ok

Makefile

@@ -10,7 +10,7 @@ OBJ =
 all: $(BIN) $(OBJ)
 export LDFLAGS= -pthread -lm 
 
-xgboost: regrank/xgboost_regrank_main.cpp regrank/*.h booster/*.h booster/*/*.hpp booster/*.hpp
+xgboost: regrank/xgboost_regrank_main.cpp regrank/*.h regrank/*.hpp booster/*.h booster/*/*.hpp booster/*.hpp
 
 
 $(BIN) : 

demo/rank/mq2008.conf

@@ -2,9 +2,9 @@
 # choose the tree booster, 0: tree, 1: linear
 booster_type = 0 
 
-objective="rank:pairwise"
+#objective="rank:pairwise"
 #objective="rank:softmax"
-#objective="lambdarank:map"
+objective="lambdarank:map"
 #objective="lambdarank:ndcg"
 
 # Tree Booster Parameters
@@ -13,7 +13,7 @@ bst:eta = 0.1
 # minimum loss reduction required to make a further partition
 bst:gamma = 1.0 
 # minimum sum of instance weight(hessian) needed in a child
-bst:min_child_weight = 1 
+bst:min_child_weight = 0.1
 # maximum depth of a tree
 bst:max_depth = 6
 eval_metric = "ndcg"

regrank/xgboost_regrank_obj.hpp

@@ -261,7 +261,7 @@ namespace xgboost{
                 }
             }
             virtual const char* DefaultEvalMetric(void) {
-                return "ndcg";
+                return "map";
             }    
         private:
             inline void AddGradient( unsigned pid, unsigned nid, 
@@ -285,23 +285,36 @@ namespace xgboost{
         };
     };
     
-
     namespace regrank{
         class LambdaRankObj : public IObjFunction{
         public:
-            LambdaRankObj(void){}
-
+            LambdaRankObj(void){
+                loss_.loss_type = LossType::kLogisticRaw;
+            }
             virtual ~LambdaRankObj(){}
-
             virtual void SetParam(const char *name, const char *val){
                 if( !strcmp( "loss_type", name ) )       loss_.loss_type = atoi( val );
                 if( !strcmp( "fix_list_weight", name ) ) fix_list_weight_ = (float)atof( val );
-
             }
         private:
             LossType loss_;
             float fix_list_weight_;
         protected:
+            /*! \brief helper information in a list */
+            struct ListEntry{
+                /*! \brief the predict score we in the data */
+                float pred;
+                /*! \brief the actual label of the entry */
+                float label;
+                /*! \brief row index in the data matrix */                
+                unsigned rindex;
+                // constructor
+                ListEntry(float pred, float label, unsigned rindex): pred(pred),label(label),rindex(rindex){}
+                // comparator by prediction
+                inline bool operator<(const ListEntry &p) const{
+                    return pred > p.pred;
+                }
+            };
             
             class Triple{
             public:
@@ -431,8 +444,6 @@ namespace xgboost{
                     i = j;
                 }
             }                        
- 
-
         public:
             virtual void GetGradient(const std::vector<float>& preds,
                 const DMatrix::Info &info,
@@ -449,7 +460,6 @@ namespace xgboost{
                     GetGroupGradient(preds, info.labels, group_index, grad, hess, pairs, i);
                 }
             }            
-
             virtual const char* DefaultEvalMetric(void) {
                 return "auc";
             }