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source: branches/ProblemInstancesRegressionAndClassification/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationProblemData.cs @ 7603

Last change on this file since 7603 was 7603, checked in by sforsten, 13 years ago

#1784:

  • first implementation of regression problem instances with one instance to test
File size: 19.8 KB
RevLine 
[5559]1#region License Information
2/* HeuristicLab
[7259]3 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[5559]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.Generic;
[5601]24using System.IO;
[5559]25using System.Linq;
26using HeuristicLab.Common;
[5601]27using HeuristicLab.Core;
28using HeuristicLab.Data;
29using HeuristicLab.Parameters;
[5559]30using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
[7603]31using HeuristicLab.Problems.Instances;
[5559]32
33namespace HeuristicLab.Problems.DataAnalysis {
34  [StorableClass]
[5601]35  [Item("ClassificationProblemData", "Represents an item containing all data defining a classification problem.")]
[7134]36  public class ClassificationProblemData : DataAnalysisProblemData, IClassificationProblemData, IStorableContent {
[6666]37    protected const string TargetVariableParameterName = "TargetVariable";
38    protected const string ClassNamesParameterName = "ClassNames";
39    protected const string ClassificationPenaltiesParameterName = "ClassificationPenalties";
[7266]40    protected const int MaximumNumberOfClasses = 100;
41    protected const int InspectedRowsToDetermineTargets = 2000;
[5601]42
[7134]43    public string Filename { get; set; }
44
[5559]45    #region default data
46    private static string[] defaultVariableNames = new string[] { "sample", "clump thickness", "cell size", "cell shape", "marginal adhesion", "epithelial cell size", "bare nuclei", "chromatin", "nucleoli", "mitoses", "class" };
47    private static double[,] defaultData = new double[,]{
48     {1000025,5,1,1,1,2,1,3,1,1,2      },
49     {1002945,5,4,4,5,7,10,3,2,1,2     },
50     {1015425,3,1,1,1,2,2,3,1,1,2      },
51     {1016277,6,8,8,1,3,4,3,7,1,2      },
52     {1017023,4,1,1,3,2,1,3,1,1,2      },
53     {1017122,8,10,10,8,7,10,9,7,1,4   },
54     {1018099,1,1,1,1,2,10,3,1,1,2     },
55     {1018561,2,1,2,1,2,1,3,1,1,2      },
56     {1033078,2,1,1,1,2,1,1,1,5,2      },
57     {1033078,4,2,1,1,2,1,2,1,1,2      },
58     {1035283,1,1,1,1,1,1,3,1,1,2      },
59     {1036172,2,1,1,1,2,1,2,1,1,2      },
60     {1041801,5,3,3,3,2,3,4,4,1,4      },
61     {1043999,1,1,1,1,2,3,3,1,1,2      },
62     {1044572,8,7,5,10,7,9,5,5,4,4     },
63     {1047630,7,4,6,4,6,1,4,3,1,4      },
64     {1048672,4,1,1,1,2,1,2,1,1,2      },
65     {1049815,4,1,1,1,2,1,3,1,1,2      },
66     {1050670,10,7,7,6,4,10,4,1,2,4    },
67     {1050718,6,1,1,1,2,1,3,1,1,2      },
68     {1054590,7,3,2,10,5,10,5,4,4,4    },
69     {1054593,10,5,5,3,6,7,7,10,1,4    },
70     {1056784,3,1,1,1,2,1,2,1,1,2      },
71     {1057013,8,4,5,1,2,2,7,3,1,4      },
72     {1059552,1,1,1,1,2,1,3,1,1,2      },
73     {1065726,5,2,3,4,2,7,3,6,1,4      },
74     {1066373,3,2,1,1,1,1,2,1,1,2      },
75     {1066979,5,1,1,1,2,1,2,1,1,2      },
76     {1067444,2,1,1,1,2,1,2,1,1,2      },
77     {1070935,1,1,3,1,2,1,1,1,1,2      },
78     {1070935,3,1,1,1,1,1,2,1,1,2      },
79     {1071760,2,1,1,1,2,1,3,1,1,2      },
80     {1072179,10,7,7,3,8,5,7,4,3,4     },
81     {1074610,2,1,1,2,2,1,3,1,1,2      },
82     {1075123,3,1,2,1,2,1,2,1,1,2      },
83     {1079304,2,1,1,1,2,1,2,1,1,2      },
84     {1080185,10,10,10,8,6,1,8,9,1,4   },
85     {1081791,6,2,1,1,1,1,7,1,1,2      },
86     {1084584,5,4,4,9,2,10,5,6,1,4     },
87     {1091262,2,5,3,3,6,7,7,5,1,4      },
88     {1096800,6,6,6,9,6,4,7,8,1,2      },
89     {1099510,10,4,3,1,3,3,6,5,2,4     },
90     {1100524,6,10,10,2,8,10,7,3,3,4   },
91     {1102573,5,6,5,6,10,1,3,1,1,4     },
92     {1103608,10,10,10,4,8,1,8,10,1,4  },
93     {1103722,1,1,1,1,2,1,2,1,2,2      },
94     {1105257,3,7,7,4,4,9,4,8,1,4      },
95     {1105524,1,1,1,1,2,1,2,1,1,2      },
96     {1106095,4,1,1,3,2,1,3,1,1,2      },
97     {1106829,7,8,7,2,4,8,3,8,2,4      },
98     {1108370,9,5,8,1,2,3,2,1,5,4      },
99     {1108449,5,3,3,4,2,4,3,4,1,4      },
100     {1110102,10,3,6,2,3,5,4,10,2,4    },
101     {1110503,5,5,5,8,10,8,7,3,7,4     },
102     {1110524,10,5,5,6,8,8,7,1,1,4     },
103     {1111249,10,6,6,3,4,5,3,6,1,4     },
104     {1112209,8,10,10,1,3,6,3,9,1,4    },
105     {1113038,8,2,4,1,5,1,5,4,4,4      },
106     {1113483,5,2,3,1,6,10,5,1,1,4     },
107     {1113906,9,5,5,2,2,2,5,1,1,4      },
108     {1115282,5,3,5,5,3,3,4,10,1,4     },
109     {1115293,1,1,1,1,2,2,2,1,1,2      },
110     {1116116,9,10,10,1,10,8,3,3,1,4   },
111     {1116132,6,3,4,1,5,2,3,9,1,4      },
112     {1116192,1,1,1,1,2,1,2,1,1,2      },
113     {1116998,10,4,2,1,3,2,4,3,10,4    },
114     {1117152,4,1,1,1,2,1,3,1,1,2      },
115     {1118039,5,3,4,1,8,10,4,9,1,4     },
116     {1120559,8,3,8,3,4,9,8,9,8,4      },
117     {1121732,1,1,1,1,2,1,3,2,1,2      },
118     {1121919,5,1,3,1,2,1,2,1,1,2      },
119     {1123061,6,10,2,8,10,2,7,8,10,4   },
120     {1124651,1,3,3,2,2,1,7,2,1,2      },
121     {1125035,9,4,5,10,6,10,4,8,1,4    },
122     {1126417,10,6,4,1,3,4,3,2,3,4     },
123     {1131294,1,1,2,1,2,2,4,2,1,2      },
124     {1132347,1,1,4,1,2,1,2,1,1,2      },
125     {1133041,5,3,1,2,2,1,2,1,1,2      },
126     {1133136,3,1,1,1,2,3,3,1,1,2      },
127     {1136142,2,1,1,1,3,1,2,1,1,2      },
128     {1137156,2,2,2,1,1,1,7,1,1,2      },
129     {1143978,4,1,1,2,2,1,2,1,1,2      },
130     {1143978,5,2,1,1,2,1,3,1,1,2      },
131     {1147044,3,1,1,1,2,2,7,1,1,2      },
132     {1147699,3,5,7,8,8,9,7,10,7,4     },
133     {1147748,5,10,6,1,10,4,4,10,10,4  },
134     {1148278,3,3,6,4,5,8,4,4,1,4      },
135     {1148873,3,6,6,6,5,10,6,8,3,4     },
136     {1152331,4,1,1,1,2,1,3,1,1,2      },
137     {1155546,2,1,1,2,3,1,2,1,1,2      },
138     {1156272,1,1,1,1,2,1,3,1,1,2      },
139     {1156948,3,1,1,2,2,1,1,1,1,2      },
140     {1157734,4,1,1,1,2,1,3,1,1,2      },
141     {1158247,1,1,1,1,2,1,2,1,1,2      },
142     {1160476,2,1,1,1,2,1,3,1,1,2      },
143     {1164066,1,1,1,1,2,1,3,1,1,2      },
144     {1165297,2,1,1,2,2,1,1,1,1,2      },
145     {1165790,5,1,1,1,2,1,3,1,1,2      },
146     {1165926,9,6,9,2,10,6,2,9,10,4    },
147     {1166630,7,5,6,10,5,10,7,9,4,4    },
148     {1166654,10,3,5,1,10,5,3,10,2,4   },
149     {1167439,2,3,4,4,2,5,2,5,1,4      },
150     {1167471,4,1,2,1,2,1,3,1,1,2      },
151     {1168359,8,2,3,1,6,3,7,1,1,4      },
152     {1168736,10,10,10,10,10,1,8,8,8,4 },
153     {1169049,7,3,4,4,3,3,3,2,7,4      },
154     {1170419,10,10,10,8,2,10,4,1,1,4  },
155     {1170420,1,6,8,10,8,10,5,7,1,4    },
156     {1171710,1,1,1,1,2,1,2,3,1,2      },
157     {1171710,6,5,4,4,3,9,7,8,3,4      },
158     {1171795,1,3,1,2,2,2,5,3,2,2      },
159     {1171845,8,6,4,3,5,9,3,1,1,4      },
160     {1172152,10,3,3,10,2,10,7,3,3,4   },
161     {1173216,10,10,10,3,10,8,8,1,1,4  },
162     {1173235,3,3,2,1,2,3,3,1,1,2      },
163     {1173347,1,1,1,1,2,5,1,1,1,2      },
164     {1173347,8,3,3,1,2,2,3,2,1,2      },
165     {1173509,4,5,5,10,4,10,7,5,8,4    },
166     {1173514,1,1,1,1,4,3,1,1,1,2      },
167     {1173681,3,2,1,1,2,2,3,1,1,2      },
168     {1174057,1,1,2,2,2,1,3,1,1,2      },
169     {1174057,4,2,1,1,2,2,3,1,1,2      },
170     {1174131,10,10,10,2,10,10,5,3,3,4 },
171     {1174428,5,3,5,1,8,10,5,3,1,4     },
172     {1175937,5,4,6,7,9,7,8,10,1,4     },
173     {1176406,1,1,1,1,2,1,2,1,1,2      },
174     {1176881,7,5,3,7,4,10,7,5,5,4        }
175};
[6672]176    private static readonly Dataset defaultDataset;
177    private static readonly IEnumerable<string> defaultAllowedInputVariables;
178    private static readonly string defaultTargetVariable;
[6666]179
[6672]180    private static readonly ClassificationProblemData emptyProblemData;
[6666]181    public static ClassificationProblemData EmptyProblemData {
182      get { return EmptyProblemData; }
183    }
184
[5559]185    static ClassificationProblemData() {
186      defaultDataset = new Dataset(defaultVariableNames, defaultData);
187      defaultDataset.Name = "Wisconsin classification problem";
188      defaultDataset.Description = "subset from to ..";
189
190      defaultAllowedInputVariables = defaultVariableNames.Except(new List<string>() { "sample", "class" });
191      defaultTargetVariable = "class";
[6666]192
193      var problemData = new ClassificationProblemData();
194      problemData.Parameters.Clear();
195      problemData.Name = "Empty Classification ProblemData";
196      problemData.Description = "This ProblemData acts as place holder before the correct problem data is loaded.";
197      problemData.isEmpty = true;
198
199      problemData.Parameters.Add(new FixedValueParameter<Dataset>(DatasetParameterName, "", new Dataset()));
200      problemData.Parameters.Add(new FixedValueParameter<ReadOnlyCheckedItemList<StringValue>>(InputVariablesParameterName, ""));
201      problemData.Parameters.Add(new FixedValueParameter<IntRange>(TrainingPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
202      problemData.Parameters.Add(new FixedValueParameter<IntRange>(TestPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
203      problemData.Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>()));
204      problemData.Parameters.Add(new FixedValueParameter<StringMatrix>(ClassNamesParameterName, "", new StringMatrix(0, 0).AsReadOnly()));
205      problemData.Parameters.Add(new FixedValueParameter<DoubleMatrix>(ClassificationPenaltiesParameterName, "", (DoubleMatrix)new DoubleMatrix(0, 0).AsReadOnly()));
206      emptyProblemData = problemData;
[5559]207    }
208    #endregion
209
[5601]210    #region parameter properties
[6440]211    public ConstrainedValueParameter<StringValue> TargetVariableParameter {
212      get { return (ConstrainedValueParameter<StringValue>)Parameters[TargetVariableParameterName]; }
[5601]213    }
214    public IFixedValueParameter<StringMatrix> ClassNamesParameter {
215      get { return (IFixedValueParameter<StringMatrix>)Parameters[ClassNamesParameterName]; }
216    }
217    public IFixedValueParameter<DoubleMatrix> ClassificationPenaltiesParameter {
218      get { return (IFixedValueParameter<DoubleMatrix>)Parameters[ClassificationPenaltiesParameterName]; }
219    }
220    #endregion
221
[5649]222    #region properties
[5559]223    public string TargetVariable {
[5601]224      get { return TargetVariableParameter.Value.Value; }
225    }
[5559]226
[5601]227    private List<double> classValues;
228    public List<double> ClassValues {
229      get {
230        if (classValues == null) {
[6740]231          classValues = Dataset.GetDoubleValues(TargetVariableParameter.Value.Value).Distinct().ToList();
[5601]232          classValues.Sort();
[5559]233        }
[5601]234        return classValues;
[5559]235      }
236    }
[5601]237    IEnumerable<double> IClassificationProblemData.ClassValues {
238      get { return ClassValues; }
[5559]239    }
240
241    public int Classes {
[5601]242      get { return ClassValues.Count; }
[5559]243    }
244
[5601]245    private List<string> classNames;
246    public List<string> ClassNames {
247      get {
248        if (classNames == null) {
249          classNames = new List<string>();
250          for (int i = 0; i < ClassNamesParameter.Value.Rows; i++)
251            classNames.Add(ClassNamesParameter.Value[i, 0]);
252        }
253        return classNames;
[5559]254      }
255    }
[5601]256    IEnumerable<string> IClassificationProblemData.ClassNames {
257      get { return ClassNames; }
[5559]258    }
259
[5601]260    private Dictionary<Tuple<double, double>, double> classificationPenaltiesCache = new Dictionary<Tuple<double, double>, double>();
[5559]261    #endregion
262
263
264    [StorableConstructor]
265    protected ClassificationProblemData(bool deserializing) : base(deserializing) { }
[5601]266    [StorableHook(HookType.AfterDeserialization)]
267    private void AfterDeserialization() {
268      RegisterParameterEvents();
269    }
[5559]270
[5601]271    protected ClassificationProblemData(ClassificationProblemData original, Cloner cloner)
272      : base(original, cloner) {
273      RegisterParameterEvents();
[5559]274    }
[6666]275    public override IDeepCloneable Clone(Cloner cloner) {
276      if (this == emptyProblemData) return emptyProblemData;
277      return new ClassificationProblemData(this, cloner);
278    }
[5559]279
[5601]280    public ClassificationProblemData() : this(defaultDataset, defaultAllowedInputVariables, defaultTargetVariable) { }
[5559]281    public ClassificationProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable)
282      : base(dataset, allowedInputVariables) {
[6186]283      var validTargetVariableValues = CheckVariablesForPossibleTargetVariables(dataset).Select(x => new StringValue(x).AsReadOnly()).ToList();
284      var target = validTargetVariableValues.Where(x => x.Value == targetVariable).DefaultIfEmpty(validTargetVariableValues.First()).First();
285
286      Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>(validTargetVariableValues), target));
[5847]287      Parameters.Add(new FixedValueParameter<StringMatrix>(ClassNamesParameterName, ""));
288      Parameters.Add(new FixedValueParameter<DoubleMatrix>(ClassificationPenaltiesParameterName, ""));
[5559]289
[5601]290      ResetTargetVariableDependentMembers();
291      RegisterParameterEvents();
[5559]292    }
293
[6186]294    private static IEnumerable<string> CheckVariablesForPossibleTargetVariables(Dataset dataset) {
[6223]295      int maxSamples = Math.Min(InspectedRowsToDetermineTargets, dataset.Rows);
[6740]296      var validTargetVariables = (from v in dataset.DoubleVariables
297                                  let distinctValues = dataset.GetDoubleValues(v)
[6654]298                                    .Take(maxSamples)
299                                    .Distinct()
300                                    .Count()
301                                  where distinctValues < MaximumNumberOfClasses
302                                  select v).ToArray();
[6186]303
304      if (!validTargetVariables.Any())
[6223]305        throw new ArgumentException("Import of classification problem data was not successful, because no target variable was found." +
306          " A target variable must have at most " + MaximumNumberOfClasses + " distinct values to be applicable to classification.");
[6186]307      return validTargetVariables;
308    }
309
310
[5601]311    private void ResetTargetVariableDependentMembers() {
[6654]312      DeregisterParameterEvents();
[5559]313
[5601]314      classNames = null;
315      ((IStringConvertibleMatrix)ClassNamesParameter.Value).Columns = 1;
316      ((IStringConvertibleMatrix)ClassNamesParameter.Value).Rows = ClassValues.Count;
317      for (int i = 0; i < Classes; i++)
318        ClassNamesParameter.Value[i, 0] = "Class " + ClassValues[i];
319      ClassNamesParameter.Value.ColumnNames = new List<string>() { "ClassNames" };
320      ClassNamesParameter.Value.RowNames = ClassValues.Select(s => "ClassValue: " + s);
[5559]321
[5601]322      classificationPenaltiesCache.Clear();
323      ((ValueParameter<DoubleMatrix>)ClassificationPenaltiesParameter).ReactOnValueToStringChangedAndValueItemImageChanged = false;
324      ((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Rows = Classes;
325      ((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Columns = Classes;
326      ClassificationPenaltiesParameter.Value.RowNames = ClassNames.Select(name => "Actual " + name);
327      ClassificationPenaltiesParameter.Value.ColumnNames = ClassNames.Select(name => "Estimated " + name);
328      for (int i = 0; i < Classes; i++) {
329        for (int j = 0; j < Classes; j++) {
330          if (i != j) ClassificationPenaltiesParameter.Value[i, j] = 1;
331          else ClassificationPenaltiesParameter.Value[i, j] = 0;
[5559]332        }
333      }
[5601]334      ((ValueParameter<DoubleMatrix>)ClassificationPenaltiesParameter).ReactOnValueToStringChangedAndValueItemImageChanged = true;
335      RegisterParameterEvents();
[5559]336    }
337
338    public string GetClassName(double classValue) {
[5601]339      if (!ClassValues.Contains(classValue)) throw new ArgumentException();
340      int index = ClassValues.IndexOf(classValue);
341      return ClassNames[index];
[5559]342    }
343    public double GetClassValue(string className) {
[5601]344      if (!ClassNames.Contains(className)) throw new ArgumentException();
345      int index = ClassNames.IndexOf(className);
346      return ClassValues[index];
[5559]347    }
348    public void SetClassName(double classValue, string className) {
349      if (!classValues.Contains(classValue)) throw new ArgumentException();
[5601]350      int index = ClassValues.IndexOf(classValue);
351      ClassNames[index] = className;
352      ClassNamesParameter.Value[index, 0] = className;
[5559]353    }
354
355    public double GetClassificationPenalty(string correctClassName, string estimatedClassName) {
356      return GetClassificationPenalty(GetClassValue(correctClassName), GetClassValue(estimatedClassName));
357    }
358    public double GetClassificationPenalty(double correctClassValue, double estimatedClassValue) {
359      var key = Tuple.Create(correctClassValue, estimatedClassValue);
[5601]360      if (!classificationPenaltiesCache.ContainsKey(key)) {
361        int correctClassIndex = ClassValues.IndexOf(correctClassValue);
362        int estimatedClassIndex = ClassValues.IndexOf(estimatedClassValue);
363        classificationPenaltiesCache[key] = ClassificationPenaltiesParameter.Value[correctClassIndex, estimatedClassIndex];
364      }
365      return classificationPenaltiesCache[key];
[5559]366    }
367    public void SetClassificationPenalty(string correctClassName, string estimatedClassName, double penalty) {
368      SetClassificationPenalty(GetClassValue(correctClassName), GetClassValue(estimatedClassName), penalty);
369    }
370    public void SetClassificationPenalty(double correctClassValue, double estimatedClassValue, double penalty) {
371      var key = Tuple.Create(correctClassValue, estimatedClassValue);
[5601]372      int correctClassIndex = ClassValues.IndexOf(correctClassValue);
373      int estimatedClassIndex = ClassValues.IndexOf(estimatedClassValue);
374
375      ClassificationPenaltiesParameter.Value[correctClassIndex, estimatedClassIndex] = penalty;
[5559]376    }
377
[5601]378    #region events
379    private void RegisterParameterEvents() {
380      TargetVariableParameter.ValueChanged += new EventHandler(TargetVariableParameter_ValueChanged);
381      ClassNamesParameter.Value.Reset += new EventHandler(Parameter_ValueChanged);
382      ClassNamesParameter.Value.ItemChanged += new EventHandler<EventArgs<int, int>>(MatrixParameter_ItemChanged);
383      ClassificationPenaltiesParameter.Value.Reset += new EventHandler(Parameter_ValueChanged);
384      ClassificationPenaltiesParameter.Value.ItemChanged += new EventHandler<EventArgs<int, int>>(MatrixParameter_ItemChanged);
[5559]385    }
[6654]386    private void DeregisterParameterEvents() {
[5601]387      TargetVariableParameter.ValueChanged -= new EventHandler(TargetVariableParameter_ValueChanged);
388      ClassNamesParameter.Value.Reset -= new EventHandler(Parameter_ValueChanged);
389      ClassNamesParameter.Value.ItemChanged -= new EventHandler<EventArgs<int, int>>(MatrixParameter_ItemChanged);
390      ClassificationPenaltiesParameter.Value.Reset -= new EventHandler(Parameter_ValueChanged);
391      ClassificationPenaltiesParameter.Value.ItemChanged -= new EventHandler<EventArgs<int, int>>(MatrixParameter_ItemChanged);
[5559]392    }
[5601]393
394    private void TargetVariableParameter_ValueChanged(object sender, EventArgs e) {
395      classValues = null;
396      ResetTargetVariableDependentMembers();
397      OnChanged();
398    }
399    private void Parameter_ValueChanged(object sender, EventArgs e) {
400      OnChanged();
401    }
402    private void MatrixParameter_ItemChanged(object sender, EventArgs<int, int> e) {
403      OnChanged();
404    }
405    #endregion
406
407    #region Import from file
408    public static ClassificationProblemData ImportFromFile(string fileName) {
409      TableFileParser csvFileParser = new TableFileParser();
410      csvFileParser.Parse(fileName);
411
412      Dataset dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values);
413      dataset.Name = Path.GetFileName(fileName);
414
[6740]415      ClassificationProblemData problemData = new ClassificationProblemData(dataset, dataset.DoubleVariables.Skip(1), dataset.DoubleVariables.First());
[5601]416      problemData.Name = "Data imported from " + Path.GetFileName(fileName);
417      return problemData;
418    }
419    #endregion
[5559]420  }
421}
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