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

)

        # throw informative error message if any categorical predictors have 1
        # category
        num_cats = [x.data.size for x in self.fixed_terms.values()]
        if any(np.array(num_cats) == 0):
            raise ValueError("At least one categorical predictor contains only 1 category!")

        # only set priors if there is at least one term in the model
        if self.terms:
            # Get and scale default priors if none are defined yet
            if self.taylor is not None:
                taylor = self.taylor
            else:
                taylor = 5 if self.family.name == "gaussian" else 1
            scaler = PriorScaler(self, taylor=taylor)
            scaler.scale()

        # For bernoulli models with n_trials = 1 (most common use case),
        # tell user which event is being modeled
        if self.family.name == "bernoulli" and np.max(self.y.data) < 1.01:
            event = next(i for i, x in enumerate(self.y.data.flatten()) if x > 0.99)
            warnings.warn(
                "Modeling the probability that {}=='{}'".format(
                    self.y.name, str(self.clean_data[self.y.name].iloc[event])
                )
            )

        self._set_backend(backend)
        self.backend.build(self)
        self.built = True

["fixed"] * len(fixed_slopes)
            + ["intercept"] * len(fixed_intercepts)
            + ["random"] * len(random_terms)
        )

        # initialize them in order
        for t, term_type in zip(term_list, term_types):
            if t.prior.scale is None:
                # pylint: disable=protected-access
                if not t.prior._auto_scale or not self.model.auto_scale:
                    continue
                t.prior.scale = "wide"

            # Convert scale names to float
            if isinstance(t.prior.scale, str):
                t.prior.scale = PriorScaler.names[t.prior.scale]

            # scale it!
            getattr(self, "_scale_%s" % term_type)(t)