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source: branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredNestedTreeSizeEvaluator.cs @ 12147

Last change on this file since 12147 was 12147, checked in by mkommend, 9 years ago

#2175: Added rounding of quality values to multi-objective sym reg evaluators.

File size: 5.2 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2014 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 HeuristicLab.Parameters;
30using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
31
32namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
33  [Item("Pearson R² & Nested Tree size Evaluator", "Calculates the Pearson R² and the nested tree size of a symbolic regression solution.")]
34  [StorableClass]
35  public class PearsonRSquaredNestedTreeSizeEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
36    private const string useConstantOptimizationParameterName = "Use constant optimization";
37    public IFixedValueParameter<BoolValue> UseConstantOptimizationParameter {
38      get { return (IFixedValueParameter<BoolValue>)Parameters[useConstantOptimizationParameterName]; }
39    }
40    public bool UseConstantOptimization {
41      get { return UseConstantOptimizationParameter.Value.Value; }
42      set { UseConstantOptimizationParameter.Value.Value = value; }
43    }
44
45    [StorableConstructor]
46    protected PearsonRSquaredNestedTreeSizeEvaluator(bool deserializing) : base(deserializing) { }
47    protected PearsonRSquaredNestedTreeSizeEvaluator(PearsonRSquaredNestedTreeSizeEvaluator original, Cloner cloner)
48      : base(original, cloner) {
49    }
50    public override IDeepCloneable Clone(Cloner cloner) {
51      return new PearsonRSquaredNestedTreeSizeEvaluator(this, cloner);
52    }
53
54    public PearsonRSquaredNestedTreeSizeEvaluator()
55      : base() {
56      Parameters.Add(new FixedValueParameter<BoolValue>(useConstantOptimizationParameterName, "", new BoolValue(false)));
57    }
58
59    public override IEnumerable<bool> Maximization { get { return new bool[2] { true, false }; } }
60
61    public override IOperation InstrumentedApply() {
62      IEnumerable<int> rows = GenerateRowsToEvaluate();
63      var solution = SymbolicExpressionTreeParameter.ActualValue;
64      var problemData = ProblemDataParameter.ActualValue;
65      var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
66      var estimationLimits = EstimationLimitsParameter.ActualValue;
67      var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
68
69      if (UseConstantOptimization) {
70        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows, applyLinearScaling, 5, estimationLimits.Upper, estimationLimits.Lower);
71      }
72
73      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
74      QualitiesParameter.ActualValue = new DoubleArray(qualities);
75      return base.InstrumentedApply();
76    }
77
78    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
79      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
80      r2 = Math.Round(r2, 3);
81      return new double[2] { r2, solution.IterateNodesPostfix().Sum(n => n.GetLength()) };
82    }
83
84    public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
85      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
86      EstimationLimitsParameter.ExecutionContext = context;
87      ApplyLinearScalingParameter.ExecutionContext = context;
88
89      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
90
91      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
92      EstimationLimitsParameter.ExecutionContext = null;
93      ApplyLinearScalingParameter.ExecutionContext = null;
94
95      return quality;
96    }
97  }
98}
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