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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredNumberOfVariablesEvaluator.cs @ 13300

Last change on this file since 13300 was 13300, checked in by gkronber, 8 years ago

#2175 made some changes while reviewing the code

File size: 4.9 KB
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[11407]1#region License Information
2/* HeuristicLab
[12849]3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[11407]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
[12147]22using System;
[11407]23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30
31namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
[11883]32  [Item("Pearson R² & Number of Variables Evaluator", "Calculates the Pearson R² and the number of used variables of a symbolic regression solution.")]
[11407]33  [StorableClass]
[11883]34  public class PearsonRSquaredNumberOfVariablesEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
[11407]35    [StorableConstructor]
[11883]36    protected PearsonRSquaredNumberOfVariablesEvaluator(bool deserializing) : base(deserializing) { }
37    protected PearsonRSquaredNumberOfVariablesEvaluator(PearsonRSquaredNumberOfVariablesEvaluator original, Cloner cloner)
[11407]38      : base(original, cloner) {
39    }
40    public override IDeepCloneable Clone(Cloner cloner) {
[11883]41      return new PearsonRSquaredNumberOfVariablesEvaluator(this, cloner);
[11407]42    }
43
[12848]44    public PearsonRSquaredNumberOfVariablesEvaluator() : base() { }
[11407]45
[13300]46    public override IEnumerable<bool> Maximization { get { return new bool[2] { true, false }; } } // maximize R² and minimize the number of variables
[11407]47
48    public override IOperation InstrumentedApply() {
49      IEnumerable<int> rows = GenerateRowsToEvaluate();
50      var solution = SymbolicExpressionTreeParameter.ActualValue;
[11883]51      var problemData = ProblemDataParameter.ActualValue;
52      var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
53      var estimationLimits = EstimationLimitsParameter.ActualValue;
54      var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
55
56      if (UseConstantOptimization) {
[13211]57        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows, applyLinearScaling, ConstantOptimizationIterations, estimationLimits.Upper, estimationLimits.Lower);
[11883]58      }
[12848]59      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value, DecimalPlaces);
[11407]60      QualitiesParameter.ActualValue = new DoubleArray(qualities);
61      return base.InstrumentedApply();
62    }
63
[12848]64    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling, int decimalPlaces) {
[11407]65      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
[12848]66      if (decimalPlaces >= 0)
67        r2 = Math.Round(r2, decimalPlaces);
[13300]68      return new double[2] { r2, solution.IterateNodesPostfix().OfType<VariableTreeNode>().Count() }; // count the number of variables
[11407]69    }
70
71    public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
72      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
73      EstimationLimitsParameter.ExecutionContext = context;
74      ApplyLinearScalingParameter.ExecutionContext = context;
[13300]75      // DecimalPlaces parameter is a FixedValueParameter and doesn't need the context.
[11407]76
[12848]77      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value, DecimalPlaces);
[11407]78
79      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
80      EstimationLimitsParameter.ExecutionContext = null;
81      ApplyLinearScalingParameter.ExecutionContext = null;
82
83      return quality;
84    }
85  }
86}
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