#region License Information
/* HeuristicLab
* Copyright (C) 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.IO;
using System.Linq;
using HEAL.Attic;
using HeuristicLab.Algorithms.GeneticAlgorithm;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Problems.DataAnalysis;
using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
using HeuristicLab.Problems.Instances.DataAnalysis;
using HeuristicLab.Selection;
using Microsoft.VisualStudio.TestTools.UnitTesting;
namespace HeuristicLab.Tests {
[TestClass]
public class ShapeConstrainedRegressionSampleTest {
private const string SampleFileName = "GP_Shape_Constrained_Regression";
private static readonly ProtoBufSerializer serializer = new ProtoBufSerializer();
[TestMethod]
[TestCategory("Samples.Execute")]
[TestProperty("Time", "long")]
public void RunShapeConstrainedRegressionSampleTest() {
var ga = CreateShapeConstrainedRegressionSample();
ga.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(ga);
if (Environment.Is64BitProcess) {
Assert.AreEqual(0.035536903914644882, SamplesUtils.GetDoubleResult(ga, "BestQuality"), 1E-8);
Assert.AreEqual(26.707437555596698, SamplesUtils.GetDoubleResult(ga, "CurrentAverageQuality"), 1E-8);
Assert.AreEqual(3294.1754151628993, SamplesUtils.GetDoubleResult(ga, "CurrentWorstQuality"), 1E-8);
Assert.AreEqual(150200, SamplesUtils.GetIntResult(ga, "EvaluatedSolutions"));
} else {
Assert.AreEqual(0.317642788600248, SamplesUtils.GetDoubleResult(ga, "BestQuality"), 1E-8);
Assert.AreEqual(40.9805778810063, SamplesUtils.GetDoubleResult(ga, "CurrentAverageQuality"), 1E-8);
Assert.AreEqual(3359.91748220025, SamplesUtils.GetDoubleResult(ga, "CurrentWorstQuality"), 1E-8);
Assert.AreEqual(150200, SamplesUtils.GetIntResult(ga, "EvaluatedSolutions"));
}
}
[TestMethod]
[TestCategory("Samples.Create")]
[TestProperty("Time", "medium")]
public void CreateShapeConstrainedRegressionSampleTest() {
var ga = CreateShapeConstrainedRegressionSample();
string path = Path.Combine(SamplesUtils.SamplesDirectory, SampleFileName + SamplesUtils.SampleFileExtension);
serializer.Serialize(ga, path);
}
public static GeneticAlgorithm CreateShapeConstrainedRegressionSample() {
var alg = new GeneticAlgorithm();
var provider = new FeynmanSmallInstanceProvider(0);
var instance = provider.GetDataDescriptors().Where(x => x.Name.Contains("Radiated gravitational wave power: -32/5*G**4/c**5*(m1*m2)**2*(m1+m2)/r**5 | no noise")).Single();
var problem = new ShapeConstrainedRegressionSingleObjectiveProblem();
problem.Load(provider.LoadData(instance));
var problemData = (IShapeConstrainedRegressionProblemData)problem.ProblemData;
problemData.ShapeConstraints.Add(new ShapeConstraint(new Interval(double.NegativeInfinity, 0), 1.0));
problemData.ShapeConstraints.Add(new ShapeConstraint("G", 1, new Interval(double.NegativeInfinity, 0), 1.0));
problemData.ShapeConstraints.Add(new ShapeConstraint("c", 1, new Interval(0, double.PositiveInfinity), 1.0));
problemData.ShapeConstraints.Add(new ShapeConstraint("m1", 1, new Interval(double.NegativeInfinity, 0), 1.0));
problemData.ShapeConstraints.Add(new ShapeConstraint("m2", 1, new Interval(double.NegativeInfinity, 0), 1.0));
problemData.ShapeConstraints.Add(new ShapeConstraint("r", 1, new Interval(0, double.PositiveInfinity), 1.0));
problemData.VariableRanges.SetInterval("G", new Interval(1, 2));
problemData.VariableRanges.SetInterval("c", new Interval(1, 2));
problemData.VariableRanges.SetInterval("m1", new Interval(1, 5));
problemData.VariableRanges.SetInterval("m2", new Interval(1, 5));
problemData.VariableRanges.SetInterval("r", new Interval(1, 2));
problem.ProblemData = problemData;
#region Algorithm Configuration
alg.Name = "Genetic Programming - Shape-constrained Regression";
alg.Description = "A standard genetic programming algorithm to solve a shape constrained regression problem (Radiated gravitational wave power - Feynman instance)";
alg.Problem = problem;
SamplesUtils.ConfigureGeneticAlgorithmParameters
(alg, popSize: 500, elites: 1, maxGens: 300, mutationRate: 0.15, tournGroupSize: 3);
alg.Seed.Value = 0;
#endregion
alg.Engine = new ParallelEngine.ParallelEngine();
return alg;
}
}
}