pyriemann.classification.MeanField

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

Classification by Minimum Distance to Mean Field.

Classification by Minimum Distance to Mean Field [1], defining several power means for each class.

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

Exponents of power means.

method_label{‘sum_means’, ‘inf_means’}, default=’sum_means’

Method to combine labels:

  • sum_means: it assigns the covariance to the class whom the sum of distances to means of the field is the lowest;

  • inf_means: it assigns the covariance to the class of the closest mean of the field.

metricstring | dict, default=’riemann’

The type of metric used for distance estimation during prediction. See distance for the list of supported metric.

See also

MDM

Notes

New in version 0.3.

References

1

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.

Attributes
classes_ndarray, shape (n_classes,)

Labels for each class.

covmeans_dict of n_powers lists of n_classes ndarrays of shape (n_channels, n_channels)

Centroids for each power and each class.

__init__(power_list=[-1, 0, 1], method_label='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 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_predict(X, y)

Fit and predict in one function.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters
Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

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 matrices.

Returns
predndarray of int, shape (n_matrices,)

Predictions for each matrix according to the closest means field.

predict_proba(X)

Predict proba using softmax.

Parameters
Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD 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.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters
Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns
scorefloat

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

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)

Get the distance to each means field.

Parameters
Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

Returns
distndarray, shape (n_matrices, n_classes)

Distance to each means field according to the metric.