pyriemann.classification.TSClassifier¶

class pyriemann.classification.TSClassifier(metric='riemann', tsupdate=False, clf=LogisticRegression())¶

Classification in the tangent space.

Project SPD matrices in the tangent space and apply a classifier. This is a simple helper to pipeline the tangent space projection and a classifier.

Parameters:

metricstring | dict, default=”riemann”: The type of metric used for reference matrix estimation (for the list of supported metrics see pyriemann.utils.mean.mean_covariance()) and for tangent space map (see pyriemann.utils.tangent_space.tangent_space()). The metric can be a dict with two keys, “mean” and “map” in order to pass different metrics.
tsupdatebool, default=False: Activate tangent space update for covariate shift correction between training and test, as described in [1]. This is not compatible with online implementation. Performance are better when the number of matrices for prediction is higher.
clfsklearn classifier, default=LogisticRegression(): The classifier to apply in the tangent space.

Attributes:

classes_ndarray, shape (n_classes,): Labels for each class.

See also

TangentSpace

Notes

Added in version 0.2.4.

References

[1]

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.

__init__(metric='riemann', tsupdate=False, clf=LogisticRegression())¶: Init.

fit(X, y, sample_weight=None)¶

Fit TsClassifier.

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:

selfTSClassifier instance: The TSClassifier instance.

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.

predict(X)¶

Get the predictions.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels): Set of SPD matrices.

Returns:

predndarray of int, shape (n_matrices,): Predictions for each matrix.

predict_proba(X)¶

Get the probability.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels): Set of SPD matrices.

Returns:

predndarray of ifloat, shape (n_matrices, n_classes): Predictions for each matrix.

score(X, y, sample_weight=None)¶

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

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels): Test set of SPD matrices.
yndarray, shape (n_matrices,): True labels for each matrix.
sample_weightNone | ndarray, shape (n_matrices,), default=None: Weights for each matrix.

Returns:

scorefloat: Mean accuracy of clf.predict(X) wrt. y.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → TSClassifier¶

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight 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.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → TSClassifier¶

Configure whether metadata should be requested to be passed to the score method.

The options for each parameter are:

True: metadata is requested, and passed to score 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 score.
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.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.

Examples using `pyriemann.classification.TSClassifier`¶

Motor imagery classification

pyriemann.classification.TSClassifier¶

Examples using pyriemann.classification.TSClassifier¶

Examples using `pyriemann.classification.TSClassifier`¶