old_contents: from __future__ import division
  from __future__ import print_function
  import sys
  sys.path.insert(1,"../../")
  import h2o
  from tests import pyunit_utils
  from h2o.estimators import H2OGradientBoostingEstimator
  
  
  def pyunit_make_metrics():
    fr = h2o.import_file(pyunit_utils.locate("smalldata/logreg/prostate.csv"))
    fr["CAPSULE"] = fr["CAPSULE"].asfactor()
    fr["RACE"] = fr["RACE"].asfactor()
    fr.describe()
  
    response = "AGE"
    predictors = range(1,fr.ncol)
    model = H2OGradientBoostingEstimator()
    model.train(x=predictors,y=response,training_frame=fr)
  
    p = model.model_performance(train=True)
  #  p = make_metrics(predicted,actual,"gaussian")
  
  if __name__ == "__main__":
    pyunit_utils.standalone_test(pyunit_make_metrics)
  else:
    pyunit_make_metrics()
new_contents: from __future__ import division
  from __future__ import print_function
  import sys
  sys.path.insert(1,"../../")
  import h2o
  from tests import pyunit_utils
  from h2o.estimators import H2OGradientBoostingEstimator
  
  
  def pyunit_make_metrics():
    fr = h2o.import_file(pyunit_utils.locate("smalldata/logreg/prostate.csv"))
    fr["CAPSULE"] = fr["CAPSULE"].asfactor()
    fr["RACE"] = fr["RACE"].asfactor()
    fr.describe()
  
    response = "AGE"
    predictors = range(1,fr.ncol)
  #  model = H2OGradientBoostingEstimator()
  #  model.train(x=predictors,y=response,training_frame=fr)
  
  #  p = model.model_performance(train=True)
  #  p = make_metrics(predicted,actual,"gaussian")
  
  if __name__ == "__main__":
    pyunit_utils.standalone_test(pyunit_make_metrics)
  else:
    pyunit_make_metrics()
  
subject: Disable unused test for now.