source: trunk/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.cs

Last change on this file was 18220, checked in by gkronber, 6 months ago

#3136: reintegrated structure-template GP branch into trunk

File size: 5.3 KB
Line 
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.Regression {
30  [Item("Pearson R² Evaluator", "Calculates the square of the pearson correlation coefficient (also known as coefficient of determination) of a symbolic regression solution.")]
31  [StorableType("6FAEC6C2-C747-452A-A60D-29AE37898A90")]
32  public class SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
33    [StorableConstructor]
34    protected SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(StorableConstructorFlag _) : base(_) { }
35    protected SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator original, Cloner cloner)
36      : base(original, cloner) {
37    }
38    public override IDeepCloneable Clone(Cloner cloner) {
39      return new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(this, cloner);
40    }
41
42    public SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator() : base() { }
43
44    public override bool Maximization { get { return true; } }
45
46    public override IOperation InstrumentedApply() {
47      var solution = SymbolicExpressionTreeParameter.ActualValue;
48      IEnumerable<int> rows = GenerateRowsToEvaluate();
49
50      double quality = Calculate(
51        solution, ProblemDataParameter.ActualValue,
52        rows, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
53        ApplyLinearScalingParameter.ActualValue.Value,
54        EstimationLimitsParameter.ActualValue.Lower,
55        EstimationLimitsParameter.ActualValue.Upper);
56      QualityParameter.ActualValue = new DoubleValue(quality);
57
58      return base.InstrumentedApply();
59    }
60
61    public static double Calculate(
62      ISymbolicExpressionTree tree,
63      IRegressionProblemData problemData,
64      IEnumerable<int> rows,
65      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
66      bool applyLinearScaling,
67      double lowerEstimationLimit,
68      double upperEstimationLimit) {
69      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows);
70      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
71      OnlineCalculatorError errorState;
72
73      double r;
74      if (applyLinearScaling) {
75        var rCalculator = new OnlinePearsonsRCalculator();
76        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, rCalculator, problemData.Dataset.Rows);
77        errorState = rCalculator.ErrorState;
78        r = rCalculator.R;
79      } else {
80        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
81        r = OnlinePearsonsRCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
82      }
83      if (errorState != OnlineCalculatorError.None) return double.NaN;
84      return r*r;
85    }
86
87    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
88      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
89      EstimationLimitsParameter.ExecutionContext = context;
90      ApplyLinearScalingParameter.ExecutionContext = context;
91
92      double r2 = Calculate(
93         tree, problemData, rows,
94         SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
95         ApplyLinearScalingParameter.ActualValue.Value,
96         EstimationLimitsParameter.ActualValue.Lower,
97         EstimationLimitsParameter.ActualValue.Upper);
98
99      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
100      EstimationLimitsParameter.ExecutionContext = null;
101      ApplyLinearScalingParameter.ExecutionContext = null;
102
103      return r2;
104    }
105
106    public override double Evaluate(
107      ISymbolicExpressionTree tree,
108      IRegressionProblemData problemData,
109      IEnumerable<int> rows,
110      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
111      bool applyLinearScaling = true,
112      double lowerEstimationLimit = double.MinValue,
113      double upperEstimationLimit = double.MaxValue) {
114      return Calculate(
115        tree, problemData, rows,
116        interpreter, applyLinearScaling,
117        lowerEstimationLimit, upperEstimationLimit);
118    }
119  }
120}
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