Scikit learn 隐藏随机搜索CV输出

我正在终端中执行pylint，以稍微清理一下python脚本。在我的脚本中，我还使用RandomizedSearchCV。我该怎么做才能使pylint的结果不显示随机搜索CV结果的不同组合？或者如何抑制RandomizedSearchCV的输出

下面是我的.py脚本中导致此问题的代码片段，以及我在终端中执行时看到的开始/结束的屏幕截图

LOGGER.info("Fine tune model and fit it (Model 2)")
# with warnings.catch_warnings():
#     warnings.filterwarnings("ignore")

new_model = RandomizedSearchCV(lr_alt, parameters, cv=4, n_iter=15)

# with warnings.catch_warnings():
#     warnings.filterwarnings("ignore")
new_model.fit(train_features_x, train_y)

无法加载图像，但以下是终端中的开始代码片段：

(env-stats404-w20-HW5) Franciscos-MacBook-Pro:FRANCISCO-AVALOS franciscoavalosjr$ pylint main.py 
************* Module main
main.py:69:0: C0103: Argument name "Product" doesn't conform to snake_case naming style (invalid-name)
main.py:80:4: R1705: Unnecessary "elif" after "return" (no-else-return)
main.py:89:75: W0108: Lambda may not be necessary (unnecessary-lambda)
Traceback (most recent call last):
  File "/Users/franciscoavalosjr/opt/anaconda3/envs/env-stats404-w20-HW5/bin/pylint", line 8, in <module>
    sys.exit(run_pylint())
  File "/Users/franciscoavalosjr/opt/anaconda3/envs/env-stats404-w20-HW5/lib/python3.7/site-packages/pylint/__init__.py", line 23, in run_pylint
    PylintRun(sys.argv[1:])

---

结果python不喜欢我分配新专栏的方式。修复方法是使用新形成的列创建一个新变量，而不是将其添加到我的dataframe中。前后代码如下：

原始代码： #DF_多数=子_数据[子_数据['balance']==0] #DF_MINORITY=子_数据[子_数据['balance']==1]

# NEW_MAJORITY_NUMBER = ((DF_MINORITY.shape[0]/0.075) - DF_MINORITY.shape[0])
# NEW_MAJORITY_NUMBER = int(round(NEW_MAJORITY_NUMBER))

# DF_MAJORITY_DOWNSAMPLED = resample(DF_MAJORITY, replace=False, n_samples=NEW_MAJORITY_NUMBER,
#                                    random_state=29)

# DF_DOWNSAMPLED = pd.concat([DF_MAJORITY_DOWNSAMPLED, DF_MINORITY])

新代码：

BALANCE = SUB_DATA.loc[:, 'Delivery Status'].apply(lambda x: classify_shipping(x))
BALANCE = pd.DataFrame(BALANCE)

DF_MAJORITY = BALANCE[BALANCE['Delivery Status'] == 0]
DF_MINORITY = BALANCE[BALANCE['Delivery Status'] == 1]


NEW_MAJORITY_NUMBER = ((DF_MINORITY.shape[0]/0.075) - DF_MINORITY.shape[0])
NEW_MAJORITY_NUMBER = int(round(NEW_MAJORITY_NUMBER))

DF_MAJORITY_DOWNSAMPLED = resample(DF_MAJORITY, replace=False,
                                   n_samples=NEW_MAJORITY_NUMBER, random_state=29)
DF_DOWNSAMPLED = pd.concat([DF_MAJORITY_DOWNSAMPLED, DF_MINORITY])

---

展示你的一些想法code@VaidøtasIvøška，我在这里包含了代码片段。如果这有帮助，你想隐藏什么？你们得到了一个递归错误，而且我也并没有看到随机搜索结果的组合，你们说有没有一种方法可以将递归代码从显示中删除？这就是我最终想要删除的。您应该修复递归错误，而不是隐藏它

BALANCE = SUB_DATA.loc[:, 'Delivery Status'].apply(lambda x: classify_shipping(x))
BALANCE = pd.DataFrame(BALANCE)

DF_MAJORITY = BALANCE[BALANCE['Delivery Status'] == 0]
DF_MINORITY = BALANCE[BALANCE['Delivery Status'] == 1]


NEW_MAJORITY_NUMBER = ((DF_MINORITY.shape[0]/0.075) - DF_MINORITY.shape[0])
NEW_MAJORITY_NUMBER = int(round(NEW_MAJORITY_NUMBER))

DF_MAJORITY_DOWNSAMPLED = resample(DF_MAJORITY, replace=False,
                                   n_samples=NEW_MAJORITY_NUMBER, random_state=29)
DF_DOWNSAMPLED = pd.concat([DF_MAJORITY_DOWNSAMPLED, DF_MINORITY])