Skip to main content

Opinionated scaffolding library for machine learning projects

Project description

https://travis-ci.org/carlomazzaferro/neu.svg?branch=master https://img.shields.io/pypi/v/test-ml.svg Documentation Status Coverage

Treat your machine learning models like any other software asset: properly test them and fail builds if they don’t meet your desired performance.

$ cd docs && make clean && make html

Open then index.html in the newly created docs/_build folder and you’re good to go.

Overview

This library enables you to easily test machine learning artifacts. Specify a set of target metric, and the rest is taken care of.

Features

  • Rich CLI capabilities that enable you to configure metrics, input data, performance cut-offs, and more

  • Small, statically typed codebase, and extensive docstrings

  • Public API enabling embedding this library in any build process

  • Easily extensible with custom loaders, runners, and metrics

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.1.0 (2018-11-02)

  • First release on PyPI.

0.2.0 (2018-11-13)

  • Major feature implementation and documentation

  • Static typing

  • Testing - 78% coverage

0.3.0 (2018-11-20)

  • Major internals refactoring

  • API unchanged, although external API was made more clear and documented

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

test-ml-0.1.0.tar.gz (24.1 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page