1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2016 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("Regression Ensemble Model", "A regression model that contains an ensemble of multiple regression models")]
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35 | [Creatable(CreatableAttribute.Categories.DataAnalysisEnsembles, Priority = 100)]
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36 | public sealed class RegressionEnsembleModel : RegressionModel, IRegressionEnsembleModel {
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37 | public override IEnumerable<string> VariablesUsedForPrediction {
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38 | get { return models.SelectMany(x => x.VariablesUsedForPrediction).Distinct().OrderBy(x => x); }
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39 | }
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40 |
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41 | private List<IRegressionModel> models;
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42 | public IEnumerable<IRegressionModel> Models {
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43 | get { return new List<IRegressionModel>(models); }
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44 | }
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45 |
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46 | [Storable(Name = "Models")]
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47 | private IEnumerable<IRegressionModel> StorableModels {
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48 | get { return models; }
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49 | set { models = value.ToList(); }
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50 | }
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51 |
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52 | private List<double> modelWeights;
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53 | public IEnumerable<double> ModelWeights {
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54 | get { return modelWeights; }
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55 | }
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56 |
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57 | [Storable(Name = "ModelWeights")]
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58 | private IEnumerable<double> StorableModelWeights {
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59 | get { return modelWeights; }
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60 | set { modelWeights = value.ToList(); }
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61 | }
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62 |
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63 | [Storable]
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64 | private bool averageModelEstimates = true;
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65 | public bool AverageModelEstimates {
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66 | get { return averageModelEstimates; }
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67 | set {
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68 | if (averageModelEstimates != value) {
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69 | averageModelEstimates = value;
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70 | OnChanged();
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71 | }
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72 | }
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73 | }
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74 |
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75 | #region backwards compatiblity 3.3.5
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76 | [Storable(Name = "models", AllowOneWay = true)]
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77 | private List<IRegressionModel> OldStorableModels {
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78 | set { models = value; }
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79 | }
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80 | #endregion
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81 |
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82 | [StorableHook(HookType.AfterDeserialization)]
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83 | private void AfterDeserialization() {
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84 | // BackwardsCompatibility 3.3.14
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85 | #region Backwards compatible code, remove with 3.4
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86 | if (modelWeights == null || !modelWeights.Any())
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87 | modelWeights = new List<double>(models.Select(m => 1.0));
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88 | #endregion
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89 | }
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90 |
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91 | [StorableConstructor]
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92 | private RegressionEnsembleModel(bool deserializing) : base(deserializing) { }
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93 | private RegressionEnsembleModel(RegressionEnsembleModel original, Cloner cloner)
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94 | : base(original, cloner) {
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95 | this.models = original.Models.Select(cloner.Clone).ToList();
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96 | this.modelWeights = new List<double>(original.ModelWeights);
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97 | this.averageModelEstimates = original.averageModelEstimates;
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98 | }
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99 | public override IDeepCloneable Clone(Cloner cloner) {
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100 | return new RegressionEnsembleModel(this, cloner);
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101 | }
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102 |
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103 | public RegressionEnsembleModel() : this(Enumerable.Empty<IRegressionModel>()) { }
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104 | public RegressionEnsembleModel(IEnumerable<IRegressionModel> models) : this(models, models.Select(m => 1.0)) { }
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105 | public RegressionEnsembleModel(IEnumerable<IRegressionModel> models, IEnumerable<double> modelWeights)
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106 | : base(string.Empty) {
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107 | this.name = ItemName;
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108 | this.description = ItemDescription;
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109 |
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110 | this.models = new List<IRegressionModel>(models);
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111 | this.modelWeights = new List<double>(modelWeights);
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112 |
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113 | if (this.models.Any()) this.TargetVariable = this.models.First().TargetVariable;
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114 | }
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115 |
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116 | public void Add(IRegressionModel model) {
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117 | if (string.IsNullOrEmpty(TargetVariable)) TargetVariable = model.TargetVariable;
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118 | Add(model, 1.0);
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119 | }
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120 | public void Add(IRegressionModel model, double weight) {
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121 | if (string.IsNullOrEmpty(TargetVariable)) TargetVariable = model.TargetVariable;
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122 |
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123 | models.Add(model);
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124 | modelWeights.Add(weight);
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125 | OnChanged();
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126 | }
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127 |
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128 | public void AddRange(IEnumerable<IRegressionModel> models) {
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129 | AddRange(models, models.Select(m => 1.0));
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130 | }
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131 | public void AddRange(IEnumerable<IRegressionModel> models, IEnumerable<double> weights) {
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132 | if (string.IsNullOrEmpty(TargetVariable)) TargetVariable = models.First().TargetVariable;
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133 |
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134 | this.models.AddRange(models);
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135 | modelWeights.AddRange(weights);
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136 | OnChanged();
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137 | }
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138 |
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139 | public void Remove(IRegressionModel model) {
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140 | var index = models.IndexOf(model);
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141 | models.RemoveAt(index);
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142 | modelWeights.RemoveAt(index);
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143 |
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144 | if (!models.Any()) TargetVariable = string.Empty;
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145 | OnChanged();
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146 | }
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147 | public void RemoveRange(IEnumerable<IRegressionModel> models) {
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148 | foreach (var model in models) {
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149 | var index = this.models.IndexOf(model);
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150 | this.models.RemoveAt(index);
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151 | modelWeights.RemoveAt(index);
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152 | }
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153 |
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154 | if (!models.Any()) TargetVariable = string.Empty;
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155 | OnChanged();
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156 | }
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157 |
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158 | public double GetModelWeight(IRegressionModel model) {
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159 | var index = models.IndexOf(model);
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160 | return modelWeights[index];
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161 | }
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162 | public void SetModelWeight(IRegressionModel model, double weight) {
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163 | var index = models.IndexOf(model);
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164 | modelWeights[index] = weight;
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165 | OnChanged();
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166 | }
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167 |
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168 | #region evaluation
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169 | public IEnumerable<IEnumerable<double>> GetEstimatedValueVectors(IDataset dataset, IEnumerable<int> rows) {
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170 | var estimatedValuesEnumerators = (from model in models
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171 | let weight = GetModelWeight(model)
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172 | select model.GetEstimatedValues(dataset, rows).Select(e => weight * e)
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173 | .GetEnumerator()).ToList();
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174 |
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175 | while (estimatedValuesEnumerators.All(en => en.MoveNext())) {
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176 | yield return from enumerator in estimatedValuesEnumerators
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177 | select enumerator.Current;
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178 | }
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179 | }
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180 |
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181 | public override IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
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182 | double weightsSum = modelWeights.Sum();
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183 | var summedEstimates = from estimatedValuesVector in GetEstimatedValueVectors(dataset, rows)
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184 | select estimatedValuesVector.DefaultIfEmpty(double.NaN).Sum();
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185 |
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186 | if (AverageModelEstimates)
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187 | return summedEstimates.Select(v => v / weightsSum);
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188 | else
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189 | return summedEstimates;
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190 |
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191 | }
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192 |
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193 | public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows, Func<int, IRegressionModel, bool> modelSelectionPredicate) {
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194 | var estimatedValuesEnumerators = GetEstimatedValueVectors(dataset, rows).GetEnumerator();
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195 | var rowsEnumerator = rows.GetEnumerator();
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196 |
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197 | while (rowsEnumerator.MoveNext() & estimatedValuesEnumerators.MoveNext()) {
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198 | var estimatedValueEnumerator = estimatedValuesEnumerators.Current.GetEnumerator();
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199 | int currentRow = rowsEnumerator.Current;
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200 | double weightsSum = 0.0;
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201 | double filteredEstimatesSum = 0.0;
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202 |
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203 | for (int m = 0; m < models.Count; m++) {
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204 | estimatedValueEnumerator.MoveNext();
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205 | var model = models[m];
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206 | if (!modelSelectionPredicate(currentRow, model)) continue;
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207 |
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208 | filteredEstimatesSum += estimatedValueEnumerator.Current;
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209 | weightsSum += modelWeights[m];
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210 | }
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211 |
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212 | if (AverageModelEstimates)
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213 | yield return filteredEstimatesSum / weightsSum;
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214 | else
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215 | yield return filteredEstimatesSum;
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216 | }
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217 | }
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218 |
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219 | #endregion
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220 |
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221 | public event EventHandler Changed;
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222 | private void OnChanged() {
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223 | var handler = Changed;
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224 | if (handler != null)
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225 | handler(this, EventArgs.Empty);
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226 | }
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227 |
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228 |
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229 | public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
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230 | return new RegressionEnsembleSolution(this, new RegressionEnsembleProblemData(problemData));
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231 | }
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232 | }
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233 | }
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