1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2010 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.Operators;
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28 | using HeuristicLab.Optimization;
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29 | using HeuristicLab.Parameters;
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30 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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31 |
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32 | namespace HeuristicLab.Analysis {
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33 | /// <summary>
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34 | /// An operator for analyzing the solution diversity in a population.
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35 | /// </summary>
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36 | [Item("PopulationDiversityAnalyzer", "An operator for analyzing the solution diversity in a population.")]
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37 | [StorableClass]
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38 | public abstract class PopulationDiversityAnalyzer<T> : SingleSuccessorOperator, IAnalyzer where T : class, IItem {
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39 | public LookupParameter<BoolValue> MaximizationParameter {
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40 | get { return (LookupParameter<BoolValue>)Parameters["Maximization"]; }
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41 | }
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42 | public ScopeTreeLookupParameter<T> SolutionParameter {
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43 | get { return (ScopeTreeLookupParameter<T>)Parameters["Solution"]; }
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44 | }
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45 | public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
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46 | get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
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47 | }
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48 | public ValueLookupParameter<ResultCollection> ResultsParameter {
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49 | get { return (ValueLookupParameter<ResultCollection>)Parameters["Results"]; }
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50 | }
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51 | public ValueParameter<BoolValue> StoreHistoryParameter {
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52 | get { return (ValueParameter<BoolValue>)Parameters["StoreHistory"]; }
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53 | }
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54 | public ValueParameter<IntValue> UpdateIntervalParameter {
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55 | get { return (ValueParameter<IntValue>)Parameters["UpdateInterval"]; }
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56 | }
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57 | public LookupParameter<IntValue> UpdateCounterParameter {
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58 | get { return (LookupParameter<IntValue>)Parameters["UpdateCounter"]; }
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59 | }
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60 |
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61 | [StorableConstructor]
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62 | protected PopulationDiversityAnalyzer(bool deserializing) : base(deserializing) { }
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63 | protected PopulationDiversityAnalyzer(PopulationDiversityAnalyzer<T> original, Cloner cloner) : base(original, cloner) { }
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64 | public PopulationDiversityAnalyzer()
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65 | : base() {
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66 | Parameters.Add(new LookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem."));
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67 | Parameters.Add(new ScopeTreeLookupParameter<T>("Solution", "The solutions whose diversity should be analyzed."));
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68 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The qualities of the solutions which should be analyzed."));
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69 | Parameters.Add(new ValueLookupParameter<ResultCollection>("Results", "The result collection where the population diversity analysis results should be stored."));
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70 | Parameters.Add(new ValueParameter<BoolValue>("StoreHistory", "True if the history of the population diversity analysis should be stored.", new BoolValue(false)));
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71 | Parameters.Add(new ValueParameter<IntValue>("UpdateInterval", "The interval in which the population diversity analysis should be applied.", new IntValue(1)));
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72 | Parameters.Add(new LookupParameter<IntValue>("UpdateCounter", "The value which counts how many times the operator was called since the last update.", "PopulationDiversityAnalyzerUpdateCounter"));
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73 | }
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74 |
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75 | public override IOperation Apply() {
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76 | int updateInterval = UpdateIntervalParameter.Value.Value;
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77 | IntValue updateCounter = UpdateCounterParameter.ActualValue;
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78 | // if counter does not yet exist then initialize it with update interval
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79 | // to make sure the solutions are analyzed on the first application of this operator
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80 | if (updateCounter == null) {
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81 | updateCounter = new IntValue(updateInterval);
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82 | UpdateCounterParameter.ActualValue = updateCounter;
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83 | } else updateCounter.Value++;
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84 |
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85 | //analyze solutions only every 'updateInterval' times
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86 | if (updateCounter.Value == updateInterval) {
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87 | updateCounter.Value = 0;
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88 |
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89 | bool max = MaximizationParameter.ActualValue.Value;
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90 | ItemArray<T> solutions = SolutionParameter.ActualValue;
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91 | ItemArray<DoubleValue> qualities = QualityParameter.ActualValue;
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92 | bool storeHistory = StoreHistoryParameter.Value.Value;
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93 | int count = solutions.Length;
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94 |
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95 | if (count > 1) {
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96 | // sort solutions by quality
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97 | T[] sortedSolutions = null;
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98 | if (max)
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99 | sortedSolutions = solutions
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100 | .Select((x, index) => new { Solution = x, Quality = qualities[index] })
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101 | .OrderByDescending(x => x.Quality)
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102 | .Select(x => x.Solution)
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103 | .ToArray();
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104 | else
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105 | sortedSolutions = solutions
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106 | .Select((x, index) => new { Solution = x, Quality = qualities[index] })
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107 | .OrderBy(x => x.Quality)
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108 | .Select(x => x.Solution)
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109 | .ToArray();
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110 |
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111 | // calculate solution similarities
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112 | double[,] similarities = CalculateSimilarities(sortedSolutions);
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113 |
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114 | // calculate minimum, average and maximum similarities
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115 | double similarity;
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116 | double[] minSimilarities = new double[count];
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117 | double[] avgSimilarities = new double[count];
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118 | double[] maxSimilarities = new double[count];
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119 | for (int i = 0; i < count; i++) {
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120 | minSimilarities[i] = 1;
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121 | avgSimilarities[i] = 0;
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122 | maxSimilarities[i] = 0;
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123 | for (int j = 0; j < count; j++) {
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124 | if (i != j) {
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125 | similarity = similarities[i, j];
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126 |
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127 | if ((similarity < 0) || (similarity > 1))
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128 | throw new InvalidOperationException("Solution similarities have to be in the interval [0;1].");
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129 |
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130 | if (minSimilarities[i] > similarity) minSimilarities[i] = similarity;
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131 | avgSimilarities[i] += similarity;
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132 | if (maxSimilarities[i] < similarity) maxSimilarities[i] = similarity;
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133 | }
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134 | }
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135 | avgSimilarities[i] = avgSimilarities[i] / (count - 1);
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136 | }
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137 | double avgMinSimilarity = minSimilarities.Average();
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138 | double avgAvgSimilarity = avgSimilarities.Average();
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139 | double avgMaxSimilarity = maxSimilarities.Average();
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140 |
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141 | // fetch results collection
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142 | ResultCollection results;
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143 | if (!ResultsParameter.ActualValue.ContainsKey("Population Diversity Analysis Results")) {
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144 | results = new ResultCollection();
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145 | ResultsParameter.ActualValue.Add(new Result("Population Diversity Analysis Results", results));
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146 | } else {
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147 | results = (ResultCollection)ResultsParameter.ActualValue["Population Diversity Analysis Results"].Value;
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148 | }
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149 |
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150 | // store similarities
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151 | HeatMap similaritiesHeatMap = new HeatMap(similarities, "Solution Similarities", 0.0, 1.0);
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152 | if (!results.ContainsKey("Solution Similarities"))
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153 | results.Add(new Result("Solution Similarities", similaritiesHeatMap));
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154 | else
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155 | results["Solution Similarities"].Value = similaritiesHeatMap;
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156 |
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157 | // store similarities history
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158 | if (storeHistory) {
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159 | if (!results.ContainsKey("Solution Similarities History")) {
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160 | HeatMapHistory history = new HeatMapHistory();
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161 | history.Add(similaritiesHeatMap);
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162 | results.Add(new Result("Solution Similarities History", history));
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163 | } else {
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164 | ((HeatMapHistory)results["Solution Similarities History"].Value).Add(similaritiesHeatMap);
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165 | }
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166 | }
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167 |
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168 | // store average minimum, average and maximum similarity
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169 | if (!results.ContainsKey("Average Minimum Solution Similarity"))
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170 | results.Add(new Result("Average Minimum Solution Similarity", new DoubleValue(avgMinSimilarity)));
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171 | else
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172 | ((DoubleValue)results["Average Minimum Solution Similarity"].Value).Value = avgMinSimilarity;
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173 |
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174 | if (!results.ContainsKey("Average Average Solution Similarity"))
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175 | results.Add(new Result("Average Average Solution Similarity", new DoubleValue(avgAvgSimilarity)));
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176 | else
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177 | ((DoubleValue)results["Average Average Solution Similarity"].Value).Value = avgAvgSimilarity;
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178 |
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179 | if (!results.ContainsKey("Average Maximum Solution Similarity"))
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180 | results.Add(new Result("Average Maximum Solution Similarity", new DoubleValue(avgMaxSimilarity)));
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181 | else
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182 | ((DoubleValue)results["Average Maximum Solution Similarity"].Value).Value = avgMaxSimilarity;
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183 |
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184 | // store average minimum, average and maximum solution similarity data table
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185 | DataTable minAvgMaxSimilarityDataTable;
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186 | if (!results.ContainsKey("Average Minimum/Average/Maximum Solution Similarity")) {
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187 | minAvgMaxSimilarityDataTable = new DataTable("Average Minimum/Average/Maximum Solution Similarity");
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188 | results.Add(new Result("Average Minimum/Average/Maximum Solution Similarity", minAvgMaxSimilarityDataTable));
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189 | minAvgMaxSimilarityDataTable.Rows.Add(new DataRow("Average Minimum Solution Similarity", null));
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190 | minAvgMaxSimilarityDataTable.Rows["Average Minimum Solution Similarity"].VisualProperties.StartIndexZero = true;
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191 | minAvgMaxSimilarityDataTable.Rows.Add(new DataRow("Average Average Solution Similarity", null));
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192 | minAvgMaxSimilarityDataTable.Rows["Average Average Solution Similarity"].VisualProperties.StartIndexZero = true;
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193 | minAvgMaxSimilarityDataTable.Rows.Add(new DataRow("Average Maximum Solution Similarity", null));
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194 | minAvgMaxSimilarityDataTable.Rows["Average Maximum Solution Similarity"].VisualProperties.StartIndexZero = true;
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195 | } else {
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196 | minAvgMaxSimilarityDataTable = (DataTable)results["Average Minimum/Average/Maximum Solution Similarity"].Value;
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197 | }
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198 | minAvgMaxSimilarityDataTable.Rows["Average Minimum Solution Similarity"].Values.Add(avgMinSimilarity);
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199 | minAvgMaxSimilarityDataTable.Rows["Average Average Solution Similarity"].Values.Add(avgAvgSimilarity);
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200 | minAvgMaxSimilarityDataTable.Rows["Average Maximum Solution Similarity"].Values.Add(avgMaxSimilarity);
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201 |
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202 | // store minimum, average, maximum similarities data table
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203 | DataTable minAvgMaxSimilaritiesDataTable = new DataTable("Minimum/Average/Maximum Solution Similarities");
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204 | minAvgMaxSimilaritiesDataTable.Rows.Add(new DataRow("Minimum Solution Similarity", null, minSimilarities));
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205 | minAvgMaxSimilaritiesDataTable.Rows["Minimum Solution Similarity"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
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206 | minAvgMaxSimilaritiesDataTable.Rows.Add(new DataRow("Average Solution Similarity", null, avgSimilarities));
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207 | minAvgMaxSimilaritiesDataTable.Rows["Average Solution Similarity"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
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208 | minAvgMaxSimilaritiesDataTable.Rows.Add(new DataRow("Maximum Solution Similarity", null, maxSimilarities));
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209 | minAvgMaxSimilaritiesDataTable.Rows["Maximum Solution Similarity"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
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210 | if (!results.ContainsKey("Minimum/Average/Maximum Solution Similarities")) {
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211 | results.Add(new Result("Minimum/Average/Maximum Solution Similarities", minAvgMaxSimilaritiesDataTable));
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212 | } else {
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213 | results["Minimum/Average/Maximum Solution Similarities"].Value = minAvgMaxSimilaritiesDataTable;
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214 | }
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215 |
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216 | // store minimum, average, maximum similarities history
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217 | if (storeHistory) {
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218 | if (!results.ContainsKey("Minimum/Average/Maximum Solution Similarities History")) {
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219 | DataTableHistory history = new DataTableHistory();
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220 | history.Add(minAvgMaxSimilaritiesDataTable);
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221 | results.Add(new Result("Minimum/Average/Maximum Solution Similarities History", history));
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222 | } else {
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223 | ((DataTableHistory)results["Minimum/Average/Maximum Solution Similarities History"].Value).Add(minAvgMaxSimilaritiesDataTable);
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224 | }
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225 | }
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226 | }
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227 | }
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228 | return base.Apply();
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229 | }
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230 |
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231 | protected abstract double[,] CalculateSimilarities(T[] solutions);
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232 | }
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233 | }
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