code: def test_helper(Estimator):
      df = h2o.import_file(
          pyunit_utils.locate("smalldata/gbm_test/titanic.csv")
      )
      y = "survived"
      df[y] = df[y].asfactor()
  
      mod_default = fast_estimator(Estimator)
      mod_default.train(y=y, training_frame=df)
  
      assert_not_eq(
          get_ks(mod_default, df), ks_score(mod_default, df, y)
      )  # default histogram is not precise enough
  
      mod_glbins = fast_estimator(Estimator, gainslift_bins=df.nrow)
      mod_glbins.train(y=y, training_frame=df)
  
      assert_eq(get_ks(mod_glbins, df), ks_score(mod_glbins, df, y))
  
file_path: h2o-3/h2o-py/tests/testdir_misc/pyunit_gainslift_bins.py
imports: import sys
  import h2o
  import pandas
  from tests import pyunit_utils
  from h2o.estimators import *
  from h2o.utils.threading import local_context
label_desc: Use image node_id 6 for calling a global function with example usage: test_helper(Estimator) without return types