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

Last change on this file since 16453 was 16453, checked in by jkarder, 12 months ago

#2520: updated year of copyrights

File size: 7.3 KB
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1
2#region License Information
3/* HeuristicLab
4 * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
5 *
6 * This file is part of HeuristicLab.
7 *
8 * HeuristicLab is free software: you can redistribute it and/or modify
9 * it under the terms of the GNU General Public License as published by
10 * the Free Software Foundation, either version 3 of the License, or
11 * (at your option) any later version.
12 *
13 * HeuristicLab is distributed in the hope that it will be useful,
14 * but WITHOUT ANY WARRANTY; without even the implied warranty of
15 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
16 * GNU General Public License for more details.
17 *
18 * You should have received a copy of the GNU General Public License
19 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
20 */
21#endregion
22
23using System;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Optimization;
29using HeuristicLab.Parameters;
30using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
31using HeuristicLab.PluginInfrastructure;
32using HeuristicLab.Problems.DataAnalysis;
33
34namespace HeuristicLab.Algorithms.DataAnalysis {
35  /// <summary>
36  ///Gaussian process regression data analysis algorithm.
37  /// </summary>
38  [Item("Gaussian Process Regression", "Gaussian process regression data analysis algorithm.")]
39  [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 160)]
40  [StorableClass]
41  public sealed class GaussianProcessRegression : GaussianProcessBase, IStorableContent, IDataAnalysisAlgorithm<IRegressionProblem> {
42    public string Filename { get; set; }
43
44    public override Type ProblemType { get { return typeof(IRegressionProblem); } }
45    public new IRegressionProblem Problem {
46      get { return (IRegressionProblem)base.Problem; }
47      set { base.Problem = value; }
48    }
49
50    private const string ModelParameterName = "Model";
51    private const string CreateSolutionParameterName = "CreateSolution";
52
53
54    #region parameter properties
55    public IConstrainedValueParameter<IGaussianProcessRegressionModelCreator> GaussianProcessModelCreatorParameter {
56      get { return (IConstrainedValueParameter<IGaussianProcessRegressionModelCreator>)Parameters[ModelCreatorParameterName]; }
57    }
58    public IFixedValueParameter<GaussianProcessRegressionSolutionCreator> GaussianProcessSolutionCreatorParameter {
59      get { return (IFixedValueParameter<GaussianProcessRegressionSolutionCreator>)Parameters[SolutionCreatorParameterName]; }
60    }
61    public IFixedValueParameter<BoolValue> CreateSolutionParameter {
62      get { return (IFixedValueParameter<BoolValue>)Parameters[CreateSolutionParameterName]; }
63    }
64    #endregion
65    #region properties
66    public bool CreateSolution {
67      get { return CreateSolutionParameter.Value.Value; }
68      set { CreateSolutionParameter.Value.Value = value; }
69    }
70    #endregion
71
72    [StorableConstructor]
73    private GaussianProcessRegression(bool deserializing) : base(deserializing) { }
74    private GaussianProcessRegression(GaussianProcessRegression original, Cloner cloner)
75      : base(original, cloner) {
76      RegisterEventHandlers();
77    }
78    public GaussianProcessRegression()
79      : base(new RegressionProblem()) {
80      this.name = ItemName;
81      this.description = ItemDescription;
82
83      var modelCreators = ApplicationManager.Manager.GetInstances<IGaussianProcessRegressionModelCreator>();
84      var defaultModelCreator = modelCreators.First(c => c is GaussianProcessRegressionModelCreator);
85
86      // GP regression and classification algorithms only differ in the model and solution creators,
87      // thus we use a common base class and use operator parameters to implement the specific versions.
88      // Different model creators can be implemented,
89      // but the solution creator is implemented in a generic fashion already and we don't allow derived solution creators
90      Parameters.Add(new ConstrainedValueParameter<IGaussianProcessRegressionModelCreator>(ModelCreatorParameterName, "The operator to create the Gaussian process model.",
91        new ItemSet<IGaussianProcessRegressionModelCreator>(modelCreators), defaultModelCreator));
92      // the solution creator cannot be changed
93      Parameters.Add(new FixedValueParameter<GaussianProcessRegressionSolutionCreator>(SolutionCreatorParameterName, "The solution creator for the algorithm",
94        new GaussianProcessRegressionSolutionCreator()));
95      Parameters[SolutionCreatorParameterName].Hidden = true;
96      // TODO: it would be better to deactivate the solution creator when this parameter is changed
97      Parameters.Add(new FixedValueParameter<BoolValue>(CreateSolutionParameterName, "Flag that indicates if a solution should be produced at the end of the run", new BoolValue(true)));
98      Parameters[CreateSolutionParameterName].Hidden = true;
99
100      ParameterizedModelCreators();
101      ParameterizeSolutionCreator(GaussianProcessSolutionCreatorParameter.Value);
102      RegisterEventHandlers();
103    }
104
105
106    [StorableHook(HookType.AfterDeserialization)]
107    private void AfterDeserialization() {
108      // BackwardsCompatibility3.3
109      #region Backwards compatible code, remove with 3.4
110      if (!Parameters.ContainsKey(CreateSolutionParameterName)) {
111        Parameters.Add(new FixedValueParameter<BoolValue>(CreateSolutionParameterName, "Flag that indicates if a solution should be produced at the end of the run", new BoolValue(true)));
112        Parameters[CreateSolutionParameterName].Hidden = true;
113      }
114      #endregion
115      RegisterEventHandlers();
116    }
117
118    public override IDeepCloneable Clone(Cloner cloner) {
119      return new GaussianProcessRegression(this, cloner);
120    }
121
122    #region events
123    private void RegisterEventHandlers() {
124      GaussianProcessModelCreatorParameter.ValueChanged += ModelCreatorParameter_ValueChanged;
125    }
126
127    private void ModelCreatorParameter_ValueChanged(object sender, EventArgs e) {
128      ParameterizedModelCreator(GaussianProcessModelCreatorParameter.Value);
129    }
130    #endregion
131
132    private void ParameterizedModelCreators() {
133      foreach (var creator in GaussianProcessModelCreatorParameter.ValidValues) {
134        ParameterizedModelCreator(creator);
135      }
136    }
137
138    private void ParameterizedModelCreator(IGaussianProcessRegressionModelCreator modelCreator) {
139      modelCreator.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
140      modelCreator.MeanFunctionParameter.ActualName = MeanFunctionParameterName;
141      modelCreator.CovarianceFunctionParameter.ActualName = CovarianceFunctionParameterName;
142
143      // parameter names fixed by the algorithm
144      modelCreator.ModelParameter.ActualName = ModelParameterName;
145      modelCreator.HyperparameterParameter.ActualName = HyperparameterParameterName;
146      modelCreator.HyperparameterGradientsParameter.ActualName = HyperparameterGradientsParameterName;
147      modelCreator.NegativeLogLikelihoodParameter.ActualName = NegativeLogLikelihoodParameterName;
148    }
149
150    private void ParameterizeSolutionCreator(GaussianProcessRegressionSolutionCreator solutionCreator) {
151      solutionCreator.ModelParameter.ActualName = ModelParameterName;
152      solutionCreator.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
153    }
154  }
155}
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