Python sklearn.covariance.GraphLassoCV() Examples

The following are 3 code examples of sklearn.covariance.GraphLassoCV(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module sklearn.covariance , or try the search function .
Example #1
Source File: abide_utils.py    From gcn_metric_learning with MIT License 5 votes vote down vote up 
def subject_connectivity(timeseries, subject, atlas_name, kind, save=True, save_path=root_folder):
    """
        timeseries   : timeseries table for subject (timepoints x regions)
        subject      : the subject short ID
        atlas_name   : name of the atlas used
        kind         : the kind of connectivity to be used, e.g. lasso, partial correlation, correlation
        save         : save the connectivity matrix to a file
        save_path    : specify path to save the matrix if different from subject folder

    returns:
        connectivity : connectivity matrix (regions x regions)
    """

    print("Estimating %s matrix for subject %s" % (kind, subject))

    if kind == 'lasso':
        # Graph Lasso estimator
        covariance_estimator = GraphLassoCV(verbose=1)
        covariance_estimator.fit(timeseries)
        connectivity = covariance_estimator.covariance_
        print('Covariance matrix has shape {0}.'.format(connectivity.shape))

    elif kind in ['tangent', 'partial correlation', 'correlation']:
        conn_measure = connectome.ConnectivityMeasure(kind=kind)
        connectivity = conn_measure.fit_transform([timeseries])[0]

    if save:
        subject_file = os.path.join(save_path, subject,
                                    subject + '_' + atlas_name + '_' + kind.replace(' ', '_') + '.mat')
        sio.savemat(subject_file, {'connectivity': connectivity})

    return connectivity
Example #2
Source File: abide_utils.py    From gcn_metric_learning with MIT License 5 votes vote down vote up 
def group_connectivity(timeseries, subject_list, atlas_name, kind, save=True, save_path=root_folder):
    """
        timeseries   : list of timeseries tables for subjects (timepoints x regions)
        subject_list : the subject short IDs list
        atlas_name   : name of the atlas used
        kind         : the kind of connectivity to be used, e.g. lasso, partial correlation, correlation
        save         : save the connectivity matrix to a file
        save_path    : specify path to save the matrix if different from subject folder

    returns:
        connectivity : connectivity matrix (regions x regions)
    """

    if kind == 'lasso':
        # Graph Lasso estimator
        covariance_estimator = GraphLassoCV(verbose=1)
        connectivity_matrices = []

        for i, ts in enumerate(timeseries):
            covariance_estimator.fit(ts)
            connectivity = covariance_estimator.covariance_
            connectivity_matrices.append(connectivity)
            print('Covariance matrix has shape {0}.'.format(connectivity.shape))

    elif kind in ['tangent', 'partial correlation', 'correlation']:
        conn_measure = connectome.ConnectivityMeasure(kind=kind)
        connectivity_matrices = conn_measure.fit_transform(timeseries)

    if save:
        for i, subject in enumerate(subject_list):
            subject_file = os.path.join(save_path, subject_list[i],
                                        subject_list[i] + '_' + atlas_name + '_' + kind.replace(' ', '_') + '.mat')
            sio.savemat(subject_file, {'connectivity': connectivity_matrices[i]})
            print("Saving connectivity matrix to %s" % subject_file)

    return connectivity_matrices
Example #3
Source File: test_covariance.py    From pandas-ml with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def test_objectmapper(self):
        df = pdml.ModelFrame([])
        self.assertIs(df.covariance.EmpiricalCovariance, covariance.EmpiricalCovariance)
        self.assertIs(df.covariance.EllipticEnvelope, covariance.EllipticEnvelope)
        self.assertIs(df.covariance.GraphLasso, covariance.GraphLasso)
        self.assertIs(df.covariance.GraphLassoCV, covariance.GraphLassoCV)
        self.assertIs(df.covariance.LedoitWolf, covariance.LedoitWolf)
        self.assertIs(df.covariance.MinCovDet, covariance.MinCovDet)
        self.assertIs(df.covariance.OAS, covariance.OAS)
        self.assertIs(df.covariance.ShrunkCovariance, covariance.ShrunkCovariance)

        self.assertIs(df.covariance.shrunk_covariance, covariance.shrunk_covariance)
        self.assertIs(df.covariance.graph_lasso, covariance.graph_lasso)