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])