Influence diagram

In [1]:

import math

import pyagrum.influence_diagram as gum
import pyagrum.lib.notebook as gnb

Build a Influence Diagram

fast build with string

In [2]:

gum.fastID("A->*B->$C<-D<-*E->*G->H->*I<-D")

Out[2]:

bgum format file

In [3]:

diag = gum.loadID("res/diag.bgum")
gnb.showInfluenceDiagram(diag)

../_images/notebooks_21-Models_InfluenceDiagram_6_0.svg

In [4]:

diag

Out[4]:

the hard way :-)

In [5]:

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)

../_images/notebooks_21-Models_InfluenceDiagram_9_0.svg

In [6]:

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 [7]:

oil = gum.loadID("res/OilWildcatter.bgum")
gnb.flow.row(oil, gnb.getInference(oil))

Out[7]:

Inference in the LIMID optimizing the decisions nodes

In [8]:

# 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)
ie.makeInference()
show_decisions(ie)

Graphical inference with evidence and targets (developped nodes)

In [9]:

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 [10]:

gnb.showInference(oil, evs={"OilContents": [0.7, 0.5, 0.8]})

../_images/notebooks_21-Models_InfluenceDiagram_18_0.svg

Forced decision

In [11]:

gnb.showInference(oil, evs={"Drilling": "Yes"})

../_images/notebooks_21-Models_InfluenceDiagram_20_0.svg

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 [12]:

infdiag = gum.fastID("Chance->*Decision1->Chance2->$Utility<-Chance3<-*Decision2<-Chance->Utility")
infdiag

Out[12]:

In [13]:

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 [14]:

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 [15]:

gum.config.reset()
gnb.showInference(infdiag, engine=ie, size="7!")

../_images/notebooks_21-Models_InfluenceDiagram_26_0.svg

Many visual options can be changed when displaing an inference (especially for influence diagrams)

In [16]:

# 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!")

../_images/notebooks_21-Models_InfluenceDiagram_28_0.svg

In [17]:

# 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!")

../_images/notebooks_21-Models_InfluenceDiagram_29_0.svg

In [18]:

# 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 [19]:

# 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"]))

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