code: def test_infogram_iris():
      """
      Simple Iris test to check that core infogram is working:
       1. it generates the correct lists as Deep's original code.
       2. check and make sure the frame contains the correct information.
       3. check the admissible features contains cmi and relevance >= 0.1
      :return:
      """
      deep_rel = [0.009010006, 0.011170417, 0.755170945, 1.000000000]
      deep_cmi = [0.1038524, 0.7135458, 0.5745915, 1.0000000]
      fr = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/admissibleml_test/irisROriginal.csv"
          )
      )
      target = "Species"
      fr[target] = fr[target].asfactor()
      x = fr.names
      x.remove(target)
  
      infogram_model = H2OInfogram(
          seed=12345, distribution="multinomial"
      )  # build infogram model with default settings
      infogram_model.train(x=x, y=target, training_frame=fr)
  
      # make sure frame returning all predictors, relevance and cmi contains correct value
      pred_names, rel = infogram_model.get_all_predictor_relevance()
      x, cmi = infogram_model.get_all_predictor_cmi()
      predictor_rel_cmi_frame = (
          infogram_model.get_admissible_score_frame()
      )  # get relevance and cmi frame
      assert_list_frame_equal(cmi, rel, predictor_rel_cmi_frame)
  
      # make sure our result matches Deep's
      assert (
          deep_rel.sort() == rel.sort()
      ), "Expected: {0}, actual: {1}".format(deep_rel, rel)
      assert (
          deep_cmi.sort() == cmi.sort()
      ), "Expected: {0}, actual: {1}".format(deep_cmi, cmi)
  
      # check admissible features values >= 0.1
      admissible_rel = infogram_model.get_admissible_relevance()
      admissible_cmi = infogram_model.get_admissible_cmi()
      for index in range(0, len(admissible_rel)):
          assert admissible_rel[index] >= 0.1, (
              "Admissible relevance should equal or exceed 0.1 but is not.  Actual admissible"
              " relevance is {0}".format(admissible_rel[index])
          )
          assert admissible_cmi[index] >= 0.1, (
              "Admissible cmi should equal or exceed 0.1 but is not.  Actual admissible cmi"
              " is {0}".format(admissible_cmi[index])
          )
  
imports: import os
  import sys
  import h2o
  from h2o.estimators.infogram import H2OInfogram
  from tests import pyunit_utils
label_desc: Use image node_id 1 for calling a global function with example usage: test_infogram_iris() without return types