add in grad and hess rescale in lambdarank

demo/rank/toy.eval

@@ -1,5 +0,0 @@
-1 0:2 1:3 2:2
-0 0:2 1:3 2:2
-0 0:2 1:3 2:2
-0 0:2 1:3 2:2
-1 0:2 1:3 2:2

demo/rank/toy.eval.group

@@ -1,2 +0,0 @@
-2
-3

demo/rank/toy.test

@@ -1,5 +0,0 @@
-1 0:2 1:3 2:2
-0 0:2 1:3 2:2
-0 0:2 1:3 2:2
-0 0:2 1:3 2:2
-1 0:2 1:3 2:2

demo/rank/toy.test.group

@@ -1,2 +0,0 @@
-2
-3

demo/rank/toy.train

@@ -1,11 +0,0 @@
-1 0:1.2 1:3 2:5.6
-0 0:2.0 1:2.3 2:5.1
-0 0:3.9 1:3 2:3.1
-0 0:2 1:3.2 2:3.4
-1 0:2.1 1:4.5 2:4.2
-0 0:1.9 1:2.8 2:3.1
-1 0:3.0 1:2.0 2:1.1
-0 0:1.9 1:1.8 2:2.1
-0 0:1.1 1:2.2 2:1.4
-1 0:2.1 1:4.1 2:4.0
-0 0:1.9 1:2.2 2:1.1

demo/rank/toy.train.group

@@ -1,2 +0,0 @@
-6
-5

regrank/xgboost_regrank_obj.hpp

@@ -281,10 +281,12 @@ namespace xgboost{
 
             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:
 
             class Triple{
@@ -372,6 +374,15 @@ namespace xgboost{
                     grad[pairs[i].first] += first_order_gradient;
                     hess[pairs[i].second] += second_order_gradient;
                     grad[pairs[i].second] -= first_order_gradient;
+                    	
+		}
+
+                if( fix_list_weight_ != 0.0f ){
+                    float scale = fix_list_weight_ / (group_index[group+1] - group_index[group]);
+                    for(unsigned j = group_index[group]; j < group_index[group+1]; ++j ){
+                        grad[j] *= scale; 
+			hess[j] *= scale;
+                    }                            
                 }
             }