text: #!/usr/bin/python
  import numpy as np
  import xgboost as xgb
  ###
  # advanced: cutomsized loss function
  #
  print ('start running example to used cutomized objective function')
  
  dtrain = xgb.DMatrix('../data/agaricus.txt.train')
  dtest = xgb.DMatrix('../data/agaricus.txt.test')
  
  # 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 = {'max_depth':2, 'eta':1, 'silent':1 }
  watchlist  = [(dtest,'eval'), (dtrain,'train')]
  num_round = 2
  
  # user define objective function, given prediction, return gradient and second order gradient
  # this is loglikelihood loss
  def logregobj(preds, dtrain):
      labels = dtrain.get_label()
      preds = 1.0 / (1.0 + np.exp(-preds))
      grad = preds - labels
      hess = preds * (1.0-preds)
      return grad, hess
  
  # user defined evaluation function, return a pair metric_name, result
  # NOTE: when you do customized loss function, the default prediction value is margin
  # this may make buildin evalution metric not function properly
  # for example, we are doing logistic loss, the prediction is score before logistic transformation
  # the buildin evaluation error assumes input is after logistic transformation
  # Take this in mind when you use the customization, and maybe you need write customized evaluation function
  def evalerror(preds, dtrain):
      labels = dtrain.get_label()
      # return a pair metric_name, result
      # since preds are margin(before logistic transformation, cutoff at 0)
      return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
  
  # training with customized objective, we can also do step by step training
  # simply look at xgboost.py's implementation of train
  bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
  