1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2008 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.Text;
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25 | using System.Windows.Forms;
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26 | using HeuristicLab.PluginInfrastructure;
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27 | using System.Net;
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28 | using System.ServiceModel;
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29 | using HeuristicLab.CEDMA.DB.Interfaces;
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30 | using HeuristicLab.CEDMA.DB;
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31 | using System.ServiceModel.Description;
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32 | using System.Linq;
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33 | using HeuristicLab.CEDMA.Core;
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34 | using HeuristicLab.GP.StructureIdentification;
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35 | using HeuristicLab.Data;
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36 | using HeuristicLab.Core;
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37 |
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38 | namespace HeuristicLab.CEDMA.Server {
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39 | public class Dispatcher {
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40 | private List<Execution> dispatchQueue;
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41 | public IList<string> DispatchQueue {
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42 | get { return dispatchQueue.Select(t => "StandardGP").ToList(); }
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43 | }
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44 |
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45 | private IStore store;
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46 |
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47 | public Dispatcher(IStore store) {
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48 | this.store = store;
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49 | this.dispatchQueue = new List<Execution>();
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50 | }
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51 |
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52 | private void FillDispatchQueue() {
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53 | // find and select a dataset
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54 | var dataSetVar = new HeuristicLab.CEDMA.DB.Interfaces.Variable("DataSet");
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55 | var dataSetQuery = new Statement[] {
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56 | new Statement(dataSetVar, Ontology.PredicateInstanceOf, Ontology.TypeDataSet)
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57 | };
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58 |
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59 | var dataSetBindings = store.Query("?DataSet <"+Ontology.PredicateInstanceOf.Uri+"> <"+Ontology.TypeDataSet.Uri+"> .");
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60 |
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61 | // no datasets => do nothing
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62 | if (dataSetBindings.Count() == 0) return;
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63 |
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64 | // assume last dataset is the most interesting one
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65 | // find and select all results for this dataset
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66 | var dataSetEntity = (Entity)dataSetBindings.Last().Get("DataSet");
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67 | var targetVar = new HeuristicLab.CEDMA.DB.Interfaces.Variable("TargetVariable");
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68 | var modelVar = new HeuristicLab.CEDMA.DB.Interfaces.Variable("Model");
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69 | var modelMAPE = new HeuristicLab.CEDMA.DB.Interfaces.Variable("ModelMAPE");
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70 |
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71 | var query = "<" + dataSetEntity.Uri + "> <" + Ontology.PredicateHasModel.Uri + "> ?Model ." + Environment.NewLine +
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72 | "?Model <" + Ontology.TargetVariable.Uri + "> ?TargetVariable ." + Environment.NewLine +
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73 | "?Model <" + Ontology.ValidationMeanAbsolutePercentageError.Uri + "> ?ModelMAPE .";
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74 |
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75 |
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76 |
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77 | var bindings = store.Query(query);
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78 | DataSet dataSet = new DataSet(store, dataSetEntity);
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79 | double[] utilization = new double[dataSet.Problem.AllowedTargetVariables.Count];
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80 | int i = 0;
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81 | int totalN = bindings.Count();
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82 | foreach (int targetVariable in dataSet.Problem.AllowedTargetVariables) {
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83 | var targetVarBindings = bindings.Where(x => (int)((Literal)x.Get("TargetVariable")).Value == targetVariable);
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84 | if (targetVarBindings.Count() == 0) {
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85 | utilization[i++] = double.PositiveInfinity;
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86 | } else {
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87 | double averageMape = targetVarBindings.Average(x => (double)((Literal)x.Get("ModelMAPE")).Value);
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88 | double n = targetVarBindings.Count();
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89 | utilization[i++] = -averageMape + Math.Sqrt(Math.Log(totalN) / n) * 0.1;
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90 | }
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91 | }
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92 | int[] idx = Enumerable.Range(0, utilization.Length).ToArray();
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93 | Array.Sort(utilization, idx);
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94 | int nConfigurations = utilization.Length;
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95 | for (int j = nConfigurations - 1; j > nConfigurations * 0.8; j--) {
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96 | int targetVariable = dataSet.Problem.AllowedTargetVariables[idx[j]];
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97 | IEngine engine = CreateEngine(dataSet.Problem, targetVariable);
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98 | if (engine != null) {
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99 | QueueJob(new Execution(dataSetEntity, engine, targetVariable));
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100 | }
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101 | }
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102 | }
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103 |
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104 | private void QueueJob(Execution execution) {
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105 | dispatchQueue.Add(execution);
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106 | }
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107 |
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108 | public Execution GetNextJob() {
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109 | if (dispatchQueue.Count == 0) {
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110 | FillDispatchQueue();
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111 | }
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112 | if (dispatchQueue.Count > 0) {
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113 | Execution next = dispatchQueue[0];
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114 | dispatchQueue.RemoveAt(0);
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115 | return next;
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116 | } else
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117 | return null;
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118 | }
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119 |
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120 | internal void Start() {
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121 | FillDispatchQueue();
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122 | }
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123 |
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124 | private IEngine CreateEngine(Problem problem, int targetVariable) {
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125 | switch (problem.LearningTask) {
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126 | case LearningTask.Classification: return null;
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127 | case LearningTask.Regression: {
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128 | return CreateStandardGp(problem, targetVariable).Engine;
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129 | }
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130 | case LearningTask.TimeSeries: return null;
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131 | case LearningTask.Clustering: return null;
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132 | default: return null;
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133 | }
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134 | }
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135 |
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136 | private StandardGP CreateStandardGp(Problem problem, int targetVariable) {
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137 | ProblemInjector probInjector = new ProblemInjector(problem);
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138 | probInjector.TargetVariable = targetVariable;
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139 | StandardGP sgp = new StandardGP();
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140 | sgp.SetSeedRandomly = true;
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141 | sgp.MaxGenerations = 300;
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142 | sgp.PopulationSize = 10000;
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143 | sgp.Elites = 1;
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144 | sgp.ProblemInjector = probInjector;
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145 | return sgp;
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146 | }
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147 | }
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148 | }
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