weka.classifiers.bayes.net.search.global
Class GlobalScoreSearchAlgorithm

java.lang.Object
  weka.classifiers.bayes.net.search.SearchAlgorithm
      weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm

All Implemented Interfaces:

OptionHandler, java.io.Serializable

Direct Known Subclasses:

GeneticSearch, HillClimber, K2, SimulatedAnnealing, TAN

public class GlobalScoreSearchAlgorithm

extends SearchAlgorithm

The CVSearchAlgorithm class supports Bayes net structure search algorithms that are based on cross validation (as opposed to for example score based of conditional independence based search algorithms).

Version:

$Revision: 1.5 $

Author:

Remco Bouckaert

See Also:

Serialized Form

Field Summary

static Tag[]

TAGS_CV_TYPE

Constructor Summary

GlobalScoreSearchAlgorithm()

Method Summary

double	calcScore(BayesNet bayesNet) performCV returns the accuracy calculated using cross validation.
double	calcScoreWithExtraParent(int nNode, int nCandidateParent) Calc Node Score With Added Parent
double	calcScoreWithMissingParent(int nNode, int nCandidateParent) Calc Node Score With Parent Deleted
double	calcScoreWithReversedParent(int nNode, int nCandidateParent) Calc Node Score With Arrow reversed
double	cumulativeCV(BayesNet bayesNet) CumulativeCV returns the accuracy calculated using cumulative cross validation.
java.lang.String	CVTypeTipText()
SelectedTag	getCVType() get cross validation strategy to be used in searching for networks.
java.lang.String[]	getOptions() Gets the current settings of the search algorithm.
boolean	getUseProb()
java.lang.String	globalInfo() This will return a string describing the search algorithm.
double	kFoldCV(BayesNet bayesNet, int nNrOfFolds) kFoldCV uses k-fold cross validation to measure the accuracy of a Bayes network classifier.
double	leaveOneOutCV(BayesNet bayesNet) LeaveOneOutCV returns the accuracy calculated using Leave One Out cross validation.
java.util.Enumeration	listOptions() Returns an enumeration describing the available options
void	setCVType(SelectedTag newCVType) set cross validation strategy to be used in searching for networks.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setUseProb(boolean useProb)
java.lang.String	useProbTipText()

Methods inherited from class weka.classifiers.bayes.net.search.SearchAlgorithm

buildStructure, getMarkovBlanketClassifier, initAsNaiveBayesTipText, markovBlanketClassifierTipText, maxNrOfParentsTipText, setMarkovBlanketClassifier, toString

Methods inherited from class java.lang.Object

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

Field Detail

TAGS_CV_TYPE

public static final Tag[] TAGS_CV_TYPE

Constructor Detail

GlobalScoreSearchAlgorithm

public GlobalScoreSearchAlgorithm()

Method Detail

calcScore

public double calcScore(BayesNet bayesNet)
                 throws java.lang.Exception

performCV returns the accuracy calculated using cross validation. The dataset used is m_Instances associated with the Bayes Network.

Parameters:

bayesNet - : Bayes Network containing structure to evaluate

Returns:

accuracy (in interval 0..1) measured using cv.

Throws:

java.lang.Exception - whn m_nCVType is invalided + exceptions passed on by updateClassifier

calcScoreWithExtraParent

public double calcScoreWithExtraParent(int nNode,
                                       int nCandidateParent)
                                throws java.lang.Exception

Calc Node Score With Added Parent

Parameters:

nNode - node for which the score is calculate

nCandidateParent - candidate parent to add to the existing parent set

Returns:

log score

Throws:

java.lang.Exception

calcScoreWithMissingParent

public double calcScoreWithMissingParent(int nNode,
                                         int nCandidateParent)
                                  throws java.lang.Exception

Calc Node Score With Parent Deleted

Parameters:

nNode - node for which the score is calculate

nCandidateParent - candidate parent to delete from the existing parent set

Returns:

log score

Throws:

java.lang.Exception

calcScoreWithReversedParent

public double calcScoreWithReversedParent(int nNode,
                                          int nCandidateParent)
                                   throws java.lang.Exception

Calc Node Score With Arrow reversed

Parameters:

nNode - node for which the score is calculate

nCandidateParent - candidate parent to delete from the existing parent set

Returns:

log score

Throws:

java.lang.Exception

leaveOneOutCV

public double leaveOneOutCV(BayesNet bayesNet)
                     throws java.lang.Exception

LeaveOneOutCV returns the accuracy calculated using Leave One Out cross validation. The dataset used is m_Instances associated with the Bayes Network.

Parameters:

bayesNet - : Bayes Network containing structure to evaluate

Returns:

accuracy (in interval 0..1) measured using leave one out cv.

Throws:

java.lang.Exception - passed on by updateClassifier

cumulativeCV

public double cumulativeCV(BayesNet bayesNet)
                    throws java.lang.Exception

CumulativeCV returns the accuracy calculated using cumulative cross validation. The idea is to run through the data set and try to classify each of the instances based on the previously seen data. The data set used is m_Instances associated with the Bayes Network.

Parameters:

bayesNet - : Bayes Network containing structure to evaluate

Returns:

accuracy (in interval 0..1) measured using leave one out cv.

Throws:

java.lang.Exception - passed on by updateClassifier

kFoldCV

public double kFoldCV(BayesNet bayesNet,
                      int nNrOfFolds)
               throws java.lang.Exception

kFoldCV uses k-fold cross validation to measure the accuracy of a Bayes network classifier.

Parameters:

bayesNet - : Bayes Network containing structure to evaluate

nNrOfFolds - : the number of folds k to perform k-fold cv

Returns:

accuracy (in interval 0..1) measured using leave one out cv.

Throws:

java.lang.Exception - passed on by updateClassifier

getUseProb

public boolean getUseProb()

Returns:

use probabilities or not in accuracy estimate

setUseProb

public void setUseProb(boolean useProb)

Parameters:

useProb - : use probabilities or not in accuracy estimate

setCVType

public void setCVType(SelectedTag newCVType)

set cross validation strategy to be used in searching for networks.

Parameters:

newCVType - : cross validation strategy

getCVType

public SelectedTag getCVType()

get cross validation strategy to be used in searching for networks.

Returns:

cross validation strategy

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class SearchAlgorithm

Returns:

an enumeration of all the available options

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options. Valid options are:

For other options see search algorithm.

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class SearchAlgorithm

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the search algorithm.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class SearchAlgorithm

Returns:

an array of strings suitable for passing to setOptions

CVTypeTipText

public java.lang.String CVTypeTipText()

Returns:

a string to describe the CVType option.

useProbTipText

public java.lang.String useProbTipText()

Returns:

a string to describe the UseProb option.

globalInfo

public java.lang.String globalInfo()

This will return a string describing the search algorithm.

Returns:

The string.