Credal Networks
In [1]:
import os
%matplotlib inline
from pylab import *
import matplotlib.pyplot as plt
In [2]:
import pyAgrum as gum
import pyAgrum.lib.notebook as gnb
gnb.configuration()
Library | Version |
---|---|
OS | posix [linux] |
Python | 3.10.10 (main, Mar 5 2023, 22:26:53) [GCC 12.2.1 20230201] |
IPython | 8.13.2 |
Matplotlib | 3.7.1 |
Numpy | 1.24.3 |
pyDot | 1.4.2 |
pyAgrum | 1.8.0 |
Tue May 09 10:01:28 2023 CEST
Credal Net from BN
In [3]:
bn=gum.fastBN("A->B[3]->C<-D<-A->E->F")
bn_min=gum.BayesNet(bn)
bn_max=gum.BayesNet(bn)
for n in bn.nodes():
x=0.4*min(bn.cpt(n).min(),1-bn.cpt(n).max())
bn_min.cpt(n).translate(-x)
bn_max.cpt(n).translate(x)
cn=gum.CredalNet(bn_min,bn_max)
cn.intervalToCredal()
gnb.flow.row(bn,bn.cpt("B"),cn,bn_min.cpt("B"),bn_max.cpt("B"),captions=["Bayes Net","CPT","Credal Net","CPTmin","CPTmax"])
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0.3332 | 0.1264 | 0.5403 | |
0.0964 | 0.5542 | 0.3495 |
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0.2947 | 0.0879 | 0.5018 | |
0.0578 | 0.5156 | 0.3109 |
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0.3718 | 0.1650 | 0.5789 | |
0.1349 | 0.5927 | 0.3880 |
We can use LBP on CN (L2U) only for binary credal networks (here B is not binary). We then propose the classical binarization (but warn the user that this leads to approximation in the inference)
In [4]:
cn2=gum.CredalNet(bn_min,bn_max)
cn2.intervalToCredal()
cn2.approximatedBinarization()
cn2.computeBinaryCPTMinMax()
gnb.flow.row(cn,cn2,captions=["Credal net","Binarized credal net"])
Here, \(B\) becomes - \(B\)-b:math:i : the \(i\)-th bit of B - instrumental \(B\)-v:math:k : the indicator variable for each modality \(k\) of \(B\)
In [5]:
ie_mc=gum.CNMonteCarloSampling(cn)
ie2_lbp=gum.CNLoopyPropagation(cn2)
ie2_mc=gum.CNMonteCarloSampling(cn2)
In [6]:
gnb.sideBySide(gnb.getInference(cn,engine=ie_mc),
gnb.getInference(cn2,engine=ie2_mc),
gnb.getInference(cn2,engine=ie2_lbp))
In [7]:
gnb.sideBySide(ie_mc.CN(),ie_mc.marginalMin("F"),ie_mc.marginalMax("F"),
ie_mc.CN(),ie2_lbp.marginalMin("F"),ie2_lbp.marginalMax("F"),
ncols=3)
print(cn)
A:Range([0,1])
<> : [[0.102429 , 0.897571] , [0.238999 , 0.761001]]
B:Range([0,2])
<A:0> : [[0.294675 , 0.126435 , 0.57889] , [0.294675 , 0.164977 , 0.540348] , [0.333215 , 0.164977 , 0.501808] , [0.371758 , 0.126434 , 0.501808] , [0.333216 , 0.0878939 , 0.57889] , [0.371758 , 0.0878939 , 0.540348]]
<A:1> : [[0.0578125 , 0.554178 , 0.38801] , [0.0578125 , 0.592719 , 0.349469] , [0.096352 , 0.592719 , 0.310929] , [0.134894 , 0.554177 , 0.310929] , [0.0963536 , 0.515636 , 0.38801] , [0.134894 , 0.515636 , 0.34947]]
C:Range([0,1])
<B:0|D:0> : [[0.340507 , 0.659493] , [0.360624 , 0.639376]]
<B:1|D:0> : [[0.505574 , 0.494426] , [0.52569 , 0.47431]]
<B:2|D:0> : [[0.149982 , 0.850018] , [0.170098 , 0.829902]]
<B:0|D:1> : [[0.964798 , 0.0352021] , [0.984914 , 0.0150862]]
<B:1|D:1> : [[0.34812 , 0.65188] , [0.368237 , 0.631763]]
<B:2|D:1> : [[0.664717 , 0.335283] , [0.684834 , 0.315166]]
D:Range([0,1])
<A:0> : [[0.705049 , 0.294951] , [0.873592 , 0.126408]]
<A:1> : [[0.471767 , 0.528233] , [0.64031 , 0.35969]]
E:Range([0,1])
<A:0> : [[0.105756 , 0.894244] , [0.246766 , 0.753234]]
<A:1> : [[0.128255 , 0.871745] , [0.269265 , 0.730735]]
F:Range([0,1])
<E:0> : [[0.745444 , 0.254556] , [0.890904 , 0.109096]]
<E:1> : [[0.195918 , 0.804082] , [0.341377 , 0.658623]]
Credal Net from bif files
In [8]:
cn=gum.CredalNet("res/cn/2Umin.bif","res/cn/2Umax.bif")
cn.intervalToCredal()
In [9]:
gnb.showCN(cn,"2")
In [10]:
ie=gum.CNMonteCarloSampling(cn)
ie.insertEvidenceFile("res/cn/L2U.evi")
In [11]:
ie.setRepetitiveInd(False)
ie.setMaxTime(1)
ie.setMaxIter(1000)
ie.makeInference()
In [12]:
cn
In [13]:
gnb.showInference(cn,targets={"A","H","L","D"},engine=ie,evs={"L":[0,1],"G":[1,0]})
Comparing inference in credal networks
In [14]:
import pyAgrum as gum
def showDiffInference(model,mc,lbp):
for i in model.current_bn().nodes():
a,b=mc.marginalMin(i)[:]
c,d=mc.marginalMax(i)[:]
e,f=lbp.marginalMin(i)[:]
g,h=lbp.marginalMax(i)[:]
plt.scatter([a,b,c,d],[e,f,g,h])
cn=gum.CredalNet("res/cn/2Umin.bif","res/cn/2Umax.bif")
cn.intervalToCredal()
The two inference give quite the same result
In [15]:
ie_mc=gum.CNMonteCarloSampling(cn)
ie_mc.makeInference()
cn.computeBinaryCPTMinMax()
ie_lbp=gum.CNLoopyPropagation(cn)
ie_lbp.makeInference()
showDiffInference(cn,ie_mc,ie_lbp)
but not when evidence are inserted
In [16]:
ie_mc=gum.CNMonteCarloSampling(cn)
ie_mc.insertEvidenceFile("res/cn/L2U.evi")
ie_mc.makeInference()
ie_lbp=gum.CNLoopyPropagation(cn)
ie_lbp.insertEvidenceFile("res/cn/L2U.evi")
ie_lbp.makeInference()
showDiffInference(cn,ie_mc,ie_lbp)
Dynamical Credal Net
In [17]:
cn=gum.CredalNet("res/cn/bn_c_8.bif","res/cn/den_c_8.bif")
cn.bnToCredal(0.8,False)
In [18]:
ie=gum.CNMonteCarloSampling(cn)
ie.insertModalsFile("res/cn/modalities.modal")
ie.setRepetitiveInd(True)
ie.setMaxTime(5)
ie.setMaxIter(1000)
ie.makeInference()
In [19]:
print(ie.dynamicExpMax("temp"))
(14.203404647522472, 11.817699847864338, 12.173214728164902, 11.99476087981647, 11.966313382958862, 11.964973878078364, 11.965031829300205, 11.96501512083492, 11.965015808981818)
In [20]:
fig=figure()
ax=fig.add_subplot(111)
ax.fill_between(range(9),ie.dynamicExpMax("temp"),ie.dynamicExpMin("temp"))
Out[20]:
<matplotlib.collections.PolyCollection at 0x7f7a216b8b20>
In [21]:
ie=gum.CNMonteCarloSampling(cn)
ie.insertModalsFile("res/cn/modalities.modal")
ie.setRepetitiveInd(False)
ie.setMaxTime(5)
ie.setMaxIter(1000)
ie.makeInference()
print(ie.messageApproximationScheme())
stopped with epsilon=0
In [22]:
fig=figure()
ax=fig.add_subplot(111)
ax.fill_between(range(9),ie.dynamicExpMax("temp"),ie.dynamicExpMin("temp"))
Out[22]:
<matplotlib.collections.PolyCollection at 0x7f7a21549cf0>
In [23]:
ie=gum.CNMonteCarloSampling(cn)
ie.insertModalsFile("res/cn/modalities.modal")
ie.setRepetitiveInd(False)
ie.setMaxTime(5)
ie.setMaxIter(5000)
gnb.animApproximationScheme(ie)
ie.makeInference()
In [24]:
fig=figure()
ax=fig.add_subplot(111)
ax.fill_between(range(9),ie.dynamicExpMax("temp"),ie.dynamicExpMin("temp"));
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