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source: stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/CSV/RegressionCSVInstanceProvider.cs @ 11019

Last change on this file since 11019 was 10020, checked in by gkronber, 11 years ago

#1508: merged r9804:9805,r9808:9809,r9811:9812,r9822,r9824:9825,r9897,r9928,r9938:9941,r9964:9965,r9989,r9991:9992,r9995,r9997,r10004:10015 from trunk into stable branch.

File size: 4.9 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Collections;
24using System.Collections.Generic;
25using System.IO;
26using System.Linq;
27using HeuristicLab.Common;
28using HeuristicLab.Problems.DataAnalysis;
29
30namespace HeuristicLab.Problems.Instances.DataAnalysis {
31  public class RegressionCSVInstanceProvider : RegressionInstanceProvider {
32    public override string Name {
33      get { return "CSV File"; }
34    }
35    public override string Description {
36      get {
37        return "";
38      }
39    }
40    public override Uri WebLink {
41      get { return new Uri("http://dev.heuristiclab.com/trac/hl/core/wiki/UsersFAQ#DataAnalysisImportFileFormat"); }
42    }
43    public override string ReferencePublication {
44      get { return ""; }
45    }
46
47    public override IEnumerable<IDataDescriptor> GetDataDescriptors() {
48      return new List<IDataDescriptor>();
49    }
50    public override IRegressionProblemData LoadData(IDataDescriptor descriptor) {
51      throw new NotImplementedException();
52    }
53
54    public override bool CanImportData {
55      get { return true; }
56    }
57    public override IRegressionProblemData ImportData(string path) {
58      TableFileParser csvFileParser = new TableFileParser();
59      csvFileParser.Parse(path, csvFileParser.AreColumnNamesInFirstLine(path));
60
61      Dataset dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values);
62      string targetVar = dataset.DoubleVariables.Last();
63
64      // turn off input variables that are constant in the training partition
65      var allowedInputVars = new List<string>();
66      var trainingIndizes = Enumerable.Range(0, (csvFileParser.Rows * 2) / 3);
67      if (trainingIndizes.Count() >= 2) {
68        foreach (var variableName in dataset.DoubleVariables) {
69          if (dataset.GetDoubleValues(variableName, trainingIndizes).Range() > 0 &&
70            variableName != targetVar)
71            allowedInputVars.Add(variableName);
72        }
73      } else {
74        allowedInputVars.AddRange(dataset.DoubleVariables.Where(x => !x.Equals(targetVar)));
75      }
76
77      IRegressionProblemData regressionData = new RegressionProblemData(dataset, allowedInputVars, targetVar);
78
79      var trainingPartEnd = trainingIndizes.Last();
80      regressionData.TrainingPartition.Start = trainingIndizes.First();
81      regressionData.TrainingPartition.End = trainingPartEnd;
82      regressionData.TestPartition.Start = trainingPartEnd;
83      regressionData.TestPartition.End = csvFileParser.Rows;
84
85      regressionData.Name = Path.GetFileName(path);
86
87      return regressionData;
88    }
89
90    protected override IRegressionProblemData ImportData(string path, RegressionImportType type, TableFileParser csvFileParser) {
91      List<IList> values = csvFileParser.Values;
92      if (type.Shuffle) {
93        values = Shuffle(values);
94      }
95      Dataset dataset = new Dataset(csvFileParser.VariableNames, values);
96
97      // turn of input variables that are constant in the training partition
98      var allowedInputVars = new List<string>();
99      int trainingPartEnd = (csvFileParser.Rows * type.TrainingPercentage) / 100;
100      trainingPartEnd = trainingPartEnd > 0 ? trainingPartEnd : 1;
101      var trainingIndizes = Enumerable.Range(0, trainingPartEnd);
102      if (trainingIndizes.Count() >= 2) {
103        foreach (var variableName in dataset.DoubleVariables) {
104          if (dataset.GetDoubleValues(variableName, trainingIndizes).Range() > 0 &&
105            variableName != type.TargetVariable)
106            allowedInputVars.Add(variableName);
107        }
108      } else {
109        allowedInputVars.AddRange(dataset.DoubleVariables.Where(x => !x.Equals(type.TargetVariable)));
110      }
111
112      RegressionProblemData regressionData = new RegressionProblemData(dataset, allowedInputVars, type.TargetVariable);
113
114      regressionData.TrainingPartition.Start = 0;
115      regressionData.TrainingPartition.End = trainingPartEnd;
116      regressionData.TestPartition.Start = trainingPartEnd;
117      regressionData.TestPartition.End = csvFileParser.Rows;
118
119      regressionData.Name = Path.GetFileName(path);
120
121      return regressionData;
122    }
123  }
124}
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