| 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2010 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 System.Linq;
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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 HeuristicLab.Parameters;
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| 28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 29 | using HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Interfaces;
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| 30 | using HeuristicLab.Problems.DataAnalysis.Regression.Symbolic;
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| 31 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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| 32 |
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| 33 | namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Evaluators {
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| 34 | [Item("SymbolicVectorRegressionScaledNormalizedMseEvaluator", "Represents an operator that calculates the sum of the normalized mean squared error over all components.")]
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| 35 | [StorableClass]
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| 36 | public class SymbolicVectorRegressionScaledNormalizedMseEvaluator : SingleObjectiveSymbolicVectorRegressionEvaluator {
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| 37 | private const string AlphaParameterName = "Alpha";
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| 38 | private const string BetaParameterName = "Beta";
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| 39 |
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| 40 | #region parameter properties
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| 41 | public ILookupParameter<DoubleArray> AlphaParameter {
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| 42 | get { return (ILookupParameter<DoubleArray>)Parameters[AlphaParameterName]; }
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| 43 | }
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| 44 | public ILookupParameter<DoubleArray> BetaParameter {
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| 45 | get { return (ILookupParameter<DoubleArray>)Parameters[BetaParameterName]; }
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| 46 | }
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| 47 |
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| 48 | #endregion
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| 49 |
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| 50 | public SymbolicVectorRegressionScaledNormalizedMseEvaluator()
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| 51 | : base() {
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| 52 | Parameters.Add(new LookupParameter<DoubleArray>(AlphaParameterName, "The alpha parameter for linear scaling."));
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| 53 | Parameters.Add(new LookupParameter<DoubleArray>(BetaParameterName, "The beta parameter for linear scaling."));
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| 54 | }
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| 55 |
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| 56 | public override double Evaluate(SymbolicExpressionTree tree, ISymbolicExpressionTreeInterpreter interpreter, MultiVariateDataAnalysisProblemData problemData, IEnumerable<string> targetVariables, IEnumerable<int> rows, DoubleArray lowerEstimationBound, DoubleArray upperEstimationBound) {
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| 57 | List<string> targetVariablesList = targetVariables.ToList();
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| 58 | double nmseSum = 0.0;
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| 59 |
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| 60 | DoubleArray alpha = new DoubleArray(targetVariablesList.Count);
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| 61 | DoubleArray beta = new DoubleArray(targetVariablesList.Count);
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| 62 |
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| 63 | // use only the i-th vector component
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| 64 | List<SymbolicExpressionTreeNode> componentBranches = new List<SymbolicExpressionTreeNode>(tree.Root.SubTrees[0].SubTrees);
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| 65 | while (tree.Root.SubTrees[0].SubTrees.Count > 0) tree.Root.SubTrees[0].RemoveSubTree(0);
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| 66 | for (int i = 0; i < targetVariablesList.Count; i++) {
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| 67 | tree.Root.SubTrees[0].AddSubTree(componentBranches[i]);
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| 68 | double compAlpha;
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| 69 | double compBeta;
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| 70 | double nmse = SymbolicRegressionScaledNormalizedMeanSquaredErrorEvaluator.Calculate(interpreter, tree,
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| 71 | lowerEstimationBound[i], upperEstimationBound[i],
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| 72 | problemData.Dataset, targetVariablesList[i], rows, out compBeta, out compAlpha);
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| 73 | alpha[i] = compAlpha;
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| 74 | beta[i] = compBeta;
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| 75 | nmseSum += nmse;
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| 76 | tree.Root.SubTrees[0].RemoveSubTree(0);
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| 77 | }
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| 78 | // restore tree
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| 79 | foreach (var treeNode in componentBranches) {
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| 80 | tree.Root.SubTrees[0].AddSubTree(treeNode);
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| 81 | }
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| 82 | AlphaParameter.ActualValue = alpha;
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| 83 | BetaParameter.ActualValue = beta;
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| 84 | return nmseSum;
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| 85 | }
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| 86 | }
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| 87 | }
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