Free cookie consent management tool by TermsFeed Policy Generator

source: branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationEnsembleSolutionEstimatedClassValuesView.cs @ 7866

Last change on this file since 7866 was 7866, checked in by sforsten, 12 years ago

#1776:

  • merged r7454:7863 trunk into branch to make it compatible again
  • adjusted GetConfidence of MajorityVoteWeightCalculator to be in an interval of [0, 1]
File size: 8.4 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
21using System;
22using System.Collections.Generic;
23using System.Drawing;
24using System.Linq;
25using System.Windows.Forms;
26using HeuristicLab.Common;
27using HeuristicLab.Data;
28using HeuristicLab.MainForm;
29using HeuristicLab.MainForm.WindowsForms;
30using HeuristicLab.Problems.DataAnalysis.Interfaces.Classification;
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.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      IEnumerable<IClassificationSolution> solutions = Content.ClassificationSolutions.CheckedItems;
98      int classValuesCount = Content.ProblemData.ClassValues.Count;
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      for (int i = 0; i < indizes.Length; i++) {
108        int row = indizes[i];
109        values[i, 0] = row.ToString();
110        values[i, 1] = target[i].ToString();
111        //display only indices and target values if no models are present
112        if (solutionsCount > 0) {
113          values[i, 2] = estimatedClassValues[i].ToString();
114          values[i, 3] = (target[i].IsAlmost(estimatedClassValues[i])).ToString();
115          values[i, 4] = weightCalc.GetConfidence(solutions, indizes[i], estimatedClassValues[i]).ToString();
116
117          var groups =
118            estimatedValuesVector[i].GroupBy(x => x).Select(g => new { Key = g.Key, Count = g.Count() }).ToList();
119          for (int classIndex = 0; classIndex < Content.ProblemData.ClassValues.Count; classIndex++) {
120            var group = groups.Where(g => g.Key == Content.ProblemData.ClassValues[classIndex]).SingleOrDefault();
121            if (group == null) values[i, 5 + classIndex] = 0.ToString();
122            else values[i, 5 + classIndex] = group.Count.ToString();
123          }
124          for (int modelIndex = 0; modelIndex < estimatedValuesVector[i].Count; modelIndex++) {
125            values[i, 5 + classValuesCount + modelIndex] = estimatedValuesVector[i][modelIndex] == null
126                                                             ? string.Empty
127                                                             : estimatedValuesVector[i][modelIndex].ToString();
128          }
129        }
130      }
131
132      StringMatrix matrix = new StringMatrix(values);
133      List<string> columnNames = new List<string>() { "Id", TargetClassValuesColumnName, EstimatedClassValuesColumnName, CorrectClassificationColumnName, ConfidenceColumnName };
134      columnNames.AddRange(Content.ProblemData.ClassNames);
135      columnNames.AddRange(Content.ClassificationSolutions.CheckedItems.Select(s => s.Model.Name));//.Model.Models.Select(m => m.Name));
136      matrix.ColumnNames = columnNames;
137      matrix.SortableView = true;
138      matrixView.Content = matrix;
139    }
140
141    private IEnumerable<int> FindAllIndices(List<double?> list, double value) {
142      List<int> indices = new List<int>();
143      for (int i = 0; i < list.Count; i++) {
144        if (list[i].Equals(value))
145          indices.Add(i);
146      }
147      return indices;
148    }
149
150    private List<List<double?>> GetEstimatedValues(string samplesSelection, int[] rows, IEnumerable<IClassificationSolution> solutions) {
151      List<List<double?>> values = new List<List<double?>>();
152      int solutionIndex = 0;
153      foreach (var solution in solutions) {
154        double[] estimation = solution.GetEstimatedClassValues(rows).ToArray();
155        for (int i = 0; i < rows.Length; i++) {
156          var row = rows[i];
157          if (solutionIndex == 0) values.Add(new List<double?>());
158
159          if (samplesSelection == SamplesComboBoxAllSamples)
160            values[i].Add(estimation[i]);
161          else if (samplesSelection == SamplesComboBoxTrainingSamples && solution.ProblemData.IsTrainingSample(row))
162            values[i].Add(estimation[i]);
163          else if (samplesSelection == SamplesComboBoxTestSamples && solution.ProblemData.IsTestSample(row))
164            values[i].Add(estimation[i]);
165          else
166            values[i].Add(null);
167        }
168        solutionIndex++;
169      }
170      return values;
171    }
172
173    private void DataGridView_RowPrePaint(object sender, DataGridViewRowPrePaintEventArgs e) {
174      if (InvokeRequired) {
175        Invoke(new EventHandler<DataGridViewRowPrePaintEventArgs>(DataGridView_RowPrePaint), sender, e);
176        return;
177      }
178      var cellValue = matrixView.DataGridView[3, e.RowIndex].Value.ToString();
179      if (string.IsNullOrEmpty(cellValue)) return;
180      bool correctClassified = bool.Parse(cellValue);
181      matrixView.DataGridView.Rows[e.RowIndex].DefaultCellStyle.ForeColor = correctClassified ? Color.MediumSeaGreen : Color.Red;
182    }
183  }
184}
Note: See TracBrowser for help on using the repository browser.