On Fri, May 11, 2012 at 3:06 PM, Olivier Grisel <[email protected]>wrote:
> 2012/5/10 JAGANADH G <[email protected]>:
> > Hi all
> >
> > Is there any way to get the TF-IDF value mapped with the word vector in
> > sklearn.
> >
> > I would like to get output like
> >
> > w1 -> TF-IDF
> > w2 -> TF-IDF
>
> TF is sample-dependent but the IDF weights for each feature index are
> stored as an array attribute named `idf_` on the fitted vectorizer
> along with the `vocabulary_` that gives you the mapping from words to
> IDF weights.
>
> See the documentation for more details:
>
>
> http://scikit-learn.org/dev/modules/feature_extraction.html#text-feature-extraction
>
>
Thanks Olivier ,
I tried the same . I am pasting the code below . Am I following the correct
procedure ??
[code]
from sklearn.datasets import load_files
categories = ["pos","neg"]
mov_train =
load_files("/usr/share/nltk_data/corpora/movie_reviews",categories=categories,shuffle=True,random_state=42)
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
cvect = CountVectorizer()
train_counts = cvect.fit_transform(mov_train.data)
tfidf_tr = TfidfTransformer(use_idf=True).fit(train_counts)
for word,fr in zip(cvect.vocabulary_,tfidf_tr.idf_):
print '%r => %r' % (word, fr)
[\code]
--
**********************************
JAGANADH G
http://jaganadhg.in
*ILUGCBE*
http://ilugcbe.org.in
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