Hello,
This is my first post here (I just started with Julia for two weeks and I'm
very happy)
The problem I'm facing now is calling the sklearn python library via PyCall
This is an example of code
X = reshape(rand(20000),(10000,2))
radius = 0.05
using PyCall
@pyimport sklearn.neighbors as nb
@time balltree = nb.BallTree(X,leaf_size=30)
@time result_test =
pycall(balltree["query_radius"],PyVector{Array{Int64,1}},X,radius);
@time resultJulia_test = copy(result_test);
elapsed time: 0.005133544 seconds (2204 bytes allocated)
elapsed time: 0.097277762 seconds (680 bytes allocated)
elapsed time: 8.034163516 seconds (20968224 bytes allocated, 4.13% gc time)
The conversion to julia array takes around 100 times compared to the actual
calculation
Another thing I tried is to iterate the PyVector 'result_test'.
function iterate_Pyvector(vec)
cont = 0
for v in vec
cont += 1
end
end
@time iterate_Pyvector(result_test)
elapsed time: 7.865199943 seconds (20736288 bytes allocated)
Iteration of 'result_test' takes roughly the same time and the same memory
allocation as conversion.
This fact suggests me that conversion is actually happening.
Is there any suggestion to improve performance in both conversion and iteration
of the vector result_test?
Thanks,
Alberto
