def testSparseDotExecution(self):
a_data = sps.random(5, 9, density=.1)
b_data = sps.random(9, 10, density=.2)
a = tensor(a_data, chunk_size=2)
b = tensor(b_data, chunk_size=3)
c = dot(a, b)
res = self.executor.execute_tensor(c, concat=True)[0]
self.assertTrue(issparse(res))
np.testing.assert_allclose(res.toarray(), a_data.dot(b_data).toarray())
c2 = dot(a, b, sparse=False)
res = self.executor.execute_tensor(c2, concat=True)[0]
self.assertFalse(issparse(res))
np.testing.assert_allclose(res, a_data.dot(b_data).toarray())
c3 = tensordot(a, b.T, (-1, -1), sparse=False)
res = self.executor.execute_tensor(c3, concat=True)[0]
self.assertFalse(issparse(res))
np.testing.assert_allclose(res, a_data.dot(b_data).toarray())
c = inner(a, b.T)
res = self.executor.execute_tensor(c, concat=True)[0]
self.assertTrue(issparse(res))
np.testing.assert_allclose(res.toarray(), a_data.dot(b_data).toarray())
c = inner(a, b.T, sparse=False)
res = self.executor.execute_tensor(c, concat=True)[0]
self.assertFalse(issparse(res))
np.testing.assert_allclose(res, a_data.dot(b_data).toarray())
def assertArrayEqual(self, a, b):
if issparse(a):
a = a.toarray()
else:
a = np.asarray(a)
if issparse(b):
b = b.toarray()
else:
b = np.asarray(b)
return self.assertTrue(self._nan_equal(a, b))
def assertArrayEqual(self, a, b, almost=False):
if issparse(a):
a = a.toarray()
else:
a = np.asarray(a)
if issparse(b):
b = b.toarray()
else:
b = np.asarray(b)
if not almost:
np.testing.assert_array_equal(a, b)
else:
np.testing.assert_almost_equal(a, b)
def assertArrayEqual(self, a, b):
if issparse(a):
a = a.toarray()
else:
a = np.asarray(a)
if issparse(b):
b = b.toarray()
else:
b = np.asarray(b)
try:
return np.testing.assert_equal(a, b)
except AssertionError:
return False
def func(*args):
new_args = []
for arg in args:
if issparse(arg):
new_args.append(arg.toarray())
else:
new_args.append(arg)
return op(*new_args)
def testSparseRandintExecution(self):
size_executor = ExecutorForTest(sync_provider_type=ExecutorForTest.SyncProviderType.MOCK)
arr = tensor.random.randint(1, 2, size=(30, 50), density=.1, chunk_size=10, dtype='f4')
size_res = size_executor.execute_tensor(arr, mock=True)
self.assertAlmostEqual(arr.nbytes * 0.1, sum(tp[0] for tp in size_res))
res = self.executor.execute_tensor(arr, concat=True)[0]
self.assertTrue(issparse(res))
self.assertEqual(res.shape, (30, 50))
self.assertTrue(np.all(res.data >= 1))
self.assertTrue(np.all(res.data < 2))
self.assertAlmostEqual((res >= 1).toarray().sum(), 30 * 50 * .1, delta=20)
def test_sparse_randint_execution(setup):
# size_executor = ExecutorForTest(sync_provider_type=ExecutorForTest.SyncProviderType.MOCK)
arr = tensor.random.randint(1, 2, size=(30, 50), density=.1,
chunk_size=20, dtype='f4')
# size_res = size_executor.execute_tensor(arr, mock=True)
# assert pytest.approx(arr.nbytes * 0.1) == sum(tp[0] for tp in size_res)
res = arr.execute().fetch()
assert issparse(res) is True
assert res.shape == (30, 50)
np.testing.assert_array_less(res.data, 2)
np.testing.assert_array_less(0, res.data)
assert pytest.approx((res >= 1).toarray().sum(), 30 * 50 * .1, abs=20)
def testSparseRandintExecution(self):
arr = tensor.random.randint(1,
2,
size=(30, 50),
density=.1,
chunk_size=10,
dtype='f4')
res = self.executor.execute_tensor(arr, concat=True)[0]
self.assertTrue(issparse(res))
self.assertEqual(res.shape, (30, 50))
self.assertTrue(np.all(res.data >= 1))
self.assertTrue(np.all(res.data < 2))
self.assertAlmostEqual((res >= 1).toarray().sum(),
30 * 50 * .1,
delta=20)
