weka.classifiers.trees

Class ExtraTree

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.RandomizableClassifier

weka.classifiers.trees.ExtraTree

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.PartitionGenerator, weka.core.Randomizable, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler, weka.core.WeightedInstancesHandler

public class ExtraTree
extends weka.classifiers.RandomizableClassifier
implements java.io.Serializable, weka.core.OptionHandler, weka.core.TechnicalInformationHandler, weka.core.WeightedInstancesHandler, weka.core.PartitionGenerator

Class for generating a single Extra-Tree. Use with the RandomCommittee meta classifier to generate an Extra-Trees forest for classification or regression. This classifier requires all predictors to be numeric. Missing values are not allowed. Instance weights are taken into account. For more information, see

Pierre Geurts, Damien Ernst, Louis Wehenkel (2006). Extremely randomized trees. Machine Learning. 63(1):3-42.

BibTeX:

 @article{Geurts2006,
    author = {Pierre Geurts and Damien Ernst and Louis Wehenkel},
    journal = {Machine Learning},
    number = {1},
    pages = {3-42},
    title = {Extremely randomized trees},
    volume = {63},
    year = {2006}
 }
 

Valid options are:

 -K <number of attributes>
  Number of attributes to randomly choose at a node. If values is -1, (m - 1) will be used for regression problems, and Math.rint(sqrt(m - 1)) for classification problems, where m is the number of predictors, as specified in Geurts et al. (default -1).
 

 -N <minimum number of instances>
  The minimum number of instances required at a node for splitting to be considered. If value is -1, 5 will be used for regression problems and 2 for classification problems, as specified in Geurts et al. (default -1).
 

 -S <num>
  Random number seed.
  (default 1)
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 

Version:

$Revision: 14564 $

Author:

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

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
ExtraTree()

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(weka.core.Instances data) Builds one tree.
double[]	distributionForInstance(weka.core.Instance inst) Returns the distribution.
void	generatePartition(weka.core.Instances data) Builds the classifier to generate a partition.
weka.core.Capabilities	getCapabilities() Returns default capabilities of the classifier.
int	getK() Get the value of K.
double[]	getMembershipValues(weka.core.Instance instance) Computes array that indicates node membership.
int	getNmin() Get the value of n_min.
java.lang.String[]	getOptions() Gets options from this classifier.
java.lang.String	getRevision() Returns the revision string.
weka.core.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.
java.lang.String	globalInfo() Returns a string describing classifier
java.lang.String	kTipText() Returns the tip text for this property
java.util.Enumeration<weka.core.Option>	listOptions() Lists the command-line options for this classifier.
static void	main(java.lang.String[] args) Main method for this class.
java.lang.String	nminTipText() Returns the tip text for this property
int	numElements() Returns the number of elements in the partition.
void	setK(int k) Set the value of K.
void	setNmin(int n) Set the value of n_min.
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString() Returns classifier description.

Methods inherited from class weka.classifiers.RandomizableClassifier

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

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

ExtraTree

public ExtraTree()

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 weka.core.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 weka.core.TechnicalInformationHandler

Returns:

the technical information about this class

kTipText

public java.lang.String kTipText()

Returns the tip text for this property

Returns:

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

getK

public int getK()

Get the value of K.

Returns:

Value of K.

setK

public void setK(int k)

Set the value of K.

Parameters:

k - value to assign to K.

nminTipText

public java.lang.String nminTipText()

Returns the tip text for this property

Returns:

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

getNmin

public int getNmin()

Get the value of n_min.

Returns:

Value of n_min.

setNmin

public void setNmin(int n)

Set the value of n_min.

Parameters:

n - value to assign to n_min.

listOptions

public java.util.Enumeration<weka.core.Option> listOptions()

Lists the command-line options for this classifier.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.RandomizableClassifier

Returns:

an enumeration over all possible options

getOptions

public java.lang.String[] getOptions()

Gets options from this classifier.

Specified by:

getOptions in interface weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.RandomizableClassifier

Returns:

the options for the current setup

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options.

Valid options are:

 -K <number of attributes>
  Number of attributes to randomly choose at a node. If values is -1, (m - 1) will be used for regression problems, and Math.rint(sqrt(m - 1)) for classification problems, where m is the number of predictors, as specified in Geurts et al. (default -1).
 

 -N <minimum number of instances>
  The minimum number of instances required at a node for splitting to be considered. If value is -1, 5 will be used for regression problems and 2 for classification problems, as specified in Geurts et al. (default -1).
 

 -S <num>
  Random number seed.
  (default 1)
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.RandomizableClassifier

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

getCapabilities

public weka.core.Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface weka.classifiers.Classifier

Specified by:

getCapabilities in interface weka.core.CapabilitiesHandler

Overrides:

getCapabilities in class weka.classifiers.AbstractClassifier

Returns:

the capabilities of this classifier

buildClassifier

public void buildClassifier(weka.core.Instances data)
                     throws java.lang.Exception

Builds one tree.

Specified by:

buildClassifier in interface weka.classifiers.Classifier

Throws:

java.lang.Exception

distributionForInstance

public double[] distributionForInstance(weka.core.Instance inst)

Returns the distribution.

Specified by:

distributionForInstance in interface weka.classifiers.Classifier

Overrides:

distributionForInstance in class weka.classifiers.AbstractClassifier

toString

public java.lang.String toString()

Returns classifier description.

Overrides:

toString in class java.lang.Object

generatePartition

public void generatePartition(weka.core.Instances data)
                       throws java.lang.Exception

Builds the classifier to generate a partition.

Specified by:

generatePartition in interface weka.core.PartitionGenerator

Throws:

java.lang.Exception

getMembershipValues

public double[] getMembershipValues(weka.core.Instance instance)
                             throws java.lang.Exception

Computes array that indicates node membership. Array locations are allocated based on breadth-first exploration of the tree.

Specified by:

getMembershipValues in interface weka.core.PartitionGenerator

Throws:

java.lang.Exception

numElements

public int numElements()
                throws java.lang.Exception

Returns the number of elements in the partition.

Specified by:

numElements in interface weka.core.PartitionGenerator

Throws:

java.lang.Exception

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface weka.core.RevisionHandler

Overrides:

getRevision in class weka.classifiers.AbstractClassifier

Returns:

the revision

main

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

Main method for this class.