Free cookie consent management tool by TermsFeed Policy Generator

source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationEnsembleSolutionEstimatedClassValuesView.cs @ 6740

Last change on this file since 6740 was 6740, checked in by mkommend, 13 years ago

#1597, #1609, #1640:

  • Corrected TableFileParser to handle empty rows correctly.
  • Refactored DataSet to store values in List<List> instead of a two-dimensional array.
  • Enable importing and storing string and datetime values.
  • Changed data access methods in dataset and adapted all concerning classes.
  • Changed interpreter to store the variable values for all rows during the compilation step.
File size: 8.2 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 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
21using System;
22using System.Collections.Generic;
23using System.Drawing;
24using System.Linq;
25using System.Windows.Forms;
26using HeuristicLab.Data;
27using HeuristicLab.MainForm;
28using HeuristicLab.MainForm.WindowsForms;
29
30namespace HeuristicLab.Problems.DataAnalysis.Views {
31  [View("Estimated Class Values")]
32  [Content(typeof(ClassificationEnsembleSolution))]
33  public partial class ClassificationEnsembleSolutionEstimatedClassValuesView :
34    ClassificationSolutionEstimatedClassValuesView {
35    private const string RowColumnName = "Row";
36    private const string TargetClassValuesColumnName = "Target Variable";
37    private const string EstimatedClassValuesColumnName = "Estimated Class Values";
38    private const string CorrectClassificationColumnName = "Correct Classification";
39    private const string ConfidenceColumnName = "Confidence";
40
41    private const string SamplesComboBoxAllSamples = "All Samples";
42    private const string SamplesComboBoxTrainingSamples = "Training Samples";
43    private const string SamplesComboBoxTestSamples = "Test Samples";
44
45    public new ClassificationEnsembleSolution Content {
46      get { return (ClassificationEnsembleSolution)base.Content; }
47      set { base.Content = value; }
48    }
49
50    public ClassificationEnsembleSolutionEstimatedClassValuesView()
51      : base() {
52      InitializeComponent();
53      SamplesComboBox.Items.AddRange(new string[] { SamplesComboBoxAllSamples, SamplesComboBoxTrainingSamples, SamplesComboBoxTestSamples });
54      SamplesComboBox.SelectedIndex = 0;
55      matrixView.DataGridView.RowPrePaint += new DataGridViewRowPrePaintEventHandler(DataGridView_RowPrePaint);
56    }
57
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.TrainingIndizes.ToArray();
85            estimatedClassValues = Content.EstimatedTrainingClassValues.ToArray();
86            break;
87          }
88        case SamplesComboBoxTestSamples: {
89            indizes = Content.ProblemData.TestIndizes.ToArray();
90            estimatedClassValues = Content.EstimatedTestClassValues.ToArray();
91            break;
92          }
93        default:
94          throw new ArgumentException();
95      }
96
97      int classValuesCount = Content.ProblemData.ClassValues.Count;
98      int modelCount = Content.Model.Models.Count();
99      string[,] values = new string[indizes.Length, 5 + classValuesCount + modelCount];
100      double[] target = Content.ProblemData.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable).ToArray();
101      List<List<double?>> estimatedValuesVector = GetEstimatedValues(SamplesComboBox.SelectedItem.ToString(), indizes,
102                                                            Content.ClassificationSolutions);
103
104      for (int i = 0; i < indizes.Length; i++) {
105        int row = indizes[i];
106        values[i, 0] = row.ToString();
107        values[i, 1] = target[i].ToString();
108        values[i, 2] = estimatedClassValues[i].ToString();
109        values[i, 3] = (target[i] == estimatedClassValues[i]).ToString();
110        var groups = estimatedValuesVector[i].GroupBy(x => x).Select(g => new { Key = g.Key, Count = g.Count() }).ToList();
111        var estimationCount = groups.Where(g => g.Key != null).Select(g => g.Count).Sum();
112        values[i, 4] = (((double)groups.Where(g => g.Key == estimatedClassValues[i]).Single().Count) / estimationCount).ToString();
113        for (int classIndex = 0; classIndex < Content.ProblemData.ClassValues.Count; classIndex++) {
114          var group = groups.Where(g => g.Key == Content.ProblemData.ClassValues[classIndex]).SingleOrDefault();
115          if (group == null) values[i, 5 + classIndex] = 0.ToString();
116          else values[i, 5 + classIndex] = group.Count.ToString();
117        }
118        for (int modelIndex = 0; modelIndex < estimatedValuesVector[i].Count; modelIndex++) {
119          values[i, 5 + classValuesCount + modelIndex] = estimatedValuesVector[i][modelIndex] == null
120                                                           ? string.Empty
121                                                           : estimatedValuesVector[i][modelIndex].ToString();
122        }
123
124      }
125
126      StringMatrix matrix = new StringMatrix(values);
127      List<string> columnNames = new List<string>() { "Id", TargetClassValuesColumnName, EstimatedClassValuesColumnName, CorrectClassificationColumnName, ConfidenceColumnName };
128      columnNames.AddRange(Content.ProblemData.ClassNames);
129      columnNames.AddRange(Content.Model.Models.Select(m => m.Name));
130      matrix.ColumnNames = columnNames;
131      matrix.SortableView = true;
132      matrixView.Content = matrix;
133      UpdateColoringOfRows();
134    }
135
136    private List<List<double?>> GetEstimatedValues(string samplesSelection, int[] rows, IEnumerable<IClassificationSolution> solutions) {
137      List<List<double?>> values = new List<List<double?>>();
138      int solutionIndex = 0;
139      foreach (var solution in solutions) {
140        double[] estimation = solution.GetEstimatedClassValues(rows).ToArray();
141        for (int i = 0; i < rows.Length; i++) {
142          var row = rows[i];
143          if (solutionIndex == 0) values.Add(new List<double?>());
144
145          if (samplesSelection == SamplesComboBoxAllSamples)
146            values[i].Add(estimation[i]);
147          else if (samplesSelection == SamplesComboBoxTrainingSamples && solution.ProblemData.IsTrainingSample(row))
148            values[i].Add(estimation[i]);
149          else if (samplesSelection == SamplesComboBoxTestSamples && solution.ProblemData.IsTestSample(row))
150            values[i].Add(estimation[i]);
151          else
152            values[i].Add(null);
153        }
154        solutionIndex++;
155      }
156      return values;
157    }
158
159    private void DataGridView_RowPrePaint(object sender, DataGridViewRowPrePaintEventArgs e) {
160      if (InvokeRequired) {
161        Invoke(new EventHandler<DataGridViewRowPrePaintEventArgs>(DataGridView_RowPrePaint), sender, e);
162        return;
163      }
164      bool correctClassified = bool.Parse(matrixView.DataGridView[3, e.RowIndex].Value.ToString());
165      matrixView.DataGridView.Rows[e.RowIndex].DefaultCellStyle.ForeColor = correctClassified ? Color.MediumSeaGreen : Color.Red;
166    }
167
168    private void UpdateColoringOfRows() {
169      if (InvokeRequired) {
170        Invoke((Action)UpdateColoringOfRows);
171        return;
172      }
173      //matrixView.DataGridView.SuspendRepaint();
174      //for (int i = 0; i < matrixView.DataGridView.Rows.Count; i++) {
175      //  bool correctClassified = bool.Parse(matrixView.Content.GetValue(i, 3));
176      //  matrixView.DataGridView.Rows[i].DefaultCellStyle.ForeColor = correctClassified ? Color.MediumSeaGreen : Color.Red;
177      //}
178      //matrixView.DataGridView.ResumeRepaint(true);
179    }
180  }
181}
Note: See TracBrowser for help on using the repository browser.