def compute_clusters()

in mozetl/taar/taar_similarity.py [0:0]

• 8 lines of code
• 6 McCabe index (conditional complexity)

def compute_clusters(addons_df, num_clusters, random_seed):
    """Performs user clustering by using add-on ids as features."""

    # Build the stages of the pipeline. We need hashing to make the next
    # steps work.
    hashing_stage = HashingTF(inputCol="addon_ids", outputCol="hashed_features")
    idf_stage = IDF(inputCol="hashed_features", outputCol="features", minDocFreq=1)
    # As a future improvement, we may add a sane value for the minimum cluster size
    # to BisectingKMeans (e.g. minDivisibleClusterSize). For now, just make sure
    # to pass along the random seed if needed for tests.
    kmeans_kwargs = {"seed": random_seed} if random_seed else {}
    bkmeans_stage = BisectingKMeans(k=num_clusters, **kmeans_kwargs)
    pipeline = Pipeline(stages=[hashing_stage, idf_stage, bkmeans_stage])

    # Run the pipeline and compute the results.
    model = pipeline.fit(addons_df)
    return model.transform(addons_df).select(["client_id", "prediction"])