Evolutionary Dataset Optimisation

A library for generating artificial datasets through evolution.

The edo library provides an evolutionary algorithm that optimises any real-valued function over a subset of the space of all possible datasets that we call Evolutionary Dataset Optimisation. The output of the algorithm is a bank of effective datasets for which the provided function performs well that can then be studied.

The applications of this method are varied but an important and relevant one is in learning an algorithm's strengths and weaknesses.

When determining the quality of an algorithm, the standard route is to run the comparable algorithms on a finite set of existing (or newly simulated) datasets and calculating some metric. The algorithm(s) with the smallest value of this metric are chosen to be the best performing.

An issue with this approach is that it pays little regard to the reliability and quality of the datasets being used, which begs the question: what makes a dataset "good" for an algorithm? Or, why is it that an algorithm performs well on some datasets but not others?

By passing the objective function of the algorithm to the edo.DataOptimiser class, questions like these can be answered by studying the properties of the resultant datasets. Beyond that, a combination of objective functions could be used to determine how an algorithm performs against any number of other algorithms. A comprehensive description of the evolutionary algorithm and an examplar case study is available at https://doi.org/10.1007/s10489-019-01592-4.

Installation

The edo library requires Python 3.6+ and is pip-installable:

$ python -m pip install edo

To install from source then clone the GitHub repo:

$ git clone https://github.com/daffidwilde/edo.git
$ cd edo
$ python setup.py install

A command line tool has been developed to make using edo for larger experiments easier: https://github.com/daffidwilde/edolab

Publications and documentation

Full documentation for the library is available at https://edo.readthedocs.io.

An article on the theory behind the algorithm has been published:

Wilde, H., Knight, V. & Gillard, J. Evolutionary dataset optimisation: learning algorithm quality through evolution. Appl Intell 50, 1172-1191 (2020). https://doi.org/10.1007/s10489-019-01592-4

Citation instructions

Citing the library

Please use the following to cite the library:

@misc{edo-library,
    author = {{The EDO library developers}},
    title = {edo: <RELEASE TITLE>},
    year = <RELEASE YEAR>,
    doi = {<DOI INFORMATION>},
    url = {http://doi.org/<DOI INFORMATION>}
}

To check the relevant details (i.e. RELEASE TITLE, RELEASE YEAR and DOI NUMBER) head to the library's Zenodo page:

Citing the paper

If you wish to cite the paper, then use the following:

@article{edo-paper,
    title = {Evolutionary dataset optimisation: learning algorithm quality
             through evolution},
    author = {Wilde, Henry and Knight, Vincent and Gillard, Jonathan},
    journal = {Applied Intelligence},
    year = 2020,
    volume = 50,
    pages = {1172--1191},
    doi = {10.1007/s10489-019-01592-4},
}

Contributing to the library

Contributions are always welcome whether they come in the form of providing a fix for a current issue, reporting a bug or implementing an enhancement to the library code itself. Pull requests (PRs) will be reviewed and collaboration is encouraged.

To make a contribution via a PR, follow these steps:

1. Make a fork of the GitHub repo and clone your fork locally:

   $ git clone https://github.com/<your-username>/edo.git
   

2. Install the library in development mode. If you use Anaconda, there is a conda environment file (environment.yml) with all of the development dependencies:

   $ cd edo
   $ conda env create -f environment.yml
   $ conda activate edo-dev
   $ python setup.py develop
   

3. Make your changes and write tests to go with them. Ensure that they pass and you have 100% coverage:

   $ python -m pytest --cov=edo --cov-fail-under=100 tests
   

4. Push to your fork and open a pull request.