def get_sweep_results(sim_meta_config_files, calib_file_path, tags_data_file_path):
# Login to COMPs
comps_login()
# find total number of simulations across given experiment files
num_sims = 0
for sim_meta_config_file in sim_meta_config_files:
with open(sim_meta_config_file) as metadata_file:
metadata = json.loads(metadata_file.read())
num_sims = num_sims + len(metadata['sims'])
# Download simulations locally
# sample sim meta config file (like "C:\\Users\\Mnikolov\\Zambia-raw\\dtk-scripts\\1node\\simulations\\Sinamalina_Sinazongwe_Calibration_e9979059-33f8-e411-93f9-f0921c16b9e7.json")
#print 'Downloading simulations from experiment ' + str(sim_meta_config_files) + '...'
# simulations tag data structure: accumulates sims meta information from sims tags
tag_data = {
'ITN trajectory': [],\
'Drug coverage per round': [],\
'Temporary habitat scale': [],\
'Constant habitat scale': []\
}
# iterate through experiments
calib_output = {}
# count processed sims to updated progress
count = 0
for sim_meta_config_file in sim_meta_config_files:
# construct experiment directory structure
with open(sim_meta_config_file) as metadata_file:
metadata = json.loads(metadata_file.read())
output_path = metadata['sim_root']
exp_id = metadata['exp_id']
exp_name = metadata['exp_name']
sim_dir_map = CompsDTKOutputParser.createSimDirectoryMap(exp_id)
# get all successfully completed sims in experiment
for sim_id, sim in metadata['sims'].items():
# get path to the sim timeseries channels data
timeseries_path = os.path.join(sim_dir_map[sim_id],'output', 'InsetChart.json')
#get sim timeseries channels data; json2dict returns None if timeseries_path points to non-existing file, which is the case if the sim has not successfully finished
sim_output = json2dict(timeseries_path)
# only download successfully completed simulations
if sim_output == None:
continue
# delete all but the specified channels
for channel in sim_output['Channels'].keys():
if not channel in channels:
del(sim_output['Channels'][channel])
# process specified reports
report_channels_data = {}
if not reports_channels == None:
report_channels_data = process_reports(reports_channels, sim_dir_map, sim_id)
# record sim meta information including sim tags
tags_path = os.path.join(sim_dir_map[sim_id], 'tags.json')
f = open(tags_path, 'r')
tags = f.read()
sim_meta = ast.literal_eval(tags)
append_tag_data(sim_meta, tag_data)
# construct sim group key and sim key
x_temp_h = sim_meta_2_temp_h(sim_meta)
const_h = sim_meta_2_const_h(sim_meta)
itn_level = sim_meta_2_itn_level(sim_meta)
drug_coverage_level = sim_meta_2_drug_cov(sim_meta)
sim_key = get_sim_key(x_temp_h, const_h, itn_level, drug_coverage_level)
sim_group_key = get_sim_group_key(itn_level, drug_coverage_level)
# store sim channels data
if sim_group_key not in calib_output:
calib_output[sim_group_key] = {}
calib_output[sim_group_key][sim_key] = {
# can add/remove data entries depending on needs
'prevalence': sim_output['Channels']['New Diagnostic Prevalence']['Data'],
'reinfections': report_channels_data['reinfections'],
'meta':sim_meta,
'sim_id':sim_id
}
'''
count = count + 1
percent_complete = 100*count/(num_sims+0.0)
sys.stdout.write('\r')
sys.stdout.write('%2f %%' % percent_complete)
#sys.stdout.write('%d' % count)
sys.stdout.flush()
'''
print ""
print "Writing files..."
with open(calib_file_path, 'w') as calib_f:
json.dump(calib_output, calib_f)
print str(len(calib_output)) + ' simulation results saved to ' + calib_file_path
with open(tags_data_file_path, 'w') as tags_f:
json.dump(tag_data, tags_f)
print 'Meta data tags saved to ' + tags_data_file_path
print ""
return calib_f
sim_output = sim['output']
x_temp_h = float(sim['meta']['x_Temporary_Larval_Habitat'])
const_h_struct = ast.literal_eval(sim['meta']['scale_larval_habitats_single'])
const_h = const_h_struct[0][1][1]
itn_level_struct = ast.literal_eval(sim['meta']['add_ITN_mult'])
itn_level = itn_level_struct[0][1][0][0][1]
drug_coverage_level_struct = ast.literal_eval(sim['meta']['add_drug_multi_campaigns'])
drug_coverage_level = drug_coverage_level_struct[0][1][0]['coverage']
sim_key = get_sim_key(x_temp_h, const_h, itn_level, drug_coverage_level)
sim_group_key = get_sim_group_key(itn_level, drug_coverage_level)
sim_data = sim_output['Channels']['New Clinical Cases']['Data']
if not sim_group_key in cc:
cc[sim_group_key] = {}
cc[sim_group_key][sim_key] = sim_data
del calib_data
gc.collect()
def fit(self):
models_list_prime = calib_data_2_models_list(self.calib_data)
best_fits = {}
all_fits = {}
#all_fits = {'fit':{'min_residual':float('inf')}, }
all_fits['min_residual'] = float('inf')
all_fits['max_residual'] = 0.0
all_fits['models'] = {}
debug_p('category ' + self.category)
for idx,cluster_id in enumerate(c2c(self.category)):
models_list = copy.deepcopy(models_list_prime)
print "Processing cluster " + cluster_id + "."
debug_p('Processing cluster ' + cluster_id + " in " + self.category + ".")
itn_traj = cluster_2_itn_traj(cluster_id)
drug_cov = cluster_2_drug_cov(cluster_id)
# prune models to the ones matching prior data
cluster_models = []
for model in models_list:
model_meta = model.get_meta()
if model_meta['group_key'] == get_sim_group_key(itn_traj, drug_cov):
#debug_p('model id before kariba conversion ' + str(model.get_model_id()))
group_key = model_meta['group_key']
sim_key = model_meta['sim_key']
model = KaribaModel(model, self.calib_data[group_key][sim_key], cluster_id, all_fits = self.fit_terms)
#model = kariba_model
#debug_p('model id after kariba conversion ' + str(model.get_model_id()))
cluster_models.append(model)
surv_data = {}
all_ref_objs_found = True
for channel_code in objectives_channel_codes:
if channel_code == 'prevalence':
prev_data = c2p(cluster_id)
if prev_data:
surv_data[channel_code] = prev_data
else:
msg = 'Prevalence objective reference data was not found!\n Skipping cluster ' + cluster_id + ' fit!'
print msg
all_ref_objs_found = False
else:
msg = "Channel objective" + channel_code + " not implemented yet!\nSetting objective reference data to None."
warn_p(msg)
surv_data[channel_code] = None
# one of the reference objective channels was not found; skipping cluster fit!
if not all_ref_objs_found:
continue
ref = d2f(surv_data)
# adjust highest possible fit to account for RDT+ model in dtk not reflecting reality at the upper end
obj_prev = ref.get_obj_by_name('prevalence')
d_points = obj_prev.get_points()
obj_prev.set_points([min(point, rdt_max) for point in d_points])
fitting_set = FittingSet(cluster_id, cluster_models, ref)
if load_prevalence_mse:
fit = Fit(fitting_set, type = 'mmse_distance_cached')
else:
fit = Fit(fitting_set)
best_fit_model = fit.best_fit_mmse_distance()
min_residual = fit.get_min_residual()
max_residual = fit.get_max_residual()
if min_residual < all_fits['min_residual']:
all_fits['min_residual'] = min_residual
if max_residual > all_fits['max_residual']:
all_fits['max_residual'] = max_residual
if best_fit_model:
temp_h, const_h, itn_level, drug_coverage_level = get_model_params(best_fit_model)
best_fit_meta = best_fit_model.get_meta()
best_fits[cluster_id] = {}
best_fits[cluster_id]['habs'] = {}
best_fits[cluster_id]['habs']['const_h'] = const_h
best_fits[cluster_id]['habs']['temp_h'] = temp_h
best_fits[cluster_id]['ITN_cov'] = itn_level
best_fits[cluster_id]['category'] = self.category
best_fits[cluster_id]['MSAT_cov'] = drug_coverage_level
best_fits[cluster_id]['sim_id'] = best_fit_meta['sim_id']
best_fits[cluster_id]['sim_key'] = best_fit_meta['sim_key']
best_fits[cluster_id]['group_key'] = best_fit_meta['group_key']
best_fits[cluster_id]['fit_value'] = best_fit_model.get_fit_val()
best_fits[cluster_id]['sim_avg_reinfection_rate'] = best_fit_model.get_sim_avg_reinfection_rate()
best_fits[cluster_id]['ref_avg_reinfection_rate'] = best_fit_model.get_ref_avg_reinfection_rate()
best_fits[cluster_id]['prevalence'] = best_fit_model.get_objective_by_name('prevalence').get_points()
# redundancy; to be refactored via FitEntry class
best_fits[cluster_id]['fit'] = {}
best_fits[cluster_id]['fit']['value'] = best_fit_model.get_fit_val()
best_fits[cluster_id]['fit']['temp_h'] = temp_h
best_fits[cluster_id]['fit']['const_h'] = const_h
best_fits[cluster_id]['fit']['ITN_cov'] = itn_level
best_fits[cluster_id]['fit']['MSAT_cov'] = drug_coverage_level
best_fits[cluster_id]['fit']['sim_id'] = best_fit_meta['sim_id']
best_fits[cluster_id]['fit']['sim_key'] = best_fit_meta['sim_key']
best_fits[cluster_id]['mse'] = {}
best_fits[cluster_id]['mse']['value'] = fit.get_min_mses()['prevalence']['value'] # get mmse for objective prevalence
best_fit_mse_model = fit.get_min_mses()['prevalence']['model']
temp_h, const_h, itn_level, drug_coverage_level = get_model_params(best_fit_mse_model)
model_meta_data = best_fit_mse_model.get_meta()
best_fits[cluster_id]['mse']['temp_h'] = temp_h
best_fits[cluster_id]['mse']['const_h'] = const_h
best_fits[cluster_id]['mse']['ITN_cov'] = itn_level
best_fits[cluster_id]['mse']['MSAT_cov'] = drug_coverage_level
best_fits[cluster_id]['mse']['sim_id'] = model_meta_data['sim_id']
best_fits[cluster_id]['mse']['sim_key'] = model_meta_data['sim_key']
best_fits[cluster_id]['cc_penalty'] = {}
best_fits[cluster_id]['cc_penalty']['value'] = fit.get_min_penalties()['prevalence']['value'] # get clinical penalty for objective prevalence; at present this is just the clinical cases penalty; if reinfection is considered the code needs to be adjusted
best_fit_cc_penalty_model = fit.get_min_penalties()['prevalence']['model']
temp_h, const_h, itn_level, drug_coverage_level = get_model_params(best_fit_cc_penalty_model)
model_meta_data = best_fit_cc_penalty_model.get_meta()
best_fits[cluster_id]['cc_penalty']['temp_h'] = temp_h
best_fits[cluster_id]['cc_penalty']['const_h'] = const_h
best_fits[cluster_id]['cc_penalty']['ITN_cov'] = itn_level
best_fits[cluster_id]['cc_penalty']['MSAT_cov'] = drug_coverage_level
best_fits[cluster_id]['cc_penalty']['sim_id'] = model_meta_data['sim_id']
best_fits[cluster_id]['cc_penalty']['sim_key'] = model_meta_data['sim_key']
rho = best_fit_model.get_rho()
p_val = best_fit_model.get_p_val()
if rho and p_val :
best_fits[cluster_id]['rho'] = rho
best_fits[cluster_id]['p_val'] = p_val
debug_p('rho' + str(rho))
debug_p('p_val' + str(p_val))
else:
msg = "something went wrong and the best fit for " + cluster_id + " could not be found."
warn_p(msg)
all_fits['models'][cluster_id] = cluster_models
#all_fits['models'][cluster_id] = fit.get_fitting_set_models()
print str(idx+1) + " clusters have been processed."
debug_p( str(idx+1) + " clusters have been processed in category " + self.category)
'''
if idx > 0:
break
'''
return best_fits, all_fits
