https://github.com/erdogant/bnlearn

Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.
https://github.com/erdogant/bnlearn

bayesian-inference causal causal-models causal-networks causality-analysis dag directed-acyclic-graph machine-learning parameter-learning python sampling-methods structure-learning

Last synced: 9 months ago
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Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.

Host: GitHub
URL: https://github.com/erdogant/bnlearn
Owner: erdogant
License: other
Created: 2020-01-01T21:00:30.000Z (about 6 years ago)
Default Branch: master
Last Pushed: 2025-05-04T19:55:15.000Z (9 months ago)
Last Synced: 2025-05-04T20:36:24.896Z (9 months ago)
Topics: bayesian-inference, causal, causal-models, causal-networks, causality-analysis, dag, directed-acyclic-graph, machine-learning, parameter-learning, python, sampling-methods, structure-learning
Language: Jupyter Notebook
Homepage: https://erdogant.github.io/bnlearn
Size: 39.3 MB
Stars: 510
Watchers: 7
Forks: 49
Open Issues: 9
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Citation: CITATION.cff

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### 





``bnlearn`` is Python package for causal discovery by learning the graphical structure of Bayesian networks, parameter learning, inference, and sampling methods.

Because probabilistic graphical models can be difficult to use, ``Bnlearn`` contains the most-wanted pipelines. Navigate to [API documentations](https://erdogant.github.io/bnlearn/) for more detailed information. **⭐️ Star it if you like it ⭐️**



---

### Key Pipelines

| Feature | Description |

|--------|-------------|

| [**Causal Discovery / Structure Learning**](https://erdogant.github.io/bnlearn/pages/html/Structure%20learning.html) | Learn the model structure from data or with expert knowledge. |

| [**Parameter Learning**](https://erdogant.github.io/bnlearn/pages/html/Parameter%20learning.html) | Estimate model parameters (e.g., conditional probability distributions) from observed data. |

| [**Causal Inference**](https://pgmpy.org/examples/Causal%20Inference.html) | Compute interventional and counterfactual distributions using do-calculus. |

| [**Generate Synthetic Data**](https://erdogant.github.io/bnlearn/pages/html/Sampling.html) | Generate synthetic data. |

| [**Discretize Data**](https://erdogant.github.io/bnlearn/pages/html/Discretizing.html) | Discretize continuous datasets. |

---

### Resources and Links

- **Example Notebooks:** [Examples](https://erdogant.github.io/bnlearn/pages/html/Documentation.html#google-colab-notebooks)

- **Blog Posts:** [Medium](https://erdogant.medium.com/)

- **Documentation:** [Website](https://erdogant.github.io/bnlearn)

- **Bug Reports and Feature Requests:** [GitHub Issues](https://github.com/erdogant/bnlearn/issues)

---

### The following functions are available after installation:

| Feature | Description |

|--------|-------------|

| **Key Pipelines** | |

|  [Structure learning](https://erdogant.github.io/bnlearn/pages/html/Structure%20learning.html#exhaustivesearch) | ```bn.structure_learning.fit()``` |

| [Parameter learning](https://erdogant.github.io/bnlearn/pages/html/Parameter%20learning.html#parameter-learning-examples) | ```bn.parameter_learning.fit()``` |

| [Inference](https://erdogant.github.io/bnlearn/pages/html/Inference.html#examples-inference) | ```bn.inference.fit()``` |

| [Make predictions](https://erdogant.github.io/bnlearn/pages/html/Predict.html) | ```bn.predict()``` |

| [Generate Synthetic Data](https://erdogant.github.io/bnlearn/pages/html/Sampling.html) | ```bn.sampling()``` |

| [Compute Edge Strength](https://erdogant.github.io/bnlearn/pages/html/independence_test.html) | ```bn.independence_test()``` |

| **Key Functions** | |

| [Imputations](https://erdogant.github.io/bnlearn/pages/html/impute.html#) | ```bn.knn_imputer()``` |

| [Discretizing](https://erdogant.github.io/bnlearn/pages/html/Discretizing.html#) | ```bn.discretize()``` |

| [Check Model Parameters](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.check_model) | ```bn.check_model()``` |

| [Create DAG](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.make_DAG) | ```bn.make_DAG()``` |

| [Get Node Properties](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.get_node_properties) | ```bn.get_node_properties()``` |

| [Get Edge Properties](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.get_edge_properties) | ```bn.get_edge_properties()``` |

| [Get Parents From Edges](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.get_parents) | ```bn.get_parents()``` |

| [Generate Default CPT per Node](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.generate_cpt) | ```bn.generate_cpt()``` |

| [Generate Default CPTs for All Edges](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.build_cpts_from_structure) | ```bn.build_cpts_from_structure()``` |

| **Make Plots** | |

| [Plotting](https://erdogant.github.io/bnlearn/pages/html/Plot.html) | ```bn.plot()``` |

| [Plot Graphviz](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.plot_graphviz) | ```bn.plot_graphviz()``` |

| [Compare 2 Networks](https://erdogant.github.io/bnlearn/pages/html/Plot.html#comparison-of-two-networks) | ```bn.compare_networks()``` |

| [Load DAG (bif files)](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.import_DAG) | ```bn.import_DAG()``` |

| [Load Examples](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.import_example) | ```bn.import_example()``` |

| **Transformation Functions** | |

| [Convert DAG to Undirected](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.to_undirected) | ```bn.to_undirected()``` |

| [Convert to one-hot](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.df2onehot) | ```bn.df2onehot()``` |

| [Convert Adjacency Matrix to Vector](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.adjmat2vec) | ```bn.adjmat2vec()``` |

| [Convert Adjacency Matrix to Dictionary](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.adjmat2dict) | ```bn.adjmat2dict()``` |

| [Convert Vector to Adjacency Matrix](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.vec2adjmat) | ```bn.vec2adjmat()``` |

| [Convert DAG to Adjacency Matrix](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.dag2adjmat) | ```bn.dag2adjmat()``` |

| [Convert DataFrame to Onehot](https://erdogant.github.io/bnlearn/pages/html/Example%20Datasets.html) | ```bn.df2onehot()``` |

| [Convert Query to DataFrame](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.query2df) | ```bn.query2df()``` |

| [Convert Vector to DataFrame](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.vec2df) | ```bn.vec2df()``` |

| **Metrics** | |

| [Compute Topological Ordering](https://erdogant.github.io/bnlearn/pages/html/topological_sort.html) | ```bn.topological_sort()``` |

| [Compute Structure Scores](https://erdogant.github.io/bnlearn/pages/html/Structure_scores.html) | ```bn.structure_scores()``` |

| **General** | |

| [Save Model](https://erdogant.github.io/bnlearn/pages/html/saving%20and%20loading.html) | ```bn.save()``` |

| [Load Model](https://erdogant.github.io/bnlearn/pages/html/saving%20and%20loading.html) | ```bn.load()``` |

| [Print CPTs](https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.print_CPD) | ```bn.print_CPD()``` |

---

### Installation

##### Install bnlearn from PyPI

```bash

pip install bnlearn

```

##### Install bnlearn from github source

```bash

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

```

##### Load library

```python

# Import library

import bnlearn as bn

```

---

### Code Examples

```python

    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)

```



  



```python

# 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)

```



### Contributors

Setting up and maintaining bnlearn has been possible thanks to users and contributors. Thanks to:



  

  

  



### Maintainer

* Erdogan Taskesen, github: [erdogant](https://github.com/erdogant)

* Contributions are welcome.

* Yes! This library is entirely **free** but it runs on coffee! :) Feel free to support with a Coffee.

ecosyste.ms

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