otbDecisionTreeMachineLearningModel.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 otbDecisionTreeMachineLearningModel_h
#define otbDecisionTreeMachineLearningModel_h
 
#include "otbRequiresOpenCVCheck.h"
 
#include "itkLightObject.h"
#include "itkFixedArray.h"
#include "otbMachineLearningModel.h"
 
#include "otbOpenCVUtils.h"
 
namespace otb
{
template <class TInputValue, class TTargetValue>
class ITK_EXPORT DecisionTreeMachineLearningModel : public MachineLearningModel<TInputValue, TTargetValue>
{
public:
  typedef DecisionTreeMachineLearningModel 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;
  itkNewMacro(Self);
  itkTypeMacro(DecisionTreeMachineLearningModel, MachineLearningModel);
 
  itkGetMacro(MaxDepth, int);
  itkSetMacro(MaxDepth, int);
 
  itkGetMacro(MinSampleCount, int);
  itkSetMacro(MinSampleCount, int);
 
  itkGetMacro(RegressionAccuracy, double);
  itkSetMacro(RegressionAccuracy, double);
 
  itkGetMacro(UseSurrogates, bool);
  itkSetMacro(UseSurrogates, bool);
 
  itkGetMacro(MaxCategories, int);
  itkSetMacro(MaxCategories, int);
 
  itkGetMacro(Use1seRule, bool);
  itkSetMacro(Use1seRule, bool);
 
  itkGetMacro(TruncatePrunedTree, bool);
  itkSetMacro(TruncatePrunedTree, bool);
 
 
  /*  The array of a priori class probabilities, sorted by the class label
  * value. The parameter can be used to tune the decision tree preferences toward
  * a certain class. For example, if you want to detect some rare anomaly
  * occurrence, the training base will likely contain much more normal cases than
  * anomalies, so a very good classification performance will be achieved just by
  * considering every case as normal. To avoid this, the priors can be specified,
  * where the anomaly probability is artificially increased (up to 0.5 or even
  * greater), so the weight of the misclassified anomalies becomes much bigger,
  * and the tree is adjusted properly. You can also think about this parameter as
  * weights of prediction categories which determine relative weights that you
  * give to misclassification. That is, if the weight of the first category is 1
  * and the weight of the second category is 10, then each mistake in predicting
  * the second category is equivalent to making 10 mistakes in predicting the
  first category. */
 
  std::vector<float> GetPriors() const
  {
    return m_Priors;
  }
 
  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;
 
protected:
  DecisionTreeMachineLearningModel();
 
  ~DecisionTreeMachineLearningModel() 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;
 
private:
  DecisionTreeMachineLearningModel(const Self&) = delete;
  void operator=(const Self&) = delete;
 
  cv::Ptr<cv::ml::DTrees> m_DTreeModel;
 
  int                m_MaxDepth;
  int                m_MinSampleCount;
  double             m_RegressionAccuracy;
  bool               m_UseSurrogates;
  int                m_MaxCategories;
  bool               m_Use1seRule;
  bool               m_TruncatePrunedTree;
  std::vector<float> m_Priors;
};
} // end namespace otb
 
#ifndef OTB_MANUAL_INSTANTIATION
#include "otbDecisionTreeMachineLearningModel.hxx"
#endif
 
#endif