Skip to main content

Spines

Spines Logo

Skeletons for parameterized models.

Build Status Coverage Status Documentation Status PyPI Status Python Versions

Important: This software is still in it's early alpha phase and is constantly in flux. It will likely change significantly.

Installation

To install spines use your package manager of choice, an example using pipenv would be:

$ pipenv install spines

About

Spines is a library which provides a consistent (and hopefully familiar) framework for building predictive models. It's core Model class is similar, in structure, to some of scikit-learn's underlying Estimator classes - but with a single set of unified functions for all models, namely:

  • Construct
  • Fit
  • Train
  • Predict
  • Error
  • Score

The predict method is the only one that's required to be implemented, though the others are likely useful most of the time (and often required to take advantage of some of the additional features provided by spines).

Spines was built because the process of developing a model could be significantly aided by an intelligent framework keeping tabs on changes, storing results and helping you iterate. The purpose of spines was to give a simple (and not too opinionated) interface/skeleton for models as well as provide some helpful utilities for the model building process. To accomplish this spines provides some useful key features:

  • Standardized format for models of all types.
  • Automatic version management.
  • Storing intermediate/iterative results during the model development and training/fitting process.
  • A unified storage format for models to facilitate collaboration, training and deployment.

Documentation

The latest documentation is hosted on read the docs.

License

This project is licensed under the MIT License, for more information see the LICENSE file.

Metadata

Release files for spines 0.0.6

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for spines 0.0.6
File Size Uploaded Provenance Actions
spines-0.0.6.tar.gz 97.6 kB Without provenance Details for spines-0.0.6.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for spines 0.0.6
File Interpreter ABI Platform Actions
spines-0.0.6-py3-none-any.whl Python 3 none any Details for spines-0.0.6-py3-none-any.whl

Total release size: 189.7 kB

Release files / spines-0.0.6.tar.gz

Download URL spines-0.0.6.tar.gz
Size 97.6 kB
Tags Source
SHA-256 checksum
How to use checksums
3c7e43b0b786588b2f5ca4d736d254c4dd4484a52e3e390bd06dfb66fd6e3b2f
BLAKE2b-256 checksum
How to use checksums
f237fe1da1f554b765ea99699dd1f00f0e7d5838bd136ae09af67795e81b7df5
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.7.2

Release files / spines-0.0.6-py3-none-any.whl

Download URL spines-0.0.6-py3-none-any.whl
Size 92.0 kB
Tags Python 3
SHA-256 checksum
How to use checksums
d1500531a50be2e70094643eca265367a7c92fddcaf26565fe861c5df31885fa
BLAKE2b-256 checksum
How to use checksums
151cd675b008d676ba80bedfbdc24637aeb355d6e964ca04cbcfbda751162796
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.7.2

Security for spines 0.0.6

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page