pymc.model_graph.model_to_networkx

pymc.model_graph.model_to_networkx(model=None, *, var_names=None, formatting='plain', node_formatters=None)

Produce a networkx Digraph from a PyMC model.

Requires networkx, which may be installed most easily with:

conda install networkx

Alternatively, you may install using pip with:

pip install networkx

See https://networkx.org/documentation/stable/ for more information.

Parameters:

modelModel

The model to plot. Not required when called from inside a modelcontext.

var_namesiterable of str, optional

Subset of variables to be plotted that identify a subgraph with respect to the entire model graph

formattingstr, optional

one of { “plain” }

node_formattersdict, optional

A dictionary mapping node types to functions that return a dictionary of node attributes. Check out the networkx documentation for more information how attributes are added to nodes: https://networkx.org/documentation/stable/reference/classes/generated/networkx.Graph.add_node.html

Examples

How to plot the graph of the model.

import numpy as np
from pymc import HalfCauchy, Model, Normal, model_to_networkx

J = 8
y = np.array([28, 8, -3, 7, -1, 1, 18, 12])
sigma = np.array([15, 10, 16, 11, 9, 11, 10, 18])

with Model() as schools:

    eta = Normal("eta", 0, 1, shape=J)
    mu = Normal("mu", 0, sigma=1e6)
    tau = HalfCauchy("tau", 25)

    theta = mu + tau * eta

    obs = Normal("obs", theta, sigma=sigma, observed=y)

model_to_networkx(schools)

Add custom attributes to Free Random Variables and Observed Random Variables nodes.

node_formatters = {
    "Free Random Variable": lambda var: {"shape": "circle", "label": var.name},
    "Observed Random Variable": lambda var: {"shape": "square", "label": var.name},
}
model_to_networkx(schools, node_formatters=node_formatters)