On Wed, May 23, 2012 at 10:08 PM, Olivier Grisel
<[email protected]>wrote:
> 2012/5/14 JAGANADH G <[email protected]>:
> >
> >
> > 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)
>
> Vocabulary is a dict so the iteration order is not deterministic. Instead
> do:
>
> for word, feature_idx in cvect.vocabulary_.iteritems():
> print '%r => %r' % (word, tfidf_tr.idf_[feature_idx])
>
> (untested).
>
>
Hi Olivier,
Thanks for the solution.
It works !!!
Best regards
--
**********************************
JAGANADH G
http://jaganadhg.in
*ILUGCBE*
http://ilugcbe.org.in
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