meka.classifiers.multilabel

Class PLST

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

meka.classifiers.multilabel.LabelTransformationClassifier

meka.classifiers.multilabel.PLST

All Implemented Interfaces:

Serializable, Cloneable, MultiLabelClassifier, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler

public class PLST
extends LabelTransformationClassifier
implements weka.core.TechnicalInformationHandler

PLST - Principal Label Space Transformation. Uses SVD to generate a matrix that transforms the label space. This implementation is adapted from the MatLab implementation provided by the authors at Github
See: Farbound Tai and Hsuan-Tien Lin. Multilabel classification with principal label space transformation. Neural Computation, 24(9):2508--2542, September 2012.

Author:

Joerg Wicker (wicker@uni-mainz.de)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected weka.core.Instances	m_PatternInstances
protected weka.core.matrix.Matrix	m_Shift
protected int	m_Size
protected weka.core.matrix.Matrix	m_v

Fields inherited from class weka.classifiers.SingleClassifierEnhancer

m_Classifier

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
PLST()

Method Summary

Modifier and Type	Method and Description
protected weka.classifiers.Classifier	getDefaultClassifier() Change default classifier to CR with Linear Regression as base as this classifier uses numeric values in the compressed labels.
protected int	getDefaultSize() The default size, set to 5.
String	getModel() Returns a string representation of the model.
String[]	getOptions() Returns an array with the options of the classifier.
int	getSize() Returns the size of the compressed labels.
weka.core.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.
String	globalInfo() Returns the global information of the classifier.
Enumeration	listOptions() Returns an enumeration of the options.
static void	main(String[] args) Main method for testing.
void	setOptions(String[] options) Sets the options to the given values in the array.
void	setSize(int size) Sets the size of the compressed labels.
String	sizeTipText() The tooltip for the size.
String	toString()
weka.core.Instance	transformInstance(weka.core.Instance x) Transforms the instance in the prediction process before given to the internal multi-label or multi-target classifier.
weka.core.Instances	transformLabels(weka.core.Instances D) The method to transform the labels into another set of latent labels, typically a compression method is used, e.g., Boolean matrix decomposition in the case of MLC-BMaD, or matrix multiplication based on SVD for PLST.
double[]	transformPredictionsBack(double[] y) Transforms the predictions of the internal classifier back to the original labels.

Methods inherited from class meka.classifiers.multilabel.LabelTransformationClassifier

buildClassifier, defaultClassifierString, distributionForInstance, extractPart, getRevision, setClassifier, testCapabilities

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, defaultClassifierOptions, getCapabilities, getClassifier, getClassifierSpec, postExecution, preExecution

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

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

Methods inherited from interface meka.classifiers.multilabel.MultiLabelClassifier

debugTipText, getDebug, setDebug

Methods inherited from interface weka.classifiers.Classifier

classifyInstance, getCapabilities

Field Detail

m_Shift

protected weka.core.matrix.Matrix m_Shift

m_PatternInstances

protected weka.core.Instances m_PatternInstances

m_v

protected weka.core.matrix.Matrix m_v

m_Size

protected int m_Size

Constructor Detail

PLST

public PLST()

Method Detail

globalInfo

public String globalInfo()

Returns the global information of the classifier.

Returns:

Global information of the classfier

getDefaultClassifier

protected weka.classifiers.Classifier getDefaultClassifier()

Change default classifier to CR with Linear Regression as base as this classifier uses numeric values in the compressed labels.

Overrides:

getDefaultClassifier in class LabelTransformationClassifier

getDefaultSize

protected int getDefaultSize()

The default size, set to 5.

Returns:

the default size.

getSize

public int getSize()

Returns the size of the compressed labels.

Returns:

The size of the compressed labels, i.e., the number of columns.

setSize

public void setSize(int size)

Sets the size of the compressed labels.

Parameters:

size - The size of the compressed labels, i.e., the number of columns.

sizeTipText

public String sizeTipText()

The tooltip for the size.

Returns:

the tooltip.

getTechnicalInformation

public weka.core.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 weka.core.TechnicalInformationHandler

Returns:

the technical information about this class

listOptions

public Enumeration listOptions()

Returns an enumeration of the options.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.SingleClassifierEnhancer

Returns:

Enumeration of the options.

getOptions

public String[] getOptions()

Returns an array with the options of the classifier.

Specified by:

getOptions in interface weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.SingleClassifierEnhancer

Returns:

Array of options.

setOptions

public void setOptions(String[] options)
                throws Exception

Sets the options to the given values in the array.

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.SingleClassifierEnhancer

Parameters:

options - The options to be set.

Throws:

Exception

transformLabels

public weka.core.Instances transformLabels(weka.core.Instances D)
                                    throws Exception

The method to transform the labels into another set of latent labels, typically a compression method is used, e.g., Boolean matrix decomposition in the case of MLC-BMaD, or matrix multiplication based on SVD for PLST.

Specified by:

transformLabels in class LabelTransformationClassifier

Parameters:

D - the instances to transform into new instances with transformed labels. The Instances consist of features and original labels.

Returns:

The resulting instances. Instances consist of features and transformed labels.

Throws:

Exception

transformPredictionsBack

public double[] transformPredictionsBack(double[] y)

Transforms the predictions of the internal classifier back to the original labels.

Specified by:

transformPredictionsBack in class LabelTransformationClassifier

Parameters:

y - The predictions that should be transformed back. The array consists only of the predictions as they are returned from the internal classifier.

Returns:

The transformed predictions.

transformInstance

public weka.core.Instance transformInstance(weka.core.Instance x)
                                     throws Exception

Transforms the instance in the prediction process before given to the internal multi-label or multi-target classifier. The instance is passed having the original set of labels, these must be replaced with the transformed labels (attributes) so that the internla classifier can predict them.

Specified by:

transformInstance in class LabelTransformationClassifier

Parameters:

x - The instance to transform. Consists of features and labels.

Returns:

The transformed instance. Consists of features and transformed labels.

Throws:

Exception

getModel

public String getModel()

Returns a string representation of the model.

Specified by:

getModel in interface MultiLabelClassifier

Returns:

the model

toString

public String toString()

Overrides:

toString in class Object

main

public static void main(String[] args)
                 throws Exception

Main method for testing.

Parameters:

args - - Arguments passed from the command line

Throws:

Exception