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Commit 5766b181 authored by Tiago Peixoto's avatar Tiago Peixoto
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blockmodel: Make sure empty groups' labels are sampled uniformly

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doc/demos/inference/inference.rst
View file @ 5766b181
......@@ -581,8 +581,8 @@ random partition into 20 groups
.. testoutput:: model-averaging
Change in description length: -374.329276...
Number of accepted vertex moves: 4394
Change in description length: -360.18357...
Number of accepted vertex moves: 4743
.. note::
......@@ -605,8 +605,8 @@ random partition into 20 groups
.. testoutput:: model-averaging
Change in description length: -4.2327044...
Number of accepted vertex moves: 3887
Change in description length: 15.255168280735756
Number of accepted vertex moves: 3997
Although the above is sufficient to implement model averaging, there is a
convenience function called
......@@ -627,34 +627,45 @@ will output:
.. testoutput:: model-averaging
:options: +NORMALIZE_WHITESPACE
niter: 1 count: 0 breaks: 0 min_S: 695.34285 max_S: 710.82373 S: 710.82373 ΔS: 15.4809 moves: 26
niter: 2 count: 1 breaks: 0 min_S: 695.34285 max_S: 710.82373 S: 700.88756 ΔS: -9.93617 moves: 28
niter: 3 count: 0 breaks: 0 min_S: 695.34285 max_S: 715.67616 S: 715.67616 ΔS: 14.7886 moves: 36
niter: 4 count: 1 breaks: 0 min_S: 695.34285 max_S: 715.67616 S: 700.17714 ΔS: -15.4990 moves: 47
niter: 5 count: 2 breaks: 0 min_S: 695.34285 max_S: 715.67616 S: 699.60917 ΔS: -0.567973 moves: 26
niter: 6 count: 3 breaks: 0 min_S: 695.34285 max_S: 715.67616 S: 695.98465 ΔS: -3.62452 moves: 26
niter: 7 count: 4 breaks: 0 min_S: 695.34285 max_S: 715.67616 S: 696.61192 ΔS: 0.627269 moves: 14
niter: 8 count: 5 breaks: 0 min_S: 695.34285 max_S: 715.67616 S: 708.40482 ΔS: 11.7929 moves: 23
niter: 9 count: 6 breaks: 0 min_S: 695.34285 max_S: 715.67616 S: 706.45612 ΔS: -1.94870 moves: 27
niter: 10 count: 7 breaks: 0 min_S: 695.34285 max_S: 715.67616 S: 706.23034 ΔS: -0.225778 moves: 23
niter: 11 count: 8 breaks: 0 min_S: 695.34285 max_S: 715.67616 S: 704.95338 ΔS: -1.27696 moves: 41
niter: 12 count: 9 breaks: 0 min_S: 695.34285 max_S: 715.67616 S: 713.74824 ΔS: 8.79486 moves: 41
niter: 13 count: 0 breaks: 1 min_S: 704.05707 max_S: 704.05707 S: 704.05707 ΔS: -9.69117 moves: 35
niter: 14 count: 0 breaks: 1 min_S: 704.05707 max_S: 708.98963 S: 708.98963 ΔS: 4.93256 moves: 42
niter: 15 count: 0 breaks: 1 min_S: 703.01886 max_S: 708.98963 S: 703.01886 ΔS: -5.97077 moves: 24
niter: 16 count: 0 breaks: 1 min_S: 703.01886 max_S: 712.90264 S: 712.90264 ΔS: 9.88378 moves: 33
niter: 17 count: 0 breaks: 1 min_S: 703.01886 max_S: 722.28564 S: 722.28564 ΔS: 9.38300 moves: 48
niter: 18 count: 0 breaks: 1 min_S: 698.11815 max_S: 722.28564 S: 698.11815 ΔS: -24.1675 moves: 34
niter: 19 count: 1 breaks: 1 min_S: 698.11815 max_S: 722.28564 S: 702.54589 ΔS: 4.42774 moves: 44
niter: 20 count: 2 breaks: 1 min_S: 698.11815 max_S: 722.28564 S: 698.87992 ΔS: -3.66597 moves: 32
niter: 21 count: 3 breaks: 1 min_S: 698.11815 max_S: 722.28564 S: 699.07353 ΔS: 0.193605 moves: 17
niter: 22 count: 4 breaks: 1 min_S: 698.11815 max_S: 722.28564 S: 710.06346 ΔS: 10.9899 moves: 32
niter: 23 count: 5 breaks: 1 min_S: 698.11815 max_S: 722.28564 S: 713.65309 ΔS: 3.58963 moves: 43
niter: 24 count: 6 breaks: 1 min_S: 698.11815 max_S: 722.28564 S: 709.27203 ΔS: -4.38106 moves: 29
niter: 25 count: 7 breaks: 1 min_S: 698.11815 max_S: 722.28564 S: 700.13040 ΔS: -9.14163 moves: 21
niter: 26 count: 8 breaks: 1 min_S: 698.11815 max_S: 722.28564 S: 703.50075 ΔS: 3.37036 moves: 15
niter: 27 count: 9 breaks: 1 min_S: 698.11815 max_S: 722.28564 S: 716.09070 ΔS: 12.5899 moves: 37
niter: 28 count: 10 breaks: 2 min_S: 698.11815 max_S: 722.28564 S: 707.79215 ΔS: -8.29855 moves: 25
niter: 1 count: 0 breaks: 0 min_S: 714.83073 max_S: 722.84073 S: 722.84073 ΔS: 8.01000 moves: 41
niter: 2 count: 0 breaks: 0 min_S: 713.03117 max_S: 722.84073 S: 713.03117 ΔS: -9.80956 moves: 60
niter: 3 count: 1 breaks: 0 min_S: 713.03117 max_S: 722.84073 S: 720.23665 ΔS: 7.20547 moves: 73
niter: 4 count: 0 breaks: 0 min_S: 711.50647 max_S: 722.84073 S: 711.50647 ΔS: -8.73018 moves: 44
niter: 5 count: 1 breaks: 0 min_S: 711.50647 max_S: 722.84073 S: 715.11782 ΔS: 3.61135 moves: 38
niter: 6 count: 2 breaks: 0 min_S: 711.50647 max_S: 722.84073 S: 713.35769 ΔS: -1.76013 moves: 45
niter: 7 count: 0 breaks: 0 min_S: 708.56292 max_S: 722.84073 S: 708.56292 ΔS: -4.79477 moves: 43
niter: 8 count: 0 breaks: 0 min_S: 704.93377 max_S: 722.84073 S: 704.93377 ΔS: -3.62915 moves: 41
niter: 9 count: 1 breaks: 0 min_S: 704.93377 max_S: 722.84073 S: 705.50820 ΔS: 0.574435 moves: 41
niter: 10 count: 0 breaks: 0 min_S: 704.93377 max_S: 723.88251 S: 723.88251 ΔS: 18.3743 moves: 42
niter: 11 count: 0 breaks: 0 min_S: 700.08240 max_S: 723.88251 S: 700.08240 ΔS: -23.8001 moves: 41
niter: 12 count: 1 breaks: 0 min_S: 700.08240 max_S: 723.88251 S: 700.47455 ΔS: 0.392148 moves: 24
niter: 13 count: 0 breaks: 0 min_S: 694.69715 max_S: 723.88251 S: 694.69715 ΔS: -5.77740 moves: 43
niter: 14 count: 0 breaks: 0 min_S: 694.69715 max_S: 727.95993 S: 727.95993 ΔS: 33.2628 moves: 62
niter: 15 count: 1 breaks: 0 min_S: 694.69715 max_S: 727.95993 S: 716.30984 ΔS: -11.6501 moves: 64
niter: 16 count: 2 breaks: 0 min_S: 694.69715 max_S: 727.95993 S: 720.31379 ΔS: 4.00396 moves: 69
niter: 17 count: 3 breaks: 0 min_S: 694.69715 max_S: 727.95993 S: 707.61481 ΔS: -12.6990 moves: 59
niter: 18 count: 4 breaks: 0 min_S: 694.69715 max_S: 727.95993 S: 704.16866 ΔS: -3.44615 moves: 41
niter: 19 count: 5 breaks: 0 min_S: 694.69715 max_S: 727.95993 S: 709.60575 ΔS: 5.43710 moves: 38
niter: 20 count: 6 breaks: 0 min_S: 694.69715 max_S: 727.95993 S: 709.53325 ΔS: -0.0725008 moves: 55
niter: 21 count: 7 breaks: 0 min_S: 694.69715 max_S: 727.95993 S: 701.93006 ΔS: -7.60319 moves: 51
niter: 22 count: 8 breaks: 0 min_S: 694.69715 max_S: 727.95993 S: 710.48650 ΔS: 8.55644 moves: 32
niter: 23 count: 9 breaks: 0 min_S: 694.69715 max_S: 727.95993 S: 710.23681 ΔS: -0.249697 moves: 67
niter: 24 count: 0 breaks: 1 min_S: 714.83608 max_S: 714.83608 S: 714.83608 ΔS: 4.59928 moves: 48
niter: 25 count: 0 breaks: 1 min_S: 706.77203 max_S: 714.83608 S: 706.77203 ΔS: -8.06406 moves: 46
niter: 26 count: 1 breaks: 1 min_S: 706.77203 max_S: 714.83608 S: 712.14703 ΔS: 5.37500 moves: 55
niter: 27 count: 0 breaks: 1 min_S: 706.77203 max_S: 716.70496 S: 716.70496 ΔS: 4.55793 moves: 58
niter: 28 count: 0 breaks: 1 min_S: 704.36112 max_S: 716.70496 S: 704.36112 ΔS: -12.3438 moves: 43
niter: 29 count: 0 breaks: 1 min_S: 697.62383 max_S: 716.70496 S: 697.62383 ΔS: -6.73729 moves: 25
niter: 30 count: 1 breaks: 1 min_S: 697.62383 max_S: 716.70496 S: 703.24718 ΔS: 5.62334 moves: 32
niter: 31 count: 2 breaks: 1 min_S: 697.62383 max_S: 716.70496 S: 707.59173 ΔS: 4.34456 moves: 34
niter: 32 count: 3 breaks: 1 min_S: 697.62383 max_S: 716.70496 S: 707.00452 ΔS: -0.587214 moves: 35
niter: 33 count: 4 breaks: 1 min_S: 697.62383 max_S: 716.70496 S: 709.79274 ΔS: 2.78822 moves: 41
niter: 34 count: 5 breaks: 1 min_S: 697.62383 max_S: 716.70496 S: 711.31756 ΔS: 1.52482 moves: 43
niter: 35 count: 6 breaks: 1 min_S: 697.62383 max_S: 716.70496 S: 703.10965 ΔS: -8.20792 moves: 16
niter: 36 count: 7 breaks: 1 min_S: 697.62383 max_S: 716.70496 S: 698.20186 ΔS: -4.90778 moves: 28
niter: 37 count: 8 breaks: 1 min_S: 697.62383 max_S: 716.70496 S: 705.90392 ΔS: 7.70206 moves: 32
niter: 38 count: 9 breaks: 1 min_S: 697.62383 max_S: 716.70496 S: 704.85226 ΔS: -1.05167 moves: 21
niter: 39 count: 10 breaks: 2 min_S: 697.62383 max_S: 716.70496 S: 700.00545 ΔS: -4.84681 moves: 36
Note that the value of `wait` above was made purposefully low so that
......@@ -791,8 +802,8 @@ network as above.
.. testoutput:: nested-model-averaging
Change in description length: 6.368298...
Number of accepted vertex moves: 5316
Change in description length: 11.12196...
Number of accepted vertex moves: 6083
Similarly to the the non-nested case, we can use
:func:`~graph_tool.inference.mcmc_equilibrate` to do most of the boring
......@@ -1066,8 +1077,8 @@ evidence efficiently, as we show below, using
.. testoutput:: model-evidence
Model evidence for deg_corr = True: -599.280568166 (mean field), -744.851035413 (Bethe)
Model evidence for deg_corr = False: -637.320504421 (mean field), -669.533693635 (Bethe)
Model evidence for deg_corr = True: -594.637748079 (mean field), -756.951720315 (Bethe)
Model evidence for deg_corr = False: -606.269851822 (mean field), -680.353860482 (Bethe)
If we consider the more accurate approximation, the outcome shows a
preference for the non-degree-corrected model.
......@@ -1131,8 +1142,8 @@ approach for the same network, using the nested model.
.. testoutput:: model-evidence
Model evidence for deg_corr = True: -508.072303996 (mean field), -703.774572649 (Bethe)
Model evidence for deg_corr = False: -565.034423817 (mean field), -662.335604507 (Bethe)
Model evidence for deg_corr = True: -325.236916492 (mean field), -621.047406544 (Bethe)
Model evidence for deg_corr = False: -350.004565775 (mean field), -545.630773383 (Bethe)
The results are similar: If we consider the most accurate approximation,
the non-degree-corrected model possesses the largest evidence. Note also
......@@ -1362,8 +1373,8 @@ above).
.. testoutput:: missing-edges
likelihood-ratio for (101, 102): 0.350445
likelihood-ratio for (17, 56): 0.649555
likelihood-ratio for (101, 102): 0.350471
likelihood-ratio for (17, 56): 0.649529
From which we can conclude that edge :math:`(17, 56)` is around twice as
likely as :math:`(101, 102)` to be a missing edge.
......
src/graph/generation/sampler.hh
View file @ 5766b181
......@@ -111,14 +111,21 @@ private:
// uniform sampling from containers
template <class Iter, class RNG>
auto& uniform_sample(Iter begin, const Iter& end, RNG& rng)
{
auto N = end - begin;
std::uniform_int_distribution<size_t> i_rand(0, N - 1);
std::advance(begin, i_rand(rng));
return *begin;
}
template <class Container, class RNG>
auto& uniform_sample(Container& v, RNG& rng)
{
std::uniform_int_distribution<size_t> i_rand(0, v.size() - 1);
return v[i_rand(rng)];
return uniform_sample(v.begin(), v.end(), rng);
}
} // namespace graph_tool
#endif // SAMPLER_HH
src/graph/inference/gibbs_loop.hh
View file @ 5766b181
......@@ -136,7 +136,7 @@ auto gibbs_sweep(GibbsState state, RNG& rng_)
if (!state._parallel)
{
state.perform_move(v, s);
state.perform_move(v, s, rng);
nmoves += state.node_weight(v);
S += deltas[j];
}
......@@ -160,7 +160,7 @@ auto gibbs_sweep(GibbsState state, RNG& rng_)
if (dS > 0 && std::isinf(beta))
continue;
state.perform_move(v, s);
state.perform_move(v, s, get_rng(rngs, rng_));
nmoves++;
S += dS;
}
......
src/graph/inference/graph_blockmodel.hh
View file @ 5766b181
......@@ -118,6 +118,7 @@ public:
rebuild_neighbour_sampler();
_empty_blocks.clear();
_candidate_blocks.clear();
_candidate_blocks.push_back(null_group);
for (auto r : vertices_range(_bg))
{
if (_wr[r] == 0)
......@@ -125,9 +126,6 @@ public:
else
add_element(_candidate_blocks, _candidate_pos, r);
}
if (!_empty_blocks.empty())
add_element(_candidate_blocks, _candidate_pos,
_empty_blocks.back());
}
BlockState(const BlockState& other)
......@@ -294,11 +292,7 @@ public:
if (_vweight[v] > 0 && _wr[r] == 0)
{
// Group became empty: Remove from candidate list, unless it is the
// only empty group remaining.
if (!_empty_blocks.empty() &&
has_element(_candidate_blocks, _candidate_pos, r))
remove_element(_candidate_blocks, _candidate_pos, r);
remove_element(_candidate_blocks, _candidate_pos, r);
add_element(_empty_blocks, _empty_pos, r);
}
}
......@@ -317,25 +311,8 @@ public:
if (_vweight[v] > 0 && _wr[r] == _vweight[v])
{
// Previously empty group became occupied: Remove from empty list
// and add a new empty group to the candidate list (if available).
remove_element(_empty_blocks, _empty_pos, r);
if (has_element(_candidate_blocks, _candidate_pos, r))
{
if (!_empty_blocks.empty())
{
auto s = _empty_blocks.back();
if (!has_element(_candidate_blocks, _candidate_pos, s))
add_element(_candidate_blocks, _candidate_pos, s);
}
}
else
{
// if r is not a candidate in the first place (i.e. the move as
// done by hand by the user), we need just to add it to the list
add_element(_candidate_blocks, _candidate_pos, r);
}
add_element(_candidate_blocks, _candidate_pos, r);
}
}
......@@ -1259,7 +1236,19 @@ public:
size_t sample_block(size_t v, double c, RNG& rng)
{
// attempt random block
size_t s = uniform_sample(_candidate_blocks, rng);
size_t s;
if (_empty_blocks.empty())
{
s = uniform_sample(_candidate_blocks.begin() + 1,
_candidate_blocks.end(),
rng);
}
else
{
s = uniform_sample(_candidate_blocks, rng);
if (s == null_group)
s = uniform_sample(_empty_blocks, rng);
}
auto& sampler = _neighbour_sampler[v];
if (!std::isinf(c) && !sampler.empty())
......@@ -1269,7 +1258,8 @@ public:
double p_rand = 0;
if (c > 0)
{
size_t B = _candidate_blocks.size();
size_t B = (_empty_blocks.empty()) ?
_candidate_blocks.size() - 1 : _candidate_blocks.size();
if (is_directed::apply<g_t>::type::value)
p_rand = c * B / double(_mrp[t] + _mrm[t] + c * B);
else
......@@ -1310,7 +1300,8 @@ public:
MEntries& m_entries)
{
typedef typename graph_traits<g_t>::vertex_descriptor vertex_t;
size_t B = _candidate_blocks.size();
size_t B = (_empty_blocks.empty()) ?
_candidate_blocks.size() - 1 : _candidate_blocks.size();
double p = 0;
size_t w = 0;
......
src/graph/inference/graph_blockmodel_gibbs.hh
View file @ 5766b181
......@@ -91,16 +91,27 @@ struct Gibbs
double virtual_move_dS(size_t v, size_t nr)
{
if (nr == null_group)
{
if (_state._empty_blocks.empty())
return numeric_limits<double>::infinity();
else
nr = _state._empty_blocks.front();
}
size_t r = _state._b[v];
if (!_state.allow_move(r, nr))
return numeric_limits<double>::infinity();
return _state.virtual_move(v, r, nr, _entropy_args, _m_entries);
}
void perform_move(size_t v, size_t nr)
template <class RNG>
void perform_move(size_t v, size_t nr, RNG& rng)
{
size_t r = _state._b[v];
if (nr == null_group)
nr = uniform_sample(_state._empty_blocks, rng);
if (r == nr)
return;
......
src/graph/inference/graph_blockmodel_overlap.hh
View file @ 5766b181
......@@ -1022,6 +1022,9 @@ public:
UnityPropertyMap<int,GraphInterface::edge_t> _eweight;
UnityPropertyMap<int,GraphInterface::vertex_t> _vweight;
std::array<size_t, 0> _empty_blocks;
};
} // namespace graph_tool
......
src/graph/inference/util.hh
View file @ 5766b181
......@@ -69,7 +69,7 @@ inline double lbinom_careful(double N, double k)
}
template <class Vec, class PosMap, class Val>
void remove_element(Vec& vec, PosMap& pos, Val& val)
void remove_element(Vec& vec, PosMap& pos, Val val)
{
auto& back = vec.back();
auto& j = pos[back];
......@@ -80,14 +80,14 @@ void remove_element(Vec& vec, PosMap& pos, Val& val)
}
template <class Vec, class PosMap, class Val>
void add_element(Vec& vec, PosMap& pos, Val& val)
void add_element(Vec& vec, PosMap& pos, Val val)
{
pos[val] = vec.size();
vec.push_back(val);
}
template <class Vec, class PosMap, class Val>
bool has_element(Vec& vec, PosMap& pos, Val& val)
bool has_element(Vec& vec, PosMap& pos, Val val)
{
size_t i = pos[val];
return (i < vec.size() && vec[i] == val);
......
src/graph_tool/inference/nested_blockmodel.py
View file @ 5766b181
......@@ -501,6 +501,16 @@ class NestedBlockState(object):
else:
args = overlay(kwargs, entropy_args=eargs, c=c[l])
if l > 0:
N_ = self.levels[l].get_N()
idx_ = self.levels[l].wr.a == 0
rs = arange(len(idx_), dtype="int")
rs = rs[idx_]
rs = rs[:N_]
self.levels[l].empty_blocks.resize(len(rs))
self.levels[l].empty_blocks.a = rs
reverse_map(rs, self.levels[l].empty_pos)
ret = algo(self.levels[l], **args)
dS += ret[0]
......
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