def test_fill_diagonal():
a = tensor(np.random.rand(10, 13))
fill_diagonal(a, 10)
assert a.shape == (10, 13)
# must be Tensor
with pytest.raises(TypeError):
fill_diagonal(np.random.rand(11, 10), 1)
# at least 2-d required
with pytest.raises(ValueError):
a = tensor(np.random.rand(4))
fill_diagonal(a, 1)
# for more than 2-d, shape on each dimension should be equal
with pytest.raises(ValueError):
a = tensor(np.random.rand(11, 10, 11))
fill_diagonal(a, 1)
def testFillDiagonal(self):
a = tensor(np.random.rand(10, 13))
fill_diagonal(a, 10)
self.assertEqual(a.shape, (10, 13))
# must be Tensor
with self.assertRaises(TypeError):
fill_diagonal(np.random.rand(11, 10), 1)
# at least 2-d required
with self.assertRaises(ValueError):
a = tensor(np.random.rand(4))
fill_diagonal(a, 1)
# for more than 2-d, shape on each dimension should be equal
with self.assertRaises(ValueError):
a = tensor(np.random.rand(11, 10, 11))
fill_diagonal(a, 1)
def testFillDiagonalExecution(self):
# 2-d
raws = [
np.random.rand(30, 11),
np.random.rand(15, 15),
np.random.rand(11, 30),
sps.random(30, 11, density=0.1, format='csr')
]
def copy(x):
if hasattr(x, 'nnz'):
# sparse
return x.A
else:
return x.copy()
for raw in raws:
# test 1 chunk, wrap=False
t = tensor(raw, chunk_size=30)
fill_diagonal(t, 1)
res = self.executor.execute_tensor(t, concat=True)[0]
expected = copy(raw)
np.fill_diagonal(expected, 1)
np.testing.assert_array_equal(np.asarray(res), expected)
# test 1 chunk, wrap=True
t = tensor(raw, chunk_size=30)
fill_diagonal(t, 1, wrap=True)
res = self.executor.execute_tensor(t, concat=True)[0]
expected = copy(raw)
np.fill_diagonal(expected, 1, wrap=True)
np.testing.assert_array_equal(np.asarray(res), expected)
# test multiple chunks, wrap=False
t = tensor(raw, chunk_size=(12, 4))
fill_diagonal(t, 1)
res = self.executor.execute_tensor(t, concat=True)[0]
expected = copy(raw)
np.fill_diagonal(expected, 1)
np.testing.assert_array_equal(np.asarray(res), expected)
t = tensor(raw, chunk_size=(4, 12))
fill_diagonal(t, 1)
res = self.executor.execute_tensor(t, concat=True)[0]
expected = copy(raw)
np.fill_diagonal(expected, 1)
np.testing.assert_array_equal(np.asarray(res), expected)
# test multiple chunk, val with list type
t = tensor(raw, chunk_size=(12, 4))
fill_diagonal(t, [1, 2, 3])
res = self.executor.execute_tensor(t, concat=True)[0]
expected = copy(raw)
np.fill_diagonal(expected, [1, 2, 3])
np.testing.assert_array_equal(np.asarray(res), expected)
# test multiple chunk, val with tensor type
t = tensor(raw, chunk_size=(12, 4))
fill_diagonal(t, tensor([1, 2, 3]))
res = self.executor.execute_tensor(t, concat=True)[0]
expected = copy(raw)
np.fill_diagonal(expected, [1, 2, 3])
np.testing.assert_array_equal(np.asarray(res), expected)
# test multiple chunks, wrap=True
t = tensor(raw, chunk_size=(12, 4))
fill_diagonal(t, 1, wrap=True)
res = self.executor.execute_tensor(t, concat=True)[0]
expected = copy(raw)
np.fill_diagonal(expected, 1, wrap=True)
np.testing.assert_array_equal(np.asarray(res), expected)
t = tensor(raw, chunk_size=(4, 12))
fill_diagonal(t, 1, wrap=True)
res = self.executor.execute_tensor(t, concat=True)[0]
expected = copy(raw)
np.fill_diagonal(expected, 1, wrap=True)
np.testing.assert_array_equal(np.asarray(res), expected)
# test multiple chunk, val with list type
t = tensor(raw, chunk_size=(12, 4))
fill_diagonal(t, [1, 2, 3], wrap=True)
res = self.executor.execute_tensor(t, concat=True)[0]
expected = copy(raw)
np.fill_diagonal(expected, [1, 2, 3], wrap=True)
np.testing.assert_array_equal(np.asarray(res), expected)
# test multiple chunk, val with tensor type
t = tensor(raw, chunk_size=(12, 4))
fill_diagonal(t, tensor([[1, 2], [3, 4]]), wrap=True)
res = self.executor.execute_tensor(t, concat=True)[0]
expected = copy(raw)
np.fill_diagonal(expected, [1, 2, 3, 4], wrap=True)
np.testing.assert_array_equal(np.asarray(res), expected)
# 3-d
raw = np.random.rand(11, 11, 11)
expected = raw.copy()
np.fill_diagonal(expected, 1)
expected2 = raw.copy()
np.fill_diagonal(expected2, 1, wrap=True)
np.testing.assert_array_equal(expected, expected2)
# test 1 chunk
t = tensor(raw, chunk_size=30)
fill_diagonal(t, 1)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_array_equal(res, expected)
t = tensor(raw, chunk_size=30)
# wrap = True does not take effect when ndim > 2
fill_diagonal(t, 1, wrap=True)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_array_equal(res, expected)
# test multiple chunk
t = tensor(raw, chunk_size=(3, 4, 5))
fill_diagonal(t, 1)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_array_equal(res, expected)
t = tensor(raw, chunk_size=(3, 4, 5))
# wrap = True does not take effect when ndim > 2
fill_diagonal(t, 1, wrap=True)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_array_equal(res, expected)
# test val with list type
t = tensor(raw, chunk_size=(3, 4, 5))
fill_diagonal(t, [[1, 2], [3, 4]])
res = self.executor.execute_tensor(t, concat=True)[0]
expected = raw.copy()
np.fill_diagonal(expected, [1, 2, 3, 4])
np.testing.assert_array_equal(res, expected)
# test val with tensor type
t = tensor(raw, chunk_size=(3, 4, 5))
fill_diagonal(t, tensor([1, 2, 3]))
res = self.executor.execute_tensor(t, concat=True)[0]
expected = raw.copy()
np.fill_diagonal(expected, [1, 2, 3])
np.testing.assert_array_equal(res, expected)
# test val with tensor type which ndim == 0
t = tensor(raw, chunk_size=(3, 4, 5))
fill_diagonal(t, tensor([1, 2, 3]).sum())
res = self.executor.execute_tensor(t, concat=True)[0]
expected = raw.copy()
np.fill_diagonal(expected, 6)
np.testing.assert_array_equal(res, expected)
# test val with ndarray type which size is too long
t = tensor(raw, chunk_size=(3, 4, 5))
fill_diagonal(t, np.arange(20))
res = self.executor.execute_tensor(t, concat=True)[0]
expected = raw.copy()
np.fill_diagonal(expected, np.arange(20))
np.testing.assert_array_equal(res, expected)
