yacth/autogoal

A Python framework for program synthesis with a focus on Automated Machine Learning.

AutoGOAL

Automatic Generation, Optimization And Artificial Learning

AutoGOAL is a Python library for automatically finding the best way to solve a given task. It has been designed mainly for Automated Machine Learning (aka AutoML) but it can be used in any scenario where you have several possible ways to solve a given task.

Technically speaking, AutoGOAL is a framework for program synthesis, i.e., finding the best program to solve a given problem, provided that the user can describe the space of all possible programs. AutoGOAL provides a set of low-level components to define different spaces and efficiently search in them. In the specific context of machine learning, AutoGOAL also provides high-level components that can be used as a black-box in almost any type of problem and dataset format.

Quickstart

AutoGOAL is first and foremost a framework for Automated Machine Learning. As such, it comes pre-packaged with hundreds of low-level machine learning algorithms that can be automatically assembled into pipelines for different problems.

The core of this functionality lies in the AutoML class.

To illustrate the simplicity of its use we will load a dataset and run an automatic classifier in it.

from autogoal.datasets import cars
from autogoal.ml import AutoML

X, y = cars.load()
automl = AutoML()
automl.fit(X, y)

Sensible defaults are defined for each of the many parameters of AutoML. Make sure to read the documentation for more information.

Installation

Installation is very simple:

pip install autogoal

However, autogoal comes with a bunch of optional dependencies. You can install them all with:

pip install autogoal[contrib]

To fine-pick which dependencies you want, read the dependencies section.

Using Docker

The easiest way to get AutoGOAL up and running with all the dependencies is to pull the development Docker image, which is somewhat big:

docker pull autogoal/autogoal

Instructions for setting up Docker are available here.

Once you have the development image downloaded, you can fire up a console and use AutoGOAL interactively.

NOTE: By installing through pip you will get the latest release version of AutoGOAL, while by installing through Docker, you will get the latest development version. The development version is mostly up-to-date with the main branch, hence it will probably contain more features, but also more bugs, than the release version.

CLI

You can use AutoGOAL directly from the CLI. To see options just type:

python -m autogoal

Read more in the CLI documentation.

Demo

An online demo app is available at autogoal.github.io/demo. This app showcases the main features of AutoGOAL in interactive case studies.

To run the demo locally, simply type:

docker run -p 8501:8501 autogoal/autogoal

And navigate to localhost:8501.

Documentation

This documentation is available online at autogoal.github.io. Check the following sections:

• User Guide: Step-by-step showcase of everything you need to know to use AuoGOAL.
• Examples: The best way to learn how to use AutoGOAL by practice.
• API: Details about the public API for AutoGOAL.

The HTML version can be deployed offline by downloading the AutoGOAL Docker image and running:

docker run -p 8000:8000 autogoal/autogoal mkdocs serve -a 0.0.0.0:8000

And navigating to localhost:8000.

Publications

If you use AutoGOAL in academic research, please cite the following paper:

@article{estevez2020general,
  title={General-purpose hierarchical optimisation of machine learning pipelines with grammatical evolution},
  author={Est{\'e}vez-Velarde, Suilan and Guti{\'e}rrez, Yoan and Almeida-Cruz, Yudivi{\'a}n and Montoyo, Andr{\'e}s},
  journal={Information Sciences},
  year={2020},
  publisher={Elsevier},
  doi={10.1016/j.ins.2020.07.035}
}

The technologies and theoretical results leading up to AutoGOAL have been presented at different venues:

• Optimizing Natural Language Processing Pipelines: Opinion Mining Case Study marks the inception of the idea of using evolutionary optimization with a probabilistic search space for pipeline optimization.

• AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text applied probabilistic grammatical evolution with a custom-made grammar in the context of entity recognition in medical text.

• General-purpose Hierarchical Optimisation of Machine Learning Pipelines with Grammatical Evolution presents a more uniform framework with different grammars in different problems, from tabular datasets to natural language processing.

• Solving Heterogeneous AutoML Problems with AutoGOAL is the first actual description of AutoGOAL as a framework, unifying the ideas presented in the previous papers.

Contribution

Code is licensed under MIT. Read the details in the collaboration section.

Contributors

Suilan Estevez-Velarde 💻 ⚠️ 🤔 📖	Alejandro Piad 💻 ⚠️ 📖	Yudivián Almeida Cruz 🤔 📖	ygutierrez 🤔 📖	Ernesto Luis Estevanell Valladares 💻 ⚠️	Alexander Gonzalez 💻 ⚠️	Anshu Trivedi 💻
Alex Coto 📖	Guillermo Blanco 🐛