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Timestamp:
09/17/12 11:18:40 (12 years ago)
Author:
mkommend
Message:

#1951:

  • Added linear scaling parameter to data analysis problems.
  • Adapted interfaces, evaluators and analyzers accordingly.
  • Added OnlineBoundedMeanSquaredErrorCalculator.
  • Adapted symbolic regression sample unit test.
Location:
trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective
Files:
5 edited

Legend:

Unmodified
Added
Removed

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs

    r7259 r8664  
    4747      IEnumerable<int> rows = GenerateRowsToEvaluate();
    4848      var solution = SymbolicExpressionTreeParameter.ActualValue;
    49       double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
     49      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
    5050      QualitiesParameter.ActualValue = new DoubleArray(qualities);
    5151      return base.Apply();
    5252    }
    5353
    54     public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows) {
     54    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
    5555      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
    56       IEnumerable<double> originalValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
    57       IEnumerable<double> boundedEstimationValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
     56      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
    5857      OnlineCalculatorError errorState;
    59       double mse = OnlineMeanSquaredErrorCalculator.Calculate(originalValues, boundedEstimationValues, out errorState);
     58
     59      double mse;
     60      if (applyLinearScaling) {
     61        var mseCalculator = new OnlineMeanSquaredErrorCalculator();
     62        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, mseCalculator, problemData.Dataset.Rows);
     63        errorState = mseCalculator.ErrorState;
     64        mse = mseCalculator.MeanSquaredError;
     65      } else {
     66        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
     67        mse = OnlineMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
     68      }
    6069      if (errorState != OnlineCalculatorError.None) mse = double.NaN;
    6170      return new double[2] { mse, solution.Length };
    … …  
    6574      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
    6675      EstimationLimitsParameter.ExecutionContext = context;
     76      ApplyLinearScalingParameter.ExecutionContext = context;
    6777
    68       double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
     78      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
    6979
    7080      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
    7181      EstimationLimitsParameter.ExecutionContext = null;
     82      ApplyLinearScalingParameter.ExecutionContext = null;
    7283
    7384      return quality;

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs

    r7259 r8664  
    4747      IEnumerable<int> rows = GenerateRowsToEvaluate();
    4848      var solution = SymbolicExpressionTreeParameter.ActualValue;
    49       double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
     49      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
    5050      QualitiesParameter.ActualValue = new DoubleArray(qualities);
    5151      return base.Apply();
    5252    }
    5353
    54     public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows) {
     54    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
    5555      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
    56       IEnumerable<double> originalValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
     56      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
    5757      OnlineCalculatorError errorState;
    58       double r2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedValues, originalValues, out errorState);
    59       if (errorState != OnlineCalculatorError.None) r2 = 0.0;
    60       return new double[] { r2, solution.Length };
     58
     59      double r2;
     60      if (applyLinearScaling) {
     61        var r2Calculator = new OnlinePearsonsRSquaredCalculator();
     62        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, r2Calculator, problemData.Dataset.Rows);
     63        errorState = r2Calculator.ErrorState;
     64        r2 = r2Calculator.RSquared;
     65      } else {
     66        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
     67        r2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
     68      }
     69
     70      if (errorState != OnlineCalculatorError.None) r2 = double.NaN;
     71      return new double[2] { r2, solution.Length };
    6172    }
    6273
    … …  
    6475      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
    6576      EstimationLimitsParameter.ExecutionContext = context;
     77      ApplyLinearScalingParameter.ExecutionContext = context;
    6678
    67       double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
     79      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
    6880
    6981      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
    7082      EstimationLimitsParameter.ExecutionContext = null;
     83      ApplyLinearScalingParameter.ExecutionContext = null;
    7184
    7285      return quality;

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveProblem.cs

    r8175 r8664  
    6565      EstimationLimitsParameter.Hidden = true;
    6666
     67      ApplyLinearScalingParameter.Value.Value = true;
    6768      Maximization = new BoolArray(new bool[] { true, false });
    6869      MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth;

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer.cs

    r7259 r8664  
    2222using HeuristicLab.Common;
    2323using HeuristicLab.Core;
    24 using HeuristicLab.Data;
    2524using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    2625using HeuristicLab.Parameters;
    … …  
    3837    private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
    3938    private const string EstimationLimitsParameterName = "EstimationLimits";
    40     private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
    4139    #region parameter properties
    4240    public ILookupParameter<IRegressionProblemData> ProblemDataParameter {
    … …  
    4846    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
    4947      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
    50     }
    51     public IValueParameter<BoolValue> ApplyLinearScalingParameter {
    52       get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
    53     }
    54     #endregion
    55 
    56     #region properties
    57     public BoolValue ApplyLinearScaling {
    58       get { return ApplyLinearScalingParameter.Value; }
    5948    }
    6049    #endregion
    … …  
    6857      Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The symbolic data analysis tree interpreter for the symbolic expression tree."));
    6958      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model."));
    70       Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
    7159    }
    7260
    … …  
    7765    protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQuality) {
    7866      var model = new SymbolicRegressionModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
    79       if (ApplyLinearScaling.Value)
    80         SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue);
     67      if (ApplyLinearScalingParameter.ActualValue.Value) SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue, ProblemDataParameter.ActualValue.TargetVariable);
    8168      return new SymbolicRegressionSolution(model, (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone());
    8269    }

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveValidationBestSolutionAnalyzer.cs

    r7259 r8664  
    2222using HeuristicLab.Common;
    2323using HeuristicLab.Core;
    24 using HeuristicLab.Data;
    2524using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    2625using HeuristicLab.Parameters;
    … …  
    3635    ISymbolicDataAnalysisBoundedOperator {
    3736    private const string EstimationLimitsParameterName = "EstimationLimits";
    38     private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
    3937
    4038    #region parameter properties
    … …  
    4240      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
    4341    }
    44     public IValueParameter<BoolValue> ApplyLinearScalingParameter {
    45       get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
    46     }
    4742    #endregion
    4843
    49     #region properties
    50     public BoolValue ApplyLinearScaling {
    51       get { return ApplyLinearScalingParameter.Value; }
    52     }
    53     #endregion
    5444    [StorableConstructor]
    5545    private SymbolicRegressionMultiObjectiveValidationBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
    … …  
    5848      : base() {
    5949      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model."));
    60       Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
    6150    }
    6251    public override IDeepCloneable Clone(Cloner cloner) {
    … …  
    6655    protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQuality) {
    6756      var model = new SymbolicRegressionModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
    68       if (ApplyLinearScaling.Value)
    69         SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue);
     57      if (ApplyLinearScalingParameter.ActualValue.Value) SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue, ProblemDataParameter.ActualValue.TargetVariable);
    7058      return new SymbolicRegressionSolution(model, (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone());
    7159    }
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