vak.transforms.defaults.frame_classification.get_default_frame_classification_transform#

vak.transforms.defaults.frame_classification.get_default_frame_classification_transform(mode: str, transform_kwargs: dict) → tuple[Callable, Callable] | Callable[source]#

Get default transform for frame classification model.

Parameters:

  • mode (str) –

  • transform_kwargs (dict) –

    A dict with the following key-value pairs:
    spect_standardizervak.transforms.StandardizeSpect

    instance that has already been fit to dataset, using fit_df method. Default is None, in which case no standardization transform is applied.

    window_sizeint

    width of window in number of elements. Argument to PadToWindow transform.

    padvalfloat

    value to pad with. Added to end of array, the “right side” if 2-dimensional. Argument to PadToWindow transform. Default is 0.

    return_padding_maskbool

    if True, the dictionary returned by ItemTransform classes will include a boolean vector to use for cropping back down to size before padding. padding_mask has size equal to width of padded array, i.e. original size plus padding at the end, and has values of 1 where columns in padded are from the original array, and values of 0 where columns were added for padding.