#region License Information /* HeuristicLab * Copyright (C) 2002-2012 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 . */ #endregion using System; using System.Collections; using System.Collections.Generic; using System.Globalization; using System.IO; using System.Linq; using System.Text; using HeuristicLab.Common; using HeuristicLab.Problems.DataAnalysis; namespace HeuristicLab.Problems.Instances.DataAnalysis { public class RegressionCSVInstanceProvider : RegressionInstanceProvider { public override string Name { get { return "CSV File"; } } public override string Description { get { return ""; } } public override Uri WebLink { get { return new Uri("http://dev.heuristiclab.com/trac/hl/core/wiki/UsersFAQ#DataAnalysisImportFileFormat"); } } public override string ReferencePublication { get { return ""; } } public override IEnumerable GetDataDescriptors() { return new List(); } public override IRegressionProblemData LoadData(IDataDescriptor descriptor) { throw new NotImplementedException(); } public override bool CanImportData { get { return true; } } public override IRegressionProblemData ImportData(string path) { TableFileParser csvFileParser = new TableFileParser(); csvFileParser.Parse(path); Dataset dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values); string targetVar = dataset.DoubleVariables.Last(); // turn of input variables that are constant in the training partition var allowedInputVars = new List(); var trainingIndizes = Enumerable.Range(0, (csvFileParser.Rows * 2) / 3); foreach (var variableName in dataset.DoubleVariables) { if (trainingIndizes.Count() >= 2 && dataset.GetDoubleValues(variableName, trainingIndizes).Range() > 0 && variableName != targetVar) allowedInputVars.Add(variableName); } IRegressionProblemData regressionData = new RegressionProblemData(dataset, allowedInputVars, targetVar); var trainingPartEnd = trainingIndizes.Last(); regressionData.TrainingPartition.Start = trainingIndizes.First(); regressionData.TrainingPartition.End = trainingPartEnd; regressionData.TestPartition.Start = trainingPartEnd; regressionData.TestPartition.End = csvFileParser.Rows; regressionData.Name = Path.GetFileName(path); return regressionData; } public override IRegressionProblemData ImportData(string path, DataAnalysisImportType type) { TableFileParser csvFileParser = new TableFileParser(); csvFileParser.Parse(path); List values = csvFileParser.Values; if (type.Shuffle) { values = Shuffle(values); } Dataset dataset = new Dataset(csvFileParser.VariableNames, values); string targetVar = dataset.DoubleVariables.Last(); // turn of input variables that are constant in the training partition var allowedInputVars = new List(); int trainingPartEnd = (csvFileParser.Rows * type.Training) / 100; trainingPartEnd = trainingPartEnd > 0 ? trainingPartEnd : 1; var trainingIndizes = Enumerable.Range(0, trainingPartEnd); if (trainingIndizes.Count() >= 2) { foreach (var variableName in dataset.DoubleVariables) { if (dataset.GetDoubleValues(variableName, trainingIndizes).Range() > 0 && variableName != targetVar) allowedInputVars.Add(variableName); } } else { allowedInputVars.AddRange(dataset.DoubleVariables.Where(x => x.Equals(targetVar))); } RegressionProblemData regressionData = new RegressionProblemData(dataset, allowedInputVars, targetVar); regressionData.TrainingPartition.Start = 0; regressionData.TrainingPartition.End = trainingPartEnd; regressionData.TestPartition.Start = trainingPartEnd; regressionData.TestPartition.End = csvFileParser.Rows; regressionData.Name = Path.GetFileName(path); return regressionData; } public override bool CanExportData { get { return true; } } public override void ExportData(IRegressionProblemData instance, string path) { var strBuilder = new StringBuilder(); foreach (var variable in instance.InputVariables) { strBuilder.Append(variable + CultureInfo.CurrentCulture.TextInfo.ListSeparator); } strBuilder.Remove(strBuilder.Length - CultureInfo.CurrentCulture.TextInfo.ListSeparator.Length, CultureInfo.CurrentCulture.TextInfo.ListSeparator.Length); strBuilder.AppendLine(); var dataset = instance.Dataset; for (int i = 0; i < dataset.Rows; i++) { for (int j = 0; j < dataset.Columns; j++) { if (j > 0) strBuilder.Append(CultureInfo.CurrentCulture.TextInfo.ListSeparator); strBuilder.Append(dataset.GetValue(i, j)); } strBuilder.AppendLine(); } using (var writer = new StreamWriter(path)) { writer.Write(strBuilder); } } } }