pyriemann.classification.FgMDM

class pyriemann.classification.FgMDM(metric='riemann', tsupdate=False, n_jobs=1)

Classification by Minimum Distance to Mean with geodesic filtering.

Apply geodesic filtering described in [1], and classify using MDM. The geodesic filtering is achieved in tangent space with a Linear Discriminant Analysis, then data are projected back to the manifold and classifier with a regular MDM. This is basically a pipeline of FGDA and MDM.

Parameters:
metricstring | dict, default=”riemann”

Metric used for reference matrix estimation (for the list of supported metrics, see pyriemann.utils.mean.mean_covariance()), for distance estimation (see pyriemann.utils.distance.distance()) and for tangent space map (see pyriemann.utils.tangent_space.tangent_space()). The metric can be a dict with three keys, “mean”, “dist” and “map” in order to pass different metrics.

tsupdatebool, default=False

Activate tangent space update for covariante shift correction between training and test, as described in [2]. This is not compatible with online implementation. Performance are better when the number of matrices for prediction is higher.

n_jobsint, default=1

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.

See also

MDM
FGDA
TangentSpace

References

[1]

Riemannian geometry applied to BCI classification A. Barachant, S. Bonnet, M. Congedo and C. Jutten. 9th International Conference Latent Variable Analysis and Signal Separation (LVA/ICA 2010), LNCS vol. 6365, 2010, p. 629-636.

[2]

Classification of covariance matrices using a Riemannian-based kernel for BCI applications A. Barachant, S. Bonnet, M. Congedo and C. Jutten. Neurocomputing, Elsevier, 2013, 112, pp.172-178.

__init__(metric='riemann', tsupdate=False, n_jobs=1)

Init.

fit(X, y, sample_weight=None)

Fit FgMDM.

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:
selfFgMDM instance

The FgMDM instance.

fit_transform(X, y=None, sample_weight=None)

Fit and transform in a single function.

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

Set of SPD matrices.

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

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_centroids)

Distance to each centroid.

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 after FGDA filtering.

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 nearest centroid.

predict_proba(X)

Predict proba using softmax after FGDA filtering.

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

Set of SPD matrices.

Returns:
probndarray, shape (n_matrices, n_classes)

The softmax 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 = '$UNCHANGED$') → FgMDM

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 = '$UNCHANGED$') → FgMDM

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 centroid after FGDA filtering.

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

Set of SPD matrices.

Returns:
distndarray, shape (n_matrices, n_classes)

Distance to each centroid according to the metric.

Examples using pyriemann.classification.FgMDM

Ensemble learning on functional connectivity

Ensemble learning on functional connectivity