def test_mesh_refinement():
""" Compare fit for coarse and fine age mesh"""
# load model and fit it
dm1 = DiseaseJson(file('tests/single_low_noise.json').read())
dm1.set_param_age_mesh(arange(0,101,20))
from dismod3 import neg_binom_model
neg_binom_model.fit_emp_prior(dm1, 'prevalence', '/dev/null')
# load another copy and fit it with a finer age mesh
dm2 = DiseaseJson(file('tests/single_low_noise.json').read())
dm2.set_param_age_mesh(arange(0,101,5))
from dismod3 import neg_binom_model
neg_binom_model.fit_emp_prior(dm2, 'prevalence', '/dev/null')
# compare fits
p1 = dm1.get_mcmc('emp_prior_mean', dismod3.utils.gbd_key_for('prevalence', 'asia_southeast', 1990, 'male'))
p2 = dm2.get_mcmc('emp_prior_mean', dismod3.utils.gbd_key_for('prevalence', 'asia_southeast', 1990, 'male'))
print p1[::20]
print p2[::20]
assert np.all(abs(p1[::20] / p2[::20] - 1.) < .05), 'Prediction should be closer to data'
def fit_simulated_disease(n=300, cv=2.):
""" Test fit for simulated disease data with noise and missingness"""
# load model to test fitting
dm = DiseaseJson(file('tests/simulation_gold_standard.json').read())
# adjust any priors and covariates as desired
dm.set_param_age_mesh(arange(0,101,2))
for type in 'incidence prevalence remission excess_mortality'.split():
dm.params['global_priors']['heterogeneity'][type] = 'Very'
dm.params['covariates']['Country_level']['LDI_id']['rate']['value'] = 0
# filter and noise up data
mort_data = []
all_data = []
for d in dm.data:
d['truth'] = d['value']
d['age_weights'] = array([1.])
if d['data_type'] == 'all-cause mortality data':
mort_data.append(d)
else:
if d['value'] > 0:
se = (cv / 100.) * d['value']
Y_i = mc.rtruncnorm(d['truth'], se**-2, 0, np.inf)
d['value'] = Y_i
d['standard_error'] = se
d['effective_sample_size'] = Y_i * (1-Y_i) / se**2
all_data.append(d)
sampled_data = random.sample(all_data, n) + mort_data
dm.data = sampled_data
# fit empirical priors and compare fit to data
from dismod3 import neg_binom_model
for rate_type in 'prevalence incidence remission excess-mortality'.split():
#neg_binom_model.fit_emp_prior(dm, rate_type, iter=1000, thin=1, burn=0, dbname='/dev/null')
neg_binom_model.fit_emp_prior(dm, rate_type, iter=30000, thin=15, burn=15000, dbname='/dev/null')
check_emp_prior_fits(dm)
# fit posterior
delattr(dm, 'vars') # remove vars so that gbd_disease_model creates its own version
from dismod3 import gbd_disease_model
keys = dismod3.utils.gbd_keys(region_list=['north_america_high_income'],
year_list=[1990], sex_list=['male'])
gbd_disease_model.fit(dm, method='map', keys=keys, verbose=1) ## first generate decent initial conditions
gbd_disease_model.fit(dm, method='mcmc', keys=keys, iter=30000, thin=15, burn=15000, verbose=1, dbname='/dev/null') ## then sample the posterior via MCMC
#gbd_disease_model.fit(dm, method='mcmc', keys=keys, iter=1000, thin=1, burn=0, verbose=1, dbname='/dev/null') ## fast for dev
print 'error compared to the noisy data (coefficient of variation = %.2f)' % cv
check_posterior_fits(dm)
dm.data = all_data
for d in dm.data:
if d['data_type'] != 'all-cause mortality data':
d['noisy_data'] = d['value']
d['value'] = d['truth']
print 'error compared to the truth'
are, coverage = check_posterior_fits(dm)
print
print 'Median Absolute Relative Error of Posterior Predictions:', median(are)
print 'Pct coverage:', 100*mean(coverage)
f = open('score_%d_%f.txt' % (n, cv), 'a')
f.write('%10.10f,%10.10f\n' % (median(are), mean(coverage)))
f.close()
dm.all_data = all_data
dm.data = sampled_data
for d in dm.data:
if d['data_type'] != 'all-cause mortality data':
d['value'] = d['noisy_data']
generate_figure(dm, n, cv)
return dm
