).y
return x_sol_best
x_sol_final = solve_best_params_and_predict(
best_params)
data_creator = DELPHIDataCreator(
x_sol_final=x_sol_final,
date_day_since100=date_day_since100,
best_params=best_params,
continent=continent,
country=country,
province=province,
)
# Creating the parameters dataset for this (Continent, Country, Province)
mape_data = (
mape(fitcasesnd,
x_sol_final[15, :len(fitcasesnd)]) +
mape(fitcasesd,
x_sol_final[14, :len(fitcasesd)])) / 2
try:
mape_data_2 = (
mape(
fitcasesnd[-15:],
x_sol_final[15,
len(fitcasesnd) -
15:len(fitcasesnd)]) +
mape(
fitcasesd[-15:],
x_sol_final[14,
len(fitcasesnd) -
15:len(fitcasesd)])) / 2
except IndexError:
t_eval=t_predictions,
args=tuple(optimal_params),
).y
return x_sol_best
x_sol_final = solve_best_params_and_predict(best_params)
data_creator = DELPHIDataCreator(
x_sol_final=x_sol_final,
date_day_since100=date_day_since100,
best_params=best_params,
continent=continent,
country=country,
province=province,
)
# Creating the parameters dataset for this (Continent, Country, Province)
mape_data = (mape(fitcasesnd, x_sol_final[15, :len(fitcasesnd)]) +
mape(fitcasesd, x_sol_final[14, :len(fitcasesd)])) / 2
# mape_data_2 = (
# mape(fitcasesnd[-15:], x_sol_final[15, len(fitcasesnd)-15:len(fitcasesnd)]) +
# mape(fitcasesd[-15:], x_sol_final[14, len(fitcasesnd)-15:len(fitcasesd)])
# ) / 2
# print(fitcasesd[-15:])
print(x_sol_final[14, len(fitcasesnd) - 15:len(fitcasesnd)])
# print(mape_data_2)
df_parameters_cont_country_prov = data_creator.create_dataset_parameters(
mape_data)
list_df_global_parameters.append(df_parameters_cont_country_prov)
# Creating the datasets for predictions of this (Continent, Country, Province)
df_predictions_since_today_cont_country_prov, df_predictions_since_100_cont_country_prov = (
data_creator.create_datasets_predictions())
list_df_global_predictions_since_today.append(
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
x_sol_final = solve_best_params_and_predict(best_params)
data_creator = DELPHIDataCreator(
x_sol_final=x_sol_final, date_day_since100=date_day_since100, best_params=best_params,
continent=continent, country=country, province=province,
)
# Creating the parameters dataset for this (Continent, Country, Province)
mape_data = (
mape(fitcasesnd, x_sol_final[15, :len(fitcasesnd)]) +
mape(fitcasesd, x_sol_final[14, :len(fitcasesd)])
) / 2
# mape_data_2 = (
# mape(fitcasesnd[-15:], x_sol_final[15, len(fitcasesnd)-15:len(fitcasesnd)]) +
# mape(fitcasesd[-15:], x_sol_final[14, len(fitcasesnd)-15:len(fitcasesd)])
# ) / 2
# print(fitcasesd[-15:])
print(x_sol_final[14, len(fitcasesnd)-15:len(fitcasesnd)])
# print(mape_data_2)
df_parameters_cont_country_prov = data_creator.create_dataset_parameters(mape_data)
list_df_global_parameters.append(df_parameters_cont_country_prov)
# Creating the datasets for predictions of this (Continent, Country, Province)
df_predictions_since_today_cont_country_prov, df_predictions_since_100_cont_country_prov = (
data_creator.create_datasets_predictions()
)
return x_sol_best
best_params = parameter_list_fitted
print(f"Optimized Fitted Params: {best_params}")
x_sol_final = solve_best_params_and_predict(best_params)
if len(best_params) > n_params_without_policy_params:
best_params_base = best_params[:
n_params_without_policy_params]
best_params_policies = best_params[
n_params_without_policy_params:]
else:
best_params_base = best_params
best_params_policies = []
# Creating the parameters dataset for this (Continent, Country, Province)
mape_data = round(
(mape(fitcasesnd, x_sol_final[15, :len(fitcasesnd)]) +
mape(fitcasesd, x_sol_final[14, :len(fitcasesd)])) /
2, 3)
try:
mape_data_last_15 = round((mape(
fitcasesnd[-15:], x_sol_final[
15, len(fitcasesnd) - 15:len(fitcasesnd)]
) + mape(
fitcasesd[-15:], x_sol_final[
14, len(fitcasesnd) - 15:len(fitcasesd)])) / 2,
3)
except:
mape_data_last_15 = "Less than 15 historical points"
print("Policy: ", future_policy, "\t Enacting Time: ",
future_time, "\t Total MAPE=", mape_data,
"\t MAPE on last 15 days=", mape_data_last_15)
fun=model_covid,
y0=x_0_cases,
t_span=[t_cases_all[0], t_cases_all[-1]],
t_eval=t_cases_all,
args=tuple(optimal_params),
).y
return x_sol_best
x_sol_final = solve_best_params_and_predict(best_params)
data_creator = DELPHIDataCreator(
x_sol_final=x_sol_final, date_day_since100=date_day_since100, best_params=best_params,
continent=continent, country=country, province=province,
)
# Creating the parameters dataset for this (Continent, Country, Province)
mape_train_nondeath = (
mape(fitcasesnd, x_sol_final[15, :len(fitcasesnd)])
)
mape_train_death = (
mape(fitcasesd, x_sol_final[14, :len(fitcasesd)])
)
mape_test_nondeath = (
mape(testcasesnd, x_sol_final[15, -len(testcasesnd):])
)
mape_test_death = (
mape(testcasesd, x_sol_final[14, -len(testcasesd):])
)
df_backtest_performance_tuple = pd.DataFrame({
"continent": [continent],
"country": [country],
"province": [province],
"train_start_date": [date_day_since100],
