ballet 0.7.7

Core functionality for lightweight, collaborative data science projects

ballet

A lightweight framework for collaborative, open-source data science projects through feature engineering.

• Free software: MIT license
• Documentation: https://hdi-project.github.io/ballet
• Homepage: https://github.com/HDI-Project/ballet

Overview

Do you develop machine learning models? Do you work by yourself or on a team? Do you share notebooks or are you committing code to a shared repository? In contrast to successful, massively collaborative, open-source projects like the Linux kernel, the Rails framework, Firefox, GNU, or Tensorflow, most data science projects are developed by just a handful of people. But think if the open-source community could leverage its ingenuity and determination to collaboratively develop data science projects to predict the incidence of disease in a population, to predict whether vulnerable children will be evicted from their homes, or to predict whether learners will drop out of online courses.

Our vision is to make collaborative data science possible by making it more like open-source software development. Our approach is based on decomposing the data science process into modular patches - standalone units of contribution - that can then be intelligently combined, representing objects like "feature", "labeling function", or "prediction task definition". Collaborators work in parallel to write patches and submit them to a repo. Our software framework provides the underlying functionality to merge high-quality contributions, collect modules from the file system, and compose the accepted contributions into a single product. It also provides a familiar notebook-based development experience that is friendly to data scientists and other inexperienced open-source contributors. We don't require any computing infrastructure beyond that which is commonly used in open-source software development.

Currently, Ballet focuses on supporting collaboratively developing feature engineering pipelines, an important part of many data science projects. Individual features are represented as separate Python modules, declaring the subset of a dataframe that they operate on and a scikit-learn-style learned transformer that extracts feature values from the raw data. Ballet collects individual features and composes them into a feature engineering pipeline. At any point, a project built on Ballet can be installed for end-to-end feature engineering on new data instances for the same problem. How do we ensure the feature engineering pipeline is always useful? Ballet thoroughly validates proposed features for correctness and machine learning performance, using an extensive test suite and a novel streaming logical feature selection algorithm. Accepted features can be automatically merged by the ballet GitHub app into projects.

Next steps

Are you a data owner or project maintainer that wants to organize a collaboration?

👉 Check out the Ballet Maintainer Guide

Are you a data scientist or enthusiast that wants to join a collaboration?

👉 Check out the Ballet Contributor Guide

Want to learn about how Ballet enables Better Feature Engineering™️?

👉 Check out the Feature Engineering Guide

Want to see a demo collaboration in progress and maybe even participate yourself?

👉 Check out the ballet-predict-house-prices project

History

0.7.7 (2020-08-12)

• Support None as the transformer in a Feature, it will be automatically converted to an IdentityTransformer
• Implement ColumnSelector
• Update docs
• Various bug fixes and improvements

0.7.6 (2020-08-12)

• Re-export feature engineering primitives from various libraries
• Show type annotations in docs
• Update guides
• Various bug fixes and improvements

0.7.5 (2020-08-03)

• Make validator parameters configurable in ballet.yml file (e.g. λ_1 and λ_2 for GFSSF algorithms)
• Support dynaconf 3.x

0.7.4 (2020-07-22)

• Accept logger names, as well as logger instances, in ballet.util.log.enable
• Updated docs

0.7.3 (2020-07-21)

• Add load_data method with built-in caching to project API
• Fix bug in GFSSF accepter
• Always use encoded target during validation
• Various bug fixes and improvements

0.7.2 (2020-07-21)

• Add sample analysis notebook to project template
• Add binder url/badge to project template
• Fix bug with enabling logging with multiple loggers

0.7.1 (2020-07-20)

• Add client for easy interactive usage (ballet.b)
• Add binder setup to project template

0.7 (2020-07-17)

• Revamp project template: update project structure, create single API via FeatureEngineeringProject, use and add support for pyinvoke, revamp build into engineer_features, support repolockr bot
• Improve ballet.project.Project: can create by ascending from given path, can create from current working directory, can resolve arbitrary project symbol, exposes project's API
• Check for and notify of new release of ballet during project update (ballet update-project-template)
• Add ComputedValueTransformer to ballet.eng
• Move stacklog to separate project and install it
• Add validators that {never,always} accept submissions
• Add feature API checks to ensure that the feature can fit and transform a single row
• Add feature engineering guide to documentation and significantly expand contributor guide
• Add bot installation instructions to maintainer guide
• Add type annotations throughout
• Drop support for py35, add support for py38
• Deprecate modeling code
• Various bug fixes and improvements

0.6 (2019-11-12)

• Implement GFSSF validators and random validators
• Improve validators and allow validators to be configured in ballet.yml
• Improve project template
• Create ballet CLI
• Bug fixes and performance improvements

0.5 (2018-10-14)

• Add project template and ballet-quickstart command
• Add project structure checks and feature API checks
• Implement multi-stage validation routine driver

0.4 (2018-09-21)

• Implement Modeler for versatile modeling and evaluation
• Change project name

0.3 (2018-04-28)

• Implement PullRequestFeatureValidator
• Add util.travis, util.modutil, util.git util modules

0.2

• Implement ArrayLikeEqualityTestingMixin
• Implement collect_contrib_features

0.1

• First release on PyPI