code: def test_user_serendipity_item_feature_vector(spark_diversity_data):
      train_df, reco_df, item_feature_df = spark_diversity_data
      evaluator = SparkDiversityEvaluation(
          train_df=train_df,
          reco_df=reco_df,
          item_feature_df=item_feature_df,
          item_sim_measure="item_feature_vector",
          col_user="UserId",
          col_item="ItemId",
          col_relevance="Relevance",
      )
      actual = evaluator.user_serendipity().toPandas()
      assert_frame_equal(
          pd.DataFrame(
              dict(
                  UserId=[1, 2, 3],
                  user_serendipity=[0.2500, 0.625, 0.3333],
              )
          ),
          actual,
          check_exact=False,
          atol=TOL,
      )
  
imports: import pytest
  import numpy
  import pandas
  from pandas.util.testing import assert_frame_equal
  from recommenders.evaluation.python_evaluation import precision_at_k, recall_at_k, ndcg_at_k, map_at_k, map
label_desc: Use image node_id 27 for calling a global function with example usage: test_user_serendipity_item_feature_vector(spark_diversity_data) without return types