weka.classifiers.meta

Class WeightedInstancesHandlerWrapper

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.RandomizableSingleClassifierEnhancer

weka.classifiers.meta.WeightedInstancesHandlerWrapper

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler, WeightedInstancesHandler

public class WeightedInstancesHandlerWrapper
extends RandomizableSingleClassifierEnhancer
implements WeightedInstancesHandler

Generic wrapper around any classifier to enable weighted instances support.
Uses resampling with weights if the base classifier is not implementing the weka.core.WeightedInstancesHandler interface and there are instance weights other 1.0 present. By default, the training data is passed through to the base classifier if it can handle instance weights. However, it is possible to force the use of resampling with weights as well.

Valid options are:

 -force-resample-with-weights
  Forces resampling of weights, regardless of whether
  base classifier handles instance weights

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

 -W
  Full name of base classifier.
  (default: weka.classifiers.rules.ZeroR)

 -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).

 -num-decimal-places
  The number of decimal places for the output of numbers in the model (default 2).

 
 Options specific to classifier weka.classifiers.rules.ZeroR:
 

 -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).

 -num-decimal-places
  The number of decimal places for the output of numbers in the model (default 2).

Version:

$Revision: 12226 $

Author:

FracPete (fracpete at waikato dot ac dot nz)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static java.lang.String	FORCE_RESAMPLE_WITH_WEIGHTS command-line option for resampling with weights.

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
WeightedInstancesHandlerWrapper()

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances data) builds the classifier.
double	classifyInstance(Instance instance) Classifies the given test instance.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
java.lang.String	forceResampleWithWeightsTipText() Returns the tip text for this property
boolean	getForceResampleWithWeights() Gets the size of each subSpace, as a percentage of the training set size.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
java.lang.String	getRevision() Returns the revision string.
java.lang.String	globalInfo() Returns a string describing classifier
java.util.Enumeration<Option>	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] args) Main method for testing this class.
void	setForceResampleWithWeights(boolean value) Sets the size of each subSpace, as a percentage of the training set size.
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString() Returns a string description of the model.

Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getCapabilities, getClassifier, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, 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.core.OptionHandler

makeCopy

Field Detail

FORCE_RESAMPLE_WITH_WEIGHTS

public static final java.lang.String FORCE_RESAMPLE_WITH_WEIGHTS

command-line option for resampling with weights.

See Also:

Constant Field Values

Constructor Detail

WeightedInstancesHandlerWrapper

public WeightedInstancesHandlerWrapper()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

listOptions

public java.util.Enumeration<Option> listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class RandomizableSingleClassifierEnhancer

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.

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class RandomizableSingleClassifierEnhancer

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 RandomizableSingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

getForceResampleWithWeights

public boolean getForceResampleWithWeights()

Gets the size of each subSpace, as a percentage of the training set size.

Returns:

the subSpace size, as a percentage.

setForceResampleWithWeights

public void setForceResampleWithWeights(boolean value)

Sets the size of each subSpace, as a percentage of the training set size.

Parameters:

value - the subSpace size, as a percentage.

forceResampleWithWeightsTipText

public java.lang.String forceResampleWithWeightsTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

buildClassifier

public void buildClassifier(Instances data)
                     throws java.lang.Exception

builds the classifier.

Specified by:

buildClassifier in interface Classifier

Parameters:

data - the training data to be used for generating the classifier.

Throws:

java.lang.Exception - if the classifier could not be built successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception

Calculates the class membership probabilities for the given test instance.

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

preedicted class probability distribution

Throws:

java.lang.Exception - if distribution can't be computed successfully

classifyInstance

public double classifyInstance(Instance instance)
                        throws java.lang.Exception

Classifies the given test instance.

Specified by:

classifyInstance in interface Classifier

Overrides:

classifyInstance in class AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

the predicted most likely class for the instance or Utils.missingValue() if no prediction is made

Throws:

java.lang.Exception - if an error occurred during the prediction

toString

public java.lang.String toString()

Returns a string description of the model.

Overrides:

toString in class java.lang.Object

Returns:

the model

getRevision

public java.lang.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(java.lang.String[] args)

Main method for testing this class.

Parameters:

args - the options