source: branches/3136_Structural_GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredNumberOfVariablesEvaluator.cs @ 18103

Last change on this file since 18103 was 18103, checked in by dpiringe, 9 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: 4.9 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;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
29using HEAL.Attic;
30
31namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
32  [Item("Pearson R² & Number of Variables Evaluator", "Calculates the Pearson R² and the number of used variables of a symbolic regression solution.")]
33  [StorableType("DF68BE26-D76C-4CB7-BB38-CBBB4391FE86")]
34  public class PearsonRSquaredNumberOfVariablesEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
35    [StorableConstructor]
36    protected PearsonRSquaredNumberOfVariablesEvaluator(StorableConstructorFlag _) : base(_) { }
37    protected PearsonRSquaredNumberOfVariablesEvaluator(PearsonRSquaredNumberOfVariablesEvaluator original, Cloner cloner)
38      : base(original, cloner) {
39    }
40    public override IDeepCloneable Clone(Cloner cloner) {
41      return new PearsonRSquaredNumberOfVariablesEvaluator(this, cloner);
42    }
43
44    public PearsonRSquaredNumberOfVariablesEvaluator() : base() { }
45
46    public override IEnumerable<bool> Maximization { get { return new bool[2] { true, false }; } } // maximize R² and minimize the number of variables
47
48    public override IOperation InstrumentedApply() {
49      IEnumerable<int> rows = GenerateRowsToEvaluate();
50      var solution = SymbolicExpressionTreeParameter.ActualValue;
51      var problemData = ProblemDataParameter.ActualValue;
52      var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
53      var estimationLimits = EstimationLimitsParameter.ActualValue;
54      var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
55
56      if (UseConstantOptimization) {
57        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows, applyLinearScaling, ConstantOptimizationIterations, updateVariableWeights: ConstantOptimizationUpdateVariableWeights, lowerEstimationLimit: estimationLimits.Lower, upperEstimationLimit: estimationLimits.Upper);
58      }
59      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value, DecimalPlaces);
60      QualitiesParameter.ActualValue = new DoubleArray(qualities);
61      return base.InstrumentedApply();
62    }
63
64    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree tree, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling, int decimalPlaces) {
65      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(
66        tree, problemData, rows, interpreter, applyLinearScaling,
67        lowerEstimationLimit, upperEstimationLimit);
68      if (decimalPlaces >= 0)
69        r2 = Math.Round(r2, decimalPlaces);
70      return new double[2] { r2, tree.IterateNodesPostfix().OfType<IVariableTreeNode>().Count() }; // count the number of variables
71    }
72
73    public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
74      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
75      EstimationLimitsParameter.ExecutionContext = context;
76      ApplyLinearScalingParameter.ExecutionContext = context;
77      // DecimalPlaces parameter is a FixedValueParameter and doesn't need the context.
78
79      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value, DecimalPlaces);
80
81      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
82      EstimationLimitsParameter.ExecutionContext = null;
83      ApplyLinearScalingParameter.ExecutionContext = null;
84
85      return quality;
86    }
87  }
88}
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