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.accuracymethods; 020 021import org.dllearner.core.ComponentAnn; 022import org.dllearner.core.config.ConfigOption; 023 024@ComponentAnn(name = "Weighted Predictive Accuracy", shortName = "weighted.pred_acc", version = 0) 025public class AccMethodPredAccWeighted implements AccMethodTwoValued { 026 027 @ConfigOption(defaultValue = "false", description = "balance the weights to relative set size") 028 private boolean balanced = false; 029 @ConfigOption(defaultValue = "1", description = "weight on the positive examples") 030 private double posWeight = 1; 031 @ConfigOption(defaultValue = "1", description = "weight on the negative examples") 032 private double negWeight = 1; 033 034 public AccMethodPredAccWeighted() { 035 } 036 037 public AccMethodPredAccWeighted(boolean init) { 038 if (init) init(); 039 } 040 041 @Override 042 public void init() { 043 } 044 045 @Override 046 public double getAccOrTooWeak2(int tp, int fn, int fp, int tn, double noise) { 047 int posExamples = tp + fn; 048 int negExamples = fp + tn; 049 050 int maxNotCovered = (int) Math.ceil(noise*posExamples); 051 052 if(fn != 0 && fn >= maxNotCovered) { 053 return -1; 054 } 055 056 if (balanced) { 057 posWeight = 1/(double)posExamples; 058 negWeight = 1/(double)negExamples; 059 } 060 return ((tp*posWeight) + (tn*negWeight)) / ((posExamples*posWeight)+(negExamples*negWeight)); 061 } 062 063 public boolean isBalanced() { 064 return balanced; 065 } 066 067 public void setBalanced(boolean balanced) { 068 this.balanced = balanced; 069 } 070 071 public double getPosWeight() { 072 return posWeight; 073 } 074 075 public void setPosWeight(double posWeight) { 076 this.posWeight = posWeight; 077 } 078 079 public double getNegWeight() { 080 return negWeight; 081 } 082 083 public void setNegWeight(double negWeight) { 084 this.negWeight = negWeight; 085 } 086 087}