code: def test_infogram_breast_cancer():
      """
      Simple breast cancer data test to check that core infogram is working:
       1. cmi/relevance in Frame equals to those passed in model.output.
       2. when model and infogram parameters are specified, it uses the correct specification.
       3. cmi/relevance from Deep code aggree with ours.
      :return:
      """
      deep_rel = [
          0.0040477989,
          0.0974455315,
          0.0086303713,
          0.0041002103,
          0.0037914745,
          0.0036801151,
          0.0257819346,
          0.2808010416,
          0.0005372569,
          0.0036280018,
          0.0032444598,
          0.0002943119,
          0.0026430897,
          0.0262074332,
          0.0033317064,
          0.0068812603,
          0.0006185385,
          0.0082121491,
          0.0014562177,
          0.0081786997,
          1.0000000000,
          0.0894895310,
          0.6187801784,
          0.3302352775,
          0.0021346433,
          0.0016077771,
          0.0260198502,
          0.3404628948,
          0.0041384517,
          0.0019399743,
      ]
      deep_cmi = [
          0.00000000,
          0.31823883,
          0.52769230,
          0.00000000,
          0.00000000,
          0.00000000,
          0.01183309,
          0.67430653,
          0.00000000,
          0.00000000,
          0.45443221,
          0.00000000,
          0.24561013,
          0.87720587,
          0.31939378,
          0.19370515,
          0.00000000,
          0.16463918,
          0.00000000,
          0.00000000,
          0.44830772,
          1.00000000,
          0.00000000,
          0.00000000,
          0.62478098,
          0.00000000,
          0.00000000,
          0.00000000,
          0.00000000,
          0.64466111,
      ]
      fr = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/admissibleml_test/wdbc_changed.csv"
          )
      )
      target = "diagnosis"
      fr[target] = fr[target].asfactor()
      x = [
          "radius_mean",
          "texture_mean",
          "perimeter_mean",
          "area_mean",
          "smoothness_mean",
          "compactness_mean",
          "concavity_mean",
          "concave_points_mean",
          "symmetry_mean",
          "fractal_dimension_mean",
          "radius_se",
          "texture_se",
          "perimeter_se",
          "area_se",
          "smoothness_se",
          "compactness_se",
          "concavity_se",
          "concave_points_se",
          "symmetry_se",
          "fractal_dimension_se",
          "radius_worst",
          "texture_worst",
          "perimeter_worst",
          "area_worst",
          "smoothness_worst",
          "compactness_worst",
          "concavity_worst",
          "concave_points_worst",
          "symmetry_worst",
          "fractal_dimension_worst",
      ]
      infogram_model = H2OInfogram(seed=12345, top_n_features=50)
      infogram_model.train(x=x, y=target, training_frame=fr)
  
      # make sure our result matches Deep's
      pred_names, rel = infogram_model.get_all_predictor_relevance()
      x, cmi = infogram_model.get_all_predictor_cmi()
      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)
  
      gbm_params = {"ntrees": 3, "max_depth": 5}
      infogram_model_gbm = H2OInfogram(
          seed=12345,
          top_n_features=50,
          algorithm="gbm",
          algorithm_params=gbm_params,
      )
      infogram_model_gbm.train(x=x, y=target, training_frame=fr)
      x, cmi_gbm = infogram_model_gbm.get_all_predictor_cmi()
      assert abs(cmi_gbm[1] - cmi[1]) > 0.01, (
          "CMI from infogram model with gbm using different number of trees should"
          " be different but are not."
      )
  
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_breast_cancer() without return types