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From rok <rokros...@gmail.com>
Subject calculating the mean of SparseVector RDD
Date Wed, 07 Jan 2015 17:42:34 GMT
I have an RDD of SparseVectors and I'd like to calculate the means returning
a dense vector. I've tried doing this with the following (using pyspark,
spark v1.2.0): 

def aggregate_partition_values(vec1, vec2) :
    vec1[vec2.indices] += vec2.values
    return vec1

def aggregate_combined_vectors(vec1, vec2) : 
    if all(vec1 == vec2) : 
        # then the vector came from only one partition
        return vec1
    else:
        return vec1 + vec2

means = vals.aggregate(np.zeros(vec_len), aggregate_partition_values,
aggregate_combined_vectors)
means = means / nvals

This turns out to be really slow -- and doesn't seem to depend on how many
vectors there are so there seems to be some overhead somewhere that I'm not
understanding. Is there a better way of doing this? 



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