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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
Files:
17 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    }

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionConstantOptimizationEvaluator.cs

    r8053 r8664  
    106106        IEnumerable<int> constantOptimizationRows = GenerateRowsToEvaluate(ConstantOptimizationRowsPercentage.Value);
    107107        quality = OptimizeConstants(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, ProblemDataParameter.ActualValue,
    108            constantOptimizationRows, ConstantOptimizationImprovement.Value, ConstantOptimizationIterations.Value, 0.001,
     108           constantOptimizationRows, ApplyLinearScalingParameter.ActualValue.Value, ConstantOptimizationImprovement.Value, ConstantOptimizationIterations.Value, 0.001,
    109109           EstimationLimitsParameter.ActualValue.Upper, EstimationLimitsParameter.ActualValue.Lower,
    110110          EvaluatedTreesParameter.ActualValue, EvaluatedTreeNodesParameter.ActualValue);
    111111        if (ConstantOptimizationRowsPercentage.Value != RelativeNumberOfEvaluatedSamplesParameter.ActualValue.Value) {
    112112          var evaluationRows = GenerateRowsToEvaluate();
    113           quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, evaluationRows);
     113          quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, evaluationRows, ApplyLinearScalingParameter.ActualValue.Value);
    114114        }
    115115      } else {
    116116        var evaluationRows = GenerateRowsToEvaluate();
    117         quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, evaluationRows);
     117        quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, evaluationRows, ApplyLinearScalingParameter.ActualValue.Value);
    118118      }
    119119      QualityParameter.ActualValue = new DoubleValue(quality);
    … …  
    145145      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
    146146      EstimationLimitsParameter.ExecutionContext = context;
    147 
    148       double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
     147      ApplyLinearScalingParameter.ExecutionContext = context;
     148
     149      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
    149150
    150151      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
    151152      EstimationLimitsParameter.ExecutionContext = null;
     153      ApplyLinearScalingParameter.ExecutionContext = context;
    152154
    153155      return r2;
    … …  
    155157
    156158    public static double OptimizeConstants(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree tree, IRegressionProblemData problemData,
    157       IEnumerable<int> rows, double improvement, int iterations, double differentialStep, double upperEstimationLimit = double.MaxValue, double lowerEstimationLimit = double.MinValue, IntValue evaluatedTrees = null, IntValue evaluatedTreeNodes = null) {
     159      IEnumerable<int> rows, bool applyLinearScaling, double improvement, int iterations, double differentialStep, double upperEstimationLimit = double.MaxValue, double lowerEstimationLimit = double.MinValue, IntValue evaluatedTrees = null, IntValue evaluatedTreeNodes = null) {
    158160      List<SymbolicExpressionTreeTerminalNode> terminalNodes = tree.Root.IterateNodesPrefix().OfType<SymbolicExpressionTreeTerminalNode>().ToList();
    159161      double[] c = new double[terminalNodes.Count];
    … …  
    179181      alglib.minlmcreatev(1, c, diffstep, out state);
    180182      alglib.minlmsetcond(state, epsg, epsf, epsx, maxits);
    181       alglib.minlmoptimize(state, CreateCallBack(interpreter, tree, problemData, rows, upperEstimationLimit, lowerEstimationLimit, treeLength, evaluatedTrees, evaluatedTreeNodes), null, terminalNodes);
     183      alglib.minlmoptimize(state, CreateCallBack(interpreter, tree, problemData, rows, applyLinearScaling, upperEstimationLimit, lowerEstimationLimit, treeLength, evaluatedTrees, evaluatedTreeNodes), null, terminalNodes);
    182184      alglib.minlmresults(state, out c, out report);
    183185
    … …  
    192194    }
    193195
    194     private static alglib.ndimensional_fvec CreateCallBack(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows, double upperEstimationLimit, double lowerEstimationLimit, int treeLength, IntValue evaluatedTrees = null, IntValue evaluatedTreeNodes = null) {
     196    private static alglib.ndimensional_fvec CreateCallBack(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling, double upperEstimationLimit, double lowerEstimationLimit, int treeLength, IntValue evaluatedTrees = null, IntValue evaluatedTreeNodes = null) {
    195197      return (double[] arg, double[] fi, object obj) => {
    196198        // update constants of tree
    … …  
    203205        }
    204206
    205         double quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows);
     207        double quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
    206208
    207209        fi[0] = 1 - quality;

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveEvaluator.cs

    r8639 r8664  
    3030namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
    3131  [StorableClass]
    32   public abstract class SymbolicRegressionSingleObjectiveEvaluator : SymbolicDataAnalysisSingleObjectiveEvaluator<IRegressionProblemData>, ISymbolicRegressionSingleObjectiveEvaluator {
    33     private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
    34     public IFixedValueParameter<BoolValue> ApplyLinearScalingParameter {
    35       get { return (IFixedValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
    36     }
    37     public bool ApplyLinearScaling {
    38       get { return ApplyLinearScalingParameter.Value.Value; }
    39       set { ApplyLinearScalingParameter.Value.Value = value; }
    40     }
    41 
     32  public abstract class SymbolicRegressionSingleObjectiveEvaluator : SymbolicDataAnalysisSingleObjectiveEvaluator<IRegressionProblemData>, ISymbolicRegressionSingleObjectiveEvaluator { 
    4233    [StorableConstructor]
    4334    protected SymbolicRegressionSingleObjectiveEvaluator(bool deserializing) : base(deserializing) { }
    4435    protected SymbolicRegressionSingleObjectiveEvaluator(SymbolicRegressionSingleObjectiveEvaluator original, Cloner cloner) : base(original, cloner) { }
    45     protected SymbolicRegressionSingleObjectiveEvaluator()
    46       : base() {
    47       Parameters.Add(new FixedValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the individual should be linearly scaled before evaluating.", new BoolValue(true)));
    48       ApplyLinearScalingParameter.Hidden = true;
    49     }
    50 
    51     [StorableHook(HookType.AfterDeserialization)]
    52     private void AfterDeserialization() {
    53       if (!Parameters.ContainsKey(ApplyLinearScalingParameterName)) {
    54         Parameters.Add(new FixedValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the individual should be linearly scaled before evaluating.", new BoolValue(false)));
    55         ApplyLinearScalingParameter.Hidden = true;
    56       }
    57     }
    58 
    59     [ThreadStatic]
    60     private static double[] cache;
    61 
    62     protected static void CalculateWithScaling(IEnumerable<double> targetValues, IEnumerable<double> estimatedValues,
    63       double lowerEstimationLimit, double upperEstimationLimit,
    64       IOnlineCalculator calculator, int maxRows) {
    65       if (cache == null || cache.GetLength(0) < maxRows) {
    66         cache = new double[maxRows];
    67       }
    68 
    69       //calculate linear scaling
    70       //the static methods of the calculator could not be used as it performs a check if the enumerators have an equal amount of elements
    71       //this is not true if the cache is used
    72       int i = 0;
    73       var linearScalingCalculator = new OnlineLinearScalingParameterCalculator();
    74       var targetValuesEnumerator = targetValues.GetEnumerator();
    75       var estimatedValuesEnumerator = estimatedValues.GetEnumerator();
    76       while (targetValuesEnumerator.MoveNext() & estimatedValuesEnumerator.MoveNext()) {
    77         double target = targetValuesEnumerator.Current;
    78         double estimated = estimatedValuesEnumerator.Current;
    79         cache[i] = estimated;
    80         if (!double.IsNaN(estimated) && !double.IsInfinity(estimated))
    81           linearScalingCalculator.Add(estimated, target);
    82         i++;
    83       }
    84       if (linearScalingCalculator.ErrorState == OnlineCalculatorError.None && (targetValuesEnumerator.MoveNext() || estimatedValuesEnumerator.MoveNext()))
    85         throw new ArgumentException("Number of elements in target and estimated values enumeration do not match.");
    86 
    87       double alpha = linearScalingCalculator.Alpha;
    88       double beta = linearScalingCalculator.Beta;
    89       if (linearScalingCalculator.ErrorState != OnlineCalculatorError.None) {
    90         alpha = 0.0;
    91         beta = 1.0;
    92       }
    93 
    94       //calculate the quality by using the passed online calculator
    95       targetValuesEnumerator = targetValues.GetEnumerator();
    96       var scaledBoundedEstimatedValuesEnumerator = Enumerable.Range(0, i).Select(x => cache[x] * beta + alpha)
    97         .LimitToRange(lowerEstimationLimit, upperEstimationLimit).GetEnumerator();
    98 
    99       while (targetValuesEnumerator.MoveNext() & scaledBoundedEstimatedValuesEnumerator.MoveNext()) {
    100         calculator.Add(targetValuesEnumerator.Current, scaledBoundedEstimatedValuesEnumerator.Current);
    101       }
    102     }
     36    protected SymbolicRegressionSingleObjectiveEvaluator(): base() {}   
    10337  }
    10438}

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator.cs

    r8113 r8664  
    2020#endregion
    2121
    22 using System;
    2322using System.Collections.Generic;
    2423using HeuristicLab.Common;
    … …  
    4746      IEnumerable<int> rows = GenerateRowsToEvaluate();
    4847
    49       double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScaling);
     48      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
    5049      QualityParameter.ActualValue = new DoubleValue(quality);
    5150
    … …  
    6867        mse = OnlineMaxAbsoluteErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
    6968      }
    70       if (errorState != OnlineCalculatorError.None) return Double.NaN;
    71       else return mse;
     69      if (errorState != OnlineCalculatorError.None) return double.NaN;
     70      return mse;
    7271    }
    7372
    … …  
    7574      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
    7675      EstimationLimitsParameter.ExecutionContext = context;
     76      ApplyLinearScalingParameter.ExecutionContext = context;
    7777
    78       double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScaling);
     78      double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
    7979
    8080      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
    8181      EstimationLimitsParameter.ExecutionContext = null;
     82      ApplyLinearScalingParameter.ExecutionContext = null;
    8283
    8384      return mse;

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator.cs

    r8634 r8664  
    2020#endregion
    2121
    22 using System;
    2322using System.Collections.Generic;
    2423using HeuristicLab.Common;
    … …  
    4746      IEnumerable<int> rows = GenerateRowsToEvaluate();
    4847
    49       double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScaling);
     48      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
    5049      QualityParameter.ActualValue = new DoubleValue(quality);
    5150
    … …  
    6867        mae = OnlineMeanAbsoluteErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
    6968      }
    70       if (errorState != OnlineCalculatorError.None) return Double.NaN;
    71       else return mae;
     69      if (errorState != OnlineCalculatorError.None) return double.NaN;
     70      return mae;
    7271    }
    7372
    … …  
    7574      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
    7675      EstimationLimitsParameter.ExecutionContext = context;
     76      ApplyLinearScalingParameter.ExecutionContext = context;
    7777
    78       double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScaling);
     78      double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
    7979
    8080      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
    8181      EstimationLimitsParameter.ExecutionContext = null;
     82      ApplyLinearScalingParameter.ExecutionContext = null;
    8283
    8384      return mse;

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.cs

    r8113 r8664  
    2020#endregion
    2121
    22 using System;
    2322using System.Collections.Generic;
    2423using HeuristicLab.Common;
    … …  
    4746      IEnumerable<int> rows = GenerateRowsToEvaluate();
    4847
    49       double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScaling);
     48      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
    5049      QualityParameter.ActualValue = new DoubleValue(quality);
    5150
    … …  
    6867        mse = OnlineMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
    6968      }
    70       if (errorState != OnlineCalculatorError.None) return Double.NaN;
    71       else return mse;
     69      if (errorState != OnlineCalculatorError.None) return double.NaN;
     70      return mse;
    7271    }
    7372
    … …  
    7574      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
    7675      EstimationLimitsParameter.ExecutionContext = context;
     76      ApplyLinearScalingParameter.ExecutionContext = context;
    7777
    78       double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScaling);
     78      double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
    7979
    8080      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
    8181      EstimationLimitsParameter.ExecutionContext = null;
     82      ApplyLinearScalingParameter.ExecutionContext = null;
    8283
    8384      return mse;

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.cs

    r7672 r8664  
    4848      IEnumerable<int> rows = GenerateRowsToEvaluate();
    4949
    50       double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
     50      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
    5151      QualityParameter.ActualValue = new DoubleValue(quality);
    5252
    … …  
    5454    }
    5555
    56     public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows) {
     56    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
    5757      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
    58       IEnumerable<double> originalValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
     58      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
    5959      OnlineCalculatorError errorState;
    60       double r2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedValues, originalValues, out errorState);
    61       if (errorState != OnlineCalculatorError.None) return 0.0;
    62       else return r2;
     60
     61      double r2;
     62      if (applyLinearScaling) {
     63        var r2Calculator = new OnlinePearsonsRSquaredCalculator();
     64        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, r2Calculator, problemData.Dataset.Rows);
     65        errorState = r2Calculator.ErrorState;
     66        r2 = r2Calculator.RSquared;
     67      } else {
     68        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
     69        r2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
     70      }
     71      if (errorState != OnlineCalculatorError.None) return double.NaN;
     72      return r2;
    6373    }
    6474
    … …  
    6676      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
    6777      EstimationLimitsParameter.ExecutionContext = context;
     78      ApplyLinearScalingParameter.ExecutionContext = context;
    6879
    69       double r2 = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
     80      double r2 = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
    7081
    7182      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
    7283      EstimationLimitsParameter.ExecutionContext = null;
     84      ApplyLinearScalingParameter.ExecutionContext = null;
    7385
    7486      return r2;

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveProblem.cs

    r8175 r8664  
    6161      EstimationLimitsParameter.Hidden = true;
    6262
     63
     64      ApplyLinearScalingParameter.Value.Value = true;
    6365      Maximization.Value = true;
    6466      MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth;

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer.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    public override IDeepCloneable Clone(Cloner cloner) {
    … …  
    7664    protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
    7765      var model = new SymbolicRegressionModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
    78       if (ApplyLinearScaling.Value)
    79         SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue);
     66      if (ApplyLinearScalingParameter.ActualValue.Value) SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue, ProblemDataParameter.ActualValue.TargetVariable);
    8067      return new SymbolicRegressionSolution(model, (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone());
    8168    }

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer.cs

    r8169 r8664  
    2222using HeuristicLab.Common;
    2323using HeuristicLab.Core;
    24 using HeuristicLab.Data;
    2524using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    26 using HeuristicLab.Parameters;
    2725using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    2826
    … …  
    3432  [StorableClass]
    3533  public sealed class SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveTrainingParetoBestSolutionAnalyzer<IRegressionProblemData, ISymbolicRegressionSolution> {
    36     private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
    37     #region parameter properties
    38     public IValueParameter<BoolValue> ApplyLinearScalingParameter {
    39       get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
    40     }
    41     #endregion
    42 
    43     #region properties
    44     public BoolValue ApplyLinearScaling {
    45       get { return ApplyLinearScalingParameter.Value; }
    46     }
    47     #endregion
    4834
    4935    [StorableConstructor]
    5036    private SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
    5137    private SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer(SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
    52     public SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer()
    53       : base() {
    54       Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
    55     }
     38    public SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer() : base() { }
    5639    public override IDeepCloneable Clone(Cloner cloner) {
    5740      return new SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer(this, cloner);
    … …  
    6043    protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree) {
    6144      var model = new SymbolicRegressionModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
    62       if (ApplyLinearScaling.Value)
    63         SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue);
     45      if (ApplyLinearScalingParameter.ActualValue.Value) SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue, ProblemDataParameter.ActualValue.TargetVariable);
    6446      return new SymbolicRegressionSolution(model, (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone());
    6547    }

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer.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
    4139    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
    4240      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
    43     }
    44     public IValueParameter<BoolValue> ApplyLinearScalingParameter {
    45       get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
    46     }
    47     #endregion
    48 
    49     #region properties
    50     public BoolValue ApplyLinearScaling {
    51       get { return ApplyLinearScalingParameter.Value; }
    5241    }
    5342    #endregion
    … …  
    5948      : base() {
    6049      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model."));
    61       Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
    6250    }
    6351
    … …  
    6856    protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
    6957      var model = new SymbolicRegressionModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
    70       if (ApplyLinearScaling.Value)
    71         SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue);
     58      if (ApplyLinearScalingParameter.ActualValue.Value) SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue, ProblemDataParameter.ActualValue.TargetVariable);
    7259      return new SymbolicRegressionSolution(model, (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone());
    7360    }

  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer.cs

    r8169 r8664  
    2222using HeuristicLab.Common;
    2323using HeuristicLab.Core;
    24 using HeuristicLab.Data;
    2524using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    26 using HeuristicLab.Parameters;
    2725using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    2826
    … …  
    3432  [StorableClass]
    3533  public sealed class SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveValidationParetoBestSolutionAnalyzer<ISymbolicRegressionSolution, ISymbolicRegressionSingleObjectiveEvaluator, IRegressionProblemData> {
    36     private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
    37     #region parameter properties
    38     public IValueParameter<BoolValue> ApplyLinearScalingParameter {
    39       get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
    40     }
    41     #endregion
    42 
    43     #region properties
    44     public BoolValue ApplyLinearScaling {
    45       get { return ApplyLinearScalingParameter.Value; }
    46     }
    47     #endregion
    48 
    4934    [StorableConstructor]
    5035    private SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
    5136    private SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer(SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
    52     public SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer()
    53       : base() {
    54       Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
    55     }
     37    public SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer() : base() { }
     38
    5639    public override IDeepCloneable Clone(Cloner cloner) {
    5740      return new SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer(this, cloner);
    … …  
    6043    protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree) {
    6144      var model = new SymbolicRegressionModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
    62       if (ApplyLinearScaling.Value)
    63         SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue);
     45      if (ApplyLinearScalingParameter.ActualValue.Value) SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue, ProblemDataParameter.ActualValue.TargetVariable);
    6446      return new SymbolicRegressionSolution(model, (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone());
    6547    }

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

    r8639 r8664  
    2020#endregion
    2121
    22 using System;
    2322using System.Collections.Generic;
    2423using HeuristicLab.Common;
    … …  
    7069      return CreateRegressionSolution(problemData);
    7170    }
    72 
    73     public static void Scale(SymbolicRegressionModel model, IRegressionProblemData problemData) {
    74       var dataset = problemData.Dataset;
    75       var targetVariable = problemData.TargetVariable;
    76       var rows = problemData.TrainingIndices;
    77       var estimatedValues = model.Interpreter.GetSymbolicExpressionTreeValues(model.SymbolicExpressionTree, dataset, rows);
    78       var targetValues = dataset.GetDoubleValues(targetVariable, rows);
    79 
    80       var linearScalingCalculator = new OnlineLinearScalingParameterCalculator();
    81       var targetValuesEnumerator = targetValues.GetEnumerator();
    82       var estimatedValuesEnumerator = estimatedValues.GetEnumerator();
    83       while (targetValuesEnumerator.MoveNext() & estimatedValuesEnumerator.MoveNext()) {
    84         double target = targetValuesEnumerator.Current;
    85         double estimated = estimatedValuesEnumerator.Current;
    86         if (!double.IsNaN(estimated) && !double.IsInfinity(estimated))
    87           linearScalingCalculator.Add(estimated, target);
    88       }
    89       if (linearScalingCalculator.ErrorState == OnlineCalculatorError.None && (targetValuesEnumerator.MoveNext() || estimatedValuesEnumerator.MoveNext()))
    90         throw new ArgumentException("Number of elements in target and estimated values enumeration do not match.");
    91 
    92       double alpha = linearScalingCalculator.Alpha;
    93       double beta = linearScalingCalculator.Beta;
    94       if (linearScalingCalculator.ErrorState != OnlineCalculatorError.None) return;
    95 
    96       ConstantTreeNode alphaTreeNode = null;
    97       ConstantTreeNode betaTreeNode = null;
    98       // check if model has been scaled previously by analyzing the structure of the tree
    99       var startNode = model.SymbolicExpressionTree.Root.GetSubtree(0);
    100       if (startNode.GetSubtree(0).Symbol is Addition) {
    101         var addNode = startNode.GetSubtree(0);
    102         if (addNode.SubtreeCount == 2 && addNode.GetSubtree(0).Symbol is Multiplication && addNode.GetSubtree(1).Symbol is Constant) {
    103           alphaTreeNode = addNode.GetSubtree(1) as ConstantTreeNode;
    104           var mulNode = addNode.GetSubtree(0);
    105           if (mulNode.SubtreeCount == 2 && mulNode.GetSubtree(1).Symbol is Constant) {
    106             betaTreeNode = mulNode.GetSubtree(1) as ConstantTreeNode;
    107           }
    108         }
    109       }
    110       // if tree structure matches the structure necessary for linear scaling then reuse the existing tree nodes
    111       if (alphaTreeNode != null && betaTreeNode != null) {
    112         betaTreeNode.Value *= beta;
    113         alphaTreeNode.Value *= beta;
    114         alphaTreeNode.Value += alpha;
    115       } else {
    116         var mainBranch = startNode.GetSubtree(0);
    117         startNode.RemoveSubtree(0);
    118         var scaledMainBranch = MakeSum(MakeProduct(mainBranch, beta), alpha);
    119         startNode.AddSubtree(scaledMainBranch);
    120       }
    121     }
    122 
    123     private static ISymbolicExpressionTreeNode MakeSum(ISymbolicExpressionTreeNode treeNode, double alpha) {
    124       if (alpha.IsAlmost(0.0)) {
    125         return treeNode;
    126       } else {
    127         var addition = new Addition();
    128         var node = addition.CreateTreeNode();
    129         var alphaConst = MakeConstant(alpha);
    130         node.AddSubtree(treeNode);
    131         node.AddSubtree(alphaConst);
    132         return node;
    133       }
    134     }
    135 
    136     private static ISymbolicExpressionTreeNode MakeProduct(ISymbolicExpressionTreeNode treeNode, double beta) {
    137       if (beta.IsAlmost(1.0)) {
    138         return treeNode;
    139       } else {
    140         var multipliciation = new Multiplication();
    141         var node = multipliciation.CreateTreeNode();
    142         var betaConst = MakeConstant(beta);
    143         node.AddSubtree(treeNode);
    144         node.AddSubtree(betaConst);
    145         return node;
    146       }
    147     }
    148 
    149     private static ISymbolicExpressionTreeNode MakeConstant(double c) {
    150       var node = (ConstantTreeNode)(new Constant()).CreateTreeNode();
    151       node.Value = c;
    152       return node;
    153     }
    15471  }
    15572}
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