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:

Serializable, OptionHandler, RevisionHandler

Direct Known Subclasses:

GeneticSearch, HillClimber, K2, SimulatedAnnealing, TAN

public class GlobalScoreSearchAlgorithm
extends SearchAlgorithm

This Bayes Network learning algorithm uses cross validation to estimate classification accuracy.

Valid options are:

 -mbc
  Applies a Markov Blanket correction to the network structure, 
  after a network structure is learned. This ensures that all 
  nodes in the network are part of the Markov blanket of the 
  classifier node.
 

 -S [LOO-CV|k-Fold-CV|Cumulative-CV]
  Score type (LOO-CV,k-Fold-CV,Cumulative-CV)
 

 -Q
  Use probabilistic or 0/1 scoring.
  (default probabilistic scoring)
 

Version:

$Revision: 10154 $

Author:

Remco Bouckaert

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static Tag[]	TAGS_CV_TYPE the score types

Constructor Summary

Constructors

Constructor and Description
GlobalScoreSearchAlgorithm()

Method Summary

Methods

Modifier and Type	Method and Description
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.
String	CVTypeTipText()
SelectedTag	getCVType() get cross validation strategy to be used in searching for networks.
boolean	getMarkovBlanketClassifier()
String[]	getOptions() Gets the current settings of the search algorithm.
String	getRevision() Returns the revision string.
boolean	getUseProb()
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.
Enumeration<Option>	listOptions() Returns an enumeration describing the available options
String	markovBlanketClassifierTipText()
void	setCVType(SelectedTag newCVType) set cross validation strategy to be used in searching for networks.
void	setMarkovBlanketClassifier(boolean bMarkovBlanketClassifier)
void	setOptions(String[] options) Parses a given list of options.
void	setUseProb(boolean useProb)
String	useProbTipText()

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

buildStructure, initAsNaiveBayesTipText, maxNrOfParentsTipText, 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

the score types

Constructor Detail

GlobalScoreSearchAlgorithm

public GlobalScoreSearchAlgorithm()

Method Detail

calcScore

public double calcScore(BayesNet bayesNet)
                 throws 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:

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

calcScoreWithExtraParent

public double calcScoreWithExtraParent(int nNode,
                              int nCandidateParent)
                                throws 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:

Exception - if something goes wrong

calcScoreWithMissingParent

public double calcScoreWithMissingParent(int nNode,
                                int nCandidateParent)
                                  throws 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:

Exception - if something goes wrong

calcScoreWithReversedParent

public double calcScoreWithReversedParent(int nNode,
                                 int nCandidateParent)
                                   throws 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:

Exception - if something goes wrong

leaveOneOutCV

public double leaveOneOutCV(BayesNet bayesNet)
                     throws 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:

Exception - passed on by updateClassifier

cumulativeCV

public double cumulativeCV(BayesNet bayesNet)
                    throws 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:

Exception - passed on by updateClassifier

kFoldCV

public double kFoldCV(BayesNet bayesNet,
             int nNrOfFolds)
               throws 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:

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

setMarkovBlanketClassifier

public void setMarkovBlanketClassifier(boolean bMarkovBlanketClassifier)

Parameters:

bMarkovBlanketClassifier -

getMarkovBlanketClassifier

public boolean getMarkovBlanketClassifier()

Returns:

listOptions

public Enumeration<Option> 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(String[] options)
                throws Exception

Parses a given list of options.

Valid options are:

 -mbc
  Applies a Markov Blanket correction to the network structure, 
  after a network structure is learned. This ensures that all 
  nodes in the network are part of the Markov blanket of the 
  classifier node.
 

 -S [LOO-CV|k-Fold-CV|Cumulative-CV]
  Score type (LOO-CV,k-Fold-CV,Cumulative-CV)
 

 -Q
  Use probabilistic or 0/1 scoring.
  (default probabilistic scoring)
 

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class SearchAlgorithm

Parameters:

options - the list of options as an array of strings

Throws:

Exception - if an option is not supported

getOptions

public 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 String CVTypeTipText()

Returns:

a string to describe the CVType option.

useProbTipText

public String useProbTipText()

Returns:

a string to describe the UseProb option.

globalInfo

public String globalInfo()

This will return a string describing the search algorithm.

Returns:

The string.

markovBlanketClassifierTipText

public String markovBlanketClassifierTipText()

Returns:

a string to describe the MarkovBlanketClassifier option.

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class SearchAlgorithm

Returns:

the revision