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Ignore:
Timestamp:
03/23/11 13:52:29 (13 years ago)
Author:
gkronber
Message:

#1418 Fixed a problem with scaling of regression and classification solutions (moved scale method out of solution into the model because of leaky abstraction).

Location:
trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4
Files:
6 edited

Legend:

Unmodified
Added
Removed

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

    r5809 r5818  
    7777    protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQuality) {
    7878      var model = new SymbolicRegressionModel(bestTree, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
    79       var solution = new SymbolicRegressionSolution(model, ProblemDataParameter.ActualValue);
    8079      if (ApplyLinearScaling.Value)
    81         solution.ScaleModel();
    82       return solution;
     80        SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue);
     81      return new SymbolicRegressionSolution(model, ProblemDataParameter.ActualValue);
    8382    }
    8483  }

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

    r5809 r5818  
    6666    protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQuality) {
    6767      var model = new SymbolicRegressionModel(bestTree, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
    68       var solution = new SymbolicRegressionSolution(model, ProblemDataParameter.ActualValue);
    6968      if (ApplyLinearScaling.Value)
    70         solution.ScaleModel();
    71       return solution;
     69        SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue);
     70      return new SymbolicRegressionSolution(model, ProblemDataParameter.ActualValue);
    7271    }
    7372  }

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

    r5809 r5818  
    7676    protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
    7777      var model = new SymbolicRegressionModel(bestTree, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
    78       var solution = new SymbolicRegressionSolution(model, ProblemDataParameter.ActualValue);
    7978      if (ApplyLinearScaling.Value)
    80         solution.ScaleModel();
    81       return solution;
     79        SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue);
     80      return new SymbolicRegressionSolution(model, ProblemDataParameter.ActualValue);
    8281    }
    8382  }

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

    r5809 r5818  
    6868    protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
    6969      var model = new SymbolicRegressionModel(bestTree, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
    70       var solution = new SymbolicRegressionSolution(model, ProblemDataParameter.ActualValue);
    7170      if (ApplyLinearScaling.Value)
    72         solution.ScaleModel();
    73       return solution;
     71        SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue);
     72      return new SymbolicRegressionSolution(model, ProblemDataParameter.ActualValue);
    7473    }
    7574  }

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

    r5809 r5818  
    6666        .LimitToRange(lowerEstimationLimit, upperEstimationLimit);
    6767    }
     68
     69    public static void Scale(SymbolicRegressionModel model, IRegressionProblemData problemData) {
     70      var dataset = problemData.Dataset;
     71      var targetVariable = problemData.TargetVariable;
     72      var rows = problemData.TrainingIndizes;
     73      var estimatedValues = model.Interpreter.GetSymbolicExpressionTreeValues(model.SymbolicExpressionTree, dataset, rows);
     74      var targetValues = dataset.GetEnumeratedVariableValues(targetVariable, rows);
     75      double alpha;
     76      double beta;
     77      OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out alpha, out beta);
     78
     79      ConstantTreeNode alphaTreeNode = null;
     80      ConstantTreeNode betaTreeNode = null;
     81      // check if model has been scaled previously by analyzing the structure of the tree
     82      var startNode = model.SymbolicExpressionTree.Root.GetSubtree(0);
     83      if (startNode.GetSubtree(0).Symbol is Addition) {
     84        var addNode = startNode.GetSubtree(0);
     85        if (addNode.SubtreesCount == 2 && addNode.GetSubtree(0).Symbol is Multiplication && addNode.GetSubtree(1).Symbol is Constant) {
     86          alphaTreeNode = addNode.GetSubtree(1) as ConstantTreeNode;
     87          var mulNode = addNode.GetSubtree(0);
     88          if (mulNode.SubtreesCount == 2 && mulNode.GetSubtree(1).Symbol is Constant) {
     89            betaTreeNode = mulNode.GetSubtree(1) as ConstantTreeNode;
     90          }
     91        }
     92      }
     93      // if tree structure matches the structure necessary for linear scaling then reuse the existing tree nodes
     94      if (alphaTreeNode != null && betaTreeNode != null) {
     95        betaTreeNode.Value *= beta;
     96        alphaTreeNode.Value *= beta;
     97        alphaTreeNode.Value += alpha;
     98      } else {
     99        var mainBranch = startNode.GetSubtree(0);
     100        startNode.RemoveSubtree(0);
     101        var scaledMainBranch = MakeSum(MakeProduct(beta, mainBranch), alpha);
     102        startNode.AddSubtree(scaledMainBranch);
     103      }
     104    }
     105
     106    private static ISymbolicExpressionTreeNode MakeSum(ISymbolicExpressionTreeNode treeNode, double alpha) {
     107      if (alpha.IsAlmost(0.0)) {
     108        return treeNode;
     109      } else {
     110        var node = (new Addition()).CreateTreeNode();
     111        var alphaConst = MakeConstant(alpha);
     112        node.AddSubtree(treeNode);
     113        node.AddSubtree(alphaConst);
     114        return node;
     115      }
     116    }
     117
     118    private static ISymbolicExpressionTreeNode MakeProduct(double beta, ISymbolicExpressionTreeNode treeNode) {
     119      if (beta.IsAlmost(1.0)) {
     120        return treeNode;
     121      } else {
     122        var node = (new Multiplication()).CreateTreeNode();
     123        var betaConst = MakeConstant(beta);
     124        node.AddSubtree(treeNode);
     125        node.AddSubtree(betaConst);
     126        return node;
     127      }
     128    }
     129
     130    private static ISymbolicExpressionTreeNode MakeConstant(double c) {
     131      var node = (ConstantTreeNode)(new Constant()).CreateTreeNode();
     132      node.Value = c;
     133      return node;
     134    }
    68135  }
    69136}

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

    r5809 r5818  
    8484      ModelDepth = Model.SymbolicExpressionTree.Depth;
    8585    }
    86 
    87     public void ScaleModel() {
    88       var dataset = ProblemData.Dataset;
    89       var targetVariable = ProblemData.TargetVariable;
    90       var rows = ProblemData.TrainingIndizes;
    91       var estimatedValues = GetEstimatedValues(rows);
    92       var targetValues = dataset.GetEnumeratedVariableValues(targetVariable, rows);
    93       double alpha;
    94       double beta;
    95       OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out alpha, out beta);
    96 
    97       ConstantTreeNode alphaTreeNode = null;
    98       ConstantTreeNode betaTreeNode = null;
    99       // check if model has been scaled previously by analyzing the structure of the tree
    100       var startNode = Model.SymbolicExpressionTree.Root.GetSubtree(0);
    101       if (startNode.GetSubtree(0).Symbol is Addition) {
    102         var addNode = startNode.GetSubtree(0);
    103         if (addNode.SubtreesCount == 2 && addNode.GetSubtree(0).Symbol is Multiplication && addNode.GetSubtree(1).Symbol is Constant) {
    104           alphaTreeNode = addNode.GetSubtree(1) as ConstantTreeNode;
    105           var mulNode = addNode.GetSubtree(0);
    106           if (mulNode.SubtreesCount == 2 && mulNode.GetSubtree(1).Symbol is Constant) {
    107             betaTreeNode = mulNode.GetSubtree(1) as ConstantTreeNode;
    108           }
    109         }
    110       }
    111       // if tree structure matches the structure necessary for linear scaling then reuse the existing tree nodes
    112       if (alphaTreeNode != null && betaTreeNode != null) {
    113         betaTreeNode.Value *= beta;
    114         alphaTreeNode.Value *= beta;
    115         alphaTreeNode.Value += alpha;
    116       } else {
    117         var mainBranch = startNode.GetSubtree(0);
    118         startNode.RemoveSubtree(0);
    119         var scaledMainBranch = MakeSum(MakeProduct(beta, mainBranch), alpha);
    120         startNode.AddSubtree(scaledMainBranch);
    121       }
    122 
    123       OnModelChanged(EventArgs.Empty);
    124     }
    125 
    126     private static ISymbolicExpressionTreeNode MakeSum(ISymbolicExpressionTreeNode treeNode, double alpha) {
    127       if (alpha.IsAlmost(0.0)) {
    128         return treeNode;
    129       } else {
    130         var node = (new Addition()).CreateTreeNode();
    131         var alphaConst = MakeConstant(alpha);
    132         node.AddSubtree(treeNode);
    133         node.AddSubtree(alphaConst);
    134         return node;
    135       }
    136     }
    137 
    138     private static ISymbolicExpressionTreeNode MakeProduct(double beta, ISymbolicExpressionTreeNode treeNode) {
    139       if (beta.IsAlmost(1.0)) {
    140         return treeNode;
    141       } else {
    142         var node = (new Multiplication()).CreateTreeNode();
    143         var betaConst = MakeConstant(beta);
    144         node.AddSubtree(treeNode);
    145         node.AddSubtree(betaConst);
    146         return node;
    147       }
    148     }
    149 
    150     private static ISymbolicExpressionTreeNode MakeConstant(double c) {
    151       var node = (ConstantTreeNode)(new Constant()).CreateTreeNode();
    152       node.Value = c;
    153       return node;
    154     }
    15586  }
    15687}
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