#region License Information
/* HeuristicLab
* Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Operators;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using SVM;
namespace HeuristicLab.Problems.DataAnalysis.SupportVectorMachine {
///
/// Represents an operator that creates a support vector machine model.
///
[StorableClass]
[Item("SupportVectorMachineModelCreator", "Represents an operator that creates a support vector machine model.")]
public sealed class SupportVectorMachineModelCreator : SingleSuccessorOperator {
private const string DataAnalysisProblemDataParameterName = "DataAnalysisProblemData";
private const string SvmTypeParameterName = "SvmType";
private const string KernelTypeParameterName = "KernelType";
private const string CostParameterName = "Cost";
private const string NuParameterName = "Nu";
private const string GammaParameterName = "Gamma";
private const string EpsilonParameterName = "Epsilon";
private const string SamplesStartParameterName = "SamplesStart";
private const string SamplesEndParameterName = "SamplesEnd";
private const string ModelParameterName = "SupportVectorMachineModel";
#region parameter properties
public IValueLookupParameter DataAnalysisProblemDataParameter {
get { return (IValueLookupParameter)Parameters[DataAnalysisProblemDataParameterName]; }
}
public IValueLookupParameter SvmTypeParameter {
get { return (IValueLookupParameter)Parameters[SvmTypeParameterName]; }
}
public IValueLookupParameter KernelTypeParameter {
get { return (IValueLookupParameter)Parameters[KernelTypeParameterName]; }
}
public IValueLookupParameter NuParameter {
get { return (IValueLookupParameter)Parameters[NuParameterName]; }
}
public IValueLookupParameter CostParameter {
get { return (IValueLookupParameter)Parameters[CostParameterName]; }
}
public IValueLookupParameter GammaParameter {
get { return (IValueLookupParameter)Parameters[GammaParameterName]; }
}
public IValueLookupParameter EpsilonParameter {
get { return (IValueLookupParameter)Parameters[EpsilonParameterName]; }
}
public IValueLookupParameter SamplesStartParameter {
get { return (IValueLookupParameter)Parameters[SamplesStartParameterName]; }
}
public IValueLookupParameter SamplesEndParameter {
get { return (IValueLookupParameter)Parameters[SamplesEndParameterName]; }
}
public ILookupParameter SupportVectorMachineModelParameter {
get { return (ILookupParameter)Parameters[ModelParameterName]; }
}
#endregion
#region properties
public DataAnalysisProblemData DataAnalysisProblemData {
get { return DataAnalysisProblemDataParameter.ActualValue; }
}
public StringValue SvmType {
get { return SvmTypeParameter.Value; }
}
public StringValue KernelType {
get { return KernelTypeParameter.Value; }
}
public DoubleValue Nu {
get { return NuParameter.ActualValue; }
}
public DoubleValue Cost {
get { return CostParameter.ActualValue; }
}
public DoubleValue Gamma {
get { return GammaParameter.ActualValue; }
}
public DoubleValue Epsilon {
get { return EpsilonParameter.ActualValue; }
}
public IntValue SamplesStart {
get { return SamplesStartParameter.ActualValue; }
}
public IntValue SamplesEnd {
get { return SamplesEndParameter.ActualValue; }
}
#endregion
[StorableConstructor]
private SupportVectorMachineModelCreator(bool deserializing) : base(deserializing) { }
private SupportVectorMachineModelCreator(SupportVectorMachineModelCreator original, Cloner cloner) : base(original, cloner) { }
public override IDeepCloneable Clone(Cloner cloner) {
return new SupportVectorMachineModelCreator(this, cloner);
}
public SupportVectorMachineModelCreator()
: base() {
StringValue nuSvrType = new StringValue("NU_SVR").AsReadOnly();
StringValue rbfKernelType = new StringValue("RBF").AsReadOnly();
Parameters.Add(new ValueLookupParameter(DataAnalysisProblemDataParameterName, "The data analysis problem data to use for training."));
Parameters.Add(new ValueLookupParameter(SvmTypeParameterName, "The type of SVM to use.", nuSvrType));
Parameters.Add(new ValueLookupParameter(KernelTypeParameterName, "The kernel type to use for the SVM.", rbfKernelType));
Parameters.Add(new ValueLookupParameter(NuParameterName, "The value of the nu parameter nu-SVC, one-class SVM and nu-SVR."));
Parameters.Add(new ValueLookupParameter(CostParameterName, "The value of the C (cost) parameter of C-SVC, epsilon-SVR and nu-SVR."));
Parameters.Add(new ValueLookupParameter(GammaParameterName, "The value of the gamma parameter in the kernel function."));
Parameters.Add(new ValueLookupParameter(EpsilonParameterName, "The value of the epsilon parameter for epsilon-SVR."));
Parameters.Add(new ValueLookupParameter(SamplesStartParameterName, "The first index of the data set partition the support vector machine should use for training."));
Parameters.Add(new ValueLookupParameter(SamplesEndParameterName, "The last index of the data set partition the support vector machine should use for training."));
Parameters.Add(new LookupParameter(ModelParameterName, "The result model generated by the SVM."));
}
public override IOperation Apply() {
int start = SamplesStart.Value;
int end = SamplesEnd.Value;
IEnumerable rows =
Enumerable.Range(start, end - start)
.Where(i => i < DataAnalysisProblemData.TestSamplesStart.Value || DataAnalysisProblemData.TestSamplesEnd.Value <= i);
SupportVectorMachineModel model = TrainModel(DataAnalysisProblemData,
rows,
SvmType.Value, KernelType.Value,
Cost.Value, Nu.Value, Gamma.Value, Epsilon.Value);
SupportVectorMachineModelParameter.ActualValue = model;
return base.Apply();
}
private static SupportVectorMachineModel TrainModel(
DataAnalysisProblemData problemData,
string svmType, string kernelType,
double cost, double nu, double gamma, double epsilon) {
return TrainModel(problemData, problemData.TrainingIndizes, svmType, kernelType, cost, nu, gamma, epsilon);
}
public static SupportVectorMachineModel TrainModel(
DataAnalysisProblemData problemData,
IEnumerable trainingIndizes,
string svmType, string kernelType,
double cost, double nu, double gamma, double epsilon) {
int targetVariableIndex = problemData.Dataset.GetVariableIndex(problemData.TargetVariable.Value);
//extract SVM parameters from scope and set them
SVM.Parameter parameter = new SVM.Parameter();
parameter.SvmType = (SVM.SvmType)Enum.Parse(typeof(SVM.SvmType), svmType, true);
parameter.KernelType = (SVM.KernelType)Enum.Parse(typeof(SVM.KernelType), kernelType, true);
parameter.C = cost;
parameter.Nu = nu;
parameter.Gamma = gamma;
parameter.P = epsilon;
parameter.CacheSize = 500;
parameter.Probability = false;
SVM.Problem problem = SupportVectorMachineUtil.CreateSvmProblem(problemData, trainingIndizes);
SVM.RangeTransform rangeTransform = SVM.RangeTransform.Compute(problem);
SVM.Problem scaledProblem = Scaling.Scale(rangeTransform, problem);
var model = new SupportVectorMachineModel();
model.Model = SVM.Training.Train(scaledProblem, parameter);
model.RangeTransform = rangeTransform;
return model;
}
}
}