bn-testing

Name	bn-testing JSON
Version	0.12.2 JSON
	download
home_page	https://github.com/windisch/bn_testing
Summary	A test bench to benchmark learn algorithms for graphical models
upload_time	2023-06-08 14:45:37
maintainer
docs_url	None
author	Tobias Windisch
requires_python	>=3.8.0
license	Apache-2.0
keywords	graphical models
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # 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)
[![Documentation Status](https://readthedocs.org/projects/bn_testing/badge/?version=latest)](https://bn_testing.readthedocs.io/en/latest/?badge=latest)
[![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`

            

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