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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 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.");
// 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
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