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source: branches/2865-Tourplanning/HeuristicLab.Tests/HeuristicLab-3.3/Samples/GPSymbolicRegressionSampleTest.cs @ 15519

Last change on this file since 15519 was 14832, checked in by gkronber, 8 years ago

#2650 adapted GP unit test so that they produce the same outcomes as before reintegration of the factors branch
File size: 8.2 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2016 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 System.Linq;
24using HeuristicLab.Algorithms.GeneticAlgorithm;
25using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
26using HeuristicLab.Persistence.Default.Xml;
27using HeuristicLab.Problems.DataAnalysis;
28using HeuristicLab.Problems.DataAnalysis.Symbolic;
29using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
30using HeuristicLab.Problems.Instances.DataAnalysis;
31using HeuristicLab.Selection;
32using Microsoft.VisualStudio.TestTools.UnitTesting;
33
34namespace HeuristicLab.Tests {
35  [TestClass]
36  public class GPSymbolicRegressionSampleTest {
37    private const string SampleFileName = "SGP_SymbReg";
38
39    [TestMethod]
40    [TestCategory("Samples.Create")]
41    [TestProperty("Time", "medium")]
42    public void CreateGpSymbolicRegressionSampleTest() {
43      var ga = CreateGpSymbolicRegressionSample();
44      string path = Path.Combine(SamplesUtils.SamplesDirectory, SampleFileName + SamplesUtils.SampleFileExtension);
45      XmlGenerator.Serialize(ga, path);
46    }
47    [TestMethod]
48    [TestCategory("Samples.Execute")]
49    [TestProperty("Time", "long")]
50    public void RunGpSymbolicRegressionSampleTest() {
51      var ga = CreateGpSymbolicRegressionSample();
52      ga.SetSeedRandomly.Value = false;
53      SamplesUtils.RunAlgorithm(ga);
54      Assert.AreEqual(0.858344291534625, SamplesUtils.GetDoubleResult(ga, "BestQuality"), 1E-8);
55      Assert.AreEqual(0.56758466520692641, SamplesUtils.GetDoubleResult(ga, "CurrentAverageQuality"), 1E-8);
56      Assert.AreEqual(0, SamplesUtils.GetDoubleResult(ga, "CurrentWorstQuality"), 1E-8);
57      Assert.AreEqual(50950, SamplesUtils.GetIntResult(ga, "EvaluatedSolutions"));
58      var bestTrainingSolution = (IRegressionSolution)ga.Results["Best training solution"].Value;
59      Assert.AreEqual(0.85504801557844745, bestTrainingSolution.TrainingRSquared, 1E-8);
60      Assert.AreEqual(0.86259381948647817, bestTrainingSolution.TestRSquared, 1E-8);
61      var bestValidationSolution = (IRegressionSolution)ga.Results["Best validation solution"].Value;
62      Assert.AreEqual(0.84854338315539746, bestValidationSolution.TrainingRSquared, 1E-8);
63      Assert.AreEqual(0.8662813452656678, bestValidationSolution.TestRSquared, 1E-8);
64    }
65
66    private GeneticAlgorithm CreateGpSymbolicRegressionSample() {
67      GeneticAlgorithm ga = new GeneticAlgorithm();
68      #region Problem Configuration
69      SymbolicRegressionSingleObjectiveProblem symbRegProblem = new SymbolicRegressionSingleObjectiveProblem();
70      symbRegProblem.Name = "Tower Symbolic Regression Problem";
71      symbRegProblem.Description = "Tower Dataset (downloaded from: http://www.symbolicregression.com/?q=towerProblem)";
72      RegressionRealWorldInstanceProvider provider = new RegressionRealWorldInstanceProvider();
73      var instance = provider.GetDataDescriptors().Where(x => x.Name.Equals("Tower")).Single();
74      var towerProblemData = (RegressionProblemData)provider.LoadData(instance);
75      towerProblemData.TargetVariableParameter.Value = towerProblemData.TargetVariableParameter.ValidValues
76        .First(v => v.Value == "towerResponse");
77      towerProblemData.InputVariables.SetItemCheckedState(
78        towerProblemData.InputVariables.Single(x => x.Value == "x1"), true);
79      towerProblemData.InputVariables.SetItemCheckedState(
80        towerProblemData.InputVariables.Single(x => x.Value == "x7"), false);
81      towerProblemData.InputVariables.SetItemCheckedState(
82        towerProblemData.InputVariables.Single(x => x.Value == "x11"), false);
83      towerProblemData.InputVariables.SetItemCheckedState(
84        towerProblemData.InputVariables.Single(x => x.Value == "x16"), false);
85      towerProblemData.InputVariables.SetItemCheckedState(
86        towerProblemData.InputVariables.Single(x => x.Value == "x21"), false);
87      towerProblemData.InputVariables.SetItemCheckedState(
88        towerProblemData.InputVariables.Single(x => x.Value == "x25"), false);
89      towerProblemData.InputVariables.SetItemCheckedState(
90        towerProblemData.InputVariables.Single(x => x.Value == "towerResponse"), false);
91      towerProblemData.TrainingPartition.Start = 0;
92      towerProblemData.TrainingPartition.End = 3136;
93      towerProblemData.TestPartition.Start = 3136;
94      towerProblemData.TestPartition.End = 4999;
95      towerProblemData.Name = "Data imported from towerData.txt";
96      towerProblemData.Description = "Chemical concentration at top of distillation tower, dataset downloaded from: http://vanillamodeling.com/realproblems.html, best R² achieved with nu-SVR = 0.97";
97      symbRegProblem.ProblemData = towerProblemData;
98
99      // configure grammar
100      var grammar = new TypeCoherentExpressionGrammar();
101      grammar.ConfigureAsDefaultRegressionGrammar();
102      grammar.Symbols.OfType<VariableCondition>().Single().InitialFrequency = 0.0;
103      foreach (var varSy in grammar.Symbols.OfType<VariableBase>()) varSy.VariableChangeProbability = 1.0; // for backwards compatibilty
104      var varSymbol = grammar.Symbols.OfType<Variable>().Single();
105      varSymbol.WeightMu = 1.0;
106      varSymbol.WeightSigma = 1.0;
107      varSymbol.WeightManipulatorMu = 0.0;
108      varSymbol.WeightManipulatorSigma = 0.05;
109      varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
110      var constSymbol = grammar.Symbols.OfType<Constant>().Single();
111      constSymbol.MaxValue = 20;
112      constSymbol.MinValue = -20;
113      constSymbol.ManipulatorMu = 0.0;
114      constSymbol.ManipulatorSigma = 1;
115      constSymbol.MultiplicativeManipulatorSigma = 0.03;
116      symbRegProblem.SymbolicExpressionTreeGrammar = grammar;
117
118      // configure remaining problem parameters
119      symbRegProblem.BestKnownQuality.Value = 0.97;
120      symbRegProblem.FitnessCalculationPartition.Start = 0;
121      symbRegProblem.FitnessCalculationPartition.End = 2300;
122      symbRegProblem.ValidationPartition.Start = 2300;
123      symbRegProblem.ValidationPartition.End = 3136;
124      symbRegProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
125      symbRegProblem.MaximumSymbolicExpressionTreeLength.Value = 150;
126      symbRegProblem.MaximumSymbolicExpressionTreeDepth.Value = 12;
127      symbRegProblem.MaximumFunctionDefinitions.Value = 0;
128      symbRegProblem.MaximumFunctionArguments.Value = 0;
129
130      symbRegProblem.EvaluatorParameter.Value = new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator();
131      #endregion
132      #region Algorithm Configuration
133      ga.Problem = symbRegProblem;
134      ga.Name = "Genetic Programming - Symbolic Regression";
135      ga.Description = "A standard genetic programming algorithm to solve a symbolic regression problem (tower dataset)";
136      SamplesUtils.ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
137        ga, 1000, 1, 50, 0.15, 5);
138      var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
139      mutator.Operators.OfType<FullTreeShaker>().Single().ShakingFactor = 0.1;
140      mutator.Operators.OfType<OnePointShaker>().Single().ShakingFactor = 1.0;
141
142      ga.Analyzer.Operators.SetItemCheckedState(
143        ga.Analyzer.Operators
144        .OfType<SymbolicRegressionSingleObjectiveOverfittingAnalyzer>()
145        .Single(), false);
146      ga.Analyzer.Operators.SetItemCheckedState(
147        ga.Analyzer.Operators
148        .OfType<SymbolicDataAnalysisAlleleFrequencyAnalyzer>()
149        .First(), false);
150      #endregion
151      return ga;
152    }
153  }
154}
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