pyriemann.transfer.TLCenter

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

Centering for transfer learning.

For inputs in matrix manifold, it recenters the matrices from each domain to the identity matrix on manifold, ie it makes the mean of the matrices of each domain become the identity [1]. This operation corresponds to a whitening when the matrices represent the spatial covariance matrices of multivariate signals.

For inputs in tangent space, it recenters the tangent vectors from each domain to the origin of tangent space, ie it makes the mean of the vectors of each domain become zero.

Note

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

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”

For inputs in manifold, 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.

Attributes:
centers_dict

Dictionary with key=domain_name and value=domain_center.

Notes

Added in version 0.4.

Changed in version 0.8: Add support for tangent space centering.

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

__init__(target_domain, metric='riemann')

Init

fit(X, y_enc, sample_weight=None)

Fit TLCenter.

For each domain, it calculates the mean of matrices or vectors of this domain.

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.

sample_weightNone | ndarray, shape (n_matrices,) or shape (n_vectors,), default=None

Weights for each matrix or vector. If None, it uses equal weights.

Returns:
selfTLCenter instance

The TLCenter instance.

fit_transform(X, y_enc, sample_weight=None)

Fit TLCenter and then center each domain.

For each domain, it calculates the mean of matrices or vectors of this domain, and then recenters them to identity matrix or to null vector.

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) 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.

sample_weightNone | ndarray, shape (n_matrices,) or shape (n_vectors,), default=None

Weights for each matrix or vector. If None, it uses equal weights.

Returns:
X_newndarray, shape (n_matrices, n_channels, n_channels) or shape (n_vectors, n_ts)

Set of centered SPD matrices or tangent vectors in each domain.

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.

property recenter_

Warning

DEPRECATED: Attribute recenter_ is deprecated and will be removed in 0.10.0; please use centers_.

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.

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:
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 Introducing the set_output API for an example on how to use the API.

Parameters:
transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • “polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

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.

transform(X)

Center in the target domain.

Note

This method is designed for using at test time, recentering all inputs in target domain, or in the last fitted domain.

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

Set of SPD matrices or tangent vectors.

Returns:
X_newndarray, shape (n_matrices, n_classes) or shape (n_vectors, n_ts)

Set of centered SPD matrices or tangent vectors in target domain.

Examples using pyriemann.transfer.TLCenter

Motor imagery classification by transfer learning

Motor imagery classification by transfer learning

Comparison of pipelines for transfer learning

Comparison of pipelines for transfer learning

Data transformations in the Riemannian Procrustes Analysis

Data transformations in the Riemannian Procrustes Analysis