sklearn.covariance.GraphLassoCV

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

Sparse inverse covariance w/ crossvalidated 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, crossvalidation generator or an iterable, optional 
Determines the crossvalidation splitting strategy. Possible inputs for cv are:  None, to use the default 3fold crossvalidation,
 integer, to specify the number of folds.
 An object to be used as a crossvalidation generator.
 An iterable yielding train/test splits.
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 will change from 3fold to 5fold 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) 
Loglikelihood score on leftout data across folds. 
n_iter_ : int 
Number of iterations run for the optimal alpha. 
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.
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 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. 

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

comp_cov : arraylike, 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)
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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_ : arraylike 
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 : arraylike, 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 loglikelihood of a Gaussian data set with self.covariance_
as an estimator of its covariance matrix.
Parameters: 

X_test : arraylike, 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.