source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis.Views/3.3/ResultsView.cs @ 9714

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2010 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.ComponentModel;
using System.Drawing;
using System.Data;
using System.Linq;
using System.Text;
using System.Windows.Forms;
using HeuristicLab.MainForm;
using HeuristicLab.MainForm.WindowsForms;
using HeuristicLab.Data.Views;
using HeuristicLab.Data;
using HeuristicLab.Problems.DataAnalysis.Evaluators;
using HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis.Symbolic;

namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis.Views {
  [Content(typeof(SymbolicTimeSeriesPrognosisSolution), true)]
  [View("Time Series Prognosis Results View")]
  public partial class ResultsView : AsynchronousContentView {
    private List<string> rowNames = new List<string>() { 
      "Mean squared error",
      "Pearson's R²", 
      "Mean relative error",
      "Directional symmetry",
      "Weighted directional symmetry",
      "Theil's U" 
    };

    public ResultsView() {
      InitializeComponent();
    }

    public new SymbolicTimeSeriesPrognosisSolution Content {
      get { return (SymbolicTimeSeriesPrognosisSolution)base.Content; }
      set { base.Content = value; }
    }

    protected override void RegisterContentEvents() {
      base.RegisterContentEvents();
      Content.ModelChanged += new EventHandler(Content_ModelChanged);
      Content.ProblemDataChanged += new EventHandler(Content_ProblemDataChanged);
      Content.EstimatedValuesChanged += new EventHandler(Content_EstimatedValuesChanged);
    }
    protected override void DeregisterContentEvents() {
      base.DeregisterContentEvents();
      Content.ModelChanged -= new EventHandler(Content_ModelChanged);
      Content.ProblemDataChanged -= new EventHandler(Content_ProblemDataChanged);
      Content.EstimatedValuesChanged -= new EventHandler(Content_EstimatedValuesChanged);
    }

    private void Content_ModelChanged(object sender, EventArgs e) {
      UpdateView();
    }
    private void Content_ProblemDataChanged(object sender, EventArgs e) {
      UpdateView();
    }
    private void Content_EstimatedValuesChanged(object sender, EventArgs e) {
      UpdateView();
    }

    protected override void OnContentChanged() {
      base.OnContentChanged();
      UpdateView();
    }
    private void UpdateView() {
      if (Content != null) {
        matrixView.Content = CalculateMatrix();
      } else
        matrixView.Content = null;
    }

    public DoubleMatrix CalculateMatrix() {
      List<string> targetVariables = Content.ProblemData.TargetVariables.CheckedItems.Select(x => x.Value.Value).ToList();
      DoubleMatrix matrix = new DoubleMatrix(rowNames.Count, targetVariables.Count() * 2);
      matrix.RowNames = rowNames;
      matrix.ColumnNames = targetVariables.SelectMany(x => new List<string>() { x + " (training)", x + " (test)" });
      matrix.SortableView = false;

      int trainingStart = Content.ProblemData.TrainingSamplesStart.Value;
      int trainingEnd = Content.ProblemData.TrainingSamplesEnd.Value;
      int testStart = Content.ProblemData.TestSamplesStart.Value;
      int testEnd = Content.ProblemData.TestSamplesEnd.Value;
      // create a list of time series evaluators for each target variable
      Dictionary<string, List<IOnlineEvaluator>> trainingEvaluators =
        new Dictionary<string, List<IOnlineEvaluator>>();
      Dictionary<string, List<IOnlineEvaluator>> testEvaluators =
        new Dictionary<string, List<IOnlineEvaluator>>();
      foreach (string targetVariable in targetVariables) {
        trainingEvaluators.Add(targetVariable, new List<IOnlineEvaluator>());
        trainingEvaluators[targetVariable].Add(new OnlineMeanSquaredErrorEvaluator());
        trainingEvaluators[targetVariable].Add(new OnlinePearsonsRSquaredEvaluator());
        trainingEvaluators[targetVariable].Add(new OnlineMeanAbsolutePercentageErrorEvaluator());
        trainingEvaluators[targetVariable].Add(new OnlineDirectionalSymmetryEvaluator());
        trainingEvaluators[targetVariable].Add(new OnlineWeightedDirectionalSymmetryEvaluator());
        trainingEvaluators[targetVariable].Add(new OnlineTheilsUStatisticEvaluator());

        testEvaluators.Add(targetVariable, new List<IOnlineEvaluator>());
        testEvaluators[targetVariable].Add(new OnlineMeanSquaredErrorEvaluator());
        testEvaluators[targetVariable].Add(new OnlinePearsonsRSquaredEvaluator());
        testEvaluators[targetVariable].Add(new OnlineMeanAbsolutePercentageErrorEvaluator());
        testEvaluators[targetVariable].Add(new OnlineDirectionalSymmetryEvaluator());
        testEvaluators[targetVariable].Add(new OnlineWeightedDirectionalSymmetryEvaluator());
        testEvaluators[targetVariable].Add(new OnlineTheilsUStatisticEvaluator());
      }

      Evaluate(trainingStart, trainingEnd, trainingEvaluators);
      Evaluate(testStart, testEnd, testEvaluators);

      int columnIndex = 0;
      foreach (string targetVariable in targetVariables) {
        int rowIndex = 0;
        // training
        foreach (var evaluator in trainingEvaluators[targetVariable]) {
          matrix[rowIndex++, columnIndex] = Math.Round(evaluator.Value, 3);
        }
        columnIndex++;
        // test
        rowIndex = 0;
        foreach (var evaluator in testEvaluators[targetVariable]) {
          matrix[rowIndex++, columnIndex] = Math.Round(evaluator.Value, 3);
        }
        columnIndex++;
      }
      return matrix;
    }

    private void Evaluate(int start, int end, Dictionary<string, List<IOnlineEvaluator>> evaluators) {

      for (int row = start; row < end; row++) {
        if (string.IsNullOrEmpty(Content.ConditionalEvaluationVariable) || Content.ProblemData.Dataset[Content.ConditionalEvaluationVariable, row] != 0) {
          // prepare evaluators for each target variable for a new prediction window
          foreach (var entry in evaluators) {
            double referenceOriginalValue = Content.ProblemData.Dataset[entry.Key, row - 1];
            foreach (IOnlineTimeSeriesPrognosisEvaluator evaluator in entry.Value.OfType<IOnlineTimeSeriesPrognosisEvaluator>()) {
              evaluator.StartNewPredictionWindow(referenceOriginalValue);
            }
          }
          List<string> targetVariables = Content.ProblemData.TargetVariables.CheckedItems.Select(x => x.Value.Value).ToList();

          if (string.IsNullOrEmpty(Content.ConditionalEvaluationVariable) ||
            Content.ProblemData.Dataset[Content.ConditionalEvaluationVariable, row] > 0) {
            int timestep = 0;
            foreach (double[] x in Content.GetPrognosis(row)) {
              int targetIndex = 0;
              if (row + timestep < Content.ProblemData.Dataset.Rows) {
                foreach (var targetVariable in targetVariables) {
                  double originalValue = Content.ProblemData.Dataset[targetVariable, row + timestep];
                  double estimatedValue = x[targetIndex];
                  if (IsValidValue(originalValue) && IsValidValue(estimatedValue)) {
                    foreach (IOnlineEvaluator evaluator in evaluators[targetVariable]) {
                      evaluator.Add(originalValue, estimatedValue);
                    }
                  }
                  targetIndex++;
                }
              }
              timestep++;
            }
          }
        }
      }
    }

    private bool IsValidValue(double d) {
      return !(double.IsNaN(d) || double.IsInfinity(d));
    }
  }
}