抑制 python 警告

Question

当我在 for 循环中迭代时，我不断收到相同的警告，我想抑制它。 警告内容如下：

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 "

产生警告的代码如下：

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

我已经禁用了一个警告。 我不想禁用所有警告，我只想禁用此警告。 我正在使用 python 3.7 和 sklearn 版本 0.0

Answer 1

在脚本开头尝试此操作以忽略特定警告：

import warnings
warnings.filterwarnings("ignore", message="Numerical issues were encountered ")

Answer 2

import warnings
with warnings.catch_warnings():
    warnings.simplefilter('ignore')
    # code that produces a warning

warnings.catch_warnings()表示“无论warnings.方法在此块内运行，退出块时撤消它们”。

Answer 3

要忽略特定代码块：

import warnings

class IgnoreWarnings(object):
    def __init__(self, message):
        self.message = message
    
    def __enter__(self):
        warnings.filterwarnings("ignore", message=f".*{self.message}.*")
    
    def __exit__(self, *_):
        warnings.filterwarnings("default", message=f".*{self.message}.*")

with IgnoreWarnings("fish"):
    warnings.warn("here be fish")
    warnings.warn("here be dog")
warnings.warn("here were fish")

UserWarning: here be dog
UserWarning: here were fish

Answer 4

python contextlib 有一个上下文管理器： suppress

from contextlib import suppress

with suppress(UserWarning):
    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)))