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source: branches/2645_ContextAlgorithms/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs

Last change on this file was 15583, checked in by swagner, 7 years ago

#2640: Updated year of copyrights in license headers

File size: 4.4 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2018 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.Collections.Generic;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
30  [Item("Mean squared error & Tree size Evaluator", "Calculates the mean squared error and the tree size of a symbolic classification solution.")]
31  [StorableClass]
32  public class SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator : SymbolicClassificationMultiObjectiveEvaluator {
33    [StorableConstructor]
34    protected SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator(bool deserializing) : base(deserializing) { }
35    protected SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator(SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator original, Cloner cloner)
36      : base(original, cloner) {
37    }
38    public override IDeepCloneable Clone(Cloner cloner) {
39      return new SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator(this, cloner);
40    }
41
42    public SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator() : base() { }
43
44    public override IEnumerable<bool> Maximization { get { return new bool[2] { false, false }; } }
45
46    public override IOperation InstrumentedApply() {
47      IEnumerable<int> rows = GenerateRowsToEvaluate();
48      var solution = SymbolicExpressionTreeParameter.ActualValue;
49      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
50      QualitiesParameter.ActualValue = new DoubleArray(qualities);
51      return base.InstrumentedApply();
52    }
53
54    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable<int> rows) {
55      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
56      IEnumerable<double> originalValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
57      IEnumerable<double> boundedEstimationValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
58      OnlineCalculatorError errorState;
59      double mse = OnlineMeanSquaredErrorCalculator.Calculate(originalValues, boundedEstimationValues, out errorState);
60      if (errorState != OnlineCalculatorError.None) mse = double.NaN;
61      return new double[2] { mse, solution.Length };
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      ApplyLinearScalingParameter.ExecutionContext = context;
68
69      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
70
71      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
72      EstimationLimitsParameter.ExecutionContext = null;
73      ApplyLinearScalingParameter.ExecutionContext = null;
74
75      return quality;
76    }
77  }
78}
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