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source: branches/DataAnalysis.PopulationDiversityAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Evaluators/SymbolicRegressionMeanSquaredErrorEvaluator.cs @ 12064

Last change on this file since 12064 was 4877, checked in by swinkler, 14 years ago

Created branch for population diversity analysis for symbolic regression. (#1278)

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("SymbolicRegressionMeanSquaredErrorEvaluator", "Calculates the mean squared error of a symbolic regression solution.")]
33  [StorableClass]
34  public class SymbolicRegressionMeanSquaredErrorEvaluator : SingleObjectiveSymbolicRegressionEvaluator {
35
36    [StorableConstructor]
37    protected SymbolicRegressionMeanSquaredErrorEvaluator(bool deserializing) : base(deserializing) { }
38    protected SymbolicRegressionMeanSquaredErrorEvaluator(SymbolicRegressionMeanSquaredErrorEvaluator original, Cloner cloner)
39      : base(original, cloner) {
40    }
41    public SymbolicRegressionMeanSquaredErrorEvaluator() : base() { }
42
43    public override IDeepCloneable Clone(Cloner cloner) {
44      return new SymbolicRegressionMeanSquaredErrorEvaluator(this, cloner);
45    }
46
47    public override double Evaluate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, Dataset dataset, string targetVariable, IEnumerable<int> rows) {
48      double mse = Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, dataset, targetVariable, rows);
49      return mse;
50    }
51
52    public static double Calculate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, Dataset dataset, string targetVariable, IEnumerable<int> rows) {
53      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, dataset, rows);
54      IEnumerable<double> originalValues = dataset.GetEnumeratedVariableValues(targetVariable, rows);
55      IEnumerator<double> originalEnumerator = originalValues.GetEnumerator();
56      IEnumerator<double> estimatedEnumerator = estimatedValues.GetEnumerator();
57      OnlineMeanSquaredErrorEvaluator mseEvaluator = new OnlineMeanSquaredErrorEvaluator();
58
59      while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) {
60        double estimated = estimatedEnumerator.Current;
61        double original = originalEnumerator.Current;
62        if (double.IsNaN(estimated))
63          estimated = upperEstimationLimit;
64        else
65          estimated = Math.Min(upperEstimationLimit, Math.Max(lowerEstimationLimit, estimated));
66        mseEvaluator.Add(original, estimated);
67      }
68
69      if (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext()) {
70        throw new ArgumentException("Number of elements in original and estimated enumeration doesn't match.");
71      } else {
72        return mseEvaluator.MeanSquaredError;
73      }
74    }
75  }
76}
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