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
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 ¶
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.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 infit
.
- 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 ¶
Request metadata passed to the
score
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.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 inscore
.
- Returns:
- selfobject
The updated object.