def sampling()

in python/prophet/models.py [0:0]

• 27 lines of code
• 9 McCabe index (conditional complexity)

    def sampling(self, stan_init, stan_data, samples, **kwargs) -> dict:
        (stan_init, stan_data) = self.prepare_data(stan_init, stan_data)

        if 'inits' not in kwargs and 'init' in kwargs:
            kwargs['inits'] = self.prepare_data(kwargs['init'], stan_data)[0]

        args = dict(
            data=stan_data,
            inits=stan_init,
        )

        if 'chains' not in kwargs:
            kwargs['chains'] = 4
        iter_half = samples // 2
        kwargs['iter_sampling'] = iter_half
        if 'iter_warmup' not in kwargs:
            kwargs['iter_warmup'] = iter_half

        args.update(kwargs)

        self.stan_fit = self.model.sample(**args)
        res = self.stan_fit.draws()
        (samples, c, columns) = res.shape
        res = res.reshape((samples * c, columns))
        params = self.stan_to_dict_numpy(self.stan_fit.column_names, res)

        for par in params:
            s = params[par].shape
            if s[1] == 1:
                params[par] = params[par].reshape((s[0],))

            if par in ['delta', 'beta'] and len(s) < 2:
                params[par] = params[par].reshape((-1, 1))

        return params