[PYTHON] Refactor trainnig API to use callback

tests/python/test_basic.py

@@ -35,6 +35,22 @@ class TestBasic(unittest.TestCase):
         # assert they are the same
         assert np.sum(np.abs(preds2 - preds)) == 0
 
+    def test_record_results(self):
+        dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
+        dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
+        param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
+        # specify validations set to watch performance
+        watchlist = [(dtest, 'eval'), (dtrain, 'train')]
+        num_round = 2
+        result = {}
+        res2 = {}
+        xgb.train(param, dtrain, num_round, watchlist,
+                  callbacks=[xgb.callback.record_evaluation(result)])
+        xgb.train(param, dtrain, num_round, watchlist,
+                  evals_result=res2)
+        assert result['train']['error'][0] < 0.1
+        assert res2 == result
+
     def test_multiclass(self):
         dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
         dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
@@ -189,5 +205,5 @@ class TestBasic(unittest.TestCase):
 
         # return np.ndarray
         cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=False)
-        assert isinstance(cv, np.ndarray)
-        assert cv.shape == (10, 4)
+        assert isinstance(cv, dict)
+        assert len(cv) == (4)