def validate_fit_consistent_model():
# create model and priors
vars = consistent_model.consistent_model(model, 'all', 'total', 'all', {})
m = fit_model.fit_consistent_model(vars)
return m
def validate_fit_consistent_model():
# create model and priors
vars = consistent_model.consistent_model(model, 'all', 'total', 'all', {})
m = fit_model.fit_consistent_model(vars)
return m
def simulate_data(dm, area, sex, year):
""" generate simulated data, based on mu_pred of dm.vars[t]['data']
"""
param_type = dict(i='incidence', p='prevalence', r='remission', f='excess-mortality', rr='relative-risk', pf='prevalence_x_excess-mortality', m_with='mortality')
# TODO: abstract this block of code into data.py, it is also called in fit_posterior.py
# select data that is about areas in this region, recent years, and sex of male or total only
predict_area = area
predict_year = year
predict_sex = sex
model = dm.model
assert predict_area in model.hierarchy, 'region %s not found in area hierarchy' % predict_area
subtree = nx.traversal.bfs_tree(model.hierarchy, predict_area)
relevant_rows = [i for i, r in model.input_data.T.iteritems() \
if (r['area'] in subtree or r['area'] == 'all')\
and ((predict_year >= 1997 and r['year_end'] >= 1997) or
(predict_year <= 1997 and r['year_start'] <= 1997)) \
and r['sex'] in [predict_sex, 'total']]
model.input_data = model.input_data.ix[relevant_rows]
# replace area 'all' with predict_area
model.input_data['area'][model.input_data['area'] == 'all'] = predict_area
input_data = pandas.DataFrame()
for t in types_to_resample:
data_t = model.get_data(t)
if t == 'rr':
input_data = input_data.append(data_t) # TODO: resample rr, smr, X with appropriate noise model
else:
input_data = input_data.append(resample(data_t))
# copy m_all data over
input_data = input_data.append(dm.model.input_data[dm.model.input_data['data_type']=='m_all'])
dm.model.input_data = input_data
true = {}
for t in types_to_resample:
key = dismod3.utils.gbd_key_for(param_type[t], area, year, sex)
true[t] = dm.get_mcmc('mean', key)
dm.true = true
emp_priors = {}
for t in 'i r f p rr pf'.split():
key = dismod3.utils.gbd_key_for(param_type[t], area, year, sex)
mu = dm.get_mcmc('emp_prior_mean', key)
sigma = dm.get_mcmc('emp_prior_std', key)
if len(mu) == 101 and len(sigma) == 101:
emp_priors[t, 'mu'] = mu
emp_priors[t, 'sigma'] = sigma
dm.emp_priors = emp_priors
dm.vars = consistent_model.consistent_model(dm.model, area, sex, year, emp_priors)
return dm
[col for col in model.input_data.columns if col.startswith('x_')], axis=1)
# create model for (europe_western, male, 2005)
root_area = 'europe_western'
subtree = nx.traversal.bfs_tree(model.hierarchy, root_area)
relevant_rows = [i for i, r in model.input_data.T.iteritems() \
if r['area'] in subtree \
and r['year_end'] >= 1997 \
and r['sex'] in ['male', 'total'] \
and r['data_type'] in ['pf', 'm']]
model.input_data = model.input_data.ix[relevant_rows]
## create and fit consistent model at gbd region level
vars = consistent_model.consistent_model(model,
root_area=root_area,
root_sex='male',
root_year=2005,
priors={})
posterior_model = fit_model.fit_consistent_model(vars,
iter=1030,
burn=500,
thin=5,
tune_interval=100)
## generate estimates
predict_area = root_area
posteriors = {}
for t in 'i r f p rr pf'.split():
posteriors[t] = pl.median(covariate_model.predict_for(
model.output_template, model.hierarchy, root_area, 'male', 2005,
predict_area, 'male', 2005, vars[t]),
reload(book_graphics)
# <codecell>
### @export 'initialize-model'
types = pl.array(['i', 'r', 'f', 'p'])
model = data_simulation.simple_model(0)
model.input_data = pandas.read_csv(
'/home/j/Project/dismod/gbd/data/ssas_mx.csv')
ages = pl.array([0, 5, 15, 25, 35, 45, 55, 65, 75, 100])
for t in types:
model.parameters[t]['parameter_age_mesh'] = ages
model.vars = consistent_model.consistent_model(model, 'all', 'total', 'all',
{})
for i, k_i in enumerate(model.parameters[t]['parameter_age_mesh']):
model.vars['i']['gamma'][i].value = pl.log(k_i * .0001 + .001)
model.vars['r']['gamma'][i].value = pl.log(.1)
model.vars['f']['gamma'][i].value = pl.log(.05)
# <codecell>
### @export 'initial-rates'
#book_graphics.plot_age_patterns(model, types='i r m f p'.split(),
# yticks=dict(i=[0,.01,.02], r=[0,.05,.1], m=[0,.2,.4], f=[0,.05,.1], p=[0,.05,.1]))
def set_rate(rate_type, value):
t = {
def simulate_data(dm, area, sex, year):
""" generate simulated data, based on mu_pred of dm.vars[t]['data']
"""
param_type = dict(i='incidence',
p='prevalence',
r='remission',
f='excess-mortality',
rr='relative-risk',
pf='prevalence_x_excess-mortality',
m_with='mortality')
# TODO: abstract this block of code into data.py, it is also called in fit_posterior.py
# select data that is about areas in this region, recent years, and sex of male or total only
predict_area = area
predict_year = year
predict_sex = sex
model = dm.model
assert predict_area in model.hierarchy, 'region %s not found in area hierarchy' % predict_area
subtree = nx.traversal.bfs_tree(model.hierarchy, predict_area)
relevant_rows = [i for i, r in model.input_data.T.iteritems() \
if (r['area'] in subtree or r['area'] == 'all')\
and ((predict_year >= 1997 and r['year_end'] >= 1997) or
(predict_year <= 1997 and r['year_start'] <= 1997)) \
and r['sex'] in [predict_sex, 'total']]
model.input_data = model.input_data.ix[relevant_rows]
# replace area 'all' with predict_area
model.input_data['area'][model.input_data['area'] == 'all'] = predict_area
input_data = pandas.DataFrame()
for t in types_to_resample:
data_t = model.get_data(t)
if t == 'rr':
input_data = input_data.append(
data_t
) # TODO: resample rr, smr, X with appropriate noise model
else:
input_data = input_data.append(resample(data_t))
# copy m_all data over
input_data = input_data.append(
dm.model.input_data[dm.model.input_data['data_type'] == 'm_all'])
dm.model.input_data = input_data
true = {}
for t in types_to_resample:
key = dismod3.utils.gbd_key_for(param_type[t], area, year, sex)
true[t] = dm.get_mcmc('mean', key)
dm.true = true
emp_priors = {}
for t in 'i r f p rr pf'.split():
key = dismod3.utils.gbd_key_for(param_type[t], area, year, sex)
mu = dm.get_mcmc('emp_prior_mean', key)
sigma = dm.get_mcmc('emp_prior_std', key)
if len(mu) == 101 and len(sigma) == 101:
emp_priors[t, 'mu'] = mu
emp_priors[t, 'sigma'] = sigma
dm.emp_priors = emp_priors
dm.vars = consistent_model.consistent_model(dm.model, area, sex, year,
emp_priors)
return dm
model.parameters['r']['level_value'] = dict(age_before=100, age_after=100, value=0.)
# no covariates
model.input_data = model.input_data.drop([col for col in model.input_data.columns if col.startswith('x_')], axis=1)
# create model for (europe_western, male, 2005)
root_area = 'europe_western'
subtree = nx.traversal.bfs_tree(model.hierarchy, root_area)
relevant_rows = [i for i, r in model.input_data.T.iteritems() \
if r['area'] in subtree \
and r['year_end'] >= 1997 \
and r['sex'] in ['male', 'total'] \
and r['data_type'] in ['pf', 'm']]
model.input_data = model.input_data.ix[relevant_rows]
## create and fit consistent model at gbd region level
vars = consistent_model.consistent_model(model, root_area=root_area, root_sex='male', root_year=2005, priors={})
posterior_model = fit_model.fit_consistent_model(vars, iter=1030, burn=500, thin=5, tune_interval=100)
## generate estimates
predict_area = root_area
posteriors = {}
for t in 'i r f p rr pf'.split():
posteriors[t] = pl.median(covariate_model.predict_for(model.output_template, model.hierarchy,
root_area, 'male', 2005,
predict_area, 'male', 2005, vars[t]), axis=0)
graphics.all_plots(model, vars, {}, posteriors)
import book_graphics
reload(book_graphics)
### @export 'initialize-model'
types = pl.array(['i', 'r', 'f', 'p'])
model = data_simulation.simple_model(0)
model.input_data = pandas.read_csv('/home/j/Project/dismod/gbd/data/ssas_mx.csv')
ages = pl.array([0, 5, 15, 25, 35, 45, 55, 65, 75, 100])
for t in types:
model.parameters[t]['parameter_age_mesh'] = ages
model.vars = consistent_model.consistent_model(model, 'all', 'total', 'all', {})
for i, k_i in enumerate(model.parameters[t]['parameter_age_mesh']):
model.vars['i']['gamma'][i].value = pl.log(k_i*.0001 + .001)
model.vars['r']['gamma'][i].value = pl.log(.1)
model.vars['f']['gamma'][i].value = pl.log(.05)
### @export 'initial-rates'
#book_graphics.plot_age_patterns(model, types='i r m f p'.split(),
# yticks=dict(i=[0,.01,.02], r=[0,.05,.1], m=[0,.2,.4], f=[0,.05,.1], p=[0,.05,.1]))
def set_rate(rate_type, value):
t = {'incidence':'i', 'remission': 'r', 'excess-mortality': 'f'}[rate_type]
for i, k_i in enumerate(model.vars[t]['knots']):
model.vars[t]['gamma'][i].value = pl.log(pl.maximum(1.e-9, value[i]))
def validate_consistent_re(N=500, delta_true=.15, sigma_true=[.1,.1,.1,.1,.1],
true=dict(i=quadratic, f=constant, r=constant)):
types = pl.array(['i', 'r', 'f', 'p'])
## generate simulated data
model = data_simulation.simple_model(N)
model.input_data['effective_sample_size'] = 1.
model.input_data['value'] = 0.
# coarse knot spacing for fast testing
for t in types:
model.parameters[t]['parameter_age_mesh'] = range(0, 101, 20)
sim = consistent_model.consistent_model(model, 'all', 'total', 'all', {})
for t in 'irf':
for i, k_i in enumerate(sim[t]['knots']):
sim[t]['gamma'][i].value = pl.log(true[t](k_i))
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)
data_type = types[mc.rcategorical(pl.ones(len(types), dtype=float) / float(len(types)), size=N)]
a = pl.arange(101)
age_weights = pl.ones_like(a)
sum_wt = pl.cumsum(age_weights)
p = pl.zeros(N)
for t in types:
mu_t = sim[t]['mu_age'].value
sum_mu_wt = pl.cumsum(mu_t*age_weights)
p_t = (sum_mu_wt[age_end] - sum_mu_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_t[i] = mu_t[age_start[i]]
# copy part into p
p[data_type==t] = p_t[data_type==t]
# add covariate shifts
import dismod3
import simplejson as json
gbd_model = data.ModelData.from_gbd_jsons(json.loads(dismod3.disease_json.DiseaseJson().to_json()))
model.hierarchy = gbd_model.hierarchy
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 = {}
for t in types:
alpha[t] = alpha_true_sim(model, area_list, sigma_true)
print json.dumps(alpha, indent=2)
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():
t = data_type[i]
p[i] = p[i] * pl.exp(pl.sum([alpha[t][n] for n in nx.shortest_path(model.hierarchy, 'all', a) if n in alpha]))
n = mc.runiform(100, 10000, size=N)
model.input_data['data_type'] = data_type
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
# coarse knot spacing for fast testing
for t in types:
model.parameters[t]['parameter_age_mesh'] = range(0, 101, 20)
## Then fit the model and compare the estimates to the truth
model.vars = {}
model.vars = consistent_model.consistent_model(model, 'all', 'total', 'all', {})
#model.map, model.mcmc = fit_model.fit_consistent_model(model.vars, iter=101, burn=0, thin=1, tune_interval=100)
model.map, model.mcmc = fit_model.fit_consistent_model(model.vars, iter=10000, burn=5000, thin=25, tune_interval=100)
graphics.plot_convergence_diag(model.vars)
graphics.plot_fit(model, model.vars, {}, {})
for i, t in enumerate('i r f p rr pf'.split()):
pl.subplot(2, 3, i+1)
pl.plot(range(101), sim[t]['mu_age'].value, 'w-', label='Truth', linewidth=2)
pl.plot(range(101), sim[t]['mu_age'].value, 'r-', label='Truth', linewidth=1)
pl.show()
model.input_data['mu_pred'] = 0.
model.input_data['sigma_pred'] = 0.
for t in types:
model.input_data['mu_pred'][data_type==t] = model.vars[t]['p_pred'].stats()['mean']
model.input_data['sigma_pred'][data_type==t] = model.vars[t]['p_pred'].stats()['standard deviation']
data_simulation.add_quality_metrics(model.input_data)
model.delta = pandas.DataFrame(dict(true=[delta_true for t in types if t != 'rr']))
model.delta['mu_pred'] = [pl.exp(model.vars[t]['eta'].trace()).mean() for t in types if t != 'rr']
model.delta['sigma_pred'] = [pl.exp(model.vars[t]['eta'].trace()).std() for t in types if t != 'rr']
data_simulation.add_quality_metrics(model.delta)
model.alpha = pandas.DataFrame()
model.sigma = pandas.DataFrame()
for t in types:
alpha_t = pandas.DataFrame(index=[n for n in nx.traversal.dfs_preorder_nodes(model.hierarchy)])
alpha_t['true'] = pandas.Series(dict(alpha[t]))
alpha_t['mu_pred'] = pandas.Series([n.stats()['mean'] for n in model.vars[t]['alpha']], index=model.vars[t]['U'].columns)
alpha_t['sigma_pred'] = pandas.Series([n.stats()['standard deviation'] for n in model.vars[t]['alpha']], index=model.vars[t]['U'].columns)
alpha_t['type'] = t
model.alpha = model.alpha.append(alpha_t.dropna(), ignore_index=True)
sigma_t = pandas.DataFrame(dict(true=sigma_true))
sigma_t['mu_pred'] = [n.stats()['mean'] for n in model.vars[t]['sigma_alpha']]
sigma_t['sigma_pred'] = [n.stats()['standard deviation'] for n in model.vars[t]['sigma_alpha']]
model.sigma = model.sigma.append(sigma_t.dropna(), ignore_index=True)
data_simulation.add_quality_metrics(model.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()
for t in types:
model.mu = model.mu.append(pandas.DataFrame(dict(true=sim[t]['mu_age'].value,
mu_pred=model.vars[t]['mu_age'].stats()['mean'],
sigma_pred=model.vars[t]['mu_age'].stats()['standard deviation'])),
ignore_index=True)
data_simulation.add_quality_metrics(model.mu)
print '\nparam prediction bias: %.5f, MARE: %.3f, coverage: %.2f' % (model.mu['abs_err'].mean(),
pl.median(pl.absolute(model.mu['rel_err'].dropna())),
model.mu['covered?'].mean())
print
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
def validate_consistent_model_sim(N=500,
delta_true=.5,
true=dict(i=quadratic,
f=constant,
r=constant)):
types = pl.array(['i', 'r', 'f', 'p'])
## generate simulated data
model = data_simulation.simple_model(N)
model.input_data['effective_sample_size'] = 1.
model.input_data['value'] = 0.
for t in types:
model.parameters[t]['parameter_age_mesh'] = range(0, 101, 20)
sim = consistent_model.consistent_model(model, 'all', 'total', 'all', {})
for t in 'irf':
for i, k_i in enumerate(sim[t]['knots']):
sim[t]['gamma'][i].value = pl.log(true[t](k_i))
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)
data_type = types[mc.rcategorical(pl.ones(len(types), dtype=float) /
float(len(types)),
size=N)]
a = pl.arange(101)
age_weights = pl.ones_like(a)
sum_wt = pl.cumsum(age_weights)
p = pl.zeros(N)
for t in types:
mu_t = sim[t]['mu_age'].value
sum_mu_wt = pl.cumsum(mu_t * age_weights)
p_t = (sum_mu_wt[age_end] - sum_mu_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_t[i] = mu_t[age_start[i]]
# copy part into p
p[data_type == t] = p_t[data_type == t]
n = mc.runiform(100, 10000, size=N)
model.input_data['data_type'] = data_type
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
# coarse knot spacing for fast testing
for t in types:
model.parameters[t]['parameter_age_mesh'] = range(0, 101, 20)
## Then fit the model and compare the estimates to the truth
model.vars = {}
model.vars = consistent_model.consistent_model(model, 'all', 'total',
'all', {})
model.map, model.mcmc = fit_model.fit_consistent_model(model.vars,
iter=10000,
burn=5000,
thin=25,
tune_interval=100)
graphics.plot_convergence_diag(model.vars)
graphics.plot_fit(model, model.vars, {}, {})
for i, t in enumerate('i r f p rr pf'.split()):
pl.subplot(2, 3, i + 1)
pl.plot(a, sim[t]['mu_age'].value, 'w-', label='Truth', linewidth=2)
pl.plot(a, sim[t]['mu_age'].value, 'r-', label='Truth', linewidth=1)
#graphics.plot_one_type(model, model.vars['p'], {}, 'p')
#pl.legend(fancybox=True, shadow=True, loc='upper left')
pl.show()
model.input_data['mu_pred'] = 0.
model.input_data['sigma_pred'] = 0.
for t in types:
model.input_data['mu_pred'][
data_type == t] = model.vars[t]['p_pred'].stats()['mean']
model.input_data['sigma_pred'][data_type == t] = model.vars['p'][
'p_pred'].stats()['standard deviation']
data_simulation.add_quality_metrics(model.input_data)
model.delta = pandas.DataFrame(
dict(true=[delta_true for t in types if t != 'rr']))
model.delta['mu_pred'] = [
pl.exp(model.vars[t]['eta'].trace()).mean() for t in types if t != 'rr'
]
model.delta['sigma_pred'] = [
pl.exp(model.vars[t]['eta'].trace()).std() for t in types if t != 'rr'
]
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()
for t in types:
model.mu = model.mu.append(pandas.DataFrame(
dict(true=sim[t]['mu_age'].value,
mu_pred=model.vars[t]['mu_age'].stats()['mean'],
sigma_pred=model.vars[t]['mu_age'].stats()
['standard deviation'])),
ignore_index=True)
data_simulation.add_quality_metrics(model.mu)
print '\nparam prediction bias: %.5f, MARE: %.3f, coverage: %.2f' % (
model.mu['abs_err'].mean(),
pl.median(pl.absolute(
model.mu['rel_err'].dropna())), model.mu['covered?'].mean())
print
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
return model
