Pass tokens to CountVectorizer

Question

I have a text classification problem where i have two types of features:

• features which are n-grams (extracted by CountVectorizer)
• other textual features

Answer 1

Similar to user126350's answer, but even simpler, here's what I did.

def do_nothing(tokens):
    return tokens

pipe = Pipeline([
    ('tokenizer', MyCustomTokenizer()),
    ('vect', CountVectorizer(tokenizer=do_nothing,
                             preprocessor=None,
                             lowercase=False))
])

doc_vects = pipe.transform(my_docs)  # pass list of documents as strings

Answer 2

Summarizing the answers of @user126350 and @miroli and this link:

from sklearn.feature_extraction.text import CountVectorizer

def dummy(doc):
    return doc

cv = CountVectorizer(
        tokenizer=dummy,
        preprocessor=dummy,
    )  

docs = [
    ['hello', 'world', '.'],
    ['hello', 'world'],
    ['again', 'hello', 'world']
]

cv.fit(docs)
cv.get_feature_names()
# ['.', 'again', 'hello', 'world']

The one thing to keep in mind is to wrap the new tokenized document into a list before calling the transform() function so that it is handled as a single document instead of interpreting each token as a document:

new_doc = ['again', 'hello', 'world', '.']
v_1 = cv.transform(new_doc)
v_2 = cv.transform([new_doc])

v_1.shape
# (4, 4)

v_2.shape
# (1, 4)

Answer 3

In general, you can pass a custom tokenizer parameter to CountVectorizer. The tokenizer should be a function that takes a string and returns an array of its tokens. However, if you already have your tokens in arrays, you can simply make a dictionary of the token arrays with some arbitrary key and have your tokenizer return from that dictionary. Then, when you run CountVectorizer, just pass your dictionary keys. For example,

 # arbitrary token arrays and their keys
 custom_tokens = {"hello world": ["here", "is", "world"],
                  "it is possible": ["yes it", "is"]}

 CV = CountVectorizer(
      # so we can pass it strings
      input='content',
      # turn off preprocessing of strings to avoid corrupting our keys
      lowercase=False,
      preprocessor=lambda x: x,
      # use our token dictionary
      tokenizer=lambda key: custom_tokens[key])

 CV.fit(custom_tokens.keys())