source: branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis.Views/3.4/GradientChart.cs @ 13830

#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.Windows.Forms;
using System.Windows.Forms.DataVisualization.Charting;
using HeuristicLab.Common;
using HeuristicLab.Visualization.ChartControlsExtensions;

namespace HeuristicLab.Problems.DataAnalysis.Views {
  public partial class GradientChart : EnhancedChart {
    private ModifiableDataset sharedDataset; // used for syncronising variable values between charts
    private ModifiableDataset internalDataset; // used to cache values and speed up calculations

    public bool ShowLegend { get; set; }
    public bool ShowXAxisLabel { get; set; }
    public bool ShowYAxisLabel { get; set; }
    public bool ShowCursor { get; set; }

    private bool useMedianValues;
    public bool UseMedianValues {
      get { return useMedianValues; }
      set {
        if (value == useMedianValues) return;
        useMedianValues = value;
        OnChartPropertyChanged(this, EventArgs.Empty);
        UpdateChart();
      }
    }

    private int row;
    public int Row {
      get { return row; }
      set {
        if (row == value) return;
        row = value;
        OnChartPropertyChanged(this, EventArgs.Empty);
        UpdateChart();
      }
    }

    private double min;
    public double Min {
      get { return min; }
      set {
        if (value.IsAlmost(min)) return;
        min = value;
        OnChartPropertyChanged(this, EventArgs.Empty);
        UpdateChart();
      }
    }

    private double max;
    public double Max {
      get { return max; }
      set {
        if (value.IsAlmost(max)) return;
        max = value;
        OnChartPropertyChanged(this, EventArgs.Empty);
        UpdateChart();
      }
    }

    private int points;
    public int Points {
      get { return points; }
      set {
        if (value == points) return;
        points = value;
        OnChartPropertyChanged(this, EventArgs.Empty);
        UpdateChart();
      }
    }

    private IRegressionProblemData problemData;
    public IRegressionProblemData ProblemData {
      get { return problemData; }
      set {
        if (!SolutionsCompatibleWithProblemData(value, solutionList))
          throw new ArgumentException("The problem data provided does not contain all the variables required by the solutions.");
        problemData = value;
        UpdateDataset();
        UpdateChart();
      }
    }

    public string Target {
      get { return Solutions.First().ProblemData.TargetVariable; }
    }

    private string variable;
    public string Variable {
      get { return variable; }
      set {
        if (variable == value) return;
        if (!ProblemData.Dataset.DoubleVariables.Contains(value))
          throw new ArgumentException("The variable must be present in the problem dataset.");
        OnChartPropertyChanged(this, EventArgs.Empty);
        variable = value;
        var values = ProblemData.Dataset.GetReadOnlyDoubleValues(variable);
        min = values.Min();
        max = values.Max();
        UpdateChart();
      }
    }

    private List<IRegressionSolution> solutionList;
    public IEnumerable<IRegressionSolution> Solutions {
      get { return solutionList; }
      set {
        if (!value.Any())
          throw new ArgumentException("At least one solution must be provided.");
        if (SolutionsCompatibleWithProblemData(problemData, value))
          solutionList = new List<IRegressionSolution>(value);
        else
          throw new ArgumentException("The provided solution collection is not compatible with the existing problem data.");
        UpdateChart();
      }
    }

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

    public GradientChart() {
      InitializeComponent();
      RegisterEvents();
    }

    public void AddSolution(IRegressionSolution solution) {
      if (!SolutionsCompatibleWithProblemData(problemData, new[] { solution })) {
        throw new ArgumentException("The solution is not compatible with the problem data.");
      }
      solutionList.Add(solution);
      UpdateChart();
    }

    public void RemoveSolution(IRegressionSolution solution) {
      var removed = solutionList.RemoveAll(x => x == solution);
      if (removed > 0)
        UpdateChart();
    }

    private static bool SolutionsCompatibleWithProblemData(IRegressionProblemData pd, IEnumerable<IRegressionSolution> solutions) {
      if (pd == null || !solutions.Any()) return true;
      if (solutions.Any(x => x.ProblemData.TargetVariable != pd.TargetVariable)) return false;
      var variables = new HashSet<string>(pd.Dataset.DoubleVariables);
      return solutions.SelectMany(x => x.ProblemData.Dataset.DoubleVariables).All(variables.Contains);
    }

    public void Configure(IEnumerable<IRegressionSolution> solutions, IRegressionProblemData pd, ModifiableDataset dataset, string variable, double min, double max, int points) {
      if (!SolutionsCompatibleWithProblemData(pd, solutions))
        throw new ArgumentException("Solutions are not compatible with the problem data.");
      this.solutionList = new List<IRegressionSolution>(solutions);
      this.problemData = pd;
      this.variable = variable;
      this.sharedDataset = dataset;
      this.min = min;
      this.max = max;
      this.points = points;

      // 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)
      var variables = sharedDataset.DoubleVariables.ToList();
      sharedDataset.ItemChanged += (o, e) => {
        var rowIndex = e.Value;
        var columnIndex = e.Value2;
        var ds = (ModifiableDataset)o;
        var variableName = variables[columnIndex];
        if (variableName == Variable) return;
        var v = ds.GetDoubleValue(variableName, rowIndex);
        var values = new List<double>(Enumerable.Repeat(v, Points));
        internalDataset.ReplaceVariable(variableName, values);
      };

      // configure internal dataset. we also expand the range in order to get nice tick intervals on the x axis
      const int tics = 5;
      double xmin, xmax, xinterval;
      ChartUtil.CalculateAxisInterval(min, max, tics, out xmin, out xmax, out xinterval);
      var step = (xmax - xmin) / points;
      var xvalues = new List<double>();
      for (int i = 0; i < points; ++i) { xvalues.Add(xmin + i * step); }
      internalDataset = new ModifiableDataset(variables, variables.Select(x => x == Variable ? xvalues : new List<double>(Enumerable.Repeat(sharedDataset.GetDoubleValue(x, 0), xvalues.Count))));
    }

    public void UpdateChart() {
      // throw exceptions?
      if (sharedDataset == null || solutionList == null || !solutionList.Any())
        return;
      if (min.IsAlmost(max) || min > max || points == 0)
        return;
      Series.Clear();
      var vla = VerticalLineAnnotation;
      Annotations.Clear();
      var defaultValue = sharedDataset.GetDoubleValue(variable, 0);
      vla.Visible = ShowCursor;
      Annotations.Add(vla);
      vla.X = defaultValue;

      double axisMin, axisMax, axisInterval;
      // calculate X-axis interval
      ChartUtil.CalculateAxisInterval(min, max, 5, out axisMin, out axisMax, out axisInterval);
      var axis = ChartAreas[0].AxisX;
      axis.Minimum = axisMin;
      axis.Maximum = axisMax;
      axis.Interval = axisInterval;

      for (int i = 0; i < solutionList.Count; ++i) {
        var solution = solutionList[i];
        var series = PlotSeries(solution);
        series.Name = Target + " " + i;
        Series.Add(series);
      }
      // calculate Y-axis interval
      double ymin = 0, ymax = 0;
      foreach (var v in Series[0].Points.Select(x => x.YValues[0])) {
        if (ymin > v) ymin = v;
        if (ymax < v) ymax = v;
      }
      ChartUtil.CalculateAxisInterval(ymin, ymax, 5, out axisMin, out axisMax, out axisInterval);
      axis = ChartAreas[0].AxisY;
      axis.Minimum = axisMin;
      axis.Maximum = axisMax;
      axis.Interval = axisInterval;

      if (ShowXAxisLabel) {
        ChartAreas[0].AxisX.Title = Variable + " : " + defaultValue.ToString("N3", CultureInfo.CurrentCulture); // set axis title
      }

      AddStripLines(); // add strip lines
      if (ShowLegend)
        AddLegends();
    }

    private void UpdateDataset() {
      var variables = ProblemData.Dataset.DoubleVariables.ToList();
      var variableValues = new List<double>[variables.Count];

      if (UseMedianValues) {
        for (int i = 0; i < variables.Count; ++i) {
          var median = ProblemData.Dataset.GetDoubleValues(variables[i], ProblemData.TrainingIndices).Median();
          variableValues[i] = new List<double> { median };
        }
      } else {
        for (int i = 0; i < variables.Count; ++i) {
          var variableValue = ProblemData.Dataset.GetDoubleValue(variables[i], Row);
          variableValues[i] = new List<double> { variableValue };
        }
      }
      sharedDataset = new ModifiableDataset(variables, variableValues);
    }

    private Series PlotSeries(IRegressionSolution solution) {
      var v = sharedDataset.GetDoubleValue(variable, 0);
      var series = new Series { ChartType = SeriesChartType.Point };
      // get values from series
      var xvalues = internalDataset.GetReadOnlyDoubleValues(Variable);
      var yvalues = solution.Model.GetEstimatedValues(internalDataset, Enumerable.Range(0, internalDataset.Rows));
      int i = 0;
      foreach (var y in yvalues) {
        var x = xvalues[i++];
        series.Points.Add(new DataPoint(x, y) { MarkerSize = 2, MarkerColor = Color.DodgerBlue });
      }
      if (ShowCursor) {
        var y = solution.Model.GetEstimatedValues(sharedDataset, new[] { 0 }).Single();
        series.Points.Add(new DataPoint(v, y) { MarkerSize = 5, MarkerColor = Color.Red });
      }
      if (ShowLegend) {
        series.IsVisibleInLegend = true;
      }
      return series;
    }

    private void AddLegends() {
      Legends.Clear();
      var legend = new Legend();
      legend.Alignment = StringAlignment.Center;
      legend.LegendStyle = LegendStyle.Row;
      legend.Docking = Docking.Top;
      Legends.Add(legend);
      foreach (var s in Series) {
        s.Legend = legend.Name;
      }
    }

    private void AddStripLines() {
      var axisX = ChartAreas[0].AxisX;
      axisX.StripLines.Clear();
      axisX.StripLines.Add(new StripLine { BackColor = Color.FromArgb(30, Color.Green), IntervalOffset = axisX.Minimum, StripWidth = min - axisX.Minimum });
      axisX.StripLines.Add(new StripLine { BackColor = Color.FromArgb(30, Color.Green), IntervalOffset = max, StripWidth = axisX.Maximum - max });
    }

    private void RegisterEvents() {
      AnnotationPositionChanging += chart_AnnotationPositionChanging;
      MouseMove += chart_MouseMove;
      FormatNumber += chart_FormatNumber;
    }

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

    public event EventHandler ChartPropertyChanged;
    public void OnChartPropertyChanged(object sender, EventArgs args) {
      var changed = ChartPropertyChanged;
      if (changed == null) return;
      changed(sender, args);
    }

    private void chart_AnnotationPositionChanged(object sender, EventArgs e) {
      var annotation = VerticalLineAnnotation;
      var x = annotation.X;
      sharedDataset.SetVariableValue(x, Variable, 0);
      for (int i = 0; i < solutionList.Count; ++i) {
        var y = solutionList[i].Model.GetEstimatedValues(sharedDataset, new[] { 0 }).Single();
        var s = Series[i];
        var n = s.Points.Count;
        s.Points[n - 1] = new DataPoint(x, y) { MarkerColor = Color.Red, MarkerSize = 5 };
      }
      if (ShowXAxisLabel) {
        ChartAreas[0].AxisX.Title = Variable + " : " + x.ToString("N3", CultureInfo.CurrentCulture);
      }
      Update();
      OnVariableValueChanged(this, EventArgs.Empty);
    }

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

    private void chart_MouseMove(object sender, MouseEventArgs e) {
      this.Cursor = 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
  }
}