code: def extended_isolation_forest_metrics_large():
      print(
          "Extended Isolation Forest Anomaly Metrics Test On Large Data"
      )
  
      train = h2o.import_file(
          pyunit_utils.locate(
              "bigdata/laptop/creditcardfraud/creditcardfraud.csv"
          )
      )
  
      eif_model = H2OExtendedIsolationForestEstimator(
          ntrees=100,
          seed=0xBEEF,
          sample_size=256,
          extension_level=1,
          disable_training_metrics=False,
      )
      eif_model.train(training_frame=train)
      metrics_by_python = eif_model.predict(train).mean()
      average_mean_length = metrics_by_python[0, 1]
      average_anomaly_score = metrics_by_python[0, 0]
  
      print(metrics_by_python)
      print(eif_model)
  
      perf = eif_model.model_performance()
      assert_equals(
          perf.mean_score(),
          average_mean_length,
          "Mean score metric is not correct",
          1e-3,
      )
      assert_equals(
          perf.mean_normalized_score(),
          average_anomaly_score,
          "Anomaly score metric is not correct",
          1e-3,
      )
  
imports: from __future__ import print_function
  import sys, os
  import h2o
  from tests import pyunit_utils, assert_equals
  from h2o.estimators.extended_isolation_forest import H2OExtendedIsolationForestEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: extended_isolation_forest_metrics_large() without return types