pgmpy-rcr 0.1.6

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Key Features

Resources and Links

• Example Notebooks: Examples
• Tutorial Notebooks: Tutorials
• Blog Posts: Medium
• Documentation: Website
• Bug Reports and Feature Requests: GitHub Issues
• Questions: discord · Stack Overflow

Quickstart

Installation

pgmpy is available on both PyPI and anaconda. To install from PyPI, use:

pip install pgmpy

To install from conda-forge, use:

conda install conda-forge::pgmpy

Examples

Discrete Data

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)

Linear Gaussian Data

# 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)

Mixture Data with Arbitrary Relationships

import pyro.distributions as dist

from pgmpy.models import FunctionalBayesianNetwork
from pgmpy.factors.hybrid import FunctionalCPD

# Create a Bayesian Network with mixture of discrete and continuous variables.
func_bn = FunctionalBayesianNetwork(
    [
        ("x1", "w"),
        ("x2", "w"),
        ("x1", "y"),
        ("x2", "y"),
        ("w", "y"),
        ("y", "z"),
        ("w", "z"),
        ("y", "c"),
        ("w", "c"),
    ]
)

# Define the Functional CPDs for each node and add them to the model.
cpd_x1 = FunctionalCPD("x1", fn=lambda _: dist.Normal(0.0, 1.0))
cpd_x2 = FunctionalCPD("x2", fn=lambda _: dist.Normal(0.5, 1.2))

# Continuous mediator: w = 0.7*x1 - 0.3*x2 + ε
cpd_w = FunctionalCPD(
    "w",
    fn=lambda parents: dist.Normal(0.7 * parents["x1"] - 0.3 * parents["x2"], 0.5),
    parents=["x1", "x2"],
)

# Bernoulli target with logistic link: y ~ Bernoulli(sigmoid(-0.7 + 1.5*x1 + 0.8*x2 + 1.2*w))
cpd_y = FunctionalCPD(
    "y",
    fn=lambda parents: dist.Bernoulli(
        logits=(-0.7 + 1.5 * parents["x1"] + 0.8 * parents["x2"] + 1.2 * parents["w"])
    ),
    parents=["x1", "x2", "w"],
)

# Downstream Bernoulli influenced by y and w
cpd_z = FunctionalCPD(
    "z",
    fn=lambda parents: dist.Bernoulli(
        logits=(-1.2 + 0.8 * parents["y"] + 0.2 * parents["w"])
    ),
    parents=["y", "w"],
)

# Continuous outcome depending on y and w: c = 0.2 + 0.5*y + 0.3*w + ε
cpd_c = FunctionalCPD(
    "c",
    fn=lambda parents: dist.Normal(0.2 + 0.5 * parents["y"] + 0.3 * parents["w"], 0.7),
    parents=["y", "w"],
)

func_bn.add_cpds(cpd_x1, cpd_x2, cpd_w, cpd_y, cpd_z, cpd_c)
func_bn.check_model()

# Simulate data from the model
df_func = func_bn.simulate(n_samples=1000, seed=123)

# For learning and inference in Functional Bayesian Networks, please refer to the example notebook: https://github.com/pgmpy/pgmpy/blob/dev/examples/Functional_Bayesian_Network_Tutorial.ipynb

Contributing

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.