How to visualize a hidden Markov model in Python?

Question

I think the question is clear enough. I want to make a hidden Markov model in Python and draw a vizualization model of it. So, it’s something like this picture:

Is there any module to do that? I’ve googled it and found nothing.

Asked By: user2435611

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Answer 1

Though I’ve never worked with Hidden Markov Models, when I need to visualize a graph (directed, with labels, colors, etc.), I use Gephi, a GUI graph browser/editor and generate the graphs programmatically as GraphML files, which is an XML-based format. Python has good XML-handling tools (in the standard library and lxml). Gephi recognizes some of the <data> sub-elements as positions, colors, and labels for nodes and edges.

Answered By: Jim Pivarski

Answer 2

The dot package from graphviz is the best I’ve found. The syntax is simple, simpler than xml.

Answered By: Charles Pehlivanian

Answer 3

The Python library pomegranate has good support for Hidden Markov Models. It includes functionality for defining such models, learning it from data, doing inference, and visualizing the transitions graph (as you request here).

Below is example code for defining a model, and plotting the states and transitions. The image output will be like this:

from pomegranate import HiddenMarkovModel, State, DiscreteDistribution
from matplotlib import pyplot as plt

def build_model():

    d1 = DiscreteDistribution({'A' : 0.50, 'B' : 0.50})
    d2 = DiscreteDistribution({'A' : 0.10, 'B' : 0.90})
    d3 = DiscreteDistribution({'A' : 0.90, 'B' : 0.10})

    s1 = State(d1, name="s1")
    s2 = State(d2, name="s2")
    s3 = State(d3, name="s3")

    model = HiddenMarkovModel(name='my model')
    model.add_states(s1, s2, s3)
    model.add_transition(model.start, s1, 1.0)
    model.add_transition(s1, s1, 0.7)
    model.add_transition(s1, s2, 0.3) # s1->s2
    model.add_transition(s2, s2, 0.8)
    
    model.add_transition(s2, s3, 0.0) # no transition from s2 to s3
    model.add_transition(s1, s3, 0.1) # indirect from s1 to s3
    model.add_transition(s3, s1, 0.1) # indirect from s3 to s1
    
    model.add_transition(s3, s3, 0.9)
    model.add_transition(s3, model.end, 0.1)
    model.start.name = 'start'
    model.end.name = 'end'
    model.bake()

    return model

model = build_model()
fig, ax = plt.subplots(1)
model.plot(ax=ax, precision=2)
fig.savefig('model.png')

Answered By: Jon Nordby

How to visualize a hidden Markov model in Python?

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