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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator.cs @ 5851

Last change on this file since 5851 was 5851, checked in by gkronber, 13 years ago

#1411 added evaluated nodes parameter to symbolic data analysis evaluators.

File size: 4.2 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 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.Linq;
24using System.Collections.Generic;
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.Classification {
32  [Item("Pearson R² evaluator", "Calculates the square of the pearson correlation coefficient (also known as coefficient of determination) of a symbolic classification solution.")]
33  [StorableClass]
34  public class SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator : SymbolicClassificationSingleObjectiveEvaluator {
35    [StorableConstructor]
36    protected SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator(bool deserializing) : base(deserializing) { }
37    protected SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator(SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator original, Cloner cloner)
38      : base(original, cloner) {
39    }
40    public override IDeepCloneable Clone(Cloner cloner) {
41      return new SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator(this, cloner);
42    }
43
44    public SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator() : base() { }
45
46    public override bool Maximization { get { return true; } }
47
48    public override IOperation Apply() {
49      IEnumerable<int> rows = GenerateRowsToEvaluate();
50      var solution = SymbolicExpressionTreeParameter.ActualValue;
51      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
52      QualityParameter.ActualValue = new DoubleValue(quality);
53      AddEvaluatedNodes(solution.Length * rows.Count());
54      return base.Apply();
55    }
56
57    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable<int> rows) {
58      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
59      IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
60      double r2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedValues, originalValues);
61      return double.IsNaN(r2) ? 0.0 : r2;
62    }
63
64    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IClassificationProblemData problemData, IEnumerable<int> rows) {
65      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
66      EstimationLimitsParameter.ExecutionContext = context;
67      EvaluatedNodesParameter.ExecutionContext = context;
68
69      double r2 = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
70      AddEvaluatedNodes(tree.Length * rows.Count());
71
72      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
73      EstimationLimitsParameter.ExecutionContext = null;
74      EvaluatedNodesParameter.ExecutionContext = null;
75
76      return r2;
77    }
78  }
79}
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