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 |
|
---|
22 | using System.Linq;
|
---|
23 | using HeuristicLab.Common;
|
---|
24 | using HeuristicLab.Core;
|
---|
25 | using HeuristicLab.Data;
|
---|
26 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
|
---|
27 | using HeuristicLab.Optimization;
|
---|
28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
|
---|
29 |
|
---|
30 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
|
---|
31 | /// <summary>
|
---|
32 | /// Represents a symbolic classification solution (model + data) and attributes of the solution like accuracy and complexity
|
---|
33 | /// </summary>
|
---|
34 | [StorableClass]
|
---|
35 | [Item(Name = "SymbolicDiscriminantFunctionClassificationSolution", Description = "Represents a symbolic classification solution (model + data) and attributes of the solution like accuracy and complexity.")]
|
---|
36 | public sealed class SymbolicDiscriminantFunctionClassificationSolution : DiscriminantFunctionClassificationSolution, ISymbolicClassificationSolution {
|
---|
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 ISymbolicDiscriminantFunctionClassificationModel Model {
|
---|
50 | get { return (ISymbolicDiscriminantFunctionClassificationModel)base.Model; }
|
---|
51 | set { base.Model = value; }
|
---|
52 | }
|
---|
53 |
|
---|
54 | ISymbolicClassificationModel ISymbolicClassificationSolution.Model {
|
---|
55 | get { return Model; }
|
---|
56 | }
|
---|
57 |
|
---|
58 | ISymbolicDataAnalysisModel ISymbolicDataAnalysisSolution.Model {
|
---|
59 | get { return Model; }
|
---|
60 | }
|
---|
61 | public int ModelLength {
|
---|
62 | get { return ((IntValue)this[ModelLengthResultName].Value).Value; }
|
---|
63 | private set { ((IntValue)this[ModelLengthResultName].Value).Value = value; }
|
---|
64 | }
|
---|
65 |
|
---|
66 | public int ModelDepth {
|
---|
67 | get { return ((IntValue)this[ModelDepthResultName].Value).Value; }
|
---|
68 | private set { ((IntValue)this[ModelDepthResultName].Value).Value = value; }
|
---|
69 | }
|
---|
70 |
|
---|
71 | private ResultCollection EstimationLimitsResultCollection {
|
---|
72 | get { return (ResultCollection)this[EstimationLimitsResultsResultName].Value; }
|
---|
73 | }
|
---|
74 | public DoubleLimit EstimationLimits {
|
---|
75 | get { return (DoubleLimit)EstimationLimitsResultCollection[EstimationLimitsResultName].Value; }
|
---|
76 | }
|
---|
77 |
|
---|
78 | public int TrainingUpperEstimationLimitHits {
|
---|
79 | get { return ((IntValue)EstimationLimitsResultCollection[TrainingUpperEstimationLimitHitsResultName].Value).Value; }
|
---|
80 | private set { ((IntValue)EstimationLimitsResultCollection[TrainingUpperEstimationLimitHitsResultName].Value).Value = value; }
|
---|
81 | }
|
---|
82 | public int TestUpperEstimationLimitHits {
|
---|
83 | get { return ((IntValue)EstimationLimitsResultCollection[TestUpperEstimationLimitHitsResultName].Value).Value; }
|
---|
84 | private set { ((IntValue)EstimationLimitsResultCollection[TestUpperEstimationLimitHitsResultName].Value).Value = value; }
|
---|
85 | }
|
---|
86 | public int TrainingLowerEstimationLimitHits {
|
---|
87 | get { return ((IntValue)EstimationLimitsResultCollection[TrainingLowerEstimationLimitHitsResultName].Value).Value; }
|
---|
88 | private set { ((IntValue)EstimationLimitsResultCollection[TrainingLowerEstimationLimitHitsResultName].Value).Value = value; }
|
---|
89 | }
|
---|
90 | public int TestLowerEstimationLimitHits {
|
---|
91 | get { return ((IntValue)EstimationLimitsResultCollection[TestLowerEstimationLimitHitsResultName].Value).Value; }
|
---|
92 | private set { ((IntValue)EstimationLimitsResultCollection[TestLowerEstimationLimitHitsResultName].Value).Value = value; }
|
---|
93 | }
|
---|
94 | public int TrainingNaNEvaluations {
|
---|
95 | get { return ((IntValue)EstimationLimitsResultCollection[TrainingNaNEvaluationsResultName].Value).Value; }
|
---|
96 | private set { ((IntValue)EstimationLimitsResultCollection[TrainingNaNEvaluationsResultName].Value).Value = value; }
|
---|
97 | }
|
---|
98 | public int TestNaNEvaluations {
|
---|
99 | get { return ((IntValue)EstimationLimitsResultCollection[TestNaNEvaluationsResultName].Value).Value; }
|
---|
100 | private set { ((IntValue)EstimationLimitsResultCollection[TestNaNEvaluationsResultName].Value).Value = value; }
|
---|
101 | }
|
---|
102 |
|
---|
103 | [StorableConstructor]
|
---|
104 | private SymbolicDiscriminantFunctionClassificationSolution(bool deserializing) : base(deserializing) { }
|
---|
105 | private SymbolicDiscriminantFunctionClassificationSolution(SymbolicDiscriminantFunctionClassificationSolution original, Cloner cloner)
|
---|
106 | : base(original, cloner) {
|
---|
107 | }
|
---|
108 | public SymbolicDiscriminantFunctionClassificationSolution(ISymbolicDiscriminantFunctionClassificationModel model, IClassificationProblemData problemData)
|
---|
109 | : base(model, problemData) {
|
---|
110 | foreach (var node in model.SymbolicExpressionTree.Root.IterateNodesPrefix().OfType<SymbolicExpressionTreeTopLevelNode>())
|
---|
111 | node.SetGrammar(null);
|
---|
112 |
|
---|
113 | Add(new Result(ModelLengthResultName, "Length of the symbolic classification model.", new IntValue()));
|
---|
114 | Add(new Result(ModelDepthResultName, "Depth of the symbolic classification model.", new IntValue()));
|
---|
115 |
|
---|
116 | ResultCollection estimationLimitResults = new ResultCollection();
|
---|
117 | estimationLimitResults.Add(new Result(EstimationLimitsResultName, "", new DoubleLimit()));
|
---|
118 | estimationLimitResults.Add(new Result(TrainingUpperEstimationLimitHitsResultName, "", new IntValue()));
|
---|
119 | estimationLimitResults.Add(new Result(TestUpperEstimationLimitHitsResultName, "", new IntValue()));
|
---|
120 | estimationLimitResults.Add(new Result(TrainingLowerEstimationLimitHitsResultName, "", new IntValue()));
|
---|
121 | estimationLimitResults.Add(new Result(TestLowerEstimationLimitHitsResultName, "", new IntValue()));
|
---|
122 | estimationLimitResults.Add(new Result(TrainingNaNEvaluationsResultName, "", new IntValue()));
|
---|
123 | estimationLimitResults.Add(new Result(TestNaNEvaluationsResultName, "", new IntValue()));
|
---|
124 | Add(new Result(EstimationLimitsResultsResultName, "Results concerning the estimation limits of symbolic regression solution", estimationLimitResults));
|
---|
125 |
|
---|
126 | CalculateResults();
|
---|
127 | }
|
---|
128 |
|
---|
129 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
130 | return new SymbolicDiscriminantFunctionClassificationSolution(this, cloner);
|
---|
131 | }
|
---|
132 |
|
---|
133 | [StorableHook(HookType.AfterDeserialization)]
|
---|
134 | private void AfterDeserialization() {
|
---|
135 | if (!ContainsKey(EstimationLimitsResultsResultName)) {
|
---|
136 | ResultCollection estimationLimitResults = new ResultCollection();
|
---|
137 | estimationLimitResults.Add(new Result(EstimationLimitsResultName, "", new DoubleLimit()));
|
---|
138 | estimationLimitResults.Add(new Result(TrainingUpperEstimationLimitHitsResultName, "", new IntValue()));
|
---|
139 | estimationLimitResults.Add(new Result(TestUpperEstimationLimitHitsResultName, "", new IntValue()));
|
---|
140 | estimationLimitResults.Add(new Result(TrainingLowerEstimationLimitHitsResultName, "", new IntValue()));
|
---|
141 | estimationLimitResults.Add(new Result(TestLowerEstimationLimitHitsResultName, "", new IntValue()));
|
---|
142 | estimationLimitResults.Add(new Result(TrainingNaNEvaluationsResultName, "", new IntValue()));
|
---|
143 | estimationLimitResults.Add(new Result(TestNaNEvaluationsResultName, "", new IntValue()));
|
---|
144 | Add(new Result(EstimationLimitsResultsResultName, "Results concerning the estimation limits of symbolic regression solution", estimationLimitResults));
|
---|
145 | CalculateResults();
|
---|
146 | }
|
---|
147 | }
|
---|
148 |
|
---|
149 |
|
---|
150 | private void CalculateResults() {
|
---|
151 | ModelLength = Model.SymbolicExpressionTree.Length;
|
---|
152 | ModelDepth = Model.SymbolicExpressionTree.Depth;
|
---|
153 |
|
---|
154 | EstimationLimits.Lower = Model.LowerEstimationLimit;
|
---|
155 | EstimationLimits.Upper = Model.UpperEstimationLimit;
|
---|
156 |
|
---|
157 | TrainingUpperEstimationLimitHits = EstimatedTrainingValues.Count(x => x.IsAlmost(Model.UpperEstimationLimit));
|
---|
158 | TestUpperEstimationLimitHits = EstimatedTestValues.Count(x => x.IsAlmost(Model.UpperEstimationLimit));
|
---|
159 | TrainingLowerEstimationLimitHits = EstimatedTrainingValues.Count(x => x.IsAlmost(Model.LowerEstimationLimit));
|
---|
160 | TestLowerEstimationLimitHits = EstimatedTestValues.Count(x => x.IsAlmost(Model.LowerEstimationLimit));
|
---|
161 | TrainingNaNEvaluations = Model.Interpreter.GetSymbolicExpressionTreeValues(Model.SymbolicExpressionTree, ProblemData.Dataset, ProblemData.TrainingIndices).Count(double.IsNaN);
|
---|
162 | TestNaNEvaluations = Model.Interpreter.GetSymbolicExpressionTreeValues(Model.SymbolicExpressionTree, ProblemData.Dataset, ProblemData.TestIndices).Count(double.IsNaN);
|
---|
163 | }
|
---|
164 |
|
---|
165 | protected override void RecalculateResults() {
|
---|
166 | base.RecalculateResults();
|
---|
167 | CalculateResults();
|
---|
168 | }
|
---|
169 | }
|
---|
170 | }
|
---|