tobigue tobigue - 10 days ago 5
Python Question

How to get most informative features for scikit-learn classifiers?

The classifiers in machine learning packages like liblinear and nltk offer a method

show_most_informative_features()
, which is really helpful for debugging features:

viagra = None ok : spam = 4.5 : 1.0
hello = True ok : spam = 4.5 : 1.0
hello = None spam : ok = 3.3 : 1.0
viagra = True spam : ok = 3.3 : 1.0
casino = True spam : ok = 2.0 : 1.0
casino = None ok : spam = 1.5 : 1.0


My question is if something similar is implemented for the classifiers in scikit-learn. I searched the documentation, but couldn't find anything the like.

If there is no such function yet, does somebody know a workaround how to get to those values?

Thanks alot!

Answer

With the help of larsmans code I came up with this code for the binary case:

def show_most_informative_features(vectorizer, clf, n=20):
    feature_names = vectorizer.get_feature_names()
    coefs_with_fns = sorted(zip(clf.coef_[0], feature_names))
    top = zip(coefs_with_fns[:n], coefs_with_fns[:-(n + 1):-1])
    for (coef_1, fn_1), (coef_2, fn_2) in top:
        print "\t%.4f\t%-15s\t\t%.4f\t%-15s" % (coef_1, fn_1, coef_2, fn_2)
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