weka.classifiers.meta
Class LogitBoost

java.lang.Object
  extended by weka.classifiers.Classifier
      extended by weka.classifiers.SingleClassifierEnhancer
          extended by weka.classifiers.IteratedSingleClassifierEnhancer
              extended by weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
                  extended by weka.classifiers.meta.LogitBoost
All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, Sourcable, CapabilitiesHandler, OptionHandler, Randomizable, 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: 1.42 $
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.
 java.lang.String[] getOptions()
          Gets the current settings of the Classifier.
 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
 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, makeCopy, 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

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 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:

 -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:
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

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 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

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 CapabilitiesHandler
Overrides:
getCapabilities in class SingleClassifierEnhancer
Returns:
the capabilities of this classifier
See Also:
Capabilities

buildClassifier

public void buildClassifier(Instances data)
                     throws java.lang.Exception
Builds the boosted classifier

Overrides:
buildClassifier in class IteratedSingleClassifierEnhancer
Parameters:
data - the data to train the classifier with
Throws:
java.lang.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 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 classname in the generated code
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