1
    )
    enetcv_unconstrained.fit(X, y)
    assert min(enetcv_unconstrained.coef_) < 0

    # On same data, constrained fit has non-negative coefficients
    enetcv_constrained = ElasticNetCV(
        n_alphas=3, eps=1e-1, max_iter=max_iter, cv=2, positive=True, n_jobs=1
    )
    enetcv_constrained.fit(X, y)
    assert min(enetcv_constrained.coef_) >= 0


def test_uniform_targets():
    enet = ElasticNetCV(n_alphas=3)
    m_enet = MultiTaskElasticNetCV(n_alphas=3)
    lasso = LassoCV(n_alphas=3)
    m_lasso = MultiTaskLassoCV(n_alphas=3)

    models_single_task = (enet, lasso)
    models_multi_task = (m_enet, m_lasso)

    rng = np.random.RandomState(0)

    X_train = rng.random_sample(size=(10, 3))
    X_test = rng.random_sample(size=(10, 3))

    y1 = np.empty(10)
    y2 = np.empty((10, 2))

    for model in models_single_task:
        for y_values in (0, 5):
            y1.fill(y_values)
            with ignore_warnings(category=ConvergenceWarning):
                assert_array_equal(model.fit(X_train, y1).predict(X_test), y1)
            assert_array_equal(model.alphas_, [np.finfo(float).resolution] * 3)

    for model in models_multi_task:
        for y_values in (0, 5):
            y2[:, 0].fill(y_values)
            y2[:, 1].fill(2 * y_values)
            with ignore_warnings(category=ConvergenceWarning):
                assert_array_equal(model.fit(X_train, y2).predict(X_test), y2)
            assert_array_equal(model.alphas_, [np.finfo(float).resolution] * 3)


def test_multi_task_lasso_and_enet():
    X, y, X_test, y_test = build_dataset()
    Y = np.c_[y, y]
    # Y_test = np.c_[y_test, y_test]
    clf = MultiTaskLasso(alpha=1, tol=1e-8).fit(X, Y)
    assert 0 < clf.dual_gap_ < 1e-5
    assert_array_almost_equal(clf.coef_[0], clf.coef_[1])

    clf = MultiTaskElasticNet(alpha=1, tol=1e-8).fit(X, Y)
    assert 0 < clf.dual_gap_ < 1e-5
    assert_array_almost_equal(clf.coef_[0], clf.coef_[1])

    clf = MultiTaskElasticNet(alpha=1.0, tol=1e-8, max_iter=1)
    warning_message = (
        "Objective did not converge. You might want to "
        "increase the number of iterations."
    )
    with pytest.warns(ConvergenceWarning, match=warning_message):
        clf.fit(X, Y)


def test_lasso_readonly_data():
    X = np.array([[-1], [0], [1]])
    Y = np.array([-1, 0, 1])  # just a straight line
    T = np.array([[2], [3], [4]])  # test sample
    with TempMemmap((X, Y)) as (X, Y):
        clf = Lasso(alpha=0.5)
        clf.fit(X, Y)
        pred = clf.predict(T)
        assert_array_almost_equal(clf.coef_, [0.25])
        assert_array_almost_equal(pred, [0.5, 0.75, 1.0])
        assert_almost_equal(clf.dual_gap_, 0)


def test_multi_task_lasso_readonly_data():
    X, y, X_test, y_test = build_dataset()
    Y = np.c_[y, y]
    with TempMemmap((X, Y)) as (X, Y):
        Y = np.c_[y, y]
        clf = MultiTaskLasso(alpha=1, tol=1e-8).fit(X, Y)
        assert 0 < clf.dual_gap_ < 1e-5
        assert_array_almost_equal(clf.coef_[0], clf.coef_[1])


def test_enet_multitarget():
    n_targets = 3
    X, y, _, _ = build_dataset(
        n_samples=10, n_features=8, n_informative_features=10, n_targets=n_targets
    )
    estimator = ElasticNet(alpha=0.01)
    estimator.fit(X, y)
    coef, intercept, dual_gap = (
        estimator.coef_,
        estimator.intercept_,
        estimator.dual_gap_,
    )

    for k in range(n_targets):
        estimator.fit(X, y[:, k])
        assert_array_almost_equal(coef[k, :], estimator.coef_)
        assert_array_almost_equal(intercept[k], estimator.intercept_)
        assert_array_almost_equal(dual_gap[k], estimator.dual_gap_)


def test_multioutput_enetcv_error():
    rng = np.random.RandomState(0)
    X = rng.randn(10, 2)
    y = rng.randn(10, 2)
    clf = ElasticNetCV()
    with pytest.raises(ValueError):
        clf.fit(X, y)


def test_multitask_enet_and_lasso_cv():
    X, y, _, _ = build_dataset(n_features=50, n_targets=3)
    clf = MultiTaskElasticNetCV(cv=3).fit(X, y)
    assert_almost_equal(clf.alpha_, 0.00556, 3)
    clf = MultiTaskLassoCV(cv=3).fit(X, y)
    assert_almost_equal(clf.alpha_, 0.00278, 3)

    X, y, _, _ = build_dataset(n_targets=3)
    clf = MultiTaskElasticNetCV(
        n_alphas=10, eps=1e-3, max_iter=200, l1_ratio=[0.3, 0.5], tol=1e-3, cv=3
    )
    clf.fit(X, y)
    assert 0.5 == clf.l1_ratio_
    assert (3, X.shape[1]) == clf.coef_.shape
    assert (3,) == clf.intercept_.shape
    assert (2, 10, 3) == clf.mse_path_.shape
    assert (2, 10) == clf.alphas_.shape

    X, y, _, _ = build_dataset(n_targets=3)
    clf = MultiTaskLassoCV(n_alphas=10, eps=1e-3, max_iter=500, tol=1e-3, cv=3)
    clf.fit(X, y)
    assert (3, X.shape[1]) == clf.coef_.shape
    assert (3,) == clf.intercept_.shape
    assert (10, 3) == clf.mse_path_.shape
    assert 10 == len(clf.alphas_)


def test_1d_multioutput_enet_and_multitask_enet_cv():
    X, y, _, _ = build_dataset(n_features=10)
    y = y[:, np.newaxis]
    clf = ElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
    clf.fit(X, y[:, 0])
    clf1 = MultiTaskElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
    clf1.fit(X, y)
    assert_almost_equal(clf.l1_ratio_, clf1.l1_ratio_)
    assert_almost_equal(clf.alpha_, clf1.alpha_)
    assert_almost_equal(clf.coef_, clf1.coef_[0])
    assert_almost_equal(clf.intercept_, clf1.intercept_[0])


def test_1d_multioutput_lasso_and_multitask_lasso_cv():
    X, y, _, _ = build_dataset(n_features=10)
    y = y[:, np.newaxis]
    clf = LassoCV(n_alphas=5, eps=2e-3)
    clf.fit(X, y[:, 0])
    clf1 = MultiTaskLassoCV(n_alphas=5, eps=2e-3)
    clf1.fit(X, y)
    assert_almost_equal(clf.alpha_, clf1.alpha_)
    assert_almost_equal(clf.coef_, clf1.coef_[0])
    assert_almost_equal(clf.intercept_, clf1.intercept_[0])


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_sparse_input_dtype_enet_and_lassocv(csr_container):
    X, y, _, _ = build_dataset(n_features=10)
    clf = ElasticNetCV(n_alphas=5)
    clf.fit(csr_container(X), y)
    clf1 = ElasticNetCV(n_alphas=5)
    clf1.fit(csr_container(X, dtype=np.float32), y)
    assert_almost_equal(clf.alpha_, clf1.alpha_, decimal=6)
    assert_almost_equal(clf.coef_, clf1.coef_, decimal=6)

    clf = LassoCV(n_alphas=5)
    clf.fit(csr_container(X), y)
    clf1 = LassoCV(n_alphas=5)
    clf1.fit(csr_container(X, dtype=np.float32), y)
    assert_almost_equal(clf.alpha_, clf1.alpha_, decimal=6)
    assert_almost_equal(clf.coef_, clf1.coef_, decimal=6)


def test_elasticnet_precompute_incorrect_gram():
    # check that passing an invalid precomputed Gram matrix will raise an
    # error.
    X, y, _, _ = build_dataset()

    rng = np.random.RandomState(0)

    X_centered = X - np.average(X, axis=0)
    garbage = rng.standard_normal(X.shape)
    precompute = np.dot(garbage.T, garbage)

    clf = ElasticNet(alpha=0.01, precompute=precompute)
    msg = "Gram matrix.*did not pass validation.*"
    with pytest.raises(ValueError, match=msg):
        clf.fit(X_centered, y)


def test_elasticnet_precompute_gram_weighted_samples():
    # check the equivalence between passing a precomputed Gram matrix and
    # internal computation using sample weights.
    X, y, _, _ = build_dataset()

    rng = np.random.RandomState(0)
    sample_weight = rng.lognormal(size=y.shape)

    w_norm = sample_weight * (y.shape / np.sum(sample_weight))
    X_c = X - np.average(X, axis=0, weights=w_norm)
    X_r = X_c * np.sqrt(w_norm)[:, np.newaxis]
    gram = np.dot(X_r.T, X_r)

    clf1 = ElasticNet(alpha=0.01, precompute=gram)
    clf1.fit(X_c, y, sample_weight=sample_weight)

    clf2 = ElasticNet(alpha=0.01, precompute=False)
    clf2.fit(X, y, sample_weight=sample_weight)

    assert_allclose(clf1.coef_, clf2.coef_)


def test_elasticnet_precompute_gram():
    # Check the dtype-aware check for a precomputed Gram matrix
    # (see https://github.com/scikit-learn/scikit-learn/pull/22059
    # and https://github.com/scikit-learn/scikit-learn/issues/21997).
    # Here: (X_c.T, X_c)[2, 3] is not equal to np.dot(X_c[:, 2], X_c[:, 3])
    # but within tolerance for np.float32

    rng = np.random.RandomState(58)
    X = rng.binomial(1, 0.25, (1000, 4)).astype(np.float32)
    y = rng.rand(1000).astype(np.float32)

    X_c = X - np.average(X, axis=0)
    gram = np.dot(X_c.T, X_c)

    clf1 = ElasticNet(alpha=0.01, precompute=gram)
    clf1.fit(X_c, y)

    clf2 = ElasticNet(alpha=0.01, precompute=False)
    clf2.fit(X, y)

    assert_allclose(clf1.coef_, clf2.coef_)


def test_warm_start_convergence():
    X, y, _, _ = build_dataset()
    model = ElasticNet(alpha=1e-3, tol=1e-3).fit(X, y)
    n_iter_reference = model.n_iter_

    # This dataset is not trivial enough for the model to converge in one pass.
    assert n_iter_reference > 2

    # Check that n_iter_ is invariant to multiple calls to fit
    # when warm_start=False, all else being equal.
    model.fit(X, y)
    n_iter_cold_start = model.n_iter_
    assert n_iter_cold_start == n_iter_reference

    # Fit the same model again, using a warm start: the optimizer just performs
    # a single pass before checking that it has already converged
    model.set_params(warm_start=True)
    model.fit(X, y)
    n_iter_warm_start = model.n_iter_
    assert n_iter_warm_start == 1


def test_warm_start_convergence_with_regularizer_decrement():
    X, y = load_diabetes(return_X_y=True)

    # Train a model to converge on a lightly regularized problem
    final_alpha = 1e-5
    low_reg_model = ElasticNet(alpha=final_alpha).fit(X, y)

    # Fitting a new model on a more regularized version of the same problem.
    # Fitting with high regularization is easier it should converge faster
    # in general.
    high_reg_model = ElasticNet(alpha=final_alpha * 10).fit(X, y)
    assert low_reg_model.n_iter_ > high_reg_model.n_iter_

    # Fit the solution to the original, less regularized version of the
    # problem but from the solution of the highly regularized variant of
    # the problem as a better starting point. This should also converge
    # faster than the original model that starts from zero.
    warm_low_reg_model = deepcopy(high_reg_model)
    warm_low_reg_model.set_params(warm_start=True, alpha=final_alpha)
    warm_low_reg_model.fit(X, y)
    assert low_reg_model.n_iter_ > warm_low_reg_model.n_iter_


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_random_descent(csr_container):
    # Test that both random and cyclic selection give the same results.
    # Ensure that the test models fully converge and check a wide
    # range of conditions.

    # This uses the coordinate descent algo using the gram trick.
    X, y, _, _ = build_dataset(n_samples=50, n_features=20)
    clf_cyclic = ElasticNet(selection="cyclic", tol=1e-8)
    clf_cyclic.fit(X, y)
    clf_random = ElasticNet(selection="random", tol=1e-8, random_state=42)
    clf_random.fit(X, y)
    assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
    assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)

    # This uses the descent algo without the gram trick
    clf_cyclic = ElasticNet(selection="cyclic", tol=1e-8)
    clf_cyclic.fit(X.T, y[:20])
    clf_random = ElasticNet(selection="random", tol=1e-8, random_state=42)
    clf_random.fit(X.T, y[:20])
    assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
    assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)

    # Sparse Case
    clf_cyclic = ElasticNet(selection="cyclic", tol=1e-8)
    clf_cyclic.fit(csr_container(X), y)
    clf_random = ElasticNet(selection="random", tol=1e-8, random_state=42)
    clf_random.fit(csr_container(X), y)
    assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
    assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)

    # Multioutput case.
    new_y = np.hstack((y[:, np.newaxis], y[:, np.newaxis]))
    clf_cyclic = MultiTaskElasticNet(selection="cyclic", tol=1e-8)
    clf_cyclic.fit(X, new_y)
    clf_random = MultiTaskElasticNet(selection="random", tol=1e-8, random_state=42)
    clf_random.fit(X, new_y)
    assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
    assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)


def test_enet_path_positive():
    # Test positive parameter

    X, Y, _, _ = build_dataset(n_samples=50, n_features=50, n_targets=2)

    # For mono output
    # Test that the coefs returned by positive=True in enet_path are positive
    for path in [enet_path, lasso_path]:
        pos_path_coef = path(X, Y[:, 0], positive=True)[1]
        assert np.all(pos_path_coef >= 0)

    # For multi output, positive parameter is not allowed
    # Test that an error is raised
    for path in [enet_path, lasso_path]:
        with pytest.raises(ValueError):
            path(X, Y, positive=True)


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_sparse_dense_descent_paths(csr_container):
    # Test that dense and sparse input give the same input for descent paths.
    X, y, _, _ = build_dataset(n_samples=50, n_features=20)
    csr = csr_container(X)
    for path in [enet_path, lasso_path]:
        _, coefs, _ = path(X, y)
        _, sparse_coefs, _ = path(csr, y)
        assert_array_almost_equal(coefs, sparse_coefs)


@pytest.mark.parametrize("path_func", [enet_path, lasso_path])
def test_path_unknown_parameter(path_func):
    """Check that passing parameter not used by the coordinate descent solver
    will raise an error."""
    X, y, _, _ = build_dataset(n_samples=50, n_features=20)
    err_msg = "Unexpected parameters in params"
    with pytest.raises(ValueError, match=err_msg):
        path_func(X, y, normalize=True, fit_intercept=True)


def test_check_input_false():
    X, y, _, _ = build_dataset(n_samples=20, n_features=10)
    X = check_array(X, order="F", dtype="float64")
    y = check_array(X, order="F", dtype="float64")
    clf = ElasticNet(selection="cyclic", tol=1e-8)
    # Check that no error is raised if data is provided in the right format
    clf.fit(X, y, check_input=False)
    # With check_input=False, an exhaustive check is not made on y but its
    # dtype is still cast in _preprocess_data to X's dtype. So the test should
    # pass anyway
    X = check_array(X, order="F", dtype="float32")
    with ignore_warnings(category=ConvergenceWarning):
        clf.fit(X, y, check_input=False)
    # With no input checking, providing X in C order should result in false
    # computation
    X = check_array(X, order="C", dtype="float64")
    with pytest.raises(ValueError):
        clf.fit(X, y, check_input=False)


@pytest.mark.parametrize("check_input", [True, False])
def test_enet_copy_X_True(check_input):
    X, y, _, _ = build_dataset()
    X = X.copy(order="F")

    original_X = X.copy()
    enet = ElasticNet(copy_X=True)
    enet.fit(X, y, check_input=check_input)

    assert_array_equal(original_X, X)


def test_enet_copy_X_False_check_input_False():
    X, y, _, _ = build_dataset()
    X = X.copy(order="F")

    original_X = X.copy()
    enet = ElasticNet(copy_X=False)
    enet.fit(X, y, check_input=False)

    # No copying, X is overwritten
    assert np.any(np.not_equal(original_X, X))


def test_overrided_gram_matrix():
    X, y, _, _ = build_dataset(n_samples=20, n_features=10)
    Gram = X.T.dot(X)
    clf = ElasticNet(selection="cyclic", tol=1e-8, precompute=Gram)
    warning_message = (
        "Gram matrix was provided but X was centered"
        " to fit intercept: recomputing Gram matrix."
    )
    with pytest.warns(UserWarning, match=warning_message):
        clf.fit(X, y)


@pytest.mark.parametrize("model", [ElasticNet, Lasso])
def test_lasso_non_float_y(model):
    X = [[0, 0], [1, 1], [-1, -1]]
    y = [0, 1, 2]
    y_float = [0.0, 1.0, 2.0]

    clf = model(fit_intercept=False)
    clf.fit(X, y)
    clf_float = model(fit_intercept=False)
    clf_float.fit(X, y_float)
    assert_array_equal(clf.coef_, clf_float.coef_)


def test_enet_float_precision():
    # Generate dataset
    X, y, X_test, y_test = build_dataset(n_samples=20, n_features=10)
    # Here we have a small number of iterations, and thus the
    # ElasticNet might not converge. This is to speed up tests

    for fit_intercept in [True, False]:
        coef = {}
        intercept = {}
        for dtype in [np.float64, np.float32]:
            clf = ElasticNet(
                alpha=0.5,
                max_iter=100,
                precompute=False,
                fit_intercept=fit_intercept,
            )

            X = dtype(X)
            y = dtype(y)
            ignore_warnings(clf.fit)(X, y)

            coef[("simple", dtype)] = clf.coef_
            intercept[("simple", dtype)] = clf.intercept_

            assert clf.coef_.dtype == dtype

            # test precompute Gram array
            Gram = X.T.dot(X)
            clf_precompute = ElasticNet(
                alpha=0.5,
                max_iter=100,
                precompute=Gram,
                fit_intercept=fit_intercept,
            )
            ignore_warnings(clf_precompute.fit)(X, y)
            assert_array_almost_equal(clf.coef_, clf_precompute.coef_)
            assert_array_almost_equal(clf.intercept_, clf_precompute.intercept_)

            # test multi task enet
            multi_y = np.hstack((y[:, np.newaxis], y[:, np.newaxis]))
            clf_multioutput = MultiTaskElasticNet(
                alpha=0.5,
                max_iter=100,
                fit_intercept=fit_intercept,
            )
            clf_multioutput.fit(X, multi_y)
            coef[("multi", dtype)] = clf_multioutput.coef_
            intercept[("multi", dtype)] = clf_multioutput.intercept_
            assert clf.coef_.dtype == dtype

        for v in ["simple", "multi"]:
            assert_array_almost_equal(
                coef[(v, np.float32)], coef[(v, np.float64)], decimal=4
            )
            assert_array_almost_equal(
                intercept[(v, np.float32)], intercept[(v, np.float64)], decimal=4
            )


@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
def test_enet_l1_ratio():
    # Test that an error message is raised if an estimator that
    # uses _alpha_grid is called with l1_ratio=0
    msg = (
        "Automatic alpha grid generation is not supported for l1_ratio=0. "
        "Please supply a grid by providing your estimator with the "
        "appropriate `alphas=` argument."
    )
    X = np.array([[1, 2, 4, 5, 8], [3, 5, 7, 7, 8]]).T
    y = np.array([12, 10, 11, 21, 5])

    with pytest.raises(ValueError, match=msg):
        ElasticNetCV(l1_ratio=0, random_state=42).fit(X, y)

    with pytest.raises(ValueError, match=msg):
        MultiTaskElasticNetCV(l1_ratio=0, random_state=42).fit(X, y[:, None])

    # Test that l1_ratio=0 with alpha>0 produces user warning
    warning_message = (
        "Coordinate descent without L1 regularization may "
        "lead to unexpected results and is discouraged. "
        "Set l1_ratio > 0 to add L1 regularization."
    )
    est = ElasticNetCV(l1_ratio=[0], alphas=[1])
    with pytest.warns(UserWarning, match=warning_message):
        est.fit(X, y)

    # Test that l1_ratio=0 is allowed if we supply a grid manually
    alphas = [0.1, 10]
    estkwds = {"alphas": alphas, "random_state": 42}
    est_desired = ElasticNetCV(l1_ratio=0.00001, **estkwds)
    est = ElasticNetCV(l1_ratio=0, **estkwds)
    with ignore_warnings():
        est_desired.fit(X, y)
        est.fit(X, y)
    assert_array_almost_equal(est.coef_, est_desired.coef_, decimal=5)

    est_desired = MultiTaskElasticNetCV(l1_ratio=0.00001, **estkwds)
    est = MultiTaskElasticNetCV(l1_ratio=0, **estkwds)
    with ignore_warnings():
        est.fit(X, y[:, None])
        est_desired.fit(X, y[:, None])
    assert_array_almost_equal(est.coef_, est_desired.coef_, decimal=5)


def test_coef_shape_not_zero():
    est_no_intercept = Lasso(fit_intercept=False)
    est_no_intercept.fit(np.c_[np.ones(3)], np.ones(3))
    assert est_no_intercept.coef_.shape == (1,)


def test_warm_start_multitask_lasso():
    X, y, X_test, y_test = build_dataset()
    Y = np.c_[y, y]
    clf = MultiTaskLasso(alpha=0.1, max_iter=5, warm_start=True)
    ignore_warnings(clf.fit)(X, Y)
    ignore_warnings(clf.fit)(X, Y)  # do a second round with 5 iterations

    clf2 = MultiTaskLasso(alpha=0.1, max_iter=10)
    ignore_warnings(clf2.fit)(X, Y)
    assert_array_almost_equal(clf2.coef_, clf.coef_)


@pytest.mark.parametrize(
    "klass, n_classes, kwargs",
    [
        (Lasso, 1, dict(precompute=True)),
        (Lasso, 1, dict(precompute=False)),
    ],
)
def test_enet_coordinate_descent(klass, n_classes, kwargs):
    """Test that a warning is issued if model does not converge"""
    clf = klass(max_iter=2, **kwargs)
    n_samples = 5
    n_features = 2
    X = np.ones((n_samples, n_features)) * 1e50
    y = np.ones((n_samples, n_classes))
    if klass == Lasso:
        y = y.ravel()
    warning_message = (
        "Objective did not converge. You might want to"
        " increase the number of iterations."
    )
    with pytest.warns(ConvergenceWarning, match=warning_message):
        clf.fit(X, y)


def test_convergence_warnings():
    random_state = np.random.RandomState(0)
    X = random_state.standard_normal((1000, 500))
    y = random_state.standard_normal((1000, 3))

    # check that the model converges w/o convergence warnings
    with warnings.catch_warnings():
        warnings.simplefilter("error", ConvergenceWarning)
        MultiTaskElasticNet().fit(X, y)


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_sparse_input_convergence_warning(csr_container):
    X, y, _, _ = build_dataset(n_samples=1000, n_features=500)

    with pytest.warns(ConvergenceWarning):
        ElasticNet(max_iter=1, tol=0).fit(csr_container(X, dtype=np.float32), y)

    # check that the model converges w/o convergence warnings
    with warnings.catch_warnings():
        warnings.simplefilter("error", ConvergenceWarning)
        Lasso().fit(csr_container(X, dtype=np.float32), y)


@pytest.mark.parametrize(
    "precompute, inner_precompute",
    [
        (True, True),
        ("auto", False),
        (False, False),
    ],
)
def test_lassoCV_does_not_set_precompute(monkeypatch, precompute, inner_precompute):
    X, y, _, _ = build_dataset()
    calls = 0

    class LassoMock(Lasso):
        def fit(self, X, y):
            super().fit(X, y)
            nonlocal calls
            calls += 1
            assert self.precompute == inner_precompute

    monkeypatch.setattr("sklearn.linear_model._coordinate_descent.Lasso", LassoMock)
    clf = LassoCV(precompute=precompute)
    clf.fit(X, y)
    assert calls > 0


def test_multi_task_lasso_cv_dtype():
    n_samples, n_features = 10, 3
    rng = np.random.RandomState(42)
    X = rng.binomial(1, 0.5, size=(n_samples, n_features))
    X = X.astype(int)  # make it explicit that X is int
    y = X[:, [0, 0]].copy()
    est = MultiTaskLassoCV(n_alphas=5, fit_intercept=True).fit(X, y)
    assert_array_almost_equal(est.coef_, [[1, 0, 0]] * 2, decimal=3)


@pytest.mark.parametrize("fit_intercept", [True, False])
@pytest.mark.parametrize("alpha", [0.01])
@pytest.mark.parametrize("precompute", [False, True])
@pytest.mark.parametrize("sparse_container", [None] + CSR_CONTAINERS)
def test_enet_sample_weight_consistency(
    fit_intercept, alpha, precompute, sparse_container, global_random_seed
):
    """Test that the impact of sample_weight is consistent.

    Note that this test is stricter than the common test
    check_sample_weight_equivalence alone and also tests sparse X.
    """
    rng = np.random.RandomState(global_random_seed)
    n_samples, n_features = 10, 5

    X = rng.rand(n_samples, n_features)
    y = rng.rand(n_samples)
    if sparse_container is not None:
        X = sparse_container(X)
    params = dict(
        alpha=alpha,
        fit_intercept=fit_intercept,
        precompute=precompute,
        tol=1e-6,
        l1_ratio=0.5,
    )

    reg = ElasticNet(**params).fit(X, y)
    coef = reg.coef_.copy()
    if fit_intercept:
        intercept = reg.intercept_

    # 1) sample_weight=np.ones(..) should be equivalent to sample_weight=None
    sample_weight = np.ones_like(y)
    reg.fit(X, y, sample_weight=sample_weight)
    assert_allclose(reg.coef_, coef, rtol=1e-6)
    if fit_intercept:
        assert_allclose(reg.intercept_, intercept)

    # 2) sample_weight=None should be equivalent to sample_weight = number
    sample_weight = 123.0
    reg.fit(X, y, sample_weight=sample_weight)
    assert_allclose(reg.coef_, coef, rtol=1e-6)
    if fit_intercept:
        assert_allclose(reg.intercept_, intercept)

    # 3) scaling of sample_weight should have no effect, cf. np.average()
    sample_weight = rng.uniform(low=0.01, high=2, size=X.shape[0])
    reg = reg.fit(X, y, sample_weight=sample_weight)
    coef = reg.coef_.copy()
    if fit_intercept:
        intercept = reg.intercept_

    reg.fit(X, y, sample_weight=np.pi * sample_weight)
    assert_allclose(reg.coef_, coef, rtol=1e-6)
    if fit_intercept:
        assert_allclose(reg.intercept_, intercept)

    # 4) setting elements of sample_weight to 0 is equivalent to removing these samples
    sample_weight_0 = sample_weight.copy()
    sample_weight_0[-5:] = 0
    y[-5:] *= 1000  # to make excluding those samples important
    reg.fit(X, y, sample_weight=sample_weight_0)
    coef_0 = reg.coef_.copy()
    if fit_intercept:
        intercept_0 = reg.intercept_
    reg.fit(X[:-5], y[:-5], sample_weight=sample_weight[:-5])
    assert_allclose(reg.coef_, coef_0, rtol=1e-6)
    if fit_intercept:
        assert_allclose(reg.intercept_, intercept_0)

    # 5) check that multiplying sample_weight by 2 is equivalent to repeating
    # corresponding samples twice
    if sparse_container is not None:
        X2 = sparse.vstack([X, X[: n_samples // 2]], format="csc")
    else:
        X2 = np.concatenate([X, X[: n_samples // 2]], axis=0)
    y2 = np.concatenate([y, y[: n_samples // 2]])
    sample_weight_1 = sample_weight.copy()
    sample_weight_1[: n_samples // 2] *= 2
    sample_weight_2 = np.concatenate(
        [sample_weight, sample_weight[: n_samples // 2]], axis=0
    )

    reg1 = ElasticNet(**params).fit(X, y, sample_weight=sample_weight_1)
    reg2 = ElasticNet(**params).fit(X2, y2, sample_weight=sample_weight_2)
    assert_allclose(reg1.coef_, reg2.coef_, rtol=1e-6)


@pytest.mark.parametrize("fit_intercept", [True, False])
@pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS)
def test_enet_cv_sample_weight_correctness(
    fit_intercept, sparse_container, global_random_seed
):
    """Test that ElasticNetCV with sample weights gives correct results.

    We fit the same model twice, once with weighted training data, once with repeated
    data points in the training data and check that both models converge to the
    same solution.

    Since this model uses an internal cross-validation scheme to tune the alpha
    regularization parameter, we make sure that the repetitions only occur within
    a specific CV group. Data points belonging to other CV groups stay
    unit-weighted / "unrepeated".
    """
    rng = np.random.RandomState(global_random_seed)
    n_splits, n_samples_per_cv, n_features = 3, 10, 5
    X_with_weights = rng.rand(n_splits * n_samples_per_cv, n_features)
    beta = rng.rand(n_features)
    beta[0:2] = 0
    y_with_weights = X_with_weights @ beta + rng.rand(n_splits * n_samples_per_cv)

    if sparse_container is not None:
        X_with_weights = sparse_container(X_with_weights)
    params = dict(tol=1e-6)

    # Assign random integer weights only to the first cross-validation group.
    # The samples in the other cross-validation groups are left with unit
    # weights.

    sw = np.ones_like(y_with_weights)
    sw[:n_samples_per_cv] = rng.randint(0, 5, size=n_samples_per_cv)
    groups_with_weights = np.concatenate(
        [
            np.full(n_samples_per_cv, 0),
            np.full(n_samples_per_cv, 1),
            np.full(n_samples_per_cv, 2),
        ]
    )
    splits_with_weights = list(
        LeaveOneGroupOut().split(X_with_weights, groups=groups_with_weights)
    )
    reg_with_weights = ElasticNetCV(
        cv=splits_with_weights, fit_intercept=fit_intercept, **params
    )

    reg_with_weights.fit(X_with_weights, y_with_weights, sample_weight=sw)

    if sparse_container is not None:
        X_with_weights = X_with_weights.toarray()
    X_with_repetitions = np.repeat(X_with_weights, sw.astype(int), axis=0)
    if sparse_container is not None:
        X_with_repetitions = sparse_container(X_with_repetitions)

    y_with_repetitions = np.repeat(y_with_weights, sw.astype(int), axis=0)
    groups_with_repetitions = np.repeat(groups_with_weights, sw.astype(int), axis=0)

    splits_with_repetitions = list(
        LeaveOneGroupOut().split(X_with_repetitions, groups=groups_with_repetitions)
    )
    reg_with_repetitions = ElasticNetCV(
        cv=splits_with_repetitions, fit_intercept=fit_intercept, **params
    )
    reg_with_repetitions.fit(X_with_repetitions, y_with_repetitions)

    # Check that the alpha selection process is the same:
    assert_allclose(reg_with_weights.mse_path_, reg_with_repetitions.mse_path_)
    assert_allclose(reg_with_weights.alphas_, reg_with_repetitions.alphas_)
    assert reg_with_weights.alpha_ == pytest.approx(reg_with_repetitions.alpha_)

    # Check that the final model coefficients are the same:
    assert_allclose(reg_with_weights.coef_, reg_with_repetitions.coef_, atol=1e-10)
    assert reg_with_weights.intercept_ == pytest.approx(reg_with_repetitions.intercept_)


@pytest.mark.parametrize("sample_weight", [False, True])
def test_enet_cv_grid_search(sample_weight):
    """Test that ElasticNetCV gives same result as GridSearchCV."""
    n_samples, n_features = 200, 10
    cv = 5
    X, y = make_regression(
        n_samples=n_samples,
        n_features=n_features,
        effective_rank=10,
        n_informative=n_features - 4,
        noise=10,
        random_state=0,
    )
    if sample_weight:
        sample_weight = np.linspace(1, 5, num=n_samples)
    else:
        sample_weight = None

    alphas = np.logspace(np.log10(1e-5), np.log10(1), num=10)
    l1_ratios = [0.1, 0.5, 0.9]
    reg = ElasticNetCV(cv=cv, alphas=alphas, l1_ratio=l1_ratios)
    reg.fit(X, y, sample_weight=sample_weight)

    param = {"alpha": alphas, "l1_ratio": l1_ratios}
    gs = GridSearchCV(
        estimator=ElasticNet(),
        param_grid=param,
        cv=cv,
        scoring="neg_mean_squared_error",
    ).fit(X, y, sample_weight=sample_weight)

    assert reg.l1_ratio_ == pytest.approx(gs.best_params_["l1_ratio"])
    assert reg.alpha_ == pytest.approx(gs.best_params_["alpha"])


@pytest.mark.parametrize("fit_intercept", [True, False])
@pytest.mark.parametrize("l1_ratio", [0, 0.5, 1])
@pytest.mark.parametrize("precompute", [False, True])
@pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS)
def test_enet_cv_sample_weight_consistency(
    fit_intercept, l1_ratio, precompute, sparse_container
):
    """Test that the impact of sample_weight is consistent."""
    rng = np.random.RandomState(0)
    n_samples, n_features = 10, 5

    X = rng.rand(n_samples, n_features)
    y = X.sum(axis=1) + rng.rand(n_samples)
    params = dict(
        l1_ratio=l1_ratio,
        fit_intercept=fit_intercept,
        precompute=precompute,
        tol=1e-6,
        cv=3,
    )
    if sparse_container is not None:
        X = sparse_container(X)

    if l1_ratio == 0:
        params.pop("l1_ratio", None)
        reg = LassoCV(**params).fit(X, y)
    else:
        reg = ElasticNetCV(**params).fit(X, y)
    coef = reg.coef_.copy()
    if fit_intercept:
        intercept = reg.intercept_

    # sample_weight=np.ones(..) should be equivalent to sample_weight=None
    sample_weight = np.ones_like(y)
    reg.fit(X, y, sample_weight=sample_weight)
    assert_allclose(reg.coef_, coef, rtol=1e-6)
    if fit_intercept:
        assert_allclose(reg.intercept_, intercept)

    # sample_weight=None should be equivalent to sample_weight = number
    sample_weight = 123.0
    reg.fit(X, y, sample_weight=sample_weight)
    assert_allclose(reg.coef_, coef, rtol=1e-6)
    if fit_intercept:
        assert_allclose(reg.intercept_, intercept)

    # scaling of sample_weight should have no effect, cf. np.average()
    sample_weight = 2 * np.ones_like(y)
    reg.fit(X, y, sample_weight=sample_weight)
    assert_allclose(reg.coef_, coef, rtol=1e-6)
    if fit_intercept:
        assert_allclose(reg.intercept_, intercept)


@pytest.mark.parametrize("X_is_sparse", [False, True])
@pytest.mark.parametrize("fit_intercept", [False, True])
@pytest.mark.parametrize("sample_weight", [np.array([10, 1, 10, 1]), None])
def test_enet_alpha_max_sample_weight(X_is_sparse, fit_intercept, sample_weight):
    X = np.array([[3.0, 1.0], [2.0, 5.0], [5.0, 3.0], [1.0, 4.0]])
    beta = np.array([1, 1])
    y = X @ beta
    if X_is_sparse:
        X = sparse.csc_matrix(X)
    # Test alpha_max makes coefs zero.
    reg = ElasticNetCV(n_alphas=1, cv=2, eps=1, fit_intercept=fit_intercept)
    reg.fit(X, y, sample_weight=sample_weight)
    assert_allclose(reg.coef_, 0, atol=1e-5)
    alpha_max = reg.alpha_
    # Test smaller alpha makes coefs nonzero.
    reg = ElasticNet(alpha=0.99 * alpha_max, fit_intercept=fit_intercept)
    reg.fit(X, y, sample_weight=sample_weight)
    assert_array_less(1e-3, np.max(np.abs(reg.coef_)))


@pytest.mark.parametrize("estimator", [ElasticNetCV, LassoCV])
def test_linear_models_cv_fit_with_loky(estimator):
    # LinearModelsCV.fit performs operations on fancy-indexed memmapped
    # data when using the loky backend, causing an error due to unexpected
    # behavior of fancy indexing of read-only memmaps (cf. numpy#14132).

    # Create a problem sufficiently large to cause memmapping (1MB).
    # Unfortunately the scikit-learn and joblib APIs do not make it possible to
    # change the max_nbyte of the inner Parallel call.
    X, y = make_regression(int(1e6) // 8 + 1, 1)
    assert X.nbytes > 1e6  # 1 MB
    with joblib.parallel_backend("loky"):
        estimator(n_jobs=2, cv=3).fit(X, y)


@pytest.mark.parametrize("check_input", [True, False])
def test_enet_sample_weight_does_not_overwrite_sample_weight(check_input):
    """Check that ElasticNet does not overwrite sample_weights."""

    rng = np.random.RandomState(0)
    n_samples, n_features = 10, 5

    X = rng.rand(n_samples, n_features)
    y = rng.rand(n_samples)

    sample_weight_1_25 = 1.25 * np.ones_like(y)
    sample_weight = sample_weight_1_25.copy()

    reg = ElasticNet()
    reg.fit(X, y, sample_weight=sample_weight, check_input=check_input)

    assert_array_equal(sample_weight, sample_weight_1_25)


@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
@pytest.mark.parametrize("ridge_alpha", [1e-1, 1.0, 1e6])
def test_enet_ridge_consistency(ridge_alpha):
    # Check that ElasticNet(l1_ratio=0) converges to the same solution as Ridge
    # provided that the value of alpha is adapted.
    #
    # XXX: this test does not pass for weaker regularization (lower values of
    # ridge_alpha): it could be either a problem of ElasticNet or Ridge (less
    # likely) and depends on the dataset statistics: lower values for
    # effective_rank are more problematic in particular.

    rng = np.random.RandomState(42)
    n_samples = 300
    X, y = make_regression(
        n_samples=n_samples,
        n_features=100,
        effective_rank=10,
        n_informative=50,
        random_state=rng,
    )
    sw = rng.uniform(low=0.01, high=10, size=X.shape[0])
    alpha = 1.0
    common_params = dict(
        tol=1e-12,
    )
    ridge = Ridge(alpha=alpha, **common_params).fit(X, y, sample_weight=sw)

    alpha_enet = alpha / sw.sum()
    enet = ElasticNet(alpha=alpha_enet, l1_ratio=0, **common_params).fit(
        X, y, sample_weight=sw
    )
    assert_allclose(ridge.coef_, enet.coef_)
    assert_allclose(ridge.intercept_, enet.intercept_)


@pytest.mark.parametrize(
    "estimator",
    [
        Lasso(alpha=1.0),
        ElasticNet(alpha=1.0, l1_ratio=0.1),
    ],
)
def test_sample_weight_invariance(estimator):
    rng = np.random.RandomState(42)
    X, y = make_regression(
        n_samples=100,
        n_features=300,
        effective_rank=10,
        n_informative=50,
        random_state=rng,
    )
    sw = rng.uniform(low=0.01, high=2, size=X.shape[0])
    params = dict(tol=1e-12)

    # Check that setting some weights to 0 is equivalent to trimming the
    # samples:
    cutoff = X.shape[0] // 3
    sw_with_null = sw.copy()
    sw_with_null[:cutoff] = 0.0
    X_trimmed, y_trimmed = X[cutoff:, :], y[cutoff:]
    sw_trimmed = sw[cutoff:]

    reg_trimmed = (
        clone(estimator)
        .set_params(**params)
        .fit(X_trimmed, y_trimmed, sample_weight=sw_trimmed)
    )
    reg_null_weighted = (
        clone(estimator).set_params(**params).fit(X, y, sample_weight=sw_with_null)
    )
    assert_allclose(reg_null_weighted.coef_, reg_trimmed.coef_)
    assert_allclose(reg_null_weighted.intercept_, reg_trimmed.intercept_)

    # Check that duplicating the training dataset is equivalent to multiplying
    # the weights by 2:
    X_dup = np.concatenate([X, X], axis=0)
    y_dup = np.concatenate([y, y], axis=0)
    sw_dup = np.concatenate([sw, sw], axis=0)

    reg_2sw = clone(estimator).set_params(**params).fit(X, y, sample_weight=2 * sw)
    reg_dup = (
        clone(estimator).set_params(**params).fit(X_dup, y_dup, sample_weight=sw_dup)
    )

    assert_allclose(reg_2sw.coef_, reg_dup.coef_)
    assert_allclose(reg_2sw.intercept_, reg_dup.intercept_)


def test_read_only_buffer():
    """Test that sparse coordinate descent works for read-only buffers"""

    rng = np.random.RandomState(0)
    clf = ElasticNet(alpha=0.1, copy_X=True, random_state=rng)
    X = np.asfortranarray(rng.uniform(size=(100, 10)))
    X.setflags(write=False)

    y = rng.rand(100)
    clf.fit(X, y)


@pytest.mark.parametrize(
    "EstimatorCV",
    [ElasticNetCV, LassoCV, MultiTaskElasticNetCV, MultiTaskLassoCV],
)
def test_cv_estimators_reject_params_with_no_routing_enabled(EstimatorCV):
    """Check that the models inheriting from class:`LinearModelCV` raise an
    error when any `params` are passed when routing is not enabled.
    """
    X, y = make_regression(random_state=42)
    groups = np.array([0, 1] * (len(y) // 2))
    estimator = EstimatorCV()
    msg = "is only supported if enable_metadata_routing=True"
    with pytest.raises(ValueError, match=msg):
        estimator.fit(X, y, groups=groups)


@pytest.mark.parametrize(
    "MultiTaskEstimatorCV",
    [MultiTaskElasticNetCV, MultiTaskLassoCV],
)
@config_context(enable_metadata_routing=True)
def test_multitask_cv_estimators_with_sample_weight(MultiTaskEstimatorCV):
    """Check that for :class:`MultiTaskElasticNetCV` and
    class:`MultiTaskLassoCV` if `sample_weight` is passed and the
    CV splitter does not support `sample_weight` an error is raised.
    On the other hand if the splitter does support `sample_weight`
    while `sample_weight` is passed there is no error and process
    completes smoothly as before.
    """

    class CVSplitter(GroupsConsumerMixin, BaseCrossValidator):
        def get_n_splits(self, X=None, y=None, groups=None, metadata=None):
            pass  # pragma: nocover

    class CVSplitterSampleWeight(CVSplitter):
        def split(self, X, y=None, groups=None, sample_weight=None):
            split_index = len(X) // 2
            train_indices = list(range(0, split_index))
            test_indices = list(range(split_index, len(X)))
            yield test_indices, train_indices
            yield train_indices, test_indices

    X, y = make_regression(random_state=42, n_targets=2)
    sample_weight = np.ones(X.shape[0])

    # If CV splitter does not support sample_weight an error is raised
    splitter = CVSplitter().set_split_request(groups=True)
    estimator = MultiTaskEstimatorCV(cv=splitter)
    msg = "do not support sample weights"
    with pytest.raises(ValueError, match=msg):
        estimator.fit(X, y, sample_weight=sample_weight)

    # If CV splitter does support sample_weight no error is raised
    splitter = CVSplitterSampleWeight().set_split_request(
        groups=True, sample_weight=True
    )
    estimator = MultiTaskEstimatorCV(cv=splitter)
    estimator.fit(X, y, sample_weight=sample_weight)