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’
The metric for calculating the dispersion can be: ‘ale’, ‘alm’, ‘euclid’, ‘harmonic’, ‘identity’, ‘kullback_sym’, ‘logdet’, ‘logeuclid’, ‘riemann’, ‘wasserstein’, or a callable function.
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)¶
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.
- Returns
- selfTLStretch instance
The TLStretch instance.
- fit_transform(X, y_enc)¶
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.
- Returns
- Xndarray, shape (n_matrices, n_classes)
Set of SPD matrices with desired final dispersion.
- 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_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, 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.