Merge branch 'master' of https://github.com/pymc-devs/pymc3

Author: Tom Gilliss

.gitignore

@@ -32,3 +32,6 @@ benchmarks/env/
 benchmarks/html/
 benchmarks/results/
 .pytest_cache/
+
+# VSCode
+.vscode/

.travis.yml

@@ -18,22 +18,22 @@ before_install:
 
 install:
   - . ./scripts/create_testenv.sh
-  - pip install coveralls travis-sphinx==2.0.0
+  - pip install coveralls
+  - conda list && pip freeze
 
 env:
-  - FLOATX='float32' TESTCMD="--durations=10 --ignore=pymc3/tests/test_examples.py --cov-append --ignore=pymc3/tests/test_distributions_random.py --ignore=pymc3/tests/test_variational_inference.py --ignore=pymc3/tests/test_shared.py --ignore=pymc3/tests/test_smc.py --ignore=pymc3/tests/test_updates.py --ignore=pymc3/tests/test_posteriors.py --ignore=pymc3/tests/test_sampling.py"
-  - FLOATX='float32' RUN_PYLINT="true" TESTCMD="--durations=10 --cov-append pymc3/tests/test_distributions_random.py pymc3/tests/test_shared.py pymc3/tests/test_smc.py pymc3/tests/test_sampling.py"
-  - FLOATX='float32' TESTCMD="--durations=10 --cov-append pymc3/tests/test_examples.py pymc3/tests/test_posteriors.py"
+  - FLOATX='float32' TESTCMD="--durations=10 --ignore=pymc3/tests/test_examples.py --cov-append --ignore=pymc3/tests/test_distributions_random.py --ignore=pymc3/tests/test_variational_inference.py --ignore=pymc3/tests/test_shared.py --ignore=pymc3/tests/test_smc.py --ignore=pymc3/tests/test_updates.py --ignore=pymc3/tests/test_posteriors.py --ignore=pymc3/tests/test_sampling.py --ignore=pymc3/tests/test_parallel_sampling.py --ignore=pymc3/tests/test_dist_math.py --ignore=pymc3/tests/test_distribution_defaults.py --ignore=pymc3/tests/test_distributions_timeseries.py --ignore=pymc3/tests/test_random.py --ignore=pymc3/tests/test_gp.py"
+  - FLOATX='float32' RUN_PYLINT="true" TESTCMD="--durations=10 --cov-append pymc3/tests/test_distributions_random.py pymc3/tests/test_shared.py pymc3/tests/test_smc.py pymc3/tests/test_sampling.py pymc3/tests/test_parallel_sampling.py pymc3/tests/test_dist_math.py pymc3/tests/test_distribution_defaults.py pymc3/tests/test_distributions_timeseries.py pymc3/tests/test_random.py"
+  - FLOATX='float32' TESTCMD="--durations=10 --cov-append pymc3/tests/test_examples.py pymc3/tests/test_posteriors.py pymc3/tests/test_gp.py"
   - FLOATX='float32' TESTCMD="--durations=10 --cov-append pymc3/tests/test_variational_inference.py pymc3/tests/test_updates.py"
-  - FLOATX='float64' TESTCMD="--durations=10 --cov-append --ignore=pymc3/tests/test_examples.py --ignore=pymc3/tests/test_distributions_random.py --ignore=pymc3/tests/test_variational_inference.py --ignore=pymc3/tests/test_shared.py --ignore=pymc3/tests/test_smc.py --ignore=pymc3/tests/test_updates.py --ignore=pymc3/tests/test_posteriors.py --ignore=pymc3/tests/test_sampling.py"
-  - FLOATX='float64' TESTCMD="--durations=10 --cov-append pymc3/tests/test_distributions_random.py pymc3/tests/test_shared.py pymc3/tests/test_smc.py pymc3/tests/test_sampling.py"
-  - FLOATX='float64' BUILD_DOCS="true" TESTCMD="--durations=10 --cov-append pymc3/tests/test_examples.py pymc3/tests/test_posteriors.py"
+  - FLOATX='float64' TESTCMD="--durations=10 --cov-append --ignore=pymc3/tests/test_examples.py --ignore=pymc3/tests/test_distributions_random.py --ignore=pymc3/tests/test_variational_inference.py --ignore=pymc3/tests/test_shared.py --ignore=pymc3/tests/test_smc.py --ignore=pymc3/tests/test_updates.py --ignore=pymc3/tests/test_posteriors.py --ignore=pymc3/tests/test_sampling.py --ignore=pymc3/tests/test_parallel_sampling.py --ignore=pymc3/tests/test_dist_math.py --ignore=pymc3/tests/test_distribution_defaults.py --ignore=pymc3/tests/test_distributions_timeseries.py --ignore=pymc3/tests/test_random.py --ignore=pymc3/tests/test_gp.py"
+  - FLOATX='float64' TESTCMD="--durations=10 --cov-append pymc3/tests/test_distributions_random.py pymc3/tests/test_shared.py pymc3/tests/test_smc.py pymc3/tests/test_sampling.py pymc3/tests/test_parallel_sampling.py pymc3/tests/test_dist_math.py pymc3/tests/test_distribution_defaults.py pymc3/tests/test_distributions_timeseries.py pymc3/tests/test_random.py"
+  - FLOATX='float64' TESTCMD="--durations=10 --cov-append pymc3/tests/test_examples.py pymc3/tests/test_posteriors.py pymc3/tests/test_gp.py"
   - FLOATX='float64' TESTCMD="--durations=10 --cov-append pymc3/tests/test_variational_inference.py pymc3/tests/test_updates.py"
 
 script:
   - . ./scripts/test.sh $TESTCMD
   - . ./scripts/confirm_mpl_optional.sh
 
 after_success:
-  - if [[ "$BUILD_DOCS" == "true" ]]; then travis-sphinx deploy -c "docs.pymc.io"; fi
   - coveralls

README.rst

@@ -78,6 +78,13 @@ Or via conda-forge:
 
     conda install -c conda-forge pymc3
 
+Plotting is done using `ArviZ <https://arviz-devs.github.io/arviz/>`__
+which may be installed separately, or along with PyMC3:
+
+::
+
+    pip install pymc3[plots]
+
 The current development branch of PyMC3 can be installed from GitHub, also using ``pip``:
 
 ::
@@ -169,7 +176,7 @@ page <https://github.com/pymc-devs/pymc3/graphs/contributors>`__
 Support
 =======
 
-PyMC3 is a non-profit project under NumFOCUS umbrella. If you want to support PyMC3 financially, you can donate `here <https://www.flipcause.com/widget/widget_home/MTE4OTc=>`__.
+PyMC3 is a non-profit project under NumFOCUS umbrella. If you want to support PyMC3 financially, you can donate `here <https://numfocus.salsalabs.org/donate-to-pymc3/index.html>`__.
 
 Sponsors
 ========

RELEASE-NOTES.md

@@ -4,15 +4,37 @@
 
 ### New features
 
-### Maintenance
+- Add data container class (`Data`) that wraps the theano SharedVariable class and let the model be aware of its inputs and outputs.
+- Add function `set_data` to update variables defined as `Data`.
+- `Mixture` now supports mixtures of multidimensional probability distributions, not just lists of 1D distributions.
+- `GLM.from_formula` and `LinearComponent.from_formula` can extract variables from the calling scope. Customizable via the new `eval_env` argument. Fixing #3382.
 
+### Maintenance
 - All occurances of `sd` as a parameter name have been renamed to `sigma`. `sd` will continue to function for backwards compatibility.
 - Made `BrokenPipeError` for parallel sampling more verbose on Windows.
 - Added the `broadcast_distribution_samples` function that helps broadcasting arrays of drawn samples, taking into account the requested `size` and the inferred distribution shape. This sometimes is needed by distributions that call several `rvs` separately within their `random` method, such as the `ZeroInflatedPoisson` (Fix issue #3310).
 - The `Wald`, `Kumaraswamy`, `LogNormal`, `Pareto`, `Cauchy`, `HalfCauchy`, `Weibull` and `ExGaussian` distributions `random` method used a hidden `_random` function that was written with scalars in mind. This could potentially lead to artificial correlations between random draws. Added shape guards and broadcasting of the distribution samples to prevent this (Similar to issue #3310).
+- Added a fix to allow the imputation of single missing values of observed data, which previously would fail (Fix issue #3122).
+- Fix for #3346. The `draw_values` function was too permissive with what could be grabbed from inside `point`, which lead to an error when sampling posterior predictives of variables that depended on shared variables that had changed their shape after `pm.sample()` had been called.
+- Fix for #3354. `draw_values` now adds the theano graph descendants of `TensorConstant` or `SharedVariables` to the named relationship nodes stack, only if these descendants are `ObservedRV` or `MultiObservedRV` instances.
+- Fixed bug in broadcast_distrution_samples, which did not handle correctly cases in which some samples did not have the size tuple prepended.
+- Changed `MvNormal.random`'s usage of `tensordot` for Cholesky encoded covariances. This lead to wrong axis broadcasting and seemed to be the cause for issue #3343.
+- Fixed defect in `Mixture.random` when multidimensional mixtures were involved. The mixture component was not preserved across all the elements of the dimensions of the mixture. This meant that the correlations across elements within a given draw of the mixture were partly broken.
+- Restructured `Mixture.random` to allow better use of vectorized calls to `comp_dists.random`.
+- Added tests for mixtures of multidimensional distributions to the test suite.
+- Fixed incorrect usage of `broadcast_distribution_samples` in `DiscreteWeibull`.
+- `Mixture`'s default dtype is now determined by `theano.config.floatX`.
+- `dist_math.random_choice` now handles nd-arrays of category probabilities, and also handles sizes that are not `None`. Also removed unused `k` kwarg from `dist_math.random_choice`.
+- Changed `Categorical.mode` to preserve all the dimensions of `p` except the last one, which encodes each category's probability.
+- Changed initialization of `Categorical.p`. `p` is now normalized to sum to `1` inside `logp` and `random`, but not during initialization. This could hide negative values supplied to `p` as mentioned in #2082.
+- `Categorical` now accepts elements of `p` equal to `0`. `logp` will return `-inf` if there are `values` that index to the zero probability categories.
+- Add `sigma`, `tau`, and `sd` to signature of `NormalMixture`.
 
 ### Deprecations
 
+- `nuts_kwargs` and `step_kwargs` have been deprecated in favor of using the standard `kwargs` to pass optional step method arguments.
+- `SGFS` and `CSG` have been removed (Fix for [#3353](https://github.com/pymc-devs/pymc3/issues/3353)). They have been moved to [pymc3-experimental](https://github.com/pymc-devs/pymc3-experimental).
+
 ## PyMC3 3.6 (Dec 21 2018)
 
 This will be the last release to support Python 2.
@@ -491,4 +513,3 @@ Thus, Thomas, Chris and I are pleased to announce that PyMC3 is now in Beta.
 * maahnman <[email protected]>
 * paul sorenson <[email protected]>
 * zenourn <[email protected]>
-

docs/source/index.rst

@@ -127,7 +127,7 @@
 
         <div class="ui bottom attached segment">
             <h2 class="ui dividing header">Support and sponsors</h2>
-            <p>PyMC3 is a non-profit project under NumFOCUS umbrella. If you want to support PyMC3 financially, you <a href="https://www.flipcause.com/widget/widget_home/MTE4OTc=">can donate here</a>.</p>
+            <p>PyMC3 is a non-profit project under NumFOCUS umbrella. If you want to support PyMC3 financially, you <a href="https://numfocus.salsalabs.org/donate-to-pymc3/index.html">can donate here</a>.</p>
 
             <div class="ui equal width grid">
                 <div class="column">

docs/source/notebooks/BEST.ipynb

@@ -1002,7 +1002,7 @@
    "source": [
     "with Centered_eight:\n",
     "    fit_cp85 = pm.sample(5000, chains=2, tune=2000,\n",
-    "                         nuts_kwargs=dict(target_accept=.85))"
+    "                         target_accept=.85)"
    ]
   },
   {
@@ -1029,7 +1029,7 @@
    "source": [
     "with Centered_eight:\n",
     "    fit_cp90 = pm.sample(5000, chains=2, tune=2000,\n",
-    "                         nuts_kwargs=dict(target_accept=.90))"
+    "                         target_accept=.90)"
    ]
   },
   {
@@ -1056,7 +1056,7 @@
    "source": [
     "with Centered_eight:\n",
     "    fit_cp95 = pm.sample(5000, chains=2, tune=2000,\n",
-    "                         nuts_kwargs=dict(target_accept=.95))"
+    "                         target_accept=.95)"
    ]
   },
   {
@@ -1083,7 +1083,7 @@
    "source": [
     "with Centered_eight:\n",
     "    fit_cp99 = pm.sample(5000, chains=2, tune=2000,\n",
-    "                         nuts_kwargs=dict(target_accept=.99))"
+    "                         target_accept=.99)"
    ]
   },
   {
@@ -1350,7 +1350,7 @@
    "source": [
     "with NonCentered_eight:\n",
     "    fit_ncp80 = pm.sample(5000, chains=2, tune=1000, random_seed=SEED,\n",
-    "                         nuts_kwargs=dict(target_accept=.80))"
+    "                         target_accept=.80)"
    ]
   },
   {
@@ -1708,7 +1708,7 @@
    "source": [
     "with NonCentered_eight:\n",
     "    fit_ncp90 = pm.sample(5000, chains=2, tune=1000, random_seed=SEED,\n",
-    "                         nuts_kwargs=dict(target_accept=.90))\n",
+    "                         target_accept=.90)\n",
     "    \n",
     "# display the total number and percentage of divergent\n",
     "divergent = fit_ncp90['diverging']\n",

docs/source/notebooks/Diagnosing_biased_Inference_with_Divergences.ipynb

@@ -309,7 +309,7 @@
     "    theta = pm.Beta('theta', alpha=ab[0], beta=ab[1], shape=N)\n",
     "\n",
     "    p = pm.Binomial('y', p=theta, observed=y, n=n)\n",
-    "    trace = pm.sample(1000, tune=2000, nuts_kwargs={'target_accept': .95})\n",
+    "    trace = pm.sample(1000, tune=2000, target_accept=0.95)\n",
     "    "
    ]
   },

docs/source/notebooks/GLM-hierarchical-binominal-model.ipynb

@@ -328,7 +328,7 @@
    "source": [
     "with model_randomwalk:\n",
     "    trace_rw = pm.sample(tune=2000, cores=4, samples=200, \n",
-    "                         nuts_kwargs=dict(target_accept=.9))"
+    "                         target_accept=0.9)"
    ]
   },
   {

docs/source/notebooks/GLM-rolling-regression.ipynb

@@ -260,7 +260,7 @@
    ],
    "source": [
     "with model:\n",
-    "    tr = pm.sample(1000, tune=1000, chains=2, cores=1, nuts_kwargs={\"target_accept\":0.95})"
+    "    tr = pm.sample(1000, tune=1000, chains=2, cores=1, target_accept=0.95)"
    ]
   },
   {
@@ -595,7 +595,7 @@
    ],
    "source": [
     "with model:\n",
-    "    tr = pm.sample(1000, tune=1000, chains=2, cores=1, nuts_kwargs={\"target_accept\":0.95})"
+    "    tr = pm.sample(1000, tune=1000, chains=2, cores=1, target_accept=0.95)"
    ]
   },
   {
@@ -1084,7 +1084,7 @@
    ],
    "source": [
     "with model:\n",
-    "    tr = pm.sample(500, chains=2, cores=1, nuts_kwargs={\"target_accept\": 0.95})"
+    "    tr = pm.sample(500, chains=2, cores=1, target_accept=0.95)"
    ]
   },
   {