.. _sampling:
Sampling from the posterior distribution
----------------------------------------
When analyzing empirical networks, one should be open to the possibility
that there will be more than one fit of the SBM with similar posterior
probabilities. In such situations, one should instead `sample`
partitions from the posterior distribution, instead of simply finding
its maximum. One can then compute quantities that are averaged over the
different model fits, weighted according to their posterior
probabilities.
Full support for model averaging is implemented in ``graph-tool`` via an
efficient `Markov chain Monte Carlo (MCMC)
`_ algorithm
[peixoto-efficient-2014]_. It works by attempting to move nodes into
different groups with specific probabilities, and `accepting or
rejecting
`_
such moves so that, after a sufficiently long time, the partitions will
be observed with the desired posterior probability. The algorithm is
designed so that its run-time (i.e. each sweep of the MCMC) is linear on
the number of edges in the network, and independent on the number of
groups being used in the model, and hence is suitable for use on very
large networks.
In order to perform such moves, one needs again to operate with
:class:`~graph_tool.inference.blockmodel.BlockState` or
:class:`~graph_tool.inference.nested_blockmodel.NestedBlockState` instances, and calling
their :meth:`~graph_tool.inference.blockmodel.BlockState.mcmc_sweep` methods. For
example, the following will perform 1000 sweeps of the algorithm with
the network of characters in the novel Les Misérables, starting from a
random partition into 20 groups
.. testcode:: model-averaging
g = gt.collection.data["lesmis"]
state = gt.BlockState(g, B=20) # This automatically initializes the state
# with a random partition into B=20
# nonempty groups; The user could
# also pass an arbitrary initial
# partition using the 'b' parameter.
# Now we run 1,000 sweeps of the MCMC. Note that the number of groups
# is allowed to change, so it will eventually move from the initial
# value of B=20 to whatever is most appropriate for the data.
dS, nattempts, nmoves = state.mcmc_sweep(niter=1000)
print("Change in description length:", dS)
print("Number of accepted vertex moves:", nmoves)
.. testoutput:: model-averaging
Change in description length: -365.317522...
Number of accepted vertex moves: 38213
.. note::
Starting from a random partition is rarely the best option, since it
may take a long time for it to equilibrate. It was done above simply
as an illustration on how to initialize
:class:`~graph_tool.inference.blockmodel.BlockState` by hand. Instead, a much
better option in practice is to start from an approximation to the
"ground state" obtained with
:func:`~graph_tool.inference.minimize.minimize_blockmodel_dl`, e.g.
.. testcode:: model-averaging
state = gt.minimize_blockmodel_dl(g)
state = state.copy(B=g.num_vertices())
dS, nattempts, nmoves = state.mcmc_sweep(niter=1000)
print("Change in description length:", dS)
print("Number of accepted vertex moves:", nmoves)
.. testoutput:: model-averaging
Change in description length: 1.660677...
Number of accepted vertex moves: 40461
Although the above is sufficient to implement model averaging, there is a
convenience function called
:func:`~graph_tool.inference.mcmc.mcmc_equilibrate` that is intend to
simplify the detection of equilibration, by keeping track of the maximum
and minimum values of description length encountered and how many sweeps
have been made without a "record breaking" event. For example,
.. testcode:: model-averaging
# We will accept equilibration if 10 sweeps are completed without a
# record breaking event, 2 consecutive times.
gt.mcmc_equilibrate(state, wait=10, nbreaks=2, mcmc_args=dict(niter=10), verbose=True)
will output:
.. testoutput:: model-averaging
:options: +NORMALIZE_WHITESPACE
niter: 1 count: 0 breaks: 0 min_S: 706.26857 max_S: 708.14483 S: 708.14483 ΔS: 1.87626 moves: 418
niter: 2 count: 0 breaks: 0 min_S: 699.23453 max_S: 708.14483 S: 699.23453 ΔS: -8.91030 moves: 409
niter: 3 count: 0 breaks: 0 min_S: 699.23453 max_S: 715.33531 S: 715.33531 ΔS: 16.1008 moves: 414
niter: 4 count: 0 breaks: 0 min_S: 699.23453 max_S: 723.13301 S: 723.13301 ΔS: 7.79770 moves: 391
niter: 5 count: 1 breaks: 0 min_S: 699.23453 max_S: 723.13301 S: 702.93354 ΔS: -20.1995 moves: 411
niter: 6 count: 2 breaks: 0 min_S: 699.23453 max_S: 723.13301 S: 706.39029 ΔS: 3.45675 moves: 389
niter: 7 count: 3 breaks: 0 min_S: 699.23453 max_S: 723.13301 S: 706.80859 ΔS: 0.418293 moves: 404
niter: 8 count: 4 breaks: 0 min_S: 699.23453 max_S: 723.13301 S: 707.61960 ΔS: 0.811010 moves: 417
niter: 9 count: 5 breaks: 0 min_S: 699.23453 max_S: 723.13301 S: 706.46577 ΔS: -1.15383 moves: 392
niter: 10 count: 6 breaks: 0 min_S: 699.23453 max_S: 723.13301 S: 714.34671 ΔS: 7.88094 moves: 410
niter: 11 count: 7 breaks: 0 min_S: 699.23453 max_S: 723.13301 S: 706.43194 ΔS: -7.91477 moves: 383
niter: 12 count: 8 breaks: 0 min_S: 699.23453 max_S: 723.13301 S: 705.19434 ΔS: -1.23760 moves: 405
niter: 13 count: 9 breaks: 0 min_S: 699.23453 max_S: 723.13301 S: 702.21395 ΔS: -2.98039 moves: 423
niter: 14 count: 0 breaks: 1 min_S: 715.54878 max_S: 715.54878 S: 715.54878 ΔS: 13.3348 moves: 400
niter: 15 count: 0 breaks: 1 min_S: 715.54878 max_S: 716.65842 S: 716.65842 ΔS: 1.10964 moves: 413
niter: 16 count: 0 breaks: 1 min_S: 701.19994 max_S: 716.65842 S: 701.19994 ΔS: -15.4585 moves: 382
niter: 17 count: 1 breaks: 1 min_S: 701.19994 max_S: 716.65842 S: 715.56997 ΔS: 14.3700 moves: 394
niter: 18 count: 0 breaks: 1 min_S: 701.19994 max_S: 719.25577 S: 719.25577 ΔS: 3.68580 moves: 404
niter: 19 count: 0 breaks: 1 min_S: 701.19994 max_S: 723.78811 S: 723.78811 ΔS: 4.53233 moves: 413
niter: 20 count: 1 breaks: 1 min_S: 701.19994 max_S: 723.78811 S: 709.77340 ΔS: -14.0147 moves: 387
niter: 21 count: 2 breaks: 1 min_S: 701.19994 max_S: 723.78811 S: 714.14891 ΔS: 4.37551 moves: 419
niter: 22 count: 3 breaks: 1 min_S: 701.19994 max_S: 723.78811 S: 722.05875 ΔS: 7.90984 moves: 399
niter: 23 count: 4 breaks: 1 min_S: 701.19994 max_S: 723.78811 S: 714.32503 ΔS: -7.73371 moves: 422
niter: 24 count: 5 breaks: 1 min_S: 701.19994 max_S: 723.78811 S: 708.53927 ΔS: -5.78576 moves: 392
niter: 25 count: 6 breaks: 1 min_S: 701.19994 max_S: 723.78811 S: 714.05889 ΔS: 5.51962 moves: 404
niter: 26 count: 7 breaks: 1 min_S: 701.19994 max_S: 723.78811 S: 713.93196 ΔS: -0.126937 moves: 414
niter: 27 count: 8 breaks: 1 min_S: 701.19994 max_S: 723.78811 S: 709.49863 ΔS: -4.43333 moves: 410
niter: 28 count: 9 breaks: 1 min_S: 701.19994 max_S: 723.78811 S: 707.42167 ΔS: -2.07696 moves: 397
niter: 29 count: 0 breaks: 1 min_S: 699.89982 max_S: 723.78811 S: 699.89982 ΔS: -7.52185 moves: 388
niter: 30 count: 0 breaks: 1 min_S: 698.57305 max_S: 723.78811 S: 698.57305 ΔS: -1.32677 moves: 391
niter: 31 count: 1 breaks: 1 min_S: 698.57305 max_S: 723.78811 S: 706.02629 ΔS: 7.45324 moves: 412
niter: 32 count: 2 breaks: 1 min_S: 698.57305 max_S: 723.78811 S: 701.97778 ΔS: -4.04852 moves: 421
niter: 33 count: 3 breaks: 1 min_S: 698.57305 max_S: 723.78811 S: 707.50134 ΔS: 5.52356 moves: 410
niter: 34 count: 4 breaks: 1 min_S: 698.57305 max_S: 723.78811 S: 708.56686 ΔS: 1.06552 moves: 424
niter: 35 count: 0 breaks: 1 min_S: 698.57305 max_S: 724.07361 S: 724.07361 ΔS: 15.5067 moves: 399
niter: 36 count: 1 breaks: 1 min_S: 698.57305 max_S: 724.07361 S: 723.51969 ΔS: -0.553915 moves: 384
niter: 37 count: 2 breaks: 1 min_S: 698.57305 max_S: 724.07361 S: 702.36708 ΔS: -21.1526 moves: 406
niter: 38 count: 3 breaks: 1 min_S: 698.57305 max_S: 724.07361 S: 707.60129 ΔS: 5.23420 moves: 405
niter: 39 count: 4 breaks: 1 min_S: 698.57305 max_S: 724.07361 S: 709.67542 ΔS: 2.07413 moves: 400
niter: 40 count: 5 breaks: 1 min_S: 698.57305 max_S: 724.07361 S: 714.52753 ΔS: 4.85212 moves: 398
niter: 41 count: 6 breaks: 1 min_S: 698.57305 max_S: 724.07361 S: 707.86563 ΔS: -6.66190 moves: 409
niter: 42 count: 7 breaks: 1 min_S: 698.57305 max_S: 724.07361 S: 718.80926 ΔS: 10.9436 moves: 400
niter: 43 count: 8 breaks: 1 min_S: 698.57305 max_S: 724.07361 S: 716.37312 ΔS: -2.43615 moves: 378
niter: 44 count: 9 breaks: 1 min_S: 698.57305 max_S: 724.07361 S: 713.76944 ΔS: -2.60368 moves: 399
niter: 45 count: 10 breaks: 2 min_S: 698.57305 max_S: 724.07361 S: 715.29009 ΔS: 1.52066 moves: 421
Note that the value of ``wait`` above was made purposefully low so that
the output would not be overly long. The most appropriate value requires
experimentation, but a typically good value is ``wait=1000``.
The function :func:`~graph_tool.inference.mcmc.mcmc_equilibrate` accepts a
``callback`` argument that takes an optional function to be invoked
after each call to
:meth:`~graph_tool.inference.blockmodel.BlockState.mcmc_sweep`. This function
should accept a single parameter which will contain the actual
:class:`~graph_tool.inference.blockmodel.BlockState` instance. We will use this in
the example below to collect the posterior vertex marginals (via
:class:`~graph_tool.inference.blockmodel.BlockState.collect_vertex_marginals`),
i.e. the posterior probability that a node belongs to a given group:
.. testcode:: model-averaging
# We will first equilibrate the Markov chain
gt.mcmc_equilibrate(state, wait=1000, mcmc_args=dict(niter=10))
pv = None
def collect_marginals(s):
global pv
pv = s.collect_vertex_marginals(pv)
# Now we collect the marginals for exactly 100,000 sweeps, at
# intervals of 10 sweeps:
gt.mcmc_equilibrate(state, force_niter=10000, mcmc_args=dict(niter=10),
callback=collect_marginals)
# Now the node marginals are stored in property map pv. We can
# visualize them as pie charts on the nodes:
state.draw(pos=g.vp.pos, vertex_shape="pie", vertex_pie_fractions=pv,
edge_gradient=None, output="lesmis-sbm-marginals.svg")
.. figure:: lesmis-sbm-marginals.*
:align: center
:width: 450px
Marginal probabilities of group memberships of the network of
characters in the novel Les Misérables, according to the
degree-corrected SBM. The `pie fractions
`_ on the nodes correspond
to the probability of being in group associated with the respective
color.
We can also obtain a marginal probability on the number of groups
itself, as follows.
.. testcode:: model-averaging
h = np.zeros(g.num_vertices() + 1)
def collect_num_groups(s):
B = s.get_nonempty_B()
h[B] += 1
# Now we collect the marginals for exactly 100,000 sweeps, at
# intervals of 10 sweeps:
gt.mcmc_equilibrate(state, force_niter=10000, mcmc_args=dict(niter=10),
callback=collect_num_groups)
.. testcode:: model-averaging
:hide:
figure()
Bs = np.arange(len(h))
idx = h > 0
bar(Bs[idx], h[idx] / h.sum(), width=1, color="#ccb974")
gca().set_xticks([6,7,8,9])
xlabel("$B$")
ylabel(r"$P(B|\boldsymbol G)$")
savefig("lesmis-B-posterior.svg")
.. figure:: lesmis-B-posterior.*
:align: center
Marginal posterior probability of the number of nonempty groups for
the network of characters in the novel Les Misérables, according to
the degree-corrected SBM.
Hierarchical partitions
+++++++++++++++++++++++
We can also perform model averaging using the nested SBM, which will
give us a distribution over hierarchies. The whole procedure is fairly
analogous, but now we make use of
:class:`~graph_tool.inference.nested_blockmodel.NestedBlockState` instances.
.. note::
When using :class:`~graph_tool.inference.nested_blockmodel.NestedBlockState` instances
to perform model averaging, they need to be constructed with the
option ``sampling=True``.
Here we perform the sampling of hierarchical partitions using the same
network as above.
.. testcode:: nested-model-averaging
g = gt.collection.data["lesmis"]
state = gt.minimize_nested_blockmodel_dl(g) # Initialize he Markov
# chain from the "ground
# state"
# Before doing model averaging, the need to create a NestedBlockState
# by passing sampling = True.
# We also want to increase the maximum hierarchy depth to L = 10
# We can do both of the above by copying.
bs = state.get_bs() # Get hierarchical partition.
bs += [np.zeros(1)] * (10 - len(bs)) # Augment it to L = 10 with
# single-group levels.
state = state.copy(bs=bs, sampling=True)
# Now we run 1000 sweeps of the MCMC
dS, nattempts, nmoves = state.mcmc_sweep(niter=1000)
print("Change in description length:", dS)
print("Number of accepted vertex moves:", nmoves)
.. testoutput:: nested-model-averaging
Change in description length: 2.371018...
Number of accepted vertex moves: 56087
Similarly to the the non-nested case, we can use
:func:`~graph_tool.inference.mcmc.mcmc_equilibrate` to do most of the boring
work, and we can now obtain vertex marginals on all hierarchical levels:
.. testcode:: nested-model-averaging
# We will first equilibrate the Markov chain
gt.mcmc_equilibrate(state, wait=1000, mcmc_args=dict(niter=10))
pv = [None] * len(state.get_levels())
def collect_marginals(s):
global pv
pv = [sl.collect_vertex_marginals(pv[l]) for l, sl in enumerate(s.get_levels())]
# Now we collect the marginals for exactly 100,000 sweeps
gt.mcmc_equilibrate(state, force_niter=10000, mcmc_args=dict(niter=10),
callback=collect_marginals)
# Now the node marginals for all levels are stored in property map
# list pv. We can visualize the first level as pie charts on the nodes:
state_0 = state.get_levels()[0]
state_0.draw(pos=g.vp.pos, vertex_shape="pie", vertex_pie_fractions=pv[0],
edge_gradient=None, output="lesmis-nested-sbm-marginals.svg")
.. figure:: lesmis-nested-sbm-marginals.*
:align: center
:width: 450px
Marginal probabilities of group memberships of the network of
characters in the novel Les Misérables, according to the nested
degree-corrected SBM. The pie fractions on the nodes correspond to
the probability of being in group associated with the respective
color.
We can also obtain a marginal probability of the number of groups
itself, as follows.
.. testcode:: nested-model-averaging
h = [np.zeros(g.num_vertices() + 1) for s in state.get_levels()]
def collect_num_groups(s):
for l, sl in enumerate(s.get_levels()):
B = sl.get_nonempty_B()
h[l][B] += 1
# Now we collect the marginal distribution for exactly 100,000 sweeps
gt.mcmc_equilibrate(state, force_niter=10000, mcmc_args=dict(niter=10),
callback=collect_num_groups)
.. testcode:: nested-model-averaging
:hide:
figure()
f, ax = plt.subplots(1, 5, figsize=(10, 3))
for i, h_ in enumerate(h[:5]):
Bs = np.arange(len(h_))
idx = h_ > 0
ax[i].bar(Bs[idx], h_[idx] / h_.sum(), width=1, color="#ccb974")
ax[i].set_xticks(Bs[idx])
ax[i].set_xlabel("$B_{%d}$" % i)
ax[i].set_ylabel(r"$P(B_{%d}|\boldsymbol G)$" % i)
locator = MaxNLocator(prune='both', nbins=5)
ax[i].yaxis.set_major_locator(locator)
tight_layout()
savefig("lesmis-nested-B-posterior.svg")
.. figure:: lesmis-nested-B-posterior.*
:align: center
Marginal posterior probability of the number of nonempty groups
:math:`B_l` at each hierarchy level :math:`l` for the network of
characters in the novel Les Misérables, according to the nested
degree-corrected SBM.
Below we obtain some hierarchical partitions sampled from the posterior
distribution.
.. testcode:: nested-model-averaging
for i in range(10):
state.mcmc_sweep(niter=1000)
state.draw(output="lesmis-partition-sample-%i.svg" % i, empty_branches=False)
.. image:: lesmis-partition-sample-0.svg
:width: 200px
.. image:: lesmis-partition-sample-1.svg
:width: 200px
.. image:: lesmis-partition-sample-2.svg
:width: 200px
.. image:: lesmis-partition-sample-3.svg
:width: 200px
.. image:: lesmis-partition-sample-4.svg
:width: 200px
.. image:: lesmis-partition-sample-5.svg
:width: 200px
.. image:: lesmis-partition-sample-6.svg
:width: 200px
.. image:: lesmis-partition-sample-7.svg
:width: 200px
.. image:: lesmis-partition-sample-8.svg
:width: 200px
.. image:: lesmis-partition-sample-9.svg
:width: 200px