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
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Metadata
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Release files for bn-testing 0.12.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| bn_testing-0.12.2.tar.gz | 13.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| bn_testing-0.12.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 28.5 kB
Release files / bn_testing-0.12.2.tar.gz
| Download URL | bn_testing-0.12.2.tar.gz |
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| Size | 13.7 kB |
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| Size | 14.8 kB |
| Tags | Python 3 |
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