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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_similar_artists(output_filename, model_name="als"):
 """ generates a list of similar artists in lastfm by utiliizing the 'similar_items' api of the models """

    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_recommend = 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)

    # write out similar artists by popularity
    start = time.time()
    logging.debug("calculating top artists")

    user_count = np.ediff1d(plays.indptr)
    to_generate = sorted(np.arange(len(artists)), key=lambda x: -user_count[x])

    # write out as a TSV of artistid, otherartistid, score
    logging.debug("writing similar items")
    with tqdm.tqdm(total=len(to_generate)) as progress:
        with codecs.open(output_filename, "w", "utf8") as o:
            for artistid in to_generate:
                artist = artists[artistid]
                for other, score in model.similar_items(artistid, 11):
                    o.write("%s\t%s\t%s\n" % (artist, artists[other], score))
                progress.update(1)

    logging.debug("generated similar artists in %0.2fs",  time.time() - start)