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Question

我试图计算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(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()

我得到了这个错误：

^{pr2}$

我使用的是scikit版本0.14.1。在

Answer 1

CountVectorizer(stopWords)

应该是

^{pr2}$

除非文档中另有说明，否则始终对scikit学习对象的构造函数参数使用关键字参数。在