CountVectorizer:"我"没有出现在矢量化文本中

Question

我是scrakit-learn的新手,目前正在学习NaïveBayes(Multinomial).现在,我正在研究sklearn.feature_extraction.text中的文本向量化,出于某种原因,当我向某些文本进行矢量化时,单词"I"不会出现在输出的数组中.

码:

x_train = ['I am a Nigerian hacker', 'I like puppies']

# convert x_train to vectorized text
vectorizer_train = CountVectorizer(min_df=0)
vectorizer_train.fit(x_train)
x_train_array = vectorizer_train.transform(x_train).toarray()

# print vectorized text, feature names
print x_train_array
print vectorizer_train.get_feature_names()

输出:

1 1 0 1 0
0 0 1 0 1
[u'am', u'hacker', u'like', u'nigerian', u'puppies']

为什么"我"似乎没有出现在功能名称中？当我将其更改为"Ia"或类似的其他内容时,它确实会显示出来.

Answer 1

这是由默认值token_patternfor 引起的CountVectorizer,它会删除单个字符的标记:

>>> vectorizer_train
CountVectorizer(analyzer=u'word', binary=False, charset=None,
        charset_error=None, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=1.0, max_features=None, min_df=0,
        ngram_range=(1, 1), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None)
>>> pattern = re.compile(vectorizer_train.token_pattern, re.UNICODE)
>>> print(pattern.match("I"))
None

要保留"I",请使用不同的模式,例如

>>> vectorizer_train = CountVectorizer(min_df=0, token_pattern=r"\b\w+\b")
>>> vectorizer_train.fit(x_train)
CountVectorizer(analyzer=u'word', binary=False, charset=None,
        charset_error=None, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=1.0, max_features=None, min_df=0,
        ngram_range=(1, 1), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern='\\b\\w+\\b', tokenizer=None,
        vocabulary=None)
>>> vectorizer_train.get_feature_names()
[u'a', u'am', u'hacker', u'i', u'like', u'nigerian', u'puppies']

请注意,现在也保留了无信息的单词"a".