Added a per node loss option to the structural_svm_graph_labeling_problem's

interface.

dlib/svm/structural_graph_labeling_trainer.h

@@ -180,7 +180,8 @@ namespace dlib
                 );
 
 
-            structural_svm_graph_labeling_problem<graph_type> prob(samples, labels, num_threads);
+            std::vector<std::vector<double> > losses;
+            structural_svm_graph_labeling_problem<graph_type> prob(samples, labels, losses, num_threads);
 
             if (verbose)
                 prob.be_verbose();

dlib/svm/structural_svm_graph_labeling_problem.h

@@ -83,6 +83,46 @@ namespace dlib
         return true;
     }
 
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename T,
+        typename U
+        >
+    bool sizes_match (
+        const std::vector<std::vector<T> >& lhs,
+        const std::vector<std::vector<U> >& rhs
+    )
+    {
+        if (lhs.size() != rhs.size())
+            return false;
+
+        for (unsigned long i = 0; i < lhs.size(); ++i)
+        {
+            if (lhs[i].size() != rhs[i].size())
+                return false;
+        }
+
+        return true;
+    }
+
+// ----------------------------------------------------------------------------------------
+
+    inline bool all_values_are_nonnegative (
+        const std::vector<std::vector<double> >& x
+    )
+    {
+        for (unsigned long i = 0; i < x.size(); ++i)
+        {
+            for (unsigned long j = 0; j < x[i].size(); ++j)
+            {
+                if (x[i][j] < 0)
+                    return false;
+            }
+        }
+        return true;
+    }
+
 // ----------------------------------------------------------------------------------------
 // ----------------------------------------------------------------------------------------
 
@@ -135,18 +175,25 @@ namespace dlib
         structural_svm_graph_labeling_problem(
             const dlib::array<sample_type>& samples_,
             const std::vector<label_type>& labels_,
+            const std::vector<std::vector<double> >& losses_,
             unsigned long num_threads = 2
         ) :
             structural_svm_problem_threaded<matrix_type,feature_vector_type>(num_threads),
             samples(samples_),
-            labels(labels_)
+            labels(labels_),
+            losses(losses_)
         {
             // make sure requires clause is not broken
-            DLIB_ASSERT(is_graph_labeling_problem(samples, labels) == true,
+            DLIB_ASSERT(is_graph_labeling_problem(samples, labels) == true &&
+                        (losses.size() == 0 || sizes_match(labels, losses) == true) &&
+                        all_values_are_nonnegative(losses) == true,
                     "\t structural_svm_graph_labeling_problem::structural_svm_graph_labeling_problem()"
                     << "\n\t Invalid inputs were given to this function."
                     << "\n\t samples.size(): " << samples.size() 
                     << "\n\t labels.size():  " << labels.size() 
+                    << "\n\t losses.size():  " << losses.size() 
+                    << "\n\t sizes_match(labels,losses): " << sizes_match(labels,losses) 
+                    << "\n\t all_values_are_nonnegative(losses): " << all_values_are_nonnegative(losses) 
                     << "\n\t this: " << this );
 
             loss_pos = 1.0;
@@ -168,6 +215,9 @@ namespace dlib
             }
         }
 
+        const std::vector<std::vector<double> >& get_losses (
+        ) const { return losses; }
+
         long get_num_edge_weights (
         ) const
         { 
@@ -179,8 +229,8 @@ namespace dlib
         )
         {
             // make sure requires clause is not broken
-            DLIB_ASSERT(loss >= 0,
-                    "\t structural_svm_graph_labeling_problem::set_loss_on_positive_class()"
+            DLIB_ASSERT(loss >= 0 && get_losses().size() == 0,
+                    "\t void structural_svm_graph_labeling_problem::set_loss_on_positive_class()"
                     << "\n\t Invalid inputs were given to this function."
                     << "\n\t loss: " << loss 
                     << "\n\t this: " << this );
@@ -193,8 +243,8 @@ namespace dlib
         )
         {
             // make sure requires clause is not broken
-            DLIB_ASSERT(loss >= 0,
-                    "\t structural_svm_graph_labeling_problem::set_loss_on_negative_class()"
+            DLIB_ASSERT(loss >= 0 && get_losses().size() == 0,
+                    "\t void structural_svm_graph_labeling_problem::set_loss_on_negative_class()"
                     << "\n\t Invalid inputs were given to this function."
                     << "\n\t loss: " << loss 
                     << "\n\t this: " << this );
@@ -203,10 +253,28 @@ namespace dlib
         }
 
         double get_loss_on_negative_class (
-        ) const { return loss_neg; }
+        ) const 
+        { 
+            // make sure requires clause is not broken
+            DLIB_ASSERT(get_losses().size() == 0,
+                    "\t double structural_svm_graph_labeling_problem::get_loss_on_negative_class()"
+                    << "\n\t Invalid inputs were given to this function."
+                    << "\n\t this: " << this );
+
+            return loss_neg; 
+        }
 
         double get_loss_on_positive_class (
-        ) const { return loss_pos; }
+        ) const 
+        { 
+            // make sure requires clause is not broken
+            DLIB_ASSERT(get_losses().size() == 0,
+                    "\t double structural_svm_graph_labeling_problem::get_loss_on_positive_class()"
+                    << "\n\t Invalid inputs were given to this function."
+                    << "\n\t this: " << this );
+
+            return loss_pos; 
+        }
 
 
     private:
@@ -386,7 +454,9 @@ namespace dlib
                 const bool true_label = labels[sample_idx][node_idx];
                 if (true_label != predicted_label)
                 {
-                    if (true_label == true)
+                    if (losses.size() != 0)
+                        return losses[sample_idx][node_idx];
+                    else if (true_label == true)
                         return loss_pos;
                     else
                         return loss_neg;
@@ -400,6 +470,7 @@ namespace dlib
 
         const dlib::array<sample_type>& samples;
         const std::vector<label_type>& labels;
+        const std::vector<std::vector<double> >& losses;
 
         long node_dims;
         long edge_dims;

dlib/svm/structural_svm_graph_labeling_problem_abstract.h

@@ -55,6 +55,36 @@ namespace dlib
                     - All vectors have non-zero size.  That is, they have more than 0 dimensions.
     !*/
 
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename T,
+        typename U
+        >
+    bool sizes_match (
+        const std::vector<std::vector<T> >& lhs,
+        const std::vector<std::vector<U> >& rhs
+    );
+    /*!
+        ensures
+            - returns true if the sizes of lhs and rhs, as well as their constituent vectors
+              all match.  In particular, we return true if all of the following conditions are
+              met and false otherwise:
+                - lhs.size() == rhs.size()
+                - for all valid i:
+                    - lhs[i].size() == rhs[i].size()
+    !*/
+
+// ----------------------------------------------------------------------------------------
+
+    bool all_values_are_nonnegative (
+        const std::vector<std::vector<double> >& x
+    );
+    /*!
+        ensures
+            - returns true if all the double values contained in x are >= 0.
+    !*/
+
 // ----------------------------------------------------------------------------------------
 // ----------------------------------------------------------------------------------------
 
@@ -87,11 +117,15 @@ namespace dlib
         structural_svm_graph_labeling_problem(
             const dlib::array<sample_type>& samples,
             const std::vector<label_type>& labels,
+            const std::vector<std::vector<double> >& losses,
             unsigned long num_threads 
         );
         /*!
             requires
                 - is_graph_labeling_problem(samples,labels) == true
+                - if (losses.size() != 0) then
+                    - sizes_match(labels, losses) == true
+                    - all_values_are_nonnegative(losses) == true
             ensures
                 - This object attempts to learn a mapping from the given samples to the 
                   given labels.  In particular, it attempts to learn to predict labels[i] 
@@ -107,8 +141,29 @@ namespace dlib
                 - This object will use num_threads threads during the optimization 
                   procedure.  You should set this parameter equal to the number of 
                   available processing cores on your machine.
-                - #get_loss_on_positive_class() == 1.0
-                - #get_loss_on_negative_class() == 1.0
+                - if (losses.size() == 0) then
+                    - #get_loss_on_positive_class() == 1.0
+                    - #get_loss_on_negative_class() == 1.0
+                    - #get_losses().size() == 0
+                    - the losses argument is effectively ignored if its size is zero.
+                - else
+                    - #get_losses() == losses
+                    - Each node in the training data has its own loss value defined by
+                      the corresponding entry of losses.  In particular, this means that 
+                      the node with label labels[i][j] incurs a loss of losses[i][j] if 
+                      it is incorrectly labeled.
+                    - The get_loss_on_positive_class() and get_loss_on_negative_class()
+                      parameters are ignored.  Only get_losses() is used in this case.
+        !*/
+
+        const std::vector<std::vector<double> >& get_losses (
+        ) const;
+        /*!
+            ensures
+                - returns the losses vector given to this object's constructor. 
+                  This vector defines the per sample loss values used.  If the vector
+                  is empty then the loss values defined by get_loss_on_positive_class() and
+                  get_loss_on_positive_class() are used instead.
         !*/
 
         long get_num_edge_weights (
@@ -128,6 +183,7 @@ namespace dlib
         /*!
             requires
                 - loss >= 0
+                - get_losses().size() == 0
             ensures
                 - #get_loss_on_positive_class() == loss
         !*/
@@ -138,6 +194,7 @@ namespace dlib
         /*!
             requires
                 - loss >= 0
+                - get_losses().size() == 0
             ensures
                 - #get_loss_on_negative_class() == loss
         !*/
@@ -145,6 +202,8 @@ namespace dlib
         double get_loss_on_positive_class (
         ) const;
         /*!
+            requires
+                - get_losses().size() == 0
             ensures
                 - returns the loss incurred when a graph node which is supposed to have
                   a label of true gets misclassified.  This value controls how much we care 
@@ -155,6 +214,8 @@ namespace dlib
         double get_loss_on_negative_class (
         ) const;
         /*!
+            requires
+                - get_losses().size() == 0
             ensures
                 - returns the loss incurred when a graph node which is supposed to have
                   a label of false gets misclassified.  This value controls how much we care