[128] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2008 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 System.Text;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.Operators;
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| 29 | using HeuristicLab.Functions;
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| 30 | using HeuristicLab.DataAnalysis;
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| 31 |
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| 32 | namespace HeuristicLab.StructureIdentification {
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| 33 | public class EarlyStoppingMeanSquaredErrorEvaluator : MeanSquaredErrorEvaluator {
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[479] | 34 | private double qualityLimit;
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[128] | 35 | public override string Description {
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| 36 | get {
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[155] | 37 | return @"Evaluates 'FunctionTree' for all samples of the dataset and calculates the mean-squared-error
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[128] | 38 | for the estimated values vs. the real values of 'TargetVariable'.
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| 39 | This operator stops the computation as soon as an upper limit for the mean-squared-error is reached.";
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| 40 | }
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| 41 | }
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| 42 |
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| 43 | public EarlyStoppingMeanSquaredErrorEvaluator()
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| 44 | : base() {
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[129] | 45 | AddVariableInfo(new VariableInfo("QualityLimit", "The upper limit of the MSE which is used as early stopping criterion.", typeof(DoubleData), VariableKind.In));
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[128] | 46 | }
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| 47 |
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[479] | 48 | public override IOperation Apply(IScope scope) {
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| 49 | qualityLimit = GetVariableValue<DoubleData>("QualityLimit", scope, false).Data;
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| 50 | return base.Apply(scope);
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| 51 | }
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| 52 |
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[332] | 53 | // evaluates the function-tree for the given target-variable and the whole dataset and returns the MSE
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[482] | 54 | public override void Evaluate(int start, int end) {
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[128] | 55 | double errorsSquaredSum = 0;
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[479] | 56 | int rows = end - start;
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| 57 | for(int sample = start; sample < end; sample++) {
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| 58 | double estimated = GetEstimatedValue(sample);
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| 59 | double original = GetOriginalValue(sample);
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[480] | 60 | SetOriginalValue(sample, estimated);
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[479] | 61 | if(!double.IsNaN(original) && !double.IsInfinity(original)) {
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| 62 | double error = estimated - original;
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| 63 | errorsSquaredSum += error * error;
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[128] | 64 | }
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[136] | 65 | // check the limit and stop as soon as we hit the limit
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[334] | 66 | if(errorsSquaredSum / rows >= qualityLimit) {
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[482] | 67 | mse.Data = errorsSquaredSum / (sample - start + 1); // return estimated MSE (when the remaining errors are on average the same)
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| 68 | return;
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[200] | 69 | }
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[128] | 70 | }
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[334] | 71 | errorsSquaredSum /= rows;
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[128] | 72 | if(double.IsNaN(errorsSquaredSum) || double.IsInfinity(errorsSquaredSum)) {
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| 73 | errorsSquaredSum = double.MaxValue;
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| 74 | }
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[482] | 75 | mse.Data = errorsSquaredSum;
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[128] | 76 | }
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| 77 | }
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| 78 | }
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