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"))
def test_diagnose_divergences(self):
exe = os.path.join(datafiles_path,
'bernoulli' + EXTENSION) # fake out validation
output = os.path.join(datafiles_path, 'diagnose-good',
'corr_gauss_depth8')
sampler_args = SamplerArgs()
cmdstan_args = CmdStanArgs(
model_name='bernoulli',
model_exe=exe,
chain_ids=[1],
output_basename=output,
method_args=sampler_args,
)
fit = StanFit(args=cmdstan_args, chains=1)
# TODO - use cmdstan test files instead
expected = '\n'.join([
'Checking sampler transitions treedepth.',
'424 of 1000 (42%) transitions hit the maximum '
'treedepth limit of 8, or 2^8 leapfrog steps.',
'Trajectories that are prematurely terminated '
'due to this limit will result in slow exploration.',
'For optimal performance, increase this limit.',
])
self.assertIn(expected, fit.diagnose().replace("\r\n", "\n"))