content: """
  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>
  
  import numbers
  import warnings
  
  import numpy as np
  from scipy import optimize, sparse
  
  from .base import LinearClassifierMixin, SparseCoefMixin, BaseEstimator
  from ..feature_selection.from_model import _LearntSelectorMixin
  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,
                               squared_norm)
  from ..utils.optimize import newton_cg
  from ..utils.validation import (as_float_array, DataConversionWarning,
                                  check_X_y)
  from ..utils.fixes import expit
  from ..externals.joblib import Parallel, delayed
  from ..cross_validation 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.
      """
      c = 0.
      if w.size == X.shape[1] + 1:
          c = w[-1]
          w = w[:-1]
  
      z = safe_sparse_dot(X, w) + c
      return w, c, y * z
  
  
  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 : ndarray, 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_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(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)
  
      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 : ndarray, 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 : ndarray, 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 : ndarray, 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.
      """
      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 : ndarray, 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
      """
      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 : ndarray, 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']:
          raise ValueError("Logistic Regression supports only liblinear,"
                           " newton-cg and lbfgs 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 != 'liblinear':
          if penalty != 'l2':
              raise ValueError("Solver %s supports only l2 penalties, "
                               "got %s penalty." % (solver, penalty))
          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, copy=True,
                               class_weight=None, dual=False, penalty='l2',
                               intercept_scaling=1., multi_class='ovr',
                               random_state=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.
  
      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'}
          Numerical solver to use.
  
      coef : array-like, shape (n_features,), default None
          Initialization value for coefficients of logistic regression.
  
      copy : bool, default True
          Whether or not to produce a copy of the data. Setting this to
          True will be useful in cases, when logistic_regression_path
          is called repeatedly with the same data, as y is modified
          along the path.
  
      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))``
  
      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 and
          lbfgs solvers support only l2 penalties.
  
      intercept_scaling : float, default 1.
          This parameter is 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. 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.
  
      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.
  
      Notes
      -----
      You might get slighly different results with the solver liblinear than
      with the others since this uses LIBLINEAR which penalizes the intercept.
      """
      if isinstance(Cs, numbers.Integral):
          Cs = np.logspace(-4, 4, Cs)
  
      _check_solver_option(solver, multi_class, penalty, dual)
  
      # Preprocessing.
      X = check_array(X, accept_sparse='csr', dtype=np.float64)
      y = check_array(y, ensure_2d=False, copy=copy, dtype=None)
      _, n_features = X.shape
      check_consistent_length(X, y)
      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 class_weights is a dict (provided by the user), the weights
      # are assigned to the original labels. If it is "auto", then
      # the class_weights are assigned after masking the labels with a OvR.
      sample_weight = np.ones(X.shape[0])
      le = LabelEncoder()
  
      if isinstance(class_weight, dict):
          if solver == "liblinear":
              if classes.size == 2:
                  # Reconstruct the weights with keys 1 and -1
                  temp = {1: class_weight[pos_class],
                          -1: class_weight[classes[0]]}
                  class_weight = temp.copy()
              else:
                  raise ValueError("In LogisticRegressionCV the liblinear "
                                   "solver cannot handle multiclass with "
                                   "class_weight of type dict. Use the lbfgs, "
                                   "newton-cg solvers or set "
                                   "class_weight='auto'")
          else:
              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 = [-1, 1]
          mask = (y == pos_class)
          y[mask] = 1
          y[~mask] = -1
          # To take care of object dtypes, i.e 1 and -1 are in the form of
          # strings.
          y = as_float_array(y, copy=False)
  
      else:
          lbin = LabelBinarizer()
          Y_bin = lbin.fit_transform(y)
          if Y_bin.shape[1] == 1:
              Y_bin = np.hstack([1 - Y_bin, Y_bin])
          w0 = np.zeros((Y_bin.shape[1], n_features + int(fit_intercept)),
                        order='F')
          mask_classes = classes
  
      if class_weight == "auto":
          class_weight_ = compute_class_weight(class_weight, mask_classes, y)
          sample_weight = class_weight_[le.fit_transform(y)]
  
      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_vectors = classes.size
              if n_vectors == 2:
                  n_vectors = 1
  
              if (coef.shape[0] != n_vectors 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.
          w0 = w0.ravel()
          target = Y_bin
          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
      else:
          target = y
          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
  
      coefs = list()
  
      for C in 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.")
          elif solver == 'newton-cg':
              args = (X, target, 1. / C, sample_weight)
              w0 = newton_cg(hess, func, grad, w0, args=args, maxiter=max_iter,
                             tol=tol)
          elif solver == 'liblinear':
              coef_, intercept_, _, = _fit_liblinear(
                  X, y, C, fit_intercept, intercept_scaling, class_weight,
                  penalty, dual, verbose, max_iter, tol, random_state)
              if fit_intercept:
                  w0 = np.concatenate([coef_.ravel(), intercept_])
              else:
                  w0 = coef_.ravel()
          else:
              raise ValueError("solver must be one of {'liblinear', 'lbfgs', "
                               "'newton-cg'}, 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)
      return coefs, np.array(Cs)
  
  
  # 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, copy=True, intercept_scaling=1.,
                            multi_class='ovr'):
      """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
          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))``
  
      verbose : int
          For the liblinear and lbfgs solvers set verbose to any positive
          number for verbosity.
  
      solver : {'lbfgs', 'newton-cg', 'liblinear'}
          Decides which solver to use.
  
      penalty : str, 'l1' or 'l2'
          Used to specify the norm used in the penalization. The newton-cg 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.
          This parameter is 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. Works only for the 'lbfgs'
          solver.
  
      copy : bool, default True
          Whether or not to produce a copy of the data. Setting this to
          True will be useful in cases, when ``_log_reg_scoring_path`` is called
          repeatedly with the same data, as y is modified along the path.
  
      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.
      """
      _check_solver_option(solver, multi_class, penalty, dual)
  
      log_reg = LogisticRegression(fit_intercept=fit_intercept)
  
      X_train = X[train]
      X_test = X[test]
      y_train = y[train]
      y_test = y[test]
  
      # 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[mask] = 1
          y_test[~mask] = -1
  
      # To deal with object dtypes, we need to convert into an array of floats.
      y_test = as_float_array(y_test, copy=False)
  
      coefs, Cs = logistic_regression_path(X_train, y_train, Cs=Cs,
                                           fit_intercept=fit_intercept,
                                           solver=solver,
                                           max_iter=max_iter,
                                           class_weight=class_weight,
                                           copy=copy, pos_class=pos_class,
                                           multi_class=multi_class,
                                           tol=tol, verbose=verbose,
                                           dual=dual, penalty=penalty,
                                           intercept_scaling=intercept_scaling)
  
      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)
  
  
  class LogisticRegression(BaseEstimator, LinearClassifierMixin,
                           _LearntSelectorMixin, 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' and
      'newton-cg' solvers.)
  
      This class implements regularized logistic regression using the
      `liblinear` library, newton-cg 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 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'
          Used to specify the norm used in the penalization. The newton-cg 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.
  
      C : float, optional (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 the decision function.
  
      intercept_scaling : float, default: 1
          Useful only if solver is liblinear.
          when self.fit_intercept is True, instance vector 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.
  
      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))``
  
      max_iter : int
          Useful only for the newton-cg and lbfgs solvers. Maximum number of
          iterations taken for the solvers to converge.
  
      random_state : int seed, RandomState instance, or None (default)
          The seed of the pseudo random number generator to use when
          shuffling the data.
  
      solver : {'newton-cg', 'lbfgs', 'liblinear'}
          Algorithm to use in the optimization problem.
  
      tol : float, optional
          Tolerance for stopping criteria.
  
      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'
          solver.
  
      verbose : int
          For the liblinear and lbfgs solvers set verbose to any positive
          number for verbosity.
  
      Attributes
      ----------
      coef_ : array, shape (n_classes, n_features)
          Coefficient of the features in the decision function.
  
      intercept_ : array, shape (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.
  
      n_iter_ : int
          Maximum of the actual number of iterations across all classes.
          Valid only for the liblinear solver.
  
      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/
  
      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
  
  
      See also
      --------
      sklearn.linear_model.SGDClassifier
      """
  
      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):
  
          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
  
      def fit(self, X, y):
          """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 in the number of samples and
              n_features is the number of features.
  
          y : array-like, shape (n_samples,)
              Target vector relative to X.
  
          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")
          self.classes_ = np.unique(y)
  
          _check_solver_option(self.solver, self.multi_class, self.penalty,
                               self.dual)
  
          if self.solver == 'liblinear':
              self.coef_, self.intercept_, self.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)
              return self
  
          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:]
  
          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]
  
          for ind, class_ in enumerate(classes_):
              coef_, _ = logistic_regression_path(
                  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)
              self.coef_.append(coef_[0])
  
          self.coef_ = np.squeeze(self.coef_)
          # For the binary case, this get squeezed to a 1-D array.
          if self.coef_.ndim == 1:
              self.coef_ = self.coef_[np.newaxis, :]
  
          self.coef_ = np.asarray(self.coef_)
          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.
  
          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_``.
          """
          return self._predict_proba_lr(X)
  
      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, _LearntSelectorMixin):
      """Logistic Regression CV (aka logit, MaxEnt) classifier.
  
      This class implements logistic regression using liblinear, newton-cg or
      LBFGS optimizer. The newton-cg 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 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))``
  
      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.cross_validation` 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 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 : callabale
          Scoring function to use as cross-validation criteria. For a list of
          scoring functions that can be used, look at :mod:`sklearn.metrics`.
          The default scoring option used is accuracy_score.
  
      solver : {'newton-cg', 'lbfgs', 'liblinear'}
          Algorithm to use in the optimization problem.
  
      tol : float, optional
          Tolerance for stopping criteria.
  
      max_iter : int, optional
          Maximum number of iterations of the optimization algorithm.
  
      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))``
  
      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 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 'lbfgs'
          solver.
  
      intercept_scaling : float, default 1.
          Useful only if solver is liblinear.
          This parameter is 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.
  
      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.
          `coef_` is readonly property derived from `raw_coef_` that
          follows the internal memory layout of liblinear.
  
      intercept_ : array, shape (1,) or (n_classes,)
          Intercept (a.k.a. bias) added to the decision function.
          It is available only when parameter intercept is set to True
          and 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.
  
      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'):
          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
  
      def fit(self, X, y):
          """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 in the number of samples and
              n_features is the number of features.
  
          y : array-like, shape (n_samples,)
              Target vector relative to X.
  
          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 = check_array(X, accept_sparse='csr', dtype=np.float64)
          y = check_array(y, ensure_2d=False, dtype=None)
  
          if y.ndim == 2 and y.shape[1] == 1:
              warnings.warn(
                  "A column-vector y was passed when a 1d array was"
                  " expected. Please change the shape of y to "
                  "(n_samples, ), for example using ravel().",
                  DataConversionWarning)
              y = np.ravel(y)
  
          check_consistent_length(X, y)
  
          # init cross-validation generator
          cv = check_cv(self.cv, X, y, classifier=True)
          folds = list(cv)
  
          self._enc = LabelEncoder()
          self._enc.fit(y)
  
          labels = self.classes_ = np.unique(y)
          n_classes = len(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" % self.classes_[0])
          if n_classes == 2:
              # OvR in case of binary problems is as good as fitting
              # the higher label
              n_classes = 1
              labels = labels[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.
          iter_labels = labels
          if self.multi_class == 'multinomial':
              iter_labels = [None]
  
          if self.class_weight and not(isinstance(self.class_weight, dict) or
                                       self.class_weight in ['balanced', 'auto']):
              raise ValueError("class_weight provided should be a "
                               "dict or 'balanced'")
  
          path_func = delayed(_log_reg_scoring_path)
  
          fold_coefs_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
              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=self.class_weight, scoring=self.scoring,
                        multi_class=self.multi_class,
                        intercept_scaling=self.intercept_scaling
                        )
              for label in iter_labels
              for train, test in folds)
  
          if self.multi_class == 'multinomial':
              multi_coefs_paths, Cs, multi_scores = 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]
  
          else:
              coefs_paths, Cs, scores = zip(*fold_coefs_)
              self.Cs_ = Cs[0]
              coefs_paths = np.reshape(coefs_paths, (n_classes, len(folds),
                                       len(self.Cs_), -1))
  
          self.coefs_paths_ = dict(zip(labels, coefs_paths))
          scores = np.reshape(scores, (n_classes, len(folds), -1))
          self.scores_ = dict(zip(labels, 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, label in enumerate(iter_labels):
              if self.multi_class == 'ovr':
                  scores = self.scores_[label]
                  coefs_paths = self.coefs_paths_[label]
  
              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)
                  w, _ = logistic_regression_path(
                      X, y, pos_class=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=self.class_weight,
                      multi_class=self.multi_class,
                      verbose=max(0, self.verbose - 1))
                  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
  