source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionSolutionImpactValuesCalculator.cs @ 12641

Last change on this file since 12641 was 12641, checked in by gkronber, 6 years ago

#2392: fixed all warnings by using PearsonsRCalculator instead of PearsonsRSquaredCalculator

File size: 4.7 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.Encodings.SymbolicExpressionTreeEncoding;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
30  [StorableClass]
31  [Item("SymbolicRegressionSolutionImpactValuesCalculator", "Calculate symbolic expression tree node impact values for regression problems.")]
32  public class SymbolicRegressionSolutionImpactValuesCalculator : SymbolicDataAnalysisSolutionImpactValuesCalculator {
33    public SymbolicRegressionSolutionImpactValuesCalculator() { }
34
35    protected SymbolicRegressionSolutionImpactValuesCalculator(SymbolicRegressionSolutionImpactValuesCalculator original, Cloner cloner)
36      : base(original, cloner) { }
37    public override IDeepCloneable Clone(Cloner cloner) {
38      return new SymbolicRegressionSolutionImpactValuesCalculator(this, cloner);
39    }
40
41    [StorableConstructor]
42    protected SymbolicRegressionSolutionImpactValuesCalculator(bool deserializing) : base(deserializing) { }
43    public override double CalculateReplacementValue(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData, IEnumerable<int> rows) {
44      var regressionModel = (ISymbolicRegressionModel)model;
45      var regressionProblemData = (IRegressionProblemData)problemData;
46
47      return CalculateReplacementValue(node, regressionModel.SymbolicExpressionTree, regressionModel.Interpreter, regressionProblemData.Dataset, rows);
48    }
49
50    public override double CalculateImpactValue(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData, IEnumerable<int> rows, double originalQuality = double.NaN) {
51      double impactValue, replacementValue;
52      CalculateImpactAndReplacementValues(model, node, problemData, rows, out impactValue, out replacementValue, originalQuality);
53      return impactValue;
54    }
55
56    public override void CalculateImpactAndReplacementValues(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node,
57      IDataAnalysisProblemData problemData, IEnumerable<int> rows, out double impactValue, out double replacementValue,
58      double originalQuality = Double.NaN) {
59      var regressionModel = (ISymbolicRegressionModel)model;
60      var regressionProblemData = (IRegressionProblemData)problemData;
61
62      var dataset = regressionProblemData.Dataset;
63      var targetValues = dataset.GetDoubleValues(regressionProblemData.TargetVariable, rows);
64
65      OnlineCalculatorError errorState;
66      if (double.IsNaN(originalQuality)) {
67        var originalValues = regressionModel.GetEstimatedValues(dataset, rows);
68        originalQuality = OnlinePearsonsRCalculator.Calculate(targetValues, originalValues, out errorState);
69        if (errorState != OnlineCalculatorError.None) originalQuality = 0.0;
70      }
71
72      replacementValue = CalculateReplacementValue(regressionModel, node, regressionProblemData, rows);
73      var constantNode = new ConstantTreeNode(new Constant()) { Value = replacementValue };
74
75      var cloner = new Cloner();
76      var tempModel = cloner.Clone(regressionModel);
77      var tempModelNode = (ISymbolicExpressionTreeNode)cloner.GetClone(node);
78
79      var tempModelParentNode = tempModelNode.Parent;
80      int i = tempModelParentNode.IndexOfSubtree(tempModelNode);
81      tempModelParentNode.RemoveSubtree(i);
82      tempModelParentNode.InsertSubtree(i, constantNode);
83
84      var estimatedValues = tempModel.GetEstimatedValues(dataset, rows);
85      double newQuality = OnlinePearsonsRCalculator.Calculate(targetValues, estimatedValues, out errorState);
86      if (errorState != OnlineCalculatorError.None) newQuality = 0.0;
87
88      impactValue = (originalQuality*originalQuality) - (newQuality*newQuality);
89    }
90  }
91}
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