sklearn.covariance
.GraphicalLassoCV¶

class
sklearn.covariance.
GraphicalLassoCV
(*, alphas=4, n_refinements=4, cv=None, tol=0.0001, enet_tol=0.0001, max_iter=100, mode='cd', n_jobs=None, verbose=False, assume_centered=False)[source]¶ Sparse inverse covariance w/ crossvalidated choice of the l1 penalty.
See glossary entry for crossvalidation estimator.
Read more in the User Guide.
Changed in version v0.20: GraphLassoCV has been renamed to GraphicalLassoCV
 Parameters
 alphasint or arraylike of shape (n_alphas,), dtype=float, default=4
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. Range is (0, inf] when floats given.
 n_refinementsint, default=4
The number of times the grid is refined. Not used if explicit values of alphas are passed. Range is [1, inf).
 cvint, crossvalidation generator or iterable, default=None
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
None, to use the default 5fold crossvalidation,
integer, to specify the number of folds.
An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs
KFold
is used.Refer User Guide for the various crossvalidation strategies that can be used here.
Changed in version 0.20:
cv
default value if None changed from 3fold to 5fold. tolfloat, default=1e4
The tolerance to declare convergence: if the dual gap goes below this value, iterations are stopped. Range is (0, inf].
 enet_tolfloat, default=1e4
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
Maximum number of iterations.
 mode{‘cd’, ‘lars’}, default=’cd’
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_jobsint, default=None
number of jobs to run in parallel.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details.Changed in version v0.20:
n_jobs
default changed from 1 to None verbosebool, default=False
If verbose is True, the objective function and duality gap are printed at each iteration.
 assume_centeredbool, 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.
 Attributes
 location_ndarray of shape (n_features,)
Estimated location, i.e. the estimated mean.
 covariance_ndarray of shape (n_features, n_features)
Estimated covariance matrix.
 precision_ndarray of shape (n_features, n_features)
Estimated precision matrix (inverse covariance).
 alpha_float
Penalization parameter selected.
 cv_alphas_list of shape (n_alphas,), dtype=float
All penalization parameters explored.
Deprecated since version 0.24: The
cv_alphas_
attribute is deprecated in version 0.24 in favor ofcv_results_['alphas']
and will be removed in version 0.26. grid_scores_ndarray of shape (n_alphas, n_folds)
Loglikelihood score on leftout data across folds.
Deprecated since version 0.24: The
grid_scores_
attribute is deprecated in version 0.24 in favor ofcv_results_
and will be removed in version 0.26. cv_results_dict of ndarrays
A dict with keys:
 alphasndarray of shape (n_alphas,)
All penalization parameters explored.
 split(k)_scorendarray of shape (n_alphas,)
Loglikelihood score on leftout data across (k)th fold.
 mean_scorendarray of shape (n_alphas,)
Mean of scores over the folds.
 std_scorendarray of shape (n_alphas,)
Standard deviation of scores over the folds.
New in version 0.24.
 n_iter_int
Number of iterations run for the optimal alpha.
See also
Notes
The search for the optimal penalization parameter (alpha) is done on an iteratively refined grid: first the crossvalidated 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 wellconditioned estimate. The corresponding values of alpha then come out as missing values, but the optimum may be close to these missing values.
Examples
>>> import numpy as np >>> from sklearn.covariance import GraphicalLassoCV >>> true_cov = np.array([[0.8, 0.0, 0.2, 0.0], ... [0.0, 0.4, 0.0, 0.0], ... [0.2, 0.0, 0.3, 0.1], ... [0.0, 0.0, 0.1, 0.7]]) >>> np.random.seed(0) >>> X = np.random.multivariate_normal(mean=[0, 0, 0, 0], ... cov=true_cov, ... size=200) >>> cov = GraphicalLassoCV().fit(X) >>> np.around(cov.covariance_, decimals=3) array([[0.816, 0.051, 0.22 , 0.017], [0.051, 0.364, 0.018, 0.036], [0.22 , 0.018, 0.322, 0.094], [0.017, 0.036, 0.094, 0.69 ]]) >>> np.around(cov.location_, decimals=3) array([0.073, 0.04 , 0.038, 0.143])
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.
Getter for the precision matrix.
mahalanobis
(X)Computes the squared Mahalanobis distances of given observations.
score
(X_test[, y])Computes the loglikelihood of a Gaussian data set with
self.covariance_
as an estimator of its covariance matrix.set_params
(**params)Set the parameters of this estimator.

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_covarraylike of shape (n_features, n_features)
The covariance to compare with.
 norm{“frobenius”, “spectral”}, default=”frobenius”
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_)
. scalingbool, default=True
If True (default), the squared error norm is divided by n_features. If False, the squared error norm is not rescaled.
 squaredbool, default=True
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
 resultfloat
The Mean Squared Error (in the sense of the Frobenius norm) between
self
andcomp_cov
covariance estimators.

fit
(X, y=None)[source]¶ Fits the GraphicalLasso covariance model to X.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Data from which to compute the covariance estimate
 yIgnored
Not used, present for API consistence purpose.
 Returns
 selfobject

get_params
(deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsmapping of string to any
Parameter names mapped to their values.

get_precision
()[source]¶ Getter for the precision matrix.
 Returns
 precision_arraylike of shape (n_features, n_features)
The precision matrix associated to the current covariance object.

mahalanobis
(X)[source]¶ Computes the squared Mahalanobis distances of given observations.
 Parameters
 Xarraylike of 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
 distndarray of shape (n_samples,)
Squared Mahalanobis distances of the observations.

score
(X_test, y=None)[source]¶ Computes the loglikelihood of a Gaussian data set with
self.covariance_
as an estimator of its covariance matrix. Parameters
 X_testarraylike of 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).
 yIgnored
Not used, present for API consistence purpose.
 Returns
 resfloat
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. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.