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.Collections.Generic;
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23 | using HeuristicLab.Common;
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24 | using HeuristicLab.Core;
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25 | using HeuristicLab.Data;
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26 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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27 | using HEAL.Attic;
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28 |
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29 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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30 | [Item("Mean squared error Evaluator", "Calculates the mean squared error of a symbolic regression solution.")]
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31 | [StorableType("8D4B5243-1635-46A6-AEF9-18C9BCB725DD")]
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32 | public class SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
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33 | public override bool Maximization { get { return false; } }
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34 | [StorableConstructor]
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35 | protected SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator(StorableConstructorFlag _) : base(_) { }
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36 | protected SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator(SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator original, Cloner cloner)
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37 | : base(original, cloner) {
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38 | }
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39 | public override IDeepCloneable Clone(Cloner cloner) {
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40 | return new SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator(this, cloner);
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41 | }
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42 | public SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator() : base() { }
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43 |
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44 | public override IOperation InstrumentedApply() {
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45 | var tree = SymbolicExpressionTreeParameter.ActualValue;
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46 | IEnumerable<int> rows = GenerateRowsToEvaluate();
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47 |
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48 | double quality = Calculate(
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49 | tree, ProblemDataParameter.ActualValue,
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50 | rows, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
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51 | ApplyLinearScalingParameter.ActualValue.Value,
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52 | EstimationLimitsParameter.ActualValue.Lower,
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53 | EstimationLimitsParameter.ActualValue.Upper);
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54 | QualityParameter.ActualValue = new DoubleValue(quality);
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55 |
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56 | return base.InstrumentedApply();
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57 | }
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58 |
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59 | public static double Calculate(
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60 | ISymbolicExpressionTree tree,
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61 | IRegressionProblemData problemData,
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62 | IEnumerable<int> rows,
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63 | ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
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64 | bool applyLinearScaling,
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65 | double lowerEstimationLimit, double upperEstimationLimit) {
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66 | IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows);
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67 | IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
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68 | OnlineCalculatorError errorState;
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69 |
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70 | double mse;
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71 | if (applyLinearScaling) {
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72 | var mseCalculator = new OnlineMeanSquaredErrorCalculator();
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73 | CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, mseCalculator, problemData.Dataset.Rows);
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74 | errorState = mseCalculator.ErrorState;
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75 | mse = mseCalculator.MeanSquaredError;
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76 | } else {
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77 | IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
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78 | mse = OnlineMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
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79 | }
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80 | if (errorState != OnlineCalculatorError.None) return double.NaN;
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81 | return mse;
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82 | }
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83 |
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84 | public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
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85 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
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86 | EstimationLimitsParameter.ExecutionContext = context;
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87 | ApplyLinearScalingParameter.ExecutionContext = context;
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88 |
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89 | double mse = Calculate(
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90 | tree, problemData, rows,
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91 | SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
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92 | ApplyLinearScalingParameter.ActualValue.Value,
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93 | EstimationLimitsParameter.ActualValue.Lower,
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94 | EstimationLimitsParameter.ActualValue.Upper);
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95 |
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96 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
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97 | EstimationLimitsParameter.ExecutionContext = null;
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98 | ApplyLinearScalingParameter.ExecutionContext = null;
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99 |
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100 | return mse;
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101 | }
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102 |
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103 | public override double Evaluate(
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104 | ISymbolicExpressionTree tree,
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105 | IRegressionProblemData problemData,
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106 | IEnumerable<int> rows,
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107 | ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
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108 | bool applyLinearScaling = true,
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109 | double lowerEstimationLimit = double.MinValue,
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110 | double upperEstimationLimit = double.MaxValue) {
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111 | return Calculate(tree, problemData, rows, interpreter, applyLinearScaling, lowerEstimationLimit, upperEstimationLimit);
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112 | }
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113 | }
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114 | }
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