def test_dense_vs_sparse():
# XXX: really belongs in irm test cases, but kernels has a nice cluster
# enumeration iterator
r = rng()
n = 5
raw = ma.array(
np.random.choice(np.arange(20), size=(n, n)),
mask=np.random.choice([False, True], size=(n, n)))
dense = [relation_numpy_dataview(raw)]
sparse = [sparse_relation_dataview(_tocsr(raw))]
domains = [n]
relations = [((0, 0), gp)]
defn = irm_definition(domains, relations)
def score_fn(data):
def f(assignments):
s = irm_initialize(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
return f
product_assignments = tuple(map(list, map(permutation_iter, domains)))
dense_posterior = scores_to_probs(
np.array(map(score_fn(dense), it.product(*product_assignments))))
sparse_posterior = scores_to_probs(
np.array(map(score_fn(sparse), it.product(*product_assignments))))
assert_1d_lists_almost_equals(dense_posterior, sparse_posterior, places=3)
def test_dense_vs_sparse():
# XXX: really belongs in irm test cases, but kernels has a nice cluster
# enumeration iterator
r = rng()
n = 5
raw = ma.array(np.random.choice(np.arange(20), size=(n, n)),
mask=np.random.choice([False, True], size=(n, n)))
dense = [relation_numpy_dataview(raw)]
sparse = [sparse_relation_dataview(_tocsr(raw))]
domains = [n]
relations = [((0, 0), gp)]
defn = irm_definition(domains, relations)
def score_fn(data):
def f(assignments):
s = irm_initialize(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
return f
product_assignments = tuple(map(list, map(permutation_iter, domains)))
dense_posterior = scores_to_probs(
np.array(map(score_fn(dense), it.product(*product_assignments))))
sparse_posterior = scores_to_probs(
np.array(map(score_fn(sparse), it.product(*product_assignments))))
assert_1d_lists_almost_equals(dense_posterior, sparse_posterior, places=3)
def test_one_binary_sparse():
# 1 domain, 1 binary relation
domains = [4]
def mk_relations(model):
return [((0, 0), model)]
relsize = (domains[0], domains[0])
raw = ma.array(np.random.choice([False, True], size=relsize),
mask=np.random.choice([False, True], size=relsize))
data = [sparse_relation_dataview(_tocsr(raw))]
_test_convergence(domains, data, mk_relations(bb), mk_relations(bb),
assign)
def test_one_binary_sparse():
# 1 domain, 1 binary relation
domains = [4]
def mk_relations(model):
return [((0, 0), model)]
relsize = (domains[0], domains[0])
raw = ma.array(
np.random.choice([False, True], size=relsize),
mask=np.random.choice([False, True], size=relsize))
data = [sparse_relation_dataview(_tocsr(raw))]
_test_convergence(
domains, data, mk_relations(bb), mk_relations(bb), assign)
