def fit_emp_prior(id, param_type):
""" Fit empirical prior of specified type for specified model
Parameters
----------
id : int
The model id number for the job to fit
param_type : str, one of incidence, prevalence, remission, excess-mortality
The disease parameter to generate empirical priors for
Example
-------
>>> import fit_emp_prior
>>> fit_emp_prior.fit_emp_prior(2552, 'incidence')
"""
#dismod3.log_job_status(id, 'empirical_priors', param_type, 'Running')
# load disease model
dm = dismod3.load_disease_model(id)
#dm.data = [] # remove all data to speed up computation, for test
import dismod3.neg_binom_model as model
dir = dismod3.settings.JOB_WORKING_DIR % id
model.fit_emp_prior(dm, param_type, dbname='%s/empirical_priors/pickle/dm-%d-emp_prior-%s.pickle' % (dir, id, param_type))
# generate empirical prior plots
from pylab import subplot
for sex in dismod3.settings.gbd_sexes:
for year in dismod3.settings.gbd_years:
keys = dismod3.utils.gbd_keys(region_list=['all'], year_list=[year], sex_list=[sex], type_list=[param_type])
dismod3.tile_plot_disease_model(dm, keys, defaults={})
dm.savefig('dm-%d-emp_prior-%s-%s-%s.png' % (id, param_type, sex, year))
# TODO: put this in a separate script, which runs after all empirical priors are computed
for effect in ['alpha', 'beta', 'gamma', 'delta']:
dismod3.plotting.plot_empirical_prior_effects([dm], effect)
dm.savefig('dm-%d-emp-prior-%s-%s.png' % (id, param_type, effect))
# summarize fit quality graphically, as well as parameter posteriors
k0 = keys[0]
dm.vars = {k0: dm.vars} # hack to make posterior predictions plot
dismod3.plotting.plot_posterior_predicted_checks(dm, k0)
dm.savefig('dm-%d-emp-prior-check-%s.png' % (dm.id, param_type))
dm.vars = dm.vars[k0] # undo hack to make posterior predictions plot
# save results (do this last, because it removes things from the disease model that plotting function, etc, might need
dm.save('dm-%d-prior-%s.json' % (id, param_type))
dismod3.try_posting_disease_model(dm, ntries=5)
#dismod3.log_job_status(id, 'empirical_priors', param_type, 'Completed')
return dm
def upload_fits(id):
""" Send results of cluster fits to dismod server
Parameters
----------
id : int
The model id number
Example
-------
>>> import fit_emp_prior
>>> fit_emp_prior.fit_emp_prior(2552, 'incidence')
>>> import upload_fits
>>> upload_fits.upload_fits(2552)
"""
# load disease model
dm = dismod3.load_disease_model(id) # this merges together results from all fits
dismod3.try_posting_disease_model(dm, ntries=5)
for age in age_mesh:
x.append(age)
y.append(measure_out.model[index_dict[(dm4_type, year, age)]])
key = dismod3.gbd_key_for(dm3_type, r, year, sex)
est = dismod3.utils.interpolate(x, y, dm.get_estimate_age_mesh())
dm.set_truth(key, est)
dismod3.tile_plot_disease_model(dm, [key], defaults={})
try:
pl.savefig(dismod3.settings.JOB_WORKING_DIR % id + '/dm-%d-posterior-%s-%s-%s.png' % (id, dm3_type, sex, year)) # TODO: refactor naming into its own function
except IOError, e:
print 'Warning: could not create png. Maybe it exists already?\n%s' % e
# save results (do this last, because it removes things from the disease model that plotting function, etc, might need
dismod3.try_posting_disease_model(dm, ntries=5)
print
print '********************'
print 'computation complete'
print '********************'
def main():
import optparse
usage = 'usage: %prog [options] disease_model_id'
parser = optparse.OptionParser(usage)
(options, args) = parser.parse_args()
if len(args) != 1:
for effect in ['alpha', 'beta', 'gamma', 'delta']:
try:
dismod3.plotting.plot_empirical_prior_effects([dm], effect)
dm.savefig('dm-%d-emp-prior-%s.png' % (id, effect))
except Exception:
print 'failed to plot %s' % effect
# save table output
try:
dismod3.table.make_tables(dm)
except Exception, e:
print 'Failed to make table'
print e
# send to website
dismod3.try_posting_disease_model(dm, ntries=5)
# record that job is done
o = '%s/empirical_priors/stdout/%d_running.txt' % (dir, id)
f = open(o, 'a')
import time
f.write('\n**** JOB DONE AT %s' % time.strftime('%c'))
f.close()
def merge_data_csvs(id):
df = pandas.DataFrame()
dir = dismod3.settings.JOB_WORKING_DIR % id
#print dir
