code: def test_evaluate_classification_optimiser():
      """Test evaluate method of Optimiser class for classication."""
      reader = Reader(sep=",")
      dict = reader.train_test_split(
          Lpath=["data_for_tests/train.csv", "data_for_tests/test.csv"],
          target_name="Survived",
      )
      drift_thresholder = Drift_thresholder()
      drift_thresholder = drift_thresholder.fit_transform(dict)
  
      with pytest.warns(UserWarning) as record:
          opt = Optimiser(scoring=None, n_folds=3)
      assert len(record) == 1
      score = opt.evaluate(None, dict)
      assert -np.Inf <= score
  
      with pytest.warns(UserWarning) as record:
          opt = Optimiser(scoring="roc_auc", n_folds=3)
      assert len(record) == 1
      score = opt.evaluate(None, dict)
      assert 0.0 <= score <= 1.0
  
      with pytest.warns(UserWarning) as record:
          opt = Optimiser(scoring="wrong_scoring", n_folds=3)
      assert len(record) == 1
      with pytest.warns(UserWarning) as record:
          score = opt.evaluate(None, dict)
      assert opt.scoring == "neg_log_loss"
  
file_path: MLBox/tests/test_optimiser.py
imports: import pytest
  import numpy
  from mlbox.optimisation.optimiser import Optimiser
  from mlbox.preprocessing.drift_thresholder import Drift_thresholder
  from mlbox.preprocessing.reader import Reader
  from mlbox.optimisation import make_scorer
label_desc: Use image node_id 4 for calling a global function with example usage: test_evaluate_classification_optimiser() without return types