#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.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 ClassificationCSVInstanceProvider : ClassificationInstanceProvider {
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 IClassificationProblemData LoadData(IDataDescriptor descriptor) {
throw new NotImplementedException();
}
public override bool CanImportData {
get { return true; }
}
public override IClassificationProblemData 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 (dataset.GetDoubleValues(variableName, trainingIndizes).Range() > 0 &&
variableName != targetVar)
allowedInputVars.Add(variableName);
}
ClassificationProblemData classificationData = new ClassificationProblemData(dataset, allowedInputVars, targetVar);
int trainingPartEnd = trainingIndizes.Last();
classificationData.TrainingPartition.Start = trainingIndizes.First();
classificationData.TrainingPartition.End = trainingPartEnd;
classificationData.TestPartition.Start = trainingPartEnd;
classificationData.TestPartition.End = csvFileParser.Rows;
classificationData.Name = Path.GetFileName(path);
return classificationData;
}
public override bool CanExportData {
get { return true; }
}
public override void ExportData(IClassificationProblemData 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);
}
}
}
}