From 8196d5d680f0beeaf390d5fbbaa8d8f32207669b Mon Sep 17 00:00:00 2001
From: terrytangyuan <terrytangyuan@gmail.com>
Date: Tue, 8 Sep 2015 10:14:28 -0400
Subject: [PATCH] TST: More thorough checks for Python tests

---
 tests/python/test_models.py | 31 +++++++++++++++++++------------
 1 file changed, 19 insertions(+), 12 deletions(-)

diff --git a/tests/python/test_models.py b/tests/python/test_models.py
index b0eb7482d..8c06d9de9 100644
--- a/tests/python/test_models.py
+++ b/tests/python/test_models.py
@@ -6,27 +6,34 @@ dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
 dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
 
 def test_glm():
-	param = {'silent':1, 'objective':'binary:logistic', 'booster':'gblinear',
-         'alpha': 0.0001, 'lambda': 1 }
-    watchlist  = [(dtest,'eval'), (dtrain,'train')]
+	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)
+	assert isinstance(bst, xgb.core.Booster)
 	preds = bst.predict(dtest)
 	labels = dtest.get_label()
+	err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
+	assert err < 0.1
 
 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)
+		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)
+	assert isinstance(bst, xgb.core.Booster)
+	preds = bst.predict(dtest)
+	labels = dtest.get_label()
+	err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
+	assert err < 0.1