weka.classifiers.rules
Class PART

java.lang.Object
  weka.classifiers.Classifier
      weka.classifiers.rules.PART

All Implemented Interfaces:

AdditionalMeasureProducer, java.lang.Cloneable, OptionHandler, java.io.Serializable, Summarizable, WeightedInstancesHandler

public class PART

extends Classifier

implements OptionHandler, WeightedInstancesHandler, Summarizable, AdditionalMeasureProducer

Class for generating a PART decision list. For more information, see

Eibe Frank and Ian H. Witten (1998). Generating Accurate Rule Sets Without Global Optimization. In Shavlik, J., ed., Machine Learning: Proceedings of the Fifteenth International Conference, Morgan Kaufmann Publishers, San Francisco, CA.

Valid options are:

-C confidence
Set confidence threshold for pruning. (Default: 0.25)

-M number
Set minimum number of instances per leaf. (Default: 2)

-R
Use reduced error pruning.

-N number
Set number of folds for reduced error pruning. One fold is used as the pruning set. (Default: 3)

-B
Use binary splits for nominal attributes.

-U
Generate unpruned decision list.

-Q
The seed for reduced-error pruning.

Version:

$Revision: 1.1 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

private boolean	m_binarySplits Binary splits on nominal attributes?
private float	m_CF Confidence level
private int	m_minNumObj Minimum number of objects
private int	m_numFolds Number of folds for reduced error pruning.
private boolean	m_reducedErrorPruning Use reduced error pruning?
private MakeDecList	m_root The decision list
private int	m_Seed The seed for random number generation.
private boolean	m_unpruned Generate unpruned list?

Fields inherited from class weka.classifiers.Classifier

m_Debug

Constructor Summary

PART()

Method Summary

java.lang.String	binarySplitsTipText() Returns the tip text for this property
void	buildClassifier(Instances instances) Generates the classifier.
double	classifyInstance(Instance instance) Classifies an instance.
java.lang.String	confidenceFactorTipText() Returns the tip text for this property
double[]	distributionForInstance(Instance instance) Returns class probabilities for an instance.
java.util.Enumeration	enumerateMeasures() Returns an enumeration of the additional measure names
boolean	getBinarySplits() Get the value of binarySplits.
float	getConfidenceFactor() Get the value of CF.
double	getMeasure(java.lang.String additionalMeasureName) Returns the value of the named measure
int	getMinNumObj() Get the value of minNumObj.
int	getNumFolds() Get the value of numFolds.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
boolean	getReducedErrorPruning() Get the value of reducedErrorPruning.
int	getSeed() Get the value of Seed.
boolean	getUnpruned() Get the value of unpruned.
java.lang.String	globalInfo() Returns a string describing classifier
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for testing this class.
double	measureNumRules() Return the number of rules.
java.lang.String	minNumObjTipText() Returns the tip text for this property
java.lang.String	numFoldsTipText() Returns the tip text for this property
java.lang.String	reducedErrorPruningTipText() Returns the tip text for this property
java.lang.String	seedTipText() Returns the tip text for this property
void	setBinarySplits(boolean v) Set the value of binarySplits.
void	setConfidenceFactor(float v) Set the value of CF.
void	setMinNumObj(int v) Set the value of minNumObj.
void	setNumFolds(int v) Set the value of numFolds.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setReducedErrorPruning(boolean v) Set the value of reducedErrorPruning.
void	setSeed(int newSeed) Set the value of Seed.
void	setUnpruned(boolean newunpruned) Set the value of unpruned.
java.lang.String	toString() Returns a description of the classifier
java.lang.String	toSummaryString() Returns a superconcise version of the model
java.lang.String	unprunedTipText() Returns the tip text for this property

Methods inherited from class weka.classifiers.Classifier

debugTipText, forName, getDebug, makeCopies, setDebug

Methods inherited from class java.lang.Object

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

Field Detail

m_root

private MakeDecList m_root

The decision list

m_CF

private float m_CF

Confidence level

m_minNumObj

private int m_minNumObj

Minimum number of objects

m_reducedErrorPruning

private boolean m_reducedErrorPruning

Use reduced error pruning?

m_numFolds

private int m_numFolds

Number of folds for reduced error pruning.

m_binarySplits

private boolean m_binarySplits

Binary splits on nominal attributes?

m_unpruned

private boolean m_unpruned

Generate unpruned list?

m_Seed

private int m_Seed

The seed for random number generation.

Constructor Detail

PART

public PART()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

buildClassifier

public void buildClassifier(Instances instances)
                     throws java.lang.Exception

Generates the classifier.

Specified by:

buildClassifier in class Classifier

Parameters:

instances - set of instances serving as training data

Throws:

java.lang.Exception - if classifier can't be built successfully

classifyInstance

public double classifyInstance(Instance instance)
                        throws java.lang.Exception

Classifies an instance.

Overrides:

classifyInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

the predicted most likely class for the instance or Instance.missingValue() if no prediction is made

Throws:

java.lang.Exception - if instance can't be classified successfully

distributionForInstance

public final double[] distributionForInstance(Instance instance)
                                       throws java.lang.Exception

Returns class probabilities for an instance.

Overrides:

distributionForInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

an array containing the estimated membership probabilities of the test instance in each class or the numeric prediction

Throws:

java.lang.Exception - if the distribution can't be computed successfully

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options. Valid options are:

-C confidence
Set confidence threshold for pruning. (Default: 0.25)

-M number
Set minimum number of instances per leaf. (Default: 2)

-R
Use reduced error pruning.

-N number
Set number of folds for reduced error pruning. One fold is used as the pruning set. (Default: 3)

-B
Use binary splits for nominal attributes.

-U
Generate unpruned decision list.

-Q
The seed for reduced-error pruning.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class Classifier

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.

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class Classifier

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 Classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class Classifier

Returns:

an array of strings suitable for passing to setOptions

toString

public java.lang.String toString()

Returns a description of the classifier

toSummaryString

public java.lang.String toSummaryString()

Returns a superconcise version of the model

Specified by:

toSummaryString in interface Summarizable

Returns:

the object summarized as a string

measureNumRules

public double measureNumRules()

Return the number of rules.

Returns:

the number of rules

enumerateMeasures

public java.util.Enumeration enumerateMeasures()

Returns an enumeration of the additional measure names

Specified by:

enumerateMeasures in interface AdditionalMeasureProducer

Returns:

an enumeration of the measure names

getMeasure

public double getMeasure(java.lang.String additionalMeasureName)

Returns the value of the named measure

Specified by:

getMeasure in interface AdditionalMeasureProducer

Parameters:

additionalMeasureName - the name of the measure to query for its value

Returns:

the value of the named measure

Throws:

java.lang.IllegalArgumentException - if the named measure is not supported

confidenceFactorTipText

public java.lang.String confidenceFactorTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getConfidenceFactor

public float getConfidenceFactor()

Get the value of CF.

Returns:

Value of CF.

setConfidenceFactor

public void setConfidenceFactor(float v)

Set the value of CF.

Parameters:

v - Value to assign to CF.

minNumObjTipText

public java.lang.String minNumObjTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getMinNumObj

public int getMinNumObj()

Get the value of minNumObj.

Returns:

Value of minNumObj.

setMinNumObj

public void setMinNumObj(int v)

Set the value of minNumObj.

Parameters:

v - Value to assign to minNumObj.

reducedErrorPruningTipText

public java.lang.String reducedErrorPruningTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getReducedErrorPruning

public boolean getReducedErrorPruning()

Get the value of reducedErrorPruning.

Returns:

Value of reducedErrorPruning.

setReducedErrorPruning

public void setReducedErrorPruning(boolean v)

Set the value of reducedErrorPruning.

Parameters:

v - Value to assign to reducedErrorPruning.

unprunedTipText

public java.lang.String unprunedTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getUnpruned

public boolean getUnpruned()

Get the value of unpruned.

Returns:

Value of unpruned.

setUnpruned

public void setUnpruned(boolean newunpruned)

Set the value of unpruned.

Parameters:

newunpruned - Value to assign to unpruned.

numFoldsTipText

public java.lang.String numFoldsTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getNumFolds

public int getNumFolds()

Get the value of numFolds.

Returns:

Value of numFolds.

setNumFolds

public void setNumFolds(int v)

Set the value of numFolds.

Parameters:

v - Value to assign to numFolds.

seedTipText

public java.lang.String seedTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getSeed

public int getSeed()

Get the value of Seed.

Returns:

Value of Seed.

setSeed

public void setSeed(int newSeed)

Set the value of Seed.

Parameters:

newSeed - Value to assign to Seed.

binarySplitsTipText

public java.lang.String binarySplitsTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getBinarySplits

public boolean getBinarySplits()

Get the value of binarySplits.

Returns:

Value of binarySplits.

setBinarySplits

public void setBinarySplits(boolean v)

Set the value of binarySplits.

Parameters:

v - Value to assign to binarySplits.

main

public static void main(java.lang.String[] argv)

Main method for testing this class.