source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectiveProblem.cs @ 6802

Last change on this file since 6802 was 6802, checked in by gkronber, 10 years ago

#1081 added classes (problem, evaluators, analyzers, solution, model, online-calculators, and views) for time series prognosis problems and added an algorithm implementation to generation linear AR (auto-regressive) time series prognosis solution.

File size: 5.2 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 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.Parameters;
26using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27
28namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis {
29  [Item("Symbolic Time-Series Prognosis Problem (single objective)", "Represents a single objective symbolic time-series prognosis problem.")]
30  [StorableClass]
31  [Creatable("Problems")]
32  public class SymbolicTimeSeriesPrognosisSingleObjectiveProblem : SymbolicDataAnalysisSingleObjectiveProblem<ITimeSeriesPrognosisProblemData, ISymbolicTimeSeriesPrognosisSingleObjectiveEvaluator, ISymbolicDataAnalysisSolutionCreator>, ITimeSeriesPrognosisProblem {
33    private const double PunishmentFactor = 10;
34    private const int InitialMaximumTreeDepth = 8;
35    private const int InitialMaximumTreeLength = 25;
36    private const string EstimationLimitsParameterName = "EstimationLimits";
37    private const string EstimationLimitsParameterDescription = "The limits for the estimated value that can be returned by the symbolic regression model.";
38
39    #region parameter properties
40    public IFixedValueParameter<DoubleLimit> EstimationLimitsParameter {
41      get { return (IFixedValueParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
42    }
43    #endregion
44    #region properties
45    public DoubleLimit EstimationLimits {
46      get { return EstimationLimitsParameter.Value; }
47    }
48    #endregion
49    [StorableConstructor]
50    protected SymbolicTimeSeriesPrognosisSingleObjectiveProblem(bool deserializing) : base(deserializing) { }
51    protected SymbolicTimeSeriesPrognosisSingleObjectiveProblem(SymbolicTimeSeriesPrognosisSingleObjectiveProblem original, Cloner cloner) : base(original, cloner) { }
52    public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicTimeSeriesPrognosisSingleObjectiveProblem(this, cloner); }
53
54    public SymbolicTimeSeriesPrognosisSingleObjectiveProblem()
55      : base(new TimeSeriesPrognosisProblemData(), new SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) {
56      Parameters.Add(new FixedValueParameter<DoubleLimit>(EstimationLimitsParameterName, EstimationLimitsParameterDescription));
57
58      EstimationLimitsParameter.Hidden = true;
59
60      Maximization.Value = true;
61      MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth;
62      MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength;
63
64      InitializeOperators();
65      UpdateEstimationLimits();
66    }
67
68    private void InitializeOperators() {
69      Operators.Add(new SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer());
70      Operators.Add(new SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer());
71      Operators.Add(new SymbolicTimeSeriesPrognosisSingleObjectiveOverfittingAnalyzer());
72      ParameterizeOperators();
73    }
74
75    private void UpdateEstimationLimits() {
76      if (ProblemData.TrainingIndizes.Any()) {
77        var targetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes).ToList();
78        var mean = targetValues.Average();
79        var range = targetValues.Max() - targetValues.Min();
80        EstimationLimits.Upper = mean + PunishmentFactor * range;
81        EstimationLimits.Lower = mean - PunishmentFactor * range;
82      } else {
83        EstimationLimits.Upper = double.MaxValue;
84        EstimationLimits.Lower = double.MinValue;
85      }
86    }
87
88    protected override void OnProblemDataChanged() {
89      base.OnProblemDataChanged();
90      UpdateEstimationLimits();
91    }
92
93    protected override void ParameterizeOperators() {
94      base.ParameterizeOperators();
95      if (Parameters.ContainsKey(EstimationLimitsParameterName)) {
96        var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators);
97        foreach (var op in operators.OfType<ISymbolicDataAnalysisBoundedOperator>()) {
98          op.EstimationLimitsParameter.ActualName = EstimationLimitsParameter.Name;
99        }
100      }
101    }
102
103    public override void ImportProblemDataFromFile(string fileName) {
104      TimeSeriesPrognosisProblemData problemData = TimeSeriesPrognosisProblemData.ImportFromFile(fileName);
105      ProblemData = problemData;
106    }
107  }
108}
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