source: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/FOneScoreCalculator.cs @ 13102

Last change on this file since 13102 was 13102, checked in by gkronber, 7 years ago

#1998:

  • changed namespace and name of view
  • calculate f1 score only for solutions for binary classification problems
File size: 1.9 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 System.Linq;
25
26namespace HeuristicLab.Problems.DataAnalysis {
27  public class FOneScoreCalculator {
28    public static double Calculate(IEnumerable<double> originalValues, IEnumerable<double> estimatedValues, out OnlineCalculatorError errorState) {
29      if (originalValues.Distinct().Skip(2).Any()) {
30        throw new ArgumentException("F1 score can only be calculated for binary classification.");
31      }
32
33      var confusionMatrix = ConfusionMatrixCalculator.Calculate(originalValues, estimatedValues, out errorState);
34      if (!errorState.Equals(OnlineCalculatorError.None)) {
35        return double.NaN;
36      }
37      return CalculateFOne(confusionMatrix);
38    }
39
40    private static double CalculateFOne(double[,] confusionMatrix) {
41      double precision = confusionMatrix[0, 0] / (confusionMatrix[0, 0] + confusionMatrix[0, 1]);
42      double recall = confusionMatrix[0, 0] / (confusionMatrix[0, 0] + confusionMatrix[1, 0]);
43
44      return 2 * ((precision * recall) / (precision + recall));
45    }
46  }
47}
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