import xgboost as xgb
from sklearn.datasets import load_digits
from sklearn.cross_validation import KFold, train_test_split

def test_early_stopping_nonparallel():
	digits = load_digits(2)
	X = digits['data']
	y = digits['target']
	X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
	clf = xgb.XGBClassifier()
	clf.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="auc",
	        eval_set=[(X_test, y_test)])

# todo: parallel test for early stopping