[14818] | 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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