def test_level_filter_not_matches(logger, caplog):
enable(logger, level='DEBUG', echo=False)
logger.addFilter(LevelFilter(logging.DEBUG))
# does *not* log message at level INFO > DEBUG
with caplog.at_level(logging.INFO, logger=logger.name):
logger.info('msg')
assert not caplog.text
def test_level_filter_matches(logger, caplog):
enable(logger, level='DEBUG', echo=False)
logger.addFilter(LevelFilter(logging.CRITICAL))
# does log message at level CRITICAL
with caplog.at_level(logging.CRITICAL, logger=logger.name):
logger.critical('msg')
assert caplog.text
#!/usr/bin/env python
if __name__ == '__main__':
from ballet.util.log import enable
from ballet.validation.main import validate
import ames
enable(level='DEBUG', echo=False)
validate(ames)
def test_logging_context(logger, caplog):
enable(logger, level='DEBUG', echo=False)
with caplog.at_level(logging.DEBUG, logger=logger.name):
with LoggingContext(logger, level='INFO'):
logger.debug('msg')
assert not caplog.text
def test_enable(logger, caplog, level):
with caplog.at_level(level, logger=logger.name):
enable(logger, level, echo=True)
assert 'enabled' in caplog.text
class RandomValue(BaseTransformer):
def fit(self, X, y=None):
return self
def transform(self, X):
return np.random.random(X.shape[0])
transformer = RandomValue()
feature = Feature(input=input, transformer=transformer)
if __name__ == "__main__":
from ballet.util.log import enable
from ballet.project import Project
from ballet.validation.main import _load_class
from ballet_predict_house_prices.features import build
from ballet_predict_house_prices.load_data import load_data
enable(level='INFO')
X_df, y_df = load_data()
out = build(X_df, y_df)
X_df, y, features = out.X_df, out.y, out.features
project = Project.from_path(".")
Accepter = _load_class(project, 'validation.feature_accepter')
accepter = Accepter(X_df, y, features, feature)
assert accepter.judge()
