1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2013 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.Diagnostics;
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25 | using System.Linq;
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26 | using System.Threading;
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27 | using HeuristicLab.Core;
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28 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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29 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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30 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
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31 | using HeuristicLab.Random;
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32 | using Microsoft.VisualStudio.TestTools.UnitTesting;
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33 | using ExecutionContext = HeuristicLab.Core.ExecutionContext;
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34 |
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35 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Tests {
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36 | [TestClass()]
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37 | public class SymbolicDataAnalysisExpressionCrossoverTest {
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38 | private const int PopulationSize = 10000;
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39 | private const int MaxTreeDepth = 10;
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40 | private const int MaxTreeLength = 100;
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41 | private const int Rows = 1000;
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42 | private const int Columns = 50;
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43 |
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44 | /// <summary>
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45 | ///Gets or sets the test context which provides
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46 | ///information about and functionality for the current test run.
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47 | ///</summary>
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48 | public TestContext TestContext { get; set; }
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49 |
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50 | [TestMethod]
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51 | public void SymbolicDataAnalysisExpressionSemanticSimilarityCrossoverPerformanceTest() {
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52 | var problem = new SymbolicRegressionSingleObjectiveProblem();
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53 | var crossover = problem.OperatorsParameter.Value.OfType<SymbolicDataAnalysisExpressionSemanticSimilarityCrossover<IRegressionProblemData>>().First();
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54 | SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
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55 | }
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56 |
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57 | [TestMethod]
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58 | public void SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossoverPerformanceTest() {
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59 | var problem = new SymbolicRegressionSingleObjectiveProblem();
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60 | var crossover = problem.OperatorsParameter.Value.OfType<SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover<IRegressionProblemData>>().First();
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61 | SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
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62 | }
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63 |
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64 | [TestMethod]
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65 | public void SymbolicDataAnalysisExpressionDeterministicBestCrossoverPerformanceTest() {
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66 | var problem = new SymbolicRegressionSingleObjectiveProblem();
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67 | var crossover = problem.OperatorsParameter.Value.OfType<SymbolicDataAnalysisExpressionDeterministicBestCrossover<IRegressionProblemData>>().First();
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68 | SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
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69 | }
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70 |
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71 | [TestMethod]
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72 | public void SymbolicDataAnalysisExpressionContextAwareCrossoverPerformanceTest() {
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73 | var problem = new SymbolicRegressionSingleObjectiveProblem();
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74 | var crossover = problem.OperatorsParameter.Value.OfType<SymbolicDataAnalysisExpressionContextAwareCrossover<IRegressionProblemData>>().First();
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75 | SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
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76 | }
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77 |
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78 | [TestMethod]
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79 | public void SymbolicDataAnalysisExpressionDepthConstrainedCrossoverPerformanceTest() {
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80 | var problem = new SymbolicRegressionSingleObjectiveProblem();
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81 | var crossover = problem.OperatorsParameter.Value.OfType<SymbolicDataAnalysisExpressionDepthConstrainedCrossover<IRegressionProblemData>>().First();
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82 |
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83 | crossover.DepthRangeParameter.Value = crossover.DepthRangeParameter.ValidValues.First(s => s.Value == "HighLevel");
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84 | SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
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85 | crossover.DepthRangeParameter.Value = crossover.DepthRangeParameter.ValidValues.First(s => s.Value == "Standard");
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86 | SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
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87 | crossover.DepthRangeParameter.Value = crossover.DepthRangeParameter.ValidValues.First(s => s.Value == "LowLevel");
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88 | SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
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89 | }
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90 |
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91 |
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92 | private static void SymbolicDataAnalysisCrossoverPerformanceTest(ISymbolicDataAnalysisExpressionCrossover<IRegressionProblemData> crossover) {
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93 | var twister = new MersenneTwister(31415);
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94 | var dataset = Util.CreateRandomDataset(twister, Rows, Columns);
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95 | var grammar = new FullFunctionalExpressionGrammar();
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96 | var stopwatch = new Stopwatch();
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97 |
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98 | grammar.MaximumFunctionArguments = 0;
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99 | grammar.MaximumFunctionDefinitions = 0;
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100 | grammar.MinimumFunctionArguments = 0;
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101 | grammar.MinimumFunctionDefinitions = 0;
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102 |
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103 | var trees = Util.CreateRandomTrees(twister, dataset, grammar, PopulationSize, 1, MaxTreeLength, 0, 0);
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104 | foreach (ISymbolicExpressionTree tree in trees) {
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105 | Util.InitTree(tree, twister, new List<string>(dataset.VariableNames));
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106 | }
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107 | var problemData = new RegressionProblemData(dataset, dataset.VariableNames, dataset.VariableNames.Last());
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108 | var problem = new SymbolicRegressionSingleObjectiveProblem();
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109 | problem.ProblemData = problemData;
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110 |
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111 | var globalScope = new Scope("Global Scope");
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112 | globalScope.Variables.Add(new Core.Variable("Random", twister));
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113 | var context = new ExecutionContext(null, problem, globalScope);
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114 | context = new ExecutionContext(context, crossover, globalScope);
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115 |
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116 | stopwatch.Start();
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117 | for (int i = 0; i != PopulationSize; ++i) {
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118 | var parent0 = (ISymbolicExpressionTree)trees.SelectRandom(twister).Clone();
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119 | var scopeParent0 = new Scope();
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120 | scopeParent0.Variables.Add(new Core.Variable(crossover.ParentsParameter.ActualName, parent0));
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121 | context.Scope.SubScopes.Add(scopeParent0);
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122 |
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123 | var parent1 = (ISymbolicExpressionTree)trees.SelectRandom(twister).Clone();
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124 | var scopeParent1 = new Scope();
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125 | scopeParent1.Variables.Add(new Core.Variable(crossover.ParentsParameter.ActualName, parent1));
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126 | context.Scope.SubScopes.Add(scopeParent1);
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127 |
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128 | crossover.Execute(context, new CancellationToken());
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129 |
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130 | context.Scope.SubScopes.Remove(scopeParent0); // clean the scope in preparation for the next iteration
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131 | context.Scope.SubScopes.Remove(scopeParent1); // clean the scope in preparation for the next iteration
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132 | }
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133 | stopwatch.Stop();
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134 | double msPerCrossover = 2 * stopwatch.ElapsedMilliseconds / (double)PopulationSize;
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135 | Console.WriteLine(crossover.Name + ": " + Environment.NewLine +
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136 | msPerCrossover + " ms per crossover (~" + Math.Round(1000.0 / (msPerCrossover)) + " crossover operations / s)");
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137 |
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138 | foreach (var tree in trees)
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139 | HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Tests.Util.IsValid(tree);
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140 | }
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141 | }
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142 | }
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