LinearSelfConfigurator (LIMES 1.5.3 API)

java.lang.Object
- org.aksw.limes.core.ml.algorithm.euclid.LinearSelfConfigurator

All Implemented Interfaces:

ISelfConfigurator

Direct Known Subclasses:

BooleanSelfConfigurator
```
public class LinearSelfConfigurator
extends Object
implements ISelfConfigurator
```
Author:

Axel-C. Ngonga Ngomo (ngonga@informatik.uni-leipzig.de), Mohamed Sherif (sherif@informatik.uni-leipzig.de), Klaus Lyko (lyko@informatik.uni-leipzig.de)

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class LinearSelfConfigurator.QMeasureType

static class LinearSelfConfigurator.Strategy

Nested Classes
Modifier and Type	Class and Description
`static class`	`LinearSelfConfigurator.QMeasureType`
`static class`	`LinearSelfConfigurator.Strategy`

Field Summary

Fields
Modifier and Type	Field and Description
`AMapping`	`asked`
`int`	`ITERATIONS_MAX`
`double`	`kappa`
`double`	`learningRate`
`double`	`min_coverage`
`double`	`MIN_THRESHOLD`
`ACache`	`source`
`boolean`	`STRICT`
`ACache`	`target`

Constructor Summary

Constructors
Constructor and Description
`LinearSelfConfigurator(ACache source, ACache target)` Constructor
`LinearSelfConfigurator(ACache source, ACache target, double minCoverage, double beta)` Constructor
`LinearSelfConfigurator(ACache source, ACache target, double minCoverage, double beta, Map<String,String> measures)` Constructor

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`computeMeasure(ACache source, ACache target, String[] parameters)`
`double`	`computeNext(List<SimpleClassifier> classifiers, int index)` Aims to improve upon a particular classifier by checking whether adding a delta to its similarity worsens the total classifer
`Double`	`computeQuality(AMapping map)` Method to compute quality of a mapping.
`AMapping`	`execute(String sourceProperty, String targetProperty, String measure, double threshold)` Runs measure(sourceProperty, targetProperty) >= threshold
`AMapping`	`executeClassifier(SimpleClassifier c, double threshold)` Runs a classifier and get the mappings for it
`List<SimpleClassifier>`	`getAllInitialClassifiers()` Computes all initial classifiers that compare properties whose coverage is beyong the coverage threshold
`List<SimpleClassifier>`	`getBestInitialClassifiers()` Computes the best initial mapping for each source property
`List<SimpleClassifier>`	`getBestInitialClassifiers(Set<String> measureList)` Computes the best initial mapping for each source property
`AMapping`	`getBestOneToOneMapping(AMapping m)` Gets the best target for each source and returns it
`List<SimpleClassifier>`	`getInitialOverallClassifiers(List<SimpleClassifier> classifiers)` Updates the weights of the classifiers such that they map the initial conditions for a classifier
`LinkSpecification`	`getLinkSpecification(List<SimpleClassifier> list)` TODO FIXME this is only a basic implementation
`AMapping`	`getMapping(List<SimpleClassifier> classifiers)` Runs classifiers and retrieves the corresponding mappings
`String`	`getMeasure()`
`AMapping`	`getOverallMapping(Map<SimpleClassifier,AMapping> mappings, double threshold)` Computes the weighted linear combination of the similarity computed by the single classifiers
`static Map<String,Double>`	`getPropertyStats(ACache c, double minCoverage)` Extracts all properties from a cache that have a coverage beyond minCoverage
`static String`	`getPropertyType(ACache c, String p)`
`AMapping`	`getResults()`
`ACache`	`getSource()`
`ACache`	`getTarget()`
`String`	`getThreshold()`
`List<SimpleClassifier>`	`learnClassifer(List<SimpleClassifier> classifiers)`
`AMapping`	`minimizeToKnow(AMapping map)`
`List<SimpleClassifier>`	`normalizeClassifiers(List<SimpleClassifier> classifiers)`
`void`	`setDefaultMeasures()` set default atomic measures
`void`	`setMeasure(AMeasure measure)`
`void`	`setMeasure(IQualitativeMeasure pfm)` Set PFMs based upon name.
`void`	`setPFMType(LinearSelfConfigurator.QMeasureType qMeasureType)` Set PFMs based upon name.
`void`	`setSource(ACache source)`
`void`	`setSupervisedBatch(AMapping reference)` Set caches to trimmed caches according to the given reference mapping.
`void`	`setTarget(ACache target)`

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - STRICT
```
public boolean STRICT
```
  - ITERATIONS_MAX
```
public int ITERATIONS_MAX
```
  - MIN_THRESHOLD
```
public double MIN_THRESHOLD
```
  - source
```
public ACache source
```
  - target
```
public ACache target
```
  - learningRate
```
public double learningRate
```
  - kappa
```
public double kappa
```
  - min_coverage
```
public double min_coverage
```
  - asked
```
public AMapping asked
```
- Constructor Detail
  - LinearSelfConfigurator
```
public LinearSelfConfigurator(ACache source,
                              ACache target)
```
    Constructor
    
    Parameters:
    
    source - Source cache
    
    target - Target cache
  - LinearSelfConfigurator
```
public LinearSelfConfigurator(ACache source,
                              ACache target,
                              double minCoverage,
                              double beta,
                              Map<String,String> measures)
```
    Constructor
    
    Parameters:
    
    source - Source cache
    
    target - Target cache
    
    minCoverage - Minimal coverage for a property to be considered for linking
    
    beta - Beta value for computing F_beta
    
    measures - Atomic measures
  - LinearSelfConfigurator
```
public LinearSelfConfigurator(ACache source,
                              ACache target,
                              double minCoverage,
                              double beta)
```
    Constructor
    
    Parameters:
    
    source - Source cache
    
    target - Target cache
    
    beta - Beta value for computing F_beta
    
    minCoverage - Minimal coverage for a property to be considered for linking
- Method Detail
  - setPFMType
```
public void setPFMType(LinearSelfConfigurator.QMeasureType qMeasureType)
```
    Set PFMs based upon name. if name.equals("reference") using ReferencePseudoMeasures.class: Nikolov/D'Aquin/Motta ESWC 2012.
  - setMeasure
```
public void setMeasure(IQualitativeMeasure pfm)
```
    Set PFMs based upon name. if name.equals("reference") using ReferencePseudoMeasures.class: Nikolov/D'Aquin/Motta ESWC 2012.
  - setDefaultMeasures
```
public void setDefaultMeasures()
```
    set default atomic measures
  - computeMeasure
```
public void computeMeasure(ACache source,
                           ACache target,
                           String[] parameters)
```
    Specified by:
    
    computeMeasure in interface ISelfConfigurator
  - getThreshold
```
public String getThreshold()
```
    Specified by:
    
    getThreshold in interface ISelfConfigurator
  - getResults
```
public AMapping getResults()
```
    Specified by:
    
    getResults in interface ISelfConfigurator
  - getPropertyStats
```
public static Map<String,Double> getPropertyStats(ACache c,
                                                  double minCoverage)
```
    Extracts all properties from a cache that have a coverage beyond minCoverage
    
    Parameters:
    
    c - Input cache
    
    minCoverage - Threshold for coverage
    
    Returns:
    
    Map of property to coverage
  - getMeasure
```
public String getMeasure()
```
    Specified by:
    
    getMeasure in interface ISelfConfigurator
  - setMeasure
```
public void setMeasure(AMeasure measure)
```
    Specified by:
    
    setMeasure in interface ISelfConfigurator
  - getPropertyType
```
public static String getPropertyType(ACache c,
                                     String p)
```
  - getAllInitialClassifiers
```
public List<SimpleClassifier> getAllInitialClassifiers()
```
    Computes all initial classifiers that compare properties whose coverage is beyong the coverage threshold
    
    Returns:
    
    A map of sourceProperty to targetProperty to Classifier
  - getBestInitialClassifiers
```
public List<SimpleClassifier> getBestInitialClassifiers()
```
    Computes the best initial mapping for each source property
    
    Returns:
    
    List of classifiers that each contain the best initial mappings
  - getBestInitialClassifiers
```
public List<SimpleClassifier> getBestInitialClassifiers(Set<String> measureList)
```
    Computes the best initial mapping for each source property
    
    Parameters:
    
    measureList - Define Measures to be used by their name: eg. "jaccard", "levenshtein", "trigrams", "cosine", ...
    
    Returns:
    
    List of classifiers that each contain the best initial mappings
  - getMapping
```
public AMapping getMapping(List<SimpleClassifier> classifiers)
```
    Runs classifiers and retrieves the corresponding mappings
    
    Parameters:
    
    classifiers - List of classifiers
    
    Returns:
    
    AMapping generated by the list of classifiers
  - getOverallMapping
```
public AMapping getOverallMapping(Map<SimpleClassifier,AMapping> mappings,
                                  double threshold)
```
    Computes the weighted linear combination of the similarity computed by the single classifiers
    
    Parameters:
    
    mappings - Maps classifiers to their results
    
    threshold - Similarity threshold for exclusion
    
    Returns:
    
    Resulting overall mapping
  - normalizeClassifiers
```
public List<SimpleClassifier> normalizeClassifiers(List<SimpleClassifier> classifiers)
```
  - getInitialOverallClassifiers
```
public List<SimpleClassifier> getInitialOverallClassifiers(List<SimpleClassifier> classifiers)
```
    Updates the weights of the classifiers such that they map the initial conditions for a classifier
    
    Parameters:
    
    classifiers - Input classifiers
    
    Returns:
    
    Normed classifiers
  - computeNext
```
public double computeNext(List<SimpleClassifier> classifiers,
                          int index)
```
    Aims to improve upon a particular classifier by checking whether adding a delta to its similarity worsens the total classifer
  - learnClassifer
```
public List<SimpleClassifier> learnClassifer(List<SimpleClassifier> classifiers)
```
  - executeClassifier
```
public AMapping executeClassifier(SimpleClassifier c,
                                  double threshold)
```
    Runs a classifier and get the mappings for it
    
    Parameters:
    
    c - Classifier
    
    threshold - Threshold for similarities
    
    Returns:
    
    Corresponding mapping
  - execute
```
public AMapping execute(String sourceProperty,
                        String targetProperty,
                        String measure,
                        double threshold)
```
    Runs measure(sourceProperty, targetProperty) >= threshold
    
    Parameters:
    
    sourceProperty - Source property
    
    targetProperty - Target property
    
    measure - Similarity measure
    
    threshold - Similarity threshold
    
    Returns:
    
    Correspoding AMapping
  - getBestOneToOneMapping
```
public AMapping getBestOneToOneMapping(AMapping m)
```
    Gets the best target for each source and returns it
    
    Parameters:
    
    m -
    
    Returns:
  - computeQuality
```
public Double computeQuality(AMapping map)
```
    Method to compute quality of a mapping. Uses per default the specified PFM qMeasure. TODO: active learning variant.
    
    Parameters:
    
    map -
    
    Returns:
  - setSupervisedBatch
```
public void setSupervisedBatch(AMapping reference)
```
    Set caches to trimmed caches according to the given reference mapping.
    
    Parameters:
    
    reference -
  - minimizeToKnow
```
public AMapping minimizeToKnow(AMapping map)
```
  - getSource
```
public ACache getSource()
```
  - setSource
```
public void setSource(ACache source)
```
  - getTarget
```
public ACache getTarget()
```
  - setTarget
```
public void setTarget(ACache target)
```
  - getLinkSpecification
```
public LinkSpecification getLinkSpecification(List<SimpleClassifier> list)
```
    TODO FIXME this is only a basic implementation
    
    Parameters:
    
    list -
    
    Returns:

Class LinearSelfConfigurator

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

STRICT

ITERATIONS_MAX

MIN_THRESHOLD

source

target

learningRate

kappa

min_coverage

asked

Constructor Detail

LinearSelfConfigurator

LinearSelfConfigurator

LinearSelfConfigurator

Method Detail

setPFMType

setMeasure

setDefaultMeasures

computeMeasure

getThreshold

getResults

getPropertyStats

getMeasure

setMeasure

getPropertyType

getAllInitialClassifiers

getBestInitialClassifiers

getBestInitialClassifiers

getMapping

getOverallMapping

normalizeClassifiers

getInitialOverallClassifiers

computeNext

learnClassifer

executeClassifier

execute

getBestOneToOneMapping

computeQuality

setSupervisedBatch

minimizeToKnow

getSource

setSource

getTarget

setTarget

getLinkSpecification