code: def test_uplift_random_forest_smoke(self):
      from causalml.dataset import make_uplift_classification
  
      n_samples = 10000
      seed = 12345
  
      h2o.init(strict_version_check=False)
      # Data generation
      # Generate dataset with 50 features, treatment/control flag feature
      # and outcome feature (In this case "conversion")
      train, x_names = make_uplift_classification(
          n_samples=n_samples,  # n_samples*2 rows will be generated
          # (#n_samples for each treatment_name)
          treatment_name=["control", "treatment"],
          n_classification_features=50,
          # Dataset contains only features valid for modeling
          # Do not confuse model with irrelevant or redundant features
          n_classification_informative=50,
          random_seed=seed,
      )
  
      treatment_column = "treatment_group_key"
      response_column = "conversion"
  
      train_h2o = h2o.H2OFrame(train)
      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,
      )
  
      print(
          "KL:"
          + str(
              uplift_predict_kl.mean().as_data_frame()[
                  "uplift_predict"
              ][0]
          )
      )
      print(
          "EUC:"
          + str(
              uplift_predict_euc.mean().as_data_frame()[
                  "uplift_predict"
              ][0]
          )
      )
      print(
          "CHI:"
          + str(
              uplift_predict_chi.mean().as_data_frame()[
                  "uplift_predict"
              ][0]
          )
      )
  
      assert (
          0.007
          < uplift_predict_kl.mean().as_data_frame()["uplift_predict"][
              0
          ]
          < 0.008
      ), (
          "Not expected output: Mean uplift is suspiciously different. "
          + str(
              uplift_predict_kl.mean().as_data_frame()[
                  "uplift_predict"
              ][0]
          )
      )
  
      assert (
          0.0075
          < uplift_predict_euc.mean().as_data_frame()["uplift_predict"][
              0
          ]
          < 0.0085
      ), (
          "Not expected output: Mean uplift is suspiciously different."
          + str(
              uplift_predict_euc.mean()
              .as_data_frame()["uplift_predict"][0]
              .mean()
          )
      )
  
      assert (
          0.01
          < uplift_predict_chi.mean().as_data_frame()["uplift_predict"][
              0
          ]
          < 0.02
      ), (
          "Not expected output: Mean uplift is suspiciously different."
          + str(
              uplift_predict_chi.mean()
              .as_data_frame()["uplift_predict"][0]
              .mean()
          )
      )
  
imports: import sys, os
  import unittest
  import h2o
  from h2o.estimators import H2OUpliftRandomForestEstimator
label_desc: Use image node_id 1 for calling the UpliftDrfLargeSmoke obj's underlying member method code with example usage: obj.test_uplift_random_forest_smoke() without return types