pgmpy is a Python library for causal and probabilistic modeling using Bayesian Networks and related models. It provides a uniform API for building, learning, and analyzing models such as Bayesian Networks, Dynamic Bayesian Networks, Directed Acyclic Graphs (DAGs), and Structural Equation Models(SEMs). By integrating tools from both probabilistic inference and causal inference, pgmpy enables users to seamlessly transition between predictive and interventional analyses.
pgmpy is available on both PyPI and anaconda. To install from PyPI, use:
To install from conda-forge, use:
conda install conda-forge::pgmpy
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()
# Drop a column and predict using the learned model.
evidence_df = alarm_df.drop(columns=['FIO2'], axis=1)
pred_FIO2 = dag_fitted.predict(evidence_df)
# 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.
from pgmpy.models import LinearGausianBayesianNetwork
gaussian_bn = LinearGausianBayesianNetwork(dag.edges())
dag_fitted = gaussian_bn.fit(ecoli_df)
dag_fitted.get_cpds()
# Drop a column and predict using the learned model.
evidence_df = ecoli_df.drop(columns=['ftsJ'], axis=1)
pred_ftsJ = dag_fitted.predict(evidence_df)
We welcome all contributions --not just code-- to pgmpy. Please refer out contributing guide for more details. We also offer mentorship for new contributors and maintain a list of potential mentored projects. If you are interested in contributing to pgmpy, please join our discord server and introduce yourself. We will be happy to help you get started.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4