#region License Information /* HeuristicLab * Copyright (C) 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.Optimization; using HeuristicLab.Parameters; using HEAL.Attic; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression { [Item("Symbolic Regression Problem (single-objective)", "Represents a single objective symbolic regression problem.")] [StorableType("7DDCF683-96FC-4F70-BF4F-FE3A0B0DE6E0")] [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 100)] public class SymbolicRegressionSingleObjectiveProblem : SymbolicDataAnalysisSingleObjectiveProblem, 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 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 SymbolicRegressionSingleObjectiveProblem(StorableConstructorFlag _) : base(_) { } protected SymbolicRegressionSingleObjectiveProblem(SymbolicRegressionSingleObjectiveProblem original, Cloner cloner) : base(original, cloner) { RegisterEventHandlers(); } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionSingleObjectiveProblem(this, cloner); } public SymbolicRegressionSingleObjectiveProblem() : this(new RegressionProblemData(), new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) { } public SymbolicRegressionSingleObjectiveProblem(IRegressionProblemData problemData, ISymbolicRegressionSingleObjectiveEvaluator evaluator, ISymbolicDataAnalysisSolutionCreator solutionCreator) : base(problemData, evaluator, solutionCreator) { Parameters.Add(new FixedValueParameter(EstimationLimitsParameterName, EstimationLimitsParameterDescription)); EstimationLimitsParameter.Hidden = true; ApplyLinearScalingParameter.Value.Value = true; Maximization.Value = true; MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth; MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength; RegisterEventHandlers(); ConfigureGrammarSymbols(); InitializeOperators(); UpdateEstimationLimits(); } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { RegisterEventHandlers(); // compatibility bool changed = false; if (!Operators.OfType().Any()) { Operators.Add(new SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer()); changed = true; } if (!Operators.OfType().Any()) { Operators.Add(new SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer()); changed = true; } if (!Operators.OfType().Any()) { Operators.Add(new SolutionQualityAnalyzer()); changed = true; } if (!Operators.OfType().Any()) { Operators.Add(new ShapeConstraintsAnalyzer()); changed = true; } if (changed) { ParameterizeOperators(); } } 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 SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer()); Operators.Add(new SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer()); Operators.Add(new SymbolicRegressionSingleObjectiveOverfittingAnalyzer()); Operators.Add(new SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer()); Operators.Add(new SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer()); Operators.Add(new SolutionQualityAnalyzer()); Operators.Add(new SymbolicExpressionTreePhenotypicSimilarityCalculator()); Operators.Add(new ShapeConstraintsAnalyzer()); Operators.Add(new SymbolicRegressionPhenotypicDiversityAnalyzer(Operators.OfType()) { DiversityResultName = "Phenotypic Diversity" }); 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.QualityParameter.ActualName; if (op is SymbolicExpressionTreePhenotypicSimilarityCalculator) { var phenotypicSimilarityCalculator = (SymbolicExpressionTreePhenotypicSimilarityCalculator)op; phenotypicSimilarityCalculator.ProblemData = ProblemData; phenotypicSimilarityCalculator.Interpreter = SymbolicExpressionTreeInterpreter; } } } } }