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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.Classification/3.4/MultiObjective
Files:
5 edited

Legend:

Unmodified
Added
Removed

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs

    r7259 r8664  
    6565      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
    6666      EstimationLimitsParameter.ExecutionContext = context;
     67      ApplyLinearScalingParameter.ExecutionContext = context;
    6768
    6869      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
    … …  
    7071      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
    7172      EstimationLimitsParameter.ExecutionContext = null;
     73      ApplyLinearScalingParameter.ExecutionContext = null;
    7274
    7375      return quality;

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs

    r6740 r8664  
    2626      IEnumerable<int> rows = GenerateRowsToEvaluate();
    2727      var solution = SymbolicExpressionTreeParameter.ActualValue;
    28       double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
     28      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
    2929      QualitiesParameter.ActualValue = new DoubleArray(qualities);
    3030      return base.Apply();
    3131    }
    3232
    33     public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable<int> rows) {
     33    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
    3434      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
    35       IEnumerable<double> originalValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
     35      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
    3636      OnlineCalculatorError errorState;
    37       double r2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedValues, originalValues, out errorState);
    38       if (errorState != OnlineCalculatorError.None) r2 = 0.0;
    39       return new double[] { r2, solution.Length };
     37
     38      double r2;
     39      if (applyLinearScaling) {
     40        var r2Calculator = new OnlinePearsonsRSquaredCalculator();
     41        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, r2Calculator, problemData.Dataset.Rows);
     42        errorState = r2Calculator.ErrorState;
     43        r2 = r2Calculator.RSquared;
     44      } else {
     45        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
     46        r2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
     47      }
     48
     49      if (errorState != OnlineCalculatorError.None) r2 = double.NaN;
     50      return new double[2] { r2, solution.Length };
    4051
    4152    }
    … …  
    4556      EstimationLimitsParameter.ExecutionContext = context;
    4657
    47       double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
     58      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
    4859
    4960      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveProblem.cs

    r8594 r8664  
    6868      Parameters.Add(new ValueParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName, "", new AccuracyMaximizingThresholdsModelCreator()));
    6969
     70      ApplyLinearScalingParameter.Value.Value = false;
    7071      EstimationLimitsParameter.Hidden = true;
    7172

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer.cs

    r8594 r8664  
    2222using HeuristicLab.Common;
    2323using HeuristicLab.Core;
    24 using HeuristicLab.Data;
    2524using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    2625using HeuristicLab.Parameters;
    … …  
    3938    private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
    4039    private const string EstimationLimitsParameterName = "EstimationLimits";
    41     private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
    4240
    4341    #region parameter properties
    … …  
    5755      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
    5856    }
    59     public IValueParameter<BoolValue> ApplyLinearScalingParameter {
    60       get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
    61     }
    62     #endregion
    63 
    64     #region properties
    65     public BoolValue ApplyLinearScaling {
    66       get { return ApplyLinearScalingParameter.Value; }
    67     }
    6857    #endregion
    6958
    … …  
    7766      Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The symbolic data analysis tree interpreter for the symbolic expression tree."));
    7867      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic classification model."));
    79       Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic classification solution should be linearly scaled.", new BoolValue(false)));
    8068    }
    8169    public override IDeepCloneable Clone(Cloner cloner) {
    … …  
    9179    protected override ISymbolicClassificationSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQuality) {
    9280      var model = ModelCreatorParameter.ActualValue.CreateSymbolicClassificationModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
    93       if (ApplyLinearScaling.Value) SymbolicClassificationModel.Scale(model, ProblemDataParameter.ActualValue);
     81      if (ApplyLinearScalingParameter.ActualValue.Value) SymbolicClassificationModel.Scale(model, ProblemDataParameter.ActualValue, ProblemDataParameter.ActualValue.TargetVariable);
    9482
    9583      model.RecalculateModelParameters(ProblemDataParameter.ActualValue, ProblemDataParameter.ActualValue.TrainingIndices);

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer.cs

    r8594 r8664  
    2222using HeuristicLab.Common;
    2323using HeuristicLab.Core;
    24 using HeuristicLab.Data;
    2524using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    2625using HeuristicLab.Parameters;
    … …  
    3736    private const string ModelCreatorParameterName = "ModelCreator";
    3837    private const string EstimationLimitsParameterName = "EstimationLimits";
    39     private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
    4038
    4139    #region parameter properties
    4240    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
    4341      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
    44     }
    45     public IValueParameter<BoolValue> ApplyLinearScalingParameter {
    46       get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
    4742    }
    4843    public IValueLookupParameter<ISymbolicClassificationModelCreator> ModelCreatorParameter {
    … …  
    5449    #endregion
    5550
    56     #region properties
    57     public BoolValue ApplyLinearScaling {
    58       get { return ApplyLinearScalingParameter.Value; }
    59     }
    60     #endregion
    6151    [StorableConstructor]
    6252    private SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
    … …  
    6555      : base() {
    6656      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The loewr and upper limit for the estimated values produced by the symbolic classification model."));
    67       Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic classification solution should be linearly scaled.", new BoolValue(false)));
    6857      Parameters.Add(new ValueLookupParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName, ""));
    6958    }
    … …  
    7261    }
    7362
    74     [StorableHook(HookType.AfterDeserialization)]
    75     private void AfterDeserialization() {
    76       if (!Parameters.ContainsKey(ModelCreatorParameterName))
    77         Parameters.Add(new ValueLookupParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName, ""));
    78     }
    7963
    8064    protected override ISymbolicClassificationSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQualities) {
    8165      var model = ModelCreatorParameter.ActualValue.CreateSymbolicClassificationModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
    82       if (ApplyLinearScaling.Value) SymbolicClassificationModel.Scale(model, ProblemDataParameter.ActualValue);
     66      if (ApplyLinearScalingParameter.ActualValue.Value) SymbolicClassificationModel.Scale(model, ProblemDataParameter.ActualValue, ProblemDataParameter.ActualValue.TargetVariable);
    8367
    8468      model.RecalculateModelParameters(ProblemDataParameter.ActualValue, ProblemDataParameter.ActualValue.TrainingIndices);
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