sklearn.covariance
.graph_lasso¶

sklearn.covariance.
graph_lasso
(emp_cov, alpha, cov_init=None, mode='cd', tol=0.0001, max_iter=100, verbose=False, return_costs=False, eps=2.2204460492503131e16, return_n_iter=False)[source]¶ l1penalized covariance estimator
Parameters: emp_cov : 2D ndarray, shape (n_features, n_features)
Empirical covariance from which to compute the covariance estimate.
alpha : positive float
The regularization parameter: the higher alpha, the more regularization, the sparser the inverse covariance.
cov_init : 2D array (n_features, n_features), optional
The initial guess for the covariance.
mode : {‘cd’, ‘lars’}
The Lasso solver to use: coordinate descent or LARS. Use LARS for very sparse underlying graphs, where p > n. Elsewhere prefer cd which is more numerically stable.
tol : positive float, optional
The tolerance to declare convergence: if the dual gap goes below this value, iterations are stopped.
max_iter : integer, optional
The maximum number of iterations.
verbose : boolean, optional
If verbose is True, the objective function and dual gap are printed at each iteration.
return_costs : boolean, optional
If return_costs is True, the objective function and dual gap at each iteration are returned.
eps : float, optional
The machineprecision regularization in the computation of the Cholesky diagonal factors. Increase this for very illconditioned systems.
return_n_iter : bool, optional
Whether or not to return the number of iterations.
Returns: covariance : 2D ndarray, shape (n_features, n_features)
The estimated covariance matrix.
precision : 2D ndarray, shape (n_features, n_features)
The estimated (sparse) precision matrix.
costs : list of (objective, dual_gap) pairs
The list of values of the objective function and the dual gap at each iteration. Returned only if return_costs is True.
n_iter : int
Number of iterations. Returned only if return_n_iter is set to True.
See also
Notes
The algorithm employed to solve this problem is the GLasso algorithm, from the Friedman 2008 Biostatistics paper. It is the same algorithm as in the R glasso package.
One possible difference with the glasso R package is that the diagonal coefficients are not penalized.