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¶

from pgmpy.utils import get_example_model

# 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 LinearGaussianBayesianNetwork

gaussian_bn = LinearGaussianBayesianNetwork(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 distributions¶

from pgmpy.global_vars import config

config.set_backend("torch")

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

Next Steps¶