/usr/include/OTB-5.8/otbTrainGradientBoostedTree.txx is in libotb-dev 5.8.0+dfsg-3.
This file is owned by root:root, with mode 0o644.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | /*=========================================================================
Program: ORFEO Toolbox
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
See OTBCopyright.txt for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#ifndef otbTrainGradientBoostedTree_txx
#define otbTrainGradientBoostedTree_txx
#include "otbLearningApplicationBase.h"
namespace otb
{
namespace Wrapper
{
template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::InitGradientBoostedTreeParams()
{
AddChoice("classifier.gbt", "Gradient Boosted Tree classifier");
SetParameterDescription(
"classifier.gbt",
"This group of parameters allows setting Gradient Boosted Tree classifier parameters. "
"See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/gradient_boosted_trees.html}.");
if (m_RegressionFlag)
{
AddParameter(ParameterType_Choice, "classifier.gbt.t", "Loss Function Type");
SetParameterDescription("classifier.gbt.t","Type of loss functionused for training.");
AddChoice("classifier.gbt.t.sqr","Squared Loss");
AddChoice("classifier.gbt.t.abs","Absolute Loss");
AddChoice("classifier.gbt.t.hub","Huber Loss");
}
//WeakCount
AddParameter(ParameterType_Int, "classifier.gbt.w", "Number of boosting algorithm iterations");
SetParameterInt("classifier.gbt.w", 200);
SetParameterDescription(
"classifier.gbt.w",
"Number \"w\" of boosting algorithm iterations, with w*K being the total number of trees in "
"the GBT model, where K is the output number of classes.");
//Shrinkage
AddParameter(ParameterType_Float, "classifier.gbt.s", "Regularization parameter");
SetParameterFloat("classifier.gbt.s", 0.01);
SetParameterDescription("classifier.gbt.s", "Regularization parameter.");
//SubSamplePortion
AddParameter(ParameterType_Float, "classifier.gbt.p",
"Portion of the whole training set used for each algorithm iteration");
SetParameterFloat("classifier.gbt.p", 0.8);
SetParameterDescription(
"classifier.gbt.p",
"Portion of the whole training set used for each algorithm iteration. The subset is generated randomly.");
//MaxDepth
AddParameter(ParameterType_Int, "classifier.gbt.max", "Maximum depth of the tree");
SetParameterInt("classifier.gbt.max", 3);
SetParameterDescription(
"classifier.gbt.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.");
//UseSurrogates : don't need to be exposed !
//AddParameter(ParameterType_Empty, "classifier.gbt.sur", "Surrogate splits will be built");
//SetParameterDescription("classifier.gbt.sur","These splits allow working with missing data and compute variable importance correctly.");
}
template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::TrainGradientBoostedTree(typename ListSampleType::Pointer trainingListSample,
typename TargetListSampleType::Pointer trainingLabeledListSample,
std::string modelPath)
{
typename GradientBoostedTreeType::Pointer classifier = GradientBoostedTreeType::New();
classifier->SetRegressionMode(this->m_RegressionFlag);
classifier->SetInputListSample(trainingListSample);
classifier->SetTargetListSample(trainingLabeledListSample);
classifier->SetWeakCount(GetParameterInt("classifier.gbt.w"));
classifier->SetShrinkage(GetParameterFloat("classifier.gbt.s"));
classifier->SetSubSamplePortion(GetParameterFloat("classifier.gbt.p"));
classifier->SetMaxDepth(GetParameterInt("classifier.gbt.max"));
if (m_RegressionFlag)
{
switch (GetParameterInt("classifier.gbt.t"))
{
case 0: // SQUARED_LOSS
classifier->SetLossFunctionType(CvGBTrees::SQUARED_LOSS);
break;
case 1: // ABSOLUTE_LOSS
classifier->SetLossFunctionType(CvGBTrees::ABSOLUTE_LOSS);
break;
case 2: // HUBER_LOSS
classifier->SetLossFunctionType(CvGBTrees::HUBER_LOSS);
break;
default:
classifier->SetLossFunctionType(CvGBTrees::SQUARED_LOSS);
break;
}
}
else
{
classifier->SetLossFunctionType(CvGBTrees::DEVIANCE_LOSS);
}
classifier->Train();
classifier->Save(modelPath);
}
} //end namespace wrapper
} //end namespace otb
#endif
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