pyeeg.preprocess.MultichanWienerFilter.set_fit_request

MultichanWienerFilter.set_fit_request(*, cov_data: bool | None | str = '$UNCHANGED$', y_artifact: bool | None | str = '$UNCHANGED$', y_clean: bool | None | str = '$UNCHANGED$') → MultichanWienerFilter

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:

  • cov_data (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for cov_data parameter in fit.

  • y_artifact (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for y_artifact parameter in fit.

  • y_clean (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for y_clean parameter in fit.

Returns:

self – The updated object.

Return type:

object