source: branches/DataPreprocessing/HeuristicLab.DataPreprocessing.Views/3.4/PreprocessingFeatureCorrelationView.cs @ 10925

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2013 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.Collections.Generic;
using System.ComponentModel;
using System.Linq;
using System.Windows.Forms;
using HeuristicLab.Data;
using HeuristicLab.MainForm;
using HeuristicLab.MainForm.WindowsForms;
using HeuristicLab.PluginInfrastructure;
using System;
using HeuristicLab.DataPreprocessing;

namespace HeuristicLab.Problems.DataAnalysis.Views {
  [View("Preprocessing Feature Correlation View")]
  [Content(typeof(CorrelationMatrixContent), false)]
  public partial class PreprocessingFeatureCorrelationView : AsynchronousContentView {
    public const string ALLSAMPLES = "All Samples";
    public const string TRAININGSAMPLES = "Training Samples";
    public const string TESTSAMPLES = "Test Samples";

    public static readonly IList<string> Partitions = new List<string>() { ALLSAMPLES, TRAININGSAMPLES, TESTSAMPLES };

    protected FeatureCorrelationCalculator fcc;

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

    private FeatureCorrelationCache correlationCache;

    public PreprocessingFeatureCorrelationView() {

      correlationCache = new FeatureCorrelationCache();
      InitializeComponent();
      fcc = new FeatureCorrelationCalculator();
      var calculators = ApplicationManager.Manager.GetInstances<IDependencyCalculator>();
      var calcList = calculators.OrderBy(c => c.Name).Select(c => new { Name = c.Name, Calculator = c }).ToList();
      correlationCalcComboBox.ValueMember = "Calculator";
      correlationCalcComboBox.DisplayMember = "Name";
      correlationCalcComboBox.DataSource = calcList;
      correlationCalcComboBox.SelectedItem = calcList.First(c => c.Calculator.GetType().Equals(typeof(PearsonsRDependenceCalculator)));
      partitionComboBox.DataSource = Partitions;
      partitionComboBox.SelectedItem = TRAININGSAMPLES;
    }

    protected override void RegisterContentEvents() {
      base.RegisterContentEvents();
      fcc.ProgressCalculation += new FeatureCorrelationCalculator.ProgressCalculationHandler(Content_ProgressCalculation);
      fcc.CorrelationCalculationFinished += new FeatureCorrelationCalculator.CorrelationCalculationFinishedHandler(Content_CorrelationCalculationFinished);
    }

    protected override void DeregisterContentEvents() {
      fcc.CorrelationCalculationFinished -= new FeatureCorrelationCalculator.CorrelationCalculationFinishedHandler(Content_CorrelationCalculationFinished);
      fcc.ProgressCalculation -= new FeatureCorrelationCalculator.ProgressCalculationHandler(Content_ProgressCalculation);
      base.DeregisterContentEvents();
    }

    protected override void OnContentChanged() {
      base.OnContentChanged();
      fcc.TryCancelCalculation();
      if (Content != null) {
        fcc.ProblemData = Content.ProblemData;
        CalculateCorrelation();
      } else {
        dataView.Maximum = 0;
        dataView.Minimum = 0;
        dataView.Content = null;
        dataView.ResetVisibility();
      }
    }

    protected virtual bool[] SetInitialVariableVisibility() {
      bool[] initialVisibility = new bool[Content.ProblemData.Dataset.DoubleVariables.Count()];
      int i = 0;
      foreach (var variable in Content.ProblemData.Dataset.DoubleVariables) {
        initialVisibility[i] = Content.ProblemData.AllowedInputVariables.Contains(variable);
        i++;
      }
      return initialVisibility;
    }

    protected void CorrelationMeasureComboBox_SelectedChangeCommitted(object sender, System.EventArgs e) {
      CalculateCorrelation();
    }
    protected void PartitionComboBox_SelectedChangeCommitted(object sender, System.EventArgs e) {
      CalculateCorrelation();
    }

     protected void CalculateCorrelation() {
      if (correlationCalcComboBox.SelectedItem == null) return;
      if (partitionComboBox.SelectedItem == null) return;

      IDependencyCalculator calc = (IDependencyCalculator)correlationCalcComboBox.SelectedValue;
      string partition = (string)partitionComboBox.SelectedValue;
      dataView.Enabled = false;
      double[,] corr = correlationCache.GetCorrelation(calc, partition);
      if (corr == null) {
        fcc.CalculateElements(calc, partition);
      } else {
        fcc.TryCancelCalculation();
        var correlation = new DoubleMatrix(corr, Content.ProblemData.Dataset.DoubleVariables, Content.ProblemData.Dataset.DoubleVariables);
        UpdateDataView(correlation);
      }
    }

    protected void Content_CorrelationCalculationFinished(object sender, FeatureCorrelationCalculator.CorrelationCalculationFinishedArgs e) {
      if (InvokeRequired) {
        Invoke(new FeatureCorrelationCalculator.CorrelationCalculationFinishedHandler(Content_CorrelationCalculationFinished), sender, e);
        return;
      }
      correlationCache.SetCorrelation(e.Calculcator, e.Partition, e.Correlation);
      var correlation = new DoubleMatrix(e.Correlation, Content.ProblemData.Dataset.DoubleVariables, Content.ProblemData.Dataset.DoubleVariables);
      UpdateDataView(correlation);
    }

    protected void UpdateDataView(DoubleMatrix correlation) {
      IDependencyCalculator calc = (IDependencyCalculator)correlationCalcComboBox.SelectedValue;
      maximumLabel.Text = calc.Maximum.ToString();
      minimumLabel.Text = calc.Minimum.ToString();

      correlation.SortableView = true;
      dataView.Maximum = calc.Maximum;
      dataView.Minimum = calc.Minimum;
      dataView.Content = correlation;
      dataView.Enabled = true;
    }

    protected void Content_ProgressCalculation(object sender, ProgressChangedEventArgs e) {
      if (!progressPanel.Visible && e.ProgressPercentage != progressBar.Maximum) {
        progressPanel.Show();
      } else if (e.ProgressPercentage == progressBar.Maximum) {
        progressPanel.Hide();
      }
      progressBar.Value = e.ProgressPercentage;
    }

      [NonDiscoverableType]
      private class FeatureCorrelationCache : Object {
        private Dictionary<Tuple<IDependencyCalculator, string>, double[,]> correlationsCache;

        public FeatureCorrelationCache()
          : base() {
          InitializeCaches();
        }

        private void InitializeCaches() {
          correlationsCache = new Dictionary<Tuple<IDependencyCalculator, string>, double[,]>();
        }

        public void Reset() {
          InitializeCaches();
        }

        public double[,] GetCorrelation(IDependencyCalculator calc, string partition) {
          double[,] corr;
          var key = new Tuple<IDependencyCalculator, string>(calc, partition);
          correlationsCache.TryGetValue(key, out corr);
          return corr;
        }

        public void SetCorrelation(IDependencyCalculator calc, string partition, double[,] correlation) {
          var key = new Tuple<IDependencyCalculator, string>(calc, partition);
          correlationsCache[key] = correlation;
        }
      }
  }
}