Add GitHubAction to auto-update hooks, and other cleanups

.codecov.yml

@@ -13,13 +13,13 @@ coverage:
         # basic
         target: auto
         threshold: 1%
-        base: auto 
+        base: auto
     patch:
       default:
         # basic
         target: 50%
         threshold: 1%
-        base: auto 
+        base: auto
 
 comment:
   layout: "reach, diff, flags, files"

.github/ISSUE_TEMPLATE.md

@@ -1,4 +1,4 @@
-If you have questions about a specific use case, or you are not sure whether this is a bug or not, please post it to our discourse channel: https://discourse.pymc.io  
+If you have questions about a specific use case, or you are not sure whether this is a bug or not, please post it to our discourse channel: https://discourse.pymc.io
 
 ## Description of your problem
 

.github/workflows/autoupdate-pre-commit-config.yml

@@ -0,0 +1,33 @@
+name: "Update pre-commit config"
+
+on:
+  schedule:
+    - cron: "0 7 * * 1" # At 07:00 on each Monday.
+  workflow_dispatch:
+
+jobs:
+  update-pre-commit:
+    if: github.repository_owner == 'pymc-devs'
+    name: Autoupdate pre-commit config
+    runs-on: ubuntu-latest
+    steps:
+      - name: Set up Python
+        uses: actions/setup-python@v2
+      - name: Cache multiple paths
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/.cache/pre-commit
+            ~/.cache/pip
+          key: pre-commit-autoupdate-${{ runner.os }}-build
+      - name: Update pre-commit config packages
+        uses: technote-space/create-pr-action@v2
+        with:
+          GITHUB_TOKEN: ${{ secrets.ACTION_TRIGGER_TOKEN }}
+          EXECUTE_COMMANDS: |
+            pip install pre-commit
+            pre-commit autoupdate || (exit 0);
+            pre-commit run -a || (exit 0);
+          COMMIT_MESSAGE: "⬆️ UPGRADE: Autoupdate pre-commit config"
+          PR_BRANCH_NAME: "pre-commit-config-update-${PR_ID}"
+          PR_TITLE: "⬆️ UPGRADE: Autoupdate pre-commit config"

.github/workflows/pre-commit.yml

@@ -8,6 +8,8 @@ on:
 jobs:
   pre-commit:
     runs-on: ubuntu-latest
+    env:
+      SKIP: no-commit-to-branch
     steps:
     - uses: actions/checkout@v2
     - uses: actions/setup-python@v2

.github/workflows/pytest.yml

@@ -29,6 +29,7 @@ jobs:
             --ignore=pymc3/tests/test_shape_handling.py
             --ignore=pymc3/tests/test_shared.py
             --ignore=pymc3/tests/test_smc.py
+            --ignore=pymc3/tests/test_step.py
             --ignore=pymc3/tests/test_updates.py
             --ignore=pymc3/tests/test_variational_inference.py
           - |
@@ -47,6 +48,7 @@ jobs:
           - |
             pymc3/tests/test_distributions_timeseries.py
             pymc3/tests/test_shape_handling.py
+            pymc3/tests/test_step.py
             pymc3/tests/test_updates.py
             pymc3/tests/test_variational_inference.py
           - |

.pre-commit-config.yaml

@@ -1,24 +1,31 @@
+exclude: ^(docs/logos|pymc3/examples/data)/
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
   rev: v3.3.0
   hooks:
-    -   id: end-of-file-fixer
+    -   id: check-merge-conflict
     -   id: check-toml
+    -   id: check-yaml
+    -   id: debug-statements
+    -   id: end-of-file-fixer
+    -   id: no-commit-to-branch
+        args: [--branch, master]
+    -   id: requirements-txt-fixer
+    -   id: trailing-whitespace
 - repo: https://github.com/nbQA-dev/nbQA
-  rev: 0.4.1
+  rev: 0.5.4
   hooks:
     - id: nbqa-black
       additional_dependencies: [black==20.8b1]
     - id: nbqa-isort
       additional_dependencies: [isort==5.6.4]
     - id: nbqa-pyupgrade
       additional_dependencies: [pyupgrade==2.7.4]
-
 - repo: https://github.com/PyCQA/isort
   rev: 5.6.4
   hooks:
     - id: isort
-      name: isort (python)
+      name: isort
 - repo: https://github.com/asottile/pyupgrade
   rev: v2.7.4
   hooks:

GOVERNANCE.md

@@ -13,7 +13,7 @@ developed openly and hosted in public GitHub repositories under the
 [GitHub organization](https://github.com/pymc-devs/pymc3). Examples of
 Project Software include the PyMC3 code and the Documentation, etc. The Services run by the
 Project consist of public websites and web-services that are hosted
-at [http://pymc-devs.github.io/pymc3/](http://pymc-devs.github.io/pymc3/) 
+at [http://pymc-devs.github.io/pymc3/](http://pymc-devs.github.io/pymc3/)
 The Project is developed by a team of distributed developers, called
 Contributors. Contributors are individuals who have contributed code,
 documentation, designs or other work to one or more Project repositories.
@@ -131,7 +131,7 @@ The current Steering Council membership comprises:
 - Junpeng Lao
 - Osvaldo Martin
 - Austin Rochford
-- Adrian Seyboldt 
+- Adrian Seyboldt
 - Thomas Wiecki
 
 ### Council membership

README.rst

@@ -21,11 +21,11 @@ The future of PyMC3 & Theano
 There have been many questions and uncertainty around the future of PyMC3 since Theano
 stopped getting developed by the original authors, and we started experiments with PyMC4.
 
-We are happy to announce that PyMC3 on Theano (which we are `developing further <https://github.com/pymc-devs/Theano-PyMC>`__) 
-with a new JAX backend is the future. PyMC4 will not be developed further. 
+We are happy to announce that PyMC3 on Theano (which we are `developing further <https://github.com/pymc-devs/Theano-PyMC>`__)
+with a new JAX backend is the future. PyMC4 will not be developed further.
 
 See the `full announcement <https://pymc-devs.medium.com/the-future-of-pymc3-or-theano-is-dead-long-live-theano-d8005f8a0e9b>`__
-for more details. 
+for more details.
 
 Features
 ========
@@ -119,7 +119,7 @@ Another option is to clone the repository and install PyMC3 using
 Dependencies
 ============
 
-PyMC3 is tested on Python 3.6, 3.7, and 3.8 and depends on `Theano-PyMC <https://github.com/pymc-devs/Theano-PyMC>`__, 
+PyMC3 is tested on Python 3.6, 3.7, and 3.8 and depends on `Theano-PyMC <https://github.com/pymc-devs/Theano-PyMC>`__,
 NumPy, SciPy, and Pandas
 (see `requirements.txt <https://github.com/pymc-devs/pymc3/blob/master/requirements.txt>`__ for version
 information).

binder/requirements.txt

@@ -1,4 +1,4 @@
 -r ../requirements-dev.txt
-# this installs pymc3 itself. it is funny that this is an absolute path, 
+# this installs pymc3 itself. it is funny that this is an absolute path,
 # but reqirements-dev.txt is relative.
 .

docs/source/Gaussian_Processes.rst

@@ -21,17 +21,17 @@ choice as priors over functions due to the marginalization and conditioning
 properties of the multivariate normal distribution.  Usually, the marginal
 distribution over :math:`f(x)` is evaluated during the inference step.  The
 conditional distribution is then used for predicting the function values
-:math:`f(x_*)` at new points, :math:`x_*`.  
+:math:`f(x_*)` at new points, :math:`x_*`.
 
 The joint distribution of :math:`f(x)` and :math:`f(x_*)` is multivariate
 normal,
 
 .. math::
 
   \begin{bmatrix} f(x) \\ f(x_*) \\ \end{bmatrix} \sim
-  \text{N}\left( 
+  \text{N}\left(
     \begin{bmatrix} m(x)  \\ m(x_*)    \\ \end{bmatrix} \,,
-    \begin{bmatrix} k(x,x')    & k(x_*, x)    \\ 
+    \begin{bmatrix} k(x,x')    & k(x_*, x)    \\
                     k(x_*, x) &  k(x_*, x_*')  \\ \end{bmatrix}
           \right) \,.
 
@@ -41,21 +41,21 @@ distribution is
 
 .. math::
 
-  f(x_*) \mid f(x) \sim \text{N}\left( k(x_*, x) k(x, x)^{-1} [f(x) - m(x)] + m(x_*) ,\, 
+  f(x_*) \mid f(x) \sim \text{N}\left( k(x_*, x) k(x, x)^{-1} [f(x) - m(x)] + m(x_*) ,\,
     k(x_*, x_*) - k(x, x_*) k(x, x)^{-1} k(x, x_*) \right) \,.
 
 .. note::
 
   For more information on GPs, check out the book `Gaussian Processes for
   Machine Learning <http://www.gaussianprocess.org/gpml/>`_ by Rasmussen &
-  Williams, or `this introduction <https://www.ics.uci.edu/~welling/teaching/KernelsICS273B/gpB.pdf>`_ 
+  Williams, or `this introduction <https://www.ics.uci.edu/~welling/teaching/KernelsICS273B/gpB.pdf>`_
   by D. Mackay.
 
 PyMC3 is a great environment for working with fully Bayesian Gaussian Process
-models.  GPs in PyMC3 have a clear syntax and are highly composable, and many 
-predefined covariance functions (or kernels), mean functions, and several GP 
+models.  GPs in PyMC3 have a clear syntax and are highly composable, and many
+predefined covariance functions (or kernels), mean functions, and several GP
 implementations are included.  GPs are treated as distributions that can be
-used within larger or hierarchical models, not just as standalone regression 
+used within larger or hierarchical models, not just as standalone regression
 models.
 
 Mean and covariance functions
@@ -83,7 +83,7 @@ specify :code:`input_dim`, the total number of columns of :code:`X`, and
 :code:`active_dims`, which of those columns or dimensions the covariance
 function will act on, is because :code:`cov_func` hasn't actually seen the
 input data yet.  The :code:`active_dims` argument is optional, and defaults to
-all columns of the matrix of inputs.  
+all columns of the matrix of inputs.
 
 Covariance functions in PyMC3 closely follow the algebraic rules for kernels,
 which allows users to combine covariance functions into new ones, for example:
@@ -97,13 +97,13 @@ which allows users to combine covariance functions into new ones, for example:
 
 
     cov_func = pm.gp.cov.ExpQuad(...) * pm.gp.cov.Periodic(...)
-    
+
 - The product (or sum) of a covariance function with a scalar is a
   covariance function::
 
-    
+
     cov_func = eta**2 * pm.gp.cov.Matern32(...)
-    
+
 
 
 After the covariance function is defined, it is now a function that is
@@ -133,7 +133,7 @@ is::
 The first argument is the mean function and the second is the covariance
 function.  We've made the GP object, but we haven't made clear which function
 it is to be a prior for, what the inputs are, or what parameters it will be
-conditioned on.  
+conditioned on.
 
 .. note::
 
@@ -145,18 +145,18 @@ conditioned on.
 
 Calling the `prior` method will create a PyMC3 random variable that represents
 the latent function :math:`f(x) = \mathbf{f}`::
-  
+
 	f = gp.prior("f", X)
 
 :code:`f` is a random variable that can be used within a PyMC3 model like any
 other type of random variable.  The first argument is the name of the random
-variable representing the function we are placing the prior over.  
-The second argument is the inputs to the function that the prior is over, 
+variable representing the function we are placing the prior over.
+The second argument is the inputs to the function that the prior is over,
 :code:`X`.  The inputs are usually known and present in the data, but they can
-also be PyMC3 random variables.  If the inputs are a Theano tensor or a 
+also be PyMC3 random variables.  If the inputs are a Theano tensor or a
 PyMC3 random variable, the :code:`shape` needs to be given.
 
-Usually at this point, inference is performed on the model.  The 
+Usually at this point, inference is performed on the model.  The
 :code:`conditional` method creates the conditional, or predictive,
 distribution over the latent function at arbitrary :math:`x_*` input points,
 :math:`f(x_*)`.  To construct the conditional distribution we write::
@@ -166,7 +166,7 @@ distribution over the latent function at arbitrary :math:`x_*` input points,
 Additive GPs
 ============
 
-The GP implementation in PyMC3 is constructed so that it is easy to define 
+The GP implementation in PyMC3 is constructed so that it is easy to define
 additive GPs and sample from individual GP components.  We can write::
 
     gp1 = pm.gp.Marginal(mean_func1, cov_func1)
@@ -183,18 +183,18 @@ Consider two independent GP distributed functions, :math:`f_1(x) \sim
 
 .. math::
 
-  \begin{bmatrix} f_1 \\ f_1^* \\ f_2 \\ f_2^* 
+  \begin{bmatrix} f_1 \\ f_1^* \\ f_2 \\ f_2^*
                \\ f_1 + f_2    \\ f_1^* + f_2^* \end{bmatrix} \sim
-  \text{N}\left( 
+  \text{N}\left(
     \begin{bmatrix} m_1 \\ m_1^* \\ m_2 \\ m_2^* \\
                     m_1 + m_2    \\ m_1^* + m_2^*   \\ \end{bmatrix} \,,\,
-    \begin{bmatrix} 
+    \begin{bmatrix}
       K_1       &  K_1^*     &   0       &    0      & K_1        & K_1^*              \\
       K_1^{*^T} &  K_1^{**}  &   0       &    0      & K_1^*      & K_1^{**}           \\
       0         &  0         & K_2       & K_2^*     & K_2        & K_2^{*}            \\
       0         &  0         & K_2^{*^T} & K_2^{**}  & K_2^{*}    & K_2^{**}           \\
       K_1       &  K_1^{*}   & K_2       & K_2^{*}   & K_1 + K_2  & K_1^{*} + K_2^{*}  \\
-      K_1^{*^T} & K_1^{**} & K_2^{*^T} & K_2^{**} & K_1^{*^T}+K_2^{*^T} & K_1^{**}+K_2^{**} 
+      K_1^{*^T} & K_1^{**} & K_2^{*^T} & K_2^{**} & K_1^{*^T}+K_2^{*^T} & K_1^{**}+K_2^{**}
     \end{bmatrix}
   \right) \,.
 
@@ -220,42 +220,42 @@ other implementations.  The first block fits the GP prior.  We denote
     with pm.Model() as model:
         gp1 = pm.gp.Marginal(mean_func1, cov_func1)
         gp2 = pm.gp.Marginal(mean_func2, cov_func2)
-        
-        # gp represents f1 + f2.  
+
+        # gp represents f1 + f2.
         gp = gp1 + gp2
-        
+
         f = gp.marginal_likelihood("f", X, y, noise)
-        
+
         trace = pm.sample(1000)
 
 
-To construct the conditional distribution of :code:`gp1` or :code:`gp2`, we 
-also need to include the additional arguments, :code:`X`, :code:`y`, and 
+To construct the conditional distribution of :code:`gp1` or :code:`gp2`, we
+also need to include the additional arguments, :code:`X`, :code:`y`, and
 :code:`noise`::
 
     with model:
         # conditional distributions of f1 and f2
-        f1_star = gp1.conditional("f1_star", X_star, 
+        f1_star = gp1.conditional("f1_star", X_star,
                                   given={"X": X, "y": y, "noise": noise, "gp": gp})
-        f2_star = gp2.conditional("f2_star", X_star, 
+        f2_star = gp2.conditional("f2_star", X_star,
                                   given={"X": X, "y": y, "noise": noise, "gp": gp})
 
         # conditional of f1 + f2, `given` not required
         f_star = gp.conditional("f_star", X_star)
 
-This second block produces the conditional distributions.  Notice that extra 
+This second block produces the conditional distributions.  Notice that extra
 arguments are required for conditionals of :math:`f1` and :math:`f2`, but not
-:math:`f`.  This is because those arguments are cached when 
+:math:`f`.  This is because those arguments are cached when
 :code:`.marginal_likelihood` is called on :code:`gp`.
 
 .. note::
   When constructing conditionals, the additional arguments :code:`X`, :code:`y`,
   :code:`noise` and :code:`gp` must be provided as a dict called `given`!
 
-Since the marginal likelihoood method of :code:`gp1` or :code:`gp2` weren't called, 
-their conditionals need to be provided with the required inputs.  In the same 
+Since the marginal likelihoood method of :code:`gp1` or :code:`gp2` weren't called,
+their conditionals need to be provided with the required inputs.  In the same
 fashion as the prior, :code:`f_star`, :code:`f1_star` and :code:`f2_star` are random
-variables that can now be used like any other random variable in PyMC3.  
+variables that can now be used like any other random variable in PyMC3.
 
 Check the notebooks for detailed demonstrations of the usage of GP functionality
 in PyMC3.