https://github.com/jjfiv/fastrank

My most frequently used learning-to-rank algorithms ported to rust for efficiency. Try it: "pip install fastrank".
https://github.com/jjfiv/fastrank

coordinate-ascent learning-to-rank machine-learning random-forests ranklib rust

Last synced: about 2 months ago
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My most frequently used learning-to-rank algorithms ported to rust for efficiency. Try it: "pip install fastrank".

• Host: GitHub
• URL: https://github.com/jjfiv/fastrank
• Owner: jjfiv
• License: mit
• Created: 2019-07-14T19:16:28.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2023-10-12T11:16:54.000Z (12 months ago)
• Last Synced: 2024-07-04T20:43:26.462Z (3 months ago)
• Topics: coordinate-ascent, learning-to-rank, machine-learning, random-forests, ranklib, rust
• Language: Rust
• Homepage: https://jjfoley.me/2019/10/11/fastrank-alpha.html
• Size: 340 KB
• Stars: 48
• Watchers: 2
• Forks: 6
• Open Issues: 18
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# FastRank ![CI Status Badge](https://github.com/jjfiv/fastrank/workflows/CI/badge.svg) [![PyPI version](https://badge.fury.io/py/fastrank.svg)](https://badge.fury.io/py/fastrank)

My most frequently used learning-to-rank algorithms ported to rust for efficiency.

Read my [blog-post](https://jjfoley.me/2019/10/11/fastrank-alpha.html) announcing the first public version: 0.4. It's alpha because I think the API needs work, not because there's any sort of known correctness or compatiblity issues.

## Python Requirements / Release History

- 0.5 and earlier require only Python 3.5, but no windows builds were pushed.

- 0.6 requires Python 3.6 due to EOL for Python 3.5 becoming prevalent in the latest pip.

- 0.6.1 switched to manylinux2010 building; you might get better vectorization from a local copy.

- 0.7 maintains the requirement of Python 3.6

- 0.8 and forward will require Python 3.7 so we can use the standard @dataclass annotation and drop the attrs dependency.

## Python Usage

```shell

pip install fastrank

```

See this [Colab notebook](https://colab.research.google.com/drive/1IjF7yTin1XaNO_6mBNxAoQYTmF0nckk1) for more, or see a static version [here on Github](https://github.com/jjfiv/fastrank/blob/master/examples/FastRankDemo.ipynb).