python - Is this correct tfidf? -


i trying tfidf document. dont think giving me correct values or may doing thing wrong. please suggest. code , output below:

from sklearn.feature_extraction.text import tfidfvectorizer books = ["hello there first book read wordcount script.", "this second book read wordcount script. has additionl information.", "just third book."] vectorizer = tfidfvectorizer() response = vectorizer.fit_transform(books) feature_names = vectorizer.get_feature_names() col in response.nonzero()[1]:    print feature_names[col], '-', response[0, col]

update 1: (as suggested juanpa.arrivillaga)

vectorizer = tfidfvectorizer(smooth_idf=false)

output:

script - 0.269290317245 wordcount - 0.269290317245 - 0.269290317245 read - 0.269290317245 - 0.269290317245 - 0.269290317245 book - 0.209127954024 first - 0.354084405732 - 0.269290317245 - 0.269290317245 there - 0.354084405732 hello - 0.354084405732 information - 0.0 ...

output after update 1:

script - 0.256536760895 wordcount - 0.256536760895 - 0.256536760895 read - 0.256536760895 - 0.256536760895 - 0.256536760895 book - 0.182528018244 first - 0.383055542114 - 0.256536760895 - 0.256536760895 there - 0.383055542114 hello - 0.383055542114 information - 0.0 ...

as per understanding tfidf = tf * idf. , way manually calculate example:

document 1: "hello there first book read wordcount script." document 2: "this second book read wordcount script. has additionl information." document 3: "just third book."

tfidf hello: 

tf= 1/12(total terms in document 1)= 0.08333333333 idf= log(3(total documents)/1(no. of document term in it))= 0.47712125472 0.08333333333*0.47712125472= 0.03976008865

which different below (hello - 0.354084405732).

manual calculation after update 1:

tf = 1 idf= log(nd/df) +1 = log (3/1) +1= 0.47712125472 + 1= 1.47712  tfidf = tf*idf = 1* 1.47712= 1.47712

(not same code output "hello - 0.383055542114" after idf smoothing)

any understand whats going on highly appreciated..

here output without smoothing or normalization:

in [2]: sklearn.feature_extraction.text import tfidfvectorizer    ...: books = ["hello there first book read wordcount script.", "this second book read wordcount sc    ...: ript. has additionl information.", "just third book."]    ...: vectorizer = tfidfvectorizer(smooth_idf=false, norm=none)    ...: response = vectorizer.fit_transform(books)    ...: feature_names = vectorizer.get_feature_names()    ...: col in response.nonzero()[1]:    ...:    print(feature_names[col], '-', response[0, col])    ...: hello - 2.09861228867 there - 2.09861228867 - 1.40546510811 - 1.40546510811 first - 2.09861228867 book - 1.0 - 1.40546510811 - 1.40546510811 read - 1.40546510811 - 1.40546510811 wordcount - 1.40546510811 script - 1.40546510811 - 1.40546510811 - 1.40546510811 book - 1.0 - 1.40546510811 - 1.40546510811 read - 1.40546510811 - 1.40546510811 wordcount - 1.40546510811 script - 1.40546510811 second - 0.0 - 0.0 has - 0.0 - 0.0 additionl - 0.0 information - 0.0 book - 1.0 - 0.0 third - 0.0

so consider result "hello":

hello - 2.09861228867

now, manually:

in [3]: import math  in [4]: tf = 1  in [5]: idf = math.log(3/1) + 1  in [6]: tf*idf out[6]: 2.09861228866811

the problem manual calculation using log base 10, need use natural logarithm.

if still feel burning desire go through smoothing , normalization steps, should set correctly.


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