`sklearn.covariance`.graphical_lasso¶

sklearn.covariance.graphical_lasso(emp_cov, alpha, *, cov_init=None, mode='cd', tol=0.0001, enet_tol=0.0001, max_iter=100, verbose=False, return_costs=False, eps=2.220446049250313e-16, return_n_iter=False)[source]¶

l1-penalized covariance estimator

Read more in the User Guide.

Changed in version v0.20: graph_lasso has been renamed to graphical_lasso

Parameters

emp_covndarray of shape (n_features, n_features): Empirical covariance from which to compute the covariance estimate.
alphafloat: The regularization parameter: the higher alpha, the more regularization, the sparser the inverse covariance. Range is (0, inf].
cov_initarray of shape (n_features, n_features), default=None: The initial guess for the covariance.
mode{‘cd’, ‘lars’}, default=’cd’: 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.
tolfloat, default=1e-4: The tolerance to declare convergence: if the dual gap goes below this value, iterations are stopped. Range is (0, inf].
enet_tolfloat, default=1e-4: 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’. Range is (0, inf].
max_iterint, default=100: The maximum number of iterations.
verbosebool, default=False: If verbose is True, the objective function and dual gap are printed at each iteration.
return_costsbool, default=Flase: If return_costs is True, the objective function and dual gap at each iteration are returned.
epsfloat, default=eps: The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Default is np.finfo(np.float64).eps.
return_n_iterbool, default=False: Whether or not to return the number of iterations.

Returns

covariancendarray of shape (n_features, n_features): The estimated covariance matrix.
precisionndarray of shape (n_features, n_features): The estimated (sparse) precision matrix.
costslist 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_iterint: Number of iterations. Returned only if return_n_iter is set to True.

sklearn.covariance.graphical_lasso¶

`sklearn.covariance`.graphical_lasso¶