source: branches/PersistenceOverhaul/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectiveProblem.cs

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2015 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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
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.")]
  [StorableType("478FF590-DC38-4C50-932F-CC462D91AE22")]
  [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 140)]
  public class SymbolicTimeSeriesPrognosisSingleObjectiveProblem : SymbolicDataAnalysisSingleObjectiveProblem<ITimeSeriesPrognosisProblemData, ISymbolicTimeSeriesPrognosisSingleObjectiveEvaluator, ISymbolicDataAnalysisSolutionCreator>, 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<DoubleLimit> EstimationLimitsParameter {
      get { return (IFixedValueParameter<DoubleLimit>)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 SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) {
      Parameters.Add(new FixedValueParameter<DoubleLimit>(EstimationLimitsParameterName, EstimationLimitsParameterDescription));
      EstimationLimitsParameter.Hidden = true;

      Maximization.Value = false;
      MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth;
      MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength;

      var interpeter = new SymbolicTimeSeriesPrognosisExpressionTreeInterpreter();
      interpeter.TargetVariable = ProblemData.TargetVariable;
      SymbolicExpressionTreeInterpreter = interpeter;

      SymbolicExpressionTreeGrammarParameter.ValueChanged += (o, e) => ConfigureGrammarSymbols();
      ConfigureGrammarSymbols();

      InitializeOperators();
      UpdateEstimationLimits();
    }

    private void ConfigureGrammarSymbols() {
      var grammar = SymbolicExpressionTreeGrammar as TypeCoherentExpressionGrammar;
      if (grammar != null) grammar.ConfigureAsDefaultTimeSeriesPrognosisGrammar();
      UpdateGrammar();
    }
    protected override void UpdateGrammar() {
      base.UpdateGrammar();
      foreach (var autoregressiveSymbol in SymbolicExpressionTreeGrammar.Symbols.OfType<AutoregressiveTargetVariable>()) {
        if (!autoregressiveSymbol.Fixed) autoregressiveSymbol.VariableNames = ProblemData.TargetVariable.ToEnumerable();
      }
    }

    private void InitializeOperators() {
      Operators.Add(new SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer());
      Operators.Add(new SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer());
      Operators.Add(new SymbolicTimeSeriesPrognosisSingleObjectiveOverfittingAnalyzer());
      ParameterizeOperators();
    }

    private void UpdateEstimationLimits() {
      if (ProblemData.TrainingIndices.Any()) {
        var targetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).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();
      var interpreter = SymbolicExpressionTreeInterpreter as ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter;
      if (interpreter != null) {
        interpreter.TargetVariable = ProblemData.TargetVariable;
      }
      UpdateEstimationLimits();

    }

    protected override void ParameterizeOperators() {
      base.ParameterizeOperators();
      if (Parameters.ContainsKey(EstimationLimitsParameterName)) {
        var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators);
        foreach (var op in operators.OfType<ISymbolicDataAnalysisBoundedOperator>()) {
          op.EstimationLimitsParameter.ActualName = EstimationLimitsParameter.Name;
        }
        foreach (var op in operators.OfType<SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer>()) {
          op.MaximizationParameter.ActualName = MaximizationParameter.Name;
          op.ProblemDataParameter.ActualName = ProblemDataParameter.Name;
          op.QualityParameter.ActualName = Evaluator.QualityParameter.ActualName;
          op.SymbolicDataAnalysisTreeInterpreterParameter.ActualName = SymbolicExpressionTreeInterpreterParameter.Name;
          op.SymbolicExpressionTreeParameter.ActualName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName;
        }
      }
    }

    public override void Load(ITimeSeriesPrognosisProblemData data) {
      base.Load(data);
      this.ProblemData.TrainingPartition.Start =
        Math.Min(10, this.ProblemData.TrainingPartition.End);
    }
  }
}