pyriemann.transfer.TLDummy

class pyriemann.transfer.TLDummy

No transformation for transfer learning.

No transformation of data between the domains.

Notes

Added in version 0.4.

__init__(*args, **kwargs)

fit(X, y_enc=None)

Do nothing.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels) or shape (n_vectors, n_ts)

Set of SPD matrices or tangent vectors.

y_encNone

Not used, here for compatibility with sklearn API.

Returns:

selfTLDummy instance

The TLDummy instance.

fit_transform(X, y_enc=None)

Do nothing.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels) or shape (n_vectors, n_ts)

Set of SPD matrices or tangent vectors.

y_encNone

Not used, here for compatibility with sklearn API.

Returns:

X_newndarray, shape (n_matrices, n_channels, n_channels) or shape (n_vectors, n_ts)

Same data as in the input.

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(*, y_enc: bool | None | str = '$UNCHANGED$') → TLDummy

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:

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.

transform(X)

Do nothing.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels) or shape (n_vectors, n_ts)

Set of SPD matrices or tangent vectors.

Returns:

X_newndarray, shape (n_matrices, n_channels, n_channels) or shape (n_vectors, n_ts)

Same data as in the input.

Examples using pyriemann.transfer.TLDummy

Motor imagery classification by transfer learning

Motor imagery classification by transfer learning

Comparison of pipelines for transfer learning

Comparison of pipelines for transfer learning