def test_validate_outputs(self):
# construct runset using existing sampler output
stan = os.path.join(datafiles_path, 'bernoulli.stan')
exe = os.path.join(datafiles_path, 'bernoulli')
model = Model(exe_file=exe, stan_file=stan)
jdata = os.path.join(datafiles_path, 'bernoulli.data.json')
output = os.path.join(goodfiles_path, 'bern')
args = SamplerArgs(
model,
chain_ids=[1, 2, 3, 4],
seed=12345,
data=jdata,
output_file=output,
sampling_iters=100,
max_treedepth=11,
adapt_delta=0.95,
)
runset = RunSet(chains=4, args=args)
retcodes = runset.retcodes
for i in range(len(retcodes)):
runset.set_retcode(i, 0)
self.assertTrue(runset.check_retcodes())
runset.check_console_msgs()
runset.validate_csv_files()
self.assertEqual(4, runset.chains)
self.assertEqual(100, runset.draws)
self.assertEqual(8, len(runset.column_names))
self.assertEqual('lp__', runset.column_names[0])
def test_sample_big(self):
# construct runset using existing sampler output
stan = os.path.join(datafiles_path, 'bernoulli.stan')
exe = os.path.join(datafiles_path, 'bernoulli')
model = Model(exe_file=exe, stan_file=stan)
output = os.path.join(datafiles_path, 'runset-big', 'output_icar_nyc')
args = SamplerArgs(model, chain_ids=[1, 2], output_file=output)
runset = RunSet(chains=2, args=args)
runset.validate_csv_files()
runset.assemble_sample()
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(runset.columns, len(column_names))
self.assertEqual(runset.column_names, tuple(column_names))
self.assertEqual(runset.metric_type, 'diag_e')
self.assertEqual(runset.stepsize.shape, (2, ))
self.assertEqual(runset.metric.shape, (2, 2095))
self.assertEqual((1000, 2, 2102), runset.sample.shape)
phis = get_drawset(runset, params=['phi'])
self.assertEqual((2000, 2095), phis.shape)
phi1 = get_drawset(runset, params=['phi.1'])
self.assertEqual((2000, 1), phi1.shape)
mo_phis = get_drawset(runset, params=['phi.1', 'phi.10', 'phi.100'])
self.assertEqual((2000, 3), mo_phis.shape)
phi2095 = get_drawset(runset, params=['phi.2095'])
self.assertEqual((2000, 1), phi2095.shape)
with self.assertRaises(Exception):
get_drawset(runset, params=['phi.2096'])
with self.assertRaises(Exception):
get_drawset(runset, params=['ph'])
def test_validate_bad_hdr(self):
stan = os.path.join(datafiles_path, 'bernoulli.stan')
exe = os.path.join(datafiles_path, 'bernoulli')
model = Model(exe_file=exe, stan_file=stan)
jdata = os.path.join(datafiles_path, 'bernoulli.data.json')
output = os.path.join(badfiles_path, 'bad-hdr-bern')
args = SamplerArgs(model, chain_ids=[1,2,3,4],
seed=12345,
data=jdata,
output_file=output,
sampling_iters=100,
max_treedepth=11,
adapt_delta=0.95)
runset = RunSet(chains=4, args=args)
retcodes = runset.retcodes
for i in range(len(retcodes)):
runset.set_retcode(i, 0)
self.assertTrue(runset.check_retcodes())
with self.assertRaisesRegex(ValueError, 'header mismatch'):
runset.validate_csv_files()
def sample(
stan_model: Model,
data: Union[Dict, str] = None,
chains: int = 4,
cores: int = 1,
seed: Union[int, List[int]] = None,
chain_ids: Union[int, List[int]] = None,
inits: Union[Dict, float, str, List[str]] = None,
warmup_iters: int = None,
sampling_iters: int = None,
warmup_schedule: Tuple[float, float, float] = (0.15, 0.75, 0.10),
save_warmup: bool = False,
thin: int = None,
max_treedepth: float = None,
metric: Union[str, List[str]] = None,
step_size: Union[float, List[float]] = None,
adapt_engaged: bool = True,
adapt_delta: float = None,
csv_output_file: str = None,
show_progress: bool = False,
) -> RunSet:
"""
Run or more chains of the NUTS sampler to produce a set of draws
from the posterior distribution of a model conditioned on some data.
The caller must specify the model and data; all other arguments
are optional.
This function validates the specified configuration, composes a call to
the CmdStan ``sample`` method and spawns one subprocess per chain to run
the sampler and waits for all chains to run to completion.
The composed call to CmdStan omits arguments left unspecified (i.e., value
is ``None``) so that the default CmdStan configuration values will be used.
For each chain, the ``RunSet`` object records the command, the return code,
the paths to the sampler output files, and the corresponding subprocess
console outputs, if any.
:param stan_model: Compiled Stan model.
:param data: Values for all data variables in the model, specified either
as a dictionary with entries matching the data variables,
or as the path of a data file in JSON or Rdump format.
:param chains: Number of sampler chains, should be > 1.
:param cores: Number of processes to run in parallel. Must be an integer
between 1 and the number of CPUs in the system.
:param seed: The seed for random number generator or a list of per-chain
seeds. Must be an integer between 0 and 2^32 - 1. If unspecified,
numpy.random.RandomState() is used to generate a seed which will be
used for all chains. When the same seed is used across all chains,
the chain-id is used to advance the RNG to avoid dependent samples.
:param chain_ids: The offset for the random number generator, either
an integer or a list of unique per-chain offsets. If unspecified,
chain ids are numbered sequentially starting from 1.
:param inits: Specifies how the sampler initializes parameter values.
Initializiation is either uniform random on a range centered on 0,
exactly 0, or a dictionary or file of initial values for some or all
parameters in the model. The default initialization behavoir will
initialize all parameter values on range [-2, 2]. If these values
are too far from the expected parameter values, explicit initialization
may improve adaptation. The following value types are allowed:
* Single number ``n > 0`` - initialization range is [-n, n].
* ``0`` - all parameters are initialized to 0.
* dictionary - pairs parameter name : initial value.
* string - pathname to a JSON or Rdump file of initial parameter values.
* list of strings - per-chain pathname to data file.
:param warmup_iters: Number of iterations during warmup for each chain.
:param sampling_iters: Number of draws from the posterior for each chain.
:param warmup_schedule: Triple specifying fraction of total warmup
iterations allocated to each adaptation phase. The default schedule
is (.15, .75, .10) where:
* Phase I is "fast" adaptation to find the typical set
* Phase II is "slow" adaptation to find the metric
* Phase III is "fast" adaptation to find the step_size.
For further details, see the Stan Reference Manual, section
HMC algorithm parameters.
:param save_warmup: When True, sampler saves warmup draws as part of
the Stan csv output file.
:param thin: Period between saved samples.
:param max_treedepth: Maximum depth of trees evaluated by NUTS sampler
per iteration.
:param metric: Specification of the mass matrix, either as a
vector consisting of the diagonal elements of the covariance
matrix (``diag`` or ``diag_e``) or the full covariance matrix
(``dense`` or ``dense_e``).
If the value of the metric argument is a string other than
``diag``, ``diag_e``, ``dense``, or ``dense_e``, it must be
a valid filepath to a JSON or Rdump file which contains an entry
``inv_metric`` whose value is either the diagonal vector or
the full covariance matrix. This can be used to restart sampling
with no adaptation given the outputs of all chains from a previous run.
If the value of the metric argument is a list of paths, its
length must match the number of chains and all paths must be
unique.
:param step_size: Initial stepsize for HMC sampler. The value is either
a single number or a list of numbers which will be used as the global
or per-chain initial step_size, respectively.
The length of the list of step sizes must match the number of chains.
This feature can be used to restart sampling with no adaptation
given the outputs of all chains from a previous run.
:param adapt_engaged: When True, adapt stepsize, metric.
*Note: If True, ``warmup_iters`` must be > 0.*
:param adapt_delta: Adaptation target Metropolis acceptance rate.
The default value is 0.8. Increasing this value, which must be
strictly less than 1, causes adaptation to use smaller step sizes.
It improves the effective sample size, but may increase the time
per iteration.
:param csv_output_file: A path or file name which will be used as the
base name for the sampler output files. The csv output files
for each chain are written to file ``<basename>-<chain_id>.csv``
and the console output and error messages are written to file
``<basename>-<chain_id>.txt``.
:param show_progress: When True, command sends progress messages to
console. When False, command executes silently.
"""
if chains < 1:
raise ValueError(
'chains must be a positive integer value, found {}'.format(chains))
if chain_ids is None:
chain_ids = [x + 1 for x in range(chains)]
else:
if type(chain_ids) is int:
if chain_ids < 1:
raise ValueError('chain_id must be a positive integer value,'
' found {}'.format(chain_ids))
offset = chain_ids
chain_ids = [x + offset + 1 for x in range(chains)]
else:
if not len(chain_ids) == chains:
raise ValueError(
'chain_ids must correspond to number of chains'
' specified {} chains, found {} chain_ids'.format(
chains, len(chain_ids)))
for i in len(chain_ids):
if chain_ids[i] < 1:
raise ValueError(
'chain_id must be a positive integer value,'
' found {}'.format(chain_ids[i]))
if cores < 1:
raise ValueError(
'cores must be a positive integer value, found {}'.format(cores))
if cores > cpu_count():
print('requested {} cores, only {} available'.format(
cores, cpu_count()))
cores = cpu_count()
if data is not None:
if isinstance(data, dict):
with tempfile.NamedTemporaryFile(mode='w+',
suffix='.json',
dir=TMPDIR,
delete=False) as fd:
data_file = fd.name
print('input data tempfile: {}'.format(fd.name))
sd = StanData(data_file)
sd.write_json(data)
data_dict = data
data = data_file
if inits is not None:
if isinstance(inits, dict):
with tempfile.NamedTemporaryFile(mode='w+',
suffix='.json',
dir=TMPDIR,
delete=False) as fd:
inits_file = fd.name
print('inits tempfile: {}'.format(fd.name))
sd = StanData(inits_file)
sd.write_json(inits)
inits_dict = inits
inits = inits_file
# TODO: issue 49: inits can be initialization function
args = SamplerArgs(
model=stan_model,
chain_ids=chain_ids,
data=data,
seed=seed,
inits=inits,
warmup_iters=warmup_iters,
sampling_iters=sampling_iters,
warmup_schedule=warmup_schedule,
save_warmup=save_warmup,
thin=thin,
max_treedepth=max_treedepth,
metric=metric,
step_size=step_size,
adapt_engaged=adapt_engaged,
adapt_delta=adapt_delta,
output_file=csv_output_file,
)
runset = RunSet(args=args, chains=chains)
try:
tp = ThreadPool(cores)
for i in range(chains):
tp.apply_async(do_sample, (runset, i))
finally:
tp.close()
tp.join()
if not runset.check_retcodes():
msg = 'Error during sampling'
for i in range(chains):
if runset.retcode(i) != 0:
msg = '{}, chain {} returned error code {}'.format(
msg, i, runset.retcode(i))
raise Exception(msg)
runset.validate_csv_files()
return runset