LogitBoost (weka-dev 3.9.4 API)

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, IterativeClassifier, Sourcable, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class LogitBoost
extends RandomizableIteratedSingleClassifierEnhancer
implements Sourcable, WeightedInstancesHandler, TechnicalInformationHandler, IterativeClassifier, BatchPredictor

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.

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.

 -use-estimated-priors
  Use estimated priors rather than uniform ones.

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

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

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

 -Z <num>
  Z max threshold for responses.
  (default 3)

 -O <int>
  The size of the thread pool, for example, the number of cores in the CPU. (default 1)

 -E <int>
  The number of threads to use for batch prediction, which should be >= size of thread pool.
  (default 1)

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

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

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

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

 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).

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

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

 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).

Options after -- are passed to the designated learner.

Version:: $Revision: 15021 $
Author:: Len Trigg ([email protected]), Eibe Frank ([email protected])
See Also:: Serialized Form

Field Summary
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

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)` Method used to build the classifier.
`Classifier[][]`	`classifiers()` Returns the array of classifiers that have been built.
`double[]`	`distributionForInstance(Instance inst)` Calculates the class membership probabilities for the given test instance.
`double[][]`	`distributionsForInstances(Instances insts)` Calculates the class membership probabilities for the given test instances.
`void`	`done()` Clean up after boosting.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`double`	`getLikelihoodThreshold()` Get the value of Precision.
`int`	`getNumThreads()` Gets the number of threads.
`String[]`	`getOptions()` Gets the current settings of the Classifier.
`int`	`getPoolSize()` Gets the number of threads.
`boolean`	`getResume()` Returns true if the model is to be finalized (or has been finalized) after training.
`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`	`getUseEstimatedPriors()` Get whether resampling is turned on
`boolean`	`getUseResampling()` Get whether resampling is turned on
`int`	`getWeightThreshold()` Get the degree of weight thresholding
`double`	`getZMax()` Get the Z max threshold on the responses
`String`	`globalInfo()` Returns a string describing classifier
`boolean`	`implementsMoreEfficientBatchPrediction()` Performs efficient batch prediction
`void`	`initializeClassifier(Instances data)` Builds the boosted classifier
`String`	`likelihoodThresholdTipText()` Returns the tip text for this property
`Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(String[] argv)` Main method for testing this class.
`boolean`	`next()` Perform another iteration of boosting.
`String`	`numThreadsTipText()`
`String`	`poolSizeTipText()`
`String`	`resumeTipText()` Tool tip text for the resume property
`void`	`setLikelihoodThreshold(double newPrecision)` Set the value of Precision.
`void`	`setNumThreads(int nT)` Sets the number of threads
`void`	`setOptions(String[] options)` Parses a given list of options.
`void`	`setPoolSize(int nT)` Sets the number of threads
`void`	`setResume(boolean resume)` If called with argument true, then the next time done() is called the model is effectively "frozen" and no further iterations can be performed
`void`	`setShrinkage(double newShrinkage)` Set the value of Shrinkage.
`void`	`setUseEstimatedPriors(boolean r)` Set resampling mode
`void`	`setUseResampling(boolean r)` Set resampling mode
`void`	`setWeightThreshold(int threshold)` Set weight thresholding
`void`	`setZMax(double zMax)` Set the Z max threshold on the responses
`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`	`useEstimatedPriorsTipText()` Returns the tip text for this property
`String`	`useResamplingTipText()` Returns the tip text for this property
`String`	`weightThresholdTipText()` Returns the tip text for this property
`String`	`ZMaxTipText()` 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, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

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

Methods inherited from interface weka.classifiers.Classifier
classifyInstance

Methods inherited from interface weka.core.BatchPredictor
getBatchSize, setBatchSize

Methods inherited from interface weka.core.OptionHandler
makeCopy

- 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<Option> 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.
 
```
```
 -use-estimated-priors
  Use estimated priors rather than uniform ones.
 
```
```
 -P <percent>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)
 
```
```
 -L <num>
  Threshold on the improvement of the likelihood.
  (default -Double.MAX_VALUE)
 
```
```
 -H <num>
  Shrinkage parameter.
  (default 1)
 
```
```
 -Z <num>
  Z max threshold for responses.
  (default 3)
 
```
```
 -O <int>
  The size of the thread pool, for example, the number of cores in the CPU. (default 1)
 
```
```
 -E <int>
  The number of threads to use for batch prediction, which should be >= size of thread pool.
  (default 1)
 
```
```
 -S <num>
  Random number seed.
  (default 1)
 
```
```
 -I <num>
  Number of iterations.
  (default 10)
 
```
```
 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.DecisionStump)
 
```
```
 -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console
 
```
```
 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).
 
```
```
 Options specific to classifier weka.classifiers.trees.DecisionStump:
 
```
```
 -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console
 
```
```
 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).
 
```
    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
  - ZMaxTipText
```
public String ZMaxTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setZMax
```
public void setZMax(double zMax)
```
    Set the Z max threshold on the responses
    
    Parameters:
    
    zMax - the threshold to use
  - getZMax
```
public double getZMax()
```
    Get the Z max threshold on the responses
    
    Returns:
    
    the threshold to use
  - 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.
  - 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
  - useEstimatedPriorsTipText
```
public String useEstimatedPriorsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setUseEstimatedPriors
```
public void setUseEstimatedPriors(boolean r)
```
    Set resampling mode
    
    Parameters:
    
    r - true if resampling should be done
  - getUseEstimatedPriors
```
public boolean getUseEstimatedPriors()
```
    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
  - numThreadsTipText
```
public String numThreadsTipText()
```
    Returns:
    
    a string to describe the option
  - getNumThreads
```
public int getNumThreads()
```
    Gets the number of threads.
  - setNumThreads
```
public void setNumThreads(int nT)
```
    Sets the number of threads
  - poolSizeTipText
```
public String poolSizeTipText()
```
    Returns:
    
    a string to describe the option
  - getPoolSize
```
public int getPoolSize()
```
    Gets the number of threads.
  - setPoolSize
```
public void setPoolSize(int nT)
```
    Sets the number of threads
  - 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
```
    Method used to build the classifier.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Overrides:
    
    buildClassifier in class IteratedSingleClassifierEnhancer
    
    Parameters:
    
    data - the training data to be used for generating the bagged classifier.
    
    Throws:
    
    Exception - if the classifier could not be built successfully
  - initializeClassifier
```
public void initializeClassifier(Instances data)
                          throws Exception
```
    Builds the boosted classifier
    
    Specified by:
    
    initializeClassifier in interface IterativeClassifier
    
    Parameters:
    
    data - the data to train the classifier with
    
    Throws:
    
    Exception - if building fails, e.g., can't handle data
  - next
```
public boolean next()
             throws Exception
```
    Perform another iteration of boosting.
    
    Specified by:
    
    next in interface IterativeClassifier
    
    Returns:
    
    false if no further iterations could be performed, true otherwise
    
    Throws:
    
    Exception - if this iteration fails for unexpected reasons
  - resumeTipText
```
public String resumeTipText()
```
    Tool tip text for the resume property
    
    Returns:
    
    the tool tip text for the finalize property
  - setResume
```
public void setResume(boolean resume)
```
    If called with argument true, then the next time done() is called the model is effectively "frozen" and no further iterations can be performed
    
    Specified by:
    
    setResume in interface IterativeClassifier
    
    Parameters:
    
    resume - true if the model is to be finalized after performing iterations
  - getResume
```
public boolean getResume()
```
    Returns true if the model is to be finalized (or has been finalized) after training.
    
    Specified by:
    
    getResume in interface IterativeClassifier
    
    Returns:
    
    the current value of finalize
  - done
```
public void done()
```
    Clean up after boosting.
    
    Specified by:
    
    done in interface IterativeClassifier
  - classifiers
```
public Classifier[][] classifiers()
```
    Returns the array of classifiers that have been built.
    
    Returns:
    
    the built classifiers
  - implementsMoreEfficientBatchPrediction
```
public boolean implementsMoreEfficientBatchPrediction()
```
    Performs efficient batch prediction
    
    Specified by:
    
    implementsMoreEfficientBatchPrediction in interface BatchPredictor
    
    Overrides:
    
    implementsMoreEfficientBatchPrediction in class AbstractClassifier
    
    Returns:
    
    true, as LogitBoost can perform efficient batch prediction
  - distributionForInstance
```
public double[] distributionForInstance(Instance inst)
                                 throws Exception
```
    Calculates the class membership probabilities for the given test instance.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    inst - the instance to be classified
    
    Returns:
    
    predicted class probability distribution
    
    Throws:
    
    Exception - if instance could not be classified successfully
  - distributionsForInstances
```
public double[][] distributionsForInstances(Instances insts)
                                     throws Exception
```
    Calculates the class membership probabilities for the given test instances. Uses multi-threading if requested.
    
    Specified by:
    
    distributionsForInstances in interface BatchPredictor
    
    Overrides:
    
    distributionsForInstances in class AbstractClassifier
    
    Parameters:
    
    insts - the instances to be classified
    
    Returns:
    
    predicted class probability distributions
    
    Throws:
    
    Exception - if instances 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

Class LogitBoost

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

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.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.classifiers.Classifier

Methods inherited from interface weka.core.BatchPredictor

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

LogitBoost

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

ZMaxTipText

setZMax

getZMax

shrinkageTipText

getShrinkage

setShrinkage

likelihoodThresholdTipText

getLikelihoodThreshold

setLikelihoodThreshold

useResamplingTipText

setUseResampling

getUseResampling

useEstimatedPriorsTipText

setUseEstimatedPriors

getUseEstimatedPriors

weightThresholdTipText

setWeightThreshold

getWeightThreshold

numThreadsTipText

getNumThreads

setNumThreads

poolSizeTipText

getPoolSize

setPoolSize

getCapabilities

buildClassifier

initializeClassifier

next

resumeTipText

setResume

getResume

done

classifiers

implementsMoreEfficientBatchPrediction

distributionForInstance

distributionsForInstances

toSource

toString

getRevision

main