1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2011 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 System.Text;
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26 | using HeuristicLab.Parameters;
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27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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28 | using HeuristicLab.Common;
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29 | using HeuristicLab.Core;
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30 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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31 | using HeuristicLab.Data;
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32 |
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33 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
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34 |
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35 | [Item("SemanticSimilarityCrossover", "An operator which performs subtree swapping based on semantic similarity.")]
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36 | public sealed class SymbolicDataAnalysisExpressionSemanticSimilarityCrossover<T> : SymbolicDataAnalysisExpressionCrossover<T> where T : class, IDataAnalysisProblemData {
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37 | private const string SemanticSimilarityLowerBoundParameterName = "SemanticSimilarityLowerBound";
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38 | private const string SemanticSimilarityUpperBoundParameterName = "SemanticSimilarityUpperBound";
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39 |
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40 | #region Parameter properties
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41 | public IValueParameter<DoubleValue> SemanticSimilarityLowerBoundParameter {
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42 | get { return (IValueParameter<DoubleValue>)Parameters[SemanticSimilarityLowerBoundParameterName]; }
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43 | }
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44 | public IValueParameter<DoubleValue> SemanticSimilarityUpperBoundParameter {
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45 | get { return (IValueParameter<DoubleValue>)Parameters[SemanticSimilarityUpperBoundParameterName]; }
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46 | }
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47 | #endregion
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48 |
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49 | #region Properties
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50 | public DoubleValue SemanticSimilarityLowerBound {
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51 | get { return SemanticSimilarityLowerBoundParameter.Value; }
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52 | }
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53 | public DoubleValue SemanticSimilarityUpperBound {
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54 | get { return SemanticSimilarityUpperBoundParameter.Value; }
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55 | }
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56 | #endregion
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57 |
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58 | [StorableConstructor]
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59 | private SymbolicDataAnalysisExpressionSemanticSimilarityCrossover(bool deserializing) : base(deserializing) { }
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60 | private SymbolicDataAnalysisExpressionSemanticSimilarityCrossover(SymbolicDataAnalysisExpressionSemanticSimilarityCrossover<T> original, Cloner cloner) : base(original, cloner) { }
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61 | public SymbolicDataAnalysisExpressionSemanticSimilarityCrossover()
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62 | : base() {
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63 | Parameters.Add(new ValueLookupParameter<DoubleValue>(SemanticSimilarityLowerBoundParameterName, "The lower bound of the semantic similarity interval."));
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64 | Parameters.Add(new ValueLookupParameter<DoubleValue>(SemanticSimilarityUpperBoundParameterName, "The lower bound of the semantic similarity interval."));
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65 |
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66 | // Set some decent default values for the lower and upper bound parameters
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67 | SemanticSimilarityLowerBoundParameter.Value = new DoubleValue(0.0001);
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68 | SemanticSimilarityUpperBoundParameter.Value = new DoubleValue(10);
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69 | }
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70 | public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicDataAnalysisExpressionSemanticSimilarityCrossover<T>(this, cloner); }
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71 |
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72 | protected override ISymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) {
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73 | ISymbolicDataAnalysisExpressionTreeInterpreter interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
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74 | IEnumerable<int> rows = GenerateRowsToEvaluate();
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75 | T problemData = ProblemDataParameter.ActualValue;
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76 | return Cross(random, parent0, parent1, interpreter, problemData, rows, MaximumSymbolicExpressionTreeDepth.Value, MaximumSymbolicExpressionTreeLength.Value, SemanticSimilarityLowerBoundParameter.Value.Value, SemanticSimilarityUpperBoundParameter.Value.Value);
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77 | }
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78 | /// <summary>
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79 | /// Takes two parent individuals P0 and P1.
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80 | /// Randomly choose a node i from the first parent, then get a node j from the second parent that matches the semantic similarity criteria.
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81 | /// </summary>
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82 | public static ISymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1,
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83 | ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, T problemData, IEnumerable<int> rows, int maxDepth, int maxLength, double lower, double upper) {
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84 | var crossoverPoints0 = new List<CutPoint>();
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85 | parent0.Root.ForEachNodePostfix((n) => {
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86 | if (n.Subtrees.Any() && n != parent0.Root)
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87 | foreach (var child in n.Subtrees)
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88 | crossoverPoints0.Add(new CutPoint(n, child));
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89 | });
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90 | var crossoverPoint0 = crossoverPoints0[random.Next(crossoverPoints0.Count)];
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91 | int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
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92 | int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();
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93 |
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94 | var allowedBranches = new List<ISymbolicExpressionTreeNode>();
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95 | parent1.Root.ForEachNodePostfix((n) => {
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96 | if (n.Subtrees.Any() && n != parent1.Root)
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97 | foreach (var child in n.Subtrees)
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98 | if (crossoverPoint0.IsMatchingPointType(child) && (child.GetDepth() + level <= maxDepth) && (child.GetLength() + length <= maxLength))
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99 | allowedBranches.Add(child);
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100 | });
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101 |
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102 | // check if empty branch is allowed
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103 | if (crossoverPoint0.IsMatchingPointType(null)) allowedBranches.Add(null);
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104 |
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105 | if (allowedBranches.Count == 0)
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106 | return parent0;
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107 |
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108 | var dataset = problemData.Dataset;
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109 |
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110 | // create symbols in order to improvize an ad-hoc tree so that the child can be evaluated
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111 | var rootSymbol = new ProgramRootSymbol();
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112 | var startSymbol = new StartSymbol();
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113 | var tree0 = CreateTreeFromNode(random, crossoverPoint0.Child, rootSymbol, startSymbol);
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114 | IEnumerable<double> estimatedValues0 = interpreter.GetSymbolicExpressionTreeValues(tree0, dataset, rows);
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115 | crossoverPoint0.Child.Parent = crossoverPoint0.Parent; // restore parent
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116 | ISymbolicExpressionTreeNode selectedBranch = null;
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117 |
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118 | // pick the first node that fulfills the semantic similarity conditions
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119 | foreach (var node in allowedBranches) {
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120 | var parent = node.Parent;
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121 | var tree1 = CreateTreeFromNode(random, node, startSymbol, rootSymbol); // this will affect node.Parent
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122 | IEnumerable<double> estimatedValues1 = interpreter.GetSymbolicExpressionTreeValues(tree1, dataset, rows);
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123 | node.Parent = parent; // restore parent
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124 |
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125 | OnlineCalculatorError errorState;
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126 | double ssd = OnlineMeanAbsoluteErrorCalculator.Calculate(estimatedValues0, estimatedValues1, out errorState);
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127 |
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128 | if (lower < ssd && ssd < upper) {
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129 | selectedBranch = node;
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130 | break;
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131 | }
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132 | }
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133 |
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134 | if (selectedBranch == null)
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135 | return parent0;
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136 |
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137 | // perform the actual swap
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138 | swap(crossoverPoint0, selectedBranch);
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139 |
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140 | return parent0;
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141 | }
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142 |
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143 | private static void swap(CutPoint crossoverPoint, ISymbolicExpressionTreeNode selectedBranch) {
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144 | // perform the actual swap
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145 | if (crossoverPoint.Child != null) {
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146 | // manipulate the tree of parent0 in place
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147 | // replace the branch in tree0 with the selected branch from tree1
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148 | crossoverPoint.Parent.RemoveSubtree(crossoverPoint.ChildIndex);
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149 | if (selectedBranch != null) {
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150 | crossoverPoint.Parent.InsertSubtree(crossoverPoint.ChildIndex, selectedBranch);
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151 | }
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152 | } else {
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153 | // child is null (additional child should be added under the parent)
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154 | if (selectedBranch != null) {
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155 | crossoverPoint.Parent.AddSubtree(selectedBranch);
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156 | }
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157 | }
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158 | }
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159 |
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160 | }
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161 | }
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