Package edu.cmu.tetrad.search.score

Class DegenerateGaussianScore

java.lang.Object

edu.cmu.tetrad.search.score.DegenerateGaussianScore

All Implemented Interfaces:

Score

public class DegenerateGaussianScore
extends Object
implements Score

This implements the degenerate Gaussian BIC score for FGES. The degenerate Gaussian score replaces each discrete variable in the data with a list of 0/1 continuous indicator columns for each of the categories but one (the last one implied). This data, now all continuous, is given to the SEM BIC score and methods used to help determine conditional independence for the mixed continuous/discrete case from this information. The references is as follows:

Andrews, B., Ramsey, J., & Cooper, G. F. (2019, July). Learning high-dimensional directed acyclic graphs with mixed data-types. In The 2019 ACM SIGKDD Workshop on Causal Discovery (pp. 4-21). PMLR.

As for all scores in Tetrad, higher scores mean more dependence, and negative scores indicate independence.

Author:

Bryan Andrews

Constructor Summary

Constructors

Constructor	Description
DegenerateGaussianScore(DataSet dataSet, boolean precomputeCovariances)

Method Summary

Modifier and Type	Method	Description
int	getMaxDegree()	Returns the max degree, by default 1000.
double	getPenaltyDiscount()
int	getSampleSize()	The sample size of the data.
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, int... parents)	Calculates the sample likelihood and BIC score for i given its parents in a simple SEM model.
double	localScoreDiff(int x, int y, int[] z)	Returns the score difference of the graph.
void	setPenaltyDiscount(double penaltyDiscount)
String	toString()	A string representation of the score.

Methods inherited from class java.lang.Object

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

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

append, determines, getVariable, localScore, localScore, localScoreDiff

Constructor Details

DegenerateGaussianScore

public DegenerateGaussianScore(DataSet dataSet,
 boolean precomputeCovariances)

Method Details

localScore

public double localScore(int i,
 int... parents)

Calculates the sample likelihood and BIC score for i given its parents in a simple SEM model. s

Specified by:

localScore in interface Score

Parameters:

i - The child indes.

parents - The indices of the parents.

Returns:

The score.

localScoreDiff

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

Description copied from interface: Score

Returns the score difference of the graph.

Specified by:

localScoreDiff in interface Score

Parameters:

x - A node.

y - TAhe node.

z - A set of nodes.

Returns:

The score difference.

getVariables

public List<Node> getVariables()

Description copied from interface: Score

The variables of the score.

Specified by:

getVariables in interface Score

Returns:

This list.

isEffectEdge

public boolean isEffectEdge(double bump)

Description copied from interface: Score

Returns true iff the edge between x and y is 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.

getSampleSize

public int getSampleSize()

Description copied from interface: Score

The sample size of the data.

Specified by:

getSampleSize in interface Score

Returns:

This size.

getMaxDegree

public int getMaxDegree()

Description copied from interface: Score

Returns the max degree, by default 1000.

Specified by:

getMaxDegree in interface Score

Returns:

The max degree.

toString

public String toString()

Description copied from interface: Score

A string representation of the score.

Specified by:

toString in interface Score

Overrides:

toString in class Object

Returns:

This string.

getPenaltyDiscount

public double getPenaltyDiscount()

setPenaltyDiscount

public void setPenaltyDiscount(double penaltyDiscount)