pyriemann.transfer.TLRegressor¶

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

Transfer learning wrapper for regressors.

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

Parameters:

target_domainstr: Domain to consider as target.
estimatorBaseRegressor: The regressor to apply on matrices.
domain_weightNone | dict, default=None: Weights to combine data from each domain to train the regressor. The dict contains key=domain_name and value=weight_to_assign. If None, it uses equal weights.

See also

TLClassifier

Notes

Added in version 0.4.

__init__(target_domain, estimator, domain_weight=None)¶: Init.

fit(X, y_enc)¶

Fit TLRegressor.

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:

selfTLRegressor instance: The TLRegressor 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.

score(X, y_enc)¶

Return the coefficient of determination of the prediction.

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:

scorefloat: R2 of self.predict(X) wrt. y.

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

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:

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.

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

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:

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

Returns:

selfobject: The updated object.