class_docstr: Lasso linear model with iterative fitting along a regularization path
  
  The best model is selected by cross-validation.
  
  The optimization objective for Lasso is::
  
      (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
  
  Read more in the :ref:`User Guide <lasso>`.
  
  Parameters
  ----------
  eps : float, optional
      Length of the path. ``eps=1e-3`` means that
      ``alpha_min / alpha_max = 1e-3``.
  
  n_alphas : int, optional
      Number of alphas along the regularization path
  
  alphas : numpy array, optional
      List of alphas where to compute the models.
      If ``None`` alphas are set automatically
  
  precompute : True | False | 'auto' | array-like
      Whether to use a precomputed Gram matrix to speed up
      calculations. If set to ``'auto'`` let us decide. The Gram
      matrix can also be passed as argument.
  
  max_iter : int, optional
      The maximum number of iterations
  
  tol : float, optional
      The tolerance for the optimization: if the updates are
      smaller than ``tol``, the optimization code checks the
      dual gap for optimality and continues until it is smaller
      than ``tol``.
  
  cv : int, cross-validation generator or an iterable, optional
      Determines the cross-validation splitting strategy.
      Possible inputs for cv are:
  
      - None, to use the default 3-fold cross-validation,
      - integer, to specify the number of folds.
      - An object to be used as a cross-validation generator.
      - An iterable yielding train/test splits.
  
      For integer/None inputs, :class:`KFold` is used.
  
      Refer :ref:`User Guide <cross_validation>` for the various
      cross-validation strategies that can be used here.
  
  verbose : bool or integer
      Amount of verbosity.
  
  n_jobs : integer, optional
      Number of CPUs to use during the cross validation. If ``-1``, use
      all the CPUs.
  
  positive : bool, optional
      If positive, restrict regression coefficients to be positive
  
  selection : str, default 'cyclic'
      If set to 'random', a random coefficient is updated every iteration
      rather than looping over features sequentially by default. This
      (setting to 'random') often leads to significantly faster convergence
      especially when tol is higher than 1e-4.
  
  random_state : int, RandomState instance, or None (default)
      The seed of the pseudo random number generator that selects
      a random feature to update. Useful only when selection is set to
      'random'.
  
  fit_intercept : boolean, default True
      whether to calculate the intercept for this model. If set
      to false, no intercept will be used in calculations
      (e.g. data is expected to be already centered).
  
  normalize : boolean, optional, default False
      If ``True``, the regressors X will be normalized before regression.
      This parameter is ignored when ``fit_intercept`` is set to ``False``.
      When the regressors are normalized, note that this makes the
      hyperparameters learnt more robust and almost independent of the number
      of samples. The same property is not valid for standardized data.
      However, if you wish to standardize, please use
      :class:`preprocessing.StandardScaler` before calling ``fit`` on an estimator
      with ``normalize=False``.
  
  copy_X : boolean, optional, default True
      If ``True``, X will be copied; else, it may be overwritten.
  
  Attributes
  ----------
  alpha_ : float
      The amount of penalization chosen by cross validation
  
  coef_ : array, shape (n_features,) | (n_targets, n_features)
      parameter vector (w in the cost function formula)
  
  intercept_ : float | array, shape (n_targets,)
      independent term in decision function.
  
  mse_path_ : array, shape (n_alphas, n_folds)
      mean square error for the test set on each fold, varying alpha
  
  alphas_ : numpy array, shape (n_alphas,)
      The grid of alphas used for fitting
  
  dual_gap_ : ndarray, shape ()
      The dual gap at the end of the optimization for the optimal alpha
      (``alpha_``).
  
  n_iter_ : int
      number of iterations run by the coordinate descent solver to reach
      the specified tolerance for the optimal alpha.
  
  Notes
  -----
  See examples/linear_model/plot_lasso_model_selection.py
  for an example.
  
  To avoid unnecessary memory duplication the X argument of the fit method
  should be directly passed as a Fortran-contiguous numpy array.
  
  See also
  --------
  lars_path
  lasso_path
  LassoLars
  Lasso
  LassoLarsCV
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code: def __init__(
      self,
      eps=1e-3,
      n_alphas=100,
      alphas=None,
      fit_intercept=True,
      normalize=False,
      precompute="auto",
      max_iter=1000,
      tol=1e-4,
      copy_X=True,
      cv=None,
      verbose=False,
      n_jobs=1,
      positive=False,
      random_state=None,
      selection="cyclic",
  ):
      super(LassoCV, self).__init__(
          eps=eps,
          n_alphas=n_alphas,
          alphas=alphas,
          fit_intercept=fit_intercept,
          normalize=normalize,
          precompute=precompute,
          max_iter=max_iter,
          tol=tol,
          copy_X=copy_X,
          cv=cv,
          verbose=verbose,
          n_jobs=n_jobs,
          positive=positive,
          random_state=random_state,
          selection=selection,
      )
  
imports: import sys
  import warnings
  from abc import ABCMeta, abstractmethod
  import numpy
  from scipy import sparse
  from .base import LinearModel, _pre_fit
  from ..base import RegressorMixin
  from .base import _preprocess_data
  from ..utils import check_array, check_X_y, deprecated
  from ..utils.validation import check_random_state
  from ..model_selection import check_cv
  from ..externals.joblib import Parallel, delayed
  from ..externals import six
  from ..externals.six.moves import xrange
  from ..utils.extmath import safe_sparse_dot
  from ..utils.validation import check_is_fitted
  from ..utils.validation import column_or_1d
  from ..exceptions import ConvergenceWarning
  from .None import cd_fast
label_desc: Use image node_id 1 to create a new LassoCV object from inherited base classes: LinearModelCV, RegressorMixin with example: obj = LassoCV(eps, n_alphas, alphas, fit_intercept, normalize, precompute, max_iter, tol, copy_X, cv, verbose, n_jobs, positive, random_state, selection)