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/*
* Copyright (C) 2005-2020 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_hxx
#define otbRandomForestsMachineLearningModel_hxx
#include <fstream>
#include "itkMacro.h"
#include "otbRandomForestsMachineLearningModel.h"
#include "otbOpenCVUtils.h"
namespace otb
{
template <class TInputValue, class TOutputValue>
RandomForestsMachineLearningModel<TInputValue, TOutputValue>::RandomForestsMachineLearningModel()
:
m_RFModel(CvRTreesWrapper::create()),
m_MaxDepth(5),
m_MinSampleCount(10),
m_RegressionAccuracy(0.01),
m_ComputeSurrogateSplit(false),
m_MaxNumberOfCategories(10),
m_CalculateVariableImportance(false),
m_MaxNumberOfVariables(0),
m_MaxNumberOfTrees(100),
m_ForestAccuracy(0.01),
m_TerminationCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS), // identic for v3 ?
m_ComputeMargin(false)
{
this->m_ConfidenceIndex = true;
this->m_ProbaIndex = false;
this->m_IsRegressionSupported = true;
}
template <class TInputValue, class TOutputValue>
float RandomForestsMachineLearningModel<TInputValue, TOutputValue>::GetTrainError()
{
// TODO
cv::Mat samples;
otb::ListSampleToMat<InputListSampleType>(this->GetInputListSample(), samples);
cv::Mat labels;
otb::ListSampleToMat<TargetListSampleType>(this->GetTargetListSample(), labels);
cv::Mat var_type = cv::Mat(this->GetInputListSample()->GetMeasurementVectorSize() + 1, 1, CV_8U);
var_type.setTo(cv::Scalar(CV_VAR_NUMERICAL)); // all inputs are numerical
if (this->m_RegressionMode)
var_type.at<uchar>(this->GetInputListSample()->GetMeasurementVectorSize(), 0) = CV_VAR_NUMERICAL;
else
var_type.at<uchar>(this->GetInputListSample()->GetMeasurementVectorSize(), 0) = CV_VAR_CATEGORICAL;
return m_RFModel->calcError(cv::ml::TrainData::create(samples, cv::ml::ROW_SAMPLE, labels, cv::noArray(), cv::noArray(), cv::noArray(), var_type), false,
cv::noArray());
}
/** Train the machine learning model */
template <class TInputValue, class TOutputValue>
void RandomForestsMachineLearningModel<TInputValue, TOutputValue>::Train()
{
// convert listsample to opencv matrix
cv::Mat samples;
otb::ListSampleToMat<InputListSampleType>(this->GetInputListSample(), samples);
cv::Mat labels;
otb::ListSampleToMat<TargetListSampleType>(this->GetTargetListSample(), labels);
cv::Mat var_type = cv::Mat(this->GetInputListSample()->GetMeasurementVectorSize() + 1, 1, CV_8U);
var_type.setTo(cv::Scalar(CV_VAR_NUMERICAL)); // all inputs are numerical
if (this->m_RegressionMode)
var_type.at<uchar>(this->GetInputListSample()->GetMeasurementVectorSize(), 0) = CV_VAR_NUMERICAL;
else
var_type.at<uchar>(this->GetInputListSample()->GetMeasurementVectorSize(), 0) = CV_VAR_CATEGORICAL;
// Mat var_type = Mat(ATTRIBUTES_PER_SAMPLE + 1, 1, CV_8U );
// std::cout << "priors " << m_Priors[0] << std::endl;
// Define random forests paramneters
// FIXME do this in the constructor?
m_RFModel->setMaxDepth(m_MaxDepth);
m_RFModel->setMinSampleCount(m_MinSampleCount);
m_RFModel->setRegressionAccuracy(m_RegressionAccuracy);
m_RFModel->setUseSurrogates(m_ComputeSurrogateSplit);
m_RFModel->setMaxCategories(m_MaxNumberOfCategories);
m_RFModel->setPriors(cv::Mat(m_Priors)); // TODO
m_RFModel->setCalculateVarImportance(m_CalculateVariableImportance);
m_RFModel->setActiveVarCount(m_MaxNumberOfVariables);
m_RFModel->setTermCriteria(cv::TermCriteria(m_TerminationCriteria, m_MaxNumberOfTrees, m_ForestAccuracy));
m_RFModel->train(cv::ml::TrainData::create(samples, cv::ml::ROW_SAMPLE, labels, cv::noArray(), cv::noArray(), cv::noArray(), var_type));
}
template <class TInputValue, class TOutputValue>
typename RandomForestsMachineLearningModel<TInputValue, TOutputValue>::TargetSampleType
RandomForestsMachineLearningModel<TInputValue, TOutputValue>::DoPredict(const InputSampleType& value, ConfidenceValueType* quality,
ProbaSampleType* proba) const
{
// std::cout << "Enter predict" << std::endl;
TargetSampleType target;
// convert listsample to Mat
cv::Mat sample;
otb::SampleToMat<InputSampleType>(value, sample);
double result = m_RFModel->predict(sample);
target[0] = static_cast<TOutputValue>(result);
if (quality != nullptr)
{
if (m_ComputeMargin)
(*quality) = m_RFModel->predict_margin(sample);
else
(*quality) = m_RFModel->predict_confidence(sample);
}
if (proba != nullptr && !this->m_ProbaIndex)
itkExceptionMacro("Probability per class not available for this classifier !");
return target[0];
}
template <class TInputValue, class TOutputValue>
void RandomForestsMachineLearningModel<TInputValue, TOutputValue>::Save(const std::string& filename, const std::string& name)
{
cv::FileStorage fs(filename, cv::FileStorage::WRITE);
fs << (name.empty() ? m_RFModel->getDefaultName() : cv::String(name)) << "{";
m_RFModel->write(fs);
fs << "}";
fs.release();
}
template <class TInputValue, class TOutputValue>
void RandomForestsMachineLearningModel<TInputValue, TOutputValue>::Load(const std::string& filename, const std::string& name)
{
cv::FileStorage fs(filename, cv::FileStorage::READ);
m_RFModel->read(name.empty() ? fs.getFirstTopLevelNode() : fs[name]);
}
template <class TInputValue, class TOutputValue>
bool RandomForestsMachineLearningModel<TInputValue, TOutputValue>::CanReadFile(const std::string& file)
{
std::ifstream ifs;
ifs.open(file);
if (!ifs)
{
std::cerr << "Could not read file " << file << std::endl;
return false;
}
while (!ifs.eof())
{
std::string line;
std::getline(ifs, line);
// if (line.find(m_RFModel->getName()) != std::string::npos)
if (line.find(CV_TYPE_NAME_ML_RTREES) != std::string::npos || line.find(m_RFModel->getDefaultName()) != std::string::npos)
{
return true;
}
}
ifs.close();
return false;
}
template <class TInputValue, class TOutputValue>
bool RandomForestsMachineLearningModel<TInputValue, TOutputValue>::CanWriteFile(const std::string& itkNotUsed(file))
{
return false;
}
template <class TInputValue, class TOutputValue>
typename RandomForestsMachineLearningModel<TInputValue, TOutputValue>::VariableImportanceMatrixType
RandomForestsMachineLearningModel<TInputValue, TOutputValue>::GetVariableImportance()
{
cv::Mat cvMat = m_RFModel->getVarImportance();
VariableImportanceMatrixType itkMat(cvMat.rows, cvMat.cols);
for (int i = 0; i < cvMat.rows; i++)
{
for (int j = 0; j < cvMat.cols; j++)
{
itkMat(i, j) = cvMat.at<float>(i, j);
}
}
return itkMat;
}
template <class TInputValue, class TOutputValue>
void RandomForestsMachineLearningModel<TInputValue, TOutputValue>::PrintSelf(std::ostream& os, itk::Indent indent) const
{
// Call superclass implementation
Superclass::PrintSelf(os, indent);
}
} // end namespace otb
#endif
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