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source: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationProblemData.cs @ 8716

Last change on this file since 8716 was 8716, checked in by mkommend, 10 years ago

#1915: Corrected constructor of classification problem data to take class names into account.

File size: 18.7 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Parameters;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30
31namespace HeuristicLab.Problems.DataAnalysis {
32  [StorableClass]
33  [Item("ClassificationProblemData", "Represents an item containing all data defining a classification problem.")]
34  public class ClassificationProblemData : DataAnalysisProblemData, IClassificationProblemData, IStorableContent {
35    protected const string TargetVariableParameterName = "TargetVariable";
36    protected const string ClassNamesParameterName = "ClassNames";
37    protected const string ClassificationPenaltiesParameterName = "ClassificationPenalties";
38    protected const int MaximumNumberOfClasses = 100;
39    protected const int InspectedRowsToDetermineTargets = 2000;
40
41    public string Filename { get; set; }
42
43    #region default data
44    private static string[] defaultVariableNames = new string[] { "sample", "clump thickness", "cell size", "cell shape", "marginal adhesion", "epithelial cell size", "bare nuclei", "chromatin", "nucleoli", "mitoses", "class" };
45    private static double[,] defaultData = new double[,]{
46     {1000025,5,1,1,1,2,1,3,1,1,2      },
47     {1002945,5,4,4,5,7,10,3,2,1,2     },
48     {1015425,3,1,1,1,2,2,3,1,1,2      },
49     {1016277,6,8,8,1,3,4,3,7,1,2      },
50     {1017023,4,1,1,3,2,1,3,1,1,2      },
51     {1017122,8,10,10,8,7,10,9,7,1,4   },
52     {1018099,1,1,1,1,2,10,3,1,1,2     },
53     {1018561,2,1,2,1,2,1,3,1,1,2      },
54     {1033078,2,1,1,1,2,1,1,1,5,2      },
55     {1033078,4,2,1,1,2,1,2,1,1,2      },
56     {1035283,1,1,1,1,1,1,3,1,1,2      },
57     {1036172,2,1,1,1,2,1,2,1,1,2      },
58     {1041801,5,3,3,3,2,3,4,4,1,4      },
59     {1043999,1,1,1,1,2,3,3,1,1,2      },
60     {1044572,8,7,5,10,7,9,5,5,4,4     },
61     {1047630,7,4,6,4,6,1,4,3,1,4      },
62     {1048672,4,1,1,1,2,1,2,1,1,2      },
63     {1049815,4,1,1,1,2,1,3,1,1,2      },
64     {1050670,10,7,7,6,4,10,4,1,2,4    },
65     {1050718,6,1,1,1,2,1,3,1,1,2      },
66     {1054590,7,3,2,10,5,10,5,4,4,4    },
67     {1054593,10,5,5,3,6,7,7,10,1,4    },
68     {1056784,3,1,1,1,2,1,2,1,1,2      },
69     {1057013,8,4,5,1,2,2,7,3,1,4      },
70     {1059552,1,1,1,1,2,1,3,1,1,2      },
71     {1065726,5,2,3,4,2,7,3,6,1,4      },
72     {1066373,3,2,1,1,1,1,2,1,1,2      },
73     {1066979,5,1,1,1,2,1,2,1,1,2      },
74     {1067444,2,1,1,1,2,1,2,1,1,2      },
75     {1070935,1,1,3,1,2,1,1,1,1,2      },
76     {1070935,3,1,1,1,1,1,2,1,1,2      },
77     {1071760,2,1,1,1,2,1,3,1,1,2      },
78     {1072179,10,7,7,3,8,5,7,4,3,4     },
79     {1074610,2,1,1,2,2,1,3,1,1,2      },
80     {1075123,3,1,2,1,2,1,2,1,1,2      },
81     {1079304,2,1,1,1,2,1,2,1,1,2      },
82     {1080185,10,10,10,8,6,1,8,9,1,4   },
83     {1081791,6,2,1,1,1,1,7,1,1,2      },
84     {1084584,5,4,4,9,2,10,5,6,1,4     },
85     {1091262,2,5,3,3,6,7,7,5,1,4      },
86     {1096800,6,6,6,9,6,4,7,8,1,2      },
87     {1099510,10,4,3,1,3,3,6,5,2,4     },
88     {1100524,6,10,10,2,8,10,7,3,3,4   },
89     {1102573,5,6,5,6,10,1,3,1,1,4     },
90     {1103608,10,10,10,4,8,1,8,10,1,4  },
91     {1103722,1,1,1,1,2,1,2,1,2,2      },
92     {1105257,3,7,7,4,4,9,4,8,1,4      },
93     {1105524,1,1,1,1,2,1,2,1,1,2      },
94     {1106095,4,1,1,3,2,1,3,1,1,2      },
95     {1106829,7,8,7,2,4,8,3,8,2,4      },
96     {1108370,9,5,8,1,2,3,2,1,5,4      },
97     {1108449,5,3,3,4,2,4,3,4,1,4      },
98     {1110102,10,3,6,2,3,5,4,10,2,4    },
99     {1110503,5,5,5,8,10,8,7,3,7,4     },
100     {1110524,10,5,5,6,8,8,7,1,1,4     },
101     {1111249,10,6,6,3,4,5,3,6,1,4     },
102     {1112209,8,10,10,1,3,6,3,9,1,4    },
103     {1113038,8,2,4,1,5,1,5,4,4,4      },
104     {1113483,5,2,3,1,6,10,5,1,1,4     },
105     {1113906,9,5,5,2,2,2,5,1,1,4      },
106     {1115282,5,3,5,5,3,3,4,10,1,4     },
107     {1115293,1,1,1,1,2,2,2,1,1,2      },
108     {1116116,9,10,10,1,10,8,3,3,1,4   },
109     {1116132,6,3,4,1,5,2,3,9,1,4      },
110     {1116192,1,1,1,1,2,1,2,1,1,2      },
111     {1116998,10,4,2,1,3,2,4,3,10,4    },
112     {1117152,4,1,1,1,2,1,3,1,1,2      },
113     {1118039,5,3,4,1,8,10,4,9,1,4     },
114     {1120559,8,3,8,3,4,9,8,9,8,4      },
115     {1121732,1,1,1,1,2,1,3,2,1,2      },
116     {1121919,5,1,3,1,2,1,2,1,1,2      },
117     {1123061,6,10,2,8,10,2,7,8,10,4   },
118     {1124651,1,3,3,2,2,1,7,2,1,2      },
119     {1125035,9,4,5,10,6,10,4,8,1,4    },
120     {1126417,10,6,4,1,3,4,3,2,3,4     },
121     {1131294,1,1,2,1,2,2,4,2,1,2      },
122     {1132347,1,1,4,1,2,1,2,1,1,2      },
123     {1133041,5,3,1,2,2,1,2,1,1,2      },
124     {1133136,3,1,1,1,2,3,3,1,1,2      },
125     {1136142,2,1,1,1,3,1,2,1,1,2      },
126     {1137156,2,2,2,1,1,1,7,1,1,2      },
127     {1143978,4,1,1,2,2,1,2,1,1,2      },
128     {1143978,5,2,1,1,2,1,3,1,1,2      },
129     {1147044,3,1,1,1,2,2,7,1,1,2      },
130     {1147699,3,5,7,8,8,9,7,10,7,4     },
131     {1147748,5,10,6,1,10,4,4,10,10,4  },
132     {1148278,3,3,6,4,5,8,4,4,1,4      },
133     {1148873,3,6,6,6,5,10,6,8,3,4     },
134     {1152331,4,1,1,1,2,1,3,1,1,2      },
135     {1155546,2,1,1,2,3,1,2,1,1,2      },
136     {1156272,1,1,1,1,2,1,3,1,1,2      },
137     {1156948,3,1,1,2,2,1,1,1,1,2      },
138     {1157734,4,1,1,1,2,1,3,1,1,2      },
139     {1158247,1,1,1,1,2,1,2,1,1,2      },
140     {1160476,2,1,1,1,2,1,3,1,1,2      },
141     {1164066,1,1,1,1,2,1,3,1,1,2      },
142     {1165297,2,1,1,2,2,1,1,1,1,2      },
143     {1165790,5,1,1,1,2,1,3,1,1,2      },
144     {1165926,9,6,9,2,10,6,2,9,10,4    },
145     {1166630,7,5,6,10,5,10,7,9,4,4    },
146     {1166654,10,3,5,1,10,5,3,10,2,4   },
147     {1167439,2,3,4,4,2,5,2,5,1,4      },
148     {1167471,4,1,2,1,2,1,3,1,1,2      },
149     {1168359,8,2,3,1,6,3,7,1,1,4      },
150     {1168736,10,10,10,10,10,1,8,8,8,4 },
151     {1169049,7,3,4,4,3,3,3,2,7,4      },
152     {1170419,10,10,10,8,2,10,4,1,1,4  },
153     {1170420,1,6,8,10,8,10,5,7,1,4    },
154     {1171710,1,1,1,1,2,1,2,3,1,2      },
155     {1171710,6,5,4,4,3,9,7,8,3,4      },
156     {1171795,1,3,1,2,2,2,5,3,2,2      },
157     {1171845,8,6,4,3,5,9,3,1,1,4      },
158     {1172152,10,3,3,10,2,10,7,3,3,4   },
159     {1173216,10,10,10,3,10,8,8,1,1,4  },
160     {1173235,3,3,2,1,2,3,3,1,1,2      },
161     {1173347,1,1,1,1,2,5,1,1,1,2      },
162     {1173347,8,3,3,1,2,2,3,2,1,2      },
163     {1173509,4,5,5,10,4,10,7,5,8,4    },
164     {1173514,1,1,1,1,4,3,1,1,1,2      },
165     {1173681,3,2,1,1,2,2,3,1,1,2      },
166     {1174057,1,1,2,2,2,1,3,1,1,2      },
167     {1174057,4,2,1,1,2,2,3,1,1,2      },
168     {1174131,10,10,10,2,10,10,5,3,3,4 },
169     {1174428,5,3,5,1,8,10,5,3,1,4     },
170     {1175937,5,4,6,7,9,7,8,10,1,4     },
171     {1176406,1,1,1,1,2,1,2,1,1,2      },
172     {1176881,7,5,3,7,4,10,7,5,5,4        }
173};
174    private static readonly Dataset defaultDataset;
175    private static readonly IEnumerable<string> defaultAllowedInputVariables;
176    private static readonly string defaultTargetVariable;
177
178    private static readonly ClassificationProblemData emptyProblemData;
179    public static ClassificationProblemData EmptyProblemData {
180      get { return EmptyProblemData; }
181    }
182
183    static ClassificationProblemData() {
184      defaultDataset = new Dataset(defaultVariableNames, defaultData);
185      defaultDataset.Name = "Wisconsin classification problem";
186      defaultDataset.Description = "subset from to ..";
187
188      defaultAllowedInputVariables = defaultVariableNames.Except(new List<string>() { "sample", "class" });
189      defaultTargetVariable = "class";
190
191      var problemData = new ClassificationProblemData();
192      problemData.Parameters.Clear();
193      problemData.Name = "Empty Classification ProblemData";
194      problemData.Description = "This ProblemData acts as place holder before the correct problem data is loaded.";
195      problemData.isEmpty = true;
196
197      problemData.Parameters.Add(new FixedValueParameter<Dataset>(DatasetParameterName, "", new Dataset()));
198      problemData.Parameters.Add(new FixedValueParameter<ReadOnlyCheckedItemList<StringValue>>(InputVariablesParameterName, ""));
199      problemData.Parameters.Add(new FixedValueParameter<IntRange>(TrainingPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
200      problemData.Parameters.Add(new FixedValueParameter<IntRange>(TestPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
201      problemData.Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>()));
202      problemData.Parameters.Add(new FixedValueParameter<StringMatrix>(ClassNamesParameterName, "", new StringMatrix(0, 0).AsReadOnly()));
203      problemData.Parameters.Add(new FixedValueParameter<DoubleMatrix>(ClassificationPenaltiesParameterName, "", (DoubleMatrix)new DoubleMatrix(0, 0).AsReadOnly()));
204      emptyProblemData = problemData;
205    }
206    #endregion
207
208    #region parameter properties
209    public IConstrainedValueParameter<StringValue> TargetVariableParameter {
210      get { return (IConstrainedValueParameter<StringValue>)Parameters[TargetVariableParameterName]; }
211    }
212    public IFixedValueParameter<StringMatrix> ClassNamesParameter {
213      get { return (IFixedValueParameter<StringMatrix>)Parameters[ClassNamesParameterName]; }
214    }
215    public IFixedValueParameter<DoubleMatrix> ClassificationPenaltiesParameter {
216      get { return (IFixedValueParameter<DoubleMatrix>)Parameters[ClassificationPenaltiesParameterName]; }
217    }
218    #endregion
219
220    #region properties
221    public string TargetVariable {
222      get { return TargetVariableParameter.Value.Value; }
223    }
224
225    private List<double> classValuesCache;
226    private List<double> ClassValuesCache {
227      get {
228        if (classValuesCache == null) {
229          classValuesCache = Dataset.GetDoubleValues(TargetVariableParameter.Value.Value).Distinct().OrderBy(x => x).ToList();
230        }
231        return classValuesCache;
232      }
233    }
234    public IEnumerable<double> ClassValues {
235      get { return ClassValuesCache; }
236    }
237    public int Classes {
238      get { return ClassValuesCache.Count; }
239    }
240
241    private List<string> classNamesCache;
242    private List<string> ClassNamesCache {
243      get {
244        if (classNamesCache == null) {
245          classNamesCache = new List<string>();
246          for (int i = 0; i < ClassNamesParameter.Value.Rows; i++)
247            classNamesCache.Add(ClassNamesParameter.Value[i, 0]);
248        }
249        return classNamesCache;
250      }
251    }
252    public IEnumerable<string> ClassNames {
253      get { return ClassNamesCache; }
254    }
255    #endregion
256
257
258    [StorableConstructor]
259    protected ClassificationProblemData(bool deserializing) : base(deserializing) { }
260    [StorableHook(HookType.AfterDeserialization)]
261    private void AfterDeserialization() {
262      RegisterParameterEvents();
263    }
264
265    protected ClassificationProblemData(ClassificationProblemData original, Cloner cloner)
266      : base(original, cloner) {
267      RegisterParameterEvents();
268    }
269    public override IDeepCloneable Clone(Cloner cloner) {
270      if (this == emptyProblemData) return emptyProblemData;
271      return new ClassificationProblemData(this, cloner);
272    }
273
274    public ClassificationProblemData() : this(defaultDataset, defaultAllowedInputVariables, defaultTargetVariable) { }
275
276    public ClassificationProblemData(IClassificationProblemData classificationProblemData)
277      : this(classificationProblemData.Dataset, classificationProblemData.AllowedInputVariables, classificationProblemData.TargetVariable) {
278      TrainingPartition.Start = classificationProblemData.TrainingPartition.Start;
279      TrainingPartition.End = classificationProblemData.TrainingPartition.End;
280      TestPartition.Start = classificationProblemData.TestPartition.Start;
281      TestPartition.End = classificationProblemData.TestPartition.End;
282
283      for (int i = 0; i < classificationProblemData.ClassNames.Count(); i++)
284        ClassNamesParameter.Value[i, 0] = classificationProblemData.ClassNames.ElementAt(i);
285    }
286
287    public ClassificationProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable)
288      : base(dataset, allowedInputVariables) {
289      var validTargetVariableValues = CheckVariablesForPossibleTargetVariables(dataset).Select(x => new StringValue(x).AsReadOnly()).ToList();
290      var target = validTargetVariableValues.Where(x => x.Value == targetVariable).DefaultIfEmpty(validTargetVariableValues.First()).First();
291
292      Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>(validTargetVariableValues), target));
293      Parameters.Add(new FixedValueParameter<StringMatrix>(ClassNamesParameterName, ""));
294      Parameters.Add(new FixedValueParameter<DoubleMatrix>(ClassificationPenaltiesParameterName, ""));
295
296      ResetTargetVariableDependentMembers();
297      RegisterParameterEvents();
298    }
299
300    private static IEnumerable<string> CheckVariablesForPossibleTargetVariables(Dataset dataset) {
301      int maxSamples = Math.Min(InspectedRowsToDetermineTargets, dataset.Rows);
302      var validTargetVariables = (from v in dataset.DoubleVariables
303                                  let distinctValues = dataset.GetDoubleValues(v)
304                                    .Take(maxSamples)
305                                    .Distinct()
306                                    .Count()
307                                  where distinctValues < MaximumNumberOfClasses
308                                  select v).ToArray();
309
310      if (!validTargetVariables.Any())
311        throw new ArgumentException("Import of classification problem data was not successful, because no target variable was found." +
312          " A target variable must have at most " + MaximumNumberOfClasses + " distinct values to be applicable to classification.");
313      return validTargetVariables;
314    }
315
316
317    private void ResetTargetVariableDependentMembers() {
318      DeregisterParameterEvents();
319
320      ((IStringConvertibleMatrix)ClassNamesParameter.Value).Columns = 1;
321      ((IStringConvertibleMatrix)ClassNamesParameter.Value).Rows = ClassValuesCache.Count;
322      for (int i = 0; i < Classes; i++)
323        ClassNamesParameter.Value[i, 0] = "Class " + ClassValuesCache[i];
324      ClassNamesParameter.Value.ColumnNames = new List<string>() { "ClassNames" };
325      ClassNamesParameter.Value.RowNames = ClassValues.Select(s => "ClassValue: " + s);
326
327      ((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Rows = Classes;
328      ((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Columns = Classes;
329      ClassificationPenaltiesParameter.Value.RowNames = ClassNames.Select(name => "Actual " + name);
330      ClassificationPenaltiesParameter.Value.ColumnNames = ClassNames.Select(name => "Estimated " + name);
331      for (int i = 0; i < Classes; i++) {
332        for (int j = 0; j < Classes; j++) {
333          if (i != j) ClassificationPenaltiesParameter.Value[i, j] = 1;
334          else ClassificationPenaltiesParameter.Value[i, j] = 0;
335        }
336      }
337      RegisterParameterEvents();
338    }
339
340    public string GetClassName(double classValue) {
341      if (!ClassValuesCache.Contains(classValue)) throw new ArgumentException();
342      int index = ClassValuesCache.IndexOf(classValue);
343      return ClassNamesCache[index];
344    }
345    public double GetClassValue(string className) {
346      if (!ClassNamesCache.Contains(className)) throw new ArgumentException();
347      int index = ClassNamesCache.IndexOf(className);
348      return ClassValuesCache[index];
349    }
350    public void SetClassName(double classValue, string className) {
351      if (!ClassValuesCache.Contains(classValue)) throw new ArgumentException();
352      int index = ClassValuesCache.IndexOf(classValue);
353      ClassNamesParameter.Value[index, 0] = className;
354      // updating of class names cache is not necessary here as the parameter value fires a changed event which updates the cache
355    }
356
357    public double GetClassificationPenalty(string correctClassName, string estimatedClassName) {
358      return GetClassificationPenalty(GetClassValue(correctClassName), GetClassValue(estimatedClassName));
359    }
360    public double GetClassificationPenalty(double correctClassValue, double estimatedClassValue) {
361      int correctClassIndex = ClassValuesCache.IndexOf(correctClassValue);
362      int estimatedClassIndex = ClassValuesCache.IndexOf(estimatedClassValue);
363      return ClassificationPenaltiesParameter.Value[correctClassIndex, estimatedClassIndex];
364    }
365    public void SetClassificationPenalty(string correctClassName, string estimatedClassName, double penalty) {
366      SetClassificationPenalty(GetClassValue(correctClassName), GetClassValue(estimatedClassName), penalty);
367    }
368    public void SetClassificationPenalty(double correctClassValue, double estimatedClassValue, double penalty) {
369      int correctClassIndex = ClassValuesCache.IndexOf(correctClassValue);
370      int estimatedClassIndex = ClassValuesCache.IndexOf(estimatedClassValue);
371
372      ClassificationPenaltiesParameter.Value[correctClassIndex, estimatedClassIndex] = penalty;
373    }
374
375    #region events
376    private void RegisterParameterEvents() {
377      TargetVariableParameter.ValueChanged += new EventHandler(TargetVariableParameter_ValueChanged);
378      ClassNamesParameter.Value.Reset += new EventHandler(Parameter_ValueChanged);
379      ClassNamesParameter.Value.ItemChanged += new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
380    }
381    private void DeregisterParameterEvents() {
382      TargetVariableParameter.ValueChanged -= new EventHandler(TargetVariableParameter_ValueChanged);
383      ClassNamesParameter.Value.Reset -= new EventHandler(Parameter_ValueChanged);
384      ClassNamesParameter.Value.ItemChanged -= new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
385    }
386
387    private void TargetVariableParameter_ValueChanged(object sender, EventArgs e) {
388      classValuesCache = null;
389      classNamesCache = null;
390      ResetTargetVariableDependentMembers();
391      OnChanged();
392    }
393    private void Parameter_ValueChanged(object sender, EventArgs e) {
394      classNamesCache = null;
395      OnChanged();
396    }
397    #endregion
398  }
399}
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