JRip

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.rules.JRip

All Implemented Interfaces:

java.io.Serializable, java.lang.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: 15520 $

Author:

Xin Xu (xx5@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type	Class and Description
`class`	`JRip.Antd` The single antecedent in the rule, which is composed of an attribute and the corresponding value.
`class`	`JRip.NominalAntd` The antecedent with nominal attribute
`class`	`JRip.NumericAntd` The antecedent with numeric attribute
`class`	`JRip.RipperRule` This class implements a single rule that predicts specified class.

Field Summary
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

JRip()

Constructors
Constructor and Description
`JRip()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`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<java.lang.String>`	`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.
`java.util.ArrayList<Rule>`	`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<Option>`	`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 java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler
makeCopy

- 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<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(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<java.lang.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(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 java.util.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 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

Class JRip

Nested Class Summary

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

JRip

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

enumerateMeasures

getMeasure

foldsTipText

setFolds

getFolds

minNoTipText

setMinNo

getMinNo

seedTipText

setSeed

getSeed

optimizationsTipText

setOptimizations

getOptimizations

debugTipText

setDebug

getDebug

checkErrorRateTipText

setCheckErrorRate

getCheckErrorRate

usePruningTipText

setUsePruning

getUsePruning

getRuleset

getRuleStats

getCapabilities

buildClassifier

distributionForInstance

toString

getRevision

main