def validate_prior_similarity():
#dm = dismod3.load_disease_model(20945)
#dm.model = data.ModelData.from_gbd_jsons(json.loads(dm.to_json()))
#t = 'i'
#area, sex, year = 'europe_eastern', 'male', 2005
dm = dismod3.load_disease_model(20928)
dm.model = data.ModelData.from_gbd_jsons(json.loads(dm.to_json()))
t = 'p'
area, sex, year = 'sub-saharan_africa_central', 'male', 2005
# select data that is about areas in this region, recent years, and sex of male or total only
model = dm.model
subtree = nx.traversal.bfs_tree(model.hierarchy, area)
relevant_rows = [i for i, r in model.input_data.T.iteritems() \
if (r['area'] in subtree or r['area'] == 'all')\
and ((year == 2005 and r['year_end'] >= 1997) or r['year_start'] <= 1997) \
and r['sex'] in [sex, 'total']]
model.input_data = model.input_data.ix[relevant_rows]
# replace area 'all' with area
model.input_data['area'][model.input_data['area'] == 'all'] = area
for het in 'Slightly Moderately Very'.split():
dm.model.parameters[t]['parameter_age_mesh'] = [0, 15, 20, 25, 35, 45, 55, 65, 75, 100]
dm.model.parameters[t]['heterogeneity'] = het
setup_regional_model(dm, area, sex, year)
dm.vars = {}
dm.vars[t] = data_model.data_model(t, dm.model, t,
root_area=area, root_sex=sex, root_year=year,
mu_age=None,
mu_age_parent=dm.emp_priors[t, 'mu'],
sigma_age_parent=dm.emp_priors[t, 'sigma'],
rate_type=(t == 'rr') and 'log_normal' or 'neg_binom')
fit_model.fit_data_model(dm.vars[t], iter=10050, burn=5000, thin=50, tune_interval=100)
#2graphics.plot_one_effects(dm.vars[t], t, dm.model.hierarchy)
#pl.title(het)
graphics.plot_convergence_diag(dm.vars[t])
pl.title(het)
#graphics.plot_one_ppc(dm.vars[t], t)
#pl.title(het)
graphics.plot_one_type(dm.model, dm.vars[t], dm.emp_priors, t)
pl.title(het)
pl.show()
return dm
def fit(model):
emp_priors = model.emp_priors
## Then fit the model and compare the estimates to the truth
model.vars = {}
model.vars['p'] = data_model.data_model('p', model, 'p', 'all', 'total', 'all', None, emp_priors['p', 'mu'], emp_priors['p', 'sigma'])
model.map, model.mcmc = fit_model.fit_data_model(model.vars['p'], iter=5000, burn=2000, thin=25, tune_interval=100)
#model.map, model.mcmc = fit_model.fit_data_model(model.vars['p'], iter=101, burn=0, thin=1, tune_interval=100)
#graphics.plot_one_ppc(model.vars['p'], 'p')
#graphics.plot_convergence_diag(model.vars)
graphics.plot_one_type(model, model.vars['p'], emp_priors, 'p')
pl.plot(model.a, model.pi_age_true, 'b--', linewidth=3, alpha=.5, label='Truth')
pl.legend(fancybox=True, shadow=True, loc='upper left')
pl.title('Heterogeneity %s'%model.parameters['p']['heterogeneity'])
pl.show()
model.input_data['mu_pred'] = model.vars['p']['p_pred'].stats()['mean']
model.input_data['sigma_pred'] = model.vars['p']['p_pred'].stats()['standard deviation']
data_simulation.add_quality_metrics(model.input_data)
model.delta = pandas.DataFrame(dict(true=[model.delta_true]))
model.delta['mu_pred'] = pl.exp(model.vars['p']['eta'].trace()).mean()
model.delta['sigma_pred'] = pl.exp(model.vars['p']['eta'].trace()).std()
data_simulation.add_quality_metrics(model.delta)
print 'delta'
print model.delta
print '\ndata prediction bias: %.5f, MARE: %.3f, coverage: %.2f' % (model.input_data['abs_err'].mean(),
pl.median(pl.absolute(model.input_data['rel_err'].dropna())),
model.input_data['covered?'].mean())
model.mu = pandas.DataFrame(dict(true=model.pi_age_true,
mu_pred=model.vars['p']['mu_age'].stats()['mean'],
sigma_pred=model.vars['p']['mu_age'].stats()['standard deviation']))
data_simulation.add_quality_metrics(model.mu)
data_simulation.initialize_results(model)
data_simulation.add_to_results(model, 'delta')
data_simulation.add_to_results(model, 'mu')
data_simulation.add_to_results(model, 'input_data')
data_simulation.finalize_results(model)
print model.results
def validate_age_integrating_model_sim(N=500,
delta_true=.15,
pi_true=quadratic):
## generate simulated data
a = pl.arange(0, 101, 1)
pi_age_true = pi_true(a)
model = data_simulation.simple_model(N)
#model.parameters['p']['parameter_age_mesh'] = range(0, 101, 10)
#model.parameters['p']['smoothness'] = dict(amount='Very')
age_start = pl.array(mc.runiform(0, 100, size=N), dtype=int)
age_end = pl.array(mc.runiform(age_start, 100, size=N), dtype=int)
age_weights = pl.ones_like(a)
sum_pi_wt = pl.cumsum(pi_age_true * age_weights)
sum_wt = pl.cumsum(age_weights)
p = (sum_pi_wt[age_end] - sum_pi_wt[age_start]) / (sum_wt[age_end] -
sum_wt[age_start])
# correct cases where age_start == age_end
i = age_start == age_end
if pl.any(i):
p[i] = pi_age_true[age_start[i]]
n = mc.runiform(100, 10000, size=N)
model.input_data['age_start'] = age_start
model.input_data['age_end'] = age_end
model.input_data['effective_sample_size'] = n
model.input_data['true'] = p
model.input_data['value'] = mc.rnegative_binomial(n * p,
delta_true * n * p) / n
## Then fit the model and compare the estimates to the truth
model.vars = {}
model.vars['p'] = data_model.data_model('p', model, 'p', 'all', 'total',
'all', None, None, None)
model.map, model.mcmc = fit_model.fit_data_model(model.vars['p'],
iter=10000,
burn=5000,
thin=25,
tune_interval=100)
graphics.plot_one_ppc(model.vars['p'], 'p')
graphics.plot_convergence_diag(model.vars)
graphics.plot_one_type(model, model.vars['p'], {}, 'p')
pl.plot(a, pi_age_true, 'r:', label='Truth')
pl.legend(fancybox=True, shadow=True, loc='upper left')
pl.show()
model.input_data['mu_pred'] = model.vars['p']['p_pred'].stats()['mean']
model.input_data['sigma_pred'] = model.vars['p']['p_pred'].stats(
)['standard deviation']
data_simulation.add_quality_metrics(model.input_data)
model.delta = pandas.DataFrame(dict(true=[delta_true]))
model.delta['mu_pred'] = pl.exp(model.vars['p']['eta'].trace()).mean()
model.delta['sigma_pred'] = pl.exp(model.vars['p']['eta'].trace()).std()
data_simulation.add_quality_metrics(model.delta)
print 'delta'
print model.delta
print '\ndata prediction bias: %.5f, MARE: %.3f, coverage: %.2f' % (
model.input_data['abs_err'].mean(),
pl.median(pl.absolute(model.input_data['rel_err'].dropna())),
model.input_data['covered?'].mean())
model.mu = pandas.DataFrame(
dict(true=pi_age_true,
mu_pred=model.vars['p']['mu_age'].stats()['mean'],
sigma_pred=model.vars['p']['mu_age'].stats()
['standard deviation']))
data_simulation.add_quality_metrics(model.mu)
model.results = dict(param=[], bias=[], mare=[], mae=[], pc=[])
data_simulation.add_to_results(model, 'delta')
data_simulation.add_to_results(model, 'mu')
data_simulation.add_to_results(model, 'input_data')
model.results = pandas.DataFrame(model.results,
columns='param bias mae mare pc'.split())
print model.results
return model
def validate_ai_re(N=500, delta_true=.15, sigma_true=[.1,.1,.1,.1,.1], pi_true=quadratic, smoothness='Moderately', heterogeneity='Slightly'):
## generate simulated data
a = pl.arange(0, 101, 1)
pi_age_true = pi_true(a)
import dismod3
import simplejson as json
model = data.ModelData.from_gbd_jsons(json.loads(dismod3.disease_json.DiseaseJson().to_json()))
gbd_hierarchy = model.hierarchy
model = data_simulation.simple_model(N)
model.hierarchy = gbd_hierarchy
model.parameters['p']['parameter_age_mesh'] = range(0, 101, 10)
model.parameters['p']['smoothness'] = dict(amount=smoothness)
model.parameters['p']['heterogeneity'] = heterogeneity
age_start = pl.array(mc.runiform(0, 100, size=N), dtype=int)
age_end = pl.array(mc.runiform(age_start, 100, size=N), dtype=int)
age_weights = pl.ones_like(a)
sum_pi_wt = pl.cumsum(pi_age_true*age_weights)
sum_wt = pl.cumsum(age_weights*1.)
p = (sum_pi_wt[age_end] - sum_pi_wt[age_start]) / (sum_wt[age_end] - sum_wt[age_start])
# correct cases where age_start == age_end
i = age_start == age_end
if pl.any(i):
p[i] = pi_age_true[age_start[i]]
model.input_data['age_start'] = age_start
model.input_data['age_end'] = age_end
model.input_data['effective_sample_size'] = mc.runiform(100, 10000, size=N)
from validate_covariates import alpha_true_sim
area_list = pl.array(['all', 'super-region_3', 'north_africa_middle_east', 'EGY', 'KWT', 'IRN', 'IRQ', 'JOR', 'SYR'])
alpha = alpha_true_sim(model, area_list, sigma_true)
print alpha
model.input_data['true'] = pl.nan
model.input_data['area'] = area_list[mc.rcategorical(pl.ones(len(area_list)) / float(len(area_list)), N)]
for i, a in model.input_data['area'].iteritems():
model.input_data['true'][i] = p[i] * pl.exp(pl.sum([alpha[n] for n in nx.shortest_path(model.hierarchy, 'all', a) if n in alpha]))
p = model.input_data['true']
n = model.input_data['effective_sample_size']
model.input_data['value'] = mc.rnegative_binomial(n*p, delta_true*n*p) / n
## Then fit the model and compare the estimates to the truth
model.vars = {}
model.vars['p'] = data_model.data_model('p', model, 'p', 'north_africa_middle_east', 'total', 'all', None, None, None)
#model.map, model.mcmc = fit_model.fit_data_model(model.vars['p'], iter=1005, burn=500, thin=5, tune_interval=100)
model.map, model.mcmc = fit_model.fit_data_model(model.vars['p'], iter=10000, burn=5000, thin=25, tune_interval=100)
graphics.plot_one_ppc(model.vars['p'], 'p')
graphics.plot_convergence_diag(model.vars)
graphics.plot_one_type(model, model.vars['p'], {}, 'p')
pl.plot(range(101), pi_age_true, 'r:', label='Truth')
pl.legend(fancybox=True, shadow=True, loc='upper left')
pl.show()
model.input_data['mu_pred'] = model.vars['p']['p_pred'].stats()['mean']
model.input_data['sigma_pred'] = model.vars['p']['p_pred'].stats()['standard deviation']
data_simulation.add_quality_metrics(model.input_data)
model.delta = pandas.DataFrame(dict(true=[delta_true]))
model.delta['mu_pred'] = pl.exp(model.vars['p']['eta'].trace()).mean()
model.delta['sigma_pred'] = pl.exp(model.vars['p']['eta'].trace()).std()
data_simulation.add_quality_metrics(model.delta)
model.alpha = pandas.DataFrame(index=[n for n in nx.traversal.dfs_preorder_nodes(model.hierarchy)])
model.alpha['true'] = pandas.Series(dict(alpha))
model.alpha['mu_pred'] = pandas.Series([n.stats()['mean'] for n in model.vars['p']['alpha']], index=model.vars['p']['U'].columns)
model.alpha['sigma_pred'] = pandas.Series([n.stats()['standard deviation'] for n in model.vars['p']['alpha']], index=model.vars['p']['U'].columns)
model.alpha = model.alpha.dropna()
data_simulation.add_quality_metrics(model.alpha)
model.sigma = pandas.DataFrame(dict(true=sigma_true))
model.sigma['mu_pred'] = [n.stats()['mean'] for n in model.vars['p']['sigma_alpha']]
model.sigma['sigma_pred']=[n.stats()['standard deviation'] for n in model.vars['p']['sigma_alpha']]
data_simulation.add_quality_metrics(model.sigma)
print 'delta'
print model.delta
print '\ndata prediction bias: %.5f, MARE: %.3f, coverage: %.2f' % (model.input_data['abs_err'].mean(),
pl.median(pl.absolute(model.input_data['rel_err'].dropna())),
model.input_data['covered?'].mean())
model.mu = pandas.DataFrame(dict(true=pi_age_true,
mu_pred=model.vars['p']['mu_age'].stats()['mean'],
sigma_pred=model.vars['p']['mu_age'].stats()['standard deviation']))
data_simulation.add_quality_metrics(model.mu)
data_simulation.initialize_results(model)
data_simulation.add_to_results(model, 'delta')
data_simulation.add_to_results(model, 'mu')
data_simulation.add_to_results(model, 'input_data')
data_simulation.add_to_results(model, 'alpha')
data_simulation.add_to_results(model, 'sigma')
data_simulation.finalize_results(model)
print model.results
return model
model.parameters['p']['fixed_effects']['x_CODcorrected_Cirrhosis_ASDR'] = dict(dist='TruncatedNormal', mu=0, sigma=.01, lower=-1., upper=1.)
model.parameters['p']['fixed_effects']['x_IHME_alcohol_liters_pc_25July11'] = dict(dist='TruncatedNormal', mu=0, sigma=.01, lower=-1., upper=1.)
# create model for global prevalence
root_area = 'all'
t = 'p'
vars = data_model.data_model(t, model, t,
root_area='all', root_sex='total', root_year='all',
mu_age=None, mu_age_parent=None, sigma_age_parent=None)
m = fit_model.fit_data_model(vars, iter=4000, burn=2000, thin=20, tune_interval=100)
# generate estimates for all leaves of the hierarchy
est = {}
for n in model.hierarchy:
if len(model.hierarchy.successors(n)) == 0:
est[n] = pl.median(covariate_model.predict_for(model,
'all', 'total', 'all',
n, 'male', 2005, 0., vars, 0., 1.), axis=0)
graphics.plot_one_type(model, vars, {}, 'p')
r='europe_western'
N = len(model.hierarchy[r])
for i, n in enumerate(sorted(model.hierarchy[r], key=lambda n: est[n][-1])):
col = pl.cm.Spectral((.1+i)/N)
pl.semilogy(est[n], label=n, color=col, linewidth=2)
pl.text(100,est[n][-1], n, color=col)
pl.axis([-5,110,1e-5,.02])
def validate_prior_similarity():
#dm = dismod3.load_disease_model(20945)
#dm.model = data.ModelData.from_gbd_jsons(json.loads(dm.to_json()))
#t = 'i'
#area, sex, year = 'europe_eastern', 'male', 2005
dm = dismod3.load_disease_model(20928)
dm.model = data.ModelData.from_gbd_jsons(json.loads(dm.to_json()))
t = 'p'
area, sex, year = 'sub-saharan_africa_central', 'male', 2005
# select data that is about areas in this region, recent years, and sex of male or total only
model = dm.model
subtree = nx.traversal.bfs_tree(model.hierarchy, area)
relevant_rows = [i for i, r in model.input_data.T.iteritems() \
if (r['area'] in subtree or r['area'] == 'all')\
and ((year == 2005 and r['year_end'] >= 1997) or r['year_start'] <= 1997) \
and r['sex'] in [sex, 'total']]
model.input_data = model.input_data.ix[relevant_rows]
# replace area 'all' with area
model.input_data['area'][model.input_data['area'] == 'all'] = area
for het in 'Slightly Moderately Very'.split():
dm.model.parameters[t]['parameter_age_mesh'] = [
0, 15, 20, 25, 35, 45, 55, 65, 75, 100
]
dm.model.parameters[t]['heterogeneity'] = het
setup_regional_model(dm, area, sex, year)
dm.vars = {}
dm.vars[t] = data_model.data_model(
t,
dm.model,
t,
root_area=area,
root_sex=sex,
root_year=year,
mu_age=None,
mu_age_parent=dm.emp_priors[t, 'mu'],
sigma_age_parent=dm.emp_priors[t, 'sigma'],
rate_type=(t == 'rr') and 'log_normal' or 'neg_binom')
fit_model.fit_data_model(dm.vars[t],
iter=10050,
burn=5000,
thin=50,
tune_interval=100)
#2graphics.plot_one_effects(dm.vars[t], t, dm.model.hierarchy)
#pl.title(het)
graphics.plot_convergence_diag(dm.vars[t])
pl.title(het)
#graphics.plot_one_ppc(dm.vars[t], t)
#pl.title(het)
graphics.plot_one_type(dm.model, dm.vars[t], dm.emp_priors, t)
pl.title(het)
pl.show()
return dm
def validate_age_pattern_model_sim(N=500, delta_true=.15, pi_true=quadratic):
## generate simulated data
a = pl.arange(0, 101, 1)
pi_age_true = pi_true(a)
model = data_simulation.simple_model(N)
model.parameters['p']['parameter_age_mesh'] = range(0, 101, 10)
age_list = pl.array(mc.runiform(0, 100, size=N), dtype=int)
p = pi_age_true[age_list]
n = mc.runiform(100, 10000, size=N)
model.input_data['age_start'] = age_list
model.input_data['age_end'] = age_list
model.input_data['effective_sample_size'] = n
model.input_data['true'] = p
model.input_data['value'] = mc.rnegative_binomial(n*p, delta_true*n*p) / n
## Then fit the model and compare the estimates to the truth
model.vars = {}
model.vars['p'] = data_model.data_model('p', model, 'p', 'all', 'total', 'all', None, None, None)
model.map, model.mcmc = fit_model.fit_data_model(model.vars['p'], iter=10000, burn=5000, thin=25, tune_interval=100)
graphics.plot_one_ppc(model.vars['p'], 'p')
graphics.plot_convergence_diag(model.vars)
graphics.plot_one_type(model, model.vars['p'], {}, 'p')
pl.plot(a, pi_age_true, 'r:', label='Truth')
pl.legend(fancybox=True, shadow=True, loc='upper left')
pl.show()
model.input_data['mu_pred'] = model.vars['p']['p_pred'].stats()['mean']
model.input_data['sigma_pred'] = model.vars['p']['p_pred'].stats()['standard deviation']
data_simulation.add_quality_metrics(model.input_data)
model.delta = pandas.DataFrame(dict(true=[delta_true]))
model.delta['mu_pred'] = pl.exp(model.vars['p']['eta'].trace()).mean()
model.delta['sigma_pred'] = pl.exp(model.vars['p']['eta'].trace()).std()
data_simulation.add_quality_metrics(model.delta)
print 'delta'
print model.delta
print '\ndata prediction bias: %.5f, MARE: %.3f, coverage: %.2f' % (model.input_data['abs_err'].mean(),
pl.median(pl.absolute(model.input_data['rel_err'].dropna())),
model.input_data['covered?'].mean())
model.mu = pandas.DataFrame(dict(true=pi_age_true,
mu_pred=model.vars['p']['mu_age'].stats()['mean'],
sigma_pred=model.vars['p']['mu_age'].stats()['standard deviation']))
data_simulation.add_quality_metrics(model.mu)
model.results = dict(param=[], bias=[], mare=[], mae=[], pc=[])
data_simulation.add_to_results(model, 'delta')
data_simulation.add_to_results(model, 'mu')
data_simulation.add_to_results(model, 'input_data')
model.results = pandas.DataFrame(model.results, columns='param bias mae mare pc'.split())
print model.results
return model
def validate_ai_re(N=500,
delta_true=.15,
sigma_true=[.1, .1, .1, .1, .1],
pi_true=quadratic,
smoothness='Moderately',
heterogeneity='Slightly'):
## generate simulated data
a = pl.arange(0, 101, 1)
pi_age_true = pi_true(a)
import dismod3
import simplejson as json
model = data.ModelData.from_gbd_jsons(
json.loads(dismod3.disease_json.DiseaseJson().to_json()))
gbd_hierarchy = model.hierarchy
model = data_simulation.simple_model(N)
model.hierarchy = gbd_hierarchy
model.parameters['p']['parameter_age_mesh'] = range(0, 101, 10)
model.parameters['p']['smoothness'] = dict(amount=smoothness)
model.parameters['p']['heterogeneity'] = heterogeneity
age_start = pl.array(mc.runiform(0, 100, size=N), dtype=int)
age_end = pl.array(mc.runiform(age_start, 100, size=N), dtype=int)
age_weights = pl.ones_like(a)
sum_pi_wt = pl.cumsum(pi_age_true * age_weights)
sum_wt = pl.cumsum(age_weights * 1.)
p = (sum_pi_wt[age_end] - sum_pi_wt[age_start]) / (sum_wt[age_end] -
sum_wt[age_start])
# correct cases where age_start == age_end
i = age_start == age_end
if pl.any(i):
p[i] = pi_age_true[age_start[i]]
model.input_data['age_start'] = age_start
model.input_data['age_end'] = age_end
model.input_data['effective_sample_size'] = mc.runiform(100, 10000, size=N)
from validate_covariates import alpha_true_sim
area_list = pl.array([
'all', 'super-region_3', 'north_africa_middle_east', 'EGY', 'KWT',
'IRN', 'IRQ', 'JOR', 'SYR'
])
alpha = alpha_true_sim(model, area_list, sigma_true)
print alpha
model.input_data['true'] = pl.nan
model.input_data['area'] = area_list[mc.rcategorical(
pl.ones(len(area_list)) / float(len(area_list)), N)]
for i, a in model.input_data['area'].iteritems():
model.input_data['true'][i] = p[i] * pl.exp(
pl.sum([
alpha[n] for n in nx.shortest_path(model.hierarchy, 'all', a)
if n in alpha
]))
p = model.input_data['true']
n = model.input_data['effective_sample_size']
model.input_data['value'] = mc.rnegative_binomial(n * p,
delta_true * n * p) / n
## Then fit the model and compare the estimates to the truth
model.vars = {}
model.vars['p'] = data_model.data_model('p', model, 'p',
'north_africa_middle_east',
'total', 'all', None, None, None)
#model.map, model.mcmc = fit_model.fit_data_model(model.vars['p'], iter=1005, burn=500, thin=5, tune_interval=100)
model.map, model.mcmc = fit_model.fit_data_model(model.vars['p'],
iter=10000,
burn=5000,
thin=25,
tune_interval=100)
graphics.plot_one_ppc(model.vars['p'], 'p')
graphics.plot_convergence_diag(model.vars)
graphics.plot_one_type(model, model.vars['p'], {}, 'p')
pl.plot(range(101), pi_age_true, 'r:', label='Truth')
pl.legend(fancybox=True, shadow=True, loc='upper left')
pl.show()
model.input_data['mu_pred'] = model.vars['p']['p_pred'].stats()['mean']
model.input_data['sigma_pred'] = model.vars['p']['p_pred'].stats(
)['standard deviation']
data_simulation.add_quality_metrics(model.input_data)
model.delta = pandas.DataFrame(dict(true=[delta_true]))
model.delta['mu_pred'] = pl.exp(model.vars['p']['eta'].trace()).mean()
model.delta['sigma_pred'] = pl.exp(model.vars['p']['eta'].trace()).std()
data_simulation.add_quality_metrics(model.delta)
model.alpha = pandas.DataFrame(
index=[n for n in nx.traversal.dfs_preorder_nodes(model.hierarchy)])
model.alpha['true'] = pandas.Series(dict(alpha))
model.alpha['mu_pred'] = pandas.Series(
[n.stats()['mean'] for n in model.vars['p']['alpha']],
index=model.vars['p']['U'].columns)
model.alpha['sigma_pred'] = pandas.Series(
[n.stats()['standard deviation'] for n in model.vars['p']['alpha']],
index=model.vars['p']['U'].columns)
model.alpha = model.alpha.dropna()
data_simulation.add_quality_metrics(model.alpha)
model.sigma = pandas.DataFrame(dict(true=sigma_true))
model.sigma['mu_pred'] = [
n.stats()['mean'] for n in model.vars['p']['sigma_alpha']
]
model.sigma['sigma_pred'] = [
n.stats()['standard deviation'] for n in model.vars['p']['sigma_alpha']
]
data_simulation.add_quality_metrics(model.sigma)
print 'delta'
print model.delta
print '\ndata prediction bias: %.5f, MARE: %.3f, coverage: %.2f' % (
model.input_data['abs_err'].mean(),
pl.median(pl.absolute(model.input_data['rel_err'].dropna())),
model.input_data['covered?'].mean())
model.mu = pandas.DataFrame(
dict(true=pi_age_true,
mu_pred=model.vars['p']['mu_age'].stats()['mean'],
sigma_pred=model.vars['p']['mu_age'].stats()
['standard deviation']))
data_simulation.add_quality_metrics(model.mu)
data_simulation.initialize_results(model)
data_simulation.add_to_results(model, 'delta')
data_simulation.add_to_results(model, 'mu')
data_simulation.add_to_results(model, 'input_data')
data_simulation.add_to_results(model, 'alpha')
data_simulation.add_to_results(model, 'sigma')
data_simulation.finalize_results(model)
print model.results
return model
root_year='all',
mu_age=None,
mu_age_parent=None,
sigma_age_parent=None)
m = fit_model.fit_data_model(vars,
iter=4000,
burn=2000,
thin=20,
tune_interval=100)
# generate estimates for all leaves of the hierarchy
est = {}
for n in model.hierarchy:
if len(model.hierarchy.successors(n)) == 0:
est[n] = pl.median(covariate_model.predict_for(model, 'all', 'total',
'all', n, 'male', 2005,
0., vars, 0., 1.),
axis=0)
graphics.plot_one_type(model, vars, {}, 'p')
r = 'europe_western'
N = len(model.hierarchy[r])
for i, n in enumerate(sorted(model.hierarchy[r], key=lambda n: est[n][-1])):
col = pl.cm.Spectral((.1 + i) / N)
pl.semilogy(est[n], label=n, color=col, linewidth=2)
pl.text(100, est[n][-1], n, color=col)
pl.axis([-5, 110, 1e-5, .02])
