[7840] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2012 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 HeuristicLab.Core;
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| 26 | using HeuristicLab.Data;
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| 27 | using HeuristicLab.Optimization;
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| 28 |
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| 29 | namespace HeuristicLab.Encodings.ParameterConfigurationTreeEncoding {
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| 30 | public static class MetaOptimizationUtil {
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| 31 | /// <summary>
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| 32 | /// Removes those results from the run which are not declared in resultsToKeep
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| 33 | /// </summary>
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| 34 | public static void ClearResults(IRun run, IEnumerable<string> resultsToKeep) {
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| 35 | var resultsToRemove = new List<string>();
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| 36 | foreach (var result in run.Results) {
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| 37 | if (!resultsToKeep.Contains(result.Key))
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| 38 | resultsToRemove.Add(result.Key);
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| 39 | }
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| 40 | foreach (var result in resultsToRemove)
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| 41 | run.Results.Remove(result);
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| 42 | }
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| 43 |
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| 44 | /// <summary>
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| 45 | /// Removes those parameters from the run which are not declared in parametersToKeep
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| 46 | /// </summary>
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| 47 | public static void ClearParameters(IRun run, IEnumerable<string> parametersToKeep) {
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| 48 | var parametersToRemove = new List<string>();
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| 49 | foreach (var parameter in run.Parameters) {
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| 50 | if (!parametersToKeep.Contains(parameter.Key))
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| 51 | parametersToRemove.Add(parameter.Key);
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| 52 | }
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| 53 | foreach (var parameter in parametersToRemove)
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| 54 | run.Parameters.Remove(parameter);
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| 55 | }
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| 56 |
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| 57 | public static double Normalize(
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| 58 | ParameterConfigurationTree parameterConfigurationTree,
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| 59 | double[] referenceQualityAverages,
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| 60 | double[] referenceQualityDeviations,
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| 61 | double[] referenceEvaluatedSolutionAverages,
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| 62 | double qualityAveragesWeight,
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| 63 | double qualityDeviationsWeight,
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| 64 | double evaluatedSolutionsWeight,
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| 65 | bool maximization) {
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| 66 |
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| 67 | double[] qualityAveragesNormalized = new double[referenceQualityAverages.Length];
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| 68 | double[] qualityDeviationsNormalized = new double[referenceQualityDeviations.Length];
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| 69 | double[] evaluatedSolutionAveragesNormalized = new double[referenceEvaluatedSolutionAverages.Length];
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| 70 |
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| 71 | for (int i = 0; i < referenceQualityAverages.Length; i++) {
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| 72 | qualityAveragesNormalized[i] = parameterConfigurationTree.AverageQualities[i] / referenceQualityAverages[i];
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| 73 | qualityDeviationsNormalized[i] = parameterConfigurationTree.QualityStandardDeviations[i] / referenceQualityDeviations[i];
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| 74 | evaluatedSolutionAveragesNormalized[i] = parameterConfigurationTree.AverageEvaluatedSolutions[i] / referenceEvaluatedSolutionAverages[i];
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| 75 | }
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| 76 | parameterConfigurationTree.NormalizedQualityAverages = new DoubleArray(qualityAveragesNormalized);
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| 77 | parameterConfigurationTree.NormalizedQualityDeviations = new DoubleArray(qualityDeviationsNormalized);
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| 78 | parameterConfigurationTree.NormalizedEvaluatedSolutions = new DoubleArray(evaluatedSolutionAveragesNormalized);
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| 79 |
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| 80 | double qualityAveragesNormalizedValue = qualityAveragesNormalized.Average();
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| 81 | double qualityDeviationsNormalizedValue = qualityDeviationsNormalized.Average();
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| 82 | double evaluatedSolutionAveragesNormalizedValue = evaluatedSolutionAveragesNormalized.Average();
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| 83 |
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| 84 | // deviation and evaluatedSolutions are always minimization problems. so if maximization=true, flip the values around 1.0 (e.g. 1.15 -> 0.85)
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| 85 | if (maximization) {
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| 86 | qualityDeviationsNormalizedValue -= (qualityDeviationsNormalizedValue - 1) * 2;
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| 87 | evaluatedSolutionAveragesNormalizedValue -= (evaluatedSolutionAveragesNormalizedValue - 1) * 2;
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| 88 | }
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| 89 |
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| 90 | // apply weights
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| 91 | qualityAveragesNormalizedValue *= qualityAveragesWeight;
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| 92 | qualityDeviationsNormalizedValue *= qualityDeviationsWeight;
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| 93 | evaluatedSolutionAveragesNormalizedValue *= evaluatedSolutionsWeight;
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| 94 |
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| 95 | double weightSum = qualityAveragesWeight + qualityDeviationsWeight + evaluatedSolutionsWeight;
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| 96 | parameterConfigurationTree.Quality = new DoubleValue((qualityAveragesNormalizedValue + qualityDeviationsNormalizedValue + evaluatedSolutionAveragesNormalizedValue) / weightSum);
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| 97 |
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| 98 | return parameterConfigurationTree.Quality.Value;
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| 99 | }
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| 100 |
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| 101 | /// <summary>
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| 102 | /// Creates a new instance of algorithmType, sets the given problem and parameterizes it with the given configuration
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| 103 | /// </summary>
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| 104 | public static IAlgorithm CreateParameterizedAlgorithmInstance(ParameterConfigurationTree parameterConfigurationTree, Type algorithmType, IProblem problem, bool randomize = false, IRandom random = null) {
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| 105 | var algorithm = (IAlgorithm)Activator.CreateInstance(algorithmType);
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| 106 | algorithm.Problem = problem;
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| 107 | if (algorithm is EngineAlgorithm) {
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| 108 | ((EngineAlgorithm)algorithm).Engine = new SequentialEngine.SequentialEngine();
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| 109 | }
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| 110 | if (randomize) parameterConfigurationTree.Randomize(random);
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| 111 | parameterConfigurationTree.Parameterize(algorithm);
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| 112 | return algorithm;
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| 113 | }
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| 114 | }
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| 115 | }
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