code: def smallcatRF():
      # Training set has 26 categories from A to Z
      # Categories A, C, E, G, ... are perfect predictors of y = 1
      # Categories B, D, F, H, ... are perfect predictors of y = 0
  
      # Log.info("Importing alphabet_cattest.csv data...\n")
      alphabet = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/gbm_test/alphabet_cattest.csv"
          )
      )
      alphabet["y"] = alphabet["y"].asfactor()
      # Log.info("Summary of alphabet_cattest.csv from H2O:\n")
      # alphabet.summary()
  
      # Prepare data for scikit use
      trainData = np.loadtxt(
          pyunit_utils.locate(
              "smalldata/gbm_test/alphabet_cattest.csv"
          ),
          delimiter=",",
          skiprows=1,
          converters={0: lambda s: ord(s.decode().split('"')[1])},
      )
      trainDataResponse = trainData[:, 1]
      trainDataFeatures = trainData[:, 0]
  
      # Train H2O GBM Model:
      # Log.info("H2O GBM (Naive Split) with parameters:\nntrees = 1, max_depth = 1, nbins = 100\n")
  
      rf_h2o = H2ORandomForestEstimator(
          ntrees=1, max_depth=1, nbins=100
      )
      rf_h2o.train(x="X", y="y", training_frame=alphabet)
  
      # Train scikit GBM Model:
      # Log.info("scikit GBM with same parameters:")
      rf_sci = ensemble.RandomForestClassifier(
          n_estimators=1, criterion="entropy", max_depth=1
      )
      rf_sci.fit(trainDataFeatures[:, np.newaxis], trainDataResponse)
  
      # h2o
      rf_perf = rf_h2o.model_performance(alphabet)
      auc_h2o = rf_perf.auc()
  
      # scikit
      auc_sci = roc_auc_score(
          trainDataResponse,
          rf_sci.predict_proba(trainDataFeatures[:, np.newaxis])[:, 1],
      )
  
      # Log.info(paste("scikit AUC:", auc_sci, "\tH2O AUC:", auc_h2o))
      assert (
          auc_h2o >= auc_sci
      ), "h2o (auc) performance degradation, with respect to scikit"
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.random_forest import H2ORandomForestEstimator
  import numpy
  from sklearn import ensemble
  from sklearn.metrics import roc_auc_score
label_desc: Use image node_id 1 for calling a global function with example usage: smallcatRF() without return types