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 HeuristicLab.Analysis;
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23 | using HeuristicLab.Common;
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24 | using HeuristicLab.Core;
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25 | using HeuristicLab.Data;
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26 | using HeuristicLab.Optimization;
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27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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28 | using System;
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29 | using System.Collections.Generic;
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30 | using System.Linq;
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31 |
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32 | namespace HeuristicLab.OptimizationExpertSystem.Common {
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33 | [Item("Overall Best Recommender", "")]
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34 | [StorableClass]
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35 | public class OverallBestRecommender : ParameterizedNamedItem, IAlgorithmInstanceRecommender {
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36 |
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37 | private IFixedValueParameter<DoubleValue> NeighborhoodFactorParameter {
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38 | get { return (IFixedValueParameter<DoubleValue>)Parameters["NeighborhoodFactor"]; }
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39 | }
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40 |
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41 | [StorableConstructor]
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42 | private OverallBestRecommender(bool deserializing) : base(deserializing) { }
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43 | private OverallBestRecommender(OverallBestRecommender original, Cloner cloner) : base(original, cloner) { }
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44 | public OverallBestRecommender() { }
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45 |
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46 | public override IDeepCloneable Clone(Cloner cloner) {
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47 | return new OverallBestRecommender(this, cloner);
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48 | }
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49 |
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50 | public IRecommendationModel TrainModel(IRun[] problemInstances, KnowledgeCenter kc, string[] characteristics) {
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51 | var instances = new List<KeyValuePair<IAlgorithm, double>>();
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52 | foreach (var relevantRuns in kc.GetKnowledgeBaseByAlgorithm()) {
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53 | var algorithm = relevantRuns.Key;
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54 | var pis = relevantRuns.Value.Select(x => ((StringValue)x.Parameters["Problem Name"]).Value).Distinct()
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55 | .Select(x => Tuple.Create(x, problemInstances.SingleOrDefault(y => ((StringValue)y.Parameters["Problem Name"]).Value == x)))
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56 | .Where(x => x.Item2 != null)
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57 | .Select(x => Tuple.Create(x.Item1, ((DoubleValue)x.Item2.Parameters["BestKnownQuality"]).Value))
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58 | .ToDictionary(x => x.Item1, x => x.Item2);
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59 | var avgERT = 0.0;
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60 | var count = 0;
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61 | foreach (var problemRuns in relevantRuns.Value.GroupBy(x => ((StringValue)x.Parameters["Problem Name"]).Value)) {
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62 | double bkq;
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63 | if (!pis.TryGetValue(problemRuns.Key, out bkq)) continue;
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64 | var ert = ExpectedRuntimeHelper.CalculateErt(problemRuns.ToList(), "QualityPerEvaluations", kc.GetTarget(bkq, kc.MinimumTarget.Value, kc.Maximization), kc.Maximization).ExpectedRuntime;
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65 | if (double.IsNaN(ert)) ert = int.MaxValue;
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66 | avgERT += ert;
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67 | count++;
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68 | }
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69 | avgERT /= count;
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70 | instances.Add(new KeyValuePair<IAlgorithm, double>(algorithm, avgERT));
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71 | }
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72 |
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73 | return new FixedRankModel(instances.OrderBy(x => x.Value));
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74 | }
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75 | }
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76 | }
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