While I iterate within a for loop I continually receive the same warning, which I want to suppress. The warning reads:
C:\Users\Nick Alexander\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\preprocessing\data.py:193: UserWarning: Numerical issues were encountered when scaling the data and might not be solved. The standard deviation of the data is probably very close to 0. warnings.warn("Numerical issues were encountered "
The code that is producing the warning is as follows:
def monthly_standardize(cols, df_train, df_train_grouped, df_val, df_val_grouped, df_test, df_test_grouped):
# Disable the SettingWithCopyWarning warning
pd.options.mode.chained_assignment = None
for c in cols:
df_train[c] = df_train_grouped[c].transform(lambda x: scale(x.astype(float)))
df_val[c] = df_val_grouped[c].transform(lambda x: scale(x.astype(float)))
df_test[c] = df_test_grouped[c].transform(lambda x: scale(x.astype(float)))
return df_train, df_val, df_test
I am already disabling one warning. I don't want to disable all warnings, I just want to disable this warning. I am using python 3.7 and sklearn version 0.0
Use of @SuppressWarnings is to suppress or ignore warnings coming from the compiler, i.e., the compiler will ignore warnings if any for that piece of code. 1. @SuppressWarnings("unchecked") public class Calculator { } - Here, it will ignore all unchecked warnings coming from that class.
Implementation for suppression of deprecation in Tensorflow:- where:- 0 = all messages are logged. 1= INFO logs are removed. 2 = INFO with WARNINGS is removed.
Try this at the beginning of the script to ignore specific warnings:
import warnings
warnings.filterwarnings("ignore", message="Numerical issues were encountered ")
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