[DL-Learner SVN] SF.net SVN: dl-learner:[1591] trunk/src/dl-learner/org/dllearner

[<jen...@us...>]

Revision: 1591
          http://dl-learner.svn.sourceforge.net/dl-learner/?rev=1591&view=rev
Author:   jenslehmann
Date:     2009-02-09 16:41:44 +0000 (Mon, 09 Feb 2009)

Log Message:
-----------
refactored Score and EvaluatedDescription such that a broader range of learning problems can be implemented (DL-Learner core makes less restrictions)

Modified Paths:
--------------
    trunk/src/dl-learner/org/dllearner/algorithms/BruteForceLearner.java
    trunk/src/dl-learner/org/dllearner/algorithms/DBpediaNavigationSuggestor.java
    trunk/src/dl-learner/org/dllearner/algorithms/RandomGuesser.java
    trunk/src/dl-learner/org/dllearner/algorithms/SimpleSuggestionLearningAlgorithm.java
    trunk/src/dl-learner/org/dllearner/algorithms/celoe/CELOE.java
    trunk/src/dl-learner/org/dllearner/algorithms/el/ELLearningAlgorithm.java
    trunk/src/dl-learner/org/dllearner/algorithms/gp/GP.java
    trunk/src/dl-learner/org/dllearner/algorithms/gp/GPUtilities.java
    trunk/src/dl-learner/org/dllearner/algorithms/gp/Program.java
    trunk/src/dl-learner/org/dllearner/algorithms/hybridgp/Psi.java
    trunk/src/dl-learner/org/dllearner/algorithms/refexamples/ExampleBasedROLComponent.java
    trunk/src/dl-learner/org/dllearner/algorithms/refexamples/ExampleBasedROLearner.java
    trunk/src/dl-learner/org/dllearner/algorithms/refinement/ROLearner.java
    trunk/src/dl-learner/org/dllearner/cli/Start.java
    trunk/src/dl-learner/org/dllearner/core/EvaluatedDescription.java
    trunk/src/dl-learner/org/dllearner/core/LearningAlgorithm.java
    trunk/src/dl-learner/org/dllearner/core/LearningProblem.java
    trunk/src/dl-learner/org/dllearner/core/Score.java
    trunk/src/dl-learner/org/dllearner/gui/RunPanel.java
    trunk/src/dl-learner/org/dllearner/learningproblems/ClassScore.java
    trunk/src/dl-learner/org/dllearner/learningproblems/PosNegDefinitionLP.java
    trunk/src/dl-learner/org/dllearner/learningproblems/PosNegDefinitionLPStrict.java
    trunk/src/dl-learner/org/dllearner/learningproblems/PosNegInclusionLP.java
    trunk/src/dl-learner/org/dllearner/learningproblems/PosOnlyDefinitionLP.java
    trunk/src/dl-learner/org/dllearner/learningproblems/PosOnlyInclusionLP.java
    trunk/src/dl-learner/org/dllearner/learningproblems/ScoreThreeValued.java
    trunk/src/dl-learner/org/dllearner/learningproblems/ScoreTwoValued.java
    trunk/src/dl-learner/org/dllearner/scripts/DumbLPFinder.java
    trunk/src/dl-learner/org/dllearner/scripts/NewSample.java
    trunk/src/dl-learner/org/dllearner/scripts/Sample.java
    trunk/src/dl-learner/org/dllearner/scripts/SemanticBible.java
    trunk/src/dl-learner/org/dllearner/scripts/SemanticBibleComparison.java
    trunk/src/dl-learner/org/dllearner/scripts/TestValidation.java
    trunk/src/dl-learner/org/dllearner/scripts/WikipediaCategoryCleaner.java
    trunk/src/dl-learner/org/dllearner/scripts/improveWikipedia/ConceptSPARQLReEvaluator.java
    trunk/src/dl-learner/org/dllearner/scripts/improveWikipedia/ConceptSelector.java
    trunk/src/dl-learner/org/dllearner/scripts/improveWikipedia/WikipediaCategoryTasks.java
    trunk/src/dl-learner/org/dllearner/server/DLLearnerWS.java
    trunk/src/dl-learner/org/dllearner/tools/ore/ColumnListCellRenderer.java
    trunk/src/dl-learner/org/dllearner/tools/ore/LearningPanelDescriptor.java
    trunk/src/dl-learner/org/dllearner/tools/ore/ORE.java
    trunk/src/dl-learner/org/dllearner/tools/protege/ActionHandler.java
    trunk/src/dl-learner/org/dllearner/tools/protege/DLLearnerModel.java
    trunk/src/dl-learner/org/dllearner/tools/protege/GraphicalCoveragePanel.java
    trunk/src/dl-learner/org/dllearner/tools/protege/MoreDetailForSuggestedConceptsPanel.java
    trunk/src/dl-learner/org/dllearner/tools/protege/ReadingOntologyThread.java
    trunk/src/dl-learner/org/dllearner/utilities/owl/EvaluatedDescriptionComparator.java
    trunk/src/dl-learner/org/dllearner/utilities/owl/EvaluatedDescriptionSet.java

Added Paths:
-----------
    trunk/src/dl-learner/org/dllearner/algorithms/EvaluatedDescriptionPosNeg.java
    trunk/src/dl-learner/org/dllearner/learningproblems/ScorePosNeg.java
    trunk/src/dl-learner/org/dllearner/utilities/owl/EvaluatedDescriptionPosNegComparator.java

Modified: trunk/src/dl-learner/org/dllearner/algorithms/BruteForceLearner.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/BruteForceLearner.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/algorithms/BruteForceLearner.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -26,11 +26,9 @@
 import java.util.List;
 import java.util.Map;
 
-import org.dllearner.core.EvaluatedDescription;
 import org.dllearner.core.LearningAlgorithm;
 import org.dllearner.core.LearningProblem;
 import org.dllearner.core.ReasonerComponent;
-import org.dllearner.core.Score;
 import org.dllearner.core.configurators.BruteForceLearnerConfigurator;
 import org.dllearner.core.options.CommonConfigOptions;
 import org.dllearner.core.options.ConfigEntry;
@@ -47,6 +45,7 @@
 import org.dllearner.core.owl.ObjectSomeRestriction;
 import org.dllearner.core.owl.Thing;
 import org.dllearner.core.owl.Union;
+import org.dllearner.learningproblems.ScorePosNeg;
 
 /**
  * A brute force learning algorithm.
@@ -70,7 +69,7 @@
 	private ReasonerComponent rs;
 	
     private Description bestDefinition;
-    private Score bestScore;
+    private ScorePosNeg bestScore;
     
     //changing this wont have any effect any more
     private Integer maxLength = 7;
@@ -173,7 +172,7 @@
         double bestScorePoints = Double.NEGATIVE_INFINITY;
         int overallCount = 0;
         int count = 0;
-        Score tmp;
+        ScorePosNeg tmp;
         double score;
         
         for(int i=1; i<=maxLength && !stop; i++) {
@@ -193,8 +192,8 @@
             	} else
             		newRoot = program;
             	
-            	tmp = learningProblem.computeScore(newRoot);
-                score = tmp.getScore();
+            	tmp = (ScorePosNeg) learningProblem.computeScore(newRoot);
+                score = tmp.getScoreValue();
                 
                 // TODO: find termination criterion
                 if(score > bestScorePoints) {
@@ -288,7 +287,7 @@
     }
 
 //    @Override
-	public Score getSolutionScore() {
+	public ScorePosNeg getSolutionScore() {
 		return bestScore;
 	}
 
@@ -298,8 +297,8 @@
 	}    
     
 	@Override
-	public EvaluatedDescription getCurrentlyBestEvaluatedDescription() {
-		return new EvaluatedDescription(bestDefinition,bestScore);
+	public EvaluatedDescriptionPosNeg getCurrentlyBestEvaluatedDescription() {
+		return new EvaluatedDescriptionPosNeg(bestDefinition,bestScore);
 	}
 
 	@Override

Modified: trunk/src/dl-learner/org/dllearner/algorithms/DBpediaNavigationSuggestor.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/DBpediaNavigationSuggestor.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/algorithms/DBpediaNavigationSuggestor.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -29,7 +29,6 @@
 import org.dllearner.core.LearningAlgorithm;
 import org.dllearner.core.LearningProblem;
 import org.dllearner.core.ReasonerComponent;
-import org.dllearner.core.Score;
 import org.dllearner.core.configurators.ComponentFactory;
 import org.dllearner.core.configurators.DBpediaNavigationSuggestorConfigurator;
 import org.dllearner.core.options.BooleanConfigOption;
@@ -43,6 +42,7 @@
 import org.dllearner.learningproblems.PosNegDefinitionLP;
 import org.dllearner.learningproblems.PosNegLP;
 import org.dllearner.learningproblems.PosOnlyDefinitionLP;
+import org.dllearner.learningproblems.ScorePosNeg;
 
 /**
  * The DBpedia Navigation suggestor takes a knowledge fragment extracted
@@ -51,6 +51,9 @@
  * implement a completely new learning algorithm itself, but uses the
  * example based refinement operator learning algorithm.
  * 
+ * TODO: This should not be implemented as a learning algorithm (as it does
+ * almost nothing by itself) and maybe can be completely deleted.
+ * 
  * @author Jens Lehmann
  *
  */
@@ -180,17 +183,17 @@
 	}	
 	
 	@Override
-	public EvaluatedDescription getCurrentlyBestEvaluatedDescription() {
+	public EvaluatedDescriptionPosNeg getCurrentlyBestEvaluatedDescription() {
 		return learner.getCurrentlyBestEvaluatedDescription();
 	}
 	
 	@Override
-	public List<EvaluatedDescription> getCurrentlyBestEvaluatedDescriptions(int nrOfDescriptions, double accuracyThreshold, boolean filterNonMinimalDescriptions){
+	public List<? extends EvaluatedDescription> getCurrentlyBestEvaluatedDescriptions(int nrOfDescriptions, double accuracyThreshold, boolean filterNonMinimalDescriptions){
 		return learner.getCurrentlyBestEvaluatedDescriptions(nrOfDescriptions, accuracyThreshold, filterNonMinimalDescriptions);
 	}
 
 //	@Override
-	public Score getSolutionScore() {
+	public ScorePosNeg getSolutionScore() {
 		return learner.getSolutionScore();
 	}
 

Copied: trunk/src/dl-learner/org/dllearner/algorithms/EvaluatedDescriptionPosNeg.java (from rev 1590, trunk/src/dl-learner/org/dllearner/core/EvaluatedDescription.java)
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/EvaluatedDescriptionPosNeg.java	                        (rev 0)
+++ trunk/src/dl-learner/org/dllearner/algorithms/EvaluatedDescriptionPosNeg.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -0,0 +1,168 @@
+/**
+ * Copyright (C) 2007-2008, Jens Lehmann
+ *
+ * This file is part of DL-Learner.
+ * 
+ * DL-Learner is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation; either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * DL-Learner is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ *
+ */
+package org.dllearner.algorithms;
+
+import java.util.Set;
+
+import org.dllearner.core.EvaluatedDescription;
+import org.dllearner.core.Score;
+import org.dllearner.core.owl.Description;
+import org.dllearner.core.owl.Individual;
+import org.dllearner.learningproblems.ScorePosNeg;
+import org.dllearner.learningproblems.ScoreTwoValued;
+import org.dllearner.utilities.owl.OWLAPIDescriptionConvertVisitor;
+import org.dllearner.utilities.owl.OWLAPIRenderers;
+import org.json.JSONArray;
+import org.json.JSONException;
+import org.json.JSONObject;
+import org.semanticweb.owl.model.OWLDescription;
+
+/**
+ * This represents a class description, which has been
+ * evaluated by the learning algorithm, i.e. it has been checked
+ * which examples it covers. It can be used as return value for
+ * learning algorithms to make it easier for applications to
+ * assess how good an offered class description is and how it
+ * classifies particular examples.
+ * 
+ * @author Jens Lehmann
+ *
+ */
+public class EvaluatedDescriptionPosNeg extends EvaluatedDescription {
+	
+	private ScorePosNeg score2;
+	
+	/**
+	 * Constructs an evaluated description using its score.
+	 * @param description The description, which was evaluated.
+	 * @param score The score of the description.
+	 */
+	public EvaluatedDescriptionPosNeg(Description description, ScorePosNeg score) {
+		super(description, score);
+		score2 = (ScorePosNeg) score;
+	}
+	
+	/**
+	 * Constructs an evaluated description using example coverage.
+	 * @param description The description, which was evaluated.
+	 * @param posAsPos Positive examples classified as positive by (i.e. instance of) the description.
+	 * @param posAsNeg Positive examples classified as negative by (i.e. not instance of) the description.
+	 * @param negAsPos Negative examples classified as positive by (i.e. instance of) the description.
+	 * @param negAsNeg Negative examples classified as negative by (i.e. not instance of) the description.
+	 */
+	public EvaluatedDescriptionPosNeg(Description description, Set<Individual> posAsPos, Set<Individual> posAsNeg, Set<Individual> negAsPos, Set<Individual> negAsNeg) {
+		// usually core methods should not depend on methods outside of the core package (except utilities)
+		// in this case, this is just a convenience constructor
+		super(description, new ScoreTwoValued(posAsPos, posAsNeg, negAsPos, negAsNeg));
+		score2 = (ScorePosNeg) score;
+	}
+	
+	/**
+	 * @see org.dllearner.learningproblems.ScorePosNeg#getAccuracy()
+	 * @return Accuracy of the description.
+	 */
+	public double getAccuracy() {
+		return score2.getAccuracy();
+	}
+	
+	/**
+	 * Gets the score of this description. This can be used to get
+	 * further statistical values.
+	 * @see org.dllearner.learningproblems.ScorePosNeg
+	 * @return The score object associated with this evaluated description.
+	 */
+	public ScorePosNeg getScore() {
+		return score2;
+	}
+
+	/**
+	 * @see org.dllearner.learningproblems.ScorePosNeg#getCoveredNegatives()
+	 * @return Negative examples covered by the description.
+	 */
+	public Set<Individual> getCoveredNegatives() {
+		return score2.getCoveredNegatives();
+	}
+
+	/**
+	 * @see org.dllearner.learningproblems.ScorePosNeg#getCoveredPositives()
+	 * @return Positive examples covered by the description.
+	 */
+	public Set<Individual> getCoveredPositives() {
+		return score2.getCoveredPositives();
+	}
+
+	/**
+	 * @see org.dllearner.learningproblems.ScorePosNeg#getNotCoveredNegatives()
+	 * @return Negative examples not covered by the description.
+	 */
+	public Set<Individual> getNotCoveredNegatives() {
+		return score2.getNotCoveredNegatives();
+	}
+
+	/**
+	 * @see org.dllearner.learningproblems.ScorePosNeg#getNotCoveredPositives()
+	 * @return Positive examples not covered by the description.
+	 */
+	public Set<Individual> getNotCoveredPositives() {
+		return score2.getNotCoveredPositives();
+	}
+	
+	/**
+	 * This convenience method can be used to store and exchange evaluated
+	 * descriptions by transforming them to a JSON string.
+	 * @return A JSON representation of an evaluated description.
+	 */
+	@Override
+	public String asJSON() {
+		JSONObject object = new JSONObject();
+		try {
+			object.put("descriptionManchesterSyntax", description.toManchesterSyntaxString(null, null));
+			OWLDescription d = OWLAPIDescriptionConvertVisitor.getOWLDescription(description);
+			object.put("descriptionOWLXML", OWLAPIRenderers.toOWLXMLSyntax(d));
+			object.put("descriptionKBSyntax", description.toKBSyntaxString());
+			object.put("accuracy", score2.getAccuracy());
+			object.put("coveredPositives", getJSONArray(score2.getCoveredPositives()));
+			object.put("coveredNegatives", getJSONArray(score2.getCoveredNegatives()));
+			object.put("notCoveredPositives", getJSONArray(score2.getNotCoveredPositives()));
+			object.put("notCoveredNegatives", getJSONArray(score2.getNotCoveredNegatives()));			
+			return object.toString(3);
+		} catch (JSONException e) {
+			e.printStackTrace();
+			return null;
+		}
+	}
+	
+	@Override
+	public String toString() {
+		return description.toString() + "(accuracy: " + getAccuracy() + ")";
+	}
+	
+	// we need to use this method instead of the standard JSON array constructor,
+	// otherwise we'll get unexpected results (JSONArray does not take Individuals
+	// as arguments and does not use toString)
+	private static JSONArray getJSONArray(Set<Individual> individuals) {
+		JSONArray j = new JSONArray();
+		for(Individual i : individuals) {
+			j.put(i.getName());
+		}
+		return j;
+	}
+
+}


Property changes on: trunk/src/dl-learner/org/dllearner/algorithms/EvaluatedDescriptionPosNeg.java
___________________________________________________________________
Added: svn:mergeinfo
   + 

Modified: trunk/src/dl-learner/org/dllearner/algorithms/RandomGuesser.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/RandomGuesser.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/algorithms/RandomGuesser.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -25,17 +25,16 @@
 import org.apache.log4j.Logger;
 import org.dllearner.algorithms.gp.GPUtilities;
 import org.dllearner.algorithms.gp.Program;
-import org.dllearner.core.EvaluatedDescription;
 import org.dllearner.core.LearningAlgorithm;
 import org.dllearner.core.LearningProblem;
 import org.dllearner.core.ReasonerComponent;
-import org.dllearner.core.Score;
 import org.dllearner.core.configurators.RandomGuesserConfigurator;
 import org.dllearner.core.options.ConfigEntry;
 import org.dllearner.core.options.ConfigOption;
 import org.dllearner.core.options.IntegerConfigOption;
 import org.dllearner.core.options.InvalidConfigOptionValueException;
 import org.dllearner.core.owl.Description;
+import org.dllearner.learningproblems.ScorePosNeg;
 
 public class RandomGuesser extends LearningAlgorithm {
 
@@ -46,7 +45,7 @@
 	}
 	
     private Description bestDefinition = null;
-    private Score bestScore;
+    private ScorePosNeg bestScore;
     private double bestFitness = Double.NEGATIVE_INFINITY;
 
 	private int numberOfTrees;
@@ -121,7 +120,7 @@
 	}
 
 //	@Override
-	public Score getSolutionScore() {
+	public ScorePosNeg getSolutionScore() {
 		return bestScore;
 	}
 
@@ -131,8 +130,8 @@
 	}	
 	
 	@Override
-	public EvaluatedDescription getCurrentlyBestEvaluatedDescription() {
-		return new EvaluatedDescription(bestDefinition,bestScore);
+	public EvaluatedDescriptionPosNeg getCurrentlyBestEvaluatedDescription() {
+		return new EvaluatedDescriptionPosNeg(bestDefinition,bestScore);
 	}
 
 	@Override

Modified: trunk/src/dl-learner/org/dllearner/algorithms/SimpleSuggestionLearningAlgorithm.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/SimpleSuggestionLearningAlgorithm.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/algorithms/SimpleSuggestionLearningAlgorithm.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -23,10 +23,8 @@
 import java.util.List;
 import java.util.Set;
 
-import org.dllearner.core.EvaluatedDescription;
 import org.dllearner.core.LearningAlgorithm;
 import org.dllearner.core.ReasonerComponent;
-import org.dllearner.core.Score;
 import org.dllearner.core.configurators.SimpleSuggestionLearningAlgorithmConfigurator;
 import org.dllearner.core.options.ConfigEntry;
 import org.dllearner.core.owl.Description;
@@ -34,6 +32,7 @@
 import org.dllearner.core.owl.ObjectProperty;
 import org.dllearner.core.owl.ObjectSomeRestriction;
 import org.dllearner.core.owl.Thing;
+import org.dllearner.learningproblems.ScorePosNeg;
 
 /**
  * Algorithm for getting "simple" suggestions, e.g. it tests some of the most likely candidates on whether 
@@ -51,7 +50,7 @@
 	}
 	
 //	private boolean stop = false;
-	private Score solutionScore;
+	private ScorePosNeg solutionScore;
 	private Description bestSollution;
 	private Set<Description> simpleSuggestions;
 
@@ -66,8 +65,8 @@
 	}
 
 	@Override
-	public EvaluatedDescription getCurrentlyBestEvaluatedDescription() {
-		return new EvaluatedDescription(bestSollution, solutionScore);
+	public EvaluatedDescriptionPosNeg getCurrentlyBestEvaluatedDescription() {
+		return new EvaluatedDescriptionPosNeg(bestSollution, solutionScore);
 	}
 
 	public static String getName() {
@@ -95,7 +94,7 @@
 	}
 
 //	@Override
-	public Score getSolutionScore() {
+	public ScorePosNeg getSolutionScore() {
 		return solutionScore;
 	}
 

Modified: trunk/src/dl-learner/org/dllearner/algorithms/celoe/CELOE.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/celoe/CELOE.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/algorithms/celoe/CELOE.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -19,8 +19,8 @@
  */
 package org.dllearner.algorithms.celoe;
 
+import org.dllearner.algorithms.EvaluatedDescriptionPosNeg;
 import org.dllearner.core.ComponentInitException;
-import org.dllearner.core.EvaluatedDescription;
 import org.dllearner.core.LearningAlgorithm;
 import org.dllearner.core.ReasonerComponent;
 import org.dllearner.core.configurators.Configurator;
@@ -54,7 +54,7 @@
 	 * @see org.dllearner.core.LearningAlgorithm#getCurrentlyBestEvaluatedDescription()
 	 */
 	@Override
-	public EvaluatedDescription getCurrentlyBestEvaluatedDescription() {
+	public EvaluatedDescriptionPosNeg getCurrentlyBestEvaluatedDescription() {
 		// TODO Auto-generated method stub
 		return null;
 	}

Modified: trunk/src/dl-learner/org/dllearner/algorithms/el/ELLearningAlgorithm.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/el/ELLearningAlgorithm.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/algorithms/el/ELLearningAlgorithm.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -26,17 +26,18 @@
 import java.util.TreeSet;
 
 import org.apache.log4j.Logger;
+import org.dllearner.algorithms.EvaluatedDescriptionPosNeg;
 import org.dllearner.core.ComponentInitException;
 import org.dllearner.core.EvaluatedDescription;
 import org.dllearner.core.LearningAlgorithm;
 import org.dllearner.core.LearningProblem;
 import org.dllearner.core.ReasonerComponent;
-import org.dllearner.core.Score;
 import org.dllearner.core.configurators.Configurator;
 import org.dllearner.core.configurators.ELLearningAlgorithmConfigurator;
 import org.dllearner.core.owl.Description;
 import org.dllearner.core.owl.Thing;
 import org.dllearner.learningproblems.PosNegLP;
+import org.dllearner.learningproblems.ScorePosNeg;
 import org.dllearner.refinementoperators.ELDown2;
 import org.dllearner.utilities.owl.EvaluatedDescriptionSet;
 
@@ -168,8 +169,8 @@
 			// at least as high accuracy - if not we can save the reasoner calls
 			// for fully computing the evaluated description
 			if(bestEvaluatedDescriptions.size() == 0 || bestEvaluatedDescriptions.getWorst().getCoveredNegatives().size() >= node.getCoveredNegatives()) {
-				Score score = learningProblem.computeScore(description);
-				EvaluatedDescription ed = new EvaluatedDescription(description, score);
+				ScorePosNeg score = (ScorePosNeg) learningProblem.computeScore(description);
+				EvaluatedDescriptionPosNeg ed = new EvaluatedDescriptionPosNeg(description, score);
 				bestEvaluatedDescriptions.add(ed);
 			}
 			
@@ -216,7 +217,7 @@
 	}	
 	
 	@Override
-	public SortedSet<EvaluatedDescription> getCurrentlyBestEvaluatedDescriptions() {
+	public SortedSet<? extends EvaluatedDescription> getCurrentlyBestEvaluatedDescriptions() {
 		return bestEvaluatedDescriptions.getSet();
 	}		
 	

Modified: trunk/src/dl-learner/org/dllearner/algorithms/gp/GP.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/gp/GP.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/algorithms/gp/GP.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -29,12 +29,11 @@
 import java.util.Set;
 import java.util.Map.Entry;
 
+import org.dllearner.algorithms.EvaluatedDescriptionPosNeg;
 import org.dllearner.algorithms.hybridgp.Psi;
-import org.dllearner.core.EvaluatedDescription;
 import org.dllearner.core.LearningAlgorithm;
 import org.dllearner.core.LearningProblem;
 import org.dllearner.core.ReasonerComponent;
-import org.dllearner.core.Score;
 import org.dllearner.core.configurators.GPConfigurator;
 import org.dllearner.core.options.BooleanConfigOption;
 import org.dllearner.core.options.ConfigEntry;
@@ -46,6 +45,7 @@
 import org.dllearner.core.owl.Description;
 import org.dllearner.core.owl.Thing;
 import org.dllearner.learningproblems.PosNegLP;
+import org.dllearner.learningproblems.ScorePosNeg;
 import org.dllearner.utilities.Helper;
 
 /**
@@ -126,7 +126,7 @@
     
     private long startTime;
 
-    private Score bestScore;
+    private ScorePosNeg bestScore;
     private Description bestConcept;
     
     // private GeneticRefinementOperator psi;
@@ -532,19 +532,19 @@
 
         // nachschauen, ob ev. noch bessere Konzepte im Psi-Cache sind
         boolean betterValueFoundInPsiCache = false;
-        double bestValue = bestScore.getScore();
+        double bestValue = bestScore.getScoreValue();
         
         if(refinementProbability > 0) {
         	// das Problem ist hier, dass die gecachte Score nicht unbedingt
         	// der echten Score entsprechen muss, d.h. hier muss die
         	// Konzeptlänge mit einberechnet werden => deswegen werden
         	// neue Score-Objekte generiert
-        	Set<Entry<Description,Score>> entrySet = psi.evalCache.entrySet();
-        	for(Entry<Description,Score> entry : entrySet) {
-        		Score tmpScore = entry.getValue();
+        	Set<Entry<Description,ScorePosNeg>> entrySet = psi.evalCache.entrySet();
+        	for(Entry<Description,ScorePosNeg> entry : entrySet) {
+        		ScorePosNeg tmpScore = entry.getValue();
         		Description c = entry.getKey();
         		tmpScore = tmpScore.getModifiedLengthScore(c.getLength());
-        		double tmpScoreValue = tmpScore.getScore();
+        		double tmpScoreValue = tmpScore.getScoreValue();
         		if(tmpScoreValue>bestValue) {
         			bestValue = tmpScoreValue;
         			betterValueFoundInPsiCache = true;
@@ -947,7 +947,7 @@
     }
 
 //    @Override
-	public Score getSolutionScore() {
+	public ScorePosNeg getSolutionScore() {
 		return bestScore;
 	}
 
@@ -957,9 +957,9 @@
 	}    
     
 	@Override
-	public EvaluatedDescription getCurrentlyBestEvaluatedDescription() {
+	public EvaluatedDescriptionPosNeg getCurrentlyBestEvaluatedDescription() {
 		// return fittestIndividual.getTree();
-		return new EvaluatedDescription(bestConcept,bestScore);
+		return new EvaluatedDescriptionPosNeg(bestConcept,bestScore);
 	}
 
 	@Override

Modified: trunk/src/dl-learner/org/dllearner/algorithms/gp/GPUtilities.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/gp/GPUtilities.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/algorithms/gp/GPUtilities.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -10,7 +10,6 @@
 import org.dllearner.core.LearningProblem;
 import org.dllearner.core.ReasonerComponent;
 import org.dllearner.core.ReasoningMethodUnsupportedException;
-import org.dllearner.core.Score;
 import org.dllearner.core.owl.ObjectAllRestriction;
 import org.dllearner.core.owl.NamedClass;
 import org.dllearner.core.owl.Nothing;
@@ -24,6 +23,7 @@
 import org.dllearner.core.owl.ObjectProperty;
 import org.dllearner.core.owl.Thing;
 import org.dllearner.learningproblems.PosNegDefinitionLPStrict;
+import org.dllearner.learningproblems.ScorePosNeg;
 import org.dllearner.learningproblems.ScoreThreeValued;
 import org.dllearner.reasoning.FastRetrieval;
 import org.dllearner.reasoning.ReasonerType;
@@ -54,7 +54,7 @@
 	
     private static Random rand = new Random();
     
-    private static Score calculateFitness(LearningProblem learningProblem, Description hypothesis) {
+    private static ScorePosNeg calculateFitness(LearningProblem learningProblem, Description hypothesis) {
     	return calculateFitness(learningProblem, hypothesis, null);
     }
     
@@ -63,7 +63,7 @@
     // (macht aber nicht so viel Sinn, da man das bei richtigen Reasoning-Algorithmen
     // ohnehin mit einer Erweiterung der Wissensbasis um die Inklusion Target SUBSETOF ReturnType
     // erschlagen kann)
-	private static Score calculateFitness(LearningProblem learningProblem, Description hypothesis, Description adc) {
+	private static ScorePosNeg calculateFitness(LearningProblem learningProblem, Description hypothesis, Description adc) {
 		Description extendedHypothesis;
 		
 		// return type temporarily disabled 
@@ -87,13 +87,13 @@
 //		} else
 			extendedHypothesis = hypothesis;		
 		
-		Score score;
+		ScorePosNeg score;
 		if(adc != null)
 			// TODO: ADC-Support
 			// score = learningProblem.computeScore(extendedHypothesis, adc);
 			throw new RuntimeException("ADC not supported");
 		else
-			score = learningProblem.computeScore(extendedHypothesis);
+			score = (ScorePosNeg) learningProblem.computeScore(extendedHypothesis);
 		
 		// System.out.println(hypothesis);
 		// System.out.println(score.getScore());
@@ -143,18 +143,18 @@
     		if(Math.random()<0.5) {
     			Description mainTree = mutation(learningProblem, rs, p.getTree(),true);
     			Description adc = p.getAdc();
-    			Score score = calculateFitness(learningProblem,mainTree,adc);
+    			ScorePosNeg score = calculateFitness(learningProblem,mainTree,adc);
     			return new Program(score, mainTree, adc);
     		}
     		else {
     			Description mainTree = p.getTree();
     			Description adc = mutation(learningProblem, rs, p.getAdc(),false);
-    			Score score = calculateFitness(learningProblem,mainTree,adc);
+    			ScorePosNeg score = calculateFitness(learningProblem,mainTree,adc);
     			return new Program(score, mainTree, adc);    			
     		}
     	} else {
     		Description tree = mutation(learningProblem, rs,p.getTree(),false);
-    		Score score = calculateFitness(learningProblem, tree);
+    		ScorePosNeg score = calculateFitness(learningProblem, tree);
             return new Program(score, tree);
     	}
     }
@@ -340,7 +340,7 @@
     	// double bestScore = score.getScore()+Config.accuracyPenalty/2;
     	double bestScore = 0;
     	Map<Integer,List<String>> bestNeighbours = new TreeMap<Integer,List<String>>();
-    	Score tmpScore;
+    	ScorePosNeg tmpScore;
     	SortedSetTuple<String> tmp, tmp2;
     	// FlatABox abox = ((FastRetrievalReasoner)learningProblem.getReasoner().getFastRetrieval().getAbox();
     	// FlatABox abox = Main.getFlatAbox();
@@ -363,16 +363,16 @@
     		
     		tmp2 = FastRetrieval.calculateDisjunctionSets(stringTuple, tmp);
     		tmpScore = getScore(node.getLength()+2, learningProblem, rs, Helper.getIndividualSet(tmp2.getPosSet()),Helper.getIndividualSet(tmp2.getNegSet()));
-    		if(tmpScore.getScore()==bestScore)
+    		if(tmpScore.getScoreValue()==bestScore)
     			bestNeighbours = updateMap(bestNeighbours,1,concept,false);
-    		else if(tmpScore.getScore()>bestScore)
+    		else if(tmpScore.getScoreValue()>bestScore)
     			bestNeighbours = updateMap(bestNeighbours,1,concept,true);
     		
     		tmp2 = FastRetrieval.calculateConjunctionSets(stringTuple, tmp);
     		tmpScore = getScore(node.getLength()+2,learningProblem, rs, Helper.getIndividualSet(tmp2.getPosSet()),Helper.getIndividualSet(tmp2.getNegSet()));
-    		if(tmpScore.getScore()==bestScore)
+    		if(tmpScore.getScoreValue()==bestScore)
     			bestNeighbours = updateMap(bestNeighbours,2,concept,false);
-    		else if(tmpScore.getScore()>bestScore)
+    		else if(tmpScore.getScoreValue()>bestScore)
     			bestNeighbours = updateMap(bestNeighbours,2,concept,true);    		
     	}
     	
@@ -380,16 +380,16 @@
     	for(String role : abox.roles) {
     		tmp = FastRetrieval.calculateAllSet(abox,role,stringTuple);
     		tmpScore = getScore(node.getLength()+2,learningProblem, rs, Helper.getIndividualSet(tmp.getPosSet()),Helper.getIndividualSet(tmp.getNegSet()));
-    		if(tmpScore.getScore()==bestScore)
+    		if(tmpScore.getScoreValue()==bestScore)
     			bestNeighbours = updateMap(bestNeighbours,3,role,false);
-    		else if(tmpScore.getScore()>bestScore)
+    		else if(tmpScore.getScoreValue()>bestScore)
     			bestNeighbours = updateMap(bestNeighbours,3,role,true);    		
 
     		tmp = FastRetrieval.calculateExistsSet(abox,role,stringTuple);
     		tmpScore = getScore(node.getLength()+2,learningProblem, rs, Helper.getIndividualSet(tmp.getPosSet()),Helper.getIndividualSet(tmp.getNegSet()));
-    		if(tmpScore.getScore()==bestScore)
+    		if(tmpScore.getScoreValue()==bestScore)
     			bestNeighbours = updateMap(bestNeighbours,4,role,false);
-    		else if(tmpScore.getScore()>bestScore)
+    		else if(tmpScore.getScoreValue()>bestScore)
     			bestNeighbours = updateMap(bestNeighbours,4,role,true);    		
     	}
     	

Modified: trunk/src/dl-learner/org/dllearner/algorithms/gp/Program.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/gp/Program.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/algorithms/gp/Program.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -20,8 +20,8 @@
 
 package org.dllearner.algorithms.gp;
 
-import org.dllearner.core.Score;
 import org.dllearner.core.owl.Description;
+import org.dllearner.learningproblems.ScorePosNeg;
 
 /**
  * This class represents a program, i.e. an individual.
@@ -39,7 +39,7 @@
 
 	private Description adc;
 
-	private Score score;
+	private ScorePosNeg score;
 
 	// private Score scoreAdc;
 	
@@ -51,11 +51,11 @@
 	 * Create a new program.
 	 * 
 	 */
-	public Program(Score score, Description hypothesis) {
+	public Program(ScorePosNeg score, Description hypothesis) {
 		this(score, hypothesis, null);
 	}
 
-	public Program(Score score, Description hypothesis, Description adc) {
+	public Program(ScorePosNeg score, Description hypothesis, Description adc) {
 		// this.learningProblem = learningProblem;
 		this.score = score;
 		this.hypothesis = hypothesis;
@@ -64,7 +64,7 @@
 		// Implementierung falsch !!
 		// fitness = score.getScore() - hypothesis.getLength() * Config.percentPerLengthUnit;
 		// => in getScore() ist jetzt schon der length penalty integriert
-		fitness = score.getScore();
+		fitness = score.getScoreValue();
 		// fitnessEvaluations++;
 		
 		// System.out.println("new program: " + hypothesis);
@@ -128,7 +128,7 @@
 		return hypothesis;
 	}
 
-	public Score getScore() {
+	public ScorePosNeg getScore() {
 		return score;
 	}
 

Modified: trunk/src/dl-learner/org/dllearner/algorithms/hybridgp/Psi.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/hybridgp/Psi.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/algorithms/hybridgp/Psi.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -7,9 +7,9 @@
 
 import org.dllearner.algorithms.gp.Program;
 import org.dllearner.core.ReasonerComponent;
-import org.dllearner.core.Score;
 import org.dllearner.core.owl.Description;
 import org.dllearner.learningproblems.PosNegLP;
+import org.dllearner.learningproblems.ScorePosNeg;
 import org.dllearner.refinementoperators.PsiDown;
 import org.dllearner.refinementoperators.PsiUp;
 import org.dllearner.utilities.owl.ConceptComparator;
@@ -26,7 +26,7 @@
 	
 	// Cache, damit keine Konzepte doppelt ausgewertet werden
 	ConceptComparator conceptComparator = new ConceptComparator();
-	public SortedMap<Description,Score> evalCache = new TreeMap<Description,Score>(conceptComparator);
+	public SortedMap<Description,ScorePosNeg> evalCache = new TreeMap<Description,ScorePosNeg>(conceptComparator);
 	
 	// Cache, damit PsiDown bzw. PsiUp nicht mehrfach für gleiches Konzept
 	// aufgerufen werden
@@ -190,7 +190,7 @@
 		Description conceptModForCache = ConceptTransformation.applyEquivalenceRules(conceptMod);
 		ConceptTransformation.transformToOrderedForm(conceptModForCache, conceptComparator);
 		
-		Score score = program.getScore();
+		ScorePosNeg score = program.getScore();
 		// Eval-Cache füllen
 		evalCache.put(conceptModForCache, score);
 		
@@ -214,11 +214,11 @@
 		// versuchen Reasoner-Cache zu treffen
 		// Problem: Score hängt von Konzeptlänge ab!! => muss hier explizit
 		// reingerechnet werden
-		Score newScore = evalCache.get(newConceptMod);
+		ScorePosNeg newScore = evalCache.get(newConceptMod);
 		
 		if(newScore==null) {
 			psiReasoningStartTime = System.nanoTime();
-			newScore = learningProblem.computeScore(newConcept);
+			newScore = (ScorePosNeg) learningProblem.computeScore(newConcept);
 			psiReasoningTimeNs += System.nanoTime() - psiReasoningStartTime;
 			
 			evalCache.put(newConceptMod, newScore);

Modified: trunk/src/dl-learner/org/dllearner/algorithms/refexamples/ExampleBasedROLComponent.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/refexamples/ExampleBasedROLComponent.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/algorithms/refexamples/ExampleBasedROLComponent.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -29,12 +29,11 @@
 
 import org.apache.log4j.Level;
 import org.apache.log4j.Logger;
+import org.dllearner.algorithms.EvaluatedDescriptionPosNeg;
 import org.dllearner.core.ComponentInitException;
-import org.dllearner.core.EvaluatedDescription;
 import org.dllearner.core.LearningAlgorithm;
 import org.dllearner.core.LearningProblem;
 import org.dllearner.core.ReasonerComponent;
-import org.dllearner.core.Score;
 import org.dllearner.core.configurators.ExampleBasedROLComponentConfigurator;
 import org.dllearner.core.options.BooleanConfigOption;
 import org.dllearner.core.options.CommonConfigMappings;
@@ -52,6 +51,7 @@
 import org.dllearner.learningproblems.PosNegLP;
 import org.dllearner.learningproblems.PosOnlyDefinitionLP;
 import org.dllearner.learningproblems.PosOnlyLP;
+import org.dllearner.learningproblems.ScorePosNeg;
 import org.dllearner.reasoning.ReasonerType;
 import org.dllearner.refinementoperators.RhoDRDown;
 import org.dllearner.utilities.Files;
@@ -436,7 +436,7 @@
 	}
 	
 //	@Override
-	public Score getSolutionScore() {
+	public ScorePosNeg getSolutionScore() {
 		return algorithm.getSolutionScore();
 	}
 	
@@ -451,12 +451,12 @@
 	}	
 	
 	@Override
-	public EvaluatedDescription getCurrentlyBestEvaluatedDescription() {
-		return new EvaluatedDescription(algorithm.getBestSolution(),algorithm.getSolutionScore());
+	public EvaluatedDescriptionPosNeg getCurrentlyBestEvaluatedDescription() {
+		return new EvaluatedDescriptionPosNeg(algorithm.getBestSolution(),algorithm.getSolutionScore());
 	}
 	
 	@Override
-	public synchronized SortedSet<EvaluatedDescription> getCurrentlyBestEvaluatedDescriptions() {
+	public synchronized SortedSet<EvaluatedDescriptionPosNeg> getCurrentlyBestEvaluatedDescriptions() {
 		return algorithm.getCurrentlyBestEvaluatedDescriptions();
 	}
 

Modified: trunk/src/dl-learner/org/dllearner/algorithms/refexamples/ExampleBasedROLearner.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/refexamples/ExampleBasedROLearner.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/algorithms/refexamples/ExampleBasedROLearner.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -33,10 +33,9 @@
 import java.util.concurrent.ConcurrentSkipListSet;
 
 import org.apache.log4j.Logger;
-import org.dllearner.core.EvaluatedDescription;
+import org.dllearner.algorithms.EvaluatedDescriptionPosNeg;
 import org.dllearner.core.LearningProblem;
 import org.dllearner.core.ReasonerComponent;
-import org.dllearner.core.Score;
 import org.dllearner.core.configurators.ExampleBasedROLComponentConfigurator;
 import org.dllearner.core.owl.Description;
 import org.dllearner.core.owl.Individual;
@@ -45,6 +44,7 @@
 import org.dllearner.core.owl.Union;
 import org.dllearner.learningproblems.PosNegLP;
 import org.dllearner.learningproblems.PosOnlyDefinitionLP;
+import org.dllearner.learningproblems.ScorePosNeg;
 import org.dllearner.refinementoperators.RefinementOperator;
 import org.dllearner.refinementoperators.RhoDRDown;
 import org.dllearner.utilities.Files;
@@ -52,7 +52,7 @@
 import org.dllearner.utilities.JamonMonitorLogger;
 import org.dllearner.utilities.owl.ConceptComparator;
 import org.dllearner.utilities.owl.ConceptTransformation;
-import org.dllearner.utilities.owl.EvaluatedDescriptionComparator;
+import org.dllearner.utilities.owl.EvaluatedDescriptionPosNegComparator;
 
 import com.jamonapi.Monitor;
 
@@ -184,7 +184,7 @@
 	// comparator used to create ordered sets of concepts
 	private ConceptComparator conceptComparator = new ConceptComparator();
 	// comparator for evaluated descriptions
-	private EvaluatedDescriptionComparator edComparator = new EvaluatedDescriptionComparator();
+	private EvaluatedDescriptionPosNegComparator edComparator = new EvaluatedDescriptionPosNegComparator();
 
 	// utility variables
 	private DecimalFormat df = new DecimalFormat();
@@ -1255,13 +1255,13 @@
 		return best;
 	}
 	
-	public SortedSet<EvaluatedDescription> getCurrentlyBestEvaluatedDescriptions() {
+	public SortedSet<EvaluatedDescriptionPosNeg> getCurrentlyBestEvaluatedDescriptions() {
 		Iterator<ExampleBasedNode> it = candidatesStable.descendingIterator();
 		int count = 0;
-		SortedSet<EvaluatedDescription> cbd = new TreeSet<EvaluatedDescription>(edComparator);
+		SortedSet<EvaluatedDescriptionPosNeg> cbd = new TreeSet<EvaluatedDescriptionPosNeg>(edComparator);
 		while(it.hasNext()) {
 			ExampleBasedNode eb = it.next();
-			cbd.add(new EvaluatedDescription(eb.getConcept(), getScore(eb.getConcept())));
+			cbd.add(new EvaluatedDescriptionPosNeg(eb.getConcept(), getScore(eb.getConcept())));
 			// return a maximum of 200 elements (we need a maximum, because the
 			// candidate set can be very large)
 			if (count > 200)
@@ -1304,18 +1304,18 @@
 
 	}
 
-	public Score getSolutionScore() {
+	public ScorePosNeg getSolutionScore() {
 		if (posOnly)
 			return posOnlyLearningProblem.computeScore(getBestSolution());
 		else
-			return learningProblem.computeScore(getBestSolution());
+			return (ScorePosNeg) learningProblem.computeScore(getBestSolution());
 	}
 
-	private Score getScore(Description d) {
+	private ScorePosNeg getScore(Description d) {
 		if (posOnly)
 			return posOnlyLearningProblem.computeScore(d);
 		else
-			return learningProblem.computeScore(d);
+			return (ScorePosNeg) learningProblem.computeScore(d);
 	}
 
 	public ExampleBasedNode getStartNode() {

Modified: trunk/src/dl-learner/org/dllearner/algorithms/refinement/ROLearner.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/refinement/ROLearner.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/algorithms/refinement/ROLearner.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -14,11 +14,10 @@
 
 import org.apache.log4j.Level;
 import org.apache.log4j.Logger;
-import org.dllearner.core.EvaluatedDescription;
+import org.dllearner.algorithms.EvaluatedDescriptionPosNeg;
 import org.dllearner.core.LearningAlgorithm;
 import org.dllearner.core.LearningProblem;
 import org.dllearner.core.ReasonerComponent;
-import org.dllearner.core.Score;
 import org.dllearner.core.configurators.ROLearnerConfigurator;
 import org.dllearner.core.options.BooleanConfigOption;
 import org.dllearner.core.options.CommonConfigMappings;
@@ -36,12 +35,13 @@
 import org.dllearner.core.owl.Union;
 import org.dllearner.learningproblems.PosNegLP;
 import org.dllearner.learningproblems.PosOnlyDefinitionLP;
+import org.dllearner.learningproblems.ScorePosNeg;
 import org.dllearner.refinementoperators.RhoDown;
 import org.dllearner.utilities.Files;
 import org.dllearner.utilities.Helper;
 import org.dllearner.utilities.owl.ConceptComparator;
 import org.dllearner.utilities.owl.ConceptTransformation;
-import org.dllearner.utilities.owl.EvaluatedDescriptionComparator;
+import org.dllearner.utilities.owl.EvaluatedDescriptionPosNegComparator;
 
 public class ROLearner extends LearningAlgorithm {
 	
@@ -107,7 +107,7 @@
 	private NodeComparatorStable nodeComparatorStable = new NodeComparatorStable();
 	private ConceptComparator conceptComparator = new ConceptComparator();
 	// comparator for evaluated descriptions
-	private EvaluatedDescriptionComparator edComparator = new EvaluatedDescriptionComparator();
+	private EvaluatedDescriptionPosNegComparator edComparator = new EvaluatedDescriptionPosNegComparator();
 	DecimalFormat df = new DecimalFormat();	
 	
 	private PosNegLP learningProblem;
@@ -1054,17 +1054,17 @@
 	}	
 	
 	@Override
-	public EvaluatedDescription getCurrentlyBestEvaluatedDescription() {
-		return new EvaluatedDescription(candidatesStable.last().getConcept(), getSolutionScore());
+	public EvaluatedDescriptionPosNeg getCurrentlyBestEvaluatedDescription() {
+		return new EvaluatedDescriptionPosNeg(candidatesStable.last().getConcept(), getSolutionScore());
 	}
 	
 	@Override
-	public SortedSet<EvaluatedDescription> getCurrentlyBestEvaluatedDescriptions() {
+	public SortedSet<EvaluatedDescriptionPosNeg> getCurrentlyBestEvaluatedDescriptions() {
 		int count = 0;
 		SortedSet<Node> rev = candidatesStable.descendingSet();
-		SortedSet<EvaluatedDescription> cbd = new TreeSet<EvaluatedDescription>(edComparator);
+		SortedSet<EvaluatedDescriptionPosNeg> cbd = new TreeSet<EvaluatedDescriptionPosNeg>(edComparator);
 		for(Node eb : rev) {
-			cbd.add(new EvaluatedDescription(eb.getConcept(), getSolutionScore(eb.getConcept())));
+			cbd.add(new EvaluatedDescriptionPosNeg(eb.getConcept(), getSolutionScore(eb.getConcept())));
 			// return a maximum of 200 elements (we need a maximum, because the
 			// candidate set can be very large)
 			if(count > 200)
@@ -1100,19 +1100,19 @@
 	}
 	
 //	@Override
-	public Score getSolutionScore() {
+	public ScorePosNeg getSolutionScore() {
 		if(posOnly)
 			return posOnlyLearningProblem.computeScore(getCurrentlyBestDescription());
 		else
-			return learningProblem.computeScore(getCurrentlyBestDescription());
+			return (ScorePosNeg) learningProblem.computeScore(getCurrentlyBestDescription());
 	}
 	
 	
-	public Score getSolutionScore(Description d) {
+	public ScorePosNeg getSolutionScore(Description d) {
 		if(posOnly)
 			return posOnlyLearningProblem.computeScore(d);
 		else
-			return learningProblem.computeScore(d);
+			return (ScorePosNeg) learningProblem.computeScore(d);
 	}
 
 	@Override

Modified: trunk/src/dl-learner/org/dllearner/cli/Start.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/cli/Start.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/cli/Start.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -57,7 +57,6 @@
 import org.dllearner.core.LearningProblemUnsupportedException;
 import org.dllearner.core.OntologyFormat;
 import org.dllearner.core.ReasonerComponent;
-import org.dllearner.core.Score;
 import org.dllearner.core.options.BooleanConfigOption;
 import org.dllearner.core.options.ConfigEntry;
 import org.dllearner.core.options.ConfigOption;
@@ -77,6 +76,7 @@
 import org.dllearner.learningproblems.PosNegDefinitionLP;
 import org.dllearner.learningproblems.PosNegInclusionLP;
 import org.dllearner.learningproblems.PosOnlyDefinitionLP;
+import org.dllearner.learningproblems.ScorePosNeg;
 import org.dllearner.parser.ConfParser;
 import org.dllearner.parser.KBParser;
 import org.dllearner.parser.ParseException;
@@ -840,7 +840,7 @@
 
 						logger.info("retrieval result (" + result.size() + "): " + result);
 
-						Score score = lp.computeScore(concept);
+						ScorePosNeg score = (ScorePosNeg) lp.computeScore(concept);
 						logger.info(score);
 
 					}

Modified: trunk/src/dl-learner/org/dllearner/core/EvaluatedDescription.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/core/EvaluatedDescription.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/core/EvaluatedDescription.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -1,5 +1,5 @@
 /**
- * Copyright (C) 2007-2008, Jens Lehmann
+ * Copyright (C) 2007-2009, Jens Lehmann
  *
  * This file is part of DL-Learner.
  * 
@@ -19,34 +19,25 @@
  */
 package org.dllearner.core;
 
-import java.util.Set;
-
 import org.dllearner.core.owl.Description;
-import org.dllearner.core.owl.Individual;
 import org.dllearner.kb.sparql.SparqlQueryDescriptionConvertVisitor;
-import org.dllearner.learningproblems.ScoreTwoValued;
 import org.dllearner.utilities.owl.OWLAPIDescriptionConvertVisitor;
 import org.dllearner.utilities.owl.OWLAPIRenderers;
-import org.json.JSONArray;
 import org.json.JSONException;
 import org.json.JSONObject;
 import org.semanticweb.owl.model.OWLDescription;
 
 /**
- * This represents a class description, which has been
- * evaluated by the learning algorithm, i.e. it has been checked
- * which examples it covers. It can be used as return value for
- * learning algorithms to make it easier for applications to
- * assess how good an offered class description is and how it
- * classifies particular examples.
+ * An evaluated description is a description and its score (with some
+ * convenience method and serialisation formats).
  * 
  * @author Jens Lehmann
  *
  */
 public class EvaluatedDescription {
 
-	private Description description;
-	private Score score;
+	protected Description description;
+	protected Score score;
 	
 	/**
 	 * Constructs an evaluated description using its score.
@@ -59,21 +50,6 @@
 	}
 	
 	/**
-	 * Constructs an evaluated description using example coverage.
-	 * @param description The description, which was evaluated.
-	 * @param posAsPos Positive examples classified as positive by (i.e. instance of) the description.
-	 * @param posAsNeg Positive examples classified as negative by (i.e. not instance of) the description.
-	 * @param negAsPos Negative examples classified as positive by (i.e. instance of) the description.
-	 * @param negAsNeg Negative examples classified as negative by (i.e. not instance of) the description.
-	 */
-	public EvaluatedDescription(Description description, Set<Individual> posAsPos, Set<Individual> posAsNeg, Set<Individual> negAsPos, Set<Individual> negAsNeg) {
-		this.description = description;
-		// usually core methods should not depend on methods outside of the core package (except utilities)
-		// in this case, this is just a convenience constructor
-		score = new ScoreTwoValued(posAsPos, posAsNeg, negAsPos, negAsNeg);
-	}
-
-	/**
 	 * Gets the description, which was evaluated.
 	 * @return The underlying description.
 	 */
@@ -107,56 +83,14 @@
 	}
 	
 	/**
-	 * @see org.dllearner.core.Score#getAccuracy()
-	 * @return Accuracy of the description.
+	 * @see org.dllearner.core.Score#getScoreValue()
+	 * @return Value in this score system.
 	 */
-	public double getAccuracy() {
-		return score.getAccuracy();
+	public double getScoreValue() {
+		return score.getScoreValue();
 	}
 	
 	/**
-	 * Gets the score of this description. This can be used to get
-	 * further statistical values.
-	 * @see org.dllearner.core.Score
-	 * @return The score object associated with this evaluated description.
-	 */
-	public Score getScore() {
-		return score;
-	}
-
-	/**
-	 * @see org.dllearner.core.Score#getCoveredNegatives()
-	 * @return Negative examples covered by the description.
-	 */
-	public Set<Individual> getCoveredNegatives() {
-		return score.getCoveredNegatives();
-	}
-
-	/**
-	 * @see org.dllearner.core.Score#getCoveredPositives()
-	 * @return Positive examples covered by the description.
-	 */
-	public Set<Individual> getCoveredPositives() {
-		return score.getCoveredPositives();
-	}
-
-	/**
-	 * @see org.dllearner.core.Score#getNotCoveredNegatives()
-	 * @return Negative examples not covered by the description.
-	 */
-	public Set<Individual> getNotCoveredNegatives() {
-		return score.getNotCoveredNegatives();
-	}
-
-	/**
-	 * @see org.dllearner.core.Score#getNotCoveredPositives()
-	 * @return Positive examples not covered by the description.
-	 */
-	public Set<Individual> getNotCoveredPositives() {
-		return score.getNotCoveredPositives();
-	}
-	
-	/**
 	 * Returns a SPARQL query to get instances of this description
 	 * from a SPARQL endpoint. Of course, results may be incomplete,
 	 * because no inference is done. The SPARQL query is a straightforward
@@ -169,7 +103,7 @@
 	 */
 	public String getSparqlQuery(int limit) {
 		return SparqlQueryDescriptionConvertVisitor.getSparqlQuery(description, limit);
-	}
+	}	
 	
 	/**
 	 * This convenience method can be used to store and exchange evaluated
@@ -183,32 +117,12 @@
 			OWLDescription d = OWLAPIDescriptionConvertVisitor.getOWLDescription(description);
 			object.put("descriptionOWLXML", OWLAPIRenderers.toOWLXMLSyntax(d));
 			object.put("descriptionKBSyntax", description.toKBSyntaxString());
-			object.put("accuracy", score.getAccuracy());
-			object.put("coveredPositives", getJSONArray(score.getCoveredPositives()));
-			object.put("coveredNegatives", getJSONArray(score.getCoveredNegatives()));
-			object.put("notCoveredPositives", getJSONArray(score.getNotCoveredPositives()));
-			object.put("notCoveredNegatives", getJSONArray(score.getNotCoveredNegatives()));			
+			object.put("scoreValue", score.getScoreValue());		
 			return object.toString(3);
 		} catch (JSONException e) {
 			e.printStackTrace();
 			return null;
 		}
-	}
-	
-	@Override
-	public String toString() {
-		return description.toString() + "(accuracy: " + getAccuracy() + ")";
-	}
-	
-	// we need to use this method instead of the standard JSON array constructor,
-	// otherwise we'll get unexpected results (JSONArray does not take Individuals
-	// as arguments and does not use toString)
-	private static JSONArray getJSONArray(Set<Individual> individuals) {
-		JSONArray j = new JSONArray();
-		for(Individual i : individuals) {
-			j.put(i.getName());
-		}
-		return j;
-	}
+	}	
 
 }

Modified: trunk/src/dl-learner/org/dllearner/core/LearningAlgorithm.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/core/LearningAlgorithm.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/core/LearningAlgorithm.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -207,7 +207,7 @@
 	 * first.
 	 * @return Best class descriptions found so far.
 	 */
-	public SortedSet<EvaluatedDescription> getCurrentlyBestEvaluatedDescriptions() {
+	public SortedSet<? extends EvaluatedDescription> getCurrentlyBestEvaluatedDescriptions() {
 		TreeSet<EvaluatedDescription> ds = new TreeSet<EvaluatedDescription>();
 		ds.add(getCurrentlyBestEvaluatedDescription());
 		return ds;
@@ -231,14 +231,15 @@
 	 * 
 	 * @return A list of currently best class descriptions.
 	 */
-	public synchronized List<EvaluatedDescription> getCurrentlyBestEvaluatedDescriptions(int nrOfDescriptions, double accuracyThreshold, boolean filterNonMinimalDescriptions) {
-		SortedSet<EvaluatedDescription> currentlyBest = getCurrentlyBestEvaluatedDescriptions();
+	public synchronized List<? extends EvaluatedDescription> getCurrentlyBestEvaluatedDescriptions(int nrOfDescriptions, double accuracyThreshold, boolean filterNonMinimalDescriptions) {
+		SortedSet<? extends EvaluatedDescription> currentlyBest = getCurrentlyBestEvaluatedDescriptions();
 		List<EvaluatedDescription> returnList = new LinkedList<EvaluatedDescription>();
 		for(EvaluatedDescription ed : currentlyBest) {
 			// once we hit a description with a below threshold accuracy, we simply return
 			// because learning algorithms are advised to order descriptions by accuracy,
 			// so we won't find any concept with higher accuracy in the remaining list
-			if(ed.getAccuracy() < accuracyThreshold) {
+//			if(ed.getAccuracy() < accuracyThreshold) {
+			if(ed.getScoreValue() < accuracyThreshold) {
 				return returnList;
 			}
 
@@ -270,7 +271,7 @@
 	 * @param nrOfDescriptions Maximum number of descriptions returned.
 	 * @return Return value is getCurrentlyBestDescriptions(nrOfDescriptions, 0.0, false).
 	 */
-	public synchronized List<EvaluatedDescription> getCurrentlyBestEvaluatedDescriptions(int nrOfDescriptions) {
+	public synchronized List<? extends EvaluatedDescription> getCurrentlyBestEvaluatedDescriptions(int nrOfDescriptions) {
 		return getCurrentlyBestEvaluatedDescriptions(nrOfDescriptions, 0.0, false);
 	}
 	
@@ -279,7 +280,7 @@
 	 * @param accuracyThreshold Only return solutions with this accuracy or higher.
 	 * @return Return value is getCurrentlyBestDescriptions(Integer.MAX_VALUE, accuracyThreshold, false).
 	 */
-	public synchronized  List<EvaluatedDescription> getCurrentlyBestEvaluatedDescriptions(double accuracyThreshold) {
+	public synchronized  List<? extends EvaluatedDescription> getCurrentlyBestEvaluatedDescriptions(double accuracyThreshold) {
 		return getCurrentlyBestEvaluatedDescriptions(Integer.MAX_VALUE, accuracyThreshold, false);
 	}
 		

Modified: trunk/src/dl-learner/org/dllearner/core/LearningProblem.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/core/LearningProblem.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/core/LearningProblem.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -20,6 +20,7 @@
 package org.dllearner.core;
 
 import org.dllearner.core.owl.Description;
+import org.dllearner.learningproblems.ScorePosNeg;
 
 /**
  * Base class for all learning problems.

Modified: trunk/src/dl-learner/org/dllearner/core/Score.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/core/Score.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/core/Score.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -1,5 +1,5 @@
 /**
- * Copyright (C) 2007-2008, Jens Lehmann
+ * Copyright (C) 2007-2009, Jens Lehmann
  *
  * This file is part of DL-Learner.
  * 
@@ -17,48 +17,28 @@
  * along with this program.  If not, see <http://www.gnu.org/licenses/>.
  *
  */
-
 package org.dllearner.core;
 
-import java.util.Set;
-
-import org.dllearner.core.owl.Individual;
-
 /**
  * The score class is used to store how well a class description did
- * on a learning problem.
+ * on a learning problem. Depending on the learning problem at hand,
+ * different criteria can be used. (Similar learning problems probably
+ * score class descriptions/hypothesis in a similar way.)
  * 
- * TODO: If possible this class should be abstracted further. The current
- * implementation requires positive and negative examples, i.e. does not 
- * allow arbitrary learning problems.
- * 
  * @author Jens Lehmann
  *
  */
 public abstract class Score {
-	
-	// accuracy
-	public abstract double getAccuracy();
-	
-	// example coverage
-	public abstract Set<Individual> getCoveredPositives();
-	public abstract Set<Individual> getCoveredNegatives();
-	public abstract Set<Individual> getNotCoveredPositives();
-	public abstract Set<Individual> getNotCoveredNegatives();	
-	
-	// older methods (not frequently used anymore)
-	public abstract double getScore();
+
 	/**
-	 * The score of a concept depends on how good it classifies the
-	 * examples of a learning problem and the length of the concept
-	 * itself. If a given concept is known to have equal classification
-	 * properties than the concept this score object is based on, then
-	 * this method can be used to calculate its score value by using the
-	 * length of this concept as parameter.
+	 * This method returns a value, which indicates how well a
+	 * class description solves a learning problem. Different implementations
+	 * of scoring systems can implement this differently. Higher values
+	 * are better and it is recommended to assign a score value in
+	 * the closed interval from 0 to 1 if possible. 
 	 * 
-	 * @param newLength Length of the concept.
-	 * @return Score.
+	 * @return A value indicating the quality (of a class description).
 	 */
-	public abstract Score getModifiedLengthScore(int newLength);
+	public abstract double getScoreValue();
 	
 }

Modified: trunk/src/dl-learner/org/dllearner/gui/RunPanel.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/gui/RunPanel.java	2009-02-09 12:39:09 UTC (rev 1590)
+++ trunk/src/dl-learner/org/dllearner/gui/RunPanel.java	2009-02-09 16:41:44 UTC (rev 1591)
@@ -48,6 +48,7 @@
 
 import javax.swing.*;
 
+import org.dllearner.algorithms.EvaluatedDescriptionPosNeg;
 import org.dllearner.algorithms.refexamples.ExampleBasedROLComponent;
 import org.dllearner.core.EvaluatedDescription;
 import org.dllearner.learningproblems.PosNegDefinitionLP;
@@ -377,12 +378,12 @@
 		gbc.weighty = wy;
 	}
 
-	private String getSolutionString(List<EvaluatedDescription> solutions) {
+	private String getSolutionString(List<? extends EvaluatedDescription> solutions) {
 		String baseURI = config.getReasoner().getBaseURI();
 		Map<String,String> prefixes = conf...
 
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