Commit 78d7f56c authored by Cédric Traizet's avatar Cédric Traizet

Merge branch 'regression_refactoring' into 'develop'

Regression refactoring : TrainVectorRegression

See merge request !488
parents b340ad5d 5e79e868
Pipeline #1403 passed with stage
in 23 minutes and 13 seconds
......@@ -65,6 +65,11 @@ otb_create_application(
SOURCES otbTrainRegression.cxx
LINK_LIBRARIES ${${otb-module}_LIBRARIES})
otb_create_application(
NAME TrainVectorRegression
SOURCES otbTrainVectorRegression.cxx
LINK_LIBRARIES ${${otb-module}_LIBRARIES})
otb_create_application(
NAME PredictRegression
SOURCES otbPredictRegression.cxx
......
......@@ -271,8 +271,7 @@ void ParseCSVPredictors(std::string path, ListSampleType* outputList)
elem.Fill(0.0);
for (unsigned int i=0 ; i<nbCols ; ++i)
{
iss.str(words[i]);
iss >> elem[i];
elem[i] = std::stod(words[i]);
}
outputList->PushBack(elem);
}
......
......@@ -29,11 +29,11 @@ namespace otb
namespace Wrapper
{
class TrainVectorClassifier : public TrainVectorBase
class TrainVectorClassifier : public TrainVectorBase<float, int>
{
public:
typedef TrainVectorClassifier Self;
typedef TrainVectorBase Superclass;
typedef TrainVectorBase<float, int> Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
itkNewMacro( Self )
......@@ -66,13 +66,20 @@ protected:
"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 nor vector data output.");
SetDocLimitations("");
SetDocLimitations("None");
SetDocAuthors( "OTB Team" );
SetDocSeeAlso( " " );
SetOfficialDocLink();
Superclass::DoInit();
// Add a new parameter to compute confusion matrix / contingency table
this->AddParameter(ParameterType_OutputFilename, "io.confmatout", "Output confusion matrix or contingency table");
this->SetParameterDescription("io.confmatout",
"Output file containing the confusion matrix or contingency table (.csv format)."
"The contingency table is output when we unsupervised algorithms is used otherwise the confusion matrix is output.");
this->MandatoryOff("io.confmatout");
}
void DoUpdateParameters() override
......
/*
* Copyright (C) 2005-2019 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 features to consider and a predictor.");
SetDocLongDescription(
"This application trains a regression algorithm based on "
"a predictor geometries and a list of features to consider for "
"regression.\nThis 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 with the validation predictors");
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)
......@@ -49,21 +49,22 @@ bool IsNotAlphaNum(char c)
return !std::isalnum( c );
}
class TrainVectorBase : public LearningApplicationBase<float, int>
template <class TInputValue, class TOutputValue>
class TrainVectorBase : public LearningApplicationBase<TInputValue, TOutputValue>
{
public:
/** Standard class typedefs. */
typedef TrainVectorBase Self;
typedef LearningApplicationBase<float, int> Superclass;
typedef LearningApplicationBase<TInputValue, TOutputValue> Superclass;
typedef itk::SmartPointer <Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
/** Standard macro */
itkTypeMacro(Self, Superclass);
typedef Superclass::SampleType SampleType;
typedef Superclass::ListSampleType ListSampleType;
typedef Superclass::TargetListSampleType TargetListSampleType;
typedef typename Superclass::SampleType SampleType;
typedef typename Superclass::ListSampleType ListSampleType;
typedef typename Superclass::TargetListSampleType TargetListSampleType;
typedef double ValueType;
typedef itk::VariableLengthVector <ValueType> MeasurementType;
......@@ -86,8 +87,8 @@ protected:
class SamplesWithLabel
{
public:
ListSampleType::Pointer listSample;
TargetListSampleType::Pointer labeledListSample;
typename ListSampleType::Pointer listSample;
typename TargetListSampleType::Pointer labeledListSample;
SamplesWithLabel()
{
listSample = ListSampleType::New();
......@@ -178,13 +179,18 @@ protected:
SamplesWithLabel m_TrainingSamplesWithLabel;
SamplesWithLabel m_ClassificationSamplesWithLabel;
TargetListSampleType::Pointer m_PredictedList;
typename TargetListSampleType::Pointer m_PredictedList;
FeaturesInfo m_FeaturesInfo;
void DoInit() override;
void DoUpdateParameters() override;
void DoExecute() override;
private:
/**
* Get the field of the input feature corresponding to the input field
*/
inline TOutputValue GetFeatureField(const ogr::Feature& feature, int field);
};
}
......
......@@ -837,6 +837,22 @@ if(OTB_USE_OPENCV)
${TEMP}/apTvClTrainVectorClassifierModel.rf)
endif()
#----------- TrainVectorRegression TESTS ----------------
if(OTB_USE_OPENCV)
otb_test_application(NAME apTvClTrainVectorRegression
APP TrainVectorRegression
OPTIONS -io.vd ${INPUTDATA}/Classification/apTvClSampleExtractionOut.sqlite
-feat value_0 value_1 value_2 value_3
-cfield class
-classifier rf
-io.out ${TEMP}/apTvClTrainVectorRegressionModel.rf
-io.mse ${TEMP}/apTvClTrainVectorRegressionModel.txt
TESTENVOPTIONS ${TEMP}/apTvClTrainVectorRegressionModel.txt
VALID ${ascii_comparison}
${OTBAPP_BASELINE_FILES}/apTvClTrainVectorRegressionModel.txt
${TEMP}/apTvClTrainVectorRegressionModel.txt)
endif()
#----------- TrainVectorClassifier unsupervised TESTS ----------------
if(OTB_USE_SHARK)
otb_test_application(NAME apTvClTrainVectorUnsupervised
......
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