org.aksw.limes.core.datastrutures

Class TaskData

java.lang.Object

org.aksw.limes.core.datastrutures.TaskData

All Implemented Interfaces:

Comparable<TaskData>

public class TaskData
extends Object
implements Comparable<TaskData>

This class contains all information regarding a dataset used for evaluating an algorithm.
It includes the name, cache of the source dataset, cache of the target dataset, the mapping predicted and the gold standard

Since:

1.0

Version:

1.0

Author:

Mofeed Hassan (mounir@informatik.uni-leipzig.de)

Field Summary

Fields

Modifier and Type	Field and Description
String	dataName The name of the dataset
EvaluationData	evalData
GoldStandard	goldStandard The Gold Standard used to evaluate the machine learning algorithm. It combines the reference mapping and the source and target datasets URIs
AMapping	initialMapping
AMapping	mapping The mapping generated by the machine learning
PseudoFMeasure	pseudoFMeasure The pseudo F-Measure used by the machine learning with unsupervised implementation type
ACache	source The source data to be used by the machine learning algorithm
ACache	target The target data to be used by the machine learning algorithm
AMapping	training The training data used by the machine learning with supervised implementation type

Constructor Summary

Constructors

Constructor and Description
TaskData()
TaskData(AMapping mapping)
TaskData(AMapping mapping, GoldStandard goldStandard)
TaskData(GoldStandard goldStandard, ACache source, ACache target)
TaskData(GoldStandard goldStandard, ACache source, ACache target, EvaluationData evalData)
TaskData(GoldStandard goldStandard, AMapping mapping, ACache source, ACache target)
TaskData(String name)

Method Summary

Modifier and Type	Method and Description
int	compareTo(TaskData other)
List<String>	getSourceURIs()
List<String>	getTargetURIs()

Methods inherited from class java.lang.Object

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

Field Detail

dataName

public String dataName

The name of the dataset

source

public ACache source

The source data to be used by the machine learning algorithm

target

public ACache target

The target data to be used by the machine learning algorithm

mapping

public AMapping mapping

The mapping generated by the machine learning

training

public AMapping training

The training data used by the machine learning with supervised implementation type

pseudoFMeasure

public PseudoFMeasure pseudoFMeasure

The pseudo F-Measure used by the machine learning with unsupervised implementation type

goldStandard

public GoldStandard goldStandard

The Gold Standard used to evaluate the machine learning algorithm.
It combines the reference mapping and the source and target datasets URIs

evalData

public EvaluationData evalData

initialMapping

public AMapping initialMapping

Constructor Detail

TaskData

public TaskData()

TaskData

public TaskData(GoldStandard goldStandard,
                AMapping mapping,
                ACache source,
                ACache target)

TaskData

public TaskData(GoldStandard goldStandard,
                ACache source,
                ACache target)

TaskData

public TaskData(GoldStandard goldStandard,
                ACache source,
                ACache target,
                EvaluationData evalData)

TaskData

public TaskData(AMapping mapping,
                GoldStandard goldStandard)

TaskData

public TaskData(AMapping mapping)

TaskData

public TaskData(String name)

Method Detail

getSourceURIs

public List<String> getSourceURIs()

getTargetURIs

public List<String> getTargetURIs()

compareTo

public int compareTo(TaskData other)

Specified by:

compareTo in interface Comparable<TaskData>