Merge branch 'master' into fix-4206

Author: michaelosthege

.codecov.yml

@@ -1,15 +1,23 @@
 codecov:
-  require_ci_to_pass: yes
+  require_ci_to_pass: no
 
 coverage:
   precision: 2
   round: down
   range: "70...100"
-
   status:
-    project: yes
-    patch: yes
-    changes: no
+    project:
+      default:
+        # basic
+        target: auto
+        threshold: 1%
+        base: auto 
+    patch:
+      default:
+        # basic
+        target: 50%
+        threshold: 1%
+        base: auto 
 
 comment:
   layout: "reach, diff, flags, files"

.gitignore

@@ -21,9 +21,6 @@ _build
 # Merge tool
 *.orig
 
-# Test artifacts
-mcmc.sqlite
-
 # Docker development
 # notebooks/
 
@@ -41,3 +38,6 @@ benchmarks/results/
 pytestdebug.log
 .dir-locals.el
 .pycheckers
+
+# Codespaces
+pythonenv*

.pre-commit-config.yaml

@@ -5,13 +5,16 @@ repos:
     -   id: end-of-file-fixer
     -   id: check-toml
 - repo: https://github.com/nbQA-dev/nbQA
-  rev: 0.3.5
+  rev: 0.4.1
   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/asottile/pyupgrade
-  rev: v2.7.3
+  rev: v2.7.4
   hooks:
     - id: pyupgrade
       args: [--py36-plus]

README.rst

@@ -39,7 +39,7 @@ Features
 -  **Variational inference**: `ADVI <http://www.jmlr.org/papers/v18/16-107.html>`__
    for fast approximate posterior estimation as well as mini-batch ADVI
    for large data sets.
--  Relies on `Theano <http://deeplearning.net/software/theano/>`__ which provides:
+-  Relies on `Theano <https://theano-pymc.readthedocs.io/en/latest/>`__ which provides:
     *  Computation optimization and dynamic C compilation
     *  Numpy broadcasting and advanced indexing
     *  Linear algebra operators

RELEASE-NOTES.md

@@ -3,6 +3,7 @@
 ## PyMC3 3.9.x (on deck)
 
 ### Maintenance
+- Removed non-NDArray (Text, SQLite, HDF5) backends and associated tests.
 - Switch the dependency of Theano to our own fork, [Theano-PyMC](https://github.com/pymc-devs/Theano-PyMC).
 - Mentioned the way to do any random walk with `theano.tensor.cumsum()` in `GaussianRandomWalk` docstrings (see [#4048](https://github.com/pymc-devs/pymc3/pull/4048)).
 - Fixed numerical instability in ExGaussian's logp by preventing `logpow` from returning `-inf` (see [#4050](https://github.com/pymc-devs/pymc3/pull/4050)).
@@ -12,13 +13,18 @@
 
 ### Documentation
 
+* Added a new notebook demonstrating how to incorporate sampling from a conjugate Dirichlet-multinomial posterior density in conjunction with other step methods (see [#4199](https://github.com/pymc-devs/pymc3/pull/4199)).
+
 ### New features
 - `sample_posterior_predictive_w` can now feed on `xarray.Dataset` - e.g. from `InferenceData.posterior`. (see [#4042](https://github.com/pymc-devs/pymc3/pull/4042))
 - Added `pymc3.gp.cov.Circular` kernel for Gaussian Processes on circular domains, e.g. the unit circle (see [#4082](https://github.com/pymc-devs/pymc3/pull/4082)).
 - Add MLDA, a new stepper for multilevel sampling. MLDA can be used when a hierarchy of approximate posteriors of varying accuracy is available, offering improved sampling efficiency especially in high-dimensional problems and/or where gradients are not available (see [#3926](https://github.com/pymc-devs/pymc3/pull/3926))
-- Change SMC metropolis kernel to independent metropolis kernel [#4115](https://github.com/pymc-devs/pymc3/pull/3926))
+- Change SMC metropolis kernel to independent metropolis kernel [#4115](https://github.com/pymc-devs/pymc3/pull/4115))
 - Add alternative parametrization to NegativeBinomial distribution in terms of n and p (see [#4126](https://github.com/pymc-devs/pymc3/issues/4126))
+- Add Bayesian Additive Regression Trees (BARTs) [#4183](https://github.com/pymc-devs/pymc3/pull/4183))
 - Added a new `MixtureSameFamily` distribution to handle mixtures of arbitrary dimensions in vectorized form (see [#4185](https://github.com/pymc-devs/pymc3/issues/4185)).
+- Added semantically meaningful `str` representations to PyMC3 objects for console, notebook, and GraphViz use (see [#4076](https://github.com/pymc-devs/pymc3/pull/4076), [#4065](https://github.com/pymc-devs/pymc3/pull/4065), [#4159](https://github.com/pymc-devs/pymc3/pull/4159), and [#4217](https://github.com/pymc-devs/pymc3/pull/4217))
+
 
 
 ## PyMC3 3.9.3 (11 August 2020)

docs/source/api/backends.rst

@@ -15,22 +15,6 @@ ndarray
 .. automodule:: pymc3.backends.ndarray
    :members:
 
-sqlite
-^^^^^^
-
-.. currentmodule:: pymc3.backends.sqlite
-
-.. automodule:: pymc3.backends.sqlite
-   :members:
-
-text
-^^^^
-
-.. currentmodule:: pymc3.backends.text
-
-.. automodule:: pymc3.backends.text
-   :members:
-
 tracetab
 ^^^^^^^^
 

docs/source/notebooks/AR.ipynb

@@ -718,7 +718,9 @@
     }
    ],
    "source": [
-    "%time result_pymc3 = eval_lda(transform_pymc3, beta_pymc3, docs_te.toarray(), np.arange(100))\n",
+    "%time result_pymc3 = eval_lda(\\\n",
+    "          transform_pymc3, beta_pymc3, docs_te.toarray(), np.arange(100)\\\n",
+    "      )\n",
     "print(\"Predictive log prob (pm3) = {}\".format(result_pymc3[\"lp\"]))"
    ]
   },
@@ -750,7 +752,9 @@
     "    return thetas / thetas.sum(axis=1)[:, np.newaxis]\n",
     "\n",
     "\n",
-    "%time result_sklearn = eval_lda(transform_sklearn, beta_sklearn, docs_te.toarray(), np.arange(100))\n",
+    "%time result_sklearn = eval_lda(\\\n",
+    "          transform_sklearn, beta_sklearn, docs_te.toarray(), np.arange(100)\\\n",
+    "      )\n",
     "print(\"Predictive log prob (sklearn) = {}\".format(result_sklearn[\"lp\"]))"
    ]
   },