weka.classifiers.trees
Class LMT

java.lang.Object
  weka.classifiers.Classifier
      weka.classifiers.trees.LMT

All Implemented Interfaces:

AdditionalMeasureProducer, java.lang.Cloneable, Drawable, OptionHandler, java.io.Serializable

public class LMT

extends Classifier

implements OptionHandler, AdditionalMeasureProducer, Drawable

Class for "logistic model tree" classifier. For more information, see master thesis "Logistic Model Trees" (Niels Landwehr, 2003)

Valid options are:

-B
Binary splits (convert nominal attributes to binary ones).

-R
Split on residuals instead of class values

-C
Use cross-validation for boosting at all nodes (i.e., disable heuristic)

-P
Use error on probabilities instead of misclassification error for stopping criterion of LogitBoost.

-I iterations
Set fixed number of iterations for LogitBoost (instead of using cross-validation).

-M numInstances
Set minimum number of instances at which a node can be split (default 15)

Version:

$Revision: 1.1 $

Author:

Niels Landwehr

See Also:

Serialized Form

Field Summary

protected boolean	m_convertNominal convert nominal attributes to binary ?
protected boolean	m_errorOnProbabilities use error on probabilties instead of misclassification for stopping criterion of LogitBoost?
protected boolean	m_fastRegression use heuristic that determines the number of LogitBoost iterations only once in the beginning?
protected int	m_minNumInstances minimum number of instances at which a node is considered for splitting
protected NominalToBinary	m_nominalToBinary Filter to replace nominal attributes
protected int	m_numBoostingIterations if non-zero, use fixed number of iterations for LogitBoost
protected ReplaceMissingValues	m_replaceMissing Filter to replace missing values
protected boolean	m_splitOnResiduals split on residuals?
protected LMTNode	m_tree root of the logistic model tree

Fields inherited from class weka.classifiers.Classifier

m_Debug

Fields inherited from interface weka.core.Drawable

BayesNet, NOT_DRAWABLE, TREE

Constructor Summary

LMT()
Creates an instance of LMT with standard options

Method Summary

void	buildClassifier(Instances data) Builds the classifier.
double	classifyInstance(Instance instance) Classifies an instance.
java.lang.String	convertNominalTipText() Returns the tip text for this property
double[]	distributionForInstance(Instance instance) Returns class probabilities for an instance.
java.util.Enumeration	enumerateMeasures() Returns an enumeration of the additional measure names
java.lang.String	errorOnProbabilitiesTipText() Returns the tip text for this property
java.lang.String	fastRegressionTipText() Returns the tip text for this property
boolean	getConvertNominal() Get the value of convertNominal.
boolean	getErrorOnProbabilities() Get the value of errorOnProbabilities.
boolean	getFastRegression() Get the value of fastRegression.
double	getMeasure(java.lang.String additionalMeasureName) Returns the value of the named measure
int	getMinNumInstances() Get the value of minNumInstances.
int	getNumBoostingIterations() Get the value of numBoostingIterations.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
boolean	getSplitOnResiduals() Get the value of splitOnResiduals.
java.lang.String	globalInfo() Returns a string describing classifier
java.lang.String	graph() Returns graph describing the tree.
int	graphType() Returns the type of graph this classifier represents.
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for testing this class
int	measureNumLeaves() Returns the number of leaves in the tree
int	measureTreeSize() Returns the size of the tree
java.lang.String	minNumInstancesTipText() Returns the tip text for this property
java.lang.String	numBoostingIterationsTipText() Returns the tip text for this property
void	setConvertNominal(boolean c) Set the value of convertNominal.
void	setErrorOnProbabilities(boolean c) Set the value of errorOnProbabilities.
void	setFastRegression(boolean c) Set the value of fastRegression.
void	setMinNumInstances(int c) Set the value of minNumInstances.
void	setNumBoostingIterations(int c) Set the value of numBoostingIterations.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setSplitOnResiduals(boolean c) Set the value of splitOnResiduals.
java.lang.String	splitOnResidualsTipText() Returns the tip text for this property
java.lang.String	toString() Returns a description of the classifier.

Methods inherited from class weka.classifiers.Classifier

debugTipText, forName, getDebug, makeCopies, setDebug

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_replaceMissing

protected ReplaceMissingValues m_replaceMissing

Filter to replace missing values

m_nominalToBinary

protected NominalToBinary m_nominalToBinary

Filter to replace nominal attributes

m_tree

protected LMTNode m_tree

root of the logistic model tree

m_fastRegression

protected boolean m_fastRegression

use heuristic that determines the number of LogitBoost iterations only once in the beginning?

m_convertNominal

protected boolean m_convertNominal

convert nominal attributes to binary ?

m_splitOnResiduals

protected boolean m_splitOnResiduals

split on residuals?

m_errorOnProbabilities

protected boolean m_errorOnProbabilities

use error on probabilties instead of misclassification for stopping criterion of LogitBoost?

m_minNumInstances

protected int m_minNumInstances

minimum number of instances at which a node is considered for splitting

m_numBoostingIterations

protected int m_numBoostingIterations

if non-zero, use fixed number of iterations for LogitBoost

Constructor Detail

LMT

public LMT()

Creates an instance of LMT with standard options

Method Detail

buildClassifier

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

Builds the classifier.

Specified by:

buildClassifier in class Classifier

Parameters:

data - set of instances serving as training data

Throws:

java.lang.Exception - if classifier can't be built successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception

Returns class probabilities for an instance.

Overrides:

distributionForInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

an array containing the estimated membership probabilities of the test instance in each class or the numeric prediction

Throws:

java.lang.Exception - if distribution can't be computed successfully

classifyInstance

public double classifyInstance(Instance instance)
                        throws java.lang.Exception

Classifies an instance.

Overrides:

classifyInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

the predicted most likely class for the instance or Instance.missingValue() if no prediction is made

Throws:

java.lang.Exception - if instance can't be classified successfully

toString

public java.lang.String toString()

Returns a description of the classifier.

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

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

getConvertNominal

public boolean getConvertNominal()

Get the value of convertNominal.

Returns:

Value of convertNominal.

getSplitOnResiduals

public boolean getSplitOnResiduals()

Get the value of splitOnResiduals.

Returns:

Value of splitOnResiduals.

getFastRegression

public boolean getFastRegression()

Get the value of fastRegression.

Returns:

Value of fastRegression.

getErrorOnProbabilities

public boolean getErrorOnProbabilities()

Get the value of errorOnProbabilities.

Returns:

Value of errorOnProbabilities.

getNumBoostingIterations

public int getNumBoostingIterations()

Get the value of numBoostingIterations.

Returns:

Value of numBoostingIterations.

getMinNumInstances

public int getMinNumInstances()

Get the value of minNumInstances.

Returns:

Value of minNumInstances.

setConvertNominal

public void setConvertNominal(boolean c)

Set the value of convertNominal.

Parameters:

c - Value to assign to convertNominal.

setSplitOnResiduals

public void setSplitOnResiduals(boolean c)

Set the value of splitOnResiduals.

Parameters:

c - Value to assign to splitOnResiduals.

setFastRegression

public void setFastRegression(boolean c)

Set the value of fastRegression.

Parameters:

c - Value to assign to fastRegression.

setErrorOnProbabilities

public void setErrorOnProbabilities(boolean c)

Set the value of errorOnProbabilities.

Parameters:

c - Value to assign to errorOnProbabilities.

setNumBoostingIterations

public void setNumBoostingIterations(int c)

Set the value of numBoostingIterations.

Parameters:

c - Value to assign to numBoostingIterations.

setMinNumInstances

public void setMinNumInstances(int c)

Set the value of minNumInstances.

Parameters:

c - Value to assign to minNumInstances.

graphType

public int graphType()

Returns the type of graph this classifier represents.

Specified by:

graphType in interface Drawable

Returns:

Drawable.TREE

graph

public java.lang.String graph()
                       throws java.lang.Exception

Returns graph describing the tree.

Specified by:

graph in interface Drawable

Returns:

the graph described by a string

Throws:

java.lang.Exception - if graph can't be computed

measureTreeSize

public int measureTreeSize()

Returns the size of the tree

Returns:

the size of the tree

measureNumLeaves

public int measureNumLeaves()

Returns the number of leaves in the tree

Returns:

the number of leaves in the tree

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:

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

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

convertNominalTipText

public java.lang.String convertNominalTipText()

Returns the tip text for this property

Returns:

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

splitOnResidualsTipText

public java.lang.String splitOnResidualsTipText()

Returns the tip text for this property

Returns:

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

fastRegressionTipText

public java.lang.String fastRegressionTipText()

Returns the tip text for this property

Returns:

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

errorOnProbabilitiesTipText

public java.lang.String errorOnProbabilitiesTipText()

Returns the tip text for this property

Returns:

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

numBoostingIterationsTipText

public java.lang.String numBoostingIterationsTipText()

Returns the tip text for this property

Returns:

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

minNumInstancesTipText

public java.lang.String minNumInstancesTipText()

Returns the tip text for this property

Returns:

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

main

public static void main(java.lang.String[] argv)
                 throws java.lang.Exception

Main method for testing this class

Throws:

java.lang.Exception