moabb.evaluations.WithinSessionSplitter

class moabb.evaluations.WithinSessionSplitter(n_folds: int = 5, shuffle: bool = True, random_state: int | None = None, cv_class: type[~sklearn.model_selection._split.BaseCrossValidator] = <class 'sklearn.model_selection._split.StratifiedKFold'>, **cv_kwargs: dict)

Data splitter for within session evaluation.

Within-session evaluation uses k-fold cross_validation to determine train and test sets for each subject in each session. This splitter assumes that all data from all subjects is already known and loaded.

The inner cross-validation strategy can be changed by passing the cv_class and cv_kwargs arguments. By default, it uses StratifiedKFold.

Parameters:

• n_folds (int, default=5) – Number of folds. Must be at least 2. If

• shuffle (bool, default=True) – Whether to shuffle each class’s samples before splitting into batches. Note that the samples within each split will not be shuffled.

• random_state (int, RandomState instance or None, default=None) – Controls the randomness of splits. Only used when shuffle is True. Pass an int for reproducible output across multiple function calls.

• cv_class (cros-validation class, default=StratifiedKFold) – Inner cross-validation strategy for splitting the sessions.

• cv_kwargs (dict) – Additional arguments to pass to the inner cross-validation strategy.

get_n_splits(metadata)

Returns the number of splitting iterations in the cross-validator.

split(y, metadata)

Generate indices to split data into training and test set.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.

• y (array-like of shape (n_samples,)) – The target variable for supervised learning problems.

• groups (array-like of shape (n_samples,), default=None) – Group labels for the samples used while splitting the dataset into train/test set.

Yields:

• train (ndarray) – The training set indices for that split.

• test (ndarray) – The testing set indices for that split.