Dear all,
Thanks and apologies in advance if this is something silly on my part. I can
blockmodel this dataset using a vector of covariates (two int32’s) without
hierarchy no problem, but when I try to add hierarchy, I get an error
reproducible on two different platforms (OS X and ubuntu, one without OpenMP
support, one with; one from within an ipython notebook, one from without; both
Python 2.7.12):
RuntimeError Traceback (most recent call last)
<ipython-input-441-ae6b18f3b139> in <module>()
1 # infer hierarchical blockmodel with edge covariates
----> 2 get_ipython().magic(u'time hbm_edgecovariates =
gt.inference.minimize_nested_blockmodel_dl(g, deg_corr=False, layers=True,
state_args=dict(ec=g.ep.weight, layers=False))')
//anaconda/envs/geopandas_test_env/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc
in magic(self, arg_s)
2156 magic_name, _, magic_arg_s = arg_s.partition(' ')
2157 magic_name = magic_name.lstrip(prefilter.ESC_MAGIC)
-> 2158 return self.run_line_magic(magic_name, magic_arg_s)
2159
2160
#-------------------------------------------------------------------------
//anaconda/envs/geopandas_test_env/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc
in run_line_magic(self, magic_name, line)
2077 kwargs['local_ns'] = sys._getframe(stack_depth).f_locals
2078 with self.builtin_trap:
-> 2079 result = fn(*args,**kwargs)
2080 return result
2081
<decorator-gen-59> in time(self, line, cell, local_ns)
//anaconda/envs/geopandas_test_env/lib/python2.7/site-packages/IPython/core/magic.pyc
in <lambda>(f, *a, **k)
186 # but it's overkill for just that one bit of state.
187 def magic_deco(arg):
--> 188 call = lambda f, *a, **k: f(*a, **k)
189
190 if callable(arg):
//anaconda/envs/geopandas_test_env/lib/python2.7/site-packages/IPython/core/magics/execution.pyc
in time(self, line, cell, local_ns)
1178 else:
1179 st = clock2()
-> 1180 exec(code, glob, local_ns)
1181 end = clock2()
1182 out = None
<timed exec> in <module>()
/usr/local/lib/python2.7/site-packages/graph_tool/inference/minimize.pyc in
minimize_nested_blockmodel_dl(g, B_min, B_max, b_min, b_max, Bs, bs, deg_corr,
overlap, nonoverlap_init, layers, hierarchy_minimize_args, state_args,
bisection_args, mcmc_args, anneal_args, mcmc_equilibrate_args, shrink_args,
mcmc_multilevel_args, verbose)
513 verbose=verbose,
514 **dmask(hierarchy_minimize_args,
--> 515 ["B_max", "B_min", "bisection_args",
"verbose"]))
516
517 return state
/usr/local/lib/python2.7/site-packages/graph_tool/inference/nested_blockmodel.pyc
in hierarchy_minimize(state, B_min, B_max, b_min, b_max, frozen_levels,
sparse_thres, bisection_args, verbose)
749 bstate = state.find_new_level(l,
bisection_args=bisection_args,
750 B_min=B_min, B_max=B_max,
--> 751 b_min=b_min, b_max=b_max)
752 else:
753 bstate = state.find_new_level(l,
bisection_args=bisection_args)
/usr/local/lib/python2.7/site-packages/graph_tool/inference/nested_blockmodel.pyc
in find_new_level(self, l, sparse_thres, bisection_args, B_min, B_max, b_min,
b_max)
482
483 # find new state
--> 484 state = bisection_minimize([min_state, max_state],
**bisection_args)
485
486 if _bm_test():
/usr/local/lib/python2.7/site-packages/graph_tool/inference/bisection.pyc in
bisection_minimize(init_states, random_bisection, mcmc_multilevel_args,
extra_entropy_args, verbose)
139 for state in init_states:
140 b_cache[state.B] = (get_ent(state, mcmc_multilevel_args,
--> 141 extra_entropy_args),
142 state.copy())
143
/usr/local/lib/python2.7/site-packages/graph_tool/inference/bisection.pyc in
get_ent(state, mcmc_multilevel_args, extra_entropy_args)
68 mcmc_args = mcmc_equilibrate_args.get("mcmc_args", {})
69 entropy_args = mcmc_args.get("entropy_args", {})
---> 70 S = state.entropy(**dict(entropy_args, **extra_entropy_args))
71 return S
72
/usr/local/lib/python2.7/site-packages/graph_tool/inference/layered_blockmodel.pyc
in entropy(self, adjacency, dl, partition_dl, degree_dl, degree_dl_kind,
edges_dl, dense, multigraph, deg_entropy, exact, **kwargs)
616 dense=dense, multigraph=multigraph,
617 deg_entropy=deg_entropy, exact=exact,
--> 618 **dict(kwargs, test=False))
619
620 if dl and edges_dl:
/usr/local/lib/python2.7/site-packages/graph_tool/inference/blockmodel.pyc in
entropy(self, adjacency, dl, partition_dl, degree_dl, degree_dl_kind, edges_dl,
dense, multigraph, deg_entropy, exact, **kwargs)
845 callback = kwargs.pop("callback", None)
846 if callback is not None:
--> 847 S += callback(self)
848
849 if kwargs.pop("test", True) and _bm_test():
/usr/local/lib/python2.7/site-packages/graph_tool/inference/nested_blockmodel.pyc
in <lambda>(s)
431 callback=lambda s: get_edges_dl(s,
432
self.hstate_args,
--> 433
self.hentropy_args))
434 mcmc_args = dict(mcmc_args, entropy_args=entropy_args,
435
disable_callback_test=isinstance(self.levels[0],
/usr/local/lib/python2.7/site-packages/graph_tool/inference/nested_blockmodel.pyc
in get_edges_dl(state, hstate_args, hentropy_args)
38 bstate = state.get_block_state(b=bclabel, **hstate_args)
39 return bstate.entropy(**dict(hentropy_args, dl=True, edges_dl=False,
---> 40 multigraph=True))
41
42
/usr/local/lib/python2.7/site-packages/graph_tool/inference/layered_blockmodel.pyc
in entropy(self, adjacency, dl, partition_dl, degree_dl, degree_dl_kind,
edges_dl, dense, multigraph, deg_entropy, exact, **kwargs)
628
629 if dl:
--> 630 S += self.__get_layer_entropy()
631
632 if _bm_test() and kwargs.get("test", True):
/usr/local/lib/python2.7/site-packages/graph_tool/inference/layered_blockmodel.pyc
in __get_layer_entropy(self)
590 be = group_vector_property([ec, ec])
591 lstate = OverlapBlockState(g, b=be, deg_corr=False)
--> 592 self.__layer_entropy = lstate.entropy(dl=True,
edges_dl=False) - \
593 lstate.entropy(dl=False)
594 else:
/usr/local/lib/python2.7/site-packages/graph_tool/inference/overlap_blockmodel.pyc
in entropy(self, adjacency, dl, partition_dl, degree_dl, degree_dl_kind,
edges_dl, dense, multigraph, deg_entropy, exact, **kwargs)
581 multigraph=multigraph,
582 deg_entropy=deg_entropy, exact=exact,
--> 583 **kwargs)
584
585
/usr/local/lib/python2.7/site-packages/graph_tool/inference/blockmodel.pyc in
entropy(self, adjacency, dl, partition_dl, degree_dl, degree_dl_kind, edges_dl,
dense, multigraph, deg_entropy, exact, **kwargs)
824 if dl:
825 if partition_dl:
--> 826 S += self._state.get_partition_dl()
827
828 if edges_dl:
RuntimeError: Error in function boost::math::lgamma<double>(double): numeric
overflow
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