我用Scikit学习建立了Predictive模型。我已经使用Flask，joblib部署了这个模型。每当我预测带有加载模型的新传入请求时，它在控制台上的打印消息都会显示核心上的总时间。
现在，我要禁用此打印件。当模型预测新的传入数据时，如何抑制给定的消息。
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答案 0 :(得分：3)
使用sklearn.linear_model.LinearRegression时我有类似的抱怨。在这个方法中，我可以在.fit（）方法或构造函数中找到 no 详细参数。所以python代码：
import numpy as np
from sklearn.linear_model import LinearRegression as LR
lr = LR()
y = np.random.rand(100) + 0.01 * np.arange(100) # noisy line to fit
x = np.arange(100)
x = np.reshape(y,[100,1])
lr.fit(x,y)
我得到令人讨厌的输出：
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
因为fit（）方法返回指向自身的指针，并且此文本表示该对象。抑制此问题的一种简单方法是修改上述代码：
a = lr.fit(x,y)
从而将指针指向某个变量而不是屏幕。
答案 1 :(得分：1)
将Last login: Mon Jul 6 20:27:54 on ttys000
cd '/Users/abc/Desktop/' && '/usr/bin/pythonw' '/Users/abc/Desktop/autoTag.py' && echo Exit status: $? && exit 1
abc-MacBook-Pro:~ abc$ cd '/Users/abc/Desktop/' && '/usr/bin/pythonw' '/Users/abc/Desktop/autoTag.py' && echo Exit status: $? && exit 1
Traceback (most recent call last):
File "/Users/abc/Desktop/autoTag.py", line 6, in <module>
response = urllib2.urlopen(request, json.dumps(input))
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 127, in urlopen
return _opener.open(url, data, timeout)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 410, in open
response = meth(req, response)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 523, in http_response
'http', request, response, code, msg, hdrs)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 448, in error
return self._call_chain(*args)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 382, in _call_chain
result = func(*args)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 531, in http_error_default
raise HTTPError(req.get_full_url(), code, msg, hdrs, fp)
urllib2.HTTPError: HTTP Error 401: Unauthorized
参数更改为verbose。
verbose：int，optional：详细级别：如果非零，则进度 邮件已打印。超过50，输出被发送到stdout。该 消息的频率随着详细程度而增加。如果它 超过10个，报告所有迭代。
例如使用SVM模型
False答案 2 :(得分：1)
如果由于verbose = True在模型的构造函数中而产生了不必要的输出，则可以稍后将其更改为成员变量：
from sklearn.some_category import SomeModel as M
m = M(verbose=True)
m.fit(x, y) # has output
m.verbose = False
m.predict(x) # no output