Python学习包，用于学习贝叶斯网络的图形结构、参数学习、推理和采样方法。
bnlearn的Python项目详细描述
贝叶斯网络的图形结构
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bnlearn是一个Python包，用于学习贝叶斯网络的图形结构、参数学习、推理和采样方法。这件作品的灵感来源于R包(bnlearn.com网站)多年来这对我很有用。尽管有非常好的Python包用于概率图形模型，但是仍然很难（有时甚至不必要）构建某些管道。bnlearnforpython（这个包）构建在pgmpy包上，包含最需要的管道。导航到API documentations以获取更多详细信息。在
方法概述
学习贝叶斯网络可以分为两个问题，这两个问题都在本软件包中实现：
- 结构学习：给定一组数据样本，估计一个捕获变量之间依赖关系的DAG。在
- 参数学习：给定一组数据样本和一个捕获变量之间依赖关系的DAG，估计单个变量的（条件）概率分布。在
导入bn后，以下功能可用。
^{pr2}$还包括以下方法：
- 推论
- 抽样
- 比较两个网络
- 加载bif文件
- 有向图到无向图的转换
目录
安装
- 从pybnpi学习（推荐）。bnlearn与python3.6+兼容，可在Linux、macosx和Windows上运行。在
- 它是根据麻省理工学院的许可证发行的。在
要求
- 创造一个新的环境是明智的。在
condacreate-nenv_bnlearnpython=3.8condaactivateenv_bnlearncondainstall-cankurankanpgmpy# You may need to deactivate and then activate your environment otherwise the packages may not been recognized.condadeactivatecondaactivateenv_bnlearn# The packages below are handled by the requirements in the bnlearn pip installer. So you dont need to do them manually.pipinstallsklearnpandastqdmfuncsigsstatsmodelscommunitypackaging
快速入门
pip install bnlearn
- 或者，从GitHub源安装bnlearn：
git clone https://github.com/erdogant/bnlearn.git
cd bnlearn
pip install -U .
导入bnlearn包
importbnlearnasbn
示例：结构学习
# Example dataframe sprinkler_data.csv can be loaded with: df=bn.import_example()# df = pd.read_csv('sprinkler_data.csv')model=bn.structure_learning.fit(df)G=bn.plot(model)
df看起来像这样
Cloudy Sprinkler Rain Wet_Grass
0 0 1 0 1
1 1 1 1 1
2 1 0 1 1
3 0 0 1 1
4 1 0 1 1
.. ... ... ... ...
995 0 0 0 0
996 1 0 0 0
997 0 0 1 0
998 1 1 0 1
999 1 0 1 1
在
- 选择各种MethodType和ScoringType：
model_hc_bic=bn.structure_learning.fit(df,methodtype='hc',scoretype='bic')model_hc_k2=bn.structure_learning.fit(df,methodtype='hc',scoretype='k2')model_hc_bdeu=bn.structure_learning.fit(df,methodtype='hc',scoretype='bdeu')model_ex_bic=bn.structure_learning.fit(df,methodtype='ex',scoretype='bic')model_ex_k2=bn.structure_learning.fit(df,methodtype='ex',scoretype='k2')model_ex_bdeu=bn.structure_learning.fit(df,methodtype='ex',scoretype='bdeu')
示例：参数学习
# Import dataframedf=bn.import_example()# As an example we set the CPD at False which returns an "empty" DAGmodel=bn.import_DAG('sprinkler',CPD=False)# Now we learn the parameters of the DAG using the dfmodel_update=bn.parameter_learning.fit(model,df)# Make plotG=bn.plot(model_update)
示例：推理
model=bn.import_DAG('sprinkler')q_1=bn.inference.fit(model,variables=['Rain'],evidence={'Cloudy':1,'Sprinkler':0,'Wet_Grass':1})q_2=bn.inference.fit(model,variables=['Rain'],evidence={'Cloudy':1})
示例：采样以创建数据帧
model=bn.import_DAG('sprinkler')df=bn.sampling(model,n=1000)
- 模型输出：
[bnlearn] Model correct: True
CPD of Cloudy:
+-----------+-----+
| Cloudy(0) | 0.5 |
+-----------+-----+
| Cloudy(1) | 0.5 |
+-----------+-----+
CPD of Sprinkler:
+--------------+-----------+-----------+
| Cloudy | Cloudy(0) | Cloudy(1) |
+--------------+-----------+-----------+
| Sprinkler(0) | 0.5 | 0.9 |
+--------------+-----------+-----------+
| Sprinkler(1) | 0.5 | 0.1 |
+--------------+-----------+-----------+
CPD of Rain:
+---------+-----------+-----------+
| Cloudy | Cloudy(0) | Cloudy(1) |
+---------+-----------+-----------+
| Rain(0) | 0.8 | 0.2 |
+---------+-----------+-----------+
| Rain(1) | 0.2 | 0.8 |
+---------+-----------+-----------+
CPD of Wet_Grass:
+--------------+--------------+--------------+--------------+--------------+
| Sprinkler | Sprinkler(0) | Sprinkler(0) | Sprinkler(1) | Sprinkler(1) |
+--------------+--------------+--------------+--------------+--------------+
| Rain | Rain(0) | Rain(1) | Rain(0) | Rain(1) |
+--------------+--------------+--------------+--------------+--------------+
| Wet_Grass(0) | 1.0 | 0.1 | 0.1 | 0.01 |
+--------------+--------------+--------------+--------------+--------------+
| Wet_Grass(1) | 0.0 | 0.9 | 0.9 | 0.99 |
+--------------+--------------+--------------+--------------+--------------+
[bnlearn] Nodes: ['Cloudy', 'Sprinkler', 'Rain', 'Wet_Grass']
[bnlearn] Edges: [('Cloudy', 'Sprinkler'), ('Cloudy', 'Rain'), ('Sprinkler', 'Wet_Grass'), ('Rain', 'Wet_Grass')]
[bnlearn] Independencies:
(Cloudy _|_ Wet_Grass | Rain, Sprinkler)
(Sprinkler _|_ Rain | Cloudy)
(Rain _|_ Sprinkler | Cloudy)
(Wet_Grass _|_ Cloudy | Rain, Sprinkler)
示例：从bif文件加载DAG
bif_file='sprinkler'bif_file='alarm'bif_file='andes'bif_file='asia'bif_file='pathfinder'bif_file='sachs'bif_file='miserables'bif_file='filepath/to/model.bif'# Loading example datasetmodel=bn.import_DAG(bif_file)
示例：比较网络
# Load asia DAGmodel=bn.import_DAG('asia')# plot ground truthG=bn.plot(model)# Samplingdf=bn.sampling(model,n=10000)# Structure learning of sampled datasetmodel_sl=bn.structure_learning.fit(df,methodtype='hc',scoretype='bic')# Plot based on structure learning of sampled databn.plot(model_sl,pos=G['pos'])# Compare networks and make plotbn.compare_networks(model,model_sl,pos=G['pos'])
实际情况图
在
基于结构学习的图
在
图形比较基本真实与结构学习
在
引文
如果这对你的研究有用，请在你的出版物中引用bnlearn。以下是BibTeX条目示例：
@misc{erdogant2019bnlearn,title={bnlearn},author={Erdogan Taskesen},year={2019},howpublished={\url{https://github.com/erdogant/bnlearn}},}
参考文献
- https://erdogant.github.io/bnlearn/
- http://pgmpy.org
- https://programtalk.com/python-examples/pgmpy.factors.discrete.TabularCPD/
- http://www.bnlearn.com/
- http://www.bnlearn.com/bnrepository/
维护人员
- 项目
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