ContinuousPC

class otagrum.ContinuousPC(*args)

ContinuousPC learner.

Parameters:

data2-d sequence of float

The data

maxConditioningSetSizeint, optional

The maximum conditioning set for independecy test

alphafloat

Hypothesis test threshold

Notes

PC algorithm is a constrained method allowing to learn the structure of a bayesian network. It consists in three steps : skeleton learning, V-structure searching and constraint propagation.

Methods

PDAGtoDot(pdag)	Displays the PDAG in DOT format.
getPValue(*args)	P-value accessor.
getSepset(x, y)	Separator set indices accessor.
getSepsetNames(x, y)	Separator set names accessor.
getTTest(*args)	T-Test statistics accessor.
getTrace()	Returns a summary of the learning algorithm containing which conditional indepences have been found and the associated T-test and p-values.
getVerbosity()	Returns the verbosity flag value.
idFromName(n)	Id accessor.
isRemoved(*args)	Test if an arc is removed.
learnDAG()	Learn the DAG.
learnJunctionTree()	Learn the junction tree.
learnPDAG()	Learn the PDAG.
learnSkeleton()	Learn the skeleton.
setVerbosity(verbose)	Change the value of verbosity flag.
skeletonToDot(skeleton)	Displays the skeleton graph to dot

__init__(*args)

PDAGtoDot(pdag)

Displays the PDAG in DOT format.

Parameters:

pdagpyAgrum.MixedGraph

dag

Returns:

dotstr

Dot format representation

getPValue(*args)

P-value accessor.

Parameters:

xstr

A variable’s name

ystr

A variable’s name

xint

A variable’s id

yint

A variable’s id

Returns:

p_valuefloat

P-value of edge x - y

getSepset(x, y)

Separator set indices accessor.

Parameters:

xint

Node id

yint

Node id

Returns:

sep_setopenturns.Indices

Indices of variables in the separator set of variables x and y

getSepsetNames(x, y)

Separator set names accessor.

Parameters:

xstr

A variable’s name

ystr

A variables’s name

Returns:

sepset_namesopenturns.Description

The names of variables in the separator set of variables x and y

getTTest(*args)

T-Test statistics accessor.

Parameters:

xstr

A variable’s name

ystr

A variable’s name

xint

A variable’s id

yint

A variable’s id

Returns:

ttestfloat

T-Test statistics value of edge x - y

getTrace()

Returns a summary of the learning algorithm containing which conditional indepences have been found and the associated T-test and p-values.

Returns:

traceopenturns.Description

Trace of the learning algorithm

getVerbosity()

Returns the verbosity flag value.

Returns:

verbosebool

Verbosity flag value

idFromName(n)

Id accessor.

Parameters:

nstr

Node name

Returns:

idint

Id of the node

isRemoved(*args)

Test if an arc is removed.

Parameters:

xstr

A variable’s name

ystr

A variable’s name

xint

A variable’s id

yint

A variable’s id

Returns:

removedbool

Whether the arc x-y has been removed, this is equivalent to x and y being independent

learnDAG()

Learn the DAG.

Returns:

dagNamedDAG

the learned DAG

Notes

This step starts with the learned PDAG and orient the remaining undirected edges by avoiding to add new V-structures unless it implies to create an oriented circle.

learnJunctionTree()

Learn the junction tree.

Returns:

njtNamedJunctionTree

the learned junction tree

Notes

In order to learn the junction tree, the PDAG is moralized and then triangulated giving an undirected graph. Each clique in this graph is then associated to a node in the junction tree and there is an edge between nodes sharing common variables. Finally to each edge is associated a separator containing the intersection of variables in the two linked nodes.

learnPDAG()

Learn the PDAG.

Returns:

gum::MixedGraphpyAgrum.MixedGraph

the learned PDAG

Notes

This step starts with the learned skeleton and looks at each triplet of variables X, Y, Z such that X-Z-Y and there is no edge between X and Y. If Z is in the sepset of X and Y, that is if it was in the conditional set that allowed to remove the edge between X and Y then we orient the triplet as X->Z<-Y.

learnSkeleton()

Learn the skeleton.

Returns:

gum::UndiGraphpyAgrum.UndiGraph

The learned skeleton

Notes

This step consists in taking the complete undirected graph on the set of variables and to remove an edge if the linked variables are independent conditionally to a subset of their neighbors.

setVerbosity(verbose)

Change the value of verbosity flag.

Parameters:

verbosebool

New verbosity flag value. If True, a lot of details about the learning procedure are printed.

skeletonToDot(skeleton)

Displays the skeleton graph to dot

Parameters:

pdagpyAgrum.UndiGraph

dag

Returns:

dotstr

Dot format representation