如何保存Python NLTK对齐模型以供以后使用？

Question

在Python语言中，我使用NLTK's alignment module在平行文本之间创建单词对齐。对齐bitexts可能是一个耗时的过程，尤其是在大量语料库上完成时。如果有一天批量进行比对，并在以后使用这些比对，那就太好了。

from nltk import IBMModel1 as ibm
biverses = [list of AlignedSent objects]
model = ibm(biverses, 20)

with open(path + "eng-taq_model.txt", 'w') as f:
    f.write(model.train(biverses, 20))  // makes empty file

一旦创建了模型，我如何(1)将其保存到磁盘并(2)稍后重用它？

Answer 1

直接的答案是对其进行腌制，请参阅https://wiki.python.org/moin/UsingPickle

但是因为IBMModel1返回一个lambda函数，所以不可能用默认的pickle / cPickle (参见https://github.com/nltk/nltk/blob/develop/nltk/align/ibm1.py#L74和https://github.com/nltk/nltk/blob/develop/nltk/align/ibm1.py#L104)来pickle它。

所以我们将使用dill。首先，安装dill，请参阅Can Python pickle lambda functions?

$ pip install dill
$ python
>>> import dill as pickle

然后：

>>> import dill
>>> import dill as pickle
>>> from nltk.corpus import comtrans
>>> from nltk.align import IBMModel1
>>> bitexts = comtrans.aligned_sents()[:100]
>>> ibm = IBMModel1(bitexts, 20)
>>> with open('model1.pk', 'wb') as fout:
...     pickle.dump(ibm, fout)
...
>>> exit()

要使用酸洗模型，请执行以下操作：

>>> import dill as pickle
>>> from nltk.corpus import comtrans
>>> bitexts = comtrans.aligned_sents()[:100]
>>> with open('model1.pk', 'rb') as fin:
...     ibm = pickle.load(fin)
... 
>>> aligned_sent = ibm.align(bitexts[0])
>>> aligned_sent.words
['Wiederaufnahme', 'der', 'Sitzungsperiode']

如果您尝试pickle IBMModel1对象，它是一个lambda函数，您将得到以下结果：

>>> import cPickle as pickle
>>> from nltk.corpus import comtrans
>>> from nltk.align import IBMModel1
>>> bitexts = comtrans.aligned_sents()[:100]
>>> ibm = IBMModel1(bitexts, 20)
>>> with open('model1.pk', 'wb') as fout:
...     pickle.dump(ibm, fout)
... 
Traceback (most recent call last):
  File "<stdin>", line 2, in <module>
  File "/usr/lib/python2.7/copy_reg.py", line 70, in _reduce_ex
    raise TypeError, "can't pickle %s objects" % base.__name__
TypeError: can't pickle function objects

(注:以上代码片段来自NLTK版本3.0.0)

在使用NLTK3.0.0的python3中，您也将面临同样的问题，因为IBMModel1返回一个λ函数：

alvas@ubi:~$ python3
Python 3.4.0 (default, Apr 11 2014, 13:05:11) 
[GCC 4.8.2] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pickle
>>> from nltk.corpus import comtrans
>>> from nltk.align import IBMModel1
>>> bitexts = comtrans.aligned_sents()[:100]
>>> ibm = IBMModel1(bitexts, 20)
>>> with open('mode1.pk', 'wb') as fout:
...     pickle.dump(ibm, fout)
... 
Traceback (most recent call last):
  File "<stdin>", line 2, in <module>
_pickle.PicklingError: Can't pickle <function IBMModel1.train.<locals>.<lambda> at 0x7fa37cf9d620>: attribute lookup <lambda> on nltk.align.ibm1 failed'

>>> import dill
>>> with open('model1.pk', 'wb') as fout:
...     dill.dump(ibm, fout)
... 
>>> exit()

alvas@ubi:~$ python3
Python 3.4.0 (default, Apr 11 2014, 13:05:11) 
[GCC 4.8.2] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import dill
>>> from nltk.corpus import comtrans
>>> with open('model1.pk', 'rb') as fin:
...     ibm = dill.load(fin)
... 
>>> bitexts = comtrans.aligned_sents()[:100]
>>> aligned_sent = ibm.aligned(bitexts[0])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'IBMModel1' object has no attribute 'aligned'
>>> aligned_sent = ibm.align(bitexts[0])
>>> aligned_sent.words
['Wiederaufnahme', 'der', 'Sitzungsperiode']

(注:在python3中，pickle是cPickle，参见http://docs.pythonsprints.com/python3_porting/py-porting.html)

Answer 2

您讨论了保存对齐器模型，但您的问题似乎更多地是关于保存您已对齐的比特表达式：“如果有一天批量进行对齐，并在以后使用这些对齐，那就太好了。”我将回答这个问题。

在nltk环境中，使用类似语料库的资源的最佳方式是使用语料库阅读器访问它。NLTK没有提供语料库编写器，但是NLTK的AlignedCorpusReader支持的格式非常容易生成：(NLTK3版本)

model = ibm(biverses, 20)  # As in your question

out = open("folder/newalignedtext.txt", "w")
for pair in biverses:
    asent = model.align(pair)
    out.write(" ".join(asent.words)+"\n")
    out.write(" ".join(asent.mots)+"\n")
    out.write(str(asent.alignment)+"\n")

out.close()

就这样。您可以稍后重新加载并使用与comtrans语料库完全相同的对齐句子：

from nltk.corpus.reader import AlignedCorpusReader

mycorpus = AlignedCorpusReader(r"folder", r".*\.txt")
biverses_reloaded = mycorpus.aligned_sents()

正如您所看到的，您不需要对齐器对象本身。对齐的句子可以用语料库阅读器加载，除非你想研究嵌入的概率，否则对齐器本身是非常无用的。

评论：我不确定我是否会把对齐器对象称为“模型”。在NLTK2中，对齐器没有设置为对齐新文本--它甚至没有align()方法。在NLTK3中，函数align()可以对齐新文本，但只能在Python2中使用；在Python3中，它被破坏了，显然是因为比较不同类型的对象的规则收紧了。但是，如果您希望能够处理和重新加载校正器，我很乐意将其添加到我的答案中；根据我所看到的，可以使用普通的cPickle来完成这项工作。

Answer 3

如果您愿意，您可以将其存储为AlignedSent列表：

from nltk.align import IBMModel1 as IBM
from nltk.align import AlignedSent
import dill as pickle

biverses = [list of AlignedSent objects]
model = ibm(biverses, 20)

for sent in range(len(biverses)):
     biverses[sent].alignment = model.align(biverses[sent]).alignment

在此之后，您可以使用dill将其保存为泡菜：

with open('alignedtext.pk', 'wb') as arquive:
     pickle.dump(biverses, arquive)