def testCosineDistancesExecution(self):
raw_dense_x = np.random.rand(25, 10)
raw_dense_y = np.random.rand(17, 10)
raw_sparse_x = sps.random(25, 10, density=0.5, format='csr', random_state=0)
raw_sparse_y = sps.random(17, 10, density=0.4, format='csr', random_state=1)
for raw_x, raw_y in [
(raw_dense_x, raw_dense_y),
(raw_sparse_x, raw_sparse_y)
]:
for chunk_size in (25, 6):
x = mt.tensor(raw_x, chunk_size=chunk_size)
y = mt.tensor(raw_y, chunk_size=chunk_size)
d = cosine_distances(x, y)
result = self.executor.execute_tensor(d, concat=True)[0]
expected = sk_cosine_distances(raw_x, raw_y)
np.testing.assert_almost_equal(np.asarray(result), expected)
d = cosine_distances(x)
result = self.executor.execute_tensor(d, concat=True)[0]
expected = sk_cosine_distances(raw_x)
np.testing.assert_almost_equal(np.asarray(result), expected)
def test_cosine_distances_execution(setup, raw_x, raw_y, chunk_size):
x = mt.tensor(raw_x, chunk_size=chunk_size)
y = mt.tensor(raw_y, chunk_size=chunk_size)
d = cosine_distances(x, y)
result = d.execute().fetch()
expected = sk_cosine_distances(raw_x, raw_y)
np.testing.assert_almost_equal(np.asarray(result), expected)
d = cosine_distances(x)
result = d.execute().fetch()
expected = sk_cosine_distances(raw_x)
np.testing.assert_almost_equal(np.asarray(result), expected)