__init __（）得到了一个意想不到的关键字参数＆＃39; stop_words＆＃39;

Question

我试图使用scikit-learn版本0.14.1来计算tf-idf。这是我的代码：

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from nltk.corpus import stopwords
import numpy as np
import numpy.linalg as LA

train_set = ["The sky is blue.", "The sun is bright."] #Documents
test_set = ["The sun in the sky is bright sun."] #Query
stopWords = stopwords.words('english')

vectorizer = CountVectorizer(stop_words = stopWords)
#print vectorizer
transformer = TfidfTransformer() 
#print transformer

trainVectorizerArray = vectorizer.fit_transform(train_set).toarray()
testVectorizerArray = vectorizer.transform(test_set).toarray()
print 'Fit Vectorizer to train set', trainVectorizerArray
print 'Transform Vectorizer to test set', testVectorizerArray

transformer.fit(trainVectorizerArray)
print
print transformer.transform(trainVectorizerArray).toarray()

transformer.fit(testVectorizerArray)
print
tfidf = transformer.transform(testVectorizerArray)
print tfidf.todense()

我收到了这个错误：

Traceback (most recent call last):
File "tfidf.py", line 12, in <module>
vectorizer = CountVectorizer(stop_words = stopWords)
TypeError: __init__() got an unexpected keyword argument 'stop_words'

我不明白'stop_words'有什么问题，需要帮助吗？

Answer 1

错误是我的，我已经按照安装 sklearn 的在线教程获得了0.10版本。根据错误，我认为sklearn版本0.10不支持stop_words。 将其更新为版本0.14.1 后，它工作正常!!