code: def test_explain():
      counts = csr_matrix(
          [
              [1, 1, 0, 1, 0, 0],
              [0, 1, 1, 1, 0, 0],
              [1, 4, 1, 0, 7, 0],
              [1, 1, 0, 0, 0, 0],
              [9, 0, 4, 1, 0, 1],
              [0, 1, 0, 0, 0, 1],
              [0, 0, 2, 0, 1, 1],
          ],
          dtype=np.float32,
      )
      user_items = counts.T.tocsr()
  
      model = AlternatingLeastSquares(
          factors=4,
          regularization=20,
          alpha=2.0,
          use_native=False,
          use_cg=False,
          use_gpu=False,
          iterations=100,
          random_state=23,
      )
      model.fit(user_items, show_progress=False)
  
      userid = 0
  
      # Assert recommendation is the the same if we recompute user vectors
      # TODO: this doesn't quite work with N=10 (because we returns items that should have been
      # filtered with large negative score?) also seems like the dtype is different between
      # recalculate and not
      ids, scores = model.recommend(userid, user_items[userid], N=3)
      recalculated_ids, recalculated_scores = model.recommend(
          userid, user_items[userid], N=3, recalculate_user=True
      )
      for item1, score1, item2, score2 in zip(
          ids, scores, recalculated_ids, recalculated_scores
      ):
          assert item1 == item2
          assert pytest.approx(score1, abs=1e-4) == score2
  
      # Assert explanation makes sense
      top_rec, score = recalculated_ids[0], recalculated_scores[0]
      score_explained, contributions, W = model.explain(
          userid, user_items, itemid=top_rec
      )
      scores = [s for _, s in contributions]
      items = [i for i, _ in contributions]
      assert pytest.approx(score, abs=1e-4) == score_explained
      assert pytest.approx(score, abs=1e-4) == sum(scores)
      assert scores == sorted(scores, reverse=True)
  
      assert scores == sorted(
          scores, reverse=True
      ), "Scores not in order"
      assert [0, 2, 3, 4] == sorted(items), "Items not seen by user"
  
      # Assert explanation with precomputed user weights is correct
      top_score_explained, top_contributions, W = model.explain(
          userid, user_items, itemid=top_rec, user_weights=W, N=2
      )
      top_scores = [s for _, s in top_contributions]
      top_items = [i for i, _ in top_contributions]
  
      assert len(top_contributions) == 2
      assert pytest.approx(score, abs=1e-4) == top_score_explained
      assert scores[:2] == top_scores
      assert items[:2] == top_items
  
imports: import pickle
  import unittest
  import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from recommender_base_test import RecommenderBaseTestMixin, get_checker_board
  from scipy.sparse import coo_matrix, csr_matrix, random
  import implicit
  from implicit.als import AlternatingLeastSquares
  from implicit.gpu import HAS_CUDA
label_desc: Use image node_id 7 for calling a global function with example usage: test_explain() without return types