pyriemann.transfer.TLEstimator

class pyriemann.transfer.TLEstimator(target_domain, estimator, domain_weight=None)

Transfer learning wrapper for estimators.

This is a wrapper for any BaseEstimator (classifier or regressor) that converts extended labels used in transfer learning into the usual y array to train a classifier/regressor of choice.

Parameters:
target_domainstr

Domain to consider as target.

estimatorBaseEstimator

The estimator to apply on data. It can be any regressor or classifier from pyRiemann.

domain_weightNone | dict, default=None

Weights to combine data from each domain to train the estimator. The dict contains key=domain_name and value=weight_to_assign. If None, it uses equal weights.

See also

TLClassifier
TLRegressor

Notes

Added in version 0.4.

__init__(target_domain, estimator, domain_weight=None)

Init.

fit(X, y_enc)

Fit TLEstimator.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels) or shape (n_vectors, n_ts)

Set of SPD matrices or tangent vectors.

y_encndarray, shape (n_matrices,) or shape (n_vectors,)

Extended labels for each matrix or vector.

Returns:
selfTLEstimator instance

The TLEstimator 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) or shape (n_vectors, n_ts)

Set of SPD matrices or tangent vectors.

Returns:
predndarray, shape (n_matrices,) or shape (n_vectors,)

Predictions according to the estimator.

set_fit_request(*, y_enc: bool | None | str = '$UNCHANGED$') → TLEstimator

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.

Added 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:
y_encstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for y_enc 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.

Examples using pyriemann.transfer.TLEstimator

Motor imagery classification by transfer learning

Motor imagery classification by transfer learning

Comparison of pipelines for transfer learning

Comparison of pipelines for transfer learning