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https://github.com/lmcinnes/enstop

Ensemble topic modelling with pLSA
https://github.com/lmcinnes/enstop

dimensionality-reduction matrix-factorization plsa topic-modeling

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Ensemble topic modelling with pLSA

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README 

        
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EnsTop

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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.



.. code:: python



    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.



.. code:: python



    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_



------------

Installation

------------



The easiest way to install EnsTop is via pip



.. code:: bash



    pip install enstop



To manually install this package:



.. code:: bash



    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 <./notebooks>`_.



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.



-------

License

-------



The EnsTop package is 2-clause BSD licensed.



------------

Contributing

------------



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.