How to use the bambi.priors.PriorFactory function in bambi

def test_prior_factory_init_from_config():
    config_file = join(dirname(__file__), 'data', 'sample_priors.json')
    pf = PriorFactory(config_file)
    for d in ['dists', 'terms', 'families']:
        assert hasattr(pf, d)
        assert isinstance(getattr(pf, d), dict)
    config_dict = json.load(open(config_file, 'r'))
    pf = PriorFactory(config_dict)
    for d in ['dists', 'terms', 'families']:
        assert hasattr(pf, d)
        assert isinstance(getattr(pf, d), dict)
    assert 'feta' in pf.dists
    assert 'hard' in pf.families
    assert 'yellow' in pf.terms

def test_prior_factory_init_from_default_config():
    pf = PriorFactory()
    for d in ['dists', 'terms', 'families']:
        assert hasattr(pf, d)
        assert isinstance(getattr(pf, d), dict)
    assert 'normal' in pf.dists
    assert 'fixed' in pf.terms
    assert 'gaussian' in pf.families

def test_prior_retrieval():
    config_file = join(dirname(__file__), 'data', 'sample_priors.json')
    pf = PriorFactory(config_file)
    prior = pf.get(dist='asiago')
    assert prior.name == 'Asiago'
    assert isinstance(prior, Prior)
    assert prior.args['hardness'] == 10
    with pytest.raises(KeyError):
        assert prior.args['holes'] == 4
    family = pf.get(family='hard')
    assert isinstance(family, Family)
    assert family.link == 'grate'
    backup = family.prior.args['backup']
    assert isinstance(backup, Prior)
    assert backup.args['flavor'] == 10000
    prior = pf.get(term='yellow')
    assert prior.name == 'Swiss'

def test_prior_factory_init_from_config():
    config_file = join(dirname(__file__), 'data', 'sample_priors.json')
    pf = PriorFactory(config_file)
    for d in ['dists', 'terms', 'families']:
        assert hasattr(pf, d)
        assert isinstance(getattr(pf, d), dict)
    config_dict = json.load(open(config_file, 'r'))
    pf = PriorFactory(config_dict)
    for d in ['dists', 'terms', 'families']:
        assert hasattr(pf, d)
        assert isinstance(getattr(pf, d), dict)
    assert 'feta' in pf.dists
    assert 'hard' in pf.families
    assert 'yellow' in pf.terms

def __init__(
        self,
        data=None,
        default_priors=None,
        auto_scale=True,
        dropna=False,
        taylor=None,
        noncentered=True,
    ):

        if isinstance(data, str):
            data = pd.read_csv(data, sep=None, engine="python")

        self.default_priors = PriorFactory(default_priors)

        obj_cols = data.select_dtypes(["object"]).columns
        data[obj_cols] = data[obj_cols].apply(lambda x: x.astype("category"))
        self.data = data
        # Some random effects stuff later requires us to make guesses about
        # column groupings into terms based on patsy's naming scheme.
        if re.search(r"[\[\]]+", "".join(data.columns)):
            warnings.warn(
                "At least one of the column names in the specified "
                "dataset contain square brackets ('[' or ']')."
                "This may cause unexpected behavior if you specify "
                "models with random effects. You are encouraged to "
                "rename your columns to avoid square brackets."
            )
        self.reset()