1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System;
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23 | using System.Collections.Generic;
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24 | using System.Linq;
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25 | using HeuristicLab.Common;
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26 | using HeuristicLab.Core;
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27 | using HeuristicLab.Data;
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28 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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29 | using HEAL.Attic;
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30 |
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31 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification.SingleObjective {
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32 | [Item("Bounded Mean squared error Evaluator", "Calculates the bounded mean squared error of a symbolic classification solution (estimations above or below the class values are only penaltilized linearly.")]
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33 | [StorableType("3C4C86ED-3F6A-464F-B7B6-326A220085EC")]
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34 | public class SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator : SymbolicClassificationSingleObjectiveEvaluator {
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35 | [StorableConstructor]
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36 | protected SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator(StorableConstructorFlag _) : base(_) { }
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37 | protected SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator(SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator original, Cloner cloner) : base(original, cloner) { }
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38 | public override IDeepCloneable Clone(Cloner cloner) {
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39 | return new SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator(this, cloner);
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40 | }
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41 |
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42 | public SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator() : base() { }
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43 |
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44 | public override bool Maximization { get { return false; } }
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45 |
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46 | public override IOperation InstrumentedApply() {
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47 | IEnumerable<int> rows = GenerateRowsToEvaluate();
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48 | var solution = SymbolicExpressionTreeParameter.ActualValue;
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49 | double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
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50 | QualityParameter.ActualValue = new DoubleValue(quality);
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51 | return base.InstrumentedApply();
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52 | }
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53 |
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54 | public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
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55 | IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
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56 | IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
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57 | OnlineCalculatorError errorState;
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58 |
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59 | double lowestClassValue = problemData.ClassValues.OrderBy(x => x).First();
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60 | double upmostClassValue = problemData.ClassValues.OrderByDescending(x => x).First();
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61 |
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62 | double boundedMse;
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63 | if (applyLinearScaling) {
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64 | var boundedMseCalculator = new OnlineBoundedMeanSquaredErrorCalculator(lowestClassValue, upmostClassValue);
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65 | CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, boundedMseCalculator, problemData.Dataset.Rows);
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66 | errorState = boundedMseCalculator.ErrorState;
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67 | boundedMse = boundedMseCalculator.BoundedMeanSquaredError;
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68 | } else {
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69 | IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
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70 | boundedMse = OnlineBoundedMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, lowestClassValue, upmostClassValue, out errorState);
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71 | }
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72 | if (errorState != OnlineCalculatorError.None) return Double.NaN;
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73 | return boundedMse;
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74 | }
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75 |
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76 | public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IClassificationProblemData problemData, IEnumerable<int> rows) {
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77 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
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78 | EstimationLimitsParameter.ExecutionContext = context;
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79 | ApplyLinearScalingParameter.ExecutionContext = context;
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80 |
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81 | double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
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82 |
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83 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
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84 | EstimationLimitsParameter.ExecutionContext = null;
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85 | ApplyLinearScalingParameter.ExecutionContext = null;
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86 |
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87 | return mse;
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88 | }
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89 | }
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90 | }
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