Changeset 8430 for branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleSolution.cs
- Timestamp:
- 08/08/12 14:04:17 (12 years ago)
- Location:
- branches/HeuristicLab.TimeSeries
- Files:
-
- 3 edited
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branches/HeuristicLab.TimeSeries
- Property svn:ignore
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old new 20 20 bin 21 21 protoc.exe 22 _ReSharper.HeuristicLab.TimeSeries-3.3
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branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis
- Property svn:mergeinfo changed
/trunk/sources/HeuristicLab.Problems.DataAnalysis merged: 7921,7969,8113,8121,8126,8139,8151-8153,8167,8174,8246,8355
- Property svn:mergeinfo changed
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branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleSolution.cs
r7268 r8430 37 37 [Creatable("Data Analysis - Ensembles")] 38 38 public sealed class ClassificationEnsembleSolution : ClassificationSolution, IClassificationEnsembleSolution { 39 private readonly Dictionary<int, double> trainingEvaluationCache = new Dictionary<int, double>(); 40 private readonly Dictionary<int, double> testEvaluationCache = new Dictionary<int, double>(); 41 39 42 public new IClassificationEnsembleModel Model { 40 43 get { return (IClassificationEnsembleModel)base.Model; } … … 85 88 } 86 89 90 trainingEvaluationCache = new Dictionary<int, double>(original.ProblemData.TrainingIndices.Count()); 91 testEvaluationCache = new Dictionary<int, double>(original.ProblemData.TestIndices.Count()); 92 87 93 classificationSolutions = cloner.Clone(original.classificationSolutions); 88 94 RegisterClassificationSolutionsEventHandler(); … … 128 134 } 129 135 136 trainingEvaluationCache = new Dictionary<int, double>(problemData.TrainingIndices.Count()); 137 testEvaluationCache = new Dictionary<int, double>(problemData.TestIndices.Count()); 138 130 139 RegisterClassificationSolutionsEventHandler(); 131 140 classificationSolutions.AddRange(solutions); … … 148 157 public override IEnumerable<double> EstimatedTrainingClassValues { 149 158 get { 150 var rows = ProblemData.TrainingIndizes; 151 var estimatedValuesEnumerators = (from model in Model.Models 152 select new { Model = model, EstimatedValuesEnumerator = model.GetEstimatedClassValues(ProblemData.Dataset, rows).GetEnumerator() }) 153 .ToList(); 154 var rowsEnumerator = rows.GetEnumerator(); 155 // aggregate to make sure that MoveNext is called for all enumerators 156 while (rowsEnumerator.MoveNext() & estimatedValuesEnumerators.Select(en => en.EstimatedValuesEnumerator.MoveNext()).Aggregate(true, (acc, b) => acc & b)) { 157 int currentRow = rowsEnumerator.Current; 158 159 var selectedEnumerators = from pair in estimatedValuesEnumerators 160 where RowIsTrainingForModel(currentRow, pair.Model) && !RowIsTestForModel(currentRow, pair.Model) 161 select pair.EstimatedValuesEnumerator; 162 yield return AggregateEstimatedClassValues(selectedEnumerators.Select(x => x.Current)); 159 var rows = ProblemData.TrainingIndices; 160 var rowsToEvaluate = rows.Except(trainingEvaluationCache.Keys); 161 var rowsEnumerator = rowsToEvaluate.GetEnumerator(); 162 var valuesEnumerator = GetEstimatedValues(rowsToEvaluate, (r, m) => RowIsTrainingForModel(r, m) && !RowIsTestForModel(r, m)).GetEnumerator(); 163 164 while (rowsEnumerator.MoveNext() & valuesEnumerator.MoveNext()) { 165 trainingEvaluationCache.Add(rowsEnumerator.Current, valuesEnumerator.Current); 163 166 } 167 168 return rows.Select(row => trainingEvaluationCache[row]); 164 169 } 165 170 } … … 167 172 public override IEnumerable<double> EstimatedTestClassValues { 168 173 get { 169 var rows = ProblemData.TestIndizes; 170 var estimatedValuesEnumerators = (from model in Model.Models 171 select new { Model = model, EstimatedValuesEnumerator = model.GetEstimatedClassValues(ProblemData.Dataset, rows).GetEnumerator() }) 172 .ToList(); 173 var rowsEnumerator = ProblemData.TestIndizes.GetEnumerator(); 174 // aggregate to make sure that MoveNext is called for all enumerators 175 while (rowsEnumerator.MoveNext() & estimatedValuesEnumerators.Select(en => en.EstimatedValuesEnumerator.MoveNext()).Aggregate(true, (acc, b) => acc & b)) { 176 int currentRow = rowsEnumerator.Current; 177 178 var selectedEnumerators = from pair in estimatedValuesEnumerators 179 where RowIsTestForModel(currentRow, pair.Model) 180 select pair.EstimatedValuesEnumerator; 181 182 yield return AggregateEstimatedClassValues(selectedEnumerators.Select(x => x.Current)); 174 var rows = ProblemData.TestIndices; 175 var rowsToEvaluate = rows.Except(testEvaluationCache.Keys); 176 var rowsEnumerator = rowsToEvaluate.GetEnumerator(); 177 var valuesEnumerator = GetEstimatedValues(rowsToEvaluate, RowIsTestForModel).GetEnumerator(); 178 179 while (rowsEnumerator.MoveNext() & valuesEnumerator.MoveNext()) { 180 testEvaluationCache.Add(rowsEnumerator.Current, valuesEnumerator.Current); 183 181 } 182 183 return rows.Select(row => testEvaluationCache[row]); 184 } 185 } 186 187 private IEnumerable<double> GetEstimatedValues(IEnumerable<int> rows, Func<int, IClassificationModel, bool> modelSelectionPredicate) { 188 var estimatedValuesEnumerators = (from model in Model.Models 189 select new { Model = model, EstimatedValuesEnumerator = model.GetEstimatedClassValues(ProblemData.Dataset, rows).GetEnumerator() }) 190 .ToList(); 191 var rowsEnumerator = rows.GetEnumerator(); 192 // aggregate to make sure that MoveNext is called for all enumerators 193 while (rowsEnumerator.MoveNext() & estimatedValuesEnumerators.Select(en => en.EstimatedValuesEnumerator.MoveNext()).Aggregate(true, (acc, b) => acc & b)) { 194 int currentRow = rowsEnumerator.Current; 195 196 var selectedEnumerators = from pair in estimatedValuesEnumerators 197 where modelSelectionPredicate(currentRow, pair.Model) 198 select pair.EstimatedValuesEnumerator; 199 200 yield return AggregateEstimatedClassValues(selectedEnumerators.Select(x => x.Current)); 184 201 } 185 202 } … … 196 213 197 214 public override IEnumerable<double> GetEstimatedClassValues(IEnumerable<int> rows) { 198 return from xs in GetEstimatedClassValueVectors(ProblemData.Dataset, rows) 199 select AggregateEstimatedClassValues(xs); 215 var rowsToEvaluate = rows.Except(evaluationCache.Keys); 216 var rowsEnumerator = rowsToEvaluate.GetEnumerator(); 217 var valuesEnumerator = (from xs in GetEstimatedClassValueVectors(ProblemData.Dataset, rowsToEvaluate) 218 select AggregateEstimatedClassValues(xs)) 219 .GetEnumerator(); 220 221 while (rowsEnumerator.MoveNext() & valuesEnumerator.MoveNext()) { 222 evaluationCache.Add(rowsEnumerator.Current, valuesEnumerator.Current); 223 } 224 225 return rows.Select(row => evaluationCache[row]); 200 226 } 201 227 … … 223 249 224 250 protected override void OnProblemDataChanged() { 251 trainingEvaluationCache.Clear(); 252 testEvaluationCache.Clear(); 253 evaluationCache.Clear(); 254 225 255 IClassificationProblemData problemData = new ClassificationProblemData(ProblemData.Dataset, 226 256 ProblemData.AllowedInputVariables, … … 251 281 public void AddClassificationSolutions(IEnumerable<IClassificationSolution> solutions) { 252 282 classificationSolutions.AddRange(solutions); 283 284 trainingEvaluationCache.Clear(); 285 testEvaluationCache.Clear(); 286 evaluationCache.Clear(); 253 287 } 254 288 public void RemoveClassificationSolutions(IEnumerable<IClassificationSolution> solutions) { 255 289 classificationSolutions.RemoveRange(solutions); 290 291 trainingEvaluationCache.Clear(); 292 testEvaluationCache.Clear(); 293 evaluationCache.Clear(); 256 294 } 257 295 … … 275 313 trainingPartitions[solution.Model] = solution.ProblemData.TrainingPartition; 276 314 testPartitions[solution.Model] = solution.ProblemData.TestPartition; 315 316 trainingEvaluationCache.Clear(); 317 testEvaluationCache.Clear(); 318 evaluationCache.Clear(); 277 319 } 278 320 … … 282 324 trainingPartitions.Remove(solution.Model); 283 325 testPartitions.Remove(solution.Model); 326 327 trainingEvaluationCache.Clear(); 328 testEvaluationCache.Clear(); 329 evaluationCache.Clear(); 284 330 } 285 331 }
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