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Commit 9068e0de authored by Guillaume Pasero's avatar Guillaume Pasero
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TEST: add regression tests with bilinear and polynomial functions

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Modules/Learning/Supervised/test/otbMachineLearningRegressionTests.cxx
+ 310
− 43
View file @ 9068e0de
......@@ -219,23 +219,17 @@ int testRegression(SampleGeneratorType& sg, RegressionType& rgrsn, RegressionTes
rmse = sqrt( rmse / static_cast<double>(param.count) );
std::cout << "RMSE = "<< rmse << std::endl;
if(rmse > param.eps)
{
std::cout << "Failed : RMSE above expected precision !" << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
#ifdef OTB_USE_LIBSVM
int otbLibSVMRegressionLinearMonovariate(int itkNotUsed(argc),
char * itkNotUsed(argv) [])
MachineLearningModelRegressionType::Pointer getLibSVMRegressionModel()
{
LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
RegressionTestParam param;
param.vMin = -0.5;
param.vMax = 0.5;
param.count = 200;
param.eps = 0.1;
typedef otb::LibSVMMachineLearningModel<InputValueRegressionType,
TargetValueRegressionType>
libsvmType;
......@@ -245,23 +239,59 @@ int otbLibSVMRegressionLinearMonovariate(int itkNotUsed(argc),
regression->SetKernelType(RBF);
regression->SetEpsilon(1e-5);
regression->SetParameterOptimization(true);
return testRegression(lfsg,regression,param);
return regression.GetPointer();
}
#endif
#ifdef OTB_USE_OPENCV
int otbNeuralNetworkRegressionLinearMonovariate(int itkNotUsed(argc),
char * itkNotUsed(argv) [])
int otbLibSVMRegressionTests(int itkNotUsed(argc),
char * itkNotUsed(argv) [])
{
LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
int status = EXIT_SUCCESS;
int ret;
MachineLearningModelRegressionType::Pointer regression;
RegressionTestParam param;
param.vMin = -0.5;
param.vMax = 0.5;
param.count = 20000;
param.count = 200;
param.eps = 0.1;
std::cout << "Testing regression on a linear monovariate function" << std::endl;
LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
regression = getLibSVMRegressionModel();
ret = testRegression(lfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
std::cout << "Testing regression on a bilinear function" << std::endl;
BilinearFunctionSampleGenerator<PrecisionType> bfsg(2.0,-1.0,1.0);
regression = getLibSVMRegressionModel();
ret = testRegression(bfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
std::cout << "Testing regression on a polynomial function" << std::endl;
std::vector<PrecisionType> coeffs;
coeffs.push_back(0.0);
coeffs.push_back(-1.0);
coeffs.push_back(0.0);
coeffs.push_back(4.0);
PolynomialFunctionSampleGenerator<PrecisionType> pfsg(coeffs);
regression = getLibSVMRegressionModel();
ret = testRegression(pfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
return status;
}
#endif
#ifdef OTB_USE_OPENCV
MachineLearningModelRegressionType::Pointer getNeuralNetworkRegressionModel(unsigned int nbInputVar)
{
typedef otb::NeuralNetworkMachineLearningModel<InputValueRegressionType,
TargetValueRegressionType>
NeuralNetworkType;
......@@ -270,7 +300,7 @@ int otbNeuralNetworkRegressionLinearMonovariate(int itkNotUsed(argc),
regression->SetRegressionMode(1);
regression->SetTrainMethod(CvANN_MLP_TrainParams::BACKPROP);
std::vector<unsigned int> layerSizes;
layerSizes.push_back(1);
layerSizes.push_back(nbInputVar);
layerSizes.push_back(5);
layerSizes.push_back(1);
regression->SetLayerSizes(layerSizes);
......@@ -284,22 +314,57 @@ int otbNeuralNetworkRegressionLinearMonovariate(int itkNotUsed(argc),
regression->SetTermCriteriaType(CV_TERMCRIT_EPS);
regression->SetEpsilon(1e-5);
regression->SetMaxIter(1e4);
return testRegression(lfsg,regression,param);
return regression.GetPointer();
}
int otbSVMRegressionLinearMonovariate(int itkNotUsed(argc),
char * itkNotUsed(argv) [])
int otbNeuralNetworkRegressionTests(int itkNotUsed(argc),
char * itkNotUsed(argv) [])
{
LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
int status = EXIT_SUCCESS;
int ret;
MachineLearningModelRegressionType::Pointer regression;
RegressionTestParam param;
param.vMin = -0.5;
param.vMax = 0.5;
param.count = 200;
param.count = 20000;
param.eps = 0.1;
std::cout << "Testing regression on a linear monovariate function" << std::endl;
LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
regression = getNeuralNetworkRegressionModel(1);
ret = testRegression(lfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
std::cout << "Testing regression on a bilinear function" << std::endl;
BilinearFunctionSampleGenerator<PrecisionType> bfsg(2.0,-1.0,1.0);
regression = getNeuralNetworkRegressionModel(2);
ret = testRegression(bfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
std::cout << "Testing regression on a polynomial function" << std::endl;
std::vector<PrecisionType> coeffs;
coeffs.push_back(0.0);
coeffs.push_back(-1.0);
coeffs.push_back(0.0);
coeffs.push_back(4.0);
PolynomialFunctionSampleGenerator<PrecisionType> pfsg(coeffs);
regression = getNeuralNetworkRegressionModel(1);
ret = testRegression(pfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
return status;
}
MachineLearningModelRegressionType::Pointer getSVMRegressionModel()
{
typedef otb::SVMMachineLearningModel<InputValueRegressionType,
TargetValueRegressionType>
SVMType;
......@@ -313,14 +378,15 @@ int otbSVMRegressionLinearMonovariate(int itkNotUsed(argc),
regression->SetMaxIter(100000);
regression->SetEpsilon(1e-5);
regression->SetParameterOptimization(true);
return testRegression(lfsg,regression,param);
return regression.GetPointer();
}
int otbDecisionTreeRegressionLinearMonovariate(int itkNotUsed(argc),
int otbSVMRegressionTests(int itkNotUsed(argc),
char * itkNotUsed(argv) [])
{
LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
int status = EXIT_SUCCESS;
int ret;
MachineLearningModelRegressionType::Pointer regression;
RegressionTestParam param;
param.vMin = -0.5;
......@@ -328,19 +394,56 @@ int otbDecisionTreeRegressionLinearMonovariate(int itkNotUsed(argc),
param.count = 200;
param.eps = 0.1;
std::cout << "Testing regression on a linear monovariate function" << std::endl;
LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
regression = getSVMRegressionModel();
ret = testRegression(lfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
std::cout << "Testing regression on a bilinear function" << std::endl;
BilinearFunctionSampleGenerator<PrecisionType> bfsg(2.0,-1.0,1.0);
regression = getSVMRegressionModel();
ret = testRegression(bfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
std::cout << "Testing regression on a polynomial function" << std::endl;
std::vector<PrecisionType> coeffs;
coeffs.push_back(0.0);
coeffs.push_back(-1.0);
coeffs.push_back(0.0);
coeffs.push_back(4.0);
PolynomialFunctionSampleGenerator<PrecisionType> pfsg(coeffs);
regression = getSVMRegressionModel();
ret = testRegression(pfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
return status;
}
MachineLearningModelRegressionType::Pointer getDecisionTreeRegressionModel()
{
typedef otb::DecisionTreeMachineLearningModel<InputValueRegressionType,
TargetValueRegressionType>
DTreeType;
DTreeType::Pointer regression = DTreeType::New();
regression->SetRegressionMode(true);
return testRegression(lfsg,regression,param);
regression->SetRegressionAccuracy(0.005);
return regression.GetPointer();
}
int otbGradientBoostedTreeRegressionLinearMonovariate(int itkNotUsed(argc),
int otbDecisionTreeRegressionTests(int itkNotUsed(argc),
char * itkNotUsed(argv) [])
{
LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
int status = EXIT_SUCCESS;
int ret;
MachineLearningModelRegressionType::Pointer regression;
RegressionTestParam param;
param.vMin = -0.5;
......@@ -348,20 +451,61 @@ int otbGradientBoostedTreeRegressionLinearMonovariate(int itkNotUsed(argc),
param.count = 200;
param.eps = 0.1;
std::cout << "Testing regression on a linear monovariate function" << std::endl;
LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
regression = getDecisionTreeRegressionModel();
ret = testRegression(lfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
std::cout << "Testing regression on a bilinear function" << std::endl;
BilinearFunctionSampleGenerator<PrecisionType> bfsg(2.0,-1.0,1.0);
regression = getDecisionTreeRegressionModel();
// increase the number of training samples for bilinear function
param.count = 1000;
ret = testRegression(bfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
std::cout << "Testing regression on a polynomial function" << std::endl;
std::vector<PrecisionType> coeffs;
coeffs.push_back(0.0);
coeffs.push_back(-1.0);
coeffs.push_back(0.0);
coeffs.push_back(4.0);
PolynomialFunctionSampleGenerator<PrecisionType> pfsg(coeffs);
param.count = 200;
regression = getDecisionTreeRegressionModel();
ret = testRegression(pfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
return status;
}
MachineLearningModelRegressionType::Pointer getGradientBoostedTreeRegressionModel()
{
typedef otb::GradientBoostedTreeMachineLearningModel<InputValueRegressionType,
TargetValueRegressionType>
GBTreeType;
GBTreeType::Pointer regression = GBTreeType::New();
regression->SetRegressionMode(true);
regression->SetShrinkage(0.1);
regression->SetSubSamplePortion(0.8);
regression->SetLossFunctionType(CvGBTrees::SQUARED_LOSS);
return testRegression(lfsg,regression,param);
return regression.GetPointer();
}
int otbKNearestNeighborsRegressionLinearMonovariate(int itkNotUsed(argc),
int otbGradientBoostedTreeRegressionTests(int itkNotUsed(argc),
char * itkNotUsed(argv) [])
{
LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
int status = EXIT_SUCCESS;
int ret;
MachineLearningModelRegressionType::Pointer regression;
RegressionTestParam param;
param.vMin = -0.5;
......@@ -369,20 +513,59 @@ int otbKNearestNeighborsRegressionLinearMonovariate(int itkNotUsed(argc),
param.count = 200;
param.eps = 0.1;
std::cout << "Testing regression on a linear monovariate function" << std::endl;
LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
regression = getGradientBoostedTreeRegressionModel();
ret = testRegression(lfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
std::cout << "Testing regression on a bilinear function" << std::endl;
BilinearFunctionSampleGenerator<PrecisionType> bfsg(2.0,-1.0,1.0);
// increase number of training samples for bilinear function
param.count = 1000;
regression = getGradientBoostedTreeRegressionModel();
ret = testRegression(bfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
std::cout << "Testing regression on a polynomial function" << std::endl;
std::vector<PrecisionType> coeffs;
coeffs.push_back(0.0);
coeffs.push_back(-1.0);
coeffs.push_back(0.0);
coeffs.push_back(4.0);
PolynomialFunctionSampleGenerator<PrecisionType> pfsg(coeffs);
param.count = 200;
regression = getGradientBoostedTreeRegressionModel();
ret = testRegression(pfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
return status;
}
MachineLearningModelRegressionType::Pointer getKNearestNeighborsRegressionModel()
{
typedef otb::KNearestNeighborsMachineLearningModel<InputValueRegressionType,
TargetValueRegressionType>
KNNType;
KNNType::Pointer regression = KNNType::New();
regression->SetRegressionMode(true);
regression->SetK(1);
return testRegression(lfsg,regression,param);
return regression.GetPointer();
}
int otbRandomForestsRegressionLinearMonovariate(int itkNotUsed(argc),
int otbKNearestNeighborsRegressionTests(int itkNotUsed(argc),
char * itkNotUsed(argv) [])
{
LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
int status = EXIT_SUCCESS;
int ret;
MachineLearningModelRegressionType::Pointer regression;
RegressionTestParam param;
param.vMin = -0.5;
......@@ -390,12 +573,96 @@ int otbRandomForestsRegressionLinearMonovariate(int itkNotUsed(argc),
param.count = 200;
param.eps = 0.1;
std::cout << "Testing regression on a linear monovariate function" << std::endl;
LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
regression = getKNearestNeighborsRegressionModel();
ret = testRegression(lfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
std::cout << "Testing regression on a bilinear function" << std::endl;
BilinearFunctionSampleGenerator<PrecisionType> bfsg(2.0,-1.0,1.0);
regression = getKNearestNeighborsRegressionModel();
ret = testRegression(bfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
std::cout << "Testing regression on a polynomial function" << std::endl;
std::vector<PrecisionType> coeffs;
coeffs.push_back(0.0);
coeffs.push_back(-1.0);
coeffs.push_back(0.0);
coeffs.push_back(4.0);
PolynomialFunctionSampleGenerator<PrecisionType> pfsg(coeffs);
regression = getKNearestNeighborsRegressionModel();
ret = testRegression(pfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
return status;
}
MachineLearningModelRegressionType::Pointer getRandomForestsRegressionModel()
{
typedef otb::RandomForestsMachineLearningModel<InputValueRegressionType,
TargetValueRegressionType>
RFType;
RFType::Pointer regression = RFType::New();
regression->SetRegressionMode(true);
regression->SetRegressionAccuracy(0.005);
return regression.GetPointer();
}
int otbRandomForestsRegressionTests(int itkNotUsed(argc),
char * itkNotUsed(argv) [])
{
int status = EXIT_SUCCESS;
int ret;
MachineLearningModelRegressionType::Pointer regression;
RegressionTestParam param;
param.vMin = -0.5;
param.vMax = 0.5;
param.count = 200;
param.eps = 0.1;
return testRegression(lfsg,regression,param);
std::cout << "Testing regression on a linear monovariate function" << std::endl;
LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
regression = getRandomForestsRegressionModel();
ret = testRegression(lfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
std::cout << "Testing regression on a bilinear function" << std::endl;
BilinearFunctionSampleGenerator<PrecisionType> bfsg(2.0,-1.0,1.0);
// increase number of training samples for bilinear function
param.count = 1000;
regression = getRandomForestsRegressionModel();
ret = testRegression(bfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
std::cout << "Testing regression on a polynomial function" << std::endl;
std::vector<PrecisionType> coeffs;
coeffs.push_back(0.0);
coeffs.push_back(-1.0);
coeffs.push_back(0.0);
coeffs.push_back(4.0);
PolynomialFunctionSampleGenerator<PrecisionType> pfsg(coeffs);
param.count = 200;
regression = getRandomForestsRegressionModel();
ret = testRegression(pfsg,regression,param);
if (ret == EXIT_FAILURE)
{
status = EXIT_FAILURE;
}
return status;
}
#endif
Modules/Learning/Supervised/test/otbSupervisedTestDriver.cxx
+ 7
− 7
View file @ 9068e0de
......@@ -28,7 +28,7 @@ void RegisterTests()
#ifdef OTB_USE_LIBSVM
REGISTER_TEST(otbLibSVMMachineLearningModelNew);
REGISTER_TEST(otbLibSVMMachineLearningModel);
REGISTER_TEST(otbLibSVMRegressionLinearMonovariate);
REGISTER_TEST(otbLibSVMRegressionTests);
#endif
#ifdef OTB_USE_OPENCV
......@@ -49,12 +49,12 @@ void RegisterTests()
REGISTER_TEST(otbDecisionTreeMachineLearningModel);
REGISTER_TEST(otbGradientBoostedTreeMachineLearningModelNew);
REGISTER_TEST(otbGradientBoostedTreeMachineLearningModel);
REGISTER_TEST(otbNeuralNetworkRegressionLinearMonovariate);
REGISTER_TEST(otbSVMRegressionLinearMonovariate);
REGISTER_TEST(otbDecisionTreeRegressionLinearMonovariate);
REGISTER_TEST(otbGradientBoostedTreeRegressionLinearMonovariate);
REGISTER_TEST(otbKNearestNeighborsRegressionLinearMonovariate);
REGISTER_TEST(otbRandomForestsRegressionLinearMonovariate);
REGISTER_TEST(otbNeuralNetworkRegressionTests);
REGISTER_TEST(otbSVMRegressionTests);
REGISTER_TEST(otbDecisionTreeRegressionTests);
REGISTER_TEST(otbGradientBoostedTreeRegressionTests);
REGISTER_TEST(otbKNearestNeighborsRegressionTests);
REGISTER_TEST(otbRandomForestsRegressionTests);
#endif
REGISTER_TEST(otbImageClassificationFilterNew);
......
Modules/Learning/Supervised/test/tests-libsvm.cmake
+ 2
− 2
View file @ 9068e0de
......@@ -23,6 +23,6 @@ otb_add_test(NAME leTuLibSVMMachineLearningModelCanRead COMMAND otbSupervisedTes
)
set_property(TEST leTuLibSVMMachineLearningModelCanRead PROPERTY DEPENDS leTvLibSVMMachineLearningModel)
otb_add_test(NAME leTvLibSVMMachineLearningModelRegLinMono COMMAND otbSupervisedTestDriver
otbLibSVMRegressionLinearMonovariate
otb_add_test(NAME leTvLibSVMMachineLearningModelReg COMMAND otbSupervisedTestDriver
otbLibSVMRegressionTests
)
Modules/Learning/Supervised/test/tests-opencv.cmake
+ 12
− 12
View file @ 9068e0de
......@@ -11,28 +11,28 @@ otb_add_test(NAME leTvANNMachineLearningModel COMMAND otbSupervisedTestDriver
)
# ------------------ Regression tests --------------------
otb_add_test(NAME leTvANNMachineLearningModelRegLinMono COMMAND otbSupervisedTestDriver
otbNeuralNetworkRegressionLinearMonovariate
otb_add_test(NAME leTvANNMachineLearningModelReg COMMAND otbSupervisedTestDriver
otbNeuralNetworkRegressionTests
)
otb_add_test(NAME leTvSVMMachineLearningModelRegLinMono COMMAND otbSupervisedTestDriver
otbSVMRegressionLinearMonovariate
otb_add_test(NAME leTvSVMMachineLearningModelReg COMMAND otbSupervisedTestDriver
otbSVMRegressionTests
)
otb_add_test(NAME leTvDecisionTreeMachineLearningModelRegLinMono COMMAND otbSupervisedTestDriver
otbDecisionTreeRegressionLinearMonovariate
otb_add_test(NAME leTvDecisionTreeMachineLearningModelReg COMMAND otbSupervisedTestDriver
otbDecisionTreeRegressionTests
)
otb_add_test(NAME leTvGradientBoostedTreeMachineLearningModelRegLinMono COMMAND otbSupervisedTestDriver
otbGradientBoostedTreeRegressionLinearMonovariate
otb_add_test(NAME leTvGradientBoostedTreeMachineLearningModelReg COMMAND otbSupervisedTestDriver
otbGradientBoostedTreeRegressionTests
)
otb_add_test(NAME leTvKNearestNeighborsMachineLearningModelRegLinMono COMMAND otbSupervisedTestDriver
otbKNearestNeighborsRegressionLinearMonovariate
otb_add_test(NAME leTvKNearestNeighborsMachineLearningModelReg COMMAND otbSupervisedTestDriver
otbKNearestNeighborsRegressionTests
)
otb_add_test(NAME leTvRandomForestsMachineLearningModelRegLinMono COMMAND otbSupervisedTestDriver
otbRandomForestsRegressionLinearMonovariate
otb_add_test(NAME leTvRandomForestsMachineLearningModelReg COMMAND otbSupervisedTestDriver
otbRandomForestsRegressionTests
)
# --------------------------------------------------------------
......
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