def test_manhattan_distances_execution(setup, x, y, is_sparse):
if is_sparse:
rx, ry = raw_sparse_x, raw_sparse_y
else:
rx, ry = raw_x, raw_y
sv = [True, False] if not is_sparse else [True]
for sum_over_features in sv:
d = manhattan_distances(x, y, sum_over_features)
result = d.execute().fetch()
expected = sk_manhattan_distances(rx,
ry,
sum_over_features=sum_over_features)
np.testing.assert_almost_equal(result, expected)
d = manhattan_distances(x, sum_over_features=sum_over_features)
result = d.execute().fetch()
expected = sk_manhattan_distances(rx,
sum_over_features=sum_over_features)
np.testing.assert_almost_equal(result, expected)
def testManhattanDistancesExecution(self):
raw_x = np.random.rand(20, 5)
raw_y = np.random.rand(21, 5)
x1 = mt.tensor(raw_x, chunk_size=30)
y1 = mt.tensor(raw_y, chunk_size=30)
x2 = mt.tensor(raw_x, chunk_size=11)
y2 = mt.tensor(raw_y, chunk_size=12)
raw_sparse_x = sps.random(20,
5,
density=0.4,
format='csr',
random_state=0)
raw_sparse_y = sps.random(21,
5,
density=0.3,
format='csr',
random_state=0)
x3 = mt.tensor(raw_sparse_x, chunk_size=30)
y3 = mt.tensor(raw_sparse_y, chunk_size=30)
x4 = mt.tensor(raw_sparse_x, chunk_size=11)
y4 = mt.tensor(raw_sparse_y, chunk_size=12)
for x, y, is_sparse in [(x1, y1, False), (x2, y2, False),
(x3, y3, True), (x4, y4, True)]:
if is_sparse:
rx, ry = raw_sparse_x, raw_sparse_y
else:
rx, ry = raw_x, raw_y
sv = [True, False] if not is_sparse else [True]
for sum_over_features in sv:
d = manhattan_distances(x, y, sum_over_features)
result = self.executor.execute_tensor(d, concat=True)[0]
expected = sk_manhattan_distances(rx, ry, sum_over_features)
np.testing.assert_almost_equal(result, expected)
d = manhattan_distances(x, sum_over_features=sum_over_features)
result = self.executor.execute_tensor(d, concat=True)[0]
expected = sk_manhattan_distances(
rx, sum_over_features=sum_over_features)
np.testing.assert_almost_equal(result, expected)