def _test_convergence(domains,
data,
reg_relations,
brute_relations,
kernel,
burnin_niters=10000,
skip=10,
ntries=50,
nsamples=1000,
places=2):
r = rng()
reg_defn = irm_definition(domains, reg_relations)
brute_defn = irm_definition(domains, brute_relations)
def score_fn(assignments):
s = irm_initialize(
brute_defn, data, r=r,
domain_assignments=assignments)
assign = sum(s.score_assignment(i) for i in xrange(len(assignments)))
likelihood = s.score_likelihood(r)
return assign + likelihood
product_assignments = tuple(map(list, map(permutation_iter, domains)))
posterior = scores_to_probs(
np.array(map(score_fn, it.product(*product_assignments))))
s = irm_initialize(reg_defn, data, r=r)
bounded_states = [irm_bind(s, i, data) for i in xrange(len(domains))]
# burnin
start = time.time()
last = start
for i in xrange(burnin_niters):
for bs in bounded_states:
kernel(bs, r)
if not ((i + 1) % 1000):
print 'burning finished iteration', (i + 1), \
'in', (time.time() - last), 'seconds'
last = time.time()
print 'finished burnin of', burnin_niters, \
'iters in', (time.time() - start), 'seconds'
idmap = {C: i for i, C in enumerate(it.product(*product_assignments))}
#print idmap
def sample_fn():
for _ in xrange(skip):
for bs in bounded_states:
kernel(bs, r)
key = tuple(tuple(permutation_canonical(bs.assignments()))
for bs in bounded_states)
return idmap[key]
assert_discrete_dist_approx(
sample_fn, posterior,
ntries=ntries, nsamples=nsamples,
kl_places=places)
def test_compare_to_mixture_model():
r = rng()
N, D = 4, 5
Y = np.random.uniform(size=(N, D)) > 0.8
Y_rec = np.array([tuple(y) for y in Y], dtype=[('', bool)] * D)
mm_view = rec_numpy_dataview(Y_rec)
irm_view = relation_numpy_dataview(Y)
mm_def = mm_definition(N, [bb] * D)
irm_def = irm_definition([N, D], [((0, 1), bb)])
perms = list(permutation_iter(N))
assignment = perms[np.random.randint(0, len(perms))]
mm_s = mm_initialize(mm_def, mm_view, r=r, assignment=assignment)
irm_s = irm_initialize(irm_def,
[irm_view],
r=r,
domain_assignments=[
assignment,
range(D),
])
def assert_suff_stats_equal():
assert set(mm_s.groups()) == set(irm_s.groups(0))
assert irm_s.groups(1) == range(D)
groups = mm_s.groups()
for g in groups:
for i in xrange(D):
a = mm_s.get_suffstats(g, i)
b = irm_s.get_suffstats(0, [g, i])
if b is None:
b = {'heads': 0L, 'tails': 0L}
assert a['heads'] == b['heads'] and a['tails'] == b['tails']
assert_suff_stats_equal()
assert_almost_equals(
mm_s.score_assignment(), irm_s.score_assignment(0), places=3)
bound_mm_s = mm_bind(mm_s, mm_view)
bound_irm_s = irm_bind(irm_s, 0, [irm_view])
# XXX: doesn't really have to be true, just is true of impl
assert not bound_mm_s.empty_groups()
assert not bound_irm_s.empty_groups()
bound_mm_s.create_group(r)
bound_irm_s.create_group(r)
gid_a = bound_mm_s.remove_value(0, r)
gid_b = bound_irm_s.remove_value(0, r)
assert gid_a == gid_b
assert_suff_stats_equal()
x0, y0 = bound_mm_s.score_value(0, r)
x1, y1 = bound_irm_s.score_value(0, r)
assert x0 == x1 # XXX: not really a requirement
# XXX: should really normalize and then check
for a, b in zip(y0, y1):
assert_almost_equals(a, b, places=2)
def test_compare_to_mixture_model():
r = rng()
N, D = 4, 5
Y = np.random.uniform(size=(N, D)) > 0.8
Y_rec = np.array([tuple(y) for y in Y], dtype=[('', bool)] * D)
mm_view = rec_numpy_dataview(Y_rec)
irm_view = relation_numpy_dataview(Y)
mm_def = mm_definition(N, [bb] * D)
irm_def = irm_definition([N, D], [((0, 1), bb)])
perms = list(permutation_iter(N))
assignment = perms[np.random.randint(0, len(perms))]
mm_s = mm_initialize(mm_def, mm_view, r=r, assignment=assignment)
irm_s = irm_initialize(irm_def, [irm_view],
r=r,
domain_assignments=[
assignment,
range(D),
])
def assert_suff_stats_equal():
assert set(mm_s.groups()) == set(irm_s.groups(0))
assert irm_s.groups(1) == range(D)
groups = mm_s.groups()
for g in groups:
for i in xrange(D):
a = mm_s.get_suffstats(g, i)
b = irm_s.get_suffstats(0, [g, i])
if b is None:
b = {'heads': 0L, 'tails': 0L}
assert a['heads'] == b['heads'] and a['tails'] == b['tails']
assert_suff_stats_equal()
assert_almost_equals(mm_s.score_assignment(),
irm_s.score_assignment(0),
places=3)
bound_mm_s = mm_bind(mm_s, mm_view)
bound_irm_s = irm_bind(irm_s, 0, [irm_view])
# XXX: doesn't really have to be true, just is true of impl
assert not bound_mm_s.empty_groups()
assert not bound_irm_s.empty_groups()
bound_mm_s.create_group(r)
bound_irm_s.create_group(r)
gid_a = bound_mm_s.remove_value(0, r)
gid_b = bound_irm_s.remove_value(0, r)
assert gid_a == gid_b
assert_suff_stats_equal()
x0, y0 = bound_mm_s.score_value(0, r)
x1, y1 = bound_irm_s.score_value(0, r)
assert x0 == x1 # XXX: not really a requirement
# XXX: should really normalize and then check
for a, b in zip(y0, y1):
assert_almost_equals(a, b, places=2)
def _test_convergence(domains,
data,
reg_relations,
brute_relations,
kernel,
burnin_niters=10000,
skip=10,
ntries=50,
nsamples=1000,
places=2):
r = rng()
reg_defn = irm_definition(domains, reg_relations)
brute_defn = irm_definition(domains, brute_relations)
def score_fn(assignments):
s = irm_initialize(brute_defn,
data,
r=r,
domain_assignments=assignments)
assign = sum(s.score_assignment(i) for i in xrange(len(assignments)))
likelihood = s.score_likelihood(r)
return assign + likelihood
product_assignments = tuple(map(list, map(permutation_iter, domains)))
posterior = scores_to_probs(
np.array(map(score_fn, it.product(*product_assignments))))
s = irm_initialize(reg_defn, data, r=r)
bounded_states = [irm_bind(s, i, data) for i in xrange(len(domains))]
# burnin
start = time.time()
last = start
for i in xrange(burnin_niters):
for bs in bounded_states:
kernel(bs, r)
if not ((i + 1) % 1000):
print 'burning finished iteration', (i + 1), \
'in', (time.time() - last), 'seconds'
last = time.time()
print 'finished burnin of', burnin_niters, \
'iters in', (time.time() - start), 'seconds'
idmap = {C: i for i, C in enumerate(it.product(*product_assignments))}
#print idmap
def sample_fn():
for _ in xrange(skip):
for bs in bounded_states:
kernel(bs, r)
key = tuple(
tuple(permutation_canonical(bs.assignments()))
for bs in bounded_states)
return idmap[key]
assert_discrete_dist_approx(sample_fn,
posterior,
ntries=ntries,
nsamples=nsamples,
kl_places=places)
