weka.classifiers.meta
Class LogitBoost

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.SingleClassifierEnhancer
              |
              +--weka.classifiers.IteratedSingleClassifierEnhancer
                    |
                    +--weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
                          |
                          +--weka.classifiers.meta.LogitBoost

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, Randomizable, java.io.Serializable, Sourcable, WeightedInstancesHandler

public class LogitBoost

extends RandomizableIteratedSingleClassifierEnhancer

implements Sourcable, WeightedInstancesHandler

Class for performing additive logistic regression.. This class performs classification using a regression scheme as the base learner, and can handle multi-class problems. For more information, see

Friedman, J., T. Hastie and R. Tibshirani (1998) Additive Logistic Regression: a Statistical View of Boosting download postscript.

Valid options are:

-D
Turn on debugging output.

-W classname
Specify the full class name of a weak learner as the basis for boosting (required).

-I num
Set the number of boost iterations (default 10).

-Q
Use resampling instead of reweighting.

-S seed
Random number seed for resampling (default 1).

-P num
Set the percentage of weight mass used to build classifiers (default 100).

-F num
Set number of folds for the internal cross-validation (default 0 -- no cross-validation).

-R num
Set number of runs for the internal cross-validation (default 1).

-L num
Set the threshold for the improvement of the average loglikelihood (default -Double.MAX_VALUE).

-H num
Set the value of the shrinkage parameter (default 1).

Options after -- are passed to the designated learner.

Version:

$Revision: 1.31 $

Author:

Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

LogitBoost()
Constructor.

Method Summary

void	buildClassifier(Instances data) Builds the boosted classifier
Classifier[][]	classifiers() Returns the array of classifiers that have been built.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
double	getLikelihoodThreshold() Get the value of Precision.
int	getNumFolds() Get the value of NumFolds.
int	getNumRuns() Get the value of NumRuns.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
double	getShrinkage() Get the value of Shrinkage.
boolean	getUseResampling() Get whether resampling is turned on
int	getWeightThreshold() Get the degree of weight thresholding
java.lang.String	globalInfo() Returns a string describing classifier
java.lang.String	likelihoodThresholdTipText() Returns the tip text for this property
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	numFoldsTipText() Returns the tip text for this property
java.lang.String	numRunsTipText() Returns the tip text for this property
void	setLikelihoodThreshold(double newPrecision) Set the value of Precision.
void	setNumFolds(int newNumFolds) Set the value of NumFolds.
void	setNumRuns(int newNumRuns) Set the value of NumRuns.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setShrinkage(double newShrinkage) Set the value of Shrinkage.
void	setUseResampling(boolean r) Set resampling mode
void	setWeightThreshold(int threshold) Set weight thresholding
java.lang.String	shrinkageTipText() Returns the tip text for this property
java.lang.String	toSource(java.lang.String className) Returns the boosted model as Java source code.
java.lang.String	toString() Returns description of the boosted classifier.
java.lang.String	useResamplingTipText() Returns the tip text for this property
java.lang.String	weightThresholdTipText() Returns the tip text for this property

Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

getNumIterations, numIterationsTipText, setNumIterations

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getClassifier, setClassifier

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, setDebug

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

LogitBoost

public LogitBoost()

Constructor.

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class RandomizableIteratedSingleClassifierEnhancer

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. Valid options are:

-D
Turn on debugging output.

-W classname
Specify the full class name of a weak learner as the basis for boosting (required).

-I num
Set the number of boost iterations (default 10).

-Q
Use resampling instead of reweighting.

-S seed
Random number seed for resampling (default 1).

-P num
Set the percentage of weight mass used to build classifiers (default 100).

-F num
Set number of folds for the internal cross-validation (default 0 -- no cross-validation).

-R num
Set number of runs for the internal cross-validation (default 1.

-L num
Set the threshold for the improvement of the average loglikelihood (default -Double.MAX_VALUE).

-H num
Set the value of the shrinkage parameter (default 1).

Options after -- are passed to the designated learner.

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class RandomizableIteratedSingleClassifierEnhancer

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 RandomizableIteratedSingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

shrinkageTipText

public java.lang.String shrinkageTipText()

Returns the tip text for this property

Returns:

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

getShrinkage

public double getShrinkage()

Get the value of Shrinkage.

Returns:

Value of Shrinkage.

setShrinkage

public void setShrinkage(double newShrinkage)

Set the value of Shrinkage.

Parameters:

newShrinkage - Value to assign to Shrinkage.

likelihoodThresholdTipText

public java.lang.String likelihoodThresholdTipText()

Returns the tip text for this property

Returns:

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

getLikelihoodThreshold

public double getLikelihoodThreshold()

Get the value of Precision.

Returns:

Value of Precision.

setLikelihoodThreshold

public void setLikelihoodThreshold(double newPrecision)

Set the value of Precision.

Parameters:

newPrecision - Value to assign to Precision.

numRunsTipText

public java.lang.String numRunsTipText()

Returns the tip text for this property

Returns:

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

getNumRuns

public int getNumRuns()

Get the value of NumRuns.

Returns:

Value of NumRuns.

setNumRuns

public void setNumRuns(int newNumRuns)

Set the value of NumRuns.

Parameters:

newNumRuns - Value to assign to NumRuns.

numFoldsTipText

public java.lang.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.

useResamplingTipText

public java.lang.String useResamplingTipText()

Returns the tip text for this property

Returns:

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

setUseResampling

public void setUseResampling(boolean r)

Set resampling mode

getUseResampling

public boolean getUseResampling()

Get whether resampling is turned on

Returns:

true if resampling output is on

weightThresholdTipText

public java.lang.String weightThresholdTipText()

Returns the tip text for this property

Returns:

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

setWeightThreshold

public void setWeightThreshold(int threshold)

Set weight thresholding

getWeightThreshold

public int getWeightThreshold()

Get the degree of weight thresholding

Returns:

the percentage of weight mass used for training

buildClassifier

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

Builds the boosted classifier

Overrides:

buildClassifier in class IteratedSingleClassifierEnhancer

Parameters:

data - the training data to be used for generating the bagged classifier.

Throws:

java.lang.Exception - if the classifier could not be built successfully

classifiers

public Classifier[][] classifiers()

Returns the array of classifiers that have been built.

distributionForInstance

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

Calculates the class membership probabilities for the given test instance.

Overrides:

distributionForInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

predicted class probability distribution

Throws:

java.lang.Exception - if instance could not be classified successfully

toSource

public java.lang.String toSource(java.lang.String className)
                          throws java.lang.Exception

Returns the boosted model as Java source code.

Specified by:

toSource in interface Sourcable

Parameters:

className - the name that should be given to the source class.

Returns:

the tree as Java source code

Throws:

java.lang.Exception - if something goes wrong

toString

public java.lang.String toString()

Returns description of the boosted classifier.

Overrides:

toString in class java.lang.Object

Returns:

description of the boosted classifier as a string

main

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

Main method for testing this class.

Parameters:

argv - the options