Free cookie consent management tool by TermsFeed Policy Generator

source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Evaluators/SymbolicRegressionPearsonsRSquaredEvaluator.cs @ 12467

Last change on this file since 12467 was 5275, checked in by gkronber, 14 years ago

Merged changes from trunk to data analysis exploration branch and added fractional distance metric evaluator. #1142

File size: 3.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2010 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;
28using HeuristicLab.Problems.DataAnalysis.Evaluators;
29using HeuristicLab.Problems.DataAnalysis.Symbolic;
30
31namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic {
32  [Item("SymbolicRegressionPearsonsRSquaredEvaluator", "Calculates the pearson r² correlation coefficient of a symbolic regression solution.")]
33  [StorableClass]
34  public class SymbolicRegressionPearsonsRSquaredEvaluator : SingleObjectiveSymbolicRegressionEvaluator {
35    [StorableConstructor]
36    protected SymbolicRegressionPearsonsRSquaredEvaluator(bool deserializing) : base(deserializing) { }
37    protected SymbolicRegressionPearsonsRSquaredEvaluator(SymbolicRegressionPearsonsRSquaredEvaluator original, Cloner cloner)
38      : base(original, cloner) {
39    }
40    public SymbolicRegressionPearsonsRSquaredEvaluator() : base() { }
41
42    public override IDeepCloneable Clone(Cloner cloner) {
43      return new SymbolicRegressionPearsonsRSquaredEvaluator(this, cloner);
44    }
45    public override double Evaluate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, Dataset dataset, string targetVariable, IEnumerable<int> rows) {
46      double mse = Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, dataset, targetVariable, rows);
47      return mse;
48    }
49
50    public static double Calculate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, Dataset dataset, string targetVariable, IEnumerable<int> rows) {
51      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, dataset, rows);
52      IEnumerable<double> originalValues = dataset.GetEnumeratedVariableValues(targetVariable, rows);
53      IEnumerator<double> originalEnumerator = originalValues.GetEnumerator();
54      IEnumerator<double> estimatedEnumerator = estimatedValues.GetEnumerator();
55      OnlinePearsonsRSquaredEvaluator r2Evaluator = new OnlinePearsonsRSquaredEvaluator();
56
57      while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) {
58        double estimated = estimatedEnumerator.Current;
59        double original = originalEnumerator.Current;
60        if (double.IsNaN(estimated))
61          estimated = upperEstimationLimit;
62        else
63          estimated = Math.Min(upperEstimationLimit, Math.Max(lowerEstimationLimit, estimated));
64        r2Evaluator.Add(original, estimated);
65      }
66
67      if (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext()) {
68        throw new ArgumentException("Number of elements in original and estimated enumeration doesn't match.");
69      } else {
70        return r2Evaluator.RSquared;
71      }
72    }
73  }
74}
Note: See TracBrowser for help on using the repository browser.