source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis.Views/3.4/GaussianProcessRegressionSolutionEstimatedValuesView.cs @ 13439

Last change on this file since 13439 was 13439, checked in by gkronber, 6 years ago

#2542: added estimated values view specific for Gaussian processes that shows predicted variance

File size: 5.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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.Linq;
23using System.Windows.Forms;
24using HeuristicLab.Data;
25using HeuristicLab.Data.Views;
26using HeuristicLab.MainForm;
27using HeuristicLab.Problems.DataAnalysis.Views;
28
29namespace HeuristicLab.Algorithms.DataAnalysis.Views {
30  [View("Estimated Values")]
31  [Content(typeof(GaussianProcessRegressionSolution), false)]
32  public partial class GaussianProcessRegressionSolutionEstimatedValuesView : RegressionSolutionEstimatedValuesView {
33    private const string TARGETVARIABLE_SERIES_NAME = "Target Variable";
34    private const string ESTIMATEDVALUES_SERIES_NAME = "Estimated Values (all)";
35    private const string ESTIMATEDVALUES_TRAINING_SERIES_NAME = "Estimated Values (training)";
36    private const string ESTIMATEDVALUES_TEST_SERIES_NAME = "Estimated Values (test)";
37    private const string ESTIMATEDVARIANCE_TRAINING_SERIES_NAME = "Estimated Variance (training)";
38    private const string ESTIMATEDVARIANCE_TEST_SERIES_NAME = "Estimated Variance (test)";
39
40    public new GaussianProcessRegressionSolution Content {
41      get { return (GaussianProcessRegressionSolution)base.Content; }
42      set {
43        base.Content = value;
44      }
45    }
46
47    public GaussianProcessRegressionSolutionEstimatedValuesView()
48      : base() {
49      InitializeComponent();
50    }
51
52    #region events
53    protected override void RegisterContentEvents() {
54      base.RegisterContentEvents();
55      Content.ModelChanged += new EventHandler(Content_ModelChanged);
56      Content.ProblemDataChanged += new EventHandler(Content_ProblemDataChanged);
57    }
58
59    protected override void DeregisterContentEvents() {
60      base.DeregisterContentEvents();
61      Content.ModelChanged -= new EventHandler(Content_ModelChanged);
62      Content.ProblemDataChanged -= new EventHandler(Content_ProblemDataChanged);
63    }
64
65    private void Content_ProblemDataChanged(object sender, EventArgs e) {
66      OnContentChanged();
67    }
68
69    private void Content_ModelChanged(object sender, EventArgs e) {
70      OnContentChanged();
71    }
72
73    protected override void OnContentChanged() {
74      base.OnContentChanged();
75      UpdateEstimatedValues();
76    }
77
78    private void UpdateEstimatedValues() {
79      if (InvokeRequired) Invoke((Action)UpdateEstimatedValues);
80      else {
81        StringMatrix matrix = null;
82        if (Content != null) {
83          string[,] values = new string[Content.ProblemData.Dataset.Rows, 9];
84
85          var trainingRows = Content.ProblemData.TrainingIndices;
86          var testRows = Content.ProblemData.TestIndices;
87
88          double[] target = Content.ProblemData.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable).ToArray();
89          var estimated = Content.EstimatedValues.GetEnumerator();
90          var estimated_training = Content.EstimatedTrainingValues.GetEnumerator();
91          var estimated_test = Content.EstimatedTestValues.GetEnumerator();
92          var estimated_var_training = Content.GetEstimatedVariance(trainingRows).GetEnumerator();
93          var estimated_var_test = Content.GetEstimatedVariance(testRows).GetEnumerator();
94
95          foreach (var row in Content.ProblemData.TrainingIndices) {
96            estimated_training.MoveNext();
97            estimated_var_training.MoveNext();
98            values[row, 3] = estimated_training.Current.ToString();
99            values[row, 7] = estimated_var_training.Current.ToString();
100          }
101
102          foreach (var row in Content.ProblemData.TestIndices) {
103            estimated_test.MoveNext();
104            estimated_var_test.MoveNext();
105            values[row, 4] = estimated_test.Current.ToString();
106            values[row, 8] = estimated_var_test.Current.ToString();
107          }
108
109          foreach (var row in Enumerable.Range(0, Content.ProblemData.Dataset.Rows)) {
110            estimated.MoveNext();
111            double est = estimated.Current;
112            double res = Math.Abs(est - target[row]);
113            values[row, 0] = row.ToString();
114            values[row, 1] = target[row].ToString();
115            values[row, 2] = est.ToString();
116            values[row, 5] = Math.Abs(res).ToString();
117            values[row, 6] = Math.Abs(res / est).ToString();
118          }
119
120          matrix = new StringMatrix(values);
121          matrix.ColumnNames = new string[] { "Id", TARGETVARIABLE_SERIES_NAME, ESTIMATEDVALUES_SERIES_NAME, ESTIMATEDVALUES_TRAINING_SERIES_NAME, ESTIMATEDVALUES_TEST_SERIES_NAME, "Absolute Error (all)", "Relative Error (all)", ESTIMATEDVARIANCE_TRAINING_SERIES_NAME, ESTIMATEDVARIANCE_TEST_SERIES_NAME };
122          matrix.SortableView = true;
123        }
124        matrixView.Content = matrix;
125      }
126    }
127    #endregion
128  }
129}
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