I have created a NMF topic model in python the code snippet for which is as follows:def select_vectorizer(req_ngram_range=[1,2]):    ngram_lengths = req_ngram_range    vectorizer = TfidfVectorizer(analyzer='word', ngram_range=(ngram_lengths), stop_words='english', min_df=2)    #print(User specified custom stopwords: {} ....format(str(custom_stopwords)[1:-1]))    return vectorizervectorizer = select_vectorizer([2,5])X = vectorizer.fit_transform(new_review_list)clf = decomposition.NMF(n_components=20, random_state=3, alpha = .1).fit(X)vocab = vectorizer.get_feature_names()print_top_words(clf, vocab, num_top_words)which created 20 topics like the following:Topic #0:[u'blocks available', u'delivery blocks available', u'notifications blocks', u'notifications blocks available', u'new blocks', u'know blocks available', u'new blocks available', u'know blocks', u'open blocks available', u'available work', u'zero blocks', u'like blocks', u'notification blocks', u'day blocks', u'slow blocks', u'10 blocks', u'option set', u'logged 10', u'notification blocks available', u'day blocks available']Topic #1:['something', 'something'] and so on..Now I want to supply new text data to this fitted NMF model such that the model tells us which topic this new text belongs to. I have searched a lot, somebody suggested using transform, but I do not know how to use it. For eg: if I have a new text, like:new_feedback = This is a new tester feedbackThen how do I find which topic out of the 20 topics created earlier, does this new_feedback fall into? Please help!!UPDATE: On trying to do what's mentioned in the below comment, I receive the following error:clf.transform(vectorizer.transform(new_feedback))  File /usr/local/lib/python2.7/site-packages/sklearn/feature_extraction/text.py, line 1380, in transform    X = super(TfidfVectorizer, self).transform(raw_documents)  File /usr/local/lib/python2.7/site-packages/sklearn/feature_extraction/text.py, line 884, in transform    Iterable over raw text documents expected, ValueError: Iterable over raw text documents expected, string object received.
How to use Non Negative Matric Factorization (NMF)'s transform method to project topics on new text data
python;nlp;scikit learn;topic model