def sample(
self,
data: Union[Dict, str] = None,
chains: Union[int, None] = None,
cores: Union[int, None] = None,
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,
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,
fixed_param: bool = False,
csv_basename: str = None,
show_progress: Union[bool, str] = False,
) -> CmdStanMCMC:
"""
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.
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.
Unspecified arguments are not included in the call to CmdStan, i.e.,
those arguments will have CmdStan default values.
For each chain, the ``CmdStanMCMC`` object records the command,
the return code, the sampler output file paths, and the corresponding
subprocess console outputs, if any.
: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.
If none then set automatically to `chains` but no more
than `total_cpu_count - 2`
:param seed: The seed for random number generator. 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] on the
_unconstrained_ support. If the expected parameter values are
too far from this range, this option 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 data file.
* list of strings - per-chain pathname to data file.
:param warmup_iters: Number of warmup iterations for each chain.
:param sampling_iters: Number of draws from the posterior for each
chain.
: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.
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.
:param adapt_engaged: When True, adapt stepsize and 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 fixed_param: When True, call CmdStan with argument
"algorithm=fixed_param" which runs the sampler without
updating the Markov Chain, thus the values of all parameters and
transformed parameters are constant across all draws and
only those values in the generated quantities block that are
produced by RNG functions may change. This provides
a way to use Stan programs to generate simulated data via the
generated quantities block. This option must be used when the
parameters block is empty. Default value is False.
:param csv_basename: A path or file name which will be used as the
basename 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: Use tqdm progress bar to show sampling progress.
If show_progress=='notebook' use tqdm_notebook
(needs nodejs for jupyter).
:return: CmdStanMCMC object
"""
if chains is None:
if fixed_param:
chains = 1
else:
chains = 4
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]))
cores_avail = cpu_count()
if cores is None:
cores = max(min(cores_avail - 2, chains), 1)
if cores < 1:
raise ValueError(
'cores must be a positive integer value, found {}'.format(
cores))
if cores > cores_avail:
self._logger.warning('requested %u cores, only %u available',
cores, cpu_count())
cores = cores_avail
refresh = None
if show_progress:
try:
import tqdm
# progress bar updates - 100 per warmup, sampling
if fixed_param or not adapt_engaged or warmup_iters == 0:
num_updates = 100
w_iters = 0
else:
num_updates = 200
if warmup_iters is None:
w_iters = 1000
else:
w_iters = warmup_iters
s_iters = sampling_iters
if s_iters is None:
s_iters = 1000
refresh = max(int((s_iters + w_iters) // num_updates), 1)
# disable logger for console (temporary) - use tqdm
self._logger.propagate = False
except ImportError:
self._logger.warning(
('tqdm not installed, progress information is not '
'shown. Please install tqdm with '
"'pip install tqdm'"))
show_progress = False
# TODO: issue 49: inits can be initialization function
sampler_args = SamplerArgs(
warmup_iters=warmup_iters,
sampling_iters=sampling_iters,
save_warmup=save_warmup,
thin=thin,
max_treedepth=max_treedepth,
metric=metric,
step_size=step_size,
adapt_engaged=adapt_engaged,
adapt_delta=adapt_delta,
fixed_param=fixed_param,
)
with MaybeDictToFilePath(data, inits) as (_data, _inits):
args = CmdStanArgs(
self._name,
self._exe_file,
chain_ids=chain_ids,
data=_data,
seed=seed,
inits=_inits,
output_basename=csv_basename,
method_args=sampler_args,
refresh=refresh,
)
runset = RunSet(args=args, chains=chains)
pbar = None
pbar_dict = {}
with ThreadPoolExecutor(max_workers=cores) as executor:
for i in range(chains):
if show_progress:
if (isinstance(show_progress, str)
and show_progress.lower() == 'notebook'):
try:
tqdm_pbar = tqdm.tqdm_notebook
except ImportError:
msg = (
'Cannot import tqdm.tqdm_notebook.\n'
'Functionality is only supported on the '
'Jupyter Notebook and compatible platforms'
'.\nPlease follow the instructions in '
'https://github.com/tqdm/tqdm/issues/394#'
'issuecomment-384743637 and remember to '
'stop & start your jupyter server.')
self._logger.warning(msg)
tqdm_pbar = tqdm.tqdm
else:
tqdm_pbar = tqdm.tqdm
# enable dynamic_ncols for advanced users
# currently hidden feature
dynamic_ncols = os.environ.get('TQDM_DYNAMIC_NCOLS',
'False')
if dynamic_ncols.lower() in ['0', 'false']:
dynamic_ncols = False
else:
dynamic_ncols = True
pbar = [
# warmup
tqdm_pbar(
desc='Chain {} - warmup'.format(i + 1),
position=i * 2,
total=sampler_args.warmup_iters,
dynamic_ncols=dynamic_ncols,
),
# sampling
tqdm_pbar(
desc='Chain {} - sample'.format(i + 1),
position=i * 2 + 1,
total=sampler_args.sampling_iters,
dynamic_ncols=dynamic_ncols,
),
]
future = executor.submit(self._run_cmdstan, runset, i,
pbar)
pbar_dict[future] = pbar
if show_progress:
for future in as_completed(pbar_dict):
pbar = pbar_dict[future]
for pbar_item in pbar:
# close here to just to be sure
pbar_item.close()
if show_progress:
# re-enable logger for console
self._logger.propagate = True
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 RuntimeError(msg)
mcmc = CmdStanMCMC(runset, fixed_param)
mcmc._validate_csv_files()
return mcmc
def generate_quantities(
self,
data: Union[Dict, str] = None,
mcmc_sample: Union[CmdStanMCMC, List[str]] = None,
seed: int = None,
gq_csv_basename: str = None,
) -> CmdStanGQ:
"""
Wrapper for generated quantities call. Given a CmdStanMCMC object
containing a sample from the fitted model, along with the
corresponding dataset for that fit, run just the generated quantities
block of the model in order to get additional quantities of interest.
: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 mcmc_sample: Can be either a CmdStanMCMC object returned by
CmdStanPy's `sample` method or a list of stan-csv files generated
by fitting the model to the data using any Stan interface.
:param seed: The seed for random number generator. 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.
*NOTE: Specifying the seed will guarantee the same result for
multiple invocations of this method with the same inputs. However
this will not reproduce results from the sample method given
the same inputs because the RNG will be in a different state.*
:param gq_csv_basename: A path or file name which will be used as the
basename 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``.
:return: CmdStanGQ object
"""
sample_csv_files = []
sample_drawset = None
chains = 0
if isinstance(mcmc_sample, CmdStanMCMC):
sample_csv_files = mcmc_sample.runset.csv_files
sample_drawset = mcmc_sample.get_drawset()
chains = mcmc_sample.chains
elif isinstance(mcmc_sample, list):
sample_csv_files = mcmc_sample
else:
raise ValueError('mcmc_sample must be either CmdStanMCMC object'
' or list of paths to sample csv_files')
try:
chains = len(sample_csv_files)
if sample_drawset is None: # assemble sample from csv files
sampler_args = SamplerArgs()
args = CmdStanArgs(
self._name,
self._exe_file,
chain_ids=[x + 1 for x in range(chains)],
method_args=sampler_args,
)
runset = RunSet(args=args, chains=chains)
runset._csv_files = sample_csv_files
sample_fit = CmdStanMCMC(runset)
sample_fit._validate_csv_files()
sample_drawset = sample_fit.get_drawset()
except Exception as e:
raise ValueError('Invalid mcmc_sample, error:\n\t{}\n\t'
' while processing files\n\t{}'.format(
str(e), '\n\t'.join(sample_csv_files)))
generate_quantities_args = GenerateQuantitiesArgs(
csv_files=sample_csv_files)
generate_quantities_args.validate(chains)
with MaybeDictToFilePath(data, None) as (_data, _inits):
args = CmdStanArgs(
self._name,
self._exe_file,
chain_ids=[x + 1 for x in range(chains)],
data=_data,
seed=seed,
output_basename=gq_csv_basename,
method_args=generate_quantities_args,
)
runset = RunSet(args=args, chains=chains)
cores_avail = cpu_count()
cores = max(min(cores_avail - 2, chains), 1)
with ThreadPoolExecutor(max_workers=cores) as executor:
for i in range(chains):
executor.submit(self._run_cmdstan, runset, i)
if not runset._check_retcodes():
msg = 'Error during generate_quantities'
for i in range(chains):
if runset._retcode(i) != 0:
msg = '{}, chain {} returned error code {}'.format(
msg, i, runset._retcode(i))
raise RuntimeError(msg)
quantities = CmdStanGQ(runset=runset, mcmc_sample=sample_drawset)
quantities._set_attrs_gq_csv_files(sample_csv_files[0])
return quantities
def optimize(
self,
data: Union[Dict, str] = None,
seed: int = None,
inits: Union[Dict, float, str] = None,
csv_basename: str = None,
algorithm: str = None,
init_alpha: float = None,
iter: int = None,
) -> CmdStanMLE:
"""
Wrapper for optimize call
: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 seed: The seed for random number generator. 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.
: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] on the
_unconstrained_ support. If the expected parameter values are
too far from this range, this option may improve estimation.
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 data file.
:param csv_basename: A path or file name which will be used as the
basename for the CmdStan output files. The csv output files
are written to file ``<basename>-0.csv`` and the console output
and error messages are written to file ``<basename>-0.txt``.
:param algorithm: Algorithm to use. One of: "BFGS", "LBFGS", "Newton"
:param init_alpha: Line search step size for first iteration
:param iter: Total number of iterations
:return: CmdStanMLE object
"""
optimize_args = OptimizeArgs(algorithm=algorithm,
init_alpha=init_alpha,
iter=iter)
with MaybeDictToFilePath(data, inits) as (_data, _inits):
args = CmdStanArgs(
self._name,
self._exe_file,
chain_ids=None,
data=_data,
seed=seed,
inits=_inits,
output_basename=csv_basename,
method_args=optimize_args,
)
dummy_chain_id = 0
runset = RunSet(args=args, chains=1)
self._run_cmdstan(runset, dummy_chain_id)
if not runset._check_retcodes():
msg = 'Error during optimizing'
if runset._retcode(dummy_chain_id) != 0:
msg = '{}, error code {}'.format(
msg, runset._retcode(dummy_chain_id))
raise RuntimeError(msg)
mle = CmdStanMLE(runset)
mle._set_mle_attrs(runset.csv_files[0])
return mle
