code: def calculate_recommendations(output_filename, model_name="als"):
      """Generates artist recommendations for each user in the dataset"""
      # train the model based off input params
      artists, users, plays = get_lastfm()
  
      # create a model from the input data
      model = get_model(model_name)
  
      # if we're training an ALS based model, weight input for last.fm
      # by bm25
      if model_name.endswith("als"):
          # lets weight these models by bm25weight.
          logging.debug("weighting matrix by bm25_weight")
          plays = bm25_weight(plays, K1=100, B=0.8)
  
          # also disable building approximate recommend index
          model.approximate_similar_items = False
  
      # this is actually disturbingly expensive:
      plays = plays.tocsr()
      user_plays = plays.T.tocsr()
  
      logging.debug("training model %s", model_name)
      start = time.time()
      model.fit(user_plays)
      logging.debug(
          "trained model '%s' in %0.2fs",
          model_name,
          time.time() - start,
      )
  
      # generate recommendations for each user and write out to a file
      start = time.time()
      with tqdm.tqdm(total=len(users)) as progress:
          with codecs.open(output_filename, "w", "utf8") as o:
              batch_size = 1000
              to_generate = np.arange(len(users))
              for startidx in range(0, len(to_generate), batch_size):
                  batch = to_generate[startidx : startidx + batch_size]
                  ids, scores = model.recommend(
                      batch,
                      user_plays[batch],
                      filter_already_liked_items=True,
                  )
                  for i, userid in enumerate(batch):
                      username = users[userid]
                      for other, score in zip(ids[i], scores[i]):
                          o.write(
                              f"{username}\t{artists[other]}\t{score}\n"
                          )
                  progress.update(len(batch))
      logging.debug(
          "generated recommendations in %0.2fs", time.time() - start
      )
  
file_path: implicit/examples/lastfm.py
imports: import argparse
  import codecs
  import logging
  import time
  import numpy
  import tqdm
  from implicit.als import AlternatingLeastSquares
  from implicit.approximate_als import AnnoyAlternatingLeastSquares, FaissAlternatingLeastSquares, NMSLibAlternatingLeastSquares
  from implicit.bpr import BayesianPersonalizedRanking
  from implicit.datasets.lastfm import get_lastfm
  from implicit.lmf import LogisticMatrixFactorization
  from implicit.nearest_neighbours import BM25Recommender, CosineRecommender, TFIDFRecommender, bm25_weight
label_desc: Use image node_id 3 for calling a global function with example usage: calculate_recommendations(output_filename, model_name) without return types