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

Last change on this file since 11407 was 11407, checked in by mkommend, 8 years ago

#2175: Added nested tree size evaluator and simplification operator.

File size: 4.2 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.Collections.Generic;
23using System.Linq;
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 & Nested Tree size Evaluator", "Calculates the Pearson R² and the nested tree size of a symbolic regression solution.")]
32  [StorableClass]
33  public class SymbolicRegressionMultiObjectivePearsonRSquaredNestedTreeSizeEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
34    [StorableConstructor]
35    protected SymbolicRegressionMultiObjectivePearsonRSquaredNestedTreeSizeEvaluator(bool deserializing) : base(deserializing) { }
36    protected SymbolicRegressionMultiObjectivePearsonRSquaredNestedTreeSizeEvaluator(SymbolicRegressionMultiObjectivePearsonRSquaredNestedTreeSizeEvaluator original, Cloner cloner)
37      : base(original, cloner) {
38    }
39    public override IDeepCloneable Clone(Cloner cloner) {
40      return new SymbolicRegressionMultiObjectivePearsonRSquaredNestedTreeSizeEvaluator(this, cloner);
41    }
42
43    public SymbolicRegressionMultiObjectivePearsonRSquaredNestedTreeSizeEvaluator() : 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      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
51      QualitiesParameter.ActualValue = new DoubleArray(qualities);
52      return base.InstrumentedApply();
53    }
54
55    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
56      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
57      return new double[2] { r2, solution.IterateNodesPostfix().Sum(n => n.GetLength()) };
58    }
59
60    public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
61      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
62      EstimationLimitsParameter.ExecutionContext = context;
63      ApplyLinearScalingParameter.ExecutionContext = context;
64
65      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
66
67      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
68      EstimationLimitsParameter.ExecutionContext = null;
69      ApplyLinearScalingParameter.ExecutionContext = null;
70
71      return quality;
72    }
73  }
74}
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