使用scikit-learn时出现属性错误

Question

我正在尝试使用scikit使用余弦相似性来查找类似的问题。我正在试图在互联网上提供这个示例代码。 Link1和Link2

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."] 
test_set = ["The sun in the sky is bright."] 
stopWords = stopwords.words('english') 

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

trainVectorizerArray = vectorizer.fit_transform(train_set).toarray() 
trainVectorizerArray = vectorizer. 
testVectorizerArray = vectorizer.transform(test_set).toarray() 
print 'Fit Vectorizer to train set', trainVectorizerArray 
print 'Transform Vectorizer to test set', testVectorizerArray 
cx = lambda a, b : round(np.inner(a, b)/(LA.norm(a)*LA.norm(b)), 3) 

for vector in trainVectorizerArray: 
    print vector 
    for testV in testVectorizerArray: 
     print testV 
     cosine = cx(vector, testV) 
     print cosine 

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

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

我总是得到这个错误

Traceback (most recent call last): 
File "C:\Users\Animesh\Desktop\NLP\ngrams2.py", line 14, in <module> 
trainVectorizerArray = vectorizer.fit_transform(train_set).toarray() 
File "C:\Python27\lib\site-packages\scikit_learn-0.13.1-py2.7-win32.egg\sklearn \feature_extraction\text.py", line 740, in fit_transform 
raise ValueError("empty vocabulary; training set may have" 
ValueError: empty vocabulary; training set may have contained only stop words or min_df (resp. max_df) may be too high (resp. too low). 

我甚至可以检查代码上this link。我有错误AttributeError: 'CountVectorizer' object has no attribute 'vocabulary'。

如何解决这个问题？

我在Windows 7 32位和scikit_learn 0.13.1上使用Python 2.7.3。

Answer 1

由于我正在运行开发（0.14之前版本）版本，其中feature_extraction.text模块被彻底检查，所以我没有收到相同的错误消息。但我怀疑，你可以解决这一问题：

vectorizer = CountVectorizer(stop_words=stopWords, min_df=1) 

的min_df参数使CountVectorizer扔掉发生在极少数人的文件（因为它不会有任何预测值）任何条款。默认情况下，它被设置为2，这意味着所有的术语都会被扔掉，所以你会得到一个空的词汇表。