1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2012 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 | using System;
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22 | using System.Collections.Generic;
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23 | using System.Drawing;
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24 | using System.Linq;
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25 | using System.Windows.Forms;
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26 | using HeuristicLab.Common;
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27 | using HeuristicLab.Data;
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28 | using HeuristicLab.MainForm;
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29 | using HeuristicLab.MainForm.WindowsForms;
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30 | using HeuristicLab.Problems.DataAnalysis.Interfaces;
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31 |
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32 | namespace HeuristicLab.Problems.DataAnalysis.Views {
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33 | [View("Estimated Class Values")]
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34 | [Content(typeof(ClassificationEnsembleSolution))]
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35 | public partial class ClassificationEnsembleSolutionEstimatedClassValuesView :
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36 | ClassificationSolutionEstimatedClassValuesView {
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37 | private const string RowColumnName = "Row";
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38 | private const string TargetClassValuesColumnName = "Target Variable";
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39 | private const string EstimatedClassValuesColumnName = "Estimated Class Values";
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40 | private const string CorrectClassificationColumnName = "Correct Classification";
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41 | private const string ConfidenceColumnName = "Confidence";
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42 |
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43 | private const string SamplesComboBoxAllSamples = "All Samples";
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44 | private const string SamplesComboBoxTrainingSamples = "Training Samples";
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45 | private const string SamplesComboBoxTestSamples = "Test Samples";
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46 |
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47 | public new ClassificationEnsembleSolution Content {
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48 | get { return (ClassificationEnsembleSolution)base.Content; }
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49 | set { base.Content = value; }
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50 | }
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51 |
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52 | public ClassificationEnsembleSolutionEstimatedClassValuesView()
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53 | : base() {
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54 | InitializeComponent();
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55 | SamplesComboBox.Items.AddRange(new string[] { SamplesComboBoxAllSamples, SamplesComboBoxTrainingSamples, SamplesComboBoxTestSamples });
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56 | SamplesComboBox.SelectedIndex = 0;
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57 | matrixView.DataGridView.RowPrePaint += new DataGridViewRowPrePaintEventHandler(DataGridView_RowPrePaint);
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58 | }
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59 |
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60 | private void SamplesComboBox_SelectedIndexChanged(object sender, EventArgs e) {
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61 | UpdateEstimatedValues();
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62 | }
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63 |
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64 | protected override void UpdateEstimatedValues() {
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65 | if (InvokeRequired) {
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66 | Invoke((Action)UpdateEstimatedValues);
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67 | return;
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68 | }
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69 | if (Content == null) {
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70 | matrixView.Content = null;
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71 | return;
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72 | }
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73 |
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74 | int[] indizes;
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75 | double[] estimatedClassValues;
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76 |
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77 | switch (SamplesComboBox.SelectedItem.ToString()) {
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78 | case SamplesComboBoxAllSamples: {
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79 | indizes = Enumerable.Range(0, Content.ProblemData.Dataset.Rows).ToArray();
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80 | estimatedClassValues = Content.EstimatedClassValues.ToArray();
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81 | break;
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82 | }
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83 | case SamplesComboBoxTrainingSamples: {
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84 | indizes = Content.ProblemData.TrainingIndizes.ToArray();
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85 | estimatedClassValues = Content.EstimatedTrainingClassValues.ToArray();
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86 | break;
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87 | }
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88 | case SamplesComboBoxTestSamples: {
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89 | indizes = Content.ProblemData.TestIndizes.ToArray();
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90 | estimatedClassValues = Content.EstimatedTestClassValues.ToArray();
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91 | break;
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92 | }
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93 | default:
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94 | throw new ArgumentException();
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95 | }
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96 |
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97 | IEnumerable<IClassificationSolution> solutions = Content.ClassificationSolutions.CheckedItems;
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98 | int classValuesCount = Content.ProblemData.ClassValues.Count;
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99 | int solutionsCount = solutions.Count();
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100 | string[,] values = new string[indizes.Length, 5 + classValuesCount + solutionsCount];
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101 | double[] target = Content.ProblemData.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable).ToArray();
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102 | List<List<double?>> estimatedValuesVector = GetEstimatedValues(SamplesComboBox.SelectedItem.ToString(), indizes,
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103 | solutions);
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104 |
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105 | IClassificationEnsembleSolutionWeightCalculator weightCalc = Content.WeightCalculator;
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106 |
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107 | // needed to calculate average confidences of correct and wrong estimated classes
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108 | bool correctClassified;
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109 | double[] confidence = new double[2];
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110 | int[] classified = new int[2];
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111 | double curConfidence;
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112 |
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113 | double[] confidences = null;
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114 | if (SamplesComboBox.SelectedItem.ToString() == SamplesComboBoxAllSamples) {
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115 | confidences = weightCalc.GetConfidence(solutions, indizes, estimatedClassValues).ToArray();
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116 | }
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117 |
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118 | for (int i = 0; i < indizes.Length; i++) {
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119 | int row = indizes[i];
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120 | values[i, 0] = row.ToString();
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121 | values[i, 1] = target[i].ToString();
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122 | //display only indices and target values if no models are present
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123 | if (solutionsCount > 0) {
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124 | values[i, 2] = estimatedClassValues[i].ToString();
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125 | correctClassified = target[i].IsAlmost(estimatedClassValues[i]);
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126 | values[i, 3] = correctClassified.ToString();
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127 | if (SamplesComboBox.SelectedItem.ToString() == SamplesComboBoxAllSamples) {
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128 | curConfidence = confidences[i];
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129 | } else {
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130 | curConfidence = weightCalc.GetConfidence(GetRelevantSolutions(SamplesComboBox.SelectedItem.ToString(), solutions, row),
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131 | indizes[i], estimatedClassValues[i]);
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132 | }
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133 | if (correctClassified) {
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134 | confidence[0] += curConfidence;
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135 | classified[0]++;
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136 | } else {
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137 | confidence[1] += curConfidence;
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138 | classified[1]++;
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139 | }
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140 |
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141 | values[i, 4] = curConfidence.ToString();
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142 |
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143 | var groups =
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144 | estimatedValuesVector[i].GroupBy(x => x).Select(g => new { Key = g.Key, Count = g.Count() }).ToList();
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145 | for (int classIndex = 0; classIndex < Content.ProblemData.ClassValues.Count; classIndex++) {
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146 | var group = groups.Where(g => g.Key == Content.ProblemData.ClassValues[classIndex]).SingleOrDefault();
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147 | if (group == null) values[i, 5 + classIndex] = 0.ToString();
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148 | else values[i, 5 + classIndex] = group.Count.ToString();
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149 | }
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150 | for (int modelIndex = 0; modelIndex < estimatedValuesVector[i].Count; modelIndex++) {
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151 | values[i, 5 + classValuesCount + modelIndex] = estimatedValuesVector[i][modelIndex] == null
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152 | ? string.Empty
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153 | : estimatedValuesVector[i][modelIndex].ToString();
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154 | }
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155 | }
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156 | }
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157 |
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158 | CorrectClassifiedConfidence.Text = (confidence[0] / (double)classified[0]).ToString();
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159 | WrongClassifiedConfidence.Text = (confidence[1] / (double)classified[1]).ToString();
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160 |
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161 | StringMatrix matrix = new StringMatrix(values);
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162 | List<string> columnNames = new List<string>() { "Id", TargetClassValuesColumnName, EstimatedClassValuesColumnName, CorrectClassificationColumnName, ConfidenceColumnName };
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163 | columnNames.AddRange(Content.ProblemData.ClassNames);
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164 | columnNames.AddRange(Content.ClassificationSolutions.CheckedItems.Select(s => s.Model.Name));//.Model.Models.Select(m => m.Name));
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165 | matrix.ColumnNames = columnNames;
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166 | matrix.SortableView = true;
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167 | matrixView.Content = matrix;
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168 | }
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169 |
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170 | protected IEnumerable<IClassificationSolution> GetRelevantSolutions(string samplesSelection, IEnumerable<IClassificationSolution> solutions, int curRow) {
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171 | if (samplesSelection == SamplesComboBoxAllSamples)
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172 | return solutions;
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173 | else if (samplesSelection == SamplesComboBoxTrainingSamples)
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174 | return solutions.Where(s => s.ProblemData.IsTrainingSample(curRow));
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175 | else if (samplesSelection == SamplesComboBoxTestSamples)
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176 | return solutions.Where(s => s.ProblemData.IsTestSample(curRow));
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177 | else
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178 | return new List<IClassificationSolution>();
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179 | }
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180 |
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181 | private IEnumerable<int> FindAllIndices(List<double?> list, double value) {
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182 | List<int> indices = new List<int>();
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183 | for (int i = 0; i < list.Count; i++) {
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184 | if (list[i].Equals(value))
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185 | indices.Add(i);
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186 | }
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187 | return indices;
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188 | }
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189 |
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190 | private List<List<double?>> GetEstimatedValues(string samplesSelection, int[] rows, IEnumerable<IClassificationSolution> solutions) {
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191 | List<List<double?>> values = new List<List<double?>>();
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192 | int solutionIndex = 0;
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193 | foreach (var solution in solutions) {
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194 | double[] estimation = solution.GetEstimatedClassValues(rows).ToArray();
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195 | for (int i = 0; i < rows.Length; i++) {
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196 | var row = rows[i];
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197 | if (solutionIndex == 0) values.Add(new List<double?>());
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198 |
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199 | if (samplesSelection == SamplesComboBoxAllSamples)
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200 | values[i].Add(estimation[i]);
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201 | else if (samplesSelection == SamplesComboBoxTrainingSamples && solution.ProblemData.IsTrainingSample(row))
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202 | values[i].Add(estimation[i]);
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203 | else if (samplesSelection == SamplesComboBoxTestSamples && solution.ProblemData.IsTestSample(row))
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204 | values[i].Add(estimation[i]);
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205 | else
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206 | values[i].Add(null);
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207 | }
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208 | solutionIndex++;
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209 | }
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210 | return values;
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211 | }
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212 |
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213 | private void DataGridView_RowPrePaint(object sender, DataGridViewRowPrePaintEventArgs e) {
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214 | if (InvokeRequired) {
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215 | Invoke(new EventHandler<DataGridViewRowPrePaintEventArgs>(DataGridView_RowPrePaint), sender, e);
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216 | return;
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217 | }
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218 | var cellValue = matrixView.DataGridView[3, e.RowIndex].Value.ToString();
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219 | if (string.IsNullOrEmpty(cellValue)) return;
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220 | bool correctClassified = bool.Parse(cellValue);
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221 | matrixView.DataGridView.Rows[e.RowIndex].DefaultCellStyle.ForeColor = correctClassified ? Color.MediumSeaGreen : Color.Red;
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222 | }
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223 | }
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224 | }
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