self._compiler_options.add_include_path(path)
# try to detect models w/out parameters, needed for sampler
- if not cmdstan_version_before(
- 2, 27
- ): # unknown end of version range
+ if (not cmdstan_version_before(2, 27)) and cmdstan_version_before(
+ 2, 36
+ ):
try:
model_info = self.src_info()
if 'parameters' in model_info:
diff --git a/docs/_modules/cmdstanpy/stanfit.html b/docs/_modules/cmdstanpy/stanfit.html
index 0a305973..a55746b7 100644
--- a/docs/_modules/cmdstanpy/stanfit.html
+++ b/docs/_modules/cmdstanpy/stanfit.html
@@ -5,7 +5,7 @@
- cmdstanpy.stanfit — CmdStanPy 1.2.4 documentation
+ cmdstanpy.stanfit — CmdStanPy 1.2.5 documentation
@@ -61,7 +61,7 @@
diff --git a/docs/_modules/cmdstanpy/stanfit/gq.html b/docs/_modules/cmdstanpy/stanfit/gq.html
index 6ac85ff6..7c353d60 100644
--- a/docs/_modules/cmdstanpy/stanfit/gq.html
+++ b/docs/_modules/cmdstanpy/stanfit/gq.html
@@ -5,7 +5,7 @@
- cmdstanpy.stanfit.gq — CmdStanPy 1.2.4 documentation
+ cmdstanpy.stanfit.gq — CmdStanPy 1.2.5 documentation
@@ -61,7 +61,7 @@
diff --git a/docs/_modules/cmdstanpy/stanfit/laplace.html b/docs/_modules/cmdstanpy/stanfit/laplace.html
index c3345722..2960b1ce 100644
--- a/docs/_modules/cmdstanpy/stanfit/laplace.html
+++ b/docs/_modules/cmdstanpy/stanfit/laplace.html
@@ -5,7 +5,7 @@
- cmdstanpy.stanfit.laplace — CmdStanPy 1.2.4 documentation
+ cmdstanpy.stanfit.laplace — CmdStanPy 1.2.5 documentation
@@ -61,7 +61,7 @@
diff --git a/docs/_modules/cmdstanpy/stanfit/mcmc.html b/docs/_modules/cmdstanpy/stanfit/mcmc.html
index c60d0ad0..2e3186fe 100644
--- a/docs/_modules/cmdstanpy/stanfit/mcmc.html
+++ b/docs/_modules/cmdstanpy/stanfit/mcmc.html
@@ -5,7 +5,7 @@
- cmdstanpy.stanfit.mcmc — CmdStanPy 1.2.4 documentation
+ cmdstanpy.stanfit.mcmc — CmdStanPy 1.2.5 documentation
@@ -61,7 +61,7 @@
@@ -586,7 +586,7 @@ Source code for cmdstanpy.stanfit.mcmc
self._step_size[chain] = float(step_size.strip())
if self._metadata.cmdstan_config['metric'] != 'unit_e':
line = fd.readline().strip() # metric type
- line = fd.readline().lstrip(' #\t')
+ line = fd.readline().lstrip(' #\t').rstrip()
num_unconstrained_params = len(line.split(','))
if chain == 0: # can't allocate w/o num params
if self.metric_type == 'diag_e':
@@ -603,18 +603,21 @@ Source code for cmdstanpy.stanfit.mcmc
),
dtype=float,
)
- if self.metric_type == 'diag_e':
- xs = line.split(',')
- self._metric[chain, :] = [float(x) for x in xs]
- else:
- xs = line.split(',')
- self._metric[chain, 0, :] = [float(x) for x in xs]
- for i in range(1, num_unconstrained_params):
- line = fd.readline().lstrip(' #\t').strip()
+ if line:
+ if self.metric_type == 'diag_e':
xs = line.split(',')
- self._metric[chain, i, :] = [
+ self._metric[chain, :] = [float(x) for x in xs]
+ else:
+ xs = line.strip().split(',')
+ self._metric[chain, 0, :] = [
float(x) for x in xs
]
+ for i in range(1, num_unconstrained_params):
+ line = fd.readline().lstrip(' #\t').rstrip()
+ xs = line.split(',')
+ self._metric[chain, i, :] = [
+ float(x) for x in xs
+ ]
else: # unit_e changed in 2.34 to have an extra line
pos = fd.tell()
line = fd.readline().strip()
diff --git a/docs/_modules/cmdstanpy/stanfit/metadata.html b/docs/_modules/cmdstanpy/stanfit/metadata.html
index 0f97f42c..369f414a 100644
--- a/docs/_modules/cmdstanpy/stanfit/metadata.html
+++ b/docs/_modules/cmdstanpy/stanfit/metadata.html
@@ -5,7 +5,7 @@
- cmdstanpy.stanfit.metadata — CmdStanPy 1.2.4 documentation
+ cmdstanpy.stanfit.metadata — CmdStanPy 1.2.5 documentation
@@ -61,7 +61,7 @@
diff --git a/docs/_modules/cmdstanpy/stanfit/mle.html b/docs/_modules/cmdstanpy/stanfit/mle.html
index ae38b763..848cc738 100644
--- a/docs/_modules/cmdstanpy/stanfit/mle.html
+++ b/docs/_modules/cmdstanpy/stanfit/mle.html
@@ -5,7 +5,7 @@
- cmdstanpy.stanfit.mle — CmdStanPy 1.2.4 documentation
+ cmdstanpy.stanfit.mle — CmdStanPy 1.2.5 documentation
@@ -61,7 +61,7 @@
diff --git a/docs/_modules/cmdstanpy/stanfit/pathfinder.html b/docs/_modules/cmdstanpy/stanfit/pathfinder.html
index 2a932c1a..ecb0b175 100644
--- a/docs/_modules/cmdstanpy/stanfit/pathfinder.html
+++ b/docs/_modules/cmdstanpy/stanfit/pathfinder.html
@@ -5,7 +5,7 @@
- cmdstanpy.stanfit.pathfinder — CmdStanPy 1.2.4 documentation
+ cmdstanpy.stanfit.pathfinder — CmdStanPy 1.2.5 documentation
@@ -61,7 +61,7 @@
diff --git a/docs/_modules/cmdstanpy/stanfit/runset.html b/docs/_modules/cmdstanpy/stanfit/runset.html
index 325fc953..a6245077 100644
--- a/docs/_modules/cmdstanpy/stanfit/runset.html
+++ b/docs/_modules/cmdstanpy/stanfit/runset.html
@@ -5,7 +5,7 @@
- cmdstanpy.stanfit.runset — CmdStanPy 1.2.4 documentation
+ cmdstanpy.stanfit.runset — CmdStanPy 1.2.5 documentation
@@ -61,7 +61,7 @@
diff --git a/docs/_modules/cmdstanpy/stanfit/vb.html b/docs/_modules/cmdstanpy/stanfit/vb.html
index 413b3bcc..9d095e6a 100644
--- a/docs/_modules/cmdstanpy/stanfit/vb.html
+++ b/docs/_modules/cmdstanpy/stanfit/vb.html
@@ -5,7 +5,7 @@
- cmdstanpy.stanfit.vb — CmdStanPy 1.2.4 documentation
+ cmdstanpy.stanfit.vb — CmdStanPy 1.2.5 documentation
@@ -61,7 +61,7 @@
diff --git a/docs/_modules/cmdstanpy/utils.html b/docs/_modules/cmdstanpy/utils.html
index 02f913ce..2bfb937d 100644
--- a/docs/_modules/cmdstanpy/utils.html
+++ b/docs/_modules/cmdstanpy/utils.html
@@ -5,7 +5,7 @@
- cmdstanpy.utils — CmdStanPy 1.2.4 documentation
+ cmdstanpy.utils — CmdStanPy 1.2.5 documentation
@@ -61,7 +61,7 @@
diff --git a/docs/_modules/cmdstanpy/utils/cmdstan.html b/docs/_modules/cmdstanpy/utils/cmdstan.html
index d986c878..6146ec75 100644
--- a/docs/_modules/cmdstanpy/utils/cmdstan.html
+++ b/docs/_modules/cmdstanpy/utils/cmdstan.html
@@ -5,7 +5,7 @@
- cmdstanpy.utils.cmdstan — CmdStanPy 1.2.4 documentation
+ cmdstanpy.utils.cmdstan — CmdStanPy 1.2.5 documentation
@@ -61,7 +61,7 @@
diff --git a/docs/_modules/index.html b/docs/_modules/index.html
index bb9ceb7f..3665c474 100644
--- a/docs/_modules/index.html
+++ b/docs/_modules/index.html
@@ -5,7 +5,7 @@
- Overview: module code — CmdStanPy 1.2.4 documentation
+ Overview: module code — CmdStanPy 1.2.5 documentation
@@ -61,7 +61,7 @@
diff --git a/docs/_modules/stanio/json.html b/docs/_modules/stanio/json.html
index 425cbb5b..d0d63a18 100644
--- a/docs/_modules/stanio/json.html
+++ b/docs/_modules/stanio/json.html
@@ -5,7 +5,7 @@
- stanio.json — CmdStanPy 1.2.4 documentation
+ stanio.json — CmdStanPy 1.2.5 documentation
@@ -61,7 +61,7 @@
@@ -180,7 +180,7 @@ Source code for stanio.json
uj_version = tuple(map(int, json.__version__.split(".")))
if uj_version < (5, 5, 0):
- raise ImportError("ujson version too old")
+ raise ImportError("ujson version too old") # pragma: no cover
UJSON_AVAILABLE = True
except:
UJSON_AVAILABLE = False
@@ -220,7 +220,7 @@ Source code for stanio.json
if numpy_val.dtype.kind in "iuf":
return numpy_val.tolist()
if numpy_val.dtype.kind == "c":
- return np.stack([numpy_val.real, numpy_val.imag], axis=-1).tolist()
+ return np.stack([np.asarray(numpy_val.real), np.asarray(numpy_val.imag)], axis=-1).tolist()
if numpy_val.dtype.kind == "b":
return numpy_val.astype(int).tolist()
diff --git a/docs/_sources/changes.rst.txt b/docs/_sources/changes.rst.txt
index 8427effa..34f0d70e 100644
--- a/docs/_sources/changes.rst.txt
+++ b/docs/_sources/changes.rst.txt
@@ -7,6 +7,13 @@ What's New
For full changes, see the `Releases page `_ on GitHub.
+CmdStanPy 1.2.5
+---------------
+
+- Fixed issues that arose when running a model with no parameters using CmdStan 2.36.0+
+
+Reminder: The next non-bugfix release of CmdStanPy will be version 2.0, which will remove all existing deprecations.
+
CmdStanPy 1.2.4
---------------
diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js
index e5749d0d..d1b7d71c 100644
--- a/docs/_static/documentation_options.js
+++ b/docs/_static/documentation_options.js
@@ -1,6 +1,6 @@
var DOCUMENTATION_OPTIONS = {
URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
- VERSION: '1.2.4',
+ VERSION: '1.2.5',
LANGUAGE: 'en',
COLLAPSE_INDEX: false,
BUILDER: 'html',
diff --git a/docs/api.html b/docs/api.html
index a64284b4..6ff0ec1d 100644
--- a/docs/api.html
+++ b/docs/api.html
@@ -6,7 +6,7 @@
- API Reference — CmdStanPy 1.2.4 documentation
+ API Reference — CmdStanPy 1.2.5 documentation
@@ -64,7 +64,7 @@
@@ -1360,27 +1360,27 @@ CmdStanModel
Parameters:
-model_name (Optional[str]) – Model name, used for output file names.
+
model_name (Optional[str]) – 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.
stan_file (Optional[Union[str, PathLike]]) – Path to Stan program file.
exe_file (Optional[Union[str, PathLike]]) – Path to compiled executable file. Optional, unless
+
stan_file (Optional[Union[str, PathLike]]) – Path to Stan program file.
exe_file (Optional[Union[str, PathLike]]) – 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).
force_compile (bool) – If True, always compile, even if there
+
force_compile (bool) – If True, always compile, even if there
is an existing executable file for this model.
stanc_options (Optional[Dict[str, Any]]) – Options for stanc compiler, specified as a Python
+
stanc_options (Optional[Dict[str, Any]]) – Options for stanc compiler, specified as a Python
dictionary containing Stanc3 compiler option name, value pairs.
Optional.
cpp_options (Optional[Dict[str, Any]]) – Options for C++ compiler, specified as a Python
+
cpp_options (Optional[Dict[str, Any]]) – Options for C++ compiler, specified as a Python
dictionary containing C++ compiler option name, value pairs.
Optional.
user_header (Optional[Union[str, PathLike]]) – A path to a header file to include during C++
+
user_header (Optional[Union[str, PathLike]]) – A path to a header file to include during C++
compilation.
Optional.
compile (Optional[Union[bool, Literal['force']]]) – Whether or not to compile the model. Default is True.
+
compile (Optional[Union[bool, Literal['force']]]) – 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
@@ -1394,7 +1394,7 @@
CmdStanModel
- Return type:
--
+
-
@@ -1413,17 +1413,17 @@ CmdStanModel
- Parameters:
-force (bool) – When True, always compile, even if the executable file
+
force (bool) – 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.
stanc_options (Optional[Dict[str, Any]]) – Options for stanc compiler.
cpp_options (Optional[Dict[str, Any]]) – Options for C++ compiler.
user_header (Optional[Union[str, PathLike]]) – A path to a header file to include during C++
+
stanc_options (Optional[Dict[str, Any]]) – Options for stanc compiler.
cpp_options (Optional[Dict[str, Any]]) – Options for C++ compiler.
user_header (Optional[Union[str, PathLike]]) – A path to a header file to include during C++
compilation.
override_options (bool) – When True, override existing option.
+
override_options (bool) – When True, override existing option.
When False, add/replace existing options. Default is False.
_internal (bool) –
_internal (bool) –
- Return type:
@@ -1441,7 +1441,7 @@ CmdStanModel
- Return type:
--
+
-
@@ -1455,15 +1455,15 @@ CmdStanModel
- Parameters:
-overwrite_file (bool) – If True, save the updated code to disk, rather
+
overwrite_file (bool) – If True, save the updated code to disk, rather
than printing it. By default False
canonicalize (Union[bool, str, Iterable[str]]) – Whether or not the compiler should ‘canonicalize’
+
canonicalize (Union[bool, str, Iterable[str]]) – Whether or not the compiler should ‘canonicalize’
the Stan model, removing things like deprecated syntax. Default is
False. If True, all canonicalizations are run. If it is a list of
strings, those options are passed to stanc (new in Stan 2.29)
max_line_length (int) – Set the wrapping point for the formatter. The
+
max_line_length (int) – Set the wrapping point for the formatter. The
default value is 78, which wraps most lines by the 80th character.
backup (bool) – If True, create a stanfile.bak backup before
+
backup (bool) – If True, create a stanfile.bak backup before
writing to the file. Only disable this if you’re sure you have other
copies of the file or are using a version control system like Git.
@@ -1498,37 +1498,37 @@ CmdStanModel
- Parameters:
-data (Optional[Union[Mapping[str, Any], str, PathLike]]) – Values for all data variables in the model, specified
+
data (Optional[Union[Mapping[str, Any], str, PathLike]]) – 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.
previous_fit (Optional[Union[Fit, List[str]]]) – Can be either a CmdStanMCMC,
+
previous_fit (Optional[Union[Fit, List[str]]]) – Can be either a CmdStanMCMC,
CmdStanMLE, or CmdStanVB or a list of
stan-csv files generated by fitting the model to the data
using any Stan interface.
seed (Optional[int]) – The seed for random number generator. Must be an integer
+
seed (Optional[int]) – The seed for random number generator. Must be an integer
between 0 and 2^32 - 1. If unspecified,
-numpy.random.default_rng()
+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.
gq_output_dir (Optional[Union[str, PathLike]]) – Name of the directory in which the CmdStan output
+
gq_output_dir (Optional[Union[str, PathLike]]) – 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.
sig_figs (Optional[int]) – Numerical precision used for output CSV and text files.
+
sig_figs (Optional[int]) – 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.
show_console (bool) – If True, stream CmdStan messages sent to
+
show_console (bool) – If True, stream CmdStan messages sent to
stdout and stderr to the console. Default is False.
refresh (Optional[int]) – Specify the number of iterations CmdStan will take
+
refresh (Optional[int]) – Specify the number of iterations CmdStan will take
between progress messages. Default value is 100.
time_fmt (str) – A format string passed to
-strftime() to decide the file names for
+
time_fmt (str) – A format string passed to
+strftime() to decide the file names for
output CSVs. Defaults to “%Y%m%d%H%M%S”
timeout (Optional[float]) – Duration at which generation times out in seconds.
mcmc_sample (Optional[Union[CmdStanMCMC, List[str]]]) –
timeout (Optional[float]) – Duration at which generation times out in seconds.
mcmc_sample (Optional[Union[CmdStanMCMC, List[str]]]) –
- Returns:
@@ -1547,47 +1547,47 @@ CmdStanModel
- Parameters:
-data (Optional[Union[Mapping[str, Any], str, PathLike]]) – Values for all data variables in the model, specified
+
data (Optional[Union[Mapping[str, Any], str, PathLike]]) – 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.
mode (Optional[Union[CmdStanMLE, str, PathLike]]) –
The mode around which to place the approximation, either
+mode (Optional[Union[CmdStanMLE, str, PathLike]]) –
The mode around which to place the approximation, either
A 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.
draws (Optional[int]) – Number of approximate draws to return.
+
draws (Optional[int]) – Number of approximate draws to return.
Defaults to 1000
jacobian (bool) – Whether or not to enable the Jacobian adjustment
+
jacobian (bool) – 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.
output_dir (Optional[Union[str, PathLike]]) – Name of the directory to which CmdStan output
+
output_dir (Optional[Union[str, PathLike]]) – 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.
sig_figs (Optional[int]) – Numerical precision used for output CSV and text files.
+
sig_figs (Optional[int]) – 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.
save_profile (bool) – Whether or not to profile auto-diff operations in
+
save_profile (bool) – 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.
show_console (bool) – If True, stream CmdStan messages sent to stdout
+
show_console (bool) – If True, stream CmdStan messages sent to stdout
and stderr to the console. Default is False.
refresh (Optional[int]) – Specify the number of iterations CmdStan will take
+
refresh (Optional[int]) – Specify the number of iterations CmdStan will take
between progress messages. Default value is 100.
time_fmt (str) – A format string passed to
-strftime() to decide the file names for
+
time_fmt (str) – A format string passed to
+strftime() to decide the file names for
output CSVs. Defaults to “%Y%m%d%H%M%S”
timeout (Optional[float]) – Duration at which Pathfinder times
+
timeout (Optional[float]) – Duration at which Pathfinder times
out in seconds. Defaults to None.
opt_args (Optional[Dict[str, Any]]) – Dictionary of additional arguments
+
opt_args (Optional[Dict[str, Any]]) – Dictionary of additional arguments
which will be passed to optimize()
- Returns:
@@ -1614,17 +1614,17 @@ CmdStanModel
- Parameters:
-params (Union[Dict[str, Any], str, PathLike]) –
Values for all parameters in the model, specified
+
params (Union[Dict[str, Any], str, PathLike]) –
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.
data (Optional[Union[Mapping[str, Any], str, PathLike]]) – Values for all data variables in the model, specified
+
data (Optional[Union[Mapping[str, Any], str, PathLike]]) – 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.
jacobian (bool) – Whether or not to enable the Jacobian adjustment
+
jacobian (bool) – Whether or not to enable the Jacobian adjustment
for constrained parameters. Defaults to True.
sig_figs (Optional[int]) – Numerical precision used for output CSV and text files.
+
sig_figs (Optional[int]) – 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.
@@ -1635,7 +1635,7 @@ CmdStanModelReturn type:
--
+
-
@@ -1664,13 +1664,13 @@ CmdStanModel
- Parameters:
-data (Optional[Union[Mapping[str, Any], str, PathLike]]) – Values for all data variables in the model, specified
+
data (Optional[Union[Mapping[str, Any], str, PathLike]]) – 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.
seed (Optional[int]) – The seed for random number generator. Must be an integer
+
seed (Optional[int]) – The seed for random number generator. Must be an integer
between 0 and 2^32 - 1. If unspecified,
-numpy.random.default_rng() is used to generate a seed.
inits (Optional[Union[Mapping[str, Any], float, str, PathLike]]) –
Specifies how the sampler initializes parameter values.
+numpy.random.default_rng() is used to generate a seed.
inits (Optional[Union[Mapping[str, Any], float, str, PathLike]]) –
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
@@ -1685,44 +1685,44 @@
CmdStanModelOptional[Union[str, PathLike]]) – Name of the directory to which CmdStan output
+
output_dir (Optional[Union[str, PathLike]]) – 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.
sig_figs (Optional[int]) – Numerical precision used for output CSV and text files.
+
sig_figs (Optional[int]) – 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.
save_profile (bool) – Whether or not to profile auto-diff operations in
+
save_profile (bool) – 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.
algorithm (Optional[str]) – Algorithm to use. One of: ‘BFGS’, ‘LBFGS’, ‘Newton’
init_alpha (Optional[float]) – Line search step size for first iteration
tol_obj (Optional[float]) – Convergence tolerance on changes in objective
+
algorithm (Optional[str]) – Algorithm to use. One of: ‘BFGS’, ‘LBFGS’, ‘Newton’
init_alpha (Optional[float]) – Line search step size for first iteration
tol_obj (Optional[float]) – Convergence tolerance on changes in objective
function value
tol_rel_obj (Optional[float]) – Convergence tolerance on relative changes
+
tol_rel_obj (Optional[float]) – Convergence tolerance on relative changes
in objective function value
tol_grad (Optional[float]) – Convergence tolerance on the norm of the gradient
tol_rel_grad (Optional[float]) – Convergence tolerance on the relative
+
tol_grad (Optional[float]) – Convergence tolerance on the norm of the gradient
tol_rel_grad (Optional[float]) – Convergence tolerance on the relative
norm of the gradient
tol_param (Optional[float]) – Convergence tolerance on changes in parameter value
history_size (Optional[int]) – Size of the history for LBFGS Hessian
+
tol_param (Optional[float]) – Convergence tolerance on changes in parameter value
history_size (Optional[int]) – Size of the history for LBFGS Hessian
approximation. The value should be less than the dimensionality
of the parameter space. 5-10 usually sufficient
save_iterations (bool) – When True, save intermediate approximations
+
save_iterations (bool) – When True, save intermediate approximations
to the output CSV file. Default is False.
require_converged (bool) – Whether or not to raise an error if Stan
+
require_converged (bool) – Whether or not to raise an error if Stan
reports that “The algorithm may not have converged”.
show_console (bool) – If True, stream CmdStan messages sent to
+
show_console (bool) – If True, stream CmdStan messages sent to
stdout and stderr to the console. Default is False.
refresh (Optional[int]) – Specify the number of iterations cmdstan will take
+
refresh (Optional[int]) – Specify the number of iterations cmdstan will take
between progress messages. Default value is 100.
time_fmt (str) – A format string passed to
-strftime() to decide the file names for
+
time_fmt (str) – A format string passed to
+strftime() to decide the file names for
output CSVs. Defaults to “%Y%m%d%H%M%S”
timeout (Optional[float]) – Duration at which optimization times out in seconds.
jacobian (bool) – Whether or not to use the Jacobian adjustment for
+
timeout (Optional[float]) – Duration at which optimization times out in seconds.
jacobian (bool) – 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).
CmdStanModel
- Parameters:
-data (Optional[Union[Mapping[str, Any], str, PathLike]]) – Values for all data variables in the model, specified
+
data (Optional[Union[Mapping[str, Any], str, PathLike]]) – 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.
num_paths (Optional[int]) – Number of single-path Pathfinders to run.
+
num_paths (Optional[int]) – Number of single-path Pathfinders to run.
Default is 4, when the number of paths is 1 then no importance
sampling is done.
draws (Optional[int]) – Number of approximate draws to return.
num_single_draws (Optional[int]) – Number of draws each single-pathfinder will
+
draws (Optional[int]) – Number of approximate draws to return.
num_single_draws (Optional[int]) – Number of draws each single-pathfinder will
draw.
If num_paths is 1, only one of this and draws should be
used.
max_lbfgs_iters (Optional[int]) – Maximum number of L-BFGS iterations.
num_elbo_draws (Optional[int]) – Number of Monte Carlo draws to evaluate ELBO.
psis_resample (bool) – Whether or not to use Pareto Smoothed Importance
+
max_lbfgs_iters (Optional[int]) – Maximum number of L-BFGS iterations.
num_elbo_draws (Optional[int]) – Number of Monte Carlo draws to evaluate ELBO.
psis_resample (bool) – 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.
calculate_lp (bool) – Whether or not to calculate the log probability
+
calculate_lp (bool) – Whether or not to calculate the log probability
for approximate draws. If False, this also implies that
psis_resample will be set to False.
seed (Optional[int]) – The seed for random number generator. Must be an integer
+
seed (Optional[int]) – The seed for random number generator. Must be an integer
between 0 and 2^32 - 1. If unspecified,
-numpy.random.default_rng() is used to generate a seed.
inits (Optional[Union[Dict[str, float], float, str, PathLike]]) –
Specifies how the algorithm initializes parameter values.
+numpy.random.default_rng() is used to generate a seed.
inits (Optional[Union[Dict[str, float], float, str, PathLike]]) –
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
@@ -1783,44 +1783,44 @@
CmdStanModelOptional[float]) – For internal L-BFGS: Line search step size for
+
init_alpha (Optional[float]) – For internal L-BFGS: Line search step size for
first iteration
tol_obj (Optional[float]) – For internal L-BFGS: Convergence tolerance on changes
+
tol_obj (Optional[float]) – For internal L-BFGS: Convergence tolerance on changes
in objective function value
tol_rel_obj (Optional[float]) – For internal L-BFGS: Convergence tolerance on
+
tol_rel_obj (Optional[float]) – For internal L-BFGS: Convergence tolerance on
relative changes in objective function value
tol_grad (Optional[float]) – For internal L-BFGS: Convergence tolerance on the
+
tol_grad (Optional[float]) – For internal L-BFGS: Convergence tolerance on the
norm of the gradient
tol_rel_grad (Optional[float]) – For internal L-BFGS: Convergence tolerance on
+
tol_rel_grad (Optional[float]) – For internal L-BFGS: Convergence tolerance on
the relative norm of the gradient
tol_param (Optional[float]) – For internal L-BFGS: Convergence tolerance on changes
+
tol_param (Optional[float]) – For internal L-BFGS: Convergence tolerance on changes
in parameter value
history_size (Optional[int]) – For internal L-BFGS: Size of the history for LBFGS
+
history_size (Optional[int]) – 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
output_dir (Optional[Union[str, PathLike]]) – Name of the directory to which CmdStan output
+
output_dir (Optional[Union[str, PathLike]]) – 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.
sig_figs (Optional[int]) – Numerical precision used for output CSV and text files.
+
sig_figs (Optional[int]) – 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.
save_profile (bool) – Whether or not to profile auto-diff operations in
+
save_profile (bool) – 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.
show_console (bool) – If True, stream CmdStan messages sent to stdout
+
show_console (bool) – If True, stream CmdStan messages sent to stdout
and stderr to the console. Default is False.
refresh (Optional[int]) – Specify the number of iterations CmdStan will take
+
refresh (Optional[int]) – Specify the number of iterations CmdStan will take
between progress messages. Default value is 100.
time_fmt (str) – A format string passed to
-strftime() to decide the file names for
+
time_fmt (str) – A format string passed to
+strftime() to decide the file names for
output CSVs. Defaults to “%Y%m%d%H%M%S”
timeout (Optional[float]) – Duration at which Pathfinder times
+
timeout (Optional[float]) – Duration at which Pathfinder times
out in seconds. Defaults to None.
num_threads (Optional[int]) – Number of threads to request for parallel execution.
+
num_threads (Optional[int]) – Number of threads to request for parallel execution.
A number other than 1 requires the model to have been compiled
with STAN_THREADS=True.
@@ -1864,17 +1864,17 @@ CmdStanModel
- Parameters:
-data (Optional[Union[Mapping[str, Any], str, PathLike]]) – Values for all data variables in the model, specified
+
data (Optional[Union[Mapping[str, Any], str, PathLike]]) – 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.
chains (Optional[int]) – Number of sampler chains, must be a positive integer.
parallel_chains (Optional[int]) – Number of processes to run in parallel. Must be
-a positive integer. Defaults to multiprocessing.cpu_count(),
+
chains (Optional[int]) – Number of sampler chains, must be a positive integer.
parallel_chains (Optional[int]) – Number of processes to run in parallel. Must be
+a positive integer. Defaults to 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.
threads_per_chain (Optional[int]) – The number of threads to use in parallelized
+
threads_per_chain (Optional[int]) – 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,
@@ -1882,16 +1882,16 @@
CmdStanModelparallel_chains * threads_per_chain, where the default
value for parallel_chains is the number of cpus, not chains.
seed (Optional[Union[int, List[int]]]) – The seed for random number generator. Must be an integer
+
seed (Optional[Union[int, List[int]]]) – The seed for random number generator. Must be an integer
between 0 and 2^32 - 1. If unspecified,
-numpy.random.default_rng()
+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.
chain_ids (Optional[Union[int, List[int]]]) – The offset for the random number generator, either
+
chain_ids (Optional[Union[int, List[int]]]) – 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.
inits (Optional[Union[Mapping[str, Any], float, str, List[str], List[Mapping[str, Any]]]]) –
Specifies how the sampler initializes parameter values.
+
inits (Optional[Union[Mapping[str, Any], float, str, List[str], List[Mapping[str, Any]]]]) –
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
@@ -1908,16 +1908,16 @@
CmdStanModelOptional[int]) – Number of warmup iterations for each chain.
iter_sampling (Optional[int]) – Number of draws from the posterior for each
+
iter_warmup (Optional[int]) – Number of warmup iterations for each chain.
iter_sampling (Optional[int]) – Number of draws from the posterior for each
chain.
save_warmup (bool) – When True, sampler saves warmup draws as part of
+
save_warmup (bool) – When True, sampler saves warmup draws as part of
the Stan CSV output file.
thin (Optional[int]) – Period between recorded iterations. Default is 1, i.e.,
+
thin (Optional[int]) – Period between recorded iterations. Default is 1, i.e.,
all iterations are recorded.
max_treedepth (Optional[int]) – Maximum depth of trees evaluated by NUTS sampler
+
max_treedepth (Optional[int]) – Maximum depth of trees evaluated by NUTS sampler
per iteration.
metric (Optional[Union[str, Dict[str, Any], List[str], List[Dict[str, Any]]]]) –
Specification of the mass matrix, either as a
+
metric (Optional[Union[str, Dict[str, Any], List[str], List[Dict[str, Any]]]]) –
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’).
@@ -1937,28 +1937,28 @@ CmdStanModelOptional[Union[float, List[float]]]) – Initial step size for HMC sampler. The value is
+
step_size (Optional[Union[float, List[float]]]) – 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.
adapt_engaged (bool) – When True, adapt step size and metric.
adapt_delta (Optional[float]) – Adaptation target Metropolis acceptance rate.
+
adapt_engaged (bool) – When True, adapt step size and metric.
adapt_delta (Optional[float]) – 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.
adapt_init_phase (Optional[int]) – Iterations for initial phase of adaptation
+
adapt_init_phase (Optional[int]) – Iterations for initial phase of adaptation
during which step size is adjusted so that the chain converges
towards the typical set.
adapt_metric_window (Optional[int]) – The second phase of adaptation tunes
+
adapt_metric_window (Optional[int]) – 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.
adapt_step_size (Optional[int]) – Number of iterations given over to adjusting
+
adapt_step_size (Optional[int]) – Number of iterations given over to adjusting
the step size given the tuned metric during the final phase of
adaptation.
fixed_param (bool) – When True, call CmdStan with argument
+
fixed_param (bool) – 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
@@ -1966,37 +1966,37 @@
CmdStanModelFalse.
output_dir (Optional[Union[str, PathLike]]) – Name of the directory to which CmdStan output
+
output_dir (Optional[Union[str, PathLike]]) – 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.
sig_figs (Optional[int]) – Numerical precision used for output CSV and text files.
+
sig_figs (Optional[int]) – 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.
save_latent_dynamics (bool) – Whether or not to output the position and
+
save_latent_dynamics (bool) – 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.
save_profile (bool) – Whether or not to profile auto-diff operations in
+
save_profile (bool) – 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.
show_progress (bool) – If True, display progress bar to track
+
show_progress (bool) – If True, display progress bar to track
progress for warmup and sampling iterations. Default is True,
unless package tqdm progress bar encounter errors.
show_console (bool) – If True, stream CmdStan messages sent to stdout
+
show_console (bool) – If True, stream CmdStan messages sent to stdout
and stderr to the console. Default is False.
refresh (Optional[int]) – Specify the number of iterations CmdStan will take
+
refresh (Optional[int]) – Specify the number of iterations CmdStan will take
between progress messages. Default value is 100.
time_fmt (str) – A format string passed to
-strftime() to decide the file names for
+
time_fmt (str) – A format string passed to
+strftime() to decide the file names for
output CSVs. Defaults to “%Y%m%d%H%M%S”
force_one_process_per_chain (Optional[bool]) –
If True, run multiple chains in
+
force_one_process_per_chain (Optional[bool]) –
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).
@@ -2004,7 +2004,7 @@ CmdStanModelOptional[float]) – Duration at which sampling times out in seconds.
timeout (Optional[float]) – Duration at which sampling times out in seconds.
- Returns:
@@ -2024,7 +2024,7 @@ CmdStanModel
- Return type:
--
+
-
@@ -2053,56 +2053,56 @@ CmdStanModel
- Parameters:
-data (Optional[Union[Mapping[str, Any], str, PathLike]]) – Values for all data variables in the model, specified
+
data (Optional[Union[Mapping[str, Any], str, PathLike]]) – 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.
seed (Optional[int]) – The seed for random number generator. Must be an integer
+
seed (Optional[int]) – The seed for random number generator. Must be an integer
between 0 and 2^32 - 1. If unspecified,
-numpy.random.default_rng()
+numpy.random.default_rng()
is used to generate a seed which will be used for all chains.
inits (Optional[float]) – Specifies how the sampler initializes parameter values.
+
inits (Optional[float]) – 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].
output_dir (Optional[Union[str, PathLike]]) – Name of the directory to which CmdStan output
+
output_dir (Optional[Union[str, PathLike]]) – 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.
sig_figs (Optional[int]) – Numerical precision used for output CSV and text files.
+
sig_figs (Optional[int]) – 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.
save_latent_dynamics (bool) – Whether or not to save diagnostics.
+
save_latent_dynamics (bool) – 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’.
save_profile (bool) – Whether or not to profile auto-diff operations in
+
save_profile (bool) – 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.
algorithm (Optional[str]) – Algorithm to use. One of: ‘meanfield’, ‘fullrank’.
grad_samples (Optional[int]) – Number of MC draws for computing the gradient.
+
algorithm (Optional[str]) – Algorithm to use. One of: ‘meanfield’, ‘fullrank’.
grad_samples (Optional[int]) – Number of MC draws for computing the gradient.
Default is 10. If problems arise, try doubling current value.
elbo_samples (Optional[int]) – Number of MC draws for estimate of ELBO.
adapt_engaged (bool) – Whether eta adaptation is engaged.
adapt_iter (Optional[int]) – Number of iterations for eta adaptation.
tol_rel_obj (Optional[float]) – Relative tolerance parameter for convergence.
eval_elbo (Optional[int]) – Number of iterations between ELBO evaluations.
draws (Optional[int]) – Number of approximate posterior output draws
+
elbo_samples (Optional[int]) – Number of MC draws for estimate of ELBO.
adapt_engaged (bool) – Whether eta adaptation is engaged.
adapt_iter (Optional[int]) – Number of iterations for eta adaptation.
tol_rel_obj (Optional[float]) – Relative tolerance parameter for convergence.
eval_elbo (Optional[int]) – Number of iterations between ELBO evaluations.
draws (Optional[int]) – Number of approximate posterior output draws
to save.
require_converged (bool) – Whether or not to raise an error if Stan
+
require_converged (bool) – Whether or not to raise an error if Stan
reports that “The algorithm may not have converged”.
show_console (bool) – If True, stream CmdStan messages sent to
+
show_console (bool) – If True, stream CmdStan messages sent to
stdout and stderr to the console. Default is False.
refresh (Optional[int]) – Specify the number of iterations CmdStan will take
+
refresh (Optional[int]) – Specify the number of iterations CmdStan will take
between progress messages. Default value is 100.
time_fmt (str) – A format string passed to
-strftime() to decide the file names for
+
time_fmt (str) – A format string passed to
+strftime() to decide the file names for
output CSVs. Defaults to “%Y%m%d%H%M%S”
timeout (Optional[float]) – Duration at which variational Bayesian inference times
+
timeout (Optional[float]) – Duration at which variational Bayesian inference times
out in seconds.
- Returns:
@@ -2116,19 +2116,19 @@ CmdStanModel
-
-property cpp_options: Dict[str, Union[bool, int]]¶
+property cpp_options: Dict[str, Union[bool, int]]¶
Options to C++ compilers.
-
-property exe_file: Optional[Union[str, PathLike]]¶
+property exe_file: Optional[Union[str, PathLike]]¶
Full path to Stan exe file.
-
-property name: str¶
+property name: 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.
@@ -2136,19 +2136,19 @@ CmdStanModel
-
-property stan_file: Optional[Union[str, PathLike]]¶
+property stan_file: Optional[Union[str, PathLike]]¶
Full path to Stan program file.
-
-property stanc_options: Dict[str, Union[bool, int, str]]¶
+property stanc_options: Dict[str, Union[bool, int, str]]¶
Options to stanc compilers.
-
-property user_header: str¶
+property user_header: str¶
The user header file if it exists, otherwise empty
@@ -2188,7 +2188,7 @@ CmdStanMCMC
- Return type:
--
+
-
@@ -2207,15 +2207,15 @@ CmdStanMCMC
- Parameters:
-inc_warmup (bool) – When True and the warmup draws are present in
+
inc_warmup (bool) – When True and the warmup draws are present in
the output, i.e., the sampler was run with save_warmup=True,
then the warmup draws are included. Default value is False.
concat_chains (bool) – When True return a 2D array flattening all
+
concat_chains (bool) – When True return a 2D array flattening all
all draws from all chains. Default value is False.
- Return type:
--
+
-
@@ -2235,14 +2235,14 @@ CmdStanMCMC
- Parameters:
-vars (Optional[Union[List[str], str]]) – optional list of variable names.
inc_warmup (bool) – When True and the warmup draws are present in
+
vars (Optional[Union[List[str], str]]) – optional list of variable names.
inc_warmup (bool) – When True and the warmup draws are present in
the output, i.e., the sampler was run with save_warmup=True,
then the warmup draws are included. Default value is False.
- Return type:
--
+
-
@@ -2258,14 +2258,14 @@ CmdStanMCMC
- Parameters:
-vars (Optional[Union[List[str], str]]) – optional list of variable names.
inc_warmup (bool) – When True and the warmup draws are present in
+
vars (Optional[Union[List[str], str]]) – optional list of variable names.
inc_warmup (bool) – When True and the warmup draws are present in
the output, i.e., the sampler was run with save_warmup=True,
then the warmup draws are included. Default value is False.
- Return type:
--
+
-
@@ -2284,7 +2284,7 @@ CmdStanMCMC
- Return type:
--
+
-
@@ -2298,7 +2298,7 @@ CmdStanMCMC
- Parameters:
-dir (Optional[str]) – directory path
+-
- Return type:
None
@@ -2340,14 +2340,14 @@ CmdStanMCMC
- Parameters:
-var (str) – variable name
inc_warmup (bool) – When True and the warmup draws are present in
+
var (str) – variable name
inc_warmup (bool) – When True and the warmup draws are present in
the output, i.e., the sampler was run with save_warmup=True,
then the warmup draws are included. Default value is False.
- Return type:
--
+
-
@@ -2367,7 +2367,7 @@ CmdStanMCMC
- Return type:
--
+
-
@@ -2384,10 +2384,10 @@ CmdStanMCMC
- Parameters:
-percentiles (Sequence[int]) – Ordered non-empty sequence of percentiles to report.
+
percentiles (Sequence[int]) – Ordered non-empty sequence of percentiles to report.
Must be integers from (1, 99), inclusive. Defaults to
(5, 50, 95)
sig_figs (int) – Number of significant figures to report.
+
sig_figs (int) – Number of significant figures to report.
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.
If precision above 6 is requested, sample must have been produced
@@ -2399,26 +2399,26 @@
CmdStanMCMCpandas.DataFrame
- Return type:
--
+
-
-
-property column_names: Tuple[str, ...]¶
+property column_names: Tuple[str, ...]¶
Names of all outputs from the sampler, comprising sampler parameters
and all components of all model parameters, transformed parameters,
and quantities of interest. Corresponds to Stan CSV file header row,
@@ -2427,14 +2427,14 @@
CmdStanMCMC
-
-property divergences: Optional[ndarray]¶
+property divergences: Optional[ndarray]¶
Per-chain total number of post-warmup divergent iterations.
When sampler algorithm ‘fixed_param’ is specified, returns None.
-
-property max_treedepths: Optional[ndarray]¶
+property max_treedepths: Optional[ndarray]¶
Per-chain total number of post-warmup iterations where the NUTS sampler
reached the maximum allowed treedepth.
When sampler algorithm ‘fixed_param’ is specified, returns None.
@@ -2450,14 +2450,14 @@ CmdStanMCMC
-
-property metric: Optional[ndarray]¶
+property metric: Optional[ndarray]¶
Metric used by sampler for each chain.
When sampler algorithm ‘fixed_param’ is specified, metric is None.
-
-property metric_type: Optional[str]¶
+property metric_type: Optional[str]¶
Metric type used for adaptation, either ‘diag_e’ or ‘dense_e’, according
to CmdStan arg ‘metric’.
When sampler algorithm ‘fixed_param’ is specified, metric_type is None.
@@ -2465,27 +2465,27 @@ CmdStanMCMC
-
-property num_draws_sampling: int¶
+property num_draws_sampling: int¶
Number of sampling (post-warmup) draws per chain, i.e.,
thinned sampling iterations.
-
-property num_draws_warmup: int¶
+property num_draws_warmup: int¶
Number of warmup draws per chain, i.e., thinned warmup iterations.
-
-property step_size: Optional[ndarray]¶
+property step_size: Optional[ndarray]¶
Step size used by sampler for each chain.
When sampler algorithm ‘fixed_param’ is specified, step size is None.
@@ -2513,7 +2513,7 @@ CmdStanMLE
- Parameters:
-dir (Optional[str]) – directory path
+-
- Return type:
None
@@ -2536,16 +2536,16 @@ CmdStanMLE
- Parameters:
-var (str) – variable name
inc_iterations (bool) – When True and the intermediate estimates
+
var (str) – variable name
inc_iterations (bool) – When True and the intermediate estimates
are included in the output, i.e., the optimizer was run with
save_iterations=True, then intermediate estimates are included.
Default value is False.
warn (bool) –
warn (bool) –
- Return type:
--
+
-
@@ -2561,13 +2561,13 @@ CmdStanMLE
- Parameters:
-inc_iterations (bool) – When True and the intermediate estimates
+
inc_iterations (bool) – When True and the intermediate estimates
are included in the output, i.e., the optimizer was run with
save_iterations=True, then intermediate estimates are included.
Default value is False.
- Return type:
-Dict[str, Union[ndarray, float]]
+-
@@ -2578,7 +2578,7 @@ CmdStanMLE
-
-property column_names: Tuple[str, ...]¶
+property column_names: Tuple[str, ...]¶
Names of estimated quantities, includes joint log probability,
and all parameters, transformed parameters, and generated quantities.
CmdStanMLE
-
-property optimized_iterations_np: Optional[ndarray]¶
+property optimized_iterations_np: Optional[ndarray]¶
Returns all saved iterations from the optimizer and final estimate
as a numpy.ndarray which contains all optimizer outputs, i.e.,
the value for lp__ as well as all Stan program variables.
@@ -2601,7 +2601,7 @@ CmdStanMLE
-
-property optimized_iterations_pd: Optional[DataFrame]¶
+property optimized_iterations_pd: Optional[DataFrame]¶
Returns all saved iterations from the optimizer and final estimate
as a pandas.DataFrame which contains all optimizer outputs, i.e.,
the value for lp__ as well as all Stan program variables.
@@ -2609,14 +2609,14 @@ CmdStanMLE
-
-property optimized_params_dict: Dict[str, float64]¶
+property optimized_params_dict: Dict[str, float64]¶
Returns all estimates from the optimizer, including lp__ as a
Python Dict. Only returns estimate from final iteration.
-
-property optimized_params_np: ndarray¶
+property optimized_params_np: ndarray¶
Returns all final estimates from the optimizer as a numpy.ndarray
which contains all optimizer outputs, i.e., the value for lp__
as well as all Stan program variables.
@@ -2624,7 +2624,7 @@ CmdStanMLE
-
-property optimized_params_pd: DataFrame¶
+property optimized_params_pd: DataFrame¶
Returns all final estimates from the optimizer as a pandas.DataFrame
which contains all optimizer outputs, i.e., the value for lp__
as well as all Stan program variables.
@@ -2654,7 +2654,7 @@ CmdStanLaplace
- Return type:
--
+
-
CmdStanLaplace
- Parameters:
-vars (Optional[Union[List[str], str]]) – optional list of variable names.
+vars (Optional[Union[List[str], str]]) – optional list of variable names.
- Return type:
--
+
-
@@ -2687,7 +2687,7 @@ CmdStanLaplace
- Return type:
--
+
-
@@ -2701,7 +2701,7 @@ CmdStanLaplace
- Parameters:
-dir (Optional[str]) – directory path
+-
- Return type:
None
@@ -2723,10 +2723,10 @@ CmdStanLaplacefit.a is a synonym for fit.stan_variable("a")
@@ -2747,7 +2747,7 @@ CmdStanLaplaceFalse
- Return type:
--
+
-
@@ -2758,7 +2758,7 @@ CmdStanLaplace
-
-property column_names: Tuple[str, ...]¶
+property column_names: Tuple[str, ...]¶
Names of all outputs from the sampler, comprising sampler parameters
and all components of all model parameters, transformed parameters,
and quantities of interest. Corresponds to Stan CSV file header row,
@@ -2803,15 +2803,15 @@
CmdStanPathfinder
- Parameters:
-
- Returns:
The initial values for the parameters of the model.
- Return type:
-Union[List[Dict[str, ndarray]], Dict[str, ndarray]]
+-
If chains is 1, a dictionary is returned, otherwise a list
@@ -2827,7 +2827,7 @@
CmdStanPathfinder
- Return type:
--
+
-
@@ -2842,7 +2842,7 @@ CmdStanPathfinder
- Return type:
--
+
-
@@ -2856,7 +2856,7 @@ CmdStanPathfinder
- Parameters:
-dir (Optional[str]) – directory path
+-
- Return type:
None
@@ -2878,10 +2878,10 @@ CmdStanPathfinderfit.a is a synonym for fit.stan_variable("a")
@@ -2901,14 +2901,14 @@ CmdStanPathfinder
- Return type:
--
+
-
-
-property column_names: Tuple[str, ...]¶
+property column_names: Tuple[str, ...]¶
Names of all outputs from the sampler, comprising sampler parameters
and all components of all model parameters, transformed parameters,
and quantities of interest. Corresponds to Stan CSV file header row,
@@ -2917,7 +2917,7 @@
CmdStanPathfinder
-
-property is_resampled: bool¶
+property is_resampled: bool¶
Returns True if the draws were resampled from several Pathfinder
approximations, False otherwise.
@@ -2954,7 +2954,7 @@ CmdStanVB
- Parameters:
-dir (Optional[str]) – directory path
+-
- Return type:
None
@@ -2989,14 +2989,14 @@ CmdStanVB
- Parameters:
-var (str) – variable name
mean (Optional[bool]) – if True, return the variational mean. Otherwise,
+
var (str) – variable name
mean (Optional[bool]) – if True, return the variational mean. Otherwise,
return the variational sample. The default behavior will
change in a future release to return the variational sample.
- Return type:
--
+
-
@@ -3016,17 +3016,17 @@ CmdStanVB
- Parameters:
--
+
-
- Return type:
-Dict[str, Union[ndarray, float]]
+-
-
-property column_names: Tuple[str, ...]¶
+property column_names: Tuple[str, ...]¶
Names of information items returned by sampler for each draw.
Includes approximation information and names of model parameters
and computed quantities.
@@ -3034,7 +3034,7 @@ CmdStanVB
-
-property columns: int¶
+property columns: int¶
Total number of information items returned by sampler.
Includes approximation information and names of model parameters
and computed quantities.
@@ -3042,7 +3042,7 @@ CmdStanVB
-
-property eta: float¶
+property eta: float¶
Step size scaling parameter ‘eta’
@@ -3056,31 +3056,31 @@ CmdStanVB
-
-property variational_params_dict: Dict[str, ndarray]¶
+property variational_params_dict: Dict[str, ndarray]¶
Returns inferred parameter means as Dict.
-
-property variational_params_np: ndarray¶
+property variational_params_np: ndarray¶
Returns inferred parameter means as numpy array.
-
-property variational_params_pd: DataFrame¶
+property variational_params_pd: DataFrame¶
Returns inferred parameter means as pandas DataFrame.
-
-property variational_sample: ndarray¶
+property variational_sample: ndarray¶
Returns the set of approximate posterior output draws.
-
-property variational_sample_pd: DataFrame¶
+property variational_sample_pd: DataFrame¶
Returns the set of approximate posterior output draws as
a pandas DataFrame.
@@ -3118,19 +3118,19 @@ CmdStanGQ
- Parameters:
-inc_warmup (bool) – When True and the warmup draws are present in
+
inc_warmup (bool) – When True and the warmup draws are present in
the output, i.e., the sampler was run with save_warmup=True,
then the warmup draws are included. Default value is False.
concat_chains (bool) – When True return a 2D array flattening all
+
concat_chains (bool) – When True return a 2D array flattening all
all draws from all chains. Default value is False.
inc_sample (bool) – When True include all columns in the previous_fit
+
inc_sample (bool) – When True include all columns in the previous_fit
draws array as well, excepting columns for variables already present
in the generated quantities drawset. Default value is False.
inc_iterations (bool) –
inc_iterations (bool) –
- Return type:
--
+
-
@@ -3150,15 +3150,15 @@ CmdStanGQ
- Parameters:
-vars (Optional[Union[List[str], str]]) – optional list of variable names.
inc_warmup (bool) – When True and the warmup draws are present in
+
vars (Optional[Union[List[str], str]]) – optional list of variable names.
inc_warmup (bool) – When True and the warmup draws are present in
the output, i.e., the sampler was run with save_warmup=True,
then the warmup draws are included. Default value is False.
inc_sample (bool) –
inc_sample (bool) –
- Return type:
--
+
-
@@ -3169,9 +3169,9 @@ CmdStanGQ
-
-draws_xr(vars: Optional[Union[str, List[str]]] = None, inc_warmup: bool = False, inc_sample: bool = False) NoReturn[source]¶
+draws_xr(vars: Optional[Union[str, List[str]]] = None, inc_warmup: bool = False, inc_sample: bool = False) NoReturn[source]¶
-
-draws_xr(vars: Optional[Union[str, List[str]]] = None, inc_warmup: bool = False, inc_sample: bool = False) Dataset
+draws_xr(vars: Optional[Union[str, List[str]]] = None, inc_warmup: bool = False, inc_sample: bool = False) Dataset
Returns the generated quantities draws as a xarray Dataset.
This method can only be called when the underlying fit was made
through sampling, it cannot be used on MLE or VB outputs.
@@ -3200,7 +3200,7 @@ CmdStanGQ
- Parameters:
-dir (Optional[str]) – directory path
+-
- Return type:
None
@@ -3242,14 +3242,14 @@ CmdStanGQ
- Parameters:
-
- Return type:
--
+
-
@@ -3265,12 +3265,12 @@ CmdStanGQ
- Parameters:
-kwargs (bool) – Additional keyword arguments are passed to the underlying
+
kwargs (bool) – Additional keyword arguments are passed to the underlying
fit’s stan_variable method if the variable is not a generated
quantity.
- Return type:
--
+
-
@@ -3281,19 +3281,19 @@ CmdStanGQ
-
-property chain_ids: List[int]¶
+property chain_ids: List[int]¶
Chain ids.
-
-property column_names: Tuple[str, ...]¶
+property column_names: Tuple[str, ...]¶
Names of generated quantities of interest.
@@ -3325,19 +3325,19 @@ compile_stan_file
- Parameters:
-force (bool) – When True, always compile, even if the executable file
+
force (bool) – 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.
stanc_options (Optional[Dict[str, Any]]) – Options for stanc compiler.
cpp_options (Optional[Dict[str, Any]]) – Options for C++ compiler.
user_header (Optional[Union[str, PathLike]]) – A path to a header file to include during C++
+
stanc_options (Optional[Dict[str, Any]]) – Options for stanc compiler.
cpp_options (Optional[Dict[str, Any]]) – Options for C++ compiler.
user_header (Optional[Union[str, PathLike]]) – A path to a header file to include during C++
compilation.
- Return type:
--
+
-
@@ -3353,19 +3353,19 @@ format_stan_file
- Parameters:
-stan_file (Union[str, PathLike]) – Path to Stan program file.
overwrite_file (bool) – If True, save the updated code to disk, rather
+
stan_file (Union[str, PathLike]) – Path to Stan program file.
overwrite_file (bool) – If True, save the updated code to disk, rather
than printing it. By default False
canonicalize (Union[bool, str, Iterable[str]]) – Whether or not the compiler should ‘canonicalize’
+
canonicalize (Union[bool, str, Iterable[str]]) – Whether or not the compiler should ‘canonicalize’
the Stan model, removing things like deprecated syntax. Default is
False. If True, all canonicalizations are run. If it is a list of
strings, those options are passed to stanc (new in Stan 2.29)
max_line_length (int) – Set the wrapping point for the formatter. The
+
max_line_length (int) – Set the wrapping point for the formatter. The
default value is 78, which wraps most lines by the 80th character.
backup (bool) – If True, create a stanfile.bak backup before
+
backup (bool) – If True, create a stanfile.bak backup before
writing to the file. Only disable this if you’re sure you have other
copies of the file or are using a version control system like Git.
stanc_options (Optional[Dict[str, Any]]) – Additional options to pass to the stanc compiler.
stanc_options (Optional[Dict[str, Any]]) – Additional options to pass to the stanc compiler.
- Return type:
@@ -3383,10 +3383,10 @@ show_versions
- Parameters:
-output (bool) –
+output (bool) –
- Return type:
--
+
-
@@ -3400,7 +3400,7 @@ cmdstan_path
- Return type:
--
+
-
@@ -3418,27 +3418,27 @@ install_cmdstan
- Parameters:
-version (Optional[str]) – CmdStan version string, e.g. “2.29.2”.
+
version (Optional[str]) – CmdStan version string, e.g. “2.29.2”.
Defaults to latest CmdStan release.
If git is installed, a git tag or branch of stan-dev/cmdstan
can be specified, e.g. “git:develop”.
dir (Optional[str]) – Path to install directory. Defaults to hidden directory
+
dir (Optional[str]) – Path to install directory. Defaults to hidden directory
$HOME/.cmdstan.
If no directory is specified and the above directory does not
exist, directory $HOME/.cmdstan will be created and populated.
overwrite (bool) – Boolean value; when True, will overwrite and
+
overwrite (bool) – Boolean value; when True, will overwrite and
rebuild an existing CmdStan installation. Default is False.
compiler (bool) – Boolean value; when True on WINDOWS ONLY, use the
+
compiler (bool) – Boolean value; when True on WINDOWS ONLY, use the
C++ compiler from the install_cxx_toolchain command or install
one if none is found.
progress (bool) – Boolean value; when True, show a progress bar for
+
progress (bool) – Boolean value; when True, show a progress bar for
downloading and unpacking CmdStan. Default is False.
verbose (bool) – Boolean value; when True, show console output from all
+
verbose (bool) – Boolean value; when True, show console output from all
intallation steps, i.e., download, build, and test CmdStan release.
Default is False.
cores (int) – Integer, number of cores to use in the make command.
+
cores (int) – Integer, number of cores to use in the make command.
Default is 1 core.
interactive (bool) –
Boolean value; if true, ignore all other arguments
+
interactive (bool) –
Boolean value; if true, ignore all other arguments
to this function and run in an interactive mode, prompting the user
to provide the other information manually through the standard input.
This flag should only be used in interactive environments,
@@ -3450,7 +3450,7 @@
install_cmdstanBoolean value; True for success.
- Return type:
--
+
-
@@ -3468,11 +3468,11 @@ rebuild_cmdstan
- Parameters:
-verbose (bool) – Boolean value; when True, show output from make command.
+
verbose (bool) – Boolean value; when True, show output from make command.
Default is False.
progress (bool) – Boolean value; when True display progress progress bar.
+
progress (bool) – Boolean value; when True display progress progress bar.
Default is True.
cores (int) – Integer, number of cores to use in the make command.
+
cores (int) – Integer, number of cores to use in the make command.
Default is 1 core.
set_cmdstan_path
- Parameters:
-path (str) –
+path (str) –
- Return type:
None
@@ -3513,7 +3513,7 @@ cmdstan_versionhttps://github.com/stan-dev/cmdstanpy/pull/321#issuecomment-733817554
set_make_env
- Parameters:
-make (str) –
+make (str) –
- Return type:
None
@@ -3552,15 +3552,15 @@ from_csv
- Parameters:
-
- Returns:
either a CmdStanMCMC, CmdStanMLE, or CmdStanVB object
- Return type:
-Optional[Union[CmdStanMCMC, CmdStanMLE, CmdStanVB, CmdStanPathfinder, CmdStanLaplace]]
+Optional[Union[CmdStanMCMC, CmdStanMLE, CmdStanVB, CmdStanPathfinder, CmdStanLaplace]]
write_stan_jsoncmdstanpy.write_stan_json(path, data)[source]¶
Dump a mapping of strings to data to a JSON file.
Values can be any numeric type, a boolean (converted to int),
-or any collection compatible with numpy.asarray(), e.g a
-pandas.Series.
+or any collection compatible with numpy.asarray(), e.g a
+pandas.Series.
Produces a file compatible with the
Json Format for Cmdstan
- Parameters:
-path (str) – File path for the created json. Will be overwritten if
+
path (str) – File path for the created json. Will be overwritten if
already in existence.
data (Mapping[str, Any]) – A mapping from strings to values. This can be a dictionary
-or something more exotic like an xarray.Dataset. This will be
+
data (Mapping[str, Any]) – A mapping from strings to values. This can be a dictionary
+or something more exotic like an xarray.Dataset. This will be
copied before type conversion, not modified
What’s New — CmdStanPy 1.2.4 documentation
+ What’s New — CmdStanPy 1.2.5 documentation
@@ -64,7 +64,7 @@
@@ -172,6 +172,11 @@
-
-property cmdstan_config: Dict[str, Any]¶
+property cmdstan_config: Dict[str, Any]¶
Returns a dictionary containing a set of name, value pairs
parsed out of the Stan CSV file header. These include the
command configuration and the CSV file header row information.
@@ -758,13 +758,13 @@
InferenceMetadata
-
-property method_vars: Dict[str, Variable]¶
+property method_vars: Dict[str, Variable]¶
Method variable names always end in __, e.g. lp__.
-
-property stan_vars: Dict[str, Variable]¶
+property stan_vars: Dict[str, Variable]¶
These are the user-defined variables in the Stan program.
@@ -785,10 +785,10 @@ RunSetParameters:
-
@@ -800,10 +800,10 @@ RunSetnum_chains and leave chain idx off CSV files.
RunSetChecks console messages for each CmdStan run.
RunSetMoves CSV files to specified directory.
- Parameters:
-dir (Optional[str]) – directory path
+-
- Return type:
None
@@ -839,25 +839,25 @@ RunSet
-
-property chain_ids: List[int]¶
+property chain_ids: List[int]¶
Chain ids.
-
-property csv_files: List[str]¶
+property csv_files: List[str]¶
List of paths to CmdStan output files.
-
-property diagnostic_files: List[str]¶
+property diagnostic_files: List[str]¶
List of paths to CmdStan hamiltonian diagnostic files.
@@ -869,19 +869,19 @@ RunSet
-
-property model: str¶
+property model: str¶
Stan model name.
-
-property one_process_per_chain: bool¶
+property one_process_per_chain: bool¶
When True, for each chain, call CmdStan in its own subprocess.
When False, use CmdStan’s num_chains arg to run parallel chains.
Always True if CmdStan < 2.28.
@@ -890,13 +890,13 @@
RunSet
-
-property profile_files: List[str]¶
+property profile_files: List[str]¶
List of paths to CmdStan profiler files.
-
-property stdout_files: List[str]¶
+property stdout_files: List[str]¶
List of paths to transcript of CmdStan messages sent to the console.
Transcripts include config information, progress, and error messages.
@@ -913,9 +913,9 @@ CompilerOptions
- Parameters:
-
@@ -959,7 +959,7 @@ CompilerOptions
- Parameters:
-path (str) –
+path (str) –
- Return type:
None
@@ -973,11 +973,11 @@ CompilerOptions
- Parameters:
-filename_in_msg (str, optional) – filename to be displayed in stanc3 error messages
+
filename_in_msg (str, optional) – filename to be displayed in stanc3 error messages
(if different from actual filename on disk), by default None
- Return type:
--
+
-
@@ -988,7 +988,7 @@ CompilerOptions
- Return type:
--
+
-
@@ -1043,19 +1043,19 @@ CompilerOptions
-
-property cpp_options: Dict[str, Union[bool, int]]¶
+property cpp_options: Dict[str, Union[bool, int]]¶
C++ compiler options.
-
-property stanc_options: Dict[str, Union[bool, int, str, Iterable[str]]]¶
+property stanc_options: Dict[str, Union[bool, int, str, Iterable[str]]]¶
Stanc compiler options.
@@ -1073,18 +1073,18 @@ CmdStanArgs
- Parameters:
-model_name (str) –
method_args (Union[SamplerArgs, OptimizeArgs, GenerateQuantitiesArgs, VariationalArgs, LaplaceArgs, PathfinderArgs]) –
save_latent_dynamics (bool) –
save_profile (bool) –
model_name (str) –
method_args (Union[SamplerArgs, OptimizeArgs, GenerateQuantitiesArgs, VariationalArgs, LaplaceArgs, PathfinderArgs]) –
save_latent_dynamics (bool) –
save_profile (bool) –
CmdStanArgs
- Parameters:
-
- Return type:
--
+
-
@@ -1136,20 +1136,20 @@ SamplerArgs
- Parameters:
-save_warmup (bool) –
metric (Optional[Union[str, Dict[str, Any], List[str], List[Dict[str, Any]]]]) –
adapt_engaged (bool) –
fixed_param (bool) –
num_chains (int) –
save_warmup (bool) –
metric (Optional[Union[str, Dict[str, Any], List[str], List[Dict[str, Any]]]]) –
adapt_engaged (bool) –
fixed_param (bool) –
num_chains (int) –
SamplerArgs
- Parameters:
-
- Return type:
--
+
-
@@ -1181,7 +1181,7 @@ SamplerArgs
- Parameters:
--
+
-
- Return type:
None
@@ -1201,17 +1201,17 @@ OptimizeArgs
- Parameters:
-
@@ -1222,12 +1222,12 @@ OptimizeArgs
- Parameters:
-
- Return type:
--
+
-
@@ -1238,7 +1238,7 @@ OptimizeArgs
- Parameters:
--
+
-
- Return type:
None
@@ -1258,9 +1258,9 @@ LaplaceArgs
- Parameters:
-
@@ -1271,12 +1271,12 @@ LaplaceArgs
- Parameters:
-
- Return type:
--
+
-
@@ -1287,7 +1287,7 @@ LaplaceArgs
- Parameters:
--
+
-
- Return type:
None
@@ -1307,21 +1307,21 @@ PathfinderArgs
- Parameters:
-
@@ -1332,12 +1332,12 @@ PathfinderArgs
- Parameters:
-
- Return type:
--
+
-
@@ -1348,7 +1348,7 @@ PathfinderArgs
- Parameters:
--
+
-
- Return type:
None
@@ -1368,16 +1368,16 @@ VariationalArgs
- Parameters:
-
@@ -1388,12 +1388,12 @@ VariationalArgs
- Parameters:
-
- Return type:
--
+
-
@@ -1404,7 +1404,7 @@ VariationalArgs
- Parameters:
--
+
-
- Return type:
None
diff --git a/docs/objects.inv b/docs/objects.inv
index c6fcd7f0..a527a0ef 100644
Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/py-modindex.html b/docs/py-modindex.html
index abbeddcf..adb60d8f 100644
--- a/docs/py-modindex.html
+++ b/docs/py-modindex.html
@@ -5,7 +5,7 @@
- Python Module Index — CmdStanPy 1.2.4 documentation
+ Python Module Index — CmdStanPy 1.2.5 documentation
@@ -68,7 +68,7 @@
diff --git a/docs/search.html b/docs/search.html
index 01bdc183..1fd53e12 100644
--- a/docs/search.html
+++ b/docs/search.html
@@ -5,7 +5,7 @@
- Search — CmdStanPy 1.2.4 documentation
+ Search — CmdStanPy 1.2.5 documentation
@@ -67,7 +67,7 @@
diff --git a/docs/searchindex.js b/docs/searchindex.js
index f122d687..fb35c443 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["api", "changes", "community", "index", "installation", "internal_api", "users-guide", "users-guide/examples", "users-guide/examples/MCMC Sampling", "users-guide/examples/Maximum Likelihood Estimation", "users-guide/examples/Pathfinder", "users-guide/examples/Run Generated Quantities", "users-guide/examples/Using External C++", "users-guide/examples/VI as Sampler Inits", "users-guide/examples/Variational Inference", "users-guide/hello_world", "users-guide/outputs", "users-guide/overview", "users-guide/workflow"], "filenames": ["api.rst", "changes.rst", "community.rst", "index.rst", "installation.rst", "internal_api.rst", "users-guide.rst", "users-guide/examples.rst", "users-guide/examples/MCMC Sampling.ipynb", "users-guide/examples/Maximum Likelihood Estimation.ipynb", "users-guide/examples/Pathfinder.ipynb", "users-guide/examples/Run Generated Quantities.ipynb", "users-guide/examples/Using External C++.ipynb", "users-guide/examples/VI as Sampler Inits.ipynb", "users-guide/examples/Variational Inference.ipynb", "users-guide/hello_world.rst", "users-guide/outputs.rst", "users-guide/overview.rst", "users-guide/workflow.rst"], "titles": ["API Reference", "What\u2019s New", "Community", "cmdstanpy \u2013 Python interface to CmdStan", "Installation", "Internal API Reference", "User\u2019s Guide", "CmdStanPy Examples", "MCMC Sampling", "Maximum Likelihood Estimation", "Variational Inference using Pathfinder", "Generating new quantities of interest.", "Advanced Topic: Using External C++ Functions", "Using Variational Estimates to Initialize the NUTS-HMC Sampler", "Variational Inference using ADVI", "\u201cHello, World!\u201d", "Controlling Outputs", "Overview", "CmdStanPy Workflow"], "terms": {"The": [0, 1, 3, 4, 5, 8, 9, 10, 11, 12, 13, 14, 16, 18], "follow": [0, 4, 5, 9, 12, 14, 15, 16, 18], "document": [0, 1, 5, 12, 14], "public": [0, 5], "cmdstanpi": [0, 2, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], "It": [0, 3, 4, 8, 11, 13, 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\ No newline at end of file
diff --git a/docs/users-guide.html b/docs/users-guide.html
index 697cacdf..f4b3c299 100644
--- a/docs/users-guide.html
+++ b/docs/users-guide.html
@@ -6,7 +6,7 @@
- User’s Guide — CmdStanPy 1.2.4 documentation
+ User’s Guide — CmdStanPy 1.2.5 documentation
@@ -64,7 +64,7 @@
diff --git a/docs/users-guide/examples.html b/docs/users-guide/examples.html
index fe32d649..4e745032 100644
--- a/docs/users-guide/examples.html
+++ b/docs/users-guide/examples.html
@@ -6,7 +6,7 @@
- CmdStanPy Examples — CmdStanPy 1.2.4 documentation
+ CmdStanPy Examples — CmdStanPy 1.2.5 documentation
@@ -64,7 +64,7 @@
diff --git a/docs/users-guide/examples/MCMC Sampling.html b/docs/users-guide/examples/MCMC Sampling.html
index 6f2ef310..033bf194 100644
--- a/docs/users-guide/examples/MCMC Sampling.html
+++ b/docs/users-guide/examples/MCMC Sampling.html
@@ -6,7 +6,7 @@
- MCMC Sampling — CmdStanPy 1.2.4 documentation
+ MCMC Sampling — CmdStanPy 1.2.5 documentation
@@ -65,7 +65,7 @@
diff --git a/docs/users-guide/examples/Maximum Likelihood Estimation.html b/docs/users-guide/examples/Maximum Likelihood Estimation.html
index f70471cf..b869fe4d 100644
--- a/docs/users-guide/examples/Maximum Likelihood Estimation.html
+++ b/docs/users-guide/examples/Maximum Likelihood Estimation.html
@@ -6,7 +6,7 @@
- Maximum Likelihood Estimation — CmdStanPy 1.2.4 documentation
+ Maximum Likelihood Estimation — CmdStanPy 1.2.5 documentation
@@ -65,7 +65,7 @@
@@ -304,8 +304,8 @@ Maximum Likelihood Estimation
-14:28:43 - cmdstanpy - INFO - Chain [1] start processing
-14:28:43 - cmdstanpy - INFO - Chain [1] done processing
+20:13:38 - cmdstanpy - INFO - Chain [1] start processing
+20:13:38 - cmdstanpy - INFO - Chain [1] done processing
@@ -314,7 +314,7 @@ Maximum Likelihood Estimation
('lp__', 'theta')
-OrderedDict([('lp__', np.float64(-5.00402)), ('theta', np.float64(0.200012))])
+OrderedDict([('lp__', np.float64(-5.00402)), ('theta', np.float64(0.2))])
@@ -348,7 +348,7 @@ Maximum Likelihood Estimation\n",
" 0 \n",
" -5.00402 \n",
- " 0.200012 \n",
+ " 0.2 \n",
" \n",
" \n",
"\n",
"
"
],
"text/plain": [
- " lp__ theta\n",
- "0 -5.00402 0.200012"
+ " lp__ theta\n",
+ "0 -5.00402 0.2"
]
},
"execution_count": 1,
@@ -148,7 +148,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.19"
+ "version": "3.9.20"
}
},
"nbformat": 4,
diff --git a/docs/users-guide/examples/Pathfinder.html b/docs/users-guide/examples/Pathfinder.html
index 16c31d46..2241a3e9 100644
--- a/docs/users-guide/examples/Pathfinder.html
+++ b/docs/users-guide/examples/Pathfinder.html
@@ -6,7 +6,7 @@
- Variational Inference using Pathfinder — CmdStanPy 1.2.4 documentation
+ Variational Inference using Pathfinder — CmdStanPy 1.2.5 documentation
@@ -65,7 +65,7 @@
@@ -322,8 +322,8 @@ Example: variational inference with Pathfinder for model
-14:28:45 - cmdstanpy - INFO - Chain [1] start processing
-14:28:45 - cmdstanpy - INFO - Chain [1] done processing
+20:13:40 - cmdstanpy - INFO - Chain [1] start processing
+20:13:40 - cmdstanpy - INFO - Chain [1] done processing
@@ -342,11 +342,11 @@ Example: variational inference with Pathfinder for model
CmdStanPathfinder: model=bernoulli['method=pathfinder']
csv_files:
- /tmp/tmpcggr6zzu/bernoulli0sflv0au/bernoulli-20240617142845.csv
+ /tmp/tmpbblpkqzz/bernoulliinr4ztho/bernoulli-20241211201340.csv
output_files:
- /tmp/tmpcggr6zzu/bernoulli0sflv0au/bernoulli-20240617142845_0-stdout.txt
+ /tmp/tmpbblpkqzz/bernoulliinr4ztho/bernoulli-20241211201340_0-stdout.txt
Metadata:
-{'stan_version_major': 2, 'stan_version_minor': 35, 'stan_version_patch': 0, 'model': 'bernoulli_model', 'start_datetime': '2024-06-17 14:28:45 UTC', 'method': 'pathfinder', 'init_alpha': 0.001, 'tol_obj': 1e-12, 'tol_rel_obj': 10000, 'tol_grad': 1e-08, 'tol_rel_grad': 10000000.0, 'tol_param': 1e-08, 'history_size': 5, 'num_psis_draws': 1000, 'num_paths': 4, 'save_single_paths': 0, 'psis_resample': 1, 'calculate_lp': 1, 'max_lbfgs_iters': 1000, 'num_draws': 1000, 'num_elbo_draws': 25, 'id': 1, 'data_file': '/home/runner/.cmdstan/cmdstan-2.35.0/examples/bernoulli/bernoulli.data.json', 'init': 2, 'seed': 97812, 'diagnostic_file': '', 'refresh': 100, 'sig_figs': -1, 'profile_file': 'profile.csv', 'save_cmdstan_config': 0, 'num_threads': 1, 'stanc_version': 'stanc3 v2.35.0', 'stancflags': '', 'raw_header': 'lp_approx__,lp__,theta', 'column_names': ('lp_approx__', 'lp__', 'theta')}
+{'stan_version_major': 2, 'stan_version_minor': 36, 'stan_version_patch': 0, 'model': 'bernoulli_model', 'start_datetime': '2024-12-11 20:13:40 UTC', 'method': 'pathfinder', 'init_alpha': 0.001, 'tol_obj': 1e-12, 'tol_rel_obj': 10000, 'tol_grad': 1e-08, 'tol_rel_grad': 10000000.0, 'tol_param': 1e-08, 'history_size': 5, 'num_psis_draws': 1000, 'num_paths': 4, 'save_single_paths': 0, 'psis_resample': 1, 'calculate_lp': 1, 'max_lbfgs_iters': 1000, 'num_draws': 1000, 'num_elbo_draws': 25, 'id': 1, 'data_file': '/home/runner/.cmdstan/cmdstan-2.36.0/examples/bernoulli/bernoulli.data.json', 'init': 2, 'seed': 40737, 'diagnostic_file': '', 'refresh': 100, 'sig_figs': -1, 'profile_file': 'profile.csv', 'save_cmdstan_config': 0, 'num_threads': 1, 'stanc_version': 'stanc3 v2.36.0', 'stancflags': '', 'raw_header': 'lp_approx__,lp__,theta', 'column_names': ('lp_approx__', 'lp__', 'theta')}
@@ -424,7 +424,7 @@ Pathfinders as initialization for the MCMC sampler
-[{'theta': array(0.369154)}, {'theta': array(0.1015)}, {'theta': array(0.168767)}, {'theta': array(0.0580899)}]
+[{'theta': array(0.336095)}, {'theta': array(0.0803562)}, {'theta': array(0.217729)}, {'theta': array(0.304769)}]
The create_inits takes two arguments:
@@ -446,7 +446,7 @@ Pathfinders as initialization for the MCMC sampler
-[{'theta': array(0.214343)}, {'theta': array(0.168289)}, {'theta': array(0.213014)}]
+[{'theta': array(0.125923)}, {'theta': array(0.135961)}, {'theta': array(0.270804)}]
diff --git a/docs/users-guide/examples/Pathfinder.ipynb b/docs/users-guide/examples/Pathfinder.ipynb
index 1eb1cb00..622a3b03 100644
--- a/docs/users-guide/examples/Pathfinder.ipynb
+++ b/docs/users-guide/examples/Pathfinder.ipynb
@@ -42,10 +42,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:28:45.142416Z",
- "iopub.status.busy": "2024-06-17T14:28:45.142219Z",
- "iopub.status.idle": "2024-06-17T14:28:45.512973Z",
- "shell.execute_reply": "2024-06-17T14:28:45.512371Z"
+ "iopub.execute_input": "2024-12-11T20:13:39.875020Z",
+ "iopub.status.busy": "2024-12-11T20:13:39.874523Z",
+ "iopub.status.idle": "2024-12-11T20:13:40.241920Z",
+ "shell.execute_reply": "2024-12-11T20:13:40.241314Z"
}
},
"outputs": [],
@@ -59,10 +59,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:28:45.516350Z",
- "iopub.status.busy": "2024-06-17T14:28:45.515712Z",
- "iopub.status.idle": "2024-06-17T14:28:45.605228Z",
- "shell.execute_reply": "2024-06-17T14:28:45.604619Z"
+ "iopub.execute_input": "2024-12-11T20:13:40.244358Z",
+ "iopub.status.busy": "2024-12-11T20:13:40.244062Z",
+ "iopub.status.idle": "2024-12-11T20:13:40.303552Z",
+ "shell.execute_reply": "2024-12-11T20:13:40.302852Z"
}
},
"outputs": [
@@ -70,14 +70,14 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "14:28:45 - cmdstanpy - INFO - Chain [1] start processing\n"
+ "20:13:40 - cmdstanpy - INFO - Chain [1] start processing\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "14:28:45 - cmdstanpy - INFO - Chain [1] done processing\n"
+ "20:13:40 - cmdstanpy - INFO - Chain [1] done processing\n"
]
}
],
@@ -96,10 +96,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:28:45.608280Z",
- "iopub.status.busy": "2024-06-17T14:28:45.607724Z",
- "iopub.status.idle": "2024-06-17T14:28:45.611807Z",
- "shell.execute_reply": "2024-06-17T14:28:45.611173Z"
+ "iopub.execute_input": "2024-12-11T20:13:40.305853Z",
+ "iopub.status.busy": "2024-12-11T20:13:40.305585Z",
+ "iopub.status.idle": "2024-12-11T20:13:40.309404Z",
+ "shell.execute_reply": "2024-12-11T20:13:40.308737Z"
}
},
"outputs": [
@@ -109,11 +109,11 @@
"text": [
"CmdStanPathfinder: model=bernoulli['method=pathfinder']\n",
" csv_files:\n",
- "\t/tmp/tmpcggr6zzu/bernoulli0sflv0au/bernoulli-20240617142845.csv\n",
+ "\t/tmp/tmpbblpkqzz/bernoulliinr4ztho/bernoulli-20241211201340.csv\n",
" output_files:\n",
- "\t/tmp/tmpcggr6zzu/bernoulli0sflv0au/bernoulli-20240617142845_0-stdout.txt\n",
+ "\t/tmp/tmpbblpkqzz/bernoulliinr4ztho/bernoulli-20241211201340_0-stdout.txt\n",
"Metadata:\n",
- "{'stan_version_major': 2, 'stan_version_minor': 35, 'stan_version_patch': 0, 'model': 'bernoulli_model', 'start_datetime': '2024-06-17 14:28:45 UTC', 'method': 'pathfinder', 'init_alpha': 0.001, 'tol_obj': 1e-12, 'tol_rel_obj': 10000, 'tol_grad': 1e-08, 'tol_rel_grad': 10000000.0, 'tol_param': 1e-08, 'history_size': 5, 'num_psis_draws': 1000, 'num_paths': 4, 'save_single_paths': 0, 'psis_resample': 1, 'calculate_lp': 1, 'max_lbfgs_iters': 1000, 'num_draws': 1000, 'num_elbo_draws': 25, 'id': 1, 'data_file': '/home/runner/.cmdstan/cmdstan-2.35.0/examples/bernoulli/bernoulli.data.json', 'init': 2, 'seed': 97812, 'diagnostic_file': '', 'refresh': 100, 'sig_figs': -1, 'profile_file': 'profile.csv', 'save_cmdstan_config': 0, 'num_threads': 1, 'stanc_version': 'stanc3 v2.35.0', 'stancflags': '', 'raw_header': 'lp_approx__,lp__,theta', 'column_names': ('lp_approx__', 'lp__', 'theta')}\n",
+ "{'stan_version_major': 2, 'stan_version_minor': 36, 'stan_version_patch': 0, 'model': 'bernoulli_model', 'start_datetime': '2024-12-11 20:13:40 UTC', 'method': 'pathfinder', 'init_alpha': 0.001, 'tol_obj': 1e-12, 'tol_rel_obj': 10000, 'tol_grad': 1e-08, 'tol_rel_grad': 10000000.0, 'tol_param': 1e-08, 'history_size': 5, 'num_psis_draws': 1000, 'num_paths': 4, 'save_single_paths': 0, 'psis_resample': 1, 'calculate_lp': 1, 'max_lbfgs_iters': 1000, 'num_draws': 1000, 'num_elbo_draws': 25, 'id': 1, 'data_file': '/home/runner/.cmdstan/cmdstan-2.36.0/examples/bernoulli/bernoulli.data.json', 'init': 2, 'seed': 40737, 'diagnostic_file': '', 'refresh': 100, 'sig_figs': -1, 'profile_file': 'profile.csv', 'save_cmdstan_config': 0, 'num_threads': 1, 'stanc_version': 'stanc3 v2.36.0', 'stancflags': '', 'raw_header': 'lp_approx__,lp__,theta', 'column_names': ('lp_approx__', 'lp__', 'theta')}\n",
"\n"
]
}
@@ -145,10 +145,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:28:45.614201Z",
- "iopub.status.busy": "2024-06-17T14:28:45.613831Z",
- "iopub.status.idle": "2024-06-17T14:28:45.621465Z",
- "shell.execute_reply": "2024-06-17T14:28:45.620922Z"
+ "iopub.execute_input": "2024-12-11T20:13:40.311649Z",
+ "iopub.status.busy": "2024-12-11T20:13:40.311183Z",
+ "iopub.status.idle": "2024-12-11T20:13:40.318298Z",
+ "shell.execute_reply": "2024-12-11T20:13:40.317654Z"
}
},
"outputs": [
@@ -172,10 +172,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:28:45.623877Z",
- "iopub.status.busy": "2024-06-17T14:28:45.623503Z",
- "iopub.status.idle": "2024-06-17T14:28:45.627637Z",
- "shell.execute_reply": "2024-06-17T14:28:45.627029Z"
+ "iopub.execute_input": "2024-12-11T20:13:40.320277Z",
+ "iopub.status.busy": "2024-12-11T20:13:40.319917Z",
+ "iopub.status.idle": "2024-12-11T20:13:40.323910Z",
+ "shell.execute_reply": "2024-12-11T20:13:40.323392Z"
}
},
"outputs": [
@@ -199,10 +199,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:28:45.629975Z",
- "iopub.status.busy": "2024-06-17T14:28:45.629590Z",
- "iopub.status.idle": "2024-06-17T14:28:45.633652Z",
- "shell.execute_reply": "2024-06-17T14:28:45.633027Z"
+ "iopub.execute_input": "2024-12-11T20:13:40.326053Z",
+ "iopub.status.busy": "2024-12-11T20:13:40.325587Z",
+ "iopub.status.idle": "2024-12-11T20:13:40.329783Z",
+ "shell.execute_reply": "2024-12-11T20:13:40.329233Z"
}
},
"outputs": [
@@ -235,10 +235,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:28:45.635938Z",
- "iopub.status.busy": "2024-06-17T14:28:45.635593Z",
- "iopub.status.idle": "2024-06-17T14:28:45.639932Z",
- "shell.execute_reply": "2024-06-17T14:28:45.639293Z"
+ "iopub.execute_input": "2024-12-11T20:13:40.331971Z",
+ "iopub.status.busy": "2024-12-11T20:13:40.331435Z",
+ "iopub.status.idle": "2024-12-11T20:13:40.335599Z",
+ "shell.execute_reply": "2024-12-11T20:13:40.335081Z"
}
},
"outputs": [
@@ -246,7 +246,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[{'theta': array(0.369154)}, {'theta': array(0.1015)}, {'theta': array(0.168767)}, {'theta': array(0.0580899)}]\n"
+ "[{'theta': array(0.336095)}, {'theta': array(0.0803562)}, {'theta': array(0.217729)}, {'theta': array(0.304769)}]\n"
]
}
],
@@ -270,10 +270,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:28:45.642382Z",
- "iopub.status.busy": "2024-06-17T14:28:45.642017Z",
- "iopub.status.idle": "2024-06-17T14:28:45.645658Z",
- "shell.execute_reply": "2024-06-17T14:28:45.645014Z"
+ "iopub.execute_input": "2024-12-11T20:13:40.337655Z",
+ "iopub.status.busy": "2024-12-11T20:13:40.337271Z",
+ "iopub.status.idle": "2024-12-11T20:13:40.340768Z",
+ "shell.execute_reply": "2024-12-11T20:13:40.340238Z"
}
},
"outputs": [
@@ -281,7 +281,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[{'theta': array(0.214343)}, {'theta': array(0.168289)}, {'theta': array(0.213014)}]\n"
+ "[{'theta': array(0.125923)}, {'theta': array(0.135961)}, {'theta': array(0.270804)}]\n"
]
}
],
@@ -307,7 +307,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.19"
+ "version": "3.9.20"
}
},
"nbformat": 4,
diff --git a/docs/users-guide/examples/Run Generated Quantities.html b/docs/users-guide/examples/Run Generated Quantities.html
index e2c56026..ceeaba1c 100644
--- a/docs/users-guide/examples/Run Generated Quantities.html
+++ b/docs/users-guide/examples/Run Generated Quantities.html
@@ -6,7 +6,7 @@
- Generating new quantities of interest. — CmdStanPy 1.2.4 documentation
+ Generating new quantities of interest. — CmdStanPy 1.2.5 documentation
@@ -65,7 +65,7 @@
@@ -371,32 +371,32 @@ Example: add posterior predictive checks to
-14:28:47 - cmdstanpy - INFO - CmdStan start processing
+20:13:42 - cmdstanpy - INFO - CmdStan start processing
-
+
-
+
-
+
-
+
@@ -411,7 +411,7 @@ Example: add posterior predictive checks to
-14:28:47 - cmdstanpy - INFO - CmdStan done processing.
+20:13:42 - cmdstanpy - INFO - CmdStan done processing.
@@ -457,38 +457,41 @@ Example: add posterior predictive checks to
-14:28:47 - cmdstanpy - INFO - compiling stan file /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli_ppc.stan to exe file /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli_ppc
-14:28:54 - cmdstanpy - INFO - compiled model executable: /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli_ppc
+20:13:42 - cmdstanpy - INFO - compiling stan file /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli_ppc.stan to exe file /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli_ppc
+20:13:48 - cmdstanpy - INFO - compiled model executable: /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli_ppc
@@ -554,14 +557,14 @@ Example: add posterior predictive checks to
-14:28:54 - cmdstanpy - INFO - Chain [1] start processing
-14:28:54 - cmdstanpy - INFO - Chain [2] start processing
-14:28:54 - cmdstanpy - INFO - Chain [1] done processing
-14:28:54 - cmdstanpy - INFO - Chain [3] start processing
-14:28:54 - cmdstanpy - INFO - Chain [2] done processing
-14:28:54 - cmdstanpy - INFO - Chain [4] start processing
-14:28:54 - cmdstanpy - INFO - Chain [3] done processing
-14:28:54 - cmdstanpy - INFO - Chain [4] done processing
+20:13:48 - cmdstanpy - INFO - Chain [1] start processing
+20:13:48 - cmdstanpy - INFO - Chain [2] start processing
+20:13:49 - cmdstanpy - INFO - Chain [2] done processing
+20:13:49 - cmdstanpy - INFO - Chain [3] start processing
+20:13:49 - cmdstanpy - INFO - Chain [1] done processing
+20:13:49 - cmdstanpy - INFO - Chain [4] start processing
+20:13:49 - cmdstanpy - INFO - Chain [3] done processing
+20:13:49 - cmdstanpy - INFO - Chain [4] done processing
The generate_quantities method returns a CmdStanGQ object which contains the values for all variables in the generated quantities block of the program bernoulli_ppc.stan. Unlike the output from the sample method, it doesn’t contain any information on the joint log probability density, sampler state, or parameters or transformed parameter values.
@@ -581,10 +584,10 @@ Example: add posterior predictive checks to
-14:28:54 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
-14:28:54 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
-14:28:54 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
-14:28:54 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
+20:13:49 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
+20:13:49 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
+20:13:49 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
+20:13:49 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
@@ -593,18 +596,18 @@ Example: add posterior predictive checks to
(1000, 4, 10) ('y_rep[1]', 'y_rep[2]', 'y_rep[3]', 'y_rep[4]', 'y_rep[5]', 'y_rep[6]', 'y_rep[7]', 'y_rep[8]', 'y_rep[9]', 'y_rep[10]')
-[[0. 0. 0. 0. 0. 1. 0. 1. 0. 1.]
- [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
- [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
- [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]]
[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
- [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
- [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
- [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.]]
-[[1. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
- [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
- [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
- [1. 0. 1. 0. 1. 1. 1. 1. 0. 0.]]
+ [1. 0. 0. 0. 1. 1. 0. 0. 1. 1.]
+ [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.]
+ [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.]]
+[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
+ [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.]
+ [0. 1. 1. 0. 0. 0. 0. 0. 0. 0.]
+ [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
+[[0. 1. 1. 0. 0. 0. 0. 0. 0. 0.]
+ [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.]
+ [1. 1. 1. 0. 1. 1. 0. 0. 1. 0.]
+ [0. 1. 1. 0. 1. 0. 0. 0. 0. 0.]]
We can also use draws_pd(inc_sample=True) to get a pandas DataFrame which combines the input drawset with the generated quantities.
@@ -624,7 +627,7 @@ Example: add posterior predictive checks to
-14:28:54 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
+20:13:49 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
0.0
0.0
0.0
- 1.0
0.0
- 1.0
0.0
- 1.0
+ 0.0
+ 0.0
+ 0.0
1
- -6.90772
- 1.000000
- 1.02818
+ -8.81925
+ 0.917252
+ 0.941231
1.0
1.0
0.0
- 6.99126
- 0.324645
+ 8.91663
+ 0.064611
1.0
2.0
...
@@ -732,22 +735,22 @@ Example: add posterior predictive checks to
For models as simple as the bernoulli models here, it would be trivial to re-run the sampler and generate a new sample which contains both the estimate of the parameters theta as well as y_rep values. For models which are difficult to fit, i.e., when producing a sample is computationally expensive, the generate_quantities method is preferred.
diff --git a/docs/users-guide/examples/Run Generated Quantities.ipynb b/docs/users-guide/examples/Run Generated Quantities.ipynb
index 04e838cf..b31f9a65 100644
--- a/docs/users-guide/examples/Run Generated Quantities.ipynb
+++ b/docs/users-guide/examples/Run Generated Quantities.ipynb
@@ -42,10 +42,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:28:47.251333Z",
- "iopub.status.busy": "2024-06-17T14:28:47.251128Z",
- "iopub.status.idle": "2024-06-17T14:28:47.687087Z",
- "shell.execute_reply": "2024-06-17T14:28:47.686341Z"
+ "iopub.execute_input": "2024-12-11T20:13:41.967145Z",
+ "iopub.status.busy": "2024-12-11T20:13:41.966923Z",
+ "iopub.status.idle": "2024-12-11T20:13:42.391307Z",
+ "shell.execute_reply": "2024-12-11T20:13:42.390555Z"
}
},
"outputs": [
@@ -94,10 +94,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:28:47.690233Z",
- "iopub.status.busy": "2024-06-17T14:28:47.689743Z",
- "iopub.status.idle": "2024-06-17T14:28:47.701004Z",
- "shell.execute_reply": "2024-06-17T14:28:47.700351Z"
+ "iopub.execute_input": "2024-12-11T20:13:42.393966Z",
+ "iopub.status.busy": "2024-12-11T20:13:42.393427Z",
+ "iopub.status.idle": "2024-12-11T20:13:42.445962Z",
+ "shell.execute_reply": "2024-12-11T20:13:42.445251Z"
}
},
"outputs": [
@@ -132,10 +132,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:28:47.703664Z",
- "iopub.status.busy": "2024-06-17T14:28:47.703288Z",
- "iopub.status.idle": "2024-06-17T14:28:47.865942Z",
- "shell.execute_reply": "2024-06-17T14:28:47.865284Z"
+ "iopub.execute_input": "2024-12-11T20:13:42.448297Z",
+ "iopub.status.busy": "2024-12-11T20:13:42.447879Z",
+ "iopub.status.idle": "2024-12-11T20:13:42.587350Z",
+ "shell.execute_reply": "2024-12-11T20:13:42.586654Z"
}
},
"outputs": [
@@ -143,13 +143,13 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "14:28:47 - cmdstanpy - INFO - CmdStan start processing\n"
+ "20:13:42 - cmdstanpy - INFO - CmdStan start processing\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "9107fc3141d64beda5d3521a003c3e56",
+ "model_id": "9a12c29e8925418e81e8aa7f0de1735b",
"version_major": 2,
"version_minor": 0
},
@@ -163,7 +163,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "19576b01f6f9420d8f720a1e31538277",
+ "model_id": "bac16aad807549edb87a181134d61dfa",
"version_major": 2,
"version_minor": 0
},
@@ -177,7 +177,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "5605b669ea224de6a5b4667615f623d5",
+ "model_id": "311b017484f74d0cbf2480870d6c7079",
"version_major": 2,
"version_minor": 0
},
@@ -191,7 +191,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "7cc7b0472add42e0bf5beb41a01b6be8",
+ "model_id": "ee0ff6c9100b4a5c9d90bc1df2d4c10a",
"version_major": 2,
"version_minor": 0
},
@@ -234,7 +234,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "14:28:47 - cmdstanpy - INFO - CmdStan done processing.\n"
+ "20:13:42 - cmdstanpy - INFO - CmdStan done processing.\n"
]
},
{
@@ -262,10 +262,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:28:47.868728Z",
- "iopub.status.busy": "2024-06-17T14:28:47.868305Z",
- "iopub.status.idle": "2024-06-17T14:28:47.918070Z",
- "shell.execute_reply": "2024-06-17T14:28:47.917359Z"
+ "iopub.execute_input": "2024-12-11T20:13:42.589677Z",
+ "iopub.status.busy": "2024-12-11T20:13:42.589410Z",
+ "iopub.status.idle": "2024-12-11T20:13:42.643547Z",
+ "shell.execute_reply": "2024-12-11T20:13:42.642824Z"
}
},
"outputs": [
@@ -293,51 +293,54 @@
" Mean \n",
" MCSE \n",
" StdDev \n",
+ " MAD \n",
" 5% \n",
" 50% \n",
" 95% \n",
- " N_Eff \n",
- " N_Eff/s \n",
+ " ESS_bulk \n",
+ " ESS_tail \n",
" R_hat \n",
"
\n",
" \n",
" \n",
" \n",
" lp__ \n",
- " -7.289450 \n",
- " 0.021530 \n",
- " 0.713553 \n",
- " -8.717780 \n",
- " -7.01163 \n",
- " -6.750090 \n",
- " 1098.43 \n",
- " 21968.7 \n",
- " 1.00177 \n",
+ " -7.278850 \n",
+ " 0.017433 \n",
+ " 0.751723 \n",
+ " 0.329078 \n",
+ " -8.75763 \n",
+ " -6.988810 \n",
+ " -6.750330 \n",
+ " 1878.92 \n",
+ " 2316.36 \n",
+ " 1.00333 \n",
" \n",
" \n",
" theta \n",
- " 0.249577 \n",
- " 0.003296 \n",
- " 0.121900 \n",
- " 0.079791 \n",
- " 0.23454 \n",
- " 0.477362 \n",
- " 1367.84 \n",
- " 27356.8 \n",
- " 1.00060 \n",
+ " 0.247887 \n",
+ " 0.002998 \n",
+ " 0.119387 \n",
+ " 0.122025 \n",
+ " 0.07774 \n",
+ " 0.233396 \n",
+ " 0.465563 \n",
+ " 1525.63 \n",
+ " 2038.89 \n",
+ " 1.00384 \n",
" \n",
" \n",
"\n",
"
-14:28:58 - cmdstanpy - INFO - Chain [1] start processing
-14:28:58 - cmdstanpy - INFO - Chain [1] done processing
+20:13:53 - cmdstanpy - INFO - Chain [1] start processing
+20:13:53 - cmdstanpy - INFO - Chain [1] done processing
The class `CmdStanVB <https://mc-stan.org/cmdstanpy/api.html#cmdstanvb>`__ provides the following properties to access information about the parameter names, estimated means, and the sample:
@@ -355,7 +355,7 @@ Example: variational inference for model
-0.232085
+0.219985
@@ -389,10 +389,10 @@ Example: variational inference for model
-14:28:58 - cmdstanpy - INFO - compiling stan file /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/eta_should_fail.stan to exe file /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/eta_should_fail
-14:29:07 - cmdstanpy - INFO - compiled model executable: /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/eta_should_fail
-14:29:07 - cmdstanpy - INFO - Chain [1] start processing
-14:29:07 - cmdstanpy - INFO - Chain [1] done processing
+20:13:53 - cmdstanpy - INFO - compiling stan file /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/eta_should_fail.stan to exe file /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/eta_should_fail
+20:14:02 - cmdstanpy - INFO - compiled model executable: /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/eta_should_fail
+20:14:02 - cmdstanpy - INFO - Chain [1] start processing
+20:14:02 - cmdstanpy - INFO - Chain [1] done processing
@@ -439,9 +439,9 @@ Example: variational inference for model
-14:29:08 - cmdstanpy - INFO - Chain [1] start processing
-14:29:08 - cmdstanpy - INFO - Chain [1] done processing
-14:29:08 - cmdstanpy - WARNING - The algorithm may not have converged.
+20:14:02 - cmdstanpy - INFO - Chain [1] start processing
+20:14:02 - cmdstanpy - INFO - Chain [1] done processing
+20:14:02 - cmdstanpy - WARNING - The algorithm may not have converged.
Proceeding because require_converged is set to False
@@ -463,8 +463,8 @@ Example: variational inference for model API documentation for a full description of all arguments.
diff --git a/docs/users-guide/examples/Variational Inference.ipynb b/docs/users-guide/examples/Variational Inference.ipynb
index 4dbf6f06..416b3916 100644
--- a/docs/users-guide/examples/Variational Inference.ipynb
+++ b/docs/users-guide/examples/Variational Inference.ipynb
@@ -38,10 +38,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:28:58.100981Z",
- "iopub.status.busy": "2024-06-17T14:28:58.100364Z",
- "iopub.status.idle": "2024-06-17T14:28:58.580389Z",
- "shell.execute_reply": "2024-06-17T14:28:58.579576Z"
+ "iopub.execute_input": "2024-12-11T20:13:53.085362Z",
+ "iopub.status.busy": "2024-12-11T20:13:53.084852Z",
+ "iopub.status.idle": "2024-12-11T20:13:53.524825Z",
+ "shell.execute_reply": "2024-12-11T20:13:53.524175Z"
}
},
"outputs": [
@@ -49,14 +49,14 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "14:28:58 - cmdstanpy - INFO - Chain [1] start processing\n"
+ "20:13:53 - cmdstanpy - INFO - Chain [1] start processing\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "14:28:58 - cmdstanpy - INFO - Chain [1] done processing\n"
+ "20:13:53 - cmdstanpy - INFO - Chain [1] done processing\n"
]
}
],
@@ -95,10 +95,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:28:58.583770Z",
- "iopub.status.busy": "2024-06-17T14:28:58.583309Z",
- "iopub.status.idle": "2024-06-17T14:28:58.587467Z",
- "shell.execute_reply": "2024-06-17T14:28:58.586792Z"
+ "iopub.execute_input": "2024-12-11T20:13:53.527556Z",
+ "iopub.status.busy": "2024-12-11T20:13:53.527178Z",
+ "iopub.status.idle": "2024-12-11T20:13:53.530898Z",
+ "shell.execute_reply": "2024-12-11T20:13:53.530331Z"
},
"scrolled": true
},
@@ -120,10 +120,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:28:58.590018Z",
- "iopub.status.busy": "2024-06-17T14:28:58.589624Z",
- "iopub.status.idle": "2024-06-17T14:28:58.593359Z",
- "shell.execute_reply": "2024-06-17T14:28:58.592668Z"
+ "iopub.execute_input": "2024-12-11T20:13:53.532888Z",
+ "iopub.status.busy": "2024-12-11T20:13:53.532510Z",
+ "iopub.status.idle": "2024-12-11T20:13:53.536135Z",
+ "shell.execute_reply": "2024-12-11T20:13:53.535603Z"
}
},
"outputs": [
@@ -131,7 +131,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "0.232085\n"
+ "0.219985\n"
]
}
],
@@ -144,10 +144,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:28:58.595924Z",
- "iopub.status.busy": "2024-06-17T14:28:58.595544Z",
- "iopub.status.idle": "2024-06-17T14:28:58.598883Z",
- "shell.execute_reply": "2024-06-17T14:28:58.598240Z"
+ "iopub.execute_input": "2024-12-11T20:13:53.538290Z",
+ "iopub.status.busy": "2024-12-11T20:13:53.537908Z",
+ "iopub.status.idle": "2024-12-11T20:13:53.540998Z",
+ "shell.execute_reply": "2024-12-11T20:13:53.540423Z"
}
},
"outputs": [
@@ -177,10 +177,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:28:58.601541Z",
- "iopub.status.busy": "2024-06-17T14:28:58.601174Z",
- "iopub.status.idle": "2024-06-17T14:29:08.222715Z",
- "shell.execute_reply": "2024-06-17T14:29:08.222017Z"
+ "iopub.execute_input": "2024-12-11T20:13:53.543197Z",
+ "iopub.status.busy": "2024-12-11T20:13:53.542831Z",
+ "iopub.status.idle": "2024-12-11T20:14:02.860813Z",
+ "shell.execute_reply": "2024-12-11T20:14:02.860080Z"
},
"tags": [
"raises-exception"
@@ -191,28 +191,28 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "14:28:58 - cmdstanpy - INFO - compiling stan file /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/eta_should_fail.stan to exe file /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/eta_should_fail\n"
+ "20:13:53 - cmdstanpy - INFO - compiling stan file /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/eta_should_fail.stan to exe file /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/eta_should_fail\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "14:29:07 - cmdstanpy - INFO - compiled model executable: /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/eta_should_fail\n"
+ "20:14:02 - cmdstanpy - INFO - compiled model executable: /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/eta_should_fail\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "14:29:07 - cmdstanpy - INFO - Chain [1] start processing\n"
+ "20:14:02 - cmdstanpy - INFO - Chain [1] start processing\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "14:29:07 - cmdstanpy - INFO - Chain [1] done processing\n"
+ "20:14:02 - cmdstanpy - INFO - Chain [1] done processing\n"
]
},
{
@@ -245,10 +245,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:29:08.225483Z",
- "iopub.status.busy": "2024-06-17T14:29:08.225271Z",
- "iopub.status.idle": "2024-06-17T14:29:08.269361Z",
- "shell.execute_reply": "2024-06-17T14:29:08.268748Z"
+ "iopub.execute_input": "2024-12-11T20:14:02.863237Z",
+ "iopub.status.busy": "2024-12-11T20:14:02.863022Z",
+ "iopub.status.idle": "2024-12-11T20:14:02.906243Z",
+ "shell.execute_reply": "2024-12-11T20:14:02.905531Z"
}
},
"outputs": [
@@ -256,21 +256,21 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "14:29:08 - cmdstanpy - INFO - Chain [1] start processing\n"
+ "20:14:02 - cmdstanpy - INFO - Chain [1] start processing\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "14:29:08 - cmdstanpy - INFO - Chain [1] done processing\n"
+ "20:14:02 - cmdstanpy - INFO - Chain [1] done processing\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "14:29:08 - cmdstanpy - WARNING - The algorithm may not have converged.\n",
+ "20:14:02 - cmdstanpy - WARNING - The algorithm may not have converged.\n",
"Proceeding because require_converged is set to False\n"
]
}
@@ -291,10 +291,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-06-17T14:29:08.272039Z",
- "iopub.status.busy": "2024-06-17T14:29:08.271809Z",
- "iopub.status.idle": "2024-06-17T14:29:08.279234Z",
- "shell.execute_reply": "2024-06-17T14:29:08.278595Z"
+ "iopub.execute_input": "2024-12-11T20:14:02.908643Z",
+ "iopub.status.busy": "2024-12-11T20:14:02.908207Z",
+ "iopub.status.idle": "2024-12-11T20:14:02.915448Z",
+ "shell.execute_reply": "2024-12-11T20:14:02.914822Z"
}
},
"outputs": [
@@ -304,8 +304,8 @@
"OrderedDict([('lp__', np.float64(0.0)),\n",
" ('log_p__', np.float64(0.0)),\n",
" ('log_g__', np.float64(0.0)),\n",
- " ('mu[1]', np.float64(-0.009621)),\n",
- " ('mu[2]', np.float64(0.0106908))])"
+ " ('mu[1]', np.float64(0.01902)),\n",
+ " ('mu[2]', np.float64(-0.0121132))])"
]
},
"execution_count": 7,
@@ -341,7 +341,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.19"
+ "version": "3.9.20"
}
},
"nbformat": 4,
diff --git a/docs/users-guide/hello_world.html b/docs/users-guide/hello_world.html
index 61abca68..48ae53a7 100644
--- a/docs/users-guide/hello_world.html
+++ b/docs/users-guide/hello_world.html
@@ -6,7 +6,7 @@
- “Hello, World!” — CmdStanPy 1.2.4 documentation
+ “Hello, World!” — CmdStanPy 1.2.5 documentation
@@ -64,7 +64,7 @@
@@ -370,7 +370,7 @@ The Stan model# inspect compiled model
In [6]: print(model.exe_info())
-{'stan_version_major': '2', 'stan_version_minor': '35', 'stan_version_patch': '0', 'STAN_THREADS': 'false', 'STAN_MPI': 'false', 'STAN_OPENCL': 'false', 'STAN_NO_RANGE_CHECKS': 'false', 'STAN_CPP_OPTIMS': 'false'}
+{'stan_version_major': '2', 'stan_version_minor': '36', 'stan_version_patch': '0', 'STAN_THREADS': 'false', 'STAN_MPI': 'false', 'STAN_OPENCL': 'false', 'STAN_NO_RANGE_CHECKS': 'false', 'STAN_CPP_OPTIMS': 'false'}
@@ -444,13 +444,13 @@ Accessing the results
# access model variable by name
In [9]: print(fit.stan_variable('theta'))
-[0.224491 0.125899 0.28874 ... 0.344788 0.337899 0.384419]
+[0.266313 0.398688 0.176153 ... 0.185479 0.261594 0.0543699]
In [10]: print(fit.draws_pd('theta')[:3])
theta
-0 0.224491
-1 0.125899
-2 0.288740
+0 0.266313
+1 0.398688
+2 0.176153
In [11]: print(fit.draws_xr('theta'))
<xarray.Dataset> Size: 40kB
@@ -459,9 +459,9 @@ Accessing the results * chain (chain) int64 32B 1 2 3 4
* draw (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999
Data variables:
- theta (chain, draw) float64 32kB 0.2245 0.1259 0.2887 ... 0.3379 0.3844
+ theta (chain, draw) float64 32kB 0.2663 0.3987 0.1762 ... 0.2616 0.05437
Attributes:
- stan_version: 2.35.0
+ stan_version: 2.36.0
model: bernoulli_model
num_draws_sampling: 1000
@@ -490,17 +490,17 @@ Accessing the resultsIn [15]: fit.draws_pd()
Out[15]:
chain__ iter__ draw__ ... divergent__ energy__ theta
-0 1.0 1.0 1.0 ... 0.0 7.73101 0.224491
-1 1.0 2.0 2.0 ... 0.0 7.66064 0.125899
-2 1.0 3.0 3.0 ... 0.0 7.99122 0.288740
-3 1.0 4.0 4.0 ... 0.0 7.64745 0.149887
-4 1.0 5.0 5.0 ... 0.0 8.33723 0.373402
+0 1.0 1.0 1.0 ... 0.0 6.77449 0.266313
+1 1.0 2.0 2.0 ... 0.0 7.34089 0.398688
+2 1.0 3.0 3.0 ... 0.0 7.58750 0.176153
+3 1.0 4.0 4.0 ... 0.0 7.44928 0.124428
+4 1.0 5.0 5.0 ... 0.0 7.57075 0.129482
... ... ... ... ... ... ... ...
-3995 4.0 996.0 3996.0 ... 0.0 7.24381 0.326292
-3996 4.0 997.0 3997.0 ... 0.0 7.92789 0.341451
-3997 4.0 998.0 3998.0 ... 0.0 7.06679 0.344788
-3998 4.0 999.0 3999.0 ... 0.0 7.04801 0.337899
-3999 4.0 1000.0 4000.0 ... 0.0 8.92492 0.384419
+3995 4.0 996.0 3996.0 ... 0.0 6.96443 0.306744
+3996 4.0 997.0 3997.0 ... 0.0 7.32427 0.185479
+3997 4.0 998.0 3998.0 ... 0.0 7.41252 0.185479
+3998 4.0 999.0 3999.0 ... 0.0 6.87178 0.261594
+3999 4.0 1000.0 4000.0 ... 0.0 9.23901 0.054370
[4000 rows x 11 columns]
@@ -512,13 +512,13 @@ Accessing the resultsdiag_e
In [17]: print(fit.metric)
-[[0.549616]
- [0.443372]
- [0.47257 ]
- [0.443371]]
+[[0.406074]
+ [0.448938]
+ [0.426418]
+ [0.508836]]
In [18]: print(fit.step_size)
-[0.82683 1.20311 1.03741 1.02497]
+[1.2098 0.862289 1.14032 0.912742]
The CmdStanMCMC object also provides access to metadata about the model and the sampler run.
@@ -526,7 +526,7 @@ Accessing the resultsbernoulli_model
In [20]: print(fit.metadata.cmdstan_config['seed'])
-92628
+75306
@@ -541,11 +541,11 @@ CmdStan utilities: In [21]: fit.summary()
Out[21]:
- Mean MCSE StdDev ... N_Eff N_Eff/s R_hat
-lp__ -7.279690 0.019540 0.751782 ... 1480.28 34425.1 1.00131
-theta 0.245071 0.003002 0.118943 ... 1569.59 36502.0 1.00091
+ Mean MCSE StdDev ... ESS_bulk ESS_tail R_hat
+lp__ -7.302450 0.028292 0.829225 ... 1548.39 1417.96 1.00105
+theta 0.248121 0.003227 0.120999 ... 1291.12 1252.88 1.00219
-[2 rows x 9 columns]
+[2 rows x 10 columns]
CmdStan is distributed with a second posterior analysis utility
@@ -555,8 +555,6 @@
CmdStan utilities: diagnose()
method runs this utility and prints the output to the console.
In [22]: print(fit.diagnose())
-Processing csv files: /tmp/tmpz6k203wo/bernoulli4w8qmwpw/bernoulli-20240617142916_1.csv, /tmp/tmpz6k203wo/bernoulli4w8qmwpw/bernoulli-20240617142916_2.csv, /tmp/tmpz6k203wo/bernoulli4w8qmwpw/bernoulli-20240617142916_3.csv, /tmp/tmpz6k203wo/bernoulli4w8qmwpw/bernoulli-20240617142916_4.csv
-
Checking sampler transitions treedepth.
Treedepth satisfactory for all transitions.
@@ -566,9 +564,9 @@ CmdStan utilities: Checking E-BFMI - sampler transitions HMC potential energy.
E-BFMI satisfactory.
-Effective sample size satisfactory.
+Rank-normalized split effective sample size satisfactory for all parameters.
-Split R-hat values satisfactory all parameters.
+Rank-normalized split R-hat values satisfactory for all parameters.
Processing complete, no problems detected.
diff --git a/docs/users-guide/outputs.html b/docs/users-guide/outputs.html
index 0f5207ec..75e571a8 100644
--- a/docs/users-guide/outputs.html
+++ b/docs/users-guide/outputs.html
@@ -6,7 +6,7 @@
- Controlling Outputs — CmdStanPy 1.2.4 documentation
+ Controlling Outputs — CmdStanPy 1.2.5 documentation
@@ -64,7 +64,7 @@
@@ -307,15 +307,15 @@ CSV File OutputsIn [7]: print(fit)
CmdStanMCMC: model=bernoulli chains=4['method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
csv_files:
- /tmp/tmpz6k203wo/bernoulliojkfgzo6/bernoulli-20240617142916_1.csv
- /tmp/tmpz6k203wo/bernoulliojkfgzo6/bernoulli-20240617142916_2.csv
- /tmp/tmpz6k203wo/bernoulliojkfgzo6/bernoulli-20240617142916_3.csv
- /tmp/tmpz6k203wo/bernoulliojkfgzo6/bernoulli-20240617142916_4.csv
+ /tmp/tmprj9uktub/bernoulli_c76olso/bernoulli-20241211201410_1.csv
+ /tmp/tmprj9uktub/bernoulli_c76olso/bernoulli-20241211201410_2.csv
+ /tmp/tmprj9uktub/bernoulli_c76olso/bernoulli-20241211201410_3.csv
+ /tmp/tmprj9uktub/bernoulli_c76olso/bernoulli-20241211201410_4.csv
output_files:
- /tmp/tmpz6k203wo/bernoulliojkfgzo6/bernoulli-20240617142916_0-stdout.txt
- /tmp/tmpz6k203wo/bernoulliojkfgzo6/bernoulli-20240617142916_1-stdout.txt
- /tmp/tmpz6k203wo/bernoulliojkfgzo6/bernoulli-20240617142916_2-stdout.txt
- /tmp/tmpz6k203wo/bernoulliojkfgzo6/bernoulli-20240617142916_3-stdout.txt
+ /tmp/tmprj9uktub/bernoulli_c76olso/bernoulli-20241211201410_0-stdout.txt
+ /tmp/tmprj9uktub/bernoulli_c76olso/bernoulli-20241211201410_1-stdout.txt
+ /tmp/tmprj9uktub/bernoulli_c76olso/bernoulli-20241211201410_2-stdout.txt
+ /tmp/tmprj9uktub/bernoulli_c76olso/bernoulli-20241211201410_3-stdout.txt
The output_dir argument is an optional argument which specifies
@@ -329,10 +329,10 @@
CSV File OutputsINFO:cmdstanpy:CmdStan done processing.
In [9]: !ls outputs/
-bernoulli-20240617142916_0-stdout.txt bernoulli-20240617142916_2.csv
-bernoulli-20240617142916_1-stdout.txt bernoulli-20240617142916_3-stdout.txt
-bernoulli-20240617142916_1.csv bernoulli-20240617142916_3.csv
-bernoulli-20240617142916_2-stdout.txt bernoulli-20240617142916_4.csv
+bernoulli-20241211201410_0-stdout.txt bernoulli-20241211201410_2.csv
+bernoulli-20241211201410_1-stdout.txt bernoulli-20241211201410_3-stdout.txt
+bernoulli-20241211201410_1.csv bernoulli-20241211201410_3.csv
+bernoulli-20241211201410_2-stdout.txt bernoulli-20241211201410_4.csv
Alternatively, the save_csvfiles() function moves the CSV files
@@ -345,8 +345,8 @@
CSV File OutputsIn [11]: fit.save_csvfiles(dir='some/path')
In [12]: !ls some/path
-bernoulli-20240617142917_1.csv bernoulli-20240617142917_3.csv
-bernoulli-20240617142917_2.csv bernoulli-20240617142917_4.csv
+bernoulli-20241211201411_1.csv bernoulli-20241211201411_3.csv
+bernoulli-20241211201411_2.csv bernoulli-20241211201411_4.csv
@@ -365,7 +365,7 @@ LoggingINFO:cmdstanpy:Chain [2] done processing
-This output is managed through the built-in logging module. For example, it can be disabled entirely:
+This output is managed through the built-in logging module. For example, it can be disabled entirely:
In [14]: import logging
In [15]: cmdstanpy_logger = logging.getLogger("cmdstanpy")
@@ -409,40 +409,40 @@ Logging ....: for line in logs.readlines():
....: print(line.strip())
....:
-14:29:17 - cmdstanpy - DEBUG - cmd: /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli info
+20:14:11 - cmdstanpy - DEBUG - cmd: /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli info
cwd: None
-14:29:17 - cmdstanpy - INFO - CmdStan start processing
-14:29:17 - cmdstanpy - DEBUG - idx 0
-14:29:17 - cmdstanpy - DEBUG - idx 1
-14:29:17 - cmdstanpy - DEBUG - running CmdStan, num_threads: 1
-14:29:17 - cmdstanpy - DEBUG - idx 2
-14:29:17 - cmdstanpy - DEBUG - idx 3
-14:29:17 - cmdstanpy - DEBUG - running CmdStan, num_threads: 1
-14:29:17 - cmdstanpy - DEBUG - CmdStan args: ['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=1', 'random', 'seed=11039', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmpz6k203wo/bernoulliwu4xc0ot/bernoulli-20240617142917_1.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
-14:29:17 - cmdstanpy - DEBUG - running CmdStan, num_threads: 1
-14:29:17 - cmdstanpy - DEBUG - running CmdStan, num_threads: 1
-14:29:17 - cmdstanpy - DEBUG - CmdStan args: ['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=2', 'random', 'seed=11039', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmpz6k203wo/bernoulliwu4xc0ot/bernoulli-20240617142917_2.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
-14:29:17 - cmdstanpy - INFO - Chain [1] start processing
-14:29:17 - cmdstanpy - DEBUG - CmdStan args: ['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=3', 'random', 'seed=11039', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmpz6k203wo/bernoulliwu4xc0ot/bernoulli-20240617142917_3.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
-14:29:17 - cmdstanpy - DEBUG - CmdStan args: ['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=4', 'random', 'seed=11039', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmpz6k203wo/bernoulliwu4xc0ot/bernoulli-20240617142917_4.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
-14:29:17 - cmdstanpy - INFO - Chain [2] start processing
-14:29:17 - cmdstanpy - INFO - Chain [3] start processing
-14:29:17 - cmdstanpy - INFO - Chain [4] start processing
-14:29:17 - cmdstanpy - INFO - Chain [1] done processing
-14:29:17 - cmdstanpy - INFO - Chain [4] done processing
-14:29:17 - cmdstanpy - INFO - Chain [3] done processing
-14:29:17 - cmdstanpy - INFO - Chain [2] done processing
-14:29:17 - cmdstanpy - DEBUG - runset
+20:14:11 - cmdstanpy - INFO - CmdStan start processing
+20:14:11 - cmdstanpy - DEBUG - idx 0
+20:14:11 - cmdstanpy - DEBUG - idx 1
+20:14:11 - cmdstanpy - DEBUG - running CmdStan, num_threads: 1
+20:14:11 - cmdstanpy - DEBUG - idx 2
+20:14:11 - cmdstanpy - DEBUG - running CmdStan, num_threads: 1
+20:14:11 - cmdstanpy - DEBUG - idx 3
+20:14:11 - cmdstanpy - DEBUG - CmdStan args: ['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=1', 'random', 'seed=6352', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmprj9uktub/bernoullil140l7_8/bernoulli-20241211201411_1.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
+20:14:11 - cmdstanpy - DEBUG - running CmdStan, num_threads: 1
+20:14:11 - cmdstanpy - DEBUG - CmdStan args: ['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=2', 'random', 'seed=6352', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmprj9uktub/bernoullil140l7_8/bernoulli-20241211201411_2.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
+20:14:11 - cmdstanpy - DEBUG - running CmdStan, num_threads: 1
+20:14:11 - cmdstanpy - INFO - Chain [1] start processing
+20:14:11 - cmdstanpy - DEBUG - CmdStan args: ['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=3', 'random', 'seed=6352', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmprj9uktub/bernoullil140l7_8/bernoulli-20241211201411_3.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
+20:14:11 - cmdstanpy - INFO - Chain [2] start processing
+20:14:11 - cmdstanpy - DEBUG - CmdStan args: ['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=4', 'random', 'seed=6352', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmprj9uktub/bernoullil140l7_8/bernoulli-20241211201411_4.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
+20:14:11 - cmdstanpy - INFO - Chain [3] start processing
+20:14:11 - cmdstanpy - INFO - Chain [4] start processing
+20:14:11 - cmdstanpy - INFO - Chain [1] done processing
+20:14:11 - cmdstanpy - INFO - Chain [3] done processing
+20:14:11 - cmdstanpy - INFO - Chain [2] done processing
+20:14:11 - cmdstanpy - INFO - Chain [4] done processing
+20:14:11 - cmdstanpy - DEBUG - runset
RunSet: chains=4, chain_ids=[1, 2, 3, 4], num_processes=4
cmd (chain 1):
-['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=1', 'random', 'seed=11039', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmpz6k203wo/bernoulliwu4xc0ot/bernoulli-20240617142917_1.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
+['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=1', 'random', 'seed=6352', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmprj9uktub/bernoullil140l7_8/bernoulli-20241211201411_1.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
retcodes=[0, 0, 0, 0]
per-chain output files (showing chain 1 only):
csv_file:
-/tmp/tmpz6k203wo/bernoulliwu4xc0ot/bernoulli-20240617142917_1.csv
+/tmp/tmprj9uktub/bernoullil140l7_8/bernoulli-20241211201411_1.csv
console_msgs (if any):
-/tmp/tmpz6k203wo/bernoulliwu4xc0ot/bernoulli-20240617142917_0-stdout.txt
-14:29:17 - cmdstanpy - DEBUG - Chain 1 console:
+/tmp/tmprj9uktub/bernoullil140l7_8/bernoulli-20241211201411_0-stdout.txt
+20:14:11 - cmdstanpy - DEBUG - Chain 1 console:
method = sample (Default)
sample
num_samples = 1000 (Default)
@@ -474,9 +474,9 @@ Loggingfile = users-guide/examples/bernoulli.data.json
init = 2 (Default)
random
-seed = 11039
+seed = 6352
output
-file = /tmp/tmpz6k203wo/bernoulliwu4xc0ot/bernoulli-20240617142917_1.csv
+file = /tmp/tmprj9uktub/bernoullil140l7_8/bernoulli-20241211201411_1.csv
diagnostic_file = (Default)
refresh = 100 (Default)
sig_figs = -1 (Default)
diff --git a/docs/users-guide/overview.html b/docs/users-guide/overview.html
index a9ce71a9..07d5d013 100644
--- a/docs/users-guide/overview.html
+++ b/docs/users-guide/overview.html
@@ -6,7 +6,7 @@
- Overview — CmdStanPy 1.2.4 documentation
+ Overview — CmdStanPy 1.2.5 documentation
@@ -64,7 +64,7 @@
diff --git a/docs/users-guide/workflow.html b/docs/users-guide/workflow.html
index bbc081b0..ea9a56ff 100644
--- a/docs/users-guide/workflow.html
+++ b/docs/users-guide/workflow.html
@@ -6,7 +6,7 @@
- CmdStanPy Workflow — CmdStanPy 1.2.4 documentation
+ CmdStanPy Workflow — CmdStanPy 1.2.5 documentation
@@ -64,7 +64,7 @@
@@ -466,7 +466,7 @@ Output dataCmdStanMCMC and CmdStanGQ return the sample contents
in tabular form, see draws() and draws_pd().
Similarly, the draws_xr() method returns the sample
-contents as an xarray.Dataset which is a mapping from variable names to their respective values.
+contents as an xarray.Dataset which is a mapping from variable names to their respective values.