Package edu.cmu.tetrad.search.score

Class GraphScore

java.lang.Object

edu.cmu.tetrad.search.score.GraphScore

All Implemented Interfaces:

Score

public class GraphScore
extends Object
implements Score

Implements a pscudo-"score" that implmenets implements Chickering and Meek's (2002) locally consistent score criterion. This is not a true score; rather, a -1 is returned in case mseparation holds and a 1 in case mseparation does not hold. This is only meant to be used in the context of FGES, and allows the search to follow its path prescribed by the locally consistent scoring criterion. For a reference to the latter, pleasee this article:

Chickering (2002) "Optimal structure identification with greedy search" Journal of Machine Learning Research.

For further discussion of using m-separation in the GES search, see:

Nandy, P., Hauser, A., & Maathuis, M. H. (2018). High-dimensional consistency in score-based and hybrid structure learning. The Annals of Statistics, 46(6A), 3151-3183.

For more discussion please see:

Shen, X., Zhu, S., Zhang, J., Hu, S., & Chen, Z. (2022, August). Reframed GES with a neural conditional dependence measure. In Uncertainty in Artificial Intelligence (pp. 1782-1791). PMLR.

Version:

$Id: $Id

Author:

josephramsey

See Also:

• Fges

Constructor Summary

Constructors

Constructor	Description
GraphScore(IndependenceFacts facts)	Constructs a GraphScore from a list of independence facts.
GraphScore(Graph dag)	Constructs a GraphScore from a DAG.

Method Summary

Modifier and Type	Method	Description
Graph	getDag()	Returns a copy of the DAG being searched over.
int	getMaxDegree()	Returns the max degree, by default 1000.
int	getSampleSize()	getSampleSize.
List<Node>	getVariables()	The variables of the score.
boolean	isEffectEdge(double bump)	Returns true iff the edge between x and y is an effect edge.
double	localScore(int i)	Returns the local score of the gien node in the graph.
double	localScore(int i, int parent)	localScore.
double	localScore(int y, int[] z)	Calculates the sample likelihood and BIC score for y given its z in a simple SEM model.
double	localScoreDiff(int x, int y)	Returns the local score difference of the graph.
double	localScoreDiff(int x, int y, int[] z)	Returns the score difference of the graph.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface edu.cmu.tetrad.search.score.Score

append, determines, getVariable, toString

Constructor Details

GraphScore

public GraphScore(Graph dag)

Constructs a GraphScore from a DAG.

Parameters:

dag - A directed acyclic graph.

GraphScore

public GraphScore(IndependenceFacts facts)

Constructs a GraphScore from a list of independence facts.

Parameters:

facts - A list known independence facts; a lookup will be donw from these facts.

See Also:

• IndependenceFacts

Method Details

localScore

public double localScore(int y,
 int[] z)

Calculates the sample likelihood and BIC score for y given its z in a simple SEM model.

Specified by:

localScore in interface Score

Parameters:

y - a int

z - an array of objects

Returns:

this score.

localScoreDiff

public double localScoreDiff(int x,
 int y,
 int[] z)

Returns the score difference of the graph.

Returns a "score difference", which amounts to a conditional local scoring criterion results. Only difference methods is implemented, since the other methods don't make sense here.

Specified by:

localScoreDiff in interface Score

Parameters:

x - A node.

y - TAhe node.

z - A set of nodes.

Returns:

The score difference.

localScoreDiff

public double localScoreDiff(int x,
 int y)

Returns the local score difference of the graph.

The "unconditional difference." Only difference methods is implemented, since the other methods don't make sense here.

Specified by:

localScoreDiff in interface Score

Parameters:

x - A node.

y - The node.

Returns:

The local score difference.

localScore

public double localScore(int i,
 int parent)

localScore.

Specified by:

localScore in interface Score

Parameters:

i - a int

parent - a int

Returns:

a double

Throws:

UnsupportedOperationException - Since the method doesn't make sense here.

localScore

public double localScore(int i)

Returns the local score of the gien node in the graph.

Specified by:

localScore in interface Score

Parameters:

i - A node.

Returns:

The local score.

isEffectEdge

public boolean isEffectEdge(double bump)

Returns true iff the edge between x and y is an effect edge.

Returns a judgment for FGES whether a score with the bump is for an effect edge.

Specified by:

isEffectEdge in interface Score

Parameters:

bump - The bump.

Returns:

True iff the edge between x and y is an effect edge.

See Also:

• Fges

getVariables

public List<Node> getVariables()

The variables of the score.

Returns the list of variables.

Specified by:

getVariables in interface Score

Returns:

This list.

getMaxDegree

public int getMaxDegree()

Returns the max degree, by default 1000.

Returns the maximum degree, which is set to 1000.

Specified by:

getMaxDegree in interface Score

Returns:

The max degree.

getSampleSize

public int getSampleSize()

getSampleSize.

Specified by:

getSampleSize in interface Score

Returns:

a int

Throws:

UnsupportedOperationException - Since the method doesn't make sense here.

getDag

public Graph getDag()

Returns a copy of the DAG being searched over.

Returns:

This DAG.