weka.classifiers.rules
Class JRip

java.lang.Object
  extended by weka.classifiers.AbstractClassifier
      extended by weka.classifiers.rules.JRip
All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, Classifier, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class JRip
extends AbstractClassifier
implements AdditionalMeasureProducer, WeightedInstancesHandler, TechnicalInformationHandler

This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W. Cohen as an optimized version of IREP.

The algorithm is briefly described as follows:

Initialize RS = {}, and for each class from the less prevalent one to the more frequent one, DO:

1. Building stage:
Repeat 1.1 and 1.2 until the descrition length (DL) of the ruleset and examples is 64 bits greater than the smallest DL met so far, or there are no positive examples, or the error rate >= 50%.

1.1. Grow phase:
Grow one rule by greedily adding antecedents (or conditions) to the rule until the rule is perfect (i.e. 100% accurate). The procedure tries every possible value of each attribute and selects the condition with highest information gain: p(log(p/t)-log(P/T)).

1.2. Prune phase:
Incrementally prune each rule and allow the pruning of any final sequences of the antecedents;The pruning metric is (p-n)/(p+n) -- but it's actually 2p/(p+n) -1, so in this implementation we simply use p/(p+n) (actually (p+1)/(p+n+2), thus if p+n is 0, it's 0.5).

2. Optimization stage:
after generating the initial ruleset {Ri}, generate and prune two variants of each rule Ri from randomized data using procedure 1.1 and 1.2. But one variant is generated from an empty rule while the other is generated by greedily adding antecedents to the original rule. Moreover, the pruning metric used here is (TP+TN)/(P+N).Then the smallest possible DL for each variant and the original rule is computed. The variant with the minimal DL is selected as the final representative of Ri in the ruleset.After all the rules in {Ri} have been examined and if there are still residual positives, more rules are generated based on the residual positives using Building Stage again.
3. Delete the rules from the ruleset that would increase the DL of the whole ruleset if it were in it. and add resultant ruleset to RS.
ENDDO

Note that there seem to be 2 bugs in the original ripper program that would affect the ruleset size and accuracy slightly. This implementation avoids these bugs and thus is a little bit different from Cohen's original implementation. Even after fixing the bugs, since the order of classes with the same frequency is not defined in ripper, there still seems to be some trivial difference between this implementation and the original ripper, especially for audiology data in UCI repository, where there are lots of classes of few instances.

Details please see:

William W. Cohen: Fast Effective Rule Induction. In: Twelfth International Conference on Machine Learning, 115-123, 1995.

PS. We have compared this implementation with the original ripper implementation in aspects of accuracy, ruleset size and running time on both artificial data "ab+bcd+defg" and UCI datasets. In all these aspects it seems to be quite comparable to the original ripper implementation. However, we didn't consider memory consumption optimization in this implementation.

BibTeX:

 @inproceedings{Cohen1995,
    author = {William W. Cohen},
    booktitle = {Twelfth International Conference on Machine Learning},
    pages = {115-123},
    publisher = {Morgan Kaufmann},
    title = {Fast Effective Rule Induction},
    year = {1995}
 }
 

Valid options are:

 -F <number of folds>
  Set number of folds for REP
  One fold is used as pruning set.
  (default 3)
 -N <min. weights>
  Set the minimal weights of instances
  within a split.
  (default 2.0)
 -O <number of runs>
  Set the number of runs of
  optimizations. (Default: 2)
 -D
  Set whether turn on the
  debug mode (Default: false)
 -S <seed>
  The seed of randomization
  (Default: 1)
 -E
  Whether NOT check the error rate>=0.5
  in stopping criteria  (default: check)
 -P
  Whether NOT use pruning
  (default: use pruning)

Version:
$Revision: 6041 $
Author:
Xin Xu (xx5@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:
Serialized Form

Constructor Summary
JRip()
           
 
Method Summary
 void buildClassifier(Instances instances)
          Builds Ripper in the order of class frequencies.
 java.lang.String checkErrorRateTipText()
          Returns the tip text for this property
 java.lang.String debugTipText()
          Returns the tip text for this property
 double[] distributionForInstance(Instance datum)
          Classify the test instance with the rule learner and provide the class distributions
 java.util.Enumeration enumerateMeasures()
          Returns an enumeration of the additional measure names
 java.lang.String foldsTipText()
          Returns the tip text for this property
 Capabilities getCapabilities()
          Returns default capabilities of the classifier.
 boolean getCheckErrorRate()
          Gets whether to check for error rate is in stopping criterion
 boolean getDebug()
          Gets whether debug information is output to the console
 int getFolds()
          Gets the number of folds
 double getMeasure(java.lang.String additionalMeasureName)
          Returns the value of the named measure
 double getMinNo()
          Gets the minimum total weight of the instances in a rule
 int getOptimizations()
          Gets the the number of optimization runs
 java.lang.String[] getOptions()
          Gets the current settings of the Classifier.
 java.lang.String getRevision()
          Returns the revision string.
 FastVector getRuleset()
          Get the ruleset generated by Ripper
 RuleStats getRuleStats(int pos)
          Get the statistics of the ruleset in the given position
 long getSeed()
          Gets the current seed value to use in randomizing the data
 TechnicalInformation getTechnicalInformation()
          Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
 boolean getUsePruning()
          Gets whether pruning is performed
 java.lang.String globalInfo()
          Returns a string describing classifier
 java.util.Enumeration listOptions()
          Returns an enumeration describing the available options Valid options are:
static void main(java.lang.String[] args)
          Main method.
 java.lang.String minNoTipText()
          Returns the tip text for this property
 java.lang.String optimizationsTipText()
          Returns the tip text for this property
 java.lang.String seedTipText()
          Returns the tip text for this property
 void setCheckErrorRate(boolean d)
          Sets whether to check for error rate is in stopping criterion
 void setDebug(boolean d)
          Sets whether debug information is output to the console
 void setFolds(int fold)
          Sets the number of folds to use
 void setMinNo(double m)
          Sets the minimum total weight of the instances in a rule
 void setOptimizations(int run)
          Sets the number of optimization runs
 void setOptions(java.lang.String[] options)
          Parses a given list of options.
 void setSeed(long s)
          Sets the seed value to use in randomizing the data
 void setUsePruning(boolean d)
          Sets whether pruning is performed
 java.lang.String toString()
          Prints the all the rules of the rule learner.
 java.lang.String usePruningTipText()
          Returns the tip text for this property
 
Methods inherited from class weka.classifiers.AbstractClassifier
classifyInstance, forName, makeCopies, makeCopy
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Constructor Detail

JRip

public JRip()
Method Detail

globalInfo

public java.lang.String globalInfo()
Returns a string describing classifier

Returns:
a description suitable for displaying in the explorer/experimenter gui

getTechnicalInformation

public TechnicalInformation getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

Specified by:
getTechnicalInformation in interface TechnicalInformationHandler
Returns:
the technical information about this class

listOptions

public java.util.Enumeration listOptions()
Returns an enumeration describing the available options Valid options are:

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

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

-O number
Set the number of runs of optimizations. (Default: 2)

-D
Whether turn on the debug mode -S number
The seed of randomization used in Ripper.(Default: 1)

-E
Whether NOT check the error rate >= 0.5 in stopping criteria. (default: check)

-P
Whether NOT use pruning. (default: use pruning)

Specified by:
listOptions in interface OptionHandler
Overrides:
listOptions in class AbstractClassifier
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.

Valid options are:

 -F <number of folds>
  Set number of folds for REP
  One fold is used as pruning set.
  (default 3)
 -N <min. weights>
  Set the minimal weights of instances
  within a split.
  (default 2.0)
 -O <number of runs>
  Set the number of runs of
  optimizations. (Default: 2)
 -D
  Set whether turn on the
  debug mode (Default: false)
 -S <seed>
  The seed of randomization
  (Default: 1)
 -E
  Whether NOT check the error rate>=0.5
  in stopping criteria  (default: check)
 -P
  Whether NOT use pruning
  (default: use pruning)

Specified by:
setOptions in interface OptionHandler
Overrides:
setOptions in class AbstractClassifier
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 AbstractClassifier
Returns:
an array of strings suitable for passing to setOptions

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

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)
Sets the number of folds to use

Parameters:
fold - the number of folds

getFolds

public int getFolds()
Gets the number of folds

Returns:
the number of folds

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)
Sets the minimum total weight of the instances in a rule

Parameters:
m - the minimum total weight of the instances in a rule

getMinNo

public double getMinNo()
Gets the minimum total weight of the instances in a rule

Returns:
the minimum total weight of the instances in a rule

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)
Sets the seed value to use in randomizing the data

Parameters:
s - the new seed value

getSeed

public long getSeed()
Gets the current seed value to use in randomizing the data

Returns:
the seed value

optimizationsTipText

public java.lang.String optimizationsTipText()
Returns the tip text for this property

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

setOptimizations

public void setOptimizations(int run)
Sets the number of optimization runs

Parameters:
run - the number of optimization runs

getOptimizations

public int getOptimizations()
Gets the the number of optimization runs

Returns:
the number of optimization runs

debugTipText

public java.lang.String debugTipText()
Returns the tip text for this property

Overrides:
debugTipText in class AbstractClassifier
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

setDebug

public void setDebug(boolean d)
Sets whether debug information is output to the console

Overrides:
setDebug in class AbstractClassifier
Parameters:
d - whether debug information is output to the console

getDebug

public boolean getDebug()
Gets whether debug information is output to the console

Overrides:
getDebug in class AbstractClassifier
Returns:
whether debug information is output to the console

checkErrorRateTipText

public java.lang.String checkErrorRateTipText()
Returns the tip text for this property

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

setCheckErrorRate

public void setCheckErrorRate(boolean d)
Sets whether to check for error rate is in stopping criterion

Parameters:
d - whether to check for error rate is in stopping criterion

getCheckErrorRate

public boolean getCheckErrorRate()
Gets whether to check for error rate is in stopping criterion

Returns:
true if checking for error rate is in stopping criterion

usePruningTipText

public java.lang.String usePruningTipText()
Returns the tip text for this property

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

setUsePruning

public void setUsePruning(boolean d)
Sets whether pruning is performed

Parameters:
d - Whether pruning is performed

getUsePruning

public boolean getUsePruning()
Gets whether pruning is performed

Returns:
true if pruning is performed

getRuleset

public FastVector getRuleset()
Get the ruleset generated by Ripper

Returns:
the ruleset

getRuleStats

public RuleStats getRuleStats(int pos)
Get the statistics of the ruleset in the given position

Parameters:
pos - the position of the stats, assuming correct
Returns:
the statistics of the ruleset in the given position

getCapabilities

public Capabilities getCapabilities()
Returns default capabilities of the classifier.

Specified by:
getCapabilities in interface Classifier
Specified by:
getCapabilities in interface CapabilitiesHandler
Overrides:
getCapabilities in class AbstractClassifier
Returns:
the capabilities of this classifier
See Also:
Capabilities

buildClassifier

public void buildClassifier(Instances instances)
                     throws java.lang.Exception
Builds Ripper in the order of class frequencies. For each class it's built in two stages: building and optimization

Specified by:
buildClassifier in interface Classifier
Parameters:
instances - the training data
Throws:
java.lang.Exception - if classifier can't be built successfully

distributionForInstance

public double[] distributionForInstance(Instance datum)
Classify the test instance with the rule learner and provide the class distributions

Specified by:
distributionForInstance in interface Classifier
Overrides:
distributionForInstance in class AbstractClassifier
Parameters:
datum - the instance to be classified
Returns:
the distribution

toString

public java.lang.String toString()
Prints the all the rules of the rule learner.

Overrides:
toString in class java.lang.Object
Returns:
a textual description of the classifier

getRevision

public java.lang.String getRevision()
Returns the revision string.

Specified by:
getRevision in interface RevisionHandler
Overrides:
getRevision in class AbstractClassifier
Returns:
the revision

main

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

Parameters:
args - the options for the classifier