#region License Information /* HeuristicLab * Copyright (C) 2002-2016 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; using System.Collections.Generic; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression { [StorableClass] [Item("SymbolicRegressionSolutionImpactValuesCalculator", "Calculate symbolic expression tree node impact values for regression problems.")] public class SymbolicRegressionSolutionImpactValuesCalculator : SymbolicDataAnalysisSolutionImpactValuesCalculator { public SymbolicRegressionSolutionImpactValuesCalculator() { } protected SymbolicRegressionSolutionImpactValuesCalculator(SymbolicRegressionSolutionImpactValuesCalculator original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionSolutionImpactValuesCalculator(this, cloner); } [StorableConstructor] protected SymbolicRegressionSolutionImpactValuesCalculator(bool deserializing) : base(deserializing) { } public override double CalculateReplacementValue(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData, IEnumerable rows) { var regressionModel = (ISymbolicRegressionModel)model; var regressionProblemData = (IRegressionProblemData)problemData; return CalculateReplacementValue(node, regressionModel.SymbolicExpressionTree, regressionModel.Interpreter, regressionProblemData.Dataset, rows); } public override double CalculateImpactValue(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData, IEnumerable rows, double qualityForImpactsCalculation = double.NaN) { double impactValue, replacementValue, newQualityForImpactsCalculation; CalculateImpactAndReplacementValues(model, node, problemData, rows, out impactValue, out replacementValue, out newQualityForImpactsCalculation, qualityForImpactsCalculation); return impactValue; } public override void CalculateImpactAndReplacementValues(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData, IEnumerable rows, out double impactValue, out double replacementValue, out double newQualityForImpactsCalculation, double qualityForImpactsCalculation = Double.NaN) { var regressionModel = (ISymbolicRegressionModel)model; var regressionProblemData = (IRegressionProblemData)problemData; var dataset = regressionProblemData.Dataset; var targetValues = dataset.GetDoubleValues(regressionProblemData.TargetVariable, rows); OnlineCalculatorError errorState; if (double.IsNaN(qualityForImpactsCalculation)) qualityForImpactsCalculation = CalculateQualityForImpacts(regressionModel, regressionProblemData, rows); replacementValue = CalculateReplacementValue(regressionModel, node, regressionProblemData, rows); var constantNode = new ConstantTreeNode(new Constant()) { Value = replacementValue }; var cloner = new Cloner(); var tempModel = cloner.Clone(regressionModel); var tempModelNode = (ISymbolicExpressionTreeNode)cloner.GetClone(node); var tempModelParentNode = tempModelNode.Parent; int i = tempModelParentNode.IndexOfSubtree(tempModelNode); tempModelParentNode.RemoveSubtree(i); tempModelParentNode.InsertSubtree(i, constantNode); var estimatedValues = tempModel.GetEstimatedValues(dataset, rows); double r = OnlinePearsonsRCalculator.Calculate(targetValues, estimatedValues, out errorState); if (errorState != OnlineCalculatorError.None) r = 0.0; newQualityForImpactsCalculation = r * r; impactValue = qualityForImpactsCalculation - newQualityForImpactsCalculation; } public static double CalculateQualityForImpacts(ISymbolicRegressionModel model, IRegressionProblemData problemData, IEnumerable rows) { var estimatedValues = model.GetEstimatedValues(problemData.Dataset, rows); // also bounds the values var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows); OnlineCalculatorError errorState; var r = OnlinePearsonsRCalculator.Calculate(targetValues, estimatedValues, out errorState); var quality = r * r; if (errorState != OnlineCalculatorError.None) return double.NaN; return quality; } } }