"""
Logistic Regression
"""

# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
#         Fabian Pedregosa <f@bianp.net>
#         Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#         Manoj Kumar <manojkumarsivaraj334@gmail.com>
#         Lars Buitinck
#         Simon Wu <s8wu@uwaterloo.ca>
#         Arthur Mensch <arthur.mensch@m4x.org

import numbers
import warnings

import numpy as np
from scipy import optimize, sparse

from .base import LinearClassifierMixin, SparseCoefMixin, BaseEstimator
from .sag import sag_solver
from ..preprocessing import LabelEncoder, LabelBinarizer
from ..svm.base import _fit_liblinear
from ..utils import check_array, check_consistent_length, compute_class_weight
from ..utils import check_random_state
from ..utils.extmath import (logsumexp, log_logistic, safe_sparse_dot,
                             softmax, squared_norm)
from ..utils.extmath import row_norms
from ..utils.optimize import newton_cg
from ..utils.validation import check_X_y
from ..exceptions import NotFittedError
from ..utils.fixes import expit
from ..utils.multiclass import check_classification_targets
from ..externals.joblib import Parallel, delayed
from ..model_selection import check_cv
from ..externals import six
from ..metrics import SCORERS


# .. some helper functions for logistic_regression_path ..
def _intercept_dot(w, X, y):
    """Computes y * np.dot(X, w).

    It takes into consideration if the intercept should be fit or not.

    Parameters
    ----------
    w : ndarray, shape (n_features,) or (n_features + 1,)
        Coefficient vector.

    X : {array-like, sparse matrix}, shape (n_samples, n_features)
        Training data.

    y : ndarray, shape (n_samples,)
        Array of labels.

    Returns
    -------
    w : ndarray, shape (n_features,)
        Coefficient vector without the intercept weight (w[-1]) if the
        intercept should be fit. Unchanged otherwise.

    X : {array-like, sparse matrix}, shape (n_samples, n_features)
        Training data. Unchanged.

    yz : float
        y * np.dot(X, w).
    """
    c = 0.
    if w.size == X.shape[1] + 1:
        c = w[-1]
        w = w[:-1]

    z = safe_sparse_dot(X, w) + c
    yz = y * z
    return w, c, yz


def _logistic_loss_and_grad(w, X, y, alpha, sample_weight=None):
    """Computes the logistic loss and gradient.

    Parameters
    ----------
    w : ndarray, shape (n_features,) or (n_features + 1,)
        Coefficient vector.

    X : {array-like, sparse matrix}, shape (n_samples, n_features)
        Training data.

    y : ndarray, shape (n_samples,)
        Array of labels.

    alpha : float
        Regularization parameter. alpha is equal to 1 / C.

    sample_weight : array-like, shape (n_samples,) optional
        Array of weights that are assigned to individual samples.
        If not provided, then each sample is given unit weight.

    Returns
    -------
    out : float
        Logistic loss.

    grad : ndarray, shape (n_features,) or (n_features + 1,)
        Logistic gradient.
    """
    n_samples, n_features = X.shape
    grad = np.empty_like(w)

    w, c, yz = _intercept_dot(w, X, y)

    if sample_weight is None:
        sample_weight = np.ones(n_samples)

    # Logistic loss is the negative of the log of the logistic function.
    out = -np.sum(sample_weight * log_logistic(yz)) + .5 * alpha * np.dot(w, w)

    z = expit(yz)
    z0 = sample_weight * (z - 1) * y

    grad[:n_features] = safe_sparse_dot(X.T, z0) + alpha * w

    # Case where we fit the intercept.
    if grad.shape[0] > n_features:
        grad[-1] = z0.sum()
    return out, grad


def _logistic_loss(w, X, y, alpha, sample_weight=None):
    """Computes the logistic loss.

    Parameters
    ----------
    w : ndarray, shape (n_features,) or (n_features + 1,)
        Coefficient vector.

    X : {array-like, sparse matrix}, shape (n_samples, n_features)
        Training data.

    y : ndarray, shape (n_samples,)
        Array of labels.

    alpha : float
        Regularization parameter. alpha is equal to 1 / C.

    sample_weight : array-like, shape (n_samples,) optional
        Array of weights that are assigned to individual samples.
        If not provided, then each sample is given unit weight.

    Returns
    -------
    out : float
        Logistic loss.
    """
    w, c, yz = _intercept_dot(w, X, y)

    if sample_weight is None:
        sample_weight = np.ones(y.shape[0])

    # Logistic loss is the negative of the log of the logistic function.
    out = -np.sum(sample_weight * log_logistic(yz)) + .5 * alpha * np.dot(w, w)
    return out


def _logistic_grad_hess(w, X, y, alpha, sample_weight=None):
    """Computes the gradient and the Hessian, in the case of a logistic loss.

    Parameters
    ----------
    w : ndarray, shape (n_features,) or (n_features + 1,)
        Coefficient vector.

    X : {array-like, sparse matrix}, shape (n_samples, n_features)
        Training data.

    y : ndarray, shape (n_samples,)
        Array of labels.

    alpha : float
        Regularization parameter. alpha is equal to 1 / C.

    sample_weight : array-like, shape (n_samples,) optional
        Array of weights that are assigned to individual samples.
        If not provided, then each sample is given unit weight.

    Returns
    -------
    grad : ndarray, shape (n_features,) or (n_features + 1,)
        Logistic gradient.

    Hs : callable
        Function that takes the gradient as a parameter and returns the
        matrix product of the Hessian and gradient.
    """
    n_samples, n_features = X.shape
    grad = np.empty_like(w)
    fit_intercept = grad.shape[0] > n_features

    w, c, yz = _intercept_dot(w, X, y)

    if sample_weight is None:
        sample_weight = np.ones(y.shape[0])

    z = expit(yz)
    z0 = sample_weight * (z - 1) * y

    grad[:n_features] = safe_sparse_dot(X.T, z0) + alpha * w

    # Case where we fit the intercept.
    if fit_intercept:
        grad[-1] = z0.sum()

    # The mat-vec product of the Hessian
    d = sample_weight * z * (1 - z)
    if sparse.issparse(X):
        dX = safe_sparse_dot(sparse.dia_matrix((d, 0),
                             shape=(n_samples, n_samples)), X)
    else:
        # Precompute as much as possible
        dX = d[:, np.newaxis] * X

    if fit_intercept:
        # Calculate the double derivative with respect to intercept
        # In the case of sparse matrices this returns a matrix object.
        dd_intercept = np.squeeze(np.array(dX.sum(axis=0)))

    def Hs(s):
        ret = np.empty_like(s)
        ret[:n_features] = X.T.dot(dX.dot(s[:n_features]))
        ret[:n_features] += alpha * s[:n_features]

        # For the fit intercept case.
        if fit_intercept:
            ret[:n_features] += s[-1] * dd_intercept
            ret[-1] = dd_intercept.dot(s[:n_features])
            ret[-1] += d.sum() * s[-1]
        return ret

    return grad, Hs


def _multinomial_loss(w, X, Y, alpha, sample_weight):
    """Computes multinomial loss and class probabilities.

    Parameters
    ----------
    w : ndarray, shape (n_classes * n_features,) or
        (n_classes * (n_features + 1),)
        Coefficient vector.

    X : {array-like, sparse matrix}, shape (n_samples, n_features)
        Training data.

    Y : ndarray, shape (n_samples, n_classes)
        Transformed labels according to the output of LabelBinarizer.

    alpha : float
        Regularization parameter. alpha is equal to 1 / C.

    sample_weight : array-like, shape (n_samples,) optional
        Array of weights that are assigned to individual samples.
        If not provided, then each sample is given unit weight.

    Returns
    -------
    loss : float
        Multinomial loss.

    p : ndarray, shape (n_samples, n_classes)
        Estimated class probabilities.

    w : ndarray, shape (n_classes, n_features)
        Reshaped param vector excluding intercept terms.

    Reference
    ---------
    Bishop, C. M. (2006). Pattern recognition and machine learning.
    Springer. (Chapter 4.3.4)
    """
    n_classes = Y.shape[1]
    n_features = X.shape[1]
    fit_intercept = w.size == (n_classes * (n_features + 1))
    w = w.reshape(n_classes, -1)
    sample_weight = sample_weight[:, np.newaxis]
    if fit_intercept:
        intercept = w[:, -1]
        w = w[:, :-1]
    else:
        intercept = 0
    p = safe_sparse_dot(X, w.T)
    p += intercept
    p -= logsumexp(p, axis=1)[:, np.newaxis]
    loss = -(sample_weight * Y * p).sum()
    loss += 0.5 * alpha * squared_norm(w)
    p = np.exp(p, p)
    return loss, p, w


def _multinomial_loss_grad(w, X, Y, alpha, sample_weight):
    """Computes the multinomial loss, gradient and class probabilities.

    Parameters
    ----------
    w : ndarray, shape (n_classes * n_features,) or
        (n_classes * (n_features + 1),)
        Coefficient vector.

    X : {array-like, sparse matrix}, shape (n_samples, n_features)
        Training data.

    Y : ndarray, shape (n_samples, n_classes)
        Transformed labels according to the output of LabelBinarizer.

    alpha : float
        Regularization parameter. alpha is equal to 1 / C.

    sample_weight : array-like, shape (n_samples,) optional
        Array of weights that are assigned to individual samples.

    Returns
    -------
    loss : float
        Multinomial loss.

    grad : ndarray, shape (n_classes * n_features,) or
        (n_classes * (n_features + 1),)
        Ravelled gradient of the multinomial loss.

    p : ndarray, shape (n_samples, n_classes)
        Estimated class probabilities

    Reference
    ---------
    Bishop, C. M. (2006). Pattern recognition and machine learning.
    Springer. (Chapter 4.3.4)
    """
    n_classes = Y.shape[1]
    n_features = X.shape[1]
    fit_intercept = (w.size == n_classes * (n_features + 1))
    grad = np.zeros((n_classes, n_features + bool(fit_intercept)))
    loss, p, w = _multinomial_loss(w, X, Y, alpha, sample_weight)
    sample_weight = sample_weight[:, np.newaxis]
    diff = sample_weight * (p - Y)
    grad[:, :n_features] = safe_sparse_dot(diff.T, X)
    grad[:, :n_features] += alpha * w
    if fit_intercept:
        grad[:, -1] = diff.sum(axis=0)
    return loss, grad.ravel(), p


def _multinomial_grad_hess(w, X, Y, alpha, sample_weight):
    """
    Computes the gradient and the Hessian, in the case of a multinomial loss.

    Parameters
    ----------
    w : ndarray, shape (n_classes * n_features,) or
        (n_classes * (n_features + 1),)
        Coefficient vector.

    X : {array-like, sparse matrix}, shape (n_samples, n_features)
        Training data.

    Y : ndarray, shape (n_samples, n_classes)
        Transformed labels according to the output of LabelBinarizer.

    alpha : float
        Regularization parameter. alpha is equal to 1 / C.

    sample_weight : array-like, shape (n_samples,) optional
        Array of weights that are assigned to individual samples.

    Returns
    -------
    grad : array, shape (n_classes * n_features,) or
        (n_classes * (n_features + 1),)
        Ravelled gradient of the multinomial loss.

    hessp : callable
        Function that takes in a vector input of shape (n_classes * n_features)
        or (n_classes * (n_features + 1)) and returns matrix-vector product
        with hessian.

    References
    ----------
    Barak A. Pearlmutter (1993). Fast Exact Multiplication by the Hessian.
        http://www.bcl.hamilton.ie/~barak/papers/nc-hessian.pdf
    """
    n_features = X.shape[1]
    n_classes = Y.shape[1]
    fit_intercept = w.size == (n_classes * (n_features + 1))

    # `loss` is unused. Refactoring to avoid computing it does not
    # significantly speed up the computation and decreases readability
    loss, grad, p = _multinomial_loss_grad(w, X, Y, alpha, sample_weight)
    sample_weight = sample_weight[:, np.newaxis]

    # Hessian-vector product derived by applying the R-operator on the gradient
    # of the multinomial loss function.
    def hessp(v):
        v = v.reshape(n_classes, -1)
        if fit_intercept:
            inter_terms = v[:, -1]
            v = v[:, :-1]
        else:
            inter_terms = 0
        # r_yhat holds the result of applying the R-operator on the multinomial
        # estimator.
        r_yhat = safe_sparse_dot(X, v.T)
        r_yhat += inter_terms
        r_yhat += (-p * r_yhat).sum(axis=1)[:, np.newaxis]
        r_yhat *= p
        r_yhat *= sample_weight
        hessProd = np.zeros((n_classes, n_features + bool(fit_intercept)))
        hessProd[:, :n_features] = safe_sparse_dot(r_yhat.T, X)
        hessProd[:, :n_features] += v * alpha
        if fit_intercept:
            hessProd[:, -1] = r_yhat.sum(axis=0)
        return hessProd.ravel()

    return grad, hessp


def _check_solver_option(solver, multi_class, penalty, dual):
    if solver not in ['liblinear', 'newton-cg', 'lbfgs', 'sag', 'saga']:
        raise ValueError("Logistic Regression supports only liblinear,"
                         " newton-cg, lbfgs and sag solvers, got %s" % solver)

    if multi_class not in ['multinomial', 'ovr']:
        raise ValueError("multi_class should be either multinomial or "
                         "ovr, got %s" % multi_class)

    if multi_class == 'multinomial' and solver == 'liblinear':
        raise ValueError("Solver %s does not support "
                         "a multinomial backend." % solver)

    if solver not in ['liblinear', 'saga']:
        if penalty != 'l2':
            raise ValueError("Solver %s supports only l2 penalties, "
                             "got %s penalty." % (solver, penalty))
    if solver != 'liblinear':
        if dual:
            raise ValueError("Solver %s supports only "
                             "dual=False, got dual=%s" % (solver, dual))


def logistic_regression_path(X, y, pos_class=None, Cs=10, fit_intercept=True,
                             max_iter=100, tol=1e-4, verbose=0,
                             solver='lbfgs', coef=None,
                             class_weight=None, dual=False, penalty='l2',
                             intercept_scaling=1., multi_class='ovr',
                             random_state=None, check_input=True,
                             max_squared_sum=None, sample_weight=None):
    """Compute a Logistic Regression model for a list of regularization
    parameters.

    This is an implementation that uses the result of the previous model
    to speed up computations along the set of solutions, making it faster
    than sequentially calling LogisticRegression for the different parameters.
    Note that there will be no speedup with liblinear solver, since it does
    not handle warm-starting.

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

    Parameters
    ----------
    X : array-like or sparse matrix, shape (n_samples, n_features)
        Input data.

    y : array-like, shape (n_samples,)
        Input data, target values.

    Cs : int | array-like, shape (n_cs,)
        List of values for the regularization parameter or integer specifying
        the number of regularization parameters that should be used. In this
        case, the parameters will be chosen in a logarithmic scale between
        1e-4 and 1e4.

    pos_class : int, None
        The class with respect to which we perform a one-vs-all fit.
        If None, then it is assumed that the given problem is binary.

    fit_intercept : bool
        Whether to fit an intercept for the model. In this case the shape of
        the returned array is (n_cs, n_features + 1).

    max_iter : int
        Maximum number of iterations for the solver.

    tol : float
        Stopping criterion. For the newton-cg and lbfgs solvers, the iteration
        will stop when ``max{|g_i | i = 1, ..., n} <= tol``
        where ``g_i`` is the i-th component of the gradient.

    verbose : int
        For the liblinear and lbfgs solvers set verbose to any positive
        number for verbosity.

    solver : {'lbfgs', 'newton-cg', 'liblinear', 'sag', 'saga'}
        Numerical solver to use.

    coef : array-like, shape (n_features,), default None
        Initialization value for coefficients of logistic regression.
        Useless for liblinear solver.

    class_weight : dict or 'balanced', optional
        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))``.

        Note that these weights will be multiplied with sample_weight (passed
        through the fit method) if sample_weight is specified.

    dual : bool
        Dual or primal formulation. Dual formulation is only implemented for
        l2 penalty with liblinear solver. Prefer dual=False when
        n_samples > n_features.

    penalty : str, 'l1' or 'l2'
        Used to specify the norm used in the penalization. The 'newton-cg',
        'sag' and 'lbfgs' solvers support only l2 penalties.

    intercept_scaling : float, default 1.
        Useful only when the solver 'liblinear' is used
        and self.fit_intercept is set to True. In this case, x becomes
        [x, self.intercept_scaling],
        i.e. a "synthetic" feature with constant value equal to
        intercept_scaling is appended to the instance vector.
        The intercept becomes ``intercept_scaling * synthetic_feature_weight``.

        Note! the synthetic feature weight is subject to l1/l2 regularization
        as all other features.
        To lessen the effect of regularization on synthetic feature weight
        (and therefore on the intercept) intercept_scaling has to be increased.

    multi_class : str, {'ovr', 'multinomial'}
        Multiclass option can be either 'ovr' or 'multinomial'. If the option
        chosen is 'ovr', then a binary problem is fit for each label. Else
        the loss minimised is the multinomial loss fit across
        the entire probability distribution. Works only for the 'lbfgs' and
        'newton-cg' solvers.

    random_state : int seed, RandomState instance, or None (default)
        The seed of the pseudo random number generator to use when
        shuffling the data. Used only in solvers 'sag' and 'liblinear'.

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

    max_squared_sum : float, default None
        Maximum squared sum of X over samples. Used only in SAG solver.
        If None, it will be computed, going through all the samples.
        The value should be precomputed to speed up cross validation.

    sample_weight : array-like, shape(n_samples,) optional
        Array of weights that are assigned to individual samples.
        If not provided, then each sample is given unit weight.

    Returns
    -------
    coefs : ndarray, shape (n_cs, n_features) or (n_cs, n_features + 1)
        List of coefficients for the Logistic Regression model. If
        fit_intercept is set to True then the second dimension will be
        n_features + 1, where the last item represents the intercept.

    Cs : ndarray
        Grid of Cs used for cross-validation.

    n_iter : array, shape (n_cs,)
        Actual number of iteration for each Cs.

    Notes
    -----
    You might get slightly different results with the solver liblinear than
    with the others since this uses LIBLINEAR which penalizes the intercept.

    .. versionchanged:: 0.19
        The "copy" parameter was removed.
    """
    if isinstance(Cs, numbers.Integral):
        Cs = np.logspace(-4, 4, Cs)

    _check_solver_option(solver, multi_class, penalty, dual)

    # Preprocessing.
    if check_input:
        X = check_array(X, accept_sparse='csr', dtype=np.float64)
        y = check_array(y, ensure_2d=False, dtype=None)
        check_consistent_length(X, y)
    _, n_features = X.shape
    classes = np.unique(y)
    random_state = check_random_state(random_state)

    if pos_class is None and multi_class != 'multinomial':
        if (classes.size > 2):
            raise ValueError('To fit OvR, use the pos_class argument')
        # np.unique(y) gives labels in sorted order.
        pos_class = classes[1]

    # If sample weights exist, convert them to array (support for lists)
    # and check length
    # Otherwise set them to 1 for all examples
    if sample_weight is not None:
        sample_weight = np.array(sample_weight, dtype=np.float64, order='C')
        check_consistent_length(y, sample_weight)
    else:
        sample_weight = np.ones(X.shape[0])

    # If class_weights is a dict (provided by the user), the weights
    # are assigned to the original labels. If it is "balanced", then
    # the class_weights are assigned after masking the labels with a OvR.
    le = LabelEncoder()
    if isinstance(class_weight, dict) or multi_class == 'multinomial':
        class_weight_ = compute_class_weight(class_weight, classes, y)
        sample_weight *= class_weight_[le.fit_transform(y)]

    # For doing a ovr, we need to mask the labels first. for the
    # multinomial case this is not necessary.
    if multi_class == 'ovr':
        w0 = np.zeros(n_features + int(fit_intercept))
        mask_classes = np.array([-1, 1])
        mask = (y == pos_class)
        y_bin = np.ones(y.shape, dtype=np.float64)
        y_bin[~mask] = -1.
        # for compute_class_weight

        if class_weight == "balanced":
            class_weight_ = compute_class_weight(class_weight, mask_classes,
                                                 y_bin)
            sample_weight *= class_weight_[le.fit_transform(y_bin)]

    else:
        if solver not in ['sag', 'saga']:
            lbin = LabelBinarizer()
            Y_multi = lbin.fit_transform(y)
            if Y_multi.shape[1] == 1:
                Y_multi = np.hstack([1 - Y_multi, Y_multi])
        else:
            # SAG multinomial solver needs LabelEncoder, not LabelBinarizer
            le = LabelEncoder()
            Y_multi = le.fit_transform(y)

        w0 = np.zeros((classes.size, n_features + int(fit_intercept)),
                      order='F')

    if coef is not None:
        # it must work both giving the bias term and not
        if multi_class == 'ovr':
            if coef.size not in (n_features, w0.size):
                raise ValueError(
                    'Initialization coef is of shape %d, expected shape '
                    '%d or %d' % (coef.size, n_features, w0.size))
            w0[:coef.size] = coef
        else:
            # For binary problems coef.shape[0] should be 1, otherwise it
            # should be classes.size.
            n_classes = classes.size
            if n_classes == 2:
                n_classes = 1

            if (coef.shape[0] != n_classes or
                    coef.shape[1] not in (n_features, n_features + 1)):
                raise ValueError(
                    'Initialization coef is of shape (%d, %d), expected '
                    'shape (%d, %d) or (%d, %d)' % (
                        coef.shape[0], coef.shape[1], classes.size,
                        n_features, classes.size, n_features + 1))
            w0[:, :coef.shape[1]] = coef

    if multi_class == 'multinomial':
        # fmin_l_bfgs_b and newton-cg accepts only ravelled parameters.
        if solver in ['lbfgs', 'newton-cg']:
            w0 = w0.ravel()
        target = Y_multi
        if solver == 'lbfgs':
            func = lambda x, *args: _multinomial_loss_grad(x, *args)[0:2]
        elif solver == 'newton-cg':
            func = lambda x, *args: _multinomial_loss(x, *args)[0]
            grad = lambda x, *args: _multinomial_loss_grad(x, *args)[1]
            hess = _multinomial_grad_hess
        warm_start_sag = {'coef': w0.T}
    else:
        target = y_bin
        if solver == 'lbfgs':
            func = _logistic_loss_and_grad
        elif solver == 'newton-cg':
            func = _logistic_loss
            grad = lambda x, *args: _logistic_loss_and_grad(x, *args)[1]
            hess = _logistic_grad_hess
        warm_start_sag = {'coef': np.expand_dims(w0, axis=1)}

    coefs = list()
    n_iter = np.zeros(len(Cs), dtype=np.int32)
    for i, C in enumerate(Cs):
        if solver == 'lbfgs':
            try:
                w0, loss, info = optimize.fmin_l_bfgs_b(
                    func, w0, fprime=None,
                    args=(X, target, 1. / C, sample_weight),
                    iprint=(verbose > 0) - 1, pgtol=tol, maxiter=max_iter)
            except TypeError:
                # old scipy doesn't have maxiter
                w0, loss, info = optimize.fmin_l_bfgs_b(
                    func, w0, fprime=None,
                    args=(X, target, 1. / C, sample_weight),
                    iprint=(verbose > 0) - 1, pgtol=tol)
            if info["warnflag"] == 1 and verbose > 0:
                warnings.warn("lbfgs failed to converge. Increase the number "
                              "of iterations.")
            try:
                n_iter_i = info['nit'] - 1
            except:
                n_iter_i = info['funcalls'] - 1
        elif solver == 'newton-cg':
            args = (X, target, 1. / C, sample_weight)
            w0, n_iter_i = newton_cg(hess, func, grad, w0, args=args,
                                     maxiter=max_iter, tol=tol)
        elif solver == 'liblinear':
            coef_, intercept_, n_iter_i, = _fit_liblinear(
                X, target, C, fit_intercept, intercept_scaling, None,
                penalty, dual, verbose, max_iter, tol, random_state,
                sample_weight=sample_weight)
            if fit_intercept:
                w0 = np.concatenate([coef_.ravel(), intercept_])
            else:
                w0 = coef_.ravel()

        elif solver in ['sag', 'saga']:
            if multi_class == 'multinomial':
                target = target.astype(np.float64)
                loss = 'multinomial'
            else:
                loss = 'log'
            if penalty == 'l1':
                alpha = 0.
                beta = 1. / C
            else:
                alpha = 1. / C
                beta = 0.
            w0, n_iter_i, warm_start_sag = sag_solver(
                X, target, sample_weight, loss, alpha,
                beta, max_iter, tol,
                verbose, random_state, False, max_squared_sum, warm_start_sag,
                is_saga=(solver == 'saga'))

        else:
            raise ValueError("solver must be one of {'liblinear', 'lbfgs', "
                             "'newton-cg', 'sag'}, got '%s' instead" % solver)

        if multi_class == 'multinomial':
            multi_w0 = np.reshape(w0, (classes.size, -1))
            if classes.size == 2:
                multi_w0 = multi_w0[1][np.newaxis, :]
            coefs.append(multi_w0)
        else:
            coefs.append(w0.copy())

        n_iter[i] = n_iter_i

    return coefs, np.array(Cs), n_iter


# helper function for LogisticCV
def _log_reg_scoring_path(X, y, train, test, pos_class=None, Cs=10,
                          scoring=None, fit_intercept=False,
                          max_iter=100, tol=1e-4, class_weight=None,
                          verbose=0, solver='lbfgs', penalty='l2',
                          dual=False, intercept_scaling=1.,
                          multi_class='ovr', random_state=None,
                          max_squared_sum=None, sample_weight=None):
    """Computes scores across logistic_regression_path

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

    y : array-like, shape (n_samples,) or (n_samples, n_targets)
        Target labels.

    train : list of indices
        The indices of the train set.

    test : list of indices
        The indices of the test set.

    pos_class : int, None
        The class with respect to which we perform a one-vs-all fit.
        If None, then it is assumed that the given problem is binary.

    Cs : list of floats | int
        Each of the values in Cs describes the inverse of
        regularization strength. If Cs is as an int, then a grid of Cs
        values are chosen in a logarithmic scale between 1e-4 and 1e4.
        If not provided, then a fixed set of values for Cs are used.

    scoring : callable or None, optional, default: None
        A string (see model evaluation documentation) or
        a scorer callable object / function with signature
        ``scorer(estimator, X, y)``. For a list of scoring functions
        that can be used, look at :mod:`sklearn.metrics`. The
        default scoring option used is accuracy_score.

    fit_intercept : bool
        If False, then the bias term is set to zero. Else the last
        term of each coef_ gives us the intercept.

    max_iter : int
        Maximum number of iterations for the solver.

    tol : float
        Tolerance for stopping criteria.

    class_weight : dict or 'balanced', optional
        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))``

        Note that these weights will be multiplied with sample_weight (passed
        through the fit method) if sample_weight is specified.

    verbose : int
        For the liblinear and lbfgs solvers set verbose to any positive
        number for verbosity.

    solver : {'lbfgs', 'newton-cg', 'liblinear', 'sag', 'saga'}
        Decides which solver to use.

    penalty : str, 'l1' or 'l2'
        Used to specify the norm used in the penalization. The 'newton-cg',
        'sag' and 'lbfgs' solvers support only l2 penalties.

    dual : bool
        Dual or primal formulation. Dual formulation is only implemented for
        l2 penalty with liblinear solver. Prefer dual=False when
        n_samples > n_features.

    intercept_scaling : float, default 1.
        Useful only when the solver 'liblinear' is used
        and self.fit_intercept is set to True. In this case, x becomes
        [x, self.intercept_scaling],
        i.e. a "synthetic" feature with constant value equals to
        intercept_scaling is appended to the instance vector.
        The intercept becomes intercept_scaling * synthetic feature weight
        Note! the synthetic feature weight is subject to l1/l2 regularization
        as all other features.
        To lessen the effect of regularization on synthetic feature weight
        (and therefore on the intercept) intercept_scaling has to be increased.

    multi_class : str, {'ovr', 'multinomial'}
        Multiclass option can be either 'ovr' or 'multinomial'. If the option
        chosen is 'ovr', then a binary problem is fit for each label. Else
        the loss minimised is the multinomial loss fit across
        the entire probability distribution. Does not work for
        liblinear solver.

    random_state : int seed, RandomState instance, or None (default)
        The seed of the pseudo random number generator to use when
        shuffling the data. Used only in solvers 'sag' and 'liblinear'.

    max_squared_sum : float, default None
        Maximum squared sum of X over samples. Used only in SAG solver.
        If None, it will be computed, going through all the samples.
        The value should be precomputed to speed up cross validation.

    sample_weight : array-like, shape(n_samples,) optional
        Array of weights that are assigned to individual samples.
        If not provided, then each sample is given unit weight.

    Returns
    -------
    coefs : ndarray, shape (n_cs, n_features) or (n_cs, n_features + 1)
        List of coefficients for the Logistic Regression model. If
        fit_intercept is set to True then the second dimension will be
        n_features + 1, where the last item represents the intercept.

    Cs : ndarray
        Grid of Cs used for cross-validation.

    scores : ndarray, shape (n_cs,)
        Scores obtained for each Cs.

    n_iter : array, shape(n_cs,)
        Actual number of iteration for each Cs.
    """
    _check_solver_option(solver, multi_class, penalty, dual)

    X_train = X[train]
    X_test = X[test]
    y_train = y[train]
    y_test = y[test]

    if sample_weight is not None:
        sample_weight = check_array(sample_weight, ensure_2d=False)
        check_consistent_length(y, sample_weight)

        sample_weight = sample_weight[train]

    coefs, Cs, n_iter = logistic_regression_path(
        X_train, y_train, Cs=Cs, fit_intercept=fit_intercept,
        solver=solver, max_iter=max_iter, class_weight=class_weight,
        pos_class=pos_class, multi_class=multi_class,
        tol=tol, verbose=verbose, dual=dual, penalty=penalty,
        intercept_scaling=intercept_scaling, random_state=random_state,
        check_input=False, max_squared_sum=max_squared_sum,
        sample_weight=sample_weight)

    log_reg = LogisticRegression(fit_intercept=fit_intercept)

    # The score method of Logistic Regression has a classes_ attribute.
    if multi_class == 'ovr':
        log_reg.classes_ = np.array([-1, 1])
    elif multi_class == 'multinomial':
        log_reg.classes_ = np.unique(y_train)
    else:
        raise ValueError("multi_class should be either multinomial or ovr, "
                         "got %d" % multi_class)

    if pos_class is not None:
        mask = (y_test == pos_class)
        y_test = np.ones(y_test.shape, dtype=np.float64)
        y_test[~mask] = -1.

    scores = list()

    if isinstance(scoring, six.string_types):
        scoring = SCORERS[scoring]
    for w in coefs:
        if multi_class == 'ovr':
            w = w[np.newaxis, :]
        if fit_intercept:
            log_reg.coef_ = w[:, :-1]
            log_reg.intercept_ = w[:, -1]
        else:
            log_reg.coef_ = w
            log_reg.intercept_ = 0.

        if scoring is None:
            scores.append(log_reg.score(X_test, y_test))
        else:
            scores.append(scoring(log_reg, X_test, y_test))
    return coefs, Cs, np.array(scores), n_iter


class LogisticRegression(BaseEstimator, LinearClassifierMixin,
                         SparseCoefMixin):
    """Logistic Regression (aka logit, MaxEnt) classifier.

    In the multiclass case, the training algorithm uses the one-vs-rest (OvR)
    scheme if the 'multi_class' option is set to 'ovr', and uses the cross-
    entropy loss if the 'multi_class' option is set to 'multinomial'.
    (Currently the 'multinomial' option is supported only by the 'lbfgs',
    'sag' and 'newton-cg' solvers.)

    This class implements regularized logistic regression using the
    'liblinear' library, 'newton-cg', 'sag' and 'lbfgs' solvers. It can handle
    both dense and sparse input. Use C-ordered arrays or CSR matrices
    containing 64-bit floats for optimal performance; any other input format
    will be converted (and copied).

    The 'newton-cg', 'sag', and 'lbfgs' solvers support only L2 regularization
    with primal formulation. The 'liblinear' solver supports both L1 and L2
    regularization, with a dual formulation only for the L2 penalty.

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

    Parameters
    ----------
    penalty : str, 'l1' or 'l2', default: 'l2'
        Used to specify the norm used in the penalization. The 'newton-cg',
        'sag' and 'lbfgs' solvers support only l2 penalties.

        .. versionadded:: 0.19
           l1 penalty with SAGA solver (allowing 'multinomial' + L1)

    dual : bool, default: False
        Dual or primal formulation. Dual formulation is only implemented for
        l2 penalty with liblinear solver. Prefer dual=False when
        n_samples > n_features.

    C : float, default: 1.0
        Inverse of regularization strength; must be a positive float.
        Like in support vector machines, smaller values specify stronger
        regularization.

    fit_intercept : bool, default: True
        Specifies if a constant (a.k.a. bias or intercept) should be
        added to the decision function.

    intercept_scaling : float, default 1.
        Useful only when the solver 'liblinear' is used
        and self.fit_intercept is set to True. In this case, x becomes
        [x, self.intercept_scaling],
        i.e. a "synthetic" feature with constant value equal to
        intercept_scaling is appended to the instance vector.
        The intercept becomes ``intercept_scaling * synthetic_feature_weight``.

        Note! the synthetic feature weight is subject to l1/l2 regularization
        as all other features.
        To lessen the effect of regularization on synthetic feature weight
        (and therefore on the intercept) intercept_scaling has to be increased.

    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))``.

        Note that these weights will be multiplied with sample_weight (passed
        through the fit method) if sample_weight is specified.

        .. versionadded:: 0.17
           *class_weight='balanced'*

    max_iter : int, default: 100
        Useful only for the newton-cg, sag and lbfgs solvers.
        Maximum number of iterations taken for the solvers to converge.

    random_state : int seed, RandomState instance, default: None
        The seed of the pseudo random number generator to use when
        shuffling the data. Used only in solvers 'sag' and 'liblinear'.

    solver : {'newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'},
        default: 'liblinear'
        Algorithm to use in the optimization problem.

        - For small datasets, 'liblinear' is a good choice, whereas 'sag' and
            'saga' are faster for large ones.
        - For multiclass problems, only 'newton-cg', 'sag', 'saga' and 'lbfgs'
            handle multinomial loss; 'liblinear' is limited to one-versus-rest
            schemes.
        - 'newton-cg', 'lbfgs' and 'sag' only handle L2 penalty, whereas
            'liblinear' and 'saga' handle L1 penalty.

        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.

    tol : float, default: 1e-4
        Tolerance for stopping criteria.

    multi_class : str, {'ovr', 'multinomial'}, default: 'ovr'
        Multiclass option can be either 'ovr' or 'multinomial'. If the option
        chosen is 'ovr', then a binary problem is fit for each label. Else
        the loss minimised is the multinomial loss fit across
        the entire probability distribution. Does not work for liblinear
        solver.

        .. versionadded:: 0.18
           Stochastic Average Gradient descent solver for 'multinomial' case.

    verbose : int, default: 0
        For the liblinear and lbfgs solvers set verbose to any positive
        number for verbosity.

    warm_start : bool, default: False
        When set to True, reuse the solution of the previous call to fit as
        initialization, otherwise, just erase the previous solution.
        Useless for liblinear solver.

        .. versionadded:: 0.17
           *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.

    n_jobs : int, default: 1
        Number of CPU cores used when parallelizing over classes
        if multi_class='ovr'".
        If given a value of -1, all cores are used.

    Attributes
    ----------

    coef_ : array, 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_ : array, shape (1,) or (n_classes,)
        Intercept (a.k.a. bias) added to the decision function.

        If `fit_intercept` is set to False, the intercept is set to zero.
        `intercept_` is of shape(1,) when the problem is binary.

    n_iter_ : array, shape (n_classes,) or (1, )
        Actual number of iterations for all classes. If binary or multinomial,
        it returns only 1 element. For liblinear solver, only the maximum
        number of iteration across all classes is given.

    See also
    --------
    SGDClassifier : incrementally trained logistic regression (when given
        the parameter ``loss="log"``).
    sklearn.svm.LinearSVC : learns SVM models using the same algorithm.

    Notes
    -----
    The underlying C implementation uses a random number generator to
    select features when fitting the model. It is thus not uncommon,
    to have slightly different results for the same input data. If
    that happens, try with a smaller tol parameter.

    Predict output may not match that of standalone liblinear in certain
    cases. See :ref:`differences from liblinear <liblinear_differences>`
    in the narrative documentation.

    References
    ----------

    LIBLINEAR -- A Library for Large Linear Classification
        http://www.csie.ntu.edu.tw/~cjlin/liblinear/

    SAG -- Mark Schmidt, Nicolas Le Roux, and Francis Bach
        Minimizing Finite Sums with the Stochastic Average Gradient
        https://hal.inria.fr/hal-00860051/document

    SAGA -- Defazio, A., Bach F. & Lacoste-Julien S. (2014).
        SAGA: A Fast Incremental Gradient Method With Support
        for Non-Strongly Convex Composite Objectives
        https://arxiv.org/abs/1407.0202

    Hsiang-Fu Yu, Fang-Lan Huang, Chih-Jen Lin (2011). Dual coordinate descent
        methods for logistic regression and maximum entropy models.
        Machine Learning 85(1-2):41-75.
        http://www.csie.ntu.edu.tw/~cjlin/papers/maxent_dual.pdf
    """

    def __init__(self, penalty='l2', dual=False, tol=1e-4, C=1.0,
                 fit_intercept=True, intercept_scaling=1, class_weight=None,
                 random_state=None, solver='liblinear', max_iter=100,
                 multi_class='ovr', verbose=0, warm_start=False, n_jobs=1):

        self.penalty = penalty
        self.dual = dual
        self.tol = tol
        self.C = C
        self.fit_intercept = fit_intercept
        self.intercept_scaling = intercept_scaling
        self.class_weight = class_weight
        self.random_state = random_state
        self.solver = solver
        self.max_iter = max_iter
        self.multi_class = multi_class
        self.verbose = verbose
        self.warm_start = warm_start
        self.n_jobs = n_jobs

    def fit(self, X, y, sample_weight=None):
        """Fit the model according to the given training data.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            Training vector, where n_samples is the number of samples and
            n_features is the number of features.

        y : array-like, shape (n_samples,)
            Target vector relative to X.

        sample_weight : array-like, shape (n_samples,) optional
            Array of weights that are assigned to individual samples.
            If not provided, then each sample is given unit weight.

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

        Returns
        -------
        self : object
            Returns self.
        """
        if not isinstance(self.C, numbers.Number) or self.C < 0:
            raise ValueError("Penalty term must be positive; got (C=%r)"
                             % self.C)
        if not isinstance(self.max_iter, numbers.Number) or self.max_iter < 0:
            raise ValueError("Maximum number of iteration must be positive;"
                             " got (max_iter=%r)" % self.max_iter)
        if not isinstance(self.tol, numbers.Number) or self.tol < 0:
            raise ValueError("Tolerance for stopping criteria must be "
                             "positive; got (tol=%r)" % self.tol)

        X, y = check_X_y(X, y, accept_sparse='csr', dtype=np.float64,
                         order="C")
        check_classification_targets(y)
        self.classes_ = np.unique(y)
        n_samples, n_features = X.shape

        _check_solver_option(self.solver, self.multi_class, self.penalty,
                             self.dual)

        if self.solver == 'liblinear':
            self.coef_, self.intercept_, n_iter_ = _fit_liblinear(
                X, y, self.C, self.fit_intercept, self.intercept_scaling,
                self.class_weight, self.penalty, self.dual, self.verbose,
                self.max_iter, self.tol, self.random_state,
                sample_weight=sample_weight)
            self.n_iter_ = np.array([n_iter_])
            return self

        if self.solver in ['sag', 'saga']:
            max_squared_sum = row_norms(X, squared=True).max()
        else:
            max_squared_sum = None

        n_classes = len(self.classes_)
        classes_ = self.classes_
        if n_classes < 2:
            raise ValueError("This solver needs samples of at least 2 classes"
                             " in the data, but the data contains only one"
                             " class: %r" % classes_[0])

        if len(self.classes_) == 2:
            n_classes = 1
            classes_ = classes_[1:]

        if self.warm_start:
            warm_start_coef = getattr(self, 'coef_', None)
        else:
            warm_start_coef = None
        if warm_start_coef is not None and self.fit_intercept:
            warm_start_coef = np.append(warm_start_coef,
                                        self.intercept_[:, np.newaxis],
                                        axis=1)

        self.coef_ = list()
        self.intercept_ = np.zeros(n_classes)

        # Hack so that we iterate only once for the multinomial case.
        if self.multi_class == 'multinomial':
            classes_ = [None]
            warm_start_coef = [warm_start_coef]
        if warm_start_coef is None:
            warm_start_coef = [None] * n_classes

        path_func = delayed(logistic_regression_path)

        # The SAG solver releases the GIL so it's more efficient to use
        # threads for this solver.
        if self.solver in ['sag', 'saga']:
            backend = 'threading'
        else:
            backend = 'multiprocessing'
        fold_coefs_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
                               backend=backend)(
            path_func(X, y, pos_class=class_, Cs=[self.C],
                      fit_intercept=self.fit_intercept, tol=self.tol,
                      verbose=self.verbose, solver=self.solver,
                      multi_class=self.multi_class, max_iter=self.max_iter,
                      class_weight=self.class_weight, check_input=False,
                      random_state=self.random_state, coef=warm_start_coef_,
                      penalty=self.penalty,
                      max_squared_sum=max_squared_sum,
                      sample_weight=sample_weight)
            for class_, warm_start_coef_ in zip(classes_, warm_start_coef))

        fold_coefs_, _, n_iter_ = zip(*fold_coefs_)
        self.n_iter_ = np.asarray(n_iter_, dtype=np.int32)[:, 0]

        if self.multi_class == 'multinomial':
            self.coef_ = fold_coefs_[0][0]
        else:
            self.coef_ = np.asarray(fold_coefs_)
            self.coef_ = self.coef_.reshape(n_classes, n_features +
                                            int(self.fit_intercept))

        if self.fit_intercept:
            self.intercept_ = self.coef_[:, -1]
            self.coef_ = self.coef_[:, :-1]

        return self

    def predict_proba(self, X):
        """Probability estimates.

        The returned estimates for all classes are ordered by the
        label of classes.

        For a multi_class problem, if multi_class is set to be "multinomial"
        the softmax function is used to find the predicted probability of
        each class.
        Else use a one-vs-rest approach, i.e calculate the probability
        of each class assuming it to be positive using the logistic function.
        and normalize these values across all the classes.

        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]

        Returns
        -------
        T : array-like, shape = [n_samples, n_classes]
            Returns the probability of the sample for each class in the model,
            where classes are ordered as they are in ``self.classes_``.
        """
        if not hasattr(self, "coef_"):
            raise NotFittedError("Call fit before prediction")
        calculate_ovr = self.coef_.shape[0] == 1 or self.multi_class == "ovr"
        if calculate_ovr:
            return super(LogisticRegression, self)._predict_proba_lr(X)
        else:
            return softmax(self.decision_function(X), copy=False)

    def predict_log_proba(self, X):
        """Log of probability estimates.

        The returned estimates for all classes are ordered by the
        label of classes.

        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]

        Returns
        -------
        T : array-like, shape = [n_samples, n_classes]
            Returns the log-probability of the sample for each class in the
            model, where classes are ordered as they are in ``self.classes_``.
        """
        return np.log(self.predict_proba(X))


class LogisticRegressionCV(LogisticRegression, BaseEstimator,
                           LinearClassifierMixin):
    """Logistic Regression CV (aka logit, MaxEnt) classifier.

    This class implements logistic regression using liblinear, newton-cg, sag
    of lbfgs optimizer. The newton-cg, sag and lbfgs solvers support only L2
    regularization with primal formulation. The liblinear solver supports both
    L1 and L2 regularization, with a dual formulation only for the L2 penalty.

    For the grid of Cs values (that are set by default to be ten values in
    a logarithmic scale between 1e-4 and 1e4), the best hyperparameter is
    selected by the cross-validator StratifiedKFold, but it can be changed
    using the cv parameter. In the case of newton-cg and lbfgs solvers,
    we warm start along the path i.e guess the initial coefficients of the
    present fit to be the coefficients got after convergence in the previous
    fit, so it is supposed to be faster for high-dimensional dense data.

    For a multiclass problem, the hyperparameters for each class are computed
    using the best scores got by doing a one-vs-rest in parallel across all
    folds and classes. Hence this is not the true multinomial loss.

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

    Parameters
    ----------
    Cs : list of floats | int
        Each of the values in Cs describes the inverse of regularization
        strength. If Cs is as an int, then a grid of Cs values are chosen
        in a logarithmic scale between 1e-4 and 1e4.
        Like in support vector machines, smaller values specify stronger
        regularization.

    fit_intercept : bool, default: True
        Specifies if a constant (a.k.a. bias or intercept) should be
        added to the decision function.

    class_weight : dict or 'balanced', optional
        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))``.

        Note that these weights will be multiplied with sample_weight (passed
        through the fit method) if sample_weight is specified.

        .. versionadded:: 0.17
           class_weight == 'balanced'

    cv : integer or cross-validation generator
        The default cross-validation generator used is Stratified K-Folds.
        If an integer is provided, then it is the number of folds used.
        See the module :mod:`sklearn.model_selection` module for the
        list of possible cross-validation objects.

    penalty : str, 'l1' or 'l2'
        Used to specify the norm used in the penalization. The 'newton-cg',
        'sag' and 'lbfgs' solvers support only l2 penalties.

    dual : bool
        Dual or primal formulation. Dual formulation is only implemented for
        l2 penalty with liblinear solver. Prefer dual=False when
        n_samples > n_features.

    scoring : string, callable, or None
        A string (see model evaluation documentation) or
        a scorer callable object / function with signature
        ``scorer(estimator, X, y)``. For a list of scoring functions
        that can be used, look at :mod:`sklearn.metrics`. The
        default scoring option used is 'accuracy'.

    solver : {'newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'},
        default: 'liblinear'
        Algorithm to use in the optimization problem.

        - For small datasets, 'liblinear' is a good choice, whereas 'sag' and
            'saga' are faster for large ones.
        - For multiclass problems, only 'newton-cg', 'sag', 'saga' and 'lbfgs'
            handle multinomial loss; 'liblinear' is limited to one-versus-rest
            schemes.
        - 'newton-cg', 'lbfgs' and 'sag' only handle L2 penalty, whereas
            'liblinear' and 'saga' handle L1 penalty.
        - 'liblinear' might be slower in LogisticRegressionCV because it does
            not handle warm-starting.

        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.

    tol : float, optional
        Tolerance for stopping criteria.

    max_iter : int, optional
        Maximum number of iterations of the optimization algorithm.

    n_jobs : int, optional
        Number of CPU cores used during the cross-validation loop. If given
        a value of -1, all cores are used.

    verbose : int
        For the 'liblinear', 'sag' and 'lbfgs' solvers set verbose to any
        positive number for verbosity.

    refit : bool
        If set to True, the scores are averaged across all folds, and the
        coefs and the C that corresponds to the best score is taken, and a
        final refit is done using these parameters.
        Otherwise the coefs, intercepts and C that correspond to the
        best scores across folds are averaged.

    multi_class : str, {'ovr', 'multinomial'}
        Multiclass option can be either 'ovr' or 'multinomial'. If the option
        chosen is 'ovr', then a binary problem is fit for each label. Else
        the loss minimised is the multinomial loss fit across
        the entire probability distribution. Works only for the 'newton-cg',
        'sag' and 'lbfgs' solver.

        .. versionadded:: 0.18
           Stochastic Average Gradient descent solver for 'multinomial' case.

    intercept_scaling : float, default 1.
        Useful only when the solver 'liblinear' is used
        and self.fit_intercept is set to True. In this case, x becomes
        [x, self.intercept_scaling],
        i.e. a "synthetic" feature with constant value equal to
        intercept_scaling is appended to the instance vector.
        The intercept becomes ``intercept_scaling * synthetic_feature_weight``.

        Note! the synthetic feature weight is subject to l1/l2 regularization
        as all other features.
        To lessen the effect of regularization on synthetic feature weight
        (and therefore on the intercept) intercept_scaling has to be increased.

    random_state : int seed, RandomState instance, or None (default)
        The seed of the pseudo random number generator to use when
        shuffling the data.

    Attributes
    ----------
    coef_ : array, 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_ : array, shape (1,) or (n_classes,)
        Intercept (a.k.a. bias) added to the decision function.

        If `fit_intercept` is set to False, the intercept is set to zero.
        `intercept_` is of shape(1,) when the problem is binary.

    Cs_ : array
        Array of C i.e. inverse of regularization parameter values used
        for cross-validation.

    coefs_paths_ : array, shape ``(n_folds, len(Cs_), n_features)`` or \
                   ``(n_folds, len(Cs_), n_features + 1)``
        dict with classes as the keys, and the path of coefficients obtained
        during cross-validating across each fold and then across each Cs
        after doing an OvR for the corresponding class as values.
        If the 'multi_class' option is set to 'multinomial', then
        the coefs_paths are the coefficients corresponding to each class.
        Each dict value has shape ``(n_folds, len(Cs_), n_features)`` or
        ``(n_folds, len(Cs_), n_features + 1)`` depending on whether the
        intercept is fit or not.

    scores_ : dict
        dict with classes as the keys, and the values as the
        grid of scores obtained during cross-validating each fold, after doing
        an OvR for the corresponding class. If the 'multi_class' option
        given is 'multinomial' then the same scores are repeated across
        all classes, since this is the multinomial class.
        Each dict value has shape (n_folds, len(Cs))

    C_ : array, shape (n_classes,) or (n_classes - 1,)
        Array of C that maps to the best scores across every class. If refit is
        set to False, then for each class, the best C is the average of the
        C's that correspond to the best scores for each fold.

    n_iter_ : array, shape (n_classes, n_folds, n_cs) or (1, n_folds, n_cs)
        Actual number of iterations for all classes, folds and Cs.
        In the binary or multinomial cases, the first dimension is equal to 1.

    See also
    --------
    LogisticRegression

    """

    def __init__(self, Cs=10, fit_intercept=True, cv=None, dual=False,
                 penalty='l2', scoring=None, solver='lbfgs', tol=1e-4,
                 max_iter=100, class_weight=None, n_jobs=1, verbose=0,
                 refit=True, intercept_scaling=1., multi_class='ovr',
                 random_state=None):
        self.Cs = Cs
        self.fit_intercept = fit_intercept
        self.cv = cv
        self.dual = dual
        self.penalty = penalty
        self.scoring = scoring
        self.tol = tol
        self.max_iter = max_iter
        self.class_weight = class_weight
        self.n_jobs = n_jobs
        self.verbose = verbose
        self.solver = solver
        self.refit = refit
        self.intercept_scaling = intercept_scaling
        self.multi_class = multi_class
        self.random_state = random_state

    def fit(self, X, y, sample_weight=None):
        """Fit the model according to the given training data.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            Training vector, where n_samples is the number of samples and
            n_features is the number of features.

        y : array-like, shape (n_samples,)
            Target vector relative to X.

        sample_weight : array-like, shape (n_samples,) optional
            Array of weights that are assigned to individual samples.
            If not provided, then each sample is given unit weight.

        Returns
        -------
        self : object
            Returns self.
        """
        _check_solver_option(self.solver, self.multi_class, self.penalty,
                             self.dual)

        if not isinstance(self.max_iter, numbers.Number) or self.max_iter < 0:
            raise ValueError("Maximum number of iteration must be positive;"
                             " got (max_iter=%r)" % self.max_iter)
        if not isinstance(self.tol, numbers.Number) or self.tol < 0:
            raise ValueError("Tolerance for stopping criteria must be "
                             "positive; got (tol=%r)" % self.tol)

        X, y = check_X_y(X, y, accept_sparse='csr', dtype=np.float64,
                         order="C")
        check_classification_targets(y)

        class_weight = self.class_weight

        # Encode for string labels
        label_encoder = LabelEncoder().fit(y)
        y = label_encoder.transform(y)
        if isinstance(class_weight, dict):
            class_weight = dict((label_encoder.transform([cls])[0], v)
                                for cls, v in class_weight.items())

        # The original class labels
        classes = self.classes_ = label_encoder.classes_
        encoded_labels = label_encoder.transform(label_encoder.classes_)

        if self.solver in ['sag', 'saga']:
            max_squared_sum = row_norms(X, squared=True).max()
        else:
            max_squared_sum = None

        # init cross-validation generator
        cv = check_cv(self.cv, y, classifier=True)
        folds = list(cv.split(X, y))

        # Use the label encoded classes
        n_classes = len(encoded_labels)

        if n_classes < 2:
            raise ValueError("This solver needs samples of at least 2 classes"
                             " in the data, but the data contains only one"
                             " class: %r" % classes[0])

        if n_classes == 2:
            # OvR in case of binary problems is as good as fitting
            # the higher label
            n_classes = 1
            encoded_labels = encoded_labels[1:]
            classes = classes[1:]

        # We need this hack to iterate only once over labels, in the case of
        # multi_class = multinomial, without changing the value of the labels.
        if self.multi_class == 'multinomial':
            iter_encoded_labels = iter_classes = [None]
        else:
            iter_encoded_labels = encoded_labels
            iter_classes = classes

        # compute the class weights for the entire dataset y
        if class_weight == "balanced":
            class_weight = compute_class_weight(class_weight,
                                                np.arange(len(self.classes_)),
                                                y)
            class_weight = dict(enumerate(class_weight))

        path_func = delayed(_log_reg_scoring_path)

        # The SAG solver releases the GIL so it's more efficient to use
        # threads for this solver.
        if self.solver in ['sag', 'saga']:
            backend = 'threading'
        else:
            backend = 'multiprocessing'
        fold_coefs_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
                               backend=backend)(
            path_func(X, y, train, test, pos_class=label, Cs=self.Cs,
                      fit_intercept=self.fit_intercept, penalty=self.penalty,
                      dual=self.dual, solver=self.solver, tol=self.tol,
                      max_iter=self.max_iter, verbose=self.verbose,
                      class_weight=class_weight, scoring=self.scoring,
                      multi_class=self.multi_class,
                      intercept_scaling=self.intercept_scaling,
                      random_state=self.random_state,
                      max_squared_sum=max_squared_sum,
                      sample_weight=sample_weight
                      )
            for label in iter_encoded_labels
            for train, test in folds)

        if self.multi_class == 'multinomial':
            multi_coefs_paths, Cs, multi_scores, n_iter_ = zip(*fold_coefs_)
            multi_coefs_paths = np.asarray(multi_coefs_paths)
            multi_scores = np.asarray(multi_scores)

            # This is just to maintain API similarity between the ovr and
            # multinomial option.
            # Coefs_paths in now n_folds X len(Cs) X n_classes X n_features
            # we need it to be n_classes X len(Cs) X n_folds X n_features
            # to be similar to "ovr".
            coefs_paths = np.rollaxis(multi_coefs_paths, 2, 0)

            # Multinomial has a true score across all labels. Hence the
            # shape is n_folds X len(Cs). We need to repeat this score
            # across all labels for API similarity.
            scores = np.tile(multi_scores, (n_classes, 1, 1))
            self.Cs_ = Cs[0]
            self.n_iter_ = np.reshape(n_iter_, (1, len(folds),
                                                len(self.Cs_)))

        else:
            coefs_paths, Cs, scores, n_iter_ = zip(*fold_coefs_)
            self.Cs_ = Cs[0]
            coefs_paths = np.reshape(coefs_paths, (n_classes, len(folds),
                                                   len(self.Cs_), -1))
            self.n_iter_ = np.reshape(n_iter_, (n_classes, len(folds),
                                                len(self.Cs_)))

        self.coefs_paths_ = dict(zip(classes, coefs_paths))
        scores = np.reshape(scores, (n_classes, len(folds), -1))
        self.scores_ = dict(zip(classes, scores))

        self.C_ = list()
        self.coef_ = np.empty((n_classes, X.shape[1]))
        self.intercept_ = np.zeros(n_classes)

        # hack to iterate only once for multinomial case.
        if self.multi_class == 'multinomial':
            scores = multi_scores
            coefs_paths = multi_coefs_paths

        for index, (cls, encoded_label) in enumerate(
                zip(iter_classes, iter_encoded_labels)):

            if self.multi_class == 'ovr':
                # The scores_ / coefs_paths_ dict have unencoded class
                # labels as their keys
                scores = self.scores_[cls]
                coefs_paths = self.coefs_paths_[cls]

            if self.refit:
                best_index = scores.sum(axis=0).argmax()

                C_ = self.Cs_[best_index]
                self.C_.append(C_)
                if self.multi_class == 'multinomial':
                    coef_init = np.mean(coefs_paths[:, best_index, :, :],
                                        axis=0)
                else:
                    coef_init = np.mean(coefs_paths[:, best_index, :], axis=0)

                # Note that y is label encoded and hence pos_class must be
                # the encoded label / None (for 'multinomial')
                w, _, _ = logistic_regression_path(
                    X, y, pos_class=encoded_label, Cs=[C_], solver=self.solver,
                    fit_intercept=self.fit_intercept, coef=coef_init,
                    max_iter=self.max_iter, tol=self.tol,
                    penalty=self.penalty,
                    class_weight=class_weight,
                    multi_class=self.multi_class,
                    verbose=max(0, self.verbose - 1),
                    random_state=self.random_state,
                    check_input=False, max_squared_sum=max_squared_sum,
                    sample_weight=sample_weight)
                w = w[0]

            else:
                # Take the best scores across every fold and the average of all
                # coefficients corresponding to the best scores.
                best_indices = np.argmax(scores, axis=1)
                w = np.mean([coefs_paths[i][best_indices[i]]
                             for i in range(len(folds))], axis=0)
                self.C_.append(np.mean(self.Cs_[best_indices]))

            if self.multi_class == 'multinomial':
                self.C_ = np.tile(self.C_, n_classes)
                self.coef_ = w[:, :X.shape[1]]
                if self.fit_intercept:
                    self.intercept_ = w[:, -1]
            else:
                self.coef_[index] = w[: X.shape[1]]
                if self.fit_intercept:
                    self.intercept_[index] = w[-1]

        self.C_ = np.asarray(self.C_)
        return self
