source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Views/3.3/ResultsView.cs @ 6318

#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.VectorRegression;

namespace HeuristicLab.Problems.DataAnalysis.VectorRegression.Views {
  [Content(typeof(IMultiTargetRegressionSolution), false)]
  [View("Multi-target Results View")]
  public partial class ResultsView : AsynchronousContentView {
    private List<string> rowNames = new List<string>() { 
      "Mean squared error (training)", "Mean squared error (test)",
      "Pearson's R² (training)", "Pearson's R² (test)", 
      "Mean relative error (training)", "Mean relative error (test)" };

    public ResultsView() {
      InitializeComponent();
    }

    public new IMultiTargetRegressionSolution Content {
      get { return (IMultiTargetRegressionSolution)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) {
        DoubleMatrix matrix = new DoubleMatrix(rowNames.Count, Content.TargetVariables.Count());
        matrix.RowNames = rowNames;
        matrix.ColumnNames = Content.TargetVariables;
        matrix.SortableView = false;

        int columnIndex = 0;
        foreach (string targetVariable in Content.TargetVariables) {
          DataAnalysisSolution targetVariableSolution = Content.GetModelFor(targetVariable);

          IEnumerable<double> originalTrainingValues = targetVariableSolution.ProblemData.Dataset.GetVariableValues(targetVariable, targetVariableSolution.ProblemData.TrainingSamplesStart.Value, targetVariableSolution.ProblemData.TrainingSamplesEnd.Value);
          IEnumerable<double> originalTestValues = targetVariableSolution.ProblemData.Dataset.GetVariableValues(targetVariable, targetVariableSolution.ProblemData.TestSamplesStart.Value, targetVariableSolution.ProblemData.TestSamplesEnd.Value);

          matrix[0, columnIndex] = SimpleMSEEvaluator.Calculate(originalTrainingValues, targetVariableSolution.EstimatedTrainingValues);
          matrix[1, columnIndex] = SimpleMSEEvaluator.Calculate(originalTestValues, targetVariableSolution.EstimatedTestValues);
          matrix[2, columnIndex] = SimpleRSquaredEvaluator.Calculate(originalTrainingValues, targetVariableSolution.EstimatedTrainingValues);
          matrix[3, columnIndex] = SimpleRSquaredEvaluator.Calculate(originalTestValues, targetVariableSolution.EstimatedTestValues);
          matrix[4, columnIndex] = SimpleMeanAbsolutePercentageErrorEvaluator.Calculate(originalTrainingValues, targetVariableSolution.EstimatedTrainingValues);
          matrix[5, columnIndex] = SimpleMeanAbsolutePercentageErrorEvaluator.Calculate(originalTestValues, targetVariableSolution.EstimatedTestValues);
          columnIndex++;
        }

        matrixView.Content = matrix;
      } else
        matrixView.Content = null;
    }
  }
}