def run_all(fit_empirical_priors=False):
""" Enqueues all jobs necessary to fit model
Example
-------
>>> import run_all
>>> run_all.run_all()
"""
if fit_empirical_priors:
# fit empirical priors (by pooling data from all regions)
import emp_priors, graphics
emp_priors.admin_err_and_bias(recompute=True)
emp_priors.llin_discard_rate(recompute=True)
emp_priors.neg_binom(recompute=True)
emp_priors.survey_design(recompute=True)
graphics.plot_neg_binom_fits()
#fit each region individually for this model
from data import Data
data = Data()
post_names = []
dir = settings.PATH
for ii, r in enumerate(sorted(data.countries)):
o = '%s/%s-stdout.txt' % (dir, r[0:3])
e = '%s/%s-stderr.txt' % (dir, r[0:3])
name_str = 'ITN%s-%d' % (r[0:3].strip(), ii)
post_names.append(name_str)
call_str = 'qsub -cwd -o %s -e %s ' % (o,e) \
+ '-N %s ' % name_str \
+ 'fit.sh %d' % ii
subprocess.call(call_str, shell=True)
# TODO: after all posteriors have finished running, notify me via email
hold_str = '-hold_jid %s ' % ','.join(post_names)
o = '%s/summarize.stdout' % dir
e = '%s/summarize.stderr' % dir
call_str = 'qsub -cwd -o %s -e %s ' % (o,e) \
+ hold_str \
+ '-N netsdone ' \
+ 'fit.sh summarize'
subprocess.call(call_str, shell=True)
def run_all(fit_empirical_priors=False):
""" Enqueues all jobs necessary to fit model
Example
-------
>>> import run_all
>>> run_all.run_all()
"""
if fit_empirical_priors:
# fit empirical priors (by pooling data from all regions)
import emp_priors, graphics
emp_priors.admin_err_and_bias(recompute=True)
emp_priors.llin_discard_rate(recompute=True)
emp_priors.neg_binom(recompute=True)
emp_priors.survey_design(recompute=True)
graphics.plot_neg_binom_fits()
#fit each region individually for this model
from data import Data
data = Data()
post_names = []
dir = settings.PATH
for ii, r in enumerate(sorted(data.countries)):
o = '%s/%s-stdout.txt' % (dir, r[0:3])
e = '%s/%s-stderr.txt' % (dir, r[0:3])
name_str = 'ITN%s-%d' % (r[0:3].strip(), ii)
post_names.append(name_str)
call_str = 'qsub -cwd -o %s -e %s ' % (o,e) \
+ '-N %s ' % name_str \
+ 'fit.sh %d' % ii
subprocess.call(call_str, shell=True)
# TODO: after all posteriors have finished running, notify me via email
hold_str = '-hold_jid %s ' % ','.join(post_names)
o = '%s/summarize.stdout' % dir
e = '%s/summarize.stderr' % dir
call_str = 'qsub -cwd -o %s -e %s ' % (o,e) \
+ hold_str \
+ '-N netsdone ' \
+ 'fit.sh summarize'
subprocess.call(call_str, shell=True)
def main(country_id):
from settings import year_start, year_end
c = sorted(data.countries)[country_id]
print c
# get population data for this country, to calculate LLINs per capita
pop = data.population_for(c, year_start, year_end)
### setup the model variables
vars = []
#######################
### compartmental model
###
#######################
# Empirical Bayesian priors
prior = emp_priors.llin_discard_rate()
pi = Beta('Pr[net is lost]', prior['alpha'], prior['beta'])
vars += [pi]
prior = emp_priors.admin_err_and_bias()
e_d = Normal('bias in admin dist data',
prior['eps']['mu'], prior['eps']['tau'])
s_d = Normal('error in admin dist data',
prior['sigma']['mu'], prior['sigma']['tau'])
beta = Normal('relative weights of next year to current year in admin dist data',
prior['beta']['mu'], prior['beta']['tau'])
vars += [s_d, e_d, beta]
prior = emp_priors.neg_binom()
eta = Normal('coverage parameter', prior['eta']['mu'], prior['eta']['tau'], value=prior['eta']['mu'])
alpha = Gamma('dispersion parameter', prior['alpha']['alpha'], prior['alpha']['beta'], value=prior['alpha']['mu'])
vars += [eta, alpha]
prior = emp_priors.survey_design()
gamma = Normal('survey design factor for coverage data', prior['mu'], prior['tau'])
vars += [gamma]
# Fully Bayesian priors
s_m = Lognormal('error_in_llin_ship', log(.05), .5**-2, value=.05)
vars += [s_m]
s_rb = Lognormal('recall bias factor', log(.05), .5**-2, value=.05)
vars += [s_rb]
mu_N = .001 * pop
std_N = where(arange(year_start, year_end) <= 2003, .2, 2.)
log_delta = Normal('log(llins distributed)', mu=log(mu_N), tau=std_N**-2, value=log(mu_N))
delta = Lambda('llins distributed', lambda x=log_delta: exp(x))
log_mu = Normal('log(llins shipped)', mu=log(mu_N), tau=std_N**-2, value=log(mu_N))
mu = Lambda('llins shipped', lambda x=log_mu: exp(x))
log_Omega = Normal('log(non-llin household net stock)',
mu=log(mu_N), tau=2.**-2, value=log(mu_N))
Omega = Lambda('non-llin household net stock', lambda x=log_Omega: exp(x))
vars += [log_delta, delta, log_mu, mu, log_Omega, Omega]
@deterministic(name='llin warehouse net stock')
def Psi(mu=mu, delta=delta):
Psi = zeros(year_end-year_start)
for t in range(year_end - year_start - 1):
Psi[t+1] = Psi[t] + mu[t] - delta[t]
return Psi
@deterministic(name='1-year-old household llin stock')
def Theta1(delta=delta):
Theta1 = zeros(year_end-year_start)
Theta1[1:] = delta[:-1]
return Theta1
@deterministic(name='2-year-old household llin stock')
def Theta2(Theta1=Theta1, pi=pi):
Theta2 = zeros(year_end-year_start)
Theta2[1:] = Theta1[:-1] * (1 - pi) ** .5
return Theta2
@deterministic(name='3-year-old household llin stock')
def Theta3(Theta2=Theta2, pi=pi):
Theta3 = zeros(year_end-year_start)
Theta3[1:] = Theta2[:-1] * (1 - pi)
return Theta3
@deterministic(name='household llin stock')
def Theta(Theta1=Theta1, Theta2=Theta2, Theta3=Theta3):
return Theta1 + Theta2 + Theta3
@deterministic(name='household itn stock')
def itns_owned(Theta=Theta, Omega=Omega):
return Theta + Omega
@deterministic(name='llin coverage')
def llin_coverage(Theta=Theta, pop=pop,
eta=eta, alpha=alpha):
return 1. - (alpha / (eta*Theta/pop + alpha))**alpha
@deterministic(name='itn coverage')
def itn_coverage(llin=Theta, non_llin=Omega, pop=pop,
eta=eta, alpha=alpha):
return 1. - (alpha / (eta*(llin + non_llin)/pop + alpha))**alpha
vars += [Psi, Theta, Theta1, Theta2, Theta3, itns_owned, llin_coverage, itn_coverage]
# set initial conditions on nets manufactured to have no stockouts
if min(Psi.value) < 0:
log_mu.value = log(maximum(1., mu.value - 2*min(Psi.value)))
#####################
### additional priors
###
#####################
@potential
def positive_stocks(Theta=Theta, Psi=Psi, Omega=Omega):
if any(Psi < 0) or any(Theta < 0) or any(Omega < 0):
return sum(minimum(Psi,0)) + sum(minimum(Theta, 0)) + sum(minimum(Omega, 0))
else:
return 0.
vars += [positive_stocks]
proven_capacity_std = .5
@potential
def proven_capacity(delta=delta, Omega=Omega, tau=proven_capacity_std**-2):
total_dist = delta[:-1] + .5*(Omega[1:] + Omega[:-1])
max_log_d = log(maximum(1.,[max(total_dist[:(i+1)]) for i in range(len(total_dist))]))
amt_below_cap = minimum(log(maximum(total_dist,1.)) - max_log_d, 0.)
return normal_like(amt_below_cap, 0., tau)
vars += [proven_capacity]
itn_composition_std = .5
@potential
def itn_composition(llin=Theta, non_llin=Omega, tau=itn_composition_std**-2):
frac_llin = llin / (llin + non_llin)
return normal_like(frac_llin[[0,1,2,6,7,8,9,10,11]],
[0., 0., 0., 1., 1., 1., 1., 1., 1.], tau)
vars += [itn_composition]
smooth_std = .5
@potential
def smooth_coverage(itn_coverage=itn_coverage, tau=smooth_std**-2):
return normal_like(diff(log(itn_coverage)), 0., tau)
vars += [smooth_coverage]
#####################
### statistical model
###
#####################
### nets shipped to country (reported by manufacturers)
manufacturing_obs = []
for d in data.llin_manu:
if d['country'] != c:
continue
@observed
@stochastic(name='manufactured_%s_%s' % (d['country'], d['year']))
def obs(value=max(1., float(d['manu_itns'])), year=int(d['year']), mu=mu, s_m=s_m):
return normal_like(log(value), log(max(1., mu[year - year_start])), 1. / s_m**2)
manufacturing_obs.append(obs)
# also take this opportinuty to set better initial values for the MCMC
cur_val = copy.copy(mu.value)
cur_val[int(d['year']) - year_start] = min(d['manu_itns'], 10.)
log_mu.value = log(maximum(1., cur_val))
vars += [manufacturing_obs]
### nets distributed in country (reported by NMCP)
# store admin data for this country for each year
data_dict = {}
for d in data.admin_llin:
if d['country'] != c:
continue
data_dict[d['year']] = max(1., d['program_llins'])
admin_distribution_obs = []
for year, d in data_dict.items():
@observed
@stochastic(name='administrative_distribution_%s' % year)
def obs(value=log(d), year=year,
delta=delta, s_d=s_d, e_d=e_d, beta=beta):
pred = log(max(1., delta[year - year_start] + beta*delta[year+1 - year_start])) + e_d
return normal_like(value, pred, 1. / s_d**2)
admin_distribution_obs.append(obs)
# also take this opportinuty to set better initial values for the MCMC
cur_val = copy.copy(delta.value)
cur_val[year - year_start] = d
log_delta.value = log(cur_val)
vars += [admin_distribution_obs]
### nets distributed in country (observed in household survey)
household_distribution_obs = []
for d in data.hh_llin_flow:
if d['country'] != c:
continue
d2_i = d['total_llins']
estimate_year = int(d['year'])
mean_survey_date = time.strptime(d['mean_svydate'], '%d-%b-%y')
survey_year = mean_survey_date[0] + mean_survey_date[1]/12.
s_d2_i = float(d['total_st'])
@observed
@stochastic(name='household_distribution_%s_%s' % (d['country'], d['year']))
def obs(value=d2_i,
estimate_year=estimate_year,
survey_year=survey_year,
survey_err=s_d2_i,
delta=delta, pi=pi, s_rb=s_rb):
return normal_like(
value,
delta[estimate_year - year_start] * (1 - pi) ** (survey_year - estimate_year - .5),
1./ (survey_err*(1+s_rb))**2)
household_distribution_obs.append(obs)
# also take this opportinuty to set better initial values for the MCMC
cur_val = copy.copy(delta.value)
cur_val[estimate_year - year_start] = d2_i / (1 - pi.value)**(survey_year - estimate_year - .5)
log_delta.value = log(cur_val)
vars += [household_distribution_obs]
### net stock in households (from survey)
household_stock_obs = []
for d in data.hh_llin_stock:
if d['country'] != c:
continue
mean_survey_date = time.strptime(d['mean_svydate'], '%d-%b-%y')
d['year'] = mean_survey_date[0] + mean_survey_date[1]/12.
@observed
@stochastic(name='LLIN_HH_Stock_%s_%s' % (d['country'], d['survey_year2']))
def obs(value=d['svyindex_llins'],
year=d['year'],
std_err=d['svyindexllins_se'],
Theta=Theta):
year_part = year-floor(year)
Theta_i = (1-year_part) * Theta[floor(year)-year_start] + year_part * Theta[ceil(year)-year_start]
return normal_like(value, Theta_i, 1. / std_err**2)
household_stock_obs.append(obs)
vars += [household_stock_obs]
### llin and itn coverage (from survey and survey reports)
coverage_obs = []
for d in data.llin_coverage:
if d['country'] != c:
continue
if d['llins0_se']: # data from survey, includes standard error
d['coverage'] = 1. - float(d['per_0llins'])
d['coverage_se'] = float(d['llins0_se'])
mean_survey_date = time.strptime(d['mean_svydate'], '%d-%b-%y')
d['year'] = mean_survey_date[0] + mean_survey_date[1]/12.
@observed
@stochastic(name='LLIN_Coverage_%s_%s' % (d['country'], d['survey_year2']))
def obs(value=d['coverage'],
year=d['survey_year2'],
std_err=d['coverage_se'],
coverage=llin_coverage):
year_part = year-floor(year)
coverage_i = (1-year_part) * coverage[floor(year)-year_start] + year_part * coverage[ceil(year)-year_start]
return normal_like(value, coverage_i, 1. / std_err**2)
else: # data is imputed from under 5 usage, so estimate standard error
d['coverage'] = 1. - float(d['per_0llins'])
N = d['sample_size'] or 1000
d['sampling_error'] = d['coverage']*(1-d['coverage'])/sqrt(N)
d['coverage_se'] = d['sampling_error']*gamma.value
mean_survey_date = time.strptime(d['mean_svydate'], '%d-%b-%y')
d['year'] = mean_survey_date[0] + mean_survey_date[1]/12.
@observed
@stochastic(name='LLIN_Coverage_Imputation_%s_%s' % (d['country'], d['year']))
def obs(value=d['coverage'],
year=d['year'],
sampling_error=d['sampling_error'],
design_factor=gamma,
coverage=llin_coverage):
year_part = year-floor(year)
coverage_i = (1-year_part) * coverage[floor(year)-year_start] + year_part * coverage[ceil(year)-year_start]
return normal_like(value, coverage_i, 1. / (design_factor * sampling_error)**2)
coverage_obs.append(obs)
for d in data.itn_coverage:
if d['country'] != c:
continue
d['coverage'] = 1. - float(d['per_0itns'])
if d['itns0_se']: # data from survey, includes standard error
d['coverage_se'] = d['itns0_se']
mean_survey_date = time.strptime(d['mean_svydate'], '%d-%b-%y')
d['year'] = mean_survey_date[0] + mean_survey_date[1]/12.
@observed
@stochastic(name='ITN_Coverage_%s_%s' % (d['country'], d['year']))
def obs(value=d['coverage'],
year=d['year'],
std_err=d['coverage_se'],
coverage=itn_coverage):
year_part = year-floor(year)
coverage_i = (1-year_part) * coverage[floor(year)-year_start] + year_part * coverage[ceil(year)-year_start]
return normal_like(value, coverage_i, 1. / std_err**2)
else: # data from survey report, must calculate standard error
mean_survey_date = time.strptime(d['mean_svydate'], '%d-%b-%y')
d['year'] = mean_survey_date[0] + mean_survey_date[1]/12.
N = d['sample_size'] or 1000
d['sampling_error'] = d['coverage']*(1-d['coverage'])/sqrt(N)
d['coverage_se'] = d['sampling_error']*gamma.value
@observed
@stochastic(name='ITN_Coverage_Report_%s_%s' % (d['country'], d['year']))
def obs(value=d['coverage'],
year=d['year'],
sampling_error=d['sampling_error'],
design_factor=gamma,
coverage=itn_coverage):
year_part = year-floor(year)
coverage_i = (1-year_part) * coverage[floor(year)-year_start] + year_part * coverage[ceil(year)-year_start]
return normal_like(value, coverage_i, 1. / (design_factor * sampling_error)**2)
coverage_obs.append(obs)
# also take this opportinuty to set better initial values for the MCMC
t = floor(d['year'])-year_start
cur_val = copy.copy(Omega.value)
cur_val[t] = max(.0001*pop[t], log(1-d['coverage']) * pop[t] / eta.value - Theta.value[t])
log_Omega.value = log(cur_val)
vars += [coverage_obs]
#################
### fit the model
###
#################
print 'running fit for net model in %s...' % c
if settings.TESTING:
map = MAP(vars)
map.fit(method='fmin', iterlim=100, verbose=1)
else:
# just optimize some variables, to get reasonable initial conditions
map = MAP([log_mu,
positive_stocks,
manufacturing_obs])
map.fit(method='fmin_powell', verbose=1)
map = MAP([log_delta,
positive_stocks,
admin_distribution_obs, household_distribution_obs,
household_stock_obs])
map.fit(method='fmin_powell', verbose=1)
map = MAP([log_mu, log_delta, log_Omega,
positive_stocks, #itn_composition,
coverage_obs])
map.fit(method='fmin_powell', verbose=1)
for stoch in [s_m, s_d, e_d, pi, eta, alpha]:
print '%s: %s' % (str(stoch), str(stoch.value))
if settings.METHOD == 'MCMC':
mc = MCMC(vars, verbose=1, db='pickle', dbname=settings.PATH + 'bednet_model_%s_%d_%s.pickle' % (c, country_id, time.strftime('%Y_%m_%d_%H_%M')))
mc.use_step_method(Metropolis, s_m, proposal_sd=.001)
mc.use_step_method(Metropolis, eta, proposal_sd=.001)
try:
if settings.TESTING:
iter = 100
thin = 1
burn = 0
else:
iter = settings.NUM_SAMPLES
thin = settings.THIN
burn = settings.BURN
mc.sample(iter*thin+burn, burn, thin)
except KeyError:
pass
mc.db.commit()
elif settings.METHOD == 'NormApprox':
na = NormApprox(vars)
na.fit(method='fmin_powell', tol=.00001, verbose=1)
for stoch in [s_m, s_d, e_d, pi]:
print '%s: %s' % (str(stoch), str(stoch.value))
na.sample(1000)
else:
assert 0, 'Unknown estimation method'
# save results in output file
col_headings = [
'Country', 'Year', 'Population',
'LLINs Shipped (Thousands)', 'LLINs Shipped Lower CI', 'LLINs Shipped Upper CI',
'LLINs Distributed (Thousands)', 'LLINs Distributed Lower CI', 'LLINs Distributed Upper CI',
'LLINs Not Owned Warehouse (Thousands)', 'LLINs Not Owned Lower CI', 'LLINs Not Owned Upper CI',
'LLINs Owned (Thousands)', 'LLINs Owned Lower CI', 'LLINs Owned Upper CI',
'non-LLIN ITNs Owned (Thousands)', 'non-LLIN ITNs Owned Lower CI', 'non-LLIN ITNs Owned Upper CI',
'ITNs Owned (Thousands)', 'ITNs Owned Lower CI', 'ITNs Owned Upper CI',
'LLIN Coverage (Percent)', 'LLIN Coverage Lower CI', 'LLIN Coverage Upper CI',
'ITN Coverage (Percent)', 'ITN Coverage Lower CI', 'ITN Coverage Upper CI',
]
try: # sleep for a random time interval to avoid collisions when writing results
print 'sleeping...'
time.sleep(random.random()*30)
print '...woke up'
except: # but let user cancel with cntl-C if there is a rush
print '...work up early'
if not settings.CSV_NAME in os.listdir(settings.PATH):
f = open(settings.PATH + settings.CSV_NAME, 'a')
f.write('%s\n' % ','.join(col_headings))
else:
f = open(settings.PATH + settings.CSV_NAME, 'a')
for t in range(year_end - year_start):
f.write('%s,%d,%d,' % (c,year_start + t,pop[t]))
if t == year_end - year_start - 1:
val = [-99, -99, -99]
val += [-99, -99, -99]
else:
val = [mu.stats()['mean'][t]/1000] + list(mu.stats()['95% HPD interval'][t]/1000)
val += [delta.stats()['mean'][t]/1000] + list(delta.stats()['95% HPD interval'][t]/1000)
val += [Psi.stats()['mean'][t]/1000] + list(Psi.stats()['95% HPD interval'][t]/1000)
val += [Theta.stats()['mean'][t]/1000] + list(Theta.stats()['95% HPD interval'][t]/1000)
val += [Omega.stats()['mean'][t]/1000] + list(Omega.stats()['95% HPD interval'][t]/1000)
val += [itns_owned.stats()['mean'][t]/1000] + list(itns_owned.stats()['95% HPD interval'][t]/1000)
val += [100*llin_coverage.stats()['mean'][t]] + list(100*llin_coverage.stats()['95% HPD interval'][t])
val += [100*itn_coverage.stats()['mean'][t]] + list(100*itn_coverage.stats()['95% HPD interval'][t])
f.write(','.join(['%.2f']*(len(col_headings)-3)) % tuple(val))
f.write('\n')
f.close()
f = open(settings.PATH + 'traces/itn_coverage_%s_%d_%s.csv' % (c, country_id, time.strftime('%Y_%m_%d_%H_%M')), 'w')
f.write(','.join(['itn_hhcov_%d' % year for year in range(year_start, year_end)]))
f.write('\n')
for row in itn_coverage.trace():
f.write(','.join(['%.4f' % cell for cell in row]))
f.write('\n')
f.close()
f = open(settings.PATH + 'traces/itn_stock_%s_%d_%s.csv' % (c, country_id, time.strftime('%Y_%m_%d_%H_%M')), 'w')
for row in itns_owned.trace():
f.write(','.join(['%.4f' % cell for cell in row]))
f.write('\n')
f.close()
graphics.plot_posterior(country_id, c, pop,
s_m, s_d, e_d, pi, mu, delta, Psi, Theta, Omega, gamma, eta, alpha, s_rb,
manufacturing_obs, admin_distribution_obs, household_distribution_obs,
itn_coverage, llin_coverage, itns_owned, data
)
def main(country_id):
from settings import year_start, year_end
c = sorted(data.countries)[country_id]
print c
# get population data for this country, to calculate LLINs per capita
pop = data.population_for(c, year_start, year_end)
### setup the model variables
vars = []
#######################
### compartmental model
###
#######################
# Empirical Bayesian priors
prior = emp_priors.llin_discard_rate()
pi = Beta('Pr[net is lost]', prior['alpha'], prior['beta'])
vars += [pi]
prior = emp_priors.admin_err_and_bias()
e_d = Normal('bias in admin dist data', prior['eps']['mu'],
prior['eps']['tau'])
s_d = Normal('error in admin dist data', prior['sigma']['mu'],
prior['sigma']['tau'])
beta = Normal(
'relative weights of next year to current year in admin dist data',
prior['beta']['mu'], prior['beta']['tau'])
vars += [s_d, e_d, beta]
prior = emp_priors.neg_binom()
eta = Normal('coverage parameter',
prior['eta']['mu'],
prior['eta']['tau'],
value=prior['eta']['mu'])
alpha = Gamma('dispersion parameter',
prior['alpha']['alpha'],
prior['alpha']['beta'],
value=prior['alpha']['mu'])
vars += [eta, alpha]
prior = emp_priors.survey_design()
gamma = Normal('survey design factor for coverage data', prior['mu'],
prior['tau'])
vars += [gamma]
# Fully Bayesian priors
s_m = Lognormal('error_in_llin_ship', log(.05), .5**-2, value=.05)
vars += [s_m]
s_rb = Lognormal('recall bias factor', log(.05), .5**-2, value=.05)
vars += [s_rb]
mu_N = .001 * pop
std_N = where(arange(year_start, year_end) <= 2003, .2, 2.)
log_delta = Normal('log(llins distributed)',
mu=log(mu_N),
tau=std_N**-2,
value=log(mu_N))
delta = Lambda('llins distributed', lambda x=log_delta: exp(x))
log_mu = Normal('log(llins shipped)',
mu=log(mu_N),
tau=std_N**-2,
value=log(mu_N))
mu = Lambda('llins shipped', lambda x=log_mu: exp(x))
log_Omega = Normal('log(non-llin household net stock)',
mu=log(mu_N),
tau=2.**-2,
value=log(mu_N))
Omega = Lambda('non-llin household net stock', lambda x=log_Omega: exp(x))
vars += [log_delta, delta, log_mu, mu, log_Omega, Omega]
@deterministic(name='llin warehouse net stock')
def Psi(mu=mu, delta=delta):
Psi = zeros(year_end - year_start)
for t in range(year_end - year_start - 1):
Psi[t + 1] = Psi[t] + mu[t] - delta[t]
return Psi
@deterministic(name='1-year-old household llin stock')
def Theta1(delta=delta):
Theta1 = zeros(year_end - year_start)
Theta1[1:] = delta[:-1]
return Theta1
@deterministic(name='2-year-old household llin stock')
def Theta2(Theta1=Theta1, pi=pi):
Theta2 = zeros(year_end - year_start)
Theta2[1:] = Theta1[:-1] * (1 - pi)**.5
return Theta2
@deterministic(name='3-year-old household llin stock')
def Theta3(Theta2=Theta2, pi=pi):
Theta3 = zeros(year_end - year_start)
Theta3[1:] = Theta2[:-1] * (1 - pi)
return Theta3
@deterministic(name='household llin stock')
def Theta(Theta1=Theta1, Theta2=Theta2, Theta3=Theta3):
return Theta1 + Theta2 + Theta3
@deterministic(name='household itn stock')
def itns_owned(Theta=Theta, Omega=Omega):
return Theta + Omega
@deterministic(name='llin coverage')
def llin_coverage(Theta=Theta, pop=pop, eta=eta, alpha=alpha):
return 1. - (alpha / (eta * Theta / pop + alpha))**alpha
@deterministic(name='itn coverage')
def itn_coverage(llin=Theta,
non_llin=Omega,
pop=pop,
eta=eta,
alpha=alpha):
return 1. - (alpha / (eta * (llin + non_llin) / pop + alpha))**alpha
vars += [
Psi, Theta, Theta1, Theta2, Theta3, itns_owned, llin_coverage,
itn_coverage
]
# set initial conditions on nets manufactured to have no stockouts
if min(Psi.value) < 0:
log_mu.value = log(maximum(1., mu.value - 2 * min(Psi.value)))
#####################
### additional priors
###
#####################
@potential
def positive_stocks(Theta=Theta, Psi=Psi, Omega=Omega):
if any(Psi < 0) or any(Theta < 0) or any(Omega < 0):
return sum(minimum(Psi, 0)) + sum(minimum(Theta, 0)) + sum(
minimum(Omega, 0))
else:
return 0.
vars += [positive_stocks]
proven_capacity_std = .5
@potential
def proven_capacity(delta=delta, Omega=Omega, tau=proven_capacity_std**-2):
total_dist = delta[:-1] + .5 * (Omega[1:] + Omega[:-1])
max_log_d = log(
maximum(
1.,
[max(total_dist[:(i + 1)]) for i in range(len(total_dist))]))
amt_below_cap = minimum(log(maximum(total_dist, 1.)) - max_log_d, 0.)
return normal_like(amt_below_cap, 0., tau)
vars += [proven_capacity]
itn_composition_std = .5
@potential
def itn_composition(llin=Theta,
non_llin=Omega,
tau=itn_composition_std**-2):
frac_llin = llin / (llin + non_llin)
return normal_like(frac_llin[[0, 1, 2, 6, 7, 8, 9, 10, 11]],
[0., 0., 0., 1., 1., 1., 1., 1., 1.], tau)
vars += [itn_composition]
smooth_std = .5
@potential
def smooth_coverage(itn_coverage=itn_coverage, tau=smooth_std**-2):
return normal_like(diff(log(itn_coverage)), 0., tau)
vars += [smooth_coverage]
#####################
### statistical model
###
#####################
### nets shipped to country (reported by manufacturers)
manufacturing_obs = []
for d in data.llin_manu:
if d['country'] != c:
continue
@observed
@stochastic(name='manufactured_%s_%s' % (d['country'], d['year']))
def obs(value=max(1., float(d['manu_itns'])),
year=int(d['year']),
mu=mu,
s_m=s_m):
return normal_like(log(value), log(max(1., mu[year - year_start])),
1. / s_m**2)
manufacturing_obs.append(obs)
# also take this opportinuty to set better initial values for the MCMC
cur_val = copy.copy(mu.value)
cur_val[int(d['year']) - year_start] = min(d['manu_itns'], 10.)
log_mu.value = log(maximum(1., cur_val))
vars += [manufacturing_obs]
### nets distributed in country (reported by NMCP)
# store admin data for this country for each year
data_dict = {}
for d in data.admin_llin:
if d['country'] != c:
continue
data_dict[d['year']] = max(1., d['program_llins'])
admin_distribution_obs = []
for year, d in data_dict.items():
@observed
@stochastic(name='administrative_distribution_%s' % year)
def obs(value=log(d),
year=year,
delta=delta,
s_d=s_d,
e_d=e_d,
beta=beta):
pred = log(
max(
1., delta[year - year_start] +
beta * delta[year + 1 - year_start])) + e_d
return normal_like(value, pred, 1. / s_d**2)
admin_distribution_obs.append(obs)
# also take this opportinuty to set better initial values for the MCMC
cur_val = copy.copy(delta.value)
cur_val[year - year_start] = d
log_delta.value = log(cur_val)
vars += [admin_distribution_obs]
### nets distributed in country (observed in household survey)
household_distribution_obs = []
for d in data.hh_llin_flow:
if d['country'] != c:
continue
d2_i = d['total_llins']
estimate_year = int(d['year'])
mean_survey_date = time.strptime(d['mean_svydate'], '%d-%b-%y')
survey_year = mean_survey_date[0] + mean_survey_date[1] / 12.
s_d2_i = float(d['total_st'])
@observed
@stochastic(name='household_distribution_%s_%s' %
(d['country'], d['year']))
def obs(value=d2_i,
estimate_year=estimate_year,
survey_year=survey_year,
survey_err=s_d2_i,
delta=delta,
pi=pi,
s_rb=s_rb):
return normal_like(
value, delta[estimate_year - year_start] *
(1 - pi)**(survey_year - estimate_year - .5),
1. / (survey_err * (1 + s_rb))**2)
household_distribution_obs.append(obs)
# also take this opportinuty to set better initial values for the MCMC
cur_val = copy.copy(delta.value)
cur_val[estimate_year -
year_start] = d2_i / (1 - pi.value)**(survey_year -
estimate_year - .5)
log_delta.value = log(cur_val)
vars += [household_distribution_obs]
### net stock in households (from survey)
household_stock_obs = []
for d in data.hh_llin_stock:
if d['country'] != c:
continue
mean_survey_date = time.strptime(d['mean_svydate'], '%d-%b-%y')
d['year'] = mean_survey_date[0] + mean_survey_date[1] / 12.
@observed
@stochastic(name='LLIN_HH_Stock_%s_%s' %
(d['country'], d['survey_year2']))
def obs(value=d['svyindex_llins'],
year=d['year'],
std_err=d['svyindexllins_se'],
Theta=Theta):
year_part = year - floor(year)
Theta_i = (1 - year_part) * Theta[
floor(year) - year_start] + year_part * Theta[ceil(year) -
year_start]
return normal_like(value, Theta_i, 1. / std_err**2)
household_stock_obs.append(obs)
vars += [household_stock_obs]
### llin and itn coverage (from survey and survey reports)
coverage_obs = []
for d in data.llin_coverage:
if d['country'] != c:
continue
if d['llins0_se']: # data from survey, includes standard error
d['coverage'] = 1. - float(d['per_0llins'])
d['coverage_se'] = float(d['llins0_se'])
mean_survey_date = time.strptime(d['mean_svydate'], '%d-%b-%y')
d['year'] = mean_survey_date[0] + mean_survey_date[1] / 12.
@observed
@stochastic(name='LLIN_Coverage_%s_%s' %
(d['country'], d['survey_year2']))
def obs(value=d['coverage'],
year=d['survey_year2'],
std_err=d['coverage_se'],
coverage=llin_coverage):
year_part = year - floor(year)
coverage_i = (1 - year_part) * coverage[
floor(year) -
year_start] + year_part * coverage[ceil(year) - year_start]
return normal_like(value, coverage_i, 1. / std_err**2)
else: # data is imputed from under 5 usage, so estimate standard error
d['coverage'] = 1. - float(d['per_0llins'])
N = d['sample_size'] or 1000
d['sampling_error'] = d['coverage'] * (1 - d['coverage']) / sqrt(N)
d['coverage_se'] = d['sampling_error'] * gamma.value
mean_survey_date = time.strptime(d['mean_svydate'], '%d-%b-%y')
d['year'] = mean_survey_date[0] + mean_survey_date[1] / 12.
@observed
@stochastic(name='LLIN_Coverage_Imputation_%s_%s' %
(d['country'], d['year']))
def obs(value=d['coverage'],
year=d['year'],
sampling_error=d['sampling_error'],
design_factor=gamma,
coverage=llin_coverage):
year_part = year - floor(year)
coverage_i = (1 - year_part) * coverage[
floor(year) -
year_start] + year_part * coverage[ceil(year) - year_start]
return normal_like(value, coverage_i,
1. / (design_factor * sampling_error)**2)
coverage_obs.append(obs)
for d in data.itn_coverage:
if d['country'] != c:
continue
d['coverage'] = 1. - float(d['per_0itns'])
if d['itns0_se']: # data from survey, includes standard error
d['coverage_se'] = d['itns0_se']
mean_survey_date = time.strptime(d['mean_svydate'], '%d-%b-%y')
d['year'] = mean_survey_date[0] + mean_survey_date[1] / 12.
@observed
@stochastic(name='ITN_Coverage_%s_%s' % (d['country'], d['year']))
def obs(value=d['coverage'],
year=d['year'],
std_err=d['coverage_se'],
coverage=itn_coverage):
year_part = year - floor(year)
coverage_i = (1 - year_part) * coverage[
floor(year) -
year_start] + year_part * coverage[ceil(year) - year_start]
return normal_like(value, coverage_i, 1. / std_err**2)
else: # data from survey report, must calculate standard error
mean_survey_date = time.strptime(d['mean_svydate'], '%d-%b-%y')
d['year'] = mean_survey_date[0] + mean_survey_date[1] / 12.
N = d['sample_size'] or 1000
d['sampling_error'] = d['coverage'] * (1 - d['coverage']) / sqrt(N)
d['coverage_se'] = d['sampling_error'] * gamma.value
@observed
@stochastic(name='ITN_Coverage_Report_%s_%s' %
(d['country'], d['year']))
def obs(value=d['coverage'],
year=d['year'],
sampling_error=d['sampling_error'],
design_factor=gamma,
coverage=itn_coverage):
year_part = year - floor(year)
coverage_i = (1 - year_part) * coverage[
floor(year) -
year_start] + year_part * coverage[ceil(year) - year_start]
return normal_like(value, coverage_i,
1. / (design_factor * sampling_error)**2)
coverage_obs.append(obs)
# also take this opportinuty to set better initial values for the MCMC
t = floor(d['year']) - year_start
cur_val = copy.copy(Omega.value)
cur_val[t] = max(
.0001 * pop[t],
log(1 - d['coverage']) * pop[t] / eta.value - Theta.value[t])
log_Omega.value = log(cur_val)
vars += [coverage_obs]
#################
### fit the model
###
#################
print 'running fit for net model in %s...' % c
if settings.TESTING:
map = MAP(vars)
map.fit(method='fmin', iterlim=100, verbose=1)
else:
# just optimize some variables, to get reasonable initial conditions
map = MAP([log_mu, positive_stocks, manufacturing_obs])
map.fit(method='fmin_powell', verbose=1)
map = MAP([
log_delta, positive_stocks, admin_distribution_obs,
household_distribution_obs, household_stock_obs
])
map.fit(method='fmin_powell', verbose=1)
map = MAP([
log_mu,
log_delta,
log_Omega,
positive_stocks, #itn_composition,
coverage_obs
])
map.fit(method='fmin_powell', verbose=1)
for stoch in [s_m, s_d, e_d, pi, eta, alpha]:
print '%s: %s' % (str(stoch), str(stoch.value))
if settings.METHOD == 'MCMC':
mc = MCMC(vars,
verbose=1,
db='pickle',
dbname=settings.PATH + 'bednet_model_%s_%d_%s.pickle' %
(c, country_id, time.strftime('%Y_%m_%d_%H_%M')))
mc.use_step_method(Metropolis, s_m, proposal_sd=.001)
mc.use_step_method(Metropolis, eta, proposal_sd=.001)
try:
if settings.TESTING:
iter = 100
thin = 1
burn = 0
else:
iter = settings.NUM_SAMPLES
thin = settings.THIN
burn = settings.BURN
mc.sample(iter * thin + burn, burn, thin)
except KeyError:
pass
mc.db.commit()
elif settings.METHOD == 'NormApprox':
na = NormApprox(vars)
na.fit(method='fmin_powell', tol=.00001, verbose=1)
for stoch in [s_m, s_d, e_d, pi]:
print '%s: %s' % (str(stoch), str(stoch.value))
na.sample(1000)
else:
assert 0, 'Unknown estimation method'
# save results in output file
col_headings = [
'Country',
'Year',
'Population',
'LLINs Shipped (Thousands)',
'LLINs Shipped Lower CI',
'LLINs Shipped Upper CI',
'LLINs Distributed (Thousands)',
'LLINs Distributed Lower CI',
'LLINs Distributed Upper CI',
'LLINs Not Owned Warehouse (Thousands)',
'LLINs Not Owned Lower CI',
'LLINs Not Owned Upper CI',
'LLINs Owned (Thousands)',
'LLINs Owned Lower CI',
'LLINs Owned Upper CI',
'non-LLIN ITNs Owned (Thousands)',
'non-LLIN ITNs Owned Lower CI',
'non-LLIN ITNs Owned Upper CI',
'ITNs Owned (Thousands)',
'ITNs Owned Lower CI',
'ITNs Owned Upper CI',
'LLIN Coverage (Percent)',
'LLIN Coverage Lower CI',
'LLIN Coverage Upper CI',
'ITN Coverage (Percent)',
'ITN Coverage Lower CI',
'ITN Coverage Upper CI',
]
try: # sleep for a random time interval to avoid collisions when writing results
print 'sleeping...'
time.sleep(random.random() * 30)
print '...woke up'
except: # but let user cancel with cntl-C if there is a rush
print '...work up early'
if not settings.CSV_NAME in os.listdir(settings.PATH):
f = open(settings.PATH + settings.CSV_NAME, 'a')
f.write('%s\n' % ','.join(col_headings))
else:
f = open(settings.PATH + settings.CSV_NAME, 'a')
for t in range(year_end - year_start):
f.write('%s,%d,%d,' % (c, year_start + t, pop[t]))
if t == year_end - year_start - 1:
val = [-99, -99, -99]
val += [-99, -99, -99]
else:
val = [mu.stats()['mean'][t] / 1000] + list(
mu.stats()['95% HPD interval'][t] / 1000)
val += [delta.stats()['mean'][t] / 1000] + list(
delta.stats()['95% HPD interval'][t] / 1000)
val += [Psi.stats()['mean'][t] / 1000] + list(
Psi.stats()['95% HPD interval'][t] / 1000)
val += [Theta.stats()['mean'][t] / 1000] + list(
Theta.stats()['95% HPD interval'][t] / 1000)
val += [Omega.stats()['mean'][t] / 1000] + list(
Omega.stats()['95% HPD interval'][t] / 1000)
val += [itns_owned.stats()['mean'][t] / 1000] + list(
itns_owned.stats()['95% HPD interval'][t] / 1000)
val += [100 * llin_coverage.stats()['mean'][t]] + list(
100 * llin_coverage.stats()['95% HPD interval'][t])
val += [100 * itn_coverage.stats()['mean'][t]] + list(
100 * itn_coverage.stats()['95% HPD interval'][t])
f.write(','.join(['%.2f'] * (len(col_headings) - 3)) % tuple(val))
f.write('\n')
f.close()
f = open(
settings.PATH + 'traces/itn_coverage_%s_%d_%s.csv' %
(c, country_id, time.strftime('%Y_%m_%d_%H_%M')), 'w')
f.write(','.join(
['itn_hhcov_%d' % year for year in range(year_start, year_end)]))
f.write('\n')
for row in itn_coverage.trace():
f.write(','.join(['%.4f' % cell for cell in row]))
f.write('\n')
f.close()
f = open(
settings.PATH + 'traces/itn_stock_%s_%d_%s.csv' %
(c, country_id, time.strftime('%Y_%m_%d_%H_%M')), 'w')
for row in itns_owned.trace():
f.write(','.join(['%.4f' % cell for cell in row]))
f.write('\n')
f.close()
graphics.plot_posterior(country_id, c, pop, s_m, s_d, e_d, pi, mu, delta,
Psi, Theta, Omega, gamma, eta, alpha, s_rb,
manufacturing_obs, admin_distribution_obs,
household_distribution_obs, itn_coverage,
llin_coverage, itns_owned, data)
