def get_least_distinct_words ( vocab , topic_word_distrib , doc_topic_distrib , doc_lengths , n = None ) : return _words_by_distinctiveness_score ( vocab , topic_word_distrib , doc_topic_distrib , doc_lengths , n , least_to_most = True )
extract only words from topics without probability in topic modeling in python
Order the words from vocab by distinctiveness score ( Chuang et al . 2012 ) from least to most distinctive . Optionally only return the n least distinctive words .
def get_least_distinct_words(vocab, topic_word_distrib, doc_topic_distrib, doc_lengths, n=None):
    """
    Order the words from `vocab` by "distinctiveness score" (Chuang et al. 2012) from least to most distinctive.
    Optionally only return the `n` least distinctive words.

    J. Chuang, C. Manning, J. Heer 2012: "Termite: Visualization Techniques for Assessing Textual Topic Models"
    """
    return _words_by_distinctiveness_score(vocab, topic_word_distrib, doc_topic_distrib, doc_lengths, n,
                                           least_to_most=True)