user99889 user99889 - 1 year ago 646
Python Question

Hierarchical Dirichlet Process Gensim topic number independent of corpus size

I am using the Gensim HDP module on a set of documents.

>>> hdp = models.HdpModel(corpusB, id2word=dictionaryB)
>>> topics = hdp.print_topics(topics=-1, topn=20)
>>> len(topics)
>>> hdp = models.HdpModel(corpusA, id2word=dictionaryA)
>>> topics = hdp.print_topics(topics=-1, topn=20)
>>> len(topics)
>>> len(corpusA)
>>> len(corpusB)

Why is the number of topics independent of corpus length?

Answer Source

@user3907335 is exactly correct here: HDP will calculate as many topics as the assigned truncation level. However, it may be the case that many of these topics have basically zero probability of occurring. To help with this in my own work, I wrote a handy little function that performs a rough estimate of the probability weight associated with each topic. Note that this is a rough metric only: it does not account for the probability associated with each word. Even so, it provides a pretty good metric for which topics are meaningful and which aren't:

import pandas as pd
import numpy as np 

def topic_prob_extractor(hdp=None, topn=None):
    topic_list = hdp.show_topics(topics=-1, topn=topn)
    topics = [int(x.split(':')[0].split(' ')[1]) for x in topic_list]
    split_list = [x.split(' ') for x in topic_list]
    weights = []
    for lst in split_list:
        sub_list = []
        for entry in lst: 
            if '*' in entry: 
    sums = [np.sum(x) for x in weights]
    return pd.DataFrame({'topic_id' : topics, 'weight' : sums})

I assume that you already know how to calculate an HDP model. Once you have an hdp model calculated by gensim you call the function as follows:

topic_weights = topic_prob_extractor(hdp, 500)