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.

8.1.2. nilearn.connectome.GroupSparseCovariance

class nilearn.connectome.GroupSparseCovariance(alpha=0.1, tol=0.001, max_iter=10, verbose=0, memory=Memory(location=None), memory_level=0)

Covariance and precision matrix estimator.

Parameters:

alpha : float

regularization parameter. With normalized covariances matrices and number of samples, sensible values lie in the [0, 1] range(zero is no regularization: output is not sparse)

tol : positive float, optional

The tolerance to declare convergence: if the dual gap goes below this value, iterations are stopped

max_iter : int, optional

maximum number of iterations. The default value (10) is rather conservative.

verbose : int, optional

verbosity level. Zero means “no message”.

memory : instance of joblib.Memory or string, optional

Used to cache the masking process. By default, no caching is done. If a string is given, it is the path to the caching directory.

memory_level : int, optional

Caching aggressiveness. Higher values mean more caching.

Notes

The model used has been introduced in:

Gael Varoquaux, et al. Brain Covariance Selection: Better Individual Functional Connectivity Models Using Population Prior’.

The algorithm used is based on what is described in:

Jean Honorio and Dimitris Samaras. “Simultaneous and Group-Sparse Multi-Task Learning of Gaussian Graphical Models”. http://arxiv.org/abs/1207.4255.

Attributes

`covariances_`	(numpy.ndarray, shape (n_features, n_features, n_subjects)) empirical covariance matrices.
`precisions_`	(numpy.ndarraye, shape (n_features, n_features, n_subjects)) precisions matrices estimated using the group-sparse algorithm.

__init__(alpha=0.1, tol=0.001, max_iter=10, verbose=0, memory=Memory(location=None), memory_level=0)

Initialize self. See help(type(self)) for accurate signature.

fit(subjects, y=None)

Fits the group sparse precision model according to the given training data and parameters.

Parameters:

subjects : list 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:

self : GroupSparseCovariance instance

the object itself. Useful for chaining operations.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep : bool, default=True

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.

set_params(**params)

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:

**params : dict

Estimator parameters.

Returns:

self : object

Estimator instance.