Index: /branches/Sliding Window GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Views/3.4/Exporters/SymbolicSolutionExcelExporter.cs
===================================================================
--- /branches/Sliding Window GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Views/3.4/Exporters/SymbolicSolutionExcelExporter.cs	(revision 10379)
+++ /branches/Sliding Window GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Views/3.4/Exporters/SymbolicSolutionExcelExporter.cs	(revision 10379)
@@ -0,0 +1,359 @@
+﻿#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 HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Views;
+using OfficeOpenXml;
+using OfficeOpenXml.Drawing.Chart;
+
+namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Views {
+  public class SymbolicSolutionExcelExporter : IDataAnalysisSolutionExporter {
+    private const string TRAININGSTART = "TrainingStart";
+    private const string TRAININGEND = "TrainingEnd";
+    private const string TESTSTART = "TestStart";
+    private const string TESTEND = "TestEnd";
+
+
+    public string FileTypeFilter {
+      get { return "Excel 2007 file (*.xlsx)|*.xlsx"; }
+    }
+    public bool Supports(IDataAnalysisSolution solution) {
+      return solution is ISymbolicDataAnalysisSolution &&
+        solution is IRegressionSolution;
+    }
+
+    public void Export(IDataAnalysisSolution solution, string fileName) {
+      var symbSolution = solution as ISymbolicDataAnalysisSolution;
+      if (symbSolution == null) throw new NotSupportedException("This solution cannot be exported to Excel");
+      var formatter = new SymbolicDataAnalysisExpressionExcelFormatter();
+      var formula = formatter.Format(symbSolution.Model.SymbolicExpressionTree, solution.ProblemData.Dataset);
+      ExportChart(fileName, symbSolution, formula);
+    }
+
+    private void ExportChart(string fileName, ISymbolicDataAnalysisSolution solution, string formula) {
+      FileInfo newFile = new FileInfo(fileName);
+      if (newFile.Exists) {
+        newFile.Delete();
+        newFile = new FileInfo(fileName);
+      }
+      var formulaParts = formula.Split(new string[] { Environment.NewLine }, StringSplitOptions.None);
+
+      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);
+
+        if (solution is IRegressionSolution) {
+          ExcelWorksheet estimatedWorksheet = package.Workbook.Worksheets.Add("Estimated Values");
+          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;
+
+      modelWorksheet.Cells[row, 1].Value = "Estimation Limits Lower";
+      modelWorksheet.Cells[row, 2].Value = Math.Max(solution.Model.LowerEstimationLimit, -9.99999999999999E+307); // minimal value supported by excel
+      modelWorksheet.Names.Add("EstimationLimitLower", modelWorksheet.Cells[row, 2]);
+      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000E+00";
+      row++;
+
+      modelWorksheet.Cells[row, 1].Value = "Estimation Limits Upper";
+      modelWorksheet.Cells[row, 2].Value = Math.Min(solution.Model.UpperEstimationLimit, 9.99999999999999E+307);  // maximal value supported by excel
+      modelWorksheet.Names.Add("EstimationLimitUpper", modelWorksheet.Cells[row, 2]);
+      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000E+00";
+      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);
+      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000";
+      row++;
+
+      modelWorksheet.Cells[row, 1].Value = "Pearson's R² (test)";
+      modelWorksheet.Cells[row, 2].Formula = string.Format("POWER(PEARSON({0},{1}),2)", excelTestTarget, excelTestEstimated);
+      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000";
+      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]);
+      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000E+00";
+      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]);
+      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000E+00";
+      row++;
+
+      modelWorksheet.Cells[row, 1].Value = "Mean absolute error (training)";
+      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTrainingAbsoluteError);
+      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000E+00";
+      row++;
+
+      modelWorksheet.Cells[row, 1].Value = "Mean absolute error (test)";
+      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTestAbsoluteError);
+      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000E+00";
+      row++;
+
+      modelWorksheet.Cells[row, 1].Value = "Mean error (training)";
+      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTrainingMeanError);
+      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000E+00";
+      row++;
+
+      modelWorksheet.Cells[row, 1].Value = "Mean error (test)";
+      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTestMeanError);
+      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000E+00";
+      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);
+      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000E+00";
+      row++;
+
+      modelWorksheet.Cells[row, 1].Value = "Normalized Mean Squared error  (test)";
+      modelWorksheet.Cells[row, 2].Formula = string.Format("TestMSE / VAR({0})", excelTestTarget);
+      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000E+00";
+
+      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["B2:B" + (rows + 1)].Style.Numberformat.Format = "0.000";
+
+      estimatedWorksheet.Cells["C2:C" + (rows + 1)].Formula = "J2";
+      estimatedWorksheet.Cells["C2:C" + (rows + 1)].Style.Numberformat.Format = "0.000";
+      estimatedWorksheet.Cells["D2:D" + (rows + 1)].Formula = "ABS(B2 - C2)";
+      estimatedWorksheet.Cells["D2:D" + (rows + 1)].Style.Numberformat.Format = "0.000";
+      estimatedWorksheet.Cells["E2:E" + (rows + 1)].Formula = "ABS(D2 / B2)";
+      estimatedWorksheet.Cells["E2:E" + (rows + 1)].Style.Numberformat.Format = "0.000";
+      estimatedWorksheet.Cells["F2:F" + (rows + 1)].Formula = "C2 - B2";
+      estimatedWorksheet.Cells["F2:F" + (rows + 1)].Style.Numberformat.Format = "0.000";
+      estimatedWorksheet.Cells["G2:G" + (rows + 1)].Formula = "POWER(F2, 2)";
+      estimatedWorksheet.Cells["G2:G" + (rows + 1)].Style.Numberformat.Format = "0.000";
+
+      estimatedWorksheet.Cells["I2:I" + (rows + 1)].Style.Numberformat.Format = "0.000";
+      estimatedWorksheet.Cells["J2:J" + (rows + 1)].Formula = "IFERROR(IF(I2 > Model!EstimationLimitUpper, Model!EstimationLimitUpper, IF(I2 < Model!EstimationLimitLower, Model!EstimationLimitLower, I2)), AVERAGE(Model!EstimationLimitLower, Model!EstimationLimitUpper))";
+      estimatedWorksheet.Cells["J2:J" + (rows + 1)].Style.Numberformat.Format = "0.000";
+    }
+
+    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) {
+      //remark the performance of EPPlus drops dramatically 
+      //if the data is not written row wise (from left to right) due the internal indices used.
+      var variableNames = dataset.VariableNames.Select((v, i) => new { variable = v, index = i + 1 }).ToDictionary(v => v.variable, v => v.index);
+      var nameMapping = list.Select(x => x.Split('=')[0].Trim()).ToArray();
+
+      for (int row = 1; row <= dataset.Rows + 1; row++) {
+        for (int column = 1; column < nameMapping.Length + 1; column++) {
+          int variableIndex = variableNames[nameMapping[column - 1]];
+          inputsWorksheet.Cells[row, column].Formula = datasetWorksheet.Cells[row, variableIndex].FullAddress;
+        }
+      }
+    }
+
+    private void WriteDatasetToExcel(ExcelWorksheet datasetWorksheet, IDataAnalysisProblemData problemData) {
+      //remark the performance of EPPlus drops dramatically 
+      //if the data is not written row wise (from left to right) due the internal indices used.
+      Dataset dataset = problemData.Dataset;
+      var variableNames = dataset.VariableNames.ToList();
+      var doubleVariables = new HashSet<string>(dataset.DoubleVariables);
+
+      for (int col = 1; col <= variableNames.Count; col++)
+        datasetWorksheet.Cells[1, col].Value = variableNames[col - 1];
+
+      for (int row = 0; row < dataset.Rows; row++) {
+        for (int col = 0; col < variableNames.Count; col++) {
+          if (doubleVariables.Contains(variableNames[col]))
+            datasetWorksheet.Cells[row + 2, col + 1].Value = dataset.GetDoubleValue(variableNames[col], row);
+          else
+            datasetWorksheet.Cells[row + 2, col + 1].Value = dataset.GetValue(row, col);
+        }
+      }
+    }
+  }
+}
