"""
return ( # type: ignore
self._metadata.cmdstan_config.get("num_paths", 4) > 1
- and self._metadata.cmdstan_config.get('psis_resample', 1) == 1
- and self._metadata.cmdstan_config.get('calculate_lp', 1) == 1
+ and self._metadata.cmdstan_config.get('psis_resample', 1)
+ in (1, 'true')
+ and self._metadata.cmdstan_config.get('calculate_lp', 1)
+ in (1, 'true')
)
[docs] def save_csvfiles(self, dir: Optional[str] = None) -> None:
diff --git a/docs/_modules/cmdstanpy/stanfit/runset.html b/docs/_modules/cmdstanpy/stanfit/runset.html
index 2fc8301c..12af4654 100644
--- a/docs/_modules/cmdstanpy/stanfit/runset.html
+++ b/docs/_modules/cmdstanpy/stanfit/runset.html
@@ -5,7 +5,7 @@
- cmdstanpy.stanfit.runset — CmdStanPy 1.2.2 documentation
+ cmdstanpy.stanfit.runset — CmdStanPy 1.2.3 documentation
@@ -61,7 +61,7 @@
@@ -235,18 +235,26 @@ Source code for cmdstanpy.stanfit.runset
self._base_outfile = (
f'{args.model_name}-{datetime.now().strftime(time_fmt)}'
)
- # per-process console messages
+ # per-process outputs
self._stdout_files = [''] * self._num_procs
+ self._profile_files = [''] * self._num_procs # optional
if one_process_per_chain:
for i in range(chains):
self._stdout_files[i] = self.file_path("-stdout.txt", id=i)
+ if args.save_profile:
+ self._profile_files[i] = self.file_path(
+ ".csv", extra="-profile", id=chain_ids[i]
+ )
else:
self._stdout_files[0] = self.file_path("-stdout.txt")
+ if args.save_profile:
+ self._profile_files[0] = self.file_path(
+ ".csv", extra="-profile"
+ )
# per-chain output files
self._csv_files: List[str] = [''] * chains
self._diagnostic_files = [''] * chains # optional
- self._profile_files = [''] * chains # optional
if chains == 1:
self._csv_files[0] = self.file_path(".csv")
@@ -254,10 +262,6 @@ Source code for cmdstanpy.stanfit.runset
self._diagnostic_files[0] = self.file_path(
".csv", extra="-diagnostic"
)
- if args.save_profile:
- self._profile_files[0] = self.file_path(
- ".csv", extra="-profile"
- )
else:
for i in range(chains):
self._csv_files[i] = self.file_path(".csv", id=chain_ids[i])
@@ -265,10 +269,6 @@ Source code for cmdstanpy.stanfit.runset
self._diagnostic_files[i] = self.file_path(
".csv", extra="-diagnostic", id=chain_ids[i]
)
- if args.save_profile:
- self._profile_files[i] = self.file_path(
- ".csv", extra="-profile", id=chain_ids[i]
- )
def __repr__(self) -> str:
repr = 'RunSet: chains={}, chain_ids={}, num_processes={}'.format(
diff --git a/docs/_modules/cmdstanpy/stanfit/vb.html b/docs/_modules/cmdstanpy/stanfit/vb.html
index 49c5f0c0..0bb4ff9a 100644
--- a/docs/_modules/cmdstanpy/stanfit/vb.html
+++ b/docs/_modules/cmdstanpy/stanfit/vb.html
@@ -5,7 +5,7 @@
- cmdstanpy.stanfit.vb — CmdStanPy 1.2.2 documentation
+ cmdstanpy.stanfit.vb — CmdStanPy 1.2.3 documentation
@@ -61,7 +61,7 @@
diff --git a/docs/_modules/cmdstanpy/utils.html b/docs/_modules/cmdstanpy/utils.html
index ea1958ef..2c92fc88 100644
--- a/docs/_modules/cmdstanpy/utils.html
+++ b/docs/_modules/cmdstanpy/utils.html
@@ -5,7 +5,7 @@
- cmdstanpy.utils — CmdStanPy 1.2.2 documentation
+ cmdstanpy.utils — CmdStanPy 1.2.3 documentation
@@ -61,7 +61,7 @@
diff --git a/docs/_modules/cmdstanpy/utils/cmdstan.html b/docs/_modules/cmdstanpy/utils/cmdstan.html
index 46683793..f39ab1f1 100644
--- a/docs/_modules/cmdstanpy/utils/cmdstan.html
+++ b/docs/_modules/cmdstanpy/utils/cmdstan.html
@@ -5,7 +5,7 @@
- cmdstanpy.utils.cmdstan — CmdStanPy 1.2.2 documentation
+ cmdstanpy.utils.cmdstan — CmdStanPy 1.2.3 documentation
@@ -61,7 +61,7 @@
diff --git a/docs/_modules/index.html b/docs/_modules/index.html
index f1dce97b..e34d7057 100644
--- a/docs/_modules/index.html
+++ b/docs/_modules/index.html
@@ -5,7 +5,7 @@
- Overview: module code — CmdStanPy 1.2.2 documentation
+ Overview: module code — CmdStanPy 1.2.3 documentation
@@ -61,7 +61,7 @@
diff --git a/docs/_modules/stanio/json.html b/docs/_modules/stanio/json.html
index 551304fb..a69b7e96 100644
--- a/docs/_modules/stanio/json.html
+++ b/docs/_modules/stanio/json.html
@@ -5,7 +5,7 @@
- stanio.json — CmdStanPy 1.2.2 documentation
+ stanio.json — CmdStanPy 1.2.3 documentation
@@ -61,7 +61,7 @@
diff --git a/docs/_sources/changes.rst.txt b/docs/_sources/changes.rst.txt
index 15feeb75..651c46f0 100644
--- a/docs/_sources/changes.rst.txt
+++ b/docs/_sources/changes.rst.txt
@@ -7,6 +7,15 @@ What's New
For full changes, see the `Releases page `_ on GitHub.
+
+CmdStanPy 1.2.3
+---------------
+
+- Updated the logic around reading Stan CSV files to support CmdStan 2.35.0+
+- Fixed an issue where the ``profile_files`` member of the RunSet object was not correct when running multiple chains in the same process.
+
+Reminder: The next non-bugfix release of CmdStanPy will be version 2.0, which will remove all existing deprecations.
+
CmdStanPy 1.2.2
---------------
diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js
index 809835b6..19fee609 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: '1.2.2',
+ VERSION: '1.2.3',
LANGUAGE: 'en',
COLLAPSE_INDEX: false,
BUILDER: 'html',
diff --git a/docs/api.html b/docs/api.html
index 3ca6aa91..55fb874c 100644
--- a/docs/api.html
+++ b/docs/api.html
@@ -6,7 +6,7 @@
- API Reference — CmdStanPy 1.2.2 documentation
+ API Reference — CmdStanPy 1.2.3 documentation
@@ -64,7 +64,7 @@
@@ -1619,7 +1619,7 @@ CmdStanModelReturn type:
-
+
@@ -2226,7 +2226,7 @@ CmdStanMCMCReturn type:
-
+
@@ -2249,7 +2249,7 @@ CmdStanMCMCReturn type:
-
+
@@ -2383,7 +2383,7 @@ CmdStanMCMCpandas.DataFrame
Return type:
-
+
@@ -2585,7 +2585,7 @@ 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.
@@ -2608,7 +2608,7 @@ 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.
@@ -2652,7 +2652,7 @@ CmdStanLaplacevars (Optional[Union[List[str], str]]) – optional list of variable names.
Return type:
-
+
@@ -3052,7 +3052,7 @@ CmdStanVB
-property variational_params_pd: DataFrame¶
+property variational_params_pd: DataFrame¶
Returns inferred parameter means as pandas DataFrame.
CmdStanVB
-property variational_sample_pd: DataFrame¶
+property variational_sample_pd: DataFrame¶
Returns the set of approximate posterior output draws as
a pandas DataFrame.
CmdStanGQReturn type:
-
+
@@ -3155,7 +3155,7 @@ 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) 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.
@@ -3531,7 +3531,7 @@ write_stan_jsonnumpy.asarray(), e.g a
-pandas.Series.
+pandas.Series.
Produces a file compatible with the
Json Format for Cmdstan
@@ -3540,7 +3540,7 @@ write_stan_jsonstr) – 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
+or something more exotic like an xarray.Dataset. This will be
copied before type conversion, not modified
What’s New — CmdStanPy 1.2.2 documentation
+ What’s New — CmdStanPy 1.2.3 documentation
@@ -64,7 +64,7 @@
@@ -172,6 +172,11 @@
-
-
-
-
-
-
-16:29:42 - cmdstanpy - INFO - Chain [1] done processing
+15:16:07 - cmdstanpy - INFO - Chain [1] start processing
+15:16:07 - cmdstanpy - INFO - Chain [1] done processing
@@ -321,7 +314,7 @@ Maximum Likelihood Estimation
('lp__', 'theta')
-OrderedDict([('lp__', -5.00402), ('theta', 0.199999)])
+OrderedDict([('lp__', -5.00402), ('theta', 0.200001)])
@@ -355,7 +348,7 @@ Maximum Likelihood Estimation\n",
" 0 \n",
" -5.00402 \n",
- " 0.199999 \n",
+ " 0.200001 \n",
" \n",
" \n",
"\n",
@@ -105,7 +105,7 @@
],
"text/plain": [
" lp__ theta\n",
- "0 -5.00402 0.199999"
+ "0 -5.00402 0.200001"
]
},
"execution_count": 1,
@@ -148,7 +148,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.18"
+ "version": "3.9.19"
}
},
"nbformat": 4,
diff --git a/docs/users-guide/examples/Pathfinder.html b/docs/users-guide/examples/Pathfinder.html
index c0e27d42..6bf48608 100644
--- a/docs/users-guide/examples/Pathfinder.html
+++ b/docs/users-guide/examples/Pathfinder.html
@@ -6,7 +6,7 @@
- Variational Inference using Pathfinder — CmdStanPy 1.2.2 documentation
+ Variational Inference using Pathfinder — CmdStanPy 1.2.3 documentation
@@ -65,7 +65,7 @@
@@ -317,20 +317,13 @@ Example: variational inference with Pathfinder for model
-
-
-
-
-16:29:44 - cmdstanpy - INFO - Chain [1] start processing
-
-
-16:29:44 - cmdstanpy - INFO - Chain [1] done processing
+15:16:09 - cmdstanpy - INFO - Chain [1] start processing
+15:16:09 - cmdstanpy - INFO - Chain [1] done processing
@@ -349,11 +342,11 @@ Example: variational inference with Pathfinder for model
CmdStanPathfinder: model=bernoulli['method=pathfinder']
csv_files:
- /tmp/tmpimv2ege0/bernoullifbcxwakw/bernoulli-20240326162944.csv
+ /tmp/tmpy5plbz4g/bernoullio19cm8ts/bernoulli-20240603151609.csv
output_files:
- /tmp/tmpimv2ege0/bernoullifbcxwakw/bernoulli-20240326162944_0-stdout.txt
+ /tmp/tmpy5plbz4g/bernoullio19cm8ts/bernoulli-20240603151609_0-stdout.txt
Metadata:
-{'stan_version_major': 2, 'stan_version_minor': 34, 'stan_version_patch': 1, 'model': 'bernoulli_model', 'start_datetime': '2024-03-26 16:29:44 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-2.34.1/examples/bernoulli/bernoulli.data.json', 'init': 2, 'seed': 50890, 'diagnostic_file': '', 'refresh': 100, 'sig_figs': -1, 'profile_file': 'profile.csv', 'save_cmdstan_config': 0, 'num_threads': 1, 'stanc_version': 'stanc3 v2.34.0', 'stancflags': '', 'raw_header': 'lp_approx__,lp__,theta', 'column_names': ('lp_approx__', 'lp__', 'theta')}
+{'stan_version_major': 2, 'stan_version_minor': 35, 'stan_version_patch': 0, 'model': 'bernoulli_model', 'start_datetime': '2024-06-03 15:16:09 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': 'false', 'psis_resample': 'true', 'calculate_lp': 'true', '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': 37888, 'diagnostic_file': '', 'refresh': 100, 'sig_figs': -1, 'profile_file': 'profile.csv', 'save_cmdstan_config': 'false', 'num_threads': 1, 'stanc_version': 'stanc3 v2.35.0', 'stancflags': '', 'raw_header': 'lp_approx__,lp__,theta', 'column_names': ('lp_approx__', 'lp__', 'theta')}
@@ -431,7 +424,7 @@ Pathfinders as initialization for the MCMC sampler
-[{'theta': array(0.278046)}, {'theta': array(0.189515)}, {'theta': array(0.28219)}, {'theta': array(0.174821)}]
+[{'theta': array(0.244849)}, {'theta': array(0.0384281)}, {'theta': array(0.148251)}, {'theta': array(0.532504)}]
The create_inits takes two arguments:
@@ -453,7 +446,7 @@ Pathfinders as initialization for the MCMC sampler
-[{'theta': array(0.151519)}, {'theta': array(0.156614)}, {'theta': array(0.066908)}]
+[{'theta': array(0.295373)}, {'theta': array(0.223648)}, {'theta': array(0.238005)}]
diff --git a/docs/users-guide/examples/Pathfinder.ipynb b/docs/users-guide/examples/Pathfinder.ipynb
index daeee884..6f695652 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": "2024-03-26T16:29:43.878098Z",
- "iopub.status.busy": "2024-03-26T16:29:43.877555Z",
- "iopub.status.idle": "2024-03-26T16:29:44.225458Z",
- "shell.execute_reply": "2024-03-26T16:29:44.224871Z"
+ "iopub.execute_input": "2024-06-03T15:16:09.012959Z",
+ "iopub.status.busy": "2024-06-03T15:16:09.012455Z",
+ "iopub.status.idle": "2024-06-03T15:16:09.394972Z",
+ "shell.execute_reply": "2024-06-03T15:16:09.394222Z"
}
},
"outputs": [],
@@ -59,10 +59,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-03-26T16:29:44.228431Z",
- "iopub.status.busy": "2024-03-26T16:29:44.227964Z",
- "iopub.status.idle": "2024-03-26T16:29:44.313140Z",
- "shell.execute_reply": "2024-03-26T16:29:44.312554Z"
+ "iopub.execute_input": "2024-06-03T15:16:09.398404Z",
+ "iopub.status.busy": "2024-06-03T15:16:09.397898Z",
+ "iopub.status.idle": "2024-06-03T15:16:09.489569Z",
+ "shell.execute_reply": "2024-06-03T15:16:09.488916Z"
}
},
"outputs": [
@@ -70,14 +70,14 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "16:29:44 - cmdstanpy - INFO - Chain [1] start processing\n"
+ "15:16:09 - cmdstanpy - INFO - Chain [1] start processing\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "16:29:44 - cmdstanpy - INFO - Chain [1] done processing\n"
+ "15:16:09 - cmdstanpy - INFO - Chain [1] done processing\n"
]
}
],
@@ -96,10 +96,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-03-26T16:29:44.316072Z",
- "iopub.status.busy": "2024-03-26T16:29:44.315710Z",
- "iopub.status.idle": "2024-03-26T16:29:44.319485Z",
- "shell.execute_reply": "2024-03-26T16:29:44.318839Z"
+ "iopub.execute_input": "2024-06-03T15:16:09.492418Z",
+ "iopub.status.busy": "2024-06-03T15:16:09.492083Z",
+ "iopub.status.idle": "2024-06-03T15:16:09.496146Z",
+ "shell.execute_reply": "2024-06-03T15:16:09.495479Z"
}
},
"outputs": [
@@ -109,11 +109,11 @@
"text": [
"CmdStanPathfinder: model=bernoulli['method=pathfinder']\n",
" csv_files:\n",
- "\t/tmp/tmpimv2ege0/bernoullifbcxwakw/bernoulli-20240326162944.csv\n",
+ "\t/tmp/tmpy5plbz4g/bernoullio19cm8ts/bernoulli-20240603151609.csv\n",
" output_files:\n",
- "\t/tmp/tmpimv2ege0/bernoullifbcxwakw/bernoulli-20240326162944_0-stdout.txt\n",
+ "\t/tmp/tmpy5plbz4g/bernoullio19cm8ts/bernoulli-20240603151609_0-stdout.txt\n",
"Metadata:\n",
- "{'stan_version_major': 2, 'stan_version_minor': 34, 'stan_version_patch': 1, 'model': 'bernoulli_model', 'start_datetime': '2024-03-26 16:29:44 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-2.34.1/examples/bernoulli/bernoulli.data.json', 'init': 2, 'seed': 50890, 'diagnostic_file': '', 'refresh': 100, 'sig_figs': -1, 'profile_file': 'profile.csv', 'save_cmdstan_config': 0, 'num_threads': 1, 'stanc_version': 'stanc3 v2.34.0', 'stancflags': '', 'raw_header': 'lp_approx__,lp__,theta', 'column_names': ('lp_approx__', 'lp__', 'theta')}\n",
+ "{'stan_version_major': 2, 'stan_version_minor': 35, 'stan_version_patch': 0, 'model': 'bernoulli_model', 'start_datetime': '2024-06-03 15:16:09 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': 'false', 'psis_resample': 'true', 'calculate_lp': 'true', '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': 37888, 'diagnostic_file': '', 'refresh': 100, 'sig_figs': -1, 'profile_file': 'profile.csv', 'save_cmdstan_config': 'false', 'num_threads': 1, 'stanc_version': 'stanc3 v2.35.0', '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": "2024-03-26T16:29:44.322023Z",
- "iopub.status.busy": "2024-03-26T16:29:44.321649Z",
- "iopub.status.idle": "2024-03-26T16:29:44.328711Z",
- "shell.execute_reply": "2024-03-26T16:29:44.328190Z"
+ "iopub.execute_input": "2024-06-03T15:16:09.498714Z",
+ "iopub.status.busy": "2024-06-03T15:16:09.498384Z",
+ "iopub.status.idle": "2024-06-03T15:16:09.506298Z",
+ "shell.execute_reply": "2024-06-03T15:16:09.505764Z"
}
},
"outputs": [
@@ -172,10 +172,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-03-26T16:29:44.331257Z",
- "iopub.status.busy": "2024-03-26T16:29:44.330886Z",
- "iopub.status.idle": "2024-03-26T16:29:44.335028Z",
- "shell.execute_reply": "2024-03-26T16:29:44.334351Z"
+ "iopub.execute_input": "2024-06-03T15:16:09.508565Z",
+ "iopub.status.busy": "2024-06-03T15:16:09.508374Z",
+ "iopub.status.idle": "2024-06-03T15:16:09.512501Z",
+ "shell.execute_reply": "2024-06-03T15:16:09.511929Z"
}
},
"outputs": [
@@ -199,10 +199,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-03-26T16:29:44.337482Z",
- "iopub.status.busy": "2024-03-26T16:29:44.337102Z",
- "iopub.status.idle": "2024-03-26T16:29:44.341146Z",
- "shell.execute_reply": "2024-03-26T16:29:44.340523Z"
+ "iopub.execute_input": "2024-06-03T15:16:09.515022Z",
+ "iopub.status.busy": "2024-06-03T15:16:09.514578Z",
+ "iopub.status.idle": "2024-06-03T15:16:09.518838Z",
+ "shell.execute_reply": "2024-06-03T15:16:09.518209Z"
}
},
"outputs": [
@@ -235,10 +235,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-03-26T16:29:44.343774Z",
- "iopub.status.busy": "2024-03-26T16:29:44.343296Z",
- "iopub.status.idle": "2024-03-26T16:29:44.347590Z",
- "shell.execute_reply": "2024-03-26T16:29:44.346955Z"
+ "iopub.execute_input": "2024-06-03T15:16:09.521282Z",
+ "iopub.status.busy": "2024-06-03T15:16:09.520928Z",
+ "iopub.status.idle": "2024-06-03T15:16:09.525033Z",
+ "shell.execute_reply": "2024-06-03T15:16:09.524349Z"
}
},
"outputs": [
@@ -246,7 +246,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[{'theta': array(0.278046)}, {'theta': array(0.189515)}, {'theta': array(0.28219)}, {'theta': array(0.174821)}]\n"
+ "[{'theta': array(0.244849)}, {'theta': array(0.0384281)}, {'theta': array(0.148251)}, {'theta': array(0.532504)}]\n"
]
}
],
@@ -270,10 +270,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-03-26T16:29:44.350084Z",
- "iopub.status.busy": "2024-03-26T16:29:44.349681Z",
- "iopub.status.idle": "2024-03-26T16:29:44.353504Z",
- "shell.execute_reply": "2024-03-26T16:29:44.352850Z"
+ "iopub.execute_input": "2024-06-03T15:16:09.527587Z",
+ "iopub.status.busy": "2024-06-03T15:16:09.527198Z",
+ "iopub.status.idle": "2024-06-03T15:16:09.531009Z",
+ "shell.execute_reply": "2024-06-03T15:16:09.530368Z"
}
},
"outputs": [
@@ -281,7 +281,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[{'theta': array(0.151519)}, {'theta': array(0.156614)}, {'theta': array(0.066908)}]\n"
+ "[{'theta': array(0.295373)}, {'theta': array(0.223648)}, {'theta': array(0.238005)}]\n"
]
}
],
@@ -307,7 +307,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.18"
+ "version": "3.9.19"
}
},
"nbformat": 4,
diff --git a/docs/users-guide/examples/Run Generated Quantities.html b/docs/users-guide/examples/Run Generated Quantities.html
index fd0543f3..d264cec3 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 1.2.2 documentation
+ Generating new quantities of interest. — CmdStanPy 1.2.3 documentation
@@ -65,7 +65,7 @@
@@ -320,13 +320,13 @@ Example: add posterior predictive checks to
data {
int<lower=0> N;
- array[N] int<lower=0,upper=1> y;
+ array[N] int<lower=0, upper=1> y;
}
parameters {
- real<lower=0,upper=1> theta;
+ real<lower=0, upper=1> theta;
}
model {
- theta ~ beta(1,1); // uniform prior on interval 0,1
+ theta ~ beta(1, 1); // uniform prior on interval 0,1
y ~ bernoulli(theta);
}
@@ -371,56 +371,32 @@ Example: add posterior predictive checks to
-16:29:46 - cmdstanpy - INFO - CmdStan start processing
+15:16:11 - cmdstanpy - INFO - CmdStan start processing
-
-
-
-
-
-
-16:29:46 - cmdstanpy - INFO - CmdStan done processing.
+15:16:11 - cmdstanpy - INFO - CmdStan done processing.
-16:29:46 - 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
-
-
-16:30:00 - cmdstanpy - INFO - compiled model executable: /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli_ppc
+15:16:11 - 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
+15:16:18 - cmdstanpy - INFO - compiled model executable: /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli_ppc
-
-
-
-
-16:30:00 - cmdstanpy - INFO - Chain [1] start processing
-
-
-16:30:00 - cmdstanpy - INFO - Chain [2] start processing
-
-16:30:00 - cmdstanpy - INFO - Chain [1] done processing
-
-16:30:00 - cmdstanpy - INFO - Chain [3] start processing
-
-16:30:00 - cmdstanpy - INFO - Chain [2] done processing
-
-16:30:00 - cmdstanpy - INFO - Chain [4] start processing
-
-16:30:00 - cmdstanpy - INFO - Chain [3] done processing
-
-16:30:00 - cmdstanpy - INFO - Chain [4] done processing
+15:16:18 - cmdstanpy - INFO - Chain [1] start processing
+15:16:18 - cmdstanpy - INFO - Chain [2] start processing
+15:16:18 - cmdstanpy - INFO - Chain [2] done processing
+15:16:18 - cmdstanpy - INFO - Chain [3] start processing
+15:16:18 - cmdstanpy - INFO - Chain [1] done processing
+15:16:18 - cmdstanpy - INFO - Chain [4] start processing
+15:16:18 - cmdstanpy - INFO - Chain [3] done processing
+15:16:18 - 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.
-16:30:00 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
-
-
-
-
-
-
-
-16:30:00 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
-
-
-
-
-
-
-
-16:30:00 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
-
-
-
-
-
-
-
-16:30:00 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
+15:16:18 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
+15:16:18 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
+15:16:18 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
+15:16:18 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
@@ -694,18 +593,18 @@ 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. 1. 1. 0. 0. 0. 0.]
- [0. 0. 0. 0. 1. 1. 0. 0. 0. 0.]
- [0. 0. 0. 0. 1. 1. 0. 0. 0. 0.]
- [0. 0. 0. 0. 1. 1. 0. 0. 0. 0.]]
-[[0. 0. 0. 1. 0. 0. 0. 0. 1. 1.]
- [0. 0. 0. 1. 0. 0. 0. 0. 1. 1.]
- [0. 0. 0. 1. 0. 0. 0. 0. 1. 1.]
- [0. 0. 0. 1. 0. 0. 0. 0. 0. 1.]]
-[[0. 0. 0. 0. 0. 0. 0. 0. 1. 1.]
- [0. 0. 0. 0. 0. 0. 0. 0. 1. 1.]
- [0. 0. 0. 0. 0. 0. 0. 0. 1. 1.]
- [0. 0. 0. 0. 0. 0. 0. 0. 1. 1.]]
+[[0. 1. 0. 1. 0. 1. 0. 1. 0. 1.]
+ [0. 1. 0. 1. 0. 1. 1. 1. 1. 1.]
+ [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.]
+ [0. 1. 0. 1. 0. 1. 0. 1. 0. 1.]]
+[[0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]
+ [0. 1. 0. 1. 0. 1. 1. 1. 0. 0.]
+ [0. 1. 0. 0. 0. 1. 1. 1. 0. 0.]
+ [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]]
+[[1. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
+ [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
+ [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
+ [1. 0. 0. 0. 1. 0. 1. 1. 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.
@@ -725,7 +624,7 @@ Example: add posterior predictive checks to
-16:30:00 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
+15:16:18 - cmdstanpy - WARNING - Sample doesn't contain draws from warmup iterations, rerun sampler with "save_warmup=True".
@@ -785,42 +684,44 @@ Example: add posterior predictive checks to 0.0
0.0
0.0
- 1.0
- 1.0
2
- -6.77679
- 0.998779
- 1.17203
+ -6.75063
+ 0.995885
+ 0.871561
2.0
3.0
0.0
- 6.94984
- 0.220845
+ 6.78282
+ 0.259094
1.0
3.0
...
+ 1.0
0.0
0.0
0.0
- 0.0
- 0.0
- 0.0
- 0.0
+ 1.0
0.0
1.0
1.0
+ 0.0
+ 0.0
@@ -862,7 +761,7 @@ Example: add posterior predictive checks to 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 80477c75..2c40a829 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": "2024-03-26T16:29:46.036648Z",
- "iopub.status.busy": "2024-03-26T16:29:46.036452Z",
- "iopub.status.idle": "2024-03-26T16:29:46.441221Z",
- "shell.execute_reply": "2024-03-26T16:29:46.440481Z"
+ "iopub.execute_input": "2024-06-03T15:16:11.130769Z",
+ "iopub.status.busy": "2024-06-03T15:16:11.130570Z",
+ "iopub.status.idle": "2024-06-03T15:16:11.580932Z",
+ "shell.execute_reply": "2024-06-03T15:16:11.580097Z"
}
},
"outputs": [
@@ -55,13 +55,13 @@
"text": [
"data {\n",
" int N;\n",
- " array[N] int y;\n",
+ " array[N] int y;\n",
"}\n",
"parameters {\n",
- " real theta;\n",
+ " real theta;\n",
"}\n",
"model {\n",
- " theta ~ beta(1,1); // uniform prior on interval 0,1\n",
+ " theta ~ beta(1, 1); // uniform prior on interval 0,1\n",
" y ~ bernoulli(theta);\n",
"}\n",
"\n"
@@ -94,10 +94,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-03-26T16:29:46.444455Z",
- "iopub.status.busy": "2024-03-26T16:29:46.443867Z",
- "iopub.status.idle": "2024-03-26T16:29:46.502957Z",
- "shell.execute_reply": "2024-03-26T16:29:46.502392Z"
+ "iopub.execute_input": "2024-06-03T15:16:11.584396Z",
+ "iopub.status.busy": "2024-06-03T15:16:11.583656Z",
+ "iopub.status.idle": "2024-06-03T15:16:11.623878Z",
+ "shell.execute_reply": "2024-06-03T15:16:11.623146Z"
}
},
"outputs": [
@@ -132,10 +132,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-03-26T16:29:46.505763Z",
- "iopub.status.busy": "2024-03-26T16:29:46.505069Z",
- "iopub.status.idle": "2024-03-26T16:29:46.709551Z",
- "shell.execute_reply": "2024-03-26T16:29:46.708948Z"
+ "iopub.execute_input": "2024-06-03T15:16:11.626769Z",
+ "iopub.status.busy": "2024-06-03T15:16:11.626259Z",
+ "iopub.status.idle": "2024-06-03T15:16:11.764092Z",
+ "shell.execute_reply": "2024-06-03T15:16:11.763407Z"
}
},
"outputs": [
@@ -143,13 +143,13 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "16:29:46 - cmdstanpy - INFO - CmdStan start processing\n"
+ "15:16:11 - cmdstanpy - INFO - CmdStan start processing\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "d8220583290146688fa0dcd1925ad1e5",
+ "model_id": "4cfbf0700df24ca996b29e5e17fcff1d",
"version_major": 2,
"version_minor": 0
},
@@ -163,7 +163,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "46749833be2f497f927fdb925199d231",
+ "model_id": "e380ccf194c34b2fb539d4aa4d457eb9",
"version_major": 2,
"version_minor": 0
},
@@ -177,7 +177,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "f52d3bec872b4454b3ab4912a4ccd42a",
+ "model_id": "0b26c6fe294946a7b1858e300334b22b",
"version_major": 2,
"version_minor": 0
},
@@ -191,7 +191,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "da5c161a59bc4cdaa557c3421c211f32",
+ "model_id": "2f22749b12ea4e46a23a088fd606ca3f",
"version_major": 2,
"version_minor": 0
},
@@ -234,7 +234,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "16:29:46 - cmdstanpy - INFO - CmdStan done processing.\n"
+ "15:16:11 - cmdstanpy - INFO - CmdStan done processing.\n"
]
},
{
@@ -262,10 +262,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-03-26T16:29:46.712397Z",
- "iopub.status.busy": "2024-03-26T16:29:46.711862Z",
- "iopub.status.idle": "2024-03-26T16:29:46.756228Z",
- "shell.execute_reply": "2024-03-26T16:29:46.755501Z"
+ "iopub.execute_input": "2024-06-03T15:16:11.767175Z",
+ "iopub.status.busy": "2024-06-03T15:16:11.766711Z",
+ "iopub.status.idle": "2024-06-03T15:16:11.814023Z",
+ "shell.execute_reply": "2024-06-03T15:16:11.813251Z"
}
},
"outputs": [
@@ -304,40 +304,40 @@
" \n",
" \n",
" lp__ \n",
- " -7.297320 \n",
- " 0.017912 \n",
- " 0.755274 \n",
- " -8.851160 \n",
- " -6.996700 \n",
- " -6.750000 \n",
- " 1778.01 \n",
- " 36286.0 \n",
- " 1.00036 \n",
+ " -7.265470 \n",
+ " 0.017901 \n",
+ " 0.71385 \n",
+ " -8.753410 \n",
+ " -6.974640 \n",
+ " -6.749960 \n",
+ " 1590.15 \n",
+ " 30002.8 \n",
+ " 1.00045 \n",
" \n",
" \n",
" theta \n",
- " 0.250792 \n",
- " 0.003315 \n",
- " 0.121648 \n",
- " 0.076211 \n",
- " 0.240263 \n",
- " 0.470491 \n",
- " 1346.53 \n",
- " 27480.2 \n",
- " 1.00109 \n",
+ " 0.249812 \n",
+ " 0.002907 \n",
+ " 0.11983 \n",
+ " 0.080009 \n",
+ " 0.234871 \n",
+ " 0.474189 \n",
+ " 1699.67 \n",
+ " 32069.3 \n",
+ " 1.00081 \n",
" \n",
" \n",
"\n",
"
"
],
"text/plain": [
- " Mean MCSE StdDev 5% 50% 95% N_Eff \\\n",
- "lp__ -7.297320 0.017912 0.755274 -8.851160 -6.996700 -6.750000 1778.01 \n",
- "theta 0.250792 0.003315 0.121648 0.076211 0.240263 0.470491 1346.53 \n",
+ " Mean MCSE StdDev 5% 50% 95% N_Eff \\\n",
+ "lp__ -7.265470 0.017901 0.71385 -8.753410 -6.974640 -6.749960 1590.15 \n",
+ "theta 0.249812 0.002907 0.11983 0.080009 0.234871 0.474189 1699.67 \n",
"\n",
" N_Eff/s R_hat \n",
- "lp__ 36286.0 1.00036 \n",
- "theta 27480.2 1.00109 "
+ "lp__ 30002.8 1.00045 \n",
+ "theta 32069.3 1.00081 "
]
},
"execution_count": 4,
@@ -361,10 +361,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-03-26T16:29:46.759240Z",
- "iopub.status.busy": "2024-03-26T16:29:46.758703Z",
- "iopub.status.idle": "2024-03-26T16:30:00.640995Z",
- "shell.execute_reply": "2024-03-26T16:30:00.640392Z"
+ "iopub.execute_input": "2024-06-03T15:16:11.817140Z",
+ "iopub.status.busy": "2024-06-03T15:16:11.816593Z",
+ "iopub.status.idle": "2024-06-03T15:16:18.438529Z",
+ "shell.execute_reply": "2024-06-03T15:16:18.437911Z"
}
},
"outputs": [
@@ -372,14 +372,14 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "16:29:46 - 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\n"
+ "15:16:11 - 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\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "16:30:00 - cmdstanpy - INFO - compiled model executable: /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli_ppc\n"
+ "15:16:18 - cmdstanpy - INFO - compiled model executable: /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli_ppc\n"
]
},
{
@@ -430,10 +430,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-03-26T16:30:00.643833Z",
- "iopub.status.busy": "2024-03-26T16:30:00.643524Z",
- "iopub.status.idle": "2024-03-26T16:30:00.701815Z",
- "shell.execute_reply": "2024-03-26T16:30:00.701030Z"
+ "iopub.execute_input": "2024-06-03T15:16:18.441163Z",
+ "iopub.status.busy": "2024-06-03T15:16:18.440895Z",
+ "iopub.status.idle": "2024-06-03T15:16:18.502457Z",
+ "shell.execute_reply": "2024-06-03T15:16:18.501873Z"
}
},
"outputs": [
@@ -441,56 +441,56 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "16:30:00 - cmdstanpy - INFO - Chain [1] start processing\n"
+ "15:16:18 - cmdstanpy - INFO - Chain [1] start processing\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "16:30:00 - cmdstanpy - INFO - Chain [2] start processing\n"
+ "15:16:18 - cmdstanpy - INFO - Chain [2] start processing\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "16:30:00 - cmdstanpy - INFO - Chain [1] done processing\n"
+ "15:16:18 - cmdstanpy - INFO - Chain [2] done processing\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "16:30:00 - cmdstanpy - INFO - Chain [3] start processing\n"
+ "15:16:18 - cmdstanpy - INFO - Chain [3] start processing\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "16:30:00 - cmdstanpy - INFO - Chain [2] done processing\n"
+ "15:16:18 - cmdstanpy - INFO - Chain [1] done processing\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "16:30:00 - cmdstanpy - INFO - Chain [4] start processing\n"
+ "15:16:18 - cmdstanpy - INFO - Chain [4] start processing\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "16:30:00 - cmdstanpy - INFO - Chain [3] done processing\n"
+ "15:16:18 - cmdstanpy - INFO - Chain [3] done processing\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "16:30:00 - cmdstanpy - INFO - Chain [4] done processing\n"
+ "15:16:18 - cmdstanpy - INFO - Chain [4] done processing\n"
]
}
],
@@ -512,10 +512,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-03-26T16:30:00.704656Z",
- "iopub.status.busy": "2024-03-26T16:30:00.704243Z",
- "iopub.status.idle": "2024-03-26T16:30:00.728113Z",
- "shell.execute_reply": "2024-03-26T16:30:00.727557Z"
+ "iopub.execute_input": "2024-06-03T15:16:18.505505Z",
+ "iopub.status.busy": "2024-06-03T15:16:18.505010Z",
+ "iopub.status.idle": "2024-06-03T15:16:18.528731Z",
+ "shell.execute_reply": "2024-06-03T15:16:18.528055Z"
}
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diff --git a/docs/users-guide/examples/Using External C++.html b/docs/users-guide/examples/Using External C++.html
index 6b17cd33..112b3c7f 100644
--- a/docs/users-guide/examples/Using External C++.html
+++ b/docs/users-guide/examples/Using External C++.html
@@ -6,7 +6,7 @@
- Advanced Topic: Using External C++ Functions — CmdStanPy 1.2.2 documentation
+ Advanced Topic: Using External C++ Functions — CmdStanPy 1.2.3 documentation
@@ -65,7 +65,7 @@
diff --git a/docs/users-guide/examples/VI as Sampler Inits.html b/docs/users-guide/examples/VI as Sampler Inits.html
index 72a53041..d233dc86 100644
--- a/docs/users-guide/examples/VI as Sampler Inits.html
+++ b/docs/users-guide/examples/VI as Sampler Inits.html
@@ -6,7 +6,7 @@
- Using Variational Estimates to Initialize the NUTS-HMC Sampler — CmdStanPy 1.2.2 documentation
+ Using Variational Estimates to Initialize the NUTS-HMC Sampler — CmdStanPy 1.2.3 documentation
@@ -65,7 +65,7 @@
diff --git a/docs/users-guide/examples/Variational Inference.html b/docs/users-guide/examples/Variational Inference.html
index de350720..eb49fc11 100644
--- a/docs/users-guide/examples/Variational Inference.html
+++ b/docs/users-guide/examples/Variational Inference.html
@@ -6,7 +6,7 @@
- Variational Inference using ADVI — CmdStanPy 1.2.2 documentation
+ Variational Inference using ADVI — CmdStanPy 1.2.3 documentation
@@ -65,7 +65,7 @@
@@ -306,20 +306,13 @@ Example: variational inference for model
-
-
-
-
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-
-
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+15:16:22 - cmdstanpy - INFO - Chain [1] start processing
+15:16:22 - 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:
@@ -362,7 +355,7 @@ Example: variational inference for model
-0.228927
+0.235248
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-16:30:04 - 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
-
-
-
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-
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+15:16:22 - 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
+15:16:31 - cmdstanpy - INFO - compiled model executable: /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/eta_should_fail
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@@ -462,28 +434,14 @@ Example: variational inference for model
-
-
-
-
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-
-
-
-
-
-
-
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-
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+15:16:31 - cmdstanpy - INFO - Chain [1] start processing
+15:16:32 - cmdstanpy - INFO - Chain [1] done processing
+15:16:32 - cmdstanpy - WARNING - The algorithm may not have converged.
Proceeding because require_converged is set to False
@@ -505,8 +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 809a35ff..f85efa34 100644
--- a/docs/users-guide/examples/Variational Inference.ipynb
+++ b/docs/users-guide/examples/Variational Inference.ipynb
@@ -38,10 +38,10 @@
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@@ -49,14 +49,14 @@
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@@ -131,7 +131,7 @@
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@@ -191,28 +191,28 @@
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+ "15:16:22 - 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"
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- "16:30:21 - cmdstanpy - INFO - compiled model executable: /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/eta_should_fail\n"
+ "15:16:31 - cmdstanpy - INFO - compiled model executable: /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/eta_should_fail\n"
]
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- "16:30:21 - cmdstanpy - INFO - Chain [1] start processing\n"
+ "15:16:31 - cmdstanpy - INFO - Chain [1] start processing\n"
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+ "15:16:31 - cmdstanpy - INFO - Chain [1] done processing\n"
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@@ -245,10 +245,10 @@
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@@ -256,21 +256,21 @@
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{
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+ "15:16:32 - cmdstanpy - INFO - Chain [1] done processing\n"
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{
"name": "stderr",
"output_type": "stream",
"text": [
- "16:30:21 - cmdstanpy - WARNING - The algorithm may not have converged.\n",
+ "15:16:32 - cmdstanpy - WARNING - The algorithm may not have converged.\n",
"Proceeding because require_converged is set to False\n"
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@@ -291,10 +291,10 @@
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@@ -304,8 +304,8 @@
"OrderedDict([('lp__', 0.0),\n",
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" ('log_g__', 0.0),\n",
- " ('mu[1]', -0.0227764),\n",
- " ('mu[2]', -0.057944)])"
+ " ('mu[1]', 0.0107279),\n",
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@@ -341,7 +341,7 @@
"name": "python",
"nbconvert_exporter": "python",
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diff --git a/docs/users-guide/hello_world.html b/docs/users-guide/hello_world.html
index db22686e..18e66b17 100644
--- a/docs/users-guide/hello_world.html
+++ b/docs/users-guide/hello_world.html
@@ -6,7 +6,7 @@
- “Hello, World!” — CmdStanPy 1.2.2 documentation
+ “Hello, World!” — CmdStanPy 1.2.3 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': '34', 'stan_version_patch': '1', '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': '35', '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'))
-[0.174379 0.142577 0.104175 ... 0.296568 0.275516 0.289409]
+[0.346358 0.363043 0.457438 ... 0.184323 0.446334 0.167843]
In [10]: print(fit.draws_pd('theta')[:3])
theta
-0 0.174379
-1 0.142577
-2 0.104175
+0 0.346358
+1 0.363043
+2 0.457438
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 0.1744 0.1426 0.1042 ... 0.2755 0.2894
+ theta (chain, draw) float64 32kB 0.3464 0.363 0.4574 ... 0.4463 0.1678
Attributes:
- stan_version: 2.34.1
+ stan_version: 2.35.0
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 6.96995 0.174379
-1 1.0 2.0 2.0 ... 0.0 7.23168 0.142577
-2 1.0 3.0 3.0 ... 0.0 8.29890 0.104175
-3 1.0 4.0 4.0 ... 0.0 8.29236 0.083123
-4 1.0 5.0 5.0 ... 0.0 8.59298 0.076468
+0 1.0 1.0 1.0 ... 0.0 7.67257 0.346358
+1 1.0 2.0 2.0 ... 0.0 7.13510 0.363043
+2 1.0 3.0 3.0 ... 0.0 7.85056 0.457438
+3 1.0 4.0 4.0 ... 0.0 7.67333 0.214156
+4 1.0 5.0 5.0 ... 0.0 7.98950 0.327391
... ... ... ... ... ... ... ...
-3995 4.0 996.0 3996.0 ... 0.0 7.11186 0.362054
-3996 4.0 997.0 3997.0 ... 0.0 7.09438 0.333609
-3997 4.0 998.0 3998.0 ... 0.0 6.90865 0.296568
-3998 4.0 999.0 3999.0 ... 0.0 6.79980 0.275516
-3999 4.0 1000.0 4000.0 ... 0.0 6.79844 0.289409
+3995 4.0 996.0 3996.0 ... 0.0 7.21214 0.297294
+3996 4.0 997.0 3997.0 ... 0.0 6.84100 0.230654
+3997 4.0 998.0 3998.0 ... 0.0 6.91366 0.184323
+3998 4.0 999.0 3999.0 ... 0.0 7.93451 0.446334
+3999 4.0 1000.0 4000.0 ... 0.0 7.60918 0.167843
[4000 rows x 11 columns]
@@ -512,13 +512,13 @@ Accessing the resultsdiag_e
In [17]: print(fit.metric)
-[[0.548827]
- [0.618353]
- [0.488154]
- [0.506284]]
+[[0.558439]
+ [0.419612]
+ [0.570816]
+ [0.471435]]
In [18]: print(fit.step_size)
-[0.916475 0.945805 0.890823 1.05791 ]
+[0.776642 1.19095 0.971258 1.03637 ]
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'])
-5650
+90219
@@ -542,8 +542,8 @@ CmdStan utilities: In [21]: fit.summary()
Out[21]:
Mean MCSE StdDev ... N_Eff N_Eff/s R_hat
-lp__ -7.244140 0.016066 0.686744 ... 1827.16 45679.0 1.00054
-theta 0.249347 0.003168 0.116642 ... 1356.00 33900.0 1.00428
+lp__ -7.259150 0.021195 0.728839 ... 1182.45 23185.4 1.00573
+theta 0.249476 0.003148 0.118158 ... 1408.51 27617.8 1.00640
[2 rows x 9 columns]
@@ -555,7 +555,7 @@ CmdStan utilities: diagnose()
method runs this utility and prints the output to the console.
In [22]: print(fit.diagnose())
-Processing csv files: /tmp/tmpgvv426mx/bernoullig33uprfe/bernoulli-20240326163037_1.csv, /tmp/tmpgvv426mx/bernoullig33uprfe/bernoulli-20240326163037_2.csv, /tmp/tmpgvv426mx/bernoullig33uprfe/bernoulli-20240326163037_3.csv, /tmp/tmpgvv426mx/bernoullig33uprfe/bernoulli-20240326163037_4.csv
+Processing csv files: /tmp/tmpxrifw9p7/bernoullix5j71i2y/bernoulli-20240603151640_1.csv, /tmp/tmpxrifw9p7/bernoullix5j71i2y/bernoulli-20240603151640_2.csv, /tmp/tmpxrifw9p7/bernoullix5j71i2y/bernoulli-20240603151640_3.csv, /tmp/tmpxrifw9p7/bernoullix5j71i2y/bernoulli-20240603151640_4.csv
Checking sampler transitions treedepth.
Treedepth satisfactory for all transitions.
diff --git a/docs/users-guide/outputs.html b/docs/users-guide/outputs.html
index 7f925524..6ec9b383 100644
--- a/docs/users-guide/outputs.html
+++ b/docs/users-guide/outputs.html
@@ -6,7 +6,7 @@
- Controlling Outputs — CmdStanPy 1.2.2 documentation
+ Controlling Outputs — CmdStanPy 1.2.3 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/tmpgvv426mx/bernoulliigzvjuln/bernoulli-20240326163037_1.csv
- /tmp/tmpgvv426mx/bernoulliigzvjuln/bernoulli-20240326163037_2.csv
- /tmp/tmpgvv426mx/bernoulliigzvjuln/bernoulli-20240326163037_3.csv
- /tmp/tmpgvv426mx/bernoulliigzvjuln/bernoulli-20240326163037_4.csv
+ /tmp/tmpxrifw9p7/bernoullixihd21f3/bernoulli-20240603151640_1.csv
+ /tmp/tmpxrifw9p7/bernoullixihd21f3/bernoulli-20240603151640_2.csv
+ /tmp/tmpxrifw9p7/bernoullixihd21f3/bernoulli-20240603151640_3.csv
+ /tmp/tmpxrifw9p7/bernoullixihd21f3/bernoulli-20240603151640_4.csv
output_files:
- /tmp/tmpgvv426mx/bernoulliigzvjuln/bernoulli-20240326163037_0-stdout.txt
- /tmp/tmpgvv426mx/bernoulliigzvjuln/bernoulli-20240326163037_1-stdout.txt
- /tmp/tmpgvv426mx/bernoulliigzvjuln/bernoulli-20240326163037_2-stdout.txt
- /tmp/tmpgvv426mx/bernoulliigzvjuln/bernoulli-20240326163037_3-stdout.txt
+ /tmp/tmpxrifw9p7/bernoullixihd21f3/bernoulli-20240603151640_0-stdout.txt
+ /tmp/tmpxrifw9p7/bernoullixihd21f3/bernoulli-20240603151640_1-stdout.txt
+ /tmp/tmpxrifw9p7/bernoullixihd21f3/bernoulli-20240603151640_2-stdout.txt
+ /tmp/tmpxrifw9p7/bernoullixihd21f3/bernoulli-20240603151640_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-20240326163037_0-stdout.txt bernoulli-20240326163037_2.csv
-bernoulli-20240326163037_1-stdout.txt bernoulli-20240326163037_3-stdout.txt
-bernoulli-20240326163037_1.csv bernoulli-20240326163037_3.csv
-bernoulli-20240326163037_2-stdout.txt bernoulli-20240326163037_4.csv
+bernoulli-20240603151640_0-stdout.txt bernoulli-20240603151640_2.csv
+bernoulli-20240603151640_1-stdout.txt bernoulli-20240603151640_3-stdout.txt
+bernoulli-20240603151640_1.csv bernoulli-20240603151640_3.csv
+bernoulli-20240603151640_2-stdout.txt bernoulli-20240603151640_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-20240326163037_1.csv bernoulli-20240326163037_3.csv
-bernoulli-20240326163037_2.csv bernoulli-20240326163037_4.csv
+bernoulli-20240603151640_1.csv bernoulli-20240603151640_3.csv
+bernoulli-20240603151640_2.csv bernoulli-20240603151640_4.csv
@@ -359,10 +359,10 @@ LoggingINFO:cmdstanpy:Chain [2] start processing
INFO:cmdstanpy:Chain [3] start processing
INFO:cmdstanpy:Chain [4] start processing
-INFO:cmdstanpy:Chain [1] done processing
-INFO:cmdstanpy:Chain [4] done processing
INFO:cmdstanpy:Chain [3] done processing
INFO:cmdstanpy:Chain [2] done processing
+INFO:cmdstanpy:Chain [1] done processing
+INFO:cmdstanpy:Chain [4] done processing
This output is managed through the built-in logging module. For example, it can be disabled entirely:
@@ -409,48 +409,48 @@ Logging ....: for line in logs.readlines():
....: print(line.strip())
....:
-16:30:38 - cmdstanpy - DEBUG - cmd: /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli info
+15:16:41 - cmdstanpy - DEBUG - cmd: /home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli info
cwd: None
-16:30:38 - cmdstanpy - INFO - CmdStan start processing
-16:30:38 - cmdstanpy - DEBUG - idx 0
-16:30:38 - cmdstanpy - DEBUG - idx 1
-16:30:38 - cmdstanpy - DEBUG - running CmdStan, num_threads: 1
-16:30:38 - cmdstanpy - DEBUG - idx 2
-16:30:38 - cmdstanpy - DEBUG - running CmdStan, num_threads: 1
-16:30:38 - cmdstanpy - DEBUG - idx 3
-16:30:38 - cmdstanpy - DEBUG - CmdStan args: ['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=1', 'random', 'seed=23084', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmpgvv426mx/bernoulli1wek20la/bernoulli-20240326163038_1.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
-16:30:38 - cmdstanpy - DEBUG - running CmdStan, num_threads: 1
-16:30:38 - cmdstanpy - DEBUG - CmdStan args: ['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=2', 'random', 'seed=23084', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmpgvv426mx/bernoulli1wek20la/bernoulli-20240326163038_2.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
-16:30:38 - cmdstanpy - DEBUG - running CmdStan, num_threads: 1
-16:30:38 - cmdstanpy - INFO - Chain [1] start processing
-16:30:38 - cmdstanpy - DEBUG - CmdStan args: ['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=3', 'random', 'seed=23084', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmpgvv426mx/bernoulli1wek20la/bernoulli-20240326163038_3.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
-16:30:38 - cmdstanpy - INFO - Chain [2] start processing
-16:30:38 - cmdstanpy - DEBUG - CmdStan args: ['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=4', 'random', 'seed=23084', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmpgvv426mx/bernoulli1wek20la/bernoulli-20240326163038_4.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
-16:30:38 - cmdstanpy - INFO - Chain [3] start processing
-16:30:38 - cmdstanpy - INFO - Chain [4] start processing
-16:30:38 - cmdstanpy - INFO - Chain [1] done processing
-16:30:38 - cmdstanpy - INFO - Chain [2] done processing
-16:30:38 - cmdstanpy - INFO - Chain [3] done processing
-16:30:38 - cmdstanpy - INFO - Chain [4] done processing
-16:30:38 - cmdstanpy - DEBUG - runset
+15:16:41 - cmdstanpy - INFO - CmdStan start processing
+15:16:41 - cmdstanpy - DEBUG - idx 0
+15:16:41 - cmdstanpy - DEBUG - running CmdStan, num_threads: 1
+15:16:41 - cmdstanpy - DEBUG - idx 1
+15:16:41 - cmdstanpy - DEBUG - CmdStan args: ['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=1', 'random', 'seed=3968', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmpxrifw9p7/bernoulliofh4vxfv/bernoulli-20240603151641_1.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
+15:16:41 - cmdstanpy - DEBUG - running CmdStan, num_threads: 1
+15:16:41 - cmdstanpy - INFO - Chain [1] start processing
+15:16:41 - cmdstanpy - DEBUG - CmdStan args: ['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=2', 'random', 'seed=3968', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmpxrifw9p7/bernoulliofh4vxfv/bernoulli-20240603151641_2.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
+15:16:41 - cmdstanpy - DEBUG - idx 2
+15:16:41 - cmdstanpy - INFO - Chain [2] start processing
+15:16:41 - cmdstanpy - DEBUG - running CmdStan, num_threads: 1
+15:16:41 - cmdstanpy - DEBUG - idx 3
+15:16:41 - cmdstanpy - DEBUG - CmdStan args: ['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=3', 'random', 'seed=3968', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmpxrifw9p7/bernoulliofh4vxfv/bernoulli-20240603151641_3.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
+15:16:41 - cmdstanpy - DEBUG - running CmdStan, num_threads: 1
+15:16:41 - cmdstanpy - INFO - Chain [3] start processing
+15:16:41 - cmdstanpy - DEBUG - CmdStan args: ['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=4', 'random', 'seed=3968', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmpxrifw9p7/bernoulliofh4vxfv/bernoulli-20240603151641_4.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
+15:16:41 - cmdstanpy - INFO - Chain [4] start processing
+15:16:41 - cmdstanpy - INFO - Chain [1] done processing
+15:16:41 - cmdstanpy - INFO - Chain [2] done processing
+15:16:41 - cmdstanpy - INFO - Chain [4] done processing
+15:16:41 - cmdstanpy - INFO - Chain [3] done processing
+15:16:41 - 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=23084', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmpgvv426mx/bernoulli1wek20la/bernoulli-20240326163038_1.csv', 'method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
+['/home/runner/work/cmdstanpy/cmdstanpy/docsrc/users-guide/examples/bernoulli', 'id=1', 'random', 'seed=3968', 'data', 'file=users-guide/examples/bernoulli.data.json', 'output', 'file=/tmp/tmpxrifw9p7/bernoulliofh4vxfv/bernoulli-20240603151641_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/tmpgvv426mx/bernoulli1wek20la/bernoulli-20240326163038_1.csv
+/tmp/tmpxrifw9p7/bernoulliofh4vxfv/bernoulli-20240603151641_1.csv
console_msgs (if any):
-/tmp/tmpgvv426mx/bernoulli1wek20la/bernoulli-20240326163038_0-stdout.txt
-16:30:38 - cmdstanpy - DEBUG - Chain 1 console:
+/tmp/tmpxrifw9p7/bernoulliofh4vxfv/bernoulli-20240603151641_0-stdout.txt
+15:16:41 - cmdstanpy - DEBUG - Chain 1 console:
method = sample (Default)
sample
num_samples = 1000 (Default)
num_warmup = 1000 (Default)
-save_warmup = 0 (Default)
+save_warmup = false (Default)
thin = 1 (Default)
adapt
-engaged = 1 (Default)
+engaged = true (Default)
gamma = 0.05 (Default)
delta = 0.8 (Default)
kappa = 0.75 (Default)
@@ -458,7 +458,7 @@ Logginginit_buffer = 75 (Default)
term_buffer = 50 (Default)
window = 25 (Default)
-save_metric = 0 (Default)
+save_metric = false (Default)
algorithm = hmc (Default)
hmc
engine = nuts (Default)
@@ -474,14 +474,14 @@ Loggingfile = users-guide/examples/bernoulli.data.json
init = 2 (Default)
random
-seed = 23084
+seed = 3968
output
-file = /tmp/tmpgvv426mx/bernoulli1wek20la/bernoulli-20240326163038_1.csv
+file = /tmp/tmpxrifw9p7/bernoulliofh4vxfv/bernoulli-20240603151641_1.csv
diagnostic_file = (Default)
refresh = 100 (Default)
sig_figs = -1 (Default)
profile_file = profile.csv (Default)
-save_cmdstan_config = 0 (Default)
+save_cmdstan_config = false (Default)
num_threads = 1 (Default)
@@ -513,9 +513,9 @@ LoggingIteration: 1900 / 2000 [ 95%] (Sampling)
Iteration: 2000 / 2000 [100%] (Sampling)
-Elapsed Time: 0.004 seconds (Warm-up)
+Elapsed Time: 0.005 seconds (Warm-up)
0.013 seconds (Sampling)
-0.017 seconds (Total)
+0.018 seconds (Total)
diff --git a/docs/users-guide/overview.html b/docs/users-guide/overview.html
index ccbf4db6..1eff384b 100644
--- a/docs/users-guide/overview.html
+++ b/docs/users-guide/overview.html
@@ -6,7 +6,7 @@
- Overview — CmdStanPy 1.2.2 documentation
+ Overview — CmdStanPy 1.2.3 documentation
@@ -64,7 +64,7 @@
diff --git a/docs/users-guide/workflow.html b/docs/users-guide/workflow.html
index 332cac28..c10831f2 100644
--- a/docs/users-guide/workflow.html
+++ b/docs/users-guide/workflow.html
@@ -6,7 +6,7 @@
- CmdStanPy Workflow — CmdStanPy 1.2.2 documentation
+ CmdStanPy Workflow — CmdStanPy 1.2.3 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.