我正在尝试将 nltk.tag.hmm.HiddenMarkovModelTagger 序列化为 pickle ,以便在需要时使用它而无需重新训练。但是,从 .pkl 加载后,我的 HMM 看起来未经训练。我的两个问题是:
- 我做错了什么?
- 序列化 HMM 是个好主意吗 什么时候有大数据集?
代码如下:
In [1]: import nltk
In [2]: from nltk.probability import *
In [3]: from nltk.util import unique_list
In [4]: import json
In [5]: with open('data.json') as data_file:
...: corpus = json.load(data_file)
...:
In [6]: corpus = [[tuple(l) for l in sentence] for sentence in corpus]
In [7]: tag_set = unique_list(tag for sent in corpus for (word,tag) in sent)
In [8]: symbols = unique_list(word for sent in corpus for (word,tag) in sent)
In [9]: trainer = nltk.tag.HiddenMarkovModelTrainer(tag_set, symbols)
In [10]: train_corpus = corpus[:4]
In [11]: test_corpus = [corpus[4]]
In [12]: hmm = trainer.train_supervised(train_corpus, estimator=LaplaceProbDist)
In [13]: print('%.2f%%' % (100 * hmm.evaluate(test_corpus)))
100.00%
如您所见,HMM 已经过训练。现在我 pickle 它:
In [14]: import pickle
In [16]: output = open('hmm.pkl', 'wb')
In [17]: pickle.dump(hmm, output)
In [18]: output.close()
重置和加载后模型看起来比一盒石头还笨:
In [19]: %reset
Once deleted, variables cannot be recovered. Proceed (y/[n])? y
In [20]: import pickle
In [21]: import json
In [22]: with open('data.json') as data_file:
....: corpus = json.load(data_file)
....:
In [23]: test_corpus = [corpus[4]]
In [24]: pkl_file = open('hmm.pkl', 'rb')
In [25]: hmm = pickle.load(pkl_file)
In [26]: pkl_file.close()
In [27]: type(hmm)
Out[27]: nltk.tag.hmm.HiddenMarkovModelTagger
In [28]: print('%.2f%%' % (100 * hmm.evaluate(test_corpus)))
0.00%
最佳答案
1) In[22]之后,需要添加-
corpus = [[tuple(l) for l in sentence] for sentence in corpus]
2) 每次为了测试目的重新训练模型会很耗时。 所以,最好 pickle.dump 你的模型并加载它。
关于python - 从 pickle 加载的 HMM 看起来未经训练,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/38681698/