sklearn.covariance.graph_lasso()

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.

enet_tol : positive float, optional

The tolerance for the elastic net solver used to calculate the descent direction. This parameter controls the accuracy of the search direction for a given column update, not of the overall parameter estimate. Only used for mode=?cd?.

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 machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems.

return_n_iter : bool, optional

Whether or not to return the number of iterations.