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source: branches/HiveHiveEngine/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs @ 10479

Last change on this file since 10479 was 6740, checked in by mkommend, 13 years ago

#1597, #1609, #1640:

  • Corrected TableFileParser to handle empty rows correctly.
  • Refactored DataSet to store values in List<List> instead of a two-dimensional array.
  • Enable importing and storing string and datetime values.
  • Changed data access methods in dataset and adapted all concerning classes.
  • Changed interpreter to store the variable values for all rows during the compilation step.
File size: 3.3 KB
Line 
1using System.Collections.Generic;
2using HeuristicLab.Common;
3using HeuristicLab.Core;
4using HeuristicLab.Data;
5using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
6using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
7
8namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
9  [Item("Pearson R² & Tree size Evaluator", "Calculates the Pearson R² and the tree size of a symbolic classification solution.")]
10  [StorableClass]
11  public class SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator : SymbolicClassificationMultiObjectiveEvaluator {
12    [StorableConstructor]
13    protected SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator(bool deserializing) : base(deserializing) { }
14    protected SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator(SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator original, Cloner cloner)
15      : base(original, cloner) {
16    }
17    public override IDeepCloneable Clone(Cloner cloner) {
18      return new SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator(this, cloner);
19    }
20
21    public SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator() : base() { }
22
23    public override IEnumerable<bool> Maximization { get { return new bool[2] { true, false }; } }
24
25    public override IOperation Apply() {
26      IEnumerable<int> rows = GenerateRowsToEvaluate();
27      var solution = SymbolicExpressionTreeParameter.ActualValue;
28      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
29      QualitiesParameter.ActualValue = new DoubleArray(qualities);
30      return base.Apply();
31    }
32
33    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable<int> rows) {
34      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
35      IEnumerable<double> originalValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
36      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 };
40
41    }
42
43    public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IClassificationProblemData problemData, IEnumerable<int> rows) {
44      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
45      EstimationLimitsParameter.ExecutionContext = context;
46
47      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
48
49      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
50      EstimationLimitsParameter.ExecutionContext = null;
51
52      return quality;
53    }
54  }
55}
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