Thanks for the quick reply,
Katharina
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Katharina Baum, PhD Postdoctoral Fellow, Proteome and Genome Research Unit Luxembourg Institute of Health Department of Oncology 1A-B, rue Thomas Edison, L-1445 Strassen Luxembourg Fax: +352 26970-719 Email: Katharina.Baum@lih.lu Website: https://www.lih.lu/
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From: graph-tool <graph-tool-bounces@skewed.de> on behalf of Tiago de Paula Peixoto <tiago@skewed.de>
Sent: 26 March 2018 15:14
To: graph-tool@skewed.de
Subject: Re: [graph-tool] get_edges_prob() changes the entropy of the state object
On 26.03.2018 12:49, Katharina Baum wrote:
> I currently started using graph-tool: nice software, excellent
> documentation, thank you!
>
> However, I stumbled on some (for me) unexpected behavior when using the
> get_edges_prob() function with a BlockState of a weighted network
> (graph-tool version 2.26, Python 2.7 as well as Python 3.6). When calling
> the get_edges_prob() function to a state, its entropy is altered, and
> subsequent calls of get_edges_prob() deliver different results.
>
> "Luckily", I could reproduce the observed behavior with a dataset from the
> graph-tool collection (with arguably small alterations, but the introduced
> differences are a lot bigger in my networks).
>
> import graph_tool as gt
> import graph_tool.collection as gtc
> import graph_tool.inference as gti
>
> g=gtc.data["celegansneural"]
> state=gti.minimize_blockmodel_dl(g,state_args=dict(recs=[g.ep.value],rec_types=['real-normal']))
>
> original_entropy=state.entropy()
> edge_prob=[]
> for i in range(10000):
> edge_prob.append(state.get_edges_prob(missing=[],spurious=[(0,2)]))
>
> original_entropy
> state.entropy() #entropy is different from original!
> edge_prob[0] #first call of get_edges_prob() delivers other results than last
> edge_prob[-1]
>
> For me, this is really unexpected. What is happening there, and/or how this
> can be fixed?
> Smaller further experiments showed that this also happens to
> NestedBlockStates (of course), but seems not to happen for models lacking
> edge covariates...
This seems indeed like a bug. I suspect it has to do with the "real-normal"
model. Can you open an issue in the website with the above example so I can
keep track of this and fix it?
Could you also test this with "real-exponential" instead of "real-normal"
and also with the current git version?
Best,
Tiago
--
Tiago de Paula Peixoto <tiago@skewed.de>
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