Source: scikit-learn
Version: 1.1.2+dfsg-7
Severity: serious
Tags: ftbfs
User: debian-pyt...@lists.debian.org
Usertags: python3.11
Hi Maintainer
scikit-learn FTBFS with Python 3.11 as a supported version. I've
copied what I hope is the relevant part of the log below.
Regards
Graham
==================================== ERRORS ====================================
_________________ ERROR at setup of test_load_empty_lfw_people _________________
def setup_module():
"""Test fixture run once and common to all tests of this module"""
Image = pytest.importorskip("PIL.Image")
global SCIKIT_LEARN_DATA, SCIKIT_LEARN_EMPTY_DATA, LFW_HOME
SCIKIT_LEARN_DATA = tempfile.mkdtemp(prefix="scikit_learn_lfw_test_")
LFW_HOME = os.path.join(SCIKIT_LEARN_DATA, "lfw_home")
SCIKIT_LEARN_EMPTY_DATA =
tempfile.mkdtemp(prefix="scikit_learn_empty_test_")
if not os.path.exists(LFW_HOME):
os.makedirs(LFW_HOME)
random_state = random.Random(42)
np_rng = np.random.RandomState(42)
# generate some random jpeg files for each person
counts = {}
for name in FAKE_NAMES:
folder_name = os.path.join(LFW_HOME, "lfw_funneled", name)
if not os.path.exists(folder_name):
os.makedirs(folder_name)
n_faces = np_rng.randint(1, 5)
counts[name] = n_faces
for i in range(n_faces):
file_path = os.path.join(folder_name, name + "_%04d.jpg" % i)
uniface = np_rng.randint(0, 255, size=(250, 250, 3))
img = Image.fromarray(uniface.astype(np.uint8))
img.save(file_path)
# add some random file pollution to test robustness
with open(os.path.join(LFW_HOME, "lfw_funneled", ".test.swp"),
"wb") as f:
f.write(b"Text file to be ignored by the dataset loader.")
# generate some pairing metadata files using the same format as LFW
with open(os.path.join(LFW_HOME, "pairsDevTrain.txt"), "wb") as f:
f.write(b"10\n")
more_than_two = [name for name, count in counts.items() if
count >= 2]
for i in range(5):
name = random_state.choice(more_than_two)
first, second = random_state.sample(range(counts[name]), 2)
f.write(("%s\t%d\t%d\n" % (name, first, second)).encode())
for i in range(5):
first_name, second_name = random_state.sample(FAKE_NAMES, 2)
> first_index = random_state.choice(np.arange(counts[first_name]))
sklearn/datasets/tests/test_lfw.py:87:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <random.Random object at 0x6d1b4e0>, seq = array([0, 1, 2])
def choice(self, seq):
"""Choose a random element from a non-empty sequence."""
> if not seq:
E ValueError: The truth value of an array with more than one
element is ambiguous. Use a.any() or a.all()
/usr/lib/python3.11/random.py:369: ValueError
_________________ ERROR at setup of test_load_fake_lfw_people __________________
def setup_module():
"""Test fixture run once and common to all tests of this module"""
Image = pytest.importorskip("PIL.Image")
global SCIKIT_LEARN_DATA, SCIKIT_LEARN_EMPTY_DATA, LFW_HOME
SCIKIT_LEARN_DATA = tempfile.mkdtemp(prefix="scikit_learn_lfw_test_")
LFW_HOME = os.path.join(SCIKIT_LEARN_DATA, "lfw_home")
SCIKIT_LEARN_EMPTY_DATA =
tempfile.mkdtemp(prefix="scikit_learn_empty_test_")
if not os.path.exists(LFW_HOME):
os.makedirs(LFW_HOME)
random_state = random.Random(42)
np_rng = np.random.RandomState(42)
# generate some random jpeg files for each person
counts = {}
for name in FAKE_NAMES:
folder_name = os.path.join(LFW_HOME, "lfw_funneled", name)
if not os.path.exists(folder_name):
os.makedirs(folder_name)
n_faces = np_rng.randint(1, 5)
counts[name] = n_faces
for i in range(n_faces):
file_path = os.path.join(folder_name, name + "_%04d.jpg" % i)
uniface = np_rng.randint(0, 255, size=(250, 250, 3))
img = Image.fromarray(uniface.astype(np.uint8))
img.save(file_path)
# add some random file pollution to test robustness
with open(os.path.join(LFW_HOME, "lfw_funneled", ".test.swp"),
"wb") as f:
f.write(b"Text file to be ignored by the dataset loader.")
# generate some pairing metadata files using the same format as LFW
with open(os.path.join(LFW_HOME, "pairsDevTrain.txt"), "wb") as f:
f.write(b"10\n")
more_than_two = [name for name, count in counts.items() if
count >= 2]
for i in range(5):
name = random_state.choice(more_than_two)
first, second = random_state.sample(range(counts[name]), 2)
f.write(("%s\t%d\t%d\n" % (name, first, second)).encode())
for i in range(5):
first_name, second_name = random_state.sample(FAKE_NAMES, 2)
> first_index = random_state.choice(np.arange(counts[first_name]))
sklearn/datasets/tests/test_lfw.py:87:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <random.Random object at 0x6d1b4e0>, seq = array([0, 1, 2])
def choice(self, seq):
"""Choose a random element from a non-empty sequence."""
> if not seq:
E ValueError: The truth value of an array with more than one
element is ambiguous. Use a.any() or a.all()
/usr/lib/python3.11/random.py:369: ValueError
_________ ERROR at setup of test_load_fake_lfw_people_too_restrictive __________
def setup_module():
"""Test fixture run once and common to all tests of this module"""
Image = pytest.importorskip("PIL.Image")
global SCIKIT_LEARN_DATA, SCIKIT_LEARN_EMPTY_DATA, LFW_HOME
SCIKIT_LEARN_DATA = tempfile.mkdtemp(prefix="scikit_learn_lfw_test_")
LFW_HOME = os.path.join(SCIKIT_LEARN_DATA, "lfw_home")
SCIKIT_LEARN_EMPTY_DATA =
tempfile.mkdtemp(prefix="scikit_learn_empty_test_")
if not os.path.exists(LFW_HOME):
os.makedirs(LFW_HOME)
random_state = random.Random(42)
np_rng = np.random.RandomState(42)
# generate some random jpeg files for each person
counts = {}
for name in FAKE_NAMES:
folder_name = os.path.join(LFW_HOME, "lfw_funneled", name)
if not os.path.exists(folder_name):
os.makedirs(folder_name)
n_faces = np_rng.randint(1, 5)
counts[name] = n_faces
for i in range(n_faces):
file_path = os.path.join(folder_name, name + "_%04d.jpg" % i)
uniface = np_rng.randint(0, 255, size=(250, 250, 3))
img = Image.fromarray(uniface.astype(np.uint8))
img.save(file_path)
# add some random file pollution to test robustness
with open(os.path.join(LFW_HOME, "lfw_funneled", ".test.swp"),
"wb") as f:
f.write(b"Text file to be ignored by the dataset loader.")
# generate some pairing metadata files using the same format as LFW
with open(os.path.join(LFW_HOME, "pairsDevTrain.txt"), "wb") as f:
f.write(b"10\n")
more_than_two = [name for name, count in counts.items() if
count >= 2]
for i in range(5):
name = random_state.choice(more_than_two)
first, second = random_state.sample(range(counts[name]), 2)
f.write(("%s\t%d\t%d\n" % (name, first, second)).encode())
for i in range(5):
first_name, second_name = random_state.sample(FAKE_NAMES, 2)
> first_index = random_state.choice(np.arange(counts[first_name]))
sklearn/datasets/tests/test_lfw.py:87:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <random.Random object at 0x6d1b4e0>, seq = array([0, 1, 2])
def choice(self, seq):
"""Choose a random element from a non-empty sequence."""
> if not seq:
E ValueError: The truth value of an array with more than one
element is ambiguous. Use a.any() or a.all()
/usr/lib/python3.11/random.py:369: ValueError
_________________ ERROR at setup of test_load_empty_lfw_pairs __________________
def setup_module():
"""Test fixture run once and common to all tests of this module"""
Image = pytest.importorskip("PIL.Image")
global SCIKIT_LEARN_DATA, SCIKIT_LEARN_EMPTY_DATA, LFW_HOME
SCIKIT_LEARN_DATA = tempfile.mkdtemp(prefix="scikit_learn_lfw_test_")
LFW_HOME = os.path.join(SCIKIT_LEARN_DATA, "lfw_home")
SCIKIT_LEARN_EMPTY_DATA =
tempfile.mkdtemp(prefix="scikit_learn_empty_test_")
if not os.path.exists(LFW_HOME):
os.makedirs(LFW_HOME)
random_state = random.Random(42)
np_rng = np.random.RandomState(42)
# generate some random jpeg files for each person
counts = {}
for name in FAKE_NAMES:
folder_name = os.path.join(LFW_HOME, "lfw_funneled", name)
if not os.path.exists(folder_name):
os.makedirs(folder_name)
n_faces = np_rng.randint(1, 5)
counts[name] = n_faces
for i in range(n_faces):
file_path = os.path.join(folder_name, name + "_%04d.jpg" % i)
uniface = np_rng.randint(0, 255, size=(250, 250, 3))
img = Image.fromarray(uniface.astype(np.uint8))
img.save(file_path)
# add some random file pollution to test robustness
with open(os.path.join(LFW_HOME, "lfw_funneled", ".test.swp"),
"wb") as f:
f.write(b"Text file to be ignored by the dataset loader.")
# generate some pairing metadata files using the same format as LFW
with open(os.path.join(LFW_HOME, "pairsDevTrain.txt"), "wb") as f:
f.write(b"10\n")
more_than_two = [name for name, count in counts.items() if
count >= 2]
for i in range(5):
name = random_state.choice(more_than_two)
first, second = random_state.sample(range(counts[name]), 2)
f.write(("%s\t%d\t%d\n" % (name, first, second)).encode())
for i in range(5):
first_name, second_name = random_state.sample(FAKE_NAMES, 2)
> first_index = random_state.choice(np.arange(counts[first_name]))
sklearn/datasets/tests/test_lfw.py:87:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <random.Random object at 0x6d1b4e0>, seq = array([0, 1, 2])
def choice(self, seq):
"""Choose a random element from a non-empty sequence."""
> if not seq:
E ValueError: The truth value of an array with more than one
element is ambiguous. Use a.any() or a.all()
/usr/lib/python3.11/random.py:369: ValueError
__________________ ERROR at setup of test_load_fake_lfw_pairs __________________
def setup_module():
"""Test fixture run once and common to all tests of this module"""
Image = pytest.importorskip("PIL.Image")
global SCIKIT_LEARN_DATA, SCIKIT_LEARN_EMPTY_DATA, LFW_HOME
SCIKIT_LEARN_DATA = tempfile.mkdtemp(prefix="scikit_learn_lfw_test_")
LFW_HOME = os.path.join(SCIKIT_LEARN_DATA, "lfw_home")
SCIKIT_LEARN_EMPTY_DATA =
tempfile.mkdtemp(prefix="scikit_learn_empty_test_")
if not os.path.exists(LFW_HOME):
os.makedirs(LFW_HOME)
random_state = random.Random(42)
np_rng = np.random.RandomState(42)
# generate some random jpeg files for each person
counts = {}
for name in FAKE_NAMES:
folder_name = os.path.join(LFW_HOME, "lfw_funneled", name)
if not os.path.exists(folder_name):
os.makedirs(folder_name)
n_faces = np_rng.randint(1, 5)
counts[name] = n_faces
for i in range(n_faces):
file_path = os.path.join(folder_name, name + "_%04d.jpg" % i)
uniface = np_rng.randint(0, 255, size=(250, 250, 3))
img = Image.fromarray(uniface.astype(np.uint8))
img.save(file_path)
# add some random file pollution to test robustness
with open(os.path.join(LFW_HOME, "lfw_funneled", ".test.swp"),
"wb") as f:
f.write(b"Text file to be ignored by the dataset loader.")
# generate some pairing metadata files using the same format as LFW
with open(os.path.join(LFW_HOME, "pairsDevTrain.txt"), "wb") as f:
f.write(b"10\n")
more_than_two = [name for name, count in counts.items() if
count >= 2]
for i in range(5):
name = random_state.choice(more_than_two)
first, second = random_state.sample(range(counts[name]), 2)
f.write(("%s\t%d\t%d\n" % (name, first, second)).encode())
for i in range(5):
first_name, second_name = random_state.sample(FAKE_NAMES, 2)
> first_index = random_state.choice(np.arange(counts[first_name]))
sklearn/datasets/tests/test_lfw.py:87:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <random.Random object at 0x6d1b4e0>, seq = array([0, 1, 2])
def choice(self, seq):
"""Choose a random element from a non-empty sequence."""
> if not seq:
E ValueError: The truth value of an array with more than one
element is ambiguous. Use a.any() or a.all()
/usr/lib/python3.11/random.py:369: ValueError
