Ensemble topic modelling with pLSA
EnsTop provides an ensemble based approach to topic modelling using pLSA. It makes use of a high performance numba based pLSA implementation to run multiple bootstrapped topic models in parallel, and then clusters the resulting outputs to determine a set of stable topics. It can then refit the document vectors against these topics embed documents into the stable topic space.
Why use EnsTop?
There are a number of advantages to using an ensemble approach to topic modelling. The most obvious is that it produces better more stable topics. A close second, however, is that, by making use of HDBSCAN for clustering topics, it can learn a “natural” number of topics. That is, while the user needs to specify an estimated number of topics, the actual number of topics produced will be determined by how many stable topics are produced over many bootstrapped runs. In practice this can either be more, or less, than the estimated number of topics.
Despite all of these extra features the ensemble topic approach is still very efficient, especially in multi-core environments (due the the embarrassingly parallel nature of the ensemble). A run with a reasonable size ensemble can be completed in around the same time it might take to fit an LDA model, and usually produces superior quality results.
In addition to this EnsTop comes with a pLSA implementation that can be used standalone (and not as part of an ensemble). So if all you are loosing for is a good fast pLSA implementation (that can run considerably faster than many LDA implementations) then EnsTop is the library for you.
How to use EnsTop
EnsTop follows the sklearn API (and inherits from sklearn base classes), so if you use sklearn for LDA or NMF then you already know how to use Enstop. General usage is very straightforward. The following example uses EnsTop to model topics from the classic 20-Newsgroups dataset, using sklearn’s CountVectorizer to generate the required count matrix.
from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import CountVectorizer from enstop import EnsembleTopics news = fetch_20newsgroups(subset='all') data = CountVectorizer().fit_transform(news.data) model = EnsembleTopics(n_components=20).fit(data) topics = model.components_ doc_vectors = model.embedding_
How to use pLSA
EnsTop also provides a simple to use but fast and effective pLSA implementation out of the box. As with the ensemble topic modeller it follows the sklearn API, and usage is very similar.
from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import CountVectorizer from enstop import PLSA news = fetch_20newsgroups(subset='all') data = CountVectorizer().fit_transform(news.data) model = PLSA(n_components=20).fit(data) topics = model.components_ doc_vectors = model.embedding_
The easiest way to install EnsTop is via pip
pip install enstop
To manually install this package:
wget https://github.com/lmcinnes/enstop/archive/master.zip unzip master.zip rm master.zip cd enstop-master python setup.py install
Help and Support
Some basic example notebooks are available here.
Documentation is coming. This project is still very young. If you need help, or have problems please open an issue and I will try to provide any help and guidance that I can. Please also check the docstrings on the code, which provide some descriptions of the parameters.
The EnsTop package is 2-clause BSD licensed.
Contributions are more than welcome! There are lots of opportunities for potential projects, so please get in touch if you would like to help out. Everything from code to notebooks to examples and documentation are all equally valuable so please don’t feel you can’t contribute. To contribute please fork the project make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch.
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