source: branches/2974_Constants_Optimization/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionSolution.cs @ 17193

Last change on this file since 17193 was 17193, checked in by mkommend, 6 weeks ago

#2974: Merged trunk changes into branch.

File size: 9.4 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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 System.Linq;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
27using HeuristicLab.Optimization;
28using HEAL.Attic;
29
30namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
31  /// <summary>
32  /// Represents a symbolic regression solution (model + data) and attributes of the solution like accuracy and complexity
33  /// </summary>
34  [StorableType("88E56AF9-AD72-47E4-A613-8875703BD927")]
35  [Item(Name = "SymbolicRegressionSolution", Description = "Represents a symbolic regression solution (model + data) and attributes of the solution like accuracy and complexity.")]
36  public sealed class SymbolicRegressionSolution : RegressionSolution, ISymbolicRegressionSolution {
37    private const string ModelLengthResultName = "Model Length";
38    private const string ModelDepthResultName = "Model Depth";
39
40    private const string EstimationLimitsResultsResultName = "Estimation Limits Results";
41    private const string EstimationLimitsResultName = "Estimation Limits";
42    private const string TrainingUpperEstimationLimitHitsResultName = "Training Upper Estimation Limit Hits";
43    private const string TestLowerEstimationLimitHitsResultName = "Test Lower Estimation Limit Hits";
44    private const string TrainingLowerEstimationLimitHitsResultName = "Training Lower Estimation Limit Hits";
45    private const string TestUpperEstimationLimitHitsResultName = "Test Upper Estimation Limit Hits";
46    private const string TrainingNaNEvaluationsResultName = "Training NaN Evaluations";
47    private const string TestNaNEvaluationsResultName = "Test NaN Evaluations";
48
49    public new ISymbolicRegressionModel Model {
50      get { return (ISymbolicRegressionModel)base.Model; }
51      set { base.Model = value; }
52    }
53    ISymbolicDataAnalysisModel ISymbolicDataAnalysisSolution.Model {
54      get { return (ISymbolicDataAnalysisModel)base.Model; }
55    }
56    public int ModelLength {
57      get { return ((IntValue)this[ModelLengthResultName].Value).Value; }
58      private set { ((IntValue)this[ModelLengthResultName].Value).Value = value; }
59    }
60
61    public int ModelDepth {
62      get { return ((IntValue)this[ModelDepthResultName].Value).Value; }
63      private set { ((IntValue)this[ModelDepthResultName].Value).Value = value; }
64    }
65
66    private ResultCollection EstimationLimitsResultCollection {
67      get { return (ResultCollection)this[EstimationLimitsResultsResultName].Value; }
68    }
69    public DoubleLimit EstimationLimits {
70      get { return (DoubleLimit)EstimationLimitsResultCollection[EstimationLimitsResultName].Value; }
71    }
72
73    public int TrainingUpperEstimationLimitHits {
74      get { return ((IntValue)EstimationLimitsResultCollection[TrainingUpperEstimationLimitHitsResultName].Value).Value; }
75      private set { ((IntValue)EstimationLimitsResultCollection[TrainingUpperEstimationLimitHitsResultName].Value).Value = value; }
76    }
77    public int TestUpperEstimationLimitHits {
78      get { return ((IntValue)EstimationLimitsResultCollection[TestUpperEstimationLimitHitsResultName].Value).Value; }
79      private set { ((IntValue)EstimationLimitsResultCollection[TestUpperEstimationLimitHitsResultName].Value).Value = value; }
80    }
81    public int TrainingLowerEstimationLimitHits {
82      get { return ((IntValue)EstimationLimitsResultCollection[TrainingLowerEstimationLimitHitsResultName].Value).Value; }
83      private set { ((IntValue)EstimationLimitsResultCollection[TrainingLowerEstimationLimitHitsResultName].Value).Value = value; }
84    }
85    public int TestLowerEstimationLimitHits {
86      get { return ((IntValue)EstimationLimitsResultCollection[TestLowerEstimationLimitHitsResultName].Value).Value; }
87      private set { ((IntValue)EstimationLimitsResultCollection[TestLowerEstimationLimitHitsResultName].Value).Value = value; }
88    }
89    public int TrainingNaNEvaluations {
90      get { return ((IntValue)EstimationLimitsResultCollection[TrainingNaNEvaluationsResultName].Value).Value; }
91      private set { ((IntValue)EstimationLimitsResultCollection[TrainingNaNEvaluationsResultName].Value).Value = value; }
92    }
93    public int TestNaNEvaluations {
94      get { return ((IntValue)EstimationLimitsResultCollection[TestNaNEvaluationsResultName].Value).Value; }
95      private set { ((IntValue)EstimationLimitsResultCollection[TestNaNEvaluationsResultName].Value).Value = value; }
96    }
97
98    [StorableConstructor]
99    private SymbolicRegressionSolution(StorableConstructorFlag _) : base(_) { }
100    private SymbolicRegressionSolution(SymbolicRegressionSolution original, Cloner cloner)
101      : base(original, cloner) {
102    }
103    public SymbolicRegressionSolution(ISymbolicRegressionModel model, IRegressionProblemData problemData)
104      : base(model, problemData) {
105      foreach (var node in model.SymbolicExpressionTree.Root.IterateNodesPrefix().OfType<SymbolicExpressionTreeTopLevelNode>())
106        node.SetGrammar(null);
107
108      Add(new Result(ModelLengthResultName, "Length of the symbolic regression model.", new IntValue()));
109      Add(new Result(ModelDepthResultName, "Depth of the symbolic regression model.", new IntValue()));
110
111      ResultCollection estimationLimitResults = new ResultCollection();
112      estimationLimitResults.Add(new Result(EstimationLimitsResultName, "", new DoubleLimit()));
113      estimationLimitResults.Add(new Result(TrainingUpperEstimationLimitHitsResultName, "", new IntValue()));
114      estimationLimitResults.Add(new Result(TestUpperEstimationLimitHitsResultName, "", new IntValue()));
115      estimationLimitResults.Add(new Result(TrainingLowerEstimationLimitHitsResultName, "", new IntValue()));
116      estimationLimitResults.Add(new Result(TestLowerEstimationLimitHitsResultName, "", new IntValue()));
117      estimationLimitResults.Add(new Result(TrainingNaNEvaluationsResultName, "", new IntValue()));
118      estimationLimitResults.Add(new Result(TestNaNEvaluationsResultName, "", new IntValue()));
119      Add(new Result(EstimationLimitsResultsResultName, "Results concerning the estimation limits of symbolic regression solution", estimationLimitResults));
120      RecalculateResults();
121    }
122
123    public override IDeepCloneable Clone(Cloner cloner) {
124      return new SymbolicRegressionSolution(this, cloner);
125    }
126
127    [StorableHook(HookType.AfterDeserialization)]
128    private void AfterDeserialization() {
129      if (!ContainsKey(EstimationLimitsResultsResultName)) {
130        ResultCollection estimationLimitResults = new ResultCollection();
131        estimationLimitResults.Add(new Result(EstimationLimitsResultName, "", new DoubleLimit()));
132        estimationLimitResults.Add(new Result(TrainingUpperEstimationLimitHitsResultName, "", new IntValue()));
133        estimationLimitResults.Add(new Result(TestUpperEstimationLimitHitsResultName, "", new IntValue()));
134        estimationLimitResults.Add(new Result(TrainingLowerEstimationLimitHitsResultName, "", new IntValue()));
135        estimationLimitResults.Add(new Result(TestLowerEstimationLimitHitsResultName, "", new IntValue()));
136        estimationLimitResults.Add(new Result(TrainingNaNEvaluationsResultName, "", new IntValue()));
137        estimationLimitResults.Add(new Result(TestNaNEvaluationsResultName, "", new IntValue()));
138        Add(new Result(EstimationLimitsResultsResultName, "Results concerning the estimation limits of symbolic regression solution", estimationLimitResults));
139        CalculateResults();
140      }
141    }
142
143    protected override void RecalculateResults() {
144      base.RecalculateResults();
145      CalculateResults();
146    }
147
148    private void CalculateResults() {
149      ModelLength = Model.SymbolicExpressionTree.Length;
150      ModelDepth = Model.SymbolicExpressionTree.Depth;
151
152      EstimationLimits.Lower = Model.LowerEstimationLimit;
153      EstimationLimits.Upper = Model.UpperEstimationLimit;
154
155      TrainingUpperEstimationLimitHits = EstimatedTrainingValues.Count(x => x.IsAlmost(Model.UpperEstimationLimit));
156      TestUpperEstimationLimitHits = EstimatedTestValues.Count(x => x.IsAlmost(Model.UpperEstimationLimit));
157      TrainingLowerEstimationLimitHits = EstimatedTrainingValues.Count(x => x.IsAlmost(Model.LowerEstimationLimit));
158      TestLowerEstimationLimitHits = EstimatedTestValues.Count(x => x.IsAlmost(Model.LowerEstimationLimit));
159      TrainingNaNEvaluations = Model.Interpreter.GetSymbolicExpressionTreeValues(Model.SymbolicExpressionTree, ProblemData.Dataset, ProblemData.TrainingIndices).Count(double.IsNaN);
160      TestNaNEvaluations = Model.Interpreter.GetSymbolicExpressionTreeValues(Model.SymbolicExpressionTree, ProblemData.Dataset, ProblemData.TestIndices).Count(double.IsNaN);
161    }
162  }
163}
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