Substantial reorganization and improvement of blockmodel inference

This adds support for exact likelihood calculation, correct posterior
sampling of models with nonempty groups, much improved "distributed"
degree-corrected prior, as well as continuous edge covariates.

doc/test_inference.py

@@ -10,10 +10,11 @@ if not verbose:
     out = open(os.devnull, 'w')
 else:
     out = sys.stdout
-
+from collections import OrderedDict
+import itertools
 from graph_tool.all import *
 import numpy.random
-from numpy.random import randint
+from numpy.random import randint, normal, random
 
 numpy.random.seed(42)
 seed_rng(42)
@@ -23,137 +24,216 @@ graph_tool.inference.set_test(True)
 g = collection.data["football"]
 ec = g.new_ep("int", randint(0, 10, g.num_edges()))
 
-def gen_state(directed, deg_corr, layers, overlap):
+rec_p = g.new_ep("double", random(g.num_edges()))
+rec_s = g.new_ep("double", normal(0, 10, g.num_edges()))
+
+
+def gen_state(directed, deg_corr, layers, overlap, rec, allow_empty):
     u = GraphView(g, directed=directed)
     if layers != False:
         state = graph_tool.inference.LayeredBlockState(u, B=u.num_vertices(),
                                                        deg_corr=deg_corr,
                                                        ec=ec.copy(),
+                                                       rec=rec,
                                                        overlap=overlap,
-                                                       layers=layers == True)
+                                                       layers=layers == True,
+                                                       allow_empty=allow_empty)
     elif overlap:
         state = graph_tool.inference.OverlapBlockState(u, B=2 * u.num_edges(),
+                                                       rec=rec,
                                                        deg_corr=deg_corr)
     else:
         state = graph_tool.inference.BlockState(u, B=u.num_vertices(),
-                                                deg_corr=deg_corr)
+                                                rec=rec,
+                                                deg_corr=deg_corr,
+                                                allow_empty=allow_empty)
     return state
 
 
-for directed in [True, False]:
-    for overlap in [False, True]:
-        for layered in [False, "covariates", True]:
-            for deg_corr in [False, True]:
-                for dl in [False, True]:
+pranges = [("directed", [False, True]),
+           ("overlap", [False, True]),
+           ("layered", [False, "covariates", True]),
+           ("rec", [None, "positive", "signed"]),
+           ("deg_corr", [False, True]),
+           ("dl", [False, True]),
+           ("degree_dl_kind", ["uniform", "distributed", "entropy"]),
+           ("allow_empty", [False, True]),
+           ("exact", [True, False])]
+
+pranges = OrderedDict(pranges)
+
+def iter_ranges(ranges):
+    for vals in itertools.product(*[v for k, v in ranges.items()]):
+        yield zip(ranges.keys(), vals)
+
+
+for pvals in iter_ranges(pranges):
+    params = OrderedDict(pvals)
+
+    locals().update(params)
+
+    if not deg_corr and degree_dl_kind != "uniform":
+        continue
+
+    if (rec is not None or layered != False) and not exact:
+        continue
+
+    print(params, file=out)
+
+    rec_ = None
+    if rec == "positive":
+        rec_ = rec_p
+    elif rec == "signed":
+        rec_ = rec_s
+
+    print("\t mcmc (unweighted)", file=out)
+    state = gen_state(directed, deg_corr, layered, overlap, rec_, allow_empty)
+
+    print("\t\t",
+          state.mcmc_sweep(beta=0,
+                           entropy_args=dict(dl=dl,
+                                             degree_dl_kind=degree_dl_kind,
+                                             exact=exact)),
+          state.get_nonempty_B(), file=out)
+
+    if overlap:
+        print("\t\t",
+              state.mcmc_sweep(beta=0, bundled=True,
+                               entropy_args=dict(dl=dl,
+                                                 degree_dl_kind=degree_dl_kind,
+                                                 exact=exact)),
+              state.get_nonempty_B(), file=out)
+
+    state = gen_state(directed, deg_corr, layered, overlap, rec_, allow_empty)
+
+    if not overlap:
+        print("\t mcmc", file=out)
+        bstate = state.get_block_state(vweight=True,  deg_corr=deg_corr)
+
+        print("\t\t",
+              bstate.mcmc_sweep(beta=0,
+                                entropy_args=dict(dl=dl,
+                                                  degree_dl_kind=degree_dl_kind,
+                                                  exact=exact)),
+              bstate.get_nonempty_B(), file=out)
+
+        print("\t\t",
+              bstate.mcmc_sweep(beta=0,
+                                entropy_args=dict(dl=dl,
+                                                  degree_dl_kind=degree_dl_kind,
+                                                  exact=exact)),
+              bstate.get_nonempty_B(), file=out)
+
+        print("\t\t",
+              bstate.gibbs_sweep(beta=0,
+                                 entropy_args=dict(dl=dl,
+                                                   degree_dl_kind=degree_dl_kind,
+                                                   exact=exact)),
+              bstate.get_nonempty_B(), file=out)
+
+    print("\t merge", file=out)
+
+    state = gen_state(directed, deg_corr, layered, overlap, rec_, allow_empty)
+
+    if not overlap:
+        bstate = state.get_block_state(vweight=True, deg_corr=deg_corr)
+
+        print("\t\t",
+              bstate.merge_sweep(50,
+                                 entropy_args=dict(dl=dl,
+                                                   degree_dl_kind=degree_dl_kind,
+                                                   multigraph=False,
+                                                   exact=exact)),
+              file=out)
+
+        bstate = bstate.copy()
+
+        print("\t\t",
+              bstate.mcmc_sweep(beta=0,
+                                entropy_args=dict(dl=dl,
+                                                  degree_dl_kind=degree_dl_kind,
+                                                  exact=exact)),
+              file=out)
+        print("\t\t",
+              bstate.gibbs_sweep(beta=0,
+                                 entropy_args=dict(dl=dl,
+                                                   degree_dl_kind=degree_dl_kind,
+                                                   exact=exact)),
+              file=out)
+    else:
+        print("\t\t",
+              state.merge_sweep(50,
+                                entropy_args=dict(dl=dl,
+                                                  degree_dl_kind=degree_dl_kind,
+                                                  multigraph=False,
+                                                  exact=exact)),
+              file=out)
 
-                    print("\ndirected:", directed, "overlap:", overlap,
-                          "layered:", layered, "deg-corr:", deg_corr, "dl:", dl,
-                          file=out)
+    print("\t shrink", file=out)
 
+    state = gen_state(directed, deg_corr, layered, overlap, rec_, allow_empty)
+    state = state.shrink(B=5, entropy_args=dict(dl=dl,
+                                                degree_dl_kind=degree_dl_kind,
+                                                multigraph=False,
+                                                exact=exact))
+    print("\t\t", state.B, "\n", file=out)
 
-                    print("\t mcmc (unweighted)", file=out)
-                    state = gen_state(directed, deg_corr, layered, overlap)
 
-                    print("\t\t", state.mcmc_sweep(beta=0, allow_empty=True,
-                                                   entropy_args=dict(dl=dl)),
-                          (state.wr.a > 0).sum(), file=out)
-                    if overlap:
-                        print("\t\t", state.mcmc_sweep(beta=0, bundled=True,
-                                                       allow_empty=True,
-                                                       entropy_args=dict(dl=dl)),
-                              (state.wr.a > 0).sum(),
-                              file=out)
+pranges = [("directed", [False, True]),
+           ("overlap", [False, True]),
+           ("layered", [False, "covariates", True]),
+           ("rec", [None, "positive", "signed"]),
+           ("deg_corr", [False, True]),
+           ("degree_dl_kind", ["uniform", "distributed", "entropy"]),
+           ("exact", [True])]
 
-                    state = gen_state(directed, deg_corr, layered, overlap)
+pranges = OrderedDict(pranges)
 
-                    if not overlap:
-                        print("\t mcmc", file=out)
-                        bstate = state.get_block_state(vweight=True,
-                                                       deg_corr=deg_corr)
+for pvals in iter_ranges(pranges):
+    params = OrderedDict(pvals)
 
-                        print("\t\t",
-                              bstate.mcmc_sweep(beta=0,
-                                                allow_empty=True,
-                                                entropy_args=dict(dl=dl,
-                                                                  multigraph=False)),
-                              (bstate.wr.a > 0).sum(), file=out)
+    locals().update(params)
 
-                        print("\t\t",
-                              bstate.mcmc_sweep(beta=0, allow_empty=True,
-                                                entropy_args=dict(dl=dl,
-                                                                  multigraph=False)),
-                              (bstate.wr.a > 0).sum(), file=out)
+    if not deg_corr and degree_dl_kind != "uniform":
+        continue
 
-                        print("\t\t",
-                              bstate.gibbs_sweep(beta=0, allow_empty=True,
-                                                 entropy_args=dict(dl=dl,
-                                                                   multigraph=False)),
-                              (bstate.wr.a > 0).sum(), file=out)
+    print(params, file=out)
 
-                    print("\t merge", file=out)
+    rec_ = None
+    if rec == "positive":
+        rec_ = rec_p
+    elif rec == "signed":
+        rec_ = rec_s
 
-                    state = gen_state(directed, deg_corr, layered, overlap)
+    if layered != False:
+        state_args = dict(ec=ec, layers=(layered == True), rec=rec_)
+    else:
+        state_args = dict(rec=rec_)
 
-                    if not overlap:
-                        bstate = state.get_block_state(vweight=True,
-                                                       deg_corr=deg_corr)
+    entropy_args = dict(exact=exact)
+
+    state = minimize_blockmodel_dl(GraphView(g, directed=directed),
+                                   verbose=(1, "\t") if verbose else False,
+                                   deg_corr=deg_corr,
+                                   overlap=overlap,
+                                   layers=layered != False,
+                                   state_args=state_args,
+                                   mcmc_args=dict(entropy_args=entropy_args))
+
+    print(state.B, state.entropy(), file=out)
+
+    state = minimize_nested_blockmodel_dl(GraphView(g, directed=directed),
+                                          verbose=(2, "\t") if verbose else False,
+                                          deg_corr=deg_corr,
+                                          overlap=overlap,
+                                          layers=layered != False,
+                                          state_args=state_args,
+                                          mcmc_args=dict(entropy_args=entropy_args))
+    if verbose:
+        state.print_summary()
+
+    print(state.entropy(), "\n", file=out)
 
-                        print("\t\t",
-                              bstate.merge_sweep(50,
-                                                 entropy_args=dict(dl=dl,
-                                                                   multigraph=False)),
-                              file=out)
-
-                        bstate = bstate.copy()
-
-                        print("\t\t",
-                              bstate.mcmc_sweep(beta=0, allow_empty=True,
-                                                entropy_args=dict(dl=dl,
-                                                                  multigraph=False)),
-                              file=out)
-                        print("\t\t",
-                              bstate.gibbs_sweep(beta=0, allow_empty=True,
-                                                 entropy_args=dict(dl=dl,
-                                                                multigraph=False)),
-                              file=out)
-                    else:
-                        print("\t\t",
-                              state.merge_sweep(50,
-                                                entropy_args=dict(dl=dl,
-                                                                  multigraph=False)),
-                              file=out)
-
-                    print("\t shrink", file=out)
-
-                    state = gen_state(directed, deg_corr, layered, overlap)
-                    state = state.shrink(B=5, entropy_args=dict(dl=dl,
-                                                                multigraph=False))
-                    print("\t\t", state.B, file=out)
-
-for directed in [True, False]:
-    for overlap in [False, True]:
-        for layered in [False, "covariates", True]:
-            for deg_corr in [False, True]:
-                print("\ndirected:", directed, "overlap:", overlap,
-                      "layered:", layered, "deg-corr:", deg_corr, file=out)
-
-                state_args = dict(ec=ec, layers=(layered == True)) if layered != False else {}
-                state = minimize_blockmodel_dl(GraphView(g, directed=directed),
-                                               verbose=(1, "\t") if verbose else False,
-                                               deg_corr=deg_corr,
-                                               overlap=overlap,
-                                               layers=layered != False,
-                                               state_args=state_args)
-                print(state.B, state.entropy(), file=out)
-
-                state = minimize_nested_blockmodel_dl(GraphView(g, directed=directed),
-                                                      verbose=(1, "\t") if verbose else False,
-                                                      deg_corr=deg_corr,
-                                                      overlap=overlap,
-                                                      layers=layered != False,
-                                                      state_args=state_args)
-                if verbose:
-                    state.print_summary()
-                print(state.entropy(), file=out)
+graph_tool.inference.set_test(False)
 
 print("OK")
\ No newline at end of file

doc/test_inference_mcmc.py

@@ -16,7 +16,6 @@ verbose = __name__ == "__main__"
 g = collection.data["football"]
 B = 10
 
-
 for directed in [True, False]:
     clf()
     g.set_directed(directed)
@@ -96,32 +95,35 @@ for directed in [True, False]:
 
     savefig("test_mcmc_move_prob_directed%s.pdf" % directed)
 
-g = collection.data["karate"]
-B = 3
+g = collection.data["lesmis"]
+B = 6
 
 for directed in [True, False]:
-    clf()
     g.set_directed(directed)
 
     hists = {}
 
-    state = minimize_blockmodel_dl(g, deg_corr=False, B_min=B, B_max=B)
+    state = minimize_blockmodel_dl(g, deg_corr=True, B_min=B, B_max=B)
     state = state.copy(B=B+1)
 
     cs = list(reversed([numpy.inf, 1, 0.1, 0.01, 0.001, "gibbs"]))
 
     for i, c in enumerate(cs):
         if c != "gibbs":
-            mcmc_args=dict(beta=1, c=c, niter=600, allow_empty=True)
+            mcmc_args=dict(beta=1, c=c, niter=100, allow_vacate=True)
         else:
-            mcmc_args=dict(beta=1, niter=600, allow_empty=True)
+            mcmc_args=dict(beta=1, niter=100, allow_vacate=True)
         if i == 0:
-            mcmc_equilibrate(state, mcmc_args=mcmc_args, gibbs=c=="gibbs",
-                             wait=1000,
+            mcmc_equilibrate(state,
+                             mcmc_args=mcmc_args,
+                             gibbs=c=="gibbs",
+                             wait=100,
                              verbose=(1, "c = %s (t) " % str(c))  if verbose else False)
-        hists[c] = mcmc_equilibrate(state, mcmc_args=mcmc_args,
+        hists[c] = mcmc_equilibrate(state,
+                                    mcmc_args=mcmc_args,
                                     gibbs=c=="gibbs",
-                                    force_niter=2000,
+                                    wait=2000,
+                                    nbreaks=12,
                                     verbose=(1, "c = %s " % str(c)) if verbose else False,
                                     history=True)
 
@@ -146,20 +148,30 @@ for directed in [True, False]:
                        "(%s, %s) are not the same, with a p-value: %g (D=%g)") %
                       (str(directed), str(c1), str(c2), p, D))
 
-    bins = None
-    for c in cs:
-        hist = hists[c]
-        h = histogram(list(zip(*hist))[0], 1000000)
-        plot(h[-1][:-1], numpy.cumsum(h[0]), "-", label="c=%s" % str(c))
-
-        if c != numpy.inf:
-            hist = hists[numpy.inf]
-            h2 = histogram(list(zip(*hist))[0], bins=h[-1])
-            res = abs(numpy.cumsum(h2[0]) - numpy.cumsum(h[0]))
-            i = res.argmax()
-            axvline(h[-1][i], color="grey")
-
-    legend(loc="lower right")
-    savefig("test_mcmc_directed%s.pdf" % directed)
+    for cum in [True, False]:
+        clf()
+        bins = None
+        for c in cs:
+            hist = hists[c]
+            if cum:
+                h = histogram(list(zip(*hist))[0], 1000000, density=True)
+                plot(h[-1][:-1], numpy.cumsum(h[0]), "-", label="c=%s" % str(c))
+
+                if c != numpy.inf:
+                    hist = hists[numpy.inf]
+                    h2 = histogram(list(zip(*hist))[0], bins=h[-1], density=True)
+                    res = abs(numpy.cumsum(h2[0]) - numpy.cumsum(h[0]))
+                    i = res.argmax()
+                    axvline(h[-1][i], color="grey")
+            else:
+                h = histogram(list(zip(*hist))[0], 40)
+                plot(h[-1][:-1], h[0], "s-", label="c=%s" % str(c))
+                gca().set_yscale("log")
+
+        if cum:
+            legend(loc="lower right")
+        else:
+            legend(loc="best")
+        savefig("test_mcmc_directed%s-cum%s.pdf" % (directed, str(cum)))
 
 print("OK")
\ No newline at end of file

src/graph/inference/Makefile.am

@@ -17,6 +17,7 @@ libgraph_tool_inference_la_LDFLAGS = $(MOD_LDFLAGS)
 libgraph_tool_inference_la_SOURCES = \
     cache.cc \
     graph_blockmodel.cc \
+    graph_blockmodel_imp.cc \
     graph_blockmodel_em.cc \
     graph_blockmodel_exhaustive.cc \
     graph_blockmodel_gibbs.cc \
@@ -44,6 +45,7 @@ libgraph_tool_inference_la_SOURCES = \
     graph_blockmodel_overlap_multicanonical.cc \
     graph_blockmodel_overlap_vacate.cc \
     graph_inference.cc \
+    int_part.cc \
     spence.cc
 
 libgraph_tool_inference_la_include_HEADERS = \
@@ -70,4 +72,5 @@ libgraph_tool_inference_la_include_HEADERS = \
     merge_loop.hh \
     multicanonical_loop.hh \
     parallel_rng.hh \
+    int_part.hh \
     util.hh

src/graph/inference/bundled_vacate_loop.hh

@@ -36,6 +36,9 @@ namespace graph_tool
 template <class MergeState, class RNG>
 auto bundled_vacate_sweep(MergeState& state, RNG& rng)
 {
+    if (state._nmerges == 0)
+        return make_pair(double(0), size_t(0));
+
     // individual bundles can move in different directions
     auto get_best_move = [&] (auto& bundle, auto& past_moves)
         {
@@ -134,7 +137,6 @@ auto bundled_vacate_sweep(MergeState& state, RNG& rng)
             double dS = 0;
             for (auto& bundle : bundles)
             {
-
                 gt_hash_set<size_t> past_moves(forbidden_moves);
                 auto best_move = get_best_move(bundle, past_moves);
                 if (get<0>(best_move) == state._null_move)
@@ -196,7 +198,7 @@ auto bundled_vacate_sweep(MergeState& state, RNG& rng)
     double S = 0;
     size_t nmerges = 0;
     gt_hash_set<size_t> vacated;
-    while (nmerges != state._nmerges && !queue.empty())
+    while (nmerges < state._nmerges && !queue.empty())
     {
         auto pos = queue.top();
         queue.pop();

src/graph/inference/cache.cc

@@ -28,12 +28,15 @@ vector<double> __lgamma_cache;
 
 void init_safelog(size_t x)
 {
-    size_t old_size = __safelog_cache.size();
-    if (x >= old_size)
+    #pragma omp critical (_safelog_)
     {
-        __safelog_cache.resize(x + 1);
-        for (size_t i = old_size; i < __safelog_cache.size(); ++i)
-            __safelog_cache[i] = safelog(double(i));
+        size_t old_size = __safelog_cache.size();
+        if (x >= old_size)
+        {
+            __safelog_cache.resize(x + 1);
+            for (size_t i = old_size; i < __safelog_cache.size(); ++i)
+                __safelog_cache[i] = safelog(double(i));
+        }
     }
 }
 
@@ -45,12 +48,15 @@ void clear_safelog()
 
 void init_xlogx(size_t x)
 {
-    size_t old_size = __xlogx_cache.size();
-    if (x >= old_size)
+    #pragma omp critical (_xlogx_)
     {
-        __xlogx_cache.resize(x + 1);
-        for (size_t i = old_size; i < __xlogx_cache.size(); ++i)
-            __xlogx_cache[i] = i * safelog(i);
+        size_t old_size = __xlogx_cache.size();
+        if (x >= old_size)
+        {
+            __xlogx_cache.resize(x + 1);
+            for (size_t i = old_size; i < __xlogx_cache.size(); ++i)
+                __xlogx_cache[i] = i * safelog(i);
+        }
     }
 }
 
@@ -61,12 +67,19 @@ void clear_xlogx()
 
 void init_lgamma(size_t x)
 {
-    size_t old_size = __lgamma_cache.size();
-    if (x >= old_size)
+    using namespace boost::math::policies;
+    #pragma omp critical (_lgamma_)
     {
-        __lgamma_cache.resize(x + 1);
-        for (size_t i = old_size; i < __lgamma_cache.size(); ++i)
-            __lgamma_cache[i] = lgamma(i);
+
+        size_t old_size = __lgamma_cache.size();
+        if (x >= old_size)
+        {
+            __lgamma_cache.resize(x + 1);
+            if (old_size == 0)
+                __lgamma_cache[0] = numeric_limits<double>::infinity();
+            for (size_t i = std::max(old_size, size_t(1)); i < __lgamma_cache.size(); ++i)
+                __lgamma_cache[i] = boost::math::lgamma(i);
+        }
     }
 }
 

src/graph/inference/cache.hh

@@ -23,6 +23,8 @@
 #include <vector>
 #include <cmath>
 
+#include <boost/math/special_functions/gamma.hpp>
+
 namespace graph_tool
 {
 using namespace std;

src/graph/inference/gibbs_loop.hh

@@ -90,8 +90,7 @@ auto gibbs_sweep(GibbsState state, RNG& rng_)
                  if (state.node_weight(v) == 0)
                      return;
 
-                 vector<size_t>& moves = state.get_moves(v);
-                 auto& weights = state.get_weights(v);
+                 auto& moves = state.get_moves(v);
 
                  probs.resize(moves.size());
                  deltas.resize(moves.size());
@@ -114,13 +113,13 @@ auto gibbs_sweep(GibbsState state, RNG& rng_)
                          if (std::isinf(deltas[j]))
                              probs[j] = 0;
                          else
-                             probs[j] = exp((-deltas[j] + dS_min) * beta) * weights[j];
+                             probs[j] = exp((-deltas[j] + dS_min) * beta);
                      }
                  }
                  else
                  {
                      for (size_t j = 0; j < moves.size(); ++j)
-                         probs[j] = (deltas[j] == dS_min) ? weights[j] : 0;
+                         probs[j] = (deltas[j] == dS_min) ? 1 : 0;
                  }
 
                  Sampler<size_t> sampler(idx, probs);

src/graph/inference/graph_blockmodel.cc

@@ -27,17 +27,22 @@
 using namespace boost;
 using namespace graph_tool;