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Ignore:
Timestamp:
03/17/15 14:35:41 (9 years ago)
Author:
mkommend
Message:

#2175: Merged trunk changes into complexity branch.

Location:
branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression
Files:
3 edited

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  • branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression

  • branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4

  • branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionPruningOperator.cs

    r12130 r12214  
    2222#endregion
    2323
     24using System.Collections.Generic;
    2425using System.Linq;
    2526using HeuristicLab.Common;
    2627using HeuristicLab.Core;
    27 using HeuristicLab.Parameters;
     28using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    2829using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    2930
     
    3233  [Item("SymbolicRegressionPruningOperator", "An operator which prunes symbolic regression trees.")]
    3334  public class SymbolicRegressionPruningOperator : SymbolicDataAnalysisExpressionPruningOperator {
    34     private const string ImpactValuesCalculatorParameterName = "ImpactValuesCalculator";
    35 
    3635    protected SymbolicRegressionPruningOperator(SymbolicRegressionPruningOperator original, Cloner cloner)
    3736      : base(original, cloner) {
     
    4443    protected SymbolicRegressionPruningOperator(bool deserializing) : base(deserializing) { }
    4544
    46     public SymbolicRegressionPruningOperator() {
    47       var impactValuesCalculator = new SymbolicRegressionSolutionImpactValuesCalculator();
    48       Parameters.Add(new ValueParameter<ISymbolicDataAnalysisSolutionImpactValuesCalculator>(ImpactValuesCalculatorParameterName, "The impact values calculator to be used for figuring out the node impacts.", impactValuesCalculator));
     45    public SymbolicRegressionPruningOperator(ISymbolicDataAnalysisSolutionImpactValuesCalculator impactValuesCalculator)
     46      : base(impactValuesCalculator) {
    4947    }
    5048
    51     protected override ISymbolicDataAnalysisModel CreateModel() {
    52       return new SymbolicRegressionModel(SymbolicExpressionTree, Interpreter, EstimationLimits.Lower, EstimationLimits.Upper);
     49    protected override ISymbolicDataAnalysisModel CreateModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData, DoubleLimit estimationLimits) {
     50      return new SymbolicRegressionModel(tree, interpreter, estimationLimits.Lower, estimationLimits.Upper);
    5351    }
    5452
     
    5654      var regressionModel = (IRegressionModel)model;
    5755      var regressionProblemData = (IRegressionProblemData)ProblemData;
    58       var trainingIndices = Enumerable.Range(FitnessCalculationPartition.Start, FitnessCalculationPartition.Size);
    59       var estimatedValues = regressionModel.GetEstimatedValues(ProblemData.Dataset, trainingIndices); // also bounds the values
    60       var targetValues = ProblemData.Dataset.GetDoubleValues(regressionProblemData.TargetVariable, trainingIndices);
     56      var rows = Enumerable.Range(FitnessCalculationPartition.Start, FitnessCalculationPartition.Size);
     57      return Evaluate(regressionModel, regressionProblemData, rows);
     58    }
     59
     60    private static double Evaluate(IRegressionModel model, IRegressionProblemData problemData,
     61      IEnumerable<int> rows) {
     62      var estimatedValues = model.GetEstimatedValues(problemData.Dataset, rows); // also bounds the values
     63      var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
    6164      OnlineCalculatorError errorState;
    6265      var quality = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, estimatedValues, out errorState);
     
    6467      return quality;
    6568    }
     69
     70    public static ISymbolicExpressionTree Prune(ISymbolicExpressionTree tree, SymbolicRegressionSolutionImpactValuesCalculator impactValuesCalculator, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IRegressionProblemData problemData, DoubleLimit estimationLimits, IEnumerable<int> rows, double nodeImpactThreshold = 0.0, bool pruneOnlyZeroImpactNodes = false) {
     71      var clonedTree = (ISymbolicExpressionTree)tree.Clone();
     72      var model = new SymbolicRegressionModel(clonedTree, interpreter, estimationLimits.Lower, estimationLimits.Upper);
     73      var nodes = clonedTree.IterateNodesPrefix().ToList();
     74      double quality = Evaluate(model, problemData, rows);
     75
     76      for (int i = 0; i < nodes.Count; ++i) {
     77        var node = nodes[i];
     78        if (node is ConstantTreeNode) continue;
     79
     80        double impactValue, replacementValue;
     81        impactValuesCalculator.CalculateImpactAndReplacementValues(model, node, problemData, rows, out impactValue, out replacementValue, quality);
     82
     83        if (pruneOnlyZeroImpactNodes) {
     84          if (!impactValue.IsAlmost(0.0)) continue;
     85        } else if (nodeImpactThreshold < impactValue) {
     86          continue;
     87        }
     88
     89        var constantNode = (ConstantTreeNode)node.Grammar.GetSymbol("Constant").CreateTreeNode();
     90        constantNode.Value = replacementValue;
     91
     92        ReplaceWithConstant(node, constantNode);
     93        i += node.GetLength() - 1; // skip subtrees under the node that was folded
     94
     95        quality -= impactValue;
     96      }
     97      return model.SymbolicExpressionTree;
     98    }
    6699  }
    67100}
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