#region License Information /* HeuristicLab * Copyright (C) 2002-2011 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 HeuristicLab.Analysis; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Optimization; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis.Symbolic; namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Analyzers { /// /// An operator that analyzes the validation best scaled symbolic regression solution. /// [Item("FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer", "An operator that analyzes the validation best scaled symbolic regression solution.")] [StorableClass] public sealed class FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer : SymbolicRegressionValidationAnalyzer, ISymbolicRegressionAnalyzer { private const string ApplyLinearScalingParameterName = "ApplyLinearScaling"; private const string MaximizationParameterName = "Maximization"; private const string CalculateSolutionComplexityParameterName = "CalculateSolutionComplexity"; private const string BestSolutionParameterName = "Best solution (validation)"; private const string BestSolutionQualityParameterName = "Best solution quality (validation)"; private const string BestSolutionLengthParameterName = "Best solution length (validation)"; private const string BestSolutionHeightParameterName = "Best solution height (validiation)"; private const string CurrentBestValidationQualityParameterName = "Current best validation quality"; private const string BestSolutionQualityValuesParameterName = "Validation Quality"; private const string ResultsParameterName = "Results"; private const string VariableFrequenciesParameterName = "VariableFrequencies"; private const string BestKnownQualityParameterName = "BestKnownQuality"; private const string GenerationsParameterName = "Generations"; #region parameter properties public ILookupParameter MaximizationParameter { get { return (ILookupParameter)Parameters[MaximizationParameterName]; } } public IValueParameter CalculateSolutionComplexityParameter { get { return (IValueParameter)Parameters[CalculateSolutionComplexityParameterName]; } } public ILookupParameter BestSolutionParameter { get { return (ILookupParameter)Parameters[BestSolutionParameterName]; } } public ILookupParameter GenerationsParameter { get { return (ILookupParameter)Parameters[GenerationsParameterName]; } } public ILookupParameter BestSolutionQualityParameter { get { return (ILookupParameter)Parameters[BestSolutionQualityParameterName]; } } public ILookupParameter BestSolutionLengthParameter { get { return (ILookupParameter)Parameters[BestSolutionLengthParameterName]; } } public ILookupParameter BestSolutionHeightParameter { get { return (ILookupParameter)Parameters[BestSolutionHeightParameterName]; } } public ILookupParameter ResultsParameter { get { return (ILookupParameter)Parameters[ResultsParameterName]; } } public ILookupParameter BestKnownQualityParameter { get { return (ILookupParameter)Parameters[BestKnownQualityParameterName]; } } public ILookupParameter VariableFrequenciesParameter { get { return (ILookupParameter)Parameters[VariableFrequenciesParameterName]; } } public IValueLookupParameter ApplyLinearScalingParameter { get { return (IValueLookupParameter)Parameters[ApplyLinearScalingParameterName]; } } #endregion #region properties public BoolValue Maximization { get { return MaximizationParameter.ActualValue; } } public BoolValue CalculateSolutionComplexity { get { return CalculateSolutionComplexityParameter.Value; } set { CalculateSolutionComplexityParameter.Value = value; } } public ResultCollection Results { get { return ResultsParameter.ActualValue; } } public DataTable VariableFrequencies { get { return VariableFrequenciesParameter.ActualValue; } } public IntValue Generations { get { return GenerationsParameter.ActualValue; } } public DoubleValue BestSolutionQuality { get { return BestSolutionQualityParameter.ActualValue; } } public IntValue BestSolutionLength { get { return BestSolutionLengthParameter.ActualValue; } set { BestSolutionLengthParameter.ActualValue = value; } } public IntValue BestSolutionHeight { get { return BestSolutionHeightParameter.ActualValue; } set { BestSolutionHeightParameter.ActualValue = value; } } public BoolValue ApplyLinearScaling { get { return ApplyLinearScalingParameter.ActualValue; } set { ApplyLinearScalingParameter.ActualValue = value; } } #endregion [StorableConstructor] private FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer(bool deserializing) : base(deserializing) { } private FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer(FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { } public FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer() : base() { Parameters.Add(new ValueLookupParameter(ApplyLinearScalingParameterName, "The switch determines if the best solution should be linearly scaled on the whole training set.", new BoolValue(true))); Parameters.Add(new LookupParameter(MaximizationParameterName, "The direction of optimization.")); Parameters.Add(new ValueParameter(CalculateSolutionComplexityParameterName, "Determines if the length and height of the validation best solution should be calculated.", new BoolValue(true))); Parameters.Add(new LookupParameter(BestSolutionParameterName, "The best symbolic regression solution.")); Parameters.Add(new LookupParameter(GenerationsParameterName, "The number of generations calculated so far.")); Parameters.Add(new LookupParameter(BestSolutionQualityParameterName, "The quality of the best symbolic regression solution.")); Parameters.Add(new LookupParameter(BestSolutionLengthParameterName, "The length of the best symbolic regression solution.")); Parameters.Add(new LookupParameter(BestSolutionHeightParameterName, "The height of the best symbolic regression solution.")); Parameters.Add(new LookupParameter(ResultsParameterName, "The result collection where the best symbolic regression solution should be stored.")); Parameters.Add(new LookupParameter(BestKnownQualityParameterName, "The best known (validation) quality achieved on the data set.")); Parameters.Add(new LookupParameter(VariableFrequenciesParameterName, "The variable frequencies table to use for the calculation of variable impacts")); } public override IDeepCloneable Clone(Cloner cloner) { return new FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer(this, cloner); } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { #region compatibility remove before releasing 3.4 if (!Parameters.ContainsKey("Evaluator")) { Parameters.Add(new LookupParameter("Evaluator", "The evaluator which should be used to evaluate the solution on the validation set.")); } if (!Parameters.ContainsKey(MaximizationParameterName)) { Parameters.Add(new LookupParameter(MaximizationParameterName, "The direction of optimization.")); } if (!Parameters.ContainsKey(CalculateSolutionComplexityParameterName)) { Parameters.Add(new ValueParameter(CalculateSolutionComplexityParameterName, "Determines if the length and height of the validation best solution should be calculated.", new BoolValue(false))); } if (!Parameters.ContainsKey(BestSolutionLengthParameterName)) { Parameters.Add(new LookupParameter(BestSolutionLengthParameterName, "The length of the best symbolic regression solution.")); } if (!Parameters.ContainsKey(BestSolutionHeightParameterName)) { Parameters.Add(new LookupParameter(BestSolutionHeightParameterName, "The height of the best symbolic regression solution.")); } if (!Parameters.ContainsKey(ApplyLinearScalingParameterName)) { Parameters.Add(new ValueLookupParameter(ApplyLinearScalingParameterName, "The switch determines if the best solution should be linearly scaled on the whole training set.", new BoolValue(true))); } #endregion } protected override void Analyze(SymbolicExpressionTree[] trees, double[] validationQuality) { double bestQuality = Maximization.Value ? double.NegativeInfinity : double.PositiveInfinity; SymbolicExpressionTree bestTree = null; for (int i = 0; i < trees.Length; i++) { double quality = validationQuality[i]; if ((Maximization.Value && quality > bestQuality) || (!Maximization.Value && quality < bestQuality)) { bestQuality = quality; bestTree = trees[i]; } } // if the best validation tree is better than the current best solution => update bool newBest = BestSolutionQuality == null || (Maximization.Value && bestQuality > BestSolutionQuality.Value) || (!Maximization.Value && bestQuality < BestSolutionQuality.Value); if (newBest) { double upperEstimationLimit = UpperEstimationLimit != null ? UpperEstimationLimit.Value : double.PositiveInfinity; double lowerEstimationLimit = LowerEstimationLimit != null ? LowerEstimationLimit.Value : double.NegativeInfinity; string targetVariable = ProblemData.TargetVariable.Value; if (ApplyLinearScaling.Value) { // calculate scaling parameters and only for the best tree using the full training set double alpha, beta; SymbolicRegressionScaledMeanSquaredErrorEvaluator.Calculate(SymbolicExpressionTreeInterpreter, bestTree, lowerEstimationLimit, upperEstimationLimit, ProblemData.Dataset, targetVariable, ProblemData.TrainingIndizes, out beta, out alpha); // scale tree for solution bestTree = SymbolicRegressionSolutionLinearScaler.Scale(bestTree, alpha, beta); } var model = new SymbolicRegressionModel((ISymbolicExpressionTreeInterpreter)SymbolicExpressionTreeInterpreter.Clone(), bestTree); var solution = new SymbolicRegressionSolution((DataAnalysisProblemData)ProblemData.Clone(), model, lowerEstimationLimit, upperEstimationLimit); solution.Name = BestSolutionParameterName; solution.Description = "Best solution on validation partition found over the whole run."; BestSolutionParameter.ActualValue = solution; BestSolutionQualityParameter.ActualValue = new DoubleValue(bestQuality); if (CalculateSolutionComplexity.Value) { BestSolutionLength = new IntValue(solution.Model.SymbolicExpressionTree.Size); BestSolutionHeight = new IntValue(solution.Model.SymbolicExpressionTree.Height); if (!Results.ContainsKey(BestSolutionLengthParameterName)) { Results.Add(new Result(BestSolutionLengthParameterName, "Length of the best solution on the validation set", new IntValue())); Results.Add(new Result(BestSolutionHeightParameterName, "Height of the best solution on the validation set", new IntValue())); } Results[BestSolutionLengthParameterName].Value = BestSolutionLength; Results[BestSolutionHeightParameterName].Value = BestSolutionHeight; } BestSymbolicRegressionSolutionAnalyzer.UpdateBestSolutionResults(solution, ProblemData, Results, Generations, VariableFrequencies); } if (!Results.ContainsKey(BestSolutionQualityValuesParameterName)) { Results.Add(new Result(BestSolutionQualityValuesParameterName, new DataTable(BestSolutionQualityValuesParameterName, BestSolutionQualityValuesParameterName))); Results.Add(new Result(BestSolutionQualityParameterName, new DoubleValue())); Results.Add(new Result(CurrentBestValidationQualityParameterName, new DoubleValue())); } Results[BestSolutionQualityParameterName].Value = new DoubleValue(BestSolutionQualityParameter.ActualValue.Value); Results[CurrentBestValidationQualityParameterName].Value = new DoubleValue(bestQuality); DataTable validationValues = (DataTable)Results[BestSolutionQualityValuesParameterName].Value; AddValue(validationValues, BestSolutionQualityParameter.ActualValue.Value, BestSolutionQualityParameterName, BestSolutionQualityParameterName); AddValue(validationValues, bestQuality, CurrentBestValidationQualityParameterName, CurrentBestValidationQualityParameterName); } private static void AddValue(DataTable table, double data, string name, string description) { DataRow row; table.Rows.TryGetValue(name, out row); if (row == null) { row = new DataRow(name, description); row.Values.Add(data); table.Rows.Add(row); } else { row.Values.Add(data); } } } }