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+import sys, os%0Asys.path.insert(1, os.path.join(%22..%22,%22..%22,%22..%22))%0Aimport h2o%0Afrom tests import pyunit_utils%0Afrom h2o.estimators.gbm import H2OGradientBoostingEstimator%0A%0A%0Adef gbm_quantiles_global_with_only_categorical_colums():%0A  prostate_train = h2o.import_file(path=pyunit_utils.locate(%22smalldata/logreg/prostate_train.csv%22))%0A  prostate_train = prostate_train.drop(%22AGE%22)%0A%0A  for col_name in prostate_train.names:%0A    prostate_train%5Bcol_name%5D = prostate_train%5Bcol_name%5D.ascharacter().asfactor()%0A%0A  gbm_h2o = H2OGradientBoostingEstimator(histogram_type=%22quantiles_global%22)%0A  gbm_h2o.train(y=%22CAPSULE%22, training_frame=prostate_train)%0A%0A%0Aif __name__ == %22__main__%22:%0A  pyunit_utils.standalone_test(gbm_quantiles_global_with_only_categorical_colums)%0Aelse:%0A    gbm_quantiles_global_with_only_categorical_colums()%0A

Add test showing QuantilesGlobal works with categorical-only dataset