1 | #region License Information
|
---|
2 | /* HeuristicLab
|
---|
3 | * Copyright (C) 2002-2010 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 |
|
---|
22 | using System.Linq;
|
---|
23 | using HeuristicLab.Common;
|
---|
24 | using HeuristicLab.Core;
|
---|
25 | using HeuristicLab.Data;
|
---|
26 | using HeuristicLab.Operators;
|
---|
27 | using HeuristicLab.Optimization;
|
---|
28 | using HeuristicLab.Parameters;
|
---|
29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
|
---|
30 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
|
---|
31 |
|
---|
32 | namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic {
|
---|
33 | /// <summary>
|
---|
34 | /// An operator for visualizing the best symbolic regression solution based on the validation set.
|
---|
35 | /// </summary>
|
---|
36 | [Item("BestSymbolicExpressionTreeVisualizer", "An operator for visualizing the best symbolic regression solution based on the validation set.")]
|
---|
37 | [StorableClass]
|
---|
38 | public sealed class BestValidationSymbolicRegressionSolutionVisualizer : SingleSuccessorOperator, ISingleObjectiveSolutionsVisualizer, ISolutionsVisualizer {
|
---|
39 | private const string SymbolicRegressionModelParameterName = "SymbolicRegressionModel";
|
---|
40 | private const string DataAnalysisProblemDataParameterName = "DataAnalysisProblemData";
|
---|
41 | private const string BestValidationSolutionParameterName = "BestValidationSolution";
|
---|
42 | private const string QualityParameterName = "Quality";
|
---|
43 | public ILookupParameter<ItemArray<SymbolicExpressionTree>> SymbolicExpressionTreeParameter {
|
---|
44 | get { return (ILookupParameter<ItemArray<SymbolicExpressionTree>>)Parameters[SymbolicRegressionModelParameterName]; }
|
---|
45 | }
|
---|
46 | public ILookupParameter<DataAnalysisProblemData> DataAnalysisProblemDataParameter {
|
---|
47 | get { return (ILookupParameter<DataAnalysisProblemData>)Parameters[DataAnalysisProblemDataParameterName]; }
|
---|
48 | }
|
---|
49 | public ILookupParameter<SymbolicRegressionSolution> BestValidationSolutionParameter {
|
---|
50 | get { return (ILookupParameter<SymbolicRegressionSolution>)Parameters[BestValidationSolutionParameterName]; }
|
---|
51 | }
|
---|
52 | ILookupParameter ISolutionsVisualizer.VisualizationParameter {
|
---|
53 | get { return BestValidationSolutionParameter; }
|
---|
54 | }
|
---|
55 |
|
---|
56 | public ILookupParameter<ItemArray<DoubleValue>> QualityParameter {
|
---|
57 | get { return (ILookupParameter<ItemArray<DoubleValue>>)Parameters[QualityParameterName]; }
|
---|
58 | }
|
---|
59 |
|
---|
60 | public BestValidationSymbolicRegressionSolutionVisualizer()
|
---|
61 | : base() {
|
---|
62 | Parameters.Add(new SubScopesLookupParameter<SymbolicExpressionTree>(SymbolicRegressionModelParameterName, "The symbolic regression solutions from which the best solution should be visualized."));
|
---|
63 | Parameters.Add(new SubScopesLookupParameter<DoubleValue>(QualityParameterName, "The quality of the symbolic regression solutions."));
|
---|
64 | Parameters.Add(new LookupParameter<DataAnalysisProblemData>(DataAnalysisProblemDataParameterName, "The symbolic regression problme data on which the best solution should be evaluated."));
|
---|
65 | Parameters.Add(new LookupParameter<SymbolicRegressionSolution>(BestValidationSolutionParameterName, "The best symbolic expression tree based on the validation data for the symbolic regression problem."));
|
---|
66 | Parameters.Add(new LookupParameter<ResultCollection>("Results"));
|
---|
67 | }
|
---|
68 |
|
---|
69 | public override IOperation Apply() {
|
---|
70 | ItemArray<SymbolicExpressionTree> expressions = SymbolicExpressionTreeParameter.ActualValue;
|
---|
71 | DataAnalysisProblemData problemData = DataAnalysisProblemDataParameter.ActualValue;
|
---|
72 | ItemArray<DoubleValue> qualities = QualityParameter.ActualValue;
|
---|
73 |
|
---|
74 | var bestExpressionIndex = (from index in Enumerable.Range(0, qualities.Count())
|
---|
75 | select new { Index = index, Quality = qualities[index] }).OrderBy(x => x.Quality).Select(x => x.Index).First();
|
---|
76 |
|
---|
77 | var bestExpression = expressions[bestExpressionIndex];
|
---|
78 | SymbolicRegressionSolution bestSolution = BestValidationSolutionParameter.ActualValue;
|
---|
79 | if (bestSolution == null) BestValidationSolutionParameter.ActualValue = CreateDataAnalysisSolution(problemData, bestExpression);
|
---|
80 | else {
|
---|
81 | bestSolution.Model = CreateModel(problemData, bestExpression);
|
---|
82 | }
|
---|
83 | // ((ResultCollection)Parameters["Results"].ActualValue).Add(new Result("ValidationMSE", new DoubleValue(3.15)));
|
---|
84 | return base.Apply();
|
---|
85 | }
|
---|
86 |
|
---|
87 | private SymbolicRegressionModel CreateModel(DataAnalysisProblemData problemData, SymbolicExpressionTree expression) {
|
---|
88 | return new SymbolicRegressionModel(expression, problemData.InputVariables.Select(x => x.Value));
|
---|
89 | }
|
---|
90 |
|
---|
91 | private SymbolicRegressionSolution CreateDataAnalysisSolution(DataAnalysisProblemData problemData, SymbolicExpressionTree expression) {
|
---|
92 | return new SymbolicRegressionSolution(problemData, CreateModel(problemData, expression));
|
---|
93 | }
|
---|
94 | }
|
---|
95 | }
|
---|