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Ignore:
Timestamp:
07/09/15 13:07:30 (10 years ago)
Author:
abeham
Message:

#2208: merged trunk changes

Location:
branches/HeuristicLab.Problems.Orienteering
Files:
46 edited
1 copied

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  • branches/HeuristicLab.Problems.Orienteering

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleModel.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    6767    }
    6868
    69     public IEnumerable<IEnumerable<double>> GetEstimatedClassValueVectors(Dataset dataset, IEnumerable<int> rows) {
     69    public IEnumerable<IEnumerable<double>> GetEstimatedClassValueVectors(IDataset dataset, IEnumerable<int> rows) {
    7070      var estimatedValuesEnumerators = (from model in models
    7171                                        select model.GetEstimatedClassValues(dataset, rows).GetEnumerator())
    … …  
    8282    #region IClassificationModel Members
    8383
    84     public IEnumerable<double> GetEstimatedClassValues(Dataset dataset, IEnumerable<int> rows) {
     84    public IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
    8585      foreach (var estimatedValuesVector in GetEstimatedClassValueVectors(dataset, rows)) {
    8686        // return the class which is most often occuring

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleProblemData.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    7979      this.TestPartition.Start = classificationProblemData.TestPartition.Start;
    8080      this.TestPartition.End = classificationProblemData.TestPartition.End;
     81      this.PositiveClass = classificationProblemData.PositiveClass;
    8182    }
    8283

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleSolution.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    3535  [StorableClass]
    3636  [Item("Classification Ensemble Solution", "A classification solution that contains an ensemble of multiple classification models")]
    37   [Creatable("Data Analysis - Ensembles")]
     37  [Creatable(CreatableAttribute.Categories.DataAnalysisEnsembles, Priority = 110)]
    3838  public sealed class ClassificationEnsembleSolution : ClassificationSolutionBase, IClassificationEnsembleSolution {
    3939    private readonly Dictionary<int, double> trainingEvaluationCache = new Dictionary<int, double>();
    … …  
    231231    }
    232232
    233     public IEnumerable<IEnumerable<double>> GetEstimatedClassValueVectors(Dataset dataset, IEnumerable<int> rows) {
     233    public IEnumerable<IEnumerable<double>> GetEstimatedClassValueVectors(IDataset dataset, IEnumerable<int> rows) {
    234234      if (!Model.Models.Any()) yield break;
    235235      var estimatedValuesEnumerators = (from model in Model.Models

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationProblem.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    2727  [StorableClass]
    2828  [Item("Classification Problem", "A general classification problem.")]
    29   [Creatable("Problems")]
    3029  public class ClassificationProblem : DataAnalysisProblem<IClassificationProblemData>, IClassificationProblem, IStorableContent {
    3130    public string Filename { get; set; }

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationProblemData.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    3636    protected const string ClassNamesParameterName = "ClassNames";
    3737    protected const string ClassificationPenaltiesParameterName = "ClassificationPenalties";
     38    protected const string PositiveClassParameterName = "PositiveClass";
    3839    protected const int MaximumNumberOfClasses = 100;
    3940    protected const int InspectedRowsToDetermineTargets = 2000;
    … …  
    213214      get { return (IFixedValueParameter<StringMatrix>)Parameters[ClassNamesParameterName]; }
    214215    }
     216    public IConstrainedValueParameter<StringValue> PositiveClassParameter {
     217      get { return (IConstrainedValueParameter<StringValue>)Parameters[PositiveClassParameterName]; }
     218    }
    215219    public IFixedValueParameter<DoubleMatrix> ClassificationPenaltiesParameter {
    216220      get { return (IFixedValueParameter<DoubleMatrix>)Parameters[ClassificationPenaltiesParameterName]; }
    … …  
    262266      get { return ClassNamesCache; }
    263267    }
     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    }
    264277    #endregion
    265278
    … …  
    270283    private void AfterDeserialization() {
    271284      RegisterParameterEvents();
     285      // BackwardsCompatibility3.4
     286      #region Backwards compatible code, remove with 3.5
     287      if (!Parameters.ContainsKey(PositiveClassParameterName)) {
     288        var validValues = new ItemSet<StringValue>(ClassNames.Select(s => new StringValue(s).AsReadOnly()));
     289        Parameters.Add(new ConstrainedValueParameter<StringValue>(PositiveClassParameterName,
     290          "The positive class which is used for quality measure calculation (e.g., specifity, sensitivity,...)", validValues, validValues.First()));
     291      }
     292      #endregion
     293
    272294    }
    273295
    … …  
    290312      TestPartition.End = classificationProblemData.TestPartition.End;
    291313
     314      PositiveClass = classificationProblemData.PositiveClass;
     315
    292316      for (int i = 0; i < classificationProblemData.ClassNames.Count(); i++)
    293317        ClassNamesParameter.Value[i, 0] = classificationProblemData.ClassNames.ElementAt(i);
    … …  
    300324    }
    301325
    302     public ClassificationProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable, IEnumerable<ITransformation> transformations = null)
     326    public ClassificationProblemData(IDataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable, IEnumerable<ITransformation> transformations = null)
    303327      : base(dataset, allowedInputVariables, transformations ?? Enumerable.Empty<ITransformation>()) {
    304328      var validTargetVariableValues = CheckVariablesForPossibleTargetVariables(dataset).Select(x => new StringValue(x).AsReadOnly()).ToList();
    … …  
    307331      Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>(validTargetVariableValues), target));
    308332      Parameters.Add(new FixedValueParameter<StringMatrix>(ClassNamesParameterName, ""));
     333      Parameters.Add(new ConstrainedValueParameter<StringValue>(PositiveClassParameterName, "The positive class which is used for quality measure calculation (e.g., specifity, sensitivity,...)"));
    309334      Parameters.Add(new FixedValueParameter<DoubleMatrix>(ClassificationPenaltiesParameterName, ""));
    310335
    … …  
    313338    }
    314339
    315     public static IEnumerable<string> CheckVariablesForPossibleTargetVariables(Dataset dataset) {
     340    public static IEnumerable<string> CheckVariablesForPossibleTargetVariables(IDataset dataset) {
    316341      int maxSamples = Math.Min(InspectedRowsToDetermineTargets, dataset.Rows);
    317342      var validTargetVariables = (from v in dataset.DoubleVariables
    … …  
    339364      ClassNamesParameter.Value.ColumnNames = new List<string>() { "ClassNames" };
    340365      ClassNamesParameter.Value.RowNames = ClassValues.Select(s => "ClassValue: " + s);
     366
     367      PositiveClassParameter.ValidValues.Clear();
     368      foreach (var className in ClassNames) {
     369        PositiveClassParameter.ValidValues.Add(new StringValue(className).AsReadOnly());
     370      }
    341371
    342372      ((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Rows = Classes;
    … …  
    411441    }
    412442    private void Parameter_ValueChanged(object sender, EventArgs e) {
     443      var oldPositiveClass = PositiveClass;
     444      var oldClassNames = classNamesCache;
     445      var index = oldClassNames.IndexOf(oldPositiveClass);
     446
    413447      classNamesCache = null;
    414448      ClassificationPenaltiesParameter.Value.RowNames = ClassNames.Select(name => "Actual " + name);
    415449      ClassificationPenaltiesParameter.Value.ColumnNames = ClassNames.Select(name => "Estimated " + name);
     450
     451      PositiveClassParameter.ValidValues.Clear();
     452      foreach (var className in ClassNames) {
     453        PositiveClassParameter.ValidValues.Add(new StringValue(className).AsReadOnly());
     454      }
     455      PositiveClassParameter.Value = PositiveClassParameter.ValidValues.ElementAt(index);
     456
    416457      OnChanged();
    417458    }
    … …  
    435476      if (!newClassValues.SequenceEqual(ClassValues)) {
    436477        errorMessage = errorMessage + string.Format("The class values differ in the provided classification problem data.");
    437         return false;
     478        returnValue = false;
     479      }
     480
     481      var newPositivieClassName = classificationProblemData.PositiveClass;
     482      if (newPositivieClassName != PositiveClass) {
     483        errorMessage = errorMessage + string.Format("The positive class differs in the provided classification problem data.");
     484        returnValue = false;
    438485      }
    439486
    … …  
    452499        ClassNamesParameter.Value[i, 0] = classificationProblemData.ClassNames.ElementAt(i);
    453500
     501      PositiveClass = classificationProblemData.PositiveClass;
     502
    454503      for (int i = 0; i < Classes; i++) {
    455504        for (int j = 0; j < Classes; j++) {

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationSolution.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    3030  /// </summary>
    3131  [StorableClass]
    32   public abstract class ClassificationSolution : ClassificationSolutionBase {
     32  public class ClassificationSolution : ClassificationSolutionBase {
    3333    protected readonly Dictionary<int, double> evaluationCache;
    3434
    … …  
    4646      evaluationCache = new Dictionary<int, double>(problemData.Dataset.Rows);
    4747      CalculateClassificationResults();
     48    }
     49
     50    public override IDeepCloneable Clone(Cloner cloner) {
     51      return new ClassificationSolution(this, cloner);
    4852    }
    4953

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationSolutionBase.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    3434    private const string TrainingNormalizedGiniCoefficientResultName = "Normalized Gini Coefficient (training)";
    3535    private const string TestNormalizedGiniCoefficientResultName = "Normalized Gini Coefficient (test)";
     36    private const string ClassificationPerformanceMeasuresResultName = "Classification Performance Measures";
    3637
    3738    public new IClassificationModel Model {
    … …  
    6263      protected set { ((DoubleValue)this[TestNormalizedGiniCoefficientResultName].Value).Value = value; }
    6364    }
     65    public ClassificationPerformanceMeasuresResultCollection ClassificationPerformanceMeasures {
     66      get { return ((ClassificationPerformanceMeasuresResultCollection)this[ClassificationPerformanceMeasuresResultName].Value); }
     67      protected set { (this[ClassificationPerformanceMeasuresResultName].Value) = value; }
     68    }
    6469    #endregion
    6570
    … …  
    7580      Add(new Result(TrainingNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the model on the training partition.", new DoubleValue()));
    7681      Add(new Result(TestNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the model on the test partition.", new DoubleValue()));
     82      Add(new Result(ClassificationPerformanceMeasuresResultName, @"Classification performance measures.\n
     83                              In a multiclass classification all misclassifications of the negative class will be treated as true negatives except on positive class estimations.",
     84                            new ClassificationPerformanceMeasuresResultCollection()));
    7785    }
    7886
    … …  
    8391      if (!this.ContainsKey(TestNormalizedGiniCoefficientResultName))
    8492        Add(new Result(TestNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the model on the test partition.", new DoubleValue()));
     93      if (!this.ContainsKey(ClassificationPerformanceMeasuresResultName)) {
     94        Add(new Result(ClassificationPerformanceMeasuresResultName, @"Classification performance measures.\n
     95                              In a multiclass classification all misclassifications of the negative class will be treated as true negatives except on positive class estimations.",
     96                              new ClassificationPerformanceMeasuresResultCollection()));
     97        CalculateClassificationResults();
     98      }
    8599    }
    86100
    … …  
    88102      double[] estimatedTrainingClassValues = EstimatedTrainingClassValues.ToArray(); // cache values
    89103      double[] originalTrainingClassValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToArray();
     104
    90105      double[] estimatedTestClassValues = EstimatedTestClassValues.ToArray(); // cache values
    91106      double[] originalTestClassValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices).ToArray();
     107
     108      var positiveClassName = ProblemData.PositiveClass;
     109      double positiveClassValue = ProblemData.GetClassValue(positiveClassName);
     110      ClassificationPerformanceMeasuresCalculator trainingPerformanceCalculator = new ClassificationPerformanceMeasuresCalculator(positiveClassName, positiveClassValue);
     111      ClassificationPerformanceMeasuresCalculator testPerformanceCalculator = new ClassificationPerformanceMeasuresCalculator(positiveClassName, positiveClassValue);
    92112
    93113      OnlineCalculatorError errorState;
    … …  
    107127      TrainingNormalizedGiniCoefficient = trainingNormalizedGini;
    108128      TestNormalizedGiniCoefficient = testNormalizedGini;
     129
     130      trainingPerformanceCalculator.Calculate(originalTrainingClassValues, estimatedTrainingClassValues);
     131      if (trainingPerformanceCalculator.ErrorState == OnlineCalculatorError.None)
     132        ClassificationPerformanceMeasures.SetTrainingResults(trainingPerformanceCalculator);
     133
     134      testPerformanceCalculator.Calculate(originalTestClassValues, estimatedTestClassValues);
     135      if (testPerformanceCalculator.ErrorState == OnlineCalculatorError.None)
     136        ClassificationPerformanceMeasures.SetTestResults(testPerformanceCalculator);
    109137    }
    110138

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationModel.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    111111
    112112
    113     public IEnumerable<double> GetEstimatedValues(Dataset dataset, IEnumerable<int> rows) {
     113    public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
    114114      return model.GetEstimatedValues(dataset, rows);
    115115    }
    116116
    117     public IEnumerable<double> GetEstimatedClassValues(Dataset dataset, IEnumerable<int> rows) {
     117    public IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
    118118      if (!Thresholds.Any() && !ClassValues.Any()) throw new ArgumentException("No thresholds and class values were set for the current classification model.");
    119119      foreach (var x in GetEstimatedValues(dataset, rows)) {

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolution.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolutionBase.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    105105      TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
    106106
    107       double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    108       TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
    109       double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    110       TestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
     107      double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
     108      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR*trainingR : double.NaN;
     109      double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
     110      TestRSquared = errorState == OnlineCalculatorError.None ? testR*testR : double.NaN;
    111111
    112112      double trainingNormalizedGini = NormalizedGiniCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/AccuracyMaximizationThresholdCalculator.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/NormalDistributionCutPointsThresholdCalculator.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/ThresholdCalculator.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Clustering/ClusteringProblem.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    2727  [StorableClass]
    2828  [Item("Clustering Problem", "A general clustering problem.")]
    29   [Creatable("Problems")]
    3029  public class ClusteringProblem : DataAnalysisProblem<IClusteringProblemData>, IClusteringProblem {
    3130    [StorableConstructor]

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Clustering/ClusteringProblemData.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Clustering/ClusteringSolution.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/DataAnalysisProblem.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/DataAnalysisProblemData.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    6363      get { return isEmpty; }
    6464    }
    65     public Dataset Dataset {
     65    public IDataset Dataset {
    6666      get { return DatasetParameter.Value; }
    6767    }
    … …  
    126126    }
    127127
    128     protected DataAnalysisProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, IEnumerable<ITransformation> transformations = null) {
     128    protected DataAnalysisProblemData(IDataset dataset, IEnumerable<string> allowedInputVariables, IEnumerable<ITransformation> transformations = null) {
    129129      if (dataset == null) throw new ArgumentNullException("The dataset must not be null.");
    130130      if (allowedInputVariables == null) throw new ArgumentNullException("The allowedInputVariables must not be null.");
    … …  
    144144      var transformationsList = new ItemList<ITransformation>(transformations ?? Enumerable.Empty<ITransformation>());
    145145
    146       Parameters.Add(new FixedValueParameter<Dataset>(DatasetParameterName, "", dataset));
     146      Parameters.Add(new FixedValueParameter<Dataset>(DatasetParameterName, "", (Dataset)dataset));
    147147      Parameters.Add(new FixedValueParameter<ReadOnlyCheckedItemList<StringValue>>(InputVariablesParameterName, "", inputVariables.AsReadOnly()));
    148148      Parameters.Add(new FixedValueParameter<IntRange>(TrainingPartitionParameterName, "", new IntRange(trainingPartitionStart, trainingPartitionEnd)));

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/DataAnalysisSolution.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    2121
    2222using System;
     23using System.Collections.Generic;
    2324using System.Drawing;
    2425using HeuristicLab.Common;
    … …  
    106107    }
    107108
     109    //mkommend avoid unnecessary event registration for result name changes
     110    protected override void RegisterItemEvents(IEnumerable<IResult> items) { }
     111    protected override void DeregisterItemEvents(IEnumerable<IResult> items) { }
     112
    108113    #region INamedItem Members
    109114    [Storable]

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/ConstantRegressionModel.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    5151    }
    5252
    53     public IEnumerable<double> GetEstimatedValues(Dataset dataset, IEnumerable<int> rows) {
     53    public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
    5454      return rows.Select(row => Constant);
    5555    }

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/ConstantRegressionSolution.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionEnsembleModel.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    8080    }
    8181
    82     public IEnumerable<IEnumerable<double>> GetEstimatedValueVectors(Dataset dataset, IEnumerable<int> rows) {
     82    public IEnumerable<IEnumerable<double>> GetEstimatedValueVectors(IDataset dataset, IEnumerable<int> rows) {
    8383      var estimatedValuesEnumerators = (from model in models
    8484                                        select model.GetEstimatedValues(dataset, rows).GetEnumerator())
    … …  
    9595    #region IRegressionModel Members
    9696
    97     public IEnumerable<double> GetEstimatedValues(Dataset dataset, IEnumerable<int> rows) {
     97    public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
    9898      foreach (var estimatedValuesVector in GetEstimatedValueVectors(dataset, rows)) {
    9999        yield return estimatedValuesVector.Average();

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionEnsembleProblemData.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionEnsembleSolution.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    3535  [StorableClass]
    3636  [Item("Regression Ensemble Solution", "A regression solution that contains an ensemble of multiple regression models")]
    37   [Creatable("Data Analysis - Ensembles")]
     37  [Creatable(CreatableAttribute.Categories.DataAnalysisEnsembles, Priority = 100)]
    3838  public sealed class RegressionEnsembleSolution : RegressionSolutionBase, IRegressionEnsembleSolution {
    3939    private readonly Dictionary<int, double> trainingEvaluationCache = new Dictionary<int, double>();
    … …  
    232232    }
    233233
    234     public IEnumerable<IEnumerable<double>> GetEstimatedValueVectors(Dataset dataset, IEnumerable<int> rows) {
     234    public IEnumerable<IEnumerable<double>> GetEstimatedValueVectors(IDataset dataset, IEnumerable<int> rows) {
    235235      if (!Model.Models.Any()) yield break;
    236236      var estimatedValuesEnumerators = (from model in Model.Models

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionProblem.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    2727  [StorableClass]
    2828  [Item("Regression Problem", "A general regression problem.")]
    29   [Creatable("Problems")]
    3029  public class RegressionProblem : DataAnalysisProblem<IRegressionProblemData>, IRegressionProblem, IStorableContent {
    3130    public string Filename { get; set; }

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionProblemData.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    137137    }
    138138
    139     public RegressionProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable, IEnumerable<ITransformation> transformations = null)
     139    public RegressionProblemData(IDataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable, IEnumerable<ITransformation> transformations = null)
    140140      : base(dataset, allowedInputVariables, transformations ?? Enumerable.Empty<ITransformation>()) {
    141141      var variables = InputVariables.Select(x => x.AsReadOnly()).ToList();

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolution.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    3030  /// </summary>
    3131  [StorableClass]
    32   public abstract class RegressionSolution : RegressionSolutionBase {
     32  public class RegressionSolution : RegressionSolutionBase {
    3333    protected readonly Dictionary<int, double> evaluationCache;
    3434
    … …  
    4242      evaluationCache = new Dictionary<int, double>(original.evaluationCache);
    4343    }
    44     protected RegressionSolution(IRegressionModel model, IRegressionProblemData problemData)
     44    public RegressionSolution(IRegressionModel model, IRegressionProblemData problemData)
    4545      : base(model, problemData) {
    4646      evaluationCache = new Dictionary<int, double>(problemData.Dataset.Rows);
    … …  
    4848    }
    4949
     50    public override IDeepCloneable Clone(Cloner cloner) {
     51      return new RegressionSolution(this, cloner);
     52    }
    5053
    5154    public override IEnumerable<double> EstimatedValues {
    … …  
    6063
    6164    public override IEnumerable<double> GetEstimatedValues(IEnumerable<int> rows) {
    62       var rowsToEvaluate = rows.Except(evaluationCache.Keys);
     65      var rowsToEvaluate = rows.Where(row => !evaluationCache.ContainsKey(row));
    6366      var rowsEnumerator = rowsToEvaluate.GetEnumerator();
    6467      var valuesEnumerator = Model.GetEstimatedValues(ProblemData.Dataset, rowsToEvaluate).GetEnumerator();

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolutionBase.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    2020#endregion
    2121
     22using System;
    2223using System.Collections.Generic;
    2324using HeuristicLab.Common;
    … …  
    3940    protected const string TrainingNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (training)";
    4041    protected const string TestNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (test)";
    41     protected const string TrainingMeanErrorResultName = "Mean error (training)";
    42     protected const string TestMeanErrorResultName = "Mean error (test)";
     42    protected const string TrainingRootMeanSquaredErrorResultName = "Root mean squared error (training)";
     43    protected const string TestRootMeanSquaredErrorResultName = "Root mean squared error (test)";
     44
     45    // BackwardsCompatibility3.3
     46    #region Backwards compatible code, remove with 3.5
     47    private const string TrainingMeanErrorResultName = "Mean error (training)";
     48    private const string TestMeanErrorResultName = "Mean error (test)";
     49    #endregion
     50
    4351
    4452    protected const string TrainingMeanSquaredErrorResultDescription = "Mean of squared errors of the model on the training partition";
    … …  
    5260    protected const string TrainingNormalizedMeanSquaredErrorResultDescription = "Normalized mean of squared errors of the model on the training partition";
    5361    protected const string TestNormalizedMeanSquaredErrorResultDescription = "Normalized mean of squared errors of the model on the test partition";
    54     protected const string TrainingMeanErrorResultDescription = "Mean of errors of the model on the training partition";
    55     protected const string TestMeanErrorResultDescription = "Mean of errors of the model on the test partition";
     62    protected const string TrainingRootMeanSquaredErrorResultDescription = "Root mean of squared errors of the model on the training partition";
     63    protected const string TestRootMeanSquaredErrorResultDescription = "Root mean of squared errors of the model on the test partition";
    5664
    5765    public new IRegressionModel Model {
    … …  
    111119      private set { ((DoubleValue)this[TestNormalizedMeanSquaredErrorResultName].Value).Value = value; }
    112120    }
    113     public double TrainingMeanError {
    114       get { return ((DoubleValue)this[TrainingMeanErrorResultName].Value).Value; }
    115       private set { ((DoubleValue)this[TrainingMeanErrorResultName].Value).Value = value; }
    116     }
    117     public double TestMeanError {
    118       get { return ((DoubleValue)this[TestMeanErrorResultName].Value).Value; }
    119       private set { ((DoubleValue)this[TestMeanErrorResultName].Value).Value = value; }
    120     }
     121    public double TrainingRootMeanSquaredError {
     122      get { return ((DoubleValue)this[TrainingRootMeanSquaredErrorResultName].Value).Value; }
     123      private set { ((DoubleValue)this[TrainingRootMeanSquaredErrorResultName].Value).Value = value; }
     124    }
     125    public double TestRootMeanSquaredError {
     126      get { return ((DoubleValue)this[TestRootMeanSquaredErrorResultName].Value).Value; }
     127      private set { ((DoubleValue)this[TestRootMeanSquaredErrorResultName].Value).Value = value; }
     128    }
     129
     130    // BackwardsCompatibility3.3
     131    #region Backwards compatible code, remove with 3.5
     132    private double TrainingMeanError {
     133      get {
     134        if (!ContainsKey(TrainingMeanErrorResultName)) return double.NaN;
     135        return ((DoubleValue)this[TrainingMeanErrorResultName].Value).Value;
     136      }
     137      set {
     138        if (ContainsKey(TrainingMeanErrorResultName))
     139          ((DoubleValue)this[TrainingMeanErrorResultName].Value).Value = value;
     140      }
     141    }
     142    private double TestMeanError {
     143      get {
     144        if (!ContainsKey(TestMeanErrorResultName)) return double.NaN;
     145        return ((DoubleValue)this[TestMeanErrorResultName].Value).Value;
     146      }
     147      set {
     148        if (ContainsKey(TestMeanErrorResultName))
     149          ((DoubleValue)this[TestMeanErrorResultName].Value).Value = value;
     150      }
     151    }
     152    #endregion
    121153    #endregion
    122154
    … …  
    138170      Add(new Result(TrainingNormalizedMeanSquaredErrorResultName, TrainingNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
    139171      Add(new Result(TestNormalizedMeanSquaredErrorResultName, TestNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
    140       Add(new Result(TrainingMeanErrorResultName, TrainingMeanErrorResultDescription, new DoubleValue()));
    141       Add(new Result(TestMeanErrorResultName, TestMeanErrorResultDescription, new DoubleValue()));
     172      Add(new Result(TrainingRootMeanSquaredErrorResultName, TrainingRootMeanSquaredErrorResultDescription, new DoubleValue()));
     173      Add(new Result(TestRootMeanSquaredErrorResultName, TestRootMeanSquaredErrorResultDescription, new DoubleValue()));
    142174    }
    143175
    … …  
    145177    private void AfterDeserialization() {
    146178      // BackwardsCompatibility3.4
    147 
    148179      #region Backwards compatible code, remove with 3.5
    149 
    150180      if (!ContainsKey(TrainingMeanAbsoluteErrorResultName)) {
    151181        OnlineCalculatorError errorState;
    … …  
    162192      }
    163193
    164       if (!ContainsKey(TrainingMeanErrorResultName)) {
    165         OnlineCalculatorError errorState;
    166         Add(new Result(TrainingMeanErrorResultName, "Mean of errors of the model on the training partition", new DoubleValue()));
    167         double trainingME = OnlineMeanErrorCalculator.Calculate(EstimatedTrainingValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices), out errorState);
    168         TrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
    169       }
    170       if (!ContainsKey(TestMeanErrorResultName)) {
    171         OnlineCalculatorError errorState;
    172         Add(new Result(TestMeanErrorResultName, "Mean of errors of the model on the test partition", new DoubleValue()));
    173         double testME = OnlineMeanErrorCalculator.Calculate(EstimatedTestValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices), out errorState);
    174         TestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
     194      if (!ContainsKey(TrainingRootMeanSquaredErrorResultName)) {
     195        OnlineCalculatorError errorState;
     196        Add(new Result(TrainingRootMeanSquaredErrorResultName, TrainingRootMeanSquaredErrorResultDescription, new DoubleValue()));
     197        double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(EstimatedTrainingValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices), out errorState);
     198        TrainingRootMeanSquaredError = errorState == OnlineCalculatorError.None ? Math.Sqrt(trainingMSE) : double.NaN;
     199      }
     200
     201      if (!ContainsKey(TestRootMeanSquaredErrorResultName)) {
     202        OnlineCalculatorError errorState;
     203        Add(new Result(TestRootMeanSquaredErrorResultName, TestRootMeanSquaredErrorResultDescription, new DoubleValue()));
     204        double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(EstimatedTestValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices), out errorState);
     205        TestRootMeanSquaredError = errorState == OnlineCalculatorError.None ? Math.Sqrt(testMSE) : double.NaN;
    175206      }
    176207      #endregion
    … …  
    198229      TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
    199230
    200       double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    201       TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
    202       double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    203       TestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
     231      double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
     232      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR*trainingR : double.NaN;
     233      double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
     234      TestRSquared = errorState == OnlineCalculatorError.None ? testR*testR : double.NaN;
    204235
    205236      double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    … …  
    213244      TestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? testNMSE : double.NaN;
    214245
    215       double trainingME = OnlineMeanErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    216       TrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
    217       double testME = OnlineMeanErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    218       TestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
     246      TrainingRootMeanSquaredError = Math.Sqrt(TrainingMeanSquaredError);
     247      TestRootMeanSquaredError = Math.Sqrt(TestMeanSquaredError);
     248
     249      // BackwardsCompatibility3.3
     250      #region Backwards compatible code, remove with 3.5
     251      if (ContainsKey(TrainingMeanErrorResultName)) {
     252        double trainingME = OnlineMeanErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
     253        TrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
     254      }
     255      if (ContainsKey(TestMeanErrorResultName)) {
     256        double testME = OnlineMeanErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
     257        TestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
     258      }
     259      #endregion
    219260    }
    220261  }

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/Models/ConstantTimeSeriesPrognosisModel.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    3939    public ConstantTimeSeriesPrognosisModel(double constant) : base(constant) { }
    4040
    41     public IEnumerable<IEnumerable<double>> GetPrognosedValues(Dataset dataset, IEnumerable<int> rows, IEnumerable<int> horizons) {
     41    public IEnumerable<IEnumerable<double>> GetPrognosedValues(IDataset dataset, IEnumerable<int> rows, IEnumerable<int> horizons) {
    4242      return horizons.Select(horizon => Enumerable.Repeat(Constant, horizon));
    4343    }

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/Models/TimeSeriesPrognosisAutoRegressiveModel.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    5858    }
    5959
    60     public IEnumerable<IEnumerable<double>> GetPrognosedValues(Dataset dataset, IEnumerable<int> rows, IEnumerable<int> horizons) {
     60    public IEnumerable<IEnumerable<double>> GetPrognosedValues(IDataset dataset, IEnumerable<int> rows, IEnumerable<int> horizons) {
    6161      var rowsEnumerator = rows.GetEnumerator();
    6262      var horizonsEnumerator = horizons.GetEnumerator();
    … …  
    9191    }
    9292
    93     public IEnumerable<double> GetEstimatedValues(Dataset dataset, IEnumerable<int> rows) {
     93    public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
    9494      var targetVariables = dataset.GetReadOnlyDoubleValues(TargetVariable);
    9595      foreach (int row in rows) {

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisProblem.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    2727  [StorableClass]
    2828  [Item("Time-series Prognosis Problem", "A general time-series prognosis problem.")]
    29   [Creatable("Problems")]
    3029  public class TimeSeriesPrognosisProblem : DataAnalysisProblem<ITimeSeriesPrognosisProblemData>, ITimeSeriesPrognosisProblem {
    3130    [StorableConstructor]

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisProblemData.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    15821582      TrainingPartition.Start = 50;
    15831583    }
    1584     public TimeSeriesPrognosisProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable, IEnumerable<ITransformation> transformations = null)
     1584    public TimeSeriesPrognosisProblemData(IDataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable, IEnumerable<ITransformation> transformations = null)
    15851585      : base(dataset, allowedInputVariables, targetVariable, transformations ?? Enumerable.Empty<ITransformation>()) {
    15861586      Parameters.Add(new FixedValueParameter<IntValue>(TrainingHorizonParameterName, "Specifies the horizon (how far the prognosis reaches in the future) for each training sample.", new IntValue(1)));

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisResults.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    394394      double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    395395      PrognosisTrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
    396       double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    397       PrognosisTrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
     396      double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
     397      PrognosisTrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR*trainingR : double.NaN;
    398398      double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    399399      PrognosisTrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
    … …  
    430430      double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    431431      PrognosisTestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
    432       double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    433       PrognosisTestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
     432      double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
     433      PrognosisTestRSquared = errorState == OnlineCalculatorError.None ? testR*testR : double.NaN;
    434434      double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    435435      PrognosisTestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolution.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Transformations/CopyColumnTransformation.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    2929
    3030namespace HeuristicLab.Problems.DataAnalysis {
     31  [StorableClass]
    3132  [Item("CopyColumnTransformation", "Represents a transformation which represents a copied Column.")]
    3233  public class CopyColumnTransformation : Transformation {

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Transformations/ExponentialTransformation.cs

    r11114 r12694  
    11using System;
    22using System.Collections.Generic;
     3using System.Linq;
    34using HeuristicLab.Common;
    45using HeuristicLab.Core;
    … …  
    89
    910namespace HeuristicLab.Problems.DataAnalysis {
     11  [StorableClass]
    1012  [Item("Exponential Transformation", "f(x) = b ^ x | Represents a exponential transformation.")]
    1113  public class ExponentialTransformation : Transformation<double> {
    … …  
    4446
    4547    public override IEnumerable<double> Apply(IEnumerable<double> data) {
    46       foreach (double d in data) {
    47         yield return Math.Pow(Base, d);
    48       }
     48      return data.Select(d => Math.Pow(Base, d));
    4949    }
    5050

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Transformations/LinearTransformation.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    3131
    3232namespace HeuristicLab.Problems.DataAnalysis {
     33  [StorableClass]
    3334  [Item("Linear Transformation", "f(x) = k * x + d | Represents a linear transformation with multiplication and addition.")]
    3435  public class LinearTransformation : Transformation<double> {
    … …  
    8081
    8182    public override IEnumerable<double> Apply(IEnumerable<double> data) {
    82       return data.Select(e => e * Multiplier + Addend);
     83      var m = Multiplier;
     84      var a = Addend;
     85      return data.Select(e => e * m + a);
    8386    }
    8487
    8588    public override bool Check(IEnumerable<double> data, out string errorMsg) {
    8689      errorMsg = null;
    87       if (Multiplier == 0.0) {
     90      if (Multiplier.IsAlmost(0.0)) {
    8891        errorMsg = String.Format("Multiplicand is 0, all {0} entries will be set to {1}. Inverse apply will not be possible (division by 0).", data.Count(), Addend);
    8992        return false;

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Transformations/LogarithmicTransformation.cs

    r11114 r12694  
    99
    1010namespace HeuristicLab.Problems.DataAnalysis {
     11  [StorableClass]
    1112  [Item("Logarithmic Transformation", "f(x) = log(x, b) | Represents a logarithmic transformation.")]
    1213  public class LogarithmicTransformation : Transformation<double> {
    … …  
    4344
    4445    public override IEnumerable<double> Apply(IEnumerable<double> data) {
    45       foreach (double i in data) {
    46         if (i > 0.0)
    47           yield return Math.Log(i, Base);
    48         else
    49           yield return i;
    50       }
     46      var b = Base;
     47      return data.Select(d => d > 0.0 ? Math.Log(d, b) : d);
    5148    }
    5249

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Transformations/PowerTransformation.cs

    r11114 r12694  
    11using System;
    22using System.Collections.Generic;
     3using System.Linq;
    34using HeuristicLab.Common;
    45using HeuristicLab.Core;
    … …  
    89
    910namespace HeuristicLab.Problems.DataAnalysis {
     11  [StorableClass]
    1012  [Item("Power Transformation", "f(x) = x ^ exp | Represents a power transformation.")]
    1113  public class PowerTransformation : Transformation<double> {
    … …  
    4244
    4345    public override IEnumerable<double> Apply(IEnumerable<double> data) {
    44       foreach (double i in data) {
    45         yield return Math.Pow(i, Exponent);
    46       }
     46      return data.Select(i => Math.Pow(i, Exponent));
    4747    }
    4848

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Transformations/ReciprocalTransformation.cs

    r11114 r12694  
    77using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    88namespace HeuristicLab.Problems.DataAnalysis {
     9  [StorableClass]
    910  [Item("Reciprocal Transformation", "f(x) = 1 / x | Represents a reciprocal transformation.")]
    1011  public class ReciprocalTransformation : Transformation<double> {
    … …  
    1617    #endregion
    1718
    18     //TODO: is a special case of Linear
    1919    [StorableConstructor]
    2020    protected ReciprocalTransformation(bool deserializing) : base(deserializing) { }
    … …  
    3131
    3232    public override IEnumerable<double> Apply(IEnumerable<double> data) {
    33       foreach (double i in data) {
    34         if (i > 0.0)
    35           yield return 1.0 / i;
    36         else
    37           yield return i;
    38       }
     33      return data.Select(d => d > 0 ? 1.0 / d : d);
    3934    }
    4035

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Transformations/ShiftStandardDistributionTransformation.cs

    r11114 r12694  
    88
    99namespace HeuristicLab.Problems.DataAnalysis {
     10  [StorableClass]
    1011  [Item("Shift Standard Distribution Transformation", "f(x) = ((x - m_org) / s_org ) * s_tar + m_tar | Represents Transformation to unit standard deviation and additional linear transformation to a target Mean and Standard deviation")]
    1112  public class ShiftStandardDistributionTransformation : Transformation<double> {
    … …  
    6970    }
    7071
    71     // http://en.wikipedia.org/wiki/Standard_deviation
    72     // http://www.statistics4u.info/fundstat_germ/ee_ztransform.html
    73     // https://www.uni-due.de/~bm0061/vorl12.pdf p5
    7472    public override IEnumerable<double> Apply(IEnumerable<double> data) {
    7573      ConfigureParameters(data);
    76       if (OriginalStandardDeviation == 0.0) {
    77         foreach (var e in data) {
    78           yield return e;
    79         }
    80         yield break;
     74      if (OriginalStandardDeviation.IsAlmost(0.0)) {
     75        return data;
    8176      }
    82 
    83       foreach (var e in data) {
    84         double unitNormalDistributedValue = (e - OriginalMean) / OriginalStandardDeviation;
    85         yield return unitNormalDistributedValue * StandardDeviation + Mean;
    86       }
     77      var old_m = OriginalMean;
     78      var old_s = OriginalStandardDeviation;
     79      var m = Mean;
     80      var s = StandardDeviation;
     81      return data
     82        .Select(d => (d - old_m) / old_s) // standardized
     83        .Select(d => d * s + m);
    8784    }
    8885
    … …  
    9087      ConfigureParameters(data);
    9188      errorMsg = "";
    92       if (OriginalStandardDeviation == 0.0) {
     89      if (OriginalStandardDeviation.IsAlmost(0.0)) {
    9390        errorMsg = "Standard deviaton for the original data is 0.0, Transformation cannot be applied onto these values.";
    9491        return false;

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Transformations/ShiftToRangeTransformation.cs

    r11114 r12694  
    88
    99namespace HeuristicLab.Problems.DataAnalysis {
     10  [StorableClass]
    1011  [Item("Shift to Range Transformation", "f(x) = k * x + d, start <= f(x) <= end | Represents a linear Transformation using Parameters defining a target range")]
    1112  public class ShiftToRangeTransformation : LinearTransformation {

  • branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Transformations/Transformation.cs

    r11185 r12694  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    3131
    3232  [Item("Transformation", "Represents the base class for a transformation.")]
    33   [StorableClass]
    3433  public abstract class Transformation : ParameterizedNamedItem, ITransformation {
    3534    protected const string ColumnParameterName = "Column";
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