bn-testing 0.12.2

A test bench to benchmark learn algorithms for graphical models

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

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