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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.
*/
#include "otbTrainVectorBase.h"
namespace otb
{
namespace Wrapper
{
class TrainVectorRegression : public TrainVectorBase<float, float>
{
public:
typedef TrainVectorRegression Self;
typedef TrainVectorBase<float, float> Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
itkNewMacro(Self) itkTypeMacro(Self, Superclass)
typedef Superclass::SampleType SampleType;
typedef Superclass::ListSampleType ListSampleType;
typedef Superclass::TargetListSampleType TargetListSampleType;
protected:
TrainVectorRegression()
{
this->m_RegressionFlag = true;
}
void DoInit() override
{
SetName("TrainVectorRegression");
SetDescription(
"Train a regression algorithm based on geometries with "
"list of predictor to consider and a label (dependent variable).");
SetDocLongDescription(
"This application trains a regression algorithm based on "
"geometries containing list of predictors to consider for "
"regression as well as groundtruth labels.\n"
"This application is based on LibSVM, OpenCV Machine "
"Learning (2.3.1 and later), and Shark ML The output of this application "
"is a text model file, whose format corresponds to the ML model type "
"chosen. There is no image or vector data output.");
SetDocLimitations("None");
SetDocAuthors("OTB Team");
SetDocSeeAlso("TrainVectorClassifier");
SetOfficialDocLink();
Superclass::DoInit();
AddParameter(ParameterType_Float, "io.mse", "Mean Square Error");
SetParameterDescription("io.mse", "Mean square error computed using the validation dataset");
SetParameterRole("io.mse", Role_Output);
this->MandatoryOff("io.mse");
}
void DoUpdateParameters() override
{
Superclass::DoUpdateParameters();
}
double ComputeMSE(const TargetListSampleType& list1, const TargetListSampleType& list2)
{
assert(list1.Size() == list2.Size());
double mse = 0.;
for (TargetListSampleType::InstanceIdentifier i = 0; i < list1.Size(); ++i)
{
auto elem1 = list1.GetMeasurementVector(i);
auto elem2 = list2.GetMeasurementVector(i);
mse += (elem1[0] - elem2[0]) * (elem1[0] - elem2[0]);
}
mse /= static_cast<double>(list1.Size());
return mse;
}
void DoExecute() override
{
m_FeaturesInfo.SetClassFieldNames(GetChoiceNames("cfield"), GetSelectedItems("cfield"));
if (m_FeaturesInfo.m_SelectedCFieldIdx.empty() && GetClassifierCategory() == Supervised)
{
otbAppLogFATAL(<< "No field has been selected for data labelling!");
}
Superclass::DoExecute();
otbAppLogINFO("Computing training performances");
auto mse = ComputeMSE(*m_ClassificationSamplesWithLabel.labeledListSample, *m_PredictedList);
otbAppLogINFO("Mean Square Error = " << mse);
this->SetParameterFloat("io.mse", mse);
}
private:
};
}
}
OTB_APPLICATION_EXPORT(otb::Wrapper::TrainVectorRegression)
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