weka.classifiers.bayes.net.search.local

Class GeneticSearch

java.lang.Object

weka.classifiers.bayes.net.search.SearchAlgorithm

weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm

weka.classifiers.bayes.net.search.local.GeneticSearch

All Implemented Interfaces:

Serializable, OptionHandler, RevisionHandler

public class GeneticSearch
extends LocalScoreSearchAlgorithm

This Bayes Network learning algorithm uses genetic search for finding a well scoring Bayes network structure. Genetic search works by having a population of Bayes network structures and allow them to mutate and apply cross over to get offspring. The best network structure found during the process is returned.

Valid options are:

 -L <integer>
  Population size

 -A <integer>
  Descendant population size

 -U <integer>
  Number of runs

 -M
  Use mutation.
  (default true)

 -C
  Use cross-over.
  (default true)

 -O
  Use tournament selection (true) or maximum subpopulatin (false).
  (default false)

 -R <seed>
  Random number seed

 -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 [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES]
  Score type (BAYES, BDeu, MDL, ENTROPY and AIC)

Version:

$Revision: 1.5 $

Author:

Remco Bouckaert ([email protected])

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm

TAGS_SCORE_TYPE

Constructor Summary

Constructors

Constructor and Description
GeneticSearch()

Method Summary

Methods

Modifier and Type	Method and Description
String	descendantPopulationSizeTipText()
int	getDescendantPopulationSize()
String[]	getOptions() Gets the current settings of the search algorithm.
int	getPopulationSize()
String	getRevision() Returns the revision string.
int	getRuns()
int	getSeed()
boolean	getUseCrossOver()
boolean	getUseMutation()
boolean	getUseTournamentSelection()
String	globalInfo() This will return a string describing the classifier.
Enumeration	listOptions() Returns an enumeration describing the available options.
String	populationSizeTipText()
String	runsTipText()
String	seedTipText()
void	setDescendantPopulationSize(int iDescendantPopulationSize)
void	setOptions(String[] options) Parses a given list of options.
void	setPopulationSize(int iPopulationSize)
void	setRuns(int nRuns) Sets the number of runs
void	setSeed(int nSeed) Sets the random number seed
void	setUseCrossOver(boolean bUseCrossOver)
void	setUseMutation(boolean bUseMutation)
void	setUseTournamentSelection(boolean bUseTournamentSelection)
String	useCrossOverTipText()
String	useMutationTipText()
String	useTournamentSelectionTipText()

Methods inherited from class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm

buildStructure, calcNodeScore, calcScoreWithExtraParent, calcScoreWithMissingParent, getMarkovBlanketClassifier, getScoreType, logScore, markovBlanketClassifierTipText, scoreTypeTipText, setMarkovBlanketClassifier, setScoreType

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

initAsNaiveBayesTipText, maxNrOfParentsTipText, toString

Methods inherited from class java.lang.Object

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

Constructor Detail

GeneticSearch

public GeneticSearch()

Method Detail

getRuns

public int getRuns()

Returns:

number of runs

setRuns

public void setRuns(int nRuns)

Sets the number of runs

Parameters:

nRuns - The number of runs to set

listOptions

public Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class LocalScoreSearchAlgorithm

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:

 -L <integer>
  Population size

 -A <integer>
  Descendant population size

 -U <integer>
  Number of runs

 -M
  Use mutation.
  (default true)

 -C
  Use cross-over.
  (default true)

 -O
  Use tournament selection (true) or maximum subpopulatin (false).
  (default false)

 -R <seed>
  Random number seed

 -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 [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES]
  Score type (BAYES, BDeu, MDL, ENTROPY and AIC)

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class LocalScoreSearchAlgorithm

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 LocalScoreSearchAlgorithm

Returns:

an array of strings suitable for passing to setOptions

getUseCrossOver

public boolean getUseCrossOver()

Returns:

whether cross-over is used

getUseMutation

public boolean getUseMutation()

Returns:

whether mutation is used

getDescendantPopulationSize

public int getDescendantPopulationSize()

Returns:

descendant population size

getPopulationSize

public int getPopulationSize()

Returns:

population size

setUseCrossOver

public void setUseCrossOver(boolean bUseCrossOver)

Parameters:

bUseCrossOver - sets whether cross-over is used

setUseMutation

public void setUseMutation(boolean bUseMutation)

Parameters:

bUseMutation - sets whether mutation is used

getUseTournamentSelection

public boolean getUseTournamentSelection()

Returns:

whether Tournament Selection (true) or Maximum Sub-Population (false) should be used

setUseTournamentSelection

public void setUseTournamentSelection(boolean bUseTournamentSelection)

Parameters:

bUseTournamentSelection - sets whether Tournament Selection or Maximum Sub-Population should be used

setDescendantPopulationSize

public void setDescendantPopulationSize(int iDescendantPopulationSize)

Parameters:

iDescendantPopulationSize - sets descendant population size

setPopulationSize

public void setPopulationSize(int iPopulationSize)

Parameters:

iPopulationSize - sets population size

getSeed

public int getSeed()

Returns:

random number seed

setSeed

public void setSeed(int nSeed)

Sets the random number seed

Parameters:

nSeed - The number of the seed to set

globalInfo

public String globalInfo()

This will return a string describing the classifier.

Overrides:

globalInfo in class LocalScoreSearchAlgorithm

Returns:

The string.

runsTipText

public String runsTipText()

Returns:

a string to describe the Runs option.

seedTipText

public String seedTipText()

Returns:

a string to describe the Seed option.

populationSizeTipText

public String populationSizeTipText()

Returns:

a string to describe the Population Size option.

descendantPopulationSizeTipText

public String descendantPopulationSizeTipText()

Returns:

a string to describe the Descendant Population Size option.

useMutationTipText

public String useMutationTipText()

Returns:

a string to describe the Use Mutation option.

useCrossOverTipText

public String useCrossOverTipText()

Returns:

a string to describe the Use Cross-Over option.

useTournamentSelectionTipText

public String useTournamentSelectionTipText()

Returns:

a string to describe the Use Tournament Selection option.

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class LocalScoreSearchAlgorithm

Returns:

the revision