ctfidf breaks down when specifying a vocabulary in CountVectorizer

Nobody has claimed this yet.

In some cases, the stop_words parameter of the CountVectorizer is not enough to prevent certain non-desired words from coming through. For example, one may have the desire to filter out non-verbs like abbreviations before coming up with topic representations.

This can be done by specifying a vocubulary in the CountVectorizer object
[sklearn docs](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html)

However, a problem that occurs then is that ctfidf breaks down due to division by zero in line 82 of _ctfidf.py:
` idf = np.log((avg_nr_samples / df)+1)`
because it could be that some words in the vocabulary actually never occur.

I would therefore propose to change the line above to
` idf = np.log((avg_nr_samples / np.maximum(df, 1))+1)`

This solution does not change behaviour in normal cases and gives the optionality to specify a vocabulary when creating topic representations

Start in _ctfidf.py at line 82 and inspect how df is produced when CountVectorizer receives a vocabulary containing unused words. Confirm the division-by-zero failure, then verify that the c-TF-IDF calculation remains unchanged for normally occurring vocabulary terms and handles unused terms without failing.

Written by the indexing model from the issue text.