pyriemann.transfer.TLRotate

class pyriemann.transfer.TLRotate(target_domain, weights=None, metric='euclid', n_jobs=1)

Rotate data for transfer learning.

Rotate the data points from each source domain so to match its class means with those from the target domain. The loss function for this matching was first proposed in [1] and the optimization procedure for mininimizing it follows the presentation from [2].

Note

The data points from each domain must have been re-centered to the identity before calculating the rotation.

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.

weightsNone | array, shape (n_classes,), default=None

Weights to assign for each class. If None, then give the same weight for each class.

metric{“euclid”, “riemann”}, default=”euclid”

Metric for the distance to minimize between class means.

n_jobsint, default=1

The number of jobs to use for the computation. This works by computing the rotation matrix for each source domain in parallel. If -1 all CPUs are used.

Notes

New in version 0.4.

References

[1]

Riemannian Procrustes analysis: transfer learning for brain-computer interfaces PLC Rodrigues et al, IEEE Transactions on Biomedical Engineering, vol. 66, no. 8, pp. 2390-2401, December, 2018

[2]

An introduction to optimization on smooth manifolds N. Boumal. To appear with Cambridge University Press. June, 2022

Attributes:
rotations_dict

Dictionary with key=domain_name and value=domain_rotation_matrix.

__init__(target_domain, weights=None, metric='euclid', n_jobs=1)

Init

fit(X, y_enc, sample_weight=None)

Fit TLRotate.

Calculate the rotations matrices to transform each source domain into the target 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:
selfTLRotate instance

The TLRotate instance.

fit_transform(X, y_enc, sample_weight=None)

Fit TLRotate and then transform data points.

Calculate the rotation matrix for matching each source domain to the target domain.

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 SPD matrices after rotation step.

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 = '$UNCHANGED$') → TLRotate

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 = '$UNCHANGED$') → TLRotate

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)

Rotate the data points in the target domain.

The rotations are done from source to target, so in this step the data points suffer no transformation at all.

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)

Same set of SPD matrices as in the input.