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Commit 26b147db authored by Tiago Peixoto's avatar Tiago Peixoto
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minimize_(nested_)blockmodel_dl(): Add random_bisection optional parameter

parent 64471e25
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Showing with 52 additions and 33 deletions
src/graph_tool/community/blockmodel.py
View file @ 26b147db
......@@ -1923,9 +1923,12 @@ def fibo_n_floor(x):
n = floor(log(x * sqrt(5) + 0.5) / log(phi))
return int(n)
def get_mid(a, b):
n = fibo_n_floor(b - a)
return b - fibo(n - 1)
def get_mid(a, b, random=False):
if random:
return a + numpy.random.randint(b - a + 1)
else:
n = fibo_n_floor(b - a)
return b - fibo(n - 1)
def is_fibo(x):
return fibo(fibo_n_floor(x)) == x
......@@ -1940,8 +1943,9 @@ def minimize_blockmodel_dl(g, deg_corr=True, overlap=False, ec=None,
greedy_cooling=True, sequential=True, parallel=False,
r=2, nmerge_sweeps=10, max_B=None, min_B=None,
mid_B=None, checkpoint=None, minimize_state=None,
exhaustive=False, init_states=None, max_BE=None,
verbose=False, **kwargs):
random_bisection=False, exhaustive=False,
init_states=None, max_BE=None, verbose=False,
**kwargs):
r"""Find the block partition of an unspecified size which minimizes the description
length of the network, according to the stochastic blockmodel ensemble which
best describes it.
......@@ -2061,6 +2065,9 @@ def minimize_blockmodel_dl(g, deg_corr=True, overlap=False, ec=None,
:class:`~graph_tool.community.MinimizeState`
instance which will be passed to the callback of the ``checkpoint``
option above, and can be stored by :mod:`pickle`.
random_bisection : ``bool`` (optional, default: ``False``)
If ``True``, the best value of ``B`` will be found by performing a
random bisection, instead of a Fibonacci search.
exhaustive : ``bool`` (optional, default: ``False``)
If ``True``, the best value of ``B`` will be found by testing all possible
values, instead of performing a bisection search.
......@@ -2265,7 +2272,7 @@ def minimize_blockmodel_dl(g, deg_corr=True, overlap=False, ec=None,
print("max_B:", max_B)
if mid_B is None:
mid_B = get_mid(min_B, max_B)
mid_B = get_mid(min_B, max_B, random_bisection)
greedy = greedy_cooling
shrink = True
......@@ -2422,10 +2429,10 @@ def minimize_blockmodel_dl(g, deg_corr=True, overlap=False, ec=None,
if f_mid > f_min or f_mid > f_max:
if f_min < f_max:
max_B = mid_B
mid_B = get_mid(min_B, mid_B)
mid_B = get_mid(min_B, mid_B, random_bisection)
else:
min_B = mid_B
mid_B = get_mid(mid_B, max_B)
mid_B = get_mid(mid_B, max_B, random_bisection)
else:
break
......@@ -2435,9 +2442,9 @@ def minimize_blockmodel_dl(g, deg_corr=True, overlap=False, ec=None,
# Fibonacci search
while True:
if max_B - mid_B > mid_B - min_B:
x = get_mid(mid_B, max_B)
x = get_mid(mid_B, max_B, random_bisection)
else:
x = get_mid(min_B, mid_B)
x = get_mid(min_B, mid_B, random_bisection)
f_x = get_state_dl(B=x, **kwargs)
f_mid = get_state_dl(B=mid_B, **kwargs)
......
src/graph_tool/community/nested_blockmodel.py
View file @ 26b147db
......@@ -655,7 +655,8 @@ def replace_level(l, state, min_B=None, max_B=None, max_b=None, nsweeps=10,
sequential=True, parallel=False, dl=False, dense=False,
multigraph=True, sparse_thresh=100, verbose=False,
checkpoint=None, minimize_state=None, dl_ent=False,
propagate_clabel=True, confine_layers=False):
propagate_clabel=True, confine_layers=False,
random_bisection=False):
r"""Replaces level l with another state with a possibly different number of
groups. This may change not only the state at level l, but also the one at
level l + 1, which needs to be 'rebuilt' because of the label changes at
......@@ -740,6 +741,7 @@ def replace_level(l, state, min_B=None, max_B=None, max_b=None, nsweeps=10,
parallel=parallel,
greedy_cooling=True, epsilon=epsilon,
confine_layers=confine_layers,
random_bisection=random_bisection,
verbose=verbose=="full")
if _bm_test():
assert istate._BlockState__check_clabel(), "invalid clabel!"
......@@ -775,6 +777,7 @@ def replace_level(l, state, min_B=None, max_B=None, max_b=None, nsweeps=10,
nonoverlap_init=False,
init_states=init_states,
verbose=verbose=="full",
random_bisection=random_bisection,
##exaustive=g.num_vertices() <= 100,
#minimize_state=minimize_state.minimize_state, >>>>>> HERE <<<<<
checkpoint=checkpoint,
......@@ -895,12 +898,12 @@ def get_checkpoint_wrap(checkpoint, state, minimize_state, dl_ent):
def nested_tree_sweep(state, min_B=None, max_B=None, max_b=None, nsweeps=10,
epsilon=0., r=2., nmerge_sweeps=10, adaptive_sweeps=True,
c=0, dl=False, dense=False, multigraph=True,
propagate_clabel=True, sequential=True, parallel=False,
sparse_thresh=100, checkpoint=None, minimize_state=None,
frozen_levels=None, confine_layers=False, verbose=False,
**kwargs):
epsilon=0., r=2., random_bisection=False,
nmerge_sweeps=10, adaptive_sweeps=True, c=0, dl=False,
dense=False, multigraph=True, propagate_clabel=True,
sequential=True, parallel=False, sparse_thresh=100,
checkpoint=None, minimize_state=None, frozen_levels=None,
confine_layers=False, verbose=False, **kwargs):
r"""Performs one greedy sweep in the entire hierarchy tree, attempting to
decrease its description length.
......@@ -937,7 +940,8 @@ def nested_tree_sweep(state, min_B=None, max_B=None, max_b=None, nsweeps=10,
max_B=max_B, max_b=max_b, checkpoint=checkpoint,
minimize_state=minimize_state, dl_ent=dl_ent,
confine_layers=confine_layers,
propagate_clabel=propagate_clabel)
propagate_clabel=propagate_clabel,
random_bisection=random_bisection)
#_Si = state.entropy(dense=dense, multigraph=dense)
dS = 0
......@@ -1082,10 +1086,11 @@ def nested_tree_sweep(state, min_B=None, max_B=None, max_b=None, nsweeps=10,
def init_nested_state(g, Bs, ec=None, deg_corr=True, overlap=False,
layers=False, confine_layers=False, dl=False, dense=False,
multigraph=True, eweight=None, vweight=None, clabel=None,
nsweeps=10, epsilon=0., r=2, nmerge_sweeps=10,
adaptive_sweeps=True, c=0, sequential=True,
parallel=False, sparse_thresh=100, checkpoint=None,
minimize_state=None, max_BE=1000, verbose=False, **kwargs):
random_bisection=False, nsweeps=10, epsilon=0., r=2,
nmerge_sweeps=10, adaptive_sweeps=True, c=0,
sequential=True, parallel=False, sparse_thresh=100,
checkpoint=None, minimize_state=None, max_BE=1000,
verbose=False, **kwargs):
r"""Initializes a nested block hierarchy with sizes given by ``Bs``.
The meaning of the parameters are the same as in
......@@ -1103,7 +1108,8 @@ def init_nested_state(g, Bs, ec=None, deg_corr=True, overlap=False,
This algorithm has worst-case complexity of :math:`O(V\ln^2 V \times L)`,
where :math:`V` is the number of nodes in the network, and :math:`L` is the
depth of the hierarchy. """
depth of the hierarchy.
"""
dl_ent = kwargs.get("dl_ent", False)
ignore_degrees = kwargs.get("ignore_degrees", None)
......@@ -1184,6 +1190,7 @@ def init_nested_state(g, Bs, ec=None, deg_corr=True, overlap=False,
multigraph=(l > 0 and g.num_vertices() < sparse_thresh) or multigraph,
sequential=sequential,
parallel=parallel,
random_bisection=random_bisection,
verbose=verbose != False,
checkpoint=chkp,
minimize_state=minimize_state.minimize_state)
......@@ -1217,12 +1224,12 @@ def minimize_nested_blockmodel_dl(g, Bs=None, bs=None, min_B=None, max_B=None,
multigraph=True, dense=False, eweight=None,
vweight=None, clabel=None,
propagate_clabel=True, frozen_levels=None,
nsweeps=10, adaptive_sweeps=True,
epsilon=1e-3, c=0, nmerge_sweeps=10, r=2,
sparse_thresh=100, sequential=True,
parallel=False, verbose=False,
checkpoint=None, minimize_state=None,
**kwargs):
random_bisection=False, nsweeps=10,
adaptive_sweeps=True, epsilon=1e-3, c=0,
nmerge_sweeps=10, r=2, sparse_thresh=100,
sequential=True, parallel=False,
verbose=False, checkpoint=None,
minimize_state=None, **kwargs):
r"""Find the block hierarchy of an unspecified size which minimizes the description
length of the network, according to the nested stochastic blockmodel ensemble which
best describes it.
......@@ -1282,6 +1289,9 @@ def minimize_nested_blockmodel_dl(g, Bs=None, bs=None, min_B=None, max_B=None,
frozen_levels : :class:`list` (optional, default: ``None``)
List of levels (``int``s) which will remain unmodified during the
algorithm.
random_bisection : ``bool`` (optional, default: ``False``)
If ``True``, the best value of ``B`` will be found by performing a
random bisection, instead of a Fibonacci search.
nsweeps : ``int`` (optional, default: ``10``)
The number of sweeps done after each merge step to reach the local
minimum.
......@@ -1461,6 +1471,7 @@ def minimize_nested_blockmodel_dl(g, Bs=None, bs=None, min_B=None, max_B=None,
vweight=vweight,
clabel=clabel if isinstance(clabel, PropertyMap) else None,
propagate_clabel=propagate_clabel,
random_bisection=random_bisection,
nsweeps=nsweeps,
adaptive_sweeps=adaptive_sweeps,
epsilon=epsilon, c=c,
......@@ -1527,8 +1538,9 @@ def minimize_nested_blockmodel_dl(g, Bs=None, bs=None, min_B=None, max_B=None,
eweight=eweight,
propagate_clabel=propagate_clabel,
vweight=vweight, clabel=clabel,
verbose=verbose, nsweeps=nsweeps,
nmerge_sweeps=nmerge_sweeps,
verbose=verbose,
random_bisection=random_bisection,
nsweeps=nsweeps, nmerge_sweeps=nmerge_sweeps,
adaptive_sweeps=adaptive_sweeps, dl=dl,
dense=dense, multigraph=multigraph,
epsilon=epsilon, sparse_thresh=sparse_thresh,
......@@ -1547,8 +1559,8 @@ def minimize_nested_blockmodel_dl(g, Bs=None, bs=None, min_B=None, max_B=None,
chkp = get_checkpoint_wrap(checkpoint, state, minimize_state, dl_ent)
dS = nested_tree_sweep(state, min_B=min_B, max_B=max_B, max_b=max_b,
verbose=verbose, nsweeps=nsweeps,
nmerge_sweeps=nmerge_sweeps,
verbose=verbose, random_bisection=random_bisection,
nsweeps=nsweeps, nmerge_sweeps=nmerge_sweeps,
adaptive_sweeps=adaptive_sweeps, r=r,
epsilon=epsilon, dense=dense, dl=dl,
multigraph=multigraph, sequential=sequential,
......
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