#region License Information /* HeuristicLab * Copyright (C) 2002-2013 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.Drawing; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis.Symbolic { /// /// Abstract base class for symbolic data analysis models /// [StorableClass] public abstract class SymbolicDataAnalysisModel : NamedItem, ISymbolicDataAnalysisModel { public static new Image StaticItemImage { get { return HeuristicLab.Common.Resources.VSImageLibrary.Function; } } #region properties [Storable] private double lowerEstimationLimit; public double LowerEstimationLimit { get { return lowerEstimationLimit; } } [Storable] private double upperEstimationLimit; public double UpperEstimationLimit { get { return upperEstimationLimit; } } [Storable] private ISymbolicExpressionTree symbolicExpressionTree; public ISymbolicExpressionTree SymbolicExpressionTree { get { return symbolicExpressionTree; } } [Storable] private ISymbolicDataAnalysisExpressionTreeInterpreter interpreter; public ISymbolicDataAnalysisExpressionTreeInterpreter Interpreter { get { return interpreter; } } #endregion [StorableConstructor] protected SymbolicDataAnalysisModel(bool deserializing) : base(deserializing) { } protected SymbolicDataAnalysisModel(SymbolicDataAnalysisModel original, Cloner cloner) : base(original, cloner) { this.symbolicExpressionTree = cloner.Clone(original.symbolicExpressionTree); this.interpreter = cloner.Clone(original.interpreter); this.lowerEstimationLimit = original.lowerEstimationLimit; this.upperEstimationLimit = original.upperEstimationLimit; } protected SymbolicDataAnalysisModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, double lowerEstimationLimit, double upperEstimationLimit) : base() { this.name = ItemName; this.description = ItemDescription; this.symbolicExpressionTree = tree; this.interpreter = interpreter; this.lowerEstimationLimit = lowerEstimationLimit; this.upperEstimationLimit = upperEstimationLimit; } #region Scaling protected void Scale(IDataAnalysisProblemData problemData, string targetVariable) { var dataset = problemData.Dataset; var rows = problemData.TrainingIndices; var estimatedValues = Interpreter.GetSymbolicExpressionTreeValues(SymbolicExpressionTree, dataset, rows); var targetValues = dataset.GetDoubleValues(targetVariable, rows); var linearScalingCalculator = new OnlineLinearScalingParameterCalculator(); var targetValuesEnumerator = targetValues.GetEnumerator(); var estimatedValuesEnumerator = estimatedValues.GetEnumerator(); while (targetValuesEnumerator.MoveNext() & estimatedValuesEnumerator.MoveNext()) { double target = targetValuesEnumerator.Current; double estimated = estimatedValuesEnumerator.Current; if (!double.IsNaN(estimated) && !double.IsInfinity(estimated)) linearScalingCalculator.Add(estimated, target); } if (linearScalingCalculator.ErrorState == OnlineCalculatorError.None && (targetValuesEnumerator.MoveNext() || estimatedValuesEnumerator.MoveNext())) throw new ArgumentException("Number of elements in target and estimated values enumeration do not match."); double alpha = linearScalingCalculator.Alpha; double beta = linearScalingCalculator.Beta; if (linearScalingCalculator.ErrorState != OnlineCalculatorError.None) return; ConstantTreeNode alphaTreeNode = null; ConstantTreeNode betaTreeNode = null; // check if model has been scaled previously by analyzing the structure of the tree var startNode = SymbolicExpressionTree.Root.GetSubtree(0); if (startNode.GetSubtree(0).Symbol is Addition) { var addNode = startNode.GetSubtree(0); if (addNode.SubtreeCount == 2 && addNode.GetSubtree(0).Symbol is Multiplication && addNode.GetSubtree(1).Symbol is Constant) { alphaTreeNode = addNode.GetSubtree(1) as ConstantTreeNode; var mulNode = addNode.GetSubtree(0); if (mulNode.SubtreeCount == 2 && mulNode.GetSubtree(1).Symbol is Constant) { betaTreeNode = mulNode.GetSubtree(1) as ConstantTreeNode; } } } // if tree structure matches the structure necessary for linear scaling then reuse the existing tree nodes if (alphaTreeNode != null && betaTreeNode != null) { betaTreeNode.Value *= beta; alphaTreeNode.Value *= beta; alphaTreeNode.Value += alpha; } else { var mainBranch = startNode.GetSubtree(0); startNode.RemoveSubtree(0); var scaledMainBranch = MakeSum(MakeProduct(mainBranch, beta), alpha); startNode.AddSubtree(scaledMainBranch); } } private static ISymbolicExpressionTreeNode MakeSum(ISymbolicExpressionTreeNode treeNode, double alpha) { if (alpha.IsAlmost(0.0)) { return treeNode; } else { var addition = new Addition(); var node = addition.CreateTreeNode(); var alphaConst = MakeConstant(alpha); node.AddSubtree(treeNode); node.AddSubtree(alphaConst); return node; } } private static ISymbolicExpressionTreeNode MakeProduct(ISymbolicExpressionTreeNode treeNode, double beta) { if (beta.IsAlmost(1.0)) { return treeNode; } else { var multipliciation = new Multiplication(); var node = multipliciation.CreateTreeNode(); var betaConst = MakeConstant(beta); node.AddSubtree(treeNode); node.AddSubtree(betaConst); return node; } } private static ISymbolicExpressionTreeNode MakeConstant(double c) { var node = (ConstantTreeNode)(new Constant()).CreateTreeNode(); node.Value = c; return node; } #endregion } }