Learning Bayesian Belief Networks (BBNs) with LASSO. Example code is as below.
from lassobbn.learn import learn_parameters, learn_structure, to_bbn, to_join_tree, posteriors_to_df
# Step 1. Learn the structure
df_path = './data/data-binary.csv'
meta_path = './data/data-binary-complete.json'
parents = learn_structure(df_path, meta_path, n_way=2, ignore_neg_gt=-0.01, ignore_pos_lt=0.05)
# Step 2. Learn the parameters
d, g, p = learn_parameters(df_path, parents)
# Step 3. Get the BBN
bbn = to_bbn(d, g, p)
# Step 4. Get the Join Tree
jt = to_join_tree(bbn)You can then use Py-BBN to create a BBN and join tree (JT) instance and perform exact inference.
pip install lassobbn| turing_bbn | pyspark-bbn |
|---|---|
 |
 |
- turing_bbn is a C++17 implementation of py-bbn; take your causal and probabilistic inferences to the next computing level!
- pyspark-bbn is a is a scalable, massively parallel processing MPP framework for learning structures and parameters of Bayesian Belief Networks BBNs using Apache Spark.
@misc{alemi_2021,
title={lasso-bbn},
url={https://lasso-bbn.readthedocs.io/},
author={F. Alemi, J. Vang},
year={2021},
month={Aug}}
Copyright 2021 Farrokh Alemi and Jee Vang
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Copyright 2021 Daytchia Vang