Vote (interruptible-weka 0.1.4 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.MultipleClassifiersCombiner
  - - weka.classifiers.RandomizableMultipleClassifiersCombiner
    - - weka.classifiers.meta.Vote

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, Classifier, Aggregateable<Classifier>, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, EnvironmentHandler, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler

public class Vote
extends RandomizableMultipleClassifiersCombiner
implements TechnicalInformationHandler, EnvironmentHandler, Aggregateable<Classifier>

Class for combining classifiers. Different combinations of probability estimates for classification are available.

For more information see:

Ludmila I. Kuncheva (2004). Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons, Inc..

J. Kittler, M. Hatef, Robert P.W. Duin, J. Matas (1998). On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(3):226-239.

Valid options are:

 -P <path to serialized classifier>
  Full path to serialized classifier to include.
  May be specified multiple times to include
  multiple serialized classifiers. Note: it does
  not make sense to use pre-built classifiers in
  a cross-validation.

 -R <AVG|PROD|MAJ|MIN|MAX|MED>
  The combination rule to use
  (default: AVG)

 -print
  Print the individual models in the output

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

 -B <classifier specification>
  Full class name of classifier to include, followed
  by scheme options. May be specified multiple times.
  (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).

 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).

BibTeX:

 @book{Kuncheva2004,
    author = {Ludmila I. Kuncheva},
    publisher = {John Wiley and Sons, Inc.},
    title = {Combining Pattern Classifiers: Methods and Algorithms},
    year = {2004}
 }

 @article{Kittler1998,
    author = {J. Kittler and M. Hatef and Robert P.W. Duin and J. Matas},
    journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
    number = {3},
    pages = {226-239},
    title = {On combining classifiers},
    volume = {20},
    year = {1998}
 }

See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`AVERAGE_RULE` combination rule: Average of Probabilities
`protected java.util.List<java.lang.String>`	`m_classifiersToLoad` List of file paths to serialized models to load
`protected int`	`m_CombinationRule` Combination Rule variable
`protected boolean`	`m_dontPrintModels` Print the individual models in the output
`protected Environment`	`m_env` Environment variables
`protected java.util.List<Classifier>`	`m_preBuiltClassifiers` List of de-serialized pre-built classifiers to include in the ensemble
`protected Instances`	`m_structure` Structure of the training data
`static int`	`MAJORITY_VOTING_RULE` combination rule: Majority Voting (only nominal classes)
`static int`	`MAX_RULE` combination rule: Maximum Probability
`static int`	`MEDIAN_RULE` combination rule: Median Probability (only numeric class)
`static int`	`MIN_RULE` combination rule: Minimum Probability
`static int`	`PRODUCT_RULE` combination rule: Product of Probabilities (only nominal classes)
`static Tag[]`	`TAGS_RULES` combination rules

Fields inherited from class weka.classifiers.RandomizableMultipleClassifiersCombiner
m_Seed

Fields inherited from class weka.classifiers.MultipleClassifiersCombiner
m_Classifiers

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

Vote()

Constructors
Constructor and Description
`Vote()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`addPreBuiltClassifier(Classifier c)` Add a prebuilt classifier to the list for use in the ensemble
`Classifier`	`aggregate(Classifier toAggregate)` Aggregate an object with this one
`void`	`buildClassifier(Instances data)` Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
`double`	`classifyInstance(Instance instance)` Classifies the given test instance.
`protected double`	`classifyInstanceMedian(Instance instance)` Classifies the given test instance, returning the median from all classifiers.
`java.lang.String`	`combinationRuleTipText()` Returns the tip text for this property
`double[]`	`distributionForInstance(Instance instance)` Classifies a given instance using the selected combination rule.
`protected double[]`	`distributionForInstanceAverage(Instance instance)` Classifies a given instance using the Average of Probabilities combination rule.
`protected double[]`	`distributionForInstanceMajorityVoting(Instance instance)` Classifies a given instance using the Majority Voting combination rule.
`protected double[]`	`distributionForInstanceMax(Instance instance)` Classifies a given instance using the Maximum Probability combination rule.
`protected double[]`	`distributionForInstanceMin(Instance instance)` Classifies a given instance using the Minimum Probability combination rule.
`protected double[]`	`distributionForInstanceProduct(Instance instance)` Classifies a given instance using the Product of Probabilities combination rule.
`java.lang.String`	`doNotPrintModelsTipText()` Returns the tip text for this property
`void`	`finalizeAggregation()` Call to complete the aggregation process.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`SelectedTag`	`getCombinationRule()` Gets the combination rule used
`boolean`	`getDoNotPrintModels()` Get whether to print the individual ensemble models in the output
`java.lang.String[]`	`getOptions()` Gets the current settings of Vote.
`java.io.File[]`	`getPreBuiltClassifiers()` Get the paths to pre-built serialized classifiers to load and include in the ensemble
`java.lang.String`	`getRevision()` Returns the revision string.
`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.
`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[] argv)` Main method for testing this class.
`java.lang.String`	`preBuiltClassifiersTipText()` Returns the tip text for this property
`void`	`removePreBuiltClassifier(Classifier c)` Remove a prebuilt classifier from the list to use in the ensemble
`void`	`setCombinationRule(SelectedTag newRule)` Sets the combination rule to use.
`void`	`setDoNotPrintModels(boolean print)` Set whether to print the individual ensemble models in the output
`void`	`setEnvironment(Environment env)` Set environment variable values to substitute in the paths of serialized models to load
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setPreBuiltClassifiers(java.io.File[] preBuilt)` Set the paths to pre-built serialized classifiers to load and include in the ensemble
`java.lang.String`	`toString()` Output a representation of this classifier

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

Methods inherited from class weka.classifiers.MultipleClassifiersCombiner
classifiersTipText, getClassifier, getClassifiers, getClassifierSpec, postExecution, preExecution, setClassifiers

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
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - AVERAGE_RULE
```
public static final int AVERAGE_RULE
```
    combination rule: Average of Probabilities
    
    See Also:
    
    Constant Field Values
  - PRODUCT_RULE
```
public static final int PRODUCT_RULE
```
    combination rule: Product of Probabilities (only nominal classes)
    
    See Also:
    
    Constant Field Values
  - MAJORITY_VOTING_RULE
```
public static final int MAJORITY_VOTING_RULE
```
    combination rule: Majority Voting (only nominal classes)
    
    See Also:
    
    Constant Field Values
  - MIN_RULE
```
public static final int MIN_RULE
```
    combination rule: Minimum Probability
    
    See Also:
    
    Constant Field Values
  - MAX_RULE
```
public static final int MAX_RULE
```
    combination rule: Maximum Probability
    
    See Also:
    
    Constant Field Values
  - MEDIAN_RULE
```
public static final int MEDIAN_RULE
```
    combination rule: Median Probability (only numeric class)
    
    See Also:
    
    Constant Field Values
  - TAGS_RULES
```
public static final Tag[] TAGS_RULES
```
    combination rules
  - m_CombinationRule
```
protected int m_CombinationRule
```
    Combination Rule variable
  - m_classifiersToLoad
```
protected java.util.List<java.lang.String> m_classifiersToLoad
```
    List of file paths to serialized models to load
  - m_preBuiltClassifiers
```
protected java.util.List<Classifier> m_preBuiltClassifiers
```
    List of de-serialized pre-built classifiers to include in the ensemble
  - m_env
```
protected transient Environment m_env
```
    Environment variables
  - m_structure
```
protected Instances m_structure
```
    Structure of the training data
  - m_dontPrintModels
```
protected boolean m_dontPrintModels
```
    Print the individual models in the output
- Constructor Detail
  - Vote
```
public Vote()
```
- 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 RandomizableMultipleClassifiersCombiner
    
    Returns:
    
    an enumeration of all the available options.
  - getOptions
```
public java.lang.String[] getOptions()
```
    Gets the current settings of Vote.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class RandomizableMultipleClassifiersCombiner
    
    Returns:
    
    an array of strings suitable for passing to setOptions()
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options.
    Valid options are:
```
 -P <path to serialized classifier>
  Full path to serialized classifier to include.
  May be specified multiple times to include
  multiple serialized classifiers. Note: it does
  not make sense to use pre-built classifiers in
  a cross-validation.
 
```
```
 -R <AVG|PROD|MAJ|MIN|MAX|MED>
  The combination rule to use
  (default: AVG)
 
```
```
 -print
  Print the individual models in the output
 
```
```
 -S <num>
  Random number seed.
  (default 1)
 
```
```
 -B <classifier specification>
  Full class name of classifier to include, followed
  by scheme options. May be specified multiple times.
  (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).
 
```
```
 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).
 
```
    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
  - getTechnicalInformation
```
public 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.
    
    Specified by:
    
    getTechnicalInformation in interface TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - 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 MultipleClassifiersCombiner
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - 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 interface 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
  - addPreBuiltClassifier
```
public void addPreBuiltClassifier(Classifier c)
```
    Add a prebuilt classifier to the list for use in the ensemble
    
    Parameters:
    
    c - a prebuilt Classifier to add.
  - removePreBuiltClassifier
```
public void removePreBuiltClassifier(Classifier c)
```
    Remove a prebuilt classifier from the list to use in the ensemble
    
    Parameters:
    
    c - the classifier to remove
  - 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
  - classifyInstanceMedian
```
protected double classifyInstanceMedian(Instance instance)
                                 throws java.lang.Exception
```
    Classifies the given test instance, returning the median from all classifiers. Can assume that class is numeric.
    
    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
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
```
    Classifies a given instance using the selected combination rule.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    the distribution
    
    Throws:
    
    java.lang.Exception - if instance could not be classified successfully
  - distributionForInstanceAverage
```
protected double[] distributionForInstanceAverage(Instance instance)
                                           throws java.lang.Exception
```
    Classifies a given instance using the Average of Probabilities combination rule.
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    the distribution
    
    Throws:
    
    java.lang.Exception - if instance could not be classified successfully
  - distributionForInstanceProduct
```
protected double[] distributionForInstanceProduct(Instance instance)
                                           throws java.lang.Exception
```
    Classifies a given instance using the Product of Probabilities combination rule. Can assume that class is nominal.
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    the distribution
    
    Throws:
    
    java.lang.Exception - if instance could not be classified successfully
  - distributionForInstanceMajorityVoting
```
protected double[] distributionForInstanceMajorityVoting(Instance instance)
                                                  throws java.lang.Exception
```
    Classifies a given instance using the Majority Voting combination rule. Can assume that class is nominal.
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    the distribution
    
    Throws:
    
    java.lang.Exception - if instance could not be classified successfully
  - distributionForInstanceMax
```
protected double[] distributionForInstanceMax(Instance instance)
                                       throws java.lang.Exception
```
    Classifies a given instance using the Maximum Probability combination rule.
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    the distribution
    
    Throws:
    
    java.lang.Exception - if instance could not be classified successfully
  - distributionForInstanceMin
```
protected double[] distributionForInstanceMin(Instance instance)
                                       throws java.lang.Exception
```
    Classifies a given instance using the Minimum Probability combination rule.
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    the distribution
    
    Throws:
    
    java.lang.Exception - if instance could not be classified successfully
  - combinationRuleTipText
```
public java.lang.String combinationRuleTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getCombinationRule
```
public SelectedTag getCombinationRule()
```
    Gets the combination rule used
    
    Returns:
    
    the combination rule used
  - setCombinationRule
```
public void setCombinationRule(SelectedTag newRule)
```
    Sets the combination rule to use. Values other than
    
    Parameters:
    
    newRule - the combination rule method to use
  - preBuiltClassifiersTipText
```
public java.lang.String preBuiltClassifiersTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setPreBuiltClassifiers
```
public void setPreBuiltClassifiers(java.io.File[] preBuilt)
```
    Set the paths to pre-built serialized classifiers to load and include in the ensemble
    
    Parameters:
    
    preBuilt - an array of File paths to serialized models
  - getPreBuiltClassifiers
```
public java.io.File[] getPreBuiltClassifiers()
```
    Get the paths to pre-built serialized classifiers to load and include in the ensemble
    
    Returns:
    
    an array of File paths to serialized models
  - doNotPrintModelsTipText
```
public java.lang.String doNotPrintModelsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDoNotPrintModels
```
public void setDoNotPrintModels(boolean print)
```
    Set whether to print the individual ensemble models in the output
    
    Parameters:
    
    print - true if the individual models are to be printed
  - getDoNotPrintModels
```
public boolean getDoNotPrintModels()
```
    Get whether to print the individual ensemble models in the output
    
    Returns:
    
    true if the individual models are to be printed
  - toString
```
public java.lang.String toString()
```
    Output a representation of this classifier
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a string representation of the classifier
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClassifier
    
    Returns:
    
    the revision
  - setEnvironment
```
public void setEnvironment(Environment env)
```
    Set environment variable values to substitute in the paths of serialized models to load
    
    Specified by:
    
    setEnvironment in interface EnvironmentHandler
    
    Parameters:
    
    env - the environment variables to use
  - aggregate
```
public Classifier aggregate(Classifier toAggregate)
                     throws java.lang.Exception
```
    Aggregate an object with this one
    
    Specified by:
    
    aggregate in interface Aggregateable<Classifier>
    
    Parameters:
    
    toAggregate - the object to aggregate
    
    Returns:
    
    the result of aggregation
    
    Throws:
    
    java.lang.Exception - if the supplied object can't be aggregated for some reason
  - finalizeAggregation
```
public void finalizeAggregation()
                         throws java.lang.Exception
```
    Call to complete the aggregation process. Allows implementers to do any final processing based on how many objects were aggregated.
    
    Specified by:
    
    finalizeAggregation in interface Aggregateable<Classifier>
    
    Throws:
    
    java.lang.Exception - if the aggregation can't be finalized for some reason
  - 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]

Class Vote

Field Summary

Fields inherited from class weka.classifiers.RandomizableMultipleClassifiersCombiner

Fields inherited from class weka.classifiers.MultipleClassifiersCombiner

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableMultipleClassifiersCombiner

Methods inherited from class weka.classifiers.MultipleClassifiersCombiner

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Field Detail

AVERAGE_RULE

PRODUCT_RULE

MAJORITY_VOTING_RULE

MIN_RULE

MAX_RULE

MEDIAN_RULE

TAGS_RULES

m_CombinationRule

m_classifiersToLoad

m_preBuiltClassifiers

m_env

m_structure

m_dontPrintModels

Constructor Detail

Vote

Method Detail

globalInfo

listOptions

getOptions

setOptions

getTechnicalInformation

getCapabilities

buildClassifier

addPreBuiltClassifier

removePreBuiltClassifier

classifyInstance

classifyInstanceMedian

distributionForInstance

distributionForInstanceAverage

distributionForInstanceProduct

distributionForInstanceMajorityVoting

distributionForInstanceMax

distributionForInstanceMin

combinationRuleTipText

getCombinationRule

setCombinationRule

preBuiltClassifiersTipText

setPreBuiltClassifiers

getPreBuiltClassifiers

doNotPrintModelsTipText

setDoNotPrintModels

getDoNotPrintModels

toString

getRevision

setEnvironment

aggregate

finalizeAggregation

main