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.Data;
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28 | using HeuristicLab.Parameters;
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29 | using HeuristicLab.Persistence;
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30 |
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31 | namespace HeuristicLab.Problems.DataAnalysis {
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32 | [StorableType("4d9ed3cf-b3f8-4daf-9cab-74fb3942dd8c")]
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33 | [Item("ClassificationProblemData", "Represents an item containing all data defining a classification problem.")]
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34 | public class ClassificationProblemData : DataAnalysisProblemData, IClassificationProblemData, IStorableContent {
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35 | protected const string TargetVariableParameterName = "TargetVariable";
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36 | protected const string ClassNamesParameterName = "ClassNames";
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37 | protected const string ClassificationPenaltiesParameterName = "ClassificationPenalties";
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38 | protected const string PositiveClassParameterName = "PositiveClass";
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39 | protected const int MaximumNumberOfClasses = 100;
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40 | protected const int InspectedRowsToDetermineTargets = 2000;
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41 |
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42 | public string Filename { get; set; }
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43 |
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44 | #region default data
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45 | private static string[] defaultVariableNames = new string[] { "sample", "clump thickness", "cell size", "cell shape", "marginal adhesion", "epithelial cell size", "bare nuclei", "chromatin", "nucleoli", "mitoses", "class" };
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46 | private static double[,] defaultData = new double[,]{
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47 | {1000025,5,1,1,1,2,1,3,1,1,2 },
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48 | {1002945,5,4,4,5,7,10,3,2,1,2 },
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49 | {1015425,3,1,1,1,2,2,3,1,1,2 },
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50 | {1016277,6,8,8,1,3,4,3,7,1,2 },
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51 | {1017023,4,1,1,3,2,1,3,1,1,2 },
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52 | {1017122,8,10,10,8,7,10,9,7,1,4 },
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53 | {1018099,1,1,1,1,2,10,3,1,1,2 },
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54 | {1018561,2,1,2,1,2,1,3,1,1,2 },
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55 | {1033078,2,1,1,1,2,1,1,1,5,2 },
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56 | {1033078,4,2,1,1,2,1,2,1,1,2 },
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57 | {1035283,1,1,1,1,1,1,3,1,1,2 },
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58 | {1036172,2,1,1,1,2,1,2,1,1,2 },
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59 | {1041801,5,3,3,3,2,3,4,4,1,4 },
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60 | {1043999,1,1,1,1,2,3,3,1,1,2 },
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61 | {1044572,8,7,5,10,7,9,5,5,4,4 },
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62 | {1047630,7,4,6,4,6,1,4,3,1,4 },
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63 | {1048672,4,1,1,1,2,1,2,1,1,2 },
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64 | {1049815,4,1,1,1,2,1,3,1,1,2 },
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65 | {1050670,10,7,7,6,4,10,4,1,2,4 },
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66 | {1050718,6,1,1,1,2,1,3,1,1,2 },
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67 | {1054590,7,3,2,10,5,10,5,4,4,4 },
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68 | {1054593,10,5,5,3,6,7,7,10,1,4 },
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69 | {1056784,3,1,1,1,2,1,2,1,1,2 },
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70 | {1057013,8,4,5,1,2,2,7,3,1,4 },
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71 | {1059552,1,1,1,1,2,1,3,1,1,2 },
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72 | {1065726,5,2,3,4,2,7,3,6,1,4 },
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73 | {1066373,3,2,1,1,1,1,2,1,1,2 },
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74 | {1066979,5,1,1,1,2,1,2,1,1,2 },
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75 | {1067444,2,1,1,1,2,1,2,1,1,2 },
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76 | {1070935,1,1,3,1,2,1,1,1,1,2 },
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77 | {1070935,3,1,1,1,1,1,2,1,1,2 },
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78 | {1071760,2,1,1,1,2,1,3,1,1,2 },
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79 | {1072179,10,7,7,3,8,5,7,4,3,4 },
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80 | {1074610,2,1,1,2,2,1,3,1,1,2 },
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81 | {1075123,3,1,2,1,2,1,2,1,1,2 },
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82 | {1079304,2,1,1,1,2,1,2,1,1,2 },
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83 | {1080185,10,10,10,8,6,1,8,9,1,4 },
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84 | {1081791,6,2,1,1,1,1,7,1,1,2 },
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85 | {1084584,5,4,4,9,2,10,5,6,1,4 },
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86 | {1091262,2,5,3,3,6,7,7,5,1,4 },
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87 | {1096800,6,6,6,9,6,4,7,8,1,2 },
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88 | {1099510,10,4,3,1,3,3,6,5,2,4 },
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89 | {1100524,6,10,10,2,8,10,7,3,3,4 },
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90 | {1102573,5,6,5,6,10,1,3,1,1,4 },
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91 | {1103608,10,10,10,4,8,1,8,10,1,4 },
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92 | {1103722,1,1,1,1,2,1,2,1,2,2 },
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93 | {1105257,3,7,7,4,4,9,4,8,1,4 },
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94 | {1105524,1,1,1,1,2,1,2,1,1,2 },
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95 | {1106095,4,1,1,3,2,1,3,1,1,2 },
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96 | {1106829,7,8,7,2,4,8,3,8,2,4 },
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97 | {1108370,9,5,8,1,2,3,2,1,5,4 },
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98 | {1108449,5,3,3,4,2,4,3,4,1,4 },
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99 | {1110102,10,3,6,2,3,5,4,10,2,4 },
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100 | {1110503,5,5,5,8,10,8,7,3,7,4 },
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101 | {1110524,10,5,5,6,8,8,7,1,1,4 },
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102 | {1111249,10,6,6,3,4,5,3,6,1,4 },
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103 | {1112209,8,10,10,1,3,6,3,9,1,4 },
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104 | {1113038,8,2,4,1,5,1,5,4,4,4 },
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105 | {1113483,5,2,3,1,6,10,5,1,1,4 },
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106 | {1113906,9,5,5,2,2,2,5,1,1,4 },
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107 | {1115282,5,3,5,5,3,3,4,10,1,4 },
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108 | {1115293,1,1,1,1,2,2,2,1,1,2 },
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109 | {1116116,9,10,10,1,10,8,3,3,1,4 },
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110 | {1116132,6,3,4,1,5,2,3,9,1,4 },
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111 | {1116192,1,1,1,1,2,1,2,1,1,2 },
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112 | {1116998,10,4,2,1,3,2,4,3,10,4 },
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113 | {1117152,4,1,1,1,2,1,3,1,1,2 },
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114 | {1118039,5,3,4,1,8,10,4,9,1,4 },
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115 | {1120559,8,3,8,3,4,9,8,9,8,4 },
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116 | {1121732,1,1,1,1,2,1,3,2,1,2 },
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117 | {1121919,5,1,3,1,2,1,2,1,1,2 },
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118 | {1123061,6,10,2,8,10,2,7,8,10,4 },
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119 | {1124651,1,3,3,2,2,1,7,2,1,2 },
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120 | {1125035,9,4,5,10,6,10,4,8,1,4 },
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121 | {1126417,10,6,4,1,3,4,3,2,3,4 },
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122 | {1131294,1,1,2,1,2,2,4,2,1,2 },
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123 | {1132347,1,1,4,1,2,1,2,1,1,2 },
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124 | {1133041,5,3,1,2,2,1,2,1,1,2 },
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125 | {1133136,3,1,1,1,2,3,3,1,1,2 },
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126 | {1136142,2,1,1,1,3,1,2,1,1,2 },
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127 | {1137156,2,2,2,1,1,1,7,1,1,2 },
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128 | {1143978,4,1,1,2,2,1,2,1,1,2 },
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129 | {1143978,5,2,1,1,2,1,3,1,1,2 },
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130 | {1147044,3,1,1,1,2,2,7,1,1,2 },
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131 | {1147699,3,5,7,8,8,9,7,10,7,4 },
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132 | {1147748,5,10,6,1,10,4,4,10,10,4 },
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133 | {1148278,3,3,6,4,5,8,4,4,1,4 },
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134 | {1148873,3,6,6,6,5,10,6,8,3,4 },
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135 | {1152331,4,1,1,1,2,1,3,1,1,2 },
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136 | {1155546,2,1,1,2,3,1,2,1,1,2 },
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137 | {1156272,1,1,1,1,2,1,3,1,1,2 },
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138 | {1156948,3,1,1,2,2,1,1,1,1,2 },
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139 | {1157734,4,1,1,1,2,1,3,1,1,2 },
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140 | {1158247,1,1,1,1,2,1,2,1,1,2 },
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141 | {1160476,2,1,1,1,2,1,3,1,1,2 },
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142 | {1164066,1,1,1,1,2,1,3,1,1,2 },
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143 | {1165297,2,1,1,2,2,1,1,1,1,2 },
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144 | {1165790,5,1,1,1,2,1,3,1,1,2 },
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145 | {1165926,9,6,9,2,10,6,2,9,10,4 },
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146 | {1166630,7,5,6,10,5,10,7,9,4,4 },
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147 | {1166654,10,3,5,1,10,5,3,10,2,4 },
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148 | {1167439,2,3,4,4,2,5,2,5,1,4 },
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149 | {1167471,4,1,2,1,2,1,3,1,1,2 },
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150 | {1168359,8,2,3,1,6,3,7,1,1,4 },
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151 | {1168736,10,10,10,10,10,1,8,8,8,4 },
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152 | {1169049,7,3,4,4,3,3,3,2,7,4 },
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153 | {1170419,10,10,10,8,2,10,4,1,1,4 },
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154 | {1170420,1,6,8,10,8,10,5,7,1,4 },
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155 | {1171710,1,1,1,1,2,1,2,3,1,2 },
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156 | {1171710,6,5,4,4,3,9,7,8,3,4 },
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157 | {1171795,1,3,1,2,2,2,5,3,2,2 },
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158 | {1171845,8,6,4,3,5,9,3,1,1,4 },
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159 | {1172152,10,3,3,10,2,10,7,3,3,4 },
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160 | {1173216,10,10,10,3,10,8,8,1,1,4 },
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161 | {1173235,3,3,2,1,2,3,3,1,1,2 },
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162 | {1173347,1,1,1,1,2,5,1,1,1,2 },
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163 | {1173347,8,3,3,1,2,2,3,2,1,2 },
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164 | {1173509,4,5,5,10,4,10,7,5,8,4 },
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165 | {1173514,1,1,1,1,4,3,1,1,1,2 },
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166 | {1173681,3,2,1,1,2,2,3,1,1,2 },
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167 | {1174057,1,1,2,2,2,1,3,1,1,2 },
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168 | {1174057,4,2,1,1,2,2,3,1,1,2 },
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169 | {1174131,10,10,10,2,10,10,5,3,3,4 },
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170 | {1174428,5,3,5,1,8,10,5,3,1,4 },
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171 | {1175937,5,4,6,7,9,7,8,10,1,4 },
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172 | {1176406,1,1,1,1,2,1,2,1,1,2 },
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173 | {1176881,7,5,3,7,4,10,7,5,5,4 }
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174 | };
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175 | private static readonly Dataset defaultDataset;
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176 | private static readonly IEnumerable<string> defaultAllowedInputVariables;
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177 | private static readonly string defaultTargetVariable;
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178 |
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179 | private static readonly ClassificationProblemData emptyProblemData;
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180 | public static ClassificationProblemData EmptyProblemData {
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181 | get { return EmptyProblemData; }
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182 | }
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183 |
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184 | static ClassificationProblemData() {
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185 | defaultDataset = new Dataset(defaultVariableNames, defaultData);
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186 | defaultDataset.Name = "Wisconsin classification problem";
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187 | defaultDataset.Description = "subset from to ..";
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188 |
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189 | defaultAllowedInputVariables = defaultVariableNames.Except(new List<string>() { "sample", "class" });
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190 | defaultTargetVariable = "class";
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191 |
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192 | var problemData = new ClassificationProblemData();
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193 | problemData.Parameters.Clear();
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194 | problemData.Name = "Empty Classification ProblemData";
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195 | problemData.Description = "This ProblemData acts as place holder before the correct problem data is loaded.";
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196 | problemData.isEmpty = true;
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197 |
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198 | problemData.Parameters.Add(new FixedValueParameter<Dataset>(DatasetParameterName, "", new Dataset()));
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199 | problemData.Parameters.Add(new FixedValueParameter<ReadOnlyCheckedItemList<StringValue>>(InputVariablesParameterName, ""));
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200 | problemData.Parameters.Add(new FixedValueParameter<IntRange>(TrainingPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
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201 | problemData.Parameters.Add(new FixedValueParameter<IntRange>(TestPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
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202 | problemData.Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>()));
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203 | problemData.Parameters.Add(new FixedValueParameter<StringMatrix>(ClassNamesParameterName, "", new StringMatrix(0, 0).AsReadOnly()));
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204 | problemData.Parameters.Add(new FixedValueParameter<DoubleMatrix>(ClassificationPenaltiesParameterName, "", (DoubleMatrix)new DoubleMatrix(0, 0).AsReadOnly()));
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205 | emptyProblemData = problemData;
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206 | }
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207 | #endregion
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208 |
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209 | #region parameter properties
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210 | public IConstrainedValueParameter<StringValue> TargetVariableParameter {
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211 | get { return (IConstrainedValueParameter<StringValue>)Parameters[TargetVariableParameterName]; }
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212 | }
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213 | public IFixedValueParameter<StringMatrix> ClassNamesParameter {
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214 | get { return (IFixedValueParameter<StringMatrix>)Parameters[ClassNamesParameterName]; }
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215 | }
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216 | public IConstrainedValueParameter<StringValue> PositiveClassParameter {
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217 | get { return (IConstrainedValueParameter<StringValue>)Parameters[PositiveClassParameterName]; }
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218 | }
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219 | public IFixedValueParameter<DoubleMatrix> ClassificationPenaltiesParameter {
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220 | get { return (IFixedValueParameter<DoubleMatrix>)Parameters[ClassificationPenaltiesParameterName]; }
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221 | }
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222 | #endregion
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223 |
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224 | #region properties
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225 | public string TargetVariable {
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226 | get { return TargetVariableParameter.Value.Value; }
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227 | set {
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228 | if (value == null) throw new ArgumentNullException("targetVariable", "The provided value for the targetVariable is null.");
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229 | if (value == TargetVariable) return;
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230 |
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231 |
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232 | var matchingParameterValue = TargetVariableParameter.ValidValues.FirstOrDefault(v => v.Value == value);
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233 | if (matchingParameterValue == null) throw new ArgumentException("The provided value is not valid as the targetVariable.", "targetVariable");
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234 | TargetVariableParameter.Value = matchingParameterValue;
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235 | }
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236 | }
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237 |
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238 | private List<double> classValuesCache;
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239 | private List<double> ClassValuesCache {
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240 | get {
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241 | if (classValuesCache == null) {
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242 | classValuesCache = Dataset.GetDoubleValues(TargetVariableParameter.Value.Value).Distinct().OrderBy(x => x).ToList();
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243 | }
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244 | return classValuesCache;
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245 | }
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246 | }
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247 | public IEnumerable<double> ClassValues {
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248 | get { return ClassValuesCache; }
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249 | }
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250 | public int Classes {
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251 | get { return ClassValuesCache.Count; }
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252 | }
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253 |
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254 | private List<string> classNamesCache;
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255 | private List<string> ClassNamesCache {
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256 | get {
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257 | if (classNamesCache == null) {
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258 | classNamesCache = new List<string>();
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259 | for (int i = 0; i < ClassNamesParameter.Value.Rows; i++)
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260 | classNamesCache.Add(ClassNamesParameter.Value[i, 0]);
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261 | }
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262 | return classNamesCache;
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263 | }
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264 | }
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265 | public IEnumerable<string> ClassNames {
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266 | get { return ClassNamesCache; }
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267 | }
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268 |
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269 | public string PositiveClass {
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270 | get { return PositiveClassParameter.Value.Value; }
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271 | set {
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272 | var matchingValue = PositiveClassParameter.ValidValues.SingleOrDefault(x => x.Value == value);
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273 | if (matchingValue == null) throw new ArgumentException(string.Format("{0} cannot be set as positive class.", value));
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274 | PositiveClassParameter.Value = matchingValue;
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275 | }
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276 | }
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277 | #endregion
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278 |
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279 |
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280 | [StorableConstructor]
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281 | protected ClassificationProblemData(StorableConstructorFlag deserializing) : base(deserializing) { }
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282 | [StorableHook(HookType.AfterDeserialization)]
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283 | private void AfterDeserialization() {
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284 | RegisterParameterEvents();
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285 |
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286 | classNamesCache = new List<string>();
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287 | for (int i = 0; i < ClassNamesParameter.Value.Rows; i++)
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288 | classNamesCache.Add(ClassNamesParameter.Value[i, 0]);
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289 |
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290 | // BackwardsCompatibility3.4
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291 | #region Backwards compatible code, remove with 3.5
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292 | if (!Parameters.ContainsKey(PositiveClassParameterName)) {
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293 | var validValues = new ItemSet<StringValue>(ClassNames.Select(s => new StringValue(s).AsReadOnly()));
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294 | Parameters.Add(new ConstrainedValueParameter<StringValue>(PositiveClassParameterName,
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295 | "The positive class which is used for quality measure calculation (e.g., specifity, sensitivity,...)", validValues, validValues.First()));
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296 | }
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297 | #endregion
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298 |
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299 | }
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300 |
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301 | protected ClassificationProblemData(ClassificationProblemData original, Cloner cloner)
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302 | : base(original, cloner) {
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303 | RegisterParameterEvents();
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304 | classNamesCache = new List<string>();
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305 | for (int i = 0; i < ClassNamesParameter.Value.Rows; i++)
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306 | classNamesCache.Add(ClassNamesParameter.Value[i, 0]);
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307 | }
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308 | public override IDeepCloneable Clone(Cloner cloner) {
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309 | if (this == emptyProblemData) return emptyProblemData;
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310 | return new ClassificationProblemData(this, cloner);
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311 | }
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312 |
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313 | public ClassificationProblemData() : this(defaultDataset, defaultAllowedInputVariables, defaultTargetVariable) { }
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314 |
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315 | public ClassificationProblemData(IClassificationProblemData classificationProblemData)
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316 | : this(classificationProblemData.Dataset, classificationProblemData.AllowedInputVariables, classificationProblemData.TargetVariable) {
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317 | TrainingPartition.Start = classificationProblemData.TrainingPartition.Start;
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318 | TrainingPartition.End = classificationProblemData.TrainingPartition.End;
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319 | TestPartition.Start = classificationProblemData.TestPartition.Start;
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320 | TestPartition.End = classificationProblemData.TestPartition.End;
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321 |
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322 | PositiveClass = classificationProblemData.PositiveClass;
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323 |
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324 | for (int i = 0; i < classificationProblemData.ClassNames.Count(); i++)
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325 | ClassNamesParameter.Value[i, 0] = classificationProblemData.ClassNames.ElementAt(i);
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326 |
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327 | for (int i = 0; i < Classes; i++) {
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328 | for (int j = 0; j < Classes; j++) {
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329 | ClassificationPenaltiesParameter.Value[i, j] = classificationProblemData.GetClassificationPenalty(ClassValuesCache[i], ClassValuesCache[j]);
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330 | }
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331 | }
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332 | }
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333 |
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334 | public ClassificationProblemData(IDataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable, IEnumerable<ITransformation> transformations = null)
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335 | : base(dataset, allowedInputVariables, transformations ?? Enumerable.Empty<ITransformation>()) {
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336 | var validTargetVariableValues = CheckVariablesForPossibleTargetVariables(dataset).Select(x => new StringValue(x).AsReadOnly()).ToList();
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337 | var target = validTargetVariableValues.Where(x => x.Value == targetVariable).DefaultIfEmpty(validTargetVariableValues.First()).First();
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338 |
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339 | Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>(validTargetVariableValues), target));
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340 | Parameters.Add(new FixedValueParameter<StringMatrix>(ClassNamesParameterName, ""));
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341 | Parameters.Add(new ConstrainedValueParameter<StringValue>(PositiveClassParameterName, "The positive class which is used for quality measure calculation (e.g., specifity, sensitivity,...)"));
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342 | Parameters.Add(new FixedValueParameter<DoubleMatrix>(ClassificationPenaltiesParameterName, ""));
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343 |
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344 | RegisterParameterEvents();
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345 | ResetTargetVariableDependentMembers();
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346 | }
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347 |
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348 | public static IEnumerable<string> CheckVariablesForPossibleTargetVariables(IDataset dataset) {
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349 | int maxSamples = Math.Min(InspectedRowsToDetermineTargets, dataset.Rows);
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350 | var validTargetVariables = (from v in dataset.DoubleVariables
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351 | let distinctValues = dataset.GetDoubleValues(v)
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352 | .Take(maxSamples)
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353 | .Distinct()
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354 | .Count()
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355 | where distinctValues <= MaximumNumberOfClasses
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356 | select v).ToArray();
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357 |
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358 | if (!validTargetVariables.Any())
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359 | throw new ArgumentException("Import of classification problem data was not successful, because no target variable was found." +
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360 | " A target variable must have at most " + MaximumNumberOfClasses + " distinct values to be applicable to classification.");
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361 | return validTargetVariables;
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362 | }
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363 |
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364 |
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365 | private void ResetTargetVariableDependentMembers() {
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366 | DeregisterParameterEvents();
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367 |
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368 | ((IStringConvertibleMatrix)ClassNamesParameter.Value).Columns = 1;
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369 | ((IStringConvertibleMatrix)ClassNamesParameter.Value).Rows = ClassValuesCache.Count;
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370 | for (int i = 0; i < Classes; i++)
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371 | ClassNamesParameter.Value[i, 0] = "Class " + ClassValuesCache[i];
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372 | ClassNamesParameter.Value.ColumnNames = new List<string>() { "ClassNames" };
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373 | ClassNamesParameter.Value.RowNames = ClassValues.Select(s => "ClassValue: " + s);
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374 |
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375 | PositiveClassParameter.ValidValues.Clear();
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376 | foreach (var className in ClassNames) {
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377 | PositiveClassParameter.ValidValues.Add(new StringValue(className).AsReadOnly());
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378 | }
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379 |
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380 | ((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Rows = Classes;
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381 | ((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Columns = Classes;
|
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382 | ClassificationPenaltiesParameter.Value.RowNames = ClassNames.Select(name => "Actual " + name);
|
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383 | ClassificationPenaltiesParameter.Value.ColumnNames = ClassNames.Select(name => "Estimated " + name);
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384 | for (int i = 0; i < Classes; i++) {
|
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385 | for (int j = 0; j < Classes; j++) {
|
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386 | if (i != j) ClassificationPenaltiesParameter.Value[i, j] = 1;
|
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387 | else ClassificationPenaltiesParameter.Value[i, j] = 0;
|
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388 | }
|
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389 | }
|
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390 | RegisterParameterEvents();
|
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391 | }
|
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392 |
|
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393 | public string GetClassName(double classValue) {
|
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394 | if (!ClassValuesCache.Contains(classValue)) throw new ArgumentException();
|
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395 | int index = ClassValuesCache.IndexOf(classValue);
|
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396 | return ClassNamesCache[index];
|
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397 | }
|
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398 | public double GetClassValue(string className) {
|
---|
399 | if (!ClassNamesCache.Contains(className)) throw new ArgumentException();
|
---|
400 | int index = ClassNamesCache.IndexOf(className);
|
---|
401 | return ClassValuesCache[index];
|
---|
402 | }
|
---|
403 | public void SetClassName(double classValue, string className) {
|
---|
404 | if (!ClassValuesCache.Contains(classValue)) throw new ArgumentException();
|
---|
405 | int index = ClassValuesCache.IndexOf(classValue);
|
---|
406 | ClassNamesParameter.Value[index, 0] = className;
|
---|
407 | // updating of class names cache is not necessary here as the parameter value fires a changed event which updates the cache
|
---|
408 | }
|
---|
409 |
|
---|
410 | public double GetClassificationPenalty(string correctClassName, string estimatedClassName) {
|
---|
411 | return GetClassificationPenalty(GetClassValue(correctClassName), GetClassValue(estimatedClassName));
|
---|
412 | }
|
---|
413 | public double GetClassificationPenalty(double correctClassValue, double estimatedClassValue) {
|
---|
414 | int correctClassIndex = ClassValuesCache.IndexOf(correctClassValue);
|
---|
415 | int estimatedClassIndex = ClassValuesCache.IndexOf(estimatedClassValue);
|
---|
416 | return ClassificationPenaltiesParameter.Value[correctClassIndex, estimatedClassIndex];
|
---|
417 | }
|
---|
418 | public void SetClassificationPenalty(string correctClassName, string estimatedClassName, double penalty) {
|
---|
419 | SetClassificationPenalty(GetClassValue(correctClassName), GetClassValue(estimatedClassName), penalty);
|
---|
420 | }
|
---|
421 | public void SetClassificationPenalty(double correctClassValue, double estimatedClassValue, double penalty) {
|
---|
422 | int correctClassIndex = ClassValuesCache.IndexOf(correctClassValue);
|
---|
423 | int estimatedClassIndex = ClassValuesCache.IndexOf(estimatedClassValue);
|
---|
424 |
|
---|
425 | ClassificationPenaltiesParameter.Value[correctClassIndex, estimatedClassIndex] = penalty;
|
---|
426 | }
|
---|
427 |
|
---|
428 | #region events
|
---|
429 | private void RegisterParameterEvents() {
|
---|
430 | TargetVariableParameter.ValueChanged += new EventHandler(TargetVariableParameter_ValueChanged);
|
---|
431 | ClassNamesParameter.Value.Reset += new EventHandler(Parameter_ValueChanged);
|
---|
432 | ClassNamesParameter.Value.ItemChanged += new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
|
---|
433 | ClassificationPenaltiesParameter.Value.ItemChanged += new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
|
---|
434 | ClassificationPenaltiesParameter.Value.Reset += new EventHandler(Parameter_ValueChanged);
|
---|
435 | }
|
---|
436 | private void DeregisterParameterEvents() {
|
---|
437 | TargetVariableParameter.ValueChanged -= new EventHandler(TargetVariableParameter_ValueChanged);
|
---|
438 | ClassNamesParameter.Value.Reset -= new EventHandler(Parameter_ValueChanged);
|
---|
439 | ClassNamesParameter.Value.ItemChanged -= new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
|
---|
440 | ClassificationPenaltiesParameter.Value.ItemChanged -= new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
|
---|
441 | ClassificationPenaltiesParameter.Value.Reset -= new EventHandler(Parameter_ValueChanged);
|
---|
442 | }
|
---|
443 |
|
---|
444 | private void TargetVariableParameter_ValueChanged(object sender, EventArgs e) {
|
---|
445 | classValuesCache = null;
|
---|
446 | classNamesCache = null;
|
---|
447 | ResetTargetVariableDependentMembers();
|
---|
448 | OnChanged();
|
---|
449 | }
|
---|
450 | private void Parameter_ValueChanged(object sender, EventArgs e) {
|
---|
451 | var oldPositiveClass = PositiveClass;
|
---|
452 | var oldClassNames = classNamesCache;
|
---|
453 | var index = oldClassNames.IndexOf(oldPositiveClass);
|
---|
454 |
|
---|
455 | classNamesCache = null;
|
---|
456 | ClassificationPenaltiesParameter.Value.RowNames = ClassNames.Select(name => "Actual " + name);
|
---|
457 | ClassificationPenaltiesParameter.Value.ColumnNames = ClassNames.Select(name => "Estimated " + name);
|
---|
458 |
|
---|
459 | PositiveClassParameter.ValidValues.Clear();
|
---|
460 | foreach (var className in ClassNames) {
|
---|
461 | PositiveClassParameter.ValidValues.Add(new StringValue(className).AsReadOnly());
|
---|
462 | }
|
---|
463 | PositiveClassParameter.Value = PositiveClassParameter.ValidValues.ElementAt(index);
|
---|
464 |
|
---|
465 | OnChanged();
|
---|
466 | }
|
---|
467 | #endregion
|
---|
468 |
|
---|
469 | protected override bool IsProblemDataCompatible(IDataAnalysisProblemData problemData, out string errorMessage) {
|
---|
470 | if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
|
---|
471 | IClassificationProblemData classificationProblemData = problemData as IClassificationProblemData;
|
---|
472 | if (classificationProblemData == null)
|
---|
473 | throw new ArgumentException("The problem data is no classification problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
|
---|
474 |
|
---|
475 | var returnValue = base.IsProblemDataCompatible(classificationProblemData, out errorMessage);
|
---|
476 | //check targetVariable
|
---|
477 | if (classificationProblemData.InputVariables.All(var => var.Value != TargetVariable)) {
|
---|
478 | errorMessage = string.Format("The target variable {0} is not present in the new problem data.", TargetVariable)
|
---|
479 | + Environment.NewLine + errorMessage;
|
---|
480 | return false;
|
---|
481 | }
|
---|
482 |
|
---|
483 | var newClassValues = classificationProblemData.Dataset.GetDoubleValues(TargetVariable).Distinct().OrderBy(x => x);
|
---|
484 | if (!newClassValues.SequenceEqual(ClassValues)) {
|
---|
485 | errorMessage = errorMessage + string.Format("The class values differ in the provided classification problem data.");
|
---|
486 | returnValue = false;
|
---|
487 | }
|
---|
488 |
|
---|
489 | var newPositivieClassName = classificationProblemData.PositiveClass;
|
---|
490 | if (newPositivieClassName != PositiveClass) {
|
---|
491 | errorMessage = errorMessage + string.Format("The positive class differs in the provided classification problem data.");
|
---|
492 | returnValue = false;
|
---|
493 | }
|
---|
494 |
|
---|
495 | return returnValue;
|
---|
496 | }
|
---|
497 |
|
---|
498 | public override void AdjustProblemDataProperties(IDataAnalysisProblemData problemData) {
|
---|
499 | if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
|
---|
500 | ClassificationProblemData classificationProblemData = problemData as ClassificationProblemData;
|
---|
501 | if (classificationProblemData == null)
|
---|
502 | throw new ArgumentException("The problem data is not a classification problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
|
---|
503 |
|
---|
504 | base.AdjustProblemDataProperties(problemData);
|
---|
505 | TargetVariable = classificationProblemData.TargetVariable;
|
---|
506 | for (int i = 0; i < classificationProblemData.ClassNames.Count(); i++)
|
---|
507 | ClassNamesParameter.Value[i, 0] = classificationProblemData.ClassNames.ElementAt(i);
|
---|
508 |
|
---|
509 | PositiveClass = classificationProblemData.PositiveClass;
|
---|
510 |
|
---|
511 | for (int i = 0; i < Classes; i++) {
|
---|
512 | for (int j = 0; j < Classes; j++) {
|
---|
513 | ClassificationPenaltiesParameter.Value[i, j] = classificationProblemData.GetClassificationPenalty(ClassValuesCache[i], ClassValuesCache[j]);
|
---|
514 | }
|
---|
515 | }
|
---|
516 | }
|
---|
517 | }
|
---|
518 | }
|
---|