Aesara的PPL工具
项目描述
aeppl为用Aesara编写的
特性
将包含Aesara RandomVariable的图转换为联合对数似然图
将RandomVariable映射到约束支持空间到非约束空间（例如，扩展实数）的转换，以及自动在整个图中应用这些转换的重写
用于遍历和转换包含RandomVariable的图的工具
RandomVariable感知的格式化和LaTeX输出
示例
使用aeppl，可以从包含Aesara RandomVariable的图创建联合对数似然图
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)对数概率图是标准的Aesara图，因此我们可以使用它们来计算值
logprob_fn = aesara.function([y, s], logprob)
logprob_fn(-0.5, 1.0)
# array(-2.46287705)图也可以进行美化打印
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))))还可以计算一些从随机变量派生出来的项的联合对数概率
# 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])))安装
可以通过PyPI使用pip安装最新版本的
pip install aeppl
可以通过GitHub使用pip安装
pip install git+https://github.com/aesara-devs/aeppl
下载文件
下载适合您平台的文件。如果您不确定选择哪个，请了解更多关于安装包的信息。
源分布
aeppl-nightly-0.0.40.tar.gz (68.8 kB 查看散列值)
构建分布
aeppl_nightly-0.0.40-py3-none-any.whl (58.2 kB 查看散列值)
