pyriemann.tangentspace.FGDA

class pyriemann.tangentspace.FGDA(metric='riemann', tsupdate=False)[source]

Fisher geodesic discriminant analysis.

Fisher geodesic discriminant analysis (FGDA) projects SPD matrices in tangent space, applies a Fisher linear discriminant analysis (FLDA) to reduce dimension, and projects filtered tangent vectors back in the manifold [1].

Parameters:
metricstring | dict, default=”riemann”

The type of metric used for reference matrix estimation (for the list of supported metrics see pyriemann.geometry.mean.gmean()) and for tangent space map (see pyriemann.geometry.tangent_space.tangent_space()). The metric can be a dict with two keys, “mean” 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.

Attributes:
classes_ndarray, shape (n_classes,)

Labels for each class.

See also

FgMDM
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)[source]

Init.

fit(X, y=None, sample_weight=None)[source]

Fit (estimates) the reference matrix and the FLDA.

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

Set of SPD matrices.

yNone

Not used, here for compatibility with sklearn API.

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

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

Returns:
selfFGDA instance

The FGDA instance.

fit_transform(X, y=None, sample_weight=None)[source]

Fit and transform in a single function.

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

Set of SPD matrices.

yNone

Not used, here for compatibility with sklearn API.

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

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

Returns:
X_newndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices after filtering.

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.

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

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.

transform(X)[source]

Filtering operation.

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

Set of SPD matrices.

Returns:
X_newndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices after filtering.