def setUp(self):
"""Basic set up."""
self.dense = CDense([[1, 0, 0, 0, 5], [2, 4, 0, 0, 0], [3, 4, 5, 0,
0]])
self.dense_vector = CDense([1, 0, 0, 0, 3])
self.sparse_matrix = CSparse(self.dense)
self.sparse_vector = CSparse(self.dense_vector)
def _randomize_values_for_attack(self, x_init):
min_val = 0 + self.shift_values
max_val = 255 + self.shift_values
x_init[self.indexes_to_perturb] = CDense(
torch.randint(low=min_val,
high=max_val,
size=(1, len(self.indexes_to_perturb))))
return x_init
def test_init_array(self):
"""Test CArray initialization using arrays."""
self.logger.info("Initializing CArray with another CArray...")
arrays_list = [CArray([[2, 3], [22, 33]]),
CArray([2, 3]),
CArray([[2], [3]]),
CArray(3),
CArray([[[2, 3], [22, 33]], [[4, 5], [44, 55]]])]
for init_elem in arrays_list:
self.logger.info(init_elem)
array = CArray(init_elem)
self.assertEqual(init_elem.issparse, array.issparse)
self.assert_array_equal(array, init_elem)
self.assertEqual(init_elem.shape, array.shape)
self.assertEqual(init_elem.input_shape, array.input_shape)
self.logger.info("Initializing CArray with a CDense...")
dense_list = [CDense([[2, 3], [22, 33]]),
CDense([2, 3]),
CDense([[2], [3]]),
CDense([3]),
CDense([[[2, 3], [22, 33]], [[4, 5], [44, 55]]])]
for init_elem in dense_list:
self.logger.info(init_elem)
array = CArray(init_elem)
self.assertTrue(array.isdense)
self.assert_array_equal(array, init_elem)
self.assertEqual(array.shape, init_elem.shape)
self.assertEqual(init_elem.input_shape, array.input_shape)
self.logger.info("Initializing CArray with an ndarray...")
dense_list = [np.array([[2, 3], [22, 33]]),
np.array([2, 3]),
np.array([[2], [3]]),
np.array([3]),
np.array([[[2, 3], [22, 33]], [[4, 5], [44, 55]]])]
for init_elem in dense_list:
self.logger.info(init_elem)
array = CArray(init_elem)
self.assertTrue(array.isdense)
if init_elem.ndim <= 2:
self.assert_array_equal(array, init_elem)
self.assertEqual(array.shape, init_elem.shape)
else: # N-Dimensional ndarray
self.assertEqual(array.shape[0], init_elem.shape[0])
self.assertEqual(array.shape[1], sum(init_elem.shape[1:]))
self.assertEqual(array.input_shape, init_elem.shape)
self.logger.info("Initializing CArray with a sparse CArray...")
sparse_list = [
CArray([[2, 3], [22, 33]], tosparse=True),
CArray([2, 3], tosparse=True),
CArray([[2], [3]], tosparse=True),
CArray(3, tosparse=True),
CArray([[[2, 3], [22, 33]], [[4, 5], [44, 55]]], tosparse=True)]
for init_elem in sparse_list:
self.logger.info(init_elem)
array = CArray(init_elem)
self.assertTrue(array.issparse)
self.assert_array_equal(array, init_elem)
self.assertEqual(init_elem.input_shape, array.input_shape)
self.logger.info("Initializing CArray with a CSparse...")
sparse_list = [CSparse([[2, 3], [22, 33]]),
CSparse([2, 3]),
CSparse([[2], [3]]),
CSparse([3]),
CSparse([[[2, 3], [22, 33]], [[4, 5], [44, 55]]])]
for init_elem in sparse_list:
self.logger.info(init_elem)
array = CArray(init_elem)
self.assertTrue(array.issparse)
self.assert_array_equal(array, init_elem)
self.assertEqual(array.shape, init_elem.shape)
self.assertEqual(array.input_shape, init_elem.input_shape)
self.logger.info("Initializing CArray with a csr_matrix...")
sparse_list = [scs.csr_matrix([[2, 3], [22, 33]]),
scs.csr_matrix([2, 3]),
scs.csr_matrix([[2], [3]]),
scs.csr_matrix([3])]
for init_elem in sparse_list:
self.logger.info(init_elem)
array = CArray(init_elem)
self.assertTrue(array.issparse)
self.assert_array_equal(array.todense(), init_elem.todense())
self.assertEqual(array.shape, init_elem.shape)
self.assertEqual(array.input_shape, init_elem.shape)
def test_save_load(self):
self.logger.info("UNITTEST - CDense - save/load matrix")
test_file = fm.join(fm.abspath(__file__), 'test.txt')
# Cleaning test file
try:
fm.remove_file(test_file)
except (OSError, IOError) as e:
if e.errno != 2:
raise e
a = CDense().zeros((1000, 1000))
with self.timer():
a.save(test_file)
with self.timer():
b = CDense().load(test_file,
startrow=100,
cols=CDense(np.arange(0, 100)))
self.assertFalse((a[100:, 0:100] != b).any())
self.logger.info("UNITTEST - CDense - save/load vector")
a = CDense().zeros(1000, dtype=int)
with self.timer():
a.save(test_file, overwrite=True)
with self.timer():
b = CDense().load(test_file,
cols=list(range(100, 1000)),
dtype=int).ravel()
self.assertFalse((a[0, 100] != b).any())
if np.__version__ < '1.18':
with self.assertRaises(IndexError) as e:
CDense().load(test_file, startrow=10)
self.logger.info("Expected error: {:}".format(e.exception))
else:
with self.logger.catch_warnings():
self.logger.filterwarnings(
"ignore", message="genfromtxt: Empty input file")
a = CDense().load(test_file, startrow=10)
self.assertEqual(a.size, 0)
self.logger.info("UNITTEST - CDense - save/load row vector")
a = CDense().zeros((1, 1000))
with self.timer():
a.save(test_file, overwrite=True)
with self.timer():
b = CDense().load(test_file, cols=CDense.arange(100, 1000))
self.assertFalse((a[:, 100:] != b).any())
# For some reasons np.genfromtxt does not close the file here
# Let's handle the resource warning about unclosed file
with self.logger.catch_warnings():
self.logger.filterwarnings("ignore", message="unclosed file")
if np.__version__ < '1.18':
with self.assertRaises(IndexError) as e:
CDense().load(test_file, startrow=10)
self.logger.info("Expected error: {:}".format(e.exception))
else:
self.logger.filterwarnings(
"ignore", message="genfromtxt: Empty input file")
a = CDense().load(test_file, startrow=10)
self.assertEqual(a.size, 0)
self.logger.info("UNITTEST - CDense - save/load negative vector")
a = -CDense().zeros(1000)
a.save(test_file, overwrite=True)
with open(test_file, mode='at+') as fhandle:
with self.timer():
a.save(fhandle, overwrite=True)
b = CDense().load(test_file)
# Simulating double save \w append
a = a.atleast_2d().append(a.atleast_2d(), axis=0)
self.assertFalse((a != b).any())
a = CDense(['a', 'b'])
with self.timer():
a.save(test_file, overwrite=True)
b = CDense().load(test_file, dtype=str).ravel()
self.assertFalse((a != b).any())
# Cleaning test file
try:
fm.remove_file(test_file)
except (OSError, IOError) as e:
if e.errno != 2:
raise e
def test_init_array(self):
"""Test CArray initialization using arrays."""
self.logger.info("Initializing CArray with another CArray...")
arrays_list = [
CArray([[2, 3], [22, 33]]),
CArray([2, 3]),
CArray([[2], [3]]),
CArray(3)
]
for init_elem in arrays_list:
array = CArray(init_elem)
self.assertTrue(init_elem.issparse == array.issparse)
self.assertFalse((array != init_elem).any())
self.logger.info("Initializing CArray with a CDense or an ndarray...")
dense_list = [
CDense([[2, 3], [22, 33]]),
CDense([2, 3]),
CDense([[2], [3]]),
CDense([3]),
np.array([[2, 3], [22, 33]]),
np.array([2, 3]),
np.array([[2], [3]]),
np.array([3])
]
for init_elem in dense_list:
array = CArray(init_elem)
self.assertTrue(array.isdense)
self.assertTrue(array.shape == init_elem.shape)
self.logger.info("Initializing CArray with a sparse CArray...")
sparse_list = [
CArray([[2, 3], [22, 33]], tosparse=True),
CArray([2, 3], tosparse=True),
CArray([[2], [3]], tosparse=True),
CArray(3, tosparse=True)
]
for init_elem in sparse_list:
array = CArray(init_elem)
self.assertTrue(array.issparse)
self.assertFalse((array != init_elem).any())
self.logger.info("Initializing CArray with a CSparse or csr_matrix...")
sparse_list = [
CSparse([[2, 3], [22, 33]]),
CSparse([2, 3]),
CSparse([[2], [3]]),
CSparse([3]),
scs.csr_matrix([[2, 3], [22, 33]]),
scs.csr_matrix([2, 3]),
scs.csr_matrix([[2], [3]]),
scs.csr_matrix([3])
]
for init_elem in sparse_list:
array = CArray(init_elem)
self.assertTrue(array.issparse)
self.assertTrue(array.shape == init_elem.shape)