pyntbci.stopping.CriterionStopping

class pyntbci.stopping.CriterionStopping(estimator: ClassifierMixin, segment_time: float, fs: int, criterion: str = 'accuracy', optimization: str = 'max', n_folds: int = 4, target: float | None = None, smooth_width: float | None = None)[source]

Criterion static stopping. Fits an optimal stopping time given some criterion to optimize.

Parameters:

  • estimator (ClassifierMixin) – The classifier object that performs the classification.

  • segment_time (float) – The size of a segment of data at which classification is performed ins seconds.

  • fs (int) – The sampling frequency of the EEG data in Hz.

  • criterion (str (default: "accuracy")) – The criterion to use.

  • optimization (str (default: "max)) – The optimization to use.

  • n_folds (int (n_folds: 4)) – The number of folds to evaluate the optimization.

  • target (float (default: None)) – The targeted value for the criterion to optimize for.

  • smooth_width (float (default: None)) – The width of the smoothing applied in seconds. If None, the values of the criterion are not smoothened.

stop_time_

The trained static stopping time.

Type:

float

fit(X: ndarray[Any, dtype[_ScalarType_co]], y: ndarray[Any, dtype[_ScalarType_co]]) → ClassifierMixin[source]

The training procedure to fit the static procedure on supervised EEG data.

Parameters:

  • X (NDArray) – The matrix of EEG data of shape (n_trials, n_channels, n_samples).

  • y (NDArray) – The vector of ground-truth labels of the trials in X of shape (n_trials).

Returns:

self – Returns the instance itself.

Return type:

ClassifierMixin

predict(X: ndarray[Any, dtype[_ScalarType_co]]) → ndarray[Any, dtype[_ScalarType_co]][source]

The testing procedure to apply the estimator to novel EEG data using criterion static stopping.

Parameters:

X (NDArray) – The matrix of EEG data of shape (n_trials, n_channels, n_samples).

Returns:

y – The vector of predicted labels of the trials in X of shape (n_trials). Note, the value equals -1 if the trial cannot yet be stopped.

Return type:

NDArray

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

Request metadata passed to the score 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 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.

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:

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