A test framework to evaluate methods that learn Bayesian Networks from high-dimensional observational data.
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
FAQs
A test bench to benchmark learn algorithms for graphical models
We found that bn-testing demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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