pyriemann.transfer.TLCenter¶

class pyriemann.transfer.TLCenter(target_domain, metric='riemann')¶

Recenter data for transfer learning.

Recenter the data points from each domain to the Identity on manifold, ie make the mean of the datasets become the identity. This operation corresponds to a whitening step if the SPD matrices represent the spatial covariance matrices of multivariate signals.

Note

Using .fit() and then .transform() will give different results than .fit_transform(). In fact, .fit_transform() should be applied on the training dataset (target and source) and .transform() on the test partition of the target dataset.

Parameters:

target_domainstr

Domain to consider as target in transform() function:

if not empty, transform() recenters matrices to the specified target domain;
else, transform() recenters matrices to the last fitted domain.

metricstr, default=”riemann”

Metric used for mean estimation. For the list of supported metrics, see pyriemann.utils.mean.mean_covariance(). Note, however, that only when using the “riemann” metric that we are ensured to re-center the matrices precisely to the Identity.

Notes

New in version 0.4.

References

[1]

Transfer Learning: A Riemannian Geometry Framework With Applications to Brain–Computer Interfaces P Zanini et al, IEEE Transactions on Biomedical Engineering, vol. 65, no. 5, pp. 1107-1116, August, 2017

[2]

Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach He He and Dongrui Wu, IEEE Transactions on Biomedical Engineering, 2019

Attributes:

recenter_dict: If fit, dictionary with key=domain_name and value=domain_mean.

__init__(target_domain, metric='riemann')¶: Init

fit(X, y_enc, sample_weight=None)¶

Fit TLCenter.

For each domain, calculates the mean of matrices of this domain.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels): Set of SPD matrices.
y_encndarray, shape (n_matrices,): Extended labels for each matrix.
sample_weightNone | ndarray, shape (n_matrices,), default=None: Weights for each matrix. If None, it uses equal weights.

Returns:

selfTLCenter instance: The TLCenter instance.

fit_transform(X, y_enc, sample_weight=None)¶

Fit TLCenter and then transform matrices.

For each domain, calculates the mean of matrices of this domain and then recenters them to Identity.

Note

This method is designed for using at training time. The output for .fit_transform() will be different than using .fit() and .transform() separately.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels): Set of SPD matrices.
y_encndarray, shape (n_matrices,): Extended labels for each matrix.
sample_weightNone | ndarray, shape (n_matrices,), default=None: Weights for each matrix. If None, it uses equal weights.

Returns:

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

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.

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

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.
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_output(*, transform=None)¶

Set output container.

See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.

Parameters:

transform{“default”, “pandas”}, default=None

Configure output of transform and fit_transform.

“default”: Default output format of a transformer
“pandas”: DataFrame output
None: Transform configuration is unchanged

Returns:

selfestimator instance: Estimator instance.

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

Request metadata passed to the transform 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 transform 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 transform.
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:

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

Returns:

selfobject: The updated object.

transform(X, y_enc=None)¶

Re-center matrices in the target domain.

Note

This method is designed for using at test time, recentering all matrices in target domain.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels): Set of SPD matrices.
y_encNone: Not used, here for compatibility with sklearn API.

Returns:

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