Archana Archana - 12 days ago 5
Python Question

CountVectorizer does not print vocabulary

I have installed python 2.7, numpy 1.9.0, scipy 0.15.1 and scikit-learn 0.15.2.
Now when I do the following in python:

train_set = ("The sky is blue.", "The sun is bright.")
test_set = ("The sun in the sky is bright.",
"We can see the shining sun, the bright sun.")

from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()

print vectorizer


CountVectorizer(analyzer=u'word', binary=False, charset=None,
charset_error=None, decode_error=u'strict',
dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
lowercase=True, max_df=1.0, max_features=None, min_df=1,
ngram_range=(1, 1), preprocessor=None, stop_words=None,
strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
tokenizer=None, vocabulary=None)

vectorizer.fit_transform(train_set)
print vectorizer.vocabulary

None.


Actually it should have printed the following:

CountVectorizer(analyzer__min_n=1,
analyzer__stop_words=set(['all', 'six', 'less', 'being', 'indeed', 'over',
'move', 'anyway', 'four', 'not', 'own', 'through', 'yourselves', (...) --->
For count vectorizer

{'blue': 0, 'sun': 1, 'bright': 2, 'sky': 3} ---> for vocabulary


The above code are from the blog:
http://blog.christianperone.com/?p=1589

Could you please help me as to why I get such an error. Since the vocabulary is not indexed properly I am not able to move ahead in understanding the concept of TF-IDF. I am a newbie for python so any help would be appreciated.

Arc.

Answer

You are missing an underscore, try this way:

from sklearn.feature_extraction.text import CountVectorizer
train_set = ("The sky is blue.", "The sun is bright.")
test_set = ("The sun in the sky is bright.", 
    "We can see the shining sun, the bright sun.")

vectorizer = CountVectorizer(stop_words='english')
document_term_matrix = vectorizer.fit_transform(train_set)
print vectorizer.vocabulary_
# {u'blue': 0, u'sun': 3, u'bright': 1, u'sky': 2}

If you use the ipython shell, you can use tab completion, and you can find easier the methods and attributes of objects.