Hi
I am
using python-2.7.3, numpy-1.6.2-win32-superpack-python2.7,
scipy-0.11.0rc1-win32-superpack-python2.7,
scikit-learn-0.11.win32-py2.7
I
tried the following
>>>
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=word,
binary=False, charset=utf-8,
charset_error=strict, dtype=<type
'long'>, input=content,
lowercase=True, max_df=1.0,
max_features=None, max_n=1, min_n=1,
preprocessor=None, stop_words=None,
strip_accents=None,
token_pattern=\b\w\w+\b,
tokenizer=None, vocabulary=None)
>>>
vectorizer.fit_transform(train_set)
<2x6
sparse matrix of type '<type 'numpy.int64'>'
with 8 stored elements in COOrdinate
format>
>>>
print vectorizer.vocabulary
Traceback
(most recent call last):
File "<pyshell#6>", line 1,
in <module>
print vectorizer.vocabulary
AttributeError:
'CountVectorizer' object has no attribute 'vocabulary'
>>>
I tried to fix the parameters of CountVectorizer (analyzer = WordNGramAnalyzer,
vocabulary = dict) but
it didn’t work. Therefore I decided to install sklearn 0.9 and it works, so we
could say that everything is OK but I still would like to know what is wrong
with version sklearn 0.11
Andrés Soto
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