otbLibSVMMachineLearningModel.h

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/*
 * Copyright (C) 2005-2022 Centre National d'Etudes Spatiales (CNES)
 *
 * This file is part of Orfeo Toolbox
 *
 *     https://www.orfeo-toolbox.org/
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
 
#ifndef otbLibSVMMachineLearningModel_h
#define otbLibSVMMachineLearningModel_h
 
#include "itkLightObject.h"
#include "itkFixedArray.h"
#include "otbMachineLearningModel.h"
 
#include "svm.h"
 
namespace otb
{
template <class TInputValue, class TTargetValue>
class ITK_EXPORT LibSVMMachineLearningModel : public MachineLearningModel<TInputValue, TTargetValue>
{
public:
  typedef LibSVMMachineLearningModel Self;
  typedef MachineLearningModel<TInputValue, TTargetValue> Superclass;
  typedef itk::SmartPointer<Self>       Pointer;
  typedef itk::SmartPointer<const Self> ConstPointer;
 
  typedef typename Superclass::InputValueType       InputValueType;
  typedef typename Superclass::InputSampleType      InputSampleType;
  typedef typename Superclass::InputListSampleType  InputListSampleType;
  typedef typename Superclass::TargetValueType      TargetValueType;
  typedef typename Superclass::TargetSampleType     TargetSampleType;
  typedef typename Superclass::TargetListSampleType TargetListSampleType;
  typedef typename Superclass::ConfidenceValueType  ConfidenceValueType;
  typedef typename Superclass::ProbaSampleType      ProbaSampleType;
  typedef enum { CM_INDEX, CM_PROBA, CM_HYPER } ConfidenceMode;
 
  itkNewMacro(Self);
  itkTypeMacro(SVMMachineLearningModel, MachineLearningModel);
 
  void Train() override;
 
  void Save(const std::string& filename, const std::string& name = "") override;
 
  void Load(const std::string& filename, const std::string& name = "") override;
 
 
  bool CanReadFile(const std::string&) override;
 
  bool CanWriteFile(const std::string&) override;
 
#define otbSetSVMParameterMacro(name, alias, type)  \
  void Set##name(const type _arg)                   \
  {                                                 \
    itkDebugMacro("setting " #name " to " << _arg); \
    if (this->m_Parameters.alias != _arg)           \
    {                                               \
      this->m_Parameters.alias = _arg;              \
      this->Modified();                             \
    }                                               \
  }
 
  otbSetSVMParameterMacro(SVMType, svm_type, int);
 
  int GetSVMType(void) const
  {
    return m_Parameters.svm_type;
  }
 
  otbSetSVMParameterMacro(KernelType, kernel_type, int);
 
  int GetKernelType(void) const
  {
    return m_Parameters.kernel_type;
  }
 
  otbSetSVMParameterMacro(PolynomialKernelDegree, degree, int);
 
  int GetPolynomialKernelDegree(void) const
  {
    return m_Parameters.degree;
  }
 
  otbSetSVMParameterMacro(KernelGamma, gamma, double);
 
  double GetKernelGamma(void) const
  {
    return m_Parameters.gamma;
  }
 
  otbSetSVMParameterMacro(KernelCoef0, coef0, double);
 
  double GetKernelCoef0(void) const
  {
    return m_Parameters.coef0;
  }
 
  otbSetSVMParameterMacro(C, C, double);
 
  double GetC(void) const
  {
    return m_Parameters.C;
  }
 
  itkSetMacro(ParameterOptimization, bool);
  itkGetMacro(ParameterOptimization, bool);
 
  void SetDoProbabilityEstimates(bool prob)
  {
    m_Parameters.probability = static_cast<int>(prob);
  }
 
  bool GetDoProbabilityEstimates(void) const
  {
    return static_cast<bool>(m_Parameters.probability);
  }
 
  bool HasProbabilities(void) const;
 
  otbSetSVMParameterMacro(Epsilon, eps, double);
 
  double GetEpsilon(void) const
  {
    return m_Parameters.eps;
  }
 
  otbSetSVMParameterMacro(P, p, double);
 
  double GetP(void) const
  {
    return m_Parameters.p;
  }
 
  otbSetSVMParameterMacro(Nu, nu, double);
 
  double GetNu(void) const
  {
    return m_Parameters.nu;
  }
 
#undef otbSetSVMParameterMacro
 
  void DoShrinking(bool s)
  {
    m_Parameters.shrinking = static_cast<int>(s);
    this->Modified();
  }
 
  bool GetDoShrinking(void) const
  {
    return static_cast<bool>(m_Parameters.shrinking);
  }
 
  void SetCacheSize(int cSize)
  {
    m_Parameters.cache_size = static_cast<double>(cSize);
    this->Modified();
  }
 
  int GetCacheSize(void) const
  {
    return static_cast<int>(m_Parameters.cache_size);
  }
 
  itkSetMacro(CVFolders, unsigned int);
  itkGetMacro(CVFolders, unsigned int);
 
  itkGetMacro(InitialCrossValidationAccuracy, double);
 
  itkGetMacro(FinalCrossValidationAccuracy, double);
 
  itkSetMacro(CoarseOptimizationNumberOfSteps, unsigned int);
  itkGetMacro(CoarseOptimizationNumberOfSteps, unsigned int);
 
  itkSetMacro(FineOptimizationNumberOfSteps, unsigned int);
  itkGetMacro(FineOptimizationNumberOfSteps, unsigned int);
 
  void SetConfidenceMode(unsigned int mode)
  {
    if (m_ConfidenceMode != static_cast<ConfidenceMode>(mode))
    {
      m_ConfidenceMode        = static_cast<ConfidenceMode>(mode);
      this->m_ConfidenceIndex = this->HasProbabilities();
      this->Modified();
    }
  }
  itkGetMacro(ConfidenceMode, unsigned int);
 
  unsigned int GetNumberOfKernelParameters();
 
  double CrossValidation(void);
 
  unsigned int GetNumberOfSupportVectors(void) const
  {
    if (m_Model)
      return m_Model->l;
    return 0;
  }
 
  unsigned int GetNumberOfClasses(void) const
  {
    if (m_Model)
      return m_Model->nr_class;
    return 0;
  }
 
protected:
  LibSVMMachineLearningModel();
 
  ~LibSVMMachineLearningModel() override;
 
  TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType* quality = nullptr, ProbaSampleType* proba = nullptr) const override;
 
  void PrintSelf(std::ostream& os, itk::Indent indent) const override;
 
private:
  LibSVMMachineLearningModel(const Self&) = delete;
  void operator=(const Self&) = delete;
 
  void BuildProblem(void);
 
  void ConsistencyCheck(void);
 
  void DeleteProblem(void);
 
  void DeleteModel(void);
 
  void OptimizeParameters(void);
 
  struct svm_model* m_Model;
 
  struct svm_problem m_Problem;
 
  struct svm_parameter m_Parameters;
 
  bool m_ParameterOptimization;
 
  unsigned int m_CVFolders;
 
  double m_InitialCrossValidationAccuracy;
 
  double m_FinalCrossValidationAccuracy;
 
  unsigned int m_CoarseOptimizationNumberOfSteps;
 
  unsigned int m_FineOptimizationNumberOfSteps;
 
  ConfidenceMode m_ConfidenceMode;
 
  std::vector<double> m_TmpTarget;
};
} // end namespace otb
 
#ifndef OTB_MANUAL_INSTANTIATION
#include "otbLibSVMMachineLearningModel.hxx"
#endif
 
#endif