#region License Information
/* HeuristicLab
* Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Linq;
using System.Threading;
using HeuristicLab.Core;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
using HeuristicLab.Random;
using Microsoft.VisualStudio.TestTools.UnitTesting;
using ExecutionContext = HeuristicLab.Core.ExecutionContext;
namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Tests {
[TestClass()]
public class SymbolicDataAnalysisExpressionCrossoverTest {
private const int PopulationSize = 10000;
private const int MaxTreeDepth = 10;
private const int MaxTreeLength = 100;
private const int Rows = 1000;
private const int Columns = 50;
///
///Gets or sets the test context which provides
///information about and functionality for the current test run.
///
public TestContext TestContext { get; set; }
[TestMethod]
[TestCategory("Problems.DataAnalysis")]
[TestProperty("Time", "long")]
public void SymbolicDataAnalysisExpressionSemanticSimilarityCrossoverPerformanceTest() {
var problem = new SymbolicRegressionSingleObjectiveProblem();
var crossover = problem.OperatorsParameter.Value.OfType>().First();
SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
}
[TestMethod]
[TestCategory("Problems.DataAnalysis")]
[TestProperty("Time", "long")]
public void SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossoverPerformanceTest() {
var problem = new SymbolicRegressionSingleObjectiveProblem();
var crossover = problem.OperatorsParameter.Value.OfType>().First();
SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
}
[TestMethod]
[TestCategory("Problems.DataAnalysis")]
[TestProperty("Time", "long")]
public void SymbolicDataAnalysisExpressionDeterministicBestCrossoverPerformanceTest() {
var problem = new SymbolicRegressionSingleObjectiveProblem();
var crossover = problem.OperatorsParameter.Value.OfType>().First();
SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
}
[TestMethod]
[TestCategory("Problems.DataAnalysis")]
[TestProperty("Time", "long")]
public void SymbolicDataAnalysisExpressionContextAwareCrossoverPerformanceTest() {
var problem = new SymbolicRegressionSingleObjectiveProblem();
var crossover = problem.OperatorsParameter.Value.OfType>().First();
SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
}
[TestMethod]
[TestCategory("Problems.DataAnalysis")]
[TestProperty("Time", "long")]
public void SymbolicDataAnalysisExpressionDepthConstrainedCrossoverPerformanceTest() {
var problem = new SymbolicRegressionSingleObjectiveProblem();
var crossover = problem.OperatorsParameter.Value.OfType>().First();
crossover.DepthRangeParameter.Value = crossover.DepthRangeParameter.ValidValues.First(s => s.Value == "HighLevel");
SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
crossover.DepthRangeParameter.Value = crossover.DepthRangeParameter.ValidValues.First(s => s.Value == "Standard");
SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
crossover.DepthRangeParameter.Value = crossover.DepthRangeParameter.ValidValues.First(s => s.Value == "LowLevel");
SymbolicDataAnalysisCrossoverPerformanceTest(crossover);
}
private static void SymbolicDataAnalysisCrossoverPerformanceTest(ISymbolicDataAnalysisExpressionCrossover crossover) {
var twister = new MersenneTwister(31415);
var dataset = Util.CreateRandomDataset(twister, Rows, Columns);
var grammar = new FullFunctionalExpressionGrammar();
var stopwatch = new Stopwatch();
grammar.MaximumFunctionArguments = 0;
grammar.MaximumFunctionDefinitions = 0;
grammar.MinimumFunctionArguments = 0;
grammar.MinimumFunctionDefinitions = 0;
var trees = Util.CreateRandomTrees(twister, dataset, grammar, PopulationSize, 1, MaxTreeLength, 0, 0);
foreach (ISymbolicExpressionTree tree in trees) {
Util.InitTree(tree, twister, new List(dataset.VariableNames));
}
var problemData = new RegressionProblemData(dataset, dataset.VariableNames, dataset.VariableNames.Last());
var problem = new SymbolicRegressionSingleObjectiveProblem();
problem.ProblemData = problemData;
var globalScope = new Scope("Global Scope");
globalScope.Variables.Add(new Core.Variable("Random", twister));
var context = new ExecutionContext(null, problem, globalScope);
context = new ExecutionContext(context, crossover, globalScope);
stopwatch.Start();
for (int i = 0; i != PopulationSize; ++i) {
var parent0 = (ISymbolicExpressionTree)trees.SampleRandom(twister).Clone();
var scopeParent0 = new Scope();
scopeParent0.Variables.Add(new Core.Variable(crossover.ParentsParameter.ActualName, parent0));
context.Scope.SubScopes.Add(scopeParent0);
var parent1 = (ISymbolicExpressionTree)trees.SampleRandom(twister).Clone();
var scopeParent1 = new Scope();
scopeParent1.Variables.Add(new Core.Variable(crossover.ParentsParameter.ActualName, parent1));
context.Scope.SubScopes.Add(scopeParent1);
crossover.Execute(context, new CancellationToken());
context.Scope.SubScopes.Remove(scopeParent0); // clean the scope in preparation for the next iteration
context.Scope.SubScopes.Remove(scopeParent1); // clean the scope in preparation for the next iteration
}
stopwatch.Stop();
double msPerCrossover = 2 * stopwatch.ElapsedMilliseconds / (double)PopulationSize;
Console.WriteLine(crossover.Name + ": " + Environment.NewLine +
msPerCrossover + " ms per crossover (~" + Math.Round(1000.0 / (msPerCrossover)) + " crossover operations / s)");
foreach (var tree in trees)
HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Tests.Util.IsValid(tree);
}
}
}