Changeset 13948 for branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis

Timestamp:

06/29/16 10:36:52 (9 years ago)

Author:

pfleck

Message:

#2597

• Merged recent trunk changes.
• Adapted VariablesUsedForPrediction property for RegressionSolutionTargetResponseGradientView.
• Fixed a reference (.dll to project ref).

Location:

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis

Files:

• . (modified) (1 prop)
• 3.4/HeuristicLab.Problems.DataAnalysis-3.4.csproj (modified) (2 diffs)
• 3.4/Implementation/Classification/ClassificationEnsembleModel.cs (modified) (5 diffs)
• 3.4/Implementation/Classification/ClassificationModel.cs (copied) (copied from trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationModel.cs)
• 3.4/Implementation/Classification/DiscriminantFunctionClassificationModel.cs (modified) (4 diffs)
• 3.4/Implementation/ConstantModel.cs (modified) (4 diffs)
• 3.4/Implementation/DataAnalysisModel.cs (copied) (copied from trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/DataAnalysisModel.cs)
• 3.4/Implementation/DataAnalysisSolution.cs (modified) (2 diffs)
• 3.4/Implementation/Regression/ConstantRegressionModel.cs (modified) (3 diffs)
• 3.4/Implementation/Regression/RegressionEnsembleModel.cs (modified) (7 diffs)
• 3.4/Implementation/Regression/RegressionModel.cs (copied) (copied from trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionModel.cs)
• 3.4/Implementation/TimeSeriesPrognosis/Models/TimeSeriesPrognosisAutoRegressiveModel.cs (modified) (5 diffs)
• 3.4/Interfaces/Classification/IClassificationModel.cs (modified) (1 diff)
• 3.4/Interfaces/IDataAnalysisModel.cs (modified) (1 diff)
• 3.4/Interfaces/IDataAnalysisSolution.cs (modified) (3 diffs)
• 3.4/Interfaces/IDependencyCalculator.cs (modified) (2 diffs)
• 3.4/Interfaces/Regression/IRegressionModel.cs (modified) (1 diff)
• 3.4/OnlineCalculators/DependencyCalculator/HoeffdingsDependenceCalculator.cs (modified) (1 diff)
• 3.4/OnlineCalculators/DependencyCalculator/PearsonsRDependenceCalculator.cs (modified) (1 diff)
• 3.4/OnlineCalculators/DependencyCalculator/PearsonsRSquaredDependenceCalculator.cs (modified) (2 diffs)
• 3.4/OnlineCalculators/DependencyCalculator/SpearmansRankCorrelationCoefficientCalculator.cs (modified) (3 diffs)

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis/3.4/HeuristicLab.Problems.DataAnalysis-3.4.csproj

@@ -182 +182 @@
     <Compile Include="Implementation\Classification\ConstantClassificationSolution.cs" />
     <Compile Include="Implementation\Classification\DiscriminantFunctionClassificationSolutionBase.cs" />
+    <Compile Include="Implementation\Classification\ClassificationModel.cs" />
     <Compile Include="Implementation\Clustering\ClusteringProblem.cs" />
     <Compile Include="Implementation\Clustering\ClusteringProblemData.cs" />
     <Compile Include="Implementation\Clustering\ClusteringSolution.cs" />
     <Compile Include="Implementation\ConstantModel.cs" />
+    <Compile Include="Implementation\DataAnalysisModel.cs" />
     <Compile Include="Implementation\Regression\ConfidenceBoundRegressionSolution.cs" />
     <Compile Include="Implementation\Regression\ConstantRegressionModel.cs" />
@@ -194 +196 @@
     </Compile>
     <Compile Include="Implementation\Regression\RegressionEnsembleSolution.cs" />
+    <Compile Include="Implementation\Regression\RegressionModel.cs" />
     <Compile Include="Implementation\Regression\RegressionSolutionVariableImpactsCalculator.cs" />
     <Compile Include="Implementation\TimeSeriesPrognosis\Models\ConstantTimeSeriesPrognosisModel.cs" />

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

@@ -32 +32 @@
   [StorableClass]
   [Item("ClassificationEnsembleModel", "A classification model that contains an ensemble of multiple classification models")]
-  public class ClassificationEnsembleModel : NamedItem, IClassificationEnsembleModel {
+  public class ClassificationEnsembleModel : ClassificationModel, IClassificationEnsembleModel {
+    public override IEnumerable<string> VariablesUsedForPrediction {
+      get { return models.SelectMany(x => x.VariablesUsedForPrediction).Distinct().OrderBy(x => x); }
+    }
 
     [Storable]
@@ -49 +52 @@
     public ClassificationEnsembleModel() : this(Enumerable.Empty<IClassificationModel>()) { }
     public ClassificationEnsembleModel(IEnumerable<IClassificationModel> models)
-      : base() {
+      : base(string.Empty) {
       this.name = ItemName;
       this.description = ItemDescription;
       this.models = new List<IClassificationModel>(models);
+
+      if (this.models.Any()) this.TargetVariable = this.models.First().TargetVariable;
     }
 
@@ -59 +64 @@
     }
 
-    #region IClassificationEnsembleModel Members
     public void Add(IClassificationModel model) {
+      if (string.IsNullOrEmpty(TargetVariable)) TargetVariable = model.TargetVariable;
       models.Add(model);
     }
     public void Remove(IClassificationModel model) {
       models.Remove(model);
+      if (!models.Any()) TargetVariable = string.Empty;
     }
 
@@ -78 +84 @@
     }
 
-    #endregion
 
-    #region IClassificationModel Members
-
-    public IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
+    public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
       foreach (var estimatedValuesVector in GetEstimatedClassValueVectors(dataset, rows)) {
         // return the class which is most often occuring
@@ -94 +97 @@
     }
 
-    IClassificationSolution IClassificationModel.CreateClassificationSolution(IClassificationProblemData problemData) {
+    public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
       return new ClassificationEnsembleSolution(models, new ClassificationEnsembleProblemData(problemData));
     }
-    #endregion
+
+
   }
 }

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

@@ -33 +33 @@
   [StorableClass]
   [Item("DiscriminantFunctionClassificationModel", "Represents a classification model that uses a discriminant function and classification thresholds.")]
-  public class DiscriminantFunctionClassificationModel : NamedItem, IDiscriminantFunctionClassificationModel {
+  public class DiscriminantFunctionClassificationModel : ClassificationModel, IDiscriminantFunctionClassificationModel {
+    public override IEnumerable<string> VariablesUsedForPrediction {
+      get { return model.VariablesUsedForPrediction; }
+    }
+
     [Storable]
     private IRegressionModel model;
@@ -73 +77 @@
 
     public DiscriminantFunctionClassificationModel(IRegressionModel model, IDiscriminantFunctionThresholdCalculator thresholdCalculator)
-      : base() {
+      : base(model.TargetVariable) {
       this.name = ItemName;
       this.description = ItemDescription;
+
       this.model = model;
       this.classValues = new double[0];
@@ -115 +120 @@
     }
 
-    public IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
+    public override 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)) {
@@ -135 +140 @@
     #endregion
 
-    public virtual IDiscriminantFunctionClassificationSolution CreateDiscriminantFunctionClassificationSolution(IClassificationProblemData problemData) {
+    public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
+      return CreateDiscriminantFunctionClassificationSolution(problemData);
+    }
+    public virtual IDiscriminantFunctionClassificationSolution CreateDiscriminantFunctionClassificationSolution(
+      IClassificationProblemData problemData) {
       return new DiscriminantFunctionClassificationSolution(this, new ClassificationProblemData(problemData));
-    }
-
-    public virtual IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
-      return CreateDiscriminantFunctionClassificationSolution(problemData);
     }
   }

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/ConstantModel.cs

@@ -31 +31 @@
   [StorableClass]
   [Item("Constant Model", "A model that always returns the same constant value regardless of the presented input data.")]
-  public class ConstantModel : NamedItem, IRegressionModel, IClassificationModel, ITimeSeriesPrognosisModel, IStringConvertibleValue {
+  public class ConstantModel : RegressionModel, IClassificationModel, ITimeSeriesPrognosisModel, IStringConvertibleValue {
+    public override IEnumerable<string> VariablesUsedForPrediction { get { return Enumerable.Empty<string>(); } }
+
+
     [Storable]
-    private double constant;
+    private readonly double constant;
     public double Constant {
       get { return constant; }
@@ -45 +48 @@
       this.constant = original.constant;
     }
+
     public override IDeepCloneable Clone(Cloner cloner) { return new ConstantModel(this, cloner); }
 
-    public ConstantModel(double constant)
-      : base() {
+    public ConstantModel(double constant, string targetVariable = "Target")
+      : base(targetVariable) {
       this.name = ItemName;
       this.description = ItemDescription;
@@ -55 +59 @@
     }
 
-    public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
+    public override IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
       return rows.Select(row => Constant);
     }
@@ -65 +69 @@
     }
 
-    public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
+    public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
       return new ConstantRegressionSolution(this, new RegressionProblemData(problemData));
     }

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/DataAnalysisSolution.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -111 +111 @@
     protected override void DeregisterItemEvents(IEnumerable<IResult> items) { }
 
-    public virtual IEnumerable<string> GetUsedVariablesForPrediction() {
-      return this.ProblemData.AllowedInputVariables;
-    }
-
     #region INamedItem Members
     [Storable]

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/ConstantRegressionModel.cs

@@ -32 +32 @@
   [Item("Constant Regression Model", "A model that always returns the same constant value regardless of the presented input data.")]
   [Obsolete]
-  public class ConstantRegressionModel : NamedItem, IRegressionModel, IStringConvertibleValue {
+  public class ConstantRegressionModel : RegressionModel, IStringConvertibleValue {
+    public override IEnumerable<string> VariablesUsedForPrediction { get { return Enumerable.Empty<string>(); } }
+
     [Storable]
     private double constant;
@@ -46 +48 @@
       this.constant = original.constant;
     }
+
     public override IDeepCloneable Clone(Cloner cloner) { return new ConstantRegressionModel(this, cloner); }
 
-    public ConstantRegressionModel(double constant)
-      : base() {
+    public ConstantRegressionModel(double constant, string targetVariable = "Target")
+      : base(targetVariable) {
       this.name = ItemName;
       this.description = ItemDescription;
@@ -56 +59 @@
     }
 
-    public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
+    public override IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
       return rows.Select(row => Constant);
     }
 
-    public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
+    public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
       return new ConstantRegressionSolution(new ConstantModel(constant), new RegressionProblemData(problemData));
     }

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionEnsembleModel.cs

@@ -33 +33 @@
   [StorableClass]
   [Item("RegressionEnsembleModel", "A regression model that contains an ensemble of multiple regression models")]
-  public sealed class RegressionEnsembleModel : NamedItem, IRegressionEnsembleModel {
+  public sealed class RegressionEnsembleModel : RegressionModel, IRegressionEnsembleModel {
+    public override IEnumerable<string> VariablesUsedForPrediction {
+      get { return models.SelectMany(x => x.VariablesUsedForPrediction).Distinct().OrderBy(x => x); }
+    }
 
     private List<IRegressionModel> models;
@@ -100 +103 @@
     public RegressionEnsembleModel(IEnumerable<IRegressionModel> models) : this(models, models.Select(m => 1.0)) { }
     public RegressionEnsembleModel(IEnumerable<IRegressionModel> models, IEnumerable<double> modelWeights)
-      : base() {
+      : base(string.Empty) {
       this.name = ItemName;
       this.description = ItemDescription;
 
-
       this.models = new List<IRegressionModel>(models);
       this.modelWeights = new List<double>(modelWeights);
+
+      if (this.models.Any()) this.TargetVariable = this.models.First().TargetVariable;
     }
 
     public void Add(IRegressionModel model) {
+      if (string.IsNullOrEmpty(TargetVariable)) TargetVariable = model.TargetVariable;
       Add(model, 1.0);
     }
     public void Add(IRegressionModel model, double weight) {
+      if (string.IsNullOrEmpty(TargetVariable)) TargetVariable = model.TargetVariable;
+
       models.Add(model);
       modelWeights.Add(weight);
@@ -122 +129 @@
     }
     public void AddRange(IEnumerable<IRegressionModel> models, IEnumerable<double> weights) {
+      if (string.IsNullOrEmpty(TargetVariable)) TargetVariable = models.First().TargetVariable;
+
       this.models.AddRange(models);
       modelWeights.AddRange(weights);
@@ -131 +140 @@
       models.RemoveAt(index);
       modelWeights.RemoveAt(index);
+
+      if (!models.Any()) TargetVariable = string.Empty;
       OnChanged();
     }
@@ -139 +150 @@
         modelWeights.RemoveAt(index);
       }
+
+      if (!models.Any()) TargetVariable = string.Empty;
       OnChanged();
     }
@@ -165 +178 @@
     }
 
-    public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
+    public override IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
       double weightsSum = modelWeights.Sum();
       var summedEstimates = from estimatedValuesVector in GetEstimatedValueVectors(dataset, rows)
@@ -213 +226 @@
 
 
-    public RegressionEnsembleSolution CreateRegressionSolution(IRegressionProblemData problemData) {
+    public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
       return new RegressionEnsembleSolution(this, new RegressionEnsembleProblemData(problemData));
-    }
-    IRegressionSolution IRegressionModel.CreateRegressionSolution(IRegressionProblemData problemData) {
-      return CreateRegressionSolution(problemData);
     }
   }

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

@@ -30 +30 @@
   [StorableClass]
   [Item("Autoregressive TimeSeries Model", "A linear autoregressive time series model used to predict future values.")]
-  public class TimeSeriesPrognosisAutoRegressiveModel : NamedItem, ITimeSeriesPrognosisModel {
+  public class TimeSeriesPrognosisAutoRegressiveModel : RegressionModel, ITimeSeriesPrognosisModel {
+    public override IEnumerable<string> VariablesUsedForPrediction {
+      get { return Enumerable.Empty<string>(); } // what to return here?
+    }
+
     [Storable]
     public double[] Phi { get; private set; }
     [Storable]
     public double Constant { get; private set; }
-    [Storable]
-    public string TargetVariable { get; private set; }
 
     public int TimeOffset { get { return Phi.Length; } }
@@ -46 +48 @@
       this.Phi = (double[])original.Phi.Clone();
       this.Constant = original.Constant;
-      this.TargetVariable = original.TargetVariable;
     }
     public override IDeepCloneable Clone(Cloner cloner) {
@@ -52 +53 @@
     }
     public TimeSeriesPrognosisAutoRegressiveModel(string targetVariable, double[] phi, double constant)
-      : base("AR(1) Model") {
+      : base(targetVariable, "AR(1) Model") {
       Phi = (double[])phi.Clone();
       Constant = constant;
-      TargetVariable = targetVariable;
     }
 
@@ -91 +91 @@
     }
 
-    public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
+    public override IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
       var targetVariables = dataset.GetReadOnlyDoubleValues(TargetVariable);
       foreach (int row in rows) {
@@ -111 +111 @@
       return new TimeSeriesPrognosisSolution(this, new TimeSeriesPrognosisProblemData(problemData));
     }
-    public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
+    public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
       throw new NotSupportedException();
     }

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/Classification/IClassificationModel.cs

@@ -25 +25 @@
     IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows);
     IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData);
+    string TargetVariable { get; }
   }
 }

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/IDataAnalysisModel.cs

@@ -20 +20 @@
 #endregion
 
+using System.Collections.Generic;
 using HeuristicLab.Core;
 
 namespace HeuristicLab.Problems.DataAnalysis {
   public interface IDataAnalysisModel : INamedItem {
+    IEnumerable<string> VariablesUsedForPrediction { get; }
   }
 }

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/IDataAnalysisSolution.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -21 +21 @@
 
 using System;
-using System.Collections.Generic;
 using HeuristicLab.Common;
 using HeuristicLab.Core;
@@ -29 +28 @@
     IDataAnalysisModel Model { get; }
     IDataAnalysisProblemData ProblemData { get; set; }
-    IEnumerable<string> GetUsedVariablesForPrediction();
 
     event EventHandler ModelChanged;

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/IDependencyCalculator.cs

@@ -20 +20 @@
 #endregion
 
+using System;
 using System.Collections.Generic;
 
@@ -29 +30 @@
 
     double Calculate(IEnumerable<double> originalValues, IEnumerable<double> estimatedValues, out OnlineCalculatorError errorState);
+    double Calculate(IEnumerable<Tuple<double, double>> values, out OnlineCalculatorError errorState);
   }
 }

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/Regression/IRegressionModel.cs

@@ -25 +25 @@
     IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows);
     IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData);
+    string TargetVariable { get; }
   }
 

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/DependencyCalculator/HoeffdingsDependenceCalculator.cs

@@ -41 +41 @@
       if (errorState != OnlineCalculatorError.None) return double.NaN;
       return d;
+    }
+
+    public double Calculate(IEnumerable<Tuple<double, double>> values, out OnlineCalculatorError errorState) {
+      return HoeffD(values.Select(v => v.Item1), values.Select(v => v.Item2), out errorState);
     }
 

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/DependencyCalculator/PearsonsRDependenceCalculator.cs

@@ -35 +35 @@
       return OnlinePearsonsRCalculator.Calculate(originalValues, estimatedValues, out errorState);
     }
+
+    public double Calculate(IEnumerable<Tuple<double, double>> values, out OnlineCalculatorError errorState) {
+      var calculator = new OnlinePearsonsRCalculator();
+      foreach (var tuple in values) {
+        calculator.Add(tuple.Item1, tuple.Item2);
+        if (calculator.ErrorState != OnlineCalculatorError.None) break;
+      }
+      errorState = calculator.ErrorState;
+      return calculator.R;
+    }
   }
 }

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/DependencyCalculator/PearsonsRSquaredDependenceCalculator.cs

@@ -20 +20 @@
 #endregion
 
+using System;
 using System.Collections.Generic;
 
@@ -35 +36 @@
       return r * r;
     }
+
+    public double Calculate(IEnumerable<Tuple<double, double>> values, out OnlineCalculatorError errorState) {
+      var calculator = new OnlinePearsonsRCalculator();
+      foreach (var tuple in values) {
+        calculator.Add(tuple.Item1, tuple.Item2);
+        if (calculator.ErrorState != OnlineCalculatorError.None) break;
+      }
+      errorState = calculator.ErrorState;
+      var r = calculator.R;
+      return r * r;
+    }
   }
 }

branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/DependencyCalculator/SpearmansRankCorrelationCoefficientCalculator.cs

@@ -20 +20 @@
 #endregion
 
+using System;
 using System.Collections.Generic;
 using System.Linq;
@@ -33 +34 @@
 
     public double Calculate(IEnumerable<double> originalValues, IEnumerable<double> estimatedValues, out OnlineCalculatorError errorState) {
-      return SpearmansRankCorrelationCoefficientCalculator.CalculateSpearmansRank(originalValues, estimatedValues, out errorState);
+      return CalculateSpearmansRank(originalValues, estimatedValues, out errorState);
+    }
+    public double Calculate(IEnumerable<Tuple<double, double>> values, out OnlineCalculatorError errorState) {
+      return CalculateSpearmansRank(values.Select(v => v.Item1), values.Select(v => v.Item2), out errorState);
     }
 
@@ -50 +54 @@
       return rs;
     }
+
+
   }
 }