remove dependency on bst

demo/binary_classification/mushroom.conf

@@ -6,13 +6,13 @@ objective = binary:logistic
 
 # Tree Booster Parameters
 # step size shrinkage
-bst:eta = 1.0 
+eta = 1.0 
 # minimum loss reduction required to make a further partition
-bst:gamma = 1.0 
+gamma = 1.0 
 # minimum sum of instance weight(hessian) needed in a child
-bst:min_child_weight = 1 
+min_child_weight = 1 
 # maximum depth of a tree
-bst:max_depth = 3 
+max_depth = 3 
 
 # Task Parameters
 # the number of round to do boosting

demo/kaggle-higgs/higgs-numpy.py

@@ -42,8 +42,8 @@ param = {}
 param['objective'] = 'binary:logitraw'
 # scale weight of positive examples
 param['scale_pos_weight'] = sum_wneg/sum_wpos
-param['bst:eta'] = 0.1 
-param['bst:max_depth'] = 6
+param['eta'] = 0.1 
+param['max_depth'] = 6
 param['eval_metric'] = 'auc'
 param['silent'] = 1
 param['nthread'] = 16

demo/multiclass_classification/train.py

@@ -25,8 +25,8 @@ param = {}
 # use softmax multi-class classification
 param['objective'] = 'multi:softmax'
 # scale weight of positive examples
-param['bst:eta'] = 0.1
-param['bst:max_depth'] = 6
+param['eta'] = 0.1
+param['max_depth'] = 6
 param['silent'] = 1
 param['nthread'] = 4
 param['num_class'] = 6

demo/rank/mq2008.conf

@@ -5,13 +5,13 @@ objective="rank:pairwise"
 
 # Tree Booster Parameters
 # step size shrinkage
-bst:eta = 0.1 
+eta = 0.1 
 # minimum loss reduction required to make a further partition
-bst:gamma = 1.0 
+gamma = 1.0 
 # minimum sum of instance weight(hessian) needed in a child
-bst:min_child_weight = 0.1
+min_child_weight = 0.1
 # maximum depth of a tree
-bst:max_depth = 6
+max_depth = 6
 
 # Task parameters
 # the number of round to do boosting

demo/regression/machine.conf

@@ -7,13 +7,13 @@ objective = reg:linear
 
 # Tree Booster Parameters
 # step size shrinkage
-bst:eta = 1.0 
+eta = 1.0 
 # minimum loss reduction required to make a further partition
-bst:gamma = 1.0 
+gamma = 1.0 
 # minimum sum of instance weight(hessian) needed in a child
-bst:min_child_weight = 1 
+min_child_weight = 1 
 # maximum depth of a tree
-bst:max_depth = 3 
+max_depth = 3 
 
 # Task parameters
 # the number of round to do boosting

src/learner/learner-inl.hpp

@@ -79,6 +79,11 @@ class BoostLearner {
    * \param val  value of the parameter
    */
   inline void SetParam(const char *name, const char *val) {
+    // in this version, bst: prefix is no longer required 
+    if (strncmp(name, "bst:", 4) != 0) {
+      std::string n = "bst:"; n += name;
+      this->SetParam(n.c_str(), val);
+    }
     if (!strcmp(name, "silent")) silent = atoi(val);
     if (!strcmp(name, "prob_buffer_row")) prob_buffer_row = static_cast<float>(atof(val));
     if (!strcmp(name, "eval_metric")) evaluator_.AddEval(val);

wrapper/python-example/demo.py

@@ -13,7 +13,7 @@ dtrain = xgb.DMatrix('agaricus.txt.train')
 dtest = xgb.DMatrix('agaricus.txt.test')
 
 # specify parameters via map, definition are same as c++ version
-param = {'bst:max_depth':2, 'bst:eta':1, 'silent':1, 'objective':'binary:logistic' }
+param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
 
 # specify validations set to watch performance
 evallist  = [(dtest,'eval'), (dtrain,'train')]
@@ -75,7 +75,7 @@ print ('start running example to used cutomized objective function')
 # note: for customized objective function, we leave objective as default
 # note: what we are getting is margin value in prediction
 # you must know what you are doing
-param = {'bst:max_depth':2, 'bst:eta':1, 'silent':1 }
+param = {'max_depth':2, 'eta':1, 'silent':1 }
 
 # user define objective function, given prediction, return gradient and second order gradient
 # this is loglikelihood loss
@@ -107,7 +107,7 @@ bst = xgb.train(param, dtrain, num_round, evallist, logregobj, evalerror)
 #
 print ('start running example to start from a initial prediction')
 # specify parameters via map, definition are same as c++ version
-param = {'bst:max_depth':2, 'bst:eta':1, 'silent':1, 'objective':'binary:logistic' }
+param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
 # train xgboost for 1 round
 bst = xgb.train( param, dtrain, 1, evallist )
 # Note: we need the margin value instead of transformed prediction in set_base_margin