def test_exceptions_inside_log_in_smbo(smbo_run_mock, backend, dask_client):
# Below importing and shutdown is a workaround, to make sure
# we reset the port to collect messages. Randomly, when running
# this test with multiple other test at the same time causes this
# test to fail. This resets the singletons of the logging class
import logging
logging.shutdown()
automl = autosklearn.automl.AutoML(
backend,
20,
5,
metric=accuracy,
dask_client=dask_client,
)
dataset_name = 'test_exceptions_inside_log'
# Create a custom exception to prevent other errors to slip in
class MyException(Exception):
pass
X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
# The first call is on dummy predictor failure
message = str(np.random.randint(100)) + '_run_smbo'
smbo_run_mock.side_effect = MyException(message)
with pytest.raises(MyException):
automl.fit(
X_train,
Y_train,
task=MULTICLASS_CLASSIFICATION,
dataset_name=dataset_name,
)
# make sure that the logfile was created
import shutil
shutil.copytree(backend.temporary_directory, '/tmp/trydebug')
logger_name = 'AutoML(%d):%s' % (1, dataset_name)
logfile = os.path.join(backend.temporary_directory, logger_name + '.log')
assert os.path.exists(logfile), automl._clean_logger()
with open(logfile) as f:
assert message in f.read(), automl._clean_logger()
# Speed up the closing after forced crash
automl._clean_logger()
def test_exceptions_inside_log_in_smbo(smbo_run_mock, backend, dask_client):
# Below importing and shutdown is a workaround, to make sure
# we reset the port to collect messages. Randomly, when running
# this test with multiple other test at the same time causes this
# test to fail. This resets the singletons of the logging class
import logging
logging.shutdown()
automl = autosklearn.automl.AutoML(
backend,
20,
5,
metric=accuracy,
dask_client=dask_client,
)
dataset_name = 'test_exceptions_inside_log'
# Create a custom exception to prevent other errors to slip in
class MyException(Exception):
pass
X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
# The first call is on dummy predictor failure
message = str(np.random.randint(100)) + '_run_smbo'
smbo_run_mock.side_effect = MyException(message)
with pytest.raises(MyException):
automl.fit(
X_train,
Y_train,
task=MULTICLASS_CLASSIFICATION,
dataset_name=dataset_name,
)
# make sure that the logfile was created
logger_name = 'AutoML(%d):%s' % (1, dataset_name)
logger = logging.getLogger(logger_name)
logfile = os.path.join(backend.temporary_directory, logger_name + '.log')
assert os.path.exists(logfile), print_debug_information(automl) + str(automl._clean_logger())
# Give some time for the error message to be printed in the
# log file
found_message = False
for incr_tolerance in range(5):
with open(logfile) as f:
lines = f.readlines()
if any(message in line for line in lines):
found_message = True
break
else:
time.sleep(incr_tolerance)
# Speed up the closing after forced crash
automl._clean_logger()
if not found_message:
pytest.fail("Did not find {} in the log file {} for logger {}/{}/{}".format(
message,
print_debug_information(automl),
vars(automl._logger.logger),
vars(logger),
vars(logging.getLogger())
))