Bug in get_edges_prob for layered SBM

graph-tool
1

Hi,

There seems to be problem with get_edges_prob for the layered SBM. Here is a
minimal example:

import graph_tool.all as gt

import numpy as np

gr=gt.generate_sbm(b=np.array([0]*500+[1]*500),probs=np.array([[10000,200],[
200,10000]]))

etype=gr.new_edge_property('int')

gr.ep.etype=etype

t=0

for e in gr.edges():

    gr.ep.etype[e]=t%4

    t+=1

state = gt.minimize_nested_blockmodel_dl(gr,
deg_corr=True,layers=True,state_args=dict(ec=gr.ep.etype,layers=True))

print(state.get_edges_prob(missing=[[2,32,0]],spurious=[]))

print(state.get_edges_prob(missing=[[2,32,0],[3,4,2]],spurious=[]))

print(state.get_edges_prob(missing=[[2,32,0],[3,4,2],[36,7,0]],spurious=[]))

pr=state.get_edges_prob(missing=[[2,32,0],[3,4,2]],spurious=gr.get_edges([gr
.ep.etype])[:3])

Output:

0.0

-7.883180576649465

-7.883180576649465

attachment.html (8.74 KB)

2

Hi,

Thanks for the bug report, and the succinct example!

Could you please open an issue for it in gitlab, so we can keep track of
this more easily?

Thanks!

Best,
Tiago