[645] | 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 HeuristicLab.Core;
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| 24 | using HeuristicLab.Data;
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[2328] | 25 | using HeuristicLab.Common;
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[2578] | 26 | using HeuristicLab.GP.Interfaces;
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| 27 | using System.Linq;
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| 28 | using HeuristicLab.DataAnalysis;
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[645] | 29 |
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[668] | 30 | namespace HeuristicLab.GP.StructureIdentification.Classification {
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[702] | 31 | public class ClassificationMeanSquaredErrorEvaluator : GPClassificationEvaluatorBase {
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| 32 | private const double EPSILON = 1.0E-7;
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[645] | 33 | public override string Description {
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| 34 | get {
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| 35 | return @"Evaluates 'FunctionTree' for all samples of 'DataSet' and calculates the mean-squared-error
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| 36 | for the estimated values vs. the real values of 'TargetVariable'.";
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| 37 | }
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| 38 | }
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| 39 |
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| 40 | public ClassificationMeanSquaredErrorEvaluator()
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| 41 | : base() {
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[702] | 42 | AddVariableInfo(new VariableInfo("MSE", "The mean squared error of the model", typeof(DoubleData), VariableKind.New));
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[645] | 43 | }
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| 44 |
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[2578] | 45 | public override void Evaluate(IScope scope, IFunctionTree tree, ITreeEvaluator evaluator, Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end) {
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[645] | 46 | double errorsSquaredSum = 0;
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[2578] | 47 | double[] estimatedValues = evaluator.Evaluate(dataset, tree, Enumerable.Range(start, end - start)).ToArray();
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[712] | 48 | for (int sample = start; sample < end; sample++) {
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| 49 | double original = dataset.GetValue(sample, targetVariable);
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| 50 | if (!double.IsNaN(original) && !double.IsInfinity(original)) {
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[2578] | 51 | double error = estimatedValues[sample - start] - original;
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[645] | 52 | // between classes use squared error
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| 53 | // on the lower end and upper end only add linear error if the absolute error is larger than 1
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| 54 | // the error>1.0 constraint is needed for balance because in the interval ]-1, 1[ the squared error is smaller than the absolute error
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[2328] | 55 | if ((original.IsAlmost(classes[0]) && error < -1.0) ||
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| 56 | (original.IsAlmost(classes[classes.Length - 1]) && error > 1.0)) {
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[645] | 57 | errorsSquaredSum += Math.Abs(error); // only add linear error below the smallest class or above the largest class
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| 58 | } else {
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| 59 | errorsSquaredSum += error * error;
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| 60 | }
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| 61 | }
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[2578] | 62 |
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[645] | 63 | }
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| 64 |
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| 65 | errorsSquaredSum /= (end - start);
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[712] | 66 | if (double.IsNaN(errorsSquaredSum) || double.IsInfinity(errorsSquaredSum)) {
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[645] | 67 | errorsSquaredSum = double.MaxValue;
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| 68 | }
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[702] | 69 |
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| 70 | DoubleData mse = GetVariableValue<DoubleData>("MSE", scope, false, false);
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[712] | 71 | if (mse == null) {
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[702] | 72 | mse = new DoubleData();
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| 73 | scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("MSE"), mse));
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| 74 | }
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| 75 |
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[645] | 76 | mse.Data = errorsSquaredSum;
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| 77 | }
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| 78 | }
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| 79 | }
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