From ecd14f9f926d7b54078fca6b2c0c6d57fe2b2aae Mon Sep 17 00:00:00 2001
From: Jordi Inglada <jordi.inglada@cesbio.cnes.fr>
Date: Wed, 21 Oct 2015 11:46:24 +0200
Subject: [PATCH] ENH: use the random fores confidence estimation in the
MachineLearningModel
---
.../otbRandomForestsMachineLearningModel.h | 9 ++--
.../otbRandomForestsMachineLearningModel.txx | 50 +++++++++----------
2 files changed, 30 insertions(+), 29 deletions(-)
diff --git a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
index ec62b76135..6f8a38d5fb 100644
--- a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
@@ -24,8 +24,9 @@
#include "itkFixedArray.h"
#include "otbMachineLearningModel.h"
#include "itkVariableSizeMatrix.h"
+#include "otbCvRTrees.h"
-class CvRTrees;
+class CvRTreesWrapper;
namespace otb
{
@@ -53,7 +54,7 @@ public:
//opencv typedef
- typedef CvRTrees RFType;
+ typedef CvRTreesWrapper RFType;
/** Run-time type information (and related methods). */
itkNewMacro(Self);
@@ -145,7 +146,7 @@ private:
RandomForestsMachineLearningModel(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
- CvRTrees * m_RFModel;
+ CvRTreesWrapper * m_RFModel;
/** The depth of the tree. A low value will likely underfit and conversely a
* high value will likely overfit. The optimal value can be obtained using cross
* validation or other suitable methods. */
@@ -189,7 +190,7 @@ private:
* first category. */
std::vector<float> m_Priors;
/** If true then variable importance will be calculated and then it can be
- * retrieved by CvRTrees::get_var_importance(). */
+ * retrieved by CvRTreesWrapper::get_var_importance(). */
bool m_CalculateVariableImportance;
/** The size of the randomly selected subset of features at each tree node and
* that are used to find the best split(s). If you set it to 0 then the size will
diff --git a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.txx
index 78642f1212..67d21f8f49 100644
--- a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.txx
@@ -29,17 +29,17 @@ namespace otb
template <class TInputValue, class TOutputValue>
RandomForestsMachineLearningModel<TInputValue,TOutputValue>
::RandomForestsMachineLearningModel() :
- m_RFModel (new CvRTrees),
- 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)
+ m_RFModel (new CvRTreesWrapper),
+ 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)
{
this->m_ConfidenceIndex = true;
this->m_IsRegressionSupported = true;
@@ -125,7 +125,7 @@ RandomForestsMachineLearningModel<TInputValue,TOutputValue>
if (quality != NULL)
{
- (*quality) = m_RFModel->predict_prob(sample);
+ (*quality) = m_RFModel->predict_confidence(sample);
}
return target[0];
@@ -158,23 +158,23 @@ bool
RandomForestsMachineLearningModel<TInputValue,TOutputValue>
::CanReadFile(const std::string & file)
{
- std::ifstream ifs;
- ifs.open(file.c_str());
+ std::ifstream ifs;
+ ifs.open(file.c_str());
- if(!ifs)
- {
- std::cerr<<"Could not read file "<<file<<std::endl;
- return false;
- }
+ if(!ifs)
+ {
+ std::cerr<<"Could not read file "<<file<<std::endl;
+ return false;
+ }
- while (!ifs.eof())
- {
- std::string line;
- std::getline(ifs, line);
+ 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)
+ //if (line.find(m_RFModel->getName()) != std::string::npos)
+ if (line.find(CV_TYPE_NAME_ML_RTREES) != std::string::npos)
{
//std::cout<<"Reading a "<<CV_TYPE_NAME_ML_RTREES<<" model"<<std::endl;
return true;
--
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