CountVectorizer returns only zeros

Question

I am trying to extract some functions from this document, given a given set of functions.

from sklearn.feature_extraction.text import CountVectorizer
features = ['a', 'b', 'c']
doc = ['a', 'c']

vectoriser = CountVectorizer()
vectoriser.vocabulary = features
vectoriser.fit_transform(doc)

However, the output is a 2x3 array filled with zeros, not:

desired_output = [[1, 0, 0]
                  [0, 0, 1]]

Any help would be greatly appreciated

Answer 1

This is because the default marker template in CountVectorizer gets rid of any words that are just one character long. You can change the default marker template to fix this:

from sklearn.feature_extraction.text import CountVectorizer
features = ['a', 'b', 'c']
doc = ['a', 'c']

vectoriser = CountVectorizer(vocabulary=features, token_pattern=r"\b\w+\b")

vectoriser.fit_transform(doc)