Quickstart

Installation

Installing bnlearn is straightforward. It is recommended to create a new environment for the installation.

conda create -n env_bnlearn python=3.8
conda activate env_bnlearn
pip install bnlearn

Quick Examples

Let’s start by importing some data. We need a DAG and CPD (Conditional Probability Distribution).

import bnlearn as bn

# Import example dataset
df = bn.import_example()

# Learn the structure from data
model = bn.structure_learning.fit(df)

# Perform independence tests
model = bn.independence_test(model, df)

# Visualize the network
G = bn.plot(model)

Here’s another example demonstrating a complete workflow:

import bnlearn as bn

# Import a predefined DAG (Sprinkler network)
model = bn.import_DAG('sprinkler')

# Import example dataset
df = bn.import_example()

# Generate samples from the model
df = bn.sampling(model)

# Perform inference
query = bn.inference.fit(model, variables=['Rain'], evidence={'Cloudy':1, 'Wet_Grass':1})
print(query.df)

# Learn structure from data
model_sl = bn.structure_learning.fit(df)

# Learn parameters
model_pl = bn.parameter_learning.fit(model_sl, df)

# Compare networks
scores, adjmat = bn.compare_networks(model_sl, model)

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