pyriemann.classification.FgMDM

class pyriemann.classification.FgMDM(metric='riemann', tsupdate=False, n_jobs=1)

Classification by Minimum Distance to Mean with geodesic filtering.

Apply geodesic filtering described in [1], and classify using MDM. The geodesic filtering is achieved in tangent space with a Linear Discriminant Analysis, then data are projected back to the manifold and classifier with a regular MDM. This is basically a pipeline of FGDA and MDM.

Parameters

metricstring | dict, default=’riemann’

The type of metric used for reference matrix estimation (see mean_covariance for the list of supported metric), for distance estimation, and for tangent space map (see tangent_space for the list of supported metric). The metric could be a dict with three keys, mean, dist and map in order to pass different metrics for the reference matrix estimation, the distance estimation, and the tangent space mapping.

tsupdatebool, default=False

Activate tangent space update for covariante shift correction between training and test, as described in [2]. This is not compatible with online implementation. Performance are better when the number of matrices for prediction is higher.

n_jobsint, default=1

The number of jobs to use for the computation. This works by computing each of the class centroid in parallel. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.

See also

MDM

FGDA

TangentSpace

References

1

Riemannian geometry applied to BCI classification A. Barachant, S. Bonnet, M. Congedo and C. Jutten. 9th International Conference Latent Variable Analysis and Signal Separation (LVA/ICA 2010), LNCS vol. 6365, 2010, p. 629-636.

2

Classification of covariance matrices using a Riemannian-based kernel for BCI applications A. Barachant, S. Bonnet, M. Congedo and C. Jutten. Neurocomputing, Elsevier, 2013, 112, pp.172-178.

Attributes

classes_ndarray, shape (n_classes,)

Labels for each class.

__init__(metric='riemann', tsupdate=False, n_jobs=1)

Init.

fit(X, y, sample_weight=None)

Fit FgMDM.

Parameters

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

yndarray, shape (n_matrices,)

Labels for each matrix.

sample_weightNone | ndarray, shape (n_matrices,), default=None

Weights for each matrix. If None, it uses equal weights.

Returns

selfFgMDM instance

The FgMDM instance.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

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.

predict(X)

Get the predictions after FGDA filtering.

Parameters

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

Returns

predndarray of int, shape (n_matrices,)

Predictions for each matrix according to the closest centroid.

predict_proba(X)

Predict proba using softmax after FGDA filtering.

Parameters

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

Returns

probndarray, shape (n_matrices, n_classes)

The softmax probabilities for each class.

score(X, y, sample_weight=None)

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns

scorefloat

Mean accuracy of self.predict(X) wrt. y.

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.

transform(X)

Get the distance to each centroid after FGDA filtering.

Parameters

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

Returns

distndarray, shape (n_matrices, n_cluster)

The distance to each centroid according to the metric.