[10355] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[17180] | 3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[10355] | 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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[16565] | 29 | using HEAL.Attic;
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[10355] | 30 |
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| 31 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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[10432] | 32 | [Item("Mean relative error Evaluator", "Evaluator for symbolic regression models that calculates the mean relative error avg( |y' - y| / (|y| + 1))." +
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[10355] | 33 | "The +1 is necessary to handle data with the value of 0.0 correctly. " +
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| 34 | "Notice: Linear scaling is ignored for this evaluator.")]
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[16565] | 35 | [StorableType("8A5AAF93-5338-4E11-B3B2-3D9274329E5F")]
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[10355] | 36 | public class SymbolicRegressionMeanRelativeErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
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| 37 | public override bool Maximization { get { return false; } }
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| 38 | [StorableConstructor]
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[16565] | 39 | protected SymbolicRegressionMeanRelativeErrorEvaluator(StorableConstructorFlag _) : base(_) { }
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[10355] | 40 | protected SymbolicRegressionMeanRelativeErrorEvaluator(SymbolicRegressionMeanRelativeErrorEvaluator original, Cloner cloner)
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| 41 | : base(original, cloner) {
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| 42 | }
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| 43 | public override IDeepCloneable Clone(Cloner cloner) {
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| 44 | return new SymbolicRegressionMeanRelativeErrorEvaluator(this, cloner);
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| 45 | }
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| 46 | public SymbolicRegressionMeanRelativeErrorEvaluator() : base() { }
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| 47 |
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| 48 | public override IOperation InstrumentedApply() {
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[18220] | 49 | var tree = SymbolicExpressionTreeParameter.ActualValue;
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[10355] | 50 | IEnumerable<int> rows = GenerateRowsToEvaluate();
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| 51 |
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[18220] | 52 | double quality = Calculate(
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| 53 | tree, ProblemDataParameter.ActualValue, rows,
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| 54 | SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
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| 55 | EstimationLimitsParameter.ActualValue.Lower,
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| 56 | EstimationLimitsParameter.ActualValue.Upper);
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[10355] | 57 | QualityParameter.ActualValue = new DoubleValue(quality);
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| 58 |
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| 59 | return base.InstrumentedApply();
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| 60 | }
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| 61 |
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[18220] | 62 | public static double Calculate(
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| 63 | ISymbolicExpressionTree tree,
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| 64 | IRegressionProblemData problemData,
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| 65 | IEnumerable<int> rows,
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| 66 | ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
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| 67 | double lowerEstimationLimit, double upperEstimationLimit) {
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| 68 | IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows);
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[10355] | 69 | IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
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| 70 | IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
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| 71 |
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| 72 | var relResiduals = boundedEstimatedValues.Zip(targetValues, (e, t) => Math.Abs(t - e) / (Math.Abs(t) + 1.0));
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| 73 |
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| 74 | OnlineCalculatorError errorState;
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| 75 | OnlineCalculatorError varErrorState;
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| 76 | double mre;
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| 77 | double variance;
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| 78 | OnlineMeanAndVarianceCalculator.Calculate(relResiduals, out mre, out variance, out errorState, out varErrorState);
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| 79 | if (errorState != OnlineCalculatorError.None) return double.NaN;
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| 80 | return mre;
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| 81 | }
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| 82 |
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| 83 | public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
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| 84 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
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| 85 | EstimationLimitsParameter.ExecutionContext = context;
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| 86 |
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[18220] | 87 | double mre = Calculate(
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| 88 | tree, problemData, rows,
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| 89 | SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
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| 90 | EstimationLimitsParameter.ActualValue.Lower,
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| 91 | EstimationLimitsParameter.ActualValue.Upper);
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[10355] | 92 |
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| 93 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
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| 94 | EstimationLimitsParameter.ExecutionContext = null;
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| 95 |
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| 96 | return mre;
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| 97 | }
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[18220] | 98 |
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| 99 | public override double Evaluate(
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| 100 | ISymbolicExpressionTree tree,
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| 101 | IRegressionProblemData problemData,
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| 102 | IEnumerable<int> rows,
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| 103 | ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
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| 104 | bool applyLinearScaling = true,
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| 105 | double lowerEstimationLimit = double.MinValue,
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| 106 | double upperEstimationLimit = double.MaxValue) {
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| 107 | return Calculate(tree, problemData, rows, interpreter, lowerEstimationLimit, upperEstimationLimit);
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| 108 | }
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[10355] | 109 | }
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| 110 | } |
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