Hi,
I just want some n-grams--I don't necessarily want to tell CountVectorizer
my life story. It's pretty stingy about giving n-grams unless you pass it a
ton of data or something.
Am I using it wrong? Are there kwargs that I missed that would support this
kind of use case?
Thanks,
Doug
In [225]: cv = CountVectorizer(analyzer='char', stop_words=None,
ngram_range=(1,5))
In [226]: cv.fit(['Gimme n-grams!'])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-226-ccd2238d644b> in <module>()
----> 1 cv.fit(['Gimme n-grams!'])
/usr/local/lib/python2.7/site-packages/sklearn/feature_extraction/text.pyc
in fit(self, raw_documents, y)
430 self
431 """
--> 432 self.fit_transform(raw_documents)
433 return self
434
/usr/local/lib/python2.7/site-packages/sklearn/feature_extraction/text.pyc
in fit_transform(self, raw_documents, y)
518 vocab = dict(((t, i) for i, t in enumerate(sorted(terms))))
519 if not vocab:
--> 520 raise ValueError("empty vocabulary; training set may
have"
521 " contained only stop words")
522 self.vocabulary_ = vocab
ValueError: empty vocabulary; training set may have contained only stop
words
------------------------------------------------------------------------------
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