MultiClassClassifier

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.SingleClassifierEnhancer
  - - weka.classifiers.RandomizableSingleClassifierEnhancer
    - - weka.classifiers.meta.MultiClassClassifier

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

Direct Known Subclasses:: MultiClassClassifierUpdateable

public class MultiClassClassifier
extends RandomizableSingleClassifierEnhancer
implements OptionHandler, WeightedInstancesHandler

A metaclassifier for handling multi-class datasets with 2-class classifiers. This classifier is also capable of applying error correcting output codes for increased accuracy. If the base classifier cannot handle instance weights, and the instance weights are not uniform, the data will be resampled with replacement based on the weights before being passed to the base classifier.

Valid options are:

 -M <num>
  Sets the method to use. Valid values are 0 (1-against-all),
  1 (random codes), 2 (exhaustive code), and 3 (1-against-1). (default 0)

 -R <num>
  Sets the multiplier when using random codes. (default 2.0)

 -P
  Use pairwise coupling (only has an effect for 1-against1)

 -L
  Use log loss decoding for random and exhaustive codes.

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

 -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.functions.Logistic)

 
 Options specific to classifier weka.classifiers.functions.Logistic:

 -D
  Turn on debugging output.

 -R <ridge>
  Set the ridge in the log-likelihood.

 -M <number>
  Set the maximum number of iterations (default -1, until convergence).

Version:: $Revision: 15519 $
Author:: Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (len@reeltwo.com), Richard Kirkby (rkirkby@cs.waikato.ac.nz)
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`METHOD_1_AGAINST_1` 1-against-1
`static int`	`METHOD_1_AGAINST_ALL` 1-against-all
`static int`	`METHOD_ERROR_EXHAUSTIVE` exhaustive correction code
`static int`	`METHOD_ERROR_RANDOM` random correction code
`static Tag[]`	`TAGS_METHOD` The error correction modes

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

MultiClassClassifier()
Constructor.

Constructors
Constructor and Description
`MultiClassClassifier()` Constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances insts)` Builds the classifiers.
`double[]`	`distributionForInstance(Instance inst)` Returns the distribution for an instance.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getLogLossDecoding()` Whether log loss decoding is used for random or exhaustive codes.
`SelectedTag`	`getMethod()` Gets the method used.
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`double`	`getRandomWidthFactor()` Gets the multiplier when generating random codes.
`java.lang.String`	`getRevision()` Returns the revision string.
`boolean`	`getUsePairwiseCoupling()` Gets whether to use pairwise coupling with 1-vs-1 classification to improve probability estimates.
`java.lang.String`	`globalInfo()`
`double[]`	`individualPredictions(Instance inst)` Returns the individual predictions of the base classifiers for an instance.
`java.util.Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options
`java.lang.String`	`logLossDecodingTipText()`
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`java.lang.String`	`methodTipText()`
`static double[]`	`pairwiseCoupling(double[][] n, double[][] r)` Implements pairwise coupling.
`java.lang.String`	`randomWidthFactorTipText()`
`void`	`setLogLossDecoding(boolean newlogLossDecoding)` Sets whether log loss decoding is used for random or exhaustive codes.
`void`	`setMethod(SelectedTag newMethod)` Sets the method used.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setRandomWidthFactor(double newRandomWidthFactor)` Sets the multiplier when generating random codes.
`void`	`setUsePairwiseCoupling(boolean p)` Set whether to use pairwise coupling with 1-vs-1 classification to improve probability estimates.
`java.lang.String`	`toString()` Prints the classifiers.
`java.lang.String`	`usePairwiseCouplingTipText()`

Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer
getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, classifyInstance, 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
  - METHOD_1_AGAINST_ALL
```
public static final int METHOD_1_AGAINST_ALL
```
    1-against-all
    
    See Also:
    
    Constant Field Values
  - METHOD_ERROR_RANDOM
```
public static final int METHOD_ERROR_RANDOM
```
    random correction code
    
    See Also:
    
    Constant Field Values
  - METHOD_ERROR_EXHAUSTIVE
```
public static final int METHOD_ERROR_EXHAUSTIVE
```
    exhaustive correction code
    
    See Also:
    
    Constant Field Values
  - METHOD_1_AGAINST_1
```
public static final int METHOD_1_AGAINST_1
```
    1-against-1
    
    See Also:
    
    Constant Field Values
  - TAGS_METHOD
```
public static final Tag[] TAGS_METHOD
```
    The error correction modes
- Constructor Detail
  - MultiClassClassifier
```
public MultiClassClassifier()
```
    Constructor.
- Method Detail
  - 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 insts)
                     throws java.lang.Exception
```
    Builds the classifiers.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    
    insts - the training data.
    
    Throws:
    
    java.lang.Exception - if a classifier can't be built
  - individualPredictions
```
public double[] individualPredictions(Instance inst)
                               throws java.lang.Exception
```
    Returns the individual predictions of the base classifiers for an instance. Used by StackedMultiClassClassifier. Returns the probability for the second "class" predicted by each base classifier.
    
    Parameters:
    
    inst - the instance to get the prediction for
    
    Returns:
    
    the individual predictions
    
    Throws:
    
    java.lang.Exception - if the predictions can't be computed successfully
  - distributionForInstance
```
public double[] distributionForInstance(Instance inst)
                                 throws java.lang.Exception
```
    Returns the distribution for an instance.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    inst - the instance to get the distribution for
    
    Returns:
    
    the distribution
    
    Throws:
    
    java.lang.Exception - if the distribution can't be computed successfully
  - toString
```
public java.lang.String toString()
```
    Prints the classifiers.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a string representation of the classifier
  - 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.
    Valid options are:
```
 -M <num>
  Sets the method to use. Valid values are 0 (1-against-all),
  1 (random codes), 2 (exhaustive code), and 3 (1-against-1). (default 0)
 
```
```
 -R <num>
  Sets the multiplier when using random codes. (default 2.0)
```
```
 -P
  Use pairwise coupling (only has an effect for 1-against1)
```
```
 -L
  Use log loss decoding for random and exhaustive codes.
```
```
 -S <num>
  Random number seed.
  (default 1)
```
```
 -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.functions.Logistic)
```
```
 
 Options specific to classifier weka.classifiers.functions.Logistic:
 
```
```
 -D
  Turn on debugging output.
```
```
 -R <ridge>
  Set the ridge in the log-likelihood.
```
```
 -M <number>
  Set the maximum number of iterations (default -1, until convergence).
```
    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
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns:
    
    a description of the classifier suitable for displaying in the explorer/experimenter gui
  - logLossDecodingTipText
```
public java.lang.String logLossDecodingTipText()
```
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getLogLossDecoding
```
public boolean getLogLossDecoding()
```
    Whether log loss decoding is used for random or exhaustive codes.
    
    Returns:
    
    true if log loss is used
  - setLogLossDecoding
```
public void setLogLossDecoding(boolean newlogLossDecoding)
```
    Sets whether log loss decoding is used for random or exhaustive codes.
    
    Parameters:
    
    newlogLossDecoding - true if log loss is to be used
  - randomWidthFactorTipText
```
public java.lang.String randomWidthFactorTipText()
```
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getRandomWidthFactor
```
public double getRandomWidthFactor()
```
    Gets the multiplier when generating random codes. Will generate numClasses * m_RandomWidthFactor codes.
    
    Returns:
    
    the width multiplier
  - setRandomWidthFactor
```
public void setRandomWidthFactor(double newRandomWidthFactor)
```
    Sets the multiplier when generating random codes. Will generate numClasses * m_RandomWidthFactor codes.
    
    Parameters:
    
    newRandomWidthFactor - the new width multiplier
  - methodTipText
```
public java.lang.String methodTipText()
```
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getMethod
```
public SelectedTag getMethod()
```
    Gets the method used. Will be one of METHOD_1_AGAINST_ALL, METHOD_ERROR_RANDOM, METHOD_ERROR_EXHAUSTIVE, or METHOD_1_AGAINST_1.
    
    Returns:
    
    the current method.
  - setMethod
```
public void setMethod(SelectedTag newMethod)
```
    Sets the method used. Will be one of METHOD_1_AGAINST_ALL, METHOD_ERROR_RANDOM, METHOD_ERROR_EXHAUSTIVE, or METHOD_1_AGAINST_1.
    
    Parameters:
    
    newMethod - the new method.
  - setUsePairwiseCoupling
```
public void setUsePairwiseCoupling(boolean p)
```
    Set whether to use pairwise coupling with 1-vs-1 classification to improve probability estimates.
    
    Parameters:
    
    p - true if pairwise coupling is to be used
  - getUsePairwiseCoupling
```
public boolean getUsePairwiseCoupling()
```
    Gets whether to use pairwise coupling with 1-vs-1 classification to improve probability estimates.
    
    Returns:
    
    true if pairwise coupling is to be used
  - usePairwiseCouplingTipText
```
public java.lang.String usePairwiseCouplingTipText()
```
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - pairwiseCoupling
```
public static double[] pairwiseCoupling(double[][] n,
                                        double[][] r)
```
    Implements pairwise coupling.
    
    Parameters:
    
    n - the sum of weights used to train each model
    
    r - the probability estimate from each model
    
    Returns:
    
    the coupled estimates
  - 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[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - the options

Class MultiClassClassifier

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer

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.core.OptionHandler

Field Detail

METHOD_1_AGAINST_ALL

METHOD_ERROR_RANDOM

METHOD_ERROR_EXHAUSTIVE

METHOD_1_AGAINST_1

TAGS_METHOD

Constructor Detail

MultiClassClassifier

Method Detail

getCapabilities

buildClassifier

individualPredictions

distributionForInstance

toString

listOptions

setOptions

getOptions

globalInfo

logLossDecodingTipText

getLogLossDecoding

setLogLossDecoding

randomWidthFactorTipText

getRandomWidthFactor

setRandomWidthFactor

methodTipText

getMethod

setMethod

setUsePairwiseCoupling

getUsePairwiseCoupling

usePairwiseCouplingTipText

pairwiseCoupling

getRevision

main