Update unittests for XGBoost 1.0.0

Author: Beat Buesser

test_requirements.txt

@@ -8,7 +8,7 @@ mxnet
 #torch>=1.2.0
 tensorflow
 scikit-learn==0.21.3
-xgboost
+xgboost==1.0.0
 lightgbm
 # Comment out because of compatibility issues with numpy versions
 # catboost

tests/classifiers/test_xgboost.py

@@ -102,7 +102,7 @@ def setUpClass(cls):
 
     def test_predict(self):
         y_predicted = self.classifier.predict(self.x_test[0:1])
-        y_expected = [0.02563512, 0.02925956, 0.94510525]
+        y_expected = [0.00280161, 0.00359648, 0.99360186]
         for i in range(3):
             self.assertAlmostEqual(y_predicted[0, i], y_expected[i], 4)
 

tests/metrics/test_verification_decision_trees.py

@@ -66,8 +66,8 @@ def test_XGBoost(self):
         average_bound, verified_error = rt.verify(x=self.x_test, y=self.y_test, eps_init=0.3,
                                                   nb_search_steps=10, max_clique=2, max_level=2)
 
-        self.assertEqual(average_bound, 0.035996093750000006)
-        self.assertEqual(verified_error, 0.96)
+        self.assertEqual(average_bound, 0.03186914062500001)
+        self.assertEqual(verified_error, 0.99)
 
     def test_LightGBM(self):
         train_data = lightgbm.Dataset(self.x_train, label=np.argmax(self.y_train, axis=1))
@@ -110,7 +110,7 @@ def test_GradientBoosting(self):
         average_bound, verified_error = rt.verify(x=self.x_test, y=self.y_test, eps_init=0.3,
                                                   nb_search_steps=10, max_clique=2, max_level=2)
 
-        self.assertEqual(average_bound, 0.009234374999999996)
+        self.assertEqual(average_bound, 0.009117187499999995)
         self.assertEqual(verified_error, 1.0)
 
     def test_RandomForest(self):