Merge pull request #646 from stan-dev/doc/release-110

Update documentation for 1.1.0

Author: WardBrian

cmdstanpy/model.py

@@ -1600,18 +1600,17 @@ def log_prob(
         Calculate the log probability and gradient at the given parameter
         values.
 
-        NOTE: This function is **NOT** an efficient way to evaluate the log
-        density of the model. It should be used for diagnostics ONLY.
-        Please, do not use this for other purposes such as testing new
-        sampling algorithms!
-
-        Parameters
-        ----------
-        :param data: Values for all parameters in the model, specified
+        .. note:: This function is **NOT** an efficient way to evaluate the log
+            density of the model. It should be used for diagnostics ONLY.
+            Please, do not use this for other purposes such as testing new
+            sampling algorithms!
+
+        :param params: Values for all parameters in the model, specified
             either as a dictionary with entries matching the parameter
             variables, or as the path of a data file in JSON or Rdump format.
 
             These should be given on the constrained (natural) scale.
+
         :param data: Values for all data variables in the model, specified
             either as a dictionary with entries matching the data variables,
             or as the path of a data file in JSON or Rdump format.

cmdstanpy/stanfit/gq.py

@@ -198,7 +198,7 @@ def draws(
         :param concat_chains: When ``True`` return a 2D array flattening all
             all draws from all chains.  Default value is ``False``.
 
-        :param inc_sample: When ``True`` include all columns in the mcmc_sample
+        :param inc_sample: 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``.
 

docsrc/changes.rst

@@ -7,6 +7,24 @@ What's New
 
 For full changes, see the `Releases page <https://github.com/stan-dev/cmdstanpy/releases>`__ on GitHub.
 
+
+CmdStanPy 1.1.0
+---------------
+- **New functionality**
+
+  - :meth:`CmdStanModel.generate_quantities` can now accept samples from optimization and variational inference.
+    The argument ``mcmc_sample`` has been renamed ``previous_fit`` to reflect this; the former name is still accepted
+    but deprecated.
+  - :meth:`CmdStanModel.log_prob` is able to return the log probability and its gradient with respect to a set of parameters.
+    **Note** that this is *not* an efficient way of calculating this in general and should be reserved for debugging
+    and model development.
+- **Other changes**
+
+  - Improved some of the type hints in the package.
+  - Ensure draws are serialized if a fit object is pickled.
+  - :meth:`~CmdStanModel.src_info` now raises an error if the command fails, rather than returning ``{}``.
+  - CmdStanPy has transitioned all unit tests from the ``unittest`` library to use ``pytest``.
+
 CmdStanPy 1.0.8
 ---------------
 

docsrc/community.rst

@@ -29,7 +29,8 @@ Software
   interface <https://julia.arviz.org/stable/>`_) for exploratory analysis of
   Bayesian models. Includes functions for posterior analysis, data storage,
   model checking, comparison and diagnostics.
-  
- - `BridgeStan <https://github.com/roualdes/bridgestan>`_ A project which provides efficient 
-  in-memory access through Python, Julia, and R to the methods of a Stan model, including 
+
+- `BridgeStan <https://github.com/roualdes/bridgestan>`_ A project which provides efficient
+  in-memory access through Python, Julia, and R to the methods of a Stan model, including
   log densities, gradients, Hessians, and constraining and unconstraining transforms.
+

docsrc/users-guide/examples/Run Generated Quantities.ipynb

@@ -129,14 +129,15 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We run the `generate_quantities` method on `bernoulli_ppc` using existing sample `fit` as input.  The `generate_quantities` method takes the values of `theta` in the `fit` sample as the set of draws from the posterior used to generate the corresponsing `y_rep` quantities of interest.\n",
     "\n",
     "The arguments to the `generate_quantities` method are:\n",
     " + `data`  - the data used to fit the model\n",
-    " + `mcmc_sample` - either a `CmdStanMCMC` object or a list of stan-csv files\n"
+    " + `previous_fit` - either a `CmdStanMCMC`, `CmdStanVB`, or `CmdStanMLE` object or a list of stan-csv files\n"
    ]
   },
   {
@@ -145,7 +146,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "new_quantities = model_ppc.generate_quantities(data=data_file, mcmc_sample=fit)"
+    "new_quantities = model_ppc.generate_quantities(data=data_file, previous_fit=fit)"
    ]
   },
   {

docsrc/users-guide/workflow.rst

@@ -149,8 +149,8 @@ An example of each is provided in the `next section <examples.rst>`__
 
 * The :meth:`~CmdStanModel.generate_quantities` method runs Stan's
   `generate_quantities method <https://mc-stan.org/docs/cmdstan-guide/standalone-generate-quantities.html>`_
-  which generates additional quantities of interest from a mode. Its take an existing sample as input and
-  uses the parameter estimates in the sample to run the Stan program's `generated quantities block <https://mc-stan.org/docs/reference-manual/program-block-generated-quantities.html>`__.
+  which generates additional quantities of interest from a mode. Its take an existing fit as input and
+  uses the parameter estimates in the fit to run the Stan program's `generated quantities block <https://mc-stan.org/docs/reference-manual/program-block-generated-quantities.html>`__.
 
   It returns a :class:`CmdStanGQ` object.