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Timestamp:
08/14/12 14:31:44 (12 years ago)
Author:
mkommend
Message:

#1081: Added ErrorCharacteristics View for time series models.

File:
1 edited

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  • branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView.cs

    r7099 r8487  
    2020#endregion
    2121
    22 using System;
    2322using System.Collections.Generic;
    2423using System.Linq;
    2524using System.Windows.Forms;
    26 using System.Windows.Forms.DataVisualization.Charting;
    2725using HeuristicLab.MainForm;
    2826using HeuristicLab.MainForm.WindowsForms;
    … …  
    3028  [View("Error Characteristics Curve")]
    3129  [Content(typeof(ITimeSeriesPrognosisSolution))]
    32   public partial class TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView : DataAnalysisSolutionEvaluationView {
    33     protected const string TrainingSamples = "Training";
    34     protected const string TestSamples = "Test";
    35     protected const string AllSamples = "All Samples";
     30  public partial class TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView : RegressionSolutionErrorCharacteristicsCurveView {
     31
    3632
    3733    public TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView()
    3834      : base() {
    3935      InitializeComponent();
    40 
    41       cmbSamples.Items.Add(TrainingSamples);
    42       cmbSamples.Items.Add(TestSamples);
    43       cmbSamples.Items.Add(AllSamples);
    44 
    45       cmbSamples.SelectedIndex = 0;
    46 
    47       chart.CustomizeAllChartAreas();
    48       chart.ChartAreas[0].AxisX.Title = "Absolute Error";
    49       chart.ChartAreas[0].AxisX.Minimum = 0.0;
    50       chart.ChartAreas[0].AxisX.Maximum = 1.0;
    51       chart.ChartAreas[0].AxisX.IntervalAutoMode = IntervalAutoMode.VariableCount;
    52       chart.ChartAreas[0].CursorX.Interval = 0.01;
    53 
    54       chart.ChartAreas[0].AxisY.Title = "Number of Samples";
    55       chart.ChartAreas[0].AxisY.Minimum = 0.0;
    56       chart.ChartAreas[0].AxisY.Maximum = 1.0;
    57       chart.ChartAreas[0].AxisY.MajorGrid.Interval = 0.2;
    58       chart.ChartAreas[0].CursorY.Interval = 0.01;
    5936    }
    6037
    … …  
    6340      set { base.Content = value; }
    6441    }
    65     public ITimeSeriesPrognosisProblemData ProblemData {
     42    public new ITimeSeriesPrognosisProblemData ProblemData {
    6643      get {
    6744        if (Content == null) return null;
    … …  
    7047    }
    7148
    72     protected override void RegisterContentEvents() {
    73       base.RegisterContentEvents();
    74       Content.ModelChanged += new EventHandler(Content_ModelChanged);
    75       Content.ProblemDataChanged += new EventHandler(Content_ProblemDataChanged);
    76     }
    77     protected override void DeregisterContentEvents() {
    78       base.DeregisterContentEvents();
    79       Content.ModelChanged -= new EventHandler(Content_ModelChanged);
    80       Content.ProblemDataChanged -= new EventHandler(Content_ProblemDataChanged);
    81     }
     49    protected override void UpdateChart() {
     50      base.UpdateChart();
     51      if (Content == null) return;
     52      var movingAverageModel = new TimeSeriesPrognosisMovingAverageModel(10, Content.ProblemData.TargetVariable).CreateTimeSeriesPrognosisSolution(ProblemData);
     53      movingAverageModel.Name = "Moving average Model";
     54      AddRegressionSolution(movingAverageModel);
    8255
    83     protected virtual void Content_ModelChanged(object sender, EventArgs e) {
    84       if (InvokeRequired) Invoke((Action<object, EventArgs>)Content_ModelChanged, sender, e);
    85       else UpdateChart();
    86     }
    87     protected virtual void Content_ProblemDataChanged(object sender, EventArgs e) {
    88       if (InvokeRequired) Invoke((Action<object, EventArgs>)Content_ProblemDataChanged, sender, e);
    89       else {
    90         UpdateChart();
    91       }
    92     }
    93     protected override void OnContentChanged() {
    94       base.OnContentChanged();
    95       UpdateChart();
    96     }
    97 
    98     protected virtual void UpdateChart() {
    99       chart.Series.Clear();
    100       chart.Annotations.Clear();
    101       if (Content == null) return;
    102 
    103       var originalValues = GetOriginalValues();
    104       var meanModelEstimatedValues = GetMeanModelEstimatedValues(originalValues);
    105       var meanModelResiduals = GetResiduals(originalValues, meanModelEstimatedValues);
    106 
    107       meanModelResiduals.Sort();
    108       chart.ChartAreas[0].AxisX.Maximum = Math.Ceiling(meanModelResiduals.Last());
    109       chart.ChartAreas[0].CursorX.Interval = meanModelResiduals.First() / 100;
    110 
    111       Series meanModelSeries = new Series("Mean Model");
    112       meanModelSeries.ChartType = SeriesChartType.FastLine;
    113       UpdateSeries(meanModelResiduals, meanModelSeries);
    114       meanModelSeries.ToolTip = "Area over Curve: " + CalculateAreaOverCurve(meanModelSeries);
    115       chart.Series.Add(meanModelSeries);
    116 
    117       AddTimeSeriesPrognosisSolution(Content);
    118     }
    119 
    120     protected void AddTimeSeriesPrognosisSolution(ITimeSeriesPrognosisSolution solution) {
    121       if (chart.Series.Any(s => s.Name == solution.Name)) return;
    122 
    123       Series solutionSeries = new Series(solution.Name);
    124       solutionSeries.Tag = solution;
    125       solutionSeries.ChartType = SeriesChartType.FastLine;
    126       var estimatedValues = GetResiduals(GetOriginalValues(), GetPrognosedValues(solution));
    127       UpdateSeries(estimatedValues, solutionSeries);
    128       solutionSeries.ToolTip = "Area over Curve: " + CalculateAreaOverCurve(solutionSeries);
    129       chart.Series.Add(solutionSeries);
    130     }
    131 
    132     protected void UpdateSeries(List<double> residuals, Series series) {
    133       series.Points.Clear();
    134       residuals.Sort();
    135 
    136       series.Points.AddXY(0, 0);
    137       for (int i = 0; i < residuals.Count; i++) {
    138         var point = new DataPoint();
    139         if (residuals[i] > chart.ChartAreas[0].AxisX.Maximum) {
    140           point.XValue = chart.ChartAreas[0].AxisX.Maximum;
    141           point.YValues[0] = ((double)i) / residuals.Count;
    142           point.ToolTip = "Error: " + point.XValue + "\n" + "Samples: " + point.YValues[0];
    143           series.Points.Add(point);
    144           break;
    145         }
    146 
    147         point.XValue = residuals[i];
    148         point.YValues[0] = ((double)i + 1) / residuals.Count;
    149         point.ToolTip = "Error: " + point.XValue + "\n" + "Samples: " + point.YValues[0];
    150         series.Points.Add(point);
    151       }
    152 
    153       if (series.Points.Last().XValue < chart.ChartAreas[0].AxisX.Maximum) {
    154         var point = new DataPoint();
    155         point.XValue = chart.ChartAreas[0].AxisX.Maximum;
    156         point.YValues[0] = 1;
    157         point.ToolTip = "Error: " + point.XValue + "\n" + "Samples: " + point.YValues[0];
    158         series.Points.Add(point);
    159       }
    160     }
    161 
    162     protected IEnumerable<double> GetOriginalValues() {
    163       IEnumerable<double> originalValues;
    164       switch (cmbSamples.SelectedItem.ToString()) {
    165         case TrainingSamples:
    166           originalValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes);
    167           break;
    168         case TestSamples:
    169           originalValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndizes);
    170           break;
    171         case AllSamples:
    172           originalValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable);
    173           break;
    174         default:
    175           throw new NotSupportedException();
    176       }
    177       return originalValues;
    178     }
    179 
    180     protected IEnumerable<double> GetPrognosedValues(ITimeSeriesPrognosisSolution solution) {
    181       IEnumerable<double> prognosedValues;
    182       switch (cmbSamples.SelectedItem.ToString()) {
    183         case TrainingSamples:
    184           prognosedValues = solution.PrognosedTrainingValues;
    185           break;
    186         case TestSamples:
    187           prognosedValues = solution.PrognosedTestValues;
    188           break;
    189         case AllSamples:
    190           prognosedValues = solution.PrognosedValues;
    191           break;
    192         default:
    193           throw new NotSupportedException();
    194       }
    195       return prognosedValues;
    196     }
    197 
    198     protected IEnumerable<double> GetMeanModelEstimatedValues(IEnumerable<double> originalValues) {
    199       double averageTrainingTarget = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes).Average();
    200       return Enumerable.Repeat(averageTrainingTarget, originalValues.Count());
    201     }
    202 
    203     protected virtual List<double> GetResiduals(IEnumerable<double> originalValues, IEnumerable<double> estimatedValues) {
    204       return originalValues.Zip(estimatedValues, (x, y) => Math.Abs(x - y)).ToList();
    205     }
    206 
    207     private double CalculateAreaOverCurve(Series series) {
    208       if (series.Points.Count < 1) throw new ArgumentException("Could not calculate area under curve if less than 1 data points were given.");
    209 
    210       double auc = 0.0;
    211       for (int i = 1; i < series.Points.Count; i++) {
    212         double width = series.Points[i].XValue - series.Points[i - 1].XValue;
    213         double y1 = 1 - series.Points[i - 1].YValues[0];
    214         double y2 = 1 - series.Points[i].YValues[0];
    215 
    216         auc += (y1 + y2) * width / 2;
    217       }
    218 
    219       return auc;
    220     }
    221 
    222     protected void cmbSamples_SelectedIndexChanged(object sender, EventArgs e) {
    223       if (InvokeRequired) Invoke((Action<object, EventArgs>)cmbSamples_SelectedIndexChanged, sender, e);
    224       else UpdateChart();
     56      //AR1 model
     57      double alpha, beta;
     58      OnlineCalculatorError errorState;
     59      IEnumerable<double> trainingStartValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
     60      OnlineLinearScalingParameterCalculator.Calculate(ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
     61      var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(ProblemData.TargetVariable, new double[] { beta }, alpha).CreateTimeSeriesPrognosisSolution(ProblemData);
     62      AR1model.Name = "AR(1) Model";
     63      AddRegressionSolution(AR1model);
    22564    }
    22665  }
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