def fit_posterior(id, region, sex, year):
""" Fit posterior of specified region/sex/year for specified model
Parameters
----------
id : int
The model id number for the job to fit
region : str
From dismod3.settings.gbd_regions, but clean()-ed
sex : str, from dismod3.settings.gbd_sexes
year : str, from dismod3.settings.gbd_years
Example
-------
>>> import fit_posterior
>>> fit_posterior.fit_posterior(2552, 'asia_east', 'male', '2005')
"""
#print 'updating job status on server'
#dismod3.log_job_status(id, 'posterior', '%s--%s--%s' % (region, sex, year), 'Running')
dm = dismod3.load_disease_model(id)
#dm.data = [] # for testing, remove all data
keys = dismod3.utils.gbd_keys(region_list=[region], year_list=[year], sex_list=[sex], type_list=['prevalence'])
# fit the model
dir = dismod3.settings.JOB_WORKING_DIR % id
import dismod3.neg_binom_model as model
k0 = keys[0]
dm.vars = {}
dm.vars[k0] = model.setup(dm, k0, dm.data)
dm.mcmc = mc.MCMC(dm.vars)
dm.mcmc.sample(iter=50000,burn=25000,thin=50,verbose=1)
dm.map = mc.MAP(dm.vars)
dm.map.fit()
model.store_mcmc_fit(dm, k0, dm.vars[k0])
# update job status file
#print 'updating job status on server'
#dismod3.log_job_status(id, 'posterior',
# '%s--%s--%s' % (region, sex, year), 'Completed')
# save results (do this last, because it removes things from the disease model that plotting function, etc, might need
keys = dismod3.utils.gbd_keys(region_list=[region], year_list=[year], sex_list=[sex], type_list=['prevalence'])
dm.save('dm-%d-posterior-%s-%s-%s.json' % (id, region, sex, year), keys_to_save=keys)
return dm
def prior_vals(dm, type):
""" Estimate the prior distribution on param_age_mesh for a particular type
Parameters
----------
dm : DiseaseJson
type : str, one of 'prevalence', 'incidence', 'remission', 'excess-mortality'
Results
-------
vars : dict of stochastics generated by logit_normal_model
"""
import random
import dismod3.neg_binom_model as model
data = [
d for d in dm.data
if clean(d['data_type']).find(type) != -1 and not d.get('ignore') != 1
]
dm.clear_empirical_prior()
dm.fit_initial_estimate(type, data)
if len(data) >= 8:
random.seed(12345)
data = random.sample(data, 8)
X_region, X_study = model.regional_covariates('none', dm.get_covariates())
est_mesh = dm.get_estimate_age_mesh()
prior_dict = dict(alpha=pl.zeros(len(X_region)),
beta=pl.zeros(len(X_study)),
gamma=-10 * pl.ones(len(est_mesh)),
sigma_alpha=[1.],
sigma_beta=[1.],
sigma_gamma=[1.],
delta=100.,
sigma_delta=1.)
vars = model.setup(dm, key=type, data_list=data, emp_prior=prior_dict)
mc.MAP(vars).fit(method='fmin_powell', tol=.1, iterlim=100)
mc.MCMC(vars).sample(1)
return vars
def prior_vals(dm, type):
""" Estimate the prior distribution on param_age_mesh for a particular type
Parameters
----------
dm : DiseaseJson
type : str, one of 'prevalence', 'incidence', 'remission', 'excess-mortality'
Results
-------
vars : dict of stochastics generated by logit_normal_model
"""
import random
import dismod3.neg_binom_model as model
data = [d for d in dm.data if clean(d['data_type']).find(type) != -1 and not d.get('ignore') != 1]
dm.clear_empirical_prior()
dm.fit_initial_estimate(type, data)
if len(data) >= 8:
random.seed(12345)
data = random.sample(data, 8)
X_region, X_study = model.regional_covariates('none', dm.get_covariates())
est_mesh = dm.get_estimate_age_mesh()
prior_dict = dict(alpha=pl.zeros(len(X_region)),
beta=pl.zeros(len(X_study)),
gamma=-10*pl.ones(len(est_mesh)),
sigma_alpha=[1.],
sigma_beta=[1.],
sigma_gamma=[1.],
delta=100.,
sigma_delta=1.)
vars = model.setup(dm, key=type, data_list=data, emp_prior=prior_dict)
mc.MAP(vars).fit(method='fmin_powell', tol=.1, iterlim=100)
mc.MCMC(vars).sample(1)
return vars
def hep_c_fit(regions, prediction_years, data_year_start=-inf, data_year_end=inf, egypt_flag=False):
""" Fit prevalence for regions and years specified """
print "\n***************************\nfitting %s for %s (using data from years %f to %f)" % (
regions,
prediction_years,
data_year_start,
data_year_end,
)
## load model to fit
# dm = DiseaseJson(file('tests/hep_c.json').read())
id = 8788
dismod3.disease_json.create_disease_model_dir(id)
dm = dismod3.fetch_disease_model(id)
## adjust the expert priors
dm.params["global_priors"]["heterogeneity"]["prevalence"] = "Very"
dm.params["global_priors"]["smoothness"]["prevalence"]["amount"] = "Slightly"
# TODO: construct examples of adjusting other covariates
# ipdb> dm.params['global_priors'].keys()
# [u'increasing', u'unimodal', u'level_bounds', u'y_maximum', u'note', u'level_value', u'decreasing', u'parameter_age_mesh', u'heterogeneity', u'smoothness']
# ipdb> dm.params['global_priors']['smoothness']['prevalence']
# {u'age_start': 0, u'amount': u'Moderately', u'age_end': 100}
# include a study-level covariate for 'bias'
covariates_dict = dm.get_covariates()
covariates_dict["Study_level"]["bias"]["rate"]["value"] = 1
# TODO: construct additional examples of adjusting covariates
## select relevant prevalence data
# TODO: streamline data selection functions
if egypt_flag:
dm.data = [d for d in dm.data if d["country_iso3_code"] == "EGY"]
else:
dm.data = [
d
for d in dm.data
if dismod3.utils.clean(d["gbd_region"]) in regions
and float(d["year_end"]) >= data_year_start
and float(d["year_start"]) <= data_year_end
and d["country_iso3_code"] != "EGY"
]
## create, fit, and save rate model
dm.vars = {}
keys = dismod3.utils.gbd_keys(type_list=["prevalence"], region_list=regions, year_list=prediction_years)
# TODO: consider how to do this for models that use the complete disease model
# TODO: consider adding hierarchical similarity priors for the male and female models
k0 = keys[0] # looks like k0='prevalence+asia_south+1990+male'
dm.vars[k0] = neg_binom_model.setup(dm, k0, dm.data)
dm.mcmc = mc.MCMC(dm.vars)
dm.mcmc.sample(iter=50000, burn=25000, thin=50, verbose=1)
# make map object so we can compute AIC and BIC
dm.map = mc.MAP(dm.vars)
dm.map.fit()
for k in keys:
# save the results in the disease model
dm.vars[k] = dm.vars[k0]
neg_binom_model.store_mcmc_fit(dm, k, dm.vars[k])
# check autocorrelation to confirm chain has mixed
test_model.summarize_acorr(dm.vars[k]["rate_stoch"].trace())
# generate plots of results
dismod3.tile_plot_disease_model(dm, [k], defaults={"ymax": 0.15, "alpha": 0.5})
dm.savefig("dm-%d-posterior-%s.%f.png" % (dm.id, k, random()))
# summarize fit quality graphically, as well as parameter posteriors
dismod3.plotting.plot_posterior_predicted_checks(dm, k0)
dm.savefig("dm-%d-check-%s.%f.png" % (dm.id, k0, random()))
dismod3.post_disease_model(dm)
return dm
