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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 otbTrainDecisionTree_hxx
#define otbTrainDecisionTree_hxx
#include "otbLearningApplicationBase.h"
#include "otbDecisionTreeMachineLearningModel.h"
namespace otb
{
namespace Wrapper
{
template <class TInputValue, class TOutputValue>
void LearningApplicationBase<TInputValue, TOutputValue>::InitDecisionTreeParams()
{
AddChoice("classifier.dt", "Decision Tree classifier");
SetParameterDescription("classifier.dt", "http://docs.opencv.org/modules/ml/doc/decision_trees.html");
// MaxDepth
AddParameter(ParameterType_Int, "classifier.dt.max", "Maximum depth of the tree");
SetParameterInt("classifier.dt.max", 10);
SetParameterDescription("classifier.dt.max",
"The training algorithm attempts to split each node while its depth is smaller "
"than the maximum possible depth of the tree. The actual depth may be smaller "
"if the other termination criteria are met, and/or if the tree is pruned.");
// MinSampleCount
AddParameter(ParameterType_Int, "classifier.dt.min", "Minimum number of samples in each node");
SetParameterInt("classifier.dt.min", 10);
SetParameterDescription("classifier.dt.min",
"If the number of samples in a node is smaller "
"than this parameter, then this node will not be split.");
// RegressionAccuracy
AddParameter(ParameterType_Float, "classifier.dt.ra", "Termination criteria for regression tree");
SetParameterFloat("classifier.dt.ra", 0.01);
SetParameterDescription("classifier.dt.ra",
"If all absolute differences between an estimated value in a node "
"and the values of the train samples in this node are smaller than this "
"regression accuracy parameter, then the node will not be split further.");
// UseSurrogates : don't need to be exposed !
// SetParameterDescription("classifier.dt.sur","These splits allow working with missing data and compute variable importance correctly.");
// MaxCategories
AddParameter(ParameterType_Int, "classifier.dt.cat",
"Cluster possible values of a categorical variable into K <= cat clusters to find a "
"suboptimal split");
SetParameterInt("classifier.dt.cat", 10);
SetParameterDescription("classifier.dt.cat",
"Cluster possible values of a categorical variable into K <= cat clusters to find a "
"suboptimal split.");
// Use1seRule
AddParameter(ParameterType_Bool, "classifier.dt.r", "Set Use1seRule flag to false");
SetParameterDescription("classifier.dt.r",
"If true, then a pruning will be harsher. This will make a tree more compact and more "
"resistant to the training data noise but a bit less accurate.");
// TruncatePrunedTree
AddParameter(ParameterType_Bool, "classifier.dt.t", "Set TruncatePrunedTree flag to false");
SetParameterDescription("classifier.dt.t", "If true, then pruned branches are physically removed from the tree.");
// Priors are not exposed.
}
template <class TInputValue, class TOutputValue>
void LearningApplicationBase<TInputValue, TOutputValue>::TrainDecisionTree(typename ListSampleType::Pointer trainingListSample,
typename TargetListSampleType::Pointer trainingLabeledListSample,
std::string modelPath)
{
typedef otb::DecisionTreeMachineLearningModel<InputValueType, OutputValueType> DecisionTreeType;
typename DecisionTreeType::Pointer classifier = DecisionTreeType::New();
classifier->SetRegressionMode(this->m_RegressionFlag);
classifier->SetInputListSample(trainingListSample);
classifier->SetTargetListSample(trainingLabeledListSample);
classifier->SetMaxDepth(GetParameterInt("classifier.dt.max"));
classifier->SetMinSampleCount(GetParameterInt("classifier.dt.min"));
classifier->SetRegressionAccuracy(GetParameterFloat("classifier.dt.ra"));
classifier->SetMaxCategories(GetParameterInt("classifier.dt.cat"));
if (GetParameterInt("classifier.dt.r"))
{
classifier->SetUse1seRule(false);
}
if (GetParameterInt("classifier.dt.t"))
{
classifier->SetTruncatePrunedTree(false);
}
classifier->Train();
classifier->Save(modelPath);
}
} // end namespace wrapper
} // end namespace otb
#endif
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