cedalion.mlutils.features

Feature extraction from epoched fNIRS data for use with scikit-learn pipelines.

Functions

epoch_features(epochs, feature_types[, ...])

Extract scalar features from epoched data for use in ML classifiers.
cedalion.mlutils.features.epoch_features(
epochs: DataArray,
feature_types: list[Literal['slope', 'mean', 'max', 'min', 'auc']],
reltime_slices: dict[Literal['slope', 'mean', 'max', 'min', 'auc'], slice] | None = None,
)[source]

Extract scalar features from epoched data for use in ML classifiers.

For each requested feature type, a scalar value is computed over the "reltime" axis (optionally restricted to a sub-window). All non-epoch dimensions (channel, chromo, …) are then stacked into a flat "feature" dimension so the result is suitable as a 2-D feature matrix for scikit-learn estimators (rows = epochs, columns = features).

Parameters:

  • epochs – DataArray with at least an "epoch" dimension and a "reltime" dimension.

  • feature_types – One or more of "slope", "mean", "max", "min", "auc". A string is also accepted as a shorthand for a single-element list.

  • reltime_slices – Optional mapping from feature type to a slice of relative-time values used to restrict the window for that feature. Unspecified feature types use the full reltime range.

Returns:

xr.DataArray with dimensions (epoch, feature) where feature is a multi-index stacking all non-epoch, non-reltime dimensions and the feature_type label.

Raises:

ValueError – If an unrecognised feature type is requested.