source: branches/2288_HeuristicLab.VariableInteractionNetworks/HeuristicLab.VariableInteractionNetworks.Views/3.3/CreateTargetVariationExperimentDialog.cs @ 16003

#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.ComponentModel;
using System.Linq;
using System.Windows.Forms;
using HeuristicLab.Common;
using HeuristicLab.MainForm;
using HeuristicLab.Optimization;
using HeuristicLab.Problems.DataAnalysis;

namespace HeuristicLab.VariableInteractionNetworks {
  public partial class CreateTargetVariationExperimentDialog : Form {
    public Experiment Experiment { get; private set; }

    public CreateTargetVariationExperimentDialog() : this(null) { }
    public CreateTargetVariationExperimentDialog(IAlgorithm alg) {
      InitializeComponent();

      var problem = alg.Problem as IDataAnalysisProblem;
      var inputCount = problem.ProblemData.AllowedInputVariables.Count() - 1; // -1 because one will be the target
      if (combinationGroupSizeNumericUpDown.Value > inputCount)
        combinationGroupSizeNumericUpDown.Value = inputCount;
      var combinationGroupSize = (int)combinationGroupSizeNumericUpDown.Value;
      combinationCountLabel.Text = ((inputCount + 1) * EnumerableExtensions.BinomialCoefficient(inputCount, combinationGroupSize)).ToString();

      Experiment = null;
      okButton.Enabled = alg != null;
    }

    public static IEnumerable<IOptimizer> CreateVariableCombinations(IAlgorithm algorithm, int combinationGroupSize, int repetitions) {
      if (algorithm == null)
        throw new ArgumentNullException("The algorithm parameter must be a valid IAlgorithm instance.");

      var problem = (IDataAnalysisProblem)algorithm.Problem;
      var problemData = problem.ProblemData;

      var variables = problemData.AllowedInputVariables.ToList();
      int count = 1;
      foreach (var target in variables) {
        var inputs = variables.Where(x => x != target).ToList();
        var combinations = inputs.Combinations(combinationGroupSize);

        foreach (var combination in combinations) {
          var h = new HashSet<string>(combination);
          var alg = (IAlgorithm)algorithm.Clone();
          alg.Name += " {Target = " + target + ", Inputs = (" + combination.Aggregate((a, b) => a + "," + b) + ")}";
          problem = (IDataAnalysisProblem)alg.Problem;
          problemData = problem.ProblemData;
          foreach (var item in problemData.InputVariables) {
            problemData.InputVariables.SetItemCheckedState(item, h.Contains(item.Value));
          }
          SetTargetName(problemData, target);

          var batchRun = new BatchRun(string.Format("Batchrun {0}", count++)) {
            Repetitions = repetitions,
            Optimizer = alg
          };
          yield return batchRun;
        }
      }
    }

    private static string GetTargetName(IDataAnalysisProblemData problemData) {
      var regressionProblemData = problemData as IRegressionProblemData;
      if (regressionProblemData != null)
        return regressionProblemData.TargetVariable;
      var classificationProblemData = problemData as IClassificationProblemData;
      if (classificationProblemData != null)
        return classificationProblemData.TargetVariable;
      throw new ArgumentException("The provided argument must be either a regression or classification problem data.");
    }

    private static void SetTargetName(IDataAnalysisProblemData problemData, string targetName) {
      var regressionProblemData = problemData as IRegressionProblemData;
      if (regressionProblemData != null) {
        regressionProblemData.TargetVariable = targetName;
        return;
      }
      var classificationProblemData = problemData as IClassificationProblemData;
      if (classificationProblemData != null) {
        classificationProblemData.TargetVariable = targetName;
        return;
      }
      throw new ArgumentException("The provided argument must be either a regression or classification problem data.");
    }

    #region events
    private void CreateTargetVariationExperimentDialog_FormClosing(object sender, FormClosingEventArgs e) {
      if (worker.IsBusy) {
        if (DialogResult != DialogResult.OK) {
          if (worker.IsBusy) worker.CancelAsync();
        }
        e.Cancel = true;
      }
    }

    private void repetitionsNumericUpDown_Validated(object sender, EventArgs e) {
      if (repetitionsNumericUpDown.Text == string.Empty)
        repetitionsNumericUpDown.Text = repetitionsNumericUpDown.Value.ToString();
    }

    private void combinationGroupSizeNumericUpDown_Validating(object sender, System.ComponentModel.CancelEventArgs e) {
      IContentView activeView = MainFormManager.MainForm.ActiveView as IContentView;
      if (activeView == null) return;
      var algorithm = (IAlgorithm)activeView.Content;
      var problem = algorithm.Problem as IDataAnalysisProblem;
      if (problem == null) return;
      var inputCount = problem.ProblemData.AllowedInputVariables.Count() - 1;
      if (combinationGroupSizeNumericUpDown.Value > inputCount)
        combinationGroupSizeNumericUpDown.Value = inputCount;
    }

    private void combinationGroupSizeNumericUpDown_Validated(object sender, EventArgs e) {
      if (combinationGroupSizeNumericUpDown.Text == string.Empty)
        combinationGroupSizeNumericUpDown.Text = combinationGroupSizeNumericUpDown.Value.ToString();

      IContentView activeView = MainFormManager.MainForm.ActiveView as IContentView;
      if (activeView == null) return;
      var algorithm = (IAlgorithm)activeView.Content;
      var problem = (IDataAnalysisProblem)algorithm.Problem;
      var inputCount = problem.ProblemData.AllowedInputVariables.Count() - 1;
      if (combinationGroupSizeNumericUpDown.Value > inputCount)
        combinationGroupSizeNumericUpDown.Value = inputCount;
      combinationCountLabel.Text = ((inputCount + 1) * EnumerableExtensions.BinomialCoefficient(inputCount, (int)combinationGroupSizeNumericUpDown.Value)).ToString();
    }

    private void worker_DoWork(object sender, DoWorkEventArgs e) {
      IContentView activeView = (IContentView)MainFormManager.MainForm.ActiveView;
      var algorithm = (IAlgorithm)activeView.Content;
      var problem = (IDataAnalysisProblem)algorithm.Problem;
      var experiment = new Experiment("Target Variation Experiment");
      var repetitions = (int)repetitionsNumericUpDown.Value;
      var inputCount = problem.ProblemData.AllowedInputVariables.Count() - 1;
      var combinationGroupSize = (int)combinationGroupSizeNumericUpDown.Value;
      var totalNumberOfCombinations = (inputCount + 1) * EnumerableExtensions.BinomialCoefficient(inputCount, combinationGroupSize);
      int progress = 0;
      try {
        foreach (var optimizer in CreateVariableCombinations(algorithm, combinationGroupSize, repetitions)) {
          if (worker.CancellationPending)
            throw new OperationCanceledException();
          experiment.Optimizers.Add(optimizer);
          progress++;
          worker.ReportProgress((int)Math.Round(100d * progress / totalNumberOfCombinations));
        }
        Experiment = experiment;
      } catch (OperationCanceledException) {
        e.Cancel = true;
        UpdateProgress(0);
        progressBar.Visible = false;
        okButton.Enabled = true;
        combinationsLabel.Visible = true;
        combinationCountLabel.Visible = true;
      }
    }

    private void worker_ProgressChanged(object sender, ProgressChangedEventArgs e) {
      if (InvokeRequired) {
        Invoke((Action<int>)UpdateProgress, e.ProgressPercentage);
      } else {
        UpdateProgress(e.ProgressPercentage);
      }
    }

    private void UpdateProgress(int percentage) {
      progressBar.Value = percentage;
    }

    private void worker_RunWorkerCompleted(object sender, RunWorkerCompletedEventArgs e) {
      if (e.Cancelled || e.Error != null)
        return;
      DialogResult = DialogResult.OK;
      Close();
    }

    private void okButton_Click(object sender, EventArgs args) {
      IContentView activeView = MainFormManager.MainForm.ActiveView as IContentView;
      if ((activeView != null) && (activeView.Content != null) && (activeView.Content is IAlgorithm) && !activeView.Locked) {
        okButton.Enabled = false;
        combinationsLabel.Visible = false;
        combinationCountLabel.Visible = false;
        progressBar.Visible = true;
        worker.RunWorkerAsync();
      }
    }

    private void cancelButton_Click(object sender, EventArgs e) {
      if (worker.IsBusy)
        worker.CancelAsync();
    }
    #endregion
  }
}