H2OXGBoostEstimator with cv cannot report validation score correctly

[songjiaxun]

Since I upgraded my h2o python package to 3.18.0.8, the H2OXGBoostEstimator could not give me the score on validation set correctly. The details are following:

Steps to reproduce

# python version: 3.6.4 |Anaconda custom (x86_64)| (default, Dec 21 2017, 15:39:08) 
# [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]
import h2o
h2o.init()

# load iris data
data = h2o.load_dataset('iris')

# train the xgboost model with 10-fold cross-validation
from h2o.estimators import H2OXGBoostEstimator
model = H2OXGBoostEstimator(nfolds=10, keep_cross_validation_predictions=True)
model.train(y = 'Species', training_frame = data)

# get model for each fold, and print the score
cv_models = model.cross_validation_models()
for i in range(10):
    model_cv = cv_models[i]
    print (model_cv.r2(train=True, valid=True, xval=True))

The results of code above will be:

Parse progress: |█████████████████████████████████████████████████████████| 100%
xgboost Model Build progress: |███████████████████████████████████████████| 100%
{'train': 0.988033979503409, 'valid': 0.988033979503409, 'xval': None}
{'train': 0.980701555221906, 'valid': 0.980701555221906, 'xval': None}
{'train': 0.9884792314320985, 'valid': 0.9884792314320985, 'xval': None}
{'train': 0.9937521418957451, 'valid': 0.9937521418957451, 'xval': None}
{'train': 0.998061238277609, 'valid': 0.998061238277609, 'xval': None}
{'train': 0.9789531276501665, 'valid': 0.9789531276501665, 'xval': None}
{'train': 0.9909617503865926, 'valid': 0.9909617503865926, 'xval': None}
{'train': 0.9979398347526219, 'valid': 0.9979398347526219, 'xval': None}
{'train': 0.9980672446348753, 'valid': 0.9980672446348753, 'xval': None}
{'train': 0.9973039440210139, 'valid': 0.9973039440210139, 'xval': None}

As you can see, the score on train set and validation set are exactly the same. This does not happen for other estimators. If I am doing this wrong, please correct me. Thank you!