Am 05.10.21 um 05:06 schrieb Sam G:

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?

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) -- Tiago de Paula Peixoto <tiago@skewed.de>