def solve_best_params_and_predict(optimal_params):
# Variables Initialization for the ODE system
alpha, days, r_s, r_dth, p_dth, r_dthdecay, k1, k2, jump, t_jump, std_normal = optimal_params
optimal_params = [
max(alpha, dict_default_reinit_parameters["alpha"]),
days,
max(r_s, dict_default_reinit_parameters["r_s"]),
max(min(r_dth, 1),
dict_default_reinit_parameters["r_dth"]),
max(min(p_dth, 1),
dict_default_reinit_parameters["p_dth"]),
max(r_dthdecay,
dict_default_reinit_parameters["r_dthdecay"]),
max(k1, dict_default_reinit_parameters["k1"]),
max(k2, dict_default_reinit_parameters["k2"]),
max(jump, dict_default_reinit_parameters["jump"]),
max(t_jump, dict_default_reinit_parameters["t_jump"]),
max(std_normal,
dict_default_reinit_parameters["std_normal"]),
]
x_0_cases = get_initial_conditions(
params_fitted=optimal_params,
global_params_fixed=GLOBAL_PARAMS_FIXED,
)
x_sol_best = solve_ivp(
fun=model_covid,
y0=x_0_cases,
t_span=[t_predictions[0], t_predictions[-1]],
t_eval=t_predictions,
args=tuple(optimal_params),
).y
return x_sol_best
def residuals_totalcases(params) -> float:
"""
Function that makes sure the parameters are in the right range during the fitting process and computes
the loss function depending on the optimizer that has been chosen for this run as a global variable
:param params: currently fitted values of the parameters during the fitting process
:return: the value of the loss function as a float that is optimized against (in our case, minimized)
"""
# Variables Initialization for the ODE system
alpha, days, r_s, r_dth, p_dth, r_dthdecay, k1, k2, jump, t_jump, std_normal = params
# Force params values to stay in a certain range during the optimization process with re-initializations
params = (
max(alpha, dict_default_reinit_parameters["alpha"]),
days,
max(r_s, dict_default_reinit_parameters["r_s"]),
max(min(r_dth, 1),
dict_default_reinit_parameters["r_dth"]),
max(min(p_dth, 1),
dict_default_reinit_parameters["p_dth"]),
max(r_dthdecay,
dict_default_reinit_parameters["r_dthdecay"]),
max(k1, dict_default_reinit_parameters["k1"]),
max(k2, dict_default_reinit_parameters["k2"]),
max(jump, dict_default_reinit_parameters["jump"]),
max(t_jump, dict_default_reinit_parameters["t_jump"]),
max(std_normal,
dict_default_reinit_parameters["std_normal"]),
)
x_0_cases = get_initial_conditions(
params_fitted=params,
global_params_fixed=GLOBAL_PARAMS_FIXED)
x_sol_total = solve_ivp(
fun=model_covid,
y0=x_0_cases,
t_span=[t_cases[0], t_cases[-1]],
t_eval=t_cases,
args=tuple(params),
)
x_sol = x_sol_total.y
weights = list(range(1, len(cases_data_fit) + 1))
weights = [(x / len(cases_data_fit))**2 for x in weights]
if x_sol_total.status == 0:
residuals_value = get_residuals_value(
optimizer=OPTIMIZER,
balance=balance,
x_sol=x_sol,
cases_data_fit=cases_data_fit,
deaths_data_fit=deaths_data_fit,
weights=weights)
else:
residuals_value = 1e12
return residuals_value
def solve_best_params_and_predict(optimal_params):
# Variables Initialization for the ODE system
x_0_cases = get_initial_conditions(
params_fitted=optimal_params,
global_params_fixed=GLOBAL_PARAMS_FIXED
)
x_sol_best = solve_ivp(
fun=model_covid_predictions,
y0=x_0_cases,
t_span=[t_predictions[0], t_predictions[-1]],
t_eval=t_predictions,
args=tuple(optimal_params),
).y
return x_sol_best