weka.classifiers.meta

## Class 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, OptionHandler, Randomizable, RevisionHandler
  Direct Known Subclasses:
    MultiClassClassifierUpdateable

      public class MultiClassClassifier
      extends RandomizableSingleClassifierEnhancer
      implements OptionHandler

  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.

  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: 11889 $
  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

      `NUM_DECIMAL_PLACES_DEFAULT`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `MultiClassClassifier()` Constructor.

  - ### Method Summary

    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, setClassifier`

    - ### Methods inherited from class weka.classifiers.AbstractClassifier

      `batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces`

    - ### Methods inherited from class java.lang.Object

      `equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

- - ### 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