source: branches/3136_Structural_GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.cs @ 18103

Last change on this file since 18103 was 18103, checked in by dpiringe, 6 months ago

#3136

  • refactor the evaluation logic of NMSESingleObjectiveConstraintsEvaluator
  • refactor the new method Evaluate for PearsonRSquaredAverageSimilarityEvaluator
  • change the parameter order of some evaluate/calculate methods
File size: 5.4 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    //TODO: refactor like evaluate method
62    public static double Calculate(
63      ISymbolicExpressionTree tree,
64      IRegressionProblemData problemData,
65      IEnumerable<int> rows,
66      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
67      bool applyLinearScaling,
68      double lowerEstimationLimit,
69      double upperEstimationLimit) {
70      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows);
71      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
72      OnlineCalculatorError errorState;
73
74      double r;
75      if (applyLinearScaling) {
76        var rCalculator = new OnlinePearsonsRCalculator();
77        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, rCalculator, problemData.Dataset.Rows);
78        errorState = rCalculator.ErrorState;
79        r = rCalculator.R;
80      } else {
81        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
82        r = OnlinePearsonsRCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
83      }
84      if (errorState != OnlineCalculatorError.None) return double.NaN;
85      return r*r;
86    }
87
88    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
89      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
90      EstimationLimitsParameter.ExecutionContext = context;
91      ApplyLinearScalingParameter.ExecutionContext = context;
92
93      double r2 = Calculate(
94         tree, problemData, rows,
95         SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
96         ApplyLinearScalingParameter.ActualValue.Value,
97         EstimationLimitsParameter.ActualValue.Lower,
98         EstimationLimitsParameter.ActualValue.Upper);
99
100      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
101      EstimationLimitsParameter.ExecutionContext = null;
102      ApplyLinearScalingParameter.ExecutionContext = null;
103
104      return r2;
105    }
106
107    public override double Evaluate(
108      ISymbolicExpressionTree tree,
109      IRegressionProblemData problemData,
110      IEnumerable<int> rows,
111      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
112      bool applyLinearScaling = true,
113      double lowerEstimationLimit = double.MinValue,
114      double upperEstimationLimit = double.MaxValue) {
115      return Calculate(
116        tree, problemData, rows,
117        interpreter, applyLinearScaling,
118        lowerEstimationLimit, upperEstimationLimit);
119    }
120  }
121}
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