diff --git a/tests/python/test_models.py b/tests/python/test_models.py
index a12198f59..b0eb7482d 100644
--- a/tests/python/test_models.py
+++ b/tests/python/test_models.py
@@ -2,14 +2,31 @@ import numpy as np
 import xgboost as xgb
 
 dpath = 'demo/data/'
+dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
+dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
 
 def test_glm():
-	dtrain = xgb.DMatrix('../data/agaricus.txt.train')
-	dtest = xgb.DMatrix('../data/agaricus.txt.test')
 	param = {'silent':1, 'objective':'binary:logistic', 'booster':'gblinear',
          'alpha': 0.0001, 'lambda': 1 }
     watchlist  = [(dtest,'eval'), (dtrain,'train')]
 	num_round = 4
 	bst = xgb.train(param, dtrain, num_round, watchlist)
 	preds = bst.predict(dtest)
-	labels = dtest.get_label()
\ No newline at end of file
+	labels = dtest.get_label()
+
+def test_custom_objective():
+	param = {'max_depth':2, 'eta':1, 'silent':1 }
+	watchlist  = [(dtest,'eval'), (dtrain,'train')]
+	num_round = 2
+	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)
+    bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
+
+