# BN testing


[![Test Package](https://github.com/windisch/bn_testing/actions/workflows/test_package.yml/badge.svg)](https://github.com/windisch/bn_testing/actions/workflows/test_package.yml)
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[![PyPI](https://img.shields.io/pypi/v/bn_testing)](https://pypi.org/project/bn_testing/)

A test framework to evaluate methods that learn Bayesian Networks from
high-dimensional observational data.


## Sampling

Set up the graphical model and sample  data
```python
from bn_testing.models import BayesianNetwork
from bn_testing.dags import ErdosReny
from bn_testing.conditionals import PolynomialConditional


model = BayesianNetwork(
   dag=ErdosReny(p=0.01, n_nodes=100),
   conditionals=PolynomialConditional(max_terms=5)
)

df = model.sample(10000, normalize=True)
```

The observations are stored in a `pandas.DataFrame` where the columns
are the nodes of the DAG and each row is an observation. The
underlying DAG of the graphical model can be accessed with `model.dag`


A test bench to benchmark learn algorithms for graphical models. 

Socket Acquires Coana to Bring Reachability Analysis to Every Appsec Team

bn-testing

A test bench to benchmark learn algorithms for graphical models

tobias.windisch

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bn-testing

BN testing

Sampling

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