/*
 * Copyright (C) 2005-2019 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 otbTrainBoost_hxx
#define otbTrainBoost_hxx
#include "otbLearningApplicationBase.h"
#include "otbBoostMachineLearningModel.h"

namespace otb
{
namespace Wrapper
{

template <class TInputValue, class TOutputValue>
void LearningApplicationBase<TInputValue, TOutputValue>::InitBoostParams()
{
  AddChoice("classifier.boost", "Boost classifier");
  SetParameterDescription("classifier.boost", "http://docs.opencv.org/modules/ml/doc/boosting.html");
  // BoostType
  AddParameter(ParameterType_Choice, "classifier.boost.t", "Boost Type");
  AddChoice("classifier.boost.t.discrete", "Discrete AdaBoost");
  SetParameterDescription("classifier.boost.t.discrete",
                          "This procedure trains the classifiers on weighted versions of the training "
                          "sample, giving higher weight to cases that are currently misclassified. "
                          "This is done for a sequence of weighter samples, and then the final "
                          "classifier is defined as a linear combination of the classifier from "
                          "each stage.");
  AddChoice("classifier.boost.t.real",
            "Real AdaBoost (technique using confidence-rated predictions "
            "and working well with categorical data)");
  SetParameterDescription("classifier.boost.t.real", "Adaptation of the Discrete Adaboost algorithm with Real value");
  AddChoice("classifier.boost.t.logit", "LogitBoost (technique producing good regression fits)");
  SetParameterDescription("classifier.boost.t.logit",
                          "This procedure is an adaptive Newton algorithm for fitting an additive "
                          "logistic regression model. Beware it can produce numeric instability.");
  AddChoice("classifier.boost.t.gentle",
            "Gentle AdaBoost (technique setting less weight on outlier data points "
            "and, for that reason, being often good with regression data)");
  SetParameterDescription("classifier.boost.t.gentle",
                          "A modified version of the Real Adaboost algorithm, using Newton stepping "
                          "rather than exact optimization at each step.");
  SetParameterString("classifier.boost.t", "real");
  SetParameterDescription("classifier.boost.t", "Type of Boosting algorithm.");
  // Do not expose SplitCriteria
  // WeakCount
  AddParameter(ParameterType_Int, "classifier.boost.w", "Weak count");
  SetParameterInt("classifier.boost.w", 100);
  SetParameterDescription("classifier.boost.w", "The number of weak classifiers.");
  // WeightTrimRate
  AddParameter(ParameterType_Float, "classifier.boost.r", "Weight Trim Rate");
  SetParameterFloat("classifier.boost.r", 0.95);
  SetParameterDescription("classifier.boost.r",
                          "A threshold between 0 and 1 used to save computational time. "
                          "Samples with summary weight <= (1 - weight_trim_rate) do not participate in"
                          " the next iteration of training. Set this parameter to 0 to turn off this "
                          "functionality.");
  // MaxDepth : Not sure that this parameter has to be exposed.
  AddParameter(ParameterType_Int, "classifier.boost.m", "Maximum depth of the tree");
  SetParameterInt("classifier.boost.m", 1);
  SetParameterDescription("classifier.boost.m", "Maximum depth of the tree.");
}

template <class TInputValue, class TOutputValue>
void LearningApplicationBase<TInputValue, TOutputValue>::TrainBoost(typename ListSampleType::Pointer trainingListSample,
                                                                    typename TargetListSampleType::Pointer trainingLabeledListSample, std::string modelPath)
{
  typedef otb::BoostMachineLearningModel<InputValueType, OutputValueType> BoostType;
  typename BoostType::Pointer boostClassifier = BoostType::New();
  boostClassifier->SetRegressionMode(this->m_RegressionFlag);
  boostClassifier->SetInputListSample(trainingListSample);
  boostClassifier->SetTargetListSample(trainingLabeledListSample);
  boostClassifier->SetBoostType(GetParameterInt("classifier.boost.t"));
  boostClassifier->SetWeakCount(GetParameterInt("classifier.boost.w"));
  boostClassifier->SetWeightTrimRate(GetParameterFloat("classifier.boost.r"));
  boostClassifier->SetMaxDepth(GetParameterInt("classifier.boost.m"));

  boostClassifier->Train();
  boostClassifier->Save(modelPath);
}

} // end namespace wrapper
} // end namespace otb

#endif