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 TheilInequalityCoefficientEvaluator : GPEvaluatorBase {
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34 | public override string Description {
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35 | get {
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36 | return @"Evaluates 'FunctionTree' for all samples of 'Dataset' and calculates
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37 | the 'Theil inequality coefficient (scale invariant)' of estimated values vs. real values of 'TargetVariable'.";
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38 | }
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39 | }
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40 |
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41 | public TheilInequalityCoefficientEvaluator()
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42 | : base() {
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43 | AddVariableInfo(new VariableInfo("Differential", "Wether to calculate the coefficient for the predicted change vs. original change or for the absolute prediction vs. original value", typeof(BoolData), VariableKind.In));
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44 | }
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45 |
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46 | public override double Evaluate(IScope scope, IFunctionTree functionTree, int targetVariable, Dataset dataset) {
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47 | int trainingStart = GetVariableValue<IntData>("TrainingSamplesStart", scope, true).Data;
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48 | int trainingEnd = GetVariableValue<IntData>("TrainingSamplesEnd", scope, true).Data;
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49 | bool difference = GetVariableValue<BoolData>("Differential", scope, true).Data;
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50 | double errorsSquaredSum = 0.0;
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51 | double estimatedSquaredSum = 0.0;
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52 | double originalSquaredSum = 0.0;
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53 | functionTree.PrepareEvaluation(dataset);
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54 | for(int sample = trainingStart; sample < trainingEnd; sample++) {
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55 | double prevValue = 0.0;
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56 | if(difference) prevValue = dataset.GetValue(sample - 1, targetVariable);
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57 | double estimatedChange = functionTree.Evaluate(sample) - prevValue;
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58 | double originalChange = dataset.GetValue(sample, targetVariable) - prevValue;
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59 | if(!double.IsNaN(originalChange) && !double.IsInfinity(originalChange)) {
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60 | if(double.IsNaN(estimatedChange) || double.IsInfinity(estimatedChange))
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61 | estimatedChange = maximumPunishment;
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62 | else if(estimatedChange > maximumPunishment)
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63 | estimatedChange = maximumPunishment;
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64 | else if(estimatedChange < -maximumPunishment)
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65 | estimatedChange = - maximumPunishment;
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66 |
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67 | double error = estimatedChange - originalChange;
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68 | errorsSquaredSum += error * error;
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69 | estimatedSquaredSum += estimatedChange * estimatedChange;
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70 | originalSquaredSum += originalChange * originalChange;
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71 | }
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72 | }
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73 | int nSamples = trainingEnd - trainingStart;
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74 | double quality = Math.Sqrt(errorsSquaredSum / nSamples) / (Math.Sqrt(estimatedSquaredSum/nSamples) + Math.Sqrt(originalSquaredSum/nSamples));
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75 | if(double.IsNaN(quality) || double.IsInfinity(quality))
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76 | quality = double.MaxValue;
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77 | return quality;
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78 | }
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79 | }
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80 | }
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