This pyAgrum's notebook is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Probablistic Inference with pyAgrum¶

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")

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

In [3]:

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 [4]:

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

You may also want to see the graph with some posteriors

In [5]:

# 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 [6]:

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 [7]:

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