get_params() not working with XGBoost and gridsearch about h2o-3 HOT 2 OPEN

assert H2OXGBoostEstimator.available() is True

# CPU Backend is forced for the results to be comparable
h2oParamsS = {"tree_method": "exact", "seed": 123, "backend": "cpu", "ntrees": 5}

trainFile = pyunit_utils.genTrainFrame(100, 10, enumCols=0, randseed=17)
print(trainFile)
myX = trainFile.names
y='response'
myX.remove(y)

h2oParamsS["monotone_constraints"] = {
    "C1": -1,
    "C3": 1,
    "C7": 1
}

gbm_params2 = {'learn_rate':[0.01, 0.02]}

gridM = H2OGridSearch(H2OXGBoostEstimator(**h2oParamsS), hyper_params=gbm_params2)
gridM.train(x=myX, y=y, training_frame=trainFile)
gridS = gridM.get_grid(sort_by="auc", decreasing=True)
best_gmb2 = gridS.models[0]
native_params2 = best_gmb2._model_json["output"]["native_parameters"].as_data_frame()
constraints2 = (native_params2[native_params2['name'] == "monotone_constraints"])['value'].values[0]
params = best_gmb2.get_params(deep=True)

h2oModelS = H2OXGBoostEstimator(**h2oParamsS)
h2oModelS.train(x=myX, y=y, training_frame=trainFile)

native_params = h2oModelS._model_json["output"]["native_parameters"].as_data_frame()
print(native_params)

constraints = (native_params[native_params['name'] == "monotone_constraints"])['value'].values[0]

assert constraints == u'(-1,0,1,0,0,0,1,0,0,0)'