#region License Information
/* HeuristicLab
* Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis {
[Item("Symbolic Time-Series Prognosis Problem (single objective)", "Represents a single objective symbolic time-series prognosis problem.")]
[StorableClass]
[Creatable("Problems")]
public class SymbolicTimeSeriesPrognosisSingleObjectiveProblem : SymbolicDataAnalysisSingleObjectiveProblem, ITimeSeriesPrognosisProblem {
private const double PunishmentFactor = 10;
private const int InitialMaximumTreeDepth = 8;
private const int InitialMaximumTreeLength = 25;
private const string EstimationLimitsParameterName = "EstimationLimits";
private const string EstimationLimitsParameterDescription = "The limits for the estimated value that can be returned by the symbolic regression model.";
#region parameter properties
public IFixedValueParameter EstimationLimitsParameter {
get { return (IFixedValueParameter)Parameters[EstimationLimitsParameterName]; }
}
#endregion
#region properties
public DoubleLimit EstimationLimits {
get { return EstimationLimitsParameter.Value; }
}
#endregion
[StorableConstructor]
protected SymbolicTimeSeriesPrognosisSingleObjectiveProblem(bool deserializing) : base(deserializing) { }
protected SymbolicTimeSeriesPrognosisSingleObjectiveProblem(SymbolicTimeSeriesPrognosisSingleObjectiveProblem original, Cloner cloner) : base(original, cloner) { }
public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicTimeSeriesPrognosisSingleObjectiveProblem(this, cloner); }
public SymbolicTimeSeriesPrognosisSingleObjectiveProblem()
: base(new TimeSeriesPrognosisProblemData(), new SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) {
Parameters.Add(new FixedValueParameter(EstimationLimitsParameterName, EstimationLimitsParameterDescription));
EstimationLimitsParameter.Hidden = true;
Maximization.Value = true;
MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth;
MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength;
SymbolicExpressionTreeGrammarParameter.ValueChanged += (o, e) => ConfigureGrammarSymbols();
ConfigureGrammarSymbols();
InitializeOperators();
UpdateEstimationLimits();
}
private void ConfigureGrammarSymbols() {
var grammar = SymbolicExpressionTreeGrammar as TypeCoherentExpressionGrammar;
if (grammar != null) grammar.ConfigureAsDefaultTimeSeriesPrognosisGrammar();
}
private void InitializeOperators() {
Operators.Add(new SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer());
//Operators.Add(new SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer());
//Operators.Add(new SymbolicTimeSeriesPrognosisSingleObjectiveOverfittingAnalyzer());
ParameterizeOperators();
}
private void UpdateEstimationLimits() {
//if (ProblemData.TrainingIndizes.Any()) {
// var targetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariables, ProblemData.TrainingIndizes).ToList();
// var mean = targetValues.Average();
// var range = targetValues.Max() - targetValues.Min();
// EstimationLimits.Upper = mean + PunishmentFactor * range;
// EstimationLimits.Lower = mean - PunishmentFactor * range;
//} else {
EstimationLimits.Upper = double.MaxValue;
EstimationLimits.Lower = double.MinValue;
//}
}
protected override void OnProblemDataChanged() {
base.OnProblemDataChanged();
UpdateEstimationLimits();
}
protected override void ParameterizeOperators() {
base.ParameterizeOperators();
if (Parameters.ContainsKey(EstimationLimitsParameterName)) {
var operators = Parameters.OfType().Select(p => p.Value).OfType().Union(Operators);
foreach (var op in operators.OfType()) {
op.EstimationLimitsParameter.ActualName = EstimationLimitsParameter.Name;
}
}
}
public override void ImportProblemDataFromFile(string fileName) {
TimeSeriesPrognosisProblemData problemData = TimeSeriesPrognosisProblemData.ImportFromFile(fileName);
ProblemData = problemData;
}
}
}