LogitBoost (weka-stable 3.6.8 API)

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

All Implemented Interfaces:: Serializable, Cloneable, 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: 6094 $
Author:: Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected Attribute`	`m_ClassAttribute` The actual class attribute (for getting class names)
`protected Classifier[][]`	`m_Classifiers` Array for storing the generated base classifiers.
`protected int`	`m_NumClasses` The number of classes
`protected Instances`	`m_NumericClassData` Dummy dataset with a numeric class
`protected int`	`m_NumFolds` The number of folds for the internal cross-validation.
`protected int`	`m_NumGenerated` The number of successfully generated base classifiers.
`protected int`	`m_NumRuns` The number of runs for the internal cross-validation.
`protected double`	`m_Offset` The value by which the actual target value for the true class is offset.
`protected double`	`m_Precision` The threshold on the improvement of the likelihood
`protected Random`	`m_RandomInstance` The random number generator used
`protected double`	`m_Shrinkage` The value of the shrinkage parameter
`protected boolean`	`m_UseResampling` Use boosting with reweighting?
`protected int`	`m_WeightThreshold` Weight thresholding.
`protected Classifier`	`m_ZeroR` a ZeroR model in case no model can be built from the data
`protected static double`	`Z_MAX` A threshold for responses (Friedman suggests between 2 and 4)

Fields inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
m_Seed

Fields inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
m_NumIterations

Fields inherited from class weka.classifiers.SingleClassifierEnhancer
m_Classifier

Fields inherited from class weka.classifiers.Classifier
m_Debug

Constructor Summary

Constructors
Constructor and Description

LogitBoost()
Constructor.

Constructors
Constructor and Description
`LogitBoost()` Constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances data)` Builds the boosted classifier
`Classifier[][]`	`classifiers()` Returns the array of classifiers that have been built.
`protected String`	`defaultClassifierString()` String describing default classifier.
`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
`protected Instances`	`selectWeightQuantile(Instances data, double quantile)` Select only instances with weights that contribute to the specified quantile of the weight distribution
`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, getClassifierSpec, setClassifier

Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - m_Classifiers
```
protected Classifier[][] m_Classifiers
```
    Array for storing the generated base classifiers. Note: we are hiding the variable from IteratedSingleClassifierEnhancer
  - m_NumClasses
```
protected int m_NumClasses
```
    The number of classes
  - m_NumGenerated
```
protected int m_NumGenerated
```
    The number of successfully generated base classifiers.
  - m_NumFolds
```
protected int m_NumFolds
```
    The number of folds for the internal cross-validation.
  - m_NumRuns
```
protected int m_NumRuns
```
    The number of runs for the internal cross-validation.
  - m_WeightThreshold
```
protected int m_WeightThreshold
```
    Weight thresholding. The percentage of weight mass used in training
  - Z_MAX
```
protected static final double Z_MAX
```
    A threshold for responses (Friedman suggests between 2 and 4)
    
    See Also:
    
    Constant Field Values
  - m_NumericClassData
```
protected Instances m_NumericClassData
```
    Dummy dataset with a numeric class
  - m_ClassAttribute
```
protected Attribute m_ClassAttribute
```
    The actual class attribute (for getting class names)
  - m_UseResampling
```
protected boolean m_UseResampling
```
    Use boosting with reweighting?
  - m_Precision
```
protected double m_Precision
```
    The threshold on the improvement of the likelihood
  - m_Shrinkage
```
protected double m_Shrinkage
```
    The value of the shrinkage parameter
  - m_RandomInstance
```
protected Random m_RandomInstance
```
    The random number generator used
  - m_Offset
```
protected double m_Offset
```
    The value by which the actual target value for the true class is offset.
  - m_ZeroR
```
protected Classifier m_ZeroR
```
    a ZeroR model in case no model can be built from the data
- 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
  - defaultClassifierString
```
protected String defaultClassifierString()
```
    String describing default classifier.
    
    Overrides:
    
    defaultClassifierString in class SingleClassifierEnhancer
    
    Returns:
    
    the default classifier classname
  - selectWeightQuantile
```
protected Instances selectWeightQuantile(Instances data,
                                         double quantile)
```
    Select only instances with weights that contribute to the specified quantile of the weight distribution
    
    Parameters:
    
    data - the input instances
    
    quantile - the specified quantile eg 0.9 to select 90% of the weight mass
    
    Returns:
    
    the selected instances
  - 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 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
    
    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.
    
    Overrides:
    
    distributionForInstance in class Classifier
    
    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 Classifier
    
    Returns:
    
    the revision
  - main
```
public static void main(String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - the options

Class LogitBoost

Field Summary

Fields inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

Fields inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

Fields inherited from class weka.classifiers.SingleClassifierEnhancer

Fields inherited from class weka.classifiers.Classifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Field Detail

m_Classifiers

m_NumClasses

m_NumGenerated

m_NumFolds

m_NumRuns

m_WeightThreshold

Z_MAX

m_NumericClassData

m_ClassAttribute

m_UseResampling

m_Precision

m_Shrinkage

m_RandomInstance

m_Offset

m_ZeroR

Constructor Detail

LogitBoost

Method Detail

globalInfo

getTechnicalInformation

defaultClassifierString

selectWeightQuantile

listOptions

setOptions

getOptions

shrinkageTipText

getShrinkage

setShrinkage

likelihoodThresholdTipText

getLikelihoodThreshold

setLikelihoodThreshold

numRunsTipText

getNumRuns

setNumRuns

numFoldsTipText

getNumFolds

setNumFolds

useResamplingTipText

setUseResampling

getUseResampling

weightThresholdTipText

setWeightThreshold

getWeightThreshold

getCapabilities

buildClassifier

classifiers

distributionForInstance

toSource

toString

getRevision

main