1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2015 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;
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23 | using System.Collections.Generic;
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24 | using System.Linq;
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25 | using HeuristicLab.Common;
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26 | using HeuristicLab.Core;
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27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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28 |
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29 | namespace HeuristicLab.Problems.DataAnalysis {
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30 | /// <summary>
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31 | /// Represents regression solutions that contain an ensemble of multiple regression models
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32 | /// </summary>
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33 | [StorableClass]
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34 | [Item("RegressionEnsembleModel", "A regression model that contains an ensemble of multiple regression models")]
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35 | public class RegressionEnsembleModel : NamedItem, IRegressionEnsembleModel {
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36 |
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37 | private List<IRegressionModel> models;
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38 | public IEnumerable<IRegressionModel> Models {
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39 | get { return new List<IRegressionModel>(models); }
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40 | }
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41 |
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42 | [Storable(Name = "Models")]
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43 | private IEnumerable<IRegressionModel> StorableModels {
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44 | get { return models; }
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45 | set { models = value.ToList(); }
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46 | }
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47 |
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48 | #region backwards compatiblity 3.3.5
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49 | [Storable(Name = "models", AllowOneWay = true)]
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50 | private List<IRegressionModel> OldStorableModels {
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51 | set { models = value; }
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52 | }
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53 | #endregion
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54 |
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55 | [StorableConstructor]
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56 | protected RegressionEnsembleModel(bool deserializing) : base(deserializing) { }
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57 | protected RegressionEnsembleModel(RegressionEnsembleModel original, Cloner cloner)
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58 | : base(original, cloner) {
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59 | this.models = original.Models.Select(m => cloner.Clone(m)).ToList();
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60 | }
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61 |
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62 | public RegressionEnsembleModel() : this(Enumerable.Empty<IRegressionModel>()) { }
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63 | public RegressionEnsembleModel(IEnumerable<IRegressionModel> models)
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64 | : base() {
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65 | this.name = ItemName;
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66 | this.description = ItemDescription;
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67 | this.models = new List<IRegressionModel>(models);
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68 | }
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69 |
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70 | public override IDeepCloneable Clone(Cloner cloner) {
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71 | return new RegressionEnsembleModel(this, cloner);
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72 | }
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73 |
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74 | #region IRegressionEnsembleModel Members
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75 |
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76 | public void Add(IRegressionModel model) {
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77 | models.Add(model);
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78 | }
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79 | public void Remove(IRegressionModel model) {
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80 | models.Remove(model);
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81 | }
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82 |
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83 | public IEnumerable<IEnumerable<double>> GetEstimatedValueVectors(IDataset dataset, IEnumerable<int> rows) {
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84 | var estimatedValuesEnumerators = (from model in models
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85 | select model.GetEstimatedValues(dataset, rows).GetEnumerator())
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86 | .ToList();
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87 |
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88 | while (estimatedValuesEnumerators.All(en => en.MoveNext())) {
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89 | yield return from enumerator in estimatedValuesEnumerators
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90 | select enumerator.Current;
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91 | }
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92 | }
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93 |
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94 | public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows, Func<int, IRegressionModel, bool> modelSelectionPredicate) {
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95 | var estimatedValuesEnumerators = GetEstimatedValueVectors(dataset, rows).GetEnumerator();
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96 | var rowsEnumerator = rows.GetEnumerator();
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97 |
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98 | // aggregate to make sure that MoveNext is called for all enumerators
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99 | while (rowsEnumerator.MoveNext() & estimatedValuesEnumerators.MoveNext()) {
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100 | int currentRow = rowsEnumerator.Current;
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101 |
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102 | var filteredEstimates = models.Zip(estimatedValuesEnumerators.Current,
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103 | (m, e) => new { Model = m, EstimatedValue = e }).Where(f => modelSelectionPredicate(currentRow, f.Model));
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104 |
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105 | yield return filteredEstimates.Select(f => f.EstimatedValue).DefaultIfEmpty(double.NaN).Average();
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106 | }
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107 | }
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108 |
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109 | #endregion
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110 |
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111 | #region IRegressionModel Members
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112 | public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
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113 | foreach (var estimatedValuesVector in GetEstimatedValueVectors(dataset, rows)) {
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114 | yield return estimatedValuesVector.Average();
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115 | }
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116 | }
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117 |
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118 | public RegressionEnsembleSolution CreateRegressionSolution(IRegressionProblemData problemData) {
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119 | return new RegressionEnsembleSolution(this.Models, new RegressionEnsembleProblemData(problemData));
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120 | }
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121 | IRegressionSolution IRegressionModel.CreateRegressionSolution(IRegressionProblemData problemData) {
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122 | return CreateRegressionSolution(problemData);
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123 | }
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124 | #endregion
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125 | }
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126 | }
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