bnlearn - Library for Bayesian network learning and inference

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. Navigate to API documentations for more detailed information.

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Blogs

Read the blogs to get a structured overview of bayesian methods and detailed usage of bnlearn.

• Step-by-step guide for structure learning.
• Step-by-step guide for knowledge-driven models.

Documentation pages

On the documentation pages you can find detailed information about the working of the bnlearn with many examples.

Installation

It is advisable to create a new environment (e.g. with Conda).

conda create -n env_bnlearn python=3.8
conda activate env_bnlearn

Install bnlearn from PyPI

pip install bnlearn

The following functions are available after installation:

# Import library
import bnlearn as bn

# Structure learning
bn.structure_learning.fit()

# Compute edge strength with the test statistic
bn.independence_test(model, df, test='chi_square', prune=True)

# Parameter learning
bn.parameter_learning.fit()

# Inference
bn.inference.fit()

# Make predictions
bn.predict()

# Based on a DAG, you can sample the number of samples you want.
bn.sampling()

# Load well known examples to play arround with or load your own .bif file.
bn.import_DAG()

# Load simple dataframe of sprinkler dataset.
bn.import_example()

# Compare 2 graphs
bn.compare_networks()

# Plot graph
bn.plot()

# To make the directed grapyh undirected
bn.to_undirected()

# Convert to one-hot datamatrix
bn.df2onehot()

# Derive the topological ordering of the (entire) graph 
bn.topological_sort()

# 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

Method overview

Learning a Bayesian network can be split into the underneath problems which are all implemented in this package:

• Structure learning: Given the data: Estimate a DAG that captures the dependencies between the variables.

  • There are multiple manners to perform structure learning.
    • Exhaustivesearch
    • Hillclimbsearch
    • NaiveBayes
    • TreeSearch
      • Chow-liu
      • Tree-augmented Naive Bayes (TAN)

• Parameter learning: Given the data and DAG: Estimate the (conditional) probability distributions of the individual variables.

• Inference: Given the learned model: Determine the exact probability values for your queries.

Examples

A structured overview of all examples are now available on the documentation pages.

Structure learning

• Example: Learn structure on the Sprinkler dataset based on a simple dataframe

• Example: Comparison method and scoring types types for structure learning

• Example: Learn structure on more complex dataset (Asia)

Parameter learning

• Example: Parameter learning using a DAG and dataframe

Inferences

• Example: Make predictions on a dataframe using inference

Sampling

• Example: Sampling to create datasets

Complete examples

• Example: Create a Bayesian Network, learn its parameters from data and perform the inference

• Example: Use case in the medical domain

• Example: Use case Titanic

Plotting

• Example: Interactive plotting

• Example: Static plotting

• Example: Comparison of two networks

Various

• Example: Saving and loading of bnlearn models

• Example: Data conversions such as creating sparse datamatrix from source-target and weights

• Example: Load DAG from BIF files

Various basic examples

    import bnlearn as bn
    # Example dataframe sprinkler_data.csv can be loaded with: 
    df = bn.import_example()
    # df = pd.read_csv('sprinkler_data.csv')

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

    model = bn.structure_learning.fit(df)
    # Compute edge strength with the chi_square test statistic
    model = bn.independence_test(model, df)
    G = bn.plot(model)

• Choosing various methodtypes and scoringtypes:

    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')
    model_cl      = bn.structure_learning.fit(df, methodtype='cl', root_node='Wet_Grass')
    model_tan     = bn.structure_learning.fit(df, methodtype='tan', root_node='Wet_Grass', class_node='Rain')

Example: Parameter Learning

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

Example: Inference

    import bnlearn as bn
    model = bn.import_DAG('sprinkler')
    query = bn.inference.fit(model, variables=['Rain'], evidence={'Cloudy':1,'Sprinkler':0, 'Wet_Grass':1})
    print(query)
    print(query.df)
    
    # Lets try another inference
    query = bn.inference.fit(model, variables=['Rain'], evidence={'Cloudy':1})
    print(query)
    print(query.df)

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References

• https://erdogant.github.io/bnlearn/
• http://pgmpy.org
• https://programtalk.com/python-examples/pgmpy.factors.discrete.TabularCPD/
• http://www.bnlearn.com/bnrepository/

Contribute

• All kinds of contributions are welcome!

Citation

Please cite bnlearn in your publications if this is useful for your research. See column right for citation information.

Maintainer

• Erdogan Taskesen, github: erdogant
• Contributions are welcome.
• If you wish to buy me a Coffee for this work, it is very appreciated :)