From a Bayesian network to a Classifier¶

This notebook shows how to build a classifier from an Bayesian network (and not from a database).

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In [1]:

import pyAgrum as gum
import pyAgrum.skbn as skbn
import pyAgrum.lib.notebook as gnb

In [2]:

bn=gum.loadBN("res/alarm.dsl")
gnb.showBN(bn,size="10")
print(bn.variable("HR"))

No description has been provided for this image

HR:Labelized({LOW|NORMAL|HIGH})

Let's say that you would like to use this Bayesian network to learn a classifier for the class HR (3 classes)

In [3]:

#generating the base of 100 values for testing purpose
print(f"LL(alarm-100)={gum.generateSample(bn,100,'out/alarm-100.csv')}")

LL(alarm-100)=-1577.1656065552986

In [4]:

bnc=skbn.BNClassifier()
bnc.fromTrainedModel(bn,targetAttribute="HR")
print(f"Binary classifier : {bnc.isBinaryClassifier}")
gnb.showBN(bnc.MarkovBlanket)

xTrain, yTrain = bnc.XYfromCSV(filename ='out/alarm-100.csv' )
print(f"predicted : {list(bnc.predict(xTrain))}")
print(f"in base   : {yTrain.to_list()}")

Binary classifier : False

predicted : ['HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'LOW', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'NORMAL', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'NORMAL', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH']
in base   : ['HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'LOW', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'NORMAL', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'NORMAL', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH']

In [5]:

print(list(bnc.predict(X ='out/alarm-100.csv')))

['HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'LOW', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'NORMAL', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'NORMAL', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'HIGH', 'NORMAL', 'HIGH', 'HIGH', 'HIGH', 'HIGH']

In [6]:

scoreCSV1 = bnc.score('out/alarm-100.csv', y = yTrain)
print("{0:.2f}% good predictions".format(100*scoreCSV1))

100.00% good predictions

From a Bayesian network to a Binary classifier¶

By targetting a specific label, one can create a binary classifier to predict this very target.

In [7]:

bnc=skbn.BNClassifier()
bnc.fromTrainedModel(bn,targetAttribute="HR",targetModality="LOW")
print(f"Binary classifier : {bnc.isBinaryClassifier}")
gnb.showBN(bnc.MarkovBlanket)

xTrain, yTrain = bnc.XYfromCSV(filename ='out/alarm-100.csv' )
print(f"predicted : {list(bnc.predict(xTrain))}")
print(f"in base   : {yTrain.to_list()}")

Binary classifier : True

predicted : [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]
in base   : [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]

In [8]:

print(list(bnc.predict(X ='out/alarm-100.csv')))

[False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]

In [9]:

scoreCSV1 = bnc.score('out/alarm-100.csv', y = yTrain)
print("{0:.2f}% good predictions".format(100*scoreCSV1))

100.00% good predictions

In [10]:

print(f"LL(alarm-1000)={gum.generateSample(bn,1000,'out/alarm-1000.csv',with_labels=True)}")
bnc.showROC_PR('out/alarm-1000.csv')

LL(alarm-1000)=-15114.588901720348

In [11]:

import pyAgrum.lib.bn2roc as bn2roc
bn2roc.animROC(bnc.bn,'out/alarm-1000.csv',target="HR",label="LOW")

Out[11]:

interactive(children=(IntSlider(value=50, description='rate'), Output(layout=Layout(height='250px'))), _dom_cl…

In [12]:

bn2roc.animPR(bnc.bn,'out/alarm-1000.csv',target="HR",label="LOW")

Out[12]:

interactive(children=(IntSlider(value=50, description='rate'), Output(layout=Layout(height='250px'))), _dom_cl…

In [ ]:

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Generated on 2023-07-26 05:22