def test_save_country_level_posterior(): """ Test exporting country level posterior output """ # load model to test fitting dm = DiseaseJson(file('tests/dismoditis.json').read()) # fit posterior where there is data from dismod3 import gbd_disease_model keys = dismod3.utils.gbd_keys(region_list=['asia_southeast'], year_list=[1990], sex_list=['male']) gbd_disease_model.fit(dm, method='map', keys=keys, verbose=1) ## first generate decent initial conditions gbd_disease_model.fit(dm, method='mcmc', keys=keys, iter=100, thin=1, burn=0, verbose=1, dbname='/dev/null') ## then sample the posterior via MCMC # make a rate_type_list rate_type_list = ['incidence', 'prevalence', 'remission', 'excess-mortality'] # job working directory job_wd = dismod3.settings.JOB_WORKING_DIR % dm.id # directory to save the file dir = job_wd + '/posterior/country_level_posterior_dm-' + str(dm.id) + '/' import os from shutil import rmtree if os.path.exists(dir): rmtree(dir) os.makedirs(dir) # save country level posterior in csv file from fit_posterior import save_country_level_posterior save_country_level_posterior(dm, 'asia_southeast', '1990', 'male', rate_type_list) # zip the csv file from upload_fits import zip_country_level_posterior_files zip_country_level_posterior_files(dm.id)
def test_dismoditis_wo_prevalence(): """ Test fit for simple example""" # load model to test fitting dm = DiseaseJson(file('tests/dismoditis.json').read()) # remove all prevalence data dm.data = [d for d in dm.data if d['parameter'] != 'prevalence data'] # fit empirical priors neg_binom_model.fit_emp_prior(dm, 'incidence', '/dev/null') check_emp_prior_fits(dm) neg_binom_model.fit_emp_prior(dm, 'excess-mortality', '/dev/null') check_emp_prior_fits(dm) # fit posterior delattr(dm, 'vars') # remove vars so that gbd_disease_model creates its own version from dismod3 import gbd_disease_model keys = dismod3.utils.gbd_keys(region_list=['asia_southeast'], year_list=[1990], sex_list=['male']) #gbd_disease_model.fit(dm, method='map', keys=keys, verbose=1) ## first generate decent initial conditions gbd_disease_model.fit(dm, method='mcmc', keys=keys, iter=1000, thin=5, burn=5000, verbose=1, dbname='/dev/null') ## then sample the posterior via MCMC # compare fit to data check_posterior_fits(dm)
def test_simulated_disease(): """ Test fit for simulated disease data""" # load model to test fitting dm = DiseaseJson(file('tests/test_disease_1.json').read()) # filter and noise up data cov = .5 data = [] for d in dm.data: d['truth'] = d['value'] if dismod3.utils.clean(d['gbd_region']) == 'north_america_high_income': if d['data_type'] == 'all-cause mortality data': data.append(d) else: se = (cov * d['value']) d['value'] = mc.rtruncnorm(d['truth'], se**-2, 0, np.inf) d['age_start'] -= 5 d['age_end'] = d['age_start']+9 d['age_weights'] = np.ones(d['age_end']-d['age_start']+1) d['age_weights'] /= float(len(d['age_weights'])) d['standard_error'] = se data.append(d) dm.data = data # fit empirical priors and compare fit to data from dismod3 import neg_binom_model for rate_type in 'prevalence incidence remission excess-mortality'.split(): neg_binom_model.fit_emp_prior(dm, rate_type, '/dev/null') check_emp_prior_fits(dm) # fit posterior delattr(dm, 'vars') # remove vars so that gbd_disease_model creates its own version from dismod3 import gbd_disease_model keys = dismod3.utils.gbd_keys(region_list=['north_america_high_income'], year_list=[1990], sex_list=['male']) gbd_disease_model.fit(dm, method='map', keys=keys, verbose=1) ## first generate decent initial conditions gbd_disease_model.fit(dm, method='mcmc', keys=keys, iter=1000, thin=5, burn=5000, verbose=1, dbname='/dev/null') ## then sample the posterior via MCMC print 'error compared to the noisy data (coefficient of variation = %.2f)' % cov check_posterior_fits(dm) for d in dm.data: d['value'] = d['truth'] d['age_start'] += 5 d['age_end'] = d['age_start'] d['age_weights'] = np.ones(d['age_end']-d['age_start']+1) d['age_weights'] /= float(len(d['age_weights'])) print 'error compared to the truth' check_posterior_fits(dm) return dm
def test_hep_c(): """ Test fit for subset of hep_c data data is filtered to include only prevalence with region == 'europe_western' and sex == 'all' """ # load model to test fitting dm = DiseaseJson(file('tests/hep_c_europe_western.json').read()) # fit empirical priors neg_binom_model.fit_emp_prior(dm, 'prevalence', '/dev/null') # fit posterior delattr(dm, 'vars') # remove vars so that gbd_disease_model creates its own version from dismod3 import gbd_disease_model keys = dismod3.utils.gbd_keys(region_list=['europe_western'], year_list=[1990], sex_list=['male']) gbd_disease_model.fit(dm, method='map', keys=keys, verbose=1) ## first generate decent initial conditions gbd_disease_model.fit(dm, method='mcmc', keys=keys, iter=1000, thin=5, burn=5000, verbose=1, dbname='/dev/null') ## then sample the posterior via MCMC # check that prevalence is smooth near age zero prediction = dm.get_mcmc('mean', 'prevalence+europe_western+1990+male') print prediction return dm assert prediction[100] < .1, 'prediction should not shoot up in oldest ages'
def test_dismoditis(): """ Test fit for simple example""" # load model to test fitting dm = DiseaseJson(file('tests/dismoditis.json').read()) for d in dm.data: d['standard_error'] = .01 # fit empirical priors neg_binom_model.fit_emp_prior(dm, 'prevalence', '/dev/null') check_emp_prior_fits(dm) neg_binom_model.fit_emp_prior(dm, 'incidence', '/dev/null') check_emp_prior_fits(dm) neg_binom_model.fit_emp_prior(dm, 'excess-mortality', '/dev/null') check_emp_prior_fits(dm) # fit posterior where there is no data delattr(dm, 'vars') # remove vars so that gbd_disease_model creates its own version from dismod3 import gbd_disease_model keys = dismod3.utils.gbd_keys(region_list=['north_america_high_income'], year_list=[1990], sex_list=['male']) gbd_disease_model.fit(dm, method='map', keys=keys, verbose=1) ## first generate decent initial conditions gbd_disease_model.fit(dm, method='mcmc', keys=keys, iter=1000, thin=5, burn=5000, verbose=1, dbname='/dev/null') ## then sample the posterior via MCMC check_posterior_fits(dm) # check that prevalence is smooth near age zero prediction = dm.get_mcmc('mean', 'prevalence+north_america_high_income+1990+male') assert prediction[1]-prediction[0] < .01, 'prediction should be smooth near zero'
def test_triangle_pattern(): """ Test fit for empirical prior to data showing a linearly increasing age pattern""" # load model to test fitting dm = DiseaseJson(file('tests/single_low_noise.json').read()) # create linear age pattern data import copy d = dm.data.pop() for a in range(10, 100, 20): d = copy.copy(d) d['age_start'] = a d['age_end'] = a d['parameter_value'] = .01*min(a, 100-a) d['value'] = .01*min(a, 100-a) dm.data.append(d) # fit empirical priors from dismod3 import neg_binom_model neg_binom_model.fit_emp_prior(dm, 'prevalence', '/dev/null') # compare fit to data check_emp_prior_fits(dm) # fit posterior delattr(dm, 'vars') # remove vars so that gbd_disease_model creates its own version from dismod3 import gbd_disease_model keys = dismod3.utils.gbd_keys(region_list=['asia_southeast'], year_list=[1990], sex_list=['male']) gbd_disease_model.fit(dm, method='map', keys=keys, verbose=1) ## first generate decent initial conditions gbd_disease_model.fit(dm, method='mcmc', keys=keys, iter=1000, thin=5, burn=5000, verbose=1, dbname='/dev/null') ## then sample the posterior via MCMC # compare fit to data check_posterior_fits(dm)
def fit_model(dm, region, year, sex): """ Fit the empirical priors, and the posterior for a specific region/year/sex """ # fit empirical priors for rate_type in 'prevalence incidence remission excess-mortality'.split(): neg_binom_model.fit_emp_prior(dm, rate_type, '/dev/null') # fit posterior delattr(dm, 'vars') # remove vars so that gbd_disease_model creates its own version from dismod3 import gbd_disease_model keys = dismod3.utils.gbd_keys(region_list=[region], year_list=[year], sex_list=[sex]) gbd_disease_model.fit(dm, method='map', keys=keys, verbose=1) ## first generate decent initial conditions gbd_disease_model.fit(dm, method='mcmc', keys=keys, iter=1000, thin=5, burn=5000, verbose=1, dbname='/dev/null') ## then sample the posterior via MCMC
def fit_simulated_disease(n=300, cv=2.): """ Test fit for simulated disease data with noise and missingness""" # load model to test fitting dm = DiseaseJson(file('tests/simulation_gold_standard.json').read()) # adjust any priors and covariates as desired dm.set_param_age_mesh(arange(0,101,2)) for type in 'incidence prevalence remission excess_mortality'.split(): dm.params['global_priors']['heterogeneity'][type] = 'Very' dm.params['covariates']['Country_level']['LDI_id']['rate']['value'] = 0 # filter and noise up data mort_data = [] all_data = [] for d in dm.data: d['truth'] = d['value'] d['age_weights'] = array([1.]) if d['data_type'] == 'all-cause mortality data': mort_data.append(d) else: if d['value'] > 0: se = (cv / 100.) * d['value'] Y_i = mc.rtruncnorm(d['truth'], se**-2, 0, np.inf) d['value'] = Y_i d['standard_error'] = se d['effective_sample_size'] = Y_i * (1-Y_i) / se**2 all_data.append(d) sampled_data = random.sample(all_data, n) + mort_data dm.data = sampled_data # fit empirical priors and compare fit to data from dismod3 import neg_binom_model for rate_type in 'prevalence incidence remission excess-mortality'.split(): #neg_binom_model.fit_emp_prior(dm, rate_type, iter=1000, thin=1, burn=0, dbname='/dev/null') neg_binom_model.fit_emp_prior(dm, rate_type, iter=30000, thin=15, burn=15000, dbname='/dev/null') check_emp_prior_fits(dm) # fit posterior delattr(dm, 'vars') # remove vars so that gbd_disease_model creates its own version from dismod3 import gbd_disease_model keys = dismod3.utils.gbd_keys(region_list=['north_america_high_income'], year_list=[1990], sex_list=['male']) gbd_disease_model.fit(dm, method='map', keys=keys, verbose=1) ## first generate decent initial conditions gbd_disease_model.fit(dm, method='mcmc', keys=keys, iter=30000, thin=15, burn=15000, verbose=1, dbname='/dev/null') ## then sample the posterior via MCMC #gbd_disease_model.fit(dm, method='mcmc', keys=keys, iter=1000, thin=1, burn=0, verbose=1, dbname='/dev/null') ## fast for dev print 'error compared to the noisy data (coefficient of variation = %.2f)' % cv check_posterior_fits(dm) dm.data = all_data for d in dm.data: if d['data_type'] != 'all-cause mortality data': d['noisy_data'] = d['value'] d['value'] = d['truth'] print 'error compared to the truth' are, coverage = check_posterior_fits(dm) print print 'Median Absolute Relative Error of Posterior Predictions:', median(are) print 'Pct coverage:', 100*mean(coverage) f = open('score_%d_%f.txt' % (n, cv), 'a') f.write('%10.10f,%10.10f\n' % (median(are), mean(coverage))) f.close() dm.all_data = all_data dm.data = sampled_data for d in dm.data: if d['data_type'] != 'all-cause mortality data': d['value'] = d['noisy_data'] generate_figure(dm, n, cv) return dm
# store the ground truth values, for plotting (these are _not_ used in fitting the model) dm.set_truth(key % 'remission', r) dm.set_truth(key % 'incidence', i) dm.set_truth(key % 'prevalence', p) dm.set_truth(key % 'case-fatality', f) dm.set_truth(key % 'relative-risk', (m + f) / m) dm.set_truth(key % 'duration', X) dm.set_truth(key % 'yld', X * i) print '\nfitting model...' dm.params['estimate_type'] = 'Borrow strength within each region' keys = model.gbd_keys(region_list=['asia_southeast'], year_list=[1990, 2005], sex_list=['female', 'male']) print ' beginning MLE+NA fit...' model.fit(dm, method='norm_approx', keys=keys, verbose=1) print '...model fit complete.' # save relevant statistics of the simulation experiment total_yld = sum(i * X) est_yld = sum(dm.get_mcmc('median', key % 'yld')) est_yld_upper_ui = sum(dm.get_mcmc('upper_ui', key % 'yld')) est_yld_lower_ui = sum(dm.get_mcmc('lower_ui', key % 'yld')) total_yld_str = """ Dashed = Truth Dotted = MLE Solid = Median True YLD = %.2f
def fit_posterior(id, region, sex, year): """ Fit posterior of specified region/sex/year for specified model Parameters ---------- id : int The model id number for the job to fit region : str From dismod3.settings.gbd_regions, but clean()-ed sex : str, from dismod3.settings.gbd_sexes year : str, from dismod3.settings.gbd_years Example ------- >>> import fit_posterior >>> fit_posterior.fit_posterior(2552, 'asia_east', 'male', '2005') """ #print 'updating job status on server' #dismod3.log_job_status(id, 'posterior', '%s--%s--%s' % (region, sex, year), 'Running') dm = dismod3.load_disease_model(id) #dm.data = [] # for testing, remove all data keys = dismod3.utils.gbd_keys(region_list=[region], year_list=[year], sex_list=[sex]) # fit the model dir = dismod3.settings.JOB_WORKING_DIR % id import dismod3.gbd_disease_model as model model.fit(dm, method='map', keys=keys, verbose=1) ## first generate decent initial conditions ## then sample the posterior via MCMC model.fit(dm, method='mcmc', keys=keys, iter=50000, thin=25, burn=25000, verbose=1, dbname='%s/posterior/pickle/dm-%d-posterior-%s-%s-%s.pickle' % (dir, id, region, sex, year)) # generate plots of results dismod3.tile_plot_disease_model(dm, keys, defaults={}) dm.savefig('dm-%d-posterior-%s.png' % (id, '+'.join(['all', region, sex, year]))) # TODO: refactor naming into its own function (disease_json.save_image perhaps) for param_type in dismod3.settings.output_data_types: keys = dismod3.utils.gbd_keys(region_list=[region], year_list=[year], sex_list=[sex], type_list=[param_type]) dismod3.tile_plot_disease_model(dm, keys, defaults={}) dm.savefig('dm-%d-posterior-%s-%s-%s-%s.png' % (id, dismod3.utils.clean(param_type), region, sex, year)) # TODO: refactor naming into its own function # summarize fit quality graphically, as well as parameter posteriors for k in dismod3.utils.gbd_keys(region_list=[region], year_list=[year], sex_list=[sex]): if dm.vars[k].get('data'): dismod3.plotting.plot_posterior_predicted_checks(dm, k) dm.savefig('dm-%d-check-%s.png' % (dm.id, k)) # save results (do this last, because it removes things from the disease model that plotting function, etc, might need keys = dismod3.utils.gbd_keys(region_list=[region], year_list=[year], sex_list=[sex]) dm.save('dm-%d-posterior-%s-%s-%s.json' % (id, region, sex, year), keys_to_save=keys) # make a rate_type_list rate_type_list = ['incidence', 'prevalence', 'remission', 'excess-mortality', 'mortality', 'relative-risk', 'duration', 'incidence_x_duration'] # save country level posterior save_country_level_posterior(dm, region, year, sex) # update job status file #print 'updating job status on server' #dismod3.log_job_status(id, 'posterior', # '%s--%s--%s' % (region, sex, year), 'Completed') return dm
dm.set_truth(key % 'incidence', i) dm.set_truth(key % 'prevalence', p) dm.set_truth(key % 'case-fatality', f) dm.set_truth(key % 'relative-risk', (m + f) / m) dm.set_truth(key % 'duration', X) dm.set_truth(key % 'yld', X * i) print '\nfitting model...' dm.params['estimate_type'] = 'Borrow strength within each region' keys = model.gbd_keys(region_list=['asia_southeast'], year_list=[1990, 2005], sex_list=['female', 'male']) print ' beginning MLE+NA fit...' model.fit(dm, method='norm_approx', keys=keys, verbose=1) print '...model fit complete.' # save relevant statistics of the simulation experiment total_yld = sum(i * X) est_yld = sum(dm.get_mcmc('median', key % 'yld')) est_yld_upper_ui = sum(dm.get_mcmc('upper_ui', key % 'yld')) est_yld_lower_ui = sum(dm.get_mcmc('lower_ui', key % 'yld')) total_yld_str = """ Dashed = Truth Dotted = MLE Solid = Median True YLD = %.2f