pyriemann.classification.TSclassifier¶

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

Classification in the tangent space.

Project data in the tangent space and apply a classifier on the projected data. This is a simple helper to pipeline the tangent space projection and a classifier. Default classifier is LogisticRegression

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 [2]. 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.

See also

TangentSpace

Notes

New in version 0.2.4.

Attributes:

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

__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 according to the closest centroid.

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 according to the closest centroid.

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) w.r.t. y.

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

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:

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¶

Request metadata passed to the score 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 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.

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:

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.