https://github.com/aesara-devs/aeppl
Tools for an Aesara-based PPL.
https://github.com/aesara-devs/aeppl
bayesian-statistics modeling-and-simulation ppl probability statistics symbolic-computation term-rewriting
Last synced: 16 days ago
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Tools for an Aesara-based PPL.
- Host: GitHub
- URL: https://github.com/aesara-devs/aeppl
- Owner: aesara-devs
- License: mit
- Created: 2021-05-26T22:17:30.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-04-29T07:33:35.000Z (12 months ago)
- Last Synced: 2024-05-01T14:46:53.387Z (12 months ago)
- Topics: bayesian-statistics, modeling-and-simulation, ppl, probability, statistics, symbolic-computation, term-rewriting
- Language: Python
- Homepage: https://aeppl.readthedocs.io
- Size: 736 KB
- Stars: 62
- Watchers: 2
- Forks: 22
- Open Issues: 44
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Security: .github/SECURITY.md
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README
# AePPL
[![Pypi][pypi-badge]][pypi]
[![Downloads][downloads-badge]][releases]
[![Contributors][contributors-badge]][contributors]
[![Gitter][gitter-badge]][gitter]
[![Discord][discord-badge]][discord]
[![Twitter][twitter-badge]][twitter]
Aeppl provides tools for a[e]PPL written in [Aesara](https://github.com/aesara-devs/aesara).
*Build arbitrarily complex probabilistic models. If it is mathematically defined, AePPL will support it.*
[Features](#features) •
[Get started](#get-started) •
[Install](#install) •
[Get help](#get-help) •
[Contribute](#contribute)
## 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
## Get started
Using `aeppl`, one can create a joint log-density graph from a graph
containing Aesara `RandomVariable`s:
``` python
import aesara
from aesara import tensor as at
from aeppl import joint_logprob, pprint
srng = at.random.RandomStream()
# A simple scale mixture model
S_rv = srng.invgamma(0.5, 0.5)
Y_rv = srng.normal(0.0, at.sqrt(S_rv))
# Compute the joint log-probability
logprob, (y, s) = joint_logprob(Y_rv, S_rv)
```
Log-density graphs are standard Aesara graphs, so we can compute
compile them to compute values:
``` python
logprob_fn = aesara.function([y, s], logprob)
logprob_fn(-0.5, 1.0)
# array(-2.46287705)
```
AePPL provides utilities to pretty-print the log-density graphs:
``` 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-densities can be computed for some terms that are *derived* from
`RandomVariable`s, as well:
``` python
# Create a switching model from a Bernoulli distributed index
Z_rv = srng.normal([-100, 100], 1.0, name="Z")
I_rv = srng.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])))
```
Take a look at the [documentation][documentation-examples] for more examples.
## Install
The latest release of `aeppl` can be installed from PyPI using `pip`:
``` bash
pip install aeppl
```
Or via conda-forge:
``` bash
conda install -c conda-forge aeppl
```
The nightly (bleeding edge) version of `aeppl` can be installed using `pip`:
``` bash
pip install aeppl-nightly
```
## Get help
Report bugs by opening an [issue][issues]. If you have a question regarding the usage of AePPL, start a [discussion][discussions] or visit our [Discord server][discord] and [Gitter room][gitter] chats.
## Contribute
AePPL welcomes contributions. To start contributing, take a look at the open [issues][issues].
If you want to implement a new feature, open a [discussion][discussions] or come chat with us on [Discord][discord] or [Gitter][gitter].
[contributors]: https://github.com/aesara-devs/aeppl/graphs/contributors
[contributors-badge]: https://img.shields.io/github/contributors/aesara-devs/aeppl?style=flat-square&logo=github&logoColor=white&color=ECEFF4
[discussions]: https://github.com/aesara-devs/aeppl/discussions
[documentation-examples]: https://aeppl.readthedocs.io/en/latest/examples.html
[downloads-badge]: https://img.shields.io/pypi/dm/aeppl?style=flat-square&logo=pypi&logoColor=white&color=8FBCBB
[discord]: https://discord.gg/h3sjmPYuGJ
[discord-badge]: https://img.shields.io/discord/1072170173785723041?color=81A1C1&logo=discord&logoColor=white&style=flat-square
[gitter]: https://gitter.im/aesara-devs/aeppl
[gitter-badge]: https://img.shields.io/gitter/room/aesara-devs/aeppl?color=81A1C1&logo=matrix&logoColor=white&style=flat-square
[issues]: https://github.com/aesara-devs/aeppl/issues
[releases]: https://github.com/aesara-devs/aeppl/releases
[twitter]: https://twitter.com/AesaraDevs
[twitter-badge]: https://img.shields.io/twitter/follow/AesaraDevs?style=social
[pypi]: https://pypi.org/project/aeppl/
[pypi-badge]: https://img.shields.io/pypi/v/aeppl?color=ECEFF4&logo=python&logoColor=white&style=flat-square