在Python Mapper Reducer中使用CountVectorizer

我正在尝试使用python mapper reducer函数应用标记器。我有以下代码，但我不断得到错误。reducer在列表中输出值，我将值传递给矢量器

from mrjob.job import MRJob
from sklearn.cross_validation import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer

class bagOfWords(MRJob):

def mapper(self, _, line):
    cat, phrase, phraseid, sentiment = line.split(',')
    yield (cat, phraseid, sentiment), phrase

def reducer(self, keys, values):

    yield keys, list(values)

#Output: ["Train", "--", "2"] ["A series of escapades demonstrating the adage that    what is good for the goose", "A series", "A", "series"]

def mapper(self, keys, values):
    vectorizer = CountVectorizer(min_df=0)
    vectorizer.fit(values)
    x = vectorizer.transform(values)
    x=x.toarray()       
    yield keys, (x)


if __name__ == '__main__':
    bagOfWords.run()

ValueError：空词汇表；也许文档中只包含停止词

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感谢大家提供的帮助。

CountVectorizer是有状态的：您需要在完整数据集上匹配同一个实例来构建词汇表，因此这不适合并行处理

相反，您可以使用无状态的

hashingvectorier

（无需调整，您可以直接调用

transform

）