1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2014 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.Collections;
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26 | using HeuristicLab.Common;
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27 | using HeuristicLab.Core;
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28 | using HeuristicLab.Data;
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29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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30 |
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31 | namespace HeuristicLab.Problems.DataAnalysis {
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32 | /// <summary>
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33 | /// Represents regression solutions that contain an ensemble of multiple regression models
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34 | /// </summary>
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35 | [StorableClass]
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36 | [Item("Regression Ensemble Solution", "A regression solution that contains an ensemble of multiple regression models")]
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37 | [Creatable("Data Analysis - Ensembles")]
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38 | public sealed class RegressionEnsembleSolution : RegressionSolutionBase, IRegressionEnsembleSolution {
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39 | private readonly Dictionary<int, double> trainingEvaluationCache = new Dictionary<int, double>();
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40 | private readonly Dictionary<int, double> testEvaluationCache = new Dictionary<int, double>();
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41 | private readonly Dictionary<int, double> evaluationCache = new Dictionary<int, double>();
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42 |
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43 | public new IRegressionEnsembleModel Model {
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44 | get { return (IRegressionEnsembleModel)base.Model; }
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45 | }
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46 |
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47 | public new RegressionEnsembleProblemData ProblemData {
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48 | get { return (RegressionEnsembleProblemData)base.ProblemData; }
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49 | set { base.ProblemData = value; }
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50 | }
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51 |
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52 | private readonly ItemCollection<IRegressionSolution> regressionSolutions;
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53 | public IItemCollection<IRegressionSolution> RegressionSolutions {
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54 | get { return regressionSolutions; }
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55 | }
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56 |
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57 | [Storable]
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58 | private readonly Dictionary<IRegressionModel, IntRange> trainingPartitions;
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59 | [Storable]
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60 | private readonly Dictionary<IRegressionModel, IntRange> testPartitions;
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61 |
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62 | [StorableConstructor]
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63 | private RegressionEnsembleSolution(bool deserializing)
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64 | : base(deserializing) {
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65 | regressionSolutions = new ItemCollection<IRegressionSolution>();
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66 | }
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67 | [StorableHook(HookType.AfterDeserialization)]
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68 | private void AfterDeserialization() {
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69 | foreach (var model in Model.Models) {
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70 | IRegressionProblemData problemData = (IRegressionProblemData)ProblemData.Clone();
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71 | problemData.TrainingPartition.Start = trainingPartitions[model].Start;
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72 | problemData.TrainingPartition.End = trainingPartitions[model].End;
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73 | problemData.TestPartition.Start = testPartitions[model].Start;
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74 | problemData.TestPartition.End = testPartitions[model].End;
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75 |
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76 | regressionSolutions.Add(model.CreateRegressionSolution(problemData));
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77 | }
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78 | RegisterRegressionSolutionsEventHandler();
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79 | }
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80 |
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81 | private RegressionEnsembleSolution(RegressionEnsembleSolution original, Cloner cloner)
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82 | : base(original, cloner) {
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83 | trainingPartitions = new Dictionary<IRegressionModel, IntRange>();
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84 | testPartitions = new Dictionary<IRegressionModel, IntRange>();
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85 | foreach (var pair in original.trainingPartitions) {
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86 | trainingPartitions[cloner.Clone(pair.Key)] = cloner.Clone(pair.Value);
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87 | }
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88 | foreach (var pair in original.testPartitions) {
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89 | testPartitions[cloner.Clone(pair.Key)] = cloner.Clone(pair.Value);
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90 | }
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91 |
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92 | trainingEvaluationCache = new Dictionary<int, double>(original.ProblemData.TrainingIndices.Count());
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93 | testEvaluationCache = new Dictionary<int, double>(original.ProblemData.TestIndices.Count());
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94 |
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95 | regressionSolutions = cloner.Clone(original.regressionSolutions);
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96 | RegisterRegressionSolutionsEventHandler();
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97 | }
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98 |
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99 | public RegressionEnsembleSolution()
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100 | : base(new RegressionEnsembleModel(), RegressionEnsembleProblemData.EmptyProblemData) {
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101 | trainingPartitions = new Dictionary<IRegressionModel, IntRange>();
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102 | testPartitions = new Dictionary<IRegressionModel, IntRange>();
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103 | regressionSolutions = new ItemCollection<IRegressionSolution>();
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104 |
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105 | RegisterRegressionSolutionsEventHandler();
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106 | }
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107 |
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108 | public RegressionEnsembleSolution(IRegressionProblemData problemData)
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109 | : this(Enumerable.Empty<IRegressionModel>(), problemData) {
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110 | }
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111 |
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112 | public RegressionEnsembleSolution(IEnumerable<IRegressionModel> models, IRegressionProblemData problemData)
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113 | : this(models, problemData,
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114 | models.Select(m => (IntRange)problemData.TrainingPartition.Clone()),
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115 | models.Select(m => (IntRange)problemData.TestPartition.Clone())
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116 | ) { }
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117 |
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118 | public RegressionEnsembleSolution(IEnumerable<IRegressionModel> models, IRegressionProblemData problemData, IEnumerable<IntRange> trainingPartitions, IEnumerable<IntRange> testPartitions)
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119 | : base(new RegressionEnsembleModel(Enumerable.Empty<IRegressionModel>()), new RegressionEnsembleProblemData(problemData)) {
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120 | this.trainingPartitions = new Dictionary<IRegressionModel, IntRange>();
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121 | this.testPartitions = new Dictionary<IRegressionModel, IntRange>();
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122 | this.regressionSolutions = new ItemCollection<IRegressionSolution>();
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123 |
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124 | List<IRegressionSolution> solutions = new List<IRegressionSolution>();
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125 | var modelEnumerator = models.GetEnumerator();
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126 | var trainingPartitionEnumerator = trainingPartitions.GetEnumerator();
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127 | var testPartitionEnumerator = testPartitions.GetEnumerator();
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128 |
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129 | while (modelEnumerator.MoveNext() & trainingPartitionEnumerator.MoveNext() & testPartitionEnumerator.MoveNext()) {
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130 | var p = (IRegressionProblemData)problemData.Clone();
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131 | p.TrainingPartition.Start = trainingPartitionEnumerator.Current.Start;
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132 | p.TrainingPartition.End = trainingPartitionEnumerator.Current.End;
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133 | p.TestPartition.Start = testPartitionEnumerator.Current.Start;
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134 | p.TestPartition.End = testPartitionEnumerator.Current.End;
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135 |
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136 | solutions.Add(modelEnumerator.Current.CreateRegressionSolution(p));
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137 | }
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138 | if (modelEnumerator.MoveNext() | trainingPartitionEnumerator.MoveNext() | testPartitionEnumerator.MoveNext()) {
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139 | throw new ArgumentException();
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140 | }
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141 |
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142 | trainingEvaluationCache = new Dictionary<int, double>(problemData.TrainingIndices.Count());
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143 | testEvaluationCache = new Dictionary<int, double>(problemData.TestIndices.Count());
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144 |
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145 | RegisterRegressionSolutionsEventHandler();
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146 | regressionSolutions.AddRange(solutions);
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147 | }
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148 |
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149 | public override IDeepCloneable Clone(Cloner cloner) {
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150 | return new RegressionEnsembleSolution(this, cloner);
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151 | }
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152 | private void RegisterRegressionSolutionsEventHandler() {
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153 | regressionSolutions.ItemsAdded += new CollectionItemsChangedEventHandler<IRegressionSolution>(regressionSolutions_ItemsAdded);
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154 | regressionSolutions.ItemsRemoved += new CollectionItemsChangedEventHandler<IRegressionSolution>(regressionSolutions_ItemsRemoved);
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155 | regressionSolutions.CollectionReset += new CollectionItemsChangedEventHandler<IRegressionSolution>(regressionSolutions_CollectionReset);
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156 | }
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157 |
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158 | #region Evaluation
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159 | public override IEnumerable<double> EstimatedValues {
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160 | get { return GetEstimatedValues(Enumerable.Range(0, ProblemData.Dataset.Rows)); }
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161 | }
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162 |
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163 | public override IEnumerable<double> EstimatedTrainingValues {
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164 | get {
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165 | var rows = ProblemData.TrainingIndices;
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166 | var rowsToEvaluate = rows.Except(trainingEvaluationCache.Keys);
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167 | var rowsEnumerator = rowsToEvaluate.GetEnumerator();
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168 | var valuesEnumerator = GetEstimatedValues(rowsToEvaluate, (r, m) => RowIsTrainingForModel(r, m) && !RowIsTestForModel(r, m)).GetEnumerator();
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169 |
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170 | while (rowsEnumerator.MoveNext() & valuesEnumerator.MoveNext()) {
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171 | trainingEvaluationCache.Add(rowsEnumerator.Current, valuesEnumerator.Current);
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172 | }
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173 |
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174 | return rows.Select(row => trainingEvaluationCache[row]);
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175 | }
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176 | }
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177 |
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178 | public override IEnumerable<double> EstimatedTestValues {
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179 | get {
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180 | var rows = ProblemData.TestIndices;
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181 | var rowsToEvaluate = rows.Except(testEvaluationCache.Keys);
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182 | var rowsEnumerator = rowsToEvaluate.GetEnumerator();
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183 | var valuesEnumerator = GetEstimatedValues(rowsToEvaluate, RowIsTestForModel).GetEnumerator();
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184 |
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185 | while (rowsEnumerator.MoveNext() & valuesEnumerator.MoveNext()) {
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186 | testEvaluationCache.Add(rowsEnumerator.Current, valuesEnumerator.Current);
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187 | }
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188 |
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189 | return rows.Select(row => testEvaluationCache[row]);
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190 | }
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191 | }
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192 |
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193 | private IEnumerable<double> GetEstimatedValues(IEnumerable<int> rows, Func<int, IRegressionModel, bool> modelSelectionPredicate) {
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194 | var estimatedValuesEnumerators = (from model in Model.Models
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195 | select new { Model = model, EstimatedValuesEnumerator = model.GetEstimatedValues(ProblemData.Dataset, rows).GetEnumerator() })
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196 | .ToList();
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197 | var rowsEnumerator = rows.GetEnumerator();
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198 | // aggregate to make sure that MoveNext is called for all enumerators
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199 | while (rowsEnumerator.MoveNext() & estimatedValuesEnumerators.Select(en => en.EstimatedValuesEnumerator.MoveNext()).Aggregate(true, (acc, b) => acc & b)) {
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200 | int currentRow = rowsEnumerator.Current;
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201 |
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202 | var selectedEnumerators = from pair in estimatedValuesEnumerators
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203 | where modelSelectionPredicate(currentRow, pair.Model)
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204 | select pair.EstimatedValuesEnumerator;
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205 |
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206 | yield return AggregateEstimatedValues(selectedEnumerators.Select(x => x.Current));
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207 | }
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208 | }
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209 |
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210 | private bool RowIsTrainingForModel(int currentRow, IRegressionModel model) {
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211 | return trainingPartitions == null || !trainingPartitions.ContainsKey(model) ||
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212 | (trainingPartitions[model].Start <= currentRow && currentRow < trainingPartitions[model].End);
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213 | }
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214 |
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215 | private bool RowIsTestForModel(int currentRow, IRegressionModel model) {
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216 | return testPartitions == null || !testPartitions.ContainsKey(model) ||
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217 | (testPartitions[model].Start <= currentRow && currentRow < testPartitions[model].End);
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218 | }
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219 |
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220 | public override IEnumerable<double> GetEstimatedValues(IEnumerable<int> rows) {
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221 | var rowsToEvaluate = rows.Except(evaluationCache.Keys);
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222 | var rowsEnumerator = rowsToEvaluate.GetEnumerator();
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223 | var valuesEnumerator = (from xs in GetEstimatedValueVectors(ProblemData.Dataset, rowsToEvaluate)
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224 | select AggregateEstimatedValues(xs))
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225 | .GetEnumerator();
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226 |
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227 | while (rowsEnumerator.MoveNext() & valuesEnumerator.MoveNext()) {
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228 | evaluationCache.Add(rowsEnumerator.Current, valuesEnumerator.Current);
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229 | }
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230 |
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231 | return rows.Select(row => evaluationCache[row]);
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232 | }
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233 |
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234 | public IEnumerable<IEnumerable<double>> GetEstimatedValueVectors(Dataset dataset, IEnumerable<int> rows) {
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235 | if (!Model.Models.Any()) yield break;
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236 | var estimatedValuesEnumerators = (from model in Model.Models
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237 | select model.GetEstimatedValues(dataset, rows).GetEnumerator())
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238 | .ToList();
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239 |
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240 | while (estimatedValuesEnumerators.All(en => en.MoveNext())) {
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241 | yield return from enumerator in estimatedValuesEnumerators
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242 | select enumerator.Current;
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243 | }
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244 | }
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245 |
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246 | private double AggregateEstimatedValues(IEnumerable<double> estimatedValues) {
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247 | return estimatedValues.DefaultIfEmpty(double.NaN).Average();
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248 | }
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249 | #endregion
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250 |
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251 | protected override void OnProblemDataChanged() {
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252 | trainingEvaluationCache.Clear();
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253 | testEvaluationCache.Clear();
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254 | evaluationCache.Clear();
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255 | IRegressionProblemData problemData = new RegressionProblemData(ProblemData.Dataset,
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256 | ProblemData.AllowedInputVariables,
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257 | ProblemData.TargetVariable);
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258 | problemData.TrainingPartition.Start = ProblemData.TrainingPartition.Start;
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259 | problemData.TrainingPartition.End = ProblemData.TrainingPartition.End;
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260 | problemData.TestPartition.Start = ProblemData.TestPartition.Start;
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261 | problemData.TestPartition.End = ProblemData.TestPartition.End;
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262 |
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263 | foreach (var solution in RegressionSolutions) {
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264 | if (solution is RegressionEnsembleSolution)
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265 | solution.ProblemData = ProblemData;
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266 | else
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267 | solution.ProblemData = problemData;
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268 | }
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269 | foreach (var trainingPartition in trainingPartitions.Values) {
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270 | trainingPartition.Start = ProblemData.TrainingPartition.Start;
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271 | trainingPartition.End = ProblemData.TrainingPartition.End;
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272 | }
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273 | foreach (var testPartition in testPartitions.Values) {
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274 | testPartition.Start = ProblemData.TestPartition.Start;
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275 | testPartition.End = ProblemData.TestPartition.End;
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276 | }
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277 |
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278 | base.OnProblemDataChanged();
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279 | }
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280 |
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281 | public void AddRegressionSolutions(IEnumerable<IRegressionSolution> solutions) {
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282 | regressionSolutions.AddRange(solutions);
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283 |
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284 | trainingEvaluationCache.Clear();
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285 | testEvaluationCache.Clear();
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286 | evaluationCache.Clear();
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287 | }
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288 | public void RemoveRegressionSolutions(IEnumerable<IRegressionSolution> solutions) {
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289 | regressionSolutions.RemoveRange(solutions);
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290 |
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291 | trainingEvaluationCache.Clear();
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292 | testEvaluationCache.Clear();
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293 | evaluationCache.Clear();
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294 | }
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295 |
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296 | private void regressionSolutions_ItemsAdded(object sender, CollectionItemsChangedEventArgs<IRegressionSolution> e) {
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297 | foreach (var solution in e.Items) AddRegressionSolution(solution);
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298 | RecalculateResults();
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299 | }
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300 | private void regressionSolutions_ItemsRemoved(object sender, CollectionItemsChangedEventArgs<IRegressionSolution> e) {
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301 | foreach (var solution in e.Items) RemoveRegressionSolution(solution);
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302 | RecalculateResults();
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303 | }
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304 | private void regressionSolutions_CollectionReset(object sender, CollectionItemsChangedEventArgs<IRegressionSolution> e) {
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305 | foreach (var solution in e.OldItems) RemoveRegressionSolution(solution);
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306 | foreach (var solution in e.Items) AddRegressionSolution(solution);
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307 | RecalculateResults();
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308 | }
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309 |
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310 | private void AddRegressionSolution(IRegressionSolution solution) {
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311 | if (Model.Models.Contains(solution.Model)) throw new ArgumentException();
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312 | Model.Add(solution.Model);
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313 | trainingPartitions[solution.Model] = solution.ProblemData.TrainingPartition;
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314 | testPartitions[solution.Model] = solution.ProblemData.TestPartition;
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315 |
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316 | trainingEvaluationCache.Clear();
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317 | testEvaluationCache.Clear();
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318 | evaluationCache.Clear();
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319 | }
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320 |
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321 | private void RemoveRegressionSolution(IRegressionSolution solution) {
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322 | if (!Model.Models.Contains(solution.Model)) throw new ArgumentException();
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323 | Model.Remove(solution.Model);
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324 | trainingPartitions.Remove(solution.Model);
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325 | testPartitions.Remove(solution.Model);
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326 |
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327 | trainingEvaluationCache.Clear();
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328 | testEvaluationCache.Clear();
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329 | evaluationCache.Clear();
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330 | }
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331 | }
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332 | }
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