我尝试使用 scikit-learn 版本 0.14.1 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()
我收到此错误:
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？
最佳答案
所以错误是我的，我按照在线教程安装了sklearn，它给了我0.10版本。根据错误，我认为 sklearn 版本 0.10 不支持 stop_words。 更新到版本0.14.1后，它工作正常!!
关于python - __init__() 获得意外的关键字参数 'stop_words'，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/22790764/