source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs @ 13670

Last change on this file since 13670 was 13670, checked in by mkommend, 5 years ago

#2584: Added parameter in constant optimization that determines whether variable weights should be modified.

File size: 4.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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 HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29
30namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
31  [Item("Pearson R² & Tree size Evaluator", "Calculates the Pearson R² and the tree size of a symbolic regression solution.")]
32  [StorableClass]
33  public class SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
34    [StorableConstructor]
35    protected SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator(bool deserializing) : base(deserializing) { }
36    protected SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator(SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator original, Cloner cloner)
37      : base(original, cloner) {
38    }
39    public override IDeepCloneable Clone(Cloner cloner) {
40      return new SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator(this, cloner);
41    }
42
43    public SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator() : base() { }
44
45    public override IEnumerable<bool> Maximization { get { return new bool[2] { true, false }; } }
46
47    public override IOperation InstrumentedApply() {
48      IEnumerable<int> rows = GenerateRowsToEvaluate();
49      var solution = 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, solution, problemData, rows, applyLinearScaling, ConstantOptimizationIterations, updateVariableWeights: ConstantOptimizationUpdateVariableWeights, lowerEstimationLimit: estimationLimits.Lower, upperEstimationLimit: estimationLimits.Upper);
57      }
58      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value, DecimalPlaces);
59      QualitiesParameter.ActualValue = new DoubleArray(qualities);
60      return base.InstrumentedApply();
61    }
62
63    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling, int decimalPlaces) {
64      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
65      if (decimalPlaces >= 0)
66        r2 = Math.Round(r2, decimalPlaces);
67      return new double[2] { r2, solution.Length };
68    }
69
70    public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
71      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
72      EstimationLimitsParameter.ExecutionContext = context;
73      ApplyLinearScalingParameter.ExecutionContext = context;
74
75      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value, DecimalPlaces);
76
77      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
78      EstimationLimitsParameter.ExecutionContext = null;
79      ApplyLinearScalingParameter.ExecutionContext = null;
80
81      return quality;
82    }
83  }
84}
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