"""CmdStanModel"""
import os
import platform
import re
import shutil
import subprocess
import sys
import tempfile
import threading
from collections import OrderedDict
from concurrent.futures import ThreadPoolExecutor
from io import StringIO
from multiprocessing import cpu_count
from typing import (
Any,
Callable,
Dict,
Iterable,
List,
Literal,
Mapping,
Optional,
TypeVar,
Union,
)
import pandas as pd
from tqdm.auto import tqdm
from cmdstanpy import (
_CMDSTAN_REFRESH,
_CMDSTAN_SAMPLING,
_CMDSTAN_WARMUP,
_TMPDIR,
compilation,
)
from cmdstanpy.cmdstan_args import (
CmdStanArgs,
GenerateQuantitiesArgs,
LaplaceArgs,
Method,
OptimizeArgs,
PathfinderArgs,
SamplerArgs,
VariationalArgs,
)
from cmdstanpy.stanfit import (
CmdStanGQ,
CmdStanLaplace,
CmdStanMCMC,
CmdStanMLE,
CmdStanPathfinder,
CmdStanVB,
RunSet,
from_csv,
)
from cmdstanpy.utils import (
cmdstan_path,
cmdstan_version_before,
do_command,
get_logger,
returncode_msg,
)
from cmdstanpy.utils.filesystem import temp_inits, temp_single_json
from . import progress as progbar
OptionalPath = Union[str, os.PathLike, None]
Fit = TypeVar('Fit', CmdStanMCMC, CmdStanMLE, CmdStanVB)
[docs]class CmdStanModel:
# overview, omitted from doc comment in order to improve Sphinx docs.
# A CmdStanModel object encapsulates the Stan program and provides
# methods for compilation and inference.
"""
The constructor method allows model instantiation given either the
Stan program source file or the compiled executable, or both.
This will compile the model if provided a Stan file and no executable,
:param model_name: Model name, used for output file names.
Optional, default is the base filename of the Stan program file.
Deprecated: In version 2.0.0, model name cannot be
specified and will always be taken from executable.
:param stan_file: Path to Stan program file.
:param exe_file: Path to compiled executable file. Optional, unless
no Stan program file is specified. If both the program file and
the compiled executable file are specified, the base filenames
must match, (but different directory locations are allowed).
:param force_compile: If ``True``, always compile, even if there
is an existing executable file for this model.
:param stanc_options: Options for stanc compiler, specified as a Python
dictionary containing Stanc3 compiler option name, value pairs.
Optional.
:param cpp_options: Options for C++ compiler, specified as a Python
dictionary containing C++ compiler option name, value pairs.
Optional.
:param user_header: A path to a header file to include during C++
compilation.
Optional.
:param compile: Whether or not to compile the model. Default is ``True``.
If set to the string ``"force"``, it will always compile even if
an existing executable is found.
Deprecated: Use ``force_compile`` instead. The ability to instantiate
a CmdStanModel without an executable will be removed in version 2.0.0.
"""
def __init__(
self,
model_name: Optional[str] = None,
stan_file: OptionalPath = None,
exe_file: OptionalPath = None,
force_compile: bool = False,
stanc_options: Optional[Dict[str, Any]] = None,
cpp_options: Optional[Dict[str, Any]] = None,
user_header: OptionalPath = None,
*,
compile: Union[bool, Literal['force'], None] = None,
) -> None:
"""
Initialize object given constructor args.
:param model_name: Deprecated. Model name, used for output file names.
:param stan_file: Path to Stan program file.
:param exe_file: Path to compiled executable file.
:param force_compile: Whether or not to force recompilation if
executable file already exists.
:param stanc_options: Options for stanc compiler.
:param cpp_options: Options for C++ compiler.
:param user_header: A path to a header file to include during C++
compilation.
:param compile: Deprecated. Whether or not to compile the model.
"""
self._name = ''
self._stan_file = None
self._exe_file = None
self._compiler_options = compilation.CompilerOptions(
stanc_options=stanc_options,
cpp_options=cpp_options,
user_header=user_header,
)
self._compiler_options.validate()
self._fixed_param = False
if compile is None:
compile = True
else:
get_logger().warning(
"CmdStanModel(compile=...) is deprecated and will be "
"removed in the next major version. The constructor will "
"always ensure a model has a compiled executable.\n"
"If you wish to force recompilation, use force_compile=True "
"instead."
)
if force_compile:
compile = 'force'
if model_name is not None:
get_logger().warning(
"CmdStanModel(model_name=...) is deprecated and will be "
"removed in the next major version."
)
if not model_name.strip():
raise ValueError(
'Invalid value for argument model name, found "{}"'.format(
model_name
)
)
self._name = model_name.strip()
if stan_file is None:
if exe_file is None:
raise ValueError(
'Missing model file arguments, you must specify '
'either Stan source or executable program file or both.'
)
else:
self._stan_file = os.path.realpath(os.path.expanduser(stan_file))
if not os.path.exists(self._stan_file):
raise ValueError('no such file {}'.format(self._stan_file))
_, filename = os.path.split(stan_file)
if len(filename) < 6 or not filename.endswith('.stan'):
raise ValueError(
'invalid stan filename {}'.format(self._stan_file)
)
if not self._name:
self._name, _ = os.path.splitext(filename)
# if program has include directives, record path
with open(self._stan_file, 'r') as fd:
program = fd.read()
if '#include' in program:
path, _ = os.path.split(self._stan_file)
self._compiler_options.add_include_path(path)
# try to detect models w/out parameters, needed for sampler
if (not cmdstan_version_before(2, 27)) and cmdstan_version_before(
2, 36
):
try:
model_info = self.src_info()
if 'parameters' in model_info:
self._fixed_param |= len(model_info['parameters']) == 0
except ValueError as e:
if compile:
raise
get_logger().debug(e)
if exe_file is not None:
self._exe_file = os.path.realpath(os.path.expanduser(exe_file))
if not os.path.exists(self._exe_file):
raise ValueError('no such file {}'.format(self._exe_file))
_, exename = os.path.split(self._exe_file)
if not self._name:
self._name, _ = os.path.splitext(exename)
else:
if self._name != os.path.splitext(exename)[0]:
raise ValueError(
'Name mismatch between Stan file and compiled'
' executable, expecting basename: {}'
' found: {}.'.format(self._name, exename)
)
if platform.system() == 'Windows':
try:
do_command(['where.exe', 'tbb.dll'], fd_out=None)
except RuntimeError:
# Add tbb to the $PATH on Windows
libtbb = os.environ.get('STAN_TBB')
if libtbb is None:
libtbb = os.path.join(
cmdstan_path(), 'stan', 'lib', 'stan_math', 'lib', 'tbb'
)
get_logger().debug("Adding TBB (%s) to PATH", libtbb)
os.environ['PATH'] = ';'.join(
list(
OrderedDict.fromkeys(
[libtbb] + os.environ.get('PATH', '').split(';')
)
)
)
else:
get_logger().debug("TBB already found in load path")
if compile and self._exe_file is None:
self.compile(force=str(compile).lower() == 'force', _internal=True)
def __repr__(self) -> str:
repr = 'CmdStanModel: name={}'.format(self._name)
repr = '{}\n\t stan_file={}'.format(repr, self._stan_file)
repr = '{}\n\t exe_file={}'.format(repr, self._exe_file)
repr = '{}\n\t compiler_options={}'.format(repr, self._compiler_options)
return repr
@property
def name(self) -> str:
"""
Model name used in output filename templates. Default is basename
of Stan program or exe file, unless specified in call to constructor
via argument ``model_name``.
"""
return self._name
@property
def stan_file(self) -> OptionalPath:
"""Full path to Stan program file."""
return self._stan_file
@property
def exe_file(self) -> OptionalPath:
"""Full path to Stan exe file."""
return self._exe_file
[docs] def exe_info(self) -> Dict[str, str]:
"""
Run model with option 'info'. Parse output statements, which all
have form 'key = value' into a Dict.
If exe file compiled with CmdStan < 2.27, option 'info' isn't
available and the method returns an empty dictionary.
"""
result: Dict[str, str] = {}
if self.exe_file is None:
return result
try:
info = StringIO()
do_command(cmd=[str(self.exe_file), 'info'], fd_out=info)
lines = info.getvalue().split('\n')
for line in lines:
kv_pair = [x.strip() for x in line.split('=')]
if len(kv_pair) != 2:
continue
result[kv_pair[0]] = kv_pair[1]
return result
except RuntimeError as e:
get_logger().debug(e)
return result
[docs] def src_info(self) -> Dict[str, Any]:
"""
Run stanc with option '--info'.
If stanc is older than 2.27 or if the stan
file cannot be found, returns an empty dictionary.
"""
if self.stan_file is None or cmdstan_version_before(2, 27):
return {}
return compilation.src_info(str(self.stan_file), self._compiler_options)
# TODO(2.0) remove
@property
def stanc_options(self) -> Dict[str, Union[bool, int, str]]:
"""Options to stanc compilers."""
return self._compiler_options._stanc_options
@property
def cpp_options(self) -> Dict[str, Union[bool, int]]:
"""Options to C++ compilers."""
return self._compiler_options._cpp_options
@property
def user_header(self) -> str:
"""The user header file if it exists, otherwise empty"""
return self._compiler_options._user_header
[docs] def code(self) -> Optional[str]:
"""Return Stan program as a string."""
if not self._stan_file:
raise RuntimeError('Please specify source file')
code = None
try:
with open(self._stan_file, 'r') as fd:
code = fd.read()
except IOError:
get_logger().error(
'Cannot read file Stan file: %s', self._stan_file
)
return code
# TODO(2.0): remove
[docs] def compile(
self,
force: bool = False,
stanc_options: Optional[Dict[str, Any]] = None,
cpp_options: Optional[Dict[str, Any]] = None,
user_header: OptionalPath = None,
override_options: bool = False,
*,
_internal: bool = False,
) -> None:
"""
Deprecated: To compile a model, use the :class:`~cmdstanpy.CmdStanModel`
constructor or :func:`cmdstanpy.compile_stan_file()`.
Compile the given Stan program file. Translates the Stan code to
C++, then calls the C++ compiler.
By default, this function compares the timestamps on the source and
executable files; if the executable is newer than the source file, it
will not recompile the file, unless argument ``force`` is ``True``
or unless the compiler options have been changed.
:param force: When ``True``, always compile, even if the executable file
is newer than the source file. Used for Stan models which have
``#include`` directives in order to force recompilation when changes
are made to the included files.
:param stanc_options: Options for stanc compiler.
:param cpp_options: Options for C++ compiler.
:param user_header: A path to a header file to include during C++
compilation.
:param override_options: When ``True``, override existing option.
When ``False``, add/replace existing options. Default is ``False``.
"""
if not _internal:
get_logger().warning(
"CmdStanModel.compile() is deprecated and will be removed in "
"the next major version. To compile a model, use the "
"CmdStanModel() constructor or cmdstanpy.compile_stan_file()."
)
if not self._stan_file:
raise RuntimeError('Please specify source file')
compiler_options = None
if (
stanc_options is not None
or cpp_options is not None
or user_header is not None
):
compiler_options = compilation.CompilerOptions(
stanc_options=stanc_options,
cpp_options=cpp_options,
user_header=user_header,
)
compiler_options.validate()
if compiler_options != self._compiler_options:
force = True
if self._compiler_options is None:
self._compiler_options = compiler_options
elif override_options:
self._compiler_options = compiler_options
else:
self._compiler_options.add(compiler_options)
self._exe_file = compilation.compile_stan_file(
str(self.stan_file),
force=force,
stanc_options=self._compiler_options.stanc_options,
cpp_options=self._compiler_options.cpp_options,
user_header=self._compiler_options.user_header,
)
[docs] def optimize(
self,
data: Union[Mapping[str, Any], str, os.PathLike, None] = None,
seed: Optional[int] = None,
inits: Union[Mapping[str, Any], float, str, os.PathLike, None] = None,
output_dir: OptionalPath = None,
sig_figs: Optional[int] = None,
save_profile: bool = False,
algorithm: Optional[str] = None,
init_alpha: Optional[float] = None,
tol_obj: Optional[float] = None,
tol_rel_obj: Optional[float] = None,
tol_grad: Optional[float] = None,
tol_rel_grad: Optional[float] = None,
tol_param: Optional[float] = None,
history_size: Optional[int] = None,
iter: Optional[int] = None,
save_iterations: bool = False,
require_converged: bool = True,
show_console: bool = False,
refresh: Optional[int] = None,
time_fmt: str = "%Y%m%d%H%M%S",
timeout: Optional[float] = None,
jacobian: bool = False,
# would be nice to move this further up, but that's a breaking change
) -> 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 :class:`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. 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,
:func:`numpy.random.default_rng` 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 sig_figs: Numerical precision used for output CSV and text files.
Must be an integer between 1 and 18. If unspecified, the default
precision for the system file I/O is used; the usual value is 6.
Introduced in CmdStan-2.25.
:param save_profile: Whether or not to profile auto-diff operations in
labelled blocks of code. If ``True``, CSV outputs are written to
file '<model_name>-<YYYYMMDDHHMM>-profile-<chain_id>'.
Introduced in CmdStan-2.26.
:param algorithm: Algorithm to use. One of: 'BFGS', 'LBFGS', 'Newton'
:param init_alpha: Line search step size for first iteration
:param tol_obj: Convergence tolerance on changes in objective
function value
:param tol_rel_obj: Convergence tolerance on relative changes
in objective function value
:param tol_grad: Convergence tolerance on the norm of the gradient
:param tol_rel_grad: Convergence tolerance on the relative
norm of the gradient
:param tol_param: Convergence tolerance on changes in parameter value
:param history_size: Size of the history for LBFGS Hessian
approximation. The value should be less than the dimensionality
of the parameter space. 5-10 usually sufficient
:param iter: Total number of iterations
:param save_iterations: When ``True``, save intermediate approximations
to the output CSV file. Default is ``False``.
:param require_converged: Whether or not to raise an error if Stan
reports that "The algorithm may not have converged".
:param show_console: If ``True``, stream CmdStan messages sent to
stdout and stderr to the console. Default is ``False``.
:param refresh: Specify the number of iterations cmdstan will take
between progress messages. Default value is 100.
:param time_fmt: A format string passed to
:meth:`~datetime.datetime.strftime` to decide the file names for
output CSVs. Defaults to "%Y%m%d%H%M%S"
:param timeout: Duration at which optimization times out in seconds.
:param jacobian: Whether or not to use the Jacobian adjustment for
constrained variables in optimization. By default this is false,
meaning optimization yields the Maximum Likehood Estimate (MLE).
Setting it to true yields the Maximum A Posteriori Estimate (MAP).
:return: CmdStanMLE object
"""
optimize_args = OptimizeArgs(
algorithm=algorithm,
init_alpha=init_alpha,
tol_obj=tol_obj,
tol_rel_obj=tol_rel_obj,
tol_grad=tol_grad,
tol_rel_grad=tol_rel_grad,
tol_param=tol_param,
history_size=history_size,
iter=iter,
save_iterations=save_iterations,
jacobian=jacobian,
)
if jacobian and cmdstan_version_before(2, 32, self.exe_info()):
raise ValueError(
"Jacobian adjustment for optimization is only supported "
"in CmdStan 2.32 and above."
)
with temp_single_json(data) as _data, temp_inits(
inits, allow_multiple=False
) as _inits:
args = CmdStanArgs(
self._name,
self._exe_file,
chain_ids=None,
data=_data,
seed=seed,
inits=_inits,
output_dir=output_dir,
sig_figs=sig_figs,
save_profile=save_profile,
method_args=optimize_args,
refresh=refresh,
)
dummy_chain_id = 0
runset = RunSet(args=args, chains=1, time_fmt=time_fmt)
self._run_cmdstan(
runset,
dummy_chain_id,
show_console=show_console,
timeout=timeout,
)
runset.raise_for_timeouts()
if not runset._check_retcodes():
msg = "Error during optimization! Command '{}' failed: {}".format(
' '.join(runset.cmd(0)), runset.get_err_msgs()
)
if 'Line search failed' in msg and not require_converged:
get_logger().warning(msg)
else:
raise RuntimeError(msg)
mle = CmdStanMLE(runset)
return mle
# pylint: disable=too-many-arguments
[docs] def sample(
self,
data: Union[Mapping[str, Any], str, os.PathLike, None] = None,
chains: Optional[int] = None,
parallel_chains: Optional[int] = None,
threads_per_chain: Optional[int] = None,
seed: Union[int, List[int], None] = None,
chain_ids: Union[int, List[int], None] = None,
inits: Union[
Mapping[str, Any],
float,
str,
List[str],
List[Mapping[str, Any]],
None,
] = None,
iter_warmup: Optional[int] = None,
iter_sampling: Optional[int] = None,
save_warmup: bool = False,
thin: Optional[int] = None,
max_treedepth: Optional[int] = None,
metric: Union[
str, Dict[str, Any], List[str], List[Dict[str, Any]], None
] = None,
step_size: Union[float, List[float], None] = None,
adapt_engaged: bool = True,
adapt_delta: Optional[float] = None,
adapt_init_phase: Optional[int] = None,
adapt_metric_window: Optional[int] = None,
adapt_step_size: Optional[int] = None,
fixed_param: bool = False,
output_dir: OptionalPath = None,
sig_figs: Optional[int] = None,
save_latent_dynamics: bool = False,
save_profile: bool = False,
show_progress: bool = True,
show_console: bool = False,
refresh: Optional[int] = None,
time_fmt: str = "%Y%m%d%H%M%S",
timeout: Optional[float] = None,
*,
force_one_process_per_chain: Optional[bool] = None,
) -> CmdStanMCMC:
"""
Run or more chains of the NUTS-HMC 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 :class:`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 :func:`multiprocessing.cpu_count`,
i.e., it will only run as many chains in parallel as there are
cores on the machine. Note that CmdStan 2.28 and higher can run
all chains in parallel providing that the model was compiled with
threading support.
: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. For such models,
CmdStan version 2.28 and higher will run all chains in parallel
from within a single process. The total number of threads used
will be ``parallel_chains * threads_per_chain``, where the default
value for parallel_chains is the number of cpus, not chains.
:param seed: The seed for random number generator. Must be an integer
between 0 and 2^32 - 1. If unspecified,
:func:`numpy.random.default_rng`
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.
* list of dictionaries - per-chain initial values.
: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 recorded iterations. Default is 1, i.e.,
all iterations are recorded.
: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.
If the value of the metric argument is a Python dict object, it
must contain an entry 'inv_metric' which specifies either the
diagnoal or dense matrix.
If the value of the metric argument is a list of Python dicts,
its length must match the number of chains and all dicts must
containan entry 'inv_metric' and all 'inv_metric' entries must
have the same shape.
:param step_size: Initial step size 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 step size 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 step size 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. 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 sig_figs: Numerical precision used for output CSV and text files.
Must be an integer between 1 and 18. If unspecified, the default
precision for the system file I/O is used; the usual value is 6.
Introduced in CmdStan-2.25.
:param save_latent_dynamics: Whether or not to output the position and
momentum information for the model parameters (unconstrained).
If ``True``, CSV outputs are written to an output file
'<model_name>-<YYYYMMDDHHMM>-diagnostic-<chain_id>',
e.g. 'bernoulli-201912081451-diagnostic-1.csv', see
https://mc-stan.org/docs/cmdstan-guide/stan_csv.html,
section "Diagnostic CSV output file" for details.
:param save_profile: Whether or not to profile auto-diff operations in
labelled blocks of code. If ``True``, CSV outputs are written to
file '<model_name>-<YYYYMMDDHHMM>-profile-<chain_id>'.
Introduced in CmdStan-2.26, see
https://mc-stan.org/docs/cmdstan-guide/stan_csv.html,
section "Profiling CSV output file" for details.
:param show_progress: If ``True``, display progress bar to track
progress for warmup and sampling iterations. Default is ``True``,
unless package tqdm progress bar encounter errors.
:param show_console: If ``True``, stream CmdStan messages sent to stdout
and stderr to the console. Default is ``False``.
:param refresh: Specify the number of iterations CmdStan will take
between progress messages. Default value is 100.
:param time_fmt: A format string passed to
:meth:`~datetime.datetime.strftime` to decide the file names for
output CSVs. Defaults to "%Y%m%d%H%M%S"
:param force_one_process_per_chain: If ``True``, run multiple chains in
distinct processes regardless of model ability to run parallel
chains (CmdStan 2.28+ feature). If ``False``, always run multiple
chains in one process (does not check that this is valid).
If None (Default): Check that CmdStan version is >=2.28, and that
model was compiled with STAN_THREADS=True, and utilize the
parallel chain functionality if those conditions are met.
:param timeout: Duration at which sampling times out in seconds.
:return: CmdStanMCMC object
"""
if fixed_param is None:
fixed_param = self._fixed_param
if chains is None:
chains = 4
if chains < 1:
raise ValueError(
'Chains must be a positive integer value, found {}.'.format(
chains
)
)
if parallel_chains is None:
parallel_chains = max(min(cpu_count(), chains), 1)
elif parallel_chains > chains:
get_logger().info(
'Requested %u parallel_chains but only %u required, '
'will run all chains in parallel.',
parallel_chains,
chains,
)
parallel_chains = chains
elif parallel_chains < 1:
raise ValueError(
'Argument parallel_chains must be a positive integer, '
'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, '
'found {}.'.format(threads_per_chain)
)
parallel_procs = parallel_chains
num_threads = threads_per_chain
one_process_per_chain = True
info_dict = self.exe_info()
stan_threads = info_dict.get('STAN_THREADS', 'false').lower()
# run multi-chain sampler unless algo is fixed_param or 1 chain
if chains == 1:
force_one_process_per_chain = True
if (
force_one_process_per_chain is None
and not cmdstan_version_before(2, 28, info_dict)
and stan_threads == 'true'
):
one_process_per_chain = False
num_threads = parallel_chains * num_threads
parallel_procs = 1
if force_one_process_per_chain is False:
if not cmdstan_version_before(2, 28, info_dict):
one_process_per_chain = False
num_threads = parallel_chains * num_threads
parallel_procs = 1
if stan_threads == 'false':
get_logger().warning(
'Stan program not compiled for threading, '
'process will run chains sequentially. '
'For multi-chain parallelization, recompile '
'the model with argument '
'"cpp_options={\'STAN_THREADS\':\'TRUE\'}.'
)
else:
get_logger().warning(
'Installed version of CmdStan cannot multi-process '
'chains, will run %d processes. '
'Run "install_cmdstan" to upgrade to latest version.',
chains,
)
os.environ['STAN_NUM_THREADS'] = str(num_threads)
if chain_ids is None:
chain_ids = [i + 1 for i 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 = [i + chain_ids for i in range(chains)]
else:
if not one_process_per_chain:
for i, j in zip(chain_ids, chain_ids[1:]):
if i != j - 1:
raise ValueError(
'chain_ids must be sequential list of integers,'
' found {}.'.format(chain_ids)
)
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)
)
sampler_args = SamplerArgs(
num_chains=1 if one_process_per_chain else chains,
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 temp_single_json(data) as _data, temp_inits(
inits, id=chain_ids[0]
) as _inits:
cmdstan_inits: Union[str, List[str], int, float, None]
if one_process_per_chain and isinstance(inits, list): # legacy
cmdstan_inits = [
f"{_inits[:-5]}_{i}.json" for i in chain_ids # type: ignore
]
else:
cmdstan_inits = _inits
args = CmdStanArgs(
self._name,
self._exe_file,
chain_ids=chain_ids,
data=_data,
seed=seed,
inits=cmdstan_inits,
output_dir=output_dir,
sig_figs=sig_figs,
save_latent_dynamics=save_latent_dynamics,
save_profile=save_profile,
method_args=sampler_args,
refresh=refresh,
)
if show_console:
show_progress = False
else:
show_progress = show_progress and progbar.allow_show_progress()
get_logger().info('CmdStan start processing')
progress_hook: Optional[Callable[[str, int], None]] = None
if show_progress:
iter_total = 0
if iter_warmup is None:
iter_total += _CMDSTAN_WARMUP
else:
iter_total += iter_warmup
if iter_sampling is None:
iter_total += _CMDSTAN_SAMPLING
else:
iter_total += iter_sampling
if refresh is None:
refresh = _CMDSTAN_REFRESH
iter_total = iter_total // refresh + 2
progress_hook = self._wrap_sampler_progress_hook(
chain_ids=chain_ids,
total=iter_total,
)
runset = RunSet(
args=args,
chains=chains,
chain_ids=chain_ids,
time_fmt=time_fmt,
one_process_per_chain=one_process_per_chain,
)
with ThreadPoolExecutor(max_workers=parallel_procs) as executor:
for i in range(runset.num_procs):
executor.submit(
self._run_cmdstan,
runset=runset,
idx=i,
show_progress=show_progress,
show_console=show_console,
progress_hook=progress_hook,
timeout=timeout,
)
if show_progress and progress_hook is not None:
progress_hook("Done", -1) # -1 == all chains finished
# advance terminal window cursor past progress bars
term_size: os.terminal_size = shutil.get_terminal_size(
fallback=(80, 24)
)
if term_size is not None and term_size[0] > 0:
for i in range(chains):
sys.stdout.write(' ' * term_size[0])
sys.stdout.flush()
sys.stdout.write('\n')
get_logger().info('CmdStan done processing.')
runset.raise_for_timeouts()
get_logger().debug('runset\n%s', repr(runset))
# hack needed to parse CSV files if model has no params
# needed if exe is supplied without stan file
with open(runset.stdout_files[0], 'r') as fd:
console_msgs = fd.read()
get_logger().debug('Chain 1 console:\n%s', console_msgs)
if 'running fixed_param sampler' in console_msgs:
get_logger().debug("Detected fixed param model")
sampler_args.fixed_param = True
runset._args.method_args = sampler_args
errors = runset.get_err_msgs()
if not runset._check_retcodes():
msg = (
f'Error during sampling:\n{errors}\n'
f'Command and output files:\n{repr(runset)}'
)
if not show_console:
msg += (
'\nConsider re-running with show_console=True if the'
' above output is unclear!'
)
raise RuntimeError(msg)
if errors:
msg = f'Non-fatal error during sampling:\n{errors}'
if not show_console:
msg += (
'\nConsider re-running with show_console=True if the'
' above output is unclear!'
)
get_logger().warning(msg)
mcmc = CmdStanMCMC(runset)
return mcmc
[docs] def generate_quantities(
self,
data: Union[Mapping[str, Any], str, os.PathLike, None] = None,
previous_fit: Union[Fit, List[str], None] = None,
seed: Optional[int] = None,
gq_output_dir: OptionalPath = None,
sig_figs: Optional[int] = None,
show_console: bool = False,
refresh: Optional[int] = None,
time_fmt: str = "%Y%m%d%H%M%S",
timeout: Optional[float] = None,
*,
mcmc_sample: Union[CmdStanMCMC, List[str], None] = None,
) -> CmdStanGQ[Fit]:
"""
Run CmdStan's generate_quantities method which runs the generated
quantities block of a model given an existing sample.
This function takes one of the Stan fit objects
:class:`CmdStanMCMC`, :class:`CmdStanMLE`, or :class:`CmdStanVB`
and the data required for the model and calls to the CmdStan
``generate_quantities`` method to generate additional quantities of
interest.
The :class:`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 previous_fit: Can be either a :class:`CmdStanMCMC`,
:class:`CmdStanMLE`, or :class:`CmdStanVB` 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,
:func:`numpy.random.default_rng`
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.
:param sig_figs: Numerical precision used for output CSV and text files.
Must be an integer between 1 and 18. If unspecified, the default
precision for the system file I/O is used; the usual value is 6.
Introduced in CmdStan-2.25.
:param show_console: If ``True``, stream CmdStan messages sent to
stdout and stderr to the console. Default is ``False``.
:param refresh: Specify the number of iterations CmdStan will take
between progress messages. Default value is 100.
:param time_fmt: A format string passed to
:meth:`~datetime.datetime.strftime` to decide the file names for
output CSVs. Defaults to "%Y%m%d%H%M%S"
:param timeout: Duration at which generation times out in seconds.
:return: CmdStanGQ object
"""
# TODO(2.0): remove
if mcmc_sample is not None:
if previous_fit:
raise ValueError(
"Cannot supply both 'previous_fit' and "
"deprecated argument 'mcmc_sample'"
)
get_logger().warning(
"Argument name `mcmc_sample` is deprecated, please "
"rename to `previous_fit`."
)
previous_fit = mcmc_sample # type: ignore
if isinstance(previous_fit, (CmdStanMCMC, CmdStanMLE, CmdStanVB)):
fit_object = previous_fit
fit_csv_files = previous_fit.runset.csv_files
elif isinstance(previous_fit, list):
if len(previous_fit) < 1:
raise ValueError(
'Expecting list of Stan CSV files, found empty list'
)
try:
fit_csv_files = previous_fit
fit_object = from_csv(fit_csv_files) # type: ignore
except ValueError as e:
raise ValueError(
'Invalid sample from Stan CSV files, error:\n\t{}\n\t'
' while processing files\n\t{}'.format(
repr(e), '\n\t'.join(previous_fit)
)
) from e
else:
raise ValueError(
'Previous fit must be either CmdStanPy fit object'
' or list of paths to Stan CSV files.'
)
if isinstance(fit_object, CmdStanMCMC):
chains = fit_object.chains
chain_ids = fit_object.chain_ids
if fit_object._save_warmup:
get_logger().warning(
'Sample contains saved warmup draws which will be used '
'to generate additional quantities of interest.'
)
elif isinstance(fit_object, CmdStanMLE):
if cmdstan_version_before(2, 31):
raise RuntimeError(
"Method generate_quantities was not "
"available for non-HMC until CmdStan 2.31"
)
chains = 1
chain_ids = [1]
if fit_object._save_iterations:
get_logger().warning(
'MLE contains saved iterations which will be used '
'to generate additional quantities of interest.'
)
else: # isinstance(fit_object, CmdStanVB)
if cmdstan_version_before(2, 31):
raise RuntimeError(
"Method generate_quantities was not "
"available for non-HMC until CmdStan 2.31"
)
chains = 1
chain_ids = [1]
generate_quantities_args = GenerateQuantitiesArgs(
csv_files=fit_csv_files
)
generate_quantities_args.validate(chains)
with temp_single_json(data) as _data:
args = CmdStanArgs(
self._name,
self._exe_file,
chain_ids=chain_ids,
data=_data,
seed=seed,
output_dir=gq_output_dir,
sig_figs=sig_figs,
method_args=generate_quantities_args,
refresh=refresh,
)
runset = RunSet(
args=args, chains=chains, chain_ids=chain_ids, time_fmt=time_fmt
)
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,
show_console=show_console,
timeout=timeout,
)
runset.raise_for_timeouts()
errors = runset.get_err_msgs()
if errors:
msg = (
f'Error during generate_quantities:\n{errors}\n'
f'Command and output files:\n{repr(runset)}'
)
if not show_console:
msg += (
'\nConsider re-running with show_console=True if the'
' above output is unclear!'
)
raise RuntimeError(msg)
quantities = CmdStanGQ(runset=runset, previous_fit=fit_object)
return quantities
[docs] def variational(
self,
data: Union[Mapping[str, Any], str, os.PathLike, None] = None,
seed: Optional[int] = None,
inits: Optional[float] = None,
output_dir: OptionalPath = None,
sig_figs: Optional[int] = None,
save_latent_dynamics: bool = False,
save_profile: bool = False,
algorithm: Optional[str] = None,
iter: Optional[int] = None,
grad_samples: Optional[int] = None,
elbo_samples: Optional[int] = None,
eta: Optional[float] = None,
adapt_engaged: bool = True,
adapt_iter: Optional[int] = None,
tol_rel_obj: Optional[float] = None,
eval_elbo: Optional[int] = None,
draws: Optional[int] = None,
require_converged: bool = True,
show_console: bool = False,
refresh: Optional[int] = None,
time_fmt: str = "%Y%m%d%H%M%S",
timeout: Optional[float] = None,
*,
output_samples: Optional[int] = None,
) -> 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 :class:`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,
:func:`numpy.random.default_rng`
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 sig_figs: Numerical precision used for output CSV and text files.
Must be an integer between 1 and 18. If unspecified, the default
precision for the system file I/O is used; the usual value is 6.
Introduced in CmdStan-2.25.
:param save_latent_dynamics: Whether or not to save diagnostics.
If ``True``, CSV outputs are written to output file
'<model_name>-<YYYYMMDDHHMM>-diagnostic-<chain_id>',
e.g. 'bernoulli-201912081451-diagnostic-1.csv'.
:param save_profile: Whether or not to profile auto-diff operations in
labelled blocks of code. If ``True``, CSV outputs are written to
file '<model_name>-<YYYYMMDDHHMM>-profile-<chain_id>'.
Introduced in CmdStan-2.26.
: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.
Default is 10. If problems arise, try doubling current value.
:param elbo_samples: Number of MC draws for estimate of ELBO.
:param eta: Step size 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 draws: 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".
:param show_console: If ``True``, stream CmdStan messages sent to
stdout and stderr to the console. Default is ``False``.
:param refresh: Specify the number of iterations CmdStan will take
between progress messages. Default value is 100.
:param time_fmt: A format string passed to
:meth:`~datetime.datetime.strftime` to decide the file names for
output CSVs. Defaults to "%Y%m%d%H%M%S"
:param timeout: Duration at which variational Bayesian inference times
out in seconds.
:return: CmdStanVB object
"""
# TODO(2.0): remove
if output_samples is not None:
if draws is not None:
raise ValueError(
"Cannot supply both 'draws' and deprecated argument "
"'output_samples'"
)
get_logger().warning(
"Argument name `output_samples` is deprecated, please "
"rename to `draws`."
)
draws = output_samples
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=draws,
)
with temp_single_json(data) as _data, temp_inits(
inits, allow_multiple=False
) as _inits:
args = CmdStanArgs(
self._name,
self._exe_file,
chain_ids=None,
data=_data,
seed=seed,
inits=_inits,
output_dir=output_dir,
sig_figs=sig_figs,
save_latent_dynamics=save_latent_dynamics,
save_profile=save_profile,
method_args=variational_args,
refresh=refresh,
)
dummy_chain_id = 0
runset = RunSet(args=args, chains=1, time_fmt=time_fmt)
self._run_cmdstan(
runset,
dummy_chain_id,
show_console=show_console,
timeout=timeout,
)
runset.raise_for_timeouts()
# treat failure to converge as failure
transcript_file = runset.stdout_files[dummy_chain_id]
pat = re.compile(r'The algorithm may not have converged.', re.M)
with open(transcript_file, 'r') as transcript:
contents = transcript.read()
if len(re.findall(pat, contents)) > 0:
if require_converged:
raise RuntimeError(
'The algorithm may not have converged.\n'
'If you would like to inspect the output, '
're-call with require_converged=False'
)
# else:
get_logger().warning(
'%s\n%s',
'The algorithm may not have converged.',
'Proceeding because require_converged is set to False',
)
if not runset._check_retcodes():
transcript_file = runset.stdout_files[dummy_chain_id]
with open(transcript_file, 'r') as transcript:
contents = transcript.read()
pat = re.compile(
r'stan::variational::normal_meanfield::calc_grad:', re.M
)
if len(re.findall(pat, contents)) > 0:
if grad_samples is None:
grad_samples = 10
msg = (
'Variational algorithm gradient calculation failed. '
'Double the value of argument "grad_samples", '
'current value is {}.'.format(grad_samples)
)
else:
msg = 'Error during variational inference: {}'.format(
runset.get_err_msgs()
)
raise RuntimeError(msg)
# pylint: disable=invalid-name
vb = CmdStanVB(runset)
return vb
[docs] def pathfinder(
self,
data: Union[Mapping[str, Any], str, os.PathLike, None] = None,
*,
init_alpha: Optional[float] = None,
tol_obj: Optional[float] = None,
tol_rel_obj: Optional[float] = None,
tol_grad: Optional[float] = None,
tol_rel_grad: Optional[float] = None,
tol_param: Optional[float] = None,
history_size: Optional[int] = None,
num_paths: Optional[int] = None,
max_lbfgs_iters: Optional[int] = None,
draws: Optional[int] = None,
num_single_draws: Optional[int] = None,
num_elbo_draws: Optional[int] = None,
psis_resample: bool = True,
calculate_lp: bool = True,
# arguments standard to all methods
seed: Optional[int] = None,
inits: Union[Dict[str, float], float, str, os.PathLike, None] = None,
output_dir: OptionalPath = None,
sig_figs: Optional[int] = None,
save_profile: bool = False,
show_console: bool = False,
refresh: Optional[int] = None,
time_fmt: str = "%Y%m%d%H%M%S",
timeout: Optional[float] = None,
num_threads: Optional[int] = None,
) -> CmdStanPathfinder:
"""
Run CmdStan's Pathfinder variational inference algorithm.
: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 num_paths: Number of single-path Pathfinders to run.
Default is 4, when the number of paths is 1 then no importance
sampling is done.
:param draws: Number of approximate draws to return.
:param num_single_draws: Number of draws each single-pathfinder will
draw.
If ``num_paths`` is 1, only one of this and ``draws`` should be
used.
:param max_lbfgs_iters: Maximum number of L-BFGS iterations.
:param num_elbo_draws: Number of Monte Carlo draws to evaluate ELBO.
:param psis_resample: Whether or not to use Pareto Smoothed Importance
Sampling on the result of the individual Pathfinders. If False, the
result contains the draws from each path.
:param calculate_lp: Whether or not to calculate the log probability
for approximate draws. If False, this also implies that
``psis_resample`` will be set to False.
:param seed: The seed for random number generator. Must be an integer
between 0 and 2^32 - 1. If unspecified,
:func:`numpy.random.default_rng` is used to generate a seed.
:param inits: Specifies how the algorithm 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-path pathname to data file.
* list of dictionaries - per-path initial values.
:param init_alpha: For internal L-BFGS: Line search step size for
first iteration
:param tol_obj: For internal L-BFGS: Convergence tolerance on changes
in objective function value
:param tol_rel_obj: For internal L-BFGS: Convergence tolerance on
relative changes in objective function value
:param tol_grad: For internal L-BFGS: Convergence tolerance on the
norm of the gradient
:param tol_rel_grad: For internal L-BFGS: Convergence tolerance on
the relative norm of the gradient
:param tol_param: For internal L-BFGS: Convergence tolerance on changes
in parameter value
:param history_size: For internal L-BFGS: Size of the history for LBFGS
Hessian approximation. The value should be less than the
dimensionality of the parameter space. 5-10 is usually sufficient
: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 sig_figs: Numerical precision used for output CSV and text files.
Must be an integer between 1 and 18. If unspecified, the default
precision for the system file I/O is used; the usual value is 6.
Introduced in CmdStan-2.25.
:param save_profile: Whether or not to profile auto-diff operations in
labelled blocks of code. If ``True``, CSV outputs are written to
file '<model_name>-<YYYYMMDDHHMM>-profile-<path_id>'.
Introduced in CmdStan-2.26, see
https://mc-stan.org/docs/cmdstan-guide/stan_csv.html,
section "Profiling CSV output file" for details.
:param show_console: If ``True``, stream CmdStan messages sent to stdout
and stderr to the console. Default is ``False``.
:param refresh: Specify the number of iterations CmdStan will take
between progress messages. Default value is 100.
:param time_fmt: A format string passed to
:meth:`~datetime.datetime.strftime` to decide the file names for
output CSVs. Defaults to "%Y%m%d%H%M%S"
:param timeout: Duration at which Pathfinder times
out in seconds. Defaults to None.
:param num_threads: Number of threads to request for parallel execution.
A number other than ``1`` requires the model to have been compiled
with STAN_THREADS=True.
:return: A :class:`CmdStanPathfinder` object
References
----------
Zhang, L., Carpenter, B., Gelman, A., & Vehtari, A. (2022). Pathfinder:
Parallel quasi-Newton variational inference. Journal of Machine Learning
Research, 23(306), 1–49. Retrieved from
http://jmlr.org/papers/v23/21-0889.html
"""
exe_info = self.exe_info()
if cmdstan_version_before(2, 33, exe_info):
raise ValueError(
"Method 'pathfinder' not available for CmdStan versions "
"before 2.33"
)
if (not psis_resample or not calculate_lp) and cmdstan_version_before(
2, 34, exe_info
):
raise ValueError(
"Arguments 'psis_resample' and 'calculate_lp' are only "
"available for CmdStan versions 2.34 and later"
)
if num_threads is not None:
if (
num_threads != 1
and exe_info.get('STAN_THREADS', '').lower() != 'true'
):
raise ValueError(
"Model must be compiled with 'STAN_THREADS=true' to use"
" 'num_threads' argument"
)
os.environ['STAN_NUM_THREADS'] = str(num_threads)
if num_paths == 1:
if num_single_draws is None:
num_single_draws = draws
if draws is not None and num_single_draws != draws:
raise ValueError(
"Cannot specify both 'draws' and 'num_single_draws'"
" when 'num_paths' is 1"
)
pathfinder_args = PathfinderArgs(
init_alpha=init_alpha,
tol_obj=tol_obj,
tol_rel_obj=tol_rel_obj,
tol_grad=tol_grad,
tol_rel_grad=tol_rel_grad,
tol_param=tol_param,
history_size=history_size,
num_psis_draws=draws,
num_paths=num_paths,
max_lbfgs_iters=max_lbfgs_iters,
num_draws=num_single_draws,
num_elbo_draws=num_elbo_draws,
psis_resample=psis_resample,
calculate_lp=calculate_lp,
)
with temp_single_json(data) as _data, temp_inits(inits) as _inits:
args = CmdStanArgs(
self._name,
self._exe_file,
chain_ids=None,
data=_data,
seed=seed,
inits=_inits,
output_dir=output_dir,
sig_figs=sig_figs,
save_profile=save_profile,
method_args=pathfinder_args,
refresh=refresh,
)
dummy_chain_id = 0
runset = RunSet(args=args, chains=1, time_fmt=time_fmt)
self._run_cmdstan(
runset,
dummy_chain_id,
show_console=show_console,
timeout=timeout,
)
runset.raise_for_timeouts()
if not runset._check_retcodes():
msg = "Error during Pathfinder! Command '{}' failed: {}".format(
' '.join(runset.cmd(0)), runset.get_err_msgs()
)
raise RuntimeError(msg)
return CmdStanPathfinder(runset)
[docs] def log_prob(
self,
params: Union[Dict[str, Any], str, os.PathLike],
data: Union[Mapping[str, Any], str, os.PathLike, None] = None,
*,
jacobian: bool = True,
sig_figs: Optional[int] = None,
) -> pd.DataFrame:
"""
Calculate the log probability and gradient at the given parameter
values.
.. note:: This function is **NOT** an efficient way to evaluate the log
density of the model. It should be used for diagnostics ONLY.
Please, do not use this for other purposes such as testing new
sampling algorithms!
:param params: Values for all parameters in the model, specified
either as a dictionary with entries matching the parameter
variables, or as the path of a data file in JSON or Rdump format.
These should be given on the constrained (natural) scale.
: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 jacobian: Whether or not to enable the Jacobian adjustment
for constrained parameters. Defaults to ``True``.
:param sig_figs: Numerical precision used for output CSV and text files.
Must be an integer between 1 and 18. If unspecified, the default
precision for the system file I/O is used; the usual value is 6.
:return: A pandas.DataFrame containing columns "lp__" and additional
columns for the gradient values. These gradients will be for the
unconstrained parameters of the model.
"""
if cmdstan_version_before(2, 31, self.exe_info()):
raise ValueError(
"Method 'log_prob' not available for CmdStan versions "
"before 2.31"
)
with temp_single_json(data) as _data, temp_single_json(
params
) as _params:
cmd = [
str(self.exe_file),
"log_prob",
f"constrained_params={_params}",
f"jacobian={int(jacobian)}",
]
if _data is not None:
cmd += ["data", f"file={_data}"]
output_dir = tempfile.mkdtemp(prefix=self.name, dir=_TMPDIR)
output = os.path.join(output_dir, "output.csv")
cmd += ["output", f"file={output}"]
if sig_figs is not None:
cmd.append(f"sig_figs={sig_figs}")
get_logger().debug("Cmd: %s", str(cmd))
proc = subprocess.run(
cmd, capture_output=True, check=False, text=True
)
if proc.returncode:
get_logger().error(
"'log_prob' command failed!\nstdout:%s\nstderr:%s",
proc.stdout,
proc.stderr,
)
raise RuntimeError(
"Method 'log_prob' failed with return code "
+ str(proc.returncode)
)
result = pd.read_csv(output, comment="#")
return result
[docs] def laplace_sample(
self,
data: Union[Mapping[str, Any], str, os.PathLike, None] = None,
mode: Union[CmdStanMLE, str, os.PathLike, None] = None,
draws: Optional[int] = None,
*,
jacobian: bool = True, # NB: Different than optimize!
seed: Optional[int] = None,
output_dir: OptionalPath = None,
sig_figs: Optional[int] = None,
save_profile: bool = False,
show_console: bool = False,
refresh: Optional[int] = None,
time_fmt: str = "%Y%m%d%H%M%S",
timeout: Optional[float] = None,
opt_args: Optional[Dict[str, Any]] = None,
) -> CmdStanLaplace:
"""
Run a Laplace approximation around the posterior mode.
: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 mode: The mode around which to place the approximation, either
* A :class:`CmdStanMLE` object
* A path to a CSV file containing the output of an optimization run.
* ``None`` - use default optimizer settings and/or any ``opt_args``.
:param draws: Number of approximate draws to return.
Defaults to 1000
:param jacobian: Whether or not to enable the Jacobian adjustment
for constrained parameters. Defaults to ``True``.
Note: This must match the argument used in the creation of
``mode``, if supplied.
: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 sig_figs: Numerical precision used for output CSV and text files.
Must be an integer between 1 and 18. If unspecified, the default
precision for the system file I/O is used; the usual value is 6.
Introduced in CmdStan-2.25.
:param save_profile: Whether or not to profile auto-diff operations in
labelled blocks of code. If ``True``, CSV outputs are written to
file '<model_name>-<YYYYMMDDHHMM>-profile-<path_id>'.
Introduced in CmdStan-2.26, see
https://mc-stan.org/docs/cmdstan-guide/stan_csv.html,
section "Profiling CSV output file" for details.
:param show_console: If ``True``, stream CmdStan messages sent to stdout
and stderr to the console. Default is ``False``.
:param refresh: Specify the number of iterations CmdStan will take
between progress messages. Default value is 100.
:param time_fmt: A format string passed to
:meth:`~datetime.datetime.strftime` to decide the file names for
output CSVs. Defaults to "%Y%m%d%H%M%S"
:param timeout: Duration at which Pathfinder times
out in seconds. Defaults to None.
:param opt_args: Dictionary of additional arguments
which will be passed to :meth:`~CmdStanModel.optimize`
:return: A :class:`CmdStanLaplace` object.
"""
if cmdstan_version_before(2, 32, self.exe_info()):
raise ValueError(
"Method 'laplace_sample' not available for CmdStan versions "
"before 2.32"
)
if opt_args is not None and mode is not None:
raise ValueError(
"Cannot specify both 'opt_args' and 'mode' arguments"
)
if mode is None:
optimize_args = {
"seed": seed,
"sig_figs": sig_figs,
"jacobian": jacobian,
"save_profile": save_profile,
"show_console": show_console,
"refresh": refresh,
"time_fmt": time_fmt,
"timeout": timeout,
"output_dir": output_dir,
}
optimize_args.update(opt_args or {})
optimize_args['time_fmt'] = 'opt-' + time_fmt
try:
cmdstan_mode: CmdStanMLE = self.optimize(
data=data,
**optimize_args, # type: ignore
)
except Exception as e:
raise RuntimeError(
"Failed to run optimizer on model. "
"Consider supplying a mode or additional optimizer args"
) from e
elif not isinstance(mode, CmdStanMLE):
cmdstan_mode = from_csv(mode) # type: ignore # we check below
else:
cmdstan_mode = mode
if cmdstan_mode.runset.method != Method.OPTIMIZE:
raise ValueError(
"Mode must be a CmdStanMLE or a path to an optimize CSV"
)
mode_jacobian = (
cmdstan_mode.runset._args.method_args.jacobian # type: ignore
)
if mode_jacobian != jacobian:
raise ValueError(
"Jacobian argument to optimize and laplace must match!\n"
f"Laplace was run with jacobian={jacobian},\n"
f"but optimize was run with jacobian={mode_jacobian}"
)
laplace_args = LaplaceArgs(
cmdstan_mode.runset.csv_files[0], draws, jacobian
)
with temp_single_json(data) as _data:
args = CmdStanArgs(
self._name,
self._exe_file,
chain_ids=None,
data=_data,
seed=seed,
output_dir=output_dir,
sig_figs=sig_figs,
save_profile=save_profile,
method_args=laplace_args,
refresh=refresh,
)
dummy_chain_id = 0
runset = RunSet(args=args, chains=1, time_fmt=time_fmt)
self._run_cmdstan(
runset,
dummy_chain_id,
show_console=show_console,
timeout=timeout,
)
runset.raise_for_timeouts()
return CmdStanLaplace(runset, cmdstan_mode)
def _run_cmdstan(
self,
runset: RunSet,
idx: int,
show_progress: bool = False,
show_console: bool = False,
progress_hook: Optional[Callable[[str, int], None]] = None,
timeout: Optional[float] = None,
) -> None:
"""
Helper function which encapsulates call to CmdStan.
Uses subprocess POpen object to run the process.
Records stdout, stderr messages, and process returncode.
Args 'show_progress' and 'show_console' allow use of progress bar,
streaming output to console, respectively.
"""
get_logger().debug('idx %d', idx)
get_logger().debug(
'running CmdStan, num_threads: %s',
str(os.environ.get('STAN_NUM_THREADS')),
)
logger_prefix = 'CmdStan'
console_prefix = ''
if runset.one_process_per_chain:
logger_prefix = 'Chain [{}]'.format(runset.chain_ids[idx])
console_prefix = 'Chain [{}] '.format(runset.chain_ids[idx])
cmd = runset.cmd(idx)
get_logger().debug('CmdStan args: %s', cmd)
if not show_progress:
get_logger().info('%s start processing', logger_prefix)
try:
fd_out = open(runset.stdout_files[idx], 'w')
proc = subprocess.Popen(
cmd,
bufsize=1,
stdin=subprocess.DEVNULL,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT, # avoid buffer overflow
env=os.environ,
universal_newlines=True,
)
timer: Optional[threading.Timer]
if timeout:
def _timer_target() -> None:
# Abort if the process has already terminated.
if proc.poll() is not None:
return
proc.terminate()
runset._set_timeout_flag(idx, True)
timer = threading.Timer(timeout, _timer_target)
timer.daemon = True
timer.start()
else:
timer = None
while proc.poll() is None:
if proc.stdout is not None:
line = proc.stdout.readline()
fd_out.write(line)
line = line.strip()
if show_console:
print(f'{console_prefix}{line}')
elif progress_hook is not None:
progress_hook(line, idx)
stdout, _ = proc.communicate()
retcode = proc.returncode
runset._set_retcode(idx, retcode)
if timer:
timer.cancel()
if stdout:
fd_out.write(stdout)
if show_console:
lines = stdout.split('\n')
for line in lines:
print(f'{console_prefix}{line}')
fd_out.close()
except OSError as e:
msg = 'Failed with error {}\n'.format(str(e))
raise RuntimeError(msg) from e
finally:
fd_out.close()
if not show_progress:
get_logger().info('%s done processing', logger_prefix)
if retcode != 0:
retcode_summary = returncode_msg(retcode)
serror = ''
try:
serror = os.strerror(retcode)
except (ArithmeticError, ValueError):
pass
get_logger().error(
'%s error: %s %s', logger_prefix, retcode_summary, serror
)
@staticmethod
@progbar.wrap_callback
def _wrap_sampler_progress_hook(
chain_ids: List[int],
total: int,
) -> Optional[Callable[[str, int], None]]:
"""
Sets up tqdm callback for CmdStan sampler console msgs.
CmdStan progress messages start with "Iteration", for single chain
process, "Chain [id] Iteration" for multi-chain processing.
For the latter, manage array of pbars, update accordingly.
"""
pat = re.compile(r'Chain \[(\d*)\] (Iteration.*)')
pbars: Dict[int, tqdm] = {
chain_id: tqdm(
total=total,
bar_format="{desc} |{bar}| {elapsed} {postfix[0][value]}",
postfix=[{"value": "Status"}],
desc=f'chain {chain_id}',
colour='yellow',
)
for chain_id in chain_ids
}
def progress_hook(line: str, idx: int) -> None:
if line == "Done":
for pbar in pbars.values():
pbar.postfix[0]["value"] = 'Sampling completed'
pbar.update(total - pbar.n)
pbar.close()
else:
match = pat.match(line)
if match:
idx = int(match.group(1))
mline = match.group(2).strip()
elif line.startswith("Iteration"):
mline = line
idx = chain_ids[idx]
else:
return
if 'Sampling' in mline:
pbars[idx].colour = 'blue'
pbars[idx].update(1)
pbars[idx].postfix[0]["value"] = mline
return progress_hook