There is the example code which cause the same infinite looping:
*import numpy as np*
*from graph_tool.all import **
*gg = collection.data['football']*
*dummy_type = np.array(map(lambda x: int('state' in x.lower()),
*dummy_weights = gg.new_edge_property('double')*
*dummy_weights.a = edge_endpoint_property(gg, gg.vp.value, "source").a -
edge_endpoint_property(gg, gg.vp.value, "target").a*
*test = minimize_nested_blockmodel_dl(gg, deg_corr=False, b_min =
dummy_type, B_max = 50, B_min=5,*
* state_args=dict(clabel =
* recs =
* rec_types =
* ), **
My investigation showed that the issue occurs only if I have simultaneously
*clabel constraint* and *real-normal covariates* for edges. If I remove one
of them - everything is fine, If I change the covariates type from normal
to any other -- again, no looping. This specific *clabel* forces for
bisection search degenerated bounds (min_state = max_state), and normal
covariates some how affects on entropy calculus.
As I understand, the problem is in potentially wrong entropy calculation
As we saw from outputs, code is trying to replace (N=2, B=1) with (N=2,
B=2) and is getting lower entropy.
I found that only in case of normal covariates you have subtraction for
entropy, hence potentially smaller entropy
for more sophisticated model.
Hope, it helps.
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