Probablistic Inference with pyAgrum¶

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In this notebook, we will show different basic features for probabilistic inference on Bayesian networks using pyAgrum.

First we need some external modules:

In [1]:

import os 

%matplotlib inline
from pylab import *
import matplotlib.pyplot as plt

Basic inference and display¶

Then we import pyAgrum and the pyAgrum's notebook module, that offers very usefull methods when writting a notebook.

This first example shows how you can load a BayesNet and show it as graph. Note that pyAgrum handles serveral BayesNet file format such as DSL, BIF and UAI.

In [2]:

import pyAgrum as gum
import pyAgrum.lib.notebook as gnb
bn=gum.loadBN("res/alarm.dsl")
gnb.showBN(bn,size="9")

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In [3]:

print(bn)

BN{nodes: 37, arcs: 46, domainSize: 10^16.2389, dim: 509, mem: 5Ko 896o}

From there, it is easy to get a posterior using an inference engine :

In [4]:

ie=gum.LazyPropagation(bn)
ie.makeInference()
print(ie.posterior(bn.idFromName("CATECHOL")))

  CATECHOL         |
NORMAL   |HIGH     |
---------|---------|
 0.0512  | 0.9488  |

But since we are in notebook, why not use pyAgrum notebook's methods ?

In [5]:

gnb.showPosterior(bn,evs={},target='CATECHOL')

You may also want to see the graph with some posteriors

In [6]:

# due to matplotlib, format is forced to png.
gnb.showInference(bn,evs={},targets={"VENTALV","CATECHOL","HR","MINVOLSET"},size="11")

.. and then observe the impact of evidence :

In [7]:

gnb.showInference(bn,
                  evs={"CO":1,"VENTLUNG":1},
                  targets={"VENTALV",
                           "CATECHOL",
                           "HR",
                           "MINVOLSET",
                           "ANAPHYLAXIS",
                           "STROKEVOLUME",
                           "ERRLOWOUTPUT",
                           "HBR",
                           "PULMEMBOLUS",
                           "HISTORY",
                           "BP",
                           "PRESS",
                           "CO"},
                  size="10")

You can even compute all posteriors by leaving the targets parameter empty (which is its default value).

In [8]:

gnb.showInference(bn,evs={"CO":1,"VENTLUNG":1},size="14")