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Releases: jjfiv/fastrank

Fastrank 0.8 (M1 support, Python 3.7)

25 Mar 22:17
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We revamped how CI scripts work and upgraded Maturin in order to get "Apple Silicon" support.

What's Changed

Full Changelog: 0.7.0...0.8.0

The 'predict_scores' release

22 Mar 19:50
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  • CModel now has predict_scores that returns a sparse representation of Dict[int, float] where the position in the arrays you've loaded correspond to the score.
  • We also have predict_dense_scores which returns a List[float] with the same semantics. If you have subsampled queries, this may make less sense than the aforementioned method.
  • We have some better testing covering these features.
  • Note: trying again because of a glitch in automatic releases ('fix' for #32 insufficient)

0.6.1: move to manylinux2010

06 Mar 01:02
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Rather than require the arbitrary libc etc. from the gh-actions publish, align with manylinux2010. Fixed some documentation nits along the way.

0.6.0: Windows, fast-float and python3.6

05 Mar 23:36
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Updates in 0.6.0 are:

  • Minimum python version now 3.6 -- 3.5 started failing on CI, so it's gone now.
  • support for faster float parsing -- on my machine the msn30k dataset took 90s to load, and now only takes 60s. Thanks rust libraries!
  • Windows supported - now that I have regular access to a windows machine, I will make sure PyPI has windows builds.

First public alpha: 0.4

12 Oct 00:26
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FastRank

FastRank is an implementation of CoordinateAscent[1] from Ranklib that you can pip install; written in Rust and uses threads for efficiency; it will scale much better than the Java version to large datasets and many features.

It also has RandomForests, and someday LambdaMART (others depending on interest). I've been thinking a lot about what the limits of coordinate ascent are (linearity), and will probably play with that in future versions.

This is ready for production use in the sense that I used it for my TREC submission this year. The python API could use some thoughts and experiences (post issues on Github).

pip install fastrank

[1] Metzler, D., & Croft, W. B. (2007). Linear feature-based models for information retrieval. Information Retrieval, 10(3), 257-274.