1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2016 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.Linq;
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24 | using HeuristicLab.Common;
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25 | using HeuristicLab.Core;
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26 | using HeuristicLab.Data;
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27 | using HeuristicLab.Encodings.RealVectorEncoding;
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28 | using HeuristicLab.Parameters;
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29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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30 | using HeuristicLab.Problems.DataAnalysis;
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31 |
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32 | // ReSharper disable once CheckNamespace
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33 | namespace HeuristicLab.Algorithms.EGO {
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34 |
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35 | [StorableClass]
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36 | [Item("ExpectedQuantileImprovement", "Noisy InfillCriterion, Extension of the Expected Improvement as described in \n Noisy expectedimprovement and on - line computation time allocation for the optimization of simulators with tunable fidelitys\r\nPicheny, V., Ginsbourger, D., Richet, Y")]
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37 | public class ExpectedQuantileImprovement : ExpectedImprovement {
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38 |
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39 | #region Parameternames
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40 | public const string AlphaParameterName = "Alpha";
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41 | public const string MaxEvaluationsParameterName = "MaxEvaluations";
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42 | #endregion
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43 |
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44 | #region Parameters
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45 | public IFixedValueParameter<DoubleValue> AlphaParameter => Parameters[AlphaParameterName] as IFixedValueParameter<DoubleValue>;
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46 | public IValueParameter<IntValue> MaxEvaluationsParameter => Parameters[MaxEvaluationsParameterName] as IValueParameter<IntValue>;
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47 | #endregion
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48 |
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49 | #region Properties
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50 |
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51 | public int MaxEvaluations => MaxEvaluationsParameter.Value.Value;
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52 | public double Alpha => AlphaParameter.Value.Value;
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53 | [Storable]
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54 | private double Tau;
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55 |
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56 | #endregion
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57 |
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58 | #region HL-Constructors, Serialization and Cloning
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59 | [StorableConstructor]
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60 | private ExpectedQuantileImprovement(bool deserializing) : base(deserializing) { }
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61 |
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62 | private ExpectedQuantileImprovement(ExpectedQuantileImprovement original, Cloner cloner) : base(original, cloner) {
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63 | Tau = original.Tau;
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64 | }
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65 |
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66 | public ExpectedQuantileImprovement() {
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67 | Parameters.Add(new FixedValueParameter<DoubleValue>(AlphaParameterName, "The Alpha value specifiying the robustness of the \"effective best solution\". Recommended value is 1.0", new DoubleValue(1.0)));
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68 | Parameters.Add(new ValueParameter<IntValue>(MaxEvaluationsParameterName, "The maximum number of evaluations allowed for EGO", new IntValue(100)));
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69 | MaxEvaluationsParameter.Hidden = true;
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70 | }
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71 | public override IDeepCloneable Clone(Cloner cloner) {
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72 | return new ExpectedQuantileImprovement(this, cloner);
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73 | }
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74 | #endregion
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75 |
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76 | public override double Evaluate(RealVector vector) {
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77 | var model = RegressionSolution.Model as IConfidenceRegressionModel;
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78 | var s2 = model.GetVariance(vector);
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79 |
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80 | var yhat = model.GetEstimation(vector) + Alpha * Math.Sqrt(Tau * s2 / (Tau + s2));
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81 | var s = Math.Sqrt(s2 * s2 / (Tau + s2));
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82 |
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83 | return GetEstimatedImprovement(YMin, yhat, s, ExploitationWeight);
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84 | }
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85 |
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86 | protected override void Initialize() {
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87 | if (ExpensiveMaximization) throw new NotImplementedException("AugmentedExpectedImprovement for maximization not yet implemented");
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88 | var solution = RegressionSolution as IConfidenceRegressionSolution;
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89 | if (solution == null) throw new ArgumentException("can not calculate Augmented EI without a regression solution providing confidence values");
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90 |
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91 | Tau = RegressionSolution.EstimatedTrainingValues.Zip(RegressionSolution.ProblemData.TargetVariableTrainingValues, (d, d1) => Math.Abs(d - d1)).Average();
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92 | Tau = Tau * Tau / (MaxEvaluations - RegressionSolution.ProblemData.Dataset.Rows + 1);
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93 |
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94 | var xss = new RealVector(Encoding.Length);
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95 | var xssIndex = solution.EstimatedTrainingVariances.Zip(solution.EstimatedTrainingVariances, (m, s2) => m + Alpha * Math.Sqrt(s2)).ArgMin(x => x);
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96 | var i = solution.ProblemData.TrainingIndices.ToArray()[xssIndex];
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97 | for (var j = 0; j < Encoding.Length; j++) xss[j] = solution.ProblemData.Dataset.GetDoubleValue(i, j);
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98 |
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99 | YMin = RegressionSolution.Model.GetEstimation(xss);
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100 | }
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101 |
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102 | }
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103 | }
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