Package: autogoal
AutoGOAL is a Python library for automatically finding the best way to solve a given task.
It has been designed mainly forAutomated Machine Learning(akaAutoML)
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.⭐ QuickstartAutoGOAL 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 theAutoMLclass.To illustrate the simplicity of its use we will load a dataset and run an automatic classifier in it.
The following code will run for approximately 5 minutes on a classic dataset.fromautogoal.datasetsimportcarsfromautogoal.kbimport(MatrixContinuousDense,Supervised,VectorCategorical)fromautogoal.mlimportAutoML# Load datasetX,y=cars.load()# Instantiate AutoML and define input/output typesautoml=AutoML(input=(MatrixContinuousDense,Supervised[VectorCategorical]),output=VectorCategorical)# Run the pipeline search processautoml.fit(X,y)# Report the best pipelineprint(automl.best_pipeline_)print(automl.best_score_)Sensible defaults are defined for each of the many parameters ofAutoML.
Make sure toread the documentationfor more information.⚙️ InstallationThe 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/autogoalInstructions for setting up Docker are availablehere.Once you have the development image downloaded, you can fire up a console and use AutoGOAL interactively.If you prefer to not use Docker, or you don't want all the dependencies, you can also install AutoGOAL directly with pip:pip install autogoalThis will install the core library but you won't be able to use any of the underlying machine learning algorithms until you install the corresponding optional dependencies. You can install them all with:pip install autogoal[contrib]To fine-pick which dependencies you want, read thedependencies section.⚠️NOTE: By installing throughpipyou 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 themainbranch, hence it will probably contain more features, but also more bugs, than the release version.💻 CLIYou can use AutoGOAL directly from the CLI. To see options just type:autogoalUsing the CLI you can train and use AutoML models, download datasets and inspect the contrib libraries without writing a single line of code.Read more in theCLI documentation.🤩 DemoAn online demo app is available atautogoal.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/autogoalAnd navigate tolocalhost:8501.⚖️ API stabilityWe make a conscious effort to maintain a consistent public API across versions, but the private API can change at any time.
In general, everything you can import fromautogoalwithout underscores is considered public.For example:# "clean" imports are part of the public APIfromautogoalimportoptimizefromautogoal.mlimportAutoMLfromautogoal.contrib.sklearnimportfind_classes# public members of public types as wellautoml=AutoMLautoml.fit(...)# underscored imports are part of the private APIfromautogoal.ml._automlimport...fromautogoal.contrib.sklearn._generatedimport...# as well as private members of any typeautoml._input_type(...)These are our consistency rules:Major breaking changes are introduced between major version updates, e.g.,x.0andy.0. These can be additions, removals, or modifications of any kind in any part of the API.Between minor version updates, e.g.,1.xand1.y, you can expect to find new functionality, but anything you can use from the   public API will still be there with a consistent semantic (save for bugfixes).Between micro version updates, e.g.,1.3.xand1.3.y, the public API is frozen even for additions.The private API can be changed at all times.⚠️ While AutoGOAL is on public beta (versions0.x) the public API is considered unstable and thus everything can change. However, we try to keep breaking changes to a minimum.📚 DocumentationThis documentation is available online atautogoal.github.io. Check the following sections:User Guide: Step-by-step showcase of everything you need to know to use AutoGOAL.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 theAutoGOAL Docker imageand running:docker run -p 8000:8000 autogoal/autogoal mkdocs serve -a 0.0.0.0:8000And navigating tolocalhost:8000.📃 PublicationsIf 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 Studymarks 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 Textapplied 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 Evolutionpresents a more uniform framework with different grammars in different problems, from tabular datasets to natural language processing.Solving Heterogeneous AutoML Problems with AutoGOALis the first actual description of AutoGOAL as a framework, unifying the ideas presented in the previous papers.🤝 ContributionCode is licensed under MIT. Read the details in thecollaboration section.This project follows theall-contributorsspecification. Any contribution will be given credit, from fixing typos, to reporting bugs, to implementing new core functionalities.Here are all the current contributions.🙏 Thanks!Suilan Estevez-Velarde💻⚠️🤔📖Alejandro Piad💻⚠️📖Yudivián Almeida Cruz🤔📖ygutierrez🤔📖Ernesto Luis Estevanell Valladares💻⚠️Alexander Gonzalez💻⚠️Anshu Trivedi💻Alex Coto📖Guillermo Blanco🐛💻📖yacth🐛💻Brandon Fergerson🐛Aditya Nikhil🐛lucas-FP🐛💻Leynier Gutiérrez González📖Ender Minyard📖