weka.classifiers.meta
Class MultiScheme

java.lang.Object
  extended byweka.classifiers.Classifier
      extended byweka.classifiers.MultipleClassifiersCombiner
          extended byweka.classifiers.RandomizableMultipleClassifiersCombiner
              extended byweka.classifiers.meta.MultiScheme
All Implemented Interfaces:
java.lang.Cloneable, OptionHandler, Randomizable, java.io.Serializable

public class MultiScheme
extends RandomizableMultipleClassifiersCombiner

Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data. Performance is measured based on percent correct (classification) or mean-squared error (regression).

Valid options from the command line are:

-D
Turn on debugging output.

-S seed
Random number seed (default 1).

-B classifierstring
Classifierstring should contain the full class name of a scheme included for selection followed by options to the classifier (required, option should be used once for each classifier).

-X num_folds
Use cross validation error as the basis for classifier selection. (default 0, is to use error on the training data instead)

Version:
$Revision: 1.17 $
Author:
Len Trigg (trigg@cs.waikato.ac.nz)
See Also:
Serialized Form

Field Summary
protected  Classifier m_Classifier
          The classifier that had the best performance on training data.
protected  int m_ClassifierIndex
          The index into the vector for the selected scheme
protected  int m_NumXValFolds
          Number of folds to use for cross validation (0 means use training error for selection)
 
Fields inherited from class weka.classifiers.RandomizableMultipleClassifiersCombiner
m_Seed
 
Fields inherited from class weka.classifiers.MultipleClassifiersCombiner
m_Classifiers
 
Fields inherited from class weka.classifiers.Classifier
m_Debug
 
Constructor Summary
MultiScheme()
           
 
Method Summary
 void buildClassifier(Instances data)
          Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
 java.lang.String classifiersTipText()
          Returns the tip text for this property
 java.lang.String debugTipText()
          Returns the tip text for this property
 double[] distributionForInstance(Instance instance)
          Returns class probabilities.
 Classifier getClassifier(int index)
          Gets a single classifier from the set of available classifiers.
 Classifier[] getClassifiers()
          Gets the list of possible classifers to choose from.
protected  java.lang.String getClassifierSpec(int index)
          Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
 boolean getDebug()
          Get whether debugging is turned on
 int getNumFolds()
          Gets the number of folds for cross-validation.
 java.lang.String[] getOptions()
          Gets the current settings of the Classifier.
 int getSeed()
          Gets the random number seed.
 java.lang.String globalInfo()
          Returns a string describing classifier
 java.util.Enumeration listOptions()
          Returns an enumeration describing the available options.
static void main(java.lang.String[] argv)
          Main method for testing this class.
 java.lang.String numFoldsTipText()
          Returns the tip text for this property
 java.lang.String seedTipText()
          Returns the tip text for this property
 void setClassifiers(Classifier[] classifiers)
          Sets the list of possible classifers to choose from.
 void setDebug(boolean debug)
          Set debugging mode
 void setNumFolds(int numFolds)
          Sets the number of folds for cross-validation.
 void setOptions(java.lang.String[] options)
          Parses a given list of options.
 void setSeed(int seed)
          Sets the seed for random number generation.
 java.lang.String toString()
          Output a representation of this classifier
 
Methods inherited from class weka.classifiers.Classifier
classifyInstance, forName, makeCopies
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Field Detail

m_Classifier

protected Classifier m_Classifier
The classifier that had the best performance on training data.


m_ClassifierIndex

protected int m_ClassifierIndex
The index into the vector for the selected scheme


m_NumXValFolds

protected int m_NumXValFolds
Number of folds to use for cross validation (0 means use training error for selection)

Constructor Detail

MultiScheme

public MultiScheme()
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 listOptions()
Returns an enumeration describing the available options.

Specified by:
listOptions in interface OptionHandler
Overrides:
listOptions in class RandomizableMultipleClassifiersCombiner
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:

-D
Turn on debugging output.

-S seed
Random number seed (default 1).

-B classifierstring
Classifierstring should contain the full class name of a scheme included for selection followed by options to the classifier (required, option should be used once for each classifier).

-X num_folds
Use cross validation error as the basis for classifier selection. (default 0, is to use error on the training data instead)

Specified by:
setOptions in interface OptionHandler
Overrides:
setOptions in class RandomizableMultipleClassifiersCombiner
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 RandomizableMultipleClassifiersCombiner
Returns:
an array of strings suitable for passing to setOptions

classifiersTipText

public java.lang.String classifiersTipText()
Returns the tip text for this property

Overrides:
classifiersTipText in class MultipleClassifiersCombiner
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

setClassifiers

public void setClassifiers(Classifier[] classifiers)
Sets the list of possible classifers to choose from.

Overrides:
setClassifiers in class MultipleClassifiersCombiner
Parameters:
classifiers - an array of classifiers with all options set.

getClassifiers

public Classifier[] getClassifiers()
Gets the list of possible classifers to choose from.

Overrides:
getClassifiers in class MultipleClassifiersCombiner
Returns:
the array of Classifiers

getClassifier

public Classifier getClassifier(int index)
Gets a single classifier from the set of available classifiers.

Overrides:
getClassifier in class MultipleClassifiersCombiner
Parameters:
index - the index of the classifier wanted
Returns:
the Classifier

getClassifierSpec

protected java.lang.String getClassifierSpec(int index)
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier

Overrides:
getClassifierSpec in class MultipleClassifiersCombiner
Parameters:
index - the index of the classifier string to retrieve, starting from 0.
Returns:
the classifier string, or the empty string if no classifier has been assigned (or the index given is out of range).

seedTipText

public java.lang.String seedTipText()
Returns the tip text for this property

Overrides:
seedTipText in class RandomizableMultipleClassifiersCombiner
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

setSeed

public void setSeed(int seed)
Sets the seed for random number generation.

Specified by:
setSeed in interface Randomizable
Overrides:
setSeed in class RandomizableMultipleClassifiersCombiner
Parameters:
seed - the random number seed

getSeed

public int getSeed()
Gets the random number seed.

Specified by:
getSeed in interface Randomizable
Overrides:
getSeed in class RandomizableMultipleClassifiersCombiner
Returns:
the random number seed

numFoldsTipText

public java.lang.String numFoldsTipText()
Returns the tip text for this property

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

getNumFolds

public int getNumFolds()
Gets the number of folds for cross-validation. A number less than 2 specifies using training error rather than cross-validation.

Returns:
the number of folds for cross-validation

setNumFolds

public void setNumFolds(int numFolds)
Sets the number of folds for cross-validation. A number less than 2 specifies using training error rather than cross-validation.

Parameters:
numFolds - the number of folds for cross-validation

debugTipText

public java.lang.String debugTipText()
Returns the tip text for this property

Overrides:
debugTipText in class Classifier
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

setDebug

public void setDebug(boolean debug)
Set debugging mode

Overrides:
setDebug in class Classifier
Parameters:
debug - true if debug output should be printed

getDebug

public boolean getDebug()
Get whether debugging is turned on

Overrides:
getDebug in class Classifier
Returns:
true if debugging output is on

buildClassifier

public void buildClassifier(Instances data)
                     throws java.lang.Exception
Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.

Specified by:
buildClassifier in class Classifier
Parameters:
data - the training data to be used for generating the boosted classifier.
Throws:
java.lang.Exception - if the classifier could not be built successfully

distributionForInstance

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

Overrides:
distributionForInstance in class Classifier
Parameters:
instance - the instance to be classified
Returns:
an array containing the estimated membership probabilities of the test instance in each class or the numeric prediction
Throws:
java.lang.Exception - if instance could not be classified successfully

toString

public java.lang.String toString()
Output a representation of this classifier


main

public static void main(java.lang.String[] argv)
Main method for testing this class.

Parameters:
argv - should contain the following arguments: -t training file [-T test file] [-c class index]