def test_not_an_array_array_function():
not_array = _NotAnArray(np.ones(10))
msg = "Don't want to call array_function sum!"
with raises(TypeError, match=msg):
np.sum(not_array)
# always returns True
assert np.may_share_memory(not_array, None)
def test_not_an_array_array_function():
if np_version < parse_version('1.17'):
raise SkipTest("array_function protocol not supported in numpy <1.17")
not_array = _NotAnArray(np.ones(10))
msg = "Don't want to call array_function sum!"
assert_raises_regex(TypeError, msg, np.sum, not_array)
# always returns True
assert np.may_share_memory(not_array, None)
def test_check_array():
# accept_sparse == False
# raise error on sparse inputs
X = [[1, 2], [3, 4]]
X_csr = sp.csr_matrix(X)
assert_raises(TypeError, check_array, X_csr)
# ensure_2d=False
X_array = check_array([0, 1, 2], ensure_2d=False)
assert X_array.ndim == 1
# ensure_2d=True with 1d array
assert_raise_message(ValueError,
'Expected 2D array, got 1D array instead',
check_array, [0, 1, 2],
ensure_2d=True)
# ensure_2d=True with scalar array
assert_raise_message(ValueError,
'Expected 2D array, got scalar array instead',
check_array,
10,
ensure_2d=True)
# don't allow ndim > 3
X_ndim = np.arange(8).reshape(2, 2, 2)
assert_raises(ValueError, check_array, X_ndim)
check_array(X_ndim, allow_nd=True) # doesn't raise
# dtype and order enforcement.
X_C = np.arange(4).reshape(2, 2).copy("C")
X_F = X_C.copy("F")
X_int = X_C.astype(np.int)
X_float = X_C.astype(np.float)
Xs = [X_C, X_F, X_int, X_float]
dtypes = [np.int32, np.int, np.float, np.float32, None, np.bool, object]
orders = ['C', 'F', None]
copys = [True, False]
for X, dtype, order, copy in product(Xs, dtypes, orders, copys):
X_checked = check_array(X, dtype=dtype, order=order, copy=copy)
if dtype is not None:
assert X_checked.dtype == dtype
else:
assert X_checked.dtype == X.dtype
if order == 'C':
assert X_checked.flags['C_CONTIGUOUS']
assert not X_checked.flags['F_CONTIGUOUS']
elif order == 'F':
assert X_checked.flags['F_CONTIGUOUS']
assert not X_checked.flags['C_CONTIGUOUS']
if copy:
assert X is not X_checked
else:
# doesn't copy if it was already good
if (X.dtype == X_checked.dtype and X_checked.flags['C_CONTIGUOUS']
== X.flags['C_CONTIGUOUS']
and X_checked.flags['F_CONTIGUOUS']
== X.flags['F_CONTIGUOUS']):
assert X is X_checked
# allowed sparse != None
X_csc = sp.csc_matrix(X_C)
X_coo = X_csc.tocoo()
X_dok = X_csc.todok()
X_int = X_csc.astype(np.int)
X_float = X_csc.astype(np.float)
Xs = [X_csc, X_coo, X_dok, X_int, X_float]
accept_sparses = [['csr', 'coo'], ['coo', 'dok']]
for X, dtype, accept_sparse, copy in product(Xs, dtypes, accept_sparses,
copys):
with warnings.catch_warnings(record=True) as w:
X_checked = check_array(X,
dtype=dtype,
accept_sparse=accept_sparse,
copy=copy)
if (dtype is object or sp.isspmatrix_dok(X)) and len(w):
message = str(w[0].message)
messages = [
"object dtype is not supported by sparse matrices",
"Can't check dok sparse matrix for nan or inf."
]
assert message in messages
else:
assert len(w) == 0
if dtype is not None:
assert X_checked.dtype == dtype
else:
assert X_checked.dtype == X.dtype
if X.format in accept_sparse:
# no change if allowed
assert X.format == X_checked.format
else:
# got converted
assert X_checked.format == accept_sparse[0]
if copy:
assert X is not X_checked
else:
# doesn't copy if it was already good
if X.dtype == X_checked.dtype and X.format == X_checked.format:
assert X is X_checked
# other input formats
# convert lists to arrays
X_dense = check_array([[1, 2], [3, 4]])
assert isinstance(X_dense, np.ndarray)
# raise on too deep lists
assert_raises(ValueError, check_array, X_ndim.tolist())
check_array(X_ndim.tolist(), allow_nd=True) # doesn't raise
# convert weird stuff to arrays
X_no_array = _NotAnArray(X_dense)
result = check_array(X_no_array)
assert isinstance(result, np.ndarray)
# deprecation warning if string-like array with dtype="numeric"
expected_warn_regex = r"converted to decimal numbers if dtype='numeric'"
X_str = [['11', '12'], ['13', 'xx']]
for X in [X_str, np.array(X_str, dtype='U'), np.array(X_str, dtype='S')]:
with pytest.warns(FutureWarning, match=expected_warn_regex):
check_array(X, dtype="numeric")
# deprecation warning if byte-like array with dtype="numeric"
X_bytes = [[b'a', b'b'], [b'c', b'd']]
for X in [X_bytes, np.array(X_bytes, dtype='V1')]:
with pytest.warns(FutureWarning, match=expected_warn_regex):
check_array(X, dtype="numeric")
def test_check_array():
# accept_sparse == False
# raise error on sparse inputs
X = [[1, 2], [3, 4]]
X_csr = sp.csr_matrix(X)
with pytest.raises(TypeError):
check_array(X_csr)
# ensure_2d=False
X_array = check_array([0, 1, 2], ensure_2d=False)
assert X_array.ndim == 1
# ensure_2d=True with 1d array
with pytest.raises(ValueError,
match="Expected 2D array,"
" got 1D array instead"):
check_array([0, 1, 2], ensure_2d=True)
# ensure_2d=True with scalar array
with pytest.raises(ValueError,
match="Expected 2D array,"
" got scalar array instead"):
check_array(10, ensure_2d=True)
# don't allow ndim > 3
X_ndim = np.arange(8).reshape(2, 2, 2)
with pytest.raises(ValueError):
check_array(X_ndim)
check_array(X_ndim, allow_nd=True) # doesn't raise
# dtype and order enforcement.
X_C = np.arange(4).reshape(2, 2).copy("C")
X_F = X_C.copy("F")
X_int = X_C.astype(int)
X_float = X_C.astype(float)
Xs = [X_C, X_F, X_int, X_float]
dtypes = [np.int32, int, float, np.float32, None, bool, object]
orders = ['C', 'F', None]
copys = [True, False]
for X, dtype, order, copy in product(Xs, dtypes, orders, copys):
X_checked = check_array(X, dtype=dtype, order=order, copy=copy)
if dtype is not None:
assert X_checked.dtype == dtype
else:
assert X_checked.dtype == X.dtype
if order == 'C':
assert X_checked.flags['C_CONTIGUOUS']
assert not X_checked.flags['F_CONTIGUOUS']
elif order == 'F':
assert X_checked.flags['F_CONTIGUOUS']
assert not X_checked.flags['C_CONTIGUOUS']
if copy:
assert X is not X_checked
else:
# doesn't copy if it was already good
if (X.dtype == X_checked.dtype and X_checked.flags['C_CONTIGUOUS']
== X.flags['C_CONTIGUOUS']
and X_checked.flags['F_CONTIGUOUS']
== X.flags['F_CONTIGUOUS']):
assert X is X_checked
# allowed sparse != None
X_csc = sp.csc_matrix(X_C)
X_coo = X_csc.tocoo()
X_dok = X_csc.todok()
X_int = X_csc.astype(int)
X_float = X_csc.astype(float)
Xs = [X_csc, X_coo, X_dok, X_int, X_float]
accept_sparses = [['csr', 'coo'], ['coo', 'dok']]
for X, dtype, accept_sparse, copy in product(Xs, dtypes, accept_sparses,
copys):
with warnings.catch_warnings(record=True) as w:
X_checked = check_array(X,
dtype=dtype,
accept_sparse=accept_sparse,
copy=copy)
if (dtype is object or sp.isspmatrix_dok(X)) and len(w):
# XXX unreached code as of v0.22
message = str(w[0].message)
messages = [
"object dtype is not supported by sparse matrices",
"Can't check dok sparse matrix for nan or inf."
]
assert message in messages
else:
assert len(w) == 0
if dtype is not None:
assert X_checked.dtype == dtype
else:
assert X_checked.dtype == X.dtype
if X.format in accept_sparse:
# no change if allowed
assert X.format == X_checked.format
else:
# got converted
assert X_checked.format == accept_sparse[0]
if copy:
assert X is not X_checked
else:
# doesn't copy if it was already good
if X.dtype == X_checked.dtype and X.format == X_checked.format:
assert X is X_checked
# other input formats
# convert lists to arrays
X_dense = check_array([[1, 2], [3, 4]])
assert isinstance(X_dense, np.ndarray)
# raise on too deep lists
with pytest.raises(ValueError):
check_array(X_ndim.tolist())
check_array(X_ndim.tolist(), allow_nd=True) # doesn't raise
# convert weird stuff to arrays
X_no_array = _NotAnArray(X_dense)
result = check_array(X_no_array)
assert isinstance(result, np.ndarray)
csr_matrix(np.random.RandomState(42).randint(2, size=(10, 10))),
[[0, 1], [1, 0]],
[[0, 1]],
csr_matrix(np.array([[0, 1], [1, 0]])),
csr_matrix(np.array([[0, 1], [1, 0]], dtype=bool)),
csr_matrix(np.array([[0, 1], [1, 0]], dtype=np.int8)),
csr_matrix(np.array([[0, 1], [1, 0]], dtype=np.uint8)),
csr_matrix(np.array([[0, 1], [1, 0]], dtype=float)),
csr_matrix(np.array([[0, 1], [1, 0]], dtype=np.float32)),
csr_matrix(np.array([[0, 0], [0, 0]])),
csr_matrix(np.array([[0, 1]])),
# Only valid when data is dense
[[-1, 1], [1, -1]],
np.array([[-1, 1], [1, -1]]),
np.array([[-3, 3], [3, -3]]),
_NotAnArray(np.array([[-3, 3], [3, -3]])),
],
"multiclass": [
[1, 0, 2, 2, 1, 4, 2, 4, 4, 4],
np.array([1, 0, 2]),
np.array([1, 0, 2], dtype=np.int8),
np.array([1, 0, 2], dtype=np.uint8),
np.array([1, 0, 2], dtype=float),
np.array([1, 0, 2], dtype=np.float32),
np.array([[1], [0], [2]]),
_NotAnArray(np.array([1, 0, 2])),
[0, 1, 2],
["a", "b", "c"],
np.array(["a", "b", "c"]),
np.array(["a", "b", "c"], dtype=object),
np.array(["a", "b", "c"], dtype=object),
def test_check_array(setup):
# accept_sparse == False
# raise error on sparse inputs
X = [[1, 2], [3, 4]]
X_csr = sp.csr_matrix(X)
with pytest.raises(TypeError):
check_array(X_csr)
X_csr = mt.tensor(sp.csr_matrix(X))
with pytest.raises(TypeError):
check_array(X_csr)
# ensure_2d=False
X_array = check_array([0, 1, 2], ensure_2d=False)
assert X_array.ndim == 1
# ensure_2d=True with 1d array
assert_raise_message(ValueError,
'Expected 2D array, got 1D array instead',
check_array, [0, 1, 2],
ensure_2d=True)
assert_raise_message(ValueError,
'Expected 2D array, got 1D array instead',
check_array,
mt.tensor([0, 1, 2]),
ensure_2d=True)
# ensure_2d=True with scalar array
assert_raise_message(ValueError,
'Expected 2D array, got scalar array instead',
check_array,
10,
ensure_2d=True)
# don't allow ndim > 3
X_ndim = mt.arange(8).reshape(2, 2, 2)
with pytest.raises(ValueError):
check_array(X_ndim)
check_array(X_ndim, allow_nd=True) # doesn't raise
# dtype and order enforcement.
X_C = mt.arange(4).reshape(2, 2).copy("C")
X_F = X_C.copy("F")
X_int = X_C.astype(mt.int)
X_float = X_C.astype(mt.float)
Xs = [X_C, X_F, X_int, X_float]
dtypes = [mt.int32, mt.int, mt.float, mt.float32, None, mt.bool, object]
orders = ['C', 'F', None]
copy_flags = [True, False]
for X, dtype, order, copy in product(Xs, dtypes, orders, copy_flags):
X_checked = check_array(X,
dtype=dtype,
order=order,
copy=copy,
force_all_finite=False)
if dtype is not None:
assert X_checked.dtype == dtype
else:
assert X_checked.dtype == X.dtype
if order == 'C':
assert X_checked.flags['C_CONTIGUOUS']
assert not X_checked.flags['F_CONTIGUOUS']
elif order == 'F':
assert X_checked.flags['F_CONTIGUOUS']
assert not X_checked.flags['C_CONTIGUOUS']
if copy:
assert X is not X_checked
else:
# doesn't copy if it was already good
if (X.dtype == X_checked.dtype and X_checked.flags['C_CONTIGUOUS']
== X.flags['C_CONTIGUOUS']
and X_checked.flags['F_CONTIGUOUS']
== X.flags['F_CONTIGUOUS']):
assert X is X_checked
# other input formats
# convert lists to arrays
X_dense = check_array([[1, 2], [3, 4]])
assert isinstance(X_dense, Tensor)
# raise on too deep lists
with pytest.raises(ValueError):
check_array(X_ndim.to_numpy().tolist())
check_array(X_ndim.to_numpy().tolist(), allow_nd=True) # doesn't raise
# convert weird stuff to arrays
X_no_array = _NotAnArray(X_dense.to_numpy())
result = check_array(X_no_array)
assert isinstance(result, Tensor)
# deprecation warning if string-like array with dtype="numeric"
expected_warn_regex = r"converted to decimal numbers if dtype='numeric'"
X_str = [['11', '12'], ['13', 'xx']]
for X in [X_str, mt.array(X_str, dtype='U'), mt.array(X_str, dtype='S')]:
with pytest.warns(FutureWarning, match=expected_warn_regex):
check_array(X, dtype="numeric")
# deprecation warning if byte-like array with dtype="numeric"
X_bytes = [[b'a', b'b'], [b'c', b'd']]
for X in [X_bytes, mt.array(X_bytes, dtype='V1')]:
with pytest.warns(FutureWarning, match=expected_warn_regex):
check_array(X, dtype="numeric")
# test finite
X = [[1.0, np.nan], [2.0, 3.0]]
with pytest.raises(ValueError):
_ = check_array(X).execute()
csr_matrix(np.random.RandomState(42).randint(2, size=(10, 10))),
[[0, 1], [1, 0]],
[[0, 1]],
csr_matrix(np.array([[0, 1], [1, 0]])),
csr_matrix(np.array([[0, 1], [1, 0]], dtype=bool)),
csr_matrix(np.array([[0, 1], [1, 0]], dtype=np.int8)),
csr_matrix(np.array([[0, 1], [1, 0]], dtype=np.uint8)),
csr_matrix(np.array([[0, 1], [1, 0]], dtype=float)),
csr_matrix(np.array([[0, 1], [1, 0]], dtype=np.float32)),
csr_matrix(np.array([[0, 0], [0, 0]])),
csr_matrix(np.array([[0, 1]])),
# Only valid when data is dense
[[-1, 1], [1, -1]],
np.array([[-1, 1], [1, -1]]),
np.array([[-3, 3], [3, -3]]),
_NotAnArray(np.array([[-3, 3], [3, -3]])),
],
"multiclass": [
[1, 0, 2, 2, 1, 4, 2, 4, 4, 4],
np.array([1, 0, 2]),
np.array([1, 0, 2], dtype=np.int8),
np.array([1, 0, 2], dtype=np.uint8),
np.array([1, 0, 2], dtype=float),
np.array([1, 0, 2], dtype=np.float32),
np.array([[1], [0], [2]]),
_NotAnArray(np.array([1, 0, 2])),
[0, 1, 2],
["a", "b", "c"],
np.array(["a", "b", "c"]),
np.array(["a", "b", "c"], dtype=object),
np.array(["a", "b", "c"], dtype=object),
