code: def test_oof_mode_metric(self):
      model = LogisticRegression(
          random_state=0, solver="liblinear", multi_class="ovr"
      )
      scorer = make_scorer(accuracy_score)
      scores = cross_val_score(
          model,
          X_train,
          y=y_train,
          cv=n_folds,
          scoring=scorer,
          n_jobs=1,
          verbose=0,
      )
      mean_str_1 = "%.8f" % np.mean(scores)
      std_str_1 = "%.8f" % np.std(scores)
  
      models = [
          LogisticRegression(
              random_state=0, solver="liblinear", multi_class="ovr"
          )
      ]
      S_train, S_test = stacking(
          models,
          X_train,
          y_train,
          X_test,
          regression=False,
          n_folds=n_folds,
          save_dir=temp_dir,
          mode="oof",
          random_state=0,
          verbose=0,
          stratified=True,
      )
  
      # Load mean score and std from file
      # Normally if cleaning is performed there is only one .log.txt file at given moment
      # But if we have no cleaning there may be more then one file so we take the latest
      file_name = sorted(
          glob.glob(os.path.join(temp_dir, "*.log.txt"))
      )[
          -1
      ]  # take the latest file
      with open(file_name) as f:
          for line in f:
              if "MEAN" in line:
                  split = line.strip().split()
                  break
  
      mean_str_2 = split[1][1:-1]
      std_str_2 = split[3][1:-1]
  
      assert_equal(mean_str_1, mean_str_2)
      assert_equal(std_str_1, std_str_2)
  
imports: from __future__ import print_function
  from __future__ import division
  import unittest
  from numpy.testing import assert_array_equal
  from numpy.testing import assert_equal
  import os
  import glob
  import numpy
  import scipy.stats
  from sklearn.model_selection import cross_val_predict
  from sklearn.model_selection import cross_val_score
  from sklearn.model_selection import StratifiedKFold
  from sklearn.datasets import make_classification
  from sklearn.metrics import accuracy_score
  from sklearn.metrics import log_loss
  from sklearn.metrics import make_scorer
  from sklearn.linear_model import LogisticRegression
  from sklearn.naive_bayes import GaussianNB
  from vecstack import stacking
label_desc: Use image node_id 15 for calling the TestFuncClassificationBinary obj's underlying member method code with example usage: obj.test_oof_mode_metric() without return types