text: #===============================================================================
  # Copyright 2014-2021 Intel Corporation
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #===============================================================================
  
  import daal4py as d4p
  import numpy as np
  import scipy.sparse as sparse
  import scipy.optimize as optimize
  import numbers
  import warnings
  
  from .logistic_loss import (_daal4py_loss_and_grad,
                              _daal4py_logistic_loss_extra_args,
                              _daal4py_cross_entropy_loss_extra_args,
                              _daal4py_loss_, _daal4py_grad_,
                              _daal4py_grad_hess_)
  import sklearn.linear_model._logistic as logistic_module
  
  from sklearn import __version__ as sklearn_version
  from distutils.version import LooseVersion
  from sklearn.utils import (check_array,
                             check_consistent_length,
                             compute_class_weight,
                             check_random_state)
  from sklearn.utils.validation import _check_sample_weight, check_is_fitted
  from sklearn.linear_model._sag import sag_solver
  from sklearn.utils.optimize import _newton_cg, _check_optimize_result
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.linear_model._logistic import (
      _check_solver,
      _check_multi_class,
      _fit_liblinear,
      _logistic_loss_and_grad,
      _logistic_loss,
      _logistic_grad_hess,
      _multinomial_loss,
      _multinomial_loss_grad,
      _multinomial_grad_hess,
      _LOGISTIC_SOLVER_CONVERGENCE_MSG,
      LogisticRegression as LogisticRegression_original)
  from sklearn.preprocessing import (LabelEncoder, LabelBinarizer)
  from sklearn.linear_model._base import (LinearClassifierMixin, SparseCoefMixin,
                                          BaseEstimator)
  from .._utils import (daal_check_version, getFPType,
                        get_patch_message, sklearn_check_version)
  import logging
  
  
  # Code adapted from sklearn.linear_model.logistic version 0.21
  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='warn',
                                 random_state=None, check_input=True,
                                 max_squared_sum=None, sample_weight=None,
                                 l1_ratio=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,) or (n_samples, n_targets)
          Input data, target values.
  
      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 : 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.
  
      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', 'l2', or 'elasticnet'
          Used to specify the norm used in the penalization. The 'newton-cg',
          'sag' and 'lbfgs' solvers support only l2 penalties. 'elasticnet' is
          only supported by the 'saga' solver.
  
      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', 'auto'}, default: 'ovr'
          If the option chosen is 'ovr', then a binary problem is fit for each
          label. For 'multinomial' the loss minimised is the multinomial loss fit
          across the entire probability distribution, *even when the data is
          binary*. 'multinomial' is unavailable when solver='liblinear'.
          'auto' selects 'ovr' if the data is binary, or if solver='liblinear',
          and otherwise selects 'multinomial'.
  
          .. versionadded:: 0.18
             Stochastic Average Gradient descent solver for 'multinomial' case.
          .. versionchanged:: 0.20
              Default will change from 'ovr' to 'auto' in 0.22.
  
      random_state : int, RandomState instance or None, optional, default None
          The seed of the pseudo random number generator to use when shuffling
          the data.  If int, random_state is the seed used by the random number
          generator; If RandomState instance, random_state is the random number
          generator; If None, the random number generator is the RandomState
          instance used by `np.random`. Used when ``solver`` == 'sag' or
          '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.
  
      l1_ratio : float or None, optional (default=None)
          The Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``. Only
          used if ``penalty='elasticnet'``. Setting ``l1_ratio=0`` is equivalent
          to using ``penalty='l2'``, while setting ``l1_ratio=1`` is equivalent
          to using ``penalty='l1'``. For ``0 < l1_ratio <1``, the penalty is a
          combination of L1 and L2.
  
      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. For
          ``multiclass='multinomial'``, the shape is (n_classes, n_cs,
          n_features) or (n_classes, n_cs, n_features + 1).
  
      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)
  
      solver = _check_solver(solver, penalty, dual)
  
      # Preprocessing.
      if check_input:
          X = check_array(X, accept_sparse='csr', dtype=np.float64,
                          accept_large_sparse=solver != 'liblinear')
          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)
  
      multi_class = _check_multi_class(multi_class, solver, len(classes))
      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]
  
      daal_ready = solver in ['lbfgs', 'newton-cg'] and not sparse.issparse(X)
      daal_ready = daal_ready and sample_weight is None and class_weight is None
  
      if not daal_ready:
          if sklearn_check_version('0.24'):
              sample_weight = _check_sample_weight(sample_weight, X,
                                                   dtype=X.dtype,
                                                   copy=True)
          else:
              sample_weight = _check_sample_weight(sample_weight, X,
                                                   dtype=X.dtype)
      # 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') and \
              not daal_ready:
          class_weight_ = compute_class_weight(class_weight, classes=classes, y=y)
          if not np.allclose(class_weight_, np.ones_like(class_weight_)):
              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), dtype=X.dtype)
          mask_classes = np.array([-1, 1])
          mask = (y == pos_class)
          y_bin = np.ones(y.shape, dtype=X.dtype)
          y_bin[~mask] = -1.
          # for compute_class_weight
  
          if class_weight == "balanced" and not daal_ready:
              class_weight_ = compute_class_weight(class_weight, classes=mask_classes,
                                                   y=y_bin)
              if not np.allclose(class_weight_, np.ones_like(class_weight_)):
                  sample_weight *= class_weight_[le.fit_transform(y_bin)]
  
          if daal_ready:
              w0 = np.zeros(n_features + 1, dtype=X.dtype)
              y_bin[~mask] = 0.
          else:
              w0 = np.zeros(n_features + int(fit_intercept), dtype=X.dtype)
  
      else:
          if solver not in ['sag', 'saga']:
              if daal_ready:
                  Y_multi = le.fit_transform(y).astype(X.dtype, copy=False)
              else:
                  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).astype(X.dtype, copy=False)
  
          if daal_ready:
              w0 = np.zeros((classes.size, n_features + 1),
                            order='C', dtype=X.dtype)
          else:
              w0 = np.zeros((classes.size, n_features + int(fit_intercept)),
                            order='F', dtype=X.dtype)
  
      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))
              if daal_ready:
                  w0[-coef.size:] = \
                      np.roll(coef, 1, -1) if coef.size != n_features else coef
              else:
                  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))
  
              if daal_ready:
                  w0[:, -coef.shape[1]:] = \
                      np.roll(coef, 1, -1) if coef.shape[1] != n_features else coef
              else:
                  if n_classes == 1:
                      w0[0, :coef.shape[1]] = -coef
                      w0[1, :coef.shape[1]] = coef
                  else:
                      w0[:, :coef.shape[1]] = coef
  
      C_daal_multiplier = 1
      # commented out because this is Py3 feature
      #def _map_to_binary_logistic_regression():
      #    nonlocal C_daal_multiplier
      #    nonlocal w0
      #    C_daal_multiplier = 2
      #    w0 *= 2
  
      if multi_class == 'multinomial':
          # fmin_l_bfgs_b and newton-cg accepts only ravelled parameters.
          if solver in ['lbfgs', 'newton-cg']:
              if daal_ready and classes.size == 2:
                  w0 = w0[-1:, :]
              w0 = w0.ravel()
          target = Y_multi
          if solver == 'lbfgs':
              if daal_ready:
                  if classes.size == 2:
                      # _map_to_binary_logistic_regression()
                      C_daal_multiplier = 2
                      w0 *= 2
                      daal_extra_args_func = _daal4py_logistic_loss_extra_args
                  else:
                      daal_extra_args_func = _daal4py_cross_entropy_loss_extra_args
                  func = _daal4py_loss_and_grad
              else:
                  def func(x, *args):
                      return _multinomial_loss_grad(x, *args)[0:2]
          elif solver == 'newton-cg':
              if daal_ready:
                  if classes.size == 2:
                      # _map_to_binary_logistic_regression()
                      C_daal_multiplier = 2
                      w0 *= 2
                      daal_extra_args_func = _daal4py_logistic_loss_extra_args
                  else:
                      daal_extra_args_func = _daal4py_cross_entropy_loss_extra_args
                  func = _daal4py_loss_
                  grad = _daal4py_grad_
                  hess = _daal4py_grad_hess_
              else:
                  def func(x, *args):
                      return _multinomial_loss(x, *args)[0]
  
                  def grad(x, *args):
                      return _multinomial_loss_grad(x, *args)[1]
                  hess = _multinomial_grad_hess
          warm_start_sag = {'coef': w0.T}
      else:
          target = y_bin
          if solver == 'lbfgs':
              if daal_ready:
                  func = _daal4py_loss_and_grad
                  daal_extra_args_func = _daal4py_logistic_loss_extra_args
              else:
                  func = _logistic_loss_and_grad
          elif solver == 'newton-cg':
              if daal_ready:
                  daal_extra_args_func = _daal4py_logistic_loss_extra_args
                  func = _daal4py_loss_
                  grad = _daal4py_grad_
                  hess = _daal4py_grad_hess_
              else:
                  func = _logistic_loss
  
                  def grad(x, *args):
                      return _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':
              if daal_ready:
                  extra_args = daal_extra_args_func(classes.size, w0, X, target,
                                                    0., 0.5 / C / C_daal_multiplier,
                                                    fit_intercept, value=True,
                                                    gradient=True, hessian=False)
              else:
                  extra_args = (X, target, 1. / C, sample_weight)
  
              iprint = [-1, 50, 1, 100, 101][
                  np.searchsorted(np.array([0, 1, 2, 3]), verbose)]
              opt_res = optimize.minimize(
                  func, w0, method="L-BFGS-B", jac=True,
                  args=extra_args,
                  options={"iprint": iprint, "gtol": tol, "maxiter": max_iter}
              )
              n_iter_i = _check_optimize_result(
                  solver, opt_res, max_iter,
                  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
              w0, loss = opt_res.x, opt_res.fun
              if daal_ready and C_daal_multiplier == 2:
                  w0 *= 0.5
          elif solver == 'newton-cg':
              if daal_ready:
                  def make_ncg_funcs(f, value=False, gradient=False, hessian=False):
                      daal_penaltyL2 = 0.5 / C / C_daal_multiplier
                      _obj_, X_, y_, n_samples = daal_extra_args_func(
                          classes.size, w0, X, target, 0., daal_penaltyL2, fit_intercept,
                          value=value, gradient=gradient, hessian=hessian)
  
                      def _func_(x, *args):
                          return f(x, _obj_, *args)
                      return _func_, (X_, y_, n_samples, daal_penaltyL2)
  
                  loss_func, extra_args = make_ncg_funcs(func, value=True)
                  grad_func, _ = make_ncg_funcs(grad, gradient=True)
                  grad_hess_func, _ = make_ncg_funcs(hess, gradient=True)
                  w0, n_iter_i = _newton_cg(grad_hess_func, loss_func, grad_func,
                                            w0, args=extra_args,
                                            maxiter=max_iter, tol=tol)
              else:
                  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(X.dtype, copy=False)
                  loss = 'multinomial'
              else:
                  loss = 'log'
              # alpha is for L2-norm, beta is for L1-norm
              if penalty == 'l1':
                  alpha = 0.
                  beta = 1. / C
              elif penalty == 'l2':
                  alpha = 1. / C
                  beta = 0.
              else:  # Elastic-Net penalty
                  alpha = (1. / C) * (1 - l1_ratio)
                  beta = (1. / C) * l1_ratio
  
              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':
              if daal_ready:
                  if classes.size == 2:
                      multi_w0 = w0[np.newaxis, :]
                  else:
                      multi_w0 = np.reshape(w0, (classes.size, -1))
              else:
                  n_classes = max(2, classes.size)
                  multi_w0 = np.reshape(w0, (n_classes, -1))
                  if n_classes == 2:
                      multi_w0 = multi_w0[1][np.newaxis, :]
              coefs.append(np.require(multi_w0, requirements='O'))
          else:
              coefs.append(np.require(w0, requirements='O'))
  
          n_iter[i] = n_iter_i
  
      if daal_ready:
          if fit_intercept:
              for i, ci in enumerate(coefs):
                  coefs[i] = np.roll(ci, -1, -1)
          else:
              for i, ci in enumerate(coefs):
                  coefs[i] = np.delete(ci, 0, axis=-1)
  
      if daal_ready:
          logging.info(
              "sklearn.linear_model.LogisticRegression."
              "fit: " + get_patch_message("daal"))
      else:
          logging.info(
              "sklearn.linear_model.LogisticRegression."
              "fit: " + get_patch_message("sklearn"))
  
      return np.array(coefs), np.array(Cs), n_iter
  
  
  def daal4py_predict(self, X, resultsToEvaluate):
      check_is_fitted(self)
      X = check_array(X, accept_sparse='csr', dtype=[np.float64, np.float32])
      try:
          fptype = getFPType(X)
      except ValueError:
          fptype = None
  
      multinomial = self.multi_class in ["multinomial", "warn"] or \
          self.classes_.size == 2 or resultsToEvaluate == 'computeClassLabels'
  
      if daal_check_version(((2021, 'P', 1))) and fptype is not None and \
              not sparse.issparse(X) and multinomial and not sparse.issparse(self.coef_):
          logging.info(
              "sklearn.linear_model.LogisticRegression."
              "predict: " + get_patch_message("daal"))
          n_features = self.coef_.shape[1]
          if X.shape[1] != n_features:
              raise ValueError(
                  (f'X has {X.shape[1]} features, '
                   f'but LogisticRegression is expecting {n_features} features as input'))
          builder = d4p.logistic_regression_model_builder(X.shape[1], len(self.classes_))
          builder.set_beta(self.coef_, self.intercept_)
          predict = d4p.logistic_regression_prediction(
              nClasses=len(self.classes_),
              fptype=fptype,
              method='defaultDense',
              resultsToEvaluate=resultsToEvaluate)
          res = predict.compute(X, builder.model)
          if resultsToEvaluate == 'computeClassLabels':
              res = res.prediction
              if not np.array_equal(self.classes_, np.arange(0, len(self.classes_))) or \
                      self.classes_.dtype != X.dtype:
                  res = self.classes_.take(np.asarray(res, dtype=np.intp))
          elif resultsToEvaluate == 'computeClassProbabilities':
              res = res.probabilities
          elif resultsToEvaluate == 'computeClassLogProbabilities':
              res = res.logProbabilities
          else:
              raise ValueError('resultsToEvaluate must be in [computeClassLabels, \
                               computeClassProbabilities, computeClassLogProbabilities]')
          if res.shape[1] == 1:
              res = np.ravel(res)
          return res
  
      if resultsToEvaluate == 'computeClassLabels':
          logging.info(
              "sklearn.linear_model.LogisticRegression."
              "predict: " + get_patch_message("sklearn"))
          return LogisticRegression_original.predict(self, X)
      if resultsToEvaluate == 'computeClassProbabilities':
          logging.info(
              "sklearn.linear_model.LogisticRegression."
              "predict_proba: " + get_patch_message("sklearn"))
          return LogisticRegression_original.predict_proba(self, X)
      if resultsToEvaluate == 'computeClassLogProbabilities':
          logging.info(
              "sklearn.linear_model.LogisticRegression."
              "predict_log_proba: " + get_patch_message("sklearn"))
          return LogisticRegression_original.predict_log_proba(self, X)
      raise ValueError('resultsToEvaluate must be in [computeClassLabels, \
                       computeClassProbabilities, computeClassLogProbabilities]')
  
  
  if (LooseVersion(sklearn_version) >= LooseVersion("0.24")):
      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='auto',
                                   random_state=None, check_input=True,
                                   max_squared_sum=None, sample_weight=None,
                                   l1_ratio=None):
          return __logistic_regression_path(X, y, pos_class=pos_class,
                                            Cs=Cs, fit_intercept=fit_intercept,
                                            max_iter=max_iter, tol=tol, verbose=verbose,
                                            solver=solver, coef=coef,
                                            class_weight=class_weight,
                                            dual=dual, penalty=penalty,
                                            intercept_scaling=intercept_scaling,
                                            multi_class=multi_class,
                                            random_state=random_state,
                                            check_input=check_input,
                                            max_squared_sum=max_squared_sum,
                                            sample_weight=sample_weight,
                                            l1_ratio=l1_ratio)
  
      class LogisticRegression(LogisticRegression_original):
          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='lbfgs', max_iter=100,
                       multi_class='auto', verbose=0, warm_start=False, n_jobs=None,
                       l1_ratio=None):
              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 = 1 if n_jobs is not None else None
              self.l1_ratio = l1_ratio
  
          def fit(self, X, y, sample_weight=None):
              which, what = logistic_module, '_logistic_regression_path'
              replacer = logistic_regression_path
              descriptor = getattr(which, what, None)
              setattr(which, what, replacer)
              clf = super().fit(X, y, sample_weight)
              setattr(which, what, descriptor)
              return clf
  
          def predict(self, X):
              return daal4py_predict(self, X, 'computeClassLabels')
  
          def predict_log_proba(self, X):
              return daal4py_predict(self, X, 'computeClassLogProbabilities')
  
          def predict_proba(self, X):
              return daal4py_predict(self, X, 'computeClassProbabilities')
  
  
  else:
      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='auto',
                                   random_state=None, check_input=True,
                                   max_squared_sum=None, sample_weight=None,
                                   l1_ratio=None):
          return __logistic_regression_path(X, y, pos_class=pos_class,
                                            Cs=Cs, fit_intercept=fit_intercept,
                                            max_iter=max_iter, tol=tol, verbose=verbose,
                                            solver=solver, coef=coef,
                                            class_weight=class_weight,
                                            dual=dual, penalty=penalty,
                                            intercept_scaling=intercept_scaling,
                                            multi_class=multi_class,
                                            random_state=random_state,
                                            check_input=check_input,
                                            max_squared_sum=max_squared_sum,
                                            sample_weight=sample_weight,
                                            l1_ratio=l1_ratio)
  
      class LogisticRegression(LogisticRegression_original):
          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='lbfgs', max_iter=100,
                       multi_class='auto', verbose=0, warm_start=False, n_jobs=None,
                       l1_ratio=None):
  
              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 = 1 if n_jobs is not None else None
              self.l1_ratio = l1_ratio
  
          def fit(self, X, y, sample_weight=None):
              which, what = logistic_module, '_logistic_regression_path'
              replacer = logistic_regression_path
              descriptor = getattr(which, what, None)
              setattr(which, what, replacer)
              clf = super().fit(X, y, sample_weight)
              setattr(which, what, descriptor)
              return clf
  
          def predict(self, X):
              return daal4py_predict(self, X, 'computeClassLabels')
  
          def predict_log_proba(self, X):
              return daal4py_predict(self, X, 'computeClassLogProbabilities')
  
          def predict_proba(self, X):
              return daal4py_predict(self, X, 'computeClassProbabilities')
  