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Python package for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.

Project description

bnlearn

Python PyPI Version License

  • Bnlearn is Python package for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods. This work is inspired by the R package (bnlearn.com) that has been very usefull to me for many years. Although there are very good Python packages for probabilistic graphical models, it still can remain difficult (and somethimes unnecessarily) to (re)build certain pipelines. Bnlearn for python (this package) is build on the pgmpy package and contains the most-wanted pipelines.

Method overview

Learning a Bayesian network can be split into two problems which are both implemented in this package:

  • Structure learning: Given a set of data samples, estimate a DAG that captures the dependencies between the variables.
  • Parameter learning: Given a set of data samples and a DAG that captures the dependencies between the variables, estimate the (conditional) probability distributions of the individual variables.

The following functions are available:

 .structure_learning()
 .parameter_learning()
 .inference()
  # Based on a DAG, you can sample the number of samples you want.
 .sampling()
  # Load five well known examples to play arround with or load your own .bif file.
 .import_DAG()
  # Load simple dataframe of sprinkler dataset.
 .import_example
  # Compare 2 graphs
 .compare_networks()
  # Plot graph
 .plot()
  # To make the directed grapyh undirected
 .to_undirected()

See below for the exact working of the functions

The following methods are also included:

  • inference
  • sampling
  • comparing two networks
  • loading bif files
  • conversion of directed to undirected graphs

Contents

Installation

  • Install bnlearn from PyPI (recommended). bnlearn is compatible with Python 3.6+ and runs on Linux, MacOS X and Windows.
  • It is distributed under the MIT license.

Requirements

  • It is advisable to create a new environment.
  • Pgmpy requires an older version of networkx and matplotlib.
conda create -n env_BNLEARN python=3.6
conda activate env_BNLEARN
conda install pytorch

# The packages below are handled bij de requirements in the bnlearn pip installer. So you dont need to do them manually.
pip install sklearn pandas tqdm funcsigs pgmpy statsmodels community
pip install networkx==v1.11
pip install matplotlib==2.2.3

Quick Start

pip install bnlearn
  • Alternatively, install bnlearn from the GitHub source:
git clone https://github.com/erdogant/bnlearn.git
cd bnlearn
python setup.py install

Import bnlearn package

import bnlearn as bnlearn

Example: Structure Learning

# Example dataframe sprinkler_data.csv can be loaded with: 
df = pd.import_example()
# df = pd.read_csv('sprinkler_data.csv')
model = bnlearn.structure_learning(df)
G = bnlearn.plot(model)

df looks like this

     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

  • Choosing various methodtypes and scoringtypes:
model_hc_bic  = bnlearn.structure_learning(df, methodtype='hc', scoretype='bic')
model_hc_k2   = bnlearn.structure_learning(df, methodtype='hc', scoretype='k2')
model_hc_bdeu = bnlearn.structure_learning(df, methodtype='hc', scoretype='bdeu')
model_ex_bic  = bnlearn.structure_learning(df, methodtype='ex', scoretype='bic')
model_ex_k2   = bnlearn.structure_learning(df, methodtype='ex', scoretype='k2')
model_ex_bdeu = bnlearn.structure_learning(df, methodtype='ex', scoretype='bdeu')

Example: Parameter Learning

# Import dataframe
df = pd.import_example()
# As an example we set the CPD at False which returns an "empty" DAG
model = bnlearn.import_DAG('sprinkler', CPD=False)
# Now we learn the parameters of the DAG using the df
model_update = bnlearn.parameter_learning(model, df)
# Make plot
G = bnlearn.plot(model)

Example: Inference

model = bnlearn.import_DAG('sprinkler')
q_1 = bnlearn.inference(model, variables=['Rain'], evidence={'Cloudy':1,'Sprinkler':0, 'Wet_Grass':1})
q_2 = bnlearn.inference(model, variables=['Rain'], evidence={'Cloudy':1})

Example: Sampling to create dataframe

model = bnlearn.import_DAG('sprinkler')
df = bnlearn.sampling(model, n=1000)
  • Output of the model:
[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)

Example: Loading DAG from bif files

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 dataset
model = bnlearn.import_DAG(bif_file)

Example: Comparing networks

# Load asia DAG
model=bnlearn.import_DAG('asia')
# plot ground truth
G=bnlearn.plot(model)

# Sampling
df=bnlearn.sampling(model, n=10000)
# Structure learning of sampled dataset
model_sl = bnlearn.structure_learning(df, methodtype='hc', scoretype='bic')
# Plot based on structure learning of sampled data
bnlearn.plot(model_sl, pos=G['pos'])

# Compare networks and make plot
bnlearn.compare_networks(model['adjmat'], model_sl['adjmat'], pos=G['pos'])

Graph of ground truth

Graph based on Structure learning

Graph comparison ground truth vs. structure learning

Citation

Please cite bnlearn in your publications if this is useful for your research. Here is an example BibTeX entry:

@misc{erdogant2019bnlearn,
  title={bnlearn},
  author={Erdogan Taskesen},
  year={2019},
  howpublished={\url{https://github.com/erdogant/bnlearn}},
}

References

Maintainers

Contribute

  • All kinds of contributions are welcome!

© Copyright

See LICENSE for details.

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