pyriemann.classification.MeanField

class pyriemann.classification.MeanField(power_list=[-1, 0, 1], method_label='', method_combination='sum_means', metric='riemann', n_jobs=1)

Classification by Mean Field.

The Mean Field estimates several power means for each class. Then, it can be used as a classifier, which computes the minimum distance to the mean field [1]; or as a feature extractor, which must be pipelined with another classifier [2].

Parameters:
power_listlist of float, default=[-1,0,+1]

Exponents of power means.

method_combination{“sum_means”, “inf_means”, None}, default=”sum_means”

Method to combine distances from the different means of the field:

  • sum_means: the classifier assigns the matrix to the class whom the sum of distances to means of the field is the lowest [1];

  • inf_means: the classifier assigns the matrix to the class of the nearest mean of the field [1];

  • None: the transformer extracts all distances, without combination [2].

Changed in version 0.10.

metricstring, default=”riemann”

Metric used for distance estimation during prediction. For the list of supported metrics, see pyriemann.utils.distance.distance().

Attributes:
classes_ndarray, shape (n_classes,)

Labels for each class.

covmeans_ndarray, shape (n_classes, n_powers, n_channels, n_channels)

Centroids for each class and each power.

Changed in version 0.10.

See also

MDM

Notes

Added in version 0.3.

References

[1] (1,2,3)

The Riemannian Minimum Distance to Means Field Classifier M Congedo, PLC Rodrigues, C Jutten. BCI 2019 - 8th International Brain-Computer Interface Conference, Sep 2019, Graz, Austria.

[2] (1,2)

The Riemannian Means Field Classifier for EEG-Based BCI Data A Andreev, G Cattan, M Congedo. MDPI Sensors journal, April 2025

__init__(power_list=[-1, 0, 1], method_label='', method_combination='sum_means', metric='riemann', n_jobs=1)

Init.

fit(X, y, sample_weight=None)

Fit (estimates) the centroids.

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:
selfMeanField instance

The MeanField instance.

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) or ndarray, shape (n_matrices, n_classes x n_powers)

Distance to each mean field 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 mean field.

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, 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.

yndarray, shape (n_matrices,)

True labels for each matrix.

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

Weights for each matrix.

Returns:
scorefloat

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

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

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight 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 = '$UNCHANGED$') → MeanField

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:
selfobject

The updated object.

transform(X)

Get the distance to each mean field.

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

Set of SPD/HPD matrices.

Returns:
distndarray, shape (n_matrices, n_classes) or ndarray, shape (n_matrices, n_classes x n_powers)

Distance to each mean field according to the metric.