Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.connectome.GroupSparseCovarianceCV#

class nilearn.connectome.GroupSparseCovarianceCV(alphas=4, n_refinements=4, cv=None, tol_cv=0.01, max_iter_cv=50, tol=0.001, max_iter=100, verbose=0, n_jobs=1, debug=False, early_stopping=True)[source]#

Sparse inverse covariance w/ cross-validated choice of the parameter.

A cross-validated value for the regularization parameter is first determined using several calls to group_sparse_covariance. Then a final optimization is run to get a value for the precision matrices, using the selected value of the parameter. Different values of tolerance and of maximum iteration number can be used in these two phases (see the tol and tol_cv keyword below for example).

Parameters:
alphasinteger, default=4

initial number of points in the grid of regularization parameter values. Each step of grid refinement adds that many points as well.

n_refinementsinteger, default=4

number of times the initial grid should be refined.

cvinteger, default=3

number of folds in a K-fold cross-validation scheme.

tol_cvfloat, default=1e-2

tolerance used to get the optimal alpha value. It has the same meaning as the tol parameter in group_sparse_covariance.

max_iter_cvinteger, default=50

maximum number of iterations for each optimization, during the alpha- selection phase.

tolfloat, default=1e-3

tolerance used during the final optimization for determining precision matrices value.

max_iterinteger, default=100

maximum number of iterations in the final optimization.

verboseinteger, default=0

verbosity level. 0 means nothing is printed to the user.

n_jobsinteger, default=1

maximum number of cpu cores to use. The number of cores actually used at the same time cannot exceed the number of folds in folding strategy (that is, the value of cv).

debugbool, default=False

if True, activates some internal checks for consistency. Only useful for nilearn developers, not users.

early_stoppingbool, default=True

if True, reduce computation time by using a heuristic to reduce the number of iterations required to get the optimal value for alpha. Be aware that this can lead to slightly different values for the optimal alpha compared to early_stopping=False.

See also

GroupSparseCovariance
sklearn.covariance.GraphicalLassoCV

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.

Attributes:
covariances_numpy.ndarray, shape (n_features, n_features, n_subjects)

covariance matrices, one per subject.

precisions_numpy.ndarray, shape (n_features, n_features, n_subjects)

precision matrices, one per subject. All matrices have the same sparsity pattern (if a coefficient is zero for a given matrix, it is also zero for every other.)

alpha_float

penalization parameter value selected.

cv_alphas_list of floats

all values of the penalization parameter explored.

cv_scores_numpy.ndarray, shape (n_alphas, n_folds)

scores obtained on test set for each value of the penalization parameter explored.

__init__(alphas=4, n_refinements=4, cv=None, tol_cv=0.01, max_iter_cv=50, tol=0.001, max_iter=100, verbose=0, n_jobs=1, debug=False, early_stopping=True)[source]#
fit(subjects, y=None)[source]#

Compute cross-validated group-sparse precisions.

Parameters:
subjectslist of numpy.ndarray with shapes (n_samples, n_features)

input subjects. Each subject is a 2D array, whose columns contain signals. Sample number can vary from subject to subject, but all subjects must have the same number of features (i.e. of columns.)

Returns:
selfGroupSparseCovarianceCV

the object instance itself.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)#

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:
paramsdict

Parameter names mapped to their values.

set_fit_request(*, subjects='$UNCHANGED$')#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
subjectsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for subjects parameter in fit.

Returns:
selfobject

The updated object.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). 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:
selfestimator instance

Estimator instance.

Examples using nilearn.connectome.GroupSparseCovarianceCV#

Connectivity structure estimation on simulated data

Connectivity structure estimation on simulated data

Group Sparse inverse covariance for multi-subject connectome

Group Sparse inverse covariance for multi-subject connectome