chg all settings to obj

.gitignore

@@ -18,3 +18,8 @@
 *model
 xgboost
 *pyc
+*train
+*test
+*group
+*rar
+*vali

demo/binary_classification/mushroom.conf

@@ -2,7 +2,7 @@
 # choose the tree booster, 0: tree, 1: linear
 booster_type = 0 
 # choose logistic regression loss function for binary classification
-loss_type = 2 
+objective = binary:logistic
 
 # Tree Booster Parameters
 # step size shrinkage

demo/kaggle-higgs/higgs-numpy.py

@@ -31,8 +31,9 @@ xgmat = xgb.DMatrix( data, label=label, missing = -999.0, weight=weight )
 
 # setup parameters for xgboost
 param = {}
-# use logistic regression loss
-param['loss_type'] = 3
+# use logistic regression loss, use raw prediction before logistic transformation
+# since we only need the rank
+param['objective'] = 'binary:logitraw'
 # scale weight of positive examples
 param['scale_pos_weight'] = sum_wneg/sum_wpos
 param['bst:eta'] = 0.1 

demo/kaggle-higgs/speedtest.py

@@ -33,7 +33,7 @@ xgmat = xgb.DMatrix( data, label=label, missing = -999.0, weight=weight )
 # setup parameters for xgboost
 param = {}
 # use logistic regression loss
-param['loss_type'] = 1
+param['objective'] = 'binary:logitraw'
 # scale weight of positive examples
 param['scale_pos_weight'] = sum_wneg/sum_wpos
 param['bst:eta'] = 0.1

demo/rank/runexp.sh

@@ -3,6 +3,5 @@ python trans_data.py test.txt mq2008.test mq2008.test.group
 python trans_data.py vali.txt mq2008.vali mq2008.vali.group
 
 ../../xgboost mq2008.conf
-
-../../xgboost mq2008.conf task=pred model_in=0002.model
+../../xgboost mq2008.conf task=pred model_in=0004.model
 

demo/regression/machine.conf

@@ -1,9 +1,9 @@
 # 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
+# this is the only difference with classification, use reg:linear to do linear classification
+# when labels are in [0,1] we can also use reg:logistic
+objective = reg:linear
 
 # Tree Booster Parameters
 # step size shrinkage

regrank/xgboost_regrank.h

@@ -25,7 +25,7 @@ namespace xgboost{
             RegRankBoostLearner(void){
                 silent = 0;
                 obj_ = NULL;
-                name_obj_ = "reg";
+                name_obj_ = "reg:linear";
             }
             /*!
              * \brief a regression booter associated with training and evaluating data

regrank/xgboost_regrank_data.h

@@ -129,7 +129,9 @@ namespace xgboost{
                     if( fs.Read(&nwt, sizeof(unsigned) ) != 0 ){
                         utils::Assert( nwt == 0 || nwt == data.NumRow(), "invalid weight" );
                         info.weights.resize( nwt );
-                        utils::Assert( fs.Read(&info.weights[0], sizeof(unsigned) * nwt) != 0, "Load weight file");
+                        if( nwt != 0 ){
+                            utils::Assert( fs.Read(&info.weights[0], sizeof(unsigned) * nwt) != 0, "Load weight file");
+                        }
                     }
                 }
                 fs.Close();

regrank/xgboost_regrank_obj.h

@@ -109,15 +109,16 @@ namespace xgboost{
 namespace xgboost{
     namespace regrank{        
        inline IObjFunction* CreateObjFunction( const char *name ){
-            if( !strcmp("reg", name ) ) return new RegressionObj();
-            if( !strcmp("rank:pairwise", name ) ) return new PairwiseRankObj();
-            if( !strcmp("rank:softmax", name ) ) return new SoftmaxRankObj();
-            if( !strcmp("softmax", name ) ) return new SoftmaxMultiClassObj();
-            //            if (!strcmp("lambdarank:map", name)) return new LambdaRankObj_MAP();
-            //            if (!strcmp("lambdarank:ndcg", name)) return new LambdaRankObj_NDCG();
-            utils::Error("unknown objective function type");
-            return NULL;
-        }
+           if( !strcmp("reg:linear", name ) )     return new RegressionObj( LossType::kLinearSquare );
+           if( !strcmp("reg:logistic", name ) )    return new RegressionObj( LossType::kLogisticNeglik );
+           if( !strcmp("binary:logistic", name ) ) return new RegressionObj( LossType::kLogisticClassify );
+           if( !strcmp("binary:logitraw", name ) ) return new RegressionObj( LossType::kLogisticRaw );
+           if( !strcmp("multi:softmax", name ) )      return new SoftmaxMultiClassObj();
+           if( !strcmp("rank:pairwise", name ) ) return new PairwiseRankObj();
+           if( !strcmp("rank:softmax", name ) ) return new SoftmaxRankObj();
+           utils::Error("unknown objective function type");
+           return NULL;
+       }
     };
 };
 #endif

regrank/xgboost_regrank_obj.hpp

@@ -14,8 +14,8 @@ namespace xgboost{
     namespace regrank{        
         class RegressionObj : public IObjFunction{
         public:
-            RegressionObj(void){
-                loss.loss_type = LossType::kLinearSquare;
+            RegressionObj( int loss_type ){
+                loss.loss_type = loss_type;
             }
             virtual ~RegressionObj(){}
             virtual void SetParam(const char *name, const char *val){