#region License Information /* HeuristicLab * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System; using System.Collections.Generic; using System.Drawing; using System.Linq; using System.Windows.Forms; using System.Windows.Forms.DataVisualization.Charting; using HeuristicLab.MainForm; using HeuristicLab.Problems.DataAnalysis.Views; namespace HeuristicLab.Algorithms.DataAnalysis.Views { [View("Line Chart (95% confidence interval)")] [Content(typeof(GaussianProcessRegressionSolution))] public partial class GaussianProcessRegressionSolutionLineChartView : DataAnalysisSolutionEvaluationView { private const string TARGETVARIABLE_SERIES_NAME = "Target Variable"; private const string ESTIMATEDVALUES_TRAINING_SERIES_NAME = "Estimated Values (training)"; private const string ESTIMATEDVALUES_TEST_SERIES_NAME = "Estimated Values (test)"; private const string ESTIMATEDVALUES_ALL_SERIES_NAME = "Estimated Values (all samples)"; public new GaussianProcessRegressionSolution Content { get { return (GaussianProcessRegressionSolution)base.Content; } set { base.Content = value; } } public GaussianProcessRegressionSolutionLineChartView() : base() { InitializeComponent(); //configure axis this.chart.CustomizeAllChartAreas(); this.chart.ChartAreas[0].CursorX.IsUserSelectionEnabled = true; this.chart.ChartAreas[0].AxisX.ScaleView.Zoomable = true; this.chart.ChartAreas[0].AxisX.IsStartedFromZero = true; this.chart.ChartAreas[0].CursorX.Interval = 1; this.chart.ChartAreas[0].CursorY.IsUserSelectionEnabled = true; this.chart.ChartAreas[0].AxisY.ScaleView.Zoomable = true; this.chart.ChartAreas[0].CursorY.Interval = 0; } private void RedrawChart() { this.chart.Series.Clear(); if (Content != null) { this.chart.ChartAreas[0].AxisX.Minimum = 0; this.chart.ChartAreas[0].AxisX.Maximum = Content.ProblemData.Dataset.Rows - 1; // training series this.chart.Series.Add(ESTIMATEDVALUES_TRAINING_SERIES_NAME); this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].LegendText = ESTIMATEDVALUES_TRAINING_SERIES_NAME; this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].ChartType = SeriesChartType.Range; this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].EmptyPointStyle.Color = this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Color; var mean = Content.EstimatedTrainingValues.ToArray(); var s2 = Content.EstimatedTrainingVariance.ToArray(); var lower = mean.Zip(s2, GetLowerConfBound).ToArray(); var upper = mean.Zip(s2, GetUpperConfBound).ToArray(); this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Points.DataBindXY(Content.ProblemData.TrainingIndices.ToArray(), lower, upper); this.InsertEmptyPoints(this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME]); this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Tag = Content; // test series this.chart.Series.Add(ESTIMATEDVALUES_TEST_SERIES_NAME); this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].LegendText = ESTIMATEDVALUES_TEST_SERIES_NAME; this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].ChartType = SeriesChartType.Range; mean = Content.EstimatedTestValues.ToArray(); s2 = Content.EstimatedTestVariance.ToArray(); lower = mean.Zip(s2, GetLowerConfBound).ToArray(); upper = mean.Zip(s2, GetUpperConfBound).ToArray(); this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].Points.DataBindXY(Content.ProblemData.TestIndices.ToArray(), lower, upper); this.InsertEmptyPoints(this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME]); this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].Tag = Content; // series of remaining points int[] allIndices = Enumerable.Range(0, Content.ProblemData.Dataset.Rows).Except(Content.ProblemData.TrainingIndices).Except(Content.ProblemData.TestIndices).ToArray(); mean = Content.EstimatedValues.ToArray(); s2 = Content.EstimatedVariance.ToArray(); lower = mean.Zip(s2, GetLowerConfBound).ToArray(); upper = mean.Zip(s2, GetUpperConfBound).ToArray(); List allLower = allIndices.Select(index => lower[index]).ToList(); List allUpper = allIndices.Select(index => upper[index]).ToList(); this.chart.Series.Add(ESTIMATEDVALUES_ALL_SERIES_NAME); this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME].LegendText = ESTIMATEDVALUES_ALL_SERIES_NAME; this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME].ChartType = SeriesChartType.Range; if (allIndices.Count() > 0) { this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME].Points.DataBindXY(allIndices, allLower, allUpper); this.InsertEmptyPoints(this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME]); } this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME].Tag = Content; // target this.chart.Series.Add(TARGETVARIABLE_SERIES_NAME); this.chart.Series[TARGETVARIABLE_SERIES_NAME].LegendText = Content.ProblemData.TargetVariable; this.chart.Series[TARGETVARIABLE_SERIES_NAME].ChartType = SeriesChartType.FastLine; this.chart.Series[TARGETVARIABLE_SERIES_NAME].Points.DataBindXY(Enumerable.Range(0, Content.ProblemData.Dataset.Rows).ToArray(), Content.ProblemData.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable).ToArray()); this.ToggleSeriesData(this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME]); // the series have been added in different order than in the normal line chart // --> adapt coloring; chart.ApplyPaletteColors(); this.chart.Palette = ChartColorPalette.None; var s0Color = chart.Series[0].Color; var s1Color = chart.Series[1].Color; var s2Color = chart.Series[2].Color; var s3Color = chart.Series[3].Color; this.chart.PaletteCustomColors = new Color[] { s1Color, s2Color, s3Color, s0Color }; UpdateCursorInterval(); this.UpdateStripLines(); } } private void InsertEmptyPoints(Series series) { int i = 0; while (i < series.Points.Count - 1) { if (series.Points[i].IsEmpty) { ++i; continue; } var p1 = series.Points[i]; var p2 = series.Points[i + 1]; // check for consecutive indices if ((int)p2.XValue - (int)p1.XValue != 1) { // insert an empty point between p1 and p2 so that the line will be invisible (transparent) var p = new DataPoint((int)((p1.XValue + p2.XValue) / 2), new double[] { 0.0, 0.0 }) { IsEmpty = true }; // insert series.Points.Insert(i + 1, p); } ++i; } } private void UpdateCursorInterval() { var estimatedValues = this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Points.Select(x => x.YValues[0]).DefaultIfEmpty(1.0); var targetValues = this.chart.Series[TARGETVARIABLE_SERIES_NAME].Points.Select(x => x.YValues[0]).DefaultIfEmpty(1.0); double estimatedValuesRange = estimatedValues.Max() - estimatedValues.Min(); double targetValuesRange = targetValues.Max() - targetValues.Min(); double interestingValuesRange = Math.Min(Math.Max(targetValuesRange, 1.0), Math.Max(estimatedValuesRange, 1.0)); double digits = (int)Math.Log10(interestingValuesRange) - 3; double yZoomInterval = Math.Max(Math.Pow(10, digits), 10E-5); this.chart.ChartAreas[0].CursorY.Interval = yZoomInterval; } #region events protected override void RegisterContentEvents() { base.RegisterContentEvents(); Content.ModelChanged += new EventHandler(Content_ModelChanged); Content.ProblemDataChanged += new EventHandler(Content_ProblemDataChanged); } protected override void DeregisterContentEvents() { base.DeregisterContentEvents(); Content.ModelChanged -= new EventHandler(Content_ModelChanged); Content.ProblemDataChanged -= new EventHandler(Content_ProblemDataChanged); } protected override void OnContentChanged() { base.OnContentChanged(); RedrawChart(); } private void Content_ProblemDataChanged(object sender, EventArgs e) { RedrawChart(); } private void Content_ModelChanged(object sender, EventArgs e) { RedrawChart(); } private void Chart_MouseDoubleClick(object sender, MouseEventArgs e) { HitTestResult result = chart.HitTest(e.X, e.Y); if (result.ChartArea != null && (result.ChartElementType == ChartElementType.PlottingArea || result.ChartElementType == ChartElementType.Gridlines) || result.ChartElementType == ChartElementType.StripLines) { foreach (var axis in result.ChartArea.Axes) axis.ScaleView.ZoomReset(int.MaxValue); } } #endregion private void UpdateStripLines() { this.chart.ChartAreas[0].AxisX.StripLines.Clear(); int[] attr = new int[Content.ProblemData.Dataset.Rows + 1]; // add a virtual last row that is again empty to simplify loop further down foreach (var row in Content.ProblemData.TrainingIndices) { attr[row] += 1; } foreach (var row in Content.ProblemData.TestIndices) { attr[row] += 2; } int start = 0; int curAttr = attr[start]; for (int row = 0; row < attr.Length; row++) { if (attr[row] != curAttr) { switch (curAttr) { case 0: break; case 1: this.CreateAndAddStripLine("Training", start, row, Color.FromArgb(40, Color.Green), Color.Transparent); break; case 2: this.CreateAndAddStripLine("Test", start, row, Color.FromArgb(40, Color.Red), Color.Transparent); break; case 3: this.CreateAndAddStripLine("Training and Test", start, row, Color.FromArgb(40, Color.Green), Color.FromArgb(40, Color.Red), ChartHatchStyle.WideUpwardDiagonal); break; default: // should not happen break; } curAttr = attr[row]; start = row; } } } private void CreateAndAddStripLine(string title, int start, int end, Color color, Color secondColor, ChartHatchStyle hatchStyle = ChartHatchStyle.None) { StripLine stripLine = new StripLine(); stripLine.BackColor = color; stripLine.BackSecondaryColor = secondColor; stripLine.BackHatchStyle = hatchStyle; stripLine.Text = title; stripLine.Font = new Font("Times New Roman", 12, FontStyle.Bold); // strip range is [start .. end] inclusive, but we evaluate [start..end[ (end is exclusive) // the strip should be by one longer (starting at start - 0.5 and ending at end + 0.5) stripLine.StripWidth = end - start; stripLine.IntervalOffset = start - 0.5; // start slightly to the left of the first point to clearly indicate the first point in the partition this.chart.ChartAreas[0].AxisX.StripLines.Add(stripLine); } private void ToggleSeriesData(Series series) { if (series.Points.Count > 0) { //checks if series is shown if (this.chart.Series.Any(s => s != series && s.Points.Count > 0)) { ClearPointsQuick(series.Points); } } else if (Content != null) { IEnumerable indices = null; IEnumerable mean = null; IEnumerable s2 = null; double[] lower = null; double[] upper = null; switch (series.Name) { case ESTIMATEDVALUES_ALL_SERIES_NAME: indices = Enumerable.Range(0, Content.ProblemData.Dataset.Rows).Except(Content.ProblemData.TrainingIndices).Except(Content.ProblemData.TestIndices).ToArray(); mean = Content.EstimatedValues.ToArray(); s2 = Content.EstimatedVariance.ToArray(); lower = mean.Zip(s2, GetLowerConfBound).ToArray(); upper = mean.Zip(s2, GetUpperConfBound).ToArray(); lower = indices.Select(index => lower[index]).ToArray(); upper = indices.Select(index => upper[index]).ToArray(); break; case ESTIMATEDVALUES_TRAINING_SERIES_NAME: indices = Content.ProblemData.TrainingIndices.ToArray(); mean = Content.EstimatedTrainingValues.ToArray(); s2 = Content.EstimatedTrainingVariance.ToArray(); lower = mean.Zip(s2, GetLowerConfBound).ToArray(); upper = mean.Zip(s2, GetUpperConfBound).ToArray(); break; case ESTIMATEDVALUES_TEST_SERIES_NAME: indices = Content.ProblemData.TestIndices.ToArray(); mean = Content.EstimatedTestValues.ToArray(); s2 = Content.EstimatedTestVariance.ToArray(); lower = mean.Zip(s2, GetLowerConfBound).ToArray(); upper = mean.Zip(s2, GetUpperConfBound).ToArray(); break; } if (indices.Count() > 0) { series.Points.DataBindXY(indices, lower, upper); this.InsertEmptyPoints(series); chart.Legends[series.Legend].ForeColor = Color.Black; UpdateCursorInterval(); chart.Refresh(); } } } private double GetLowerConfBound(double m, double s) { return m - 1.96 * Math.Sqrt(s); } private double GetUpperConfBound(double m, double s) { return m + 1.96 * Math.Sqrt(s); } // workaround as per http://stackoverflow.com/questions/5744930/datapointcollection-clear-performance private static void ClearPointsQuick(DataPointCollection points) { points.SuspendUpdates(); while (points.Count > 0) points.RemoveAt(points.Count - 1); points.ResumeUpdates(); } private void chart_MouseMove(object sender, MouseEventArgs e) { HitTestResult result = chart.HitTest(e.X, e.Y); if (result.ChartElementType == ChartElementType.LegendItem && result.Series.Name != TARGETVARIABLE_SERIES_NAME) Cursor = Cursors.Hand; else Cursor = Cursors.Default; } private void chart_MouseDown(object sender, MouseEventArgs e) { HitTestResult result = chart.HitTest(e.X, e.Y); if (result.ChartElementType == ChartElementType.LegendItem && result.Series.Name != TARGETVARIABLE_SERIES_NAME) { ToggleSeriesData(result.Series); } } private void chart_CustomizeLegend(object sender, CustomizeLegendEventArgs e) { if (chart.Series.Count != 4) return; e.LegendItems[0].Cells[1].ForeColor = this.chart.Series[ESTIMATEDVALUES_TRAINING_SERIES_NAME].Points.Count == 0 ? Color.Gray : Color.Black; e.LegendItems[1].Cells[1].ForeColor = this.chart.Series[ESTIMATEDVALUES_TEST_SERIES_NAME].Points.Count == 0 ? Color.Gray : Color.Black; e.LegendItems[2].Cells[1].ForeColor = this.chart.Series[ESTIMATEDVALUES_ALL_SERIES_NAME].Points.Count == 0 ? Color.Gray : Color.Black; e.LegendItems[3].Cells[1].ForeColor = this.chart.Series[TARGETVARIABLE_SERIES_NAME].Points.Count == 0 ? Color.Gray : Color.Black; } } }