semvec/pypi-packages huggingface dataset:
bn-testing 0.12.2
A test bench to benchmark learn algorithms for graphical models
License: Apache-2.0
Requires Python: >=3.8.0
Keywords: graphical models

# BN testing


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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`