Question

我根据我选择的主题使用TextBlob的NaiveBayesclassifier进行文本分析。

数据量巨大（约3000个条目）。

虽然我能够得到一个结果，但是如果不再调用该函数并等待数小时直到处理完成，我将无法将其保存以备将来使用。

我尝试通过以下方法进行酸洗

ab = NaiveBayesClassifier(data)

import pickle

object = ab
file = open('f.obj','w') #tried to use 'a' in place of 'w' ie. append
pickle.dump(object,file)

我收到了一个错误，如下所示：

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Python27\lib\pickle.py", line 1370, in dump
    Pickler(file, protocol).dump(obj)
  File "C:\Python27\lib\pickle.py", line 224, in dump
    self.save(obj)
  File "C:\Python27\lib\pickle.py", line 331, in save
    self.save_reduce(obj=obj, *rv)
  File "C:\Python27\lib\pickle.py", line 419, in save_reduce
    save(state)
  File "C:\Python27\lib\pickle.py", line 286, in save
    f(self, obj) # Call unbound method with explicit self
  File "C:\Python27\lib\pickle.py", line 649, in save_dict
    self._batch_setitems(obj.iteritems())
  File "C:\Python27\lib\pickle.py", line 663, in _batch_setitems
    save(v)
  File "C:\Python27\lib\pickle.py", line 286, in save
    f(self, obj) # Call unbound method with explicit self
  File "C:\Python27\lib\pickle.py", line 600, in save_list
    self._batch_appends(iter(obj))
  File "C:\Python27\lib\pickle.py", line 615, in _batch_appends
    save(x)
  File "C:\Python27\lib\pickle.py", line 286, in save
    f(self, obj) # Call unbound method with explicit self
  File "C:\Python27\lib\pickle.py", line 562, in save_tuple
    save(element)
  File "C:\Python27\lib\pickle.py", line 286, in save
    f(self, obj) # Call unbound method with explicit self
  File "C:\Python27\lib\pickle.py", line 649, in save_dict
    self._batch_setitems(obj.iteritems())
  File "C:\Python27\lib\pickle.py", line 662, in _batch_setitems
    save(k)
  File "C:\Python27\lib\pickle.py", line 286, in save
    f(self, obj) # Call unbound method with explicit self
  File "C:\Python27\lib\pickle.py", line 501, in save_unicode
    self.memoize(obj)
  File "C:\Python27\lib\pickle.py", line 247, in memoize
    self.memo[id(obj)] = memo_len, obj
MemoryError

我也试过过sPickle，但也导致了错误，例如：

#saving object with function sPickle.s_dump
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Python27\lib\site-packages\sPickle.py", line 22, in s_dump
    for elt in iterable_to_pickle:
TypeError: 'NaiveBayesClassifier' object is not iterable

#saving object with function sPickle.s_dump_elt
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Python27\lib\site-packages\sPickle.py", line 28, in s_dump_elt
    pickled_elt_str = dumps(elt_to_pickle)
MemoryError: out of memory

有谁能告诉我要保存对象需要做什么？

或者无论如何保存分类器的结果以供将来使用？

Answer 1

我自己解决了这个问题。

首先使用 64位版本的Python （适用于2.6到3.4的所有版本）

64位版本解决了所有内存问题

使用cPickle

import cPickle as pickle

其次打开你的文件

file = open('file_name.pickle','wb') #same as what Robert said in the above post

在文件上写入对象

pickle.dump(object,file)

你的对象将被转储到一个文件中。但你必须检查你的对象使用了什么内存。 pickleing也占用了内存空间，因此至少有25％的内存可用于被腌制的对象

对我来说，我的笔记本电脑有一个8 GB的RAM，所以内存足以只有一个对象。

（我的分类器很重，有3000个字符串实例，每个字符串包含大约15-30个单词的句子。情绪/主题的数量是22个。）

因此，如果您的笔记本电脑死锁（或者，一般而言，停止工作），那么您可能需要关闭电源并重新开始并尝试使用较小的号码。实例或更少的没有。情感/主题。

在这里，cPickle是非常有用的bcz它比任何其他的腌制模块快得多，并且我建议使用它。

Answer 2

您需要将“wb”用于二进制格式：

file = open('f.obj','wb')

Answer 3

对于Python> 3.0，似乎cPickle不再存在，但是默认的pickle可以完成工作，只需确保使用适合您的python安装的协议即可。对于python> 3.4，请使用以下代码：

import pickle
with open(r"blobClassifier.pickle",'wb') as file:
    pickle.dump(cl_Title, file, protocol=pickle.HIGHEST_PROTOCOL,fix_imports=False)

如何保存分类器textblob NaiveBayesClassifier的结果？

3 个答案: