Getting Started¶
Install¶
pgmpy supports Python >= 3.10. For installation through pypi:
pip install pgmpy
For installation through anaconda, use the command:
conda install -c conda-forge pgmpy
For installing the latest dev branch from GitHub, use the command:
pip install git+https://github.com/pgmpy/pgmpy.git@dev
Quickstart¶
Discrete Bayesian Network¶
from pgmpy.utils import get_example_model
# Load a Discrete Bayesian Network and simulate data.
discrete_bn = get_example_model('alarm')
alarm_df = discrete_bn.simulate(n_samples=100)
# Learn a network from simulated data.
from pgmpy.estimators import PC
dag = PC(data=alarm_df).estimate(ci_test='chi_square', return_type='dag')
# Learn the parameters from the data.
dag_fitted = dag.fit(alarm_df)
dag_fitted.get_cpds()
Gaussian Bayesian Network¶
# Load an example Gaussian Bayesian Network and simulate data
gaussian_bn = get_example_model('ecoli70')
ecoli_df = gaussian_bn.simulate(n_samples=100)
# Learn the network from simulated data.
from pgmpy.estimators import PC
dag = PC(data=ecoli_df).estimate(ci_test='pearsonr', return_type='dag')
# Learn the parameters from the data.
gaussian_bn = LinearGausianBayesianNetwork(dag.edges())
dag_fitted = gaussian_bn.fit(ecoli_df)
dag_fitted.get_cpds()
