REPTree (weka-dev 3.7.6-SNAPSHOT API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.trees.REPTree

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Sourcable, AdditionalMeasureProducer, CapabilitiesHandler, Drawable, OptionHandler, RevisionHandler, WeightedInstancesHandler
```
public class REPTree
extends AbstractClassifier
implements OptionHandler, WeightedInstancesHandler, Drawable, AdditionalMeasureProducer, Sourcable
```
Fast decision tree learner. Builds a decision/regression tree using information gain/variance and prunes it using reduced-error pruning (with backfitting). Only sorts values for numeric attributes once. Missing values are dealt with by splitting the corresponding instances into pieces (i.e. as in C4.5).
Valid options are:
```
 -M <minimum number of instances>
  Set minimum number of instances per leaf (default 2).
```
```
 -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).
```
```
 -N <number of folds>
  Number of folds for reduced error pruning (default 3).
```
```
 -S <seed>
  Seed for random data shuffling (default 1).
```
```
 -P
  No pruning.
```
```
 -L
  Maximum tree depth (default -1, no maximum)
```
Version:

$Revision: 8034 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:
Serialized Form

Field Summary
- Fields inherited from interface weka.core.Drawable
  BayesNet, Newick, NOT_DRAWABLE, TREE

Constructor Summary

Constructors
Constructor and Description

REPTree()

Constructors
Constructor and Description
`REPTree()`

Method Summary

Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances data)` Builds classifier.
`double[]`	`distributionForInstance(Instance instance)` Computes class distribution of an instance using the tree.
`Enumeration`	`enumerateMeasures()` Returns an enumeration of the additional measure names.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`double`	`getInitialCount()` Get the value of InitialCount.
`int`	`getMaxDepth()` Get the value of MaxDepth.
`double`	`getMeasure(String additionalMeasureName)` Returns the value of the named measure.
`double`	`getMinNum()` Get the value of MinNum.
`double`	`getMinVarianceProp()` Get the value of MinVarianceProp.
`boolean`	`getNoPruning()` Get the value of NoPruning.
`int`	`getNumFolds()` Get the value of NumFolds.
`String[]`	`getOptions()` Gets options from this classifier.
`String`	`getRevision()` Returns the revision string.
`int`	`getSeed()` Get the value of Seed.
`boolean`	`getSpreadInitialCount()` Get the value of SpreadInitialCount.
`String`	`globalInfo()` Returns a string describing classifier
`String`	`graph()` Outputs the decision tree as a graph
`int`	`graphType()` Returns the type of graph this classifier represents.
`String`	`initialCountTipText()` Returns the tip text for this property
`Enumeration`	`listOptions()` Lists the command-line options for this classifier.
`static void`	`main(String[] argv)` Main method for this class.
`String`	`maxDepthTipText()` Returns the tip text for this property
`String`	`minNumTipText()` Returns the tip text for this property
`String`	`minVariancePropTipText()` Returns the tip text for this property
`String`	`noPruningTipText()` Returns the tip text for this property
`String`	`numFoldsTipText()` Returns the tip text for this property
`int`	`numNodes()` Computes size of the tree.
`String`	`seedTipText()` Returns the tip text for this property
`void`	`setInitialCount(double newInitialCount)` Set the value of InitialCount.
`void`	`setMaxDepth(int newMaxDepth)` Set the value of MaxDepth.
`void`	`setMinNum(double newMinNum)` Set the value of MinNum.
`void`	`setMinVarianceProp(double newMinVarianceProp)` Set the value of MinVarianceProp.
`void`	`setNoPruning(boolean newNoPruning)` Set the value of NoPruning.
`void`	`setNumFolds(int newNumFolds)` Set the value of NumFolds.
`void`	`setOptions(String[] options)` Parses a given list of options.
`void`	`setSeed(int newSeed)` Set the value of Seed.
`void`	`setSpreadInitialCount(boolean newSpreadInitialCount)` Set the value of SpreadInitialCount.
`String`	`spreadInitialCountTipText()` Returns the tip text for this property
`String`	`toSource(String className)` Returns the tree as if-then statements.
`String`	`toString()` Outputs the decision tree.

Methods inherited from class weka.classifiers.AbstractClassifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - REPTree
```
public REPTree()
```
- Method Detail
  - globalInfo
```
public String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    a description suitable for displaying in the explorer/experimenter gui
  - noPruningTipText
```
public String noPruningTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNoPruning
```
public boolean getNoPruning()
```
    Get the value of NoPruning.
    
    Returns:
    Value of NoPruning.
  - setNoPruning
```
public void setNoPruning(boolean newNoPruning)
```
    Set the value of NoPruning.
    
    Parameters:
    newNoPruning - Value to assign to NoPruning.
  - minNumTipText
```
public String minNumTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getMinNum
```
public double getMinNum()
```
    Get the value of MinNum.
    
    Returns:
    Value of MinNum.
  - setMinNum
```
public void setMinNum(double newMinNum)
```
    Set the value of MinNum.
    
    Parameters:
    newMinNum - Value to assign to MinNum.
  - minVariancePropTipText
```
public String minVariancePropTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getMinVarianceProp
```
public double getMinVarianceProp()
```
    Get the value of MinVarianceProp.
    
    Returns:
    Value of MinVarianceProp.
  - setMinVarianceProp
```
public void setMinVarianceProp(double newMinVarianceProp)
```
    Set the value of MinVarianceProp.
    
    Parameters:
    newMinVarianceProp - Value to assign to MinVarianceProp.
  - 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
  - getSeed
```
public int getSeed()
```
    Get the value of Seed.
    
    Returns:
    Value of Seed.
  - setSeed
```
public void setSeed(int newSeed)
```
    Set the value of Seed.
    
    Parameters:
    newSeed - Value to assign to Seed.
  - numFoldsTipText
```
public String numFoldsTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNumFolds
```
public int getNumFolds()
```
    Get the value of NumFolds.
    
    Returns:
    Value of NumFolds.
  - setNumFolds
```
public void setNumFolds(int newNumFolds)
```
    Set the value of NumFolds.
    
    Parameters:
    newNumFolds - Value to assign to NumFolds.
  - maxDepthTipText
```
public String maxDepthTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getMaxDepth
```
public int getMaxDepth()
```
    Get the value of MaxDepth.
    
    Returns:
    Value of MaxDepth.
  - setMaxDepth
```
public void setMaxDepth(int newMaxDepth)
```
    Set the value of MaxDepth.
    
    Parameters:
    newMaxDepth - Value to assign to MaxDepth.
  - initialCountTipText
```
public String initialCountTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getInitialCount
```
public double getInitialCount()
```
    Get the value of InitialCount.
    
    Returns:
    Value of InitialCount.
  - setInitialCount
```
public void setInitialCount(double newInitialCount)
```
    Set the value of InitialCount.
    
    Parameters:
    newInitialCount - Value to assign to InitialCount.
  - spreadInitialCountTipText
```
public String spreadInitialCountTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getSpreadInitialCount
```
public boolean getSpreadInitialCount()
```
    Get the value of SpreadInitialCount.
    
    Returns:
    Value of SpreadInitialCount.
  - setSpreadInitialCount
```
public void setSpreadInitialCount(boolean newSpreadInitialCount)
```
    Set the value of SpreadInitialCount.
    
    Parameters:
    newSpreadInitialCount - Value to assign to SpreadInitialCount.
  - listOptions
```
public Enumeration listOptions()
```
    Lists the command-line options for this classifier.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class AbstractClassifier
    
    Returns:
    an enumeration over all commandline options
  - getOptions
```
public String[] getOptions()
```
    Gets options from this classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class AbstractClassifier
    
    Returns:
    the options for the current setup
  - setOptions
```
public void setOptions(String[] options)
                throws Exception
```
    Parses a given list of options.
    Valid options are:
```
 -M <minimum number of instances>
  Set minimum number of instances per leaf (default 2).
```
```
 -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).
```
```
 -N <number of folds>
  Number of folds for reduced error pruning (default 3).
```
```
 -S <seed>
  Seed for random data shuffling (default 1).
```
```
 -P
  No pruning.
```
```
 -L
  Maximum tree depth (default -1, no maximum)
```
    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
  - numNodes
```
public int numNodes()
```
    Computes size of the tree.
    
    Returns:
    the number of nodes
  - enumerateMeasures
```
public 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(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
  - 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 data)
                     throws Exception
```
    Builds classifier.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    data - the data to train with
    
    Throws:
    
    Exception - if building fails
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws Exception
```
    Computes class distribution of an instance using the tree.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    instance - the instance to compute the distribution for
    
    Returns:
    the computed class probabilities
    
    Throws:
    
    Exception - if computation fails
  - toSource
```
public String toSource(String className)
                throws Exception
```
    Returns the tree as if-then statements.
    
    Specified by:
    
    toSource in interface Sourcable
    
    Parameters:
    className - the name for the generated class
    
    Returns:
    the tree as a Java if-then type statement
    
    Throws:
    
    Exception - if something goes wrong
  - graphType
```
public int graphType()
```
    Returns the type of graph this classifier represents.
    
    Specified by:
    
    graphType in interface Drawable
    
    Returns:
    Drawable.TREE
  - graph
```
public String graph()
             throws Exception
```
    Outputs the decision tree as a graph
    
    Specified by:
    
    graph in interface Drawable
    
    Returns:
    the tree as a graph
    
    Throws:
    
    Exception - if generation fails
  - toString
```
public String toString()
```
    Outputs the decision tree.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    a string representation 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[] argv)
```
    Main method for this class.
    
    Parameters:
    argv - the commandline options

Class REPTree

Field Summary

Fields inherited from interface weka.core.Drawable

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Constructor Detail

REPTree

Method Detail

globalInfo

noPruningTipText

getNoPruning

setNoPruning

minNumTipText

getMinNum

setMinNum

minVariancePropTipText

getMinVarianceProp

setMinVarianceProp

seedTipText

getSeed

setSeed

numFoldsTipText

getNumFolds

setNumFolds

maxDepthTipText

getMaxDepth

setMaxDepth

initialCountTipText

getInitialCount

setInitialCount

spreadInitialCountTipText

getSpreadInitialCount

setSpreadInitialCount

listOptions

getOptions

setOptions

numNodes

enumerateMeasures

getMeasure

getCapabilities

buildClassifier

distributionForInstance

toSource

graphType

graph

toString

getRevision

main