Merge pull request #62 from pymc-devs/master

sthagen · web-flow · commit 39d95eb5367d · 2021-03-23T18:29:32.000+01:00
Sync Fork from Upstream Repo
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -14,12 +14,12 @@ repos:
         exclude: ^requirements-dev\.txt$
     -   id: trailing-whitespace
 - repo: https://github.com/PyCQA/isort
-  rev: 5.7.0
+  rev: 5.8.0
   hooks:
     - id: isort
       name: isort
 - repo: https://github.com/asottile/pyupgrade
-  rev: v2.10.0
+  rev: v2.11.0
   hooks:
     - id: pyupgrade
       args: [--py37-plus]
diff --git a/README.rst b/README.rst
@@ -3,7 +3,7 @@
     :alt: PyMC3 logo
     :align: center
 
-|Build Status| |Coverage| |NumFOCUS_badge| |Binder| |Dockerhub|
+|Build Status| |Coverage| |NumFOCUS_badge| |Binder| |Dockerhub| |DOIzenodo|
 
 PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning
 focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI)
@@ -74,10 +74,16 @@ To install PyMC3 on your system, follow the instructions on the appropriate inst
 
 Citing PyMC3
 ============
+Please choose from the following:
 
-Salvatier J., Wiecki T.V., Fonnesbeck C. (2016) Probabilistic programming
-in Python using PyMC3. PeerJ Computer Science 2:e55
-`DOI: 10.7717/peerj-cs.55 <https://doi.org/10.7717/peerj-cs.55>`__.
+- |DOIpaper| *Probabilistic programming in Python using PyMC3*, Salvatier J., Wiecki T.V., Fonnesbeck C. (2016)
+- |DOIzenodo| A DOI for all versions.
+- DOIs for specific versions are shown on Zenodo and under `Releases <https://github.com/pymc-devs/pymc3/releases>`_
+
+.. |DOIpaper| image:: https://img.shields.io/badge/DOI-10.7717%2Fpeerj--cs.55-blue
+     :target: https://doi.org/10.7717/peerj-cs.55
+.. |DOIzenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4603970.svg
+   :target: https://doi.org/10.5281/zenodo.4603970
 
 Contact
 =======
diff --git a/RELEASE-NOTES.md b/RELEASE-NOTES.md
@@ -3,6 +3,8 @@
 ## PyMC3 vNext (4.0.0)
 ### Breaking Changes
 - ⚠ Theano-PyMC has been replaced with Aesara, so all external references to `theano`, `tt`, and `pymc3.theanof` need to be replaced with `aesara`, `aet`, and `pymc3.aesaraf` (see [4471](https://github.com/pymc-devs/pymc3/pull/4471)).
+- ArviZ `plots` and `stats` *wrappers* were removed. The functions are now just available by their original names (see [#4549](https://github.com/pymc-devs/pymc3/pull/4471) and `3.11.2` release notes).
+- ...
 
 ### New Features
 - The `CAR` distribution has been added to allow for use of conditional autoregressions which often are used in spatial and network models.
diff --git a/docs/source/api/plots.rst b/docs/source/api/plots.rst
@@ -7,8 +7,7 @@ Plots
 Plots are delegated to the
 `ArviZ <https://arviz-devs.github.io/arviz/index.html>`_.
 library, a general purpose library for
-"exploratory analysis of Bayesian models."
-Refer to its documentation to use the plotting functions directly.
+"exploratory analysis of Bayesian models".
 
-.. automodule:: pymc3.plots.posteriorplot
-   :members:
+Functions from the `arviz.plots` module are available through ``pymc3.<function>`` or ``pymc3.plots.<function>``,
+but for their API documentation please refer to the :ref:`ArviZ documentation <arviz:plot_api>`.
diff --git a/docs/source/api/stats.rst b/docs/source/api/stats.rst
@@ -1,8 +1,13 @@
 *****
 Stats
 *****
+
+.. currentmodule:: pymc3.stats
+
 Statistics and diagnostics are delegated to the
 `ArviZ <https://arviz-devs.github.io/arviz/index.html>`_.
 library, a general purpose library for
-"exploratory analysis of Bayesian models."
-Refer to its documentation to use the diagnostics functions directly.
+"exploratory analysis of Bayesian models".
+
+Functions from the `arviz.stats` module are available through ``pymc3.<function>`` or ``pymc3.stats.<function>``,
+but for their API documentation please refer to the :ref:`ArviZ documentation <arviz:stats_api>`.
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -115,8 +115,12 @@
 
         <div class="ui vertical segment">
             <h2 class="ui dividing header">Citing PyMC3</h2>
-            <p>Salvatier J., Wiecki T.V., Fonnesbeck C. (2016) Probabilistic programming in Python using PyMC3. PeerJ
-                Computer Science 2:e55 <a href="https://doi.org/10.7717/peerj-cs.55">DOI: 10.7717/peerj-cs.55</a>.</p>
+            <p>Please choose from the following:</p>
+            <ul>
+                <li><a href="https://doi.org/10.7717/peerj-cs.55" rel="nofollow"><img alt="DOIpaper" src="https://camo.githubusercontent.com/6a7e1c555ea828c2f9253f7cff0868debe9fdc711694424b913bf95f6d2da9dd/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f444f492d31302e37373137253246706565726a2d2d63732e35352d626c7565" data-canonical-src="https://img.shields.io/badge/DOI-10.7717%2Fpeerj--cs.55-blue" style="max-width:100%;"></a> <em>Probabilistic programming in Python using PyMC3</em>, Salvatier J., Wiecki T.V., Fonnesbeck C. (2016)</li>
+                <li><a href="https://doi.org/10.5281/zenodo.4603970" rel="nofollow"><img alt="DOIzenodo" src="https://camo.githubusercontent.com/2b33f91dc16d9e7ad37aede0119d197776fbef41b8080c7e38a3629df3d9c201/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e343630333937302e737667" data-canonical-src="https://zenodo.org/badge/DOI/10.5281/zenodo.4603970.svg" style="max-width:100%;"></a> A DOI for all versions.</li>
+                <li>DOIs for specific versions are shown on Zenodo and under <a href="https://github.com/pymc-devs/pymc3/releases">Releases</a></li>
+            </ul>
             <p>See <a href="https://scholar.google.de/scholar?oi=bibs&hl=en&authuser=1&cites=6936955228135731011">Google Scholar</a> for a continuously updated list of papers citing PyMC3.</p>
         </div>
 
diff --git a/pymc3/__init__.py b/pymc3/__init__.py
@@ -62,6 +62,7 @@ def __set_compiler_flags():
 from pymc3.plots import *
 from pymc3.sampling import *
 from pymc3.smc import *
+from pymc3.stats import *
 from pymc3.step_methods import *
 from pymc3.tests import test
 from pymc3.tuning import *
diff --git a/pymc3/distributions/continuous.py b/pymc3/distributions/continuous.py
@@ -4223,6 +4223,28 @@ class Interpolated(BoundedContinuous):
     Both parameters ``x_points`` and values ``pdf_points`` are not variables, but
     plain array-like objects, so they are constant and cannot be sampled.
 
+    .. plot::
+
+        import matplotlib.pyplot as plt
+        import numpy as np
+        import pymc3 as pm
+        import arviz as az
+        from scipy.stats import gamma
+        plt.style.use('arviz-darkgrid')
+        rv = gamma(1.99)
+        x = np.linspace(rv.ppf(0.01),rv.ppf(0.99), 1000)
+        points = np.linspace(x[0], x[-1], 50)
+        pdf = rv.pdf(points)
+        interpolated = pm.Interpolated.dist(points, pdf)
+        fig, ax = plt.subplots(1, 1)
+        ax.plot(x, rv.pdf(x), 'C0', linestyle = '--',  label='Original Gamma pdf',alpha=0.8,lw=2)
+        ax.plot(points, pdf, color='black', marker='o', label='Lattice Points',alpha=0.5,linestyle='')
+        ax.plot(x, np.exp(interpolated.logp(x).eval()),'C1',label='Interpolated pdf',alpha=0.8,lw=3)
+        r = interpolated.random(size=1000)
+        ax.hist(r, density=True, alpha=0.4,align ='mid',color='grey')
+        ax.legend(loc='best', frameon=False)
+        plt.show()
+
     ========  ===========================================
     Support   :math:`x \in [x\_points[0], x\_points[-1]]`
     ========  ===========================================
diff --git a/pymc3/distributions/posterior_predictive.py b/pymc3/distributions/posterior_predictive.py
@@ -342,7 +342,7 @@ def __init__(self, vars, trace: _TraceDict, samples, model: Model | None, size=N
         self.size = size
         self.logger = logging.getLogger("posterior_predictive")
 
-    def __enter__(self) -> "_PosteriorPredictiveSampler":
+    def __enter__(self) -> _PosteriorPredictiveSampler:
         self._tok = vectorized_ppc.set(posterior_predictive_draw_values)
         return self
 
diff --git a/pymc3/plots/__init__.py b/pymc3/plots/__init__.py
@@ -1,4 +1,4 @@
-#   Copyright 2020 The PyMC Developers
+#   Copyright 2021 The PyMC Developers
 #
 #   Licensed under the Apache License, Version 2.0 (the "License");
 #   you may not use this file except in compliance with the License.
@@ -12,19 +12,61 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
-"""PyMC3 Plotting.
+"""Alias for the `plots` submodule from ArviZ.
 
-Plots are delegated to the `ArviZ <https://arviz-devs.github.io/arviz/>`_ library, a general purpose library for
-exploratory analysis of Bayesian models. For more details, see https://arviz-devs.github.io/arviz/.
-
-Only `plot_posterior_predictive_glm` is kept in the PyMC code base for now, but it will move to ArviZ once the latter adds features for regression plots.
+Plots are delegated to the ArviZ library, a general purpose library for
+"exploratory analysis of Bayesian models."
+See https://arviz-devs.github.io/arviz/ for details on plots.
 """
 import functools
 import sys
 import warnings
 
 import arviz as az
 
+# Makes this module as identical to arviz.plots as possible
+for attr in az.plots.__all__:
+    obj = getattr(az.plots, attr)
+    if not attr.startswith("__"):
+        setattr(sys.modules[__name__], attr, obj)
+
+
+def alias_deprecation(func, alias: str):
+    original = func.__name__
+
+    @functools.wraps(func)
+    def wrapped(*args, **kwargs):
+        raise DeprecationWarning(
+            f"The function `{alias}` from PyMC3 was an alias for `{original}` from ArviZ. "
+            "It was removed in PyMC3 4.0. "
+            f"Switch to `pymc3.{original}` or `arviz.{original}`."
+        )
+
+    return wrapped
+
+
+# Aliases of ArviZ functions
+autocorrplot = alias_deprecation(az.plot_autocorr, alias="autocorrplot")
+forestplot = alias_deprecation(az.plot_forest, alias="forestplot")
+kdeplot = alias_deprecation(az.plot_kde, alias="kdeplot")
+energyplot = alias_deprecation(az.plot_energy, alias="energyplot")
+densityplot = alias_deprecation(az.plot_density, alias="densityplot")
+pairplot = alias_deprecation(az.plot_pair, alias="pairplot")
+traceplot = alias_deprecation(az.plot_trace, alias="traceplot")
+compareplot = alias_deprecation(az.plot_compare, alias="compareplot")
+
+
 from pymc3.plots.posteriorplot import plot_posterior_predictive_glm
 
-__all__ = ["plot_posterior_predictive_glm"]
+__all__ = tuple(az.plots.__all__) + (
+    "autocorrplot",
+    "compareplot",
+    "forestplot",
+    "kdeplot",
+    "plot_posterior",
+    "traceplot",
+    "energyplot",
+    "densityplot",
+    "pairplot",
+    "plot_posterior_predictive_glm",
+)
diff --git a/pymc3/stats/__init__.py b/pymc3/stats/__init__.py
@@ -0,0 +1,31 @@
+#   Copyright 2021 The PyMC Developers
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+"""Alias for the `stats` submodule from ArviZ.
+
+Diagnostics and auxiliary statistical functions are delegated to the ArviZ library, a general
+purpose library for "exploratory analysis of Bayesian models."
+See https://arviz-devs.github.io/arviz/ for details.
+"""
+import sys
+
+import arviz as az
+
+for attr in az.stats.__all__:
+    obj = getattr(az.stats, attr)
+    if not attr.startswith("__"):
+        setattr(sys.modules[__name__], attr, obj)
+
+
+__all__ = tuple(az.stats.__all__)
