pyntbci.classifiers.Ensemble

class pyntbci.classifiers.Ensemble(estimator: ClassifierMixin, gate: ClassifierMixin)[source]

Ensemble classifier. It wraps an ensemble classifier around another classifier object. The classifiers are applied to each item in a databank separately. A gating function combines the outputs of the individual classifications to arrive at a single final combined classification.

Parameters:

estimator (ClassifierMixin) – The classifier object that is applied to each item in the databank.
gate (ClassifierMixin) – The gate that is used to combine the scores obtained from each individual estimator.

models_

A list containing all models learned for each of the databanks.

Type:: list[ClassifierMixin]

decision_function(X: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → ndarray[tuple[Any, ...], dtype[_ScalarT]][source]

Apply the classifier to get classification scores for X.

Parameters:: X (NDArray) – The matrix of EEG data of shape (n_trials, n_channels, n_samples, n_items).
Returns:: scores – The matrix of scores of shape (n_trials, n_classes).
Return type:: NDArray

fit(X: ndarray[tuple[Any, ...], dtype[_ScalarT]], y: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → ClassifierMixin[source]

The training procedure to apply an ensemble classifier on supervised EEG data.

Parameters:

X (NDArray) – The matrix of EEG data of shape (n_trials, n_channels, n_samples, n_items).
y (NDArray) – The vector of ground-truth labels of the trials in X of shape (n_trials). Note, these denote the index at which to find the associated stimulus!

Returns:

self – Returns the instance itself.

Return type:

ClassifierMixin

predict(X: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → ndarray[tuple[Any, ...], dtype[_ScalarT]][source]

The testing procedure to apply the ensemble classifier to novel EEG data.

Parameters:: X (NDArray) – The matrix of EEG data of shape (n_trials, n_channels, n_samples, n_items).
Returns:: y – The vector of predicted labels of the trials in X of shape (n_trials). Note, these denote the index at which to find the associated stimulus!
Return type:: NDArray

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → Ensemble

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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.
Returns:: self – The updated object.
Return type:: object