running minimize_blockmodel_dl() in parallel

graph-tool
1

hi,

here is a simple example where i run minimize_blockmodel_dl() 10 times in parallel using multiprocessing and collect the entropy. when i run this, i get the same value of entropy every single time.

import multiprocessing as mp
import numpy as np
import time
import graph_tool.all as gt

# load graph
g = gt.collection.data["celegansneural"]

N_iter = 10

def get_sbm_entropy():
    np.random.seed()
    state = gt.minimize_blockmodel_dl(g)
    return state.entropy()

def _parallel_mc(iter=N_iter):
    pool = mp.Pool(10)

    future_res = [pool.apply_async(get_sbm_entropy) for _ in range(iter)]
    res = [f.get() for f in future_res]

    return res

def parallel_monte_carlo(iter=N_iter):
    entropies = _parallel_mc(iter)

    return entropies

parallel_monte_carlo()

result: [8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546]

ultimately i would like to use this to keep entropy as well as the block membership vector for each iteration

any ideas?

cheers,
-sam

2

The line np.random.seed() initializes numpy's RNG, which different from
the one used in graph-tool (which is in C++). You need to replace that
line with:

    gt.seed_rng(0)