def test_simple():
domains = [5, 6]
relations = [((0, 1), bb)]
relsize = (domains[0], domains[1])
raw_data = [
ma.array(np.random.choice([False, True], size=relsize),
mask=np.random.choice([False, True], size=relsize))
]
def csr(raw):
n, m = raw.shape
def indices():
for i, j in it.product(range(n), range(m)):
if not raw.mask[i, j]:
yield i, j
data = [raw[i, j] for i, j in indices()]
i = list(map(op.itemgetter(0), indices()))
j = list(map(op.itemgetter(1), indices()))
return coo_matrix((data, (i, j)), shape=raw.shape).tocsr()
defn = model_definition(domains, relations)
data = map(numpy_dataview, raw_data)
sparse_data = map(sparse_2d_dataview, map(csr, raw_data))
r = rng()
s = initialize(defn, data, r=r)
assert s and bind(s, 0, data) and bind(s, 1, data)
s1 = initialize(defn, sparse_data, r=r)
assert s1 and bind(s1, 0, sparse_data) and bind(s1, 1, sparse_data)
def entity_data_positions(domain, eid):
def f(domains, reln):
for pos0 in xrange(reln.shape[0]):
for pos1 in xrange(reln.shape[1]):
if reln.mask[pos0, pos1]:
continue
if (domains[0] == domain and pos0 == eid) or \
(domains[1] == domain and pos1 == eid):
yield [pos0, pos1]
return list(
it.chain.from_iterable(
f(domains, reln)
for (domains, _), reln in zip(relations, raw_data)))
def test(s):
for did, nentities in enumerate(domains):
for eid in xrange(nentities):
a = entity_data_positions(did, eid)
b = s.entity_data_positions(did, eid, data)
assert sorted(a) == sorted(b)
test(s)
test(s1)
def test_simple():
domains = [5, 6]
relations = [((0, 1), bb)]
relsize = (domains[0], domains[1])
raw_data = [
ma.array(np.random.choice([False, True], size=relsize), mask=np.random.choice([False, True], size=relsize))
]
def csr(raw):
n, m = raw.shape
def indices():
for i, j in it.product(range(n), range(m)):
if not raw.mask[i, j]:
yield i, j
data = [raw[i, j] for i, j in indices()]
i = list(map(op.itemgetter(0), indices()))
j = list(map(op.itemgetter(1), indices()))
return coo_matrix((data, (i, j)), shape=raw.shape).tocsr()
defn = model_definition(domains, relations)
data = map(numpy_dataview, raw_data)
sparse_data = map(sparse_2d_dataview, map(csr, raw_data))
r = rng()
s = initialize(defn, data, r=r)
assert s and bind(s, 0, data) and bind(s, 1, data)
s1 = initialize(defn, sparse_data, r=r)
assert s1 and bind(s1, 0, sparse_data) and bind(s1, 1, sparse_data)
def entity_data_positions(domain, eid):
def f(domains, reln):
for pos0 in xrange(reln.shape[0]):
for pos1 in xrange(reln.shape[1]):
if reln.mask[pos0, pos1]:
continue
if (domains[0] == domain and pos0 == eid) or (domains[1] == domain and pos1 == eid):
yield [pos0, pos1]
return list(it.chain.from_iterable(f(domains, reln) for (domains, _), reln in zip(relations, raw_data)))
def test(s):
for did, nentities in enumerate(domains):
for eid in xrange(nentities):
a = entity_data_positions(did, eid)
b = s.entity_data_positions(did, eid, data)
assert sorted(a) == sorted(b)
test(s)
test(s1)
def _assert_structure_equals(defn, s1, s2, views, r):
assert_equals(s1.ndomains(), s2.ndomains())
assert_equals(s1.nrelations(), s2.nrelations())
for did in xrange(s1.ndomains()):
assert_equals(s1.nentities(did), s2.nentities(did))
assert_equals(s1.ngroups(did), s2.ngroups(did))
assert_equals(s1.assignments(did),
s2.assignments(did))
assert_equals(set(s1.groups(did)),
set(s2.groups(did)))
assert_close(s1.get_domain_hp(did),
s2.get_domain_hp(did))
assert_almost_equals(s1.score_assignment(did),
s2.score_assignment(did))
for rid in xrange(s1.nrelations()):
assert_close(s1.get_relation_hp(rid),
s2.get_relation_hp(rid))
dids = defn.relations()[rid]
groups = [s1.groups(did) for did in dids]
for gids in it.product(*groups):
ss1 = s1.get_suffstats(rid, gids)
ss2 = s2.get_suffstats(rid, gids)
if ss1 is None:
assert_is_none(ss2)
else:
assert_close(ss1, ss2)
assert_almost_equals(s1.score_likelihood(r),
s2.score_likelihood(r))
before = list(s1.assignments(0))
bound = model.bind(s1, 0, views)
gid = bound.remove_value(0, r)
assert_equals(s1.assignments(0)[0], -1)
assert_equals(before, s2.assignments(0))
bound.add_value(gid, 0, r) # restore
def run(self, r, niters=10000):
"""Run the specified mixturemodel kernel for `niters`, in a single
thread.
Parameters
----------
r : random state
niters : int
"""
validator.validate_type(r, rng, param_name='r')
validator.validate_positive(niters, param_name='niters')
inds = xrange(len(self._defn.domains()))
models = [bind(self._latent, i, self._views) for i in inds]
for _ in xrange(niters):
for name, config in self._kernel_config:
if name == 'assign':
for idx in config.keys():
gibbs.assign(models[idx], r)
elif name == 'assign_resample':
for idx, v in config.iteritems():
gibbs.assign_resample(models[idx], v['m'], r)
elif name == 'slice_cluster_hp':
for idx, v in config.iteritems():
slice.hp(models[idx], r, cparam=v['cparam'])
elif name == 'grid_relation_hp':
gibbs.hp(models[0], config, r)
elif name == 'slice_relation_hp':
slice.hp(models[0], r, hparams=config['hparams'])
elif name == 'theta':
slice.theta(models[0], r, tparams=config['tparams'])
else:
assert False, "should not be reached"
def test_slice_theta_irm():
N = 10
defn = model_definition([N], [((0, 0), bbnc)])
data = np.random.random(size=(N, N)) < 0.8
view = numpy_dataview(data)
r = rng()
prior = {'alpha': 1.0, 'beta': 9.0}
s = initialize(
defn,
[view],
r=r,
cluster_hps=[{'alpha': 2.0}],
relation_hps=[prior],
domain_assignments=[[0] * N])
bs = bind(s, 0, [view])
params = {0: {'p': 0.05}}
heads = len([1 for y in data.flatten() if y])
tails = len([1 for y in data.flatten() if not y])
alpha1 = prior['alpha'] + heads
beta1 = prior['beta'] + tails
def sample_fn():
theta(bs, r, tparams=params)
return s.get_suffstats(0, [0, 0])['p']
rv = beta(alpha1, beta1)
assert_1d_cont_dist_approx_sps(sample_fn, rv, nsamples=50000)
def _assert_structure_equals(defn, s1, s2, views, r):
assert_equals(s1.ndomains(), s2.ndomains())
assert_equals(s1.nrelations(), s2.nrelations())
for did in xrange(s1.ndomains()):
assert_equals(s1.nentities(did), s2.nentities(did))
assert_equals(s1.ngroups(did), s2.ngroups(did))
assert_equals(s1.assignments(did), s2.assignments(did))
assert_equals(set(s1.groups(did)), set(s2.groups(did)))
assert_close(s1.get_domain_hp(did), s2.get_domain_hp(did))
assert_almost_equals(s1.score_assignment(did), s2.score_assignment(did))
for rid in xrange(s1.nrelations()):
assert_close(s1.get_relation_hp(rid), s2.get_relation_hp(rid))
dids = defn.relations()[rid]
groups = [s1.groups(did) for did in dids]
for gids in it.product(*groups):
ss1 = s1.get_suffstats(rid, gids)
ss2 = s2.get_suffstats(rid, gids)
if ss1 is None:
assert_is_none(ss2)
else:
assert_close(ss1, ss2)
assert_almost_equals(s1.score_likelihood(r), s2.score_likelihood(r))
before = list(s1.assignments(0))
bound = model.bind(s1, 0, views)
gid = bound.remove_value(0, r)
assert_equals(s1.assignments(0)[0], -1)
assert_equals(before, s2.assignments(0))
bound.add_value(gid, 0, r) # restore
def latent(groups, entities_per_group, features, r):
N = groups * entities_per_group
defn = model_definition([N], [((0, 0), bb)] * features)
# generate fake data
views = []
for i in xrange(features):
Y = np.random.random(size=(N, N)) <= 0.5
view = numpy_dataview(Y)
views.append(view)
# assign entities to their respective groups
assignment = [[g] * entities_per_group for g in xrange(groups)]
assignment = list(it.chain.from_iterable(assignment))
latent = bind(initialize(defn, views, r, domain_assignments=[assignment]),
0, views)
latent.create_group(r) # perftest() doesnt modify group assignments
return latent
def latent(groups, entities_per_group, features, r):
N = groups * entities_per_group
defn = model_definition([N], [((0, 0), bb)] * features)
# generate fake data
views = []
for i in xrange(features):
Y = np.random.random(size=(N, N)) <= 0.5
view = numpy_dataview(Y)
views.append(view)
# assign entities to their respective groups
assignment = [[g] * entities_per_group for g in xrange(groups)]
assignment = list(it.chain.from_iterable(assignment))
latent = bind(
initialize(defn, views, r, domain_assignments=[assignment]), 0, views)
latent.create_group(r) # perftest() doesnt modify group assignments
return latent
