Embedded Topic Model

This package was made to easily run embedded topic modelling on a given corpus.

ETM is a topic model that marries the probabilistic topic modelling of Latent Dirichlet Allocation with the contextual information brought by word embeddings-most specifically, word2vec. ETM models topics as points in the word embedding space, arranging together topics and words with similar context. As such, ETM can either learn word embeddings alongside topics, or be given pretrained embeddings to discover the topic patterns on the corpus.

ETM was originally published by Adji B. Dieng, Francisco J. R. Ruiz, and David M. Blei on a article titled "Topic Modeling in Embedding Spaces" in 2019. This code is an adaptation of the original provided with the article and is not affiliated in any manner with the original authors. Most of the original code was kept here, with some changes here and there, mostly for ease of usage. This package was created to facilitate research purposes. If you want a more stable and feature-rich package to train ETM and other models, take a look at OCTIS.

With the tools provided here, you can run ETM on your dataset using simple steps.

Index

• :beer: Installation
• :wrench: Usage
  • :microscope: Examples
• :books: Citation
• :heart: Contributing
• :v: Acknowledgements
• :pushpin: License

:beer: Installation

You can install the package using pip by running: pip install -U embedded_topic_model

:wrench: Usage

To use ETM on your corpus, you must first preprocess the documents into a format understandable by the model. This package has a quick-use preprocessing script. The only requirement is that the corpus must be composed by a list of strings, where each string corresponds to a document in the corpus.

You can preprocess your corpus as follows:

from embedded_topic_model.utils import preprocessing
import json

# Loading a dataset in JSON format. As said, documents must be composed by string sentences
corpus_file = 'datasets/example_dataset.json'
documents_raw = json.load(open(corpus_file, 'r'))
documents = [document['body'] for document in documents_raw]

# Preprocessing the dataset
vocabulary, train_dataset, _, = preprocessing.create_etm_datasets(
    documents, 
    min_df=0.01, 
    max_df=0.75, 
    train_size=0.85, 
)

Then, you can train word2vec embeddings to use with the ETM model. This is optional, and if you're not interested on training your embeddings, you can either pass a pretrained word2vec embeddings file for ETM or learn the embeddings using ETM itself. If you want ETM to learn its word embeddings, just pass train_embeddings=True as an instance parameter.

To pretrain the embeddings, you can do the following:

from embedded_topic_model.utils import embedding

# Training word2vec embeddings
embeddings_mapping = embedding.create_word2vec_embedding_from_dataset(documents)

To create and fit the model using the training data, execute:

from embedded_topic_model.models.etm import ETM

# Training an ETM instance
etm_instance = ETM(
    vocabulary,
    embeddings=embeddings_mapping, # You can pass here the path to a word2vec file or
                                   # a KeyedVectors instance
    num_topics=8,
    epochs=100,
    debug_mode=True,
    train_embeddings=False, # Optional. If True, ETM will learn word embeddings jointly with
                            # topic embeddings. By default, is False. If 'embeddings' argument
                            # is being passed, this argument must not be True
)

etm_instance.fit(train_dataset)

You can get the topic words with this method. Note that you can select how many word per topic you're interest in:

t_w_mtx = etm_instance.get_topics(top_n_words=20)

You can get the topic word matrix with this method. Note that it will return all word for each topic:

t_w_mtx = etm_instance.get_topic_word_matrix()

You can get the topic word distribution matrix and the document topic distribution matrix with the following methods, both return a normalized distribution matrix:

t_w_dist_mtx = etm_instance.get_topic_word_dist()
d_t_dist_mtx = etm_instance.get_document_topic_dist()

Also, to obtain topic coherence or topic diversity of the model, you can do as follows:

topics = etm_instance.get_topics(20)
topic_coherence = etm_instance.get_topic_coherence()
topic_diversity = etm_instance.get_topic_diversity()

You can also predict topics for unseen documents with the following.

from embedded_topic_model.utils import preprocessing
from embedded_topic_model.models.etm import ETM

corpus_file = 'datasets/example_dataset.json'
documents_raw = json.load(open(corpus_file, 'r'))
documents = [document['body'] for document in documents_raw]

# Splits into train/test datasets
train = documents[:len(documents)-100]
test = documents[len(documents)-100:]

# Model fitting
# ...

# The vocabulary must be the same one created during preprocessing of the training dataset (see above)
preprocessed_test = preprocessing.create_bow_dataset(test, vocabulary)
# Transforms test dataset and returns normalized document topic distribution
test_d_t_dist = etm_instance.transform(preprocessed_test)
print(f'test_d_t_dist: {test_d_t_dist}')

For further details, see examples.

:microscope: Examples

:books: Citation

To cite ETM, use the original article's citation:

@article{dieng2019topic,
    title = {Topic modeling in embedding spaces},
    author = {Dieng, Adji B and Ruiz, Francisco J R and Blei, David M},
    journal = {arXiv preprint arXiv: 1907.04907},
    year = {2019}
}

:heart: Contributing

Contributions are always welcomed :heart:! You can take a look at to see some guidelines. Feel free to contact through issues, to elaborate on desired enhancements and to check if work is already being done on the matter.

:v: Acknowledgements

Credits given to Adji B. Dieng, Francisco J. R. Ruiz, and David M. Blei for the original work.

:pushpin: License

Licensed under MIT license.

Changelog

This changelog was inspired by the keep-a-changelog project and follows semantic versioning.

[1.2.1] - 2023-09-06

Changed

• (#cf35c3) fixes minimum python version to be python>=3.9

[1.2.0] - 2023-09-06

Added

• (#61730d, #224995, #331fc0) adds support for macOS MPS devices and updates outdated numpy/sklearn code - thanks to @d-jiao
• (#c48016, #2fe517, #c965b1, #5578ca, #5b0d85) adds security guidelines and request templates

Changed

• (#331fc0) updates actions pipeline, supported python versions and internal dependencies to the latest available like torch, gensim, among others. Support for python<=3.8 was dropped as a result. Numerous security vulnerabilities were solved

[1.1.0] - 2023-09-05

Added

• (#3f27ee) adds transform method
• (#f98f3f) adds example jupyter notebook
• (#683bec) adds contributing and conduct guidelines

Changed

• (#f98f3f, #c918a4) updates documentation

[1.0.2] - 2021-06-23

Changed

• deactivates debug mode by default
• documents get_most_similar_words method

[1.0.1] - 2021-02-15

Changed

• optimizes original word2vec TXT file input for model training
• updates README.md

[1.0.0] - 2021-02-15

Added

• adds support for original word2vec pretrained embeddings files on both formats (BIN/TXT)

Changed

• optimizes handling of gensim's word2vec mapping file for better memory usage

[0.1.1] - 2021-02-01

Added

• support for python 3.6

[0.1.0] - 2021-02-01

Added

• ETM training with partially tested support for original ETM features.
• ETM corpus preprocessing scripts - including word2vec embeddings training - adapted from the original code.
• adds methods to retrieve document-topic and topic-word probability distributions from the trained model.
• adds docstrings for tested API methods.
• adds unit and integration tests for ETM and preprocessing scripts.