1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System.Collections.Generic;
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23 | using System.Linq;
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24 | using HeuristicLab.Core;
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25 | using HeuristicLab.Data;
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26 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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27 | using HeuristicLab.Parameters;
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28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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29 | using HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Interfaces;
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30 | using HeuristicLab.Problems.DataAnalysis.Regression.Symbolic;
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31 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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32 |
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33 | namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Evaluators {
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34 | [Item("SymbolicVectorRegressionScaledMseEvaluator", "Represents an operator that calculates the scaled mean squared error for all components independently.")]
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35 | [StorableClass]
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36 | public class SymbolicVectorRegressionScaledMseEvaluator : SymbolicVectorRegressionEvaluator, IMultiObjectiveSymbolicVectorRegressionEvaluator {
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37 | private const string QualitiesParameterName = "ScaledMeanSquaredErrors";
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38 | private const string AlphaParameterName = "Alpha";
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39 | private const string BetaParameterName = "Beta";
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40 |
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41 | #region parameter properties
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42 | public ILookupParameter<DoubleArray> QualitiesParameter {
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43 | get { return (ILookupParameter<DoubleArray>)Parameters[QualitiesParameterName]; }
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44 | }
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45 | public ILookupParameter<DoubleArray> AlphaParameter {
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46 | get { return (ILookupParameter<DoubleArray>)Parameters[AlphaParameterName]; }
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47 | }
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48 | public ILookupParameter<DoubleArray> BetaParameter {
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49 | get { return (ILookupParameter<DoubleArray>)Parameters[BetaParameterName]; }
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50 | }
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51 |
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52 | #endregion
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53 |
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54 | public SymbolicVectorRegressionScaledMseEvaluator()
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55 | : base() {
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56 | Parameters.Add(new LookupParameter<DoubleArray>(QualitiesParameterName, "The mean squared errors for each component of the symbolic vector regression solution encoded as a symbolic expression tree."));
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57 | Parameters.Add(new LookupParameter<DoubleArray>(AlphaParameterName, "The alpha parameter for linear scaling."));
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58 | Parameters.Add(new LookupParameter<DoubleArray>(BetaParameterName, "The beta parameter for linear scaling."));
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59 | }
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60 |
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61 | public override void Evaluate(SymbolicExpressionTree tree, ISymbolicExpressionTreeInterpreter interpreter, MultiVariateDataAnalysisProblemData problemData, IEnumerable<string> targetVariables, IEnumerable<int> rows, DoubleArray lowerEstimationBound, DoubleArray upperEstimationBound) {
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62 | List<string> targetVariablesList = targetVariables.ToList();
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63 | DoubleArray qualities = new DoubleArray(targetVariables.Count());
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64 | DoubleArray alpha = new DoubleArray(qualities.Length);
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65 | DoubleArray beta = new DoubleArray(qualities.Length);
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66 | // use only the i-th vector component
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67 | List<SymbolicExpressionTreeNode> componentBranches = new List<SymbolicExpressionTreeNode>(tree.Root.SubTrees[0].SubTrees);
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68 | while (tree.Root.SubTrees[0].SubTrees.Count > 0) tree.Root.SubTrees[0].RemoveSubTree(0);
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69 |
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70 | for (int i = 0; i < targetVariables.Count(); i++) {
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71 | tree.Root.SubTrees[0].AddSubTree(componentBranches[i]);
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72 |
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73 | double compAlpha;
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74 | double compBeta;
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75 | double mse = SymbolicRegressionScaledMeanSquaredErrorEvaluator.Calculate(interpreter, tree,
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76 | lowerEstimationBound[i], upperEstimationBound[i],
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77 | problemData.Dataset, targetVariablesList[i], rows, out compAlpha, out compBeta);
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78 |
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79 | qualities[i] = mse;
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80 | alpha[i] = compAlpha;
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81 | beta[i] = compBeta;
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82 | tree.Root.SubTrees[0].RemoveSubTree(0);
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83 | }
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84 | // restore tree
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85 | foreach (var treeNode in componentBranches) {
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86 | tree.Root.SubTrees[0].AddSubTree(treeNode);
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87 | }
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88 |
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89 | QualitiesParameter.ActualValue = qualities;
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90 | AlphaParameter.ActualValue = alpha;
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91 | BetaParameter.ActualValue = beta;
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92 | }
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93 | }
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94 | }
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