Skip to content

Latest commit

 

History

History
89 lines (70 loc) · 3.02 KB

README.md

File metadata and controls

89 lines (70 loc) · 3.02 KB
Raw

Biterm Topic Model

This is a simple Python implementation of the awesome Biterm Topic Model. This model is accurate in short text classification. It explicitly models the word co-occurrence patterns in the whole corpus to solve the problem of sparse word co-occurrence at document-level.

Simply install by:

pip install biterm

Load some short texts and vectorize them via sklearn.

    from sklearn.feature_extraction.text import CountVectorizer

    texts = open('./data/reuters.titles').read().splitlines()[:50]
    vec = CountVectorizer(stop_words='english')
    X = vec.fit_transform(texts).toarray()

Get the vocabulary and the biterms from the texts.

    from biterm.utility import vec_to_biterms

    vocab = np.array(vec.get_feature_names())
    biterms = vec_to_biterms(X)

Create a BTM and pass the biterms to train it.

    from biterm.btm import oBTM

    btm = oBTM(num_topics=20, V=vocab)
    topics = btm.fit_transform(biterms, iterations=100)

Save a topic plot using pyLDAvis and explore the results! (also see simple_btml.py)

    from biterm.btm import oBTM

    btm = oBTM(num_topics=20, V=vocab)
    topics = btm.fit_transform(biterms, iterations=100)

pyLDAvis Visualization

Inference is done with Gibbs Sampling and it's not really fast. The implementation is not meant for production. But if you have to classify a lot of texts you can try using online learning.

import numpy as np
import pyLDAvis
from biterm.btm import oBTM 
from sklearn.feature_extraction.text import CountVectorizer
from biterm.utility import vec_to_biterms, topic_summuary # helper functions

if __name__ == "__main__":

    texts = open('./data/reuters.titles').read().splitlines() # path of data file

    # vectorize texts
    vec = CountVectorizer(stop_words='english')
    X = vec.fit_transform(texts).toarray()

    # get vocabulary
    vocab = np.array(vec.get_feature_names())

    # get biterms
    biterms = vec_to_biterms(X)

    # create btm
    btm = oBTM(num_topics=20, V=vocab)

    print("\n\n Train Online BTM ..")
    for i in range(0, len(biterms), 100): # prozess chunk of 200 texts
        biterms_chunk = biterms[i:i + 100]
        btm.fit(biterms_chunk, iterations=50)
    topics = btm.transform(biterms)

    print("\n\n Visualize Topics ..")
    vis = pyLDAvis.prepare(btm.phi_wz.T, topics, np.count_nonzero(X, axis=1), vocab, np.sum(X, axis=0))
    pyLDAvis.save_html(vis, './vis/online_btm.html')  # path to output

    print("\n\n Topic coherence ..")
    topic_summuary(btm.phi_wz.T, X, vocab, 10)

    print("\n\n Texts & Topics ..")
    for i in range(len(texts)):
        print("{} (topic: {})".format(texts[i], topics[i].argmax()))

Use the Cython version to speed up performance. Therefore, you can download the repo and build the cbtm.pyx for the operating system of your choice. Afterwards use from biterm.cbtm import oBTM to use the cythonic version.