weka.classifiers.rules
Class Ridor

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

All Implemented Interfaces:

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

public class Ridor

extends Classifier

implements OptionHandler, AdditionalMeasureProducer, WeightedInstancesHandler

The implementation of a RIpple-DOwn Rule learner. It generates the default rule first and then the exceptions for the default rule with the least (weighted) error rate. Then it generates the "best" exceptions for each exception and iterates until pure. Thus it performs a tree-like expansion of exceptions and the leaf has only default rule but no exceptions.
The exceptions are a set of rules that predict the class other than class in default rule. IREP is used to find out the exceptions.

There are five inner classes defined in this class.
The first is Ridor_node, which implements one node in the Ridor tree. It's basically composed of a default class and a set of exception rules to the default class.
The second inner class is RidorRule, which implements a single exception rule using REP.
The last three inner classes are only used in RidorRule. They are Antd, NumericAntd and NominalAntd, which all implement a single antecedent in the RidorRule.
The Antd class is an abstract class, which has two subclasses, NumericAntd and NominalAntd, to implement the corresponding abstract functions. These two subclasses implement the functions related to a antecedent with a nominal attribute and a numeric attribute respectively.

Version:

$Revision: 1.10 $

See Also:

Serialized Form

Nested Class Summary

private class	Ridor.Antd The single antecedent in the rule, which is composed of an attribute and the corresponding value.
private class	Ridor.NominalAntd The antecedent with nominal attribute
private class	Ridor.NumericAntd The antecedent with numeric attribute
private class	Ridor.Ridor_node Private class implementing the single node of Ridor.
private class	Ridor.RidorRule This class implements a single rule that predicts the 2-class distribution.

Field Summary

private Attribute	m_Class The class attribute of the data
private double	m_Cover Statistics of the data
private double	m_Err Statistics of the data
private int	m_Folds The number of folds to split data into Grow and Prune for IREP
private boolean	m_IsAllErr Whether use error rate on all the data
private boolean	m_IsMajority Whether use majority class as default class
private double	m_MinNo The minimal number of instance weights within a split
private java.util.Random	m_Random Random object for randomization
private Ridor.Ridor_node	m_Root The root of Ridor
private int	m_Seed The seed to perform randomization
private int	m_Shuffle The number of shuffles performed on the data for randomization

Fields inherited from class weka.classifiers.Classifier

m_Debug

Constructor Summary

Ridor()

Method Summary

void	buildClassifier(Instances instances) Builds a ripple-down manner rule learner.
private double	classify(Ridor.Ridor_node node, Instance datum) Classify the test instance with one node of Ridor
double	classifyInstance(Instance datum) Classify the test instance with the rule learner
java.util.Enumeration	enumerateMeasures() Returns an enumeration of the additional measure names
java.lang.String	foldsTipText() Returns the tip text for this property
int	getFolds()
boolean	getMajorityClass()
double	getMeasure(java.lang.String additionalMeasureName) Returns the value of the named measure
double	getMinNo()
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
int	getSeed()
int	getShuffle()
boolean	getWholeDataErr()
java.lang.String	globalInfo() Returns a string describing classifier
java.util.Enumeration	listOptions() Returns an enumeration describing the available options Valid options are: -F number Set number of folds for reduced error pruning.
static void	main(java.lang.String[] args) Main method.
java.lang.String	majorityClassTipText() Returns the tip text for this property
java.lang.String	minNoTipText() Returns the tip text for this property
private double	numRules() Measure the number of rules in total in the model
java.lang.String	seedTipText() Returns the tip text for this property
void	setFolds(int fold)
void	setMajorityClass(boolean m)
void	setMinNo(double m)
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setSeed(int s)
void	setShuffle(int sh)
void	setWholeDataErr(boolean a)
java.lang.String	shuffleTipText() Returns the tip text for this property
java.lang.String	toString() Prints the all the rules of the rule learner.
java.lang.String	wholeDataErrTipText() Returns the tip text for this property

Methods inherited from class weka.classifiers.Classifier

debugTipText, distributionForInstance, forName, getDebug, makeCopies, setDebug

Methods inherited from class java.lang.Object

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

Field Detail

m_Folds

private int m_Folds

The number of folds to split data into Grow and Prune for IREP

m_Shuffle

private int m_Shuffle

The number of shuffles performed on the data for randomization

m_Random

private java.util.Random m_Random

Random object for randomization

m_Seed

private int m_Seed

The seed to perform randomization

m_IsAllErr

private boolean m_IsAllErr

Whether use error rate on all the data

m_IsMajority

private boolean m_IsMajority

Whether use majority class as default class

m_Root

private Ridor.Ridor_node m_Root

The root of Ridor

m_Class

private Attribute m_Class

The class attribute of the data

m_Cover

private double m_Cover

Statistics of the data

m_Err

private double m_Err

Statistics of the data

m_MinNo

private double m_MinNo

The minimal number of instance weights within a split

Constructor Detail

Ridor

public Ridor()

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

Builds a ripple-down manner rule learner.

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 datum)

Classify the test instance with the rule learner

Overrides:

classifyInstance in class Classifier

Parameters:

datum - the instance to be classified

Returns:

the classification

classify

private double classify(Ridor.Ridor_node node,
                        Instance datum)

Classify the test instance with one node of Ridor

Parameters:

node - the node of Ridor to classify the test instance

Returns:

the classification

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options Valid options are:

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

-S number
Set number of shuffles for randomization. (Default: 10)

-A
Set flag of whether use the error rate of all the data to select the default class in each step. If not set, the learner will only use the error rate in the pruning data

-M
Set flag of whether use the majority class as the default class in each step instead of choosing default class based on the error rate (if the flag is not set)

-N number
Set the minimal weights of instances within a split. (Default: 2)

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

foldsTipText

public java.lang.String foldsTipText()

Returns the tip text for this property

Returns:

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

setFolds

public void setFolds(int fold)

getFolds

public int getFolds()

shuffleTipText

public java.lang.String shuffleTipText()

Returns the tip text for this property

Returns:

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

setShuffle

public void setShuffle(int sh)

getShuffle

public int getShuffle()

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

setSeed

public void setSeed(int s)

getSeed

public int getSeed()

wholeDataErrTipText

public java.lang.String wholeDataErrTipText()

Returns the tip text for this property

Returns:

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

setWholeDataErr

public void setWholeDataErr(boolean a)

getWholeDataErr

public boolean getWholeDataErr()

majorityClassTipText

public java.lang.String majorityClassTipText()

Returns the tip text for this property

Returns:

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

setMajorityClass

public void setMajorityClass(boolean m)

getMajorityClass

public boolean getMajorityClass()

minNoTipText

public java.lang.String minNoTipText()

Returns the tip text for this property

Returns:

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

setMinNo

public void setMinNo(double m)

getMinNo

public double getMinNo()

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

numRules

private double numRules()

Measure the number of rules in total in the model

Returns:

the number of rules

toString

public java.lang.String toString()

Prints the all the rules of the rule learner.

Returns:

a textual description of the classifier

main

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

Main method.

Parameters:

args - the options for the classifier