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source: branches/HeuristicLab.Classification/HeuristicLab.Problems.DataAnalysis.Classification/3.3/Symbolic/SymbolicClassificationMeanSquaredErrorEvaluator.cs @ 4366

Last change on this file since 4366 was 4366, checked in by mkommend, 14 years ago

added draft version of classification (ticket #939)

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 System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29using HeuristicLab.Problems.DataAnalysis.Evaluators;
30using HeuristicLab.Problems.DataAnalysis.Symbolic;
31
32namespace HeuristicLab.Problems.DataAnalysis.Classification {
33  [Item("SymbolicClassificationMeanSquaredErrorEvaluator", "Calculates the mean squared error of a symbolic classification solution.")]
34  [StorableClass]
35  public class SymbolicClassifacitionMeanSquaredErrorEvaluator : SingleObjectiveSymbolicClassificationEvaluator {
36
37    public SymbolicClassifacitionMeanSquaredErrorEvaluator()
38      : base() {
39    }
40
41    public override double Evaluate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, Dataset dataset, string targetVariable, IEnumerable<double> sortedClassValues, IEnumerable<int> rows) {
42      double mse = Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, dataset, targetVariable, sortedClassValues, rows);
43      return mse;
44    }
45
46    public static double Calculate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, Dataset dataset, string targetVariable, IEnumerable<double> sortedClassValues, IEnumerable<int> rows) {
47      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, dataset, rows);
48      IEnumerable<double> originalValues = dataset.GetEnumeratedVariableValues(targetVariable, rows);
49      IEnumerator<double> originalEnumerator = originalValues.GetEnumerator();
50      IEnumerator<double> estimatedEnumerator = estimatedValues.GetEnumerator();
51      OnlineMeanSquaredErrorEvaluator mseEvaluator = new OnlineMeanSquaredErrorEvaluator();
52
53
54      double firstClassValue = sortedClassValues.First();
55      double lastClassValue = sortedClassValues.Last();
56      while (originalEnumerator.MoveNext() && estimatedEnumerator.MoveNext()) {
57        double estimated = estimatedEnumerator.Current;
58        double original = originalEnumerator.Current;
59        if (double.IsNaN(estimated))
60          estimated = upperEstimationLimit;
61        else if (estimated < original && original.IsAlmost(firstClassValue))
62          estimated = original;
63        else if (estimated > original && original.IsAlmost(lastClassValue))
64          estimated = original;
65        else
66          estimated = Math.Min(upperEstimationLimit, Math.Max(lowerEstimationLimit, estimated));
67        mseEvaluator.Add(original, estimated);
68      }
69
70      if (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext()) {
71        throw new ArgumentException("Number of elements in original and estimated enumeration doesn't match.");
72      } else {
73        return mseEvaluator.MeanSquaredError;
74      }
75    }
76  }
77}
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