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 dseparation holds and a 1 in case dseparation 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 d-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.

Author:

josephramsey

See Also:

• Fges

Constructor Summary

Constructors

Constructor	Description
GraphScore(IndependenceFacts facts)	Constructor.
GraphScore(Graph dag)	Constructor

Method Summary

Modifier and Type	Method	Description
boolean	determines(List<Node> z, Node y)
Graph	getDag()	Returns a copy of the DAG being searched over.
DataModel	getData()
DataSet	getDataSet()
int	getMaxDegree()	Returns the maximum degree, which is set to 1000.
int	getSampleSize()	The sample size of the data.
List<Node>	getVariables()	Returns the list of variables.
boolean	isEffectEdge(double bump)	Returns a judgment for FGES as to whether a score with the bump is for an effect edge.
double	localScore(int i)
double	localScore(int i, int parent)
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)	The "unconditional difference."
double	localScoreDiff(int x, int y, int[] z)	Returns a "score difference", which amounts to a conditional local scoring criterion results

Methods inherited from class java.lang.Object

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

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

append, getVariable, toString

Constructor Details

GraphScore

public GraphScore(Graph dag)

Constructor

Parameters:

dag - A directed acyclic graph.

GraphScore

public GraphScore(IndependenceFacts facts)

Constructor.

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 - The node.

z - The parents.

Returns:

this score.

localScoreDiff

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

Returns a "score difference", which amounts to a conditional local scoring criterion results

Specified by:

localScoreDiff in interface Score

Returns:

The "difference".

localScoreDiff

public double localScoreDiff(int x,
 int y)

The "unconditional difference."

Specified by:

localScoreDiff in interface Score

Returns:

This.

localScore

public double localScore(int i,
 int parent)

Specified by:

localScore in interface Score

Throws:

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

localScore

public double localScore(int i)

Specified by:

localScore in interface Score

Throws:

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

isEffectEdge

public boolean isEffectEdge(double bump)

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

Specified by:

isEffectEdge in interface Score

Parameters:

bump - The bump

Returns:

True if so.

See Also:

• Fges

getDataSet

public DataSet getDataSet()

Throws:

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

getVariables

public List<Node> getVariables()

Returns the list of variables.

Specified by:

getVariables in interface Score

Returns:

This list.

getMaxDegree

public int getMaxDegree()

Returns the maximum degree, which is set to 1000.

Specified by:

getMaxDegree in interface Score

Returns:

1000.

determines

public boolean determines(List<Node> z,
 Node y)

Specified by:

determines in interface Score

Throws:

javax.help.UnsupportedOperationException - Since this method doesn't make sense here.

getData

public DataModel getData()

Throws:

javax.help.UnsupportedOperationException - Since this "score" does not use data.

getSampleSize

public int getSampleSize()

Description copied from interface: Score

The sample size of the data.

Specified by:

getSampleSize in interface Score

Returns:

This size.

Throws:

javax.help.UnsupportedOperationException - Since this score does not use data.

getDag

public Graph getDag()

Returns a copy of the DAG being searched over.

Returns:

This DAG.