Package weka.classifiers.meta
Class 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:
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: 15520 $
- Author:
- Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
- See Also:
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Field Summary
Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionvoidbuildClassifier(Instances data) Method used to build the classifier.Classifier[][]Returns the array of classifiers that have been built.double[]Calculates the class membership probabilities for the given test instance.double[][]Calculates the class membership probabilities for the given test instances.voiddone()Clean up after boosting.Returns default capabilities of the classifier.doubleGet the value of Precision.intGets the number of threads.String[]Gets the current settings of the Classifier.intGets the number of threads.booleanReturns true if the model is to be finalized (or has been finalized) after training.Returns the revision string.doubleGet the value of Shrinkage.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.booleanGet whether resampling is turned onbooleanGet whether resampling is turned onintGet the degree of weight thresholdingdoublegetZMax()Get the Z max threshold on the responsesReturns a string describing classifierbooleanPerforms efficient batch predictionvoidBuilds the boosted classifierReturns the tip text for this propertyReturns an enumeration describing the available options.static voidMain method for testing this class.booleannext()Perform another iteration of boosting.Tool tip text for the resume propertyvoidsetLikelihoodThreshold(double newPrecision) Set the value of Precision.voidsetNumThreads(int nT) Sets the number of threadsvoidsetOptions(String[] options) Parses a given list of options.voidsetPoolSize(int nT) Sets the number of threadsvoidsetResume(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 performedvoidsetShrinkage(double newShrinkage) Set the value of Shrinkage.voidsetUseEstimatedPriors(boolean r) Set resampling modevoidsetUseResampling(boolean r) Set resampling modevoidsetWeightThreshold(int threshold) Set weight thresholdingvoidsetZMax(double zMax) Set the Z max threshold on the responsesReturns the tip text for this propertyReturns the boosted model as Java source code.toString()Returns description of the boosted classifier.Returns the tip text for this propertyReturns the tip text for this propertyReturns the tip text for this propertyReturns the tip text for this propertyMethods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
getSeed, seedTipText, setSeedMethods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
getNumIterations, numIterationsTipText, setNumIterationsMethods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, postExecution, preExecution, setClassifierMethods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlacesMethods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, waitMethods inherited from interface weka.core.BatchPredictor
getBatchSize, setBatchSizeMethods inherited from interface weka.classifiers.Classifier
classifyInstance
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Constructor Details
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LogitBoost
public LogitBoost()Constructor.
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Method Details
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globalInfo
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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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:
getTechnicalInformationin interfaceTechnicalInformationHandler- Returns:
- the technical information about this class
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listOptions
Returns an enumeration describing the available options.- Specified by:
listOptionsin interfaceOptionHandler- Overrides:
listOptionsin classRandomizableIteratedSingleClassifierEnhancer- Returns:
- an enumeration of all the available options.
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setOptions
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:
setOptionsin interfaceOptionHandler- Overrides:
setOptionsin classRandomizableIteratedSingleClassifierEnhancer- Parameters:
options- the list of options as an array of strings- Throws:
Exception- if an option is not supported
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getOptions
Gets the current settings of the Classifier.- Specified by:
getOptionsin interfaceOptionHandler- Overrides:
getOptionsin classRandomizableIteratedSingleClassifierEnhancer- Returns:
- an array of strings suitable for passing to setOptions
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ZMaxTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setZMax
public void setZMax(double zMax) Set the Z max threshold on the responses- Parameters:
zMax- the threshold to use
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getZMax
public double getZMax()Get the Z max threshold on the responses- Returns:
- the threshold to use
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shrinkageTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getShrinkage
public double getShrinkage()Get the value of Shrinkage.- Returns:
- Value of Shrinkage.
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setShrinkage
public void setShrinkage(double newShrinkage) Set the value of Shrinkage.- Parameters:
newShrinkage- Value to assign to Shrinkage.
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likelihoodThresholdTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getLikelihoodThreshold
public double getLikelihoodThreshold()Get the value of Precision.- Returns:
- Value of Precision.
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setLikelihoodThreshold
public void setLikelihoodThreshold(double newPrecision) Set the value of Precision.- Parameters:
newPrecision- Value to assign to Precision.
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useResamplingTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setUseResampling
public void setUseResampling(boolean r) Set resampling mode- Parameters:
r- true if resampling should be done
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getUseResampling
public boolean getUseResampling()Get whether resampling is turned on- Returns:
- true if resampling output is on
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useEstimatedPriorsTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setUseEstimatedPriors
public void setUseEstimatedPriors(boolean r) Set resampling mode- Parameters:
r- true if resampling should be done
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getUseEstimatedPriors
public boolean getUseEstimatedPriors()Get whether resampling is turned on- Returns:
- true if resampling output is on
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weightThresholdTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setWeightThreshold
public void setWeightThreshold(int threshold) Set weight thresholding- Parameters:
threshold- the percentage of weight mass used for training
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getWeightThreshold
public int getWeightThreshold()Get the degree of weight thresholding- Returns:
- the percentage of weight mass used for training
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numThreadsTipText
- Returns:
- a string to describe the option
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getNumThreads
public int getNumThreads()Gets the number of threads. -
setNumThreads
public void setNumThreads(int nT) Sets the number of threads -
poolSizeTipText
- Returns:
- a string to describe the option
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getPoolSize
public int getPoolSize()Gets the number of threads. -
setPoolSize
public void setPoolSize(int nT) Sets the number of threads -
getCapabilities
Returns default capabilities of the classifier.- Specified by:
getCapabilitiesin interfaceCapabilitiesHandler- Specified by:
getCapabilitiesin interfaceClassifier- Overrides:
getCapabilitiesin classSingleClassifierEnhancer- Returns:
- the capabilities of this classifier
- See Also:
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buildClassifier
Method used to build the classifier.- Specified by:
buildClassifierin interfaceClassifier- Overrides:
buildClassifierin classIteratedSingleClassifierEnhancer- Parameters:
data- the training data to be used for generating the bagged classifier.- Throws:
Exception- if the classifier could not be built successfully
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initializeClassifier
Builds the boosted classifier- Specified by:
initializeClassifierin interfaceIterativeClassifier- Parameters:
data- the data to train the classifier with- Throws:
Exception- if building fails, e.g., can't handle data
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next
Perform another iteration of boosting.- Specified by:
nextin interfaceIterativeClassifier- Returns:
- false if no further iterations could be performed, true otherwise
- Throws:
Exception- if this iteration fails for unexpected reasons
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resumeTipText
Tool tip text for the resume property- Returns:
- the tool tip text for the finalize property
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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:
setResumein interfaceIterativeClassifier- Parameters:
resume- true if the model is to be finalized after performing iterations
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getResume
public boolean getResume()Returns true if the model is to be finalized (or has been finalized) after training.- Specified by:
getResumein interfaceIterativeClassifier- Returns:
- the current value of finalize
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done
public void done()Clean up after boosting.- Specified by:
donein interfaceIterativeClassifier
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classifiers
Returns the array of classifiers that have been built.- Returns:
- the built classifiers
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implementsMoreEfficientBatchPrediction
public boolean implementsMoreEfficientBatchPrediction()Performs efficient batch prediction- Specified by:
implementsMoreEfficientBatchPredictionin interfaceBatchPredictor- Overrides:
implementsMoreEfficientBatchPredictionin classAbstractClassifier- Returns:
- true, as LogitBoost can perform efficient batch prediction
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distributionForInstance
Calculates the class membership probabilities for the given test instance.- Specified by:
distributionForInstancein interfaceClassifier- Overrides:
distributionForInstancein classAbstractClassifier- Parameters:
inst- the instance to be classified- Returns:
- predicted class probability distribution
- Throws:
Exception- if instance could not be classified successfully
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distributionsForInstances
Calculates the class membership probabilities for the given test instances. Uses multi-threading if requested.- Specified by:
distributionsForInstancesin interfaceBatchPredictor- Overrides:
distributionsForInstancesin classAbstractClassifier- Parameters:
insts- the instances to be classified- Returns:
- predicted class probability distributions
- Throws:
Exception- if instances could not be classified successfully
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toSource
Returns the boosted model as Java source code. -
toString
Returns description of the boosted classifier. -
getRevision
Returns the revision string.- Specified by:
getRevisionin interfaceRevisionHandler- Overrides:
getRevisionin classAbstractClassifier- Returns:
- the revision
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main
Main method for testing this class.- Parameters:
argv- the options
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