From 938a62a9a466f667158a78c05eaba48a45d24bec Mon Sep 17 00:00:00 2001
From: Guillaume Pasero <guillaume.pasero@c-s.fr>
Date: Mon, 10 Apr 2017 19:12:51 +0200
Subject: [PATCH] REFAC: support hyperplanes and proba output through the
confidence index
---
.../include/otbLibSVMMachineLearningModel.h | 25 ++++++
.../include/otbLibSVMMachineLearningModel.txx | 77 ++++++++++++-------
2 files changed, 74 insertions(+), 28 deletions(-)
diff --git a/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h b/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h
index 33e4ee9b91..a79416de81 100644
--- a/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h
@@ -48,6 +48,17 @@ public:
typedef typename Superclass::TargetListSampleType TargetListSampleType;
typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
+ /** enum to choose the way confidence is computed
+ * CM_INDEX : compute the difference between highest and second highest probability
+ * CM_PROBA : returns probabilities for all classes
+ * The given pointer needs to store 'nbClass' values
+ * This mode requires that ConfidenceValueType is double
+ * CM_HYPER : returns hyperplanes distances*
+ * The given pointer needs to store 'nbClass * (nbClass-1) / 2' values
+ * This mode requires that ConfidenceValueType is double
+ */
+ typedef enum {CM_INDEX,CM_PROBA,CM_HYPER} ConfidenceMode;
+
/** Run-time type information (and related methods). */
itkNewMacro(Self);
itkTypeMacro(SVMMachineLearningModel, MachineLearningModel);
@@ -225,6 +236,17 @@ public:
itkSetMacro(FineOptimizationNumberOfSteps, unsigned int);
itkGetMacro(FineOptimizationNumberOfSteps, unsigned int);
+ void SetConfidenceMode(unsigned int mode)
+ {
+ if (mode != m_ConfidenceMode)
+ {
+ m_ConfidenceMode = mode;
+ this->m_ConfidenceIndex = this->HasProbabilities();
+ this->Modified();
+ }
+ }
+ itkGetMacro(ConfidenceMode, unsigned int);
+
unsigned int GetNumberOfKernelParameters();
double CrossValidation(void);
@@ -283,6 +305,9 @@ private:
/** Number of steps for the fine search */
unsigned int m_FineOptimizationNumberOfSteps;
+ /** Output mode for confidence index (see enum ) */
+ ConfidenceMode m_ConfidenceMode;
+
/** Temporary array to store cross-validation results */
std::vector<double> m_TmpTarget;
diff --git a/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx
index 6a10eb4934..dedfe1787e 100644
--- a/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx
@@ -53,6 +53,8 @@ LibSVMMachineLearningModel<TInputValue,TOutputValue>
this->m_FinalCrossValidationAccuracy = 0.;
this->m_CoarseOptimizationNumberOfSteps = 5;
this->m_FineOptimizationNumberOfSteps = 5;
+ this->m_ConfidenceMode =
+ LibSVMMachineLearningModel<TInputValue,TOutputValue>::CM_INDEX;
this->m_Parameters.nr_weight = 0;
this->m_Parameters.weight_label = ITK_NULLPTR;
@@ -129,38 +131,49 @@ LibSVMMachineLearningModel<TInputValue,TOutputValue>
{
itkExceptionMacro("Confidence index not available for this classifier !");
}
- if (svm_type == C_SVC || svm_type == NU_SVC)
+ if (this->m_ConfidenceMode == CM_INDEX)
{
- // Eventually allocate space for probabilities
- int nr_class = svm_get_nr_class(m_Model);
- double *prob_estimates = new double[nr_class];
- // predict
- target[0] = static_cast<TargetValueType>(svm_predict_probability(m_Model, x, prob_estimates));
- double maxProb = 0.0;
- double secProb = 0.0;
- for (unsigned int i=0 ; i< nr_class ; ++i)
+ if (svm_type == C_SVC || svm_type == NU_SVC)
{
- if (maxProb < prob_estimates[i])
+ // Eventually allocate space for probabilities
+ int nr_class = svm_get_nr_class(m_Model);
+ double *prob_estimates = new double[nr_class];
+ // predict
+ target[0] = static_cast<TargetValueType>(svm_predict_probability(m_Model, x, prob_estimates));
+ double maxProb = 0.0;
+ double secProb = 0.0;
+ for (unsigned int i=0 ; i< nr_class ; ++i)
{
- secProb = maxProb;
- maxProb = prob_estimates[i];
+ if (maxProb < prob_estimates[i])
+ {
+ secProb = maxProb;
+ maxProb = prob_estimates[i];
+ }
+ else if (secProb < prob_estimates[i])
+ {
+ secProb = prob_estimates[i];
+ }
}
- else if (secProb < prob_estimates[i])
- {
- secProb = prob_estimates[i];
- }
- }
- (*quality) = static_cast<ConfidenceValueType>(maxProb - secProb);
+ (*quality) = static_cast<ConfidenceValueType>(maxProb - secProb);
- delete[] prob_estimates;
+ delete[] prob_estimates;
+ }
+ else
+ {
+ target[0] = static_cast<TargetValueType>(svm_predict(m_Model, x));
+ // Prob. model for test data: target value = predicted value + z
+ // z: Laplace distribution e^(-|z|/sigma)/(2sigma)
+ // sigma is output as confidence index
+ (*quality) = svm_get_svr_probability(m_Model);
+ }
+ }
+ else if (this->m_ConfidenceMode == CM_PROBA)
+ {
+ target[0] = static_cast<TargetValueType>(svm_predict_probability(m_Model, x, quality));
}
- else
+ else if (this->m_ConfidenceMode == CM_HYPER)
{
- target[0] = static_cast<TargetValueType>(svm_predict(m_Model, x));
- // Prob. model for test data: target value = predicted value + z
- // z: Laplace distribution e^(-|z|/sigma)/(2sigma)
- // sigma is output as confidence index
- (*quality) = svm_get_svr_probability(m_Model);
+ target[0] = static_cast<TargetValueType>(svm_predict_values(m_Model, x, quality));
}
}
else
@@ -252,10 +265,18 @@ bool
LibSVMMachineLearningModel<TInputValue,TOutputValue>
::HasProbabilities(void) const
{
- bool ret = static_cast<bool>(svm_check_probability_model(m_Model));
- if (svm_get_svm_type(m_Model) == ONE_CLASS)
+ bool modelHasProba = static_cast<bool>(svm_check_probability_model(m_Model));
+ int type = svm_get_svm_type(m_Model);
+ int cmMode = this->m_ConfidenceMode;
+ bool ret = false;
+ if (type == EPSILON_SVR || type == NU_SVR)
+ {
+ ret = (modelHasProba && cmMode == CM_INDEX);
+ }
+ else if (type == C_SVC || type == NU_SVC)
{
- ret = false;
+ ret = (modelHasProba && (cmMode == CM_INDEX || cmMode == CM_PROBA)) ||
+ (cmMode == CM_HYPER);
}
return ret;
}
--
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