class_docstr: Sparse inverse covariance w/ cross-validated choice of the l1 penalty
  via quadratic approximation.
  
  This takes advantage of "path" mode in QuicGraphicalLasso.
  See sklearn.covariance.graph_lasso.GraphLassoCV.
  
  Parameters
  -----------
  lam : scalar or 2D ndarray, shape (n_features, n_features) (default=0.5)
      Regularization parameters per element of the inverse covariance matrix.
      The next parameter 'lams' scale this matrix as the lasso path learned.
  
      If a scalar lambda is used, a penalty matrix will be generated
      containing lambda for all values in both upper and lower triangles
      and zeros along the diagonal.  This differs from the scalar graphical
      lasso by the diagonal. To replicate the scalar formulation you must
      manualy pass in lam * np.ones((n_features, n_features)).
  
  lams : integer, or list positive float, optional
      If an integer is given, it fixes the number of points on the
      grids of alpha to be used. If a list is given, it gives the
      grid to be used. See the notes in the class docstring for
      more details.
  
  cv : int, cross-validation generator or an iterable, optional
      Determines the cross-validation splitting strategy.
      Possible inputs for cv are:
  
      - None, use the default 3-fold, 3-trial RepeatedKFold cross-validation,
      - integer, to specify the number of folds (3-trial RepeatedKFold).
      - tuple, (n_folds, n_trials)
      - An object to be used as a cross-validation generator.
      - An iterable yielding train/test splits.
  
  n_refinements : strictly positive integer
      The number of times the grid is refined. Not used if explicit
      values of alphas are passed.
  
  n_jobs : int, optional
      number of jobs to run in parallel (default 1).
  
  sc : sparkContext (default=None)
      If not None and a valid SparkContext, the cross-validation iterations
      will be performed in parallel via Apache Spark.  In this case n_jobs
      will be unused.
  
  tol : float (default=1e-6)
      Convergence threshold.
  
  max_iter : int (default=1000)
      Maximum number of Newton iterations.
  
  Theta0 : 2D ndarray, shape (n_features, n_features) (default=None)
      Initial guess for the inverse covariance matrix. If not provided, the
      diagonal identity matrix is used.
  
  Sigma0 : 2D ndarray, shape (n_features, n_features) (default=None)
      Initial guess for the covariance matrix. If not provided the diagonal
      identity matrix is used.
  
  method : 'quic' or 'bigquic', ... (default=quic)
      Currently only 'quic' is supported.
  
  score_metric : one of 'log_likelihood' (default), 'frobenius', 'spectral',
                'kl', or 'quadratic'
      Used for computing self.score().
  
  init_method : one of 'corrcoef', 'cov', 'spearman', 'kendalltau',
      or a custom function.
      Computes initial covariance and scales lambda appropriately.
      Using the custom function extends graphical model estimation to
      distributions beyond the multivariate Gaussian.
      The `spearman` or `kendalltau` options extend inverse covariance
      estimation to nonparanormal and transelliptic graphical models.
      Custom function must return ((n_features, n_features) ndarray, float)
      where the scalar parameter will be used to scale the penalty lam.
  
  auto_scale : bool
      If True, will compute self.lam_scale_ = max off-diagonal value when
      init_method='cov'.
      If false, then self.lam_scale_ = 1.
      lam_scale_ is used to scale user-supplied self.lam during fit.
  
  backend : string, optional (default=threading)
      Joblib parallelization backend.
      Not used when using the sparkContext (sc).
  
  Attributes
  ----------
  covariance_ : 2D ndarray, shape (n_features, n_features)
      Estimated covariance matrix
  
  precision_ : 2D ndarray, shape (n_features, n_features)
      Estimated pseudo-inverse matrix.
  
  lam_ : float
      Penalization parameter selected.
  
  cv_lams_ : list of float
      All penalization parameters explored.
  
  grid_scores_: 2D numpy.ndarray (n_alphas, n_folds)
      Log-likelihood score on left-out data across folds.
  
  n_iter_ : int
      Number of iterations run for the optimal alpha.
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code: def __init__(
      self,
      lam=1.0,
      lams=4,
      n_refinements=4,
      cv=None,
      tol=1e-6,
      max_iter=1000,
      Theta0=None,
      Sigma0=None,
      method="quic",
      verbose=0,
      n_jobs=1,
      sc=None,
      score_metric="log_likelihood",
      init_method="corrcoef",
      auto_scale=True,
      backend="threading",
  ):
      # GridCV params
      self.n_jobs = n_jobs
      self.sc = sc
      self.cv = cv
      self.lam = lam
      self.lams = lams
      self.n_refinements = n_refinements
  
      # quic-specific params
      self.tol = tol
      self.max_iter = max_iter
      self.Theta0 = Theta0
      self.Sigma0 = Sigma0
      self.method = method
      self.verbose = verbose
      self.backend = backend
  
      super(QuicGraphicalLassoCV, self).__init__(
          score_metric=score_metric,
          init_method=init_method,
          auto_scale=auto_scale,
      )
  
imports: from __future__ import absolute_import
  import sys
  import time
  import operator
  import numpy
  from functools import partial
  from sklearn.covariance import EmpiricalCovariance
  from sklearn.utils import check_array, as_float_array, deprecated
  from numpy.testing import assert_array_almost_equal
  from joblib import Parallel, delayed
  from sklearn.model_selection import cross_val_score, RepeatedKFold
  from .None import pyquic
  from .inverse_covariance import InverseCovarianceEstimator, _init_coefs, _compute_error, _validate_path
label_desc: Use image node_id 1 to create a new QuicGraphicalLassoCV object from inherited base classes: InverseCovarianceEstimator with example: obj = QuicGraphicalLassoCV(lam, lams, n_refinements, cv, tol, max_iter, Theta0, Sigma0, method, verbose, n_jobs, sc, score_metric, init_method, auto_scale, backend)