def test_simulated_disease():
""" Test fit for simulated disease data"""
# load model to test fitting
dm = DiseaseJson(file('tests/test_disease_1.json').read())
# filter and noise up data
cov = .5
data = []
for d in dm.data:
d['truth'] = d['value']
if dismod3.utils.clean(d['gbd_region']) == 'north_america_high_income':
if d['data_type'] == 'all-cause mortality data':
data.append(d)
else:
se = (cov * d['value'])
d['value'] = mc.rtruncnorm(d['truth'], se**-2, 0, np.inf)
d['age_start'] -= 5
d['age_end'] = d['age_start']+9
d['age_weights'] = np.ones(d['age_end']-d['age_start']+1)
d['age_weights'] /= float(len(d['age_weights']))
d['standard_error'] = se
data.append(d)
dm.data = 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, '/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=1000, thin=5, burn=5000, verbose=1, dbname='/dev/null') ## then sample the posterior via MCMC
print 'error compared to the noisy data (coefficient of variation = %.2f)' % cov
check_posterior_fits(dm)
for d in dm.data:
d['value'] = d['truth']
d['age_start'] += 5
d['age_end'] = d['age_start']
d['age_weights'] = np.ones(d['age_end']-d['age_start']+1)
d['age_weights'] /= float(len(d['age_weights']))
print 'error compared to the truth'
check_posterior_fits(dm)
return dm
def step(self):
# The right-hand sides for the linear constraints
self.rhs = dict(zip(self.constraint_offdiags,
[np.asarray(np.dot(pm.utils.value(od), self.g.value)).squeeze() for od in self.constraint_offdiags]))
for i in xrange(self.n):
try:
lb, ub, rhs = self.get_bounds(i)
except ConstraintError:
warnings.warn('Bounds could not be set, this element is very highly constrained')
continue
newgs = np.hstack((self.g.value[i], pm.rtruncnorm(0,1,lb,ub,size=self.n_draws)))
lpls = np.hstack((self.get_likelihood_only(), np.empty(self.n_draws)))
for j, newg in enumerate(newgs[1:]):
self.set_g_value(newg, i)
# The newgs are drawn from the prior, taking the canstraints into account, so
# accept them based on the 'likelihood children' only.
try:
lpls[j+1] = self.get_likelihood_only()
except pm.ZeroProbability:
lpls[j+1] = -np.inf
lpls -= pm.flib.logsum(lpls)
newg = newgs[pm.rcategorical(np.exp(lpls))]
self.set_g_value(newg, i)
for od in self.constraint_offdiags:
rhs[od] += np.asarray(pm.utils.value(od))[:,i].squeeze() * newg
self.rhs = rhs
def propose(self):
tau = 1. / (self.adaptive_scale_factor * self.proposal_sd) ** 2
self.stochastic.value = pm.rtruncnorm(
self.stochastic.value,
tau,
self.low_bound,
self.up_bound)
def step(self):
# TODO: Propose from not the prior, and tune using the asf's.
# The right-hand sides for the linear constraints
self.rhs = dict(zip(self.constraint_offdiags,
[np.asarray(np.dot(pm.utils.value(od), self.g.value)).squeeze() for od in self.constraint_offdiags]))
this_round = np.zeros(self.n, dtype='int')
for i in xrange(self.n):
self.check_constraints()
# Jump an element of g.
lb, ub, rhs = self.get_bounds(i)
# Propose a new value
curg = self.g.value[i]
tau = 1./self.adaptive_scale_factor[i]**2
newg = pm.rtruncnorm(curg,tau,lb,ub)[0]
# The Hastings factor
hf = pm.truncnorm_like(curg,newg,tau,lb,ub)-pm.truncnorm_like(newg,curg,tau,lb,ub)
# The difference in prior log-probabilities of g
dpri = .5*(curg**2 - newg**2)
# Get the current log-likelihood of the non-constraint children.
lpl = self.get_likelihood_only()
cv = {}
for od in self.all_offdiags:
for c in od.children:
cv[c] = c.value.copy()
# Inter the proposed value and get the proposed log-likelihood.
self.set_g_value(newg, i)
try:
lpl_p = self.get_likelihood_only()
except pm.ZeroProbability:
self.reject(i, cv)
self.check_constraints()
this_round[i] = -1
continue
# M-H acceptance
if np.log(np.random.random()) < lpl_p - lpl + hf + dpri:
self.accepted[i] += 1
this_round[i] = 1
for od in self.constraint_offdiags:
rhs[od] += np.asarray(pm.utils.value(od))[:,i].squeeze() * newg
self.rhs = rhs
self.check_constraints()
else:
self.reject(i, cv)
self.check_constraints()
this_round[i] = -1
def generate_and_append_data(data, data_type, truth, age_intervals, condition,
gbd_region, country, year, sex, effective_sample_size, cov=0.):
""" create simulated data"""
for a0, a1 in age_intervals:
d = { 'condition': condition,
'data_type': data_type,
'gbd_region': gbd_region,
'region': country,
'year_start': year,
'year_end': year,
'sex': sex,
'age_start': a0,
'age_end': a1,
'id': len(data),}
holdout = 0
d['ignore'] = holdout
d['test_set'] = holdout
ages = range(a0, a1 + 1)
if data_type == 'incidence_x_duration':
pop = 1. * np.ones_like(ages)
else:
pop = np.array([population_by_age[(country, str(year), sex)][a] for a in ages])
if np.sum(pop) > 0:
pop /= float(np.sum(pop)) # normalize the pop weights to sum to 1
else:
pop = np.ones_like(ages) / float(len(ages)) # for countries where pop is zero, fill in constant structure
d['age_weights'] = list(pop)
p0 = dismod3.utils.rate_for_range(truth, ages, pop)
d['truth'] = p0
if p0 == 0 or cov == 0:
p1 = p0
d['value'] = p1
d['effective_sample_size'] = effective_sample_size
else:
p1 = mc.rtruncnorm(p0, (cov/100. * p0)**-2, 0, np.inf)
assert not np.isnan(p1)
d['value'] = p1
d['standard_error'] = p0 * cov/100.
data.append(d)
def generate_and_append_data(data,
data_type,
truth,
age_intervals,
condition,
gbd_region,
country,
year,
sex,
effective_sample_size,
cov=0.):
""" create simulated data"""
for a0, a1 in age_intervals:
d = {
'condition': condition,
'data_type': data_type,
'gbd_region': gbd_region,
'region': country,
'year_start': year,
'year_end': year,
'sex': sex,
'age_start': a0,
'age_end': a1,
'id': len(data),
}
holdout = 0
d['ignore'] = holdout
d['test_set'] = holdout
ages = range(a0, a1 + 1)
if data_type == 'incidence_x_duration':
pop = 1. * np.ones_like(ages)
else:
pop = np.array([
population_by_age[(country, str(year), sex)][a] for a in ages
])
if np.sum(pop) > 0:
pop /= float(
np.sum(pop)) # normalize the pop weights to sum to 1
else:
pop = np.ones_like(ages) / float(
len(ages)
) # for countries where pop is zero, fill in constant structure
d['age_weights'] = list(pop)
p0 = dismod3.utils.rate_for_range(truth, ages, pop)
d['truth'] = p0
if p0 == 0 or cov == 0:
p1 = p0
d['value'] = p1
d['effective_sample_size'] = effective_sample_size
else:
p1 = mc.rtruncnorm(p0, (cov / 100. * p0)**-2, 0, np.inf)
assert not np.isnan(p1)
d['value'] = p1
d['standard_error'] = p0 * cov / 100.
data.append(d)
def propose(self):
tau = 1. / (self.adaptive_scale_factor * self.proposal_sd)**2
self.stochastic.value = pm.rtruncnorm(self.stochastic.value, tau,
self.low_bound, self.up_bound)
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
