sklearn.covariance.GraphLassoCV

class sklearn.covariance.GraphLassoCV(*args, **kwargs) [source]

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

This class implements the Graphical Lasso algorithm.

Read more in the User Guide.

Parameters:

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. Changed in version 0.20: cv default value if None will change from 3-fold to 5-fold in v0.22. 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 or None, optional (default=None) number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details. 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.

Attributes:

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.

See also

graph_lasso, GraphLasso

Notes

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.

Methods

error_norm(comp_cov[, norm, scaling, squared])	Computes the Mean Squared Error between two covariance estimators.
fit(X[, y])	Fits the GraphicalLasso covariance model to X.
get_params([deep])	Get parameters for this estimator.
get_precision()	Getter for the precision matrix.
mahalanobis(X)	Computes the squared Mahalanobis distances of given observations.
score(X_test[, y])	Computes the log-likelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix.
set_params(**params)	Set the parameters of this estimator.

__init__(*args, **kwargs) [source]

DEPRECATED: The ‘GraphLassoCV’ was renamed to ‘GraphicalLassoCV’ in version 0.20 and will be removed in 0.22.

error_norm(comp_cov, norm=’frobenius’, scaling=True, squared=True) [source]

Computes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm).

Parameters:

comp_cov : array-like, shape = [n_features, n_features] The covariance to compare with. norm : str The type of norm used to compute the error. Available error types: - ‘frobenius’ (default): sqrt(tr(A^t.A)) - ‘spectral’: sqrt(max(eigenvalues(A^t.A)) where A is the error (comp_cov - self.covariance_). scaling : bool If True (default), the squared error norm is divided by n_features. If False, the squared error norm is not rescaled. squared : bool Whether to compute the squared error norm or the error norm. If True (default), the squared error norm is returned. If False, the error norm is returned.

Returns:

The Mean Squared Error (in the sense of the Frobenius norm) between `self` and `comp_cov` covariance estimators.

fit(X, y=None) [source]

Fits the GraphicalLasso covariance model to X.

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

get_params(deep=True) [source]

Get parameters for this estimator.

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

get_precision() [source]

Getter for the precision matrix.

Returns:	precision_ : array-like The precision matrix associated to the current covariance object.

grid_scores

DEPRECATED: Attribute grid_scores was deprecated in version 0.19 and will be removed in 0.21. Use grid_scores_ instead

mahalanobis(X) [source]

Computes the squared Mahalanobis distances of given observations.

Parameters:	X : array-like, shape = [n_samples, n_features] The observations, the Mahalanobis distances of the which we compute. Observations are assumed to be drawn from the same distribution than the data used in fit.
Returns:	dist : array, shape = [n_samples,] Squared Mahalanobis distances of the observations.

score(X_test, y=None) [source]

Computes the log-likelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix.

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

set_params(**params) [source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:	self