pyriemann.transfer.TLStretch

class pyriemann.transfer.TLStretch(target_domain, final_dispersion=1.0, centered_data=False, metric='riemann')

Stretch data for transfer learning.

Change the dispersion of the datapoints around their geometric mean for each dataset so that they all have the same desired value.

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.

dispersionfloat, default=1.0

Target value for the dispersion of the data points.

centered_databool, default=False

Whether the data has been re-centered to the Identity beforehand.

metricstr, default=”riemann”

Metric used for calculating the dispersion. For the list of supported metrics, see pyriemann.utils.distance.distance().

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

Attributes:
dispersions_dict

Dictionary with key=domain_name and value=domain_dispersion.

__init__(target_domain, final_dispersion=1.0, centered_data=False, metric='riemann')

Init

fit(X, y_enc, sample_weight=None)

Fit TLStretch.

Calculate the dispersion around the mean for each 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:
selfTLStretch instance

The TLStretch instance.

fit_transform(X, y_enc, sample_weight=None)

Fit TLStretch and then transform data points.

Calculate the dispersion around the mean for each domain and then stretch the data points to the desired final dispersion.

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 with desired final dispersion.

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 = '$UNCHANGED$') → TLStretch

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 = '$UNCHANGED$') → TLStretch

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)

Stretch the data points in the target domain.

Note

The stretching operation is properly defined only for the riemann metric.

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 SPD matrices with desired final dispersion.