otbRandomForestsMachineLearningModel.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 otbRandomForestsMachineLearningModel_h
#define otbRandomForestsMachineLearningModel_h
 
#include "otbRequiresOpenCVCheck.h"
 
#include "itkLightObject.h"
#include "itkFixedArray.h"
#include "otbMachineLearningModel.h"
#include "itkVariableSizeMatrix.h"
#include "otbCvRTreesWrapper.h"
 
namespace otb
{
 
template <class TInputValue, class TTargetValue>
class ITK_EXPORT RandomForestsMachineLearningModel : public MachineLearningModel<TInputValue, TTargetValue>
{
public:
  typedef RandomForestsMachineLearningModel 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;
  // Other
  typedef itk::VariableSizeMatrix<float> VariableImportanceMatrixType;
 
 
  // opencv typedef
  typedef CvRTreesWrapper RFType;
 
  itkNewMacro(Self);
  itkTypeMacro(RandomForestsMachineLearningModel, 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;
 
  // Setters of RT parameters (documentation get from opencv doxygen 2.4)
  itkGetMacro(MaxDepth, int);
  itkSetMacro(MaxDepth, int);
 
  itkGetMacro(MinSampleCount, int);
  itkSetMacro(MinSampleCount, int);
 
  itkGetMacro(RegressionAccuracy, double);
  itkSetMacro(RegressionAccuracy, double);
 
  itkGetMacro(ComputeSurrogateSplit, bool);
  itkSetMacro(ComputeSurrogateSplit, bool);
 
  itkGetMacro(MaxNumberOfCategories, int);
  itkSetMacro(MaxNumberOfCategories, int);
 
  std::vector<float> GetPriors() const
  {
    return m_Priors;
  }
 
  void SetPriors(const std::vector<float>& priors)
  {
    m_Priors = priors;
  }
 
  itkGetMacro(CalculateVariableImportance, bool);
  itkSetMacro(CalculateVariableImportance, bool);
 
  itkGetMacro(MaxNumberOfVariables, int);
  itkSetMacro(MaxNumberOfVariables, int);
 
  itkGetMacro(MaxNumberOfTrees, int);
  itkSetMacro(MaxNumberOfTrees, int);
 
  itkGetMacro(ForestAccuracy, float);
  itkSetMacro(ForestAccuracy, float);
 
  itkGetMacro(TerminationCriteria, int);
  itkSetMacro(TerminationCriteria, int);
 
  itkGetMacro(ComputeMargin, bool);
  itkSetMacro(ComputeMargin, bool);
 
  VariableImportanceMatrixType GetVariableImportance();
 
  float GetTrainError();
 
protected:
  RandomForestsMachineLearningModel();
 
  ~RandomForestsMachineLearningModel() override = default;
 
  TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType* quality = nullptr, ProbaSampleType* proba = nullptr) const override;
 
  void PrintSelf(std::ostream& os, itk::Indent indent) const override;
 
  /* /\** Input list sample *\/ */
  /* typename InputListSampleType::Pointer m_InputListSample; */
 
  /* /\** Target list sample *\/ */
  /* typename TargetListSampleType::Pointer m_TargetListSample; */
 
private:
  RandomForestsMachineLearningModel(const Self&) = delete;
  void operator=(const Self&) = delete;
 
  cv::Ptr<CvRTreesWrapper> m_RFModel;
 
  int m_MaxDepth;
 
  int m_MinSampleCount;
 
  float m_RegressionAccuracy;
  bool  m_ComputeSurrogateSplit;
 
  int m_MaxNumberOfCategories;
 
  std::vector<float> m_Priors;
 
  bool m_CalculateVariableImportance;
 
  int m_MaxNumberOfVariables;
 
  int m_MaxNumberOfTrees;
 
  float m_ForestAccuracy;
 
  int m_TerminationCriteria;
 
  bool m_ComputeMargin;
};
} // end namespace otb
 
#ifndef OTB_MANUAL_INSTANTIATION
#include "otbRandomForestsMachineLearningModel.hxx"
#endif
 
#endif