class_docstr: Logistic Regression CV (aka logit, MaxEnt) classifier.
  
  See glossary entry for :term:`cross-validation estimator`.
  
  This class implements logistic regression using liblinear, newton-cg, sag
  of lbfgs optimizer. The newton-cg, sag and lbfgs solvers support only L2
  regularization with primal formulation. The liblinear solver supports both
  L1 and L2 regularization, with a dual formulation only for the L2 penalty.
  Elastic-Net penalty is only supported by the saga solver.
  
  For the grid of `Cs` values and `l1_ratios` values, the best hyperparameter
  is selected by the cross-validator
  :class:`~sklearn.model_selection.StratifiedKFold`, but it can be changed
  using the :term:`cv` parameter. The 'newton-cg', 'sag', 'saga' and 'lbfgs'
  solvers can warm-start the coefficients (see :term:`Glossary<warm_start>`).
  
  Read more in the :ref:`User Guide <logistic_regression>`.
  
  Parameters
  ----------
  Cs : int or list of floats, default=10
      Each of the values in Cs describes the inverse of regularization
      strength. If Cs is as an int, then a grid of Cs values are chosen
      in a logarithmic scale between 1e-4 and 1e4.
      Like in support vector machines, smaller values specify stronger
      regularization.
  
  fit_intercept : bool, default=True
      Specifies if a constant (a.k.a. bias or intercept) should be
      added to the decision function.
  
  cv : int or cross-validation generator, default=None
      The default cross-validation generator used is Stratified K-Folds.
      If an integer is provided, then it is the number of folds used.
      See the module :mod:`sklearn.model_selection` module for the
      list of possible cross-validation objects.
  
      .. versionchanged:: 0.22
          ``cv`` default value if None changed from 3-fold to 5-fold.
  
  dual : bool, default=False
      Dual (constrained) or primal (regularized, see also
      :ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation
      is only implemented for l2 penalty with liblinear solver. Prefer dual=False when
      n_samples > n_features.
  
  penalty : {'l1', 'l2', 'elasticnet'}, default='l2'
      Specify the norm of the penalty:
  
      - `'l2'`: add a L2 penalty term (used by default);
      - `'l1'`: add a L1 penalty term;
      - `'elasticnet'`: both L1 and L2 penalty terms are added.
  
      .. warning::
         Some penalties may not work with some solvers. See the parameter
         `solver` below, to know the compatibility between the penalty and
         solver.
  
  scoring : str or callable, default=None
      A string (see model evaluation documentation) or
      a scorer callable object / function with signature
      ``scorer(estimator, X, y)``. For a list of scoring functions
      that can be used, look at :mod:`sklearn.metrics`. The
      default scoring option used is 'accuracy'.
  
  solver : {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'
  
      Algorithm to use in the optimization problem. Default is 'lbfgs'.
      To choose a solver, you might want to consider the following aspects:
  
          - For small datasets, 'liblinear' is a good choice, whereas 'sag'
            and 'saga' are faster for large ones;
          - For multiclass problems, only 'newton-cg', 'sag', 'saga' and
            'lbfgs' handle multinomial loss;
          - 'liblinear' might be slower in :class:`LogisticRegressionCV`
            because it does not handle warm-starting. 'liblinear' is
            limited to one-versus-rest schemes.
          - 'newton-cholesky' is a good choice for `n_samples` >> `n_features`,
            especially with one-hot encoded categorical features with rare
            categories. Note that it is limited to binary classification and the
            one-versus-rest reduction for multiclass classification. Be aware that
            the memory usage of this solver has a quadratic dependency on
            `n_features` because it explicitly computes the Hessian matrix.
  
      .. warning::
         The choice of the algorithm depends on the penalty chosen.
         Supported penalties by solver:
  
         - 'lbfgs'           -   ['l2']
         - 'liblinear'       -   ['l1', 'l2']
         - 'newton-cg'       -   ['l2']
         - 'newton-cholesky' -   ['l2']
         - 'sag'             -   ['l2']
         - 'saga'            -   ['elasticnet', 'l1', 'l2']
  
      .. note::
         'sag' and 'saga' fast convergence is only guaranteed on features
         with approximately the same scale. You can preprocess the data with
         a scaler from :mod:`sklearn.preprocessing`.
  
      .. versionadded:: 0.17
         Stochastic Average Gradient descent solver.
      .. versionadded:: 0.19
         SAGA solver.
      .. versionadded:: 1.2
         newton-cholesky solver.
  
  tol : float, default=1e-4
      Tolerance for stopping criteria.
  
  max_iter : int, default=100
      Maximum number of iterations of the optimization algorithm.
  
  class_weight : dict or 'balanced', default=None
      Weights associated with classes in the form ``{class_label: weight}``.
      If not given, all classes are supposed to have weight one.
  
      The "balanced" mode uses the values of y to automatically adjust
      weights inversely proportional to class frequencies in the input data
      as ``n_samples / (n_classes * np.bincount(y))``.
  
      Note that these weights will be multiplied with sample_weight (passed
      through the fit method) if sample_weight is specified.
  
      .. versionadded:: 0.17
         class_weight == 'balanced'
  
  n_jobs : int, default=None
      Number of CPU cores used during the cross-validation loop.
      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
      ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
      for more details.
  
  verbose : int, default=0
      For the 'liblinear', 'sag' and 'lbfgs' solvers set verbose to any
      positive number for verbosity.
  
  refit : bool, default=True
      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.
  
  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 : {'auto, 'ovr', 'multinomial'}, default='auto'
      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:: 0.22
          Default changed from 'ovr' to 'auto' in 0.22.
  
  random_state : int, RandomState instance, default=None
      Used when `solver='sag'`, 'saga' or 'liblinear' to shuffle the data.
      Note that this only applies to the solver and not the cross-validation
      generator. See :term:`Glossary <random_state>` for details.
  
  l1_ratios : list of float, default=None
      The list of Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``.
      Only used if ``penalty='elasticnet'``. A value of 0 is equivalent to
      using ``penalty='l2'``, while 1 is equivalent to using
      ``penalty='l1'``. For ``0 < l1_ratio <1``, the penalty is a combination
      of L1 and L2.
  
  Attributes
  ----------
  classes_ : ndarray of shape (n_classes, )
      A list of class labels known to the classifier.
  
  coef_ : ndarray of shape (1, n_features) or (n_classes, n_features)
      Coefficient of the features in the decision function.
  
      `coef_` is of shape (1, n_features) when the given problem
      is binary.
  
  intercept_ : ndarray of shape (1,) or (n_classes,)
      Intercept (a.k.a. bias) added to the decision function.
  
      If `fit_intercept` is set to False, the intercept is set to zero.
      `intercept_` is of shape(1,) when the problem is binary.
  
  Cs_ : ndarray of shape (n_cs)
      Array of C i.e. inverse of regularization parameter values used
      for cross-validation.
  
  l1_ratios_ : ndarray of shape (n_l1_ratios)
      Array of l1_ratios used for cross-validation. If no l1_ratio is used
      (i.e. penalty is not 'elasticnet'), this is set to ``[None]``
  
  coefs_paths_ : ndarray of shape (n_folds, n_cs, n_features) or                    (n_folds, n_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, n_cs, n_features)`` or
      ``(n_folds, n_cs, n_features + 1)`` depending on whether the
      intercept is fit or not. If ``penalty='elasticnet'``, the shape is
      ``(n_folds, n_cs, n_l1_ratios_, n_features)`` or
      ``(n_folds, n_cs, n_l1_ratios_, n_features + 1)``.
  
  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, n_cs)`` or ``(n_folds, n_cs, n_l1_ratios)`` if
      ``penalty='elasticnet'``.
  
  C_ : ndarray of 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.
      `C_` is of shape(n_classes,) when the problem is binary.
  
  l1_ratio_ : ndarray of shape (n_classes,) or (n_classes - 1,)
      Array of l1_ratio that maps to the best scores across every class. If
      refit is set to False, then for each class, the best l1_ratio is the
      average of the l1_ratio's that correspond to the best scores for each
      fold.  `l1_ratio_` is of shape(n_classes,) when the problem is binary.
  
  n_iter_ : ndarray of shape (n_classes, n_folds, n_cs) or (1, n_folds, n_cs)
      Actual number of iterations for all classes, folds and Cs.
      In the binary or multinomial cases, the first dimension is equal to 1.
      If ``penalty='elasticnet'``, the shape is ``(n_classes, n_folds,
      n_cs, n_l1_ratios)`` or ``(1, n_folds, n_cs, n_l1_ratios)``.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  See Also
  --------
  LogisticRegression : Logistic regression without tuning the
      hyperparameter `C`.
  
  Examples
  --------
  >>> from sklearn.datasets import load_iris
  >>> from sklearn.linear_model import LogisticRegressionCV
  >>> X, y = load_iris(return_X_y=True)
  >>> clf = LogisticRegressionCV(cv=5, random_state=0).fit(X, y)
  >>> clf.predict(X[:2, :])
  array([0, 0])
  >>> clf.predict_proba(X[:2, :]).shape
  (2, 3)
  >>> clf.score(X, y)
  0.98...
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code: 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=None,
      verbose=0,
      refit=True,
      intercept_scaling=1.0,
      multi_class="auto",
      random_state=None,
      l1_ratios=None,
  ):
      self.Cs = Cs
      self.fit_intercept = fit_intercept
      self.cv = cv
      self.dual = dual
      self.penalty = penalty
      self.scoring = scoring
      self.tol = tol
      self.max_iter = max_iter
      self.class_weight = class_weight
      self.n_jobs = n_jobs
      self.verbose = verbose
      self.solver = solver
      self.refit = refit
      self.intercept_scaling = intercept_scaling
      self.multi_class = multi_class
      self.random_state = random_state
      self.l1_ratios = l1_ratios
  
imports: import numbers
  import warnings
  from numbers import Integral, Real
  import numpy
  from joblib import effective_n_jobs
  from scipy import optimize
  from sklearn.metrics import get_scorer_names
  from .._loss.loss import HalfBinomialLoss, HalfMultinomialLoss
  from ..base import _fit_context
  from ..metrics import get_scorer
  from ..model_selection import check_cv
  from ..preprocessing import LabelBinarizer, LabelEncoder
  from ..svm._base import _fit_liblinear
  from ..utils import Bunch, check_array, check_consistent_length, check_random_state, compute_class_weight
  from ..utils._param_validation import Interval, StrOptions
  from ..utils.extmath import row_norms, softmax
  from ..utils.metadata_routing import MetadataRouter, MethodMapping, _raise_for_params, _routing_enabled, process_routing
  from ..utils.multiclass import check_classification_targets
  from ..utils.optimize import _check_optimize_result, _newton_cg
  from ..utils.parallel import Parallel, delayed
  from ..utils.validation import _check_method_params, _check_sample_weight, check_is_fitted
  from ._base import BaseEstimator, LinearClassifierMixin, SparseCoefMixin
  from ._glm.glm import NewtonCholeskySolver
  from ._linear_loss import LinearModelLoss
  from ._sag import sag_solver
label_desc: Use image node_id 1 to create a new LogisticRegressionCV object from inherited base classes: LogisticRegression, LinearClassifierMixin, BaseEstimator with example: obj = LogisticRegressionCV()