Reputation: 1087
I have built a few off-the-shelf classifiers from sklearn
and there are some expected scenarios where I know the classifier is bound to perform badly and not predict anything correctly. The sklearn.svm package runs without an error but raises the following warning.
~/anaconda/lib/python3.5/site-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.
'precision', 'predicted', average, warn_for)
I wish to suppress this warning and instead replace with a message to stdout
, say for instance, "poor classifier performance"
.
Is there any way to suppress warnings
in general?
Upvotes: 0
Views: 437
Reputation: 26717
Suppressing all warnings is easy with -Wignore
(see warning flag docs)
The warnings
module can do some finer-tuning with filters (ignore just your warning type).
Capturing just your warning (assuming there isn't some API in the module to tweak it) and doing something special could be done using the warnings.catch_warnings
context manager and code adapted from "Testing Warnings":
import warnings
class MyWarning(Warning):
pass
def something():
warnings.warn("magic warning", MyWarning)
with warnings.catch_warnings(record=True) as w:
# Trigger a warning.
something()
# Verify some things
if ((len(w) == 1)
and issubclass(w[0].category, MyWarning)
and "magic" in str(w[-1].message)):
print('something magical')
Upvotes: 2