AutoTM
Automatic parameters selection for topic models (ARTM approach) using evolutionary algorithms. AutoTM provides necessary tools to preprocess english and russian text datasets and tune topic models.
What is AutoTM?
Topic modeling is one of the basic methods for EDA of unlabelled text data. While ARTM (additive regularization for topic models) approach provides the significant flexibility and quality comparative or better that neural approaches it is hard to tune such models due to amount of hyperparameters and their combinations.
To overcome the tuning problems AutoTM presents an easy way to represent a learning strategy to train specific models for input corporas.
Optimization procedure is done by genetic algorithm which operators are specifically tuned for the task. To speed up the procedure we also implemented surrogate modeling that, for some iterations, approximate fitness function to reduce computation costs on training topic models.
Installation
! Note: The functionality of topic models training is available only for linux distributions.
Via pip:
pip install autotm
From source:
pip install -r requirements.txt
python -m spacy download en_core_web_sm
export PYTHONPATH="${PYTHONPATH}:/path/to/src"
Quickstart
The notebook with an example is available in examples folder.
Running from the command line
To fit a model:
autotmctl --verbose fit --config conf/config.yaml --in data/sample_corpora/sample_dataset_lenta.csv
To predict with a fitted model:
autotmctl predict --in data/sample_corpora/sample_dataset_lenta.csv --model model.artm
Backlog:
- Add tests
- Add new multi-stage
Citation
@article{10.1093/jigpal/jzac019,
author = {Khodorchenko, Maria and Butakov, Nikolay and Sokhin, Timur and Teryoshkin, Sergey},
title = "{ Surrogate-based optimization of learning strategies for additively regularized topic models}",
journal = {Logic Journal of the IGPL},
year = {2022},
month = {02},
issn = {1367-0751},
doi = {10.1093/jigpal/jzac019},
url = {https://doi.org/10.1093/jigpal/jzac019},}