TypeError：'WordListCorpusReader'對象在使用nltk.classify.apply_features時沒有屬性'__getitem__'

Question

我按照本教程學習this site上的NaiveBayes。我的代碼是：

from nltk.corpus import names 
from nltk.classify import apply_features 

def gender_features(word): 
    return {'last_letter': word[-1]} 

labeled_names = ([(name, 'male') for name in names.words('male.txt')] + 
[(name, 'female') for name in names.words('female.txt')]) 

feature_sets = [(gender_features(n), gender) for (n, gender) in labeled_names] 

#train_set, test_set = feature_sets[500:], feature_sets[:500] 
train_set = apply_features(gender_features, names[500:]) 
test_set = apply_features(gender_features, names[:500]) 

classifier = NaiveBayesClassifier.train(train_set) 

print classifier.classify(gender_features('Neo')) 

使用train_set用了apply_features工作正常。任何人都知道我能如何解決它？謝謝。

Answer 1

首先，我認爲這是在本教程的一個錯字上http://www.nltk.org/book/ch06.html

詞表語料庫不能像一個列表訪問。

>>> from nltk.corpus import names 
>>> names[:5] 
Traceback (most recent call last): 
    File "<stdin>", line 1, in <module> 
TypeError: 'LazyCorpusLoader' object has no attribute '__getitem__' 
>>> names.words()[:5] 
[u'Abagael', u'Abagail', u'Abbe', u'Abbey', u'Abbi'] 

下一頁在這裏看到的是什麼呢apply_features（https://github.com/nltk/nltk/blob/develop/nltk/classify/util.py#L28）。

基本上，給定[('input_1', 'label_1'), ...('input_N', 'label_N')]的元組列表，它返回[(feature_func(tok), label) for (tok, label) in toks]。例如。

# To get the input list of tuples for apply_features, we do this: 
>>> [(word,'female') for word in names.words('female.txt')[:10]] 
[(u'Abagael', 'female'), (u'Abagail', 'female'), (u'Abbe', 'female'), (u'Abbey', 'female'), (u'Abbi', 'female'), (u'Abbie', 'female'), (u'Abby', 'female'), (u'Abigael', 'female'), (u'Abigail', 'female'), (u'Abigale', 'female')] 

# Let's get 250 from female and 250 from male names. 
>>> train_female = [(word,'female') for word in names.words('female.txt')[:250]] 
>>> train_male = [(word,'male') for word in names.words('male.txt')[:250]] 
>>> train_data = train_female + train_male 
>>> apply_features(gender_features, train_data) 
[({'last_letter': u'l'}, 'female'), ({'last_letter': u'l'}, 'female'), ...] 

完整的代碼，以獲得Naivebayes在NLTK工作的名稱文集：

from nltk.corpus import names 
from nltk.classify import apply_features, NaiveBayesClassifier 

def gender_features(word): 
    return {'last_letter': word[-1]} 


train_female = [(word,'female') for word in names.words('female.txt')[:250]] 
train_male = [(word,'male') for word in names.words('male.txt')[:250]] 
train_data = train_female + train_male 
train_set = apply_features(gender_features, train_data) 

# Do like wise for the test set. 
''' 
test_female = [(word,'female') for word in names.words('female.txt')[250:]] 
test_male = [(word,'male') for word in names.words('male.txt')[250:]] 
test_data = test_female + test_male 
test_set = apply_features(gender_features, test_data) 
''' 

classifier = NaiveBayesClassifier.train(train_set) 
print classifier.classify(gender_features('Neo')) 

[出]：

'male'