def run_model_mp(parameters_i):
print(mp.current_process())
# Calculate and save the aggregated log-likelihood - total, children and adult (median of 5 runs)
# likelihoods = []
# likelihoods_by_age = []
likelihoods_by_subdist = []
for i in range(m):
# Run model and get results
# model_results = model.run_coupled_model(parameters_i, parameters_v, prep_data, season) # TODO: CHANGE BACK TO NETWORK MODEL
model_results = model.run_coupled_model(parameters_i,
parameters_v,
prep_data,
season,
homogenous=True)
# Calculate log likelihood
# By clinic and age
# log_likelihood = model.log_likelihood_influenza(model_results['lambdas'],
# data_for_fit_i['by_clinic_age'], season=season)
# # By age
# log_likelihood_age = model.log_likelihood_agg_age_influenza(model_results, data_for_fit_i, season=season)
# By subdist and age
log_likelihood_age_subdist = model.log_likelihood_agg_by_subdist_influenza(
model_results['lambdas'], data_for_fit_i['by_subdist'], season,
prep_data)
# Add to the lists
# likelihoods.append(log_likelihood)
# likelihoods_by_age.append(log_likelihood_age)
likelihoods_by_subdist.append(log_likelihood_age_subdist)
# Calculate medians
# med_likelihood = np.argsort(np.array(likelihoods))[len(likelihoods)//2]
# med_likelihood_age = np.argsort(np.array(likelihoods_by_age))[len(likelihoods_by_age)//2]
med_likelihood_subdist = np.argsort(
np.array(likelihoods_by_subdist))[len(likelihoods_by_subdist) // 2]
# Return parameters, log-likelihoods and MSEs
return {
'parameters': parameters_i,
'log_likelihood_subdist':
likelihoods_by_subdist[med_likelihood_subdist]
}
def run_model_mp(x):
print(mp.current_process())
# Run the model for all 7 seasons
model_results_all_seasons = [model.run_coupled_model(parameters_i, parameters_v, prep_data, season) for season in model.seasons]
# Run the model for 1 season
# model_results = model.run_coupled_model(parameters_i, parameters_v, prep_data, season, vacc_start=vacc_start)
# Calculate likelihood for each season
likelihood_by_season = model.calculate_likelihood_lists_all_seasons(model_results_all_seasons, data_for_fit_i, prep_data, single=True)
# Filter model results - keep only relevant fields
model_results_filtered = [{'new_Is': res['new_Is'], 'new_Is_by_age': res['new_Is_by_age'], 'Is_by_clinic_age': res['Is_by_clinic_age'],
'new_Iv': res['new_Iv'], 'new_Iv_by_age': res['new_Iv_by_age'], 'Iv_by_clinic_age': res['Iv_by_clinic_age']}
for res in model_results_all_seasons]
return {'model_results': model_results_filtered, 'likelihood_by_season': likelihood_by_season}