Function get_coef

mne/decoding/base.py:687–791

Retrieve the coefficients of an estimator ending with a Linear Model. This is typically useful to retrieve "spatial filters" or "spatial patterns" of decoding models :footcite:`HaufeEtAl2014`. Parameters ---------- estimator : object | None An estimator from scikit-lear

(
    estimator, attr="filters_", inverse_transform=False, *, step_name=None, verbose=None
)

@verbose
def get_coef(
    estimator, attr="filters_", inverse_transform=False, *, step_name=None, verbose=None
):
    """Retrieve the coefficients of an estimator ending with a Linear Model.

    This is typically useful to retrieve "spatial filters" or "spatial
    patterns" of decoding models :footcite:`HaufeEtAl2014`.

    Parameters
    ----------
    estimator : object | None
        An estimator from scikit-learn.
    attr : str
        The name of the coefficient attribute to retrieve, typically
        ``'filters_'`` (default) or ``'patterns_'``.
    inverse_transform : bool
        If True, returns the coefficients after inverse transforming them with
        the transformer steps of the estimator.
    step_name : str | None
        Name of the sklearn's pipeline step to get the coef from.
        If inverse_transform is True, the inverse transformations
        will be applied using transformers before this step.
        If None, the last step will be used. Defaults to None.

        .. versionadded:: 1.11
    %(verbose)s

    Returns
    -------
    coef : array
        The coefficients.

    References
    ----------
    .. footbibliography::
    """
    # Get the coefficients of the last estimator in case of nested pipeline
    est = estimator
    logger.debug(f"Getting coefficients from estimator: {est.__class__.__name__}")

    if step_name is not None:
        if not hasattr(estimator, "named_steps"):
            raise ValueError("step_name can only be used with a pipeline estimator.")
        try:
            est = est.get_params(deep=True)[step_name]
        except KeyError:
            raise ValueError(f"Step '{step_name}' is not part of the pipeline.")
    else:
        while hasattr(est, "steps"):
            est = est.steps[-1][1]

    squeeze_first_dim = False

    # If SlidingEstimator, loop across estimators
    if hasattr(est, "estimators_"):
        coef = list()
        for ei, this_est in enumerate(est.estimators_):
            if ei == 0: