Question

I am trying to get Highest frequency terms out of vectors in scikit-learn.
From example It can be done using this for each Categories but i want it for each files inside categories.

https://github.com/scikit-learn/scikit-learn/blob/master/examples/document_classification_20newsgroups.py

    if opts.print_top10:
        print "top 10 keywords per class:"
        for i, category in enumerate(categories):
            top10 = np.argsort(clf.coef_[i])[-10:]
            print trim("%s: %s" % (
            category, " ".join(feature_names[top10])))

I want to do this for each files from testing dataset instead of each categories.
Where should i be looking?

Thanks

EDIT: s/discrimitive/highest frequency/g (Sorry for the confusions)

Answer 1

You can use the result of transform together with get_feature_names to obtain the term counts for a given document.

X = vectorizer.transform(docs)
terms = np.array(vectorizer.get_feature_names())
terms_for_first_doc = zip(terms, X.toarray()[0])