Changeset 7183 for branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SingleObjective

Timestamp:

12/13/11 13:52:22 (13 years ago)

Author:

gkronber

Message:

#1081 implemented remaining metrics for time series prognosis solutions, added estimation limits fixed training and validation best solution analyzers, implemented overfitting analyzer.

Location:

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SingleObjective

Files:

• SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator.cs (modified) (1 diff)
• SymbolicTimeSeriesPrognosisSingleObjectiveProblem.cs (modified) (2 diffs)
• SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer.cs (modified) (1 diff)
• SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer.cs (modified) (4 diffs)

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator.cs

@@ -93 +93 @@
         meanCalculator.Add(calc.MeanSquaredError);
       }
-      //int i = 0;
-      //foreach (var targetVariable in problemData.TargetVariables) {
-      //  var predictedContinuations = allPredictedContinuations.Select(v => v.ElementAt(i));
-      //  for (int h = 0; h < horizon; h++) {
-      //    OnlineCalculatorError errorState;
-      //    meanCalculator.Add(OnlineMeanSquaredErrorCalculator.Calculate(predictedContinuations
-      //                                                                    .Select(x => x.ElementAt(h))
-      //                                                                    .LimitToRange(lowerEstimationLimit,
-      //                                                                                  upperEstimationLimit),
-      //                                                                  actualContinuations.Select(x => x.ElementAt(h)),
-      //                                                                  out errorState));
-      //    if (errorState != OnlineCalculatorError.None) return double.NaN;
-      //  }
-      //}
+
       return meanCalculator.MeanErrorState == OnlineCalculatorError.None ? meanCalculator.Mean : double.MaxValue;
     }

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectiveProblem.cs

@@ -76 +76 @@
     private void InitializeOperators() {
       Operators.Add(new SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer());
-      //Operators.Add(new SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer());
-      //Operators.Add(new SymbolicTimeSeriesPrognosisSingleObjectiveOverfittingAnalyzer());
+      Operators.Add(new SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer());
+      Operators.Add(new SymbolicTimeSeriesPrognosisSingleObjectiveOverfittingAnalyzer());
       ParameterizeOperators();
     }
 
     private void UpdateEstimationLimits() {
-      //if (ProblemData.TrainingIndizes.Any()) {
-      //  var targetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariables, ProblemData.TrainingIndizes).ToList();
-      //  var mean = targetValues.Average();
-      //  var range = targetValues.Max() - targetValues.Min();
-      //  EstimationLimits.Upper = mean + PunishmentFactor * range;
-      //  EstimationLimits.Lower = mean - PunishmentFactor * range;
-      //} else {
       EstimationLimits.Upper = double.MaxValue;
       EstimationLimits.Lower = double.MinValue;
-      //}
     }
 
@@ -106 +98 @@
           op.EstimationLimitsParameter.ActualName = EstimationLimitsParameter.Name;
         }
-        foreach (var op in operators.OfType<SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer>())
-        {
+        foreach (var op in operators.OfType<SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer>()) {
           op.MaximizationParameter.ActualName = MaximizationParameter.Name;
           op.ProblemDataParameter.ActualName = ProblemDataParameter.Name;

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer.cs

@@ -76 +76 @@
 
     protected override ISymbolicTimeSeriesPrognosisSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
-      var model = new SymbolicTimeSeriesPrognosisModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, ProblemDataParameter.ActualValue.TargetVariables.ToArray());
+      var model = new SymbolicTimeSeriesPrognosisModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, ProblemDataParameter.ActualValue.TargetVariables.ToArray(), EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
       if (ApplyLinearScaling.Value)
         SymbolicTimeSeriesPrognosisModel.Scale(model, ProblemDataParameter.ActualValue);

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer.cs

@@ -20 +20 @@
 #endregion
 
+using System.Linq;
 using HeuristicLab.Common;
 using HeuristicLab.Core;
@@ -33 +34 @@
   [Item("SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer", "An operator that analyzes the validation best symbolic time-series prognosis solution for single objective symbolic time-series prognosis problems.")]
   [StorableClass]
-  public sealed class SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveValidationBestSolutionAnalyzer<ISymbolicTimeSeriesPrognosisSolution, ISymbolicTimeSeriesPrognosisSingleObjectiveEvaluator, ITimeSeriesPrognosisProblemData>,
-    ISymbolicDataAnalysisBoundedOperator {
+  public sealed class SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveValidationBestSolutionAnalyzer<ISymbolicTimeSeriesPrognosisSolution, ISymbolicTimeSeriesPrognosisSingleObjectiveEvaluator, ITimeSeriesPrognosisProblemData>, ISymbolicDataAnalysisBoundedOperator {
     private const string EstimationLimitsParameterName = "EstimationLimits";
     private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
-
     #region parameter properties
     public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
@@ -61 +60 @@
       Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
     }
-
     public override IDeepCloneable Clone(Cloner cloner) {
       return new SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer(this, cloner);
@@ -67 +65 @@
 
     protected override ISymbolicTimeSeriesPrognosisSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
-      var model = new SymbolicTimeSeriesPrognosisModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicTimeSeriesPrognosisInterpreterParameter.ActualValue);
+      var model = new SymbolicTimeSeriesPrognosisModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, ProblemDataParameter.ActualValue.TargetVariables.ToArray(), EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
       if (ApplyLinearScaling.Value)
         SymbolicTimeSeriesPrognosisModel.Scale(model, ProblemDataParameter.ActualValue);