To try the examples run:

python -m hpopt.examples.movie_reviews

Basic requirements are Python 3.5 or greater.

The sklearn_opinion example requires sklearn, nltk and the movie_reviews corpus. To install these requirements, follow the instructions here and here.

If you have pip installed, some quick steps are:

pip install -U sklearn
pip install -U nltk
python

>>> import nltk
>>> nltk.download("movie_reviews")

We have added a docker-compose.yml configuration that will run the framework inside our custom machine learning image. To try it just type:

docker-compose up

Please cite this work with the following this BibTeX:

@inproceedings{estevez-velarde-etal-2019-automl,
    title = "{A}uto{ML} Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text",
    author = "Estevez-Velarde, Suilan  and
      Guti{\'e}rrez, Yoan  and
      Montoyo, Andr{\'e}s  and
      Almeida-Cruz, Yudivi{\'a}n",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1428",
    pages = "4356--4365",
    abstract = "The process of extracting knowledge from natural language text poses a complex problem that requires both a combination of machine learning techniques and proper feature selection. Recent advances in Automatic Machine Learning (AutoML) provide effective tools to explore large sets of algorithms, hyper-parameters and features to find out the most suitable combination of them. This paper proposes a novel AutoML strategy based on probabilistic grammatical evolution, which is evaluated on the health domain by facing the knowledge discovery challenge in Spanish text documents. Our approach achieves state-of-the-art results and provides interesting insights into the best combination of parameters and algorithms to use when dealing with this challenge. Source code is provided for the research community.",
}

Licensed under the MIT open source license.

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