Oh ok thank you. How can I check regex of sklearn and modify it?



On Thu, Nov 19, 2015 at 11:40 AM, Andreas Mueller <t3k...@gmail.com> wrote:

> Yeah but if the regexp is different you will get different results.
>
>
>
> On 11/19/2015 02:20 PM, Ehsan Asgari wrote:
>
> 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>
>> 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
>>>>>
>>>>>
>>>>>
>>>>>
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>>>>
>>>> --
>>>> _____________________________________
>>>>
>>>> PhD Candidate in Neuroscience | UC Berkeley <http://hwni.org/>
>>>> Editor and Web Director | Berkeley Science Review
>>>> <http://sciencereview.berkeley.edu/>
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