def is_feature_overweighted_towards_class(feature_size_by_class_df,
    threshold=0.99, feature_level=True):
    """
    The intended use case is for seeing whether a categorical feature very 
    strongly points towards a particular class value, as, if this is true, 
    the feature is either highly predictive or leaking data.

    Parameters
    ----------

    feature_size_by_class_df : pandas.DataFrame
        DataFrame on which this function will operate. This is presumed to 
        be a result from the function `get_feature_size_by_class`. Additionally, 
        the feature represented in `feature_size_by_class_df` should be discrete, 
        i.e. comes from a finite set of values.

    threshold : float, with value between 0 and 1 (default=.99)
        Proportion of rows allowed that are allowed to be taken up by single
        `class_col` & `feature` value grouping.

    feature_level : bool (default=True)
        Whether to perform at the feature level, or feature-value level.

    Return
    ------

    `bool` indicating whether all class values see all feature values.
    """
    ratios = feature_size_by_class_df.unstack(0).ratio
    if feature_level:
        booleans = ratios.sum() >= threshold
    else:
        booleans = (ratios >= threshold).any()
    return booleans.any()
