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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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 issubclass(model.__class__, AlternatingLeastSquares):
        # 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()

    logging.debug("training model %s", model_name)
    start = time.time()
    model.fit(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()
    user_plays = plays.T.tocsr()
    with tqdm.tqdm(total=len(users)) as progress:
        with codecs.open(output_filename, "w", "utf8") as o:
            for userid, username in enumerate(users):
                for artistid, score in model.recommend(userid, user_plays):
                    o.write("%s\t%s\t%s\n" % (username, artists[artistid], score))
                progress.update(1)
    logging.debug("generated recommendations in %0.2fs",  time.time() - start)