Dynamic Bayesian Networks

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aGrUM

interactive online version

In [1]:
import pyAgrum as gum
import pyAgrum.lib.notebook as gnb
import pyAgrum.lib.dynamicBN as gdyn

Building a 2TBN

Note the naming convention for a 2TBN : a variable with a name \(A\) is present at t=0 with the name \(A0\) and at time t as \(At\).

In [2]:
# hard coded BN
#twodbn=gum.BayesNet()
#a0,b0,c0,at,bt,ct=[twodbn.add(gum.LabelizedVariable(s,s,6))
#                   for s in ["a0","b0","c0","at","bt","ct"]]
#d0,dt=[twodbn.add(gum.LabelizedVariable(s,s,3))
#       for s in ["d0","dt"]]

#twodbn.addArc(a0,b0)

#twodbn.addArc(c0,d0)
#twodbn.addArc(c0,at)

#twodbn.addArc(a0,at)
#twodbn.addArc(a0,bt)
#twodbn.addArc(a0,dt)
#twodbn.addArc(b0,bt)
#twodbn.addArc(c0,ct)
#twodbn.addArc(d0,ct)
#twodbn.addArc(d0,dt)

#twodbn.addArc(at,ct)
#twodbn.generateCPTs()

# fast BN version

twodbn=gum.fastBN("d0[3]->ct<-at<-a0->b0->bt<-a0->dt[3]<-d0<-c0->ct;c0->at",6)
twodbn
Out[2]:
G d0 d0 ct ct d0->ct dt dt d0->dt bt bt at at at->ct c0 c0 c0->d0 c0->ct c0->at a0 a0 a0->bt a0->at a0->dt b0 b0 a0->b0 b0->bt

2TBN

The dbn above actually is a 2TBN and is not correctly shown as a BN. Using the naming convention, it can be shown as a 2TBN.

In [3]:
gdyn.showTimeSlices(twodbn)
../_images/notebooks_22-Models_dynamicBn_8_0.svg

unrolling 2TBN

A dBN is ‘unrolled’ using the 2TBN and the time period size. For a couple \(a_0\),\(a_t\) in the 2TBN, the unrolled dBN will include \(a_0, a_1, \cdots, a_{T-1}\)

In [4]:
T=5

dbn=gdyn.unroll2TBN(twodbn,T)
gdyn.showTimeSlices(dbn,size="10")
../_images/notebooks_22-Models_dynamicBn_11_0.svg

We can infer on bn just as on a normal bn. Following the naming convention in 2TBN, the variables in a dbN are named using the convention \(a_i\) where \(i\) is the number of their time slice.

In [5]:
gnb.flow.clear()
for i in range(T):
    gnb.flow.add_html(gnb.getPosterior(dbn,target="d{}".format(i),evs={}),"$P(d{})$".format(i))
gnb.flow.display()

$P(d0)$

$P(d1)$

$P(d2)$

$P(d3)$

$P(d4)$

dynamic inference : following variables

gdyn.plotFollow directly ask for the 2TBN, unroll it and add evidence evs. Then it shows the dynamic of variable \(a\) for instance by plotting \(a_0,a_1,\cdots,a_{T-1}\).

In [6]:
import matplotlib.pyplot as plt

plt.rcParams['figure.figsize'] = (10, 2)
gdyn.plotFollow(["a","b","c","d"],twodbn,T=51,evs={'a9':2,'a30':0,'c14':0,'b40':0,'c50':3})
../_images/notebooks_22-Models_dynamicBn_16_0.svg
../_images/notebooks_22-Models_dynamicBn_16_1.svg
../_images/notebooks_22-Models_dynamicBn_16_2.svg
../_images/notebooks_22-Models_dynamicBn_16_3.svg

nsDBN (Non-Stationnary Dynamic Bayesian network)

In [7]:
T=15

dbn=gdyn.unroll2TBN(twodbn,T)
gdyn.showTimeSlices(dbn)
../_images/notebooks_22-Models_dynamicBn_18_0.svg

Non-stationnaty DBN allows to express that the dBN do not follow the same 2TBN during all steps. A unrolled dbn is a classical BayesNet and then can be changed as you want after unrolling.

In [8]:
##### new P(ct|c0)
pot=gum.Potential().add(twodbn["ct"]).add(twodbn["c0"])
pot.fillWith([1,0,0,0.1]*9).normalizeAsCPT() # 36 valeurs normalized as CPT
Out[8]:
ct
c0
0
1
2
3
4
5
0
0.47620.00000.00000.04760.47620.0000
1
0.00000.08330.83330.00000.00000.0833
2
0.47620.00000.00000.04760.47620.0000
3
0.00000.08330.83330.00000.00000.0833
4
0.47620.00000.00000.04760.47620.0000
5
0.00000.08330.83330.00000.00000.0833
In [9]:
# from steps 5 to 10, $C_t$ only depends on $C_{t-1}$ and follows this new CPT
for i in range(5,11):
    dbn.eraseArc(f"d{i-1}",f"c{i}")
    dbn.eraseArc(f"a{i}",f"c{i}")
    dbn.cpt(f"c{i}").fillWith(pot,["ct","c0"]) # ct in pot <- first var of cpt, c0 in pot<-second var in cpt

gdyn.showTimeSlices(dbn,size="14")
../_images/notebooks_22-Models_dynamicBn_21_0.svg
In [10]:
plt.rcParams['figure.figsize'] = (10, 2)
gdyn.plotFollowUnrolled(["a","b","c","d"],dbn,T=15,evs={'a9':2,'c14':0})
../_images/notebooks_22-Models_dynamicBn_22_0.svg
../_images/notebooks_22-Models_dynamicBn_22_1.svg
../_images/notebooks_22-Models_dynamicBn_22_2.svg
../_images/notebooks_22-Models_dynamicBn_22_3.svg
In [ ]: