source: branches/2520_PersistenceReintegration/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/GaussianProcessClassificationSolutionCreator.cs @ 16453

Last change on this file since 16453 was 16453, checked in by jkarder, 10 months ago

#2520: updated year of copyrights

File size: 6.2 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2019 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 HeuristicLab.Common;
23using HeuristicLab.Core;
24using HeuristicLab.Data;
25using HeuristicLab.Operators;
26using HeuristicLab.Optimization;
27using HeuristicLab.Parameters;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29using HeuristicLab.Problems.DataAnalysis;
30
31namespace HeuristicLab.Algorithms.DataAnalysis {
32  [StorableClass]
33  [Item(Name = "GaussianProcessClassificationSolutionCreator",
34    Description = "Creates a Gaussian process solution from a trained model.")]
35  public sealed class GaussianProcessClassificationSolutionCreator : SingleSuccessorOperator {
36    private const string ProblemDataParameterName = "ProblemData";
37    private const string ModelParameterName = "GaussianProcessClassificationModel";
38    private const string SolutionParameterName = "Solution";
39    private const string ResultsParameterName = "Results";
40    private const string TrainingAccuracyResultName = "Accuracy (training)";
41    private const string TestAccuracyResultName = "Accuracy (test)";
42    private const string CreateSolutionParameterName = "CreateSolution";
43
44    #region Parameter Properties
45    public ILookupParameter<IClassificationProblemData> ProblemDataParameter {
46      get { return (ILookupParameter<IClassificationProblemData>)Parameters[ProblemDataParameterName]; }
47    }
48    public ILookupParameter<IDiscriminantFunctionClassificationSolution> SolutionParameter {
49      get { return (ILookupParameter<IDiscriminantFunctionClassificationSolution>)Parameters[SolutionParameterName]; }
50    }
51    public ILookupParameter<IGaussianProcessModel> ModelParameter {
52      get { return (ILookupParameter<IGaussianProcessModel>)Parameters[ModelParameterName]; }
53    }
54    public ILookupParameter<ResultCollection> ResultsParameter {
55      get { return (ILookupParameter<ResultCollection>)Parameters[ResultsParameterName]; }
56    }
57    public ILookupParameter<BoolValue> CreateSolutionParameter {
58      get { return (ILookupParameter<BoolValue>)Parameters[CreateSolutionParameterName]; }
59    }
60    #endregion
61
62    [StorableConstructor]
63    private GaussianProcessClassificationSolutionCreator(bool deserializing) : base(deserializing) { }
64    private GaussianProcessClassificationSolutionCreator(GaussianProcessClassificationSolutionCreator original, Cloner cloner) : base(original, cloner) { }
65    public GaussianProcessClassificationSolutionCreator()
66      : base() {
67      // in
68      Parameters.Add(new LookupParameter<IClassificationProblemData>(ProblemDataParameterName, "The classification problem data for the Gaussian process solution."));
69      Parameters.Add(new LookupParameter<IGaussianProcessModel>(ModelParameterName, "The Gaussian process classification model to use for the solution."));
70      Parameters.Add(new LookupParameter<BoolValue>(CreateSolutionParameterName, "Flag that indicates if a solution should be produced at the end of the run"));
71
72      // in & out
73      Parameters.Add(new LookupParameter<ResultCollection>(ResultsParameterName, "The result collection of the algorithm."));
74      // out
75      Parameters.Add(new LookupParameter<IDiscriminantFunctionClassificationSolution>(SolutionParameterName, "The produced Gaussian process solution."));
76    }
77
78    [StorableHook(HookType.AfterDeserialization)]
79    private void AfterDeserialization() {
80      // BackwardsCompatibility3.3
81      #region Backwards compatible code, remove with 3.4
82      if (!Parameters.ContainsKey(CreateSolutionParameterName)) {
83        Parameters.Add(new LookupParameter<BoolValue>(CreateSolutionParameterName, "Flag that indicates if a solution should be produced at the end of the run"));
84      }
85      #endregion
86    }
87
88    public override IDeepCloneable Clone(Cloner cloner) {
89      return new GaussianProcessClassificationSolutionCreator(this, cloner);
90    }
91
92    public override IOperation Apply() {
93      if (ModelParameter.ActualValue != null && CreateSolutionParameter.ActualValue.Value == true) {
94        var m = (IGaussianProcessModel)ModelParameter.ActualValue.Clone();
95        m.FixParameters();
96        var data = (IClassificationProblemData)ProblemDataParameter.ActualValue.Clone();
97        var model = new DiscriminantFunctionClassificationModel(m, new NormalDistributionCutPointsThresholdCalculator());
98        model.RecalculateModelParameters(data, data.TrainingIndices);
99        var s = model.CreateDiscriminantFunctionClassificationSolution(data);
100
101        SolutionParameter.ActualValue = s;
102        var results = ResultsParameter.ActualValue;
103        if (!results.ContainsKey(SolutionParameterName)) {
104          results.Add(new Result(SolutionParameterName, "The Gaussian process classification solution", s));
105          results.Add(new Result(TrainingAccuracyResultName,
106                                 "The accuracy of the Gaussian process solution on the training partition.",
107                                 new DoubleValue(s.TrainingAccuracy)));
108          results.Add(new Result(TestAccuracyResultName,
109                                 "The accuracy of the Gaussian process solution on the test partition.",
110                                 new DoubleValue(s.TestAccuracy)));
111        } else {
112          results[SolutionParameterName].Value = s;
113          results[TrainingAccuracyResultName].Value = new DoubleValue(s.TrainingAccuracy);
114          results[TestAccuracyResultName].Value = new DoubleValue(s.TestAccuracy);
115        }
116      }
117      return base.Apply();
118    }
119  }
120}
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