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Add accuracy, mean F1 and mean IoU to trainer

This commit is contained in:
Simon Giraudot 2017-03-02 16:08:28 +01:00
parent 4feca8dec0
commit cea79f650a
2 changed files with 59 additions and 16 deletions
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5
Classification/examples/Classification/example_point_set_classifier.cpp
View File

@ -123,7 +123,10 @@ int main (int argc, char** argv)
<< trainer.f1_score(psc.label(i)) << " ; "
<< trainer.IoU(psc.label(i)) << std::endl;
}
std::cerr << "Accuracy = " << trainer.accuracy() << std::endl
<< "Mean F1 score = " << trainer.mean_f1_score() << std::endl
<< "Mean IoU = " << trainer.mean_IoU() << std::endl;
psc.run_with_graphcut (psc.neighborhood().k_neighbor_query(12), 0.5);

70
Classification/include/CGAL/Classification/Trainer.h
View File

@ -73,6 +73,9 @@ private:
std::vector<double> m_precision;
std::vector<double> m_recall;
std::vector<double> m_iou; // intersection over union
double m_accuracy;
double m_mean_iou;
double m_mean_f1;
public:
@ -330,7 +333,6 @@ public:
best_weights[k] = att->weight();
}
}
current_att->set_weight(current_att->weight() * tr.factor);
}
@ -397,7 +399,7 @@ public:
the true positives and the false positives.
*/
double precision (Label_handle label)
double precision (Label_handle label) const
{
std::size_t label_idx = (std::size_t)(-1);
for (std::size_t i = 0; i < m_classifier->number_of_labels(); ++ i)
@ -420,7 +422,7 @@ public:
the true positives and the false negatives.
*/
double recall (Label_handle label)
double recall (Label_handle label) const
{
std::size_t label_idx = (std::size_t)(-1);
for (std::size_t i = 0; i < m_classifier->number_of_labels(); ++ i)
@ -446,7 +448,7 @@ public:
\f]
*/
double f1_score (Label_handle label)
double f1_score (Label_handle label) const
{
std::size_t label_idx = (std::size_t)(-1);
for (std::size_t i = 0; i < m_classifier->number_of_labels(); ++ i)
@ -462,8 +464,7 @@ public:
/ (m_precision[label_idx] + m_recall[label_idx]);
}
/*!
/*!
\brief Returns the intersection over union of the training for the
given label.
@ -471,7 +472,7 @@ public:
the sum of the true positives, of the false positives and of the
false negatives.
*/
double IoU (Label_handle label)
double IoU (Label_handle label) const
{
std::size_t label_idx = (std::size_t)(-1);
for (std::size_t i = 0; i < m_classifier->number_of_labels(); ++ i)
@ -485,6 +486,27 @@ public:
return m_iou[label_idx];
}
/*!
\brief Returns the accuracy of the training.
Accuracy is the total number of true positives divided by the
total number of provided inliers.
*/
double accuracy() const { return m_accuracy; }
/*!
\brief Returns the mean \f$F_1\f$ score of the training over all
labels (see `f1_score()`).
*/
double mean_f1_score() const { return m_mean_f1; }
/*!
\brief Returns the mean IoU of the training over all labels (see
`IoU()`).
*/
double mean_IoU() const { return m_mean_iou; }
/// @}
/// \cond SKIP_IN_MANUAL
@ -555,8 +577,8 @@ private:
for(std::size_t l = 0; l < m_classifier->number_of_labels(); ++ l)
{
double value = m_classifier->classification_value (m_classifier->label(l),
m_training_sets[j][k]);
double value = m_classifier->energy_of (m_classifier->label(l),
m_training_sets[j][k]);
if(val_class_best > value)
{
@ -591,8 +613,8 @@ private:
std::vector<std::pair<double, std::size_t> > values;
for(std::size_t l = 0; l < m_classifier->number_of_labels(); ++ l)
values.push_back (std::make_pair (m_classifier->classification_value (m_classifier->label(l),
m_training_sets[j][k]),
values.push_back (std::make_pair (m_classifier->energy_of (m_classifier->label(l),
m_training_sets[j][k]),
l));
std::sort (values.begin(), values.end());
@ -636,6 +658,9 @@ private:
std::vector<std::size_t> false_positives (m_classifier->number_of_labels());
std::vector<std::size_t> false_negatives (m_classifier->number_of_labels());
std::size_t sum_true_positives = 0;
std::size_t total = 0;
for (std::size_t j = 0; j < m_classifier->number_of_labels(); ++ j)
{
for (std::size_t k = 0; k < m_training_sets[j].size(); ++ k)
@ -645,8 +670,8 @@ private:
for(std::size_t l = 0; l < m_classifier->number_of_labels(); ++ l)
{
double value = m_classifier->classification_value (m_classifier->label(l),
m_training_sets[j][k]);
double value = m_classifier->energy_of (m_classifier->label(l),
m_training_sets[j][k]);
if(val_class_best > value)
{
@ -654,9 +679,12 @@ private:
nb_class_best = l;
}
}
++ total;
if (nb_class_best == j)
++ true_positives[j];
{
++ true_positives[j];
++ sum_true_positives;
}
else
{
++ false_negatives[j];
@ -671,12 +699,24 @@ private:
m_precision.clear();
m_recall.clear();
m_mean_iou = 0.;
m_mean_f1 = 0.;
for (std::size_t j = 0; j < m_classifier->number_of_labels(); ++ j)
{
m_precision.push_back (true_positives[j] / double(true_positives[j] + false_positives[j]));
m_recall.push_back (true_positives[j] / double(true_positives[j] + false_negatives[j]));
m_iou.push_back (true_positives[j] / double(true_positives[j] + false_positives[j] + false_negatives[j]));
m_mean_iou += m_iou.back();
m_mean_f1 += 2. * (m_precision.back() * m_recall.back())
/ (m_precision.back() + m_recall.back());
}
m_mean_iou /= m_classifier->number_of_labels();
m_mean_f1 /= m_classifier->number_of_labels();
m_accuracy = sum_true_positives / double(total);
}