REFAC: port SVM parameter optimization

Modules/Learning/SVMLearning/src/CMakeLists.txt

-#
-# Copyright (C) 2005-2017 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.
-#
-
-set(OTBSVMLearning_SRC
-  otbExhaustiveExponentialOptimizer.cxx
-  )
-
-add_library(OTBSVMLearning ${OTBSVMLearning_SRC})
-target_link_libraries(OTBSVMLearning 
-  ${OTBVectorDataBase_LIBRARIES}
-  ${OTBImageBase_LIBRARIES}
-  ${OTBLibSVM_LIBRARIES}
-  ${OTBStreaming_LIBRARIES}
-  ${OTBCommon_LIBRARIES}
-
-  )
-
-otb_module_target(OTBSVMLearning)

Modules/Learning/Supervised/include/otbExhaustiveExponentialOptimizer.h

@@ -42,9 +42,9 @@ namespace otb
  *
  * \ingroup Numerics Optimizers
  *
- * \ingroup OTBSVMLearning
+ * \ingroup OTBSupervised
  */
-class ITK_EXPORT ExhaustiveExponentialOptimizer :
+class ITK_ABI_EXPORT ExhaustiveExponentialOptimizer :
   public itk::SingleValuedNonLinearOptimizer
 {
 public:
@@ -59,7 +59,7 @@ public:
   itkNewMacro(Self);
 
   /** Run-time type information (and related methods). */
-  itkTypeMacro(ExhaustiveExponentialOptimizer,           SingleValuedNonLinearOptimizer);
+  itkTypeMacro(ExhaustiveExponentialOptimizer,itk::SingleValuedNonLinearOptimizer);
 
   void    StartOptimization(void) ITK_OVERRIDE;
 

Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h

@@ -225,6 +225,10 @@ public:
   itkSetMacro(FineOptimizationNumberOfSteps, unsigned int);
   itkGetMacro(FineOptimizationNumberOfSteps, unsigned int);
 
+  unsigned int GetNumberOfKernelParameters();
+
+  double CrossValidation(void);
+
 protected:
   /** Constructor */
   LibSVMMachineLearningModel();
@@ -250,8 +254,6 @@ private:
 
   void DeleteModel(void);
 
-  double CrossValidation(void);
-
   void OptimizeParameters(void);
 
   /** Container to hold the SVM model itself */

Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx

@@ -379,6 +379,38 @@ LibSVMMachineLearningModel<TInputValue,TOutputValue>
   m_Model = ITK_NULLPTR;
 }
 
+template <class TInputValue, class TOutputValue>
+unsigned int
+LibSVMMachineLearningModel<TInputValue,TOutputValue>
+::GetNumberOfKernelParameters()
+{
+  unsigned int nb = 1;
+  switch(this->GetKernelType())
+    {
+    case LINEAR:
+      // C
+      nb = 1;
+      break;
+    case POLY:
+      // C, gamma and coef0
+      nb = 3;
+      break;
+    case RBF:
+      // C and gamma
+      nb = 2;
+      break;
+    case SIGMOID:
+      // C, gamma and coef0
+      nb = 3;
+      break;
+    default:
+      // C
+      nb = 1;
+      break;
+    }
+  return nb;
+}
+
 template <class TInputValue, class TOutputValue>
 double
 LibSVMMachineLearningModel<TInputValue,TOutputValue>
@@ -397,7 +429,7 @@ LibSVMMachineLearningModel<TInputValue,TOutputValue>
   double total_correct = 0.;
   for (int i = 0; i < length; ++i)
     {
-    if (target[i] == m_Problem.y[i])
+    if (m_TmpTarget[i] == m_Problem.y[i])
       {
       ++total_correct;
       }
@@ -413,57 +445,25 @@ void
 LibSVMMachineLearningModel<TInputValue,TOutputValue>
 ::OptimizeParameters()
 {
-  typedef SVMCrossValidationCostFunction<this> CrossValidationFunctionType;
+  typedef SVMCrossValidationCostFunction<LibSVMMachineLearningModel<TInputValue,TOutputValue> > CrossValidationFunctionType;
   typename CrossValidationFunctionType::Pointer crossValidationFunction = CrossValidationFunctionType::New();
   crossValidationFunction->SetModel(this);
 
   typename CrossValidationFunctionType::ParametersType initialParameters, coarseBestParameters, fineBestParameters;
 
-  switch (this->GetKernelType())
-    {
-    case LINEAR:
-      // C
-      initialParameters.SetSize(1);
-      initialParameters[0] = this->GetC();
-      break;
-
-    case POLY:
-      // C, gamma and coef0
-      initialParameters.SetSize(3);
-      initialParameters[0] = this->GetC();
-      initialParameters[1] = this->GetKernelGamma();
-      initialParameters[2] = this->GetKernelCoef0();
-      break;
-
-    case RBF:
-      // C and gamma
-      initialParameters.SetSize(2);
-      initialParameters[0] = this->GetC();
-      initialParameters[1] = this->GetKernelGamma();
-      break;
-
-    case SIGMOID:
-      // C, gamma and coef0
-      initialParameters.SetSize(3);
-      initialParameters[0] = this->GetC();
-      initialParameters[1] = this->GetKernelGamma();
-      initialParameters[2] = this->GetKernelCoef0();
-      break;
-
-    default:
-      // Only C
-      initialParameters.SetSize(1);
-      initialParameters[0] = this->GetC();
-      break;
-    }
+  unsigned int nbParams = this->GetNumberOfKernelParameters();
+  initialParameters.SetSize(nbParams);
+  initialParameters[0] = this->GetC();
+  if (nbParams > 1) initialParameters[1] = this->GetKernelGamma();
+  if (nbParams > 2) initialParameters[2] = this->GetKernelCoef0();
 
   m_InitialCrossValidationAccuracy = crossValidationFunction->GetValue(initialParameters);
   m_FinalCrossValidationAccuracy = m_InitialCrossValidationAccuracy;
 
   otbMsgDebugMacro(<< "Initial accuracy : " << m_InitialCrossValidationAccuracy
-                   << ", Parameters Optimization" << m_ParametersOptimization);
+                   << ", Parameters Optimization" << m_ParameterOptimization);
 
-  if (m_ParametersOptimization)
+  if (m_ParameterOptimization)
     {
     otbMsgDebugMacro(<< "Model parameters optimization");
     typename ExhaustiveExponentialOptimizer::Pointer coarseOptimizer = ExhaustiveExponentialOptimizer::New();
@@ -503,38 +503,9 @@ LibSVMMachineLearningModel<TInputValue,TOutputValue>
 
     m_FinalCrossValidationAccuracy = fineOptimizer->GetMaximumMetricValue();
 
-    switch (this->GetKernelType())
-      {
-      case LINEAR:
-        // C
-        this->SetC(fineBestParameters[0]);
-        break;
-
-      case POLY:
-        // C, gamma and coef0
-        this->SetC(fineBestParameters[0]);
-        this->SetKernelGamma(fineBestParameters[1]);
-        this->SetKernelCoef0(fineBestParameters[2]);
-        break;
-
-      case RBF:
-        // C and gamma
-        this->SetC(fineBestParameters[0]);
-        this->SetKernelGamma(fineBestParameters[1]);
-        break;
-
-      case SIGMOID:
-        // C, gamma and coef0
-        this->SetC(fineBestParameters[0]);
-        this->SetKernelGamma(fineBestParameters[1]);
-        this->SetKernelCoef0(fineBestParameters[2]);
-        break;
-
-      default:
-        // Only C
-        this->SetC(fineBestParameters[0]);
-        break;
-      }
+    this->SetC(fineBestParameters[0]);
+    if (nbParams > 1) this->SetKernelGamma(fineBestParameters[1]);
+    if (nbParams > 2) this->SetKernelCoef0(fineBestParameters[2]);
     }
 }
 

Modules/Learning/Supervised/include/otbSVMCrossValidationCostFunction.h

@@ -21,7 +21,6 @@
 #ifndef otbSVMCrossValidationCostFunction_h
 #define otbSVMCrossValidationCostFunction_h
 
-#include "otbSVMModel.h"
 #include "itkSingleValuedCostFunction.h"
 
 namespace otb

Modules/Learning/Supervised/include/otbSVMCrossValidationCostFunction.txx

@@ -22,6 +22,7 @@
 #define otbSVMCrossValidationCostFunction_txx
 
 #include "otbSVMCrossValidationCostFunction.h"
+#include "otbMacro.h"
 
 namespace otb
 {
@@ -95,67 +96,18 @@ SVMCrossValidationCostFunction<TModel>
     {
     itkExceptionMacro(<< "Model is null, can not evaluate number of parameters.");
     }
-
-  switch (m_Model->GetKernelType())
-    {
-    case LINEAR:
-      // C
-      return 1;
-
-    case POLY:
-      // C, gamma and coef0
-      return 3;
-
-    case RBF:
-      // C and gamma
-      return 2;
-
-    case SIGMOID:
-      // C, gamma and coef0
-      return 3;
-
-    default:
-      // C
-      return 1;
-    }
+  return m_Model->GetNumberOfKernelParameters();
 }
 
 template<class TModel>
 void
 SVMCrossValidationCostFunction<TModel>
-::UpdateParameters(struct svm_parameter& svm_parameters, const ParametersType& parameters) const
+::UpdateParameters(const ParametersType& parameters) const
 {
-  switch (m_Model->GetKernelType())
-    {
-    case LINEAR:
-      // C
-      m_Model->SetC(parameters[0]);
-      break;
-
-    case POLY:
-      // C, gamma and coef0
-      m_Model->SetC(parameters[0]);
-      m_Model->SetKernelGamma(parameters[1]);
-      m_Model->SetKernelCoef0(parameters[2]);
-      break;
-
-    case RBF:
-      // C and gamma
-      m_Model->SetC(parameters[0]);
-      m_Model->SetKernelGamma(parameters[1]);
-      break;
-
-    case SIGMOID:
-      // C, gamma and coef0
-      m_Model->SetC(parameters[0]);
-      m_Model->SetKernelGamma(parameters[1]);
-      m_Model->SetKernelCoef0(parameters[2]);
-      break;
-
-    default:
-      m_Model->SetC(parameters[0]);
-      break;
-    }
+  unsigned int nbParams = m_Model->GetNumberOfKernelParameters();
+  m_Model->SetC(parameters[0]);
+  if (nbParams > 1) m_Model->SetKernelGamma(parameters[1]);
+  if (nbParams > 2) m_Model->SetKernelCoef0(parameters[2]);
 }
 
 } // namespace otb

Modules/Learning/Supervised/src/CMakeLists.txt

@@ -20,6 +20,7 @@
 
 set(OTBSupervised_SRC
   otbMachineLearningModelFactoryBase.cxx
+  otbExhaustiveExponentialOptimizer.cxx
   )
 
 if(OTB_USE_OPENCV)