Suppress warnings does not work

[CBrauer]

Please tell me how to suppress warnings in my Jupyter notebook.
I tried:

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
import warnings
warnings.filterwarnings("ignore")

feature_names = ['BoxRatio', 'Thrust', 'Acceleration', 'Velocity', 'OnBalRun', 'vwapGain', 'Expect', 'Trin']
response_name = ['Altitude']

df_train = pd.read_csv("/HedgeTools/Datasets/rocket-train-classify.csv")

for name in feature_names:
    print('feature name: ', name)
    X_train = df_train[name].values
    f = Fitter(X_train)
    f.fit(progress=True)
    f.summary(plot=True)
    print('best transform: ', f.get_best())

and I'm getting

WARNING:root:SKIPPED alpha distribution (taking more than 30 seconds)
WARNING:root:SKIPPED beta distribution (taking more than 30 seconds)
WARNING:root:SKIPPED arcsine distribution (taking more than 30 seconds)
WARNING:root:SKIPPED anglit distribution (taking more than 30 seconds)
WARNING:root:SKIPPED argus distribution (taking more than 30 seconds)
WARNING:root:SKIPPED betaprime distribution (taking more than 30 seconds)
WARNING:root:SKIPPED bradford distribution (taking more than 30 seconds)
WARNING:root:SKIPPED burr distribution (taking more than 30 seconds)
WARNING:root:SKIPPED burr12 distribution (taking more than 30 seconds)
WARNING:root:SKIPPED chi2 distribution (taking more than 30 seconds)

Charles

[meetsaiya]

There's an attribute with which can increase the run time. It's bydefault set to 30. Kindly check documentation. I believe if you have big data set or its computationally intensive then you can change that. However, I believe it must be set some value because if the curve doesn't really fit it runs into an open ended diverging fit which make computers angry.

[NSGregory]

This can be addressed using the logging module (the messages are generated by the logging module). The error messages are generated from the "root" logger, which can be accessed by:

logging.getLogger()

You can change the threshold for the root logger by:

logging.getLogger().setLevel(logging.CRITICAL)

Where the parameter for setLevel has several values which can be read about in the logger manual. If you enter a string as a parameter for getLogger(), it will get the associated logger. It seems that fitter does not have a 'fitter' logger, but rather uses root, which is why that parameter is left blank in this example.

[ghayward]

For me it helped out first try raising the the timeout = ## argument in the Fitter() method. See if that works... but if it never converges, and just can't solve, then I'm less sure what to do.