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

Last change on this file since 18103 was 18103, checked in by dpiringe, 8 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: 5.0 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 HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
28using HEAL.Attic;
29
30namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
31  [Item("Mean squared error & Tree size Evaluator", "Calculates the mean squared error and the tree size of a symbolic regression solution.")]
32  [StorableType("B1EFB303-9C37-4CBB-8269-BDBC223D9086")]
33  public class SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
34    [StorableConstructor]
35    protected SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(StorableConstructorFlag _) : base(_) { }
36    protected SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator original, Cloner cloner)
37      : base(original, cloner) {
38    }
39    public override IDeepCloneable Clone(Cloner cloner) {
40      return new SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(this, cloner);
41    }
42
43    public SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator() : base() { }
44
45    public override IEnumerable<bool> Maximization { get { return new bool[2] { false, false }; } }
46
47    public override IOperation InstrumentedApply() {
48      IEnumerable<int> rows = GenerateRowsToEvaluate();
49      var tree = SymbolicExpressionTreeParameter.ActualValue;
50      var problemData = ProblemDataParameter.ActualValue;
51      var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
52      var estimationLimits = EstimationLimitsParameter.ActualValue;
53      var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
54
55      if (UseConstantOptimization) {
56        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, tree, problemData, rows, applyLinearScaling, ConstantOptimizationIterations, updateVariableWeights: ConstantOptimizationUpdateVariableWeights, lowerEstimationLimit: estimationLimits.Lower, upperEstimationLimit: estimationLimits.Upper);
57      }
58
59      double[] qualities = Calculate(
60        tree, ProblemDataParameter.ActualValue,
61        rows, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
62        ApplyLinearScalingParameter.ActualValue.Value,
63        EstimationLimitsParameter.ActualValue.Lower,
64        EstimationLimitsParameter.ActualValue.Upper,
65        DecimalPlaces);
66      QualitiesParameter.ActualValue = new DoubleArray(qualities);
67      return base.InstrumentedApply();
68    }
69
70    public static double[] Calculate(
71      ISymbolicExpressionTree tree,
72      IRegressionProblemData problemData,
73      IEnumerable<int> rows,
74      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
75      bool applyLinearScaling,
76      double lowerEstimationLimit, double upperEstimationLimit,
77      int decimalPlaces) {
78      var mse = SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.Calculate(
79        tree, problemData, rows,
80        interpreter, applyLinearScaling,
81        lowerEstimationLimit,
82        upperEstimationLimit);
83
84      if (decimalPlaces >= 0)
85        mse = Math.Round(mse, decimalPlaces);
86
87      return new double[2] { mse, tree.Length };
88    }
89
90    public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
91      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
92      EstimationLimitsParameter.ExecutionContext = context;
93      ApplyLinearScalingParameter.ExecutionContext = context;
94
95      double[] quality = Calculate(
96        tree, problemData, rows,
97        SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
98        ApplyLinearScalingParameter.ActualValue.Value,
99        EstimationLimitsParameter.ActualValue.Lower,
100        EstimationLimitsParameter.ActualValue.Upper, DecimalPlaces);
101
102      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
103      EstimationLimitsParameter.ExecutionContext = null;
104      ApplyLinearScalingParameter.ExecutionContext = null;
105
106      return quality;
107    }
108  }
109}
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