Hello,
I think I was able to figure it out using the code from the "Characterizing
the posterior distribution" section. I start by equilibrating a
degree-corrected NestedBlockState with edge weights and then inferring
partition modes with ModeClusterState. Is this the same as running
minimize_nested_blockmodel_dl? Are the minimize* functions not used anymore?
Also, is it normal to have inferred only one mode?
Thanks for your help,
Eli
On Wed, Mar 2, 2022 at 4:48 PM Eli Draizen <ed...@virginia.edu> wrote:
> Hi Tiago,
>
> Just checking to see the status on this. I've tried the newer version
> (2.44) but am still having the same issues. Your proposed solution of
> copying the state and adding the overlap work for the non-nested block
> models, but I was having trouble with the nested version. I am also now
> running into an issue where I can only run non degree corrected sbms,
> otherwise it hangs (logs attached). If you have any suggestions, I would
> really appreciate it.
>
> Thanks,
> Eli
>
> -> = copied state adding the following options, then reran mcmc
> + = defined in minimization function followed by mcmc
>
> NDC = yes
> NDC+Overlap = slow
> NDC->Overlap = yes
> NDC+Nested = slow
> NDC+Nested+Overlap = slow
> NDC+Nested->Overlap (not implemented)
>
> NDC+Nested+Overlap -> DC = slow
>
> DC+Overlap+Nested = hangs (This is the goal)
> DC+Overlap = hangs
> DC+Nested->Overlap (not implemented)
>
>
>
> On Thu, Nov 11, 2021 at 3:16 AM Tiago de Paula Peixoto <ti...@skewed.de>
> wrote:
>
>> Am 11.11.21 um 03:03 schrieb Eli Draizen:
>> > Hi everyone,
>> >
>> > I was wondering if it would be possible to provide some more examples
>> of
>> > how to run a nested mixed membership SBM with edge weights. The new
>> > version seems to have removed the "overlap=True" option for state_args
>> > in the minimize_* functions.
>>
>> Indeed, I will add more examples about this. Could you please open an
>> issue in the website so I don't forget?
>>
>> > Is this the correct way to do it now?
>> >
>> > import graph_tool as gta
>> > import numpy as np
>> > g = .... # build graph
>> > e_score = .... #Set edge weights
>> > state_args = dict(
>> > deg_corr=deg_corr,
>> > base_type=gta.inference.overlap_blockmodel.OverlapBlockState,
>> > B=2*g.num_edges(), #B_max
>> > deg_corr=True,
>> > recs=[e_score],
>> > rec_types=["real-normal"])
>> > state = gta.inference.minimize_nested_blockmodel_dl(
>> > g,
>> > state_args=state_args,
>> > multilevel_mcmc_args=dict(verbose=True))
>> > # improve solution with merge-split
>> > state = state.copy(bs=state.get_bs() + [np.zeros(1)] * 4,
>> sampling=True)
>> >
>> > for i in range(100):
>> > if i%10==0: print(".", end="")
>> > ret = state.multiflip_mcmc_sweep(niter=10, beta=np.inf,
>> > verbose=True)
>> This is correct. But note that the "sampling=True" option is no longer
>> needed.
>>
>> > I am currently running this for a fully connected bipartite graph with
>> > 3454 nodes and 55008 edges. I understand it would take longer than the
>> > non-overlapping version, but do you have any suggestions on how to
>> speed
>> > it up? The non-overlapping version takes about 15 minutes, while the
>> > overlapping version is still running after 1 day.
>> The new version will contain a much faster code for the overlapping case!
>>
>> But in the mean-time, what you can do is to fit the non-overlapping
>> model first, and use that as a starting point to the MCMC with overlap.
>> You do that simply by doing:
>>
>> state = state.copy(state_args=dict(overlap=True))
>>
>> Best,
>> Tiago
>>
>>
>>
>>
>> --
>> Tiago de Paula Peixoto <ti...@skewed.de>
>> _______________________________________________
>> graph-tool mailing list -- graph-tool@skewed.de
>> To unsubscribe send an email to graph-tool-le...@skewed.de
>>
>
>
> --
> PhD Candidate, Phil Bourne's lab
> University of Virginia
>
--
PhD Candidate, Phil Bourne's lab
University of Virginia
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