CELOEConfigurator (DL-Learner Javadoc)

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org.dllearner.core.configurators
Class CELOEConfigurator

java.lang.Object
  org.dllearner.core.configurators.RefinementOperatorConfigurator
      org.dllearner.core.configurators.CELOEConfigurator

All Implemented Interfaces:: Configurator

public class CELOEConfigurator
extends RefinementOperatorConfigurator
implements Configurator
extends RefinementOperatorConfigurator
implements Configurator

automatically generated, do not edit manually. run org.dllearner.scripts.ConfigJavaGenerator to update

Constructor Summary
`CELOEConfigurator(CELOE cELOE)`

Method Summary
`Set<String>`	`getAllowedConcepts()` allowedConcepts concepts the algorithm is allowed to use.
`int`	`getCardinalityLimit()` cardinalityLimit Gives the maximum number used in cardinality restrictions..
`static CELOE`	`getCELOE(AbstractLearningProblem learningProblem, AbstractReasonerComponent reasoningService)`
`double`	`getExpansionPenaltyFactor()` expansionPenaltyFactor heuristic penalty per syntactic construct used (lower = finds more complex expression, but might miss simple ones).
`boolean`	`getFilterDescriptionsFollowingFromKB()` filterDescriptionsFollowingFromKB If true, then the results will not contain suggestions, which already follow logically from the knowledge base.
`Set<String>`	`getIgnoredConcepts()` ignoredConcepts concepts the algorithm must ignore.
`boolean`	`getInstanceBasedDisjoints()` instanceBasedDisjoints Specifies whether to use real disjointness checks or instance based ones (no common instances) in the refinement operator..
`int`	`getMaxClassDescriptionTests()` maxClassDescriptionTests The maximum number of candidate hypothesis the algorithm is allowed to test (0 = no limit).
`int`	`getMaxDepth()` maxDepth maximum depth of description.
`int`	`getMaxExecutionTimeInSeconds()` maxExecutionTimeInSeconds algorithm will stop after specified seconds.
`int`	`getMaxNrOfResults()` maxNrOfResults Sets the maximum number of results one is interested in.
`double`	`getNoisePercentage()` noisePercentage the (approximated) percentage of noise within the examples.
`boolean`	`getReplaceSearchTree()` replaceSearchTree specifies whether to replace the search tree in the log file after each run or append the new search tree.
`boolean`	`getReuseExistingDescription()` reuseExistingDescription If true, the algorithm tries to find a good starting point close to an existing definition/super class of the given class in the knowledge base..
`String`	`getSearchTreeFile()` searchTreeFile file to use for the search tree.
`boolean`	`getSingleSuggestionMode()` singleSuggestionMode Use this if you are interested in only one suggestion and your learning problem has many (more than 1000) examples..
`boolean`	`getTerminateOnNoiseReached()` terminateOnNoiseReached specifies whether to terminate when noise criterion is met.
`boolean`	`getUseAllConstructor()` useAllConstructor specifies whether the universal concept constructor is used in the learning algorithm.
`boolean`	`getUseBooleanDatatypes()` useBooleanDatatypes specifies whether boolean datatypes are used in the learning algorothm.
`boolean`	`getUseCardinalityRestrictions()` useCardinalityRestrictions specifies whether CardinalityRestrictions is used in the learning algorithm.
`boolean`	`getUseDataHasValueConstructor()` useDataHasValueConstructor specifies whether the hasValue constructor is used in the learning algorithm in combination with data properties.
`boolean`	`getUseDoubleDatatypes()` useDoubleDatatypes specifies whether double datatypes are used in the learning algorothm.
`boolean`	`getUseExistsConstructor()` useExistsConstructor specifies whether the existential concept constructor is used in the learning algorithm.
`boolean`	`getUseHasValueConstructor()` useHasValueConstructor specifies whether the hasValue constructor is used in the learning algorithm.
`boolean`	`getUseNegation()` useNegation specifies whether negation is used in the learning algorothm.
`int`	`getValueFrequencyThreshold()` valueFrequencyThreshold specifies how often an object must occur as value in order to be considered for hasValue restrictions.
`boolean`	`getWriteSearchTree()` writeSearchTree specifies whether to write a search tree.
`boolean`	`isReinitNecessary()` true, if this component needs reinitializsation.
`void`	`setAllowedConcepts(Set<String> allowedConcepts)`
`void`	`setCardinalityLimit(int cardinalityLimit)`
`void`	`setExpansionPenaltyFactor(double expansionPenaltyFactor)`
`void`	`setFilterDescriptionsFollowingFromKB(boolean filterDescriptionsFollowingFromKB)`
`void`	`setIgnoredConcepts(Set<String> ignoredConcepts)`
`void`	`setInstanceBasedDisjoints(boolean instanceBasedDisjoints)`
`void`	`setMaxClassDescriptionTests(int maxClassDescriptionTests)`
`void`	`setMaxDepth(int maxDepth)`
`void`	`setMaxExecutionTimeInSeconds(int maxExecutionTimeInSeconds)`
`void`	`setMaxNrOfResults(int maxNrOfResults)`
`void`	`setNoisePercentage(double noisePercentage)`
`void`	`setReplaceSearchTree(boolean replaceSearchTree)`
`void`	`setReuseExistingDescription(boolean reuseExistingDescription)`
`void`	`setSearchTreeFile(String searchTreeFile)`
`void`	`setSingleSuggestionMode(boolean singleSuggestionMode)`
`void`	`setTerminateOnNoiseReached(boolean terminateOnNoiseReached)`
`void`	`setUseAllConstructor(boolean useAllConstructor)`
`void`	`setUseBooleanDatatypes(boolean useBooleanDatatypes)`
`void`	`setUseCardinalityRestrictions(boolean useCardinalityRestrictions)`
`void`	`setUseDataHasValueConstructor(boolean useDataHasValueConstructor)`
`void`	`setUseDoubleDatatypes(boolean useDoubleDatatypes)`
`void`	`setUseExistsConstructor(boolean useExistsConstructor)`
`void`	`setUseHasValueConstructor(boolean useHasValueConstructor)`
`void`	`setUseNegation(boolean useNegation)`
`void`	`setValueFrequencyThreshold(int valueFrequencyThreshold)`
`void`	`setWriteSearchTree(boolean writeSearchTree)`

Methods inherited from class org.dllearner.core.configurators.RefinementOperatorConfigurator
`getUseStringDatatypes`

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

Constructor Detail

CELOEConfigurator

public CELOEConfigurator(CELOE cELOE)

Parameters:: cELOE - see CELOE

Method Detail

getCELOE

public static CELOE getCELOE(AbstractLearningProblem learningProblem,
                             AbstractReasonerComponent reasoningService)
                      throws LearningProblemUnsupportedException

Parameters:: reasoningService - see reasoningService; learningProblem - see learningProblem
Returns:: CELOE
Throws:: LearningProblemUnsupportedException - see

getUseAllConstructor

public boolean getUseAllConstructor()

useAllConstructor specifies whether the universal concept constructor is used in the learning algorithm. mandatory: false| reinit necessary: true default value: true

Specified by:: getUseAllConstructor in class RefinementOperatorConfigurator

Returns:: boolean

getUseExistsConstructor

public boolean getUseExistsConstructor()

useExistsConstructor specifies whether the existential concept constructor is used in the learning algorithm. mandatory: false| reinit necessary: true default value: true

Specified by:: getUseExistsConstructor in class RefinementOperatorConfigurator

Returns:: boolean

getUseHasValueConstructor

public boolean getUseHasValueConstructor()

useHasValueConstructor specifies whether the hasValue constructor is used in the learning algorithm. mandatory: false| reinit necessary: true default value: false

Overrides:: getUseHasValueConstructor in class RefinementOperatorConfigurator

Returns:: boolean

getUseDataHasValueConstructor

public boolean getUseDataHasValueConstructor()

useDataHasValueConstructor specifies whether the hasValue constructor is used in the learning algorithm in combination with data properties. mandatory: false| reinit necessary: true default value: false

Overrides:: getUseDataHasValueConstructor in class RefinementOperatorConfigurator

Returns:: boolean

getValueFrequencyThreshold

public int getValueFrequencyThreshold()

valueFrequencyThreshold specifies how often an object must occur as value in order to be considered for hasValue restrictions. mandatory: false| reinit necessary: true default value: 3

Overrides:: getValueFrequencyThreshold in class RefinementOperatorConfigurator

Returns:: int

getUseCardinalityRestrictions

public boolean getUseCardinalityRestrictions()

useCardinalityRestrictions specifies whether CardinalityRestrictions is used in the learning algorithm. mandatory: false| reinit necessary: true default value: true

Specified by:: getUseCardinalityRestrictions in class RefinementOperatorConfigurator

Returns:: boolean

getCardinalityLimit

public int getCardinalityLimit()

cardinalityLimit Gives the maximum number used in cardinality restrictions.. mandatory: false| reinit necessary: true default value: 5

Overrides:: getCardinalityLimit in class RefinementOperatorConfigurator

Returns:: int

getUseNegation

public boolean getUseNegation()

useNegation specifies whether negation is used in the learning algorothm. mandatory: false| reinit necessary: true default value: false

Specified by:: getUseNegation in class RefinementOperatorConfigurator

Returns:: boolean

getUseBooleanDatatypes

public boolean getUseBooleanDatatypes()

useBooleanDatatypes specifies whether boolean datatypes are used in the learning algorothm. mandatory: false| reinit necessary: true default value: true

Specified by:: getUseBooleanDatatypes in class RefinementOperatorConfigurator

Returns:: boolean

getUseDoubleDatatypes

public boolean getUseDoubleDatatypes()

useDoubleDatatypes specifies whether double datatypes are used in the learning algorothm. mandatory: false| reinit necessary: true default value: true

Overrides:: getUseDoubleDatatypes in class RefinementOperatorConfigurator

Returns:: boolean

getMaxExecutionTimeInSeconds

public int getMaxExecutionTimeInSeconds()

maxExecutionTimeInSeconds algorithm will stop after specified seconds. mandatory: false| reinit necessary: true default value: 10

Returns:: int

getNoisePercentage

public double getNoisePercentage()

noisePercentage the (approximated) percentage of noise within the examples. mandatory: false| reinit necessary: true default value: 0.0

Returns:: double

getTerminateOnNoiseReached

public boolean getTerminateOnNoiseReached()

terminateOnNoiseReached specifies whether to terminate when noise criterion is met. mandatory: false| reinit necessary: true default value: false

Returns:: boolean

getMaxDepth

public int getMaxDepth()

maxDepth maximum depth of description. mandatory: false| reinit necessary: true default value: 7

Returns:: int

getMaxNrOfResults

public int getMaxNrOfResults()

maxNrOfResults Sets the maximum number of results one is interested in. (Setting this to a lower value may increase performance as the learning algorithm has to store/evaluate/beautify less descriptions).. mandatory: false| reinit necessary: true default value: 10

Returns:: int

getMaxClassDescriptionTests

public int getMaxClassDescriptionTests()

maxClassDescriptionTests The maximum number of candidate hypothesis the algorithm is allowed to test (0 = no limit). The algorithm will stop afterwards. (The real number of tests can be slightly higher, because this criterion usually won't be checked after each single test.). mandatory: false| reinit necessary: true default value: 0

Returns:: int

getSingleSuggestionMode

public boolean getSingleSuggestionMode()

singleSuggestionMode Use this if you are interested in only one suggestion and your learning problem has many (more than 1000) examples.. mandatory: false| reinit necessary: true default value: false

Returns:: boolean

getInstanceBasedDisjoints

public boolean getInstanceBasedDisjoints()

instanceBasedDisjoints Specifies whether to use real disjointness checks or instance based ones (no common instances) in the refinement operator.. mandatory: false| reinit necessary: true default value: true

Specified by:: getInstanceBasedDisjoints in class RefinementOperatorConfigurator

Returns:: boolean

getFilterDescriptionsFollowingFromKB

public boolean getFilterDescriptionsFollowingFromKB()

filterDescriptionsFollowingFromKB If true, then the results will not contain suggestions, which already follow logically from the knowledge base. Be careful, since this requires a potentially expensive consistency check for candidate solutions.. mandatory: false| reinit necessary: true default value: false

Returns:: boolean

getReuseExistingDescription

public boolean getReuseExistingDescription()

reuseExistingDescription If true, the algorithm tries to find a good starting point close to an existing definition/super class of the given class in the knowledge base.. mandatory: false| reinit necessary: true default value: false

Returns:: boolean

getWriteSearchTree

public boolean getWriteSearchTree()

writeSearchTree specifies whether to write a search tree. mandatory: false| reinit necessary: true default value: false

Returns:: boolean

getSearchTreeFile

public String getSearchTreeFile()

searchTreeFile file to use for the search tree. mandatory: false| reinit necessary: true default value: log/searchTree.txt

Returns:: String

getReplaceSearchTree

public boolean getReplaceSearchTree()

replaceSearchTree specifies whether to replace the search tree in the log file after each run or append the new search tree. mandatory: false| reinit necessary: true default value: false

Returns:: boolean

getExpansionPenaltyFactor

public double getExpansionPenaltyFactor()

expansionPenaltyFactor heuristic penalty per syntactic construct used (lower = finds more complex expression, but might miss simple ones). mandatory: false| reinit necessary: true default value: 0.1

Returns:: double

getAllowedConcepts

public Set<String> getAllowedConcepts()

allowedConcepts concepts the algorithm is allowed to use. mandatory: false| reinit necessary: true default value: null

Returns:: Set(String)

getIgnoredConcepts

public Set<String> getIgnoredConcepts()

ignoredConcepts concepts the algorithm must ignore. mandatory: false| reinit necessary: true default value: null

Returns:: Set(String)

setUseAllConstructor

public void setUseAllConstructor(boolean useAllConstructor)

Parameters:: useAllConstructor - specifies whether the universal concept constructor is used in the learning algorithm. mandatory: false| reinit necessary: true default value: true

setUseExistsConstructor

public void setUseExistsConstructor(boolean useExistsConstructor)

Parameters:: useExistsConstructor - specifies whether the existential concept constructor is used in the learning algorithm. mandatory: false| reinit necessary: true default value: true

setUseHasValueConstructor

public void setUseHasValueConstructor(boolean useHasValueConstructor)

Parameters:: useHasValueConstructor - specifies whether the hasValue constructor is used in the learning algorithm. mandatory: false| reinit necessary: true default value: false

setUseDataHasValueConstructor

public void setUseDataHasValueConstructor(boolean useDataHasValueConstructor)

Parameters:: useDataHasValueConstructor - specifies whether the hasValue constructor is used in the learning algorithm in combination with data properties. mandatory: false| reinit necessary: true default value: false

setValueFrequencyThreshold

public void setValueFrequencyThreshold(int valueFrequencyThreshold)

Parameters:: valueFrequencyThreshold - specifies how often an object must occur as value in order to be considered for hasValue restrictions. mandatory: false| reinit necessary: true default value: 3

setUseCardinalityRestrictions

public void setUseCardinalityRestrictions(boolean useCardinalityRestrictions)

Parameters:: useCardinalityRestrictions - specifies whether CardinalityRestrictions is used in the learning algorithm. mandatory: false| reinit necessary: true default value: true

setCardinalityLimit

public void setCardinalityLimit(int cardinalityLimit)

Parameters:: cardinalityLimit - Gives the maximum number used in cardinality restrictions.. mandatory: false| reinit necessary: true default value: 5

setUseNegation

public void setUseNegation(boolean useNegation)

Parameters:: useNegation - specifies whether negation is used in the learning algorothm. mandatory: false| reinit necessary: true default value: false

setUseBooleanDatatypes

public void setUseBooleanDatatypes(boolean useBooleanDatatypes)

Parameters:: useBooleanDatatypes - specifies whether boolean datatypes are used in the learning algorothm. mandatory: false| reinit necessary: true default value: true

setUseDoubleDatatypes

public void setUseDoubleDatatypes(boolean useDoubleDatatypes)

Parameters:: useDoubleDatatypes - specifies whether double datatypes are used in the learning algorothm. mandatory: false| reinit necessary: true default value: true

setMaxExecutionTimeInSeconds

public void setMaxExecutionTimeInSeconds(int maxExecutionTimeInSeconds)

Parameters:: maxExecutionTimeInSeconds - algorithm will stop after specified seconds. mandatory: false| reinit necessary: true default value: 10

setNoisePercentage

public void setNoisePercentage(double noisePercentage)

Parameters:: noisePercentage - the (approximated) percentage of noise within the examples. mandatory: false| reinit necessary: true default value: 0.0

setTerminateOnNoiseReached

public void setTerminateOnNoiseReached(boolean terminateOnNoiseReached)

Parameters:: terminateOnNoiseReached - specifies whether to terminate when noise criterion is met. mandatory: false| reinit necessary: true default value: false

setMaxDepth

public void setMaxDepth(int maxDepth)

Parameters:: maxDepth - maximum depth of description. mandatory: false| reinit necessary: true default value: 7

setMaxNrOfResults

public void setMaxNrOfResults(int maxNrOfResults)

Parameters:: maxNrOfResults - Sets the maximum number of results one is interested in. (Setting this to a lower value may increase performance as the learning algorithm has to store/evaluate/beautify less descriptions).. mandatory: false| reinit necessary: true default value: 10

setMaxClassDescriptionTests

public void setMaxClassDescriptionTests(int maxClassDescriptionTests)

Parameters:: maxClassDescriptionTests - The maximum number of candidate hypothesis the algorithm is allowed to test (0 = no limit). The algorithm will stop afterwards. (The real number of tests can be slightly higher, because this criterion usually won't be checked after each single test.). mandatory: false| reinit necessary: true default value: 0

setSingleSuggestionMode

public void setSingleSuggestionMode(boolean singleSuggestionMode)

Parameters:: singleSuggestionMode - Use this if you are interested in only one suggestion and your learning problem has many (more than 1000) examples.. mandatory: false| reinit necessary: true default value: false

setInstanceBasedDisjoints

public void setInstanceBasedDisjoints(boolean instanceBasedDisjoints)

Parameters:: instanceBasedDisjoints - Specifies whether to use real disjointness checks or instance based ones (no common instances) in the refinement operator.. mandatory: false| reinit necessary: true default value: true

setFilterDescriptionsFollowingFromKB

public void setFilterDescriptionsFollowingFromKB(boolean filterDescriptionsFollowingFromKB)

Parameters:: filterDescriptionsFollowingFromKB - If true, then the results will not contain suggestions, which already follow logically from the knowledge base. Be careful, since this requires a potentially expensive consistency check for candidate solutions.. mandatory: false| reinit necessary: true default value: false

setReuseExistingDescription

public void setReuseExistingDescription(boolean reuseExistingDescription)

Parameters:: reuseExistingDescription - If true, the algorithm tries to find a good starting point close to an existing definition/super class of the given class in the knowledge base.. mandatory: false| reinit necessary: true default value: false

setWriteSearchTree

public void setWriteSearchTree(boolean writeSearchTree)

Parameters:: writeSearchTree - specifies whether to write a search tree. mandatory: false| reinit necessary: true default value: false

setSearchTreeFile

public void setSearchTreeFile(String searchTreeFile)

Parameters:: searchTreeFile - file to use for the search tree. mandatory: false| reinit necessary: true default value: log/searchTree.txt

setReplaceSearchTree

public void setReplaceSearchTree(boolean replaceSearchTree)

Parameters:: replaceSearchTree - specifies whether to replace the search tree in the log file after each run or append the new search tree. mandatory: false| reinit necessary: true default value: false

setExpansionPenaltyFactor

public void setExpansionPenaltyFactor(double expansionPenaltyFactor)

Parameters:: expansionPenaltyFactor - heuristic penalty per syntactic construct used (lower = finds more complex expression, but might miss simple ones). mandatory: false| reinit necessary: true default value: 0.1

setAllowedConcepts

public void setAllowedConcepts(Set<String> allowedConcepts)

Parameters:: allowedConcepts - concepts the algorithm is allowed to use. mandatory: false| reinit necessary: true default value: null

setIgnoredConcepts

public void setIgnoredConcepts(Set<String> ignoredConcepts)

Parameters:: ignoredConcepts - concepts the algorithm must ignore. mandatory: false| reinit necessary: true default value: null

isReinitNecessary

public boolean isReinitNecessary()

true, if this component needs reinitializsation.

Returns:: boolean