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source: stable/HeuristicLab.Tests/HeuristicLab-3.3/Samples/GPTimeSeriesSampleTest.cs

Last change on this file was 17181, checked in by swagner, 5 years ago

#2875: Merged r17180 from trunk to stable

File size: 4.2 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System.IO;
23using HEAL.Attic;
24using HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm;
25using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
26using HeuristicLab.Problems.DataAnalysis.Symbolic;
27using HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis;
28using HeuristicLab.Selection;
29using Microsoft.VisualStudio.TestTools.UnitTesting;
30
31namespace HeuristicLab.Tests {
32  [TestClass]
33  public class GPTimeSeriesSampleTest {
34    private const string SampleFileName = "OSGP_TimeSeries";
35
36    private static readonly ProtoBufSerializer serializer = new ProtoBufSerializer();
37
38    [TestMethod]
39    [TestCategory("Samples.Create")]
40    [TestProperty("Time", "medium")]
41    public void CreateGpTimeSeriesSampleTest() {
42      var ga = CreateGpTimeSeriesSample();
43      string path = Path.Combine(SamplesUtils.SamplesDirectory, SampleFileName + SamplesUtils.SampleFileExtension);
44      serializer.Serialize(ga, path);
45    }
46    [TestMethod]
47    [TestCategory("Samples.Execute")]
48    [TestProperty("Time", "long")]
49    public void RunGpTimeSeriesSampleTest() {
50      var osga = CreateGpTimeSeriesSample();
51      osga.SetSeedRandomly.Value = false;
52      SamplesUtils.RunAlgorithm(osga);
53
54      Assert.AreEqual(0.015441526903606416, SamplesUtils.GetDoubleResult(osga, "BestQuality"), 1E-8);
55      Assert.AreEqual(0.017420834241279298, SamplesUtils.GetDoubleResult(osga, "CurrentAverageQuality"), 1E-8);
56      Assert.AreEqual(0.065195703753298972, SamplesUtils.GetDoubleResult(osga, "CurrentWorstQuality"), 1E-8);
57      Assert.AreEqual(92000, SamplesUtils.GetIntResult(osga, "EvaluatedSolutions"));
58    }
59
60    public static OffspringSelectionGeneticAlgorithm CreateGpTimeSeriesSample() {
61      var problem = new SymbolicTimeSeriesPrognosisSingleObjectiveProblem();
62      problem.Name = "Symbolic time series prognosis problem (Mackey Glass t=17)";
63      problem.ProblemData.Name = "Mackey Glass t=17";
64      problem.MaximumSymbolicExpressionTreeLength.Value = 125;
65      problem.MaximumSymbolicExpressionTreeDepth.Value = 12;
66      problem.EvaluatorParameter.Value.HorizonParameter.Value.Value = 10;
67      problem.ApplyLinearScaling.Value = true;
68
69      foreach (var symbol in problem.SymbolicExpressionTreeGrammar.Symbols) {
70        if (symbol is Exponential || symbol is Logarithm) {
71          symbol.Enabled = false;
72        } else if (symbol is AutoregressiveTargetVariable) {
73          symbol.Enabled = true;
74          var autoRegressiveSymbol = symbol as AutoregressiveTargetVariable;
75          autoRegressiveSymbol.MinLag = -30;
76          autoRegressiveSymbol.MaxLag = -1;
77        }
78        if (symbol is VariableBase) {
79          var varSy = symbol as VariableBase;
80          varSy.VariableChangeProbability = 1.0; // backwards compatibility
81        }
82      }
83
84      var osga = new OffspringSelectionGeneticAlgorithm();
85      osga.Name = "Genetic Programming - Time Series Prediction (Mackey-Glass-17)";
86      osga.Description = "A genetic programming algorithm for creating a time-series model for the Mackey-Glass-17 time series.";
87      osga.Problem = problem;
88      SamplesUtils.ConfigureOsGeneticAlgorithmParameters<GenderSpecificSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>
89        (osga, popSize: 100, elites: 1, maxGens: 25, mutationRate: 0.15);
90      osga.MaximumSelectionPressure.Value = 100;
91      return osga;
92
93    }
94  }
95}
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