Medium "How to actually forecast COVID-19" embed

optim.py

def smape_resid_transform(true, pred, eps=1e-5):
    return (true - pred) / (np.abs(true) + np.abs(pred) + eps)

class HiddenCurveFitter(BaseFitter):
...
    def residual(self, params, t_vals, data, model):
        model.params = params

        initial_conditions = model.get_initial_conditions(data)

        (S, E, I, Iv, R, Rv, D, Dv), history = model.predict(t_vals, initial_conditions, history=False)
        (new_exposed,
         new_infected_invisible, new_infected_visible,
         new_recovered_invisible,
         new_recovered_visible,
         new_dead_invisible, new_dead_visible) = model.compute_daily_values(S, E, I, Iv, R, Rv, D, Dv)

        new_infected_visible = new_infected_visible
        new_dead_visible = new_dead_visible
        new_recovered_visible = new_recovered_visible

        true_daily_cases = data[self.new_cases_col].values[1:]
        true_daily_deaths = data[self.new_deaths_col].values[1:]
        true_daily_recoveries = data[self.new_recoveries_col].values[1:]

        resid_I_new = smape_resid_transform(true_daily_cases, new_infected_visible)
        resid_D_new = smape_resid_transform(true_daily_deaths, new_dead_visible)
        resid_R_new = smape_resid_transform(true_daily_recoveries, new_recovered_visible)

        if self.weights:
            residuals = np.concatenate([
                self.weights['I'] * resid_I_new,
                self.weights['D'] * resid_D_new,
                self.weights['R'] * resid_R_new,
            ]).flatten()
        else:
            residuals = np.concatenate([
                resid_I_new,
                resid_D_new,
                resid_R_new,
            ]).flatten()

        return residuals