1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System;
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23 | using System.Collections;
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24 | using System.Collections.Generic;
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25 | using System.IO;
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26 | using System.Linq;
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27 | using HeuristicLab.Common;
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28 | using HeuristicLab.Problems.DataAnalysis;
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29 |
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30 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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31 | public class ClusteringCSVInstanceProvider : ClusteringInstanceProvider {
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32 | public override string Name {
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33 | get { return "CSV File"; }
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34 | }
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35 | public override string Description {
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36 | get {
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37 | return "";
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38 | }
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39 | }
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40 | public override Uri WebLink {
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41 | get { return new Uri("http://dev.heuristiclab.com/trac/hl/core/wiki/UsersFAQ#DataAnalysisImportFileFormat"); }
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42 | }
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43 | public override string ReferencePublication {
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44 | get { return ""; }
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45 | }
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46 |
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47 | public override IEnumerable<IDataDescriptor> GetDataDescriptors() {
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48 | return new List<IDataDescriptor>();
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49 | }
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50 |
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51 | public override IClusteringProblemData LoadData(IDataDescriptor descriptor) {
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52 | throw new NotImplementedException();
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53 | }
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54 |
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55 | public override bool CanImportData {
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56 | get { return true; }
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57 | }
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58 | public override IClusteringProblemData ImportData(string path) {
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59 | var csvFileParser = new TableFileParser();
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60 | csvFileParser.Parse(path, csvFileParser.AreColumnNamesInFirstLine(path));
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61 |
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62 | Dataset dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values);
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63 | string targetVar = dataset.DoubleVariables.Last();
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64 |
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65 | // turn of input variables that are constant in the training partition
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66 | var allowedInputVars = new List<string>();
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67 | var trainingIndizes = Enumerable.Range(0, (csvFileParser.Rows * 2) / 3);
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68 | if (trainingIndizes.Count() >= 2) {
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69 | foreach (var variableName in dataset.DoubleVariables) {
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70 | if (dataset.GetDoubleValues(variableName, trainingIndizes).Range() > 0 &&
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71 | variableName != targetVar)
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72 | allowedInputVars.Add(variableName);
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73 | }
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74 | } else {
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75 | allowedInputVars.AddRange(dataset.DoubleVariables.Where(x => !x.Equals(targetVar)));
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76 | }
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77 |
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78 | ClusteringProblemData clusteringData = new ClusteringProblemData(dataset, allowedInputVars);
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79 |
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80 | int trainingPartEnd = trainingIndizes.Last();
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81 | clusteringData.TrainingPartition.Start = trainingIndizes.First();
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82 | clusteringData.TrainingPartition.End = trainingPartEnd;
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83 | clusteringData.TestPartition.Start = trainingPartEnd;
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84 | clusteringData.TestPartition.End = csvFileParser.Rows;
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85 |
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86 | clusteringData.Name = Path.GetFileName(path);
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87 |
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88 | return clusteringData;
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89 | }
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90 |
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91 | protected override IClusteringProblemData ImportData(string path, DataAnalysisImportType type, TableFileParser csvFileParser) {
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92 | List<IList> values = csvFileParser.Values;
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93 | if (type.Shuffle) {
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94 | values = Shuffle(values);
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95 | }
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96 |
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97 | Dataset dataset = new Dataset(csvFileParser.VariableNames, values);
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98 | string targetVar = dataset.DoubleVariables.Last();
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99 |
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100 | // turn of input variables that are constant in the training partition
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101 | var allowedInputVars = new List<string>();
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102 | int trainingPartEnd = (csvFileParser.Rows * type.TrainingPercentage) / 100;
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103 | var trainingIndizes = Enumerable.Range(0, trainingPartEnd);
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104 | if (trainingIndizes.Count() >= 2) {
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105 | foreach (var variableName in dataset.DoubleVariables) {
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106 | if (dataset.GetDoubleValues(variableName, trainingIndizes).Range() > 0 &&
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107 | variableName != targetVar)
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108 | allowedInputVars.Add(variableName);
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109 | }
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110 | } else {
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111 | allowedInputVars.AddRange(dataset.DoubleVariables.Where(x => !x.Equals(targetVar)));
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112 | }
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113 |
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114 | ClusteringProblemData clusteringData = new ClusteringProblemData(dataset, allowedInputVars);
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115 |
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116 | clusteringData.TrainingPartition.Start = 0;
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117 | clusteringData.TrainingPartition.End = trainingPartEnd;
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118 | clusteringData.TestPartition.Start = trainingPartEnd;
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119 | clusteringData.TestPartition.End = csvFileParser.Rows;
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120 |
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121 | clusteringData.Name = Path.GetFileName(path);
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122 |
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123 | return clusteringData;
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124 | }
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125 | }
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126 | }
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