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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