old_contents: #!/usr/bin/python
  import numpy as np
  import xgboost as xgb
  
  ### load data in do training
  dtrain = xgb.DMatrix('../data/agaricus.txt.train')
  param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
  num_round = 2
  
  print ('running cross validation')
  # do cross validation, this will print result out as
  # [iteration]  metric_name:mean_value+std_value
  # std_value is standard deviation of the metric
  xgb.cv(param, dtrain, num_round, nfold=5,
         metrics={'error'}, seed = 0,
         callbacks=[xgb.callback.print_evaluation(show_stdv=True)])
  
  print ('running cross validation, disable standard deviation display')
  # do cross validation, this will print result out as
  # [iteration]  metric_name:mean_value+std_value
  # std_value is standard deviation of the metric
  res = xgb.cv(param, dtrain, num_boost_round=10, nfold=5,
               metrics={'error'}, seed = 0,
               callbacks=[xgb.callback.print_evaluation(show_stdv=False),
                          xgb.callback.early_stop(3)])
  print (res)
  print ('running cross validation, with preprocessing function')
  # define the preprocessing function
  # used to return the preprocessed training, test data, and parameter
  # we can use this to do weight rescale, etc.
  # as a example, we try to set scale_pos_weight
  def fpreproc(dtrain, dtest, param):
      label = dtrain.get_label()
      ratio = float(np.sum(label == 0)) / np.sum(label==1)
      param['scale_pos_weight'] = ratio
      return (dtrain, dtest, param)
  
  # do cross validation, for each fold
  # the dtrain, dtest, param will be passed into fpreproc
  # then the return value of fpreproc will be used to generate
  # results of that fold
  xgb.cv(param, dtrain, num_round, nfold=5,
         metrics={'auc'}, seed = 0, fpreproc = fpreproc)
  
  ###
  # you can also do cross validation with cutomized loss function
  # See custom_objective.py
  ##
  print ('running cross validation, with cutomsized loss function')
  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
  def evalerror(preds, dtrain):
      labels = dtrain.get_label()
      return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
  
  param = {'max_depth':2, 'eta':1, 'silent':1}
  # train with customized objective
  xgb.cv(param, dtrain, num_round, nfold = 5, seed = 0,
         obj = logregobj, feval=evalerror)
  
new_contents: #!/usr/bin/python
  import numpy as np
  import xgboost as xgb
  
  ### load data in do training
  dtrain = xgb.DMatrix('../data/agaricus.txt.train')
  param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
  num_round = 2
  
  print ('running cross validation')
  # do cross validation, this will print result out as
  # [iteration]  metric_name:mean_value+std_value
  # std_value is standard deviation of the metric
  xgb.cv(param, dtrain, num_round, nfold=5,
         metrics={'error'}, seed = 0,
         callbacks=[xgb.callback.print_evaluation(show_stdv=True)])
  
  print ('running cross validation, disable standard deviation display')
  # do cross validation, this will print result out as
  # [iteration]  metric_name:mean_value
  res = xgb.cv(param, dtrain, num_boost_round=10, nfold=5,
               metrics={'error'}, seed = 0,
               callbacks=[xgb.callback.print_evaluation(show_stdv=False),
                          xgb.callback.early_stop(3)])
  print (res)
  print ('running cross validation, with preprocessing function')
  # define the preprocessing function
  # used to return the preprocessed training, test data, and parameter
  # we can use this to do weight rescale, etc.
  # as a example, we try to set scale_pos_weight
  def fpreproc(dtrain, dtest, param):
      label = dtrain.get_label()
      ratio = float(np.sum(label == 0)) / np.sum(label==1)
      param['scale_pos_weight'] = ratio
      return (dtrain, dtest, param)
  
  # do cross validation, for each fold
  # the dtrain, dtest, param will be passed into fpreproc
  # then the return value of fpreproc will be used to generate
  # results of that fold
  xgb.cv(param, dtrain, num_round, nfold=5,
         metrics={'auc'}, seed = 0, fpreproc = fpreproc)
  
  ###
  # you can also do cross validation with cutomized loss function
  # See custom_objective.py
  ##
  print ('running cross validation, with cutomsized loss function')
  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
  def evalerror(preds, dtrain):
      labels = dtrain.get_label()
      return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
  
  param = {'max_depth':2, 'eta':1, 'silent':1}
  # train with customized objective
  xgb.cv(param, dtrain, num_round, nfold = 5, seed = 0,
         obj = logregobj, feval=evalerror)
  
subject: Fix comment in cross_validation.py (#1923)