code: def extended_isolation_forest_scoring_history():
      print("Extended Isolation Forest Scoring History Test")
  
      train = h2o.import_file(
          pyunit_utils.locate("smalldata/anomaly/single_blob.csv")
      )
  
      eif_model = H2OExtendedIsolationForestEstimator(
          ntrees=10, seed=0xBEEF, sample_size=255, extension_level=1
      )
      eif_model.train(training_frame=train)
      print(eif_model.scoring_history())
      assert_equals(
          None,
          eif_model.scoring_history(),
          "No scoring history by default",
      )
  
      eif_model = H2OExtendedIsolationForestEstimator(
          ntrees=10,
          seed=0xBEEF,
          sample_size=255,
          extension_level=1,
          score_each_iteration=True,
          disable_training_metrics=False,
      )
      eif_model.train(training_frame=train)
      print(eif_model.scoring_history())
      assert_equals(
          11,
          len(eif_model.scoring_history()),
          "There should be one empty row and one row for each tree",
      )
  
      eif_model = H2OExtendedIsolationForestEstimator(
          ntrees=10,
          seed=0xBEEF,
          sample_size=255,
          extension_level=1,
          score_tree_interval=3,
          disable_training_metrics=False,
      )
      eif_model.train(training_frame=train)
      print(eif_model.scoring_history())
      assert_equals(
          5,
          len(eif_model.scoring_history()),
          "There should be one empty row and one row for each interval",
      )
  
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_scoring_history() without return types