Use sklearn TfidfVectorizer with already tokenized inputs?

Try initializing the TfidfVectorizer object with the parameter lowercase=False (assuming this is actually desired as you've lowercased your tokens in previous stages).

tokenized_list_of_sentences = [['this', 'is', 'one', 'basketball'], ['this', 'is', 'a', 'football']]

def identity_tokenizer(text):
    return text

tfidf = TfidfVectorizer(tokenizer=identity_tokenizer, stop_words='english', lowercase=False)    
tfidf.fit_transform(tokenized_list_of_sentences)

Note that I changed the sentences as they apparently only contained stop words which caused another error due to an empty vocabulary.

Try preprocessor instead of tokenizer.

    return lambda x: strip_accents(x.lower())
AttributeError: 'list' object has no attribute 'lower'

If x in the above error message is a list, then doing x.lower() to a list will throw the error.

Your two examples are all stopwords so to make this example return something, throw in a few random words. Here's an example:

tokenized_sentences = [['this', 'is', 'one', 'cat', 'or', 'dog'],
                       ['this', 'is', 'another', 'dog']]

tfidf = TfidfVectorizer(preprocessor=' '.join, stop_words='english')
tfidf.fit_transform(tokenized_sentences)

Returns:

<2x2 sparse matrix of type '<class 'numpy.float64'>'
    with 3 stored elements in Compressed Sparse Row format>

Features:

>>> tfidf.get_feature_names()
['cat', 'dog']

UPDATE: maybe use lambdas on tokenizer and preprocessor?

tokenized_sentences = [['this', 'is', 'one', 'cat', 'or', 'dog'],
                       ['this', 'is', 'another', 'dog']]

tfidf = TfidfVectorizer(tokenizer=lambda x: x,
                        preprocessor=lambda x: x, stop_words='english')
tfidf.fit_transform(tokenized_sentences)

<2x2 sparse matrix of type '<class 'numpy.float64'>'
    with 3 stored elements in Compressed Sparse Row format>
>>> tfidf.get_feature_names()
['cat', 'dog']