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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectiveProblem.cs @ 8798

Last change on this file since 8798 was 8798, checked in by mkommend, 11 years ago

#1081: Reintegrated time series modeling branch into trunk.

File size: 6.7 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 SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) {
56      Parameters.Add(new FixedValueParameter<DoubleLimit>(EstimationLimitsParameterName, EstimationLimitsParameterDescription));
57      EstimationLimitsParameter.Hidden = true;
58
59      Maximization.Value = false;
60      MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth;
61      MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength;
62
63      var interpeter = new SymbolicTimeSeriesPrognosisExpressionTreeInterpreter();
64      interpeter.TargetVariable = ProblemData.TargetVariable;
65      SymbolicExpressionTreeInterpreter = interpeter;
66
67      SymbolicExpressionTreeGrammarParameter.ValueChanged += (o, e) => ConfigureGrammarSymbols();
68      ConfigureGrammarSymbols();
69
70      InitializeOperators();
71      UpdateEstimationLimits();
72    }
73
74    private void ConfigureGrammarSymbols() {
75      var grammar = SymbolicExpressionTreeGrammar as TypeCoherentExpressionGrammar;
76      if (grammar != null) grammar.ConfigureAsDefaultTimeSeriesPrognosisGrammar();
77      UpdateGrammar();
78    }
79    protected override void UpdateGrammar() {
80      base.UpdateGrammar();
81      foreach (var autoregressiveSymbol in SymbolicExpressionTreeGrammar.Symbols.OfType<AutoregressiveTargetVariable>()) {
82        if (!autoregressiveSymbol.Fixed) autoregressiveSymbol.VariableNames = ProblemData.TargetVariable.ToEnumerable();
83      }
84    }
85
86    private void InitializeOperators() {
87      Operators.Add(new SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer());
88      Operators.Add(new SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer());
89      Operators.Add(new SymbolicTimeSeriesPrognosisSingleObjectiveOverfittingAnalyzer());
90      ParameterizeOperators();
91    }
92
93    private void UpdateEstimationLimits() {
94      if (ProblemData.TrainingIndices.Any()) {
95        var targetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList();
96        var mean = targetValues.Average();
97        var range = targetValues.Max() - targetValues.Min();
98        EstimationLimits.Upper = mean + PunishmentFactor * range;
99        EstimationLimits.Lower = mean - PunishmentFactor * range;
100      } else {
101        EstimationLimits.Upper = double.MaxValue;
102        EstimationLimits.Lower = double.MinValue;
103      }
104    }
105
106    protected override void OnProblemDataChanged() {
107      base.OnProblemDataChanged();
108      var interpreter = SymbolicExpressionTreeInterpreter as ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter;
109      if (interpreter != null) {
110        interpreter.TargetVariable = ProblemData.TargetVariable;
111      }
112      UpdateEstimationLimits();
113
114    }
115
116    protected override void ParameterizeOperators() {
117      base.ParameterizeOperators();
118      if (Parameters.ContainsKey(EstimationLimitsParameterName)) {
119        var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators);
120        foreach (var op in operators.OfType<ISymbolicDataAnalysisBoundedOperator>()) {
121          op.EstimationLimitsParameter.ActualName = EstimationLimitsParameter.Name;
122        }
123        foreach (var op in operators.OfType<SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer>()) {
124          op.MaximizationParameter.ActualName = MaximizationParameter.Name;
125          op.ProblemDataParameter.ActualName = ProblemDataParameter.Name;
126          op.QualityParameter.ActualName = Evaluator.QualityParameter.ActualName;
127          op.SymbolicDataAnalysisTreeInterpreterParameter.ActualName = SymbolicExpressionTreeInterpreterParameter.Name;
128          op.SymbolicExpressionTreeParameter.ActualName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName;
129        }
130      }
131    }
132  }
133}
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