code: def test_make_leaderboard_uplift():
      data = h2o.upload_file(
          pyunit_utils.locate("smalldata/uplift/criteo_uplift_13k.csv")
      )
      predictors = ["f1", "f2", "f3", "f4", "f5", "f6", "f7", "f8"]
      response = "conversion"
      data[response] = data[response].asfactor()
      treatment_column = "treatment"
      data[treatment_column] = data[treatment_column].asfactor()
      train, valid = data.split_frame(ratios=[0.8], seed=1234)
      uplift1 = H2OUpliftRandomForestEstimator(
          ntrees=10,
          max_depth=5,
          treatment_column=treatment_column,
          uplift_metric="KL",
          min_rows=10,
          seed=1234,
          auuc_type="qini",
      )
      uplift1.train(
          x=predictors,
          y=response,
          training_frame=train,
          validation_frame=valid,
      )
  
      uplift2 = H2OUpliftRandomForestEstimator(
          ntrees=10,
          max_depth=5,
          treatment_column=treatment_column,
          uplift_metric="KL",
          min_rows=10,
          seed=123,
          auuc_type="qini",
      )
      uplift2.train(
          x=predictors,
          y=response,
          training_frame=train,
          validation_frame=valid,
      )
      try:
          print(h2o.make_leaderboard([uplift1, uplift2]))
          assert (
              False
          ), "Should have failed - no support for unsupervised models"
      except h2o.exceptions.H2OServerError:
          pass
  
file_path: h2o-3/h2o-py/tests/testdir_misc/explain/pyunit_make_leaderboard.py
imports: import os
  import sys
  import h2o
  import h2o.exceptions
  from tests import pyunit_utils, dataset_prostate, CustomMaeFunc
  from h2o.automl import H2OAutoML
  from h2o.estimators import *
  from h2o.grid import H2OGridSearch
label_desc: Use image node_id 5 for calling a global function with example usage: test_make_leaderboard_uplift() without return types