Changeset 14000

Timestamp:

07/05/16 14:05:46 (8 years ago)

Author:

mkommend

Message:

#2604: Removed default ctor arguments for the target variable in regression and classification models.

Location:

trunk/sources

Files:

• HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithmStatic.cs (modified) (1 diff)
• HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/RegressionTreeModel.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/ConstantModel.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/ConstantRegressionModel.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/Models/ConstantTimeSeriesPrognosisModel.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisResults.cs (modified) (4 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs (modified) (1 diff)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithmStatic.cs

@@ -96 +96 @@
         weights = new List<double>();
         // add constant model
-        models.Add(new ConstantModel(f0));
+        models.Add(new ConstantModel(f0, problemData.TargetVariable));
         weights.Add(1.0);
       }

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/RegressionTreeModel.cs

@@ -160 +160 @@
     }
 
-    internal RegressionTreeModel(TreeNode[] tree, string target = "Target")
-      : base(target, "RegressionTreeModel", "Represents a decision tree for regression.") {
+    internal RegressionTreeModel(TreeNode[] tree, string targetVariable)
+      : base(targetVariable, "RegressionTreeModel", "Represents a decision tree for regression.") {
       this.tree = tree;
     }

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/ConstantModel.cs

@@ -51 +51 @@
     public override IDeepCloneable Clone(Cloner cloner) { return new ConstantModel(this, cloner); }
 
-    public ConstantModel(double constant, string targetVariable = "Target")
+    public ConstantModel(double constant, string targetVariable)
       : base(targetVariable) {
       this.name = ItemName;

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/ConstantRegressionModel.cs

@@ -51 +51 @@
     public override IDeepCloneable Clone(Cloner cloner) { return new ConstantRegressionModel(this, cloner); }
 
-    public ConstantRegressionModel(double constant, string targetVariable = "Target")
+    public ConstantRegressionModel(double constant, string targetVariable)
       : base(targetVariable) {
       this.name = ItemName;

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/Models/ConstantTimeSeriesPrognosisModel.cs

@@ -39 +39 @@
     }
 
-    public ConstantTimeSeriesPrognosisModel(double constant, string targetVariable = "Target") : base(constant, targetVariable) { }
+    public ConstantTimeSeriesPrognosisModel(double constant, string targetVariable) : base(constant, targetVariable) { }
 
     public IEnumerable<IEnumerable<double>> GetPrognosedValues(IDataset dataset, IEnumerable<int> rows, IEnumerable<int> horizons) {

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisResults.cs

@@ -373 +373 @@
       //mean model
       double trainingMean = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).Average();
-      var meanModel = new ConstantModel(trainingMean);
+      var meanModel = new ConstantModel(trainingMean, problemData.TargetVariable);
 
       //AR1 model
@@ -395 +395 @@
       PrognosisTrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
       double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
-      PrognosisTrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR*trainingR : double.NaN;
+      PrognosisTrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR * trainingR : double.NaN;
       double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
       PrognosisTrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
@@ -431 +431 @@
       PrognosisTestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
       double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
-      PrognosisTestRSquared = errorState == OnlineCalculatorError.None ? testR*testR : double.NaN;
+      PrognosisTestRSquared = errorState == OnlineCalculatorError.None ? testR * testR : double.NaN;
       double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
       PrognosisTestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;
@@ -448 +448 @@
         //mean model
         double trainingMean = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).Average();
-        var meanModel = new ConstantModel(trainingMean);
+        var meanModel = new ConstantModel(trainingMean, problemData.TargetVariable);
 
         //AR1 model

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs

@@ -150 +150 @@
       OnlineCalculatorError errorState;
       double trainingMean = ProblemData.TrainingIndices.Any() ? ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).Average() : double.NaN;
-      var meanModel = new ConstantModel(trainingMean);
+      var meanModel = new ConstantModel(trainingMean,ProblemData.TargetVariable);
 
       double alpha, beta;