erdogant/bnlearn

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Key Features

Feature	Description	Medium	Gumroad/Podcast
Causal Discovery - Overview and Starters Guide	Learn the basics of causal modelling.	link	---
Structure Learning	Learn the model structure from data or with expert knowledge.	link	Podcast
Causal Predictions	Learn to make causal predictions and explore interventions.	link	---
Parameter Learning	Estimate model parameters (e.g., conditional probability distributions) from observed data and combine data with expert knowledge.	link	Podcast
Causal Inference	Compute interventional and counterfactual distributions using Bayesian inference.	link	Podcast
Generate Synthetic Data	Generate synthetic data using Bayesian sampling.	link	Podcast
Discretize Data	Discretize continuous datasets for Bayesian network modelling.	---	---
Data Types	Discrete, continuous and mixed datasets.	---	---
Comparisons	Compare causal discovery libraries and their ability to find hidden causal relationships.	link	Podcast
LLMs and Causal Discovery	Benchmark large language models against Bayesian methods for causal discovery.	link	---

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Resources and Links

• Example Notebooks: Examples
• Blog Posts: Medium
• Documentation: Website
• Bug Reports and Feature Requests: GitHub Issues

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The following functions are available after installation:

Feature	Description
Key Pipelines
Structure learning	bn.structure_learning.fit()
Parameter learning	bn.parameter_learning.fit()
Inference	bn.inference.fit()
Make predictions	bn.predict()
Generate Synthetic Data	bn.sampling()
Compute Edge Strength	bn.independence_test()
Key Functions
Imputations	bn.knn_imputer()
Discretizing	bn.discretize()
Check Model Parameters	bn.check_model()
Create DAG	bn.make_DAG()
Get Node Properties	bn.get_node_properties()
Get Edge Properties	bn.get_edge_properties()
Get Parents From Edges	bn.get_parents()
Generate Default CPT per Node	bn.generate_cpt()
Generate Default CPTs for All Edges	bn.build_cpts_from_structure()
Make Plots
Plotting	bn.plot()
Plot Graphviz	bn.plot_graphviz()
Compare 2 Networks	bn.compare()
Load DAG (bif files)	bn.import_BIF()
Load Examples	bn.import_example()
Transformation Functions
Convert DAG to Undirected	bn.to_undirected()
Convert to one-hot	bn.df2onehot()
Convert Adjacency Matrix to Vector	bn.adjmat2vec()
Convert Adjacency Matrix to Dictionary	bn.adjmat2dict()
Convert Vector to Adjacency Matrix	bn.vec2adjmat()
Convert DAG to Adjacency Matrix	bn.dag2adjmat()
Convert DataFrame to Onehot	bn.df2onehot()
Convert Query to DataFrame	bn.query2df()
Convert Vector to DataFrame	bn.vec2df()
Metrics
Compute Topological Ordering	bn.topological_sort()
Compute Structure Scores	bn.structure_scores()
General
Save Model	bn.save()
Load Model	bn.load()
Print CPTs	bn.print_CPD()

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Installation

Python 3.10 or newer is required.

Installation: uv-pypi

uv venv
.venv\Scripts\activate
uv pip install bnlearn

Installation: uv-local

uv venv
.venv\Scripts\activate
git clone https://github.com/erdogant/bnlearn.git
cd bnlearn
uv pip install -U -e .

Installation: conda-pypi

conda create -n env_bnlearn python=3.12
conda activate env_bnlearn
pip install bnlearn

Installation: GitHub source

pip install git+https://github.com/erdogant/bnlearn
uv pip install git+https://github.com/erdogant/bnlearn

Run tests (uv)

uv pip install -e ".[dev]"
uv run pytest

Run tests (pip)

pip install -e ".[dev]"
pytest -v

Install skill:

npx skills add erdogant/bnlearn --skill bnlearn

Load library

# Import library
import bnlearn as bn

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Code 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')

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)

# Example: Structure Learning

    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_BIF('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_BIF('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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Star history

Contributors

Thank the contributors!

Maintainer

• Erdogan Taskesen, github: erdogant
• Contributions are welcome.
• Yes! This library is entirely free but it runs on coffee! :) Feel free to support with a Coffee.