Asthma

import matplotlib.pyplot as plt

# import the computation tools of aGrUM
import pyAgrum as gum

# import the graphical display functions
import pyAgrum.lib.notebook as gnb

The model

# load the "asthma" Bayesian network
bn=gum.loadBN('res/asthma.bif')

# display the Bayesian network
gnb.showBN(bn,nodeColor={n:0.9 for n in bn.names()},cmapNode=plt.get_cmap('Blues'))

# display the conditional probability table of asthme given pollution
gnb.showPotential(bn.cpt(bn.idFromName('asthma')),digits=4)

	asthma
pollution	crisis	no_crisis
1	0.1000	0.9000
2	0.2113	0.7887
3	0.1972	0.8028
4	0.2824	0.7176
5	0.4746	0.5254
6	0.5230	0.4770
7	0.6341	0.3659
8	0.8257	0.1743
9	0.8537	0.1463
10	0.9091	0.0909

Some inference

# display the probability distribution of Variable "trafic"
gnb.showPosterior (bn, {}, "traffic" )

# display the probability distribution of Variable "pollution"
gnb.showPosterior ( bn, {}, "pollution")

# display the distribution somewhat differently
gum.getPosterior ( bn, target="pollution")

pollution
1	2	3	4	5	6	7	8	9	10
0.0000	0.0355	0.4233	0.2788	0.1475	0.0629	0.0164	0.0219	0.0082	0.0055

# more interesting: display the posterior distribution of "asthme"
# given that we observed that time is 8:00 and weather is cloudy
gnb.showPosterior (bn, evs={'hour' : 8, 'weather' : 'cloudy'}, target="asthma" )

# show the complete model on morning (from 8 to 12)
gnb.showInference(bn,evs={'hour' : [0 if i< 8 or i> 12 else 1 for i in range(24)]})

# show the posterior distributions of all the variables given that
# we observed that heure=8 and meteo=nuageux.
# the tables in beige represent the observations
gnb.flow.row(gnb.getInference(bn,size="9",evs={'hour' : 8, 'weather' : 'cloudy'}),
               gnb.getInference(bn,size="9",evs={'hour': 7, 'accident' : 'yes'}),
              captions=["When time is 8:00 and weatcher is cloudy","When time is 7:00 and there hase been an accident"])

When time is 8:00 and weatcher is cloudy

When time is 7:00 and there hase been an accident