code: def test_LeaveOneOut(toy_data):
      loo = LeaveOneOut()
      with pytest.raises(ValueError):
          next(
              loo.split(toy_data)
          )  # each user only has 1 item so trainsets fail
  
      reader = Reader("ml-100k")
  
      data_path = (
          os.path.dirname(os.path.realpath(__file__))
          + "/u1_ml100k_test"
      )
      data = Dataset.load_from_file(file_path=data_path, reader=reader)
  
      # Test random_state parameter
      # If random_state is None, you get different split each time (conditioned
      # by rng of course)
      loo = LeaveOneOut(random_state=None)
      testsets_a = [testset for (_, testset) in loo.split(data)]
      testsets_b = [testset for (_, testset) in loo.split(data)]
      assert testsets_a != testsets_b
      # Repeated called to split when random_state is set lead to the same folds
      loo = LeaveOneOut(random_state=1)
      testsets_a = [testset for (_, testset) in loo.split(data)]
      testsets_b = [testset for (_, testset) in loo.split(data)]
      assert testsets_a == testsets_b
  
      # Make sure only one rating per user is present in the testset
      loo = LeaveOneOut()
      for _, testset in loo.split(data):
          cnt = Counter([uid for (uid, _, _) in testset])
          assert all(val == 1 for val in cnt.values())
  
      # test the min_n_ratings parameter
      loo = LeaveOneOut(min_n_ratings=5)
      for trainset, _ in loo.split(data):
          assert all(
              len(ratings) >= 5 for ratings in trainset.ur.values()
          )
  
      loo = LeaveOneOut(min_n_ratings=10)
      for trainset, _ in loo.split(data):
          assert all(
              len(ratings) >= 10 for ratings in trainset.ur.values()
          )
  
      loo = LeaveOneOut(min_n_ratings=10000)  # too high
      with pytest.raises(ValueError):
          next(loo.split(data))
  
file_path: Surprise/tests/test_split.py
imports: import os
  from collections import Counter
  from copy import copy
  import numpy
  import pytest
  from surprise import Dataset, Reader
  from surprise.model_selection import KFold, LeaveOneOut, PredefinedKFold, RepeatedKFold, ShuffleSplit, train_test_split
  from surprise.model_selection.split import get_cv
label_desc: Use image node_id 5 for calling a global function with example usage: test_LeaveOneOut(toy_data) without return types