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Ignore:
Timestamp:
07/06/11 21:13:34 (13 years ago)
Author:
gkronber
Message:

#1450 implemented menu item to merge all data analysis solutions in a run collection into ensemble solutions and fixed a few bugs related to ensemble solutions.

Location:
trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4
Files:
7 edited

Legend:

Unmodified
Added
Removed
  • trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleModel.cs

    r6239 r6520  
    5858
    5959    #region IClassificationEnsembleModel Members
     60    public void Add(IClassificationModel model) {
     61      models.Add(model);
     62    }
    6063
    6164    public IEnumerable<IEnumerable<double>> GetEstimatedClassValueVectors(Dataset dataset, IEnumerable<int> rows) {
  • trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleProblemData.cs

    r6239 r6520  
    3737    public override IEnumerable<int> TrainingIndizes {
    3838      get {
    39         return Enumerable.Range(TrainingPartition.Start, TestPartition.End - TestPartition.Start);
     39        return Enumerable.Range(TrainingPartition.Start, TrainingPartition.End - TrainingPartition.Start);
    4040      }
    4141    }
  • trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleSolution.cs

    r6302 r6520  
    7979      this.trainingPartitions = new Dictionary<IClassificationModel, IntRange>();
    8080      this.testPartitions = new Dictionary<IClassificationModel, IntRange>();
    81       var modelEnumerator = models.GetEnumerator();
    82       var trainingPartitionEnumerator = trainingPartitions.GetEnumerator();
    83       var testPartitionEnumerator = testPartitions.GetEnumerator();
    84       while (modelEnumerator.MoveNext() & trainingPartitionEnumerator.MoveNext() & testPartitionEnumerator.MoveNext()) {
    85         this.trainingPartitions[modelEnumerator.Current] = (IntRange)trainingPartitionEnumerator.Current.Clone();
    86         this.testPartitions[modelEnumerator.Current] = (IntRange)testPartitionEnumerator.Current.Clone();
    87       }
    88       if (modelEnumerator.MoveNext() | trainingPartitionEnumerator.MoveNext() | testPartitionEnumerator.MoveNext()) {
    89         throw new ArgumentException();
    90       }
     81      AddModelsAndParitions(models,
     82        trainingPartitions,
     83        testPartitions);
    9184      RecalculateResults();
    9285    }
     
    169162      .First();
    170163    }
     164
     165    public void AddModelsAndParitions(IEnumerable<IClassificationSolution> solutions) {
     166      foreach (var solution in solutions) {
     167        var ensembleSolution = solution as ClassificationEnsembleSolution;
     168        if (ensembleSolution != null) {
     169          var data = from m in ensembleSolution.Model.Models
     170                     let train = ensembleSolution.trainingPartitions[m]
     171                     let test = ensembleSolution.testPartitions[m]
     172                     select new { m, train, test };
     173
     174          foreach (var d in data) {
     175            Model.Add(d.m);
     176            trainingPartitions[d.m] = (IntRange)d.train.Clone();
     177            testPartitions[d.m] = (IntRange)d.test.Clone();
     178          }
     179        } else {
     180          Model.Add(solution.Model);
     181          trainingPartitions[solution.Model] = (IntRange)solution.ProblemData.TrainingPartition.Clone();
     182          testPartitions[solution.Model] = (IntRange)solution.ProblemData.TestPartition.Clone();
     183        }
     184      }
     185
     186      RecalculateResults();
     187    }
     188
     189    private void AddModelsAndParitions(IEnumerable<IClassificationModel> models, IEnumerable<IntRange> trainingPartitions, IEnumerable<IntRange> testPartitions) {
     190      var modelEnumerator = models.GetEnumerator();
     191      var trainingPartitionEnumerator = trainingPartitions.GetEnumerator();
     192      var testPartitionEnumerator = testPartitions.GetEnumerator();
     193
     194      while (modelEnumerator.MoveNext() & trainingPartitionEnumerator.MoveNext() & testPartitionEnumerator.MoveNext()) {
     195        this.trainingPartitions[modelEnumerator.Current] = (IntRange)trainingPartitionEnumerator.Current.Clone();
     196        this.testPartitions[modelEnumerator.Current] = (IntRange)testPartitionEnumerator.Current.Clone();
     197      }
     198      if (modelEnumerator.MoveNext() | trainingPartitionEnumerator.MoveNext() | testPartitionEnumerator.MoveNext()) {
     199        throw new ArgumentException();
     200      }
     201    }
    171202  }
    172203}
  • trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionEnsembleModel.cs

    r5809 r6520  
    5858    #region IRegressionEnsembleModel Members
    5959
     60    public void Add(IRegressionModel model) {
     61      models.Add(model);
     62    }
     63
    6064    public IEnumerable<IEnumerable<double>> GetEstimatedValueVectors(Dataset dataset, IEnumerable<int> rows) {
    6165      var estimatedValuesEnumerators = (from model in models
  • trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionEnsembleSolution.cs

    r6302 r6520  
    6464      trainingPartitions = new Dictionary<IRegressionModel, IntRange>();
    6565      testPartitions = new Dictionary<IRegressionModel, IntRange>();
    66       foreach (var model in models) {
    67         trainingPartitions[model] = (IntRange)problemData.TrainingPartition.Clone();
    68         testPartitions[model] = (IntRange)problemData.TestPartition.Clone();
    69       }
     66      AddModelsAndParitions(models,
     67        from m in models select (IntRange)problemData.TrainingPartition.Clone(),
     68        from m in models select (IntRange)problemData.TestPartition.Clone());
    7069      RecalculateResults();
    7170    }
     
    7574      this.trainingPartitions = new Dictionary<IRegressionModel, IntRange>();
    7675      this.testPartitions = new Dictionary<IRegressionModel, IntRange>();
    77       var modelEnumerator = models.GetEnumerator();
    78       var trainingPartitionEnumerator = trainingPartitions.GetEnumerator();
    79       var testPartitionEnumerator = testPartitions.GetEnumerator();
    80       while (modelEnumerator.MoveNext() & trainingPartitionEnumerator.MoveNext() & testPartitionEnumerator.MoveNext()) {
    81         this.trainingPartitions[modelEnumerator.Current] = (IntRange)trainingPartitionEnumerator.Current.Clone();
    82         this.testPartitions[modelEnumerator.Current] = (IntRange)testPartitionEnumerator.Current.Clone();
    83       }
    84       if (modelEnumerator.MoveNext() | trainingPartitionEnumerator.MoveNext() | testPartitionEnumerator.MoveNext()) {
    85         throw new ArgumentException();
    86       }
     76      AddModelsAndParitions(models, trainingPartitions, testPartitions);
    8777      RecalculateResults();
    8878    }
     
    160150      return estimatedValues.DefaultIfEmpty(double.NaN).Average();
    161151    }
     152
     153
     154    public void AddModelsAndParitions(IEnumerable<IRegressionSolution> solutions) {
     155      foreach (var solution in solutions) {
     156        var ensembleSolution = solution as RegressionEnsembleSolution;
     157        if (ensembleSolution != null) {
     158          var data = from m in ensembleSolution.Model.Models
     159                     let train = ensembleSolution.trainingPartitions[m]
     160                     let test = ensembleSolution.testPartitions[m]
     161                     select new { m, train, test };
     162
     163          foreach (var d in data) {
     164            Model.Add(d.m);
     165            trainingPartitions[d.m] = (IntRange)d.train.Clone();
     166            testPartitions[d.m] = (IntRange)d.test.Clone();
     167          }
     168        } else {
     169          Model.Add(solution.Model);
     170          trainingPartitions[solution.Model] = (IntRange)solution.ProblemData.TrainingPartition.Clone();
     171          testPartitions[solution.Model] = (IntRange)solution.ProblemData.TestPartition.Clone();
     172        }
     173      }
     174
     175      RecalculateResults();
     176    }
     177
     178    private void AddModelsAndParitions(IEnumerable<IRegressionModel> models, IEnumerable<IntRange> trainingPartitions, IEnumerable<IntRange> testPartitions) {
     179      var modelEnumerator = models.GetEnumerator();
     180      var trainingPartitionEnumerator = trainingPartitions.GetEnumerator();
     181      var testPartitionEnumerator = testPartitions.GetEnumerator();
     182
     183      while (modelEnumerator.MoveNext() & trainingPartitionEnumerator.MoveNext() & testPartitionEnumerator.MoveNext()) {
     184        this.trainingPartitions[modelEnumerator.Current] = (IntRange)trainingPartitionEnumerator.Current.Clone();
     185        this.testPartitions[modelEnumerator.Current] = (IntRange)testPartitionEnumerator.Current.Clone();
     186      }
     187      if (modelEnumerator.MoveNext() | trainingPartitionEnumerator.MoveNext() | testPartitionEnumerator.MoveNext()) {
     188        throw new ArgumentException();
     189      }
     190    }
    162191  }
    163192}
  • trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/Classification/IClassificationEnsembleModel.cs

    r5809 r6520  
    2323namespace HeuristicLab.Problems.DataAnalysis {
    2424  public interface IClassificationEnsembleModel : IClassificationModel {
     25    void Add(IClassificationModel model);
    2526    IEnumerable<IClassificationModel> Models { get; }
    2627    IEnumerable<IEnumerable<double>> GetEstimatedClassValueVectors(Dataset dataset, IEnumerable<int> rows);
  • trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/Regression/IRegressionEnsembleModel.cs

    r5809 r6520  
    2323namespace HeuristicLab.Problems.DataAnalysis {
    2424  public interface IRegressionEnsembleModel : IRegressionModel {
     25    void Add(IRegressionModel model);
    2526    IEnumerable<IRegressionModel> Models { get; }
    2627    IEnumerable<IEnumerable<double>> GetEstimatedValueVectors(Dataset dataset, IEnumerable<int> rows);
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