[1806] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2009 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.DataAnalysis;
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[1848] | 29 | using System.Threading;
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[1806] | 30 |
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| 31 | namespace HeuristicLab.SupportVectorMachines {
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| 32 | public class SupportVectorCreator : OperatorBase {
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[1848] | 33 | private Thread trainingThread;
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| 34 | private object locker = new object();
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| 35 | private bool abortRequested = false;
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[1806] | 36 |
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| 37 | public SupportVectorCreator()
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| 38 | : base() {
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| 39 | //Dataset infos
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| 40 | AddVariableInfo(new VariableInfo("Dataset", "Dataset with all samples on which to apply the function", typeof(Dataset), VariableKind.In));
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| 41 | AddVariableInfo(new VariableInfo("AllowedFeatures", "List of indexes of allowed features", typeof(ItemList<IntData>), VariableKind.In));
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| 42 | AddVariableInfo(new VariableInfo("TargetVariable", "Index of the column of the dataset that holds the target variable", typeof(IntData), VariableKind.In));
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| 43 | AddVariableInfo(new VariableInfo("SamplesStart", "Start index of samples in dataset to evaluate", typeof(IntData), VariableKind.In));
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| 44 | AddVariableInfo(new VariableInfo("SamplesEnd", "End index of samples in dataset to evaluate", typeof(IntData), VariableKind.In));
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| 45 |
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| 46 | //SVM parameters
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| 47 | AddVariableInfo(new VariableInfo("SVMType", "String describing which SVM type is used. Valid inputs are: C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR",
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| 48 | typeof(StringData), VariableKind.In));
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| 49 | AddVariableInfo(new VariableInfo("SVMKernelType", "String describing which SVM kernel is used. Valid inputs are: LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED",
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| 50 | typeof(StringData), VariableKind.In));
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| 51 | AddVariableInfo(new VariableInfo("SVMCost", "Cost parameter (C) of C-SVC, epsilon-SVR and nu-SVR", typeof(DoubleData), VariableKind.In));
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| 52 | AddVariableInfo(new VariableInfo("SVMNu", "Nu parameter of nu-SVC, one-class SVM and nu-SVR", typeof(DoubleData), VariableKind.In));
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| 53 | AddVariableInfo(new VariableInfo("SVMGamma", "Gamma parameter in kernel function", typeof(DoubleData), VariableKind.In));
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[1837] | 54 | AddVariableInfo(new VariableInfo("SVMModel", "Represent the model learned by the SVM", typeof(SVMModel), VariableKind.New | VariableKind.Out));
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[1906] | 55 | // AddVariableInfo(new VariableInfo("SVMRangeTransform", "The applied transformation during the learning the model", typeof(SVMRangeTransform), VariableKind.New | VariableKind.Out));
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[1848] | 56 | }
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[1806] | 57 |
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[1848] | 58 | public override void Abort() {
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| 59 | abortRequested = true;
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| 60 | lock (locker) {
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| 61 | if (trainingThread != null && trainingThread.ThreadState == ThreadState.Running) {
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| 62 | trainingThread.Abort();
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| 63 | }
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| 64 | }
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[1806] | 65 | }
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| 66 |
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| 67 | public override IOperation Apply(IScope scope) {
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[1848] | 68 | abortRequested = false;
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[1806] | 69 | Dataset dataset = GetVariableValue<Dataset>("Dataset", scope, true);
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| 70 | ItemList<IntData> allowedFeatures = GetVariableValue<ItemList<IntData>>("AllowedFeatures", scope, true);
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| 71 | int targetVariable = GetVariableValue<IntData>("TargetVariable", scope, true).Data;
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| 72 | int start = GetVariableValue<IntData>("SamplesStart", scope, true).Data;
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| 73 | int end = GetVariableValue<IntData>("SamplesEnd", scope, true).Data;
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| 74 |
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| 75 | string svmType = GetVariableValue<StringData>("SVMType", scope, true).Data;
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| 76 | string svmKernelType = GetVariableValue<StringData>("SVMKernelType", scope, true).Data;
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| 77 |
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| 78 | //extract SVM parameters from scope and set them
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| 79 | SVM.Parameter parameter = new SVM.Parameter();
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| 80 | parameter.SvmType = (SVM.SvmType)Enum.Parse(typeof(SVM.SvmType), svmType, true);
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| 81 | parameter.KernelType = (SVM.KernelType)Enum.Parse(typeof(SVM.KernelType), svmKernelType, true);
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| 82 | parameter.C = GetVariableValue<DoubleData>("SVMCost", scope, true).Data;
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| 83 | parameter.Nu = GetVariableValue<DoubleData>("SVMNu", scope, true).Data;
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| 84 | parameter.Gamma = GetVariableValue<DoubleData>("SVMGamma", scope, true).Data;
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| 85 |
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| 86 | SVM.Problem problem = SVMHelper.CreateSVMProblem(dataset, allowedFeatures, targetVariable, start, end);
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| 87 | SVM.RangeTransform rangeTransform = SVM.Scaling.DetermineRange(problem);
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| 88 | SVM.Problem scaledProblem = SVM.Scaling.Scale(problem, rangeTransform);
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| 89 |
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[1848] | 90 | SVM.Model model = StartTraining(scaledProblem, parameter);
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| 91 | if (!abortRequested) {
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| 92 | //persist variables in scope
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| 93 | SVMModel modelData = new SVMModel();
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[1906] | 94 | modelData.Model = model;
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| 95 | modelData.RangeTransform = rangeTransform;
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[1848] | 96 | scope.AddVariable(new Variable(scope.TranslateName("SVMModel"), modelData));
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[1906] | 97 | //SVMRangeTransform rangeTransformData = new SVMRangeTransform();
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| 98 | //rangeTransformData.Data = rangeTransform;
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| 99 | //scope.AddVariable(new Variable(scope.TranslateName("SVMRangeTransform"), rangeTransformData));
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[1851] | 100 | return null;
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| 101 | } else {
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| 102 | return new AtomicOperation(this, scope);
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[1848] | 103 | }
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[1806] | 104 | }
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| 105 |
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[1848] | 106 | private SVM.Model StartTraining(SVM.Problem scaledProblem, SVM.Parameter parameter) {
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| 107 | SVM.Model model = null;
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| 108 | lock (locker) {
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| 109 | if (!abortRequested) {
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| 110 | trainingThread = new Thread(() => {
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| 111 | model = SVM.Training.Train(scaledProblem, parameter);
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| 112 | });
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| 113 | trainingThread.Start();
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| 114 | }
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| 115 | }
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| 116 | trainingThread.Join();
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| 117 | trainingThread = null;
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| 118 | return model;
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| 119 | }
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[1806] | 120 | }
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| 121 | }
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