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_recarray_numpy_dataview_pickle():
# not masked, int32
y = np.array([(1, 2, 3, 4, 5), (5, 4, 3, 2, 1)],
dtype=[('', np.int32)] * 5)
view = recarray_numpy_dataview(y)
bstr = pickle.dumps(view)
view1 = pickle.loads(bstr)
assert_list_equal(list(v for v in view), list(v for v in view1))
# not masked, float32
y = np.array([(1, 2, 3, 4, 5), (5, 4, 3, 2, 1)],
dtype=[('', np.float32)] * 5)
view = recarray_numpy_dataview(y)
bstr = pickle.dumps(view)
view1 = pickle.loads(bstr)
assert_1d_lists_almost_equals(
list(v for v in view), list(v for v in view1))
# masked, int32
y = np.array([(1, 2, 3, 4, 5), (5, 4, 3, 2, 1)],
dtype=[('', np.int32)] * 5)
mask = [(False, False, False, False, True),
(True, False, True, True, False)]
y = ma.array(y, mask=mask)
view = recarray_numpy_dataview(y)
bstr = pickle.dumps(view)
view1 = pickle.loads(bstr)
# XXX(stephentu): masked arrays suck and don't implement equality
r = repr(list(v for v in view))
r1 = repr(list(v for v in view1))
assert_equals(r, r1)
# masked, float32
y = np.array([(1, 2, 3, 4, 5), (5, 4, 3, 2, 1)],
dtype=[('', np.float32)] * 5)
mask = [(False, False, False, False, False),
(False, False, False, False, False)]
y = ma.array(y, mask=mask)
view = recarray_numpy_dataview(y)
bstr = pickle.dumps(view)
view1 = pickle.loads(bstr)
assert_1d_lists_almost_equals(
list(v.data for v in view),
list(v.data for v in view1))
def test_recarray_numpy_dataview_pickle():
# not masked, int32
y = np.array([(1, 2, 3, 4, 5), (5, 4, 3, 2, 1)],
dtype=[('', np.int32)] * 5)
view = recarray_numpy_dataview(y)
bstr = pickle.dumps(view)
view1 = pickle.loads(bstr)
assert_list_equal(list(v for v in view), list(v for v in view1))
# not masked, float32
y = np.array([(1, 2, 3, 4, 5), (5, 4, 3, 2, 1)],
dtype=[('', np.float32)] * 5)
view = recarray_numpy_dataview(y)
bstr = pickle.dumps(view)
view1 = pickle.loads(bstr)
assert_1d_lists_almost_equals(list(v for v in view),
list(v for v in view1))
# masked, int32
y = np.array([(1, 2, 3, 4, 5), (5, 4, 3, 2, 1)],
dtype=[('', np.int32)] * 5)
mask = [(False, False, False, False, True),
(True, False, True, True, False)]
y = ma.array(y, mask=mask)
view = recarray_numpy_dataview(y)
bstr = pickle.dumps(view)
view1 = pickle.loads(bstr)
# XXX(stephentu): masked arrays suck and don't implement equality
r = repr(list(v for v in view))
r1 = repr(list(v for v in view1))
assert_equals(r, r1)
# masked, float32
y = np.array([(1, 2, 3, 4, 5), (5, 4, 3, 2, 1)],
dtype=[('', np.float32)] * 5)
mask = [(False, False, False, False, False),
(False, False, False, False, False)]
y = ma.array(y, mask=mask)
view = recarray_numpy_dataview(y)
bstr = pickle.dumps(view)
view1 = pickle.loads(bstr)
assert_1d_lists_almost_equals(list(v.data for v in view),
list(v.data for v in view1))
