aeppl-nightly

|Tests Status| |Coverage| |Gitter|

aeppl provides tools for a[e]PPL written in Aesara <https://github.com/pymc-devs/aesara>_.

Features

• Convert graphs containing Aesara RandomVariable\s into joint log-probability graphs
• Transforms for RandomVariable\s that map constrained support spaces to unconstrained spaces (e.g. the extended real numbers), and a rewrite that automatically applies these transformations throughout a graph
• Tools for traversing and transforming graphs containing RandomVariable\s
• RandomVariable-aware pretty printing and LaTeX output

Examples

Using aeppl, one can create a joint log-probability graph from a graph containing Aesara RandomVariable\s:

.. code-block:: python

import aesara from aesara import tensor as at

from aeppl import joint_logprob, pprint

A simple scale mixture model

S_rv = at.random.invgamma(0.5, 0.5) Y_rv = at.random.normal(0.0, at.sqrt(S_rv))

Compute the joint log-probability

logprob, (y, s) = joint_logprob(Y_rv, S_rv)

Log-probability graphs are standard Aesara graphs, so we can compute values with them:

.. code-block:: python

logprob_fn = aesara.function([y, s], logprob)

logprob_fn(-0.5, 1.0)

array(-2.46287705)

Graphs can also be pretty printed:

.. code-block:: python

from aeppl import pprint, latex_pprint

Print the original graph

print(pprint(Y_rv))

b ~ invgamma(0.5, 0.5) in R, a ~ N(0.0, sqrt(b)**2) in R

a

print(latex_pprint(Y_rv))

\begin{equation}

\begin{gathered}

b \sim \operatorname{invgamma}\left(0.5, 0.5\right), \in \mathbb{R}

\

a \sim \operatorname{N}\left(0.0, {\sqrt{b}}^{2}\right), \in \mathbb{R}

\end{gathered}

\

a

\end{equation}

Simplify the graph so that it's easier to read

from aesara.graph.rewriting.utils import rewrite_graph from aesara.tensor.rewriting.basic import topo_constant_folding

logprob = rewrite_graph(logprob, custom_rewrite=topo_constant_folding)

print(pprint(logprob))

s in R, y in R

(switch(s >= 0.0,

((-0.9189385175704956 +

switch(s == 0, -inf, (-1.5 * log(s)))) - (0.5 / s)),

-inf) +

((-0.9189385332046727 + (-0.5 * ((y / sqrt(s)) ** 2))) - log(sqrt(s))))

Joint log-probabilities can be computed for some terms that are derived from RandomVariable\s, as well:

.. code-block:: python

Create a switching model from a Bernoulli distributed index

Z_rv = at.random.normal([-100, 100], 1.0, name="Z") I_rv = at.random.bernoulli(0.5, name="I")

M_rv = Z_rv[I_rv] M_rv.name = "M"

Compute the joint log-probability for the mixture

logprob, (m, z, i) = joint_logprob(M_rv, Z_rv, I_rv)

logprob = rewrite_graph(logprob, custom_rewrite=topo_constant_folding)

print(pprint(logprob))

i in Z, m in R, a in Z

(switch((0 <= i and i <= 1), -0.6931472, -inf) +

((-0.9189385332046727 + (-0.5 * (((m - [-100 100][a]) / [1. 1.][a]) ** 2))) -

log([1. 1.][a])))

Installation

The latest release of aeppl can be installed from PyPI using pip:

::

pip install aeppl

The current development branch of aeppl can be installed from GitHub, also using pip:

::

pip install git+https://github.com/aesara-devs/aeppl

.. |Tests Status| image:: https://github.com/aesara-devs/aeppl/actions/workflows/test.yml/badge.svg?branch=main :target: https://github.com/aesara-devs/aeppl/actions/workflows/test.yml .. |Coverage| image:: https://codecov.io/gh/aesara-devs/aeppl/branch/main/graph/badge.svg?token=L2i59LsFc0 :target: https://codecov.io/gh/aesara-devs/aeppl .. |Gitter| image:: https://badges.gitter.im/aesara-devs/aeppl.svg :alt: Join the chat at https://gitter.im/aesara-devs/aeppl :target: https://gitter.im/aesara-devs/aeppl?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge