No, but actually there is no punctuation in my text, only space between terms.
Best, Ehsan On Thu, Nov 19, 2015 at 11:13 AM, Fred Mailhot <fred.mail...@gmail.com> wrote: > Have you checked that your other program tokenizes the same way as the > default sklearn tokenization? > > > On 19 November 2015 at 11:09, Ehsan Asgari <asg...@berkeley.edu> wrote: > >> Hi, >> >> Thank you, but it didn't work. >> I checked len(tf.vocabulary_) and it is also 1900 instead of 1914. >> I have another program that counts distinct terms and it is 1914 there. >> >> Best, >> Ehsan >> >> >> >> On Thu, Nov 19, 2015 at 9:36 AM, Andreas Mueller <t3k...@gmail.com> >> wrote: >> >>> You should set min_df=1 and max_df=1.0 (which should be the default, but >>> it depends on your scikit-learn version). >>> How did you determine that your vocabulary size should be 1860? >>> >>> >>> >>> On 11/19/2015 12:31 PM, Ehsan Asgari wrote: >>> >>> Hi, >>> >>> Thank you for your reply. I changed my delimeters from tab to space and >>> most of the problem has been solved (1900 index term from 1914). However, >>> still there are few words that are excluded. I didn't set any parameter as >>> you can see in the code. >>> >>> tf = TfidfVectorizer(ngram_range=(1,ngram),use_idf=False) >>>> tf_matrix = tf.fit_transform(corpus) >>>> feature_names = tf.get_feature_names() >>>> >>>> Should I play with the min_df and max_df? >>> >>> Best, >>> Ehsan >>> >>> On Nov 19, 2015, at 9:01 AM, Chris Holdgraf < <choldg...@berkeley.edu> >>> choldg...@berkeley.edu> wrote: >>> >>> If you vocab is indeed being cut down, could it be because some words >>> don't pass through the word frequency cutoff filters? (min_df, max_df) >>> >>> On Thu, Nov 19, 2015 at 8:55 AM, Andreas Mueller < <t3k...@gmail.com> >>> t3k...@gmail.com> wrote: >>> >>>> Hi Ehsan. >>>> Which version of scikit-learn are you using? >>>> And why do you think the vocabulary size is 1860? >>>> What is len(tf.vocabulary_)? >>>> >>>> Andy >>>> >>>> On 11/18/2015 11:45 PM, Ehsan Asgari wrote: >>>> >>>> Hi, >>>> >>>> I am using TfidfVectorizer of sklearn.feature_extraction.text for >>>> generating tf-idf matrix of a corpus. However, when I look at the features >>>> extracted from my corpus it seems that it has reduced my vocabulary size >>>> from 1860 to 598! I tried to play with max_df, min_df, and max_features. >>>> But nothing changed. >>>> >>>> tf = TfidfVectorizer(ngram_range=(1,ngram),use_idf=False) >>>> tf_matrix = tf.fit_transform(corpus) >>>> feature_names = tf.get_feature_names() >>>> >>>> Does someone have an idea how to solve this problem? >>>> >>>> Thank you, >>>> >>>> Ehsan >>>> >>>> >>>> >>>> >>>> ------------------------------------------------------------------------------ >>>> >>>> >>>> >>>> _______________________________________________ >>>> Scikit-learn-general mailing >>>> listScikit-learn-general@lists.sourceforge.nethttps://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>>> >>>> >>>> >>>> >>>> ------------------------------------------------------------------------------ >>>> >>>> _______________________________________________ >>>> Scikit-learn-general mailing list >>>> Scikit-learn-general@lists.sourceforge.net >>>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>>> >>>> >>> >>> >>> -- >>> _____________________________________ >>> >>> PhD Candidate in Neuroscience | UC Berkeley <http://hwni.org/> >>> Editor and Web Director | Berkeley Science Review >>> <http://sciencereview.berkeley.edu/> >>> _____________________________________ >>> >>> >>> ------------------------------------------------------------------------------ >>> >>> _______________________________________________ >>> Scikit-learn-general mailing list >>> Scikit-learn-general@lists.sourceforge.net >>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>> >>> >>> >>> ------------------------------------------------------------------------------ >>> >>> >>> >>> _______________________________________________ >>> Scikit-learn-general mailing >>> listScikit-learn-general@lists.sourceforge.nethttps://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>> >>> >>> >>> >>> ------------------------------------------------------------------------------ >>> >>> _______________________________________________ >>> Scikit-learn-general mailing list >>> Scikit-learn-general@lists.sourceforge.net >>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>> >>> >> >> >> ------------------------------------------------------------------------------ >> >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> > > > ------------------------------------------------------------------------------ > > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > >
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