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)