pyriemann.transfer.MDWM

class pyriemann.transfer.MDWM(domain_tradeoff, target_domain, metric='riemann', n_jobs=1)

Classification by Minimum Distance to Weighted Mean.

Classification by nearest centroid. For each of the given classes, a centroid is estimated, according to the chosen metric, as a weighted mean of SPD matrices from the source domain, combined with the class centroid of the target domain [1] [2]. For classification, a given new matrix is attibuted to the class whose centroid is the nearest according to the chosen metric.

Parameters:
domain_tradeofffloat

Coefficient in [0,1] controlling the transfer, ie the trade-off between source and target domains. At 0, there is no transfer, only matrices acquired from the source domain are used. At 1, this is a calibration-free system as no matrices are required from the source domain.

target_domainstring

Name of the target domain in extended labels.

metricstring | dict, default=”riemann”

Metric used for mean estimation (for the list of supported metrics, see pyriemann.utils.mean.mean_covariance()) and for distance estimation (see pyriemann.utils.distance.distance()). The metric can be a dict with two keys, “mean” and “distance” in order to pass different metrics.

n_jobsint, default=1

The number of jobs to use for the computation. This works by computing each of the class centroid in parallel. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.

Attributes:
classes_ndarray, shape (n_classes,)

Labels for each class.

covmeans_ndarray, shape (n_classes, n_channels, n_channels)

Centroids for each class.

See also

MDM

Notes

Added in version 0.4.

References

[1]

Transfer learning for SSVEP-based BCI using Riemannian similarities between users E. Kalunga, S. Chevallier and Q. Barthelemy, in 26th European Signal Processing Conference (EUSIPCO), pp. 1685-1689. IEEE, 2018.

[2]

Minimizing Subject-dependent Calibration for BCI with Riemannian Transfer Learning S. Khazem, S. Chevallier, Q. Barthelemy, K. Haroun and C. Nous, 10th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 523-526. IEEE, 2021.

__init__(domain_tradeoff, target_domain, metric='riemann', n_jobs=1)

Init.

fit(X, y_enc, sample_weight=None)

Fit (estimates) the centroids.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices from source and target domain.

y_encndarray, shape (n_matrices,)

Extended labels for each matrix.

sample_weightNone | ndarray, shape (n_matrices_source,), default=None

Weights for each matrix from the source domains. If None, it uses equal weights.

Returns:
selfMDWM instance

The MDWM instance.

fit_predict(**kwargs)

Warning

DEPRECATED: fit_predict() is deprecated and will be removed in 0.10.0; please use fit().predict().

fit_transform(X, y, sample_weight=None)

Fit and transform in a single function.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD/HPD matrices.

yndarray, shape (n_matrices,)

Labels for each matrix.

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

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

Returns:
distndarray, shape (n_matrices, n_classes)

Distance to each centroid according to the metric.

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.

predict(X)

Get the predictions.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD/HPD matrices.

Returns:
predndarray of int, shape (n_matrices,)

Predictions for each matrix according to the nearest centroid.

predict_proba(X)

Predict proba using softmax of negative squared distances.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD/HPD matrices.

Returns:
probndarray, shape (n_matrices, n_classes)

Probabilities for each class.

score(X, y_enc, sample_weight=None)

Return the mean accuracy on the given test data and labels.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels)

Test set of SPD matrices.

y_encndarray, shape (n_matrices,)

Extended true labels for each matrix.

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

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

Returns:
scorefloat

Mean accuracy of clf.predict(X) wrt. y_enc.

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

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.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$', y_enc: bool | None | str = '$UNCHANGED$') → MDWM

Request metadata passed to the score 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 score 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 score.

  • 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 score.

y_encstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for y_enc parameter in score.

Returns:
selfobject

The updated object.

transform(X)

Get the distance to each centroid.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD/HPD matrices.

Returns:
distndarray, shape (n_matrices, n_classes)

Distance to each centroid according to the metric.

Examples using pyriemann.transfer.MDWM

Comparison of pipelines for transfer learning

Comparison of pipelines for transfer learning