weka.classifiers.trees

Class RandomTree

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.trees.RandomTree

All Implemented Interfaces:

Serializable, Cloneable, Classifier, CapabilitiesHandler, Drawable, OptionHandler, Randomizable, RevisionHandler, WeightedInstancesHandler

public class RandomTree
extends AbstractClassifier
implements OptionHandler, WeightedInstancesHandler, Randomizable, Drawable

Class for constructing a tree that considers K randomly chosen attributes at each node. Performs no pruning. Also has an option to allow estimation of class probabilities based on a hold-out set (backfitting).

Valid options are:

 -K <number of attributes>
  Number of attributes to randomly investigate
  (<0 = int(log_2(#attributes)+1)).

 -M <minimum number of instances>
  Set minimum number of instances per leaf.

 -S <num>
  Seed for random number generator.
  (default 1)

 -depth <num>
  The maximum depth of the tree, 0 for unlimited.
  (default 0)

 -N <num>
  Number of folds for backfitting (default 0, no backfitting).

 -U
  Allow unclassified instances.

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

Version:

$Revision: 8034 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Richard Kirkby (rkirkby@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
RandomTree()

Method Summary

Methods

Modifier and Type	Method and Description
String	allowUnclassifiedInstancesTipText() Returns the tip text for this property
void	backfitData(Instances data) Backfits the given data into the tree.
void	buildClassifier(Instances data) Builds classifier.
double[]	distributionForInstance(Instance instance) Computes class distribution of an instance using the decision tree.
boolean	getAllowUnclassifiedInstances() Get the value of NumFolds.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
int	getKValue() Get the value of K.
int	getMaxDepth() Get the maximum depth of trh tree, 0 for unlimited.
double	getMinNum() Get the value of MinNum.
int	getNumFolds() Get the value of NumFolds.
String[]	getOptions() Gets options from this classifier.
String	getRevision() Returns the revision string.
int	getSeed() Gets the seed for the random number generations
String	globalInfo() Returns a string describing classifier
String	graph() Returns graph describing the tree.
int	graphType() Returns the type of graph this classifier represents.
String	KValueTipText() 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	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	setAllowUnclassifiedInstances(boolean newAllowUnclassifiedInstances) Set the value of AllowUnclassifiedInstances.
void	setKValue(int k) Set the value of K.
void	setMaxDepth(int value) Set the maximum depth of the tree, 0 for unlimited.
void	setMinNum(double newMinNum) Set the value of MinNum.
void	setNumFolds(int newNumFolds) Set the value of NumFolds.
void	setOptions(String[] options) Parses a given list of options.
void	setSeed(int seed) Set the seed for random number generation.
String	toGraph() Outputs the decision tree as a graph
int	toGraph(StringBuffer text, int num) Outputs one node for graph.
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

RandomTree

public RandomTree()

Method Detail

globalInfo

public String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

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.

KValueTipText

public String KValueTipText()

Returns the tip text for this property

Returns:

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

getKValue

public int getKValue()

Get the value of K.

Returns:

Value of K.

setKValue

public void setKValue(int k)

Set the value of K.

Parameters:

k - Value to assign to K.

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(int seed)

Set the seed for random number generation.

Specified by:

setSeed in interface Randomizable

Parameters:

seed - the seed

getSeed

public int getSeed()

Gets the seed for the random number generations

Specified by:

getSeed in interface Randomizable

Returns:

the seed for the random number generation

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 maximum depth of trh tree, 0 for unlimited.

Returns:

the maximum depth.

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.

allowUnclassifiedInstancesTipText

public String allowUnclassifiedInstancesTipText()

Returns the tip text for this property

Returns:

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

getAllowUnclassifiedInstances

public boolean getAllowUnclassifiedInstances()

Get the value of NumFolds.

Returns:

Value of NumFolds.

setAllowUnclassifiedInstances

public void setAllowUnclassifiedInstances(boolean newAllowUnclassifiedInstances)

Set the value of AllowUnclassifiedInstances.

Parameters:

newAllowUnclassifiedInstances - Value to assign to AllowUnclassifiedInstances.

setMaxDepth

public void setMaxDepth(int value)

Set the maximum depth of the tree, 0 for unlimited.

Parameters:

value - the maximum depth.

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 possible 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:

 -K <number of attributes>
  Number of attributes to randomly investigate
  (<0 = int(log_2(#attributes)+1)).

 -M <minimum number of instances>
  Set minimum number of instances per leaf.

 -S <num>
  Seed for random number generator.
  (default 1)

 -depth <num>
  The maximum depth of the tree, 0 for unlimited.
  (default 0)

 -N <num>
  Number of folds for backfitting (default 0, no backfitting).

 -U
  Allow unclassified instances.

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

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

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 something goes wrong or the data doesn't fit

backfitData

public void backfitData(Instances data)
                 throws Exception

Backfits the given data into the tree.

Throws:

Exception

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws Exception

Computes class distribution of an instance using the decision 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 distribution

Throws:

Exception - if computation fails

toGraph

public String toGraph()

Outputs the decision tree as a graph

Returns:

the tree as a graph

toGraph

public int toGraph(StringBuffer text,
          int num)
            throws Exception

Outputs one node for graph.

Parameters:

text - the buffer to append the output to

num - unique node id

Returns:

the next node id

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

numNodes

public int numNodes()

Computes size of the tree.

Returns:

the number of nodes

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 parameters

graph

public String graph()
             throws Exception

Returns graph describing the tree.

Specified by:

graph in interface Drawable

Returns:

the graph describing the tree

Throws:

Exception - if graph can't be computed

graphType

public int graphType()

Returns the type of graph this classifier represents.

Specified by:

graphType in interface Drawable

Returns:

Drawable.TREE