Package edu.cmu.tetrad.search.score

Class GicScores

java.lang.Object

edu.cmu.tetrad.search.score.GicScores

All Implemented Interfaces:

Score

public class GicScores
extends Object
implements Score

Implements scores motivated by the Generalized Information Criterion (GIC) approach as given in Kim et al. (2012).

Kim, Y., Kwon, S., & Choi, H. (2012). Consistent model selection criteria on high dimensions. The Journal of Machine Learning Research, 13(1), 1037-1057.

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

Version:

$Id: $Id

Author:

josephramsey

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static enum	GicScores.RuleType	Gives the options for the rules to use for calculating the scores.

Constructor Summary

Constructors

Constructor	Description
GicScores(DataSet dataSet, boolean precomputeCovariances)	Constructs the score using a covariance matrix.
GicScores(ICovarianceMatrix covariances)	Constructs the score using a covariance matrix.

Method Summary

Modifier and Type	Method	Description
boolean	determines(List<Node> z, Node y)	Returns true iff the score determines the edge between x and y.
ICovarianceMatrix	getCovariances()	Returns the sample size.
DataSet	getDataSet()	Returns the dataset.
int	getMaxDegree()	Returns the max degree, by default 1000.
double	getPenaltyDiscount()	Returns the penalty discount.
int	getSampleSize()	Returns the sample size.
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.
boolean	isVerbose()	Returns true if verbose output should be sent to out.
double	localScore(int i, int... parents)	Calculates the sample likelihood and BIC score for index 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	setLambda(double lambda)	Sets the lambda parameter.
void	setPenaltyDiscount(double penaltyDiscount)	Sets the penalty discount.
void	setRuleType(GicScores.RuleType ruleType)	Sets the rule type.
void	setUsePseudoInverse(boolean usePseudoInverse)	Sets whether to use the pseudo-inverse when calculating the score.
void	setVariables(List<Node> variables)	Sets the variables of the dataset.
void	setVerbose(boolean verbose)	Sets whether verbose output should be sent to out.
String	toString()	Returns a string for this object.

Methods inherited from class java.lang.Object

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

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

append, getVariable, localScore, localScore, localScoreDiff

Constructor Details

GicScores

public GicScores(ICovarianceMatrix covariances)

Constructs the score using a covariance matrix.

Parameters:

covariances - The covariance matrix.

GicScores

public GicScores(DataSet dataSet,
 boolean precomputeCovariances)

Constructs the score using a covariance matrix.

Parameters:

dataSet - The continuous dataset to analyze.

precomputeCovariances - Whether the covariances should be precomputed or computed on the fly. True if

Method Details

localScoreDiff

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

Returns the score difference of the graph.

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

Specified by:

localScoreDiff in interface Score

Parameters:

x - A node.

y - TAhe node.

z - A set of nodes.

Returns:

The score difference.

localScore

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

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

Specified by:

localScore in interface Score

Parameters:

i - The node.

parents - The parents.

Returns:

The score.

getCovariances

public ICovarianceMatrix getCovariances()

Returns the sample size.

Returns:

This size.

getSampleSize

public int getSampleSize()

Returns the sample size.

Specified by:

getSampleSize in interface Score

Returns:

This size.

isEffectEdge

public boolean isEffectEdge(double bump)

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

Returns true if an edge with this bump 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.

getDataSet

public DataSet getDataSet()

Returns the dataset.

Returns:

The dataset.

isVerbose

public boolean isVerbose()

Returns true if verbose output should be sent to out.

Returns:

True if verbose output should be sent to out.

setVerbose

public void setVerbose(boolean verbose)

Sets whether verbose output should be sent to out.

Parameters:

verbose - True if verbose output should be sent to out.

getVariables

public List<Node> getVariables()

The variables of the score.

Returns the variables of the dataset.

Specified by:

getVariables in interface Score

Returns:

This list.

setVariables

public void setVariables(List<Node> variables)

Sets the variables of the dataset.

Parameters:

variables - The variables of the dataset.

getMaxDegree

public int getMaxDegree()

Returns the max degree, by default 1000.

Returns the max degree of the graph for some algorithms.

Specified by:

getMaxDegree in interface Score

Returns:

The max degree.

determines

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

Returns true iff the score determines the edge between x and y.

Returns a judgment of whether the variable in z determine y exactly.

Specified by:

determines in interface Score

Parameters:

z - The set of nodes.

y - The node.

Returns:

True iff the score determines the edge between x and y.

setRuleType

public void setRuleType(GicScores.RuleType ruleType)

Sets the rule type.

Parameters:

ruleType - The rule type.

See Also:

• GicScores.RuleType

setLambda

public void setLambda(double lambda)

Sets the lambda parameter.

Parameters:

lambda - The lambda parameter.

getPenaltyDiscount

public double getPenaltyDiscount()

Returns the penalty discount.

Returns:

The penalty discount.

setPenaltyDiscount

public void setPenaltyDiscount(double penaltyDiscount)

Sets the penalty discount.

Parameters:

penaltyDiscount - The penalty discount.

toString

public String toString()

Returns a string for this object.

Specified by:

toString in interface Score

Overrides:

toString in class Object

Returns:

A string for this object.

setUsePseudoInverse

public void setUsePseudoInverse(boolean usePseudoInverse)

Sets whether to use the pseudo-inverse when calculating the score.

Parameters:

usePseudoInverse - True if so.