https://github.com/vlukiyanov/pt-avitm

PyTorch implementation of AVITM (Autoencoding Variational Inference For Topic Models)
https://github.com/vlukiyanov/pt-avitm

autoencoder avitm pytorch topic-modeling

Last synced: about 1 month ago
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PyTorch implementation of AVITM (Autoencoding Variational Inference For Topic Models)

• Host: GitHub
• URL: https://github.com/vlukiyanov/pt-avitm
• Owner: vlukiyanov
• License: mit
• Created: 2018-12-29T20:20:28.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2022-07-15T18:41:14.000Z (over 2 years ago)
• Last Synced: 2024-08-03T02:11:12.156Z (4 months ago)
• Topics: autoencoder, avitm, pytorch, topic-modeling
• Language: Python
• Size: 2.68 MB
• Stars: 35
• Watchers: 3
• Forks: 6
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# pt-avitm

[![Build Status](https://travis-ci.org/vlukiyanov/pt-avitm.svg?branch=master)](https://travis-ci.org/vlukiyanov/pt-avitm) [![codecov](https://codecov.io/gh/vlukiyanov/pt-avitm/branch/master/graph/badge.svg)](https://codecov.io/gh/vlukiyanov/pt-avitm)

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PyTorch implementation of a version of the Autoencoding Variational Inference For Topic Models (AVITM) algorithm. Compatible with PyTorch 1.0.0 and Python 3.6 or 3.7 with or without CUDA.

This follows (*or attempts to; note this implementation is unofficial*) the algorithm described in "Autoencoding Variational Inference For Topic Models" of Akash Srivastava, Charles Sutton (https://arxiv.org/abs/1703.01488).

## Examples

You can find a number of examples in the examples directory, see also Usage below.

## Usage

The simplest way to use the library is using the sklearn-compatible API, as below.

```python

import sklearn.datasets

from sklearn.feature_extraction.text import CountVectorizer

from sklearn.pipeline import make_pipeline

from ptavitm.sklearn_api import ProdLDATransformer

texts = sklearn.datasets.fetch_20newsgroups()['data']

pipeline = make_pipeline(

    CountVectorizer(

        stop_words='english',

        max_features=2500,

        max_df=0.9

    ),

    ProdLDATransformer()

)

pipeline.fit(texts)

result = pipeline.transform(texts)

```

## Other implementations of AVITM and similar

* Original TensorFlow: https://github.com/akashgit/autoencoding_vi_for_topic_models 

* PyTorch: https://github.com/hyqneuron/pytorch-avitm