cedalion.mlutils package

Submodules

cedalion.mlutils.cv module

cedalion.mlutils.cv.create_cv_splits(
df_stim: DataFrame,
n_splits: int,
) → Generator[tuple[DataFrame, DataFrame], None, None][source]

Split stimulus events into train and test sets for cross-validation.

Parameters:

  • df_stim – Stimulus events, sorted by onset times with ordered index.

  • n_splits – number of folds

Yields:

For each fold, the stimulis data frame split into train and test set.

The test trials are consecutive and not randomized.

cedalion.mlutils.cv.mask_design_matrix(
dms: ~cedalion.models.glm.design_matrix.DesignMatrix,
df_stim_test: ~pandas.core.frame.DataFrame,
before: ~pint.Annotated[~pint.Quantity,
'[time]'] = <Quantity(5,
'second')>,
after: ~pint.Annotated[~pint.Quantity,
'[time]'] = <Quantity(20,
'second')>,
) → DesignMatrix[source]

Mask a segment of the design matrix by setting it to zero.

When using GLM parameters as features, the fit must not have access to the test trials. This function zeros out a contiguous segment of the design matrix, ensuring that the model cannot explain the time course in the masked segment for any choice of parameters. The segment extends from the earliest to the latest trial in df_stim_test, padded by additional time specified by the before and after parameters. Because the masked segment is continuous, the train-test split must be chosen such that the test trials are consecutive.

Parameters:

  • dms – The design matrix to mask

  • df_stim_test – test set of stimulus events.

  • before – time to pad before the earlist test trial

  • after – time to pad after the latest test trial

Returns:

A copy of the design matrix with the masked segment set to zero.

cedalion.mlutils.features module

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]

Module contents