Influence diagram
|
|
|
In [1]:
import os
%matplotlib inline
from pylab import *
import matplotlib.pyplot as plt
from IPython.display import display,HTML
import math
In [2]:
import pyAgrum as gum
import pyAgrum.lib.notebook as gnb
Build a Influence Diagram
fast build with string
In [3]:
gum.fastID("A->*B->$C<-D<-*E->*G->H->*I<-D")
Out[3]:
bifxml format file
In [4]:
diag=gum.loadID("res/diag.bifxml")
gnb.showInfluenceDiagram(diag)
In [5]:
diag
Out[5]:
the hard way :-)
In [6]:
F=diag.addChanceNode(gum.LabelizedVariable("F","F",2))
diag.addArc(diag.idFromName("decisionVar1"),F)
U=diag.addUtilityNode(gum.LabelizedVariable("U","U",1))
diag.addArc(diag.idFromName("decisionVar3"),U)
diag.addArc(diag.idFromName("F"),U)
gnb.showInfluenceDiagram(diag)
In [7]:
diag.cpt(F)[{'decisionVar1':0}]=[0.9,0.1]
diag.cpt(F)[{'decisionVar1':1}]=[0.3,0.7]
diag.utility(U)[{'F':0,'decisionVar3':0}]=2
diag.utility(U)[{'F':0,'decisionVar3':1}]=4
diag.utility(U)[{'F':1}]=[[0],[5]]
Optimization in an influence diagram (actually LIMID)
In [8]:
oil=gum.loadID("res/OilWildcatter.bifxml")
gnb.flow.row(oil,gnb.getInference(oil))
In [9]:
# a function to show results on decision nodes T and D
def show_decisions(ie):
gnb.flow.row(ie.optimalDecision("Testing"),
ie.optimalDecision("Drilling"),
f"$${ie.MEU()['mean']:5.3f}\\ (stdev : {math.sqrt(ie.MEU()['variance']):5.3f})$$",
captions=["Strategy for T",
"Strategy for D",
"MEU and its standard deviation"])
gnb.flow.row(ie.posterior("Testing"),ie.posteriorUtility("Testing"),
ie.posterior("Drilling"),ie.posteriorUtility("Drilling"),
captions=["Final decision for Testing","Final reward for Testing",
"Final decision for Drilling","Final reward for Drilling"])
ie=gum.ShaferShenoyLIMIDInference(oil)
display(HTML("<h2>Inference in the LIMID optimizing the decisions nodes</h2>"))
ie.makeInference()
show_decisions(ie)
Inference in the LIMID optimizing the decisions nodes
|
|
|
|---|---|
| 1.0000 | 0.0000 |
|
|
| |
|---|---|---|
| 1.0000 | 0.0000 | |
| 1.0000 | 0.0000 | |
| 0.0000 | 1.0000 | |
$$22.500\ (stdev : 87.457)$$
MEU and its standard deviation
|
|
|
|---|---|
| 1.0000 | 0.0000 |
|
|
|
|---|---|
| 22.5000 | 20.0000 |
|
|
|
|---|---|
| 0.5900 | 0.4100 |
|
|
|
|---|---|
| 45.0847 | -10.0000 |
Graphical inference with evidence and targets (developped nodes)
In [10]:
gnb.sideBySide(oil,
gnb.getInference(oil,evs={'TestResult':'closed'}),
gnb.getInference(oil,evs={'TestResult':'open'}),
gnb.getInference(oil,evs={'TestResult':'diffuse'}),
oil,
gnb.getInference(oil,evs={'OilContents':'Dry'}),
gnb.getInference(oil,evs={'OilContents':'Wet'}),
gnb.getInference(oil,evs={'OilContents':'Soaking'}),
ncols=4)
Soft evidence on chance node
In [11]:
gnb.showInference(oil,evs={'OilContents':[0.7,0.5,0.8]})
Forced decision
In [12]:
gnb.showInference(oil,evs={'Drilling':'Yes'})
LIMID versus Influence Diagram
The default inference for influence diagram actually an inference for LIMIDs. In order to use it for classical (and solvable) influence diagram, do not forget to add the sequence of decision nodes using addNoForgettingAssumption.
In [13]:
infdiag=gum.fastID("Chance->*Decision1->Chance2->$Utility<-Chance3<-*Decision2<-Chance->Utility")
infdiag
Out[13]:
In [14]:
ie=gum.ShaferShenoyLIMIDInference(infdiag)
try:
ie.makeInference()
except gum.GumException as e:
print(e)
[pyAgrum] Fatal error: This LIMID/Influence Diagram is not solvable.
In [15]:
ie.addNoForgettingAssumption(["Decision1","Decision2"])
gnb.sideBySide(ie.reducedLIMID(),ie.junctionTree(),gnb.getInference(infdiag,engine=ie))
Customizing visualization of the results
Using pyAgrum.config, it is possible to adapt the graphical representations for Influence Diagram (see the notebook 99-Tools_configForPyAgrum.ipynb).
In [16]:
gum.config.reset()
gnb.showInference(infdiag,engine=ie,size="7!")
Many visual options can be changed when displaing an inference (especially for influence diagrams)
In [17]:
# do not show inference time
gum.config["notebook","show_inference_time"]=False
# more digits for probabilities
gum.config["notebook","histogram_horizontal_visible_digits"]=3
gnb.showInference(infdiag,engine=ie,size="7!")
In [18]:
# specificic for influence diagram :
# more digits for utilities
gum.config["influenceDiagram","utility_visible_digits"]=5
# disabling stdev for utility and MEU
gum.config["influenceDiagram","utility_show_stdev"]=False
# showing loss (=-utility) and mEL (minimum Expected Loss) instead of MEU
gum.config["influenceDiagram","utility_show_loss"]=True
gnb.showInference(infdiag,engine=ie,size="7!")
In [19]:
# visual changes for influence diagram and inference
gum.config.reset()
gum.config.push() # keep the current state
gum.config['notebook','graph_rankdir']='LR'
gnb.sideBySide(infdiag,gnb.getInference(infdiag,engine=ie,targets=["Decision1","Chance3"]))
In [20]:
# more visual changes for influence diagram and inference
gum.config.pop() # back to the last state
#shape (https://graphviz.org/doc/info/shapes.html)
gum.config["influenceDiagram","chance_shape"] = "cylinder"
gum.config["influenceDiagram","utility_shape"] = "star"
gum.config["influenceDiagram","decision_shape"] = "box3d"
#colors
gum.config["influenceDiagram","default_chance_bgcolor"] = "green"
gum.config["influenceDiagram","default_utility_bgcolor"] = "MediumVioletRed"
gum.config["influenceDiagram","default_decision_bgcolor"] = "DarkSalmon"
gum.config["influenceDiagram","utility_show_stdev"]=False
gnb.sideBySide(infdiag,gnb.getInference(infdiag,engine=ie,targets=["Decision1","Chance3"]))