1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2008 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System;
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23 | using System.Collections.Generic;
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24 | using System.Linq;
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25 | using System.Text;
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26 | using HeuristicLab.Core;
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27 | using HeuristicLab.Data;
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28 | using HeuristicLab.Operators;
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29 | using HeuristicLab.Functions;
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30 | using HeuristicLab.DataAnalysis;
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31 |
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32 | namespace HeuristicLab.StructureIdentification {
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33 | public class MCCEvaluator : GPEvaluatorBase {
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34 | public override string Description {
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35 | get {
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36 | return @"TASK";
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37 | }
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38 | }
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39 |
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40 | public MCCEvaluator()
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41 | : base() {
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42 | AddVariableInfo(new VariableInfo("ClassSeparation", "The value of separation between negative and positive target classification values (for instance 0.5 if negative=0 and positive=1).", typeof(DoubleData), VariableKind.In));
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43 | }
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44 |
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45 | private double[] original = new double[1];
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46 | private double[] estimated = new double[1];
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47 | public override double Evaluate(IScope scope, IFunctionTree functionTree, int targetVariable, Dataset dataset) {
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48 | int trainingStart = GetVariableValue<IntData>("TrainingSamplesStart", scope, true).Data;
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49 | int trainingEnd = GetVariableValue<IntData>("TrainingSamplesEnd", scope, true).Data;
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50 | int nSamples = trainingEnd-trainingStart;
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51 | double limit = GetVariableValue<DoubleData>("ClassSeparation", scope, false).Data;
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52 | if(estimated.Length != nSamples) {
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53 | estimated = new double[nSamples];
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54 | original = new double[nSamples];
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55 | }
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56 |
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57 | double positive = 0;
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58 | double negative = 0;
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59 | double targetMean = dataset.GetMean(targetVariable, trainingStart, trainingEnd);
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60 | functionTree.PrepareEvaluation(dataset);
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61 | for(int sample = trainingStart; sample < trainingEnd; sample++) {
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62 | double est = functionTree.Evaluate(sample);
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63 | double orig = dataset.GetValue(sample, targetVariable);
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64 | if(double.IsNaN(est) || double.IsInfinity(est)) {
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65 | est = targetMean + maximumPunishment;
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66 | } else if(est > targetMean + maximumPunishment) {
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67 | est = targetMean + maximumPunishment;
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68 | } else if(est < targetMean - maximumPunishment) {
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69 | est = targetMean - maximumPunishment;
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70 | }
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71 | estimated[sample-trainingStart] = est;
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72 | original[sample-trainingStart] = orig;
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73 | if(orig >= limit) positive++;
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74 | else negative++;
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75 | }
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76 | Array.Sort(estimated, original);
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77 | double best_mcc = -1.0;
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78 | double tp = 0;
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79 | double fn = positive;
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80 | double tn = negative;
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81 | double fp = 0;
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82 | for(int i = original.Length-1; i >= 0 ; i--) {
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83 | if(original[i] >= limit) {
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84 | tp++; fn--;
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85 | } else {
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86 | tn--; fp++;
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87 | }
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88 | double mcc = (tp * tn - fp * fn) / Math.Sqrt(positive * (tp + fn) * (tn + fp) * negative);
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89 | if(best_mcc < mcc) {
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90 | best_mcc = mcc;
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91 | }
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92 | }
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93 | return best_mcc;
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94 | }
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95 | }
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96 | }
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