Keep TFIDF result for predicting new content using Scikit for Python

Question

I am using sklearn on Python to do some clustering. I\'ve trained 200,000 data, and code below works well.

corpus = open(\"token_from_xml.txt\")
vectorizer =         


        

Answer 1

If you want to store features list for testing data for use in future, you can do this:

tfidf = transformer.fit_transform(vectorizer.fit_transform(corpus))

#store the content
with open("x_result.pkl", 'wb') as handle:
                    pickle.dump(tfidf, handle)
#load the content
tfidf = pickle.load(open("x_result.pkl", "rb" ) )