#region License Information /* HeuristicLab * Copyright (C) 2002-2012 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.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification { [Item("Symbolic Classification Problem (multi objective)", "Represents a multi objective symbolic classfication problem.")] [StorableClass] [Creatable("Problems")] public class SymbolicClassificationMultiObjectiveProblem : SymbolicDataAnalysisMultiObjectiveProblem, IClassificationProblem { 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 classification model."; private const string ModelCreatorParameterName = "ModelCreator"; #region parameter properties public IFixedValueParameter EstimationLimitsParameter { get { return (IFixedValueParameter)Parameters[EstimationLimitsParameterName]; } } public IValueParameter ModelCreatorParameter { get { return (IValueParameter)Parameters[ModelCreatorParameterName]; } } #endregion #region properties public DoubleLimit EstimationLimits { get { return EstimationLimitsParameter.Value; } } public ISymbolicClassificationModelCreator ModelCreator { get { return ModelCreatorParameter.Value; } } #endregion [StorableConstructor] protected SymbolicClassificationMultiObjectiveProblem(bool deserializing) : base(deserializing) { } protected SymbolicClassificationMultiObjectiveProblem(SymbolicClassificationMultiObjectiveProblem original, Cloner cloner) : base(original, cloner) { RegisterEventHandlers(); } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicClassificationMultiObjectiveProblem(this, cloner); } public SymbolicClassificationMultiObjectiveProblem() : base(new ClassificationProblemData(), new SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) { Parameters.Add(new FixedValueParameter(EstimationLimitsParameterName, EstimationLimitsParameterDescription)); Parameters.Add(new ValueParameter(ModelCreatorParameterName, "", new AccuracyMaximizingThresholdsModelCreator())); ApplyLinearScalingParameter.Value.Value = false; EstimationLimitsParameter.Hidden = true; Maximization = new BoolArray(new bool[] { false, false }); MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth; MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength; RegisterEventHandlers(); ConfigureGrammarSymbols(); InitializeOperators(); UpdateEstimationLimits(); } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { // BackwardsCompatibility3.4 #region Backwards compatible code, remove with 3.5 if (!Parameters.ContainsKey(ModelCreatorParameterName)) Parameters.Add(new ValueParameter(ModelCreatorParameterName, "", new AccuracyMaximizingThresholdsModelCreator())); #endregion RegisterEventHandlers(); } private void RegisterEventHandlers() { SymbolicExpressionTreeGrammarParameter.ValueChanged += (o, e) => ConfigureGrammarSymbols(); ModelCreatorParameter.NameChanged += (o, e) => ParameterizeOperators(); } private void ConfigureGrammarSymbols() { var grammar = SymbolicExpressionTreeGrammar as TypeCoherentExpressionGrammar; if (grammar != null) grammar.ConfigureAsDefaultClassificationGrammar(); } private void InitializeOperators() { Operators.Add(new SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer()); Operators.Add(new SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer()); 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.ModelCreatorParameter.ActualName = ModelCreatorParameter.Name; } } } }