Fix a few sphinx warnings for new doc build (#3702)

Author: ColCarroll

docs/source/api.rst

@@ -14,7 +14,6 @@ API Reference
    api/gp
    api/plots
    api/stats
-   api/diagnostics
    api/backends
    api/math
    api/data

docs/source/api/diagnostics.rst

@@ -40,6 +40,7 @@ initialized instances of the Transform Classes, which are described
 below.
 
 .. glossary::
+
 ``stick_breaking``
     Instantiation of :class:`~pymc3.distributions.transforms.StickBreaking`
     :class:`~pymc3.distributions.transforms.Transform` class for use in the ``transform``
@@ -93,7 +94,7 @@ below.
     for use in the ``transform`` argument of a random variable.
 
 
-.. autofunction::  t_stick_breaking 
+.. autofunction::  t_stick_breaking
 
 
 Transform Base Classes

docs/source/api/distributions/transforms.rst

@@ -54,9 +54,9 @@ Hamiltonian Monte Carlo
 Sequential Monte Carlo
 ~~~~~~~~~~~~~~~~~~~~~~~
 
-.. currentmodule:: pymc3.step_methods.smc
+.. currentmodule:: pymc3.step_methods.smc.smc
 
-.. autoclass:: pymc3.step_methods.smc.SMC
+.. autoclass:: pymc3.smc.smc.SMC
    :members:
 
 

docs/source/api/inference.rst

@@ -40,6 +40,7 @@
     "nbsphinx",
     "numpydoc",
     "IPython.sphinxext.ipython_console_highlighting",
+    "IPython.sphinxext.ipython_directive",
     "sphinx.ext.autosectionlabel",
     "sphinx.ext.napoleon",
     "gallery_generator",

docs/source/conf.py

@@ -906,7 +906,7 @@ TFP, which is a tenor in tensor out function. Moreover, transition
 kernels in TFP do not flatten the tensors, see eg docstring of
 `tensorflow\_probability/python/mcmc/random\_walk\_metropolis.py <https://github.com/tensorflow/probability/blob/master/tensorflow_probability/python/mcmc/random_walk_metropolis.py>`__:
 
-.. code:: python
+.. code::
 
           new_state_fn: Python callable which takes a list of state parts and a
             seed; returns a same-type `list` of `Tensor`s, each being a perturbation
@@ -945,7 +945,7 @@ where it builds the objective function by calling:
 
 Where:
 
-.. code:: python
+.. code::
 
         op     : Operator class
         approx : Approximation class or instance

docs/source/developer_guide.rst

@@ -2325,7 +2325,7 @@
     "You can find more details in the original [Stan case study](http://mc-stan.org/documentation/case-studies/mbjoseph-CARStan.html). You might come across similar constructs in Gaussian Process, which result in a zero-mean Gaussian distribution conditioned on a covariance function.\n",
     "\n",
     "In the `Stan` Code, matrix D is generated in the model using a `transformed data{}` block:\n",
-    "```Stan\n",
+    "```\n",
     "transformed data{\n",
     "  vector[n] zeros;\n",
     "  matrix<lower = 0>[n, n] D;\n",
@@ -2359,7 +2359,7 @@
    "metadata": {},
    "source": [
     "Then in the `Stan` model:\n",
-    "```stan\n",
+    "```\n",
     "model {\n",
     "  phi ~ multi_normal_prec(zeros, tau * (D - alpha * W));\n",
     "  ...\n",
@@ -2475,7 +2475,7 @@
     "Note that in the node $\\phi \\sim \\mathcal{N}(0, [D_\\tau (I - \\alpha B)]^{-1})$, we are computing the log-likelihood for a multivariate Gaussian distribution, which might not scale well in high-dimensions. We can take advantage of the fact that the covariance matrix here $[D_\\tau (I - \\alpha B)]^{-1}$ is **sparse**, and there are faster ways to compute its log-likelihood. \n",
     "\n",
     "For example, a more efficient sparse representation of the CAR in `STAN`:\n",
-    "```stan\n",
+    "```\n",
     "functions {\n",
     "  /**\n",
     "  * Return the log probability of a proper conditional autoregressive (CAR) prior \n",
@@ -2513,7 +2513,7 @@
     "}\n",
     "```\n",
     "with the data transformed in the model:\n",
-    "```stan\n",
+    "```\n",
     "transformed data {\n",
     "  int W_sparse[W_n, 2];   // adjacency pairs\n",
     "  vector[n] D_sparse;     // diagonal of D (number of neigbors for each site)\n",
@@ -2544,7 +2544,7 @@
     "}\n",
     "```\n",
     "and the likelihood:\n",
-    "```stan\n",
+    "```\n",
     "model {\n",
     "  phi ~ sparse_car(tau, alpha, W_sparse, D_sparse, lambda, n, W_n);\n",
     "}\n",
@@ -2901,4 +2901,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

docs/source/notebooks/PyMC3_tips_and_heuristic.ipynb

@@ -219,9 +219,10 @@ def backward_val(self, x):
         return np.log(1 + np.exp(-np.abs(x))) + np.max([x, 0])
 
     def forward(self, x):
-        """Inverse operation of softplus
-        y = Log(Exp(x) - 1)
-          = Log(1 - Exp(-x)) + x
+        """Inverse operation of softplus.
+
+            y = Log(Exp(x) - 1)
+              = Log(1 - Exp(-x)) + x
         """
         return tt.log(1.0 - tt.exp(-x)) + x