source: branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis.Views/3.4/GradientChart.cs @ 13837

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2016 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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Drawing;
using System.Globalization;
using System.Linq;
using System.Threading.Tasks;
using System.Windows.Forms;
using System.Windows.Forms.DataVisualization.Charting;
using HeuristicLab.Common;
using HeuristicLab.MainForm.WindowsForms;
using HeuristicLab.Visualization.ChartControlsExtensions;

namespace HeuristicLab.Problems.DataAnalysis.Views {
  public partial class GradientChart : UserControl {
    private ModifiableDataset sharedFixedVariables; // used for syncronising variable values between charts
    private ModifiableDataset internalDataset; // holds the x values for each point drawn

    public bool ShowLegend {
      get { return chart.Legends[0].Enabled; }
      set { chart.Legends[0].Enabled = value; }
    }
    public bool ShowXAxisLabel {
      get { return chart.ChartAreas[0].AxisX.Enabled == AxisEnabled.True; }
      set { chart.ChartAreas[0].AxisX.Enabled = value ? AxisEnabled.True : AxisEnabled.False; }
    }
    public bool ShowYAxisLabel {
      get { return chart.ChartAreas[0].AxisY.Enabled == AxisEnabled.True; }
      set { chart.ChartAreas[0].AxisY.Enabled = value ? AxisEnabled.True : AxisEnabled.False; }
    }
    public bool ShowCursor {
      get { return chart.Annotations[0].Visible; }
      set { chart.Annotations[0].Visible = value; }
    }

    private int xAxisTicks = 5;
    public int XAxisTicks {
      get { return xAxisTicks; }
      set { if (xAxisTicks != value) { xAxisTicks = value; UpdateChartAsync(); } }
    }
    private int yAxisTicks = 5;
    public int YXAxisTicks {
      get { return yAxisTicks; }
      set { if (yAxisTicks != value) { yAxisTicks = value; UpdateChartAsync(); } }
    }

    private double trainingMin = double.MinValue;
    public double TrainingMin {
      get { return trainingMin; }
      set { if (!value.IsAlmost(trainingMin)) { trainingMin = value; UpdateChartAsync(); } }
    }
    private double trainingMax = double.MaxValue;
    public double TrainingMax {
      get { return trainingMax; }
      set { if (!value.IsAlmost(trainingMax)) { trainingMax = value; UpdateChartAsync(); } }
    }

    private int drawingSteps = 1000;
    public int DrawingSteps {
      get { return drawingSteps; }
      set { if (value != drawingSteps) { drawingSteps = value; UpdateChartAsync(); } }
    }

    private string freeVariable;
    public string FreeVariable {
      get { return freeVariable; }
      set {
        if (value == freeVariable) return;
        if (solutions.Any(s => !s.ProblemData.Dataset.DoubleVariables.Contains(value))) {
          throw new ArgumentException("Variable does not exist in the ProblemData of the Solutions.");
        }
        freeVariable = value;
        RecalculateInternalDataset();
        UpdateChartAsync();
      }
    }

    private readonly List<IRegressionSolution> solutions = new List<IRegressionSolution>();
    public IEnumerable<IRegressionSolution> Solutions {
      get { return solutions; }
    }

    private VerticalLineAnnotation VerticalLineAnnotation {
      get { return (VerticalLineAnnotation)chart.Annotations.SingleOrDefault(x => x is VerticalLineAnnotation); }
    }

    public GradientChart() {
      InitializeComponent();

      // Configure axis
      chart.CustomizeAllChartAreas();
      chart.ChartAreas[0].CursorX.IsUserSelectionEnabled = true;
      chart.ChartAreas[0].AxisX.ScaleView.Zoomable = true;
      chart.ChartAreas[0].CursorX.Interval = 0;

      chart.ChartAreas[0].CursorY.IsUserSelectionEnabled = true;
      chart.ChartAreas[0].AxisY.ScaleView.Zoomable = true;
      chart.ChartAreas[0].CursorY.Interval = 0;
    }

    public void Configure(IEnumerable<IRegressionSolution> solutions, ModifiableDataset sharedFixedVariables, string freeVariable, int drawingSteps) {
      if (!SolutionsCompatible(solutions))
        throw new ArgumentException("Solutions are not compatible with the problem data.");
      this.solutions.Clear();
      this.solutions.AddRange(solutions);
      this.freeVariable = freeVariable;
      this.drawingSteps = drawingSteps;

      // add an event such that whenever a value is changed in the shared dataset, 
      // this change is reflected in the internal dataset (where the value becomes a whole column)
      if (this.sharedFixedVariables != null)
        this.sharedFixedVariables.ItemChanged -= sharedFixedVariables_ItemChanged;
      this.sharedFixedVariables = sharedFixedVariables;
      this.sharedFixedVariables.ItemChanged += sharedFixedVariables_ItemChanged;

      RecalculateTrainingLimits();
      RecalculateInternalDataset();
    }

    private void sharedFixedVariables_ItemChanged(object o, EventArgs<int, int> e) {
      if (o != sharedFixedVariables) return;
      var variables = sharedFixedVariables.DoubleVariables.ToList();
      var rowIndex = e.Value;
      var columnIndex = e.Value2;

      var variableName = variables[columnIndex];
      if (variableName == FreeVariable) return;
      var v = sharedFixedVariables.GetDoubleValue(variableName, rowIndex);
      var values = new List<double>(Enumerable.Repeat(v, DrawingSteps));
      internalDataset.ReplaceVariable(variableName, values);
    }

    private void RecalculateInternalDataset() {
      // we expand the range in order to get nice tick intervals on the x axis
      double xmin, xmax, xinterval;
      ChartUtil.CalculateAxisInterval(trainingMin, trainingMax, XAxisTicks, out xmin, out xmax, out xinterval);
      double step = (xmax - xmin) / drawingSteps;

      var xvalues = new List<double>();
      for (int i = 0; i < drawingSteps; i++)
        xvalues.Add(xmin + i * step);

      var variables = sharedFixedVariables.DoubleVariables.ToList();
      internalDataset = new ModifiableDataset(variables,
        variables.Select(x => x == FreeVariable
          ? xvalues
          : Enumerable.Repeat(sharedFixedVariables.GetDoubleValue(x, 0), xvalues.Count).ToList()
        )
      );
    }

    private void RecalculateTrainingLimits() {
      trainingMin = solutions.Select(s => s.ProblemData.Dataset.GetDoubleValues(freeVariable, s.ProblemData.TrainingIndices).Min()).Max();
      trainingMax = solutions.Select(s => s.ProblemData.Dataset.GetDoubleValues(freeVariable, s.ProblemData.TrainingIndices).Max()).Min();
    }

    public async Task UpdateChartAsync() {
      // throw exceptions?
      if (sharedFixedVariables == null || solutions == null || !solutions.Any())
        return;
      if (trainingMin.IsAlmost(trainingMax) || trainingMin > trainingMax || drawingSteps == 0)
        return;

      // Set cursor
      var defaultValue = sharedFixedVariables.GetDoubleValue(freeVariable, 0);
      VerticalLineAnnotation.X = defaultValue;

      // Calculate X-axis interval
      double axisMin, axisMax, axisInterval;
      ChartUtil.CalculateAxisInterval(trainingMin, trainingMax, XAxisTicks, out axisMin, out axisMax, out axisInterval);
      var axis = chart.ChartAreas[0].AxisX;
      axis.Minimum = axisMin;
      axis.Maximum = axisMax;
      axis.Interval = axisInterval;

      // Create series <mean, conf. interval>
      var seriesDict = new Dictionary<Series, Series>();
      for (int i = 0; i < solutions.Count; ++i) {
        var solution = solutions[i];
        var series = await CreateSeriesAsync(solution);
        series.Item1.Tag = i; // for sorting
        var meanSeries = series.Item1;
        var confidenceIntervalSeries = series.Item2;
        meanSeries.Name = solution.ProblemData.TargetVariable + " " + i;
        seriesDict.Add(meanSeries, confidenceIntervalSeries);
        if (confidenceIntervalSeries != null)
          confidenceIntervalSeries.Name = "95% Conf. Interval " + meanSeries.Name;
      }

      chart.SuspendRepaint();
      chart.Series.Clear();
      // Add mean series for applying palette colors
      foreach (var series in seriesDict.Keys.OrderBy(s => (int)s.Tag)) {
        series.LegendText = series.Name;
        chart.Series.Add(series);
      }
      chart.Palette = ChartColorPalette.BrightPastel;
      chart.ApplyPaletteColors();
      chart.Palette = ChartColorPalette.None;

      foreach (var series in seriesDict.OrderBy(s => (int)s.Key.Tag)) {
        if (series.Value == null) continue;
        int idx = chart.Series.IndexOf(series.Key);
        series.Value.Color = Color.FromArgb(40, series.Key.Color);
        series.Value.IsVisibleInLegend = false;
        chart.Series.Insert(idx, series.Value);
      }
      chart.ResumeRepaint(true);


      // calculate Y-axis interval
      //double ymin = 0, ymax = 0;
      //foreach (var vs in chart.Series.SelectMany(series => series.Points.Select(s => s.YValues))) {
      //  for (int index = 0; index < vs.Length; index++) {
      //    var v = vs[index];
      //    if (ymin > v) ymin = v;
      //    if (ymax < v) ymax = v;
      //  }
      //}
      //ChartUtil.CalculateAxisInterval(ymin, ymax, YXAxisTicks, out axisMin, out axisMax, out axisInterval);
      //axis = chart.ChartAreas[0].AxisY;
      //axis.Minimum = axisMin;
      //axis.Maximum = axisMax;
      //axis.Interval = axisInterval;
      //chart.ChartAreas[0].RecalculateAxesScale();

      // set axis title
      chart.ChartAreas[0].AxisX.Title = FreeVariable + " : " + defaultValue.ToString("N3", CultureInfo.CurrentCulture);

      UpdateStripLines();
    }

    private Task<Tuple<Series, Series>> CreateSeriesAsync(IRegressionSolution solution) {
      return Task.Run(() => {
        var xvalues = internalDataset.GetDoubleValues(FreeVariable).ToList();
        var yvalues = solution.Model.GetEstimatedValues(internalDataset, Enumerable.Range(0, internalDataset.Rows)).ToList();

        var series = new Series { ChartType = SeriesChartType.Line };
        series.Points.DataBindXY(xvalues, yvalues);

        var confidenceBoundSolution = solution as IConfidenceBoundRegressionSolution;
        Series confidenceIntervalSeries = null;
        if (confidenceBoundSolution != null) {
          var variances = confidenceBoundSolution.Model.GetEstimatedVariances(internalDataset, Enumerable.Range(0, internalDataset.Rows)).ToList();

          var lower = yvalues.Zip(variances, (m, s2) => m - 1.96 * Math.Sqrt(s2)).ToList();
          var upper = yvalues.Zip(variances, (m, s2) => m + 1.96 * Math.Sqrt(s2)).ToList();

          confidenceIntervalSeries = new Series { ChartType = SeriesChartType.Range, YValuesPerPoint = 2 };
          confidenceIntervalSeries.Points.DataBindXY(xvalues, lower, upper);
        }

        return Tuple.Create(series, confidenceIntervalSeries);
      });
    }

    public void AddSolution(IRegressionSolution solution) {
      if (!SolutionsCompatible(solutions.Concat(new[] { solution })))
        throw new ArgumentException("The solution is not compatible with the problem data.");
      if (solutions.Contains(solution)) return;
      solutions.Add(solution);
      RecalculateTrainingLimits();
      UpdateChartAsync();
    }
    public void RemoveSolution(IRegressionSolution solution) {
      if (!solutions.Remove(solution)) return;
      RecalculateTrainingLimits();
      UpdateChartAsync();
    }

    private static bool SolutionsCompatible(IEnumerable<IRegressionSolution> solutions) {
      foreach (var solution1 in solutions) {
        var variables1 = solution1.ProblemData.Dataset.DoubleVariables;
        foreach (var solution2 in solutions) {
          if (solution1 == solution2)
            continue;
          var variables2 = solution2.ProblemData.Dataset.DoubleVariables;
          if (!variables1.All(variables2.Contains))
            return false;
        }
      }
      return true;
    }

    private void UpdateStripLines() {
      var axisX = chart.ChartAreas[0].AxisX;
      var lowerStripLine = axisX.StripLines[0];
      var upperStripLine = axisX.StripLines[1];

      lowerStripLine.IntervalOffset = axisX.Minimum;
      lowerStripLine.StripWidth = trainingMin - axisX.Minimum;

      upperStripLine.IntervalOffset = trainingMax;
      upperStripLine.StripWidth = axisX.Maximum - trainingMax;
    }

    #region events
    public event EventHandler VariableValueChanged;
    public void OnVariableValueChanged(object sender, EventArgs args) {
      var changed = VariableValueChanged;
      if (changed == null) return;
      changed(sender, args);
    }

    private void chart_AnnotationPositionChanged(object sender, EventArgs e) {
      var annotation = VerticalLineAnnotation;
      var x = annotation.X;
      sharedFixedVariables.SetVariableValue(x, FreeVariable, 0);

      chart.ChartAreas[0].AxisX.Title = FreeVariable + " : " + x.ToString("N3", CultureInfo.CurrentCulture);
      chart.Update();

      OnVariableValueChanged(this, EventArgs.Empty);
    }

    private void chart_AnnotationPositionChanging(object sender, AnnotationPositionChangingEventArgs e) {
      //var step = (trainingMax - trainingMin) / drawingSteps;
      //e.NewLocationX = step * (long)Math.Round(e.NewLocationX / step);
      //var axisX = chart.ChartAreas[0].AxisX;
      //if (e.NewLocationX > axisX.Maximum)
      //  e.NewLocationX = axisX.Maximum;
      //if (e.NewLocationX < axisX.Minimum)
      //  e.NewLocationX = axisX.Minimum;

      var annotation = VerticalLineAnnotation;
      var x = annotation.X;
      sharedFixedVariables.SetVariableValue(x, FreeVariable, 0);

      chart.ChartAreas[0].AxisX.Title = FreeVariable + " : " + x.ToString("N3", CultureInfo.CurrentCulture);
      chart.Update();

      OnVariableValueChanged(this, EventArgs.Empty);
    }

    private void chart_MouseMove(object sender, MouseEventArgs e) {
      chart.Cursor = chart.HitTest(e.X, e.Y).ChartElementType == ChartElementType.Annotation ? Cursors.VSplit : Cursors.Default;
    }

    private void chart_FormatNumber(object sender, FormatNumberEventArgs e) {
      if (e.ElementType == ChartElementType.AxisLabels) {
        switch (e.Format) {
          case "CustomAxisXFormat":
            break;
          case "CustomAxisYFormat":
            var v = e.Value;
            e.LocalizedValue = string.Format("{0,5}", v);
            break;
          default:
            break;
        }
      }
    }

    private void GradientChart_DragDrop(object sender, DragEventArgs e) {
      var data = e.Data.GetData(HeuristicLab.Common.Constants.DragDropDataFormat);
      if (data != null) {
        var solution = data as IRegressionSolution;
        if (!Solutions.Contains(solution))
          AddSolution(solution);
      }
    }
    private void GradientChart_DragEnter(object sender, DragEventArgs e) {
      if (!e.Data.GetDataPresent(HeuristicLab.Common.Constants.DragDropDataFormat)) return;
      e.Effect = DragDropEffects.None;

      var data = e.Data.GetData(HeuristicLab.Common.Constants.DragDropDataFormat);
      var regressionSolution = data as IRegressionSolution;
      if (regressionSolution != null) {
        e.Effect = DragDropEffects.Copy;
      }
    }
    #endregion
  }
}