#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 System.Collections.Generic; using System.Linq; using HeuristicLab.Analysis; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Operators; using HeuristicLab.Optimization; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis.Symbolic; namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Analyzers { /// /// A base class for operators that analyze the validation fitness of symbolic regression models. /// [Item("SymbolicRegressionValidationAnalyzer", "A base class for operators that analyze the validation fitness of symbolic regression models.")] [StorableClass] public abstract class SymbolicRegressionValidationAnalyzer : SingleSuccessorOperator { private const string RandomParameterName = "Random"; private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree"; private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter"; private const string ProblemDataParameterName = "ProblemData"; private const string ValidationSamplesStartParameterName = "SamplesStart"; private const string ValidationSamplesEndParameterName = "SamplesEnd"; private const string UpperEstimationLimitParameterName = "UpperEstimationLimit"; private const string LowerEstimationLimitParameterName = "LowerEstimationLimit"; private const string EvaluatorParameterName = "Evaluator"; private const string RelativeNumberOfEvaluatedSamplesParameterName = "RelativeNumberOfEvaluatedSamples"; #region parameter properties public ILookupParameter RandomParameter { get { return (ILookupParameter)Parameters[RandomParameterName]; } } public ScopeTreeLookupParameter SymbolicExpressionTreeParameter { get { return (ScopeTreeLookupParameter)Parameters[SymbolicExpressionTreeParameterName]; } } public IValueLookupParameter SymbolicExpressionTreeInterpreterParameter { get { return (IValueLookupParameter)Parameters[SymbolicExpressionTreeInterpreterParameterName]; } } public ILookupParameter EvaluatorParameter { get { return (ILookupParameter)Parameters[EvaluatorParameterName]; } } public IValueLookupParameter ProblemDataParameter { get { return (IValueLookupParameter)Parameters[ProblemDataParameterName]; } } public IValueLookupParameter ValidationSamplesStartParameter { get { return (IValueLookupParameter)Parameters[ValidationSamplesStartParameterName]; } } public IValueLookupParameter ValidationSamplesEndParameter { get { return (IValueLookupParameter)Parameters[ValidationSamplesEndParameterName]; } } public IValueParameter RelativeNumberOfEvaluatedSamplesParameter { get { return (IValueParameter)Parameters[RelativeNumberOfEvaluatedSamplesParameterName]; } } public IValueLookupParameter UpperEstimationLimitParameter { get { return (IValueLookupParameter)Parameters[UpperEstimationLimitParameterName]; } } public IValueLookupParameter LowerEstimationLimitParameter { get { return (IValueLookupParameter)Parameters[LowerEstimationLimitParameterName]; } } #endregion #region properties public IRandom Random { get { return RandomParameter.ActualValue; } } public ItemArray SymbolicExpressionTree { get { return SymbolicExpressionTreeParameter.ActualValue; } } public ISymbolicExpressionTreeInterpreter SymbolicExpressionTreeInterpreter { get { return SymbolicExpressionTreeInterpreterParameter.ActualValue; } } public ISymbolicRegressionEvaluator Evaluator { get { return EvaluatorParameter.ActualValue; } } public DataAnalysisProblemData ProblemData { get { return ProblemDataParameter.ActualValue; } } public IntValue ValidiationSamplesStart { get { return ValidationSamplesStartParameter.ActualValue; } } public IntValue ValidationSamplesEnd { get { return ValidationSamplesEndParameter.ActualValue; } } public PercentValue RelativeNumberOfEvaluatedSamples { get { return RelativeNumberOfEvaluatedSamplesParameter.Value; } } public DoubleValue UpperEstimationLimit { get { return UpperEstimationLimitParameter.ActualValue; } } public DoubleValue LowerEstimationLimit { get { return LowerEstimationLimitParameter.ActualValue; } } #endregion [StorableConstructor] protected SymbolicRegressionValidationAnalyzer(bool deserializing) : base(deserializing) { } protected SymbolicRegressionValidationAnalyzer(SymbolicRegressionValidationAnalyzer original, Cloner cloner) : base(original, cloner) { } public SymbolicRegressionValidationAnalyzer() : base() { Parameters.Add(new LookupParameter(RandomParameterName, "The random generator to use.")); Parameters.Add(new LookupParameter(EvaluatorParameterName, "The evaluator which should be used to evaluate the solution on the validation set.")); Parameters.Add(new ScopeTreeLookupParameter(SymbolicExpressionTreeParameterName, "The symbolic expression trees to analyze.")); Parameters.Add(new ValueLookupParameter(SymbolicExpressionTreeInterpreterParameterName, "The interpreter that should be used for the analysis of symbolic expression trees.")); Parameters.Add(new ValueLookupParameter(ProblemDataParameterName, "The problem data for which the symbolic expression tree is a solution.")); Parameters.Add(new ValueLookupParameter(ValidationSamplesStartParameterName, "The first index of the validation partition of the data set.")); Parameters.Add(new ValueLookupParameter(ValidationSamplesEndParameterName, "The last index of the validation partition of the data set.")); Parameters.Add(new ValueParameter(RelativeNumberOfEvaluatedSamplesParameterName, "The relative number of samples of the dataset partition, which should be randomly chosen for evaluation between the start and end index.", new PercentValue(1))); Parameters.Add(new ValueLookupParameter(UpperEstimationLimitParameterName, "The upper estimation limit that was set for the evaluation of the symbolic expression trees.")); Parameters.Add(new ValueLookupParameter(LowerEstimationLimitParameterName, "The lower estimation limit that was set for the evaluation of the symbolic expression trees.")); } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { } public override IOperation Apply() { var trees = SymbolicExpressionTree.ToArray(); string targetVariable = ProblemData.TargetVariable.Value; // select a random subset of rows in the validation set int validationStart = ValidiationSamplesStart.Value; int validationEnd = ValidationSamplesEnd.Value; int seed = Random.Next(); int count = (int)((validationEnd - validationStart) * RelativeNumberOfEvaluatedSamples.Value); if (count == 0) count = 1; IEnumerable rows = RandomEnumerable.SampleRandomNumbers(seed, validationStart, validationEnd, count) .Where(row => row < ProblemData.TestSamplesStart.Value || ProblemData.TestSamplesEnd.Value <= row); double upperEstimationLimit = UpperEstimationLimit != null ? UpperEstimationLimit.Value : double.PositiveInfinity; double lowerEstimationLimit = LowerEstimationLimit != null ? LowerEstimationLimit.Value : double.NegativeInfinity; double[] validationQuality = new double[trees.Count()]; for (int i = 0; i < validationQuality.Length; i++) { validationQuality[i] = Evaluator.Evaluate(SymbolicExpressionTreeInterpreter, trees[i], lowerEstimationLimit, upperEstimationLimit, ProblemData.Dataset, targetVariable, rows); } Analyze(trees, validationQuality); return base.Apply(); } protected abstract void Analyze(SymbolicExpressionTree[] trees, double[] validationQuality); } }