weka.classifiers.rules
Class ConjunctiveRule

java.lang.Object
  extended byweka.classifiers.Classifier
      extended byweka.classifiers.DistributionClassifier
          extended byweka.classifiers.rules.ConjunctiveRule
All Implemented Interfaces:
java.lang.Cloneable, OptionHandler, java.io.Serializable, WeightedInstancesHandler

public class ConjunctiveRule
extends DistributionClassifier
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.

See Also:
Serialized Form

Field Summary
protected  FastVector m_Antds
          The vector of antecedents of this rule
protected  double[] m_Cnsqt
          The consequent of this rule
protected  double[] m_DefDstr
          The default rule distribution of the data not covered
 
Constructor Summary
ConjunctiveRule()
           
 
Method Summary
 void buildClassifier(Instances instances)
          Builds a single rule learner with REP dealing with nominal classes or numeric classes.
 double[] distributionForInstance(Instance instance)
          Computes class distribution for the given instance.
 boolean getExclusive()
           
 int getFolds()
           
 double getMinNo()
           
 int getNumAntds()
           
 java.lang.String[] getOptions()
          Gets the current settings of the Classifier.
 boolean getRandomized()
           
 long getSeed()
           
 boolean hasAntds()
          Whether this rule has antecedents, i.e.
 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:
static void main(java.lang.String[] args)
          Main method.
 void setExclusive(boolean e)
           
 void setFolds(int folds)
          The access functions for parameters
 void setMinNo(double m)
           
 void setNumAntds(int n)
           
 void setOptions(java.lang.String[] options)
          Parses a given list of options.
 void setRandomized(boolean r)
           
 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.DistributionClassifier
calculateEntropy, calculateLabeledInstanceMargin, calculateMargin, classifyInstance
 
Methods inherited from class weka.classifiers.Classifier
forName, makeCopies
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Field Detail

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

Constructor Detail

ConjunctiveRule

public ConjunctiveRule()
Method Detail

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
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
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
Returns:
an array of strings suitable for passing to setOptions

setFolds

public void setFolds(int folds)
The access functions for parameters


getFolds

public int getFolds()

setSeed

public void setSeed(long s)

getSeed

public long getSeed()

getRandomized

public boolean getRandomized()

setRandomized

public void setRandomized(boolean r)

getExclusive

public boolean getExclusive()

setExclusive

public void setExclusive(boolean e)

setMinNo

public void setMinNo(double m)

getMinNo

public double getMinNo()

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.

Specified by:
distributionForInstance in class DistributionClassifier
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

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