adams.optimise.genetic.fitnessfunctions
Class AbstractWEKAFitnessFunction

java.lang.Object
  adams.core.ConsoleObject
      adams.core.option.AbstractOptionHandler
          adams.optimise.AbstractFitnessFunction
              adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction

All Implemented Interfaces:

Debuggable, Destroyable, OptionHandler, SizeOfHandler, FitnessFunction, Serializable

Direct Known Subclasses:

AttributeSelection

public abstract class AbstractWEKAFitnessFunction
extends AbstractFitnessFunction

Perform attribute selection using WEKA classification.

Author:

dale

See Also:

Serialized Form

Nested Class Summary

static class

AbstractWEKAFitnessFunction.Measure The measure to use for evaluating.

Field Summary

protected weka.classifiers.Classifier	m_Classifier the classifier to use if no serialized model is given.
protected String	m_ClassIndex the class index.
protected int	m_CrossValidationSeed the cross-validation seed.
protected PlaceholderFile	m_Dataset the filename of the data to use for cross-validation.
protected int	m_Folds the number of folds for cross-validation.
protected boolean	m_init initialised?
protected weka.core.Instances	m_Instances the data to use for cross-validation.
protected AbstractWEKAFitnessFunction.Measure	m_Measure the measure to use for evaluating the fitness.
protected PlaceholderDirectory	m_OutputDirectory the directory to store the generated ARFF files in.

Fields inherited from class adams.core.option.AbstractOptionHandler

m_DebugLevel, m_OptionManager

Constructor Summary

AbstractWEKAFitnessFunction()

Method Summary

String	classifierTipText() Returns the tip text for this property.
String	classIndexTipText() Returns the tip text for this property.
String	crossValidationSeedTipText() Returns the tip text for this property.
String	datasetTipText() Returns the tip text for this property.
void	defineOptions() Adds options to the internal list of options.
String	foldsTipText() Returns the tip text for this property.
weka.classifiers.Classifier	getClassifier() Returns the currently set classifier.
String	getClassIndex() Returns the current class index.
int	getCrossValidationSeed() Returns the current seed value for cross-validation.
PlaceholderFile	getDataset() Returns the currently set filename of the dataset for cross-validation.
int	getFolds() Returns the number of folds to use in cross-validation.
weka.core.Instances	getInstances() Returns the currently set dataset for cross-validation.
AbstractWEKAFitnessFunction.Measure	getMeasure() Returns the current measure for evaluating the fitness.
PlaceholderDirectory	getOutputDirectory() Returns the currently set directory for the generated ARFF files.
String	globalInfo()
protected void	init()
String	measureTipText() Returns the tip text for this property.
String	outputDirectoryTipText() Returns the tip text for this property.
void	setClassifier(weka.classifiers.Classifier value) Sets the classifier to use (if no serialized model is used).
void	setClassIndex(String value) Sets the class index.
void	setCrossValidationSeed(int value) Sets the seed value to use for cross-validation.
void	setDataset(PlaceholderFile value) Sets the filename of the dataset to use for cross-validation.
void	setFolds(int value) Sets the number of folds to use in cross-validation.
void	setInstances(weka.core.Instances value) Sets the data to use for cross-validation.
void	setMeasure(AbstractWEKAFitnessFunction.Measure value) Sets the measure used for evaluating the fitness.
void	setOutputDirectory(PlaceholderDirectory value) Sets the directory for the generated ARFF files.

Methods inherited from class adams.optimise.AbstractFitnessFunction

newBest

Methods inherited from class adams.core.option.AbstractOptionHandler

cleanUpOptions, debug, debug, debugLevelTipText, destroy, finishInit, getDebugLevel, getOptionManager, initialize, isDebugOn, newOptionManager, reset, setDebugLevel, toCommandLine, toString

Methods inherited from class adams.core.ConsoleObject

getDebugging, getSystemErr, getSystemOut, sizeOf

Methods inherited from class java.lang.Object

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

Methods inherited from interface adams.optimise.FitnessFunction

evaluate

Field Detail

m_Instances

protected weka.core.Instances m_Instances

the data to use for cross-validation.

m_Dataset

protected PlaceholderFile m_Dataset

the filename of the data to use for cross-validation.

m_Classifier

protected weka.classifiers.Classifier m_Classifier

the classifier to use if no serialized model is given.

m_OutputDirectory

protected PlaceholderDirectory m_OutputDirectory

the directory to store the generated ARFF files in.

m_Folds

protected int m_Folds

the number of folds for cross-validation.

m_CrossValidationSeed

protected int m_CrossValidationSeed

the cross-validation seed.

m_ClassIndex

protected String m_ClassIndex

the class index.

m_Measure

protected AbstractWEKAFitnessFunction.Measure m_Measure

the measure to use for evaluating the fitness.

m_init

protected boolean m_init

initialised?

Constructor Detail

AbstractWEKAFitnessFunction

public AbstractWEKAFitnessFunction()

Method Detail

defineOptions

public void defineOptions()

Adds options to the internal list of options.

Specified by:

defineOptions in interface OptionHandler

Overrides:

defineOptions in class AbstractOptionHandler

setFolds

public void setFolds(int value)

Sets the number of folds to use in cross-validation.

Parameters:

value - the number of folds

getFolds

public int getFolds()

Returns the number of folds to use in cross-validation.

Returns:

the number of folds

foldsTipText

public String foldsTipText()

Returns the tip text for this property.

Returns:

tip text for this property suitable for displaying in the GUI or for listing the options.

setCrossValidationSeed

public void setCrossValidationSeed(int value)

Sets the seed value to use for cross-validation.

Parameters:

value - the seed to use

getCrossValidationSeed

public int getCrossValidationSeed()

Returns the current seed value for cross-validation.

Returns:

the seed value

crossValidationSeedTipText

public String crossValidationSeedTipText()

Returns the tip text for this property.

Returns:

tip text for this property suitable for displaying in the GUI or for listing the options.

setInstances

public void setInstances(weka.core.Instances value)

Sets the data to use for cross-validation.

Parameters:

value - the dataset

getInstances

public weka.core.Instances getInstances()

Returns the currently set dataset for cross-validation.

Returns:

the dataset

setDataset

public void setDataset(PlaceholderFile value)

Sets the filename of the dataset to use for cross-validation.

Parameters:

value - the filename

getDataset

public PlaceholderFile getDataset()

Returns the currently set filename of the dataset for cross-validation.

Returns:

the filename

datasetTipText

public String datasetTipText()

Returns the tip text for this property.

Returns:

tip text for this property suitable for displaying in the GUI or for listing the options.

setClassifier

public void setClassifier(weka.classifiers.Classifier value)

Sets the classifier to use (if no serialized model is used).

Parameters:

value - the classifier

getClassifier

public weka.classifiers.Classifier getClassifier()

Returns the currently set classifier.

Returns:

the classifier

classifierTipText

public String classifierTipText()

Returns the tip text for this property.

Returns:

tip text for this property suitable for displaying in the GUI or for listing the options.

setOutputDirectory

public void setOutputDirectory(PlaceholderDirectory value)

Sets the directory for the generated ARFF files.

Parameters:

value - the directory

getOutputDirectory

public PlaceholderDirectory getOutputDirectory()

Returns the currently set directory for the generated ARFF files.

Returns:

the directory

outputDirectoryTipText

public String outputDirectoryTipText()

Returns the tip text for this property.

Returns:

tip text for this property suitable for displaying in the GUI or for listing the options.

setClassIndex

public void setClassIndex(String value)

Sets the class index.

Parameters:

value - the class index

getClassIndex

public String getClassIndex()

Returns the current class index.

Returns:

the class index

classIndexTipText

public String classIndexTipText()

Returns the tip text for this property.

Returns:

tip text for this property suitable for displaying in the GUI or for listing the options.

setMeasure

public void setMeasure(AbstractWEKAFitnessFunction.Measure value)

Sets the measure used for evaluating the fitness.

Parameters:

value - the fitness measure

getMeasure

public AbstractWEKAFitnessFunction.Measure getMeasure()

Returns the current measure for evaluating the fitness.

Returns:

the measure

measureTipText

public String measureTipText()

Returns the tip text for this property.

Returns:

tip text for this property suitable for displaying in the GUI or for listing the options.

globalInfo

public String globalInfo()

Specified by:

globalInfo in class AbstractOptionHandler

init

protected void init()