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## weka.classifiers.rules
Class JRip

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

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, AdditionalMeasureProducer, OptionHandler, WeightedInstancesHandler

    public class JRip

extends Classifier
implements OptionHandler, AdditionalMeasureProducer, WeightedInstancesHandler

This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which is 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 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.

If wrapped by other classes, typical usage of this class is:
`JRip rip = new JRip(); Instances data = ... // Data from somewhere double[] orderedClasses = ... // Get the ordered class counts for the data double expFPRate = ... // Calculate the expected FP/(FP+FN) rate double classIndex = ... // The class index for which ruleset is built // DL of default rule, no theory DL, only data DL double defDL = RuleStats.dataDL(expFPRate, 0.0, data.sumOfWeights(), 0.0, orderedClasses[(int)classIndex]); rip.rulesetForOneClass(expFPRate, data, classIndex, defDL); RuleStats rulesetStats = rip.getRuleStats(0); // Can get heaps of information from RuleStats, e.g. combined DL, // simpleStats, etc. double comDL = rulesetStats.combinedDL(expFPRate, classIndex); int whichRule = ... // Want simple stats of which rule? double[] simpleStats = rulesetStats.getSimpleStats(whichRule); ...` Details please see "Fast Effective Rule Induction", William W. Cohen, 'Machine Learning: Proceedings of the Twelfth International Conference' (ML95).

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.

Version:
  $Revision: 4610 $
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
`boolean` | `getCheckErrorRate()`
`boolean` | `getDebug()` Get whether debugging is turned on.
`int` | `getFolds()`
`double` | `getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure
`double` | `getMinNo()`
`int` | `getOptimizations()`
`java.lang.String[]` | `getOptions()` Gets the current settings of the Classifier.
`FastVector` | `getRuleset()` Get the ruleset generated by Ripper
`RuleStats` | `getRuleStats(int pos)` Get the statistics of the ruleset in the given position
`long` | `getSeed()`
`boolean` | `getUsePruning()`
`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)`
`void` | `setDebug(boolean d)` Set debugging mode.
`void` | `setFolds(int fold)`
`void` | `setMinNo(double m)`
`void` | `setOptimizations(int run)`
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setSeed(long s)`
`void` | `setUsePruning(boolean d)`
`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.Classifier
`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

### 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 `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

### 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:
    `measureName` - 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)

### getFolds

    public int getFolds()

### 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()

### 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()

### 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)

### getOptimizations

    public int getOptimizations()

### debugTipText

    public java.lang.String debugTipText()

  Returns the tip text for this property

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

### setDebug

    public void setDebug(boolean d)

  Description copied from class: `Classifier`
  Set debugging mode.

  Overrides:
    `setDebug` in class `Classifier`
  Parameters:
    `d` - true if debug output should be printed

### getDebug

    public boolean getDebug()

  Description copied from class: `Classifier`
  Get whether debugging is turned on.

  Overrides:
    `getDebug` in class `Classifier`
  Returns:
    true if debugging output is on

### 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)

### getCheckErrorRate

    public boolean getCheckErrorRate()

### 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)

### getUsePruning

    public boolean getUsePruning()

### 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

### 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 class `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

  Overrides:
    `distributionForInstance` in class `Classifier`
  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

### main

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

  Main method.

  Parameters:
    `args` - the options for the classifier

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