weka.classifiers.meta
Class PartitionedStacking

java.lang.Object
  weka.classifiers.AbstractClassifier
      weka.classifiers.MultipleClassifiersCombiner
          weka.classifiers.ParallelMultipleClassifiersCombiner
              weka.classifiers.meta.PartitionedStacking

All Implemented Interfaces:

Serializable, Cloneable, weka.classifiers.Classifier, weka.core.CapabilitiesHandler, weka.core.OptionHandler, weka.core.RevisionHandler

public class PartitionedStacking
extends weka.classifiers.ParallelMultipleClassifiersCombiner

Builds the base-classifiers on subsets of the data defined by ranges that correspond to the base-classifiers. The base-classifiers expect the class attribute to be the last attribute in the range of attributes that is defined for them.
The predictions of the base-classifiers and the original class attribute are used to generated a new meta-dataset that is used as input for the meta-level classifier.

Valid options are:

 -R <range>
  The attributes ranges to use for training the base-classifiers.
  Must be specified as often as there are base-classifiers.
  This is a comma separated list of attribute indices, with
  "first" and "last" valid values. Specify an inclusive
  range with "-". E.g: "first-3,5,6-10,last".

 -M <classifier specification>
  Full class name of the classifier to use for the meta-level,
  followed by scheme options.
  (default: "weka.classifiers.trees.M5P")

 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)

 -B <classifier specification>
  Full class name of classifier to include, followed
  by scheme options. May be specified multiple times.
  (default: "weka.classifiers.rules.ZeroR")

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Version:

$Revision: 4521 $

Author:

fracpete (fracpete at waikato dot ac dot nz)

See Also:

Serialized Form

Field Summary

protected weka.classifiers.Classifier	m_MetaLevelClassifier the meta-level classifier.
protected weka.core.Instances	m_MetaLevelData the header for the meta-level data.
protected weka.core.Range[]	m_Ranges the attribute ranges for the base-classifiers.
protected weka.filters.unsupervised.attribute.Remove[]	m_Remove the filters for removing the unwanted attributes for the base classifiers.

Fields inherited from class weka.classifiers.ParallelMultipleClassifiersCombiner

m_completed, m_executorPool, m_failed, m_numExecutionSlots

Fields inherited from class weka.classifiers.MultipleClassifiersCombiner

m_Classifiers

Fields inherited from class weka.classifiers.AbstractClassifier

m_Debug

Constructor Summary

PartitionedStacking()

Method Summary

void	buildClassifier(weka.core.Instances data) Builds the classifier.
protected void	buildClassifiers(weka.core.Instances data) Does the actual construction of the base-classifiers.
double	classifyInstance(weka.core.Instance instance) Classifies the given test instance.
weka.core.Capabilities	getCapabilities() Returns combined capabilities of the base classifiers, i.e., the capabilities all of them have in common.
weka.classifiers.Classifier	getMetaLevelClassifier() Returns the meta-level classifier.
String[]	getOptions() Gets the current settings of the Classifier.
weka.core.Range[]	getRanges() Returns the attribute ranges for the base-classifiers.
String	getRevision() Returns the revision string.
String	globalInfo() Returns a string describing this classifier.
Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(String[] args) Main method for running this classifier.
String	metaLevelClassifierTipText() Returns the tip text for this property.
String	rangesTipText() Returns the tip text for this property.
void	setMetaLevelClassifier(weka.classifiers.Classifier value) Sets the meta-level classifier.
void	setOptions(String[] options) Parses a given list of options.
void	setRanges(weka.core.Range[] value) Sets the attribute ranges for the base-classifiers.

Methods inherited from class weka.classifiers.ParallelMultipleClassifiersCombiner

completedClassifier, getNumExecutionSlots, numExecutionSlotsTipText, setNumExecutionSlots, startExecutorPool

Methods inherited from class weka.classifiers.MultipleClassifiersCombiner

classifiersTipText, getClassifier, getClassifiers, getClassifierSpec, setClassifiers

Methods inherited from class weka.classifiers.AbstractClassifier

debugTipText, distributionForInstance, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object

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

Field Detail

m_MetaLevelClassifier

protected weka.classifiers.Classifier m_MetaLevelClassifier

the meta-level classifier.

m_Ranges

protected weka.core.Range[] m_Ranges

the attribute ranges for the base-classifiers.

m_Remove

protected weka.filters.unsupervised.attribute.Remove[] m_Remove

the filters for removing the unwanted attributes for the base classifiers.

m_MetaLevelData

protected weka.core.Instances m_MetaLevelData

the header for the meta-level data.

Constructor Detail

PartitionedStacking

public PartitionedStacking()

Method Detail

globalInfo

public String globalInfo()

Returns a string describing this classifier.

Returns:

a description of the classifier suitable for displaying in the explorer/experimenter gui

listOptions

public Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.ParallelMultipleClassifiersCombiner

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(String[] options)
                throws Exception

Parses a given list of options.

Valid options are:

 -R <range>
  The attributes ranges to use for training the base-classifiers.
  Must be specified as often as there are base-classifiers.
  This is a comma separated list of attribute indices, with
  "first" and "last" valid values. Specify an inclusive
  range with "-". E.g: "first-3,5,6-10,last".

 -M <classifier specification>
  Full class name of the classifier to use for the meta-level,
  followed by scheme options.
  (default: "weka.classifiers.trees.M5P")

 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)

 -B <classifier specification>
  Full class name of classifier to include, followed
  by scheme options. May be specified multiple times.
  (default: "weka.classifiers.rules.ZeroR")

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.ParallelMultipleClassifiersCombiner

Parameters:

options - the list of options as an array of strings

Throws:

Exception - if an option is not supported

getOptions

public String[] getOptions()

Gets the current settings of the Classifier.

Specified by:

getOptions in interface weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.ParallelMultipleClassifiersCombiner

Returns:

an array of strings suitable for passing to setOptions

setRanges

public void setRanges(weka.core.Range[] value)

Sets the attribute ranges for the base-classifiers.

Parameters:

value - the ranges

getRanges

public weka.core.Range[] getRanges()

Returns the attribute ranges for the base-classifiers.

Returns:

the ranges

rangesTipText

public String rangesTipText()

Returns the tip text for this property.

Returns:

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

setMetaLevelClassifier

public void setMetaLevelClassifier(weka.classifiers.Classifier value)

Sets the meta-level classifier.

Parameters:

value - the meta-level classifier

getMetaLevelClassifier

public weka.classifiers.Classifier getMetaLevelClassifier()

Returns the meta-level classifier.

Returns:

the meta-level classifier

metaLevelClassifierTipText

public String metaLevelClassifierTipText()

Returns the tip text for this property.

Returns:

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

getCapabilities

public weka.core.Capabilities getCapabilities()

Returns combined capabilities of the base classifiers, i.e., the capabilities all of them have in common.

Specified by:

getCapabilities in interface weka.classifiers.Classifier

Specified by:

getCapabilities in interface weka.core.CapabilitiesHandler

Overrides:

getCapabilities in class weka.classifiers.MultipleClassifiersCombiner

Returns:

the capabilities of the base classifiers

buildClassifiers

protected void buildClassifiers(weka.core.Instances data)
                         throws Exception

Does the actual construction of the base-classifiers.

Overrides:

buildClassifiers in class weka.classifiers.ParallelMultipleClassifiersCombiner

Parameters:

data - the data to use as basis for base-classifiers

Throws:

Exception - if something goes wrong during the training process

buildClassifier

public void buildClassifier(weka.core.Instances data)
                     throws Exception

Builds the classifier.

Specified by:

buildClassifier in interface weka.classifiers.Classifier

Overrides:

buildClassifier in class weka.classifiers.ParallelMultipleClassifiersCombiner

Parameters:

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

Throws:

Exception - if the classifier could not be built successfully

classifyInstance

public double classifyInstance(weka.core.Instance instance)
                        throws Exception

Classifies the given test instance. The instance has to belong to a dataset when it's being classified.

Specified by:

classifyInstance in interface weka.classifiers.Classifier

Overrides:

classifyInstance in class weka.classifiers.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:

Exception - if an error occurred during the prediction

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface weka.core.RevisionHandler

Overrides:

getRevision in class weka.classifiers.AbstractClassifier

Returns:

the revision

main

public static void main(String[] args)

Main method for running this classifier.

Parameters:

args - the parameters, use -h to display them