使用Python3.x创建贝叶斯网络并学习参数
使用Python3.x创建贝叶斯网络并学习参数
提问于 2015-02-10 20:11:09
我正在为Windows上的python3.x寻找最合适的工具来创建一个贝叶斯网络，从数据中学习它的参数并执行推理。
我想要定义自己的网络结构如下：
它取自this论文。
除了"Size“和"GraspPose”之外，所有变量都是离散的(并且只能采用两种可能的状态)，这两个变量是连续的，应该建模为高斯混合。
作者使用期望最大化算法来学习条件概率表的参数，并使用联合树算法来计算精确推理。
据我所知，所有这些都是用墨菲的贝叶斯网络工具箱在MatLab中实现的。
我尝试在python中搜索类似的内容，结果如下：
- Python贝叶斯网络工具箱http://sourceforge.net/projects/pbnt.berlios/ (http://pbnt.berlios.de/)。网站不工作，项目似乎不受支持。
- BayesPy https://github.com/bayespy/bayespy我想这才是我真正需要的，但我找不到一些与我的情况类似的例子，了解如何构建网络structure.
- PyMC似乎是一个强大的模块，但我有问题导入到Windows64，Python3.3上。安装开发版本时出现错误
警告(theano.configdefaults)：未检测到g++！Theano将无法执行优化的C实现(针对CPU和GPU)，并将默认使用Python实现。性能将严重下降。要删除此警告，请将Theano标志cxx设置为空字符串。
更新：
- libpgm (http://pythonhosted.org/libpgm/)。这正是我需要的，不幸的是Python3.x不支持
- 非常有趣的正在开发的库: PGMPY。不幸的是，目前还不支持连续变量和从数据中学习。https://github.com/pgmpy/pgmpy/
任何建议和具体的例子都将受到高度赞赏。
回答 5
Stack Overflow用户
发布于 2015-10-20 23:53:57
看起来pomegranate最近更新了，加入了贝叶斯网络。我自己还没有试过，但界面看起来不错，而且很滑稽。
Stack Overflow用户
发布于 2020-06-18 22:59:32
试试bnlearn库，它包含许多函数，可以从数据中学习参数并执行推理。
pip install bnlearn您的用例将如下所示：
# Import the library
import bnlearn
# Define the network structure
edges = [('task', 'size'),
('lat var', 'size'),
('task', 'fill level'),
('task', 'object shape'),
('task', 'side graspable'),
('size', 'GrasPose'),
('task', 'GrasPose'),
('fill level', 'GrasPose'),
('object shape', 'GrasPose'),
('side graspable', 'GrasPose'),
('GrasPose', 'latvar'),
]
# Make the actual Bayesian DAG
DAG = bnlearn.make_DAG(edges)
# DAG is stored in adjacency matrix
print(DAG['adjmat'])
# target task size lat var ... side graspable GrasPose latvar
# source ...
# task False True False ... True True False
# size False False False ... False True False
# lat var False True False ... False False False
# fill level False False False ... False True False
# object shape False False False ... False True False
# side graspable False False False ... False True False
# GrasPose False False False ... False False True
# latvar False False False ... False False False
#
# [8 rows x 8 columns]
# No CPDs are in the DAG. Lets see what happens if we print it.
bnlearn.print_CPD(DAG)
# >[BNLEARN.print_CPD] No CPDs to print. Use bnlearn.plot(DAG) to make a plot.
# Plot DAG. Note that it can be differently orientated if you re-make the plot.
bnlearn.plot(DAG)
现在我们需要数据来学习它的参数。假设这些都存储在您的df中。数据文件中的变量名必须存在于DAG中。
# Read data
df = pd.read_csv('path_to_your_data.csv')
# Learn the parameters and store CPDs in the DAG. Use the methodtype your desire. Options are maximumlikelihood or bayes.
DAG = bnlearn.parameter_learning.fit(DAG, df, methodtype='maximumlikelihood')
# CPDs are present in the DAG at this point.
bnlearn.print_CPD(DAG)
# Start making inferences now. As an example:
q1 = bnlearn.inference.fit(DAG, variables=['lat var'], evidence={'fill level':1, 'size':0, 'task':1})下面是一个演示数据集(sprinkler)的工作示例。你可以玩这个。
# Import example dataset
df = bnlearn.import_example('sprinkler')
print(df)
# Cloudy Sprinkler Rain Wet_Grass
# 0 0 0 0 0
# 1 1 0 1 1
# 2 0 1 0 1
# 3 1 1 1 1
# 4 1 1 1 1
# .. ... ... ... ...
# 995 1 0 1 1
# 996 1 0 1 1
# 997 1 0 1 1
# 998 0 0 0 0
# 999 0 1 1 1
# [1000 rows x 4 columns]
# Define the network structure
edges = [('Cloudy', 'Sprinkler'),
('Cloudy', 'Rain'),
('Sprinkler', 'Wet_Grass'),
('Rain', 'Wet_Grass')]
# Make the actual Bayesian DAG
DAG = bnlearn.make_DAG(edges)
# Print the CPDs
bnlearn.print_CPD(DAG)
# [BNLEARN.print_CPD] No CPDs to print. Use bnlearn.plot(DAG) to make a plot.
# Plot the DAG
bnlearn.plot(DAG)
# Parameter learning on the user-defined DAG and input data
DAG = bnlearn.parameter_learning.fit(DAG, df)
# Print the learned CPDs
bnlearn.print_CPD(DAG)
# [BNLEARN.print_CPD] Independencies:
# (Cloudy _|_ Wet_Grass | Rain, Sprinkler)
# (Sprinkler _|_ Rain | Cloudy)
# (Rain _|_ Sprinkler | Cloudy)
# (Wet_Grass _|_ Cloudy | Rain, Sprinkler)
# [BNLEARN.print_CPD] Nodes: ['Cloudy', 'Sprinkler', 'Rain', 'Wet_Grass']
# [BNLEARN.print_CPD] Edges: [('Cloudy', 'Sprinkler'), ('Cloudy', 'Rain'), ('Sprinkler', 'Wet_Grass'), ('Rain', 'Wet_Grass')]
# CPD of Cloudy:
# +-----------+-------+
# | Cloudy(0) | 0.494 |
# +-----------+-------+
# | Cloudy(1) | 0.506 |
# +-----------+-------+
# CPD of Sprinkler:
# +--------------+--------------------+--------------------+
# | Cloudy | Cloudy(0) | Cloudy(1) |
# +--------------+--------------------+--------------------+
# | Sprinkler(0) | 0.4807692307692308 | 0.7075098814229249 |
# +--------------+--------------------+--------------------+
# | Sprinkler(1) | 0.5192307692307693 | 0.2924901185770751 |
# +--------------+--------------------+--------------------+
# CPD of Rain:
# +---------+--------------------+---------------------+
# | Cloudy | Cloudy(0) | Cloudy(1) |
# +---------+--------------------+---------------------+
# | Rain(0) | 0.6518218623481782 | 0.33695652173913043 |
# +---------+--------------------+---------------------+
# | Rain(1) | 0.3481781376518219 | 0.6630434782608695 |
# +---------+--------------------+---------------------+
# CPD of Wet_Grass:
# +--------------+--------------------+---------------------+---------------------+---------------------+
# | Rain | Rain(0) | Rain(0) | Rain(1) | Rain(1) |
# +--------------+--------------------+---------------------+---------------------+---------------------+
# | Sprinkler | Sprinkler(0) | Sprinkler(1) | Sprinkler(0) | Sprinkler(1) |
# +--------------+--------------------+---------------------+---------------------+---------------------+
# | Wet_Grass(0) | 0.7553816046966731 | 0.33755274261603374 | 0.25588235294117645 | 0.37910447761194027 |
# +--------------+--------------------+---------------------+---------------------+---------------------+
# | Wet_Grass(1) | 0.2446183953033268 | 0.6624472573839663 | 0.7441176470588236 | 0.6208955223880597 |
# +--------------+--------------------+---------------------+---------------------+---------------------+
# Make inference
q1 = bnlearn.inference.fit(DAG, variables=['Wet_Grass'], evidence={'Rain':1, 'Sprinkler':0, 'Cloudy':1})
# +--------------+------------------+
# | Wet_Grass | phi(Wet_Grass) |
# +==============+==================+
# | Wet_Grass(0) | 0.2559 |
# +--------------+------------------+
# | Wet_Grass(1) | 0.7441 |
# +--------------+------------------+
print(q1.values)
# array([0.25588235, 0.74411765])有关更多示例，请访问bnlearn：https://erdogant.github.io/bnlearn
Stack Overflow用户
发布于 2018-10-03 13:45:11
我正在寻找一个类似的库，我发现pomegranate是一个很好的库。谢谢James Atwood
下面是一个如何使用它的示例。
from pomegranate import *
import numpy as np
mydb=np.array([[1,2,3],[1,2,4],[1,2,5],[1,2,6],[1,3,8],[2,3,8],[1,2,4]])
bnet = BayesianNetwork.from_samples(mydb)
print(bnet.node_count())
print(bnet.probability([[1,2,3]]))
print (bnet.probability([[1,2,8]]))页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/28431350
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