[14741] | 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("ExpectedImprovementMeassure", "Extension of the Expected Improvement to a weighted version by ANDRAS SÓBESTER , STEPHEN J. LEARY and ANDY J. KEANE in \n On the Design of Optimization Strategies Based on Global Response Surface Approximation Models")]
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| 37 | public class ExpectedImprovement : InfillCriterionBase {
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| 38 |
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| 39 | #region ParameterNames
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| 40 | private const string ExploitationWeightParameterName = "ExploitationWeight";
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| 41 | #endregion
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| 42 |
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| 43 | #region ParameterProperties
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| 44 | public IFixedValueParameter<DoubleValue> ExploitationWeightParameter
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| 45 | {
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| 46 | get { return Parameters[ExploitationWeightParameterName] as IFixedValueParameter<DoubleValue>; }
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| 47 | }
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| 48 | #endregion
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| 49 |
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| 50 | #region Properties
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| 51 | private double ExploitationWeight
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| 52 | {
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| 53 | get { return ExploitationWeightParameter.Value.Value; }
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| 54 | }
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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 ExpectedImprovement(bool deserializing) : base(deserializing) { }
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| 61 | [StorableHook(HookType.AfterDeserialization)]
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| 62 | private void AfterDeserialization() {
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| 63 | RegisterEventhandlers();
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| 64 | }
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| 65 | private ExpectedImprovement(ExpectedImprovement original, Cloner cloner) : base(original, cloner) {
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| 66 | RegisterEventhandlers();
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| 67 | }
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| 68 | public ExpectedImprovement() {
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| 69 | Parameters.Add(new FixedValueParameter<DoubleValue>(ExploitationWeightParameterName, "A value between 0 and 1 indicating the focus on exploration (0) or exploitation (1)", new DoubleValue(0.5)));
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| 70 | RegisterEventhandlers();
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| 71 | }
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| 72 | public override IDeepCloneable Clone(Cloner cloner) {
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| 73 | return new ExpectedImprovement(this, cloner);
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| 74 | }
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| 75 | #endregion
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| 76 |
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| 77 | public override double Evaluate(IRegressionSolution solution, RealVector vector, bool maximization) {
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| 78 | if (maximization) throw new NotImplementedException("Expected Improvement for maximization not yet implemented");
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| 79 | var model = solution.Model as IConfidenceRegressionModel;
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| 80 | if (model == null) throw new ArgumentException("can not calculate EI without confidence measure");
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| 81 | var yhat = model.GetEstimation(vector);
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| 82 | var min = solution.ProblemData.TargetVariableTrainingValues.Min();
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| 83 | var s = Math.Sqrt(model.GetVariance(vector));
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| 84 | return GetEstimatedImprovement(min, yhat, s, ExploitationWeight);
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| 85 | }
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| 86 |
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| 87 | public override bool Maximization(bool expensiveProblemMaximization) {
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| 88 | return true;
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| 89 | }
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| 90 |
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| 91 | #region Eventhandling
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| 92 | private void RegisterEventhandlers() {
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| 93 | DeregisterEventhandlers();
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| 94 | ExploitationWeightParameter.Value.ValueChanged += OnExploitationWeightChanged;
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| 95 | }
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| 96 | private void DeregisterEventhandlers() {
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| 97 | ExploitationWeightParameter.Value.ValueChanged -= OnExploitationWeightChanged;
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| 98 | }
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| 99 | private void OnExploitationWeightChanged(object sender, EventArgs e) {
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| 100 | ExploitationWeightParameter.Value.ValueChanged -= OnExploitationWeightChanged;
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| 101 | ExploitationWeightParameter.Value.Value = Math.Max(0, Math.Min(ExploitationWeight, 1));
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| 102 | ExploitationWeightParameter.Value.ValueChanged += OnExploitationWeightChanged;
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| 103 | }
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| 104 | #endregion
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| 105 |
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| 106 | #region Helpers
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| 107 | private static double GetEstimatedImprovement(double ymin, double yhat, double s, double w) {
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| 108 | if (Math.Abs(s) < double.Epsilon) return 0;
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| 109 | var val = (ymin - yhat) / s;
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| 110 | var res = w * (ymin - yhat) * StandardNormalDistribution(val) + (1 - w) * s * StandardNormalDensity(val);
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| 111 | return double.IsInfinity(res) || double.IsNaN(res) ? 0 : res;
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| 112 | }
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| 113 |
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| 114 | private static double StandardNormalDensity(double x) {
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| 115 | if (Math.Abs(x) > 10) return 0;
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| 116 | return Math.Exp(-0.5 * x * x) / Math.Sqrt(2 * Math.PI);
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| 117 | }
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| 118 |
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| 119 | //taken from https://www.johndcook.com/blog/2009/01/19/stand-alone-error-function-erf/
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| 120 | private static double StandardNormalDistribution(double x) {
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| 121 | if (x > 10) return 1;
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| 122 | if (x < -10) return 0;
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| 123 | const double a1 = 0.254829592;
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| 124 | const double a2 = -0.284496736;
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| 125 | const double a3 = 1.421413741;
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| 126 | const double a4 = -1.453152027;
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| 127 | const double a5 = 1.061405429;
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| 128 | const double p = 0.3275911;
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| 129 | var sign = x < 0 ? -1 : 1;
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| 130 | x = Math.Abs(x) / Math.Sqrt(2.0);
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| 131 | var t = 1.0 / (1.0 + p * x);
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| 132 | var y = 1.0 - ((((a5 * t + a4) * t + a3) * t + a2) * t + a1) * t * Math.Exp(-x * x);
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| 133 | return 0.5 * (1.0 + sign * y);
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| 134 | }
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| 135 | #endregion
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| 136 | }
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| 137 | }
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