001/** 002 * Copyright (C) 2007 - 2016, Jens Lehmann 003 * 004 * This file is part of DL-Learner. 005 * 006 * DL-Learner is free software; you can redistribute it and/or modify 007 * it under the terms of the GNU General Public License as published by 008 * the Free Software Foundation; either version 3 of the License, or 009 * (at your option) any later version. 010 * 011 * DL-Learner is distributed in the hope that it will be useful, 012 * but WITHOUT ANY WARRANTY; without even the implied warranty of 013 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 014 * GNU General Public License for more details. 015 * 016 * You should have received a copy of the GNU General Public License 017 * along with this program. If not, see <http://www.gnu.org/licenses/>. 018 */ 019package org.dllearner.algorithms.qtl.heuristics; 020 021import java.util.Set; 022 023import org.dllearner.algorithms.qtl.datastructures.impl.EvaluatedRDFResourceTree; 024import org.dllearner.core.ComponentAnn; 025import org.dllearner.core.ComponentInitException; 026import org.dllearner.learningproblems.Heuristics; 027import org.dllearner.learningproblems.QueryTreeScore; 028import org.semanticweb.owlapi.model.OWLIndividual; 029 030/** 031 * A simple heuristic based which just takes the accuracy into account. 032 * @author Lorenz Buehmann 033 * 034 */ 035@ComponentAnn(name = "QueryTreeHeuristic", shortName = "qtree_heuristic_simple", version = 0.1) 036public class QueryTreeHeuristicSimple extends QueryTreeHeuristic { 037 038 /* (non-Javadoc) 039 * @see org.dllearner.core.Component#init() 040 */ 041 @Override 042 public void init() throws ComponentInitException { 043 initialized = true; 044 } 045 046 @Override 047 public double getScore(EvaluatedRDFResourceTree tree){ 048 QueryTreeScore treeScore = tree.getTreeScore(); 049 050 Set<OWLIndividual> truePositives = treeScore.getCoveredPositives(); 051 Set<OWLIndividual> trueNegatives = treeScore.getNotCoveredNegatives(); 052 Set<OWLIndividual> falsePositives = treeScore.getNotCoveredPositives(); 053 Set<OWLIndividual> falseNegatives = treeScore.getCoveredNegatives(); 054 055 double tp = truePositives.size(); 056 double tn = trueNegatives.size(); 057 double fp = falsePositives.size(); 058 double fn = falseNegatives.size(); 059 060 double score = 0; 061 switch(heuristicType){ 062 case FMEASURE : 063 score = Heuristics.getFScore(tp/(tp+fn), tp/(tp+fp), posExamplesWeight);break; 064 case PRED_ACC : 065 score = (1/posExamplesWeight * tp + tn) / (1/posExamplesWeight * (tp + fn) + (tn + fp));break; 066 case ENTROPY :{ 067 double total = tp + fn; 068 double pp = tp / total; 069 double pn = fn / total; 070 score = pp * Math.log(pp) + pn * Math.log(pn); 071 break;} 072 case MATTHEWS_CORRELATION : // a measure between -1 and 1 073 double denominator = Math.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)); 074 if(denominator == 0) { // 0 means not better than random prediction 075 return 0; 076// denominator = 1; 077 } 078 score = (tp * tn - fp * fn) / denominator;break; 079 case YOUDEN_INDEX : score = tp / (tp + fn) + tn / (fp + tn) - 1;break; 080 default: 081 break; 082 083 } 084 085 return score; 086 } 087}