bnlearn 1.1.0

Key Features

Resources and Links

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

The following functions are available after installation:

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

Installation of bnlearn skills

Installation of bnlearn skills for various harnesses. Default is .claude

Install skill (default claude)

bnlearn install skill 

Install skill: harnass options {claude, opencode agents or any other name}

bnlearn install skill --harness opencode

Load library

# Import library
import bnlearn as bn

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_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)

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.