code: def football():
      df = h2o.import_file(
          "https://h2o-public-test-data.s3.amazonaws.com/mli-testing/manual-test/small-dataset/binomial/football_prediction.csv"
      )
      df["FTR"] = df["FTR"].asfactor()
      x = df.columns
      y = "FTR"
      x.remove(y)
      x.remove("Date")
  
      # Split the dataset into train and test
      train, test = df.split_frame(ratios=[0.8], seed=1234)
  
      rfit = H2ORuleFitEstimator(
          min_rule_length=1,
          max_rule_length=3,
          max_num_rules=10,
          seed=1234,
          model_type="rules_and_linear",
      )
      rfit.train(training_frame=train, x=x, y=y, validation_frame=test)
  
      df[y] = df[y].append_levels(["extra_level"])
  
      # Split the dataset into train and test
      train, test = df.split_frame(ratios=[0.8], seed=1234)
  
      rfit_multi = H2ORuleFitEstimator(
          min_rule_length=1,
          max_rule_length=3,
          max_num_rules=10,
          seed=1234,
          model_type="rules_and_linear",
          distribution="multinomial",
      )
      rfit_multi.train(
          training_frame=train, x=x, y=y, validation_frame=test
      )
  
      # Print rules and metrics for comparision:
      print("Binomial model rules:")
      print(rfit.rule_importance())
  
      print("Multinomial model rules:")
      print(rfit_multi.rule_importance())
  
      print("Binomial train RMSE vs. multinomial train RMSE:")
      print(str(rfit.rmse()) + " vs. " + str(rfit_multi.rmse()))
      print("Binomial train MSE vs. multinomial train MSE: ")
      print(str(rfit.mse()) + " vs. " + str(rfit_multi.mse()))
      print("Binomial valid RMSE vs. multinomial valid RMSE: ")
      print(
          str(rfit.rmse(valid=True))
          + " vs. "
          + str(rfit_multi.rmse(valid=True))
      )
      print("Binomial valid MSE vs. multinomial valid MSE: ")
      print(
          str(rfit.mse(valid=True))
          + " vs. "
          + str(rfit_multi.mse(valid=True))
      )
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.rulefit import H2ORuleFitEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: football() without return types