class_docstr: Mixin to test a bunch of common functionality in models
  deriving from RecommenderBase
code: def test_recalculate_user(self):
      item_users = get_checker_board(50)
      user_items = item_users.T.tocsr()
  
      model = self._get_model()
      model.fit(item_users, show_progress=False)
  
      try:
          userids = np.arange(50)
          batch_ids, batch_scores = model.recommend(
              userids, user_items[userids], N=1, recalculate_user=True
          )
      except NotImplementedError:
          # some models don't support recalculating user on the fly, and that's ok
          return
  
      for userid in range(item_users.shape[1]):
          ids, scores = model.recommend(userid, user_items[userid], N=1)
          self.assertEqual(len(ids), 1)
          user_vector = user_items[userid]
  
          # we should get the same item if we recalculate_user
          ids_from_liked, scores_from_liked = model.recommend(
              userid=0,
              user_items=user_vector,
              N=1,
              recalculate_user=True,
          )
          self.assertEqual(ids[0], ids_from_liked[0])
  
          # TODO: if we set regularization for the model to be sufficiently high, the
          # scores from recalculate_user are slightly different. Investigate
          # (could be difference between CG and cholesky optimizers?)
          self.assertAlmostEqual(
              scores[0], scores_from_liked[0], places=3
          )
  
          # we should also get the same from the batch recommend call already done
          self.assertEqual(batch_ids[userid][0], ids_from_liked[0])
          self.assertAlmostEqual(
              batch_scores[userid][0], scores_from_liked[0], places=3
          )
  
imports: import os
  import pickle
  import random
  import tempfile
  import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from scipy.sparse import coo_matrix, csr_matrix
  from implicit.evaluation import precision_at_k
  from implicit.nearest_neighbours import ItemItemRecommender
  from implicit.utils import ParameterWarning
label_desc: Use image node_id 4 for calling the RecommenderBaseTestMixin obj's underlying member method code with example usage: obj.test_recalculate_user() without return types