/*
 * 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:
  /** Standard class typedefs. */
  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;

  /** Run-time type information (and related methods). */
  itkNewMacro(Self);
  itkTypeMacro(RandomForestsMachineLearningModel, MachineLearningModel);

  /** Train the machine learning model */
  void Train() override;

  /** Save the model to file */
  void Save(const std::string& filename, const std::string& name = "") override;

  /** Load the model from file */
  void Load(const std::string& filename, const std::string& name = "") override;

  /**\name Classification model file compatibility tests */
  //@{
  /** Is the input model file readable and compatible with the corresponding classifier ? */
  bool CanReadFile(const std::string&) override;

  /** Is the input model file writable and compatible with the corresponding classifier ? */
  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);

  /** Returns a matrix containing variable importance */
  VariableImportanceMatrixType GetVariableImportance();

  float GetTrainError();

protected:
  /** Constructor */
  RandomForestsMachineLearningModel();

  /** Destructor */
  ~RandomForestsMachineLearningModel() override = default;

  /** Predict values using the model */
  TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType* quality = nullptr, ProbaSampleType* proba = nullptr) const override;

  /** PrintSelf method */
  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;

  /** The depth of the tree. A low value will likely underfit and conversely a
   * high value will likely overfit. The optimal value can be obtained using cross
   * validation or other suitable methods. */
  int m_MaxDepth;
  /** minimum samples required at a leaf node for it to be split. A reasonable
   * value is a small percentage of the total data e.g. 1%. */
  int m_MinSampleCount;
  /** Termination criteria for regression trees. If all absolute differences
   * between an estimated value in a node and values of train samples in this node
   * are less than this parameter then the node will not be split */
  float m_RegressionAccuracy;
  bool  m_ComputeSurrogateSplit;
  /** Cluster possible values of a categorical variable into
   * \f$ K \leq MaxCategories \f$
   * clusters to find a suboptimal split. If a discrete variable,
   * on which the training procedure tries to make a split, takes more than
   * max_categories values, the precise best subset estimation may take a very
   * long time because the algorithm is exponential. Instead, many decision
   * trees engines (including ML) try to find sub-optimal split in this case by
   * clustering all the samples into max categories clusters that is some
   * categories are merged together. The clustering is applied only in n>2-class
   * classification problems for categorical variables with N > max_categories
   * possible values. In case of regression and 2-class classification the
   * optimal split can be found efficiently without employing clustering, thus
   * the parameter is not used in these cases.
   */
  int m_MaxNumberOfCategories;
  /** 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> m_Priors;
  /** If true then variable importance will be calculated and then it can be
   * retrieved by CvRTreesWrapper::get_var_importance(). */
  bool m_CalculateVariableImportance;
  /** The size of the randomly selected subset of features at each tree node and
   * that are used to find the best split(s). If you set it to 0 then the size will
   * be set to the square root of the total number of features. */
  int m_MaxNumberOfVariables;
  /** The maximum number of trees in the forest (surprise, surprise). Typically
   * the more trees you have the better the accuracy. However, the improvement in
   * accuracy generally diminishes and asymptotes pass a certain number of
   * trees. Also to keep in mind, the number of tree increases the prediction time
   *linearly. */
  int m_MaxNumberOfTrees;
  /** Sufficient accuracy (OOB error) */
  float m_ForestAccuracy;
  /** The type of the termination criteria */
  int m_TerminationCriteria;
  /** Whether to compute margin (difference in probability between the
   * 2 most voted classes) instead of confidence (probability of the most
   * voted class) in prediction*/
  bool m_ComputeMargin;
};
} // end namespace otb

#ifndef OTB_MANUAL_INSTANTIATION
#include "otbRandomForestsMachineLearningModel.hxx"
#endif

#endif
