def test_check_retcodes(self):
exe = os.path.join(DATAFILES_PATH, 'bernoulli' + EXTENSION)
jdata = os.path.join(DATAFILES_PATH, 'bernoulli.data.json')
sampler_args = SamplerArgs()
chain_ids = [1, 2, 3, 4] # default
cmdstan_args = CmdStanArgs(
model_name='bernoulli',
model_exe=exe,
chain_ids=chain_ids,
data=jdata,
method_args=sampler_args,
)
runset = RunSet(args=cmdstan_args, chains=4)
retcodes = runset._retcodes
self.assertEqual(4, len(retcodes))
for i in range(len(retcodes)):
self.assertEqual(-1, runset._retcode(i))
runset._set_retcode(0, 0)
self.assertEqual(0, runset._retcode(0))
for i in range(1, len(retcodes)):
self.assertEqual(-1, runset._retcode(i))
self.assertFalse(runset._check_retcodes())
for i in range(1, len(retcodes)):
runset._set_retcode(i, 0)
self.assertTrue(runset._check_retcodes())
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')
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, chains=4)
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'),
]
self.assertEqual(4, runset.chains)
retcodes = runset._retcodes
for i in range(len(retcodes)):
runset._set_retcode(i, 0)
self.assertTrue(runset._check_retcodes())
fit = CmdStanMCMC(runset)
self.assertEqual(100, fit.num_draws)
self.assertEqual(len(BERNOULLI_COLS), len(fit.column_names))
self.assertEqual('lp__', fit.column_names[0])
drawset = fit.get_drawset()
self.assertEqual(
drawset.shape,
(fit.runset.chains * fit.num_draws, len(fit.column_names)),
)
_ = fit.summary()
self.assertTrue(True)
# 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_validate_bad_run(self):
exe = os.path.join(DATAFILES_PATH, 'bernoulli' + EXTENSION)
jdata = os.path.join(DATAFILES_PATH, 'bernoulli.data.json')
sampler_args = SamplerArgs(max_treedepth=11, adapt_delta=0.95)
# some chains had errors
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, chains=4)
for i in range(4):
runset._set_retcode(i, 0)
self.assertTrue(runset._check_retcodes())
# errors reported
runset._stderr_files = [
os.path.join(DATAFILES_PATH, 'runset-bad',
'bad-transcript-bern-1.txt'),
os.path.join(DATAFILES_PATH, 'runset-bad',
'bad-transcript-bern-2.txt'),
os.path.join(DATAFILES_PATH, 'runset-bad',
'bad-transcript-bern-3.txt'),
os.path.join(DATAFILES_PATH, 'runset-bad',
'bad-transcript-bern-4.txt'),
]
self.assertEqual(len(runset._get_err_msgs()), 4)
# csv file headers inconsistent
runset._csv_files = [
os.path.join(DATAFILES_PATH, 'runset-bad', 'bad-hdr-bern-1.csv'),
os.path.join(DATAFILES_PATH, 'runset-bad', 'bad-hdr-bern-2.csv'),
os.path.join(DATAFILES_PATH, 'runset-bad', 'bad-hdr-bern-3.csv'),
os.path.join(DATAFILES_PATH, 'runset-bad', 'bad-hdr-bern-4.csv'),
]
with self.assertRaisesRegex(ValueError, 'header mismatch'):
CmdStanMCMC(runset)
# bad draws
runset._csv_files = [
os.path.join(DATAFILES_PATH, 'runset-bad', 'bad-draws-bern-1.csv'),
os.path.join(DATAFILES_PATH, 'runset-bad', 'bad-draws-bern-2.csv'),
os.path.join(DATAFILES_PATH, 'runset-bad', 'bad-draws-bern-3.csv'),
os.path.join(DATAFILES_PATH, 'runset-bad', 'bad-draws-bern-4.csv'),
]
with self.assertRaisesRegex(ValueError, 'draws'):
CmdStanMCMC(runset)
# mismatch - column headers, draws
runset._csv_files = [
os.path.join(DATAFILES_PATH, 'runset-bad', 'bad-cols-bern-1.csv'),
os.path.join(DATAFILES_PATH, 'runset-bad', 'bad-cols-bern-2.csv'),
os.path.join(DATAFILES_PATH, 'runset-bad', 'bad-cols-bern-3.csv'),
os.path.join(DATAFILES_PATH, 'runset-bad', 'bad-cols-bern-4.csv'),
]
with self.assertRaisesRegex(ValueError,
'bad draw, expecting 9 items, found 8'):
CmdStanMCMC(runset)
def variational(
self,
data: Union[Dict, str] = None,
seed: int = None,
inits: float = None,
output_dir: str = None,
save_diagnostics: bool = False,
algorithm: str = None,
iter: int = None,
grad_samples: int = None,
elbo_samples: int = None,
eta: Real = None,
adapt_engaged: bool = True,
adapt_iter: int = None,
tol_rel_obj: Real = None,
eval_elbo: int = None,
output_samples: int = None,
require_converged: bool = True,
) -> CmdStanVB:
"""
Run CmdStan's variational inference algorithm to approximate
the posterior distribution of the model conditioned on the data.
This function validates the specified configuration, composes a call to
the CmdStan ``variational`` method and spawns one subprocess to run the
optimizer and waits for it to run to completion.
Unspecified arguments are not included in the call to CmdStan, i.e.,
those arguments will have CmdStan default values.
The ``CmdStanVB`` object records the command, the return code,
and the paths to the variational method output csv and console files.
The output files are written either to a specified output directory
or to a temporary directory which is deleted upon session exit.
Output files are either written to a temporary directory or to the
specified output directory. Output filenames correspond to the template
'<model_name>-<YYYYMMDDHHMM>-<chain_id>' plus the file suffix which is
either '.csv' for the CmdStan output or '.txt' for
the console messages, e.g. 'bernoulli-201912081451-1.csv'.
Output files written to the temporary directory contain an additional
8-character random string, e.g. 'bernoulli-201912081451-1-5nm6as7u.csv'.
: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.
Initialization is uniform random on a range centered on 0 with
default range of 2. Specifying a single number n > 0 changes
the initialization range to [-n, n].
:param output_dir: Name of the directory to which CmdStan output
files are written. If unspecified, output files will be written
to a temporary directory which is deleted upon session exit.
:param save_diagnostics: Whether or not to save diagnostics. If True,
csv output files are written to an output file with filename
template '<model_name>-<YYYYMMDDHHMM>-diagnostic-<chain_id>',
e.g. 'bernoulli-201912081451-diagnostic-1.csv'.
:param algorithm: Algorithm to use. One of: 'meanfield', 'fullrank'.
:param iter: Maximum number of ADVI iterations.
:param grad_samples: Number of MC draws for computing the gradient.
:param elbo_samples: Number of MC draws for estimate of ELBO.
:param eta: Stepsize scaling parameter.
:param adapt_engaged: Whether eta adaptation is engaged.
:param adapt_iter: Number of iterations for eta adaptation.
:param tol_rel_obj: Relative tolerance parameter for convergence.
:param eval_elbo: Number of iterations between ELBO evaluations.
:param output_samples: Number of approximate posterior output draws
to save.
:param require_converged: Whether or not to raise an error if stan
reports that "The algorithm may not have converged".
:return: CmdStanVB object
"""
variational_args = VariationalArgs(
algorithm=algorithm,
iter=iter,
grad_samples=grad_samples,
elbo_samples=elbo_samples,
eta=eta,
adapt_engaged=adapt_engaged,
adapt_iter=adapt_iter,
tol_rel_obj=tol_rel_obj,
eval_elbo=eval_elbo,
output_samples=output_samples,
)
with MaybeDictToFilePath(data, inits) as (_data, _inits):
args = CmdStanArgs(
self._name,
self._exe_file,
chain_ids=None,
data=_data,
seed=seed,
inits=_inits,
output_dir=output_dir,
save_diagnostics=save_diagnostics,
method_args=variational_args,
)
dummy_chain_id = 0
runset = RunSet(args=args, chains=1)
self._run_cmdstan(runset, dummy_chain_id)
# treat failure to converge as failure
transcript_file = runset.stdout_files[dummy_chain_id]
valid = True
pat = re.compile(r'The algorithm may not have converged.', re.M)
with open(transcript_file, 'r') as transcript:
contents = transcript.read()
errors = re.findall(pat, contents)
if len(errors) > 0:
valid = False
if require_converged and not valid:
raise RuntimeError('The algorithm may not have converged.')
if not runset._check_retcodes():
msg = 'Error during variational inference.\n{}'.format(
runset.get_err_msgs())
raise RuntimeError(msg)
# pylint: disable=invalid-name
vb = CmdStanVB(runset)
return vb
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')
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'),
]
self.assertEqual(4, runset.chains)
retcodes = runset._retcodes
for i in range(len(retcodes)):
runset._set_retcode(i, 0)
self.assertTrue(runset._check_retcodes())
fit = CmdStanMCMC(runset)
self.assertEqual(100, fit.num_draws)
self.assertEqual(len(BERNOULLI_COLS), len(fit.column_names))
self.assertEqual('lp__', fit.column_names[0])
drawset = fit.get_drawset()
self.assertEqual(
drawset.shape,
(fit.runset.chains * fit.num_draws, len(fit.column_names)),
)
summary = fit.summary()
self.assertIn('5%', list(summary.columns))
self.assertIn('50%', list(summary.columns))
self.assertIn('95%', list(summary.columns))
self.assertNotIn('1%', list(summary.columns))
self.assertNotIn('99%', list(summary.columns))
summary = fit.summary(percentiles=[1, 45, 99])
self.assertIn('1%', list(summary.columns))
self.assertIn('45%', list(summary.columns))
self.assertIn('99%', list(summary.columns))
self.assertNotIn('5%', list(summary.columns))
self.assertNotIn('50%', list(summary.columns))
self.assertNotIn('95%', list(summary.columns))
with self.assertRaises(ValueError):
fit.summary(percentiles=[])
with self.assertRaises(ValueError):
fit.summary(percentiles=[-1])
diagnostics = fit.diagnose()
self.assertIn('Treedepth satisfactory for all transitions.',
diagnostics)
self.assertIn('No divergent transitions found.', diagnostics)
self.assertIn('E-BFMI satisfactory for all transitions.', diagnostics)
self.assertIn('Effective sample size satisfactory.', diagnostics)
def sample(
self,
data: Union[Dict, str] = None,
chains: Union[int, None] = None,
parallel_chains: Union[int, None] = None,
threads_per_chain: 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,
iter_warmup: int = None,
iter_sampling: 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,
adapt_init_phase: int = None,
adapt_metric_window: int = None,
adapt_step_size: int = None,
fixed_param: bool = False,
output_dir: str = None,
save_diagnostics: bool = False,
show_progress: Union[bool, str] = False,
validate_csv: bool = True,
) -> 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
console outputs, if any. The output files are written either to a
specified output directory or to a temporary directory which is deleted
upon session exit.
Output files are either written to a temporary directory or to the
specified output directory. Ouput filenames correspond to the template
'<model_name>-<YYYYMMDDHHMM>-<chain_id>' plus the file suffix which is
either '.csv' for the CmdStan output or '.txt' for
the console messages, e.g. 'bernoulli-201912081451-1.csv'.
Output files written to the temporary directory contain an additional
8-character random string, e.g. 'bernoulli-201912081451-1-5nm6as7u.csv'.
: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, must be a positive integer.
:param parallel_chains: Number of processes to run in parallel. Must be
a positive integer. Defaults to ``multiprocessing.cpu_count()``.
:param threads_per_chain: The number of threads to use in parallelized
sections within an MCMC chain (e.g., when using the Stan functions
``reduce_sum()`` or ``map_rect()``). This will only have an effect
if the model was compiled with threading support. The total number
of threads used will be ``parallel_chains * threads_per_chain``.
: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.
Initialization 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 behavior 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 iter_warmup: Number of warmup iterations for each chain.
:param iter_sampling: 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.
: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
which improves the effective sample size, but may increase the time
per iteration.
:param adapt_init_phase: Iterations for initial phase of adaptation
during which step size is adjusted so that the chain converges
towards the typical set.
:param adapt_metric_window: The second phase of adaptation tunes
the metric and stepsize in a series of intervals. This parameter
specifies the number of iterations used for the first tuning
interval; window size increases for each subsequent interval.
:param adapt_step_size: Number of iterations given over to adjusting
the step size given the tuned metric during the final phase of
adaptation.
: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 output_dir: Name of the directory to which CmdStan output
files are written. If unspecified, output files will be written
to a temporary directory which is deleted upon session exit.
:param save_diagnostics: Whether or not to save diagnostics. If True,
csv output files are written to an output file with filename
template '<model_name>-<YYYYMMDDHHMM>-diagnostic-<chain_id>',
e.g. 'bernoulli-201912081451-diagnostic-1.csv'.
:param show_progress: Use tqdm progress bar to show sampling progress.
If show_progress=='notebook' use tqdm_notebook
(needs nodejs for jupyter).
:param validate_csv: If ``False``, skip scan of sample csv output file.
When sample is large or disk i/o is slow, will speed up processing.
Default is ``True`` - sample csv files are scanned for completeness
and consistency.
: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 isinstance(chain_ids, int):
if chain_ids < 1:
raise ValueError(
'Chain_id must be a positive integer value,'
' found {}.'.format(chain_ids))
chain_ids = [chain_ids + i for i 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 chain_id in chain_ids:
if chain_id < 0:
raise ValueError(
'Chain_id must be a non-negative integer value,'
' found {}.'.format(chain_id))
if parallel_chains is None:
parallel_chains = max(min(cpu_count(), chains), 1)
elif parallel_chains > chains:
self._logger.info(
'Requesting %u parallel_chains for %u chains,'
' running all chains in parallel.',
parallel_chains,
chains,
)
parallel_chains = chains
elif parallel_chains < 1:
raise ValueError(
'Argument parallel_chains must be a positive integer value, '
'found {}.'.format(parallel_chains))
if threads_per_chain is None:
threads_per_chain = 1
if threads_per_chain < 1:
raise ValueError(
'Argument threads_per_chain must be a positive integer value, '
'found {}.'.format(threads_per_chain))
self._logger.debug('total threads: %u',
parallel_chains * threads_per_chain)
os.environ['STAN_NUM_THREADS'] = str(threads_per_chain)
refresh = None
if show_progress:
try:
import tqdm
self._logger.propagate = False
except ImportError:
self._logger.warning(
('Package tqdm not installed, cannot show progress '
'information. Please install tqdm with '
"'pip install tqdm'"))
show_progress = False
# TODO: issue 49: inits can be initialization function
sampler_args = SamplerArgs(
iter_warmup=iter_warmup,
iter_sampling=iter_sampling,
save_warmup=save_warmup,
thin=thin,
max_treedepth=max_treedepth,
metric=metric,
step_size=step_size,
adapt_engaged=adapt_engaged,
adapt_delta=adapt_delta,
adapt_init_phase=adapt_init_phase,
adapt_metric_window=adapt_metric_window,
adapt_step_size=adapt_step_size,
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_dir=output_dir,
save_diagnostics=save_diagnostics,
method_args=sampler_args,
refresh=refresh,
logger=self._logger,
)
runset = RunSet(args=args, chains=chains, chain_ids=chain_ids)
pbar = None
all_pbars = []
with ThreadPoolExecutor(max_workers=parallel_chains) 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 = tqdm_pbar(
desc='Chain {} - warmup'.format(i + 1),
position=i,
total=1, # Will set total from Stan's output
dynamic_ncols=dynamic_ncols,
)
all_pbars.append(pbar)
executor.submit(self._run_cmdstan, runset, i, pbar)
# Closing all progress bars
for pbar in all_pbars:
pbar.close()
if show_progress:
# re-enable logger for console
self._logger.propagate = True
if not runset._check_retcodes():
msg = 'Error during sampling.\n{}'.format(
runset.get_err_msgs())
raise RuntimeError(msg)
mcmc = CmdStanMCMC(runset, validate_csv, logger=self._logger)
return mcmc
def generate_quantities(
self,
data: Union[Dict, str] = None,
mcmc_sample: Union[CmdStanMCMC, List[str]] = None,
seed: int = None,
gq_output_dir: str = None,
) -> CmdStanGQ:
"""
Run CmdStan's generate_quantities method which runs the generated
quantities block of a model given an existing sample.
This function takes a CmdStanMCMC object and the dataset used to
generate that sample and calls to the CmdStan ``generate_quantities``
method to generate additional quantities of interest.
The ``CmdStanGQ`` object records the command, the return code,
and the paths to the generate method output csv and console files.
The output files are written either to a specified output directory
or to a temporary directory which is deleted upon session exit.
Output files are either written to a temporary directory or to the
specified output directory. Output filenames correspond to the template
'<model_name>-<YYYYMMDDHHMM>-<chain_id>' plus the file suffix which is
either '.csv' for the CmdStan output or '.txt' for
the console messages, e.g. 'bernoulli-201912081451-1.csv'.
Output files written to the temporary directory contain an additional
8-character random string, e.g. 'bernoulli-201912081451-1-5nm6as7u.csv'.
: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
the ``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_output_dir: Name of the directory in which the CmdStan output
files are saved. If unspecified, files will be written to a
temporary directory which is deleted upon session exit.
: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.draws_pd()
chains = mcmc_sample.chains
chain_ids = mcmc_sample.chain_ids
elif isinstance(mcmc_sample, list):
if len(mcmc_sample) < 1:
raise ValueError('MCMC sample cannot be empty list')
sample_csv_files = mcmc_sample
chains = len(sample_csv_files)
chain_ids = [x + 1 for x in range(chains)]
else:
raise ValueError('MCMC sample must be either CmdStanMCMC object'
' or list of paths to sample csv_files.')
try:
if sample_drawset is None: # assemble sample from csv files
config = {}
# scan 1st csv file to get config
try:
config = scan_sampler_csv(sample_csv_files[0])
except ValueError:
config = scan_sampler_csv(sample_csv_files[0], True)
conf_iter_sampling = None
if 'num_samples' in config:
conf_iter_sampling = int(config['num_samples'])
conf_iter_warmup = None
if 'num_warmup' in config:
conf_iter_warmup = int(config['num_warmup'])
conf_thin = None
if 'thin' in config:
conf_thin = int(config['thin'])
sampler_args = SamplerArgs(
iter_sampling=conf_iter_sampling,
iter_warmup=conf_iter_warmup,
thin=conf_thin,
)
args = CmdStanArgs(
self._name,
self._exe_file,
chain_ids=chain_ids,
method_args=sampler_args,
)
runset = RunSet(args=args, chains=chains, chain_ids=chain_ids)
runset._csv_files = sample_csv_files
sample_fit = CmdStanMCMC(runset)
sample_drawset = sample_fit.draws_pd()
except ValueError as exc:
raise ValueError('Invalid mcmc_sample, error:\n\t{}\n\t'
' while processing files\n\t{}'.format(
repr(exc),
'\n\t'.join(sample_csv_files))) from exc
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=chain_ids,
data=_data,
seed=seed,
output_dir=gq_output_dir,
method_args=generate_quantities_args,
)
runset = RunSet(args=args, chains=chains, chain_ids=chain_ids)
parallel_chains_avail = cpu_count()
parallel_chains = max(min(parallel_chains_avail - 2, chains), 1)
with ThreadPoolExecutor(max_workers=parallel_chains) as executor:
for i in range(chains):
executor.submit(self._run_cmdstan, runset, i)
if not runset._check_retcodes():
msg = 'Error during generate_quantities.\n{}'.format(
runset.get_err_msgs())
raise RuntimeError(msg)
quantities = CmdStanGQ(runset=runset, mcmc_sample=sample_drawset)
return quantities
def test_validate_bad_run(self):
exe = os.path.join(datafiles_path, 'bernoulli' + EXTENSION)
jdata = os.path.join(datafiles_path, 'bernoulli.data.json')
sampler_args = SamplerArgs(sampling_iters=100,
max_treedepth=11,
adapt_delta=0.95)
# some chains had errors
output = os.path.join(badfiles_path, 'bad-transcript-bern')
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,
)
runset = RunSet(args=cmdstan_args, chains=4)
with self.assertRaisesRegex(Exception, 'Exception'):
runset._check_console_msgs()
# csv file headers inconsistent
output = os.path.join(badfiles_path, 'bad-hdr-bern')
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,
)
runset = RunSet(args=cmdstan_args, chains=4)
retcodes = runset._retcodes
for i in range(len(retcodes)):
runset._set_retcode(i, 0)
self.assertTrue(runset._check_retcodes())
fit = CmdStanMCMC(runset)
with self.assertRaisesRegex(ValueError, 'header mismatch'):
fit._validate_csv_files()
# bad draws
output = os.path.join(badfiles_path, 'bad-draws-bern')
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,
)
runset = RunSet(args=cmdstan_args, chains=4)
retcodes = runset._retcodes
for i in range(len(retcodes)):
runset._set_retcode(i, 0)
self.assertTrue(runset._check_retcodes())
fit = CmdStanMCMC(runset)
with self.assertRaisesRegex(ValueError, 'draws'):
fit._validate_csv_files()
# mismatch - column headers, draws
output = os.path.join(badfiles_path, 'bad-cols-bern')
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,
)
runset = RunSet(args=cmdstan_args, chains=4)
retcodes = runset._retcodes
for i in range(len(retcodes)):
runset._set_retcode(i, 0)
self.assertTrue(runset._check_retcodes())
fit = CmdStanMCMC(runset)
with self.assertRaisesRegex(ValueError, 'bad draw'):
fit._validate_csv_files()
def optimize(
self,
data: Union[Dict, str] = None,
seed: int = None,
inits: Union[Dict, float, str] = None,
output_dir: str = None,
algorithm: str = None,
init_alpha: float = None,
iter: int = None,
) -> CmdStanMLE:
"""
Run the specified CmdStan optimize algorithm to produce a
penalized maximum likelihood estimate of the model parameters.
This function validates the specified configuration, composes a call to
the CmdStan ``optimize`` method and spawns one subprocess to run the
optimizer and waits for it to run to completion.
Unspecified arguments are not included in the call to CmdStan, i.e.,
those arguments will have CmdStan default values.
The ``CmdStanMLE`` object records the command, the return code,
and the paths to the optimize method output csv and console files.
The output files are written either to a specified output directory
or to a temporary directory which is deleted upon session exit.
Output files are either written to a temporary directory or to the
specified output directory. Ouput filenames correspond to the template
'<model_name>-<YYYYMMDDHHMM>-<chain_id>' plus the file suffix which is
either '.csv' for the CmdStan output or '.txt' for
the console messages, e.g. 'bernoulli-201912081451-1.csv'.
Output files written to the temporary directory contain an additional
8-character random string, e.g. 'bernoulli-201912081451-1-5nm6as7u.csv'.
: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.
:param inits: Specifies how the sampler initializes parameter values.
Initialization 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 behavior
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 output_dir: Name of the directory to which CmdStan output
files are written. If unspecified, output files will be written
to a temporary directory which is deleted upon session exit.
: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_dir=output_dir,
save_diagnostics=False,
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 optimization.\n{}'.format(
runset.get_err_msgs())
raise RuntimeError(msg)
mle = CmdStanMLE(runset)
return mle
def generate_quantities(
self,
data: Union[Dict, str] = None,
mcmc_sample: Union[CmdStanMCMC, List[str]] = None,
seed: int = None,
gq_output_dir: str = None,
) -> CmdStanGQ:
"""
Run CmdStan's generate_quantities method which runs the generated
quantities block of a model given an existing sample.
This function takes a CmdStanMCMC object and the dataset used to
generate that sample and calls to the CmdStan ``generate_quantities``
method to generate additional quantities of interest.
The ``CmdStanGQ`` object records the command, the return code,
and the paths to the generate method output csv and console files.
The output files are written either to a specified output directory
or to a temporary directory which is deleted upon session exit.
Output filenames are composed of the model name, a timestamp
in the form YYYYMMDDhhmm and the chain id, plus the corresponding
filetype suffix, either '.csv' for the CmdStan output or '.txt' for
the console messages, e.g. `bernoulli_ppc-201912081451-1.csv`. Output
files written to the temporary directory contain an additional
8-character random string, e.g.
`bernoulli_ppc-201912081451-1-5nm6as7u.csv`.
: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_output_dir: Name of the directory in which the CmdStan output
files are saved. If unspecified, files will be written to a
temporary directory which is deleted upon session exit.
: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 ValueError as e:
raise ValueError(
'Invalid mcmc_sample, error:\n\t{}\n\t'
' while processing files\n\t{}'.format(
repr(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_dir=gq_output_dir,
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 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,
output_dir: str = None,
save_diagnostics: bool = False,
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
console outputs, if any. The output files are written either to a
specified output directory or to a temporary directory which is deleted
upon session exit.
The output filenames are composed of the model name, a timestamp
in the form YYYYMMDDhhmm and the chain id, plus the corresponding
filetype suffix, either '.csv' for the CmdStan output or '.txt' for
the console messages, e.g. `bernoulli-201912081451-1.csv`. Output files
written to the temporary directory contain an additional 8-character
random string, e.g. `bernoulli-201912081451-1-5nm6as7u.csv`.
: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.
Initialization 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 behavior 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 output_dir: Name of the directory to with the CmdStan output
files are written. If unspecified, output files will be written
to a temporary directory which is deleted upon session exit.
:param save_diagnostics: Whether or not to save diagnostics. If True,
csv output files are written to
``<basename>-diagnostic-<chain_id>.csv.``, where ``<basename>``
is set with ``csv_basename``.
: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 isinstance(chain_ids, 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
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_dir=output_dir,
save_diagnostics=save_diagnostics,
method_args=sampler_args,
refresh=refresh,
)
runset = RunSet(args=args, chains=chains)
pbar = None
all_pbars = []
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 = tqdm_pbar(
desc='Chain {} - warmup'.format(i + 1),
position=i,
total=1, # Will set total from Stan's output
dynamic_ncols=dynamic_ncols,
)
all_pbars.append(pbar)
executor.submit(self._run_cmdstan, runset, i, pbar)
# Closing all progress bars
for pbar in all_pbars:
pbar.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 variational(
self,
data: Union[Dict, str] = None,
seed: int = None,
inits: float = None,
csv_basename: str = None,
algorithm: str = None,
iter: int = None,
grad_samples: int = None,
elbo_samples: int = None,
eta: Real = None,
adapt_iter: int = None,
tol_rel_obj: Real = None,
eval_elbo: int = None,
output_samples: int = None,
) -> CmdStanVB:
"""
Run CmdStan's variational inference algorithm to approximate
the posterior distribution of the model conditioned on the data.
: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 uniform random on a range centered on ``0`` with
default range of ``2``. Specifying a single number ``n > 0`` changes
the initialization range to ``[-n, n]``.
: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: "meanfield", "fullrank".
:param iter: Maximum number of ADVI iterations.
:param grad_samples: Number of MC draws for computing the gradient.
:param elbo_samples: Number of MC draws for estimate of ELBO.
:param eta: Stepsize scaling parameter.
:param adapt_iter: Number of iterations for eta adaptation.
:param tol_rel_obj: Relative tolerance parameter for convergence.
:param eval_elbo: Number of interations between ELBO evaluations.
:param output_samples: Number of approximate posterior output draws
to save.
:return: CmdStanVB object
"""
variational_args = VariationalArgs(
algorithm=algorithm,
iter=iter,
grad_samples=grad_samples,
elbo_samples=elbo_samples,
eta=eta,
adapt_iter=adapt_iter,
tol_rel_obj=tol_rel_obj,
eval_elbo=eval_elbo,
output_samples=output_samples,
)
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=variational_args,
)
dummy_chain_id = 0
runset = RunSet(args=args, chains=1)
self._run_cmdstan(runset, dummy_chain_id)
# treat failure to converge as failure
transcript_file = runset.console_files[dummy_chain_id]
valid = True
pat = re.compile(r'The algorithm may not have converged.', re.M)
with open(transcript_file, 'r') as transcript:
contents = transcript.read()
errors = re.findall(pat, contents)
if len(errors) > 0:
valid = False
if not valid:
raise RuntimeError('The algorithm may not have converged.')
if not runset._check_retcodes():
msg = 'Error during variational inference'
if runset._retcode(dummy_chain_id) != 0:
msg = '{}, error code {}'.format(
msg, runset._retcode(dummy_chain_id)
)
raise RuntimeError(msg)
# pylint: disable=invalid-name
vb = CmdStanVB(runset)
vb._set_variational_attrs(runset.csv_files[0])
return vb
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 ValueError as e:
raise ValueError(
'Invalid mcmc_sample, error:\n\t{}\n\t'
' while processing files\n\t{}'.format(
repr(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
