source: branches/ExportSymbolicDataAnalysisSolutions/HeuristicLab.Problems.DataAnalysis.Symbolic.Views/3.4/MenuItems/ExportSymbolicSolutionToExcelMenuItem.cs @ 9580

#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;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Windows.Forms;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Views;
using HeuristicLab.MainForm;
using HeuristicLab.Optimizer;
using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
using OfficeOpenXml;
using OfficeOpenXml.Drawing.Chart;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Views {
  public class ExportSymbolicSolutionToExcelMenuItem : MainForm.WindowsForms.MenuItem, IOptimizerUserInterfaceItemProvider {
    private const string TRAININGSTART = "TrainingStart";
    private const string TRAININGEND = "TrainingEnd";
    private const string TESTSTART = "TestStart";
    private const string TESTEND = "TestEnd";

    public override string Name {
      get { return "Export Symbolic Solution To Excel"; }
    }
    public override IEnumerable<string> Structure {
      get { return new string[] { "&Edit" }; }
    }
    public override int Position {
      get { return 2500; }
    }
    public override string ToolTipText {
      get { return "Create excel file of symbolic data analysis solutions."; }
    }

    protected override void OnToolStripItemSet(EventArgs e) {
      ToolStripItem.Enabled = false;
    }
    protected override void OnActiveViewChanged(object sender, EventArgs e) {
      IContentView activeView = MainFormManager.MainForm.ActiveView as IContentView;
      ToolStripItem.Enabled = activeView != null && activeView.Content as ISymbolicDataAnalysisSolution != null;
    }

    public override void Execute() {
      IContentView activeView = MainFormManager.MainForm.ActiveView as IContentView;
      var solution = activeView.Content as ISymbolicDataAnalysisSolution;
      var formatter = new SymbolicDataAnalysisExpressionExcelFormatter();
      var formula = formatter.Format(solution.Model.SymbolicExpressionTree);
      var formulaParts = formula.Split(new string[] { Environment.NewLine }, StringSplitOptions.None);

      SaveFileDialog saveFileDialog = new SaveFileDialog();
      saveFileDialog.Filter = "Excel Workbook|*.xlsx";
      saveFileDialog.Title = "Save an Excel File";
      if (saveFileDialog.ShowDialog() == DialogResult.OK) {
        string fileName = saveFileDialog.FileName;
        FileInfo newFile = new FileInfo(fileName);
        if (newFile.Exists) {
          newFile.Delete();
          newFile = new FileInfo(fileName);
        }
        using (ExcelPackage package = new ExcelPackage(newFile)) {
          ExcelWorksheet modelWorksheet = package.Workbook.Worksheets.Add("Model");
          FormatModelSheet(modelWorksheet, solution, formulaParts);

          ExcelWorksheet datasetWorksheet = package.Workbook.Worksheets.Add("Dataset");
          WriteDatasetToExcel(datasetWorksheet, solution.ProblemData);

          ExcelWorksheet inputsWorksheet = package.Workbook.Worksheets.Add("Inputs");
          WriteInputSheet(inputsWorksheet, datasetWorksheet, formulaParts.Skip(2), solution.ProblemData.Dataset);

          ExcelWorksheet estimatedWorksheet = package.Workbook.Worksheets.Add("Estimated Values");
          if (solution is IRegressionSolution) {
            WriteEstimatedWorksheet(estimatedWorksheet, datasetWorksheet, formulaParts, solution as IRegressionSolution);
          }

          ExcelWorksheet chartsWorksheet = package.Workbook.Worksheets.Add("Charts");
          AddCharts(chartsWorksheet);

          package.Workbook.Properties.Title = "Excel Export";
          package.Workbook.Properties.Author = "HEAL";
          package.Workbook.Properties.Comments = "Excel export of a symbolic data analysis solution from HeuristicLab";

          package.Save();
        }
      }
    }

    private void FormatModelSheet(ExcelWorksheet modelWorksheet, ISymbolicDataAnalysisSolution solution, IEnumerable<string> formulaParts) {
      int row = 1;
      modelWorksheet.Cells[row, 1].Value = "Model";
      modelWorksheet.Cells[row, 2].Value = solution.Name;

      foreach (var part in formulaParts) {
        modelWorksheet.Cells[row, 4].Value = part;
        row++;
      }

      row = 2;
      modelWorksheet.Cells[row, 1].Value = "Model Depth";
      modelWorksheet.Cells[row, 2].Value = solution.Model.SymbolicExpressionTree.Depth;
      row++;

      modelWorksheet.Cells[row, 1].Value = "Model Length";
      modelWorksheet.Cells[row, 2].Value = solution.Model.SymbolicExpressionTree.Length;
      row += 2;

      var regSolution = solution as SymbolicRegressionSolution;
      if (regSolution != null) {
        modelWorksheet.Cells[row, 1].Value = "Estimation Limits Lower";
        modelWorksheet.Cells[row, 2].Value = regSolution.EstimationLimits.Lower;
        modelWorksheet.Names.Add("EstimationLimitLower", modelWorksheet.Cells[row, 2]);
        row++;

        modelWorksheet.Cells[row, 1].Value = "Estimation Limits Upper";
        modelWorksheet.Cells[row, 2].Value = regSolution.EstimationLimits.Upper;
        modelWorksheet.Names.Add("EstimationLimitUpper", modelWorksheet.Cells[row, 2]);
        row += 2;
      }

      modelWorksheet.Cells[row, 1].Value = "Trainings Partition Start";
      modelWorksheet.Cells[row, 2].Value = solution.ProblemData.TrainingPartition.Start;
      modelWorksheet.Names.Add(TRAININGSTART, modelWorksheet.Cells[row, 2]);
      row++;

      modelWorksheet.Cells[row, 1].Value = "Trainings Partition End";
      modelWorksheet.Cells[row, 2].Value = solution.ProblemData.TrainingPartition.End;
      modelWorksheet.Names.Add(TRAININGEND, modelWorksheet.Cells[row, 2]);
      row++;

      modelWorksheet.Cells[row, 1].Value = "Test Partition Start";
      modelWorksheet.Cells[row, 2].Value = solution.ProblemData.TestPartition.Start;
      modelWorksheet.Names.Add(TESTSTART, modelWorksheet.Cells[row, 2]);
      row++;

      modelWorksheet.Cells[row, 1].Value = "Test Partition End";
      modelWorksheet.Cells[row, 2].Value = solution.ProblemData.TestPartition.End;
      modelWorksheet.Names.Add(TESTEND, modelWorksheet.Cells[row, 2]);
      row += 2;

      string excelTrainingTarget = Indirect("B", true);
      string excelTrainingEstimated = Indirect("C", true);
      string excelTrainingAbsoluteError = Indirect("D", true);
      string excelTrainingRelativeError = Indirect("E", true);
      string excelTrainingMeanError = Indirect("F", true);
      string excelTrainingMSE = Indirect("G", true);

      string excelTestTarget = Indirect("B", false);
      string excelTestEstimated = Indirect("C", false);
      string excelTestAbsoluteError = Indirect("D", false);
      string excelTestRelativeError = Indirect("E", false);
      string excelTestMeanError = Indirect("F", false);
      string excelTestMSE = Indirect("G", false);

      modelWorksheet.Cells[row, 1].Value = "Pearson's R² (training)";
      modelWorksheet.Cells[row, 2].Formula = string.Format("POWER(PEARSON({0},{1}),2)", excelTrainingTarget, excelTrainingEstimated);
      row++;

      modelWorksheet.Cells[row, 1].Value = "Pearson's R² (test)";
      modelWorksheet.Cells[row, 2].Formula = string.Format("POWER(PEARSON({0},{1}),2)", excelTestTarget, excelTestEstimated);
      row++;

      modelWorksheet.Cells[row, 1].Value = "Mean Squared Error (training)";
      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTrainingMSE);
      modelWorksheet.Names.Add("TrainingMSE", modelWorksheet.Cells[row, 2]);
      row++;

      modelWorksheet.Cells[row, 1].Value = "Mean Squared Error (test)";
      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTestMSE);
      modelWorksheet.Names.Add("TestMSE", modelWorksheet.Cells[row, 2]);
      row++;

      modelWorksheet.Cells[row, 1].Value = "Mean absolute error (training)";
      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTrainingAbsoluteError);
      row++;

      modelWorksheet.Cells[row, 1].Value = "Mean absolute error (test)";
      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTestAbsoluteError);
      row++;

      modelWorksheet.Cells[row, 1].Value = "Mean error (training)";
      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTrainingMeanError);
      row++;

      modelWorksheet.Cells[row, 1].Value = "Mean error (test)";
      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTestMeanError);
      row++;

      modelWorksheet.Cells[row, 1].Value = "Average relative error (training)";
      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTrainingRelativeError);
      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.00%";
      row++;

      modelWorksheet.Cells[row, 1].Value = "Average relative error (test)";
      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTestRelativeError);
      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.00%";
      row++;

      modelWorksheet.Cells[row, 1].Value = "Normalized Mean Squared error (training)";
      modelWorksheet.Cells[row, 2].Formula = string.Format("TrainingMSE / VAR({0})", excelTrainingTarget);
      row++;

      modelWorksheet.Cells[row, 1].Value = "Normalized Mean Squared error  (test)";
      modelWorksheet.Cells[row, 2].Formula = string.Format("TestMSE / VAR({0})", excelTestTarget);

      modelWorksheet.Cells["A1:B" + row].AutoFitColumns();

      AddModelTreePicture(modelWorksheet, solution.Model);
    }

    private string Indirect(string column, bool training) {
      if (training) {
        return string.Format("INDIRECT(\"'Estimated Values'!{0}\"&{1}+2&\":{0}\"&{2}+1)", column, TRAININGSTART, TRAININGEND);
      } else {
        return string.Format("INDIRECT(\"'Estimated Values'!{0}\"&{1}+2&\":{0}\"&{2}+1)", column, TESTSTART, TESTEND);
      }
    }

    private void AddCharts(ExcelWorksheet chartsWorksheet) {
      chartsWorksheet.Names.AddFormula("AllId", "OFFSET('Estimated Values'!$A$1,1,0, COUNTA('Estimated Values'!$A:$A)-1)");
      chartsWorksheet.Names.AddFormula("AllTarget", "OFFSET('Estimated Values'!$B$1,1,0, COUNTA('Estimated Values'!$B:$B)-1)");
      chartsWorksheet.Names.AddFormula("AllEstimated", "OFFSET('Estimated Values'!$C$1,1,0, COUNTA('Estimated Values'!$C:$C)-1)");
      chartsWorksheet.Names.AddFormula("TrainingId", "OFFSET('Estimated Values'!$A$1,Model!TrainingStart + 1,0, Model!TrainingEnd - Model!TrainingStart)");
      chartsWorksheet.Names.AddFormula("TrainingTarget", "OFFSET('Estimated Values'!$B$1,Model!TrainingStart + 1,0, Model!TrainingEnd - Model!TrainingStart)");
      chartsWorksheet.Names.AddFormula("TrainingEstimated", "OFFSET('Estimated Values'!$C$1,Model!TrainingStart + 1,0, Model!TrainingEnd - Model!TrainingStart)");
      chartsWorksheet.Names.AddFormula("TestId", "OFFSET('Estimated Values'!$A$1,Model!TestStart + 1,0, Model!TestEnd - Model!TestStart)");
      chartsWorksheet.Names.AddFormula("TestTarget", "OFFSET('Estimated Values'!$B$1,Model!TestStart + 1,0, Model!TestEnd - Model!TestStart)");
      chartsWorksheet.Names.AddFormula("TestEstimated", "OFFSET('Estimated Values'!$C$1,Model!TestStart + 1,0, Model!TestEnd - Model!TestStart)");

      var scatterPlot = chartsWorksheet.Drawings.AddChart("scatterPlot", eChartType.XYScatter);
      scatterPlot.SetSize(800, 400);
      scatterPlot.SetPosition(0, 0);
      scatterPlot.Title.Text = "Scatter Plot";
      var seriesAll = scatterPlot.Series.Add("AllTarget", "AllEstimated");
      seriesAll.Header = "All";
      var seriesTraining = scatterPlot.Series.Add("TrainingTarget", "TrainingEstimated");
      seriesTraining.Header = "Training";
      var seriesTest = scatterPlot.Series.Add("TestTarget", "TestEstimated");
      seriesTest.Header = "Test";

      var lineChart = chartsWorksheet.Drawings.AddChart("lineChart", eChartType.XYScatterLinesNoMarkers);
      lineChart.SetSize(800, 400);
      lineChart.SetPosition(400, 0);
      lineChart.Title.Text = "LineChart";
      var lineTarget = lineChart.Series.Add("AllTarget", "AllId");
      lineTarget.Header = "Target";
      var lineAll = lineChart.Series.Add("AllEstimated", "AllId");
      lineAll.Header = "All";
      var lineTraining = lineChart.Series.Add("TrainingEstimated", "TrainingId");
      lineTraining.Header = "Training";
      var lineTest = lineChart.Series.Add("TestEstimated", "TestId");
      lineTest.Header = "Test";
    }

    private void AddModelTreePicture(ExcelWorksheet modelWorksheet, ISymbolicDataAnalysisModel model) {
      SymbolicExpressionTreeChart modelTreePicture = new SymbolicExpressionTreeChart();
      modelTreePicture.Tree = model.SymbolicExpressionTree;
      string tmpFilename = Path.GetTempFileName();
      modelTreePicture.Width = 1000;
      modelTreePicture.Height = 500;
      modelTreePicture.SaveImageAsEmf(tmpFilename);

      FileInfo fi = new FileInfo(tmpFilename);
      var excelModelTreePic = modelWorksheet.Drawings.AddPicture("ModelTree", fi);
      excelModelTreePic.SetSize(50);
      excelModelTreePic.SetPosition(2, 0, 6, 0);
    }

    private void WriteEstimatedWorksheet(ExcelWorksheet estimatedWorksheet, ExcelWorksheet datasetWorksheet, string[] formulaParts, IRegressionSolution solution) {
      string preparedFormula = PrepareFormula(formulaParts);
      int rows = solution.ProblemData.Dataset.Rows;
      estimatedWorksheet.Cells[1, 1].Value = "Id";
      estimatedWorksheet.Cells[1, 2].Value = "Target Variable";
      estimatedWorksheet.Cells[1, 3].Value = "Estimated Values";
      estimatedWorksheet.Cells[1, 4].Value = "Absolute Error";
      estimatedWorksheet.Cells[1, 5].Value = "Relative Error";
      estimatedWorksheet.Cells[1, 6].Value = "Error";
      estimatedWorksheet.Cells[1, 7].Value = "Squared Error";
      estimatedWorksheet.Cells[1, 9].Value = "Unbounded Estimated Values";
      estimatedWorksheet.Cells[1, 10].Value = "Bounded Estimated Values";

      estimatedWorksheet.Cells[1, 1, 1, 10].AutoFitColumns();

      int targetIndex = solution.ProblemData.Dataset.VariableNames.ToList().FindIndex(x => x.Equals(solution.ProblemData.TargetVariable)) + 1;
      for (int i = 0; i < rows; i++) {
        estimatedWorksheet.Cells[i + 2, 1].Value = i;
        estimatedWorksheet.Cells[i + 2, 2].Formula = datasetWorksheet.Cells[i + 2, targetIndex].FullAddress;
        estimatedWorksheet.Cells[i + 2, 9].Formula = string.Format(preparedFormula, i + 2);
      }

      estimatedWorksheet.Cells["C2:C" + (rows + 1)].Formula = "J2";

      estimatedWorksheet.Cells["D2:D" + (rows + 1)].Formula = "ABS(B2 - C2)";
      estimatedWorksheet.Cells["E2:E" + (rows + 1)].Formula = "ABS(D2 / B2)";
      estimatedWorksheet.Cells["F2:F" + (rows + 1)].Formula = "C2 - B2";
      estimatedWorksheet.Cells["G2:G" + (rows + 1)].Formula = "POWER(F2, 2)";

      estimatedWorksheet.Cells["J2:J" + (rows + 1)].Formula = "IF(I2 > Model!EstimationLimitUpper, Model!EstimationLimitUpper, IF(I2 < Model!EstimationLimitLower, Model!EstimationLimitLower, I2))";
    }

    private string PrepareFormula(string[] formulaParts) {
      string preparedFormula = formulaParts[0];
      foreach (var part in formulaParts.Skip(2)) {
        var varMap = part.Split(new string[] { " = " }, StringSplitOptions.None);
        var columnName = "$" + varMap[1] + "1";
        preparedFormula = preparedFormula.Replace(columnName, "Inputs!$" + varMap[1] + "{0}");   //{0} will be replaced later with the row number 
      }
      return preparedFormula;
    }

    private void WriteInputSheet(ExcelWorksheet inputsWorksheet, ExcelWorksheet datasetWorksheet, IEnumerable<string> list, Dataset dataset) {
      int rows = dataset.Rows;
      var variableNames = dataset.VariableNames.ToList();
      int cur = 1;
      foreach (var variableMapping in list) {
        var varMap = variableMapping.Split(new string[] { " = " }, StringSplitOptions.None);
        if (varMap.Count() != 2) throw new ArgumentException("variableMapping is not correct");
        int column = variableNames.FindIndex(x => x.Equals(varMap[0])) + 1;
        inputsWorksheet.Cells[1, cur].Value = varMap[0];
        for (int i = 2; i <= rows + 1; i++) {
          inputsWorksheet.Cells[i, cur].Formula = datasetWorksheet.Cells[i, column].FullAddress;
        }
        cur++;
      }
    }

    private void WriteDatasetToExcel(ExcelWorksheet datasetWorksheet, IDataAnalysisProblemData problemData) {
      Dataset dataset = problemData.Dataset;
      var variableNames = dataset.VariableNames.ToList();
      for (int col = 1; col <= variableNames.Count; col++) {
        datasetWorksheet.Cells[1, col].Value = variableNames[col - 1];
        if (dataset.DoubleVariables.Contains(variableNames[col - 1])) {
          datasetWorksheet.Cells[2, col].LoadFromCollection(dataset.GetDoubleValues(variableNames[col - 1]));
        } else {
          var coll = Enumerable.Range(0, dataset.Rows).Select(x => dataset.GetValue(x, col - 1));
          datasetWorksheet.Cells[2, col].LoadFromCollection(coll);
        }
      }
    }
  }
}