[8623] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[12009] | 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[8623] | 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 HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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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 | [StorableClass]
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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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[13283] | 42 | private const string CreateSolutionParameterName = "CreateSolution";
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[8623] | 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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[13283] | 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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[8623] | 60 | #endregion
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| 61 |
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| 62 | [StorableConstructor]
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| 63 | private GaussianProcessClassificationSolutionCreator(bool deserializing) : base(deserializing) { }
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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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[13283] | 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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[8623] | 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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[13283] | 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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[8623] | 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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[13283] | 93 | if (ModelParameter.ActualValue != null && CreateSolutionParameter.ActualValue.Value == true) {
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[8623] | 94 | var m = (IGaussianProcessModel)ModelParameter.ActualValue.Clone();
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[8982] | 95 | m.FixParameters();
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[8623] | 96 | var data = (IClassificationProblemData)ProblemDataParameter.ActualValue.Clone();
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[8679] | 97 | var model = new DiscriminantFunctionClassificationModel(m, new NormalDistributionCutPointsThresholdCalculator());
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[8623] | 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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