Package weka.classifiers.rules

Class JRip

java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.rules.JRip
All Implemented Interfaces:
Serializable, Cloneable, Classifier, AdditionalMeasureProducer, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, 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: 15519 $
Author:
Xin Xu (xx5@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:

  • Constructor Details

    • JRip

      public JRip()

  • Method Details

    • globalInfo

      public 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 Enumeration<Option> 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(String[] options) throws 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:
      Exception - if an option is not supported

    • getOptions

      public 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 Enumeration<String> 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(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:
      IllegalArgumentException - if the named measure is not supported

    • foldsTipText

      public 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 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 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 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 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 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 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 ArrayList<Rule> 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 CapabilitiesHandler
      Specified by:
      getCapabilities in interface Classifier
      Overrides:
      getCapabilities in class AbstractClassifier
      Returns:
      the capabilities of this classifier
      See Also:

    • buildClassifier

      public void buildClassifier(Instances instances) throws 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:
      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 String toString()
      Prints the all the rules of the rule learner.
      Overrides:
      toString in class Object
      Returns:
      a textual description of the classifier

    • getRevision

      public 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(String[] args)
      Main method.
      Parameters:
      args - the options for the classifier