weka.classifiers.rules
Class ConjunctiveRule

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

All Implemented Interfaces:

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

public class ConjunctiveRule

extends Classifier

implements OptionHandler, WeightedInstancesHandler

This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.

A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. In this case, the consequent is the distribution of the available classes (or numeric value) in the dataset. If the test instance is not covered by this rule, then it's predicted using the default class distributions/value of the data not covered by the rule in the training data.
This learner selects an antecedent by computing the Information Gain of each antecendent and prunes the generated rule using Reduced Error Prunning (REP).

For classification, the Information of one antecedent is the weighted average of the entropies of both the data covered and not covered by the rule.
For regression, the Information is the weighted average of the mean-squared errors of both the data covered and not covered by the rule.

In pruning, weighted average of accuracy rate of the pruning data is used for classification while the weighted average of the mean-squared errors of the pruning data is used for regression.

Version:

$Revision: 1.10 $

See Also:

Serialized Form

Nested Class Summary

private class	ConjunctiveRule.Antd The single antecedent in the rule, which is composed of an attribute and the corresponding value.
(package private) class	ConjunctiveRule.NominalAntd The antecedent with nominal attribute
private class	ConjunctiveRule.NumericAntd The antecedent with numeric attribute

Field Summary

protected FastVector	m_Antds The vector of antecedents of this rule
private Attribute	m_ClassAttribute The class attribute of the data
protected double[]	m_Cnsqt The consequent of this rule
protected double[]	m_DefDstr The default rule distribution of the data not covered
private int	m_Folds The number of folds to split data into Grow and Prune for REP
private boolean	m_IsExclude Whether to use exlusive expressions for nominal attributes
private double	m_MinNo The minimal number of instance weights within a split
private int	m_NumAntds The number of antecedents in pre-pruning
private int	m_NumClasses Number of classes in the training data
private java.util.Random	m_Random The Random object used for randomization
private long	m_Seed The seed to perform randomization
private FastVector	m_Targets The predicted classes recorded for each antecedent in the growing data

Fields inherited from class weka.classifiers.Classifier

m_Debug

Constructor Summary

ConjunctiveRule()

Method Summary

void	buildClassifier(Instances instances) Builds a single rule learner with REP dealing with nominal classes or numeric classes.
private double	computeAccu(Instances data, int clas) Private function to compute number of accurate instances based on the specified predicted class
private Instances[]	computeInfoGain(Instances instances, double defInfo, ConjunctiveRule.Antd antd) Compute the best information gain for the specified antecedent
double[]	distributionForInstance(Instance instance) Computes class distribution for the given instance.
java.lang.String	exclusiveTipText() Returns the tip text for this property
java.lang.String	foldsTipText() Returns the tip text for this property
boolean	getExclusive()
int	getFolds()
double	getMinNo()
int	getNumAntds()
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
long	getSeed()
java.lang.String	globalInfo() Returns a string describing classifier
private void	grow(Instances data) Build one rule using the growing data
boolean	hasAntds() Whether this rule has antecedents, i.e. whether it is a default rule
boolean	isCover(Instance datum) Whether the instance covered by this rule
java.util.Enumeration	listOptions() Returns an enumeration describing the available options Valid options are: -N number Set number of folds for REP.
static void	main(java.lang.String[] args) Main method.
private double	meanSquaredError(Instances data, double mean) Private function to compute the squared error of the specified data and the specified mean
java.lang.String	minNoTipText() Returns the tip text for this property
java.lang.String	numAntdsTipText() Returns the tip text for this property
private void	prune(Instances pruneData) Prune the rule using the pruning data.
java.lang.String	seedTipText() Returns the tip text for this property
void	setExclusive(boolean e)
void	setFolds(int folds)
void	setMinNo(double m)
void	setNumAntds(int n)
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setSeed(long s)
java.lang.String	toString() Prints this rule
java.lang.String	toString(java.lang.String att, java.lang.String cl) Prints this rule with the specified class label

Methods inherited from class weka.classifiers.Classifier

classifyInstance, 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_Folds

private int m_Folds

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

m_ClassAttribute

private Attribute m_ClassAttribute

The class attribute of the data

m_Antds

protected FastVector m_Antds

The vector of antecedents of this rule

m_DefDstr

protected double[] m_DefDstr

The default rule distribution of the data not covered

m_Cnsqt

protected double[] m_Cnsqt

The consequent of this rule

m_NumClasses

private int m_NumClasses

Number of classes in the training data

m_Seed

private long m_Seed

The seed to perform randomization

m_Random

private java.util.Random m_Random

The Random object used for randomization

m_Targets

private FastVector m_Targets

The predicted classes recorded for each antecedent in the growing data

m_IsExclude

private boolean m_IsExclude

Whether to use exlusive expressions for nominal attributes

m_MinNo

private double m_MinNo

The minimal number of instance weights within a split

m_NumAntds

private int m_NumAntds

The number of antecedents in pre-pruning

Constructor Detail

ConjunctiveRule

public ConjunctiveRule()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options Valid options are:

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

-R
Set if NOT randomize the data before split to growing and pruning data. If NOT set, the seed of randomization is specified by the -S option. (Default: randomize)

-S
Seed of randomization. (Default: 1)

-E
Set whether consider the exclusive expressions for nominal attribute split. (Default: false)

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

-P number
Set the number of antecedents allowed in the rule if pre-pruning is used. If this value is other than -1, then pre-pruning will be used, otherwise the rule uses REP. (Default: -1)

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 folds)

getFolds

public int getFolds()

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(long s)

getSeed

public long getSeed()

exclusiveTipText

public java.lang.String exclusiveTipText()

Returns the tip text for this property

Returns:

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

getExclusive

public boolean getExclusive()

setExclusive

public void setExclusive(boolean e)

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()

numAntdsTipText

public java.lang.String numAntdsTipText()

Returns the tip text for this property

Returns:

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

setNumAntds

public void setNumAntds(int n)

getNumAntds

public int getNumAntds()

buildClassifier

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

Builds a single rule learner with REP dealing with nominal classes or numeric classes. For nominal classes, this rule learner predicts a distribution on the classes. For numeric classes, this learner predicts a single value.

Specified by:

buildClassifier in class Classifier

Parameters:

instances - the training data

Throws:

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

distributionForInstance

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

Computes class distribution for the given instance.

Overrides:

distributionForInstance in class Classifier

Parameters:

instance - the instance for which distribution is to be computed

Returns:

the class distribution for the given instance

Throws:

java.lang.Exception - if distribution could not be computed successfully

isCover

public boolean isCover(Instance datum)

Whether the instance covered by this rule

Returns:

the boolean value indicating whether the instance is covered by this rule

hasAntds

public boolean hasAntds()

Whether this rule has antecedents, i.e. whether it is a default rule

Returns:

the boolean value indicating whether the rule has antecedents

grow

private void grow(Instances data)

Build one rule using the growing data

Parameters:

data - the growing data used to build the rule

computeInfoGain

private Instances[] computeInfoGain(Instances instances,
                                    double defInfo,
                                    ConjunctiveRule.Antd antd)

Compute the best information gain for the specified antecedent

Parameters:

defInfo - the default information of data

antd - the specific antecedent

Returns:

the data covered and not covered by the antecedent

prune

private void prune(Instances pruneData)

Prune the rule using the pruning data. The weighted average of accuracy rate/mean-squared error is used to prune the rule.

Parameters:

pruneData - the pruning data used to prune the rule

computeAccu

private double computeAccu(Instances data,
                           int clas)

Private function to compute number of accurate instances based on the specified predicted class

Parameters:

data - the data in question

clas - the predicted class

Returns:

the default accuracy number

meanSquaredError

private double meanSquaredError(Instances data,
                                double mean)

Private function to compute the squared error of the specified data and the specified mean

Parameters:

data - the data in question

mean - the specified mean

Returns:

the default mean-squared error

toString

public java.lang.String toString(java.lang.String att,
                                 java.lang.String cl)

Prints this rule with the specified class label

Parameters:

att - the string standing for attribute in the consequent of this rule

cl - the string standing for value in the consequent of this rule

Returns:

a textual description of this rule with the specified class label

toString

public java.lang.String toString()

Prints this rule

Returns:

a textual description of this rule

main

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

Main method.

Parameters:

args - the options for the classifier