[7477] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[12012] | 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[7477] | 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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[7508] | 27 | using HeuristicLab.Core;
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[7477] | 28 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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[7508] | 29 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
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[7477] | 30 | using HeuristicLab.Random;
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| 31 | using Microsoft.VisualStudio.TestTools.UnitTesting;
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| 32 | using ExecutionContext = HeuristicLab.Core.ExecutionContext;
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| 33 |
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[9764] | 34 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Tests {
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[7477] | 35 | [TestClass()]
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| 36 | public class SymbolicDataAnalysisExpressionCrossoverTest {
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| 37 | private const int PopulationSize = 10000;
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| 38 | private const int MaxTreeDepth = 10;
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| 39 | private const int MaxTreeLength = 100;
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| 40 | private const int Rows = 1000;
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| 41 | private const int Columns = 50;
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| 42 |
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| 43 | /// <summary>
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| 44 | ///Gets or sets the test context which provides
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| 45 | ///information about and functionality for the current test run.
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| 46 | ///</summary>
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| 47 | public TestContext TestContext { get; set; }
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| 48 |
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| 49 | [TestMethod]
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[9785] | 50 | [TestCategory("Problems.DataAnalysis")]
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| 51 | [TestProperty("Time", "long")]
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[7477] | 52 | public void SymbolicDataAnalysisExpressionSemanticSimilarityCrossoverPerformanceTest() {
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[7508] | 53 | var problem = new SymbolicRegressionSingleObjectiveProblem();
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| 54 | var crossover = problem.OperatorsParameter.Value.OfType<SymbolicDataAnalysisExpressionSemanticSimilarityCrossover<IRegressionProblemData>>().First();
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| 55 | SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
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[7477] | 56 | }
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| 57 |
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| 58 | [TestMethod]
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[9785] | 59 | [TestCategory("Problems.DataAnalysis")]
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| 60 | [TestProperty("Time", "long")]
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[7477] | 61 | public void SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossoverPerformanceTest() {
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[7508] | 62 | var problem = new SymbolicRegressionSingleObjectiveProblem();
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| 63 | var crossover = problem.OperatorsParameter.Value.OfType<SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover<IRegressionProblemData>>().First();
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| 64 | SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
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[7477] | 65 | }
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| 66 |
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| 67 | [TestMethod]
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[9785] | 68 | [TestCategory("Problems.DataAnalysis")]
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| 69 | [TestProperty("Time", "long")]
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[7477] | 70 | public void SymbolicDataAnalysisExpressionDeterministicBestCrossoverPerformanceTest() {
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[7508] | 71 | var problem = new SymbolicRegressionSingleObjectiveProblem();
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| 72 | var crossover = problem.OperatorsParameter.Value.OfType<SymbolicDataAnalysisExpressionDeterministicBestCrossover<IRegressionProblemData>>().First();
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| 73 | SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
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[7477] | 74 | }
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| 75 |
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| 76 | [TestMethod]
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[9785] | 77 | [TestCategory("Problems.DataAnalysis")]
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| 78 | [TestProperty("Time", "long")]
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[7477] | 79 | public void SymbolicDataAnalysisExpressionContextAwareCrossoverPerformanceTest() {
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[7508] | 80 | var problem = new SymbolicRegressionSingleObjectiveProblem();
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| 81 | var crossover = problem.OperatorsParameter.Value.OfType<SymbolicDataAnalysisExpressionContextAwareCrossover<IRegressionProblemData>>().First();
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| 82 | SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
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[7477] | 83 | }
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| 84 |
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| 85 | [TestMethod]
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[9785] | 86 | [TestCategory("Problems.DataAnalysis")]
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| 87 | [TestProperty("Time", "long")]
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[7477] | 88 | public void SymbolicDataAnalysisExpressionDepthConstrainedCrossoverPerformanceTest() {
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| 89 | var problem = new SymbolicRegressionSingleObjectiveProblem();
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| 90 | var crossover = problem.OperatorsParameter.Value.OfType<SymbolicDataAnalysisExpressionDepthConstrainedCrossover<IRegressionProblemData>>().First();
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| 91 |
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[7508] | 92 | crossover.DepthRangeParameter.Value = crossover.DepthRangeParameter.ValidValues.First(s => s.Value == "HighLevel");
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| 93 | SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
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| 94 | crossover.DepthRangeParameter.Value = crossover.DepthRangeParameter.ValidValues.First(s => s.Value == "Standard");
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| 95 | SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
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| 96 | crossover.DepthRangeParameter.Value = crossover.DepthRangeParameter.ValidValues.First(s => s.Value == "LowLevel");
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| 97 | SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
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[7477] | 98 | }
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| 99 |
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| 100 |
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[7508] | 101 | private static void SymbolicDataAnalysisCrossoverPerformanceTest(ISymbolicDataAnalysisExpressionCrossover<IRegressionProblemData> crossover) {
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[7477] | 102 | var twister = new MersenneTwister(31415);
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| 103 | var dataset = Util.CreateRandomDataset(twister, Rows, Columns);
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| 104 | var grammar = new FullFunctionalExpressionGrammar();
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| 105 | var stopwatch = new Stopwatch();
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| 106 |
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| 107 | grammar.MaximumFunctionArguments = 0;
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| 108 | grammar.MaximumFunctionDefinitions = 0;
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| 109 | grammar.MinimumFunctionArguments = 0;
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| 110 | grammar.MinimumFunctionDefinitions = 0;
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| 111 |
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| 112 | var trees = Util.CreateRandomTrees(twister, dataset, grammar, PopulationSize, 1, MaxTreeLength, 0, 0);
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| 113 | foreach (ISymbolicExpressionTree tree in trees) {
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| 114 | Util.InitTree(tree, twister, new List<string>(dataset.VariableNames));
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| 115 | }
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| 116 | var problemData = new RegressionProblemData(dataset, dataset.VariableNames, dataset.VariableNames.Last());
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| 117 | var problem = new SymbolicRegressionSingleObjectiveProblem();
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| 118 | problem.ProblemData = problemData;
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| 119 |
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| 120 | var globalScope = new Scope("Global Scope");
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| 121 | globalScope.Variables.Add(new Core.Variable("Random", twister));
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| 122 | var context = new ExecutionContext(null, problem, globalScope);
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| 123 | context = new ExecutionContext(context, crossover, globalScope);
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| 124 |
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| 125 | stopwatch.Start();
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| 126 | for (int i = 0; i != PopulationSize; ++i) {
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[12515] | 127 | var parent0 = (ISymbolicExpressionTree)trees.SampleRandom(twister).Clone();
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[7477] | 128 | var scopeParent0 = new Scope();
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| 129 | scopeParent0.Variables.Add(new Core.Variable(crossover.ParentsParameter.ActualName, parent0));
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| 130 | context.Scope.SubScopes.Add(scopeParent0);
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| 131 |
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[12515] | 132 | var parent1 = (ISymbolicExpressionTree)trees.SampleRandom(twister).Clone();
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[7477] | 133 | var scopeParent1 = new Scope();
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| 134 | scopeParent1.Variables.Add(new Core.Variable(crossover.ParentsParameter.ActualName, parent1));
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| 135 | context.Scope.SubScopes.Add(scopeParent1);
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| 136 |
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| 137 | crossover.Execute(context, new CancellationToken());
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| 138 |
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| 139 | context.Scope.SubScopes.Remove(scopeParent0); // clean the scope in preparation for the next iteration
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| 140 | context.Scope.SubScopes.Remove(scopeParent1); // clean the scope in preparation for the next iteration
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| 141 | }
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| 142 | stopwatch.Stop();
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| 143 | double msPerCrossover = 2 * stopwatch.ElapsedMilliseconds / (double)PopulationSize;
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[7508] | 144 | Console.WriteLine(crossover.Name + ": " + Environment.NewLine +
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[7477] | 145 | msPerCrossover + " ms per crossover (~" + Math.Round(1000.0 / (msPerCrossover)) + " crossover operations / s)");
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| 146 |
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| 147 | foreach (var tree in trees)
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[9764] | 148 | HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Tests.Util.IsValid(tree);
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[7477] | 149 | }
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| 150 | }
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| 151 | }
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