我正在按照这个网站上的教程学习 NaiveBayes。我的代码如下：
from nltk.corpus import namesfrom nltk.classify import apply_featuresdef 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'))如果不使用 apply_features，直接使用 train_set 是可以正常工作的。有人知道我该如何解决这个问题吗？谢谢。
回答：
首先，我认为http://www.nltk.org/book/ch06.html上的教程中有一个拼写错误。
词列表语料库不能像列表一样被访问。
>>> 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']接下来看看 apply_features 函数的作用（https://github.com/nltk/nltk/blob/develop/nltk/classify/util.py#L28）。
基本来说，给定一个包含元组的列表，如 [('input_1', 'label_1'), ...('input_N', 'label_N')]，它会返回 [(feature_func(tok), label) for (tok, label) in toks]。例如：
# 为了获取 apply_features 的输入元组列表，我们这样做：>>> [(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')]# 让我们从女性和男性名字中各取250个。>>> 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'), ...]以下是让 NaiveBayes 在 NLTK 的名字语料库上工作的完整代码：
from nltk.corpus import namesfrom nltk.classify import apply_features, NaiveBayesClassifierdef 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_maletrain_set = apply_features(gender_features, train_data)# 对测试集做同样的处理。'''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_maletest_set = apply_features(gender_features, test_data)'''classifier = NaiveBayesClassifier.train(train_set)print classifier.classify(gender_features('Neo'))[out]:
'male'