"""
Ridge regression
"""

# Author: Mathieu Blondel <mathieu@mblondel.org>
#         Reuben Fletcher-Costin <reuben.fletchercostin@gmail.com>
#         Fabian Pedregosa <fabian@fseoane.net>
#         Michael Eickenberg <michael.eickenberg@nsup.org>
# License: BSD 3 clause


from abc import ABCMeta, abstractmethod
from functools import partial
from numbers import Integral, Real
import warnings

import numpy as np
import numbers
from scipy import linalg
from scipy import sparse
from scipy import optimize
from scipy.sparse import linalg as sp_linalg

from ._base import LinearClassifierMixin, LinearModel
from ._base import _preprocess_data, _rescale_data
from ._sag import sag_solver
from ..base import MultiOutputMixin, RegressorMixin, is_classifier
from ..utils.extmath import safe_sparse_dot
from ..utils.extmath import row_norms
from ..utils import check_array
from ..utils import check_consistent_length
from ..utils import check_scalar
from ..utils import compute_sample_weight
from ..utils import column_or_1d
from ..utils.validation import check_is_fitted
from ..utils.validation import _check_sample_weight
from ..utils._param_validation import Interval
from ..utils._param_validation import StrOptions
from ..preprocessing import LabelBinarizer
from ..model_selection import GridSearchCV
from ..metrics import check_scoring
from ..metrics import get_scorer_names
from ..exceptions import ConvergenceWarning
from ..utils.sparsefuncs import mean_variance_axis


def _get_rescaled_operator(X, X_offset, sample_weight_sqrt):
    """Create LinearOperator for matrix products with implicit centering.

    Matrix product `LinearOperator @ coef` returns `(X - X_offset) @ coef`.
    """

    def matvec(b):
        return X.dot(b) - sample_weight_sqrt * b.dot(X_offset)

    def rmatvec(b):
        return X.T.dot(b) - X_offset * b.dot(sample_weight_sqrt)

    X1 = sparse.linalg.LinearOperator(shape=X.shape, matvec=matvec, rmatvec=rmatvec)
    return X1


def _solve_sparse_cg(
    X,
    y,
    alpha,
    max_iter=None,
    tol=1e-4,
    verbose=0,
    X_offset=None,
    X_scale=None,
    sample_weight_sqrt=None,
):
    if sample_weight_sqrt is None:
        sample_weight_sqrt = np.ones(X.shape[0], dtype=X.dtype)

    n_samples, n_features = X.shape

    if X_offset is None or X_scale is None:
        X1 = sp_linalg.aslinearoperator(X)
    else:
        X_offset_scale = X_offset / X_scale
        X1 = _get_rescaled_operator(X, X_offset_scale, sample_weight_sqrt)

    coefs = np.empty((y.shape[1], n_features), dtype=X.dtype)

    if n_features > n_samples:

        def create_mv(curr_alpha):
            def _mv(x):
                return X1.matvec(X1.rmatvec(x)) + curr_alpha * x

            return _mv

    else:

        def create_mv(curr_alpha):
            def _mv(x):
                return X1.rmatvec(X1.matvec(x)) + curr_alpha * x

            return _mv

    for i in range(y.shape[1]):
        y_column = y[:, i]

        mv = create_mv(alpha[i])
        if n_features > n_samples:
            # kernel ridge
            # w = X.T * inv(X X^t + alpha*Id) y
            C = sp_linalg.LinearOperator(
                (n_samples, n_samples), matvec=mv, dtype=X.dtype
            )
            # FIXME atol
            try:
                coef, info = sp_linalg.cg(C, y_column, tol=tol, atol="legacy")
            except TypeError:
                # old scipy
                coef, info = sp_linalg.cg(C, y_column, tol=tol)
            coefs[i] = X1.rmatvec(coef)
        else:
            # linear ridge
            # w = inv(X^t X + alpha*Id) * X.T y
            y_column = X1.rmatvec(y_column)
            C = sp_linalg.LinearOperator(
                (n_features, n_features), matvec=mv, dtype=X.dtype
            )
            # FIXME atol
            try:
                coefs[i], info = sp_linalg.cg(
                    C, y_column, maxiter=max_iter, tol=tol, atol="legacy"
                )
            except TypeError:
                # old scipy
                coefs[i], info = sp_linalg.cg(C, y_column, maxiter=max_iter, tol=tol)

        if info < 0:
            raise ValueError("Failed with error code %d" % info)

        if max_iter is None and info > 0 and verbose:
            warnings.warn(
                "sparse_cg did not converge after %d iterations." % info,
                ConvergenceWarning,
            )

    return coefs


def _solve_lsqr(
    X,
    y,
    *,
    alpha,
    fit_intercept=True,
    max_iter=None,
    tol=1e-4,
    X_offset=None,
    X_scale=None,
    sample_weight_sqrt=None,
):
    """Solve Ridge regression via LSQR.

    We expect that y is always mean centered.
    If X is dense, we expect it to be mean centered such that we can solve
        ||y - Xw||_2^2 + alpha * ||w||_2^2

    If X is sparse, we expect X_offset to be given such that we can solve
        ||y - (X - X_offset)w||_2^2 + alpha * ||w||_2^2

    With sample weights S=diag(sample_weight), this becomes
        ||sqrt(S) (y - (X - X_offset) w)||_2^2 + alpha * ||w||_2^2
    and we expect y and X to already be rescaled, i.e. sqrt(S) @ y, sqrt(S) @ X. In
    this case, X_offset is the sample_weight weighted mean of X before scaling by
    sqrt(S). The objective then reads
       ||y - (X - sqrt(S) X_offset) w)||_2^2 + alpha * ||w||_2^2
    """
    if sample_weight_sqrt is None:
        sample_weight_sqrt = np.ones(X.shape[0], dtype=X.dtype)

    if sparse.issparse(X) and fit_intercept:
        X_offset_scale = X_offset / X_scale
        X1 = _get_rescaled_operator(X, X_offset_scale, sample_weight_sqrt)
    else:
        # No need to touch anything
        X1 = X

    n_samples, n_features = X.shape
    coefs = np.empty((y.shape[1], n_features), dtype=X.dtype)
    n_iter = np.empty(y.shape[1], dtype=np.int32)

    # According to the lsqr documentation, alpha = damp^2.
    sqrt_alpha = np.sqrt(alpha)

    for i in range(y.shape[1]):
        y_column = y[:, i]
        info = sp_linalg.lsqr(
            X1, y_column, damp=sqrt_alpha[i], atol=tol, btol=tol, iter_lim=max_iter
        )
        coefs[i] = info[0]
        n_iter[i] = info[2]

    return coefs, n_iter


def _solve_cholesky(X, y, alpha):
    # w = inv(X^t X + alpha*Id) * X.T y
    n_features = X.shape[1]
    n_targets = y.shape[1]

    A = safe_sparse_dot(X.T, X, dense_output=True)
    Xy = safe_sparse_dot(X.T, y, dense_output=True)

    one_alpha = np.array_equal(alpha, len(alpha) * [alpha[0]])

    if one_alpha:
        A.flat[:: n_features + 1] += alpha[0]
        return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T
    else:
        coefs = np.empty([n_targets, n_features], dtype=X.dtype)
        for coef, target, current_alpha in zip(coefs, Xy.T, alpha):
            A.flat[:: n_features + 1] += current_alpha
            coef[:] = linalg.solve(A, target, assume_a="pos", overwrite_a=False).ravel()
            A.flat[:: n_features + 1] -= current_alpha
        return coefs


def _solve_cholesky_kernel(K, y, alpha, sample_weight=None, copy=False):
    # dual_coef = inv(X X^t + alpha*Id) y
    n_samples = K.shape[0]
    n_targets = y.shape[1]

    if copy:
        K = K.copy()

    alpha = np.atleast_1d(alpha)
    one_alpha = (alpha == alpha[0]).all()
    has_sw = isinstance(sample_weight, np.ndarray) or sample_weight not in [1.0, None]

    if has_sw:
        # Unlike other solvers, we need to support sample_weight directly
        # because K might be a pre-computed kernel.
        sw = np.sqrt(np.atleast_1d(sample_weight))
        y = y * sw[:, np.newaxis]
        K *= np.outer(sw, sw)

    if one_alpha:
        # Only one penalty, we can solve multi-target problems in one time.
        K.flat[:: n_samples + 1] += alpha[0]

        try:
            # Note: we must use overwrite_a=False in order to be able to
            #       use the fall-back solution below in case a LinAlgError
            #       is raised
            dual_coef = linalg.solve(K, y, assume_a="pos", overwrite_a=False)
        except np.linalg.LinAlgError:
            warnings.warn(
                "Singular matrix in solving dual problem. Using "
                "least-squares solution instead."
            )
            dual_coef = linalg.lstsq(K, y)[0]

        # K is expensive to compute and store in memory so change it back in
        # case it was user-given.
        K.flat[:: n_samples + 1] -= alpha[0]

        if has_sw:
            dual_coef *= sw[:, np.newaxis]

        return dual_coef
    else:
        # One penalty per target. We need to solve each target separately.
        dual_coefs = np.empty([n_targets, n_samples], K.dtype)

        for dual_coef, target, current_alpha in zip(dual_coefs, y.T, alpha):
            K.flat[:: n_samples + 1] += current_alpha

            dual_coef[:] = linalg.solve(
                K, target, assume_a="pos", overwrite_a=False
            ).ravel()

            K.flat[:: n_samples + 1] -= current_alpha

        if has_sw:
            dual_coefs *= sw[np.newaxis, :]

        return dual_coefs.T


def _solve_svd(X, y, alpha):
    U, s, Vt = linalg.svd(X, full_matrices=False)
    idx = s > 1e-15  # same default value as scipy.linalg.pinv
    s_nnz = s[idx][:, np.newaxis]
    UTy = np.dot(U.T, y)
    d = np.zeros((s.size, alpha.size), dtype=X.dtype)
    d[idx] = s_nnz / (s_nnz**2 + alpha)
    d_UT_y = d * UTy
    return np.dot(Vt.T, d_UT_y).T


def _solve_lbfgs(
    X,
    y,
    alpha,
    positive=True,
    max_iter=None,
    tol=1e-4,
    X_offset=None,
    X_scale=None,
    sample_weight_sqrt=None,
):
    """Solve ridge regression with LBFGS.

    The main purpose is fitting with forcing coefficients to be positive.
    For unconstrained ridge regression, there are faster dedicated solver methods.
    Note that with positive bounds on the coefficients, LBFGS seems faster
    than scipy.optimize.lsq_linear.
    """
    n_samples, n_features = X.shape

    options = {}
    if max_iter is not None:
        options["maxiter"] = max_iter
    config = {
        "method": "L-BFGS-B",
        "tol": tol,
        "jac": True,
        "options": options,
    }
    if positive:
        config["bounds"] = [(0, np.inf)] * n_features

    if X_offset is not None and X_scale is not None:
        X_offset_scale = X_offset / X_scale
    else:
        X_offset_scale = None

    if sample_weight_sqrt is None:
        sample_weight_sqrt = np.ones(X.shape[0], dtype=X.dtype)

    coefs = np.empty((y.shape[1], n_features), dtype=X.dtype)

    for i in range(y.shape[1]):
        x0 = np.zeros((n_features,))
        y_column = y[:, i]

        def func(w):
            residual = X.dot(w) - y_column
            if X_offset_scale is not None:
                residual -= sample_weight_sqrt * w.dot(X_offset_scale)
            f = 0.5 * residual.dot(residual) + 0.5 * alpha[i] * w.dot(w)
            grad = X.T @ residual + alpha[i] * w
            if X_offset_scale is not None:
                grad -= X_offset_scale * residual.dot(sample_weight_sqrt)

            return f, grad

        result = optimize.minimize(func, x0, **config)
        if not result["success"]:
            warnings.warn(
                "The lbfgs solver did not converge. Try increasing max_iter "
                f"or tol. Currently: max_iter={max_iter} and tol={tol}",
                ConvergenceWarning,
            )
        coefs[i] = result["x"]

    return coefs


def _get_valid_accept_sparse(is_X_sparse, solver):
    if is_X_sparse and solver in ["auto", "sag", "saga"]:
        return "csr"
    else:
        return ["csr", "csc", "coo"]


def ridge_regression(
    X,
    y,
    alpha,
    *,
    sample_weight=None,
    solver="auto",
    max_iter=None,
    tol=1e-4,
    verbose=0,
    positive=False,
    random_state=None,
    return_n_iter=False,
    return_intercept=False,
    check_input=True,
):
    """Solve the ridge equation by the method of normal equations.

    Read more in the :ref:`User Guide <ridge_regression>`.

    Parameters
    ----------
    X : {ndarray, sparse matrix, LinearOperator} of shape \
        (n_samples, n_features)
        Training data.

    y : ndarray of shape (n_samples,) or (n_samples, n_targets)
        Target values.

    alpha : float or array-like of shape (n_targets,)
        Constant that multiplies the L2 term, controlling regularization
        strength. `alpha` must be a non-negative float i.e. in `[0, inf)`.

        When `alpha = 0`, the objective is equivalent to ordinary least
        squares, solved by the :class:`LinearRegression` object. For numerical
        reasons, using `alpha = 0` with the `Ridge` object is not advised.
        Instead, you should use the :class:`LinearRegression` object.

        If an array is passed, penalties are assumed to be specific to the
        targets. Hence they must correspond in number.

    sample_weight : float or array-like of shape (n_samples,), default=None
        Individual weights for each sample. If given a float, every sample
        will have the same weight. If sample_weight is not None and
        solver='auto', the solver will be set to 'cholesky'.

        .. versionadded:: 0.17

    solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', \
            'sag', 'saga', 'lbfgs'}, default='auto'
        Solver to use in the computational routines:

        - 'auto' chooses the solver automatically based on the type of data.

        - 'svd' uses a Singular Value Decomposition of X to compute the Ridge
          coefficients. It is the most stable solver, in particular more stable
          for singular matrices than 'cholesky' at the cost of being slower.

        - 'cholesky' uses the standard scipy.linalg.solve function to
          obtain a closed-form solution via a Cholesky decomposition of
          dot(X.T, X)

        - 'sparse_cg' uses the conjugate gradient solver as found in
          scipy.sparse.linalg.cg. As an iterative algorithm, this solver is
          more appropriate than 'cholesky' for large-scale data
          (possibility to set `tol` and `max_iter`).

        - 'lsqr' uses the dedicated regularized least-squares routine
          scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative
          procedure.

        - 'sag' uses a Stochastic Average Gradient descent, and 'saga' uses
          its improved, unbiased version named SAGA. Both methods also use an
          iterative procedure, and are often faster than other solvers when
          both n_samples and n_features are large. Note that 'sag' and
          'saga' fast convergence is only guaranteed on features with
          approximately the same scale. You can preprocess the data with a
          scaler from sklearn.preprocessing.

        - 'lbfgs' uses L-BFGS-B algorithm implemented in
          `scipy.optimize.minimize`. It can be used only when `positive`
          is True.

        All solvers except 'svd' support both dense and sparse data. However, only
        'lsqr', 'sag', 'sparse_cg', and 'lbfgs' support sparse input when
        `fit_intercept` is True.

        .. versionadded:: 0.17
           Stochastic Average Gradient descent solver.
        .. versionadded:: 0.19
           SAGA solver.

    max_iter : int, default=None
        Maximum number of iterations for conjugate gradient solver.
        For the 'sparse_cg' and 'lsqr' solvers, the default value is determined
        by scipy.sparse.linalg. For 'sag' and saga solver, the default value is
        1000. For 'lbfgs' solver, the default value is 15000.

    tol : float, default=1e-4
        Precision of the solution. Note that `tol` has no effect for solvers 'svd' and
        'cholesky'.

        .. versionchanged:: 1.2
           Default value changed from 1e-3 to 1e-4 for consistency with other linear
           models.

    verbose : int, default=0
        Verbosity level. Setting verbose > 0 will display additional
        information depending on the solver used.

    positive : bool, default=False
        When set to ``True``, forces the coefficients to be positive.
        Only 'lbfgs' solver is supported in this case.

    random_state : int, RandomState instance, default=None
        Used when ``solver`` == 'sag' or 'saga' to shuffle the data.
        See :term:`Glossary <random_state>` for details.

    return_n_iter : bool, default=False
        If True, the method also returns `n_iter`, the actual number of
        iteration performed by the solver.

        .. versionadded:: 0.17

    return_intercept : bool, default=False
        If True and if X is sparse, the method also returns the intercept,
        and the solver is automatically changed to 'sag'. This is only a
        temporary fix for fitting the intercept with sparse data. For dense
        data, use sklearn.linear_model._preprocess_data before your regression.

        .. versionadded:: 0.17

    check_input : bool, default=True
        If False, the input arrays X and y will not be checked.

        .. versionadded:: 0.21

    Returns
    -------
    coef : ndarray of shape (n_features,) or (n_targets, n_features)
        Weight vector(s).

    n_iter : int, optional
        The actual number of iteration performed by the solver.
        Only returned if `return_n_iter` is True.

    intercept : float or ndarray of shape (n_targets,)
        The intercept of the model. Only returned if `return_intercept`
        is True and if X is a scipy sparse array.

    Notes
    -----
    This function won't compute the intercept.

    Regularization improves the conditioning of the problem and
    reduces the variance of the estimates. Larger values specify stronger
    regularization. Alpha corresponds to ``1 / (2C)`` in other linear
    models such as :class:`~sklearn.linear_model.LogisticRegression` or
    :class:`~sklearn.svm.LinearSVC`. If an array is passed, penalties are
    assumed to be specific to the targets. Hence they must correspond in
    number.
    """
    return _ridge_regression(
        X,
        y,
        alpha,
        sample_weight=sample_weight,
        solver=solver,
        max_iter=max_iter,
        tol=tol,
        verbose=verbose,
        positive=positive,
        random_state=random_state,
        return_n_iter=return_n_iter,
        return_intercept=return_intercept,
        X_scale=None,
        X_offset=None,
        check_input=check_input,
    )


def _ridge_regression(
    X,
    y,
    alpha,
    sample_weight=None,
    solver="auto",
    max_iter=None,
    tol=1e-4,
    verbose=0,
    positive=False,
    random_state=None,
    return_n_iter=False,
    return_intercept=False,
    X_scale=None,
    X_offset=None,
    check_input=True,
    fit_intercept=False,
):

    has_sw = sample_weight is not None

    if solver == "auto":
        if positive:
            solver = "lbfgs"
        elif return_intercept:
            # sag supports fitting intercept directly
            solver = "sag"
        elif not sparse.issparse(X):
            solver = "cholesky"
        else:
            solver = "sparse_cg"

    if solver not in ("sparse_cg", "cholesky", "svd", "lsqr", "sag", "saga", "lbfgs"):
        raise ValueError(
            "Known solvers are 'sparse_cg', 'cholesky', 'svd'"
            " 'lsqr', 'sag', 'saga' or 'lbfgs'. Got %s." % solver
        )

    if positive and solver != "lbfgs":
        raise ValueError(
            "When positive=True, only 'lbfgs' solver can be used. "
            f"Please change solver {solver} to 'lbfgs' "
            "or set positive=False."
        )

    if solver == "lbfgs" and not positive:
        raise ValueError(
            "'lbfgs' solver can be used only when positive=True. "
            "Please use another solver."
        )

    if return_intercept and solver != "sag":
        raise ValueError(
            "In Ridge, only 'sag' solver can directly fit the "
            "intercept. Please change solver to 'sag' or set "
            "return_intercept=False."
        )

    if check_input:
        _dtype = [np.float64, np.float32]
        _accept_sparse = _get_valid_accept_sparse(sparse.issparse(X), solver)
        X = check_array(X, accept_sparse=_accept_sparse, dtype=_dtype, order="C")
        y = check_array(y, dtype=X.dtype, ensure_2d=False, order=None)
    check_consistent_length(X, y)

    n_samples, n_features = X.shape

    if y.ndim > 2:
        raise ValueError("Target y has the wrong shape %s" % str(y.shape))

    ravel = False
    if y.ndim == 1:
        y = y.reshape(-1, 1)
        ravel = True

    n_samples_, n_targets = y.shape

    if n_samples != n_samples_:
        raise ValueError(
            "Number of samples in X and y does not correspond: %d != %d"
            % (n_samples, n_samples_)
        )

    if has_sw:
        sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)

        if solver not in ["sag", "saga"]:
            # SAG supports sample_weight directly. For other solvers,
            # we implement sample_weight via a simple rescaling.
            X, y, sample_weight_sqrt = _rescale_data(X, y, sample_weight)

    # Some callers of this method might pass alpha as single
    # element array which already has been validated.
    if alpha is not None and not isinstance(alpha, np.ndarray):
        alpha = check_scalar(
            alpha,
            "alpha",
            target_type=numbers.Real,
            min_val=0.0,
            include_boundaries="left",
        )

    # There should be either 1 or n_targets penalties
    alpha = np.asarray(alpha, dtype=X.dtype).ravel()
    if alpha.size not in [1, n_targets]:
        raise ValueError(
            "Number of targets and number of penalties do not correspond: %d != %d"
            % (alpha.size, n_targets)
        )

    if alpha.size == 1 and n_targets > 1:
        alpha = np.repeat(alpha, n_targets)

    n_iter = None
    if solver == "sparse_cg":
        coef = _solve_sparse_cg(
            X,
            y,
            alpha,
            max_iter=max_iter,
            tol=tol,
            verbose=verbose,
            X_offset=X_offset,
            X_scale=X_scale,
            sample_weight_sqrt=sample_weight_sqrt if has_sw else None,
        )

    elif solver == "lsqr":
        coef, n_iter = _solve_lsqr(
            X,
            y,
            alpha=alpha,
            fit_intercept=fit_intercept,
            max_iter=max_iter,
            tol=tol,
            X_offset=X_offset,
            X_scale=X_scale,
            sample_weight_sqrt=sample_weight_sqrt if has_sw else None,
        )

    elif solver == "cholesky":
        if n_features > n_samples:
            K = safe_sparse_dot(X, X.T, dense_output=True)
            try:
                dual_coef = _solve_cholesky_kernel(K, y, alpha)

                coef = safe_sparse_dot(X.T, dual_coef, dense_output=True).T
            except linalg.LinAlgError:
                # use SVD solver if matrix is singular
                solver = "svd"
        else:
            try:
                coef = _solve_cholesky(X, y, alpha)
            except linalg.LinAlgError:
                # use SVD solver if matrix is singular
                solver = "svd"

    elif solver in ["sag", "saga"]:
        # precompute max_squared_sum for all targets
        max_squared_sum = row_norms(X, squared=True).max()

        coef = np.empty((y.shape[1], n_features), dtype=X.dtype)
        n_iter = np.empty(y.shape[1], dtype=np.int32)
        intercept = np.zeros((y.shape[1],), dtype=X.dtype)
        for i, (alpha_i, target) in enumerate(zip(alpha, y.T)):
            init = {
                "coef": np.zeros((n_features + int(return_intercept), 1), dtype=X.dtype)
            }
            coef_, n_iter_, _ = sag_solver(
                X,
                target.ravel(),
                sample_weight,
                "squared",
                alpha_i,
                0,
                max_iter,
                tol,
                verbose,
                random_state,
                False,
                max_squared_sum,
                init,
                is_saga=solver == "saga",
            )
            if return_intercept:
                coef[i] = coef_[:-1]
                intercept[i] = coef_[-1]
            else:
                coef[i] = coef_
            n_iter[i] = n_iter_

        if intercept.shape[0] == 1:
            intercept = intercept[0]
        coef = np.asarray(coef)

    elif solver == "lbfgs":
        coef = _solve_lbfgs(
            X,
            y,
            alpha,
            positive=positive,
            tol=tol,
            max_iter=max_iter,
            X_offset=X_offset,
            X_scale=X_scale,
            sample_weight_sqrt=sample_weight_sqrt if has_sw else None,
        )

    if solver == "svd":
        if sparse.issparse(X):
            raise TypeError("SVD solver does not support sparse inputs currently")
        coef = _solve_svd(X, y, alpha)

    if ravel:
        # When y was passed as a 1d-array, we flatten the coefficients.
        coef = coef.ravel()

    if return_n_iter and return_intercept:
        return coef, n_iter, intercept
    elif return_intercept:
        return coef, intercept
    elif return_n_iter:
        return coef, n_iter
    else:
        return coef


class _BaseRidge(LinearModel, metaclass=ABCMeta):

    _parameter_constraints: dict = {
        "alpha": [Interval(Real, 0, None, closed="left"), np.ndarray],
        "fit_intercept": ["boolean"],
        "copy_X": ["boolean"],
        "max_iter": [Interval(Integral, 1, None, closed="left"), None],
        "tol": [Interval(Real, 0, None, closed="left")],
        "solver": [
            StrOptions(
                {"auto", "svd", "cholesky", "lsqr", "sparse_cg", "sag", "saga", "lbfgs"}
            )
        ],
        "positive": ["boolean"],
        "random_state": ["random_state"],
    }

    @abstractmethod
    def __init__(
        self,
        alpha=1.0,
        *,
        fit_intercept=True,
        copy_X=True,
        max_iter=None,
        tol=1e-4,
        solver="auto",
        positive=False,
        random_state=None,
    ):
        self.alpha = alpha
        self.fit_intercept = fit_intercept
        self.copy_X = copy_X
        self.max_iter = max_iter
        self.tol = tol
        self.solver = solver
        self.positive = positive
        self.random_state = random_state

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

        if self.solver == "lbfgs" and not self.positive:
            raise ValueError(
                "'lbfgs' solver can be used only when positive=True. "
                "Please use another solver."
            )

        if self.positive:
            if self.solver not in ["auto", "lbfgs"]:
                raise ValueError(
                    f"solver='{self.solver}' does not support positive fitting. Please"
                    " set the solver to 'auto' or 'lbfgs', or set `positive=False`"
                )
            else:
                solver = self.solver
        elif sparse.issparse(X) and self.fit_intercept:
            if self.solver not in ["auto", "lbfgs", "lsqr", "sag", "sparse_cg"]:
                raise ValueError(
                    "solver='{}' does not support fitting the intercept "
                    "on sparse data. Please set the solver to 'auto' or "
                    "'lsqr', 'sparse_cg', 'sag', 'lbfgs' "
                    "or set `fit_intercept=False`".format(self.solver)
                )
            if self.solver in ["lsqr", "lbfgs"]:
                solver = self.solver
            elif self.solver == "sag" and self.max_iter is None and self.tol > 1e-4:
                warnings.warn(
                    '"sag" solver requires many iterations to fit '
                    "an intercept with sparse inputs. Either set the "
                    'solver to "auto" or "sparse_cg", or set a low '
                    '"tol" and a high "max_iter" (especially if inputs are '
                    "not standardized)."
                )
                solver = "sag"
            else:
                solver = "sparse_cg"
        else:
            solver = self.solver

        if sample_weight is not None:
            sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)

        # when X is sparse we only remove offset from y
        X, y, X_offset, y_offset, X_scale = _preprocess_data(
            X,
            y,
            self.fit_intercept,
            copy=self.copy_X,
            sample_weight=sample_weight,
        )

        if solver == "sag" and sparse.issparse(X) and self.fit_intercept:
            self.coef_, self.n_iter_, self.intercept_ = _ridge_regression(
                X,
                y,
                alpha=self.alpha,
                sample_weight=sample_weight,
                max_iter=self.max_iter,
                tol=self.tol,
                solver="sag",
                positive=self.positive,
                random_state=self.random_state,
                return_n_iter=True,
                return_intercept=True,
                check_input=False,
            )
            # add the offset which was subtracted by _preprocess_data
            self.intercept_ += y_offset

        else:
            if sparse.issparse(X) and self.fit_intercept:
                # required to fit intercept with sparse_cg and lbfgs solver
                params = {"X_offset": X_offset, "X_scale": X_scale}
            else:
                # for dense matrices or when intercept is set to 0
                params = {}

            self.coef_, self.n_iter_ = _ridge_regression(
                X,
                y,
                alpha=self.alpha,
                sample_weight=sample_weight,
                max_iter=self.max_iter,
                tol=self.tol,
                solver=solver,
                positive=self.positive,
                random_state=self.random_state,
                return_n_iter=True,
                return_intercept=False,
                check_input=False,
                fit_intercept=self.fit_intercept,
                **params,
            )
            self._set_intercept(X_offset, y_offset, X_scale)

        return self


class Ridge(MultiOutputMixin, RegressorMixin, _BaseRidge):
    """Linear least squares with l2 regularization.

    Minimizes the objective function::

    ||y - Xw||^2_2 + alpha * ||w||^2_2

    This model solves a regression model where the loss function is
    the linear least squares function and regularization is given by
    the l2-norm. Also known as Ridge Regression or Tikhonov regularization.
    This estimator has built-in support for multi-variate regression
    (i.e., when y is a 2d-array of shape (n_samples, n_targets)).

    Read more in the :ref:`User Guide <ridge_regression>`.

    Parameters
    ----------
    alpha : {float, ndarray of shape (n_targets,)}, default=1.0
        Constant that multiplies the L2 term, controlling regularization
        strength. `alpha` must be a non-negative float i.e. in `[0, inf)`.

        When `alpha = 0`, the objective is equivalent to ordinary least
        squares, solved by the :class:`LinearRegression` object. For numerical
        reasons, using `alpha = 0` with the `Ridge` object is not advised.
        Instead, you should use the :class:`LinearRegression` object.

        If an array is passed, penalties are assumed to be specific to the
        targets. Hence they must correspond in number.

    fit_intercept : bool, default=True
        Whether to fit the intercept for this model. If set
        to false, no intercept will be used in calculations
        (i.e. ``X`` and ``y`` are expected to be centered).

    copy_X : bool, default=True
        If True, X will be copied; else, it may be overwritten.

    max_iter : int, default=None
        Maximum number of iterations for conjugate gradient solver.
        For 'sparse_cg' and 'lsqr' solvers, the default value is determined
        by scipy.sparse.linalg. For 'sag' solver, the default value is 1000.
        For 'lbfgs' solver, the default value is 15000.

    tol : float, default=1e-4
        Precision of the solution. Note that `tol` has no effect for solvers 'svd' and
        'cholesky'.

        .. versionchanged:: 1.2
           Default value changed from 1e-3 to 1e-4 for consistency with other linear
           models.

    solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', \
            'sag', 'saga', 'lbfgs'}, default='auto'
        Solver to use in the computational routines:

        - 'auto' chooses the solver automatically based on the type of data.

        - 'svd' uses a Singular Value Decomposition of X to compute the Ridge
          coefficients. It is the most stable solver, in particular more stable
          for singular matrices than 'cholesky' at the cost of being slower.

        - 'cholesky' uses the standard scipy.linalg.solve function to
          obtain a closed-form solution.

        - 'sparse_cg' uses the conjugate gradient solver as found in
          scipy.sparse.linalg.cg. As an iterative algorithm, this solver is
          more appropriate than 'cholesky' for large-scale data
          (possibility to set `tol` and `max_iter`).

        - 'lsqr' uses the dedicated regularized least-squares routine
          scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative
          procedure.

        - 'sag' uses a Stochastic Average Gradient descent, and 'saga' uses
          its improved, unbiased version named SAGA. Both methods also use an
          iterative procedure, and are often faster than other solvers when
          both n_samples and n_features are large. Note that 'sag' and
          'saga' fast convergence is only guaranteed on features with
          approximately the same scale. You can preprocess the data with a
          scaler from sklearn.preprocessing.

        - 'lbfgs' uses L-BFGS-B algorithm implemented in
          `scipy.optimize.minimize`. It can be used only when `positive`
          is True.

        All solvers except 'svd' support both dense and sparse data. However, only
        'lsqr', 'sag', 'sparse_cg', and 'lbfgs' support sparse input when
        `fit_intercept` is True.

        .. versionadded:: 0.17
           Stochastic Average Gradient descent solver.
        .. versionadded:: 0.19
           SAGA solver.

    positive : bool, default=False
        When set to ``True``, forces the coefficients to be positive.
        Only 'lbfgs' solver is supported in this case.

    random_state : int, RandomState instance, default=None
        Used when ``solver`` == 'sag' or 'saga' to shuffle the data.
        See :term:`Glossary <random_state>` for details.

        .. versionadded:: 0.17
           `random_state` to support Stochastic Average Gradient.

    Attributes
    ----------
    coef_ : ndarray of shape (n_features,) or (n_targets, n_features)
        Weight vector(s).

    intercept_ : float or ndarray of shape (n_targets,)
        Independent term in decision function. Set to 0.0 if
        ``fit_intercept = False``.

    n_iter_ : None or ndarray of shape (n_targets,)
        Actual number of iterations for each target. Available only for
        sag and lsqr solvers. Other solvers will return None.

        .. versionadded:: 0.17

    n_features_in_ : int
        Number of features seen during :term:`fit`.

        .. versionadded:: 0.24

    feature_names_in_ : ndarray of shape (`n_features_in_`,)
        Names of features seen during :term:`fit`. Defined only when `X`
        has feature names that are all strings.

        .. versionadded:: 1.0

    See Also
    --------
    RidgeClassifier : Ridge classifier.
    RidgeCV : Ridge regression with built-in cross validation.
    :class:`~sklearn.kernel_ridge.KernelRidge` : Kernel ridge regression
        combines ridge regression with the kernel trick.

    Notes
    -----
    Regularization improves the conditioning of the problem and
    reduces the variance of the estimates. Larger values specify stronger
    regularization. Alpha corresponds to ``1 / (2C)`` in other linear
    models such as :class:`~sklearn.linear_model.LogisticRegression` or
    :class:`~sklearn.svm.LinearSVC`.

    Examples
    --------
    >>> from sklearn.linear_model import Ridge
    >>> import numpy as np
    >>> n_samples, n_features = 10, 5
    >>> rng = np.random.RandomState(0)
    >>> y = rng.randn(n_samples)
    >>> X = rng.randn(n_samples, n_features)
    >>> clf = Ridge(alpha=1.0)
    >>> clf.fit(X, y)
    Ridge()
    """

    def __init__(
        self,
        alpha=1.0,
        *,
        fit_intercept=True,
        copy_X=True,
        max_iter=None,
        tol=1e-4,
        solver="auto",
        positive=False,
        random_state=None,
    ):
        super().__init__(
            alpha=alpha,
            fit_intercept=fit_intercept,
            copy_X=copy_X,
            max_iter=max_iter,
            tol=tol,
            solver=solver,
            positive=positive,
            random_state=random_state,
        )

    def fit(self, X, y, sample_weight=None):
        """Fit Ridge regression model.

        Parameters
        ----------
        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
            Training data.

        y : ndarray of shape (n_samples,) or (n_samples, n_targets)
            Target values.

        sample_weight : float or ndarray of shape (n_samples,), default=None
            Individual weights for each sample. If given a float, every sample
            will have the same weight.

        Returns
        -------
        self : object
            Fitted estimator.
        """
        self._validate_params()

        _accept_sparse = _get_valid_accept_sparse(sparse.issparse(X), self.solver)
        X, y = self._validate_data(
            X,
            y,
            accept_sparse=_accept_sparse,
            dtype=[np.float64, np.float32],
            multi_output=True,
            y_numeric=True,
        )
        return super().fit(X, y, sample_weight=sample_weight)


class _RidgeClassifierMixin(LinearClassifierMixin):
    def _prepare_data(self, X, y, sample_weight, solver):
        """Validate `X` and `y` and binarize `y`.

        Parameters
        ----------
        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
            Training data.

        y : ndarray of shape (n_samples,)
            Target values.

        sample_weight : float or ndarray of shape (n_samples,), default=None
            Individual weights for each sample. If given a float, every sample
            will have the same weight.

        solver : str
            The solver used in `Ridge` to know which sparse format to support.

        Returns
        -------
        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
            Validated training data.

        y : ndarray of shape (n_samples,)
            Validated target values.

        sample_weight : ndarray of shape (n_samples,)
            Validated sample weights.

        Y : ndarray of shape (n_samples, n_classes)
            The binarized version of `y`.
        """
        accept_sparse = _get_valid_accept_sparse(sparse.issparse(X), solver)
        X, y = self._validate_data(
            X,
            y,
            accept_sparse=accept_sparse,
            multi_output=True,
            y_numeric=False,
        )

        self._label_binarizer = LabelBinarizer(pos_label=1, neg_label=-1)
        Y = self._label_binarizer.fit_transform(y)
        if not self._label_binarizer.y_type_.startswith("multilabel"):
            y = column_or_1d(y, warn=True)

        sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
        if self.class_weight:
            sample_weight = sample_weight * compute_sample_weight(self.class_weight, y)
        return X, y, sample_weight, Y

    def predict(self, X):
        """Predict class labels for samples in `X`.

        Parameters
        ----------
        X : {array-like, spare matrix} of shape (n_samples, n_features)
            The data matrix for which we want to predict the targets.

        Returns
        -------
        y_pred : ndarray of shape (n_samples,) or (n_samples, n_outputs)
            Vector or matrix containing the predictions. In binary and
            multiclass problems, this is a vector containing `n_samples`. In
            a multilabel problem, it returns a matrix of shape
            `(n_samples, n_outputs)`.
        """
        check_is_fitted(self, attributes=["_label_binarizer"])
        if self._label_binarizer.y_type_.startswith("multilabel"):
            # Threshold such that the negative label is -1 and positive label
            # is 1 to use the inverse transform of the label binarizer fitted
            # during fit.
            scores = 2 * (self.decision_function(X) > 0) - 1
            return self._label_binarizer.inverse_transform(scores)
        return super().predict(X)

    @property
    def classes_(self):
        """Classes labels."""
        return self._label_binarizer.classes_

    def _more_tags(self):
        return {"multilabel": True}


class RidgeClassifier(_RidgeClassifierMixin, _BaseRidge):
    """Classifier using Ridge regression.

    This classifier first converts the target values into ``{-1, 1}`` and
    then treats the problem as a regression task (multi-output regression in
    the multiclass case).

    Read more in the :ref:`User Guide <ridge_regression>`.

    Parameters
    ----------
    alpha : float, default=1.0
        Regularization strength; must be a positive float. Regularization
        improves the conditioning of the problem and reduces the variance of
        the estimates. Larger values specify stronger regularization.
        Alpha corresponds to ``1 / (2C)`` in other linear models such as
        :class:`~sklearn.linear_model.LogisticRegression` or
        :class:`~sklearn.svm.LinearSVC`.

    fit_intercept : bool, default=True
        Whether to calculate the intercept for this model. If set to false, no
        intercept will be used in calculations (e.g. data is expected to be
        already centered).

    copy_X : bool, default=True
        If True, X will be copied; else, it may be overwritten.

    max_iter : int, default=None
        Maximum number of iterations for conjugate gradient solver.
        The default value is determined by scipy.sparse.linalg.

    tol : float, default=1e-4
        Precision of the solution. Note that `tol` has no effect for solvers 'svd' and
        'cholesky'.

        .. versionchanged:: 1.2
           Default value changed from 1e-3 to 1e-4 for consistency with other linear
           models.

    class_weight : dict or 'balanced', default=None
        Weights associated with classes in the form ``{class_label: weight}``.
        If not given, all classes are supposed to have weight one.

        The "balanced" mode uses the values of y to automatically adjust
        weights inversely proportional to class frequencies in the input data
        as ``n_samples / (n_classes * np.bincount(y))``.

    solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', \
            'sag', 'saga', 'lbfgs'}, default='auto'
        Solver to use in the computational routines:

        - 'auto' chooses the solver automatically based on the type of data.

        - 'svd' uses a Singular Value Decomposition of X to compute the Ridge
          coefficients. It is the most stable solver, in particular more stable
          for singular matrices than 'cholesky' at the cost of being slower.

        - 'cholesky' uses the standard scipy.linalg.solve function to
          obtain a closed-form solution.

        - 'sparse_cg' uses the conjugate gradient solver as found in
          scipy.sparse.linalg.cg. As an iterative algorithm, this solver is
          more appropriate than 'cholesky' for large-scale data
          (possibility to set `tol` and `max_iter`).

        - 'lsqr' uses the dedicated regularized least-squares routine
          scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative
          procedure.

        - 'sag' uses a Stochastic Average Gradient descent, and 'saga' uses
          its unbiased and more flexible version named SAGA. Both methods
          use an iterative procedure, and are often faster than other solvers
          when both n_samples and n_features are large. Note that 'sag' and
          'saga' fast convergence is only guaranteed on features with
          approximately the same scale. You can preprocess the data with a
          scaler from sklearn.preprocessing.

          .. versionadded:: 0.17
             Stochastic Average Gradient descent solver.
          .. versionadded:: 0.19
             SAGA solver.

        - 'lbfgs' uses L-BFGS-B algorithm implemented in
          `scipy.optimize.minimize`. It can be used only when `positive`
          is True.

    positive : bool, default=False
        When set to ``True``, forces the coefficients to be positive.
        Only 'lbfgs' solver is supported in this case.

    random_state : int, RandomState instance, default=None
        Used when ``solver`` == 'sag' or 'saga' to shuffle the data.
        See :term:`Glossary <random_state>` for details.

    Attributes
    ----------
    coef_ : ndarray of shape (1, n_features) or (n_classes, n_features)
        Coefficient of the features in the decision function.

        ``coef_`` is of shape (1, n_features) when the given problem is binary.

    intercept_ : float or ndarray of shape (n_targets,)
        Independent term in decision function. Set to 0.0 if
        ``fit_intercept = False``.

    n_iter_ : None or ndarray of shape (n_targets,)
        Actual number of iterations for each target. Available only for
        sag and lsqr solvers. Other solvers will return None.

    classes_ : ndarray of shape (n_classes,)
        The classes labels.

    n_features_in_ : int
        Number of features seen during :term:`fit`.

        .. versionadded:: 0.24

    feature_names_in_ : ndarray of shape (`n_features_in_`,)
        Names of features seen during :term:`fit`. Defined only when `X`
        has feature names that are all strings.

        .. versionadded:: 1.0

    See Also
    --------
    Ridge : Ridge regression.
    RidgeClassifierCV :  Ridge classifier with built-in cross validation.

    Notes
    -----
    For multi-class classification, n_class classifiers are trained in
    a one-versus-all approach. Concretely, this is implemented by taking
    advantage of the multi-variate response support in Ridge.

    Examples
    --------
    >>> from sklearn.datasets import load_breast_cancer
    >>> from sklearn.linear_model import RidgeClassifier
    >>> X, y = load_breast_cancer(return_X_y=True)
    >>> clf = RidgeClassifier().fit(X, y)
    >>> clf.score(X, y)
    0.9595...
    """

    _parameter_constraints: dict = {
        **_BaseRidge._parameter_constraints,
        "class_weight": [dict, StrOptions({"balanced"}), None],
    }

    def __init__(
        self,
        alpha=1.0,
        *,
        fit_intercept=True,
        copy_X=True,
        max_iter=None,
        tol=1e-4,
        class_weight=None,
        solver="auto",
        positive=False,
        random_state=None,
    ):
        super().__init__(
            alpha=alpha,
            fit_intercept=fit_intercept,
            copy_X=copy_X,
            max_iter=max_iter,
            tol=tol,
            solver=solver,
            positive=positive,
            random_state=random_state,
        )
        self.class_weight = class_weight

    def fit(self, X, y, sample_weight=None):
        """Fit Ridge classifier model.

        Parameters
        ----------
        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
            Training data.

        y : ndarray of shape (n_samples,)
            Target values.

        sample_weight : float or ndarray of shape (n_samples,), default=None
            Individual weights for each sample. If given a float, every sample
            will have the same weight.

            .. versionadded:: 0.17
               *sample_weight* support to RidgeClassifier.

        Returns
        -------
        self : object
            Instance of the estimator.
        """
        self._validate_params()

        X, y, sample_weight, Y = self._prepare_data(X, y, sample_weight, self.solver)

        super().fit(X, Y, sample_weight=sample_weight)
        return self


def _check_gcv_mode(X, gcv_mode):
    if gcv_mode in ["eigen", "svd"]:
        return gcv_mode
    # if X has more rows than columns, use decomposition of X^T.X,
    # otherwise X.X^T
    if X.shape[0] > X.shape[1]:
        return "svd"
    return "eigen"


def _find_smallest_angle(query, vectors):
    """Find the column of vectors that is most aligned with the query.

    Both query and the columns of vectors must have their l2 norm equal to 1.

    Parameters
    ----------
    query : ndarray of shape (n_samples,)
        Normalized query vector.

    vectors : ndarray of shape (n_samples, n_features)
        Vectors to which we compare query, as columns. Must be normalized.
    """
    abs_cosine = np.abs(query.dot(vectors))
    index = np.argmax(abs_cosine)
    return index


class _X_CenterStackOp(sparse.linalg.LinearOperator):
    """Behaves as centered and scaled X with an added intercept column.

    This operator behaves as
    np.hstack([X - sqrt_sw[:, None] * X_mean, sqrt_sw[:, None]])
    """

    def __init__(self, X, X_mean, sqrt_sw):
        n_samples, n_features = X.shape
        super().__init__(X.dtype, (n_samples, n_features + 1))
        self.X = X
        self.X_mean = X_mean
        self.sqrt_sw = sqrt_sw

    def _matvec(self, v):
        v = v.ravel()
        return (
            safe_sparse_dot(self.X, v[:-1], dense_output=True)
            - self.sqrt_sw * self.X_mean.dot(v[:-1])
            + v[-1] * self.sqrt_sw
        )

    def _matmat(self, v):
        return (
            safe_sparse_dot(self.X, v[:-1], dense_output=True)
            - self.sqrt_sw[:, None] * self.X_mean.dot(v[:-1])
            + v[-1] * self.sqrt_sw[:, None]
        )

    def _transpose(self):
        return _XT_CenterStackOp(self.X, self.X_mean, self.sqrt_sw)


class _XT_CenterStackOp(sparse.linalg.LinearOperator):
    """Behaves as transposed centered and scaled X with an intercept column.

    This operator behaves as
    np.hstack([X - sqrt_sw[:, None] * X_mean, sqrt_sw[:, None]]).T
    """

    def __init__(self, X, X_mean, sqrt_sw):
        n_samples, n_features = X.shape
        super().__init__(X.dtype, (n_features + 1, n_samples))
        self.X = X
        self.X_mean = X_mean
        self.sqrt_sw = sqrt_sw

    def _matvec(self, v):
        v = v.ravel()
        n_features = self.shape[0]
        res = np.empty(n_features, dtype=self.X.dtype)
        res[:-1] = safe_sparse_dot(self.X.T, v, dense_output=True) - (
            self.X_mean * self.sqrt_sw.dot(v)
        )
        res[-1] = np.dot(v, self.sqrt_sw)
        return res

    def _matmat(self, v):
        n_features = self.shape[0]
        res = np.empty((n_features, v.shape[1]), dtype=self.X.dtype)
        res[:-1] = safe_sparse_dot(self.X.T, v, dense_output=True) - self.X_mean[
            :, None
        ] * self.sqrt_sw.dot(v)
        res[-1] = np.dot(self.sqrt_sw, v)
        return res


class _IdentityRegressor:
    """Fake regressor which will directly output the prediction."""

    def decision_function(self, y_predict):
        return y_predict

    def predict(self, y_predict):
        return y_predict


class _IdentityClassifier(LinearClassifierMixin):
    """Fake classifier which will directly output the prediction.

    We inherit from LinearClassifierMixin to get the proper shape for the
    output `y`.
    """

    def __init__(self, classes):
        self.classes_ = classes

    def decision_function(self, y_predict):
        return y_predict


class _RidgeGCV(LinearModel):
    """Ridge regression with built-in Leave-one-out Cross-Validation.

    This class is not intended to be used directly. Use RidgeCV instead.

    Notes
    -----

    We want to solve (K + alpha*Id)c = y,
    where K = X X^T is the kernel matrix.

    Let G = (K + alpha*Id).

    Dual solution: c = G^-1y
    Primal solution: w = X^T c

    Compute eigendecomposition K = Q V Q^T.
    Then G^-1 = Q (V + alpha*Id)^-1 Q^T,
    where (V + alpha*Id) is diagonal.
    It is thus inexpensive to inverse for many alphas.

    Let loov be the vector of prediction values for each example
    when the model was fitted with all examples but this example.

    loov = (KG^-1Y - diag(KG^-1)Y) / diag(I-KG^-1)

    Let looe be the vector of prediction errors for each example
    when the model was fitted with all examples but this example.

    looe = y - loov = c / diag(G^-1)

    The best score (negative mean squared error or user-provided scoring) is
    stored in the `best_score_` attribute, and the selected hyperparameter in
    `alpha_`.

    References
    ----------
    http://cbcl.mit.edu/publications/ps/MIT-CSAIL-TR-2007-025.pdf
    https://www.mit.edu/~9.520/spring07/Classes/rlsslides.pdf
    """

    def __init__(
        self,
        alphas=(0.1, 1.0, 10.0),
        *,
        fit_intercept=True,
        scoring=None,
        copy_X=True,
        gcv_mode=None,
        store_cv_values=False,
        is_clf=False,
        alpha_per_target=False,
    ):
        self.alphas = alphas
        self.fit_intercept = fit_intercept
        self.scoring = scoring
        self.copy_X = copy_X
        self.gcv_mode = gcv_mode
        self.store_cv_values = store_cv_values
        self.is_clf = is_clf
        self.alpha_per_target = alpha_per_target

    @staticmethod
    def _decomp_diag(v_prime, Q):
        # compute diagonal of the matrix: dot(Q, dot(diag(v_prime), Q^T))
        return (v_prime * Q**2).sum(axis=-1)

    @staticmethod
    def _diag_dot(D, B):
        # compute dot(diag(D), B)
        if len(B.shape) > 1:
            # handle case where B is > 1-d
            D = D[(slice(None),) + (np.newaxis,) * (len(B.shape) - 1)]
        return D * B

    def _compute_gram(self, X, sqrt_sw):
        """Computes the Gram matrix XX^T with possible centering.

        Parameters
        ----------
        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
            The preprocessed design matrix.

        sqrt_sw : ndarray of shape (n_samples,)
            square roots of sample weights

        Returns
        -------
        gram : ndarray of shape (n_samples, n_samples)
            The Gram matrix.
        X_mean : ndarray of shape (n_feature,)
            The weighted mean of ``X`` for each feature.

        Notes
        -----
        When X is dense the centering has been done in preprocessing
        so the mean is 0 and we just compute XX^T.

        When X is sparse it has not been centered in preprocessing, but it has
        been scaled by sqrt(sample weights).

        When self.fit_intercept is False no centering is done.

        The centered X is never actually computed because centering would break
        the sparsity of X.
        """
        center = self.fit_intercept and sparse.issparse(X)
        if not center:
            # in this case centering has been done in preprocessing
            # or we are not fitting an intercept.
            X_mean = np.zeros(X.shape[1], dtype=X.dtype)
            return safe_sparse_dot(X, X.T, dense_output=True), X_mean
        # X is sparse
        n_samples = X.shape[0]
        sample_weight_matrix = sparse.dia_matrix(
            (sqrt_sw, 0), shape=(n_samples, n_samples)
        )
        X_weighted = sample_weight_matrix.dot(X)
        X_mean, _ = mean_variance_axis(X_weighted, axis=0)
        X_mean *= n_samples / sqrt_sw.dot(sqrt_sw)
        X_mX = sqrt_sw[:, None] * safe_sparse_dot(X_mean, X.T, dense_output=True)
        X_mX_m = np.outer(sqrt_sw, sqrt_sw) * np.dot(X_mean, X_mean)
        return (
            safe_sparse_dot(X, X.T, dense_output=True) + X_mX_m - X_mX - X_mX.T,
            X_mean,
        )

    def _compute_covariance(self, X, sqrt_sw):
        """Computes covariance matrix X^TX with possible centering.

        Parameters
        ----------
        X : sparse matrix of shape (n_samples, n_features)
            The preprocessed design matrix.

        sqrt_sw : ndarray of shape (n_samples,)
            square roots of sample weights

        Returns
        -------
        covariance : ndarray of shape (n_features, n_features)
            The covariance matrix.
        X_mean : ndarray of shape (n_feature,)
            The weighted mean of ``X`` for each feature.

        Notes
        -----
        Since X is sparse it has not been centered in preprocessing, but it has
        been scaled by sqrt(sample weights).

        When self.fit_intercept is False no centering is done.

        The centered X is never actually computed because centering would break
        the sparsity of X.
        """
        if not self.fit_intercept:
            # in this case centering has been done in preprocessing
            # or we are not fitting an intercept.
            X_mean = np.zeros(X.shape[1], dtype=X.dtype)
            return safe_sparse_dot(X.T, X, dense_output=True), X_mean
        # this function only gets called for sparse X
        n_samples = X.shape[0]
        sample_weight_matrix = sparse.dia_matrix(
            (sqrt_sw, 0), shape=(n_samples, n_samples)
        )
        X_weighted = sample_weight_matrix.dot(X)
        X_mean, _ = mean_variance_axis(X_weighted, axis=0)
        X_mean = X_mean * n_samples / sqrt_sw.dot(sqrt_sw)
        weight_sum = sqrt_sw.dot(sqrt_sw)
        return (
            safe_sparse_dot(X.T, X, dense_output=True)
            - weight_sum * np.outer(X_mean, X_mean),
            X_mean,
        )

    def _sparse_multidot_diag(self, X, A, X_mean, sqrt_sw):
        """Compute the diagonal of (X - X_mean).dot(A).dot((X - X_mean).T)
        without explicitly centering X nor computing X.dot(A)
        when X is sparse.

        Parameters
        ----------
        X : sparse matrix of shape (n_samples, n_features)

        A : ndarray of shape (n_features, n_features)

        X_mean : ndarray of shape (n_features,)

        sqrt_sw : ndarray of shape (n_features,)
            square roots of sample weights

        Returns
        -------
        diag : np.ndarray, shape (n_samples,)
            The computed diagonal.
        """
        intercept_col = scale = sqrt_sw
        batch_size = X.shape[1]
        diag = np.empty(X.shape[0], dtype=X.dtype)
        for start in range(0, X.shape[0], batch_size):
            batch = slice(start, min(X.shape[0], start + batch_size), 1)
            X_batch = np.empty(
                (X[batch].shape[0], X.shape[1] + self.fit_intercept), dtype=X.dtype
            )
            if self.fit_intercept:
                X_batch[:, :-1] = X[batch].A - X_mean * scale[batch][:, None]
                X_batch[:, -1] = intercept_col[batch]
            else:
                X_batch = X[batch].A
            diag[batch] = (X_batch.dot(A) * X_batch).sum(axis=1)
        return diag

    def _eigen_decompose_gram(self, X, y, sqrt_sw):
        """Eigendecomposition of X.X^T, used when n_samples <= n_features."""
        # if X is dense it has already been centered in preprocessing
        K, X_mean = self._compute_gram(X, sqrt_sw)
        if self.fit_intercept:
            # to emulate centering X with sample weights,
            # ie removing the weighted average, we add a column
            # containing the square roots of the sample weights.
            # by centering, it is orthogonal to the other columns
            K += np.outer(sqrt_sw, sqrt_sw)
        eigvals, Q = linalg.eigh(K)
        QT_y = np.dot(Q.T, y)
        return X_mean, eigvals, Q, QT_y

    def _solve_eigen_gram(self, alpha, y, sqrt_sw, X_mean, eigvals, Q, QT_y):
        """Compute dual coefficients and diagonal of G^-1.

        Used when we have a decomposition of X.X^T (n_samples <= n_features).
        """
        w = 1.0 / (eigvals + alpha)
        if self.fit_intercept:
            # the vector containing the square roots of the sample weights (1
            # when no sample weights) is the eigenvector of XX^T which
            # corresponds to the intercept; we cancel the regularization on
            # this dimension. the corresponding eigenvalue is
            # sum(sample_weight).
            normalized_sw = sqrt_sw / np.linalg.norm(sqrt_sw)
            intercept_dim = _find_smallest_angle(normalized_sw, Q)
            w[intercept_dim] = 0  # cancel regularization for the intercept

        c = np.dot(Q, self._diag_dot(w, QT_y))
        G_inverse_diag = self._decomp_diag(w, Q)
        # handle case where y is 2-d
        if len(y.shape) != 1:
            G_inverse_diag = G_inverse_diag[:, np.newaxis]
        return G_inverse_diag, c

    def _eigen_decompose_covariance(self, X, y, sqrt_sw):
        """Eigendecomposition of X^T.X, used when n_samples > n_features
        and X is sparse.
        """
        n_samples, n_features = X.shape
        cov = np.empty((n_features + 1, n_features + 1), dtype=X.dtype)
        cov[:-1, :-1], X_mean = self._compute_covariance(X, sqrt_sw)
        if not self.fit_intercept:
            cov = cov[:-1, :-1]
        # to emulate centering X with sample weights,
        # ie removing the weighted average, we add a column
        # containing the square roots of the sample weights.
        # by centering, it is orthogonal to the other columns
        # when all samples have the same weight we add a column of 1
        else:
            cov[-1] = 0
            cov[:, -1] = 0
            cov[-1, -1] = sqrt_sw.dot(sqrt_sw)
        nullspace_dim = max(0, n_features - n_samples)
        eigvals, V = linalg.eigh(cov)
        # remove eigenvalues and vectors in the null space of X^T.X
        eigvals = eigvals[nullspace_dim:]
        V = V[:, nullspace_dim:]
        return X_mean, eigvals, V, X

    def _solve_eigen_covariance_no_intercept(
        self, alpha, y, sqrt_sw, X_mean, eigvals, V, X
    ):
        """Compute dual coefficients and diagonal of G^-1.

        Used when we have a decomposition of X^T.X
        (n_samples > n_features and X is sparse), and not fitting an intercept.
        """
        w = 1 / (eigvals + alpha)
        A = (V * w).dot(V.T)
        AXy = A.dot(safe_sparse_dot(X.T, y, dense_output=True))
        y_hat = safe_sparse_dot(X, AXy, dense_output=True)
        hat_diag = self._sparse_multidot_diag(X, A, X_mean, sqrt_sw)
        if len(y.shape) != 1:
            # handle case where y is 2-d
            hat_diag = hat_diag[:, np.newaxis]
        return (1 - hat_diag) / alpha, (y - y_hat) / alpha

    def _solve_eigen_covariance_intercept(
        self, alpha, y, sqrt_sw, X_mean, eigvals, V, X
    ):
        """Compute dual coefficients and diagonal of G^-1.

        Used when we have a decomposition of X^T.X
        (n_samples > n_features and X is sparse),
        and we are fitting an intercept.
        """
        # the vector [0, 0, ..., 0, 1]
        # is the eigenvector of X^TX which
        # corresponds to the intercept; we cancel the regularization on
        # this dimension. the corresponding eigenvalue is
        # sum(sample_weight), e.g. n when uniform sample weights.
        intercept_sv = np.zeros(V.shape[0])
        intercept_sv[-1] = 1
        intercept_dim = _find_smallest_angle(intercept_sv, V)
        w = 1 / (eigvals + alpha)
        w[intercept_dim] = 1 / eigvals[intercept_dim]
        A = (V * w).dot(V.T)
        # add a column to X containing the square roots of sample weights
        X_op = _X_CenterStackOp(X, X_mean, sqrt_sw)
        AXy = A.dot(X_op.T.dot(y))
        y_hat = X_op.dot(AXy)
        hat_diag = self._sparse_multidot_diag(X, A, X_mean, sqrt_sw)
        # return (1 - hat_diag), (y - y_hat)
        if len(y.shape) != 1:
            # handle case where y is 2-d
            hat_diag = hat_diag[:, np.newaxis]
        return (1 - hat_diag) / alpha, (y - y_hat) / alpha

    def _solve_eigen_covariance(self, alpha, y, sqrt_sw, X_mean, eigvals, V, X):
        """Compute dual coefficients and diagonal of G^-1.

        Used when we have a decomposition of X^T.X
        (n_samples > n_features and X is sparse).
        """
        if self.fit_intercept:
            return self._solve_eigen_covariance_intercept(
                alpha, y, sqrt_sw, X_mean, eigvals, V, X
            )
        return self._solve_eigen_covariance_no_intercept(
            alpha, y, sqrt_sw, X_mean, eigvals, V, X
        )

    def _svd_decompose_design_matrix(self, X, y, sqrt_sw):
        # X already centered
        X_mean = np.zeros(X.shape[1], dtype=X.dtype)
        if self.fit_intercept:
            # to emulate fit_intercept=True situation, add a column
            # containing the square roots of the sample weights
            # by centering, the other columns are orthogonal to that one
            intercept_column = sqrt_sw[:, None]
            X = np.hstack((X, intercept_column))
        U, singvals, _ = linalg.svd(X, full_matrices=0)
        singvals_sq = singvals**2
        UT_y = np.dot(U.T, y)
        return X_mean, singvals_sq, U, UT_y

    def _solve_svd_design_matrix(self, alpha, y, sqrt_sw, X_mean, singvals_sq, U, UT_y):
        """Compute dual coefficients and diagonal of G^-1.

        Used when we have an SVD decomposition of X
        (n_samples > n_features and X is dense).
        """
        w = ((singvals_sq + alpha) ** -1) - (alpha**-1)
        if self.fit_intercept:
            # detect intercept column
            normalized_sw = sqrt_sw / np.linalg.norm(sqrt_sw)
            intercept_dim = _find_smallest_angle(normalized_sw, U)
            # cancel the regularization for the intercept
            w[intercept_dim] = -(alpha**-1)
        c = np.dot(U, self._diag_dot(w, UT_y)) + (alpha**-1) * y
        G_inverse_diag = self._decomp_diag(w, U) + (alpha**-1)
        if len(y.shape) != 1:
            # handle case where y is 2-d
            G_inverse_diag = G_inverse_diag[:, np.newaxis]
        return G_inverse_diag, c

    def fit(self, X, y, sample_weight=None):
        """Fit Ridge regression model with gcv.

        Parameters
        ----------
        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
            Training data. Will be cast to float64 if necessary.

        y : ndarray of shape (n_samples,) or (n_samples, n_targets)
            Target values. Will be cast to float64 if necessary.

        sample_weight : float or ndarray of shape (n_samples,), default=None
            Individual weights for each sample. If given a float, every sample
            will have the same weight.

        Returns
        -------
        self : object
        """
        X, y = self._validate_data(
            X,
            y,
            accept_sparse=["csr", "csc", "coo"],
            dtype=[np.float64],
            multi_output=True,
            y_numeric=True,
        )

        # alpha_per_target cannot be used in classifier mode. All subclasses
        # of _RidgeGCV that are classifiers keep alpha_per_target at its
        # default value: False, so the condition below should never happen.
        assert not (self.is_clf and self.alpha_per_target)

        if sample_weight is not None:
            sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)

        self.alphas = np.asarray(self.alphas)

        X, y, X_offset, y_offset, X_scale = _preprocess_data(
            X,
            y,
            self.fit_intercept,
            copy=self.copy_X,
            sample_weight=sample_weight,
        )

        gcv_mode = _check_gcv_mode(X, self.gcv_mode)

        if gcv_mode == "eigen":
            decompose = self._eigen_decompose_gram
            solve = self._solve_eigen_gram
        elif gcv_mode == "svd":
            if sparse.issparse(X):
                decompose = self._eigen_decompose_covariance
                solve = self._solve_eigen_covariance
            else:
                decompose = self._svd_decompose_design_matrix
                solve = self._solve_svd_design_matrix

        n_samples = X.shape[0]

        if sample_weight is not None:
            X, y, sqrt_sw = _rescale_data(X, y, sample_weight)
        else:
            sqrt_sw = np.ones(n_samples, dtype=X.dtype)

        X_mean, *decomposition = decompose(X, y, sqrt_sw)

        scorer = check_scoring(self, scoring=self.scoring, allow_none=True)
        error = scorer is None

        n_y = 1 if len(y.shape) == 1 else y.shape[1]
        n_alphas = 1 if np.ndim(self.alphas) == 0 else len(self.alphas)

        if self.store_cv_values:
            self.cv_values_ = np.empty((n_samples * n_y, n_alphas), dtype=X.dtype)

        best_coef, best_score, best_alpha = None, None, None

        for i, alpha in enumerate(np.atleast_1d(self.alphas)):
            G_inverse_diag, c = solve(float(alpha), y, sqrt_sw, X_mean, *decomposition)
            if error:
                squared_errors = (c / G_inverse_diag) ** 2
                if self.alpha_per_target:
                    alpha_score = -squared_errors.mean(axis=0)
                else:
                    alpha_score = -squared_errors.mean()
                if self.store_cv_values:
                    self.cv_values_[:, i] = squared_errors.ravel()
            else:
                predictions = y - (c / G_inverse_diag)
                if self.store_cv_values:
                    self.cv_values_[:, i] = predictions.ravel()

                if self.is_clf:
                    identity_estimator = _IdentityClassifier(classes=np.arange(n_y))
                    alpha_score = scorer(
                        identity_estimator, predictions, y.argmax(axis=1)
                    )
                else:
                    identity_estimator = _IdentityRegressor()
                    if self.alpha_per_target:
                        alpha_score = np.array(
                            [
                                scorer(identity_estimator, predictions[:, j], y[:, j])
                                for j in range(n_y)
                            ]
                        )
                    else:
                        alpha_score = scorer(
                            identity_estimator, predictions.ravel(), y.ravel()
                        )

            # Keep track of the best model
            if best_score is None:
                # initialize
                if self.alpha_per_target and n_y > 1:
                    best_coef = c
                    best_score = np.atleast_1d(alpha_score)
                    best_alpha = np.full(n_y, alpha)
                else:
                    best_coef = c
                    best_score = alpha_score
                    best_alpha = alpha
            else:
                # update
                if self.alpha_per_target and n_y > 1:
                    to_update = alpha_score > best_score
                    best_coef[:, to_update] = c[:, to_update]
                    best_score[to_update] = alpha_score[to_update]
                    best_alpha[to_update] = alpha
                elif alpha_score > best_score:
                    best_coef, best_score, best_alpha = c, alpha_score, alpha

        self.alpha_ = best_alpha
        self.best_score_ = best_score
        self.dual_coef_ = best_coef
        self.coef_ = safe_sparse_dot(self.dual_coef_.T, X)

        if sparse.issparse(X):
            X_offset = X_mean * X_scale
        else:
            X_offset += X_mean * X_scale
        self._set_intercept(X_offset, y_offset, X_scale)

        if self.store_cv_values:
            if len(y.shape) == 1:
                cv_values_shape = n_samples, n_alphas
            else:
                cv_values_shape = n_samples, n_y, n_alphas
            self.cv_values_ = self.cv_values_.reshape(cv_values_shape)

        return self


class _BaseRidgeCV(LinearModel):

    _parameter_constraints: dict = {
        "alphas": ["array-like", Interval(Real, 0, None, closed="neither")],
        "fit_intercept": ["boolean"],
        "scoring": [StrOptions(set(get_scorer_names())), callable, None],
        "cv": ["cv_object"],
        "gcv_mode": [StrOptions({"auto", "svd", "eigen"}), None],
        "store_cv_values": ["boolean"],
        "alpha_per_target": ["boolean"],
    }

    def __init__(
        self,
        alphas=(0.1, 1.0, 10.0),
        *,
        fit_intercept=True,
        scoring=None,
        cv=None,
        gcv_mode=None,
        store_cv_values=False,
        alpha_per_target=False,
    ):
        self.alphas = alphas
        self.fit_intercept = fit_intercept
        self.scoring = scoring
        self.cv = cv
        self.gcv_mode = gcv_mode
        self.store_cv_values = store_cv_values
        self.alpha_per_target = alpha_per_target

    def fit(self, X, y, sample_weight=None):
        """Fit Ridge regression model with cv.

        Parameters
        ----------
        X : ndarray of shape (n_samples, n_features)
            Training data. If using GCV, will be cast to float64
            if necessary.

        y : ndarray of shape (n_samples,) or (n_samples, n_targets)
            Target values. Will be cast to X's dtype if necessary.

        sample_weight : float or ndarray of shape (n_samples,), default=None
            Individual weights for each sample. If given a float, every sample
            will have the same weight.

        Returns
        -------
        self : object
            Fitted estimator.

        Notes
        -----
        When sample_weight is provided, the selected hyperparameter may depend
        on whether we use leave-one-out cross-validation (cv=None or cv='auto')
        or another form of cross-validation, because only leave-one-out
        cross-validation takes the sample weights into account when computing
        the validation score.
        """
        cv = self.cv

        check_scalar_alpha = partial(
            check_scalar,
            target_type=numbers.Real,
            min_val=0.0,
            include_boundaries="neither",
        )

        if isinstance(self.alphas, (np.ndarray, list, tuple)):
            n_alphas = 1 if np.ndim(self.alphas) == 0 else len(self.alphas)
            if n_alphas != 1:
                for index, alpha in enumerate(self.alphas):
                    alpha = check_scalar_alpha(alpha, f"alphas[{index}]")
            else:
                self.alphas[0] = check_scalar_alpha(self.alphas[0], "alphas")
        alphas = np.asarray(self.alphas)

        if cv is None:
            estimator = _RidgeGCV(
                alphas,
                fit_intercept=self.fit_intercept,
                scoring=self.scoring,
                gcv_mode=self.gcv_mode,
                store_cv_values=self.store_cv_values,
                is_clf=is_classifier(self),
                alpha_per_target=self.alpha_per_target,
            )
            estimator.fit(X, y, sample_weight=sample_weight)
            self.alpha_ = estimator.alpha_
            self.best_score_ = estimator.best_score_
            if self.store_cv_values:
                self.cv_values_ = estimator.cv_values_
        else:
            if self.store_cv_values:
                raise ValueError("cv!=None and store_cv_values=True are incompatible")
            if self.alpha_per_target:
                raise ValueError("cv!=None and alpha_per_target=True are incompatible")

            parameters = {"alpha": alphas}
            solver = "sparse_cg" if sparse.issparse(X) else "auto"
            model = RidgeClassifier if is_classifier(self) else Ridge
            gs = GridSearchCV(
                model(
                    fit_intercept=self.fit_intercept,
                    solver=solver,
                ),
                parameters,
                cv=cv,
                scoring=self.scoring,
            )
            gs.fit(X, y, sample_weight=sample_weight)
            estimator = gs.best_estimator_
            self.alpha_ = gs.best_estimator_.alpha
            self.best_score_ = gs.best_score_

        self.coef_ = estimator.coef_
        self.intercept_ = estimator.intercept_
        self.n_features_in_ = estimator.n_features_in_
        if hasattr(estimator, "feature_names_in_"):
            self.feature_names_in_ = estimator.feature_names_in_

        return self


class RidgeCV(MultiOutputMixin, RegressorMixin, _BaseRidgeCV):
    """Ridge regression with built-in cross-validation.

    See glossary entry for :term:`cross-validation estimator`.

    By default, it performs efficient Leave-One-Out Cross-Validation.

    Read more in the :ref:`User Guide <ridge_regression>`.

    Parameters
    ----------
    alphas : array-like of shape (n_alphas,), default=(0.1, 1.0, 10.0)
        Array of alpha values to try.
        Regularization strength; must be a positive float. Regularization
        improves the conditioning of the problem and reduces the variance of
        the estimates. Larger values specify stronger regularization.
        Alpha corresponds to ``1 / (2C)`` in other linear models such as
        :class:`~sklearn.linear_model.LogisticRegression` or
        :class:`~sklearn.svm.LinearSVC`.
        If using Leave-One-Out cross-validation, alphas must be positive.

    fit_intercept : bool, default=True
        Whether to calculate the intercept for this model. If set
        to false, no intercept will be used in calculations
        (i.e. data is expected to be centered).

    scoring : str, callable, default=None
        A string (see model evaluation documentation) or
        a scorer callable object / function with signature
        ``scorer(estimator, X, y)``.
        If None, the negative mean squared error if cv is 'auto' or None
        (i.e. when using leave-one-out cross-validation), and r2 score
        otherwise.

    cv : int, cross-validation generator or an iterable, default=None
        Determines the cross-validation splitting strategy.
        Possible inputs for cv are:

        - None, to use the efficient Leave-One-Out cross-validation
        - integer, to specify the number of folds.
        - :term:`CV splitter`,
        - An iterable yielding (train, test) splits as arrays of indices.

        For integer/None inputs, if ``y`` is binary or multiclass,
        :class:`~sklearn.model_selection.StratifiedKFold` is used, else,
        :class:`~sklearn.model_selection.KFold` is used.

        Refer :ref:`User Guide <cross_validation>` for the various
        cross-validation strategies that can be used here.

    gcv_mode : {'auto', 'svd', 'eigen'}, default='auto'
        Flag indicating which strategy to use when performing
        Leave-One-Out Cross-Validation. Options are::

            'auto' : use 'svd' if n_samples > n_features, otherwise use 'eigen'
            'svd' : force use of singular value decomposition of X when X is
                dense, eigenvalue decomposition of X^T.X when X is sparse.
            'eigen' : force computation via eigendecomposition of X.X^T

        The 'auto' mode is the default and is intended to pick the cheaper
        option of the two depending on the shape of the training data.

    store_cv_values : bool, default=False
        Flag indicating if the cross-validation values corresponding to
        each alpha should be stored in the ``cv_values_`` attribute (see
        below). This flag is only compatible with ``cv=None`` (i.e. using
        Leave-One-Out Cross-Validation).

    alpha_per_target : bool, default=False
        Flag indicating whether to optimize the alpha value (picked from the
        `alphas` parameter list) for each target separately (for multi-output
        settings: multiple prediction targets). When set to `True`, after
        fitting, the `alpha_` attribute will contain a value for each target.
        When set to `False`, a single alpha is used for all targets.

        .. versionadded:: 0.24

    Attributes
    ----------
    cv_values_ : ndarray of shape (n_samples, n_alphas) or \
            shape (n_samples, n_targets, n_alphas), optional
        Cross-validation values for each alpha (only available if
        ``store_cv_values=True`` and ``cv=None``). After ``fit()`` has been
        called, this attribute will contain the mean squared errors if
        `scoring is None` otherwise it will contain standardized per point
        prediction values.

    coef_ : ndarray of shape (n_features) or (n_targets, n_features)
        Weight vector(s).

    intercept_ : float or ndarray of shape (n_targets,)
        Independent term in decision function. Set to 0.0 if
        ``fit_intercept = False``.

    alpha_ : float or ndarray of shape (n_targets,)
        Estimated regularization parameter, or, if ``alpha_per_target=True``,
        the estimated regularization parameter for each target.

    best_score_ : float or ndarray of shape (n_targets,)
        Score of base estimator with best alpha, or, if
        ``alpha_per_target=True``, a score for each target.

        .. versionadded:: 0.23

    n_features_in_ : int
        Number of features seen during :term:`fit`.

        .. versionadded:: 0.24

    feature_names_in_ : ndarray of shape (`n_features_in_`,)
        Names of features seen during :term:`fit`. Defined only when `X`
        has feature names that are all strings.

        .. versionadded:: 1.0

    See Also
    --------
    Ridge : Ridge regression.
    RidgeClassifier : Classifier based on ridge regression on {-1, 1} labels.
    RidgeClassifierCV : Ridge classifier with built-in cross validation.

    Examples
    --------
    >>> from sklearn.datasets import load_diabetes
    >>> from sklearn.linear_model import RidgeCV
    >>> X, y = load_diabetes(return_X_y=True)
    >>> clf = RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1]).fit(X, y)
    >>> clf.score(X, y)
    0.5166...
    """

    def fit(self, X, y, sample_weight=None):
        """Fit Ridge regression model with cv.

        Parameters
        ----------
        X : ndarray of shape (n_samples, n_features)
            Training data. If using GCV, will be cast to float64
            if necessary.

        y : ndarray of shape (n_samples,) or (n_samples, n_targets)
            Target values. Will be cast to X's dtype if necessary.

        sample_weight : float or ndarray of shape (n_samples,), default=None
            Individual weights for each sample. If given a float, every sample
            will have the same weight.

        Returns
        -------
        self : object
            Fitted estimator.

        Notes
        -----
        When sample_weight is provided, the selected hyperparameter may depend
        on whether we use leave-one-out cross-validation (cv=None or cv='auto')
        or another form of cross-validation, because only leave-one-out
        cross-validation takes the sample weights into account when computing
        the validation score.
        """
        self._validate_params()

        super().fit(X, y, sample_weight=sample_weight)
        return self


class RidgeClassifierCV(_RidgeClassifierMixin, _BaseRidgeCV):
    """Ridge classifier with built-in cross-validation.

    See glossary entry for :term:`cross-validation estimator`.

    By default, it performs Leave-One-Out Cross-Validation. Currently,
    only the n_features > n_samples case is handled efficiently.

    Read more in the :ref:`User Guide <ridge_regression>`.

    Parameters
    ----------
    alphas : array-like of shape (n_alphas,), default=(0.1, 1.0, 10.0)
        Array of alpha values to try.
        Regularization strength; must be a positive float. Regularization
        improves the conditioning of the problem and reduces the variance of
        the estimates. Larger values specify stronger regularization.
        Alpha corresponds to ``1 / (2C)`` in other linear models such as
        :class:`~sklearn.linear_model.LogisticRegression` or
        :class:`~sklearn.svm.LinearSVC`.

    fit_intercept : bool, default=True
        Whether to calculate the intercept for this model. If set
        to false, no intercept will be used in calculations
        (i.e. data is expected to be centered).

    scoring : str, callable, default=None
        A string (see model evaluation documentation) or
        a scorer callable object / function with signature
        ``scorer(estimator, X, y)``.

    cv : int, cross-validation generator or an iterable, default=None
        Determines the cross-validation splitting strategy.
        Possible inputs for cv are:

        - None, to use the efficient Leave-One-Out cross-validation
        - integer, to specify the number of folds.
        - :term:`CV splitter`,
        - An iterable yielding (train, test) splits as arrays of indices.

        Refer :ref:`User Guide <cross_validation>` for the various
        cross-validation strategies that can be used here.

    class_weight : dict or 'balanced', default=None
        Weights associated with classes in the form ``{class_label: weight}``.
        If not given, all classes are supposed to have weight one.

        The "balanced" mode uses the values of y to automatically adjust
        weights inversely proportional to class frequencies in the input data
        as ``n_samples / (n_classes * np.bincount(y))``.

    store_cv_values : bool, default=False
        Flag indicating if the cross-validation values corresponding to
        each alpha should be stored in the ``cv_values_`` attribute (see
        below). This flag is only compatible with ``cv=None`` (i.e. using
        Leave-One-Out Cross-Validation).

    Attributes
    ----------
    cv_values_ : ndarray of shape (n_samples, n_targets, n_alphas), optional
        Cross-validation values for each alpha (only if ``store_cv_values=True`` and
        ``cv=None``). After ``fit()`` has been called, this attribute will
        contain the mean squared errors if `scoring is None` otherwise it
        will contain standardized per point prediction values.

    coef_ : ndarray of shape (1, n_features) or (n_targets, n_features)
        Coefficient of the features in the decision function.

        ``coef_`` is of shape (1, n_features) when the given problem is binary.

    intercept_ : float or ndarray of shape (n_targets,)
        Independent term in decision function. Set to 0.0 if
        ``fit_intercept = False``.

    alpha_ : float
        Estimated regularization parameter.

    best_score_ : float
        Score of base estimator with best alpha.

        .. versionadded:: 0.23

    classes_ : ndarray of shape (n_classes,)
        The classes labels.

    n_features_in_ : int
        Number of features seen during :term:`fit`.

        .. versionadded:: 0.24

    feature_names_in_ : ndarray of shape (`n_features_in_`,)
        Names of features seen during :term:`fit`. Defined only when `X`
        has feature names that are all strings.

        .. versionadded:: 1.0

    See Also
    --------
    Ridge : Ridge regression.
    RidgeClassifier : Ridge classifier.
    RidgeCV : Ridge regression with built-in cross validation.

    Notes
    -----
    For multi-class classification, n_class classifiers are trained in
    a one-versus-all approach. Concretely, this is implemented by taking
    advantage of the multi-variate response support in Ridge.

    Examples
    --------
    >>> from sklearn.datasets import load_breast_cancer
    >>> from sklearn.linear_model import RidgeClassifierCV
    >>> X, y = load_breast_cancer(return_X_y=True)
    >>> clf = RidgeClassifierCV(alphas=[1e-3, 1e-2, 1e-1, 1]).fit(X, y)
    >>> clf.score(X, y)
    0.9630...
    """

    _parameter_constraints: dict = {
        **_BaseRidgeCV._parameter_constraints,
        "class_weight": [dict, StrOptions({"balanced"}), None],
    }
    for param in ("gcv_mode", "alpha_per_target"):
        _parameter_constraints.pop(param)

    def __init__(
        self,
        alphas=(0.1, 1.0, 10.0),
        *,
        fit_intercept=True,
        scoring=None,
        cv=None,
        class_weight=None,
        store_cv_values=False,
    ):
        super().__init__(
            alphas=alphas,
            fit_intercept=fit_intercept,
            scoring=scoring,
            cv=cv,
            store_cv_values=store_cv_values,
        )
        self.class_weight = class_weight

    def fit(self, X, y, sample_weight=None):
        """Fit Ridge classifier with cv.

        Parameters
        ----------
        X : ndarray of shape (n_samples, n_features)
            Training vectors, where `n_samples` is the number of samples
            and `n_features` is the number of features. When using GCV,
            will be cast to float64 if necessary.

        y : ndarray of shape (n_samples,)
            Target values. Will be cast to X's dtype if necessary.

        sample_weight : float or ndarray of shape (n_samples,), default=None
            Individual weights for each sample. If given a float, every sample
            will have the same weight.

        Returns
        -------
        self : object
            Fitted estimator.
        """
        self._validate_params()

        # `RidgeClassifier` does not accept "sag" or "saga" solver and thus support
        # csr, csc, and coo sparse matrices. By using solver="eigen" we force to accept
        # all sparse format.
        X, y, sample_weight, Y = self._prepare_data(X, y, sample_weight, solver="eigen")

        # If cv is None, gcv mode will be used and we used the binarized Y
        # since y will not be binarized in _RidgeGCV estimator.
        # If cv is not None, a GridSearchCV with some RidgeClassifier
        # estimators are used where y will be binarized. Thus, we pass y
        # instead of the binarized Y.
        target = Y if self.cv is None else y
        super().fit(X, target, sample_weight=sample_weight)
        return self

    def _more_tags(self):
        return {
            "multilabel": True,
            "_xfail_checks": {
                "check_sample_weights_invariance": (
                    "zero sample_weight is not equivalent to removing samples"
                ),
            },
        }
