def get_cached_mse(self):
if self.all_fits:
if scale_fit_terms:
min_term = unroll_term(self.all_fits[self.cluster_id][self.sim_key]['min_terms'], fit_terms_types['mse'])
max_term = unroll_term(self.all_fits[self.cluster_id][self.sim_key]['max_terms'], fit_terms_types['mse'])
return val_scale(self.all_fits[self.cluster_id][self.sim_key]['fit_terms'], max_term, min_term)
else:
return self.all_fits[self.cluster_id][self.sim_key]['fit_terms']['mse']
else:
error_loading_fit_terms('mse')
def __init__(self, model, sim_data, cluster_id, reinfection_penalty = 0.0, reinfection_penalty_weight = 0.0, clinical_cases_penalty = 0.0, clinical_cases_penalty_weight = 0.0, all_fits = None):
self.cluster_id = cluster_id
self.reinfection_penalty = reinfection_penalty
self.reinfection_penalty_term = reinfection_penalty
self.reinfection_penalty_weight = reinfection_penalty_weight
self.ref_reinfection_num_points = 0
self.rho = None
self.p_val = None
self.clinical_cases_penalty = clinical_cases_penalty
self.clinical_cases_penalty_term = clinical_cases_penalty
self.clinical_cases_penalty_weight = clinical_cases_penalty_weight
self.ref_clinical_cases_num_points = 0
self.sim_data = sim_data
# pre calculated fits
self.all_fits = all_fits
self.ref_avg_reinfection_rate = 0.0
self.sim_avg_reinfection_rate = 0.0
#debug_p('model id during kariba conversion prior model assignment ' + str(model.get_model_id()))
self.model = model
model_meta = self.model.get_meta()
self.sim_key = model_meta['sim_key']
#debug_p('model id during kariba conversion after model assignment ' + str(self.model.get_model_id()))
# get reinfection rates from sim data, compute reinfection penalty and model penalties
if not reinf_weight == 0:
model_report_channels = sim_report_channels_model_format(reports_channels, self.sim_data)
if not load_reinf_penalty:
self.set_reinfection_penalty(model_report_channels['reinfections'], self.cluster_id)
else:
if self.all_fits:
self.reinfection_penalty = self.all_fits[self.cluster_id][self.sim_key]['reinf_penalty']
self.reinfection_penalty_weight = reinf_weight
else:
error_loading_fit_terms('reinfection penalty')
if not load_cc_penalty:
if 'ls_folded_norm' in cc_penalty_model:
self.set_clinical_cases_penalty_by_ls(self.sim_data['cc'], self.cluster_id)
if 'ls_folded_no_norm' in cc_penalty_model:
self.set_clinical_cases_penalty_by_ls_no_norm(self.sim_data['cc'], self.cluster_id)
if 'corr' in cc_penalty_model:
self.set_clinical_cases_penalty_by_corr(self.sim_data['cc'], self.cluster_id)
else:
if self.all_fits:
max_term = 0
min_term = 0
if 'ls_folded_norm' in cc_penalty_model:
self.clinical_cases_penalty = self.all_fits[self.cluster_id][self.sim_key]['fit_terms']['cc_penalty']['ls_norm']
if scale_fit_terms:
max_term = unroll_term(self.all_fits[self.cluster_id][self.sim_key]['max_terms'], fit_terms_types['ls_norm'])
min_term = unroll_term(self.all_fits[self.cluster_id][self.sim_key]['min_terms'], fit_terms_types['ls_norm'])
elif 'ls_norm_not_folded' in cc_penalty_model:
self.clinical_cases_penalty = self.all_fits[self.cluster_id][self.sim_key]['fit_terms']['cc_penalty']['ls_norm_not_folded']
if scale_fit_terms:
# change path in fit_terms_types for ls_norm_not_folded if we use that again; need to add corresponding entry as well
# if we are not using that feature again, remove these lines altogether; this is just a placeholder
max_term = unroll_term(self.all_fits[self.cluster_id][self.sim_key]['max_terms'], fit_terms_types['ls_norm'])
min_term = unroll_term(self.all_fits[self.cluster_id][self.sim_key]['min_terms'], fit_terms_types['ls_norm'])
if 'ls_no_norm' in cc_penalty_model:
self.clinical_cases_penalty = self.all_fits[self.cluster_id][self.sim_key]['fit_terms']['cc_penalty']['ls_no_norm']
if scale_fit_terms:
# change path in fit_terms_types for ls_norm_not_folded if we use that again; need to add corresponding entry as well
# if we are not using that feature again, remove these lines altogether; this is just a placeholder
max_term = unroll_term(self.all_fits[self.cluster_id][self.sim_key]['max_terms'], fit_terms_types['ls_norm'])
min_term = unroll_term(self.all_fits[self.cluster_id][self.sim_key]['min_terms'], fit_terms_types['ls_norm'])
if 'corr_folded' in cc_penalty_model:
self.clinical_cases_penalty = self.all_fits[self.cluster_id][self.sim_key]['fit_terms']['cc_penalty']['corr_folded']['penalty']
if scale_fit_terms:
max_term = unroll_term(self.all_fits[self.cluster_id][self.sim_key]['max_terms'], fit_terms_types['corr_folded'])
min_term = unroll_term(self.all_fits[self.cluster_id][self.sim_key]['min_terms'], fit_terms_types['corr_folded'])
self.rho = self.all_fits[self.cluster_id][self.sim_key]['fit_terms']['cc_penalty']['corr_folded']['rho']
self.p_val = self.all_fits[self.cluster_id][self.sim_key]['fit_terms']['cc_penalty']['corr_folded']['p_val']
if 'corr_not_folded' in cc_penalty_model:
self.clinical_cases_penalty = self.all_fits[self.cluster_id][self.sim_key]['fit_terms']['cc_penalty']['corr_not_folded']['penalty']
if scale_fit_terms:
# change path in fit_terms_types for ls_norm_not_folded if we use that again; need to add corresponding entry as well
# if we are not using that feature again, remove these lines altogether; this is just a placeholder
max_term = unroll_term(self.all_fits[self.cluster_id][self.sim_key]['max_terms'], fit_terms_types['corr_not_folded'])
min_term = unroll_term(self.all_fits[self.cluster_id][self.sim_key]['min_terms'], fit_terms_types['corr_not_folded'])
self.rho = self.all_fits[self.cluster_id][self.sim_key]['fit_terms']['cc_penalty']['corr_not_folded']['rho']
self.p_val = self.all_fits[self.cluster_id][self.sim_key]['fit_terms']['cc_penalty']['corr_not_folded']['p_val']
self.clinical_cases_penalty_term = self.clinical_cases_penalty
if scale_fit_terms: # should have found proper min_term and max_term if scale_fit_terms is True
self.clinical_cases_penalty = val_scale(self.clinical_cases_penalty, max_term, min_term)
self.clinical_cases_penalty_weight = cc_weight
else:
error_loading_fit_terms('clinical cases penalty')
self.set_model_penalties()