def testCreateSparseExecution(self):
mat = sps.csr_matrix([[0, 0, 2], [2, 0, 0]])
t = tensor(mat, dtype='f8', chunk_size=2)
res = self.executor.execute_tensor(t)
self.assertIsInstance(res[0], SparseNDArray)
self.assertEqual(res[0].dtype, np.float64)
np.testing.assert_array_equal(res[0].toarray(), mat[..., :2].toarray())
np.testing.assert_array_equal(res[1].toarray(), mat[..., 2:].toarray())
t2 = ones_like(t, dtype='f4')
res = self.executor.execute_tensor(t2)
expected = sps.csr_matrix([[0, 0, 1], [1, 0, 0]])
self.assertIsInstance(res[0], SparseNDArray)
self.assertEqual(res[0].dtype, np.float32)
np.testing.assert_array_equal(res[0].toarray(),
expected[..., :2].toarray())
np.testing.assert_array_equal(res[1].toarray(), expected[...,
2:].toarray())
t3 = tensor(np.array([[0, 0, 2], [2, 0, 0]]), chunk_size=2).tosparse()
res = self.executor.execute_tensor(t3)
self.assertIsInstance(res[0], SparseNDArray)
self.assertEqual(res[0].dtype, np.int_)
np.testing.assert_array_equal(res[0].toarray(), mat[..., :2].toarray())
np.testing.assert_array_equal(res[1].toarray(), mat[..., 2:].toarray())
def testCreateSparseExecution(self):
mat = sps.csr_matrix([[0, 0, 2], [2, 0, 0]])
t = tensor(mat, dtype='f8', chunk_size=2)
res = self.executor.execute_tensor(t)
self.assertIsInstance(res[0], SparseNDArray)
self.assertEqual(res[0].dtype, np.float64)
np.testing.assert_array_equal(res[0].toarray(), mat[..., :2].toarray())
np.testing.assert_array_equal(res[1].toarray(), mat[..., 2:].toarray())
t2 = ones_like(t, dtype='f4')
res = self.executor.execute_tensor(t2)
expected = sps.csr_matrix([[0, 0, 1], [1, 0, 0]])
self.assertIsInstance(res[0], SparseNDArray)
self.assertEqual(res[0].dtype, np.float32)
np.testing.assert_array_equal(res[0].toarray(),
expected[..., :2].toarray())
np.testing.assert_array_equal(res[1].toarray(), expected[...,
2:].toarray())
t3 = tensor(np.array([[0, 0, 2], [2, 0, 0]]), chunk_size=2).tosparse()
res = self.executor.execute_tensor(t3)
self.assertIsInstance(res[0], SparseNDArray)
self.assertEqual(res[0].dtype, np.int_)
np.testing.assert_array_equal(res[0].toarray(), mat[..., :2].toarray())
np.testing.assert_array_equal(res[1].toarray(), mat[..., 2:].toarray())
# test missing argument
t4 = tensor(np.array([[0, 0, 2], [2, 0, 0]]),
chunk_size=2).tosparse(missing=2)
t4 = t4 + 1
expected = mat.toarray()
raw = expected.copy()
expected[raw == 0] += 1
expected[raw != 0] = 0
res = self.executor.execute_tensor(t4, concat=True)[0]
self.assertIsInstance(res, SparseNDArray)
self.assertEqual(res.dtype, np.int_)
np.testing.assert_array_equal(res.toarray(), expected)
# test missing argument that is np.nan
t5 = tensor(np.array([[np.nan, np.nan, 2], [2, np.nan, -999]]),
chunk_size=2).tosparse(missing=[np.nan, -999])
t5 = (t5 + 1).todense(fill_value=np.nan)
expected = mat.toarray().astype(float)
expected[expected != 0] += 1
expected[expected == 0] = np.nan
res = self.executor.execute_tensor(t5, concat=True)[0]
self.assertEqual(res.dtype, np.float64)
np.testing.assert_array_equal(res, expected)
def test_create_sparse_execution(setup):
mat = sps.csr_matrix([[0, 0, 2], [2, 0, 0]])
t = tensor(mat, dtype='f8', chunk_size=2)
res = t.execute().fetch()
assert isinstance(res, SparseNDArray)
assert res.dtype == np.float64
np.testing.assert_array_equal(res.toarray(), mat.toarray())
t2 = ones_like(t, dtype='f4')
res = t2.execute().fetch()
expected = sps.csr_matrix([[0, 0, 1], [1, 0, 0]])
assert isinstance(res, SparseNDArray)
assert res.dtype == np.float32
np.testing.assert_array_equal(res.toarray(), expected.toarray())
t3 = tensor(np.array([[0, 0, 2], [2, 0, 0]]), chunk_size=2).tosparse()
res = t3.execute().fetch()
assert isinstance(res, SparseNDArray)
assert res.dtype == np.int_
np.testing.assert_array_equal(res.toarray(), mat.toarray())
# test missing argument
t4 = tensor(np.array([[0, 0, 2], [2, 0, 0]]), chunk_size=2).tosparse(missing=2)
t4 = t4 + 1
expected = mat.toarray()
raw = expected.copy()
expected[raw == 0] += 1
expected[raw != 0] = 0
res = t4.execute().fetch()
assert isinstance(res, SparseNDArray)
assert res.dtype == np.int_
np.testing.assert_array_equal(res.toarray(), expected)
# test missing argument that is np.nan
t5 = tensor(np.array([[np.nan, np.nan, 2], [2, np.nan, -999]]),
chunk_size=2).tosparse(missing=[np.nan, -999])
t5 = (t5 + 1).todense(fill_value=np.nan)
expected = mat.toarray().astype(float)
expected[expected != 0] += 1
expected[expected == 0] = np.nan
res = t5.execute().fetch()
assert res.dtype == np.float64
np.testing.assert_array_equal(res, expected)