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source: branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectivePearsonRSquaredTreeComplexityEvaluator.cs @ 11407

Last change on this file since 11407 was 11310, checked in by mkommend, 10 years ago

#2175: Merged trunk changes into complexity branch.

File size: 5.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2013 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 HeuristicLab.Parameters;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29
30namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
31  [Item("Pearson R² & Tree Complexity Evaluator", "Calculates the Pearson R² and the tree complexity of a symbolic regression solution.")]
32  [StorableClass]
33  public class SymbolicRegressionMultiObjectivePearsonRSquaredTreeComplexityEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
34    private string useConstantOptimizationParameterName = "Use constant optimization";
35
36    public IFixedValueParameter<BoolValue> UseConstantOptimizationParameter {
37      get { return (IFixedValueParameter<BoolValue>)Parameters[useConstantOptimizationParameterName]; }
38    }
39
40    public bool UseConstantOptimization {
41      get { return UseConstantOptimizationParameter.Value.Value; }
42      set { UseConstantOptimizationParameter.Value.Value = value; }
43    }
44
45    [StorableConstructor]
46    protected SymbolicRegressionMultiObjectivePearsonRSquaredTreeComplexityEvaluator(bool deserializing) : base(deserializing) { }
47    protected SymbolicRegressionMultiObjectivePearsonRSquaredTreeComplexityEvaluator(SymbolicRegressionMultiObjectivePearsonRSquaredTreeComplexityEvaluator original, Cloner cloner)
48      : base(original, cloner) {
49    }
50    public override IDeepCloneable Clone(Cloner cloner) {
51      return new SymbolicRegressionMultiObjectivePearsonRSquaredTreeComplexityEvaluator(this, cloner);
52    }
53
54    public SymbolicRegressionMultiObjectivePearsonRSquaredTreeComplexityEvaluator()
55      : base() {
56      Parameters.Add(new FixedValueParameter<BoolValue>(useConstantOptimizationParameterName, "", new BoolValue(false)));
57
58    }
59
60    [StorableHook(HookType.AfterDeserialization)]
61    private void AfterDeserialization() {
62      if (!Parameters.ContainsKey(useConstantOptimizationParameterName)) {
63        Parameters.Add(new FixedValueParameter<BoolValue>(useConstantOptimizationParameterName, "", new BoolValue(false)));
64      }
65    }
66
67    public override IEnumerable<bool> Maximization { get { return new bool[2] { true, false }; } }
68
69    public override IOperation InstrumentedApply() {
70      IEnumerable<int> rows = GenerateRowsToEvaluate();
71      var solution = SymbolicExpressionTreeParameter.ActualValue;
72      var problemData = ProblemDataParameter.ActualValue;
73      var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
74      var estimationLimits = EstimationLimitsParameter.ActualValue;
75      var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
76
77      if (UseConstantOptimization) {
78        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows, applyLinearScaling, 5, estimationLimits.Upper, estimationLimits.Lower);
79      }
80      double[] qualities = Calculate(interpreter, solution, estimationLimits.Lower, estimationLimits.Upper, problemData, rows, applyLinearScaling);
81      QualitiesParameter.ActualValue = new DoubleArray(qualities);
82      return base.InstrumentedApply();
83    }
84
85    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
86      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
87      return new double[2] { r2, SymbolicDataAnalysisModelComplexityAnalyzer.CalculateComplexity(solution.Root.GetSubtree(0).GetSubtree(0)) };
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(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
96
97      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
98      EstimationLimitsParameter.ExecutionContext = null;
99      ApplyLinearScalingParameter.ExecutionContext = null;
100
101      return quality;
102    }
103  }
104}
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