public class LogitBoost extends RandomizableIteratedSingleClassifierEnhancer implements Sourcable, WeightedInstancesHandler, TechnicalInformationHandler, IterativeClassifier, BatchPredictor
@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.
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT
Constructor and Description |
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LogitBoost()
Constructor.
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Modifier and Type | Method and Description |
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void |
buildClassifier(Instances data)
Method used to build the classifier.
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Classifier[][] |
classifiers()
Returns the array of classifiers that have been built.
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double[] |
distributionForInstance(Instance inst)
Calculates the class membership probabilities for the given test instance.
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double[][] |
distributionsForInstances(Instances insts)
Calculates the class membership probabilities for the given test instances.
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void |
done()
Clean up after boosting.
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Capabilities |
getCapabilities()
Returns default capabilities of the classifier.
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double |
getLikelihoodThreshold()
Get the value of Precision.
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int |
getNumThreads()
Gets the number of threads.
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java.lang.String[] |
getOptions()
Gets the current settings of the Classifier.
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int |
getPoolSize()
Gets the number of threads.
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boolean |
getResume()
Returns true if the model is to be finalized (or has been finalized) after
training.
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java.lang.String |
getRevision()
Returns the revision string.
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double |
getShrinkage()
Get the value of Shrinkage.
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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.
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boolean |
getUseEstimatedPriors()
Get whether resampling is turned on
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boolean |
getUseResampling()
Get whether resampling is turned on
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int |
getWeightThreshold()
Get the degree of weight thresholding
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double |
getZMax()
Get the Z max threshold on the responses
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java.lang.String |
globalInfo()
Returns a string describing classifier
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boolean |
implementsMoreEfficientBatchPrediction()
Performs efficient batch prediction
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void |
initializeClassifier(Instances data)
Builds the boosted classifier
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java.lang.String |
likelihoodThresholdTipText()
Returns the tip text for this property
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java.util.Enumeration<Option> |
listOptions()
Returns an enumeration describing the available options.
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static void |
main(java.lang.String[] argv)
Main method for testing this class.
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boolean |
next()
Perform another iteration of boosting.
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java.lang.String |
numThreadsTipText() |
java.lang.String |
poolSizeTipText() |
java.lang.String |
resumeTipText()
Tool tip text for the resume property
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void |
setLikelihoodThreshold(double newPrecision)
Set the value of Precision.
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void |
setNumThreads(int nT)
Sets the number of threads
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void |
setOptions(java.lang.String[] options)
Parses a given list of options.
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void |
setPoolSize(int nT)
Sets the number of threads
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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
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void |
setShrinkage(double newShrinkage)
Set the value of Shrinkage.
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void |
setUseEstimatedPriors(boolean r)
Set resampling mode
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void |
setUseResampling(boolean r)
Set resampling mode
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void |
setWeightThreshold(int threshold)
Set weight thresholding
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void |
setZMax(double zMax)
Set the Z max threshold on the responses
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java.lang.String |
shrinkageTipText()
Returns the tip text for this property
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java.lang.String |
toSource(java.lang.String className)
Returns the boosted model as Java source code.
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java.lang.String |
toString()
Returns description of the boosted classifier.
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java.lang.String |
useEstimatedPriorsTipText()
Returns the tip text for this property
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java.lang.String |
useResamplingTipText()
Returns the tip text for this property
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java.lang.String |
weightThresholdTipText()
Returns the tip text for this property
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java.lang.String |
ZMaxTipText()
Returns the tip text for this property
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getSeed, seedTipText, setSeed
getNumIterations, numIterationsTipText, setNumIterations
classifierTipText, getClassifier, postExecution, preExecution, setClassifier
batchSizeTipText, classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
classifyInstance
getBatchSize, setBatchSize
makeCopy
public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public java.util.Enumeration<Option> listOptions()
listOptions
in interface OptionHandler
listOptions
in class RandomizableIteratedSingleClassifierEnhancer
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-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.
setOptions
in interface OptionHandler
setOptions
in class RandomizableIteratedSingleClassifierEnhancer
options
- the list of options as an array of stringsjava.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class RandomizableIteratedSingleClassifierEnhancer
public java.lang.String ZMaxTipText()
public void setZMax(double zMax)
zMax
- the threshold to usepublic double getZMax()
public java.lang.String shrinkageTipText()
public double getShrinkage()
public void setShrinkage(double newShrinkage)
newShrinkage
- Value to assign to Shrinkage.public java.lang.String likelihoodThresholdTipText()
public double getLikelihoodThreshold()
public void setLikelihoodThreshold(double newPrecision)
newPrecision
- Value to assign to Precision.public java.lang.String useResamplingTipText()
public void setUseResampling(boolean r)
r
- true if resampling should be donepublic boolean getUseResampling()
public java.lang.String useEstimatedPriorsTipText()
public void setUseEstimatedPriors(boolean r)
r
- true if resampling should be donepublic boolean getUseEstimatedPriors()
public java.lang.String weightThresholdTipText()
public void setWeightThreshold(int threshold)
threshold
- the percentage of weight mass used for trainingpublic int getWeightThreshold()
public java.lang.String numThreadsTipText()
public int getNumThreads()
public void setNumThreads(int nT)
public java.lang.String poolSizeTipText()
public int getPoolSize()
public void setPoolSize(int nT)
public Capabilities getCapabilities()
getCapabilities
in interface Classifier
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class SingleClassifierEnhancer
Capabilities
public void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier
in interface Classifier
buildClassifier
in class IteratedSingleClassifierEnhancer
data
- the training data to be used for generating the
bagged classifier.java.lang.Exception
- if the classifier could not be built successfullypublic void initializeClassifier(Instances data) throws java.lang.Exception
initializeClassifier
in interface IterativeClassifier
data
- the data to train the classifier withjava.lang.Exception
- if building fails, e.g., can't handle datapublic boolean next() throws java.lang.Exception
next
in interface IterativeClassifier
java.lang.Exception
- if this iteration fails for unexpected reasonspublic java.lang.String resumeTipText()
public void setResume(boolean resume)
setResume
in interface IterativeClassifier
resume
- true if the model is to be finalized after performing iterationspublic boolean getResume()
getResume
in interface IterativeClassifier
public void done()
done
in interface IterativeClassifier
public Classifier[][] classifiers()
public boolean implementsMoreEfficientBatchPrediction()
implementsMoreEfficientBatchPrediction
in interface BatchPredictor
implementsMoreEfficientBatchPrediction
in class AbstractClassifier
public double[] distributionForInstance(Instance inst) throws java.lang.Exception
distributionForInstance
in interface Classifier
distributionForInstance
in class AbstractClassifier
inst
- the instance to be classifiedjava.lang.Exception
- if instance could not be classified successfullypublic double[][] distributionsForInstances(Instances insts) throws java.lang.Exception
distributionsForInstances
in interface BatchPredictor
distributionsForInstances
in class AbstractClassifier
insts
- the instances to be classifiedjava.lang.Exception
- if instances could not be classified successfullypublic java.lang.String toSource(java.lang.String className) throws java.lang.Exception
public java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class AbstractClassifier
public static void main(java.lang.String[] argv)
argv
- the options