def test_validate_big_run(self):
exe = os.path.join(datafiles_path,
'bernoulli' + EXTENSION) # fake out validation
output = os.path.join(datafiles_path, 'runset-big', 'output_icar_nyc')
sampler_args = SamplerArgs()
cmdstan_args = CmdStanArgs(
model_name='bernoulli',
model_exe=exe,
chain_ids=[1, 2],
seed=12345,
output_basename=output,
method_args=sampler_args,
)
fit = StanFit(args=cmdstan_args, chains=2)
fit._validate_csv_files()
sampler_state = [
'lp__',
'accept_stat__',
'stepsize__',
'treedepth__',
'n_leapfrog__',
'divergent__',
'energy__',
]
phis = ['phi.{}'.format(str(x + 1)) for x in range(2095)]
column_names = sampler_state + phis
self.assertEqual(fit.columns, len(column_names))
self.assertEqual(fit.column_names, tuple(column_names))
self.assertEqual(fit.metric_type, 'diag_e')
self.assertEqual(fit.stepsize.shape, (2, ))
self.assertEqual(fit.metric.shape, (2, 2095))
self.assertEqual((1000, 2, 2102), fit.sample.shape)
phis = fit.get_drawset(params=['phi'])
self.assertEqual((2000, 2095), phis.shape)
phi1 = fit.get_drawset(params=['phi.1'])
self.assertEqual((2000, 1), phi1.shape)
mo_phis = fit.get_drawset(params=['phi.1', 'phi.10', 'phi.100'])
self.assertEqual((2000, 3), mo_phis.shape)
phi2095 = fit.get_drawset(params=['phi.2095'])
self.assertEqual((2000, 1), phi2095.shape)
with self.assertRaises(Exception):
fit.get_drawset(params=['phi.2096'])
with self.assertRaises(Exception):
fit.get_drawset(params=['ph'])
def test_validate_good_run(self):
# construct fit using existing sampler output
exe = os.path.join(datafiles_path, 'bernoulli' + EXTENSION)
jdata = os.path.join(datafiles_path, 'bernoulli.data.json')
output = os.path.join(goodfiles_path, 'bern')
sampler_args = SamplerArgs(sampling_iters=100,
max_treedepth=11,
adapt_delta=0.95)
cmdstan_args = CmdStanArgs(
model_name='bernoulli',
model_exe=exe,
chain_ids=[1, 2, 3, 4],
seed=12345,
data=jdata,
output_basename=output,
method_args=sampler_args,
)
fit = StanFit(args=cmdstan_args, chains=4)
retcodes = fit._retcodes
for i in range(len(retcodes)):
fit._set_retcode(i, 0)
self.assertTrue(fit._check_retcodes())
fit._check_console_msgs()
fit._validate_csv_files()
self.assertEqual(4, fit.chains)
self.assertEqual(100, fit.draws)
self.assertEqual(8, len(fit.column_names))
self.assertEqual('lp__', fit.column_names[0])
df = fit.get_drawset()
self.assertEqual(df.shape,
(fit.chains * fit.draws, len(fit.column_names)))
_ = fit.summary()
# TODO - use cmdstan test files instead
expected = '\n'.join([
'Checking sampler transitions treedepth.',
'Treedepth satisfactory for all transitions.',
'\nChecking sampler transitions for divergences.',
'No divergent transitions found.',
'\nChecking E-BFMI - sampler transitions HMC potential energy.',
'E-BFMI satisfactory for all transitions.',
'\nEffective sample size satisfactory.',
])
self.assertIn(expected, fit.diagnose().replace("\r\n", "\n"))