added unittest for training continuation

tests/python/test_training_continuation.py

@@ -0,0 +1,52 @@
+import xgboost as xgb
+import numpy as np
+from sklearn.cross_validation import KFold, train_test_split
+from sklearn.metrics import mean_squared_error
+from sklearn.grid_search import GridSearchCV
+from sklearn.datasets import load_iris, load_digits, load_boston
+import unittest
+
+rng = np.random.RandomState(1337)
+
+class TestTrainingContinuation(unittest.TestCase):
+
+    xgb_params = {
+        'colsample_bytree': 0.7,
+        'silent': 1,
+        'nthread': 1,
+    }
+
+    def test_training_continuation(self):
+        digits = load_digits(2)
+        X = digits['data']
+        y = digits['target']
+
+        dtrain = xgb.DMatrix(X,label=y)
+
+        gbdt_01 = xgb.train(self.xgb_params, dtrain, num_boost_round=10)
+        ntrees_01 = len(gbdt_01.get_dump())
+        assert ntrees_01 == 10
+
+        gbdt_02 = xgb.train(self.xgb_params, dtrain, num_boost_round=0)
+        gbdt_02.save_model('xgb_tc.model')
+
+        gbdt_02a = xgb.train(self.xgb_params, dtrain, num_boost_round=10, xgb_model=gbdt_02)
+        gbdt_02b = xgb.train(self.xgb_params, dtrain, num_boost_round=10, xgb_model="xgb_tc.model")
+        ntrees_02a = len(gbdt_02a.get_dump())
+        ntrees_02b = len(gbdt_02b.get_dump())
+        assert ntrees_02a == 10
+        assert ntrees_02b == 10
+        assert mean_squared_error(y, gbdt_01.predict(dtrain)) == mean_squared_error(y, gbdt_02a.predict(dtrain))
+        assert mean_squared_error(y, gbdt_01.predict(dtrain)) == mean_squared_error(y, gbdt_02b.predict(dtrain))
+
+        gbdt_03 = xgb.train(self.xgb_params, dtrain, num_boost_round=3)
+        gbdt_03.save_model('xgb_tc.model')
+
+        gbdt_03a = xgb.train(self.xgb_params, dtrain, num_boost_round=7, xgb_model=gbdt_03)
+        gbdt_03b = xgb.train(self.xgb_params, dtrain, num_boost_round=7, xgb_model="xgb_tc.model")
+        ntrees_03a = len(gbdt_03a.get_dump())
+        ntrees_03b = len(gbdt_03b.get_dump())
+        assert ntrees_03a == 10
+        assert ntrees_03b == 10
+        assert mean_squared_error(y, gbdt_03a.predict(dtrain)) == mean_squared_error(y, gbdt_03b.predict(dtrain))
+