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¶

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:

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: