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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis.Views/3.4/GaussianProcessRegressionSolutionLineChartView.cs @ 8484

Last change on this file since 8484 was 8484, checked in by gkronber, 12 years ago

#1902 changed interface for covariance functions to improve readability, fixed several bugs in the covariance functions and in the line chart for Gaussian process models.
File size: 15.4 KB
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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 System.Windows.Forms.DataVisualization.Charting;
27using HeuristicLab.MainForm;
28using HeuristicLab.Problems.DataAnalysis.Views;
29
30namespace HeuristicLab.Algorithms.DataAnalysis.Views {
31  [View("Line Chart 2")]
32  [Content(typeof(GaussianProcessRegressionSolution))]
33  public partial class GaussianProcessRegressionSolutionLineChartView : DataAnalysisSolutionEvaluationView {
34    private const string TARGETVARIABLE_SERIES_NAME = "Target Variable";
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 ESTIMATEDVALUES_ALL_SERIES_NAME = "Estimated Values (all samples)";
38
39    public new GaussianProcessRegressionSolution Content {
40      get { return (GaussianProcessRegressionSolution)base.Content; }
41      set { base.Content = value; }
42    }
43
44    public GaussianProcessRegressionSolutionLineChartView()
45      : base() {
46      InitializeComponent();
47      //configure axis
48      this.chart.CustomizeAllChartAreas();
49      this.chart.ChartAreas[0].CursorX.IsUserSelectionEnabled = true;
50      this.chart.ChartAreas[0].AxisX.ScaleView.Zoomable = true;
51      this.chart.ChartAreas[0].AxisX.IsStartedFromZero = true;
52      this.chart.ChartAreas[0].CursorX.Interval = 1;
53
54      this.chart.ChartAreas[0].CursorY.IsUserSelectionEnabled = true;
55      this.chart.ChartAreas[0].AxisY.ScaleView.Zoomable = true;
56      this.chart.ChartAreas[0].CursorY.Interval = 0;
57    }
58
59    private void RedrawChart() {
60      this.chart.Series.Clear();
61      if (Content != null) {
62        this.chart.ChartAreas[0].AxisX.Minimum = 0;
63        this.chart.ChartAreas[0].AxisX.Maximum = Content.ProblemData.Dataset.Rows - 1;
64
65        // training series
66        this.chart.Series.Add(ESTIMATEDVALUES_TRAINING_SERIES_NAME);
67        this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].LegendText = ESTIMATEDVALUES_TRAINING_SERIES_NAME;
68        this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].ChartType = SeriesChartType.Range;
69        this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].EmptyPointStyle.Color = this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Color;
70        var mean = Content.EstimatedTrainingValues.ToArray();
71        var s2 = Content.EstimatedTrainingVariance.ToArray();
72        var lower = mean.Zip(s2, (m, s) => m - 1.96 * Math.Sqrt(s)).ToArray();
73        var upper = mean.Zip(s2, (m, s) => m + 1.96 * Math.Sqrt(s)).ToArray();
74        this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Points.DataBindXY(Content.ProblemData.TrainingIndices.ToArray(), lower, upper);
75        this.InsertEmptyPoints(this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME]);
76        this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Tag = Content;
77
78        // test series
79        this.chart.Series.Add(ESTIMATEDVALUES_TEST_SERIES_NAME);
80        this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].LegendText = ESTIMATEDVALUES_TEST_SERIES_NAME;
81        this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].ChartType = SeriesChartType.Range;
82
83        mean = Content.EstimatedTestValues.ToArray();
84        s2 = Content.EstimatedTestVariance.ToArray();
85        lower = mean.Zip(s2, (m, s) => m - 1.96 * Math.Sqrt(s)).ToArray();
86        upper = mean.Zip(s2, (m, s) => m + 1.96 * Math.Sqrt(s)).ToArray();
87        this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].Points.DataBindXY(Content.ProblemData.TestIndices.ToArray(), lower, upper);
88        this.InsertEmptyPoints(this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME]);
89        this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].Tag = Content;
90
91        // series of remaining points
92        int[] allIndices = Enumerable.Range(0, Content.ProblemData.Dataset.Rows).Except(Content.ProblemData.TrainingIndices).Except(Content.ProblemData.TestIndices).ToArray();
93        mean = Content.EstimatedValues.ToArray();
94        s2 = Content.EstimatedVariance.ToArray();
95        lower = mean.Zip(s2, (m, s) => m - 1.96 * Math.Sqrt(s)).ToArray();
96        upper = mean.Zip(s2, (m, s) => m + 1.96 * Math.Sqrt(s)).ToArray();
97        List<double> allLower = allIndices.Select(index => lower[index]).ToList();
98        List<double> allUpper = allIndices.Select(index => upper[index]).ToList();
99        this.chart.Series.Add(ESTIMATEDVALUES_ALL_SERIES_NAME);
100        this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME].LegendText = ESTIMATEDVALUES_ALL_SERIES_NAME;
101        this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME].ChartType = SeriesChartType.Range;
102        if (allIndices.Count() > 0) {
103          this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME].Points.DataBindXY(allIndices, allLower, allUpper);
104          this.InsertEmptyPoints(this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME]);
105        }
106        this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME].Tag = Content;
107
108        // target
109        this.chart.Series.Add(TARGETVARIABLE_SERIES_NAME);
110        this.chart.Series[TARGETVARIABLE_SERIES_NAME].LegendText = Content.ProblemData.TargetVariable;
111        this.chart.Series[TARGETVARIABLE_SERIES_NAME].ChartType = SeriesChartType.FastLine;
112        this.chart.Series[TARGETVARIABLE_SERIES_NAME].Points.DataBindXY(Enumerable.Range(0, Content.ProblemData.Dataset.Rows).ToArray(),
113          Content.ProblemData.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable).ToArray());
114
115        this.ToggleSeriesData(this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME]);
116
117        UpdateCursorInterval();
118        this.UpdateStripLines();
119      }
120    }
121
122    private void InsertEmptyPoints(Series series) {
123      int i = 0;
124      while (i < series.Points.Count - 1) {
125        if (series.Points[i].IsEmpty) {
126          ++i;
127          continue;
128        }
129
130        var p1 = series.Points[i];
131        var p2 = series.Points[i + 1];
132        // check for consecutive indices
133        if ((int)p2.XValue - (int)p1.XValue != 1) {
134          // insert an empty point between p1 and p2 so that the line will be invisible (transparent)
135          var p = new DataPoint((int)((p1.XValue + p2.XValue) / 2), new double[] { 0.0, 0.0 }) { IsEmpty = true };
136          // insert
137          series.Points.Insert(i + 1, p);
138        }
139        ++i;
140      }
141    }
142
143    private void UpdateCursorInterval() {
144      var estimatedValues = this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Points.Select(x => x.YValues[0]).DefaultIfEmpty(1.0);
145      var targetValues = this.chart.Series[TARGETVARIABLE_SERIES_NAME].Points.Select(x => x.YValues[0]).DefaultIfEmpty(1.0);
146      double estimatedValuesRange = estimatedValues.Max() - estimatedValues.Min();
147      double targetValuesRange = targetValues.Max() - targetValues.Min();
148      double interestingValuesRange = Math.Min(Math.Max(targetValuesRange, 1.0), Math.Max(estimatedValuesRange, 1.0));
149      double digits = (int)Math.Log10(interestingValuesRange) - 3;
150      double yZoomInterval = Math.Max(Math.Pow(10, digits), 10E-5);
151      this.chart.ChartAreas[0].CursorY.Interval = yZoomInterval;
152    }
153
154    #region events
155    protected override void RegisterContentEvents() {
156      base.RegisterContentEvents();
157      Content.ModelChanged += new EventHandler(Content_ModelChanged);
158      Content.ProblemDataChanged += new EventHandler(Content_ProblemDataChanged);
159    }
160    protected override void DeregisterContentEvents() {
161      base.DeregisterContentEvents();
162      Content.ModelChanged -= new EventHandler(Content_ModelChanged);
163      Content.ProblemDataChanged -= new EventHandler(Content_ProblemDataChanged);
164    }
165
166    protected override void OnContentChanged() {
167      base.OnContentChanged();
168      RedrawChart();
169    }
170    private void Content_ProblemDataChanged(object sender, EventArgs e) {
171      RedrawChart();
172    }
173    private void Content_ModelChanged(object sender, EventArgs e) {
174      RedrawChart();
175    }
176
177
178
179    private void Chart_MouseDoubleClick(object sender, MouseEventArgs e) {
180      HitTestResult result = chart.HitTest(e.X, e.Y);
181      if (result.ChartArea != null && (result.ChartElementType == ChartElementType.PlottingArea ||
182                                       result.ChartElementType == ChartElementType.Gridlines) ||
183                                       result.ChartElementType == ChartElementType.StripLines) {
184        foreach (var axis in result.ChartArea.Axes)
185          axis.ScaleView.ZoomReset(int.MaxValue);
186      }
187    }
188    #endregion
189
190    private void UpdateStripLines() {
191      this.chart.ChartAreas[0].AxisX.StripLines.Clear();
192
193      int[] attr = new int[Content.ProblemData.Dataset.Rows + 1]; // add a virtual last row that is again empty to simplify loop further down
194      foreach (var row in Content.ProblemData.TrainingIndices) {
195        attr[row] += 1;
196      }
197      foreach (var row in Content.ProblemData.TestIndices) {
198        attr[row] += 2;
199      }
200      int start = 0;
201      int curAttr = attr[start];
202      for (int row = 0; row < attr.Length; row++) {
203        if (attr[row] != curAttr) {
204          switch (curAttr) {
205            case 0: break;
206            case 1:
207              this.CreateAndAddStripLine("Training", start, row, Color.FromArgb(40, Color.Green), Color.Transparent);
208              break;
209            case 2:
210              this.CreateAndAddStripLine("Test", start, row, Color.FromArgb(40, Color.Red), Color.Transparent);
211              break;
212            case 3:
213              this.CreateAndAddStripLine("Training and Test", start, row, Color.FromArgb(40, Color.Green), Color.FromArgb(40, Color.Red), ChartHatchStyle.WideUpwardDiagonal);
214              break;
215            default:
216              // should not happen
217              break;
218          }
219          curAttr = attr[row];
220          start = row;
221        }
222      }
223    }
224
225    private void CreateAndAddStripLine(string title, int start, int end, Color color, Color secondColor, ChartHatchStyle hatchStyle = ChartHatchStyle.None) {
226      StripLine stripLine = new StripLine();
227      stripLine.BackColor = color;
228      stripLine.BackSecondaryColor = secondColor;
229      stripLine.BackHatchStyle = hatchStyle;
230      stripLine.Text = title;
231      stripLine.Font = new Font("Times New Roman", 12, FontStyle.Bold);
232      // strip range is [start .. end] inclusive, but we evaluate [start..end[ (end is exclusive)
233      // the strip should be by one longer (starting at start - 0.5 and ending at end + 0.5)
234      stripLine.StripWidth = end - start;
235      stripLine.IntervalOffset = start - 0.5; // start slightly to the left of the first point to clearly indicate the first point in the partition
236      this.chart.ChartAreas[0].AxisX.StripLines.Add(stripLine);
237    }
238
239    private void ToggleSeriesData(Series series) {
240      if (series.Points.Count > 0) {  //checks if series is shown
241        if (this.chart.Series.Any(s => s != series && s.Points.Count > 0)) {
242          ClearPointsQuick(series.Points);
243        }
244      } else if (Content != null) {
245
246        IEnumerable<int> indices = null;
247        IEnumerable<double> mean = null;
248        IEnumerable<double> s2 = null;
249        double[] lower = null;
250        double[] upper = null;
251        switch (series.Name) {
252          case ESTIMATEDVALUES_ALL_SERIES_NAME:
253            indices = Enumerable.Range(0, Content.ProblemData.Dataset.Rows).Except(Content.ProblemData.TrainingIndices).Except(Content.ProblemData.TestIndices).ToArray();
254            mean = Content.EstimatedValues.ToArray();
255            s2 = Content.EstimatedVariance.ToArray();
256            lower = mean.Zip(s2, (m, s) => m - 1.96 * Math.Sqrt(s)).ToArray();
257            upper = mean.Zip(s2, (m, s) => m + 1.96 * Math.Sqrt(s)).ToArray();
258            lower = indices.Select(index => lower[index]).ToArray();
259            upper = indices.Select(index => upper[index]).ToArray();
260            break;
261          case ESTIMATEDVALUES_TRAINING_SERIES_NAME:
262            indices = Content.ProblemData.TrainingIndices.ToArray();
263            mean = Content.EstimatedTrainingValues.ToArray();
264            s2 = Content.EstimatedTrainingVariance.ToArray();
265            lower = mean.Zip(s2, (m, s) => m - 1.96 * Math.Sqrt(s)).ToArray();
266            upper = mean.Zip(s2, (m, s) => m + 1.96 * Math.Sqrt(s)).ToArray();
267            break;
268          case ESTIMATEDVALUES_TEST_SERIES_NAME:
269            indices = Content.ProblemData.TestIndices.ToArray();
270            mean = Content.EstimatedTestValues.ToArray();
271            s2 = Content.EstimatedTestVariance.ToArray();
272            lower = mean.Zip(s2, (m, s) => m - s).ToArray();
273            upper = mean.Zip(s2, (m, s) => m + s).ToArray();
274            break;
275        }
276        if (indices.Count() > 0) {
277          series.Points.DataBindXY(indices, lower, upper);
278          this.InsertEmptyPoints(series);
279          chart.Legends[series.Legend].ForeColor = Color.Black;
280          UpdateCursorInterval();
281          chart.Refresh();
282        }
283      }
284    }
285
286    // workaround as per http://stackoverflow.com/questions/5744930/datapointcollection-clear-performance
287    private static void ClearPointsQuick(DataPointCollection points) {
288      points.SuspendUpdates();
289      while (points.Count > 0)
290        points.RemoveAt(points.Count - 1);
291      points.ResumeUpdates();
292    }
293
294    private void chart_MouseMove(object sender, MouseEventArgs e) {
295      HitTestResult result = chart.HitTest(e.X, e.Y);
296      if (result.ChartElementType == ChartElementType.LegendItem && result.Series.Name != TARGETVARIABLE_SERIES_NAME)
297        Cursor = Cursors.Hand;
298      else
299        Cursor = Cursors.Default;
300    }
301    private void chart_MouseDown(object sender, MouseEventArgs e) {
302      HitTestResult result = chart.HitTest(e.X, e.Y);
303      if (result.ChartElementType == ChartElementType.LegendItem && result.Series.Name != TARGETVARIABLE_SERIES_NAME) {
304        ToggleSeriesData(result.Series);
305      }
306    }
307
308    private void chart_CustomizeLegend(object sender, CustomizeLegendEventArgs e) {
309      if (chart.Series.Count != 4) return;
310      e.LegendItems[0].Cells[1].ForeColor = this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Points.Count == 0 ? Color.Gray : Color.Black;
311      e.LegendItems[1].Cells[1].ForeColor = this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].Points.Count == 0 ? Color.Gray : Color.Black;
312      e.LegendItems[2].Cells[1].ForeColor = this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME].Points.Count == 0 ? Color.Gray : Color.Black;
313      e.LegendItems[3].Cells[1].ForeColor = this.chart.Series[TARGETVARIABLE_SERIES_NAME].Points.Count == 0 ? Color.Gray : Color.Black;
314    }
315  }
316}
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