def main(_):
util.print('Loading model {} with dataset {}.'.format(
FLAGS.model, FLAGS.dataset))
if FLAGS.model == 'radon':
model_config = models.get_radon(state_code=FLAGS.dataset)
elif FLAGS.model == 'radon_stddvs':
model_config = models.get_radon_model_stddvs(state_code=FLAGS.dataset)
elif FLAGS.model == '8schools':
model_config = models.get_eight_schools()
elif FLAGS.model == 'german_credit_gammascale':
model_config = models.get_german_credit_gammascale()
elif FLAGS.model == 'german_credit_lognormalcentered':
model_config = models.get_german_credit_lognormalcentered()
else:
raise Exception('unknown model {}'.format(FLAGS.model))
description = FLAGS.model + '_{}'.format(FLAGS.dataset)
experiments_dir = os.path.join(
FLAGS.results_dir,
'num_leapfrog_steps={}'.format(FLAGS.num_leapfrog_steps))
if not tf.gfile.Exists(experiments_dir):
tf.gfile.MakeDirs(experiments_dir)
if FLAGS.method == 'baseline':
run_baseline(description,
model_config=model_config,
experiments_dir=experiments_dir,
num_samples=FLAGS.num_samples,
burnin=FLAGS.burnin,
num_adaptation_steps=FLAGS.num_adaptation_steps,
num_optimization_steps=FLAGS.num_optimization_steps,
tau=FLAGS.tau,
num_leapfrog_steps=FLAGS.num_leapfrog_steps,
description=description)
elif FLAGS.method == 'vip':
run_vip(description,
model_config=model_config,
experiments_dir=experiments_dir,
use_iaf_posterior=FLAGS.use_iaf_posterior,
num_samples=FLAGS.num_samples,
burnin=FLAGS.burnin,
num_adaptation_steps=FLAGS.num_adaptation_steps,
num_optimization_steps=FLAGS.num_optimization_steps,
num_mc_samples=FLAGS.num_mc_samples,
tau=FLAGS.tau,
num_leapfrog_steps=FLAGS.num_leapfrog_steps,
description=description)
else:
raise Exception('No such method')
def test_eight_schools(self):
(model, model_args, observed, to_cp, to_ncp,
make_to_cp) = models.get_eight_schools()
self._sanity_check_conversion(model, model_args, observed, to_cp,
to_ncp, make_to_cp)