Add test.

tests/python-gpu/test_gpu_basic_models.py

@@ -12,25 +12,15 @@
 class TestGPUBasicModels(unittest.TestCase):
     cputest = test_bm.TestModels()
 
-    def test_eta_decay_gpu_hist(self):
-        self.cputest.run_eta_decay('gpu_hist')
-
-    def test_deterministic_gpu_hist(self):
-        kRows = 1000
-        kCols = 64
-        kClasses = 4
-        # Create large values to force rounding.
-        X = np.random.randn(kRows, kCols) * 1e4
-        y = np.random.randint(0, kClasses, size=kRows)
-
+    def run_cls(self, X, y, deterministic):
         cls = xgb.XGBClassifier(tree_method='gpu_hist',
-                                deterministic_histogram=True,
+                                deterministic_histogram=deterministic,
                                 single_precision_histogram=True)
         cls.fit(X, y)
         cls.get_booster().save_model('test_deterministic_gpu_hist-0.json')
 
         cls = xgb.XGBClassifier(tree_method='gpu_hist',
-                                deterministic_histogram=True,
+                                deterministic_histogram=deterministic,
                                 single_precision_histogram=True)
         cls.fit(X, y)
         cls.get_booster().save_model('test_deterministic_gpu_hist-1.json')
@@ -40,7 +30,24 @@ def test_deterministic_gpu_hist(self):
         with open('test_deterministic_gpu_hist-1.json', 'r') as fd:
             model_1 = fd.read()
 
-        assert hash(model_0) == hash(model_1)
-
         os.remove('test_deterministic_gpu_hist-0.json')
         os.remove('test_deterministic_gpu_hist-1.json')
+
+        return hash(model_0), hash(model_1)
+
+    def test_eta_decay_gpu_hist(self):
+        self.cputest.run_eta_decay('gpu_hist')
+
+    def test_deterministic_gpu_hist(self):
+        kRows = 1000
+        kCols = 64
+        kClasses = 4
+        # Create large values to force rounding.
+        X = np.random.randn(kRows, kCols) * 1e4
+        y = np.random.randint(0, kClasses, size=kRows) * 1e4
+
+        model_0, model_1 = self.run_cls(X, y, True)
+        assert hash(model_0) == hash(model_1)
+
+        model_0, model_1 = self.run_cls(X, y, False)
+        assert hash(model_0) != hash(model_1)