def _validate_csv_files(self) -> None:
"""
Checks that csv output files for all chains are consistent.
Populates attributes for draws, column_names, num_params, metric_type.
Raises exception when inconsistencies detected.
"""
dzero = {}
for i in range(self.runset.chains):
if i == 0:
dzero = check_sampler_csv(
path=self.runset.csv_files[i],
is_fixed_param=self._is_fixed_param,
iter_sampling=self._iter_sampling,
iter_warmup=self._iter_warmup,
save_warmup=self._save_warmup,
thin=self._thin,
)
else:
drest = check_sampler_csv(
path=self.runset.csv_files[i],
is_fixed_param=self._is_fixed_param,
iter_sampling=self._iter_sampling,
iter_warmup=self._iter_warmup,
save_warmup=self._save_warmup,
thin=self._thin,
)
for key in dzero:
if (key not in ['id', 'diagnostic_file']
and dzero[key] != drest[key]):
raise ValueError(
'csv file header mismatch, '
'file {}, key {} is {}, expected {}'.format(
self.runset.csv_files[i],
key,
dzero[key],
drest[key],
))
self._draws_sampling = dzero['draws_sampling']
if self._save_warmup:
self._draws_warmup = dzero['draws_warmup']
else:
self._draws_warmup = 0
self._column_names = dzero['column_names']
if not self._is_fixed_param:
self._num_params = dzero['num_params']
self._metric_type = dzero.get('metric')
def test_check_sampler_csv_1(self):
csv_good = os.path.join(DATAFILES_PATH, 'bernoulli_output_1.csv')
dict = check_sampler_csv(csv_good)
self.assertEqual('bernoulli_model', dict['model'])
self.assertEqual(10, dict['num_samples'])
self.assertFalse('save_warmup' in dict)
self.assertEqual(10, dict['draws'])
self.assertEqual(8, len(dict['column_names']))
def test_check_sampler_csv_1(self):
csv_good = os.path.join(DATAFILES_PATH, 'bernoulli_output_1.csv')
dict = check_sampler_csv(
path=csv_good,
is_fixed_param=False,
iter_warmup=100,
iter_sampling=10,
thin=1,
)
self.assertEqual('bernoulli_model', dict['model'])
self.assertEqual(10, dict['num_samples'])
self.assertFalse('save_warmup' in dict)
self.assertEqual(10, dict['draws_sampling'])
self.assertEqual(8, len(dict['column_names']))
with self.assertRaisesRegex(
ValueError, 'config error, expected thin = 2'
):
check_sampler_csv(
path=csv_good, iter_warmup=100, iter_sampling=20, thin=2
)
with self.assertRaisesRegex(
ValueError, 'config error, expected save_warmup'
):
check_sampler_csv(
path=csv_good,
iter_warmup=100,
iter_sampling=10,
save_warmup=True,
)
with self.assertRaisesRegex(ValueError, 'expected 1000 draws'):
check_sampler_csv(path=csv_good, iter_warmup=100)
def test_good(self):
# construct fit using existing sampler output
exe = os.path.join(DATAFILES_PATH, 'bernoulli' + EXTENSION)
jdata = os.path.join(DATAFILES_PATH, 'bernoulli.data.json')
sampler_args = SamplerArgs(iter_sampling=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_dir=DATAFILES_PATH,
method_args=sampler_args,
)
runset = RunSet(args=cmdstan_args)
runset._csv_files = [
os.path.join(DATAFILES_PATH, 'runset-good', 'bern-1.csv'),
os.path.join(DATAFILES_PATH, 'runset-good', 'bern-2.csv'),
os.path.join(DATAFILES_PATH, 'runset-good', 'bern-3.csv'),
os.path.join(DATAFILES_PATH, 'runset-good', 'bern-4.csv'),
]
retcodes = runset._retcodes
for i in range(len(retcodes)):
runset._set_retcode(i, 0)
config = check_sampler_csv(
path=runset.csv_files[i],
is_fixed_param=False,
iter_sampling=100,
iter_warmup=1000,
save_warmup=False,
thin=1,
)
expected = 'Metadata:\n{}\n'.format(config)
metadata = InferenceMetadata(config)
actual = '{}'.format(metadata)
self.assertEqual(expected, actual)
self.assertEqual(config, metadata.cmdstan_config)
hmc_vars = {
'lp__',
'accept_stat__',
'stepsize__',
'treedepth__',
'n_leapfrog__',
'divergent__',
'energy__',
}
sampler_vars_cols = metadata.sampler_vars_cols
self.assertEqual(hmc_vars, sampler_vars_cols.keys())
bern_model_vars = {'theta'}
self.assertEqual(bern_model_vars, metadata.stan_vars_dims.keys())
self.assertEqual((), metadata.stan_vars_dims['theta'])
self.assertEqual(bern_model_vars, metadata.stan_vars_cols.keys())
self.assertEqual((7, ), metadata.stan_vars_cols['theta'])
def validate_csv_files(self) -> None:
"""
Checks that csv output files for all chains are consistent.
Populates attributes for metadata, draws, metric, step size.
Raises exception when inconsistencies detected.
"""
dzero = {}
for i in range(self.chains):
if i == 0:
dzero = check_sampler_csv(
path=self.runset.csv_files[i],
is_fixed_param=self._is_fixed_param,
iter_sampling=self._iter_sampling,
iter_warmup=self._iter_warmup,
save_warmup=self._save_warmup,
thin=self._thin,
)
else:
drest = check_sampler_csv(
path=self.runset.csv_files[i],
is_fixed_param=self._is_fixed_param,
iter_sampling=self._iter_sampling,
iter_warmup=self._iter_warmup,
save_warmup=self._save_warmup,
thin=self._thin,
)
for key in dzero:
if (key not in [
'id',
'diagnostic_file',
'metric_file',
'stepsize',
'init',
'seed',
] and dzero[key] != drest[key]):
raise ValueError(
'csv file header mismatch, '
'file {}, key {} is {}, expected {}'.format(
self.runset.csv_files[i],
key,
dzero[key],
drest[key],
))
self._metadata = InferenceMetadata(dzero)
def test_check_sampler_csv_thin(self):
stan = os.path.join(DATAFILES_PATH, 'bernoulli.stan')
bern_model = CmdStanModel(stan_file=stan)
bern_model.compile()
jdata = os.path.join(DATAFILES_PATH, 'bernoulli.data.json')
bern_fit = bern_model.sample(
data=jdata,
chains=1,
cores=1,
seed=12345,
sampling_iters=490,
warmup_iters=490,
thin=7,
max_treedepth=11,
adapt_delta=0.98,
)
csv_file = bern_fit.runset.csv_files[0]
dict = check_sampler_csv(csv_file)
self.assertEqual(dict['num_samples'], 490)
self.assertEqual(dict['thin'], 7)
self.assertEqual(dict['draws'], 70)
self.assertEqual(dict['seed'], 12345)
self.assertEqual(dict['max_depth'], 11)
self.assertEqual(dict['delta'], 0.98)
def test_check_sampler_csv_metric_1(self):
csv_bad = os.path.join(DATAFILES_PATH, 'output_bad_metric_1.csv')
with self.assertRaisesRegex(Exception, 'expecting metric'):
check_sampler_csv(csv_bad)
def test_check_sampler_csv_4(self):
csv_bad = os.path.join(DATAFILES_PATH, 'output_bad_rows.csv')
with self.assertRaisesRegex(Exception, 'found 9'):
check_sampler_csv(csv_bad)
def test_check_sampler_csv_3(self):
csv_bad = os.path.join(DATAFILES_PATH, 'output_bad_cols.csv')
with self.assertRaisesRegex(Exception, '8 items'):
check_sampler_csv(csv_bad)
def test_check_sampler_csv_2(self):
csv_bad = os.path.join(DATAFILES_PATH, 'no_such_file.csv')
with self.assertRaises(Exception):
check_sampler_csv(csv_bad)
def test_check_sampler_csv_3(self):
csv_bad = os.path.join(datafiles_path, 'output_bad_cols.csv')
with self.assertRaisesRegex(Exception, '8 items'):
dict = check_sampler_csv(csv_bad)
def test_check_sampler_csv_metric_4(self):
csv_bad = os.path.join(datafiles_path, 'output_bad_metric_4.csv')
with self.assertRaisesRegex(
Exception, 'invalid or missing mass matrix specification'):
dict = check_sampler_csv(csv_bad)
def test_check_sampler_csv_metric_2(self):
csv_bad = os.path.join(datafiles_path, 'output_bad_metric_2.csv')
with self.assertRaisesRegex(Exception, 'invalid stepsize'):
dict = check_sampler_csv(csv_bad)
def test_check_sampler_csv_metric_1(self):
csv_bad = os.path.join(datafiles_path, 'output_bad_metric_1.csv')
with self.assertRaisesRegex(Exception, 'expecting metric'):
dict = check_sampler_csv(csv_bad)
def test_check_sampler_csv_4(self):
csv_bad = os.path.join(datafiles_path, 'output_bad_rows.csv')
with self.assertRaisesRegex(Exception, 'found 9'):
dict = check_sampler_csv(csv_bad)
def test_check_sampler_csv_metric_2(self):
csv_bad = os.path.join(DATAFILES_PATH, 'output_bad_metric_2.csv')
with self.assertRaisesRegex(Exception, 'invalid stepsize'):
check_sampler_csv(csv_bad)
def test_check_sampler_csv_metric_4(self):
csv_bad = os.path.join(DATAFILES_PATH, 'output_bad_metric_4.csv')
with self.assertRaisesRegex(
Exception, 'invalid or missing mass matrix specification'
):
check_sampler_csv(csv_bad)
def test_check_sampler_csv_2(self):
csv_bad = os.path.join(datafiles_path, 'no_such_file.csv')
with self.assertRaises(Exception):
dict = check_sampler_csv(csv_bad)