#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 . * * Author: Sabine Winkler */ #endregion using System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Encodings.IntegerVectorEncoding; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis; using HeuristicLab.Problems.DataAnalysis.Symbolic; using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression; namespace HeuristicLab.Problems.GrammaticalEvolution { [Item("Grammatical Evolution Symbolic Regression Problem (single objective)", "Represents grammatical evolution for single objective symbolic regression problems.")] [StorableClass] public class GESymbolicRegressionSingleObjectiveProblem : GESymbolicDataAnalysisSingleObjectiveProblem, IRegressionProblem { private const double PunishmentFactor = 10; private const int InitialMaximumTreeLength = 30; 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 GESymbolicRegressionSingleObjectiveProblem(bool deserializing) : base(deserializing) { } protected GESymbolicRegressionSingleObjectiveProblem(GESymbolicRegressionSingleObjectiveProblem original, Cloner cloner) : base(original, cloner) { RegisterEventHandlers(); } public override IDeepCloneable Clone(Cloner cloner) { return new GESymbolicRegressionSingleObjectiveProblem(this, cloner); } public GESymbolicRegressionSingleObjectiveProblem() : base(new RegressionProblemData(), new GESymbolicRegressionSingleObjectiveEvaluator(), new UniformRandomIntegerVectorCreator()) { Parameters.Add(new FixedValueParameter(EstimationLimitsParameterName, EstimationLimitsParameterDescription)); EstimationLimitsParameter.Hidden = true; ApplyLinearScalingParameter.Value.Value = true; Maximization.Value = Evaluator.Maximization; MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength; RegisterEventHandlers(); InitializeOperators(); UpdateEstimationLimits(); } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { RegisterEventHandlers(); } private void RegisterEventHandlers() { // when the ge evaluator itself changes EvaluatorParameter.ValueChanged += (sender, args) => { // register a new hander for the symbreg evaluator in the ge evaluator // hacky because we the evaluator does not have an event for changes of the maximization property EvaluatorParameter.Value.EvaluatorParameter.ValueChanged += (_, __) => Maximization.Value = Evaluator.Maximization; }; EvaluatorParameter.Value.EvaluatorParameter.ValueChanged += (sender, args) => Maximization.Value = Evaluator.Maximization; } private void InitializeOperators() { Operators.Add(new SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer()); Operators.Add(new SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer()); Operators.Add(new SymbolicRegressionSingleObjectiveOverfittingAnalyzer()); Operators.Add(new SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer()); Operators.Add(new SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer()); 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; } } } } }