Hierarchical Pipeline Optimization

To try the examples run:

python -m hpopt.examples.movie_reviews

Requirements

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")

Docker support

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

How to cite

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.",
}

License

Licensed under the MIT open source license.

MIT License

Copyright (c) 2019 Knowledge Learning Project

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.