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source: trunk/HeuristicLab.Tests/HeuristicLab-3.3/Samples/GPSymbolicRegressionSampleTest.cs @ 17507

Last change on this file since 17507 was 17180, checked in by swagner, 5 years ago

#2875: Removed years in copyrights
File size: 8.2 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 System.Linq;
24using HEAL.Attic;
25using HeuristicLab.Algorithms.GeneticAlgorithm;
26using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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    private static readonly ProtoBufSerializer serializer = new ProtoBufSerializer();
40
41    [TestMethod]
42    [TestCategory("Samples.Create")]
43    [TestProperty("Time", "medium")]
44    public void CreateGpSymbolicRegressionSampleTest() {
45      var ga = CreateGpSymbolicRegressionSample();
46      string path = Path.Combine(SamplesUtils.SamplesDirectory, SampleFileName + SamplesUtils.SampleFileExtension);
47      serializer.Serialize(ga, path);
48    }
49    [TestMethod]
50    [TestCategory("Samples.Execute")]
51    [TestProperty("Time", "long")]
52    public void RunGpSymbolicRegressionSampleTest() {
53      var ga = CreateGpSymbolicRegressionSample();
54      ga.SetSeedRandomly.Value = false;
55      SamplesUtils.RunAlgorithm(ga);
56      Assert.AreEqual(0.858344291534625, SamplesUtils.GetDoubleResult(ga, "BestQuality"), 1E-8);
57      Assert.AreEqual(0.56758466520692641, SamplesUtils.GetDoubleResult(ga, "CurrentAverageQuality"), 1E-8);
58      Assert.AreEqual(0, SamplesUtils.GetDoubleResult(ga, "CurrentWorstQuality"), 1E-8);
59      Assert.AreEqual(50950, SamplesUtils.GetIntResult(ga, "EvaluatedSolutions"));
60      var bestTrainingSolution = (IRegressionSolution)ga.Results["Best training solution"].Value;
61      Assert.AreEqual(0.85504801557844745, bestTrainingSolution.TrainingRSquared, 1E-8);
62      Assert.AreEqual(0.86259381948647817, bestTrainingSolution.TestRSquared, 1E-8);
63      var bestValidationSolution = (IRegressionSolution)ga.Results["Best validation solution"].Value;
64      Assert.AreEqual(0.84854338315539746, bestValidationSolution.TrainingRSquared, 1E-8);
65      Assert.AreEqual(0.8662813452656678, bestValidationSolution.TestRSquared, 1E-8);
66    }
67
68    private GeneticAlgorithm CreateGpSymbolicRegressionSample() {
69      GeneticAlgorithm ga = new GeneticAlgorithm();
70      #region Problem Configuration
71      SymbolicRegressionSingleObjectiveProblem symbRegProblem = new SymbolicRegressionSingleObjectiveProblem();
72      symbRegProblem.Name = "Tower Symbolic Regression Problem";
73      symbRegProblem.Description = "Tower Dataset (downloaded from: http://www.symbolicregression.com/?q=towerProblem)";
74      RegressionRealWorldInstanceProvider provider = new RegressionRealWorldInstanceProvider();
75      var instance = provider.GetDataDescriptors().Where(x => x.Name.Equals("Tower")).Single();
76      var towerProblemData = (RegressionProblemData)provider.LoadData(instance);
77      towerProblemData.TargetVariableParameter.Value = towerProblemData.TargetVariableParameter.ValidValues
78        .First(v => v.Value == "towerResponse");
79      towerProblemData.InputVariables.SetItemCheckedState(
80        towerProblemData.InputVariables.Single(x => x.Value == "x1"), true);
81      towerProblemData.InputVariables.SetItemCheckedState(
82        towerProblemData.InputVariables.Single(x => x.Value == "x7"), false);
83      towerProblemData.InputVariables.SetItemCheckedState(
84        towerProblemData.InputVariables.Single(x => x.Value == "x11"), false);
85      towerProblemData.InputVariables.SetItemCheckedState(
86        towerProblemData.InputVariables.Single(x => x.Value == "x16"), false);
87      towerProblemData.InputVariables.SetItemCheckedState(
88        towerProblemData.InputVariables.Single(x => x.Value == "x21"), false);
89      towerProblemData.InputVariables.SetItemCheckedState(
90        towerProblemData.InputVariables.Single(x => x.Value == "x25"), false);
91      towerProblemData.InputVariables.SetItemCheckedState(
92        towerProblemData.InputVariables.Single(x => x.Value == "towerResponse"), false);
93      towerProblemData.TrainingPartition.Start = 0;
94      towerProblemData.TrainingPartition.End = 3136;
95      towerProblemData.TestPartition.Start = 3136;
96      towerProblemData.TestPartition.End = 4999;
97      towerProblemData.Name = "Data imported from towerData.txt";
98      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";
99      symbRegProblem.ProblemData = towerProblemData;
100
101      // configure grammar
102      var grammar = new TypeCoherentExpressionGrammar();
103      grammar.ConfigureAsDefaultRegressionGrammar();
104      grammar.Symbols.OfType<VariableCondition>().Single().InitialFrequency = 0.0;
105      foreach (var varSy in grammar.Symbols.OfType<VariableBase>()) varSy.VariableChangeProbability = 1.0; // for backwards compatibilty
106      var varSymbol = grammar.Symbols.OfType<Variable>().Single();
107      varSymbol.WeightMu = 1.0;
108      varSymbol.WeightSigma = 1.0;
109      varSymbol.WeightManipulatorMu = 0.0;
110      varSymbol.WeightManipulatorSigma = 0.05;
111      varSymbol.MultiplicativeWeightManipulatorSigma = 0.03;
112      var constSymbol = grammar.Symbols.OfType<Constant>().Single();
113      constSymbol.MaxValue = 20;
114      constSymbol.MinValue = -20;
115      constSymbol.ManipulatorMu = 0.0;
116      constSymbol.ManipulatorSigma = 1;
117      constSymbol.MultiplicativeManipulatorSigma = 0.03;
118      symbRegProblem.SymbolicExpressionTreeGrammar = grammar;
119
120      // configure remaining problem parameters
121      symbRegProblem.BestKnownQuality.Value = 0.97;
122      symbRegProblem.FitnessCalculationPartition.Start = 0;
123      symbRegProblem.FitnessCalculationPartition.End = 2300;
124      symbRegProblem.ValidationPartition.Start = 2300;
125      symbRegProblem.ValidationPartition.End = 3136;
126      symbRegProblem.RelativeNumberOfEvaluatedSamples.Value = 1;
127      symbRegProblem.MaximumSymbolicExpressionTreeLength.Value = 150;
128      symbRegProblem.MaximumSymbolicExpressionTreeDepth.Value = 12;
129      symbRegProblem.MaximumFunctionDefinitions.Value = 0;
130      symbRegProblem.MaximumFunctionArguments.Value = 0;
131
132      symbRegProblem.EvaluatorParameter.Value = new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator();
133      #endregion
134      #region Algorithm Configuration
135      ga.Problem = symbRegProblem;
136      ga.Name = "Genetic Programming - Symbolic Regression";
137      ga.Description = "A standard genetic programming algorithm to solve a symbolic regression problem (tower dataset)";
138      SamplesUtils.ConfigureGeneticAlgorithmParameters<TournamentSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>(
139        ga, 1000, 1, 50, 0.15, 5);
140      var mutator = (MultiSymbolicExpressionTreeManipulator)ga.Mutator;
141      mutator.Operators.OfType<FullTreeShaker>().Single().ShakingFactor = 0.1;
142      mutator.Operators.OfType<OnePointShaker>().Single().ShakingFactor = 1.0;
143
144      ga.Analyzer.Operators.SetItemCheckedState(
145        ga.Analyzer.Operators
146        .OfType<SymbolicRegressionSingleObjectiveOverfittingAnalyzer>()
147        .Single(), false);
148      ga.Analyzer.Operators.SetItemCheckedState(
149        ga.Analyzer.Operators
150        .OfType<SymbolicDataAnalysisAlleleFrequencyAnalyzer>()
151        .First(), false);
152      #endregion
153      return ga;
154    }
155  }
156}
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