org.dllearner.core

Class AbstractLearningProblem<T extends Score,V extends org.semanticweb.owlapi.model.OWLObject,W extends EvaluatedHypothesis<V,T>>

java.lang.Object

org.dllearner.core.AbstractComponent

org.dllearner.core.AbstractLearningProblem<T,V,W>

All Implemented Interfaces:

Component, LearningProblem

Direct Known Subclasses:

AbstractClassExpressionLearningProblem, PropertyAxiomLearningProblem

public abstract class AbstractLearningProblem<T extends Score,V extends org.semanticweb.owlapi.model.OWLObject,W extends EvaluatedHypothesis<V,T>>
extends AbstractComponent
implements LearningProblem

Base class for all learning problems. See also the wiki page for DL-Learner-Architecture.

Author:

Jens Lehmann

Constructor Summary

Constructors

Constructor and Description
AbstractLearningProblem()
AbstractLearningProblem(AbstractReasonerComponent reasoner) Constructs a learning problem using a reasoning service for querying the background knowledge.

Method Summary

Modifier and Type	Method and Description
void	changeReasonerComponent(AbstractReasonerComponent reasoner) Method to exchange the reasoner underlying the learning problem.
T	computeScore(V hypothesis) Computes the Score of a given hypothesis with respect to this learning problem.
abstract T	computeScore(V hypothesis, double noise) Computes the Score of a given hypothesis with respect to this learning problem.
W	evaluate(V hypothesis) Evaluates the hypothesis by computing the score and returning an evaluated hypothesis of the correct type (ClassLearningProblem returns EvaluatedDescriptionClass instead of generic EvaluatedDescription).
W	evaluate(V hypothesis, double noise) Evaluates the hypothesis by computing the score and returning an evaluated hypothesis of the correct type (ClassLearningProblem returns EvaluatedDescriptionClass instead of generic EvaluatedDescription).
double	getAccuracyOrTooWeak(V object) This method returns a value, which indicates how accurate a hypothesis solves a learning problem.
abstract double	getAccuracyOrTooWeak(V hypothesis, double noise) This method computes the accuracy and returns -1 instead of the accuracy if the accuracy of the hypothesis is below the given threshold and the accuracy of all more special w.r.t.
AbstractReasonerComponent	getReasoner() Implementations of learning problems can use this class variable to perform reasoner operations.
void	setReasoner(AbstractReasonerComponent reasoner)

Methods inherited from class org.dllearner.core.AbstractComponent

isInitialized

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.dllearner.core.Component

init

Constructor Detail

AbstractLearningProblem

public AbstractLearningProblem()

AbstractLearningProblem

public AbstractLearningProblem(AbstractReasonerComponent reasoner)

Constructs a learning problem using a reasoning service for querying the background knowledge. It can be used for evaluating solution candidates.

Parameters:

reasoner - The reasoning service used as background knowledge.

Method Detail

changeReasonerComponent

public void changeReasonerComponent(AbstractReasonerComponent reasoner)

Method to exchange the reasoner underlying the learning problem. Implementations, which do not only use the provided reasoning service class variable, must make sure that a call to this method indeed changes the reasoning service.

Parameters:

reasoner - New reasoning service.

computeScore

public T computeScore(V hypothesis)

Computes the Score of a given hypothesis with respect to this learning problem. This can (but does not need to) be used by learning algorithms to measure how good the hypothesis fits the learning problem. Score objects are used to store e.g. covered examples, accuracy etc., so often it is more efficient to only create score objects for promising hypotheses.

Parameters:

hypothesis - A hypothesis (as solution candidate for this learning problem).

Returns:

A Score object.

computeScore

public abstract T computeScore(V hypothesis,
                               double noise)

Computes the Score of a given hypothesis with respect to this learning problem. This can (but does not need to) be used by learning algorithms to measure how good the hypothesis fits the learning problem. Score objects are used to store e.g. covered examples, accuracy etc., so often it is more efficient to only create score objects for promising hypotheses.

Parameters:

hypothesis - A hypothesis (as solution candidate for this learning problem).

noise - the (approximated) value of noise within the examples

Returns:

A Score object.

evaluate

public W evaluate(V hypothesis)

Evaluates the hypothesis by computing the score and returning an evaluated hypothesis of the correct type (ClassLearningProblem returns EvaluatedDescriptionClass instead of generic EvaluatedDescription).

Parameters:

hypothesis - Hypothesis to evaluate.

Returns:

an evaluated hypothesis

evaluate

public W evaluate(V hypothesis,
                  double noise)

Evaluates the hypothesis by computing the score and returning an evaluated hypothesis of the correct type (ClassLearningProblem returns EvaluatedDescriptionClass instead of generic EvaluatedDescription).

Parameters:

hypothesis - Hypothesis to evaluate.

noise - the (approximated) value of noise within the examples

Returns:

an evaluated hypothesis

getAccuracyOrTooWeak

public double getAccuracyOrTooWeak(V object)

This method returns a value, which indicates how accurate a hypothesis solves a learning problem. There can be different ways to compute accuracy depending on the type of learning problem and other factors. However, all implementations are required to return a value between 0 and 1, where 1 stands for the highest possible accuracy and 0 for the lowest possible accuracy.

Returns:

A value between 0 and 1 indicating the quality (of a hypothesis). or -1 as described above.

getAccuracyOrTooWeak

public abstract double getAccuracyOrTooWeak(V hypothesis,
                                            double noise)

This method computes the accuracy and returns -1 instead of the accuracy if

1. the accuracy of the hypothesis is below the given threshold and
2. the accuracy of all more special w.r.t. subsumption hypotheses is below the given threshold.

This is used for efficiency reasons, i.e. -1 can be returned instantly if it is clear that the hypothesis and all its refinements are not sufficiently accurate.

Returns:

A value between 0 and 1 indicating the quality (of a hypothesis) or -1 as described above.

getReasoner

public AbstractReasonerComponent getReasoner()

Implementations of learning problems can use this class variable to perform reasoner operations.

setReasoner

@Autowired(required=false)
public void setReasoner(AbstractReasonerComponent reasoner)