Metadata-Version: 2.1
Name: bn-testing
Version: 0.1.1
Summary: A test bench to benchmark learn algorithms for graphical models
Home-page: https://github.com/windisch/bn-test-bench
Author: Tobias Windisch
Author-email: tobias.windisch@posteo.de
License: GNU GPL3
Keywords: graphical models
Platform: UNKNOWN
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=1.20.0)
Requires-Dist: pandas (>=1.3.0)
Requires-Dist: networkx (>=2.5)
Requires-Dist: tqdm (>4.6.0)

# BN testing

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


## Sampling

Set up the graphical model and sample  data
```python
from bn_testing.dags import GroupedGaussianBN

model = GroupedGaussianBN(n_nodes=200, n_groups=10)
df = model.sample(10000)
```
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`


