# GraphLassoCV¶

class ibex.sklearn.covariance.GraphLassoCV(alphas=4, n_refinements=4, cv=None, tol=0.0001, enet_tol=0.0001, max_iter=100, mode='cd', n_jobs=1, verbose=False, assume_centered=False)

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

Note

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

Sparse inverse covariance w/ cross-validated choice of the l1 penalty

Read more in the User Guide.

alphas : 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.
n_refinements : strictly positive integer
The number of times the grid is refined. Not used if explicit values of alphas are passed.
cv : int, cross-validation generator or an iterable, optional

Determines the cross-validation splitting strategy. Possible inputs for cv are:

• None, to use the default 3-fold cross-validation,
• integer, to specify the number of folds.
• An object to be used as a cross-validation generator.
• An iterable yielding train/test splits.

For integer/None inputs KFold is used.

Refer User Guide for the various cross-validation strategies that can be used here.

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
Maximum number of iterations.
mode : {‘cd’, ‘lars’}
The Lasso solver to use: coordinate descent or LARS. Use LARS for very sparse underlying graphs, where number of features is greater than number of samples. Elsewhere prefer cd which is more numerically stable.
n_jobs : int, optional
number of jobs to run in parallel (default 1).
verbose : boolean, optional
If verbose is True, the objective function and duality gap are printed at each iteration.
assume_centered : Boolean
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_ : numpy.ndarray, shape (n_features, n_features)
Estimated covariance matrix.
precision_ : numpy.ndarray, shape (n_features, n_features)
Estimated precision matrix (inverse covariance).
alpha_ : float
Penalization parameter selected.
cv_alphas_ : 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.

graph_lasso, GraphLasso

The search for the optimal penalization parameter (alpha) is done on an iteratively refined grid: first the cross-validated scores on a grid are computed, then a new refined grid is centered around the maximum, and so on.

One of the challenges which is faced here is that the solvers can fail to converge to a well-conditioned estimate. The corresponding values of alpha then come out as missing values, but the optimum may be close to these missing values.

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 covariance 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.