source: branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationEnsembleSolutionEstimatedClassValuesView.cs @ 8814

Last change on this file since 8814 was 8814, checked in by sforsten, 7 years ago

#1776:

  • improved performance of confidence calculation
  • fixed bug in median confidence calculation
  • fixed bug in average confidence calculation
  • confidence calculation is now easier for training and test
  • removed obsolete view ClassificationEnsembleSolutionConfidenceAccuracyDependence
File size: 9.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Collections.Generic;
24using System.Drawing;
25using System.Linq;
26using System.Windows.Forms;
27using HeuristicLab.Common;
28using HeuristicLab.Data;
29using HeuristicLab.MainForm;
30using HeuristicLab.MainForm.WindowsForms;
31
32namespace HeuristicLab.Problems.DataAnalysis.Views {
33  [View("Estimated Class Values")]
34  [Content(typeof(ClassificationEnsembleSolution))]
35  public partial class ClassificationEnsembleSolutionEstimatedClassValuesView :
36    ClassificationSolutionEstimatedClassValuesView {
37    private const string RowColumnName = "Row";
38    private const string TargetClassValuesColumnName = "Target Variable";
39    private const string EstimatedClassValuesColumnName = "Estimated Class Values";
40    private const string CorrectClassificationColumnName = "Correct Classification";
41    private const string ConfidenceColumnName = "Confidence";
42
43    private const string SamplesComboBoxAllSamples = "All Samples";
44    private const string SamplesComboBoxTrainingSamples = "Training Samples";
45    private const string SamplesComboBoxTestSamples = "Test Samples";
46
47    public new ClassificationEnsembleSolution Content {
48      get { return (ClassificationEnsembleSolution)base.Content; }
49      set { base.Content = value; }
50    }
51
52    public ClassificationEnsembleSolutionEstimatedClassValuesView()
53      : base() {
54      InitializeComponent();
55      SamplesComboBox.Items.AddRange(new string[] { SamplesComboBoxAllSamples, SamplesComboBoxTrainingSamples, SamplesComboBoxTestSamples });
56      SamplesComboBox.SelectedIndex = 0;
57      matrixView.DataGridView.RowPrePaint += new DataGridViewRowPrePaintEventHandler(DataGridView_RowPrePaint);
58    }
59
60    private void SamplesComboBox_SelectedIndexChanged(object sender, EventArgs e) {
61      UpdateEstimatedValues();
62    }
63
64    protected override void UpdateEstimatedValues() {
65      if (InvokeRequired) {
66        Invoke((Action)UpdateEstimatedValues);
67        return;
68      }
69      if (Content == null) {
70        matrixView.Content = null;
71        return;
72      }
73
74      int[] indizes;
75      double[] estimatedClassValues;
76
77      switch (SamplesComboBox.SelectedItem.ToString()) {
78        case SamplesComboBoxAllSamples: {
79            indizes = Enumerable.Range(0, Content.ProblemData.Dataset.Rows).ToArray();
80            estimatedClassValues = Content.EstimatedClassValues.ToArray();
81            break;
82          }
83        case SamplesComboBoxTrainingSamples: {
84            indizes = Content.ProblemData.TrainingIndices.ToArray();
85            estimatedClassValues = Content.EstimatedTrainingClassValues.ToArray();
86            break;
87          }
88        case SamplesComboBoxTestSamples: {
89            indizes = Content.ProblemData.TestIndices.ToArray();
90            estimatedClassValues = Content.EstimatedTestClassValues.ToArray();
91            break;
92          }
93        default:
94          throw new ArgumentException();
95      }
96
97      IEnumerable<IClassificationSolution> solutions = Content.ClassificationSolutions.CheckedItems;
98      int classValuesCount = Content.ProblemData.Classes;
99      int solutionsCount = solutions.Count();
100      string[,] values = new string[indizes.Length, 5 + classValuesCount + solutionsCount];
101      double[] target = Content.ProblemData.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable).ToArray();
102      List<List<double?>> estimatedValuesVector = GetEstimatedValues(SamplesComboBox.SelectedItem.ToString(), indizes,
103                                                            solutions);
104
105      IClassificationEnsembleSolutionWeightCalculator weightCalc = Content.WeightCalculator;
106
107      // needed to calculate average confidences of correct and wrong estimated classes
108      bool correctClassified;
109      double[] confidence = new double[2];
110      int[] classified = new int[2];
111      double curConfidence;
112
113      double[] confidences = null;
114
115      if (SamplesComboBox.SelectedItem.ToString().Equals(SamplesComboBoxAllSamples)) {
116        confidences = weightCalc.GetConfidence(solutions,
117                                               indizes,
118                                               estimatedClassValues,
119                                               weightCalc.GetAllClassDelegate()).ToArray();
120      } else if (SamplesComboBox.SelectedItem.ToString().Equals(SamplesComboBoxTrainingSamples)) {
121        confidences = weightCalc.GetConfidence(solutions,
122                                               indizes,
123                                               estimatedClassValues,
124                                               weightCalc.GetTrainingClassDelegate()).ToArray();
125      } else if (SamplesComboBox.SelectedItem.ToString().Equals(SamplesComboBoxTestSamples)) {
126        confidences = weightCalc.GetConfidence(solutions,
127                                               indizes,
128                                               estimatedClassValues,
129                                               weightCalc.GetTestClassDelegate()).ToArray();
130      }
131
132      for (int i = 0; i < indizes.Length; i++) {
133        int row = indizes[i];
134        values[i, 0] = row.ToString();
135        values[i, 1] = target[i].ToString();
136        //display only indices and target values if no models are present
137        if (solutionsCount > 0) {
138          values[i, 2] = estimatedClassValues[i].ToString();
139          correctClassified = target[i].IsAlmost(estimatedClassValues[i]);
140          values[i, 3] = correctClassified.ToString();
141          curConfidence = confidences[i];
142          if (correctClassified) {
143            confidence[0] += curConfidence;
144            classified[0]++;
145          } else {
146            confidence[1] += curConfidence;
147            classified[1]++;
148          }
149
150          values[i, 4] = curConfidence.ToString();
151
152          var groups =
153            estimatedValuesVector[i].GroupBy(x => x).Select(g => new { Key = g.Key, Count = g.Count() }).ToList();
154
155          for (int classIndex = 0; classIndex < Content.ProblemData.Classes; classIndex++) {
156            var group = groups.Where(g => g.Key == Content.ProblemData.ClassValues.ElementAt(classIndex)).SingleOrDefault();
157            if (group == null) values[i, 5 + classIndex] = 0.ToString();
158            else values[i, 5 + classIndex] = group.Count.ToString();
159          }
160          for (int modelIndex = 0; modelIndex < estimatedValuesVector[i].Count; modelIndex++) {
161            values[i, 5 + classValuesCount + modelIndex] = estimatedValuesVector[i][modelIndex] == null
162                                                             ? string.Empty
163                                                             : estimatedValuesVector[i][modelIndex].ToString();
164          }
165        }
166      }
167
168      CorrectClassifiedConfidence.Text = (confidence[0] / (double)classified[0]).ToString();
169      WrongClassifiedConfidence.Text = (confidence[1] / (double)classified[1]).ToString();
170
171      StringMatrix matrix = new StringMatrix(values);
172      List<string> columnNames = new List<string>() { "Id", TargetClassValuesColumnName, EstimatedClassValuesColumnName, CorrectClassificationColumnName, ConfidenceColumnName };
173      columnNames.AddRange(Content.ProblemData.ClassNames);
174      columnNames.AddRange(Content.ClassificationSolutions.CheckedItems.Select(s => s.Model.Name));//.Model.Models.Select(m => m.Name));
175      matrix.ColumnNames = columnNames;
176      matrix.SortableView = true;
177      matrixView.Content = matrix;
178    }
179
180    private List<List<double?>> GetEstimatedValues(string samplesSelection, int[] rows, IEnumerable<IClassificationSolution> solutions) {
181      List<List<double?>> values = new List<List<double?>>();
182      int solutionIndex = 0;
183      foreach (var solution in solutions) {
184        double[] estimation = solution.GetEstimatedClassValues(rows).ToArray();
185        for (int i = 0; i < rows.Length; i++) {
186          var row = rows[i];
187          if (solutionIndex == 0) values.Add(new List<double?>());
188
189          if (samplesSelection == SamplesComboBoxAllSamples)
190            values[i].Add(estimation[i]);
191          else if (samplesSelection == SamplesComboBoxTrainingSamples && solution.ProblemData.IsTrainingSample(row))
192            values[i].Add(estimation[i]);
193          else if (samplesSelection == SamplesComboBoxTestSamples && solution.ProblemData.IsTestSample(row))
194            values[i].Add(estimation[i]);
195          else
196            values[i].Add(null);
197        }
198        solutionIndex++;
199      }
200      return values;
201    }
202
203    private void DataGridView_RowPrePaint(object sender, DataGridViewRowPrePaintEventArgs e) {
204      if (InvokeRequired) {
205        Invoke(new EventHandler<DataGridViewRowPrePaintEventArgs>(DataGridView_RowPrePaint), sender, e);
206        return;
207      }
208      var cellValue = matrixView.DataGridView[3, e.RowIndex].Value.ToString();
209      if (string.IsNullOrEmpty(cellValue)) return;
210      bool correctClassified = bool.Parse(cellValue);
211      matrixView.DataGridView.Rows[e.RowIndex].DefaultCellStyle.ForeColor = correctClassified ? Color.MediumSeaGreen : Color.Red;
212    }
213  }
214}
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