def fetch_propublica(subset='train'):
    """
    Load files and data from the propublica dataset 

    Parameters
    ----------
    subset : 'train' or 'test', 'all'
        Select which dataset to load 

    Returns
    -------
    X : 2-d ndarray 

    y : 2-d ndarray 

    attribute_map : dict of dicts
        Denotes the protected attributes of the category of protected 
        attribute (e.g. "Race") to measure causal fairness 
        maps the attribute name to the column and value that correspond 
        to that attribute 
        e.g. one-hot encoding {"Purple": {"col": 0, "val": 1}, 
                               "Green": {"col": 1, "val": 1}}

        e.g. categorical encoding {"Purple": {"col": 0, "val: 1"}, 
                                   "Green": {"col": 0, "val: 2"}}                   

        Note: these MUST be mutually exclusive categories! 

    is_categorical : boolean 
        True if the y-values are categorical 
        False otherwise (indicating a one-hot encoding) 


    Examples
    --------
    >>> from fklearn.datasets import fetch_propublica 

    """
    pass
