How to visualize a Bayesian network model constructed with pomegranate

Question

I want to visualize a Bayesian network created with pomegranate with the following code.

import math
from pomegranate import *
import networkx as nx
import matplotlib.pyplot as plt
import pandas as pd

SOmeone has an idea of how I can to this using matplotlib or pygraphvis?

df = pd.DataFrame({'A':[0,0,0,1,0], 'B':[0,0,1,0,0], 'C':[1,1,0,0,1], 'D':[0,1,0,1,1]})
print(df)
df.head()

model = BayesianNetwork.from_samples(df.to_numpy(), state_names=df.columns.values, algorithm='exact')

print(model)

Answer 1

I do not know for Pomegranate, but if I may, using pyAgrum,

import pyAgrum as gum
import pandas as pd
import pyAgrum.lib.notebook as gnb 

df = pd.DataFrame({'A':[0,0,0,1,0], 'B':[0,0,1,0,0], 'C':[1,1,0,0,1], 'D':[0,1,0,1,1]})
gum.BNLearner(df).useAprioriSmoothing(1e-5).useScoreLog2Likelihood().learnBN()

which returns in a jupyter notebook :

For more information on the learned BN,

bn=gum.BNLearner(df).useAprioriSmoothing(1e-).useScoreLog2Likelihood().learnBN()
gnb.sideBySide(bn,gnb.getInference(bn))
gnb.sideBySide(*[bn.cpt(i) for i in bn.nodes()])