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source: trunk/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationProblemData.cs @ 17845

Last change on this file since 17845 was 17845, checked in by bburlacu, 3 years ago

#3102: Add constructor taking original problem data and new dataset.

File size: 24.2 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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 HEAL.Attic;
30
31namespace HeuristicLab.Problems.DataAnalysis {
32  [StorableType("1C8DCCCF-4E2A-421D-9C61-7C017D584054")]
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 string PositiveClassParameterName = "PositiveClass";
39    protected const int MaximumNumberOfClasses = 100;
40    protected const int InspectedRowsToDetermineTargets = 2000;
41
42    public string Filename { get; set; }
43
44    #region default data
45    private static string[] defaultVariableNames = new string[] { "sample", "clump thickness", "cell size", "cell shape", "marginal adhesion", "epithelial cell size", "bare nuclei", "chromatin", "nucleoli", "mitoses", "class" };
46    private static double[,] defaultData = new double[,]{
47     {1000025,5,1,1,1,2,1,3,1,1,2      },
48     {1002945,5,4,4,5,7,10,3,2,1,2     },
49     {1015425,3,1,1,1,2,2,3,1,1,2      },
50     {1016277,6,8,8,1,3,4,3,7,1,2      },
51     {1017023,4,1,1,3,2,1,3,1,1,2      },
52     {1017122,8,10,10,8,7,10,9,7,1,4   },
53     {1018099,1,1,1,1,2,10,3,1,1,2     },
54     {1018561,2,1,2,1,2,1,3,1,1,2      },
55     {1033078,2,1,1,1,2,1,1,1,5,2      },
56     {1033078,4,2,1,1,2,1,2,1,1,2      },
57     {1035283,1,1,1,1,1,1,3,1,1,2      },
58     {1036172,2,1,1,1,2,1,2,1,1,2      },
59     {1041801,5,3,3,3,2,3,4,4,1,4      },
60     {1043999,1,1,1,1,2,3,3,1,1,2      },
61     {1044572,8,7,5,10,7,9,5,5,4,4     },
62     {1047630,7,4,6,4,6,1,4,3,1,4      },
63     {1048672,4,1,1,1,2,1,2,1,1,2      },
64     {1049815,4,1,1,1,2,1,3,1,1,2      },
65     {1050670,10,7,7,6,4,10,4,1,2,4    },
66     {1050718,6,1,1,1,2,1,3,1,1,2      },
67     {1054590,7,3,2,10,5,10,5,4,4,4    },
68     {1054593,10,5,5,3,6,7,7,10,1,4    },
69     {1056784,3,1,1,1,2,1,2,1,1,2      },
70     {1057013,8,4,5,1,2,2,7,3,1,4      },
71     {1059552,1,1,1,1,2,1,3,1,1,2      },
72     {1065726,5,2,3,4,2,7,3,6,1,4      },
73     {1066373,3,2,1,1,1,1,2,1,1,2      },
74     {1066979,5,1,1,1,2,1,2,1,1,2      },
75     {1067444,2,1,1,1,2,1,2,1,1,2      },
76     {1070935,1,1,3,1,2,1,1,1,1,2      },
77     {1070935,3,1,1,1,1,1,2,1,1,2      },
78     {1071760,2,1,1,1,2,1,3,1,1,2      },
79     {1072179,10,7,7,3,8,5,7,4,3,4     },
80     {1074610,2,1,1,2,2,1,3,1,1,2      },
81     {1075123,3,1,2,1,2,1,2,1,1,2      },
82     {1079304,2,1,1,1,2,1,2,1,1,2      },
83     {1080185,10,10,10,8,6,1,8,9,1,4   },
84     {1081791,6,2,1,1,1,1,7,1,1,2      },
85     {1084584,5,4,4,9,2,10,5,6,1,4     },
86     {1091262,2,5,3,3,6,7,7,5,1,4      },
87     {1096800,6,6,6,9,6,4,7,8,1,2      },
88     {1099510,10,4,3,1,3,3,6,5,2,4     },
89     {1100524,6,10,10,2,8,10,7,3,3,4   },
90     {1102573,5,6,5,6,10,1,3,1,1,4     },
91     {1103608,10,10,10,4,8,1,8,10,1,4  },
92     {1103722,1,1,1,1,2,1,2,1,2,2      },
93     {1105257,3,7,7,4,4,9,4,8,1,4      },
94     {1105524,1,1,1,1,2,1,2,1,1,2      },
95     {1106095,4,1,1,3,2,1,3,1,1,2      },
96     {1106829,7,8,7,2,4,8,3,8,2,4      },
97     {1108370,9,5,8,1,2,3,2,1,5,4      },
98     {1108449,5,3,3,4,2,4,3,4,1,4      },
99     {1110102,10,3,6,2,3,5,4,10,2,4    },
100     {1110503,5,5,5,8,10,8,7,3,7,4     },
101     {1110524,10,5,5,6,8,8,7,1,1,4     },
102     {1111249,10,6,6,3,4,5,3,6,1,4     },
103     {1112209,8,10,10,1,3,6,3,9,1,4    },
104     {1113038,8,2,4,1,5,1,5,4,4,4      },
105     {1113483,5,2,3,1,6,10,5,1,1,4     },
106     {1113906,9,5,5,2,2,2,5,1,1,4      },
107     {1115282,5,3,5,5,3,3,4,10,1,4     },
108     {1115293,1,1,1,1,2,2,2,1,1,2      },
109     {1116116,9,10,10,1,10,8,3,3,1,4   },
110     {1116132,6,3,4,1,5,2,3,9,1,4      },
111     {1116192,1,1,1,1,2,1,2,1,1,2      },
112     {1116998,10,4,2,1,3,2,4,3,10,4    },
113     {1117152,4,1,1,1,2,1,3,1,1,2      },
114     {1118039,5,3,4,1,8,10,4,9,1,4     },
115     {1120559,8,3,8,3,4,9,8,9,8,4      },
116     {1121732,1,1,1,1,2,1,3,2,1,2      },
117     {1121919,5,1,3,1,2,1,2,1,1,2      },
118     {1123061,6,10,2,8,10,2,7,8,10,4   },
119     {1124651,1,3,3,2,2,1,7,2,1,2      },
120     {1125035,9,4,5,10,6,10,4,8,1,4    },
121     {1126417,10,6,4,1,3,4,3,2,3,4     },
122     {1131294,1,1,2,1,2,2,4,2,1,2      },
123     {1132347,1,1,4,1,2,1,2,1,1,2      },
124     {1133041,5,3,1,2,2,1,2,1,1,2      },
125     {1133136,3,1,1,1,2,3,3,1,1,2      },
126     {1136142,2,1,1,1,3,1,2,1,1,2      },
127     {1137156,2,2,2,1,1,1,7,1,1,2      },
128     {1143978,4,1,1,2,2,1,2,1,1,2      },
129     {1143978,5,2,1,1,2,1,3,1,1,2      },
130     {1147044,3,1,1,1,2,2,7,1,1,2      },
131     {1147699,3,5,7,8,8,9,7,10,7,4     },
132     {1147748,5,10,6,1,10,4,4,10,10,4  },
133     {1148278,3,3,6,4,5,8,4,4,1,4      },
134     {1148873,3,6,6,6,5,10,6,8,3,4     },
135     {1152331,4,1,1,1,2,1,3,1,1,2      },
136     {1155546,2,1,1,2,3,1,2,1,1,2      },
137     {1156272,1,1,1,1,2,1,3,1,1,2      },
138     {1156948,3,1,1,2,2,1,1,1,1,2      },
139     {1157734,4,1,1,1,2,1,3,1,1,2      },
140     {1158247,1,1,1,1,2,1,2,1,1,2      },
141     {1160476,2,1,1,1,2,1,3,1,1,2      },
142     {1164066,1,1,1,1,2,1,3,1,1,2      },
143     {1165297,2,1,1,2,2,1,1,1,1,2      },
144     {1165790,5,1,1,1,2,1,3,1,1,2      },
145     {1165926,9,6,9,2,10,6,2,9,10,4    },
146     {1166630,7,5,6,10,5,10,7,9,4,4    },
147     {1166654,10,3,5,1,10,5,3,10,2,4   },
148     {1167439,2,3,4,4,2,5,2,5,1,4      },
149     {1167471,4,1,2,1,2,1,3,1,1,2      },
150     {1168359,8,2,3,1,6,3,7,1,1,4      },
151     {1168736,10,10,10,10,10,1,8,8,8,4 },
152     {1169049,7,3,4,4,3,3,3,2,7,4      },
153     {1170419,10,10,10,8,2,10,4,1,1,4  },
154     {1170420,1,6,8,10,8,10,5,7,1,4    },
155     {1171710,1,1,1,1,2,1,2,3,1,2      },
156     {1171710,6,5,4,4,3,9,7,8,3,4      },
157     {1171795,1,3,1,2,2,2,5,3,2,2      },
158     {1171845,8,6,4,3,5,9,3,1,1,4      },
159     {1172152,10,3,3,10,2,10,7,3,3,4   },
160     {1173216,10,10,10,3,10,8,8,1,1,4  },
161     {1173235,3,3,2,1,2,3,3,1,1,2      },
162     {1173347,1,1,1,1,2,5,1,1,1,2      },
163     {1173347,8,3,3,1,2,2,3,2,1,2      },
164     {1173509,4,5,5,10,4,10,7,5,8,4    },
165     {1173514,1,1,1,1,4,3,1,1,1,2      },
166     {1173681,3,2,1,1,2,2,3,1,1,2      },
167     {1174057,1,1,2,2,2,1,3,1,1,2      },
168     {1174057,4,2,1,1,2,2,3,1,1,2      },
169     {1174131,10,10,10,2,10,10,5,3,3,4 },
170     {1174428,5,3,5,1,8,10,5,3,1,4     },
171     {1175937,5,4,6,7,9,7,8,10,1,4     },
172     {1176406,1,1,1,1,2,1,2,1,1,2      },
173     {1176881,7,5,3,7,4,10,7,5,5,4        }
174};
175    private static readonly Dataset defaultDataset;
176    private static readonly IEnumerable<string> defaultAllowedInputVariables;
177    private static readonly string defaultTargetVariable;
178
179    private static readonly ClassificationProblemData emptyProblemData;
180    public static ClassificationProblemData EmptyProblemData {
181      get { return EmptyProblemData; }
182    }
183
184    static ClassificationProblemData() {
185      defaultDataset = new Dataset(defaultVariableNames, defaultData);
186      defaultDataset.Name = "Wisconsin classification problem";
187      defaultDataset.Description = "subset from to ..";
188
189      defaultAllowedInputVariables = defaultVariableNames.Except(new List<string>() { "sample", "class" });
190      defaultTargetVariable = "class";
191
192      var problemData = new ClassificationProblemData();
193      problemData.Parameters.Clear();
194      problemData.Name = "Empty Classification ProblemData";
195      problemData.Description = "This ProblemData acts as place holder before the correct problem data is loaded.";
196      problemData.isEmpty = true;
197
198      problemData.Parameters.Add(new FixedValueParameter<Dataset>(DatasetParameterName, "", new Dataset()));
199      problemData.Parameters.Add(new FixedValueParameter<ReadOnlyCheckedItemList<StringValue>>(InputVariablesParameterName, ""));
200      problemData.Parameters.Add(new FixedValueParameter<IntRange>(TrainingPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
201      problemData.Parameters.Add(new FixedValueParameter<IntRange>(TestPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
202      problemData.Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>()));
203      problemData.Parameters.Add(new FixedValueParameter<StringMatrix>(ClassNamesParameterName, "", new StringMatrix(0, 0).AsReadOnly()));
204      problemData.Parameters.Add(new FixedValueParameter<DoubleMatrix>(ClassificationPenaltiesParameterName, "", (DoubleMatrix)new DoubleMatrix(0, 0).AsReadOnly()));
205      emptyProblemData = problemData;
206    }
207    #endregion
208
209    #region parameter properties
210    public IConstrainedValueParameter<StringValue> TargetVariableParameter {
211      get { return (IConstrainedValueParameter<StringValue>)Parameters[TargetVariableParameterName]; }
212    }
213    public IFixedValueParameter<StringMatrix> ClassNamesParameter {
214      get { return (IFixedValueParameter<StringMatrix>)Parameters[ClassNamesParameterName]; }
215    }
216    public IConstrainedValueParameter<StringValue> PositiveClassParameter {
217      get { return (IConstrainedValueParameter<StringValue>)Parameters[PositiveClassParameterName]; }
218    }
219    public IFixedValueParameter<DoubleMatrix> ClassificationPenaltiesParameter {
220      get { return (IFixedValueParameter<DoubleMatrix>)Parameters[ClassificationPenaltiesParameterName]; }
221    }
222    #endregion
223
224    #region properties
225    public string TargetVariable {
226      get { return TargetVariableParameter.Value.Value; }
227      set {
228        if (value == null) throw new ArgumentNullException("targetVariable", "The provided value for the targetVariable is null.");
229        if (value == TargetVariable) return;
230
231
232        var matchingParameterValue = TargetVariableParameter.ValidValues.FirstOrDefault(v => v.Value == value);
233        if (matchingParameterValue == null) throw new ArgumentException("The provided value is not valid as the targetVariable.", "targetVariable");
234        TargetVariableParameter.Value = matchingParameterValue;
235      }
236    }
237
238    private List<double> classValuesCache;
239    private List<double> ClassValuesCache {
240      get {
241        if (classValuesCache == null) {
242          classValuesCache = Dataset.GetDoubleValues(TargetVariableParameter.Value.Value).Distinct().OrderBy(x => x).ToList();
243        }
244        return classValuesCache;
245      }
246    }
247    public IEnumerable<double> ClassValues {
248      get { return ClassValuesCache; }
249    }
250    public int Classes {
251      get { return ClassValuesCache.Count; }
252    }
253
254    private List<string> classNamesCache;
255    private List<string> ClassNamesCache {
256      get {
257        if (classNamesCache == null) {
258          classNamesCache = new List<string>();
259          for (int i = 0; i < ClassNamesParameter.Value.Rows; i++)
260            classNamesCache.Add(ClassNamesParameter.Value[i, 0]);
261        }
262        return classNamesCache;
263      }
264    }
265    public IEnumerable<string> ClassNames {
266      get { return ClassNamesCache; }
267    }
268
269    public string PositiveClass {
270      get { return PositiveClassParameter.Value.Value; }
271      set {
272        var matchingValue = PositiveClassParameter.ValidValues.SingleOrDefault(x => x.Value == value);
273        if (matchingValue == null) throw new ArgumentException(string.Format("{0} cannot be set as positive class.", value));
274        PositiveClassParameter.Value = matchingValue;
275      }
276    }
277    #endregion
278
279
280    [StorableConstructor]
281    protected ClassificationProblemData(StorableConstructorFlag _) : base(_) { }
282    [StorableHook(HookType.AfterDeserialization)]
283    private void AfterDeserialization() {
284      RegisterParameterEvents();
285
286      classNamesCache = new List<string>();
287      for (int i = 0; i < ClassNamesParameter.Value.Rows; i++)
288        classNamesCache.Add(ClassNamesParameter.Value[i, 0]);
289
290      // BackwardsCompatibility3.4
291      #region Backwards compatible code, remove with 3.5
292      if (!Parameters.ContainsKey(PositiveClassParameterName)) {
293        var validValues = new ItemSet<StringValue>(ClassNames.Select(s => new StringValue(s).AsReadOnly()));
294        Parameters.Add(new ConstrainedValueParameter<StringValue>(PositiveClassParameterName,
295          "The positive class which is used for quality measure calculation (e.g., specifity, sensitivity,...)", validValues, validValues.First()));
296      }
297      #endregion
298
299    }
300
301    protected ClassificationProblemData(ClassificationProblemData original, Cloner cloner)
302      : base(original, cloner) {
303      RegisterParameterEvents();
304      classNamesCache = new List<string>();
305      for (int i = 0; i < ClassNamesParameter.Value.Rows; i++)
306        classNamesCache.Add(ClassNamesParameter.Value[i, 0]);
307    }
308
309    public override IDeepCloneable Clone(Cloner cloner) {
310      if (this == emptyProblemData) return emptyProblemData;
311      return new ClassificationProblemData(this, cloner);
312    }
313
314    public ClassificationProblemData() : this(defaultDataset, defaultAllowedInputVariables, defaultTargetVariable, Enumerable.Empty<string>()) { }
315
316    public ClassificationProblemData(IClassificationProblemData classificationProblemData)
317      : this(classificationProblemData, classificationProblemData.Dataset) {
318    }
319
320    /// <summary>
321    /// This method satisfies a common use case: making a copy of the problem but providing a different dataset.
322    /// One must be careful here that the dataset passed is not modified, as that would invalidate the problem data internals.
323    /// Passing a ModifiableDataset to this constructor is therefore discouraged.
324    /// </summary>
325    /// <param name="classificationProblemData">The original instance of classification problem data.</param>
326    /// <param name="dataset">The new dataset.</param>
327    public ClassificationProblemData(IClassificationProblemData classificationProblemData, IDataset dataset)
328    : this(classificationProblemData.Dataset, classificationProblemData.AllowedInputVariables, classificationProblemData.TargetVariable, classificationProblemData.ClassNames, classificationProblemData.PositiveClass) {
329
330      TrainingPartition.Start = classificationProblemData.TrainingPartition.Start;
331      TrainingPartition.End = classificationProblemData.TrainingPartition.End;
332      TestPartition.Start = classificationProblemData.TestPartition.Start;
333      TestPartition.End = classificationProblemData.TestPartition.End;
334
335      for (int i = 0; i < Classes; i++) {
336        for (int j = 0; j < Classes; j++) {
337          ClassificationPenaltiesParameter.Value[i, j] = classificationProblemData.GetClassificationPenalty(ClassValuesCache[i], ClassValuesCache[j]);
338        }
339      }
340    }
341
342    public ClassificationProblemData(IDataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable,
343      IEnumerable<string> classNames = null,
344      string positiveClass = null, // can be null in which case it's set as the first class name
345      IEnumerable<ITransformation> transformations = null)
346      : base(dataset, allowedInputVariables, transformations ?? Enumerable.Empty<ITransformation>()) {
347      var validTargetVariableValues = CheckVariablesForPossibleTargetVariables(dataset).Select(x => new StringValue(x).AsReadOnly()).ToList();
348      var target = validTargetVariableValues.Where(x => x.Value == targetVariable).DefaultIfEmpty(validTargetVariableValues.First()).First();
349
350      Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>(validTargetVariableValues), target));
351      Parameters.Add(new FixedValueParameter<StringMatrix>(ClassNamesParameterName, "", new StringMatrix()));
352      Parameters.Add(new ConstrainedValueParameter<StringValue>(PositiveClassParameterName, "The positive class which is used for quality measure calculation (e.g., specifity, sensitivity,...)"));
353      Parameters.Add(new FixedValueParameter<DoubleMatrix>(ClassificationPenaltiesParameterName, ""));
354
355      RegisterParameterEvents();
356      ResetTargetVariableDependentMembers(); // correctly set the values of the parameters added above
357
358      // set the class names
359      if (classNames != null && classNames.Any()) {
360        // better to allocate lists because we use these multiple times below
361        var names = classNames.ToList();
362        var values = ClassValuesCache;
363
364        if (names.Count != values.Count) {
365          throw new ArgumentException();
366        }
367
368        ((IStringConvertibleMatrix)ClassNamesParameter.Value).Columns = 1;
369        ((IStringConvertibleMatrix)ClassNamesParameter.Value).Rows = names.Count;
370
371        for (int i = 0; i < names.Count; ++i) {
372          SetClassName(values[i], names[i]);
373        }
374      }
375
376      // set the positive class value
377      if (positiveClass != null) {
378        PositiveClass = positiveClass;
379      }
380    }
381
382    public static IEnumerable<string> CheckVariablesForPossibleTargetVariables(IDataset dataset) {
383      int maxSamples = Math.Min(InspectedRowsToDetermineTargets, dataset.Rows);
384      var validTargetVariables = (from v in dataset.DoubleVariables
385                                  let distinctValues = dataset.GetDoubleValues(v)
386                                    .Take(maxSamples)
387                                    .Distinct()
388                                    .Count()
389                                  where distinctValues <= MaximumNumberOfClasses
390                                  select v).ToArray();
391
392      if (!validTargetVariables.Any())
393        throw new ArgumentException("Import of classification problem data was not successful, because no target variable was found." +
394          " A target variable must have at most " + MaximumNumberOfClasses + " distinct values to be applicable to classification.");
395      return validTargetVariables;
396    }
397
398
399    private void ResetTargetVariableDependentMembers() {
400      DeregisterParameterEvents();
401
402      ((IStringConvertibleMatrix)ClassNamesParameter.Value).Columns = 1;
403      ((IStringConvertibleMatrix)ClassNamesParameter.Value).Rows = ClassValuesCache.Count;
404      for (int i = 0; i < Classes; i++)
405        ClassNamesParameter.Value[i, 0] = "Class " + ClassValuesCache[i];
406      ClassNamesParameter.Value.ColumnNames = new List<string>() { "ClassNames" };
407      ClassNamesParameter.Value.RowNames = ClassValues.Select(s => "ClassValue: " + s);
408
409      PositiveClassParameter.ValidValues.Clear();
410      foreach (var className in ClassNames) {
411        PositiveClassParameter.ValidValues.Add(new StringValue(className).AsReadOnly());
412      }
413
414      ((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Rows = Classes;
415      ((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Columns = Classes;
416      ClassificationPenaltiesParameter.Value.RowNames = ClassNames.Select(name => "Actual " + name);
417      ClassificationPenaltiesParameter.Value.ColumnNames = ClassNames.Select(name => "Estimated " + name);
418      for (int i = 0; i < Classes; i++) {
419        for (int j = 0; j < Classes; j++) {
420          if (i != j) ClassificationPenaltiesParameter.Value[i, j] = 1;
421          else ClassificationPenaltiesParameter.Value[i, j] = 0;
422        }
423      }
424      RegisterParameterEvents();
425    }
426
427    public string GetClassName(double classValue) {
428      if (!ClassValuesCache.Contains(classValue)) throw new ArgumentException();
429      int index = ClassValuesCache.IndexOf(classValue);
430      return ClassNamesCache[index];
431    }
432    public double GetClassValue(string className) {
433      if (!ClassNamesCache.Contains(className)) throw new ArgumentException();
434      int index = ClassNamesCache.IndexOf(className);
435      return ClassValuesCache[index];
436    }
437    public void SetClassName(double classValue, string className) {
438      if (!ClassValuesCache.Contains(classValue)) throw new ArgumentException();
439      int index = ClassValuesCache.IndexOf(classValue);
440      ClassNamesParameter.Value[index, 0] = className;
441      // updating of class names cache is not necessary here as the parameter value fires a changed event which updates the cache
442    }
443
444    public double GetClassificationPenalty(string correctClassName, string estimatedClassName) {
445      return GetClassificationPenalty(GetClassValue(correctClassName), GetClassValue(estimatedClassName));
446    }
447    public double GetClassificationPenalty(double correctClassValue, double estimatedClassValue) {
448      int correctClassIndex = ClassValuesCache.IndexOf(correctClassValue);
449      int estimatedClassIndex = ClassValuesCache.IndexOf(estimatedClassValue);
450      return ClassificationPenaltiesParameter.Value[correctClassIndex, estimatedClassIndex];
451    }
452    public void SetClassificationPenalty(string correctClassName, string estimatedClassName, double penalty) {
453      SetClassificationPenalty(GetClassValue(correctClassName), GetClassValue(estimatedClassName), penalty);
454    }
455    public void SetClassificationPenalty(double correctClassValue, double estimatedClassValue, double penalty) {
456      int correctClassIndex = ClassValuesCache.IndexOf(correctClassValue);
457      int estimatedClassIndex = ClassValuesCache.IndexOf(estimatedClassValue);
458
459      ClassificationPenaltiesParameter.Value[correctClassIndex, estimatedClassIndex] = penalty;
460    }
461
462    #region events
463    private void RegisterParameterEvents() {
464      TargetVariableParameter.ValueChanged += new EventHandler(TargetVariableParameter_ValueChanged);
465      ClassNamesParameter.Value.Reset += new EventHandler(Parameter_ValueChanged);
466      ClassNamesParameter.Value.ItemChanged += new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
467      ClassificationPenaltiesParameter.Value.ItemChanged += new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
468      ClassificationPenaltiesParameter.Value.Reset += new EventHandler(Parameter_ValueChanged);
469    }
470    private void DeregisterParameterEvents() {
471      TargetVariableParameter.ValueChanged -= new EventHandler(TargetVariableParameter_ValueChanged);
472      ClassNamesParameter.Value.Reset -= new EventHandler(Parameter_ValueChanged);
473      ClassNamesParameter.Value.ItemChanged -= new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
474      ClassificationPenaltiesParameter.Value.ItemChanged -= new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
475      ClassificationPenaltiesParameter.Value.Reset -= new EventHandler(Parameter_ValueChanged);
476    }
477
478    private void TargetVariableParameter_ValueChanged(object sender, EventArgs e) {
479      classValuesCache = null;
480      classNamesCache = null;
481      ResetTargetVariableDependentMembers();
482      OnChanged();
483    }
484    private void Parameter_ValueChanged(object sender, EventArgs e) {
485      var oldPositiveClass = PositiveClass;
486      var oldClassNames = classNamesCache;
487      var index = oldClassNames.IndexOf(oldPositiveClass);
488
489      classNamesCache = null;
490      ClassificationPenaltiesParameter.Value.RowNames = ClassNames.Select(name => "Actual " + name);
491      ClassificationPenaltiesParameter.Value.ColumnNames = ClassNames.Select(name => "Estimated " + name);
492
493      PositiveClassParameter.ValidValues.Clear();
494      foreach (var className in ClassNames) {
495        PositiveClassParameter.ValidValues.Add(new StringValue(className).AsReadOnly());
496      }
497      PositiveClassParameter.Value = PositiveClassParameter.ValidValues.ElementAt(index);
498
499      OnChanged();
500    }
501    #endregion
502  }
503}
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