weka.classifiers.meta
Class LogitBoost

java.lang.Object
  weka.classifiers.AbstractClassifier
      weka.classifiers.SingleClassifierEnhancer
          weka.classifiers.IteratedSingleClassifierEnhancer
              weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
                  weka.classifiers.meta.LogitBoost

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Sourcable, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class LogitBoost
extends RandomizableIteratedSingleClassifierEnhancer
implements Sourcable, WeightedInstancesHandler, TechnicalInformationHandler

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

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

Can do efficient internal cross-validation to determine appropriate number of iterations.

BibTeX:

 @techreport{Friedman1998,
    address = {Stanford University},
    author = {J. Friedman and T. Hastie and R. Tibshirani},
    title = {Additive Logistic Regression: a Statistical View of Boosting},
    year = {1998},
    PS = {http://www-stat.stanford.edu/\~jhf/ftp/boost.ps}
 }
 

Valid options are:

 -Q
  Use resampling instead of reweighting for boosting.

 -P <percent>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)

 -F <num>
  Number of folds for internal cross-validation.
  (default 0 -- no cross-validation)

 -R <num>
  Number of runs for internal cross-validation.
  (default 1)

 -L <num>
  Threshold on the improvement of the likelihood.
  (default -Double.MAX_VALUE)

 -H <num>
  Shrinkage parameter.
  (default 1)

 -S <num>
  Random number seed.
  (default 1)

 -I <num>
  Number of iterations.
  (default 10)

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

 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.DecisionStump)

 
 Options specific to classifier weka.classifiers.trees.DecisionStump:
 

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

Options after -- are passed to the designated learner.

Version:

$Revision: 8034 $

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.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
double	getLikelihoodThreshold() Get the value of Precision.
int	getNumFolds() Get the value of NumFolds.
int	getNumRuns() Get the value of NumRuns.
String[]	getOptions() Gets the current settings of the Classifier.
String	getRevision() Returns the revision string.
double	getShrinkage() Get the value of Shrinkage.
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	getUseResampling() Get whether resampling is turned on
int	getWeightThreshold() Get the degree of weight thresholding
String	globalInfo() Returns a string describing classifier
String	likelihoodThresholdTipText() Returns the tip text for this property
Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(String[] argv) Main method for testing this class.
String	numFoldsTipText() Returns the tip text for this property
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(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
String	shrinkageTipText() Returns the tip text for this property
String	toSource(String className) Returns the boosted model as Java source code.
String	toString() Returns description of the boosted classifier.
String	useResamplingTipText() Returns the tip text for this property
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.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

LogitBoost

public LogitBoost()

Constructor.

Method Detail

globalInfo

public String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

getTechnicalInformation

public 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 TechnicalInformationHandler

Returns:

the technical information about this class

listOptions

public 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(String[] options)
                throws Exception

Parses a given list of options.

Valid options are:

 -Q
  Use resampling instead of reweighting for boosting.

 -P <percent>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)

 -F <num>
  Number of folds for internal cross-validation.
  (default 0 -- no cross-validation)

 -R <num>
  Number of runs for internal cross-validation.
  (default 1)

 -L <num>
  Threshold on the improvement of the likelihood.
  (default -Double.MAX_VALUE)

 -H <num>
  Shrinkage parameter.
  (default 1)

 -S <num>
  Random number seed.
  (default 1)

 -I <num>
  Number of iterations.
  (default 10)

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

 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.DecisionStump)

 
 Options specific to classifier weka.classifiers.trees.DecisionStump:
 

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

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:

Exception - if an option is not supported

getOptions

public 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 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 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 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 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 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

Parameters:

r - true if resampling should be done

getUseResampling

public boolean getUseResampling()

Get whether resampling is turned on

Returns:

true if resampling output is on

weightThresholdTipText

public 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

Parameters:

threshold - the percentage of weight mass used for training

getWeightThreshold

public int getWeightThreshold()

Get the degree of weight thresholding

Returns:

the percentage of weight mass used for training

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 SingleClassifierEnhancer

Returns:

the capabilities of this classifier

See Also:

Capabilities

buildClassifier

public void buildClassifier(Instances data)
                     throws Exception

Builds the boosted classifier

Specified by:

buildClassifier in interface Classifier

Overrides:

buildClassifier in class IteratedSingleClassifierEnhancer

Parameters:

data - the data to train the classifier with

Throws:

Exception - if building fails, e.g., can't handle data

classifiers

public Classifier[][] classifiers()

Returns the array of classifiers that have been built.

Returns:

the built classifiers

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws Exception

Calculates the class membership probabilities for the given test instance.

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

predicted class probability distribution

Throws:

Exception - if instance could not be classified successfully

toSource

public String toSource(String className)
                throws Exception

Returns the boosted model as Java source code.

Specified by:

toSource in interface Sourcable

Parameters:

className - the classname in the generated code

Returns:

the tree as Java source code

Throws:

Exception - if something goes wrong

toString

public String toString()

Returns description of the boosted classifier.

Overrides:

toString in class Object

Returns:

description of the boosted classifier as a string

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 testing this class.

Parameters:

argv - the options