code: def test_sparse_to_df(python_dataset):
      # initialize the splitter
      header = {
          "col_user": DEFAULT_USER_COL,
          "col_item": DEFAULT_ITEM_COL,
          "col_rating": DEFAULT_RATING_COL,
      }
  
      # instantiate the the affinity matrix
      am = AffinityMatrix(df=python_dataset, **header)
  
      # generate the sparse matrix representation
      X, _, _ = am.gen_affinity_matrix()
  
      # use the inverse function to generate a pandas df from a sparse matrix ordered by userID
      df = am.map_back_sparse(X, kind="ratings")
  
      # tests: check that the two dataframes have the same elements in the same positions.
      assert (
          df.userID.values.all()
          == python_dataset.sort_values(
              by=["userID"]
          ).userID.values.all()
      )
  
      assert (
          df.itemID.values.all()
          == python_dataset.sort_values(
              by=["userID"]
          ).itemID.values.all()
      )
  
      assert (
          df.rating.values.all()
          == python_dataset.sort_values(
              by=["userID"]
          ).rating.values.all()
      )
  
file_path: recommenders/tests/unit/recommenders/datasets/test_sparse.py
imports: import pandas
  import numpy
  import pytest
  from recommenders.datasets.sparse import AffinityMatrix
  from recommenders.utils.constants import DEFAULT_USER_COL, DEFAULT_ITEM_COL, DEFAULT_RATING_COL, DEFAULT_TIMESTAMP_COL
label_desc: Use image node_id 4 for calling a global function with example usage: test_sparse_to_df(python_dataset) without return types