cleanup code

demo/rank/mq2008.conf

@@ -1,29 +1,25 @@
 # General Parameters, see comment for each definition
 # choose the tree booster, 0: tree, 1: linear
 booster_type = 0 
-# this is the only difference with classification, use 0: linear regression
-# when labels are in [0,1] we can also use 1: logistic regression
-loss_type = 0
 
 #objective="rank:pairwise"
 #objective="rank:softmax"
 #objective="lambdarank:map"
 #objective="lambdarank:ndcg"
 
-num_feature=50
 # Tree Booster Parameters
 # step size shrinkage
-bst:eta = 1.0 
+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 
 # maximum depth of a tree
 bst:max_depth = 3 
-
+eval_metric='ndcg'
 # Task parameters
 # the number of round to do boosting
-num_round = 2
+num_round = 4
 # 0 means do not save any model except the final round model
 save_period = 0 
 # The path of training data

demo/rank/trans_data.py

@@ -6,20 +6,22 @@ def save_data(group_data,output_feature,output_group):
 
     output_group.write(str(len(group_data))+"\n")
     for data in group_data:
-	output_feature.write(data[0] + " " + " ".join(data[2:]) + "\n")
+        # only include nonzero features
+        feats = [ p for p in data[2:] if float(p.split(':')[1]) != 0.0 ]        
+	output_feature.write(data[0] + " " + " ".join(feats) + "\n")
 
 if __name__ == "__main__":
     if len(sys.argv) != 4:
         print "Usage: python trans_data.py [Ranksvm Format Input] [Output Feature File] [Output Group File]"
 	sys.exit(0)
 
-    input = open(sys.argv[1])
+    fi = open(sys.argv[1])
     output_feature = open(sys.argv[2],"w")
     output_group = open(sys.argv[3],"w")
     
     group_data = []
     group = ""
-    for line in input:
+    for line in fi:
 	if not line:
 	    break
 	if "#" in line:
@@ -33,8 +35,7 @@ if __name__ == "__main__":
 
     save_data(group_data,output_feature,output_group)
 
-    input.close()
+    fi.close()
     output_feature.close()
     output_group.close()
 
-    

regrank/xgboost_regrank_eval.h

@@ -104,6 +104,7 @@ namespace xgboost{
         public:
             EvalAMS(const char *name){
                 name_ = name;
+                // note: ams@0 will automatically select which ratio to go
                 utils::Assert( sscanf(name, "ams@%f", &ratio_ ) == 1, "invalid ams format" );
             }            
             virtual float Eval(const std::vector<float> &preds,
@@ -152,14 +153,9 @@ namespace xgboost{
             float ratio_;
         };
 
-        /*! \brief Error */
+        /*! \brief Error for multi-class classification, need exact match */
         struct EvalMatchError : public IEvaluator{
         public:
-            EvalMatchError(const char *name){
-                name_ = name;
-                abs_ = 0;
-                if(!strcmp("mabserror", name)) abs_ =1;
-            }            
             virtual float Eval(const std::vector<float> &preds,
                                const DMatrix::Info &info) const {
                 const unsigned ndata = static_cast<unsigned>(preds.size());
@@ -168,19 +164,14 @@ namespace xgboost{
                 for (unsigned i = 0; i < ndata; ++i){
                     const float wt = info.GetWeight(i);
                     int label = static_cast<int>(info.labels[i]);
-                    if( label < 0 && abs_ != 0 ) label = -label-1;
-                    if (static_cast<int>(preds[i]) != label ){
-                        sum += wt;
-                    }
+                    if (static_cast<int>(preds[i]) != label ) sum += wt;
                     wsum += wt;
                 }
                 return sum / wsum;
             }
             virtual const char *Name(void) const{
-                return name_.c_str();
+                return "merror";
             }
-            int abs_;
-            std::string name_;
         };
 
 
@@ -328,7 +319,7 @@ namespace xgboost{
                 float idcg = this->CalcDCG(rec);
                 std::sort(rec.begin(), rec.end(), CmpSecond);
                 float dcg = this->CalcDCG(rec);
-		if( idcg == 0.0f ) return 0.0f;
+                if( idcg == 0.0f ) return 0.0f;
                 else return dcg/idcg;
             }
         };
@@ -366,8 +357,7 @@ namespace xgboost{
                 }
                 if (!strcmp(name, "rmse"))    evals_.push_back(new EvalRMSE());
                 if (!strcmp(name, "error"))   evals_.push_back(new EvalError());
-                if (!strcmp(name, "merror"))   evals_.push_back(new EvalMatchError("merror"));
-                if (!strcmp(name, "mabserror"))   evals_.push_back(new EvalMatchError("mabserror"));
+                if (!strcmp(name, "merror"))   evals_.push_back(new EvalMatchError());
                 if (!strcmp(name, "logloss")) evals_.push_back(new EvalLogLoss());
                 if (!strcmp(name, "auc"))    evals_.push_back(new EvalAuc());
                 if (!strncmp(name, "ams@",4))  evals_.push_back(new EvalAMS(name));