old_contents: import numpy as np
  import pandas as pd
  import xgboost as xgb
  import time
  import random
  from sklearn.model_selection import StratifiedKFold
  
  #For sampling rows from input file
  random_seed = 9
  subset = 0.4
  
  n_rows = 1183747;
  train_rows = int(n_rows * subset)
  random.seed(random_seed)
  skip = sorted(random.sample(xrange(1,n_rows + 1),n_rows-train_rows))
  data = pd.read_csv("../data/train_numeric.csv", index_col=0, dtype=np.float32, skiprows=skip)
  y = data['Response'].values
  del data['Response']
  X = data.values
  
  param = {}
  param['objective'] = 'binary:logistic'
  param['eval_metric'] = 'auc'
  param['max_depth'] = 5
  param['eta'] = 0.3
  param['silent'] = 0
  param['tree_method'] = 'gpu_exact'
  
  num_round = 20
  
  cv = StratifiedKFold(y, n_folds=5)
  
  for i, (train, test) in enumerate(cv):
      dtrain = xgb.DMatrix(X[train], label=y[train])
      tmp = time.time()
      bst = xgb.train(param, dtrain, num_round)
      boost_time = time.time() - tmp
      res = bst.eval(xgb.DMatrix(X[test], label=y[test]))
      print("Fold {}: {}, Boost Time {}".format(i, res, str(boost_time)))
      del bst
  
  
new_contents: import numpy as np
  import pandas as pd
  import xgboost as xgb
  import time
  import random
  from sklearn.model_selection import StratifiedKFold
  
  #For sampling rows from input file
  random_seed = 9
  subset = 0.4
  
  n_rows = 1183747;
  train_rows = int(n_rows * subset)
  random.seed(random_seed)
  skip = sorted(random.sample(range(1,n_rows + 1),n_rows-train_rows))
  data = pd.read_csv("../data/train_numeric.csv", index_col=0, dtype=np.float32, skiprows=skip)
  y = data['Response'].values
  del data['Response']
  X = data.values
  
  param = {}
  param['objective'] = 'binary:logistic'
  param['eval_metric'] = 'auc'
  param['max_depth'] = 5
  param['eta'] = 0.3
  param['silent'] = 0
  param['tree_method'] = 'gpu_exact'
  
  num_round = 20
  
  skf = StratifiedKFold(n_splits=5)
  
  for i, (train, test) in enumerate(skf.split(X, y)):
      dtrain = xgb.DMatrix(X[train], label=y[train])
      tmp = time.time()
      bst = xgb.train(param, dtrain, num_round)
      boost_time = time.time() - tmp
      res = bst.eval(xgb.DMatrix(X[test], label=y[test]))
      print("Fold {}: {}, Boost Time {}".format(i, res, str(boost_time)))
      del bst
  
  
subject: Update bosch.py (#2482)