def test_args_good(self):
exe = os.path.join(datafiles_path, 'bernoulli')
jdata = os.path.join(datafiles_path, 'bernoulli.data.json')
sampler_args = SamplerArgs()
cmdstan_args = CmdStanArgs(
model_name='bernoulli',
model_exe=exe,
chain_ids=[1, 2, 3, 4],
data=jdata,
method_args=sampler_args,
)
self.assertEqual(cmdstan_args.method, Method.SAMPLE)
cmd = cmdstan_args.compose_command(idx=0, csv_file='bern-output-1.csv')
self.assertIn('id=1 random seed=', ' '.join(cmd))
self.assertIn('data file=', ' '.join(cmd))
self.assertIn('output file=', ' '.join(cmd))
self.assertIn('method=sample algorithm=hmc', ' '.join(cmd))
cmdstan_args = CmdStanArgs(
model_name='bernoulli',
model_exe=exe,
chain_ids=[7, 11, 18, 29],
data=jdata,
method_args=sampler_args,
)
cmd = cmdstan_args.compose_command(idx=0, csv_file='bern-output-1.csv')
self.assertIn('id=7 random seed=', ' '.join(cmd))
def test_args_inits(self):
exe = os.path.join(datafiles_path, 'bernoulli')
jdata = os.path.join(datafiles_path, 'bernoulli.data.json')
sampler_args = SamplerArgs()
jinits = os.path.join(datafiles_path, 'bernoulli.init.json')
jinits1 = os.path.join(datafiles_path, 'bernoulli.init_1.json')
jinits2 = os.path.join(datafiles_path, 'bernoulli.init_2.json')
cmdstan_args = CmdStanArgs(
model_name='bernoulli',
model_exe=exe,
chain_ids=[1, 2, 3, 4],
data=jdata,
inits=jinits,
method_args=sampler_args,
)
cmd = cmdstan_args.compose_command(idx=0, csv_file='bern-output-1.csv')
self.assertIn('init=', ' '.join(cmd))
cmdstan_args = CmdStanArgs(
model_name='bernoulli',
model_exe=exe,
chain_ids=[1, 2],
data=jdata,
inits=[jinits1, jinits2],
method_args=sampler_args,
)
cmd = cmdstan_args.compose_command(idx=0, csv_file='bern-output-1.csv')
self.assertIn('bernoulli.init_1.json', ' '.join(cmd))
cmd = cmdstan_args.compose_command(idx=1, csv_file='bern-output-1.csv')
self.assertIn('bernoulli.init_2.json', ' '.join(cmd))
cmdstan_args = CmdStanArgs(
model_name='bernoulli',
model_exe=exe,
chain_ids=[1, 2, 3, 4],
data=jdata,
inits=0,
method_args=sampler_args,
)
cmd = cmdstan_args.compose_command(idx=0, csv_file='bern-output-1.csv')
self.assertIn('init=0', ' '.join(cmd))
cmdstan_args = CmdStanArgs(
model_name='bernoulli',
model_exe=exe,
chain_ids=[1, 2, 3, 4],
data=jdata,
inits=3.33,
method_args=sampler_args,
)
cmd = cmdstan_args.compose_command(idx=0, csv_file='bern-output-1.csv')
self.assertIn('init=3.33', ' '.join(cmd))
def test_compose(self):
exe = os.path.join(datafiles_path, 'bernoulli')
sampler_args = SamplerArgs()
cmdstan_args = CmdStanArgs(
model_name='bernoulli',
model_exe=exe,
chain_ids=[1, 2, 3, 4],
method_args=sampler_args,
)
with self.assertRaises(ValueError):
cmdstan_args.compose_command(idx=4, csv_file='foo')
with self.assertRaises(ValueError):
cmdstan_args.compose_command(idx=-1, csv_file='foo')
def __init__(self,
args: CmdStanArgs,
chains: int = 4,
logger: logging.Logger = None) -> None:
"""Initialize object."""
self._args = args
self._chains = chains
self._logger = logger or get_logger()
if chains < 1:
raise ValueError('chains must be positive integer value, '
'found {i]}'.format(chains))
self._csv_files = []
if args.output_basename is None:
csv_basename = 'stan-{}-{}'.format(args.model_name, args.method)
for i in range(chains):
fd_name = create_named_text_file(
dir=TMPDIR,
prefix='{}-{}-'.format(csv_basename, i + 1),
suffix='.csv',
)
self._csv_files.append(fd_name)
else:
for i in range(chains):
self._csv_files.append('{}-{}.csv'.format(
args.output_basename, i + 1))
self._console_files = []
for i in range(chains):
txt_file = ''.join(
[os.path.splitext(self._csv_files[i])[0], '.txt'])
self._console_files.append(txt_file)
self._cmds = [
args.compose_command(i, self._csv_files[i]) for i in range(chains)
]
self._retcodes = [-1 for _ in range(chains)]
def test_no_chains(self):
# we don't have chains for optimize
exe = os.path.join(datafiles_path, 'bernoulli')
sampler_args = FixedParamArgs()
jinits = os.path.join(datafiles_path, 'bernoulli.init.json')
cmdstan_args = CmdStanArgs(
model_name='bernoulli',
model_exe=exe,
chain_ids=None,
inits=jinits,
method_args=sampler_args,
)
self.assertIn('init=', cmdstan_args.compose_command(None, 'out.csv'))
with self.assertRaises(ValueError):
CmdStanArgs(
model_name='bernoulli',
model_exe=exe,
chain_ids=None,
seed=[1, 2, 3],
inits=jinits,
method_args=sampler_args,
)
with self.assertRaises(ValueError):
CmdStanArgs(
model_name='bernoulli',
model_exe=exe,
chain_ids=None,
inits=[jinits],
method_args=sampler_args,
)
def test_args_good(self):
exe = os.path.join(DATAFILES_PATH, 'bernoulli')
jdata = os.path.join(DATAFILES_PATH, 'bernoulli.data.json')
sampler_args = SamplerArgs()
cmdstan_args = CmdStanArgs(
model_name='bernoulli',
model_exe=exe,
chain_ids=[1, 2, 3, 4],
data=jdata,
method_args=sampler_args,
refresh=10,
)
self.assertEqual(cmdstan_args.method, Method.SAMPLE)
cmd = cmdstan_args.compose_command(idx=0, csv_file='bern-output-1.csv')
self.assertIn('id=1 random seed=', ' '.join(cmd))
self.assertIn('data file=', ' '.join(cmd))
self.assertIn('output file=', ' '.join(cmd))
self.assertIn('method=sample algorithm=hmc', ' '.join(cmd))
self.assertIn('refresh=10', ' '.join(cmd))
cmdstan_args = CmdStanArgs(
model_name='bernoulli',
model_exe=exe,
chain_ids=[7, 11, 18, 29],
data=jdata,
method_args=sampler_args,
)
cmd = cmdstan_args.compose_command(idx=0, csv_file='bern-output-1.csv')
self.assertIn('id=7 random seed=', ' '.join(cmd))
dirname = 'tmp' + str(time())
if os.path.exists(dirname):
os.rmdir(dirname)
CmdStanArgs(
model_name='bernoulli',
model_exe='bernoulli.exe',
chain_ids=[1, 2, 3, 4],
output_dir=dirname,
method_args=sampler_args,
)
self.assertTrue(os.path.exists(dirname))
os.rmdir(dirname)
def __init__(self,
args: CmdStanArgs,
chains: int = 4,
logger: logging.Logger = None) -> None:
"""Initialize object."""
self._args = args
self._is_optimizing = isinstance(self._args.method_args, OptimizeArgs)
self._chains = chains
self._logger = logger or get_logger()
if chains < 1:
raise ValueError('chains must be positive integer value, '
'found {i]}'.format(chains))
self.csv_files = []
"""per-chain sample csv files."""
if args.output_basename is None:
csv_basename = 'stan-{}-draws'.format(args.model_name)
for i in range(chains):
fd = tempfile.NamedTemporaryFile(
mode='w+',
prefix='{}-{}-'.format(csv_basename, i + 1),
suffix='.csv',
dir=TMPDIR,
delete=False,
)
self.csv_files.append(fd.name)
else:
for i in range(chains):
self.csv_files.append('{}-{}.csv'.format(
args.output_basename, i + 1))
self.console_files = []
"""per-chain sample console output files."""
for i in range(chains):
txt_file = ''.join(
[os.path.splitext(self.csv_files[i])[0], '.txt'])
self.console_files.append(txt_file)
self.cmds = [
args.compose_command(i, self.csv_files[i]) for i in range(chains)
]
"""per-chain sampler command."""
self._retcodes = [-1 for _ in range(chains)]
self._draws = None
self._column_names = None
self._num_params = None # metric dim(s)
self._metric_type = None
self._metric = None
self._stepsize = None
self._sample = None
self._first_draw = None
def test_args_sig_figs(self):
sampler_args = SamplerArgs()
cmdstan_path() # sets os.environ['CMDSTAN']
if cmdstan_version_before(2, 25):
with LogCapture() as log:
logging.getLogger()
CmdStanArgs(
model_name='bernoulli',
model_exe='bernoulli.exe',
chain_ids=[1, 2, 3, 4],
sig_figs=12,
method_args=sampler_args,
)
expect = (
'Argument "sig_figs" invalid for CmdStan versions < 2.25, '
'using version {} in directory {}').format(
os.path.basename(cmdstan_path()),
os.path.dirname(cmdstan_path()),
)
log.check_present(('cmdstanpy', 'WARNING', expect))
else:
cmdstan_args = CmdStanArgs(
model_name='bernoulli',
model_exe='bernoulli.exe',
chain_ids=[1, 2, 3, 4],
sig_figs=12,
method_args=sampler_args,
)
cmd = cmdstan_args.compose_command(idx=0,
csv_file='bern-output-1.csv')
self.assertIn('sig_figs=', ' '.join(cmd))
with self.assertRaises(ValueError):
CmdStanArgs(
model_name='bernoulli',
model_exe='bernoulli.exe',
chain_ids=[1, 2, 3, 4],
sig_figs=-1,
method_args=sampler_args,
)
with self.assertRaises(ValueError):
CmdStanArgs(
model_name='bernoulli',
model_exe='bernoulli.exe',
chain_ids=[1, 2, 3, 4],
sig_figs=20,
method_args=sampler_args,
)
def __init__(self,
args: CmdStanArgs,
chains: int = 4,
logger: logging.Logger = None) -> None:
"""Initialize object."""
self._args = args
self._chains = chains
self._logger = logger or get_logger()
if chains < 1:
raise ValueError('chains must be positive integer value, '
'found {}'.format(chains))
self._retcodes = [-1 for _ in range(chains)]
# output and console messages are written to a text file:
# ``<model_name>-<YYYYMMDDHHMM>-<chain_id>.txt``
now = datetime.now()
now_str = now.strftime('%Y%m%d%H%M')
file_basename = '-'.join([args.model_name, now_str])
if args.output_dir is not None:
output_dir = args.output_dir
else:
output_dir = TMPDIR
self._csv_files = []
self._diagnostic_files = [None for _ in range(chains)]
self._console_files = []
self._cmds = []
for i in range(chains):
if args.output_dir is None:
csv_file = create_named_text_file(
dir=output_dir,
prefix='{}-{}-'.format(file_basename, i + 1),
suffix='.csv',
)
else:
csv_file = os.path.join(
output_dir, '{}-{}.{}'.format(file_basename, i + 1, 'csv'))
self._csv_files.append(csv_file)
txt_file = ''.join([os.path.splitext(csv_file)[0], '.txt'])
self._console_files.append(txt_file)
if args.save_diagnostics:
if args.output_dir is None:
diag_file = create_named_text_file(
dir=TMPDIR,
prefix='{}-diagnostic-{}-'.format(
file_basename, i + 1),
suffix='.csv',
)
else:
diag_file = os.path.join(
output_dir,
'{}-diagnostic-{}.{}'.format(file_basename, i + 1,
'csv'),
)
self._diagnostic_files.append(diag_file)
self._cmds.append(
args.compose_command(i, self._csv_files[i],
self._diagnostic_files[i]))
else:
self._cmds.append(args.compose_command(i, self._csv_files[i]))
def __init__(
self,
args: CmdStanArgs,
chains: int = 4,
chain_ids: List[int] = None,
logger: logging.Logger = None,
) -> None:
"""Initialize object."""
self._args = args
self._chains = chains
self._logger = logger or get_logger()
if chains < 1:
raise ValueError('chains must be positive integer value, '
'found {}'.format(chains))
if chain_ids is None:
chain_ids = [x + 1 for x in range(chains)]
elif len(chain_ids) != chains:
raise ValueError(
'mismatch between number of chains and chain_ids, '
'found {} chains, but {} chain_ids'.format(
chains, len(chain_ids)))
self._chain_ids = chain_ids
self._retcodes = [-1 for _ in range(chains)]
# stdout, stderr are written to text files
# prefix: ``<model_name>-<YYYYMMDDHHMM>-<chain_id>``
# suffixes: ``-stdout.txt``, ``-stderr.txt``
now = datetime.now()
now_str = now.strftime('%Y%m%d%H%M')
file_basename = '-'.join([args.model_name, now_str])
if args.output_dir is not None:
output_dir = args.output_dir
else:
output_dir = _TMPDIR
self._csv_files = [None for _ in range(chains)]
self._diagnostic_files = [None for _ in range(chains)]
self._stdout_files = [None for _ in range(chains)]
self._stderr_files = [None for _ in range(chains)]
self._cmds = []
for i in range(chains):
if args.output_dir is None:
csv_file = create_named_text_file(
dir=output_dir,
prefix='{}-{}-'.format(file_basename, str(chain_ids[i])),
suffix='.csv',
)
else:
csv_file = os.path.join(
output_dir,
'{}-{}.{}'.format(file_basename, str(chain_ids[i]), 'csv'),
)
self._csv_files[i] = csv_file
stdout_file = ''.join(
[os.path.splitext(csv_file)[0], '-stdout.txt'])
self._stdout_files[i] = stdout_file
stderr_file = ''.join(
[os.path.splitext(csv_file)[0], '-stderr.txt'])
self._stderr_files[i] = stderr_file
if args.save_diagnostics:
if args.output_dir is None:
diag_file = create_named_text_file(
dir=_TMPDIR,
prefix='{}-diagnostic-{}-'.format(
file_basename, str(chain_ids[i])),
suffix='.csv',
)
else:
diag_file = os.path.join(
output_dir,
'{}-diagnostic-{}.{}'.format(file_basename,
str(chain_ids[i]), 'csv'),
)
self._diagnostic_files[i] = diag_file
self._cmds.append(
args.compose_command(i, self._csv_files[i],
self._diagnostic_files[i]))
else:
self._cmds.append(args.compose_command(i, self._csv_files[i]))
