Changeset 7100 for branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SingleObjective

Timestamp:

11/29/11 20:05:38 (13 years ago)

Author:

gkronber

Message:

#1081 worked on multi-variate time series prognosis

Location:

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

Files:

• . (modified) (1 prop)
• SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator.cs (modified) (2 diffs)
• SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator.cs (modified) (2 diffs)
• SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectiveProblem.cs (modified) (2 diffs)
• SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer.cs (modified) (4 diffs)
• SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer.cs (modified) (1 diff)

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

@@ -3 +3 @@
 HeuristicLabProblemsDataAnalysisSymbolicTimeSeriesPrognosisPlugin.cs
 obj
+Plugin.cs

@@ -3 +3 @@
 HeuristicLabProblemsDataAnalysisSymbolicTimeSeriesPrognosisPlugin.cs
 obj
+Plugin.cs

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

@@ -48 +48 @@
       IEnumerable<int> rows = GenerateRowsToEvaluate();
 
-      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
+      double quality = Calculate(SymbolicTimeSeriesPrognosisInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
       QualityParameter.ActualValue = new DoubleValue(quality);
 
@@ -56 +56 @@
     public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows) {
       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
-      IEnumerable<double> originalValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
-      IEnumerable<double> boundedEstimationValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
-      OnlineCalculatorError errorState;
-      double mse = OnlineMeanSquaredErrorCalculator.Calculate(originalValues, boundedEstimationValues, out errorState);
-      if (errorState != OnlineCalculatorError.None) return double.NaN;
-      else return mse;
+      OnlineMeanAndVarianceCalculator meanCalculator = new OnlineMeanAndVarianceCalculator();
+      foreach (var targetVariable in problemData.TargetVariables) {
+        IEnumerable<double> originalValues = problemData.Dataset.GetDoubleValues(targetVariable, rows);
+        IEnumerable<double> boundedEstimationValues = estimatedValues.LimitToRange(lowerEstimationLimit,
+                                                                                   upperEstimationLimit);
+        OnlineCalculatorError errorState;
+        meanCalculator.Add(OnlineMeanSquaredErrorCalculator.Calculate(originalValues, boundedEstimationValues, out errorState));
+        if (errorState != OnlineCalculatorError.None) return double.NaN;
+      }
+      return meanCalculator.Mean;
     }
 
     public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows) {
-      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
+      SymbolicTimeSeriesPrognosisInterpreterParameter.ExecutionContext = context;
       EstimationLimitsParameter.ExecutionContext = context;
 
-      double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
+      double mse = Calculate(SymbolicTimeSeriesPrognosisInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
 
 
-      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
+      SymbolicTimeSeriesPrognosisInterpreterParameter.ExecutionContext = null;
       EstimationLimitsParameter.ExecutionContext = null;
 

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

@@ -48 +48 @@
       IEnumerable<int> rows = GenerateRowsToEvaluate();
 
-      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
+      double quality = Calculate(SymbolicTimeSeriesPrognosisInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
       QualityParameter.ActualValue = new DoubleValue(quality);
 
@@ -56 +56 @@
     public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows) {
       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
-      IEnumerable<double> originalValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
-      OnlineCalculatorError errorState;
-      double r2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedValues, originalValues, out errorState);
-      if (errorState != OnlineCalculatorError.None) return 0.0;
-      else return r2;
+      var meanCalculator = new OnlineMeanAndVarianceCalculator();
+      foreach (var targetVariable in problemData.TargetVariables) {
+        IEnumerable<double> originalValues = problemData.Dataset.GetDoubleValues(targetVariable, rows);
+        OnlineCalculatorError errorState;
+        meanCalculator.Add(OnlinePearsonsRSquaredCalculator.Calculate(estimatedValues, originalValues, out errorState));
+        if (errorState != OnlineCalculatorError.None) return 0.0;
+      }
+      return meanCalculator.Mean;
     }
 
     public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows) {
-      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
+      SymbolicTimeSeriesPrognosisInterpreterParameter.ExecutionContext = context;
       EstimationLimitsParameter.ExecutionContext = context;
 
-      double r2 = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
+      double r2 = Calculate(SymbolicTimeSeriesPrognosisInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
 
-      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
+      SymbolicTimeSeriesPrognosisInterpreterParameter.ExecutionContext = null;
       EstimationLimitsParameter.ExecutionContext = null;
 

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

@@ -69 +69 @@
       UpdateEstimationLimits();
     }
-    
+
     private void ConfigureGrammarSymbols() {
       var grammar = SymbolicExpressionTreeGrammar as TypeCoherentExpressionGrammar;
@@ -77 +77 @@
     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.TargetVariable, 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;
-      }
+      //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;
+      //}
     }
 

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

@@ -34 +34 @@
   [StorableClass]
   public sealed class SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveTrainingBestSolutionAnalyzer<ISymbolicTimeSeriesPrognosisSolution>,
-  ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator {
+  ISymbolicTimeSeriesPrognosisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator {
     private const string ProblemDataParameterName = "ProblemData";
-    private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
+    private const string SymbolicTimeSeriesPrognosisInterpreterParameterName = "SymbolicTimeSeriesPrognosisInterpreter";
     private const string EstimationLimitsParameterName = "EstimationLimits";
     private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
@@ -43 +43 @@
       get { return (ILookupParameter<ITimeSeriesPrognosisProblemData>)Parameters[ProblemDataParameterName]; }
     }
-    public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
-      get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
+    public ILookupParameter<ISymbolicTimeSeriesPrognosisInterpreter> SymbolicTimeSeriesPrognosisInterpreterParameter {
+      get { return (ILookupParameter<ISymbolicTimeSeriesPrognosisInterpreter>)Parameters[SymbolicTimeSeriesPrognosisInterpreterParameterName]; }
     }
     public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
@@ -66 +66 @@
       : base() {
       Parameters.Add(new LookupParameter<ITimeSeriesPrognosisProblemData>(ProblemDataParameterName, "The problem data for the symbolic regression solution."));
-      Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The symbolic data analysis tree interpreter for the symbolic expression tree."));
+      Parameters.Add(new LookupParameter<ISymbolicTimeSeriesPrognosisInterpreter>(SymbolicTimeSeriesPrognosisInterpreterParameterName, "The symbolic time series prognosis interpreter for the symbolic expression tree."));
       Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model."));
       Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
@@ -75 +75 @@
 
     protected override ISymbolicTimeSeriesPrognosisSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
-      var model = new SymbolicTimeSeriesPrognosisModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
+      var model = new SymbolicTimeSeriesPrognosisModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicTimeSeriesPrognosisInterpreterParameter.ActualValue);
       if (ApplyLinearScaling.Value)
         SymbolicTimeSeriesPrognosisModel.Scale(model, ProblemDataParameter.ActualValue);

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

@@ -67 +67 @@
 
     protected override ISymbolicTimeSeriesPrognosisSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
-      var model = new SymbolicTimeSeriesPrognosisModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
+      var model = new SymbolicTimeSeriesPrognosisModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicTimeSeriesPrognosisInterpreterParameter.ActualValue);
       if (ApplyLinearScaling.Value)
         SymbolicTimeSeriesPrognosisModel.Scale(model, ProblemDataParameter.ActualValue);

@@ -3 +3 @@
 HeuristicLabProblemsDataAnalysisSymbolicTimeSeriesPrognosisPlugin.cs
 obj
+Plugin.cs