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source: stable/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/GaussianProcessClassification.cs @ 13283

Last change on this file since 13283 was 13283, checked in by gkronber, 7 years ago

#2385: merged r12934,r13204,r13205,r13270 from trunk to stable

File size: 7.4 KB
Line 
1
2#region License Information
3/* HeuristicLab
4 * Copyright (C) 2002-2015 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 least-squares classification data analysis algorithm.
37  /// </summary>
38  [Item("Gaussian Process Least-Squares Classification", "Gaussian process least-squares classification data analysis algorithm.")]
39  [Creatable(CreatableAttribute.Categories.DataAnalysisClassification, Priority = 160)]
40  [StorableClass]
41  public sealed class GaussianProcessClassification : GaussianProcessBase, IStorableContent {
42    public string Filename { get; set; }
43
44    public override Type ProblemType { get { return typeof(IClassificationProblem); } }
45    public new IClassificationProblem Problem {
46      get { return (IClassificationProblem)base.Problem; }
47      set { base.Problem = value; }
48    }
49
50    private const string ModelParameterName = "Model";
51    private const string CreateSolutionParameterName = "CreateSolution";
52
53    #region parameter properties
54    public IConstrainedValueParameter<IGaussianProcessClassificationModelCreator> GaussianProcessModelCreatorParameter {
55      get { return (IConstrainedValueParameter<IGaussianProcessClassificationModelCreator>)Parameters[ModelCreatorParameterName]; }
56    }
57    public IFixedValueParameter<GaussianProcessClassificationSolutionCreator> GaussianProcessSolutionCreatorParameter {
58      get { return (IFixedValueParameter<GaussianProcessClassificationSolutionCreator>)Parameters[SolutionCreatorParameterName]; }
59    }
60    public IFixedValueParameter<BoolValue> CreateSolutionParameter {
61      get { return (IFixedValueParameter<BoolValue>)Parameters[CreateSolutionParameterName]; }
62    }
63    #endregion
64    #region properties
65    public bool CreateSolution {
66      get { return CreateSolutionParameter.Value.Value; }
67      set { CreateSolutionParameter.Value.Value = value; }
68    }
69    #endregion
70
71    [StorableConstructor]
72    private GaussianProcessClassification(bool deserializing) : base(deserializing) { }
73    private GaussianProcessClassification(GaussianProcessClassification original, Cloner cloner)
74      : base(original, cloner) {
75      RegisterEventHandlers();
76    }
77    public GaussianProcessClassification()
78      : base(new ClassificationProblem()) {
79      this.name = ItemName;
80      this.description = ItemDescription;
81
82      var modelCreators = ApplicationManager.Manager.GetInstances<IGaussianProcessClassificationModelCreator>();
83      var defaultModelCreator = modelCreators.First(c => c is GaussianProcessClassificationModelCreator);
84
85      // GP regression and classification algorithms only differ in the model and solution creators,
86      // thus we use a common base class and use operator parameters to implement the specific versions.
87      // Different model creators can be implemented,
88      // but the solution creator is implemented in a generic fashion already and we don't allow derived solution creators
89      Parameters.Add(new ConstrainedValueParameter<IGaussianProcessClassificationModelCreator>(ModelCreatorParameterName, "The operator to create the Gaussian process model.",
90        new ItemSet<IGaussianProcessClassificationModelCreator>(modelCreators), defaultModelCreator));
91      // this parameter is not intended to be changed,
92      Parameters.Add(new FixedValueParameter<GaussianProcessClassificationSolutionCreator>(SolutionCreatorParameterName, "The solution creator for the algorithm",
93        new GaussianProcessClassificationSolutionCreator()));
94      Parameters[SolutionCreatorParameterName].Hidden = true;
95      // TODO: it would be better to deactivate the solution creator when this parameter is changed
96      Parameters.Add(new FixedValueParameter<BoolValue>(CreateSolutionParameterName, "Flag that indicates if a solution should be produced at the end of the run", new BoolValue(true)));
97      Parameters[CreateSolutionParameterName].Hidden = true;
98
99      ParameterizedModelCreators();
100      ParameterizeSolutionCreator(GaussianProcessSolutionCreatorParameter.Value);
101      RegisterEventHandlers();
102    }
103
104
105    [StorableHook(HookType.AfterDeserialization)]
106    private void AfterDeserialization() {
107      // BackwardsCompatibility3.3
108      #region Backwards compatible code, remove with 3.4
109      if (!Parameters.ContainsKey(CreateSolutionParameterName)) {
110        Parameters.Add(new FixedValueParameter<BoolValue>(CreateSolutionParameterName, "Flag that indicates if a solution should be produced at the end of the run", new BoolValue(true)));
111        Parameters[CreateSolutionParameterName].Hidden = true;
112      }
113      #endregion
114      RegisterEventHandlers();
115    }
116
117    public override IDeepCloneable Clone(Cloner cloner) {
118      return new GaussianProcessClassification(this, cloner);
119    }
120
121    #region events
122    private void RegisterEventHandlers() {
123      GaussianProcessModelCreatorParameter.ValueChanged += ModelCreatorParameter_ValueChanged;
124    }
125
126    private void ModelCreatorParameter_ValueChanged(object sender, EventArgs e) {
127      ParameterizedModelCreator(GaussianProcessModelCreatorParameter.Value);
128    }
129    #endregion
130
131    private void ParameterizedModelCreators() {
132      foreach (var creator in GaussianProcessModelCreatorParameter.ValidValues) {
133        ParameterizedModelCreator(creator);
134      }
135    }
136
137    private void ParameterizedModelCreator(IGaussianProcessClassificationModelCreator modelCreator) {
138      modelCreator.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
139      modelCreator.MeanFunctionParameter.ActualName = MeanFunctionParameterName;
140      modelCreator.CovarianceFunctionParameter.ActualName = CovarianceFunctionParameterName;
141
142      // parameter names fixed by the algorithm
143      modelCreator.ModelParameter.ActualName = ModelParameterName;
144      modelCreator.HyperparameterParameter.ActualName = HyperparameterParameterName;
145      modelCreator.HyperparameterGradientsParameter.ActualName = HyperparameterGradientsParameterName;
146      modelCreator.NegativeLogLikelihoodParameter.ActualName = NegativeLogLikelihoodParameterName;
147    }
148
149    private void ParameterizeSolutionCreator(GaussianProcessClassificationSolutionCreator solutionCreator) {
150      solutionCreator.ModelParameter.ActualName = ModelParameterName;
151      solutionCreator.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
152    }
153  }
154}
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