Starting with minimize_nested_blockmodel_dl indeed speeds up the process and
now I'm more confident that the model will eventually run:
state=minimize_nested_blockmodel_dl(g,
state_args=dict(base_type=LayeredBlockState, state_args=dict(deg_corr=True,
overlap=True, layers=True, ec=g.ep.layer, recs=[g.ep.weight],
rec_types=["discrete-binomial"])))
dS, nmoves=0, 0
for i in range(100):
ret=state.multiflip_mcmc_sweep(niter=10)
dS+=ret[0]
nmoves+=ret[1]
print("Change in description length:", dS)
print("Number of accepted vertex moves:", nmoves)
mcmc_equilibrate(state, wait=1000, mcmc_args=dict(niter=10), verbose=True)
bs=[]
def collect_partitions(s):
global bs
bs.append(s.get_bs())
mcmc_equilibrate(state, force_niter=10000, mcmc_args=dict(niter=10),
verbose=True, callback=collect_partitions)
pm=PartitionModeState(bs, nested=True, converge=True)
pv=pm.get_marginal(g)
bs=pm.get_max_nested()
state=state.copy(bs=bs)
Coming back to my initial question concerning the layer-specific partitions, it
seems that I was too optimistic about being able to decompose this information
from get_edge_blocks. I have already spent time reading the docs and the use of
_be_ in source codes to convert the block membership of each half-edge to
layer-specific block membership of each node, so I need to ask for more
instructions.
There are a few previous posts about extracting this information but none of
the answers addresses this decomposition issue beyond get_edge_blocks. I
presume there is a relatively straightforward way to do this?
I also thought that there could have been a more convenient way of doing this
by applying set_edge_filter to state.g and get_majority_blocks to extract this
information, but yeah, didn't work.
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