weka.classifiers.trees.m5
Class M5Base2

java.lang.Object
  weka.classifiers.AbstractClassifier
      weka.classifiers.trees.m5.M5Base2

All Implemented Interfaces:

Serializable, Cloneable, weka.classifiers.Classifier, weka.core.AdditionalMeasureProducer, weka.core.CapabilitiesHandler, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler

Direct Known Subclasses:

M5P2

public abstract class M5Base2
extends weka.classifiers.AbstractClassifier
implements weka.core.AdditionalMeasureProducer, weka.core.TechnicalInformationHandler

M5Base. Implements base routines for generating M5 Model trees and rules.

The original algorithm M5 was invented by Quinlan:
Quinlan J. R. (1992). Learning with continuous classes. Proceedings of the Australian Joint Conference on Artificial Intelligence. 343--348. World Scientific, Singapore.

Yong Wang made improvements and created M5':
Wang, Y and Witten, I. H. (1997). Induction of model trees for predicting continuous classes. Proceedings of the poster papers of the European Conference on Machine Learning. University of Economics, Faculty of Informatics and Statistics, Prague.

Valid options are:

-U
Use unsmoothed predictions.

-R
Build regression tree/rule rather than model tree/rule

Version:

$Revision: 4584 $

Author:

Mark Hall (mhall@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

protected double	m_minNumInstances The minimum number of instances to allow at a leaf node
protected boolean	m_regressionTree Make a regression tree/rule instead of a model tree/rule
protected weka.core.FastVector	m_ruleSet the rule set
protected boolean	m_saveInstances Save instances at each node in an M5 tree for visualization purposes.
protected boolean	m_useUnpruned Do not prune tree/rules

Fields inherited from class weka.classifiers.AbstractClassifier

m_Debug

Constructor Summary

M5Base2()
Constructor

Method Summary

void	buildClassifier(weka.core.Instances data) Generates the classifier.
String	buildRegressionTreeTipText() Returns the tip text for this property
double	classifyInstance(weka.core.Instance inst) Calculates a prediction for an instance using a set of rules or an M5 model tree
Enumeration	enumerateMeasures() Returns an enumeration of the additional measure names
String	generateRulesTipText() Returns the tip text for this property
boolean	getBuildRegressionTree() Get the value of regressionTree.
weka.core.Capabilities	getCapabilities() Returns default capabilities of the classifier, i.e., of LinearRegression.
protected boolean	getGenerateRules() get whether rules are being generated rather than a tree
RuleNode2	getM5RootNode()
double	getMeasure(String additionalMeasureName) Returns the value of the named measure
double	getMinNumInstances() Get the minimum number of instances to allow at a leaf node
String[]	getOptions() Gets the current settings of the classifier.
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.
boolean	getUnpruned() Get whether unpruned tree/rules are being generated
boolean	getUseUnsmoothed() Get whether or not smoothing is being used
String	globalInfo() returns information about the classifier
Enumeration	listOptions() Returns an enumeration describing the available options
double	measureNumRules() return the number of rules
String	minNumInstancesTipText() Returns the tip text for this property
void	setBuildRegressionTree(boolean newregressionTree) Set the value of regressionTree.
protected void	setGenerateRules(boolean u) Generate rules (decision list) rather than a tree
void	setMinNumInstances(double minNum) Set the minimum number of instances to allow at a leaf node
void	setOptions(String[] options) Parses a given list of options.
void	setUnpruned(boolean unpruned) Use unpruned tree/rules
void	setUseUnsmoothed(boolean s) Use unsmoothed predictions
String	toString() Returns a description of the classifier
String	unprunedTipText() Returns the tip text for this property
String	useUnsmoothedTipText() Returns the tip text for this property

Methods inherited from class weka.classifiers.AbstractClassifier

debugTipText, distributionForInstance, forName, getDebug, getRevision, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object

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

Field Detail

m_ruleSet

protected weka.core.FastVector m_ruleSet

the rule set

m_saveInstances

protected boolean m_saveInstances

Save instances at each node in an M5 tree for visualization purposes.

m_regressionTree

protected boolean m_regressionTree

Make a regression tree/rule instead of a model tree/rule

m_useUnpruned

protected boolean m_useUnpruned

Do not prune tree/rules

m_minNumInstances

protected double m_minNumInstances

The minimum number of instances to allow at a leaf node

Constructor Detail

M5Base2

public M5Base2()

Constructor

Method Detail

globalInfo

public String globalInfo()

returns information about the 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

listOptions

public Enumeration listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.AbstractClassifier

Returns:

an enumeration of all the available options

setOptions

public void setOptions(String[] options)
                throws Exception

Parses a given list of options.

Valid options are:

-U
Use unsmoothed predictions.

-R
Build a regression tree rather than a model tree.

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.AbstractClassifier

Parameters:

options - the list of options as an array of strings

Throws:

Exception - if an option is not supported

getOptions

public String[] getOptions()

Gets the current settings of the classifier.

Specified by:

getOptions in interface weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.AbstractClassifier

Returns:

an array of strings suitable for passing to setOptions

unprunedTipText

public String unprunedTipText()

Returns the tip text for this property

Returns:

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

setUnpruned

public void setUnpruned(boolean unpruned)

Use unpruned tree/rules

Parameters:

unpruned - true if unpruned tree/rules are to be generated

getUnpruned

public boolean getUnpruned()

Get whether unpruned tree/rules are being generated

Returns:

true if unpruned tree/rules are to be generated

generateRulesTipText

public String generateRulesTipText()

Returns the tip text for this property

Returns:

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

setGenerateRules

protected void setGenerateRules(boolean u)

Generate rules (decision list) rather than a tree

Parameters:

u - true if rules are to be generated

getGenerateRules

protected boolean getGenerateRules()

get whether rules are being generated rather than a tree

Returns:

true if rules are to be generated

useUnsmoothedTipText

public String useUnsmoothedTipText()

Returns the tip text for this property

Returns:

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

setUseUnsmoothed

public void setUseUnsmoothed(boolean s)

Use unsmoothed predictions

Parameters:

s - true if unsmoothed predictions are to be used

getUseUnsmoothed

public boolean getUseUnsmoothed()

Get whether or not smoothing is being used

Returns:

true if unsmoothed predictions are to be used

buildRegressionTreeTipText

public String buildRegressionTreeTipText()

Returns the tip text for this property

Returns:

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

getBuildRegressionTree

public boolean getBuildRegressionTree()

Get the value of regressionTree.

Returns:

Value of regressionTree.

setBuildRegressionTree

public void setBuildRegressionTree(boolean newregressionTree)

Set the value of regressionTree.

Parameters:

newregressionTree - Value to assign to regressionTree.

minNumInstancesTipText

public String minNumInstancesTipText()

Returns the tip text for this property

Returns:

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

setMinNumInstances

public void setMinNumInstances(double minNum)

Set the minimum number of instances to allow at a leaf node

Parameters:

minNum - the minimum number of instances

getMinNumInstances

public double getMinNumInstances()

Get the minimum number of instances to allow at a leaf node

Returns:

a double value

getCapabilities

public weka.core.Capabilities getCapabilities()

Returns default capabilities of the classifier, i.e., of LinearRegression.

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 Exception

Generates the classifier.

Specified by:

buildClassifier in interface weka.classifiers.Classifier

Parameters:

data - set of instances serving as training data

Throws:

Exception - if the classifier has not been generated successfully

classifyInstance

public double classifyInstance(weka.core.Instance inst)
                        throws Exception

Calculates a prediction for an instance using a set of rules or an M5 model tree

Specified by:

classifyInstance in interface weka.classifiers.Classifier

Overrides:

classifyInstance in class weka.classifiers.AbstractClassifier

Parameters:

inst - the instance whos class value is to be predicted

Returns:

the prediction

Throws:

Exception - if a prediction can't be made.

toString

public String toString()

Returns a description of the classifier

Overrides:

toString in class Object

Returns:

a description of the classifier as a String

enumerateMeasures

public Enumeration enumerateMeasures()

Returns an enumeration of the additional measure names

Specified by:

enumerateMeasures in interface weka.core.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 weka.core.AdditionalMeasureProducer

Parameters:

additionalMeasureName - the name of the measure to query for its value

Returns:

the value of the named measure

Throws:

Exception - if the named measure is not supported

measureNumRules

public double measureNumRules()

return the number of rules

Returns:

the number of rules (same as # linear models & # leaves in the tree)

getM5RootNode

public RuleNode2 getM5RootNode()