Hi all, I have a NestedBlockState with LayeredBlockState as basis, two layers.
I would like to predict edges that are missing in one or the other layer
(besides, is this even possible?). However, given a list of missing edges (and
a list of spurious ones) I tried this
a_state.get_edges_prob(missing, spurious)
and get this error
AttributeError Traceback (most recent call last)
<ipython-input-68-b3bc5280d3da> in <module>
----> 1 a_state.get_edges_prob([missing], spurious)
~/anaconda3/envs/experimental/lib/python3.8/site-packages/graph_tool/inference/nested_blockmodel.py
in get_edges_prob(self, missing, spurious, entropy_args)
441 lstate._state.clear_egroups()
442
--> 443 L += lstate.get_edges_prob(missing, spurious,
entropy_args=eargs)
444 if isinstance(self.levels[0], LayeredBlockState):
445 missing = [(lstate.b[u], lstate.b[v], l_) for u, v, l_
in missing]
~/anaconda3/envs/experimental/lib/python3.8/site-packages/graph_tool/inference/blockmodel.py
in get_edges_prob(self, missing, spurious, entropy_args)
1204 pos[v] = self.b[v]
1205
-> 1206 self.remove_vertex(pos.keys())
1207
1208 try:
~/anaconda3/envs/experimental/lib/python3.8/site-packages/graph_tool/inference/blockmodel.py
in remove_vertex(self, v)
1144 twice.
1145 """
-> 1146 self._state.remove_vertex(int(v))
1147
1148 def add_vertex(self, v, r):
AttributeError: 'graph_tool::BlockState<boost::undirected_adaptor<b' object has
no attribute 'remove_vertex'
I'm not sure what's happening here, but as far as I know gt.BlockState (and
gt.NestedBlockState and gt.LayeredBlockState) have a remove_vertex method
defined, right?
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