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source: branches/3044_variableScaling/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator.cs @ 17387

Last change on this file since 17387 was 17180, checked in by swagner, 5 years ago

#2875: Removed years in copyrights

File size: 4.7 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System.Collections.Generic;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
27using HEAL.Attic;
28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
30  [Item("Pearson R² evaluator", "Calculates the square of the pearson correlation coefficient (also known as coefficient of determination) of a symbolic classification solution.")]
31  [StorableType("F6F480F9-21DC-4D22-9D5C-5951906BEB79")]
32  public class SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator : SymbolicClassificationSingleObjectiveEvaluator {
33    [StorableConstructor]
34    protected SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator(StorableConstructorFlag _) : base(_) { }
35    protected SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator(SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator original, Cloner cloner)
36      : base(original, cloner) {
37    }
38    public override IDeepCloneable Clone(Cloner cloner) {
39      return new SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator(this, cloner);
40    }
41
42    public SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator() : base() { }
43
44    public override bool Maximization { get { return true; } }
45
46    public override IOperation InstrumentedApply() {
47      IEnumerable<int> rows = GenerateRowsToEvaluate();
48      var solution = SymbolicExpressionTreeParameter.ActualValue;
49      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
50      QualityParameter.ActualValue = new DoubleValue(quality);
51      return base.InstrumentedApply();
52    }
53
54    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
55      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
56      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
57      OnlineCalculatorError errorState;
58
59      double r;
60      if (applyLinearScaling) {
61        var rCalculator = new OnlinePearsonsRCalculator();
62        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, rCalculator, problemData.Dataset.Rows);
63        errorState = rCalculator.ErrorState;
64        r = rCalculator.R;
65      } else {
66        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
67        r = OnlinePearsonsRCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
68      }
69      if (errorState != OnlineCalculatorError.None) return double.NaN;
70      return r*r;
71    }
72
73    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IClassificationProblemData problemData, IEnumerable<int> rows) {
74      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
75      EstimationLimitsParameter.ExecutionContext = context;
76      ApplyLinearScalingParameter.ExecutionContext = context;
77
78      double r2 = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
79
80      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
81      EstimationLimitsParameter.ExecutionContext = null;
82      ApplyLinearScalingParameter.ExecutionContext = null;
83
84      return r2;
85    }
86  }
87}
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