code: def uplift_random_forest_smoke():
      seed = 12345
      treatment_column = "treatment"
      response_column = "outcome"
      x_names = ["feature_" + str(x) for x in range(1, 13)]
  
      train_h2o = h2o.upload_file(
          pyunit_utils.locate("smalldata/uplift/upliftml_train.csv")
      )
      train_h2o[treatment_column] = train_h2o[
          treatment_column
      ].asfactor()
      train_h2o[response_column] = train_h2o[response_column].asfactor()
  
      uplift_predict_kl = uplift_train_predict(
          "KL",
          x_names,
          treatment_column,
          response_column,
          train_h2o,
          seed,
      )
      uplift_predict_euc = uplift_train_predict(
          "euclidean",
          x_names,
          treatment_column,
          response_column,
          train_h2o,
          seed,
      )
      uplift_predict_chi = uplift_train_predict(
          "chi_squared",
          x_names,
          treatment_column,
          response_column,
          train_h2o,
          seed,
      )
  
      assert 0.08 < uplift_predict_kl.mean() < 0.09, (
          "Not expected output: Mean prediction is suspiciously different. "
          + str(uplift_predict_kl.mean())
      )
  
      assert 0.08 < uplift_predict_euc.mean() < 0.09, (
          "Not expected output: Mean prediction is suspiciously different. "
          + str(uplift_predict_euc.mean())
      )
  
      assert 0.08 < uplift_predict_chi.mean() < 0.09, (
          "Not expected output: Mean prediction is suspiciously different. "
          + str(uplift_predict_chi.mean())
      )
  
      perf_kl, uplift_kl = uplift_train_performance_and_plot(
          "KL",
          x_names,
          treatment_column,
          response_column,
          train_h2o,
          seed,
      )
      perf_euc, uplift_euc = uplift_train_performance_and_plot(
          "euclidean",
          x_names,
          treatment_column,
          response_column,
          train_h2o,
          seed,
      )
      perf_chi, uplift_chi = uplift_train_performance_and_plot(
          "chi_squared",
          x_names,
          treatment_column,
          response_column,
          train_h2o,
          seed,
      )
  
      assert 93 < np.mean(uplift_kl) < 94, (
          "Not expected output: Mean uplift is suspiciously different. "
          + str(np.mean(uplift_kl))
      )
  
      assert 85 < np.mean(uplift_euc) < 86, (
          "Not expected output: Mean uplift is suspiciously different. "
          + str(np.mean(uplift_euc))
      )
  
      assert 405 < np.mean(uplift_chi) < 406, (
          "Not expected output: Mean uplift is suspiciously different. "
          + str(np.mean(uplift_chi))
      )
  
      assert (
          398 < perf_kl.auuc() < 399
      ), "Not expected output: AUUC is suspiciously different. " + str(
          perf_kl.auuc()
      )
  
      assert (
          5 < perf_kl.qini() < 6
      ), "Not expected output: Qini is suspiciously different. " + str(
          perf_kl.qini()
      )
  
      assert (
          403 < perf_euc.auuc() < 404
      ), "Not expected output: AUUC is suspiciously different. " + str(
          perf_euc.auuc()
      )
  
      assert (
          7 < perf_euc.qini() < 8
      ), "Not expected output: Qini is suspiciously different. " + str(
          perf_euc.qini()
      )
  
      assert (
          409 < perf_chi.auuc() < 410
      ), "Not expected output: AUUC is suspiciously different. " + str(
          perf_chi.auuc()
      )
  
      assert (
          9 < perf_chi.qini() < 10
      ), "Not expected output: Qini is suspiciously different. " + str(
          perf_chi.qini()
      )
  
imports: import sys, os
  import h2o
  from tests import pyunit_utils
  from h2o.estimators import H2OUpliftRandomForestEstimator
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: uplift_random_forest_smoke() without return types