GraphLasso

class ibex.sklearn.covariance.GraphLasso(alpha=0.01, mode='cd', tol=0.0001, enet_tol=0.0001, max_iter=100, verbose=False, assume_centered=False)

Bases: sklearn.covariance.graph_lasso_.GraphLasso, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Sparse inverse covariance estimation with an l1-penalized estimator.

Read more in the User Guide.

alpha : positive float, default 0.01
The regularization parameter: the higher alpha, the more regularization, the sparser the inverse 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.
tol : positive float, default 1e-4
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, default 100
The maximum number of iterations.
verbose : boolean, default False
If verbose is True, the objective function and dual gap are plotted at each iteration.
assume_centered : boolean, default False
If True, data are not centered before computation. Useful when working with data whose mean is almost, but not exactly zero. If False, data are centered before computation.
covariance_ : array-like, shape (n_features, n_features)
Estimated covariance matrix
precision_ : array-like, shape (n_features, n_features)
Estimated pseudo inverse matrix.
n_iter_ : int
Number of iterations run.

graph_lasso, GraphLassoCV

fit(X, y=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fits the GraphLasso model to X.

X : ndarray, shape (n_samples, n_features)
Data from which to compute the covariance estimate

y : (ignored)

score(X_test, y=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Computes the log-likelihood of a Gaussian data set with

self.covariance_ as an estimator of its covariance matrix.

X_test : array-like, shape = [n_samples, n_features]
Test data of which we compute the likelihood, where n_samples is the number of samples and n_features is the number of features. X_test is assumed to be drawn from the same distribution than the data used in fit (including centering).

y : not used, present for API consistence purpose.

res : float
The likelihood of the data set with self.covariance_ as an estimator of its covariance matrix.