Aeppl provides tools for a[e]PPL written in Aesara.
Build arbitrarily complex probabilistic models. If it is mathematically defined, AePPL will support it.
Features • Get started • Install • Get help • Contribute
- Convert graphs containing Aesara
RandomVariables into joint log-probability graphs - Transforms for
RandomVariables 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
RandomVariables -
RandomVariable-aware pretty printing and LaTeX output
Using aeppl, one can create a joint log-density graph from a graph
containing Aesara RandomVariables:
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:
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:
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
RandomVariables, as well:
# 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 for more examples.
The latest release of aeppl can be installed from PyPI using pip:
pip install aepplOr via conda-forge:
conda install -c conda-forge aepplThe nightly (bleeding edge) version of aeppl can be installed using pip:
pip install aeppl-nightlyReport bugs by opening an issue. If you have a question regarding the usage of AePPL, start a discussion or visit our Discord server and Gitter room chats.
AePPL welcomes contributions. To start contributing, take a look at the open issues.
If you want to implement a new feature, open a discussion or come chat with us on Discord or Gitter.
