Changeset 12969 for branches/gteufl/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification

Timestamp:

09/25/15 14:39:59 (9 years ago)

Author:

gkronber

Message:

#2478 merged all changes from trunk to branch before trunk-reintegration

Location:

branches/gteufl

Files:

• . (modified) (3 props)
• HeuristicLab.Problems.DataAnalysis (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleModel.cs (modified) (3 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleProblemData.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleSolution.cs (modified) (5 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationPerformanceMeasures.cs (copied) (copied from trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationPerformanceMeasures.cs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationProblem.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationProblemData.cs (modified) (12 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationSolution.cs (modified) (3 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationSolutionBase.cs (modified) (7 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationModel.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolution.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolutionBase.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/AccuracyMaximizationThresholdCalculator.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/NormalDistributionCutPointsThresholdCalculator.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/ThresholdCalculator.cs (modified) (1 diff)

branches/gteufl

@@ -8 +8 @@
 FxCopResults.txt
 Google.ProtocolBuffers-0.9.1.dll
+Google.ProtocolBuffers-2.4.1.473.dll
 HeuristicLab 3.3.5.1.ReSharper.user
 HeuristicLab 3.3.6.0.ReSharper.user
 HeuristicLab.4.5.resharper.user
 HeuristicLab.ExtLibs.6.0.ReSharper.user
+HeuristicLab.Scripting.Development
 HeuristicLab.resharper.user
 ProtoGen.exe
@@ -17 +19 @@
 _ReSharper.HeuristicLab
 _ReSharper.HeuristicLab 3.3
+_ReSharper.HeuristicLab 3.3 Tests
 _ReSharper.HeuristicLab.ExtLibs
 bin
 protoc.exe
-_ReSharper.HeuristicLab 3.3 Tests
-Google.ProtocolBuffers-2.4.1.473.dll
+obj

@@ -8 +8 @@
 FxCopResults.txt
 Google.ProtocolBuffers-0.9.1.dll
+Google.ProtocolBuffers-2.4.1.473.dll
 HeuristicLab 3.3.5.1.ReSharper.user
 HeuristicLab 3.3.6.0.ReSharper.user
 HeuristicLab.4.5.resharper.user
 HeuristicLab.ExtLibs.6.0.ReSharper.user
+HeuristicLab.Scripting.Development
 HeuristicLab.resharper.user
 ProtoGen.exe
@@ -17 +19 @@
 _ReSharper.HeuristicLab
 _ReSharper.HeuristicLab 3.3
+_ReSharper.HeuristicLab 3.3 Tests
 _ReSharper.HeuristicLab.ExtLibs
 bin
 protoc.exe
-_ReSharper.HeuristicLab 3.3 Tests
-Google.ProtocolBuffers-2.4.1.473.dll
+obj

branches/gteufl/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleModel.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -67 +67 @@
     }
 
-    public IEnumerable<IEnumerable<double>> GetEstimatedClassValueVectors(Dataset dataset, IEnumerable<int> rows) {
+    public IEnumerable<IEnumerable<double>> GetEstimatedClassValueVectors(IDataset dataset, IEnumerable<int> rows) {
       var estimatedValuesEnumerators = (from model in models
                                         select model.GetEstimatedClassValues(dataset, rows).GetEnumerator())
@@ -82 +82 @@
     #region IClassificationModel Members
 
-    public IEnumerable<double> GetEstimatedClassValues(Dataset dataset, IEnumerable<int> rows) {
+    public IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
       foreach (var estimatedValuesVector in GetEstimatedClassValueVectors(dataset, rows)) {
         // return the class which is most often occuring

branches/gteufl/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleProblemData.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -79 +79 @@
       this.TestPartition.Start = classificationProblemData.TestPartition.Start;
       this.TestPartition.End = classificationProblemData.TestPartition.End;
+      this.PositiveClass = classificationProblemData.PositiveClass;
     }
 

branches/gteufl/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleSolution.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -35 +35 @@
   [StorableClass]
   [Item("Classification Ensemble Solution", "A classification solution that contains an ensemble of multiple classification models")]
-  [Creatable("Data Analysis - Ensembles")]
+  [Creatable(CreatableAttribute.Categories.DataAnalysisEnsembles, Priority = 110)]
   public sealed class ClassificationEnsembleSolution : ClassificationSolutionBase, IClassificationEnsembleSolution {
     private readonly Dictionary<int, double> trainingEvaluationCache = new Dictionary<int, double>();
@@ -49 +49 @@
     }
 
+    [Storable]
     private readonly ItemCollection<IClassificationSolution> classificationSolutions;
     public IItemCollection<IClassificationSolution> ClassificationSolutions {
@@ -66 +67 @@
     [StorableHook(HookType.AfterDeserialization)]
     private void AfterDeserialization() {
-      foreach (var model in Model.Models) {
-        IClassificationProblemData problemData = (IClassificationProblemData)ProblemData.Clone();
-        problemData.TrainingPartition.Start = trainingPartitions[model].Start;
-        problemData.TrainingPartition.End = trainingPartitions[model].End;
-        problemData.TestPartition.Start = testPartitions[model].Start;
-        problemData.TestPartition.End = testPartitions[model].End;
-
-        classificationSolutions.Add(model.CreateClassificationSolution(problemData));
+      if (!classificationSolutions.Any()) {
+        foreach (var model in Model.Models) {
+          IClassificationProblemData problemData = (IClassificationProblemData)ProblemData.Clone();
+          problemData.TrainingPartition.Start = trainingPartitions[model].Start;
+          problemData.TrainingPartition.End = trainingPartitions[model].End;
+          problemData.TestPartition.Start = testPartitions[model].Start;
+          problemData.TestPartition.End = testPartitions[model].End;
+
+          classificationSolutions.Add(model.CreateClassificationSolution(problemData));
+        }
       }
       RegisterClassificationSolutionsEventHandler();
@@ -231 +234 @@
     }
 
-    public IEnumerable<IEnumerable<double>> GetEstimatedClassValueVectors(Dataset dataset, IEnumerable<int> rows) {
+    public IEnumerable<IEnumerable<double>> GetEstimatedClassValueVectors(IDataset dataset, IEnumerable<int> rows) {
       if (!Model.Models.Any()) yield break;
       var estimatedValuesEnumerators = (from model in Model.Models

branches/gteufl/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationProblem.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -27 +27 @@
   [StorableClass]
   [Item("Classification Problem", "A general classification problem.")]
-  [Creatable("Problems")]
   public class ClassificationProblem : DataAnalysisProblem<IClassificationProblemData>, IClassificationProblem, IStorableContent {
     public string Filename { get; set; }

branches/gteufl/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationProblemData.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -36 +36 @@
     protected const string ClassNamesParameterName = "ClassNames";
     protected const string ClassificationPenaltiesParameterName = "ClassificationPenalties";
+    protected const string PositiveClassParameterName = "PositiveClass";
     protected const int MaximumNumberOfClasses = 100;
     protected const int InspectedRowsToDetermineTargets = 2000;
@@ -213 +214 @@
       get { return (IFixedValueParameter<StringMatrix>)Parameters[ClassNamesParameterName]; }
     }
+    public IConstrainedValueParameter<StringValue> PositiveClassParameter {
+      get { return (IConstrainedValueParameter<StringValue>)Parameters[PositiveClassParameterName]; }
+    }
     public IFixedValueParameter<DoubleMatrix> ClassificationPenaltiesParameter {
       get { return (IFixedValueParameter<DoubleMatrix>)Parameters[ClassificationPenaltiesParameterName]; }
@@ -221 +225 @@
     public string TargetVariable {
       get { return TargetVariableParameter.Value.Value; }
+      set {
+        if (value == null) throw new ArgumentNullException("targetVariable", "The provided value for the targetVariable is null.");
+        if (value == TargetVariable) return;
+
+
+        var matchingParameterValue = TargetVariableParameter.ValidValues.FirstOrDefault(v => v.Value == value);
+        if (matchingParameterValue == null) throw new ArgumentException("The provided value is not valid as the targetVariable.", "targetVariable");
+        TargetVariableParameter.Value = matchingParameterValue;
+      }
     }
 
@@ -253 +266 @@
       get { return ClassNamesCache; }
     }
+
+    public string PositiveClass {
+      get { return PositiveClassParameter.Value.Value; }
+      set {
+        var matchingValue = PositiveClassParameter.ValidValues.SingleOrDefault(x => x.Value == value);
+        if (matchingValue == null) throw new ArgumentException(string.Format("{0} cannot be set as positive class.", value));
+        PositiveClassParameter.Value = matchingValue;
+      }
+    }
     #endregion
 
@@ -261 +283 @@
     private void AfterDeserialization() {
       RegisterParameterEvents();
+      // BackwardsCompatibility3.4
+      #region Backwards compatible code, remove with 3.5
+      if (!Parameters.ContainsKey(PositiveClassParameterName)) {
+        var validValues = new ItemSet<StringValue>(ClassNames.Select(s => new StringValue(s).AsReadOnly()));
+        Parameters.Add(new ConstrainedValueParameter<StringValue>(PositiveClassParameterName,
+          "The positive class which is used for quality measure calculation (e.g., specifity, sensitivity,...)", validValues, validValues.First()));
+      }
+      #endregion
+
     }
 
@@ -281 +312 @@
       TestPartition.End = classificationProblemData.TestPartition.End;
 
+      PositiveClass = classificationProblemData.PositiveClass;
+
       for (int i = 0; i < classificationProblemData.ClassNames.Count(); i++)
         ClassNamesParameter.Value[i, 0] = classificationProblemData.ClassNames.ElementAt(i);
@@ -291 +324 @@
     }
 
-    public ClassificationProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable)
-      : base(dataset, allowedInputVariables) {
+    public ClassificationProblemData(IDataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable, IEnumerable<ITransformation> transformations = null)
+      : base(dataset, allowedInputVariables, transformations ?? Enumerable.Empty<ITransformation>()) {
       var validTargetVariableValues = CheckVariablesForPossibleTargetVariables(dataset).Select(x => new StringValue(x).AsReadOnly()).ToList();
       var target = validTargetVariableValues.Where(x => x.Value == targetVariable).DefaultIfEmpty(validTargetVariableValues.First()).First();
@@ -298 +331 @@
       Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>(validTargetVariableValues), target));
       Parameters.Add(new FixedValueParameter<StringMatrix>(ClassNamesParameterName, ""));
+      Parameters.Add(new ConstrainedValueParameter<StringValue>(PositiveClassParameterName, "The positive class which is used for quality measure calculation (e.g., specifity, sensitivity,...)"));
       Parameters.Add(new FixedValueParameter<DoubleMatrix>(ClassificationPenaltiesParameterName, ""));
 
@@ -304 +338 @@
     }
 
-    public static IEnumerable<string> CheckVariablesForPossibleTargetVariables(Dataset dataset) {
+    public static IEnumerable<string> CheckVariablesForPossibleTargetVariables(IDataset dataset) {
       int maxSamples = Math.Min(InspectedRowsToDetermineTargets, dataset.Rows);
       var validTargetVariables = (from v in dataset.DoubleVariables
@@ -330 +364 @@
       ClassNamesParameter.Value.ColumnNames = new List<string>() { "ClassNames" };
       ClassNamesParameter.Value.RowNames = ClassValues.Select(s => "ClassValue: " + s);
+
+      PositiveClassParameter.ValidValues.Clear();
+      foreach (var className in ClassNames) {
+        PositiveClassParameter.ValidValues.Add(new StringValue(className).AsReadOnly());
+      }
 
       ((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Rows = Classes;
@@ -402 +441 @@
     }
     private void Parameter_ValueChanged(object sender, EventArgs e) {
+      var oldPositiveClass = PositiveClass;
+      var oldClassNames = classNamesCache;
+      var index = oldClassNames.IndexOf(oldPositiveClass);
+
       classNamesCache = null;
       ClassificationPenaltiesParameter.Value.RowNames = ClassNames.Select(name => "Actual " + name);
       ClassificationPenaltiesParameter.Value.ColumnNames = ClassNames.Select(name => "Estimated " + name);
+
+      PositiveClassParameter.ValidValues.Clear();
+      foreach (var className in ClassNames) {
+        PositiveClassParameter.ValidValues.Add(new StringValue(className).AsReadOnly());
+      }
+      PositiveClassParameter.Value = PositiveClassParameter.ValidValues.ElementAt(index);
+
       OnChanged();
     }
     #endregion
+
+    protected override bool IsProblemDataCompatible(IDataAnalysisProblemData problemData, out string errorMessage) {
+      if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
+      IClassificationProblemData classificationProblemData = problemData as IClassificationProblemData;
+      if (classificationProblemData == null)
+        throw new ArgumentException("The problem data is no classification problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
+
+      var returnValue = base.IsProblemDataCompatible(classificationProblemData, out errorMessage);
+      //check targetVariable
+      if (classificationProblemData.InputVariables.All(var => var.Value != TargetVariable)) {
+        errorMessage = string.Format("The target variable {0} is not present in the new problem data.", TargetVariable)
+                       + Environment.NewLine + errorMessage;
+        return false;
+      }
+
+      var newClassValues = classificationProblemData.Dataset.GetDoubleValues(TargetVariable).Distinct().OrderBy(x => x);
+      if (!newClassValues.SequenceEqual(ClassValues)) {
+        errorMessage = errorMessage + string.Format("The class values differ in the provided classification problem data.");
+        returnValue = false;
+      }
+
+      var newPositivieClassName = classificationProblemData.PositiveClass;
+      if (newPositivieClassName != PositiveClass) {
+        errorMessage = errorMessage + string.Format("The positive class differs in the provided classification problem data.");
+        returnValue = false;
+      }
+
+      return returnValue;
+    }
+
+    public override void AdjustProblemDataProperties(IDataAnalysisProblemData problemData) {
+      if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
+      ClassificationProblemData classificationProblemData = problemData as ClassificationProblemData;
+      if (classificationProblemData == null)
+        throw new ArgumentException("The problem data is not a classification problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
+
+      base.AdjustProblemDataProperties(problemData);
+      TargetVariable = classificationProblemData.TargetVariable;
+      for (int i = 0; i < classificationProblemData.ClassNames.Count(); i++)
+        ClassNamesParameter.Value[i, 0] = classificationProblemData.ClassNames.ElementAt(i);
+
+      PositiveClass = classificationProblemData.PositiveClass;
+
+      for (int i = 0; i < Classes; i++) {
+        for (int j = 0; j < Classes; j++) {
+          ClassificationPenaltiesParameter.Value[i, j] = classificationProblemData.GetClassificationPenalty(ClassValuesCache[i], ClassValuesCache[j]);
+        }
+      }
+    }
   }
 }

branches/gteufl/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationSolution.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -30 +30 @@
   /// </summary>
   [StorableClass]
-  public abstract class ClassificationSolution : ClassificationSolutionBase {
+  public class ClassificationSolution : ClassificationSolutionBase {
     protected readonly Dictionary<int, double> evaluationCache;
 
@@ -46 +46 @@
       evaluationCache = new Dictionary<int, double>(problemData.Dataset.Rows);
       CalculateClassificationResults();
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new ClassificationSolution(this, cloner);
     }
 

branches/gteufl/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationSolutionBase.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -34 +34 @@
     private const string TrainingNormalizedGiniCoefficientResultName = "Normalized Gini Coefficient (training)";
     private const string TestNormalizedGiniCoefficientResultName = "Normalized Gini Coefficient (test)";
+    private const string ClassificationPerformanceMeasuresResultName = "Classification Performance Measures";
 
     public new IClassificationModel Model {
@@ -62 +63 @@
       protected set { ((DoubleValue)this[TestNormalizedGiniCoefficientResultName].Value).Value = value; }
     }
+    public ClassificationPerformanceMeasuresResultCollection ClassificationPerformanceMeasures {
+      get { return ((ClassificationPerformanceMeasuresResultCollection)this[ClassificationPerformanceMeasuresResultName].Value); }
+      protected set { (this[ClassificationPerformanceMeasuresResultName].Value) = value; }
+    }
     #endregion
 
@@ -75 +80 @@
       Add(new Result(TrainingNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the model on the training partition.", new DoubleValue()));
       Add(new Result(TestNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the model on the test partition.", new DoubleValue()));
+      Add(new Result(ClassificationPerformanceMeasuresResultName, @"Classification performance measures.\n
+                              In a multiclass classification all misclassifications of the negative class will be treated as true negatives except on positive class estimations.",
+                            new ClassificationPerformanceMeasuresResultCollection()));
     }
 
@@ -83 +91 @@
       if (!this.ContainsKey(TestNormalizedGiniCoefficientResultName))
         Add(new Result(TestNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the model on the test partition.", new DoubleValue()));
+      if (!this.ContainsKey(ClassificationPerformanceMeasuresResultName)) {
+        Add(new Result(ClassificationPerformanceMeasuresResultName, @"Classification performance measures.\n
+                              In a multiclass classification all misclassifications of the negative class will be treated as true negatives except on positive class estimations.",
+                              new ClassificationPerformanceMeasuresResultCollection()));
+        CalculateClassificationResults();
+      }
     }
 
@@ -88 +102 @@
       double[] estimatedTrainingClassValues = EstimatedTrainingClassValues.ToArray(); // cache values
       double[] originalTrainingClassValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToArray();
+
       double[] estimatedTestClassValues = EstimatedTestClassValues.ToArray(); // cache values
       double[] originalTestClassValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices).ToArray();
+
+      var positiveClassName = ProblemData.PositiveClass;
+      double positiveClassValue = ProblemData.GetClassValue(positiveClassName);
+      ClassificationPerformanceMeasuresCalculator trainingPerformanceCalculator = new ClassificationPerformanceMeasuresCalculator(positiveClassName, positiveClassValue);
+      ClassificationPerformanceMeasuresCalculator testPerformanceCalculator = new ClassificationPerformanceMeasuresCalculator(positiveClassName, positiveClassValue);
 
       OnlineCalculatorError errorState;
@@ -107 +127 @@
       TrainingNormalizedGiniCoefficient = trainingNormalizedGini;
       TestNormalizedGiniCoefficient = testNormalizedGini;
+
+      trainingPerformanceCalculator.Calculate(originalTrainingClassValues, estimatedTrainingClassValues);
+      if (trainingPerformanceCalculator.ErrorState == OnlineCalculatorError.None)
+        ClassificationPerformanceMeasures.SetTrainingResults(trainingPerformanceCalculator);
+
+      testPerformanceCalculator.Calculate(originalTestClassValues, estimatedTestClassValues);
+      if (testPerformanceCalculator.ErrorState == OnlineCalculatorError.None)
+        ClassificationPerformanceMeasures.SetTestResults(testPerformanceCalculator);
     }
 

branches/gteufl/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationModel.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -111 +111 @@
 
 
-    public IEnumerable<double> GetEstimatedValues(Dataset dataset, IEnumerable<int> rows) {
+    public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
       return model.GetEstimatedValues(dataset, rows);
     }
 
-    public IEnumerable<double> GetEstimatedClassValues(Dataset dataset, IEnumerable<int> rows) {
+    public IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
       if (!Thresholds.Any() && !ClassValues.Any()) throw new ArgumentException("No thresholds and class values were set for the current classification model.");
       foreach (var x in GetEstimatedValues(dataset, rows)) {

branches/gteufl/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolution.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.

branches/gteufl/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolutionBase.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -105 +105 @@
       TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
 
-      double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
-      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
-      double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
-      TestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
+      double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
+      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR*trainingR : double.NaN;
+      double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
+      TestRSquared = errorState == OnlineCalculatorError.None ? testR*testR : double.NaN;
 
       double trainingNormalizedGini = NormalizedGiniCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);

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

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.

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

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.

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

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.

@@ -8 +8 @@
 FxCopResults.txt
 Google.ProtocolBuffers-0.9.1.dll
+Google.ProtocolBuffers-2.4.1.473.dll
 HeuristicLab 3.3.5.1.ReSharper.user
 HeuristicLab 3.3.6.0.ReSharper.user
 HeuristicLab.4.5.resharper.user
 HeuristicLab.ExtLibs.6.0.ReSharper.user
+HeuristicLab.Scripting.Development
 HeuristicLab.resharper.user
 ProtoGen.exe
@@ -17 +19 @@
 _ReSharper.HeuristicLab
 _ReSharper.HeuristicLab 3.3
+_ReSharper.HeuristicLab 3.3 Tests
 _ReSharper.HeuristicLab.ExtLibs
 bin
 protoc.exe
-_ReSharper.HeuristicLab 3.3 Tests
-Google.ProtocolBuffers-2.4.1.473.dll
+obj