1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using HeuristicLab.Common;
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23 | using HeuristicLab.Core;
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24 | using HeuristicLab.Data;
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25 | using HeuristicLab.Operators;
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26 | using HeuristicLab.Optimization;
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27 | using HeuristicLab.Parameters;
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28 | using HEAL.Attic;
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29 | using HeuristicLab.Problems.DataAnalysis;
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30 |
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31 | namespace HeuristicLab.Algorithms.DataAnalysis {
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32 | [StorableType("A2DE805A-046B-40C5-AB14-2AF633A2FB2F")]
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33 | [Item(Name = "GaussianProcessClassificationSolutionCreator",
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34 | Description = "Creates a Gaussian process solution from a trained model.")]
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35 | public sealed class GaussianProcessClassificationSolutionCreator : SingleSuccessorOperator {
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36 | private const string ProblemDataParameterName = "ProblemData";
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37 | private const string ModelParameterName = "GaussianProcessClassificationModel";
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38 | private const string SolutionParameterName = "Solution";
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39 | private const string ResultsParameterName = "Results";
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40 | private const string TrainingAccuracyResultName = "Accuracy (training)";
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41 | private const string TestAccuracyResultName = "Accuracy (test)";
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42 | private const string CreateSolutionParameterName = "CreateSolution";
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43 |
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44 | #region Parameter Properties
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45 | public ILookupParameter<IClassificationProblemData> ProblemDataParameter {
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46 | get { return (ILookupParameter<IClassificationProblemData>)Parameters[ProblemDataParameterName]; }
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47 | }
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48 | public ILookupParameter<IDiscriminantFunctionClassificationSolution> SolutionParameter {
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49 | get { return (ILookupParameter<IDiscriminantFunctionClassificationSolution>)Parameters[SolutionParameterName]; }
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50 | }
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51 | public ILookupParameter<IGaussianProcessModel> ModelParameter {
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52 | get { return (ILookupParameter<IGaussianProcessModel>)Parameters[ModelParameterName]; }
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53 | }
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54 | public ILookupParameter<ResultCollection> ResultsParameter {
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55 | get { return (ILookupParameter<ResultCollection>)Parameters[ResultsParameterName]; }
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56 | }
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57 | public ILookupParameter<BoolValue> CreateSolutionParameter {
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58 | get { return (ILookupParameter<BoolValue>)Parameters[CreateSolutionParameterName]; }
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59 | }
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60 | #endregion
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61 |
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62 | [StorableConstructor]
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63 | private GaussianProcessClassificationSolutionCreator(StorableConstructorFlag _) : base(_) { }
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64 | private GaussianProcessClassificationSolutionCreator(GaussianProcessClassificationSolutionCreator original, Cloner cloner) : base(original, cloner) { }
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65 | public GaussianProcessClassificationSolutionCreator()
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66 | : base() {
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67 | // in
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68 | Parameters.Add(new LookupParameter<IClassificationProblemData>(ProblemDataParameterName, "The classification problem data for the Gaussian process solution."));
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69 | Parameters.Add(new LookupParameter<IGaussianProcessModel>(ModelParameterName, "The Gaussian process classification model to use for the solution."));
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70 | Parameters.Add(new LookupParameter<BoolValue>(CreateSolutionParameterName, "Flag that indicates if a solution should be produced at the end of the run"));
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71 |
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72 | // in & out
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73 | Parameters.Add(new LookupParameter<ResultCollection>(ResultsParameterName, "The result collection of the algorithm."));
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74 | // out
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75 | Parameters.Add(new LookupParameter<IDiscriminantFunctionClassificationSolution>(SolutionParameterName, "The produced Gaussian process solution."));
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76 | }
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77 |
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78 | [StorableHook(HookType.AfterDeserialization)]
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79 | private void AfterDeserialization() {
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80 | // BackwardsCompatibility3.3
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81 | #region Backwards compatible code, remove with 3.4
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82 | if (!Parameters.ContainsKey(CreateSolutionParameterName)) {
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83 | Parameters.Add(new LookupParameter<BoolValue>(CreateSolutionParameterName, "Flag that indicates if a solution should be produced at the end of the run"));
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84 | }
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85 | #endregion
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86 | }
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87 |
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88 | public override IDeepCloneable Clone(Cloner cloner) {
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89 | return new GaussianProcessClassificationSolutionCreator(this, cloner);
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90 | }
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91 |
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92 | public override IOperation Apply() {
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93 | if (ModelParameter.ActualValue != null && CreateSolutionParameter.ActualValue.Value == true) {
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94 | var m = (IGaussianProcessModel)ModelParameter.ActualValue.Clone();
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95 | m.FixParameters();
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96 | var data = (IClassificationProblemData)ProblemDataParameter.ActualValue.Clone();
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97 | var model = new DiscriminantFunctionClassificationModel(m, new NormalDistributionCutPointsThresholdCalculator());
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98 | model.RecalculateModelParameters(data, data.TrainingIndices);
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99 | var s = model.CreateDiscriminantFunctionClassificationSolution(data);
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100 |
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101 | SolutionParameter.ActualValue = s;
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102 | var results = ResultsParameter.ActualValue;
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103 | if (!results.ContainsKey(SolutionParameterName)) {
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104 | results.Add(new Result(SolutionParameterName, "The Gaussian process classification solution", s));
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105 | results.Add(new Result(TrainingAccuracyResultName,
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106 | "The accuracy of the Gaussian process solution on the training partition.",
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107 | new DoubleValue(s.TrainingAccuracy)));
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108 | results.Add(new Result(TestAccuracyResultName,
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109 | "The accuracy of the Gaussian process solution on the test partition.",
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110 | new DoubleValue(s.TestAccuracy)));
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111 | } else {
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112 | results[SolutionParameterName].Value = s;
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113 | results[TrainingAccuracyResultName].Value = new DoubleValue(s.TrainingAccuracy);
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114 | results[TestAccuracyResultName].Value = new DoubleValue(s.TestAccuracy);
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115 | }
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116 | }
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117 | return base.Apply();
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118 | }
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119 | }
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120 | }
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