1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2019 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 HeuristicLab.Common;
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25 | using HeuristicLab.Data;
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26 | using HeuristicLab.Optimization;
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27 | using HEAL.Attic;
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28 | using HEAL.Attic;
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29 |
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30 | namespace HeuristicLab.Problems.DataAnalysis {
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31 | [StorableType("03116F5E-ABBF-4966-9428-E3DC67D1D03D")]
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32 | public abstract class RegressionSolutionBase : DataAnalysisSolution, IRegressionSolution {
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33 | protected const string TrainingMeanSquaredErrorResultName = "Mean squared error (training)";
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34 | protected const string TestMeanSquaredErrorResultName = "Mean squared error (test)";
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35 | protected const string TrainingMeanAbsoluteErrorResultName = "Mean absolute error (training)";
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36 | protected const string TestMeanAbsoluteErrorResultName = "Mean absolute error (test)";
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37 | protected const string TrainingSquaredCorrelationResultName = "Pearson's R² (training)";
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38 | protected const string TestSquaredCorrelationResultName = "Pearson's R² (test)";
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39 | protected const string TrainingRelativeErrorResultName = "Average relative error (training)";
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40 | protected const string TestRelativeErrorResultName = "Average relative error (test)";
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41 | protected const string TrainingNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (training)";
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42 | protected const string TestNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (test)";
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43 | protected const string TrainingRootMeanSquaredErrorResultName = "Root mean squared error (training)";
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44 | protected const string TestRootMeanSquaredErrorResultName = "Root mean squared error (test)";
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45 |
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46 | // BackwardsCompatibility3.3
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47 | #region Backwards compatible code, remove with 3.5
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48 | private const string TrainingMeanErrorResultName = "Mean error (training)";
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49 | private const string TestMeanErrorResultName = "Mean error (test)";
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50 | #endregion
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51 |
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52 |
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53 | protected const string TrainingMeanSquaredErrorResultDescription = "Mean of squared errors of the model on the training partition";
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54 | protected const string TestMeanSquaredErrorResultDescription = "Mean of squared errors of the model on the test partition";
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55 | protected const string TrainingMeanAbsoluteErrorResultDescription = "Mean of absolute errors of the model on the training partition";
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56 | protected const string TestMeanAbsoluteErrorResultDescription = "Mean of absolute errors of the model on the test partition";
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57 | protected const string TrainingSquaredCorrelationResultDescription = "Squared Pearson's correlation coefficient of the model output and the actual values on the training partition";
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58 | protected const string TestSquaredCorrelationResultDescription = "Squared Pearson's correlation coefficient of the model output and the actual values on the test partition";
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59 | protected const string TrainingRelativeErrorResultDescription = "Average of the relative errors of the model output and the actual values on the training partition";
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60 | protected const string TestRelativeErrorResultDescription = "Average of the relative errors of the model output and the actual values on the test partition";
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61 | protected const string TrainingNormalizedMeanSquaredErrorResultDescription = "Normalized mean of squared errors of the model on the training partition";
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62 | protected const string TestNormalizedMeanSquaredErrorResultDescription = "Normalized mean of squared errors of the model on the test partition";
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63 | protected const string TrainingRootMeanSquaredErrorResultDescription = "Root mean of squared errors of the model on the training partition";
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64 | protected const string TestRootMeanSquaredErrorResultDescription = "Root mean of squared errors of the model on the test partition";
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65 |
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66 | public new IRegressionModel Model {
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67 | get { return (IRegressionModel)base.Model; }
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68 | protected set { base.Model = value; }
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69 | }
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70 |
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71 | public new IRegressionProblemData ProblemData {
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72 | get { return (IRegressionProblemData)base.ProblemData; }
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73 | set {
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74 | if (value == null) throw new ArgumentNullException("The problemData must not be null.");
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75 | string errorMessage = string.Empty;
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76 | if (!Model.IsProblemDataCompatible(value, out errorMessage)) throw new ArgumentException(errorMessage);
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77 |
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78 | base.ProblemData = value;
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79 | }
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80 | }
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81 |
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82 | public abstract IEnumerable<double> EstimatedValues { get; }
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83 | public abstract IEnumerable<double> EstimatedTrainingValues { get; }
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84 | public abstract IEnumerable<double> EstimatedTestValues { get; }
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85 | public abstract IEnumerable<double> GetEstimatedValues(IEnumerable<int> rows);
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86 |
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87 | #region Results
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88 | public double TrainingMeanSquaredError {
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89 | get { return ((DoubleValue)this[TrainingMeanSquaredErrorResultName].Value).Value; }
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90 | private set { ((DoubleValue)this[TrainingMeanSquaredErrorResultName].Value).Value = value; }
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91 | }
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92 | public double TestMeanSquaredError {
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93 | get { return ((DoubleValue)this[TestMeanSquaredErrorResultName].Value).Value; }
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94 | private set { ((DoubleValue)this[TestMeanSquaredErrorResultName].Value).Value = value; }
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95 | }
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96 | public double TrainingMeanAbsoluteError {
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97 | get { return ((DoubleValue)this[TrainingMeanAbsoluteErrorResultName].Value).Value; }
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98 | private set { ((DoubleValue)this[TrainingMeanAbsoluteErrorResultName].Value).Value = value; }
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99 | }
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100 | public double TestMeanAbsoluteError {
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101 | get { return ((DoubleValue)this[TestMeanAbsoluteErrorResultName].Value).Value; }
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102 | private set { ((DoubleValue)this[TestMeanAbsoluteErrorResultName].Value).Value = value; }
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103 | }
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104 | public double TrainingRSquared {
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105 | get { return ((DoubleValue)this[TrainingSquaredCorrelationResultName].Value).Value; }
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106 | private set { ((DoubleValue)this[TrainingSquaredCorrelationResultName].Value).Value = value; }
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107 | }
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108 | public double TestRSquared {
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109 | get { return ((DoubleValue)this[TestSquaredCorrelationResultName].Value).Value; }
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110 | private set { ((DoubleValue)this[TestSquaredCorrelationResultName].Value).Value = value; }
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111 | }
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112 | public double TrainingRelativeError {
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113 | get { return ((DoubleValue)this[TrainingRelativeErrorResultName].Value).Value; }
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114 | private set { ((DoubleValue)this[TrainingRelativeErrorResultName].Value).Value = value; }
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115 | }
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116 | public double TestRelativeError {
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117 | get { return ((DoubleValue)this[TestRelativeErrorResultName].Value).Value; }
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118 | private set { ((DoubleValue)this[TestRelativeErrorResultName].Value).Value = value; }
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119 | }
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120 | public double TrainingNormalizedMeanSquaredError {
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121 | get { return ((DoubleValue)this[TrainingNormalizedMeanSquaredErrorResultName].Value).Value; }
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122 | private set { ((DoubleValue)this[TrainingNormalizedMeanSquaredErrorResultName].Value).Value = value; }
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123 | }
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124 | public double TestNormalizedMeanSquaredError {
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125 | get { return ((DoubleValue)this[TestNormalizedMeanSquaredErrorResultName].Value).Value; }
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126 | private set { ((DoubleValue)this[TestNormalizedMeanSquaredErrorResultName].Value).Value = value; }
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127 | }
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128 | public double TrainingRootMeanSquaredError {
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129 | get { return ((DoubleValue)this[TrainingRootMeanSquaredErrorResultName].Value).Value; }
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130 | private set { ((DoubleValue)this[TrainingRootMeanSquaredErrorResultName].Value).Value = value; }
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131 | }
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132 | public double TestRootMeanSquaredError {
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133 | get { return ((DoubleValue)this[TestRootMeanSquaredErrorResultName].Value).Value; }
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134 | private set { ((DoubleValue)this[TestRootMeanSquaredErrorResultName].Value).Value = value; }
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135 | }
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136 |
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137 | // BackwardsCompatibility3.3
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138 | #region Backwards compatible code, remove with 3.5
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139 | private double TrainingMeanError {
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140 | get {
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141 | if (!ContainsKey(TrainingMeanErrorResultName)) return double.NaN;
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142 | return ((DoubleValue)this[TrainingMeanErrorResultName].Value).Value;
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143 | }
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144 | set {
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145 | if (ContainsKey(TrainingMeanErrorResultName))
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146 | ((DoubleValue)this[TrainingMeanErrorResultName].Value).Value = value;
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147 | }
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148 | }
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149 | private double TestMeanError {
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150 | get {
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151 | if (!ContainsKey(TestMeanErrorResultName)) return double.NaN;
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152 | return ((DoubleValue)this[TestMeanErrorResultName].Value).Value;
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153 | }
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154 | set {
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155 | if (ContainsKey(TestMeanErrorResultName))
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156 | ((DoubleValue)this[TestMeanErrorResultName].Value).Value = value;
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157 | }
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158 | }
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159 | #endregion
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160 | #endregion
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161 |
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162 | [StorableConstructor]
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163 | protected RegressionSolutionBase(StorableConstructorFlag _) : base(_) { }
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164 | protected RegressionSolutionBase(RegressionSolutionBase original, Cloner cloner)
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165 | : base(original, cloner) {
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166 | }
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167 | protected RegressionSolutionBase(IRegressionModel model, IRegressionProblemData problemData)
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168 | : base(model, problemData) {
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169 | Add(new Result(TrainingMeanSquaredErrorResultName, TrainingMeanSquaredErrorResultDescription, new DoubleValue()));
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170 | Add(new Result(TestMeanSquaredErrorResultName, TestMeanSquaredErrorResultDescription, new DoubleValue()));
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171 | Add(new Result(TrainingMeanAbsoluteErrorResultName, TrainingMeanAbsoluteErrorResultDescription, new DoubleValue()));
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172 | Add(new Result(TestMeanAbsoluteErrorResultName, TestMeanAbsoluteErrorResultDescription, new DoubleValue()));
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173 | Add(new Result(TrainingSquaredCorrelationResultName, TrainingSquaredCorrelationResultDescription, new DoubleValue()));
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174 | Add(new Result(TestSquaredCorrelationResultName, TestSquaredCorrelationResultDescription, new DoubleValue()));
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175 | Add(new Result(TrainingRelativeErrorResultName, TrainingRelativeErrorResultDescription, new PercentValue()));
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176 | Add(new Result(TestRelativeErrorResultName, TestRelativeErrorResultDescription, new PercentValue()));
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177 | Add(new Result(TrainingNormalizedMeanSquaredErrorResultName, TrainingNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
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178 | Add(new Result(TestNormalizedMeanSquaredErrorResultName, TestNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
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179 | Add(new Result(TrainingRootMeanSquaredErrorResultName, TrainingRootMeanSquaredErrorResultDescription, new DoubleValue()));
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180 | Add(new Result(TestRootMeanSquaredErrorResultName, TestRootMeanSquaredErrorResultDescription, new DoubleValue()));
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181 | }
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182 |
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183 | [StorableHook(HookType.AfterDeserialization)]
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184 | private void AfterDeserialization() {
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185 | if (string.IsNullOrEmpty(Model.TargetVariable))
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186 | Model.TargetVariable = this.ProblemData.TargetVariable;
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187 |
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188 | // BackwardsCompatibility3.4
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189 | #region Backwards compatible code, remove with 3.5
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190 | if (!ContainsKey(TrainingMeanAbsoluteErrorResultName)) {
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191 | OnlineCalculatorError errorState;
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192 | Add(new Result(TrainingMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the training partition", new DoubleValue()));
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193 | double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(EstimatedTrainingValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices), out errorState);
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194 | TrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
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195 | }
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196 |
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197 | if (!ContainsKey(TestMeanAbsoluteErrorResultName)) {
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198 | OnlineCalculatorError errorState;
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199 | Add(new Result(TestMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the test partition", new DoubleValue()));
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200 | double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(EstimatedTestValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices), out errorState);
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201 | TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
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202 | }
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203 |
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204 | if (!ContainsKey(TrainingRootMeanSquaredErrorResultName)) {
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205 | OnlineCalculatorError errorState;
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206 | Add(new Result(TrainingRootMeanSquaredErrorResultName, TrainingRootMeanSquaredErrorResultDescription, new DoubleValue()));
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207 | double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(EstimatedTrainingValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices), out errorState);
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208 | TrainingRootMeanSquaredError = errorState == OnlineCalculatorError.None ? Math.Sqrt(trainingMSE) : double.NaN;
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209 | }
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210 |
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211 | if (!ContainsKey(TestRootMeanSquaredErrorResultName)) {
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212 | OnlineCalculatorError errorState;
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213 | Add(new Result(TestRootMeanSquaredErrorResultName, TestRootMeanSquaredErrorResultDescription, new DoubleValue()));
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214 | double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(EstimatedTestValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices), out errorState);
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215 | TestRootMeanSquaredError = errorState == OnlineCalculatorError.None ? Math.Sqrt(testMSE) : double.NaN;
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216 | }
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217 | #endregion
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218 | }
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219 |
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220 | protected override void RecalculateResults() {
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221 | CalculateRegressionResults();
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222 | }
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223 |
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224 | protected void CalculateRegressionResults() {
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225 | IEnumerable<double> estimatedTrainingValues = EstimatedTrainingValues; // cache values
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226 | IEnumerable<double> originalTrainingValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices);
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227 | IEnumerable<double> estimatedTestValues = EstimatedTestValues; // cache values
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228 | IEnumerable<double> originalTestValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices);
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229 |
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230 | OnlineCalculatorError errorState;
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231 | double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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232 | TrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMSE : double.NaN;
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233 | double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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234 | TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
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235 |
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236 | double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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237 | TrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
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238 | double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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239 | TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
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240 |
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241 | double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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242 | TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR * trainingR : double.NaN;
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243 | double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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244 | TestRSquared = errorState == OnlineCalculatorError.None ? testR * testR : double.NaN;
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245 |
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246 | double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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247 | TrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
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248 | double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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249 | TestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;
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250 |
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251 | double trainingNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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252 | TrainingNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingNMSE : double.NaN;
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253 | double testNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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254 | TestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? testNMSE : double.NaN;
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255 |
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256 | TrainingRootMeanSquaredError = Math.Sqrt(TrainingMeanSquaredError);
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257 | TestRootMeanSquaredError = Math.Sqrt(TestMeanSquaredError);
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258 |
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259 | // BackwardsCompatibility3.3
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260 | #region Backwards compatible code, remove with 3.5
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261 | if (ContainsKey(TrainingMeanErrorResultName)) {
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262 | double trainingME = OnlineMeanErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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263 | TrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
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264 | }
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265 | if (ContainsKey(TestMeanErrorResultName)) {
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266 | double testME = OnlineMeanErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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267 | TestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
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268 | }
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269 | #endregion
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270 | }
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271 | }
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272 | }
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