Use sklearn TfidfVectorizer with already tokenized inputs?

Question

I have a list of tokenized sentences and would like to fit a tfidf Vectorizer. I tried the following:

tokenized_list_of_sentences = [[\'this\', \'is\', \'one\'],         


        

Answer 1

Like @Jarad said just use a "passthrough" function for your analyzer but it needs to ignore stopwords. You can get stop words from sklearn:

>>> from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS

or from nltk:

>>> import nltk
>>> nltk.download('stopwords')
>>> from nltk.corpus import stopwords
>>> stop_words = set(stopwords.words('english'))

or combine both sets:

stop_words = stop_words.union(ENGLISH_STOP_WORDS)

But then your examples contain only stop words (because all your words are in the sklearn.ENGLISH_STOP_WORDS set).

Noetheless @Jarad's examples work:

>>> tokenized_list_of_sentences =  [
...     ['this', 'is', 'one', 'cat', 'or', 'dog'],
...     ['this', 'is', 'another', 'dog']]
>>> from sklearn.feature_extraction.text import TfidfVectorizer
>>> tfidf = TfidfVectorizer(analyzer=lambda x:[w for w in x if w not in stop_words])
>>> tfidf_vectors = tfidf.fit_transform(tokenized_list_of_sentences)

I like pd.DataFrames for browsing TF-IDF vectors:

>>> import pandas as pd
>>> pd.DataFrame(tfidf_vectors.todense(), columns=tfidf.vocabulary_)
        cat       dog 
0  0.814802  0.579739
1  0.000000  1.000000