code: def test__split_evaluator_extractor(
      eval_name, split_kwargs, create_split_logs_and_evaluator
  ):
      logs, base_evaluator = create_split_logs_and_evaluator(eval_name)
  
      expected_df = pd.DataFrame(
          {"roc_auc": [0.48, 0.52], eval_name: [0, 1]}
      )
      actual_df = split_evaluator_extractor(
          logs,
          base_extractor=base_evaluator,
          split_values=[0, 1],
          **split_kwargs,
      ).reset_index(drop=True)
  
      pd.testing.assert_frame_equal(actual_df, expected_df)
  
file_path: fklearn/tests/metrics/test_pd_extractors.py
imports: from datetime import timedelta
  import numpy
  import pandas
  import pytest
  from sklearn.datasets import fetch_california_housing
  from fklearn.data.datasets import make_tutorial_data
  from fklearn.metrics.pd_extractors import combined_evaluator_extractor, evaluator_extractor, extract, split_evaluator_extractor, split_evaluator_extractor_iteration, temporal_split_evaluator_extractor
  from fklearn.training.regression import linear_regression_learner
  from fklearn.validation.evaluators import combined_evaluators, r2_evaluator, spearman_evaluator, split_evaluator, temporal_split_evaluator, mse_evaluator
  from fklearn.validation.splitters import forward_stability_curve_time_splitter, out_of_time_and_space_splitter, stability_curve_time_splitter, time_learning_curve_splitter
  from fklearn.validation.validator import validator
label_desc: Use image node_id 3 for calling a global function with example usage: test__split_evaluator_extractor(eval_name, split_kwargs, create_split_logs_and_evaluator) without return types