weka.classifiers.m5
Class M5Prime

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.m5.M5Prime

All Implemented Interfaces:

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

public final class M5Prime

extends Classifier

implements OptionHandler, AdditionalMeasureProducer

Class for contructing and evaluating model trees; M5' algorithm.

Reference: 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:

-O
Type of model to be used. (l: linear regression, r: regression tree, m: model tree) (default: m)

-U
Use unsmoothed tree.

-F factor
Set pruning factor (default: 2).

-V <0|1|2>
Verbosity (default: 0).

Author:

Yong Wang (yongwang@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

static int	MODEL_LINEAR_REGRESSION
static int	MODEL_MODEL_TREE
static int	MODEL_REGRESSION_TREE
static Tag[]	TAGS_MODEL_TYPES

Constructor Summary

M5Prime()

Method Summary

void	buildClassifier(Instances inst) Construct a model tree by training instances
double	classifyInstance(Instance ins) Classifies the given test instance.
java.util.Enumeration	enumerateMeasures() Returns an enumeration of the additional measure names
double	getMeasure(java.lang.String additionalMeasureName) Returns the value of the named measure
SelectedTag	getModelType() Get the value of Model.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
double	getPruningFactor() Get the value of PruningFactor.
boolean	getUseUnsmoothed() Get the value of UseUnsmoothed.
int	getVerbosity() Get the value of Verbosity.
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for M5' algorithm
double	measureNumLeaves() return the number of leaves in the tree
double	measureNumLinearModels() return the number of linear models
double	measureNumRules() return the number of rules
void	setModelType(SelectedTag newMethod) Set the value of Model.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setPruningFactor(double v) Set the value of PruningFactor.
void	setUseUnsmoothed(boolean v) Set the value of UseUnsmoothed.
void	setVerbosity(int v) Set the value of Verbosity.
java.lang.String	toString() Converts the output of the training process into a string

Methods inherited from class weka.classifiers.Classifier

forName, makeCopies

Methods inherited from class java.lang.Object

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

Field Detail

MODEL_LINEAR_REGRESSION

public static final int MODEL_LINEAR_REGRESSION

MODEL_REGRESSION_TREE

public static final int MODEL_REGRESSION_TREE

MODEL_MODEL_TREE

public static final int MODEL_MODEL_TREE

TAGS_MODEL_TYPES

public static final Tag[] TAGS_MODEL_TYPES

Constructor Detail

M5Prime

public M5Prime()

Method Detail

buildClassifier

public final void buildClassifier(Instances inst)
                           throws java.lang.Exception

Construct a model tree by training instances

Overrides:

buildClassifier in class Classifier

Parameters:

inst - training instances

options - information for constructing the model tree, mostly from command line options

Returns:

the root of the model tree

Throws:

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

classifyInstance

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

Classifies the given test instance.

Overrides:

classifyInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

the predicted class for the instance

Throws:

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

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options. Valid options are:

-O
Type of model to be used. (l: linear regression, r: regression tree, m: model tree) (default: m)

-U
Use unsmoothed tree.

-F factor
Set pruning factor (default: 2).

-V <0|1|2>
Verbosity (default: 0).

Specified by:

listOptions in interface OptionHandler

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

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

Returns:

an array of strings suitable for passing to setOptions

toString

public final java.lang.String toString()

Converts the output of the training process into a string

Overrides:

toString in class java.lang.Object

Returns:

the converted string

measureNumLinearModels

public double measureNumLinearModels()

return the number of linear models

Returns:

the number of linear models

measureNumLeaves

public double measureNumLeaves()

return the number of leaves in the tree

Returns:

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

measureNumRules

public double measureNumRules()

return the number of rules

Returns:

the number of rules (same as # linear models & # 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:

measureName - 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

getUseUnsmoothed

public boolean getUseUnsmoothed()

Get the value of UseUnsmoothed.

Returns:

Value of UseUnsmoothed.

setUseUnsmoothed

public void setUseUnsmoothed(boolean v)

Set the value of UseUnsmoothed.

Parameters:

v - Value to assign to UseUnsmoothed.

getPruningFactor

public double getPruningFactor()

Get the value of PruningFactor.

Returns:

Value of PruningFactor.

setPruningFactor

public void setPruningFactor(double v)

Set the value of PruningFactor.

Parameters:

v - Value to assign to PruningFactor.

getModelType

public SelectedTag getModelType()

Get the value of Model.

Returns:

Value of Model.

setModelType

public void setModelType(SelectedTag newMethod)

Set the value of Model.

Parameters:

v - Value to assign to Model.

getVerbosity

public int getVerbosity()

Get the value of Verbosity.

Returns:

Value of Verbosity.

setVerbosity

public void setVerbosity(int v)

Set the value of Verbosity.

Parameters:

v - Value to assign to Verbosity.

main

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

Main method for M5' algorithm

Parameters:

argv - command line arguments