#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 .
*/
#endregion
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
[Item("Symbolic Regression Problem (multi-objective)", "Represents a multi objective symbolic regression problem.")]
[StorableClass("89555BC1-26BD-445E-8E06-C27B4602479E")]
[Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 110)]
public class SymbolicRegressionMultiObjectiveProblem : SymbolicDataAnalysisMultiObjectiveProblem, IRegressionProblem {
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 lower and upper limit 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 SymbolicRegressionMultiObjectiveProblem(bool deserializing) : base(deserializing) { }
protected SymbolicRegressionMultiObjectiveProblem(SymbolicRegressionMultiObjectiveProblem original, Cloner cloner)
: base(original, cloner) {
RegisterEventHandlers();
}
public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionMultiObjectiveProblem(this, cloner); }
public SymbolicRegressionMultiObjectiveProblem()
: base(new RegressionProblemData(), new SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) {
Parameters.Add(new FixedValueParameter(EstimationLimitsParameterName, EstimationLimitsParameterDescription));
EstimationLimitsParameter.Hidden = true;
ApplyLinearScalingParameter.Value.Value = true;
Maximization = new BoolArray(new bool[] { true, false });
MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth;
MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength;
RegisterEventHandlers();
ConfigureGrammarSymbols();
InitializeOperators();
UpdateEstimationLimits();
}
[StorableHook(HookType.AfterDeserialization)]
private void AfterDeserialization() {
RegisterEventHandlers();
}
private void RegisterEventHandlers() {
SymbolicExpressionTreeGrammarParameter.ValueChanged += (o, e) => ConfigureGrammarSymbols();
}
private void ConfigureGrammarSymbols() {
var grammar = SymbolicExpressionTreeGrammar as TypeCoherentExpressionGrammar;
if (grammar != null) grammar.ConfigureAsDefaultRegressionGrammar();
}
private void InitializeOperators() {
Operators.Add(new SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer());
Operators.Add(new SymbolicRegressionMultiObjectiveValidationBestSolutionAnalyzer());
Operators.Add(new SymbolicExpressionTreePhenotypicSimilarityCalculator());
Operators.Add(new SymbolicRegressionPhenotypicDiversityAnalyzer(Operators.OfType()));
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();
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;
}
}
foreach (var op in Operators.OfType()) {
op.SolutionVariableName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName;
op.QualityVariableName = Evaluator.QualitiesParameter.ActualName;
if (op is SymbolicExpressionTreePhenotypicSimilarityCalculator) {
var phenotypicSimilarityCalculator = (SymbolicExpressionTreePhenotypicSimilarityCalculator)op;
phenotypicSimilarityCalculator.ProblemData = ProblemData;
phenotypicSimilarityCalculator.Interpreter = SymbolicExpressionTreeInterpreter;
}
}
}
}
}