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Probablistic Inference with pyAgrum

Creative Commons LicenseaGrUMinteractive online version

In this notebook, we will show different basic features for probabilistic inference on Bayesian networks using pyagrum.

First we need some external modules:

Basic inference and display

Section titled “Basic inference and display”

Then we import pyagrum and the pyAgrum’s notebook module, that offers very usefull methods when writting a notebook.

This first example shows how you can load a BayesNet and show it as graph. Note that pyAgrum handles serveral BayesNet file format such as DSL, BIF and UAI.

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


bn = gum.loadBN("res/alarm.bgum")
gnb.showBN(bn, size="9")

svg

print(bn)
BN{nodes: 37, arcs: 46, domainSize: 10^16.2389, dim: 509, mem: 5Ko 896o}

From there, it is easy to get a posterior using an inference engine :

ie = gum.LazyPropagation(bn)
ie.makeInference()
print(ie.posterior(bn.idFromName("CATECHOL")))
  CATECHOL         │
NORMAL   │HIGH     │
─────────│─────────│
 0.0512  │ 0.9488  │

But since we are in notebook, why not use pyAgrum notebook’s methods ?

gnb.showPosterior(bn, evs={}, target="CATECHOL")
PyAgrum inline image

You may also want to see the graph with some posteriors

## due to matplotlib, format is forced to png.
gnb.showInference(bn, evs={}, targets={"VENTALV", "CATECHOL", "HR", "MINVOLSET"}, size="11")

svg

.. and then observe the impact of evidence :

gnb.showInference(
  bn,
  evs={"CO": 1, "VENTLUNG": 1},
  targets={
    "VENTALV",
    "CATECHOL",
    "HR",
    "MINVOLSET",
    "ANAPHYLAXIS",
    "STROKEVOLUME",
    "ERRLOWOUTPUT",
    "HBR",
    "PULMEMBOLUS",
    "HISTORY",
    "BP",
    "PRESS",
    "CO",
  },
  size="10",
)

svg

You can even compute all posteriors by leaving the targets parameter empty (which is its default value).

gnb.showInference(bn, evs={"CO": 1, "VENTLUNG": 1}, size="14")

svg

Showing the information graph

Section titled “Showing the information graph”

To have a global view of the knowledge brought by the inference, you can also draw the entropy of all nodes

import pyagrum.explain as explain


explain.showInformation(bn, {}, size="14")
G VENTMACH VENTMACH VENTTUBE VENTTUBE VENTMACH->VENTTUBE EXPCO2 EXPCO2 ERRCAUTER ERRCAUTER HREKG HREKG ERRCAUTER->HREKG HRSAT HRSAT ERRCAUTER->HRSAT ARTCO2 ARTCO2 ARTCO2->EXPCO2 CATECHOL CATECHOL ARTCO2->CATECHOL KINKEDTUBE KINKEDTUBE VENTLUNG VENTLUNG KINKEDTUBE->VENTLUNG PRESS PRESS KINKEDTUBE->PRESS STROKEVOLUME STROKEVOLUME CO CO STROKEVOLUME->CO FIO2 FIO2 PVSAT PVSAT FIO2->PVSAT PAP PAP VENTLUNG->EXPCO2 VENTALV VENTALV VENTLUNG->VENTALV MINVOL MINVOL VENTLUNG->MINVOL HRBP HRBP SAO2 SAO2 SAO2->CATECHOL HISTORY HISTORY ERRLOWOUTPUT ERRLOWOUTPUT ERRLOWOUTPUT->HRBP DISCONNECT DISCONNECT DISCONNECT->VENTTUBE PULMEMBOLUS PULMEMBOLUS PULMEMBOLUS->PAP SHUNT SHUNT PULMEMBOLUS->SHUNT LVFAILURE LVFAILURE LVFAILURE->STROKEVOLUME LVFAILURE->HISTORY LVEDVOLUME LVEDVOLUME LVFAILURE->LVEDVOLUME INSUFFANESTH INSUFFANESTH INSUFFANESTH->CATECHOL PVSAT->SAO2 INTUBATION INTUBATION INTUBATION->VENTLUNG INTUBATION->SHUNT INTUBATION->VENTALV INTUBATION->PRESS INTUBATION->MINVOL ANAPHYLAXIS ANAPHYLAXIS TPR TPR ANAPHYLAXIS->TPR BP BP CO->BP SHUNT->SAO2 MINVOLSET MINVOLSET MINVOLSET->VENTMACH HR HR HR->HREKG HR->HRSAT HR->HRBP HR->CO CATECHOL->HR HYPOVOLEMIA HYPOVOLEMIA HYPOVOLEMIA->STROKEVOLUME HYPOVOLEMIA->LVEDVOLUME VENTTUBE->VENTLUNG VENTTUBE->PRESS VENTALV->ARTCO2 VENTALV->PVSAT TPR->CATECHOL TPR->BP CVP CVP LVEDVOLUME->CVP PCWP PCWP LVEDVOLUME->PCWP
PyAgrum inline image

… and then observe the impact of an evidence on the whole bayes network :

explain.showInformation(bn, {"CO": 0}, size="9")
G VENTMACH VENTMACH VENTTUBE VENTTUBE VENTMACH->VENTTUBE EXPCO2 EXPCO2 ERRCAUTER ERRCAUTER HREKG HREKG ERRCAUTER->HREKG HRSAT HRSAT ERRCAUTER->HRSAT ARTCO2 ARTCO2 ARTCO2->EXPCO2 CATECHOL CATECHOL ARTCO2->CATECHOL KINKEDTUBE KINKEDTUBE VENTLUNG VENTLUNG KINKEDTUBE->VENTLUNG PRESS PRESS KINKEDTUBE->PRESS STROKEVOLUME STROKEVOLUME CO CO STROKEVOLUME->CO FIO2 FIO2 PVSAT PVSAT FIO2->PVSAT PAP PAP VENTLUNG->EXPCO2 VENTALV VENTALV VENTLUNG->VENTALV MINVOL MINVOL VENTLUNG->MINVOL HRBP HRBP SAO2 SAO2 SAO2->CATECHOL HISTORY HISTORY ERRLOWOUTPUT ERRLOWOUTPUT ERRLOWOUTPUT->HRBP DISCONNECT DISCONNECT DISCONNECT->VENTTUBE PULMEMBOLUS PULMEMBOLUS PULMEMBOLUS->PAP SHUNT SHUNT PULMEMBOLUS->SHUNT LVFAILURE LVFAILURE LVFAILURE->STROKEVOLUME LVFAILURE->HISTORY LVEDVOLUME LVEDVOLUME LVFAILURE->LVEDVOLUME INSUFFANESTH INSUFFANESTH INSUFFANESTH->CATECHOL PVSAT->SAO2 INTUBATION INTUBATION INTUBATION->VENTLUNG INTUBATION->SHUNT INTUBATION->VENTALV INTUBATION->PRESS INTUBATION->MINVOL ANAPHYLAXIS ANAPHYLAXIS TPR TPR ANAPHYLAXIS->TPR BP BP CO->BP SHUNT->SAO2 MINVOLSET MINVOLSET MINVOLSET->VENTMACH HR HR HR->HREKG HR->HRSAT HR->HRBP HR->CO CATECHOL->HR HYPOVOLEMIA HYPOVOLEMIA HYPOVOLEMIA->STROKEVOLUME HYPOVOLEMIA->LVEDVOLUME VENTTUBE->VENTLUNG VENTTUBE->PRESS VENTALV->ARTCO2 VENTALV->PVSAT TPR->CATECHOL TPR->BP CVP CVP LVEDVOLUME->CVP PCWP PCWP LVEDVOLUME->PCWP
PyAgrum inline image

Exploring the junction tree

Section titled “Exploring the junction tree”

Lazy Propagation, like several other inference algorithms, uses a junction tree to propagate information.

You can show the junction tree used by Lazy Propagation with pyAgrum:

jt = ie.junctionTree()
gnb.showJunctionTree(bn, size="12")

svg

## another representation of the junction, more convenient for investigating the flow of data in the jt
## the size/width of cliques and separators are proportionnal to the number of nodes in the factor.
jt.map()
0 0~16 0--0~16 1 1~32 1--1~32 2 2~33 2--2~33 3 3~4 3--3~4 4 4~22 4--4~22 5 5~22 5--5~22 6 6~23 6--6~23 7 7~26 7--7~26 8 8~17 8--8~17 10 10~14 10--10~14 11 11~16 11--11~16 12 12~13 12--12~13 13 13~30 13--13~30 14 14~26 14--14~26 16 16~17 16--16~17 17 17~24 17--17~24 19 19~27 19--19~27 20 20~33 20--20~33 22 22~33 22--22~33 23 23~27 23--23~27 23~31 23--23~31 24 24~26 24--24~26 26 26~27 26--26~27 27 30 30~31 30--30~31 31 31~32 31--31~32 32 32~33 32--32~33 33 19~27--27 12~13--13 2~33--33 23~27--27 22~33--33 11~16--16 24~26--26 31~32--32 10~14--14 26~27--27 13~30--30 5~22--22 7~26--26 20~33--33 16~17--17 32~33--33 23~31--31 8~17--17 1~32--32 3~4--4 4~22--22 17~24--24 14~26--26 30~31--31 6~23--23 0~16--16

Introspection in junction trees

Section titled “Introspection in junction trees”

One can easily walk through the junction tree.

for n in jt.nodes():
  print([bn.variable(n).name() for n in jt.clique(n)])
['CVP', 'LVEDVOLUME']
['FIO2', 'VENTALV', 'PVSAT']
['ARTCO2', 'EXPCO2', 'VENTLUNG']
['VENTMACH', 'MINVOLSET']
['VENTMACH', 'DISCONNECT', 'VENTTUBE']
['PRESS', 'KINKEDTUBE', 'INTUBATION', 'VENTTUBE']
['ANAPHYLAXIS', 'TPR']
['HRBP', 'ERRLOWOUTPUT', 'HR']
['LVFAILURE', 'HISTORY']
['HREKG', 'HR', 'ERRCAUTER']
['PCWP', 'LVEDVOLUME']
['PAP', 'PULMEMBOLUS']
['SHUNT', 'INTUBATION', 'PULMEMBOLUS']
['HRSAT', 'HR', 'ERRCAUTER']
['LVFAILURE', 'HYPOVOLEMIA', 'LVEDVOLUME']
['HYPOVOLEMIA', 'STROKEVOLUME', 'LVFAILURE']
['CO', 'BP', 'TPR']
['INTUBATION', 'VENTLUNG', 'MINVOL']
['KINKEDTUBE', 'INTUBATION', 'VENTTUBE', 'VENTLUNG']
['INSUFFANESTH', 'TPR', 'ARTCO2', 'SAO2', 'CATECHOL']
['CO', 'STROKEVOLUME', 'HR']
['CO', 'CATECHOL', 'HR']
['CO', 'TPR', 'CATECHOL']
['INTUBATION', 'SHUNT', 'PVSAT', 'SAO2']
['INTUBATION', 'ARTCO2', 'PVSAT', 'SAO2']
['ARTCO2', 'VENTALV', 'INTUBATION', 'PVSAT']
['VENTALV', 'INTUBATION', 'ARTCO2', 'VENTLUNG']
for e in jt.edges():
  print(f"Separator for {e} : {jt.clique(e[0]).intersection(jt.clique(e[1]))}")
Separator for (13, 30) : {18, 2}
Separator for (2, 33) : {26, 22}
Separator for (3, 4) : {16}
Separator for (26, 27) : {34, 30}
Separator for (7, 26) : {31}
Separator for (12, 13) : {4}
Separator for (31, 32) : {27, 26, 2}
Separator for (23, 31) : {26, 28}
Separator for (5, 22) : {0, 2, 20}
Separator for (17, 24) : {13}
Separator for (19, 27) : {34, 14}
Separator for (24, 26) : {34, 31}
Separator for (32, 33) : {25, 2, 26}
Separator for (6, 23) : {14}
Separator for (23, 27) : {14, 30}
Separator for (11, 16) : {15}
Separator for (10, 14) : {7, 31}
Separator for (8, 17) : {9}
Separator for (0, 16) : {15}
Separator for (1, 32) : {25, 27}
Separator for (20, 33) : {2, 22}
Separator for (4, 22) : {20}
Separator for (14, 26) : {31}
Separator for (22, 33) : {2, 22}
Separator for (30, 31) : {2, 27, 28}
Separator for (16, 17) : {1, 9}
jt.hasRunningIntersection()
True

Introspecting junction trees and friends

Section titled “Introspecting junction trees and friends”

The junction tree created by a LazyPropagation is optimized for the query (see RelevanceReasonning notebook). But you can also introspect a junction tree directly from a BN or a graph using the JunctionTreeGenerator’s class.

bn = gum.fastBN("0->1->2<-3->4->5->6<-2->7")
jtg = gum.JunctionTreeGenerator()
gnb.sideBySide(
  bn,
  jtg.junctionTree(bn),
  jtg.eliminationOrder(bn),
  jtg.binaryJoinTree(bn),
  captions=[
    "A Bayesien network",
    "a junction tree for this BN",
    "its elimination order",
    "an (optimized) binary join tree",
  ],
)
G 1 1 2 2 1->2 6 6 2->6 7 7 2->7 5 5 5->6 3 3 3->2 4 4 3->4 0 0 0->1 4->5
A Bayesien network
(0) 2-5-6 2-5-6 (0) 2-5-6^(4) 2-3-5 2-5 (0) 2-5-6--(0) 2-5-6^(4) 2-3-5 (1) 2-7 2-7 (1) 2-7^(4) 2-3-5 2 (1) 2-7--(1) 2-7^(4) 2-3-5 (2) 0-1 0-1 (2) 0-1^(3) 1-2-3 1 (2) 0-1--(2) 0-1^(3) 1-2-3 (3) 1-2-3 1-2-3 (3) 1-2-3^(4) 2-3-5 2-3 (3) 1-2-3--(3) 1-2-3^(4) 2-3-5 (4) 2-3-5 2-3-5 (4) 2-3-5^(5) 3-4-5 3-5 (4) 2-3-5--(4) 2-3-5^(5) 3-4-5 (5) 3-4-5 3-4-5 (2) 0-1^(3) 1-2-3--(3) 1-2-3 (4) 2-3-5^(5) 3-4-5--(5) 3-4-5 (0) 2-5-6^(4) 2-3-5--(4) 2-3-5 (1) 2-7^(4) 2-3-5--(4) 2-3-5 (3) 1-2-3^(4) 2-3-5--(4) 2-3-5
a junction tree for this BN
[6, 7, 0, 1, 2, 3, 4, 5]
its elimination order
(0) 2-5-6 2-5-6 (0) 2-5-6^(6) 2-5 2-5 (0) 2-5-6--(0) 2-5-6^(6) 2-5 (1) 2-7 2-7 (1) 2-7^(6) 2-5 2 (1) 2-7--(1) 2-7^(6) 2-5 (2) 0-1 0-1 (2) 0-1^(3) 1-2-3 1 (2) 0-1--(2) 0-1^(3) 1-2-3 (3) 1-2-3 1-2-3 (3) 1-2-3^(4) 2-3-5 2-3 (3) 1-2-3--(3) 1-2-3^(4) 2-3-5 (4) 2-3-5 2-3-5 (4) 2-3-5^(5) 3-4-5 3-5 (4) 2-3-5--(4) 2-3-5^(5) 3-4-5 (4) 2-3-5^(6) 2-5 2-5 (4) 2-3-5--(4) 2-3-5^(6) 2-5 (5) 3-4-5 3-4-5 (6) 2-5 2-5 (2) 0-1^(3) 1-2-3--(3) 1-2-3 (4) 2-3-5^(5) 3-4-5--(5) 3-4-5 (3) 1-2-3^(4) 2-3-5--(4) 2-3-5 (1) 2-7^(6) 2-5--(6) 2-5 (0) 2-5-6^(6) 2-5--(6) 2-5 (4) 2-3-5^(6) 2-5--(6) 2-5
an (optimized) binary join tree

junction tree from graphs (using uniform domainSize)

Section titled “junction tree from graphs (using uniform domainSize)”
## creating a dag slightly different
dag = bn.dag()
dag.addArc(0, 3)
dag.addArc(0, 7)
gnb.sideBySide(
  dag,
  dag.moralGraph(),
  jtg.junctionTree(dag),
  jtg.eliminationOrder(dag),
  jtg.binaryJoinTree(dag),
  captions=[
    "A DAG",
    "its moral graph",
    "a junction tree for this dag (with partial order)",
    "its elimination order (with partial order)",
    "an (optipmized) binary jointree (with partial order)",
  ],
)
0 (0) 0 1 (1) 1 0->1 3 (3) 3 0->3 7 (7) 7 0->7 2 (2) 2 1->2 6 (6) 6 2->6 2->7 3->2 4 (4) 4 3->4 5 (5) 5 4->5 5->6
A DAG
no_name 0 (0) 0 1 (1) 1 0->1 2 (2) 2 0->2 3 (3) 3 0->3 7 (7) 7 0->7 1->2 1->3 2->3 5 (5) 5 2->5 6 (6) 6 2->6 2->7 4 (4) 4 3->4 4->5 5->6
its moral graph
(0) 2-5-6 2-5-6 (0) 2-5-6^(4) 2-3-5 2-5 (0) 2-5-6--(0) 2-5-6^(4) 2-3-5 (1) 0-1-2-3 0-1-2-3 (1) 0-1-2-3^(4) 2-3-5 2-3 (1) 0-1-2-3--(1) 0-1-2-3^(4) 2-3-5 (1) 0-1-2-3^(2) 0-2-7 0-2 (1) 0-1-2-3--(1) 0-1-2-3^(2) 0-2-7 (2) 0-2-7 0-2-7 (4) 2-3-5 2-3-5 (4) 2-3-5^(5) 3-4-5 3-5 (4) 2-3-5--(4) 2-3-5^(5) 3-4-5 (5) 3-4-5 3-4-5 (4) 2-3-5^(5) 3-4-5--(5) 3-4-5 (0) 2-5-6^(4) 2-3-5--(4) 2-3-5 (1) 0-1-2-3^(4) 2-3-5--(4) 2-3-5 (1) 0-1-2-3^(2) 0-2-7--(2) 0-2-7
a junction tree for this dag (with partial order)
[6, 1, 7, 0, 2, 3, 4, 5]
its elimination order (with partial order)
(0) 2-5-6 2-5-6 (0) 2-5-6^(4) 2-3-5 2-5 (0) 2-5-6--(0) 2-5-6^(4) 2-3-5 (1) 0-1-2-3 0-1-2-3 (1) 0-1-2-3^(4) 2-3-5 2-3 (1) 0-1-2-3--(1) 0-1-2-3^(4) 2-3-5 (1) 0-1-2-3^(2) 0-2-7 0-2 (1) 0-1-2-3--(1) 0-1-2-3^(2) 0-2-7 (2) 0-2-7 0-2-7 (4) 2-3-5 2-3-5 (4) 2-3-5^(5) 3-4-5 3-5 (4) 2-3-5--(4) 2-3-5^(5) 3-4-5 (5) 3-4-5 3-4-5 (4) 2-3-5^(5) 3-4-5--(5) 3-4-5 (0) 2-5-6^(4) 2-3-5--(4) 2-3-5 (1) 0-1-2-3^(4) 2-3-5--(4) 2-3-5 (1) 0-1-2-3^(2) 0-2-7--(2) 0-2-7
an (optipmized) binary jointree (with partial order)
## creating an undigraph slightly different
ug = bn.dag().moralGraph()
ug.addEdge(0, 7)
gnb.sideBySide(
  ug,
  jtg.junctionTree(ug),
  jtg.eliminationOrder(ug),
  jtg.binaryJoinTree(ug),
  captions=[
    "A undigraph",
    "a junction tree for this undigraph",
    "its elimination order",
    "an (optipmized) binary jointree",
  ],
)
no_name 0 (0) 0 1 (1) 1 0->1 7 (7) 7 0->7 2 (2) 2 1->2 3 (3) 3 1->3 2->3 5 (5) 5 2->5 6 (6) 6 2->6 2->7 4 (4) 4 3->4 4->5 5->6
A undigraph
(0) 2-5-6 2-5-6 (0) 2-5-6^(2) 2-3-5 2-5 (0) 2-5-6--(0) 2-5-6^(2) 2-3-5 (1) 3-4-5 3-4-5 (1) 3-4-5^(2) 2-3-5 3-5 (1) 3-4-5--(1) 3-4-5^(2) 2-3-5 (2) 2-3-5 2-3-5 (2) 2-3-5^(3) 1-2-3 2-3 (2) 2-3-5--(2) 2-3-5^(3) 1-2-3 (3) 1-2-3 1-2-3 (3) 1-2-3^(4) 1-2-7 1-2 (3) 1-2-3--(3) 1-2-3^(4) 1-2-7 (4) 1-2-7 1-2-7 (4) 1-2-7^(5) 0-1-7 1-7 (4) 1-2-7--(4) 1-2-7^(5) 0-1-7 (5) 0-1-7 0-1-7 (2) 2-3-5^(3) 1-2-3--(3) 1-2-3 (4) 1-2-7^(5) 0-1-7--(5) 0-1-7 (0) 2-5-6^(2) 2-3-5--(2) 2-3-5 (1) 3-4-5^(2) 2-3-5--(2) 2-3-5 (3) 1-2-3^(4) 1-2-7--(4) 1-2-7
a junction tree for this undigraph
[6, 4, 5, 3, 2, 1, 7, 0]
its elimination order
(0) 2-5-6 2-5-6 (0) 2-5-6^(2) 2-3-5 2-5 (0) 2-5-6--(0) 2-5-6^(2) 2-3-5 (1) 3-4-5 3-4-5 (1) 3-4-5^(2) 2-3-5 3-5 (1) 3-4-5--(1) 3-4-5^(2) 2-3-5 (2) 2-3-5 2-3-5 (2) 2-3-5^(3) 1-2-3 2-3 (2) 2-3-5--(2) 2-3-5^(3) 1-2-3 (3) 1-2-3 1-2-3 (3) 1-2-3^(4) 1-2-7 1-2 (3) 1-2-3--(3) 1-2-3^(4) 1-2-7 (4) 1-2-7 1-2-7 (4) 1-2-7^(5) 0-1-7 1-7 (4) 1-2-7--(4) 1-2-7^(5) 0-1-7 (5) 0-1-7 0-1-7 (2) 2-3-5^(3) 1-2-3--(3) 1-2-3 (4) 1-2-7^(5) 0-1-7--(5) 0-1-7 (0) 2-5-6^(2) 2-3-5--(2) 2-3-5 (1) 3-4-5^(2) 2-3-5--(2) 2-3-5 (3) 1-2-3^(4) 1-2-7--(4) 1-2-7
an (optipmized) binary jointree

Using partial order to specify the elimination order

Section titled “Using partial order to specify the elimination order”
## adding a partial order for the elimination order
po = [[1, 2, 3], [0, 4, 7], [5, 6]]
gnb.sideBySide(
  bn,
  jtg.junctionTree(bn, po),
  jtg.eliminationOrder(bn, po),
  jtg.binaryJoinTree(bn),
  captions=[
    "A Bayesien network",
    "a junction tree for this BN using partial order",
    "its elimination order following partial order",
    "an (optimized) binary join tree",
  ],
)
G 1 1 2 2 1->2 6 6 2->6 7 7 2->7 5 5 5->6 3 3 3->2 4 4 3->4 0 0 0->1 4->5
A Bayesien network
(0) 1-2-3-4 1-2-3-4 (0) 1-2-3-4^(1) 0-1-2-4 1-2-4 (0) 1-2-3-4--(0) 1-2-3-4^(1) 0-1-2-4 (1) 0-1-2-4 0-1-2-4 (1) 0-1-2-4^(2) 0-2-4-5-6-7 0-2-4 (1) 0-1-2-4--(1) 0-1-2-4^(2) 0-2-4-5-6-7 (2) 0-2-4-5-6-7 0-2-4-5-6-7 (0) 1-2-3-4^(1) 0-1-2-4--(1) 0-1-2-4 (1) 0-1-2-4^(2) 0-2-4-5-6-7--(2) 0-2-4-5-6-7
a junction tree for this BN using partial order
[3, 1, 2, 7, 0, 4, 6, 5]
its elimination order following partial order
(0) 2-5-6 2-5-6 (0) 2-5-6^(6) 2-5 2-5 (0) 2-5-6--(0) 2-5-6^(6) 2-5 (1) 2-7 2-7 (1) 2-7^(6) 2-5 2 (1) 2-7--(1) 2-7^(6) 2-5 (2) 0-1 0-1 (2) 0-1^(3) 1-2-3 1 (2) 0-1--(2) 0-1^(3) 1-2-3 (3) 1-2-3 1-2-3 (3) 1-2-3^(4) 2-3-5 2-3 (3) 1-2-3--(3) 1-2-3^(4) 2-3-5 (4) 2-3-5 2-3-5 (4) 2-3-5^(5) 3-4-5 3-5 (4) 2-3-5--(4) 2-3-5^(5) 3-4-5 (4) 2-3-5^(6) 2-5 2-5 (4) 2-3-5--(4) 2-3-5^(6) 2-5 (5) 3-4-5 3-4-5 (6) 2-5 2-5 (2) 0-1^(3) 1-2-3--(3) 1-2-3 (4) 2-3-5^(5) 3-4-5--(5) 3-4-5 (3) 1-2-3^(4) 2-3-5--(4) 2-3-5 (1) 2-7^(6) 2-5--(6) 2-5 (0) 2-5-6^(6) 2-5--(6) 2-5 (4) 2-3-5^(6) 2-5--(6) 2-5
an (optimized) binary join tree
## adding a partial order for the elimination order also for the graphs
po = [[0, 4, 7], [1, 3], [5, 6, 2]]


## creating a dag slightly different
dag = bn.dag()
dag.addArc(0, 3)
dag.addArc(0, 7)


gnb.sideBySide(
  dag,
  dag.moralGraph(),
  jtg.junctionTree(dag, po),
  jtg.eliminationOrder(dag, po),
  jtg.binaryJoinTree(dag, po),
  captions=[
    "A DAG",
    "its moral graph",
    "a junction tree for this dag (with partial order)",
    "its elimination order (with partial order)",
    "an (optimized) binary jointree (with partial order)",
  ],
)
0 (0) 0 1 (1) 1 0->1 3 (3) 3 0->3 7 (7) 7 0->7 2 (2) 2 1->2 6 (6) 6 2->6 2->7 3->2 4 (4) 4 3->4 5 (5) 5 4->5 5->6
A DAG
no_name 0 (0) 0 1 (1) 1 0->1 2 (2) 2 0->2 3 (3) 3 0->3 7 (7) 7 0->7 1->2 1->3 2->3 5 (5) 5 2->5 6 (6) 6 2->6 2->7 4 (4) 4 3->4 4->5 5->6
its moral graph
(0) 0-2-7 0-2-7 (0) 0-2-7^(1) 0-1-2-3 0-2 (0) 0-2-7--(0) 0-2-7^(1) 0-1-2-3 (1) 0-1-2-3 0-1-2-3 (1) 0-1-2-3^(4) 2-3-5 2-3 (1) 0-1-2-3--(1) 0-1-2-3^(4) 2-3-5 (2) 3-4-5 3-4-5 (2) 3-4-5^(4) 2-3-5 3-5 (2) 3-4-5--(2) 3-4-5^(4) 2-3-5 (4) 2-3-5 2-3-5 (4) 2-3-5^(5) 2-5-6 2-5 (4) 2-3-5--(4) 2-3-5^(5) 2-5-6 (5) 2-5-6 2-5-6 (0) 0-2-7^(1) 0-1-2-3--(1) 0-1-2-3 (4) 2-3-5^(5) 2-5-6--(5) 2-5-6 (2) 3-4-5^(4) 2-3-5--(4) 2-3-5 (1) 0-1-2-3^(4) 2-3-5--(4) 2-3-5
a junction tree for this dag (with partial order)
[7, 0, 4, 1, 3, 2, 6, 5]
its elimination order (with partial order)
(0) 0-2-7 0-2-7 (0) 0-2-7^(1) 0-1-2-3 0-2 (0) 0-2-7--(0) 0-2-7^(1) 0-1-2-3 (1) 0-1-2-3 0-1-2-3 (1) 0-1-2-3^(4) 2-3-5 2-3 (1) 0-1-2-3--(1) 0-1-2-3^(4) 2-3-5 (2) 3-4-5 3-4-5 (2) 3-4-5^(4) 2-3-5 3-5 (2) 3-4-5--(2) 3-4-5^(4) 2-3-5 (4) 2-3-5 2-3-5 (4) 2-3-5^(5) 2-5-6 2-5 (4) 2-3-5--(4) 2-3-5^(5) 2-5-6 (5) 2-5-6 2-5-6 (0) 0-2-7^(1) 0-1-2-3--(1) 0-1-2-3 (4) 2-3-5^(5) 2-5-6--(5) 2-5-6 (2) 3-4-5^(4) 2-3-5--(4) 2-3-5 (1) 0-1-2-3^(4) 2-3-5--(4) 2-3-5
an (optimized) binary jointree (with partial order)